diff --git a/.codespellrc b/.codespellrc index 0edc8e73d..e6d52acd3 100644 --- a/.codespellrc +++ b/.codespellrc @@ -1,3 +1,3 @@ [codespell] -ignore-words-list = ans, als, hel, boostrap, childs, te, vas, hsa, ment, cann, thi, makro, wil, rouge, PRIS, ather, MIS, medias, allready, inout, nd, fo, visibles, nothink, renderD, ond, tbe, CopyIn, notin, subtile, subtiles, dout, IST, kInf +ignore-words-list = ans, als, hel, boostrap, childs, te, vas, hsa, ment, cann, thi, makro, wil, rouge, PRIS, ather, MIS, medias, allready, inout, nd, fo, visibles, nothink, renderD, ond, tbe, CopyIn, notin, subtile, subtiles, dout, IST, kInf, datas skip = *.json, *.jsonl, *.patch, *.txt, *.lock diff --git a/docker/Dockerfile b/docker/Dockerfile index 5eb4ea67f..f318a292c 100644 --- a/docker/Dockerfile +++ b/docker/Dockerfile @@ -221,6 +221,7 @@ RUN --mount=type=cache,target=/root/.cache/pip \ # these paths invalidate the dep-install layer, but Python source changes don't. COPY python/pyproject.toml /tmp/sglang_deps/python/pyproject.toml COPY rust/sglang-grpc /tmp/sglang_deps/rust/sglang-grpc +COPY rust/sglang-mm /tmp/sglang_deps/rust/sglang-mm COPY proto /tmp/sglang_deps/proto # Install sglang dependencies (torch, transformers, etc.) @@ -257,6 +258,10 @@ RUN --mount=type=cache,target=/root/.cache/pip \ && rm -rf /tmp/sglang_deps \ && pip freeze | grep -v "^sglang==" > /sgl-workspace/constraints.txt +# distro resolves to the apt python3-distro under /usr/lib/python3, which the runtime +# stage does not COPY; force a pip copy into /usr/local so it survives the stage split. +RUN python3 -m pip install --ignore-installed --no-deps distro + ######################################################## # PARALLEL STAGE 2: DeepEP Builder (needs torch_deps) ######################################################## diff --git a/docker/rocm.Dockerfile b/docker/rocm.Dockerfile index e6da350e0..1bee164d0 100644 --- a/docker/rocm.Dockerfile +++ b/docker/rocm.Dockerfile @@ -69,6 +69,11 @@ ENV BUILD_AITER_ALL="1" ENV BUILD_MOONCAKE="1" ENV AITER_COMMIT_DEFAULT="9127c94a18e4398e1eba91f6639e910f0994ad02" +# Local source stage: with BRANCH_TYPE=local the build context is copied here and +# used instead of git clone (mirrors docker/Dockerfile's local_src stage). +FROM scratch AS local_src +COPY . /src + # =============================== # Chosen arch and args FROM ${GPU_ARCH} @@ -81,6 +86,7 @@ ENV PYTORCH_ROCM_ARCH=gfx942;gfx950 ARG SGL_REPO="https://github.com/sgl-project/sglang.git" ARG SGL_DEFAULT="main" ARG SGL_BRANCH=${SGL_DEFAULT} +ARG BRANCH_TYPE=remote # Version override for setuptools_scm (used in nightly builds) ARG SETUPTOOLS_SCM_PRETEND_VERSION="" @@ -282,16 +288,35 @@ RUN pip install IPython \ && pip install torchao==0.9.0 \ && pip install pybind11 +# Rust toolchain — needed by setuptools-rust to build the sglang-mm extension +# (sglang.srt.multimodal._core) during the sglang pip install below, and later by +# sgl-model-gateway. Must precede the sglang install. +ENV PATH="/root/.cargo/bin:${PATH}" +RUN curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y \ + && rustc --version && cargo --version +ENV CARGO_BUILD_JOBS=4 + RUN pip uninstall -y sgl_kernel sglang -RUN git clone ${SGL_REPO} \ - && cd sglang \ - && if [ "${SGL_BRANCH}" = ${SGL_DEFAULT} ]; then \ - echo "Using ${SGL_DEFAULT}, default branch."; \ - git checkout ${SGL_DEFAULT}; \ + +# Obtain sglang source: copied from the build context (BRANCH_TYPE=local) or git clone. +COPY --from=local_src /src /tmp/local_src +RUN if [ "$BRANCH_TYPE" = "local" ]; then \ + echo "Using local source (BRANCH_TYPE=local)."; \ + cp -r /tmp/local_src sglang; \ else \ - echo "Using ${SGL_BRANCH} branch."; \ - git checkout ${SGL_BRANCH}; \ + git clone ${SGL_REPO} sglang \ + && cd sglang \ + && if [ "${SGL_BRANCH}" = ${SGL_DEFAULT} ]; then \ + echo "Using ${SGL_DEFAULT}, default branch."; \ + git checkout ${SGL_DEFAULT}; \ + else \ + echo "Using ${SGL_BRANCH} branch."; \ + git checkout ${SGL_BRANCH}; \ + fi \ + && cd ..; \ fi \ + && rm -rf /tmp/local_src \ + && cd sglang \ && cd sgl-kernel \ && rm -f pyproject.toml \ && mv pyproject_rocm.toml pyproject.toml \ @@ -311,11 +336,7 @@ RUN find /sgl-workspace/sglang/python/sglang/srt/layers/quantization/configs/ \ /sgl-workspace/sglang/python/sglang/srt/layers/moe/fused_moe_triton/configs/ \ -type f -name '*MI300X*' | xargs -I {} sh -c 'vf_config=$(echo "$1" | sed "s/MI300X/MI300X_VF/"); cp "$1" "$vf_config"' -- {} -# Install Rust toolchain for sgl-model-gateway -ENV PATH="/root/.cargo/bin:${PATH}" -RUN curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y \ - && rustc --version && cargo --version -ENV CARGO_BUILD_JOBS=4 +# Rust toolchain already installed above (before the sglang install). # Build and install sgl-model-gateway RUN python3 -m pip install --no-cache-dir "maturin<1.14" \ diff --git a/python/pyproject.toml b/python/pyproject.toml index 49a4c9323..3fae86339 100755 --- a/python/pyproject.toml +++ b/python/pyproject.toml @@ -33,6 +33,7 @@ dependencies = [ "flash-attn-4==4.0.0b15", "flashinfer_python[cu13]==0.6.14", # keep it aligned with jit-cache version in Dockerfile "gguf", + "helion==0.2.6", "humming-kernels[cu13]==0.1.10", "interegular", "IPython", @@ -42,6 +43,7 @@ dependencies = [ "modelscope", "msgspec", "ninja", + "numba==0.65.1", "numpy", "nvidia-cutlass-dsl[cu13]==4.5.2", "nvidia-mathdx==25.6.0", @@ -228,5 +230,11 @@ target = "sglang.srt.grpc._core" path = "../rust/sglang-grpc/Cargo.toml" binding = "PyO3" +[[tool.setuptools-rust.ext-modules]] +target = "sglang.srt.multimodal._core" +path = "../rust/sglang-mm/Cargo.toml" +binding = "PyO3" +debug = false + [tool.kernels.dependencies] "kernels-community/sgl-flash-attn3" = 1 diff --git a/python/pyproject_other.toml b/python/pyproject_other.toml index 1fd75b814..8ad576f62 100755 --- a/python/pyproject_other.toml +++ b/python/pyproject_other.toml @@ -1,5 +1,5 @@ [build-system] -requires = ["setuptools>=61.0", "setuptools-scm>=8.0", "wheel"] +requires = ["setuptools>=61.0", "setuptools-rust>=1.10", "setuptools-scm>=8.0", "wheel"] build-backend = "setuptools.build_meta" [project] @@ -30,6 +30,7 @@ runtime_common = [ "einops", "fastapi", "gguf", + "helion==0.2.6", "interegular", "IPython", "llguidance>=1.7.6,<2.0.0", @@ -191,6 +192,13 @@ dev_mps = ["sglang[all_mps]", "sglang[test]"] [project.scripts] sglang = "sglang.cli.main:main" +# Rust-accelerated multimodal preprocessing (sglang.srt.multimodal._core). +# grpc is intentionally omitted here (it needs proto/tonic); ROCm only builds mm. +[[tool.setuptools-rust.ext-modules]] +target = "sglang.srt.multimodal._core" +path = "../rust/sglang-mm/Cargo.toml" +binding = "PyO3" + [tool.setuptools.package-data] "sglang" = [ "srt/**/*", diff --git a/python/sglang/jit_kernel/csrc/inkling/causal_conv1d.cuh b/python/sglang/jit_kernel/csrc/inkling/causal_conv1d.cuh new file mode 100644 index 000000000..814442e40 --- /dev/null +++ b/python/sglang/jit_kernel/csrc/inkling/causal_conv1d.cuh @@ -0,0 +1,217 @@ +// Depthwise causal conv1d (extend/prefill) with the W-1 prefix taps gathered +// directly from sconv_cache. +// +// Semantics: +// For packed token t in sequence s (bos = cu_seqlens[s], slot = safe_idx[s]) and +// tap iw in 0..W-1, shifted = t - (W-1) + iw: +// shifted >= bos (in-seq history) -> tap = x[shifted, d] +// shifted < bos, pp=shifted-bos+(W-1)>=0 -> tap = cache[slot, pp, d] +// (* cache_mask[s] when !IS_DECODE) +// else -> tap = 0 +// out[t,d] = act(sum_iw tap*weight[d,iw]) (+ x[t,d] if residual), fp32 accum. +// in_x / in_prefix are mutually exclusive, so the fp32 tap sum is bit-identical to +// the Triton bf16 add (one operand is always 0). +// +// Channel-independent control is shared by two channels packed as bf16x2. +// Each thread keeps a token strip and its prefix window in registers across taps. +#include // For TensorMatcher, SymbolicSize, SymbolicDevice +#include // For RuntimeCheck, div_ceil + +#include // For fp32_t / bf16_t aliases +#include // For LaunchKernel, SGL_DEVICE + +#include +#include + +#include +#include + +namespace { + +struct CausalConv1dParams { + const void* __restrict__ x; // [T, D] + const void* __restrict__ cache; // [max_slots, W-1, D] + const void* __restrict__ safe_idx; // int64 [nseq] cache slot per sequence + const void* __restrict__ cache_mask; // bool [nseq,1,1] raw metadata + const void* __restrict__ weight; // [D, W] + const void* __restrict__ cu; // int64 [nseq+1] packed sequence starts + const void* __restrict__ seq_idx; // int32 [T] sequence id per token + void* __restrict__ y; // [T, D] contiguous output + int64_t x_stride_t; + int64_t cache_stride_slot; + int64_t cache_stride_w; + int64_t cache_mask_stride; + int64_t weight_stride_d; + int64_t y_stride_t; + uint32_t T; + uint32_t D; +}; + +constexpr int kConvBlockT = 4; // tokens per thread strip +constexpr uint32_t kConvThreads = 256; // threads per block (each owns 2 channels) + +// blockIdx.x = token strip (BLOCK_T tokens); each thread owns channel pair (c0, c0+1). +// Requires bf16, D even, and unit channel/row-inner stride (host-checked). +template +__global__ void causal_conv1d_kernel(const __grid_constant__ CausalConv1dParams p) { + constexpr int BT = kConvBlockT; + constexpr int WIN = BT + (W - 1); + + __shared__ int s_bos[BT]; + __shared__ int s_slot[BT]; + __shared__ float s_m[BT]; + + const int T = static_cast(p.T); + const int t0 = static_cast(blockIdx.x) * BT; + + if (threadIdx.x < static_cast(BT)) { + const int j = static_cast(threadIdx.x); + const int t = t0 + j; + if (t < T) { + const int seq = static_cast(p.seq_idx)[t]; + s_bos[j] = static_cast(static_cast(p.cu)[seq]); + s_slot[j] = static_cast(static_cast(p.safe_idx)[seq]); + if constexpr (!IS_DECODE) { + s_m[j] = static_cast(p.cache_mask)[static_cast(seq) * p.cache_mask_stride] ? 1.0f : 0.0f; + } + } + } + __syncthreads(); + + const int c0 = (blockIdx.y * kConvThreads + threadIdx.x) * 2; // this thread's channel pair + if (c0 >= static_cast(p.D)) return; + + const int sxt = static_cast(p.x_stride_t); + const int syt = static_cast(p.y_stride_t); + const int swd = static_cast(p.weight_stride_d); + + const auto* xp = static_cast(p.x); + const auto* cp = static_cast(p.cache); + const auto* wp = static_cast(p.weight); + auto* yp = static_cast<__nv_bfloat16*>(p.y); + + // Window (bf16x2 per row), read once into registers. + __nv_bfloat162 xr[WIN]; +#pragma unroll + for (int i = 0; i < WIN; ++i) { + const int row = t0 - (W - 1) + i; + xr[i] = (row >= 0 && row < T) ? *reinterpret_cast(&xp[row * sxt + c0]) + : __float2bfloat162_rn(0.0f); + } + // Weight taps for the two channels (weight[c0, iw], weight[c0+1, iw]). + float2 wv[W]; +#pragma unroll + for (int iw = 0; iw < W; ++iw) { + wv[iw] = make_float2(__bfloat162float(wp[c0 * swd + iw]), __bfloat162float(wp[(c0 + 1) * swd + iw])); + } + +#pragma unroll + for (int j = 0; j < BT; ++j) { + const int t = t0 + j; + if (t >= T) break; + const int bos = s_bos[j]; + const float2 x_cur = __bfloat1622float2(xr[j + (W - 1)]); // tap iw == W-1 + + float acc0 = 0.0f, acc1 = 0.0f; +#pragma unroll + for (int iw = 0; iw < W; ++iw) { + float2 tap; + if (iw == W - 1) { + tap = x_cur; + } else { + const int shifted = t - (W - 1) + iw; // < T always + tap = (shifted >= bos) ? __bfloat1622float2(xr[j + iw]) : make_float2(0.0f, 0.0f); + const int prefix_pos = shifted - bos + (W - 1); + if (shifted < bos && prefix_pos >= 0 && prefix_pos < (W - 1)) { // rare: seq start + const int64_t coff = static_cast(s_slot[j]) * p.cache_stride_slot + + static_cast(prefix_pos) * p.cache_stride_w + static_cast(c0); + float2 pv = __bfloat1622float2(*reinterpret_cast(&cp[coff])); + if constexpr (!IS_DECODE) { + pv.x *= s_m[j]; + pv.y *= s_m[j]; + } + tap.x += pv.x; + tap.y += pv.y; + } + } + acc0 += tap.x * wv[iw].x; + acc1 += tap.y * wv[iw].y; + } + + if constexpr (USE_SILU) { + acc0 = __fdividef(acc0, 1.0f + __expf(-acc0)); // silu = x*sigmoid(x) + acc1 = __fdividef(acc1, 1.0f + __expf(-acc1)); + } + if constexpr (USE_RESIDUAL) { + acc0 += x_cur.x; + acc1 += x_cur.y; + } + *reinterpret_cast<__nv_bfloat162*>(&yp[t * syt + c0]) = __floats2bfloat162_rn(acc0, acc1); + } +} + +template +struct CausalConv1dKernel { + static void + run(tvm::ffi::TensorView x, + tvm::ffi::TensorView cache, + tvm::ffi::TensorView safe_idx, + tvm::ffi::TensorView cache_mask, + tvm::ffi::TensorView weight, + tvm::ffi::TensorView cu, + tvm::ffi::TensorView seq_idx, + tvm::ffi::TensorView y) { + using namespace host; + auto T = SymbolicSize{"T"}; + auto D = SymbolicSize{"D"}; + auto Wd = SymbolicSize{"W"}; + auto Km1 = SymbolicSize{"W_minus_1"}; + auto NS = SymbolicSize{"nseq"}; + auto dev = SymbolicDevice{}; + dev.set_options(); + Wd.set_value(W); + Km1.set_value(W - 1); + + // x may be a non-contiguous row view (stride_t arbitrary) but must be + // channel-contiguous. cache_mask is torch-bool (verify shape/device only). + TensorMatcher({T, D}).with_strides({-1, 1}).with_dtype().with_device(dev).verify(x); + TensorMatcher({-1, Km1, D}).with_dtype().with_device(dev).verify(cache); + TensorMatcher({NS}).with_dtype().with_device(dev).verify(safe_idx); + TensorMatcher({NS, 1, 1}).with_device(dev).verify(cache_mask); + TensorMatcher({D, Wd}).with_strides({-1, 1}).with_dtype().with_device(dev).verify(weight); + TensorMatcher({-1}).with_dtype().with_device(dev).verify(cu); + TensorMatcher({T}).with_dtype().with_device(dev).verify(seq_idx); + TensorMatcher({T, D}).with_strides({-1, 1}).with_dtype().with_device(dev).verify(y); + RuntimeCheck(cu.size(0) == NS.unwrap() + 1, "cu must have length nseq+1"); + RuntimeCheck(sizeof(DType) == 2, "causal_conv1d: bf16x2 kernel requires a 16-bit dtype"); + RuntimeCheck(D.unwrap() % 2 == 0, "causal_conv1d: D must be even for the bf16x2 kernel"); + RuntimeCheck(cache.stride(2) == 1, "causal_conv1d: sconv_cache must be channel-contiguous"); + + const auto params = CausalConv1dParams{ + .x = x.data_ptr(), + .cache = cache.data_ptr(), + .safe_idx = safe_idx.data_ptr(), + .cache_mask = cache_mask.data_ptr(), + .weight = weight.data_ptr(), + .cu = cu.data_ptr(), + .seq_idx = seq_idx.data_ptr(), + .y = y.data_ptr(), + .x_stride_t = x.stride(0), + .cache_stride_slot = cache.stride(0), + .cache_stride_w = cache.stride(1), + .cache_mask_stride = cache_mask.stride(0), + .weight_stride_d = weight.stride(0), + .y_stride_t = y.stride(0), + .T = static_cast(T.unwrap()), + .D = static_cast(D.unwrap()), + }; + + const uint32_t d_pairs = params.D / 2; + const dim3 grid{div_ceil(params.T, static_cast(kConvBlockT)), div_ceil(d_pairs, kConvThreads)}; + const dim3 block{kConvThreads}; + constexpr auto kernel = causal_conv1d_kernel; + LaunchKernel(grid, block, dev.unwrap())(kernel, params); + } +}; + +} // namespace diff --git a/python/sglang/jit_kernel/csrc/inkling/draft_extend_sconv.cuh b/python/sglang/jit_kernel/csrc/inkling/draft_extend_sconv.cuh new file mode 100644 index 000000000..f6ba6bff1 --- /dev/null +++ b/python/sglang/jit_kernel/csrc/inkling/draft_extend_sconv.cuh @@ -0,0 +1,147 @@ +// Fused draft-extend convolution-cache update. +// +// Speculative draft-extend: for each sequence b (slot ci = cache_indices[b]) the new +// conv state is the length-W1 window of the "virtual padded" stream +// virtual = [ sconv_cache[ci] (W1 rows) ++ hidden[b, 0:T] (T rows) ] +// starting at num_accepted_tokens[b]: new[w] = virtual[n_acc + w], w in 0..W1-1 +// n_acc + w < W1 -> sconv_cache[ci, n_acc + w] (initial state) +// n_acc + w >= W1 -> hidden[b*T + (n_acc + w - W1)] (a draft token) +// written back to sconv_cache[ci]. With tracking, the window at track_step[b] is also +// written to sconv_cache[mamba_track_indices[b]] wherever crossed[b]. +// Pure copy/select (BIT-EXACT). Init state loaded to registers before writes (RAW-safe); +// 2 channels/thread as bf16x2. Requires bf16 + even D + channel-contiguous. +#include // For TensorMatcher, SymbolicSize, SymbolicDevice +#include // For RuntimeCheck, div_ceil + +#include // For LaunchKernel, SGL_DEVICE + +#include +#include + +#include +#include + +namespace { + +struct DraftExtendParams { + const void* __restrict__ hidden; // [B*T, D], channel-contiguous + void* __restrict__ cache; // [pool, W1, D], in-place + const void* __restrict__ cache_indices; // int32 [B] + const void* __restrict__ num_accepted; // int32 [B] + const void* __restrict__ crossed; // bool [B] (DO_TRACK only) + const void* __restrict__ track_step; // int32 [B] (DO_TRACK only) + const void* __restrict__ track_indices; // int64 [B] (DO_TRACK only) + int64_t hs_stride_t; + int64_t cache_stride_slot; + int64_t cache_stride_w; + uint32_t D; + uint32_t T; // draft_token_num +}; + +constexpr uint32_t kDEThreads = 256; + +template +__global__ void draft_extend_kernel(const __grid_constant__ DraftExtendParams p) { + const int b = blockIdx.y; + const int c0 = (blockIdx.x * kDEThreads + threadIdx.x) * 2; + if (c0 >= static_cast(p.D)) return; + + const int ci = static_cast(p.cache_indices)[b]; + const auto* hp = static_cast(p.hidden); + auto* cp = static_cast<__nv_bfloat16*>(p.cache); + const int cw = static_cast(p.cache_stride_w); + const int T = static_cast(p.T); + const int b_off = b * T; // hidden row base for this sequence + const int64_t src_slot_base = static_cast(ci) * p.cache_stride_slot + c0; + + // Initial state -> registers (RAW-safe against the cache[ci] writes below). + __nv_bfloat162 init_reg[W1]; +#pragma unroll + for (int w = 0; w < W1; ++w) { + init_reg[w] = *reinterpret_cast(&cp[src_slot_base + static_cast(w) * cw]); + } + + // Select the window at `at` from the virtual stream and write it to cache[dst_base]. + auto emit = [&](int at, int64_t dst_base) { +#pragma unroll + for (int w = 0; w < W1; ++w) { + const int pos = at + w; + __nv_bfloat162 v; + if (pos < W1) { + v = init_reg[0]; +#pragma unroll + for (int src = 0; src < W1; ++src) { + if (src == pos) v = init_reg[src]; + } + } else { + const int row = b_off + (pos - W1); + v = *reinterpret_cast(&hp[static_cast(row) * p.hs_stride_t + c0]); + } + *reinterpret_cast<__nv_bfloat162*>(&cp[dst_base + static_cast(w) * cw]) = v; + } + }; + + const int n_acc = static_cast(p.num_accepted)[b]; + if constexpr (DO_TRACK) { + // Track window first (reads init_reg, distinct dst slot) then the main window. + if (static_cast(p.crossed)[b]) { + const int tstep = static_cast(p.track_step)[b]; + const int64_t tslot = static_cast(p.track_indices)[b]; + emit(tstep, tslot * p.cache_stride_slot + c0); + } + } + emit(n_acc, src_slot_base); +} + +template +struct DraftExtendSconvKernel { + static void + run(tvm::ffi::TensorView hidden, + tvm::ffi::TensorView cache, + tvm::ffi::TensorView cache_indices, + tvm::ffi::TensorView num_accepted, + int64_t draft_token_num, + tvm::ffi::TensorView crossed, + tvm::ffi::TensorView track_step, + tvm::ffi::TensorView track_indices) { + using namespace host; + auto BT = SymbolicSize{"B_times_T"}; + auto D = SymbolicSize{"D"}; + auto W1s = SymbolicSize{"W_minus_1"}; + auto B = SymbolicSize{"B"}; + auto dev = SymbolicDevice{}; + dev.set_options(); + W1s.set_value(W1); + + TensorMatcher({BT, D}).with_strides({-1, 1}).with_dtype().with_device(dev).verify(hidden); + TensorMatcher({-1, W1s, D}).with_dtype().with_device(dev).verify(cache); + TensorMatcher({B}).with_dtype().with_device(dev).verify(cache_indices); + TensorMatcher({B}).with_dtype().with_device(dev).verify(num_accepted); + RuntimeCheck(sizeof(DType) == 2, "draft_extend: bf16x2 kernel requires 16-bit dtype"); + RuntimeCheck(D.unwrap() % 2 == 0, "draft_extend: D must be even for the bf16x2 kernel"); + RuntimeCheck(cache.stride(2) == 1, "draft_extend: cache must be channel-contiguous"); + + const auto params = DraftExtendParams{ + .hidden = hidden.data_ptr(), + .cache = cache.data_ptr(), + .cache_indices = cache_indices.data_ptr(), + .num_accepted = num_accepted.data_ptr(), + .crossed = DO_TRACK ? crossed.data_ptr() : nullptr, + .track_step = DO_TRACK ? track_step.data_ptr() : nullptr, + .track_indices = DO_TRACK ? track_indices.data_ptr() : nullptr, + .hs_stride_t = hidden.stride(0), + .cache_stride_slot = cache.stride(0), + .cache_stride_w = cache.stride(1), + .D = static_cast(D.unwrap()), + .T = static_cast(draft_token_num), + }; + + const uint32_t d_pairs = params.D / 2; + const dim3 grid{div_ceil(d_pairs, kDEThreads), static_cast(B.unwrap())}; + const dim3 block{kDEThreads}; + constexpr auto kernel = draft_extend_kernel; + LaunchKernel(grid, block, dev.unwrap())(kernel, params); + } +}; + +} // namespace diff --git a/python/sglang/jit_kernel/csrc/inkling/fused_decode_update.cuh b/python/sglang/jit_kernel/csrc/inkling/fused_decode_update.cuh new file mode 100644 index 000000000..37a9aa5b5 --- /dev/null +++ b/python/sglang/jit_kernel/csrc/inkling/fused_decode_update.cuh @@ -0,0 +1,187 @@ +// Fused decode causal_conv1d, cache shift-update, and optional track copy. +// +// Decode: each token t is its own sequence (bos=t). Per token: +// conv: acc = sum_{iw // For TensorMatcher, SymbolicSize, SymbolicDevice +#include // For RuntimeCheck, div_ceil + +#include // For LaunchKernel, SGL_DEVICE + +#include +#include + +#include +#include + +namespace { + +struct DecodeUpdateParams { + const void* __restrict__ x; // [T, D], channel-contiguous + void* __restrict__ cache; // [pool, W-1, D], in-place update + const void* __restrict__ cache_indices; // int32 [T] (PAD == -1) + const void* __restrict__ cache_mask; // bool [T] + const void* __restrict__ weight; // [D, W] + void* __restrict__ y; // [T, D] contiguous output + const void* __restrict__ track_mask; // bool [T] (DO_TRACK only) + const void* __restrict__ track_indices; // int64 [T] (DO_TRACK only) + int64_t x_stride_t; + int64_t cache_stride_slot; + int64_t cache_stride_w; + int64_t weight_stride_d; + int64_t y_stride_t; + int64_t track_idx_stride; + uint32_t D; +}; + +constexpr uint32_t kDecThreads = 256; +constexpr int kPadSlot = -1; + +template +__global__ void fused_decode_update_kernel(const __grid_constant__ DecodeUpdateParams p) { + constexpr int W1 = W - 1; // number of cached history taps / conv-state rows + const int t = blockIdx.y; + const int ci = static_cast(p.cache_indices)[t]; + const bool valid = ci != kPadSlot; + const int slot = valid ? ci : 0; // clamp: PAD lanes still emit y (discarded), no cache write + + const int c0 = (blockIdx.x * kDecThreads + threadIdx.x) * 2; + if (c0 >= static_cast(p.D)) return; + + const float cm = static_cast(p.cache_mask)[t] ? 1.0f : 0.0f; + const auto* xp = static_cast(p.x); + const auto* wp = static_cast(p.weight); + auto* cp = static_cast<__nv_bfloat16*>(p.cache); + auto* yp = static_cast<__nv_bfloat16*>(p.y); + const int cw = static_cast(p.cache_stride_w); + const int swd = static_cast(p.weight_stride_d); + const int64_t cache_base = static_cast(slot) * p.cache_stride_slot + c0; + + // History taps -> registers (RAW-safe against the update writes below). + __nv_bfloat162 hist[W1]; +#pragma unroll + for (int w = 0; w < W1; ++w) { + hist[w] = *reinterpret_cast(&cp[cache_base + static_cast(w) * cw]); + } + const __nv_bfloat162 xv = *reinterpret_cast(&xp[static_cast(t) * p.x_stride_t + c0]); + const float2 xf = __bfloat1622float2(xv); + + float2 wv[W]; +#pragma unroll + for (int iw = 0; iw < W; ++iw) { + wv[iw] = make_float2(__bfloat162float(wp[c0 * swd + iw]), __bfloat162float(wp[(c0 + 1) * swd + iw])); + } + + // ---- conv (fp32 accum): W-1 cached taps (gated by cache_mask) + current token ---- + float acc0 = 0.0f, acc1 = 0.0f; +#pragma unroll + for (int iw = 0; iw < W1; ++iw) { + const float2 h = __bfloat1622float2(hist[iw]); + acc0 += h.x * cm * wv[iw].x; + acc1 += h.y * cm * wv[iw].y; + } + acc0 += xf.x * wv[W1].x; + acc1 += xf.y * wv[W1].y; + if constexpr (USE_SILU) { + acc0 = __fdividef(acc0, 1.0f + __expf(-acc0)); + acc1 = __fdividef(acc1, 1.0f + __expf(-acc1)); + } + if constexpr (USE_RESIDUAL) { + acc0 += xf.x; + acc1 += xf.y; + } + *reinterpret_cast<__nv_bfloat162*>(&yp[static_cast(t) * p.y_stride_t + c0]) = + __floats2bfloat162_rn(acc0, acc1); + + if (!valid) return; + + // ---- update: shift state left (gated by cache_mask), append current token ---- + const __nv_bfloat162 zero = __float2bfloat162_rn(0.0f); + int64_t track_base = 0; + bool do_tr = false; + if constexpr (DO_TRACK) { + do_tr = static_cast(p.track_mask)[t]; + if (do_tr) { + const int64_t tslot = static_cast(p.track_indices)[static_cast(t) * p.track_idx_stride]; + track_base = tslot * p.cache_stride_slot + c0; + } + } +#pragma unroll + for (int iw = 0; iw < W1; ++iw) { + const __nv_bfloat162 nv = (iw < W1 - 1) ? ((cm != 0.0f) ? hist[iw + 1] : zero) : xv; + *reinterpret_cast<__nv_bfloat162*>(&cp[cache_base + static_cast(iw) * cw]) = nv; + if constexpr (DO_TRACK) { + if (do_tr) { + *reinterpret_cast<__nv_bfloat162*>(&cp[track_base + static_cast(iw) * cw]) = nv; + } + } + } +} + +template +struct FusedDecodeUpdateKernel { + static void + run(tvm::ffi::TensorView x, + tvm::ffi::TensorView cache, + tvm::ffi::TensorView cache_indices, + tvm::ffi::TensorView cache_mask, + tvm::ffi::TensorView weight, + tvm::ffi::TensorView y, + tvm::ffi::TensorView track_mask, + tvm::ffi::TensorView track_indices) { + using namespace host; + auto T = SymbolicSize{"T"}; + auto D = SymbolicSize{"D"}; + auto Wd = SymbolicSize{"W"}; + auto W1s = SymbolicSize{"W_minus_1"}; + auto dev = SymbolicDevice{}; + dev.set_options(); + Wd.set_value(W); + W1s.set_value(W - 1); + + TensorMatcher({T, D}).with_strides({-1, 1}).with_dtype().with_device(dev).verify(x); + TensorMatcher({-1, W1s, D}).with_dtype().with_device(dev).verify(cache); + TensorMatcher({T}).with_dtype().with_device(dev).verify(cache_indices); + TensorMatcher({T}).with_device(dev).verify(cache_mask); + TensorMatcher({D, Wd}).with_strides({-1, 1}).with_dtype().with_device(dev).verify(weight); + TensorMatcher({T, D}).with_strides({-1, 1}).with_dtype().with_device(dev).verify(y); + RuntimeCheck(sizeof(DType) == 2, "fused_decode: bf16x2 kernel requires 16-bit dtype"); + RuntimeCheck(D.unwrap() % 2 == 0, "fused_decode: D must be even for the bf16x2 kernel"); + RuntimeCheck(cache.stride(2) == 1, "fused_decode: cache must be channel-contiguous"); + + const auto params = DecodeUpdateParams{ + .x = x.data_ptr(), + .cache = cache.data_ptr(), + .cache_indices = cache_indices.data_ptr(), + .cache_mask = cache_mask.data_ptr(), + .weight = weight.data_ptr(), + .y = y.data_ptr(), + .track_mask = DO_TRACK ? track_mask.data_ptr() : nullptr, + .track_indices = DO_TRACK ? track_indices.data_ptr() : nullptr, + .x_stride_t = x.stride(0), + .cache_stride_slot = cache.stride(0), + .cache_stride_w = cache.stride(1), + .weight_stride_d = weight.stride(0), + .y_stride_t = y.stride(0), + .track_idx_stride = DO_TRACK ? track_indices.stride(0) : 0, + .D = static_cast(D.unwrap()), + }; + + const uint32_t d_pairs = params.D / 2; + const dim3 grid{div_ceil(d_pairs, kDecThreads), static_cast(T.unwrap())}; + const dim3 block{kDecThreads}; + constexpr auto kernel = fused_decode_update_kernel; + LaunchKernel(grid, block, dev.unwrap())(kernel, params); + } +}; + +} // namespace diff --git a/python/sglang/jit_kernel/csrc/inkling/gather_scatter_sconv.cuh b/python/sglang/jit_kernel/csrc/inkling/gather_scatter_sconv.cuh new file mode 100644 index 000000000..840654ae7 --- /dev/null +++ b/python/sglang/jit_kernel/csrc/inkling/gather_scatter_sconv.cuh @@ -0,0 +1,109 @@ +// Fused gather and scatter into sconv_cache. +// +// For each batch element b where mask[b] is true, copy the W1 = W-1 token rows +// hidden_states[track_idx[b, w]] -> sconv_cache[dst[b], w] (w = 0..W1-1). +// Masked-out lanes are left untouched. Pure copy (no arithmetic) => BIT-EXACT. +// 2 channels/thread packed as bf16x2. Requires bf16 + even D + channel-contiguous. +#include // For TensorMatcher, SymbolicSize, SymbolicDevice +#include // For RuntimeCheck, div_ceil + +#include // For LaunchKernel, SGL_DEVICE + +#include +#include + +#include +#include + +namespace { + +struct GatherScatterParams { + const void* __restrict__ hidden; // [T, D], channel-contiguous + void* __restrict__ cache; // [pool, W1, D], in-place scatter target + const void* __restrict__ track_idx; // int32 [B, W1] + const void* __restrict__ mask; // bool [B] + const void* __restrict__ dst; // int64 [B] + int64_t hs_stride_t; + int64_t cache_stride_slot; + int64_t cache_stride_w; + int64_t track_stride_b; + int64_t track_stride_w; + int64_t dst_stride_b; + uint32_t D; +}; + +constexpr uint32_t kGSThreads = 256; + +template +__global__ void gather_scatter_kernel(const __grid_constant__ GatherScatterParams p) { + const int b = blockIdx.y; + if (!static_cast(p.mask)[b]) return; // masked-out lane: untouched + + const int c0 = (blockIdx.x * kGSThreads + threadIdx.x) * 2; + if (c0 >= static_cast(p.D)) return; + + const auto* hp = static_cast(p.hidden); + auto* cp = static_cast<__nv_bfloat16*>(p.cache); + const int64_t dst_slot = static_cast(p.dst)[static_cast(b) * p.dst_stride_b]; + const int64_t cache_base = dst_slot * p.cache_stride_slot + c0; + const int64_t track_base = static_cast(b) * p.track_stride_b; + +#pragma unroll + for (int w = 0; w < W1; ++w) { + const int64_t src_t = + static_cast(p.track_idx)[track_base + static_cast(w) * p.track_stride_w]; + const __nv_bfloat162 v = *reinterpret_cast(&hp[src_t * p.hs_stride_t + c0]); + *reinterpret_cast<__nv_bfloat162*>(&cp[cache_base + static_cast(w) * p.cache_stride_w]) = v; + } +} + +template +struct GatherScatterSconvKernel { + static void + run(tvm::ffi::TensorView hidden, + tvm::ffi::TensorView cache, + tvm::ffi::TensorView track_idx, + tvm::ffi::TensorView mask, + tvm::ffi::TensorView dst) { + using namespace host; + auto T = SymbolicSize{"T"}; + auto D = SymbolicSize{"D"}; + auto W1s = SymbolicSize{"W_minus_1"}; + auto B = SymbolicSize{"B"}; + auto dev = SymbolicDevice{}; + dev.set_options(); + W1s.set_value(W1); + + TensorMatcher({T, D}).with_strides({-1, 1}).with_dtype().with_device(dev).verify(hidden); + TensorMatcher({-1, W1s, D}).with_dtype().with_device(dev).verify(cache); + TensorMatcher({B, W1s}).with_dtype().with_device(dev).verify(track_idx); + TensorMatcher({B}).with_device(dev).verify(mask); + TensorMatcher({B}).with_dtype().with_device(dev).verify(dst); + RuntimeCheck(sizeof(DType) == 2, "gather_scatter: bf16x2 kernel requires 16-bit dtype"); + RuntimeCheck(D.unwrap() % 2 == 0, "gather_scatter: D must be even for the bf16x2 kernel"); + RuntimeCheck(cache.stride(2) == 1, "gather_scatter: cache must be channel-contiguous"); + + const auto params = GatherScatterParams{ + .hidden = hidden.data_ptr(), + .cache = cache.data_ptr(), + .track_idx = track_idx.data_ptr(), + .mask = mask.data_ptr(), + .dst = dst.data_ptr(), + .hs_stride_t = hidden.stride(0), + .cache_stride_slot = cache.stride(0), + .cache_stride_w = cache.stride(1), + .track_stride_b = track_idx.stride(0), + .track_stride_w = track_idx.stride(1), + .dst_stride_b = dst.stride(0), + .D = static_cast(D.unwrap()), + }; + + const uint32_t d_pairs = params.D / 2; + const dim3 grid{div_ceil(d_pairs, kGSThreads), static_cast(B.unwrap())}; + const dim3 block{kGSThreads}; + constexpr auto kernel = gather_scatter_kernel; + LaunchKernel(grid, block, dev.unwrap())(kernel, params); + } +}; + +} // namespace diff --git a/python/sglang/jit_kernel/csrc/inkling/inkling_all_reduce.cuh b/python/sglang/jit_kernel/csrc/inkling/inkling_all_reduce.cuh new file mode 100644 index 000000000..1441af21f --- /dev/null +++ b/python/sglang/jit_kernel/csrc/inkling/inkling_all_reduce.cuh @@ -0,0 +1,672 @@ +// Two-shot (reduce-scatter + all-gather) all-reduce over a torch +// symmetric-memory buffer. +// +// It operates IN PLACE on the peer symm buffers: the producer (e.g. the wo_ud / +// MoE-combine GEMM) writes its local shard straight into THIS rank's symm buffer +// (via get_ar_buffer), so there is no stage-in copy; the reduced result is left +// in the buffer and handed back to Python as a view, so there is no copy-out. +// +// Correctness (two-shot is race-safe in place): rank r owns the disjoint vec +// slice [local_vec_start, local_vec_finish); it reads every peer's slice, sums, +// and broadcasts the sum back to every peer's slice. Only rank r ever writes +// slice S_r (in any buffer), so there is no write-write conflict, and each +// per-element load completes before its store (data dependency). +// +// Two variants: +// * ..._kernel (v1): no in-kernel sync; the caller fences with the symm-mem +// handle's barrier() on each side (3 launches total). +// * ..._fused_kernel (v2): an in-kernel per-block system barrier (entry: +// producers done + visible; exit: broadcasts done + visible), so the whole +// all-reduce is a single launch. The barrier uses a DEDICATED symmetric +// flags buffer (independent of torch's signal pad, so no interference with +// multimem) and a device-resident monotonic epoch counter per block, which +// keeps advancing across launches -- including CUDA-graph replays -- so +// flags never go stale (spin is `flag < epoch`, epoch strictly increasing). +// +// Fusion seam: the reduced `result` Storage below is where an epilogue (RMSNorm +// / short-conv / bias) plugs in -- applied in registers before the broadcast +// store, so the normed/conv'd result never makes an extra HBM round trip. + +#include +#include + +#include +#include +#include +#include + +#include + +#include "inkling_ar_barrier.cuh" +#include +#include +#include +#include +#include + +namespace { + +template +struct InklingAllReduceTrait { + static constexpr uint32_t kVecSize = 16 / (sizeof(DType) * 2); + static constexpr uint32_t kElemsPerVec = kVecSize * 2; + using DType2 = packed_t; + using Storage = device::AlignedVector; + static_assert(sizeof(Storage) == 16 && alignof(Storage) == 16, "Storage must be 16B"); + static_assert(std::has_single_bit(kNumGPU), "kNumGPU must be a power of two"); +}; + +// Register-level fused add of two vecs (fp32 math, ONE round to DType) -- the +// exact numerics of torch.add on two bf16 tensors, so fusing the shared-expert +// partials stays bit-identical to the unfused {torch.add -> AR} chain. +template +__device__ __forceinline__ typename InklingAllReduceTrait::Storage add_vec_rn( + const typename InklingAllReduceTrait::Storage& a, + const typename InklingAllReduceTrait::Storage& b) { + using namespace device; + using Trait = InklingAllReduceTrait; // kNumGPU-independent + using DType2 = typename Trait::DType2; + typename Trait::Storage out; +#pragma unroll + for (uint32_t j = 0; j < Trait::kVecSize; ++j) { + const fp32x2_t x = cast(a[j]); + const fp32x2_t y = cast(b[j]); + fp32x2_t s; + s.x = x.x + y.x; + s.y = x.y + y.y; + out[j] = cast(s); + } + return out; +} + +// Fused-shared PROLOGUE for the pull-based kernels (v2/v3/v3b/v4): fold this +// rank's LOCAL shared-expert partials into its own symm input region before +// the ENTRY barrier, so every peer's ld_reduce / peer-read sums +// (routed_r + shared_r) across ranks. The entry barrier must then run in +// publish mode (grid_system_barrier, publish_writes=true): these are in-kernel +// stores by ALL CTAs, not prior-kernel stores, so each CTA has to +// system-publish them before the leader's release. (The per-block barrier +// cannot order this: block b's fold range is not the range peer block b +// reads.) The push-based kernels (v5 & the fused decode family) instead fold +// in registers at the push -- see the shared branch in the push loop. +template +__device__ __forceinline__ void +fold_shared_local(DType* __restrict__ buf, const DType* __restrict__ shared, uint32_t num_items) { + using Trait = InklingAllReduceTrait; + using Storage = typename Trait::Storage; + const uint32_t total_vec = num_items / Trait::kElemsPerVec; + const uint32_t stride = gridDim.x * blockDim.x; + for (uint32_t v = blockIdx.x * blockDim.x + threadIdx.x; v < total_vec; v += stride) { + Storage a, b; + a.load(buf, v); + b.load(shared, v); + add_vec_rn(a, b).store(buf, v); + } +} + +// Two-shot partition: contiguous, warp-aligned vec slice per rank. Returns +// {start, count} in vec units (empty for trailing ranks when the range is small). +template +__device__ __forceinline__ uint2 rank_vec_slice(uint32_t rank, uint32_t num_items) { + using namespace device; + using Trait = InklingAllReduceTrait; + const uint32_t total_vec = num_items / Trait::kElemsPerVec; + const uint32_t vec_per_rank = div_ceil(div_ceil(total_vec, kNumGPU), kWarpThreads) * kWarpThreads; + const uint32_t start = min(rank * vec_per_rank, total_vec); + const uint32_t finish = min(start + vec_per_rank, total_vec); + return {start, finish - start}; +} + +// Offset each peer pointer to this rank's slice, and return the slice's local +// vec count. +template +__device__ __forceinline__ uint32_t +slice_setup(DType* (&input)[kNumGPU], void* const* peer_ptrs, uint32_t rank, uint32_t num_items) { + using Trait = InklingAllReduceTrait; + const uint2 slice = rank_vec_slice(rank, num_items); + const uint32_t base = slice.x * Trait::kElemsPerVec; +#pragma unroll + for (uint32_t i = 0; i < kNumGPU; ++i) + input[i] = static_cast(peer_ptrs[i]) + base; + return slice.y; // local vec count +} + +template +__device__ __forceinline__ void two_shot_reduce_local(DType* (&input)[kNumGPU], uint32_t local_vecs) { + using namespace device; + using Trait = InklingAllReduceTrait; + using Storage = typename Trait::Storage; + using DType2 = typename Trait::DType2; + constexpr uint32_t kVecSize = Trait::kVecSize; + const uint32_t stride = gridDim.x * blockDim.x; + for (uint32_t v = blockIdx.x * blockDim.x + threadIdx.x; v < local_vecs; v += stride) { + Storage s[kNumGPU]; +#pragma unroll + for (uint32_t i = 0; i < kNumGPU; ++i) + s[i].load(input[i], v); + Storage result; +#pragma unroll + for (uint32_t j = 0; j < kVecSize; ++j) { + fp32x2_t acc = cast(s[0][j]); +#pragma unroll + for (uint32_t i = 1; i < kNumGPU; ++i) { + const fp32x2_t x = cast(s[i][j]); + acc.x += x.x; + acc.y += x.y; + } + result[j] = cast(acc); // <-- EPILOGUE SEAM + } +#pragma unroll + for (uint32_t i = 0; i < kNumGPU; ++i) + result.store(input[i], v); + } +} + +// v1: no in-kernel barrier (caller fences via hdl.barrier()). +template +__global__ __launch_bounds__(1024, 1) void inkling_two_shot_all_reduce_kernel( + void* const* __restrict__ peer_ptrs, const uint32_t rank, const uint32_t num_items) { + DType* input[kNumGPU]; + const uint32_t local_vecs = slice_setup(input, peer_ptrs, rank, num_items); + two_shot_reduce_local(input, local_vecs); +} + +// v2: single-launch, fused entry + exit system barrier. `shared` (optional): +// this rank's LOCAL shared-expert partials, folded into its own buffer before +// the entry barrier (which then must publish -- see fold_shared_local). +template +__global__ __launch_bounds__(1024, 1) void inkling_two_shot_all_reduce_fused_kernel( + void* const* __restrict__ peer_ptrs, + void* const* __restrict__ flag_ptrs, + uint32_t* __restrict__ state, + const DType* __restrict__ shared, + const uint32_t rank, + const uint32_t num_items) { + DType* input[kNumGPU]; + const uint32_t local_vecs = slice_setup(input, peer_ptrs, rank, num_items); + if (shared != nullptr) { + fold_shared_local(static_cast(peer_ptrs[rank]), shared, num_items); + } + // ENTRY: producers done + visible (publish the fold's in-kernel stores too). + inkling_ar::grid_system_barrier(state, flag_ptrs, rank, 0, /*publish_writes=*/shared != nullptr); + two_shot_reduce_local(input, local_vecs); + inkling_ar::grid_system_barrier( + state, flag_ptrs, rank, 1, /*publish_writes=*/true); // EXIT: broadcasts done + visible +} + +// Multimem one-shot all-reduce: uses the NVLink multicast ld_reduce/st hardware +// instructions on the symm buffer's multicast pointer -- the same in-switch +// reduce torch's multimem_all_reduce_ uses -- so it matches multimem for the +// tiny, latency-bound decode messages where two-shot's N peer reads lose. Reduce +// is one transaction (hardware sums all GPUs); scatter partition keeps the store +// traffic minimal. bf16-only (multimem.add supports .bf16x2 on sm90/sm100). +// kPerBlockBarrier swaps both barriers for block_system_barrier (per-block +// peer handshake, no grid funnel). Correct for the two-shot too: any peer +// block's ENTRY signal proves that peer's producer kernel completed (kernel +// serialization on its stream), and kernel end is a grid-wide join, so my +// per-block EXIT waits compose into "every peer block's broadcasts done" +// before my consumer can run. The two calls share the per-block epoch slot +// (it just advances twice per launch). +template +__global__ __launch_bounds__(1024, 1) void inkling_multimem_one_shot_fused_kernel( + DType* __restrict__ mc_ptr, // multicast base pointer (covers all peers) + DType* __restrict__ local_ptr, // this rank's LOCAL base of the same buffer + void* const* __restrict__ flag_ptrs, + uint32_t* __restrict__ state, + const DType* __restrict__ shared, // optional LOCAL shared-expert partials + const uint32_t rank, + const uint32_t num_items) { + using namespace device; + using Trait = InklingAllReduceTrait; + static_assert(std::is_same_v, "multimem.add path is bf16-only"); + constexpr uint32_t kElemsPerVec = Trait::kElemsPerVec; // 8 bf16 = 16 B + + const uint2 slice = rank_vec_slice(rank, num_items); + const uint32_t local_vecs = slice.y; + DType* mc = mc_ptr + slice.x * kElemsPerVec; + + if (shared != nullptr) { + // Fold covers the FULL range while each peer ld_reduces only its slice, so + // the per-block handshake cannot order it -- use the publishing grid + // barrier for entry even in v3b (exit stays per-block). + fold_shared_local(local_ptr, shared, num_items); + inkling_ar::grid_system_barrier(state, flag_ptrs, rank, 0, /*publish_writes=*/true); + } else if constexpr (kPerBlockBarrier) { + inkling_ar::block_system_barrier(state, flag_ptrs, rank); // ENTRY + } else { + inkling_ar::grid_system_barrier( + state, flag_ptrs, rank, 0, /*publish_writes=*/false); // ENTRY: producers done + visible + } + const uint32_t stride = gridDim.x * blockDim.x; + for (uint32_t v = blockIdx.x * blockDim.x + threadIdx.x; v < local_vecs; v += stride) { + DType* addr = mc + v * kElemsPerVec; // 16 B, 16-B aligned + uint32_t r0, r1, r2, r3; + // hardware reduce across all GPUs mapped to the multicast region. + asm volatile("multimem.ld_reduce.relaxed.sys.global.add.v4.bf16x2 {%0,%1,%2,%3}, [%4];" + : "=r"(r0), "=r"(r1), "=r"(r2), "=r"(r3) + : "l"(addr)); + // <-- EPILOGUE SEAM (norm / sconv / bias on {r0..r3} before broadcast) + // broadcast the reduced slice to every GPU. + asm volatile( + "multimem.st.relaxed.sys.global.v4.bf16x2 [%0], {%1,%2,%3,%4};" ::"l"(addr), "r"(r0), "r"(r1), "r"(r2), "r"(r3) + : "memory"); + } + if constexpr (kPerBlockBarrier) { + inkling_ar::block_system_barrier(state, flag_ptrs, rank); // EXIT (release signal publishes) + } else { + inkling_ar::grid_system_barrier( + state, flag_ptrs, rank, 1, /*publish_writes=*/true); // EXIT: broadcasts done + visible + } +} + +// One-shot PUSH all-reduce (v5): each rank multicast-STORES its full input into +// its per-rank slot of a symmetric staging area (the NVSwitch replicates the +// slot to every GPU), ONE grid barrier waits for all pushes to land, then each +// rank reduces the N staged shards LOCALLY (fp32 accum) into a LOCAL output. +// Single barrier total: the push needs no entry barrier (it publishes only this +// rank's own producer data, stream-ordered locally) and the local output needs +// no exit barrier. Staging reuse is caller-managed (A/B rotation, like v4's +// input; the next AR's barrier proves peers consumed the old buffer). +// +// vs v3/mm (two-shot): drops one full cross-GPU barrier round trip -- wins the +// latency-bound band. vs v4 (full one-shot ld_reduce): switch REPLICATION is +// cheap where the switch's reduce engine serializes N redundant full-range +// reduces, so this scales past v4's 2-row ceiling. Fabric cost: n egress, +// (N-1)*n ingress per GPU; local HBM/L2: N*n read + n write. Like v4, each rank +// holds the FULL row at the epilogue seam (natural RMSNorm-fusion base). +// bf16-only (multimem.st .bf16x2). +// +// kPerBlockBarrier selects block_system_barrier (per-block peer handshake, no +// grid funnel -- the multi-block latency winner) over the single-leader grid +// barrier. Safe here because the reduce loop reads exactly the vec ranges the +// blockIdx-matched pushes wrote. +template +__global__ __launch_bounds__(1024, 1) void inkling_multimem_push_oneshot_kernel( + const DType* __restrict__ in_ptr, // local input (producer's partial sums) + DType* __restrict__ mc_stage_ptr, // multicast staging base (slot r at r*num_items) + const DType* __restrict__ stage_ptr, // this GPU's LOCAL view of the staging base + DType* __restrict__ out_ptr, // local output + void* const* __restrict__ flag_ptrs, + uint32_t* __restrict__ state, + const DType* __restrict__ shared, // optional LOCAL shared-expert partials + const uint32_t rank, + const uint32_t num_items) { + using namespace device; + using Trait = InklingAllReduceTrait; + static_assert(std::is_same_v, "multimem path is bf16-only"); + constexpr uint32_t kElemsPerVec = Trait::kElemsPerVec; // 8 bf16 = 16 B + const uint32_t total_vec = num_items / kElemsPerVec; + const uint32_t stride = gridDim.x * blockDim.x; + + // Phase 1: push. One multicast store per vec; the switch fans it out to every + // GPU's replica of slot `rank` (including our own). With `shared`, the + // shared-expert partials fold into the pushed value in registers (fp32 add, + // one bf16 round -- torch.add numerics) at ZERO extra fabric or HBM traffic; + // both barrier flavors stay valid because push/reduce mappings are unchanged. + DType* slot = mc_stage_ptr + rank * num_items; + if (shared != nullptr) { + using Storage = typename Trait::Storage; + for (uint32_t v = blockIdx.x * blockDim.x + threadIdx.x; v < total_vec; v += stride) { + Storage a, b; + a.load(in_ptr, v); + b.load(shared, v); + const Storage s = add_vec_rn(a, b); + const uint4 d = *reinterpret_cast(&s); + asm volatile("multimem.st.relaxed.sys.global.v4.bf16x2 [%0], {%1,%2,%3,%4};" ::"l"(slot + v * kElemsPerVec), + "r"(d.x), + "r"(d.y), + "r"(d.z), + "r"(d.w) + : "memory"); + } + } else { + for (uint32_t v = blockIdx.x * blockDim.x + threadIdx.x; v < total_vec; v += stride) { + const uint4 d = *reinterpret_cast(in_ptr + v * kElemsPerVec); + asm volatile("multimem.st.relaxed.sys.global.v4.bf16x2 [%0], {%1,%2,%3,%4};" ::"l"(slot + v * kElemsPerVec), + "r"(d.x), + "r"(d.y), + "r"(d.z), + "r"(d.w) + : "memory"); + } + } + + // Single barrier: publish our pushes and wait until every rank's pushes for + // OUR ranges have landed in this GPU's local staging copy. + if constexpr (kPerBlockBarrier) { + inkling_ar::block_system_barrier(state, flag_ptrs, rank); + } else { + inkling_ar::grid_system_barrier(state, flag_ptrs, rank, 0, /*publish_writes=*/true); + } + + // Phase 2: local reduce of the N staged shards -- all-local reads (the pushes + // just landed in L2), fp32 accumulation. + using Storage = typename Trait::Storage; + using DType2 = typename Trait::DType2; + constexpr uint32_t kVecSize = Trait::kVecSize; + for (uint32_t v = blockIdx.x * blockDim.x + threadIdx.x; v < total_vec; v += stride) { + Storage s[kNumGPU]; +#pragma unroll + for (uint32_t i = 0; i < kNumGPU; ++i) + s[i].load(stage_ptr + i * num_items, v); + Storage result; +#pragma unroll + for (uint32_t j = 0; j < kVecSize; ++j) { + fp32x2_t acc = cast(s[0][j]); +#pragma unroll + for (uint32_t i = 1; i < kNumGPU; ++i) { + const fp32x2_t x = cast(s[i][j]); + acc.x += x.x; + acc.y += x.y; + } + result[j] = cast(acc); // <-- EPILOGUE SEAM (full row on-rank) + } + result.store(out_ptr, v); + } +} + +// Full one-shot: every rank ld_reduces the ENTIRE range (multicast hardware sum +// -> full result), writing it to a LOCAL output buffer. No broadcast and NO exit +// barrier -- the result is complete on this rank, and input-buffer reuse is the +// caller's responsibility (double-buffer the input). Halving the barrier count +// wins for tiny, latency-bound (decode) messages. bf16-only. +template +__global__ __launch_bounds__(1024, 1) void inkling_multimem_full_oneshot_kernel( + DType* __restrict__ mc_ptr, // multicast input base (covers all peers) + DType* __restrict__ local_in_ptr, // this rank's LOCAL base of the input + DType* __restrict__ out_ptr, // local output base + void* const* __restrict__ flag_ptrs, + uint32_t* __restrict__ state, + const DType* __restrict__ shared, // optional LOCAL shared-expert partials + const uint32_t rank, + const uint32_t num_items) { + using namespace device; + using Trait = InklingAllReduceTrait; + static_assert(std::is_same_v, "multimem.add path is bf16-only"); + constexpr uint32_t kElemsPerVec = Trait::kElemsPerVec; // 8 bf16 = 16 B + const uint32_t total_vec = num_items / kElemsPerVec; + + if (shared != nullptr) { + // Fold into this rank's (double-buffered) input region; the publishing + // entry barrier then orders it for every peer's ld_reduce. v4 fires only + // for 1-2 rows, so the extra local pass is negligible next to the + // torch.add launch it replaces. + fold_shared_local(local_in_ptr, shared, num_items); + } + inkling_ar::grid_system_barrier( + state, flag_ptrs, rank, 0, /*publish_writes=*/shared != nullptr); // ENTRY only (single barrier) + const uint32_t stride = gridDim.x * blockDim.x; + for (uint32_t v = blockIdx.x * blockDim.x + threadIdx.x; v < total_vec; v += stride) { + DType* in = mc_ptr + v * kElemsPerVec; + uint32_t r0, r1, r2, r3; + asm volatile("multimem.ld_reduce.relaxed.sys.global.add.v4.bf16x2 {%0,%1,%2,%3}, [%4];" + : "=r"(r0), "=r"(r1), "=r"(r2), "=r"(r3) + : "l"(in)); + // <-- EPILOGUE SEAM (norm / sconv / bias on {r0..r3} before the local store) + *reinterpret_cast(out_ptr + v * kElemsPerVec) = make_uint4(r0, r1, r2, r3); + } + // NO exit barrier: result is local & complete; input reuse is caller-managed. +} + +// Blocks needed to cover this rank's two-shot slice (v1/v2/v3 partition). +template +uint32_t work_num_blocks(uint32_t n, uint32_t block_size) { + using Trait = InklingAllReduceTrait; + const uint32_t total_vec = n / Trait::kElemsPerVec; + const uint32_t vec_per_rank = + host::div_ceil(host::div_ceil(total_vec, kNumGPU), device::kWarpThreads) * device::kWarpThreads; + return max(1u, host::div_ceil(vec_per_rank, block_size)); +} + +// Blocks needed to cover the FULL vec range (the full one-shot kernel reads +// the entire range on every rank, not a per-rank slice). +template +uint32_t full_range_num_blocks(uint32_t n, uint32_t block_size) { + constexpr uint32_t kElemsPerVec = InklingAllReduceTrait::kElemsPerVec; // kNumGPU-independent + return max(1u, host::div_ceil(n / kElemsPerVec, block_size)); +} + +// Max blocks that are simultaneously resident for `kernel` at `block_size`. +// The grid-level barrier REQUIRES all launched blocks to be co-resident (the +// leader waits for every block to arrive); launching more would deadlock, so +// the fused kernels cap their grid at this. Small messages need far fewer. +// Cached per (kernel, block_size, device): the occupancy query costs ~a few us +// on every eager launch of a latency-bound AR otherwise. +template +uint32_t max_resident_blocks(Kernel kernel, uint32_t block_size, DLDevice device) { + using namespace host; + static std::mutex mu; + static std::unordered_map cache; + const uint64_t key = (std::bit_cast(reinterpret_cast(kernel)) << 12) ^ + (static_cast(block_size) << 8) ^ static_cast(device.device_id); + { + std::lock_guard lk(mu); + if (auto it = cache.find(key); it != cache.end()) return it->second; + } + int sm_count = 0; + cudaDeviceGetAttribute(&sm_count, cudaDevAttrMultiProcessorCount, device.device_id); + RuntimeCheck(sm_count > 0, "failed to query multiProcessorCount"); + const uint32_t bps = runtime::get_blocks_per_sm(kernel, block_size); + RuntimeCheck(bps > 0, "kernel has zero occupancy at block_size ", block_size); + const uint32_t result = static_cast(sm_count) * bps; + std::lock_guard lk(mu); + cache.emplace(key, result); + return result; +} + +// Optional shared-expert partials: numel == 0 -> disabled (nullptr); else a +// LOCAL contiguous tensor covering num_items, folded in-kernel. +template +const DType* shared_ptr_or_null(tvm::ffi::TensorView shared, int64_t num_items) { + using namespace host; + if (shared.numel() == 0) return nullptr; + RuntimeCheck(shared.IsContiguous(), "shared must be contiguous"); + RuntimeCheck(is_type(shared.dtype()), "shared dtype mismatch"); + RuntimeCheck(shared.numel() >= num_items, "shared smaller than num_items"); + RuntimeCheck(std::bit_cast(shared.data_ptr()) % 16 == 0, "shared not 16B aligned"); + return reinterpret_cast(shared.data_ptr()); +} + +template +void validate(tvm::ffi::TensorView buf, int64_t peer_ptrs_dev, int64_t rank, int64_t num_items, uint32_t& n) { + using namespace host; + using Trait = InklingAllReduceTrait; + n = static_cast(num_items); + RuntimeCheck(buf.IsContiguous(), "buffer must be contiguous"); + RuntimeCheck(buf.device().device_type == kDLCUDA, "buffer must be on a CUDA device"); + RuntimeCheck(is_type(buf.dtype()), "buffer dtype mismatch"); + RuntimeCheck(static_cast(n) == num_items, "num_items exceeds 4G"); + RuntimeCheck(buf.numel() >= num_items, "buffer smaller than num_items"); + RuntimeCheck(n % Trait::kElemsPerVec == 0, "num_items must be a multiple of ", Trait::kElemsPerVec); + RuntimeCheck(std::bit_cast(buf.data_ptr()) % 16 == 0, "buffer not 16B aligned"); + RuntimeCheck(peer_ptrs_dev != 0, "peer_ptrs_dev is null"); + RuntimeCheck(rank >= 0 && rank < kNumGPU, "rank out of range"); +} + +template +void inkling_two_shot_all_reduce( + tvm::ffi::TensorView local_buffer, int64_t peer_ptrs_dev, int64_t rank, int64_t num_items) { + using namespace host; + uint32_t n; + validate(local_buffer, peer_ptrs_dev, rank, num_items, n); + const auto device = local_buffer.device(); + const uint32_t num_blocks = work_num_blocks(n, 1024u); // no in-kernel barrier -> uncapped + const auto stream = LaunchKernel::resolve_device(device); + LaunchKernel(num_blocks, 1024u, stream)( + inkling_two_shot_all_reduce_kernel, + reinterpret_cast(peer_ptrs_dev), + static_cast(rank), + n); +} + +template +void inkling_two_shot_all_reduce_fused( + tvm::ffi::TensorView local_buffer, + int64_t data_ptrs_dev, + int64_t flag_ptrs_dev, + int64_t state_ptr, + int64_t rank, + int64_t num_items, + int64_t nb_override, + int64_t bs_override, + tvm::ffi::TensorView shared) { + using namespace host; + uint32_t n; + validate(local_buffer, data_ptrs_dev, rank, num_items, n); + RuntimeCheck(flag_ptrs_dev != 0, "flag_ptrs_dev is null"); + RuntimeCheck(state_ptr != 0, "state_ptr is null"); + const DType* shared_ptr = shared_ptr_or_null(shared, num_items); + const auto device = local_buffer.device(); + const auto kernel = inkling_two_shot_all_reduce_fused_kernel; + const uint32_t block_size = bs_override > 0 ? static_cast(bs_override) : 1024u; + const uint32_t cap = max_resident_blocks(kernel, block_size, device); + const uint32_t num_blocks = nb_override > 0 ? min(static_cast(nb_override), cap) + : min(work_num_blocks(n, block_size), cap); + const auto stream = LaunchKernel::resolve_device(device); + LaunchKernel(num_blocks, block_size, stream)( + kernel, + reinterpret_cast(data_ptrs_dev), + reinterpret_cast(flag_ptrs_dev), + reinterpret_cast(state_ptr), + shared_ptr, + static_cast(rank), + n); +} + +template +void inkling_multimem_one_shot_fused( + tvm::ffi::TensorView local_buffer, + int64_t multicast_ptr, + int64_t flag_ptrs_dev, + int64_t state_ptr, + int64_t rank, + int64_t num_items, + int64_t nb_override, + int64_t bs_override, + int64_t per_block_barrier, + tvm::ffi::TensorView shared) { + using namespace host; + uint32_t n; + // validate uses the local buffer view only for device/dtype/shape; the kernel + // operates on the multicast pointer (plus the local view for the shared fold). + validate(local_buffer, multicast_ptr, rank, num_items, n); + RuntimeCheck(flag_ptrs_dev != 0, "flag_ptrs_dev is null"); + RuntimeCheck(state_ptr != 0, "state_ptr is null"); + RuntimeCheck(multicast_ptr % 16 == 0, "multicast_ptr not 16B aligned"); + const DType* shared_ptr = shared_ptr_or_null(shared, num_items); + const auto device = local_buffer.device(); + const auto kernel = per_block_barrier ? inkling_multimem_one_shot_fused_kernel + : inkling_multimem_one_shot_fused_kernel; + const uint32_t block_size = bs_override > 0 ? static_cast(bs_override) : 1024u; + uint32_t cap = max_resident_blocks(kernel, block_size, device); + if (per_block_barrier) cap = min(cap, inkling_ar::kMaxBarrierBlocks); + const uint32_t num_blocks = nb_override > 0 ? min(static_cast(nb_override), cap) + : min(work_num_blocks(n, block_size), cap); + const auto stream = LaunchKernel::resolve_device(device); + LaunchKernel(num_blocks, block_size, stream)( + kernel, + reinterpret_cast(multicast_ptr), + reinterpret_cast(local_buffer.data_ptr()), + reinterpret_cast(flag_ptrs_dev), + reinterpret_cast(state_ptr), + shared_ptr, + static_cast(rank), + n); +} + +template +void inkling_multimem_push_oneshot( + tvm::ffi::TensorView in_buffer, + tvm::ffi::TensorView out_buffer, + int64_t mc_stage_ptr, + int64_t local_stage_ptr, + int64_t flag_ptrs_dev, + int64_t state_ptr, + int64_t rank, + int64_t num_items, + int64_t nb_override, + int64_t bs_override, + int64_t per_block_barrier, + tvm::ffi::TensorView shared) { + using namespace host; + uint32_t n; + // in_buffer is any LOCAL contiguous bf16 tensor (need not be a symm buffer); + // validate() covers contiguity/dtype/alignment; mc_stage stands in for the + // pointer null check. + validate(in_buffer, mc_stage_ptr, rank, num_items, n); + const DType* shared_ptr = shared_ptr_or_null(shared, num_items); + RuntimeCheck(out_buffer.IsContiguous(), "out must be contiguous"); + RuntimeCheck(is_type(out_buffer.dtype()), "out dtype mismatch"); + RuntimeCheck(out_buffer.numel() >= num_items, "out smaller than num_items"); + RuntimeCheck(std::bit_cast(out_buffer.data_ptr()) % 16 == 0, "out not 16B aligned"); + RuntimeCheck(flag_ptrs_dev != 0, "flag_ptrs_dev is null"); + RuntimeCheck(state_ptr != 0, "state_ptr is null"); + RuntimeCheck(local_stage_ptr != 0, "local_stage_ptr is null"); + RuntimeCheck(mc_stage_ptr % 16 == 0, "mc_stage_ptr not 16B aligned"); + RuntimeCheck(local_stage_ptr % 16 == 0, "local_stage_ptr not 16B aligned"); + const auto device = in_buffer.device(); + const auto kernel = per_block_barrier ? inkling_multimem_push_oneshot_kernel + : inkling_multimem_push_oneshot_kernel; + const uint32_t block_size = bs_override > 0 ? static_cast(bs_override) : 1024u; + uint32_t cap = max_resident_blocks(kernel, block_size, device); + // The per-block barrier has kMaxBarrierBlocks flag/epoch slots per rank. + if (per_block_barrier) cap = min(cap, inkling_ar::kMaxBarrierBlocks); + const uint32_t num_blocks = nb_override > 0 ? min(static_cast(nb_override), cap) + : min(full_range_num_blocks(n, block_size), cap); + const auto stream = LaunchKernel::resolve_device(device); + LaunchKernel(num_blocks, block_size, stream)( + kernel, + reinterpret_cast(in_buffer.data_ptr()), + reinterpret_cast(mc_stage_ptr), + reinterpret_cast(local_stage_ptr), + reinterpret_cast(out_buffer.data_ptr()), + reinterpret_cast(flag_ptrs_dev), + reinterpret_cast(state_ptr), + shared_ptr, + static_cast(rank), + n); +} + +template +void inkling_multimem_full_oneshot( + tvm::ffi::TensorView in_buffer, + tvm::ffi::TensorView out_buffer, + int64_t multicast_ptr, + int64_t flag_ptrs_dev, + int64_t state_ptr, + int64_t rank, + int64_t num_items, + int64_t nb_override, + int64_t bs_override, + tvm::ffi::TensorView shared) { + using namespace host; + uint32_t n; + validate(in_buffer, multicast_ptr, rank, num_items, n); + RuntimeCheck(out_buffer.IsContiguous(), "out must be contiguous"); + RuntimeCheck(is_type(out_buffer.dtype()), "out dtype mismatch"); + RuntimeCheck(out_buffer.numel() >= num_items, "out smaller than num_items"); + RuntimeCheck(std::bit_cast(out_buffer.data_ptr()) % 16 == 0, "out not 16B aligned"); + RuntimeCheck(flag_ptrs_dev != 0, "flag_ptrs_dev is null"); + RuntimeCheck(state_ptr != 0, "state_ptr is null"); + RuntimeCheck(multicast_ptr % 16 == 0, "multicast_ptr not 16B aligned"); + const DType* shared_ptr = shared_ptr_or_null(shared, num_items); + const auto device = in_buffer.device(); + const auto kernel = inkling_multimem_full_oneshot_kernel; + const uint32_t block_size = bs_override > 0 ? static_cast(bs_override) : 1024u; + const uint32_t cap = max_resident_blocks(kernel, block_size, device); + const uint32_t num_blocks = nb_override > 0 ? min(static_cast(nb_override), cap) + : min(full_range_num_blocks(n, block_size), cap); + const auto stream = LaunchKernel::resolve_device(device); + LaunchKernel(num_blocks, block_size, stream)( + kernel, + reinterpret_cast(multicast_ptr), + reinterpret_cast(in_buffer.data_ptr()), + reinterpret_cast(out_buffer.data_ptr()), + reinterpret_cast(flag_ptrs_dev), + reinterpret_cast(state_ptr), + shared_ptr, + static_cast(rank), + n); +} + +} // namespace diff --git a/python/sglang/jit_kernel/csrc/inkling/inkling_ar_barrier.cuh b/python/sglang/jit_kernel/csrc/inkling/inkling_ar_barrier.cuh new file mode 100644 index 000000000..b326e93ab --- /dev/null +++ b/python/sglang/jit_kernel/csrc/inkling/inkling_ar_barrier.cuh @@ -0,0 +1,193 @@ +// Cross-GPU barrier primitives shared by the Inkling custom all-reduce kernels +// (inkling_all_reduce.cuh) and the fused AR+sconv+norm decode kernel +// (inkling_ar_fused_decode.cuh). Two designs are provided: a single-leader +// grid barrier and a per-block variant. +// +// Resources (see inkling_all_reduce.py): +// * flags: DEDICATED symmetric uint32 buffer, zero-initialized at setup: +// kNumGPU single-leader slots (one per peer), then +// kNumGPU * kMaxBarrierBlocks per-(writer, block) slots. +// * state: device-LOCAL uint32 buffer: [arrival0, arrival1, release0, +// release1, xepoch] padded to kLeaderStateWords, then kMaxBarrierBlocks +// per-block epochs. All epochs are monotonic (mod 2^32, wrap-safe compares) +// and advance under CUDA-graph replay, so flags never go stale. + +#pragma once + +#include + +namespace inkling_ar { + +constexpr uint32_t kLeaderStateWords = 8; +constexpr uint32_t kMaxBarrierBlocks = 256; + +// Grid-level system barrier across all ranks. Two levels: +// 1. Grid: every block arrives at a self-resetting device counter +// (atomicInc wraps at gridDim.x-1); the last arriver is the leader. +// 2. Cross-GPU: ONLY the leader block does the peer release/acquire +// signal/wait, so that O(1) cost is independent of gridDim.x (the reason +// the old per-block barrier was slow for many-block launches). +// The leader then bumps a release counter; followers spin on it (device scope). +// +// `st` is a device-local uint32 state buffer: [arrival0, arrival1, release0, +// release1, xepoch]. idx 0/1 selects the entry/exit instances (distinct grid +// counters so the two barriers in one kernel don't collide). xepoch is a single +// monotonic cross-GPU epoch (entry uses e, exit uses e+1) -- consistent across +// ranks (SPMD) and advancing under CUDA-graph replay, so flags never go stale. +// s_prev is read BEFORE arriving, and the leader (last arriver) bumps release +// only after all blocks arrived, so no follower can miss the bump (no deadlock). +template +__device__ __forceinline__ void grid_system_barrier( + uint32_t* __restrict__ st, void* const* __restrict__ flag_ptrs, uint32_t rank, uint32_t idx, bool publish_writes) { + // publish_writes=true (EXIT barriers): every CTA flushes its just-written + // reduced/broadcast slices to SYSTEM scope BEFORE it signals arrival, so the + // single leader's `st.release.sys` publishes ALL blocks' stores rather than + // only the leader thread's own. Without this, a multi-block launch (the tuned + // v2/v3 configs) lets a peer leave the exit barrier and read a slice a + // non-leader CTA wrote but never system-published. ONE fence per CTA suffices: + // the __syncthreads below orders every thread's stores before thread 0's + // fence (CTA-scope happens-before), and `fence.sys + relaxed arrival` is a + // release pattern, so the arrival publishes the whole CTA's stores. ENTRY + // barriers pass false: the data they gate on was written by a prior kernel + // and is already uniformly visible, which the leader's release then promotes + // for free. (The solo path needs no fence either way: its st.release.sys + // signals below are themselves release ops ordered after the __syncthreads.) + uint32_t* xepoch = st + 4; + __shared__ uint32_t s_e; + __shared__ uint32_t s_prev; + __shared__ int s_leader; + const bool solo = (gridDim.x == 1u); // token=1 etc.: the sole block IS the grid + __syncthreads(); + if (threadIdx.x == 0) { + if (solo) { + s_leader = 1; // skip the grid arrival/release bookkeeping entirely + } else { + if (publish_writes) __threadfence_system(); // release pattern with the arrive below + s_prev = *static_cast(st + 2 + idx); // pre-barrier release + // Self-resetting arrive (atomicInc semantics: wrap at gridDim.x-1). + // acq_rel: the release side pairs with the fence above (publishing this + // CTA's stores); the acquire side lets the last arriver (leader) inherit + // every earlier CTA's release pattern, so its st.release.sys to the peers + // covers the whole grid's writes. + uint32_t old; + asm volatile("atom.acq_rel.gpu.global.inc.u32 %0, [%1], %2;" + : "=r"(old) + : "l"(st + idx), "r"(gridDim.x - 1u) + : "memory"); + s_leader = (old == gridDim.x - 1u) ? 1 : 0; + } + } + __syncthreads(); + if (s_leader) { + if (threadIdx.x == 0) { + const uint32_t e = *xepoch + 1u; + *xepoch = e; + s_e = e; + } + __syncthreads(); + const uint32_t e = s_e; + // Cross-GPU arrive+wait with release/acquire at system scope. The release + // store publishes THIS (leader) thread's system-visible writes and the + // acquire spin makes the peer's visible -- far cheaper than a full + // threadfence_system here. Data written by OTHER (non-leader) CTAs is made + // system-visible by the publish_writes=true fence they each ran before + // arriving (see top), so the leader's single release covers the whole grid. + if (threadIdx.x < kNumGPU) { + const uint32_t peer = threadIdx.x; + uint32_t* remote = static_cast(flag_ptrs[peer]) + rank; + asm volatile("st.release.sys.global.u32 [%0], %1;" ::"l"(remote), "r"(e) : "memory"); + uint32_t* mine = static_cast(flag_ptrs[rank]) + peer; + uint32_t got; + do { + asm volatile("ld.acquire.sys.global.u32 %0, [%1];" : "=r"(got) : "l"(mine) : "memory"); + } while (static_cast(got - e) < 0); // wrap-safe: epoch is mod-2^32 + } + __syncthreads(); + if (!solo && threadIdx.x == 0) { + // Release-ordered bump: pairs with the followers' ld.acquire.gpu so the + // leader's acquired peer state (and its xepoch store above) is visible to + // them -- a relaxed atomicAdd would leave that handoff formally unordered. + asm volatile("red.release.gpu.global.add.u32 [%0], %1;" ::"l"(st + 2 + idx), "r"(1u) : "memory"); + } + } else { + if (threadIdx.x == 0) { + // `release` is this rank's LOCAL counter -> device-scope acquire suffices. + uint32_t* rel = st + 2 + idx; + uint32_t got; + do { + asm volatile("ld.acquire.gpu.global.u32 %0, [%1];" : "=r"(got) : "l"(rel) : "memory"); + } while (static_cast(got - s_prev) <= 0); // wrap-safe + } + __syncthreads(); + } +} + +// Device-LOCAL grid sync (no cross-GPU traffic): all blocks arrive at a +// self-resetting counter (state word 5), the last arriver bumps a release +// counter (word 6), followers spin on it -- the grid level of +// grid_system_barrier without the peer handshake. Words 5/6 are spare in the +// kLeaderStateWords block. Requires all blocks co-resident (the launch cap the +// fused kernels already apply). Used by the two-phase {AR + scattered sconv} +// kernel to publish its local scratch between the reduce and conv phases. +__device__ __forceinline__ void grid_local_sync(uint32_t* __restrict__ st) { + __syncthreads(); + if (gridDim.x > 1u) { + if (threadIdx.x == 0) { + uint32_t* arrive = st + 5; + uint32_t* release = st + 6; + const uint32_t prev = *static_cast(release); + uint32_t old; + asm volatile("atom.acq_rel.gpu.global.inc.u32 %0, [%1], %2;" + : "=r"(old) + : "l"(arrive), "r"(gridDim.x - 1u) + : "memory"); + if (old == gridDim.x - 1u) { + asm volatile("red.release.gpu.global.add.u32 [%0], %1;" ::"l"(release), "r"(1u) : "memory"); + } else { + uint32_t got; + do { + asm volatile("ld.acquire.gpu.global.u32 %0, [%1];" : "=r"(got) : "l"(release) : "memory"); + } while (static_cast(got - prev) <= 0); // wrap-safe + } + } + __syncthreads(); + } +} + +// Per-block cross-GPU barrier (no grid funnel): block b handshakes ONLY with +// block b on each peer -- one NVLink round trip per block, all blocks in +// parallel, no arrival/release atomics and no leader serialization. Valid +// whenever the consumer phase reads exactly the ranges its blockIdx-matched +// producers wrote (true for the push one-shot: its push and reduce loops use +// the same grid-stride mapping, and every rank launches the same grid). The +// signal is a release store, which covers the CTA's prior (multicast) stores +// via the preceding __syncthreads -- no explicit fence needed. Epochs live in +// per-block device-local slots (monotonic across launches and CUDA-graph +// replays, like xepoch). +template +__device__ __forceinline__ void +block_system_barrier(uint32_t* __restrict__ st, void* const* __restrict__ flag_ptrs, uint32_t rank) { + __shared__ uint32_t s_e; + __syncthreads(); // CTA stores done before the release signals below + if (threadIdx.x == 0) { + uint32_t* epoch = st + kLeaderStateWords + blockIdx.x; + const uint32_t e = *epoch + 1u; + *epoch = e; + s_e = e; + } + __syncthreads(); + const uint32_t e = s_e; + if (threadIdx.x < kNumGPU) { + const uint32_t peer = threadIdx.x; + uint32_t* remote = static_cast(flag_ptrs[peer]) + kNumGPU + rank * kMaxBarrierBlocks + blockIdx.x; + asm volatile("st.release.sys.global.u32 [%0], %1;" ::"l"(remote), "r"(e) : "memory"); + uint32_t* mine = static_cast(flag_ptrs[rank]) + kNumGPU + peer * kMaxBarrierBlocks + blockIdx.x; + uint32_t got; + do { + asm volatile("ld.acquire.sys.global.u32 %0, [%1];" : "=r"(got) : "l"(mine) : "memory"); + } while (static_cast(got - e) < 0); // wrap-safe + } + __syncthreads(); +} + +} // namespace inkling_ar diff --git a/python/sglang/jit_kernel/csrc/inkling/inkling_ar_fused_decode.cuh b/python/sglang/jit_kernel/csrc/inkling/inkling_ar_fused_decode.cuh new file mode 100644 index 000000000..926d47e74 --- /dev/null +++ b/python/sglang/jit_kernel/csrc/inkling/inkling_ar_fused_decode.cuh @@ -0,0 +1,830 @@ +// Fused decode {all-reduce -> mlp/attn sconv -> residual-add + RMSNorm} for the +// Inkling (Moonrise) small-batch decode path -- the v5 push one-shot all-reduce +// (inkling_all_reduce.cuh) with the EPILOGUE SEAM filled in by the decode short-conv +// (fused_decode_update.cuh semantics) and the fused-add RMSNorm. +// +// Replaces THREE kernels (AR + fused_decode_update + fused_add_rmsnorm) and +// their intermediate HBM round trips with ONE launch per (AR, sconv, norm) +// chain. Layout: ONE BLOCK PER TOKEN (decode rows are few and the RMSNorm needs +// a per-row cross-hidden reduction), VPT 16B vecs (8 channels each) per thread +// -- a TUNED knob: fewer/fatter threads buy load ILP and a cheaper block +// reduction; more threads buy parallelism. Phases: +// +// 0. prefetch: sconv metadata, conv history and conv weights load FIRST -- +// none depend on the producer kernel's output, and their HBM latency +// hides under the cross-GPU barrier below. (This -- not PDL -- is where +// the fused kernel's latency win comes from: the producer GEMMs never +// trigger programmatic launch early, so the PDL wait is a no-op in +// practice and the launch attribute only pipelines the launch tail.) +// 1. push: griddepcontrol.wait, then multicast-store this rank's partial +// row into staging slot (rank*T + t)*D; issue the residual load. +// 2. barrier: per-block peer handshake (block t <-> peers' block t). +// 3. reduce: fp32 sum of the kNumGPU staged shards; round to bf16 `xb` +// (bit-identical to what the unfused AR would have stored). +// 4. sconv: decode causal_conv1d on xb (W-1 cached taps gated by +// cache_mask + current token), optional SiLU, optional +xb +// residual; cache shift-update (+ optional track-copy) -- +// identical semantics to fused_decode_update_kernel. +// 5. norm: r = residual_in + y (fp32); block-reduce sum(r^2); write +// residual_out = bf16(r) and hs_out = bf16(r * rsqrt(mean+eps) +// * gamma) (fused_add_rmsnorm semantics). +// +// Staging reuse is caller-managed (A/B rotation shared with v5 -- this kernel +// IS a v5 AR occupying one rotation slot). PAD rows (cache_indices == -1) +// still compute y/hs but never write the cache, matching the unfused kernel. +// bf16-only. + +#include +#include + +#include +#include + +#include +#include + +#include "inkling_ar_barrier.cuh" +#include +#include +#include +#include + +namespace { + +constexpr int kPadSlot = -1; +constexpr uint32_t kVecElems = 8; // bf16x8 = 16 B + +// Register-level fused add of two bf16x8 vecs (fp32 math, ONE round to bf16) +// -- torch.add numerics, so folding the shared-expert partials into the push +// stays bit-identical to the unfused {torch.add -> AR} chain. +__device__ __forceinline__ uint4 add_bf16x8_rn(const uint4 a, const uint4 b) { + const auto* a2 = reinterpret_cast(&a); + const auto* b2 = reinterpret_cast(&b); + uint4 out; + auto* o2 = reinterpret_cast<__nv_bfloat162*>(&out); +#pragma unroll + for (int j = 0; j < 4; ++j) { + const float2 x = __bfloat1622float2(a2[j]); + const float2 y = __bfloat1622float2(b2[j]); + o2[j] = __floats2bfloat162_rn(x.x + y.x, x.y + y.y); + } + return out; +} + +struct ArSconvNormParams { + // AR + const void* __restrict__ in; // [T, D] partial sums (LOCAL tensor) + const void* __restrict__ shared; // optional [T, D] shared-expert partials (LOCAL) + void* __restrict__ mc_stage; // multicast staging base (>= kNumGPU*T*D elems) + const void* __restrict__ stage; // this GPU's local view of the staging base + void* const* __restrict__ flag_ptrs; + uint32_t* __restrict__ state; + // sconv (fused_decode_update semantics) + void* __restrict__ cache; // [pool, W-1, D], in-place update + const void* __restrict__ cache_indices; // int32 [T] (PAD == -1) + const void* __restrict__ cache_mask; // bool [T] + const void* __restrict__ conv_weight; // [D, W] + const void* __restrict__ track_mask; // bool [T] (DO_TRACK only) + const void* __restrict__ track_indices; // int64 [T] (DO_TRACK only) + // norm + const void* __restrict__ residual_in; // [T, D] + void* __restrict__ residual_out; // [T, D] + void* __restrict__ hs_out; // [T, D] + const void* __restrict__ norm_weight; // [D] + float eps; + // strides (elements) + int64_t in_stride_t; + int64_t shared_stride_t; + int64_t res_in_stride_t; + int64_t res_out_stride_t; + int64_t hs_stride_t; + int64_t cache_stride_slot; + int64_t cache_stride_w; + int64_t conv_weight_stride_d; + int64_t track_idx_stride; + uint32_t rank; + uint32_t T; + uint32_t D; +}; + +// VPT = 16B vecs handled per thread (tuning knob; see the header comment). +// Vec i of a thread is at index threadIdx.x + i*blockDim.x (warp-coalesced). +template +__global__ __launch_bounds__(1024, 1) void inkling_ar_sconv_norm_kernel(const __grid_constant__ ArSconvNormParams p) { + static_assert(std::is_same_v, "multimem push path is bf16-only"); + constexpr int W1 = W - 1; + const uint32_t t = blockIdx.x; + const uint32_t vecs = p.D / kVecElems; + + uint32_t c0[VPT]; + bool act[VPT]; +#pragma unroll + for (int i = 0; i < VPT; ++i) { + const uint32_t v = threadIdx.x + i * blockDim.x; + act[i] = v < vecs; + c0[i] = (act[i] ? v : 0) * kVecElems; // clamp: inactive lanes never store + } + + // ---- 0. prefetch (independent of the producer's output) ---- + const int ci = static_cast(p.cache_indices)[t]; + const bool valid = ci != kPadSlot; + const int slot_id = valid ? ci : 0; // PAD lanes still emit y, never write cache + const float cm = static_cast(p.cache_mask)[t] ? 1.0f : 0.0f; + auto* cp = static_cast<__nv_bfloat16*>(p.cache); + const auto* wp = static_cast(p.conv_weight); + uint4 hist_raw[VPT][W1]; + __nv_bfloat16 wtaps[VPT][kVecElems][W]; +#pragma unroll + for (int i = 0; i < VPT; ++i) { + if (!act[i]) continue; + const int64_t cache_base = static_cast(slot_id) * p.cache_stride_slot + c0[i]; +#pragma unroll + for (int w = 0; w < W1; ++w) { + hist_raw[i][w] = *reinterpret_cast(&cp[cache_base + w * p.cache_stride_w]); + } +#pragma unroll + for (int j = 0; j < static_cast(kVecElems); ++j) { + const int64_t wrow = static_cast(c0[i] + j) * p.conv_weight_stride_d; + if constexpr (W == 4) { + // One 8B load per channel row (bf16 x4, 8B-aligned for contiguous [D, W]). + if (p.conv_weight_stride_d == W) { + *reinterpret_cast(wtaps[i][j]) = *reinterpret_cast(wp + wrow); + continue; + } + } +#pragma unroll + for (int w = 0; w < W; ++w) + wtaps[i][j][w] = wp[wrow + w]; + } + } + + // ---- 1. push: wait for the producer's output (PDL; no-op without a PDL + // launch or an early-triggering producer), multicast-store this rank's + // partial row, and issue the residual load (it lands under the barrier). ---- + asm volatile("griddepcontrol.wait;" ::: "memory"); + const auto* in_row = static_cast(p.in) + t * p.in_stride_t; + const auto* sh_row = + p.shared == nullptr ? nullptr : static_cast(p.shared) + t * p.shared_stride_t; + auto* slot = static_cast<__nv_bfloat16*>(p.mc_stage) + (static_cast(p.rank) * p.T + t) * p.D; + uint4 res_raw[VPT]; +#pragma unroll + for (int i = 0; i < VPT; ++i) { + if (!act[i]) continue; + uint4 d = *reinterpret_cast(in_row + c0[i]); + if (sh_row != nullptr) { + d = add_bf16x8_rn(d, *reinterpret_cast(sh_row + c0[i])); + } + asm volatile("multimem.st.relaxed.sys.global.v4.bf16x2 [%0], {%1,%2,%3,%4};" ::"l"(slot + c0[i]), + "r"(d.x), + "r"(d.y), + "r"(d.z), + "r"(d.w) + : "memory"); + res_raw[i] = *reinterpret_cast( + static_cast(p.residual_in) + t * p.res_in_stride_t + c0[i]); + } + + // ---- 2. per-block barrier: all ranks' row-t pushes have landed locally ---- + inkling_ar::block_system_barrier(p.state, p.flag_ptrs, p.rank); + // Inactive lanes must NOT exit: they participate in the norm's __syncthreads + // and full-mask warp shuffles below (sumsq contribution 0). + + float r[VPT][kVecElems]; + float sumsq = 0.0f; + const auto* stage = static_cast(p.stage); +#pragma unroll + for (int i = 0; i < VPT; ++i) { + if (!act[i]) continue; + // ---- 3. reduce: fp32 sum of the kNumGPU staged shards; round to bf16 ---- + float xf[kVecElems]; +#pragma unroll + for (int j = 0; j < static_cast(kVecElems); ++j) + xf[j] = 0.0f; +#pragma unroll + for (uint32_t rr = 0; rr < kNumGPU; ++rr) { + const uint4 d = *reinterpret_cast(stage + (static_cast(rr) * p.T + t) * p.D + c0[i]); + const auto* h2 = reinterpret_cast(&d); +#pragma unroll + for (int j = 0; j < 4; ++j) { + const float2 f = __bfloat1622float2(h2[j]); + xf[2 * j] += f.x; + xf[2 * j + 1] += f.y; + } + } + // Round to bf16 exactly as the unfused AR's store would (the sconv below + // and the cache append must see the same bits the unfused path sees). + __nv_bfloat162 xb2[4]; +#pragma unroll + for (int j = 0; j < 4; ++j) + xb2[j] = __floats2bfloat162_rn(xf[2 * j], xf[2 * j + 1]); + + // ---- 4. sconv: conv over W-1 cached taps (prefetched) + current token ---- + float y[kVecElems]; +#pragma unroll + for (int j = 0; j < static_cast(kVecElems); ++j) { + const float xj = __bfloat162float(reinterpret_cast(xb2)[j]); + float acc = 0.0f; +#pragma unroll + for (int w = 0; w < W1; ++w) { + const float h = __bfloat162float(reinterpret_cast(&hist_raw[i][w])[j]); + acc += h * cm * __bfloat162float(wtaps[i][j][w]); + } + acc += xj * __bfloat162float(wtaps[i][j][W1]); + if constexpr (USE_SILU) acc = __fdividef(acc, 1.0f + __expf(-acc)); + if constexpr (USE_RESIDUAL) acc += xj; + y[j] = acc; + } + + if (valid) { + // Shift state left (gated by cache_mask), append current token (xb). + const int64_t cache_base = static_cast(slot_id) * p.cache_stride_slot + c0[i]; + int64_t track_base = 0; + bool do_tr = false; + if constexpr (DO_TRACK) { + do_tr = static_cast(p.track_mask)[t]; + if (do_tr) { + const int64_t tslot = + static_cast(p.track_indices)[static_cast(t) * p.track_idx_stride]; + track_base = tslot * p.cache_stride_slot + c0[i]; + } + } + const uint4 zero = make_uint4(0, 0, 0, 0); +#pragma unroll + for (int w = 0; w < W1; ++w) { + const uint4 nv = + (w < W1 - 1) ? ((cm != 0.0f) ? hist_raw[i][w + 1] : zero) : *reinterpret_cast(xb2); + *reinterpret_cast(&cp[cache_base + w * p.cache_stride_w]) = nv; + if constexpr (DO_TRACK) { + if (do_tr) { + *reinterpret_cast(&cp[track_base + w * p.cache_stride_w]) = nv; + } + } + } + } + + // ---- 5a. residual add (fused_add_rmsnorm semantics) ---- + // yb: round the sconv output to bf16 first -- the unfused path writes y to + // HBM as bf16 before the norm kernel reads it back. +#pragma unroll + for (int j = 0; j < static_cast(kVecElems); ++j) { + const float yb = __bfloat162float(__float2bfloat16_rn(y[j])); + r[i][j] = yb + __bfloat162float(reinterpret_cast(&res_raw[i])[j]); + sumsq += r[i][j] * r[i][j]; + } + } + + // ---- 5b. block reduction of sumsq (warp shuffle + one smem slot/warp) ---- + __shared__ float s_warp[32]; + __shared__ float s_inv; + const uint32_t lane = threadIdx.x & 31u; + const uint32_t warp = threadIdx.x >> 5; +#pragma unroll + for (int off = 16; off > 0; off >>= 1) + sumsq += __shfl_down_sync(~0u, sumsq, off); + if (lane == 0) s_warp[warp] = sumsq; + __syncthreads(); + if (warp == 0) { + const uint32_t nwarps = (blockDim.x + 31u) >> 5; + float total = (lane < nwarps && lane < 32u) ? s_warp[lane] : 0.0f; +#pragma unroll + for (int off = 16; off > 0; off >>= 1) + total += __shfl_down_sync(~0u, total, off); + if (lane == 0) s_inv = rsqrtf(total / static_cast(p.D) + p.eps); + } + __syncthreads(); + const float inv = s_inv; + + const auto* gw = static_cast(p.norm_weight); + auto* res_out = static_cast<__nv_bfloat16*>(p.residual_out) + t * p.res_out_stride_t; + auto* hs_out = static_cast<__nv_bfloat16*>(p.hs_out) + t * p.hs_stride_t; +#pragma unroll + for (int i = 0; i < VPT; ++i) { + if (!act[i]) continue; + __nv_bfloat162 ro[4], ho[4]; +#pragma unroll + for (int j = 0; j < 4; ++j) { + const float g0 = __bfloat162float(gw[c0[i] + 2 * j]); + const float g1 = __bfloat162float(gw[c0[i] + 2 * j + 1]); + ro[j] = __floats2bfloat162_rn(r[i][2 * j], r[i][2 * j + 1]); + ho[j] = __floats2bfloat162_rn(r[i][2 * j] * inv * g0, r[i][2 * j + 1] * inv * g1); + } + *reinterpret_cast(res_out + c0[i]) = *reinterpret_cast(ro); + *reinterpret_cast(hs_out + c0[i]) = *reinterpret_cast(ho); + } +} + +// --------------------------------------------------------------------------- +// Target-verify variant: {AR -> extend-style causal_conv1d -> +// save_intermediate_conv_windows -> add+RMSNorm} in one launch. Every sequence +// has exactly `q` (draft_token_num) consecutive tokens; token t belongs to +// seq = t/q with bos = seq*q. The conv's cross-token taps are RE-REDUCED from +// the v5 staging buffer (any block can rebuild any token's reduced row by +// summing the staged shards -- ~kNumGPU x 16B extra local L2 reads per tap, no +// cross-block dependency). The conv does NOT update the working cache at +// verify; instead the per-position windows are written to intermediate_out +// (consumed by update_conv_state_after_mtp_verify), whose values are exactly +// the cache prefix rows and the re-reduced x this kernel already holds. +struct ArSconvNormVerifyParams { + const void* __restrict__ in; // [T, D] partial sums (LOCAL tensor) + const void* __restrict__ shared; // optional [T, D] shared-expert partials (LOCAL) + void* __restrict__ mc_stage; // multicast staging base + const void* __restrict__ stage; // this GPU's local view of the staging base + void* const* __restrict__ flag_ptrs; + uint32_t* __restrict__ state; + const void* __restrict__ cache; // [pool, W-1, D] (read-only here) + const void* __restrict__ cache_indices; // int32 [B] per-SEQ slot (PAD == -1) + const void* __restrict__ cache_mask; // bool [B] per-SEQ prefix gate + const void* __restrict__ conv_weight; // [D, W] + void* __restrict__ inter_out; // [max_bs, q, W-1, D] + const void* __restrict__ residual_in; // [T, D] + void* __restrict__ residual_out; // [T, D] + void* __restrict__ hs_out; // [T, D] + const void* __restrict__ norm_weight; // [D] + float eps; + int64_t in_stride_t; + int64_t shared_stride_t; + int64_t res_in_stride_t; + int64_t res_out_stride_t; + int64_t hs_stride_t; + int64_t cache_stride_slot; + int64_t cache_stride_w; + int64_t conv_weight_stride_d; + int64_t inter_stride_b; + int64_t inter_stride_t; + int64_t inter_stride_w; + uint32_t rank; + uint32_t T; + uint32_t D; + uint32_t q; // draft_token_num +}; + +template +__global__ +__launch_bounds__(1024, 1) void inkling_ar_sconv_norm_verify_kernel(const __grid_constant__ ArSconvNormVerifyParams p) { + static_assert(std::is_same_v, "multimem push path is bf16-only"); + constexpr int W1 = W - 1; + const uint32_t vecs = p.D / kVecElems; + const uint32_t v = threadIdx.x; // one 16B vec (8 channels) per thread + const bool active = v < vecs; + const uint32_t c0 = (active ? v : 0) * kVecElems; + const uint32_t stride_t = gridDim.x; // grid-stride over tokens + + // Conv weights are token-independent (per channel) -- load once. + const auto* wp = static_cast(p.conv_weight); + __nv_bfloat16 wtaps[kVecElems][W]; + if (active) { +#pragma unroll + for (int j = 0; j < static_cast(kVecElems); ++j) { + const int64_t wrow = static_cast(c0 + j) * p.conv_weight_stride_d; + if constexpr (W == 4) { + if (p.conv_weight_stride_d == W) { + *reinterpret_cast(wtaps[j]) = *reinterpret_cast(wp + wrow); + continue; + } + } +#pragma unroll + for (int w = 0; w < W; ++w) + wtaps[j][w] = wp[wrow + w]; + } + } + + // ---- Phase 1: push every assigned row into staging (PDL-gated input). ---- + // A single grid barrier (below) then makes ALL rows' pushes visible on this + // GPU, so Phase 2's cross-token (neighbor) staging reads are race-free -- the + // per-block barrier only synchronized the same blockIdx across ranks and did + // NOT order block t-j's push before block t's read. + asm volatile("griddepcontrol.wait;" ::: "memory"); + auto* mc = static_cast<__nv_bfloat16*>(p.mc_stage); + const auto* in = static_cast(p.in); + const auto* sh = static_cast(p.shared); + if (active) { + for (uint32_t t = blockIdx.x; t < p.T; t += stride_t) { + uint4 d = *reinterpret_cast(in + t * p.in_stride_t + c0); + if (sh != nullptr) { + // Fold the shared-expert partials in registers (torch.add numerics); + // the staged value then matches the unfused pre-added input, so the + // cross-token re-reduces below stay bit-identical too. + d = add_bf16x8_rn(d, *reinterpret_cast(sh + t * p.shared_stride_t + c0)); + } + auto* slot = mc + (static_cast(p.rank) * p.T + t) * p.D + c0; + asm volatile("multimem.st.relaxed.sys.global.v4.bf16x2 [%0], {%1,%2,%3,%4};" ::"l"(slot), + "r"(d.x), + "r"(d.y), + "r"(d.z), + "r"(d.w) + : "memory"); + } + } + + // ---- Grid barrier: all pushes done + system-visible across all ranks. ---- + inkling_ar::grid_system_barrier( + p.state, + p.flag_ptrs, + p.rank, + 0, + /*publish_writes=*/true); + + // ---- Phase 2: reduce + conv + save_windows + add-RMSNorm per row. ---- + const auto* stage = static_cast(p.stage); + const auto* cp = static_cast(p.cache); + const auto* gw = static_cast(p.norm_weight); + __shared__ float s_warp[32]; + __shared__ float s_inv; + const uint32_t lane = threadIdx.x & 31u; + const uint32_t warp = threadIdx.x >> 5; + + auto reduce_row = [&](uint32_t row, __nv_bfloat162* out2) { + float xf[kVecElems]; +#pragma unroll + for (int j = 0; j < static_cast(kVecElems); ++j) + xf[j] = 0.0f; +#pragma unroll + for (uint32_t rr = 0; rr < kNumGPU; ++rr) { + const uint4 d = *reinterpret_cast(stage + (static_cast(rr) * p.T + row) * p.D + c0); + const auto* h2 = reinterpret_cast(&d); +#pragma unroll + for (int j = 0; j < 4; ++j) { + const float2 f = __bfloat1622float2(h2[j]); + xf[2 * j] += f.x; + xf[2 * j + 1] += f.y; + } + } +#pragma unroll + for (int j = 0; j < 4; ++j) + out2[j] = __floats2bfloat162_rn(xf[2 * j], xf[2 * j + 1]); + }; + + for (uint32_t t = blockIdx.x; t < p.T; t += stride_t) { + const uint32_t seq = t / p.q; + const uint32_t tq = t - seq * p.q; + const int bos = static_cast(seq * p.q); + const int ci = static_cast(p.cache_indices)[seq]; + const bool valid = ci != kPadSlot; + const int slot_id = valid ? ci : 0; + const float cm = (valid && static_cast(p.cache_mask)[seq]) ? 1.0f : 0.0f; + const int64_t cache_base = static_cast(slot_id) * p.cache_stride_slot + c0; + + float r[kVecElems]; + float sumsq = 0.0f; + if (active) { + uint4 pref_raw[W1]; +#pragma unroll + for (int w = 0; w < W1; ++w) { + pref_raw[w] = *reinterpret_cast(&cp[cache_base + w * p.cache_stride_w]); + } + const uint4 res_raw = *reinterpret_cast( + static_cast(p.residual_in) + t * p.res_in_stride_t + c0); + + __nv_bfloat162 xb2[4]; // own row + __nv_bfloat162 xn2[W1][4]; // neighbors t-1 .. t-(W-1), where in-seq + reduce_row(t, xb2); +#pragma unroll + for (int j = 1; j <= W1; ++j) { + const int n = static_cast(t) - j; + if (n >= bos) reduce_row(static_cast(n), xn2[j - 1]); + } + + // conv (jit causal_conv1d semantics, fp32 accum, ascending tap order). + float y[kVecElems]; +#pragma unroll + for (int j = 0; j < static_cast(kVecElems); ++j) { + const float xj = __bfloat162float(reinterpret_cast(xb2)[j]); + float acc = 0.0f; +#pragma unroll + for (int iw = 0; iw < W1; ++iw) { + const int shifted = static_cast(t) - W1 + iw; + float tap = 0.0f; + if (shifted >= bos) { + tap = __bfloat162float(reinterpret_cast(xn2[W1 - 1 - iw])[j]); + } else { + const int prefix_pos = shifted - bos + W1; + if (prefix_pos >= 0) { + tap = cm * __bfloat162float(reinterpret_cast(&pref_raw[prefix_pos])[j]); + } + } + acc += tap * __bfloat162float(wtaps[j][iw]); + } + acc += xj * __bfloat162float(wtaps[j][W1]); + if constexpr (USE_SILU) acc = __fdividef(acc, 1.0f + __expf(-acc)); + if constexpr (USE_RESIDUAL) acc += xj; + y[j] = acc; + } + + // save_intermediate_conv_windows: window after draft position tq is raw + // copies of {cache prefix rows | reduced x rows} (no cm gating). + if (valid) { + auto* op = static_cast<__nv_bfloat16*>(p.inter_out) + static_cast(seq) * p.inter_stride_b + + static_cast(tq) * p.inter_stride_t + c0; +#pragma unroll + for (int w = 0; w < W1; ++w) { + const int position = static_cast(tq) + 1 + w; + uint4 val; + if (position < W1) { + val = pref_raw[position]; + } else { + const int g = bos + position - W1; + val = (g == static_cast(t)) ? *reinterpret_cast(xb2) + : *reinterpret_cast(xn2[t - g - 1]); + } + *reinterpret_cast(op + w * p.inter_stride_w) = val; + } + } + + // residual add. +#pragma unroll + for (int j = 0; j < static_cast(kVecElems); ++j) { + const float yb = __bfloat162float(__float2bfloat16_rn(y[j])); + r[j] = yb + __bfloat162float(reinterpret_cast(&res_raw)[j]); + sumsq += r[j] * r[j]; + } + } + + // block reduction of sumsq (all threads participate; inactive contribute 0). + __syncthreads(); // protect s_warp/s_inv reuse across the token loop + float ss = sumsq; +#pragma unroll + for (int off = 16; off > 0; off >>= 1) + ss += __shfl_down_sync(~0u, ss, off); + if (lane == 0) s_warp[warp] = ss; + __syncthreads(); + if (warp == 0) { + const uint32_t nwarps = (blockDim.x + 31u) >> 5; + float total = (lane < nwarps && lane < 32u) ? s_warp[lane] : 0.0f; +#pragma unroll + for (int off = 16; off > 0; off >>= 1) + total += __shfl_down_sync(~0u, total, off); + if (lane == 0) s_inv = rsqrtf(total / static_cast(p.D) + p.eps); + } + __syncthreads(); + const float inv = s_inv; + + if (active) { + auto* res_out = static_cast<__nv_bfloat16*>(p.residual_out) + t * p.res_out_stride_t; + auto* hs_out = static_cast<__nv_bfloat16*>(p.hs_out) + t * p.hs_stride_t; + __nv_bfloat162 ro[4], ho[4]; +#pragma unroll + for (int j = 0; j < 4; ++j) { + const float g0 = __bfloat162float(gw[c0 + 2 * j]); + const float g1 = __bfloat162float(gw[c0 + 2 * j + 1]); + ro[j] = __floats2bfloat162_rn(r[2 * j], r[2 * j + 1]); + ho[j] = __floats2bfloat162_rn(r[2 * j] * inv * g0, r[2 * j + 1] * inv * g1); + } + *reinterpret_cast(res_out + c0) = *reinterpret_cast(ro); + *reinterpret_cast(hs_out + c0) = *reinterpret_cast(ho); + } + } +} + +template +struct ArSconvNormKernel { + template + static void launch(const ArSconvNormParams& params, uint32_t t_num, uint32_t vecs, DLDevice dev, bool pdl) { + using namespace host; + const uint32_t block = min(1024u, div_ceil(div_ceil(vecs, VPT), 32u) * 32u); + constexpr auto kernel = inkling_ar_sconv_norm_kernel; + LaunchKernel(dim3{t_num}, dim3{block}, dev).enable_pdl(pdl)(kernel, params); + } + + static void + run(tvm::ffi::TensorView in, + tvm::ffi::TensorView residual_in, + tvm::ffi::TensorView residual_out, + tvm::ffi::TensorView hs_out, + tvm::ffi::TensorView norm_weight, + double eps, + tvm::ffi::TensorView cache, + tvm::ffi::TensorView cache_indices, + tvm::ffi::TensorView cache_mask, + tvm::ffi::TensorView conv_weight, + tvm::ffi::TensorView track_mask, + tvm::ffi::TensorView track_indices, + int64_t mc_stage_ptr, + int64_t local_stage_ptr, + int64_t flag_ptrs_dev, + int64_t state_ptr, + int64_t rank, + int64_t enable_pdl, + int64_t vecs_per_thread, + tvm::ffi::TensorView shared) { + using namespace host; + auto T = SymbolicSize{"T"}; + auto D = SymbolicSize{"D"}; + auto Wd = SymbolicSize{"W"}; + auto W1s = SymbolicSize{"W_minus_1"}; + auto dev = SymbolicDevice{}; + dev.set_options(); + Wd.set_value(W); + W1s.set_value(W - 1); + + TensorMatcher({T, D}).with_strides({-1, 1}).with_dtype().with_device(dev).verify(in); + const bool do_shared = shared.numel() > 0; + if (do_shared) { + TensorMatcher({T, D}).with_strides({-1, 1}).with_dtype().with_device(dev).verify(shared); + RuntimeCheck(shared.stride(0) % kVecElems == 0, "shared row stride must keep 16B alignment"); + RuntimeCheck(std::bit_cast(shared.data_ptr()) % 16 == 0, "shared not 16B aligned"); + } + TensorMatcher({T, D}).with_strides({-1, 1}).with_dtype().with_device(dev).verify(residual_in); + TensorMatcher({T, D}).with_strides({-1, 1}).with_dtype().with_device(dev).verify(residual_out); + TensorMatcher({T, D}).with_strides({-1, 1}).with_dtype().with_device(dev).verify(hs_out); + TensorMatcher({D}).with_dtype().with_device(dev).verify(norm_weight); + TensorMatcher({-1, W1s, D}).with_dtype().with_device(dev).verify(cache); + TensorMatcher({T}).with_dtype().with_device(dev).verify(cache_indices); + TensorMatcher({T}).with_device(dev).verify(cache_mask); + TensorMatcher({D, Wd}).with_strides({-1, 1}).with_dtype().with_device(dev).verify(conv_weight); + const uint32_t t_num = static_cast(T.unwrap()); + const uint32_t d_num = static_cast(D.unwrap()); + const uint32_t vecs = d_num / kVecElems; + RuntimeCheck( + t_num >= 1 && t_num <= inkling_ar::kMaxBarrierBlocks, + "T must be in [1, kMaxBarrierBlocks] (one barrier slot per token)"); + RuntimeCheck(d_num % kVecElems == 0, "D must be a multiple of 8"); + RuntimeCheck(cache.stride(2) == 1, "cache must be channel-contiguous"); + RuntimeCheck(mc_stage_ptr % 16 == 0, "mc_stage_ptr not 16B aligned"); + RuntimeCheck(local_stage_ptr != 0 && local_stage_ptr % 16 == 0, "bad local_stage_ptr"); + RuntimeCheck(flag_ptrs_dev != 0, "flag_ptrs_dev is null"); + RuntimeCheck(state_ptr != 0, "state_ptr is null"); + RuntimeCheck(rank >= 0 && rank < kNumGPU, "rank out of range"); + RuntimeCheck(in.stride(0) % kVecElems == 0, "in row stride must keep 16B alignment"); + RuntimeCheck(std::bit_cast(in.data_ptr()) % 16 == 0, "in not 16B aligned"); + + const auto params = ArSconvNormParams{ + .in = in.data_ptr(), + .shared = do_shared ? shared.data_ptr() : nullptr, + .mc_stage = reinterpret_cast(mc_stage_ptr), + .stage = reinterpret_cast(local_stage_ptr), + .flag_ptrs = reinterpret_cast(flag_ptrs_dev), + .state = reinterpret_cast(state_ptr), + .cache = cache.data_ptr(), + .cache_indices = cache_indices.data_ptr(), + .cache_mask = cache_mask.data_ptr(), + .conv_weight = conv_weight.data_ptr(), + .track_mask = DO_TRACK ? track_mask.data_ptr() : nullptr, + .track_indices = DO_TRACK ? track_indices.data_ptr() : nullptr, + .residual_in = residual_in.data_ptr(), + .residual_out = residual_out.data_ptr(), + .hs_out = hs_out.data_ptr(), + .norm_weight = norm_weight.data_ptr(), + .eps = static_cast(eps), + .in_stride_t = in.stride(0), + .shared_stride_t = do_shared ? shared.stride(0) : 0, + .res_in_stride_t = residual_in.stride(0), + .res_out_stride_t = residual_out.stride(0), + .hs_stride_t = hs_out.stride(0), + .cache_stride_slot = cache.stride(0), + .cache_stride_w = cache.stride(1), + .conv_weight_stride_d = conv_weight.stride(0), + .track_idx_stride = DO_TRACK ? track_indices.stride(0) : 0, + .rank = static_cast(rank), + .T = t_num, + .D = d_num, + }; + + // vecs_per_thread (VPT) is the tuned knob; 0 -> 1. Each VPT must still fit + // one block (div_ceil(vecs, VPT) <= 1024). + const int vpt = vecs_per_thread > 0 ? static_cast(vecs_per_thread) : 1; + const bool pdl = enable_pdl != 0; + switch (vpt) { + case 1: + RuntimeCheck(vecs <= 1024, "D/8 must fit one block at VPT=1"); + launch<1>(params, t_num, vecs, dev.unwrap(), pdl); + break; + case 2: + launch<2>(params, t_num, vecs, dev.unwrap(), pdl); + break; + case 3: + launch<3>(params, t_num, vecs, dev.unwrap(), pdl); + break; + case 4: + launch<4>(params, t_num, vecs, dev.unwrap(), pdl); + break; + case 6: + launch<6>(params, t_num, vecs, dev.unwrap(), pdl); + break; + default: + RuntimeCheck(false, "unsupported vecs_per_thread (use 1/2/3/4/6)"); + } + } +}; + +// Host wrapper for the target-verify variant. DO_TRACK is accepted (to share +// the module's template-arg string) but unused -- verify never tracks. +template +struct ArSconvNormVerifyKernel { + static void + run(tvm::ffi::TensorView in, + tvm::ffi::TensorView residual_in, + tvm::ffi::TensorView residual_out, + tvm::ffi::TensorView hs_out, + tvm::ffi::TensorView norm_weight, + double eps, + tvm::ffi::TensorView cache, + tvm::ffi::TensorView cache_indices, + tvm::ffi::TensorView cache_mask, + tvm::ffi::TensorView conv_weight, + tvm::ffi::TensorView inter_out, + int64_t q, + int64_t mc_stage_ptr, + int64_t local_stage_ptr, + int64_t flag_ptrs_dev, + int64_t state_ptr, + int64_t rank, + int64_t enable_pdl, + tvm::ffi::TensorView shared) { + using namespace host; + auto T = SymbolicSize{"T"}; + auto B = SymbolicSize{"B"}; + auto D = SymbolicSize{"D"}; + auto Wd = SymbolicSize{"W"}; + auto W1s = SymbolicSize{"W_minus_1"}; + auto dev = SymbolicDevice{}; + dev.set_options(); + Wd.set_value(W); + W1s.set_value(W - 1); + + TensorMatcher({T, D}).with_strides({-1, 1}).with_dtype().with_device(dev).verify(in); + const bool do_shared = shared.numel() > 0; + if (do_shared) { + TensorMatcher({T, D}).with_strides({-1, 1}).with_dtype().with_device(dev).verify(shared); + RuntimeCheck(shared.stride(0) % kVecElems == 0, "shared row stride must keep 16B alignment"); + RuntimeCheck(std::bit_cast(shared.data_ptr()) % 16 == 0, "shared not 16B aligned"); + } + TensorMatcher({T, D}).with_strides({-1, 1}).with_dtype().with_device(dev).verify(residual_in); + TensorMatcher({T, D}).with_strides({-1, 1}).with_dtype().with_device(dev).verify(residual_out); + TensorMatcher({T, D}).with_strides({-1, 1}).with_dtype().with_device(dev).verify(hs_out); + TensorMatcher({D}).with_dtype().with_device(dev).verify(norm_weight); + TensorMatcher({-1, W1s, D}).with_dtype().with_device(dev).verify(cache); + TensorMatcher({B}).with_dtype().with_device(dev).verify(cache_indices); + TensorMatcher({B}).with_device(dev).verify(cache_mask); + TensorMatcher({D, Wd}).with_strides({-1, 1}).with_dtype().with_device(dev).verify(conv_weight); + const uint32_t t_num = static_cast(T.unwrap()); + const uint32_t b_num = static_cast(B.unwrap()); + const uint32_t d_num = static_cast(D.unwrap()); + RuntimeCheck(q > 0 && t_num == b_num * static_cast(q), "T must equal B * draft_token_num"); + RuntimeCheck( + t_num >= 1 && t_num <= inkling_ar::kMaxBarrierBlocks, + "T must be in [1, kMaxBarrierBlocks] (one barrier slot per token)"); + RuntimeCheck(d_num % kVecElems == 0, "D must be a multiple of 8"); + RuntimeCheck(d_num / kVecElems <= 1024, "D/8 must fit one block"); + RuntimeCheck(cache.stride(2) == 1, "cache must be channel-contiguous"); + // inter_out: [max_bs, q, W-1, D], channel-contiguous, batch B rows used. + auto MB = SymbolicSize{"max_bs"}; + auto Qs = SymbolicSize{"q"}; + Qs.set_value(q); + TensorMatcher({MB, Qs, W1s, D}).with_dtype().with_device(dev).verify(inter_out); + RuntimeCheck(MB.unwrap() >= b_num, "inter_out batch dim too small"); + RuntimeCheck(inter_out.stride(3) == 1, "inter_out must be channel-contiguous"); + RuntimeCheck(mc_stage_ptr % 16 == 0, "mc_stage_ptr not 16B aligned"); + RuntimeCheck(local_stage_ptr != 0 && local_stage_ptr % 16 == 0, "bad local_stage_ptr"); + RuntimeCheck(flag_ptrs_dev != 0 && state_ptr != 0, "null barrier resources"); + RuntimeCheck(rank >= 0 && rank < kNumGPU, "rank out of range"); + RuntimeCheck(in.stride(0) % kVecElems == 0, "in row stride must keep 16B alignment"); + RuntimeCheck(std::bit_cast(in.data_ptr()) % 16 == 0, "in not 16B aligned"); + + const auto params = ArSconvNormVerifyParams{ + .in = in.data_ptr(), + .shared = do_shared ? shared.data_ptr() : nullptr, + .mc_stage = reinterpret_cast(mc_stage_ptr), + .stage = reinterpret_cast(local_stage_ptr), + .flag_ptrs = reinterpret_cast(flag_ptrs_dev), + .state = reinterpret_cast(state_ptr), + .cache = cache.data_ptr(), + .cache_indices = cache_indices.data_ptr(), + .cache_mask = cache_mask.data_ptr(), + .conv_weight = conv_weight.data_ptr(), + .inter_out = inter_out.data_ptr(), + .residual_in = residual_in.data_ptr(), + .residual_out = residual_out.data_ptr(), + .hs_out = hs_out.data_ptr(), + .norm_weight = norm_weight.data_ptr(), + .eps = static_cast(eps), + .in_stride_t = in.stride(0), + .shared_stride_t = do_shared ? shared.stride(0) : 0, + .res_in_stride_t = residual_in.stride(0), + .res_out_stride_t = residual_out.stride(0), + .hs_stride_t = hs_out.stride(0), + .cache_stride_slot = cache.stride(0), + .cache_stride_w = cache.stride(1), + .conv_weight_stride_d = conv_weight.stride(0), + .inter_stride_b = inter_out.stride(0), + .inter_stride_t = inter_out.stride(1), + .inter_stride_w = inter_out.stride(2), + .rank = static_cast(rank), + .T = t_num, + .D = d_num, + .q = static_cast(q), + }; + + const uint32_t block = min(1024u, div_ceil(d_num / kVecElems, 32u) * 32u); + constexpr auto kernel = inkling_ar_sconv_norm_verify_kernel; + // The kernel grid-strides over tokens with ONE grid_system_barrier between + // the push and the neighbor-reading reduce, so all blocks must be + // co-resident (else the leader waits forever). Cap the grid at the + // occupancy limit; the token loop covers any remaining rows. + const uint32_t bps = host::runtime::get_blocks_per_sm(kernel, block); + const uint32_t cap = host::runtime::get_sm_count(dev.unwrap().device_id) * max(1u, bps); + const uint32_t grid = min(t_num, cap); + LaunchKernel(dim3{grid}, dim3{block}, dev.unwrap()).enable_pdl(enable_pdl != 0)(kernel, params); + } +}; + +} // namespace diff --git a/python/sglang/jit_kernel/csrc/inkling/inkling_ar_scattered_sconv.cuh b/python/sglang/jit_kernel/csrc/inkling/inkling_ar_scattered_sconv.cuh new file mode 100644 index 000000000..5bd4a56ac --- /dev/null +++ b/python/sglang/jit_kernel/csrc/inkling/inkling_ar_scattered_sconv.cuh @@ -0,0 +1,2034 @@ +// Fused {all-reduce + scattered short-conv} for Inkling (--enable-scattered-sconv +// with SGLANG_OPT_USE_INKLING_FUSED_AR_SCONV): the v3/v3b two-shot multimem +// all-reduce (inkling_all_reduce.cuh) with its slice partition changed from the +// flat vec range to a per-rank HIDDEN-CHANNEL shard, and the EPILOGUE SEAM +// filled in by the extend-style causal short-conv. +// +// The two-shot structure maps 1:1 onto the scattered-sconv chain +// all_gather(sconv_shard(reduce_scatter(x))) == this kernel: +// * reduce phase == reduce_scatter: rank r `multimem.ld_reduce`s its channel +// columns [r*Hc, (r+1)*Hc) for every token (the switch sums all peers); +// * epilogue == sconv on the shard: the conv along tokens is fully local +// to the rank (channelwise conv + channel shard => each rank owns the full +// token history of its channels; conv-state cache and weights are sharded); +// * broadcast == all_gather: `multimem.st` of the POST-conv values into +// the OUT region reassembles [T, H] on every rank. +// +// OUT-OF-PLACE: broadcasts land in a dedicated OUT region, NOT the input +// slices, because the conv taps of later tokens re-read the *reduced pre-conv* +// values of earlier tokens (in-place would clobber them). One OUT region (no +// A/B) is safe for the same reason in-place v3 is: this kernel keeps BOTH +// barriers, so the next fused call's ENTRY barrier proves every peer's +// consumers (which run before that call on the peer's stream) already read the +// previous OUT contents. +// +// This file holds a family of kernels for the same {reduce, conv, broadcast} +// chain, tuned for different shapes -- the primary (chunked/tiled) kernel +// below, plus streaming, column-decode, one-shot, and banded variants further +// down, each with its own design comment at its definition. +// +// Cross-token conv taps: taps that precede a token's SEQUENCE start come from +// the (sharded) conv-state cache gated by cache_mask -- exactly the prefix +// semantics of causal_conv1d.cuh. The reduced pre-conv x is also stored to a +// LOCAL [T, Hc] scratch, consumed in-kernel by the fused cache-update / +// prefix-cache track (Phase 3) -- there is no separate kernel call. +// +// Numerics: `multimem.ld_reduce` rounds to bf16 in the switch -- identical to +// the unfused torch reduce_scatter_out staging; conv accumulates in fp32 over +// those bf16 values and rounds once at the broadcast store, matching the +// unfused {RS -> causal_conv1d -> AG} chain bit-for-bit. bf16-only. + +#include +#include + +#include +#include + +#include +#include + +#include "inkling_ar_barrier.cuh" +#include +#include +#include +#include +#include +#include + +namespace { + +constexpr uint32_t kSsVecElems = 8; // bf16x8 = 16 B +constexpr int kSsPadSlot = -1; +constexpr uint32_t kSsMaxHcW = 6144; // smem weight stage capacity (Hc * W) +constexpr uint32_t kSsTileElems = 17920; // smem tile capacity (35 KB; 48 KB static limit incl. weights) + +struct ArScatteredSconvParams { + const void* __restrict__ mc_in; // multicast base of [T, H] partial sums + void* __restrict__ mc_out; // multicast base of the [T, H] OUT region + void* __restrict__ x_scratch; // LOCAL [T, Hc] reduced pre-conv x + void* __restrict__ cache; // [slots, W-1, Hc] sharded conv cache (in-place) + const void* __restrict__ safe_idx; // int64 [B] clamped cache slots + const void* __restrict__ cache_mask; // bool [B] has_initial_state & valid + const void* __restrict__ ci; // int32 [B] raw cache slots (PAD == -1) + const void* __restrict__ has_init; // bool [B] + const void* __restrict__ cu; // int64 [B+1] query_start_loc + const void* __restrict__ si; // int32 [T] token -> sequence index + const void* __restrict__ weight; // bf16 [Hc, W] sharded depthwise taps + const void* __restrict__ track_rows; // int64 [B, W-1] gather rows (or null) + const void* __restrict__ track_mask; // bool [B] (or null) + const void* __restrict__ track_dst; // int64 [B] (or null) + // Fused add+RMSNorm tail (decode/verify): consumes the gathered OUT rows + // locally after the exit barrier. norm_gamma == null -> phase skipped. + const void* __restrict__ out_local; // this rank's [T, H] view of OUT + const void* __restrict__ norm_gamma; // bf16 [H] (or null) + void* __restrict__ norm_residual; // bf16 [T, H] in/out (residual' = out + residual) + void* __restrict__ norm_out; // bf16 [T, H] normed hidden + void* const* __restrict__ flag_ptrs; + uint32_t* __restrict__ state; + int64_t cache_stride_slot; + int64_t cache_stride_w; + int64_t track_dst_stride; + float norm_eps; + uint32_t rank; + uint32_t T; + uint32_t H; // full hidden (row stride of mc_in / mc_out) + uint32_t Hc; // per-rank channel shard (row stride of x_scratch) + uint32_t B; + uint32_t chunk_rows; // token rows per CTA chunk + uint32_t cvec_chunks; // cvec-range splits per token chunk + uint32_t track_from_cache; // decode: track = post-update window (nv), not scratch gathers + uint32_t use_tile; // chunked mode: stage tiles in smem, not global scratch + uint32_t need_scratch; // verify consumes x_scratch externally -> keep writing it + // FULL-WIDTH mode (non-scattered sconv): cache is the replicated + // [slots, W-1, H] tensor. Conv taps read this rank's columns at + // cache_col0 (= rank * Hc); phase 3 updates ALL H columns on every rank + // (window rows re-ld_reduced full-width -- B * (W-1) rows, negligible) so + // the replicated cache stays coherent for full-width consumers (decode). + uint32_t cache_col0; // column offset of this rank's shard in the cache + uint32_t full_update; // phase 3 spans all H columns (replicated cache) +}; + +// Fused add+RMSNorm tail, shared by the chunked and streaming kernels. Works +// under BOTH exit barrier modes: each block's exit acquire made its +// peer-block's remote writes visible in the local memory hierarchy; the +// gpu-scope grid_local_sync (per-block mode) propagates that visibility to +// every local block, so any block may then read any full OUT row (the same +// reasoning that lets post-kernel consumers read OUT after a per-block exit +// barrier). One block per token row: residual' = OUT + residual (written back +// to norm_residual), norm_out = residual' * rsqrt(mean(residual'^2) + eps) +// * gamma -- flashinfer FusedAddRMSNorm semantics. No per-thread value stash: +// pass 2 re-reads the just-written bf16 residual (register pressure would +// otherwise spill the conv phases). +__device__ __forceinline__ void ss_fused_norm_tail(const ArScatteredSconvParams& p) { + const auto* outp = static_cast(p.out_local); + const auto* gamma = static_cast(p.norm_gamma); + auto* resid = static_cast<__nv_bfloat16*>(p.norm_residual); + auto* nout = static_cast<__nv_bfloat16*>(p.norm_out); + const uint32_t hvecs = p.H / kSsVecElems; + __shared__ float red[32]; + for (uint32_t r = blockIdx.x; r < p.T; r += gridDim.x) { + const int64_t base = static_cast(r) * p.H; + float ssq = 0.0f; + for (uint32_t i = threadIdx.x; i < hvecs; i += blockDim.x) { + const uint32_t c = i * kSsVecElems; + const uint4 ov = *reinterpret_cast(outp + base + c); + const uint4 rv = *reinterpret_cast(resid + base + c); + const auto* oh = reinterpret_cast(&ov); + const auto* rh = reinterpret_cast(&rv); + __nv_bfloat16 sb[kSsVecElems]; +#pragma unroll + for (int j = 0; j < static_cast(kSsVecElems); ++j) { + const float v = __bfloat162float(oh[j]) + __bfloat162float(rh[j]); + ssq += v * v; + sb[j] = __float2bfloat16(v); + } + *reinterpret_cast(resid + base + c) = *reinterpret_cast(sb); + } + // Block-reduce ssq (warp shuffle + one shared round). +#pragma unroll + for (int off = 16; off > 0; off >>= 1) { + ssq += __shfl_down_sync(0xffffffffu, ssq, off); + } + if ((threadIdx.x & 31u) == 0) red[threadIdx.x >> 5] = ssq; + __syncthreads(); + if (threadIdx.x < 32) { + float v = threadIdx.x < (blockDim.x >> 5) ? red[threadIdx.x] : 0.0f; +#pragma unroll + for (int off = 16; off > 0; off >>= 1) { + v += __shfl_down_sync(0xffffffffu, v, off); + } + if (threadIdx.x == 0) red[0] = v; + } + __syncthreads(); + const float rms = rsqrtf(red[0] / static_cast(p.H) + p.norm_eps); + for (uint32_t i = threadIdx.x; i < hvecs; i += blockDim.x) { + const uint32_t c = i * kSsVecElems; + const uint4 sv = *reinterpret_cast(resid + base + c); + const uint4 gv = *reinterpret_cast(gamma + c); + const auto* sh = reinterpret_cast(&sv); + const auto* gh = reinterpret_cast(&gv); + __nv_bfloat16 ob[kSsVecElems]; +#pragma unroll + for (int j = 0; j < static_cast(kSsVecElems); ++j) { + ob[j] = __float2bfloat16(__bfloat162float(sh[j]) * rms * __bfloat162float(gh[j])); + } + *reinterpret_cast(nout + base + c) = *reinterpret_cast(ob); + } + __syncthreads(); // red[] reuse across rows + } +} + +template +__global__ +__launch_bounds__(1024, 1) void inkling_ar_scattered_sconv_kernel(const __grid_constant__ ArScatteredSconvParams p) { + static_assert(std::is_same_v, "multimem path is bf16-only"); + constexpr int W1 = W - 1; + + // ---- Weight staging: the [Hc, W] taps into smem BEFORE the entry + // barrier, so the global loads complete under the barrier spin. Stage B + // otherwise re-loads every channel's taps per (token, cvec) item. + __shared__ alignas(16) __nv_bfloat16 smem_w[kSsMaxHcW]; + // Tile stage: the reduced (halo + tile) lives in smem in chunked mode -- + // the global x_scratch round-trip (plus its 4x tap-read amplification, + // ~60 MB/site at T=4096) was eating the fusion's HBM savings vs the + // unfused chain. Phase 3 re-ld_reduces its few rows instead. + __shared__ alignas(16) __nv_bfloat16 smem_x[kSsTileElems]; + // Below ~8K tokens the per-block copy outweighs the stage-B savings + // (little barrier spin to hide it under); read taps from global there. + const bool use_smw = p.T >= 8192; + if (use_smw) { + const auto* wg = static_cast(p.weight); + const uint32_t nw = p.Hc * W; + for (uint32_t i = threadIdx.x; i < nw; i += blockDim.x) + smem_w[i] = wg[i]; + __syncthreads(); // smem_w visible to the whole block + } + + // ---- ENTRY barrier: all peers' producer partials are visible ---- + if constexpr (kPerBlockBarrier) { + inkling_ar::block_system_barrier(p.state, p.flag_ptrs, p.rank); + } else { + inkling_ar::grid_system_barrier(p.state, p.flag_ptrs, p.rank, 0, /*publish_writes=*/false); + } + + const uint32_t stride = gridDim.x * blockDim.x; + const auto* si = static_cast(p.si); + const auto* cu = static_cast(p.cu); + const auto* safe_idx = static_cast(p.safe_idx); + const auto* cmask = static_cast(p.cache_mask); + const auto* cache = static_cast(p.cache); + const auto* mc_in = static_cast(p.mc_in); + auto* mc_out = static_cast<__nv_bfloat16*>(p.mc_out); + auto* scratch = static_cast<__nv_bfloat16*>(p.x_scratch); + + // PIPELINED CHUNK design (single grid sync). The conv's only cross-thread + // dependency is W-1 token rows backward, so each CTA owns a contiguous + // (token-chunk x cvec-range) tile: stage A ld_reduces the tile plus its own + // W-1 halo rows into the shared scratch (all loads independent), a CTA-local + // __syncthreads publishes them, and stage B convolves + broadcasts. CTAs + // never wait on each other -- one CTA's broadcast overlaps the next CTA's + // reduce, restoring the reduce<->broadcast pipelining a global drain would + // forfeit. Halo rows overlap neighbouring chunks' main rows; both write the + // same ld_reduce value to the same scratch slot (benign). The single + // grid_local_sync below only fences the (tiny) phase-3 cache update. + const uint32_t cvecs = p.Hc / kSsVecElems; + const uint32_t token_chunks = (p.T + p.chunk_rows - 1) / p.chunk_rows; + const uint32_t total_chunks = token_chunks * p.cvec_chunks; + const uint32_t cvec_per = (cvecs + p.cvec_chunks - 1) / p.cvec_chunks; + const uint32_t gtid = blockIdx.x * blockDim.x + threadIdx.x; + + for (uint32_t chunk = blockIdx.x; chunk < total_chunks; chunk += gridDim.x) { + const uint32_t tc = chunk / p.cvec_chunks; + const uint32_t cc = chunk % p.cvec_chunks; + const uint32_t t0 = tc * p.chunk_rows; + const uint32_t t1 = min(t0 + p.chunk_rows, p.T); + const uint32_t c0 = min(cc * cvec_per, cvecs); + const uint32_t c1 = min(c0 + cvec_per, cvecs); + const uint32_t ncv = c1 - c0; + if (ncv == 0) continue; + const uint32_t h0 = t0 > W1 ? t0 - W1 : 0; // include halo rows + + // ---- Stage A: independent ld_reduces of (halo + tile) into the smem + // tile (chunked mode) or global scratch (zero-halo / verify) ---- + const uint32_t ncv8 = ncv * kSsVecElems; // smem tile row stride (elems) + { + const uint32_t rows = t1 - h0; + const uint32_t items = rows * ncv; + for (uint32_t i = threadIdx.x; i < items; i += blockDim.x) { + const uint32_t t = h0 + i / ncv; + const uint32_t cv = i % ncv; + const uint32_t lc = (c0 + cv) * kSsVecElems; + const __nv_bfloat16* addr = mc_in + static_cast(t) * p.H + p.rank * p.Hc + lc; + uint4 v; + asm volatile("multimem.ld_reduce.relaxed.sys.global.add.v4.bf16x2 {%0,%1,%2,%3}, [%4];" + : "=r"(v.x), "=r"(v.y), "=r"(v.z), "=r"(v.w) + : "l"(addr)); + if (p.use_tile) { + *reinterpret_cast(smem_x + (t - h0) * ncv8 + cv * kSsVecElems) = v; + if (p.need_scratch && t >= t0) { + *reinterpret_cast(scratch + static_cast(t) * p.Hc + lc) = v; + } + } else { + *reinterpret_cast(scratch + static_cast(t) * p.Hc + lc) = v; + } + } + } + __syncthreads(); // CTA-local: this chunk's scratch rows are visible + + // ---- Stage B: conv from scratch + broadcast ---- + { + const uint32_t rows = t1 - t0; + const uint32_t items = rows * ncv; + for (uint32_t i = threadIdx.x; i < items; i += blockDim.x) { + const uint32_t t = t0 + i / ncv; + const uint32_t lc = (c0 + i % ncv) * kSsVecElems; + const uint32_t col = p.rank * p.Hc + lc; + const int s = si[t]; + const int64_t bos = cu[s]; + const bool cm = cmask[s]; + const int64_t cache_base = safe_idx[s] * p.cache_stride_slot + p.cache_col0 + lc; + const uint32_t cv8 = lc - c0 * kSsVecElems; // smem col offset (elems) + const uint4 xt = p.use_tile ? *reinterpret_cast(smem_x + (t - h0) * ncv8 + cv8) + : *reinterpret_cast(scratch + static_cast(t) * p.Hc + lc); + + uint4 taps[W1]; +#pragma unroll + for (int k = 0; k < W1; ++k) { + const int64_t pos = static_cast(t) - (W1 - k); + if (pos >= bos) { + taps[k] = p.use_tile ? *reinterpret_cast(smem_x + (pos - h0) * ncv8 + cv8) + : *reinterpret_cast(scratch + pos * p.Hc + lc); + } else { + const int64_t prow = pos - bos + W1; // prefix row in [0, W1) + taps[k] = cm ? *reinterpret_cast(&cache[cache_base + prow * p.cache_stride_w]) + : make_uint4(0, 0, 0, 0); + } + } + + // Taps from the smem stage: one 8B vector per channel at W == 4 + // (vs 32 scalar global loads), converted at use. + const auto* wsrc = use_smw ? smem_w : static_cast(p.weight); + uint2 wraw[kSsVecElems]; +#pragma unroll + for (int j = 0; j < static_cast(kSsVecElems); ++j) { + if constexpr (W == 4) { + wraw[j] = *reinterpret_cast(&wsrc[(lc + j) * W]); + } + } + const auto wt = [&](int j, int w) -> float { + if constexpr (W == 4) { + return __bfloat162float(reinterpret_cast(&wraw[j])[w]); + } else { + return __bfloat162float(wsrc[(lc + j) * W + w]); + } + }; + const auto* xh = reinterpret_cast(&xt); + __nv_bfloat162 yb[4]; +#pragma unroll + for (int j2 = 0; j2 < 4; ++j2) { + float yj[2]; +#pragma unroll + for (int h = 0; h < 2; ++h) { + const int j = 2 * j2 + h; + const float xj = __bfloat162float(xh[j]); + float acc = xj * wt(j, W1); +#pragma unroll + for (int k = 0; k < W1; ++k) { + const float tap = __bfloat162float(reinterpret_cast(&taps[k])[j]); + acc += tap * wt(j, k); + } + if constexpr (USE_SILU) acc = __fdividef(acc, 1.0f + __expf(-acc)); + if constexpr (USE_RESIDUAL) acc += xj; + yj[h] = acc; + } + yb[j2] = __floats2bfloat162_rn(yj[0], yj[1]); + } + + __nv_bfloat16* addr = mc_out + static_cast(t) * p.H + col; + asm volatile("multimem.st.relaxed.sys.global.v4.bf16x2 [%0], {%1,%2,%3,%4};" ::"l"(addr), + "r"(reinterpret_cast(yb)[0]), + "r"(reinterpret_cast(yb)[1]), + "r"(reinterpret_cast(yb)[2]), + "r"(reinterpret_cast(yb)[3]) + : "memory"); + } + } + __syncthreads(); // don't start the next chunk's stage A over this scratch... (regions disjoint; kept for clarity) + } + + // ---- EXIT barrier first: peers' consumers wait only on the broadcasts, + // not on our rank-local cache update (phase 3 below reads only local + // scratch/metadata and writes the rank-sharded cache). ---- + if constexpr (kPerBlockBarrier) { + inkling_ar::block_system_barrier(p.state, p.flag_ptrs, p.rank); + } else { + inkling_ar::grid_system_barrier(p.state, p.flag_ptrs, p.rank, 1, /*publish_writes=*/true); + } + // WAR fence before phase 3 overwrites cache prefix rows that other local + // blocks' stage-B taps may still be reading; the grid exit barrier already + // grid-joins local blocks, so only the per-block mode needs it. It also + // provides the cross-block visibility the norm phase relies on. + if constexpr (kPerBlockBarrier) { + inkling_ar::grid_local_sync(p.state); + } + + // ---- Phase 3: fused update_sconv_cache + prefix-cache track. All source + // rows live in the LOCAL x_scratch (it holds every token of the shard), so + // this is pure local traffic. One thread owns all W-1 rows of one + // (sequence, channel-vec) pair -- RAW-safe load-all-then-store, mirroring + // the standalone kernel. + { + auto* wcache = static_cast<__nv_bfloat16*>(p.cache); + const auto* cip = static_cast(p.ci); + const auto* hinit = static_cast(p.has_init); + // Full-width mode spans all H columns (replicated cache -- every rank + // re-reduces the window rows full-width); scattered spans the shard. + const uint32_t ucv = p.full_update ? p.H / kSsVecElems : cvecs; + const uint32_t items3 = p.B * ucv; + for (uint32_t it = gtid; it < items3; it += stride) { + const uint32_t b = it / ucv; + const uint32_t lc = (it % ucv) * kSsVecElems; + const uint32_t ccol = p.full_update ? lc : p.cache_col0 + lc; + const uint32_t mcol = p.full_update ? lc : p.rank * p.Hc + lc; + const int slot = cip[b]; + const int64_t qlen = cu[b + 1] - cu[b]; + const bool updated = slot != kSsPadSlot && qlen > 0; + uint4 nv_reg[W1]; // post-update window, reused by from-cache tracking + if (updated) { + const bool hs = hinit[b]; + const int64_t cb = static_cast(slot) * p.cache_stride_slot + ccol; + uint4 old_reg[W1]; +#pragma unroll + for (int w = 0; w < W1; ++w) { + old_reg[w] = *reinterpret_cast(&wcache[cb + w * p.cache_stride_w]); + } + const uint4 zero = make_uint4(0, 0, 0, 0); +#pragma unroll + for (int w = 0; w < W1; ++w) { + uint4 nv; + if (qlen >= W1 - w) { + const int64_t row = cu[b + 1] - W1 + w; + if (p.full_update || (p.use_tile && !p.need_scratch)) { + // Full-width columns never live in the local scratch; tiles + // are per-CTA and scratch wasn't written. Re-reduce from the + // pristine multicast input (bit-identical on a fixed + // NVSwitch topology). + const __nv_bfloat16* a3 = mc_in + row * p.H + mcol; + asm volatile("multimem.ld_reduce.relaxed.sys.global.add.v4.bf16x2 {%0,%1,%2,%3}, [%4];" + : "=r"(nv.x), "=r"(nv.y), "=r"(nv.z), "=r"(nv.w) + : "l"(a3)); + } else { + nv = *reinterpret_cast(scratch + row * p.Hc + lc); + } + } else { + uint4 shift = zero; +#pragma unroll + for (int src = 0; src < W1; ++src) { + if (src == w + qlen) shift = old_reg[src]; + } + nv = hs ? shift : zero; + } + nv_reg[w] = nv; + *reinterpret_cast(&wcache[cb + w * p.cache_stride_w]) = nv; + } + } + if (p.track_mask != nullptr) { + const auto* tmask = static_cast(p.track_mask); + if (tmask[b]) { + const int64_t dst = static_cast(p.track_dst)[static_cast(b) * p.track_dst_stride]; + const int64_t db = dst * p.cache_stride_slot + ccol; + if (p.track_from_cache) { + // Decode: snapshot the post-update window (mirrors the unfused + // fused_causal_conv1d_update_decode track-copy semantics). + if (updated) { +#pragma unroll + for (int w = 0; w < W1; ++w) { + *reinterpret_cast(&wcache[db + w * p.cache_stride_w]) = nv_reg[w]; + } + } + } else { + const auto* trows = static_cast(p.track_rows); +#pragma unroll + for (int w = 0; w < W1; ++w) { + const int64_t row = trows[static_cast(b) * W1 + w]; + uint4 nv; + if (p.full_update || (p.use_tile && !p.need_scratch)) { + const __nv_bfloat16* a4 = mc_in + row * p.H + mcol; + asm volatile("multimem.ld_reduce.relaxed.sys.global.add.v4.bf16x2 {%0,%1,%2,%3}, [%4];" + : "=r"(nv.x), "=r"(nv.y), "=r"(nv.z), "=r"(nv.w) + : "l"(a4)); + } else { + nv = *reinterpret_cast(scratch + row * p.Hc + lc); + } + *reinterpret_cast(&wcache[db + w * p.cache_stride_w]) = nv; + } + } + } + } + } + } + + // ---- Fused add+RMSNorm tail (decode/verify/extend). See ss_fused_norm_tail. + if (p.norm_gamma != nullptr) { + ss_fused_norm_tail(p); + } +} + +// Occupancy cap (same rationale as inkling_all_reduce.cuh: the grid barrier +// requires all blocks co-resident). +template +uint32_t ss_max_resident_blocks(Kernel kernel, uint32_t block_size, DLDevice device) { + using namespace host; + static std::mutex mu; + static std::unordered_map cache; + const uint64_t key = (std::bit_cast(reinterpret_cast(kernel)) << 12) ^ + (static_cast(block_size) << 8) ^ static_cast(device.device_id); + { + std::lock_guard lk(mu); + if (auto it = cache.find(key); it != cache.end()) return it->second; + } + int sm_count = 0; + cudaDeviceGetAttribute(&sm_count, cudaDevAttrMultiProcessorCount, device.device_id); + RuntimeCheck(sm_count > 0, "failed to query multiProcessorCount"); + const uint32_t bps = runtime::get_blocks_per_sm(kernel, block_size); + RuntimeCheck(bps > 0, "kernel has zero occupancy at block_size ", block_size); + const uint32_t result = static_cast(sm_count) * bps; + std::lock_guard lk(mu); + cache.emplace(key, result); + return result; +} + +// --------------------------------------------------------------------------- +// STREAMING rolling-window variant: v3's exact per-element dataflow +// (ld_reduce -> st, no staging, full memory-level parallelism) with the conv +// carried in registers. Each thread walks a token range down ONE cvec column +// holding the last W-1 reduced vectors; every step is +// v = ld_reduce(x[t, col]); y = conv(regs, v); st(out[t, col], y); shift. +// Warm-up re-reduces the W-1 halo rows once per walk (+ (W-1)/L remote +// traffic). No smem tile, no __syncthreads, no x_scratch, no A/B phases -- +// this removes the stage split that serialized the reduce/broadcast streams. +// --------------------------------------------------------------------------- + +template +__global__ +__launch_bounds__(1024, 1) void inkling_ar_stream_sconv_kernel(const __grid_constant__ ArScatteredSconvParams p) { + static_assert(std::is_same_v, "multimem path is bf16-only"); + constexpr int W1 = W - 1; + + if constexpr (kPerBlockBarrier) { + inkling_ar::block_system_barrier(p.state, p.flag_ptrs, p.rank); + } else { + inkling_ar::grid_system_barrier(p.state, p.flag_ptrs, p.rank, 0, false); + } + + const auto* si = static_cast(p.si); + const auto* cu = static_cast(p.cu); + const auto* safe_idx = static_cast(p.safe_idx); + const auto* cmask = static_cast(p.cache_mask); + const auto* cache = static_cast(p.cache); + const auto* wp = static_cast(p.weight); + const auto* mc_in = static_cast(p.mc_in); + auto* mc_out = static_cast<__nv_bfloat16*>(p.mc_out); + auto* scratch = static_cast<__nv_bfloat16*>(p.x_scratch); + + const uint32_t cvecs = p.Hc / kSsVecElems; + const uint32_t L = p.chunk_rows; // walk length (tokens per thread-walk) + const uint32_t walks_t = (p.T + L - 1) / L; + const uint32_t total = walks_t * cvecs; + const uint32_t gstride = gridDim.x * blockDim.x; + const uint32_t gtid = blockIdx.x * blockDim.x + threadIdx.x; + + for (uint32_t wk = gtid; wk < total; wk += gstride) { + const uint32_t tw = wk / cvecs; // token-walk index + const uint32_t cv = wk % cvecs; + const uint32_t lc = cv * kSsVecElems; // column offset in the shard + const uint32_t col = p.rank * p.Hc + lc; // global column + const uint32_t tw0 = tw * L; + const uint32_t tw1 = min(tw0 + L, p.T); + + // Load the walk's tap window: re-reduce the W-1 halo rows (rows < tw0) + // where they exist; sequence-prefix rows come from the cache below. + uint4 taps[W1]; // taps[k] = reduced x at row t - (W1 - k) +#pragma unroll + for (int k = 0; k < W1; ++k) { + const int64_t pos = static_cast(tw0) - (W1 - k); + if (pos >= 0) { + const __nv_bfloat16* a = mc_in + pos * p.H + col; + asm volatile("multimem.ld_reduce.relaxed.sys.global.add.v4.bf16x2 {%0,%1,%2,%3}, [%4];" + : "=r"(taps[k].x), "=r"(taps[k].y), "=r"(taps[k].z), "=r"(taps[k].w) + : "l"(a)); + } else { + taps[k] = make_uint4(0, 0, 0, 0); + } + } + + // Per-channel taps as packed bf16x4 (8B) loads. + uint2 wraw[kSsVecElems]; +#pragma unroll + for (int j = 0; j < static_cast(kSsVecElems); ++j) { + if constexpr (W == 4) { + wraw[j] = *reinterpret_cast(&wp[(lc + j) * W]); + } + } + const auto wt = [&](int j, int w) -> float { + if constexpr (W == 4) { + return __bfloat162float(reinterpret_cast(&wraw[j])[w]); + } else { + return __bfloat162float(wp[(lc + j) * W + w]); + } + }; + + for (uint32_t t = tw0; t < tw1; ++t) { + const int sq = si[t]; + const int64_t bos = cu[sq]; + uint4 v; + const __nv_bfloat16* a = mc_in + static_cast(t) * p.H + col; + asm volatile("multimem.ld_reduce.relaxed.sys.global.add.v4.bf16x2 {%0,%1,%2,%3}, [%4];" + : "=r"(v.x), "=r"(v.y), "=r"(v.z), "=r"(v.w) + : "l"(a)); + if (p.need_scratch) { + *reinterpret_cast(scratch + static_cast(t) * p.Hc + lc) = v; + } + + // Sequence-prefix taps override the rolling window near bos. + const bool cm = cmask[sq]; + const int64_t cb = safe_idx[sq] * p.cache_stride_slot + p.cache_col0 + lc; + uint4 tp[W1]; +#pragma unroll + for (int k = 0; k < W1; ++k) { + const int64_t pos = static_cast(t) - (W1 - k); + if (pos >= bos) { + tp[k] = taps[k]; + } else { + const int64_t prow = pos - bos + W1; + tp[k] = cm ? *reinterpret_cast(&cache[cb + prow * p.cache_stride_w]) : make_uint4(0, 0, 0, 0); + } + } + + const auto* xh = reinterpret_cast(&v); + __nv_bfloat162 yb[4]; +#pragma unroll + for (int j2 = 0; j2 < 4; ++j2) { + float yj[2]; +#pragma unroll + for (int hh = 0; hh < 2; ++hh) { + const int j = 2 * j2 + hh; + const float xj = __bfloat162float(xh[j]); + float acc = xj * wt(j, W1); +#pragma unroll + for (int k = 0; k < W1; ++k) { + const float tap = __bfloat162float(reinterpret_cast(&tp[k])[j]); + acc += tap * wt(j, k); + } + if constexpr (USE_SILU) acc = __fdividef(acc, 1.0f + __expf(-acc)); + if constexpr (USE_RESIDUAL) acc += xj; + yj[hh] = acc; + } + yb[j2] = __floats2bfloat162_rn(yj[0], yj[1]); + } + __nv_bfloat16* ao = mc_out + static_cast(t) * p.H + col; + asm volatile("multimem.st.relaxed.sys.global.v4.bf16x2 [%0], {%1,%2,%3,%4};" ::"l"(ao), + "r"(reinterpret_cast(yb)[0]), + "r"(reinterpret_cast(yb)[1]), + "r"(reinterpret_cast(yb)[2]), + "r"(reinterpret_cast(yb)[3]) + : "memory"); + + // Shift the rolling window. +#pragma unroll + for (int k = 0; k < W1 - 1; ++k) + taps[k] = taps[k + 1]; + taps[W1 - 1] = v; + } + } + + // Exit barrier + phase 3 + norm tail: identical to the chunked kernel. + if constexpr (kPerBlockBarrier) { + inkling_ar::block_system_barrier(p.state, p.flag_ptrs, p.rank); + } else { + inkling_ar::grid_system_barrier(p.state, p.flag_ptrs, p.rank, 1, true); + } + if constexpr (kPerBlockBarrier) { + inkling_ar::grid_local_sync(p.state); + } + { + auto* wcache = static_cast<__nv_bfloat16*>(p.cache); + const auto* cip = static_cast(p.ci); + const auto* hinit = static_cast(p.has_init); + // Full-width mode spans all H columns (replicated cache); see the + // chunked kernel's phase 3. + const uint32_t ucv = p.full_update ? p.H / kSsVecElems : cvecs; + const uint32_t items3 = p.B * ucv; + for (uint32_t it = gtid; it < items3; it += gstride) { + const uint32_t b = it / ucv; + const uint32_t lc = (it % ucv) * kSsVecElems; + const uint32_t ccol = p.full_update ? lc : p.cache_col0 + lc; + const uint32_t mcol = p.full_update ? lc : p.rank * p.Hc + lc; + const int slot = cip[b]; + const int64_t qlen = cu[b + 1] - cu[b]; + const bool updated = slot != kSsPadSlot && qlen > 0; + uint4 nv_reg[W1]; + if (updated) { + const bool hs = hinit[b]; + const int64_t cb = static_cast(slot) * p.cache_stride_slot + ccol; + uint4 old_reg[W1]; +#pragma unroll + for (int w = 0; w < W1; ++w) { + old_reg[w] = *reinterpret_cast(&wcache[cb + w * p.cache_stride_w]); + } + const uint4 zero = make_uint4(0, 0, 0, 0); +#pragma unroll + for (int w = 0; w < W1; ++w) { + uint4 nv; + if (qlen >= W1 - w) { + const int64_t row = cu[b + 1] - W1 + w; + const __nv_bfloat16* a3 = mc_in + row * p.H + mcol; + asm volatile("multimem.ld_reduce.relaxed.sys.global.add.v4.bf16x2 {%0,%1,%2,%3}, [%4];" + : "=r"(nv.x), "=r"(nv.y), "=r"(nv.z), "=r"(nv.w) + : "l"(a3)); + } else { + uint4 shift = zero; +#pragma unroll + for (int src = 0; src < W1; ++src) { + if (src == w + qlen) shift = old_reg[src]; + } + nv = hs ? shift : zero; + } + nv_reg[w] = nv; + *reinterpret_cast(&wcache[cb + w * p.cache_stride_w]) = nv; + } + } + if (p.track_mask != nullptr) { + const auto* tmask = static_cast(p.track_mask); + if (tmask[b]) { + const int64_t dst = static_cast(p.track_dst)[static_cast(b) * p.track_dst_stride]; + const int64_t db = dst * p.cache_stride_slot + ccol; + if (p.track_from_cache) { + if (updated) { +#pragma unroll + for (int w = 0; w < W1; ++w) { + *reinterpret_cast(&wcache[db + w * p.cache_stride_w]) = nv_reg[w]; + } + } + } else { + const auto* trows = static_cast(p.track_rows); +#pragma unroll + for (int w = 0; w < W1; ++w) { + const int64_t row = trows[static_cast(b) * W1 + w]; + uint4 nv; + const __nv_bfloat16* a4 = mc_in + row * p.H + mcol; + asm volatile("multimem.ld_reduce.relaxed.sys.global.add.v4.bf16x2 {%0,%1,%2,%3}, [%4];" + : "=r"(nv.x), "=r"(nv.y), "=r"(nv.z), "=r"(nv.w) + : "l"(a4)); + *reinterpret_cast(&wcache[db + w * p.cache_stride_w]) = nv; + } + } + } + } + } + } + + if (p.norm_gamma != nullptr) { + ss_fused_norm_tail(p); + } +} + +template +struct ArScatteredSconvKernel { + static void + run(tvm::ffi::TensorView in_buffer, // this rank's [T, H] view of the input symm region + tvm::ffi::TensorView x_scratch, // LOCAL [T, Hc] bf16 + tvm::ffi::TensorView cache, // [slots, W-1, Hc] bf16 + tvm::ffi::TensorView safe_idx, // int64 [B] + tvm::ffi::TensorView cache_mask, // bool [B] + tvm::ffi::TensorView ci, // int32 [B] raw slots (PAD == -1) + tvm::ffi::TensorView has_init, // bool [B] + tvm::ffi::TensorView cu, // int64 [B+1] + tvm::ffi::TensorView si, // int32 [T] + tvm::ffi::TensorView weight, // bf16 [Hc, W] + tvm::ffi::TensorView track_rows, // int64 [B, W-1] (numel 0 -> no track-by-rows) + tvm::ffi::TensorView track_mask, // bool [B] (numel 0 -> no tracking at all) + tvm::ffi::TensorView track_dst, // int64 [B] (possibly strided) + tvm::ffi::TensorView out_local, // this rank's [T, H] view of OUT (norm reads) + tvm::ffi::TensorView norm_gamma, // bf16 [H] (numel 0 -> no fused norm) + tvm::ffi::TensorView norm_residual, // bf16 [T, H] in/out + tvm::ffi::TensorView norm_out, // bf16 [T, H] + int64_t mc_in, + int64_t mc_out, + int64_t flag_ptrs_dev, + int64_t state_ptr, + int64_t rank, + int64_t nb_override, + int64_t bs_override, + bool per_block_barrier, + bool track_from_cache, + double norm_eps, + bool need_scratch, + bool use_stream, + int64_t stream_walk, + bool full_update, + int64_t cache_col0) { + using namespace host; + RuntimeCheck(in_buffer.ndim() == 2, "in must be [T, H]"); + const uint32_t T = static_cast(in_buffer.size(0)); + const uint32_t H = static_cast(in_buffer.size(1)); + const uint32_t Hc = static_cast(x_scratch.size(1)); + RuntimeCheck(Hc * kNumGPU == H, "Hc * world must equal H"); + RuntimeCheck(Hc % kSsVecElems == 0, "Hc must be a multiple of 8"); + RuntimeCheck(x_scratch.size(0) == T, "x_scratch rows must equal T"); + RuntimeCheck(weight.size(0) == Hc && weight.size(1) == W, "weight must be [Hc, W]"); + RuntimeCheck(weight.stride(1) == 1 && weight.stride(0) == W, "weight must be contiguous [Hc, W] (smem stage)"); + RuntimeCheck(Hc * W <= kSsMaxHcW, "Hc * W exceeds the smem weight stage"); + if (full_update) { + // Non-scattered mode: the replicated [slots, W-1, H] cache. Conv taps + // read this rank's columns at cache_col0; phase 3 updates all H. + RuntimeCheck(cache.size(1) == W - 1 && cache.size(2) == H, "full-width cache must be [slots, W-1, H]"); + RuntimeCheck( + cache_col0 % kSsVecElems == 0 && cache_col0 + Hc <= H, + "cache_col0 must be an aligned in-range column offset"); + RuntimeCheck(!need_scratch, "verify (need_scratch) unsupported full-width"); + } else { + RuntimeCheck(cache.size(1) == W - 1 && cache.size(2) == Hc, "cache must be [slots, W-1, Hc]"); + RuntimeCheck(cache_col0 == 0, "cache_col0 is full-width-mode only"); + } + RuntimeCheck(cache.stride(2) == 1, "cache must be channel-contiguous"); + RuntimeCheck(si.size(0) >= T, "si must cover T tokens"); + RuntimeCheck(mc_in % 16 == 0 && mc_out % 16 == 0, "multicast ptrs must be 16B aligned"); + RuntimeCheck(flag_ptrs_dev != 0 && state_ptr != 0, "barrier resources are null"); + RuntimeCheck(rank >= 0 && rank < kNumGPU, "rank out of range"); + if (T == 0) return; + + // Track: mask numel > 0 turns tracking on; rows are required only for the + // extend (gather) mode. Decode passes empty rows + track_from_cache. + const bool do_track = track_mask.numel() > 0; + if (do_track && !track_from_cache) { + RuntimeCheck(track_rows.numel() > 0, "extend track needs gather rows"); + } + const bool do_norm = norm_gamma.numel() > 0; + if (do_norm) { + RuntimeCheck(norm_gamma.numel() == H, "norm_gamma must be [H]"); + RuntimeCheck( + norm_residual.numel() == static_cast(T) * H && norm_out.numel() == static_cast(T) * H, + "norm residual/out must be [T, H]"); + RuntimeCheck(out_local.numel() == static_cast(T) * H, "out_local must be [T, H]"); + } + const auto params = ArScatteredSconvParams{ + .mc_in = reinterpret_cast(mc_in), + .mc_out = reinterpret_cast(mc_out), + .x_scratch = x_scratch.data_ptr(), + .cache = cache.data_ptr(), + .safe_idx = safe_idx.data_ptr(), + .cache_mask = cache_mask.data_ptr(), + .ci = ci.data_ptr(), + .has_init = has_init.data_ptr(), + .cu = cu.data_ptr(), + .si = si.data_ptr(), + .weight = weight.data_ptr(), + .track_rows = do_track && !track_from_cache ? track_rows.data_ptr() : nullptr, + .track_mask = do_track ? track_mask.data_ptr() : nullptr, + .track_dst = do_track ? track_dst.data_ptr() : nullptr, + .out_local = do_norm ? out_local.data_ptr() : nullptr, + .norm_gamma = do_norm ? norm_gamma.data_ptr() : nullptr, + .norm_residual = do_norm ? norm_residual.data_ptr() : nullptr, + .norm_out = do_norm ? norm_out.data_ptr() : nullptr, + .flag_ptrs = reinterpret_cast(flag_ptrs_dev), + .state = reinterpret_cast(state_ptr), + .cache_stride_slot = cache.stride(0), + .cache_stride_w = cache.stride(1), + .track_dst_stride = do_track ? track_dst.stride(0) : 0, + .norm_eps = static_cast(norm_eps), + .rank = static_cast(rank), + .T = T, + .H = H, + .Hc = Hc, + .B = static_cast(safe_idx.size(0)), + .chunk_rows = 1, + .cvec_chunks = 1, + .track_from_cache = track_from_cache ? 1u : 0u, + .use_tile = 0, + .need_scratch = 1, + .cache_col0 = static_cast(cache_col0), + .full_update = full_update ? 1u : 0u, + }; + + const auto device = in_buffer.device(); + const uint32_t block_size = bs_override > 0 ? static_cast(bs_override) : 256u; + const uint32_t cvecs = Hc / kSsVecElems; + const auto launch = [&](auto kernel) { + uint32_t cap = ss_max_resident_blocks(kernel, block_size, device); + if (per_block_barrier) cap = min(cap, inkling_ar::kMaxBarrierBlocks); + if (nb_override > 0) cap = min(static_cast(nb_override), cap); + // Chunk geometry. Small T: ONE token chunk (zero halo) split across the + // cvec range only -- cvec splits carry no halo and no dependency. Large + // T: token chunks sized for a small halo fraction AND >= ~3 chunks per + // CTA, so CTAs drift out of phase and one CTA's broadcast overlaps + // another's reduce (simultaneous single-chunk CTAs would keep the + // reduce/broadcast phases globally aligned). + const uint32_t min_rows = 16u * (W - 1); // halo overhead <= ~6% + uint32_t chunk_rows; + if (T <= 2u * min_rows * max(1u, cap / cvecs)) { + chunk_rows = T; // zero-halo mode: parallelism from cvec splits + } else { + chunk_rows = max(min_rows, host::div_ceil(T, 3u * cap)); + } + const uint32_t token_chunks = host::div_ceil(T, chunk_rows); + const uint32_t cvec_chunks = + max(1u, + min(cvecs, + max(cap / max(1u, token_chunks), + host::div_ceil(cvecs * min(chunk_rows, T) * 2u, block_size * max(1u, token_chunks))))); + // Smem-tile mode for the chunked path: cap the per-chunk cvec range so + // (chunk_rows + W-1) rows fit the 48 KB tile; zero-halo (small T) keeps + // the global-scratch path (its traffic is negligible there). + uint32_t cvec_chunks2 = cvec_chunks; + // Tile mode only where the scratch traffic dominates (the forced + // finer cvec split makes waves ragged at mid T; see T=4096 spot). + bool use_tile = chunk_rows < T && T >= 8192; + if (use_tile) { + const uint32_t max_cvec8 = kSsTileElems / (chunk_rows + W - 1); + const uint32_t max_cv = max_cvec8 / kSsVecElems; + if (max_cv == 0) { + use_tile = false; + } else { + cvec_chunks2 = max(cvec_chunks, host::div_ceil(cvecs, max_cv)); + } + } + auto pp = params; + pp.chunk_rows = chunk_rows; + pp.cvec_chunks = cvec_chunks2; + pp.use_tile = use_tile ? 1u : 0u; + pp.need_scratch = need_scratch ? 1u : 0u; + const uint32_t num_blocks = min(cap, token_chunks * cvec_chunks2); + const auto stream = LaunchKernel::resolve_device(device); + LaunchKernel(num_blocks, block_size, stream)(kernel, pp); + }; + if (use_stream) { + // Streaming rolling-window path: one walk of L tokens per thread down a + // cvec column; nb/bs set the walk length (L = ceil(T*cvecs/threads)). + const auto launch_stream = [&](auto kernel) { + uint32_t cap = ss_max_resident_blocks(kernel, block_size, device); + if (per_block_barrier) cap = min(cap, inkling_ar::kMaxBarrierBlocks); + if (nb_override > 0) cap = min(static_cast(nb_override), cap); + const uint32_t threads = cap * block_size; + uint32_t L = + stream_walk > 0 ? static_cast(stream_walk) : max(48u, host::div_ceil(T * cvecs, threads)); + const uint32_t walks = host::div_ceil(T, L) * cvecs; + const uint32_t nblk = min(cap, host::div_ceil(walks, block_size)); + auto pp = params; + pp.chunk_rows = L; + pp.use_tile = 0; + pp.need_scratch = need_scratch ? 1u : 0u; + const auto stream = LaunchKernel::resolve_device(device); + LaunchKernel(nblk, block_size, stream)(kernel, pp); + }; + if (per_block_barrier) { + launch_stream(inkling_ar_stream_sconv_kernel); + } else { + launch_stream(inkling_ar_stream_sconv_kernel); + } + return; + } + if (per_block_barrier) { + launch(inkling_ar_scattered_sconv_kernel); + } else { + launch(inkling_ar_scattered_sconv_kernel); + } + } +}; + +// --------------------------------------------------------------------------- +// COLUMN DECODE V2: dedicated small-batch {two-shot AR + sharded sconv + +// add-RMSNorm}. Pure column design (no window replication) pays exactly two +// cross-rank rounds; this kernel strips everything else: ONE BLOCK PER TOKEN +// ROW with block-scoped barriers (block t <-> peer block t), all metadata / +// cache-window / weight / residual loads issued BEFORE the entry barrier so +// they land under the barrier spin, conv from registers, inline cache +// shift-update (+ track), then the full-row norm after the exit barrier. +// Decode rows are single-token sequences: every tap is cache prefix (no +// si/cu). +// --------------------------------------------------------------------------- + +struct ColDecodeParams { + const void* __restrict__ mc_in; // multicast base of [T, H] partials + void* __restrict__ mc_out; // multicast base of the [T, H] OUT region + const void* __restrict__ out_local; // this rank's [T, H] OUT view + void* const* __restrict__ flag_ptrs; + uint32_t* __restrict__ state; + void* __restrict__ cache; // [pool, W-1, Hc] shard, in-place + const void* __restrict__ cache_indices; // int32 [T] (PAD == -1) + const void* __restrict__ cache_mask; // bool [T] + const void* __restrict__ weight; // bf16 [Hc, W] shard + const void* __restrict__ track_mask; // bool [T] (or null) + const void* __restrict__ track_dst; // int64 [T] (or null) + const void* __restrict__ residual_in; // [T, H] + void* __restrict__ residual_out; // [T, H] + void* __restrict__ hs_out; // [T, H] + const void* __restrict__ norm_weight; // [H] + float eps; + int64_t cache_stride_slot; + int64_t cache_stride_w; + int64_t track_dst_stride; + uint32_t rank; + uint32_t T; + uint32_t H; + uint32_t Hc; +}; + +template +__global__ __launch_bounds__(1024, 1) void inkling_ar_col_decode_kernel(const __grid_constant__ ColDecodeParams p) { + static_assert(std::is_same_v, "multimem path is bf16-only"); + constexpr int W1 = W - 1; + const uint32_t t = blockIdx.x; + const uint32_t vecs = p.H / kSsVecElems; // full-row vecs (norm) + const uint32_t ovecs = p.Hc / kSsVecElems; // own-shard vecs (reduce/conv) + + // Full-row lane map (norm) and own-shard lane map (reduce/conv). + uint32_t c0[VPT]; + bool act[VPT]; +#pragma unroll + for (int i = 0; i < VPT; ++i) { + const uint32_t v = threadIdx.x + i * blockDim.x; + act[i] = v < vecs; + c0[i] = (act[i] ? v : 0) * kSsVecElems; + } + const uint32_t ov = threadIdx.x; // one own-vec per thread (ovecs <= blockDim) + const bool own = ov < ovecs; + const uint32_t olc = (own ? ov : 0) * kSsVecElems; + + // ---- 0. prefetch: everything independent of peers' partials ---- + const int ci = static_cast(p.cache_indices)[t]; + const bool valid = ci != kSsPadSlot; + const int slot = valid ? ci : 0; + const float cm = static_cast(p.cache_mask)[t] ? 1.0f : 0.0f; + auto* cp = static_cast<__nv_bfloat16*>(p.cache); + const auto* wp = static_cast(p.weight); + uint4 hist[W1]; + if (own) { + const int64_t cb = static_cast(slot) * p.cache_stride_slot + olc; +#pragma unroll + for (int w = 0; w < W1; ++w) { + hist[w] = *reinterpret_cast(&cp[cb + w * p.cache_stride_w]); + } + } + // taps: uint2 per channel at W==4; per-thread 8 channels -> 8 uint2 loads + uint2 wr8[kSsVecElems]; +#pragma unroll + for (int j = 0; j < static_cast(kSsVecElems); ++j) { + if (own) { + if constexpr (W == 4) { + wr8[j] = *reinterpret_cast(&wp[(olc + j) * W]); + } + } + } + uint4 res_raw[VPT]; +#pragma unroll + for (int i = 0; i < VPT; ++i) { + if (act[i]) { + res_raw[i] = *reinterpret_cast( + static_cast(p.residual_in) + static_cast(t) * p.H + c0[i]); + } + } + asm volatile("griddepcontrol.wait;" ::: "memory"); + + // ---- 1. entry: peers' producer partials visible (block t <-> peer t) ---- + inkling_ar::block_system_barrier(p.state, p.flag_ptrs, p.rank); + + // ---- 2. reduce own columns + conv from registers + broadcast ---- + const auto* mc_in = static_cast(p.mc_in); + auto* mc_out = static_cast<__nv_bfloat16*>(p.mc_out); + uint4 xb; + if (own) { + const __nv_bfloat16* a = mc_in + static_cast(t) * p.H + p.rank * p.Hc + olc; + asm volatile("multimem.ld_reduce.relaxed.sys.global.add.v4.bf16x2 {%0,%1,%2,%3}, [%4];" + : "=r"(xb.x), "=r"(xb.y), "=r"(xb.z), "=r"(xb.w) + : "l"(a)); + const auto* xh = reinterpret_cast(&xb); + __nv_bfloat162 yb[4]; +#pragma unroll + for (int j2 = 0; j2 < 4; ++j2) { + float yj[2]; +#pragma unroll + for (int hh = 0; hh < 2; ++hh) { + const int j = 2 * j2 + hh; + const float xj = __bfloat162float(xh[j]); + const auto wt = [&](int w) -> float { + if constexpr (W == 4) { + return __bfloat162float(reinterpret_cast(&wr8[j])[w]); + } else { + return __bfloat162float(wp[(olc + j) * W + w]); + } + }; + float acc = xj * wt(W1); +#pragma unroll + for (int k = 0; k < W1; ++k) { + const float tap = __bfloat162float(reinterpret_cast(&hist[k])[j]); + acc += tap * cm * wt(k); + } + if constexpr (USE_SILU) acc = __fdividef(acc, 1.0f + __expf(-acc)); + if constexpr (USE_RESIDUAL) acc += xj; + yj[hh] = acc; + } + yb[j2] = __floats2bfloat162_rn(yj[0], yj[1]); + } + __nv_bfloat16* ao = mc_out + static_cast(t) * p.H + p.rank * p.Hc + olc; + asm volatile("multimem.st.relaxed.sys.global.v4.bf16x2 [%0], {%1,%2,%3,%4};" ::"l"(ao), + "r"(reinterpret_cast(yb)[0]), + "r"(reinterpret_cast(yb)[1]), + "r"(reinterpret_cast(yb)[2]), + "r"(reinterpret_cast(yb)[3]) + : "memory"); + + // ---- 3. inline cache shift-update (+ decode track snapshot) ---- + if (valid) { + const int64_t cb = static_cast(slot) * p.cache_stride_slot + olc; + const uint4 zero = make_uint4(0, 0, 0, 0); + uint4 nv[W1]; +#pragma unroll + for (int w = 0; w < W1; ++w) { + nv[w] = (w < W1 - 1) ? ((cm != 0.0f) ? hist[w + 1] : zero) : xb; + *reinterpret_cast(&cp[cb + w * p.cache_stride_w]) = nv[w]; + } + if (p.track_mask != nullptr && static_cast(p.track_mask)[t]) { + const int64_t dst = static_cast(p.track_dst)[static_cast(t) * p.track_dst_stride]; + const int64_t db = dst * p.cache_stride_slot + olc; +#pragma unroll + for (int w = 0; w < W1; ++w) { + *reinterpret_cast(&cp[db + w * p.cache_stride_w]) = nv[w]; + } + } + } + } + + // ---- 4. exit: all ranks' row-t broadcasts landed locally ---- + inkling_ar::block_system_barrier(p.state, p.flag_ptrs, p.rank); + + // ---- 5. full-row add+RMSNorm (flashinfer semantics) ---- + const auto* outp = static_cast(p.out_local); + float r[VPT][kSsVecElems]; + float sumsq = 0.0f; +#pragma unroll + for (int i = 0; i < VPT; ++i) { + if (!act[i]) continue; + const uint4 ov4 = *reinterpret_cast(outp + static_cast(t) * p.H + c0[i]); + const auto* oh = reinterpret_cast(&ov4); + const auto* rh = reinterpret_cast(&res_raw[i]); +#pragma unroll + for (int j = 0; j < static_cast(kSsVecElems); ++j) { + const float v = __bfloat162float(oh[j]) + __bfloat162float(rh[j]); + r[i][j] = v; + sumsq += v * v; + } + } + __shared__ float s_warp[32]; + __shared__ float s_inv; + const uint32_t lane = threadIdx.x & 31u; + const uint32_t warp = threadIdx.x >> 5; +#pragma unroll + for (int off = 16; off > 0; off >>= 1) + sumsq += __shfl_down_sync(~0u, sumsq, off); + if (lane == 0) s_warp[warp] = sumsq; + __syncthreads(); + if (warp == 0) { + const uint32_t nwarps = (blockDim.x + 31u) >> 5; + float total = (lane < nwarps && lane < 32u) ? s_warp[lane] : 0.0f; +#pragma unroll + for (int off = 16; off > 0; off >>= 1) + total += __shfl_down_sync(~0u, total, off); + if (lane == 0) s_inv = rsqrtf(total / static_cast(p.H) + p.eps); + } + __syncthreads(); + const float inv = s_inv; + const auto* gw = static_cast(p.norm_weight); + auto* ro = static_cast<__nv_bfloat16*>(p.residual_out) + static_cast(t) * p.H; + auto* ho = static_cast<__nv_bfloat16*>(p.hs_out) + static_cast(t) * p.H; +#pragma unroll + for (int i = 0; i < VPT; ++i) { + if (!act[i]) continue; + __nv_bfloat162 rr[4], hh2[4]; +#pragma unroll + for (int j = 0; j < 4; ++j) { + const float g0 = __bfloat162float(gw[c0[i] + 2 * j]); + const float g1 = __bfloat162float(gw[c0[i] + 2 * j + 1]); + rr[j] = __floats2bfloat162_rn(r[i][2 * j], r[i][2 * j + 1]); + hh2[j] = __floats2bfloat162_rn(r[i][2 * j] * inv * g0, r[i][2 * j + 1] * inv * g1); + } + *reinterpret_cast(ro + c0[i]) = *reinterpret_cast(rr); + *reinterpret_cast(ho + c0[i]) = *reinterpret_cast(hh2); + } +} + +template +struct ColDecodeKernel { + template + static void launch(const ColDecodeParams& params, uint32_t t_num, uint32_t vecs, DLDevice dev) { + using namespace host; + const uint32_t block = min(1024u, host::div_ceil(host::div_ceil(vecs, VPT), 32u) * 32u); + constexpr auto kernel = inkling_ar_col_decode_kernel; + const auto stream = LaunchKernel::resolve_device(dev); + LaunchKernel(dim3{t_num}, dim3{block}, stream)(kernel, params); + } + + static void + run(tvm::ffi::TensorView in_buffer, // [T, H] view of the input symm region + tvm::ffi::TensorView out_local, // [T, H] view of the OUT symm region + tvm::ffi::TensorView residual_in, + tvm::ffi::TensorView residual_out, + tvm::ffi::TensorView hs_out, + tvm::ffi::TensorView norm_weight, + double eps, + tvm::ffi::TensorView cache, // [pool, W-1, Hc] shard + tvm::ffi::TensorView cache_indices, // int32 [T] + tvm::ffi::TensorView cache_mask, // bool [T] + tvm::ffi::TensorView weight, // bf16 [Hc, W] shard + tvm::ffi::TensorView track_mask, // bool [T] (numel 0 -> off) + tvm::ffi::TensorView track_dst, // int64 [T] + int64_t mc_in, + int64_t mc_out, + int64_t flag_ptrs_dev, + int64_t state_ptr, + int64_t rank, + int64_t vecs_per_thread) { + using namespace host; + const uint32_t T = static_cast(in_buffer.size(0)); + const uint32_t H = static_cast(in_buffer.size(1)); + const uint32_t Hc = static_cast(cache.size(2)); + const uint32_t vecs = H / kSsVecElems; + RuntimeCheck(Hc * kNumGPU == H, "Hc * world must equal H"); + RuntimeCheck(T >= 1 && T <= inkling_ar::kMaxBarrierBlocks, "T must fit one barrier slot per token"); + RuntimeCheck( + weight.size(0) == Hc && weight.size(1) == W && weight.stride(1) == 1 && weight.stride(0) == W, + "weight must be contiguous [Hc, W]"); + RuntimeCheck(cache.stride(2) == 1, "cache must be channel-contiguous"); + RuntimeCheck(norm_weight.numel() == H, "norm_weight must be [H]"); + RuntimeCheck(mc_in % 16 == 0 && mc_out % 16 == 0, "mc ptrs must be 16B aligned"); + RuntimeCheck(flag_ptrs_dev != 0 && state_ptr != 0, "null barrier resources"); + const bool do_track = track_mask.numel() > 0; + const auto params = ColDecodeParams{ + .mc_in = reinterpret_cast(mc_in), + .mc_out = reinterpret_cast(mc_out), + .out_local = out_local.data_ptr(), + .flag_ptrs = reinterpret_cast(flag_ptrs_dev), + .state = reinterpret_cast(state_ptr), + .cache = cache.data_ptr(), + .cache_indices = cache_indices.data_ptr(), + .cache_mask = cache_mask.data_ptr(), + .weight = weight.data_ptr(), + .track_mask = do_track ? track_mask.data_ptr() : nullptr, + .track_dst = do_track ? track_dst.data_ptr() : nullptr, + .residual_in = residual_in.data_ptr(), + .residual_out = residual_out.data_ptr(), + .hs_out = hs_out.data_ptr(), + .norm_weight = norm_weight.data_ptr(), + .eps = static_cast(eps), + .cache_stride_slot = cache.stride(0), + .cache_stride_w = cache.stride(1), + .track_dst_stride = do_track ? track_dst.stride(0) : 0, + .rank = static_cast(rank), + .T = T, + .H = H, + .Hc = Hc, + }; + const int vpt = vecs_per_thread > 0 ? static_cast(vecs_per_thread) : 1; + const auto dev = in_buffer.device(); + // Own-shard lanes use one vec per thread: block must cover Hc/8. + RuntimeCheck( + host::div_ceil(vecs, static_cast(vpt)) >= Hc / kSsVecElems, + "block too small for the own-shard lanes"); + switch (vpt) { + case 1: + launch<1>(params, T, vecs, dev); + break; + case 2: + launch<2>(params, T, vecs, dev); + break; + case 3: + launch<3>(params, T, vecs, dev); + break; + case 4: + launch<4>(params, T, vecs, dev); + break; + default: + RuntimeCheck(false, "unsupported vecs_per_thread (1/2/3/4)"); + } + } +}; + +// --------------------------------------------------------------------------- +// TOKEN-BANDED fused {v3 AR + full-width sconv}: v3's contiguous slice +// geometry (rank r owns a token band x full H -- v3-class switch-transaction +// efficiency) with the conv fused in. Unlike the column-sharded kernel above, +// weights and conv-state cache stay FULL-width (no --enable-scattered-sconv +// dependency): this fuses the PRODUCTION {v3 all-reduce + sconv} chain. +// Phase 1: contiguous multimem.ld_reduce of the band plus W-1 halo rows +// into a local scratch (all loads independent). +// sync: grid_local_sync publishes the scratch. +// Phase 2: full-width conv reading taps from the (local) scratch; the +// post-conv band broadcasts contiguously via multimem.st. +// Phase 3: conv-state update + prefix-cache track, run IDENTICALLY on every +// rank (each re-ld_reduces just the B*(W-1) sequence-end / track +// rows -- tiny), so every rank's full-width cache stays complete +// exactly as the unfused chain leaves it. +// --------------------------------------------------------------------------- + +struct ArBandedSconvParams { + const void* __restrict__ mc_in; // multicast base of [T, H] partial sums + void* __restrict__ mc_out; // multicast base of the [T, H] OUT region + void* __restrict__ scratch; // LOCAL [tpr + W-1, H] band scratch + void* __restrict__ cache; // [slots, W-1, H] conv cache (in-place); + // SCATTERED mode (Hc < H): [slots, W-1, Hc] shard + // Scattered-cache mode: full-width window staging [B, W-1, H]. Each rank + // pushes its Hc-column shard of every active slot's window pre-barrier; + // phase 2 prefix taps read this instead of the (sharded) cache. + void* __restrict__ mc_wstage; // multicast base (null in full-width mode) + const void* __restrict__ wstage; // this rank's local view + const void* __restrict__ safe_idx; // int64 [B] + const void* __restrict__ cache_mask; // bool [B] + const void* __restrict__ ci; // int32 [B] raw cache slots (PAD == -1) + const void* __restrict__ has_init; // bool [B] + const void* __restrict__ cu; // int64 [B+1] + const void* __restrict__ si; // int32 [T] + const void* __restrict__ weight; // bf16 [H, W] + const void* __restrict__ track_rows; // int64 [B, W-1] gather rows (or null) + const void* __restrict__ track_mask; // bool [B] (or null) + const void* __restrict__ track_dst; // int64 [B] (or null) + void* const* __restrict__ flag_ptrs; + uint32_t* __restrict__ state; + int64_t cache_stride_slot; + int64_t cache_stride_w; + int64_t track_dst_stride; + uint32_t rank; + uint32_t T; + uint32_t H; + uint32_t Hc; // == H in full-width mode; < H = scattered (sharded cache) + uint32_t B; + uint32_t debug_phase; // 0=full, 1=phase1 only, 2=+copy-broadcast, 3=+conv (no phase3) +}; + +template +__global__ +__launch_bounds__(1024, 1) void inkling_ar_banded_sconv_kernel(const __grid_constant__ ArBandedSconvParams p) { + static_assert(std::is_same_v, "multimem path is bf16-only"); + constexpr int W1 = W - 1; + const bool scattered = p.Hc < p.H; + + // ---- Scattered mode: push my cache-window shard for every active slot + // into the full-width staged region BEFORE the entry barrier (the barrier + // handshake publishes it alongside the producers' partials). ---- + if (scattered) { + const uint32_t pstride = gridDim.x * blockDim.x; + const uint32_t ptid = blockIdx.x * blockDim.x + threadIdx.x; + const auto* sidx = static_cast(p.safe_idx); + const auto* csh = static_cast(p.cache); + auto* wst = static_cast<__nv_bfloat16*>(p.mc_wstage); + const uint32_t cvecs = p.Hc / kSsVecElems; + const uint32_t items = p.B * W1 * cvecs; + for (uint32_t it = ptid; it < items; it += pstride) { + const uint32_t b = it / (W1 * cvecs); + const uint32_t w = (it / cvecs) % W1; + const uint32_t lc = (it % cvecs) * kSsVecElems; + const uint4 v = *reinterpret_cast(&csh[sidx[b] * p.cache_stride_slot + w * p.cache_stride_w + lc]); + __nv_bfloat16* addr = wst + (static_cast(b) * W1 + w) * p.H + p.rank * p.Hc + lc; + asm volatile("multimem.st.relaxed.sys.global.v4.bf16x2 [%0], {%1,%2,%3,%4};" ::"l"(addr), + "r"(v.x), + "r"(v.y), + "r"(v.z), + "r"(v.w) + : "memory"); + } + } + + if constexpr (kPerBlockBarrier) { + inkling_ar::block_system_barrier(p.state, p.flag_ptrs, p.rank); + } else { + // publish_writes only needed for the scattered window push above. + inkling_ar::grid_system_barrier(p.state, p.flag_ptrs, p.rank, 0, scattered); + } + + const uint32_t stride = gridDim.x * blockDim.x; + const uint32_t gtid = blockIdx.x * blockDim.x + threadIdx.x; + const uint32_t vrow = p.H / kSsVecElems; // 16B vecs per row + const uint32_t tpr = (p.T + kNumGPU - 1) / kNumGPU; + const uint32_t t_lo = min(p.rank * tpr, p.T); + const uint32_t t_hi = min(t_lo + tpr, p.T); + const int64_t base = static_cast(t_lo) - W1; // scratch row 0 = base + const uint32_t halo_lo = static_cast(base > 0 ? base : 0); + const auto* si = static_cast(p.si); + const auto* cu = static_cast(p.cu); + const auto* safe_idx = static_cast(p.safe_idx); + const auto* cmask = static_cast(p.cache_mask); + auto* cache = static_cast<__nv_bfloat16*>(p.cache); + const auto* wp = static_cast(p.weight); + const auto* mc_in = static_cast(p.mc_in); + auto* mc_out = static_cast<__nv_bfloat16*>(p.mc_out); + auto* scratch = static_cast<__nv_bfloat16*>(p.scratch); + + // ---- Phase 1: contiguous streamed reduce of [halo_lo, t_hi) x H ---- + { + const uint32_t v0 = halo_lo * vrow; + const uint32_t v1 = t_hi * vrow; + for (uint32_t i = v0 + gtid; i < v1; i += stride) { + const __nv_bfloat16* addr = mc_in + static_cast(i) * kSsVecElems; + uint4 v; + asm volatile("multimem.ld_reduce.relaxed.sys.global.add.v4.bf16x2 {%0,%1,%2,%3}, [%4];" + : "=r"(v.x), "=r"(v.y), "=r"(v.z), "=r"(v.w) + : "l"(addr)); + *reinterpret_cast(scratch + (static_cast(i) - base * vrow) * kSsVecElems) = v; + } + } + + // ---- scratch visible to every block ---- + inkling_ar::grid_local_sync(p.state); + + // ---- Phase 2: full-width conv from scratch; contiguous broadcast ---- + if (p.debug_phase != 1) { + const uint32_t v0 = t_lo * vrow; + const uint32_t v1 = t_hi * vrow; + for (uint32_t i = v0 + gtid; i < v1; i += stride) { + const uint32_t t = i / vrow; + const uint32_t c = (i % vrow) * kSsVecElems; + if (p.debug_phase == 2) { // raw copy broadcast: no conv/taps/weights + const int64_t srow2 = static_cast(t) - base; + const uint4 xr = *reinterpret_cast(scratch + (srow2 * vrow + i % vrow) * kSsVecElems); + __nv_bfloat16* addr2 = mc_out + static_cast(t) * p.H + c; + asm volatile("multimem.st.relaxed.sys.global.v4.bf16x2 [%0], {%1,%2,%3,%4};" ::"l"(addr2), + "r"(xr.x), + "r"(xr.y), + "r"(xr.z), + "r"(xr.w) + : "memory"); + continue; + } + const int s = si[t]; + const int64_t bos = cu[s]; + const bool cm = cmask[s]; + const int64_t cache_base = safe_idx[s] * p.cache_stride_slot + c; + const int64_t srow = static_cast(t) - base; + const uint4 xt = *reinterpret_cast(scratch + (srow * vrow + i % vrow) * kSsVecElems); + + // Scattered mode: prefix taps come from the staged full-width windows + // (published pre-barrier); the persistent cache holds only Hc columns. + const auto* wstage = static_cast(p.wstage); + uint4 taps[W1]; +#pragma unroll + for (int k = 0; k < W1; ++k) { + const int64_t pos = static_cast(t) - (W1 - k); + if (pos >= bos) { + taps[k] = *reinterpret_cast(scratch + ((pos - base) * vrow + i % vrow) * kSsVecElems); + } else { + const int64_t prow = pos - bos + W1; + if (!cm) { + taps[k] = make_uint4(0, 0, 0, 0); + } else if (scattered) { + taps[k] = *reinterpret_cast(wstage + (static_cast(s) * W1 + prow) * p.H + c); + } else { + taps[k] = *reinterpret_cast(&cache[cache_base + prow * p.cache_stride_w]); + } + } + } + + float wt[kSsVecElems][W]; +#pragma unroll + for (int j = 0; j < static_cast(kSsVecElems); ++j) { +#pragma unroll + for (int w = 0; w < W; ++w) { + wt[j][w] = __bfloat162float(wp[static_cast(c + j) * W + w]); + } + } + const auto* xh = reinterpret_cast(&xt); + __nv_bfloat162 yb[4]; +#pragma unroll + for (int j2 = 0; j2 < 4; ++j2) { + float yj[2]; +#pragma unroll + for (int h = 0; h < 2; ++h) { + const int j = 2 * j2 + h; + const float xj = __bfloat162float(xh[j]); + float acc = xj * wt[j][W1]; +#pragma unroll + for (int k = 0; k < W1; ++k) { + const float tap = __bfloat162float(reinterpret_cast(&taps[k])[j]); + acc += tap * wt[j][k]; + } + if constexpr (USE_SILU) acc = __fdividef(acc, 1.0f + __expf(-acc)); + if constexpr (USE_RESIDUAL) acc += xj; + yj[h] = acc; + } + yb[j2] = __floats2bfloat162_rn(yj[0], yj[1]); + } + + __nv_bfloat16* addr = mc_out + static_cast(t) * p.H + c; + asm volatile("multimem.st.relaxed.sys.global.v4.bf16x2 [%0], {%1,%2,%3,%4};" ::"l"(addr), + "r"(reinterpret_cast(yb)[0]), + "r"(reinterpret_cast(yb)[1]), + "r"(reinterpret_cast(yb)[2]), + "r"(reinterpret_cast(yb)[3]) + : "memory"); + } + } + + // ---- WAR fence: all prefix-tap reads done before the state update ---- + inkling_ar::grid_local_sync(p.state); + if (p.debug_phase == 0) + + // ---- Phase 3 (AFTER phase 2 + a second sync: phase 2 reads the OLD cache + // prefix rows that this phase overwrites): conv-state update + track, + // identical on every rank (each re-ld_reduces just the B*(W-1) sequence-end + // / track rows from the pristine input -- tiny). One thread owns all W-1 + // rows of one (sequence, channel-vec) pair -- mirrors update_sconv_cache's + // RAW-safe load-all-then-store ordering. + { + const auto* ci = static_cast(p.ci); + const auto* hinit = static_cast(p.has_init); + // Scattered mode: this rank owns only Hc columns of the cache; iterate + // its shard with the shard-local column co and the global column c for + // the mc_in re-reduces. + const uint32_t uvecs = scattered ? p.Hc / kSsVecElems : vrow; + const uint32_t items3 = p.B * uvecs; + for (uint32_t it = gtid; it < items3; it += stride) { + const uint32_t b = it / uvecs; + const uint32_t co = (it % uvecs) * kSsVecElems; + const uint32_t c = scattered ? p.rank * p.Hc + co : co; + const int slot = ci[b]; + const int64_t qlen = cu[b + 1] - cu[b]; + if (slot != kSsPadSlot && qlen > 0) { + const bool hs = hinit[b]; + const int64_t cb = static_cast(slot) * p.cache_stride_slot + co; + uint4 old_reg[W1]; +#pragma unroll + for (int w = 0; w < W1; ++w) { + old_reg[w] = *reinterpret_cast(&cache[cb + w * p.cache_stride_w]); + } + const uint4 zero = make_uint4(0, 0, 0, 0); +#pragma unroll + for (int w = 0; w < W1; ++w) { + uint4 nv; + if (qlen >= W1 - w) { + const int64_t row = cu[b + 1] - W1 + w; + const __nv_bfloat16* addr = mc_in + row * p.H + c; + asm volatile("multimem.ld_reduce.relaxed.sys.global.add.v4.bf16x2 {%0,%1,%2,%3}, [%4];" + : "=r"(nv.x), "=r"(nv.y), "=r"(nv.z), "=r"(nv.w) + : "l"(addr)); + } else { + uint4 shift = zero; +#pragma unroll + for (int src = 0; src < W1; ++src) { + if (src == w + qlen) shift = old_reg[src]; + } + nv = hs ? shift : zero; + } + *reinterpret_cast(&cache[cb + w * p.cache_stride_w]) = nv; + } + } + // Prefix-cache track: windows of x at chunk-aligned rows -> track slot. + if (p.track_rows != nullptr) { + const auto* tmask = static_cast(p.track_mask); + if (tmask[b]) { + const auto* trows = static_cast(p.track_rows); + const int64_t dst = static_cast(p.track_dst)[static_cast(b) * p.track_dst_stride]; + const int64_t db = dst * p.cache_stride_slot + co; +#pragma unroll + for (int w = 0; w < W1; ++w) { + const int64_t row = trows[static_cast(b) * W1 + w]; + uint4 nv; + const __nv_bfloat16* addr = mc_in + row * p.H + c; + asm volatile("multimem.ld_reduce.relaxed.sys.global.add.v4.bf16x2 {%0,%1,%2,%3}, [%4];" + : "=r"(nv.x), "=r"(nv.y), "=r"(nv.z), "=r"(nv.w) + : "l"(addr)); + *reinterpret_cast(&cache[db + w * p.cache_stride_w]) = nv; + } + } + } + } + } + + if constexpr (kPerBlockBarrier) { + inkling_ar::block_system_barrier(p.state, p.flag_ptrs, p.rank); + } else { + inkling_ar::grid_system_barrier(p.state, p.flag_ptrs, p.rank, 1, true); + } +} + +template +struct ArBandedSconvKernel { + static void + run(tvm::ffi::TensorView in_buffer, // [T, H] view of the input symm region + tvm::ffi::TensorView scratch, // LOCAL [tpr + W-1, H] bf16 + tvm::ffi::TensorView cache, // [slots, W-1, H] bf16 (in-place) + tvm::ffi::TensorView safe_idx, // int64 [B] + tvm::ffi::TensorView cache_mask, // bool [B] + tvm::ffi::TensorView ci, // int32 [B] + tvm::ffi::TensorView has_init, // bool [B] + tvm::ffi::TensorView cu, // int64 [B+1] + tvm::ffi::TensorView si, // int32 [T] + tvm::ffi::TensorView weight, // bf16 [H, W] + tvm::ffi::TensorView track_rows, // int64 [B, W-1] (numel 0 -> no track) + tvm::ffi::TensorView track_mask, // bool [B] + tvm::ffi::TensorView track_dst, // int64 [B] (possibly strided) + int64_t mc_in, + int64_t mc_out, + int64_t flag_ptrs_dev, + int64_t state_ptr, + int64_t rank, + int64_t nb_override, + int64_t bs_override, + bool per_block_barrier, + int64_t debug_phase, + int64_t mc_wstage, // 0 -> full-width cache mode + int64_t local_wstage) { // this rank's view of the [B, W-1, H] staging + using namespace host; + RuntimeCheck(in_buffer.ndim() == 2, "in must be [T, H]"); + const uint32_t T = static_cast(in_buffer.size(0)); + const uint32_t H = static_cast(in_buffer.size(1)); + const uint32_t B = static_cast(safe_idx.size(0)); + const uint32_t Hc = static_cast(cache.size(2)); + const bool scattered = Hc < H; + RuntimeCheck(H % kSsVecElems == 0, "H must be a multiple of 8"); + RuntimeCheck(weight.size(0) == H && weight.size(1) == W, "weight must be [H, W]"); + RuntimeCheck(cache.size(1) == W - 1, "cache must be [slots, W-1, Hc|H]"); + if (scattered) { + RuntimeCheck(Hc * kNumGPU == H, "sharded cache: Hc * world must equal H"); + RuntimeCheck(Hc % kSsVecElems == 0, "Hc must be a multiple of 8"); + RuntimeCheck( + mc_wstage != 0 && local_wstage != 0 && mc_wstage % 16 == 0, "scattered mode needs the window staging region"); + } else { + RuntimeCheck(Hc == H, "full-width cache must be [slots, W-1, H]"); + } + RuntimeCheck(cache.stride(2) == 1, "cache must be channel-contiguous"); + RuntimeCheck(si.size(0) >= T, "si must cover T tokens"); + const uint32_t tpr = host::div_ceil(T, kNumGPU); + RuntimeCheck(scratch.size(0) >= tpr + W - 1 && scratch.size(1) == H, "scratch must be [tpr + W-1, H]"); + RuntimeCheck(mc_in % 16 == 0 && mc_out % 16 == 0, "multicast ptrs must be 16B aligned"); + RuntimeCheck(flag_ptrs_dev != 0 && state_ptr != 0, "barrier resources are null"); + RuntimeCheck(rank >= 0 && rank < kNumGPU, "rank out of range"); + const bool do_track = track_rows.numel() > 0; + if (T == 0) return; + + const auto params = ArBandedSconvParams{ + .mc_in = reinterpret_cast(mc_in), + .mc_out = reinterpret_cast(mc_out), + .scratch = scratch.data_ptr(), + .cache = cache.data_ptr(), + .mc_wstage = reinterpret_cast(mc_wstage), + .wstage = reinterpret_cast(local_wstage), + .safe_idx = safe_idx.data_ptr(), + .cache_mask = cache_mask.data_ptr(), + .ci = ci.data_ptr(), + .has_init = has_init.data_ptr(), + .cu = cu.data_ptr(), + .si = si.data_ptr(), + .weight = weight.data_ptr(), + .track_rows = do_track ? track_rows.data_ptr() : nullptr, + .track_mask = do_track ? track_mask.data_ptr() : nullptr, + .track_dst = do_track ? track_dst.data_ptr() : nullptr, + .flag_ptrs = reinterpret_cast(flag_ptrs_dev), + .state = reinterpret_cast(state_ptr), + .cache_stride_slot = cache.stride(0), + .cache_stride_w = cache.stride(1), + .track_dst_stride = do_track ? track_dst.stride(0) : 0, + .rank = static_cast(rank), + .T = T, + .H = H, + .Hc = Hc, + .B = B, + .debug_phase = static_cast(debug_phase), + }; + + const auto device = in_buffer.device(); + const uint32_t block_size = bs_override > 0 ? static_cast(bs_override) : 512u; + const auto launch = [&](auto kernel) { + const uint32_t items = tpr * (H / kSsVecElems); + uint32_t cap = ss_max_resident_blocks(kernel, block_size, device); + if (per_block_barrier) cap = min(cap, inkling_ar::kMaxBarrierBlocks); + const uint32_t want = max(1u, host::div_ceil(items, block_size)); + const uint32_t num_blocks = nb_override > 0 ? min(static_cast(nb_override), cap) : min(want, cap); + const auto stream = LaunchKernel::resolve_device(device); + LaunchKernel(num_blocks, block_size, stream)(kernel, params); + }; + if (per_block_barrier) { + launch(inkling_ar_banded_sconv_kernel); + } else { + launch(inkling_ar_banded_sconv_kernel); + } + } +}; + +// --------------------------------------------------------------------------- +// ONE-SHOT decode {all-reduce + scattered sconv + add-RMSNorm}: the v5 push +// one-shot pattern (inkling_ar_fused_decode.cuh) adapted to the SHARDED conv +// cache. The two-shot column kernel above pays TWO cross-rank sync rounds per +// site (entry + exit) because the post-conv shard exchange needs a publish, +// which is markedly slower than a full-width one-shot at decode size. This +// variant removes the exchange entirely: alongside its partial row, each +// rank also multicast-pushes its CACHE-WINDOW SHARD (W-1 rows x Hc columns +// per active slot -- tiny at decode T), so after ONE per-block barrier every +// rank holds the full partials AND full-width conv windows, convs ALL H +// channels redundantly, and finishes the add+RMSNorm locally. The persistent +// cache stays sharded: each rank shift-updates (and tracks) only its own +// columns. Requires the FULL-width conv weight as a caller-supplied argument +// (not wired to a production call site; exercised by the validation/bench +// harness only). +// +// Staging: partials occupy one v5 rotation slot ([world, T, D], same reuse- +// distance rule as ar_sconv_norm). Windows stage in a rotating half of the +// (decode-idle) scattered OUT region ([T, W-1, D] per half). +// PAD rows (cache_indices == -1) compute y/hs but never write the cache; a +// row's staged window is garbage for PAD/fresh rows and is masked by +// cache_mask at the conv, matching the unfused decode kernel. +// --------------------------------------------------------------------------- + +struct SsconvNormDecodeParams { + const void* __restrict__ in; // [T, D] partial sums (LOCAL tensor) + void* __restrict__ mc_stage; // multicast partial staging (>= world*T*D) + const void* __restrict__ stage; // local view of the partial staging + void* __restrict__ mc_wstage; // multicast window staging (>= T*(W-1)*D) + const void* __restrict__ wstage; // local view of the window staging + void* const* __restrict__ flag_ptrs; + uint32_t* __restrict__ state; + void* __restrict__ cache; // [pool, W-1, Hc] SHARDED, in-place + const void* __restrict__ cache_indices; // int32 [T] (PAD == -1) + const void* __restrict__ cache_mask; // bool [T] + const void* __restrict__ conv_weight; // [D, W] FULL width + const void* __restrict__ track_mask; // bool [T] (or null) + const void* __restrict__ track_indices; // int64 [T] (or null) + const void* __restrict__ residual_in; // [T, D] + void* __restrict__ residual_out; // [T, D] + void* __restrict__ hs_out; // [T, D] + const void* __restrict__ norm_weight; // [D] + float eps; + int64_t in_stride_t; + int64_t res_in_stride_t; + int64_t res_out_stride_t; + int64_t hs_stride_t; + int64_t cache_stride_slot; + int64_t cache_stride_w; + int64_t conv_weight_stride_d; + int64_t track_idx_stride; + uint32_t rank; + uint32_t T; + uint32_t D; // full hidden + uint32_t Hc; // per-rank channel shard +}; + +template +__global__ +__launch_bounds__(1024, 1) void inkling_ar_ssconv_norm_decode_kernel(const __grid_constant__ SsconvNormDecodeParams p) { + static_assert(std::is_same_v, "multimem push path is bf16-only"); + constexpr int W1 = W - 1; + const uint32_t t = blockIdx.x; + const uint32_t vecs = p.D / kSsVecElems; + const uint32_t shard_lo = p.rank * p.Hc; + + uint32_t c0[VPT]; + bool act[VPT]; + bool mine[VPT]; // vec falls in this rank's cache shard +#pragma unroll + for (int i = 0; i < VPT; ++i) { + const uint32_t v = threadIdx.x + i * blockDim.x; + act[i] = v < vecs; + c0[i] = (act[i] ? v : 0) * kSsVecElems; + mine[i] = act[i] && c0[i] >= shard_lo && c0[i] < shard_lo + p.Hc; + } + + // ---- 0. prefetch (independent of the producer's output) ---- + const int ci = static_cast(p.cache_indices)[t]; + const bool valid = ci != kSsPadSlot; + const int slot_id = valid ? ci : 0; + const float cm = static_cast(p.cache_mask)[t] ? 1.0f : 0.0f; + auto* cp = static_cast<__nv_bfloat16*>(p.cache); + const auto* wp = static_cast(p.conv_weight); + uint4 hist_raw[VPT][W1]; // MY shard's window (mine[i] lanes only) + __nv_bfloat16 wtaps[VPT][kSsVecElems][W]; +#pragma unroll + for (int i = 0; i < VPT; ++i) { + if (!act[i]) continue; + if (mine[i]) { + const int64_t cache_base = static_cast(slot_id) * p.cache_stride_slot + (c0[i] - shard_lo); +#pragma unroll + for (int w = 0; w < W1; ++w) { + hist_raw[i][w] = *reinterpret_cast(&cp[cache_base + w * p.cache_stride_w]); + } + } +#pragma unroll + for (int j = 0; j < static_cast(kSsVecElems); ++j) { + const int64_t wrow = static_cast(c0[i] + j) * p.conv_weight_stride_d; + if constexpr (W == 4) { + if (p.conv_weight_stride_d == W) { + *reinterpret_cast(wtaps[i][j]) = *reinterpret_cast(wp + wrow); + continue; + } + } +#pragma unroll + for (int w = 0; w < W; ++w) + wtaps[i][j][w] = wp[wrow + w]; + } + } + + // ---- 1. push: this rank's partial row + its window shard ---- + asm volatile("griddepcontrol.wait;" ::: "memory"); + const auto* in_row = static_cast(p.in) + t * p.in_stride_t; + auto* slot = static_cast<__nv_bfloat16*>(p.mc_stage) + (static_cast(p.rank) * p.T + t) * p.D; + auto* wslot = static_cast<__nv_bfloat16*>(p.mc_wstage) + static_cast(t) * W1 * p.D; + uint4 res_raw[VPT]; +#pragma unroll + for (int i = 0; i < VPT; ++i) { + if (!act[i]) continue; + const uint4 d = *reinterpret_cast(in_row + c0[i]); + asm volatile("multimem.st.relaxed.sys.global.v4.bf16x2 [%0], {%1,%2,%3,%4};" ::"l"(slot + c0[i]), + "r"(d.x), + "r"(d.y), + "r"(d.z), + "r"(d.w) + : "memory"); + if (mine[i]) { +#pragma unroll + for (int w = 0; w < W1; ++w) { + const uint4 h = hist_raw[i][w]; + asm volatile("multimem.st.relaxed.sys.global.v4.bf16x2 [%0], {%1,%2,%3,%4};" ::"l"( + wslot + static_cast(w) * p.D + c0[i]), + "r"(h.x), + "r"(h.y), + "r"(h.z), + "r"(h.w) + : "memory"); + } + } + res_raw[i] = *reinterpret_cast( + static_cast(p.residual_in) + t * p.res_in_stride_t + c0[i]); + } + + // ---- 2. per-block barrier: all ranks' row-t pushes have landed locally ---- + inkling_ar::block_system_barrier(p.state, p.flag_ptrs, p.rank); + // Inactive lanes must NOT exit: they participate in the norm reduction. + + float r[VPT][kSsVecElems]; + float sumsq = 0.0f; + const auto* stage = static_cast(p.stage); + const auto* wstage = static_cast(p.wstage) + static_cast(t) * W1 * p.D; +#pragma unroll + for (int i = 0; i < VPT; ++i) { + if (!act[i]) continue; + // ---- 3. reduce the world partial copies; round to bf16 ---- + float xf[kSsVecElems]; +#pragma unroll + for (int j = 0; j < static_cast(kSsVecElems); ++j) + xf[j] = 0.0f; +#pragma unroll + for (uint32_t rr = 0; rr < kNumGPU; ++rr) { + const uint4 d = *reinterpret_cast(stage + (static_cast(rr) * p.T + t) * p.D + c0[i]); + const auto* h2 = reinterpret_cast(&d); +#pragma unroll + for (int j = 0; j < 4; ++j) { + const float2 f = __bfloat1622float2(h2[j]); + xf[2 * j] += f.x; + xf[2 * j + 1] += f.y; + } + } + __nv_bfloat162 xb2[4]; +#pragma unroll + for (int j = 0; j < 4; ++j) + xb2[j] = __floats2bfloat162_rn(xf[2 * j], xf[2 * j + 1]); + + // ---- 4. sconv over the FULL width from the staged windows ---- + uint4 taps[W1]; +#pragma unroll + for (int w = 0; w < W1; ++w) { + taps[w] = *reinterpret_cast(wstage + static_cast(w) * p.D + c0[i]); + } + float y[kSsVecElems]; +#pragma unroll + for (int j = 0; j < static_cast(kSsVecElems); ++j) { + const float xj = __bfloat162float(reinterpret_cast(xb2)[j]); + float acc = 0.0f; +#pragma unroll + for (int w = 0; w < W1; ++w) { + const float h = __bfloat162float(reinterpret_cast(&taps[w])[j]); + acc += h * cm * __bfloat162float(wtaps[i][j][w]); + } + acc += xj * __bfloat162float(wtaps[i][j][W1]); + if constexpr (USE_SILU) acc = __fdividef(acc, 1.0f + __expf(-acc)); + if constexpr (USE_RESIDUAL) acc += xj; + y[j] = acc; + } + + // ---- 4b. cache shift-update (+track) for MY shard columns only ---- + if (valid && mine[i]) { + const int64_t cache_base = static_cast(slot_id) * p.cache_stride_slot + (c0[i] - shard_lo); + int64_t track_base = 0; + bool do_tr = false; + if (p.track_mask != nullptr) { + do_tr = static_cast(p.track_mask)[t]; + if (do_tr) { + const int64_t tslot = + static_cast(p.track_indices)[static_cast(t) * p.track_idx_stride]; + track_base = tslot * p.cache_stride_slot + (c0[i] - shard_lo); + } + } + const uint4 zero = make_uint4(0, 0, 0, 0); +#pragma unroll + for (int w = 0; w < W1; ++w) { + const uint4 nv = + (w < W1 - 1) ? ((cm != 0.0f) ? hist_raw[i][w + 1] : zero) : *reinterpret_cast(xb2); + *reinterpret_cast(&cp[cache_base + w * p.cache_stride_w]) = nv; + if (do_tr) { + *reinterpret_cast(&cp[track_base + w * p.cache_stride_w]) = nv; + } + } + } + + // ---- 5a. residual add (fused_add_rmsnorm semantics) ---- +#pragma unroll + for (int j = 0; j < static_cast(kSsVecElems); ++j) { + const float yb = __bfloat162float(__float2bfloat16_rn(y[j])); + r[i][j] = yb + __bfloat162float(reinterpret_cast(&res_raw[i])[j]); + sumsq += r[i][j] * r[i][j]; + } + } + + // ---- 5b. block reduction of sumsq ---- + __shared__ float s_warp[32]; + __shared__ float s_inv; + const uint32_t lane = threadIdx.x & 31u; + const uint32_t warp = threadIdx.x >> 5; +#pragma unroll + for (int off = 16; off > 0; off >>= 1) + sumsq += __shfl_down_sync(~0u, sumsq, off); + if (lane == 0) s_warp[warp] = sumsq; + __syncthreads(); + if (warp == 0) { + const uint32_t nwarps = (blockDim.x + 31u) >> 5; + float total = (lane < nwarps && lane < 32u) ? s_warp[lane] : 0.0f; +#pragma unroll + for (int off = 16; off > 0; off >>= 1) + total += __shfl_down_sync(~0u, total, off); + if (lane == 0) s_inv = rsqrtf(total / static_cast(p.D) + p.eps); + } + __syncthreads(); + const float inv = s_inv; + + const auto* gw = static_cast(p.norm_weight); + auto* res_out = static_cast<__nv_bfloat16*>(p.residual_out) + t * p.res_out_stride_t; + auto* hs_out = static_cast<__nv_bfloat16*>(p.hs_out) + t * p.hs_stride_t; +#pragma unroll + for (int i = 0; i < VPT; ++i) { + if (!act[i]) continue; + __nv_bfloat162 ro[4], ho[4]; +#pragma unroll + for (int j = 0; j < 4; ++j) { + const float g0 = __bfloat162float(gw[c0[i] + 2 * j]); + const float g1 = __bfloat162float(gw[c0[i] + 2 * j + 1]); + ro[j] = __floats2bfloat162_rn(r[i][2 * j], r[i][2 * j + 1]); + ho[j] = __floats2bfloat162_rn(r[i][2 * j] * inv * g0, r[i][2 * j + 1] * inv * g1); + } + *reinterpret_cast(res_out + c0[i]) = *reinterpret_cast(ro); + *reinterpret_cast(hs_out + c0[i]) = *reinterpret_cast(ho); + } +} + +template +struct SsconvNormDecodeKernel { + template + static void launch(const SsconvNormDecodeParams& params, uint32_t t_num, uint32_t vecs, DLDevice dev) { + using namespace host; + const uint32_t block = min(1024u, host::div_ceil(host::div_ceil(vecs, VPT), 32u) * 32u); + constexpr auto kernel = inkling_ar_ssconv_norm_decode_kernel; + const auto stream = LaunchKernel::resolve_device(dev); + LaunchKernel(dim3{t_num}, dim3{block}, stream)(kernel, params); + } + + static void + run(tvm::ffi::TensorView in, + tvm::ffi::TensorView residual_in, + tvm::ffi::TensorView residual_out, + tvm::ffi::TensorView hs_out, + tvm::ffi::TensorView norm_weight, + double eps, + tvm::ffi::TensorView cache, // [pool, W-1, Hc] SHARDED + tvm::ffi::TensorView cache_indices, // int32 [T] + tvm::ffi::TensorView cache_mask, // bool [T] + tvm::ffi::TensorView conv_weight, // [D, W] FULL width + tvm::ffi::TensorView track_mask, // bool [T] (numel 0 -> no track) + tvm::ffi::TensorView track_indices, // int64 [T] + int64_t mc_stage_ptr, + int64_t local_stage_ptr, + int64_t mc_wstage_ptr, + int64_t local_wstage_ptr, + int64_t flag_ptrs_dev, + int64_t state_ptr, + int64_t rank, + int64_t vecs_per_thread) { + using namespace host; + RuntimeCheck(in.ndim() == 2, "in must be [T, D]"); + const uint32_t t_num = static_cast(in.size(0)); + const uint32_t d_num = static_cast(in.size(1)); + const uint32_t hc = static_cast(cache.size(2)); + const uint32_t vecs = d_num / kSsVecElems; + RuntimeCheck(hc * kNumGPU == d_num, "Hc * world must equal D"); + RuntimeCheck(hc % kSsVecElems == 0, "Hc must be a multiple of 8"); + RuntimeCheck(t_num >= 1 && t_num <= inkling_ar::kMaxBarrierBlocks, "T must be in [1, kMaxBarrierBlocks]"); + RuntimeCheck(d_num % kSsVecElems == 0, "D must be a multiple of 8"); + RuntimeCheck(cache.size(1) == W - 1, "cache must be [pool, W-1, Hc]"); + RuntimeCheck(cache.stride(2) == 1, "cache must be channel-contiguous"); + RuntimeCheck(conv_weight.size(0) == d_num && conv_weight.size(1) == W, "conv_weight must be FULL [D, W]"); + RuntimeCheck(norm_weight.numel() == d_num, "norm_weight must be [D]"); + RuntimeCheck(mc_stage_ptr % 16 == 0 && mc_wstage_ptr % 16 == 0, "staging ptrs must be 16B aligned"); + RuntimeCheck(local_stage_ptr != 0 && local_wstage_ptr != 0, "null staging"); + RuntimeCheck(flag_ptrs_dev != 0 && state_ptr != 0, "null barrier resources"); + RuntimeCheck(rank >= 0 && rank < kNumGPU, "rank out of range"); + const bool do_track = track_mask.numel() > 0; + + const auto params = SsconvNormDecodeParams{ + .in = in.data_ptr(), + .mc_stage = reinterpret_cast(mc_stage_ptr), + .stage = reinterpret_cast(local_stage_ptr), + .mc_wstage = reinterpret_cast(mc_wstage_ptr), + .wstage = reinterpret_cast(local_wstage_ptr), + .flag_ptrs = reinterpret_cast(flag_ptrs_dev), + .state = reinterpret_cast(state_ptr), + .cache = cache.data_ptr(), + .cache_indices = cache_indices.data_ptr(), + .cache_mask = cache_mask.data_ptr(), + .conv_weight = conv_weight.data_ptr(), + .track_mask = do_track ? track_mask.data_ptr() : nullptr, + .track_indices = do_track ? track_indices.data_ptr() : nullptr, + .residual_in = residual_in.data_ptr(), + .residual_out = residual_out.data_ptr(), + .hs_out = hs_out.data_ptr(), + .norm_weight = norm_weight.data_ptr(), + .eps = static_cast(eps), + .in_stride_t = in.stride(0), + .res_in_stride_t = residual_in.stride(0), + .res_out_stride_t = residual_out.stride(0), + .hs_stride_t = hs_out.stride(0), + .cache_stride_slot = cache.stride(0), + .cache_stride_w = cache.stride(1), + .conv_weight_stride_d = conv_weight.stride(0), + .track_idx_stride = do_track ? track_indices.stride(0) : 0, + .rank = static_cast(rank), + .T = t_num, + .D = d_num, + .Hc = hc, + }; + + const int vpt = vecs_per_thread > 0 ? static_cast(vecs_per_thread) : 1; + const auto dev = in.device(); + switch (vpt) { + case 1: + RuntimeCheck(vecs <= 1024, "D/8 must fit one block at VPT=1"); + launch<1>(params, t_num, vecs, dev); + break; + case 2: + launch<2>(params, t_num, vecs, dev); + break; + case 3: + launch<3>(params, t_num, vecs, dev); + break; + case 4: + launch<4>(params, t_num, vecs, dev); + break; + default: + RuntimeCheck(false, "unsupported vecs_per_thread (use 1/2/3/4)"); + } + } +}; + +} // namespace diff --git a/python/sglang/jit_kernel/csrc/inkling/inkling_attn_prologue_fused.cuh b/python/sglang/jit_kernel/csrc/inkling/inkling_attn_prologue_fused.cuh new file mode 100644 index 000000000..e09a54b0b --- /dev/null +++ b/python/sglang/jit_kernel/csrc/inkling/inkling_attn_prologue_fused.cuh @@ -0,0 +1,1441 @@ +// Fused attention prologues for Inkling: after the qkvr projection, ONE +// kernel does {k_sconv + v_sconv + per-head q/k RMSNorm + the KV-cache +// store}, in three variants -- TARGET-VERIFY (fixed q tokens/seq + both +// save_intermediate_conv_windows), DECODE (conv from the working cache + +// in-block shift-update + track), and EXTEND (varlen sequences via si/cu + +// a tiny trailing conv-cache-update/track kernel). Replaces 2x causal_conv1d +// + save_windows or update_sconv_cache + fused qk-norm + the backend's +// set_kv_buffer scatter (attention then runs with save_kv_cache=False). +// rel_logits_proj overlaps on the alt stream. +// +// Layout: ONE BLOCK PER TOKEN; one 16B vec (8 channels) per thread. Lane +// roles by vec index: [0, Dq/8) q-norm lanes, then Dkv/8 k lanes, then Dkv/8 +// v lanes. head_dim=128 -> a head is 16 CONTIGUOUS lanes, reduced with +// width-16 warp shuffles (Dq/8 and Dkv/8 are multiples of 16, so head groups +// never straddle warps). The convs read cross-token taps directly from the +// (strided) qkvr tensor; per-seq prefixes from the read-only conv caches; the +// per-position windows go to the intermediate buffers exactly like +// save_intermediate_conv_windows (raw copies, no gating). PAD sequences +// (cache_indices == -1) skip prefix/window IO but still emit outputs and the +// KV store (mirroring the unfused path, which stores pad rows too). + +#include +#include + +#include + +#include +#include + +#include +#include +#include +#include +#include + +namespace { + +constexpr int kPadSlot = -1; +constexpr uint32_t kVecElems = 8; +constexpr uint32_t kHeadDim = 128; +constexpr uint32_t kHeadLanes = kHeadDim / kVecElems; // 16 +constexpr uint32_t kMXFP8Block = 32; +constexpr uint32_t kMXFP8BlockLanes = kMXFP8Block / kVecElems; // 4 +constexpr float kE4M3Max = 448.0f; + +// Per-head (16-lane) sum-reduce -> rsqrt(mean(ss)+eps), broadcast to all lanes +// of the group. A head is 16 CONTIGUOUS lanes on a 16-aligned boundary (Dq/8 +// and Dkv/8 are multiples of 16), and the q / k / v roles also start on +// 16-aligned boundaries -- so each 16-lane half-warp is a single (role, head) +// group whose lanes all reach (or all skip) this reduction. The mask names +// EXACTLY that half-warp (not the whole warp), so it stays valid even when the +// k and v roles split a warp (odd num_tp_kv_heads, e.g. 1 KV head/rank) or the +// q/k boundary falls mid-warp -- an xor butterfly then leaves every lane in the +// group with the full sum (no cross-role shuffle, no exited-lane in the mask). +__device__ __forceinline__ float head_rmsnorm_inv(float ss, float eps) { + const unsigned hmask = 0xFFFFu << (threadIdx.x & 16u); // this thread's 16-lane group +#pragma unroll + for (int off = 8; off > 0; off >>= 1) + ss += __shfl_xor_sync(hmask, ss, off, 16); + return rsqrtf(ss / static_cast(kHeadDim) + eps); +} + +__device__ __forceinline__ uint8_t mxfp8_scale_byte(float local_amax, float* descale) { + const unsigned qmask = 0xFu << (threadIdx.x & 28u); +#pragma unroll + for (int off = 1; off < static_cast(kMXFP8BlockLanes); off <<= 1) { + local_amax = fmaxf(local_amax, __shfl_xor_sync(qmask, local_amax, off, kMXFP8BlockLanes)); + } + const float amax = fmaxf(local_amax, 1.0e-30f); + float scale_biased = ceilf(log2f(amax / kE4M3Max)) + 127.0f; + scale_biased = fminf(fmaxf(scale_biased, 0.0f), 254.0f); + *descale = exp2f(scale_biased - 127.0f); + return static_cast(scale_biased); +} + +__device__ __forceinline__ void store_mxfp8_vec(const float (&x)[kVecElems], void* dst, uint8_t* sf, uint32_t c) { + float local_amax = 0.0f; +#pragma unroll + for (int j = 0; j < static_cast(kVecElems); ++j) { + local_amax = fmaxf(local_amax, fabsf(x[j])); + } + float descale; + const uint8_t sf_byte = mxfp8_scale_byte(local_amax, &descale); + if ((c & (kMXFP8Block - 1)) == 0) *sf = sf_byte; + + union { + __nv_fp8x2_e4m3 fp8x2[4]; + uint2 raw; + } u; +#pragma unroll + for (int j = 0; j < 4; ++j) { + const float x0 = fminf(fmaxf(x[2 * j] / descale, -kE4M3Max), kE4M3Max); + const float x1 = fminf(fmaxf(x[2 * j + 1] / descale, -kE4M3Max), kE4M3Max); + u.fp8x2[j] = __nv_fp8x2_e4m3(make_float2(x0, x1)); + } + *reinterpret_cast(static_cast(dst) + c) = u.raw; +} + +struct AttnPrologueParams { + const void* __restrict__ qkvr; // [T, row_stride] packed projection output + // sconv (verify) per K/V path + const void* __restrict__ k_cache; // [pool, W-1, Dkv] + const void* __restrict__ v_cache; + const void* __restrict__ cache_indices; // int32 [B] (PAD == -1) + const void* __restrict__ cache_mask; // bool [B] + const void* __restrict__ k_weight; // [Dkv, W] + const void* __restrict__ v_weight; + void* __restrict__ k_inter; // [max_bs, q, W-1, Dkv] + void* __restrict__ v_inter; + // norms + const void* __restrict__ q_gamma; // [head_dim] + const void* __restrict__ k_gamma; // [head_dim] + const void* __restrict__ log_tau; // fp32 [T] per-token q scale (null -> off) + float eps; + // outputs + void* __restrict__ q_out; // [T, Dq] + void* __restrict__ k_out; // [T, Dkv] + void* __restrict__ v_out; // [T, Dkv] + const void* __restrict__ loc; // int64 [T] KV slots + void* __restrict__ k_buf; // [slots, Hkv, head_dim] + void* __restrict__ v_buf; + void* __restrict__ sfq; // uint8 [T, Hq, head_dim/32] when USE_MXFP8 + void* __restrict__ sfk; // uint8 BlockScaledBasicChunk layout + void* __restrict__ sfv; + int64_t qkvr_stride_t; + int64_t q_off; // elem offsets of the q/k/v slices within a qkvr row + int64_t k_off; + int64_t v_off; + int64_t cache_stride_slot; + int64_t cache_stride_w; + int64_t weight_stride_d; + int64_t inter_stride_b; + int64_t inter_stride_t; + int64_t inter_stride_w; + int64_t kv_buf_stride; // elems per KV slot row (= Hkv * head_dim) + uint32_t T; + uint32_t q; // draft_token_num + uint32_t dq; + uint32_t dkv; + uint32_t page_size; +}; + +template +__global__ __launch_bounds__(1024, 1) void inkling_attn_prologue_kernel(const __grid_constant__ AttnPrologueParams p) { + static_assert(std::is_same_v); + static_assert(!USE_MXFP8 || DO_STORE, "MXFP8 prologue quantization owns the KV store"); + constexpr int W1 = W - 1; + constexpr uint32_t SF = kHeadDim / kMXFP8Block; + const uint32_t t = blockIdx.x; + const uint32_t seq = t / p.q; + const int bos = static_cast(seq * p.q); + const uint32_t tq = t - seq * p.q; + const uint32_t nq = p.dq / kVecElems; + const uint32_t nkv = p.dkv / kVecElems; + const uint32_t vi = threadIdx.x; + const auto* base = static_cast(p.qkvr); + const int64_t row = static_cast(t) * p.qkvr_stride_t; + + const int ci = static_cast(p.cache_indices)[seq]; + const bool valid = ci != kPadSlot; + const int slot_id = valid ? ci : 0; + const float cm = (valid && static_cast(p.cache_mask)[seq]) ? 1.0f : 0.0f; + + // PDL: as in the decode kernel, the immediately-preceding qkvr GEMM only + // produces qkvr -- gammas, tau, conv weights/cache prefix and metadata are + // prefetched before PDLWaitPrimary; every qkvr read stays behind the wait. + if (vi < nq) { + // ---------------- q path: per-head RMSNorm only ---------------- + const uint32_t c = vi * kVecElems; + const uint4 gqraw = *reinterpret_cast(static_cast(p.q_gamma) + (c % kHeadDim)); + const auto* gq = reinterpret_cast(&gqraw); + const bool do_tau = p.log_tau != nullptr; + float tau = 0.0f; + if (do_tau) tau = static_cast(p.log_tau)[t]; + device::PDLWaitPrimary(); + const uint4 raw = *reinterpret_cast(base + row + p.q_off + c); + float x[kVecElems]; + float ss = 0.0f; +#pragma unroll + for (int j = 0; j < 8; ++j) { + x[j] = __bfloat162float(reinterpret_cast(&raw)[j]); + ss += x[j] * x[j]; + } + const float inv = head_rmsnorm_inv(ss, p.eps); + __nv_bfloat162 o[4]; +#pragma unroll + for (int j = 0; j < 4; ++j) { + o[j] = __floats2bfloat162_rn( + x[2 * j] * inv * __bfloat162float(gq[2 * j]), x[2 * j + 1] * inv * __bfloat162float(gq[2 * j + 1])); + } + if (do_tau) { + // Fused log-scaling tau: multiply the bf16-ROUNDED normed q (matching + // the unfused {norm kernel -> apply_log_scaling_tau} rounding exactly); + // on the MXFP8 path this scales BEFORE quantization. +#pragma unroll + for (int j = 0; j < 4; ++j) { + const float2 f = __bfloat1622float2(o[j]); + o[j] = __floats2bfloat162_rn(f.x * tau, f.y * tau); + } + } + if constexpr (USE_MXFP8) { + float q_quant[kVecElems]; +#pragma unroll + for (int j = 0; j < 4; ++j) { + const float2 f = __bfloat1622float2(o[j]); + q_quant[2 * j] = f.x; + q_quant[2 * j + 1] = f.y; + } + const uint32_t sf_idx = static_cast(t) * (p.dq / kMXFP8Block) + c / kMXFP8Block; + store_mxfp8_vec( + q_quant, + static_cast(p.q_out) + static_cast(t) * p.dq, + static_cast(p.sfq) + sf_idx, + c); + } else { + *reinterpret_cast(static_cast<__nv_bfloat16*>(p.q_out) + static_cast(t) * p.dq + c) = + *reinterpret_cast(o); + } + device::PDLTriggerSecondary(); + return; + } + if (vi >= nq + 2 * nkv) return; + + // ---------------- k / v paths: conv + save_windows (+ k norm) + store ---- + const bool is_k = vi < nq + nkv; + const uint32_t ch = (is_k ? vi - nq : vi - nq - nkv) * kVecElems; + const int64_t x_off = is_k ? p.k_off : p.v_off; + const auto* cp = static_cast(is_k ? p.k_cache : p.v_cache); + const auto* wp = static_cast(is_k ? p.k_weight : p.v_weight); + auto* ip = static_cast<__nv_bfloat16*>(is_k ? p.k_inter : p.v_inter); + const int64_t cache_base = static_cast(slot_id) * p.cache_stride_slot + ch; + + uint4 pref[W1]; + __nv_bfloat16 wt[kVecElems][W]; +#pragma unroll + for (int w = 0; w < W1; ++w) { + pref[w] = *reinterpret_cast(&cp[cache_base + w * p.cache_stride_w]); + } +#pragma unroll + for (int j = 0; j < 8; ++j) { + const int64_t wrow = static_cast(ch + j) * p.weight_stride_d; + if constexpr (W == 4) { + if (p.weight_stride_d == W) { + *reinterpret_cast(wt[j]) = *reinterpret_cast(wp + wrow); + continue; + } + } +#pragma unroll + for (int w = 0; w < W; ++w) + wt[j][w] = wp[wrow + w]; + } + + uint4 gkraw = make_uint4(0, 0, 0, 0); + if (is_k) { + gkraw = *reinterpret_cast(static_cast(p.k_gamma) + (ch % kHeadDim)); + } + device::PDLWaitPrimary(); + // In-seq neighbor rows (pre-conv x straight from qkvr) + own row. + const uint4 xcur = *reinterpret_cast(base + row + x_off + ch); + uint4 xn[W1]; +#pragma unroll + for (int j = 1; j <= W1; ++j) { + const int n = static_cast(t) - j; + if (n >= bos) { + xn[j - 1] = *reinterpret_cast(base + static_cast(n) * p.qkvr_stride_t + x_off + ch); + } + } + + float y[kVecElems]; +#pragma unroll + for (int j = 0; j < 8; ++j) { + const float xj = __bfloat162float(reinterpret_cast(&xcur)[j]); + float acc = 0.0f; +#pragma unroll + for (int iw = 0; iw < W1; ++iw) { + const int shifted = static_cast(t) - W1 + iw; + float tap = 0.0f; + if (shifted >= bos) { + tap = __bfloat162float(reinterpret_cast(&xn[W1 - 1 - iw])[j]); + } else { + const int prefix_pos = shifted - bos + W1; + if (prefix_pos >= 0) { + tap = cm * __bfloat162float(reinterpret_cast(&pref[prefix_pos])[j]); + } + } + acc += tap * __bfloat162float(wt[j][iw]); + } + acc += xj * __bfloat162float(wt[j][W1]); + if constexpr (USE_SILU) acc = __fdividef(acc, 1.0f + __expf(-acc)); + if constexpr (USE_RESIDUAL) acc += xj; + y[j] = acc; + } + + if (valid) { // save_intermediate_conv_windows (raw copies) + auto* op = ip + static_cast(seq) * p.inter_stride_b + static_cast(tq) * p.inter_stride_t + ch; +#pragma unroll + for (int w = 0; w < W1; ++w) { + const int position = static_cast(tq) + 1 + w; + uint4 val; + if (position < W1) { + val = pref[position]; + } else { + const int g = bos + position - W1; + val = (g == static_cast(t)) ? xcur : xn[t - g - 1]; + } + *reinterpret_cast(op + w * p.inter_stride_w) = val; + } + } + + __nv_bfloat162 o[4]; + if (is_k) { + // per-head RMSNorm on the conv output (16-lane groups). Round to bf16 + // FIRST: the unfused pipeline writes the conv output to memory as bf16 + // before the norm kernel reads it back. + float ss = 0.0f; +#pragma unroll + for (int j = 0; j < 8; ++j) { + y[j] = __bfloat162float(__float2bfloat16_rn(y[j])); + ss += y[j] * y[j]; + } + const float inv = head_rmsnorm_inv(ss, p.eps); + const auto* gk = reinterpret_cast(&gkraw); +#pragma unroll + for (int j = 0; j < 4; ++j) { + o[j] = __floats2bfloat162_rn( + y[2 * j] * inv * __bfloat162float(gk[2 * j]), y[2 * j + 1] * inv * __bfloat162float(gk[2 * j + 1])); + } + } else { +#pragma unroll + for (int j = 0; j < 4; ++j) + o[j] = __floats2bfloat162_rn(y[2 * j], y[2 * j + 1]); + } + const uint4 ov = *reinterpret_cast(o); + auto* out = static_cast<__nv_bfloat16*>(is_k ? p.k_out : p.v_out); + *reinterpret_cast(out + static_cast(t) * p.dkv + ch) = ov; + // Fused KV store (DO_STORE). Only used for full-attention layers writing a + // plain bf16 [slots, Hkv, head_dim] pool indexed directly by out_cache_loc; + // SWA/local layers keep the backend store (swa_out_cache_loc + its own pool), + // so the caller passes DO_STORE=false and save_kv_cache=True there. + if (DO_STORE) { + const int64_t kv_slot = static_cast(p.loc)[t]; + if (kv_slot >= 0) { // SWA full->swa translation can yield -1 sentinels + if constexpr (USE_MXFP8) { + float xo[kVecElems]; +#pragma unroll + for (int j = 0; j < 4; ++j) { + const float2 f = __bfloat1622float2(o[j]); + xo[2 * j] = f.x; + xo[2 * j + 1] = f.y; + } + auto* buf = static_cast(is_k ? p.k_buf : p.v_buf); + auto* sfb = static_cast(is_k ? p.sfk : p.sfv); + const int64_t po = kv_slot % static_cast(p.page_size); + const int64_t sf_base = ((kv_slot / static_cast(p.page_size)) * (p.dkv / kHeadDim) + ch / kHeadDim) * + (kMXFP8Block * (p.page_size / kMXFP8Block) * SF) + + (po % kMXFP8Block) * ((p.page_size / kMXFP8Block) * SF) + (po / kMXFP8Block) * SF + + (ch % kHeadDim) / kMXFP8Block; + store_mxfp8_vec(xo, buf + kv_slot * p.kv_buf_stride, sfb + sf_base, ch); + } else { + auto* buf = static_cast<__nv_bfloat16*>(is_k ? p.k_buf : p.v_buf); + *reinterpret_cast(buf + kv_slot * p.kv_buf_stride + ch) = ov; + } + } + } + device::PDLTriggerSecondary(); +} + +template +struct AttnPrologueKernel { + static void + run(tvm::ffi::TensorView qkvr, + tvm::ffi::TensorView k_cache, + tvm::ffi::TensorView v_cache, + tvm::ffi::TensorView cache_indices, + tvm::ffi::TensorView cache_mask, + tvm::ffi::TensorView k_weight, + tvm::ffi::TensorView v_weight, + tvm::ffi::TensorView k_inter, + tvm::ffi::TensorView v_inter, + tvm::ffi::TensorView q_gamma, + tvm::ffi::TensorView k_gamma, + double eps, + tvm::ffi::TensorView q_out, + tvm::ffi::TensorView k_out, + tvm::ffi::TensorView v_out, + tvm::ffi::TensorView loc, + tvm::ffi::TensorView k_buf, + tvm::ffi::TensorView v_buf, + tvm::ffi::TensorView sfq, + tvm::ffi::TensorView sfk, + tvm::ffi::TensorView sfv, + int64_t q_off, + int64_t k_off, + int64_t v_off, + int64_t q_num, + int64_t do_store, + int64_t page_size, + tvm::ffi::TensorView log_tau) { + using namespace host; + const uint32_t T = static_cast(qkvr.size(0)); + const uint32_t B = static_cast(cache_indices.size(0)); + const uint32_t dq = static_cast(q_out.size(1)); + const uint32_t dkv = static_cast(k_out.size(1)); + RuntimeCheck(q_num > 0 && T == B * static_cast(q_num), "T != B*q"); + RuntimeCheck(dq % kHeadDim == 0 && dkv % kHeadDim == 0, "dims % head_dim"); + RuntimeCheck((dq / kVecElems) % kHeadLanes == 0, "q lanes must tile heads"); + RuntimeCheck( + qkvr.stride(1) == 1 && qkvr.stride(0) % kVecElems == 0, "qkvr must be row-major with 16B-aligned rows"); + RuntimeCheck( + q_off % kVecElems == 0 && k_off % kVecElems == 0 && v_off % kVecElems == 0, + "slice offsets must be 16B aligned"); + RuntimeCheck(k_buf.stride(0) == v_buf.stride(0), "kv buf stride mismatch"); + RuntimeCheck(k_cache.stride(2) == 1 && v_cache.stride(2) == 1, "conv caches must be channel-contiguous"); + RuntimeCheck( + k_inter.stride(3) == 1 && v_inter.stride(3) == 1 && k_inter.stride(0) == v_inter.stride(0) && + k_inter.stride(1) == v_inter.stride(1) && k_inter.stride(2) == v_inter.stride(2), + "inter buffers must be channel-contiguous with equal strides"); + const uint32_t lanes = dq / kVecElems + 2 * (dkv / kVecElems); + RuntimeCheck(lanes <= 1024, "token lanes must fit one block"); + if constexpr (USE_MXFP8) { + RuntimeCheck(do_store, "MXFP8 fused prologue requires do_store=True"); + RuntimeCheck(dq % kMXFP8Block == 0 && dkv % kMXFP8Block == 0, "MXFP8 dims must tile 32-element scale blocks"); + RuntimeCheck(page_size > 0 && page_size % kMXFP8Block == 0, "MXFP8 page size must tile 32-token scale blocks"); + RuntimeCheck(is_type(q_out.dtype()), "MXFP8 q_out must be fp8_e4m3"); + RuntimeCheck( + is_type(k_buf.dtype()) && is_type(v_buf.dtype()), + "MXFP8 KV buffers must be fp8_e4m3"); + RuntimeCheck( + is_type(sfq.dtype()) && is_type(sfk.dtype()) && is_type(sfv.dtype()), + "MXFP8 scale buffers must be passed as uint8 views"); + RuntimeCheck(q_out.stride(1) == 1 && q_out.stride(0) == dq, "MXFP8 q_out must be contiguous"); + RuntimeCheck( + sfq.stride(2) == 1 && sfq.stride(1) == kHeadDim / kMXFP8Block, "MXFP8 sfq must be contiguous [T, Hq, 4]"); + const int64_t hkv = dkv / kHeadDim; + const int64_t sf_dim = kHeadDim / kMXFP8Block; + const int64_t page_chunks = page_size / kMXFP8Block; + RuntimeCheck(sfk.ndim() == 5 && sfv.ndim() == 5, "MXFP8 SFK/SFV must be 5D interleaved"); + RuntimeCheck( + sfk.size(1) == hkv && sfv.size(1) == hkv && sfk.size(2) == kMXFP8Block && sfv.size(2) == kMXFP8Block && + sfk.size(3) == page_chunks && sfv.size(3) == page_chunks && sfk.size(4) == sf_dim && + sfv.size(4) == sf_dim, + "MXFP8 SFK/SFV must use [pages, Hkv, 32, page/32, 4] layout"); + RuntimeCheck( + sfk.stride(4) == 1 && sfv.stride(4) == 1 && sfk.stride(3) == sf_dim && sfv.stride(3) == sf_dim && + sfk.stride(2) == page_chunks * sf_dim && sfv.stride(2) == page_chunks * sf_dim && + sfk.stride(1) == kMXFP8Block * page_chunks * sf_dim && + sfv.stride(1) == kMXFP8Block * page_chunks * sf_dim, + "MXFP8 SFK/SFV must be contiguous BlockScaledBasicChunk layout"); + RuntimeCheck(k_buf.stride(0) % kMXFP8Block == 0, "MXFP8 kv buf row alignment"); + } else { + RuntimeCheck(k_buf.stride(0) % kVecElems == 0, "kv buf rows must be 16B aligned"); + RuntimeCheck(is_type(q_out.dtype()), "q_out dtype mismatch"); + } + + const bool do_tau = log_tau.numel() > 0; + if (do_tau) { + RuntimeCheck(is_type(log_tau.dtype()), "log_tau must be fp32"); + RuntimeCheck(log_tau.IsContiguous(), "log_tau must be contiguous"); + RuntimeCheck(log_tau.numel() >= qkvr.size(0), "log_tau smaller than T"); + } + const auto params = AttnPrologueParams{ + .qkvr = qkvr.data_ptr(), + .k_cache = k_cache.data_ptr(), + .v_cache = v_cache.data_ptr(), + .cache_indices = cache_indices.data_ptr(), + .cache_mask = cache_mask.data_ptr(), + .k_weight = k_weight.data_ptr(), + .v_weight = v_weight.data_ptr(), + .k_inter = k_inter.data_ptr(), + .v_inter = v_inter.data_ptr(), + .q_gamma = q_gamma.data_ptr(), + .k_gamma = k_gamma.data_ptr(), + .log_tau = do_tau ? log_tau.data_ptr() : nullptr, + .eps = static_cast(eps), + .q_out = q_out.data_ptr(), + .k_out = k_out.data_ptr(), + .v_out = v_out.data_ptr(), + .loc = loc.data_ptr(), + .k_buf = k_buf.data_ptr(), + .v_buf = v_buf.data_ptr(), + .sfq = USE_MXFP8 ? sfq.data_ptr() : nullptr, + .sfk = USE_MXFP8 ? sfk.data_ptr() : nullptr, + .sfv = USE_MXFP8 ? sfv.data_ptr() : nullptr, + .qkvr_stride_t = qkvr.stride(0), + .q_off = q_off, + .k_off = k_off, + .v_off = v_off, + .cache_stride_slot = k_cache.stride(0), + .cache_stride_w = k_cache.stride(1), + .weight_stride_d = k_weight.stride(0), + .inter_stride_b = k_inter.stride(0), + .inter_stride_t = k_inter.stride(1), + .inter_stride_w = k_inter.stride(2), + .kv_buf_stride = k_buf.stride(0), + .T = T, + .q = static_cast(q_num), + .dq = dq, + .dkv = dkv, + .page_size = static_cast(page_size), + }; + RuntimeCheck( + k_cache.stride(0) == v_cache.stride(0) && k_cache.stride(1) == v_cache.stride(1) && + k_weight.stride(0) == v_weight.stride(0), + "k/v cache+weight strides must match"); + const uint32_t block = div_ceil(lanes, 32u) * 32u; + const auto kernel = do_store + ? inkling_attn_prologue_kernel + : inkling_attn_prologue_kernel; + LaunchKernel(dim3{T}, dim3{block}, qkvr.device()).enable_pdl(USE_PDL)(kernel, params); + } +}; + +// --------------------------------------------------------------------------- +// DECODE variant: {k/v decode-conv + conv-cache shift-update (+ track) + +// qk-norm + KV store}. Decode is one token per sequence, so the conv taps come +// from the working conv cache (W-1 history + current token) -- no cross-token +// reads, no AR, no barrier: every token is an independent block. Mirrors +// fused_decode_update.cuh's conv + shift-update + prefix-cache track-copy, then +// adds the per-head q/k RMSNorm and the bf16 KV-cache store (so attention runs +// save_kv_cache=False). Replaces 2x fused_decode_update + qk-norm + set_kv_buffer. +struct AttnPrologueDecodeParams { + const void* __restrict__ qkvr; + void* __restrict__ k_cache; // [pool, W-1, Dkv], in-place shift-update + void* __restrict__ v_cache; + const void* __restrict__ cache_indices; // int32 [T] per-token slot (PAD == -1) + const void* __restrict__ cache_mask; // bool [T] per-token history gate + const void* __restrict__ k_weight; // [Dkv, W] + const void* __restrict__ v_weight; + const void* __restrict__ track_mask; // bool [T] (DO_TRACK) + const void* __restrict__ track_indices; // int64 [T] (DO_TRACK) + const void* __restrict__ q_gamma; + const void* __restrict__ k_gamma; + const void* __restrict__ log_tau; // fp32 [T] per-token q scale (null -> off) + float eps; + void* __restrict__ q_out; + void* __restrict__ k_out; + void* __restrict__ v_out; + const void* __restrict__ loc; + void* __restrict__ k_buf; + void* __restrict__ v_buf; + void* __restrict__ sfq; // uint8 [T, Hq, head_dim/32] when USE_MXFP8 + void* __restrict__ sfk; // uint8 BlockScaledBasicChunk layout + void* __restrict__ sfv; + int64_t qkvr_stride_t; + int64_t q_off; + int64_t k_off; + int64_t v_off; + int64_t cache_stride_slot; + int64_t cache_stride_w; + int64_t weight_stride_d; + int64_t track_idx_stride; + int64_t kv_buf_stride; + uint32_t T; + uint32_t dq; + uint32_t dkv; + uint32_t page_size; +}; + +template < + typename DType, + int W, + bool USE_SILU, + bool USE_RESIDUAL, + bool DO_TRACK, + bool DO_STORE, + bool USE_MXFP8, + bool USE_PDL> +__global__ __launch_bounds__(1024, 1) void inkling_attn_prologue_decode_kernel( + const __grid_constant__ AttnPrologueDecodeParams p) { + static_assert(std::is_same_v); + static_assert(!USE_MXFP8 || DO_STORE, "MXFP8 decode prologue quantization owns the KV store"); + constexpr int W1 = W - 1; + const uint32_t t = blockIdx.x; + const uint32_t nq = p.dq / kVecElems; + const uint32_t nkv = p.dkv / kVecElems; + const uint32_t vi = threadIdx.x; + const auto* base = static_cast(p.qkvr); + const int64_t row = static_cast(t) * p.qkvr_stride_t; + + // PDL: the ONLY input the immediately-preceding kernel (the qkvr + // projection GEMM) produces is qkvr itself. Gammas, tau, conv weights, + // metadata and the conv-cache history (last written a full step earlier) + // are prefetched BEFORE PDLWaitPrimary so their latency hides under the + // primary's tail; every qkvr read stays behind the wait. + if (vi < nq) { + // -------- q path: per-head RMSNorm only -------- + const uint32_t c = vi * kVecElems; + const uint4 gqraw = *reinterpret_cast(static_cast(p.q_gamma) + (c % kHeadDim)); + const auto* gq = reinterpret_cast(&gqraw); + const bool do_tau = p.log_tau != nullptr; + float tau = 0.0f; + if (do_tau) tau = static_cast(p.log_tau)[t]; + device::PDLWaitPrimary(); + const uint4 raw = *reinterpret_cast(base + row + p.q_off + c); + float x[kVecElems]; + float ss = 0.0f; +#pragma unroll + for (int j = 0; j < 8; ++j) { + x[j] = __bfloat162float(reinterpret_cast(&raw)[j]); + ss += x[j] * x[j]; + } + const float inv = head_rmsnorm_inv(ss, p.eps); + __nv_bfloat162 o[4]; +#pragma unroll + for (int j = 0; j < 4; ++j) { + o[j] = __floats2bfloat162_rn( + x[2 * j] * inv * __bfloat162float(gq[2 * j]), x[2 * j + 1] * inv * __bfloat162float(gq[2 * j + 1])); + } + if (do_tau) { + // Fused log-scaling tau: multiply the bf16-ROUNDED normed q (matching + // the unfused {norm kernel -> apply_log_scaling_tau} rounding exactly); + // on the MXFP8 path this scales BEFORE quantization. +#pragma unroll + for (int j = 0; j < 4; ++j) { + const float2 f = __bfloat1622float2(o[j]); + o[j] = __floats2bfloat162_rn(f.x * tau, f.y * tau); + } + } + if constexpr (USE_MXFP8) { + float q_quant[kVecElems]; +#pragma unroll + for (int j = 0; j < 4; ++j) { + const float2 f = __bfloat1622float2(o[j]); + q_quant[2 * j] = f.x; + q_quant[2 * j + 1] = f.y; + } + const uint32_t sf_idx = static_cast(t) * (p.dq / kMXFP8Block) + c / kMXFP8Block; + store_mxfp8_vec( + q_quant, + static_cast(p.q_out) + static_cast(t) * p.dq, + static_cast(p.sfq) + sf_idx, + c); + } else { + *reinterpret_cast(static_cast<__nv_bfloat16*>(p.q_out) + static_cast(t) * p.dq + c) = + *reinterpret_cast(o); + } + device::PDLTriggerSecondary(); + return; + } + if (vi >= nq + 2 * nkv) return; + + // -------- k / v paths: decode-conv + cache update (+ track) (+ k-norm) + store -------- + const bool is_k = vi < nq + nkv; + const uint32_t ch = (is_k ? vi - nq : vi - nq - nkv) * kVecElems; + const int64_t x_off = is_k ? p.k_off : p.v_off; + auto* cp = static_cast<__nv_bfloat16*>(is_k ? p.k_cache : p.v_cache); + const auto* wp = static_cast(is_k ? p.k_weight : p.v_weight); + + const int ci = static_cast(p.cache_indices)[t]; + const bool valid = ci != kPadSlot; + const int slot_id = valid ? ci : 0; // PAD lanes still emit y/store, never write conv cache + const float cm = static_cast(p.cache_mask)[t] ? 1.0f : 0.0f; + const int64_t cache_base = static_cast(slot_id) * p.cache_stride_slot + ch; + + // History taps + current token -> registers BEFORE any cache write (RAW-safe). + uint4 hist[W1]; +#pragma unroll + for (int w = 0; w < W1; ++w) { + hist[w] = *reinterpret_cast(&cp[cache_base + w * p.cache_stride_w]); + } + __nv_bfloat16 wt[kVecElems][W]; +#pragma unroll + for (int j = 0; j < 8; ++j) { + const int64_t wrow = static_cast(ch + j) * p.weight_stride_d; + if constexpr (W == 4) { + if (p.weight_stride_d == W) { + *reinterpret_cast(wt[j]) = *reinterpret_cast(wp + wrow); + continue; + } + } +#pragma unroll + for (int w = 0; w < W; ++w) + wt[j][w] = wp[wrow + w]; + } + uint4 gkraw = make_uint4(0, 0, 0, 0); + if (is_k) { + gkraw = *reinterpret_cast(static_cast(p.k_gamma) + (ch % kHeadDim)); + } + device::PDLWaitPrimary(); + const uint4 xv = *reinterpret_cast(base + row + x_off + ch); + + // conv (fused_decode_update semantics): W-1 cached taps (cm-gated) + current. + float y[kVecElems]; +#pragma unroll + for (int j = 0; j < 8; ++j) { + const float xj = __bfloat162float(reinterpret_cast(&xv)[j]); + float acc = 0.0f; +#pragma unroll + for (int w = 0; w < W1; ++w) { + const float h = __bfloat162float(reinterpret_cast(&hist[w])[j]); + acc += h * cm * __bfloat162float(wt[j][w]); + } + acc += xj * __bfloat162float(wt[j][W1]); + if constexpr (USE_SILU) acc = __fdividef(acc, 1.0f + __expf(-acc)); + if constexpr (USE_RESIDUAL) acc += xj; + y[j] = acc; + } + + // cache shift-update (valid lanes): new[iw] = (iw(p.track_mask)[t]; + if (do_tr) { + const int64_t tslot = + static_cast(p.track_indices)[static_cast(t) * p.track_idx_stride]; + track_base = tslot * p.cache_stride_slot + ch; + } + } + const uint4 zero = make_uint4(0, 0, 0, 0); +#pragma unroll + for (int w = 0; w < W1; ++w) { + const uint4 nv = (w < W1 - 1) ? ((cm != 0.0f) ? hist[w + 1] : zero) : xv; + *reinterpret_cast(&cp[cache_base + w * p.cache_stride_w]) = nv; + if constexpr (DO_TRACK) { + if (do_tr) *reinterpret_cast(&cp[track_base + w * p.cache_stride_w]) = nv; + } + } + } + + // k-norm (round to bf16 first, matching the unfused HBM round trip); v passes through. + __nv_bfloat162 o[4]; + if (is_k) { + float ss = 0.0f; +#pragma unroll + for (int j = 0; j < 8; ++j) { + y[j] = __bfloat162float(__float2bfloat16_rn(y[j])); + ss += y[j] * y[j]; + } + const float inv = head_rmsnorm_inv(ss, p.eps); + const auto* gk = reinterpret_cast(&gkraw); +#pragma unroll + for (int j = 0; j < 4; ++j) { + o[j] = __floats2bfloat162_rn( + y[2 * j] * inv * __bfloat162float(gk[2 * j]), y[2 * j + 1] * inv * __bfloat162float(gk[2 * j + 1])); + } + } else { +#pragma unroll + for (int j = 0; j < 4; ++j) + o[j] = __floats2bfloat162_rn(y[2 * j], y[2 * j + 1]); + } + const uint4 ov = *reinterpret_cast(o); + auto* out = static_cast<__nv_bfloat16*>(is_k ? p.k_out : p.v_out); + *reinterpret_cast(out + static_cast(t) * p.dkv + ch) = ov; + if (DO_STORE && valid) { + const int64_t kv_slot = static_cast(p.loc)[t]; + if (kv_slot >= 0) { + if constexpr (USE_MXFP8) { + float xo[kVecElems]; +#pragma unroll + for (int j = 0; j < 4; ++j) { + const float2 f = __bfloat1622float2(o[j]); + xo[2 * j] = f.x; + xo[2 * j + 1] = f.y; + } + auto* buf = static_cast(is_k ? p.k_buf : p.v_buf); + auto* sfb = static_cast(is_k ? p.sfk : p.sfv); + const int64_t po = kv_slot % static_cast(p.page_size); + const int64_t sf_base = ((kv_slot / static_cast(p.page_size)) * (p.dkv / kHeadDim) + ch / kHeadDim) * + (kMXFP8Block * (p.page_size / kMXFP8Block) * (kHeadDim / kMXFP8Block)) + + (po % kMXFP8Block) * ((p.page_size / kMXFP8Block) * (kHeadDim / kMXFP8Block)) + + (po / kMXFP8Block) * (kHeadDim / kMXFP8Block) + (ch % kHeadDim) / kMXFP8Block; + store_mxfp8_vec(xo, buf + kv_slot * p.kv_buf_stride, sfb + sf_base, ch); + } else { + auto* buf = static_cast<__nv_bfloat16*>(is_k ? p.k_buf : p.v_buf); + *reinterpret_cast(buf + kv_slot * p.kv_buf_stride + ch) = ov; + } + } + } + device::PDLTriggerSecondary(); +} + +template +struct AttnPrologueDecodeKernel { + static void + run(tvm::ffi::TensorView qkvr, + tvm::ffi::TensorView k_cache, + tvm::ffi::TensorView v_cache, + tvm::ffi::TensorView cache_indices, + tvm::ffi::TensorView cache_mask, + tvm::ffi::TensorView k_weight, + tvm::ffi::TensorView v_weight, + tvm::ffi::TensorView track_mask, + tvm::ffi::TensorView track_indices, + tvm::ffi::TensorView q_gamma, + tvm::ffi::TensorView k_gamma, + double eps, + tvm::ffi::TensorView q_out, + tvm::ffi::TensorView k_out, + tvm::ffi::TensorView v_out, + tvm::ffi::TensorView loc, + tvm::ffi::TensorView k_buf, + tvm::ffi::TensorView v_buf, + tvm::ffi::TensorView sfq, + tvm::ffi::TensorView sfk, + tvm::ffi::TensorView sfv, + int64_t q_off, + int64_t k_off, + int64_t v_off, + int64_t do_track, + int64_t do_store, + int64_t page_size, + tvm::ffi::TensorView log_tau) { + using namespace host; + const uint32_t T = static_cast(qkvr.size(0)); + const uint32_t dq = static_cast(q_out.size(1)); + const uint32_t dkv = static_cast(k_out.size(1)); + RuntimeCheck(dq % kHeadDim == 0 && dkv % kHeadDim == 0, "dims % head_dim"); + RuntimeCheck((dq / kVecElems) % kHeadLanes == 0, "q lanes must tile heads"); + RuntimeCheck( + qkvr.stride(1) == 1 && qkvr.stride(0) % kVecElems == 0, "qkvr must be row-major with 16B-aligned rows"); + RuntimeCheck( + q_off % kVecElems == 0 && k_off % kVecElems == 0 && v_off % kVecElems == 0, + "slice offsets must be 16B aligned"); + RuntimeCheck(k_cache.stride(2) == 1 && v_cache.stride(2) == 1, "conv caches must be channel-contiguous"); + RuntimeCheck( + k_cache.stride(0) == v_cache.stride(0) && k_cache.stride(1) == v_cache.stride(1) && + k_weight.stride(0) == v_weight.stride(0), + "k/v cache+weight strides must match"); + const uint32_t lanes = dq / kVecElems + 2 * (dkv / kVecElems); + RuntimeCheck(lanes <= 1024, "token lanes must fit one block"); + RuntimeCheck(k_buf.stride(0) == v_buf.stride(0), "kv buf stride mismatch"); + if constexpr (USE_MXFP8) { + RuntimeCheck(do_store, "MXFP8 fused decode prologue requires do_store=True"); + RuntimeCheck(dq % kMXFP8Block == 0 && dkv % kMXFP8Block == 0, "MXFP8 dims must tile 32-element scale blocks"); + RuntimeCheck(page_size > 0 && page_size % kMXFP8Block == 0, "MXFP8 page size must tile 32-token scale blocks"); + RuntimeCheck(is_type(q_out.dtype()), "MXFP8 q_out must be fp8_e4m3"); + RuntimeCheck( + is_type(k_buf.dtype()) && is_type(v_buf.dtype()), + "MXFP8 KV buffers must be fp8_e4m3"); + RuntimeCheck( + is_type(sfq.dtype()) && is_type(sfk.dtype()) && is_type(sfv.dtype()), + "MXFP8 scale buffers must be passed as uint8 views"); + RuntimeCheck(q_out.stride(1) == 1 && q_out.stride(0) == dq, "MXFP8 q_out must be contiguous"); + RuntimeCheck( + sfq.stride(2) == 1 && sfq.stride(1) == kHeadDim / kMXFP8Block, "MXFP8 sfq must be contiguous [T, Hq, 4]"); + const int64_t hkv = dkv / kHeadDim; + const int64_t sf_dim = kHeadDim / kMXFP8Block; + const int64_t page_chunks = page_size / kMXFP8Block; + RuntimeCheck(sfk.ndim() == 5 && sfv.ndim() == 5, "MXFP8 SFK/SFV must be 5D interleaved"); + RuntimeCheck( + sfk.size(1) == hkv && sfv.size(1) == hkv && sfk.size(2) == kMXFP8Block && sfv.size(2) == kMXFP8Block && + sfk.size(3) == page_chunks && sfv.size(3) == page_chunks && sfk.size(4) == sf_dim && + sfv.size(4) == sf_dim, + "MXFP8 SFK/SFV must use [pages, Hkv, 32, page/32, 4] layout"); + RuntimeCheck( + sfk.stride(4) == 1 && sfv.stride(4) == 1 && sfk.stride(3) == sf_dim && sfv.stride(3) == sf_dim && + sfk.stride(2) == page_chunks * sf_dim && sfv.stride(2) == page_chunks * sf_dim && + sfk.stride(1) == kMXFP8Block * page_chunks * sf_dim && + sfv.stride(1) == kMXFP8Block * page_chunks * sf_dim, + "MXFP8 SFK/SFV must be contiguous BlockScaledBasicChunk layout"); + RuntimeCheck(k_buf.stride(0) % kMXFP8Block == 0, "MXFP8 kv buf row alignment"); + } else { + RuntimeCheck(k_buf.stride(0) % kVecElems == 0, "kv buf rows must be 16B aligned"); + RuntimeCheck(is_type(q_out.dtype()), "q_out dtype mismatch"); + } + + const bool do_tau = log_tau.numel() > 0; + if (do_tau) { + RuntimeCheck(is_type(log_tau.dtype()), "log_tau must be fp32"); + RuntimeCheck(log_tau.IsContiguous(), "log_tau must be contiguous"); + RuntimeCheck(log_tau.numel() >= qkvr.size(0), "log_tau smaller than T"); + } + const auto params = AttnPrologueDecodeParams{ + .qkvr = qkvr.data_ptr(), + .k_cache = k_cache.data_ptr(), + .v_cache = v_cache.data_ptr(), + .cache_indices = cache_indices.data_ptr(), + .cache_mask = cache_mask.data_ptr(), + .k_weight = k_weight.data_ptr(), + .v_weight = v_weight.data_ptr(), + .track_mask = do_track ? track_mask.data_ptr() : nullptr, + .track_indices = do_track ? track_indices.data_ptr() : nullptr, + .q_gamma = q_gamma.data_ptr(), + .k_gamma = k_gamma.data_ptr(), + .log_tau = do_tau ? log_tau.data_ptr() : nullptr, + .eps = static_cast(eps), + .q_out = q_out.data_ptr(), + .k_out = k_out.data_ptr(), + .v_out = v_out.data_ptr(), + .loc = loc.data_ptr(), + .k_buf = k_buf.data_ptr(), + .v_buf = v_buf.data_ptr(), + .sfq = USE_MXFP8 ? sfq.data_ptr() : nullptr, + .sfk = USE_MXFP8 ? sfk.data_ptr() : nullptr, + .sfv = USE_MXFP8 ? sfv.data_ptr() : nullptr, + .qkvr_stride_t = qkvr.stride(0), + .q_off = q_off, + .k_off = k_off, + .v_off = v_off, + .cache_stride_slot = k_cache.stride(0), + .cache_stride_w = k_cache.stride(1), + .weight_stride_d = k_weight.stride(0), + .track_idx_stride = do_track ? track_indices.stride(0) : 0, + .kv_buf_stride = k_buf.stride(0), + .T = T, + .dq = dq, + .dkv = dkv, + .page_size = static_cast(page_size), + }; + const uint32_t block = div_ceil(lanes, 32u) * 32u; + auto pick = [&](auto tr, auto st, auto mx) { + return inkling_attn_prologue_decode_kernel< + DType, + W, + USE_SILU, + USE_RESIDUAL, + decltype(tr)::value, + decltype(st)::value, + decltype(mx)::value, + USE_PDL>; + }; + const bool tr = do_track != 0, st = do_store != 0; + const auto kernel = USE_MXFP8 ? (tr ? pick(std::true_type{}, std::true_type{}, std::true_type{}) + : pick(std::false_type{}, std::true_type{}, std::true_type{})) + : (tr ? (st ? pick(std::true_type{}, std::true_type{}, std::false_type{}) + : pick(std::true_type{}, std::false_type{}, std::false_type{})) + : (st ? pick(std::false_type{}, std::true_type{}, std::false_type{}) + : pick(std::false_type{}, std::false_type{}, std::false_type{}))); + LaunchKernel(dim3{T}, dim3{block}, qkvr.device()).enable_pdl(USE_PDL)(kernel, params); + } +}; + +// --------------------------------------------------------------------------- +// EXTEND variant: {k/v extend-conv + per-head q/k RMSNorm + KV store} in the +// main kernel (one block per token, varlen sequences via si/cu -- the verify +// kernel's dataflow with the fixed q-per-seq mapping generalized), plus a +// TINY trailing kernel for the conv-cache update + prefix-cache track. The +// update cannot ride the main kernel: early tokens of a sequence read the OLD +// cache prefix rows from other blocks while the seq-end block would overwrite +// them, and with grid = T (up to 16K blocks) there is no co-residency for a +// grid barrier. The trailing kernel is B*(W-1) rows over both caches -- +// microseconds -- and both launches are on one stream, so ordering is free. +// Replaces {2x causal_conv1d + apply_qk_norm + 2x update_sconv_cache (+track) +// + the backend KV store}. +struct AttnPrologueExtendParams { + const void* __restrict__ qkvr; // [T, row_stride] packed projection output + const void* __restrict__ k_cache; // [pool, W-1, Dkv] (read-only here) + const void* __restrict__ v_cache; + const void* __restrict__ cache_indices; // int32 [B] (PAD == -1) + const void* __restrict__ cache_mask; // bool [B] has_init & valid + const void* __restrict__ cu; // int64 [B+1] query_start_loc + const void* __restrict__ si; // int32 [T] token -> sequence + const void* __restrict__ k_weight; // [Dkv, W] + const void* __restrict__ v_weight; + const void* __restrict__ q_gamma; // [head_dim] + const void* __restrict__ k_gamma; // [head_dim] + const void* __restrict__ log_tau; // fp32 [T] per-token q scale (null -> off) + float eps; + void* __restrict__ q_out; // [T, Dq] + void* __restrict__ k_out; // [T, Dkv] + void* __restrict__ v_out; // [T, Dkv] + const void* __restrict__ loc; // int64 [T] KV slots + void* __restrict__ k_buf; // [slots, Hkv, head_dim] + void* __restrict__ v_buf; + void* __restrict__ sfq; // uint8 [T, Hq, head_dim/32] when USE_MXFP8 + void* __restrict__ sfk; // uint8 BlockScaledBasicChunk layout + void* __restrict__ sfv; + int64_t qkvr_stride_t; + int64_t q_off; + int64_t k_off; + int64_t v_off; + int64_t cache_stride_slot; + int64_t cache_stride_w; + int64_t weight_stride_d; + int64_t kv_buf_stride; + uint32_t T; + uint32_t dq; + uint32_t dkv; + uint32_t page_size; +}; + +template +__global__ __launch_bounds__(1024, 1) void inkling_attn_prologue_extend_kernel( + const __grid_constant__ AttnPrologueExtendParams p) { + static_assert(std::is_same_v); + static_assert(!USE_MXFP8 || DO_STORE, "MXFP8 prologue quantization owns the KV store"); + constexpr int W1 = W - 1; + const uint32_t t = blockIdx.x; + const uint32_t seq = static_cast(static_cast(p.si)[t]); + const int bos = static_cast(static_cast(p.cu)[seq]); + const uint32_t nq = p.dq / kVecElems; + const uint32_t nkv = p.dkv / kVecElems; + const uint32_t vi = threadIdx.x; + const auto* base = static_cast(p.qkvr); + const int64_t row = static_cast(t) * p.qkvr_stride_t; + + const int ci = static_cast(p.cache_indices)[seq]; + const bool valid = ci != kPadSlot; + const int slot_id = valid ? ci : 0; + const float cm = static_cast(p.cache_mask)[seq] ? 1.0f : 0.0f; + + // PDL + load restructure (same as the decode/verify kernels): the + // immediately-preceding qkvr GEMM only produces qkvr; gammas, tau, conv + // weights/prefix and the si/cu/ci/cm metadata are prefetched before + // PDLWaitPrimary, every qkvr read stays behind it. The TRAILING + // kv_conv_update launch stays non-PDL: it overwrites cache rows this + // kernel reads, so it must keep full completion ordering. + if (vi < nq) { + // ---------------- q path: per-head RMSNorm only ---------------- + const uint32_t c = vi * kVecElems; + const uint4 gqraw = *reinterpret_cast(static_cast(p.q_gamma) + (c % kHeadDim)); + const auto* gq = reinterpret_cast(&gqraw); + const bool do_tau = p.log_tau != nullptr; + float tau = 0.0f; + if (do_tau) tau = static_cast(p.log_tau)[t]; + device::PDLWaitPrimary(); + const uint4 raw = *reinterpret_cast(base + row + p.q_off + c); + float x[kVecElems]; + float ss = 0.0f; +#pragma unroll + for (int j = 0; j < 8; ++j) { + x[j] = __bfloat162float(reinterpret_cast(&raw)[j]); + ss += x[j] * x[j]; + } + const float inv = head_rmsnorm_inv(ss, p.eps); + __nv_bfloat162 o[4]; +#pragma unroll + for (int j = 0; j < 4; ++j) { + o[j] = __floats2bfloat162_rn( + x[2 * j] * inv * __bfloat162float(gq[2 * j]), x[2 * j + 1] * inv * __bfloat162float(gq[2 * j + 1])); + } + if (do_tau) { + // Fused log-scaling tau: multiply the bf16-ROUNDED normed q (matching + // the unfused {norm kernel -> apply_log_scaling_tau} rounding exactly); + // on the MXFP8 path this scales BEFORE quantization. +#pragma unroll + for (int j = 0; j < 4; ++j) { + const float2 f = __bfloat1622float2(o[j]); + o[j] = __floats2bfloat162_rn(f.x * tau, f.y * tau); + } + } + if constexpr (USE_MXFP8) { + float q_quant[kVecElems]; +#pragma unroll + for (int j = 0; j < 4; ++j) { + const float2 f = __bfloat1622float2(o[j]); + q_quant[2 * j] = f.x; + q_quant[2 * j + 1] = f.y; + } + const uint32_t sf_idx = static_cast(t) * (p.dq / kMXFP8Block) + c / kMXFP8Block; + store_mxfp8_vec( + q_quant, + static_cast(p.q_out) + static_cast(t) * p.dq, + static_cast(p.sfq) + sf_idx, + c); + } else { + *reinterpret_cast(static_cast<__nv_bfloat16*>(p.q_out) + static_cast(t) * p.dq + c) = + *reinterpret_cast(o); + } + device::PDLTriggerSecondary(); + return; + } + if (vi >= nq + 2 * nkv) return; + + // ---------------- k / v paths: varlen conv (+ k norm) + store ---------- + const bool is_k = vi < nq + nkv; + const uint32_t ch = (is_k ? vi - nq : vi - nq - nkv) * kVecElems; + const int64_t x_off = is_k ? p.k_off : p.v_off; + const auto* cp = static_cast(is_k ? p.k_cache : p.v_cache); + const auto* wp = static_cast(is_k ? p.k_weight : p.v_weight); + const int64_t cache_base = static_cast(slot_id) * p.cache_stride_slot + ch; + + uint4 pref[W1]; + __nv_bfloat16 wt[kVecElems][W]; +#pragma unroll + for (int w = 0; w < W1; ++w) { + pref[w] = *reinterpret_cast(&cp[cache_base + w * p.cache_stride_w]); + } +#pragma unroll + for (int j = 0; j < 8; ++j) { + const int64_t wrow = static_cast(ch + j) * p.weight_stride_d; + if constexpr (W == 4) { + if (p.weight_stride_d == W) { + *reinterpret_cast(wt[j]) = *reinterpret_cast(wp + wrow); + continue; + } + } +#pragma unroll + for (int w = 0; w < W; ++w) + wt[j][w] = wp[wrow + w]; + } + + uint4 gkraw = make_uint4(0, 0, 0, 0); + if (is_k) { + gkraw = *reinterpret_cast(static_cast(p.k_gamma) + (ch % kHeadDim)); + } + device::PDLWaitPrimary(); + // In-seq neighbor rows (pre-conv x straight from qkvr) + own row. + const uint4 xcur = *reinterpret_cast(base + row + x_off + ch); + uint4 xn[W1]; +#pragma unroll + for (int j = 1; j <= W1; ++j) { + const int n = static_cast(t) - j; + if (n >= bos) { + xn[j - 1] = *reinterpret_cast(base + static_cast(n) * p.qkvr_stride_t + x_off + ch); + } + } + + float y[kVecElems]; +#pragma unroll + for (int j = 0; j < 8; ++j) { + const float xj = __bfloat162float(reinterpret_cast(&xcur)[j]); + float acc = 0.0f; +#pragma unroll + for (int iw = 0; iw < W1; ++iw) { + const int shifted = static_cast(t) - W1 + iw; + float tap = 0.0f; + if (shifted >= bos) { + tap = __bfloat162float(reinterpret_cast(&xn[W1 - 1 - iw])[j]); + } else { + const int prefix_pos = shifted - bos + W1; + if (prefix_pos >= 0) { + tap = cm * __bfloat162float(reinterpret_cast(&pref[prefix_pos])[j]); + } + } + acc += tap * __bfloat162float(wt[j][iw]); + } + acc += xj * __bfloat162float(wt[j][W1]); + if constexpr (USE_SILU) acc = __fdividef(acc, 1.0f + __expf(-acc)); + if constexpr (USE_RESIDUAL) acc += xj; + y[j] = acc; + } + + __nv_bfloat162 o[4]; + if (is_k) { + // per-head RMSNorm on the conv output (16-lane groups). Round to bf16 + // FIRST: the unfused pipeline writes the conv output to memory as bf16 + // before the norm kernel reads it back. + float ss = 0.0f; +#pragma unroll + for (int j = 0; j < 8; ++j) { + y[j] = __bfloat162float(__float2bfloat16_rn(y[j])); + ss += y[j] * y[j]; + } + const float inv = head_rmsnorm_inv(ss, p.eps); + const auto* gk = reinterpret_cast(&gkraw); +#pragma unroll + for (int j = 0; j < 4; ++j) { + o[j] = __floats2bfloat162_rn( + y[2 * j] * inv * __bfloat162float(gk[2 * j]), y[2 * j + 1] * inv * __bfloat162float(gk[2 * j + 1])); + } + } else { +#pragma unroll + for (int j = 0; j < 4; ++j) + o[j] = __floats2bfloat162_rn(y[2 * j], y[2 * j + 1]); + } + const uint4 ov = *reinterpret_cast(o); + auto* out = static_cast<__nv_bfloat16*>(is_k ? p.k_out : p.v_out); + *reinterpret_cast(out + static_cast(t) * p.dkv + ch) = ov; + if (DO_STORE) { + const int64_t kv_slot = static_cast(p.loc)[t]; + if (kv_slot >= 0) { // SWA full->swa translation can yield -1 sentinels + if constexpr (USE_MXFP8) { + float xo[kVecElems]; +#pragma unroll + for (int j = 0; j < 4; ++j) { + const float2 f = __bfloat1622float2(o[j]); + xo[2 * j] = f.x; + xo[2 * j + 1] = f.y; + } + auto* buf = static_cast(is_k ? p.k_buf : p.v_buf); + auto* sfb = static_cast(is_k ? p.sfk : p.sfv); + const int64_t po = kv_slot % static_cast(p.page_size); + constexpr uint32_t SF = kHeadDim / kMXFP8Block; + const int64_t sf_base = ((kv_slot / static_cast(p.page_size)) * (p.dkv / kHeadDim) + ch / kHeadDim) * + (kMXFP8Block * (p.page_size / kMXFP8Block) * SF) + + (po % kMXFP8Block) * ((p.page_size / kMXFP8Block) * SF) + (po / kMXFP8Block) * SF + + (ch % kHeadDim) / kMXFP8Block; + store_mxfp8_vec(xo, buf + kv_slot * p.kv_buf_stride, sfb + sf_base, ch); + } else { + auto* buf = static_cast<__nv_bfloat16*>(is_k ? p.k_buf : p.v_buf); + *reinterpret_cast(buf + kv_slot * p.kv_buf_stride + ch) = ov; + } + } + } + device::PDLTriggerSecondary(); +} + +// Trailing conv-cache update + prefix-cache track for BOTH k/v caches. One +// thread owns all W-1 rows of one (sequence, role, channel-vec) triple -- +// RAW-safe load-all-then-store, mirroring update_sconv_cache (and the +// AR-sconv kernel's phase 3, with the pre-conv x read straight from qkvr). +struct KvConvUpdateParams { + const void* __restrict__ qkvr; + void* __restrict__ k_cache; // [pool, W-1, Dkv], in-place + void* __restrict__ v_cache; + const void* __restrict__ cache_indices; // int32 [B] (PAD == -1) + const void* __restrict__ has_init; // bool [B] + const void* __restrict__ cu; // int64 [B+1] + const void* __restrict__ track_rows; // int64 [B, W-1] (DO_TRACK) + const void* __restrict__ track_mask; // bool [B] (DO_TRACK) + const void* __restrict__ track_dst; // int64 [B] (DO_TRACK) + int64_t qkvr_stride_t; + int64_t k_off; + int64_t v_off; + int64_t cache_stride_slot; + int64_t cache_stride_w; + int64_t track_dst_stride; + uint32_t B; + uint32_t dkv; +}; + +template +__global__ void inkling_kv_conv_update_kernel(const __grid_constant__ KvConvUpdateParams p) { + constexpr int W1 = W - 1; + const uint32_t nkv = p.dkv / kVecElems; + const uint32_t items = p.B * 2u * nkv; + const uint32_t idx = blockIdx.x * blockDim.x + threadIdx.x; + if (idx >= items) return; + const uint32_t b = idx / (2u * nkv); + const uint32_t r = idx % (2u * nkv); + const bool is_k = r < nkv; + const uint32_t ch = (is_k ? r : r - nkv) * kVecElems; + const auto* base = static_cast(p.qkvr); + const int64_t x_off = is_k ? p.k_off : p.v_off; + auto* cp = static_cast<__nv_bfloat16*>(is_k ? p.k_cache : p.v_cache); + const auto* cu = static_cast(p.cu); + const int slot = static_cast(p.cache_indices)[b]; + const int64_t qlen = cu[b + 1] - cu[b]; + if (slot != kPadSlot && qlen > 0) { + const bool hs = static_cast(p.has_init)[b]; + const int64_t cb = static_cast(slot) * p.cache_stride_slot + ch; + uint4 old_reg[W1]; +#pragma unroll + for (int w = 0; w < W1; ++w) { + old_reg[w] = *reinterpret_cast(&cp[cb + w * p.cache_stride_w]); + } + const uint4 zero = make_uint4(0, 0, 0, 0); +#pragma unroll + for (int w = 0; w < W1; ++w) { + uint4 nv; + if (qlen >= W1 - w) { + const int64_t row = cu[b + 1] - W1 + w; + nv = *reinterpret_cast(base + row * p.qkvr_stride_t + x_off + ch); + } else { + uint4 shift = zero; +#pragma unroll + for (int src = 0; src < W1; ++src) { + if (src == w + qlen) shift = old_reg[src]; + } + nv = hs ? shift : zero; + } + *reinterpret_cast(&cp[cb + w * p.cache_stride_w]) = nv; + } + } + if constexpr (DO_TRACK) { + if (static_cast(p.track_mask)[b]) { + const int64_t dst = static_cast(p.track_dst)[static_cast(b) * p.track_dst_stride]; + const int64_t db = dst * p.cache_stride_slot + ch; + const auto* trows = static_cast(p.track_rows); +#pragma unroll + for (int w = 0; w < W1; ++w) { + const int64_t row = trows[static_cast(b) * W1 + w]; + *reinterpret_cast(&cp[db + w * p.cache_stride_w]) = + *reinterpret_cast(base + row * p.qkvr_stride_t + x_off + ch); + } + } + } +} + +template +struct AttnPrologueExtendKernel { + static void + run(tvm::ffi::TensorView qkvr, + tvm::ffi::TensorView k_cache, + tvm::ffi::TensorView v_cache, + tvm::ffi::TensorView cache_indices, // int32 [B] raw slots (PAD == -1) + tvm::ffi::TensorView cache_mask, // bool [B] has_init & valid + tvm::ffi::TensorView has_init, // bool [B] + tvm::ffi::TensorView cu, // int64 [B+1] + tvm::ffi::TensorView si, // int32 [T] + tvm::ffi::TensorView k_weight, + tvm::ffi::TensorView v_weight, + tvm::ffi::TensorView track_rows, // int64 [B, W-1] (numel 0 -> no track) + tvm::ffi::TensorView track_mask, // bool [B] + tvm::ffi::TensorView track_dst, // int64 [B] (possibly strided) + tvm::ffi::TensorView q_gamma, + tvm::ffi::TensorView k_gamma, + double eps, + tvm::ffi::TensorView q_out, + tvm::ffi::TensorView k_out, + tvm::ffi::TensorView v_out, + tvm::ffi::TensorView loc, + tvm::ffi::TensorView k_buf, + tvm::ffi::TensorView v_buf, + tvm::ffi::TensorView sfq, + tvm::ffi::TensorView sfk, + tvm::ffi::TensorView sfv, + int64_t q_off, + int64_t k_off, + int64_t v_off, + int64_t do_store, + int64_t page_size, + int64_t do_cache_update, + tvm::ffi::TensorView log_tau) { + using namespace host; + const uint32_t T = static_cast(qkvr.size(0)); + const uint32_t B = static_cast(cache_indices.size(0)); + const uint32_t dq = static_cast(q_out.size(1)); + const uint32_t dkv = static_cast(k_out.size(1)); + RuntimeCheck(dq % kHeadDim == 0 && dkv % kHeadDim == 0, "dims % head_dim"); + RuntimeCheck((dq / kVecElems) % kHeadLanes == 0, "q lanes must tile heads"); + RuntimeCheck( + qkvr.stride(1) == 1 && qkvr.stride(0) % kVecElems == 0, "qkvr must be row-major with 16B-aligned rows"); + RuntimeCheck( + q_off % kVecElems == 0 && k_off % kVecElems == 0 && v_off % kVecElems == 0, + "slice offsets must be 16B aligned"); + RuntimeCheck(si.size(0) >= T, "si must cover T tokens"); + RuntimeCheck(cu.size(0) == B + 1, "cu must be [B+1]"); + RuntimeCheck(cache_mask.size(0) == B && has_init.size(0) == B, "per-seq arrays must be [B]"); + RuntimeCheck(k_cache.stride(2) == 1 && v_cache.stride(2) == 1, "conv caches must be channel-contiguous"); + RuntimeCheck( + k_cache.stride(0) == v_cache.stride(0) && k_cache.stride(1) == v_cache.stride(1) && + k_weight.stride(0) == v_weight.stride(0), + "k/v cache+weight strides must match"); + const uint32_t lanes = dq / kVecElems + 2 * (dkv / kVecElems); + RuntimeCheck(lanes <= 1024, "token lanes must fit one block"); + RuntimeCheck(k_buf.stride(0) == v_buf.stride(0), "kv buf stride mismatch"); + const bool do_track = track_mask.numel() > 0; + if (do_track) { + RuntimeCheck(track_rows.numel() > 0, "extend track needs gather rows"); + } + if constexpr (USE_MXFP8) { + RuntimeCheck(do_store, "MXFP8 fused extend prologue requires do_store=True"); + RuntimeCheck(dq % kMXFP8Block == 0 && dkv % kMXFP8Block == 0, "MXFP8 dims must tile 32-element scale blocks"); + RuntimeCheck(page_size > 0 && page_size % kMXFP8Block == 0, "MXFP8 page size must tile 32-token scale blocks"); + RuntimeCheck(is_type(q_out.dtype()), "MXFP8 q_out must be fp8_e4m3"); + RuntimeCheck( + is_type(k_buf.dtype()) && is_type(v_buf.dtype()), + "MXFP8 KV buffers must be fp8_e4m3"); + RuntimeCheck( + is_type(sfq.dtype()) && is_type(sfk.dtype()) && is_type(sfv.dtype()), + "MXFP8 scale buffers must be passed as uint8 views"); + RuntimeCheck(q_out.stride(1) == 1 && q_out.stride(0) == dq, "MXFP8 q_out must be contiguous"); + RuntimeCheck( + sfq.stride(2) == 1 && sfq.stride(1) == kHeadDim / kMXFP8Block, "MXFP8 sfq must be contiguous [T, Hq, 4]"); + const int64_t hkv = dkv / kHeadDim; + const int64_t sf_dim = kHeadDim / kMXFP8Block; + const int64_t page_chunks = page_size / kMXFP8Block; + RuntimeCheck(sfk.ndim() == 5 && sfv.ndim() == 5, "MXFP8 SFK/SFV must be 5D interleaved"); + RuntimeCheck( + sfk.size(1) == hkv && sfv.size(1) == hkv && sfk.size(2) == kMXFP8Block && sfv.size(2) == kMXFP8Block && + sfk.size(3) == page_chunks && sfv.size(3) == page_chunks && sfk.size(4) == sf_dim && + sfv.size(4) == sf_dim, + "MXFP8 SFK/SFV must use [pages, Hkv, 32, page/32, 4] layout"); + RuntimeCheck( + sfk.stride(4) == 1 && sfv.stride(4) == 1 && sfk.stride(3) == sf_dim && sfv.stride(3) == sf_dim && + sfk.stride(2) == page_chunks * sf_dim && sfv.stride(2) == page_chunks * sf_dim && + sfk.stride(1) == kMXFP8Block * page_chunks * sf_dim && + sfv.stride(1) == kMXFP8Block * page_chunks * sf_dim, + "MXFP8 SFK/SFV must be contiguous BlockScaledBasicChunk layout"); + RuntimeCheck(k_buf.stride(0) % kMXFP8Block == 0, "MXFP8 kv buf row alignment"); + } else { + RuntimeCheck(k_buf.stride(0) % kVecElems == 0, "kv buf rows must be 16B aligned"); + RuntimeCheck(is_type(q_out.dtype()), "q_out dtype mismatch"); + } + if (T == 0) return; + + const bool do_tau = log_tau.numel() > 0; + if (do_tau) { + RuntimeCheck(is_type(log_tau.dtype()), "log_tau must be fp32"); + RuntimeCheck(log_tau.IsContiguous(), "log_tau must be contiguous"); + RuntimeCheck(log_tau.numel() >= qkvr.size(0), "log_tau smaller than T"); + } + const auto params = AttnPrologueExtendParams{ + .qkvr = qkvr.data_ptr(), + .k_cache = k_cache.data_ptr(), + .v_cache = v_cache.data_ptr(), + .cache_indices = cache_indices.data_ptr(), + .cache_mask = cache_mask.data_ptr(), + .cu = cu.data_ptr(), + .si = si.data_ptr(), + .k_weight = k_weight.data_ptr(), + .v_weight = v_weight.data_ptr(), + .q_gamma = q_gamma.data_ptr(), + .k_gamma = k_gamma.data_ptr(), + .log_tau = do_tau ? log_tau.data_ptr() : nullptr, + .eps = static_cast(eps), + .q_out = q_out.data_ptr(), + .k_out = k_out.data_ptr(), + .v_out = v_out.data_ptr(), + .loc = loc.data_ptr(), + .k_buf = k_buf.data_ptr(), + .v_buf = v_buf.data_ptr(), + .sfq = USE_MXFP8 ? sfq.data_ptr() : nullptr, + .sfk = USE_MXFP8 ? sfk.data_ptr() : nullptr, + .sfv = USE_MXFP8 ? sfv.data_ptr() : nullptr, + .qkvr_stride_t = qkvr.stride(0), + .q_off = q_off, + .k_off = k_off, + .v_off = v_off, + .cache_stride_slot = k_cache.stride(0), + .cache_stride_w = k_cache.stride(1), + .weight_stride_d = k_weight.stride(0), + .kv_buf_stride = k_buf.stride(0), + .T = T, + .dq = dq, + .dkv = dkv, + .page_size = static_cast(page_size), + }; + const uint32_t block = div_ceil(lanes, 32u) * 32u; + const auto kernel = + do_store ? inkling_attn_prologue_extend_kernel + : inkling_attn_prologue_extend_kernel; + LaunchKernel(dim3{T}, dim3{block}, qkvr.device()).enable_pdl(USE_PDL)(kernel, params); + + // DRAFT_EXTEND_V2 passes do_cache_update=false: its conv state must + // reflect only num_accept_tokens, so the caller runs the accept-gated + // update (_update_sconv_cache_for_draft_extend) instead of this + // seq-end-window trailing kernel. + if (!do_cache_update) return; + const auto uparams = KvConvUpdateParams{ + .qkvr = qkvr.data_ptr(), + .k_cache = k_cache.data_ptr(), + .v_cache = v_cache.data_ptr(), + .cache_indices = cache_indices.data_ptr(), + .has_init = has_init.data_ptr(), + .cu = cu.data_ptr(), + .track_rows = do_track ? track_rows.data_ptr() : nullptr, + .track_mask = do_track ? track_mask.data_ptr() : nullptr, + .track_dst = do_track ? track_dst.data_ptr() : nullptr, + .qkvr_stride_t = qkvr.stride(0), + .k_off = k_off, + .v_off = v_off, + .cache_stride_slot = k_cache.stride(0), + .cache_stride_w = k_cache.stride(1), + .track_dst_stride = do_track ? track_dst.stride(0) : 0, + .B = B, + .dkv = dkv, + }; + const uint32_t uitems = B * 2u * (dkv / kVecElems); + const uint32_t ublock = 256; + const uint32_t ugrid = div_ceil(uitems, ublock); + const auto ukernel = do_track ? inkling_kv_conv_update_kernel : inkling_kv_conv_update_kernel; + LaunchKernel(dim3{ugrid}, dim3{ublock}, qkvr.device())(ukernel, uparams); + } +}; + +} // namespace diff --git a/python/sglang/jit_kernel/csrc/inkling/inkling_rel_proj.cuh b/python/sglang/jit_kernel/csrc/inkling/inkling_rel_proj.cuh new file mode 100644 index 000000000..a77efdc78 --- /dev/null +++ b/python/sglang/jit_kernel/csrc/inkling/inkling_rel_proj.cuh @@ -0,0 +1,145 @@ +// Latency-lean rel_logits projection for SMALL token counts: +// out[t, h, :] = bf16(sum_d fp32(r[t, h, d]) * fp32(proj[d, :])) with an +// optional per-token tau prescale folded in registers (the shipped prescale +// semantics: r*tau rounds to bf16 BEFORE the dot, matching +// {row_scale -> einsum} exactly). +// +// At t=1 the cuBLAS GEMM ([16,16]@[16,1024]) is pure launch + entry overhead +// (~1.6 us for ~64 KB of traffic); this kernel is a no-smem no-sync grid of +// independent 8-wide dots reading proj straight from L2 (32 KB, hot across +// decode steps), so its floor is the launch itself. An earlier smem-staged +// bandwidth-oriented kernel lost to cuBLAS at EVERY size -- this one is only +// dispatched inside its measured small-t band; large t stays on cuBLAS. + +#include // For TensorMatcher, SymbolicSize, SymbolicDevice +#include // For RuntimeCheck, div_ceil + +#include // For bf16_t/fp32_t aliases +#include // For LaunchKernel, PDL helpers +#include // For AlignedVector (16B loads) + +#include +#include + +#include + +namespace { + +constexpr uint32_t kRpVec = 8; // bf16x8 = 16 B +constexpr uint32_t kRpBlock = 256; + +template +__global__ __launch_bounds__(kRpBlock, 1) void rel_proj_small_t_kernel( + const bf16_t* __restrict__ r, // [t, h, kDRel], token rows strided + const fp32_t* __restrict__ tau, // [t]; unread when !kHasTau + const bf16_t* __restrict__ proj, // [kDRel, e] contiguous + bf16_t* __restrict__ out, // [t, h, e] contiguous + const int64_t r_stride_t, // elems between token rows + const uint32_t h, + const uint32_t e, + const uint32_t t) { + using namespace device; + PDLWaitPrimary(); + const uint32_t evecs = e / kRpVec; + const uint32_t total = t * h * evecs; + for (uint32_t idx = blockIdx.x * blockDim.x + threadIdx.x; idx < total; idx += gridDim.x * blockDim.x) { + const uint32_t ev = idx % evecs; + const uint32_t th = idx / evecs; + const uint32_t ti = th / h; + const uint32_t hi = th % h; + + // r[ti, hi, :] once into registers (2x 16B for kDRel=16), tau folded + // with the prescale rounding (bf16 round before the dot). + const bf16_t* rrow = r + static_cast(ti) * r_stride_t + static_cast(hi) * kDRel; + float rv[kDRel]; +#pragma unroll + for (int d = 0; d < kDRel; d += static_cast(kRpVec)) { + AlignedVector a; + a.load(rrow, d / static_cast(kRpVec)); +#pragma unroll + for (int k = 0; k < static_cast(kRpVec); ++k) { + if constexpr (kHasTau) { + rv[d + k] = static_cast(static_cast(static_cast(a[k]) * tau[ti])); + } else { + rv[d + k] = static_cast(a[k]); + } + } + } + + float acc[kRpVec] = {}; +#pragma unroll + for (int d = 0; d < kDRel; ++d) { + AlignedVector p; + p.load(proj + static_cast(d) * e, ev); +#pragma unroll + for (int k = 0; k < static_cast(kRpVec); ++k) { + acc[k] += rv[d] * static_cast(p[k]); + } + } + + AlignedVector o; +#pragma unroll + for (int k = 0; k < static_cast(kRpVec); ++k) { + o[k] = static_cast(acc[k]); + } + o.store(out, idx); + } + PDLTriggerSecondary(); +} + +template +void rel_proj_small_t( + tvm::ffi::TensorView r, + tvm::ffi::TensorView tau, // numel-0 sentinel = no prescale + tvm::ffi::TensorView proj, + tvm::ffi::TensorView out) { + using namespace host; + auto T = SymbolicSize{"t"}; + auto H = SymbolicSize{"h"}; + auto D = SymbolicSize{"d_rel"}; + auto E = SymbolicSize{"e"}; + auto dev = SymbolicDevice{}; + dev.set_options(); + + TensorMatcher({T, H, D}).with_dtype().with_device(dev).with_strides({-1, D, 1}).verify(r); + TensorMatcher({D, E}).with_dtype().with_device(dev).verify(proj); + TensorMatcher({T, H, E}).with_dtype().with_device(dev).verify(out); + + const uint32_t t = static_cast(T.unwrap()); + const uint32_t h = static_cast(H.unwrap()); + const uint32_t e = static_cast(E.unwrap()); + RuntimeCheck(D.unwrap() == kDRel, "d_rel must be ", kDRel); + static_assert(kDRel % static_cast(kRpVec) == 0, "d_rel must be a vector multiple (r loads are 16B)"); + RuntimeCheck(e % kRpVec == 0, "e must be a multiple of ", kRpVec); + RuntimeCheck((r.stride(0) * 2) % 16 == 0, "r token stride must keep 16B alignment"); + RuntimeCheck(std::bit_cast(r.data_ptr()) % 16 == 0, "r not 16B aligned"); + RuntimeCheck(std::bit_cast(proj.data_ptr()) % 16 == 0, "proj not 16B aligned"); + + const bool has_tau = tau.numel() > 0; + if (has_tau) { + TensorMatcher({T}).with_dtype().with_device(dev).verify(tau); + } + + const uint32_t total = t * h * (e / kRpVec); + const uint32_t grid = div_ceil(total, kRpBlock); + auto launch = [&](auto kernel) { + LaunchKernel(grid, kRpBlock, dev.unwrap()) + .enable_pdl(kUsePDL)( + kernel, + static_cast(r.data_ptr()), + has_tau ? static_cast(tau.data_ptr()) : nullptr, + static_cast(proj.data_ptr()), + static_cast(out.data_ptr()), + r.stride(0), + h, + e, + t); + }; + if (has_tau) { + launch(rel_proj_small_t_kernel); + } else { + launch(rel_proj_small_t_kernel); + } +} + +} // namespace diff --git a/python/sglang/jit_kernel/csrc/inkling/inkling_row_scale.cuh b/python/sglang/jit_kernel/csrc/inkling/inkling_row_scale.cuh new file mode 100644 index 000000000..0f424b382 --- /dev/null +++ b/python/sglang/jit_kernel/csrc/inkling/inkling_row_scale.cuh @@ -0,0 +1,116 @@ +// Vectorized per-row scale for the Inkling log-scaling tau paths: +// out[row, :] = bf16(fp32(x[row, :]) * tau[row]) -- the apply_log_scaling_tau +// contract (fp32 multiply, one bf16 round), replacing the scalar triton +// kernel (per-ELEMENT int64 div/mod + tau load; ~1.7 us at 512 B in-graph, +// ~2.5x off the copy floor at 16k rows) with 16 B vector loads/stores and one +// row divide per vector. x may be row-strided (a slice of the packed qkvr +// projection); out is contiguous. + +#include // For TensorMatcher, SymbolicSize, SymbolicDevice +#include // For RuntimeCheck, div_ceil + +#include // For get_blocks_per_sm / get_sm_count +#include // For bf16_t/fp32_t aliases +#include // For LaunchKernel, PDL helpers +#include // For AlignedVector (16B loads) + +#include +#include + +#include + +namespace { + +constexpr uint32_t kRsVec = 8; // bf16x8 = 16 B +constexpr uint32_t kRsBlock = 256; + +// kHasTau=false is the pure row-compaction flavor (tau may be nullptr): same +// vectorized strided-rows -> contiguous copy, no multiply. It replaces the +// TensorIterator copy hidden inside einsum's reshape of the strided r operand +// (measured ~2.3 us slower per call at decode sizes). +template +__global__ __launch_bounds__(kRsBlock, 1) void row_scale_kernel( + const bf16_t* __restrict__ x, // [rows, inner], row-strided + const fp32_t* __restrict__ tau, // [rows]; unread when !kHasTau + bf16_t* __restrict__ out, // [rows, inner] contiguous + const int64_t x_stride_row, // elems + const uint32_t inner, + const uint32_t rows) { + using namespace device; + PDLWaitPrimary(); + const uint32_t vrow = inner / kRsVec; + const uint32_t total = rows * vrow; + for (uint32_t idx = blockIdx.x * blockDim.x + threadIdx.x; idx < total; idx += gridDim.x * blockDim.x) { + const uint32_t row = idx / vrow; + const uint32_t v = idx % vrow; + AlignedVector a; + a.load(x + static_cast(row) * x_stride_row, v); + if constexpr (kHasTau) { + const float tv = tau[row]; +#pragma unroll + for (int k = 0; k < static_cast(kRsVec); ++k) { + a[k] = static_cast(static_cast(a[k]) * tv); + } + } + a.store(out, idx); + } + PDLTriggerSecondary(); +} + +template +void row_scale_launch( + tvm::ffi::TensorView x, + const fp32_t* tau_ptr, + tvm::ffi::TensorView out, + host::SymbolicSize& R, + host::SymbolicSize& N, + host::SymbolicDevice& dev) { + using namespace host; + TensorMatcher({R, N}).with_dtype().with_device(dev).with_strides({-1, 1}).verify(x); + TensorMatcher({R, N}).with_dtype().with_device(dev).verify(out); + + const uint32_t rows = static_cast(R.unwrap()); + const uint32_t inner = static_cast(N.unwrap()); + RuntimeCheck(inner % kRsVec == 0, "inner must be a multiple of ", kRsVec); + RuntimeCheck((x.stride(0) * 2) % 16 == 0, "x row stride must keep 16B alignment"); + RuntimeCheck(std::bit_cast(x.data_ptr()) % 16 == 0, "x not 16B aligned"); + + const auto kernel = row_scale_kernel; + const uint32_t sm = runtime::get_sm_count(dev.unwrap().device_id); + const uint32_t bps = runtime::get_blocks_per_sm(kernel, kRsBlock); + const uint32_t want = div_ceil(rows * (inner / kRsVec), kRsBlock); + const uint32_t grid = std::min(sm * std::max(1u, bps), std::max(1u, want)); + LaunchKernel(grid, kRsBlock, dev.unwrap()) + .enable_pdl(kUsePDL)( + kernel, + static_cast(x.data_ptr()), + tau_ptr, + static_cast(out.data_ptr()), + x.stride(0), + inner, + rows); +} + +template +void row_scale(tvm::ffi::TensorView x, tvm::ffi::TensorView tau, tvm::ffi::TensorView out) { + using namespace host; + auto R = SymbolicSize{"rows"}; + auto N = SymbolicSize{"inner"}; + auto dev = SymbolicDevice{}; + dev.set_options(); + TensorMatcher({R}).with_dtype().with_device(dev).verify(tau); + row_scale_launch(x, static_cast(tau.data_ptr()), out, R, N, dev); +} + +// Pure compaction: out = contiguous copy of the row-strided x (no tau). +template +void row_compact(tvm::ffi::TensorView x, tvm::ffi::TensorView out) { + using namespace host; + auto R = SymbolicSize{"rows"}; + auto N = SymbolicSize{"inner"}; + auto dev = SymbolicDevice{}; + dev.set_options(); + row_scale_launch(x, nullptr, out, R, N, dev); +} + +} // namespace diff --git a/python/sglang/jit_kernel/csrc/inkling/update_sconv_cache.cuh b/python/sglang/jit_kernel/csrc/inkling/update_sconv_cache.cuh new file mode 100644 index 000000000..7467cc971 --- /dev/null +++ b/python/sglang/jit_kernel/csrc/inkling/update_sconv_cache.cuh @@ -0,0 +1,138 @@ +// Update the convolution cache from an extend/prefill token stream. +// +// For each sequence b with slot ci = cache_indices[b] and query range +// [start, end) (query_start_loc), the new conv state is the last W1 = W-1 entries of +// the virtual stream [ old_state (W1 rows, gated by has_initial_state) ++ x[start:end] ]: +// new_state[w] = virtual[qlen + w] for w in 0..W1-1 (qlen = end - start) +// qlen + w >= W1 -> x[end - W1 + w, d] (a "current" token) +// qlen + w < W1 -> old_cache[slot, w + qlen, d] * has_state (shifted state) +// PAD (ci == -1) or empty (qlen <= 0) lanes are left untouched. This is a pure +// select/copy (no arithmetic) => must be BIT-EXACT (bf16 values moved verbatim). +// +// RAW-safe: each thread loads all W1 old_cache rows into registers BEFORE writing any, +// so the in-place writes never clobber a not-yet-read shift source. 2 channels/thread +// are packed as bf16x2 (32-bit) to halve the moves. Requires bf16 + even D. +#include // For TensorMatcher, SymbolicSize, SymbolicDevice +#include // For RuntimeCheck, div_ceil + +#include // For LaunchKernel, SGL_DEVICE + +#include +#include + +#include +#include + +namespace { + +struct UpdateSconvParams { + const void* __restrict__ x; // [T, D], channel-contiguous + void* __restrict__ cache; // [max_slots, W1, D], in-place update + const void* __restrict__ cache_indices; // int32 [B] + const void* __restrict__ has_state; // bool [B] + const void* __restrict__ qsl; // int32 [B+1] query_start_loc + int64_t x_stride_t; + int64_t cache_stride_slot; + int64_t cache_stride_w; + uint32_t D; +}; + +constexpr uint32_t kUpdThreads = 256; // threads/block, each owns a channel pair +constexpr int kPadSlot = -1; + +template +__global__ void update_sconv_cache_kernel(const __grid_constant__ UpdateSconvParams p) { + const int b = blockIdx.y; + const int ci = static_cast(p.cache_indices)[b]; + const int start = static_cast(p.qsl)[b]; + const int end = static_cast(p.qsl)[b + 1]; + const int qlen = end - start; + if (ci == kPadSlot || qlen <= 0) return; // PAD / empty lane: untouched + + const int c0 = (blockIdx.x * kUpdThreads + threadIdx.x) * 2; + if (c0 >= static_cast(p.D)) return; + + const bool hs = static_cast(p.has_state)[b]; + const auto* xp = static_cast(p.x); + auto* cp = static_cast<__nv_bfloat16*>(p.cache); + const int cw = static_cast(p.cache_stride_w); + const int64_t slot_base = static_cast(ci) * p.cache_stride_slot + c0; + + // Load all old-state rows into registers first (RAW-safe against the writes below). + __nv_bfloat162 old_reg[W1]; +#pragma unroll + for (int w = 0; w < W1; ++w) { + old_reg[w] = *reinterpret_cast(&cp[slot_base + static_cast(w) * cw]); + } + const __nv_bfloat162 zero = __float2bfloat162_rn(0.0f); + +#pragma unroll + for (int w = 0; w < W1; ++w) { + __nv_bfloat162 nv; + if (qlen >= (W1 - w)) { + // current token from x: index end - W1 + w >= start >= 0 + const int x_idx = end - W1 + w; + nv = *reinterpret_cast(&xp[static_cast(x_idx) * p.x_stride_t + c0]); + } else { + // shifted state old_cache[w + qlen] (w+qlen in [0, W1)), gated by has_state + __nv_bfloat162 shift = zero; +#pragma unroll + for (int src = 0; src < W1; ++src) { + if (src == w + qlen) shift = old_reg[src]; + } + nv = hs ? shift : zero; + } + *reinterpret_cast<__nv_bfloat162*>(&cp[slot_base + static_cast(w) * cw]) = nv; + } +} + +template +struct UpdateSconvCacheKernel { + static void + run(tvm::ffi::TensorView x, + tvm::ffi::TensorView cache, + tvm::ffi::TensorView cache_indices, + tvm::ffi::TensorView has_state, + tvm::ffi::TensorView qsl) { + using namespace host; + auto T = SymbolicSize{"T"}; + auto D = SymbolicSize{"D"}; + auto W1s = SymbolicSize{"W_minus_1"}; + auto B = SymbolicSize{"B"}; + auto dev = SymbolicDevice{}; + dev.set_options(); + W1s.set_value(W1); + + // x channel-contiguous (may be a non-contiguous row view); cache contiguous + // [slots, W1, D]. cache_indices/qsl int32, has_state torch-bool (shape/device only). + TensorMatcher({T, D}).with_strides({-1, 1}).with_dtype().with_device(dev).verify(x); + TensorMatcher({-1, W1s, D}).with_dtype().with_device(dev).verify(cache); + TensorMatcher({B}).with_dtype().with_device(dev).verify(cache_indices); + TensorMatcher({B}).with_device(dev).verify(has_state); + TensorMatcher({-1}).with_dtype().with_device(dev).verify(qsl); + RuntimeCheck(qsl.size(0) == B.unwrap() + 1, "qsl must have length B+1"); + RuntimeCheck(sizeof(DType) == 2, "update_sconv_cache: bf16x2 kernel requires 16-bit dtype"); + RuntimeCheck(D.unwrap() % 2 == 0, "update_sconv_cache: D must be even for the bf16x2 kernel"); + RuntimeCheck(cache.stride(2) == 1, "update_sconv_cache: cache must be channel-contiguous"); + + const auto params = UpdateSconvParams{ + .x = x.data_ptr(), + .cache = cache.data_ptr(), + .cache_indices = cache_indices.data_ptr(), + .has_state = has_state.data_ptr(), + .qsl = qsl.data_ptr(), + .x_stride_t = x.stride(0), + .cache_stride_slot = cache.stride(0), + .cache_stride_w = cache.stride(1), + .D = static_cast(D.unwrap()), + }; + + const uint32_t d_pairs = params.D / 2; + const dim3 grid{div_ceil(d_pairs, kUpdThreads), static_cast(B.unwrap())}; + const dim3 block{kUpdThreads}; + constexpr auto kernel = update_sconv_cache_kernel; + LaunchKernel(grid, block, dev.unwrap())(kernel, params); + } +}; + +} // namespace diff --git a/python/sglang/jit_kernel/csrc/moe/inkling_gate_topk_renorm.cuh b/python/sglang/jit_kernel/csrc/moe/inkling_gate_topk_renorm.cuh new file mode 100644 index 000000000..13246f9c8 --- /dev/null +++ b/python/sglang/jit_kernel/csrc/moe/inkling_gate_topk_renorm.cuh @@ -0,0 +1,1139 @@ +#pragma once + +#include + +#include +#include +#include + +#include +#include +#include +#include + +namespace { + +// Fixed constants for the Inkling model +static constexpr int kInklingRoutedExperts = 256; +static constexpr int kInklingSharedExperts = 2; +static constexpr int kInklingTotalExperts = kInklingRoutedExperts + kInklingSharedExperts; +static constexpr int kInklingTopK = 6; +static constexpr int kInklingTopPow2 = 8; +static constexpr int kInklingWarpSize = 32; +static constexpr int kInklingValuesPerLane = kInklingRoutedExperts / kInklingWarpSize; + +__device__ __forceinline__ float inkling_sigmoid(float x) { + return 1.0f / (1.0f + __expf(-x)); +} + +__device__ __forceinline__ bool inkling_score_better(float score, int idx, float best_score, int best_idx) { + return score > best_score || (score == best_score && idx < best_idx); +} + +// Order-preserving uint32 key of an fp32 (same total order as the triton +// kernel's fpval_to_key): flip sign bit for positives, all bits for negatives. +__device__ __forceinline__ uint32_t inkling_fp_key(float f) { + const uint32_t u = __float_as_uint(f); + return u ^ (static_cast(-static_cast(u >> 31)) | 0x80000000u); +} + +// FlashInfer routed-MoE pack: low 16 bits = bf16(weight) bits (round-to-nearest-even, +// same as torch/triton `.to(bfloat16)`), high 16 bits = int16 expert id. +__device__ __forceinline__ int32_t inkling_pack_routed(int32_t id, float w) { + const uint32_t wbits = static_cast(__bfloat16_as_ushort(__float2bfloat16(w))); + return static_cast((static_cast(id) << 16) | wbits); +} + +template +__launch_bounds__(kInklingWarpSize* WarpsPerBlock) __global__ void inkling_gate_topk_renorm_kernel( + const float* __restrict__ logits, + const float* __restrict__ bias, + const float* __restrict__ global_scale, + float* __restrict__ routed_w, + float* __restrict__ shared_w, + int64_t* __restrict__ indices, + int32_t* __restrict__ packed, + int64_t M, + int64_t logits_stride_m, + float route_scale) { + const int lane = threadIdx.x; + const int warp_in_block = threadIdx.y; + const int64_t row = static_cast(blockIdx.x) * WarpsPerBlock + warp_in_block; + if (row >= M) { + return; + } + + const int64_t row_base = row * logits_stride_m; + float local_scores[kInklingValuesPerLane]; +#pragma unroll + for (int i = 0; i < kInklingValuesPerLane; ++i) { + const int expert = lane + i * kInklingWarpSize; + const float raw = logits[row_base + expert]; + local_scores[i] = inkling_sigmoid(raw) + bias[expert]; + } + + int selected_idx[kInklingTopK]; +#pragma unroll + for (int k = 0; k < kInklingTopK; ++k) { + float best_score = -FLT_MAX; + int best_idx = INT_MAX; +#pragma unroll + for (int i = 0; i < kInklingValuesPerLane; ++i) { + const int expert = lane + i * kInklingWarpSize; + const float score = local_scores[i]; + if (inkling_score_better(score, expert, best_score, best_idx)) { + best_score = score; + best_idx = expert; + } + } + +#pragma unroll + for (int offset = kInklingWarpSize / 2; offset > 0; offset >>= 1) { + const float other_score = __shfl_xor_sync(0xffffffff, best_score, offset); + const int other_idx = __shfl_xor_sync(0xffffffff, best_idx, offset); + if (inkling_score_better(other_score, other_idx, best_score, best_idx)) { + best_score = other_score; + best_idx = other_idx; + } + } + + selected_idx[k] = best_idx; + if (best_idx % kInklingWarpSize == lane) { + local_scores[best_idx / kInklingWarpSize] = -FLT_MAX; + } + __syncwarp(); + } + + if (lane != 0) { + return; + } + + float active[kInklingTopPow2]; +#pragma unroll + for (int i = 0; i < kInklingTopK; ++i) { + active[i] = inkling_sigmoid(logits[row_base + selected_idx[i]]); + } +#pragma unroll + for (int i = 0; i < kInklingSharedExperts; ++i) { + active[kInklingTopK + i] = inkling_sigmoid(logits[row_base + kInklingRoutedExperts + i]); + } + + float sum = 0.0f; +#pragma unroll + for (int i = 0; i < kInklingTopPow2; ++i) { + sum += active[i]; + } + const float scale = route_scale * global_scale[0] / sum; + +#pragma unroll + for (int i = 0; i < kInklingTopK; ++i) { + const float w = active[i] * scale; + if constexpr (ReturnPacked) { + packed[row * kInklingTopK + i] = inkling_pack_routed(selected_idx[i], w); + } else { + routed_w[row * kInklingTopK + i] = w; + indices[row * kInklingTopK + i] = static_cast(selected_idx[i]); + } + } +#pragma unroll + for (int i = 0; i < kInklingSharedExperts; ++i) { + shared_w[row * kInklingSharedExperts + i] = active[kInklingTopK + i] * scale; + } +} + +template +void launch_inkling_gate_topk_renorm( + const float* logits, + const float* bias, + const float* global_scale, + float* routed_w, + float* shared_w, + int64_t* indices, + int32_t* packed, + int64_t tokens, + int64_t logits_stride_m, + float route_scale, + DLDevice device) { + using namespace host; + const dim3 block(kInklingWarpSize, WarpsPerBlock); + const dim3 grid(static_cast(div_ceil(tokens, static_cast(WarpsPerBlock)))); + LaunchKernel(grid, block, device)( + inkling_gate_topk_renorm_kernel, + logits, + bias, + global_scale, + routed_w, + shared_w, + indices, + packed, + tokens, + logits_stride_m, + route_scale); +} + +template +void dispatch_inkling_gate_topk_renorm( + const float* logits, + const float* bias, + const float* global_scale, + float* routed_w, + float* shared_w, + int64_t* indices, + int32_t* packed, + int64_t tokens, + int64_t logits_stride_m, + float route_scale, + DLDevice device) { + if (tokens <= 64) { + launch_inkling_gate_topk_renorm<1, ReturnPacked>( + logits, bias, global_scale, routed_w, shared_w, indices, packed, tokens, logits_stride_m, route_scale, device); + } else if (tokens <= 1024) { + launch_inkling_gate_topk_renorm<4, ReturnPacked>( + logits, bias, global_scale, routed_w, shared_w, indices, packed, tokens, logits_stride_m, route_scale, device); + } else { + launch_inkling_gate_topk_renorm<8, ReturnPacked>( + logits, bias, global_scale, routed_w, shared_w, indices, packed, tokens, logits_stride_m, route_scale, device); + } +} + +// Warp-per-row gate kernel with optional expert-per-block GEMV fusion. + +// Inkling gate GEMM shape: x [M, 6144] bf16 x W [264 (row-padded), 6144] bf16. +static constexpr int kInklingHidden = 6144; +static constexpr int kInklingGemvThreads = 256; +static constexpr int kInklingGemvWarps = kInklingGemvThreads / kInklingWarpSize; +// fp32 logits row pitch: 264 floats = 1056 bytes, a multiple of 32B, so every +// row supports the widest vector loads. Matches the production [M, 264] +// padded-GEMM output that InklingGate slices to [:, :258]. +static constexpr int kInklingLogitsPad = 264; +// The fused GEMV epilogue runs in a single block (8 warps looping over rows). +static constexpr int kInklingFusedMaxTokens = 64; + +// Widest vector loads: 32B on Blackwell, 16B before. +static constexpr int kVecF32 = static_cast(device::kMaxVecBytes / sizeof(float)); +static constexpr int kVecBf16 = static_cast(device::kMaxVecBytes / sizeof(bf16_t)); +// v2 per-lane expert layout: expert = lane * 8 + j (contiguous per lane so one +// or two wide loads cover a lane's slice; the warp covers 256 experts). +static constexpr int kLanePitch = kInklingValuesPerLane; +static_assert(kLanePitch == 8 && kLanePitch % kVecF32 == 0); + +// Launch-invariant gate inputs (weights): safe to read before the PDL wait. +struct InklingGateStatics { + float bias[kLanePitch]; // selection bias for experts lane*8 + j + float scale; // route_scale * global_scale[0] +}; + +__device__ __forceinline__ InklingGateStatics inkling_gate_load_statics( + const float* __restrict__ bias, const float* __restrict__ global_scale, float route_scale, int lane) { + InklingGateStatics st; +#pragma unroll + for (int i = 0; i < kLanePitch / kVecF32; ++i) { + device::AlignedVector v; + v.load(bias, lane * (kLanePitch / kVecF32) + i); +#pragma unroll + for (int j = 0; j < kVecF32; ++j) { + st.bias[i * kVecF32 + j] = v[j]; + } + } + st.scale = route_scale * SGLANG_LDG(global_scale); + return st; +} + +// Whole-warp gate for one token row: top-6 of sigmoid(logit)+bias over experts +// 0..255 (exact fp32 compare, ties -> smaller expert id, matching the triton +// kernel), then renorm over sigmoid(raw selected) ++ sigmoid(shared 256..257). +// `row` must be device::kMaxVecBytes-aligned. Raw logits ride along in registers, so +// nothing is re-gathered from memory. The epilogue is spread over 8 lanes. +template +__device__ __forceinline__ void inkling_gate_row( + const float* __restrict__ row, + const InklingGateStatics& st, + int lane, + int64_t m, + float* __restrict__ routed_w, + int32_t* __restrict__ indices, + int32_t* __restrict__ packed, + float* __restrict__ shared_w) { + float raw[kLanePitch]; + float sel[kLanePitch]; +#pragma unroll + for (int i = 0; i < kLanePitch / kVecF32; ++i) { + device::AlignedVector v; + v.load(row, lane * (kLanePitch / kVecF32) + i); +#pragma unroll + for (int j = 0; j < kVecF32; ++j) { + raw[i * kVecF32 + j] = v[j]; + sel[i * kVecF32 + j] = inkling_sigmoid(v[j]) + st.bias[i * kVecF32 + j]; + } + } + + // Shared-expert logits live at columns 256/257; lane 0 fetches, all receive. + float sh0 = 0.0f; + float sh1 = 0.0f; + if (lane == 0) { + const float2 sh = *reinterpret_cast(row + kInklingRoutedExperts); + sh0 = sh.x; + sh1 = sh.y; + } + sh0 = __shfl_sync(0xffffffff, sh0, 0); + sh1 = __shfl_sync(0xffffffff, sh1, 0); + + int sel_idx[kInklingTopK]; + float sel_raw[kInklingTopK]; +#pragma unroll + for (int k = 0; k < kInklingTopK; ++k) { + float best_s = -FLT_MAX; + int best_j = 0; +#pragma unroll + for (int j = 0; j < kLanePitch; ++j) { + // ascending j => first strict max keeps the smallest expert id + if (sel[j] > best_s) { + best_s = sel[j]; + best_j = j; + } + } + // Cross-lane argmax via one hardware redux + ballot. Exact fp32 order via + // the monotonic key; ties pick the lowest lane, which is the smallest + // expert id under the lane-major layout (expert = lane*8 + j). + const uint32_t key = inkling_fp_key(best_s); + int win_lane; +#if SGL_CUDA_ARCH >= 800 + const uint32_t key_max = __reduce_max_sync(0xffffffff, key); +#else + uint32_t key_max = key; +#pragma unroll + for (int offset = kInklingWarpSize / 2; offset > 0; offset >>= 1) { + key_max = max(key_max, __shfl_xor_sync(0xffffffff, key_max, offset)); + } +#endif + win_lane = __ffs(__ballot_sync(0xffffffff, key == key_max)) - 1; + // The owning lane retires the winner and forwards (expert id, raw logit); + // every local-array index stays static so nothing spills to local memory. + float win_raw = 0.0f; + int win_idx = 0; + if (lane == win_lane) { + win_idx = lane * kLanePitch + best_j; +#pragma unroll + for (int j = 0; j < kLanePitch; ++j) { + if (j == best_j) { + sel[j] = -FLT_MAX; + win_raw = raw[j]; + } + } + } + sel_idx[k] = __shfl_sync(0xffffffff, win_idx, win_lane); + sel_raw[k] = __shfl_sync(0xffffffff, win_raw, win_lane); + } + + // Renorm, replicated on all lanes (registers only, no cross-lane traffic). + float active[kInklingTopK + kInklingSharedExperts]; +#pragma unroll + for (int k = 0; k < kInklingTopK; ++k) { + active[k] = inkling_sigmoid(sel_raw[k]); + } + active[kInklingTopK] = inkling_sigmoid(sh0); + active[kInklingTopK + 1] = inkling_sigmoid(sh1); + float sum = 0.0f; +#pragma unroll + for (int i = 0; i < kInklingTopK + kInklingSharedExperts; ++i) { + sum += active[i]; + } + const float scale = st.scale / sum; + + // Lane a < 8 owns active slot a (static-index select, then one store each). + float my_active = 0.0f; + int my_idx = 0; +#pragma unroll + for (int a = 0; a < kInklingTopK + kInklingSharedExperts; ++a) { + if (a == lane) { + my_active = active[a]; + my_idx = a < kInklingTopK ? sel_idx[a] : 0; + } + } + const float w = my_active * scale; + if (lane < kInklingTopK) { + if constexpr (kPacked) { + packed[m * kInklingTopK + lane] = inkling_pack_routed(my_idx, w); + } else { + routed_w[m * kInklingTopK + lane] = w; + indices[m * kInklingTopK + lane] = my_idx; + } + } else if (lane < kInklingTopK + kInklingSharedExperts) { + shared_w[m * kInklingSharedExperts + (lane - kInklingTopK)] = w; + } +} + +template +__launch_bounds__(kInklingWarpSize* kWarpsPerBlock) __global__ void inkling_gate_topk_renorm_v2_kernel( + const float* __restrict__ logits, + const float* __restrict__ bias, + const float* __restrict__ global_scale, + float* __restrict__ routed_w, + int32_t* __restrict__ indices, + int32_t* __restrict__ packed, + float* __restrict__ shared_w, + int64_t M, + int64_t logits_stride_m, + float route_scale) { + const int lane = threadIdx.x; + const int64_t m = static_cast(blockIdx.x) * kWarpsPerBlock + threadIdx.y; + const InklingGateStatics st = inkling_gate_load_statics(bias, global_scale, route_scale, lane); + device::PDLWaitPrimary(); + if (m < M) { + inkling_gate_row(logits + m * logits_stride_m, st, lane, m, routed_w, indices, packed, shared_w); + } + device::PDLTriggerSecondary(); +} + +// Vectorized fp32 dot of one x row against one smem-staged W row, over the +// vector-index range [v_lo, v_hi) with stride 32 (one warp lane per vector). +__device__ __forceinline__ float +inkling_gemv_partial(const bf16_t* __restrict__ x_row, const bf16_t* __restrict__ w_row, int v_lo, int v_hi, int lane) { + float acc = 0.0f; + for (int v = v_lo + lane; v < v_hi; v += kInklingWarpSize) { + device::AlignedVector xv; + device::AlignedVector wv; + xv.load(x_row, v); + wv.load(w_row, v); +#pragma unroll + for (int p = 0; p < kVecBf16 / 2; ++p) { + const float2 xf = __bfloat1622float2(reinterpret_cast(xv.data())[p]); + const float2 wf = __bfloat1622float2(reinterpret_cast(wv.data())[p]); + acc = fmaf(xf.x, wf.x, acc); + acc = fmaf(xf.y, wf.y, acc); + } + } + return acc; +} + +// Experts-per-block GEMV: block b computes logits[:, b*kEpb : b*kEpb+kEpb] = +// x @ W[those experts].T. The W rows are staged to smem before the PDL wait +// (weights are launch-invariant), so the weight fetch overlaps the producer +// kernel's tail, and every x vector load is reused for kEpb experts. Token +// assignment: warp-per-token when M >= 8; for smaller M the warps split the +// hidden dim (partials combined through smem) so a single token still uses +// the whole block. With kFused, the last block to finish (atomic ticket) runs +// the gate epilogue over the workspace and resets the ticket so CUDA-graph +// replays need no re-initialization. +template +__launch_bounds__(kInklingGemvThreads) __global__ void inkling_gate_gemv_kernel( + const bf16_t* __restrict__ x, // [M, 6144] + const bf16_t* __restrict__ weight, // [>=258, 6144] + const float* __restrict__ bias, + const float* __restrict__ global_scale, + float* __restrict__ logits, // [M, kInklingLogitsPad] + float* __restrict__ routed_w, + int32_t* __restrict__ indices, + int32_t* __restrict__ packed, + float* __restrict__ shared_w, + int32_t* __restrict__ ticket, + int64_t M, + float route_scale) { + constexpr int kHiddenVecs = kInklingHidden / kVecBf16; + __shared__ alignas(device::kMaxVecBytes) bf16_t w_rows[kEpb][kInklingHidden]; + __shared__ float s_part[kInklingGemvWarps][kEpb]; + + const int tid = threadIdx.x; + const int lane = tid % kInklingWarpSize; + const int warp = tid / kInklingWarpSize; + const int e0 = blockIdx.x * kEpb; + const int n_e = min(kEpb, kInklingTotalExperts - e0); // tail block: fewer experts + + // M <= 2: each warp's W slice fits in registers, so it is preloaded there + // before the PDL wait -- no smem staging round-trip on the critical path. + // Sized for the worst case among the reg_sliced wpt values {8, 4}: wpt=4 + // gives the larger per-warp slice (kHiddenVecs/4 vectors). Derived from + // kHiddenVecs (not hardcoded) because it depends on kMaxVecBytes, which is + // 32B on Blackwell but only 16B pre-Blackwell -- a fixed literal sized for + // Blackwell's narrower kHiddenVecs silently drops the back half of the + // hidden dim on pre-Blackwell architectures. + constexpr int kRegVecs = (kHiddenVecs / 4 + kInklingWarpSize - 1) / kInklingWarpSize; + const bool reg_sliced = M <= 2; + const int wpt = M <= 1 ? 8 : (M <= 2 ? 4 : (M <= 4 ? 2 : 1)); + const int v_span = kHiddenVecs / wpt; + const int slice = warp % wpt; + device::AlignedVector w_reg[kEpb][kRegVecs]; + if (reg_sliced) { +#pragma unroll + for (int j = 0; j < kEpb; ++j) { + if (j < n_e) { +#pragma unroll + for (int r = 0; r < kRegVecs; ++r) { + const int v = slice * v_span + r * kInklingWarpSize + lane; + if (v < (slice + 1) * v_span) { + w_reg[j][r].load(weight + static_cast(e0 + j) * kInklingHidden, v); + } + } + } + } + } else { +#pragma unroll + for (int j = 0; j < kEpb; ++j) { + if (j < n_e) { + for (int v = tid; v < kHiddenVecs; v += kInklingGemvThreads) { + device::AlignedVector wv; + wv.load(weight + static_cast(e0 + j) * kInklingHidden, v); + wv.store(w_rows[j], v); + } + } + } + } + device::PDLWaitPrimary(); + if (!reg_sliced) { // uniform: publish the smem-staged W rows + __syncthreads(); + } + + if (reg_sliced) { + const int m = warp / wpt; + if (m < M) { + const bf16_t* x_row = x + static_cast(m) * kInklingHidden; +#pragma unroll + for (int j = 0; j < kEpb; ++j) { + float acc = 0.0f; +#pragma unroll + for (int r = 0; r < kRegVecs; ++r) { + const int v = slice * v_span + r * kInklingWarpSize + lane; + if (v < (slice + 1) * v_span) { + device::AlignedVector xv; + xv.load(x_row, v); +#pragma unroll + for (int p = 0; p < kVecBf16 / 2; ++p) { + const float2 xf = __bfloat1622float2(reinterpret_cast(xv.data())[p]); + const float2 wf = __bfloat1622float2(reinterpret_cast(w_reg[j][r].data())[p]); + acc = fmaf(xf.x, wf.x, acc); + acc = fmaf(xf.y, wf.y, acc); + } + } + } + const float r = device::warp::reduce_sum(acc); + if (lane == 0) { + s_part[warp][j] = r; + } + } + } + __syncthreads(); + if (warp == 0) { + const int total = static_cast(M) * kEpb; + if (lane < total) { + const int m_out = lane / kEpb; + const int j_out = lane % kEpb; + float r = 0.0f; + for (int s = 0; s < wpt; ++s) { + r += s_part[m_out * wpt + s][j_out]; + } + if (j_out < n_e) { + logits[static_cast(m_out) * kInklingLogitsPad + e0 + j_out] = r; + } + } + } + } else if (M >= kInklingGemvWarps) { + for (int64_t m = warp; m < M; m += kInklingGemvWarps) { + const bf16_t* x_row = x + m * kInklingHidden; + float acc[kEpb]; +#pragma unroll + for (int j = 0; j < kEpb; ++j) { + acc[j] = 0.0f; + } + // Single pass over the x row; each vector feeds all kEpb experts. + for (int v = lane; v < kHiddenVecs; v += kInklingWarpSize) { + device::AlignedVector xv; + xv.load(x_row, v); +#pragma unroll + for (int j = 0; j < kEpb; ++j) { + device::AlignedVector wv; + wv.load(w_rows[j], v); +#pragma unroll + for (int p = 0; p < kVecBf16 / 2; ++p) { + const float2 xf = __bfloat1622float2(reinterpret_cast(xv.data())[p]); + const float2 wf = __bfloat1622float2(reinterpret_cast(wv.data())[p]); + acc[j] = fmaf(xf.x, wf.x, acc[j]); + acc[j] = fmaf(xf.y, wf.y, acc[j]); + } + } + } +#pragma unroll + for (int j = 0; j < kEpb; ++j) { + const float r = device::warp::reduce_sum(acc[j]); + if (lane == 0 && j < n_e) { + logits[m * kInklingLogitsPad + e0 + j] = r; + } + } + } + } else { + // 2 < M < 8: warps_per_token warps split the hidden dim of one token, + // reading W from the smem staging. + const int m = warp / wpt; + if (m < M) { + const bf16_t* x_row = x + static_cast(m) * kInklingHidden; +#pragma unroll + for (int j = 0; j < kEpb; ++j) { + const float p = inkling_gemv_partial(x_row, w_rows[j], slice * v_span, (slice + 1) * v_span, lane); + const float r = device::warp::reduce_sum(p); + if (lane == 0) { + s_part[warp][j] = r; + } + } + } + __syncthreads(); + // One lane per (token, expert) folds the wpt partials and stores. + if (warp == 0) { + const int total = static_cast(M) * kEpb; + if (lane < total) { + const int m_out = lane / kEpb; + const int j_out = lane % kEpb; + float r = 0.0f; + for (int s = 0; s < wpt; ++s) { + r += s_part[m_out * wpt + s][j_out]; + } + if (j_out < n_e) { + logits[static_cast(m_out) * kInklingLogitsPad + e0 + j_out] = r; + } + } + } + } + + if constexpr (!kFused) { + device::PDLTriggerSecondary(); + return; + } + + // Single-pass fused epilogue (CUB-style threadfence + ticket pattern). + __shared__ int s_ticket; + __syncthreads(); + __threadfence(); + if (tid == 0) { + s_ticket = atomicAdd(ticket, 1); + } + __syncthreads(); + if (s_ticket != static_cast(gridDim.x) - 1) { + device::PDLTriggerSecondary(); + return; + } + __threadfence(); // acquire side: other blocks' workspace stores are visible + const InklingGateStatics st = inkling_gate_load_statics(bias, global_scale, route_scale, lane); + for (int64_t m = warp; m < M; m += kInklingGemvWarps) { + inkling_gate_row(logits + m * kInklingLogitsPad, st, lane, m, routed_w, indices, packed, shared_w); + } + __syncthreads(); + if (tid == 0) { + *ticket = 0; // replay-safe self-reset (CUDA graphs) + } + device::PDLTriggerSecondary(); +} + +template +void dispatch_inkling_gate_topk_renorm_v2( + const float* logits, + const float* bias, + const float* global_scale, + float* routed_w, + int32_t* indices, + int32_t* packed, + float* shared_w, + int64_t tokens, + int64_t logits_stride_m, + float route_scale, + bool enable_pdl, + int64_t warps_per_block, + DLDevice device) { + using namespace host; + if (warps_per_block == 0) { + // Four warps balance occupancy and per-row parallelism. + warps_per_block = 4; + } + const dim3 block(kInklingWarpSize, static_cast(warps_per_block)); + const dim3 grid(static_cast(div_ceil(tokens, warps_per_block))); + auto launch = [&](auto kernel) { + LaunchKernel(grid, block, device) + .enable_pdl(enable_pdl)( + kernel, + logits, + bias, + global_scale, + routed_w, + indices, + packed, + shared_w, + tokens, + logits_stride_m, + route_scale); + }; + switch (warps_per_block) { + case 1: + return enable_pdl ? launch(inkling_gate_topk_renorm_v2_kernel<1, kPacked, true>) + : launch(inkling_gate_topk_renorm_v2_kernel<1, kPacked, false>); + case 2: + return enable_pdl ? launch(inkling_gate_topk_renorm_v2_kernel<2, kPacked, true>) + : launch(inkling_gate_topk_renorm_v2_kernel<2, kPacked, false>); + case 4: + return enable_pdl ? launch(inkling_gate_topk_renorm_v2_kernel<4, kPacked, true>) + : launch(inkling_gate_topk_renorm_v2_kernel<4, kPacked, false>); + case 8: + return enable_pdl ? launch(inkling_gate_topk_renorm_v2_kernel<8, kPacked, true>) + : launch(inkling_gate_topk_renorm_v2_kernel<8, kPacked, false>); + default: + ::host::panic({}, "warps_per_block must be one of {0, 1, 2, 4, 8}"); + } +} + +template +void dispatch_inkling_gate_gemv( + const bf16_t* x, + const bf16_t* weight, + const float* bias, + const float* global_scale, + float* logits, + float* routed_w, + int32_t* indices, + int32_t* packed, + float* shared_w, + int32_t* ticket, + int64_t tokens, + float route_scale, + bool enable_pdl, + int64_t experts_per_block, + DLDevice device) { + using namespace host; + if (experts_per_block == 0) { // auto policy, tuned on B200 + experts_per_block = 2; + } + const dim3 block(kInklingGemvThreads); + const dim3 grid(static_cast(div_ceil(kInklingTotalExperts, static_cast(experts_per_block)))); + auto launch = [&](auto kernel) { + LaunchKernel(grid, block, device) + .enable_pdl(enable_pdl)( + kernel, + x, + weight, + bias, + global_scale, + logits, + routed_w, + indices, + packed, + shared_w, + ticket, + tokens, + route_scale); + }; + switch (experts_per_block) { + case 1: + return enable_pdl ? launch(inkling_gate_gemv_kernel<1, kFused, kPacked, true>) + : launch(inkling_gate_gemv_kernel<1, kFused, kPacked, false>); + case 2: + return enable_pdl ? launch(inkling_gate_gemv_kernel<2, kFused, kPacked, true>) + : launch(inkling_gate_gemv_kernel<2, kFused, kPacked, false>); + default: + // 4 experts/block would need >48KB static smem (dynamic smem territory). + ::host::panic({}, "experts_per_block must be one of {0, 1, 2}"); + } +} + +void check_gate_row_alignment(const void* ptr, int64_t stride_m) { + host::RuntimeCheck( + reinterpret_cast(ptr) % device::kMaxVecBytes == 0 && + (stride_m * static_cast(sizeof(float))) % device::kMaxVecBytes == 0, + "logits rows must be device::kMaxVecBytes-aligned (production pitch is 264 floats)"); +} + +} // namespace + +void inkling_gate_topk_renorm( + tvm::ffi::TensorView logits, + tvm::ffi::TensorView bias, + tvm::ffi::TensorView global_scale, + tvm::ffi::TensorView routed_w, + tvm::ffi::TensorView shared_w, + tvm::ffi::TensorView indices, + double route_scale) { + using namespace host; + + SymbolicSize M{"tokens"}; + SymbolicDevice device_; + device_.set_options(); + + TensorMatcher({M, kInklingTotalExperts}) + .with_strides({-1, 1}) + .with_dtype() + .with_device(device_) + .verify(logits); + TensorMatcher({kInklingRoutedExperts}).with_dtype().with_device(device_).verify(bias); + TensorMatcher({1}).with_dtype().with_device(device_).verify(global_scale); + TensorMatcher({M, kInklingTopK}).with_dtype().with_device(device_).verify(routed_w); + TensorMatcher({M, kInklingSharedExperts}).with_dtype().with_device(device_).verify(shared_w); + TensorMatcher({M, kInklingTopK}).with_dtype().with_device(device_).verify(indices); + + RuntimeCheck(logits.stride(1) == 1, "logits must be contiguous along the expert dimension"); + RuntimeCheck(bias.stride(0) == 1, "bias must be contiguous"); + RuntimeCheck(routed_w.stride(1) == 1, "routed_w must be contiguous along top-k dimension"); + RuntimeCheck(shared_w.stride(1) == 1, "shared_w must be contiguous along shared dimension"); + RuntimeCheck(indices.stride(1) == 1, "indices must be contiguous along top-k dimension"); + + const int64_t tokens = M.unwrap(); + if (tokens == 0) { + return; + } + + const auto* logits_ptr = static_cast(logits.data_ptr()); + const auto* bias_ptr = static_cast(bias.data_ptr()); + const auto* global_scale_ptr = static_cast(global_scale.data_ptr()); + auto* routed_w_ptr = static_cast(routed_w.data_ptr()); + auto* shared_w_ptr = static_cast(shared_w.data_ptr()); + auto* indices_ptr = static_cast(indices.data_ptr()); + const float route_scale_f = static_cast(route_scale); + const DLDevice device = device_.unwrap(); + + dispatch_inkling_gate_topk_renorm( + logits_ptr, + bias_ptr, + global_scale_ptr, + routed_w_ptr, + shared_w_ptr, + indices_ptr, + nullptr, + tokens, + logits.stride(0), + route_scale_f, + device); +} + +// Packed variant: emits packed[M, kInklingTopK] int32 ((id<<16)|bf16 weight) instead of +// the routed_w + indices pair; shared_w still written. +void inkling_gate_topk_renorm_packed( + tvm::ffi::TensorView logits, + tvm::ffi::TensorView bias, + tvm::ffi::TensorView global_scale, + tvm::ffi::TensorView packed, + tvm::ffi::TensorView shared_w, + double route_scale) { + using namespace host; + + SymbolicSize M{"tokens"}; + SymbolicDevice device_; + device_.set_options(); + + TensorMatcher({M, kInklingTotalExperts}) + .with_strides({-1, 1}) + .with_dtype() + .with_device(device_) + .verify(logits); + TensorMatcher({kInklingRoutedExperts}).with_dtype().with_device(device_).verify(bias); + TensorMatcher({1}).with_dtype().with_device(device_).verify(global_scale); + TensorMatcher({M, kInklingTopK}).with_dtype().with_device(device_).verify(packed); + TensorMatcher({M, kInklingSharedExperts}).with_dtype().with_device(device_).verify(shared_w); + + RuntimeCheck(logits.stride(1) == 1, "logits must be contiguous along the expert dimension"); + RuntimeCheck(bias.stride(0) == 1, "bias must be contiguous"); + RuntimeCheck(packed.stride(1) == 1, "packed must be contiguous along top-k dimension"); + RuntimeCheck(shared_w.stride(1) == 1, "shared_w must be contiguous along shared dimension"); + + const int64_t tokens = M.unwrap(); + if (tokens == 0) { + return; + } + + const auto* logits_ptr = static_cast(logits.data_ptr()); + const auto* bias_ptr = static_cast(bias.data_ptr()); + const auto* global_scale_ptr = static_cast(global_scale.data_ptr()); + auto* packed_ptr = static_cast(packed.data_ptr()); + auto* shared_w_ptr = static_cast(shared_w.data_ptr()); + const float route_scale_f = static_cast(route_scale); + const DLDevice device = device_.unwrap(); + + dispatch_inkling_gate_topk_renorm( + logits_ptr, + bias_ptr, + global_scale_ptr, + nullptr, + shared_w_ptr, + nullptr, + packed_ptr, + tokens, + logits.stride(0), + route_scale_f, + device); +} + +// Uses int32 indices for MoeRunner, optional PDL, and tunable warps (0 = auto). +// Logit rows must be device::kMaxVecBytes-aligned (the standard pitch is 264). +void inkling_gate_topk_renorm_v2( + tvm::ffi::TensorView logits, + tvm::ffi::TensorView bias, + tvm::ffi::TensorView global_scale, + tvm::ffi::TensorView routed_w, + tvm::ffi::TensorView shared_w, + tvm::ffi::TensorView indices, + double route_scale, + bool enable_pdl, + int64_t warps_per_block) { + using namespace host; + + SymbolicSize M{"tokens"}; + SymbolicDevice device_; + device_.set_options(); + + TensorMatcher({M, kInklingTotalExperts}) + .with_strides({-1, 1}) + .with_dtype() + .with_device(device_) + .verify(logits); + TensorMatcher({kInklingRoutedExperts}).with_dtype().with_device(device_).verify(bias); + TensorMatcher({1}).with_dtype().with_device(device_).verify(global_scale); + TensorMatcher({M, kInklingTopK}).with_dtype().with_device(device_).verify(routed_w); + TensorMatcher({M, kInklingSharedExperts}).with_dtype().with_device(device_).verify(shared_w); + TensorMatcher({M, kInklingTopK}).with_dtype().with_device(device_).verify(indices); + + RuntimeCheck(logits.stride(1) == 1, "logits must be contiguous along the expert dimension"); + RuntimeCheck(bias.stride(0) == 1, "bias must be contiguous"); + RuntimeCheck( + routed_w.stride(1) == 1 && shared_w.stride(1) == 1 && indices.stride(1) == 1, "outputs must be contiguous"); + check_gate_row_alignment(logits.data_ptr(), logits.stride(0)); + + const int64_t tokens = M.unwrap(); + if (tokens == 0) { + return; + } + + dispatch_inkling_gate_topk_renorm_v2( + static_cast(logits.data_ptr()), + static_cast(bias.data_ptr()), + static_cast(global_scale.data_ptr()), + static_cast(routed_w.data_ptr()), + static_cast(indices.data_ptr()), + nullptr, + static_cast(shared_w.data_ptr()), + tokens, + logits.stride(0), + static_cast(route_scale), + enable_pdl, + warps_per_block, + device_.unwrap()); +} + +void inkling_gate_topk_renorm_v2_packed( + tvm::ffi::TensorView logits, + tvm::ffi::TensorView bias, + tvm::ffi::TensorView global_scale, + tvm::ffi::TensorView packed, + tvm::ffi::TensorView shared_w, + double route_scale, + bool enable_pdl, + int64_t warps_per_block) { + using namespace host; + + SymbolicSize M{"tokens"}; + SymbolicDevice device_; + device_.set_options(); + + TensorMatcher({M, kInklingTotalExperts}) + .with_strides({-1, 1}) + .with_dtype() + .with_device(device_) + .verify(logits); + TensorMatcher({kInklingRoutedExperts}).with_dtype().with_device(device_).verify(bias); + TensorMatcher({1}).with_dtype().with_device(device_).verify(global_scale); + TensorMatcher({M, kInklingTopK}).with_dtype().with_device(device_).verify(packed); + TensorMatcher({M, kInklingSharedExperts}).with_dtype().with_device(device_).verify(shared_w); + + RuntimeCheck(logits.stride(1) == 1, "logits must be contiguous along the expert dimension"); + RuntimeCheck(bias.stride(0) == 1, "bias must be contiguous"); + RuntimeCheck(packed.stride(1) == 1 && shared_w.stride(1) == 1, "outputs must be contiguous"); + check_gate_row_alignment(logits.data_ptr(), logits.stride(0)); + + const int64_t tokens = M.unwrap(); + if (tokens == 0) { + return; + } + + dispatch_inkling_gate_topk_renorm_v2( + static_cast(logits.data_ptr()), + static_cast(bias.data_ptr()), + static_cast(global_scale.data_ptr()), + nullptr, + nullptr, + static_cast(packed.data_ptr()), + static_cast(shared_w.data_ptr()), + tokens, + logits.stride(0), + static_cast(route_scale), + enable_pdl, + warps_per_block, + device_.unwrap()); +} + +// Standalone gate GEMV: logits[:, :258] = x @ W[:258].T (fp32 accumulate), for +// the PDL split pair (GEMV kernel -> v2 gate kernel). `logits` must use the +// production [M, 264] padded layout; columns 258..263 are left untouched. +void inkling_gate_gemv( + tvm::ffi::TensorView x, + tvm::ffi::TensorView weight, + tvm::ffi::TensorView logits, + bool enable_pdl, + int64_t experts_per_block) { + using namespace host; + + SymbolicSize M{"tokens"}; + SymbolicDevice device_; + device_.set_options(); + + TensorMatcher({M, kInklingHidden}).with_dtype().with_device(device_).verify(x); + TensorMatcher({-1, kInklingHidden}).with_dtype().with_device(device_).verify(weight); + TensorMatcher({M, kInklingLogitsPad}).with_dtype().with_device(device_).verify(logits); + + RuntimeCheck(weight.size(0) >= kInklingTotalExperts, "gate weight must cover 258 experts"); + RuntimeCheck(x.stride(1) == 1 && x.stride(0) == kInklingHidden, "x must be contiguous"); + RuntimeCheck(weight.stride(1) == 1 && weight.stride(0) == kInklingHidden, "weight must be contiguous"); + RuntimeCheck(logits.stride(1) == 1 && logits.stride(0) == kInklingLogitsPad, "logits must be contiguous"); + + const int64_t tokens = M.unwrap(); + if (tokens == 0) { + return; + } + + dispatch_inkling_gate_gemv( + static_cast(x.data_ptr()), + static_cast(weight.data_ptr()), + nullptr, + nullptr, + static_cast(logits.data_ptr()), + nullptr, + nullptr, + nullptr, + nullptr, + nullptr, + tokens, + 0.0f, + enable_pdl, + experts_per_block, + device_.unwrap()); +} + +namespace { + +// Shared verification for the fused GEMV entry points; returns tokens. +int64_t verify_inkling_gate_gemv_fused_common( + tvm::ffi::TensorView x, + tvm::ffi::TensorView weight, + tvm::ffi::TensorView bias, + tvm::ffi::TensorView global_scale, + tvm::ffi::TensorView workspace, + tvm::ffi::TensorView ticket, + tvm::ffi::TensorView shared_w, + host::SymbolicSize& M, + host::SymbolicDevice& device_) { + using namespace host; + + TensorMatcher({M, kInklingHidden}).with_dtype().with_device(device_).verify(x); + TensorMatcher({-1, kInklingHidden}).with_dtype().with_device(device_).verify(weight); + TensorMatcher({kInklingRoutedExperts}).with_dtype().with_device(device_).verify(bias); + TensorMatcher({1}).with_dtype().with_device(device_).verify(global_scale); + TensorMatcher({-1, kInklingLogitsPad}).with_dtype().with_device(device_).verify(workspace); + TensorMatcher({1}).with_dtype().with_device(device_).verify(ticket); + TensorMatcher({M, kInklingSharedExperts}).with_dtype().with_device(device_).verify(shared_w); + + const int64_t tokens = M.unwrap(); + RuntimeCheck(weight.size(0) >= kInklingTotalExperts, "gate weight must cover 258 experts"); + RuntimeCheck(tokens <= kInklingFusedMaxTokens, "fused gate GEMV supports at most 64 tokens"); + RuntimeCheck(workspace.size(0) >= tokens, "workspace too small"); + RuntimeCheck(x.stride(1) == 1 && x.stride(0) == kInklingHidden, "x must be contiguous"); + RuntimeCheck(weight.stride(1) == 1 && weight.stride(0) == kInklingHidden, "weight must be contiguous"); + RuntimeCheck(workspace.stride(1) == 1 && workspace.stride(0) == kInklingLogitsPad, "workspace must be contiguous"); + RuntimeCheck(bias.stride(0) == 1, "bias must be contiguous"); + RuntimeCheck(shared_w.stride(1) == 1, "shared_w must be contiguous"); + return tokens; +} + +} // namespace + +// Fully fused gate: GEMV + top-k + renorm in a single launch. `workspace` is a +// [>=M, 264] fp32 scratch and `ticket` a zero-initialized int32[1] that the +// kernel resets after use (both may be cached across calls / graph replays). +void inkling_gate_gemv_fused( + tvm::ffi::TensorView x, + tvm::ffi::TensorView weight, + tvm::ffi::TensorView bias, + tvm::ffi::TensorView global_scale, + tvm::ffi::TensorView workspace, + tvm::ffi::TensorView ticket, + tvm::ffi::TensorView routed_w, + tvm::ffi::TensorView shared_w, + tvm::ffi::TensorView indices, + double route_scale, + bool enable_pdl, + int64_t experts_per_block) { + using namespace host; + + SymbolicSize M{"tokens"}; + SymbolicDevice device_; + device_.set_options(); + + const int64_t tokens = + verify_inkling_gate_gemv_fused_common(x, weight, bias, global_scale, workspace, ticket, shared_w, M, device_); + TensorMatcher({M, kInklingTopK}).with_dtype().with_device(device_).verify(routed_w); + TensorMatcher({M, kInklingTopK}).with_dtype().with_device(device_).verify(indices); + RuntimeCheck(routed_w.stride(1) == 1 && indices.stride(1) == 1, "outputs must be contiguous"); + if (tokens == 0) { + return; + } + + dispatch_inkling_gate_gemv( + static_cast(x.data_ptr()), + static_cast(weight.data_ptr()), + static_cast(bias.data_ptr()), + static_cast(global_scale.data_ptr()), + static_cast(workspace.data_ptr()), + static_cast(routed_w.data_ptr()), + static_cast(indices.data_ptr()), + nullptr, + static_cast(shared_w.data_ptr()), + static_cast(ticket.data_ptr()), + tokens, + static_cast(route_scale), + enable_pdl, + experts_per_block, + device_.unwrap()); +} + +void inkling_gate_gemv_fused_packed( + tvm::ffi::TensorView x, + tvm::ffi::TensorView weight, + tvm::ffi::TensorView bias, + tvm::ffi::TensorView global_scale, + tvm::ffi::TensorView workspace, + tvm::ffi::TensorView ticket, + tvm::ffi::TensorView packed, + tvm::ffi::TensorView shared_w, + double route_scale, + bool enable_pdl, + int64_t experts_per_block) { + using namespace host; + + SymbolicSize M{"tokens"}; + SymbolicDevice device_; + device_.set_options(); + + const int64_t tokens = + verify_inkling_gate_gemv_fused_common(x, weight, bias, global_scale, workspace, ticket, shared_w, M, device_); + TensorMatcher({M, kInklingTopK}).with_dtype().with_device(device_).verify(packed); + RuntimeCheck(packed.stride(1) == 1, "packed must be contiguous"); + if (tokens == 0) { + return; + } + + dispatch_inkling_gate_gemv( + static_cast(x.data_ptr()), + static_cast(weight.data_ptr()), + static_cast(bias.data_ptr()), + static_cast(global_scale.data_ptr()), + static_cast(workspace.data_ptr()), + nullptr, + nullptr, + static_cast(packed.data_ptr()), + static_cast(shared_w.data_ptr()), + static_cast(ticket.data_ptr()), + tokens, + static_cast(route_scale), + enable_pdl, + experts_per_block, + device_.unwrap()); +} diff --git a/python/sglang/jit_kernel/csrc/trtllm_lora_temp/moe_lora_merged_align_kernel.cu b/python/sglang/jit_kernel/csrc/trtllm_lora_temp/moe_lora_merged_align_kernel.cu index e2f672088..d7690c00d 100644 --- a/python/sglang/jit_kernel/csrc/trtllm_lora_temp/moe_lora_merged_align_kernel.cu +++ b/python/sglang/jit_kernel/csrc/trtllm_lora_temp/moe_lora_merged_align_kernel.cu @@ -20,11 +20,9 @@ limitations under the License. // and compute the merged virtual id inline (mirrors _fused_virtual_topk_ids), // so virtual_topk_ids is never materialized to global memory. // -// Commit 1 scope: pure fusion (inline virtual id), NO EP skip. Output is -// bucket-for-bucket equivalent to the old path (dropped/-1 tokens still land in -// the sentinel bucket 0), so it can be asserted equal to the old kernels. -// Only the `64 < num_buckets <= 1024` branch is implemented here; other expert -// counts keep the old path (handled by the Python dispatcher). +// Shared-outer and compact EP routing support up to 1024 effective buckets, +// using fused scatter for eligible shapes and two kernels otherwise. Larger +// domains keep the old path through the Python dispatcher. #include #include diff --git a/python/sglang/jit_kernel/flash_attention.py b/python/sglang/jit_kernel/flash_attention.py index 22c7dc9bc..c5e77b6f8 100644 --- a/python/sglang/jit_kernel/flash_attention.py +++ b/python/sglang/jit_kernel/flash_attention.py @@ -41,6 +41,11 @@ def flash_attn_with_kvcache( sinks=None, score_mod=None, aux_tensors=None, + sfq=None, + sfk=None, + sfv=None, + rel_bias=None, + rel_bias_prep_cache=None, ver=3, out=None, ): @@ -202,6 +207,11 @@ def flash_attn_with_kvcache( sinks=sinks, score_mod=score_mod, aux_tensors=aux_tensors, + sfq=sfq, + sfk=sfk, + sfv=sfv, + rel_bias=rel_bias, + rel_bias_prep_cache=rel_bias_prep_cache, return_softmax_lse=return_softmax_lse, ) else: @@ -236,6 +246,11 @@ def flash_attn_varlen_func( sinks=None, score_mod=None, aux_tensors=None, + sfq=None, + sfk=None, + sfv=None, + rel_bias=None, + rel_bias_prep_cache=None, ver=3, out=None, ): @@ -294,6 +309,14 @@ def flash_attn_varlen_func( pack_gqa=pack_gqa, score_mod=score_mod, aux_tensors=aux_tensors, + q_descale=q_descale, + k_descale=k_descale, + v_descale=v_descale, + sfq=sfq, + sfk=sfk, + sfv=sfv, + rel_bias=rel_bias, + rel_bias_prep_cache=rel_bias_prep_cache, return_softmax_lse=return_softmax_lse, ) else: diff --git a/python/sglang/jit_kernel/flash_attention_v4.py b/python/sglang/jit_kernel/flash_attention_v4.py index 46b49d177..f9979f3e8 100644 --- a/python/sglang/jit_kernel/flash_attention_v4.py +++ b/python/sglang/jit_kernel/flash_attention_v4.py @@ -1,5 +1,6 @@ from __future__ import annotations +import os from typing import Callable, Optional, Tuple, Union import torch @@ -7,7 +8,14 @@ import torch from sglang.kernel_api_logging import debug_kernel_api try: - from flash_attn.cute import flash_attn_varlen_func as _flash_attn_varlen_func + if os.environ.get("SGLANG_INKLING_FA4_USE_PIP") == "1": + # A/B debug escape hatch: route through the pip flash-attn-4 package + # (dev's stack). rel_bias is vendored-only, so SHEARED must be 0. + from flash_attn.cute import flash_attn_varlen_func as _flash_attn_varlen_func + else: + from sglang.jit_kernel.flash_attn.cute import ( + flash_attn_varlen_func as _flash_attn_varlen_func, + ) except Exception as _e: # pragma: no cover _flash_attn_varlen_func = None _flash_attn_import_error = _e @@ -41,12 +49,30 @@ def flash_attn_varlen_func( pack_gqa: Optional[bool] = None, score_mod: Optional[Callable] = None, aux_tensors: Optional[list] = None, + q_descale: Optional[ + torch.Tensor + ] = None, # legacy per-tensor FP8 descale scalar (fp8_e4m3/e5m2 KV) + k_descale: Optional[torch.Tensor] = None, # legacy per-tensor FP8 descale scalar + v_descale: Optional[torch.Tensor] = None, # legacy per-tensor FP8 descale scalar + sfq: Optional[ + torch.Tensor + ] = None, # MXFP8 UE8M0 per-32-elem block scales (block-scaled QK^T) + sfk: Optional[ + torch.Tensor + ] = None, # MXFP8 UE8M0 per-32-elem block scales (block-scaled QK^T) + sfv: Optional[ + torch.Tensor + ] = None, # MXFP8 UE8M0 per-32-elem block scales (in-kernel V dequant) + rel_bias: Optional[torch.Tensor] = None, + rel_bias_prep_cache: Optional[dict] = None, return_softmax_lse: bool = False, + **_: object, ): if _flash_attn_varlen_func is None: # pragma: no cover raise ImportError( - "Vendored FlashAttention CUTE is not available (cannot import " - "flash_attn.cute). Please check your source tree." + "FlashAttention-4 CUTE is not available. Install flash-attn-4 with " + "its CUDA/CUTE dependencies, or run from a source tree where the " + "vendored FA4 package is importable." ) from _flash_attn_import_error q, k, v = [_maybe_contiguous(t) for t in (q, k, v)] @@ -62,6 +88,32 @@ def flash_attn_varlen_func( if window_size == (-1, -1): window_size = (None, None) + # sf* = MXFP8 UE8M0 block scale factors (per-32-element), for the + # block-scaled QK^T / V-dequant path. *_descale = the legacy per-tensor + # FP8 descale scalars (kv_cache_dtype fp8_e4m3/fp8_e5m2). Only one group is + # ever populated for a given call. Non-None kwargs only, so bf16/other calls + # don't hand these to the kernel. + sf_kwargs = {} + if sfq is not None: + sf_kwargs["sfq"] = sfq + if sfk is not None: + sf_kwargs["sfk"] = sfk + if sfv is not None: + sf_kwargs["sfv"] = sfv + + descale_kwargs = {} + if q_descale is not None: + descale_kwargs["q_descale"] = q_descale + if k_descale is not None: + descale_kwargs["k_descale"] = k_descale + if v_descale is not None: + descale_kwargs["v_descale"] = v_descale + + rel_bias_kwargs = {} + if rel_bias is not None: + rel_bias_kwargs["rel_bias"] = rel_bias + if rel_bias_prep_cache is not None: + rel_bias_kwargs["rel_bias_prep_cache"] = rel_bias_prep_cache result = _flash_attn_varlen_func( q=q, k=k, @@ -83,6 +135,9 @@ def flash_attn_varlen_func( score_mod=score_mod, aux_tensors=aux_tensors, return_lse=return_softmax_lse, + **sf_kwargs, + **descale_kwargs, + **rel_bias_kwargs, ) if return_softmax_lse: @@ -126,6 +181,11 @@ def flash_attn_with_kvcache( sinks: Optional[torch.Tensor] = None, score_mod: Optional[Callable] = None, aux_tensors: Optional[list] = None, + sfq: Optional[torch.Tensor] = None, + sfk: Optional[torch.Tensor] = None, + sfv: Optional[torch.Tensor] = None, + rel_bias: Optional[torch.Tensor] = None, + rel_bias_prep_cache: Optional[dict] = None, return_softmax_lse: bool = False, **_: object, ): @@ -137,9 +197,6 @@ def flash_attn_with_kvcache( raise NotImplementedError( "FA4 path does not support non-consecutive batch indices or left padding." ) - if q_descale is not None or k_descale is not None or v_descale is not None: - raise NotImplementedError("FA4 path does not support descale.") - if isinstance(cache_seqlens, int): cache_seqlens = torch.full( (k_cache.shape[0],), cache_seqlens, dtype=torch.int32, device=k_cache.device @@ -157,11 +214,19 @@ def flash_attn_with_kvcache( causal=causal, softcap=softcap if softcap != 0.0 else None, window_size=window_size, - num_splits=num_splits if num_splits != 0 else 1, + num_splits=num_splits, pack_gqa=pack_gqa, learnable_sink=sinks, score_mod=score_mod, aux_tensors=aux_tensors, + q_descale=q_descale, + k_descale=k_descale, + v_descale=v_descale, + sfq=sfq, + sfk=sfk, + sfv=sfv, + rel_bias=rel_bias, + rel_bias_prep_cache=rel_bias_prep_cache, return_softmax_lse=True, ) diff --git a/python/sglang/jit_kernel/flash_attn/__init__.py b/python/sglang/jit_kernel/flash_attn/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/python/sglang/jit_kernel/flash_attn/cute/.flake8 b/python/sglang/jit_kernel/flash_attn/cute/.flake8 new file mode 100644 index 000000000..bae5b85c0 --- /dev/null +++ b/python/sglang/jit_kernel/flash_attn/cute/.flake8 @@ -0,0 +1,4 @@ +[flake8] +max-line-length = 100 +# W503: line break before binary operator +ignore = E731, E741, F841, W503 diff --git a/python/sglang/jit_kernel/flash_attn/cute/AUTHORS b/python/sglang/jit_kernel/flash_attn/cute/AUTHORS new file mode 100644 index 000000000..8e5cac41c --- /dev/null +++ b/python/sglang/jit_kernel/flash_attn/cute/AUTHORS @@ -0,0 +1,8 @@ +Tri Dao +Jay Shah +Ted Zadouri +Markus Hoehnerbach +Vijay Thakkar +Timmy Liu +Driss Guessous +Reuben Stern diff --git a/python/sglang/jit_kernel/flash_attn/cute/LICENSE b/python/sglang/jit_kernel/flash_attn/cute/LICENSE new file mode 100644 index 000000000..5860e4b33 --- /dev/null +++ b/python/sglang/jit_kernel/flash_attn/cute/LICENSE @@ -0,0 +1,29 @@ +BSD 3-Clause License + +Copyright (c) 2022, the respective contributors, as shown by the AUTHORS file. +All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + +* Redistributions of source code must retain the above copyright notice, this + list of conditions and the following disclaimer. + +* Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + +* Neither the name of the copyright holder nor the names of its + contributors may be used to endorse or promote products derived from + this software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/python/sglang/jit_kernel/flash_attn/cute/MANIFEST.in b/python/sglang/jit_kernel/flash_attn/cute/MANIFEST.in new file mode 100644 index 000000000..329d71b31 --- /dev/null +++ b/python/sglang/jit_kernel/flash_attn/cute/MANIFEST.in @@ -0,0 +1,5 @@ +global-exclude *.egg-info/* +prune flash_attn_4.egg-info +prune flash_attn.egg-info +prune build +prune dist diff --git a/python/sglang/jit_kernel/flash_attn/cute/README.md b/python/sglang/jit_kernel/flash_attn/cute/README.md new file mode 100644 index 000000000..c7f1b32eb --- /dev/null +++ b/python/sglang/jit_kernel/flash_attn/cute/README.md @@ -0,0 +1,33 @@ +# FlashAttention-4 (CuTeDSL) + +FlashAttention-4 is a CuTeDSL-based implementation of FlashAttention for Hopper and Blackwell GPUs. + +## Installation + +```sh +pip install flash-attn-4 +``` + +If you're on CUDA 13, install with the `cu13` extra for best performance: + +```sh +pip install "flash-attn-4[cu13]" +``` + +## Usage + +```python +from flash_attn.cute import flash_attn_func, flash_attn_varlen_func + +out = flash_attn_func(q, k, v, causal=True) +``` + +## Development + +```sh +git clone https://github.com/Dao-AILab/flash-attention.git +cd flash-attention +pip install -e "flash_attn/cute[dev]" # CUDA 12.x +pip install -e "flash_attn/cute[dev,cu13]" # CUDA 13.x (e.g. B200) +pytest tests/cute/ +``` diff --git a/python/sglang/jit_kernel/flash_attn/cute/__init__.py b/python/sglang/jit_kernel/flash_attn/cute/__init__.py new file mode 100644 index 000000000..be32e149b --- /dev/null +++ b/python/sglang/jit_kernel/flash_attn/cute/__init__.py @@ -0,0 +1,18 @@ +"""Flash Attention CUTE (CUDA Template Engine) implementation.""" + +from importlib.metadata import PackageNotFoundError, version + +try: + __version__ = version("fa4") +except PackageNotFoundError: + __version__ = "0.0.0" + +from .interface import ( + flash_attn_func, + flash_attn_varlen_func, +) + +__all__ = [ + "flash_attn_func", + "flash_attn_varlen_func", +] diff --git a/python/sglang/jit_kernel/flash_attn/cute/ampere_helpers.py b/python/sglang/jit_kernel/flash_attn/cute/ampere_helpers.py new file mode 100644 index 000000000..9a3ac3540 --- /dev/null +++ b/python/sglang/jit_kernel/flash_attn/cute/ampere_helpers.py @@ -0,0 +1,122 @@ +# Copyright (c) 2025, Tri Dao. +from typing import Callable, Optional, Type + +import cutlass +import cutlass.cute as cute + + +def get_smem_layout_atom( + dtype: Type[cutlass.Numeric], k_dim: int +) -> cute.ComposedLayout: + dtype_byte = cutlass.const_expr(dtype.width // 8) + bytes_per_row = cutlass.const_expr(k_dim * dtype_byte) + smem_k_block_size = ( + cutlass.const_expr( + 128 + if bytes_per_row % 128 == 0 + else ( + 64 + if bytes_per_row % 64 == 0 + else (32 if bytes_per_row % 32 == 0 else 16) + ) + ) + // dtype_byte + ) + swizzle_bits = ( + 4 + if smem_k_block_size == 128 + else (3 if smem_k_block_size == 64 else (2 if smem_k_block_size == 32 else 1)) + ) + swizzle_base = 2 if dtype_byte == 4 else (3 if dtype_byte == 2 else 4) + return cute.make_composed_layout( + cute.make_swizzle(swizzle_bits, swizzle_base, swizzle_base), + 0, + cute.make_ordered_layout( + (8 if cutlass.const_expr(k_dim % 32 == 0) else 16, smem_k_block_size), + order=(1, 0), + ), + ) + + +@cute.jit +def gemm( + tiled_mma: cute.TiledMma, + acc: cute.Tensor, + tCrA: cute.Tensor, + tCrB: cute.Tensor, + tCsA: cute.Tensor, + tCsB: cute.Tensor, + smem_thr_copy_A: cute.TiledCopy, + smem_thr_copy_B: cute.TiledCopy, + hook_fn: Optional[Callable] = None, + A_in_regs: cutlass.Constexpr[bool] = False, + B_in_regs: cutlass.Constexpr[bool] = False, + swap_AB: cutlass.Constexpr[bool] = False, +) -> None: + if cutlass.const_expr(swap_AB): + gemm( + tiled_mma, + acc, + tCrB, + tCrA, + tCsB, + tCsA, + smem_thr_copy_B, + smem_thr_copy_A, + hook_fn, + A_in_regs=B_in_regs, + B_in_regs=A_in_regs, + swap_AB=False, + ) + else: + tCrA_copy_view = smem_thr_copy_A.retile(tCrA) + tCrB_copy_view = smem_thr_copy_B.retile(tCrB) + if cutlass.const_expr(not A_in_regs): + cute.copy( + smem_thr_copy_A, tCsA[None, None, 0], tCrA_copy_view[None, None, 0] + ) + if cutlass.const_expr(not B_in_regs): + cute.copy( + smem_thr_copy_B, tCsB[None, None, 0], tCrB_copy_view[None, None, 0] + ) + for k in cutlass.range_constexpr(cute.size(tCsA.shape[2])): + if k < cute.size(tCsA.shape[2]) - 1: + if cutlass.const_expr(not A_in_regs): + cute.copy( + smem_thr_copy_A, + tCsA[None, None, k + 1], + tCrA_copy_view[None, None, k + 1], + ) + if cutlass.const_expr(not B_in_regs): + cute.copy( + smem_thr_copy_B, + tCsB[None, None, k + 1], + tCrB_copy_view[None, None, k + 1], + ) + cute.gemm(tiled_mma, acc, tCrA[None, None, k], tCrB[None, None, k], acc) + if cutlass.const_expr(k == 0 and hook_fn is not None): + hook_fn() + + +@cute.jit +def gemm_rs( + tiled_mma: cute.TiledMma, + acc: cute.Tensor, + tCrA: cute.Tensor, + tCrB: cute.Tensor, + tCsB: cute.Tensor, + smem_thr_copy_B: cute.TiledCopy, + hook_fn: Optional[Callable] = None, +) -> None: + tCrB_copy_view = smem_thr_copy_B.retile(tCrB) + cute.copy(smem_thr_copy_B, tCsB[None, None, 0], tCrB_copy_view[None, None, 0]) + for k in cutlass.range_constexpr(cute.size(tCrA.shape[2])): + if cutlass.const_expr(k < cute.size(tCrA.shape[2]) - 1): + cute.copy( + smem_thr_copy_B, + tCsB[None, None, k + 1], + tCrB_copy_view[None, None, k + 1], + ) + cute.gemm(tiled_mma, acc, tCrA[None, None, k], tCrB[None, None, k], acc) + if cutlass.const_expr(k == 0 and hook_fn is not None): + hook_fn() diff --git a/python/sglang/jit_kernel/flash_attn/cute/barrier.py b/python/sglang/jit_kernel/flash_attn/cute/barrier.py new file mode 100644 index 000000000..13cc1cb78 --- /dev/null +++ b/python/sglang/jit_kernel/flash_attn/cute/barrier.py @@ -0,0 +1,76 @@ +import cutlass +import cutlass.cute as cute +from cutlass import Int32 +from cutlass._mlir.dialects import llvm +from cutlass.cutlass_dsl import T, dsl_user_op + + +@dsl_user_op +def ld_acquire(lock_ptr: cute.Pointer, *, loc=None, ip=None) -> cutlass.Int32: + lock_ptr_i64 = lock_ptr.toint(loc=loc, ip=ip).ir_value() + state = llvm.inline_asm( + T.i32(), + [lock_ptr_i64], + "ld.global.acquire.gpu.b32 $0, [$1];", + "=r,l", + has_side_effects=True, + is_align_stack=False, + asm_dialect=llvm.AsmDialect.AD_ATT, + ) + return cutlass.Int32(state) + + +@dsl_user_op +def red_relaxed( + lock_ptr: cute.Pointer, val: cutlass.Constexpr[Int32], *, loc=None, ip=None +) -> None: + lock_ptr_i64 = lock_ptr.toint(loc=loc, ip=ip).ir_value() + llvm.inline_asm( + None, + [lock_ptr_i64, Int32(val).ir_value(loc=loc, ip=ip)], + "red.relaxed.gpu.global.add.s32 [$0], $1;", + "l,r", + has_side_effects=True, + is_align_stack=False, + asm_dialect=llvm.AsmDialect.AD_ATT, + ) + + +@dsl_user_op +def red_release( + lock_ptr: cute.Pointer, val: cutlass.Constexpr[Int32], *, loc=None, ip=None +) -> None: + lock_ptr_i64 = lock_ptr.toint(loc=loc, ip=ip).ir_value() + llvm.inline_asm( + None, + [lock_ptr_i64, Int32(val).ir_value(loc=loc, ip=ip)], + "red.release.gpu.global.add.s32 [$0], $1;", + "l,r", + has_side_effects=True, + is_align_stack=False, + asm_dialect=llvm.AsmDialect.AD_ATT, + ) + + +@cute.jit +def wait_eq( + lock_ptr: cute.Pointer, thread_idx: int | Int32, flag_offset: int, val: Int32 +) -> None: + flag_ptr = lock_ptr + flag_offset + if thread_idx == 0: + read_val = Int32(0) + while read_val != val: + read_val = ld_acquire(flag_ptr) + + +@cute.jit +def arrive_inc( + lock_ptr: cute.Pointer, + thread_idx: int | Int32, + flag_offset: int, + val: cutlass.Constexpr[Int32], +) -> None: + flag_ptr = lock_ptr + flag_offset + if thread_idx == 0: + red_release(flag_ptr, val) + # red_relaxed(flag_ptr, val) diff --git a/python/sglang/jit_kernel/flash_attn/cute/bench_utils.py b/python/sglang/jit_kernel/flash_attn/cute/bench_utils.py new file mode 100644 index 000000000..537cc2e85 --- /dev/null +++ b/python/sglang/jit_kernel/flash_attn/cute/bench_utils.py @@ -0,0 +1,261 @@ +"""Shared benchmark utilities: attention_ref, cuDNN helpers, flops calculation.""" + +import math + +import torch + +try: + import cudnn +except ImportError: + cudnn = None + + +# ── FLOPS calculation ──────────────────────────────────────────────────────── + + +def flops( + batch, + nheads, + seqlen_q, + seqlen_k, + headdim, + headdim_v, + causal=False, + window_size=(None, None), + has_qv=False, +): + if causal: + avg_seqlen = (max(0, seqlen_k - seqlen_q) + seqlen_k) / 2 + else: + if window_size == (None, None): + avg_seqlen = seqlen_k + else: + row_idx = torch.arange(seqlen_q, device="cuda") + col_left = ( + torch.maximum( + row_idx + seqlen_k - seqlen_q - window_size[0], torch.tensor(0) + ) + if window_size[0] is not None + else torch.zeros_like(row_idx) + ) + col_right = ( + torch.minimum( + row_idx + seqlen_k - seqlen_q + window_size[1], + torch.tensor(seqlen_k - 1), + ) + if window_size[1] is not None + else torch.full_like(row_idx, seqlen_k - 1) + ) + avg_seqlen = (col_right - col_left + 1).float().mean().item() + eff_headdim = headdim + headdim_v if has_qv else headdim + return batch * nheads * 2 * seqlen_q * avg_seqlen * (eff_headdim + headdim_v) + + +# ── Bandwidth calculation ──────────────────────────────────────────────────── + + +def bandwidth_fwd_bytes( + batch, + nheads, + nheads_kv, + seqlen_q, + seqlen_k, + headdim, + headdim_v, + dtype_bytes=2, + has_qv=False, +): + """HBM traffic for one attention pass: read Q,K,V + write O.""" + q = batch * nheads * seqlen_q * headdim + qv = batch * nheads * seqlen_q * headdim_v if has_qv else 0 + k = batch * nheads_kv * seqlen_k * headdim + v = batch * nheads_kv * seqlen_k * headdim_v + o = batch * nheads * seqlen_q * headdim_v + return (q + qv + k + v + o) * dtype_bytes + + +def bandwidth_bwd_bytes( + batch, nheads, nheads_kv, seqlen_q, seqlen_k, headdim, headdim_v, dtype_bytes=2 +): + """HBM traffic for one attention pass: read Q,K,V,dO + write dQ,dK,dV.""" + q = batch * nheads * seqlen_q * headdim + k = batch * nheads_kv * seqlen_k * headdim + v = batch * nheads_kv * seqlen_k * headdim_v + do = batch * nheads * seqlen_q * headdim_v + dq = q + dk = k + dv = v + return (q + k + v + do + dq + dk + dv) * dtype_bytes + + +# ── Reference attention ───────────────────────────────────────────────────── + +_attention_ref_mask_cache = {} + + +def attention_ref(q, k, v, causal=False): + """Standard attention reference implementation. + + Args: + q, k, v: (batch, seqlen, nheads, headdim) tensors. + causal: whether to apply causal mask. + """ + softmax_scale = 1.0 / math.sqrt(q.shape[-1]) + scores = torch.einsum("bthd,bshd->bhts", q * softmax_scale, k) + if causal: + if scores.shape[-2] not in _attention_ref_mask_cache: + mask = torch.tril( + torch.ones(scores.shape[-2:], device=scores.device, dtype=torch.bool), + diagonal=0, + ) + _attention_ref_mask_cache[scores.shape[-2]] = mask + else: + mask = _attention_ref_mask_cache[scores.shape[-2]] + scores = scores.masked_fill(mask, float("-inf")) + attn = torch.softmax(scores, dim=-1) + return torch.einsum("bhts,bshd->bthd", attn, v) + + +# ── cuDNN graph helpers ───────────────────────────────────────────────────── + +_TORCH_TO_CUDNN_DTYPE = { + torch.float16: "HALF", + torch.bfloat16: "BFLOAT16", + torch.float32: "FLOAT", + torch.int32: "INT32", + torch.int64: "INT64", +} + + +def _build_cudnn_graph(io_dtype, tensors, build_fn): + """Build a cuDNN graph. Returns (graph, variant_pack, workspace).""" + assert cudnn is not None, "cuDNN is not available" + cudnn_dtype = getattr(cudnn.data_type, _TORCH_TO_CUDNN_DTYPE[io_dtype]) + graph = cudnn.pygraph( + io_data_type=cudnn_dtype, + intermediate_data_type=cudnn.data_type.FLOAT, + compute_data_type=cudnn.data_type.FLOAT, + ) + graph_tensors = {name: graph.tensor_like(t.detach()) for name, t in tensors.items()} + variant_pack = build_fn(graph, graph_tensors) + graph.validate() + graph.build_operation_graph() + graph.create_execution_plans([cudnn.heur_mode.A, cudnn.heur_mode.FALLBACK]) + graph.check_support() + graph.build_plans() + workspace = torch.empty( + graph.get_workspace_size(), device="cuda", dtype=torch.uint8 + ) + return graph, variant_pack, workspace + + +def cudnn_fwd_setup(q, k, v, causal=False, window_size_left=None): + """Build a cuDNN forward SDPA graph. + + Args: + q, k, v: (batch, nheads, seqlen, headdim) tensors (cuDNN layout). + causal: whether to apply causal mask. + window_size_left: sliding window size (None for no window). + + Returns: + (fwd_fn, o_gpu, stats_gpu) where fwd_fn is a zero-arg callable. + """ + b, nheads, seqlen_q, headdim = q.shape + headdim_v = v.shape[-1] + o_gpu = torch.empty(b, nheads, seqlen_q, headdim_v, dtype=q.dtype, device=q.device) + stats_gpu = torch.empty( + b, nheads, seqlen_q, 1, dtype=torch.float32, device=q.device + ) + + def build(graph, gt): + o, stats = graph.sdpa( + name="sdpa", + q=gt["q"], + k=gt["k"], + v=gt["v"], + is_inference=False, + attn_scale=1.0 / math.sqrt(headdim), + use_causal_mask=causal or window_size_left is not None, + sliding_window_length=( + window_size_left + if window_size_left is not None and not causal + else None + ), + ) + o.set_output(True).set_dim(o_gpu.shape).set_stride(o_gpu.stride()) + stats.set_output(True).set_data_type(cudnn.data_type.FLOAT) + return {gt["q"]: q, gt["k"]: k, gt["v"]: v, o: o_gpu, stats: stats_gpu} + + graph, variant_pack, workspace = _build_cudnn_graph( + q.dtype, {"q": q, "k": k, "v": v}, build + ) + + def fwd_fn(): + graph.execute(variant_pack, workspace) + return o_gpu + + return fwd_fn, o_gpu, stats_gpu + + +def cudnn_bwd_setup(q, k, v, o, g, lse, causal=False, window_size_left=None): + """Build a cuDNN backward SDPA graph. + + Args: + q, k, v, o, g, lse: (batch, nheads, seqlen, dim) tensors (cuDNN layout). + causal: whether to apply causal mask. + window_size_left: sliding window size (None for no window). + + Returns: + bwd_fn: zero-arg callable that returns (dq, dk, dv). + """ + headdim = q.shape[-1] + dq_gpu, dk_gpu, dv_gpu = ( + torch.empty_like(q), + torch.empty_like(k), + torch.empty_like(v), + ) + + def build(graph, gt): + dq, dk, dv = graph.sdpa_backward( + name="sdpa_backward", + q=gt["q"], + k=gt["k"], + v=gt["v"], + o=gt["o"], + dO=gt["g"], + stats=gt["lse"], + attn_scale=1.0 / math.sqrt(headdim), + use_causal_mask=causal or window_size_left is not None, + sliding_window_length=( + window_size_left + if window_size_left is not None and not causal + else None + ), + use_deterministic_algorithm=False, + ) + dq.set_output(True).set_dim(dq_gpu.shape).set_stride(dq_gpu.stride()) + dk.set_output(True).set_dim(dk_gpu.shape).set_stride(dk_gpu.stride()) + dv.set_output(True).set_dim(dv_gpu.shape).set_stride(dv_gpu.stride()) + return { + gt["q"]: q, + gt["k"]: k, + gt["v"]: v, + gt["o"]: o, + gt["g"]: g, + gt["lse"]: lse, + dq: dq_gpu, + dk: dk_gpu, + dv: dv_gpu, + } + + graph, variant_pack, workspace = _build_cudnn_graph( + q.dtype, + {"q": q, "k": k, "v": v, "o": o, "g": g, "lse": lse}, + build, + ) + + def bwd_fn(): + graph.execute(variant_pack, workspace) + return dq_gpu, dk_gpu, dv_gpu + + return bwd_fn diff --git a/python/sglang/jit_kernel/flash_attn/cute/benchmark.py b/python/sglang/jit_kernel/flash_attn/cute/benchmark.py new file mode 100644 index 000000000..8f5884777 --- /dev/null +++ b/python/sglang/jit_kernel/flash_attn/cute/benchmark.py @@ -0,0 +1,281 @@ +# Copyright (c) 2023, Tri Dao. +"""Useful functions for writing test code.""" + +import torch +import torch.utils.benchmark as benchmark + + +def benchmark_forward( + fn, + *inputs, + repeats=10, + desc="", + verbose=True, + amp=False, + amp_dtype=torch.float16, + **kwinputs, +): + """Use Pytorch Benchmark on the forward pass of an arbitrary function.""" + if verbose: + print(desc, "- Forward pass") + + def amp_wrapper(*inputs, **kwinputs): + with torch.autocast(device_type="cuda", dtype=amp_dtype, enabled=amp): + fn(*inputs, **kwinputs) + + t = benchmark.Timer( + stmt="fn_amp(*inputs, **kwinputs)", + globals={"fn_amp": amp_wrapper, "inputs": inputs, "kwinputs": kwinputs}, + num_threads=torch.get_num_threads(), + ) + m = t.timeit(repeats) + if verbose: + print(m) + return t, m + + +def benchmark_backward( + fn, + *inputs, + grad=None, + repeats=10, + desc="", + verbose=True, + amp=False, + amp_dtype=torch.float16, + **kwinputs, +): + """Use Pytorch Benchmark on the backward pass of an arbitrary function.""" + if verbose: + print(desc, "- Backward pass") + with torch.autocast(device_type="cuda", dtype=amp_dtype, enabled=amp): + y = fn(*inputs, **kwinputs) + if type(y) is tuple: + y = y[0] + if grad is None: + grad = torch.randn_like(y) + else: + if grad.shape != y.shape: + raise RuntimeError("Grad shape does not match output shape") + + def f(*inputs, y, grad): + # Set .grad to None to avoid extra operation of gradient accumulation + for x in inputs: + if isinstance(x, torch.Tensor): + x.grad = None + y.backward(grad, retain_graph=True) + + t = benchmark.Timer( + stmt="f(*inputs, y=y, grad=grad)", + globals={"f": f, "inputs": inputs, "y": y, "grad": grad}, + num_threads=torch.get_num_threads(), + ) + m = t.timeit(repeats) + if verbose: + print(m) + return t, m + + +def benchmark_combined( + fn, + *inputs, + grad=None, + repeats=10, + desc="", + verbose=True, + amp=False, + amp_dtype=torch.float16, + **kwinputs, +): + """Use Pytorch Benchmark on the forward+backward pass of an arbitrary function.""" + if verbose: + print(desc, "- Forward + Backward pass") + with torch.autocast(device_type="cuda", dtype=amp_dtype, enabled=amp): + y = fn(*inputs, **kwinputs) + if type(y) is tuple: + y = y[0] + if grad is None: + grad = torch.randn_like(y) + else: + if grad.shape != y.shape: + raise RuntimeError("Grad shape does not match output shape") + + def f(grad, *inputs, **kwinputs): + for x in inputs: + if isinstance(x, torch.Tensor): + x.grad = None + with torch.autocast(device_type="cuda", dtype=amp_dtype, enabled=amp): + y = fn(*inputs, **kwinputs) + if type(y) is tuple: + y = y[0] + y.backward(grad, retain_graph=True) + + t = benchmark.Timer( + stmt="f(grad, *inputs, **kwinputs)", + globals={ + "f": f, + "fn": fn, + "inputs": inputs, + "grad": grad, + "kwinputs": kwinputs, + }, + num_threads=torch.get_num_threads(), + ) + m = t.timeit(repeats) + if verbose: + print(m) + return t, m + + +def benchmark_fwd_bwd( + fn, + *inputs, + grad=None, + repeats=10, + desc="", + verbose=True, + amp=False, + amp_dtype=torch.float16, + **kwinputs, +): + """Use Pytorch Benchmark on the forward+backward pass of an arbitrary function.""" + return ( + benchmark_forward( + fn, + *inputs, + repeats=repeats, + desc=desc, + verbose=verbose, + amp=amp, + amp_dtype=amp_dtype, + **kwinputs, + ), + benchmark_backward( + fn, + *inputs, + grad=grad, + repeats=repeats, + desc=desc, + verbose=verbose, + amp=amp, + amp_dtype=amp_dtype, + **kwinputs, + ), + ) + + +def benchmark_all( + fn, + *inputs, + grad=None, + repeats=10, + desc="", + verbose=True, + amp=False, + amp_dtype=torch.float16, + **kwinputs, +): + """Use Pytorch Benchmark on the forward+backward pass of an arbitrary function.""" + return ( + benchmark_forward( + fn, + *inputs, + repeats=repeats, + desc=desc, + verbose=verbose, + amp=amp, + amp_dtype=amp_dtype, + **kwinputs, + ), + benchmark_backward( + fn, + *inputs, + grad=grad, + repeats=repeats, + desc=desc, + verbose=verbose, + amp=amp, + amp_dtype=amp_dtype, + **kwinputs, + ), + benchmark_combined( + fn, + *inputs, + grad=grad, + repeats=repeats, + desc=desc, + verbose=verbose, + amp=amp, + amp_dtype=amp_dtype, + **kwinputs, + ), + ) + + +def pytorch_profiler( + fn, + *inputs, + trace_filename=None, + backward=False, + amp=False, + amp_dtype=torch.float16, + cpu=False, + verbose=True, + **kwinputs, +): + """Wrap benchmark functions in Pytorch profiler to see CUDA information.""" + if backward: + with torch.autocast(device_type="cuda", dtype=amp_dtype, enabled=amp): + out = fn(*inputs, **kwinputs) + if type(out) is tuple: + out = out[0] + g = torch.randn_like(out) + for _ in range(30): # Warm up + if backward: + for x in inputs: + if isinstance(x, torch.Tensor): + x.grad = None + with torch.autocast(device_type="cuda", dtype=amp_dtype, enabled=amp): + out = fn(*inputs, **kwinputs) + if type(out) is tuple: + out = out[0] + # Backward should be done outside autocast + if backward: + out.backward(g, retain_graph=True) + activities = ([torch.profiler.ProfilerActivity.CPU] if cpu else []) + [ + torch.profiler.ProfilerActivity.CUDA + ] + with torch.profiler.profile( + activities=activities, + record_shapes=True, + # profile_memory=True, + with_stack=True, + ) as prof: + if backward: + for x in inputs: + if isinstance(x, torch.Tensor): + x.grad = None + with torch.autocast(device_type="cuda", dtype=amp_dtype, enabled=amp): + out = fn(*inputs, **kwinputs) + if type(out) is tuple: + out = out[0] + if backward: + out.backward(g, retain_graph=True) + if verbose: + # print(prof.key_averages().table(sort_by="self_cuda_time_total", row_limit=50)) + print(prof.key_averages().table(row_limit=50)) + if trace_filename is not None: + prof.export_chrome_trace(trace_filename) + + +def benchmark_memory(fn, *inputs, desc="", verbose=True, **kwinputs): + torch.cuda.empty_cache() + torch.cuda.reset_peak_memory_stats() + torch.cuda.synchronize() + fn(*inputs, **kwinputs) + torch.cuda.synchronize() + mem = torch.cuda.max_memory_allocated() / ((2**20) * 1000) + if verbose: + print(f"{desc} max memory: {mem}GB") + torch.cuda.empty_cache() + return mem diff --git a/python/sglang/jit_kernel/flash_attn/cute/benchmark_flash_attention_fp8.py b/python/sglang/jit_kernel/flash_attn/cute/benchmark_flash_attention_fp8.py new file mode 100644 index 000000000..fcb344fc0 --- /dev/null +++ b/python/sglang/jit_kernel/flash_attn/cute/benchmark_flash_attention_fp8.py @@ -0,0 +1,487 @@ +# Benchmark FP8 attention for FA4 (CuTe-DSL) on SM100. +# +# Run (recommended): +# python -m flash_attn.cute.benchmark_flash_attention_fp8 +# +# Notes: +# - This is intended to be used while bringing up FP8 support for SM100. +# - FP8 correctness depends on descales + max-offset scaling being implemented in the SM100 kernel. +# This script optionally checks output vs a BF16 PyTorch baseline on dequantized FP8 inputs. +# +# Adapted from: `hopper/benchmark_flash_attention_fp8.py` + +from __future__ import annotations + +import argparse +import inspect +import math +import time +from typing import Iterable + +import torch +from einops import rearrange + +from sglang.jit_kernel.flash_attn.cute.benchmark import benchmark_forward +from sglang.jit_kernel.flash_attn.cute.interface import ( + _flash_attn_fwd as flash_attn_cute_fwd, +) + +try: + import cudnn +except ImportError: + cudnn = None + + +def _torch_float8_dtype(name: str) -> torch.dtype: + if name in ("fp8", "fp8_e4m3", "fp8_e4m3fn"): + return torch.float8_e4m3fn + if name in ("fp8_e5m2", "fp8_e5m2fn"): + return torch.float8_e5m2 + raise ValueError(f"Unsupported fp8 dtype name: {name}") + + +def _parse_int_list(csv: str) -> list[int]: + out: list[int] = [] + for part in csv.split(","): + part = part.strip() + if not part: + continue + out.append(int(part)) + return out + + +def attention_pytorch(qkv: torch.Tensor, causal: bool) -> torch.Tensor: + """ + qkv: (batch, seqlen, 3, nheads, headdim) + out: (batch, seqlen, nheads, headdim) + """ + batch_size, seqlen, _, nheads, d = qkv.shape + q, k, v = qkv.unbind(dim=2) + q = rearrange(q, "b t h d -> (b h) t d") + k = rearrange(k, "b s h d -> (b h) d s") + softmax_scale = 1.0 / math.sqrt(d) + scores = torch.empty( + batch_size * nheads, seqlen, seqlen, dtype=qkv.dtype, device=qkv.device + ) + scores = rearrange( + torch.baddbmm(scores, q, k, beta=0, alpha=softmax_scale), + "(b h) t s -> b h t s", + h=nheads, + ) + if causal: + causal_mask = torch.triu( + torch.full((seqlen, seqlen), -10000.0, device=scores.device), 1 + ) + scores = scores + causal_mask.to(dtype=scores.dtype) + attention = torch.softmax(scores, dim=-1) + output = torch.einsum("bhts,bshd->bthd", attention, v) + return output.to(dtype=qkv.dtype) + + +def flops(batch: int, seqlen: int, headdim: int, nheads: int, causal: bool) -> int: + # Matches the hopper benchmark’s convention. + return 4 * batch * seqlen**2 * nheads * headdim // (2 if causal else 1) + + +def efficiency(flop: int, seconds: float) -> float: + return (flop / seconds / 1e12) if not math.isnan(seconds) else 0.0 + + +def time_fwd(fn, *args, repeats: int, **kwargs) -> float: + time.sleep(1) # reduce residual throttling effects between benchmarks + _, m = benchmark_forward(fn, *args, repeats=repeats, verbose=False, **kwargs) + return float(m.mean) + + +def convert_to_cudnn_type(torch_type): + if torch_type == torch.float16: + return cudnn.data_type.HALF + if torch_type == torch.bfloat16: + return cudnn.data_type.BFLOAT16 + if torch_type == torch.float32: + return cudnn.data_type.FLOAT + if torch_type == torch.int32: + return cudnn.data_type.INT32 + if torch_type == torch.int64: + return cudnn.data_type.INT64 + if torch_type == torch.float8_e4m3fn: + return cudnn.data_type.FP8_E4M3 + if torch_type == torch.float8_e5m2: + return cudnn.data_type.FP8_E5M2 + raise ValueError("Unsupported tensor data type.") + + +def cudnn_sdpa_fp8_setup(qkv: torch.Tensor, seqlen_q: int, seqlen_k: int, causal: bool): + """Minimal cudnn.fp8 sdpa runner (optional).""" + assert cudnn is not None, "cudnn python bindings not available" + b, _, _, nheads, headdim = qkv.shape + o_gpu = torch.zeros( + b, seqlen_q, nheads, headdim, dtype=qkv.dtype, device=qkv.device + ) + o_gpu_transposed = torch.as_strided( + o_gpu, + [b, nheads, seqlen_q, headdim], + [nheads * seqlen_q * headdim, headdim, nheads * headdim, 1], + ) + amax_s_gpu = torch.empty(1, 1, 1, 1, dtype=torch.float32, device=qkv.device) + amax_o_gpu = torch.empty(1, 1, 1, 1, dtype=torch.float32, device=qkv.device) + + graph = cudnn.pygraph( + io_data_type=convert_to_cudnn_type(qkv.dtype), + intermediate_data_type=cudnn.data_type.FLOAT, + compute_data_type=cudnn.data_type.FLOAT, + ) + + new_q = torch.as_strided( + qkv, + [b, nheads, seqlen_q, headdim], + [seqlen_q * nheads * headdim * 3, headdim, headdim * nheads * 3, 1], + storage_offset=0, + ) + q = graph.tensor( + name="Q", + dim=list(new_q.shape), + stride=list(new_q.stride()), + data_type=convert_to_cudnn_type(qkv.dtype), + ) + + new_k = torch.as_strided( + qkv, + [b, nheads, seqlen_k, headdim], + [seqlen_k * nheads * headdim * 3, headdim, headdim * nheads * 3, 1], + storage_offset=nheads * headdim, + ) + k = graph.tensor( + name="K", + dim=list(new_k.shape), + stride=list(new_k.stride()), + data_type=convert_to_cudnn_type(qkv.dtype), + ) + + new_v = torch.as_strided( + qkv, + [b, nheads, seqlen_k, headdim], + [seqlen_k * nheads * headdim * 3, headdim, headdim * nheads * 3, 1], + storage_offset=nheads * headdim * 2, + ) + v = graph.tensor( + name="V", + dim=list(new_v.shape), + stride=list(new_v.stride()), + data_type=convert_to_cudnn_type(qkv.dtype), + ) + + def _scale_tensor(): + return graph.tensor( + dim=[1, 1, 1, 1], stride=[1, 1, 1, 1], data_type=cudnn.data_type.FLOAT + ) + + default_scale_gpu = torch.ones(1, 1, 1, 1, dtype=torch.float32, device="cuda") + descale_q = _scale_tensor() + descale_k = _scale_tensor() + descale_v = _scale_tensor() + descale_s = _scale_tensor() + scale_s = _scale_tensor() + scale_o = _scale_tensor() + + o, _, amax_s, amax_o = graph.sdpa_fp8( + q=q, + k=k, + v=v, + descale_q=descale_q, + descale_k=descale_k, + descale_v=descale_v, + descale_s=descale_s, + scale_s=scale_s, + scale_o=scale_o, + is_inference=True, + attn_scale=1.0 / math.sqrt(headdim), + use_causal_mask=causal, + name="sdpa", + ) + o.set_output(True).set_dim(o_gpu_transposed.shape).set_stride( + o_gpu_transposed.stride() + ) + amax_s.set_output(False).set_dim(amax_s_gpu.shape).set_stride(amax_s_gpu.stride()) + amax_o.set_output(False).set_dim(amax_o_gpu.shape).set_stride(amax_o_gpu.stride()) + + graph.validate() + graph.build_operation_graph() + graph.create_execution_plans([cudnn.heur_mode.A, cudnn.heur_mode.FALLBACK]) + graph.check_support() + graph.build_plans() + + variant_pack = { + q: new_q, + k: new_k, + v: new_v, + descale_q: default_scale_gpu, + descale_k: default_scale_gpu, + descale_v: default_scale_gpu, + descale_s: default_scale_gpu, + scale_s: default_scale_gpu, + scale_o: default_scale_gpu, + o: o_gpu_transposed, + amax_s: amax_s_gpu, + amax_o: amax_o_gpu, + } + workspace = torch.empty( + graph.get_workspace_size(), device="cuda", dtype=torch.uint8 + ) + + def run(): + graph.execute(variant_pack, workspace) + return o_gpu + + return run + + +def _maybe_pass_descales(callable_, **kwargs): + sig = inspect.signature(callable_) + return {k: v for k, v in kwargs.items() if k in sig.parameters} + + +def main(argv: Iterable[str] | None = None) -> int: + parser = argparse.ArgumentParser() + parser.add_argument("--repeats", type=int, default=30) + parser.add_argument("--dim", type=int, default=2048) + parser.add_argument("--headdims", default="64,128") + parser.add_argument("--dtype", default="fp8_e4m3fn") + parser.add_argument("--seed", type=int, default=0) + parser.add_argument( + "--check", + action=argparse.BooleanOptionalAction, + default=True, + help="Enable correctness checks vs BF16 PyTorch baseline.", + ) + parser.add_argument( + "--check-quantization-only", + action="store_true", + help="Check FP8 kernel vs dequantized-FP8 baseline (quantization error only).", + ) + parser.add_argument("--atol-bf16", type=float, default=0.10) + parser.add_argument("--rtol-bf16", type=float, default=0.10) + parser.add_argument("--atol-fp8", type=float, default=0.50) + parser.add_argument("--rtol-fp8", type=float, default=0.50) + parser.add_argument("--run-cudnn", action="store_true") + args = parser.parse_args(list(argv) if argv is not None else None) + + if not torch.cuda.is_available(): + raise RuntimeError("CUDA is required") + major, minor = torch.cuda.get_device_capability() + if major != 10: + raise RuntimeError( + f"This benchmark is for SM100 (compute capability 10.x). Got {major}.{minor}." + ) + + torch.manual_seed(args.seed) + device = "cuda" + fp8_dtype = _torch_float8_dtype(args.dtype) + headdim_vals = _parse_int_list(args.headdims) + bs_seqlen_vals = [ + (32, 512), + (16, 1024), + (8, 2048), + (4, 4096), + (2, 8192), + (1, 16384), + ] + + methods = ["Pytorch", "FA4-CuTe-BF16", "FA4-CuTe-FP8"] + ( + ["cuDNN-FP8"] if args.run_cudnn and cudnn is not None else [] + ) + + fp8_failures = [] + + for headdim in headdim_vals: + for causal in (False, True): + for batch, seqlen in bs_seqlen_vals: + torch.cuda.empty_cache() + nheads = args.dim // headdim + if args.dim % headdim != 0: + raise ValueError( + f"--dim must be divisible by headdim ({args.dim=} {headdim=})" + ) + + q_bf16 = torch.randn( + batch, seqlen, nheads, headdim, device=device, dtype=torch.bfloat16 + ) + k_bf16 = torch.randn( + batch, seqlen, nheads, headdim, device=device, dtype=torch.bfloat16 + ) + v_bf16 = torch.randn( + batch, seqlen, nheads, headdim, device=device, dtype=torch.bfloat16 + ) + qkv_bf16 = torch.stack([q_bf16, k_bf16, v_bf16], dim=2) + + times = {} + speeds = {} + + out_ref_bf16 = None + try: + out_ref_bf16 = attention_pytorch( + qkv_bf16, causal=causal + ) # warmup / reference + t = time_fwd( + attention_pytorch, qkv_bf16, causal=causal, repeats=args.repeats + ) + times["Pytorch"] = t + except RuntimeError as e: + if "out of memory" in str(e).lower(): + times["Pytorch"] = float("nan") + out_ref_bf16 = None + else: + raise + + # FA4 / CuTe BF16 baseline + try: + softmax_scale = headdim**-0.5 + out_fa4_bf16, _ = flash_attn_cute_fwd( + q_bf16, + k_bf16, + v_bf16, + softmax_scale=softmax_scale, + causal=causal, + ) # warmup / compile + t = time_fwd( + flash_attn_cute_fwd, + q_bf16, + k_bf16, + v_bf16, + softmax_scale=softmax_scale, + causal=causal, + repeats=args.repeats, + ) + times["FA4-CuTe-BF16"] = t + if args.check and out_ref_bf16 is not None: + torch.testing.assert_close( + out_fa4_bf16, + out_ref_bf16, + atol=args.atol_bf16, + rtol=args.rtol_bf16, + ) + except Exception as e: + # Treat as fatal: BF16 kernel should be usable for basic sanity checking. + raise RuntimeError("FA4-CuTe BF16 baseline failed") from e + + # FA4 / CuTe FP8 + q_fp8 = q_bf16.to(fp8_dtype) + k_fp8 = k_bf16.to(fp8_dtype) + v_fp8 = v_bf16.to(fp8_dtype) + + # Placeholder descales (FA3-style: per-(batch, kv_head)). + q_descale = torch.ones( + batch, nheads, device=device, dtype=torch.float32 + ) + k_descale = torch.ones( + batch, nheads, device=device, dtype=torch.float32 + ) + v_descale = torch.ones( + batch, nheads, device=device, dtype=torch.float32 + ) + + # Optional: FP8 reference baseline (dequantized FP8 -> PyTorch) for quantization-error-only checks + out_ref_fp8 = None + if args.check and args.check_quantization_only: + try: + # Dequantize FP8 inputs back to BF16 (applying descales) + q_ref_fp8 = ( + q_fp8.to(torch.bfloat16) * q_descale[:, None, :, None] + ).to(torch.bfloat16) + k_ref_fp8 = ( + k_fp8.to(torch.bfloat16) * k_descale[:, None, :, None] + ).to(torch.bfloat16) + v_ref_fp8 = ( + v_fp8.to(torch.bfloat16) * v_descale[:, None, :, None] + ).to(torch.bfloat16) + qkv_ref_fp8 = torch.stack( + [q_ref_fp8, k_ref_fp8, v_ref_fp8], dim=2 + ) + out_ref_fp8 = attention_pytorch(qkv_ref_fp8, causal=causal) + except RuntimeError as e: + if "out of memory" in str(e).lower(): + out_ref_fp8 = None + else: + raise + + fa4_kwargs = dict(softmax_scale=softmax_scale, causal=causal) + fa4_kwargs.update( + _maybe_pass_descales( + flash_attn_cute_fwd, + q_descale=q_descale, + k_descale=k_descale, + v_descale=v_descale, + ) + ) + + try: + # Warmup/compile (will raise until FP8 is implemented) + out_fa4_fp8, _ = flash_attn_cute_fwd( + q_fp8, k_fp8, v_fp8, **fa4_kwargs + ) + t = time_fwd( + flash_attn_cute_fwd, + q_fp8, + k_fp8, + v_fp8, + repeats=args.repeats, + **fa4_kwargs, + ) + times["FA4-CuTe-FP8"] = t + if args.check: + # Choose baseline: quantization-only (dequantized FP8) or full (BF16) + if args.check_quantization_only: + ref_baseline = out_ref_fp8 + else: + ref_baseline = out_ref_bf16 + + if ref_baseline is not None: + torch.testing.assert_close( + out_fa4_fp8, + ref_baseline, + atol=args.atol_fp8, + rtol=args.rtol_fp8, + ) + except Exception as e: + fp8_failures.append((causal, headdim, batch, seqlen, repr(e))) + times["FA4-CuTe-FP8"] = float("nan") + + if args.run_cudnn and cudnn is not None: + qkv_fp8 = qkv_bf16.to(fp8_dtype) + runner = cudnn_sdpa_fp8_setup( + qkv_fp8, seqlen, seqlen, causal=causal + ) + _ = runner() # warmup + t = time_fwd(lambda: runner(), repeats=args.repeats) + times["cuDNN-FP8"] = t + + print( + f"### causal={causal}, headdim={headdim}, batch={batch}, seqlen={seqlen} ###" + ) + for method in methods: + t = times.get(method, float("nan")) + speeds[method] = efficiency( + flops(batch, seqlen, headdim, nheads, causal), t + ) + if math.isnan(t): + print(f"{method} fwd: (skipped)") + else: + print( + f"{method} fwd: {speeds[method]:.2f} TFLOPs/s, {t * 1e3:.3f} ms" + ) + if math.isnan(times.get("FA4-CuTe-FP8", float("nan"))): + print("FA4-CuTe-FP8 status: FAILED") + + if fp8_failures: + print(f"\nFP8 failures: {len(fp8_failures)} (showing first 5)") + for causal, headdim, batch, seqlen, err in fp8_failures[:5]: + print( + f"- causal={causal} headdim={headdim} batch={batch} seqlen={seqlen}: {err}" + ) + return 1 + + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/python/sglang/jit_kernel/flash_attn/cute/blackwell_helpers.py b/python/sglang/jit_kernel/flash_attn/cute/blackwell_helpers.py new file mode 100644 index 000000000..f53b66e94 --- /dev/null +++ b/python/sglang/jit_kernel/flash_attn/cute/blackwell_helpers.py @@ -0,0 +1,1224 @@ +# Copyright (c) 2025, Tri Dao. +from typing import Optional, Tuple + +import cutlass +import cutlass.cute as cute +from cutlass import Boolean, Int32, const_expr +from cutlass._mlir.dialects import llvm +from cutlass.cute.nvgpu import tcgen05 + +import sglang.jit_kernel.flash_attn.cute.mma_sm100_desc as sm100_desc + + +def _tcgen05_mma_kind(op: cute.nvgpu.tcgen05.mma.MmaOp) -> str: + if isinstance(op, tcgen05.mma.MmaF16BF16Op): + return "f16" + if isinstance(op, tcgen05.mma.MmaTF32Op): + return "tf32" + if isinstance(op, tcgen05.mma.MmaI8Op): + return "i8" + # cutlass-dsl >=4.5.2 builds plain FP8 MMAs as MmaF8F6F4Op (make_trivial_tiled_mma's + # _F8F6F4_TYPES branch); <4.4.x returned the now-legacy MmaFP8Op. Both map to kind::f8f6f4. + if isinstance(op, (tcgen05.mma.MmaFP8Op, tcgen05.mma.MmaF8F6F4Op)): + return "f8f6f4" + if isinstance(op, tcgen05.mma.MmaMXF8Op): + return "mxf8f6f4" + if isinstance(op, tcgen05.mma.MmaMXF4Op): + return "mxf4" + if isinstance(op, tcgen05.mma.MmaMXF4NVF4Op): + return "mxf4nvf4" + raise TypeError(f"Unsupported tcgen05 MMA op kind: {type(op).__name__}") + + +@cute.jit +def gemm_w_idx( + tiled_mma: cute.TiledMma, + acc: cute.Tensor, + tCrA: cute.Tensor, + tCrB: cute.Tensor, + A_idx: Optional[Int32] = None, + B_idx: Optional[Int32] = None, + zero_init: bool | Boolean = False, + swap_AB: bool = False, + num_unroll_groups: int = 1, +) -> None: + if const_expr(swap_AB): + return gemm_w_idx( + tiled_mma, acc, tCrB, tCrA, B_idx, A_idx, zero_init=zero_init, swap_AB=False + ) + else: + rA = tCrA if const_expr(A_idx is None) else tCrA[None, None, None, A_idx] + rB = tCrB if const_expr(B_idx is None) else tCrB[None, None, None, B_idx] + + mma_atom = cute.make_mma_atom(tiled_mma.op) + for k in cutlass.range( + cute.size(tCrA.shape[2]), + unroll=cute.size(tCrA.shape[2]) // num_unroll_groups, + ): + mma_atom.set(tcgen05.Field.ACCUMULATE, not zero_init or k != 0) + cute.gemm(mma_atom, acc, rA[None, None, k], rB[None, None, k], acc) + + +@cute.jit +def gemm_ptx_w_idx( + tiled_mma: cute.TiledMma, + acc: cute.Tensor, + tCrA: cute.Tensor, + tCrB: cute.Tensor, + sA: Optional[cute.Tensor], + sB: cute.Tensor, + A_idx: Optional[Int32] = None, + B_idx: Optional[Int32] = None, + zero_init: bool | Boolean = False, + cta_group: int = 1, + **kwargs, +) -> None: + rA = tCrA if const_expr(A_idx is None) else tCrA[None, None, None, A_idx] + rB = tCrB if const_expr(B_idx is None) else tCrB[None, None, None, B_idx] + sA_cur = None + if const_expr(sA is not None): + sA_cur = sA if const_expr(A_idx is None) else sA[None, None, None, A_idx] + sB_cur = sB if const_expr(B_idx is None) else sB[None, None, None, B_idx] + mma_atom = cute.make_mma_atom(tiled_mma.op) + acc_tmem_addr = acc.iterator.toint() + gemm_ptx_partial( + mma_atom.op, + acc_tmem_addr, + rA, + rB, + sA_cur, + sB_cur, + zero_init=zero_init, + cta_group=cta_group, + **kwargs, + ) + + +@cute.jit +def gemm( + tiled_mma: cute.TiledMma, + acc: cute.Tensor, + tCrA: cute.Tensor, + tCrB: cute.Tensor, + zero_init: bool | Boolean = False, +) -> None: + mma_atom = cute.make_mma_atom(tiled_mma.op) + for k in cutlass.range_constexpr(cute.size(tCrA.shape[2])): + mma_atom.set(tcgen05.Field.ACCUMULATE, not zero_init or k != 0) + cute.gemm(mma_atom, acc, tCrA[None, None, k], tCrB[None, None, k], acc) + + +def i64_to_i32x2(i: int) -> Tuple[int, int]: + """Convert a 64-bit integer to a tuple of two 32-bit integers.""" + return i & 0xFFFF_FFFF, (i >> 32) & 0xFFFF_FFFF + + +@cute.jit +def gemm_ptx( + op: cute.nvgpu.tcgen05.mma.MmaOp, + acc: cute.Tensor, + tCrA: cute.Tensor, + tCrB: cute.Tensor, + sA: Optional[cute.Tensor], + sB: cute.Tensor, + zero_init: bool | Boolean = False, +) -> None: + is_ts = op.a_src == cute.nvgpu.tcgen05.OperandSource.TMEM + if const_expr(not is_ts): + assert sA is not None, "sA must be provided when a_src is not TMEM" + sA_layout = sA.layout if sA is not None else None + sB_layout = sB.layout + idesc: int = const_expr(sm100_desc.mma_op_to_idesc(op)) + kind = _tcgen05_mma_kind(op) + if const_expr(not is_ts): + sA_swizzle = sA.iterator.type.swizzle_type + smem_desc_base_a: int = const_expr( + sm100_desc.make_smem_desc_base( + cute.recast_layout(128, op.a_dtype.width, sA_layout[0]), + sA_swizzle, + ( + sm100_desc.Major.K + if const_expr( + op.a_major_mode == cute.nvgpu.tcgen05.mma.OperandMajorMode.K + ) + else sm100_desc.Major.MN + ), + ) + ) + smem_desc_base_a_lo, smem_desc_a_hi = i64_to_i32x2(smem_desc_base_a) + smem_desc_base_a_lo = const_expr(smem_desc_base_a_lo) + smem_desc_a_hi = const_expr(smem_desc_a_hi) + else: + smem_desc_base_a = None + smem_desc_base_a_lo, smem_desc_a_hi = None, None + sB_swizzle = sB.iterator.type.swizzle_type + smem_desc_base_b: int = const_expr( + sm100_desc.make_smem_desc_base( + cute.recast_layout(128, op.b_dtype.width, sB_layout[0]), + sB_swizzle, + ( + sm100_desc.Major.K + if const_expr( + op.b_major_mode == cute.nvgpu.tcgen05.mma.OperandMajorMode.K + ) + else sm100_desc.Major.MN + ), + ) + ) + smem_desc_base_b_lo, smem_desc_b_hi = i64_to_i32x2(smem_desc_base_b) + smem_desc_base_b_lo = const_expr(smem_desc_base_b_lo) + smem_desc_b_hi = const_expr(smem_desc_b_hi) + + if const_expr(not is_ts): + smem_desc_start_a_lo = Int32( + smem_desc_base_a_lo + ) | sm100_desc.make_smem_desc_start_addr(sA[None, None, 0].iterator) + else: + smem_desc_start_a_lo = None + smem_desc_start_b_lo = Int32( + smem_desc_base_b_lo + ) | sm100_desc.make_smem_desc_start_addr(sB[None, None, 0].iterator) + for k in cutlass.range_constexpr(cute.size(tCrA.shape[2])): + if const_expr(not is_ts): + smem_desc_a_lo = smem_desc_start_a_lo + ( + (cute.crd2idx((0, 0, k), sA_layout) * sA.element_type.width // 8) >> 4 + ) + smem_desc_b_lo = smem_desc_start_b_lo + ( + (cute.crd2idx((0, 0, k), sB_layout) * sB.element_type.width // 8) >> 4 + ) + # with cute.arch.elect_one(): + # cute.printf("smem_desc_a_lo = {}, smem_desc_b_lo = {}", smem_desc_a_lo, smem_desc_b_lo) + # cute.printf("smem_desc_a_lo_correct = {}, smem_desc_b_lo_correct = {}", smem_desc_a_lo_correct, smem_desc_b_lo_correct) + with cute.arch.elect_one(): + if const_expr(not is_ts): + llvm.inline_asm( + None, + [ + acc.iterator.toint().ir_value(), + smem_desc_a_lo.ir_value(), + smem_desc_b_lo.ir_value(), + Int32(not zero_init or k != 0).ir_value(), + ], + "{\n\t" + ".reg .pred p;\n\t" + ".reg .b64 smem_desc_a, smem_desc_b;\n\t" + ".reg .b32 idesc;\n\t" + f"mov.b32 idesc, {hex(idesc)};\n\t" + f"mov.b64 smem_desc_a, {{$1, {hex(smem_desc_a_hi)}}};\n\t" + f"mov.b64 smem_desc_b, {{$2, {hex(smem_desc_b_hi)}}};\n\t" + "setp.ne.b32 p, $3, 0;\n\t" + f"tcgen05.mma.cta_group::1.kind::{kind} [$0], smem_desc_a, smem_desc_b, idesc, p;\n\t" + "}\n", + "r,r,r,r", + has_side_effects=True, + is_align_stack=False, + asm_dialect=llvm.AsmDialect.AD_ATT, + ) + else: + llvm.inline_asm( + None, + [ + acc.iterator.toint().ir_value(), + tCrA[None, None, k].iterator.toint().ir_value(), + smem_desc_b_lo.ir_value(), + Int32(not zero_init or k != 0).ir_value(), + ], + "{\n\t" + ".reg .pred p;\n\t" + ".reg .b64 smem_desc_b;\n\t" + f"mov.b64 smem_desc_b, {{$2, {hex(smem_desc_b_hi)}}};\n\t" + "setp.ne.b32 p, $3, 0;\n\t" + f"tcgen05.mma.cta_group::1.kind::{kind} [$0], [$1], smem_desc_b, {hex(idesc)}, p;\n\t" + "}\n", + "r,r,r,r", + has_side_effects=True, + is_align_stack=False, + asm_dialect=llvm.AsmDialect.AD_ATT, + ) + + +@cute.jit +def gemm_ptx_loop( + op: cute.nvgpu.tcgen05.mma.MmaOp, + acc: cute.Tensor, + tCrA: cute.Tensor, + tCrB: cute.Tensor, + sA: Optional[cute.Tensor], + sB: cute.Tensor, + zero_init: bool | Boolean = False, +) -> None: + is_ts = op.a_src == cute.nvgpu.tcgen05.OperandSource.TMEM + if const_expr(not is_ts): + assert sA is not None, "sA must be provided when a_src is not TMEM" + sA_layout = sA.layout if sA is not None else tCrA.layout + sB_layout = sB.layout + idesc: int = const_expr(sm100_desc.mma_op_to_idesc(op)) + kind = _tcgen05_mma_kind(op) + if const_expr(not is_ts): + sA_swizzle = sA.iterator.type.swizzle_type + smem_desc_base_a: int = const_expr( + sm100_desc.make_smem_desc_base( + cute.recast_layout(128, op.a_dtype.width, sA_layout[0]), + sA_swizzle, + ( + sm100_desc.Major.K + if const_expr( + op.a_major_mode == cute.nvgpu.tcgen05.mma.OperandMajorMode.K + ) + else sm100_desc.Major.MN + ), + ) + ) + smem_desc_base_a_lo, smem_desc_a_hi = i64_to_i32x2(smem_desc_base_a) + smem_desc_base_a_lo = const_expr(smem_desc_base_a_lo) + smem_desc_a_hi = const_expr(smem_desc_a_hi) + else: + smem_desc_base_a = None + smem_desc_base_a_lo, smem_desc_a_hi = None, None + sB_swizzle = sB.iterator.type.swizzle_type + smem_desc_base_b: int = const_expr( + sm100_desc.make_smem_desc_base( + cute.recast_layout(128, op.b_dtype.width, sB_layout[0]), + sB_swizzle, + ( + sm100_desc.Major.K + if const_expr( + op.b_major_mode == cute.nvgpu.tcgen05.mma.OperandMajorMode.K + ) + else sm100_desc.Major.MN + ), + ) + ) + smem_desc_base_b_lo, smem_desc_b_hi = i64_to_i32x2(smem_desc_base_b) + smem_desc_base_b_lo = const_expr(smem_desc_base_b_lo) + smem_desc_b_hi = const_expr(smem_desc_b_hi) + + if const_expr(not is_ts): + offset_a = [ + (cute.crd2idx((0, 0, k), sA_layout) * sA.element_type.width // 8) >> 4 + for k in cutlass.range_constexpr(cute.size(tCrA.shape[2])) + ] + else: + offset_a = [ + cute.crd2idx((0, 0, k), sA_layout) * op.a_dtype.width // 32 + for k in cutlass.range_constexpr(cute.size(tCrA.shape[2])) + ] + offset_a_diff = [ + offset_a[k] - offset_a[k - 1] + for k in cutlass.range_constexpr(1, cute.size(tCrA.shape[2])) + ] + offset_b = [ + (cute.crd2idx((0, 0, k), sB_layout) * sB.element_type.width // 8) >> 4 + for k in cutlass.range_constexpr(cute.size(tCrB.shape[2])) + ] + offset_b_diff = [ + offset_b[k] - offset_b[k - 1] + for k in cutlass.range_constexpr(1, cute.size(tCrB.shape[2])) + ] + + if const_expr(not is_ts): + smem_desc_start_a_lo = Int32( + smem_desc_base_a_lo + | sm100_desc.make_smem_desc_start_addr(sA[None, None, 0].iterator) + ) + else: + smem_desc_start_a_lo = None + smem_desc_start_b_lo = Int32( + smem_desc_base_b_lo + | sm100_desc.make_smem_desc_start_addr(sB[None, None, 0].iterator) + ) + pred_str = "p" if isinstance(zero_init, Boolean) else "0" if zero_init else "1" + if const_expr(not is_ts): + llvm.inline_asm( + None, + [ + acc.iterator.toint().ir_value(), + Int32(cute.arch.make_warp_uniform(smem_desc_start_a_lo)).ir_value(), + Int32(cute.arch.make_warp_uniform(smem_desc_start_b_lo)).ir_value(), + Int32(not zero_init).ir_value(), + ], + "{\n\t" + ".reg .pred leader_thread;\n\t" + ".reg .pred p;\n\t" + ".reg .b32 idesc;\n\t" + ".reg .b32 smem_desc_a_lo, smem_desc_b_lo;\n\t" + ".reg .b32 smem_desc_a_hi, smem_desc_b_hi;\n\t" + ".reg .b64 smem_desc_a, smem_desc_b;\n\t" + "elect.sync _|leader_thread, -1;\n\t" + f"mov.b32 idesc, {hex(idesc)};\n\t" + "mov.b32 smem_desc_a_lo, $1;\n\t" + "mov.b32 smem_desc_b_lo, $2;\n\t" + f"mov.b32 smem_desc_a_hi, {hex(smem_desc_a_hi)};\n\t" + f"mov.b32 smem_desc_b_hi, {hex(smem_desc_b_hi)};\n\t" + f"mov.b64 smem_desc_a, {{smem_desc_a_lo, smem_desc_a_hi}};\n\t" + f"mov.b64 smem_desc_b, {{smem_desc_b_lo, smem_desc_b_hi}};\n\t" + "setp.ne.b32 p, $3, 0;\n\t" + f"@leader_thread tcgen05.mma.cta_group::1.kind::{kind} [$0], smem_desc_a, smem_desc_b, idesc, {pred_str};\n\t" + + "".join( + ( + f"add.u32 smem_desc_a_lo, smem_desc_a_lo, {hex(offset_a_diff[k - 1])};\n\t" + f"add.u32 smem_desc_b_lo, smem_desc_b_lo, {hex(offset_b_diff[k - 1])};\n\t" + f"mov.b64 smem_desc_a, {{smem_desc_a_lo, smem_desc_a_hi}};\n\t" + f"mov.b64 smem_desc_b, {{smem_desc_b_lo, smem_desc_b_hi}};\n\t" + f"@leader_thread tcgen05.mma.cta_group::1.kind::{kind} [$0], smem_desc_a, smem_desc_b, idesc, 1;\n\t" + ) + for k in cutlass.range_constexpr(1, cute.size(tCrA.shape[2])) + ) + + "}\n", + "r,r,r,r", + has_side_effects=True, + is_align_stack=False, + asm_dialect=llvm.AsmDialect.AD_ATT, + ) + else: + llvm.inline_asm( + None, + [ + acc.iterator.toint().ir_value(), + Int32(tCrA[None, None, 0].iterator.toint()).ir_value(), + Int32(smem_desc_start_b_lo).ir_value(), + Int32(not zero_init).ir_value(), + ], + "{\n\t" + ".reg .pred leader_thread;\n\t" + ".reg .pred p;\n\t" + ".reg .b32 idesc;\n\t" + ".reg .b32 tmem_a;\n\t" + ".reg .b32 smem_desc_b_lo;\n\t" + ".reg .b32 smem_desc_b_hi;\n\t" + ".reg .b64 smem_desc_b;\n\t" + "elect.sync _|leader_thread, -1;\n\t" + f"mov.b32 idesc, {hex(idesc)};\n\t" + "mov.b32 tmem_a, $1;\n\t" + "mov.b32 smem_desc_b_lo, $2;\n\t" + f"mov.b32 smem_desc_b_hi, {hex(smem_desc_b_hi)};\n\t" + f"mov.b64 smem_desc_b, {{smem_desc_b_lo, smem_desc_b_hi}};\n\t" + "setp.ne.b32 p, $3, 0;\n\t" + f"@leader_thread tcgen05.mma.cta_group::1.kind::{kind} [$0], [tmem_a], smem_desc_b, idesc, {pred_str};\n\t" + + "".join( + ( + # f"add.u32 tmem_a, tmem_a, {hex(offset_a_diff[k - 1])};\n\t" + f"add.u32 smem_desc_b_lo, smem_desc_b_lo, {hex(offset_b_diff[k - 1])};\n\t" + f"mov.b64 smem_desc_b, {{smem_desc_b_lo, smem_desc_b_hi}};\n\t" + # f"@leader_thread tcgen05.mma.cta_group::1.kind::f16 [$0], [tmem_a], smem_desc_b, idesc, 1;\n\t" + f"@leader_thread tcgen05.mma.cta_group::1.kind::{kind} [$0], [tmem_a + {hex(offset_a[k])}], smem_desc_b, idesc, 1;\n\t" + ) + for k in cutlass.range_constexpr(1, cute.size(tCrA.shape[2])) + ) + + "}\n", + "r,r,r,r", + has_side_effects=True, + is_align_stack=False, + asm_dialect=llvm.AsmDialect.AD_ATT, + ) + + +@cute.jit +def gemm_ptx_partial( + op: cute.nvgpu.tcgen05.mma.MmaOp, + acc_tmem_addr: Int32, + tCrA: cute.Tensor, + tCrB: cute.Tensor, + sA: Optional[cute.Tensor], + sB: cute.Tensor, + mbar_ptr: Optional[cutlass.Pointer] = None, + mbar_phase: Optional[Int32] = None, + split_arrive: Optional[int] = None, + zero_init: bool | Boolean = False, + # sA_offset: Int32 = 0, + # acc_offset: Int32 = 0, + tA_addr: Optional[Int32] = None, + cta_group: int = 1, +) -> None: + # acc_tmem_addr += acc_offset + is_ts = op.a_src == cute.nvgpu.tcgen05.OperandSource.TMEM + if const_expr(not is_ts): + assert sA is not None, "sA must be provided when a_src is not TMEM" + sA_layout = sA.layout if sA is not None else tCrA.layout + sB_layout = sB.layout + idesc: int = const_expr(sm100_desc.mma_op_to_idesc(op)) + kind = _tcgen05_mma_kind(op) + if const_expr(not is_ts): + sA_swizzle = sA.iterator.type.swizzle_type + smem_desc_base_a: int = const_expr( + sm100_desc.make_smem_desc_base( + cute.recast_layout(128, op.a_dtype.width, sA_layout[0]), + sA_swizzle, + ( + sm100_desc.Major.K + if const_expr( + op.a_major_mode == cute.nvgpu.tcgen05.mma.OperandMajorMode.K + ) + else sm100_desc.Major.MN + ), + ) + ) + smem_desc_base_a_lo, smem_desc_a_hi = i64_to_i32x2(smem_desc_base_a) + smem_desc_base_a_lo = const_expr(smem_desc_base_a_lo) + smem_desc_a_hi = const_expr(smem_desc_a_hi) + else: + smem_desc_base_a = None + smem_desc_base_a_lo, smem_desc_a_hi = None, None + sB_swizzle = sB.iterator.type.swizzle_type + smem_desc_base_b: int = const_expr( + sm100_desc.make_smem_desc_base( + cute.recast_layout(128, op.b_dtype.width, sB_layout[0]), + sB_swizzle, + ( + sm100_desc.Major.K + if const_expr( + op.b_major_mode == cute.nvgpu.tcgen05.mma.OperandMajorMode.K + ) + else sm100_desc.Major.MN + ), + ) + ) + smem_desc_base_b_lo, smem_desc_b_hi = i64_to_i32x2(smem_desc_base_b) + smem_desc_base_b_lo = const_expr(smem_desc_base_b_lo) + smem_desc_b_hi = const_expr(smem_desc_b_hi) + + tCrA_layout = ( + tCrA.layout + if const_expr(not is_ts) + else cute.recast_layout(32, tCrA.element_type.width, tCrA.layout) + ) + offset_a = [ + cute.crd2idx((0, 0, k), tCrA_layout) for k in range(cute.size(tCrA.shape[2])) + ] + offset_a_diff = [ + offset_a[k] - offset_a[k - 1] for k in range(1, cute.size(tCrA.shape[2])) + ] + offset_b = [ + cute.crd2idx((0, 0, k), tCrB.layout) for k in range(cute.size(tCrB.shape[2])) + ] + offset_b_diff = [ + offset_b[k] - offset_b[k - 1] for k in range(1, cute.size(tCrB.shape[2])) + ] + + if const_expr(not is_ts): + smem_desc_start_a_lo = Int32( + smem_desc_base_a_lo + | sm100_desc.make_smem_desc_start_addr(sA[None, None, 0].iterator) + ) + # ) + sA_offset + else: + smem_desc_start_a_lo = None + smem_desc_start_b_lo = Int32( + smem_desc_base_b_lo + | sm100_desc.make_smem_desc_start_addr(sB[None, None, 0].iterator) + ) + pred_str = "p" if isinstance(zero_init, Boolean) else "0" if zero_init else "1" + if const_expr(not is_ts): + assert mbar_ptr is None, "mbar_ptr must be None when a_src is not TMEM" + llvm.inline_asm( + None, + [ + # acc.iterator.toint().ir_value(), + Int32(cute.arch.make_warp_uniform(smem_desc_start_a_lo)).ir_value(), + Int32(cute.arch.make_warp_uniform(smem_desc_start_b_lo)).ir_value(), + Int32(not zero_init).ir_value(), + Int32(cute.arch.make_warp_uniform(acc_tmem_addr)).ir_value(), + ], + "{\n\t" + ".reg .pred leader_thread;\n\t" + ".reg .pred p;\n\t" + ".reg .b32 idesc;\n\t" + ".reg .b32 tmem_acc;\n\t" + ".reg .b32 smem_desc_a_lo_start, smem_desc_b_lo_start;\n\t" + ".reg .b32 smem_desc_a_lo, smem_desc_b_lo;\n\t" + ".reg .b32 smem_desc_a_hi, smem_desc_b_hi;\n\t" + ".reg .b64 smem_desc_a, smem_desc_b;\n\t" + "elect.sync _|leader_thread, -1;\n\t" + f"mov.b32 idesc, {hex(idesc)};\n\t" + # f"mov.b32 tmem_acc, {hex(acc_tmem_addr)};\n\t" + f"mov.b32 tmem_acc, $3;\n\t" + "mov.b32 smem_desc_a_lo_start, $0;\n\t" + "mov.b32 smem_desc_b_lo_start, $1;\n\t" + f"mov.b32 smem_desc_a_hi, {hex(smem_desc_a_hi)};\n\t" + f"mov.b32 smem_desc_b_hi, {hex(smem_desc_b_hi)};\n\t" + f"mov.b64 smem_desc_a, {{smem_desc_a_lo_start, smem_desc_a_hi}};\n\t" + f"mov.b64 smem_desc_b, {{smem_desc_b_lo_start, smem_desc_b_hi}};\n\t" + "setp.ne.b32 p, $2, 0;\n\t" + f"@leader_thread tcgen05.mma.cta_group::{cta_group}.kind::{kind} [tmem_acc], smem_desc_a, smem_desc_b, idesc, {pred_str};\n\t" + + "".join( + ( + # f"add.u32 smem_desc_a_lo, smem_desc_a_lo, {hex(offset_a_diff[k - 1])};\n\t" + # f"add.u32 smem_desc_b_lo, smem_desc_b_lo, {hex(offset_b_diff[k - 1])};\n\t" + f"add.u32 smem_desc_a_lo, smem_desc_a_lo_start, {hex(offset_a[k])};\n\t" + f"add.u32 smem_desc_b_lo, smem_desc_b_lo_start, {hex(offset_b[k])};\n\t" + f"mov.b64 smem_desc_a, {{smem_desc_a_lo, smem_desc_a_hi}};\n\t" + f"mov.b64 smem_desc_b, {{smem_desc_b_lo, smem_desc_b_hi}};\n\t" + f"@leader_thread tcgen05.mma.cta_group::{cta_group}.kind::{kind} [tmem_acc], smem_desc_a, smem_desc_b, idesc, 1;\n\t" + ) + for k in range(1, cute.size(tCrA.shape[2])) + ) + + "}\n", + # "r,r,r", + "r,r,r,r", + has_side_effects=True, + is_align_stack=False, + asm_dialect=llvm.AsmDialect.AD_ATT, + ) + else: + # For TS gemm, somehow tCrA.iterator.toint() returns 0 no matter what, so we need to + # explicitly pass in the tA_addr for correctness. + tA_addr = tCrA[None, None, 0].iterator.toint() if tA_addr is None else tA_addr + input_args = [ + # Int32(cute.arch.make_warp_uniform(tCrA[None, None, 0].iterator.toint())).ir_value(), + Int32(cute.arch.make_warp_uniform(tA_addr)).ir_value(), + Int32(cute.arch.make_warp_uniform(smem_desc_start_b_lo)).ir_value(), + Int32(not zero_init).ir_value(), + Int32(cute.arch.make_warp_uniform(acc_tmem_addr)).ir_value(), + ] + if const_expr(mbar_ptr is not None): + assert ( + mbar_phase is not None + ), "mbar_phase must be provided when mbar_ptr is not None" + assert ( + split_arrive is not None + ), "split_arrive must be provided when mbar_ptr is not None" + split_arrive_idx = split_arrive // op.shape_mnk[2] + input_args.append(mbar_ptr.toint().ir_value()) + input_args.append(Int32(mbar_phase).ir_value()) + mbar_wait_str = ( + ".reg .pred P1; \n\t" + "LAB_WAIT: \n\t" + "mbarrier.try_wait.parity.shared::cta.b64 P1, [$4], $5, 10000000; \n\t" + "@P1 bra DONE; \n\t" + "bra LAB_WAIT; \n\t" + "DONE: \n\t" + ) + else: + mbar_wait_str = "" + llvm.inline_asm( + None, + # [ + # # acc.iterator.toint().ir_value(), + # Int32(tCrA[None, None, 0].iterator.toint()).ir_value(), + # Int32(smem_desc_start_b_lo).ir_value(), + # Int32(not zero_init).ir_value(), + # ], + input_args, + "{\n\t" + ".reg .pred leader_thread;\n\t" + ".reg .pred p;\n\t" + ".reg .b32 idesc;\n\t" + ".reg .b32 tmem_acc;\n\t" + ".reg .b32 tmem_a;\n\t" + ".reg .b32 smem_desc_b_lo_start;\n\t" + ".reg .b32 smem_desc_b_lo;\n\t" + ".reg .b32 smem_desc_b_hi;\n\t" + ".reg .b64 smem_desc_b;\n\t" + "elect.sync _|leader_thread, -1;\n\t" + f"mov.b32 idesc, {hex(idesc)};\n\t" + # f"mov.b32 tmem_acc, {hex(acc_tmem_addr)};\n\t" + f"mov.b32 tmem_acc, $3;\n\t" + f"mov.b32 tmem_a, $0;\n\t" + f"mov.b32 smem_desc_b_lo_start, $1;\n\t" + f"mov.b32 smem_desc_b_hi, {hex(smem_desc_b_hi)};\n\t" + f"mov.b64 smem_desc_b, {{smem_desc_b_lo_start, smem_desc_b_hi}};\n\t" + "setp.ne.b32 p, $2, 0;\n\t" + f"@leader_thread tcgen05.mma.cta_group::{cta_group}.kind::{kind} [tmem_acc], [tmem_a], smem_desc_b, idesc, {pred_str};\n\t" + + "".join( + ( + # f"add.u32 tmem_a, tmem_a, {hex(offset_a_diff[k - 1])};\n\t" + # f"add.u32 smem_desc_b_lo, smem_desc_b_lo, {hex(offset_b_diff[k - 1])};\n\t" + f"add.u32 smem_desc_b_lo, smem_desc_b_lo_start, {hex(offset_b[k])};\n\t" + f"mov.b64 smem_desc_b, {{smem_desc_b_lo, smem_desc_b_hi}};\n\t" + # f"@leader_thread tcgen05.mma.cta_group::1.kind::f16 [tmem_acc], [tmem_a], smem_desc_b, idesc, 1;\n\t" + f"@leader_thread tcgen05.mma.cta_group::{cta_group}.kind::{kind} [tmem_acc], [tmem_a + {hex(offset_a[k])}], smem_desc_b, idesc, 1;\n\t" + ) + for k in range( + 1, + ( + cute.size(tCrA.shape[2]) + if const_expr(mbar_ptr is None) + else split_arrive_idx + ), + ) + ) + + mbar_wait_str + + ( + "".join( + ( + f"add.u32 smem_desc_b_lo, smem_desc_b_lo, {hex(offset_b_diff[k - 1])};\n\t" + f"mov.b64 smem_desc_b, {{smem_desc_b_lo, smem_desc_b_hi}};\n\t" + f"@leader_thread tcgen05.mma.cta_group::{cta_group}.kind::{kind} [tmem_acc], [tmem_a + {hex(offset_a[k])}], smem_desc_b, idesc, 1;\n\t" + ) + for k in range(split_arrive_idx, cute.size(tCrA.shape[2])) + ) + if const_expr(mbar_ptr is not None) + else "" + ) + + "}\n", + "r,r,r,r" if const_expr(mbar_ptr is None) else "r,r,r,r,r,r", + has_side_effects=True, + is_align_stack=False, + asm_dialect=llvm.AsmDialect.AD_ATT, + ) + + +@cute.jit +def gemm_ptx_partial1( + op: cute.nvgpu.tcgen05.mma.MmaOp, + acc_tmem_addr: cutlass.Constexpr[int], + tCrA: cute.Tensor, + tCrB: cute.Tensor, + sA_base_addr_for_desc: Int32, + sA_addr_offset_for_desc: cutlass.Constexpr[int], + sA_stage: Int32, + sB_base_addr_for_desc: Int32, + sB_addr_offset_for_desc: cutlass.Constexpr[int], + sB_stage: Int32, + sA_layout: Optional[cute.Layout], + sB_layout: Optional[cute.Layout], + sA_swizzle: Optional[cute.Swizzle], + sB_swizzle: cute.Swizzle, + zero_init: bool | Boolean = False, +) -> None: + is_ts = op.a_src == cute.nvgpu.tcgen05.OperandSource.TMEM + if const_expr(not is_ts): + assert ( + sA_layout is not None + ), "sA_layout must be provided when a_src is not TMEM" + assert ( + sA_swizzle is not None + ), "sA_swizzle must be provided when a_src is not TMEM" + idesc: int = const_expr(sm100_desc.mma_op_to_idesc(op)) + kind = _tcgen05_mma_kind(op) + if const_expr(not is_ts): + smem_desc_base_a: int = const_expr( + sm100_desc.make_smem_desc_base( + cute.recast_layout(128, op.a_dtype.width, sA_layout[0]), + sA_swizzle, + ( + sm100_desc.Major.K + if const_expr( + op.a_major_mode == cute.nvgpu.tcgen05.mma.OperandMajorMode.K + ) + else sm100_desc.Major.MN + ), + ) + ) + smem_desc_base_a_lo, smem_desc_a_hi = i64_to_i32x2(smem_desc_base_a) + smem_desc_base_a_lo = const_expr(smem_desc_base_a_lo) + smem_desc_a_hi = const_expr(smem_desc_a_hi) + else: + smem_desc_base_a = None + smem_desc_base_a_lo, smem_desc_a_hi = None, None + smem_desc_base_b: int = const_expr( + sm100_desc.make_smem_desc_base( + cute.recast_layout(128, op.b_dtype.width, sB_layout[0]), + sB_swizzle, + ( + sm100_desc.Major.K + if const_expr( + op.b_major_mode == cute.nvgpu.tcgen05.mma.OperandMajorMode.K + ) + else sm100_desc.Major.MN + ), + ) + ) + smem_desc_base_b_lo, smem_desc_b_hi = i64_to_i32x2(smem_desc_base_b) + smem_desc_base_b_lo = const_expr(smem_desc_base_b_lo) + smem_desc_b_hi = const_expr(smem_desc_b_hi) + mask = [Int32(0)] * 4 + + if const_expr(not is_ts): + offset_a = [ + (cute.crd2idx((0, 0, k), sA_layout) * op.a_dtype.width // 8) >> 4 + for k in range(cute.size(tCrA.shape[2])) + ] + else: + offset_a = [ + cute.crd2idx((0, 0, k), sA_layout) * op.a_dtype.width // 32 + for k in range(cute.size(tCrA.shape[2])) + ] + offset_a_diff = [ + offset_a[k] - offset_a[k - 1] for k in range(1, cute.size(tCrA.shape[2])) + ] + offset_b = [ + (cute.crd2idx((0, 0, k), sB_layout) * op.b_dtype.width // 8) >> 4 + for k in range(cute.size(tCrB.shape[2])) + ] + offset_b_diff = [ + offset_b[k] - offset_b[k - 1] for k in range(1, cute.size(tCrB.shape[2])) + ] + + if const_expr(not is_ts): + # smem_desc_start_a_lo = Int32(smem_desc_base_a_lo | sm100_desc.make_smem_desc_start_addr(sA[None, None, 0].iterator)) + smem_desc_start_a_lo = const_expr(smem_desc_base_a_lo) + else: + smem_desc_start_a_lo = None + # smem_desc_start_b_lo = Int32(smem_desc_base_b_lo | sm100_desc.make_smem_desc_start_addr(sB[None, None, 0].iterator)) + smem_desc_start_b_lo = const_expr(smem_desc_base_b_lo) + pred_str = "p" if isinstance(zero_init, Boolean) else "0" if zero_init else "1" + if const_expr(not is_ts): + llvm.inline_asm( + None, + [ + # acc.iterator.toint().ir_value(), + # Int32(cute.arch.make_warp_uniform(smem_desc_start_a_lo)).ir_value(), + Int32(sA_base_addr_for_desc).ir_value(), + Int32(sA_stage).ir_value(), + # Int32(cute.arch.make_warp_uniform(smem_desc_start_b_lo)).ir_value(), + Int32(sB_base_addr_for_desc).ir_value(), + Int32(sB_stage).ir_value(), + Int32(not zero_init).ir_value(), + mask[0].ir_value(), + mask[1].ir_value(), + mask[2].ir_value(), + mask[3].ir_value(), + ], + "{\n\t" + ".reg .pred leader_thread;\n\t" + ".reg .pred p;\n\t" + ".reg .b32 idesc;\n\t" + ".reg .b32 tmem_acc;\n\t" + ".reg .b32 smem_desc_a_lo, smem_desc_b_lo;\n\t" + ".reg .b32 smem_desc_a_hi, smem_desc_b_hi;\n\t" + ".reg .b64 smem_desc_a, smem_desc_b;\n\t" + "elect.sync _|leader_thread, -1;\n\t" + f"mov.b32 idesc, {hex(idesc)};\n\t" + f"mov.b32 tmem_acc, {hex(acc_tmem_addr)};\n\t" + # "mov.b32 smem_desc_a_lo, $0;\n\t" + # f"add.u32 smem_desc_a_lo, $0, {hex(smem_desc_start_a_lo)};\n\t" + f"mad.lo.u32 smem_desc_a_lo, $1, {hex(sA_addr_offset_for_desc)}, $0;\n\t" + # "mov.b32 smem_desc_b_lo, $2;\n\t" + f"mad.lo.u32 smem_desc_b_lo, $3, {hex(sB_addr_offset_for_desc)}, $2;\n\t" + f"mov.b32 smem_desc_a_hi, {hex(smem_desc_a_hi)};\n\t" + f"mov.b32 smem_desc_b_hi, {hex(smem_desc_b_hi)};\n\t" + f"mov.b64 smem_desc_a, {{smem_desc_a_lo, smem_desc_a_hi}};\n\t" + f"mov.b64 smem_desc_b, {{smem_desc_b_lo, smem_desc_b_hi}};\n\t" + "setp.ne.b32 p, $4, 0;\n\t" + f"@leader_thread tcgen05.mma.cta_group::1.kind::{kind} [tmem_acc], smem_desc_a, smem_desc_b, idesc, {{$5, $6, $7, $8}}, {pred_str};\n\t" + + "".join( + ( + f"add.u32 smem_desc_a_lo, smem_desc_a_lo, {hex(offset_a_diff[k - 1])};\n\t" + f"add.u32 smem_desc_b_lo, smem_desc_b_lo, {hex(offset_b_diff[k - 1])};\n\t" + f"mov.b64 smem_desc_a, {{smem_desc_a_lo, smem_desc_a_hi}};\n\t" + f"mov.b64 smem_desc_b, {{smem_desc_b_lo, smem_desc_b_hi}};\n\t" + f"@leader_thread tcgen05.mma.cta_group::1.kind::{kind} [tmem_acc], smem_desc_a, smem_desc_b, idesc, {{$5, $6, $7, $8}}, 1;\n\t" + ) + for k in range(1, cute.size(tCrA.shape[2])) + ) + + "}\n", + "r,r,r,r,r,r,r,r,r", + has_side_effects=True, + is_align_stack=False, + asm_dialect=llvm.AsmDialect.AD_ATT, + ) + else: + llvm.inline_asm( + None, + [ + # acc.iterator.toint().ir_value(), + Int32(tCrA[None, None, 0].iterator.toint()).ir_value(), + Int32(smem_desc_start_b_lo).ir_value(), + Int32(not zero_init).ir_value(), + mask[0].ir_value(), + mask[1].ir_value(), + mask[2].ir_value(), + mask[3].ir_value(), + ], + "{\n\t" + ".reg .pred leader_thread;\n\t" + ".reg .pred p;\n\t" + ".reg .b32 idesc;\n\t" + ".reg .b32 tmem_a;\n\t" + ".reg .b32 smem_desc_b_lo;\n\t" + ".reg .b32 smem_desc_b_hi;\n\t" + ".reg .b64 smem_desc_b;\n\t" + "elect.sync _|leader_thread, -1;\n\t" + f"mov.b32 idesc, {hex(idesc)};\n\t" + f"mov.b32 tmem_a, $1;\n\t" + f"mov.b32 smem_desc_b_lo, $2;\n\t" + f"mov.b32 smem_desc_b_hi, {hex(smem_desc_b_hi)};\n\t" + f"mov.b64 smem_desc_b, {{smem_desc_b_lo, smem_desc_b_hi}};\n\t" + "setp.ne.b32 p, $3, 0;\n\t" + f"@leader_thread tcgen05.mma.cta_group::1.kind::{kind} [$0], [tmem_a], smem_desc_b, idesc, {{$4, $5, $6, $7}}, {pred_str};\n\t" + + "".join( + ( + f"add.u32 tmem_a, tmem_a, {hex(offset_a_diff[k - 1])};\n\t" + f"add.u32 smem_desc_b_lo, smem_desc_b_lo, {hex(offset_b_diff[k - 1])};\n\t" + f"mov.b64 smem_desc_b, {{smem_desc_b_lo, smem_desc_b_hi}};\n\t" + f"@leader_thread tcgen05.mma.cta_group::1.kind::{kind} [$0], [tmem_a], smem_desc_b, idesc, {{$4, $5, $6, $7}}, 1;\n\t" + ) + for k in range(1, cute.size(tCrA.shape[2])) + ) + + "}\n", + "r,r,r,r,r,r,r,r", + has_side_effects=True, + is_align_stack=False, + asm_dialect=llvm.AsmDialect.AD_ATT, + ) + + +@cute.jit +def gemm_ptx_precomputed( + acc_tmem_addr: Int32, + smem_desc_start_a: Int32, # If TS, then this is the tmem start address for A + smem_desc_start_b: Int32, + idesc: int, + smem_desc_base_a: Optional[int], + smem_desc_base_b: int, + tCrA_layout: cute.Layout, + tCrB_layout: cute.Layout, + mbar_ptr: Optional[cutlass.Pointer] = None, + mbar_phase: Optional[Int32] = None, + zero_init: bool | Boolean = False, + cta_group: int = 1, + kind: str = "f16", +) -> None: + # acc_tmem_addr += acc_offset + is_ts = const_expr(smem_desc_base_a is None) + num_k_tile = cute.size(tCrA_layout.shape[2]) + if const_expr(not is_ts): + smem_desc_base_a_lo, smem_desc_a_hi = i64_to_i32x2(smem_desc_base_a) + else: + smem_desc_base_a_lo, smem_desc_a_hi = None, None + smem_desc_base_b_lo, smem_desc_b_hi = i64_to_i32x2(smem_desc_base_b) + + tCrA_layout = ( + tCrA_layout + if const_expr(not is_ts) + # else cute.recast_layout(32, tCrA.element_type.width, tCrA_layout) + # currently hard-coding the width to 16 + else cute.recast_layout(32, 16, tCrA_layout) + ) + offset_a = [cute.crd2idx((0, 0, k), tCrA_layout) for k in range(num_k_tile)] + offset_a_diff = [offset_a[k] - offset_a[k - 1] for k in range(1, num_k_tile)] + offset_b = [cute.crd2idx((0, 0, k), tCrB_layout) for k in range(num_k_tile)] + offset_b_diff = [offset_b[k] - offset_b[k - 1] for k in range(1, num_k_tile)] + + smem_desc_start_a_lo = None + if const_expr(not is_ts): + smem_desc_start_a_lo = Int32(smem_desc_base_a_lo | smem_desc_start_a) + # smem_desc_start_a_lo = smem_desc_start_a + smem_desc_start_b_lo = Int32(smem_desc_base_b_lo | smem_desc_start_b) + pred_str = "p" if isinstance(zero_init, Boolean) else "0" if zero_init else "1" + if const_expr(not is_ts): + assert mbar_ptr is None, "mbar_ptr must be None when a_src is not TMEM" + llvm.inline_asm( + None, + [ + # acc.iterator.toint().ir_value(), + Int32(cute.arch.make_warp_uniform(smem_desc_start_a_lo)).ir_value(), + Int32(cute.arch.make_warp_uniform(smem_desc_start_b_lo)).ir_value(), + Int32(not zero_init).ir_value(), + Int32(cute.arch.make_warp_uniform(acc_tmem_addr)).ir_value(), + ], + "{\n\t" + ".reg .pred leader_thread;\n\t" + ".reg .pred p;\n\t" + ".reg .b32 idesc;\n\t" + ".reg .b32 tmem_acc;\n\t" + ".reg .b32 smem_desc_a_lo_start, smem_desc_b_lo_start;\n\t" + ".reg .b32 smem_desc_a_lo, smem_desc_b_lo;\n\t" + ".reg .b32 smem_desc_a_hi, smem_desc_b_hi;\n\t" + ".reg .b64 smem_desc_a, smem_desc_b;\n\t" + "elect.sync _|leader_thread, -1;\n\t" + f"mov.b32 idesc, {hex(idesc)};\n\t" + # f"mov.b32 tmem_acc, {hex(acc_tmem_addr)};\n\t" + f"mov.b32 tmem_acc, $3;\n\t" + "mov.b32 smem_desc_a_lo_start, $0;\n\t" + "mov.b32 smem_desc_b_lo_start, $1;\n\t" + f"mov.b32 smem_desc_a_hi, {hex(smem_desc_a_hi)};\n\t" + f"mov.b32 smem_desc_b_hi, {hex(smem_desc_b_hi)};\n\t" + f"mov.b64 smem_desc_a, {{smem_desc_a_lo_start, smem_desc_a_hi}};\n\t" + f"mov.b64 smem_desc_b, {{smem_desc_b_lo_start, smem_desc_b_hi}};\n\t" + "setp.ne.b32 p, $2, 0;\n\t" + f"@leader_thread tcgen05.mma.cta_group::{cta_group}.kind::{kind} [tmem_acc], smem_desc_a, smem_desc_b, idesc, {pred_str};\n\t" + + "".join( + ( + # f"add.u32 smem_desc_a_lo, smem_desc_a_lo, {hex(offset_a_diff[k - 1])};\n\t" + # f"add.u32 smem_desc_b_lo, smem_desc_b_lo, {hex(offset_b_diff[k - 1])};\n\t" + f"add.s32 smem_desc_a_lo, smem_desc_a_lo_start, {hex(offset_a[k])};\n\t" + f"add.s32 smem_desc_b_lo, smem_desc_b_lo_start, {hex(offset_b[k])};\n\t" + f"mov.b64 smem_desc_a, {{smem_desc_a_lo, smem_desc_a_hi}};\n\t" + f"mov.b64 smem_desc_b, {{smem_desc_b_lo, smem_desc_b_hi}};\n\t" + f"@leader_thread tcgen05.mma.cta_group::{cta_group}.kind::{kind} [tmem_acc], smem_desc_a, smem_desc_b, idesc, 1;\n\t" + ) + for k in range(1, num_k_tile) + ) + + "}\n", + # "r,r,r", + "r,r,r,r", + has_side_effects=True, + is_align_stack=False, + asm_dialect=llvm.AsmDialect.AD_ATT, + ) + else: + input_args = [ + Int32(cute.arch.make_warp_uniform(smem_desc_start_a)).ir_value(), + Int32(cute.arch.make_warp_uniform(smem_desc_start_b_lo)).ir_value(), + Int32(not zero_init).ir_value(), + Int32(cute.arch.make_warp_uniform(acc_tmem_addr)).ir_value(), + ] + if const_expr(mbar_ptr is not None): + assert ( + mbar_phase is not None + ), "mbar_phase must be provided when mbar_ptr is not None" + input_args.append(mbar_ptr.toint().ir_value()) + input_args.append(Int32(mbar_phase).ir_value()) + mbar_wait_str = ( + ".reg .pred P1; \n\t" + "LAB_WAIT: \n\t" + "mbarrier.try_wait.parity.shared::cta.b64 P1, [$4], $5, 10000000; \n\t" + "@P1 bra DONE; \n\t" + "bra LAB_WAIT; \n\t" + "DONE: \n\t" + ) + else: + mbar_wait_str = "" + llvm.inline_asm( + None, + # [ + # # acc.iterator.toint().ir_value(), + # Int32(tCrA_layout[None, None, 0].iterator.toint()).ir_value(), + # Int32(smem_desc_start_b_lo).ir_value(), + # Int32(not zero_init).ir_value(), + # ], + input_args, + "{\n\t" + ".reg .pred leader_thread;\n\t" + ".reg .pred p;\n\t" + ".reg .b32 idesc;\n\t" + ".reg .b32 tmem_acc;\n\t" + ".reg .b32 tmem_a;\n\t" + ".reg .b32 smem_desc_b_lo_start;\n\t" + ".reg .b32 smem_desc_b_lo;\n\t" + ".reg .b32 smem_desc_b_hi;\n\t" + ".reg .b64 smem_desc_b;\n\t" + "elect.sync _|leader_thread, -1;\n\t" + f"mov.b32 idesc, {hex(idesc)};\n\t" + # f"mov.b32 tmem_acc, {hex(acc_tmem_addr)};\n\t" + f"mov.b32 tmem_acc, $3;\n\t" + f"mov.b32 tmem_a, $0;\n\t" + f"mov.b32 smem_desc_b_lo_start, $1;\n\t" + f"mov.b32 smem_desc_b_hi, {hex(smem_desc_b_hi)};\n\t" + f"mov.b64 smem_desc_b, {{smem_desc_b_lo_start, smem_desc_b_hi}};\n\t" + "setp.ne.b32 p, $2, 0;\n\t" + f"@leader_thread tcgen05.mma.cta_group::{cta_group}.kind::{kind} [tmem_acc], [tmem_a], smem_desc_b, idesc, {pred_str};\n\t" + + "".join( + ( + # f"add.u32 tmem_a, tmem_a, {hex(offset_a_diff[k - 1])};\n\t" + # f"add.u32 smem_desc_b_lo, smem_desc_b_lo, {hex(offset_b_diff[k - 1])};\n\t" + f"add.u32 smem_desc_b_lo, smem_desc_b_lo_start, {hex(offset_b[k])};\n\t" + f"mov.b64 smem_desc_b, {{smem_desc_b_lo, smem_desc_b_hi}};\n\t" + # f"@leader_thread tcgen05.mma.cta_group::1.kind::f16 [tmem_acc], [tmem_a], smem_desc_b, idesc, 1;\n\t" + f"@leader_thread tcgen05.mma.cta_group::{cta_group}.kind::{kind} [tmem_acc], [tmem_a + {hex(offset_a[k])}], smem_desc_b, idesc, 1;\n\t" + ) + for k in range( + 1, + num_k_tile if const_expr(mbar_ptr is None) else num_k_tile // 4 * 3, + ) + ) + + mbar_wait_str + + ( + "".join( + ( + # f"add.u32 smem_desc_b_lo, smem_desc_b_lo, {hex(offset_b_diff[k - 1])};\n\t" + f"add.u32 smem_desc_b_lo, smem_desc_b_lo_start, {hex(offset_b[k])};\n\t" + f"mov.b64 smem_desc_b, {{smem_desc_b_lo, smem_desc_b_hi}};\n\t" + f"@leader_thread tcgen05.mma.cta_group::{cta_group}.kind::{kind} [tmem_acc], [tmem_a + {hex(offset_a[k])}], smem_desc_b, idesc, 1;\n\t" + ) + for k in range(num_k_tile // 4 * 3, num_k_tile) + ) + if const_expr(mbar_ptr is not None) + else "" + ) + + "}\n", + "r,r,r,r" if const_expr(mbar_ptr is None) else "r,r,r,r,r,r", + has_side_effects=True, + is_align_stack=False, + asm_dialect=llvm.AsmDialect.AD_ATT, + ) + + +@cute.jit +def declare_ptx_smem_desc( + smem_desc_start_a: Int32, # If TS, then this is the tmem start address for A + smem_desc_base_a: Optional[int], + tCrA_layout: cute.Layout, + var_name_prefix: str = "smem_desc", +) -> None: + is_ts = const_expr(smem_desc_base_a is None) + num_k_tile = cute.size(tCrA_layout.shape[2]) + smem_desc_base_a_lo, smem_desc_a_hi = None, None + if const_expr(not is_ts): + smem_desc_base_a_lo, smem_desc_a_hi = i64_to_i32x2(smem_desc_base_a) + tCrA_layout = ( + tCrA_layout + if const_expr(not is_ts) + # else cute.recast_layout(32, tCrA.element_type.width, tCrA_layout) + # currently hard-coding the width to 16 + else cute.recast_layout(32, 16, tCrA_layout) + ) + offset_a = [cute.crd2idx((0, 0, k), tCrA_layout) for k in range(num_k_tile)] + smem_desc_start_a_lo = None + if const_expr(not is_ts): + smem_desc_start_a_lo = Int32(smem_desc_base_a_lo | smem_desc_start_a) + if const_expr(not is_ts): + llvm.inline_asm( + None, + [Int32(cute.arch.make_warp_uniform(smem_desc_start_a_lo)).ir_value()], + f".reg .b32 {var_name_prefix}_lo;\n\t" + f".reg .b64 {var_name_prefix}_<{num_k_tile}>;\n\t" + f"mov.b64 {var_name_prefix}_0, {{$0, {hex(smem_desc_a_hi)}}};\n\t" + + "".join( + ( + f"add.s32 {var_name_prefix}_lo, $0, {hex(offset_a[k])};\n\t" + f"mov.b64 {var_name_prefix}_{k}, {{{var_name_prefix}_lo, {hex(smem_desc_a_hi)}}};\n\t" + ) + for k in range(1, num_k_tile) + ), + "r", + has_side_effects=True, + is_align_stack=False, + asm_dialect=llvm.AsmDialect.AD_ATT, + ) + + +@cute.jit +def declare_ptx_idesc( + op: cute.nvgpu.tcgen05.mma.MmaOp, var_name: str = "idesc" +) -> None: + idesc = const_expr(sm100_desc.mma_op_to_idesc(op)) + llvm.inline_asm( + None, + [], + f".reg .b32 {var_name};\n\t" # noqa + f"mov.b32 {var_name}, {hex(idesc)};\n\t", + constraints="", + has_side_effects=True, + is_align_stack=False, + asm_dialect=llvm.AsmDialect.AD_ATT, + ) + + +@cute.jit +def gemm_ptx_precomputed_varname( + acc_tmem_addr: Int32, + smem_desc_start_b: Int32, + # idesc: int, + smem_desc_base_b: int, + tCrB_layout: cute.Layout, + smem_var_name_prefix: str, + idesc_var_name: str, + smem_offset: int, + zero_init: bool | Boolean = False, + cta_group: int = 1, + kind: str = "f16", +) -> None: + is_ts = False + num_k_tile = cute.size(tCrB_layout.shape[2]) + smem_desc_base_b_lo, smem_desc_b_hi = i64_to_i32x2(smem_desc_base_b) + offset_b = [cute.crd2idx((0, 0, k), tCrB_layout) for k in range(num_k_tile)] + + smem_desc_start_b_lo = Int32(smem_desc_base_b_lo | smem_desc_start_b) + pred_str = "p" if isinstance(zero_init, Boolean) else "0" if zero_init else "1" + if const_expr(not is_ts): + llvm.inline_asm( + None, + [ + Int32(cute.arch.make_warp_uniform(smem_desc_start_b_lo)).ir_value(), + Int32(not zero_init).ir_value(), + Int32(cute.arch.make_warp_uniform(acc_tmem_addr)).ir_value(), + ], + "{\n\t" ".reg .pred leader_thread;\n\t" ".reg .pred p;\n\t" + # ".reg .b32 idesc;\n\t" + ".reg .b32 tmem_acc;\n\t" + ".reg .b32 smem_desc_b_lo_start;\n\t" + ".reg .b32 smem_desc_a_lo, smem_desc_b_lo;\n\t" + ".reg .b32 smem_desc_a_hi, smem_desc_b_hi;\n\t" + # ".reg .b64 smem_desc_b;\n\t" + f".reg .b64 smem_desc_b_<{num_k_tile}>;\n\t" + "elect.sync _|leader_thread, -1;\n\t" + # f"mov.b32 idesc, {hex(idesc)};\n\t" + # f"mov.b32 tmem_acc, {hex(acc_tmem_addr)};\n\t" + f"mov.b32 tmem_acc, $2;\n\t" + "mov.b32 smem_desc_b_lo_start, $0;\n\t" + f"mov.b32 smem_desc_b_hi, {hex(smem_desc_b_hi)};\n\t" + f"mov.b64 {{smem_desc_a_lo, smem_desc_a_hi}}, {smem_var_name_prefix}_0;\n\t" + f"add.s32 smem_desc_a_lo, smem_desc_a_lo, {smem_offset};\n\t" + f"mov.b64 {smem_var_name_prefix}_0, {{smem_desc_a_lo, smem_desc_a_hi}};\n\t" + f"mov.b64 smem_desc_b_0, {{smem_desc_b_lo_start, smem_desc_b_hi}};\n\t" + + "".join( + ( + f"mov.b64 {{smem_desc_a_lo, smem_desc_a_hi}}, {smem_var_name_prefix}_{k};\n\t" + f"add.s32 smem_desc_a_lo, smem_desc_a_lo, {smem_offset};\n\t" + f"add.s32 smem_desc_b_lo, smem_desc_b_lo_start, {hex(offset_b[k])};\n\t" + f"mov.b64 {smem_var_name_prefix}_{k}, {{smem_desc_a_lo, smem_desc_a_hi}};\n\t" + f"mov.b64 smem_desc_b_{k}, {{smem_desc_b_lo, smem_desc_b_hi}};\n\t" + ) + for k in range(1, num_k_tile) + ) + + "setp.ne.b32 p, $1, 0;\n\t" + # f"@leader_thread tcgen05.mma.cta_group::{cta_group}.kind::f16 [tmem_acc], {smem_var_name_prefix}_0, smem_desc_b, idesc, {pred_str};\n\t" + f"@leader_thread tcgen05.mma.cta_group::{cta_group}.kind::{kind} [tmem_acc], {smem_var_name_prefix}_0, smem_desc_b_0, {idesc_var_name}, {pred_str};\n\t" + + "".join( + ( + # f"mov.b64 {{smem_desc_a_lo, smem_desc_a_hi}}, {smem_var_name_prefix}_{k};\n\t" + # f"add.s32 smem_desc_a_lo, smem_desc_a_lo, {smem_offset};\n\t" + # f"add.s32 smem_desc_b_lo, smem_desc_b_lo_start, {hex(offset_b[k])};\n\t" + # f"mov.b64 {smem_var_name_prefix}_{k}, {{smem_desc_a_lo, smem_desc_a_hi}};\n\t" + # f"mov.b64 smem_desc_b, {{smem_desc_b_lo, smem_desc_b_hi}};\n\t" + # f"@leader_thread tcgen05.mma.cta_group::{cta_group}.kind::f16 [tmem_acc], {smem_var_name_prefix}_{k}, smem_desc_b, idesc, 1;\n\t" + # f"@leader_thread tcgen05.mma.cta_group::{cta_group}.kind::f16 [tmem_acc], {smem_var_name_prefix}_{k}, smem_desc_b, {idesc_var_name}, 1;\n\t" + f"@leader_thread tcgen05.mma.cta_group::{cta_group}.kind::{kind} [tmem_acc], {smem_var_name_prefix}_{k}, smem_desc_b_{k}, {idesc_var_name}, 1;\n\t" + ) + for k in range(1, num_k_tile) + ) + + "}\n", + "r,r,r", + has_side_effects=True, + is_align_stack=False, + asm_dialect=llvm.AsmDialect.AD_ATT, + ) + + +@cute.jit +def gemm_blockscaled( + tiled_mma: cute.TiledMma, + acc: cute.Tensor, + tCrA: cute.Tensor, + tCrB: cute.Tensor, + tCtSFA: cute.Tensor, + tCtSFB: cute.Tensor, + zero_init: bool | Boolean = True, + sB: Optional[cute.Tensor] = None, + **kwargs, +) -> None: + """Blockscaled GEMM using cute.gemm with per-kblock SFA/SFB TMEM addresses.""" + num_kblocks = cute.size(tCrA.shape[2]) + for kblock_idx in cutlass.range(num_kblocks, unroll_full=True): + sf_kblock_coord = (None, None, kblock_idx) + tiled_mma.set(tcgen05.Field.SFA, tCtSFA[sf_kblock_coord].iterator) + tiled_mma.set(tcgen05.Field.SFB, tCtSFB[sf_kblock_coord].iterator) + tiled_mma.set(tcgen05.Field.ACCUMULATE, not zero_init or kblock_idx != 0) + cute.gemm( + tiled_mma, + acc, + tCrA[None, None, kblock_idx], + tCrB[None, None, kblock_idx], + acc, + ) + + +@cute.jit +def gemm_blockscaled_w_idx( + tiled_mma: cute.TiledMma, + acc: cute.Tensor, + tCrA: cute.Tensor, + tCrB: cute.Tensor, + tCtSFA: cute.Tensor, + tCtSFB: cute.Tensor, + A_idx: Optional[Int32] = None, + B_idx: Optional[Int32] = None, + zero_init: bool | Boolean = True, +) -> None: + """Blockscaled GEMM with optional A/B stage indexing.""" + rA = tCrA if const_expr(A_idx is None) else tCrA[None, None, None, A_idx] + rB = tCrB if const_expr(B_idx is None) else tCrB[None, None, None, B_idx] + gemm_blockscaled(tiled_mma, acc, rA, rB, tCtSFA, tCtSFB, zero_init=zero_init) diff --git a/python/sglang/jit_kernel/flash_attn/cute/block_info.py b/python/sglang/jit_kernel/flash_attn/cute/block_info.py new file mode 100644 index 000000000..b53a48ee0 --- /dev/null +++ b/python/sglang/jit_kernel/flash_attn/cute/block_info.py @@ -0,0 +1,203 @@ +# Copyright (c) 2025, Jay Shah, Ganesh Bikshandi, Ying Zhang, Vijay Thakkar, Pradeep Ramani, Tri Dao. +from dataclasses import dataclass +from typing import Optional, Tuple + +import cutlass +import cutlass.cute as cute +from cutlass import Int32, const_expr + +from sglang.jit_kernel.flash_attn.cute.seqlen_info import SeqlenInfoQK, SeqlenInfoQKNewK + + +@dataclass(frozen=True) +class BlockInfo: + tile_m: cutlass.Constexpr[int] + tile_n: cutlass.Constexpr[int] + is_causal: cutlass.Constexpr[bool] + is_local: cutlass.Constexpr[bool] = False + is_split_kv: cutlass.Constexpr[bool] = False + window_size_left: Optional[Int32] = None + window_size_right: Optional[Int32] = None + qhead_per_kvhead_packgqa: cutlass.Constexpr[int] = 1 + + @cute.jit + def get_n_idx_left_right( + self, + seqlen_info: SeqlenInfoQK, + m_idx: Int32, + ) -> Tuple[Int32, Int32]: + m_idx_actual = m_idx // self.qhead_per_kvhead_packgqa + if const_expr( + self.is_causal or (self.is_local and self.window_size_right is not None) + ): + n_idx_right = m_idx_actual + 1 + seqlen_info.seqlen_k - seqlen_info.seqlen_q + if const_expr(self.window_size_right is not None): + n_idx_right += self.window_size_right + else: + n_idx_right = seqlen_info.seqlen_k + if const_expr(self.is_local and self.window_size_left is not None): + n_idx_left = ( + m_idx_actual + + seqlen_info.seqlen_k + - seqlen_info.seqlen_q + - self.window_size_left + ) + n_idx_left = cutlass.max(n_idx_left, 0) + else: + n_idx_left = 0 + # inclusive n_idx_left, exclusive n_idx_right + # e.g. for causal, return (0, m_idx + 1) + return n_idx_left, n_idx_right + + @cute.jit + def get_n_block_min_max( + self, + seqlen_info: SeqlenInfoQK, + m_block: Int32, + split_idx: Int32 = 0, + num_splits: Int32 = 1, + half_tile_m: bool = False, + absolute: bool = False, + half_tile_n: bool = False, + ) -> Tuple[Int32, Int32]: + tile_m = self.tile_m // 2 if const_expr(half_tile_m) else self.tile_m + tile_n = self.tile_n // 2 if const_expr(half_tile_n) else self.tile_n + n_block_max = cute.ceil_div(seqlen_info.seqlen_k, tile_n) + if const_expr( + self.is_causal or (self.is_local and self.window_size_right is not None) + ): + m_idx_max = (m_block + 1) * tile_m + if const_expr(self.qhead_per_kvhead_packgqa > 1): + m_idx_max = cute.ceil_div(m_idx_max, self.qhead_per_kvhead_packgqa) + n_idx = m_idx_max + seqlen_info.seqlen_k - seqlen_info.seqlen_q + n_idx_right = ( + n_idx if const_expr(self.is_causal) else n_idx + self.window_size_right + ) + n_block_max = min(n_block_max, cute.ceil_div(n_idx_right, tile_n)) + n_block_min = 0 + if const_expr(self.is_local and self.window_size_left is not None): + m_idx_min = m_block * tile_m + if const_expr(self.qhead_per_kvhead_packgqa > 1): + m_idx_min = m_idx_min // self.qhead_per_kvhead_packgqa + n_idx = m_idx_min + seqlen_info.seqlen_k - seqlen_info.seqlen_q + n_idx_left = n_idx - self.window_size_left + n_block_min = cutlass.max(n_idx_left // tile_n, 0) + if cutlass.const_expr(self.is_split_kv and not absolute): + num_n_blocks_per_split = ( + Int32(0) + if n_block_max <= n_block_min + else (n_block_max - n_block_min + num_splits - 1) // num_splits + ) + n_block_min = n_block_min + split_idx * num_n_blocks_per_split + n_block_max = cutlass.min(n_block_min + num_n_blocks_per_split, n_block_max) + return n_block_min, n_block_max + + @cute.jit + def get_m_block_min_max( + self, seqlen_info: SeqlenInfoQK, n_block: Int32 + ) -> Tuple[Int32, Int32]: + m_block_max = cute.ceil_div(seqlen_info.seqlen_q, self.tile_m) + m_block_min = 0 + if const_expr( + self.is_causal or (self.is_local and self.window_size_right is not None) + ): + n_idx_min = n_block * self.tile_n + m_idx = n_idx_min + seqlen_info.seqlen_q - seqlen_info.seqlen_k + m_idx_right = ( + m_idx if const_expr(self.is_causal) else m_idx - self.window_size_right + ) + m_block_min = max(m_block_min, m_idx_right // self.tile_m) + if const_expr(self.is_local and self.window_size_left is not None): + n_idx_max = (n_block + 1) * self.tile_n + m_idx = n_idx_max + seqlen_info.seqlen_q - seqlen_info.seqlen_k + m_idx_left = m_idx + self.window_size_left + m_block_max = min(m_block_max, cute.ceil_div(m_idx_left, self.tile_m)) + return m_block_min, m_block_max + + @cute.jit + def get_n_block_k_new_min_max( + self, + seqlen_info: SeqlenInfoQKNewK, + m_block: Int32, + split_idx: Int32 = 0, + num_splits: Int32 = 1, + ) -> Tuple[Int32, Int32]: + """Get the block range for new K tokens (append KV). + + First computes the full n_block range via get_n_block_min_max, then maps + those blocks into the new-K index space by subtracting seqlen_k_og. + """ + n_block_min, n_block_max = self.get_n_block_min_max( + seqlen_info, + m_block, + split_idx, + num_splits, + ) + idx_k_new_min = cutlass.max( + n_block_min * self.tile_n - seqlen_info.seqlen_k_og, 0 + ) + idx_k_new_max = cutlass.min( + n_block_max * self.tile_n - seqlen_info.seqlen_k_og, + seqlen_info.seqlen_k_new, + ) + n_block_new_min = idx_k_new_min // self.tile_n + n_block_new_max = ( + cute.ceil_div(idx_k_new_max, self.tile_n) + if idx_k_new_max > idx_k_new_min + else n_block_new_min + ) + return n_block_new_min, n_block_new_max + + @cute.jit + def get_n_block_min_causal_local_mask( + self, + seqlen_info: SeqlenInfoQK, + m_block: Int32, + n_block_min: Int32, + ) -> Int32: + """If we have separate iterations with causal or local masking at the start, where do we stop""" + m_idx_min = m_block * self.tile_m + if const_expr(self.qhead_per_kvhead_packgqa > 1): + m_idx_min = m_idx_min // self.qhead_per_kvhead_packgqa + n_idx = m_idx_min + seqlen_info.seqlen_k - seqlen_info.seqlen_q + n_idx_right = ( + n_idx + if const_expr(not self.is_local or self.window_size_right is None) + else n_idx + self.window_size_right + ) + return cutlass.max(n_block_min, n_idx_right // self.tile_n) + + @cute.jit + def get_n_block_min_before_local_mask( + self, + seqlen_info: SeqlenInfoQK, + m_block: Int32, + n_block_min: Int32, + ) -> Int32: + """If we have separate iterations with local masking at the end, where do we stop the non-masked iterations""" + if const_expr(not self.is_local or self.window_size_left is None): + return n_block_min + else: + m_idx_max = (m_block + 1) * self.tile_m + if const_expr(self.qhead_per_kvhead_packgqa > 1): + m_idx_max = cute.ceil_div(m_idx_max, self.qhead_per_kvhead_packgqa) + n_idx = m_idx_max + seqlen_info.seqlen_k - seqlen_info.seqlen_q + n_idx_left = n_idx - self.window_size_left + return cutlass.max(n_block_min, cute.ceil_div(n_idx_left, self.tile_n)) + + @cute.jit + def get_n_block_max_for_m_block( + self, + seqlen_info: SeqlenInfoQK, + m_block: Int32, + ) -> Int32: + n_block_max = cute.ceil_div(seqlen_info.seqlen_k, self.tile_n) + if const_expr(self.is_causal or self.window_size_right is not None): + m_idx_max = (m_block + 1) * self.tile_m + if const_expr(self.qhead_per_kvhead_packgqa > 1): + m_idx_max = cute.ceil_div(m_idx_max, self.qhead_per_kvhead_packgqa) + n_idx_right = m_idx_max + seqlen_info.seqlen_k - seqlen_info.seqlen_q + if const_expr(self.window_size_right is not None): + n_idx_right += self.window_size_right + n_block_max = min(n_block_max, cute.ceil_div(n_idx_right, self.tile_n)) + return n_block_max diff --git a/python/sglang/jit_kernel/flash_attn/cute/block_sparse_utils.py b/python/sglang/jit_kernel/flash_attn/cute/block_sparse_utils.py new file mode 100644 index 000000000..01ce30b9e --- /dev/null +++ b/python/sglang/jit_kernel/flash_attn/cute/block_sparse_utils.py @@ -0,0 +1,1632 @@ +""" +Block-sparse runtime utilities for CUTE DSL kernels. + +This module contains runtime execution functions for block-sparse attention kernels. +These utilities are used by CUTE DSL kernels to produce and consume block-sparse loads. +""" + +import math +from functools import partial +from typing import Callable, Optional, Tuple + +import cutlass +import cutlass.cute as cute +from cutlass import Float32, Int32, const_expr +from quack import copy_utils + +# Import data structures from block_sparsity +from sglang.jit_kernel.flash_attn.cute.block_sparsity import BlockSparseTensors +from sglang.jit_kernel.flash_attn.cute.named_barrier import NamedBarrierBwd +from sglang.jit_kernel.flash_attn.cute.seqlen_info import SeqlenInfoQK +from sglang.jit_kernel.flash_attn.cute.utils import AuxData + + +@cute.jit +def _get_curr_blocksparse_tensors_varlen( + head_idx: cutlass.Int32, + m_block: cutlass.Int32, + blocksparse_tensors: BlockSparseTensors, + seqlen_info: SeqlenInfoQK, +) -> Tuple[cutlass.Int32, cute.Tensor, cutlass.Int32, Optional[cute.Tensor]]: + """Varlen path: tensors are 2D [nheads, total_m_blocks] / [nheads, total_n_blocks].""" + mask_block_cnt, mask_block_idx, full_block_cnt, full_block_idx, *_ = ( + blocksparse_tensors + ) + curr_m_block = seqlen_info.m_block_offset + m_block + curr_block_idx_offset = ( + seqlen_info.block_idx_offset + m_block * seqlen_info.num_n_blocks + ) + curr_mask_block_cnt = mask_block_cnt[head_idx, curr_m_block] + curr_mask_block_idx = cute.domain_offset( + curr_block_idx_offset, mask_block_idx[head_idx, None] + ) + if const_expr(full_block_cnt is not None): + curr_full_block_cnt = full_block_cnt[head_idx, curr_m_block] + curr_full_block_idx = cute.domain_offset( + curr_block_idx_offset, full_block_idx[head_idx, None] + ) + else: + curr_full_block_cnt = Int32(0) + curr_full_block_idx = None + return ( + curr_mask_block_cnt, + curr_mask_block_idx, + curr_full_block_cnt, + curr_full_block_idx, + ) + + +@cute.jit +def _get_curr_blocksparse_tensors( + batch_idx: cutlass.Int32, + head_idx: cutlass.Int32, + m_block: cutlass.Int32, + blocksparse_tensors: BlockSparseTensors, +) -> Tuple[cutlass.Int32, cute.Tensor, cutlass.Int32, Optional[cute.Tensor]]: + """Fixed-length path: tensors are 4D [batch, nheads, m_block, n_block].""" + mask_block_cnt, mask_block_idx, full_block_cnt, full_block_idx, *_ = ( + blocksparse_tensors + ) + curr_mask_block_cnt = mask_block_cnt[batch_idx, head_idx, m_block] + curr_mask_block_idx = mask_block_idx[batch_idx, head_idx, m_block, None] + if const_expr(full_block_cnt is not None): + curr_full_block_cnt = full_block_cnt[batch_idx, head_idx, m_block] + curr_full_block_idx = full_block_idx[batch_idx, head_idx, m_block, None] + else: + curr_full_block_cnt = Int32(0) + curr_full_block_idx = None + return ( + curr_mask_block_cnt, + curr_mask_block_idx, + curr_full_block_cnt, + curr_full_block_idx, + ) + + +@cute.jit +def get_curr_blocksparse_tensors( + batch_idx: cutlass.Int32, + head_idx: cutlass.Int32, + m_block: cutlass.Int32, + blocksparse_tensors: BlockSparseTensors, + seqlen_info: SeqlenInfoQK, +) -> Tuple[cutlass.Int32, cute.Tensor, cutlass.Int32, Optional[cute.Tensor]]: + """Extract head, m_block, and batch-local blocksparsity data from blocksparse_tensors""" + if const_expr(len(blocksparse_tensors.mask_block_cnt.shape) == 2): + return _get_curr_blocksparse_tensors_varlen( + head_idx, m_block, blocksparse_tensors, seqlen_info + ) + return _get_curr_blocksparse_tensors( + batch_idx, head_idx, m_block, blocksparse_tensors + ) + + +# NOTE [SM100 block-sparse empty tiles: mbarrier contract] +# +# For block-sparse SM100 forward, a given (m_block, stage) Q tile can have zero active +# KV blocks (total_block_cnt == 0). In that case there is no seqlen_kv iteration, so +# the softmax warp-group has no row stats to publish. +# +# The correction warp-group seeds fully-masked-row stats and runs the usual correction +# epilogue so output/LSE have well-defined values. Both warp-groups must still perform +# the softmax<->correction mbarrier handshake so phases advance correctly across +# empty->empty and empty->non-empty tile sequences. +# +# In the no-sink case, this corresponds to the usual fully-masked-row convention: +# output is zero and LSE is -inf. +# +# Barrier contract (each is `mbar_ptr + + stage`): +# +# Producer/consumer pairs: +# - `mbar_softmax_corr_full` : softmax arrive -> correction wait +# - `mbar_softmax_corr_empty` : correction arrive -> softmax wait +# - `mbar_P_full_O_rescaled` : softmax arrive (+ correction arrive) -> MMA wait +# - `mbar_P_full_2` : softmax arrive -> MMA wait +# - `mbar_corr_epi_full_/empty` : correction <-> epilogue (only when epilogue is separate) +# +# Empty tile (`total_block_cnt == 0`): +# - Softmax: skips the seqlen_kv softmax path entirely (no P stores, no `mbar_P_full_*`). +# It only arrives `mbar_softmax_corr_full` once per stage as a synthetic "no work" signal. +# At the `softmax_loop` level, softmax unconditionally waits `mbar_softmax_corr_empty` +# before each tile (when block-sparse) to drain a prior correction arrival and keep +# phases aligned across non-empty -> empty transitions. +# - Correction: waits `mbar_softmax_corr_full`, seeds stats + runs `correction_epilogue(scale=0)`, +# and arrives `mbar_softmax_corr_empty` (and `mbar_corr_epi_full_/empty` when applicable). +# - No `mbar_P_full_*` barriers are arrived (no P, no MMA O); only the softmax<->correction +# (and correction<->epilogue) handshakes advance phases. +# +# Non-empty tile: +# - Softmax: runs `softmax_step` (produces P) and uses `mbar_softmax_corr_full/empty` to +# publish row_max (during seqlen_kv) and final row stats (once per tile), and to advance phases; +# arrives `mbar_P_full_*` when P is stored. +# - Correction: waits `mbar_softmax_corr_full`, may rescale/release O, arrives `mbar_softmax_corr_empty` +# to ack/advance, and arrives `mbar_P_full_O_rescaled` when MMA can proceed. +# +# Backward (SM100): +# - Empty KV tile: for a given `n_block`, `total_m_block_cnt == 0` means no Q tiles contribute. +# - Both the load and compute loops guard all pipeline work on `process_tile`, so empty tiles +# skip producer/consumer operations entirely (no per-tile mbarrier phase handshake like forward). +# - In the `not dKV_postprocess` path, dK/dV for empty KV tiles are explicitly written as zeros +# even when `process_tile == False` (see `flash_bwd_sm100.py` `should_zero_dKV`). + + +@cute.jit +def load_block_list( + block_indices: cute.Tensor, + block_begin, + block_end, + first_block_preloaded: cutlass.Constexpr, + kv_producer_state, + load_K, + load_V, + pipeline_k, + pipeline_v, + intra_wg_overlap: cutlass.Constexpr, +): + """Iterate over the sparse blocks and load K, V into the pipeline. + For the intra_wg_overlap case, we overlap the loads of K and V. And this + means we need to pipeline the last V load from the partial block case, + with the loads for the full blocks. Set first_block_preloaded when the + caller has already issued the first K load for the list. + + Q is loaded separately on its own mbarrier before this function is called. + + Note: + we iterate along the block_n indices in reverse. + + Returns: + Updated kv_producer_state after processing the block list. + + """ + block_count = block_end - block_begin + if block_count > 0: + if const_expr(not intra_wg_overlap): + for offset in cutlass.range(block_count): + n_block = block_indices[block_end - 1 - offset] + pipeline_k.producer_acquire(kv_producer_state) + load_K(src_idx=n_block, producer_state=kv_producer_state) + pipeline_v.producer_acquire(kv_producer_state) + load_V(src_idx=n_block, producer_state=kv_producer_state) + kv_producer_state.advance() + else: + n_block_first = block_indices[block_end - 1] + if const_expr(not first_block_preloaded): + pipeline_k.producer_acquire(kv_producer_state) + load_K(src_idx=n_block_first, producer_state=kv_producer_state) + + for idx in cutlass.range(block_count - 1, unroll=1): + n_block_prev = block_indices[block_end - 1 - idx] + n_block = block_indices[block_end - 2 - idx] + kv_producer_state_prev = kv_producer_state.clone() + kv_producer_state.advance() + pipeline_k.producer_acquire(kv_producer_state) + load_K(src_idx=n_block, producer_state=kv_producer_state) + pipeline_v.producer_acquire(kv_producer_state_prev) + load_V(src_idx=n_block_prev, producer_state=kv_producer_state_prev) + + return kv_producer_state + + +@cute.jit +def finish_overlap_v_load( + block_indices: cute.Tensor, + block_begin, + block_end, + load_V, + pipeline_v, + kv_producer_state, +): + """Load the final V block after overlapped K/V loads.""" + block_count = block_end - block_begin + if block_count > 0: + n_block_last = block_indices[block_begin] + pipeline_v.producer_acquire(kv_producer_state) + load_V(src_idx=n_block_last, producer_state=kv_producer_state) + kv_producer_state.advance() + + return kv_producer_state + + +@cute.jit +def sparse_tensor_m_block( + m_block, + qhead_per_kvhead: cutlass.Constexpr[int], + q_subtile_factor: cutlass.Constexpr[int], +): + """Map packed m_block indices to block-sparse tensor indices.""" + block = m_block + if const_expr(qhead_per_kvhead != 1): + block = block // qhead_per_kvhead + if const_expr(q_subtile_factor != 1): + block = block // q_subtile_factor + return block + + +@cute.jit +def produce_block_sparse_loads( + blocksparse_tensors: BlockSparseTensors, + batch_idx, + head_idx, + m_block, + seqlen_info: SeqlenInfoQK, + kv_producer_state, + load_K, + load_V, + pipeline_k, + pipeline_v, + intra_wg_overlap: cutlass.Constexpr, + qhead_per_kvhead: cutlass.Constexpr[int] = 1, + q_subtile_factor: cutlass.Constexpr[int] = 1, + split_idx: Int32 = 0, + num_splits: Int32 = 1, +): + """Iterate over the mask and full block lists for a single tile. + + Q is loaded separately on its own mbarrier before this function is called. + + The masked (partial) list may leave the last V load pending when intra-warp-group + overlap is enabled. The first full block must consume that pending V while + issuing its own K load on the next pipeline stage. + + In the intra-wg-overlap path, the last masked block leaves its V copy in flight + while we advance the producer state to start the next full K. Either the full list + overlaps that pending V load, or, if no full blocks exist, we explicitly drain it. + + Args: + qhead_per_kvhead: Pack-GQA factor. When > 1, m_block is in packed space and + must be converted to unpacked for sparse tensor indexing. + """ + m_block_sparse = sparse_tensor_m_block(m_block, qhead_per_kvhead, q_subtile_factor) + + ( + curr_mask_block_cnt, + curr_mask_block_idx, + curr_full_block_cnt, + curr_full_block_idx, + ) = get_curr_blocksparse_tensors( + batch_idx, + head_idx, + m_block_sparse, + blocksparse_tensors, + seqlen_info, + ) + + mask_begin, mask_end = split_block_range(curr_mask_block_cnt, split_idx, num_splits) + full_begin, full_end = split_block_range(curr_full_block_cnt, split_idx, num_splits) + mask_empty = mask_begin == mask_end + full_empty = full_begin == full_end + + if mask_empty: + # No masked blocks: the full list owns the initial K load. + kv_producer_state = load_block_list( + curr_full_block_idx, + full_begin, + full_end, + first_block_preloaded=False, + kv_producer_state=kv_producer_state, + load_K=load_K, + load_V=load_V, + pipeline_k=pipeline_k, + pipeline_v=pipeline_v, + intra_wg_overlap=intra_wg_overlap, + ) + + if const_expr(intra_wg_overlap) and not full_empty: + kv_producer_state = finish_overlap_v_load( + curr_full_block_idx, + full_begin, + full_end, + load_V, + pipeline_v, + kv_producer_state, + ) + else: + # Masked blocks present. When overlap is disabled this fully drains the list. + kv_producer_state = load_block_list( + curr_mask_block_idx, + mask_begin, + mask_end, + first_block_preloaded=False, + kv_producer_state=kv_producer_state, + load_K=load_K, + load_V=load_V, + pipeline_k=pipeline_k, + pipeline_v=pipeline_v, + intra_wg_overlap=intra_wg_overlap, + ) + + if full_empty: + if const_expr(intra_wg_overlap): + kv_producer_state = finish_overlap_v_load( + curr_mask_block_idx, + mask_begin, + mask_end, + load_V, + pipeline_v, + kv_producer_state, + ) + else: + if const_expr(intra_wg_overlap): + # Bridge the masked list to the full list by overlapping the pending masked V + # with the first full K load. + n_block_mask_last = curr_mask_block_idx[mask_begin] + n_block_full_first = curr_full_block_idx[full_end - 1] + kv_producer_state_prev = kv_producer_state.clone() + kv_producer_state.advance() + pipeline_k.producer_acquire(kv_producer_state) + load_K(src_idx=n_block_full_first, producer_state=kv_producer_state) + pipeline_v.producer_acquire(kv_producer_state_prev) + load_V(src_idx=n_block_mask_last, producer_state=kv_producer_state_prev) + + kv_producer_state = load_block_list( + curr_full_block_idx, + full_begin, + full_end, + first_block_preloaded=True, + kv_producer_state=kv_producer_state, + load_K=load_K, + load_V=load_V, + pipeline_k=pipeline_k, + pipeline_v=pipeline_v, + intra_wg_overlap=intra_wg_overlap, + ) + + kv_producer_state = finish_overlap_v_load( + curr_full_block_idx, + full_begin, + full_end, + load_V, + pipeline_v, + kv_producer_state, + ) + else: + # Non-overlap path with both lists: run the full list normally. + kv_producer_state = load_block_list( + curr_full_block_idx, + full_begin, + full_end, + first_block_preloaded=False, + kv_producer_state=kv_producer_state, + load_K=load_K, + load_V=load_V, + pipeline_k=pipeline_k, + pipeline_v=pipeline_v, + intra_wg_overlap=intra_wg_overlap, + ) + + return kv_producer_state + + +@cute.jit +def consume_block_sparse_loads( + blocksparse_tensors: BlockSparseTensors, + batch_idx, + head_idx, + m_block, + seqlen_info, + kv_consumer_state, + mma_pv_fn, + mma_one_n_block, + process_first_half_block, + process_last_half_block, + mask_fn, + score_mod_fn, + O_should_accumulate, + mask_mod, + fastdiv_mods, + intra_wg_overlap: cutlass.Constexpr, + warp_scheduler_barrier_sync: Callable, + warp_scheduler_barrier_arrive: Callable, + qhead_per_kvhead: cutlass.Constexpr[int] = 1, + q_subtile_factor: cutlass.Constexpr[int] = 1, + split_idx: Int32 = 0, + num_splits: Int32 = 1, +): + """Consume the mask and full block lists for a single tile on the consumer side. + + Mirrors `produce_block_sparse_loads` so that the consumer pipeline uses + the same sparse tensor indexing. + + Args: + qhead_per_kvhead: Pack-GQA factor. When > 1, m_block is in packed space and + must be converted to unpacked for sparse tensor indexing. + """ + m_block_sparse = sparse_tensor_m_block(m_block, qhead_per_kvhead, q_subtile_factor) + + ( + curr_mask_block_cnt, + curr_mask_block_idx, + curr_full_block_cnt, + curr_full_block_idx, + ) = get_curr_blocksparse_tensors( + batch_idx, + head_idx, + m_block_sparse, + blocksparse_tensors, + seqlen_info, + ) + + mask_begin, mask_end = split_block_range(curr_mask_block_cnt, split_idx, num_splits) + full_begin, full_end = split_block_range(curr_full_block_cnt, split_idx, num_splits) + split_mask_block_cnt = mask_end - mask_begin + split_full_block_cnt = full_end - full_begin + processed_any = split_mask_block_cnt + split_full_block_cnt > 0 + + if const_expr(not intra_wg_overlap): + if split_mask_block_cnt > 0: + mask_n_block = curr_mask_block_idx[mask_end - 1] + warp_scheduler_barrier_sync() + kv_consumer_state = mma_one_n_block( + kv_consumer_state, + n_block=mask_n_block, + mma_pv_fn=partial(mma_pv_fn, zero_init=not O_should_accumulate), + mask_fn=partial( + mask_fn, + mask_mod=mask_mod, + mask_seqlen=True, + fastdiv_mods=( + fastdiv_mods + if cutlass.const_expr(mask_mod is not None) + else None + ), + ), + is_first_n_block=True, + ) + O_should_accumulate = True + for i in cutlass.range(1, split_mask_block_cnt): + mask_n_block = curr_mask_block_idx[mask_end - 1 - i] + kv_consumer_state = mma_one_n_block( + kv_consumer_state, + n_block=mask_n_block, + mma_pv_fn=partial(mma_pv_fn, zero_init=not O_should_accumulate), + mask_fn=partial(mask_fn, mask_mod=mask_mod, mask_seqlen=False), + is_first_n_block=False, + ) + O_should_accumulate = True + if split_full_block_cnt == 0: + warp_scheduler_barrier_arrive() + + if split_full_block_cnt > 0: + full_n_block = curr_full_block_idx[full_end - 1] + if split_mask_block_cnt == 0: + warp_scheduler_barrier_sync() + kv_consumer_state = mma_one_n_block( + kv_consumer_state, + n_block=full_n_block, + mma_pv_fn=partial(mma_pv_fn, zero_init=not O_should_accumulate), + mask_fn=partial(mask_fn, mask_seqlen=True), + is_first_n_block=True, + ) + O_should_accumulate = True + for i in cutlass.range(1, split_full_block_cnt): + full_n_block = curr_full_block_idx[full_end - 1 - i] + kv_consumer_state = mma_one_n_block( + kv_consumer_state, + n_block=full_n_block, + mma_pv_fn=partial(mma_pv_fn, zero_init=not O_should_accumulate), + mask_fn=partial(mask_fn, mask_seqlen=False), + is_first_n_block=False, + ) + O_should_accumulate = True + else: + kv_consumer_state = mma_one_n_block( + kv_consumer_state, + n_block=full_n_block, + mma_pv_fn=partial(mma_pv_fn, zero_init=not O_should_accumulate), + mask_fn=partial(mask_fn, mask_mod=None, mask_seqlen=True), + is_first_n_block=False, + ) + O_should_accumulate = True + for i in cutlass.range(1, split_full_block_cnt): + full_n_block = curr_full_block_idx[full_end - 1 - i] + kv_consumer_state = mma_one_n_block( + kv_consumer_state, + n_block=full_n_block, + mma_pv_fn=partial(mma_pv_fn, zero_init=not O_should_accumulate), + mask_fn=partial(mask_fn, mask_mod=None, mask_seqlen=False), + is_first_n_block=False, + ) + O_should_accumulate = True + warp_scheduler_barrier_arrive() + else: + if split_mask_block_cnt > 0: + mask_n_block = curr_mask_block_idx[mask_end - 1] + kv_consumer_state = process_first_half_block( + n_block=mask_n_block, + seqlen=seqlen_info, + kv_consumer_state=kv_consumer_state, + mask_fn=partial( + mask_fn, + mask_mod=mask_mod, + mask_seqlen=True, + fastdiv_mods=( + fastdiv_mods + if cutlass.const_expr(mask_mod is not None) + else None + ), + ), + score_mod_fn=score_mod_fn, + is_first_block=True, + ) + for i in cutlass.range(1, split_mask_block_cnt): + mask_n_block = curr_mask_block_idx[mask_end - 1 - i] + kv_consumer_state = mma_one_n_block( + kv_consumer_state, + n_block=mask_n_block, + seqlen=seqlen_info, + mma_pv_fn=partial(mma_pv_fn, zero_init=not O_should_accumulate), + mask_fn=partial(mask_fn, mask_mod=mask_mod, mask_seqlen=False), + ) + O_should_accumulate = True + + if split_full_block_cnt > 0: + full_n_block = curr_full_block_idx[full_end - 1] + if split_mask_block_cnt == 0: + kv_consumer_state = process_first_half_block( + n_block=full_n_block, + seqlen=seqlen_info, + kv_consumer_state=kv_consumer_state, + mask_fn=partial(mask_fn, mask_mod=None, mask_seqlen=True), + score_mod_fn=score_mod_fn, + is_first_block=True, + ) + else: + kv_consumer_state = mma_one_n_block( + kv_consumer_state, + n_block=full_n_block, + seqlen=seqlen_info, + mma_pv_fn=partial(mma_pv_fn, zero_init=not O_should_accumulate), + mask_fn=partial(mask_fn, mask_mod=None, mask_seqlen=True), + ) + O_should_accumulate = True + for i in cutlass.range(1, split_full_block_cnt): + full_n_block = curr_full_block_idx[full_end - 1 - i] + kv_consumer_state = mma_one_n_block( + kv_consumer_state, + n_block=full_n_block, + seqlen=seqlen_info, + mma_pv_fn=partial(mma_pv_fn, zero_init=not O_should_accumulate), + mask_fn=partial(mask_fn, mask_mod=None, mask_seqlen=False), + ) + O_should_accumulate = True + + if processed_any: + kv_consumer_state = process_last_half_block( + kv_consumer_state=kv_consumer_state, + zero_init=not O_should_accumulate, + ) + O_should_accumulate = True + + return kv_consumer_state, O_should_accumulate, processed_any + + +@cute.jit +def split_block_range(block_count, split_idx: Int32, num_splits: Int32): + """Return the half-open block-list range assigned to one SplitKV partition.""" + blocks_per_split = cute.ceil_div(block_count, num_splits) + block_begin = cutlass.min(split_idx * blocks_per_split, block_count) + block_end = cutlass.min(block_begin + blocks_per_split, block_count) + return block_begin, block_end + + +@cute.jit +def load_block_list_sm100( + block_indices: cute.Tensor, + block_begin, + block_end, + load_q_with_first: cutlass.Constexpr, + q_stage: cutlass.Constexpr, + kv_producer_state, + load_Q, + load_K, + load_V, + pipeline_kv, +): + """SM100 version of load_block_list (no intra_wg_overlap, no extra_tx_count).""" + block_count = block_end - block_begin + if block_count > 0: + # First iteration: load Q alongside K if requested + n_block_first = block_indices[block_end - 1] + + if const_expr(load_q_with_first): + # SM100 loads Q0 and optionally Q1 + load_Q(block=0, stage=0) + if const_expr(q_stage == 2): + load_Q(block=1, stage=1) + + # SM100 doesn't use producer_acquire for pipeline_kv in load path + # The pipeline barriers are handled inside load_KV + load_K(block=n_block_first, producer_state=kv_producer_state, page_idx=None) + kv_producer_state.advance() + load_V(block=n_block_first, producer_state=kv_producer_state, page_idx=None) + kv_producer_state.advance() + + # Remaining blocks + for offset in cutlass.range(1, block_count): + n_block = block_indices[block_end - 1 - offset] + load_K(block=n_block, producer_state=kv_producer_state, page_idx=None) + kv_producer_state.advance() + load_V(block=n_block, producer_state=kv_producer_state, page_idx=None) + kv_producer_state.advance() + + return kv_producer_state + + +# SM100-specific tile processor using SM100 helpers +@cute.jit +def produce_block_sparse_loads_sm100( + blocksparse_tensors: BlockSparseTensors, + batch_idx, + head_idx, + m_block, + seqlen_info: SeqlenInfoQK, + split_idx: Int32, + num_splits: Int32, + kv_producer_state, + load_Q, + load_K, + load_V, + pipeline_kv, + q_stage: cutlass.Constexpr, + q_producer_phase: Int32, + qhead_per_kvhead: cutlass.Constexpr, + q_subtile_factor: cutlass.Constexpr, +): + """SM100 entry point for sparse block iteration. + + SM100 uses PipelineTmaUmma which doesn't support extra_tx_count, so we use + simplified block processing that just calls producer_acquire without extras. + + Args: + m_block: which tile of m we are processing + qhead_per_kvhead: Constexpr pack factor + """ + m_block_sparse = sparse_tensor_m_block(m_block, qhead_per_kvhead, q_subtile_factor) + + ( + curr_mask_block_cnt, + curr_mask_block_idx, + curr_full_block_cnt, + curr_full_block_idx, + ) = get_curr_blocksparse_tensors( + batch_idx, + head_idx, + m_block_sparse, + blocksparse_tensors, + seqlen_info, + ) + + mask_begin, mask_end = split_block_range(curr_mask_block_cnt, split_idx, num_splits) + full_begin, full_end = split_block_range(curr_full_block_cnt, split_idx, num_splits) + mask_empty = mask_begin == mask_end + full_empty = full_begin == full_end + + q_phase_flipped = False + + if mask_empty: + # No masked blocks: process full list with Q loading + kv_producer_state = load_block_list_sm100( + curr_full_block_idx, + full_begin, + full_end, + load_q_with_first=True, + q_stage=q_stage, + kv_producer_state=kv_producer_state, + load_Q=load_Q, + load_K=load_K, + load_V=load_V, + pipeline_kv=pipeline_kv, + ) + q_phase_flipped = not full_empty + else: + # Process masked blocks with Q loading + kv_producer_state = load_block_list_sm100( + curr_mask_block_idx, + mask_begin, + mask_end, + load_q_with_first=True, + q_stage=q_stage, + kv_producer_state=kv_producer_state, + load_Q=load_Q, + load_K=load_K, + load_V=load_V, + pipeline_kv=pipeline_kv, + ) + q_phase_flipped = True + + if not full_empty: + # Process full blocks without Q loading + kv_producer_state = load_block_list_sm100( + curr_full_block_idx, + full_begin, + full_end, + load_q_with_first=False, + q_stage=q_stage, + kv_producer_state=kv_producer_state, + load_Q=load_Q, + load_K=load_K, + load_V=load_V, + pipeline_kv=pipeline_kv, + ) + + if q_phase_flipped: + q_producer_phase ^= 1 + + return kv_producer_state, q_producer_phase + + +@cute.jit +def get_total_block_count( + blocksparse_tensors: BlockSparseTensors, + batch_idx, + head_idx, + m_block, + split_idx: Int32, + num_splits: Int32, + qhead_per_kvhead: cutlass.Constexpr, + q_subtile_factor: cutlass.Constexpr, + seqlen_info: SeqlenInfoQK, +): + m_block_sparse = sparse_tensor_m_block(m_block, qhead_per_kvhead, q_subtile_factor) + ( + curr_mask_block_cnt, + _, + curr_full_block_cnt, + _, + ) = get_curr_blocksparse_tensors( + batch_idx, + head_idx, + m_block_sparse, + blocksparse_tensors, + seqlen_info, + ) + + mask_begin, mask_end = split_block_range(curr_mask_block_cnt, split_idx, num_splits) + full_begin, full_end = split_block_range(curr_full_block_cnt, split_idx, num_splits) + return mask_end - mask_begin + full_end - full_begin + + +@cute.jit +def handle_block_sparse_empty_tile_correction_sm100( + tidx: Int32, + q_stage: cutlass.Constexpr, + m_block_size: cutlass.Constexpr, + qhead_per_kvhead, + pack_gqa: cutlass.Constexpr, + is_split_kv: cutlass.Constexpr, + learnable_sink, + mLSE, + seqlen_info, + m_block: Int32, + head_idx: Int32, + batch_idx: Int32, + split_idx: Int32, + sScale: cute.Tensor, + stats: list, + correction_epilogue: Callable, + thr_mma_pv: cute.ThrMma, + tOtO: cute.Tensor, + sO: cute.Tensor, + pipeline_sm_stats: cutlass.pipeline.PipelineAsync, + sm_stats_barrier: cutlass.pipeline.NamedBarrier, + pipeline_o_epi: cutlass.pipeline.PipelineAsync, + sm_stats_consumer_phase: Int32, + o_corr_consumer_phase: Int32, + corr_epi_producer_phase: Int32, + softmax_scale_log2: Float32, + max_offset: Float32, + max_offset_scale: Float32, + mO_cur: Optional[cute.Tensor] = None, + gO: Optional[cute.Tensor] = None, + gmem_tiled_copy_O: Optional[cute.TiledCopy] = None, +): + """Handle SM100 forward block-sparse tiles with no active KV blocks. + + This path is taken when `total_block_cnt == 0`. The softmax warp-group still + arrives `mbar_softmax_corr_full` (synthetic "no work") so the correction + warp-group can: + + - seed fully-masked-row stats (row_sum=1; row_max=-inf when tracked) for LSE + - run `correction_epilogue` with `scale=0` so the output tile is written as zeros + (independent of any prior tmem contents) + - wait on `mbar_softmax_corr_full` and arrive `mbar_softmax_corr_empty` + (and `mbar_corr_epi_*` when applicable) so phases stay aligned across tiles + + This helper intentionally does not touch `mbar_P_full_*` since no P is produced. + See NOTE [SM100 block-sparse empty tiles: mbarrier contract]. + """ + LOG2_E = Float32(math.log2(math.e)) + warp_idx = cute.arch.make_warp_uniform(cute.arch.warp_idx()) % 4 + + for stage in cutlass.range_constexpr(q_stage): + row_sum_value = Float32(1.0) + row_max_value = ( + -Float32.inf + if const_expr(mLSE is not None or learnable_sink is not None) + else None + ) + if const_expr(learnable_sink is not None): + sink_val = -Float32.inf + if const_expr(not pack_gqa): + sink_val = Float32(learnable_sink[head_idx]) + elif tidx < m_block_size: + q_head_idx = ( + (q_stage * m_block + stage) * m_block_size + tidx + ) % qhead_per_kvhead + head_idx * qhead_per_kvhead + sink_val = Float32(learnable_sink[q_head_idx]) + if sink_val != -Float32.inf and ( + const_expr(not is_split_kv) or split_idx == 0 + ): + if row_max_value == -Float32.inf: + row_max_value = sink_val * (LOG2_E / softmax_scale_log2) + row_sum_value = max_offset_scale + else: + row_sum_value = row_sum_value + cute.math.exp2( + sink_val * LOG2_E + - row_max_value * softmax_scale_log2 + + max_offset, + fastmath=True, + ) + if tidx < m_block_size: + scale_row_idx = tidx + stage * m_block_size + sScale[scale_row_idx] = row_sum_value + if const_expr(mLSE is not None or learnable_sink is not None): + sScale[scale_row_idx + q_stage * m_block_size] = row_max_value + acc_flag = row_sum_value == Float32(0.0) or row_sum_value != row_sum_value + stats[stage] = (row_sum_value, row_max_value, acc_flag) + + # See NOTE [SM100 block-sparse empty tiles: mbarrier contract]. + # pipeline_sm_stats.consumer_wait_w_index_phase(stage, sm_stats_consumer_phase) + sm_stats_barrier.arrive_and_wait_w_index(index=stage * 4 + warp_idx) + pipeline_sm_stats.consumer_release_w_index(stage) + + if const_expr(gmem_tiled_copy_O is None): + pipeline_o_epi.producer_acquire_w_index_phase( + stage, corr_epi_producer_phase + ) + + gO_stage = gO[None, None, stage] if const_expr(gO is not None) else None + correction_epilogue( + thr_mma_pv, + tOtO[None, None, None, stage], + tidx, + stage, + m_block, + seqlen_info.seqlen_q, + Float32( + 0.0 + ), # zero scale ensures empty tile writes zeros into staged outputs + sO[None, None, stage], + mO_cur, + gO_stage, + gmem_tiled_copy_O, + ) + if const_expr(gmem_tiled_copy_O is None): + pipeline_o_epi.producer_commit_w_index(stage) + + sm_stats_consumer_phase ^= 1 + corr_epi_producer_phase ^= 1 + + return ( + sm_stats_consumer_phase, + o_corr_consumer_phase, + corr_epi_producer_phase, + ) + + +@cute.jit +def softmax_block_sparse_sm100( + blocksparse_tensors: BlockSparseTensors, + batch_idx, + head_idx, + m_block, + seqlen_info: SeqlenInfoQK, + split_idx: Int32, + num_splits: Int32, + softmax_step: Callable, + mask_fn: Callable, + mask_fn_none: Callable, + mma_si_consumer_phase: Int32, + si_corr_producer_phase: Int32, + s0_s1_sequence_phase: Int32, + pipeline_sm_stats: cutlass.pipeline.PipelineAsync, + sm_stats_barrier: cutlass.pipeline.NamedBarrier, + q_stage: cutlass.Constexpr, + stage_idx: Int32, + check_m_boundary: bool, + qhead_per_kvhead: cutlass.Constexpr, + q_subtile_factor: cutlass.Constexpr[int] = 1, +): + warp_idx = cute.arch.make_warp_uniform(cute.arch.warp_idx()) % 4 + m_block_sparse = sparse_tensor_m_block(m_block, qhead_per_kvhead, q_subtile_factor) + + ( + curr_mask_block_cnt, + curr_mask_block_idx, + curr_full_block_cnt, + curr_full_block_idx, + ) = get_curr_blocksparse_tensors( + batch_idx, + head_idx, + m_block_sparse, + blocksparse_tensors, + seqlen_info, + ) + + mask_begin, mask_end = split_block_range(curr_mask_block_cnt, split_idx, num_splits) + full_begin, full_end = split_block_range(curr_full_block_cnt, split_idx, num_splits) + split_mask_block_cnt = mask_end - mask_begin + split_full_block_cnt = full_end - full_begin + total_block_cnt = split_mask_block_cnt + split_full_block_cnt + + if total_block_cnt == 0: + sm_stats_barrier.arrive_w_index(index=stage_idx * 4 + warp_idx) + else: + if split_mask_block_cnt > 0: + mask_n_block = curr_mask_block_idx[mask_end - 1] + ( + mma_si_consumer_phase, + si_corr_producer_phase, + s0_s1_sequence_phase, + ) = softmax_step( + mma_si_consumer_phase, + si_corr_producer_phase, + s0_s1_sequence_phase, + mask_n_block, + is_first=True, + mask_fn=partial( + mask_fn, mask_seqlen=True, check_q_boundary=check_m_boundary + ), + ) + for i in cutlass.range(1, split_mask_block_cnt): + mask_n_block = curr_mask_block_idx[mask_end - 1 - i] + ( + mma_si_consumer_phase, + si_corr_producer_phase, + s0_s1_sequence_phase, + ) = softmax_step( + mma_si_consumer_phase, + si_corr_producer_phase, + s0_s1_sequence_phase, + mask_n_block, + mask_fn=partial( + mask_fn, mask_seqlen=False, check_q_boundary=check_m_boundary + ), + ) + + if split_full_block_cnt > 0: + full_n_block = curr_full_block_idx[full_end - 1] + if split_mask_block_cnt == 0: + ( + mma_si_consumer_phase, + si_corr_producer_phase, + s0_s1_sequence_phase, + ) = softmax_step( + mma_si_consumer_phase, + si_corr_producer_phase, + s0_s1_sequence_phase, + full_n_block, + is_first=True, + mask_fn=partial( + mask_fn_none, + mask_seqlen=True, + check_q_boundary=check_m_boundary, + ), + ) + else: + ( + mma_si_consumer_phase, + si_corr_producer_phase, + s0_s1_sequence_phase, + ) = softmax_step( + mma_si_consumer_phase, + si_corr_producer_phase, + s0_s1_sequence_phase, + full_n_block, + is_first=False, + mask_fn=partial( + mask_fn_none, + mask_seqlen=True, + check_q_boundary=check_m_boundary, + ), + ) + for i in cutlass.range(1, split_full_block_cnt): + full_n_block = curr_full_block_idx[full_end - 1 - i] + ( + mma_si_consumer_phase, + si_corr_producer_phase, + s0_s1_sequence_phase, + ) = softmax_step( + mma_si_consumer_phase, + si_corr_producer_phase, + s0_s1_sequence_phase, + full_n_block, + mask_fn=partial( + mask_fn_none, + mask_seqlen=False, + check_q_boundary=check_m_boundary, + ), + ) + + return ( + mma_si_consumer_phase, + si_corr_producer_phase, + s0_s1_sequence_phase, + total_block_cnt == 0, + ) + + +# ============================================================================= +# Backward-specific block-sparse helpers (SM100) +# ============================================================================= +# +# In backward, iteration is transposed compared to forward: +# - Forward: outer loop over m_blocks (Q tiles), inner loop over n_blocks (KV tiles) +# - Backward: outer loop over n_blocks (KV tiles), inner loop over m_blocks (Q tiles) +# +# The backward block-sparse tensors use "Q direction" indexing: +# - q_block_cnt[batch, head, n_block] → count of m_blocks to process for this KV tile +# - q_block_idx[batch, head, n_block, :] → indices of m_blocks to process +# + + +@cute.jit +def get_total_q_block_count_bwd( + blocksparse_tensors: BlockSparseTensors, + batch_idx, + head_idx, + n_block, + subtile_factor: cutlass.Constexpr = 1, + m_block_max: int = 0, +): + """Count total tile iterations for given n_block (KV tile) in backward.""" + q_block_cnt, _, full_block_cnt, _, *_ = blocksparse_tensors + total = q_block_cnt[batch_idx, head_idx, n_block] + if const_expr(full_block_cnt is not None): + total = total + full_block_cnt[batch_idx, head_idx, n_block] + return total * subtile_factor + + +@cute.jit +def produce_block_sparse_q_loads_bwd_sm100( + blocksparse_tensors: BlockSparseTensors, + batch_idx, + head_idx, + n_block, + # Pipeline states (will be returned after advancing) + producer_state_Q_LSE, + producer_state_dO_dPsum, + # Pipelines + pipeline_Q, + pipeline_LSE, + pipeline_dO, + pipeline_dPsum, + # Load functions + load_K, + load_V, + load_Q, + load_dO, + copy_stats, + # Global tensors for LSE/dPsum + gLSE, + sLSE, + gdPsum, + sdPsum, + # TMA copy bytes for extra_tx_count + tma_copy_bytes_K, + tma_copy_bytes_V, + # Flags for which loads to perform + should_load_Q: cutlass.Constexpr, + should_load_dO: cutlass.Constexpr, + # Subtiling factor and bounds + subtile_factor: cutlass.Constexpr = 1, + m_block_max: int = 0, +): + """SM100 backward block sparse loading with subtiling. + + Returns updated (producer_state_Q_LSE, producer_state_dO_dPsum). + First iteration loads K/V alongside Q/dO; subsequent iterations load only Q/dO. + """ + ( + curr_q_cnt, + curr_q_idx, + curr_full_cnt, + curr_full_idx, + loop_count, + ) = get_block_sparse_iteration_info_bwd( + blocksparse_tensors, batch_idx, head_idx, n_block, subtile_factor, m_block_max + ) + + for iter_idx in cutlass.range(loop_count, unroll=1): + m_block, _ = get_m_block_from_iter_bwd( + iter_idx, + curr_q_cnt, + curr_q_idx, + curr_full_cnt, + curr_full_idx, + subtile_factor, + m_block_max, + ) + m_block_safe = m_block + if m_block_max > 0: + m_block_safe = cutlass.min(m_block, m_block_max - 1) + + if iter_idx == 0: + # First block: load K/V alongside Q/dO + if const_expr(should_load_Q): + pipeline_Q.producer_acquire( + producer_state_Q_LSE, extra_tx_count=tma_copy_bytes_K + ) + load_K( + tma_bar_ptr=pipeline_Q.producer_get_barrier(producer_state_Q_LSE) + ) + load_Q(m_block_safe, producer_state=producer_state_Q_LSE) + pipeline_Q.producer_commit(producer_state_Q_LSE) + pipeline_LSE.producer_acquire(producer_state_Q_LSE) + with cute.arch.elect_one(): + copy_stats( + gLSE[None, m_block_safe], + sLSE[None, producer_state_Q_LSE.index], + mbar_ptr=pipeline_LSE.producer_get_barrier( + producer_state_Q_LSE + ), + ) + producer_state_Q_LSE.advance() + if const_expr(should_load_dO): + pipeline_dO.producer_acquire( + producer_state_dO_dPsum, extra_tx_count=tma_copy_bytes_V + ) + load_V( + tma_bar_ptr=pipeline_dO.producer_get_barrier( + producer_state_dO_dPsum + ) + ) + load_dO(m_block_safe, producer_state=producer_state_dO_dPsum) + pipeline_dO.producer_commit(producer_state_dO_dPsum) + pipeline_dPsum.producer_acquire(producer_state_dO_dPsum) + with cute.arch.elect_one(): + copy_stats( + gdPsum[None, m_block_safe], + sdPsum[None, producer_state_dO_dPsum.index], + mbar_ptr=pipeline_dPsum.producer_get_barrier( + producer_state_dO_dPsum + ), + ) + producer_state_dO_dPsum.advance() + else: + # Subsequent blocks: just load Q/dO (K/V already loaded) + if const_expr(should_load_Q): + pipeline_Q.producer_acquire(producer_state_Q_LSE) + load_Q(m_block_safe, producer_state=producer_state_Q_LSE) + pipeline_Q.producer_commit(producer_state_Q_LSE) + pipeline_LSE.producer_acquire(producer_state_Q_LSE) + with cute.arch.elect_one(): + copy_stats( + gLSE[None, m_block_safe], + sLSE[None, producer_state_Q_LSE.index], + mbar_ptr=pipeline_LSE.producer_get_barrier( + producer_state_Q_LSE + ), + ) + producer_state_Q_LSE.advance() + if const_expr(should_load_dO): + pipeline_dO.producer_acquire(producer_state_dO_dPsum) + load_dO(m_block_safe, producer_state=producer_state_dO_dPsum) + pipeline_dO.producer_commit(producer_state_dO_dPsum) + pipeline_dPsum.producer_acquire(producer_state_dO_dPsum) + with cute.arch.elect_one(): + copy_stats( + gdPsum[None, m_block_safe], + sdPsum[None, producer_state_dO_dPsum.index], + mbar_ptr=pipeline_dPsum.producer_get_barrier( + producer_state_dO_dPsum + ), + ) + producer_state_dO_dPsum.advance() + + return producer_state_Q_LSE, producer_state_dO_dPsum + + +@cute.jit +def get_block_sparse_iteration_info_bwd( + blocksparse_tensors: BlockSparseTensors, + batch_idx, + head_idx, + n_block, + subtile_factor: cutlass.Constexpr = 1, + m_block_max: int = 0, +): + """Extract block-sparse iteration info for backward pass. + + Returns (curr_q_cnt, curr_q_idx, curr_full_cnt, curr_full_idx, total_count). + """ + q_cnt, q_idx, full_cnt, full_idx, *_ = blocksparse_tensors + curr_q_cnt = q_cnt[batch_idx, head_idx, n_block] + curr_q_idx = q_idx[batch_idx, head_idx, n_block, None] + + if const_expr(full_cnt is not None): + curr_full_cnt = full_cnt[batch_idx, head_idx, n_block] + curr_full_idx = full_idx[batch_idx, head_idx, n_block, None] + else: + curr_full_cnt = Int32(0) + curr_full_idx = None + + sparse_block_count = curr_q_cnt + if const_expr(full_cnt is not None): + sparse_block_count = sparse_block_count + curr_full_cnt + total_count = sparse_block_count * subtile_factor + + return curr_q_cnt, curr_q_idx, curr_full_cnt, curr_full_idx, total_count + + +@cute.jit +def get_m_block_from_iter_bwd( + iter_idx, + curr_q_cnt, + curr_q_idx: cute.Tensor, + curr_full_cnt, + curr_full_idx: Optional[cute.Tensor], + subtile_factor: cutlass.Constexpr = 1, + m_block_max: int = 0, +): + """Derive m_block index and is_full_block flag from iteration index. + + Returns (m_block, is_full_block): + - m_block: The actual Q-tile block index + - is_full_block: True if this is a full block (no mask_mod needed) + """ + sparse_iter_idx = iter_idx // subtile_factor + subtile_offset = iter_idx % subtile_factor + + sparse_m_block = Int32(0) + is_full_block = False + if const_expr(curr_full_idx is not None): + if sparse_iter_idx < curr_q_cnt: + sparse_m_block = curr_q_idx[sparse_iter_idx] + else: + sparse_m_block = curr_full_idx[sparse_iter_idx - curr_q_cnt] + is_full_block = True + else: + sparse_m_block = curr_q_idx[sparse_iter_idx] + + return sparse_m_block * subtile_factor + subtile_offset, is_full_block + + +@cute.jit +def _load_q_do_block_sm90( + m_block, + producer_state_Q, + producer_state_dO, + pipeline_Q, + pipeline_dO, + load_K, + load_V, + load_Q, + load_dO, + load_LSE, + load_dPsum, + tma_copy_bytes_K, + tma_copy_bytes_V, + Q_stage_eq_dO_stage: cutlass.Constexpr, + load_kv: bool, +): + """Load one Q/dO block, optionally loading K/V on first iteration.""" + if load_kv: + pipeline_Q.producer_acquire(producer_state_Q, extra_tx_count=tma_copy_bytes_K) + load_K(tma_bar_ptr=pipeline_Q.producer_get_barrier(producer_state_Q)) + else: + pipeline_Q.producer_acquire(producer_state_Q) + load_Q(m_block, producer_state=producer_state_Q) + load_LSE(m_block, producer_state=producer_state_Q) + + producer_state_dO_cur = ( + producer_state_dO if const_expr(not Q_stage_eq_dO_stage) else producer_state_Q + ) + if load_kv: + pipeline_dO.producer_acquire( + producer_state_dO_cur, extra_tx_count=tma_copy_bytes_V + ) + load_V(tma_bar_ptr=pipeline_dO.producer_get_barrier(producer_state_dO_cur)) + else: + pipeline_dO.producer_acquire(producer_state_dO_cur) + load_dO(m_block, producer_state=producer_state_dO_cur) + load_dPsum(m_block, producer_state=producer_state_dO_cur) + + producer_state_Q.advance() + producer_state_dO.advance() + return producer_state_Q, producer_state_dO + + +@cute.jit +def produce_block_sparse_q_loads_bwd_sm90( + blocksparse_tensors: BlockSparseTensors, + batch_idx, + head_idx, + n_block, + producer_state_Q, + producer_state_dO, + pipeline_Q, + pipeline_dO, + load_K, + load_V, + load_Q, + load_dO, + load_LSE, + load_dPsum, + tma_copy_bytes_K, + tma_copy_bytes_V, + Q_stage_eq_dO_stage: cutlass.Constexpr, + subtile_factor: cutlass.Constexpr, + m_block_max: int, +): + """SM90 backward block sparse loading with separate partial/full loops. + + K/V are loaded with the first valid block. Iterates partial blocks first, + then full blocks, matching consumer order. + + Returns updated (producer_state_Q, producer_state_dO). + """ + q_cnt, q_idx, full_cnt, full_idx, *_ = blocksparse_tensors + curr_q_cnt = q_cnt[batch_idx, head_idx, n_block] + curr_q_idx = q_idx[batch_idx, head_idx, n_block, None] + + if const_expr(full_cnt is not None): + curr_full_cnt = full_cnt[batch_idx, head_idx, n_block] + curr_full_idx = full_idx[batch_idx, head_idx, n_block, None] + else: + curr_full_cnt = Int32(0) + curr_full_idx = None + + kv_loaded = False + + for iter_idx in cutlass.range(curr_q_cnt * subtile_factor, unroll=1): + sparse_idx = iter_idx // subtile_factor + subtile_offset = iter_idx % subtile_factor + m_block = curr_q_idx[sparse_idx] * subtile_factor + subtile_offset + + if m_block < m_block_max: + producer_state_Q, producer_state_dO = _load_q_do_block_sm90( + m_block, + producer_state_Q, + producer_state_dO, + pipeline_Q, + pipeline_dO, + load_K, + load_V, + load_Q, + load_dO, + load_LSE, + load_dPsum, + tma_copy_bytes_K, + tma_copy_bytes_V, + Q_stage_eq_dO_stage, + load_kv=not kv_loaded, + ) + kv_loaded = True + + if const_expr(full_cnt is not None): + for iter_idx in cutlass.range(curr_full_cnt * subtile_factor, unroll=1): + sparse_idx = iter_idx // subtile_factor + subtile_offset = iter_idx % subtile_factor + m_block = curr_full_idx[sparse_idx] * subtile_factor + subtile_offset + + if m_block < m_block_max: + producer_state_Q, producer_state_dO = _load_q_do_block_sm90( + m_block, + producer_state_Q, + producer_state_dO, + pipeline_Q, + pipeline_dO, + load_K, + load_V, + load_Q, + load_dO, + load_LSE, + load_dPsum, + tma_copy_bytes_K, + tma_copy_bytes_V, + Q_stage_eq_dO_stage, + load_kv=not kv_loaded, + ) + kv_loaded = True + + return producer_state_Q, producer_state_dO + + +@cute.jit +def consume_block_sparse_mma_bwd_sm90( + blocksparse_tensors: BlockSparseTensors, + batch_idx, + head_idx, + n_block, + consumer_state_Q, + consumer_state_dO, + mma_one_m_block_fn, + mask, + mask_mod, + is_causal: cutlass.Constexpr, + is_local: cutlass.Constexpr, + thr_mma_SdP, + score_mod_fn=None, + score_mod_bwd_fn=None, + subtile_factor: cutlass.Constexpr = 1, + m_block_max: int = 0, + aux_data: AuxData = AuxData(), + fastdiv_mods=(None, None), +): + """SM90 backward block sparse MMA consumption with separate partial/full loops. + + Partial blocks are processed first (with mask_mod applied), then full blocks + (without mask_mod). This ensures mask_mod is only applied where needed. + + Returns updated (consumer_state_Q, consumer_state_dO). + """ + q_cnt, q_idx, full_cnt, full_idx, *_ = blocksparse_tensors + curr_q_cnt = q_cnt[batch_idx, head_idx, n_block] + curr_q_idx = q_idx[batch_idx, head_idx, n_block, None] + + if const_expr(full_cnt is not None): + curr_full_cnt = full_cnt[batch_idx, head_idx, n_block] + curr_full_idx = full_idx[batch_idx, head_idx, n_block, None] + else: + curr_full_cnt = Int32(0) + curr_full_idx = None + + dKV_accumulate = False + + mask_fn_partial = partial( + mask.apply_mask, + batch_idx=batch_idx, + head_idx=head_idx, + n_block=n_block, + thr_mma=thr_mma_SdP, + mask_seqlen=True, + mask_causal=is_causal, + mask_local=is_local, + mask_mod=mask_mod, + aux_data=aux_data, + fastdiv_mods=fastdiv_mods, + ) + + mask_fn_full = partial( + mask.apply_mask, + batch_idx=batch_idx, + head_idx=head_idx, + n_block=n_block, + thr_mma=thr_mma_SdP, + mask_seqlen=True, + mask_causal=is_causal, + mask_local=is_local, + aux_data=aux_data, + fastdiv_mods=fastdiv_mods, + ) + + for iter_idx in cutlass.range(curr_q_cnt * subtile_factor, unroll=1): + sparse_idx = iter_idx // subtile_factor + subtile_offset = iter_idx % subtile_factor + m_block = curr_q_idx[sparse_idx] * subtile_factor + subtile_offset + + if m_block < m_block_max: + consumer_state_Q, consumer_state_dO = mma_one_m_block_fn( + m_block, + consumer_state_Q, + consumer_state_dO, + mask_fn=mask_fn_partial, + score_mod_fn=score_mod_fn, + score_mod_bwd_fn=score_mod_bwd_fn, + dKV_accumulate=dKV_accumulate, + ) + dKV_accumulate = True + + if const_expr(full_cnt is not None): + for iter_idx in cutlass.range(curr_full_cnt * subtile_factor, unroll=1): + sparse_idx = iter_idx // subtile_factor + subtile_offset = iter_idx % subtile_factor + m_block = curr_full_idx[sparse_idx] * subtile_factor + subtile_offset + + if m_block < m_block_max: + consumer_state_Q, consumer_state_dO = mma_one_m_block_fn( + m_block, + consumer_state_Q, + consumer_state_dO, + mask_fn=mask_fn_full, + score_mod_fn=score_mod_fn, + score_mod_bwd_fn=score_mod_bwd_fn, + dKV_accumulate=dKV_accumulate, + ) + dKV_accumulate = True + + return consumer_state_Q, consumer_state_dO + + +@cute.jit +def _store_one_dQaccum_sm90( + m_block, + sdQaccum: cute.Tensor, + gdQaccum: cute.Tensor, + num_dQ_warp_groups: cutlass.Constexpr, + num_threads_per_warp_group: cutlass.Constexpr, + tma_copy_bytes_dQ, +): + """Store dQaccum for a single m_block.""" + for warp_group_idx in cutlass.range_constexpr(num_dQ_warp_groups): + cute.arch.cp_async_bulk_wait_group( + num_dQ_warp_groups - 1 - warp_group_idx, read=True + ) + cute.arch.barrier_arrive( + barrier_id=int(NamedBarrierBwd.dQEmptyWG0) + warp_group_idx, + number_of_threads=num_threads_per_warp_group + cute.arch.WARP_SIZE, + ) + for warp_group_idx in cutlass.range_constexpr(num_dQ_warp_groups): + cute.arch.barrier( + barrier_id=int(NamedBarrierBwd.dQFullWG0) + warp_group_idx, + number_of_threads=num_threads_per_warp_group + cute.arch.WARP_SIZE, + ) + with cute.arch.elect_one(): + copy_utils.cpasync_reduce_bulk_add_f32( + sdQaccum[None, warp_group_idx].iterator, + gdQaccum[(None, warp_group_idx), m_block].iterator, + tma_copy_bytes_dQ, + ) + cute.arch.cp_async_bulk_commit_group() + + +@cute.jit +def dQaccum_store_block_sparse_bwd_sm90( + blocksparse_tensors: BlockSparseTensors, + batch_idx, + head_idx, + n_block, + sdQaccum: cute.Tensor, + gdQaccum: cute.Tensor, + subtile_factor: cutlass.Constexpr, + m_block_max: int, + num_dQ_warp_groups: cutlass.Constexpr, + num_threads_per_warp_group: cutlass.Constexpr, + tma_copy_bytes_dQ, +): + """SM90 backward block sparse dQaccum store with separate partial/full loops. + + Iterates partial blocks first, then full blocks, matching producer/consumer order. + """ + q_cnt, q_idx, full_cnt, full_idx, *_ = blocksparse_tensors + curr_q_cnt = q_cnt[batch_idx, head_idx, n_block] + curr_q_idx = q_idx[batch_idx, head_idx, n_block, None] + + if const_expr(full_cnt is not None): + curr_full_cnt = full_cnt[batch_idx, head_idx, n_block] + curr_full_idx = full_idx[batch_idx, head_idx, n_block, None] + else: + curr_full_cnt = Int32(0) + curr_full_idx = None + + for iter_idx in cutlass.range(curr_q_cnt * subtile_factor, unroll=1): + sparse_idx = iter_idx // subtile_factor + subtile_offset = iter_idx % subtile_factor + m_block = curr_q_idx[sparse_idx] * subtile_factor + subtile_offset + + if m_block < m_block_max: + _store_one_dQaccum_sm90( + m_block, + sdQaccum, + gdQaccum, + num_dQ_warp_groups, + num_threads_per_warp_group, + tma_copy_bytes_dQ, + ) + + if const_expr(full_cnt is not None): + for iter_idx in cutlass.range(curr_full_cnt * subtile_factor, unroll=1): + sparse_idx = iter_idx // subtile_factor + subtile_offset = iter_idx % subtile_factor + m_block = curr_full_idx[sparse_idx] * subtile_factor + subtile_offset + + if m_block < m_block_max: + _store_one_dQaccum_sm90( + m_block, + sdQaccum, + gdQaccum, + num_dQ_warp_groups, + num_threads_per_warp_group, + tma_copy_bytes_dQ, + ) diff --git a/python/sglang/jit_kernel/flash_attn/cute/block_sparsity.py b/python/sglang/jit_kernel/flash_attn/cute/block_sparsity.py new file mode 100644 index 000000000..7f5d0e0e2 --- /dev/null +++ b/python/sglang/jit_kernel/flash_attn/cute/block_sparsity.py @@ -0,0 +1,722 @@ +""" +Block-sparsity utilities for FlexAttention +""" + +from typing import Callable, NamedTuple, Tuple + +import cutlass.cute as cute +import torch + +from sglang.jit_kernel.flash_attn.cute.cute_dsl_utils import ( + get_broadcast_dims, + to_cute_tensor, +) + + +def ceildiv(a: int, b: int) -> int: + return (a + b - 1) // b + + +class BlockSparseTensors(NamedTuple): + mask_block_cnt: cute.Tensor + mask_block_idx: cute.Tensor + full_block_cnt: cute.Tensor | None = None + full_block_idx: cute.Tensor | None = None + cu_total_m_blocks: cute.Tensor | None = None + cu_block_idx_offsets: cute.Tensor | None = None + dq_write_order: cute.Tensor | None = None + dq_write_order_full: cute.Tensor | None = None + + def __new_from_mlir_values__(self, values): + new_fields = [] + idx = 0 + for original in self: + if original is None: + new_fields.append(None) + else: + new_fields.append(values[idx]) + idx += 1 + return BlockSparseTensors(*new_fields) + + +class BlockSparseTensorsTorch(NamedTuple): + mask_block_cnt: torch.Tensor + mask_block_idx: torch.Tensor + full_block_cnt: torch.Tensor | None = None + full_block_idx: torch.Tensor | None = None + cu_total_m_blocks: torch.Tensor | None = None + cu_block_idx_offsets: torch.Tensor | None = None + block_size: tuple[int, int] | None = None + dq_write_order: torch.Tensor | None = None + dq_write_order_full: torch.Tensor | None = None + spt: bool | None = None + + +def _ordered_to_dense_simple( + num_blocks: torch.Tensor, + indices: torch.Tensor, + num_cols: int, +) -> torch.Tensor: + """Convert ordered sparse representation to dense binary matrix. + + Args: + num_blocks: [B, H, num_rows] count of valid entries per row + indices: [B, H, num_rows, max_entries] column indices (valid entries packed left) + num_cols: total number of columns + + Returns: + dense: [B, H, num_rows, num_cols] binary int32 matrix + """ + B, H, num_rows, max_entries = indices.shape + device = indices.device + dense = torch.zeros(B, H, num_rows, num_cols + 1, dtype=torch.int32, device=device) + col_range = torch.arange(max_entries, device=device) + valid = col_range[None, None, None, :] < num_blocks[:, :, :, None] + safe_indices = torch.where(valid, indices.long(), num_cols) + row_idx = torch.arange(num_rows, device=device)[None, None, :, None].expand_as( + indices + ) + b_idx = torch.arange(B, device=device)[:, None, None, None].expand_as(indices) + h_idx = torch.arange(H, device=device)[None, :, None, None].expand_as(indices) + dense[b_idx, h_idx, row_idx, safe_indices] = 1 + return dense[:, :, :, :num_cols] + + +def compute_dq_write_order( + fwd_mask_cnt: torch.Tensor, + fwd_mask_idx: torch.Tensor, + fwd_full_cnt: torch.Tensor | None, + fwd_full_idx: torch.Tensor | None, + bwd_mask_cnt: torch.Tensor, + bwd_mask_idx: torch.Tensor, + bwd_full_cnt: torch.Tensor | None, + bwd_full_idx: torch.Tensor | None, + spt: bool = False, +) -> tuple[torch.Tensor, torch.Tensor | None]: + """Compute dQ write-order metadata for deterministic block-sparse backward. + + For each (n_block, i) in the backward iteration, computes the semaphore + lock value: the rank of n_block in the combined (partial + full) sorted + contributor list for the target m_block. + + Lock values are assigned in ascending n_block order (or descending if spt=True) + to guarantee deadlock-freedom with the CTA scheduling order. + + Args: + fwd_mask_cnt: [B, H, num_m_blocks] partial contributor counts per m_block + fwd_mask_idx: [B, H, num_m_blocks, max_kv] partial contributor n_block indices (ascending) + fwd_full_cnt: [B, H, num_m_blocks] full contributor counts per m_block (optional) + fwd_full_idx: [B, H, num_m_blocks, max_kv] full contributor n_block indices (optional) + bwd_mask_cnt: [B, H, num_n_blocks] partial iteration counts per n_block + bwd_mask_idx: [B, H, num_n_blocks, max_q] partial iteration m_block indices + bwd_full_cnt: [B, H, num_n_blocks] full iteration counts per n_block (optional) + bwd_full_idx: [B, H, num_n_blocks, max_q] full iteration m_block indices (optional) + spt: if True, reverse ordering (highest n_block gets lock_value=0) + + Returns: + (dq_write_order, dq_write_order_full): tensors parallel to bwd_mask_idx + and bwd_full_idx respectively, containing lock values. + """ + device = fwd_mask_idx.device + B, H, num_m, max_kv_partial = fwd_mask_idx.shape + _, _, num_n, max_q_partial = bwd_mask_idx.shape + + has_full = fwd_full_cnt is not None and fwd_full_idx is not None + + dense_partial = _ordered_to_dense_simple(fwd_mask_cnt, fwd_mask_idx, num_n) + if has_full: + dense_full = _ordered_to_dense_simple(fwd_full_cnt, fwd_full_idx, num_n) + dense = (dense_partial + dense_full).clamp(max=1) + else: + dense = dense_partial + + cumsum = dense.cumsum(dim=-1) + rank_table = (cumsum - dense).to(torch.int32) + + if spt: + total_per_m = cumsum[:, :, :, -1:] + rank_table = (total_per_m - 1 - rank_table).to(torch.int32) + + def _gather_write_order(bwd_idx, bwd_cnt): + b_i = torch.arange(B, device=device)[:, None, None, None].expand_as(bwd_idx) + h_i = torch.arange(H, device=device)[None, :, None, None].expand_as(bwd_idx) + n_i = torch.arange(bwd_idx.shape[2], device=device)[ + None, None, :, None + ].expand_as(bwd_idx) + m_vals = bwd_idx.long().clamp(0, num_m - 1) + return rank_table[b_i, h_i, m_vals, n_i].to(torch.int32) + + dq_write_order = _gather_write_order(bwd_mask_idx, bwd_mask_cnt) + + dq_write_order_full = None + if has_full and bwd_full_cnt is not None and bwd_full_idx is not None: + dq_write_order_full = _gather_write_order(bwd_full_idx, bwd_full_cnt) + + return dq_write_order, dq_write_order_full + + +def compute_dq_write_order_from_block_mask( + block_mask, + spt: bool = False, +) -> tuple[torch.Tensor, torch.Tensor | None]: + ( + _seq_q, + _seq_k, + kv_mask_cnt, + kv_mask_idx, + full_kv_cnt, + full_kv_idx, + q_mask_cnt, + q_mask_idx, + full_q_cnt, + full_q_idx, + *_, + ) = block_mask.as_tuple() + return compute_dq_write_order( + kv_mask_cnt, + kv_mask_idx, + full_kv_cnt, + full_kv_idx, + q_mask_cnt, + q_mask_idx, + full_q_cnt, + full_q_idx, + spt=spt, + ) + + +def get_sparse_q_block_size( + tensors: BlockSparseTensorsTorch | None, + seqlen_q: int, +) -> int | None: + """Return the Q sparse block size, or None when sparsity is unset or ambiguous.""" + if tensors is None: + return None + if tensors.block_size is not None: + return tensors.block_size[0] + num_m_blocks = tensors.mask_block_idx.shape[2] + min_block_size = ceildiv(seqlen_q, num_m_blocks) + max_block_size = ( + seqlen_q if num_m_blocks == 1 else (seqlen_q - 1) // (num_m_blocks - 1) + ) + if min_block_size != max_block_size: + return None + return min_block_size + + +def _expand_sparsity_tensor( + tensor: torch.Tensor, + expected_shape: Tuple[int, ...], + tensor_name: str, + context: str | None, + hint: str | Callable[[], str] | None, +) -> torch.Tensor: + """Check if we need to expand the tensor to expected shape, and do so if possible.""" + needs_expand = tensor.shape != expected_shape + if not needs_expand: + return tensor + can_expand = all( + map(lambda cur, tgt: cur == tgt or cur == 1, tensor.shape, expected_shape) + ) + if not can_expand: + context_clause = f" ({context})" if context else "" + resolved_hint = hint() if callable(hint) else hint + hint_clause = f" Hint: {resolved_hint}" if resolved_hint else "" + raise ValueError( + f"{tensor_name}{context_clause} with shape {tensor.shape} cannot be expanded to expected shape {expected_shape}." + f"{hint_clause}" + ) + return tensor.expand(*expected_shape) + + +def _check_and_expand_block( + name: str, + cnt: torch.Tensor | None, + idx: torch.Tensor | None, + expected_count_shape: Tuple[int, ...], + expected_index_shape: Tuple[int, ...], + context: str | None, + hint: str | Callable[[], str] | None, +) -> Tuple[torch.Tensor | None, torch.Tensor | None]: + if (cnt is None) != (idx is None): + raise ValueError( + f"{name}_block_cnt and {name}_block_idx must both be provided or both be None" + ) + if cnt is None or idx is None: + return None, None + if cnt.dtype != torch.int32 or idx.dtype != torch.int32: + raise ValueError(f"{name}_block tensors must have dtype torch.int32") + if cnt.device != idx.device: + raise ValueError( + f"{name}_block_cnt and {name}_block_idx must be on the same device" + ) + if not cnt.is_cuda or not idx.is_cuda: + raise ValueError(f"{name}_block tensors must live on CUDA") + expanded_cnt = _expand_sparsity_tensor( + cnt, expected_count_shape, f"{name}_block_cnt", context, hint + ) + # [Note] Allow Compact block sparse indices + # Allow the last dimension (n_blocks) of idx to be <= expected, since + # FA4 only accesses indices 0..cnt-1 per query tile. This enables compact + # index tensors that avoid O(N^2) memory at long sequence lengths. + if idx.ndim == 4 and idx.shape[3] <= expected_index_shape[3]: + expected_index_shape = (*expected_index_shape[:3], idx.shape[3]) + expanded_idx = _expand_sparsity_tensor( + idx, expected_index_shape, f"{name}_block_idx", context, hint + ) + return expanded_cnt, expanded_idx + + +def _check_and_expand_metadata_tensor( + name: str, + tensor: torch.Tensor | None, + expected_shape: Tuple[int, ...], + context: str | None, + hint: str | Callable[[], str] | None, + device: torch.device, +) -> torch.Tensor | None: + if tensor is None: + return None + if tensor.dtype != torch.int32: + raise ValueError(f"{name} must have dtype torch.int32") + if tensor.device != device: + raise ValueError(f"{name} must be on the same device as block sparse tensors") + if not tensor.is_cuda: + raise ValueError(f"{name} must live on CUDA") + return _expand_sparsity_tensor(tensor, expected_shape, name, context, hint) + + +def get_block_sparse_expected_shapes( + batch_size: int, + num_head: int, + seqlen_q: int, + seqlen_k: int, + m_block_size: int, + n_block_size: int, + q_stage: int, +) -> Tuple[Tuple[int, int, int], Tuple[int, int, int, int]]: + """Return (expected_count_shape, expected_index_shape) for block sparse normalization.""" + m_block_size_effective = q_stage * m_block_size + expected_m_blocks = ceildiv(seqlen_q, m_block_size_effective) + expected_n_blocks = ceildiv(seqlen_k, n_block_size) + expected_count_shape = (batch_size, num_head, expected_m_blocks) + expected_index_shape = (batch_size, num_head, expected_m_blocks, expected_n_blocks) + return expected_count_shape, expected_index_shape + + +def infer_block_sparse_expected_shapes( + tensors: BlockSparseTensorsTorch, + *, + batch_size: int, + num_head: int, + seqlen_q: int, + seqlen_k: int, + m_block_size: int, + n_block_size: int, + q_stage: int, + context: str, + sparse_block_size_q: int | None = None, + sparse_block_size_kv: int | None = None, +) -> Tuple[Tuple[int, int, int], Tuple[int, int, int, int], int]: + """Infer shapes and scaling for block-sparse tensors. + + Expectations: + - mask_block_cnt is (B, H, M) and mask_block_idx is (B, H, M, N). + - Batch/head dims may be 1 for broadcast, or match the requested sizes. + - sparse_block_size_kv must match tile_n. + - sparse_block_size_q must be a multiple of q_stage * tile_m. + - If sparse_block_size_q is omitted and seqlen_q/num_m_blocks is ambiguous, + the caller must provide block_size to disambiguate. TODO will make this required in a future PR. + """ + base_m_block = q_stage * m_block_size + base_n_block = n_block_size + if sparse_block_size_kv is None: + sparse_block_size_kv = base_n_block + if sparse_block_size_kv != base_n_block: + raise ValueError( + f"Block sparse tensors{context} require BLOCK_SIZE_KV={base_n_block}." + ) + if tensors.mask_block_idx is None: + raise ValueError( + "mask_block_cnt and mask_block_idx must be provided for block sparsity." + ) + num_m_blocks = tensors.mask_block_idx.shape[2] + + if sparse_block_size_q is None: + sparse_block_size_q = get_sparse_q_block_size(tensors, seqlen_q) + if sparse_block_size_q is None and base_m_block != 1: + raise ValueError( + f"Block sparse tensors{context} require explicit sparse_block_size[0] " + f"to disambiguate block size for seqlen_q={seqlen_q} and num_m_blocks={num_m_blocks}." + ) + if sparse_block_size_q is None: + sparse_block_size_q = ceildiv(seqlen_q, num_m_blocks) + + if sparse_block_size_q % base_m_block != 0: + raise ValueError( + f"Block sparse tensors{context} have block size {sparse_block_size_q}, " + f"which must be a multiple of {base_m_block}." + ) + + expected_m_blocks = ceildiv(seqlen_q, sparse_block_size_q) + expected_n_blocks = ceildiv(seqlen_k, sparse_block_size_kv) + q_subtile_factor = sparse_block_size_q // base_m_block + expected_count_shape = (batch_size, num_head, expected_m_blocks) + expected_index_shape = (batch_size, num_head, expected_m_blocks, expected_n_blocks) + + mask_block_cnt = tensors.mask_block_cnt + mask_block_idx = tensors.mask_block_idx + if mask_block_cnt is None or mask_block_idx is None: + raise ValueError( + "mask_block_cnt and mask_block_idx must be provided for block sparsity." + ) + if mask_block_cnt.ndim != 3 or mask_block_idx.ndim != 4: + raise ValueError( + f"Block sparse tensors{context} must have shapes (B, H, M) and (B, H, M, N)." + ) + for dim_name, cur, tgt in ( + ("batch", mask_block_cnt.shape[0], expected_count_shape[0]), + ("head", mask_block_cnt.shape[1], expected_count_shape[1]), + ): + if cur != tgt and cur != 1: + raise ValueError( + f"Block sparse tensors{context} {dim_name} dim must be {tgt} or 1." + ) + for dim_name, cur, tgt in ( + ("batch", mask_block_idx.shape[0], expected_index_shape[0]), + ("head", mask_block_idx.shape[1], expected_index_shape[1]), + ): + if cur != tgt and cur != 1: + raise ValueError( + f"Block sparse tensors{context} {dim_name} dim must be {tgt} or 1." + ) + if mask_block_cnt.shape[2] != mask_block_idx.shape[2]: + raise ValueError( + f"Block sparse tensors{context} must share the same m-block dimension." + ) + # [Note] Allow Compact block sparse indices: FA4 only accesses indices 0..cnt-1 + # per query tile, so idx.shape[3] can be <= expected_n_blocks. + if mask_block_idx.shape[3] > expected_n_blocks: + raise ValueError( + f"Block sparse tensors{context} n-block dimension must be <= {expected_n_blocks}." + ) + if expected_m_blocks != num_m_blocks: + raise ValueError( + f"Block sparse tensors{context} m-block dimension {num_m_blocks} does not match " + f"sparse_block_size_q={sparse_block_size_q}. " + f"Set BlockSparseTensorsTorch.block_size to match the BlockMask BLOCK_SIZE." + ) + return expected_count_shape, expected_index_shape, q_subtile_factor + + +def get_block_sparse_expected_shapes_bwd( + batch_size: int, + num_head: int, + seqlen_q: int, + seqlen_k: int, + m_block_size: int, + n_block_size: int, + subtile_factor: int, +) -> Tuple[Tuple[int, int, int], Tuple[int, int, int, int]]: + """Return (expected_count_shape, expected_index_shape) for backward block sparse normalization. + + Backward uses Q-direction indexing (transposed from forward), where shapes are + indexed by N-blocks first, then M-blocks. The sparse_block_size_q is determined + by subtile_factor * m_block_size. + """ + sparse_block_size_q = subtile_factor * m_block_size + expected_m_blocks = ceildiv(seqlen_q, sparse_block_size_q) + expected_n_blocks = ceildiv(seqlen_k, n_block_size) + expected_count_shape = (batch_size, num_head, expected_n_blocks) + expected_index_shape = (batch_size, num_head, expected_n_blocks, expected_m_blocks) + return expected_count_shape, expected_index_shape + + +def normalize_block_sparse_tensors( + tensors: BlockSparseTensorsTorch, + *, + expected_count_shape: Tuple[int, ...], + expected_index_shape: Tuple[int, ...], + context: str | None = None, + hint: str | Callable[[], str] | None = None, +) -> BlockSparseTensorsTorch: + if tensors.mask_block_cnt is None or tensors.mask_block_idx is None: + raise ValueError( + "mask_block_cnt and mask_block_idx must be provided for block sparsity." + ) + + mask_cnt, mask_idx = _check_and_expand_block( + "mask", + tensors.mask_block_cnt, + tensors.mask_block_idx, + expected_count_shape, + expected_index_shape, + context, + hint, + ) + if mask_cnt is None or mask_idx is None: + raise ValueError( + "mask_block_cnt and mask_block_idx must be provided for block sparsity." + ) + + full_cnt, full_idx = _check_and_expand_block( + "full", + tensors.full_block_cnt, + tensors.full_block_idx, + expected_count_shape, + expected_index_shape, + context, + hint, + ) + if full_cnt is not None and mask_cnt.device != full_cnt.device: + raise ValueError("All block sparse tensors must be on the same device") + + dq_write_order = _check_and_expand_metadata_tensor( + "dq_write_order", + tensors.dq_write_order, + tuple(mask_idx.shape), + context, + hint, + mask_cnt.device, + ) + dq_write_order_full = _check_and_expand_metadata_tensor( + "dq_write_order_full", + tensors.dq_write_order_full, + tuple(full_idx.shape) if full_idx is not None else expected_index_shape, + context, + hint, + mask_cnt.device, + ) + spt = tensors.spt + if spt is not None and not isinstance(spt, bool): + raise ValueError("spt must be a bool when provided") + if spt is not None and dq_write_order is None: + raise ValueError("spt requires dq_write_order to be provided") + + return BlockSparseTensorsTorch( + mask_block_cnt=mask_cnt, + mask_block_idx=mask_idx, + full_block_cnt=full_cnt, + full_block_idx=full_idx, + cu_total_m_blocks=tensors.cu_total_m_blocks, + cu_block_idx_offsets=tensors.cu_block_idx_offsets, + block_size=tensors.block_size, + dq_write_order=dq_write_order, + dq_write_order_full=dq_write_order_full, + spt=spt, + ) + + +def is_block_sparsity_enabled(tensors: BlockSparseTensorsTorch) -> bool: + return any(t is not None for t in (tensors.full_block_cnt, tensors.mask_block_cnt)) + + +def get_block_sparse_broadcast_pattern( + tensors: BlockSparseTensorsTorch, +) -> Tuple[Tuple[bool, ...], ...] | None: + """Return broadcast pattern for block sparse tensors by checking actual strides. + + Returns a tuple of broadcast patterns (one per tensor) where each pattern + is a tuple of bools indicating which dims have stride=0. + This is used in compile keys to ensure kernels are recompiled when + broadcast patterns change, since CuTe's mark_layout_dynamic() keeps + stride=0 as static. + + The tensors should already be expanded/normalized before calling this function. + + Returns None if block sparsity is not enabled. + """ + if not is_block_sparsity_enabled(tensors): + return None + + patterns = [] + for tensor in ( + tensors.mask_block_cnt, + tensors.mask_block_idx, + tensors.full_block_cnt, + tensors.full_block_idx, + tensors.dq_write_order, + tensors.dq_write_order_full, + ): + if tensor is not None: + patterns.append(get_broadcast_dims(tensor)) + else: + patterns.append(None) + return tuple(patterns) + + +def normalize_block_sparse_config( + tensors: BlockSparseTensorsTorch, + *, + batch_size: int, + num_head: int, + seqlen_q: int, + seqlen_k: int, + block_size: tuple[int, int], + q_stage: int, +) -> tuple[BlockSparseTensorsTorch, Tuple[Tuple[bool, ...], ...] | None, int]: + """Validate the block-sparse config, infer expected shapes, and normalize. + + Handles both fixed-length (3D `[B, H, M]` / 4D `[B, H, M, N]`) and varlen + (2D `[H, total_m_blocks]` / `[H, total_n_blocks]`) layouts. Varlen is + detected by `tensors.cu_total_m_blocks is not None` and forces + `q_subtile_factor == 1` (TODO: potentially remove this restriction). + """ + m_block_size, n_block_size = block_size + if tensors.block_size is None: + sparse_block_size_q, sparse_block_size_kv = None, n_block_size + else: + sparse_block_size_q, sparse_block_size_kv = tensors.block_size + if sparse_block_size_kv != n_block_size: + raise ValueError( + f"Block sparsity requires sparse_block_size[1]={n_block_size} to match tile_n." + ) + if tensors.cu_total_m_blocks is not None: + base_m_block = q_stage * m_block_size + if sparse_block_size_q is not None and sparse_block_size_q != base_m_block: + raise ValueError( + f"Varlen block sparsity requires sparse_block_size[0]={base_m_block} " + f"(= q_stage * tile_m); got {sparse_block_size_q}." + ) + total_m_blocks = tensors.mask_block_cnt.shape[-1] + total_n_blocks = tensors.mask_block_idx.shape[-1] + expected_count_shape = (num_head, total_m_blocks) + expected_index_shape = (num_head, total_n_blocks) + q_subtile_factor = 1 + else: + expected_count_shape, expected_index_shape, q_subtile_factor = ( + infer_block_sparse_expected_shapes( + tensors, + batch_size=batch_size, + num_head=num_head, + seqlen_q=seqlen_q, + seqlen_k=seqlen_k, + m_block_size=m_block_size, + n_block_size=n_block_size, + q_stage=q_stage, + context="forward", + sparse_block_size_q=sparse_block_size_q, + sparse_block_size_kv=sparse_block_size_kv, + ) + ) + normalized_tensors = normalize_block_sparse_tensors( + tensors, + expected_count_shape=expected_count_shape, + expected_index_shape=expected_index_shape, + ) + return ( + normalized_tensors, + get_block_sparse_broadcast_pattern(normalized_tensors), + q_subtile_factor, + ) + + +def normalize_block_sparse_config_bwd( + tensors: BlockSparseTensorsTorch, + *, + batch_size: int, + num_head: int, + seqlen_q: int, + seqlen_k: int, + block_size: tuple[int, int], + subtile_factor: int, +) -> tuple[BlockSparseTensorsTorch, Tuple[Tuple[bool, ...], ...] | None]: + m_block_size, n_block_size = block_size + if tensors.block_size is None: + sparse_block_size_q, sparse_block_size_kv = ( + subtile_factor * m_block_size, + n_block_size, + ) + else: + sparse_block_size_q, sparse_block_size_kv = tensors.block_size + if sparse_block_size_q != subtile_factor * m_block_size: + raise ValueError( + f"Block sparsity expects sparse_block_size_q={subtile_factor * m_block_size} " + f"for subtile_factor={subtile_factor}." + ) + if sparse_block_size_kv != n_block_size: + raise ValueError( + f"Block sparsity expects sparse_block_size[1]={n_block_size} to match tile_n." + ) + expected_count_shape, expected_index_shape = get_block_sparse_expected_shapes_bwd( + batch_size, + num_head, + seqlen_q, + seqlen_k, + m_block_size, + n_block_size, + subtile_factor, + ) + normalized_tensors = normalize_block_sparse_tensors( + tensors, + expected_count_shape=expected_count_shape, + expected_index_shape=expected_index_shape, + context="_flash_attn_bwd", + hint=lambda: ( + f"Backward expects Q-direction block-sparse tensors (q_mask_cnt/q_mask_idx, " + f"and optionally full_q_cnt/full_q_idx). Regenerate the backward BlockMask with " + f"BLOCK_SIZE=({subtile_factor * m_block_size}, {n_block_size})." + ), + ) + return normalized_tensors, get_block_sparse_broadcast_pattern(normalized_tensors) + + +def to_cute_block_sparse_tensors( + tensors: BlockSparseTensorsTorch, enable_tvm_ffi: bool = True +) -> BlockSparseTensors | None: + """Convert torch block sparsity tensors to CuTe tensors, optionally for tvm ffi""" + if not is_block_sparsity_enabled(tensors): + return None + mask_block_cnt_tensor, mask_block_idx_tensor = [ + to_cute_tensor( + t, assumed_align=4, leading_dim=-1, enable_tvm_ffi=enable_tvm_ffi + ) + for t in (tensors.mask_block_cnt, tensors.mask_block_idx) + ] + full_block_cnt_tensor, full_block_idx_tensor = [ + ( + to_cute_tensor( + t, assumed_align=4, leading_dim=-1, enable_tvm_ffi=enable_tvm_ffi + ) + if t is not None + else None + ) + for t in (tensors.full_block_cnt, tensors.full_block_idx) + ] + cu_total_m_blocks_tensor, cu_block_idx_offsets_tensor = [ + ( + to_cute_tensor( + t, assumed_align=4, leading_dim=0, enable_tvm_ffi=enable_tvm_ffi + ) + if t is not None + else None + ) + for t in (tensors.cu_total_m_blocks, tensors.cu_block_idx_offsets) + ] + dq_write_order_tensor, dq_write_order_full_tensor = [ + ( + to_cute_tensor( + t, assumed_align=4, leading_dim=-1, enable_tvm_ffi=enable_tvm_ffi + ) + if t is not None + else None + ) + for t in (tensors.dq_write_order, tensors.dq_write_order_full) + ] + + return BlockSparseTensors( + mask_block_cnt_tensor, + mask_block_idx_tensor, + full_block_cnt_tensor, + full_block_idx_tensor, + cu_total_m_blocks_tensor, + cu_block_idx_offsets_tensor, + dq_write_order_tensor, + dq_write_order_full_tensor, + ) + + +def fast_sampling(mask_mod): + """Convenience decorator to mark mask_mod as safe for 5-point fast sampling""" + mask_mod.use_fast_sampling = True + return mask_mod diff --git a/python/sglang/jit_kernel/flash_attn/cute/cache_utils.py b/python/sglang/jit_kernel/flash_attn/cute/cache_utils.py new file mode 100644 index 000000000..129224c7e --- /dev/null +++ b/python/sglang/jit_kernel/flash_attn/cute/cache_utils.py @@ -0,0 +1,289 @@ +# Manage Ahead-of-Time (AOT) compiled kernels +import ctypes +import fcntl +import hashlib +import os +import pickle +import sys +import tempfile +import time +from functools import lru_cache +from getpass import getuser +from pathlib import Path +from typing import Hashable, TypeAlias + +import cutlass +import cutlass.cute as cute +import tvm_ffi +from cutlass.cutlass_dsl import JitCompiledFunction + +from sglang.jit_kernel.flash_attn.cute.fa_logging import fa_log + +# Pre-load cute DSL runtime libraries with RTLD_GLOBAL so that their symbols +# (e.g. _cudaLibraryLoadData) are visible to .so modules loaded later via dlopen. +# Upstream cute.runtime.load_module loads these without RTLD_GLOBAL, which causes +# "undefined symbol" errors when loading cached kernels from disk. +for _lib_path in cute.runtime.find_runtime_libraries(enable_tvm_ffi=False): + if Path(_lib_path).exists(): + ctypes.CDLL(_lib_path, mode=ctypes.RTLD_GLOBAL) + +CompileKeyType: TypeAlias = tuple[Hashable, ...] +CallableFunction: TypeAlias = JitCompiledFunction | tvm_ffi.Function + +# Enable cache via `FLASH_ATTENTION_CUTE_DSL_CACHE_ENABLED=1` +CUTE_DSL_CACHE_ENABLED: bool = ( + os.getenv("FLASH_ATTENTION_CUTE_DSL_CACHE_ENABLED", "0") == "1" +) + + +# Customize cache dir via `FLASH_ATTENTION_CUTE_DSL_CACHE_DIR`, default is +# `/tmp/${USER}/flash_attention_cute_dsl_cache`` +CUTE_DSL_CACHE_DIR: str | None = os.getenv("FLASH_ATTENTION_CUTE_DSL_CACHE_DIR", None) + + +def get_cache_path() -> Path: + if CUTE_DSL_CACHE_DIR is not None: + cache_dir = Path(CUTE_DSL_CACHE_DIR) + else: + cache_dir = ( + Path(tempfile.gettempdir()) / getuser() / "flash_attention_cute_dsl_cache" + ) + cache_dir.mkdir(parents=True, exist_ok=True) + return cache_dir + + +@lru_cache(maxsize=1) +def _compute_source_fingerprint() -> str: + """ + Hash all CuTe Python sources plus runtime ABI stamps into a short fingerprint. + + The fingerprint changes whenever: + - Any .py file under flash_attn/cute is added, removed, renamed, or modified. + - The Python minor version changes (e.g. 3.13 -> 3.14). + - The cutlass or tvm_ffi package version changes. + + Computed once per process and cached. + """ + cute_root = Path(__file__).resolve().parent + h = hashlib.sha256() + + h.update(f"py{sys.version_info.major}.{sys.version_info.minor}".encode()) + h.update(f"cutlass={cutlass.__version__}".encode()) + h.update(f"tvm_ffi={tvm_ffi.__version__}".encode()) + + for src in sorted(cute_root.rglob("*.py")): + if not src.is_file(): + continue + h.update(src.relative_to(cute_root).as_posix().encode()) + content = src.read_bytes() + h.update(len(content).to_bytes(8, "little")) + h.update(content) + + return h.hexdigest() + + +class FileLock: + """Context manager for advisory file locks using fcntl.flock. + + Supports exclusive (write) and shared (read) locks. + Always blocks with polling until the lock is acquired or timeout is reached. + + Usage: + with FileLock(lock_path, exclusive=True, timeout=15, label="abc"): + # do work under lock + """ + + def __init__( + self, + lock_path: Path, + exclusive: bool, + timeout: float = 15, + label: str = "", + ): + """ + Args: + lock_path: Path to the lock file on disk. + exclusive: True for exclusive (write) lock, False for shared (read) lock. + timeout: Max seconds to wait for lock acquisition before raising RuntimeError. + label: Optional human-readable label for error messages. + """ + self.lock_path: Path = lock_path + self.exclusive: bool = exclusive + self.timeout: float = timeout + self.label: str = label + self._fd: int = -1 + + @property + def _lock_label(self) -> str: + kind = "exclusive" if self.exclusive else "shared" + return f"{kind} {self.label}" if self.label else kind + + def __enter__(self) -> "FileLock": + open_flags = ( + os.O_WRONLY | os.O_CREAT if self.exclusive else os.O_RDONLY | os.O_CREAT + ) + lock_type = fcntl.LOCK_EX if self.exclusive else fcntl.LOCK_SH + + self._fd = os.open(str(self.lock_path), open_flags) + + deadline = time.monotonic() + self.timeout + acquired = False + while time.monotonic() < deadline: + try: + fcntl.flock(self._fd, lock_type | fcntl.LOCK_NB) + acquired = True + break + except OSError: + time.sleep(0.1) + if not acquired: + os.close(self._fd) + self._fd = None + raise RuntimeError( + f"Timed out after {self.timeout}s waiting for " + f"{self._lock_label} lock: {self.lock_path}" + ) + + return self + + def __exit__(self, exc_type, exc_val, exc_tb) -> None: + if self._fd is not None: + fcntl.flock(self._fd, fcntl.LOCK_UN) + os.close(self._fd) + self._fd = None + + +class JITCache: + """ + In-memory cache for compiled functions. + """ + + def __init__(self): + self.cache: dict[CompileKeyType, CallableFunction] = {} + + def __setitem__(self, key: CompileKeyType, fn: JitCompiledFunction) -> None: + self.cache[key] = fn + + def __getitem__(self, key: CompileKeyType) -> CallableFunction: + return self.cache[key] + + def __contains__(self, key: CompileKeyType) -> bool: + return key in self.cache + + def clear(self) -> None: + """ + Clear in-memory cache of compiled functions + """ + self.cache.clear() + + +class JITPersistentCache(JITCache): + """ + In-memory cache for compiled functions, which is also backed by persistent storage. + Use cutedsl ahead-of-time (AOT) compilation, only supporting enable_tvm_ffi=True + """ + + EXPORT_FUNCTION_PREFIX = "func" + LOCK_TIMEOUT_SECONDS = 15 + + def __init__(self, cache_path: Path): + super().__init__() + cache_path.mkdir(parents=True, exist_ok=True) + self.cache_path: Path = cache_path + + def __setitem__(self, key: CompileKeyType, fn: JitCompiledFunction) -> None: + JITCache.__setitem__(self, key, fn) + self._try_export_to_storage(key, fn) + + def __getitem__(self, key: CompileKeyType) -> CallableFunction: + # Use __contains__ to try populating in-memory cache with persistent storage + self.__contains__(key) + return JITCache.__getitem__(self, key) + + def __contains__(self, key: CompileKeyType) -> bool: + # Checks in-memory cache first, then tries loading from storage. + # When returning True, guarantees the in-memory cache is populated. + if JITCache.__contains__(self, key): + return True + return self._try_load_from_storage(key) + + def _try_load_from_storage(self, key: CompileKeyType) -> bool: + """ + Try to load a function from persistent storage into in-memory cache. + Returns True if loaded successfully, False if not found on disk. + Holds a shared lock during loading to prevent concurrent writes. + """ + sha256_hex = self._key_to_hash(key) + obj_path = self.cache_path / f"{sha256_hex}.o" + with FileLock( + self._lock_path(sha256_hex), + exclusive=False, + timeout=self.LOCK_TIMEOUT_SECONDS, + label=sha256_hex, + ): + if obj_path.exists(): + fa_log(1, f"Loading compiled function from disk: {obj_path}") + m = cute.runtime.load_module(str(obj_path), enable_tvm_ffi=True) + fn = getattr(m, self.EXPORT_FUNCTION_PREFIX) + JITCache.__setitem__(self, key, fn) + return True + else: + fa_log(1, f"Cache miss on disk for key hash {sha256_hex}") + return False + + def _try_export_to_storage( + self, key: CompileKeyType, fn: JitCompiledFunction + ) -> None: + """Export a compiled function to persistent storage under exclusive lock.""" + sha256_hex = self._key_to_hash(key) + with FileLock( + self._lock_path(sha256_hex), + exclusive=True, + timeout=self.LOCK_TIMEOUT_SECONDS, + label=sha256_hex, + ): + obj_path = self.cache_path / f"{sha256_hex}.o" + if obj_path.exists(): + # Another process already exported. + fa_log(1, f"Skipping export, already on disk: {obj_path}") + return + fa_log(1, f"Exporting compiled function to disk: {obj_path}") + fn.export_to_c( + object_file_path=str(obj_path), + function_name=self.EXPORT_FUNCTION_PREFIX, + ) + fa_log(1, f"Successfully exported compiled function to disk: {obj_path}") + + def _key_to_hash(self, key: CompileKeyType) -> str: + return hashlib.sha256(pickle.dumps(key)).hexdigest() + + def _lock_path(self, sha256_hex: str) -> Path: + return self.cache_path / f"{sha256_hex}.lock" + + def clear(self) -> None: + """ + Not only clear the in-memory cache. Also purge persistent compilation cache. + """ + fa_log(1, f"Clearing persistent cache at {self.cache_path}") + super().clear() + for child in self.cache_path.iterdir(): + child.unlink() + + +def get_jit_cache(name: str | None = None) -> JITCache: + """ + JIT cache factory. + `name` is an optional identifier to create subdirectories to manage cache. + + When persistent caching is enabled, artifacts are namespaced under a + source fingerprint directory so that code or dependency changes + automatically invalidate stale entries. + """ + if CUTE_DSL_CACHE_ENABLED: + path = get_cache_path() / _compute_source_fingerprint() + if name: + path = path / name + fa_log(1, f"Creating persistent JIT cache at {path}") + return JITPersistentCache(path) + else: + fa_log(1, "Persistent cache disabled, using in-memory JIT cache") + return JITCache() diff --git a/python/sglang/jit_kernel/flash_attn/cute/compute_block_sparsity.py b/python/sglang/jit_kernel/flash_attn/cute/compute_block_sparsity.py new file mode 100644 index 000000000..43b196a7b --- /dev/null +++ b/python/sglang/jit_kernel/flash_attn/cute/compute_block_sparsity.py @@ -0,0 +1,591 @@ +from functools import partial +from typing import Callable, Optional, Tuple + +import cutlass +import cutlass.cute as cute +import torch +from cutlass import Boolean, Int8, Int32, const_expr + +from sglang.jit_kernel.flash_attn.cute.block_sparse_utils import ( + get_curr_blocksparse_tensors, +) +from sglang.jit_kernel.flash_attn.cute.block_sparsity import ( + BlockSparseTensors, + BlockSparseTensorsTorch, + to_cute_block_sparse_tensors, +) +from sglang.jit_kernel.flash_attn.cute.cute_dsl_utils import ( + get_aux_tensor_metadata, + to_cute_aux_tensor, + to_cute_tensor, +) +from sglang.jit_kernel.flash_attn.cute.mask import call_mask_mod +from sglang.jit_kernel.flash_attn.cute.seqlen_info import SeqlenInfoQK +from sglang.jit_kernel.flash_attn.cute.testing import is_fake_mode +from sglang.jit_kernel.flash_attn.cute.utils import ( + AuxData, + get_batch_from_cu_tensor, + hash_callable, + scalar_to_ssa, + ssa_to_scalar, +) + + +class BlockSparsityKernel: + """Block sparsity kernel for FlexAttention. + + This kernel computes `mask_mod` for every token of each block + to determine if an n block is full, masked, or neither. + + Writes block counts and indices to a BlockSparseTensors object. + + When use_fast_sampling=True, uses 5-point sampling (4 corners + center) + which is much faster but only suitable for masks where this is sufficient. + + TODO: + - optimize mask_mod evaluation + - transposed tensors for bwd pass + """ + + def __init__( + self, + mask_mod: Callable, + tile_mn: Tuple[int, int], + compute_full_blocks: bool = True, + use_aux_tensors: bool = False, + use_fast_sampling: bool = False, + ): + self.mask_mod = mask_mod + self.tile_mn = tile_mn + self.compute_full_blocks = compute_full_blocks + self.use_aux_tensors = use_aux_tensors + self.use_fast_sampling = use_fast_sampling + + @cute.jit + def __call__( + self, + blocksparse_tensors: BlockSparseTensors, + seqlen_q: Int32, + seqlen_k: Int32, + mCuSeqlensQ: Optional[cute.Tensor] = None, + mCuSeqlensK: Optional[cute.Tensor] = None, + mSeqUsedQ: Optional[cute.Tensor] = None, + mSeqUsedK: Optional[cute.Tensor] = None, + aux_data: AuxData = AuxData(), + ): + ( + mask_cnt, + mask_idx, + full_cnt, + full_idx, + mCuTotalMBlocks, + mCuBlockIdxOffsets, + *_, + ) = blocksparse_tensors + + self.is_varlen_q = const_expr(mCuSeqlensQ is not None) + + if const_expr(self.compute_full_blocks): + assert ( + full_cnt is not None and full_idx is not None + ), "full block tensors must be provided when computing full blocks" + if const_expr(not self.is_varlen_q): + batch_size, num_heads, num_m_blocks, _ = mask_idx.shape + total_m_blocks = batch_size * num_m_blocks + else: + assert const_expr( + mCuTotalMBlocks is not None + ), "mCuTotalMBlocks must be provided when varlen q" + num_heads, total_m_blocks = mask_cnt.shape # num_m_blocks is total_m_blocks + batch_size = mCuSeqlensQ.shape[0] - 1 + + if const_expr(self.use_fast_sampling): + num_threads = 5 + self.num_warps = 1 + else: + num_threads = self.tile_mn[0] + self.num_warps = (num_threads + 32 - 1) // 32 + + if const_expr(not self.is_varlen_q): + grid = [num_m_blocks, num_heads, batch_size] + else: + grid = [total_m_blocks, num_heads, 1] + + self.kernel( + blocksparse_tensors, + seqlen_q, + seqlen_k, + batch_size, + mCuSeqlensQ, + mCuSeqlensK, + mSeqUsedQ, + mSeqUsedK, + mCuTotalMBlocks, + mCuBlockIdxOffsets, + aux_data, + ).launch(grid=grid, block=[num_threads, 1, 1]) + + @cute.kernel + def kernel( + self, + blocksparse_tensors: BlockSparseTensors, + seqlen_q: Int32, + seqlen_k: Int32, + batch_size: Int32, + mCuSeqlensQ: Optional[cute.Tensor] = None, + mCuSeqlensK: Optional[cute.Tensor] = None, + mSeqUsedQ: Optional[cute.Tensor] = None, + mSeqUsedK: Optional[cute.Tensor] = None, + mCuTotalMBlocks: Optional[cute.Tensor] = None, + mCuBlockIdxOffsets: Optional[cute.Tensor] = None, + aux_data: AuxData = AuxData(), + ): + tidx, _, _ = cute.arch.thread_idx() + warp_idx = cute.arch.warp_idx() + lane_id = cute.arch.lane_idx() + + ssa = partial(scalar_to_ssa, dtype=Int32) + + @cute.struct + class SharedStorage: + reduction_buffer_smem: cute.struct.Align[ + cute.struct.MemRange[cutlass.Int8, 2 * self.num_warps], 1024 + ] + + smem = cutlass.utils.SmemAllocator() + storage = smem.allocate(SharedStorage, 16) + + reduction_buffer = storage.reduction_buffer_smem.get_tensor( + cute.make_layout((self.num_warps, 2)) + ) + SeqlenInfoCls = partial( + SeqlenInfoQK.create, + seqlen_q_static=seqlen_q, + seqlen_k_static=seqlen_k, + mCuSeqlensQ=mCuSeqlensQ, + mCuSeqlensK=mCuSeqlensK, + mSeqUsedQ=mSeqUsedQ, + mSeqUsedK=mSeqUsedK, + mCuTotalMBlocks=mCuTotalMBlocks, + mCuBlockIdxOffsets=mCuBlockIdxOffsets, + tile_m=self.tile_mn[0], + tile_n=self.tile_mn[1], + ) + + if const_expr(not self.is_varlen_q): + m_block, head_idx, batch_idx = cute.arch.block_idx() + else: + global_m_block, head_idx, _ = cute.arch.block_idx() + batch_idx = get_batch_from_cu_tensor(global_m_block, mCuTotalMBlocks) + m_block = global_m_block - mCuTotalMBlocks[batch_idx] + + seqlen = SeqlenInfoCls(batch_idx) + seqlen_q = seqlen.seqlen_q + seqlen_k = seqlen.seqlen_k + global_m_block = seqlen.m_block_offset + m_block + + num_n_blocks = (seqlen_k + self.tile_mn[1] - 1) // self.tile_mn[1] + + _, curr_mask_idx, _, curr_full_idx = get_curr_blocksparse_tensors( + batch_idx, head_idx, m_block, blocksparse_tensors, seqlen + ) + + num_mask_blocks = Int32(0) + num_full_blocks = Int32(0) + + m_base = m_block * self.tile_mn[0] + if const_expr(self.use_fast_sampling): + # Loop-invariant per-thread q_idx for the 5 sample points + # (tidx 0, 1: top corners; 2, 3: bottom corners; 4: center). + q_idx_sample = m_base + if tidx == 2 or tidx == 3: + q_idx_sample = cutlass.min(m_base + self.tile_mn[0] - 1, seqlen_q - 1) + elif tidx == 4: + q_idx_sample = ( + m_base + cutlass.min(seqlen_q - m_base, self.tile_mn[0]) // 2 + ) + else: + q_idx_thread = m_base + tidx + thread_in_bounds = Boolean( + tidx < self.tile_mn[0] and q_idx_thread < seqlen_q + ) + + for n_block in cutlass.range(num_n_blocks): + n_base = n_block * self.tile_mn[1] + + if const_expr(self.use_fast_sampling): + # 5-point sampling (4 corners + center). Interior n_blocks + # (n_base + tile_n <= seqlen_k) skip the OOB clamp on the right / + # center samples. + is_interior = (n_base + self.tile_mn[1]) <= seqlen_k + n_right = Int32(0) + n_mid = Int32(0) + if is_interior: + n_right = n_base + self.tile_mn[1] - 1 + n_mid = n_base + self.tile_mn[1] // 2 + else: + n_right = cutlass.min(n_base + self.tile_mn[1] - 1, seqlen_k - 1) + n_mid = ( + n_base + cutlass.min(seqlen_k - n_base, self.tile_mn[1]) // 2 + ) + + kv_idx = n_base + if tidx == 1 or tidx == 3: + kv_idx = n_right + elif tidx == 4: + kv_idx = n_mid + + thread_result = Boolean(False) + thread_is_valid = Boolean(False) + if tidx < 5: + thread_is_valid = Boolean(True) + thread_result = ssa_to_scalar( + call_mask_mod( + self.mask_mod, + ssa(batch_idx), + ssa(head_idx), + ssa(q_idx_sample), + ssa(kv_idx), + seqlen, + aux_data, + ) + ) + + has_unmasked = cute.arch.vote_any_sync(thread_result & thread_is_valid) + has_masked = cute.arch.vote_any_sync( + Boolean(not thread_result) & thread_is_valid + ) + + else: + # Full path. Interior blocks (n_base + tile_n <= seqlen_k) drop the + # per-element bound check; the boundary block (at most one) keeps it. + thread_has_unmasked = Boolean(False) + thread_has_masked = Boolean(False) + kv_idx = Int32(0) + is_interior = (n_base + self.tile_mn[1]) <= seqlen_k + + if is_interior: + if thread_in_bounds: + for c in cutlass.range(self.tile_mn[1], unroll_full=True): + mask_val = ssa_to_scalar( + call_mask_mod( + self.mask_mod, + ssa(batch_idx), + ssa(head_idx), + ssa(q_idx_thread), + ssa(n_base + c), + seqlen, + aux_data, + ) + ) + thread_has_unmasked |= Boolean(mask_val) + thread_has_masked |= Boolean(not mask_val) + else: + if thread_in_bounds: + for c in cutlass.range(self.tile_mn[1], unroll_full=True): + kv_idx = n_base + c + if kv_idx < seqlen_k: + mask_val = ssa_to_scalar( + call_mask_mod( + self.mask_mod, + ssa(batch_idx), + ssa(head_idx), + ssa(q_idx_thread), + ssa(kv_idx), + seqlen, + aux_data, + ) + ) + thread_has_unmasked |= Boolean(mask_val) + thread_has_masked |= Boolean(not mask_val) + + warp_unmasked = cute.arch.vote_any_sync( + thread_has_unmasked & thread_in_bounds + ) + warp_masked = cute.arch.vote_any_sync( + thread_has_masked & thread_in_bounds + ) + if lane_id == 0: + reduction_buffer[warp_idx, 0] = ( + Int8(1) if warp_unmasked else Int8(0) + ) + reduction_buffer[warp_idx, 1] = Int8(1) if warp_masked else Int8(0) + cute.arch.sync_threads() + + # Cross-warp OR via warp 0; thread 0 (lane 0 of warp 0) holds the result. + has_unmasked = Boolean(False) + has_masked = Boolean(False) + if warp_idx == 0: + lane_unmasked = Boolean(False) + lane_masked = Boolean(False) + if lane_id < self.num_warps: + lane_unmasked = reduction_buffer[lane_id, 0] != Int8(0) + lane_masked = reduction_buffer[lane_id, 1] != Int8(0) + has_unmasked = cute.arch.vote_any_sync(lane_unmasked) + has_masked = cute.arch.vote_any_sync(lane_masked) + + # Only thread 0 updates the output arrays (common to both paths) + if tidx == 0: + # Block classification based on what we found: + # - If has_masked and has_unmasked: partial block (needs masking) + # - If only has_unmasked: full block (no masking needed) + # - If only has_masked: skip this block entirely + is_partial = Boolean(has_masked and has_unmasked) + is_full = Boolean(has_unmasked and (not has_masked)) + + if is_partial: + curr_mask_idx[num_mask_blocks] = n_block + num_mask_blocks += 1 + elif is_full and const_expr(self.compute_full_blocks): + curr_full_idx[num_full_blocks] = n_block + num_full_blocks += 1 + + # Only thread 0 writes back the counts + if tidx == 0: + mask_cnt, _, full_cnt, *_ = blocksparse_tensors + if const_expr(self.is_varlen_q): + mask_cnt[head_idx, global_m_block] = num_mask_blocks + if const_expr(self.compute_full_blocks): + full_cnt[head_idx, global_m_block] = num_full_blocks + else: + mask_cnt[batch_idx, head_idx, m_block] = num_mask_blocks + if const_expr(self.compute_full_blocks): + full_cnt[batch_idx, head_idx, m_block] = num_full_blocks + + +def compute_block_sparsity( + tile_m, + tile_n, + batch_size, + num_heads, + seqlen_q, + seqlen_k, + mask_mod: Callable, + aux_tensors: Optional[list], + device, + aux_scalars: Optional[tuple] = None, + cu_seqlens_q: Optional[torch.Tensor] = None, + cu_seqlens_k: Optional[torch.Tensor] = None, + seqused_q: Optional[torch.Tensor] = None, + seqused_k: Optional[torch.Tensor] = None, + cu_total_m_blocks: Optional[torch.Tensor] = None, + cu_block_idx_offsets: Optional[torch.Tensor] = None, + compute_full_blocks: bool = True, + use_fast_sampling: bool = False, +) -> BlockSparseTensorsTorch: + """ + Computes block sparsity for a given `mask_mod`. + + Args: + tile_m: The tile size for the m dimension. + tile_n: The tile size for the n dimension. + batch_size: The batch size. + num_heads: The number of heads. + seqlen_q: The sequence length for the query. + seqlen_k: The sequence length for the key. + mask_mod: The `mask_mod` callable to use. + aux_tensors: A list of auxiliary tensors. + device: The device to use. + cu_seqlens_q: Cumulative q sequence lengths for varlen + cu_seqlens_k: Cumulative k sequence lengths for varlen + seqused_q: Per-batch effective q sequence lengths + seqused_k: Per-batch effective k sequence lengths + cu_total_m_blocks: Cumulative total m blocks tensor for varlen q + cu_block_idx_offsets: Cumulative offsets into the packed mask_block_idx / + full_block_idx tensors per batch (== cumsum of M_b * N_b). + compute_full_blocks: Whether to compute full blocks. If False, only partially-masked blocks are computed. + use_fast_sampling: Whether to use 5-point sampling (4 corners + center). This is much faster, but only suitable for masks where this check is sufficient. + + Returns: + BlockSparseTensorsTorch + """ + aux_scalars = tuple(aux_scalars) if aux_scalars else None + + # Check if mask_mod is marked as suitable for 5-point sampling + use_fast_sampling = getattr(mask_mod, "use_fast_sampling", use_fast_sampling) + + num_m_blocks = (seqlen_q + tile_m - 1) // tile_m + num_n_blocks = (seqlen_k + tile_n - 1) // tile_n + + if cu_seqlens_q is not None: + assert ( + cu_total_m_blocks is not None + ), "total m blocks must be provided when varlen q" + total_m_blocks = cu_total_m_blocks[-1].item() + if cu_block_idx_offsets is None and ( + cu_seqlens_k is not None or seqused_k is not None + ): + # Derive cu_block_idx_offsets from per-batch K seqlens. + cu_block_idx_offsets_list = [0] + for batch_idx in range(batch_size): + batch_seqlen_q = ( + cu_seqlens_q[batch_idx + 1].item() - cu_seqlens_q[batch_idx].item() + ) + if cu_seqlens_k is not None: + batch_seqlen_k = ( + cu_seqlens_k[batch_idx + 1].item() + - cu_seqlens_k[batch_idx].item() + ) + else: + batch_seqlen_k = seqused_k[batch_idx].item() + num_m_blocks_batch = (batch_seqlen_q + tile_m - 1) // tile_m + num_n_blocks_batch = (batch_seqlen_k + tile_n - 1) // tile_n + cu_block_idx_offsets_list.append( + cu_block_idx_offsets_list[-1] + + num_m_blocks_batch * num_n_blocks_batch + ) + cu_block_idx_offsets = torch.tensor( + cu_block_idx_offsets_list, dtype=torch.int32, device=device + ) + if cu_block_idx_offsets is not None: + total_n_blocks = cu_block_idx_offsets[-1].item() + else: + # Uniform-K varlen-Q: every batch has the same K seqlen. + total_n_blocks = total_m_blocks * num_n_blocks + + mask_block_cnt = torch.zeros( + (num_heads, total_m_blocks), device=device, dtype=torch.int32 + ) + mask_block_idx = torch.zeros( + (num_heads, total_n_blocks), device=device, dtype=torch.int32 + ) + full_block_cnt = ( + torch.zeros((num_heads, total_m_blocks), device=device, dtype=torch.int32) + if compute_full_blocks + else None + ) + full_block_idx = ( + torch.zeros((num_heads, total_n_blocks), device=device, dtype=torch.int32) + if compute_full_blocks + else None + ) + else: + total_m_blocks = batch_size * num_m_blocks + total_n_blocks = batch_size * num_m_blocks * num_n_blocks + + mask_block_cnt = torch.zeros( + (batch_size, num_heads, num_m_blocks), device=device, dtype=torch.int32 + ) + mask_block_idx = torch.zeros( + (batch_size, num_heads, num_m_blocks, num_n_blocks), + device=device, + dtype=torch.int32, + ) + full_block_cnt = ( + torch.zeros( + (batch_size, num_heads, num_m_blocks), device=device, dtype=torch.int32 + ) + if compute_full_blocks + else None + ) + full_block_idx = ( + torch.zeros( + (batch_size, num_heads, num_m_blocks, num_n_blocks), + device=device, + dtype=torch.int32, + ) + if compute_full_blocks + else None + ) + + blocksparse_tensors_torch = BlockSparseTensorsTorch( + mask_block_cnt=mask_block_cnt, + mask_block_idx=mask_block_idx, + full_block_cnt=full_block_cnt, + full_block_idx=full_block_idx, + cu_total_m_blocks=cu_total_m_blocks, + cu_block_idx_offsets=cu_block_idx_offsets, + block_size=(tile_m, tile_n), + ) + + mask_mod_hash = hash_callable(mask_mod) + if aux_tensors is not None: + aux_tensor_metadata = get_aux_tensor_metadata(aux_tensors) + else: + aux_tensor_metadata = None + aux_scalar_metadata = ( + tuple(type(s) for s in aux_scalars) if aux_scalars is not None else None + ) + + compile_key = ( + tile_m, + tile_n, + mask_mod_hash, + aux_tensor_metadata, + aux_scalar_metadata, + compute_full_blocks, + cu_seqlens_q is None, + cu_seqlens_k is None, + seqused_q is None, + seqused_k is None, + aux_tensors is not None, + use_fast_sampling, + ) + if compile_key not in compute_block_sparsity.compile_cache: + ( + cu_seqlens_q_tensor, + cu_seqlens_k_tensor, + seqused_q_tensor, + seqused_k_tensor, + ) = [ + to_cute_tensor(t, assumed_align=4, leading_dim=0) if t is not None else None + for t in ( + cu_seqlens_q, + cu_seqlens_k, + seqused_q, + seqused_k, + ) + ] + blocksparse_tensors = to_cute_block_sparse_tensors( + blocksparse_tensors_torch, enable_tvm_ffi=True + ) + if aux_tensors is not None: + cute_aux_tensors = [to_cute_aux_tensor(buf) for buf in aux_tensors] + else: + cute_aux_tensors = None + kernel = BlockSparsityKernel( + mask_mod, + tile_mn=(tile_m, tile_n), + compute_full_blocks=compute_full_blocks, + use_aux_tensors=aux_tensors is not None, + use_fast_sampling=use_fast_sampling, + ) + + compute_block_sparsity.compile_cache[compile_key] = cute.compile( + kernel, + blocksparse_tensors, + seqlen_q, + seqlen_k, + cu_seqlens_q_tensor, + cu_seqlens_k_tensor, + seqused_q_tensor, + seqused_k_tensor, + AuxData(cute_aux_tensors, aux_scalars), + options="--enable-tvm-ffi", + ) + + if not is_fake_mode(): + compute_block_sparsity.compile_cache[compile_key]( + ( + blocksparse_tensors_torch.mask_block_cnt, + blocksparse_tensors_torch.mask_block_idx, + blocksparse_tensors_torch.full_block_cnt, + blocksparse_tensors_torch.full_block_idx, + blocksparse_tensors_torch.cu_total_m_blocks, + blocksparse_tensors_torch.cu_block_idx_offsets, + blocksparse_tensors_torch.dq_write_order, + blocksparse_tensors_torch.dq_write_order_full, + ), + seqlen_q, + seqlen_k, + cu_seqlens_q, + cu_seqlens_k, + seqused_q, + seqused_k, + AuxData(aux_tensors, aux_scalars), + ) + + return blocksparse_tensors_torch + + +compute_block_sparsity.compile_cache = {} diff --git a/python/sglang/jit_kernel/flash_attn/cute/copy_utils.py b/python/sglang/jit_kernel/flash_attn/cute/copy_utils.py new file mode 100644 index 000000000..45119da56 --- /dev/null +++ b/python/sglang/jit_kernel/flash_attn/cute/copy_utils.py @@ -0,0 +1,402 @@ +# Copyright (c) 2025, Wentao Guo, Ted Zadouri, Tri Dao. + +import math +from typing import Callable, Optional, Type + +import cutlass +import cutlass.cute as cute +import cutlass.pipeline +import cutlass.utils.blackwell_helpers as sm100_utils +from cutlass import Float32, Int32, const_expr +from cutlass._mlir.dialects import llvm +from cutlass.cute.nvgpu import cpasync +from cutlass.cutlass_dsl import T, dsl_user_op + + +@dsl_user_op +def cvt_copy( + atom: cute.CopyAtom, + src: cute.Tensor, + dst: cute.Tensor, + *, + pred: Optional[cute.Tensor] = None, + loc=None, + ip=None, + **kwargs, +) -> None: + assert ( + isinstance(src.iterator, cute.Pointer) + and src.memspace == cute.AddressSpace.rmem + ) + if const_expr(src.element_type != dst.element_type): + src_cvt = cute.make_fragment_like(src, dst.element_type, loc=loc, ip=ip) + src_cvt.store(src.load().to(dst.element_type)) + src = src_cvt + cute.copy(atom, src, dst, pred=pred, loc=loc, ip=ip, **kwargs) + + +@dsl_user_op +def load_s2r(src: cute.Tensor, *, loc=None, ip=None) -> cute.Tensor: + dst = cute.make_fragment_like(src, src.element_type, loc=loc, ip=ip) + cute.autovec_copy(src, dst, loc=loc, ip=ip) + return dst + + +@dsl_user_op +def get_copy_atom( + dtype: Type[cutlass.Numeric], + num_copy_elems: int, + is_async: bool = False, + *, + loc=None, + ip=None, +) -> cute.CopyAtom: + num_copy_bits = const_expr(min(128, num_copy_elems * dtype.width)) + copy_op = cpasync.CopyG2SOp() if is_async else cute.nvgpu.CopyUniversalOp() + return cute.make_copy_atom(copy_op, dtype, num_bits_per_copy=num_copy_bits) + + +@dsl_user_op +def make_tmem_copy( + tmem_copy_atom: cute.CopyAtom, num_wg: int = 1, *, loc=None, ip=None +) -> cute.CopyAtom: + num_dp, num_bits, num_rep, _ = sm100_utils.get_tmem_copy_properties(tmem_copy_atom) + assert num_dp == 32 + assert num_bits == 32 + tiler_mn = (cute.make_layout((128 * num_rep * num_wg // 32, 32), stride=(32, 1)),) + layout_tv = cute.make_layout( + ((32, 4, num_wg), (num_rep, 32)), + stride=((0, 1, 4 * num_rep), (4, 4 * num_rep * num_wg)), + ) + return cute.make_tiled_copy(tmem_copy_atom, layout_tv, tiler_mn) + + +@dsl_user_op +def copy( + src: cute.Tensor, + dst: cute.Tensor, + *, + pred: Optional[cute.Tensor] = None, + num_copy_elems: int = 1, + is_async: bool = False, + loc=None, + ip=None, + **kwargs, +) -> None: + copy_atom = get_copy_atom(src.element_type, num_copy_elems, is_async) + cute.copy(copy_atom, src, dst, pred=pred, loc=loc, ip=ip, **kwargs) + + +def tiled_copy_1d( + dtype: Type[cutlass.Numeric], + num_threads: int, + num_copy_elems: int = 1, + is_async: bool = False, +) -> cute.TiledCopy: + num_copy_bits = num_copy_elems * dtype.width + copy_op = cpasync.CopyG2SOp() if is_async else cute.nvgpu.CopyUniversalOp() + copy_atom = cute.make_copy_atom(copy_op, dtype, num_bits_per_copy=num_copy_bits) + thr_layout = cute.make_layout(num_threads) + val_layout = cute.make_layout(num_copy_elems) + return cute.make_tiled_copy_tv(copy_atom, thr_layout, val_layout) + + +def tiled_copy_2d( + dtype: Type[cutlass.Numeric], + major_mode_size: int, + num_threads: int, + is_async: bool = False, +) -> cute.TiledCopy: + num_copy_bits = math.gcd(major_mode_size, 128 // dtype.width) * dtype.width + copy_elems = num_copy_bits // dtype.width + copy_op = cpasync.CopyG2SOp() if is_async else cute.nvgpu.CopyUniversalOp() + copy_atom = cute.make_copy_atom(copy_op, dtype, num_bits_per_copy=num_copy_bits) + gmem_threads_per_row = major_mode_size // copy_elems + assert num_threads % gmem_threads_per_row == 0 + thr_layout = cute.make_ordered_layout( + (num_threads // gmem_threads_per_row, gmem_threads_per_row), + order=(1, 0), + ) + val_layout = cute.make_layout((1, copy_elems)) + return cute.make_tiled_copy_tv(copy_atom, thr_layout, val_layout) + + +@dsl_user_op +def atomic_add_fp32x4( + a: Float32, + b: Float32, + c: Float32, + d: Float32, + gmem_ptr: cute.Pointer, + *, + loc=None, + ip=None, +) -> None: + gmem_ptr_i64 = gmem_ptr.toint(loc=loc, ip=ip).ir_value() + # cache_hint = cutlass.Int64(0x12F0000000000000) + llvm.inline_asm( + None, + [ + gmem_ptr_i64, + Float32(a).ir_value(loc=loc, ip=ip), + Float32(b).ir_value(loc=loc, ip=ip), + Float32(c).ir_value(loc=loc, ip=ip), + Float32(d).ir_value(loc=loc, ip=ip), + ], + # [gmem_ptr_i64, Float32(a).ir_value(loc=loc, ip=ip), cache_hint.ir_value()], + "{\n\t" + # ".reg .b128 abcd;\n\t" + # "mov.b128 abcd, {$1, $2, $3, $4};\n\t" + ".reg .v4 .f32 abcd;\n\t" + # "mov.b128 abcd, {$1, $2, $3, $4};\n\t" + "mov.f32 abcd.x, $1;\n\t" + "mov.f32 abcd.y, $2;\n\t" + "mov.f32 abcd.z, $3;\n\t" + "mov.f32 abcd.w, $4;\n\t" + "red.global.add.v4.f32 [$0], abcd;\n\t" + # "red.global.add.L2::cache_hint.v4.f32 [$0], abcd, 0x14F0000000000000;\n\t" + "}\n", + # "red.global.add.L2::cache_hint.f32 [$0], $1, 0x12F0000000000000;", + # "red.global.add.L2::cache_hint.f32 [$0], $1, $2;", + "l,f,f,f,f", + # "l,f,l", + has_side_effects=True, + is_align_stack=False, + asm_dialect=llvm.AsmDialect.AD_ATT, + ) + + +@dsl_user_op +def set_block_rank( + smem_ptr: cute.Pointer, peer_cta_rank_in_cluster: Int32, *, loc=None, ip=None +) -> Int32: + """Map the given smem pointer to the address at another CTA rank in the cluster.""" + smem_ptr_i32 = smem_ptr.toint(loc=loc, ip=ip).ir_value() + return Int32( + llvm.inline_asm( + T.i32(), + [smem_ptr_i32, peer_cta_rank_in_cluster.ir_value()], + "mapa.shared::cluster.u32 $0, $1, $2;", + "=r,r,r", + has_side_effects=False, + is_align_stack=False, + asm_dialect=llvm.AsmDialect.AD_ATT, + ) + ) + + +@dsl_user_op +def store_shared_remote_fp32x4( + a: Float32, + b: Float32, + c: Float32, + d: Float32, + smem_ptr: cute.Pointer, + mbar_ptr: cute.Pointer, + peer_cta_rank_in_cluster: Int32, + *, + loc=None, + ip=None, +) -> None: + remote_smem_ptr_i32 = set_block_rank( + smem_ptr, peer_cta_rank_in_cluster, loc=loc, ip=ip + ).ir_value() + remote_mbar_ptr_i32 = set_block_rank( + mbar_ptr, peer_cta_rank_in_cluster, loc=loc, ip=ip + ).ir_value() + llvm.inline_asm( + None, + [ + remote_smem_ptr_i32, + remote_mbar_ptr_i32, + Float32(a).ir_value(loc=loc, ip=ip), + Float32(b).ir_value(loc=loc, ip=ip), + Float32(c).ir_value(loc=loc, ip=ip), + Float32(d).ir_value(loc=loc, ip=ip), + ], + "{\n\t" + ".reg .v4 .f32 abcd;\n\t" + "mov.f32 abcd.x, $2;\n\t" + "mov.f32 abcd.y, $3;\n\t" + "mov.f32 abcd.z, $4;\n\t" + "mov.f32 abcd.w, $5;\n\t" + "st.async.shared::cluster.mbarrier::complete_tx::bytes.v4.f32 [$0], abcd, [$1];\n\t" + "}\n", + "r,r,f,f,f,f", + has_side_effects=True, + is_align_stack=False, + asm_dialect=llvm.AsmDialect.AD_ATT, + ) + + +@dsl_user_op +def cpasync_bulk_s2cluster( + smem_src_ptr: cute.Pointer, + smem_dst_ptr: cute.Pointer, + mbar_ptr: cute.Pointer, + size: int | Int32, + peer_cta_rank_in_cluster: Int32, + *, + loc=None, + ip=None, +): + smem_src_ptr_i32 = smem_src_ptr.toint(loc=loc, ip=ip).ir_value() + smem_dst_ptr_i32 = set_block_rank( + smem_dst_ptr, peer_cta_rank_in_cluster, loc=loc, ip=ip + ).ir_value() + mbar_ptr_i32 = set_block_rank( + mbar_ptr, peer_cta_rank_in_cluster, loc=loc, ip=ip + ).ir_value() + llvm.inline_asm( + None, + [ + smem_dst_ptr_i32, + smem_src_ptr_i32, + mbar_ptr_i32, + Int32(size).ir_value(loc=loc, ip=ip), + ], + "cp.async.bulk.shared::cluster.shared::cta.mbarrier::complete_tx::bytes [$0], [$1], $3, [$2];", + "r,r,r,r", + has_side_effects=True, + is_align_stack=False, + asm_dialect=llvm.AsmDialect.AD_ATT, + ) + + +@dsl_user_op +def cpasync_bulk_g2s( + gmem_ptr: cute.Pointer, + smem_ptr: cute.Pointer, + tma_bar_ptr: cute.Pointer, + size: int | Int32, + *, + loc=None, + ip=None, +): + gmem_ptr_i64 = gmem_ptr.toint(loc=loc, ip=ip).ir_value() + smem_ptr_i32 = smem_ptr.toint(loc=loc, ip=ip).ir_value() + mbar_ptr_i32 = tma_bar_ptr.toint(loc=loc, ip=ip).ir_value() + llvm.inline_asm( + None, + [gmem_ptr_i64, smem_ptr_i32, mbar_ptr_i32, Int32(size).ir_value()], + "cp.async.bulk.shared::cta.global.mbarrier::complete_tx::bytes [$1], [$0], $3, [$2];", + "l,r,r,r", + has_side_effects=True, + is_align_stack=False, + asm_dialect=llvm.AsmDialect.AD_ATT, + ) + + +@dsl_user_op +def cpasync_reduce_bulk_add_f32( + smem_ptr: cute.Pointer, + gmem_ptr: cute.Pointer, + store_bytes: int | Int32, + *, + loc=None, + ip=None, +): + smem_ptr_i32 = smem_ptr.toint(loc=loc, ip=ip).ir_value() + # cache_hint = cutlass.Int64(0x14F0000000000000) # EVICT_LAST + llvm.inline_asm( + None, + [gmem_ptr.llvm_ptr, smem_ptr_i32, Int32(store_bytes).ir_value()], + "cp.reduce.async.bulk.global.shared::cta.bulk_group.add.f32 [$0], [$1], $2;", + "l,r,r", + # [gmem_ptr.llvm_ptr, smem_ptr_i32, Int32(store_bytes).ir_value(), cache_hint.ir_value()], + # "cp.reduce.async.bulk.global.shared::cta.bulk_group.L2::cache_hint.add.f32 [$0], [$1], $2, $3;", + # "l,r,r,l", + has_side_effects=True, + is_align_stack=False, + asm_dialect=llvm.AsmDialect.AD_ATT, + ) + + +def cpasync_bulk_get_copy_fn( + src_tensor: cute.Tensor, + dst_tensor: cute.Tensor, + single_stage: bool = False, + **kwargs, +) -> Callable: + # src_is_smem = const_expr( + # isinstance(src_tensor.iterator, cute.Pointer) + # and src_tensor.memspace == cute.AddressSpace.smem + # ) + group_rank_src = const_expr(cute.rank(src_tensor) - (1 if not single_stage else 0)) + group_rank_dst = const_expr(cute.rank(dst_tensor) - (1 if not single_stage else 0)) + # ((atom_v, rest_v), STAGE), ((atom_v, rest_v), RestK) + src = cute.group_modes(src_tensor, 0, group_rank_src) + dst = cute.group_modes(dst_tensor, 0, group_rank_dst) + + def copy_bulk(src_idx, dst_idx, **new_kwargs): + size = const_expr(cute.size(src.shape[:-1]) * src.element_type.width // 8) + cpasync_bulk_g2s( + src[None, src_idx].iterator, + dst[None, dst_idx].iterator, + size=size, + **new_kwargs, + **kwargs, + ) + + def copy_bulk_single_stage(**new_kwargs): + size = const_expr(cute.size(src.shape) * src.element_type.width // 8) + cpasync_bulk_g2s(src.iterator, dst.iterator, size=size, **new_kwargs, **kwargs) + + return copy_bulk if const_expr(not single_stage) else copy_bulk_single_stage + + +def tma_get_copy_fn( + atom: cute.CopyAtom, + cta_coord: cute.Coord, + cta_layout: cute.Layout, + src_tensor: cute.Tensor, + dst_tensor: cute.Tensor, + filter_zeros: bool = False, + single_stage: bool = False, + **kwargs, +) -> Callable: + src_is_smem = const_expr( + isinstance(src_tensor.iterator, cute.Pointer) + and src_tensor.memspace == cute.AddressSpace.smem + ) + smem_tensor, gmem_tensor = ( + (src_tensor, dst_tensor) if src_is_smem else (dst_tensor, src_tensor) + ) + group_rank_smem = const_expr( + cute.rank(smem_tensor) - (1 if not single_stage else 0) + ) + group_rank_gmem = const_expr( + cute.rank(gmem_tensor) - (1 if not single_stage else 0) + ) + # ((atom_v, rest_v), STAGE), ((atom_v, rest_v), RestK) + s, g = cpasync.tma_partition( + atom, + cta_coord, + cta_layout, + cute.group_modes(smem_tensor, 0, group_rank_smem), + cute.group_modes(gmem_tensor, 0, group_rank_gmem), + ) + if const_expr(filter_zeros): + s = cute.filter_zeros(s) + g = cute.filter_zeros(g) + src, dst = (s, g) if src_is_smem else (g, s) + + def copy_tma(src_idx, dst_idx, **new_kwargs): + cute.copy(atom, src[None, src_idx], dst[None, dst_idx], **new_kwargs, **kwargs) + + def copy_tma_single_stage(**new_kwargs): + cute.copy(atom, src, dst, **new_kwargs, **kwargs) + + return (copy_tma if const_expr(not single_stage) else copy_tma_single_stage), s, g + + +def tma_producer_copy_fn(copy: Callable, pipeline: cutlass.pipeline.PipelineAsync): + def copy_fn(src_idx, producer_state: cutlass.pipeline.PipelineState, **new_kwargs): + copy( + src_idx=src_idx, + dst_idx=producer_state.index, + tma_bar_ptr=pipeline.producer_get_barrier(producer_state), + **new_kwargs, + ) + + return copy_fn diff --git a/python/sglang/jit_kernel/flash_attn/cute/cu_blocks_kernels.py b/python/sglang/jit_kernel/flash_attn/cute/cu_blocks_kernels.py new file mode 100644 index 000000000..1e48004a5 --- /dev/null +++ b/python/sglang/jit_kernel/flash_attn/cute/cu_blocks_kernels.py @@ -0,0 +1,123 @@ +from typing import Callable + +import cuda.bindings.driver as cuda +import cutlass +import cutlass.cute as cute +from cutlass import Int32, const_expr + + +class CuSeqlensToBlocksKernel: + """Single-CTA prep for block-packed shear scheduling: computes the cumulative + per-batch group-block counts and the block -> batch index map in one launch.""" + + def __init__( + self, + tile: int = 128, + num_threads: int = 1024, + seqlen_multiple: int = 1, + use_pdl: bool = False, + ): + self.tile = tile + self.num_threads = num_threads + assert num_threads % 32 == 0 + self.num_warps = num_threads // cute.arch.WARP_SIZE + self.seqlen_multiple = seqlen_multiple + self.use_pdl = use_pdl + + @cute.jit + def __call__( + self, + mCuBlocks: cute.Tensor, + mCuSeqlens: cute.Tensor, + mBlocksToBatchIdx: cute.Tensor, + # Always keep stream as the last parameter (EnvStream: obtained implicitly via TVM FFI). + stream: cuda.CUstream = None, + ): + @cute.struct + class SharedStorage: + warp_block_count: cute.struct.MemRange[Int32, self.num_warps] + cu_blocks: cute.struct.MemRange[Int32, self.num_threads + 1] + + self.kernel( + mCuBlocks, + mCuSeqlens, + mBlocksToBatchIdx, + SharedStorage, + ).launch( + grid=[1, 1, 1], + block=[self.num_threads, 1, 1], + stream=stream, + use_pdl=self.use_pdl, + ) + + @cute.kernel + def kernel( + self, + mCuBlocks: cute.Tensor, + mCuSeqlens: cute.Tensor, + mBlocksToBatchIdx: cute.Tensor, + SharedStorage: cutlass.Constexpr[Callable], + ): + if const_expr(self.use_pdl): + cute.arch.griddepcontrol_wait() + cute.arch.griddepcontrol_launch_dependents() + + batch_size = mCuBlocks.shape[0] - 1 + batch_idx = cute.arch.thread_idx()[0] + lane_idx = cute.arch.lane_idx() + warp_idx = cute.arch.make_warp_uniform(cute.arch.warp_idx()) + + smem = cutlass.utils.SmemAllocator() + storage = smem.allocate(SharedStorage) + warp_block_count = storage.warp_block_count.get_tensor( + cute.make_layout(self.num_warps) + ) + sCuBlocks = storage.cu_blocks.get_tensor(cute.make_layout(self.num_threads + 1)) + + if batch_idx == 0: + mCuBlocks[0] = 0 + sCuBlocks[0] = 0 + + seqlen = Int32(0) + if batch_idx < batch_size: + seqlen = mCuSeqlens[batch_idx + 1] - mCuSeqlens[batch_idx] + seqlen *= self.seqlen_multiple + num_blocks = (seqlen + self.tile - 1) // self.tile + + total_blocks_for_batch = num_blocks + for delta in (1, 2, 4, 8, 16): + other = cute.arch.shuffle_sync_up( + total_blocks_for_batch, delta, mask_and_clamp=0 + ) + if lane_idx >= delta: + total_blocks_for_batch += other + + if lane_idx == 31: + warp_block_count[warp_idx] = total_blocks_for_batch + + cute.arch.sync_threads() + + if warp_idx * 32 < batch_size: + for idx in cutlass.range(warp_idx): + total_blocks_for_batch += warp_block_count[idx] + + if batch_idx < batch_size: + mCuBlocks[batch_idx + 1] = total_blocks_for_batch + sCuBlocks[batch_idx + 1] = total_blocks_for_batch + + cute.arch.sync_threads() + + total_blocks = sCuBlocks[batch_size] + num_iters = (total_blocks + self.num_threads - 1) // self.num_threads + for it in cutlass.range(num_iters, unroll=1): + block = it * self.num_threads + batch_idx + if block < total_blocks: + lo = Int32(0) + hi = Int32(batch_size) + while lo < hi: + mid = (lo + hi) // 2 + if sCuBlocks[mid + 1] <= block: + lo = mid + 1 + else: + hi = mid + mBlocksToBatchIdx[block] = lo diff --git a/python/sglang/jit_kernel/flash_attn/cute/cute_dsl_ptxas.py b/python/sglang/jit_kernel/flash_attn/cute/cute_dsl_ptxas.py new file mode 100644 index 000000000..f9be93152 --- /dev/null +++ b/python/sglang/jit_kernel/flash_attn/cute/cute_dsl_ptxas.py @@ -0,0 +1,159 @@ +""" +System ptxas replacement for CUTLASS DSL. +Environment variables: + CUTE_DSL_PTXAS_PATH - Path to ptxas (e.g., /usr/local/cuda/bin/ptxas) + CUTE_DSL_PTXAS_VERBOSE - Set to 1 for verbose output +""" + +import ctypes +import os +import re +import subprocess +import sys +from pathlib import Path + +import cutlass + +CUTE_DSL_PTXAS_PATH = os.environ.get("CUTE_DSL_PTXAS_PATH", None) +VERBOSE = os.environ.get("CUTE_DSL_PTXAS_VERBOSE", "0") == "1" + +_original_load_cuda_library = None +_user_wanted_ptx = False # True if user originally set CUTE_DSL_KEEP_PTX=1 + + +def _log(msg): + if VERBOSE: + print(f"[ptxas] {msg}", file=sys.stderr) + + +def _get_ptx(compiled_func) -> tuple[str, Path] | None: + """Find and read PTX file, stripping null bytes.""" + func_name = getattr(compiled_func, "function_name", None) + if not func_name: + return None + + dump_dir = os.environ.get("CUTE_DSL_DUMP_DIR", Path.cwd()) + for ptx_path in Path(dump_dir).glob(f"*{func_name}*.ptx"): + content = ptx_path.read_text().rstrip("\x00") + if ".entry " in content and content.rstrip().endswith("}"): + _log(f"Found PTX: {ptx_path}") + return content, ptx_path + return None + + +def _compile_ptx(ptx_path: Path, ptx_content: str) -> bytes: + """Compile PTX to cubin using system ptxas.""" + # Extract arch from PTX + match = re.search(r"\.target\s+(sm_\d+[a-z]?)", ptx_content) + arch = match.group(1) if match else "sm_90a" + + # Write stripped content back if needed + if ptx_path.read_text() != ptx_content: + ptx_path.write_text(ptx_content) + + # Compile + cubin_tmp = ptx_path.with_suffix(".cubin.tmp") + try: + assert CUTE_DSL_PTXAS_PATH is not None + result = subprocess.run( + [ + CUTE_DSL_PTXAS_PATH, + f"-arch={arch}", + "-O3", + "-o", + str(cubin_tmp), + str(ptx_path), + ], + capture_output=True, + text=True, + ) + if result.returncode != 0: + raise RuntimeError(f"ptxas failed: {result.stderr}") + + cubin_data = cubin_tmp.read_bytes() + _log(f"Compiled {ptx_path.name} -> {len(cubin_data)} bytes ({arch})") + + # Save cubin if CUTE_DSL_KEEP_CUBIN is set + if os.environ.get("CUTE_DSL_KEEP_CUBIN", "0") == "1": + cubin_out = ptx_path.with_suffix(".cubin") + cubin_out.write_bytes(cubin_data) + _log(f"Saved: {cubin_out}") + + return cubin_data + finally: + cubin_tmp.unlink(missing_ok=True) + + +def _patched_load_cuda_library(self): + """Replacement for _load_cuda_library that uses system ptxas.""" + + result = _get_ptx(self) + if not result: + _log("PTX not found, falling back to embedded ptxas") + return _original_load_cuda_library(self) + + ptx_content, ptx_path = result + + try: + cubin = _compile_ptx(ptx_path, ptx_content) + except Exception as e: + _log(f"Compilation failed ({e}), falling back to embedded ptxas") + return _original_load_cuda_library(self) + + # Load cubin + import cuda.bindings.runtime as cuda_runtime + + err, library = cuda_runtime.cudaLibraryLoadData(cubin, None, None, 0, None, None, 0) + if err != cuda_runtime.cudaError_t.cudaSuccess: + _log(f"cudaLibraryLoadData failed ({err}), falling back to embedded ptxas") + return _original_load_cuda_library(self) + + # Register kernels on all devices + _, cuda_load_to_device = self._get_cuda_init_and_load() + lib_ptr = ctypes.c_void_p(int(library)) + dev_id = ctypes.c_int32(0) + err_val = ctypes.c_int32(0) + args = (ctypes.c_void_p * 3)( + ctypes.cast(ctypes.pointer(lib_ptr), ctypes.c_void_p), + ctypes.cast(ctypes.pointer(dev_id), ctypes.c_void_p), + ctypes.cast(ctypes.pointer(err_val), ctypes.c_void_p), + ) + + for dev in range(self.num_devices): + dev_id.value = dev + cuda_load_to_device(args) + if err_val.value != 0: + _log("cuda_load_to_device failed, falling back to embedded ptxas") + return _original_load_cuda_library(self) + + _log(f"Loaded kernel from {ptx_path.name}") + + # Delete PTX if user didn't originally want it kept + if not _user_wanted_ptx: + ptx_path.unlink(missing_ok=True) + + return [cuda_runtime.cudaLibrary_t(lib_ptr.value)] + + +def patch(): + """Install system ptxas hook. Call before importing cutlass.""" + global _original_load_cuda_library, _user_wanted_ptx + + assert CUTE_DSL_PTXAS_PATH is not None + if not os.path.isfile(CUTE_DSL_PTXAS_PATH) or not os.access( + CUTE_DSL_PTXAS_PATH, os.X_OK + ): + raise RuntimeError(f"ptxas not found: {CUTE_DSL_PTXAS_PATH}") + + # Track if user originally wanted PTX kept + _user_wanted_ptx = os.environ.get("CUTE_DSL_KEEP_PTX", "0") == "1" + # os.environ['CUTE_DSL_KEEP_PTX'] = '1' + assert ( + os.environ.get("CUTE_DSL_KEEP_PTX", "0") == "1" + ), "Require CUTE_DSL_KEEP_PTX=1 to use system's ptxas" + + cls = cutlass.cutlass_dsl.cuda_jit_executor.CudaDialectJitCompiledFunction + _original_load_cuda_library = cls._load_cuda_library + cls._load_cuda_library = _patched_load_cuda_library + _log("Patch applied") + return diff --git a/python/sglang/jit_kernel/flash_attn/cute/cute_dsl_utils.py b/python/sglang/jit_kernel/flash_attn/cute/cute_dsl_utils.py new file mode 100644 index 000000000..c719db663 --- /dev/null +++ b/python/sglang/jit_kernel/flash_attn/cute/cute_dsl_utils.py @@ -0,0 +1,176 @@ +# Copyright (c) 2025, Tri Dao. + +from functools import lru_cache +from typing import Tuple + +import torch + +try: + from triton.tools.disasm import extract +except ImportError: + extract = None + +import cutlass +import cutlass.cute as cute +from cutlass.cute.runtime import from_dlpack +from cutlass.cutlass_dsl import NumericMeta + +StaticTypes = (cutlass.Constexpr, NumericMeta, int, bool, str, float, type(None)) + + +load_cubin_module_data_og = cutlass.base_dsl.runtime.cuda.load_cubin_module_data +cute_compile_og = cute.compile + + +torch2cute_dtype_map = { + torch.float16: cutlass.Float16, + torch.bfloat16: cutlass.BFloat16, + torch.float32: cutlass.Float32, + torch.float8_e4m3fn: cutlass.Float8E4M3FN, + torch.float8_e5m2: cutlass.Float8E5M2, +} + + +@lru_cache +def get_max_active_clusters(cluster_size): + return cutlass.utils.HardwareInfo().get_max_active_clusters( + cluster_size=cluster_size + ) + + +@lru_cache +def get_device_capacity(device: torch.device = None) -> Tuple[int, int]: + return torch.cuda.get_device_capability(device) + + +def assume_strides_aligned(t, align=16): + """Assume all strides except the last are divisible by `align` bytes (128 + bits by default; 4 bytes for the packed UE8M0 scale-factor tensors). + + Python int strides (e.g., stride=0 from GQA expand) are kept as-is + since they're static and don't need alignment assumptions. + """ + divby = (align * 8) // t.element_type.width + strides = tuple( + s if isinstance(s, int) else cute.assume(s, divby=divby) for s in t.stride[:-1] + ) + return (*strides, t.stride[-1]) + + +def assume_tensor_aligned(t, align=16): + """Rebuild a tensor with aligned stride assumptions. Passes through None.""" + if t is None: + return None + return cute.make_tensor( + t.iterator, + cute.make_layout(t.shape, stride=assume_strides_aligned(t, align=align)), + ) + + +def to_cute_tensor( + t, assumed_align=16, leading_dim=-1, fully_dynamic=False, enable_tvm_ffi=True +): + """Convert torch tensor to cute tensor for TVM FFI. leading_dim=-1 defaults to t.ndim-1.""" + if t is None: + return None + # NOTE: torch 2.9.1 doesn't support fp8 via DLPack but 2.11.0 nightly does + # currently export raw bytes as uint8 and tell cutlass correct type + # can directly export as fp8 when torch supports it + if t.dtype in (torch.float8_e4m3fn, torch.float8_e5m2): + tensor = from_dlpack( + t.view(torch.uint8).detach(), + assumed_align=assumed_align, + enable_tvm_ffi=enable_tvm_ffi, + ) + tensor.element_type = ( + cutlass.Float8E4M3FN + if t.dtype == torch.float8_e4m3fn + else cutlass.Float8E5M2 + ) + else: + tensor = from_dlpack( + t.detach(), assumed_align=assumed_align, enable_tvm_ffi=enable_tvm_ffi + ) + if fully_dynamic: + return tensor.mark_layout_dynamic() + if leading_dim == -1: + leading_dim = t.ndim - 1 + return tensor.mark_layout_dynamic(leading_dim=leading_dim) + + +def to_cute_aux_tensor(t, enable_tvm_ffi=True): + """Convert torch tensor to cute tensor for TVM FFI, tailored to FlexAttention aux tensors. + This allows the user to specify alignment and leading dimension for aux tensors used in + custom score_mod callables. + """ + assumed_align: int = getattr(t, "__assumed_align__", None) + leading_dim: int = getattr(t, "__leading_dim__", None) + fully_dynamic: bool = leading_dim is None + + return to_cute_tensor( + t, + assumed_align=assumed_align, + leading_dim=leading_dim, + fully_dynamic=fully_dynamic, + enable_tvm_ffi=enable_tvm_ffi, + ) + + +def get_aux_tensor_metadata(aux_tensors): + return tuple( + ( + getattr(t, "__assumed_align__", 0), + getattr(t, "__leading_dim__", -1), + hasattr(t, "__leading_dim__"), + ) + for t in aux_tensors + ) + + +def get_broadcast_dims(tensor: torch.Tensor) -> Tuple[bool, ...]: + """Return tuple of bools indicating which dims have stride=0 (broadcast). + + This is useful for compile keys since CuTe's mark_layout_dynamic() keeps + stride=0 as static, meaning kernels compiled with different broadcast + patterns are not interchangeable. + """ + return tuple(s == 0 for s in tensor.stride()) + + +# credit: monellz (https://github.com/NVIDIA/cutlass/issues/2658#issuecomment-3630564264) +def dump_kernel_attributes(compiled_kernel): + import torch + from cuda.bindings import driver + from cutlass.utils import HardwareInfo + + device_id = torch.cuda.current_device() + hardware_info = HardwareInfo(device_id=device_id) + cubin_data = compiled_kernel.artifacts.CUBIN + assert ( + cubin_data is not None + ), "cubin_data is None, need '--keep-cubin' option when compiling" + cuda_library = hardware_info._checkCudaErrors( + driver.cuLibraryLoadData(cubin_data, None, None, 0, None, None, 0) + ) + kernels = hardware_info._checkCudaErrors( + driver.cuLibraryEnumerateKernels(1, cuda_library) + ) + kernel = hardware_info._checkCudaErrors(driver.cuKernelGetFunction(kernels[0])) + # more metrics: https://docs.nvidia.com/cuda/cuda-driver-api/group__CUDA__EXEC.html#group__CUDA__EXEC_1g5e92a1b0d8d1b82cb00dcfb2de15961b + local_size_bytes = hardware_info._checkCudaErrors( + driver.cuFuncGetAttribute( + driver.CUfunction_attribute.CU_FUNC_ATTRIBUTE_LOCAL_SIZE_BYTES, + kernel, + ) + ) + num_regs = hardware_info._checkCudaErrors( + driver.cuFuncGetAttribute( + driver.CUfunction_attribute.CU_FUNC_ATTRIBUTE_NUM_REGS, + kernel, + ) + ) + + print("--- Kernel Info ---") + print(f"local_size_bytes: {local_size_bytes}") + print(f"num_regs: {num_regs}") + print("--- End Kernel Info ---") diff --git a/python/sglang/jit_kernel/flash_attn/cute/fa_logging.py b/python/sglang/jit_kernel/flash_attn/cute/fa_logging.py new file mode 100644 index 000000000..63189cd5d --- /dev/null +++ b/python/sglang/jit_kernel/flash_attn/cute/fa_logging.py @@ -0,0 +1,97 @@ +# Copyright (c) 2025, Tri Dao. + +"""Unified FlashAttention logging controlled by a single ``FA_LOG_LEVEL`` env var. + +Host-side messages go through Python ``logging`` (logger name ``flash_attn``). +A default ``StreamHandler`` is attached automatically when ``FA_LOG_LEVEL >= 1`` +so that standalone scripts get output without extra setup; applications that +configure their own logging can remove or replace it via the standard API. + +FA_LOG_LEVEL mapping:: + + 0 off nothing logged + 1 host host-side summaries only (no kernel printf) + 2 kernel host + curated kernel traces + 3 max host + all kernel traces (noisy, perf hit) + +Set via environment variable:: + + FA_LOG_LEVEL=1 python train.py + +Device-side ``cute.printf`` calls are compile-time eliminated via +``cutlass.const_expr`` when the log level is below the callsite threshold, +so there is zero performance cost when device logging is off. +Changing the log level after kernel compilation requires a recompile +(the level participates in the forward compile key). +""" + +import logging +import os +import sys + +import cutlass.cute as cute +from cutlass import const_expr + +_LOG_LEVEL_NAMES = {"off": 0, "host": 1, "kernel": 2, "max": 3} + + +def _parse_log_level(raw: str) -> int: + if raw in _LOG_LEVEL_NAMES: + return _LOG_LEVEL_NAMES[raw] + try: + level = int(raw) + except ValueError: + return 0 + return max(0, min(level, 3)) + + +_fa_log_level: int = _parse_log_level(os.environ.get("FA_LOG_LEVEL", "0")) + +_logger = logging.getLogger("flash_attn") +_logger.addHandler(logging.NullHandler()) +_default_handler: logging.Handler | None = None + + +def _configure_default_handler() -> None: + global _default_handler + if _fa_log_level >= 1: + if _default_handler is None: + _default_handler = logging.StreamHandler(sys.stdout) + _default_handler.setFormatter(logging.Formatter("[FA] %(message)s")) + _logger.addHandler(_default_handler) + _logger.setLevel(logging.DEBUG) + else: + if _default_handler is not None: + _logger.removeHandler(_default_handler) + _default_handler = None + _logger.setLevel(logging.WARNING) + + +_configure_default_handler() + + +def get_fa_log_level() -> int: + return _fa_log_level + + +def set_fa_log_level(level: int | str) -> None: + """Set the FA log level programmatically. + + Host logging takes effect immediately. Device logging changes only + affect kernels compiled after this call (new compile-key selection). + """ + global _fa_log_level + if isinstance(level, str): + level = _parse_log_level(level) + _fa_log_level = max(0, min(int(level), 3)) + _configure_default_handler() + + +def fa_log(level: int, msg: str): + if _fa_log_level >= level: + _logger.info(msg) + + +def fa_printf(level: int, fmt, *args): + if const_expr(_fa_log_level >= level): + cute.printf(fmt, *args) diff --git a/python/sglang/jit_kernel/flash_attn/cute/fast_math.py b/python/sglang/jit_kernel/flash_attn/cute/fast_math.py new file mode 100644 index 000000000..c56ea89e7 --- /dev/null +++ b/python/sglang/jit_kernel/flash_attn/cute/fast_math.py @@ -0,0 +1,21 @@ +# Copyright (c) 2025, Tri Dao. + +import cutlass +import cutlass.cute as cute +from cutlass import Int32 + + +@cute.jit +def clz(x: Int32) -> Int32: + # for i in cutlass.range_constexpr(32): + # if (1 << (31 - i)) & x: + # return Int32(i) + # return Int32(32) + # Early exit is not supported yet + res = Int32(32) + done = False + for i in cutlass.range(32): + if ((1 << (31 - i)) & x) and not done: + res = Int32(i) + done = True + return res diff --git a/python/sglang/jit_kernel/flash_attn/cute/flash_fwd.py b/python/sglang/jit_kernel/flash_attn/cute/flash_fwd.py new file mode 100644 index 000000000..93f4dd044 --- /dev/null +++ b/python/sglang/jit_kernel/flash_attn/cute/flash_fwd.py @@ -0,0 +1,1531 @@ +# Copyright (c) 2025, Jay Shah, Ganesh Bikshandi, Ying Zhang, Vijay Thakkar, Pradeep Ramani, Tri Dao. +# A reimplementation of +# https://github.com/Dao-AILab/flash-attention/blob/main/hopper/flash_fwd_kernel_sm80.h +# and https://github.com/Dao-AILab/flash-attention/blob/main/hopper/flash_fwd_kernel_sm90.h +# from Cutlass C++ to Cute-DSL. +# Built on Cute-DSL example: https://github.com/NVIDIA/cutlass/blob/main/examples/python/CuTeDSL/ampere/flash_attention_v2.py + +import math +from functools import partial +from types import SimpleNamespace +from typing import Callable, Optional, Type + +import cuda.bindings.driver as cuda +import cutlass +import cutlass.cute as cute +import cutlass.utils as utils_basic +from cutlass import Float32, Int32, const_expr +from cutlass.base_dsl.arch import Arch +from cutlass.cute.nvgpu import cpasync, warp +from cutlass.cutlass_dsl import BaseDSL +from quack import copy_utils, layout_utils + +from sglang.jit_kernel.flash_attn.cute import ampere_helpers as sm80_utils +from sglang.jit_kernel.flash_attn.cute import utils +from sglang.jit_kernel.flash_attn.cute.block_info import BlockInfo +from sglang.jit_kernel.flash_attn.cute.block_sparsity import BlockSparseTensors +from sglang.jit_kernel.flash_attn.cute.cute_dsl_utils import assume_tensor_aligned +from sglang.jit_kernel.flash_attn.cute.mask import AttentionMask +from sglang.jit_kernel.flash_attn.cute.named_barrier import NamedBarrierFwd +from sglang.jit_kernel.flash_attn.cute.pack_gqa import PackGQA +from sglang.jit_kernel.flash_attn.cute.seqlen_info import SeqlenInfoQK +from sglang.jit_kernel.flash_attn.cute.softmax import Softmax, apply_score_mod_inner +from sglang.jit_kernel.flash_attn.cute.tile_scheduler import ( + SingleTileScheduler, + SingleTileVarlenScheduler, + TileSchedulerArguments, +) +from sglang.jit_kernel.flash_attn.cute.utils import AuxData + + +class FlashAttentionForwardBase: + + def __init__( + self, + dtype: Type[cutlass.Numeric], + head_dim: int, + head_dim_v: Optional[int] = None, + qhead_per_kvhead: int = 1, + is_causal: bool = False, + is_local: bool = False, + pack_gqa: bool = True, + tile_m: int = 128, + tile_n: int = 128, + num_stages: int = 1, + num_threads: int = 128, + Q_in_regs: bool = False, + score_mod: Optional[cutlass.Constexpr] = None, + mask_mod: Optional[cutlass.Constexpr] = None, + has_aux_tensors: bool = False, + q_subtile_factor: int | None = None, + is_split_kv: bool = False, + ): + """Initializes the configuration for a flash attention kernel. + + All contiguous dimensions must be at least 16 bytes aligned, which means that the head dimension + should be a multiple of 8. + + :param head_dim: head dimension + :type head_dim: int + :param tile_m: m block size + :type tile_m: int + :param tile_n: n block size + :type tile_n: int + :param num_threads: number of threads + :type num_threads: int + :param is_causal: is causal + :param score_mod: A callable that takes the attention scores and applies a modification. + Callable signature: ``score_mod(scores, batch_idx, head_idx, q_idx, kv_idx, aux_tensors) -> Any`` + :param mask_mod: A callable that takes the attention scores and returns a boolean representing whether that score should be masked. + Callable signature: ``mask_mod(batch_idx, head_idx, q_idx, kv_idx, aux_tensors) -> Boolean`` + """ + self.dtype = dtype + # padding head_dim to a multiple of 16 as k_block_size + hdim_multiple_of = 16 + self.tile_hdim = int(math.ceil(head_dim / hdim_multiple_of) * hdim_multiple_of) + head_dim_v = head_dim_v if head_dim_v is not None else head_dim + self.same_hdim_kv = head_dim == head_dim_v + self.tile_hdimv = int( + math.ceil(head_dim_v / hdim_multiple_of) * hdim_multiple_of + ) + # Can save registers (and hence be faster) if we don't have to check hdim predication + self.check_hdim_oob = head_dim != self.tile_hdim + self.check_hdim_v_oob = head_dim_v != self.tile_hdimv + self.qhead_per_kvhead = qhead_per_kvhead + self.is_causal = is_causal + self.is_local = is_local + self.pack_gqa = pack_gqa + self.tile_m = tile_m + self.tile_n = tile_n + self.num_threads = num_threads + self.num_stages = num_stages + self.q_subtile_factor = q_subtile_factor + self.is_split_kv = is_split_kv + self.Q_in_regs = Q_in_regs + self.score_mod = score_mod + self.mask_mod = mask_mod + self.qk_acc_dtype = Float32 + self.score_vec_size: cutlass.Constexpr = getattr( + score_mod, "__vec_size__", 1 if cutlass.const_expr(has_aux_tensors) else 2 + ) + if self.score_vec_size > 2: + raise ValueError( + f"score_mod vec_size {self.score_vec_size} not supported on Sm80/90/120 " + "due to accumulator thread ownership pattern." + ) + self.mask_vec_size: cutlass.Constexpr = getattr(mask_mod, "__vec_size__", 1) + if self.mask_vec_size > 1: + raise ValueError( + f"mask_mod vec_size {self.mask_vec_size} not supported on Sm80/90/120 " + "due to accumulator thread ownership pattern." + ) + self.arch = BaseDSL._get_dsl().get_arch_enum() + + @staticmethod + def can_implement( + dtype, + head_dim, + head_dim_v, + tile_m, + tile_n, + num_stages, + num_threads, + is_causal, + Q_in_regs=False, + ) -> bool: + """Check if the kernel can be implemented with the given parameters. + + :param dtype: data type + :type dtype: cutlass.Numeric + :param head_dim: head dimension + :type head_dim: int + :param tile_m: m block size + :type tile_m: int + :param tile_n: n block size + :type tile_n: int + :param num_threads: number of threads + :type num_threads: int + :param is_causal: is causal + :type is_causal: bool + + :return: True if the kernel can be implemented, False otherwise + :rtype: bool + """ + if dtype not in [cutlass.Float16, cutlass.BFloat16]: + return False + if head_dim % 8 != 0: + return False + if head_dim_v % 8 != 0: + return False + if tile_n % 16 != 0: + return False + if num_threads % 32 != 0: + return False + # Check if block size setting is out of shared memory capacity + # Shared memory usage: Q tile + (K tile + V tile) where K and V use the same tile size + smem_usage_Q = tile_m * head_dim * 2 + smem_usage_K = tile_n * head_dim * num_stages * 2 + smem_usage_V = tile_n * head_dim_v * num_stages * 2 + smem_usage_QV = ( + (smem_usage_Q + smem_usage_V) + if not Q_in_regs + else max(smem_usage_Q, smem_usage_V) + ) + smem_usage = smem_usage_QV + smem_usage_K + # TODO: sm86 and sm89 + smem_capacity = utils_basic.get_smem_capacity_in_bytes("sm_80") + if smem_usage > smem_capacity: + return False + # Check if twice the block size is divisible by the number of threads + if (tile_m * 2) % num_threads != 0: + return False + return True + + def _check_type( + self, + mQ_type: Type[cutlass.Numeric], + mK_type: Type[cutlass.Numeric], + mV_type: Type[cutlass.Numeric], + mO_type: Type[cutlass.Numeric], + mLSE_type: Type[cutlass.Numeric] | None, + mCuSeqlensQ_type: Type[cutlass.Numeric] | None, + mCuSeqlensK_type: Type[cutlass.Numeric] | None, + mSeqUsedQ_type: Type[cutlass.Numeric] | None, + mSeqUsedK_type: Type[cutlass.Numeric] | None, + ): + # Get the data type and check if it is fp16 or bf16 + if const_expr(self.is_split_kv): + # SplitKV writes float32 partial outputs; Q/K/V still fp16/bf16. + if const_expr(not (mQ_type == mK_type == mV_type)): + raise TypeError("Q/K/V must have the same data type") + if const_expr(mO_type != Float32): + raise TypeError("SplitKV partial output (mO) must be Float32") + elif const_expr(not (mQ_type == mK_type == mV_type == mO_type)): + raise TypeError("All tensors must have the same data type") + if const_expr(mQ_type not in [cutlass.Float16, cutlass.BFloat16]): + raise TypeError("Only Float16 or BFloat16 is supported") + if const_expr(mLSE_type not in [None, Float32]): + raise TypeError("LSE tensor must be Float32") + if const_expr(mCuSeqlensQ_type not in [None, Int32]): + raise TypeError("cu_seqlens_q tensor must be Int32") + if const_expr(mCuSeqlensK_type not in [None, Int32]): + raise TypeError("cu_seqlens_k tensor must be Int32") + if const_expr(mSeqUsedQ_type not in [None, Int32]): + raise TypeError("seqused_q tensor must be Int32") + if const_expr(mSeqUsedK_type not in [None, Int32]): + raise TypeError("seqused_k tensor must be Int32") + assert mQ_type == self.dtype + + def _setup_attributes(self): + # /////////////////////////////////////////////////////////////////////////////// + # Shared memory layout: Q/K/V + # /////////////////////////////////////////////////////////////////////////////// + ( + sQ_layout_atom, + sK_layout_atom, + sV_layout_atom, + sO_layout_atom, + sP_layout_atom, + ) = self._get_smem_layout_atom() + self.sQ_layout = cute.tile_to_shape( + sQ_layout_atom, + (self.tile_m, self.tile_hdim), + (0, 1), + ) + self.sK_layout = cute.tile_to_shape( + sK_layout_atom, + (self.tile_n, self.tile_hdim, self.num_stages), + (0, 1, 2), + ) + self.sV_layout = cute.tile_to_shape( + sV_layout_atom, + (self.tile_n, self.tile_hdimv, self.num_stages), + (0, 1, 2), + ) + self.sO_layout = cute.tile_to_shape( + sO_layout_atom, + (self.tile_m, self.tile_hdimv), + (0, 1), + ) + if const_expr(sP_layout_atom is not None): + self.sP_layout = cute.tile_to_shape( + sP_layout_atom, + (self.tile_m, self.tile_n), + (0, 1), + ) + else: + self.sP_layout = None + + # /////////////////////////////////////////////////////////////////////////////// + # GMEM Tiled copy: + # /////////////////////////////////////////////////////////////////////////////// + # Thread layouts for copies + universal_copy_bits = 128 + async_copy_elems = universal_copy_bits // self.dtype.width + # atom_async_copy: async copy atom for QKV load + atom_async_copy = cute.make_copy_atom( + cpasync.CopyG2SOp(cache_mode=cpasync.LoadCacheMode.GLOBAL), + self.dtype, + num_bits_per_copy=universal_copy_bits, + ) + # atom_universal_copy: universal copy atom for O store + atom_universal_copy = cute.make_copy_atom( + cute.nvgpu.CopyUniversalOp(), + self.dtype, + num_bits_per_copy=universal_copy_bits, + ) + # tQ_layout and tK_layout: thread layout for QK load + tQK_shape_dim_1 = sQ_layout_atom.outer.shape[1] // async_copy_elems + assert ( + self.num_Q_load_threads % tQK_shape_dim_1 == 0 + ), "num_threads must be divisible by tQK_shape_dim_1" + assert ( + self.num_producer_threads % tQK_shape_dim_1 == 0 + ), "num_threads must be divisible by tQK_shape_dim_1" + tQ_layout = cute.make_ordered_layout( + (self.num_Q_load_threads // tQK_shape_dim_1, tQK_shape_dim_1), + order=(1, 0), + ) + tK_layout = cute.make_ordered_layout( + (self.num_producer_threads // tQK_shape_dim_1, tQK_shape_dim_1), + order=(1, 0), + ) + # So that we don't have to check if we overshoot kBlockM when we load Q + assert self.tile_m % tQ_layout.shape[0] == 0 + tV_shape_dim_1 = sV_layout_atom.outer.shape[1] // async_copy_elems + tV_layout = cute.make_ordered_layout( + (self.num_producer_threads // tV_shape_dim_1, tV_shape_dim_1), + order=(1, 0), + ) + # TODO: need a different layout for O if O dtype is not the same as V dtype + # tO_layout: thread layout for O store + tO_layout = cute.make_ordered_layout( + (self.num_epilogue_threads // tV_shape_dim_1, tV_shape_dim_1), + order=(1, 0), + ) + # So that we don't have to check if we overshoot kBlockM when we store O + assert self.tile_m % tO_layout.shape[0] == 0 + + # Value layouts for copies + vQKV_layout = cute.make_layout((1, async_copy_elems)) + vO_layout = vQKV_layout + + self.gmem_tiled_copy_Q = cute.make_tiled_copy_tv( + atom_async_copy, tQ_layout, vQKV_layout + ) + self.gmem_tiled_copy_K = cute.make_tiled_copy_tv( + atom_async_copy, tK_layout, vQKV_layout + ) + self.gmem_tiled_copy_V = cute.make_tiled_copy_tv( + atom_async_copy, tV_layout, vQKV_layout + ) + # gmem_tiled_copy_O: tiled copy for O store + self.gmem_tiled_copy_O = cute.make_tiled_copy_tv( + atom_universal_copy, tO_layout, vO_layout + ) + + def _get_smem_layout_atom(self): + raise NotImplementedError() + + def _get_tiled_mma(self): + raise NotImplementedError() + + def _get_shared_storage_cls(self): + raise NotImplementedError() + + @cute.jit + def __call__( + self, + mQ: cute.Tensor, + mK: cute.Tensor, + mV: cute.Tensor, + mO: cute.Tensor, + mLSE: Optional[cute.Tensor], + softmax_scale: Float32, + # Always keep stream as the last parameter (EnvStream: obtained implicitly via TVM FFI). + stream: cuda.CUstream = None, + ): + """Configures and launches the flash attention kernel. + + mQ/mK/mV/mO has same data types(supports fp16 and bf16) and same layout: + (batch_size, seqlen_q, num_head, head_dim):(_, _, _, 1) + """ + raise NotImplementedError() + + @cute.jit + def epilogue( + self, + acc_O: cute.Tensor, + lse: cute.Tensor, + mO: cute.Tensor, + mLSE: Optional[cute.Tensor], + sO: cute.Tensor, + seqlen: SeqlenInfoQK, + gmem_tiled_copy_O: cute.TiledCopy, + tma_atom_O: Optional[cute.CopyAtom], + tiled_mma: cute.TiledMma, + tidx: Int32, + m_block: Int32, + head_idx: Int32, + batch_idx: Int32, + split_idx: Int32 = 0, + ): + if const_expr(self.is_split_kv): + self.epilogue_split( + acc_O, + lse, + mO, + mLSE, + seqlen, + tiled_mma, + tidx, + m_block, + head_idx, + batch_idx, + split_idx, + ) + return + # store acc_O + rO = cute.make_fragment_like(acc_O, self.dtype) + rO.store(acc_O.load().to(self.dtype)) + # Make sure all threads have finished reading V + cute.arch.barrier( + barrier_id=int(NamedBarrierFwd.Epilogue), + number_of_threads=self.num_epilogue_threads, + ) + smem_copy_atom_O = utils.get_smem_store_atom( + self.arch.major * 10 + self.arch.minor, self.dtype + ) + smem_thr_copy_O = cute.make_tiled_copy_C(smem_copy_atom_O, tiled_mma).get_slice( + tidx + ) + taccOrO = smem_thr_copy_O.retile(rO) + taccOsO = smem_thr_copy_O.partition_D(sO) + # taccOsO = copy_utils.partition_D_position_independent(smem_thr_copy_O, sO) + # copy acc O from rmem to smem with the smem copy atom + cute.copy(smem_copy_atom_O, taccOrO, taccOsO) + + cO = cute.make_identity_tensor((self.tile_m, self.tile_hdimv)) + pack_gqa = PackGQA( + self.tile_m, self.tile_hdimv, self.check_hdim_v_oob, self.qhead_per_kvhead + ) + + # Write LSE from rmem -> gmem + if const_expr(mLSE is not None): + mLSE_cur = seqlen.offset_batch_Q(mLSE, batch_idx, dim=2)[None, head_idx] + if const_expr(not self.pack_gqa): + gLSE = cute.local_tile(mLSE_cur, (self.tile_m,), (m_block,)) + gLSE_expanded_layout = cute.append( + gLSE.layout, cute.make_layout((self.tile_hdimv,), stride=(0,)) + ) + gLSE_expanded = cute.make_tensor(gLSE.iterator, gLSE_expanded_layout) + thr_mma = tiled_mma.get_slice(tidx) + taccOgLSE = layout_utils.reshape_acc_to_mn( + thr_mma.partition_C(gLSE_expanded) + ) + assert cute.size(taccOgLSE, mode=[0]) == cute.size(lse) + taccOcO = layout_utils.reshape_acc_to_mn(thr_mma.partition_C(cO)) + t0accOcO = layout_utils.reshape_acc_to_mn( + thr_mma.get_slice(0).partition_C(cO) + ) + # Only the thread corresponding to column 0 writes out the lse to gmem + if taccOcO[0][1] == 0: + for m in cutlass.range( + cute.size(taccOgLSE.shape[1]), unroll_full=True + ): + if ( + t0accOcO[m, 0][0] + < seqlen.seqlen_q - m_block * self.tile_m - taccOcO[0][0] + ): + taccOgLSE[m, 0] = lse[m] + else: + pack_gqa.store_LSE( + mLSE_cur, lse, tiled_mma, tidx, m_block, seqlen.seqlen_q + ) + + ragged = self.use_tma_O and (seqlen.has_cu_seqlens_q or seqlen.has_seqused_q) + mO_cur = seqlen.offset_batch_Q(mO, batch_idx, dim=3, ragged=ragged)[ + None, None, head_idx + ] + # thr_mma = tiled_mma.get_slice(tidx) + # taccOgO = thr_mma.partition_C(gO) + # cute.autovec_copy(rO, taccOgO) + # sync to make sure all smem stores are done + if const_expr(self.use_tma_O): + # ensure smem writes are visible to TMA + cute.arch.fence_view_async_shared() + cute.arch.barrier_arrive( + barrier_id=int(NamedBarrierFwd.Epilogue), + number_of_threads=self.num_epilogue_threads + cute.arch.WARP_SIZE, + ) + gO = cute.local_tile(mO_cur, (self.tile_m, self.tile_hdimv), (m_block, 0)) + store_O, _, _ = copy_utils.tma_get_copy_fn( + tma_atom_O, 0, cute.make_layout(1), sO, gO, single_stage=True + ) + warp_idx = cute.arch.make_warp_uniform(cute.arch.warp_idx()) + if warp_idx == 4: + cute.arch.barrier( + barrier_id=int(NamedBarrierFwd.Epilogue), + number_of_threads=self.num_epilogue_threads + cute.arch.WARP_SIZE, + ) + store_O() + cute.arch.cp_async_bulk_commit_group() + cute.arch.cp_async_bulk_wait_group(0, read=True) + else: + cute.arch.barrier( + barrier_id=int(NamedBarrierFwd.Epilogue), + number_of_threads=self.num_epilogue_threads, + ) + gmem_thr_copy_O = gmem_tiled_copy_O.get_slice(tidx) + tOsO = gmem_thr_copy_O.partition_S(sO) + tOrO = cute.make_fragment_like(tOsO, self.dtype) + # load acc O from smem to rmem for wider vectorization + cute.autovec_copy(tOsO, tOrO) + if const_expr(not self.pack_gqa): + gO = cute.local_tile( + mO_cur, (self.tile_m, self.tile_hdimv), (m_block, 0) + ) + tOgO = gmem_thr_copy_O.partition_D(gO) + tOcO = gmem_thr_copy_O.partition_S(cO) + t0OcO = gmem_tiled_copy_O.get_slice(0).partition_S(cO) + tOpO = utils.predicate_k(tOcO, limit=mO.shape[1]) + # copy acc O from rmem to gmem + for rest_m in cutlass.range_constexpr(cute.size(tOrO.shape[1])): + if ( + t0OcO[0, rest_m, 0][0] + < seqlen.seqlen_q - m_block * self.tile_m - tOcO[0][0] + ): + cute.copy( + gmem_tiled_copy_O, + tOrO[None, rest_m, None], + tOgO[None, rest_m, None], + pred=( + tOpO[None, rest_m, None] + if const_expr(self.check_hdim_v_oob) + else None + ), + ) + else: + pack_gqa.store_O( + mO_cur, tOrO, gmem_tiled_copy_O, tidx, m_block, seqlen.seqlen_q + ) + + @cute.jit + def epilogue_split( + self, + acc_O: cute.Tensor, + lse: cute.Tensor, + mO: cute.Tensor, # (s_q, dv, h, b, num_splits) or (total_q, dv, h, num_splits) + mLSE: Optional[ + cute.Tensor + ], # (s_q, h, b, num_splits) or (total_q, h, num_splits) + seqlen: SeqlenInfoQK, + tiled_mma: cute.TiledMma, + tidx: Int32, + m_block: Int32, + head_idx: Int32, + batch_idx: Int32, + split_idx: Int32, + ): + thr_mma = tiled_mma.get_slice(tidx) + cO = cute.make_identity_tensor((self.tile_m, self.tile_hdimv)) + taccOcO = layout_utils.reshape_acc_to_mn(thr_mma.partition_C(cO)) + acc_O_mn = layout_utils.reshape_acc_to_mn(acc_O) + + if const_expr(not self.pack_gqa): + # Write LSE: only the thread that owns column 0 of each row writes. + if const_expr(mLSE is not None): + mLSE_cur = seqlen.offset_batch_Q(mLSE, batch_idx, dim=2)[ + None, head_idx, split_idx + ] + gLSE = cute.local_tile(mLSE_cur, (self.tile_m,), (m_block,)) + if taccOcO[0][1] == 0: + for r in cutlass.range( + cute.size(taccOcO, mode=[0]), unroll_full=True + ): + row = taccOcO[r, 0][0] + if m_block * self.tile_m + row < seqlen.seqlen_q: + gLSE[row] = lse[r] + + # Write O partials (float32) directly from the accumulator. + mO_cur = seqlen.offset_batch_Q(mO, batch_idx, dim=3)[ + None, None, head_idx, split_idx + ] + gO = cute.local_tile(mO_cur, (self.tile_m, self.tile_hdimv), (m_block, 0)) + taccOgO = layout_utils.reshape_acc_to_mn(thr_mma.partition_C(gO)) + for r in cutlass.range(cute.size(acc_O_mn, mode=[0]), unroll_full=True): + if m_block * self.tile_m + taccOcO[r, 0][0] < seqlen.seqlen_q: + for c in cutlass.range( + cute.size(acc_O_mn, mode=[1]), unroll_full=True + ): + if ( + const_expr(not self.check_hdim_v_oob) + or taccOcO[r, c][1] < mO.shape[1] + ): + taccOgO[r, c] = acc_O_mn[r, c] + else: + # pack_gqa: mO/mLSE arrive pre-packed by pack_gqa_layout — mode 0 is the + # hierarchical (qhead_per_kvhead, seqlen_q) row and the head mode indexes + # KV heads. An integer row coordinate decomposes colexicographically as + # (row % qhead, row // qhead), which is exactly the kernel's packed row + # (q-head fastest), so we index mode 0 with the tile row directly. + row_limit = seqlen.seqlen_q * self.qhead_per_kvhead + if const_expr(mLSE is not None): + mLSE_cur = seqlen.offset_batch_Q(mLSE, batch_idx, dim=2)[ + None, head_idx, split_idx + ] + if taccOcO[0][1] == 0: + for r in cutlass.range( + cute.size(taccOcO, mode=[0]), unroll_full=True + ): + row = m_block * self.tile_m + taccOcO[r, 0][0] + if row < row_limit: + mLSE_cur[row] = lse[r] + mO_cur = seqlen.offset_batch_Q(mO, batch_idx, dim=3)[ + None, None, head_idx, split_idx + ] + for r in cutlass.range(cute.size(acc_O_mn, mode=[0]), unroll_full=True): + row = m_block * self.tile_m + taccOcO[r, 0][0] + if row < row_limit: + for c in cutlass.range( + cute.size(acc_O_mn, mode=[1]), unroll_full=True + ): + if ( + const_expr(not self.check_hdim_v_oob) + or taccOcO[r, c][1] < mO.shape[1] + ): + mO_cur[row, taccOcO[r, c][1]] = acc_O_mn[r, c] + + @cute.jit + def advance_pipeline(self, pipeline_index): + return pipeline_index + 1 if pipeline_index < self.num_stages - 1 else 0 + + @cute.jit + def load_Q( + self, + gmem_thr_copy: cute.TiledCopy, + gQ: cute.Tensor, + sQ: cute.Tensor, + block: Int32, + seqlen: Int32, + headdim: Int32, + ): + tQsQ, tQgQ = gmem_thr_copy.partition_D(sQ), gmem_thr_copy.partition_S(gQ) + cQ = cute.make_identity_tensor((self.tile_m, self.tile_hdim)) + tQcQ = gmem_thr_copy.partition_S(cQ) + t0QcQ = gmem_thr_copy.get_slice(0).partition_S(cQ) + tQpQ = utils.predicate_k(tQcQ, limit=headdim) + for m in cutlass.range_constexpr(cute.size(tQsQ.shape[1])): + # Instead of using tQcQ, we using t0QcQ and subtract the offset from the limit + # (seqlen - block * kBlockM). This is because the entries of t0QcQ are known at compile time. + if t0QcQ[0, m, 0][0] < seqlen - block * self.tile_m - tQcQ[0][0]: + cute.copy( + gmem_thr_copy, + tQgQ[None, m, None], + tQsQ[None, m, None], + pred=( + tQpQ[None, m, None] if const_expr(self.check_hdim_oob) else None + ), + ) + # We don't need to clear the sQ smem tiles since we'll only write out the valid outputs + + @cute.jit + def load_K( + self, + gmem_tiled_copy: cute.TiledCopy, + tKgK: cute.Tensor, + tKsK: cute.Tensor, + tKcK: cute.Tensor, + t0KcK: cute.Tensor, + tKpK: cute.Tensor, + block: Int32, + smem_pipe_write: Int32, + seqlen: Int32, + need_predicates: cutlass.Constexpr, + ): + # Do we need to check if we overshoot kBlockN when we load K? + is_even_n_smem_k = self.tile_n % gmem_tiled_copy.tiler_mn[0].shape == 0 + if const_expr(need_predicates or not is_even_n_smem_k): + # Instead of using tKcK, we using t0KcK and subtract the offset from the limit + # (seqlen - block * kBlockN). This is because the entries of t0KcK are known at compile time. + if const_expr(is_even_n_smem_k): + seqlen_limit = seqlen - block * self.tile_n + else: + if const_expr(not need_predicates): + seqlen_limit = self.tile_n + else: + seqlen_limit = cutlass.min( + seqlen - block * self.tile_n, self.tile_n + ) + seqlen_limit -= tKcK[0][0] + for n in cutlass.range_constexpr(cute.size(tKsK.shape[1])): + if t0KcK[0, n, 0][0] < seqlen_limit: + cute.copy( + gmem_tiled_copy, + tKgK[None, n, None, block], + tKsK[ + None, + n, + None, + smem_pipe_write if const_expr(self.num_stages > 1) else 0, + ], + pred=( + tKpK[None, n, None] + if const_expr(self.check_hdim_oob) + else None + ), + ) + # We don't need to clear the sK smem tiles since we'll mask out the scores anyway. + else: + cute.copy( + gmem_tiled_copy, + tKgK[None, None, None, block], + tKsK[ + None, + None, + None, + smem_pipe_write if const_expr(self.num_stages > 1) else 0, + ], + pred=tKpK if const_expr(self.check_hdim_oob) else None, + ) + + @cute.jit + def load_V( + self, + gmem_tiled_copy: cute.TiledCopy, + tVgV: cute.Tensor, + tVsV: cute.Tensor, + tVcV: cute.Tensor, + t0VcV: cute.Tensor, + tVpV: cute.Tensor, + block: Int32, + smem_pipe_write: Int32, + seqlen: Int32, + need_predicates: cutlass.Constexpr, + ): + # Do we need to check if we overshoot kBlockN when we load V? + is_even_n_smem_v = self.tile_n % gmem_tiled_copy.tiler_mn[0].shape == 0 + if const_expr(need_predicates or not is_even_n_smem_v): + for n in cutlass.range_constexpr(cute.size(tVsV.shape[1])): + # If kBlockN doesn't evenly divide the tiled copy, only the last `n` needs to be checked + if ( + is_even_n_smem_v + or n < cute.size(tVsV.shape[1]) - 1 + or tVcV[0, n, 0][0] < self.tile_n + ): + predicate = ( + tVpV[None, n, None] + if const_expr(self.check_hdim_v_oob) + else None + ) + if const_expr(need_predicates): + seqlen_limit = seqlen - block * self.tile_n - tVcV[0][0] + predicate_n = t0VcV[0, n, 0][0] < seqlen_limit + predicate = cute.make_fragment_like(tVpV[None, 0, None]) + for k in cutlass.range_constexpr(cute.size(predicate.shape[1])): + for i in cutlass.range_constexpr( + cute.size(predicate.shape[0]) + ): + predicate[i, k] = ( + tVpV[i, n, k] + if const_expr(self.check_hdim_v_oob) + else True + ) and predicate_n + cute.copy( + gmem_tiled_copy, + tVgV[None, n, None, block], + tVsV[ + None, + n, + None, + smem_pipe_write if const_expr(self.num_stages > 1) else 0, + ], + pred=predicate, + ) + else: + cute.copy( + gmem_tiled_copy, + tVgV[None, None, None, block], + tVsV[ + None, + None, + None, + smem_pipe_write if const_expr(self.num_stages > 1) else 0, + ], + pred=tVpV if const_expr(self.check_hdim_v_oob) else None, + ) + + +class FlashAttentionForwardSm80(FlashAttentionForwardBase): + def _get_smem_layout_atom(self): + sQ_layout_atom = sm80_utils.get_smem_layout_atom(self.dtype, self.tile_hdim) + sK_layout_atom = sQ_layout_atom + sV_layout_atom = sm80_utils.get_smem_layout_atom(self.dtype, self.tile_hdimv) + sO_layout_atom = sV_layout_atom + sP_layout_atom = None + return ( + sQ_layout_atom, + sK_layout_atom, + sV_layout_atom, + sO_layout_atom, + sP_layout_atom, + ) + + def _get_tiled_mma(self): + tiled_mma_qk = cute.make_tiled_mma( + warp.MmaF16BF16Op(self.dtype, Float32, (16, 8, 16)), + (self.num_threads // 32, 1, 1), + permutation_mnk=(self.num_threads // 32 * 16, 16, 16), + ) + tiled_mma_pv = cute.make_tiled_mma( + warp.MmaF16BF16Op(self.dtype, Float32, (16, 8, 16)), + (self.num_threads // 32, 1, 1), + permutation_mnk=(self.num_threads // 32 * 16, 16, 16), + ) + return tiled_mma_qk, tiled_mma_pv + + def _get_shared_storage_cls(self): + sQ_struct, sK_struct, sV_struct = [ + cute.struct.Align[ + cute.struct.MemRange[self.dtype, cute.cosize(layout)], 1024 + ] + for layout in (self.sQ_layout, self.sK_layout, self.sV_layout) + ] + cosize_sQV = max(cute.cosize(self.sQ_layout), cute.cosize(self.sV_layout)) + sQV_struct = cute.struct.Align[ + cute.struct.MemRange[self.dtype, cosize_sQV], 1024 + ] + + @cute.struct + class SharedStorageQKV: + sV: sV_struct + sQ: sQ_struct + sK: sK_struct + + @cute.struct + class SharedStorageSharedQV: + sQ: sQV_struct + sK: sK_struct + + return ( + SharedStorageQKV + if const_expr(not self.Q_in_regs) + else SharedStorageSharedQV + ) + + @cute.jit + def __call__( + self, + mQ: cute.Tensor, + mK: cute.Tensor, + mV: cute.Tensor, + mO: cute.Tensor, + mLSE: Optional[cute.Tensor], + softmax_scale: Float32, + mCuSeqlensQ: Optional[cute.Tensor] = None, + mCuSeqlensK: Optional[cute.Tensor] = None, + mSeqUsedQ: Optional[cute.Tensor] = None, + mSeqUsedK: Optional[cute.Tensor] = None, + mPageTable: Optional[cute.Tensor] = None, + window_size_left: Optional[Int32] = None, + window_size_right: Optional[Int32] = None, + learnable_sink: Optional[cute.Tensor] = None, + blocksparse_tensors: Optional[BlockSparseTensors] = None, + aux_data: AuxData = AuxData(), + # Always keep stream as the last parameter (EnvStream: obtained implicitly via TVM FFI). + stream: cuda.CUstream = None, + ): + """Configures and launches the flash attention kernel. + + mQ/mK/mV/mO has same data types(supports fp16 and bf16) and same layout: + (batch_size, seqlen_q, num_head, head_dim):(_, _, _, 1) + """ + assert learnable_sink is None, "Learnable sink is not supported in this kernel" + self._check_type( + *( + t.element_type if t is not None else None + for t in ( + mQ, + mK, + mV, + mO, + mLSE, + mCuSeqlensQ, + mCuSeqlensK, + mSeqUsedQ, + mSeqUsedK, + ) + ) + ) + tiled_mma_qk, tiled_mma_pv = self._get_tiled_mma() + self.num_mma_threads = tiled_mma_pv.size + self.num_producer_threads = self.num_threads + self.num_Q_load_threads = self.num_threads + self.num_epilogue_threads = self.num_threads + # self.use_tma_O = self.arch >= 90 and mCuSeqlensQ is None + self.use_tma_O = self.arch >= Arch.sm_90 + self._setup_attributes() + SharedStorage = self._get_shared_storage_cls() + mQ, mK, mV, mO = [assume_tensor_aligned(t) for t in (mQ, mK, mV, mO)] + # Layout permutation: 4D non-varlen vs 3D varlen + QO_layout_transpose = ( + [1, 3, 2, 0] if const_expr(mCuSeqlensQ is None) else [0, 2, 1] + ) + KV_layout_transpose = ( + [1, 3, 2, 0] if const_expr(mCuSeqlensK is None) else [0, 2, 1] + ) + mQ, mO = [ + cute.make_tensor( + t.iterator, cute.select(t.layout, mode=QO_layout_transpose) + ) + for t in (mQ, mO) + ] + mK, mV = [ + cute.make_tensor( + t.iterator, cute.select(t.layout, mode=KV_layout_transpose) + ) + for t in (mK, mV) + ] + if const_expr(mLSE is not None): + LSE_layout_transpose = ( + [2, 1, 0] if const_expr(mCuSeqlensQ is None) else [1, 0] + ) + mLSE = cute.make_tensor( + mLSE.iterator, cute.select(mLSE.layout, mode=LSE_layout_transpose) + ) + # TileScheduler for varlen, simple grid for non-varlen + if const_expr(mCuSeqlensQ is not None or mSeqUsedQ is not None): + TileScheduler = SingleTileVarlenScheduler + else: + TileScheduler = SingleTileScheduler + num_batch = ( + mCuSeqlensQ.shape[0] - 1 + if const_expr(mCuSeqlensQ is not None) + else mQ.shape[3] + ) + tile_sched_args = TileSchedulerArguments( + num_block=cute.ceil_div(mQ.shape[0], self.tile_m), + num_head=cute.size(mQ.shape[2]), + num_batch=num_batch, + num_splits=1, + seqlen_k=0, + headdim=mQ.shape[1], + headdim_v=mV.shape[1], + total_q=( + cute.size(mQ.shape[0]) + if const_expr(mCuSeqlensQ is not None) + else cute.size(mQ.shape[0]) * cute.size(mQ.shape[3]) + ), + tile_shape_mn=(self.tile_m, self.tile_n), + qhead_per_kvhead_packgqa=( + self.qhead_per_kvhead if const_expr(self.pack_gqa) else 1 + ), + mCuSeqlensQ=mCuSeqlensQ, + mSeqUsedQ=mSeqUsedQ, + ) + tile_sched_params = TileScheduler.to_underlying_arguments(tile_sched_args) + grid_dim = TileScheduler.get_grid_shape(tile_sched_params) + softmax_scale_log2, softmax_scale = utils.compute_softmax_scale_log2( + softmax_scale, self.score_mod + ) + fastdiv_mods = utils.compute_fastdiv_mods( + mQ, mK, self.qhead_per_kvhead, self.pack_gqa, aux_data.tensors + ) + + self.kernel( + mQ, + mK, + mV, + mO, + mLSE, + mCuSeqlensQ, + mCuSeqlensK, + mSeqUsedQ, + mSeqUsedK, + softmax_scale_log2, + softmax_scale, + window_size_left, + window_size_right, + self.sQ_layout, + self.sK_layout, + self.sV_layout, + self.sO_layout, + self.sP_layout, + self.gmem_tiled_copy_Q, + self.gmem_tiled_copy_K, + self.gmem_tiled_copy_V, + self.gmem_tiled_copy_O, + tiled_mma_qk, + tiled_mma_pv, + SharedStorage, + tile_sched_params, + TileScheduler, + aux_data, + fastdiv_mods, + ).launch( + grid=grid_dim, + block=[self.num_threads, 1, 1], + smem=SharedStorage.size_in_bytes(), + stream=stream, + ) + + @cute.kernel + def kernel( + self, + mQ: cute.Tensor, + mK: cute.Tensor, + mV: cute.Tensor, + mO: cute.Tensor, + mLSE: Optional[cute.Tensor], + mCuSeqlensQ: Optional[cute.Tensor], + mCuSeqlensK: Optional[cute.Tensor], + mSeqUsedQ: Optional[cute.Tensor], + mSeqUsedK: Optional[cute.Tensor], + softmax_scale_log2: Float32, + softmax_scale: Optional[Float32], + window_size_left: Optional[Int32], + window_size_right: Optional[Int32], + sQ_layout: cute.ComposedLayout, + sK_layout: cute.ComposedLayout, + sV_layout: cute.ComposedLayout, + sO_layout: cute.ComposedLayout, + sP_layout: cute.ComposedLayout | None, + gmem_tiled_copy_Q: cute.TiledCopy, + gmem_tiled_copy_K: cute.TiledCopy, + gmem_tiled_copy_V: cute.TiledCopy, + gmem_tiled_copy_O: cute.TiledCopy, + tiled_mma_qk: cute.TiledMma, + tiled_mma_pv: cute.TiledMma, + SharedStorage: cutlass.Constexpr, + tile_sched_params, + TileScheduler: cutlass.Constexpr[Callable], + aux_data: AuxData = AuxData(), + fastdiv_mods=None, + ): + # Thread index, block index + tidx, _, _ = cute.arch.thread_idx() + + tile_scheduler = TileScheduler.create(tile_sched_params) + work_tile = tile_scheduler.initial_work_tile_info() + m_block, num_head, batch_size, _ = work_tile.tile_idx + + block_info = BlockInfo( + self.tile_m, + self.tile_n, + self.is_causal, + self.is_local, + False, # is_split_kv + window_size_left, + window_size_right, + qhead_per_kvhead_packgqa=( + self.qhead_per_kvhead if const_expr(self.pack_gqa) else 1 + ), + ) + seqlen = SeqlenInfoQK.create( + batch_idx=batch_size, + seqlen_q_static=mQ.shape[0], + seqlen_k_static=mK.shape[0], + mCuSeqlensQ=mCuSeqlensQ, + mCuSeqlensK=mCuSeqlensK, + mSeqUsedQ=mSeqUsedQ, + mSeqUsedK=mSeqUsedK, + ) + n_block_min, n_block_max = block_info.get_n_block_min_max(seqlen, m_block) + # For varlen, wasted grid tiles (where batch_idx >= num_batch) will have + # seqlen_q=seqlen_k=0 and n_block_max=0. Clamp to 0 so we don't use a + # negative block index for K/V loads; the load/store predicates already + # guard all memory accesses when seqlen is 0. + n_block = cutlass.max(n_block_max - 1, 0) + + # /////////////////////////////////////////////////////////////////////////////// + # Get the appropriate tiles for this thread block. + # /////////////////////////////////////////////////////////////////////////////// + blkQ_shape = (self.tile_m, self.tile_hdim) + blkK_shape = (self.tile_n, self.tile_hdim) + blkV_shape = (self.tile_n, self.tile_hdimv) + num_head_kv = num_head // self.qhead_per_kvhead + if const_expr(not seqlen.has_cu_seqlens_q): + mQ_cur = mQ[None, None, num_head, batch_size] + else: + mQ_cur = cute.domain_offset((seqlen.offset_q, 0), mQ[None, None, num_head]) + if const_expr(not seqlen.has_cu_seqlens_k): + mK_cur = mK[None, None, num_head_kv, batch_size] + mV_cur = mV[None, None, num_head_kv, batch_size] + else: + mK_cur = cute.domain_offset( + (seqlen.offset_k, 0), mK[None, None, num_head_kv] + ) + mV_cur = cute.domain_offset( + (seqlen.offset_k, 0), mV[None, None, num_head_kv] + ) + gQ = cute.local_tile(mQ_cur, blkQ_shape, (m_block, 0)) + gK = cute.local_tile(mK_cur, blkK_shape, (None, 0)) + gV = cute.local_tile(mV_cur, blkV_shape, (None, 0)) + + # /////////////////////////////////////////////////////////////////////////////// + # Get shared memory buffer + # /////////////////////////////////////////////////////////////////////////////// + smem = cutlass.utils.SmemAllocator() + storage = smem.allocate(SharedStorage) + sQ = storage.sQ.get_tensor(sQ_layout) + sK = storage.sK.get_tensor(sK_layout) + if const_expr(not self.Q_in_regs): + sV = storage.sV.get_tensor(sV_layout) + else: + sV = cute.make_tensor( + cute.recast_ptr(sQ.iterator, dtype=self.dtype), sV_layout + ) + # Transpose view of V to tensor with layout (head_dim_v, tile_n) for tiled mma + sVt = layout_utils.transpose_view(sV) + + gmem_thr_copy_K = gmem_tiled_copy_K.get_slice(tidx) + gmem_thr_copy_V = gmem_tiled_copy_V.get_slice(tidx) + # (CPY_Atom, CPY_N, CPY_K, n_block) + tKsK, tKgK = gmem_thr_copy_K.partition_D(sK), gmem_thr_copy_K.partition_S(gK) + # (CPY_Atom, CPY_N, CPY_K, n_block) + tVsV, tVgV = gmem_thr_copy_V.partition_D(sV), gmem_thr_copy_V.partition_S(gV) + + # /////////////////////////////////////////////////////////////////////////////// + # Tile MMA compute thread partitions and allocate accumulators + # /////////////////////////////////////////////////////////////////////////////// + thr_mma_qk = tiled_mma_qk.get_slice(tidx) + thr_mma_pv = tiled_mma_pv.get_slice(tidx) + tSrQ = thr_mma_qk.make_fragment_A(thr_mma_qk.partition_A(sQ)) + tSrK = thr_mma_qk.make_fragment_B(thr_mma_qk.partition_B(sK[None, None, 0])) + tOrVt = thr_mma_pv.make_fragment_B(thr_mma_pv.partition_B(sVt[None, None, 0])) + acc_shape_O = thr_mma_pv.partition_shape_C((self.tile_m, self.tile_hdimv)) + acc_O = cute.make_rmem_tensor(acc_shape_O, Float32) + acc_O.fill(0.0) + + # /////////////////////////////////////////////////////////////////////////////// + # Smem copy atom tiling + # /////////////////////////////////////////////////////////////////////////////// + smem_copy_atom_QK = cute.make_copy_atom( + warp.LdMatrix8x8x16bOp(transpose=False, num_matrices=4), + self.dtype, + ) + smem_copy_atom_V = cute.make_copy_atom( + warp.LdMatrix8x8x16bOp(transpose=True, num_matrices=4), + self.dtype, + ) + smem_thr_copy_Q = utils.make_tiled_copy_A( + smem_copy_atom_QK, tiled_mma_qk + ).get_slice(tidx) + smem_thr_copy_K = utils.make_tiled_copy_B( + smem_copy_atom_QK, tiled_mma_qk + ).get_slice(tidx) + smem_thr_copy_V = utils.make_tiled_copy_B( + smem_copy_atom_V, tiled_mma_pv + ).get_slice(tidx) + + tSsQ = smem_thr_copy_Q.partition_S(sQ) + tSsK = smem_thr_copy_K.partition_S(sK) + tOsVt = smem_thr_copy_V.partition_S(sVt) + + # /////////////////////////////////////////////////////////////////////////////// + # Predicate: Mark indices that need to copy when problem_shape isn't a multiple + # of tile_shape + # /////////////////////////////////////////////////////////////////////////////// + # Construct identity layout for KV + cK = cute.make_identity_tensor((self.tile_n, self.tile_hdim)) + tKcK = gmem_thr_copy_K.partition_S(cK) + t0KcK = gmem_thr_copy_K.get_slice(0).partition_S(cK) + if const_expr(self.tile_hdim == self.tile_hdimv): + tVcV = tKcK + t0VcV = t0KcK + else: + cV = cute.make_identity_tensor((self.tile_n, self.tile_hdimv)) + tVcV = gmem_thr_copy_V.partition_S(cV) + t0VcV = gmem_thr_copy_V.get_slice(0).partition_S(cV) + # Allocate predicate tensors for m and n, here we only allocate the tile of k, and + # use "if" on the mn dimension. + # This is to reduce register pressure and gets 2-3% performance gain. + tKpK = utils.predicate_k(tKcK, limit=mK.shape[1]) + if const_expr(self.same_hdim_kv): + tVpV = tKpK + else: + tVpV = utils.predicate_k(tVcV, limit=mV.shape[1]) + + # shape: (atom_v_m * rest_m) + softmax = Softmax.create( + softmax_scale_log2, + num_rows=acc_O.shape[0][0] * acc_O.shape[1], + softmax_scale=softmax_scale, + ) + softmax.reset() + + # group parameters for compute_one_n_block + mma_params = SimpleNamespace( + thr_mma_qk=thr_mma_qk, + thr_mma_pv=thr_mma_pv, + tSrQ=tSrQ, + tSrK=tSrK, + tOrVt=tOrVt, + acc_O=acc_O, + ) + smem_copy_params = SimpleNamespace( + smem_thr_copy_Q=smem_thr_copy_Q, + smem_thr_copy_K=smem_thr_copy_K, + smem_thr_copy_V=smem_thr_copy_V, + tSsQ=tSsQ, + tSsK=tSsK, + tOsVt=tOsVt, + ) + load_K = partial( + self.load_K, + gmem_tiled_copy_K, + tKgK, + tKsK, + tKcK, + t0KcK, + tKpK, + seqlen=seqlen.seqlen_k, + ) + load_V = partial( + self.load_V, + gmem_tiled_copy_V, + tVgV, + tVsV, + tVcV, + t0VcV, + tVpV, + seqlen=seqlen.seqlen_k, + ) + + compute_one_n_block = partial( + self.compute_one_n_block, + mma_params=mma_params, + smem_copy_params=smem_copy_params, + softmax=softmax, + load_K=load_K, + load_V=load_V, + score_mod=self.score_mod, + batch_idx=batch_size, + head_idx=num_head, + m_block=m_block, + aux_data=aux_data, + fastdiv_mods=fastdiv_mods, + ) + + # /////////////////////////////////////////////////////////////////////////////// + # Prologue + # /////////////////////////////////////////////////////////////////////////////// + # Start async loads of the last mn-tile, where we take care of the mn residue + gmem_thr_copy_Q = gmem_tiled_copy_Q.get_slice(tidx) + self.load_Q( + gmem_thr_copy_Q, + gQ, + sQ, + m_block, + seqlen=seqlen.seqlen_q, + headdim=mQ.shape[1], + ) + cute.arch.cp_async_commit_group() + + def preprocess_Q(): + cute.arch.cp_async_wait_group(self.num_stages * 2 - 1) + if const_expr(self.Q_in_regs): + cute.arch.barrier() + tSrQ_copy_view = smem_thr_copy_Q.retile(tSrQ) + cute.copy(smem_thr_copy_Q, tSsQ, tSrQ_copy_view) + + # If Q_in_regs, we load Q, then load 1 stage of K, then (optionally) rotate Q and + # read from smem_q to registers, then load V. + # If !Q_in_regs, we load Q, load all stages of K & V, then (optionally) rotate Q. + if const_expr(self.Q_in_regs): + load_K(n_block, smem_pipe_write=0, need_predicates=True) + cute.arch.cp_async_commit_group() + preprocess_Q() + cute.arch.barrier() # Make sure all threads have read smem_q before loading V + + for stage in cutlass.range_constexpr(self.num_stages): + if const_expr(not self.Q_in_regs or stage > 0): + if stage == 0 or n_block - stage >= 0: + load_K( + n_block - stage, + smem_pipe_write=stage, + need_predicates=stage == 0, + ) + cute.arch.cp_async_commit_group() + if const_expr(stage < self.num_stages - 1): + if stage == 0 or n_block - stage >= 0: + load_V( + n_block - stage, + smem_pipe_write=stage, + need_predicates=stage == 0, + ) + cute.arch.cp_async_commit_group() + if const_expr(not self.Q_in_regs): + preprocess_Q() + + # /////////////////////////////////////////////////////////////////////////////// + # Mainloop + # /////////////////////////////////////////////////////////////////////////////// + # Start processing of the first n-block. + # For performance reason, we separate out two kinds of iterations: + # those that need masking on S, and those that don't. + # We need masking on S for the very last block when K and V has length not multiple of tile_n. + # We also need masking on S if it's causal, for the last several blocks. + mask = AttentionMask( + self.tile_m, + self.tile_n, + seqlen, + window_size_left, + window_size_right, + self.qhead_per_kvhead if const_expr(self.pack_gqa) else 1, + ) + mask_fn = partial( + mask.apply_mask, + batch_idx=batch_size, + head_idx=num_head, + m_block=m_block, + thr_mma=thr_mma_qk, + mask_causal=self.is_causal, + mask_local=self.is_local, + aux_data=aux_data, + fastdiv_mods=( + fastdiv_mods if const_expr(self.mask_mod is not None) else None + ), + ) + + # First iteration with seqlen masking + smem_pipe_read = Int32(0) + smem_pipe_write = Int32(self.num_stages - 1) + compute_one_n_block( + n_block, + smem_pipe_read, + smem_pipe_write, + is_first_n_block=True, + seqlen=seqlen, + mask_fn=partial(mask_fn, mask_mod=self.mask_mod, mask_seqlen=True), + ) + smem_pipe_read = self.advance_pipeline(smem_pipe_read) + smem_pipe_write = self.advance_pipeline(smem_pipe_write) + # Next couple of iterations with causal masking + if const_expr(self.is_causal or self.is_local): + n_block_min_causal_local_mask = ( + block_info.get_n_block_min_causal_local_mask( + seqlen, m_block, n_block_min + ) + ) + for n_tile in cutlass.range( + n_block_max - 1 - n_block_min_causal_local_mask, unroll=1 + ): + n_block = n_block_max - 2 - n_tile + compute_one_n_block( + n_block, + smem_pipe_read, + smem_pipe_write, + seqlen=seqlen, + mask_fn=partial(mask_fn, mask_mod=self.mask_mod, mask_seqlen=True), + ) + smem_pipe_read = self.advance_pipeline(smem_pipe_read) + smem_pipe_write = self.advance_pipeline(smem_pipe_write) + # The remaining iterations have no masking + for n_tile in cutlass.range(n_block, unroll=1): + compute_one_n_block( + n_block - n_tile - 1, + smem_pipe_read, + smem_pipe_write, + seqlen=seqlen, + is_first_n_block=False, + mask_fn=partial(mask_fn, mask_mod=self.mask_mod, mask_seqlen=False), + ) + smem_pipe_read = self.advance_pipeline(smem_pipe_read) + smem_pipe_write = self.advance_pipeline(smem_pipe_write) + # TODO: local + + # normalize acc_O by row_sum and calculate the lse + row_scale = softmax.finalize() + softmax.rescale_O(acc_O, row_scale) + + # /////////////////////////////////////////////////////////////////////////////// + # Epilogue + # /////////////////////////////////////////////////////////////////////////////// + # reuse sQ's data iterator + sO = cute.make_tensor(sQ.iterator, sO_layout) + self.epilogue( + acc_O, + softmax.row_sum, + mO, + mLSE, + sO, + seqlen, + gmem_tiled_copy_O, + None, + tiled_mma_pv, + tidx, + m_block, + num_head, + batch_size, + ) + + @cute.jit + def compute_one_n_block( + self, + n_block: Int32, + smem_pipe_read: Int32, + smem_pipe_write: Int32, + mma_params: SimpleNamespace, + smem_copy_params: SimpleNamespace, + softmax: Softmax, + load_K: Callable, + load_V: Callable, + score_mod: Callable | None, + batch_idx: cutlass.Int32, + head_idx: cutlass.Int32, + m_block: cutlass.Int32, + seqlen: SeqlenInfoQK, + aux_data: AuxData = AuxData(), + fastdiv_mods=None, + mask_fn: Optional[Callable] = None, + is_first_n_block: cutlass.Constexpr = False, + check_inf: cutlass.Constexpr = True, + ): + """Compute one n_block of S/O. + + This function provides different variants for processing the first n block versus + subsequent blocks. + """ + + def sync(): + cute.arch.cp_async_wait_group(self.num_stages * 2 - 2) + cute.arch.barrier() + + acc_shape_S = mma_params.thr_mma_qk.partition_shape_C( + (self.tile_m, self.tile_n) + ) + acc_S = cute.make_rmem_tensor(acc_shape_S, Float32) + acc_S.fill(0.0) + # wait for smem tile QK before mma calculation for S + sync() + + # need predicates for the first tile + def load_V_next(): + if self.num_stages == 1 or n_block - self.num_stages + 1 >= 0: + load_V( + n_block - self.num_stages + 1, + smem_pipe_write, + need_predicates=is_first_n_block and self.num_stages == 1, + ) + cute.arch.cp_async_commit_group() + + load_V_next() + sm80_utils.gemm( + mma_params.thr_mma_qk, + acc_S, + mma_params.tSrQ, + mma_params.tSrK, + smem_copy_params.tSsQ, + smem_copy_params.tSsK[ + None, + None, + None, + smem_pipe_read if const_expr(self.num_stages > 1) else 0, + ], + smem_copy_params.smem_thr_copy_Q, + smem_copy_params.smem_thr_copy_K, + # hook_fn=load_V_next, + A_in_regs=self.Q_in_regs, + ) + if const_expr(score_mod is not None): + self.apply_score_mod( + mma_params.thr_mma_qk, + batch_idx, + head_idx, + m_block, + acc_S, + n_block, + softmax_scale=softmax.softmax_scale, + seqlen=seqlen, + aux_data=aux_data, + fastdiv_mods=fastdiv_mods, + ) + + smem_pipe_write = self.advance_pipeline(smem_pipe_write) + + def load_K_next(): + if n_block - self.num_stages >= 0: + load_K( + n_block - self.num_stages, smem_pipe_write, need_predicates=False + ) + cute.arch.cp_async_commit_group() + + # wait for smem tile V for O + if const_expr(self.num_stages == 1): + sync() + load_K_next() + if const_expr(mask_fn is not None): + mask_fn(acc_S, n_block=n_block) + row_scale = softmax.online_softmax( + acc_S, is_first=is_first_n_block, check_inf=check_inf + ) + softmax.rescale_O(mma_params.acc_O, row_scale) + rP = cute.make_fragment_like(acc_S, self.dtype) + rP.store(acc_S.load().to(self.dtype)) + tOrP = layout_utils.reshape_acc_to_frgA(rP) + if const_expr(self.num_stages > 1): + sync() + load_K_next() + sm80_utils.gemm_rs( + mma_params.thr_mma_pv, + mma_params.acc_O, + tOrP, + mma_params.tOrVt, + smem_copy_params.tOsVt[ + None, + None, + None, + smem_pipe_read if const_expr(self.num_stages > 1) else 0, + ], + smem_copy_params.smem_thr_copy_V, + # hook_fn=load_K_next, + ) + # if const_expr(self.num_stages > 1): + # load_K_next() + + @cute.jit + def apply_score_mod( + self, + thr_mma_qk, + batch_idx, + head_idx, + m_block, + acc_S, + n_block, + softmax_scale, + seqlen, + aux_data: AuxData = AuxData(), + fastdiv_mods=None, + ): + # Prepare index tensor + cS = cute.make_identity_tensor((self.tile_m, self.tile_n)) + cS = cute.domain_offset((m_block * self.tile_m, n_block * self.tile_n), cS) + tScS = thr_mma_qk.partition_C(cS) + + apply_score_mod_inner( + acc_S, + tScS, + self.score_mod, + batch_idx, + head_idx, + softmax_scale, + self.score_vec_size, + self.qk_acc_dtype, + aux_data, + fastdiv_mods, + seqlen_info=seqlen, + constant_q_idx=None, + qhead_per_kvhead=self.qhead_per_kvhead if const_expr(self.pack_gqa) else 1, + ) + + +# SM90 forward pass moved to flash_fwd_sm90.py; re-export for backward compatibility +def __getattr__(name): + if name == "FlashAttentionForwardSm90": + from sglang.jit_kernel.flash_attn.cute.flash_fwd_sm90 import ( + FlashAttentionForwardSm90, + ) + + return FlashAttentionForwardSm90 + raise AttributeError(f"module {__name__!r} has no attribute {name!r}") diff --git a/python/sglang/jit_kernel/flash_attn/cute/flash_fwd_combine.py b/python/sglang/jit_kernel/flash_attn/cute/flash_fwd_combine.py new file mode 100644 index 000000000..5d08d11ad --- /dev/null +++ b/python/sglang/jit_kernel/flash_attn/cute/flash_fwd_combine.py @@ -0,0 +1,767 @@ +# Copyright (c) 2025, Jay Shah, Ganesh Bikshandi, Ying Zhang, Vijay Thakkar, Pradeep Ramani, Tri Dao. +# A reimplementation of https://github.com/Dao-AILab/flash-attention/blob/main/hopper/flash_fwd_combine_kernel.h +# from Cutlass C++ to Cute-DSL. +import math +from functools import partial +from typing import Optional, Type + +import cuda.bindings.driver as cuda +import cutlass +import cutlass.cute as cute +from cutlass import Boolean, Float32, Int32, const_expr +from cutlass.cute import FastDivmodDivisor +from cutlass.cute.nvgpu import cpasync + +from sglang.jit_kernel.flash_attn.cute import utils +from sglang.jit_kernel.flash_attn.cute.cute_dsl_utils import assume_tensor_aligned +from sglang.jit_kernel.flash_attn.cute.seqlen_info import SeqlenInfo + + +class FlashAttentionForwardCombine: + def __init__( + self, + dtype: Type[cutlass.Numeric], + dtype_partial: Type[cutlass.Numeric], + head_dim: int, + tile_m: int = 8, + k_block_size: int = 64, + log_max_splits: int = 4, + num_threads: int = 256, + stages: int = 4, + use_pdl: bool = False, + ): + """ + Forward combine kernel for split attention computation. + + :param dtype: output data type + :param dtype_partial: partial accumulation data type + :param head_dim: head dimension + :param tile_m: m block size + :param k_block_size: k block size + :param log_max_splits: log2 of maximum splits + :param num_threads: number of threads + :param varlen: whether using variable length sequences + :param stages: number of pipeline stages + """ + self.dtype = dtype + self.dtype_partial = dtype_partial + self.head_dim = head_dim + self.tile_m = tile_m + self.k_block_size = k_block_size + self.max_splits = 1 << log_max_splits + self.num_threads = num_threads + self.is_even_k = head_dim % k_block_size == 0 + self.stages = stages + self.use_pdl = use_pdl + + @staticmethod + def can_implement( + dtype, + dtype_partial, + head_dim, + tile_m, + k_block_size, + log_max_splits, + num_threads, + ) -> bool: + """Check if the kernel can be implemented with the given parameters.""" + if dtype not in [cutlass.Float16, cutlass.BFloat16, cutlass.Float32]: + return False + if dtype_partial not in [cutlass.Float16, cutlass.BFloat16, Float32]: + return False + if head_dim % 8 != 0: + return False + if num_threads % 32 != 0: + return False + if tile_m % 8 != 0: + return False + max_splits = 1 << log_max_splits + if max_splits > 256: + return False + if (tile_m * max_splits) % num_threads != 0: + return False + return True + + def _setup_attributes(self): + # GMEM copy setup for O partial + universal_copy_bits = 128 + async_copy_elems = universal_copy_bits // self.dtype_partial.width + assert self.k_block_size % async_copy_elems == 0 + + k_block_gmem = ( + 128 + if self.k_block_size % 128 == 0 + else (64 if self.k_block_size % 64 == 0 else 32) + ) + gmem_threads_per_row = k_block_gmem // async_copy_elems + assert self.num_threads % gmem_threads_per_row == 0 + + # Async copy atom for O partial load + atom_async_copy_partial = cute.make_copy_atom( + cpasync.CopyG2SOp(cache_mode=cpasync.LoadCacheMode.GLOBAL), + self.dtype_partial, + num_bits_per_copy=universal_copy_bits, + ) + tOpartial_layout = cute.make_ordered_layout( + (self.num_threads // gmem_threads_per_row, gmem_threads_per_row), + order=(1, 0), + ) + vOpartial_layout = cute.make_layout((1, async_copy_elems)) # 4 vals per load + self.gmem_tiled_copy_O_partial = cute.make_tiled_copy_tv( + atom_async_copy_partial, tOpartial_layout, vOpartial_layout + ) + + # GMEM copy setup for final O (use universal copy for store) + atom_universal_copy = cute.make_copy_atom( + cute.nvgpu.CopyUniversalOp(), + self.dtype, + num_bits_per_copy=async_copy_elems * self.dtype.width, + ) + self.gmem_tiled_copy_O = cute.make_tiled_copy_tv( + atom_universal_copy, + tOpartial_layout, + vOpartial_layout, # 4 vals per store + ) + + # LSE copy setup with async copy (alignment = 1) + lse_copy_bits = Float32.width # 1 element per copy, width is in bits + m_block_smem = ( + 128 + if self.tile_m % 128 == 0 + else ( + 64 + if self.tile_m % 64 == 0 + else ( + 32 + if self.tile_m % 32 == 0 + else (16 if self.tile_m % 16 == 0 else 8) + ) + ) + ) + gmem_threads_per_row_lse = m_block_smem + assert self.num_threads % gmem_threads_per_row_lse == 0 + + # Async copy atom for LSE load + atom_async_copy_lse = cute.make_copy_atom( + cpasync.CopyG2SOp(cache_mode=cpasync.LoadCacheMode.ALWAYS), + Float32, + num_bits_per_copy=lse_copy_bits, + ) + tLSE_layout = cute.make_ordered_layout( + (self.num_threads // gmem_threads_per_row_lse, gmem_threads_per_row_lse), + order=(1, 0), + ) + vLSE_layout = cute.make_layout(1) + self.gmem_tiled_copy_LSE = cute.make_tiled_copy_tv( + atom_async_copy_lse, tLSE_layout, vLSE_layout + ) + + # /////////////////////////////////////////////////////////////////////////////// + # Shared memory + # /////////////////////////////////////////////////////////////////////////////// + + # Shared memory to register copy for LSE + self.smem_threads_per_col_lse = self.num_threads // m_block_smem + assert 32 % self.smem_threads_per_col_lse == 0 # Must divide warp size + + s2r_layout_atom_lse = cute.make_ordered_layout( + ( + self.smem_threads_per_col_lse, + self.num_threads // self.smem_threads_per_col_lse, + ), + order=(0, 1), + ) + self.s2r_tiled_copy_LSE = cute.make_tiled_copy_tv( + cute.make_copy_atom(cute.nvgpu.CopyUniversalOp(), Float32), + s2r_layout_atom_lse, + cute.make_layout(1), + ) + + # LSE shared memory layout with swizzling to avoid bank conflicts + # This works for kBlockMSmem = 8, 16, 32, 64, 128, no bank conflicts + if const_expr(m_block_smem == 8): + smem_lse_swizzle = cute.make_swizzle(5, 0, 5) + elif const_expr(m_block_smem == 16): + smem_lse_swizzle = cute.make_swizzle(4, 0, 4) + else: + smem_lse_swizzle = cute.make_swizzle(3, 2, 3) + smem_layout_atom_lse = cute.make_composed_layout( + smem_lse_swizzle, + 0, + cute.make_ordered_layout((8, m_block_smem), order=(1, 0)), + ) + self.smem_layout_lse = cute.tile_to_shape( + smem_layout_atom_lse, (self.max_splits, self.tile_m), (0, 1) + ) + + # O partial shared memory layout (simple layout for pipeline stages) + self.smem_layout_o = cute.make_ordered_layout( + (self.tile_m, self.k_block_size, self.stages), order=(1, 0, 2) + ) + + @cute.jit + def __call__( + self, + mO_partial: cute.Tensor, + mLSE_partial: cute.Tensor, + mO: cute.Tensor, + mLSE: Optional[cute.Tensor] = None, + cu_seqlens: Optional[cute.Tensor] = None, + seqused: Optional[cute.Tensor] = None, + num_splits_dynamic_ptr: Optional[cute.Tensor] = None, + varlen_batch_idx: Optional[cute.Tensor] = None, + semaphore_to_reset: Optional[cute.Tensor] = None, + # Always keep stream as the last parameter (EnvStream: obtained implicitly via TVM FFI). + stream: cuda.CUstream = None, + ): + # Type checking + if const_expr(not (mO_partial.element_type == self.dtype_partial)): + raise TypeError("O partial tensor must match dtype_partial") + if const_expr(not (mO.element_type == self.dtype)): + raise TypeError("O tensor must match dtype") + if const_expr(mLSE_partial.element_type not in [Float32]): + raise TypeError("LSE partial tensor must be Float32") + if const_expr(mLSE is not None and mLSE.element_type not in [Float32]): + raise TypeError("LSE tensor must be Float32") + + # Shape validation - input tensors are in user format, need to be converted to kernel format + if const_expr(len(mO_partial.shape) not in [4, 5]): + raise ValueError( + "O partial tensor must have 4 or 5 dimensions: (num_splits, batch, seqlen, nheads, headdim) or (num_splits, total_q, nheads, headdim)" + ) + if const_expr(len(mLSE_partial.shape) not in [3, 4]): + raise ValueError( + "LSE partial tensor must have 3 or 4 dimensions: (num_splits, batch, seqlen, nheads) or (num_splits, total_q, nheads)" + ) + if const_expr(len(mO.shape) not in [3, 4]): + raise ValueError( + "O tensor must have 3 or 4 dimensions: (batch, seqlen, nheads, headdim) or (total_q, nheads, headdim)" + ) + if const_expr(mLSE is not None and len(mLSE.shape) not in [2, 3]): + raise ValueError( + "LSE tensor must have 2 or 3 dimensions: (batch, seqlen, nheads) or (total_q, nheads)" + ) + + mO_partial, mO = [assume_tensor_aligned(t) for t in (mO_partial, mO)] + # (num_splits, b, seqlen, h, d) -> (seqlen, d, num_splits, h, b) + # or (num_splits, total_q, h, d) -> (total_q, d, num_splits, h) + O_partial_layout_transpose = ( + [2, 4, 0, 3, 1] if const_expr(cu_seqlens is None) else [1, 3, 0, 2] + ) + # (b, seqlen, h, d) -> (seqlen, d, h, b) or (total_q, h, d) -> (total_q, d, h) + mO_partial = cute.make_tensor( + mO_partial.iterator, + cute.select(mO_partial.layout, mode=O_partial_layout_transpose), + ) + O_layout_transpose = ( + [1, 3, 2, 0] if const_expr(cu_seqlens is None) else [0, 2, 1] + ) + mO = cute.make_tensor( + mO.iterator, cute.select(mO.layout, mode=O_layout_transpose) + ) + # (num_splits, b, seqlen, h) -> (seqlen, num_splits, h, b) + # or (num_splits, total_q, h) -> (total_q, num_splits, h) + LSE_partial_layout_transpose = ( + [2, 0, 3, 1] if const_expr(cu_seqlens is None) else [1, 0, 2] + ) + mLSE_partial = cute.make_tensor( + mLSE_partial.iterator, + cute.select(mLSE_partial.layout, mode=LSE_partial_layout_transpose), + ) + # (b, seqlen, h) -> (seqlen, h, b) or (total_q, h) -> (total_q, h) + LSE_layout_transpose = [1, 2, 0] if const_expr(cu_seqlens is None) else [0, 1] + mLSE = ( + cute.make_tensor( + mLSE.iterator, cute.select(mLSE.layout, mode=LSE_layout_transpose) + ) + if mLSE is not None + else None + ) + + # Determine if we have variable length sequences + varlen = const_expr(cu_seqlens is not None or seqused is not None) + + self._setup_attributes() + + @cute.struct + class SharedStorage: + sLSE: cute.struct.Align[ + cute.struct.MemRange[Float32, cute.cosize(self.smem_layout_lse)], 128 + ] + sMaxValidSplit: cute.struct.Align[ + cute.struct.MemRange[Int32, self.tile_m], 128 + ] + sO: cute.struct.Align[ + cute.struct.MemRange[ + self.dtype_partial, cute.cosize(self.smem_layout_o) + ], + 128, + ] + + smem_size = SharedStorage.size_in_bytes() + + # Grid dimensions: (ceil_div(seqlen, m_block), ceil_div(head_dim, k_block), num_head * batch) + seqlen = mO_partial.shape[0] + num_head = mO_partial.shape[3] + batch_size = ( + mO_partial.shape[4] + if const_expr(cu_seqlens is None) + else Int32(cu_seqlens.shape[0] - 1) + ) + + # Create FastDivmodDivisor objects for efficient division + seqlen_divmod = FastDivmodDivisor(seqlen) + head_divmod = FastDivmodDivisor(num_head) + + grid_dim = ( + cute.ceil_div(seqlen * num_head, self.tile_m), + cute.ceil_div(self.head_dim, self.k_block_size), + batch_size, + ) + + self.kernel( + mO_partial, + mLSE_partial, + mO, + mLSE, + cu_seqlens, + seqused, + num_splits_dynamic_ptr, + varlen_batch_idx, + semaphore_to_reset, + SharedStorage, + self.smem_layout_lse, + self.smem_layout_o, + self.gmem_tiled_copy_O_partial, + self.gmem_tiled_copy_O, + self.gmem_tiled_copy_LSE, + self.s2r_tiled_copy_LSE, + seqlen_divmod, + head_divmod, + varlen, + ).launch( + grid=grid_dim, + block=[self.num_threads, 1, 1], + smem=smem_size, + stream=stream, + use_pdl=self.use_pdl, + ) + + @cute.kernel + def kernel( + self, + mO_partial: cute.Tensor, + mLSE_partial: cute.Tensor, + mO: cute.Tensor, + mLSE: Optional[cute.Tensor], + cu_seqlens: Optional[cute.Tensor], + seqused: Optional[cute.Tensor], + num_splits_dynamic_ptr: Optional[cute.Tensor], + varlen_batch_idx: Optional[cute.Tensor], + semaphore_to_reset: Optional[cute.Tensor], + SharedStorage: cutlass.Constexpr, + smem_layout_lse: cute.Layout | cute.ComposedLayout, + smem_layout_o: cute.Layout, + gmem_tiled_copy_O_partial: cute.TiledCopy, + gmem_tiled_copy_O: cute.TiledCopy, + gmem_tiled_copy_LSE: cute.TiledCopy, + s2r_tiled_copy_LSE: cute.TiledCopy, + seqlen_divmod: FastDivmodDivisor, + head_divmod: FastDivmodDivisor, + varlen: cutlass.Constexpr[bool], + ): + # Thread and block indices + tidx, _, _ = cute.arch.thread_idx() + m_block, k_block, maybe_virtual_batch = cute.arch.block_idx() + + # Map virtual batch index to real batch index (for persistent tile schedulers) + batch_idx = ( + varlen_batch_idx[maybe_virtual_batch] + if const_expr(varlen_batch_idx is not None) + else maybe_virtual_batch + ) + + # /////////////////////////////////////////////////////////////////////////////// + # Get shared memory buffer + # /////////////////////////////////////////////////////////////////////////////// + smem = cutlass.utils.SmemAllocator() + storage = smem.allocate(SharedStorage) + sLSE = storage.sLSE.get_tensor(smem_layout_lse) + sMaxValidSplit = storage.sMaxValidSplit.get_tensor((self.tile_m,)) + sO = storage.sO.get_tensor(smem_layout_o) + + # Handle semaphore reset — wait for dependent grids first + if const_expr(semaphore_to_reset is not None): + if ( + tidx == 0 + and m_block == cute.arch.grid_dim()[0] - 1 + and k_block == cute.arch.grid_dim()[1] - 1 + and maybe_virtual_batch == cute.arch.grid_dim()[2] - 1 + ): + if const_expr(self.use_pdl): + cute.arch.griddepcontrol_wait() + semaphore_to_reset[0] = 0 + + # Get number of splits (use maybe_virtual_batch for per-batch-slot splits) + num_splits = ( + num_splits_dynamic_ptr[maybe_virtual_batch] + if const_expr(num_splits_dynamic_ptr is not None) + else mLSE_partial.shape[1] + ) + # Handle variable length sequences using SeqlenInfo + seqlen_info = SeqlenInfo.create( + batch_idx=batch_idx, + seqlen_static=mO_partial.shape[0], + cu_seqlens=cu_seqlens, + seqused=seqused, + # Don't need to pass in tile size since we won't use offset_padded + ) + seqlen, offset = seqlen_info.seqlen, seqlen_info.offset + + # Extract number of heads (head index will be determined dynamically) + num_head = mO_partial.shape[3] + max_idx = seqlen * num_head + + # Early exit for single split if dynamic + if (const_expr(num_splits_dynamic_ptr is None) or num_splits > 1) and ( + const_expr(not varlen) or m_block * self.tile_m < max_idx + ): + # Wait for dependent grids (e.g., the main attention kernel that produces O_partial/LSE_partial) + if const_expr(self.use_pdl): + cute.arch.griddepcontrol_wait() + + # =============================== + # Step 1: Load LSE_partial from gmem to shared memory + # =============================== + + mLSE_partial_cur = seqlen_info.offset_batch(mLSE_partial, batch_idx, dim=3) + mLSE_partial_copy = cute.tiled_divide(mLSE_partial_cur, (1,)) + gmem_thr_copy_LSE = gmem_tiled_copy_LSE.get_slice(tidx) + tLSEsLSE = gmem_thr_copy_LSE.partition_D(sLSE) + # Create identity tensor for coordinate tracking + cLSE = cute.make_identity_tensor((self.max_splits, self.tile_m)) + tLSEcLSE = gmem_thr_copy_LSE.partition_S(cLSE) + + # Load LSE partial values + for m in cutlass.range(cute.size(tLSEcLSE, mode=[2]), unroll_full=True): + mi = tLSEcLSE[0, 0, m][1] # Get m coordinate + idx = m_block * self.tile_m + mi + if idx < max_idx: + # Calculate actual sequence position and head using FastDivmodDivisor + if const_expr(not varlen): + head_idx, m_idx = divmod(idx, seqlen_divmod) + else: + head_idx = idx // seqlen + m_idx = idx - head_idx * seqlen + mLSE_partial_cur_copy = mLSE_partial_copy[ + None, m_idx, None, head_idx + ] + for s in cutlass.range( + cute.size(tLSEcLSE, mode=[1]), unroll_full=True + ): + si = tLSEcLSE[0, s, 0][0] # Get split coordinate + if si < num_splits: + cute.copy( + gmem_thr_copy_LSE, + mLSE_partial_cur_copy[None, si], + tLSEsLSE[None, s, m], + ) + else: + tLSEsLSE[None, s, m].fill(-Float32.inf) + # Don't need to zero out the rest of the LSEs, as we will not write the output to gmem + cute.arch.cp_async_commit_group() + + # =============================== + # Step 2: Load O_partial for pipeline stages + # =============================== + + gmem_thr_copy_O_partial = gmem_tiled_copy_O_partial.get_slice(tidx) + cO = cute.make_identity_tensor((self.tile_m, self.k_block_size)) + tOcO = gmem_thr_copy_O_partial.partition_D(cO) + tOsO_partial = gmem_thr_copy_O_partial.partition_D(sO) + mO_partial_cur = seqlen_info.offset_batch(mO_partial, batch_idx, dim=4) + + # Precompute these values to avoid recomputing them in the loop + num_rows = const_expr(cute.size(tOcO, mode=[1])) + tOmidx = cute.make_rmem_tensor(num_rows, cutlass.Int32) + tOhidx = cute.make_rmem_tensor(num_rows, cutlass.Int32) + tOrOptr = cute.make_rmem_tensor(num_rows, cutlass.Int64) + for m in cutlass.range(num_rows, unroll_full=True): + mi = tOcO[0, m, 0][0] # m coordinate + idx = m_block * self.tile_m + mi + if const_expr(not varlen): + tOhidx[m], tOmidx[m] = divmod(idx, seqlen_divmod) + else: + tOhidx[m] = idx // seqlen + tOmidx[m] = idx - tOhidx[m] * seqlen + tOrOptr[m] = utils.elem_pointer( + mO_partial_cur, + (tOmidx[m], k_block * self.k_block_size, 0, tOhidx[m]), + ).toint() + if idx >= max_idx: + tOhidx[m] = -1 + + tOpO = None + if const_expr(not self.is_even_k): + tOpO = cute.make_rmem_tensor(cute.size(tOcO, mode=[2]), Boolean) + for k in cutlass.range(cute.size(tOpO), unroll_full=True): + tOpO[k] = ( + tOcO[0, 0, k][1] + < mO_partial.shape[1] - k_block * self.k_block_size + ) + # if cute.arch.thread_idx()[0] == 0 and k_block == 1: cute.print_tensor(tOpO) + + load_O_partial = partial( + self.load_O_partial, + gmem_tiled_copy_O_partial, + tOrOptr, + tOsO_partial, + tOhidx, + tOpO, + tOcO, + mO_partial_cur.layout, + ) + + # Load first few stages of O_partial + for stage in cutlass.range(self.stages - 1, unroll_full=True): + if stage < num_splits: + load_O_partial(stage, stage) + cute.arch.cp_async_commit_group() + + # =============================== + # Step 3: Load and transpose LSE from smem to registers + # =============================== + + # Wait for LSE and initial O partial stages to complete + cute.arch.cp_async_wait_group(self.stages - 1) + cute.arch.sync_threads() + # if cute.arch.thread_idx()[0] == 0: + # # cute.print_tensor(sLSE) + # for i in range(64): + # cute.printf("sLSE[%d, 0] = %f", i, sLSE[i, 0]) + # cute.arch.sync_threads() + + s2r_thr_copy_LSE = s2r_tiled_copy_LSE.get_slice(tidx) + ts2rsLSE = s2r_thr_copy_LSE.partition_S(sLSE) + ts2rrLSE = cute.make_rmem_tensor_like(ts2rsLSE) + cute.copy(s2r_tiled_copy_LSE, ts2rsLSE, ts2rrLSE) + + # =============================== + # Step 4: Compute final LSE along split dimension + # =============================== + + lse_sum = cute.make_rmem_tensor(cute.size(ts2rrLSE, mode=[2]), Float32) + ts2rcLSE = s2r_thr_copy_LSE.partition_D(cLSE) + # We compute the max valid split for each row to short-circuit the computation later + max_valid_split = cute.make_rmem_tensor( + cute.size(ts2rrLSE, mode=[2]), Int32 + ) + assert cute.size(ts2rrLSE, mode=[0]) == 1 + # Compute max, scales, and final LSE for each row + for m in cutlass.range(cute.size(ts2rrLSE, mode=[2]), unroll_full=True): + # Find max LSE value across splits + threads_per_col = const_expr(self.smem_threads_per_col_lse) + lse_max = cute.arch.warp_reduction_max( + ts2rrLSE[None, None, m] + .load() + .reduce( + cute.ReductionOp.MAX, init_val=-Float32.inf, reduction_profile=0 + ), + threads_in_group=threads_per_col, + ) + # if cute.arch.thread_idx()[0] == 0: cute.printf(lse_max) + # Find max valid split index + max_valid_idx = -1 + for s in cutlass.range(cute.size(ts2rrLSE, mode=[1]), unroll_full=True): + if ts2rrLSE[0, s, m] != -Float32.inf: + max_valid_idx = ts2rcLSE[0, s, 0][0] # Get split coordinate + # if cute.arch.thread_idx()[0] < 32: cute.printf(max_valid_idx) + max_valid_split[m] = cute.arch.warp_reduction_max( + max_valid_idx, threads_in_group=threads_per_col + ) + # Compute exp scales and sum + lse_max_cur = ( + 0.0 if lse_max == -Float32.inf else lse_max + ) # In case all local LSEs are -inf + LOG2_E = math.log2(math.e) + lse_sum_cur = 0.0 + for s in cutlass.range(cute.size(ts2rrLSE, mode=[1]), unroll_full=True): + scale = cute.math.exp2( + ts2rrLSE[0, s, m] * LOG2_E - (lse_max_cur * LOG2_E), + fastmath=True, + ) + lse_sum_cur += scale + ts2rrLSE[0, s, m] = scale # Store scale for later use + lse_sum_cur = cute.arch.warp_reduction_sum( + lse_sum_cur, threads_in_group=threads_per_col + ) + lse_sum[m] = cute.math.log(lse_sum_cur, fastmath=True) + lse_max + # Normalize scales + inv_sum = ( + 0.0 + if (lse_sum_cur == 0.0 or lse_sum_cur != lse_sum_cur) + else 1.0 / lse_sum_cur + ) + ts2rrLSE[None, None, m].store(ts2rrLSE[None, None, m].load() * inv_sum) + # Store the scales exp(lse - lse_logsum) back to smem + cute.copy(s2r_tiled_copy_LSE, ts2rrLSE, ts2rsLSE) + + # Store max valid split to smem + for m in cutlass.range(cute.size(ts2rrLSE, mode=[2]), unroll_full=True): + if ts2rcLSE[0, 0, m][0] == 0: # Only thread responsible for s=0 writes + mi = ts2rcLSE[0, 0, m][1] + if mi < self.tile_m: + sMaxValidSplit[mi] = max_valid_split[m] + + # =============================== + # Step 5: Store final LSE to gmem + # =============================== + + if const_expr(mLSE is not None): + if const_expr(cu_seqlens is None): + mLSE_cur = mLSE[None, None, batch_idx] + else: + mLSE_cur = cute.domain_offset((offset, 0), mLSE) + if k_block == 0: # Only first k_block writes LSE when mLSE is provided + for m in cutlass.range( + cute.size(ts2rrLSE, mode=[2]), unroll_full=True + ): + if ( + ts2rcLSE[0, 0, m][0] == 0 + ): # Only thread responsible for s=0 writes + mi = ts2rcLSE[0, 0, m][1] + idx = m_block * self.tile_m + mi + if idx < max_idx: + if const_expr(not varlen): + head_idx, m_idx = divmod(idx, seqlen_divmod) + else: + head_idx = idx // seqlen + m_idx = idx - head_idx * seqlen + mLSE_cur[m_idx, head_idx] = lse_sum[m] + + # =============================== + # Step 6: Read O_partial and accumulate final O + # =============================== + + cute.arch.sync_threads() + + # Get max valid split for this thread + thr_max_valid_split = sMaxValidSplit[tOcO[0, 0, 0][0]] + for m in cutlass.range(1, cute.size(tOcO, mode=[1]), unroll_full=True): + thr_max_valid_split = max( + thr_max_valid_split, sMaxValidSplit[tOcO[0, m, 0][0]] + ) + + tOrO_partial = cute.make_rmem_tensor_like(tOsO_partial[None, None, None, 0]) + tOrO = cute.make_rmem_tensor_like(tOrO_partial, Float32) + tOrO.fill(0.0) + + stage_load = self.stages - 1 + stage_compute = 0 + + # Main accumulation loop + for s in cutlass.range(thr_max_valid_split + 1, unroll=4): + # Get scales for this split + scale = cute.make_rmem_tensor(num_rows, Float32) + for m in cutlass.range(num_rows, unroll_full=True): + scale[m] = sLSE[s, tOcO[0, m, 0][0]] # Get scale from smem + + # Load next stage if needed + split_to_load = s + self.stages - 1 + if split_to_load <= thr_max_valid_split: + load_O_partial(split_to_load, stage_load) + cute.arch.cp_async_commit_group() + stage_load = 0 if stage_load == self.stages - 1 else stage_load + 1 + + # Wait for the current stage to be ready + cute.arch.cp_async_wait_group(self.stages - 1) + # We don't need __syncthreads() because each thread is just reading its own data from smem + # Copy from smem to registers + cute.autovec_copy( + tOsO_partial[None, None, None, stage_compute], tOrO_partial + ) + stage_compute = ( + 0 if stage_compute == self.stages - 1 else stage_compute + 1 + ) + + # Accumulate scaled partial results + for m in cutlass.range(num_rows, unroll_full=True): + if tOhidx[m] >= 0 and scale[m] > 0.0: + tOrO[None, m, None].store( + tOrO[None, m, None].load() + + scale[m] * tOrO_partial[None, m, None].load().to(Float32) + ) + + # =============================== + # Step 7: Write final O to gmem + # =============================== + + rO = cute.make_rmem_tensor_like(tOrO, self.dtype) + rO.store(tOrO.load().to(self.dtype)) + mO_cur = seqlen_info.offset_batch(mO, batch_idx, dim=3) + if const_expr(cu_seqlens is None): + mO_cur = mO[None, None, None, batch_idx] + else: + mO_cur = cute.domain_offset((offset, 0, 0), mO) + mO_cur = utils.domain_offset_aligned( + (0, k_block * self.k_block_size, 0), mO_cur + ) + elems_per_store = const_expr( + cute.size(gmem_tiled_copy_O.layout_tv_tiled[1]) + ) + # mO_cur_copy = cute.tiled_divide(mO_cur, (1, elems_per_store,)) + gmem_thr_copy_O = gmem_tiled_copy_O.get_slice(tidx) + # Write final results + for m in cutlass.range(num_rows, unroll_full=True): + if tOhidx[m] >= 0: + mO_cur_copy = cute.tiled_divide( + mO_cur[tOmidx[m], None, tOhidx[m]], (elems_per_store,) + ) + for k in cutlass.range(cute.size(tOcO, mode=[2]), unroll_full=True): + k_idx = tOcO[0, 0, k][1] // elems_per_store + if const_expr(self.is_even_k) or tOpO[k]: + cute.copy( + gmem_thr_copy_O, + rO[None, m, k], + mO_cur_copy[None, k_idx], + ) + + @cute.jit + def load_O_partial( + self, + gmem_tiled_copy_O_partial: cute.TiledCopy, + tOrOptr: cute.Tensor, + tOsO_partial: cute.Tensor, + tOhidx: cute.Tensor, + tOpO: Optional[cute.Tensor], + tOcO: cute.Tensor, + mO_cur_partial_layout: cute.Layout, + split: Int32, + stage: Int32, + ) -> None: + elems_per_load = const_expr( + cute.size(gmem_tiled_copy_O_partial.layout_tv_tiled[1]) + ) + tOsO_partial_cur = tOsO_partial[None, None, None, stage] + for m in cutlass.range(cute.size(tOcO, [1]), unroll_full=True): + if tOhidx[m] >= 0: + o_gmem_ptr = cute.make_ptr( + tOsO_partial.element_type, + tOrOptr[m], + cute.AddressSpace.gmem, + assumed_align=16, + ) + mO_partial_cur = cute.make_tensor( + o_gmem_ptr, cute.slice_(mO_cur_partial_layout, (0, None, None, 0)) + ) + mO_partial_cur_copy = cute.tiled_divide( + mO_partial_cur, (elems_per_load,) + ) + for k in cutlass.range(cute.size(tOcO, mode=[2]), unroll_full=True): + k_idx = tOcO[0, 0, k][1] // elems_per_load + if const_expr(tOpO is None) or tOpO[k]: + cute.copy( + gmem_tiled_copy_O_partial, + mO_partial_cur_copy[None, k_idx, split], + tOsO_partial_cur[None, m, k], + ) diff --git a/python/sglang/jit_kernel/flash_attn/cute/flash_fwd_mla_sm100.py b/python/sglang/jit_kernel/flash_attn/cute/flash_fwd_mla_sm100.py new file mode 100644 index 000000000..da0a16b6c --- /dev/null +++ b/python/sglang/jit_kernel/flash_attn/cute/flash_fwd_mla_sm100.py @@ -0,0 +1,3904 @@ +# Copyright (c) 2026, Colfax International. + +import math +import time +from functools import partial +from typing import Callable, Optional + +import cuda.bindings.driver as cuda +import cutlass +import cutlass.cute as cute +import cutlass.pipeline as pipeline +import cutlass.utils.blackwell_helpers as sm100_utils +import torch +import torch.utils.benchmark as benchmark +from cutlass import Boolean, Float32, Int32, Int64, Uint32, const_expr +from cutlass.cute import FastDivmodDivisor +from cutlass.cute.nvgpu import cpasync, tcgen05 +from cutlass.cute.runtime import from_dlpack +from cutlass.utils import ClcDynamicPersistentTileScheduler +from quack import copy_utils + +import sglang.jit_kernel.flash_attn.cute.blackwell_helpers as fa_sm100_utils +from sglang.jit_kernel.flash_attn.cute import utils as fa_utils +from sglang.jit_kernel.flash_attn.cute.block_info import BlockInfo +from sglang.jit_kernel.flash_attn.cute.cute_dsl_utils import dump_kernel_attributes +from sglang.jit_kernel.flash_attn.cute.fa_logging import fa_log, fa_printf +from sglang.jit_kernel.flash_attn.cute.mask import AttentionMask +from sglang.jit_kernel.flash_attn.cute.named_barrier import NamedBarrierFwdSm100_MLA2CTA +from sglang.jit_kernel.flash_attn.cute.pack_gqa import ( + make_packgqa_tiled_tma_atom, + pack_gqa_layout, +) +from sglang.jit_kernel.flash_attn.cute.paged_kv import PagedKVManager +from sglang.jit_kernel.flash_attn.cute.seqlen_info import SeqlenInfoQK +from sglang.jit_kernel.flash_attn.cute.softmax import SoftmaxSm100 +from sglang.jit_kernel.flash_attn.cute.testing import attention_ref +from sglang.jit_kernel.flash_attn.cute.tile_scheduler import ( + ClcState, + ParamsBase, + SchedulingMode, + SingleTileLPTScheduler, + SingleTileScheduler, + SingleTileVarlenScheduler, + TileSchedulerArguments, + TileSchedulerProtocol, +) +from sglang.jit_kernel.flash_attn.cute.topk_gather_kv import CpasyncGatherKVManager +from sglang.jit_kernel.flash_attn.cute.utils import smid + + +class FlashAttentionMLAForwardSm100: + def __init__( + self, + is_causal: bool = False, + use_cpasync_load_KV: bool = False, + topk_length: int = 2048, + is_topk_gather: bool = True, + pack_gqa: bool = False, + qhead_per_kvhead: int = 1, + nheads_kv: int = 1, + hdim: int = 64, + hdimv: int = 512, + is_varlen_q: bool = False, + disable_bitmask: bool = False, + use_clc_scheduler: bool = True, + has_qk: bool = True, + ): + self.is_causal = is_causal + self.is_local = False + self.pack_gqa = pack_gqa + self.qhead_per_kvhead = qhead_per_kvhead + self.nheads_kv = nheads_kv + self.is_varlen_q = is_varlen_q + self.use_tma_O = True + self.use_cpasync_load_KV = use_cpasync_load_KV + self.use_tma_KV = not use_cpasync_load_KV + self.topk_length = topk_length + self.is_topk_gather = is_topk_gather + if is_topk_gather: + assert pack_gqa + assert qhead_per_kvhead == 128, "require MQA 128 for DSA path" + assert use_cpasync_load_KV + # user-provided option if topk indices guaranteed in bounds + self.disable_bitmask = disable_bitmask + self.has_qk = has_qk + + # ==== tile scheduler ==== + self.is_persistent = False + self.use_clc_scheduler = use_clc_scheduler and not is_varlen_q + self.sched_stages = 1 + self.scheduling_mode = ( + SchedulingMode.CLC if self.use_clc_scheduler else SchedulingMode.STATIC + ) + + if const_expr(is_varlen_q): + self.TileScheduler = SingleTileVarlenScheduler + elif self.use_clc_scheduler: + self.TileScheduler = SingleTileLPTScheduler + else: + self.TileScheduler = SingleTileScheduler + + fa_log( + 1, + f"TileScheduler={self.TileScheduler.__name__}, scheduling_mode={self.scheduling_mode.name}", + ) + + # ==== thread info ==== + self.num_softmax_threads = 128 + self.num_epilogue_threads = 128 + self.num_load_threads = 32 + self.num_mma_threads = 32 + self.num_empty_threads = 32 if use_cpasync_load_KV else 64 + self.num_relay_threads = 32 if use_cpasync_load_KV else 0 + self.num_cpasync_load_threads = 128 if use_cpasync_load_KV else 0 + self.num_threads = ( + self.num_softmax_threads + + self.num_epilogue_threads + + self.num_load_threads + + self.num_mma_threads + + self.num_empty_threads + + self.num_relay_threads + + self.num_cpasync_load_threads + ) + self.num_warps = self.num_threads // 32 + assert self.num_warps == 12 or self.num_warps == 16 + self.softmax_warp_indices = (0, 1, 2, 3) + self.epilogue_warp_indices = (4, 5, 6, 7) + self.load_warp_id = 8 + self.mma_warp_id = 9 + self.clc_scheduler_warp_id = 10 + self.relay_warp_id = 11 + self.empty_warp_ids = tuple( + w + for w, active in [ + (self.relay_warp_id, not use_cpasync_load_KV), + (self.clc_scheduler_warp_id, not self.use_clc_scheduler), + ] + if active + ) + self.cpasync_load_warp_indices = (12, 13, 14, 15) + + # ==== register usage ==== + if self.num_warps == 16: + self.num_regs_load = 80 + self.num_regs_mma = 80 + self.num_regs_softmax = 208 + self.num_regs_epilogue = 128 + self.num_regs_cpasync = 96 if self.use_cpasync_load_KV else 0 + self.num_regs_other = 48 + else: + self.num_regs_load = 168 - 40 + self.num_regs_mma = 168 - 40 + self.num_regs_softmax = 168 + 80 + self.num_regs_epilogue = 168 - 40 + self.num_regs_cpasync = 0 + self.num_regs_other = 48 + + self.num_regs_per_thread = 168 if self.num_warps == 12 else 128 + self.num_regs_total = 504 if self.num_warps == 12 else 512 + + assert ( + self.num_regs_mma + + self.num_regs_softmax + + self.num_regs_epilogue + + self.num_regs_cpasync + <= self.num_regs_total + ) + + # ==== 2cta info ==== + self.use_2cta_instrs = True + self.cta_group = tcgen05.CtaGroup.TWO + self.cta_group_size = 2 + self.cluster_shape_mn = (2, 1) + self.cluster_shape_mnk = (2, 1, 1) + + # ==== problem shape info ==== + self.hdim = hdim + self.hdimv = hdimv + self.cta_tile_m = 64 + self.cluster_tile_m = self.cta_group_size * self.cta_tile_m + self.tile_n = 128 + assert ( + pack_gqa is False + or self.cluster_tile_m % qhead_per_kvhead == 0 + or qhead_per_kvhead % self.cluster_tile_m == 0 + ) + self.num_hdimv_splits = ( + 2 # split hdimv in half for our Qv @ V^T and P @ V mmas. + ) + assert hdimv % 32 == 0 + assert self.topk_length % self.tile_n == 0 or not self.is_topk_gather + self.epi_tile = (self.cta_tile_m, self.hdimv // self.num_hdimv_splits) + self.tile_P = (self.cta_tile_m, self.tile_n) + + # ==== MMA info ==== + self.mma_tiler_QK = ( + self.cluster_tile_m, + self.tile_n, + self.hdim, + ) + self.mma_tiler_QvV = ( + self.cluster_tile_m, + self.tile_n, + self.hdimv // self.num_hdimv_splits, + ) + self.mma_tiler_PVt = ( + self.cluster_tile_m, + self.hdimv // self.num_hdimv_splits, + self.tile_n, + ) + self.major_mode_Q = tcgen05.OperandMajorMode.K + self.major_mode_Qvi = tcgen05.OperandMajorMode.K + self.major_mode_K = tcgen05.OperandMajorMode.K + self.major_mode_Vi = tcgen05.OperandMajorMode.K + self.major_mode_Vti = tcgen05.OperandMajorMode.MN + self.major_mode_P = tcgen05.OperandMajorMode.K + self.operand_source_Q = tcgen05.OperandSource.SMEM + self.operand_source_Qvi = tcgen05.OperandSource.SMEM + self.operand_source_P = tcgen05.OperandSource.SMEM + + # ==== pipeline info ==== + self.num_stages_Q = 1 + self.num_stages_K = 1 + self.num_stages_Qv = 2 + self.num_stages_V = 4 + self.num_stages_S = 2 + # self.num_stages_P = 1 if has_qk else 2 + self.num_stages_P = 1 + self.num_stages_Oi = 1 + self.num_stages_sm_stats = 2 + self.num_stages_bitmask = 4 + assert self.num_stages_S == 2, "mainloops expect 2 stages for S" + + # ==== dtype info ==== + self.dtype_acc = Float32 + + # ==== TMEM info ==== + SM100_TMEM_CAPACITY_COLUMNS = 512 + self.tmem_alloc_cols = SM100_TMEM_CAPACITY_COLUMNS + self.tmem_cols_S = self.tile_n // self.cta_group_size + self.tmem_cols_Oi = (self.hdimv // self.num_hdimv_splits) // self.cta_group_size + self.tmem_offset_S = [ + self.tmem_cols_S * stage for stage in range(self.num_stages_S) + ] # allocate 64 TMEM columns for each stage of S + self.tmem_offset_O0 = self.tmem_cols_S * self.num_stages_S + self.tmem_offset_O1 = self.tmem_offset_O0 + self.tmem_cols_Oi + self.tmem_offsets_O = [self.tmem_offset_O0, self.tmem_offset_O1] + self.total_tmem = self.tmem_offset_O1 + self.tmem_cols_Oi + assert ( + self.total_tmem <= self.tmem_alloc_cols + ), f"Total TMEM columns allocated {self.total_tmem} exceeds capacity {self.tmem_alloc_cols}" + + def _get_shared_storage_cls(self): + self.buffer_align_bytes = 1024 + + def smem_struct_align(dtype, staged_layout, disabled=False): + if disabled: + return cute.struct.MemRange[dtype, 0] + return cute.struct.Align[ + cute.struct.MemRange[dtype, cute.cosize(staged_layout)], + self.buffer_align_bytes, + ] + + def mbar_struct(num_stages): + return cute.struct.MemRange[Int64, 2 * num_stages] + + sQ_struct, sK_struct, sQv_struct, sV_struct, sP_struct = ( + smem_struct_align(dtype, layout, disabled) + for dtype, layout, disabled in [ + (self.dtype_Q, self.sQ_layout_staged, not self.has_qk), + (self.dtype_K, self.sK_layout_staged, not self.has_qk), + (self.dtype_Qv, self.sQv_layout_staged, False), + (self.dtype_V, self.sV_layout_staged, False), + (self.dtype_P, self.sP_layout_staged, False), + ] + ) + sStats_struct = cute.struct.MemRange[Float32, cute.cosize(self.sStats_layout)] + sScale_struct = cute.struct.MemRange[Float32, cute.cosize(self.sScale_layout)] + sBitmask_struct = cute.struct.MemRange[ + Uint32, cute.cosize(self.sBitmask_layout) + ] + + ( + mbar_ptr_Q_struct, + mbar_ptr_K_struct, + mbar_ptr_Qv_struct, + mbar_ptr_V_struct, + mbar_ptr_S_struct, + mbar_ptr_P_struct, + mbar_ptr_O0_struct, + mbar_ptr_O1_struct, + mbar_sm_stats_struct, + mbar_bitmask_struct, + ) = ( + mbar_struct(n) + for n in [ + self.num_stages_Q, + self.num_stages_K, + self.num_stages_Qv, + self.num_stages_V, + self.num_stages_S, + self.num_stages_P, + self.num_stages_Oi, + self.num_stages_Oi, + self.num_stages_sm_stats, + self.num_stages_bitmask, + ] + ) + mbar_ptr_tmem_dealloc_struct = Int64 + tmem_holding_buf_struct = Int32 + + self.sched_stages = 1 + clc_response_size = self.sched_stages * 4 if self.use_clc_scheduler else 0 + clc_mbar_size = self.sched_stages * 2 if self.use_clc_scheduler else 0 + + @cute.struct + class SharedStorage: + mbar_ptr_Q: mbar_ptr_Q_struct + mbar_ptr_K: mbar_ptr_K_struct + mbar_ptr_Qv: mbar_ptr_Qv_struct + mbar_ptr_V: mbar_ptr_V_struct + mbar_ptr_S: mbar_ptr_S_struct + mbar_ptr_P: mbar_ptr_P_struct + mbar_ptr_O0: mbar_ptr_O0_struct + mbar_ptr_O1: mbar_ptr_O1_struct + mbar_ptr_K_cpasync: mbar_ptr_K_struct + mbar_ptr_V_cpasync: mbar_ptr_V_struct + mbar_ptr_sm_stats: mbar_sm_stats_struct + mbar_ptr_bitmask: mbar_bitmask_struct + mbar_ptr_tmem_dealloc: mbar_ptr_tmem_dealloc_struct + tmem_holding_buf: tmem_holding_buf_struct + clc_mbar_ptr: cute.struct.MemRange[cutlass.Int64, clc_mbar_size] + clc_response: cute.struct.MemRange[Int32, clc_response_size] + sO_empty_mbar_ptr: cutlass.Int64 + + sRowMax: sStats_struct + sRowSum: sStats_struct + sScale: sScale_struct + sBitmask: sBitmask_struct + sQv: sQv_struct + sQ: sQ_struct + sK: sK_struct + sV: sV_struct + sP: sP_struct + + # print("smem bytes = ", SharedStorage.size_in_bytes()) + + return SharedStorage + + # fmt: off + @cute.jit + def __call__( + self, + mQ: Optional[cute.Tensor], # (b, s_q, h, d) or (total_q, h, d) if there is cu_seqlens_q + mQv: cute.Tensor, # (b, s_q, h, dv) or (total_q, h, d) if there is cu_seqlens_q + mK: Optional[cute.Tensor], # (b, s_k, h_k, d) or (total_k, h_k, d) if there is cu_seqlens_k or (num_pages, page_size, h_k, d) if there is page_table + mV: cute.Tensor, # (b, s_k, h_k, dv) or (total_k, h_k, dv) if there is cu_seqlens_k or (num_pages, page_size, h_k, dv) if there is page_table + mO: cute.Tensor, # (b, s_q, h, dv) or (total_q, h, dv) if there is cu_seqlens_q + mLSE: Optional[cute.Tensor], # (b, s_q, h) or (total_q, h) if there is cu_seqlens_q + softmax_scale: Float32, + mP: Optional[cute.Tensor] = None, # (b, s_q, h, topk) or (total_q, h, topk) if there is cu_seqlens_q + mRowMax: Optional[cute.Tensor] = None, # (b, s_q, topk // tile_n, h) or (total_q, topk // tile_n, h) if there is cu_seqlens_q + mCuSeqlensQ: Optional[cute.Tensor] = None, # (b + 1) + mCuSeqlensK: Optional[cute.Tensor] = None, # (b + 1) + mSeqUsedQ: Optional[cute.Tensor] = None, # (b) + mSeqUsedK: Optional[cute.Tensor] = None, # (b) + mIndexTopk: Optional[cute.Tensor] = None, # (b, s_q, topk) or (total_q, topk) if there is cu_seqlens_q + mPageTable: Optional[cute.Tensor] = None, + window_size_left: Int32 | int | None = None, + window_size_right: Int32 | int | None = None, + # Always keep stream as the last parameter (EnvStream: obtained implicitly via TVM FFI). + stream: cuda.CUstream = None, + ): + # fmt: on + self.store_P = mP is not None + self.store_row_max = mRowMax is not None + + if const_expr(self.has_qk): + assert mQ is not None and mK is not None, "has_qk requires mQ and mK" + else: + assert mQ is None and mK is None, "not has_qk disallows mQ and mK" + + # ==== dtype info ==== + self.dtype_Q = mQ.element_type if self.has_qk else cutlass.BFloat16 + self.dtype_K = mK.element_type if self.has_qk else cutlass.BFloat16 + self.dtype_Qv = mQv.element_type + self.dtype_V = mV.element_type + self.dtype_P = mV.element_type + self.dtype_O = mO.element_type + + if const_expr(self.store_P): + assert mP.element_type == self.dtype_P + + # ==== Prepare Tensors ==== + new_stride = lambda mX: ( + *(cute.assume(s, divby=128 // mX.element_type.width) for s in mX.stride[:-1]), + mX.stride[-1], + ) + mQ, mQv, mK, mV, mO, mP = [ + cute.make_tensor(mX.iterator, cute.make_layout(mX.shape, stride=new_stride(mX))) + if mX is not None + else None + for mX in (mQ, mQv, mK, mV, mO, mP) + ] + + # (b, s, h, d) -> (s, d, h, b) or + # (total, h, d) -> (total, d, h) or + # (num_pages, page_size, h_k, d) -> (page_size, d, h_k, num_pages) + QO_layout_transpose = [1, 3, 2, 0] if const_expr(mCuSeqlensQ is None) else [0, 2, 1] + KV_layout_transpose = [1, 3, 2, 0] if const_expr(mCuSeqlensK is None) else [0, 2, 1] + mQ, mQv, mO, mP = [ + cute.make_tensor(mX.iterator, cute.select(mX.layout, mode=QO_layout_transpose)) + if mX is not None + else None + for mX in (mQ, mQv, mO, mP) + ] + mK, mV = [ + cute.make_tensor(mX.iterator, cute.select(mX.layout, mode=KV_layout_transpose)) + if mX is not None + else None + for mX in (mK, mV) + ] + # (s_k, dv, h_k, b) -> (dv, s_k, h_k, b) or + # (total_k, dv, h_k) -> (dv, total_k, h_k) + V_layout_transpose = [1, 0, 2, 3] if const_expr(mCuSeqlensK is None) else [1, 0, 2] + mVt = cute.make_tensor(mV.iterator, cute.select(mV.layout, mode=V_layout_transpose)) + # (b, s_q, topk) -> (topk, s_q, b) or (total_q, topk) -> (topk, total_q) + topk_layout_transpose = [2, 1, 0] if const_expr(mCuSeqlensQ is None) else [1, 0] + mIndexTopk = ( + cute.make_tensor( + mIndexTopk.iterator, cute.select(mIndexTopk.layout, mode=topk_layout_transpose) + ) + if mIndexTopk is not None + else None + ) + # (b, s_q, h) -> (s_q, h, b) or (total_q, h) -> (total_q, h) + LSE_layout_transpose = [1, 2, 0] if const_expr(mCuSeqlensQ is None) else [0, 1] + mLSE = ( + cute.make_tensor(mLSE.iterator, cute.select(mLSE.layout, mode=LSE_layout_transpose)) + if mLSE is not None + else None + ) + # (b, s, topk//128, h) => (s, topk//128, h, b) or + # (total, topk//128, h) == (total, topk//128, h) + rowmax_layout_transpose = [1, 2, 3, 0] if const_expr(mCuSeqlensQ is None) else [0, 1, 2] + if const_expr(mRowMax is not None): + mRowMax = cute.make_tensor( + mRowMax.iterator, cute.select(mRowMax.layout, mode=rowmax_layout_transpose) + ) + + topk_length_dynamic = mIndexTopk.shape[0] if mIndexTopk is not None else None + + self.o_layout = cutlass.utils.LayoutEnum.from_tensor(mO) + self.p_layout = cutlass.utils.LayoutEnum.ROW_MAJOR + if const_expr(self.store_P): + assert cutlass.utils.LayoutEnum.from_tensor(mP) == self.p_layout + + mO_og = mO + mP_og = mP + if const_expr(self.pack_gqa): + mQ, mQv, mO, mP, mRowMax = [ + pack_gqa_layout(mX, self.qhead_per_kvhead, self.nheads_kv, head_idx=2) + if mX is not None + else None + for mX in (mQ, mQv, mO, mP, mRowMax) + ] + if const_expr(mLSE is not None): + mLSE = pack_gqa_layout(mLSE, self.qhead_per_kvhead, self.nheads_kv, head_idx=1) + + # ==== Prepare MMAs ==== + # (local_var, dtype_a, major_a, major_b, mma_tiler, operand_source_a) + # fmt: off + _mma_specs = [ + ("tiled_mma_QK", self.dtype_Q, self.major_mode_Q, self.major_mode_K, self.mma_tiler_QK, self.operand_source_Q), + ("tiled_mma_QvV", self.dtype_Qv, self.major_mode_Qvi, self.major_mode_Vi, self.mma_tiler_QvV, self.operand_source_Qvi), + ("tiled_mma_PVt", self.dtype_P, self.major_mode_P, self.major_mode_Vti, self.mma_tiler_PVt, self.operand_source_P), + ] + tiled_mma_QK, tiled_mma_QvV, tiled_mma_PVt = ( + sm100_utils.make_trivial_tiled_mma( + dtype_a, major_a, major_b, self.dtype_acc, self.cta_group, mma_tiler[:2], operand_source_a, + ) + for _, dtype_a, major_a, major_b, mma_tiler, operand_source_a in _mma_specs + ) + # fmt: on + + # ==== Prepare SMEM layouts and TMAs ==== + # (attr, make_fn, tiled_mma, mma_tiler, dtype, num_stages) + # fmt: off + _smem_layout_specs = [ + ("sQ_layout", sm100_utils.make_smem_layout_a, tiled_mma_QK, self.mma_tiler_QK, self.dtype_Q, self.num_stages_Q), + ("sK_layout", sm100_utils.make_smem_layout_b, tiled_mma_QK, self.mma_tiler_QK, self.dtype_K, self.num_stages_K), + ("sP_layout", sm100_utils.make_smem_layout_a, tiled_mma_PVt, self.mma_tiler_PVt, self.dtype_P, self.num_stages_P), + ("sQv_layout", sm100_utils.make_smem_layout_a, tiled_mma_QvV, self.mma_tiler_QvV, self.dtype_Qv, self.num_stages_Qv), + ("sV_layout", sm100_utils.make_smem_layout_b, tiled_mma_QvV, self.mma_tiler_QvV, self.dtype_V, self.num_stages_V), + ("sVt_layout", sm100_utils.make_smem_layout_b, tiled_mma_PVt, self.mma_tiler_PVt, self.dtype_V, self.num_stages_V), + ] + for attr, make_fn, tiled_mma, mma_tiler, dtype, num_stages in _smem_layout_specs: + ab_kwarg = "a_dtype" if make_fn is sm100_utils.make_smem_layout_a else "b_dtype" + staged = make_fn( + tiled_mma=tiled_mma, + mma_tiler_mnk=mma_tiler, + num_stages=num_stages, + **{ab_kwarg: dtype}, + ) + setattr(self, f"{attr}_staged", staged) + setattr(self, attr, cute.select(staged, mode=[0, 1, 2])) + # fmt: on + + self.sStats_layout = cute.make_layout((self.cta_tile_m, self.cta_group_size)) + self.sScale_layout = cute.make_layout((self.cta_tile_m, self.num_stages_sm_stats)) + self.sBitmask_layout = cute.make_layout((self.tile_n // 32, self.num_stages_bitmask)) + + # fmt: off + for attr, dtype, layout in [ + ("tma_copy_bytes_Q", self.dtype_Q, self.sQ_layout), + ("tma_copy_bytes_K", self.dtype_K, self.sK_layout), + ("tma_copy_bytes_Qvi", self.dtype_Qv, self.sQv_layout), + ("tma_copy_bytes_Vi", self.dtype_V, self.sV_layout), + ]: + setattr(self, attr, cute.size_in_bytes(dtype, layout) * self.cta_group_size) + # fmt: on + + tma_load_op = cpasync.CopyBulkTensorTileG2SOp(self.cta_group) + cta_layout_vmnk = cute.tiled_divide( + cute.make_layout(self.cluster_shape_mnk), (tiled_mma_QK.thr_id.shape,) + ) + cta_shape = cta_layout_vmnk.shape + + def make_tma(make_fn, mX, smem_layout, mma_tiler, tiled_mma): + return make_fn(tma_load_op, mX, smem_layout, mma_tiler, tiled_mma, cta_shape) + + A, B = cute.nvgpu.make_tiled_tma_atom_A, cute.nvgpu.make_tiled_tma_atom_B + + # (atom_name, tensor_name, make_fn, m, smem_layout, mma_tiler, tiled_mma, kv_only) + # fmt: off + _tma_specs = [ + ("tma_atom_Q", "tma_tensor_Q", A, mQ, self.sQ_layout, self.mma_tiler_QK, tiled_mma_QK, False), + ("tma_atom_Qv", "tma_tensor_Qv", A, mQv, self.sQv_layout, self.mma_tiler_QvV, tiled_mma_QvV, False), + ("tma_atom_K", "tma_tensor_K", B, mK, self.sK_layout, self.mma_tiler_QK, tiled_mma_QK, True), + ("tma_atom_V", "tma_tensor_V", B, mV, self.sV_layout, self.mma_tiler_QvV, tiled_mma_QvV, True), + ("tma_atom_Vt", "tma_tensor_Vt", B, mVt, self.sVt_layout, self.mma_tiler_PVt, tiled_mma_PVt, True), + ] + _tmas = {} + for atom_name, tensor_name, make_fn, m, smem_layout, mma_tiler, tiled_mma, kv_only in _tma_specs: + _tmas[atom_name], _tmas[tensor_name] = ( + make_tma(make_fn, m, smem_layout, mma_tiler, tiled_mma) + if const_expr((not kv_only or self.use_tma_KV) and m is not None) + else (None, None) + ) + + (tma_atom_Q, tma_tensor_Q, + tma_atom_Qv, tma_tensor_Qv, + tma_atom_K, tma_tensor_K, + tma_atom_V, tma_tensor_V, + tma_atom_Vt, tma_tensor_Vt) = _tmas.values() + # fmt: on + + tma_store_op = cpasync.CopyBulkTensorTileS2GOp() + self.ragged_tma_O = ( + self.use_tma_O + and self.is_varlen_q + and self.pack_gqa + and self.cta_tile_m % self.qhead_per_kvhead == 0 + ) + make_tiled_tma_atom_fn = ( + partial(make_packgqa_tiled_tma_atom, qhead_per_kvhead=self.qhead_per_kvhead, head_idx=2) + if const_expr(self.ragged_tma_O) + else cpasync.make_tiled_tma_atom + ) + + # ==== Set up P smem -> gmem tma store ==== + + # S<3,4,3> o 0 o ((8,8),(64,2),(1,1)):((64,512),(1,4096),(0,0)) + sP_layout_out = sm100_utils.make_smem_layout_epi( + self.dtype_P, self.p_layout, self.tile_P, self.num_stages_P + ) + + if const_expr(self.store_P): + # TODO: add asserts + mP_tma = mP_og if const_expr(self.ragged_tma_O) else mP + if const_expr(self.ragged_tma_O): + mP_tma = copy_utils.create_ragged_tensor_for_tma( + mP_tma, ragged_dim=0, ptr_shift=True + ) + tma_atom_P, tma_tensor_P = make_tiled_tma_atom_fn( + tma_store_op, mP_tma, cute.select(sP_layout_out, mode=[0, 1]), self.tile_P + ) + else: + tma_atom_P = None + tma_tensor_P = None + + # ==== Set up Oi smem -> gmem tma store ==== + + self.overlap_sO_sV = True + if const_expr(self.overlap_sO_sV): + num_stages_sO = self.num_stages_V + else: + num_stages_sO = self.num_hdimv_splits + sO_layout = sm100_utils.make_smem_layout_epi( + self.dtype_O, self.o_layout, self.epi_tile, num_stages_sO + ) + + if const_expr(self.use_tma_O): + mO_tma = mO_og if const_expr(self.ragged_tma_O) else mO + if const_expr(self.ragged_tma_O): + mO_tma = copy_utils.create_ragged_tensor_for_tma( + mO_tma, ragged_dim=0, ptr_shift=True + ) + + tma_atom_O, tma_tensor_O = make_tiled_tma_atom_fn( + tma_store_op, mO_tma, cute.select(sO_layout, mode=[0, 1]), self.epi_tile + ) + else: + tma_atom_O = None + tma_tensor_O = None + + # ==== Set up Oi rmem -> gmem copy ==== + universal_copy_bits = 128 + atom_universal_copy = cute.make_copy_atom( + cute.nvgpu.CopyUniversalOp(), + self.dtype_O, + num_bits_per_copy=universal_copy_bits, + ) + thread_layout_O_r2g = cute.make_layout((64, 2), stride=(1, 64)) + value_layout_O_r2g = cute.make_layout( + (1, self.hdimv // self.num_hdimv_splits // self.cta_group_size) + ) + tiled_copy_O_r2g = cute.make_tiled_copy_tv( + atom=atom_universal_copy, + thr_layout=thread_layout_O_r2g, + val_layout=value_layout_O_r2g, + ) + + # ==== Allocate shared memory ==== + SharedStorage = self._get_shared_storage_cls() + + # ==== Tile scheduler ==== + + TileScheduler = self.TileScheduler + + tile_sched_args = TileSchedulerArguments( + num_block=cute.ceil_div(cute.size(mQv.shape[0]), self.cta_tile_m), + num_head=cute.size(mQv.shape[2]), + num_batch=cute.size(mQv.shape[3]) + if const_expr(mCuSeqlensQ is None) + else cute.size(mCuSeqlensQ.shape[0] - 1), + num_splits=1, # todo: split_kv + seqlen_k=cute.size(mV.shape[0]) + if const_expr(mPageTable is None) + else cute.size(mV.shape[0]) * cute.size(mPageTable.shape[1]), + headdim=self.hdim, + headdim_v=self.hdimv, + total_q=cute.size(mQv.shape[0]) + if const_expr(mCuSeqlensQ is not None) + else cute.size(mQv.shape[0]) * cute.size(mQv.shape[3]), + tile_shape_mn=( + self.cta_tile_m, + self.tile_n, + ), + mCuSeqlensQ=mCuSeqlensQ, + mSeqUsedQ=mSeqUsedQ, + qhead_per_kvhead_packgqa=self.qhead_per_kvhead if const_expr(self.pack_gqa) else 1, + element_size=self.dtype_K.width // 8, + is_persistent=self.is_persistent, + # lpt=self.is_causal or self.is_local, + lpt=False, + is_split_kv=False, + cluster_shape_mn=self.cluster_shape_mn, + use_cluster_idx=False, + ) + tile_sched_params = TileScheduler.to_underlying_arguments( + tile_sched_args, scheduling_mode=self.scheduling_mode + ) + self.tile_scheduler_cls = TileScheduler + grid_dim = TileScheduler.get_grid_shape(tile_sched_params) + fa_printf(1, "grid = {}", grid_dim) + + # ==== Named Barrier ==== + self.cpasync_barrier = cutlass.pipeline.NamedBarrier( + barrier_id=int(NamedBarrierFwdSm100_MLA2CTA.Cpasync), + num_threads=self.num_cpasync_load_threads, + ) + self.softmax_barrier = cutlass.pipeline.NamedBarrier( + barrier_id=int(NamedBarrierFwdSm100_MLA2CTA.Softmax), + num_threads=self.num_softmax_threads, + ) + self.epi_barrier = cutlass.pipeline.NamedBarrier( + barrier_id=int(NamedBarrierFwdSm100_MLA2CTA.Epilogue), + num_threads=self.num_epilogue_threads, + ) + # softmax -> correction + self.sm_stats_barrier_full = cutlass.pipeline.NamedBarrier( + barrier_id=int(NamedBarrierFwdSm100_MLA2CTA.SoftmaxStatsFull), + num_threads=self.num_softmax_threads + self.num_epilogue_threads, + ) + self.sm_stats_barrier_empty = cutlass.pipeline.NamedBarrier( + barrier_id=int(NamedBarrierFwdSm100_MLA2CTA.SoftmaxStatsEmpty), + num_threads=self.num_softmax_threads + self.num_epilogue_threads, + ) + + LOG2_E = math.log2(math.e) + softmax_scale_log2 = softmax_scale * LOG2_E + + # ==== Launch kernel ==== + self.kernel( + tma_tensor_Q, + tma_tensor_Qv, + tma_tensor_K if self.use_tma_KV else mK, + tma_tensor_V if self.use_tma_KV else mV, + tma_tensor_Vt if self.use_tma_KV else mVt, + tma_tensor_O if self.use_tma_O else mO, + tma_tensor_P, + mLSE, + mRowMax, + mCuSeqlensQ, + mCuSeqlensK, + mSeqUsedQ, + mSeqUsedK, + mIndexTopk, + mPageTable, + tma_atom_Q, + tma_atom_Qv, + tma_atom_K, + tma_atom_V, + tma_atom_Vt, + tma_atom_O, + tma_atom_P, + tiled_copy_O_r2g, + self.sQ_layout_staged, + self.sK_layout_staged, + self.sQv_layout_staged, + self.sV_layout_staged, + self.sVt_layout_staged, + self.sP_layout_staged, + self.sStats_layout, + self.sScale_layout, + self.sBitmask_layout, + sO_layout, + sP_layout_out, + tiled_mma_QK, + tiled_mma_QvV, + tiled_mma_PVt, + softmax_scale, + softmax_scale_log2, + topk_length_dynamic, + tile_sched_params, + SharedStorage, + ).launch( + grid=grid_dim, + block=( + self.num_threads, + 1, + 1, + ), + cluster=self.cluster_shape_mnk, + smem=SharedStorage.size_in_bytes(), + stream=stream, + ) + + @cute.kernel + def kernel( + self, + mQ: Optional[cute.Tensor], + mQv: cute.Tensor, + mK: Optional[cute.Tensor], + mV: cute.Tensor, + mVt: cute.Tensor, + mO: cute.Tensor, + mP: Optional[cute.Tensor], + mLSE: Optional[cute.Tensor], + mRowMax: Optional[cute.Tensor], + mCuSeqlensQ: Optional[cute.Tensor], + mCuSeqlensK: Optional[cute.Tensor], + mSeqUsedQ: Optional[cute.Tensor], + mSeqUsedK: Optional[cute.Tensor], + mIndexTopk: Optional[cute.Tensor], + mPageTable: Optional[cute.Tensor], + tma_atom_Q: cute.CopyAtom, + tma_atom_Qv: cute.CopyAtom, + tma_atom_K: Optional[cute.CopyAtom], + tma_atom_V: Optional[cute.CopyAtom], + tma_atom_Vt: Optional[cute.CopyAtom], + tma_atom_O: Optional[cute.CopyAtom], + tma_atom_P: Optional[cute.CopyAtom], + tiled_copy_O_r2g: cute.TiledCopy, + sQ_layout_staged: cute.ComposedLayout, + sK_layout_staged: cute.ComposedLayout, + sQv_layout_staged: cute.ComposedLayout, + sV_layout_staged: cute.ComposedLayout, + sVt_layout_staged: cute.ComposedLayout, + sP_layout_staged: cute.ComposedLayout, + sStats_layout: cute.Layout, + sScale_layout: cute.Layout, + sBitmask_layout: cute.Layout, + sO_layout: cute.ComposedLayout, + sP_layout_out: cute.ComposedLayout, + tiled_mma_QK: cute.TiledMma, + tiled_mma_QvV: cute.TiledMma, + tiled_mma_PVt: cute.TiledMma, + softmax_scale: Float32, + softmax_scale_log2: Float32, + topk_length_dynamic: Optional[Int32], + tile_sched_params: ParamsBase, + SharedStorage: cutlass.Constexpr[Callable], + ): + warp_idx = cute.arch.make_warp_uniform(cute.arch.warp_idx()) + cta_layout_vmnk = cute.tiled_divide( + cute.make_layout(self.cluster_shape_mnk), (tiled_mma_QvV.thr_id.shape,) + ) + + cta_m_block, head_idx, batch_idx = cute.arch.block_idx() + cluster_m_block = cta_m_block // self.cta_group_size + mma_tile_coord_v = cta_m_block % cute.size(tiled_mma_QvV.thr_id.shape) + is_leader_cta = mma_tile_coord_v == 0 + + # ==== Allocate SMEM ==== + smem = cutlass.utils.SmemAllocator() + storage = smem.allocate(SharedStorage) + + # ==== TMEM stuff ==== + tmem_alloc_barrier = pipeline.NamedBarrier( + barrier_id=int(NamedBarrierFwdSm100_MLA2CTA.TmemPtr), + num_threads=self.num_mma_threads + self.num_softmax_threads + self.num_epilogue_threads, + ) + tmem = cutlass.utils.TmemAllocator( + storage.tmem_holding_buf, + barrier_for_retrieve=tmem_alloc_barrier, + allocator_warp_id=self.mma_warp_id, + is_two_cta=self.use_2cta_instrs, + two_cta_tmem_dealloc_mbar_ptr=storage.mbar_ptr_tmem_dealloc, + ) + + # ==== Prefetch TMA descriptors ==== + if warp_idx == self.load_warp_id: + if const_expr(self.has_qk): + cpasync.prefetch_descriptor(tma_atom_Q) + cpasync.prefetch_descriptor(tma_atom_Qv) + if const_expr(self.use_tma_KV): + if const_expr(self.has_qk): + cpasync.prefetch_descriptor(tma_atom_K) + cpasync.prefetch_descriptor(tma_atom_V) + cpasync.prefetch_descriptor(tma_atom_Vt) + if const_expr(self.use_tma_O): + cpasync.prefetch_descriptor(tma_atom_O) + + # ==== Construct pipelines ==== + tma_warp = pipeline.CooperativeGroup(pipeline.Agent.Thread, 1) + mma_warp = pipeline.CooperativeGroup(pipeline.Agent.Thread, 1) + sm_threads = pipeline.CooperativeGroup(pipeline.Agent.Thread, self.num_softmax_threads) + epi_threads = pipeline.CooperativeGroup(pipeline.Agent.Thread, self.num_epilogue_threads) + sm_threads_cluster = pipeline.CooperativeGroup( + pipeline.Agent.Thread, self.num_softmax_threads * self.cta_group_size + ) + epi_threads_cluster = pipeline.CooperativeGroup( + pipeline.Agent.Thread, self.num_epilogue_threads * self.cta_group_size + ) + + TmaUmma = pipeline.PipelineTmaUmma + AsyncUmma = pipeline.PipelineAsyncUmma + UmmaAsync = pipeline.PipelineUmmaAsync + Async = pipeline.PipelineAsync + + def make_pipeline(cls, mbar_ptr, num_stages, producer, consumer, tx_count=None): + return cls.create( + barrier_storage=mbar_ptr.data_ptr(), + num_stages=num_stages, + producer_group=producer, + consumer_group=consumer, + defer_sync=True, + **({"cta_layout_vmnk": cta_layout_vmnk} if cls is not Async else {}), + **({"tx_count": tx_count} if tx_count is not None else {}), + ) + + # Unconditional pipelines + # fmt: off + pipeline_Q = None + if const_expr(self.has_qk): + pipeline_Q = make_pipeline(TmaUmma, storage.mbar_ptr_Q, self.num_stages_Q, tma_warp, mma_warp, self.tma_copy_bytes_Q) + pipeline_Qv = make_pipeline(TmaUmma, storage.mbar_ptr_Qv, self.num_stages_Qv, tma_warp, mma_warp, self.tma_copy_bytes_Qvi) + pipeline_S = make_pipeline(UmmaAsync, storage.mbar_ptr_S, self.num_stages_S, mma_warp, sm_threads_cluster) + pipeline_P = make_pipeline(AsyncUmma, storage.mbar_ptr_P, self.num_stages_P, sm_threads_cluster, mma_warp) + pipeline_O0 = make_pipeline(UmmaAsync, storage.mbar_ptr_O0, self.num_stages_Oi, mma_warp, epi_threads_cluster) + pipeline_O1 = make_pipeline(UmmaAsync, storage.mbar_ptr_O1, self.num_stages_Oi, mma_warp, epi_threads_cluster) + pipeline_sm_stats = make_pipeline(Async, storage.mbar_ptr_sm_stats, self.num_stages_sm_stats, sm_threads, epi_threads) + + # K/V pipelines: type and producer depend on use_tma_KV + if const_expr(self.use_tma_KV): + pipeline_K = None + if const_expr(self.has_qk): + pipeline_K = make_pipeline(TmaUmma, storage.mbar_ptr_K, self.num_stages_K, tma_warp, mma_warp, self.tma_copy_bytes_K) + pipeline_V = make_pipeline(TmaUmma, storage.mbar_ptr_V, self.num_stages_V, tma_warp, mma_warp, self.tma_copy_bytes_Vi) + pipeline_K_cpasync = pipeline_V_cpasync = pipeline_bitmask = None + else: + cpasync_load_threads = pipeline.CooperativeGroup(pipeline.Agent.Thread, self.num_cpasync_load_threads) + relay_warps_cluster = pipeline.CooperativeGroup(pipeline.Agent.Thread, self.cta_group_size) + relay_threads = pipeline.CooperativeGroup(pipeline.Agent.Thread, self.num_relay_threads) + pipeline_K = pipeline_K_cpasync = None + if const_expr(self.has_qk): + pipeline_K = make_pipeline(AsyncUmma, storage.mbar_ptr_K, self.num_stages_K, relay_warps_cluster, mma_warp) + pipeline_K_cpasync = make_pipeline(Async, storage.mbar_ptr_K_cpasync, self.num_stages_K, cpasync_load_threads, relay_threads) + pipeline_V = make_pipeline(AsyncUmma, storage.mbar_ptr_V, self.num_stages_V, relay_warps_cluster, mma_warp) + pipeline_V_cpasync = make_pipeline(Async, storage.mbar_ptr_V_cpasync, self.num_stages_V, cpasync_load_threads, relay_threads) + pipeline_bitmask = ( + make_pipeline(Async, storage.mbar_ptr_bitmask, self.num_stages_bitmask, cpasync_load_threads, sm_threads) + if const_expr(self.is_topk_gather and not self.disable_bitmask) else None + ) + # fmt: on + + sO_empty_mbar_ptr = None + if const_expr(self.use_tma_O and self.overlap_sO_sV): + sO_empty_mbar_ptr = storage.sO_empty_mbar_ptr + if warp_idx == 0: + cute.arch.mbarrier_init(sO_empty_mbar_ptr, 1) + + pipeline.pipeline_init_arrive(cluster_shape_mn=cta_layout_vmnk, is_relaxed=True) + + # ==== Get SMEM tensors ==== + # fmt: off + sQ, sK, sQv, sV, sVt, sP, sP_out = ( + store.get_tensor(layout.outer, swizzle=layout.inner) + if const_expr(store._size > 0) else None + for store, layout in [ + (storage.sQ, sQ_layout_staged), + (storage.sK, sK_layout_staged), + (storage.sQv, sQv_layout_staged), + (storage.sV, sV_layout_staged), + (storage.sV, sVt_layout_staged), # sVt reuses sV storage + (storage.sP, sP_layout_staged), + (storage.sP, sP_layout_out), + ] + ) + # fmt: on + sRowMax = storage.sRowMax.get_tensor(sStats_layout) + sRowSum = storage.sRowSum.get_tensor(sStats_layout) + sScale = storage.sScale.get_tensor(sScale_layout) + sBitmask = None + if const_expr(self.is_topk_gather): + sBitmask = storage.sBitmask.get_tensor(sBitmask_layout) + + if const_expr(self.overlap_sO_sV): + sO_iterator = sV.iterator + assert cute.cosize(sO_layout) <= cute.cosize(sV_layout_staged) + else: + sO_iterator = sQv.iterator + assert cute.cosize(sO_layout) <= cute.cosize(sQv_layout_staged) + sO = cute.make_tensor( + cute.recast_ptr(sO_iterator, sO_layout.inner, self.dtype_O), sO_layout.outer + ) + + # ==== Get thread MMAs and accumulator fragments ==== + thr_mma_QK = tiled_mma_QK.get_slice(mma_tile_coord_v) + thr_mma_QvV = tiled_mma_QvV.get_slice(mma_tile_coord_v) + thr_mma_PVt = tiled_mma_PVt.get_slice(mma_tile_coord_v) + + acc_shape_S = thr_mma_QvV.partition_shape_C(self.mma_tiler_QvV[:2]) + tStS_fake = thr_mma_QvV.make_fragment_C(cute.append(acc_shape_S, self.num_stages_S)) + + acc_shape_Oi = thr_mma_PVt.partition_shape_C(self.mma_tiler_PVt[:2]) + tOtO0_fake = thr_mma_PVt.make_fragment_C(acc_shape_Oi) + tOtO1_fake = thr_mma_PVt.make_fragment_C(acc_shape_Oi) + + block_info = BlockInfo( + self.cta_tile_m * self.cta_group_size, + self.tile_n, + is_causal=self.is_causal, + qhead_per_kvhead_packgqa=self.qhead_per_kvhead if const_expr(self.pack_gqa) else 1, + ) + SeqlenInfoCls = partial( + SeqlenInfoQK.create, + seqlen_q_static=mQv.shape[0] if const_expr(not self.pack_gqa) else mQv.shape[0][1], + seqlen_k_static=mV.shape[0] + if const_expr(mPageTable is None) + else mV.shape[0] * mPageTable.shape[1], + tile_m=self.cta_tile_m, + tile_n=self.tile_n, + mCuSeqlensQ=mCuSeqlensQ, + mCuSeqlensK=mCuSeqlensK, + mSeqUsedQ=mSeqUsedQ, + mSeqUsedK=mSeqUsedK, + ) + AttentionMaskCls = partial( + AttentionMask, + self.cta_tile_m * self.cta_group_size, + self.tile_n, + qhead_per_kvhead_packgqa=self.qhead_per_kvhead if const_expr(self.pack_gqa) else 1, + ) + + if const_expr(self.use_clc_scheduler): + clc_response_ptr = storage.clc_response.data_ptr() + clc_mbar_ptr = storage.clc_mbar_ptr.data_ptr() + + clc_pipeline_producer_group = pipeline.CooperativeGroup(pipeline.Agent.Thread) + num_clc_consumer_warps_per_cta = self.num_threads // cute.arch.WARP_SIZE + num_clc_consumer_warps = num_clc_consumer_warps_per_cta * self.cta_group_size + clc_pipeline_consumer_group = pipeline.CooperativeGroup( + pipeline.Agent.Thread, cute.arch.WARP_SIZE * num_clc_consumer_warps + ) + clc = ClcState.create( + hw_scheduler=ClcDynamicPersistentTileScheduler.create( + self.tile_scheduler_cls.clc_problem_shape(tile_sched_params), + cute.arch.block_idx(), + cute.arch.grid_dim(), + clc_response_ptr, + ), + pipeline=pipeline.PipelineClcFetchAsync.create( + barrier_storage=clc_mbar_ptr, + num_stages=self.sched_stages, + producer_group=clc_pipeline_producer_group, + consumer_group=clc_pipeline_consumer_group, + tx_count=16, + cta_layout_vmnk=cta_layout_vmnk, + ), + consumer_state=pipeline.make_pipeline_state( + pipeline.PipelineUserType.Consumer, self.sched_stages + ), + producer_state=pipeline.make_pipeline_state( + pipeline.PipelineUserType.Producer, self.sched_stages + ), + ) + tile_scheduler = self.tile_scheduler_cls.create(tile_sched_params, clc=clc) + else: + tile_scheduler = self.tile_scheduler_cls.create(tile_sched_params) + assert isinstance(tile_scheduler, TileSchedulerProtocol), ( + f"tile_scheduler is not a TileSchedulerProtocol: {type(tile_scheduler)}" + ) + + pipeline.pipeline_init_wait(cluster_shape_mn=cta_layout_vmnk) + + if const_expr(self.use_clc_scheduler): + if warp_idx == self.clc_scheduler_warp_id: + cute.arch.setmaxregister_decrease(self.num_regs_other) + if is_leader_cta: + self.clc_scheduler_warp(tile_scheduler) + else: + self.empty_warp(tile_scheduler) + for i in cutlass.range_constexpr(len(self.empty_warp_ids)): + if warp_idx == self.empty_warp_ids[i] and warp_idx != self.clc_scheduler_warp_id: + cute.arch.setmaxregister_decrease(self.num_regs_other) + self.empty_warp(tile_scheduler) + else: + for i in cutlass.range_constexpr(len(self.empty_warp_ids)): + if warp_idx == self.empty_warp_ids[i]: + cute.arch.setmaxregister_decrease(self.num_regs_other) + + if const_expr(self.use_cpasync_load_KV): + if warp_idx == self.relay_warp_id: + if const_expr(self.num_regs_load < self.num_regs_per_thread): + cute.arch.setmaxregister_decrease(self.num_regs_load) + self.relay( + pipeline_K, + pipeline_V, + pipeline_K_cpasync, + pipeline_V_cpasync, + sO_empty_mbar_ptr, + topk_length_dynamic, + block_info, + SeqlenInfoCls, + tile_scheduler=tile_scheduler, + ) + + if warp_idx in self.cpasync_load_warp_indices: + if const_expr(self.num_regs_cpasync < self.num_regs_per_thread): + cute.arch.setmaxregister_decrease(self.num_regs_cpasync) + self.load_cpasync( + mIndexTopk, + mK, + mV, + mVt, + sK, + sV, + sVt, + sBitmask, + pipeline_K, + pipeline_V, + pipeline_K_cpasync, + pipeline_V_cpasync, + pipeline_bitmask, + sO_empty_mbar_ptr, + topk_length_dynamic, + block_info, + SeqlenInfoCls, + tile_scheduler=tile_scheduler, + mPageTable=mPageTable, + ) + + if warp_idx == self.load_warp_id: + if const_expr(self.num_regs_load < self.num_regs_per_thread): + cute.arch.setmaxregister_decrease(self.num_regs_load) + self.load( + mQ, + mK, + mQv, + mV, + mVt, + sQ, + sK, + sQv, + sV, + sVt, + tma_atom_Q, + tma_atom_K, + tma_atom_Qv, + tma_atom_V, + tma_atom_Vt, + pipeline_Q, + pipeline_K, + pipeline_Qv, + pipeline_V, + sO_empty_mbar_ptr, + thr_mma_QK, + thr_mma_QvV, + thr_mma_PVt, + topk_length_dynamic, + block_info, + SeqlenInfoCls, + tile_scheduler=tile_scheduler, + mPageTable=mPageTable, + ) + + if warp_idx == self.mma_warp_id: + if const_expr(self.num_regs_mma < self.num_regs_per_thread): + cute.arch.setmaxregister_decrease(self.num_regs_mma) + # ==== Allocate TMEM ==== + tmem.allocate(self.tmem_alloc_cols) + tmem.wait_for_alloc() + tmem_ptr = tmem.retrieve_ptr(self.dtype_acc) + tStS = cute.make_tensor(tmem_ptr, tStS_fake.layout) + tOtO0 = cute.make_tensor(tmem_ptr + self.tmem_offset_O0, tOtO0_fake.layout) + tOtO1 = cute.make_tensor(tmem_ptr + self.tmem_offset_O1, tOtO1_fake.layout) + self.mma( + sQ, + sK, + sQv, + sV, + sVt, + sP, + tStS, + tOtO0, + tOtO1, + tiled_mma_QK, + tiled_mma_QvV, + tiled_mma_PVt, + pipeline_Q, + pipeline_K, + pipeline_Qv, + pipeline_V, + pipeline_S, + pipeline_P, + pipeline_O0, + pipeline_O1, + sO_empty_mbar_ptr, + is_leader_cta, + topk_length_dynamic, + block_info, + SeqlenInfoCls, + tile_scheduler=tile_scheduler, + ) + tmem.relinquish_alloc_permit() + tmem_alloc_barrier.arrive_and_wait() + tmem.free(tmem_ptr) + + if warp_idx in self.softmax_warp_indices: + cute.arch.setmaxregister_increase(self.num_regs_softmax) + tmem.wait_for_alloc() + tmem_ptr = tmem.retrieve_ptr(self.dtype_acc) + tStS = cute.make_tensor(tmem_ptr, tStS_fake.layout) + self.softmax_loop( + softmax_scale, + softmax_scale_log2, + mLSE, + mRowMax, + sRowMax, + sRowSum, + sScale, + sBitmask, + sP, + tStS, + thr_mma_QvV, + pipeline_S, + pipeline_P, + pipeline_sm_stats, + pipeline_bitmask, + sO_empty_mbar_ptr, + AttentionMaskCls, + topk_length_dynamic, + block_info, + SeqlenInfoCls, + tile_scheduler=tile_scheduler, + tma_atom_P=tma_atom_P, + mP=mP, + sP_out=sP_out, + ) + tmem_alloc_barrier.arrive() + + if warp_idx in self.epilogue_warp_indices: + if const_expr(self.num_regs_epilogue < self.num_regs_per_thread): + cute.arch.setmaxregister_decrease(self.num_regs_epilogue) + elif const_expr(self.num_regs_epilogue > self.num_regs_per_thread): + cute.arch.setmaxregister_increase(self.num_regs_epilogue) + + tmem.wait_for_alloc() + tmem_ptr = tmem.retrieve_ptr(self.dtype_acc) + tOtO0 = cute.make_tensor(tmem_ptr + self.tmem_offset_O0, tOtO0_fake.layout) + tOtO1 = cute.make_tensor(tmem_ptr + self.tmem_offset_O1, tOtO1_fake.layout) + self.correction_loop( + softmax_scale_log2, + mO, + mLSE, + tma_atom_O, + sRowMax, + sRowSum, + sScale, + sO, + tOtO0, + tOtO1, + pipeline_O0, + pipeline_O1, + pipeline_sm_stats, + sO_empty_mbar_ptr, + tiled_copy_O_r2g, + topk_length_dynamic, + block_info, + SeqlenInfoCls, + tile_scheduler=tile_scheduler, + ) + tmem_alloc_barrier.arrive() + + @cute.jit + def clc_scheduler_warp( + self, + tile_scheduler: TileSchedulerProtocol, + ): + work_tile = tile_scheduler.initial_work_tile_info() + while work_tile.is_valid_tile: + tile_scheduler.prefetch_next_work() + work_tile = tile_scheduler.advance_to_next_work() + cta_m_block, head_idx, batch_idx, _ = work_tile.tile_idx + if cute.arch.thread_idx()[0] == self.clc_scheduler_warp_id * cute.arch.WARP_SIZE: + fa_printf( + 3, + "[CLC] query sm={} cta={} (m_blk={},h={},b={},s={}) valid={}\n", + smid(), + cute.arch.block_idx()[0], + work_tile.tile_idx[0], + work_tile.tile_idx[1], + work_tile.tile_idx[2], + work_tile.tile_idx[3], + work_tile.is_valid_tile, + ) + tile_scheduler.producer_tail() + + @cute.jit + def empty_warp( + self, + tile_scheduler: TileSchedulerProtocol, + ): + work_tile = tile_scheduler.initial_work_tile_info() + while work_tile.is_valid_tile: + work_tile = tile_scheduler.advance_to_next_work() + + @cute.jit + def relay( + self, + pipeline_K: Optional[pipeline.PipelineAsyncUmma], + pipeline_V: pipeline.PipelineAsyncUmma, + pipeline_K_cpasync: Optional[pipeline.PipelineAsync], + pipeline_V_cpasync: pipeline.PipelineAsync, + sO_empty_mbar_ptr: Optional[cute.Pointer], + topk_length_dynamic: Optional[Int32], + block_info: BlockInfo, + SeqlenInfoCls: Callable, + tile_scheduler: TileSchedulerProtocol, + ): + # ==== Make pipeline states ==== + # pipeline_{K,V0,V1} producer + # pipeline_{K,V0,V1}_cpasync consumer + Producer, Consumer = pipeline.PipelineUserType.Producer, pipeline.PipelineUserType.Consumer + relay_K_fn = None + if const_expr(self.has_qk): + producer_state_K = pipeline.make_pipeline_state(Producer, stages=self.num_stages_K) + consumer_state_K = pipeline.make_pipeline_state(Consumer, stages=self.num_stages_K) + relay_K_fn = partial(self.relay_inner, pipeline_K_cpasync, pipeline_K) + + producer_state_V = pipeline.make_pipeline_state(Producer, stages=self.num_stages_V) + consumer_state_V = pipeline.make_pipeline_state(Consumer, stages=self.num_stages_V) + relay_V_fn = partial(self.relay_inner, pipeline_V_cpasync, pipeline_V) + + work_tile = tile_scheduler.initial_work_tile_info() + while work_tile.is_valid_tile: + cta_m_block, head_idx, batch_idx, _ = work_tile.tile_idx + cluster_m_block = cta_m_block // self.cta_group_size + + seqlen = SeqlenInfoCls(batch_idx) + if const_expr(self.is_topk_gather): + n_block_min = 0 + n_block_max = self.topk_length // self.tile_n + # n_block_max = topk_length_dynamic // self.tile_n + else: + n_block_min, n_block_max = block_info.get_n_block_min_max( + seqlen, + cluster_m_block, + ) + num_n_blocks = n_block_max - n_block_min + + # ==== Prologue ==== + # relay K, V0, V1 + if const_expr(self.has_qk): + consumer_state_K, producer_state_K = relay_K_fn(consumer_state_K, producer_state_K) + for _ in cutlass.range_constexpr(self.num_hdimv_splits): + consumer_state_V, producer_state_V = relay_V_fn(consumer_state_V, producer_state_V) + + # ==== Mainloop ==== + for _ in cutlass.range(num_n_blocks - 1, unroll=2): + # relay K, V0, V1, Vt0, Vt1 + if const_expr(self.has_qk): + consumer_state_K, producer_state_K = relay_K_fn( + consumer_state_K, producer_state_K + ) + for _ in cutlass.range_constexpr(2 * self.num_hdimv_splits): + consumer_state_V, producer_state_V = relay_V_fn( + consumer_state_V, producer_state_V + ) + + # ==== Epilogue === + # relay Vt0, Vt1 + for _ in cutlass.range_constexpr(self.num_hdimv_splits): + consumer_state_V, producer_state_V = relay_V_fn(consumer_state_V, producer_state_V) + + # Advance to next tile + work_tile = tile_scheduler.advance_to_next_work() + + if const_expr(self.has_qk): + pipeline_K.producer_tail(producer_state_K) + pipeline_V.producer_tail(producer_state_V) + + @cute.jit + def relay_inner( + self, + pipeline_cpasync: pipeline.PipelineAsync, + pipeline_mma: pipeline.PipelineAsyncUmma, + consumer_state: pipeline.PipelineState, + producer_state: pipeline.PipelineState, + ): + pipeline_cpasync.consumer_wait(consumer_state) + with cute.arch.elect_one(): + pipeline_mma.producer_commit(producer_state) + consumer_state.advance() + producer_state.advance() + return consumer_state, producer_state + + @cute.jit + def load_cpasync( + self, + mIndexTopk: cute.Tensor, + mK: Optional[cute.Tensor], + mV: cute.Tensor, + mVt: cute.Tensor, + sK: Optional[cute.Tensor], + sV: cute.Tensor, + sVt: cute.Tensor, + sBitmask: Optional[cute.Tensor], + pipeline_K: Optional[pipeline.PipelineAsyncUmma], + pipeline_V: pipeline.PipelineAsyncUmma, + pipeline_K_cpasync: Optional[pipeline.PipelineAsync], + pipeline_V_cpasync: pipeline.PipelineAsync, + pipeline_bitmask: pipeline.PipelineAsync, + sO_empty_mbar_ptr: Optional[cute.Pointer], + topk_length_dynamic: Optional[Int32], + block_info: BlockInfo, + SeqlenInfoCls: Callable, + tile_scheduler: TileSchedulerProtocol, + mPageTable: Optional[cute.Tensor] = None, + ): + # ==== cpasync load warpgroup ==== + # Description: loads tiles of K, V, V0, V1 from gmem to smem using cpasync + # produces: K, V, V0, V1, bitmask + # consumes: - + + # cpasync load is used for both topk gather and paged KV with page_size != tile_n + cta_rank_in_cluster = cute.arch.make_warp_uniform(cute.arch.block_idx_in_cluster()) + tidx = cute.arch.thread_idx()[0] % self.num_cpasync_load_threads + warp_idx = cute.arch.make_warp_uniform(cute.arch.warp_idx()) % ( + self.num_cpasync_load_threads // 32 + ) + + # ==== Make pipeline states ==== + # producer: acquire PipelineAsyncUmma <- mma + # producer: commit PipelineAsync -> relay + Producer = pipeline.PipelineUserType.Producer + if const_expr(self.has_qk): + producer_state_K = pipeline.make_pipeline_state(Producer, stages=self.num_stages_K) + producer_state_V = pipeline.make_pipeline_state(Producer, stages=self.num_stages_V) + if const_expr(self.is_topk_gather and not self.disable_bitmask): + producer_state_bitmask = pipeline.make_pipeline_state( + Producer, stages=self.num_stages_bitmask + ) + if const_expr(self.use_tma_O): + producer_phase_O = Int32(1) + + work_tile = tile_scheduler.initial_work_tile_info() + while work_tile.is_valid_tile: + cta_m_block, head_idx, batch_idx, _ = work_tile.tile_idx + cluster_m_block = cta_m_block // self.cta_group_size + head_idx_kv = ( + head_idx // self.qhead_per_kvhead if const_expr(not self.pack_gqa) else head_idx + ) + + seqlen = SeqlenInfoCls(batch_idx) + if const_expr(self.is_topk_gather): + n_block_min = 0 + n_block_max = self.topk_length // self.tile_n + # n_block_max = topk_length_dynamic // self.tile_n + else: + n_block_min, n_block_max = block_info.get_n_block_min_max( + seqlen, + cluster_m_block, + ) + num_n_blocks = n_block_max - n_block_min + + if const_expr(self.is_topk_gather): + # ==== Topk gather path ==== + # cluster_m_block == m_idx under MQA 128 assumption + m_idx = cluster_m_block + if const_expr(not seqlen.has_cu_seqlens_q): + mIndexTopk_cur = mIndexTopk[None, m_idx, batch_idx] + else: + offset_q = seqlen.offset_q + mIndexTopk_cur = mIndexTopk[None, m_idx + offset_q] + + if const_expr(self.is_causal): + seqlen_k_limit = m_idx + 1 + seqlen.seqlen_k - seqlen.seqlen_q + else: + seqlen_k_limit = seqlen.seqlen_k + cpasync_gather_kv_manager = CpasyncGatherKVManager.create( + mIndexTopk_cur, + cta_rank_in_cluster, + tidx, + warp_idx, + self.topk_length, + seqlen_k_limit, + self.tile_n, + self.hdim, + self.hdimv, + self.num_hdimv_splits, + self.num_cpasync_load_threads, + mV.element_type, + self.cta_group_size, + self.cpasync_barrier, + self.disable_bitmask, + sBitmask, + pipeline_bitmask, + ) + + # (seqlen_k, hdim) or (seqlen_k, hdimv) + if const_expr(self.has_qk): + mK_cur = seqlen.offset_batch_K(mK, batch_idx, dim=3)[None, None, head_idx_kv] + mV_cur = seqlen.offset_batch_K(mV, batch_idx, dim=3)[None, None, head_idx_kv] + # (hdimv, seqlen_k) + if const_expr(not seqlen.has_cu_seqlens_k): + mVt_cur = mVt[None, None, head_idx_kv, batch_idx] + else: + mVt_cur = cute.domain_offset((0, seqlen.offset_k), mVt[None, None, head_idx_kv]) + + hdimv_split_per_cta = self.hdimv // self.num_hdimv_splits // self.cta_group_size + mVt_cur = cute.tiled_divide(mVt_cur, (hdimv_split_per_cta,)) + mVt_cur = cute.logical_divide(mVt_cur, (1, self.cta_group_size, 1)) + mVt_cur = mVt_cur[(0, None), (cta_rank_in_cluster, None), (0, None)] + mVt_cur = cute.group_modes(mVt_cur, 0, 2) # ((hdimv//4, 2), seqlen_k) + + load_K = None + if const_expr(self.has_qk): + load_K = partial( + self.cpasync_gather_load_KV, + cpasync_gather_kv_manager, + pipeline_K, + pipeline_K_cpasync, + sK, + False, + "K", + mK_cur, + ) + load_V = partial( + self.cpasync_gather_load_KV, + cpasync_gather_kv_manager, + pipeline_V, + pipeline_V_cpasync, + sV, + False, + "V", + mV_cur, + ) + load_Vt = partial( + self.cpasync_gather_load_KV, + cpasync_gather_kv_manager, + pipeline_V, + pipeline_V_cpasync, + sVt, + True, + "V", + mVt_cur, + ) + + # process n_blocks in decreasing order + n_block = n_block_max - 1 + + # ==== Prologue ==== + # K, V0, V1 + cpasync_gather_kv_manager.load_index_topk(n_block, transpose=False) + if const_expr(self.has_qk): + producer_state_K = load_K(producer_state_K) + for split in cutlass.range_constexpr(self.num_hdimv_splits): + producer_state_V = load_V(producer_state_V, d_offset=split * self.hdimv // 2) + if const_expr(not self.disable_bitmask): + producer_state_bitmask = cpasync_gather_kv_manager.compute_bitmask( + producer_state_bitmask + ) + + if const_expr(self.use_tma_O and self.overlap_sO_sV): + cute.arch.mbarrier_wait(sO_empty_mbar_ptr, phase=producer_phase_O) + producer_phase_O ^= 1 + + # ==== Mainloop ==== + for _ in cutlass.range(num_n_blocks - 1, unroll=2): + # K, V0, V1 + cpasync_gather_kv_manager.load_index_topk(n_block - 1, transpose=False) + if const_expr(self.has_qk): + producer_state_K = load_K(producer_state_K) + for split in cutlass.range_constexpr(self.num_hdimv_splits): + producer_state_V = load_V( + producer_state_V, d_offset=split * self.hdimv // 2 + ) + if const_expr(not self.disable_bitmask): + producer_state_bitmask = cpasync_gather_kv_manager.compute_bitmask( + producer_state_bitmask + ) + # Vt0, Vt1 + cpasync_gather_kv_manager.load_index_topk(n_block, transpose=True) + for split in cutlass.range_constexpr(self.num_hdimv_splits): + producer_state_V = load_Vt( + producer_state_V, d_offset=split * hdimv_split_per_cta + ) + # advance n_block + n_block -= 1 + + # ==== Epilogue ==== + + # Vt0, Vt1 for n_block = 0 + cpasync_gather_kv_manager.load_index_topk(0, transpose=True) + for split in cutlass.range_constexpr(self.num_hdimv_splits): + producer_state_V = load_Vt( + producer_state_V, d_offset=split * hdimv_split_per_cta + ) + else: + # ==== Paged KV cp.async path (page_size != tile_n) ==== + page_size_divmod = FastDivmodDivisor(cute.size(mV.shape[0])) + hdimv_split = self.hdimv // self.num_hdimv_splits + hdimv_split_per_cta = hdimv_split // self.cta_group_size + + # CTA-split Vt: (dv, page_size, h_k, num_pages) -> ((dv/4, 2), page_size, h_k, num_pages) + mVt_cta = cute.tiled_divide(mVt, (hdimv_split_per_cta,)) + mVt_cta = cute.logical_divide(mVt_cta, (1, self.cta_group_size, 1, 1, 1)) + mVt_cta = mVt_cta[ + (0, None), (cta_rank_in_cluster, None), (0, None), (0, None), (0, None) + ] + mVt_cta = cute.group_modes(mVt_cta, 0, 2) + + # PagedKVManager for K (hdim=64): uses "K" mode only + if const_expr(self.has_qk): + paged_kv_K = PagedKVManager.create( + mPageTable, + mK, + mK, + page_size_divmod, + batch_idx, + head_idx_kv, + tidx, + seqlen.seqlen_k, + 0, + self.tile_n, + self.hdim, + self.hdim, + self.num_cpasync_load_threads, + mK.element_type, + arch=100, + ) + # PagedKVManager for V/Vt: "K" mode → V (non-transposed), "V" mode → Vt (transposed) + paged_kv_V = PagedKVManager.create( + mPageTable, + mV, + mVt_cta, + page_size_divmod, + batch_idx, + head_idx_kv, + tidx, + seqlen.seqlen_k, + 0, + self.tile_n, + hdimv_split, + hdimv_split_per_cta, + self.num_cpasync_load_threads, + mV.element_type, + arch=100, + ) + + if const_expr(self.has_qk): + load_K = partial( + self.cpasync_paged_load_KV, + paged_kv_K, + pipeline_K, + pipeline_K_cpasync, + sK, + False, + "K", + cta_rank_in_cluster, + ) + load_V = partial( + self.cpasync_paged_load_KV, + paged_kv_V, + pipeline_V, + pipeline_V_cpasync, + sV, + False, + "K", + cta_rank_in_cluster, + ) + load_Vt = partial( + self.cpasync_paged_load_KV, + paged_kv_V, + pipeline_V, + pipeline_V_cpasync, + sVt, + True, + "V", + cta_rank_in_cluster, + ) + + n_block_first = n_block_max - 1 + n_block = n_block_first + safe_n_block_first = n_block_first if num_n_blocks > 0 else 0 + + # ==== Prologue ==== + if const_expr(self.has_qk): + paged_kv_K.load_page_table(safe_n_block_first) + producer_state_K = load_K(n_block_first, producer_state_K) + paged_kv_V.load_page_table(safe_n_block_first) + for split in cutlass.range_constexpr(self.num_hdimv_splits): + producer_state_V = load_V( + n_block_first, producer_state_V, d_offset=split * self.hdimv // 2 + ) + + if const_expr(self.use_tma_O and self.overlap_sO_sV): + cute.arch.mbarrier_wait(sO_empty_mbar_ptr, phase=producer_phase_O) + producer_phase_O ^= 1 + + # ==== Mainloop ==== + for n_block_idx in cutlass.range(num_n_blocks - 1, unroll=2): + n_block = n_block_first - n_block_idx + # K, V0, V1 for next block in descending order + if const_expr(self.has_qk): + paged_kv_K.load_page_table(n_block - 1) + producer_state_K = load_K(n_block - 1, producer_state_K) + paged_kv_V.load_page_table(n_block - 1) + for split in cutlass.range_constexpr(self.num_hdimv_splits): + producer_state_V = load_V( + n_block - 1, producer_state_V, d_offset=split * self.hdimv // 2 + ) + # Vt0, Vt1 for current block + paged_kv_V.load_page_table(n_block) + for split in cutlass.range_constexpr(self.num_hdimv_splits): + producer_state_V = load_Vt( + n_block, producer_state_V, d_offset=split * hdimv_split_per_cta + ) + + # ==== Epilogue ==== + paged_kv_V.load_page_table(n_block_min) + for split in cutlass.range_constexpr(self.num_hdimv_splits): + producer_state_V = load_Vt( + n_block_min, producer_state_V, d_offset=split * hdimv_split_per_cta + ) + + # Advance to next tile + work_tile = tile_scheduler.advance_to_next_work() + + # note: don't use producer tail with pipeline_X_cpasync since we never use its producer_acquire. + if const_expr(self.is_topk_gather and not self.disable_bitmask): + # pipeline_bitmask invokes producer acquire in gather kv manager, + # so we should call its producer tail. + pipeline_bitmask.producer_tail(producer_state_bitmask) + + @cute.jit + def cpasync_gather_load_KV( + self, + cpasync_gather_kv_manager: CpasyncGatherKVManager, + pipeline_mma: pipeline.PipelineAsyncUmma, + pipeline_cpasync: pipeline.PipelineAsync, + sX: cute.Tensor, + transpose: bool, + K_or_V: str, + mX: cute.Tensor, + producer_state: pipeline.PipelineState, + d_offset: int = 0, + ): + stage = producer_state.index + pipeline_mma.producer_acquire(producer_state) + cpasync_gather_kv_manager.load_X( + mX, sX[None, None, None, stage], transpose, K_or_V, d_offset + ) + cute.arch.cp_async_commit_group() + pipeline_cpasync.sync_object_full.arrive_cp_async_mbarrier(stage) + producer_state.advance() + return producer_state + + @cute.jit + def cpasync_paged_load_KV( + self, + paged_kv_manager: PagedKVManager, + pipeline_mma: pipeline.PipelineAsyncUmma, + pipeline_cpasync: pipeline.PipelineAsync, + sX: cute.Tensor, + transpose: bool, + K_or_V: str, + cta_rank_in_cluster: Int32, + n_block: Int32, + producer_state: pipeline.PipelineState, + d_offset: int = 0, + ): + """Load one tile of K or V from paged gmem to smem using cp.async. + + Uses PagedKVManager for page table lookups and pointer computation, + with smem reshaping via cute.composition (same approach as CpasyncGatherKVManager). + + For non-transposed tensors (K, V0, V1): K_or_V="K", transpose=False + For transposed tensors (Vt0, Vt1): K_or_V="V", transpose=True + """ + stage = producer_state.index + pipeline_mma.producer_acquire(producer_state) + + # NOTE: load_page_table() must be called by the caller BEFORE this method. + # Calling it here (through a @cute.jit boundary) causes MLIR SSA verification + # errors because the rmem tensor writes inside load_page_table's dynamic + # cutlass.range loop cross region boundaries. This matches the SM90/SM100 + # pattern where load_page_table is called directly in the loop body. + + # Compute row pointers from cached page table entries + tPrXPtr = paged_kv_manager.compute_X_ptr(K_or_V, d_offset) + + # Reshape smem to flat 2D using composition (matches CpasyncGatherKVManager.load_X) + head_dim = ( + paged_kv_manager.head_dim_v_padded + if const_expr(K_or_V == "V") + else paged_kv_manager.head_dim_padded + ) + cta_tile_n = self.tile_n if const_expr(transpose) else self.tile_n // self.cta_group_size + order = (1, 0) if const_expr(transpose) else (0, 1) + + sX_stage = sX[None, None, None, stage] + sX_nd_layout = cute.make_ordered_layout((cta_tile_n, head_dim), order=order) + sX_nd = cute.composition(sX_stage, sX_nd_layout) + + cX = cute.make_identity_tensor((cta_tile_n, head_dim)) + tXsX = paged_kv_manager.gmem_thr_copy_KV.partition_D(sX_nd) + tXcX = paged_kv_manager.gmem_thr_copy_KV.partition_S(cX) + tXc0X = paged_kv_manager.gmem_thr_copy_KV.get_slice(0).partition_S(cX) + + base_offset = n_block * self.tile_n + if const_expr(not transpose): + base_offset += cta_tile_n * cta_rank_in_cluster + seqlenk_row_limit = ( + paged_kv_manager.seqlen_k - base_offset - tXcX[0][0] if n_block >= 0 else 0 + ) + + if const_expr(not transpose): + offset = cta_rank_in_cluster * ( + paged_kv_manager.gmem_threads_per_row // self.cta_group_size + ) + else: + offset = 0 + + for m in cutlass.range_constexpr(cute.size(tXsX, mode=[1])): + row_valid = tXc0X[0, m, 0][0] < seqlenk_row_limit + should_load = cute.make_fragment_like(tXsX[(0, None), m, 0], cute.Boolean) + should_load.fill(row_valid) + + x_ptr_i64 = fa_utils.shuffle_sync( + tPrXPtr[m // paged_kv_manager.gmem_threads_per_row], + (m + offset) % paged_kv_manager.gmem_threads_per_row, + width=paged_kv_manager.gmem_threads_per_row, + ) + x_gmem_ptr = cute.make_ptr( + paged_kv_manager.mK_paged.element_type, + x_ptr_i64, + cute.AddressSpace.gmem, + assumed_align=16, + ) + mX_cur = cute.make_tensor(x_gmem_ptr, cute.make_layout((head_dim,))) + mX_cur_copy = cute.tiled_divide(mX_cur, (paged_kv_manager.async_copy_elems,)) + + for k in cutlass.range_constexpr(cute.size(tXsX, mode=[2])): + ki = tXcX[0, 0, k][1] // paged_kv_manager.async_copy_elems + mX_cur_copy_ki = mX_cur_copy[None, ki] + tXsX_k = tXsX[None, m, k] + mX_cur_copy_ki = cute.make_tensor(mX_cur_copy_ki.iterator, tXsX_k.layout) + cute.copy( + paged_kv_manager.gmem_tiled_copy_KV, + mX_cur_copy_ki, + tXsX_k, + pred=should_load, + ) + + cute.arch.cp_async_commit_group() + pipeline_cpasync.sync_object_full.arrive_cp_async_mbarrier(stage) + producer_state.advance() + return producer_state + + @cute.jit + def load( + self, + mQ: Optional[cute.Tensor], + mK: Optional[cute.Tensor], + mQv: cute.Tensor, + mV: cute.Tensor, + mVt: cute.Tensor, + sQ: Optional[cute.Tensor], + sK: Optional[cute.Tensor], + sQv: cute.Tensor, + sV: cute.Tensor, + sVt: cute.Tensor, + tma_atom_Q: Optional[cute.CopyAtom], + tma_atom_K: Optional[cute.CopyAtom], + tma_atom_Qv: cute.CopyAtom, + tma_atom_V: cute.CopyAtom, + tma_atom_Vt: cute.CopyAtom, + pipeline_Q: Optional[pipeline.PipelineAsync], + pipeline_K: Optional[pipeline.PipelineAsync], + pipeline_Qv: pipeline.PipelineAsync, + pipeline_V: pipeline.PipelineAsync, + sO_empty_mbar_ptr: Optional[cute.Pointer], + thr_mma_QK: cute.ThrMma, + thr_mma_QvV: cute.ThrMma, + thr_mma_PVt: cute.ThrMma, + topk_length_dynamic: Optional[Int32], + block_info: BlockInfo, + SeqlenInfoCls: Callable, + tile_scheduler: TileSchedulerProtocol, + mPageTable: Optional[cute.Tensor] = None, + ): + # ==== Load warp ==== + # Description: loads tiles of Q, Qv, K, V, V0, V1 from gmem to smem using TMA + # produces: Q, Qv, K, V, V0, V1 + # consumes: - + + # ==== Make pipeline states ==== + Producer = pipeline.PipelineUserType.Producer + if const_expr(self.has_qk): + producer_state_Q = pipeline.make_pipeline_state(Producer, stages=self.num_stages_Q) + producer_state_Qv = pipeline.make_pipeline_state(Producer, stages=self.num_stages_Qv) + if const_expr(self.use_tma_KV): + if const_expr(self.has_qk): + producer_state_K = pipeline.make_pipeline_state(Producer, stages=self.num_stages_K) + producer_state_V = pipeline.make_pipeline_state(Producer, stages=self.num_stages_V) + if const_expr(self.use_tma_O): + producer_phase_O = Int32(1) + + work_tile = tile_scheduler.initial_work_tile_info() + while work_tile.is_valid_tile: + cta_m_block, head_idx, batch_idx, _ = work_tile.tile_idx + cluster_m_block = cta_m_block // self.cta_group_size + head_idx_kv = ( + head_idx // self.qhead_per_kvhead if const_expr(not self.pack_gqa) else head_idx + ) + + seqlen = SeqlenInfoCls(batch_idx) + if const_expr(self.is_topk_gather): + n_block_min = 0 + n_block_max = self.topk_length // self.tile_n + # n_block_max = topk_length_dynamic // self.tile_n + else: + n_block_min, n_block_max = block_info.get_n_block_min_max( + seqlen, + cluster_m_block, + ) + num_n_blocks = n_block_max - n_block_min + even_n_blocks = num_n_blocks % 2 == 0 and num_n_blocks > 0 + num_n_block_groups = cute.ceil_div(num_n_blocks, self.num_stages_S) + + # ==== Partition GMEM tensors ==== + # (seqlen_q, hdim or hdimv//2) + if const_expr(self.has_qk): + mQ_cur = seqlen.offset_batch_Q(mQ, batch_idx, dim=3)[None, None, head_idx] + # (mma_tile_m, hdim or hdimv//2) + gQ = cute.local_tile( + mQ_cur, + (self.mma_tiler_QK[0], self.mma_tiler_QK[2]), + (cluster_m_block, 0), + ) + tSgQ = thr_mma_QK.partition_A(gQ) + tQsQ, tQgQ = cpasync.tma_partition( + atom=tma_atom_Q, + cta_coord=0, + cta_layout=cute.make_layout(1), + smem_tensor=cute.group_modes(sQ, 0, 3), + gmem_tensor=cute.group_modes(tSgQ, 0, 3), + ) + mQv_cur = seqlen.offset_batch_Q(mQv, batch_idx, dim=3)[None, None, head_idx] + gQv = cute.local_tile( + mQv_cur, + (self.mma_tiler_QvV[0], self.mma_tiler_QvV[2]), + (cluster_m_block, None), + ) + tSgQv = thr_mma_QvV.partition_A(gQv) + tQvsQv, tQvgQv = cpasync.tma_partition( + atom=tma_atom_Qv, + cta_coord=0, + cta_layout=cute.make_layout(1), + smem_tensor=cute.group_modes(sQv, 0, 3), + gmem_tensor=cute.group_modes(tSgQv, 0, 3), + ) + + if const_expr(self.use_tma_KV): + if const_expr(mPageTable is None): + mPageTable_cur = None + # Non-paged: select batch, tile over seqlen_k + if const_expr(self.has_qk): + # (seqlen_k, hdim) + mK_cur = seqlen.offset_batch_K(mK, batch_idx, dim=3)[ + None, None, head_idx_kv + ] + # (tile_n, hdim, num_n_blocks) + gK = cute.local_tile( + mK_cur, + (self.mma_tiler_QK[1], self.mma_tiler_QK[2]), + (None, 0), + ) + # (seqlen_k, hdimv) + mV_cur = seqlen.offset_batch_K(mV, batch_idx, dim=3)[None, None, head_idx_kv] + # (hdimv, seqlen_k) + if const_expr(not seqlen.has_cu_seqlens_k): + mVt_cur = mVt[None, None, head_idx_kv, batch_idx] + else: + mVt_cur = cute.domain_offset( + (0, seqlen.offset_k), mVt[None, None, head_idx_kv] + ) + # (tile_n, hdimv//4, num_n_blocks, num_d_blocks=4) + gV = cute.local_tile( + mV_cur, + (self.mma_tiler_QvV[1], self.mma_tiler_QvV[2]), + (None, None), + ) + # (tile_n, hdimv//4, num_d_blocks=4, num_n_blocks) + gV = cute.make_tensor(gV.iterator, cute.select(gV.layout, mode=[0, 1, 3, 2])) + # (hdimv//4, tile_n, num_d_blocks=4, num_n_blocks) + gVt = cute.local_tile( + mVt_cur, + (self.mma_tiler_PVt[1], self.mma_tiler_PVt[2]), + (None, None), + ) + else: + mPageTable_cur = mPageTable[batch_idx, None] + # Paged KV: keep pages dim, index by page_idx at load time + # TMA path assumes page_size == tile_n + if const_expr(self.has_qk): + # (page_size, hdim, num_pages) + mK_cur = mK[None, None, head_idx_kv, None] + # (tile_n, hdim, num_pages) + gK = cute.local_tile( + mK_cur, + (self.mma_tiler_QK[1], self.mma_tiler_QK[2]), + (0, 0, None), + ) + # (page_size, hdimv, num_pages) + mV_cur = mV[None, None, head_idx_kv, None] + # (hdimv, page_size, num_pages) + mVt_cur = mVt[None, None, head_idx_kv, None] + # (tile_n, hdimv//4, num_d_blocks=4, num_pages) + gV = cute.local_tile( + mV_cur, + (self.mma_tiler_QvV[1], self.mma_tiler_QvV[2]), + (0, None, None), + ) + # (hdimv//4, tile_n, num_d_blocks=4, num_pages) + gVt = cute.local_tile( + mVt_cur, + (self.mma_tiler_PVt[1], self.mma_tiler_PVt[2]), + (None, 0, None), + ) + + if const_expr(self.has_qk): + tSgK = thr_mma_QK.partition_B(gK) + tKsK, tKgK = cpasync.tma_partition( + atom=tma_atom_K, + cta_coord=0, + cta_layout=cute.make_layout(1), + smem_tensor=cute.group_modes(sK, 0, 3), + gmem_tensor=cute.group_modes(tSgK, 0, 3), + ) + + tSgV = thr_mma_QvV.partition_B(gV) + tOgVt = thr_mma_PVt.partition_B(gVt) + tVsV, tVgV = cpasync.tma_partition( + atom=tma_atom_V, + cta_coord=0, + cta_layout=cute.make_layout(1), + smem_tensor=cute.group_modes(sV, 0, 3), + gmem_tensor=cute.group_modes(tSgV, 0, 3), + ) + tVtsVt, tVtgVt = cpasync.tma_partition( + atom=tma_atom_Vt, + cta_coord=0, + cta_layout=cute.make_layout(1), + smem_tensor=cute.group_modes(sVt, 0, 3), + gmem_tensor=cute.group_modes(tOgVt, 0, 3), + ) + + if const_expr(self.has_qk): + load_Q = partial(self.load_inner, tma_atom_Q, tQgQ, tQsQ, pipeline_Q) + load_Qv = partial(self.load_inner, tma_atom_Qv, tQvgQv, tQvsQv, pipeline_Qv) + + if const_expr(self.use_tma_KV): + if const_expr(self.has_qk): + load_K = partial(self.load_inner, tma_atom_K, tKgK, tKsK, pipeline_K) + load_V = partial(self.load_inner, tma_atom_V, tVgV, tVsV, pipeline_V) + load_Vt = partial(self.load_inner, tma_atom_Vt, tVtgVt, tVtsVt, pipeline_V) + + # ==== Load stationary operands ==== + + # copy Q, Qvi gmem -> smem + if const_expr(self.has_qk): + producer_state_Q = load_Q(producer_state_Q) + for dv_split in cutlass.range_constexpr(2): + producer_state_Qv = load_Qv(producer_state_Qv, block=dv_split) + + if const_expr(self.use_tma_KV): + # ==== Prologue ==== + n_block_first = n_block_max - 1 if n_block_max > 0 else 0 + block = self._get_block_idx(n_block_first, mPageTable_cur) + # copy K gmem -> smem + if const_expr(self.has_qk): + producer_state_K = load_K(producer_state_K, block=block) + # copy Vi gmem -> smem + for split in cutlass.range_constexpr(self.num_hdimv_splits): + producer_state_V = load_V(producer_state_V, block=block, split=split) + + if const_expr(self.use_tma_O and self.overlap_sO_sV): + cute.arch.mbarrier_wait(sO_empty_mbar_ptr, phase=producer_phase_O) + producer_phase_O ^= 1 + + # ==== Main loop ==== + for n_block_group in cutlass.range(num_n_block_groups - 1, unroll=1): + for stage in cutlass.range_constexpr(self.num_stages_S): + n_block = n_block_max - 1 - n_block_group * self.num_stages_S - stage + block_next = self._get_block_idx(n_block - 1, mPageTable_cur) + block = self._get_block_idx(n_block, mPageTable_cur) + if const_expr(self.has_qk): + # copy K gmem -> smem + producer_state_K = load_K(producer_state_K, block=block_next) + # copy Vi gmem -> smem + for split in cutlass.range_constexpr(self.num_hdimv_splits): + producer_state_V = load_V( + producer_state_V, block=block_next, split=split + ) + # copy Vti gmem -> smem + for split in cutlass.range_constexpr(self.num_hdimv_splits): + producer_state_V = load_Vt(producer_state_V, block=block, split=split) + + # ==== Epilogue ==== + num_final_n_blocks = self.num_stages_S if even_n_blocks else self.num_stages_S - 1 + for stage in cutlass.range(num_final_n_blocks, unroll_full=True): + n_block = num_final_n_blocks - 1 - stage + block = self._get_block_idx(n_block, mPageTable_cur) + if n_block > 0: + block_next = self._get_block_idx(n_block - 1, mPageTable_cur) + if const_expr(self.has_qk): + # copy K gmem -> smem + producer_state_K = load_K(producer_state_K, block=block_next) + # copy Vi gmem -> smem + for split in cutlass.range_constexpr(self.num_hdimv_splits): + producer_state_V = load_V( + producer_state_V, block=block_next, split=split + ) + # copy Vti gmem -> smem + for split in cutlass.range_constexpr(self.num_hdimv_splits): + producer_state_V = load_Vt(producer_state_V, block=block, split=split) + + # Advance to next tile + work_tile = tile_scheduler.advance_to_next_work() + + if const_expr(self.has_qk): + pipeline_Q.producer_tail(producer_state_Q) + pipeline_Qv.producer_tail(producer_state_Qv) + if const_expr(self.use_tma_KV): + if const_expr(self.has_qk): + pipeline_K.producer_tail(producer_state_K) + pipeline_V.producer_tail(producer_state_V) + + @cute.jit + def _get_block_idx( + self, + n_block, + mPageTable_cur: Optional[cute.Tensor], + ): + if const_expr(mPageTable_cur is not None): + return mPageTable_cur[n_block] + else: + return n_block + + @cute.jit + def load_inner( + self, + tma_atom: cute.CopyAtom, + tXgX: cute.Tensor, + tXsX: cute.Tensor, + load_pipeline: pipeline.PipelineAsync, + producer_state: pipeline.PipelineState, + block: Optional[Int32] = None, + split: Optional[Int32] = None, + ): + if const_expr(split is not None): + tXgX = tXgX[(None, split, None)] + if const_expr(block is not None): + tXgX = tXgX[(None, block)] + if const_expr(cute.rank(tXsX) != 1): + assert cute.rank(tXsX) == 2, f"wrong rank for tXsX, got {cute.rank(tXsX)}" + stage = producer_state.index + tXsX = tXsX[(None, stage)] + load_pipeline.producer_acquire(producer_state) + tma_bar_ptr = load_pipeline.producer_get_barrier(producer_state) + cute.copy(tma_atom, tXgX, tXsX, tma_bar_ptr=tma_bar_ptr) + producer_state.advance() + return producer_state + + @cute.jit + def mma( + self, + sQ: Optional[cute.Tensor], + sK: Optional[cute.Tensor], + sQv: cute.Tensor, + sV: cute.Tensor, + sVt: cute.Tensor, + sP: cute.Tensor, + tStS: cute.Tensor, + tOtO0: cute.Tensor, + tOtO1: cute.Tensor, + tiled_mma_QK: cute.TiledMma, + tiled_mma_QvV: cute.TiledMma, + tiled_mma_PVt: cute.TiledMma, + pipeline_Q: Optional[pipeline.PipelineAsync], + pipeline_K: Optional[pipeline.PipelineAsync], + pipeline_Qv: pipeline.PipelineAsync, + pipeline_V: pipeline.PipelineAsync, + pipeline_S: pipeline.PipelineAsync, + pipeline_P: pipeline.PipelineAsync, + pipeline_O0: pipeline.PipelineAsync, + pipeline_O1: pipeline.PipelineAsync, + sO_empty_mbar_ptr: Optional[cute.Pointer], + is_leader_cta: Boolean, + topk_length_dynamic: Optional[Int32], + block_info: BlockInfo, + SeqlenInfoCls: Callable, + tile_scheduler: TileSchedulerProtocol, + ): + # ==== mma warp ==== + # Description: Computes Q @ K^T, Qv @ V^T, and P @ V + # Produces: S, O + # Consumes: Q, K, Qv, V, P + + pipelines_O = [pipeline_O0, pipeline_O1] + tOtOs = [tOtO0, tOtO1] + + use_ptx_gemm_QK = not self.is_topk_gather + use_ptx_gemm_QvV = not self.is_topk_gather + use_ptx_gemm_PVt = not self.is_topk_gather + + # Operands for S = Q @ K^T + if const_expr(self.has_qk): + tSrQ = tiled_mma_QK.make_fragment_A(sQ) + tSrK = tiled_mma_QK.make_fragment_B(sK) + + # Operands for S += Qv @ V^T + tSrQv = tiled_mma_QvV.make_fragment_A(sQv) + tSrV = tiled_mma_QvV.make_fragment_B(sV) + + # Operands for Oi = P @ Vi + tOrP = tiled_mma_PVt.make_fragment_A(sP) + tOrVt = tiled_mma_PVt.make_fragment_B(sVt) + + # GEMM functions + if const_expr(self.has_qk): + if const_expr(use_ptx_gemm_QK): + gemm_QK = [ + partial( + fa_sm100_utils.gemm_ptx_partial, + tiled_mma_QK.op, + self.tmem_offset_S[stage], + zero_init=True, + cta_group=self.cta_group_size, + ) + for stage in range(self.num_stages_S) + ] + else: + gemm_QK = [ + partial( + fa_sm100_utils.gemm, + tiled_mma_QK, + tStS[None, None, None, stage], + zero_init=True, + ) + for stage in range(self.num_stages_S) + ] + if const_expr(use_ptx_gemm_QvV): + gemm_QvV = [ + partial( + fa_sm100_utils.gemm_ptx_partial, + tiled_mma_QvV.op, + self.tmem_offset_S[stage], + cta_group=self.cta_group_size, + ) + for stage in range(self.num_stages_S) + ] + else: + gemm_QvV = [ + partial( + fa_sm100_utils.gemm, + tiled_mma_QvV, + tStS[None, None, None, stage], + ) + for stage in range(self.num_stages_S) + ] + + if const_expr(use_ptx_gemm_PVt): + gemm_PVt = [ + partial( + fa_sm100_utils.gemm_ptx_partial, + tiled_mma_PVt.op, + self.tmem_offsets_O[split], + cta_group=self.cta_group_size, + ) + for split in range(self.num_hdimv_splits) + ] + else: + gemm_PVt = [ + partial( + fa_sm100_utils.gemm, + tiled_mma_PVt, + tOtOs[split], + ) + for split in range(self.num_hdimv_splits) + ] + + Consumer, Producer = pipeline.PipelineUserType.Consumer, pipeline.PipelineUserType.Producer + if const_expr(self.has_qk): + consumer_state_Q = pipeline.make_pipeline_state(Consumer, stages=self.num_stages_Q) + consumer_state_K = pipeline.make_pipeline_state(Consumer, stages=self.num_stages_K) + consumer_state_Qv = pipeline.make_pipeline_state(Consumer, stages=self.num_stages_Qv) + consumer_state_V = pipeline.make_pipeline_state(Consumer, stages=self.num_stages_V) + producer_state_S = pipeline.make_pipeline_state(Producer, stages=self.num_stages_S) + consumer_state_P = pipeline.make_pipeline_state(Consumer, stages=self.num_stages_P) + producer_state_O0 = pipeline.make_pipeline_state(Producer, stages=self.num_stages_Oi) + producer_state_O1 = pipeline.make_pipeline_state(Producer, stages=self.num_stages_Oi) + + mma_fn = self.mma_inner + if const_expr(self.has_qk): + mma_QK = partial( + mma_fn, gemm_QK, pipeline_K, tSrQ, sQ, tSrK, sK, use_ptx=use_ptx_gemm_QK + ) + mma_QvV = partial( + mma_fn, gemm_QvV, pipeline_V, tSrQv, sQv, tSrV, sV, use_ptx=use_ptx_gemm_QvV + ) + mma_PVt = partial( + mma_fn, gemm_PVt, pipeline_V, tOrP, sP, tOrVt, sVt, use_ptx=use_ptx_gemm_PVt + ) + + work_tile = tile_scheduler.initial_work_tile_info() + O_should_accumulate = False + while work_tile.is_valid_tile: + cta_m_block, head_idx, batch_idx, _ = work_tile.tile_idx + cluster_m_block = cta_m_block // self.cta_group_size + + seqlen = SeqlenInfoCls(batch_idx) + if const_expr(self.is_topk_gather): + n_block_min = 0 + # n_block_max = self.topk_length // self.tile_n + n_block_max = topk_length_dynamic // self.tile_n + else: + n_block_min, n_block_max = block_info.get_n_block_min_max( + seqlen, + cluster_m_block, + ) + num_n_blocks = n_block_max - n_block_min + even_n_blocks = num_n_blocks % 2 == 0 and num_n_blocks > 0 + num_n_block_groups = cute.ceil_div(num_n_blocks, self.num_stages_S) + + if is_leader_cta: + if const_expr(self.has_qk): + pipeline_Q.consumer_wait(consumer_state_Q) + + consumer_wait_state_Qv = consumer_state_Qv.clone() + for _ in cutlass.range_constexpr(self.num_hdimv_splits): + pipeline_Qv.consumer_wait(consumer_wait_state_Qv) + consumer_wait_state_Qv.advance() + + producer_states_O = [producer_state_O0, producer_state_O1] + + # ==== Prologue ==== + pipeline_S.producer_acquire(producer_state_S) + if const_expr(self.has_qk): + # S = Q @ K^T + consumer_state_K = mma_QK(consumer_state_K, acc_stage=0) + # S += Qvi @ Vi^T + for split in cutlass.range_constexpr(self.num_hdimv_splits): + consumer_state_V = mma_QvV( + consumer_state_V, + acc_stage=0, + a_stage=split, + zero_init=split == 0 and not self.has_qk, + ) + pipeline_S.producer_commit(producer_state_S) + producer_state_S.advance() + + # ==== Mainloop ==== + for _ in cutlass.range(num_n_block_groups - 1, unroll=1): + for stage in cutlass.range_constexpr(self.num_stages_S): + next_stage = const_expr((stage + 1) % self.num_stages_S) + pipeline_S.producer_acquire(producer_state_S) + if const_expr(self.has_qk): + # S = Q @ K^T + consumer_state_K = mma_QK(consumer_state_K, acc_stage=next_stage) + # S += Qvi @ Vi^T + for split in cutlass.range_constexpr(self.num_hdimv_splits): + consumer_state_V = mma_QvV( + consumer_state_V, + acc_stage=next_stage, + a_stage=split, + zero_init=split == 0 and not self.has_qk, + ) + pipeline_S.producer_commit(producer_state_S) + producer_state_S.advance() + # Oi += P @ Vi + pipeline_P.consumer_wait(consumer_state_P) + for split in cutlass.range_constexpr(self.num_hdimv_splits): + producer_state_Oi = producer_states_O[split] + pipelines_O[split].producer_acquire(producer_state_Oi) + consumer_state_V = mma_PVt( + consumer_state_V, + acc_stage=split, + a_stage=consumer_state_P.index, + zero_init=not O_should_accumulate, + ) + pipelines_O[split].producer_commit(producer_state_Oi) + producer_state_Oi.advance() + producer_states_O[split] = producer_state_Oi + pipeline_P.consumer_release(consumer_state_P) + consumer_state_P.advance() + O_should_accumulate = True + + # ==== Epilogue ==== + num_final_n_blocks = self.num_stages_S if even_n_blocks else self.num_stages_S - 1 + for stage in cutlass.range_constexpr(self.num_stages_S): + n_block = num_final_n_blocks - 1 - stage + if const_expr(stage == 0): + if n_block > 0: + pipeline_S.producer_acquire(producer_state_S) + if const_expr(self.has_qk): + # S = Q @ K^T + consumer_state_K = mma_QK(consumer_state_K, acc_stage=stage + 1) + # S += Qvi @ Vi^T + for split in cutlass.range_constexpr(self.num_hdimv_splits): + consumer_state_V = mma_QvV( + consumer_state_V, + acc_stage=stage + 1, + a_stage=split, + zero_init=split == 0 and not self.has_qk, + ) + pipeline_S.producer_commit(producer_state_S) + producer_state_S.advance() + if n_block >= 0: + # Oi += P @ Vi + pipeline_P.consumer_wait(consumer_state_P) + for split in cutlass.range_constexpr(self.num_hdimv_splits): + producer_state_Oi = producer_states_O[split] + pipelines_O[split].producer_acquire(producer_state_Oi) + consumer_state_V = mma_PVt( + consumer_state_V, + acc_stage=split, + a_stage=consumer_state_P.index, + zero_init=not O_should_accumulate, + ) + pipelines_O[split].producer_commit(producer_state_Oi) + producer_state_Oi.advance() + producer_states_O[split] = producer_state_Oi + pipeline_P.consumer_release(consumer_state_P) + consumer_state_P.advance() + O_should_accumulate = True + + producer_state_O0, producer_state_O1 = producer_states_O + + if const_expr(self.has_qk): + pipeline_Q.consumer_release(consumer_state_Q) + consumer_state_Q.advance() + + # if we overlap sOi with sQvi for tma store, need to acquire signal + if const_expr(self.use_tma_O and not self.overlap_sO_sV): + pipeline_O0.producer_tail(producer_state_O0.clone()) + pipeline_O1.producer_tail(producer_state_O1.clone()) + + for _ in cutlass.range_constexpr(self.num_hdimv_splits): + pipeline_Qv.consumer_release(consumer_state_Qv) + consumer_state_Qv.advance() + + # Advance to next tile + work_tile = tile_scheduler.advance_to_next_work() + O_should_accumulate = False + + pipeline_S.producer_tail(producer_state_S) + pipeline_O0.producer_tail(producer_state_O0) + pipeline_O1.producer_tail(producer_state_O1) + + @cute.jit + def mma_inner( + self, + gemm, + load_pipeline, + tCrA, + sA, + tCrB, + sB, + consumer_state: pipeline.PipelineState, + acc_stage: Optional[Int32] = None, + a_stage: Int32 = 0, + zero_init: Optional[bool] = None, + use_ptx: bool = True, + ): + if const_expr(acc_stage is not None): + gemm = gemm[acc_stage] + + tCrA_cur = tCrA[None, None, None, a_stage] + sA_cur = sA[None, None, None, a_stage] + b_stage = consumer_state.index + tCrB_cur = tCrB[None, None, None, b_stage] + sB_cur = sB[None, None, None, b_stage] + + load_pipeline.consumer_wait(consumer_state) + + kwargs = dict(tCrA=tCrA_cur, tCrB=tCrB_cur) + if const_expr(use_ptx): + kwargs |= dict(sA=sA_cur, sB=sB_cur) + if const_expr(zero_init is not None): + kwargs["zero_init"] = zero_init + gemm(**kwargs) + + load_pipeline.consumer_release(consumer_state) + consumer_state.advance() + return consumer_state + + @cute.jit + def softmax_loop( + self, + softmax_scale: Float32, + softmax_scale_log2: Float32, + mLSE: Optional[cute.Tensor], + mRowMax: Optional[cute.Tensor], + sRowMax: cute.Tensor, + sRowSum: cute.Tensor, + sScale: cute.Tensor, + sBitmask: Optional[cute.Tensor], + sP: cute.Tensor, + tStS: cute.Tensor, + thr_mma_S: cute.ThrMma, + pipeline_S: pipeline.PipelineAsync, + pipeline_P: pipeline.PipelineAsync, + pipeline_sm_stats: pipeline.PipelineAsync, + pipeline_bitmask: Optional[pipeline.PipelineAsync], + sO_empty_mbar_ptr: Optional[cute.Pointer], + AttentionMaskCls: Callable, + topk_length_dynamic: Optional[Int32], + block_info: BlockInfo, + SeqlenInfoCls: Callable, + tile_scheduler: TileSchedulerProtocol, + tma_atom_P: Optional[cute.CopyAtom] = None, + mP: Optional[cute.Tensor] = None, + sP_out: Optional[cute.Tensor] = None, + ): + # ==== softmax warpgroup ==== + # Description: computes softmax on S and writes the result to P + # Produces: P, softmax stats + # Consumes: S, bitmask (for topk sparsity) + + tidx = cute.arch.thread_idx()[0] % self.num_softmax_threads + warp_idx = cute.arch.make_warp_uniform(cute.arch.warp_idx()) % ( + self.num_softmax_threads // 32 + ) + + tSAcc = tStS[(None, None), 0, 0, 0] + tSAcc_staged = [tStS[(None, None), 0, 0, stage] for stage in range(self.num_stages_S)] + + cS = cute.make_identity_tensor(self.mma_tiler_QK[:2]) # (128, 128) + tScS = thr_mma_S.partition_C(cS)[(None, None), 0, 0] # (64, 128) + + # S tmem -> rmem copy objects + tmem_load_atom = cute.make_copy_atom( + tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(32)), + self.dtype_acc, + ) + tmem_load_tiled = tcgen05.make_tmem_copy(tmem_load_atom, tSAcc) + tmem_load_thr = tmem_load_tiled.get_slice(tidx) + # S tmem -> rmem copy operands + tStS_t2r = tmem_load_thr.partition_S(tSAcc) # (((32, 32), 1), 1, 2) + tStS_t2r_staged = [ + tmem_load_thr.partition_S(tSAcc_staged[stage]) for stage in range(self.num_stages_S) + ] + tScS_t2r = tmem_load_thr.partition_D(tScS) + tSrS_t2r = cute.make_rmem_tensor(tScS_t2r.shape, self.dtype_acc) + + # P rmem -> smem copy objects + universal_copy_bits = 128 + smem_store_atom = cute.make_copy_atom( + cute.nvgpu.CopyUniversalOp(), + self.dtype_P, + num_bits_per_copy=universal_copy_bits, + ) + smem_store_tiled = cute.make_tiled_copy_D(smem_store_atom, tmem_load_tiled) + smem_store_thr = smem_store_tiled.get_slice(tidx) + # P rmem -> smem copy operands + sP_mnp_layout = cute.make_ordered_layout( + self.tile_P + (self.num_stages_P,), order=(0, 1, 2) + ) + sP_mnp = cute.composition(sP, sP_mnp_layout) + sP_smem_view = smem_store_thr.partition_D(sP_mnp) + + Consumer, Producer = pipeline.PipelineUserType.Consumer, pipeline.PipelineUserType.Producer + consumer_state_S = pipeline.make_pipeline_state(Consumer, stages=self.num_stages_S) + producer_state_P = pipeline.make_pipeline_state(Producer, stages=self.num_stages_P) + producer_state_sm_stats = pipeline.make_pipeline_state(Producer, stages=self.num_stages_sm_stats) + consumer_state_bitmask = None + if const_expr(self.is_topk_gather and not self.disable_bitmask): + consumer_state_bitmask = pipeline.make_pipeline_state( + Consumer, stages=self.num_stages_bitmask + ) + + work_tile = tile_scheduler.initial_work_tile_info() + while work_tile.is_valid_tile: + cta_m_block, head_idx, batch_idx, _ = work_tile.tile_idx + cluster_m_block = cta_m_block // self.cta_group_size + seqlen = SeqlenInfoCls(batch_idx) + if const_expr(self.is_topk_gather): + n_block_min = 0 + n_block_max = self.topk_length // self.tile_n + # n_block_max = topk_length_dynamic // self.tile_n + else: + n_block_min, n_block_max = block_info.get_n_block_min_max( + seqlen, + cluster_m_block, + ) + num_n_blocks = n_block_max - n_block_min + even_n_blocks = num_n_blocks % 2 == 0 and num_n_blocks > 0 + num_n_block_groups = cute.ceil_div(num_n_blocks, self.num_stages_S) + + gRowMax = None + if const_expr(mRowMax is not None): + # (seqlen_q, {seqlen_k_rounded, topk} / tile_n) + if const_expr(not seqlen.has_cu_seqlens_q): + mRowMax_cur = mRowMax[None, None, head_idx, batch_idx] + else: + q_offset = ( + seqlen.offset_q if const_expr(not self.pack_gqa) else (0, seqlen.offset_q) + ) + mRowMax_cur = cute.domain_offset((q_offset, 0), mRowMax[None, None, head_idx]) + # (cta_tile_m, {seqlen_k_rounded, topk} / tile_n) + gRowMax = cute.local_tile(mRowMax_cur, (self.cta_tile_m,), (cta_m_block, None)) + + store_P = None + if const_expr(self.store_P): + # (seqlen_q, seqlen_k) + mP_cur = seqlen.offset_batch_Q(mP, batch_idx, dim=3, ragged=self.ragged_tma_O)[ + None, None, head_idx + ] + # (cta_tile_m, tile_n, num_n_blocks) + gP = cute.local_tile(mP_cur, self.tile_P, (cta_m_block, None)) + store_P, tPsP, tPgP = copy_utils.tma_get_copy_fn( + tma_atom_P, + 0, + cute.make_layout(1), + sP_out, + gP, + ) + + mask = AttentionMaskCls(seqlen) + mask_fn = partial( + mask.apply_mask_sm100, + m_block=cluster_m_block, + thr_mma=thr_mma_S, + thr_tmem_load=tmem_load_thr, + mask_causal=self.is_causal, + mask_local=self.is_local, + batch_idx=batch_idx, + head_idx=head_idx, + r2p=False, # TODO: fix r2p for 2cta + ) + disable_mask = self.disable_bitmask and self.is_topk_gather + + softmax = SoftmaxSm100.create( + softmax_scale_log2, + rescale_threshold=8.0 if const_expr(self.dtype_Q.width == 16) else 0.0, + softmax_scale=softmax_scale, + ) + softmax.reset() + + softmax_step_fn = partial( + self.softmax_step, + softmax, + sRowMax, + sScale, + sBitmask, + tStS_t2r_staged, + tSrS_t2r, + sP_smem_view, + tmem_load_thr, + smem_store_thr, + pipeline_S, + pipeline_P, + pipeline_sm_stats, + pipeline_bitmask, + tidx, + warp_idx, + store_P=store_P, + gRowMax=gRowMax, + ) + + ### first iteration ### + n_block = n_block_max - 1 + ( + consumer_state_S, + producer_state_P, + producer_state_sm_stats, + consumer_state_bitmask, + ) = softmax_step_fn( + consumer_state_S, + producer_state_P, + producer_state_sm_stats, + consumer_state_bitmask, + 0, + n_block, + mask_fn=partial(mask_fn, mask_seqlen=True) + if not const_expr(disable_mask) + else None, + is_first=True, + ) + n_block -= 1 + + ### Separate iterations with causal masking + # note: For square mma tile, can mask at most 1 n_block_group + if const_expr((self.is_causal or self.is_local) and not self.is_topk_gather): + n_block_min_causal_local_mask = block_info.get_n_block_min_causal_local_mask( + seqlen, cluster_m_block, n_block_min + ) + num_masked_n_blocks = n_block_max - 1 - n_block_min_causal_local_mask + num_masked_n_block_groups = min( + num_n_block_groups - 1, cute.ceil_div(num_masked_n_blocks, self.num_stages_S) + ) + num_n_block_groups -= num_masked_n_block_groups + for _ in cutlass.range(num_masked_n_block_groups, unroll=1): + for stage in cutlass.range_constexpr(self.num_stages_S): + ( + consumer_state_S, + producer_state_P, + producer_state_sm_stats, + consumer_state_bitmask, + ) = softmax_step_fn( + consumer_state_S, + producer_state_P, + producer_state_sm_stats, + consumer_state_bitmask, + 1 - stage, + n_block, + mask_fn=partial(mask_fn, mask_seqlen=False), + ) + n_block -= 1 + + ### Mainloop ### + for n_block_group in cutlass.range(num_n_block_groups - 1, unroll=1): + for stage in cutlass.range_constexpr(self.num_stages_S): + ( + consumer_state_S, + producer_state_P, + producer_state_sm_stats, + consumer_state_bitmask, + ) = softmax_step_fn( + consumer_state_S, + producer_state_P, + producer_state_sm_stats, + consumer_state_bitmask, + 1 - stage, + n_block, + mask_fn=partial(mask_fn, mask_seqlen=False) + if const_expr(self.is_topk_gather and not self.disable_bitmask) + else None, + ) + n_block -= 1 + + ### last iteration if even ### + # always mask to simplify logic + if even_n_blocks: + ( + consumer_state_S, + producer_state_P, + producer_state_sm_stats, + consumer_state_bitmask, + ) = softmax_step_fn( + consumer_state_S, + producer_state_P, + producer_state_sm_stats, + consumer_state_bitmask, + 1, + n_block, + mask_fn=partial(mask_fn, mask_seqlen=False) + if not const_expr(disable_mask) + else None, + ) + n_block -= 1 + + # write row max and sum to smem + sRowSum[tidx % self.cta_tile_m, warp_idx // self.cta_group_size] = softmax.row_sum[0] + if const_expr(mLSE is not None): + if tidx < self.cta_tile_m: + sRowMax[tidx, 0] = softmax.row_max[0] + self.sm_stats_barrier_full.arrive() + + # Advance to next tile + work_tile = tile_scheduler.advance_to_next_work() + self.sm_stats_barrier_empty.arrive_and_wait() + + pipeline_P.producer_tail(producer_state_P) + pipeline_sm_stats.producer_tail(producer_state_sm_stats) + + @cute.jit + def softmax_step( + self, + softmax: SoftmaxSm100, + sRowMax: cute.Tensor, + sScale: cute.Tensor, + sBitmask: Optional[cute.Tensor], + tStS_t2r_staged: cute.Tensor, + tSrS_t2r: cute.Tensor, + sP_smem_view: cute.Tensor, + tmem_load_thr: cute.CopyAtom, + smem_store_thr: cute.CopyAtom, + pipeline_S: pipeline.PipelineAsync, + pipeline_P: pipeline.PipelineAsync, + pipeline_sm_stats: pipeline.PipelineAsync, + pipeline_bitmask: Optional[pipeline.PipelineAsync], + tidx: Int32, + warp_idx: Int32, + consumer_state_S: pipeline.PipelineState, + producer_state_P: pipeline.PipelineState, + producer_state_sm_stats: pipeline.PipelineState, + consumer_state_bitmask: Optional[pipeline.PipelineState], + stage: cutlass.Constexpr[Int32], + n_block: Int32, + mask_fn: Optional[Callable] = None, + is_first: Boolean = False, + store_P: Optional[Callable] = None, + gRowMax: Optional[cute.Tensor] = None, + ): + leader_warp = warp_idx == 0 + tSrP = cute.make_rmem_tensor(tSrS_t2r.shape, self.dtype_P) + rP_smem_view = smem_store_thr.retile(tSrP) + + pipeline_S.consumer_wait(consumer_state_S) + cute.copy(tmem_load_thr, tStS_t2r_staged[stage], tSrS_t2r) + cute.arch.fence_view_async_tmem_load() + pipeline_S.consumer_release(consumer_state_S) + + rBitmask = None + if const_expr(self.is_topk_gather and not self.disable_bitmask): + assert pipeline_bitmask is not None + assert consumer_state_bitmask is not None + pipeline_bitmask.consumer_wait(consumer_state_bitmask) + rBitmask = cute.make_rmem_tensor((self.tile_n // 64,), dtype=Uint32) + bitmask_col_offset = self.tile_n // 64 if warp_idx >= 2 else 0 + for i in cutlass.range_constexpr(cute.size(rBitmask)): + rBitmask[i] = sBitmask[bitmask_col_offset + i, consumer_state_bitmask.index] + + if const_expr(mask_fn is not None): + mask_fn(tSrS_t2r, n_block=n_block, rBitmask=rBitmask) + + # compute threadwise row_max + row_max = softmax.compute_row_max_local(tSrS_t2r.load(), is_first) + # 2-thread reduce row_max through smem + assert self.cta_tile_m * self.cta_group_size == 128 + sRowMax[tidx % self.cta_tile_m, warp_idx // self.cta_group_size] = row_max + self.softmax_barrier.arrive_and_wait() + # must release after barrier sync + if const_expr(self.is_topk_gather and not self.disable_bitmask): + pipeline_bitmask.consumer_release(consumer_state_bitmask) + row_max0 = sRowMax[tidx % self.cta_tile_m, 0] + row_max1 = sRowMax[tidx % self.cta_tile_m, 1] + row_max = max(row_max0, row_max1) + + row_max, acc_scale = softmax.update_row_max_from_local(row_max, is_first) + + if const_expr(gRowMax is not None): + if tidx < self.cta_tile_m: + gRowMax[tidx, n_block] = row_max + + # note: acc_scales agree for paired threads + pipeline_sm_stats.producer_acquire(producer_state_sm_stats) + if warp_idx < self.cta_group_size: + sScale[tidx % self.cta_tile_m, producer_state_sm_stats.index] = acc_scale + pipeline_sm_stats.producer_commit(producer_state_sm_stats) + + # x -> scale_log2*x-rowmax + softmax.scale_subtract_rowmax(tSrS_t2r, row_max) + + # x -> exp2(x) + softmax.apply_exp2_convert(tSrS_t2r, tSrP) + + if const_expr(self.store_P): + if leader_warp: + cute.arch.cp_async_bulk_wait_group(self.num_stages_P - 1, read=True) + self.softmax_barrier.arrive_and_wait() + + pipeline_P.producer_acquire(producer_state_P) + cute.copy( + smem_store_thr, rP_smem_view, sP_smem_view[None, None, None, producer_state_P.index] + ) + cute.arch.fence_view_async_shared() + pipeline_P.producer_commit(producer_state_P) + # unconditionally necessary for sRowMax read to complete before next iter's store + self.softmax_barrier.arrive_and_wait() + + if const_expr(self.store_P): + if leader_warp: + store_P(src_idx=producer_state_P.index, dst_idx=n_block) + cute.arch.cp_async_bulk_commit_group() + + consumer_state_S.advance() + producer_state_P.advance() + producer_state_sm_stats.advance() + if const_expr(self.is_topk_gather and not self.disable_bitmask): + consumer_state_bitmask.advance() + + softmax.update_row_sum(tSrS_t2r.load(), acc_scale, is_first) + + return consumer_state_S, producer_state_P, producer_state_sm_stats, consumer_state_bitmask + + @cute.jit + def correction_loop( + self, + softmax_scale_log2: Float32, + mO: cute.Tensor, + mLSE: Optional[cute.Tensor], + tma_atom_O: Optional[cute.CopyAtom], + sRowMax: cute.Tensor, + sRowSum: cute.Tensor, + sScale: cute.Tensor, + sO: cute.Tensor, + tOtO0: cute.Tensor, + tOtO1: cute.Tensor, + pipeline_O0: pipeline.PipelineAsync, + pipeline_O1: pipeline.PipelineAsync, + pipeline_sm_stats: pipeline.PipelineAsync, + sO_empty_mbar_ptr: Optional[cute.Pointer], + tiled_copy_O_r2g: cute.TiledCopy, + topk_length_dynamic: Optional[Int32], + block_info: BlockInfo, + SeqlenInfoCls: Callable, + tile_scheduler: TileSchedulerProtocol, + ): + ### ==== correction/epilogue warpgroup ==== + # Correction: copy scale smem -> rmem, copy O tmem -> rmem, rescale O, store O rmem -> tmem + # Epilogue: copy O tmem -> rmem, do final scaling of O, store O rmem -> gmem, + # optionally store LSE + # Produces: - + # Consumes: O, softmax stats + + tidx = cute.arch.thread_idx()[0] % self.num_epilogue_threads + warp_idx = cute.arch.make_warp_uniform(cute.arch.warp_idx()) % ( + self.num_epilogue_threads // 32 + ) + cta_rank_in_cluster = cute.arch.make_warp_uniform(cute.arch.block_idx_in_cluster()) + leader_warp = warp_idx == 0 + + tOtO0 = tOtO0[(None, None), 0, 0] # (64, (128, 2)) + tOtO1 = tOtO1[(None, None), 0, 0] # (64, (128, 2)) + tOtOs = [tOtO0, tOtO1] + + # tuneable parameter + corr_tile_size = math.gcd(32, self.tmem_cols_Oi) + + tmem_load_atom_O = cute.make_copy_atom( + tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(corr_tile_size)), + self.dtype_acc, + ) + tmem_store_atom_O = cute.make_copy_atom( + tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(corr_tile_size)), + self.dtype_acc, + ) + thr_tmem_load_O = tcgen05.make_tmem_copy(tmem_load_atom_O, tOtO0).get_slice(tidx) + thr_tmem_store_O = tcgen05.make_tmem_copy(tmem_store_atom_O, tOtO0).get_slice(tidx) + + # ((32,1),1,4) + tOtOs_t2r = [ + thr_tmem_load_O.partition_S(tOtOs[split]) for split in range(self.num_hdimv_splits) + ] + tOtOs_r2t = [ + thr_tmem_store_O.partition_D(tOtOs[split]) for split in range(self.num_hdimv_splits) + ] + + cOi = cute.make_identity_tensor((self.cta_tile_m, self.hdimv // self.num_hdimv_splits)) + thr_tiled_copy_O_r2g = tiled_copy_O_r2g.get_slice(tidx) + tOicOi = thr_tiled_copy_O_r2g.partition_S(cOi) + + tOicOi_t2r = thr_tmem_load_O.partition_D(tOicOi[(None, None), 0, 0]) + + pipelines_O = [pipeline_O0, pipeline_O1] + + Consumer = pipeline.PipelineUserType.Consumer + consumer_state_O0 = pipeline.make_pipeline_state(Consumer, stages=self.num_stages_Oi) + consumer_state_O1 = pipeline.make_pipeline_state(Consumer, stages=self.num_stages_Oi) + consumer_state_sm_stats = pipeline.make_pipeline_state(Consumer, stages=self.num_stages_sm_stats) + + do_correction_rescale = partial( + self.correction_rescale, + thr_tmem_load_O, + thr_tmem_store_O, + tOicOi_t2r, + ) + + work_tile = tile_scheduler.initial_work_tile_info() + while work_tile.is_valid_tile: + cta_m_block, head_idx, batch_idx, _ = work_tile.tile_idx + cluster_m_block = cta_m_block // self.cta_group_size + + seqlen = SeqlenInfoCls(batch_idx) + if const_expr(self.is_topk_gather): + n_block_min = 0 + n_block_max = self.topk_length // self.tile_n + # n_block_max = topk_length_dynamic // self.tile_n + else: + n_block_min, n_block_max = block_info.get_n_block_min_max( + seqlen, + cluster_m_block, + ) + num_n_blocks = n_block_max - n_block_min + + consumer_states_O = [consumer_state_O0, consumer_state_O1] + + # acquire first signal and release immediately + pipeline_sm_stats.consumer_wait(consumer_state_sm_stats) + pipeline_sm_stats.consumer_release(consumer_state_sm_stats) + consumer_state_sm_stats.advance() + + for _ in cutlass.range(num_n_blocks - 1, unroll=1): + pipeline_sm_stats.consumer_wait(consumer_state_sm_stats) + scale = sScale[tidx % self.cta_tile_m, consumer_state_sm_stats.index] + should_rescale = cute.arch.vote_ballot_sync(scale < 1.0) != 0 + pipeline_sm_stats.consumer_release(consumer_state_sm_stats) + consumer_state_sm_stats.advance() + + for split in cutlass.range_constexpr(self.num_hdimv_splits): + consumer_state_Oi = consumer_states_O[split] + pipelines_O[split].consumer_wait(consumer_state_Oi) + if should_rescale: + do_correction_rescale( + tOtOs_t2r[split], + tOtOs_r2t[split], + scale, + ) + pipelines_O[split].consumer_release(consumer_state_Oi) + consumer_state_Oi.advance() + consumer_states_O[split] = consumer_state_Oi + + # (seqlen_q, hdimv) + mO_cur = seqlen.offset_batch_Q(mO, batch_idx, dim=3, ragged=self.ragged_tma_O)[ + None, None, head_idx + ] + # (cta_tile_m, hdimv//2, 2) + gO = cute.local_tile( + mO_cur, + (self.cta_tile_m, self.hdimv // self.num_hdimv_splits), + (cta_m_block, None), + ) + tOgO = thr_tiled_copy_O_r2g.partition_D(gO) + # ((32, 1), 1, 4) + tOrOs_t2r = [ + cute.make_rmem_tensor(tOicOi_t2r.shape, self.dtype_acc) + for split in range(self.num_hdimv_splits) + ] + tOrOs_r2g_f32 = [ + thr_tiled_copy_O_r2g.retile(tOrOs_t2r[split]) + for split in range(self.num_hdimv_splits) + ] + tOrOs_r2g = [ + cute.make_rmem_tensor_like(tOrOs_r2g_f32[split], self.dtype_O) + for split in range(self.num_hdimv_splits) + ] + if const_expr(self.use_tma_O): + tOsO = thr_tiled_copy_O_r2g.partition_D(sO) + store_O, _, _ = copy_utils.tma_get_copy_fn( + tma_atom_O, + 0, + cute.make_layout(1), + sO, + gO, + ) + + self.sm_stats_barrier_full.arrive_and_wait() + + row_sum0 = sRowSum[tidx % self.cta_tile_m, 0] + row_sum1 = sRowSum[tidx % self.cta_tile_m, 1] + row_sum = row_sum0 + row_sum1 + acc_O_mn_row_is_zero_or_nan = row_sum == 0.0 or row_sum != row_sum + scale = cute.arch.rcp_approx(row_sum if not acc_O_mn_row_is_zero_or_nan else 1.0) + + self.sm_stats_barrier_empty.arrive() + + seqlen_q = ( + seqlen.seqlen_q + if const_expr(not self.pack_gqa) + else seqlen.seqlen_q * self.qhead_per_kvhead + ) + + # compute and store lse to gmem + if const_expr(mLSE is not None): + if const_expr(not seqlen.has_cu_seqlens_q): + mLSE_cur = mLSE[None, head_idx, batch_idx] + else: + lse_offset = ( + seqlen.offset_q if const_expr(not self.pack_gqa) else (0, seqlen.offset_q) + ) + mLSE_cur = cute.domain_offset((lse_offset,), mLSE[None, head_idx]) + gLSE = cute.local_tile(mLSE_cur, (self.cta_tile_m,), (cta_m_block,)) + if tidx < self.cta_tile_m: + row_max = sRowMax[tidx, 0] + LN2 = math.log(2.0) + lse = ( + (row_max * softmax_scale_log2 + cute.math.log2(row_sum, fastmath=True)) + * LN2 + if not acc_O_mn_row_is_zero_or_nan + else -Float32.inf + ) + if tidx < seqlen_q - cta_m_block * self.cta_tile_m: + gLSE[tidx] = lse + + row_idx = cta_m_block * self.cta_tile_m + tOicOi[0][0] + + for split in cutlass.range_constexpr(self.num_hdimv_splits): + consumer_state_Oi = consumer_states_O[split] + pipelines_O[split].consumer_wait(consumer_state_Oi) + # copy Oi tmem -> rmem + cute.copy( + thr_tmem_load_O, + tOtOs_t2r[split], + tOrOs_t2r[split], + ) + + # scale and downcast Oi + tOrOs_r2g[split].store((tOrOs_r2g_f32[split].load() * scale).to(self.dtype_O)) + + if const_expr(not self.use_tma_O): + # copy Oi rmem -> gmem + if row_idx < seqlen_q: + cute.copy( + thr_tiled_copy_O_r2g, + tOrOs_r2g[split], + tOgO[None, None, None, split], + ) + else: + # copy Oi rmem -> smem + if const_expr(self.overlap_sO_sV): + # last slot for Vti is always 2, 3 + sO_idx = 2 + split + else: + sO_idx = split + cute.copy( + thr_tiled_copy_O_r2g, + tOrOs_r2g[split], + tOsO[None, None, None, sO_idx], + ) + cute.arch.fence_view_async_shared() + self.epi_barrier.arrive_and_wait() + # tma store Oi smem -> gmem + if leader_warp: + store_O(src_idx=sO_idx, dst_idx=split) + cute.arch.cp_async_bulk_commit_group() + cute.arch.cp_async_bulk_wait_group(1 - split, read=True) + if const_expr(split == 1 and self.overlap_sO_sV): + with cute.arch.elect_one(): + cute.arch.mbarrier_arrive(sO_empty_mbar_ptr) + + consumer_state_O0, consumer_state_O1 = consumer_states_O + + cute.arch.fence_view_async_tmem_load() + pipeline_O0.consumer_release(consumer_state_O0) + pipeline_O1.consumer_release(consumer_state_O1) + consumer_state_O0.advance() + consumer_state_O1.advance() + + # Advance to next tile + work_tile = tile_scheduler.advance_to_next_work() + + @cute.jit + def correction_rescale( + self, + thr_tmem_load: cute.CopyAtom, + thr_tmem_store: cute.CopyAtom, + tOcO_t2r: cute.Tensor, + tOtO_t2r: cute.Tensor, + tOtO_r2t: cute.Tensor, + scale: Float32, + ): + tOrO_t2r_frg = cute.make_rmem_tensor_like(tOcO_t2r[None, None, 0], self.dtype_acc) + + for i in cutlass.range_constexpr(cute.size(tOtO_t2r, mode=[2])): + tOtO_t2r_cur = tOtO_t2r[None, None, i] + tOtO_r2t_cur = tOtO_r2t[None, None, i] + + cute.copy(thr_tmem_load, tOtO_t2r_cur, tOrO_t2r_frg) + for j in cutlass.range(0, cute.size(tOrO_t2r_frg), 2, unroll_full=True): + tOrO_t2r_frg[j], tOrO_t2r_frg[j + 1] = cute.arch.mul_packed_f32x2( + (tOrO_t2r_frg[j], tOrO_t2r_frg[j + 1]), (scale, scale) + ) + cute.copy(thr_tmem_store, tOrO_t2r_frg, tOtO_r2t_cur) + cute.arch.fence_view_async_tmem_store() + + +def test_mla_kernel( + seqlen_q=2048, + seqlen_k=2048, + topk_length=2048, + nheads=1, + batch=1, + iter=0, + compile_cache=dict(), + validate=True, + seed=0, + gather_kv=True, + pack_gqa=False, + is_causal=False, + varlen_q=False, + varlen_k=False, + disable_bitmask=False, + has_qk=True, + store_P=False, +): + torch.manual_seed(seed) + hdim = 64 + hdimv = 512 + softmax_scale = 1.0 / math.sqrt(hdim + hdimv) if has_qk else 1.0 / math.sqrt(hdimv) + + nheads_kv = 1 + qhead_per_kvhead = nheads + seqlen_k_rounded = (seqlen_k + 128 - 1) // 128 * 128 + P_k_length = seqlen_k_rounded if not gather_kv else topk_length + + torch_stream = torch.cuda.current_stream() + stream = cuda.CUstream(torch_stream.cuda_stream) + + compile_key = ( + is_causal, + gather_kv, + topk_length if gather_kv else None, + pack_gqa, + qhead_per_kvhead, + nheads_kv, + varlen_q, + varlen_k, + disable_bitmask, + has_qk, + ) + if compile_key not in compile_cache: + total_q_dummy = batch * seqlen_q + total_k_dummy = batch * seqlen_k + + if varlen_q: + Q = torch.randn( + total_q_dummy, nheads, hdim, dtype=torch.bfloat16, device="cuda" + ) + Qv = torch.randn( + total_q_dummy, nheads, hdimv, dtype=torch.bfloat16, device="cuda" + ) + O = torch.empty( + total_q_dummy, nheads, hdimv, dtype=torch.bfloat16, device="cuda" + ) + P = torch.empty( + total_q_dummy, nheads, P_k_length, dtype=torch.bfloat16, device="cuda" + ) + lse = torch.empty(total_q_dummy, nheads, dtype=torch.float32, device="cuda") + row_max = torch.empty( + total_q_dummy, + nheads, + P_k_length // 128, + dtype=torch.float32, + device="cuda", + ) + index_topk = ( + torch.rand(total_q_dummy, topk_length, device="cuda") + .argsort(dim=-1) + .to(torch.int32) + ) + cu_seqlens_q_dummy = torch.arange( + 0, (batch + 1) * seqlen_q, seqlen_q, dtype=torch.int32, device="cuda" + ) + else: + Q = torch.randn( + batch, seqlen_q, nheads, hdim, dtype=torch.bfloat16, device="cuda" + ) + Qv = torch.randn( + batch, seqlen_q, nheads, hdimv, dtype=torch.bfloat16, device="cuda" + ) + O = torch.empty( + batch, seqlen_q, nheads, hdimv, dtype=torch.bfloat16, device="cuda" + ) + P = torch.empty( + batch, seqlen_q, nheads, P_k_length, dtype=torch.bfloat16, device="cuda" + ) + lse = torch.empty( + batch, seqlen_q, nheads, dtype=torch.float32, device="cuda" + ) + row_max = torch.empty( + batch, + seqlen_q, + nheads, + P_k_length // 128, + dtype=torch.float32, + device="cuda", + ) + index_topk = ( + torch.rand(batch, seqlen_q, topk_length, device="cuda") + .argsort(dim=-1) + .to(torch.int32) + ) + + if varlen_k: + K = torch.randn( + total_k_dummy, nheads_kv, hdim, dtype=torch.bfloat16, device="cuda" + ) + V = torch.randn( + total_k_dummy, nheads_kv, hdimv, dtype=torch.bfloat16, device="cuda" + ) + cu_seqlens_k_dummy = torch.arange( + 0, (batch + 1) * seqlen_k, seqlen_k, dtype=torch.int32, device="cuda" + ) + else: + K = torch.randn( + batch, seqlen_k, nheads_kv, hdim, dtype=torch.bfloat16, device="cuda" + ) + V = torch.randn( + batch, seqlen_k, nheads_kv, hdimv, dtype=torch.bfloat16, device="cuda" + ) + + mQ = from_dlpack(Q, assumed_align=16).mark_layout_dynamic( + leading_dim=Q.ndim - 1 + ) + mQv = from_dlpack(Qv, assumed_align=16).mark_layout_dynamic( + leading_dim=Qv.ndim - 1 + ) + mK = from_dlpack(K, assumed_align=16).mark_layout_dynamic( + leading_dim=K.ndim - 1 + ) + mV = from_dlpack(V, assumed_align=16).mark_layout_dynamic( + leading_dim=V.ndim - 1 + ) + mO = from_dlpack(O, assumed_align=16).mark_layout_dynamic( + leading_dim=O.ndim - 1 + ) + mP = from_dlpack(P, assumed_align=16).mark_layout_dynamic( + leading_dim=P.ndim - 1 + ) + mLSE = from_dlpack(lse, assumed_align=4).mark_layout_dynamic( + leading_dim=lse.ndim - 1 + ) + mRowMax = from_dlpack(row_max, assumed_align=4).mark_layout_dynamic( + leading_dim=row_max.ndim - 1 + ) + if gather_kv: + mIndexTopk = from_dlpack(index_topk, assumed_align=16).mark_layout_dynamic( + leading_dim=index_topk.ndim - 1 + ) + else: + mIndexTopk = None + + compile_kwargs = dict(mIndexTopk=mIndexTopk) + if varlen_q: + compile_kwargs["mCuSeqlensQ"] = from_dlpack( + cu_seqlens_q_dummy, assumed_align=4 + ) + if varlen_k: + compile_kwargs["mCuSeqlensK"] = from_dlpack( + cu_seqlens_k_dummy, assumed_align=4 + ) + + if not has_qk: + mQ = mK = None + + if store_P is False: + mP = mRowMax = None + + kernel = cute.compile( + FlashAttentionMLAForwardSm100( + is_causal=is_causal, + use_cpasync_load_KV=gather_kv, + topk_length=topk_length if gather_kv else 2048, + is_topk_gather=gather_kv, + pack_gqa=pack_gqa, + qhead_per_kvhead=qhead_per_kvhead, + nheads_kv=nheads_kv, + is_varlen_q=varlen_q, + disable_bitmask=disable_bitmask, + has_qk=has_qk, + ), + mQ, + mQv, + mK, + mV, + mO, + mLSE, + softmax_scale, + mP, + mRowMax, + **compile_kwargs, + stream=stream, + options="--keep-ptx --keep-cubin --generate-line-info", + ) + dump_kernel_attributes(kernel) + compile_cache[compile_key] = kernel + + # ================================================================ + # ---- Generate variable seqlens for this run ---- + if varlen_q: + torch.manual_seed(seed + 1000) + # When causal without varlen_k, every per-batch seqlen_q must not exceed seqlen_k. + max_seqlen_q = seqlen_k if (is_causal and not varlen_k) else seqlen_q + seqlens_q = torch.randint(1, max_seqlen_q + 1, (batch,), dtype=torch.int32) + cu_seqlens_q = torch.zeros(batch + 1, dtype=torch.int32, device="cuda") + cu_seqlens_q[1:] = seqlens_q.cumsum(0).to(torch.int32).cuda() + total_q = cu_seqlens_q[-1].item() + else: + seqlens_q = torch.full((batch,), seqlen_q, dtype=torch.int32) + total_q = None # unused + + if varlen_k: + torch.manual_seed(seed + 2000) + # Each batch item must have at least topk_length keys so topk gather is valid. + min_seqlen_k = topk_length if gather_kv else 1 + seqlens_k = torch.randint( + min_seqlen_k, seqlen_k + 1, (batch,), dtype=torch.int32 + ) + # When causal, every batch item needs seqlens_k[b] >= seqlens_q[b]. + if is_causal: + seqlens_k = torch.maximum(seqlens_k, seqlens_q) + cu_seqlens_k = torch.zeros(batch + 1, dtype=torch.int32, device="cuda") + cu_seqlens_k[1:] = seqlens_k.cumsum(0).to(torch.int32).cuda() + total_k = cu_seqlens_k[-1].item() + else: + seqlens_k = torch.full((batch,), seqlen_k, dtype=torch.int32) + total_k = None # unused + + torch.manual_seed(seed) # restore main seed before drawing actual tensors + + # ---- Allocate Q / Qv / O / lse ---- + if varlen_q: + Q = torch.randn(total_q, nheads, hdim, dtype=torch.bfloat16, device="cuda") + Qv = torch.randn(total_q, nheads, hdimv, dtype=torch.bfloat16, device="cuda") + O = torch.empty(total_q, nheads, hdimv, dtype=torch.bfloat16, device="cuda") + P = torch.empty( + total_q, nheads, P_k_length, dtype=torch.bfloat16, device="cuda" + ) + lse = torch.empty(total_q, nheads, dtype=torch.float32, device="cuda") + row_max = torch.empty( + total_q_dummy, P_k_length // 128, nheads, dtype=torch.float32, device="cuda" + ) + else: + Q = torch.randn( + batch, seqlen_q, nheads, hdim, dtype=torch.bfloat16, device="cuda" + ) + Qv = torch.randn( + batch, seqlen_q, nheads, hdimv, dtype=torch.bfloat16, device="cuda" + ) + O = torch.empty( + batch, seqlen_q, nheads, hdimv, dtype=torch.bfloat16, device="cuda" + ) + P = torch.empty( + batch, seqlen_q, nheads, P_k_length, dtype=torch.bfloat16, device="cuda" + ) + lse = torch.empty(batch, seqlen_q, nheads, dtype=torch.float32, device="cuda") + row_max = torch.empty( + batch, + seqlen_q, + P_k_length // 128, + nheads, + dtype=torch.float32, + device="cuda", + ) + + # ---- Allocate K / V ---- + if varlen_k: + K = torch.randn(total_k, nheads_kv, hdim, dtype=torch.bfloat16, device="cuda") + V = torch.randn(total_k, nheads_kv, hdimv, dtype=torch.bfloat16, device="cuda") + else: + K = torch.randn( + batch, seqlen_k, nheads_kv, hdim, dtype=torch.bfloat16, device="cuda" + ) + V = torch.randn( + batch, seqlen_k, nheads_kv, hdimv, dtype=torch.bfloat16, device="cuda" + ) + + # ---- Generate index_topk with per-batch valid ranges when varlen_k ---- + # index_topk shape: (total_q, topk_length) if varlen_q else (batch, seqlen_q, topk_length) + if gather_kv: + topk_parts = [] + for b in range(batch): + sl_q_b = seqlens_q[b].item() + sl_k_b = seqlens_k[b].item() + # Draw topk_length unique indices from [0, sl_k_b) for each query in this batch item. + topk_b = ( + torch.rand(sl_q_b, sl_k_b, device="cuda") + .argsort(dim=-1)[..., :topk_length] + .to(torch.int32) + ) # (sl_q_b, topk_length), all < sl_k_b + topk_parts.append(topk_b) + + if varlen_q: + index_topk = torch.cat(topk_parts, dim=0) # (total_q, topk_length) + else: + index_topk = torch.stack( + topk_parts, dim=0 + ) # (batch, seqlen_q, topk_length) + else: + index_topk = None + + # ---- Reference computation (per-batch loop covers all four varlen combos) ---- + O_ref_list, O_pt_list, lse_ref_list, lse_pt_list = [], [], [], [] + for b in range(batch): + qs = cu_seqlens_q[b].item() if varlen_q else b * seqlen_q + qe = cu_seqlens_q[b + 1].item() if varlen_q else (b + 1) * seqlen_q + ks = cu_seqlens_k[b].item() if varlen_k else b * seqlen_k + ke = cu_seqlens_k[b + 1].item() if varlen_k else (b + 1) * seqlen_k + + Q_b = ( + Q[qs:qe].unsqueeze(0) if varlen_q else Q[b : b + 1] + ) # (1, sl_q, nheads, hdim) + Qv_b = ( + Qv[qs:qe].unsqueeze(0) if varlen_q else Qv[b : b + 1] + ) # (1, sl_q, nheads, hdimv) + K_b = ( + K[ks:ke].unsqueeze(0) if varlen_k else K[b : b + 1] + ) # (1, sl_k, nheads_kv, hdim) + V_b = ( + V[ks:ke].unsqueeze(0) if varlen_k else V[b : b + 1] + ) # (1, sl_k, nheads_kv, hdimv) + if gather_kv: + topk_b = ( + index_topk[qs:qe].unsqueeze(0) if varlen_q else index_topk[b : b + 1] + ) + else: + topk_b = None + + O_b, _, lse_b = attention_ref( + Q_b if has_qk else None, + K_b if has_qk else None, + V_b, + qv=Qv_b, + causal=is_causal, + return_lse=True, + gather_kv_indices=topk_b, + ) + O_pt_b, _, lse_pt_b = attention_ref( + Q_b if has_qk else None, + K_b if has_qk else None, + V_b, + qv=Qv_b, + causal=is_causal, + upcast=False, + reorder_ops=True, + return_lse=True, + gather_kv_indices=topk_b, + ) + O_ref_list.append(O_b.squeeze(0)) + O_pt_list.append(O_pt_b.squeeze(0)) + lse_ref_list.append(lse_b.squeeze(0)) + lse_pt_list.append(lse_pt_b.squeeze(0)) + + cat_dim_o = 0 if (varlen_q) else 0 # always 0: leading token/batch dim + cat_dim_lse = -1 if (varlen_q) else -1 # always last: token dim + + if varlen_q: + O_ref = torch.cat(O_ref_list, dim=0) # (total_q, nheads, hdimv) + O_pt = torch.cat(O_pt_list, dim=0) + lse_ref = torch.cat(lse_ref_list, dim=-1) # (nheads, total_q) + lse_pt = torch.cat(lse_pt_list, dim=-1) + else: + O_ref = torch.stack(O_ref_list, dim=0) # (batch, seqlen_q, nheads, hdimv) + O_pt = torch.stack(O_pt_list, dim=0) + lse_ref = torch.stack(lse_ref_list, dim=0) # (batch, nheads, seqlen_q) + lse_pt = torch.stack(lse_pt_list, dim=0) + + rtol = 2 + atol = 2 * (O_ref + 0.3 - 0.3 - O_ref).abs().max().item() + + # ---- CuTe tensor wrappers ---- + mQ = from_dlpack(Q, assumed_align=16).mark_layout_dynamic(leading_dim=Q.ndim - 1) + mQv = from_dlpack(Qv, assumed_align=16).mark_layout_dynamic(leading_dim=Qv.ndim - 1) + mK = from_dlpack(K, assumed_align=16).mark_layout_dynamic(leading_dim=K.ndim - 1) + mV = from_dlpack(V, assumed_align=16).mark_layout_dynamic(leading_dim=V.ndim - 1) + mO = from_dlpack(O, assumed_align=16).mark_layout_dynamic(leading_dim=O.ndim - 1) + mP = from_dlpack(P, assumed_align=16).mark_layout_dynamic(leading_dim=P.ndim - 1) + mLSE = from_dlpack(lse, assumed_align=4).mark_layout_dynamic( + leading_dim=lse.ndim - 1 + ) + mRowMax = from_dlpack(row_max, assumed_align=4).mark_layout_dynamic( + leading_dim=row_max.ndim - 1 + ) + if index_topk is not None: + mIndexTopk = from_dlpack(index_topk, assumed_align=16).mark_layout_dynamic( + leading_dim=index_topk.ndim - 1 + ) + else: + mIndexTopk = None + + run_kwargs = dict(mIndexTopk=mIndexTopk) + if varlen_q: + run_kwargs["mCuSeqlensQ"] = from_dlpack(cu_seqlens_q, assumed_align=4) + if varlen_k: + run_kwargs["mCuSeqlensK"] = from_dlpack(cu_seqlens_k, assumed_align=4) + + if not has_qk: + mQ = mK = None + + if store_P is False: + mP = mRowMax = None + + # ---- Run kernel ---- + compile_cache[compile_key]( + mQ, + mQv, + mK, + mV, + mO, + mLSE, + softmax_scale, + mP, + mRowMax, + **run_kwargs, + stream=stream, + ) + + O_ref_max = O_ref.abs().max().item() + O_max = O.abs().max().item() + print(f"Pytorch O max = {O_ref_max} and our O max = {O_max}") + print(f"Pytorch max O diff: {(O_pt - O_ref).abs().max().item()}") + print(f"Pytorch mean O diff: {(O_pt - O_ref).abs().mean().item()}") + print(f"Max abs diff O, O_ref: {(O - O_ref).abs().max().item()}") + print(f"Mean abs diff O, O_ref: {(O - O_ref).abs().mean().item()}") + + lse = lse.transpose(-1, -2) + lse_ref_max = lse_ref.abs().max().item() + lse_max = lse.abs().max().item() + print(f"Pytorch LSE max = {lse_ref_max} and our LSE max = {lse_max}") + print(f"Pytorch LSE max diff: {(lse_pt - lse_ref).abs().max().item()}") + print(f"Pytorch LSE mean diff: {(lse_pt - lse_ref).abs().mean().item()}") + print(f"Max abs diff LSE: {(lse - lse_ref).abs().max().item()}") + print(f"Mean abs diff LSE: {(lse - lse_ref).abs().mean().item()}") + + if validate: + assert (O - O_ref).abs().max().item() <= rtol * ( + O_pt - O_ref + ).abs().max().item() + atol + varlen_tag = "" + if varlen_q: + varlen_tag += f", total_q:{total_q}" + if varlen_k: + varlen_tag += f", total_k:{total_k}" + print( + f"batch:{batch:3d}, nheads:{nheads:3d}, seqlen_q:{seqlen_q:5d}, seqlen_k:{seqlen_k:5d}" + f"{varlen_tag}, iter:{iter:2d} PASSED" + ) + else: + print(mO) + print( + f"batch:{batch:3d}, nheads:{nheads:3d}, seqlen_q:{seqlen_q:5d}, seqlen_k:{seqlen_k:5d}" + f", iter:{iter:2d} RUN (NOT TESTING CORRECTNESS)" + ) + + return None + + +def timeit(fn, *args, **kwargs): + # Synchronize before timing + torch.cuda.synchronize() + + # Warmup + for _ in range(10): + fn(*args, **kwargs) + + # Benchmark using PyTorch's Timer + t = benchmark.Timer( + stmt="fn(*args, **kwargs)", globals={"fn": fn, "args": args, "kwargs": kwargs} + ) + + # Time it multiple runs + measurement = t.timeit(20) # 20 repeats + avg_time = measurement.mean # Average time in seconds + + time.sleep(1) + + return avg_time + + +def benchmark_mla_kernel( + batch=1, + seqlen_q=2048, + seqlen_k=2048, + topk_length=2048, + nheads=128, + hdim=64, + hdimv=512, + compile_cache=dict(), + gather_kv=True, + is_causal=False, + disable_bitmask=False, + store_P=False, +): + assert hdim == 64, "hdim must be 64" + assert hdimv == 512, "hdimv must be 512" + + qhead_per_kvhead = nheads + nheads_kv = 1 + pack_gqa = True + softmax_scale = 1.0 / math.sqrt(hdim + hdimv) + seqlen_k_rounded = (seqlen_k + 128 - 1) // 128 * 128 + P_k_length = seqlen_k_rounded if not gather_kv else topk_length + + torch_stream = torch.cuda.current_stream() + stream = cuda.CUstream(torch_stream.cuda_stream) + + compile_key = ( + is_causal, + gather_kv, + topk_length if gather_kv else None, + pack_gqa, + qhead_per_kvhead, + nheads_kv, + disable_bitmask, + ) + if compile_key not in compile_cache: + Q = torch.randn( + batch, seqlen_q, nheads, hdim, dtype=torch.bfloat16, device="cuda" + ) + Qv = torch.randn( + batch, seqlen_q, nheads, hdimv, dtype=torch.bfloat16, device="cuda" + ) + K = torch.randn( + batch, seqlen_k, nheads_kv, hdim, dtype=torch.bfloat16, device="cuda" + ) + V = torch.randn( + batch, seqlen_k, nheads_kv, hdimv, dtype=torch.bfloat16, device="cuda" + ) + O = torch.empty( + batch, seqlen_q, nheads, hdimv, dtype=torch.bfloat16, device="cuda" + ) + P = torch.empty( + batch, seqlen_q, nheads, P_k_length, dtype=torch.bfloat16, device="cuda" + ) + index_topk = ( + torch.rand(batch, seqlen_q, topk_length, device="cuda") + .argsort(dim=-1) + .to(torch.int32) + ) + + mQ = from_dlpack(Q, assumed_align=16).mark_layout_dynamic( + leading_dim=Q.ndim - 1 + ) + mQv = from_dlpack(Qv, assumed_align=16).mark_layout_dynamic( + leading_dim=Qv.ndim - 1 + ) + mK = from_dlpack(K, assumed_align=16).mark_layout_dynamic( + leading_dim=K.ndim - 1 + ) + mV = from_dlpack(V, assumed_align=16).mark_layout_dynamic( + leading_dim=V.ndim - 1 + ) + mO = from_dlpack(O, assumed_align=16).mark_layout_dynamic( + leading_dim=O.ndim - 1 + ) + mP = from_dlpack(P, assumed_align=16).mark_layout_dynamic( + leading_dim=P.ndim - 1 + ) + if gather_kv: + mIndexTopk = from_dlpack(index_topk, assumed_align=16).mark_layout_dynamic( + leading_dim=index_topk.ndim - 1 + ) + else: + mIndexTopk = None + + mLSE = None + + if store_P is False: + mP = None + + kernel = cute.compile( + FlashAttentionMLAForwardSm100( + is_causal=is_causal, + use_cpasync_load_KV=gather_kv, + topk_length=topk_length if gather_kv else 2048, + is_topk_gather=gather_kv, + pack_gqa=pack_gqa, + qhead_per_kvhead=qhead_per_kvhead, + nheads_kv=nheads_kv, + disable_bitmask=disable_bitmask, + ), + mQ, + mQv, + mK, + mV, + mO, + mLSE, + softmax_scale, + mP=mP, + mIndexTopk=mIndexTopk, + stream=stream, + ) + compile_cache[compile_key] = kernel + + Q = torch.randn(batch, seqlen_q, nheads, hdim, dtype=torch.bfloat16, device="cuda") + Qv = torch.randn( + batch, seqlen_q, nheads, hdimv, dtype=torch.bfloat16, device="cuda" + ) + K = torch.randn( + batch, seqlen_k, nheads_kv, hdim, dtype=torch.bfloat16, device="cuda" + ) + V = torch.randn( + batch, seqlen_k, nheads_kv, hdimv, dtype=torch.bfloat16, device="cuda" + ) + O = torch.empty(batch, seqlen_q, nheads, hdimv, dtype=torch.bfloat16, device="cuda") + P = torch.empty( + batch, seqlen_q, nheads, P_k_length, dtype=torch.bfloat16, device="cuda" + ) + + index_topk = ( + torch.rand(batch, seqlen_q, topk_length, device="cuda") + .argsort(dim=-1) + .to(torch.int32) + ) + + mQ = from_dlpack(Q, assumed_align=16).mark_layout_dynamic(leading_dim=Q.ndim - 1) + mQv = from_dlpack(Qv, assumed_align=16).mark_layout_dynamic(leading_dim=Qv.ndim - 1) + mK = from_dlpack(K, assumed_align=16).mark_layout_dynamic(leading_dim=K.ndim - 1) + mV = from_dlpack(V, assumed_align=16).mark_layout_dynamic(leading_dim=V.ndim - 1) + mO = from_dlpack(O, assumed_align=16).mark_layout_dynamic(leading_dim=O.ndim - 1) + mP = from_dlpack(P, assumed_align=16).mark_layout_dynamic(leading_dim=P.ndim - 1) + if gather_kv: + mIndexTopk = from_dlpack(index_topk, assumed_align=16).mark_layout_dynamic( + leading_dim=index_topk.ndim - 1 + ) + else: + mIndexTopk = None + mLSE = None + + if store_P is False: + mP = None + + exec_time_in_s = timeit( + compile_cache[compile_key], + mQ, + mQv, + mK, + mV, + mO, + mLSE, + softmax_scale, + mP=mP, + mIndexTopk=mIndexTopk, + stream=stream, + ) + + seqlen_k_eff = topk_length if gather_kv else seqlen_k + + FLOPs = 2 * batch * nheads * seqlen_q * seqlen_k_eff * (hdim + 2 * hdimv) + if is_causal and not gather_kv: + FLOPs /= 2 + + TFLOPS = FLOPs / exec_time_in_s / 1e12 + + q_bytes = 2 * batch * nheads * seqlen_q * hdim + qv_bytes = 2 * batch * nheads * seqlen_q * hdimv + k_bytes = 2 * batch * nheads_kv * seqlen_k_eff * hdim + v_bytes = 2 * batch * nheads_kv * seqlen_k_eff * hdimv + o_bytes = 2 * batch * nheads * seqlen_q * hdimv + total_bytes = q_bytes + qv_bytes + k_bytes + v_bytes + o_bytes + TBs = total_bytes / exec_time_in_s / 1e12 + + print( + f"batch: {batch}, seqlen_q: {seqlen_q}, seqlen_k: {seqlen_k}, nheads: {nheads}, -> {exec_time_in_s * 1e3:.2f} ms, {TFLOPS:.2f} TFLOPS, {TBs:.2f} TBs" + ) + + +if __name__ == "__main__": + run_test = True + run_benchmark = True + gather_kv = True + is_causal = False + pack_gqa = True + topk_length = 2048 + varlen_q = False + varlen_k = False + disable_bitmask = False + validate = True + has_qk = True + + if run_test: + if not gather_kv: + seqlen_q_test_values = range(1, 4002, 400) + seqlen_k_test_values = range(1, 4002, 400) + else: + seqlen_q_test_values = range(1, 1001, 200) + seqlen_k_test_values = range(topk_length, 9001, 2000) + seqlen_q_test_values = [4096] + seqlen_k_test_values = [4096] + nheads_test_values = [128] + batch_test_values = [1] + test_configs = [ + ( + batch, + nheads, + seqlen_q, + seqlen_k, + ) + for batch in batch_test_values + for nheads in nheads_test_values + for seqlen_q in seqlen_q_test_values + for seqlen_k in seqlen_k_test_values + ] + iters_per_config = 1 + compile_cache = dict() + print("=" * 40) + print("Testing MLA Kernel") + print("=" * 40) + for config in test_configs: + batch, nheads, seqlen_q, seqlen_k = config + # if is_causal and seqlen_k < seqlen_q: + # continue + for iter in range(iters_per_config): + test_mla_kernel( + seqlen_q=seqlen_q, + seqlen_k=seqlen_k, + topk_length=topk_length, + nheads=nheads, + batch=batch, + iter=iter, + compile_cache=compile_cache, + validate=validate, + seed=0, + gather_kv=gather_kv, + pack_gqa=pack_gqa, + is_causal=is_causal, + varlen_q=varlen_q, + varlen_k=varlen_k, + disable_bitmask=disable_bitmask, + has_qk=has_qk, + ) + if run_benchmark: + if gather_kv: + seqlen_q_benchmark_values = [1] + seqlen_k_benchmark_values = [8192 * 2] + nheads_benchmark_values = [128] + batch_benchmark_values = [128] + else: + seqlen_q_benchmark_values = [1] + seqlen_k_benchmark_values = [8192] + nheads_benchmark_values = [128] + batch_benchmark_values = [128] + # seqlen_q_benchmark_values = [4096] + # seqlen_k_benchmark_values = [4096] + # nheads_benchmark_values = [16] + # batch_benchmark_values = [8] + benchmark_configs = [ + ( + batch, + nheads, + seqlen_q, + seqlen_k, + ) + for batch in batch_benchmark_values + for nheads in nheads_benchmark_values + for seqlen_q in seqlen_q_benchmark_values + for seqlen_k in seqlen_k_benchmark_values + ] + compile_cache = dict() + print("=" * 40) + print("Benchmarking MLA Kernel") + print("=" * 40) + for config in benchmark_configs: + batch, nheads, seqlen_q, seqlen_k = config + benchmark_mla_kernel( + batch=batch, + seqlen_q=seqlen_q, + seqlen_k=seqlen_k, + topk_length=topk_length, + nheads=nheads, + gather_kv=gather_kv, + is_causal=is_causal, + disable_bitmask=disable_bitmask, + compile_cache=compile_cache, + ) diff --git a/python/sglang/jit_kernel/flash_attn/cute/flash_fwd_sm100.py b/python/sglang/jit_kernel/flash_attn/cute/flash_fwd_sm100.py new file mode 100644 index 000000000..157f0178f --- /dev/null +++ b/python/sglang/jit_kernel/flash_attn/cute/flash_fwd_sm100.py @@ -0,0 +1,5594 @@ +# Copyright (c) 2025, Tri Dao. +# Copyright (c) 2026, Colfax International. (modifications) + +# Supported features: +# - BF16 & FP16 dtype +# - noncausal & causal attention +# - MHA, GQA, MQA +# - hdim 64, 96, 128, (192, 128). +# - varlen +# - sliding window +# - split-kv +# +# Colfax modifications: +# - relative bias +# - MXFP8 dtype +# +# Unsupported features that will be added later: +# - page size != 128 +# - more hdim (192, 256) +# +# Based on the cutlass example and cute-dsl example: +# https://github.com/NVIDIA/cutlass/tree/main/examples/77_blackwell_fmha +# https://github.com/NVIDIA/cutlass/blob/main/examples/python/CuTeDSL/blackwell/fmha.py + +import math +from functools import partial +from typing import Callable, Literal, NamedTuple, Optional, Tuple + +import cuda.bindings.driver as cuda +import cutlass +import cutlass.cute as cute +import cutlass.cute.nvgpu.tcgen05 as tcgen05 +import cutlass.pipeline as cutlass_pipeline +import cutlass.utils.blackwell_helpers as sm100_utils_basic +import cutlass.utils.blockscaled_layout as blockscaled_layout +from cutlass import Boolean, Float32, Int32, Int64, const_expr, pipeline +from cutlass.base_dsl.arch import Arch +from cutlass.cute import FastDivmodDivisor +from cutlass.cute.nvgpu import cpasync +from cutlass.cutlass_dsl import BaseDSL +from cutlass.pipeline import pipeline_init_arrive, pipeline_init_wait +from cutlass.utils import ClcDynamicPersistentTileScheduler +from quack import copy_utils, layout_utils +from quack.cute_dsl_utils import ParamsBase + +import sglang.jit_kernel.flash_attn.cute.pipeline as pipeline_custom +from sglang.jit_kernel.flash_attn.cute import blackwell_helpers as sm100_utils +from sglang.jit_kernel.flash_attn.cute import mma_sm100_desc as sm100_desc +from sglang.jit_kernel.flash_attn.cute import utils +from sglang.jit_kernel.flash_attn.cute.block_info import BlockInfo +from sglang.jit_kernel.flash_attn.cute.block_sparse_utils import ( + get_total_block_count, + handle_block_sparse_empty_tile_correction_sm100, + produce_block_sparse_loads_sm100, + softmax_block_sparse_sm100, +) +from sglang.jit_kernel.flash_attn.cute.block_sparsity import BlockSparseTensors +from sglang.jit_kernel.flash_attn.cute.cute_dsl_utils import assume_tensor_aligned +from sglang.jit_kernel.flash_attn.cute.fa_logging import fa_log, fa_printf +from sglang.jit_kernel.flash_attn.cute.mask import AttentionMask +from sglang.jit_kernel.flash_attn.cute.named_barrier import NamedBarrierFwdSm100 +from sglang.jit_kernel.flash_attn.cute.pack_gqa import PackGQA, pack_gqa_layout +from sglang.jit_kernel.flash_attn.cute.paged_kv import PagedKVManager +from sglang.jit_kernel.flash_attn.cute.seqlen_info import SeqlenInfoQK +from sglang.jit_kernel.flash_attn.cute.softmax import ( + SoftmaxSm100, + apply_score_mod_inner, +) +from sglang.jit_kernel.flash_attn.cute.tile_scheduler import ( + ClcState, + SchedulingMode, + SingleTileLPTScheduler, + SingleTileScheduler, + SingleTileVarlenScheduler, + StaticPersistentTileScheduler, + TileSchedulerArguments, + TileSchedulerProtocol, +) +from sglang.jit_kernel.flash_attn.cute.utils import ( + AuxData, + cvt_bf16x2_ue8m0x2, + smid, +) + +# === TUNING KNOBS (agent-editable) === +# Keys: (use_2cta_instrs: bool, is_causal: bool, head_dim_padded: int, is_sm103: bool) +# Values: +# ex2_emu_freq: int — how often to use emulated exp2 (0=all hardware exp2, higher=more emulation). +# SM103 has fast native exp2, so set freq=0 there. +# ex2_emu_res: int — (hd256 only) number of fragment-pairs per freq period to emulate. +# ex2_emu_start_frg: int — fragment index to start emulation from +# num_regs_softmax: int — register count for softmax warps (multiple of 8) +# num_regs_correction: int — register count for correction warps (multiple of 8) +# num_regs_other is derived: 512 - num_regs_softmax * 2 - num_regs_correction +# (hd256 exception: num_regs_other is fixed at 32, not derived) +_TUNING_CONFIG = { + (True, False, 128, False): { + "ex2_emu_freq": 10, + "ex2_emu_start_frg": 1, + "num_regs_softmax": 176, + "num_regs_correction": 88, + }, + (False, True, 128, False): { + "ex2_emu_freq": 16, + "ex2_emu_start_frg": 1, + "num_regs_softmax": 192, + "num_regs_correction": 72, + }, + (True, False, 192, False): { + "ex2_emu_freq": 16, + "ex2_emu_start_frg": 0, + "num_regs_softmax": 184, + "num_regs_correction": 80, + }, + (False, True, 192, False): { + "ex2_emu_freq": 32, + "ex2_emu_start_frg": 1, + "num_regs_softmax": 192, + "num_regs_correction": 72, + }, + (True, False, 128, True): { + "ex2_emu_freq": 0, + "ex2_emu_start_frg": 0, + "num_regs_softmax": 176, + "num_regs_correction": 80, + }, + (False, True, 128, True): { + "ex2_emu_freq": 0, + "ex2_emu_start_frg": 0, + "num_regs_softmax": 176, + "num_regs_correction": 64, + }, + (True, False, 192, True): { + "ex2_emu_freq": 0, + "ex2_emu_start_frg": 0, + "num_regs_softmax": 176, + "num_regs_correction": 64, + }, + (False, True, 192, True): { + "ex2_emu_freq": 0, + "ex2_emu_start_frg": 0, + "num_regs_softmax": 176, + "num_regs_correction": 72, + }, + (True, False, 256, False): { + "ex2_emu_freq": 14, + "ex2_emu_res": 6, + "ex2_emu_start_frg": 0, + "num_regs_softmax": 256, + "num_regs_correction": 160, + }, + (True, True, 256, False): { + "ex2_emu_freq": 14, + "ex2_emu_res": 6, + "ex2_emu_start_frg": 0, + "num_regs_softmax": 256, + "num_regs_correction": 160, + }, +} +_FP8_TUNING_CONFIG = { + (True, False, 128, False): { + "ex2_emu_freq": 10, + "ex2_emu_start_frg": 1, + "num_regs_softmax": 160, + "num_regs_correction": 72, + }, +} +_FP8_SMALL_HDIM_REGS = { + False: {"num_regs_softmax": 168, "num_regs_correction": 96, "num_regs_other": 80}, + True: {"num_regs_softmax": 152, "num_regs_correction": 96, "num_regs_other": 112}, +} +# === END TUNING KNOBS === + +# tile_to_shape order for laying block scale factors over the BasicChunk atom +# (SFK/SFQ/SFV all share it). +_SF_TILE_ORDER = (2, 1, 3, 4) + + +class DescaleTensors(NamedTuple): + q_descale: Optional[cute.Tensor] = None + k_descale: Optional[cute.Tensor] = None + v_descale: Optional[cute.Tensor] = None + + def __new_from_mlir_values__(self, values): + return DescaleTensors(*((*values, None, None, None)[:3])) + + +class SfS2TCopies(NamedTuple): + # Blockscaled QK^T scale-factor TMEM tensors + smem->TMEM tiled-copy handles, + # built per q_stage by make_sf_qk_tmem_copies. + tCtSFQ: cute.Tensor + tCtSFK: cute.Tensor + tiled_copy_sfq: cute.TiledCopy + tiled_copy_sfk: cute.TiledCopy + tCsSFQ_s2t: cute.Tensor + tCtSFQ_s2t: cute.Tensor + tCsSFK_s2t: cute.Tensor + tCtSFK_s2t: cute.Tensor + + +class FlashAttentionForwardSm100: + + def __init__( + self, + # dtype: Type[cutlass.Numeric], + head_dim: int, + head_dim_v: Optional[int] = None, + qhead_per_kvhead: cutlass.Constexpr[int] = 1, + is_causal: bool = False, + is_local: bool = False, + is_split_kv: bool = False, + pack_gqa: bool = False, + q_subtile_factor: int | None = None, + m_block_size: int = 128, + n_block_size: int = 128, + q_stage: cutlass.Constexpr[int] = 2, + is_persistent: bool = True, + score_mod: cutlass.Constexpr | None = None, + mask_mod: cutlass.Constexpr | None = None, + has_aux_tensors: cutlass.Constexpr = False, + paged_kv_non_tma: bool = False, + is_varlen_q: bool = False, + use_2cta_instrs: bool = False, + use_clc_scheduler: bool = False, + has_bias: bool = False, + bias_block_size: int = 128, + rel_extent_padded: int = 128, + qk_blockscaled: bool = False, + v_dequant: bool = False, + q_sf_interleaved: bool = False, + kv_sf_interleaved: bool = False, + ): + # MXFP8 block-scaled attention (see interface._flash_attn_fwd): + # qk_blockscaled: Q/K fp8 e4m3 + per-32 UE8M0 scales; QK^T runs as + # tcgen05 mxf8f6f4 (sm100_utils_basic.gemm_blockscaled) with the + # scales staged into TMEM by make_sf_qk_tmem_copies. + # v_dequant: V stored fp8 e4m3 + per-32 UE8M0 scales, dequantized to + # bf16 in-kernel by the correction warp (dequant_v). PV MMA is bf16. + # SFQ/SFK/SFV ride TMA when their gmem layout is the interleaved + # BlockScaledBasicChunk atom (q_sf_interleaved / kv_sf_interleaved); + # otherwise SFQ falls back to cp.async (use_cpasync_to_load_sfq) and + # SFK/SFV go through the paged non-TMA cp.async path. + self.qk_blockscaled = qk_blockscaled + # TODO(mxfp8): v_dequant is overloaded -- it means both "V is stored fp8 + # blockscaled-interleaved" and "V must be dequantized before P@V". Split + # into two flags (e.g. v_blockscaled_storage vs v_needs_dequant) if a + # non-dequant blockscaled-V (fp8 PV) path is ever added. + self.v_dequant = v_dequant + self.q_sf_interleaved = q_sf_interleaved + self.kv_sf_interleaved = kv_sf_interleaved + self.use_cpasync_to_load_sfq = self.qk_blockscaled and not self.q_sf_interleaved + self.use_tma_KV = not paged_kv_non_tma + assert not ( + qk_blockscaled and self.q_sf_interleaved and is_varlen_q + ), "if varlen q, can't have SFQ interleaved" + assert not ( + qk_blockscaled and not self.kv_sf_interleaved and self.use_tma_KV + ), "if scale KV not interleaved, can't use tma KV" + # self.dtype = dtype + # padding head_dim to a multiple of 16 as k_block_size + hdim_multiple_of = 16 + self.head_dim_padded = int( + math.ceil(head_dim / hdim_multiple_of) * hdim_multiple_of + ) + head_dim_v = head_dim_v if head_dim_v is not None else head_dim + self.same_hdim_kv = head_dim == head_dim_v + self.head_dim_v_padded = int( + math.ceil(head_dim_v / hdim_multiple_of) * hdim_multiple_of + ) + self.same_hdim_kv_padded = self.head_dim_padded == self.head_dim_v_padded + self.check_hdim_oob = head_dim != self.head_dim_padded + self.check_hdim_v_oob = head_dim_v != self.head_dim_v_padded + self.m_block_size = m_block_size + self.n_block_size = n_block_size + self.q_stage = q_stage + assert self.q_stage in [1, 2] + self.use_2cta_instrs = use_2cta_instrs + # If split_P_arrive, the softmax warps write some columns of P first, signal to the MMA warp + # to being the P @ V MMA, then write the rest of P and signal again. This allows some overlap + # between compute the last couple columns of P and the P @ V MMA. + self.split_P_arrive = n_block_size // 4 * 3 + self.split_P_arrive = int(self.split_P_arrive / 32) * 32 # multiple of 32 + assert self.split_P_arrive % 32 == 0 + assert self.split_P_arrive < self.n_block_size + self.arch = BaseDSL._get_dsl().get_arch_enum() + assert self.arch.is_family_of(Arch.sm_100f) or self.arch.is_family_of( + Arch.sm_110f + ), "Only SM 10.x and 11.x are supported" + + self.cta_group_size = 2 if self.use_2cta_instrs else 1 + # cta_tiler M includes only 1 CTA, the scheduler will take into account the cluster shape + self.cta_tiler = ( + self.q_stage * m_block_size, + n_block_size, + self.head_dim_padded, + ) + # With 2CTA, the MMA tiler M covers both CTAs, so it's cta_group_size * m_block_size. + # Each CTA owns m_block_size rows; the 2CTA MMA instruction spans both. + self.mma_tiler_qk = ( + self.cta_group_size * m_block_size, + n_block_size, + self.head_dim_padded, + ) + self.mma_tiler_pv = ( + self.cta_group_size * m_block_size, + self.head_dim_v_padded, + n_block_size, + ) + self.qk_acc_dtype = Float32 + self.pv_acc_dtype = Float32 + self.cluster_shape_mn = (2, 1) if self.use_2cta_instrs else (1, 1) + self.is_persistent = is_persistent + self.is_causal = is_causal + self.is_local = is_local + self.is_varlen_q = is_varlen_q + self.qhead_per_kvhead = qhead_per_kvhead + self.is_split_kv = is_split_kv + self.pack_gqa = pack_gqa + # relative (sheared) bias + self.has_bias = has_bias + self.rel_extent_padded = rel_extent_padded + assert rel_extent_padded % n_block_size == 0 + self.bias_n_max = rel_extent_padded // n_block_size if has_bias else 0 + self.bias_block_size = bias_block_size + self.bias_stage = ( + 2 if (self.q_stage == 2 or (self.q_stage == 1 and not is_split_kv)) else 1 + ) + assert self.bias_stage >= self.q_stage + self.use_tma_O = ( + not (self.pack_gqa and self.m_block_size % self.qhead_per_kvhead != 0) + and not (self.pack_gqa and self.is_split_kv) + and not is_varlen_q + ) + self.use_correction_warps_for_epi = not self.use_tma_O + self.q_subtile_factor = q_subtile_factor + assert not ( + self.is_split_kv and self.head_dim_v_padded >= 192 + ), "SplitKV is not supported for hdim >= 192" + self.score_mod = score_mod + self.mask_mod = mask_mod + self.score_vec_size: cutlass.Constexpr = getattr( + score_mod, "__vec_size__", 1 if cutlass.const_expr(has_aux_tensors) else 2 + ) + self.mask_vec_size: cutlass.Constexpr = getattr(mask_mod, "__vec_size__", 1) + # Does S1 need to wait for S0 to finish + # self.s0_s1_barrier = self.head_dim_padded in [64, 96] and (not self.is_causal and not self.is_local) + # NOTE: is_family_of also matches any future sm_10x with x > 3 — intentional. + # The flag gates ex2 emulation; sm_103 (B300) has fast hardware ex2 and later + # Blackwell variants are assumed to inherit this, so forward-inclusion is correct + # despite the literal `is_sm103` name. + is_sm103 = self.arch.is_family_of(Arch.sm_103f) + self.is_sm103 = is_sm103 + # enable_ex2_emu is derived: True if tuning config has freq > 0, else fallback to default logic + _default_enable_ex2_emu = ( + self.head_dim_padded <= 128 + or ( + self.head_dim_padded == 192 + and self.use_2cta_instrs + and not self.is_causal + and not self.is_local + ) + ) and not is_sm103 + self.enable_ex2_emu = _default_enable_ex2_emu + self.s0_s1_barrier = False + self.overlap_sO_sQ = ( + self.head_dim_padded == 192 and self.head_dim_v_padded >= 64 + ) or ( + self.head_dim_v_padded >= 128 + and (self.is_split_kv or (has_bias and self.q_stage == 2)) + ) + if self.q_stage == 2 and self.v_dequant: + self.overlap_sO_sQ = True + if self.overlap_sO_sQ: + self.is_persistent = False + + assert self.use_tma_KV or not ( + self.check_hdim_oob or self.check_hdim_v_oob + ), "Paged KV does not support irregular head dim" + + # ClC does not compose with these other features, so disable even if requested + self.use_clc_scheduler = ( + use_clc_scheduler and self.use_tma_KV and not self.overlap_sO_sQ + ) + self.sched_stages = 1 + if self.use_clc_scheduler: + assert ( + self.cluster_shape_mn[1] == 1 + ), f"CLC requires cluster N == 1: {self.cluster_shape_mn}" + assert self.cluster_shape_mn[0] in ( + 1, + 2, + ), f"bad CLC cluster M: {self.cluster_shape_mn}" + assert ( + self.cluster_shape_mn[0] == self.cta_group_size + ), f"CLC cluster M != cta_group_size: {self.cluster_shape_mn}, {self.cta_group_size}" + + self.scheduling_mode = ( + SchedulingMode.CLC if self.use_clc_scheduler else SchedulingMode.STATIC + ) + + if is_varlen_q: + self.TileScheduler = SingleTileVarlenScheduler + elif self.is_causal or self.is_local or self.use_clc_scheduler: + self.TileScheduler = SingleTileLPTScheduler + elif self.is_persistent: + self.TileScheduler = StaticPersistentTileScheduler + else: + self.TileScheduler = SingleTileScheduler + + fa_log( + 1, + f"TileScheduler={self.TileScheduler.__name__}, scheduling_mode={self.scheduling_mode.name}, USE_2CTA={self.use_2cta_instrs}", + ) + + self.softmax0_warp_ids = (0, 1, 2, 3) + self.softmax1_warp_ids = (4, 5, 6, 7) + self.correction_warp_ids = (8, 9, 10, 11) + self.mma_warp_id = 12 + self.epilogue_warp_ids = (13,) + self.load_warp_ids = (14,) + self.empty_warp_ids = (15,) + self.tmem_alloc_cols = cute.arch.get_max_tmem_alloc_cols("sm_100") + + self.threads_per_cta = cute.arch.WARP_SIZE * len( + ( + *self.softmax0_warp_ids, + *self.softmax1_warp_ids, + *self.correction_warp_ids, + self.mma_warp_id, + *self.load_warp_ids, + *self.epilogue_warp_ids, + *self.empty_warp_ids, + ) + ) + + self.use_tma_Q = not ( + self.pack_gqa and self.m_block_size % self.qhead_per_kvhead != 0 + ) + + if self.q_stage == 1: + if not self.use_tma_KV or not self.use_tma_Q: + self.empty_warp_ids = self.empty_warp_ids + self.load_warp_ids + self.load_warp_ids = self.softmax1_warp_ids + else: + self.empty_warp_ids = self.empty_warp_ids + self.softmax1_warp_ids + self.softmax1_warp_ids = () + elif not self.use_tma_KV: + self.load_warp_ids = (14, 15) + self.empty_warp_ids = () + + if self.use_correction_warps_for_epi: + self.empty_warp_ids = self.empty_warp_ids + self.epilogue_warp_ids + self.epilogue_warp_ids = self.correction_warp_ids + + self.clc_scheduler_warp_id = ( + self.empty_warp_ids[0] if self.use_clc_scheduler else None + ) + + self.tmem_s_offset = [0, self.n_block_size] # e.g., 0, 128 + self.tmem_o_offset = [ + self.tmem_s_offset[-1] + self.n_block_size + i * self.head_dim_v_padded + for i in range(self.q_stage) + ] # e.g., 256, 384 + self.tmem_total = self.tmem_o_offset[-1] + self.head_dim_v_padded + assert self.tmem_total <= self.tmem_alloc_cols + self.tmem_s_to_p_offset = self.n_block_size // 2 + self.tmem_p_offset = [ + self.tmem_s_offset[i] + self.tmem_s_to_p_offset for i in range(2) + ] # 0, 128 + + # vec buffer for row_max & row_sum + self.tmem_vec_offset = self.tmem_s_offset + + # Look up tuning config for register counts and ex2_emu params + _tune_key = ( + self.use_2cta_instrs, + self.is_causal, + self.head_dim_padded, + self.is_sm103, + ) + self._tune = _TUNING_CONFIG.get(_tune_key, {}) + if "ex2_emu_freq" in self._tune: + self.enable_ex2_emu = self._tune["ex2_emu_freq"] > 0 + if self.head_dim_padded < 96: + self.num_regs_softmax = 200 if not paged_kv_non_tma else 184 + self.num_regs_correction = 64 + self.num_regs_other = 48 if not paged_kv_non_tma else 80 + else: + if not paged_kv_non_tma and "num_regs_softmax" in self._tune: + self.num_regs_softmax = self._tune["num_regs_softmax"] + self.num_regs_correction = self._tune["num_regs_correction"] + elif not paged_kv_non_tma: + self.num_regs_softmax = 192 + self.num_regs_correction = 80 + else: + self.num_regs_softmax = 184 + self.num_regs_correction = 64 + if self.has_bias: + self.num_regs_softmax += 8 + self.num_regs_other = ( + 512 - self.num_regs_softmax * 2 - self.num_regs_correction + ) + + self.buffer_align_bytes = 1024 + + def _setup_attributes(self): + """Set up configurations and parameters for the FMHA kernel operation. + + This method initializes and configures various attributes required for the + execution of the fused multi-head attention kernel, mainly about the pipeline stages: + + - Sets up staging parameters for Q, K, V inputs and accumulator data + - Configures pipeline stages for softmax, correction, and epilogue operations + """ + + smem_size_q = ( + self.q_stage + * self.m_block_size + * self.head_dim_padded + * self.q_dtype.width + // 8 + ) + if self.qk_blockscaled: + smem_size_q += ( + self.q_stage + * self.m_block_size + * self.head_dim_padded + // self.qk_sf_vec_size + * self.sfq_dtype.width + // 8 + ) + smem_size_bias = ( + self.bias_stage + * self.bias_block_size + * self.n_block_size + * self.bias_dtype.width + // 8 + if self.has_bias + else 0 + ) + smem_size_o = ( + self.q_stage + * self.m_block_size + * self.head_dim_v_padded + * self.o_dtype.width + // 8 + ) + smem_size_q_o = ( + smem_size_q + smem_size_o + if not self.overlap_sO_sQ + else max(smem_size_q, smem_size_o) + ) + smem_size_q_o_bias = ( + smem_size_q_o + smem_size_bias + if not self.overlap_sO_sQ + else max(smem_size_q + smem_size_bias, smem_size_o) + ) + smem_size_k_per_stage = ( + self.n_block_size * self.head_dim_padded * self.k_dtype.width // 8 + ) + if self.qk_blockscaled: + smem_size_k_per_stage += ( + self.n_block_size + * self.head_dim_padded + // self.qk_sf_vec_size + * self.sfk_dtype.width + // 8 + ) + smem_size_v_per_stage = ( + self.n_block_size * self.head_dim_v_padded * self.v_dtype.width // 8 + ) + if self.v_dequant: + # fp8 V scale bytes + the dequantized bf16 V (the fp8 V and its + # dequant target both live in smem; K and V no longer share). + smem_size_v_per_stage += ( + self.n_block_size + * self.head_dim_v_padded + // self.v_sf_vec_size + * self.sfv_dtype.width + // 8 + ) + smem_size_v_per_stage += ( + self.n_block_size * self.head_dim_v_padded * self.v_mma_dtype.width // 8 + ) + if self.v_dequant: + # v_dequant uses separate K and V pipelines, so their smem is not shared. + smem_size_kv_per_stage = smem_size_k_per_stage + smem_size_v_per_stage + else: + smem_size_kv_per_stage = ( + max(smem_size_k_per_stage, smem_size_v_per_stage) // self.cta_group_size + ) + # Cap small head_dim from over-staging: the 224*1024 budget undercounts + # per-stage state, so at hd_padded=16 the unbounded formula picks 52 stages + # and overflows the 227 KB SMEM cap. No-op for hd_padded >= 32 (max 26). + kv_stage = min((224 * 1024 - smem_size_q_o_bias) // smem_size_kv_per_stage, 32) + if kv_stage <= 1 and self.q_stage == 1 and self.bias_stage == 2: + self.bias_stage = 1 + smem_size_bias = ( + self.bias_stage + * self.bias_block_size + * self.n_block_size + * self.bias_dtype.width + // 8 + if self.has_bias + else 0 + ) + smem_size_q_o_bias = ( + smem_size_q_o + smem_size_bias + if not self.overlap_sO_sQ + else max(smem_size_q + smem_size_bias, smem_size_o) + ) + kv_stage = min( + (224 * 1024 - smem_size_q_o_bias) // smem_size_kv_per_stage, 32 + ) + if ( + self.head_dim_padded == 192 + and self.head_dim_v_padded == 128 + and kv_stage == 2 + ): + # For hdim 192,128, we can fit 3 stages if we use uneven_kv_smem + kv_stage = 3 + v_mma_stage = kv_stage + if self.v_dtype.width == 8: + if self.v_dequant: + # Budget the fp8 K/V load pipeline (kv_stage) separately from the + # bf16 dequant buffers (v_mma_stage): sVq/sK follow kv_stage, only + # sV_dequant follows v_mma_stage, so deepening the load pipeline + # costs 8-bit bytes, not 16-bit ones. + v_mma_stage = 1 if self.has_bias and self.q_stage == 2 else 2 + smem_size_v_mma_per_stage = ( + self.n_block_size + * self.head_dim_v_padded + * self.v_mma_dtype.width + // 8 + ) + smem_size_load_per_stage = ( + smem_size_kv_per_stage - smem_size_v_mma_per_stage + ) + kv_stage = ( + 224 * 1024 + - smem_size_q_o_bias + - v_mma_stage * smem_size_v_mma_per_stage + ) // smem_size_load_per_stage + kv_stage = max(min(kv_stage, 8), 2) + else: + kv_stage = 2 + v_mma_stage = 1 if self.has_bias and self.q_stage == 2 else kv_stage + self.kv_stage = kv_stage + self.v_mma_stage = v_mma_stage + # print("kv_stage", self.kv_stage) + self.s_stage = 2 + assert self.s_stage >= self.q_stage + # For hdim 192,128 1CTA, we don't have enough smem to store all 3 stages of KV: + # 128 x 192 x 2 bytes x 3 stages = 144KB, and we need 96KB for Q. + # Instead we store smem as [smem_large, smem_small, smem_large], where smem_large is + # 128 x 192 and smem_small is 128 x 128. We set the stride between the stages to be + # 128 * 160, so that indexing the 0th and 2nd stages will get the right address, + # but for the 1st stage we need to add or subtract (depending on phase) 128 x 64. + self.uneven_kv_smem = ( + self.head_dim_padded == 192 + and self.head_dim_v_padded == 128 + and self.kv_stage == 3 + ) + self.uneven_kv_smem_offset = ( + self.n_block_size * (self.head_dim_padded - self.head_dim_v_padded) // 2 + if self.uneven_kv_smem + else 0 + ) + assert self.uneven_kv_smem_offset % 1024 == 0 + + @cute.jit + def __call__( + self, + mQ: cute.Tensor, # (b, s_q, h, d) or (total_q, h, d) if there is cu_seqlens_q + mK: cute.Tensor, # (b_k, s_k, h_k, d) or (total_k, h_k, d) if there is cu_seqlens_k or (num_pages, page_size, h_k, d) if there is page_table + mV: cute.Tensor, # (b_k, s_k, h_k, dv) or (total_k, h_k, dv) if there is cu_seqlens_k or (num_pages, page_size, h_k, dv) if there is page_table + mO: cute.Tensor, # (b, s_q, h, dv) or (total_q, h, dv) if there is cu_seqlens_q + mLSE: Optional[cute.Tensor], + softmax_scale: Float32, + mCuSeqlensQ: Optional[cute.Tensor] = None, + mCuSeqlensK: Optional[cute.Tensor] = None, + mSeqUsedQ: Optional[cute.Tensor] = None, + mSeqUsedK: Optional[cute.Tensor] = None, + mPageTable: Optional[cute.Tensor] = None, # (b_k, max_num_pages_per_seq) + window_size_left: Int32 | int | None = None, + window_size_right: Int32 | int | None = None, + learnable_sink: Optional[cute.Tensor] = None, + descale_tensors: Optional[DescaleTensors] = None, + blocksparse_tensors: Optional[BlockSparseTensors] = None, + aux_data: AuxData = AuxData(), + mBias: Optional[ + cute.Tensor + ] = None, # (b, s_q, h, rel_extent_padded) or (total_q, h, rel_extent_padded) + mSFQ: Optional[cute.Tensor] = None, # UE8M0 per-32 scales for Q (blockscaled) + mSFK: Optional[cute.Tensor] = None, # UE8M0 per-32 scales for K (blockscaled) + mSFV: Optional[cute.Tensor] = None, # UE8M0 per-32 scales for V (v_dequant) + qk_sf_vec_size: cutlass.Constexpr[Optional[int]] = None, + v_sf_vec_size: cutlass.Constexpr[Optional[int]] = None, + # Always keep stream as the last parameter (EnvStream: obtained implicitly via TVM FFI). + stream: cuda.CUstream = None, + ): + """Execute the Fused Multi-Head Attention operation on the provided tensors. + + This method prepares the input tensors for processing, validates their shapes and types, + configures the computation parameters, and launches the CUDA kernel. + + The method handles: + 1. Tensor layout transformations for specific memory access patterns + 2. Validation of tensor shapes and data types + 3. Initialization of hardware-specific parameters and memory layouts + 4. Configuration of TMA (Tensor Memory Access) operations + 5. Grid and work scheduling computation + 6. Kernel launch with appropriate parameters + """ + # setup static attributes before smem/grid/tma computation + self.q_dtype = mQ.element_type + self.k_dtype = mK.element_type + self.v_dtype = mV.element_type + self.o_dtype = mO.element_type + self.kv_size_ratio = self.v_dtype.width // self.k_dtype.width + # scale-factor setup for blockscaled / v_dequant + self.sfq_dtype = mSFQ.element_type if const_expr(mSFQ is not None) else None + self.sfk_dtype = mSFK.element_type if const_expr(mSFK is not None) else None + self.sfv_dtype = mSFV.element_type if const_expr(mSFV is not None) else None + self.qk_sf_vec_size = ( + qk_sf_vec_size if const_expr(qk_sf_vec_size is not None) else 1 + ) + self.v_sf_vec_size = ( + v_sf_vec_size if const_expr(v_sf_vec_size is not None) else 1 + ) + if const_expr(self.v_dequant): + # V is dequantized to bf16 in-kernel; PV MMA (and P) run in bf16. + self.v_mma_dtype = cutlass.BFloat16 + self.kv_size_ratio = 1 + else: + self.v_mma_dtype = self.v_dtype + self.bias_dtype = ( + mBias.element_type if const_expr(mBias is not None) else self.q_dtype + ) + mQ, mK, mV, mO = [assume_tensor_aligned(t) for t in (mQ, mK, mV, mO)] + if const_expr(mBias is not None): + mBias = assume_tensor_aligned(mBias) + mSFQ, mSFK, mSFV = [ + assume_tensor_aligned(t, align=4) if const_expr(t is not None) else None + for t in (mSFQ, mSFK, mSFV) + ] + Q_layout_transpose = ( + [1, 3, 2, 0] if const_expr(mCuSeqlensQ is None) else [0, 2, 1] + ) + mQ = cute.make_tensor( + mQ.iterator, cute.select(mQ.layout, mode=Q_layout_transpose) + ) + if const_expr(mBias is not None): + mBias = cute.make_tensor( + mBias.iterator, cute.select(mBias.layout, mode=Q_layout_transpose) + ) + # (s_k, d, h_k, b_k) or (total_k, d, h_k) if there's cu_seqlens_k or (page_size, d, h_k, num_pages) if there's page_table + KV_layout_transpose = ( + [1, 3, 2, 0] if const_expr(mCuSeqlensK is None) else [0, 2, 1] + ) + mK, mV = [ + cute.make_tensor( + t.iterator, cute.select(t.layout, mode=KV_layout_transpose) + ) + for t in (mK, mV) + ] + if const_expr(self.is_split_kv): + O_layout_transpose = ( + [2, 4, 3, 1, 0] if const_expr(mCuSeqlensQ is None) else [1, 3, 2, 0] + ) + LSE_layout_transpose = ( + [3, 2, 1, 0] if const_expr(mCuSeqlensQ is None) else [2, 1, 0] + ) + num_splits = mO.shape[0] + else: + O_layout_transpose = ( + [1, 3, 2, 0] if const_expr(mCuSeqlensQ is None) else [0, 2, 1] + ) + LSE_layout_transpose = ( + [2, 1, 0] if const_expr(mCuSeqlensQ is None) else [1, 0] + ) + num_splits = Int32(1) + mO = cute.make_tensor( + mO.iterator, cute.select(mO.layout, mode=O_layout_transpose) + ) + mLSE = ( + cute.make_tensor( + mLSE.iterator, cute.select(mLSE.layout, mode=LSE_layout_transpose) + ) + if const_expr(mLSE is not None) + else None + ) + # V's SF is laid out over (tokens-M, head_dim_v/32 sf_k) -- identical to + # K's SF (per-token, per-head-dim). Capture V's shape BEFORE the (d, s) + # transpose so the SF atom tiles the token axis as M, same as mK.shape. + mV_sf_shape = mV.shape # (page_size/s_k, dv, h, b) -- token-major, like mK + # (s, d, h, b) -> (d, s, h, b) + V_layout_transpose = ( + [1, 0, 2, 3] if const_expr(mCuSeqlensK is None) else [1, 0, 2] + ) + mV = cute.make_tensor( + mV.iterator, cute.select(mV.layout, mode=V_layout_transpose) + ) + + # Broadcast SF tensors to the blockscaled BasicChunk atom layout (or the + # transposed dense layout for the non-interleaved / paged fallback). + if const_expr(self.qk_blockscaled): + # ((32,4),(32,4)):((16,4),(0,1)) + sf_atom = blockscaled_layout.BlockScaledBasicChunk( + self.qk_sf_vec_size + ).layout + sfk_layout = cute.tile_to_shape(sf_atom, mK.shape, _SF_TILE_ORDER) + if const_expr(self.q_sf_interleaved): + sfq_layout = cute.tile_to_shape(sf_atom, mQ.shape, _SF_TILE_ORDER) + mSFQ = cute.make_tensor(mSFQ.iterator, sfq_layout) + else: + mSFQ = cute.make_tensor( + mSFQ.iterator, cute.select(mSFQ.layout, mode=Q_layout_transpose) + ) + if const_expr(self.kv_sf_interleaved): + mSFK = cute.make_tensor(mSFK.iterator, sfk_layout) + else: + assert ( + not self.use_tma_KV + ), "can't use TMA to load SFK if not interleaved in gmem" + mSFK = cute.make_tensor( + mSFK.iterator, cute.select(mSFK.layout, mode=KV_layout_transpose) + ) + if const_expr(self.v_dequant): + sfv_atom = blockscaled_layout.BlockScaledBasicChunk( + self.v_sf_vec_size + ).layout + # Tile over V's token-major shape (like sfk over mK.shape): M=tokens, + # sf_k = head_dim_v/32. sfv is now symmetric with sfk (per-token, + # per-head-dim), which is what an incremental KV cache can produce. + sfv_layout = cute.tile_to_shape(sfv_atom, mV_sf_shape, _SF_TILE_ORDER) + if const_expr(self.kv_sf_interleaved): + mSFV = cute.make_tensor(mSFV.iterator, sfv_layout) + else: + assert ( + not self.use_tma_KV + ), "can't use TMA to load SFV if not interleaved in gmem" + mSFV = cute.make_tensor( + mSFV.iterator, cute.select(mSFV.layout, mode=KV_layout_transpose) + ) + + # check type consistency + if const_expr(self.q_dtype != self.k_dtype): + raise TypeError(f"Type mismatch: {self.q_dtype} != {self.k_dtype}") + if const_expr( + not self.qk_blockscaled + and not self.v_dequant + and self.q_dtype != self.v_dtype + ): + raise TypeError(f"Type mismatch: {self.q_dtype} != {self.v_dtype}") + if const_expr(self.qk_blockscaled and self.sfq_dtype != self.sfk_dtype): + raise TypeError(f"Type mismatch: {self.sfq_dtype} != {self.sfk_dtype}") + if const_expr(self.q_dtype.width == 8): + paged_kv_non_tma = not self.use_tma_KV + if const_expr(self.head_dim_padded < 96): + fp8_regs = _FP8_SMALL_HDIM_REGS[paged_kv_non_tma] + self.num_regs_softmax = fp8_regs["num_regs_softmax"] + self.num_regs_correction = fp8_regs["num_regs_correction"] + self.num_regs_other = fp8_regs["num_regs_other"] + else: + fp8_tune = _FP8_TUNING_CONFIG.get( + ( + self.use_2cta_instrs, + self.is_causal, + self.head_dim_padded, + self.is_sm103, + ), + {}, + ) + if const_expr("ex2_emu_freq" in fp8_tune): + self._tune = {**self._tune, **fp8_tune} + self.enable_ex2_emu = self._tune["ex2_emu_freq"] > 0 + if const_expr(not paged_kv_non_tma and "num_regs_softmax" in fp8_tune): + self.num_regs_softmax = fp8_tune["num_regs_softmax"] + self.num_regs_correction = fp8_tune["num_regs_correction"] + self.num_regs_other = ( + 512 - self.num_regs_softmax * 2 - self.num_regs_correction + ) + self._setup_attributes() + # Blockscaled SF TMEM offsets: the SFs for q_stage i live in the S + # region of the OTHER stage (stage 0 SF -> col tmem_s_offset[1], stage 1 + # SF -> col tmem_s_offset[0]); no extra TMEM is allocated. With q_stage=1 + # there is a single entry pointing at tmem_s_offset[1]. + if const_expr(self.qk_blockscaled): + self.num_sfq_tmem_cols = 4 if self.qk_sf_vec_size == 32 else 8 + self.num_sfk_tmem_cols = self.num_sfq_tmem_cols + self.tmem_sfq_offset = [ + self.tmem_s_offset[1 - i] for i in range(self.q_stage) + ] + # SFK is placed immediately AFTER SFQ (adjacent, not overlapping): + # SFQ owns [off, off+num_sfq_tmem_cols), SFK the next num_sfk_tmem_cols + # columns -- both inside the alternate stage's S region. + self.tmem_sfk_offset = [ + self.tmem_sfq_offset[i] + self.num_sfq_tmem_cols + for i in range(self.q_stage) + ] + self.ex2_emu_freq = 0 + self.ex2_emu_start_frg = self._tune.get("ex2_emu_start_frg", 1) + if const_expr(self.enable_ex2_emu): + self.ex2_emu_freq = self._tune.get("ex2_emu_freq", 16) + if const_expr( + self.pack_gqa + and self.head_dim_padded > 64 + and not self.is_causal + and not self.is_local + ): + self.ex2_emu_freq = ( + 32 + if mCuSeqlensQ is not None or mSeqUsedQ is not None + else self._tune.get("ex2_emu_freq", 10) + ) + + cta_group = ( + tcgen05.CtaGroup.TWO if self.use_2cta_instrs else tcgen05.CtaGroup.ONE + ) + q_major_mode = tcgen05.OperandMajorMode.K + k_major_mode = tcgen05.OperandMajorMode.K + v_major_mode = tcgen05.OperandMajorMode.MN + self.o_layout = cutlass.utils.LayoutEnum.from_tensor(mO) + # the intermediate tensor p is from tmem & mK-major + p_source = tcgen05.OperandSource.TMEM + p_major_mode = tcgen05.OperandMajorMode.K + if const_expr(not self.qk_blockscaled): + tiled_mma_qk = sm100_utils_basic.make_trivial_tiled_mma( + self.q_dtype, + q_major_mode, + k_major_mode, + self.qk_acc_dtype, + cta_group, + self.mma_tiler_qk[:2], + ) + else: + tiled_mma_qk = sm100_utils_basic.make_blockscaled_trivial_tiled_mma( + self.q_dtype, + q_major_mode, + k_major_mode, + self.sfq_dtype, + self.qk_sf_vec_size, + cta_group, + self.mma_tiler_qk[:2], + ) + # A separate 1-CTA blockscaled MMA is used purely to compute the SFK + # TMA / smem layout (the SFB operand's tiling). + mma_inst_shape_mn_sfk = ( + self.mma_tiler_qk[0] // (2 if self.use_2cta_instrs else 1), + cute.round_up(self.mma_tiler_qk[1], 128), + ) + self.mma_tiler_qk_sfk = (*mma_inst_shape_mn_sfk, self.mma_tiler_qk[2]) + self.tiled_mma_qk_sfk = ( + sm100_utils_basic.make_blockscaled_trivial_tiled_mma( + self.q_dtype, + q_major_mode, + k_major_mode, + self.sfq_dtype, + self.qk_sf_vec_size, + tcgen05.CtaGroup.ONE, + mma_inst_shape_mn_sfk, + ) + ) + # PV MMA is always plain (v_mma_dtype == v_dtype, or bf16 under v_dequant). + tiled_mma_pv = sm100_utils_basic.make_trivial_tiled_mma( + self.v_mma_dtype, + p_major_mode, + v_major_mode, + self.pv_acc_dtype, + cta_group, + self.mma_tiler_pv[:2], + p_source, + ) + + self.cluster_shape_mnk = (*self.cluster_shape_mn, 1) + cta_layout_vmnk = cute.tiled_divide( + cute.make_layout(self.cluster_shape_mnk), (tiled_mma_qk.thr_id.shape,) + ) + if const_expr(self.qk_blockscaled): + cta_layout_sfk_vmnk = cute.tiled_divide( + cute.make_layout(self.cluster_shape_mnk), + (self.tiled_mma_qk_sfk.thr_id.shape,), + ) + + # epi_tile is per-CTA (not full 2CTA) since each CTA writes its own O portion + self.epi_tile = (self.m_block_size, self.head_dim_v_padded) + + sQ_layout = sm100_utils_basic.make_smem_layout_a( + tiled_mma_qk, self.mma_tiler_qk, self.q_dtype, self.q_stage + ) + sK_layout = sm100_utils_basic.make_smem_layout_b( + tiled_mma_qk, + self.mma_tiler_qk, + self.k_dtype, + self.kv_stage * self.kv_size_ratio, + ) + if const_expr(self.qk_blockscaled): + sSFQ_layout = blockscaled_layout.make_smem_layout_sfa( + tiled_mma_qk, self.mma_tiler_qk, self.qk_sf_vec_size, self.q_stage + ) + sSFK_layout = blockscaled_layout.make_smem_layout_sfb( + self.tiled_mma_qk_sfk, + self.mma_tiler_qk_sfk, + self.qk_sf_vec_size, + self.kv_stage, + ) + else: + sSFQ_layout = None + sSFK_layout = None + if const_expr(self.v_dequant): + # SFV is PER-HEAD-DIM (per token, head_dim_v/32 blocks): same layout + # convention as SFK. Build it with the SAME QK-style blockscaled MMA + # tiling as SFK, but sized for head_dim_v -- SFB indexed by + # (tokens-N, head_dim_v/32 K-blocks). This is symmetric with SFK and + # is what an incremental KV cache produces (a per-head-dim block only + # needs one token's worth of V). + mma_inst_shape_mn_sfv = ( + self.mma_tiler_qk[0] // (2 if self.use_2cta_instrs else 1), + cute.round_up(self.mma_tiler_qk[1], 128), + ) + mma_tiler_pv_sfv = (*mma_inst_shape_mn_sfv, self.head_dim_v_padded) + self.mma_tiler_pv_sfv = mma_tiler_pv_sfv + self.tiled_mma_pv_sfv = ( + sm100_utils_basic.make_blockscaled_trivial_tiled_mma( + self.v_dtype, + q_major_mode, + k_major_mode, + self.sfv_dtype, + self.v_sf_vec_size, + tcgen05.CtaGroup.ONE, + mma_inst_shape_mn_sfv, + ) + ) + cta_layout_sfv_vmnk = cute.tiled_divide( + cute.make_layout(self.cluster_shape_mnk), + (self.tiled_mma_pv_sfv.thr_id.shape,), + ) + sSFV_layout = blockscaled_layout.make_smem_layout_sfb( + self.tiled_mma_pv_sfv, + mma_tiler_pv_sfv, + self.v_sf_vec_size, + self.kv_stage, + ) + else: + sSFV_layout = None + tP_layout = sm100_utils_basic.make_smem_layout_a( + tiled_mma_pv, self.mma_tiler_pv, self.v_mma_dtype, self.s_stage + ) + sV_layout = sm100_utils_basic.make_smem_layout_b( + tiled_mma_pv, self.mma_tiler_pv, self.v_mma_dtype, self.v_mma_stage + ) + if const_expr(self.v_dequant): + # fp8 V lands in sVq (plain MMA layout for the fp8 dtype); the + # correction warp dequantizes it into sV (bf16). + tiled_mma_pv_vq = sm100_utils_basic.make_trivial_tiled_mma( + self.v_dtype, + p_major_mode, + v_major_mode, + self.pv_acc_dtype, + cta_group, + self.mma_tiler_pv[:2], + p_source, + ) + sVq_layout = sm100_utils_basic.make_smem_layout_b( + tiled_mma_pv_vq, self.mma_tiler_pv, self.v_dtype, self.kv_stage + ) + else: + tiled_mma_pv_vq = None + sVq_layout = None + sO_layout = sm100_utils_basic.make_smem_layout_epi( + self.o_dtype, self.o_layout, self.epi_tile, self.q_stage + ) + if const_expr(not self.v_dequant and not self.same_hdim_kv_padded): + # sK and sV are using the same physical smem so we need to adjust the stride so that they line up + stride_sK = const_expr( + max(sK_layout.outer.stride[-1], 0) + ) # take max to turn tuple to Int32 + stride_sV = const_expr(max(sV_layout.outer.stride[-1], 0)) + stage_stride = const_expr( + max(stride_sK, stride_sV) + if not self.uneven_kv_smem + else (stride_sK + stride_sV) // 2 + ) + sK_layout = cute.make_composed_layout( + sK_layout.inner, + 0, + cute.make_layout( + (*sK_layout.outer.shape[:-1], self.kv_stage), + stride=(*sK_layout.outer.stride[:-1], stage_stride), + ), + ) + sV_layout = cute.make_composed_layout( + sV_layout.inner, + 0, + cute.make_layout( + (*sV_layout.outer.shape[:-1], self.kv_stage), + stride=(*sV_layout.outer.stride[:-1], stage_stride), + ), + ) + + if const_expr(self.pack_gqa): + nheads_kv = mK.shape[2] + mQ = pack_gqa_layout(mQ, self.qhead_per_kvhead, nheads_kv, head_idx=2) + mO = pack_gqa_layout(mO, self.qhead_per_kvhead, nheads_kv, head_idx=2) + if const_expr(mBias is not None): + mBias = pack_gqa_layout( + mBias, self.qhead_per_kvhead, nheads_kv, head_idx=2 + ) + if const_expr(mLSE is not None): + mLSE = pack_gqa_layout( + mLSE, self.qhead_per_kvhead, nheads_kv, head_idx=1 + ) + if const_expr(self.qk_blockscaled): + mSFQ = pack_gqa_layout( + mSFQ, self.qhead_per_kvhead, nheads_kv, head_idx=2 + ) + + self.tma_copy_bytes = { + name: ( + cute.size_in_bytes(mX.element_type, cute.select(layout, mode=[0, 1, 2])) + if const_expr(mX is not None) + else 0 + ) + for name, mX, layout in [ + ("Q", mQ, sQ_layout), + ("K", mK, sK_layout), + ("V", mV, sV_layout if const_expr(not self.v_dequant) else sVq_layout), + ("SFQ", mSFQ, sSFQ_layout), + ("SFK", mSFK, sSFK_layout), + ("SFV", mSFV, sSFV_layout), + ] + } + for name in ("Q", "K", "V", "SFQ", "SFK", "SFV"): + self.tma_copy_bytes[name] *= self.cta_group_size + + # TMA load for Q + tma_load_op = cpasync.CopyBulkTensorTileG2SOp(cta_group) + tma_store_op = cpasync.CopyBulkTensorTileS2GOp() + + if const_expr(self.use_tma_Q): + tma_atom_Q, mQ = cute.nvgpu.make_tiled_tma_atom_A( + tma_load_op, + mQ, + cute.select(sQ_layout, mode=[0, 1, 2]), + self.mma_tiler_qk, + tiled_mma_qk, + cta_layout_vmnk.shape, + ) + gmem_tiled_copy_Q = None + else: + tma_atom_Q = None + async_copy_elems = 128 // self.q_dtype.width + num_load_threads = cute.arch.WARP_SIZE * len(self.load_warp_ids) + threads_per_row = math.gcd( + self.head_dim_padded // async_copy_elems, num_load_threads + ) + gmem_tiled_copy_Q = copy_utils.tiled_copy_2d( + self.q_dtype, + threads_per_row, + num_load_threads, + async_copy_elems, + is_async=True, + ) + + tma_atom_K = None + tma_atom_V = None + tma_atom_SFQ = None + tma_atom_SFK = None + tma_atom_SFV = None + gmem_tiled_copy_SFQ = None + if const_expr(self.use_tma_KV): + # TMA load for K + tma_atom_K, mK = cute.nvgpu.make_tiled_tma_atom_B( + tma_load_op, + mK, + cute.select(sK_layout, mode=[0, 1, 2]), + self.mma_tiler_qk, + tiled_mma_qk, + cta_layout_vmnk.shape, + ) + # TMA load for V (uses sVq_layout / fp8 mma for v_dequant) + tma_atom_V, mV = cute.nvgpu.make_tiled_tma_atom_B( + tma_load_op, + mV, + ( + cute.select(sVq_layout, mode=[0, 1, 2]) + if const_expr(self.v_dequant) + else cute.select(sV_layout, mode=[0, 1, 2]) + ), + self.mma_tiler_pv, + tiled_mma_pv_vq if const_expr(self.v_dequant) else tiled_mma_pv, + cta_layout_vmnk.shape, + ) + + if const_expr(self.qk_blockscaled): + sfq_tma_op = sm100_utils_basic.cluster_shape_to_tma_atom_A( + self.cluster_shape_mn, tiled_mma_qk.thr_id + ) + if const_expr(not self.use_cpasync_to_load_sfq): + tma_atom_SFQ, mSFQ = cute.nvgpu.make_tiled_tma_atom_A( + sfq_tma_op, + mSFQ, + cute.select(sSFQ_layout, mode=[0, 1, 2]), + self.mma_tiler_qk, + tiled_mma_qk, + cta_layout_vmnk.shape, + internal_type=cutlass.Int16, + ) + # cp.async fallback for SFQ (dense / varlen-q layout) + atom_async_copy_SFQ = cute.make_copy_atom( + cpasync.CopyG2SOp(cache_mode=cpasync.LoadCacheMode.ALWAYS), + self.sfq_dtype, + num_bits_per_copy=32, + ) + num_load_threads = cute.arch.WARP_SIZE * len(self.load_warp_ids) + thr_layout_SFQ = cute.make_ordered_layout( + (num_load_threads, 1), order=(0, 1) + ) + val_layout_SFQ = cute.make_layout((1, 4)) + gmem_tiled_copy_SFQ = cute.make_tiled_copy_tv( + atom_async_copy_SFQ, thr_layout_SFQ, val_layout_SFQ + ) + if const_expr(self.use_tma_KV): + assert ( + self.kv_sf_interleaved + ), "SFK must be interleaved in gmem to use TMA" + sfk_tma_op = sm100_utils_basic.cluster_shape_to_tma_atom_SFB( + self.cluster_shape_mn, tiled_mma_qk.thr_id + ) + tma_atom_SFK, mSFK = cute.nvgpu.make_tiled_tma_atom_B( + sfk_tma_op, + mSFK, + cute.select(sSFK_layout, mode=[0, 1, 2]), + self.mma_tiler_qk_sfk, + self.tiled_mma_qk_sfk, + cta_layout_sfk_vmnk.shape, + internal_type=cutlass.Int16, + ) + if const_expr(self.v_dequant and self.use_tma_KV): + assert self.kv_sf_interleaved, "SFV must be interleaved in gmem to use TMA" + sfv_tma_op = sm100_utils_basic.cluster_shape_to_tma_atom_SFB( + self.cluster_shape_mn, self.tiled_mma_pv_sfv.thr_id + ) + tma_atom_SFV, mSFV = cute.nvgpu.make_tiled_tma_atom_B( + sfv_tma_op, + mSFV, + cute.select(sSFV_layout, mode=[0, 1, 2]), + mma_tiler_pv_sfv, + self.tiled_mma_pv_sfv, + cta_layout_sfv_vmnk.shape, + internal_type=cutlass.Int16, + ) + + self.num_epilogue_threads = cute.arch.WARP_SIZE * len(self.epilogue_warp_ids) + if const_expr(self.use_tma_O): + tma_atom_O, mO = cpasync.make_tiled_tma_atom( + tma_store_op, mO, cute.select(sO_layout, mode=[0, 1]), self.epi_tile + ) + gmem_tiled_copy_O = None + else: + tma_atom_O = None + universal_copy_bits = 128 + async_copy_elems = universal_copy_bits // self.o_dtype.width + atom_universal_copy = cute.make_copy_atom( + cute.nvgpu.CopyUniversalOp(), + self.o_dtype, + num_bits_per_copy=universal_copy_bits, + ) + tO_shape_dim_1 = sO_layout.outer.shape[1][0] // async_copy_elems + tO_layout = cute.make_ordered_layout( + (self.num_epilogue_threads // tO_shape_dim_1, tO_shape_dim_1), + order=(1, 0), + ) + # So that we don't have to check if we overshoot kBlockM when we store O + assert self.m_block_size % tO_layout.shape[0] == 0 + vO_layout = cute.make_layout((1, async_copy_elems)) + gmem_tiled_copy_O = cute.make_tiled_copy_tv( + atom_universal_copy, tO_layout, vO_layout + ) + + if const_expr(mBias is not None): + bias_layout_enum = cutlass.utils.LayoutEnum.from_tensor(mBias) + self.bias_major_mode = bias_layout_enum.mma_major_mode() + if const_expr(self.bias_major_mode != tcgen05.OperandMajorMode.K): + raise RuntimeError("The layout of mBias is wrong") + # (bias_block_size, n_block_size, bias_stage) + sBias_layout = sm100_utils_basic.make_smem_layout_epi( + self.bias_dtype, + bias_layout_enum, + (self.bias_block_size, self.n_block_size), + self.bias_stage, + ) + sBias_size = cute.cosize(sBias_layout) + # Set after the Q/K/V cta_group_size scaling loop above so bias (non-multicast) isn't double-scaled. + self.tma_copy_bytes["bias"] = cute.size_in_bytes( + self.bias_dtype, cute.select(sBias_layout, mode=[0, 1]) + ) + tma_atom_bias, mBias = cpasync.make_tiled_tma_atom( + cpasync.CopyBulkTensorTileG2SOp(), + mBias, + cute.select(sBias_layout, mode=[0, 1]), + (self.bias_block_size, self.n_block_size), + 1, # no mcast + ) + bias_s2r_thr_layout = cute.make_ordered_layout( + (self.bias_block_size, 1), order=(1, 0) + ) + bias_s2r_val_layout = cute.make_ordered_layout( + (1, 128 // self.bias_dtype.width), order=(1, 0) + ) + bias_s2r_copy_atom = cute.make_copy_atom( + cute.nvgpu.CopyUniversalOp(), + self.bias_dtype, + num_bits_per_copy=128, + ) + bias_s2r_tiled_copy = cute.make_tiled_copy_tv( + bias_s2r_copy_atom, bias_s2r_thr_layout, bias_s2r_val_layout + ) + else: + tma_atom_bias = None + sBias_layout = None + sBias_size = 0 + bias_s2r_tiled_copy = None + + TileScheduler = self.TileScheduler + _num_block_divisor = self.cta_tiler[0] * ( + self.cta_group_size + if not self.is_persistent and self.cta_group_size > 1 + else 1 + ) + tile_sched_args = TileSchedulerArguments( + cute.ceil_div(cute.size(mQ.shape[0]), _num_block_divisor), + cute.size(mQ.shape[2]), + ( + cute.size(mQ.shape[3]) + if const_expr(mCuSeqlensQ is None) + else cute.size(mCuSeqlensQ.shape[0] - 1) + ), + num_splits, + ( + cute.size(mK.shape[0]) + if const_expr(mPageTable is None) + else mK.shape[0] * mPageTable.shape[1] + ), + mQ.shape[1], + mV.shape[ + 0 + ], # Note that this is different from Sm90 since we transpose mV in Sm100 + total_q=( + cute.size(mQ.shape[0]) + if const_expr(mCuSeqlensQ is not None) + else cute.size(mQ.shape[0]) * cute.size(mQ.shape[3]) + ), + tile_shape_mn=self.cta_tiler[:2], + mCuSeqlensQ=mCuSeqlensQ, + mSeqUsedQ=mSeqUsedQ, + qhead_per_kvhead_packgqa=( + self.qhead_per_kvhead if const_expr(self.pack_gqa) else 1 + ), + element_size=self.k_dtype.width // 8, + is_persistent=self.is_persistent, + lpt=self.is_causal or self.is_local, + is_split_kv=self.is_split_kv, + cluster_shape_mn=self.cluster_shape_mn, + use_cluster_idx=not self.is_persistent and self.cta_group_size > 1, + ) + tile_sched_params = TileScheduler.to_underlying_arguments( + tile_sched_args, scheduling_mode=self.scheduling_mode + ) + self.tile_scheduler_cls = TileScheduler + grid_dim = TileScheduler.get_grid_shape(tile_sched_params) + + sO_size = cute.cosize(sO_layout) if const_expr(not self.overlap_sO_sQ) else 0 + sQ_size = ( + cute.cosize(sQ_layout) + if const_expr(not self.overlap_sO_sQ) + else cutlass.max( + cute.cosize(sQ_layout), + cute.cosize(sO_layout) * self.o_dtype.width // self.q_dtype.width, + ) + ) + if const_expr(self.overlap_sO_sQ and self.has_bias): + sQ_size = cute.cosize(sQ_layout) + + clc_response_size = self.sched_stages * 4 if self.use_clc_scheduler else 0 + clc_mbar_size = self.sched_stages * 2 if self.use_clc_scheduler else 0 + + # MXFP8 smem/mbar sizing. + sSFQ_size = cute.cosize(sSFQ_layout) if const_expr(self.qk_blockscaled) else 0 + sSFK_size = cute.cosize(sSFK_layout) if const_expr(self.qk_blockscaled) else 0 + sSFV_size = cute.cosize(sSFV_layout) if const_expr(self.v_dequant) else 0 + sVq_size = cute.cosize(sVq_layout) if const_expr(self.v_dequant) else 0 + sV_dequant_size = cute.cosize(sV_layout) if const_expr(self.v_dequant) else 0 + use_sf_mbar = 1 if const_expr(self.qk_blockscaled and self.q_stage == 2) else 0 + use_vq_mbar = 1 if const_expr(self.v_dequant) else 0 + use_sfq_mbar = 1 if const_expr(self.use_cpasync_to_load_sfq) else 0 + + @cute.struct + class SharedStorage: + # m_barriers for pipelines + mbar_load_Q: cute.struct.MemRange[Int64, self.q_stage * 2] + mbar_load_KV: cute.struct.MemRange[Int64, self.kv_stage * 2] + mbar_S_full_P_full_O_rescaled: cute.struct.MemRange[Int64, self.q_stage * 2] + mbar_P_full_lastsplit: cute.struct.MemRange[Int64, self.q_stage * 2] + mbar_O_full: cute.struct.MemRange[Int64, self.q_stage * 2] + mbar_softmax_stats: cute.struct.MemRange[Int64, self.q_stage * 2] + # mbar_softmax_stats: cute.struct.MemRange[Int64, self.q_stage * 4 * 2] + mbar_O_epi: cute.struct.MemRange[Int64, self.q_stage * 2] + mbar_s0_s1_sequence: cute.struct.MemRange[Int64, 2 * 2] + mbar_load_bias: cute.struct.MemRange[Int64, self.bias_stage * 2] + # MXFP8 pipeline barriers (0-length when unused). + mbar_s0_s1_empty_for_sf: cute.struct.MemRange[ + Int64, self.q_stage * 2 * use_sf_mbar + ] + mbar_load_SFQ: cute.struct.MemRange[Int64, self.q_stage * 2 * use_sfq_mbar] + mbar_load_Vq: cute.struct.MemRange[Int64, self.kv_stage * 2 * use_vq_mbar] + mbar_v_upcast: cute.struct.MemRange[ + Int64, self.v_mma_stage * 2 * use_vq_mbar + ] + # Tmem dealloc cluster barrier + tmem_dealloc_mbar_ptr: Int64 + # Tmem holding buffer + tmem_holding_buf: Int32 + # Smem tensors + # store row max and row sum + sScale: cute.struct.MemRange[Float32, self.q_stage * self.m_block_size * 2] + # CLC buffers placed here to utilize padding before sO's 1024-byte alignment. + # This avoids adding bytes at the end when we're at the smem limit. + # PipelineClcFetchAsync expects 2 * sched_stages mbarriers (full + empty). + clc_mbar_ptr: cute.struct.MemRange[cutlass.Int64, clc_mbar_size] + # CLC response storage (16 bytes per stage, stored as 4 Int32s). + clc_response: cute.struct.MemRange[Int32, clc_response_size] + # Large TMA buffers with 1024-byte alignment + sO: cute.struct.Align[ + cute.struct.MemRange[self.o_dtype, sO_size], self.buffer_align_bytes + ] + sQ: cute.struct.Align[ + cute.struct.MemRange[self.q_dtype, sQ_size], self.buffer_align_bytes + ] + sK: cute.struct.Align[ + # cute.cosize(sK_layout) is correct even in the case of self.uneven_kv_smem + cute.struct.MemRange[self.k_dtype, cute.cosize(sK_layout)], + self.buffer_align_bytes, + ] + sBias: cute.struct.Align[ + cute.struct.MemRange[self.bias_dtype, sBias_size], + self.buffer_align_bytes, + ] + # MXFP8: fp8 V staging + dequantized bf16 V + SF buffers (0-length when unused) + sVq: cute.struct.Align[ + cute.struct.MemRange[ + ( + self.v_dtype + if const_expr(self.v_dequant) + else cutlass.Float8E4M3FN + ), + sVq_size, + ], + self.buffer_align_bytes, + ] + sV_dequant: cute.struct.Align[ + cute.struct.MemRange[ + ( + self.v_mma_dtype + if const_expr(self.v_dequant) + else cutlass.BFloat16 + ), + sV_dequant_size, + ], + self.buffer_align_bytes, + ] + sSFQ: cute.struct.Align[ + cute.struct.MemRange[ + ( + self.sfq_dtype + if const_expr(self.qk_blockscaled) + else cutlass.Float8E8M0FNU + ), + sSFQ_size, + ], + self.buffer_align_bytes, + ] + sSFK: cute.struct.Align[ + cute.struct.MemRange[ + ( + self.sfk_dtype + if const_expr(self.qk_blockscaled) + else cutlass.Float8E8M0FNU + ), + sSFK_size, + ], + self.buffer_align_bytes, + ] + sSFV: cute.struct.Align[ + cute.struct.MemRange[ + ( + self.sfv_dtype + if const_expr(self.v_dequant) + else cutlass.Float8E8M0FNU + ), + sSFV_size, + ], + self.buffer_align_bytes, + ] + + self.shared_storage = SharedStorage + + # rel_bias needs to match the score_mod arithmetic domain: qk is scaled first, + # then bias is added, then softmax applies the base-2 conversion. Route LOG2_E + # through the same runtime kernel argument as the score_mod path so the device + # code sees identical operand kinds (constant vs register changes codegen). + if const_expr(self.has_bias): + base_softmax_scale = softmax_scale + softmax_scale_log2, softmax_scale = utils.LOG2_E, None + else: + base_softmax_scale = None + softmax_scale_log2, softmax_scale = utils.compute_softmax_scale_log2( + softmax_scale, self.score_mod + ) + window_size_left = ( + Int32(window_size_left) if window_size_left is not None else None + ) + window_size_right = ( + Int32(window_size_right) if window_size_right is not None else None + ) + fastdiv_mods = utils.compute_fastdiv_mods( + mQ, mK, self.qhead_per_kvhead, self.pack_gqa, aux_data.tensors, mPageTable + ) + + head_divmod = None + if cutlass.const_expr(self.pack_gqa): + head_divmod = FastDivmodDivisor(self.qhead_per_kvhead) + + self.use_block_sparsity = cutlass.const_expr(blocksparse_tensors is not None) + if cutlass.const_expr(self.use_block_sparsity and mPageTable is not None): + raise NotImplementedError( + "Block sparsity + paged KV not supported on SM100" + ) + if cutlass.const_expr(self.use_block_sparsity and self.is_varlen_q): + assert const_expr( + blocksparse_tensors.cu_total_m_blocks is not None + ), "blocksparse_tensors.cu_total_m_blocks must be provided for varlen blocksparsity" + + # Launch the kernel synchronously + self.kernel( + mQ, + mK, + mV, + mO, + mLSE, + mCuSeqlensQ, + mCuSeqlensK, + mSeqUsedQ, + mSeqUsedK, + mPageTable, + tma_atom_Q, + tma_atom_K, + tma_atom_V, + tma_atom_O, + softmax_scale_log2, + softmax_scale, + window_size_left, + window_size_right, + learnable_sink, + descale_tensors, + blocksparse_tensors, + sQ_layout, + sK_layout, + tP_layout, + sV_layout, + sO_layout, + gmem_tiled_copy_Q, + gmem_tiled_copy_O, + tiled_mma_qk, + tiled_mma_pv, + tile_sched_params, + num_splits, + aux_data, + fastdiv_mods, + head_divmod, + mBias, + tma_atom_bias, + sBias_layout, + bias_s2r_tiled_copy, + base_softmax_scale, + mSFQ, + mSFK, + mSFV, + tma_atom_SFQ, + tma_atom_SFK, + tma_atom_SFV, + gmem_tiled_copy_SFQ, + sSFQ_layout, + sSFK_layout, + sSFV_layout, + sVq_layout, + tiled_mma_pv_vq, + self.tiled_mma_qk_sfk if const_expr(self.qk_blockscaled) else None, + self.tiled_mma_pv_sfv if const_expr(self.v_dequant) else None, + ).launch( + grid=grid_dim, + block=[self.threads_per_cta, 1, 1], + cluster=( + self.cluster_shape_mnk + if cute.size(self.cluster_shape_mnk) > 1 + else None + ), + stream=stream, + min_blocks_per_mp=1, + # PDL overlaps this grid with the ShearingBias producer that immediately + # precedes it on the bias path; the load warp's griddepcontrol_wait + # (before the first bias TMA) keeps every read of its output ordered. + use_pdl=self.has_bias, + ) + + def _generate_attention_mask_cls(self, window_size_left, window_size_right): + return partial( + AttentionMask, + self.m_block_size, + self.n_block_size, + window_size_left=window_size_left, + window_size_right=window_size_right, + qhead_per_kvhead_packgqa=( + self.qhead_per_kvhead if const_expr(self.pack_gqa) else 1 + ), + ) + + # GPU device kernel + @cute.kernel + def kernel( + self, + mQ: cute.Tensor, # (s_q, d, h, b) or (total_q, d, h) if there is cu_seqlens_q + mK: cute.Tensor, # (s_k, d, h_k, b_k) or (total_k, d, h_k) if there is cu_seqlens_k or (page_size, d, h_k, num_pages) if there is page_table + mV: cute.Tensor, # (d, s_k, h_k, b_k) or (d, total_k, h_k) if there is cu_seqlens_k or (d, page_size, h_k, num_pages) if there is page_table + mO: cute.Tensor, + mLSE: Optional[cute.Tensor], + mCuSeqlensQ: Optional[cute.Tensor], + mCuSeqlensK: Optional[cute.Tensor], + mSeqUsedQ: Optional[cute.Tensor], + mSeqUsedK: Optional[cute.Tensor], + mPageTable: Optional[cute.Tensor], + tma_atom_Q: Optional[cute.CopyAtom], + tma_atom_K: Optional[cute.CopyAtom], + tma_atom_V: Optional[cute.CopyAtom], + tma_atom_O: Optional[cute.CopyAtom], + softmax_scale_log2: Float32, + softmax_scale: Float32 | None, + window_size_left: Optional[Int32], + window_size_right: Optional[Int32], + learnable_sink: Optional[cute.Tensor], + descale_tensors: Optional[DescaleTensors], + blocksparse_tensors: Optional[BlockSparseTensors], + sQ_layout: cute.ComposedLayout, + sK_layout: cute.ComposedLayout, + tP_layout: cute.ComposedLayout, + sV_layout: cute.ComposedLayout, + sO_layout: cute.ComposedLayout, + gmem_tiled_copy_Q: Optional[cute.TiledCopy], + gmem_tiled_copy_O: Optional[cute.TiledCopy], + tiled_mma_qk: cute.TiledMma, + tiled_mma_pv: cute.TiledMma, + tile_sched_params: ParamsBase, + num_splits: Int32, + aux_data: AuxData = AuxData(), + fastdiv_mods=(None, None), + head_divmod=None, + mBias: Optional[cute.Tensor] = None, + tma_atom_bias: Optional[cute.CopyAtom] = None, + sBias_layout: Optional[cute.ComposedLayout] = None, + bias_s2r_tiled_copy: Optional[cute.TiledCopy] = None, + base_softmax_scale: Optional[Float32] = None, + mSFQ: Optional[cute.Tensor] = None, + mSFK: Optional[cute.Tensor] = None, + mSFV: Optional[cute.Tensor] = None, + tma_atom_SFQ: Optional[cute.CopyAtom] = None, + tma_atom_SFK: Optional[cute.CopyAtom] = None, + tma_atom_SFV: Optional[cute.CopyAtom] = None, + gmem_tiled_copy_SFQ: Optional[cute.TiledCopy] = None, + sSFQ_layout=None, + sSFK_layout=None, + sSFV_layout=None, + sVq_layout: Optional[cute.ComposedLayout] = None, + tiled_mma_pv_vq: Optional[cute.TiledMma] = None, + tiled_mma_qk_sfk: Optional[cute.TiledMma] = None, + tiled_mma_pv_sfv: Optional[cute.TiledMma] = None, + ): + """The device kernel implementation of the Fused Multi-Head Attention. + + This kernel coordinates multiple specialized warps to perform different phases of the FMHA computation: + 1. Load warp: Loads Q, K, V data from global memory to shared memory using TMA + 2. MMA warp: Performs matrix multiplications (Q*K^T and P*V) + 3. Softmax warps: Compute softmax normalization on attention scores + 4. Correction warps: Apply adjustments to intermediate results + 5. Epilogue warp: Handles final output transformation and storage + + The kernel implements a complex pipeline with overlapping computation and memory operations, + using tensor memory access (TMA) for efficient data loading, warp specialization for different + computation phases, and optional attention masking. + """ + + warp_idx = cute.arch.make_warp_uniform(cute.arch.warp_idx()) + + # Prefetch tma descriptor + if warp_idx == 0: + for tma_atom in ( + tma_atom_Q, + tma_atom_K, + tma_atom_V, + tma_atom_O, + tma_atom_SFQ, + tma_atom_SFK, + tma_atom_SFV, + ): + if const_expr(tma_atom is not None): + cpasync.prefetch_descriptor(tma_atom) + + cta_layout_vmnk = cute.tiled_divide( + cute.make_layout(self.cluster_shape_mnk), (tiled_mma_qk.thr_id.shape,) + ) + # Setup cta/thread coordinates + bidx, _, _ = cute.arch.block_idx() + if const_expr(cute.size(tiled_mma_qk.thr_id.shape) == 1): + mma_tile_coord_v = 0 + else: + mma_tile_coord_v = bidx % cute.size(tiled_mma_qk.thr_id.shape) + is_leader_cta = mma_tile_coord_v == 0 + + # Alloc + smem = cutlass.utils.SmemAllocator() + storage = smem.allocate(self.shared_storage) + + tmem_alloc_barrier = pipeline.NamedBarrier( + barrier_id=int(NamedBarrierFwdSm100.TmemPtr), + num_threads=cute.arch.WARP_SIZE + * len( + ( + self.mma_warp_id, + *self.softmax0_warp_ids, + *self.softmax1_warp_ids, + *self.correction_warp_ids, + ) + ), + ) + # Tensor memory dealloc barrier init + tmem = cutlass.utils.TmemAllocator( + storage.tmem_holding_buf, + barrier_for_retrieve=tmem_alloc_barrier, + allocator_warp_id=self.mma_warp_id, + is_two_cta=self.use_2cta_instrs, + two_cta_tmem_dealloc_mbar_ptr=storage.tmem_dealloc_mbar_ptr, + ) + + ThreadCooperativeGroup = partial( + pipeline.CooperativeGroup, pipeline.Agent.Thread + ) + mma_warp = ThreadCooperativeGroup(len([self.mma_warp_id])) + tma_warp = ThreadCooperativeGroup(1) + load_threads = ThreadCooperativeGroup( + len(self.load_warp_ids) * cute.arch.WARP_SIZE + ) + softmax_warps = ThreadCooperativeGroup(len(self.softmax0_warp_ids)) + softmax_threads = ThreadCooperativeGroup( + cute.arch.WARP_SIZE * len(self.softmax0_warp_ids) + ) + # softmax_threads = ThreadCooperativeGroup(cute.arch.WARP_SIZE) + # MXFP8 pipeline_vq consumer: the correction warps consume fp8 V by warp + # (not per-thread) to dequantize it into bf16. + correction_warps = ThreadCooperativeGroup(len(self.correction_warp_ids)) + correction_threads = ThreadCooperativeGroup( + cute.arch.WARP_SIZE * len(self.correction_warp_ids) + ) + # correction_threads = ThreadCooperativeGroup(cute.arch.WARP_SIZE) + softmax_correction_threads = ThreadCooperativeGroup( + cute.arch.WARP_SIZE * len(self.softmax0_warp_ids + self.correction_warp_ids) + ) + epilogue_threads = ThreadCooperativeGroup( + cute.arch.WARP_SIZE * len(self.epilogue_warp_ids) + ) + # For UMMA-bridging pipelines: the non-MMA side spans both CTAs in the cluster, + # so the thread count must include warps from both CTAs. + softmax_warps_cluster = ThreadCooperativeGroup( + len(self.softmax0_warp_ids) * self.cta_group_size + ) + correction_threads_cluster = ThreadCooperativeGroup( + cute.arch.WARP_SIZE * len(self.correction_warp_ids) * self.cta_group_size + ) + softmax_correction_threads_cluster = ThreadCooperativeGroup( + cute.arch.WARP_SIZE + * len(self.softmax0_warp_ids + self.correction_warp_ids) + * self.cta_group_size + ) + if const_expr(self.use_tma_Q): + # TMA SFQ rides the Q pipeline barrier, so its bytes join Q's tx_count. + pipeline_q_tx_count = ( + self.tma_copy_bytes["Q"] + self.tma_copy_bytes["SFQ"] + if const_expr(self.qk_blockscaled and not self.use_cpasync_to_load_sfq) + else self.tma_copy_bytes["Q"] + ) + pipeline_q = pipeline_custom.PipelineTmaUmma.create( + barrier_storage=storage.mbar_load_Q.data_ptr(), + num_stages=self.q_stage, + producer_group=tma_warp, + consumer_group=mma_warp, + tx_count=pipeline_q_tx_count, + cta_layout_vmnk=cta_layout_vmnk, + defer_sync=True, + ) + else: + pipeline_q = pipeline_custom.PipelineAsyncUmma.create( + barrier_storage=storage.mbar_load_Q.data_ptr(), + num_stages=self.q_stage, + producer_group=load_threads, + consumer_group=mma_warp, + cta_layout_vmnk=cta_layout_vmnk, + defer_sync=True, + ) + if const_expr(self.use_tma_KV): + pipeline_kv = pipeline_custom.PipelineTmaUmma.create( + barrier_storage=storage.mbar_load_KV.data_ptr(), + num_stages=self.kv_stage, + producer_group=tma_warp, + consumer_group=mma_warp, + tx_count=self.tma_copy_bytes["K"], + cta_layout_vmnk=cta_layout_vmnk, + defer_sync=True, + ) + else: + pipeline_kv = pipeline.PipelineAsyncUmma.create( + barrier_storage=storage.mbar_load_KV.data_ptr(), + num_stages=self.kv_stage, + producer_group=load_threads, + consumer_group=mma_warp, + cta_layout_vmnk=cta_layout_vmnk, + defer_sync=True, + ) + # This pipeline is not the typical producer-consumer pipeline. The "producer" mma warp + # uses it to signal that S is ready, and the softmax threads wait for S to be ready. + # When softmax threads write P to tmem and the correction threads have rescaled O, they + # signal as "consumer". The mma warp then waits for that signal to do the P @ V gemm. + pipeline_s_p_o = pipeline_custom.PipelineUmmaAsync.create( + barrier_storage=storage.mbar_S_full_P_full_O_rescaled.data_ptr(), + num_stages=self.q_stage, + producer_group=mma_warp, + consumer_group=softmax_correction_threads_cluster, + cta_layout_vmnk=cta_layout_vmnk, + defer_sync=True, + ) + pipeline_p_lastsplit = pipeline_custom.PipelineAsyncUmma.create( + barrier_storage=storage.mbar_P_full_lastsplit.data_ptr(), + num_stages=self.q_stage, + producer_group=softmax_warps_cluster, + consumer_group=mma_warp, + cta_layout_vmnk=cta_layout_vmnk, + defer_sync=True, + ) + # MMA warp uses this to signal to the correction warps that O is ready. + pipeline_o_acc = pipeline_custom.PipelineUmmaAsync.create( + barrier_storage=storage.mbar_O_full.data_ptr(), + num_stages=self.q_stage, + producer_group=mma_warp, + consumer_group=correction_threads_cluster, + cta_layout_vmnk=cta_layout_vmnk, + defer_sync=True, + ) + pipeline_s0_s1_sequence = None + if const_expr(self.s0_s1_barrier and self.q_stage > 1): + # This is not a typical producer-consumer pipeline. We will directly use + # pipeline_s0_s1_sequence.sync_object_full and will not use + # pipeline_s0_s1_sequence.sync_object_empty. + pipeline_s0_s1_sequence = pipeline_custom.PipelineAsync.create( + barrier_storage=storage.mbar_s0_s1_sequence.data_ptr(), + num_stages=2, + producer_group=softmax_threads, + consumer_group=softmax_threads, + defer_sync=True, + ) + pipeline_sm_stats = pipeline_custom.PipelineAsync.create( + barrier_storage=storage.mbar_softmax_stats.data_ptr(), + num_stages=self.q_stage, + producer_group=softmax_threads, + consumer_group=correction_threads, + defer_sync=True, + ) + # Should put the NamedBarrier inside the pipeline class so we'll just have pipeline_sm_stats + sm_stats_barrier = pipeline_custom.NamedBarrier( + barrier_id=int(NamedBarrierFwdSm100.SoftmaxStatsW0), + num_threads=cute.arch.WARP_SIZE * 2, + ) + pipeline_o_epi = None + if const_expr(not self.use_correction_warps_for_epi): + pipeline_o_epi = pipeline_custom.PipelineAsync.create( + barrier_storage=storage.mbar_O_epi.data_ptr(), + num_stages=self.q_stage, + producer_group=correction_threads, + consumer_group=epilogue_threads, + defer_sync=True, + ) + pipeline_bias = None + if const_expr(tma_atom_bias is not None): + pipeline_bias = pipeline_custom.PipelineTmaAsync.create( + barrier_storage=storage.mbar_load_bias.data_ptr(), + num_stages=self.bias_stage, + producer_group=tma_warp, + consumer_group=softmax_warps, + tx_count=self.tma_copy_bytes["bias"], + defer_sync=True, + ) + + # MXFP8 pipelines. + # pipeline_sfq: cp.async SFQ (only when SFQ isn't interleaved for TMA). + # pipeline_sf_overlap: q_stage==2 SF/S TMEM overlap (unused at q_stage=1). + # pipeline_vq: fp8 V (+ SFV) load -> correction warps. + # pipeline_v_mma: correction warps -> mma warp (dequantized bf16 V). + pipeline_sfq = None + pipeline_sf_overlap = None + pipeline_vq = None + pipeline_v_mma = None + if const_expr(self.use_cpasync_to_load_sfq): + pipeline_sfq = pipeline_custom.PipelineAsyncUmma.create( + barrier_storage=storage.mbar_load_SFQ.data_ptr(), + num_stages=self.q_stage, + producer_group=load_threads, + consumer_group=mma_warp, + cta_layout_vmnk=cta_layout_vmnk, + defer_sync=True, + ) + if const_expr(self.qk_blockscaled and self.q_stage == 2): + pipeline_sf_overlap = pipeline_custom.PipelineUmmaAsync.create( + barrier_storage=storage.mbar_s0_s1_empty_for_sf.data_ptr(), + num_stages=self.q_stage, + producer_group=mma_warp, + consumer_group=softmax_threads, + cta_layout_vmnk=cta_layout_vmnk, + defer_sync=True, + ) + if const_expr(self.v_dequant): + if const_expr(self.use_tma_KV): + pipeline_vq = pipeline_custom.PipelineTmaAsync.create( + barrier_storage=storage.mbar_load_Vq.data_ptr(), + num_stages=self.kv_stage, + producer_group=tma_warp, + consumer_group=correction_warps, + tx_count=self.tma_copy_bytes["V"] + self.tma_copy_bytes["SFV"], + defer_sync=True, + ) + else: + pipeline_vq = pipeline_custom.PipelineAsync.create( + barrier_storage=storage.mbar_load_Vq.data_ptr(), + num_stages=self.kv_stage, + producer_group=load_threads, + consumer_group=correction_warps, + defer_sync=True, + ) + pipeline_v_mma = pipeline_custom.PipelineAsyncUmma.create( + barrier_storage=storage.mbar_v_upcast.data_ptr(), + num_stages=self.v_mma_stage, + producer_group=correction_threads, + consumer_group=mma_warp, + cta_layout_vmnk=cta_layout_vmnk, + defer_sync=True, + ) + + # Cluster arrive after barrier init + pipeline_init_arrive(cluster_shape_mn=cta_layout_vmnk, is_relaxed=True) + + # Generate smem tensor Q/K/V/O + # (MMA, MMA_Q, MMA_D, PIPE) + sQ = storage.sQ.get_tensor(sQ_layout.outer, swizzle=sQ_layout.inner) + # (MMA, MMA_K, MMA_D, PIPE) + sK = storage.sK.get_tensor(sK_layout.outer, swizzle=sK_layout.inner) + # (MMA, MMA_K, MMA_D, PIPE) + sVq, sSFQ, sSFK, sSFV = None, None, None, None + if const_expr(self.v_dequant): + # Separate fp8-V staging (sVq) and dequantized bf16-V (sV) buffers. + sV = storage.sV_dequant.get_tensor(sV_layout.outer, swizzle=sV_layout.inner) + sVq = storage.sVq.get_tensor(sVq_layout.outer, swizzle=sVq_layout.inner) + sSFV = storage.sSFV.get_tensor(sSFV_layout) + else: + # Strip swizzle info to reuse sK's smem for V. + sV = cute.make_tensor( + cute.recast_ptr(sK.iterator, sV_layout.inner, self.v_mma_dtype), + sV_layout.outer, + ) + if const_expr(self.qk_blockscaled): + sSFQ = storage.sSFQ.get_tensor(sSFQ_layout) + sSFK = storage.sSFK.get_tensor(sSFK_layout) + if const_expr(not self.overlap_sO_sQ): + sO = storage.sO.get_tensor(sO_layout.outer, swizzle=sO_layout.inner) + else: + sO = cute.make_tensor( + cute.recast_ptr(sQ.iterator, sO_layout.inner, self.o_dtype), + sO_layout.outer, + ) + if const_expr(self.has_bias): + sBias = storage.sBias.get_tensor( + sBias_layout.outer, swizzle=sBias_layout.inner + ) + else: + sBias = sO + + sScale = storage.sScale.get_tensor( + cute.make_layout(self.q_stage * self.m_block_size * 2) + ) + + thr_mma_qk = tiled_mma_qk.get_slice(mma_tile_coord_v) + thr_mma_pv = tiled_mma_pv.get_slice(mma_tile_coord_v) + thr_mma_qk_sfk = ( + tiled_mma_qk_sfk.get_slice(mma_tile_coord_v) + if const_expr(self.qk_blockscaled) + else None + ) + thr_mma_pv_sfv = ( + tiled_mma_pv_sfv.get_slice(mma_tile_coord_v) + if const_expr(self.v_dequant) + else None + ) + thr_mma_pv_vq = ( + tiled_mma_pv_vq.get_slice(mma_tile_coord_v) + if const_expr(self.v_dequant) + else None + ) + + qk_acc_shape = thr_mma_qk.partition_shape_C(self.mma_tiler_qk[:2]) + # This is a fake tensor, by right we need to retrieve tmem_ptr. But we know that we always + # request 512 columns of tmem, so we know that it starts at 0. + tStS = thr_mma_qk.make_fragment_C(cute.append(qk_acc_shape, self.s_stage)) + pv_acc_shape = thr_mma_pv.partition_shape_C(self.mma_tiler_pv[:2]) + tOtO = thr_mma_pv.make_fragment_C(cute.append(pv_acc_shape, self.q_stage)) + tOtO = cute.make_tensor(tOtO.iterator + self.tmem_o_offset[0], tOtO.layout) + tP = cute.make_tensor(tStS.iterator, tP_layout.outer) + tOrP = thr_mma_pv.make_fragment_A(tP)[None, None, None, 0] + # Need to multiply by width ratio bc tP is in v_mma_dtype but tmem offsets are in FP32 + tP_width_ratio = Float32.width // self.v_mma_dtype.width + # Need to adjust the stage stride manually since the two stages aren't contiguous in tmem + tP_stage_stride = ( + self.tmem_p_offset[1] - self.tmem_p_offset[0] + ) * tP_width_ratio + tOrP = cute.make_tensor( + tOrP.iterator + self.tmem_p_offset[0] * tP_width_ratio, + cute.append( + tOrP.layout, + cute.make_layout((self.s_stage,), stride=(tP_stage_stride,)), + ), + ) + + block_info = BlockInfo( + # This is cta_tiler, not mma_tiler_qk, since we move by block by (2 * mma_tiler[0], mma_tiler[1]) + self.cta_tiler[0], + self.cta_tiler[1], + self.is_causal, + self.is_local, + self.is_split_kv, + window_size_left, + window_size_right, + qhead_per_kvhead_packgqa=( + self.qhead_per_kvhead if const_expr(self.pack_gqa) else 1 + ), + ) + SeqlenInfoCls = partial( + SeqlenInfoQK.create, + seqlen_q_static=( + mQ.shape[0] if const_expr(not self.pack_gqa) else mQ.shape[0][1] + ), + seqlen_k_static=( + mK.shape[0] + if const_expr(mPageTable is None) + else mK.shape[0] * mPageTable.shape[1] + ), + mCuSeqlensQ=mCuSeqlensQ, + mCuSeqlensK=mCuSeqlensK, + mSeqUsedQ=mSeqUsedQ, + mSeqUsedK=mSeqUsedK, + mCuTotalMBlocks=( + blocksparse_tensors.cu_total_m_blocks + if blocksparse_tensors is not None + else None + ), + mCuBlockIdxOffsets=( + blocksparse_tensors.cu_block_idx_offsets + if blocksparse_tensors is not None + else None + ), + ) + AttentionMaskCls = self._generate_attention_mask_cls( + window_size_left, window_size_right + ) + # Cluster wait before tensor memory alloc + pipeline_init_wait(cluster_shape_mn=cta_layout_vmnk) + + if const_expr(self.use_clc_scheduler): + clc_response_ptr = storage.clc_response.data_ptr() + clc_mbar_ptr = storage.clc_mbar_ptr.data_ptr() + + clc_pipeline_producer_group = cutlass_pipeline.CooperativeGroup( + cutlass_pipeline.Agent.Thread + ) + num_clc_consumer_warps_per_cta = self.threads_per_cta // cute.arch.WARP_SIZE + # NB on CTA0 warp15 == scheduler on CTA1 == empty but still both consume + num_clc_consumer_warps = ( + num_clc_consumer_warps_per_cta * self.cta_group_size + ) + clc_pipeline_consumer_group = cutlass_pipeline.CooperativeGroup( + cutlass_pipeline.Agent.Thread, + cute.arch.WARP_SIZE * num_clc_consumer_warps, + ) + + block_idx = cute.arch.block_idx() + clc = ClcState.create( + hw_scheduler=ClcDynamicPersistentTileScheduler.create( + self.tile_scheduler_cls.clc_problem_shape(tile_sched_params), + block_idx, + cute.arch.grid_dim(), + clc_response_ptr, + ), + pipeline=cutlass_pipeline.PipelineClcFetchAsync.create( + barrier_storage=clc_mbar_ptr, + num_stages=self.sched_stages, + producer_group=clc_pipeline_producer_group, + consumer_group=clc_pipeline_consumer_group, + tx_count=16, + cta_layout_vmnk=cta_layout_vmnk, + ), + consumer_state=cutlass_pipeline.make_pipeline_state( + cutlass_pipeline.PipelineUserType.Consumer, self.sched_stages + ), + producer_state=cutlass_pipeline.make_pipeline_state( + cutlass_pipeline.PipelineUserType.Producer, self.sched_stages + ), + ) + tile_scheduler = self.tile_scheduler_cls.create(tile_sched_params, clc=clc) + else: + tile_scheduler = self.tile_scheduler_cls.create(tile_sched_params) + assert isinstance( + tile_scheduler, TileSchedulerProtocol + ), f"tile_scheduler is not a TileSchedulerProtocol: {type(tile_scheduler)}" + + # /////////////////////////////////////////////////////////////////////////////// + # EMPTY / CLC SCHEDULER WARP + # /////////////////////////////////////////////////////////////////////////////// + if const_expr(self.use_clc_scheduler): + if warp_idx == self.clc_scheduler_warp_id: + cute.arch.setmaxregister_decrease(self.num_regs_other) + if is_leader_cta: + self.clc_scheduler_warp(tile_scheduler) + else: + self.empty_warp(tile_scheduler) + for i in cutlass.range_constexpr(len(self.empty_warp_ids)): + if ( + warp_idx == self.empty_warp_ids[i] + and warp_idx != self.clc_scheduler_warp_id + ): + cute.arch.setmaxregister_decrease(self.num_regs_other) + self.empty_warp(tile_scheduler) + else: + for i in cutlass.range_constexpr(len(self.empty_warp_ids)): + if warp_idx == self.empty_warp_ids[i]: + cute.arch.setmaxregister_decrease(self.num_regs_other) + + # /////////////////////////////////////////////////////////////////////////////// + # LOAD + # /////////////////////////////////////////////////////////////////////////////// + if warp_idx >= self.load_warp_ids[0] and warp_idx <= self.load_warp_ids[-1]: + cute.arch.setmaxregister_decrease(self.num_regs_other) + self.load( + thr_mma_qk, + thr_mma_pv if const_expr(not self.v_dequant) else thr_mma_pv_vq, + mQ, + mK, + mV, + sQ, + sK, + sV if const_expr(not self.v_dequant) else sVq, + mPageTable, + tma_atom_Q, + tma_atom_K, + tma_atom_V, + gmem_tiled_copy_Q, + pipeline_q, + pipeline_kv, + block_info, + num_splits, + SeqlenInfoCls, + blocksparse_tensors, + tile_scheduler=tile_scheduler, + mBias=mBias, + sBias=sBias, + tma_atom_bias=tma_atom_bias, + pipeline_bias=pipeline_bias, + mSFQ=mSFQ, + mSFK=mSFK, + mSFV=mSFV, + sSFQ=sSFQ, + sSFK=sSFK, + sSFV=sSFV, + sVq=sVq, + tma_atom_SFQ=tma_atom_SFQ, + tma_atom_SFK=tma_atom_SFK, + tma_atom_SFV=tma_atom_SFV, + gmem_tiled_copy_SFQ=gmem_tiled_copy_SFQ, + thr_mma_qk_sfk=thr_mma_qk_sfk, + thr_mma_pv_sfv=thr_mma_pv_sfv, + pipeline_sfq=pipeline_sfq, + pipeline_vq=pipeline_vq, + ) + + # /////////////////////////////////////////////////////////////////////////////// + # MMA + # /////////////////////////////////////////////////////////////////////////////// + if warp_idx == self.mma_warp_id: + cute.arch.setmaxregister_decrease(self.num_regs_other) + # Alloc tensor memory buffer + tmem.allocate(cute.arch.get_max_tmem_alloc_cols("sm_100")) + tmem.wait_for_alloc() + tmem_ptr = tmem.retrieve_ptr(self.qk_acc_dtype) + self.mma( + tiled_mma_qk, + tiled_mma_pv, + sQ, + sK, + sV, + tStS, + tOtO, + tOrP, + pipeline_q, + pipeline_kv, + pipeline_s_p_o, + pipeline_p_lastsplit, + pipeline_o_acc, + is_leader_cta, + block_info, + num_splits, + SeqlenInfoCls, + blocksparse_tensors, + tile_scheduler=tile_scheduler, + tmem_ptr=tmem_ptr, + sSFQ=sSFQ, + sSFK=sSFK, + sSFQ_layout=sSFQ_layout, + sSFK_layout=sSFK_layout, + pipeline_sfq=pipeline_sfq, + pipeline_sf_overlap=pipeline_sf_overlap, + pipeline_v_mma=pipeline_v_mma, + ) + # Dealloc the tensor memory buffer + tmem.relinquish_alloc_permit() + tmem_alloc_barrier.arrive_and_wait() + tmem.free(tmem_ptr) + + # /////////////////////////////////////////////////////////////////////////////// + # Epilogue + # /////////////////////////////////////////////////////////////////////////////// + if const_expr(not self.use_correction_warps_for_epi): + if ( + warp_idx >= self.epilogue_warp_ids[0] + and warp_idx <= self.epilogue_warp_ids[-1] + ): + cute.arch.setmaxregister_decrease(self.num_regs_other) + self.epilogue_s2g( + mO, + sO, + gmem_tiled_copy_O, + tma_atom_O, + pipeline_o_epi, + block_info, + num_splits, + SeqlenInfoCls, + mma_tile_coord_v, + blocksparse_tensors=blocksparse_tensors, + tile_scheduler=tile_scheduler, + ) + + # /////////////////////////////////////////////////////////////////////////////// + # Softmax + # /////////////////////////////////////////////////////////////////////////////// + if ( + const_expr(self.q_stage == 2) and warp_idx <= self.softmax1_warp_ids[-1] + ) or (const_expr(self.q_stage == 1) and warp_idx <= self.softmax0_warp_ids[-1]): + # increase register after decreasing + cute.arch.setmaxregister_increase(self.num_regs_softmax) + # sync with mma warp before retrieving tmem ptr + tmem.wait_for_alloc() + tmem_ptr = tmem.retrieve_ptr(self.qk_acc_dtype) + softmax_loop = partial( + self.softmax_loop, + softmax_scale_log2=softmax_scale_log2, + softmax_scale=softmax_scale, + descale_tensors=descale_tensors, + thr_mma_qk=thr_mma_qk, + sScale=sScale, + mLSE=mLSE, + pipeline_s_p_o=pipeline_s_p_o, + pipeline_p_lastsplit=pipeline_p_lastsplit, + pipeline_sm_stats=pipeline_sm_stats, + sm_stats_barrier=sm_stats_barrier, + pipeline_s0_s1_sequence=pipeline_s0_s1_sequence, + learnable_sink=learnable_sink, + block_info=block_info, + num_splits=num_splits, + SeqlenInfoCls=SeqlenInfoCls, + AttentionMaskCls=AttentionMaskCls, + aux_data=aux_data, + fastdiv_mods=fastdiv_mods, + head_divmod=head_divmod, + blocksparse_tensors=blocksparse_tensors, + tile_scheduler=tile_scheduler, + base_softmax_scale=base_softmax_scale, + sBias=sBias, + bias_s2r_tiled_copy=bias_s2r_tiled_copy, + pipeline_bias=pipeline_bias, + ) + + if const_expr(not self.s0_s1_barrier): + stage = Int32( + 0 + if const_expr(self.q_stage == 1) + or warp_idx < self.softmax1_warp_ids[0] + else 1 + ) + softmax_loop(stage=stage, tStS=tStS) + else: + # If there's s0_s1_barrier, it's faster to have 2 WGs having different code + if warp_idx < self.softmax1_warp_ids[0]: + softmax_loop(stage=0, tStS=tStS) + if ( + warp_idx < self.correction_warp_ids[0] + and warp_idx >= self.softmax1_warp_ids[0] + ): + softmax_loop(stage=1, tStS=tStS) + + tmem_alloc_barrier.arrive() + + # /////////////////////////////////////////////////////////////////////////////// + # Correction + # /////////////////////////////////////////////////////////////////////////////// + if warp_idx >= self.correction_warp_ids[0] and warp_idx < self.mma_warp_id: + cute.arch.setmaxregister_decrease(self.num_regs_correction) + # sync with mma warp before retrieving tmem ptr + tmem.wait_for_alloc() + tmem_ptr = tmem.retrieve_ptr(self.qk_acc_dtype) + self.correction_loop( + thr_mma_qk, + thr_mma_pv, + tStS, + tOtO, + sScale, + mO, + mLSE, + sO, + pipeline_s_p_o, + pipeline_o_acc, + pipeline_sm_stats, + sm_stats_barrier, + pipeline_o_epi, + learnable_sink, + descale_tensors, + gmem_tiled_copy_O, + tma_atom_O, + softmax_scale_log2, + block_info, + num_splits, + SeqlenInfoCls, + blocksparse_tensors, + tile_scheduler=tile_scheduler, + sVq=sVq, + sSFV=sSFV, + sV_dequant=sV, + pipeline_vq=pipeline_vq, + pipeline_v_mma=pipeline_v_mma, + ) + tmem_alloc_barrier.arrive() + + return + + @cute.jit + def load( + self, + thr_mma_qk: cute.ThrMma, + thr_mma_pv: cute.ThrMma, + mQ: cute.Tensor, + mK: cute.Tensor, + mV: cute.Tensor, + sQ: cute.Tensor, + sK: cute.Tensor, + sV: cute.Tensor, + mPageTable: Optional[cute.Tensor], + tma_atom_Q: Optional[cute.CopyAtom], + tma_atom_K: Optional[cute.CopyAtom], + tma_atom_V: Optional[cute.CopyAtom], + gmem_tiled_copy_Q: Optional[cute.TiledCopy], + pipeline_q: pipeline.PipelineAsync, + pipeline_kv: pipeline.PipelineAsync, + block_info: BlockInfo, + num_splits: Int32, + SeqlenInfoCls: Callable, + blocksparse_tensors: Optional[BlockSparseTensors], + tile_scheduler: TileSchedulerProtocol, + mBias: Optional[cute.Tensor] = None, + sBias: Optional[cute.Tensor] = None, + tma_atom_bias: Optional[cute.CopyAtom] = None, + pipeline_bias: Optional[pipeline.PipelineAsync] = None, + mSFQ: Optional[cute.Tensor] = None, + mSFK: Optional[cute.Tensor] = None, + mSFV: Optional[cute.Tensor] = None, + sSFQ: Optional[cute.Tensor] = None, + sSFK: Optional[cute.Tensor] = None, + sSFV: Optional[cute.Tensor] = None, + sVq: Optional[cute.Tensor] = None, + tma_atom_SFQ: Optional[cute.CopyAtom] = None, + tma_atom_SFK: Optional[cute.CopyAtom] = None, + tma_atom_SFV: Optional[cute.CopyAtom] = None, + gmem_tiled_copy_SFQ: Optional[cute.TiledCopy] = None, + thr_mma_qk_sfk: Optional[cute.ThrMma] = None, + thr_mma_pv_sfv: Optional[cute.ThrMma] = None, + pipeline_sfq: Optional[pipeline.PipelineAsync] = None, + pipeline_vq: Optional[pipeline.PipelineAsync] = None, + ): + num_load_threads = len(self.load_warp_ids) * cute.arch.WARP_SIZE + tidx = cute.arch.thread_idx()[0] % num_load_threads + warp_idx = cute.arch.make_warp_uniform(cute.arch.warp_idx()) + issue_kv_for_this_warp = ( + const_expr(not self.use_tma_KV or len(self.load_warp_ids) == 1) + or warp_idx == self.load_warp_ids[0] + ) + issue_q_for_this_warp = ( + const_expr(not self.use_tma_Q or len(self.load_warp_ids) == 1) + or warp_idx == self.load_warp_ids[0] + ) + q_producer_phase = Int32(1) + kv_producer_state = pipeline.make_pipeline_state( + pipeline.PipelineUserType.Producer, self.kv_stage + ) + if const_expr(self.v_dequant): + vq_producer_state = pipeline.make_pipeline_state( + pipeline.PipelineUserType.Producer, self.kv_stage + ) + bias0_producer_state = pipeline_custom.make_pipeline_state( + cutlass.pipeline.PipelineUserType.Producer, self.bias_stage // self.q_stage + ) + bias1_producer_state = pipeline_custom.make_pipeline_state( + cutlass.pipeline.PipelineUserType.Producer, self.bias_stage // self.q_stage + ) + work_tile = tile_scheduler.initial_work_tile_info() + while work_tile.is_valid_tile: + m_block, head_idx, batch_idx, split_idx = work_tile.tile_idx + seqlen = SeqlenInfoCls(batch_idx) + mQ_cur = seqlen.offset_batch_Q(mQ, batch_idx, dim=3)[None, None, head_idx] + + head_idx_kv = ( + head_idx // self.qhead_per_kvhead + if const_expr(not self.pack_gqa) + else head_idx + ) + load_SFQ_fn = None + if const_expr(self.use_cpasync_to_load_sfq): + tiler_gQ_sf = ( + (self.mma_tiler_qk[0] * self.q_stage), + self.head_dim_padded // self.qk_sf_vec_size, + ) + mSFQ_cur = seqlen.offset_batch_Q(mSFQ, batch_idx, dim=3)[ + None, None, head_idx + ] + gSFQ_sf = cute.local_tile(mSFQ_cur, tiler_gQ_sf, (m_block, 0)) + gSFQ_sf = layout_utils.select( + cute.flat_divide(gSFQ_sf, (self.mma_tiler_qk[0],)), mode=[0, 2, 1] + ) + cpasync_load_SFQ = partial( + self.cpasync_load_SFQ, + gSFQ_sf, + sSFQ, + gmem_tiled_copy_SFQ, + pipeline_sfq, + tidx=tidx, + phase=q_producer_phase, + m_block=m_block, + seqlen_q=seqlen.seqlen_q + * (self.qhead_per_kvhead if const_expr(self.pack_gqa) else 1), + ) + elif const_expr(self.qk_blockscaled and self.use_tma_Q): + tiler_gQ_sfa = ( + (self.mma_tiler_qk[0] * self.q_stage), + self.head_dim_padded // self.qk_sf_vec_size, + ) + mSFQ_cur = seqlen.offset_batch_Q(mSFQ, batch_idx, dim=3)[ + None, None, head_idx + ] + gSFQ = cute.local_tile(mSFQ_cur, tiler_gQ_sfa, (m_block, 0)) + gSFQ = layout_utils.select( + cute.flat_divide(gSFQ, (self.mma_tiler_qk[0],)), mode=[0, 2, 1] + ) + tSgSFQ = thr_mma_qk.partition_A(gSFQ) + load_SFQ_fn, _, _ = copy_utils.tma_get_copy_fn( + tma_atom_SFQ, + 0, + cute.make_layout(1), + tSgSFQ, + sSFQ, + filter_zeros=True, + ) + + if const_expr(mPageTable is None): + if const_expr(not seqlen.has_cu_seqlens_k): + mK_cur, mV_cur = [ + t[None, None, head_idx_kv, batch_idx] for t in (mK, mV) + ] + if const_expr(self.qk_blockscaled): + mSFK_cur = mSFK[None, None, head_idx_kv, batch_idx] + if const_expr(self.v_dequant): + mSFV_cur = mSFV[None, None, head_idx_kv, batch_idx] + else: + mK_cur = cute.domain_offset( + (seqlen.offset_k, 0), mK[None, None, head_idx_kv] + ) + mV_cur = cute.domain_offset( + (0, seqlen.offset_k), mV[None, None, head_idx_kv] + ) + if const_expr(self.qk_blockscaled): + mSFK_cur = cute.domain_offset( + (seqlen.offset_k, 0), mSFK[None, None, head_idx_kv] + ) + if const_expr(self.v_dequant): + # SFV is token-major (like SFK): offset the token axis. + mSFV_cur = cute.domain_offset( + (seqlen.offset_k, 0), mSFV[None, None, head_idx_kv] + ) + gK = cute.local_tile( + mK_cur, cute.select(self.mma_tiler_qk, mode=[1, 2]), (None, 0) + ) + gV = cute.local_tile( + mV_cur, cute.select(self.mma_tiler_pv, mode=[1, 2]), (0, None) + ) + if const_expr(self.qk_blockscaled): + gSFK = cute.local_tile( + mSFK_cur, + cute.select(self.mma_tiler_qk_sfk, mode=[1, 2]), + (None, 0), + ) + if const_expr(self.v_dequant): + gSFV = cute.local_tile( + mSFV_cur, + cute.select(self.mma_tiler_pv_sfv, mode=[1, 2]), + (None, 0), + ) + else: + # Need to keep batch coord None since we'll index into it with page idx + mK_cur, mV_cur = [t[None, None, head_idx_kv, None] for t in (mK, mV)] + if const_expr(self.qk_blockscaled): + mSFK_cur = mSFK[None, None, head_idx_kv, None] + if const_expr(self.v_dequant): + mSFV_cur = mSFV[None, None, head_idx_kv, None] + gK = cute.local_tile( + mK_cur, cute.select(self.mma_tiler_qk, mode=[1, 2]), (None, 0, None) + ) + gV = cute.local_tile( + mV_cur, cute.select(self.mma_tiler_pv, mode=[1, 2]), (0, None, None) + ) + if const_expr(self.qk_blockscaled): + gSFK = cute.local_tile( + mSFK_cur, + cute.select(self.mma_tiler_qk_sfk, mode=[1, 2]), + (None, 0, None), + ) + if const_expr(self.v_dequant): + gSFV = cute.local_tile( + mSFV_cur, + cute.select(self.mma_tiler_pv_sfv, mode=[1, 2]), + (None, 0, None), + ) + tSgK = thr_mma_qk.partition_B(gK) + tOgV = thr_mma_pv.partition_B(gV) + if const_expr(self.use_tma_Q): + tiler_gQ = ((self.mma_tiler_qk[0] * self.q_stage), self.head_dim_padded) + gQ = cute.local_tile(mQ_cur, tiler_gQ, (m_block, 0)) # (128 * 2, 128) + gQ = layout_utils.select( + cute.flat_divide(gQ, (self.mma_tiler_qk[0],)), mode=[0, 2, 1] + ) # (128, 128, 2) + tSgQ = thr_mma_qk.partition_A(gQ) + load_Q_fn, _, _ = copy_utils.tma_get_copy_fn( + tma_atom_Q, 0, cute.make_layout(1), tSgQ, sQ + ) + load_Q = partial( + self.load_Q, + load_Q_fn, + pipeline_q=pipeline_q, + phase=q_producer_phase, + load_SFQ_fn=load_SFQ_fn, + ) + else: + assert gmem_tiled_copy_Q is not None + load_Q = partial( + self.load_Q_non_tma, + mQ_cur, + sQ, + gmem_tiled_copy_Q, + pipeline_q, + tidx, + seqlen.seqlen_q, + m_block, + phase=q_producer_phase, + ) + + if const_expr(self.use_tma_KV): + tKsK, tKgK = cpasync.tma_partition( + tma_atom_K, + 0, # no multicast + cute.make_layout(1), + cute.group_modes(sK, 0, 3), + cute.group_modes(tSgK, 0, 3), + ) + tVsV, tVgV = cpasync.tma_partition( + tma_atom_V, + 0, # no multicast + cute.make_layout(1), + cute.group_modes(sV, 0, 3), + cute.group_modes(tOgV, 0, 3), + ) + tKsSFK, tKgSFK = None, None + tVsSFV, tVgSFV = None, None + if const_expr(self.qk_blockscaled): + tSgSFK = thr_mma_qk_sfk.partition_B(gSFK) + tKsSFK, tKgSFK = cpasync.tma_partition( + tma_atom_SFK, + 0, + cute.make_layout(1), + cute.group_modes(sSFK, 0, 3), + cute.group_modes(tSgSFK, 0, 3), + ) + tKsSFK = cute.filter_zeros(tKsSFK) + tKgSFK = cute.filter_zeros(tKgSFK) + if const_expr(self.v_dequant): + tOgSFV = thr_mma_pv_sfv.partition_B(gSFV) + tVsSFV, tVgSFV = cpasync.tma_partition( + tma_atom_SFV, + 0, + cute.make_layout(1), + cute.group_modes(sSFV, 0, 3), + cute.group_modes(tOgSFV, 0, 3), + ) + tVsSFV = cute.filter_zeros(tVsSFV) + tVgSFV = cute.filter_zeros(tVgSFV) + paged_kv_manager = None + else: + page_size = mK.shape[0] + paged_kv_manager = PagedKVManager.create( + mPageTable, + mK, + mV, + FastDivmodDivisor(page_size), + batch_idx, + head_idx_kv, + tidx, + seqlen.seqlen_k, + 0, # leftpad_k + self.n_block_size, + self.head_dim_padded, + self.head_dim_v_padded, + num_load_threads, + mK.element_type, + mSFK_paged=mSFK if const_expr(self.qk_blockscaled) else None, + mSFV_paged=mSFV if const_expr(self.v_dequant) else None, + ) + tKsK, tKgK = None, None + tVsV, tVgV = None, None + tKsSFK, tKgSFK = None, None + tVsSFV, tVgSFV = None, None + + load_K = partial( + self.load_KV, + tma_atom_K, + tKgK, + tKsK, + paged_kv_manager, + sK, + pipeline_kv=pipeline_kv, + K_or_V="K", + tma_atom_sf=tma_atom_SFK if const_expr(self.qk_blockscaled) else None, + tXgSFX=tKgSFK if const_expr(self.qk_blockscaled) else None, + tXsSFX=tKsSFK if const_expr(self.qk_blockscaled) else None, + sSFX=sSFK if const_expr(self.qk_blockscaled) else None, + stage_dilation=self.kv_size_ratio, + ) + load_V = partial( + self.load_KV, + tma_atom_V, + tVgV, + tVsV, + paged_kv_manager, + sV, + pipeline_kv=pipeline_vq if const_expr(self.v_dequant) else pipeline_kv, + K_or_V="V", + tma_atom_sf=tma_atom_SFV if const_expr(self.v_dequant) else None, + tXgSFX=tVgSFV if const_expr(self.v_dequant) else None, + tXsSFX=tVsSFV if const_expr(self.v_dequant) else None, + sSFX=sSFV if const_expr(self.v_dequant) else None, + ) + if const_expr(tma_atom_bias is not None): + # (seqlen, rel_extent_padded) + mBias_cur = seqlen.offset_batch_Q(mBias, batch_idx, dim=3)[ + None, None, head_idx + ] + # (TILE_M, TILE_N, rest_m, rest_n) + gBias = cute.local_tile( + mBias_cur, (self.bias_block_size, self.n_block_size), (None, None) + ) + # (TMA, STAGE) and (TMA, rest_m, rest_n) + tBsBias, tBgBias = cpasync.tma_partition( + tma_atom_bias, + 0, # no multicast + cute.make_layout(1), + cute.group_modes(sBias, 0, 2), + cute.group_modes(gBias, 0, 2), + ) + load_bias = partial( + self.load_bias, + tma_atom_bias, + tBgBias, + tBsBias, + pipeline_bias=pipeline_bias, + ) + + if const_expr(not self.use_block_sparsity): + n_block_min, n_block_max = block_info.get_n_block_min_max( + seqlen, m_block, split_idx, num_splits + ) + if const_expr(tma_atom_bias is not None): + _, n_block_max_abs = block_info.get_n_block_min_max( + seqlen, + m_block, + split_idx, + num_splits, + absolute=True, + ) + _, n_block_max_abs0 = block_info.get_n_block_min_max( + seqlen, + self.q_stage * m_block, + split_idx, + num_splits, + half_tile_m=self.q_stage > 1, + absolute=True, + ) + bias_idx_offset1 = n_block_max_abs - n_block_max + bias_max_idx1 = self.bias_n_max - 1 - bias_idx_offset1 + bias_idx_offset0 = n_block_max_abs0 - n_block_max + bias_max_idx0 = self.bias_n_max - 1 - max(bias_idx_offset0, 0) + dummy_first_bias_load = bias_idx_offset0 < 0 and const_expr( + self.q_stage == 2 + ) + staggered_bias_loads = ( + dummy_first_bias_load or (bias_max_idx0 > bias_max_idx1) + ) and const_expr(self.q_stage == 2) + if const_expr(not self.is_split_kv) or n_block_min < n_block_max: + n_block_first = n_block_max - 1 if n_block_max > 0 else 0 + page_idx = ( + mPageTable[batch_idx, n_block_first] + if const_expr(mPageTable is not None and self.use_tma_KV) + else None + ) + if const_expr(not self.use_tma_KV): + paged_kv_manager.load_page_table(n_block_first) + # For v_dequant, load V0 first (on vq_producer_state) so the + # correction warp's V0 dequant can start immediately. + if const_expr(self.v_dequant): + if issue_kv_for_this_warp: + load_V( + block=n_block_max - 1, + producer_state=vq_producer_state, + page_idx=page_idx, + ) + vq_producer_state.advance() + if issue_kv_for_this_warp: + load_K( + block=n_block_max - 1, + producer_state=kv_producer_state, + page_idx=page_idx, + ) # K0 + # load_K(block=n_block_max - 1, producer_state=kv_producer_state, page_idx=page_idx, extra_tx_count=self.tma_copy_bytes["Q"]) # K0 + if const_expr(self.use_cpasync_to_load_sfq): + cpasync_load_SFQ(block=0, stage=0) + if issue_q_for_this_warp: + load_Q(block=0, stage=0) + if issue_kv_for_this_warp: + kv_producer_state.advance() + if const_expr(tma_atom_bias is not None): + # Under PDL the grid launches while the ShearingBias producer + # is still running; mBias is its output, so every bias TMA + # stays behind this wait (bias_max_idx0 >= bias_max_idx1, so + # idx0 < 0 means this worktile issues no bias load at all and + # must not stall its K/V loads on the producer). Q/K/V and + # the page table are written before the producer and need no + # ordering here. + if const_expr(not self.is_split_kv) or bias_max_idx0 >= 0: + cute.arch.griddepcontrol_wait() + if issue_q_for_this_warp and ( + const_expr(not self.is_split_kv) or bias_max_idx0 >= 0 + ): + bias0_producer_state = load_bias( + m_block=self.q_stage * m_block + 0, + n_block=bias_max_idx0, + bias_producer_state=bias0_producer_state, + q_stage=0, + ) # Bias0 + if const_expr(self.q_stage == 2) and issue_q_for_this_warp: + if const_expr(self.use_cpasync_to_load_sfq): + cpasync_load_SFQ(block=1, stage=1) + load_Q(block=1, stage=1) + if const_expr(tma_atom_bias is not None): + if const_expr(not self.is_split_kv) or bias_max_idx1 >= 0: + bias1_producer_state = load_bias( + m_block=self.q_stage * m_block + 1, + n_block=bias_max_idx1, + bias_producer_state=bias1_producer_state, + q_stage=1, + ) # Bias1 + q_producer_phase ^= 1 + # For v_dequant, V0 is loaded inside the main loop on vq_producer_state + # (the correction warp's dequant_v drives the V ordering). + if const_expr(not self.v_dequant): + if issue_kv_for_this_warp: + load_V( + block=n_block_max - 1, + producer_state=kv_producer_state, + page_idx=page_idx, + ) # V0 + kv_producer_state.advance() + if const_expr(tma_atom_bias is not None): + prologue_loads = min( + bias_max_idx1, n_block_max - 1 - n_block_min + ) + tail_bias_load = ( + staggered_bias_loads + and prologue_loads < n_block_max - 1 - n_block_min + and prologue_loads >= 0 + ) + prologue_loads = max(prologue_loads, 0) + for i in cutlass.range(prologue_loads, unroll=1): + n_block = n_block_max - 2 - i + page_idx = ( + mPageTable[batch_idx, n_block] + if const_expr( + mPageTable is not None and self.use_tma_KV + ) + else None + ) + if const_expr(not self.use_tma_KV): + paged_kv_manager.load_page_table(n_block) + if issue_kv_for_this_warp: + load_K( + block=n_block, + producer_state=kv_producer_state, + page_idx=page_idx, + ) # Ki + kv_producer_state.advance() + if issue_q_for_this_warp: + bias0_producer_state = load_bias( + m_block=self.q_stage * m_block + 0, + n_block=bias_max_idx0 + - 1 + - i + + dummy_first_bias_load, + bias_producer_state=bias0_producer_state, + q_stage=0, + ) # Bias0 + if const_expr(self.q_stage == 2): + bias1_producer_state = load_bias( + m_block=self.q_stage * m_block + 1, + n_block=bias_max_idx1 - 1 - i, + bias_producer_state=bias1_producer_state, + q_stage=1, + ) # Bias1 + if issue_kv_for_this_warp: + if const_expr(self.v_dequant): + load_V( + block=n_block, + producer_state=vq_producer_state, + page_idx=page_idx, + ) # Vi + vq_producer_state.advance() + else: + load_V( + block=n_block, + producer_state=kv_producer_state, + page_idx=page_idx, + ) # Vi + kv_producer_state.advance() + if issue_q_for_this_warp and tail_bias_load: + bias0_producer_state = load_bias( + m_block=self.q_stage * m_block + 0, + n_block=bias_max_idx0 + - 1 + - prologue_loads + + dummy_first_bias_load, + bias_producer_state=bias0_producer_state, + q_stage=0, + ) # Bias0 + else: + prologue_loads = 0 + for i in cutlass.range( + prologue_loads, n_block_max - 1 - n_block_min, unroll=1 + ): + n_block = n_block_max - 2 - i + page_idx = ( + mPageTable[batch_idx, n_block] + if const_expr(mPageTable is not None and self.use_tma_KV) + else None + ) + if const_expr(not self.use_tma_KV): + paged_kv_manager.load_page_table(n_block) + if issue_kv_for_this_warp: + load_K( + block=n_block, + producer_state=kv_producer_state, + page_idx=page_idx, + ) # Ki + kv_producer_state.advance() + if const_expr(self.v_dequant): + load_V( + block=n_block, + producer_state=vq_producer_state, + page_idx=page_idx, + ) # Vi + vq_producer_state.advance() + else: + load_V( + block=n_block, + producer_state=kv_producer_state, + page_idx=page_idx, + ) # Vi + kv_producer_state.advance() + + else: + kv_producer_state, q_producer_phase = produce_block_sparse_loads_sm100( + blocksparse_tensors, + batch_idx, + head_idx, + m_block, + seqlen, + split_idx, + num_splits, + kv_producer_state, + load_Q, + load_K, + load_V, + pipeline_kv, + self.q_stage, + q_producer_phase, + self.qhead_per_kvhead if const_expr(self.pack_gqa) else 1, + self.q_subtile_factor if self.q_subtile_factor is not None else 1, + ) + + work_tile = tile_scheduler.advance_to_next_work() + # End of persistent scheduler loop + + if issue_kv_for_this_warp: + if const_expr(self.v_dequant): + pipeline_vq.producer_tail(vq_producer_state) + pipeline_kv.producer_tail(kv_producer_state) + # This is equivalent to pipeline_q.producer_tail for the TMA-Q producer warp. + if issue_q_for_this_warp: + pipeline_q.producer_acquire_w_index_phase( + self.q_stage - 1, q_producer_phase + ) + if const_expr(self.use_cpasync_to_load_sfq): + pipeline_sfq.producer_acquire_w_index_phase( + self.q_stage - 1, q_producer_phase + ) + + @cute.jit + def load_bias( + self, + tma_atom_bias: cute.CopyAtom, + tBgBias: cute.Tensor, + tBsBias: cute.Tensor, + pipeline_bias: pipeline.PipelineAsync, + m_block: Int32, + n_block: Int32, + bias_producer_state: pipeline.PipelineState, + q_stage: Int32, + ): + stage = bias_producer_state.index + q_stage + phase = bias_producer_state.phase + mbar_ptr = pipeline_bias.sync_object_full.get_barrier(stage) + pipeline_bias.producer_acquire_w_index_phase(stage, phase) + cute.copy( + tma_atom_bias, + tBgBias[None, m_block, n_block], + tBsBias[None, stage], + tma_bar_ptr=mbar_ptr, + ) + bias_producer_state.advance() + return bias_producer_state + + @cute.jit + def dequant_v( + self, + sVq: cute.Tensor, + sSFV: cute.Tensor, + sV: cute.Tensor, + pipeline_vq: pipeline.PipelineAsync, + pipeline_v_mma: pipeline.PipelineAsync, + vq_consumer_state: pipeline.PipelineState, + v_dequant_producer_state: pipeline.PipelineState, + tidx: Int32, + ): + """Correction-warp in-kernel V dequant: read one kv-tile of fp8 V (sVq) + + its PER-HEAD-DIM UE8M0 scales (sSFV, laid out like SFK: per token, + head_dim_v/32 blocks), upcast to bf16 and scale, write to sV (bf16). + Each fp8 V element V[token, dv] is scaled by sSFV[token, dv//32]. + Consumes pipeline_vq (fp8 V load), produces pipeline_v_mma (bf16 V). + + Why the CORRECTION warps do this (not the softmax/MMA warps): the + correction warps are idle during the first tile's QK/softmax -- the + first softmax signal is intentionally ignored (no correction needed on + tile 0). Issuing V0 dequant here overlaps it with QK0 + softmax0 (the + caller invokes dequant_v before the softmax wait), hiding the + fp8->bf16 upcast+scale under work already in flight without stealing + MMA or softmax cycles.""" + num_threads = cute.arch.WARP_SIZE * len(self.correction_warp_ids) + COPY_ACCESS_BITS = 128 # 16-byte vectorized copy access width + num_load_elems = COPY_ACCESS_BITS // self.v_dtype.width // 2 + num_store_elems = COPY_ACCESS_BITS // self.v_mma_dtype.width + assert num_load_elems == num_store_elems + + sVq_layout_ndp = cute.make_ordered_layout( + (*cute.select(self.mma_tiler_pv, mode=[1, 2]), self.kv_stage), + order=(1, 0, 2), + ) + sV_layout_ndp = cute.make_ordered_layout( + (*cute.select(self.mma_tiler_pv, mode=[1, 2]), self.v_mma_stage), + order=(1, 0, 2), + ) + # sSFV holds per-head-dim scales in the SFB BlockScaledBasicChunk atom + # (same layout as SFK). Its native modes are + # (token(128, hierarchical), broadcast(1), sf_block(head_dim_v/32), kv_stage). + # Read a scale via sSFV[token, 0, dv//v_sf_vec, stage] so the atom swizzle + # is honored (a naive ordered composition reads the wrong bytes). + sSFV_u8 = cute.recast_tensor(sSFV, cutlass.Uint8) + + vq_stage = vq_consumer_state.index + v_upcast_stage = v_dequant_producer_state.index + + sV_ = cute.composition(sV, sV_layout_ndp) + sVq_ = cute.composition(sVq, sVq_layout_ndp) + + tiled_copy_s2r = copy_utils.tiled_copy_2d( + self.v_dtype, + threads_per_row=4, + num_threads=num_threads, + num_copy_elems=num_load_elems, + ) + tiled_copy_r2s = copy_utils.tiled_copy_2d( + self.v_mma_dtype, + threads_per_row=4, + num_threads=num_threads, + num_copy_elems=num_store_elems, + ) + thr_copy_s2r = tiled_copy_s2r.get_slice(tidx) + thr_copy_r2s = tiled_copy_r2s.get_slice(tidx) + + tVsV_f8 = thr_copy_s2r.partition_S(sVq_)[None, None, None, vq_stage] + tVrV_f8 = cute.make_fragment_like(tVsV_f8) + tVsV_f16 = thr_copy_r2s.partition_S(sV_)[None, None, None, v_upcast_stage] + + # Identity must carry sVq_'s (dv, token) layout so partition_S aligns + # element (e,i,j) of the DATA (tVrV_f8, s2r partition of sVq_) with the + # (dv, token) coord used to pick its scale. A plain make_identity_tensor + # has dv as the fast mode while sVq_ has token fast, which mislabels every + # element -> systematic per-block scale errors. + cV = cute.make_identity_tensor(cute.select(self.mma_tiler_pv, mode=[1, 2])) + cV = cute.composition( + cV, cute.make_layout(sVq_.shape[:2], stride=sVq_.stride[:2]) + ) + tVcV = thr_copy_s2r.partition_S(cV) + + num_rows = cute.size(tVrV_f8.shape[1]) # dv-rows this thread handles + num_cols = cute.size(tVrV_f8.shape[2]) # token-cols this thread handles + + delay_v_mma_acquire = self.v_mma_stage == 1 + + pipeline_vq.consumer_wait_w_index_phase( + vq_consumer_state.index, vq_consumer_state.phase + ) + if const_expr(not delay_v_mma_acquire): + pipeline_v_mma.producer_acquire_w_index_phase( + v_dequant_producer_state.index, v_dequant_producer_state.phase + ) + + cute.copy(tiled_copy_s2r, tVsV_f8, tVrV_f8) + + num_v = cute.size(tVrV_f8.shape[0]) # vectorized elements per (row,col) + + # Partition geometry (from tVcV): the vector (e) runs along dv within + # one sf block, mode-1 (i) steps tokens by 32, mode-2 (j) steps dv + # blocks by 32. So all num_v elements of a vector share ONE scale + # sSFV[token(i), block=j], and the SFB atom keeps one token's sf_dim + # block bytes contiguous and 4B-aligned -> one 4-byte smem load per + # token row covers every block this thread touches. + assert num_cols == self.head_dim_v_padded // self.v_sf_vec_size + assert num_cols % 2 == 0 + + def _gather_scales(): + # Explicit bf16 view over the packed cvt results: flat Int32 word + # (i * num_cols//2 + p) holds bf16 scales (j=2p, 2p+1) of row i. + sc32 = cute.make_rmem_tensor((num_cols // 2) * num_rows, cutlass.Int32) + sc = cute.make_tensor( + cute.recast_ptr(sc32.iterator, dtype=cutlass.BFloat16), + cute.make_layout((num_cols, num_rows), stride=(1, num_cols)), + ) + # NOTE: recast_tensor on tiny rmem tensors mis-aliased here (a + # (1,)xInt32 -> Int16 recast returned the low half for BOTH + # entries, observed on-device), so both reinterpretations below use + # explicit make_tensor-over-recast_ptr aliases instead. + w16 = cute.make_rmem_tensor((2,), cutlass.Int16) + w32 = cute.make_tensor( + cute.recast_ptr(w16.iterator, dtype=cutlass.Int32), + cute.make_layout((1,)), + ) + # crd2idx with a dynamic stage coord silently dropped the stage + # term (observed on-device); add the stage stride explicitly. + sf_stage_stride = cute.crd2idx((0, 0, 0, 1), sSFV_u8.layout) + for i in cutlass.range_constexpr(num_rows): + tok = tVcV[0, i, 0][1] + idx = ( + cute.crd2idx((tok, 0, 0, 0), sSFV_u8.layout) + + vq_stage * sf_stage_stride + ) + w32[0] = cute.make_tensor( + cute.recast_ptr(sSFV_u8.iterator + idx, dtype=cutlass.Int32), + cute.make_layout((1,)), + )[0] + for p in cutlass.range_constexpr(num_cols // 2): + sc32[i * (num_cols // 2) + p] = cvt_bf16x2_ue8m0x2(w16[p]) + return sc # sc[j, i]: block j, token-row i + + if const_expr(delay_v_mma_acquire): + scales_all = _gather_scales() + cute.arch.fence_view_async_shared() + cute.arch.barrier( + barrier_id=int(NamedBarrierFwdSm100.Correction), + number_of_threads=len(self.correction_warp_ids) * cute.arch.WARP_SIZE, + ) + if const_expr(self.use_tma_KV): + pipeline_vq.consumer_release_w_index(vq_consumer_state.index) + else: + if cute.arch.lane_idx() == 0: + pipeline_vq.consumer_release_w_index(vq_consumer_state.index) + pipeline_v_mma.producer_acquire_w_index_phase( + v_dequant_producer_state.index, v_dequant_producer_state.phase + ) + else: + scales_all = _gather_scales() + + for i in cutlass.range_constexpr(num_rows): + tVrV_f8_frg = tVrV_f8[None, i, None] + tVsV_f16_frg = tVsV_f16[None, i, None] + tVrV_f16_frg = cute.make_fragment_like(tVsV_f16_frg) + tVrV_f16_frg.store( + tVrV_f8_frg.load().to(cutlass.Float32).to(self.v_mma_dtype) + ) + for j in cutlass.range_constexpr(num_cols): + s = scales_all[j, i] + for e in cutlass.range_constexpr(num_v): + tVrV_f16_frg[e, j] = tVrV_f16_frg[e, j] * s + cute.copy(tiled_copy_r2s, tVrV_f16_frg, tVsV_f16_frg) + + cute.arch.fence_view_async_shared() + cute.arch.barrier( + barrier_id=int(NamedBarrierFwdSm100.Correction), + number_of_threads=len(self.correction_warp_ids) * cute.arch.WARP_SIZE, + ) + if const_expr(not delay_v_mma_acquire): + if const_expr(self.use_tma_KV): + pipeline_vq.consumer_release_w_index(vq_consumer_state.index) + else: + if cute.arch.lane_idx() == 0: + pipeline_vq.consumer_release_w_index(vq_consumer_state.index) + pipeline_v_mma.producer_commit_w_index(v_dequant_producer_state.index) + vq_consumer_state.advance() + v_dequant_producer_state.advance() + return vq_consumer_state, v_dequant_producer_state + + @cute.jit + def make_sf_qk_tmem_copies( + self, + tmem_ptr, + stage, + tiled_mma_qk, + sSFQ, + sSFK, + sSFQ_layout, + sSFK_layout, + ) -> SfS2TCopies: + """Build the SFQ/SFK TMEM tensors (in the alternate stage's S region) and + the smem->TMEM tiled-copy partitions for the blockscaled QK^T MMA.""" + sfq_tmem_ptr = cute.recast_ptr( + tmem_ptr + self.tmem_sfq_offset[stage], dtype=self.sfq_dtype + ) + tCtSFQ_layout = blockscaled_layout.make_tmem_layout_sfa( + tiled_mma_qk, + self.mma_tiler_qk, + self.qk_sf_vec_size, + cute.slice_(sSFQ_layout, (None, None, None, 0)), + ) + tCtSFQ = cute.make_tensor(sfq_tmem_ptr, tCtSFQ_layout) + + sfk_tmem_ptr = cute.recast_ptr( + tmem_ptr + self.tmem_sfk_offset[stage], dtype=self.sfk_dtype + ) + tCtSFK_layout = blockscaled_layout.make_tmem_layout_sfb( + tiled_mma_qk, + self.mma_tiler_qk, + self.qk_sf_vec_size, + cute.slice_(sSFK_layout, (None, None, None, 0)), + ) + tCtSFK = cute.make_tensor(sfk_tmem_ptr, tCtSFK_layout) + + # SFQ s2t copy + tCtSFQ_compact = cute.filter_zeros(tCtSFQ) + copy_atom_s2t_sfq = cute.make_copy_atom( + tcgen05.Cp4x32x128bOp(tcgen05.CtaGroup.ONE), self.sfq_dtype + ) + tiled_copy_s2t_sfq = tcgen05.make_s2t_copy(copy_atom_s2t_sfq, tCtSFQ_compact) + thr_copy_s2t_sfq = tiled_copy_s2t_sfq.get_slice(0) + tCsSFQ_compact = cute.filter_zeros(sSFQ) + tCsSFQ_compact_s2t_ = thr_copy_s2t_sfq.partition_S(tCsSFQ_compact) + tCsSFQ_compact_s2t = tcgen05.get_s2t_smem_desc_tensor( + tiled_copy_s2t_sfq, tCsSFQ_compact_s2t_ + ) + tCtSFQ_compact_s2t = thr_copy_s2t_sfq.partition_D(tCtSFQ_compact) + + # SFK s2t copy + tCtSFK_compact = cute.filter_zeros(tCtSFK) + copy_atom_s2t_sfk = cute.make_copy_atom( + tcgen05.Cp4x32x128bOp(tcgen05.CtaGroup.ONE), self.sfk_dtype + ) + tiled_copy_s2t_sfk = tcgen05.make_s2t_copy(copy_atom_s2t_sfk, tCtSFK_compact) + thr_copy_s2t_sfk = tiled_copy_s2t_sfk.get_slice(0) + tCsSFK_compact = cute.filter_zeros(sSFK) + tCsSFK_compact_s2t_ = thr_copy_s2t_sfk.partition_S(tCsSFK_compact) + tCsSFK_compact_s2t = tcgen05.get_s2t_smem_desc_tensor( + tiled_copy_s2t_sfk, tCsSFK_compact_s2t_ + ) + tCtSFK_compact_s2t = thr_copy_s2t_sfk.partition_D(tCtSFK_compact) + + return SfS2TCopies( + tCtSFQ=tCtSFQ, + tCtSFK=tCtSFK, + tiled_copy_sfq=tiled_copy_s2t_sfq, + tiled_copy_sfk=tiled_copy_s2t_sfk, + tCsSFQ_s2t=tCsSFQ_compact_s2t, + tCtSFQ_s2t=tCtSFQ_compact_s2t, + tCsSFK_s2t=tCsSFK_compact_s2t, + tCtSFK_s2t=tCtSFK_compact_s2t, + ) + + @cute.jit + def mma( + self, + tiled_mma_qk: cute.ThrMma, + tiled_mma_pv: cute.ThrMma, + sQ: cute.Tensor, + sK: cute.Tensor, + sV: cute.Tensor, + tStS: cute.Tensor, + tOtO: cute.Tensor, + tOrP: cute.Tensor, + pipeline_q: pipeline.PipelineAsync, + pipeline_kv: pipeline.PipelineAsync, + pipeline_s_p_o: pipeline.PipelineAsync, + pipeline_p_lastsplit: pipeline.PipelineAsync, + pipeline_o_acc: pipeline.PipelineAsync, + is_leader_cta: Boolean, + block_info: BlockInfo, + num_splits: Int32, + SeqlenInfoCls: Callable, + blocksparse_tensors: Optional[BlockSparseTensors], + tile_scheduler=None, + tmem_ptr=None, + sSFQ: Optional[cute.Tensor] = None, + sSFK: Optional[cute.Tensor] = None, + sSFQ_layout=None, + sSFK_layout=None, + pipeline_sfq: Optional[pipeline.PipelineAsync] = None, + pipeline_sf_overlap: Optional[pipeline.PipelineAsync] = None, + pipeline_v_mma: Optional[pipeline.PipelineAsync] = None, + ): + tSrQ = tiled_mma_qk.make_fragment_A(sQ) + tSrK = tiled_mma_qk.make_fragment_B(sK) + tOrV = tiled_mma_pv.make_fragment_B(sV) + if const_expr(self.q_stage == 2): + tSrQs = (tSrQ[None, None, None, 0], tSrQ[None, None, None, 1]) + else: + tSrQs = (tSrQ[None, None, None, 0],) + + qk_mma_op, pv_mma_op = tiled_mma_qk.op, tiled_mma_pv.op + pv_mma_idesc = sm100_desc.mma_op_to_idesc(pv_mma_op) + v_smem_base = sm100_desc.smem_desc_base_from_tensor(sV, sm100_desc.Major.MN) + sm100_utils.declare_ptx_idesc(pv_mma_op, var_name="fa_fwd_pv_mma_idesc") + # The QK PTX-descriptor path only supports the plain MMA kinds; the + # block-scaled QK^T (MmaMXF8F6F4Op) uses cute.gemm via gemm_blockscaled + # and must not touch _tcgen05_mma_kind / the precomputed smem desc. + if const_expr(not self.qk_blockscaled): + qk_mma_idesc = sm100_desc.mma_op_to_idesc(qk_mma_op) + qk_mma_kind = sm100_utils._tcgen05_mma_kind(qk_mma_op) + q_smem_base = sm100_desc.smem_desc_base_from_tensor(sQ, sm100_desc.Major.K) + k_smem_base = sm100_desc.smem_desc_base_from_tensor(sK, sm100_desc.Major.K) + q_smem_start = [ + sm100_desc.make_smem_desc_start_addr( + sQ[None, None, None, stage].iterator + ) + for stage in range(self.q_stage) + ] + sm100_utils.declare_ptx_smem_desc( + q_smem_start[self.q_stage - 1], + q_smem_base, + tSrQ[None, None, None, 0].layout, + var_name_prefix="fa_fwd_q_smem_desc", + ) + sm100_utils.declare_ptx_idesc(qk_mma_op, var_name="fa_fwd_qk_mma_idesc") + else: + sf_copies = [ + self.make_sf_qk_tmem_copies( + tmem_ptr, stage, tiled_mma_qk, sSFQ, sSFK, sSFQ_layout, sSFK_layout + ) + for stage in range(self.q_stage) + ] + + sQ_stage_stride = (sQ.layout.stride[-1] * sQ.element_type.width // 8) >> 4 + if const_expr(self.q_stage == 1): + sQ_stage_stride = 0 + if const_expr(not self.qk_blockscaled): + gemm_Si = [ + partial( + sm100_utils.gemm_ptx_precomputed_varname, + self.tmem_s_offset[stage], + smem_desc_base_b=k_smem_base, + tCrB_layout=tSrK[None, None, None, 0].layout, + smem_var_name_prefix="fa_fwd_q_smem_desc", + idesc_var_name="fa_fwd_qk_mma_idesc", + kind=qk_mma_kind, + smem_offset=-sQ_stage_stride if stage == 0 else sQ_stage_stride, + zero_init=True, + cta_group=self.cta_group_size, + ) + for stage in range(self.q_stage) + ] + else: + # Block-scaled QK^T: cute.gemm with SFA/SFB fed from TMEM. + gemm_Si = [ + partial( + sm100_utils.gemm_blockscaled, + tiled_mma_qk, + tStS[None, None, None, stage], + tSrQs[stage], + tCtSFA=sf_copies[stage].tCtSFQ, + tCtSFB=sf_copies[stage].tCtSFK, + zero_init=True, + ) + for stage in range(self.q_stage) + ] + gemm_Pi = [ + partial( + sm100_utils.gemm_ptx_partial, + pv_mma_op, + self.tmem_o_offset[stage], + tOrP[None, None, None, stage], + sA=None, + split_arrive=self.split_P_arrive if self.split_P_arrive > 0 else None, + tA_addr=( + self.tmem_p_offset[stage] + if const_expr(self.qk_blockscaled or self.v_dequant) + else None + ), + cta_group=self.cta_group_size, + ) + for stage in range(self.q_stage) + ] + # gemm_Pi = [ + # partial( + # sm100_utils.gemm, tOtO[None, None, None, stage], tCrA=tOrP[None, None, None, stage] + # ) + # for stage in range(self.q_stage) + # ] + + mma_q_consumer_phase = Int32(0) + mma_kv_consumer_state = pipeline.make_pipeline_state( + pipeline.PipelineUserType.Consumer, self.kv_stage + ) + if const_expr(self.v_dequant): + v_dequant_consumer_state = pipeline.make_pipeline_state( + pipeline.PipelineUserType.Consumer, self.v_mma_stage + ) + P_full_O_rescaled_phase = Int32(0) + + work_tile = tile_scheduler.initial_work_tile_info() + while work_tile.is_valid_tile: + m_block, head_idx, batch_idx, split_idx = work_tile.tile_idx + seqlen = SeqlenInfoCls(batch_idx) + + block_iter_count = Int32(0) + process_tile = False + + if const_expr(self.use_block_sparsity): + block_iter_count = get_total_block_count( + blocksparse_tensors, + batch_idx, + head_idx, + m_block, + split_idx, + num_splits, + self.qhead_per_kvhead if const_expr(self.pack_gqa) else 1, + self.q_subtile_factor if self.q_subtile_factor is not None else 1, + seqlen_info=seqlen, + ) + process_tile = block_iter_count > Int32(0) + else: + n_block_min, n_block_max = block_info.get_n_block_min_max( + seqlen, m_block, split_idx, num_splits + ) + block_iter_count = n_block_max - n_block_min + if const_expr(not self.is_split_kv): + process_tile = True + else: + process_tile = n_block_min < n_block_max + + if process_tile and is_leader_cta: + for stage in cutlass.range_constexpr(self.q_stage): + # GEMM_QK00 (Q0 * K0 -> S0) or GEMM_QK01 (Q1 * K0 -> S1) + # 1. wait for Q0 / Q1 + pipeline_q.consumer_wait_w_index_phase(stage, mma_q_consumer_phase) + if const_expr(self.use_cpasync_to_load_sfq): + pipeline_sfq.consumer_wait_w_index_phase( + stage, mma_q_consumer_phase + ) + # 2. wait for K0 + if const_expr(stage == 0): + pipeline_kv.consumer_wait(mma_kv_consumer_state) + # S2T copy SFQ then SFK into TMEM (blockscaled QK^T operands) + if const_expr(self.qk_blockscaled): + cute.copy( + sf_copies[stage].tiled_copy_sfq, + sf_copies[stage].tCsSFQ_s2t[ + (None, None, None, None, stage) + ], + sf_copies[stage].tCtSFQ_s2t, + ) + cute.copy( + sf_copies[stage].tiled_copy_sfk, + sf_copies[stage].tCsSFK_s2t[ + (None, None, None, None, mma_kv_consumer_state.index) + ], + sf_copies[stage].tCtSFK_s2t, + ) + Ki_index, Ki_phase = ( + mma_kv_consumer_state.index, + mma_kv_consumer_state.phase, + ) + Ki_index *= self.kv_size_ratio + tSrKi = tSrK[None, None, None, Ki_index] + # We don't need to acquire empty S0 / S1. + # For the first iteration, we don't need to wait as we're guaranteed S0 / S1 + # are empty. For subsequent iterations, the wait happened at the end + # of the while loop. + # 3. gemm + # sm100_utils.gemm(tiled_mma_qk, tStS[None, None, None, stage], tSrQ[None, None, None, stage], tSrKi, zero_init=True) + sK_cur = sK[None, None, None, Ki_index] + if const_expr(self.uneven_kv_smem): + sK_cur = self.offset_kv_smem(sK_cur, Ki_index, Ki_phase) + if const_expr(self.qk_blockscaled): + gemm_Si[stage](tCrB=tSrKi, sB=sK_cur) + else: + gemm_Si[stage]( + smem_desc_start_b=sm100_desc.make_smem_desc_start_addr( + sK_cur.iterator + ) + ) + # gemm_Si[stage](tCrB=tSrKi) + # 4. release S0 / S1 + pipeline_s_p_o.producer_commit_w_index(stage) + mma_q_consumer_phase ^= 1 + # 5. release K0 + pipeline_kv.consumer_release(mma_kv_consumer_state) + mma_kv_consumer_state.advance() + # End of GEMM (Q1 * K0 -> S1) + # Note: Q0 & Q1 are still needed in the seqlen_kv loop + # so we need to release them after the seqlen_kv loop + + # O hasn't been accumulated yet, its first MMA calculation doesn't need to accumulate + block_loop_count = block_iter_count - 1 + O_should_accumulate = False + for i in cutlass.range(block_loop_count, unroll=1): + # GEMM_PV00 (P0 * V0 -> O0_partial), O0 needs to be accumulated in the seqlen_kv loop + # 1. wait for V0 (dequantized bf16 V via pipeline_v_mma, or fp8/bf16 V via pipeline_kv) + if const_expr(self.v_dequant): + pipeline_v_mma.consumer_wait(v_dequant_consumer_state) + Vi_index, Vi_phase = ( + v_dequant_consumer_state.index, + v_dequant_consumer_state.phase, + ) + else: + pipeline_kv.consumer_wait(mma_kv_consumer_state) + mma_kv_release_state = mma_kv_consumer_state.clone() + Vi_index, Vi_phase = ( + mma_kv_consumer_state.index, + mma_kv_consumer_state.phase, + ) + tOrVi = tOrV[None, None, None, Vi_index] + for stage in cutlass.range_constexpr(self.q_stage): + # 2. acquire corrected O0/O1_partial and P0 / P1 + # For the first iteration in this work tile, waiting for O0/O1_partial + # means that the correction warps has finished reading tO during + # the last iteration of the previous work tile. + pipeline_s_p_o.producer_acquire_w_index_phase( + stage, P_full_O_rescaled_phase + ) + # 3. gemm + # sm100_utils.gemm(tiled_mma_pv, tOtO0, tOrP0, tOrVi, zero_init=True) + # gemm_Pi[stage](tCrB=tOrVi, sB=sV[None, None, None, Vi_index], zero_init=not O_should_accumulate) + sV_cur = sV[None, None, None, Vi_index] + if const_expr(self.uneven_kv_smem): + sV_cur = self.offset_kv_smem(sV_cur, Vi_index, Vi_phase) + gemm_Pi[stage]( + tCrB=tOrVi, + sB=sV_cur, + # smem_desc_start_b=sm100_desc.make_smem_desc_start_addr(sV_cur.iterator), + zero_init=not O_should_accumulate, + mbar_ptr=( + pipeline_p_lastsplit.sync_object_full.get_barrier(stage) + if self.split_P_arrive > 0 + else None + ), + mbar_phase=P_full_O_rescaled_phase, + ) + # Don't need to signal O_full to the correction warps since the + # correction warps wait for the softmax warps anyway. By the time the softmax + # warps finished, S_i for the next iteration must have been done, so O_i-1 + # must have been done as well. + # pipeline_o_acc.producer_commit_w_index(stage) + # 4. release V(i-1) + if const_expr(stage == self.q_stage - 1): + if const_expr(self.v_dequant): + pipeline_v_mma.consumer_release( + v_dequant_consumer_state + ) + v_dequant_consumer_state.advance() + else: + pipeline_kv.consumer_release(mma_kv_release_state) + mma_kv_release_state.advance() + # End of GEMM_PV00 (P0 * V0 -> O0_partial) + + # GEMM_QK0i (Q0 * Ki -> S0) + # 1. wait for Ki (advance so consumer index points to K, not V) + if const_expr(stage == 0): + if const_expr(not self.v_dequant): + mma_kv_consumer_state.advance() + pipeline_kv.consumer_wait(mma_kv_consumer_state) + # S2T re-copy SFK for this Ki (S GEMM overwrote the SF TMEM region) + if const_expr(self.qk_blockscaled): + cute.copy( + sf_copies[stage].tiled_copy_sfk, + sf_copies[stage].tCsSFK_s2t[ + ( + None, + None, + None, + None, + mma_kv_consumer_state.index, + ) + ], + sf_copies[stage].tCtSFK_s2t, + ) + Ki_index, Ki_phase = ( + mma_kv_consumer_state.index, + mma_kv_consumer_state.phase, + ) + Ki_index *= self.kv_size_ratio + # 2. gemm + # Don't need to wait for the softmax warp to have finished reading the previous + # Si, since this gemm is scheduled after the PV gemm, which guaranteed that Si + # has been read and Pi has been written. + # sm100_utils.gemm(tiled_mma_qk, tStS[None, None, None, stage], tSrQ[None, None, None, stage], tSrK[None, None, None, Ki_index], zero_init=True) + sK_cur = sK[None, None, None, Ki_index] + if const_expr(self.uneven_kv_smem): + sK_cur = self.offset_kv_smem(sK_cur, Ki_index, Ki_phase) + if const_expr(self.qk_blockscaled): + gemm_Si[stage]( + tCrB=tSrK[None, None, None, Ki_index], sB=sK_cur + ) + else: + gemm_Si[stage]( + smem_desc_start_b=sm100_desc.make_smem_desc_start_addr( + sK_cur.iterator + ) + ) + # gemm_Si[stage](tCrB=tSrK[None, None, None, Ki_index]) + # 3. release S0 / S1 + pipeline_s_p_o.producer_commit_w_index(stage) + # End of GEMM_QK0i (Q0 * Ki -> S0) + # 4. release Ki + pipeline_kv.consumer_release(mma_kv_consumer_state) + mma_kv_consumer_state.advance() + P_full_O_rescaled_phase ^= 1 + O_should_accumulate = True + # End of seqlen_kv loop + + # release Q0 & Q1 + for stage in cutlass.range(self.q_stage): + pipeline_q.consumer_release_w_index(stage) + if const_expr(self.use_cpasync_to_load_sfq): + pipeline_sfq.consumer_release_w_index(stage) + + # GEMM_PV00 (P0 * V0 -> O0_partial), O0 needs to be accumulated in the seqlen_kv loop + # 1. wait for V0 + if const_expr(self.v_dequant): + pipeline_v_mma.consumer_wait(v_dequant_consumer_state) + Vi_index, Vi_phase = ( + v_dequant_consumer_state.index, + v_dequant_consumer_state.phase, + ) + else: + pipeline_kv.consumer_wait(mma_kv_consumer_state) + Vi_index, Vi_phase = ( + mma_kv_consumer_state.index, + mma_kv_consumer_state.phase, + ) + tOrVi = tOrV[None, None, None, Vi_index] + for stage in cutlass.range_constexpr(self.q_stage): + # 2. acquire corrected Oi_partial and Pi + pipeline_s_p_o.producer_acquire_w_index_phase( + stage, P_full_O_rescaled_phase + ) + # 3. gemm + # sm100_utils.gemm(tiled_mma_pv, tOtO0, tOrP0, tOrVi, zero_init=True) + # gemm_Pi[stage](tCrB=tOrVi, sB=sV[None, None, None, Vi_index], zero_init=not O_should_accumulate) + sV_cur = sV[None, None, None, Vi_index] + if const_expr(self.uneven_kv_smem): + sV_cur = self.offset_kv_smem(sV_cur, Vi_index, Vi_phase) + gemm_Pi[stage]( + tCrB=tOrVi, + sB=sV_cur, + # smem_desc_start_b=sm100_desc.make_smem_desc_start_addr(sV_cur.iterator), + zero_init=not O_should_accumulate, + mbar_ptr=( + pipeline_p_lastsplit.sync_object_full.get_barrier(stage) + if self.split_P_arrive > 0 + else None + ), + mbar_phase=P_full_O_rescaled_phase, + ) + # 4. release accumulated O0_partial + # We do need O_full here since for the last tile, by the time the softmax warp + # has signaled to the correction warps, the softmax warp has just finished + # computing the row sum of the current tile. It does not guarantee that the 1st + # tile of the next work tile has been computed yet. + if const_expr(not self.overlap_sO_sQ): + pipeline_o_acc.producer_commit_w_index(stage) + # End of GEMM_PV00 (P0 * V0 -> O0_partial) + P_full_O_rescaled_phase ^= 1 + # 5. release Vi_end + if const_expr(self.v_dequant): + pipeline_v_mma.consumer_release(v_dequant_consumer_state) + v_dequant_consumer_state.advance() + else: + pipeline_kv.consumer_release(mma_kv_consumer_state) + mma_kv_consumer_state.advance() + # End of GEMM_PV1(i_end) (P1 * Vi_end -> O1) + + # only signal completion after releasing all operands (overlap_sO_sQ) + if const_expr(self.overlap_sO_sQ): + for stage in cutlass.range_constexpr(self.q_stage): + pipeline_o_acc.producer_commit_w_index(stage) + + # Advance to next tile + work_tile = tile_scheduler.advance_to_next_work() + # End of persistent scheduler loop + + # We don't need pipeline_s_p_o.producer_tail() since there's no dangling mbarrier at the end + # pipeline_s_p_o.producer_acquire_w_index_phase(self.q_stage - 1, P_full_O_rescaled_phase) + # We don't need pipeline_o_acc.producer_tail() since we don't call + # pipeline_o_acc.producer_acquire() inside the loop. + + # for both softmax0 and softmax1 warp group + @cute.jit + def _kv_head_idx(self, head_idx: Int32) -> Int32: + """Map query-head tile index -> KV-head index (FA3 descale semantics).""" + if cutlass.const_expr(self.pack_gqa): + return head_idx + return head_idx // self.qhead_per_kvhead + + @cute.jit + def _load_effective_descales( + self, + descale_tensors: Optional[DescaleTensors], + batch_idx: Int32, + kv_head_idx: Int32, + ) -> Tuple[Float32, Float32]: + """Load effective QK and V descales, defaulting unspecified tensors to identity.""" + qk_descale = Float32(1.0) + v_descale = Float32(1.0) + if cutlass.const_expr(descale_tensors is not None): + if cutlass.const_expr(descale_tensors.q_descale is not None): + qk_descale = qk_descale * Float32( + descale_tensors.q_descale[batch_idx, kv_head_idx] + ) + if cutlass.const_expr(descale_tensors.k_descale is not None): + qk_descale = qk_descale * Float32( + descale_tensors.k_descale[batch_idx, kv_head_idx] + ) + if cutlass.const_expr(descale_tensors.v_descale is not None): + v_descale = Float32(descale_tensors.v_descale[batch_idx, kv_head_idx]) + return qk_descale, v_descale + + @cute.jit + def softmax_loop( + self, + stage: int | Int32, + softmax_scale_log2: Float32, + softmax_scale: Float32 | None, + descale_tensors: Optional[DescaleTensors], + thr_mma_qk: cute.ThrMma, + tStS: cute.Tensor, # ((TILE_M, TILE_N), 1, 1, q_stage) + sScale: cute.Tensor, + mLSE: Optional[cute.Tensor], + pipeline_s_p_o: pipeline.PipelineAsync, + pipeline_p_lastsplit: pipeline.PipelineAsync, + pipeline_sm_stats: pipeline.PipelineAsync, + sm_stats_barrier: pipeline.NamedBarrier, + pipeline_s0_s1_sequence: Optional[pipeline.PipelineAsync], + learnable_sink: Optional[cute.Tensor], + block_info: BlockInfo, + num_splits: Int32, + SeqlenInfoCls: Callable, + AttentionMaskCls: Callable, + aux_data: AuxData = AuxData(), + fastdiv_mods=(None, None), + head_divmod=None, + blocksparse_tensors: Optional[BlockSparseTensors] = None, + tile_scheduler=None, + base_softmax_scale: Optional[Float32] = None, + sBias: Optional[cute.Tensor] = None, + bias_s2r_tiled_copy: Optional[cute.TiledCopy] = None, + pipeline_bias: Optional[pipeline.PipelineAsync] = None, + ): + """Compute softmax on attention scores from QK matrix multiplication. + + This method handles the softmax computation for either the first or second half of the + attention matrix, depending on the 'stage' parameter. It calculates row-wise maximum + and sum values needed for stable softmax computation, applies optional masking, and + transforms raw attention scores into probability distributions. + + The implementation uses specialized memory access patterns and efficient math operations + for computing exp(x) using exp2 functions. It also coordinates pipeline + synchronization between MMA, correction, and sequence processing stages. + """ + tidx = cute.arch.thread_idx()[0] % ( + cute.arch.WARP_SIZE + # * (len(self.softmax0_warp_ids) if stage == 0 else len(self.softmax1_warp_ids) + * (len(self.softmax0_warp_ids)) + ) + warp_idx = cute.arch.make_warp_uniform(cute.arch.warp_idx()) % 4 + aux_tensors = aux_data.tensors + + cta_qk_tiler = ( + self.mma_tiler_qk[0] // thr_mma_qk.thr_id.shape, + self.mma_tiler_qk[1], + ) + tSAcc = tStS[(None, None), 0, 0, stage] # (128, 128) + tStScale = cute.composition(tSAcc, cute.make_layout((self.m_block_size, 1))) + tScS = thr_mma_qk.partition_C(cute.make_identity_tensor(self.mma_tiler_qk[:2])) + tScS = tScS[(None, None), 0, 0] # (128, 128) + tScScale = cute.composition(tScS, cute.make_layout((self.m_block_size, 1))) + + tilePlikeFP32 = self.mma_tiler_qk[1] // Float32.width * self.v_mma_dtype.width + tStP_layout = cute.composition( + tSAcc.layout, cute.make_layout((self.m_block_size, tilePlikeFP32)) + ) + tStP = cute.make_tensor(tSAcc.iterator + self.tmem_s_to_p_offset, tStP_layout) + + tmem_load_atom = cute.make_copy_atom( + tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(32)), self.qk_acc_dtype + ) + thr_tmem_load = tcgen05.make_tmem_copy(tmem_load_atom, tSAcc).get_slice(tidx) + tStS_t2r = thr_tmem_load.partition_S(tSAcc) # (((32,32),1),1,4) + + tmem_store_scale_atom = cute.make_copy_atom( + tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(1)), Float32 + ) + thr_tmem_store_scale = tcgen05.make_tmem_copy( + tmem_store_scale_atom, tStScale + ).get_slice(tidx) + tStScale_r2t = thr_tmem_store_scale.partition_D(tStScale) + tmem_store_atom = cute.make_copy_atom( + tcgen05.copy.St32x32bOp( + tcgen05.copy.Repetition( + 8 if const_expr(self.v_mma_dtype.width == 8) else 16 + ) + ), + Float32, + ) + thr_tmem_store = tcgen05.make_tmem_copy(tmem_store_atom, tStP).get_slice(tidx) + tStP_r2t = thr_tmem_store.partition_D(tStP) # (((16,32),1),1,4) + + mma_si_consumer_phase = Int32(0) + sm_stats_producer_phase = Int32(1) + s0_s1_sequence_phase = Int32(1 if stage == 0 else 0) + bias_si_consumer_state = pipeline_custom.make_pipeline_state( + cutlass.pipeline.PipelineUserType.Consumer, self.bias_stage // self.q_stage + ) + + # self.warp_scheduler_barrier_init() + + warp_idx_in_wg = cute.arch.make_warp_uniform(cute.arch.warp_idx()) % 4 + + if const_expr(bias_s2r_tiled_copy is not None): + bias_s2r_thr_copy = bias_s2r_tiled_copy.get_slice(tidx) + tS2RsBias = bias_s2r_thr_copy.partition_S(sBias) + else: + bias_s2r_thr_copy = None + tS2RsBias = None + + work_tile = tile_scheduler.initial_work_tile_info() + while work_tile.is_valid_tile: + m_block, head_idx, batch_idx, split_idx = work_tile.tile_idx + kv_head_idx = self._kv_head_idx(head_idx) + seqlen = SeqlenInfoCls(batch_idx) + n_block_min, n_block_max = block_info.get_n_block_min_max( + seqlen, m_block, split_idx, num_splits + ) + if const_expr(self.has_bias): + _, n_block_max_abs_local = block_info.get_n_block_min_max( + seqlen, + self.q_stage * m_block + stage, + split_idx, + num_splits, + half_tile_m=self.q_stage > 1, + absolute=True, + ) + bias_idx_offset = n_block_max_abs_local - n_block_max + num_bias_loads = min( + self.bias_n_max - bias_idx_offset, n_block_max - n_block_min + ) + + mask = AttentionMaskCls(seqlen) + shared_mask_kwargs = dict( + m_block=(self.q_stage * m_block + stage) * self.cta_group_size, + thr_mma=thr_mma_qk, + thr_tmem_load=thr_tmem_load, + mask_causal=self.is_causal, + mask_local=self.is_local, + batch_idx=batch_idx, + head_idx=head_idx, + aux_data=aux_data, + vec_size=self.mask_vec_size, + ) + + # Recompute fastdiv_mods if necessary + recompute_fastdiv_mods_q = cutlass.const_expr( + aux_tensors is not None + and (seqlen.has_cu_seqlens_q or seqlen.has_seqused_q) + ) + recompute_fastdiv_mods_k = cutlass.const_expr( + aux_tensors is not None + and (seqlen.has_cu_seqlens_k or seqlen.has_seqused_k) + ) + + if cutlass.const_expr(fastdiv_mods is not None): + seqlen_q_divmod, seqlen_k_divmod = fastdiv_mods + fastdiv_mods = ( + ( + seqlen_q_divmod + if not recompute_fastdiv_mods_q + else FastDivmodDivisor(seqlen.seqlen_q) + ), + ( + seqlen_k_divmod + if not recompute_fastdiv_mods_k + else FastDivmodDivisor(seqlen.seqlen_k) + ), + ) + + mask_mod = self.mask_mod if const_expr(self.mask_mod is not None) else None + mask_fn = partial( + mask.apply_mask_sm100, + mask_mod=mask_mod, + fastdiv_mods=fastdiv_mods, + head_divmod=head_divmod, + **shared_mask_kwargs, + ) + if const_expr(self.use_block_sparsity): + # Full blocks dont need mask_mod + mask_fn_none = partial( + mask.apply_mask_sm100, + mask_mod=None, + fastdiv_mods=fastdiv_mods, + head_divmod=head_divmod, + **shared_mask_kwargs, + ) + else: + mask_fn_none = None + + qk_descale, _ = self._load_effective_descales( + descale_tensors, batch_idx, kv_head_idx + ) + + max_offset = 8 if cutlass.const_expr(self.v_mma_dtype.width == 8) else 0 + if const_expr(self.has_bias): + softmax_scale_log2_eff = softmax_scale_log2 + softmax_scale_eff = base_softmax_scale * qk_descale + elif const_expr(self.score_mod is None): + softmax_scale_log2_eff = softmax_scale_log2 * qk_descale + softmax_scale_eff = None + else: + softmax_scale_log2_eff = softmax_scale_log2 + softmax_scale_eff = softmax_scale * qk_descale + + rescale_threshold = ( + 8.0 + if const_expr(self.v_mma_dtype.width == 16) + else 4.0 if const_expr(self.v_mma_dtype.width == 8) else 0.0 + ) + softmax = SoftmaxSm100.create( + softmax_scale_log2_eff, + rescale_threshold=rescale_threshold, + softmax_scale=softmax_scale_eff, + max_offset=max_offset, + ) + softmax.reset() + + if const_expr(self.use_block_sparsity): + tile_block_count = get_total_block_count( + blocksparse_tensors, + batch_idx, + head_idx, + m_block, + split_idx, + num_splits, + self.qhead_per_kvhead if const_expr(self.pack_gqa) else 1, + self.q_subtile_factor if self.q_subtile_factor is not None else 1, + seqlen_info=seqlen, + ) + has_work = tile_block_count > Int32(0) + else: + tile_block_count = n_block_max - n_block_min + has_work = const_expr(not self.is_split_kv) or tile_block_count > Int32( + 0 + ) + + softmax_step = partial( + self.softmax_step, + softmax=softmax, + thr_mma_qk=thr_mma_qk, + pipeline_s_p_o=pipeline_s_p_o, + pipeline_p_lastsplit=pipeline_p_lastsplit, + pipeline_sm_stats=pipeline_sm_stats, + sm_stats_barrier=sm_stats_barrier, + pipeline_s0_s1_sequence=pipeline_s0_s1_sequence, + thr_tmem_load=thr_tmem_load, + thr_tmem_store=thr_tmem_store, + thr_tmem_store_scale=thr_tmem_store_scale, + tStS_t2r=tStS_t2r, + tStScale_r2t=tStScale_r2t, + tStP_r2t=tStP_r2t, + sScale=sScale, + stage=stage, + batch_idx=batch_idx, + head_idx=head_idx, + m_block=(self.q_stage * m_block + stage) * self.cta_group_size, + seqlen=seqlen, + aux_data=aux_data, + fastdiv_mods=fastdiv_mods, + head_divmod=head_divmod, + bias_softmax_scale=softmax_scale_eff, + tS2RsBias=tS2RsBias, + bias_s2r_thr_copy=bias_s2r_thr_copy, + pipeline_bias=pipeline_bias, + ) + + if const_expr(self.use_block_sparsity) or has_work: + pipeline_sm_stats.producer_acquire_w_index_phase( + stage, sm_stats_producer_phase + ) + sm_stats_producer_phase ^= 1 + + # Block sparse or dense iteration + if const_expr(self.use_block_sparsity): + # When aux_tensors exist, Q indices beyond seqlen_q must be wrapped to avoid + # OOB aux_tensor access. Only edge tiles (where m_tile_end > seqlen_q) need this. + if const_expr(aux_tensors is not None): + m_tile_end = ( + (self.q_stage * m_block + stage + 1) * self.cta_group_size + ) * self.m_block_size + check_m_boundary = m_tile_end > seqlen.seqlen_q + else: + check_m_boundary = False + ( + mma_si_consumer_phase, + sm_stats_producer_phase, + s0_s1_sequence_phase, + empty_tile, + ) = softmax_block_sparse_sm100( + blocksparse_tensors, + batch_idx, + head_idx, + m_block, + seqlen, + split_idx, + num_splits, + softmax_step, + mask_fn, + mask_fn_none, + mma_si_consumer_phase, + sm_stats_producer_phase, + s0_s1_sequence_phase, + pipeline_sm_stats, + sm_stats_barrier, + self.q_stage, + Int32(stage), + check_m_boundary, + self.qhead_per_kvhead if const_expr(self.pack_gqa) else 1, + self.q_subtile_factor if self.q_subtile_factor is not None else 1, + ) + if not empty_tile: + sScale[tidx + stage * self.m_block_size] = softmax.row_sum[0] + if const_expr(mLSE is not None or learnable_sink is not None): + sScale[ + tidx + + stage * self.m_block_size + + self.q_stage * self.m_block_size + ] = softmax.row_max[0] + # if tidx == 0: + # cute.printf("softmax row sum stage %d: %f, row_max = %f\n", stage, softmax.row_sum[0], softmax.row_max[0]) + # See block_sparse_utils.py NOTE [SM100 block-sparse empty tiles: mbarrier contract]. + # pipeline_sm_stats.producer_commit_w_index(stage) + sm_stats_barrier.arrive_w_index(index=stage * 4 + warp_idx) + # if tidx == 0: cute.printf("softmax row sum stage %d: %f\n", stage, softmax.row_sum[0]) + else: + if const_expr(not self.is_split_kv) or tile_block_count > Int32(0): + if const_expr(self.has_bias) and ( + const_expr(not self.is_split_kv) or num_bias_loads > 0 + ): + ( + mma_si_consumer_phase, + sm_stats_producer_phase, + s0_s1_sequence_phase, + bias_si_consumer_state, + ) = softmax_step( + mma_si_consumer_phase, + sm_stats_producer_phase, + s0_s1_sequence_phase, + n_block_max - 1, + is_first=True, + mask_fn=partial(mask_fn, mask_seqlen=True), + apply_bias=True, + bias_emu_off=Boolean(True), + bias_si_consumer_state=bias_si_consumer_state, + ) + else: + ( + mma_si_consumer_phase, + sm_stats_producer_phase, + s0_s1_sequence_phase, + ) = softmax_step( + mma_si_consumer_phase, + sm_stats_producer_phase, + s0_s1_sequence_phase, + n_block_max - 1, + is_first=True, + mask_fn=partial(mask_fn, mask_seqlen=True), + ) + n_block_max -= 1 + # Next couple of iterations with causal masking. With bias, the sheared bias + # encodes the causal/window mask (-inf padding), so mask_fn is None there and + # the masked band is exactly the num_bias_loads blocks. + if const_expr(self.is_causal or self.is_local or self.has_bias): + if const_expr(self.has_bias): + n_block_min_causal_local_mask = max( + n_block_max + 1 - num_bias_loads, n_block_min + ) + # Boundaries of the bands the score_mod reference masks with + # mask_fn (diagonal side, and the left window edge for local + # attention); only there does the sheared bias carry -inf. + n_block_min_bias_emu_off = ( + block_info.get_n_block_min_causal_local_mask( + seqlen, m_block, n_block_min + ) + ) + n_block_left_bias_emu_off = ( + block_info.get_n_block_min_before_local_mask( + seqlen, m_block, n_block_min + ) + ) + else: + n_block_min_causal_local_mask = ( + block_info.get_n_block_min_causal_local_mask( + seqlen, m_block, n_block_min + ) + ) + for n_tile in cutlass.range( + n_block_max - n_block_min_causal_local_mask, unroll=1 + ): + n_block = n_block_max - 1 - n_tile + if const_expr(self.has_bias): + ( + mma_si_consumer_phase, + sm_stats_producer_phase, + s0_s1_sequence_phase, + bias_si_consumer_state, + ) = softmax_step( + mma_si_consumer_phase, + sm_stats_producer_phase, + s0_s1_sequence_phase, + n_block, + mask_fn=None, + apply_bias=True, + bias_emu_off=Boolean( + n_block >= n_block_min_bias_emu_off + or n_block < n_block_left_bias_emu_off + ), + bias_si_consumer_state=bias_si_consumer_state, + ) + else: + ( + mma_si_consumer_phase, + sm_stats_producer_phase, + s0_s1_sequence_phase, + ) = softmax_step( + mma_si_consumer_phase, + sm_stats_producer_phase, + s0_s1_sequence_phase, + n_block, + mask_fn=partial(mask_fn, mask_seqlen=False), + ) + n_block_max = cutlass.min( + n_block_max, n_block_min_causal_local_mask + ) + # The remaining iterations have no masking (but may still need mask_mod) + n_block_min_before_local_mask = ( + block_info.get_n_block_min_before_local_mask( + seqlen, m_block, n_block_min + ) + ) + for n_tile in cutlass.range( + n_block_max - n_block_min_before_local_mask, unroll=1 + ): + n_block = n_block_max - n_tile - 1 + if const_expr(self.mask_mod is not None): + ( + mma_si_consumer_phase, + sm_stats_producer_phase, + s0_s1_sequence_phase, + ) = softmax_step( + mma_si_consumer_phase, + sm_stats_producer_phase, + s0_s1_sequence_phase, + n_block, + mask_fn=partial(mask_fn, mask_seqlen=False), + ) + else: + ( + mma_si_consumer_phase, + sm_stats_producer_phase, + s0_s1_sequence_phase, + ) = softmax_step( + mma_si_consumer_phase, + sm_stats_producer_phase, + s0_s1_sequence_phase, + n_block, + ) + # Separate iterations with local masking on the left + if const_expr( + self.is_local and block_info.window_size_left is not None + ): + n_block_max = cutlass.min( + n_block_max, n_block_min_before_local_mask + ) + for n_tile in cutlass.range( + 0, n_block_max - n_block_min, unroll=1 + ): + n_block = n_block_max - 1 - n_tile + ( + mma_si_consumer_phase, + sm_stats_producer_phase, + s0_s1_sequence_phase, + ) = softmax_step( + mma_si_consumer_phase, + sm_stats_producer_phase, + s0_s1_sequence_phase, + n_block, + mask_fn=partial(mask_fn, mask_seqlen=False), + ) + # Now that we no longer already have the 1st iteration, need mask_seqlen=True here + + # Dense path always writes scale / signals + sScale[tidx + stage * self.m_block_size] = softmax.row_sum[0] + if const_expr(mLSE is not None or learnable_sink is not None): + sScale[ + tidx + + stage * self.m_block_size + + self.q_stage * self.m_block_size + ] = softmax.row_max[0] + # pipeline_sm_stats.producer_commit_w_index(stage) + sm_stats_barrier.arrive_w_index(index=stage * 4 + warp_idx) + + # # Write LSE to gmem + # if const_expr(mLSE is not None): + # acc_O_mn_row_is_zero_or_nan = softmax.row_sum[0] == 0.0 or softmax.row_sum[0] != softmax.row_sum[0] + # scale = ( + # cute.arch.rcp_approx(softmax.row_sum[0] if not acc_O_mn_row_is_zero_or_nan else 1.0) + # ) + # LN2 = math.log(2.0) + # lse = ( + # (softmax.row_max[0] * softmax.scale_log2 + cute.math.log2(softmax.row_sum[0], fastmath=True)) * LN2 + # if not acc_O_mn_row_is_zero_or_nan else -Float32.inf + # ) + # if const_expr(not seqlen.has_cu_seqlens_q): + # mLSE_cur = mLSE[None, head_idx, batch_idx] + # else: + # mLSE_cur = cute.domain_offset((seqlen.offset_q,), mLSE[None, head_idx]) + # gLSE = cute.local_tile(mLSE_cur, (self.m_block_size,), (m_block * 2 + stage,)) + # if tidx < seqlen.seqlen_q - (m_block * 2 + stage) * self.m_block_size: + # gLSE[tidx] = lse + + # Advance to next tile + work_tile = tile_scheduler.advance_to_next_work() + # End of persistent scheduler loop + + # This is equivalent to pipeline_sm_stats.producer_tail + pipeline_sm_stats.producer_acquire_w_index_phase(stage, sm_stats_producer_phase) + # This is equivalent to pipeline_s0_s1.producer_tail + if const_expr(self.s0_s1_barrier): + if stage == 0: + pipeline_s0_s1_sequence.sync_object_full.wait( + stage, s0_s1_sequence_phase + ) + + @cute.jit + def softmax_step( + self, + mma_si_consumer_phase: Int32, + sm_stats_producer_phase: Int32, + s0_s1_sequence_phase: Int32, + n_block: Int32, + softmax: SoftmaxSm100, + thr_mma_qk: cute.ThrMma, + pipeline_s_p_o: pipeline.PipelineAsync, + pipeline_p_lastsplit: pipeline.PipelineAsync, + pipeline_sm_stats: pipeline.PipelineAsync, + sm_stats_barrier: pipeline.NamedBarrier, + pipeline_s0_s1_sequence: Optional[pipeline.PipelineAsync], + thr_tmem_load: cute.CopyAtom, + thr_tmem_store: cute.CopyAtom, + thr_tmem_store_scale: cute.CopyAtom, + tStS_t2r: cute.Tensor, + tStScale_r2t: cute.Tensor, + tStP_r2t: cute.Tensor, + sScale: cute.Tensor, + stage: int | Int32, + batch_idx: Int32, + head_idx: Int32, + m_block: Int32, + seqlen, + aux_data: AuxData = AuxData(), + fastdiv_mods=(None, None), + head_divmod=None, + mask_fn: Optional[Callable] = None, + is_first: bool = False, + bias_softmax_scale: Optional[Float32] = None, + tS2RsBias: Optional[cute.Tensor] = None, + bias_s2r_thr_copy: Optional[cute.CopyAtom] = None, + apply_bias: bool = False, + bias_emu_off: Optional[Boolean] = None, + pipeline_bias: Optional[pipeline.PipelineAsync] = None, + bias_si_consumer_state: Optional[pipeline.PipelineState] = None, + ) -> Tuple[cute.Int32, cute.Int32, cute.Int32]: + """Perform a single step of the softmax computation on a block of attention scores. + + This method processes one block of the attention matrix, computing numerically stable + softmax by first finding the row maximum, subtracting it from all elements, applying + exponential function, and then normalizing by the sum of exponentials. It also handles + optional masking of attention scores. + + The method involves several key operations: + 1. Loading attention scores from tensor memory + 2. Applying optional masking based on position + 3. Computing row-wise maximum values for numerical stability + 4. Transforming scores using exp2(x*scale - max*scale) + 5. Computing row sums for normalization + 6. Coordinating pipeline synchronization between different processing stages + """ + warp_idx = cute.arch.make_warp_uniform(cute.arch.warp_idx()) % 4 + tilePlikeFP32 = self.mma_tiler_qk[1] // Float32.width * self.v_mma_dtype.width + tScS = thr_mma_qk.partition_C(cute.make_identity_tensor(self.mma_tiler_qk[:2])) + tScS = tScS[(None, None), 0, 0] # (128, 128) + # tScScale = cute.composition(tScS, cute.make_layout((self.m_block_size, 1))) + cta_qk_tiler = ( + self.mma_tiler_qk[0] // thr_mma_qk.thr_id.shape, + self.mma_tiler_qk[1], + ) + tScS_shape = cta_qk_tiler # (128, 128) + tScP_shape = (tScS_shape[0], tilePlikeFP32) # (128, 64) + + # Wait for Si + pipeline_s_p_o.consumer_wait_w_index_phase(stage, mma_si_consumer_phase) + tSrS_t2r = cute.make_rmem_tensor( + thr_tmem_load.partition_D(tScS).shape, self.qk_acc_dtype + ) + cute.copy(thr_tmem_load, tStS_t2r, tSrS_t2r) + # tSrS_t2r = copy_utils.load_t2r(thr_tmem_load, tScS_shape, tStS_t2r) + + if const_expr(self.has_bias and apply_bias): + tidx = cute.arch.thread_idx()[0] % ( + cute.arch.WARP_SIZE * len(self.softmax0_warp_ids) + ) + bias_si_phase = bias_si_consumer_state.phase + bias_si_stage = bias_si_consumer_state.index + stage + pipeline_bias.consumer_wait_w_index_phase(bias_si_stage, bias_si_phase) + tBrS = cute.make_tensor( + tSrS_t2r.iterator, + cute.make_fragment_like(tS2RsBias[None, None, None, 0].layout), + ) + if const_expr(self.bias_block_size == 128) or tidx < self.bias_block_size: + for i in cutlass.range_constexpr(cute.size(tS2RsBias.shape[2])): + tBrS_cur = tBrS[None, 0, i] + tS2RsBias_cur = tS2RsBias[None, 0, i, bias_si_stage] + tS2RrBias_cur = cute.make_fragment_like(tS2RsBias[None, 0, 0, 0]) + cute.copy(bias_s2r_thr_copy, tS2RsBias_cur, tS2RrBias_cur) + assert cute.size(tBrS_cur.shape) % 2 == 0 + for j in cutlass.range( + 0, cute.size(tBrS_cur.shape), 2, unroll_full=True + ): + tBrS_cur[j], tBrS_cur[j + 1] = cute.arch.fma_packed_f32x2( + (tBrS_cur[j], tBrS_cur[j + 1]), + (bias_softmax_scale, bias_softmax_scale), + ( + tS2RrBias_cur[j].to(self.qk_acc_dtype), + tS2RrBias_cur[j + 1].to(self.qk_acc_dtype), + ), + ) + cute.arch.fence_view_async_shared() + cute.arch.barrier( + barrier_id=int(NamedBarrierFwdSm100.Softmax) + stage, + number_of_threads=128, + ) + pipeline_bias.consumer_release_w_index(bias_si_stage) + + if const_expr(self.has_bias and not apply_bias): + # Blocks beyond the bias band: the score_mod reference still scales qk there + # (its bias contribution is 0), so scale to stay in the same domain. + assert cute.size(tSrS_t2r.shape) % 2 == 0 + for j in cutlass.range(0, cute.size(tSrS_t2r.shape), 2, unroll_full=True): + tSrS_t2r[j], tSrS_t2r[j + 1] = cute.arch.mul_packed_f32x2( + (tSrS_t2r[j], tSrS_t2r[j + 1]), + (bias_softmax_scale, bias_softmax_scale), + ) + + if cutlass.const_expr(self.score_mod is not None): + self.apply_score_mod( + tSrS_t2r, + thr_tmem_load, + thr_mma_qk, + batch_idx, + head_idx, + m_block, + n_block, + softmax, + seqlen, + aux_data, + fastdiv_mods, + head_divmod, + ) + + if const_expr(mask_fn is not None): + mask_fn(tSrS_t2r, n_block=n_block) + row_max, acc_scale = softmax.update_row_max(tSrS_t2r.load(), is_first) + + if const_expr(not is_first): + # tSrScale_r2t = cute.make_rmem_tensor(thr_tmem_store_scale.partition_S(tScScale).shape, Float32) + # tSrScale_r2t[0] = acc_scale + # cute.copy(thr_tmem_store_scale, tSrScale_r2t, tStScale_r2t) + # cute.arch.fence_view_async_tmem_store() + thread_idx = thr_tmem_load.thr_idx + sScale[thread_idx + stage * self.m_block_size] = acc_scale + # if thread_idx == 0: cute.printf("softmax acc_scale stage %d: %f, row_max = %f\n", stage, acc_scale, row_max) + # Notify correction wg that row_max is ready + # pipeline_sm_stats.producer_commit_w_index(stage) + sm_stats_barrier.arrive_w_index(index=stage * 4 + warp_idx) + + # if thread_idx == 0 and stage == 0: cute.print_tensor(tSrS_t2r) + softmax.scale_subtract_rowmax(tSrS_t2r, row_max) + # Sequence barrier wait + if const_expr(self.s0_s1_barrier): + pipeline_s0_s1_sequence.sync_object_full.wait(stage, s0_s1_sequence_phase) + tSrP_r2t_f32 = cute.make_rmem_tensor( + thr_tmem_store.partition_S(cute.make_identity_tensor(tScP_shape)).shape, + Float32, + ) + # P is the PV MMA's A operand: its dtype follows V's compute dtype + # (v_mma_dtype == bf16 under v_dequant; == q_dtype otherwise). + tSrP_r2t = cute.make_tensor( + cute.recast_ptr(tSrP_r2t_f32.iterator, dtype=self.v_mma_dtype), + tSrS_t2r.layout, + ) + # softmax.scale_apply_exp2_convert(tSrS_t2r, row_max, tSrP_r2t) + if const_expr(mask_fn is None and self.has_bias and apply_bias): + # The sheared bias holds -inf (the causal/local mask) only on blocks where the + # score_mod reference has a mask_fn; mirror its per-block ex2-emu gating so the + # same fragments go through the FFMA emulation and results stay bitwise equal. + if bias_emu_off: + softmax.apply_exp2_convert( + tSrS_t2r, + tSrP_r2t, + ex2_emu_freq=0, + ex2_emu_start_frg=self.ex2_emu_start_frg, + ) + else: + softmax.apply_exp2_convert( + tSrS_t2r, + tSrP_r2t, + ex2_emu_freq=self.ex2_emu_freq, + ex2_emu_start_frg=self.ex2_emu_start_frg, + ) + else: + softmax.apply_exp2_convert( + tSrS_t2r, + tSrP_r2t, + ex2_emu_freq=self.ex2_emu_freq if const_expr(mask_fn is None) else 0, + ex2_emu_start_frg=self.ex2_emu_start_frg, + ) + # Sequence barrier arrive + if const_expr(self.s0_s1_barrier): + pipeline_s0_s1_sequence.sync_object_full.arrive(1 - stage, dst=None) + # print(tSrP_r2t_f32, tStP_r2t) + # cute.copy(thr_tmem_store, tSrP_r2t_f32, tStP_r2t) + for i in cutlass.range_constexpr(cute.size(tStP_r2t.shape[2])): + cute.copy( + thr_tmem_store, tSrP_r2t_f32[None, None, i], tStP_r2t[None, None, i] + ) + if const_expr(self.split_P_arrive > 0): + split_P_arrive_idx = ( + cute.size(tStP_r2t.shape[2]) + * self.split_P_arrive + // self.n_block_size + ) + if const_expr(i + 1 == split_P_arrive_idx): + # Notify mma warp that the 1st half of P is ready + cute.arch.fence_view_async_tmem_store() + pipeline_s_p_o.consumer_release_w_index(stage) + # Notify mma warp that the 2nd half of P is ready + cute.arch.fence_view_async_tmem_store() + if const_expr(self.split_P_arrive > 0): + cute.arch.sync_warp() + with cute.arch.elect_one(): + pipeline_p_lastsplit.producer_commit_w_index(stage) + else: + pipeline_s_p_o.consumer_release_w_index(stage) + pipeline_sm_stats.producer_acquire_w_index_phase(stage, sm_stats_producer_phase) + softmax.update_row_sum(tSrS_t2r.load(), acc_scale, is_first) + # acc_scale = cute.math.exp2(acc_scale_, fastmath=True) + if const_expr(bias_si_consumer_state is not None): + if const_expr(self.has_bias and apply_bias): + bias_si_consumer_state.advance() + return ( + mma_si_consumer_phase ^ 1, + sm_stats_producer_phase ^ 1, + s0_s1_sequence_phase ^ 1, + bias_si_consumer_state, + ) + else: + return ( + mma_si_consumer_phase ^ 1, + sm_stats_producer_phase ^ 1, + s0_s1_sequence_phase ^ 1, + ) + + @cute.jit + def correction_loop( + self, + thr_mma_qk: cute.ThrMma, + thr_mma_pv: cute.ThrMma, + tStS: cute.Tensor, + tOtO: cute.Tensor, + sScale: cute.Tensor, + mO: cute.Tensor, + mLSE: cute.Tensor, + sO: cute.Tensor, + pipeline_s_p_o: pipeline.PipelineAsync, + pipeline_o_acc: pipeline.PipelineAsync, + pipeline_sm_stats: pipeline.PipelineAsync, + sm_stats_barrier: pipeline.NamedBarrier, + pipeline_o_epi: pipeline.PipelineAsync, + learnable_sink: Optional[cute.Tensor], + descale_tensors: Optional[DescaleTensors], + gmem_tiled_copy_O: cute.TiledCopy, + tma_atom_O: cute.CopyAtom, + softmax_scale_log2: Float32, + block_info: BlockInfo, + num_splits: Int32, + SeqlenInfoCls: Callable, + blocksparse_tensors: Optional[BlockSparseTensors] = None, + tile_scheduler=None, + sVq: Optional[cute.Tensor] = None, + sSFV: Optional[cute.Tensor] = None, + sV_dequant: Optional[cute.Tensor] = None, + pipeline_vq: Optional[pipeline.PipelineAsync] = None, + pipeline_v_mma: Optional[pipeline.PipelineAsync] = None, + ): + tidx = cute.arch.thread_idx()[0] % ( + cute.arch.WARP_SIZE * len(self.correction_warp_ids) + ) + warp_idx = cute.arch.make_warp_uniform(cute.arch.warp_idx()) % 4 + mma_tile_coord_v = thr_mma_qk.thr_idx + + tScS = thr_mma_qk.partition_C(cute.make_identity_tensor(self.mma_tiler_qk[:2])) + tStScale_layout = cute.composition( + tStS.layout, cute.make_layout((self.m_block_size, 1)) + ) + tStScales = tuple( + cute.make_tensor( + tStS.iterator + self.tmem_vec_offset[stage], tStScale_layout + ) + for stage in range(self.q_stage) + ) + tScScale = cute.composition(tScS, cute.make_layout((self.m_block_size, 1))) + tmem_load_v_atom = cute.make_copy_atom( + tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(1)), self.qk_acc_dtype + ) + thr_tmem_load_vec = tcgen05.make_tmem_copy( + tmem_load_v_atom, tStScales[0] + ).get_slice(tidx) + + tStScales_t2r = [ + thr_tmem_load_vec.partition_S(tStScales[stage]) + for stage in range(self.q_stage) + ] + tSrScale_t2r_shape = thr_tmem_load_vec.partition_D(tScScale).shape + + # First iter: no correction is required + # Notify mma warp that O has been rescaled + for stage in cutlass.range(self.q_stage): + pipeline_s_p_o.consumer_release_w_index(stage) + + sm_stats_consumer_phase = Int32(0) + o_corr_consumer_phase = Int32(0) + corr_epi_producer_phase = Int32(1) + if const_expr(self.v_dequant): + vq_consumer_state = pipeline.make_pipeline_state( + pipeline.PipelineUserType.Consumer, self.kv_stage + ) + v_dequant_producer_state = pipeline.make_pipeline_state( + pipeline.PipelineUserType.Producer, self.v_mma_stage + ) + + work_tile = tile_scheduler.initial_work_tile_info() + while work_tile.is_valid_tile: + m_block, head_idx, batch_idx, split_idx = work_tile.tile_idx + kv_head_idx = self._kv_head_idx(head_idx) + qk_descale, v_descale = self._load_effective_descales( + descale_tensors, batch_idx, kv_head_idx + ) + if const_expr(self.has_bias): + softmax_scale_log2_eff = softmax_scale_log2 + elif const_expr(self.score_mod is None): + softmax_scale_log2_eff = softmax_scale_log2 * qk_descale + else: + softmax_scale_log2_eff = softmax_scale_log2 + + max_offset = ( + Float32(8.0) + if cutlass.const_expr(self.v_mma_dtype.width == 8) + else Float32(0.0) + ) + max_offset_scale = ( + Float32(256.0) + if cutlass.const_expr(self.v_mma_dtype.width == 8) + else Float32(1.0) + ) + seqlen = SeqlenInfoCls(batch_idx) + n_block_min, n_block_max = block_info.get_n_block_min_max( + seqlen, m_block, split_idx, num_splits + ) + + if const_expr(self.is_split_kv): + mO_cur = seqlen.offset_batch_Q(mO, batch_idx, dim=3)[ + None, None, head_idx, split_idx + ] + else: + mO_cur = seqlen.offset_batch_Q(mO, batch_idx, dim=3)[ + None, None, head_idx + ] + gO = None + if const_expr(self.use_tma_O or not self.pack_gqa): + tiler_gO = ( + (self.mma_tiler_pv[0] * self.q_stage), + self.head_dim_v_padded, + ) + gO = cute.local_tile(mO_cur, tiler_gO, (m_block, 0)) # (128 * 2, 128) + gO = layout_utils.select( + cute.flat_divide(gO, (self.mma_tiler_pv[0],)), mode=[0, 2, 1] + ) # (128, 128, 2) + gO = cute.flat_divide( + gO, (self.mma_tiler_pv[0] // self.cta_group_size,) + )[None, mma_tile_coord_v, None, None] + + # Default LSE to -inf for invalid split_idx tiles + stats = [ + ( + 0.0, + ( + -Float32.inf + if const_expr(mLSE is not None or learnable_sink is not None) + else None + ), + True, + ) + ] * self.q_stage + + if const_expr(self.use_block_sparsity): + total_block_count = get_total_block_count( + blocksparse_tensors, + batch_idx, + head_idx, + m_block, + split_idx, + num_splits, + self.qhead_per_kvhead if const_expr(self.pack_gqa) else 1, + self.q_subtile_factor if self.q_subtile_factor is not None else 1, + seqlen_info=seqlen, + ) + has_work = total_block_count > Int32(0) + else: + total_block_count = n_block_max - n_block_min + has_work = const_expr( + not self.is_split_kv + ) or total_block_count > Int32(0) + + if const_expr(self.v_dequant): + dequant_v_fn = partial( + self.dequant_v, + sVq=sVq, + sSFV=sSFV, + sV=sV_dequant, + pipeline_vq=pipeline_vq, + pipeline_v_mma=pipeline_v_mma, + ) + + if has_work: + # V0 dequant FIRST (overlaps with QK0 gemm + softmax of the first tile) + if const_expr(self.v_dequant): + vq_consumer_state, v_dequant_producer_state = dequant_v_fn( + vq_consumer_state=vq_consumer_state, + v_dequant_producer_state=v_dequant_producer_state, + tidx=tidx, + ) + # Ignore first signal from softmax as no correction is required + # pipeline_sm_stats.consumer_wait_w_index_phase(0, sm_stats_consumer_phase) + sm_stats_barrier.arrive_and_wait_w_index(index=0 * 4 + warp_idx) + pipeline_sm_stats.consumer_release_w_index(0) + if const_expr(self.q_stage == 2): + # pipeline_sm_stats.consumer_wait_w_index_phase(1, sm_stats_consumer_phase) + sm_stats_barrier.arrive_and_wait_w_index(index=1 * 4 + warp_idx) + sm_stats_consumer_phase ^= 1 + + tSrScale_t2r = cute.make_rmem_tensor(tSrScale_t2r_shape, Float32) + for i in cutlass.range(total_block_count - 1, unroll=1): + if const_expr(self.v_dequant): + vq_consumer_state, v_dequant_producer_state = dequant_v_fn( + vq_consumer_state=vq_consumer_state, + v_dequant_producer_state=v_dequant_producer_state, + tidx=tidx, + ) + for stage in cutlass.range_constexpr(self.q_stage): + # wait for S0 / S1 + # pipeline_sm_stats.consumer_wait_w_index_phase(stage, sm_stats_consumer_phase) + sm_stats_barrier.arrive_and_wait_w_index( + index=stage * 4 + warp_idx + ) + # cute.copy(tiled_tmem_load_vec, tStScales_t2r[stage], tSrScale_t2r) + # cute.arch.fence_view_async_tmem_load() + # scale = tSrScale_t2r[0] + scale = sScale[tidx + stage * self.m_block_size] + should_rescale = cute.arch.vote_ballot_sync(scale < 1.0) != 0 + # should_rescale = True + # if tidx == 0: cute.printf("Correction scale i = %d, for stage %d: %f, should_rescale = %d\n", i, stage, scale, should_rescale) + # Don't need O_full anymore, since by the time softmax has signaled the correction + # warps, S_i must have been done, so O_i-1 must have been done as well. + # pipeline_o_acc.consumer_wait_w_index_phase(stage, o_corr_consumer_phase) + if should_rescale: + self.correction_rescale( + thr_mma_pv, tOtO[None, None, None, stage], tidx, scale + ) + # Notify mma warp that O has been rescaled + pipeline_s_p_o.consumer_release_w_index(stage) + pipeline_sm_stats.consumer_release_w_index( + self.q_stage - 1 - stage + ) + sm_stats_consumer_phase ^= 1 + # o_corr_consumer_phase ^= 1 + if const_expr(self.q_stage == 2): + pipeline_sm_stats.consumer_release_w_index(1) + # End of seqlen_corr_loop_steps + + # Even in the case of self.overlap_sO_sQ, we can write to stage 0 of sO without + # additional sync because the MMA in the top half must have been done. + # Similarly we can write to stage 1 of sO without additional sync. + learnable_sink_val = [None] * self.q_stage + if const_expr(learnable_sink is not None): + if const_expr(not self.pack_gqa): + sink_val = Float32(learnable_sink[head_idx]) + learnable_sink_val = [sink_val] * self.q_stage + else: # Each thread might have a different sink value due to different q_head + for stage in cutlass.range_constexpr(self.q_stage): + q_head_idx = ( + ( + ( + (m_block * self.q_stage + stage) + * self.cta_group_size + + mma_tile_coord_v + ) + * self.m_block_size + + tidx + ) + % self.qhead_per_kvhead + + head_idx * self.qhead_per_kvhead + ) + learnable_sink_val[stage] = Float32( + learnable_sink[q_head_idx] + ) + for stage in cutlass.range_constexpr(self.q_stage): + # pipeline_sm_stats.consumer_wait_w_index_phase(stage, sm_stats_consumer_phase) + sm_stats_barrier.arrive_and_wait_w_index(index=stage * 4 + warp_idx) + # cute.copy(tiled_tmem_load_vec, tStScales_t2r[stage], tSrScale_t2r) + # cute.arch.fence_view_async_tmem_load() + # scale = tSrScale_t2r[0] + row_sum = sScale[tidx + stage * self.m_block_size] + if const_expr(mLSE is not None or learnable_sink is not None): + row_max = sScale[ + tidx + + stage * self.m_block_size + + self.q_stage * self.m_block_size + ] + else: + row_max = None + pipeline_sm_stats.consumer_release_w_index(stage) + if const_expr(learnable_sink is not None): + LOG2_E = math.log2(math.e) + sink_val = learnable_sink_val[stage] + if const_expr(not self.is_split_kv) or split_idx == 0: + if row_max == -Float32.inf: + # It's possible to have an empty row with splitKV. + row_max = sink_val * (LOG2_E / softmax_scale_log2_eff) + row_sum = max_offset_scale + else: + row_sum += cute.math.exp2( + sink_val * LOG2_E + - row_max * softmax_scale_log2_eff + + max_offset, + fastmath=True, + ) + acc_O_mn_row_is_zero_or_nan = row_sum == 0.0 or row_sum != row_sum + stats[stage] = (row_sum, row_max, acc_O_mn_row_is_zero_or_nan) + scale = cute.arch.rcp_approx( + row_sum if not acc_O_mn_row_is_zero_or_nan else 1.0 + ) + scale = scale * v_descale + # Wait for the last O to be ready from the MMA warp + pipeline_o_acc.consumer_wait_w_index_phase( + stage, o_corr_consumer_phase + ) + if const_expr(not self.use_correction_warps_for_epi): + pipeline_o_epi.producer_acquire_w_index_phase( + stage, corr_epi_producer_phase + ) + gO_stage = ( + gO[None, None, stage] if const_expr(gO is not None) else None + ) + self.correction_epilogue( + thr_mma_pv, + tOtO[None, None, None, stage], + tidx, + stage, + m_block, + seqlen.seqlen_q, + scale, + sO[None, None, stage], + mO_cur, + gO_stage, + gmem_tiled_copy_O, + ) + # Signal for the next work tile that O buffers in tmem are already read, so + # mma warp can write to them + pipeline_s_p_o.consumer_release_w_index(stage) + if const_expr(not self.use_correction_warps_for_epi): + pipeline_o_epi.producer_commit_w_index(stage) + # if tidx == 0: cute.printf("Correction final scale for stage %d: %f\n", stage, scale) + + o_corr_consumer_phase ^= 1 + sm_stats_consumer_phase ^= 1 + corr_epi_producer_phase ^= 1 + else: + gmem_tiled_copy_O_for_empty_tile = None + if const_expr(self.use_correction_warps_for_epi): + gmem_tiled_copy_O_for_empty_tile = gmem_tiled_copy_O + if const_expr(self.use_block_sparsity): + ( + sm_stats_consumer_phase, + o_corr_consumer_phase, + corr_epi_producer_phase, + ) = handle_block_sparse_empty_tile_correction_sm100( + tidx, + self.q_stage, + self.m_block_size, + self.qhead_per_kvhead, + self.pack_gqa, + self.is_split_kv, + learnable_sink, + mLSE, + seqlen, + m_block, + head_idx, + batch_idx, + split_idx, + sScale, + stats, + self.correction_epilogue, + thr_mma_pv, + tOtO, + sO, + pipeline_sm_stats, + sm_stats_barrier, + pipeline_o_epi, + sm_stats_consumer_phase, + o_corr_consumer_phase, + corr_epi_producer_phase, + softmax_scale_log2_eff, + max_offset, + max_offset_scale, + mO_cur, + gO, + gmem_tiled_copy_O_for_empty_tile, + ) + + if const_expr(mLSE is not None): + if const_expr(not seqlen.has_cu_seqlens_q): + if const_expr(self.is_split_kv): + mLSE_cur = mLSE[None, head_idx, batch_idx, split_idx] + else: + mLSE_cur = mLSE[None, head_idx, batch_idx] + else: + offset = ( + seqlen.offset_q + if const_expr(not self.pack_gqa) + else (0, seqlen.offset_q) + ) + if const_expr(self.is_split_kv): + mLSE_cur = cute.domain_offset( + (offset,), mLSE[None, head_idx, split_idx] + ) + else: + mLSE_cur = cute.domain_offset((offset,), mLSE[None, head_idx]) + for stage in cutlass.range_constexpr(self.q_stage): + m_tile_idx = ( + m_block * self.q_stage + stage + ) * self.cta_group_size + mma_tile_coord_v + row_sum, row_max, acc_O_mn_row_is_zero_or_nan = stats[stage] + # if tidx == 0 and stage <= 1: + # cute.printf("row_sum = {}, row_max = {}, acc_O_mn_row_is_zero_or_nan = {}\n", row_sum, row_max, acc_O_mn_row_is_zero_or_nan) + LN2 = math.log(2.0) + lse = ( + ( + row_max * softmax_scale_log2_eff + + (cute.math.log2(row_sum, fastmath=True) - max_offset) + ) + * LN2 + if not acc_O_mn_row_is_zero_or_nan + else -Float32.inf + ) + seqlen_q = ( + seqlen.seqlen_q + if const_expr(not self.pack_gqa) + else seqlen.seqlen_q * self.qhead_per_kvhead + ) + if const_expr( + not self.pack_gqa + or self.m_block_size % self.qhead_per_kvhead == 0 + ): + gLSE = cute.local_tile( + mLSE_cur, (self.m_block_size,), (m_tile_idx,) + ) + if tidx < seqlen_q - m_tile_idx * self.m_block_size: + # This actually just works with PackGQA too + gLSE[tidx] = lse + else: + idx = m_tile_idx * self.m_block_size + tidx + if idx < seqlen_q: + m_idx = idx // self.qhead_per_kvhead + h_idx = idx - m_idx * self.qhead_per_kvhead + lse_ptr_i64 = utils.elem_pointer( + mLSE_cur, ((h_idx, m_idx),) + ).toint() + lse_gmem_ptr = cute.make_ptr( + mLSE_cur.element_type, + lse_ptr_i64, + cute.AddressSpace.gmem, + assumed_align=4, + ) + cute.make_tensor(lse_gmem_ptr, (1,))[0] = lse + + # Advance to next tile + work_tile = tile_scheduler.advance_to_next_work() + # End of persistent scheduler loop + + # This is equivalent to pipeline_o_epi.consumer_tail() for the correction warps + if const_expr(not self.use_correction_warps_for_epi): + pipeline_o_epi.producer_acquire_w_index_phase( + self.q_stage - 1, corr_epi_producer_phase + ) + + @cute.jit + def correction_rescale( + self, + thr_mma: cute.ThrMma, + tOtO: cute.Tensor, + tidx: Int32, + scale: Float32, + ): + """Rescale intermediate attention results based on softmax normalization factor. + + This method performs a crucial correction step in the attention computation pipeline. + When processing attention in blocks, the softmax normalization factors may change + as new blocks are processed. This method rescales previously computed partial + output values to account for updated normalization factors. + + The implementation uses efficient tensor memory operations to: + 1. Load existing partial attention output from tensor memory + 2. Apply the scaling factor to all elements + 3. Store the rescaled results back to tensor memory + """ + tOcO = thr_mma.partition_C(cute.make_identity_tensor(self.mma_tiler_pv[:2])) + corr_tile_size = 16 # tuneable parameter + tmem_load_atom = cute.make_copy_atom( + tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(corr_tile_size)), + self.pv_acc_dtype, + ) + tmem_store_atom = cute.make_copy_atom( + tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(corr_tile_size)), + self.pv_acc_dtype, + ) + tOtO_i = cute.composition( + tOtO, cute.make_layout((self.m_block_size, corr_tile_size)) + ) + tOcO_i = cute.composition( + tOcO, cute.make_layout((self.m_block_size, corr_tile_size)) + ) + thr_tmem_load = tcgen05.make_tmem_copy(tmem_load_atom, tOtO_i).get_slice(tidx) + thr_tmem_store = tcgen05.make_tmem_copy(tmem_store_atom, tOtO_i).get_slice(tidx) + tOtO_t2r = thr_tmem_load.partition_S(tOtO_i) + tOrO_t2r_shape = thr_tmem_load.partition_D(tOcO_i).shape + tOtO_r2t = thr_tmem_store.partition_D(tOtO_i) + + frg_count = self.head_dim_v_padded // corr_tile_size + tOrO_frg = cute.make_rmem_tensor((tOrO_t2r_shape, frg_count), self.pv_acc_dtype) + for i in cutlass.range_constexpr(frg_count): + tOrO_frg = cute.make_rmem_tensor(tOrO_t2r_shape, self.pv_acc_dtype) + tOtO_t2r_i = cute.make_tensor( + tOtO_t2r.iterator + i * corr_tile_size, tOtO_t2r.layout + ) + cute.copy(thr_tmem_load, tOtO_t2r_i, tOrO_frg) + for j in cutlass.range(0, cute.size(tOrO_frg), 2, unroll_full=True): + tOrO_frg[j], tOrO_frg[j + 1] = cute.arch.mul_packed_f32x2( + (tOrO_frg[j], tOrO_frg[j + 1]), (scale, scale) + ) + tOtO_r2t_i = cute.make_tensor( + tOtO_r2t.iterator + i * corr_tile_size, tOtO_r2t.layout + ) + cute.copy(thr_tmem_store, tOrO_frg, tOtO_r2t_i) + cute.arch.fence_view_async_tmem_store() + + @cute.jit + def correction_epilogue( + self, + thr_mma: cute.ThrMma, + tOtO: cute.Tensor, + tidx: Int32, + stage: Int32, + m_block: Int32, + seqlen_q: Int32, + scale: Float32, + sO: cute.Tensor, + mO_cur: Optional[cute.Tensor] = None, + gO: Optional[cute.Tensor] = None, + gmem_tiled_copy_O: Optional[cute.TiledCopy] = None, + ): + """Apply final scaling and transformation to attention output before writing to global memory. + + This correction_epilogue function handles the final processing step for attention output values. + It applies a scaling factor to the accumulated attention results and prepares the + data for efficient transfer back to global memory. + + The method performs: + 1. Loading of accumulated attention results from tensor memory + 2. Application of the final output scaling factor + 3. Type conversion if necessary (typically from higher precision accumulator to output precision) + 4. Reorganization of data for optimal memory access patterns + 5. Preparation for efficient TMA store operations + + :param thr_mma: Thread MMA operation for the computation + :type thr_mma: cute.ThrMma + :param tOtO: Tensor containing accumulated attention output + :type tOtO: cute.Tensor + :param scale: Final scaling factor to apply to the output + :type scale: Float32 + :param sO: Shared memory tensor for the final output + :type sO: cute.Tensor + """ + + corr_tile_size = 8 * 32 // self.o_dtype.width + # Use CTA 0 mapping for smem partitioning since sO is per-CTA sized + tOsO = thr_mma.get_slice(0).partition_C(sO) + tOcO = thr_mma.partition_C(cute.make_identity_tensor(self.mma_tiler_pv[:2])) + + tOtO_i = cute.logical_divide( + tOtO, cute.make_layout((self.m_block_size, corr_tile_size)) + ) + tOcO_i = cute.logical_divide( + tOcO, cute.make_layout((self.m_block_size, corr_tile_size)) + ) + tOsO_i = cute.logical_divide( + tOsO, cute.make_layout((self.m_block_size, corr_tile_size)) + ) + + epi_subtile = (self.epi_tile[0], corr_tile_size) + tmem_copy_atom = sm100_utils_basic.get_tmem_load_op( + self.mma_tiler_pv, + self.o_layout, + self.o_dtype, + self.pv_acc_dtype, + epi_subtile, + use_2cta_instrs=self.use_2cta_instrs, + ) + tiled_tmem_load = tcgen05.make_tmem_copy( + tmem_copy_atom, tOtO_i[(None, None), 0] + ) + thr_tmem_load = tiled_tmem_load.get_slice(tidx) + smem_copy_atom = sm100_utils_basic.get_smem_store_op( + self.o_layout, self.o_dtype, self.pv_acc_dtype, tiled_tmem_load + ) + tiled_smem_store = cute.make_tiled_copy_D(smem_copy_atom, tiled_tmem_load) + + tOtO_t2r = thr_tmem_load.partition_S(tOtO_i[(None, None), None]) + tOsO_s2r = copy_utils.partition_D_position_independent( + thr_tmem_load, tOsO_i[(None, None), None] + ) + tOcO_t2r = thr_tmem_load.partition_D(tOcO_i[(None, None), None]) + for i in cutlass.range( + self.head_dim_v_padded // corr_tile_size, unroll_full=True + ): + tOtO_t2r_i = tOtO_t2r[None, 0, 0, i] + tOsO_r2s_i = tOsO_s2r[None, 0, 0, i] + tOrO_frg = cute.make_rmem_tensor( + tOcO_t2r[None, 0, 0, i].shape, self.pv_acc_dtype + ) + cute.copy(tiled_tmem_load, tOtO_t2r_i, tOrO_frg) + for j in cutlass.range(0, cute.size(tOrO_frg), 2, unroll_full=True): + tOrO_frg[j], tOrO_frg[j + 1] = cute.arch.mul_packed_f32x2( + (tOrO_frg[j], tOrO_frg[j + 1]), (scale, scale) + ) + copy_utils.cvt_copy(tiled_smem_store, tOrO_frg, tOsO_r2s_i) + cute.arch.fence_view_async_shared() + + if const_expr(self.use_correction_warps_for_epi): + assert not self.use_tma_O + assert gmem_tiled_copy_O is not None + cute.arch.barrier( + barrier_id=int(NamedBarrierFwdSm100.Epilogue), + number_of_threads=len(self.epilogue_warp_ids) * cute.arch.WARP_SIZE, + ) + mma_tile_coord_v = thr_mma.thr_idx + m_tile_idx = ( + m_block * self.q_stage + stage + ) * self.cta_group_size + mma_tile_coord_v + self._store_O_to_gmem( + sO, gO, mO_cur, gmem_tiled_copy_O, tidx, seqlen_q, m_tile_idx + ) + + @cute.jit + def _store_O_to_gmem( + self, + sO_stage: cute.Tensor, + gO: Optional[cute.Tensor], + mO_cur: cute.Tensor, + gmem_tiled_copy_O: cute.TiledCopy, + tidx: Int32, + seqlen_q: Int32, + m_tile_idx: Int32, + ): + """Copy a single stage of O from smem to gmem via registers.""" + gmem_thr_copy_O = gmem_tiled_copy_O.get_slice(tidx) + tOsO = gmem_thr_copy_O.partition_S(sO_stage) + cO = cute.make_identity_tensor((self.m_block_size, self.head_dim_v_padded)) + tOcO = gmem_thr_copy_O.partition_S(cO) + t0OcO = gmem_tiled_copy_O.get_slice(0).partition_S(cO) + tOpO = copy_utils.predicate_k(tOcO, limit=mO_cur.shape[1]) + pack_gqa = PackGQA( + self.m_block_size, + self.head_dim_v_padded, + self.check_hdim_v_oob, + self.qhead_per_kvhead, + ) + + # load acc O from smem to rmem for wider vectorization + tOrO = cute.make_fragment_like(tOsO, self.o_dtype) + cute.autovec_copy(tOsO, tOrO) + # copy acc O from rmem to gmem + if const_expr(not self.pack_gqa): + assert gO is not None + tOgO = gmem_thr_copy_O.partition_D(gO) + for rest_m in cutlass.range_constexpr(cute.size(tOrO.shape[1])): + if ( + t0OcO[0, rest_m, 0][0] + < seqlen_q - m_tile_idx * self.m_block_size - tOcO[0][0] + ): + cute.copy( + gmem_tiled_copy_O, + tOrO[None, rest_m, None], + tOgO[None, rest_m, None], + pred=( + tOpO[None, rest_m, None] + if const_expr(self.check_hdim_v_oob) + else None + ), + ) + else: + pack_gqa.store_O( + mO_cur, tOrO, gmem_tiled_copy_O, tidx, m_tile_idx, seqlen_q + ) + + @cute.jit + def epilogue_s2g( + self, + mO: cute.Tensor, + sO: cute.Tensor, + gmem_tiled_copy_O: cute.TiledCopy, + tma_atom_O: Optional[cute.CopyAtom], + pipeline_o_epi: pipeline.PipelineAsync, + block_info: BlockInfo, + num_splits: int, + SeqlenInfoCls: Callable, + mma_tile_coord_v: Int32 = 0, + blocksparse_tensors: Optional[BlockSparseTensors] = None, + tile_scheduler=None, + ): + epi_consumer_phase = Int32(0) + work_tile = tile_scheduler.initial_work_tile_info() + while work_tile.is_valid_tile: + m_block, head_idx, batch_idx, split_idx = work_tile.tile_idx + seqlen = SeqlenInfoCls(batch_idx) + n_block_min, n_block_max = block_info.get_n_block_min_max( + seqlen, m_block, split_idx, num_splits + ) + has_work = ( + const_expr(self.use_block_sparsity or not self.is_split_kv) + or n_block_min < n_block_max + ) + + if has_work: + if const_expr(self.is_split_kv): + mO_cur = seqlen.offset_batch_Q(mO, batch_idx, dim=3)[ + None, None, head_idx, split_idx + ] + else: + mO_cur = seqlen.offset_batch_Q(mO, batch_idx, dim=3)[ + None, None, head_idx + ] + gO = None + if const_expr(self.use_tma_O or not self.pack_gqa): + tiler_gO = ( + (self.mma_tiler_pv[0] * self.q_stage), + self.head_dim_v_padded, + ) + gO = cute.local_tile( + mO_cur, tiler_gO, (m_block, 0) + ) # (128 * 2, 128) + gO = layout_utils.select( + cute.flat_divide(gO, (self.mma_tiler_pv[0],)), mode=[0, 2, 1] + ) # (128, 128, 2) + gO = cute.flat_divide( + gO, (self.mma_tiler_pv[0] // self.cta_group_size,) + )[None, mma_tile_coord_v, None, None] + + if const_expr(self.use_tma_O): + store_O, _, _ = copy_utils.tma_get_copy_fn( + tma_atom_O, 0, cute.make_layout(1), sO, gO + ) + for stage in cutlass.range(self.q_stage, unroll_full=True): + # wait from corr, issue tma store on smem + # 1. wait for O0 / O1 final + pipeline_o_epi.consumer_wait_w_index_phase( + stage, epi_consumer_phase + ) + # 2. copy O0 / O1 to gmem + store_O(src_idx=stage, dst_idx=stage) + cute.arch.cp_async_bulk_commit_group() + for stage in cutlass.range_constexpr(self.q_stage): + # Ensure O0 / O1 buffer is ready to be released + cute.arch.cp_async_bulk_wait_group( + self.q_stage - 1 - stage, read=True + ) + pipeline_o_epi.consumer_release_w_index(stage) + else: + tidx = cute.arch.thread_idx()[0] % ( + cute.arch.WARP_SIZE * len(self.epilogue_warp_ids) + ) + for stage in cutlass.range_constexpr(self.q_stage): + # wait from corr, issue tma store on smem + # 1. wait for O0 / O1 final + pipeline_o_epi.consumer_wait_w_index_phase( + stage, epi_consumer_phase + ) + # 2. copy O0 / O1 to gmem + m_tile_idx = ( + m_block * self.q_stage + stage + ) * self.cta_group_size + mma_tile_coord_v + gO_stage = ( + gO[None, None, stage] + if const_expr(gO is not None) + else None + ) + self._store_O_to_gmem( + sO[None, None, stage], + gO_stage, + mO_cur, + gmem_tiled_copy_O, + tidx, + seqlen.seqlen_q, + m_tile_idx, + ) + pipeline_o_epi.consumer_release_w_index(stage) + + epi_consumer_phase ^= 1 + + # Advance to next tile + work_tile = tile_scheduler.advance_to_next_work() + + @cute.jit + def clc_scheduler_warp( + self, + tile_scheduler: TileSchedulerProtocol, + ): + work_tile = tile_scheduler.initial_work_tile_info() + while work_tile.is_valid_tile: + tile_scheduler.prefetch_next_work() + work_tile = tile_scheduler.advance_to_next_work() + if ( + cute.arch.thread_idx()[0] + == self.clc_scheduler_warp_id * cute.arch.WARP_SIZE + ): + fa_printf( + 3, + "[CLC] query sm={} cta={} (m_blk={},h={},b={},s={}) valid={}\n", + smid(), + cute.arch.block_idx()[0], + work_tile.tile_idx[0], + work_tile.tile_idx[1], + work_tile.tile_idx[2], + work_tile.tile_idx[3], + work_tile.is_valid_tile, + ) + tile_scheduler.producer_tail() + + @cute.jit + def empty_warp( + self, + tile_scheduler: TileSchedulerProtocol, + ): + work_tile = tile_scheduler.initial_work_tile_info() + while work_tile.is_valid_tile: + work_tile = tile_scheduler.advance_to_next_work() + + def load_Q( + self, + load_Q_fn: Callable, + pipeline_q: pipeline.PipelineAsync, + block: Int32, + stage: int, + phase: Int32, + load_SFQ_fn: Optional[Callable] = None, + ): + pipeline_q.producer_acquire_w_index_phase(stage, phase) + load_Q_fn( + src_idx=block, + dst_idx=stage, + tma_bar_ptr=pipeline_q.sync_object_full.get_barrier(stage), + ) + # TMA SFQ (interleaved) rides the Q pipeline barrier. + if const_expr(load_SFQ_fn is not None): + load_SFQ_fn( + src_idx=block, + dst_idx=stage, + tma_bar_ptr=pipeline_q.sync_object_full.get_barrier(stage), + ) + + @cute.jit + def cpasync_load_SFQ( + self, + gSFQ: cute.Tensor, # (m_block_size, sf_dim, q_stage) + sSFQ: cute.Tensor, + gmem_tiled_copy_SFQ: cute.TiledCopy, + pipeline_sfq: pipeline.PipelineAsync, + block: Int32, + stage: int, + tidx: Int32, + phase: Int32, + m_block: Int32, + seqlen_q: Int32, + ): + """cp.async one q-tile of UE8M0 scales into the SF smem atom layout. The + copies ride pipeline_sfq's cp_async commit/arrive. Out-of-range rows are + predicated off (they contribute masked scores only).""" + gmem_thr_copy_SFQ = gmem_tiled_copy_SFQ.get_slice(tidx) + gSFQ_cpt = cute.filter_zeros(gSFQ) + sSFQ_cpt = cute.filter_zeros(sSFQ) + sSFQ_cpt_shape_mdp = ( + self.m_block_size, + self.head_dim_padded // self.qk_sf_vec_size, + self.q_stage, + ) + sSFQ_cpt_layout_mdp = cute.make_ordered_layout( + sSFQ_cpt_shape_mdp, order=(0, 1, 2) + ) + sSFQ_cpt_mdp = cute.composition(sSFQ_cpt, sSFQ_cpt_layout_mdp) + tSFQgSFQ = gmem_thr_copy_SFQ.partition_S(gSFQ_cpt) + tSFQsSFQ = gmem_thr_copy_SFQ.partition_S(sSFQ_cpt_mdp) + + cSFQ = cute.make_identity_tensor( + (self.m_block_size, self.head_dim_padded // self.qk_sf_vec_size) + ) + tSFQcSFQ = gmem_thr_copy_SFQ.partition_S(cSFQ) + tSFQc0SFQ = gmem_thr_copy_SFQ.get_slice(0).partition_S(cSFQ) + + seqlen_q_row_limit = ( + seqlen_q + - m_block * self.cta_tiler[0] + - block * self.m_block_size + - tSFQcSFQ[0][0] + if m_block >= 0 + else 0 + ) + + pipeline_sfq.producer_acquire_w_index_phase(stage, phase) + + tSFQgSFQ_cur = tSFQgSFQ[None, None, None, block] + tSFQsSFQ_cur = tSFQsSFQ[None, None, None, stage] + + for m in cutlass.range_constexpr(cute.size(tSFQsSFQ_cur, mode=[1])): + row_valid = tSFQc0SFQ[0, m, 0][0] < seqlen_q_row_limit + should_load = cute.make_fragment_like( + tSFQsSFQ_cur[(0, None), m, None], cute.Boolean + ) + should_load.fill(row_valid) + cute.copy( + gmem_thr_copy_SFQ, + tSFQgSFQ_cur[None, m, None], + tSFQsSFQ_cur[None, m, None], + pred=should_load, + ) + cute.arch.cp_async_commit_group() + pipeline_sfq.sync_object_full.arrive_cp_async_mbarrier(stage) + + def load_Q_non_tma( + self, + mQ: cute.Tensor, + sQ: cute.Tensor, + gmem_tiled_copy_Q: cute.TiledCopy, + pipeline_q: pipeline.PipelineAsync, + tidx: Int32, + seqlen_q: Int32, + m_block: Int32, + block: Int32, + stage: int, + phase: Int32, + cpasync_load_SFQ: Optional[Callable] = None, + ): + assert self.cta_group_size == 1, "cta_group_size must be 1 for non-tma Q load" + pipeline_q.producer_acquire_w_index_phase(stage, phase) + pack_gqa = PackGQA( + self.m_block_size, + self.head_dim_padded, + self.check_hdim_oob, + self.qhead_per_kvhead, + ) + sQ_stage = sQ[None, None, None, stage] + sQ_pi = cute.make_tensor( + sQ_stage.iterator, + cute.make_layout( + (sQ_stage.shape[0][0], (sQ_stage.shape[0][1], sQ_stage.shape[2])), + stride=( + sQ_stage.stride[0][0], + (sQ_stage.stride[0][1], sQ_stage.stride[2]), + ), + ), + ) + pack_gqa.load_Q( + mQ, sQ_pi, gmem_tiled_copy_Q, tidx, m_block * self.q_stage + block, seqlen_q + ) + cute.arch.cp_async_commit_group() + pipeline_q.sync_object_full.arrive_cp_async_mbarrier(stage) + + @cute.jit + def load_KV( + self, + tma_atom: Optional[cute.CopyAtom], + tXgX: Optional[cute.Tensor], + tXsX: Optional[cute.Tensor], + paged_kv_manager: Optional[PagedKVManager], + sX: cute.Tensor, + block: Int32, + pipeline_kv: pipeline.PipelineAsync, + producer_state: pipeline.PipelineState, + K_or_V: Literal["K", "V"], + page_idx: Optional[Int32] = None, + extra_tx_count: Optional[Int32] = None, + tma_atom_sf: Optional[cute.CopyAtom] = None, + tXgSFX: Optional[cute.Tensor] = None, + tXsSFX: Optional[cute.Tensor] = None, + sSFX: Optional[cute.Tensor] = None, + stage_dilation: cutlass.Constexpr[int] = 1, + ): + assert K_or_V in ("K", "V") + blockscaled: cutlass.Constexpr[bool] = all( + t is not None for t in (tma_atom_sf, tXgSFX, tXsSFX, sSFX) + ) + stage, phase = producer_state.index, producer_state.phase + sf_stage = stage + stage *= stage_dilation + sf_key = "SFK" if const_expr(K_or_V == "K") else "SFV" + # V (with its SFV) rides pipeline_vq whose tx_count already covers V+SFV. + # K rides pipeline_kv (tx_count=K), so add the K/V head-dim delta and SFK. + if const_expr(K_or_V == "V" and self.v_dequant): + extra_tx_count_kv = 0 + else: + extra_tx_count_kv = self.tma_copy_bytes[K_or_V] - self.tma_copy_bytes["K"] + if const_expr(K_or_V == "K" and blockscaled): + extra_tx_count_kv += self.tma_copy_bytes[sf_key] + extra_tx_count = ( + extra_tx_count_kv + (extra_tx_count if extra_tx_count is not None else 0) + if const_expr(self.use_tma_KV) + else None + ) + extra_kwargs = ( + {"extra_tx_count": extra_tx_count} if const_expr(self.use_tma_KV) else {} + ) + pipeline_kv.producer_acquire(producer_state, **extra_kwargs) + if const_expr(K_or_V == "K" and self.uneven_kv_smem): + # Before this round, the smem location was occupied by V, which is smaller than + # K. So we need to wait for the stage after that (stage 1) to be empty as well. + if stage == 0: + pipeline_kv.sync_object_empty.wait(1, phase) + + if const_expr(self.use_tma_KV): + assert tXgX is not None and tXsX is not None and tma_atom is not None + tXsX_cur = tXsX[None, stage] + if const_expr(self.uneven_kv_smem): + # Since this is the producer_state, the phase starts at 1, so we have to invert it + tXsX_cur = self.offset_kv_smem(tXsX_cur, stage, phase ^ 1) + # Currently we assume that page_size == n_block_size so we index into tXgX with block = 0 + tXgX_cur = ( + tXgX[None, block] + if const_expr(page_idx is None) + else tXgX[None, 0, page_idx] + ) + cute.copy( + tma_atom, + tXgX_cur, + tXsX_cur, + tma_bar_ptr=pipeline_kv.producer_get_barrier(producer_state), + ) + if const_expr(blockscaled): + tXsSFX_cur = tXsSFX[None, sf_stage] + tXgSFX_cur = ( + tXgSFX[None, block] + if const_expr(page_idx is None) + else tXgSFX[None, 0, page_idx] + ) + cute.copy( + tma_atom_sf, + tXgSFX_cur, + tXsSFX_cur, + tma_bar_ptr=pipeline_kv.producer_get_barrier(producer_state), + ) + else: + assert paged_kv_manager is not None + sX_cur = sX[None, None, None, stage] + if const_expr(self.uneven_kv_smem): + sX_cur = self.offset_kv_smem(sX_cur, stage, phase ^ 1) + paged_kv_manager.load_KV(block, sX_cur, K_or_V) + if const_expr(sSFX is not None): + paged_kv_manager.load_sf_KV( + block, sSFX[None, None, None, sf_stage], K_or_V + ) + cute.arch.cp_async_commit_group() + pipeline_kv.sync_object_full.arrive_cp_async_mbarrier(stage) + + @cute.jit + def offset_kv_smem(self, sX: cute.Tensor, stage: Int32, phase: Int32): + if const_expr(self.uneven_kv_smem): + # smem layout is [smem_large, smem_small, smem_large], and the current stride is + # (smem_large + smem_small) // 2. So for stage == 1, move right by offset if + # phase == 0, or left by offset if phase == 1. + offset = 0 if stage != 1 else self.uneven_kv_smem_offset * (1 - 2 * phase) + # Hint that the offset is 128-bit aligned so that + # ptr + offset preserves the alignment needed by cp.async. + offset = cute.assume(offset, divby=128 // self.k_dtype.width) + return cute.make_tensor(sX.iterator + offset, sX.layout) + else: + return sX + + # @cute.jit + # def warp_scheduler_barrier_init(self): + # warp_group_idx = utils.canonical_warp_group_idx(sync=False) + # if warp_group_idx == 0: + # cute.arch.barrier_arrive( + # barrier_id=int(NamedBarrierFwdSm100.WarpSchedulerWG1), number_of_threads=2 * 128, + # ) + + # def warp_scheduler_barrier_sync(self): + # cute.arch.barrier( + # barrier_id=int(NamedBarrierFwdSm100.WarpSchedulerWG1) + utils.canonical_warp_group_idx(sync=False), + # number_of_threads=2 * 128 + # ) + + # def warp_scheduler_barrier_arrive(self): + # cur_wg = utils.canonical_warp_group_idx(sync=False) + # next_wg = 1 - cur_wg + # cute.arch.barrier_arrive( + # barrier_id=int(NamedBarrierFwdSm100.WarpSchedulerWG1) + next_wg, number_of_threads=2 * 128, + # ) + + @cute.jit + def apply_score_mod( + self, + tSrS_t2r, + thr_tmem_load, + thr_mma_qk, + batch_idx, + head_idx, + m_block, + n_block, + softmax, + seqlen: SeqlenInfoQK, + aux_data: AuxData = AuxData(), + fastdiv_mods=(None, None), + head_divmod=None, + ): + """Apply score modification for SM100 (constant q_idx).""" + # Prepare index tensor with extra partition + cS = cute.make_identity_tensor((self.m_block_size, self.n_block_size)) + cS = cute.domain_offset( + (m_block * self.m_block_size, n_block * self.n_block_size), cS + ) + tScS = thr_mma_qk.partition_C(cS) + tScS = tScS[(None, None), 0, 0] + tScS_t2r = thr_tmem_load.partition_D(tScS) + + # Shared q_idx for all scores + q_idx_logical = tScS_t2r[0][0] + + # For Pack-GQA, compute the logical head index for this tile + if cutlass.const_expr(self.pack_gqa): + assert head_divmod is not None + # Building up the logical q_head idx: final_q_head = kv_head * qhead_per_kvhead + (q_physical % qhead_per_kvhead) + q_physical = q_idx_logical + q_idx_logical, head_offset = divmod(q_physical, head_divmod) + head_idx = head_idx * self.qhead_per_kvhead + head_offset + + if cutlass.const_expr(aux_data.tensors is not None): + seqlen_q_divmod, _ = fastdiv_mods + _, q_idx_logical = divmod(q_idx_logical, seqlen_q_divmod) + + apply_score_mod_inner( + tSrS_t2r, + tScS_t2r, + self.score_mod, + batch_idx, + head_idx, + softmax.softmax_scale, + self.score_vec_size, + self.qk_acc_dtype, + aux_data, + fastdiv_mods, + seqlen_info=seqlen, + constant_q_idx=q_idx_logical, + qhead_per_kvhead=( + self.qhead_per_kvhead if cutlass.const_expr(self.pack_gqa) else 1 + ), + ) diff --git a/python/sglang/jit_kernel/flash_attn/cute/flash_fwd_sm120.py b/python/sglang/jit_kernel/flash_attn/cute/flash_fwd_sm120.py new file mode 100644 index 000000000..03598802d --- /dev/null +++ b/python/sglang/jit_kernel/flash_attn/cute/flash_fwd_sm120.py @@ -0,0 +1,61 @@ +# Copyright (c) 2025, Jay Shah, Ganesh Bikshandi, Ying Zhang, Vijay Thakkar, Pradeep Ramani, Tri Dao. +# SM120 (Blackwell GeForce / DGX Spark) forward pass. +# +# SM120 uses the same SM80-era MMA instructions (mma.sync.aligned.m16n8k16) but has +# a smaller shared memory capacity (99 KB vs 163 KB on SM80). This module subclasses +# FlashAttentionForwardSm80 and overrides the SMEM capacity check accordingly. + +import cutlass +import cutlass.utils as utils_basic + +from sglang.jit_kernel.flash_attn.cute.flash_fwd import FlashAttentionForwardSm80 + + +class FlashAttentionForwardSm120(FlashAttentionForwardSm80): + # Keep arch = 80 to use CpAsync code paths (no TMA for output). + # The compilation target is determined by the GPU at compile time, not this field. + arch = 80 + + @staticmethod + def can_implement( + dtype, + head_dim, + head_dim_v, + tile_m, + tile_n, + num_stages, + num_threads, + is_causal, + Q_in_regs=False, + ) -> bool: + """Check if the kernel can be implemented on SM120. + + Same logic as SM80 but uses SM120's shared memory capacity (99 KB). + """ + if dtype not in [cutlass.Float16, cutlass.BFloat16]: + return False + if head_dim % 8 != 0: + return False + if head_dim_v % 8 != 0: + return False + if tile_n % 16 != 0: + return False + if num_threads % 32 != 0: + return False + # Shared memory usage: Q tile + (K tile + V tile) + smem_usage_Q = tile_m * head_dim * 2 + smem_usage_K = tile_n * head_dim * num_stages * 2 + smem_usage_V = tile_n * head_dim_v * num_stages * 2 + smem_usage_QV = ( + (smem_usage_Q + smem_usage_V) + if not Q_in_regs + else max(smem_usage_Q, smem_usage_V) + ) + smem_usage = smem_usage_QV + smem_usage_K + # SM120 has 99 KB shared memory (vs 163 KB on SM80) + smem_capacity = utils_basic.get_smem_capacity_in_bytes("sm_120") + if smem_usage > smem_capacity: + return False + if (tile_m * 2) % num_threads != 0: + return False + return True diff --git a/python/sglang/jit_kernel/flash_attn/cute/flash_fwd_sm90.py b/python/sglang/jit_kernel/flash_attn/cute/flash_fwd_sm90.py new file mode 100644 index 000000000..eae52665b --- /dev/null +++ b/python/sglang/jit_kernel/flash_attn/cute/flash_fwd_sm90.py @@ -0,0 +1,2127 @@ +# Copyright (c) 2025, Jay Shah, Ganesh Bikshandi, Ying Zhang, Vijay Thakkar, Pradeep Ramani, Tri Dao. +# SM90 (Hopper) forward pass for flash attention, extracted from flash_fwd.py. + +from functools import partial +from types import SimpleNamespace +from typing import Callable, Literal, Optional + +import cuda.bindings.driver as cuda +import cutlass +import cutlass.cute as cute +import cutlass.utils.hopper_helpers as sm90_utils_basic +from cutlass import Float32, Int32, const_expr, pipeline +from cutlass.base_dsl.arch import Arch +from cutlass.cute import FastDivmodDivisor +from cutlass.cute.nvgpu import cpasync, warpgroup +from cutlass.pipeline import pipeline_init_arrive, pipeline_init_wait +from cutlass.utils import LayoutEnum +from quack import copy_utils, layout_utils, sm90_utils +from quack.cute_dsl_utils import ParamsBase + +from sglang.jit_kernel.flash_attn.cute import pipeline as pipeline_custom +from sglang.jit_kernel.flash_attn.cute import utils +from sglang.jit_kernel.flash_attn.cute.block_info import BlockInfo +from sglang.jit_kernel.flash_attn.cute.block_sparse_utils import ( + consume_block_sparse_loads, + produce_block_sparse_loads, +) +from sglang.jit_kernel.flash_attn.cute.block_sparsity import BlockSparseTensors +from sglang.jit_kernel.flash_attn.cute.cute_dsl_utils import assume_tensor_aligned +from sglang.jit_kernel.flash_attn.cute.flash_fwd import FlashAttentionForwardBase +from sglang.jit_kernel.flash_attn.cute.mask import AttentionMask +from sglang.jit_kernel.flash_attn.cute.named_barrier import NamedBarrierFwd +from sglang.jit_kernel.flash_attn.cute.pack_gqa import ( + PackGQA, + make_packgqa_tiled_tma_atom, + pack_gqa_layout, +) +from sglang.jit_kernel.flash_attn.cute.paged_kv import PagedKVManager +from sglang.jit_kernel.flash_attn.cute.seqlen_info import SeqlenInfoQK +from sglang.jit_kernel.flash_attn.cute.softmax import Softmax, apply_score_mod_inner +from sglang.jit_kernel.flash_attn.cute.tile_scheduler import ( + SingleTileLPTScheduler, + SingleTileScheduler, + SingleTileVarlenScheduler, + TileSchedulerArguments, +) +from sglang.jit_kernel.flash_attn.cute.utils import AuxData + + +class FlashAttentionForwardSm90(FlashAttentionForwardBase): + def __init__( + self, + *args, + intra_wg_overlap: bool = True, + mma_pv_is_rs: bool = True, + paged_kv_non_tma: bool = False, + has_bias: bool = False, + bias_block_size: int = 128, + rel_extent_padded: int = 128, + **kwargs, + ): + super().__init__(*args, **kwargs) + self.intra_wg_overlap = intra_wg_overlap + self.mma_pv_is_rs = mma_pv_is_rs + self.buffer_align_bytes = 1024 + self.use_tma_KV = not paged_kv_non_tma + self.has_bias = has_bias + self.rel_extent_padded = rel_extent_padded + if has_bias: + assert rel_extent_padded % self.tile_n == 0 + self.bias_n_max = rel_extent_padded // self.tile_n if has_bias else 0 + self.bias_block_size = bias_block_size + assert self.use_tma_KV or not ( + self.check_hdim_oob or self.check_hdim_v_oob + ), "Paged KV does not support irregular head dim" + self.cluster_shape_mn = (1, 1) + assert self.arch.is_family_of(Arch.sm_90a), "Only SM 9.x is supported" + + def _get_smem_layout_atom(self): + sQ_layout_atom = warpgroup.make_smem_layout_atom( + sm90_utils_basic.get_smem_layout_atom( + LayoutEnum.ROW_MAJOR, self.dtype, self.tile_hdim + ), + self.dtype, + ) + sK_layout_atom = sQ_layout_atom + sV_layout_atom = warpgroup.make_smem_layout_atom( + sm90_utils_basic.get_smem_layout_atom( + LayoutEnum.ROW_MAJOR, self.dtype, self.tile_hdimv + ), + self.dtype, + ) + sO_layout_atom = sV_layout_atom + if not self.mma_pv_is_rs: + sP_layout_atom = warpgroup.make_smem_layout_atom( + sm90_utils_basic.get_smem_layout_atom( + LayoutEnum.ROW_MAJOR, self.dtype, self.tile_n + ), + self.dtype, + ) + else: + sP_layout_atom = None + return ( + sQ_layout_atom, + sK_layout_atom, + sV_layout_atom, + sO_layout_atom, + sP_layout_atom, + ) + + def _get_tiled_mma(self): + tiled_mma_qk = sm90_utils_basic.make_trivial_tiled_mma( + self.dtype, + self.dtype, + warpgroup.OperandMajorMode.K, + warpgroup.OperandMajorMode.K, + Float32, + atom_layout_mnk=(self.tile_m // 64, 1, 1), + tiler_mn=(64, self.tile_n), + ) + tiled_mma_pv = sm90_utils_basic.make_trivial_tiled_mma( + self.dtype, + self.dtype, + warpgroup.OperandMajorMode.K, + warpgroup.OperandMajorMode.MN, + Float32, + atom_layout_mnk=( + self.tile_m // 64, + 1, + 1, + ), # Might need (1, 2, 1) for hdim 512 + tiler_mn=(64, self.tile_hdimv), + a_source=( + warpgroup.OperandSource.RMEM + if self.mma_pv_is_rs + else warpgroup.OperandSource.SMEM + ), + ) + return tiled_mma_qk, tiled_mma_pv + + def _get_shared_storage_cls(self): + sQ_struct, sK_struct, sV_struct = [ + cute.struct.Align[ + cute.struct.MemRange[self.dtype, cute.cosize(layout)], + self.buffer_align_bytes, + ] + for layout in (self.sQ_layout, self.sK_layout, self.sV_layout) + ] + cosize_sQV = max(cute.cosize(self.sQ_layout), cute.cosize(self.sV_layout)) + sQV_struct = cute.struct.Align[ + cute.struct.MemRange[self.dtype, cosize_sQV], 1024 + ] + cosize_sP = ( + cute.cosize(self.sP_layout) if const_expr(self.sP_layout is not None) else 0 + ) + sP_struct = cute.struct.Align[cute.struct.MemRange[self.dtype, cosize_sP], 1024] + # 1 stage * 2 for Q pipeline (full + empty), self.num_stages*2 for K, self.num_stages*2 for V, + mbar_ptr_Q_struct = cute.struct.MemRange[cutlass.Int64, 1 * 2] + mbar_ptr_K_struct = cute.struct.MemRange[cutlass.Int64, self.num_stages * 2] + mbar_ptr_V_struct = cute.struct.MemRange[cutlass.Int64, self.num_stages * 2] + mbar_ptr_Bias_struct = cute.struct.MemRange[cutlass.Int64, self.num_stages * 2] + sBias_struct = cute.struct.Align[ + cute.struct.MemRange[ + self.bias_dtype if const_expr(self.has_bias) else cutlass.BFloat16, + cute.cosize(self.sBias_layout) if const_expr(self.has_bias) else 0, + ], + 1024, + ] + + @cute.struct + class SharedStorageQKV: + mbar_ptr_Q: mbar_ptr_Q_struct + mbar_ptr_K: mbar_ptr_K_struct + mbar_ptr_V: mbar_ptr_V_struct + sV: sV_struct + sQ: sQ_struct + sK: sK_struct + sP: sP_struct + + @cute.struct + class SharedStorageSharedQV: + mbar_ptr_Q: mbar_ptr_Q_struct + mbar_ptr_K: mbar_ptr_K_struct + mbar_ptr_V: mbar_ptr_V_struct + sQ: sQV_struct + sK: sK_struct + sP: sP_struct + + @cute.struct + class SharedStorageQKVBias: + mbar_ptr_Q: mbar_ptr_Q_struct + mbar_ptr_K: mbar_ptr_K_struct + mbar_ptr_V: mbar_ptr_V_struct + mbar_ptr_Bias: mbar_ptr_Bias_struct + sV: sV_struct + sQ: sQ_struct + sK: sK_struct + sBias: sBias_struct + sP: sP_struct + + @cute.struct + class SharedStorageSharedQVBias: + mbar_ptr_Q: mbar_ptr_Q_struct + mbar_ptr_K: mbar_ptr_K_struct + mbar_ptr_V: mbar_ptr_V_struct + mbar_ptr_Bias: mbar_ptr_Bias_struct + sQ: sQV_struct + sK: sK_struct + sBias: sBias_struct + sP: sP_struct + + if const_expr(self.has_bias): + return ( + SharedStorageQKVBias + if const_expr(not self.Q_in_regs) + else SharedStorageSharedQVBias + ) + return ( + SharedStorageQKV + if const_expr(not self.Q_in_regs) + else SharedStorageSharedQV + ) + + @cute.jit + def __call__( + self, + mQ: cute.Tensor, # (b, s_q, h, d) or (total_q, h, d) if there is cu_seqlens_q + mK: cute.Tensor, # (b_k, s_k, h_k, d) or (total_k, h_k, d) if there is cu_seqlens_k or (num_pages, page_size, h_k, d) if there is page_table + mV: cute.Tensor, # (b_k, s_k, h_k, dv) or (total_k, h_k, dv) if there is cu_seqlens_k or (num_pages, page_size, h_k, dv) if there is page_table + mO: cute.Tensor, # (b, s_q, h, dv) or (total_q, h, dv) if there is cu_seqlens_q + mLSE: Optional[cute.Tensor], + softmax_scale: Float32, + mCuSeqlensQ: Optional[cute.Tensor] = None, + mCuSeqlensK: Optional[cute.Tensor] = None, + mSeqUsedQ: Optional[cute.Tensor] = None, + mSeqUsedK: Optional[cute.Tensor] = None, + mPageTable: Optional[cute.Tensor] = None, # (b_k, max_num_pages_per_seq) + window_size_left: Int32 | int | None = None, + window_size_right: Int32 | int | None = None, + learnable_sink: Optional[cute.Tensor] = None, + blocksparse_tensors: Optional[BlockSparseTensors] = None, + aux_data: AuxData = AuxData(), + mBias: Optional[ + cute.Tensor + ] = None, # (b, s_q, h, rel_extent_padded) or (total_q, h, rel_extent_padded) + # Always keep stream as the last parameter (EnvStream: obtained implicitly via TVM FFI). + stream: cuda.CUstream = None, + ): + """Configures and launches the flash attention kernel. + + mQ/mK/mV/mO has same data types(supports fp16 and bf16) and same layout: + (batch_size, seqlen_q, num_head, head_dim):(_, _, _, 1) + """ + + self._check_type( + *( + t.element_type if t is not None else None + for t in ( + mQ, + mK, + mV, + mO, + mLSE, + mCuSeqlensQ, + mCuSeqlensK, + mSeqUsedQ, + mSeqUsedK, + ) + ) + ) + + self.varlen_q = mCuSeqlensQ is not None or mSeqUsedQ is not None + + self.bias_dtype = ( + mBias.element_type if const_expr(mBias is not None) else self.dtype + ) + mQ, mK, mV, mO = [assume_tensor_aligned(t) for t in (mQ, mK, mV, mO)] + if const_expr(mBias is not None): + mBias = assume_tensor_aligned(mBias) + Q_layout_transpose = ( + [1, 3, 2, 0] if const_expr(mCuSeqlensQ is None) else [0, 2, 1] + ) + mQ = layout_utils.select(mQ, Q_layout_transpose) + if const_expr(mBias is not None): + mBias = layout_utils.select(mBias, Q_layout_transpose) + KV_layout_transpose = ( + [1, 3, 2, 0] if const_expr(mCuSeqlensK is None) else [0, 2, 1] + ) + mK, mV = [layout_utils.select(t, KV_layout_transpose) for t in (mK, mV)] + if const_expr(self.is_split_kv): + num_splits = mO.shape[0] + O_layout_transpose = ( + [2, 4, 3, 1, 0] if const_expr(mCuSeqlensQ is None) else [1, 3, 2, 0] + ) + LSE_layout_transpose = ( + [3, 2, 1, 0] if const_expr(mCuSeqlensQ is None) else [2, 1, 0] + ) + else: + num_splits = Int32(1) + O_layout_transpose = ( + [1, 3, 2, 0] if const_expr(mCuSeqlensQ is None) else [0, 2, 1] + ) + LSE_layout_transpose = ( + [2, 1, 0] if const_expr(mCuSeqlensQ is None) else [1, 0] + ) + mO = layout_utils.select(mO, O_layout_transpose) + mLSE = ( + layout_utils.select(mLSE, LSE_layout_transpose) + if const_expr(mLSE is not None) + else None + ) + + tiled_mma_qk, tiled_mma_pv = self._get_tiled_mma() + self.num_mma_threads = tiled_mma_qk.size + self.num_threads_per_warp_group = 128 + self.num_wg_mma = self.num_mma_threads // self.num_threads_per_warp_group + assert self.num_wg_mma in [1, 2, 3] + self.num_threads = self.num_threads_per_warp_group * (self.num_wg_mma + 1) + self.num_producer_threads = 32 + self.num_Q_load_threads = self.num_threads_per_warp_group # If not TMA_Q + self.num_epilogue_threads = self.num_mma_threads + self.num_mma_regs, self.num_producer_regs = { + 1: (256, 56), + 2: (240, 24), + 3: (160, 32), + }[self.num_wg_mma] + self.use_block_sparsity = cutlass.const_expr(blocksparse_tensors is not None) + if cutlass.const_expr(self.use_block_sparsity and self.has_bias): + raise NotImplementedError( + "Block sparsity + sheared bias is not supported on SM90" + ) + + self.use_scheduler_barrier = ( + (self.num_wg_mma >= 2 and self.tile_hdim <= 128) + if const_expr(self.intra_wg_overlap) + else (self.num_wg_mma == 2) + ) + self.use_tma_Q = self.arch >= Arch.sm_90 and not ( + self.pack_gqa and self.tile_m % self.qhead_per_kvhead != 0 + ) + # Split partials are float32; store them straight from registers (no TMA O). + self.use_tma_O = self.use_tma_Q and not self.is_split_kv + # Producer needs more registers when doing cp.async Q or KV loads + if const_expr( + self.num_wg_mma == 2 and (not self.use_tma_Q or not self.use_tma_KV) + ): + self.num_mma_regs, self.num_producer_regs = 224, 40 + self.rescale_O_before_gemm = self.tile_hdimv > 128 and self.intra_wg_overlap + self._setup_attributes() + # TODO: we prob don't need most of what's in _setup_attributes + self.sQ_layout, self.sK_layout, self.sV_layout, self.sO_layout = [ + sm90_utils.make_smem_layout(elem_type, LayoutEnum.ROW_MAJOR, shape, stage) + for elem_type, shape, stage in [ + (mQ.element_type, (self.tile_m, self.tile_hdim), None), + (mK.element_type, (self.tile_n, self.tile_hdim), self.num_stages), + (mV.element_type, (self.tile_n, self.tile_hdimv), self.num_stages), + # sO is the fp16/bf16 store buffer; unused for split: mO is fp32. + (self.dtype, (self.tile_m, self.tile_hdimv), None), + ] + ] + self.sP_layout = None + if const_expr(not self.mma_pv_is_rs): + self.sP_layout = sm90_utils.make_smem_layout( + mV.element_type, LayoutEnum.ROW_MAJOR, (self.tile_m, self.tile_n) + ) + if const_expr(mBias is not None): + self.sBias_layout = sm90_utils.make_smem_layout( + self.bias_dtype, + LayoutEnum.ROW_MAJOR, + (self.bias_block_size, self.tile_n), + self.num_stages, + ) + else: + self.sBias_layout = None + + SharedStorage = self._get_shared_storage_cls() + + mQ_og, mO_og = mQ, mO + if const_expr(self.pack_gqa): + nheads_kv = mK.shape[2] + mQ = pack_gqa_layout(mQ, self.qhead_per_kvhead, nheads_kv, head_idx=2) + mO = pack_gqa_layout(mO, self.qhead_per_kvhead, nheads_kv, head_idx=2) + if const_expr(mBias is not None): + mBias = pack_gqa_layout( + mBias, self.qhead_per_kvhead, nheads_kv, head_idx=2 + ) + if const_expr(mLSE is not None): + mLSE = pack_gqa_layout( + mLSE, self.qhead_per_kvhead, nheads_kv, head_idx=1 + ) + + # TMA + gmem_tiled_copy_Q = cpasync.CopyBulkTensorTileG2SOp() + gmem_tiled_copy_KV = cpasync.CopyBulkTensorTileG2SOp() # Might multicast + gmem_tiled_copy_O = cpasync.CopyBulkTensorTileS2GOp() + gmem_tiled_copy_Bias = cpasync.CopyBulkTensorTileG2SOp() + self.tma_copy_bytes = { + name: cute.size_in_bytes(mX.element_type, cute.select(layout, mode=[0, 1])) + for name, mX, layout in [ + ("Q", mQ, self.sQ_layout), + ("K", mK, self.sK_layout), + ("V", mV, self.sV_layout), + ] + } + if const_expr(mBias is not None): + self.tma_copy_bytes["Bias"] = cute.size_in_bytes( + self.bias_dtype, cute.select(self.sBias_layout, mode=[0, 1]) + ) + make_tiled_tma_atom_fn = ( + partial( + make_packgqa_tiled_tma_atom, + qhead_per_kvhead=self.qhead_per_kvhead, + head_idx=2, + ) + if const_expr(self.pack_gqa) + else cpasync.make_tiled_tma_atom + ) + tma_atom_Q, tma_tensor_Q = None, None + if const_expr(self.use_tma_Q): + tma_atom_Q, tma_tensor_Q = make_tiled_tma_atom_fn( + gmem_tiled_copy_Q, + mQ_og if const_expr(self.pack_gqa) else mQ, + self.sQ_layout, + (self.tile_m, self.tile_hdim), # No mcast + ) + tma_atom_K, tma_tensor_K = None, None + tma_atom_V, tma_tensor_V = None, None + if const_expr(self.use_tma_KV): + tma_atom_K, tma_tensor_K = cpasync.make_tiled_tma_atom( + gmem_tiled_copy_KV, + mK, + cute.select(self.sK_layout, mode=[0, 1]), + (self.tile_n, self.tile_hdim), + 1, # No mcast for now + ) + tma_atom_V, tma_tensor_V = cpasync.make_tiled_tma_atom( + gmem_tiled_copy_KV, + mV, + cute.select(self.sV_layout, mode=[0, 1]), + (self.tile_n, self.tile_hdimv), + 1, # No mcast for now + ) + tma_atom_Bias, tma_tensor_Bias = None, None + if const_expr(mBias is not None): + tma_atom_Bias, tma_tensor_Bias = cpasync.make_tiled_tma_atom( + gmem_tiled_copy_Bias, + mBias, + cute.select(self.sBias_layout, mode=[0, 1]), + (self.bias_block_size, self.tile_n), + 1, # No mcast + ) + tma_atom_O, tma_tensor_O = None, None + if const_expr(self.use_tma_O): + mO_tma = mO_og if const_expr(self.pack_gqa) else mO + if const_expr(self.varlen_q): + mO_tma = copy_utils.create_ragged_tensor_for_tma( + mO_tma, ragged_dim=0, ptr_shift=True + ) + tma_atom_O, tma_tensor_O = make_tiled_tma_atom_fn( + gmem_tiled_copy_O, + mO_tma, + self.sO_layout, + (self.tile_m, self.tile_hdimv), # No mcast + ) + if const_expr(mCuSeqlensQ is not None or mSeqUsedQ is not None): + TileScheduler = SingleTileVarlenScheduler + else: + TileScheduler = ( + SingleTileScheduler + if const_expr(not self.is_causal or self.is_local) + else SingleTileLPTScheduler + ) + tile_sched_args = TileSchedulerArguments( + cute.ceil_div(cute.size(mQ.shape[0]), self.tile_m), + cute.size(mQ.shape[2]), + ( + cute.size(mQ.shape[3]) + if const_expr(mCuSeqlensQ is None) + else cute.size(mCuSeqlensQ.shape[0] - 1) + ), + num_splits, + ( + cute.size(mK.shape[0]) + if const_expr(mPageTable is None) + else mK.shape[0] * mPageTable.shape[1] + ), + mQ.shape[1], + mV.shape[1], + total_q=( + cute.size(mQ.shape[0]) + if const_expr(mCuSeqlensQ is not None) + else cute.size(mQ.shape[0]) * cute.size(mQ.shape[3]) + ), + tile_shape_mn=(self.tile_m, self.tile_n), + mCuSeqlensQ=mCuSeqlensQ, + mSeqUsedQ=mSeqUsedQ, + qhead_per_kvhead_packgqa=( + self.qhead_per_kvhead if const_expr(self.pack_gqa) else 1 + ), + element_size=self.dtype.width // 8, + is_persistent=False, + lpt=self.is_causal or self.is_local, + is_split_kv=self.is_split_kv, + ) + tile_sched_params = TileScheduler.to_underlying_arguments(tile_sched_args) + grid_dim = TileScheduler.get_grid_shape(tile_sched_params) + if const_expr(self.has_bias): + base_softmax_scale = softmax_scale + softmax_scale_log2, softmax_scale = utils.LOG2_E, None + else: + base_softmax_scale = None + softmax_scale_log2, softmax_scale = utils.compute_softmax_scale_log2( + softmax_scale, self.score_mod + ) + window_size_left = ( + Int32(window_size_left) if window_size_left is not None else None + ) + window_size_right = ( + Int32(window_size_right) if window_size_right is not None else None + ) + fastdiv_mods = utils.compute_fastdiv_mods( + mQ, mK, self.qhead_per_kvhead, self.pack_gqa, aux_data.tensors, mPageTable + ) + + self.kernel( + tma_tensor_Q if const_expr(self.use_tma_Q) else mQ, + tma_tensor_K if const_expr(self.use_tma_KV) else mK, + tma_tensor_V if const_expr(self.use_tma_KV) else mV, + tma_tensor_O if const_expr(self.use_tma_O) else mO, + mLSE, + mCuSeqlensQ, + mCuSeqlensK, + mSeqUsedQ, + mSeqUsedK, + mPageTable, + tma_atom_Q, + tma_atom_K, + tma_atom_V, + tma_atom_O, + tma_tensor_Bias, + tma_atom_Bias, + softmax_scale_log2, + softmax_scale, + base_softmax_scale, + window_size_left, + window_size_right, + learnable_sink, + blocksparse_tensors, + self.sQ_layout, + self.sK_layout, + self.sV_layout, + self.sO_layout, + self.sP_layout, + self.sBias_layout, + self.gmem_tiled_copy_Q, + self.gmem_tiled_copy_K, + self.gmem_tiled_copy_V, + self.gmem_tiled_copy_O, + tiled_mma_qk, + tiled_mma_pv, + tile_sched_params, + TileScheduler, + SharedStorage, + num_splits, + aux_data, + fastdiv_mods, + ).launch( + grid=grid_dim, + block=[self.num_threads, 1, 1], + stream=stream, + min_blocks_per_mp=1, + ) + + @cute.kernel + def kernel( + self, + mQ: cute.Tensor, + mK: cute.Tensor, + mV: cute.Tensor, + mO: cute.Tensor, + mLSE: Optional[cute.Tensor], + mCuSeqlensQ: Optional[cute.Tensor], + mCuSeqlensK: Optional[cute.Tensor], + mSeqUsedQ: Optional[cute.Tensor], + mSeqUsedK: Optional[cute.Tensor], + mPageTable: Optional[cute.Tensor], + tma_atom_Q: Optional[cute.CopyAtom], + tma_atom_K: Optional[cute.CopyAtom], + tma_atom_V: Optional[cute.CopyAtom], + tma_atom_O: Optional[cute.CopyAtom], + mBias: Optional[cute.Tensor], + tma_atom_Bias: Optional[cute.CopyAtom], + softmax_scale_log2: Float32, + softmax_scale: Optional[Float32], + base_softmax_scale: Optional[Float32], + window_size_left: Optional[Int32], + window_size_right: Optional[Int32], + learnable_sink: Optional[cute.Tensor], + blocksparse_tensors: Optional[BlockSparseTensors], + sQ_layout: cute.ComposedLayout, + sK_layout: cute.ComposedLayout, + sV_layout: cute.ComposedLayout, + sO_layout: cute.ComposedLayout, + sP_layout: cute.ComposedLayout | None, + sBias_layout: cute.ComposedLayout | None, + gmem_tiled_copy_Q: cute.TiledCopy, + gmem_tiled_copy_K: cute.TiledCopy, + gmem_tiled_copy_V: cute.TiledCopy, + gmem_tiled_copy_O: cute.TiledCopy, + tiled_mma_qk: cute.TiledMma, + tiled_mma_pv: cute.TiledMma, + tile_sched_params: ParamsBase, + TileScheduler: cutlass.Constexpr[Callable], + SharedStorage: cutlass.Constexpr[Callable], + num_splits: Int32 = 1, + aux_data: AuxData = AuxData(), + fastdiv_mods=None, + ): + warp_idx = cute.arch.make_warp_uniform(cute.arch.warp_idx()) + # Prefetch tma descriptor + if warp_idx == 0: + for tma_atom in ( + tma_atom_Q, + tma_atom_K, + tma_atom_V, + tma_atom_O, + tma_atom_Bias, + ): + if const_expr(tma_atom is not None): + cpasync.prefetch_descriptor(tma_atom) + + smem = cutlass.utils.SmemAllocator() + storage = smem.allocate(SharedStorage) + + # Mbarrier / pipeline init + mbar_ptr_Q = storage.mbar_ptr_Q.data_ptr() + + ThreadCooperativeGroup = partial( + pipeline.CooperativeGroup, pipeline.Agent.Thread + ) + tma_warp = ThreadCooperativeGroup(1) + load_threads = ThreadCooperativeGroup(self.num_threads_per_warp_group) + mma_warps = ThreadCooperativeGroup(self.num_mma_threads // cute.arch.WARP_SIZE) + if const_expr(self.use_tma_Q): + pipeline_q = pipeline_custom.PipelineTmaAsync.create( + barrier_storage=mbar_ptr_Q, + num_stages=1, + producer_group=tma_warp, + consumer_group=mma_warps, + tx_count=self.tma_copy_bytes["Q"], + defer_sync=True, + ) + else: + pipeline_q = pipeline_custom.PipelineCpAsync.create( + barrier_storage=mbar_ptr_Q, + num_stages=1, + producer_group=load_threads, + consumer_group=mma_warps, + defer_sync=True, + elect_one_release=True, + syncwarp_before_release=False, + ) + + if const_expr(self.use_tma_KV): + pipeline_k = pipeline_custom.PipelineTmaAsync.create( + barrier_storage=storage.mbar_ptr_K.data_ptr(), + num_stages=self.num_stages, + producer_group=tma_warp, + consumer_group=mma_warps, + tx_count=self.tma_copy_bytes["K"], + defer_sync=True, + ) + pipeline_v = pipeline_custom.PipelineTmaAsync.create( + barrier_storage=storage.mbar_ptr_V.data_ptr(), + num_stages=self.num_stages, + producer_group=tma_warp, + consumer_group=mma_warps, + tx_count=self.tma_copy_bytes["V"], + defer_sync=True, + ) + else: + pipeline_k = pipeline_custom.PipelineCpAsync.create( + barrier_storage=storage.mbar_ptr_K.data_ptr(), + num_stages=self.num_stages, + producer_group=load_threads, + consumer_group=mma_warps, + defer_sync=True, + elect_one_release=True, + syncwarp_before_release=False, + ) + pipeline_v = pipeline_custom.PipelineCpAsync.create( + barrier_storage=storage.mbar_ptr_V.data_ptr(), + num_stages=self.num_stages, + producer_group=load_threads, + consumer_group=mma_warps, + defer_sync=True, + elect_one_release=True, + syncwarp_before_release=False, + ) + if const_expr(self.has_bias): + pipeline_bias = pipeline_custom.PipelineTmaAsync.create( + barrier_storage=storage.mbar_ptr_Bias.data_ptr(), + num_stages=self.num_stages, + producer_group=tma_warp, + consumer_group=mma_warps, + tx_count=self.tma_copy_bytes["Bias"], + defer_sync=True, + ) + else: + pipeline_bias = None + + # Cluster arrive after barrier init + pipeline_init_arrive(cluster_shape_mn=self.cluster_shape_mn, is_relaxed=True) + + # /////////////////////////////////////////////////////////////////////////////// + # Get shared memory buffer + # /////////////////////////////////////////////////////////////////////////////// + sQ = storage.sQ.get_tensor(sQ_layout.outer, swizzle=sQ_layout.inner) + sK = storage.sK.get_tensor(sK_layout.outer, swizzle=sK_layout.inner) + if const_expr(not self.Q_in_regs): + sV = storage.sV.get_tensor(sV_layout.outer, swizzle=sV_layout.inner) + else: + sV = storage.sQ.get_tensor( + sV_layout.outer, swizzle=sV_layout.inner, dtype=mV.element_type + ) + # Transpose view of V to tensor with layout (head_dim_v, tile_n) for tiled mma + sVt = layout_utils.transpose_view(sV) + sP = None + if const_expr(sP_layout is not None): + sP = storage.sP.get_tensor(sP_layout.outer, swizzle=sP_layout.inner) + if const_expr(self.has_bias): + sBias = storage.sBias.get_tensor( + sBias_layout.outer, swizzle=sBias_layout.inner + ) + else: + sBias = None + # reuse sQ's data iterator + sO = storage.sQ.get_tensor( + sO_layout.outer, swizzle=sO_layout.inner, dtype=self.dtype + ) + + block_info = BlockInfo( + self.tile_m, + self.tile_n, + self.is_causal, + self.is_local, + self.is_split_kv, + window_size_left, + window_size_right, + qhead_per_kvhead_packgqa=( + self.qhead_per_kvhead if const_expr(self.pack_gqa) else 1 + ), + ) + SeqlenInfoCls = partial( + SeqlenInfoQK.create, + seqlen_q_static=( + mQ.shape[0] if const_expr(not self.pack_gqa) else mQ.shape[0][1] + ), + seqlen_k_static=( + mK.shape[0] + if const_expr(mPageTable is None) + else mK.shape[0] * mPageTable.shape[1] + ), + mCuSeqlensQ=mCuSeqlensQ, + mCuSeqlensK=mCuSeqlensK, + mSeqUsedQ=mSeqUsedQ, + mSeqUsedK=mSeqUsedK, + mCuTotalMBlocks=( + blocksparse_tensors.cu_total_m_blocks + if blocksparse_tensors is not None + else None + ), + mCuBlockIdxOffsets=( + blocksparse_tensors.cu_block_idx_offsets + if blocksparse_tensors is not None + else None + ), + # Don't need to pass in tile_mn because we won't access offset_padded + ) + AttentionMaskCls = partial( + AttentionMask, + self.tile_m, + self.tile_n, + window_size_left=window_size_left, + window_size_right=window_size_right, + qhead_per_kvhead_packgqa=( + self.qhead_per_kvhead if const_expr(self.pack_gqa) else 1 + ), + ) + TileSchedulerCls = partial(TileScheduler.create, tile_sched_params) + + # Cluster wait before starting + pipeline_init_wait(cluster_shape_mn=self.cluster_shape_mn) + + if warp_idx < 4: # Producer + cute.arch.setmaxregister_decrease(self.num_producer_regs) + self.load( + mQ, + mK, + mV, + sQ, + sK, + sV, + tma_atom_Q, + tma_atom_K, + tma_atom_V, + mBias, + sBias, + tma_atom_Bias, + pipeline_k, + pipeline_v, + pipeline_bias, + pipeline_q, + gmem_tiled_copy_Q, + mPageTable, + blocksparse_tensors, + block_info, + SeqlenInfoCls, + TileSchedulerCls, + num_splits, + ) + + else: # Consumer + cute.arch.setmaxregister_increase(self.num_mma_regs) + # /////////////////////////////////////////////////////////////////////////////// + # Tile MMA compute thread partitions and allocate accumulators + # /////////////////////////////////////////////////////////////////////////////// + tidx, _, _ = cute.arch.thread_idx() + tidx = tidx - 128 + self.mma( + tiled_mma_qk, + tiled_mma_pv, + mO, + mLSE, + sQ, + sK, + sVt, + sP, + sO, + learnable_sink, + pipeline_k, + pipeline_v, + pipeline_q, + gmem_tiled_copy_O, + tma_atom_O, + tidx, + softmax_scale_log2, + softmax_scale, + base_softmax_scale, + block_info, + SeqlenInfoCls, + AttentionMaskCls, + TileSchedulerCls, + blocksparse_tensors, + num_splits, + aux_data, + fastdiv_mods, + sBias, + pipeline_bias, + ) + + @cute.jit + def load( + self, + mQ: cute.Tensor, + mK: cute.Tensor, + mV: cute.Tensor, + sQ: cute.Tensor, + sK: cute.Tensor, + sV: cute.Tensor, + tma_atom_Q: Optional[cute.CopyAtom], + tma_atom_K: Optional[cute.CopyAtom], + tma_atom_V: Optional[cute.CopyAtom], + mBias: Optional[cute.Tensor], + sBias: Optional[cute.Tensor], + tma_atom_Bias: Optional[cute.CopyAtom], + pipeline_k: pipeline.PipelineAsync, + pipeline_v: pipeline.PipelineAsync, + pipeline_bias: Optional[pipeline.PipelineAsync], + pipeline_q: pipeline.PipelineAsync, + gmem_tiled_copy_Q: cute.TiledCopy, + mPageTable: Optional[cute.Tensor], + blocksparse_tensors: Optional[BlockSparseTensors], + block_info: BlockInfo, + SeqlenInfoCls: Callable, + TileSchedulerCls: Callable, + num_splits: Int32 = 1, + ): + warp_idx_in_wg = cute.arch.make_warp_uniform(cute.arch.warp_idx()) % 4 + tidx, _, _ = cute.arch.thread_idx() + + # TMA: only warp 0 loads. cp_async: all warps load. + # When not use_tma_Q, all 128 producer threads participate in Q loading. + is_load_warp = warp_idx_in_wg == 0 or const_expr( + not self.use_tma_KV or not self.use_tma_Q + ) + # KV loading restricted to warp 0 for TMA, all warps for non-TMA KV + is_kv_load_warp = warp_idx_in_wg == 0 or const_expr(not self.use_tma_KV) + is_bias_load_warp = warp_idx_in_wg == 0 + + if is_load_warp: + q_producer_phase = Int32(1) + kv_producer_state = pipeline.make_pipeline_state( + pipeline.PipelineUserType.Producer, self.num_stages + ) + tile_scheduler = TileSchedulerCls() + work_tile = tile_scheduler.initial_work_tile_info() + while work_tile.is_valid_tile: + # if work_tile.is_valid_tile: + m_block, head_idx, batch_idx, split_idx = work_tile.tile_idx + seqlen = SeqlenInfoCls(batch_idx) + mQ_cur = seqlen.offset_batch_Q(mQ, batch_idx, dim=3)[ + None, None, head_idx + ] + head_idx_kv = ( + head_idx // self.qhead_per_kvhead + if const_expr(not self.pack_gqa) + else head_idx + ) + + load_Q = None + if const_expr(self.use_tma_Q): + gQ = cute.local_tile( + mQ_cur, (self.tile_m, self.tile_hdim), (m_block, 0) + ) + load_Q, _, _ = copy_utils.tma_get_copy_fn( + tma_atom_Q, 0, cute.make_layout(1), gQ, sQ, single_stage=True + ) + + paged_kv_manager = None + tma_load_K_fn = None + tma_load_V_fn = None + if const_expr(self.use_tma_KV): + # === TMA path (non-paged and paged with page_size == n_block_size) === + if const_expr(mPageTable is not None): + # Paged TMA: keep page dimension indexable + mK_cur = mK[None, None, head_idx_kv, None] + mV_cur = mV[None, None, head_idx_kv, None] + gK = cute.local_tile( + mK_cur, (self.tile_n, self.tile_hdim), (0, 0, None) + ) + gV = cute.local_tile( + mV_cur, (self.tile_n, self.tile_hdimv), (0, 0, None) + ) + else: + # Non-paged TMA + mK_cur = seqlen.offset_batch_K(mK, batch_idx, dim=3)[ + None, None, head_idx_kv + ] + mV_cur = seqlen.offset_batch_K(mV, batch_idx, dim=3)[ + None, None, head_idx_kv + ] + gK = cute.local_tile( + mK_cur, (self.tile_n, self.tile_hdim), (None, 0) + ) + gV = cute.local_tile( + mV_cur, (self.tile_n, self.tile_hdimv), (None, 0) + ) + # TODO: mcast + tma_load_K_fn, _, _ = copy_utils.tma_get_copy_fn( + tma_atom_K, 0, cute.make_layout(1), gK, sK + ) + tma_load_K_fn = copy_utils.tma_producer_copy_fn( + tma_load_K_fn, pipeline_k + ) + tma_load_V_fn, _, _ = copy_utils.tma_get_copy_fn( + tma_atom_V, 0, cute.make_layout(1), gV, sV + ) + tma_load_V_fn = copy_utils.tma_producer_copy_fn( + tma_load_V_fn, pipeline_v + ) + else: + # === cp_async path (paged KV with page_size != n_block_size) === + paged_kv_manager = PagedKVManager.create( + mPageTable, + mK, + mV, + FastDivmodDivisor(mK.shape[0]), + batch_idx, + head_idx_kv, + tidx, + seqlen.seqlen_k, + 0, # leftpad_k + self.tile_n, + self.tile_hdim, + self.tile_hdimv, + self.num_threads_per_warp_group, + mK.element_type, + arch=self.arch.major * 10 + self.arch.minor, + ) + if const_expr(self.has_bias): + mBias_cur = seqlen.offset_batch_Q(mBias, batch_idx, dim=3)[ + None, None, head_idx + ] + gBias = cute.local_tile( + mBias_cur, (self.bias_block_size, self.tile_n), (None, None) + ) + tBsBias, tBgBias = cpasync.tma_partition( + tma_atom_Bias, + 0, # no multicast + cute.make_layout(1), + cute.group_modes(sBias, 0, 2), + cute.group_modes(gBias, 0, 2), + ) + load_Bias = partial( + self.load_Bias, + tma_atom_Bias, + tBgBias, + tBsBias, + pipeline_bias=pipeline_bias, + ) + + load_K = partial( + self.load_KV, + tma_load_K_fn, + paged_kv_manager, + sK, + pipeline_kv=pipeline_k, + K_or_V="K", + ) + load_V = partial( + self.load_KV, + tma_load_V_fn, + paged_kv_manager, + sV, + pipeline_kv=pipeline_v, + K_or_V="V", + ) + pack_gqa = None + if const_expr(not self.use_tma_Q): + pack_gqa = PackGQA( + self.tile_m, + self.tile_hdim, + self.check_hdim_oob, + self.qhead_per_kvhead, + ) + + if const_expr(not self.use_block_sparsity): + if const_expr(self.use_tma_Q): + if warp_idx_in_wg == 0: + pipeline_q.producer_acquire_w_index_phase( + 0, q_producer_phase + ) + load_Q( + tma_bar_ptr=pipeline_q.sync_object_full.get_barrier(0) + ) + q_producer_phase ^= 1 + else: + pipeline_q.producer_acquire_w_index_phase(0, q_producer_phase) + pack_gqa.load_Q( + mQ_cur, + sQ, + gmem_tiled_copy_Q, + tidx, + m_block, + seqlen.seqlen_q, + ) + cute.arch.cp_async_commit_group() + pipeline_q.producer_commit_w_index(0) + q_producer_phase ^= 1 + + n_block_min, n_block_max = block_info.get_n_block_min_max( + seqlen, m_block, split_idx, num_splits + ) + bias_max_idx = Int32(0) + num_bias_loads = Int32(0) + if const_expr(self.has_bias): + _, n_block_max_abs = block_info.get_n_block_min_max( + seqlen, m_block, split_idx, num_splits, absolute=True + ) + bias_idx_offset = n_block_max_abs - n_block_max + bias_max_idx = self.bias_n_max - 1 - bias_idx_offset + num_bias_loads = min( + self.bias_n_max - bias_idx_offset, n_block_max - n_block_min + ) + # if cute.arch.thread_idx()[0] == 0: + # cute.printf("m_block = %d, n_block_min: %d, n_block_max: %d", m_block, n_block_min, n_block_max) + # Empty split (n_block_min == n_block_max): no K/V to load. The + # consumer skips this tile symmetrically, so the K/V pipelines stay + # balanced. + load_kv = True + if const_expr(self.is_split_kv): + load_kv = n_block_min < n_block_max + if load_kv: + # Clamp n_block to 0 when n_block_max == 0 (can happen with causal + # + pack_gqa when seqlen_k < tile_n). TMA handles n_block=-1 + # gracefully (fills zeros), but cp.async would crash on + # out-of-bounds page table access. + n_block = ( + n_block_max - 1 + if const_expr(self.use_tma_KV) + else cutlass.max(n_block_max - 1, 0) + ) + page_idx = ( + mPageTable[batch_idx, n_block] + if const_expr(mPageTable is not None and self.use_tma_KV) + else None + ) + + # First iteration: load K on pipeline_k + if is_kv_load_warp: + pipeline_k.producer_acquire(kv_producer_state) + if const_expr(not self.use_tma_KV): + paged_kv_manager.load_page_table(n_block) + load_K( + block=n_block, + producer_state=kv_producer_state, + page_idx=page_idx, + ) + if const_expr(self.has_bias): + if is_bias_load_warp and num_bias_loads > 0: + load_Bias( + src_idx=(m_block, bias_max_idx), + producer_state=kv_producer_state, + ) + + if is_kv_load_warp: + if const_expr( + not self.intra_wg_overlap or not self.use_tma_KV + ): + pipeline_v.producer_acquire(kv_producer_state) + load_V( + block=n_block, + producer_state=kv_producer_state, + page_idx=page_idx, + ) + kv_producer_state.advance() + for i in cutlass.range( + n_block_max - 1 - n_block_min, unroll=1 + ): + n_block = n_block_max - 1 - i - 1 + page_idx = ( + mPageTable[batch_idx, n_block] + if const_expr( + mPageTable is not None and self.use_tma_KV + ) + else None + ) + if const_expr(not self.use_tma_KV): + paged_kv_manager.load_page_table(n_block) + pipeline_k.producer_acquire(kv_producer_state) + load_K( + block=n_block, + producer_state=kv_producer_state, + page_idx=page_idx, + ) + if const_expr(self.has_bias): + if is_bias_load_warp and i + 1 < num_bias_loads: + load_Bias( + src_idx=(m_block, bias_max_idx - i - 1), + producer_state=kv_producer_state, + ) + pipeline_v.producer_acquire(kv_producer_state) + load_V( + block=n_block, + producer_state=kv_producer_state, + page_idx=page_idx, + ) + kv_producer_state.advance() + else: + for i in cutlass.range( + n_block_max - 1 - n_block_min, unroll=1 + ): + n_block_prev = n_block_max - i - 1 + n_block = n_block_prev - 1 + page_idx = ( + mPageTable[batch_idx, n_block] + if const_expr(mPageTable is not None) + else None + ) + page_idx_prev = ( + mPageTable[batch_idx, n_block_prev] + if const_expr(mPageTable is not None) + else None + ) + kv_producer_state_prev = kv_producer_state.clone() + kv_producer_state.advance() + pipeline_k.producer_acquire(kv_producer_state) + load_K( + block=n_block, + producer_state=kv_producer_state, + page_idx=page_idx, + ) + if const_expr(self.has_bias): + if is_bias_load_warp and i + 1 < num_bias_loads: + load_Bias( + src_idx=(m_block, bias_max_idx - i - 1), + producer_state=kv_producer_state, + ) + pipeline_v.producer_acquire(kv_producer_state_prev) + load_V( + block=n_block_prev, + producer_state=kv_producer_state_prev, + page_idx=page_idx_prev, + ) + n_block = n_block_min + page_idx = ( + mPageTable[batch_idx, n_block] + if const_expr(mPageTable is not None) + else None + ) + pipeline_v.producer_acquire(kv_producer_state) + load_V( + block=n_block, + producer_state=kv_producer_state, + page_idx=page_idx, + ) + kv_producer_state.advance() + else: + # Block sparsity: use TMA closures directly (not paged) + # Load Q on pipeline_q, separate from K/V pipeline + if const_expr(self.use_tma_Q): + if warp_idx_in_wg == 0: + pipeline_q.producer_acquire_w_index_phase( + 0, q_producer_phase + ) + load_Q( + tma_bar_ptr=pipeline_q.sync_object_full.get_barrier(0) + ) + q_producer_phase ^= 1 + else: + pipeline_q.producer_acquire_w_index_phase(0, q_producer_phase) + pack_gqa.load_Q( + mQ_cur, + sQ, + gmem_tiled_copy_Q, + tidx, + m_block, + seqlen.seqlen_q, + ) + cute.arch.cp_async_commit_group() + pipeline_q.producer_commit_w_index(0) + q_producer_phase ^= 1 + if is_kv_load_warp: + kv_producer_state = produce_block_sparse_loads( + blocksparse_tensors, + batch_idx, + head_idx, + m_block, + seqlen, + kv_producer_state, + tma_load_K_fn, + tma_load_V_fn, + pipeline_k, + pipeline_v, + self.intra_wg_overlap, + self.qhead_per_kvhead if const_expr(self.pack_gqa) else 1, + ( + self.q_subtile_factor + if self.q_subtile_factor is not None + else 1 + ), + split_idx, + num_splits, + ) + + tile_scheduler.prefetch_next_work() + tile_scheduler.advance_to_next_work() + work_tile = tile_scheduler.get_current_work() + # End of persistent scheduler loop + + # Producer tail is only useful for cluster to avoid early exit of blocks. + # We only need producer_tail on V since that's the last that's loaded, we don't + # need it for Q (no cluster) and K. + if is_kv_load_warp: + pipeline_v.producer_tail(kv_producer_state) + + @cute.jit + def load_KV( + self, + tma_load_fn: Optional[Callable], + paged_kv_manager: Optional[PagedKVManager], + sX: cute.Tensor, + block: Int32, + pipeline_kv: pipeline.PipelineAsync, + producer_state: pipeline.PipelineState, + K_or_V: Literal["K", "V"], + page_idx: Optional[Int32] = None, + ): + if const_expr(self.use_tma_KV): + src_idx = block if const_expr(page_idx is None) else page_idx + tma_load_fn(src_idx=src_idx, producer_state=producer_state) + else: + paged_kv_manager.load_KV( + block, sX[None, None, producer_state.index], K_or_V + ) + cute.arch.cp_async_commit_group() + pipeline_kv.producer_commit(producer_state) + + @cute.jit + def load_Bias( + self, + tma_atom_Bias: cute.CopyAtom, + tBgBias: cute.Tensor, + tBsBias: cute.Tensor, + src_idx: cute.Coord, + pipeline_bias: pipeline.PipelineAsync, + producer_state: pipeline.PipelineState, + ): + pipeline_bias.producer_acquire(producer_state) + cute.copy( + tma_atom_Bias, + tBgBias[None, src_idx[0], src_idx[1]], + tBsBias[None, producer_state.index], + tma_bar_ptr=pipeline_bias.producer_get_barrier(producer_state), + ) + pipeline_bias.producer_commit(producer_state) + + @cute.jit + def apply_bias( + self, + acc_S: cute.Tensor, + thr_mma_qk: cute.ThrMma, + sBias: cute.Tensor, + pipeline_bias: pipeline.PipelineAsync, + bias_softmax_scale: Float32, + consumer_state: pipeline.PipelineState, + apply_bias: bool, + ): + if apply_bias: + pipeline_bias.consumer_wait( + consumer_state, pipeline_bias.consumer_try_wait(consumer_state) + ) + cS = cute.make_identity_tensor((self.tile_m, self.tile_n)) + tScS = thr_mma_qk.partition_C(cS) + for i in cutlass.range_constexpr(cute.size(acc_S.shape)): + row = tScS[i][0] + col = tScS[i][1] + if ( + const_expr(self.bias_block_size == self.tile_m) + or row < self.bias_block_size + ): + acc_S[i] = acc_S[i] * bias_softmax_scale + acc_S[i] = acc_S[i] + sBias[row, col, consumer_state.index].to( + self.qk_acc_dtype + ) + else: + acc_S[i] = acc_S[i] * bias_softmax_scale + pipeline_bias.consumer_release(consumer_state) + elif const_expr(self.has_bias): + for i in cutlass.range_constexpr(cute.size(acc_S.shape)): + acc_S[i] = acc_S[i] * bias_softmax_scale + + @cute.jit + def mma( + self, + tiled_mma_qk: cute.TiledMma, + tiled_mma_pv: cute.TiledMma, + mO: cute.Tensor, + mLSE: Optional[cute.Tensor], + sQ: cute.Tensor, + sK: cute.Tensor, + sVt: cute.Tensor, + sP: Optional[cute.Tensor], + sO: cute.Tensor, + learnable_sink: Optional[cute.Tensor], + pipeline_k: pipeline.PipelineAsync, + pipeline_v: pipeline.PipelineAsync, + pipeline_q: pipeline.PipelineAsync, + gmem_tiled_copy_O: cute.TiledCopy, + tma_atom_O: Optional[cute.CopyAtom], + tidx: Int32, + softmax_scale_log2: Float32, + softmax_scale: Optional[Float32], + base_softmax_scale: Optional[Float32], + block_info: BlockInfo, + SeqlenInfoCls: Callable, + AttentionMaskCls: Callable, + TileSchedulerCls: Callable, + blocksparse_tensors: Optional[BlockSparseTensors], + num_splits: Int32 = 1, + aux_data: AuxData = AuxData(), + fastdiv_mods=None, + sBias: Optional[cute.Tensor] = None, + pipeline_bias: Optional[pipeline.PipelineAsync] = None, + ): + aux_tensors = aux_data.tensors + warp_group_idx = cute.arch.make_warp_uniform( + tidx // self.num_threads_per_warp_group + ) + warp_group_thread_layout = cute.make_layout( + self.num_wg_mma, stride=self.num_threads_per_warp_group + ) + thr_mma_qk = tiled_mma_qk.get_slice(tidx) + wg_mma_qk = tiled_mma_qk.get_slice(warp_group_thread_layout(warp_group_idx)) + wg_mma_pv = tiled_mma_pv.get_slice(warp_group_thread_layout(warp_group_idx)) + _, tSrQ, tSrK = sm90_utils.partition_fragment_ABC( + wg_mma_qk, (self.tile_m, self.tile_n, self.tile_hdim), sQ, sK + ) + mma_qk_fn = partial( + sm90_utils.gemm_zero_init, + tiled_mma_qk, + (self.tile_m, self.tile_n), + tSrQ, + tSrK, + ) + acc_O, tOrP, tOrVt = sm90_utils.partition_fragment_ABC( + wg_mma_pv, (self.tile_m, self.tile_hdimv, self.tile_n), sP, sVt + ) + mma_pv_fn = partial(sm90_utils.gemm_w_idx, tiled_mma_pv, acc_O, tOrP, tOrVt) + + # /////////////////////////////////////////////////////////////////////////////// + # Smem copy atom tiling + # /////////////////////////////////////////////////////////////////////////////// + smem_copy_atom_P = utils.get_smem_store_atom( + self.arch.major * 10 + self.arch.minor, self.dtype + ) + smem_thr_copy_P = cute.make_tiled_copy_C( + smem_copy_atom_P, tiled_mma_qk + ).get_slice(tidx) + tPsP = smem_thr_copy_P.partition_D(sP) if const_expr(sP is not None) else None + smem_copy_params = SimpleNamespace(smem_thr_copy_P=smem_thr_copy_P, tPsP=tPsP) + + self.mma_init() + + q_consumer_phase = Int32(0) + kv_consumer_state = pipeline.make_pipeline_state( + pipeline.PipelineUserType.Consumer, self.num_stages + ) + + tile_scheduler = TileSchedulerCls() + work_tile = tile_scheduler.initial_work_tile_info() + softmax = Softmax.create( + softmax_scale_log2, + num_rows=acc_O.shape[0][0] * acc_O.shape[1], + softmax_scale=softmax_scale, + ) + + # For RescaleOBeforeGemm: persistent scores_scale across iterations + scores_scale = None + if const_expr(self.rescale_O_before_gemm): + scores_scale = cute.make_rmem_tensor_like(softmax.row_max, Float32) + + mma_one_n_block_all = partial( + ( + self.mma_one_n_block_intrawg_overlap + if const_expr(self.intra_wg_overlap) + else self.mma_one_n_block + ), + mma_qk_fn=mma_qk_fn, + pipeline_k=pipeline_k, + pipeline_v=pipeline_v, + acc_O=acc_O, + tOrP=tOrP, + smem_copy_params=smem_copy_params, + check_inf=True, + scores_scale=scores_scale, + thr_mma_qk=thr_mma_qk, + sBias=sBias, + pipeline_bias=pipeline_bias, + bias_softmax_scale=base_softmax_scale, + ) + + process_first_half_block = partial( + self.first_half_block_overlap, + mma_qk_fn=mma_qk_fn, + pipeline_k=pipeline_k, + tOrP=tOrP, + smem_copy_params=smem_copy_params, + scores_scale=scores_scale, + softmax=softmax, + acc_O=acc_O, + thr_mma_qk=thr_mma_qk, + sBias=sBias, + pipeline_bias=pipeline_bias, + bias_softmax_scale=base_softmax_scale, + ) + process_last_half_block = partial( + self.last_half_block_overlap, + pipeline_v=pipeline_v, + mma_pv_fn=mma_pv_fn, + scores_scale=scores_scale, + softmax=softmax, + acc_O=acc_O, + ) + while work_tile.is_valid_tile: + # if work_tile.is_valid_tile: + + # shape: (atom_v_m * rest_m) + m_block, head_idx, batch_idx, split_idx = work_tile.tile_idx + seqlen = SeqlenInfoCls(batch_idx) + + # Recompute fastdiv_mods if necessary for varlen with aux_tensors + recompute_fastdiv_mods_q = cutlass.const_expr( + aux_tensors is not None + and (seqlen.has_cu_seqlens_q or seqlen.has_seqused_q) + ) + recompute_fastdiv_mods_k = cutlass.const_expr( + aux_tensors is not None + and (seqlen.has_cu_seqlens_k or seqlen.has_seqused_k) + ) + if cutlass.const_expr(fastdiv_mods is not None): + seqlen_q_divmod, seqlen_k_divmod = fastdiv_mods + fastdiv_mods = ( + ( + seqlen_q_divmod + if not recompute_fastdiv_mods_q + else FastDivmodDivisor(seqlen.seqlen_q) + ), + ( + seqlen_k_divmod + if not recompute_fastdiv_mods_k + else FastDivmodDivisor(seqlen.seqlen_k) + ), + ) + + mask = AttentionMaskCls(seqlen) + mask_fn = partial( + mask.apply_mask, + batch_idx=batch_idx, + head_idx=head_idx, + m_block=m_block, + thr_mma=thr_mma_qk, + mask_causal=self.is_causal, + mask_local=self.is_local, + aux_data=aux_data, + fastdiv_mods=fastdiv_mods, + ) + score_mod_fn = None + if const_expr(self.score_mod is not None): + score_mod_fn = partial( + self.apply_score_mod, + thr_mma_qk, + batch_idx, + head_idx, + m_block, + softmax_scale=1.0 if const_expr(self.has_bias) else softmax_scale, + aux_data=aux_data, + fastdiv_mods=fastdiv_mods, + ) + mma_one_n_block = partial( + mma_one_n_block_all, + seqlen=seqlen, + softmax=softmax, + score_mod_fn=score_mod_fn, + ) + n_block_min, n_block_max = block_info.get_n_block_min_max( + seqlen, m_block, split_idx, num_splits + ) + num_bias_loads = Int32(0) + if const_expr(self.has_bias): + _, n_block_max_abs = block_info.get_n_block_min_max( + seqlen, m_block, split_idx, num_splits, absolute=True + ) + bias_idx_offset = n_block_max_abs - n_block_max + num_bias_loads = min( + self.bias_n_max - bias_idx_offset, n_block_max - n_block_min + ) + pipeline_q.consumer_wait_w_index_phase(0, q_consumer_phase) + # For performance reason, we separate out two kinds of iterations: + # those that need masking on S, and those that don't. + # We need masking on S for the very last block when K and V has length not multiple of tile_n. + # We also need masking on S if it's causal, for the last several blocks. + # softmax.reset() # Don't need reset as we explicitly call softmax w is_first=True + O_should_accumulate = False + has_work = True + if const_expr(self.is_split_kv): + has_work = n_block_min < n_block_max + + # ========================================== + # MAINLOOP + # ========================================== + if const_expr(not self.use_block_sparsity): + # ========================================== + # No block-sparsity (original path) + # ========================================== + if has_work: + # First iteration with seqlen masking + if const_expr(self.intra_wg_overlap): + kv_consumer_state = process_first_half_block( + n_block=n_block_max - 1, + seqlen=seqlen, + kv_consumer_state=kv_consumer_state, + mask_fn=partial(mask_fn, mask_mod=self.mask_mod), + score_mod_fn=score_mod_fn, + is_first_block=True, + apply_bias=const_expr(self.has_bias) and num_bias_loads > 0, + ) + else: + self.warp_scheduler_barrier_sync() + kv_consumer_state = mma_one_n_block( + kv_consumer_state, + n_block=n_block_max - 1, + seqlen=seqlen, + mma_pv_fn=partial(mma_pv_fn, zero_init=True), + is_first_n_block=True, + mask_fn=partial( + mask_fn, mask_mod=self.mask_mod, mask_seqlen=True + ), + apply_bias=const_expr(self.has_bias) and num_bias_loads > 0, + ) + O_should_accumulate = True + # if cute.arch.thread_idx()[0] == 128: cute.printf("m_block = {}, n_block_max = {}, n_block_min = {}", m_block, n_block_max, n_block_min) + n_block_max -= 1 + # Next couple of iterations with causal/local masking. With sheared bias, + # the bias tile carries the causal/local -inf padding for its right band. + if const_expr(self.is_causal or self.is_local or self.has_bias): + if const_expr(self.has_bias): + n_block_min_causal_local_mask = max( + n_block_max + 1 - num_bias_loads, n_block_min + ) + else: + n_block_min_causal_local_mask = ( + block_info.get_n_block_min_causal_local_mask( + seqlen, m_block, n_block_min + ) + ) + # if cute.arch.thread_idx()[0] == 128: cute.printf("n_block_min_causal_local_mask = {}", n_block_min_causal_local_mask) + for n_tile in cutlass.range( + n_block_max - n_block_min_causal_local_mask, unroll=1 + ): + kv_consumer_state = mma_one_n_block( + kv_consumer_state, + n_block=n_block_max - 1 - n_tile, + seqlen=seqlen, + mma_pv_fn=partial( + mma_pv_fn, zero_init=not O_should_accumulate + ), + mask_fn=( + None + if const_expr(self.has_bias) + else partial( + mask_fn, + mask_mod=self.mask_mod, + mask_seqlen=False, + ) + ), + apply_bias=self.has_bias, + ) + O_should_accumulate = True + n_block_max = cutlass.min( + n_block_max, n_block_min_causal_local_mask + ) + # The remaining iterations have no masking + n_block_min_before_local_mask = ( + block_info.get_n_block_min_before_local_mask( + seqlen, m_block, n_block_min + ) + ) + # if cute.arch.thread_idx()[0] == 128: cute.printf("n_block_min_before_local_mask = {}, n_block_min = {}", n_block_min_before_local_mask, n_block_min) + for n_tile in cutlass.range( + n_block_max - n_block_min_before_local_mask, unroll=1 + ): + kv_consumer_state = mma_one_n_block( + kv_consumer_state, + n_block=n_block_max - 1 - n_tile, + seqlen=seqlen, + mma_pv_fn=partial( + mma_pv_fn, zero_init=not O_should_accumulate + ), + mask_fn=partial( + mask_fn, mask_mod=self.mask_mod, mask_seqlen=False + ), + ) + O_should_accumulate = True + # Separate iterations with local masking on the left + if const_expr( + self.is_local and block_info.window_size_left is not None + ): + n_block_max = cutlass.min( + n_block_max, n_block_min_before_local_mask + ) + for n_tile in cutlass.range( + n_block_max - n_block_min, unroll=1 + ): + kv_consumer_state = mma_one_n_block( + kv_consumer_state, + n_block=n_block_max - 1 - n_tile, + seqlen=seqlen, + mma_pv_fn=partial( + mma_pv_fn, zero_init=not O_should_accumulate + ), + mask_fn=partial( + mask_fn, mask_mod=self.mask_mod, mask_seqlen=False + ), + ) + O_should_accumulate = True + # Release Q pipeline so the producer can load the next tile's Q + pipeline_q.consumer_release_w_index(0) + if has_work: + # Last "half" iteration + if const_expr(self.intra_wg_overlap): + kv_consumer_state = process_last_half_block( + kv_consumer_state=kv_consumer_state, + zero_init=not O_should_accumulate, + ) + O_should_accumulate = True + else: + self.warp_scheduler_barrier_arrive() + else: + softmax.reset() + acc_O.fill(0.0) + + else: + # ========================================== + # Block sparsity + # ========================================== + kv_consumer_state, O_should_accumulate, processed_any = ( + consume_block_sparse_loads( + blocksparse_tensors, + batch_idx, + head_idx, + m_block, + seqlen, + kv_consumer_state, + mma_pv_fn, + mma_one_n_block, + process_first_half_block, + process_last_half_block, + mask_fn, + score_mod_fn, + O_should_accumulate, + self.mask_mod, + fastdiv_mods, + self.intra_wg_overlap, + self.warp_scheduler_barrier_sync, + self.warp_scheduler_barrier_arrive, + self.qhead_per_kvhead if const_expr(self.pack_gqa) else 1, + ( + self.q_subtile_factor + if self.q_subtile_factor is not None + else 1 + ), + split_idx, + num_splits, + ) + ) + + # Release Q pipeline so the producer can load the next tile's Q + pipeline_q.consumer_release_w_index(0) + + # Handle empty case (when no blocks to process) + if not processed_any: + softmax.reset() + acc_O.fill(0.0) + + q_consumer_phase ^= 1 + + sink_val = None + if const_expr(learnable_sink is not None): + if const_expr(not self.pack_gqa): + sink_val = Float32(learnable_sink[head_idx]) + else: # Each thread might have a different sink value due to different q_head + sink_val = cute.make_rmem_tensor_like(softmax.row_max, Float32) + cS = cute.make_identity_tensor((self.tile_m, self.tile_n)) + tScS_mn = layout_utils.reshape_acc_to_mn(thr_mma_qk.partition_C(cS)) + for r in cutlass.range(cute.size(sink_val), unroll_full=True): + row = m_block * self.tile_m + tScS_mn[r][0] + q_head_idx = ( + row % self.qhead_per_kvhead + + head_idx * self.qhead_per_kvhead + ) + sink_val[r] = Float32(learnable_sink[q_head_idx]) + if const_expr(self.is_split_kv and learnable_sink is not None): + # Only split 0 folds the learnable sink into the denominator. + if const_expr(not self.pack_gqa): + sink_val = sink_val if split_idx == 0 else -Float32.inf + else: + if split_idx != 0: + sink_val.fill(-Float32.inf) + + # normalize acc_O by row_sum and calculate the lse + row_scale = softmax.finalize(sink_val=sink_val) + softmax.rescale_O(acc_O, row_scale) + + # /////////////////////////////////////////////////////////////////////////////// + # Epilogue + # /////////////////////////////////////////////////////////////////////////////// + self.epilogue( + acc_O, + softmax.row_sum, + mO, + mLSE, + sO, + seqlen, + gmem_tiled_copy_O, + tma_atom_O, + tiled_mma_pv, + tidx, + m_block, + head_idx, + batch_idx, + split_idx, + ) + + tile_scheduler.advance_to_next_work() + work_tile = tile_scheduler.get_current_work() + + @cute.jit + def first_half_block_overlap( + self, + n_block: Int32, + mma_qk_fn: Callable, + kv_consumer_state, + pipeline_k, + tOrP: cute.Tensor, + smem_copy_params: SimpleNamespace, + softmax: Softmax, + seqlen: SeqlenInfoQK, + scores_scale: Optional[cute.Tensor] = None, + acc_O: Optional[cute.Tensor] = None, + mask_fn: Callable = None, + score_mod_fn: Optional[Callable] = None, + is_first_block: bool = False, + thr_mma_qk: Optional[cute.ThrMma] = None, + sBias: Optional[cute.Tensor] = None, + pipeline_bias: Optional[pipeline.PipelineAsync] = None, + bias_softmax_scale: Optional[Float32] = None, + apply_bias: bool = False, + ): + """Processes the first half block when using intra-warpgroup-overlap""" + + pipeline_k.consumer_wait( + kv_consumer_state, pipeline_k.consumer_try_wait(kv_consumer_state) + ) + acc_S = mma_qk_fn(B_idx=kv_consumer_state.index, wg_wait=0) + pipeline_k.consumer_release(kv_consumer_state) + + if const_expr(self.has_bias): + self.apply_bias( + acc_S, + thr_mma_qk, + sBias, + pipeline_bias, + bias_softmax_scale, + kv_consumer_state, + apply_bias, + ) + + # Apply score modification if present + if const_expr(score_mod_fn is not None): + score_mod_fn(acc_S, n_block=n_block, seqlen=seqlen) + + # Apply mask; mask_seqlen always True for first block + # Caveat: if full block further right than mask block, seqlen masking is redundant; + # however, masking is being applied anyway, so essentially no perf hit + mask_fn(acc_S, n_block=n_block, mask_seqlen=True) + + row_scale = softmax.online_softmax(acc_S, is_first=is_first_block) + + tOrP_acc = layout_utils.reshape_acc_to_frgA(acc_S) + tOrP_cur = ( + tOrP + if const_expr(self.mma_pv_is_rs) + else cute.make_rmem_tensor_like(tOrP_acc, self.dtype) + ) + tOrP_cur.store(tOrP_acc.load().to(self.dtype)) + + if const_expr(not self.mma_pv_is_rs): + tPrP = smem_copy_params.smem_thr_copy_P.retile(tOrP_cur) + cute.copy(smem_copy_params.smem_thr_copy_P, tPrP, smem_copy_params.tPsP) + # Fence and barrier to make smem store visible to WGMMA + cute.arch.fence_view_async_shared() + cute.arch.sync_warp() + + # For RescaleOBeforeGemm: initialize acc_O + if const_expr(self.rescale_O_before_gemm): + acc_O.fill(0.0) + scores_scale.store(row_scale.load()) + + return kv_consumer_state + + @cute.jit + def last_half_block_overlap( + self, + kv_consumer_state, + pipeline_v, + mma_pv_fn: Callable, + zero_init: bool, + scores_scale: Optional[cute.Tensor] = None, + softmax: Optional[Softmax] = None, + acc_O: Optional[cute.Tensor] = None, + ): + """Processes the final PV GEMM when using intra-warpgroup-overlap""" + + # For RescaleOBeforeGemm: rescale O before the final PV GEMM + if const_expr(self.rescale_O_before_gemm): + softmax.rescale_O(acc_O, scores_scale) + + pipeline_v.consumer_wait( + kv_consumer_state, pipeline_v.consumer_try_wait(kv_consumer_state) + ) + mma_pv_fn(B_idx=kv_consumer_state.index, zero_init=zero_init, wg_wait=0) + pipeline_v.consumer_release(kv_consumer_state) + kv_consumer_state.advance() + return kv_consumer_state + + @cute.jit + def mma_one_n_block( + self, + smem_pipe_read: pipeline.PipelineState | pipeline_custom.PipelineStateSimple, + n_block: Int32, + mma_qk_fn: Callable, + mma_pv_fn: Callable, + pipeline_k: pipeline.PipelineAsync, + pipeline_v: pipeline.PipelineAsync, + acc_O: cute.Tensor, + tOrP: cute.Tensor, + smem_copy_params: SimpleNamespace, + softmax: Softmax, + seqlen: SeqlenInfoQK, + scores_scale: Optional[cute.Tensor] = None, # not used + score_mod_fn: Optional[Callable] = None, + mask_fn: Optional[Callable] = None, + is_first_n_block: cutlass.Constexpr = False, + check_inf: cutlass.Constexpr = True, + thr_mma_qk: Optional[cute.ThrMma] = None, + sBias: Optional[cute.Tensor] = None, + pipeline_bias: Optional[pipeline.PipelineAsync] = None, + bias_softmax_scale: Optional[Float32] = None, + apply_bias: bool = False, + ): + pipeline_k.consumer_wait( + smem_pipe_read, pipeline_k.consumer_try_wait(smem_pipe_read) + ) + # S = Q @ K.T + acc_S = mma_qk_fn(B_idx=smem_pipe_read.index, wg_wait=-1) + self.warp_scheduler_barrier_arrive() + warpgroup.wait_group(0) + pipeline_k.consumer_release(smem_pipe_read) + + if const_expr(self.has_bias): + self.apply_bias( + acc_S, + thr_mma_qk, + sBias, + pipeline_bias, + bias_softmax_scale, + smem_pipe_read, + apply_bias, + ) + + # handle score mods and masking + if const_expr(score_mod_fn is not None): + score_mod_fn(acc_S, n_block=n_block, seqlen=seqlen) + if const_expr(mask_fn is not None): + mask_fn(acc_S=acc_S, n_block=n_block) + + row_scale = softmax.online_softmax( + acc_S, is_first=is_first_n_block, check_inf=check_inf + ) + # if cute.arch.thread_idx()[0] == 0: cute.print_tensor(layout_utils.reshape_acc_to_mn(acc_S)) + tOrP_acc = layout_utils.reshape_acc_to_frgA(acc_S) + tOrP_cur = ( + tOrP + if const_expr(self.mma_pv_is_rs) + else cute.make_rmem_tensor_like(tOrP_acc, self.dtype) + ) + # tOrP.store(tOrP_acc.load().to(self.dtype)) + # the "to(self.dtype)" conversion fails to vectorize for block sizes other + # than 128 x 128, i.e. it calls convert on 1 fp32 element at a time instead of + # 2 elements. So we just call ptx directly. + utils.cvt_f16(tOrP_acc, tOrP_cur) + if const_expr(not self.mma_pv_is_rs): + tPrP = smem_copy_params.smem_thr_copy_P.retile(tOrP_cur) + cute.copy(smem_copy_params.smem_thr_copy_P, tPrP, smem_copy_params.tPsP) + softmax.rescale_O(acc_O, row_scale) + if const_expr(not self.mma_pv_is_rs): + # Fence and barrier to make sure smem store is visible to WGMMA + cute.arch.fence_view_async_shared() + cute.arch.sync_warp() # Only need syncwarp since each warp is using its own P values for MmaPV + pipeline_v.consumer_wait( + smem_pipe_read, pipeline_v.consumer_try_wait(smem_pipe_read) + ) + self.warp_scheduler_barrier_sync() + # O += P @ V + mma_pv_fn(B_idx=smem_pipe_read.index, wg_wait=0) + pipeline_v.consumer_release(smem_pipe_read) + smem_pipe_read.advance() + return smem_pipe_read + + @cute.jit + def mma_one_n_block_intrawg_overlap( + self, + smem_pipe_read: pipeline.PipelineState | pipeline_custom.PipelineStateSimple, + n_block: Int32, + mma_qk_fn: Callable, + mma_pv_fn: Callable, + pipeline_k: pipeline.PipelineAsync, + pipeline_v: pipeline.PipelineAsync, + acc_O: cute.Tensor, + tOrP: cute.Tensor, + smem_copy_params: SimpleNamespace, + softmax: Softmax, + seqlen: SeqlenInfoQK, + scores_scale: Optional[cute.Tensor] = None, + score_mod_fn: Optional[Callable] = None, + mask_fn: Optional[Callable] = None, + check_inf: cutlass.Constexpr = True, + thr_mma_qk: Optional[cute.ThrMma] = None, + sBias: Optional[cute.Tensor] = None, + pipeline_bias: Optional[pipeline.PipelineAsync] = None, + bias_softmax_scale: Optional[Float32] = None, + apply_bias: bool = False, + ): + smem_pipe_read_v = smem_pipe_read.clone() + smem_pipe_read.advance() + pipeline_k.consumer_wait( + smem_pipe_read, pipeline_k.consumer_try_wait(smem_pipe_read) + ) + self.warp_scheduler_barrier_sync() + # S = Q @ K.T + acc_S = mma_qk_fn(B_idx=smem_pipe_read.index, wg_wait=-1) + # RescaleOBeforeGemm: rescale O while QK GEMM is in flight, before PV GEMM + if const_expr(self.rescale_O_before_gemm): + softmax.rescale_O(acc_O, scores_scale) + pipeline_v.consumer_wait( + smem_pipe_read_v, pipeline_v.consumer_try_wait(smem_pipe_read_v) + ) + # O += P @ V + mma_pv_fn(B_idx=smem_pipe_read_v.index, wg_wait=-1) + self.warp_scheduler_barrier_arrive() + warpgroup.wait_group(1) + pipeline_k.consumer_release(smem_pipe_read) + + if const_expr(self.has_bias): + self.apply_bias( + acc_S, + thr_mma_qk, + sBias, + pipeline_bias, + bias_softmax_scale, + smem_pipe_read, + apply_bias, + ) + + # handle score mods and masking + if const_expr(score_mod_fn is not None): + score_mod_fn(acc_S, n_block=n_block, seqlen=seqlen) + if const_expr(mask_fn is not None): + mask_fn(acc_S=acc_S, n_block=n_block) + # if cute.arch.thread_idx()[0] == 128: cute.print_tensor(layout_utils.reshape_acc_to_mn(acc_S)) + + row_scale = softmax.online_softmax(acc_S, check_inf=check_inf) + warpgroup.wait_group(0) + pipeline_v.consumer_release(smem_pipe_read_v) + tOrP_acc = layout_utils.reshape_acc_to_frgA(acc_S) + tOrP_cur = ( + tOrP + if const_expr(self.mma_pv_is_rs) + else cute.make_rmem_tensor_like(tOrP_acc, self.dtype) + ) + # tOrP_cur.store(tOrP_acc.load().to(self.dtype)) + # the "to(self.dtype)" conversion fails to vectorize for block sizes other + # than 128 x 128, i.e. it calls convert on 1 fp32 element at a time instead of + # 2 elements. So we just call ptx directly. + utils.cvt_f16(tOrP_acc, tOrP_cur) + if const_expr(not self.mma_pv_is_rs): + tPrP = smem_copy_params.smem_thr_copy_P.retile(tOrP_cur) + cute.copy(smem_copy_params.smem_thr_copy_P, tPrP, smem_copy_params.tPsP) + if const_expr(not self.rescale_O_before_gemm): + softmax.rescale_O(acc_O, row_scale) + if const_expr(self.rescale_O_before_gemm): + scores_scale.store(row_scale.load()) + if const_expr(not self.mma_pv_is_rs): + # Fence and barrier to make sure smem store is visible to WGMMA + cute.arch.fence_view_async_shared() + cute.arch.sync_warp() # Only need syncwarp since each warp is using its own P values for MmaPV + return smem_pipe_read + + @cute.jit + def mma_init(self): + warp_group_idx = utils.canonical_warp_group_idx(sync=False) + if const_expr(self.use_scheduler_barrier): + if warp_group_idx == 1: + cute.arch.barrier_arrive( + barrier_id=int(NamedBarrierFwd.WarpSchedulerWG1), + number_of_threads=2 * self.num_threads_per_warp_group, + ) + + @cute.jit + def apply_score_mod( + self, + thr_mma_qk, + batch_idx, + head_idx, + m_block, + acc_S, + n_block, + softmax_scale, + seqlen, + aux_data: AuxData = AuxData(), + fastdiv_mods=None, + ): + # Prepare index tensor + cS = cute.make_identity_tensor((self.tile_m, self.tile_n)) + cS = cute.domain_offset((m_block * self.tile_m, n_block * self.tile_n), cS) + tScS = thr_mma_qk.partition_C(cS) + + apply_score_mod_inner( + acc_S, + tScS, + self.score_mod, + batch_idx, + head_idx, + softmax_scale, + self.score_vec_size, + self.qk_acc_dtype, + aux_data, + fastdiv_mods, + seqlen_info=seqlen, + constant_q_idx=None, + qhead_per_kvhead=self.qhead_per_kvhead if const_expr(self.pack_gqa) else 1, + ) + + def warp_scheduler_barrier_sync(self): + if const_expr(self.use_scheduler_barrier): + cute.arch.barrier( + barrier_id=int(NamedBarrierFwd.WarpSchedulerWG1) + - 1 + + utils.canonical_warp_group_idx(sync=False), + number_of_threads=2 * self.num_threads_per_warp_group, + ) + + def warp_scheduler_barrier_arrive(self): + if const_expr(self.use_scheduler_barrier): + assert self.num_wg_mma in [2, 3] + cur_wg = utils.canonical_warp_group_idx(sync=False) - 1 + if const_expr(self.num_wg_mma == 2): + next_wg = 1 - cur_wg + else: + t = cur_wg + 1 + next_wg = t % self.num_wg_mma + cute.arch.barrier_arrive( + barrier_id=int(NamedBarrierFwd.WarpSchedulerWG1) + next_wg, + number_of_threads=2 * self.num_threads_per_warp_group, + ) diff --git a/python/sglang/jit_kernel/flash_attn/cute/interface.py b/python/sglang/jit_kernel/flash_attn/cute/interface.py new file mode 100644 index 000000000..cc8ae4e21 --- /dev/null +++ b/python/sglang/jit_kernel/flash_attn/cute/interface.py @@ -0,0 +1,2216 @@ +# Copyright (c) 2025, Jay Shah, Ganesh Bikshandi, Ying Zhang, Vijay Thakkar, Pradeep Ramani, Tri Dao. +# [2025-07-04] Version in Cute-DSL, for Hopper and Blackwell. You'll need install nvidia-cutlass-dsl==4.2.0. + +import math +import os +from dataclasses import dataclass +from functools import lru_cache +from typing import Callable, Optional, Tuple + +import cutlass +import cutlass.cute as cute +import torch +from cutlass import Float32, Int32 +from quack.compile_utils import make_fake_tensor as fake_tensor + +from sglang.jit_kernel.flash_attn.cute.cache_utils import get_jit_cache +from sglang.jit_kernel.flash_attn.cute.testing import is_fake_mode +from sglang.jit_kernel.utils import is_arch_support_pdl + +if os.environ.get("CUTE_DSL_PTXAS_PATH", None) is not None: + from sglang.jit_kernel.flash_attn.cute import cute_dsl_ptxas # noqa: F401 + + # Patch to dump ptx and then use system ptxas to compile to cubin + cute_dsl_ptxas.patch() + + +from sglang.jit_kernel.flash_attn.cute import fa_logging, utils +from sglang.jit_kernel.flash_attn.cute.block_sparsity import ( + BlockSparseTensorsTorch, + get_sparse_q_block_size, + normalize_block_sparse_config, + to_cute_block_sparse_tensors, +) +from sglang.jit_kernel.flash_attn.cute.cu_blocks_kernels import CuSeqlensToBlocksKernel +from sglang.jit_kernel.flash_attn.cute.cute_dsl_utils import ( + get_aux_tensor_metadata, + to_cute_aux_tensor, + to_cute_tensor, +) +from sglang.jit_kernel.flash_attn.cute.flash_fwd import FlashAttentionForwardSm80 +from sglang.jit_kernel.flash_attn.cute.flash_fwd_combine import ( + FlashAttentionForwardCombine, +) +from sglang.jit_kernel.flash_attn.cute.flash_fwd_mla_sm100 import ( + FlashAttentionMLAForwardSm100, +) +from sglang.jit_kernel.flash_attn.cute.flash_fwd_sm90 import FlashAttentionForwardSm90 +from sglang.jit_kernel.flash_attn.cute.flash_fwd_sm100 import ( + DescaleTensors, + FlashAttentionForwardSm100, +) +from sglang.jit_kernel.flash_attn.cute.flash_fwd_sm120 import FlashAttentionForwardSm120 +from sglang.jit_kernel.flash_attn.cute.shearing_bias import ShearingBias + +# SM100 head_dim=256 2CTA kernel imports +from sglang.jit_kernel.flash_attn.cute.sm100_hd256_2cta_fmha_forward import ( + BlackwellFusedMultiHeadAttentionForward, +) +from sglang.jit_kernel.flash_attn.cute.utils import AuxData + + +def _parse_arch_str(arch_str): + """Parse arch string (e.g. 'sm_80', 'sm_90a', '80', '100') to int (e.g. 80, 90, 100).""" + import re + + match = re.match(r"^(?:sm_?|SM_?)?(\d+)(\d)([af]?)$", arch_str) + if not match: + raise ValueError(f"Invalid arch format: {arch_str}") + major, minor, _ = match.groups() + return int(major) * 10 + int(minor) + + +@lru_cache(maxsize=None) +def _get_device_arch(): + """Cached device arch check. + + Override with FLASH_ATTENTION_ARCH (e.g. 'sm_80' or '80') to select which + kernel path to use (SM80/SM90/SM100/SM120) independently of the compilation + target (CUTE_DSL_ARCH). + + For CPU-only compilation (no GPU), set both: + FLASH_ATTENTION_ARCH=sm_80 (kernel selection) + CUTE_DSL_ARCH=sm_80 (compilation target) + """ + arch_override = os.environ.get("FLASH_ATTENTION_ARCH", None) + if arch_override is not None: + return _parse_arch_str(arch_override) + major, minor = torch.cuda.get_device_capability() + return major * 10 + int(minor) + + +def _validate_head_dims( + head_dim: int, head_dim_v: int, compute_capability: int, alignment: int +) -> None: + """Validate head dimension constraints based on compute capability.""" + is_deepseek_shape = head_dim == 192 and head_dim_v == 128 + is_deepseek_mla_absorbed_shape = ( + head_dim == 64 or head_dim == head_dim_v + ) and head_dim_v == 512 + is_dedicate_kernel_shape = head_dim == 256 and head_dim_v == 256 + is_standard_range = 8 <= head_dim <= 128 and 8 <= head_dim_v <= 128 + + is_sm90_range = 8 <= head_dim <= 256 and 8 <= head_dim_v <= 256 + if compute_capability == 9: + assert ( + is_sm90_range and head_dim % alignment == 0 and head_dim_v % alignment == 0 + ), ( + f"(head_dim, head_dim_v)=({head_dim}, {head_dim_v}) is not supported on SM90. " + f"head_dim and head_dim_v must be between 8 and 256 and divisible by {alignment}." + ) + elif compute_capability in [10, 11]: + assert ( + ( + is_standard_range + or is_deepseek_shape + or is_deepseek_mla_absorbed_shape + or is_dedicate_kernel_shape + ) + and head_dim % alignment == 0 + and head_dim_v % alignment == 0 + ), ( + f"(head_dim, head_dim_v)=({head_dim}, {head_dim_v}) is not supported on SM100/SM110. " + f"head_dim and head_dim_v must be between 8 and 128 and divisible by {alignment}, or (192, 128) for DeepSeek, or (256, 256) for hd256." + ) + + +@dataclass(frozen=True) +class FwdConfig: + m_block_size: int + n_block_size: int + mma_pv_is_rs: bool + intra_wg_overlap: bool + + +def _tile_size_fwd_sm90( + head_dim, head_dim_v, is_causal, is_local, sparse_block_size_q=None +): + """Return FwdConfig for SM90 forward. + + Tile sizes and flags based on tile_size_fwd_sm90 in hopper/tile_size.h, adjusted + for the Python kernel's different register/smem tradeoffs (benchmarked on H100 SXM). + + When sparse_block_size_q is set, tile_m must divide it. For head_dim <= 96 the + optimal tile_m=192 is used when compatible, otherwise we fall back to 128. + """ + if head_dim <= 64: + # C++: 192×192 non-causal, 192×128 causal/local. + # Python: 192×128 RS+OL is consistently best across seqlens. + if sparse_block_size_q is not None and sparse_block_size_q % 192 != 0: + return FwdConfig(128, 128, True, True) + return FwdConfig(192, 128, True, True) + elif head_dim <= 96: + # C++: 192×144 noRS+OL for all cases. + # Python: RS is catastrophic with 192× tiles (~300 vs ~600 TFLOPS). + # noRS+OL is always required. Causal: 192×128 slightly better short seqlen. + if sparse_block_size_q is not None and sparse_block_size_q % 192 != 0: + return FwdConfig(128, 128, False, True) + if is_causal or is_local: + return FwdConfig(192, 128, False, True) + else: + return FwdConfig(192, 144, False, True) + elif head_dim <= 128: + return FwdConfig(128, 128, True, True) + elif head_dim <= 192: + tile_n = 96 if is_local else (128 if head_dim_v <= 128 else 112) + return FwdConfig(128, tile_n, True, True) + else: # hdim 256 + tile_n = 64 if is_local else 80 + return FwdConfig(128, tile_n, True, True) + + +def maybe_contiguous(x): + return x.contiguous() if x is not None and x.stride(-1) != 1 else x + + +def _validate_tensor(t, name, expected_shape, expected_dtype, expected_device): + assert ( + t.shape == expected_shape + ), f"{name} shape {t.shape} != expected {expected_shape}" + assert ( + t.dtype == expected_dtype + ), f"{name} dtype {t.dtype} != expected {expected_dtype}" + assert ( + t.device == expected_device + ), f"{name} device {t.device} != expected {expected_device}" + if not is_fake_mode(): + assert t.is_cuda, f"{name} must be on CUDA" + + +torch2cute_dtype_map = { + torch.float16: cutlass.Float16, + torch.bfloat16: cutlass.BFloat16, + torch.float32: cutlass.Float32, + torch.float8_e4m3fn: cutlass.Float8E4M3FN, + torch.float8_e5m2: cutlass.Float8E5M2, +} + + +_shear_bias_workspace: dict = {} + + +def _round_up_to_tile(size: int, tile_size: int) -> int: + """Return the smallest whole-tile buffer capacity that holds ``size`` rows.""" + assert tile_size > 0 + return (size + tile_size - 1) // tile_size * tile_size + + +def _shear_bias_empty(shape, dtype, device): + # Grow-only per-device workspace: the sheared-bias staging tensor is large + # (total_q x num_head x rel_extent_padded) and call shapes vary, so per-call + # torch.empty fragments the caching allocator until GPU memory is exhausted. + # Contents never persist across calls (written by the shear kernel, read by + # the fwd kernel within the same call); assumes attention calls on a device + # are serialized. Bypassed under graph capture (a capture-pool pointer must + # not leak into eager use) and fake mode (a fake tensor must not be cached). + if is_fake_mode() or torch.cuda.is_current_stream_capturing(): + return torch.empty(shape, dtype=dtype, device=device) + nbytes = math.prod(shape) * dtype.itemsize + buf = _shear_bias_workspace.get(device) + if buf is None or buf.numel() < nbytes: + buf = torch.empty(nbytes, dtype=torch.uint8, device=device) + _shear_bias_workspace[device] = buf + return buf[:nbytes].view(dtype).view(shape) + + +def num_splits_heuristic(total_mblocks, num_SMs, num_n_blocks, max_splits): + # If num_n_blocks is too small, use 1 split. For example, we never split for hdim = 128 and seqlen_k = 512. + if num_n_blocks <= 4: + return 1 + # Avoid ZeroDivisionError when batch_size or seqlen_q is 0. The empty-Q + # early-exit in _flash_attn_fwd handles correctness for those shapes; this + # guard just keeps the heuristic safe if called in other contexts. + if total_mblocks == 0: + return 1 + + # NOTE: We should revisit this heuristic after persistence is supported for split KV. + # Sometimes, it's ideal to over-schedule splits for better efficiency. + return min(num_SMs // total_mblocks, max_splits, num_n_blocks) + + +def _resolve_causal_local_window( + causal, window_size_left, window_size_right, mask_mod=None +): + """Resolve causal/local/window settings into canonical form. + + Returns (causal, local, window_size_left, window_size_right). + """ + if mask_mod is not None: + return False, False, window_size_left, window_size_right + if causal: + window_size_right = 0 + if ( + window_size_left is not None + and window_size_right is not None + and window_size_left + window_size_right < 0 + ): + window_size_left = None + window_size_right = None + if window_size_left is not None or window_size_right is not None: + if window_size_left is None and window_size_right == 0: + causal, local = True, False + window_size_right = None + else: + causal, local = False, True + else: + local = False + return causal, local, window_size_left, window_size_right + + +def _group_tile_bias(qhead_per_kvhead_packgqa=1): + return 128 + + +def _flash_attn_fwd( + q: Optional[torch.Tensor], + k: Optional[torch.Tensor], + v: torch.Tensor, + qv: Optional[torch.Tensor] = None, + cu_seqlens_q: Optional[torch.Tensor] = None, + cu_seqlens_k: Optional[torch.Tensor] = None, + seqused_q: Optional[torch.Tensor] = None, + seqused_k: Optional[torch.Tensor] = None, + max_seqlen_q: Optional[int] = None, + max_seqlen_k: Optional[int] = None, + min_seqlen_k: Optional[int] = None, + page_table: Optional[torch.Tensor] = None, + softmax_scale: Optional[float] = None, + causal: bool = False, + softcap: Optional[float] = None, + window_size_left: Optional[int] = None, + window_size_right: Optional[int] = None, + learnable_sink: Optional[torch.Tensor] = None, + tile_mn: Optional[Tuple[int, int]] = None, + mma_pv_is_rs: Optional[bool] = None, + intra_wg_overlap: Optional[bool] = None, + num_threads: int = 384, + num_splits: int = 1, + pack_gqa: Optional[bool] = None, + _arch: Optional[int] = None, + score_mod: Optional[Callable] = None, + mask_mod: Optional[Callable] = None, + block_sparse_tensors: Optional[BlockSparseTensorsTorch] = None, + return_lse: bool = False, + out: Optional[torch.Tensor] = None, + lse: Optional[torch.Tensor] = None, + aux_tensors: Optional[list[torch.Tensor]] = None, + aux_scalars: Optional[tuple] = None, + q_descale: Optional[torch.Tensor] = None, + k_descale: Optional[torch.Tensor] = None, + v_descale: Optional[torch.Tensor] = None, + gather_kv_indices: Optional[torch.Tensor] = None, + rel_bias: Optional[torch.Tensor] = None, + sfq: Optional[torch.Tensor] = None, + sfk: Optional[torch.Tensor] = None, + sfv: Optional[torch.Tensor] = None, + qk_sf_vec_size: Optional[int] = None, + v_sf_vec_size: Optional[int] = None, + rel_bias_prep_cache: Optional[dict] = None, +) -> Tuple[torch.Tensor, torch.Tensor]: + """Forward pass for FlashAttention. + + Args: + ... + score_mod: A callable that takes the attention scores and applies a modification. + mask_mod: A callable that takes token position information and selectively masks + block_sparse_tensors: A tuple of tensors used for block sparsity. + return_lse: Whether to return the log softmax of the attention scores. If set to True will always calculate + The returned LSE supports taking gradient. + out: Optional pre-allocated output tensor. If None, will be allocated internally. + lse: Optional pre-allocated log-sum-exp tensor. If None, will be allocated when needed. + aux_tensors: Some score_mods will want to read from global aux_tensors. This is how we thread them through to the inner kernel. + aux_scalars: Runtime scalar captures used by score_mod or mask_mod. + """ + aux_scalars = tuple(aux_scalars) if aux_scalars else None + q, k, v, qv = [maybe_contiguous(t) for t in (q, k, v, qv)] + assert q is not None or qv is not None + assert v is not None + q_descale, k_descale, v_descale = [ + maybe_contiguous(t) for t in (q_descale, k_descale, v_descale) + ] + q_shape = q.shape if q is not None else qv.shape + num_head, head_dim = q_shape[-2:] + if cu_seqlens_q is None: + batch_size, seqlen_q = q_shape[:2] + total_q = batch_size * seqlen_q + # SFQ can be laid out in the interleaved BlockScaledBasicChunk atom when + # the whole q tensor is dense (no varlen / seqused). + q_sf_interleaved = seqused_q is None + else: + batch_size = cu_seqlens_q.shape[0] - 1 + seqlen_q = None + total_q = q_shape[0] + q_sf_interleaved = False + if page_table is not None: + assert cu_seqlens_k is None, "page_table is not supported with cu_seqlens_k" + assert page_table.dtype == torch.int32, "page_table must be int32" + assert ( + page_table.stride(-1) == 1 + ), "page_table must be contiguous in the last dimension" + max_num_pages_per_seq = page_table.shape[1] + assert page_table.shape == (batch_size, max_num_pages_per_seq) + num_pages, page_size = v.shape[:2] + seqlen_k = num_pages * page_size + # Paged KV: SF is interleaved (TMA-loadable) only when page_size == 128 + # so a page maps exactly onto the SF atom's 128-row tile. + kv_sf_interleaved = page_size == 128 + else: + num_pages, page_size = None, None + seqlen_k = v.shape[-3] + kv_sf_interleaved = True + num_head_kv = v.shape[-2] + head_dim_v = v.shape[-1] + if cu_seqlens_k is None: + if page_table is None: + assert k is None or k.shape == (batch_size, seqlen_k, num_head_kv, head_dim) + assert v.shape == (batch_size, seqlen_k, num_head_kv, head_dim_v) + else: + assert k is None or k.shape == (num_pages, page_size, num_head_kv, head_dim) + assert v.shape == (num_pages, page_size, num_head_kv, head_dim_v) + else: + assert k is None or k.shape == (seqlen_k, num_head_kv, head_dim) + assert v.shape == (seqlen_k, num_head_kv, head_dim_v) + assert cu_seqlens_k.shape == ( + batch_size + 1, + ), "cu_seqlens_k must have shape (batch_size + 1,)" + + if cu_seqlens_q is not None: + assert cu_seqlens_q.shape == ( + batch_size + 1, + ), "cu_seqlens_q must have shape (batch_size + 1,)" + assert seqused_q is None or seqused_q.shape == ( + batch_size, + ), "seqused_q must have shape (batch_size,)" + assert seqused_k is None or seqused_k.shape == ( + batch_size, + ), "seqused_k must have shape (batch_size,)" + # MXFP8 block-scaled attention. + # qk_blockscaled (sfq given): Q/K fp8 e4m3 with per-32 UE8M0 scales; QK^T + # runs as tcgen05 mxf8f6f4 with scales fed from TMEM. + # v_blockscaled (sfv given): V stored fp8 e4m3, dequantized to bf16 + # in-kernel by the correction warp; PV MMA stays bf16. + # Paged KV with qk_blockscaled QK requires v_blockscaled. + qk_blockscaled = sfq is not None + v_blockscaled = sfv is not None + if page_table is not None and qk_blockscaled: + assert v_blockscaled, "paged KV with qk_blockscaled requires v_blockscaled" + if v_blockscaled: + assert v.dtype in [torch.float8_e4m3fn], "v_blockscaled V must be float8_e4m3fn" + assert sfv.dtype == torch.float8_e8m0fnu, "sfv must be float8_e8m0fnu" + assert ( + v_sf_vec_size is not None + ), "v_sf_vec_size must be provided for v_blockscaled" + if qk_blockscaled: + assert sfk is not None, "sfq and sfk must both be provided for qk_blockscaled" + assert ( + qk_sf_vec_size is not None + ), "qk_sf_vec_size must be provided for qk_blockscaled" + assert q is not None and q.dtype in [ + torch.float8_e4m3fn + ], "qk_blockscaled Q must be float8_e4m3fn" + assert q.dtype == k.dtype, "qk_blockscaled Q and K must have the same dtype" + assert sfq.dtype == torch.float8_e8m0fnu, "sfq must be float8_e8m0fnu" + assert sfk.dtype == torch.float8_e8m0fnu, "sfk must be float8_e8m0fnu" + if not v_blockscaled: + assert v.dtype in [ + torch.float16, + torch.bfloat16, + ], "qk_blockscaled V must be float16 or bfloat16" + else: + assert sfk is None, "sfq and sfk must both be provided for qk_blockscaled" + assert v.dtype in [ + torch.float16, + torch.bfloat16, + torch.float8_e4m3fn, + torch.float8_e5m2, + ], "inputs must be float16, bfloat16, fp8 e4m3fn, or fp8 e5m2" + + # qk_blockscaled: Q/K are fp8 (same dtype); V may be bf16 (no v_blockscaled) or + # fp8 (v_blockscaled) -- exclude V from the cross-dtype check either way. + # v_blockscaled without qk_blockscaled: Q/K bf16, V fp8 -- exclude V. + if qk_blockscaled: + input_tensors = {"q": q, "k": k, "qv": qv} + elif v_blockscaled: + input_tensors = {"q": q, "qv": qv} + else: + input_tensors = {"q": q, "k": k, "v": v, "qv": qv} + present = {name: t for name, t in input_tensors.items() if t is not None} + names = list(present.keys()) + for i in range(len(names)): + for j in range(i + 1, len(names)): + a, b = names[i], names[j] + assert ( + present[a].dtype == present[b].dtype + ), f"{a}.dtype {present[a].dtype} != {b}.dtype {present[b].dtype}" + + q_dtype = q.dtype if q is not None else qv.dtype + + for t in [cu_seqlens_q, cu_seqlens_k, seqused_q, seqused_k]: + if t is not None: + assert ( + t.dtype == torch.int32 + ), "cu_seqlens_q, cu_seqlens_k, seqused_q, seqused_k must be int32" + assert ( + t.stride(0) == 1 + ), "cu_seqlens_q, cu_seqlens_k, seqused_q, seqused_k must be contiguous" + if learnable_sink is not None: + assert learnable_sink.shape == (num_head,) + assert learnable_sink.dtype == torch.bfloat16, "learnable_sink must be bfloat16" + + if not is_fake_mode(): + assert all( + t is None or t.is_cuda + for t in ( + q, + k, + v, + qv, + q_descale, + k_descale, + v_descale, + cu_seqlens_q, + cu_seqlens_k, + seqused_q, + seqused_k, + page_table, + learnable_sink, + ) + ), "inputs must be on CUDA device" + arch = _get_device_arch() if _arch is None else _arch + assert arch // 10 in [ + 8, + 9, + 10, + 11, + 12, + ], "Unsupported compute capability. Supported: 8.x, 9.x, 10.x, 11.x, 12.x" + assert num_head % num_head_kv == 0, "num_head must be divisible by num_head_kv" + alignment = 16 // v.element_size() + if arch // 10 not in [8, 12]: + _validate_head_dims(head_dim, head_dim_v, arch // 10, alignment) + if softmax_scale is None: + softmax_scale = ( + 1.0 / math.sqrt(head_dim) + if qv is None or q is None + else 1.0 / math.sqrt(head_dim + head_dim_v) + ) + if softcap == 0.0: + softcap = None + qhead_per_kvhead = num_head // num_head_kv + if pack_gqa is None: + pack_gqa = qhead_per_kvhead > 1 + if pack_gqa: + # pack_gqa reshapes SFQ's head/token layout, which the interleaved atom + # can't express; fall back to the dense (non-interleaved) SFQ path. + q_sf_interleaved = False + + is_fp8 = v.dtype in (torch.float8_e4m3fn, torch.float8_e5m2) + requires_grad = any(t is not None and t.requires_grad for t in [q, k, v, qv]) + if is_fp8 and requires_grad: + raise NotImplementedError( + "FA4 CuTe FP8 backward is not supported yet (forward-only)." + ) + # qk_blockscaled (fp8 Q/K, bf16 V): output follows V's dtype. v_blockscaled + # (fp8 V dequanted in-kernel): output is bf16. + if qk_blockscaled: + out_torch_dtype = torch.bfloat16 if v_blockscaled else v.dtype + else: + out_torch_dtype = torch.bfloat16 if is_fp8 else q_dtype + device = v.device + q_batch_seqlen_shape = ( + (batch_size, seqlen_q) if cu_seqlens_q is None else (total_q,) + ) + + if qv is None: + lse_shape = ( + (batch_size, num_head, seqlen_q) + if cu_seqlens_q is None + else (num_head, total_q) + ) + else: + # num_head contiguous better for MQA in MLA absorbed + lse_shape = ( + (batch_size, seqlen_q, num_head) + if cu_seqlens_q is None + else (total_q, num_head) + ) + + if out is None: + out = torch.empty( + *q_batch_seqlen_shape, + num_head, + head_dim_v, + dtype=out_torch_dtype, + device=device, + ) + else: + _validate_tensor( + out, + "out", + (*q_batch_seqlen_shape, num_head, head_dim_v), + out_torch_dtype, + device, + ) + + if lse is None: + lse = ( + torch.empty(lse_shape, dtype=torch.float32, device=device) + if requires_grad or return_lse + else None + ) + elif lse is not None: + _validate_tensor(lse, "lse", lse_shape, torch.float32, device) + + if seqlen_k == 0 or total_q == 0: + out.zero_() + if lse is not None: + lse.fill_(float("-inf")) + return out, lse + + if is_fp8: + for t, name in ( + (q_descale, "q_descale"), + (k_descale, "k_descale"), + (v_descale, "v_descale"), + ): + if t is not None: + _validate_tensor( + t, name, (batch_size, num_head_kv), torch.float32, device + ) + else: + assert ( + q_descale is None and k_descale is None and v_descale is None + ), "q_descale/k_descale/v_descale are only supported for FP8 inputs" + + dtype = torch2cute_dtype_map[q_dtype] + if is_fp8: + assert ( + arch // 10 == 10 + ), "FP8 is only supported on SM100 (compute capability 10.x) for FA4 CuTe." + use_block_sparsity = block_sparse_tensors is not None + + causal, local, window_size_left, window_size_right = _resolve_causal_local_window( + causal, window_size_left, window_size_right, mask_mod + ) + + requested_use_clc_scheduler = utils._get_use_clc_scheduler_default() + requested_disable_2cta = utils._get_disable_2cta_default(is_fwd=True) + + current_stream = cute.runtime.make_fake_stream(use_tvm_ffi_env_stream=True) + + # SM80/SM120: uses SM80 MMA, 128 threads (4 warps) + if arch // 10 in [8, 12]: + num_threads = 128 + + fwd_cfg = FwdConfig(128, 128, True, True) # default + if tile_mn is None: + if arch // 10 == 12: + # SM120 tile sizes tuned for 99 KB SMEM capacity: + # D<=64: 128x128 → 48 KB (good occupancy) + # D>64: 128x64 → 64 KB (128x128 would use 96 KB, hurting occupancy) + if head_dim <= 64: + fwd_cfg = FwdConfig(128, 128, True, True) + else: + fwd_cfg = FwdConfig(128, 64, True, True) + elif arch // 10 == 8: + fwd_cfg = FwdConfig(128, 64, True, True) # SM80, should tune + elif arch // 10 == 9: + sparse_q = get_sparse_q_block_size(block_sparse_tensors, seqlen_q) + fwd_cfg = _tile_size_fwd_sm90( + head_dim, head_dim_v, causal, local, sparse_block_size_q=sparse_q + ) + else: + fwd_cfg = FwdConfig( + tile_mn[0], tile_mn[1], fwd_cfg.mma_pv_is_rs, fwd_cfg.intra_wg_overlap + ) + tile_m, tile_n = fwd_cfg.m_block_size, fwd_cfg.n_block_size + if mma_pv_is_rs is None: + mma_pv_is_rs = fwd_cfg.mma_pv_is_rs + if intra_wg_overlap is None: + intra_wg_overlap = fwd_cfg.intra_wg_overlap + + if max_seqlen_q is None: + max_seqlen_q = seqlen_q if cu_seqlens_q is None else total_q + if max_seqlen_k is None: + max_seqlen_k = seqlen_k + if cu_seqlens_k is None and seqused_k is None: + min_seqlen_k = seqlen_k + seqlen_q_packgqa = max_seqlen_q * qhead_per_kvhead + if arch // 10 in [10, 11]: + # q_stage=2 hangs on sm100 for qk_blockscaled; force q_stage=1 there. + q_stage = 1 if qk_blockscaled else (2 if seqlen_q_packgqa > tile_m else 1) + else: + q_stage = 1 + + m_block_size_effective = q_stage * tile_m + seqlen_k_loaded = ( + max_seqlen_k + if not local + else max( + 0, + min( + max_seqlen_k, + (window_size_right or max_seqlen_k) + + (window_size_left or max_seqlen_k) + + 1 + + tile_m, + ), + ) + ) + num_m_blocks = ( + seqlen_q_packgqa + m_block_size_effective - 1 + ) // m_block_size_effective + total_mblocks = batch_size * num_head_kv * num_m_blocks + num_n_blocks = (seqlen_k_loaded + tile_n - 1) // tile_n + num_SMs = ( + 132 + if is_fake_mode() + else torch.cuda.get_device_properties(device).multi_processor_count + ) + if num_splits < 1: + num_splits = num_splits_heuristic(total_mblocks, num_SMs, num_n_blocks, 128) + + # SplitKV uses float32 partial output, which doubles the O buffer size + # in shared memory, causing OOM for diff-headdim (192, 128) + if arch // 10 in [10, 11] and head_dim != head_dim_v and num_splits > 1: + if num_n_blocks >= 64 and head_dim_v != 512: + tile_n = 64 + num_n_blocks = (seqlen_k_loaded + tile_n - 1) // tile_n + num_splits = num_splits_heuristic(total_mblocks, num_SMs, num_n_blocks, 128) + else: + num_splits = 1 + + is_split_kv = num_splits > 1 + if is_split_kv: + out_partial = torch.empty( + num_splits, + *q_batch_seqlen_shape, + num_head, + head_dim_v, + dtype=torch.float32, + device=device, + ) + lse_partial = torch.empty( + num_splits, *lse_shape, dtype=torch.float32, device=device + ) + + use_2cta_instrs = ( + arch // 10 in [10, 11] + and not requested_disable_2cta + and not causal + and not local + and not is_split_kv + and cu_seqlens_q is None + and seqused_q is None + and not use_block_sparsity + and page_size in [None, 128] + and int(math.ceil(head_dim / 16) * 16) in [128, 192] + and int(math.ceil(head_dim_v / 16) * 16) == 128 + and seqlen_q_packgqa > 2 * tile_m + and (tile_m % qhead_per_kvhead == 0 or not pack_gqa) + and not qk_blockscaled + ) + + # hd=256 2CTA forward uses dedicated kernel (Blackwell family) + use_dedicated_hd256_kernel = ( + arch // 10 in [10, 11] and head_dim == 256 and head_dim_v == 256 + ) + use_2cta_instrs = use_2cta_instrs or use_dedicated_hd256_kernel + + if softcap is not None: + assert score_mod is None, "softcap and score_mod cannot be used together" + score_mod = utils.create_softcap_scoremod(softcap) + elif score_mod is not None: + if arch // 10 == 8: + raise NotImplementedError( + "Custom user-provided score_mod is not supported on SM8x architectures." + ) + + # hash score and mask mods for compile cache + score_mod_hash = utils.hash_callable(score_mod) if score_mod is not None else False + mask_mod_hash = utils.hash_callable(mask_mod) if mask_mod is not None else False + + is_varlen = ( + cu_seqlens_q is not None + or cu_seqlens_k is not None + or seqused_q is not None + or seqused_k is not None + ) + + # CLC regressed for varlen MHA and dense noncausal. Imbalanced varlen shapes + # keep more K/V blocks in flight and hurt L2; dense noncausal mostly just + # pays work-stealing overhead. + is_varlen_mha = is_varlen and qhead_per_kvhead == 1 + is_dense_noncausal = not is_varlen and not causal and not local + use_clc_scheduler = ( + requested_use_clc_scheduler and not is_varlen_mha and not is_dense_noncausal + ) + + if use_block_sparsity: + # NB: pack_gqa requires block sparse head dim == 1 (broadcasted) + head_dim_idx = 0 if block_sparse_tensors.mask_block_cnt.ndim == 2 else 1 + if pack_gqa and block_sparse_tensors.mask_block_cnt.shape[head_dim_idx] != 1: + pack_gqa = False + if cu_seqlens_q is not None: + assert ( + block_sparse_tensors.cu_total_m_blocks is not None + ), "Varlen block sparsity requires block_sparse_tensors.cu_total_m_blocks." + + # See get_broadcast_dims for why this is needed in compile key + block_sparse_broadcast_pattern = None + normalized_block_sparse_tensors = None + q_subtile_factor = None + if block_sparse_tensors is not None: + ( + normalized_block_sparse_tensors, + block_sparse_broadcast_pattern, + q_subtile_factor, + ) = normalize_block_sparse_config( + block_sparse_tensors, + batch_size=batch_size, + num_head=num_head, + seqlen_q=seqlen_q, + seqlen_k=seqlen_k, + block_size=(tile_m, tile_n), + q_stage=q_stage, + ) + if aux_tensors is not None: + aux_tensor_metadata = get_aux_tensor_metadata(aux_tensors) + else: + aux_tensor_metadata = None + aux_scalar_metadata = ( + tuple(type(s) for s in aux_scalars) if aux_scalars is not None else None + ) + + if qv is not None: + assert arch // 10 in [10, 11], "only support Blackwell arch with qv" + assert q is None or qv.shape[:-1] == q.shape[:-1] + assert qv.shape[-1] == head_dim_v + assert head_dim_v == 512 + assert q is None or head_dim == 64 + assert not local, "local not yet supported with qv" + assert ( + q_descale is None and k_descale is None and v_descale is None + ), "q_descale/k_descale/v_descale are not yet supported with qv" + assert tile_n == 128 + + assert not is_split_kv, "split kv not supported with qv" + assert learnable_sink is None + assert softcap is None + assert score_mod is None + assert mask_mod is None + + if page_table is not None: + assert ( + gather_kv_indices is None + ), "paged KV + topk sparsity not yet supported together" + + qv = maybe_contiguous(qv) + + gather_kv_length = 2048 # dummy value + sparse_kv = gather_kv_indices is not None + disable_sparse_kv_bitmask = False + if sparse_kv: + assert gather_kv_indices.shape[:-1] == qv.shape[:-2] + gather_kv_length = gather_kv_indices.shape[-1] + assert gather_kv_length % 128 == 0 + if min_seqlen_k is None or causal: + disable_sparse_kv_bitmask = False + else: + # seqlen_k_boundary = min_seqlen_k - max_seqlen_q + 1 if causal else min_seqlen_k + seqlen_k_boundary = min_seqlen_k + disable_sparse_kv_bitmask = seqlen_k_boundary >= gather_kv_length + # to be used for sparse backward + p = row_max = None + else: + assert gather_kv_indices is None, "gather_kv_indices is only supported with qv" + gather_kv_length = None + sparse_kv = None + disable_sparse_kv_bitmask = None + p = row_max = None + + # rel_bias -> sheared bias (Inkling relative attention). Produces `bias`, the column-aligned + # bias the SM100 kernel adds to pre-softmax scores via its dedicated TMA pipeline. + rel_extent = 0 + rel_extent_padded = 0 + bias = None + tile_bias = tile_m + cu_total_m_blocks_bias = None + blocks_to_batch_idx = None + if rel_bias is not None: + assert arch // 10 in [ + 9, + 10, + 11, + ], "rel_bias (sheared bias) is only supported on SM9x/10x" + qhead_per_kvhead_packgqa = qhead_per_kvhead if pack_gqa else 1 + rel_extent = rel_bias.shape[-1] + rel_extent_padded = rel_extent + 256 + assert rel_extent % 128 == 0 + assert tile_m == 128 and tile_n == 128 + assert ( + causal + or window_size_left is None + or ( + window_size_right is not None + and window_size_left + window_size_right + 1 == rel_extent + ) + ), "for relative bias, require causal or window length == rel_extent" + tile_bias = ( + (seqlen_q_packgqa + 7) // 8 * 8 if seqlen_q_packgqa < tile_m else tile_m + ) + if cu_seqlens_q is None: + bias_seqlen_q_rounded = _round_up_to_tile(seqlen_q, tile_m) + assert rel_bias.shape == (batch_size, seqlen_q, num_head, rel_extent) + bias = _shear_bias_empty( + (batch_size, bias_seqlen_q_rounded, num_head, rel_extent_padded), + rel_bias.dtype, + device, + ) + else: + assert rel_bias.shape == (total_q, num_head, rel_extent) + bias_total_q_rounded = _round_up_to_tile(total_q, tile_m) + bias = _shear_bias_empty( + (bias_total_q_rounded, num_head, rel_extent_padded), + rel_bias.dtype, + device, + ) + + rows_per_cta = 4 + group_tile_bias = _group_tile_bias(qhead_per_kvhead_packgqa) + # Decode and target verification fit each sequence in one packed-Q block. + # In that case the batch index is already a scheduler coordinate, so the + # prefix-sum/block-map preparation kernel is pure launch overhead. + max_m_blocks_leq_one = seqlen_q_packgqa <= group_tile_bias + use_pdl = is_arch_support_pdl() + bias_max_seqlen_q = max_seqlen_q if max_seqlen_q is not None else seqlen_q + bias_max_seqlen_k = max_seqlen_k if max_seqlen_k is not None else seqlen_k + + # Block-packed scheduling for the shear kernel (varlen only). + use_prepare_bias_kernel = ( + cu_seqlens_q is not None and not max_m_blocks_leq_one and batch_size <= 1024 + ) + if use_prepare_bias_kernel: + prep_cache_key = ( + group_tile_bias, + qhead_per_kvhead_packgqa, + cu_seqlens_q.data_ptr(), + ) + cached_prep = ( + rel_bias_prep_cache.get(prep_cache_key) + if rel_bias_prep_cache is not None + else None + ) + if cached_prep is not None: + cu_total_m_blocks_bias, blocks_to_batch_idx = cached_prep + else: + cu_total_m_blocks_bias = torch.empty( + batch_size + 1, dtype=torch.int32, device=device + ) + total_group_blocks_max = ( + total_q * qhead_per_kvhead_packgqa + + batch_size * (group_tile_bias - 1) + ) // group_tile_bias + blocks_to_batch_idx = torch.empty( + total_group_blocks_max, dtype=torch.int32, device=device + ) + compile_key_prepare = (group_tile_bias, qhead_per_kvhead_packgqa) + if ( + compile_key_prepare + not in _flash_attn_fwd.compile_cache_prepare_shear_bias + ): + ( + cu_total_m_blocks_bias_tensor, + cu_seqlens_q_tensor, + blocks_to_batch_idx_tensor, + ) = [ + to_cute_tensor(t, assumed_align=4, leading_dim=0) + for t in ( + cu_total_m_blocks_bias, + cu_seqlens_q, + blocks_to_batch_idx, + ) + ] + _flash_attn_fwd.compile_cache_prepare_shear_bias[ + compile_key_prepare + ] = cute.compile( + CuSeqlensToBlocksKernel( + tile=group_tile_bias, + seqlen_multiple=qhead_per_kvhead_packgqa, + use_pdl=use_pdl, + ), + cu_total_m_blocks_bias_tensor, + cu_seqlens_q_tensor, + blocks_to_batch_idx_tensor, + current_stream, + options="--enable-tvm-ffi", + ) + if not is_fake_mode(): + _flash_attn_fwd.compile_cache_prepare_shear_bias[ + compile_key_prepare + ]( + cu_total_m_blocks_bias, + cu_seqlens_q, + blocks_to_batch_idx, + ) + if rel_bias_prep_cache is not None: + rel_bias_prep_cache[prep_cache_key] = ( + cu_total_m_blocks_bias, + blocks_to_batch_idx, + ) + + shear_compile_key = ( + rel_bias.dtype, + rel_extent, + causal, + window_size_left is not None, + window_size_right is not None, + cu_seqlens_q is None, + cu_seqlens_k is None, + seqused_q is None, + seqused_k is None, + pack_gqa, + qhead_per_kvhead, + rows_per_cta, + group_tile_bias, + max_m_blocks_leq_one, + cu_total_m_blocks_bias is not None, + blocks_to_batch_idx is not None, + ) + if shear_compile_key not in _flash_attn_fwd.compile_cache_shear_bias: + ( + cu_seqlens_q_tensor, + cu_seqlens_k_tensor, + seqused_q_tensor, + seqused_k_tensor, + cu_total_m_blocks_bias_tensor, + blocks_to_batch_idx_tensor, + ) = [ + ( + to_cute_tensor(t, assumed_align=4, leading_dim=0) + if t is not None + else None + ) + for t in ( + cu_seqlens_q, + cu_seqlens_k, + seqused_q, + seqused_k, + cu_total_m_blocks_bias, + blocks_to_batch_idx, + ) + ] + _flash_attn_fwd.compile_cache_shear_bias[shear_compile_key] = cute.compile( + ShearingBias( + rel_extent, + is_causal=causal, + is_local=local, + pack_gqa=pack_gqa, + qhead_per_kvhead=qhead_per_kvhead, + rows_per_cta=rows_per_cta, + tile_m=group_tile_bias, + max_m_blocks_leq_one=max_m_blocks_leq_one, + use_pdl=use_pdl, + ), + to_cute_tensor(rel_bias), + to_cute_tensor(bias), + bias_max_seqlen_q, + bias_max_seqlen_k, + cu_seqlens_q_tensor, + cu_seqlens_k_tensor, + seqused_q_tensor, + seqused_k_tensor, + cu_total_m_blocks_bias_tensor, + blocks_to_batch_idx_tensor, + window_size_left, + window_size_right, + current_stream, + options="--enable-tvm-ffi", + ) + if not is_fake_mode(): + _flash_attn_fwd.compile_cache_shear_bias[shear_compile_key]( + rel_bias, + bias, + bias_max_seqlen_q, + bias_max_seqlen_k, + cu_seqlens_q, + cu_seqlens_k, + seqused_q, + seqused_k, + cu_total_m_blocks_bias, + blocks_to_batch_idx, + window_size_left, + window_size_right, + ) + if os.environ.get("BIAS_PREP_ONLY", "0") == "1": + # Benchmark hook: time just the shear-prep kernels, skip the attention kernel. + return out, lse + + compile_key = ( + dtype, + head_dim, + head_dim_v, + qhead_per_kvhead, + causal, + score_mod_hash, + mask_mod_hash, + use_block_sparsity, + block_sparse_broadcast_pattern, + aux_tensor_metadata, + aux_scalar_metadata, + lse is None, + cu_seqlens_q is None, + cu_seqlens_k is None, + seqused_q is None, + seqused_k is None, + page_table is not None, + window_size_left is not None, + window_size_right is not None, + learnable_sink is not None, + q_descale is not None, + k_descale is not None, + v_descale is not None, + block_sparse_tensors is None or block_sparse_tensors.cu_total_m_blocks is None, + block_sparse_tensors is None + or block_sparse_tensors.cu_block_idx_offsets is None, + tile_m, + tile_n, + q_stage, + num_threads, + is_split_kv, + pack_gqa, + arch, + page_size not in [None, tile_n], # paged KV non-TMA + use_2cta_instrs, + q_subtile_factor, + mma_pv_is_rs, + intra_wg_overlap, + use_clc_scheduler, + q is not None, + qv is not None, + p is not None, + row_max is not None, + gather_kv_length, + sparse_kv, + disable_sparse_kv_bitmask, + bias is not None, + tile_bias, + rel_extent, + qk_blockscaled, + qk_sf_vec_size, + v_blockscaled, + v_sf_vec_size, + q_sf_interleaved, + kv_sf_interleaved, + sfq.ndim if sfq is not None else None, + sfk.ndim if sfk is not None else None, + sfv.ndim if sfv is not None else None, + fa_logging.get_fa_log_level(), + ) + + if compile_key not in _flash_attn_fwd.compile_cache: + ( + cu_seqlens_q_tensor, + cu_seqlens_k_tensor, + seqused_q_tensor, + seqused_k_tensor, + learnable_sink_tensor, + ) = [ + to_cute_tensor(t, assumed_align=4, leading_dim=0) if t is not None else None + for t in (cu_seqlens_q, cu_seqlens_k, seqused_q, seqused_k, learnable_sink) + ] + page_table_tensor = ( + to_cute_tensor(page_table, assumed_align=4, leading_dim=1) + if page_table is not None + else None + ) + q_tensor, k_tensor, v_tensor, o_tensor = [ + to_cute_tensor(t) + for t in (q, k, v, out if not is_split_kv else out_partial) + ] + bias_tensor = to_cute_tensor(bias) if bias is not None else None + if qk_blockscaled: + sfq_tensor = to_cute_tensor(sfq) + sfk_tensor = to_cute_tensor(sfk) + else: + sfq_tensor = None + sfk_tensor = None + sfv_tensor = to_cute_tensor(sfv) if v_blockscaled else None + if is_split_kv: + lse_tensor = to_cute_tensor(lse_partial, assumed_align=4) + elif lse is not None: + lse_tensor = to_cute_tensor(lse, assumed_align=4) + else: + lse_tensor = None + + q_descale_tensor = ( + to_cute_tensor(q_descale, assumed_align=4, leading_dim=1) + if q_descale is not None + else None + ) + k_descale_tensor = ( + to_cute_tensor(k_descale, assumed_align=4, leading_dim=1) + if k_descale is not None + else None + ) + v_descale_tensor = ( + to_cute_tensor(v_descale, assumed_align=4, leading_dim=1) + if v_descale is not None + else None + ) + descale_tensors_tensor = ( + DescaleTensors( + q_descale=q_descale_tensor, + k_descale=k_descale_tensor, + v_descale=v_descale_tensor, + ) + if q_descale_tensor is not None + or k_descale_tensor is not None + or v_descale_tensor is not None + else None + ) + + sparse_tensors = None + if normalized_block_sparse_tensors is not None: + sparse_tensors = to_cute_block_sparse_tensors( + normalized_block_sparse_tensors + ) + + cute_aux_tensors = None + aux_tensor_metadata = None + if aux_tensors is not None: + cute_aux_tensors = [to_cute_aux_tensor(buf) for buf in aux_tensors] + + qv_tensor = to_cute_tensor(qv) if qv is not None else None + gather_kv_indices_tensor = ( + to_cute_tensor(gather_kv_indices) if gather_kv_indices is not None else None + ) + p_tensor = to_cute_tensor(p) if p is not None else None + row_max_tensor = to_cute_tensor(row_max) if row_max is not None else None + + if arch // 10 == 8: + assert page_table is None, "paged KV not supported on SM 8.0" + assert not is_split_kv, "SplitKV not supported on SM 8.0" + fa_fwd = FlashAttentionForwardSm80( + dtype, + head_dim, + head_dim_v, + qhead_per_kvhead, + is_causal=causal, + is_local=local, + pack_gqa=pack_gqa, + tile_m=tile_m, + tile_n=tile_n, + num_stages=1, + num_threads=num_threads, + Q_in_regs=False, + score_mod=score_mod, + mask_mod=mask_mod, + has_aux_tensors=aux_tensors is not None, + ) + elif arch // 10 == 9: + fa_fwd = FlashAttentionForwardSm90( + dtype, + head_dim, + head_dim_v, + qhead_per_kvhead, + is_causal=causal, + is_local=local, + is_split_kv=is_split_kv, + pack_gqa=pack_gqa, + tile_m=tile_m, + tile_n=tile_n, + # num_stages=1, + num_stages=2, + num_threads=num_threads, + Q_in_regs=False, + intra_wg_overlap=intra_wg_overlap, + mma_pv_is_rs=mma_pv_is_rs, + mask_mod=mask_mod, + score_mod=score_mod, + has_aux_tensors=aux_tensors is not None, + q_subtile_factor=q_subtile_factor, + paged_kv_non_tma=page_size not in [None, tile_n], + has_bias=bias is not None, + bias_block_size=tile_bias, + rel_extent_padded=rel_extent_padded, + ) + elif arch // 10 in [10, 11]: + if qv is not None: + paged_kv_cpasync = page_table is not None and page_size != tile_n + has_qk = q is not None + fa_fwd = FlashAttentionMLAForwardSm100( + is_causal=causal, + use_cpasync_load_KV=sparse_kv or paged_kv_cpasync, + topk_length=gather_kv_length, + is_topk_gather=sparse_kv, + pack_gqa=pack_gqa, + qhead_per_kvhead=qhead_per_kvhead, + nheads_kv=num_head_kv, + is_varlen_q=cu_seqlens_q is not None or seqused_q is not None, + disable_bitmask=disable_sparse_kv_bitmask, + has_qk=has_qk, + ) + else: + if use_dedicated_hd256_kernel: + # hd=256 2CTA forward: check for currently unsupported features + assert ( + softcap is None + ), "SM100 forward with head_dim=256 does not support softcap" + assert ( + not use_block_sparsity + ), "SM100 forward with head_dim=256 does not support block sparsity" + assert ( + learnable_sink is None + ), "SM100 forward with head_dim=256 does not support learnable_sink" + assert ( + seqused_q is None and seqused_k is None + ), "SM100 forward with head_dim=256 does not support seqused_q/seqused_k" + if page_table is not None: + assert max_seqlen_k % page_size == 0, ( + f"SM100 hd256 2CTA paged KV requires max_seqlen_k divisible by " + f"page_size ({page_size}), got max_seqlen_k={max_seqlen_k}" + ) + assert page_table.shape[1] == max_seqlen_k // page_size, ( + f"SM100 hd256 2CTA paged KV requires page_table.shape[1] == " + f"max_seqlen_k // page_size ({max_seqlen_k} // {page_size} = " + f"{max_seqlen_k // page_size}), got {page_table.shape[1]}; " + f"pass page_table[:, :{max_seqlen_k // page_size}] to slice to " + f"the actual sequence length" + ) + assert page_table.stride(0) == page_table.shape[1], ( + f"SM100 hd256 2CTA paged KV requires a fully contiguous page_table " + f"(stride(0)={page_table.stride(0)} must equal " + f"shape[1]={page_table.shape[1]})" + ) + # pack_gqa is an auto-selected optimization; disable it for hd256 kernel + pack_gqa = False + + flash_fwd_obj_cls = ( + BlackwellFusedMultiHeadAttentionForward + if use_dedicated_hd256_kernel + else FlashAttentionForwardSm100 + ) + + fa_fwd = flash_fwd_obj_cls( + head_dim, + head_dim_v, + qhead_per_kvhead=qhead_per_kvhead, + is_causal=causal, + is_local=local, + is_split_kv=is_split_kv, + pack_gqa=pack_gqa, + m_block_size=tile_m, + n_block_size=tile_n, + q_stage=q_stage, + is_persistent=not causal + and not local + and cu_seqlens_q is None + and seqused_q is None + and not is_split_kv, + score_mod=score_mod, + mask_mod=mask_mod, + has_aux_tensors=aux_tensors is not None, + paged_kv_non_tma=page_size not in [None, tile_n], + is_varlen_q=cu_seqlens_q is not None or seqused_q is not None, + q_subtile_factor=q_subtile_factor, + use_2cta_instrs=use_2cta_instrs, + use_clc_scheduler=use_clc_scheduler, + has_bias=bias is not None, + bias_block_size=tile_bias, + rel_extent_padded=rel_extent_padded, + # hd256 class doesn't take these kwargs (qk_blockscaled excludes hd256) + **( + {} + if use_dedicated_hd256_kernel + else dict( + qk_blockscaled=qk_blockscaled, + v_dequant=v_blockscaled, + q_sf_interleaved=q_sf_interleaved, + kv_sf_interleaved=kv_sf_interleaved, + ) + ), + ) + elif arch // 10 == 12: + # SM120 (Blackwell GeForce / DGX Spark): uses SM80 MMA with SM120 SMEM capacity + assert not use_block_sparsity, "Block sparsity not supported on SM 12.0" + assert page_table is None, "Paged KV not supported on SM 12.0 in this PR" + assert not is_split_kv, "SplitKV not supported on SM 12.0 in this PR" + fa_fwd = FlashAttentionForwardSm120( + dtype, + head_dim, + head_dim_v, + qhead_per_kvhead, + is_causal=causal, + is_local=local, + pack_gqa=pack_gqa, + tile_m=tile_m, + tile_n=tile_n, + num_stages=1, + num_threads=num_threads, + Q_in_regs=False, + score_mod=score_mod, + mask_mod=mask_mod, + has_aux_tensors=aux_tensors is not None, + ) + else: + raise ValueError( + f"Unsupported compute capability: {arch}. Supported: 8.x, 9.x, 10.x, 11.x, 12.x" + ) + # TODO: check @can_implement + if qv is not None: + _flash_attn_fwd.compile_cache[compile_key] = cute.compile( + fa_fwd, + q_tensor, + qv_tensor, + k_tensor, + v_tensor, + o_tensor, + lse_tensor, + softmax_scale, + p_tensor, + row_max_tensor, + cu_seqlens_q_tensor, + cu_seqlens_k_tensor, + seqused_q_tensor, + seqused_k_tensor, + gather_kv_indices_tensor, + page_table_tensor, + window_size_left, + window_size_right, + current_stream, + options="--enable-tvm-ffi", + ) + else: + compile_args = [ + fa_fwd, + q_tensor, + k_tensor, + v_tensor, + o_tensor, + lse_tensor, + softmax_scale, + cu_seqlens_q_tensor, + cu_seqlens_k_tensor, + seqused_q_tensor, + seqused_k_tensor, + page_table_tensor, + window_size_left, + window_size_right, + learnable_sink_tensor, + ] + if arch // 10 in [10, 11]: + compile_args.append(descale_tensors_tensor) + compile_args.extend( + [ + sparse_tensors, + AuxData(cute_aux_tensors, aux_scalars), + ] + ) + if arch // 10 in [9, 10, 11]: + compile_args.append(bias_tensor) # mBias + if arch // 10 in [10, 11]: + if not use_dedicated_hd256_kernel: + compile_args.extend( + [ + sfq_tensor, # mSFQ + sfk_tensor, # mSFK + sfv_tensor, # mSFV + qk_sf_vec_size, + v_sf_vec_size, + ] + ) + compile_args.append(current_stream) + _flash_attn_fwd.compile_cache[compile_key] = cute.compile( + *compile_args, options="--enable-tvm-ffi" + ) + + if not is_fake_mode(): + q_call, k_call, v_call, qv_call = [ + t.detach() if t is not None else None for t in (q, k, v, qv) + ] + if is_fp8 or qk_blockscaled: + # need uint8 workaround until we pin torch >= 2.11.0 where fp8 export + # is supported. Under qk_blockscaled/v_blockscaled some tensors stay + # bf16 (e.g. bf16 V), so view only the actual fp8 tensors. + q_call, k_call, v_call, qv_call = [ + ( + t.view(torch.uint8) + if t is not None + and t.dtype in (torch.float8_e4m3fn, torch.float8_e5m2) + else t + ) + for t in (q_call, k_call, v_call, qv_call) + ] + # SF tensors are e8m0fnu; the compile-time cute tensors keep that dtype, + # so pass them through unchanged (matches to_cute_tensor(sfq/sfk/sfv)). + sfq_call, sfk_call, sfv_call = sfq, sfk, sfv + descale_tensors = ( + DescaleTensors( + q_descale=q_descale, k_descale=k_descale, v_descale=v_descale + ) + if q_descale is not None or k_descale is not None or v_descale is not None + else None + ) + if qv is not None: + _flash_attn_fwd.compile_cache[compile_key]( + q_call, + qv_call, + k_call, + v_call, + out.detach(), + lse, + softmax_scale, + p, + row_max, + cu_seqlens_q, + cu_seqlens_k, + seqused_q, + seqused_k, + gather_kv_indices, + page_table, + window_size_left, + window_size_right, + ) + else: + call_args = [ + q_call, + k_call, + v_call, + out.detach() if not is_split_kv else out_partial, + lse_partial if is_split_kv else lse, + softmax_scale, + cu_seqlens_q, + cu_seqlens_k, + seqused_q, + seqused_k, + page_table, + window_size_left, + window_size_right, + learnable_sink, + ] + if arch // 10 in [10, 11]: + call_args.append(descale_tensors) + call_args.extend( + [ + ( + ( + normalized_block_sparse_tensors.mask_block_cnt, + normalized_block_sparse_tensors.mask_block_idx, + normalized_block_sparse_tensors.full_block_cnt, + normalized_block_sparse_tensors.full_block_idx, + normalized_block_sparse_tensors.cu_total_m_blocks, + normalized_block_sparse_tensors.cu_block_idx_offsets, + normalized_block_sparse_tensors.dq_write_order, + normalized_block_sparse_tensors.dq_write_order_full, + ) + if normalized_block_sparse_tensors is not None + else None + ), + AuxData(aux_tensors, aux_scalars), + ] + ) + if arch // 10 in [9, 10, 11]: + call_args.append(bias) # mBias + if arch // 10 in [10, 11]: + if not use_dedicated_hd256_kernel: + # qk_sf_vec_size / v_sf_vec_size are Constexpr (baked at + # compile time), so only the SF tensors go on the call. + call_args.extend( + [ + sfq_call, # mSFQ (None unless qk_blockscaled) + sfk_call, # mSFK (None unless qk_blockscaled) + sfv_call, # mSFV (None unless v_blockscaled) + ] + ) + _flash_attn_fwd.compile_cache[compile_key](*call_args) + if is_split_kv: + _flash_attn_fwd_combine( + out_partial, + lse_partial.transpose(-1, -2), + out, + lse.transpose(-1, -2) if lse is not None else None, + cu_seqlens_q, + seqused_q, + ) + return out, lse + + +_flash_attn_fwd.compile_cache = get_jit_cache("fwd") +_flash_attn_fwd.compile_cache_shear_bias = get_jit_cache("fwd_shear_bias") +_flash_attn_fwd.compile_cache_prepare_shear_bias = get_jit_cache( + "fwd_prepare_shear_bias" +) + + +class FlashAttnFunc(torch.autograd.Function): + @staticmethod + def forward( + ctx, + q: torch.Tensor, + k: torch.Tensor, + v: torch.Tensor, + qv: Optional[torch.Tensor] = None, + gather_kv_indices: Optional[torch.Tensor] = None, + softmax_scale: Optional[float] = None, + causal: bool = False, + window_size: Tuple[Optional[int], Optional[int]] = (None, None), + learnable_sink: Optional[torch.Tensor] = None, + softcap: float = 0.0, + num_splits: int = 1, + pack_gqa: Optional[bool] = None, + deterministic: bool = False, + score_mod: Optional[Callable] = None, + score_mod_bwd: Optional[Callable] = None, + mask_mod: Optional[Callable] = None, + aux_tensors: Optional[list] = None, + aux_scalars: Optional[tuple] = None, + block_sparse_tensors: Optional[BlockSparseTensorsTorch] = None, + block_sparse_tensors_bwd: Optional[BlockSparseTensorsTorch] = None, + return_lse: bool = False, + ): + aux_scalars = tuple(aux_scalars) if aux_scalars else None + shared_kv = k is v + if shared_kv and v.shape[-1] == 512: + # specialize MLA attention formula + # O = softmax(Q @ K.T + Qv @ V.T) @ V + # by setting q, k to None + qv = q if qv is None else qv + q = k = None + out, lse = _flash_attn_fwd( + q, + k, + v, + qv=qv, + softmax_scale=softmax_scale, + causal=causal, + window_size_left=window_size[0], + window_size_right=window_size[1], + learnable_sink=learnable_sink, + softcap=softcap, + num_splits=num_splits, + pack_gqa=pack_gqa, + score_mod=score_mod, + mask_mod=mask_mod, + aux_tensors=aux_tensors, + aux_scalars=aux_scalars, + block_sparse_tensors=block_sparse_tensors, + return_lse=return_lse, + gather_kv_indices=gather_kv_indices, + ) + ctx.save_for_backward(q, k, v, out, lse, *(aux_tensors or ())) + ctx.softmax_scale = softmax_scale + ctx.causal = causal + ctx.window_size = window_size + ctx.softcap = softcap + ctx.deterministic = deterministic + ctx.return_lse = return_lse + ctx.score_mod = score_mod + ctx.score_mod_bwd = score_mod_bwd + ctx.mask_mod = mask_mod + ctx.aux_scalars = aux_scalars + ctx.block_sparse_tensors_bwd = block_sparse_tensors_bwd + ctx.set_materialize_grads(False) + return out, lse + + +class FlashAttnVarlenFunc(torch.autograd.Function): + @staticmethod + def forward( + ctx, + q: Optional[torch.Tensor], + k: Optional[torch.Tensor], + v: torch.Tensor, + qv: Optional[torch.Tensor] = None, + cu_seqlens_q: Optional[torch.Tensor] = None, + cu_seqlens_k: Optional[torch.Tensor] = None, + seqused_q: Optional[torch.Tensor] = None, + seqused_k: Optional[torch.Tensor] = None, + max_seqlen_q: Optional[int] = None, + max_seqlen_k: Optional[int] = None, + min_seqlen_k: Optional[int] = None, + gather_kv_indices: Optional[torch.Tensor] = None, + page_table: Optional[torch.Tensor] = None, + softmax_scale: Optional[float] = None, + causal: bool = False, + window_size: Tuple[Optional[int], Optional[int]] = (None, None), + learnable_sink: Optional[torch.Tensor] = None, + softcap: float = 0.0, + num_splits: int = 1, + pack_gqa: Optional[bool] = None, + deterministic: bool = False, + score_mod: Optional[Callable] = None, + score_mod_bwd: Optional[Callable] = None, + mask_mod: Optional[Callable] = None, + block_sparse_tensors: Optional[list] = None, + aux_tensors: Optional[list] = None, + aux_scalars: Optional[tuple] = None, + q_descale: Optional[torch.Tensor] = None, + k_descale: Optional[torch.Tensor] = None, + v_descale: Optional[torch.Tensor] = None, + rel_bias: Optional[torch.Tensor] = None, + sfq: Optional[torch.Tensor] = None, + sfk: Optional[torch.Tensor] = None, + sfv: Optional[torch.Tensor] = None, + qk_sf_vec_size: Optional[int] = None, + v_sf_vec_size: Optional[int] = None, + rel_bias_prep_cache: Optional[dict] = None, + return_lse: bool = False, + ): + aux_scalars = tuple(aux_scalars) if aux_scalars else None + shared_kv = k is v + if shared_kv and v.shape[-1] == 512: + # specialize MLA attention formula + # O = softmax(Q @ K.T + Qv @ V.T) @ V + # by setting q, k to None + qv = q if qv is None else qv + q = k = None + out, lse = _flash_attn_fwd( + q, + k, + v, + qv=qv, + cu_seqlens_q=cu_seqlens_q, + cu_seqlens_k=cu_seqlens_k, + seqused_q=seqused_q, + seqused_k=seqused_k, + max_seqlen_q=max_seqlen_q, + max_seqlen_k=max_seqlen_k, + min_seqlen_k=min_seqlen_k, + page_table=page_table, + softmax_scale=softmax_scale, + causal=causal, + window_size_left=window_size[0], + window_size_right=window_size[1], + learnable_sink=learnable_sink, + softcap=softcap, + num_splits=num_splits, + pack_gqa=pack_gqa, + score_mod=score_mod, + mask_mod=mask_mod, + block_sparse_tensors=block_sparse_tensors, + aux_tensors=aux_tensors, + aux_scalars=aux_scalars, + q_descale=q_descale, + k_descale=k_descale, + v_descale=v_descale, + return_lse=return_lse, + gather_kv_indices=gather_kv_indices, + rel_bias=rel_bias, + sfq=sfq, + sfk=sfk, + sfv=sfv, + qk_sf_vec_size=qk_sf_vec_size, + v_sf_vec_size=v_sf_vec_size, + rel_bias_prep_cache=rel_bias_prep_cache, + ) + ctx.save_for_backward( + q, + k, + v, + out, + lse, + cu_seqlens_q, + cu_seqlens_k, + seqused_q, + seqused_k, + *(aux_tensors or ()), + ) + ctx.softmax_scale = softmax_scale + ctx.causal = causal + ctx.window_size = window_size + ctx.softcap = softcap + ctx.deterministic = deterministic + ctx.max_seqlen_q = max_seqlen_q + ctx.max_seqlen_k = max_seqlen_k + ctx.return_lse = return_lse + ctx.score_mod = score_mod + ctx.score_mod_bwd = score_mod_bwd + ctx.mask_mod = mask_mod + ctx.aux_scalars = aux_scalars + ctx.set_materialize_grads(False) + return out, lse + + +def flash_attn_func( + q: torch.Tensor, + k: torch.Tensor, + v: torch.Tensor, + qv: Optional[torch.Tensor] = None, + gather_kv_indices: Optional[torch.Tensor] = None, + softmax_scale: Optional[float] = None, + causal: bool = False, + window_size: Tuple[Optional[int], Optional[int]] = (None, None), + learnable_sink: Optional[torch.Tensor] = None, + softcap: float = 0.0, + num_splits: int = 1, + pack_gqa: Optional[bool] = None, + deterministic: bool = False, + score_mod: Optional[Callable] = None, + score_mod_bwd: Optional[Callable] = None, + mask_mod: Optional[Callable] = None, + aux_tensors: Optional[list] = None, + aux_scalars: Optional[tuple] = None, + block_sparse_tensors: Optional[BlockSparseTensorsTorch] = None, + block_sparse_tensors_bwd: Optional[BlockSparseTensorsTorch] = None, + return_lse: bool = False, +): + return FlashAttnFunc.apply( + q, + k, + v, + qv, + gather_kv_indices, + softmax_scale, + causal, + window_size, + learnable_sink, + softcap, + num_splits, + pack_gqa, + deterministic, + score_mod, + score_mod_bwd, + mask_mod, + aux_tensors, + aux_scalars, + block_sparse_tensors, + block_sparse_tensors_bwd, + return_lse, + ) + + +def flash_attn_varlen_func( + q: torch.Tensor, + k: torch.Tensor, + v: torch.Tensor, + qv: Optional[torch.Tensor] = None, + cu_seqlens_q: Optional[torch.Tensor] = None, + cu_seqlens_k: Optional[torch.Tensor] = None, + max_seqlen_q: Optional[int] = None, + max_seqlen_k: Optional[int] = None, + min_seqlen_k: Optional[int] = None, + seqused_q: Optional[torch.Tensor] = None, + seqused_k: Optional[torch.Tensor] = None, + gather_kv_indices: Optional[torch.Tensor] = None, + page_table: Optional[torch.Tensor] = None, + softmax_scale: Optional[float] = None, + causal: bool = False, + window_size: Tuple[Optional[int], Optional[int]] = (None, None), + learnable_sink: Optional[torch.Tensor] = None, + softcap: float = 0.0, + num_splits: int = 1, + pack_gqa: Optional[bool] = None, + deterministic: bool = False, + score_mod: Optional[Callable] = None, + score_mod_bwd: Optional[Callable] = None, + mask_mod: Optional[Callable] = None, + block_sparse_tensors: Optional[BlockSparseTensorsTorch] = None, + aux_tensors: Optional[list] = None, + aux_scalars: Optional[tuple] = None, + q_descale: Optional[torch.Tensor] = None, + k_descale: Optional[torch.Tensor] = None, + v_descale: Optional[torch.Tensor] = None, + rel_bias: Optional[torch.Tensor] = None, + sfq: Optional[torch.Tensor] = None, + sfk: Optional[torch.Tensor] = None, + sfv: Optional[torch.Tensor] = None, + qk_sf_vec_size: Optional[int] = None, + v_sf_vec_size: Optional[int] = None, + rel_bias_prep_cache: Optional[dict] = None, + return_lse: bool = False, +): + """ + Tensor arguments: + q: (total_q, nheads, hdim) or (batch, seqlen_q, nheads, hdim) + k: (total_k, nheads_k, hdim) or (batch, seqlen_k, nheads_k, hdim) + v: (total_k, nheads_k, hdim_v) or (batch, seqlen_k, nheads_k, hdim_v) + qv: (total_q, nheads, hdim_v) or (batch, seqlen_q, nheads, hdim_v) + cu_seqlens_q: (batch + 1) or seqused_q: (batch) + cu_seqlens_k: (batch + 1) or seqused_k: (batch) + gather_kv_indices: (total_q, gather_kv_length) or + (batch, seqlen_q, gather_kv_length) + page_table: (batch, max_num_pages_per_seq) + + Return: + out: (total_q, nheads, hdim) or (batch, seqlen_q, nheads, hdim) + lse: (nheads, total_q) or (batch, nheads, seqlen_q) if not has_qv (standard) + (total_q, nheads) or (batch, seqlen_q, nheads) if has_qv + + Explanation of some optional arguments & decisions: + + qv: we write the MLA weight absorbed formula as + O = softmax(scale * (Q @ K.T + Qv @ V.T)) @ V + where Q = q_pe, Qv = q_nope, K = pe_cache, V = kv_cache. + + lse return shape: with Qv, MQA with nheads at least divisible by 4 is typical, + so we arrange for nheads as the contiguous mode for better vectorization. + + gather_kv_indices: used for topk sparsity with MLA absorption kernel. + + min_seqlen_k: for varlen, specifies the minimum kv sequence length for any batch. + Used with gather_kv_indices to determine if we need oob masking. + """ + # Default the SF vector size to 32 (the block-scaled granularity) only for + # MXFP8 (e8m0-scaled) inputs -- other block-scaled dtypes use a different + # vec size, so gate on the e8m0 scale dtype rather than mere presence. + if qk_sf_vec_size is None and sfq is not None and sfq.dtype == torch.float8_e8m0fnu: + qk_sf_vec_size = 32 + if v_sf_vec_size is None and sfv is not None and sfv.dtype == torch.float8_e8m0fnu: + v_sf_vec_size = 32 + return FlashAttnVarlenFunc.apply( + q, + k, + v, + qv, + cu_seqlens_q, + cu_seqlens_k, + seqused_q, + seqused_k, + max_seqlen_q, + max_seqlen_k, + min_seqlen_k, + gather_kv_indices, + page_table, + softmax_scale, + causal, + window_size, + learnable_sink, + softcap, + num_splits, + pack_gqa, + deterministic, + score_mod, + score_mod_bwd, + mask_mod, + block_sparse_tensors, + aux_tensors, + aux_scalars, + q_descale, + k_descale, + v_descale, + rel_bias, + sfq, + sfk, + sfv, + qk_sf_vec_size, + v_sf_vec_size, + rel_bias_prep_cache, + return_lse, + ) + + +def _compile_fwd_combine( + dtype, + dtype_partial, + head_dim, + tile_m, + k_block_size, + log_max_splits, + has_cu_seqlens, + has_seqused, + has_lse, + has_varlen_batch_idx, + *, + use_pdl, +): + """Compile fwd combine kernel using cute fake tensors (no real GPU tensors needed).""" + sym = cute.sym_int + div = 128 // dtype_partial.width # 16-byte alignment in elements + + fa_combine = FlashAttentionForwardCombine( + dtype=dtype, + dtype_partial=dtype_partial, + head_dim=head_dim, + tile_m=tile_m, + k_block_size=k_block_size, + log_max_splits=log_max_splits, + use_pdl=use_pdl, + ) + if not fa_combine.can_implement( + dtype, + dtype_partial, + head_dim, + tile_m, + k_block_size, + log_max_splits, + num_threads=256, + ): + raise RuntimeError( + "FlashAttention combine kernel cannot be implemented with given parameters" + ) + + if has_cu_seqlens: + # Varlen: (num_splits, total_q, nheads, headdim) + num_splits, total_q, nheads = sym(), sym(), sym() + mO_partial = fake_tensor( + dtype_partial, (num_splits, total_q, nheads, head_dim), divisibility=div + ) + mLSE_partial = fake_tensor( + Float32, (num_splits, total_q, nheads), divisibility=1, leading_dim=1 + ) + mO = fake_tensor(dtype, (total_q, nheads, head_dim), divisibility=div) + mLSE = ( + fake_tensor(Float32, (total_q, nheads), divisibility=1, leading_dim=0) + if has_lse + else None + ) + else: + # Batched: (num_splits, batch, seqlen, nheads, headdim) + num_splits, batch, seqlen, nheads = sym(), sym(), sym(), sym() + mO_partial = fake_tensor( + dtype_partial, + (num_splits, batch, seqlen, nheads, head_dim), + divisibility=div, + ) + mLSE_partial = fake_tensor( + Float32, (num_splits, batch, seqlen, nheads), divisibility=1, leading_dim=2 + ) + mO = fake_tensor(dtype, (batch, seqlen, nheads, head_dim), divisibility=div) + mLSE = ( + fake_tensor(Float32, (batch, seqlen, nheads), divisibility=1, leading_dim=1) + if has_lse + else None + ) + batch = mO_partial.shape[1] + + batch_for_1d = batch if not has_cu_seqlens else sym() + batchp1 = sym() + mCuSeqlens = ( + fake_tensor(Int32, (batchp1,), divisibility=1) if has_cu_seqlens else None + ) + mSeqused = ( + fake_tensor(Int32, (batch_for_1d,), divisibility=1) if has_seqused else None + ) + mNumSplitsDynamic = None # Not parametrized in compile_key + mVarlenBatchIdx = ( + fake_tensor(Int32, (batch_for_1d,), divisibility=1) + if has_varlen_batch_idx + else None + ) + mSemaphore = None # Not parametrized in compile_key + + return cute.compile( + fa_combine, + mO_partial, + mLSE_partial, + mO, + mLSE, + mCuSeqlens, + mSeqused, + mNumSplitsDynamic, + mVarlenBatchIdx, + mSemaphore, + cute.runtime.make_fake_stream(use_tvm_ffi_env_stream=True), + options="--enable-tvm-ffi", + ) + + +def _flash_attn_fwd_combine( + out_partial: torch.Tensor, + lse_partial: torch.Tensor, + out: torch.Tensor, + lse: Optional[torch.Tensor] = None, + cu_seqlens: Optional[torch.Tensor] = None, + seqused: Optional[torch.Tensor] = None, + num_splits_dynamic_ptr: Optional[torch.Tensor] = None, + varlen_batch_idx: Optional[torch.Tensor] = None, + semaphore_to_reset: Optional[torch.Tensor] = None, +) -> None: + """Forward combine kernel for split attention computation. + + Combines partial outputs and log-sum-exp values from multiple splits + of attention computation into final outputs. + + Args: + out_partial: Partial outputs tensor (num_splits, batch, seqlen, nheads, headdim) or + (num_splits, total_q, nheads, headdim) if there's cu_seqlens + lse_partial: Partial LSE tensor (num_splits, batch, seqlen, nheads) or + (num_splits, total_q, nheads) if there's cu_seqlens + out: Output tensor (batch, seqlen, nheads, headdim) or (total_q, nheads, headdim) if there's cu_seqlens + lse: Output LSE tensor (batch, seqlen, nheads) or (total_q, nheads) if there's cu_seqlens. + cu_seqlens: Cumulative sequence lengths for variable length sequences + seqused: Used sequence lengths for each batch + num_splits_dynamic_ptr: Dynamic number of splits per batch + semaphore_to_reset: Semaphore for synchronization + k_block_size: Block size for head dimension + + Returns: + None + """ + assert out_partial.dtype in [ + torch.float16, + torch.bfloat16, + torch.float32, + ], "out_partial must be fp16, bf16, or fp32" + if not is_fake_mode(): + assert ( + out_partial.is_cuda and lse_partial.is_cuda + ), "tensors must be on CUDA device" + # Determine if this is variable length based on dimensions + is_varlen = out_partial.dim() == 4 + # Validate optional tensors + for t, name in [ + (cu_seqlens, "cu_seqlens"), + (seqused, "seqused"), + (num_splits_dynamic_ptr, "num_splits_dynamic_ptr"), + ]: + if t is not None: + if not is_fake_mode(): + assert t.is_cuda, f"{name} must be on CUDA device" + assert t.is_contiguous(), f"{name} must be contiguous" + head_dim = out_partial.shape[-1] + num_splits = out_partial.shape[0] + assert num_splits <= 256 + # If hdim is 96 or 192, it's faster to round them to 128 or 256 respectively + # so that kBlockM is smaller and we have more parallelism. + k_block_size = 64 if head_dim <= 64 else 128 + # We want kBlockM to be as small as possible to maximize parallelism. + # E.g., if hdim is 64, we want kBlockM to be 16 so that we can use 256 threads, each reading 4 elements (floats). + tile_m = 8 if k_block_size % 128 == 0 else (16 if k_block_size % 64 == 0 else 32) + log_max_splits = max(math.ceil(math.log2(num_splits)), 4) + if tile_m == 8: + # If kBlockM == 8 then the minimum number of splits is 32. + # TODO: we can deal w this by using 128 threads instead + log_max_splits = max(log_max_splits, 5) + + # Create combine kernel configuration + dtype = torch2cute_dtype_map[out.dtype] + dtype_partial = torch2cute_dtype_map[out_partial.dtype] + # Device architecture is invariant for the lifetime of this server/JIT + # cache, so PDL does not belong in the compile key. + use_pdl = is_arch_support_pdl() + compile_key = ( + dtype, + dtype_partial, + head_dim, + tile_m, + k_block_size, + log_max_splits, + cu_seqlens is not None, + seqused is not None, + lse is not None, + varlen_batch_idx is not None, + ) + if compile_key not in _flash_attn_fwd_combine.compile_cache: + _flash_attn_fwd_combine.compile_cache[compile_key] = _compile_fwd_combine( + *compile_key, use_pdl=use_pdl + ) + if not is_fake_mode(): + _flash_attn_fwd_combine.compile_cache[compile_key]( + out_partial, + lse_partial, + out, + lse, + cu_seqlens, + seqused, + num_splits_dynamic_ptr, + varlen_batch_idx, + semaphore_to_reset, + ) + + +_flash_attn_fwd_combine.compile_cache = get_jit_cache("fwd_combine") + + +def flash_attn_combine( + out_partial: torch.Tensor, + lse_partial: torch.Tensor, + out: Optional[torch.Tensor] = None, + out_dtype: Optional[torch.dtype] = None, + cu_seqlens: Optional[torch.Tensor] = None, + seqused: Optional[torch.Tensor] = None, + varlen_batch_idx: Optional[torch.Tensor] = None, + return_lse: bool = True, +) -> Tuple[torch.Tensor, Optional[torch.Tensor]]: + """Flash Attention combine function for split attention computation. + + Combines partial outputs and log-sum-exp values from multiple splits + of attention computation into final outputs. This is the main user-facing + interface for the combine kernel. + + Args: + out_partial: Partial outputs tensor with shape: + - (num_splits, batch_size, seqlen, num_heads, head_size) for regular batched input + - (num_splits, total_q, num_heads, head_size) for variable length input + lse_partial: Partial LSE tensor with shape: + - (num_splits, batch_size, seqlen, num_heads) for regular batched input + - (num_splits, total_q, num_heads) for variable length input + out: Optional output tensor. If None, will be created automatically. + out_dtype: Optional output dtype. If None, will use fp16/bf16 based on input. + cu_seqlens: Cumulative sequence lengths for variable length sequences + seqused: Used sequence lengths for each batch + varlen_batch_idx: Optional mapping from virtual batch index to real batch index + (int32 tensor of shape (batch_size,)). Used by persistent tile schedulers + that reorder batch processing for load balancing. + return_lse: Whether to return the combined LSE tensor. Default is True. + + Returns: + Tuple of (out, lse) where: + - out: Combined output tensor with shape (batch_size, seqlen, num_heads, head_size) + or (total_q, num_heads, head_size) for varlen + - lse: Combined log-sum-exp tensor with shape (batch_size, seqlen, num_heads) + or (total_q, num_heads) for varlen. None if return_lse=False + + Note: + This function expects the input tensors to be in the format produced by + split attention computation, where the first dimension is num_splits. + The permuting from user format to kernel format is now done inside the kernel. + """ + # Input validation + assert out_partial.dim() in [4, 5], "out_partial must have 4 or 5 dimensions" + # Determine if this is variable length based on dimensions + is_varlen = out_partial.dim() == 4 + if is_varlen: + # Variable length: (num_splits, total_q, num_heads, head_size) + num_splits, total_q, num_heads, head_size = out_partial.shape + batch_size = 1 # Treat as single batch for varlen + seqlen = total_q + else: + # Regular batched: (num_splits, batch_size, seqlen, num_heads, head_size) + num_splits, batch_size, seqlen, num_heads, head_size = out_partial.shape + # Determine output dtype + if out_dtype is None: + out_dtype = out_partial.dtype + # Create output if not provided + device = out_partial.device + if out is None: + if is_varlen: + out = torch.empty( + total_q, num_heads, head_size, dtype=out_dtype, device=device + ) + else: + out = torch.empty( + batch_size, seqlen, num_heads, head_size, dtype=out_dtype, device=device + ) + # Create lse output only if requested + if return_lse: + if is_varlen: + lse = torch.empty(num_heads, total_q, dtype=torch.float32, device=device) + else: + lse = torch.empty( + batch_size, num_heads, seqlen, dtype=torch.float32, device=device + ) + lse = lse.transpose(-1, -2) + else: + lse = None + _flash_attn_fwd_combine( + out_partial, + lse_partial, + out, + lse, + cu_seqlens, + seqused, + varlen_batch_idx=varlen_batch_idx, + ) + return out, lse diff --git a/python/sglang/jit_kernel/flash_attn/cute/mask.py b/python/sglang/jit_kernel/flash_attn/cute/mask.py new file mode 100644 index 000000000..0c797b970 --- /dev/null +++ b/python/sglang/jit_kernel/flash_attn/cute/mask.py @@ -0,0 +1,1841 @@ +# Copyright (c) 2025, Tri Dao. + +import enum +from dataclasses import dataclass +from typing import Callable, Optional, Tuple, TypeAlias + +import cutlass +import cutlass.cute as cute +from cutlass import Float32, Int32, Uint32, const_expr +from cutlass.cutlass_dsl import min as dsl_min +from quack import layout_utils + +import sglang.jit_kernel.flash_attn.cute.utils as utils +from sglang.jit_kernel.flash_attn.cute.block_info import BlockInfo +from sglang.jit_kernel.flash_attn.cute.seqlen_info import SeqlenInfoQK +from sglang.jit_kernel.flash_attn.cute.utils import AuxData + +MaskGenFn: TypeAlias = Callable[[int], Uint32] +MASK_R2P_CHUNK_SIZE: int = 32 + + +@cute.jit +def call_mask_mod( + mask_mod: cutlass.Constexpr, + batch_idx, + head_idx, + q_idx, + kv_idx, + seqlen_info, + aux_data: AuxData, +): + # Compatibility shim for pre-aux_scalars mask_mod callables. + if const_expr(aux_data.scalars is not None): + return mask_mod( + batch_idx, + head_idx, + q_idx, + kv_idx, + seqlen_info, + aux_data.tensors, + aux_data.scalars, + ) + return mask_mod( + batch_idx, + head_idx, + q_idx, + kv_idx, + seqlen_info, + aux_data.tensors, + ) + + +@cute.jit +def r2p_bitmask_below(limit: Int32, s: int) -> Uint32: + """32-bit R2P bitmask keeping positions < limit (exclusive upper bound). + + Positions 0..limit-1 in chunk `s` get bit=1 (keep), the rest bit=0 (mask). + Uses inline PTX to avoid shift-by-type-width UB. + """ + m = max((s + 1) * MASK_R2P_CHUNK_SIZE - limit, 0) + return utils.shr_u32(Uint32(0xFFFFFFFF), Uint32(m)) + + +@cute.jit +def r2p_bitmask_above(limit: Int32, s: int) -> Uint32: + """32-bit R2P bitmask keeping positions >= limit (inclusive lower bound). + + Positions limit..31 in chunk `s` get bit=1 (keep), the rest bit=0 (mask). + Uses inline PTX to avoid shift-by-type-width UB. + """ + n = max(limit - s * MASK_R2P_CHUNK_SIZE, 0) + return utils.shl_u32(Uint32(0xFFFFFFFF), Uint32(n)) + + +@cute.jit +def mask_r2p_lambda( + X: cute.Tensor, + mask_gen_fn: cutlass.Constexpr[MaskGenFn], + rank1: bool = False, +) -> None: + """Apply R2P masking with a custom bitmask generator. + + mask_gen_fn(chunk_idx: constexpr int) -> Uint32: + Returns a 32-bit bitmask for the chunk. Bit i set means column + chunk_idx * chunk_size + i is KEPT; bit i clear means masked to -inf. + """ + ncol = const_expr( + cute.size(X.shape[cute.rank(X) - 1]) if not rank1 else cute.size(X.shape) + ) + # 32-column chunks. The mask_gen_fn returns a Uint32 bitmask (1=keep). + CHUNK_SIZE = MASK_R2P_CHUNK_SIZE + for s in cutlass.range_constexpr(cute.ceil_div(ncol, CHUNK_SIZE)): + mask = mask_gen_fn(s) + # This needs to be range_constexpr, o/w the compiler can't generate the R2P instruction + for i in cutlass.range_constexpr(min(CHUNK_SIZE, ncol - s * CHUNK_SIZE)): + in_bound = cutlass.Boolean(mask & (Uint32(1) << i)) + c = s * CHUNK_SIZE + i + if const_expr(rank1): + X[c] = X[c] if in_bound else -Float32.inf + else: + for r in cutlass.range_constexpr(cute.size(X.shape[0])): + X[r, c] = X[r, c] if in_bound else -Float32.inf + + +@cute.jit +def sm90_col_to_r2p_idx(col_limit: Int32) -> Int32: + """Transform SM90 MMA column coordinate to R2P element index. + + SM90 MMA accumulator column indices are non-contiguous: 0, 1, 8, 9, 16, 17, ... + Element indices are contiguous: 0, 1, 2, 3, 4, 5, ... + This converts a column-space threshold to element-space for r2p_bitmask_below/above. + """ + return col_limit // 8 * 2 + min(col_limit % 8, 2) + + +@cute.jit +def row_to_r2p_idx(x: Int32, num_rep: int, num_wg: int) -> Int32: + """Convert a row coordinate to an R2P element index in the warp-group interleaved layout. + + In the SM100 backward pass, 2 warp groups share TMEM. The TMEM load atom + distributes rows in an interleaved pattern: elements 0..num_rep-1 map to + rows 0..num_rep-1 (warp group 0), elements num_rep..2*num_rep-1 map to + rows num_rep*num_wg..num_rep*num_wg+num_rep-1 (warp group 1), and so on. + Row-coordinate thresholds (causal limits, window bounds, uih_len) must be + converted to element indices before use with r2p_bitmask_above/below. + + Rows not owned by this thread (in the gap between warp groups) are clamped + to the boundary element index, which is safe because R2P thresholds are + monotonic. + + Example with num_rep=16, num_wg=2: + row 0 -> elem 0, row 15 -> elem 15, + row 16 -> elem 16 (clamped), row 31 -> elem 16 (clamped), + row 32 -> elem 16, row 33 -> elem 17, row 47 -> elem 31. + """ + return x // (num_rep * num_wg) * num_rep + min(x % (num_rep * num_wg), num_rep) + + +@cute.jit +def apply_packed_mask_chunk( + X: cute.Tensor, + chunk_idx: cutlass.Constexpr[int], + mask: Uint32, +) -> None: + """Apply one 32-bit keep mask to one 32-column chunk. + + The one-iteration chunk loop keeps the same lowering pattern as mask_r2p_lambda. + """ + ncol = const_expr(cute.size(X.shape)) + col_base = chunk_idx * MASK_R2P_CHUNK_SIZE + for s in cutlass.range_constexpr(1): + for i in cutlass.range_constexpr( + min(MASK_R2P_CHUNK_SIZE, ncol - col_base - s * MASK_R2P_CHUNK_SIZE) + ): + in_bound = cutlass.Boolean(mask & (Uint32(1) << i)) + c = col_base + s * MASK_R2P_CHUNK_SIZE + i + X[c] = X[c] if in_bound else -Float32.inf + + +@dataclass(frozen=True) +class AttentionMask: + tile_m: cutlass.Constexpr[int] + tile_n: cutlass.Constexpr[int] + seqlen_info: SeqlenInfoQK + window_size_left: Optional[Int32] = None + window_size_right: Optional[Int32] = None + qhead_per_kvhead_packgqa: cutlass.Constexpr[int] = ( + 1 # only pass in if we're doing PackGQA + ) + swap_AB: cutlass.Constexpr[bool] = False + + @property + def seqlen_q(self) -> Int32: + return self.seqlen_info.seqlen_q + + @property + def seqlen_k(self) -> Int32: + return self.seqlen_info.seqlen_k + + @cute.jit + def apply_mask( + self, + acc_S: cute.Tensor, + batch_idx: cutlass.Int32, + head_idx: cutlass.Int32, + m_block: cutlass.Int32, + n_block: cutlass.Int32, + thr_mma: cute.TiledMma, + mask_seqlen: cutlass.Constexpr[bool], + mask_causal: cutlass.Constexpr[bool], + mask_local: cutlass.Constexpr[bool] = False, + mask_mod: cutlass.Constexpr[Optional[Callable]] = None, + aux_data: AuxData = AuxData(), + fastdiv_mods=(None, None), + ) -> None: + assert not ( + mask_causal and mask_local + ), "mask_causal and mask_local cannot be both True" + acc_S_mn = layout_utils.reshape_acc_to_mn(acc_S, transpose=self.swap_AB) + acc_shape = (self.tile_m, self.tile_n) + cS = cute.make_identity_tensor( + acc_shape if not self.swap_AB else acc_shape[::-1] + ) + tScS_mn = layout_utils.reshape_acc_to_mn( + thr_mma.partition_C(cS), transpose=self.swap_AB + ) + # We use t0ScS as these indices are known at compile time. We then must subtract the + # column limit by the thread column offset. + t0ScS_mn = layout_utils.reshape_acc_to_mn( + thr_mma.get_slice(0).partition_C(cS), transpose=self.swap_AB + ) + ROW = 0 if const_expr(not self.swap_AB) else 1 + COL = 1 if const_expr(not self.swap_AB) else 0 + thr_col_offset = tScS_mn[0][COL] + # To handle edge cases of completely masked out rows where n_block_max = 0, + # we treat negative n_blocks as 0th n_block + # TODO: find more transparent solution + if n_block < 0: + n_block = 0 + seqlenk_col_limit = self.seqlen_k - n_block * self.tile_n - thr_col_offset + if const_expr(not mask_causal and not mask_local and mask_mod is None): + if const_expr(mask_seqlen): + r2p = const_expr(not self.swap_AB) + if const_expr(not r2p): + # traverse column index. + for c in cutlass.range( + cute.size(tScS_mn.shape[1]), unroll_full=True + ): + oob = t0ScS_mn[0, c][COL] >= seqlenk_col_limit + for r in cutlass.range( + cute.size(tScS_mn.shape[0]), unroll_full=True + ): + acc_S_mn[r, c] = -Float32.inf if oob else acc_S_mn[r, c] + else: + seqlenk_col_limit_r2p = sm90_col_to_r2p_idx(seqlenk_col_limit) + mask_r2p_lambda( + acc_S_mn, lambda s: r2p_bitmask_below(seqlenk_col_limit_r2p, s) + ) + + elif const_expr( + not mask_causal and not mask_local and mask_mod is not None + ): # FlexAttention mask mod + nrow = const_expr(cute.size(tScS_mn.shape[0])) + ncol = const_expr(cute.size(tScS_mn.shape[1])) + has_fastdiv = const_expr( + fastdiv_mods is not None + and fastdiv_mods[0] is not None + and fastdiv_mods[1] is not None + ) + wrap_aux_indices = const_expr( + has_fastdiv and mask_seqlen and const_expr(aux_data.tensors is not None) + ) + + for r in cutlass.range_constexpr(nrow): + # Respect swap_AB: ROW/COL determine which coordinate component corresponds to Q/KV. + local_row = tScS_mn[r, 0][ROW] + global_row_idx = local_row + m_block * self.tile_m + row_for_mod = global_row_idx + head_idx_for_mod = head_idx + if const_expr(self.qhead_per_kvhead_packgqa != 1): + head_offset = global_row_idx % self.qhead_per_kvhead_packgqa + head_idx_for_mod = ( + head_idx * self.qhead_per_kvhead_packgqa + head_offset + ) + row_for_mod = global_row_idx // self.qhead_per_kvhead_packgqa + row_for_seqlen = row_for_mod + if const_expr(wrap_aux_indices): + _, row_for_mod = divmod(row_for_mod, fastdiv_mods[0]) + + for col in cutlass.range_constexpr(ncol): + col_idx_local = t0ScS_mn[0, col][COL] + # Convert to absolute column index + global_col_idx = ( + thr_col_offset + col_idx_local + n_block * self.tile_n + ) + col_for_mod = global_col_idx + if const_expr(wrap_aux_indices): + _, col_for_mod = divmod(global_col_idx, fastdiv_mods[1]) + + batch_idx_ssa = utils.scalar_to_ssa(batch_idx, cutlass.Int32) + head_idx_ssa = utils.scalar_to_ssa(head_idx_for_mod, cutlass.Int32) + q_idx_ssa = utils.scalar_to_ssa(row_for_mod, cutlass.Int32) + kv_idx_ssa = utils.scalar_to_ssa(col_for_mod, cutlass.Int32) + mask_value = call_mask_mod( + mask_mod, + batch_idx_ssa, + head_idx_ssa, + q_idx_ssa, + kv_idx_ssa, + self.seqlen_info, + aux_data, + ) + cond = cutlass.Boolean(utils.ssa_to_scalar(mask_value)) + if const_expr(mask_seqlen): + out_of_bounds = (row_for_seqlen >= self.seqlen_q) or ( + global_col_idx >= self.seqlen_k + ) + if out_of_bounds: + acc_S_mn[r, col] = -cutlass.Float32.inf + else: + acc_S_mn[r, col] = ( + acc_S_mn[r, col] if cond else -cutlass.Float32.inf + ) + else: + acc_S_mn[r, col] = ( + acc_S_mn[r, col] if cond else -cutlass.Float32.inf + ) + + else: # Causal or local + if const_expr(not self.swap_AB): + # If PackGQA, we split the work of compute divmod among threads in the same row + threads_per_row = thr_mma.tv_layout_C.shape[0][0] + mma_m_idx = None + if const_expr(self.qhead_per_kvhead_packgqa != 1): + assert not self.swap_AB, "swap_AB with PackGQA not supported yet" + assert ( + cute.arch.WARP_SIZE % threads_per_row == 0 + ), "threads_per_row must divide WARP_SIZE" + assert cute.size(acc_S_mn.shape[0]) <= threads_per_row + tidx = thr_mma.thr_idx + mma_m_idx = ( + m_block * self.tile_m + tScS_mn[tidx % threads_per_row, 0][0] + ) // self.qhead_per_kvhead_packgqa + causal_row_offset = ( + 1 + + self.seqlen_k + - n_block * self.tile_n + - self.seqlen_q + - thr_col_offset + ) + if const_expr(mask_causal): + r2p = const_expr( + not self.swap_AB + ) # R2P trick, see apply_mask_sm100 + for r in cutlass.range( + cute.size(tScS_mn.shape[0]), unroll_full=True + ): + # get the column index limit based on current row. Only consider the row index, so the column index sets to 0. + if const_expr(self.qhead_per_kvhead_packgqa == 1): + row_idx = tScS_mn[r, 0][0] + m_block * self.tile_m + else: + row_idx = utils.shuffle_sync( + mma_m_idx, r % threads_per_row, width=threads_per_row + ) + col_limit_right = row_idx + causal_row_offset + if const_expr(mask_seqlen): + col_limit_right = cutlass.min( + col_limit_right, seqlenk_col_limit + ) + if const_expr(not r2p): + # traverse column index. + for c in cutlass.range( + cute.size(tScS_mn.shape[1]), unroll_full=True + ): + acc_S_mn[r, c] = ( + -Float32.inf + if t0ScS_mn[0, c][1] >= col_limit_right + else acc_S_mn[r, c] + ) + else: + col_limit_r2p = sm90_col_to_r2p_idx(col_limit_right) + mask_r2p_lambda( + acc_S_mn[r, None], + lambda s: r2p_bitmask_below(col_limit_r2p, s), + rank1=True, + ) + else: # Local + local_row_offset_right = ( + causal_row_offset + self.window_size_right + if const_expr(self.window_size_right is not None) + else None + ) + local_row_offset_left = ( + causal_row_offset - 1 - self.window_size_left + if const_expr(self.window_size_left is not None) + else None + ) + r2p_local = const_expr(not self.swap_AB) + for r in cutlass.range( + cute.size(tScS_mn.shape[0]), unroll_full=True + ): + if const_expr(self.qhead_per_kvhead_packgqa == 1): + row_idx = tScS_mn[r, 0][0] + m_block * self.tile_m + else: + row_idx = utils.shuffle_sync( + mma_m_idx, r % threads_per_row, width=threads_per_row + ) + if const_expr(self.window_size_right is not None): + col_limit_right = row_idx + local_row_offset_right + else: + col_limit_right = self.tile_n + if const_expr(mask_seqlen): + col_limit_right = cutlass.min( + col_limit_right, seqlenk_col_limit + ) + col_limit_left = ( + row_idx + local_row_offset_left + if const_expr(self.window_size_left is not None) + else 0 + ) + if const_expr(not r2p_local): + # traverse column index. + for c in cutlass.range( + cute.size(tScS_mn.shape[1]), unroll_full=True + ): + col_idx = t0ScS_mn[0, c][1] + if ( + col_idx >= col_limit_right + or col_idx < col_limit_left + ): + acc_S_mn[r, c] = -Float32.inf + else: + col_limit_right_r2p = sm90_col_to_r2p_idx(col_limit_right) + col_limit_left_r2p = sm90_col_to_r2p_idx(col_limit_left) + + def mask_gen_fn(s: int) -> Uint32: + return r2p_bitmask_below( + col_limit_right_r2p, s + ) & r2p_bitmask_above(col_limit_left_r2p, s) + + mask_r2p_lambda(acc_S_mn[r, None], mask_gen_fn, rank1=True) + else: # swap_AB + assert self.qhead_per_kvhead_packgqa == 1 + thr_row_offset = tScS_mn[0][ROW] + causal_row_offset = ( + seqlenk_col_limit + - self.seqlen_q + + m_block * self.tile_m + + thr_row_offset + ) + if const_expr(mask_causal): + for c in cutlass.range( + cute.size(tScS_mn.shape[1]), unroll_full=True + ): + col0 = t0ScS_mn[0, c][COL] + # If col0 is beyond the column limit, we want to mask out the entire + # column, by setting row limit to be self.tile_m. + row_limit_top = ( + self.tile_m + if col0 >= seqlenk_col_limit and mask_seqlen + else col0 - causal_row_offset + ) + for r in cutlass.range( + cute.size(tScS_mn.shape[0]), unroll_full=True + ): + acc_S_mn[r, c] = ( + -Float32.inf + if t0ScS_mn[r, 0][ROW] < row_limit_top + else acc_S_mn[r, c] + ) + else: + for c in cutlass.range( + cute.size(tScS_mn.shape[1]), unroll_full=True + ): + col0 = t0ScS_mn[0, c][COL] + # If col0 is beyond the column limit, we want to mask out the entire + # column, by setting row limit to be self.tile_m. + row_limit_top = ( + self.tile_m + if col0 >= seqlenk_col_limit and mask_seqlen + else ( + col0 - causal_row_offset - self.window_size_right + if const_expr(self.window_size_right is not None) + else 0 + ) + ) + row_limit_bot = ( + col0 - causal_row_offset + self.window_size_left + if const_expr(self.window_size_left is not None) + else self.tile_m + ) + for r in cutlass.range( + cute.size(tScS_mn.shape[0]), unroll_full=True + ): + row_idx = t0ScS_mn[r, 0][ROW] + acc_S_mn[r, c] = ( + -Float32.inf + if row_idx < row_limit_top or row_idx > row_limit_bot + else acc_S_mn[r, c] + ) + + @cute.jit + def apply_mask_mod_sm100_scalar( + self, + acc_S: cute.Tensor, + tScS_t2r: cute.Tensor, + m_block: Int32, + n_block: Int32, + mask_seqlen: cutlass.Constexpr[bool], + mask_mod: cutlass.Constexpr[Callable], + batch_idx: Int32, + head_idx: Int32, + aux_data: AuxData = AuxData(), + fastdiv_mods=(None, None), + head_divmod=None, + check_q_boundary: bool = False, + ) -> None: + """Apply a scalar FlexAttention mask_mod to an SM100 accumulator fragment. + + Each accumulator lane calls mask_mod once with logical (batch, head, q, kv) + indices. Pack-GQA rows are converted back to logical q/head indices before + the call. When aux tensors are present, indices are wrapped with fastdiv so + mask_mod never reads outside the per-example auxiliary storage. + """ + has_fastdiv = const_expr( + fastdiv_mods is not None + and fastdiv_mods[0] is not None + and fastdiv_mods[1] is not None + ) + batch_idx_ssa = utils.scalar_to_ssa(batch_idx, cutlass.Int32) + ncol = const_expr(cute.size(tScS_t2r.shape)) + + for i in cutlass.range_constexpr(ncol): + row_coord = tScS_t2r[i][0] if not self.swap_AB else tScS_t2r[i][1] + col_coord = tScS_t2r[i][1] if not self.swap_AB else tScS_t2r[i][0] + global_row = row_coord + m_block * self.tile_m + global_col = col_coord + n_block * self.tile_n + + if const_expr(self.qhead_per_kvhead_packgqa != 1): + assert head_divmod is not None + mask_row, head_offset = divmod(global_row, head_divmod) + head_idx_for_mod = ( + head_idx * self.qhead_per_kvhead_packgqa + head_offset + ) + else: + head_idx_for_mod = head_idx + mask_row = global_row + + mask_row_for_mod = mask_row + if const_expr(has_fastdiv and aux_data.tensors is not None): + if check_q_boundary: + _, mask_row_for_mod = divmod(mask_row, fastdiv_mods[0]) + global_col_for_mod = global_col + if const_expr(has_fastdiv and mask_seqlen and aux_data.tensors is not None): + _, global_col_for_mod = divmod(global_col, fastdiv_mods[1]) + + head_idx_ssa = utils.scalar_to_ssa(head_idx_for_mod, cutlass.Int32) + mask_row_ssa = utils.scalar_to_ssa(mask_row_for_mod, cutlass.Int32) + kv_idx_ssa = utils.scalar_to_ssa(global_col_for_mod, cutlass.Int32) + mask_value = call_mask_mod( + mask_mod, + batch_idx_ssa, + head_idx_ssa, + mask_row_ssa, + kv_idx_ssa, + self.seqlen_info, + aux_data, + ) + cond = cutlass.Boolean(utils.ssa_to_scalar(mask_value)) + acc_S[i] = acc_S[i] if cond else -Float32.inf + if const_expr(mask_seqlen): + acc_S[i] = -Float32.inf if global_col >= self.seqlen_k else acc_S[i] + if check_q_boundary: + acc_S[i] = -Float32.inf if mask_row >= self.seqlen_q else acc_S[i] + + @cute.jit + def apply_mask_mod_sm100_vector( + self, + acc_S: cute.Tensor, + tScS_t2r: cute.Tensor, + m_block: Int32, + n_block: Int32, + mask_seqlen: cutlass.Constexpr[bool], + mask_mod: cutlass.Constexpr[Callable], + batch_idx: Int32, + head_idx: Int32, + vec_size: cutlass.Constexpr[int], + aux_data: AuxData = AuxData(), + fastdiv_mods=(None, None), + head_divmod=None, + check_q_boundary: bool = False, + ) -> None: + """Apply a vectorized FlexAttention mask_mod to an SM100 fragment. + + mask_mod receives vec_size adjacent KV indices for one logical q row and + returns bit-packed Uint32 keep masks. Low bits correspond to lower KV + indices. The packed masks are combined with sequence-boundary checks, then + applied in 32-column chunks so the final masking lowers to R2P. + """ + has_fastdiv = const_expr( + fastdiv_mods is not None + and fastdiv_mods[0] is not None + and fastdiv_mods[1] is not None + ) + batch_idx_ssa = utils.scalar_to_ssa(batch_idx, cutlass.Int32) + ncol = const_expr(cute.size(tScS_t2r.shape)) + mask_vals_per_apply = const_expr(max(1, vec_size // 32)) + calls_per_apply = const_expr(max(1, 32 // vec_size)) + n_calls = const_expr(cute.ceil_div(ncol, vec_size)) + mask_vals = cute.make_rmem_tensor(mask_vals_per_apply, dtype=cutlass.Uint32) + + # Accumulate enough vector mask_mod calls to produce 32-bit chunks that + # apply_packed_mask_chunk can lower to R2P. + for s in cutlass.range_constexpr(n_calls): + if const_expr(s % calls_per_apply == 0): + for c in cutlass.range_constexpr(mask_vals_per_apply): + mask_vals[c] = cutlass.Uint32(0) + i = s * vec_size + row_coord = tScS_t2r[i][0] if not self.swap_AB else tScS_t2r[i][1] + col_coord = tScS_t2r[i][1] if not self.swap_AB else tScS_t2r[i][0] + global_row = row_coord + m_block * self.tile_m + global_col = col_coord + n_block * self.tile_n + if const_expr(self.qhead_per_kvhead_packgqa != 1): + assert head_divmod is not None + mask_row, head_offset = divmod(global_row, head_divmod) + head_idx_for_mod = ( + head_idx * self.qhead_per_kvhead_packgqa + head_offset + ) + else: + head_idx_for_mod = head_idx + mask_row = global_row + mask_row_for_mod = mask_row + if const_expr(has_fastdiv and aux_data.tensors is not None): + if check_q_boundary: + _, mask_row_for_mod = divmod(mask_row, fastdiv_mods[0]) + + head_idx_ssa = utils.scalar_to_ssa( + head_idx_for_mod, cutlass.Int32 + ).broadcast_to((vec_size,)) + mask_row_ssa = utils.scalar_to_ssa( + mask_row_for_mod, cutlass.Int32 + ).broadcast_to((vec_size,)) + batch_idx_ssa_call = batch_idx_ssa.broadcast_to((vec_size,)) + kv_idx_vec = cute.make_rmem_tensor(vec_size, cutlass.Int32) + + # Build the per-lane KV indices for this vectorized mask_mod call. + for j in cutlass.range_constexpr(min(vec_size, ncol - i)): + col_j_coord = ( + tScS_t2r[i + j][1] if not self.swap_AB else tScS_t2r[i + j][0] + ) + col_j_global = col_j_coord + n_block * self.tile_n + col_j_for_mod = col_j_global + if const_expr( + has_fastdiv and mask_seqlen and aux_data.tensors is not None + ): + _, col_j_for_mod = divmod(col_j_global, fastdiv_mods[1]) + kv_idx_vec[j] = col_j_for_mod + kv_idx_ssa = kv_idx_vec.load() + + # mask_value is already bit-packed by the vectorized mask_mod. + mask_value = call_mask_mod( + mask_mod, + batch_idx_ssa_call, + head_idx_ssa, + mask_row_ssa, + kv_idx_ssa, + self.seqlen_info, + aux_data, + ) + + # For vec_size < 32, multiple mask_mod calls fill one R2P chunk. + bit_offset = const_expr((s % calls_per_apply) * vec_size) + seqlen_thresh_call = ( + self.seqlen_k - global_col + if const_expr(mask_seqlen) + else cutlass.Int32(0) + ) + q_in_bounds = ( + mask_row < self.seqlen_q if check_q_boundary else cutlass.Boolean(True) + ) + for c in cutlass.range_constexpr(mask_vals_per_apply): + mask_val = mask_value[c] + if const_expr(vec_size < 32): + lane_keep = utils.shr_u32( + cutlass.Uint32(0xFFFFFFFF), + cutlass.Uint32(32 - vec_size), + ) + mask_val = mask_val & lane_keep + if const_expr(mask_seqlen): + mask_val = mask_val & r2p_bitmask_below(seqlen_thresh_call, c) + if check_q_boundary: + mask_val = mask_val if q_in_bounds else cutlass.Uint32(0) + mask_vals[c] = mask_vals[c] | (mask_val << bit_offset) + + # Apply only when the 32-bit chunk is complete, or at the tile tail. + is_last_in_apply = const_expr(s % calls_per_apply == calls_per_apply - 1) + is_last_overall = const_expr(s == n_calls - 1) + if const_expr(is_last_in_apply or is_last_overall): + apply_idx = s // calls_per_apply + for c in cutlass.range_constexpr(mask_vals_per_apply): + chunk_idx = apply_idx * mask_vals_per_apply + c + # Skip packed chunks that start past the accumulator fragment. + if const_expr(chunk_idx * 32 < ncol): + apply_packed_mask_chunk(acc_S, chunk_idx, mask_vals[c]) + + @cute.jit + def apply_mask_sm100( + self, + acc_S: cute.Tensor, + m_block: Int32, + n_block: Int32, + thr_mma: cute.TiledMma, + thr_tmem_load: cute.TiledCopy, + mask_seqlen: cutlass.Constexpr[bool], + mask_causal: cutlass.Constexpr[bool], + mask_local: cutlass.Constexpr[bool] = False, + mask_mod: cutlass.Constexpr[Optional[Callable]] = None, + batch_idx: Int32 = None, + head_idx: Int32 = None, + aux_data: AuxData = AuxData(), + fastdiv_mods=(None, None), + head_divmod=None, + vec_size: cutlass.Constexpr[int] = 1, + check_q_boundary: bool = False, + r2p: bool = True, + rBitmask: Optional[cute.Tensor] = None, + ) -> None: + assert not ( + mask_causal and mask_local + ), "mask_causal and mask_local cannot be both True" + acc_shape = (self.tile_m, self.tile_n) + cS = cute.make_identity_tensor( + acc_shape if not self.swap_AB else acc_shape[::-1] + ) + tScS = thr_mma.partition_C(cS) + tScS = tScS[(None, None), 0, 0] + tScS_t2r = thr_tmem_load.partition_D(tScS) + # To handle edge cases of completely masked out rows where n_block_max = 0, + # we treat negative n_blocks as 0th n_block + # TODO: find more transparent solution + if n_block < 0: + n_block = 0 + seqlenk_col_limit = self.seqlen_k - n_block * self.tile_n + + if const_expr(rBitmask is not None): + ncol_packed = const_expr(cute.size(rBitmask.shape[0])) + for i in cutlass.range_constexpr(ncol_packed): + col_start = 32 * i # mask is bit-packed into uint32 + curr_mask_val = rBitmask[i] + for j in cutlass.range_constexpr(32): + curr_col = col_start + j + mask = (curr_mask_val >> j) & 1 + acc_S[curr_col] = ( + acc_S[curr_col] if cutlass.Boolean(mask) else -Float32.inf + ) + + elif const_expr(not mask_causal and not mask_local and mask_mod is None): + if const_expr(mask_seqlen): + if const_expr(not r2p): + for i in cutlass.range(cute.size(tScS_t2r.shape), unroll_full=True): + # if tScS_t2r[i][1] >= seqlenk_col_limit: + # acc_S[i] = -Float32.inf + # For some reason the 2 lines above generate really bad SASS + acc_S[i] = ( + -Float32.inf + if tScS_t2r[i][1] >= seqlenk_col_limit + else acc_S[i] + ) + else: + mask_r2p_lambda( + acc_S, + lambda s: r2p_bitmask_below(seqlenk_col_limit, s), + rank1=True, + ) + + elif const_expr(not mask_causal and not mask_local and mask_mod is not None): + # FlexAttention mask_mod vectorization is gated on `mask_mod.__vec_size__`. + # vec_size == 1 returns a scalar Boolean. vec_size > 1 returns packed + # Uint32 mask fragments: one word per 32 evaluated columns. + assert ( + vec_size % 32 == 0 or 32 % vec_size == 0 + ), "vec_size must divide 32 or be a multiple of 32" + if const_expr(vec_size == 1): + self.apply_mask_mod_sm100_scalar( + acc_S, + tScS_t2r, + m_block, + n_block, + mask_seqlen, + mask_mod, + batch_idx, + head_idx, + aux_data, + fastdiv_mods, + head_divmod, + check_q_boundary, + ) + else: + self.apply_mask_mod_sm100_vector( + acc_S, + tScS_t2r, + m_block, + n_block, + mask_seqlen, + mask_mod, + batch_idx, + head_idx, + vec_size, + aux_data, + fastdiv_mods, + head_divmod, + check_q_boundary, + ) + + else: # Causal or local + causal_row_offset = self.seqlen_k - n_block * self.tile_n - self.seqlen_q + row_idx = tScS_t2r[0][0] + m_block * self.tile_m + if const_expr(self.qhead_per_kvhead_packgqa != 1): + row_idx = row_idx // self.qhead_per_kvhead_packgqa + if const_expr(mask_causal): + col_limit_right = row_idx + causal_row_offset + 1 + if const_expr(mask_seqlen): + col_limit_right = cutlass.min(col_limit_right, seqlenk_col_limit) + # if cute.arch.thread_idx()[0] % 32 == 0: + # cute.printf("tidx = %d, tidx tmem = %d, row_idx = %d, col_limit_right = %d, causal_row_offset = %d\n", cute.arch.thread_idx()[0], thr_tmem_load.thr_idx, row_idx, col_limit_right, causal_row_offset) + ncol = const_expr(cute.size(tScS_t2r.shape)) + if const_expr(not r2p): + for i in cutlass.range(ncol, unroll_full=True): + acc_S[i] = ( + -Float32.inf + if tScS_t2r[i][1] >= col_limit_right + else acc_S[i] + ) + else: + mask_r2p_lambda( + acc_S, + lambda s: r2p_bitmask_below(col_limit_right, s), + rank1=True, + ) + else: + local_row_offset_right = ( + causal_row_offset + 1 + self.window_size_right + if const_expr(self.window_size_right is not None) + else None + ) + local_row_offset_left = ( + causal_row_offset - self.window_size_left + if const_expr(self.window_size_left is not None) + else None + ) + if const_expr(self.window_size_right is not None): + col_limit_right = row_idx + local_row_offset_right + else: + col_limit_right = self.tile_n + if const_expr(mask_seqlen): + col_limit_right = cutlass.min(col_limit_right, seqlenk_col_limit) + col_limit_left = ( + row_idx + local_row_offset_left + if const_expr(self.window_size_left is not None) + else 0 + ) + if const_expr(not r2p): + # if cute.arch.thread_idx()[0] == 0 or cute.arch.thread_idx()[0] == 128: cute.printf("m_block = {}, n_block = {}, row_idx = {}, causal_row_offset = {}, col_limit_right = {}, col_limit_left = {}", m_block, n_block, row_idx, causal_row_offset, col_limit_right, col_limit_left) + for i in cutlass.range(cute.size(tScS_t2r.shape), unroll_full=True): + col_idx = tScS_t2r[i][1] + acc_S[i] = ( + -Float32.inf + if col_idx >= col_limit_right or col_idx < col_limit_left + else acc_S[i] + ) + else: + # Dual-bound R2P masking for SM100. + # Masks elements where: NOT (col_limit_left <= col < col_limit_right) + + def mask_gen_fn(s: int) -> Uint32: + return r2p_bitmask_below( + col_limit_right, s + ) & r2p_bitmask_above(col_limit_left, s) + + mask_r2p_lambda(acc_S, mask_gen_fn, rank1=True) + + @cute.jit + def apply_mask_sm100_transposed( + self, + acc_S: cute.Tensor, + tScS_t2r: cute.Tensor, + t0ScS_t2r: cute.Tensor, + m_block: cutlass.Int32, + n_block: cutlass.Int32, + mask_seqlen: cutlass.Constexpr, + mask_causal: cutlass.Constexpr, + mask_local: cutlass.Constexpr, + mask_mod: cutlass.Constexpr[Optional[Callable]] = None, + batch_idx: Int32 = None, + head_idx: Int32 = None, + aux_data: AuxData = AuxData(), + fastdiv_mods=(None, None), + is_full_block: bool = False, + check_m_boundary: bool = True, + ) -> None: + """ + Backward pass: mask S = K @ Q.T where n_block tiles seqlen_k and m_block tiles seqlen_q. + + Coordinate convention: + - ROW corresponds to Q (m_block) + - COL corresponds to KV (n_block) + + is_full_block: If True, skip mask_mod (all elements valid). Only apply seqlen masking. + check_m_boundary: If False, skip seqlen_q boundary check (optimization for non-boundary m_blocks). + When iterating m_blocks in forward order, only the last m_block may be partial. + """ + assert not ( + mask_causal and mask_local + ), "mask_causal and mask_local cannot be both True" + ROW = 0 if const_expr(not self.swap_AB) else 1 + COL = 1 if const_expr(not self.swap_AB) else 0 + # assert t0ScS_t2r[0][COL] == 0, "col0 == 0" # tmp comment for 2-cta bwd + thr_col_offset = tScS_t2r[0][COL] + seqlenk_col_limit = self.seqlen_k - n_block * self.tile_n - thr_col_offset + + if const_expr(not mask_causal and not mask_local and mask_mod is not None): + # Block sparse case with mask_mod (backward) + # + # Coordinate convention: ROW → Q (m_block), COL → KV (n_block). + # These already account for swap_AB. + # + # FULL blocks: mask_mod returns True for all elements, so skip it. + # Still need seqlen bounds check (elements may be OOB on last m_block). + # PARTIAL blocks: apply mask_mod element-wise, then seqlen bounds. + if is_full_block: + if const_expr(mask_seqlen): + if seqlenk_col_limit <= 0: + # Entire tile is OOB for K + for i in cutlass.range( + cute.size(acc_S.shape), unroll_full=True + ): + acc_S[i] = -cutlass.Float32.inf + elif check_m_boundary: + # Last m_block: check Q and K boundaries + ncol = const_expr(cute.size(tScS_t2r.shape)) + for i in cutlass.range_constexpr(ncol): + row_coord = tScS_t2r[i][ROW] + col_coord = tScS_t2r[i][COL] + global_q = row_coord + m_block * self.tile_m + global_kv = col_coord + n_block * self.tile_n + q_out_of_bounds = global_q >= self.seqlen_q + kv_out_of_bounds = global_kv >= self.seqlen_k + out_of_bounds = q_out_of_bounds or kv_out_of_bounds + acc_S[i] = ( + -cutlass.Float32.inf if out_of_bounds else acc_S[i] + ) + else: + # Partial block + has_fastdiv = const_expr( + fastdiv_mods is not None + and fastdiv_mods[0] is not None + and fastdiv_mods[1] is not None + ) + wrap_aux_indices = const_expr( + has_fastdiv + and mask_seqlen + and const_expr(aux_data.tensors is not None) + ) + batch_idx_ssa = utils.scalar_to_ssa(batch_idx, cutlass.Int32) + head_idx_ssa = utils.scalar_to_ssa(head_idx, cutlass.Int32) + + ncol = const_expr(cute.size(tScS_t2r.shape)) + for i in cutlass.range_constexpr(ncol): + row_coord = tScS_t2r[i][ROW] + col_coord = tScS_t2r[i][COL] + global_q = row_coord + m_block * self.tile_m + global_kv = col_coord + n_block * self.tile_n + + q_idx_for_mod = global_q + kv_idx_for_mod = global_kv + if const_expr(wrap_aux_indices): + _, q_idx_for_mod = divmod(global_q, fastdiv_mods[0]) + _, kv_idx_for_mod = divmod(global_kv, fastdiv_mods[1]) + + q_idx_ssa = utils.scalar_to_ssa(q_idx_for_mod, cutlass.Int32) + kv_idx_ssa = utils.scalar_to_ssa(kv_idx_for_mod, cutlass.Int32) + + mask_value = call_mask_mod( + mask_mod, + batch_idx_ssa, + head_idx_ssa, + q_idx_ssa, + kv_idx_ssa, + self.seqlen_info, + aux_data, + ) + cond = cutlass.Boolean(utils.ssa_to_scalar(mask_value)) + acc_S[i] = acc_S[i] if cond else -cutlass.Float32.inf + + if const_expr(mask_seqlen): + # check_m_boundary=False skips q check for non-boundary m_blocks + q_out_of_bounds = check_m_boundary and ( + global_q >= self.seqlen_q + ) + kv_out_of_bounds = global_kv >= self.seqlen_k + out_of_bounds = q_out_of_bounds or kv_out_of_bounds + acc_S[i] = -cutlass.Float32.inf if out_of_bounds else acc_S[i] + + elif const_expr(not mask_causal and not mask_local): + if const_expr(mask_seqlen): + if seqlenk_col_limit <= 0: + for i in cutlass.range(cute.size(acc_S.shape), unroll_full=True): + acc_S[i] = -cutlass.Float32.inf + else: # Causal or local + thr_row_offset = tScS_t2r[0][ROW] + seqlenq_row_limit = self.seqlen_q - m_block * self.tile_m - thr_row_offset + causal_offset = seqlenq_row_limit - seqlenk_col_limit + if const_expr(mask_causal): + # tidx = cute.arch.thread_idx()[0] % 256 + # if tidx < 32: + # cute.printf("tidx = {}, {} {}, {} {}", tidx, tScS_t2r[0][0], tScS_t2r[0][1], tScS_t2r[1][0], tScS_t2r[1][1]) + row_limit_top = causal_offset + if const_expr(mask_seqlen): + # If col is beyond the column limit, we want to mask out the entire + # column, by setting row limit to be self.tile_m. + if seqlenk_col_limit <= 0: + row_limit_top = self.tile_m + r2p = True + if const_expr(not r2p): + for i in cutlass.range(cute.size(acc_S.shape), unroll_full=True): + acc_S[i] = ( + -cutlass.Float32.inf + if t0ScS_t2r[i][ROW] < row_limit_top + else acc_S[i] + ) + else: + num_rep = cute.size(tScS_t2r, mode=[0]) # 16 or 32 + num_wg = 2 + row_limit = row_to_r2p_idx(row_limit_top, num_rep, num_wg) + mask_r2p_lambda( + acc_S, + lambda s: r2p_bitmask_above(row_limit, s), + rank1=True, + ) + else: + if const_expr(self.window_size_right is not None): + row_limit_top = causal_offset - self.window_size_right + else: + row_limit_top = 0 + if const_expr(self.window_size_left is not None): + row_limit_bot = causal_offset + self.window_size_left + if const_expr(mask_seqlen): + if seqlenk_col_limit <= 0: + row_limit_top = self.tile_m + r2p = True + if const_expr(not r2p): + for i in cutlass.range(cute.size(acc_S.shape), unroll_full=True): + row_idx = t0ScS_t2r[i][ROW] + local_mask = row_idx < row_limit_top + if const_expr(self.window_size_left is not None): + local_mask |= row_idx > row_limit_bot + acc_S[i] = -cutlass.Float32.inf if local_mask else acc_S[i] + else: + + def mask_gen_fn(s: int) -> Uint32: + num_rep = cute.size(tScS_t2r, mode=[0]) + num_wg = 2 + + row_limit = row_to_r2p_idx(row_limit_top, num_rep, num_wg) + mask = r2p_bitmask_above(row_limit, s) + + if const_expr(self.window_size_left is not None): + row_limit_bottom = row_to_r2p_idx( + row_limit_bot + 1, num_rep, num_wg + ) + mask = mask & r2p_bitmask_below(row_limit_bottom, s) + + return mask + + mask_r2p_lambda( + acc_S, + mask_gen_fn, + rank1=True, + ) + + +# ----------------------------------------------------------------------------- +# SM100 FMHA fused-mask policy layer (separate from generic mask primitives). +# ----------------------------------------------------------------------------- + + +class Sm100MaskEnum(enum.Enum): + """Enumeration of mask types for FMHA operations. + + - RESIDUAL_MASK: Residual mask for handling variable sequence lengths + - WINDOW_MASK: Window mask for attention which also includes causal and no mask + - WINDOW_MASK_INFERENCE: Same as the window mask, but has the limitation that the end of q is aligned with the end of k + - WINDOW_MASK_BWD: Window mask for backward pass + - WINDOW_MASK_BWD_INFERENCE: Same as the window mask for backward pass, but has the limitation that the end of q is aligned with the end of k + """ + + NO_MASK = enum.auto() + RESIDUAL_MASK = enum.auto() + CAUSAL_MASK = enum.auto() + WINDOW_MASK = enum.auto() + WINDOW_MASK_INFERENCE = enum.auto() + # Deprecated the following types + WINDOW_MASK_BWD = enum.auto() + WINDOW_MASK_BWD_INFERENCE = enum.auto() + RESIDUAL_MASK_BWD = enum.auto() + + +class Sm100FusedMask: + """A fused mask implementation for FMHA operations. + + This class handles different types of attention masks including no mask, + residual mask for variable sequence lengths, and causal mask for + autoregressive attention patterns. + + The class provides methods to: + - Calculate trip counts for different mask types + - Apply masks to attention scores + - Handle masked and unmasked trip calculations + """ + + def get_trip_count( + mask_type: Sm100MaskEnum, + blk_coord: cute.Coord, + tile_shape: cute.Shape, + seqlen_q: Int32, + seqlen_k: Int32, + window_size_left: Optional[Int32] = None, + window_size_right: Optional[Int32] = None, + ) -> Int32: + """ + Calculate the number of trips needed for the current block. + + The trip count depends on the mask type and the block coordinates. + For causal masks, it considers the autoregressive constraint. + + :param mask_type: Type of mask to use + :type mask_type: utils.Sm100MaskEnum + :param blk_coord: Block coordinates. + :type blk_coord: cute.Coord + :param tile_shape: Shape of the tile. + :type tile_shape: cute.Shape + :param seqlen_q: Query sequence length for attention computation. + :type seqlen_q: Int32 + :param seqlen_k: Key sequence length for attention computation. + :type seqlen_k: Int32 + :param window_size_left: Left-side sliding window size for attention masking. + :type window_size_left: Optional[Int32] + :param window_size_right: Right-side sliding window size for attention masking. + :type window_size_right: Optional[Int32] + + :return: Number of trips needed. + :rtype: Int32 + """ + result = 0 + offset = 0 + if cutlass.const_expr(mask_type is Sm100MaskEnum.WINDOW_MASK_INFERENCE): + offset = seqlen_k - seqlen_q + if cutlass.const_expr(mask_type is Sm100MaskEnum.WINDOW_MASK_BWD_INFERENCE): + offset = seqlen_q - seqlen_k + if cutlass.const_expr(mask_type == Sm100MaskEnum.RESIDUAL_MASK): + result = cute.ceil_div(seqlen_k, tile_shape[1]) + if cutlass.const_expr(mask_type is Sm100MaskEnum.RESIDUAL_MASK_BWD): + result = cute.ceil_div(seqlen_q, tile_shape[0]) + if cutlass.const_expr( + mask_type == Sm100MaskEnum.WINDOW_MASK + or mask_type == Sm100MaskEnum.WINDOW_MASK_INFERENCE + ): + if cutlass.const_expr(window_size_right is None): + result = cute.ceil_div(seqlen_k, tile_shape[1]) + else: + max_idx_q = (blk_coord[0] + 1) * tile_shape[0] + idx_k = max_idx_q + offset + window_size_right + tmp_blocks_k = cute.ceil_div(idx_k, tile_shape[1]) + max_blocks_k = cute.ceil_div(seqlen_k, tile_shape[1]) + result = dsl_min(max_blocks_k, tmp_blocks_k) + if cutlass.const_expr( + mask_type == Sm100MaskEnum.WINDOW_MASK_BWD + or mask_type == Sm100MaskEnum.WINDOW_MASK_BWD_INFERENCE + ): + if cutlass.const_expr(window_size_left is None): + result = cute.ceil_div(seqlen_q, tile_shape[0]) + else: + max_idx_k = (blk_coord[1] + 1) * tile_shape[1] + idx_k = max_idx_k + offset + window_size_left + tmp_blocks_q = cute.ceil_div(idx_k, tile_shape[0]) + max_blocks_q = cute.ceil_div(seqlen_q, tile_shape[0]) + result = dsl_min(max_blocks_q, tmp_blocks_q) + start_block = Sm100FusedMask.get_trip_start( + mask_type, + blk_coord, + tile_shape, + seqlen_q, + seqlen_k, + window_size_left, + window_size_right, + ) + result = result - start_block + return result + + @cute.jit + def get_trip_start_count_via_block_info( + blk_coord: cute.Coord, + tile_shape: cute.Shape, + seqlen_q: Int32, + seqlen_k: Int32, + is_causal: cutlass.Constexpr[bool] = False, + is_local: cutlass.Constexpr[bool] = False, + window_size_left: Optional[Int32] = None, + window_size_right: Optional[Int32] = None, + ) -> Tuple[Int32, Int32]: + block_info = BlockInfo( + tile_m=tile_shape[0], + tile_n=tile_shape[1], + is_causal=is_causal, + is_local=is_local and not is_causal, + window_size_left=window_size_left, + window_size_right=window_size_right, + ) + + seqlen_info = SeqlenInfoQK( + offset_q=Int32(0), + offset_k=Int32(0), + padded_offset_q=Int32(0), + padded_offset_k=Int32(0), + seqlen_q=seqlen_q, + seqlen_k=seqlen_k, + m_block_offset=Int32(0), + block_idx_offset=Int32(0), + num_n_blocks=cute.ceil_div(seqlen_k, tile_shape[1]), + has_cu_seqlens_q=False, + has_cu_seqlens_k=False, + has_seqused_q=False, + has_seqused_k=False, + ) + n_block_min, n_block_max = block_info.get_n_block_min_max( + seqlen_info, blk_coord[0] + ) + return n_block_min, n_block_max - n_block_min + + @cute.jit + def get_trip_mask_bounds_via_block_info( + blk_coord: cute.Coord, + tile_shape: cute.Shape, + seqlen_q: Int32, + seqlen_k: Int32, + is_causal: cutlass.Constexpr[bool] = False, + is_local: cutlass.Constexpr[bool] = False, + window_size_left: Optional[Int32] = None, + window_size_right: Optional[Int32] = None, + ) -> Tuple[Int32, Int32]: + """Return SM100-style mask boundaries for dense iteration. + + Returns: + - n_block_min_causal_local_mask: right-side masked region start + - n_block_min_before_local_mask: start of fully unmasked middle region + """ + block_info = BlockInfo( + tile_m=tile_shape[0], + tile_n=tile_shape[1], + is_causal=is_causal, + is_local=is_local and not is_causal, + window_size_left=window_size_left, + window_size_right=window_size_right, + ) + seqlen_info = SeqlenInfoQK( + offset_q=Int32(0), + offset_k=Int32(0), + padded_offset_q=Int32(0), + padded_offset_k=Int32(0), + seqlen_q=seqlen_q, + seqlen_k=seqlen_k, + m_block_offset=Int32(0), + block_idx_offset=Int32(0), + num_n_blocks=cute.ceil_div(seqlen_k, tile_shape[1]), + has_cu_seqlens_q=False, + has_cu_seqlens_k=False, + has_seqused_q=False, + has_seqused_k=False, + ) + n_block_min, _ = block_info.get_n_block_min_max(seqlen_info, blk_coord[0]) + n_block_min_causal_local_mask = block_info.get_n_block_min_causal_local_mask( + seqlen_info, blk_coord[0], n_block_min + ) + n_block_min_before_local_mask = block_info.get_n_block_min_before_local_mask( + seqlen_info, blk_coord[0], n_block_min + ) + return n_block_min_causal_local_mask, n_block_min_before_local_mask + + @cute.jit + def get_trip_start( + mask_type: Sm100MaskEnum, + blk_coord: cute.Coord, + tile_shape: cute.Shape, + seqlen_q: Int32, + seqlen_k: Int32, + window_size_left: Optional[Int32] = None, + window_size_right: Optional[Int32] = None, + ) -> Int32: + """ + Get the start of the trip for the current block. + + :param mask_type: Type of mask to use + :type mask_type: utils.Sm100MaskEnum + :param blk_coord: Block coordinates. + :type blk_coord: cute.Coord + :param tile_shape: Shape of the tile. + :type tile_shape: cute.Shape + :param seqlen_q: Query sequence length for attention computation. + :type seqlen_q: Int32 + :param seqlen_k: Key sequence length for attention computation. + :type seqlen_k: Int32 + :param window_size_left: Left-side sliding window size for attention masking. + :type window_size_left: Optional[Int32] + :param window_size_right: Right-side sliding window size for attention masking. + :type window_size_right: Optional[Int32] + """ + result = 0 + offset = 0 + if cutlass.const_expr(mask_type is Sm100MaskEnum.WINDOW_MASK_INFERENCE): + offset = seqlen_k - seqlen_q + if cutlass.const_expr(mask_type is Sm100MaskEnum.WINDOW_MASK_BWD_INFERENCE): + offset = seqlen_q - seqlen_k + if cutlass.const_expr( + mask_type is Sm100MaskEnum.WINDOW_MASK + or mask_type is Sm100MaskEnum.WINDOW_MASK_INFERENCE + ): + if cutlass.const_expr(window_size_left is not None): + min_idx_q = blk_coord[0] * tile_shape[0] + idx_k = min_idx_q + offset - window_size_left + tmp_blocks_k = idx_k // tile_shape[1] + result = max(tmp_blocks_k, result) + if cutlass.const_expr( + mask_type is Sm100MaskEnum.WINDOW_MASK_BWD + or mask_type is Sm100MaskEnum.WINDOW_MASK_BWD_INFERENCE + ): + if cutlass.const_expr(window_size_right is not None): + min_idx_k = blk_coord[1] * tile_shape[1] + idx_q = min_idx_k + offset - window_size_right + tmp_blocks_q = idx_q // tile_shape[0] + result = max(tmp_blocks_q, result) + return result + + @cute.jit + def get_leading_mask_id( + mask_type: Sm100MaskEnum, + blk_coord: cute.Coord, + tile_shape: cute.Shape, + seqlen_q: Int32, + seqlen_k: Int32, + window_size_left: Optional[Int32] = None, + window_size_right: Optional[Int32] = None, + ) -> Tuple[Int32, Int32]: + """ + Get the begin and end tile idx for the leading mask. + + :param mask_type: Type of mask to use + :type mask_type: utils.Sm100MaskEnum + :param blk_coord: Block coordinates. + :type blk_coord: cute.Coord + :param tile_shape: Shape of the tile. + :type tile_shape: cute.Shape + :param seqlen_q: Query sequence length for attention computation. + :type seqlen_q: Int32 + :param seqlen_k: Key sequence length for attention computation. + :type seqlen_k: Int32 + :param window_size_left: Left-side sliding window size for attention masking. + :type window_size_left: Optional[Int32] + :param window_size_right: Right-side sliding window size for attention masking. + :type window_size_right: Optional[Int32] + + :return: Tuple of (begin, end) tile idx for the leading mask. + :rtype: Tuple[Int32, Int32] + """ + offset = 0 + if cutlass.const_expr(mask_type is Sm100MaskEnum.WINDOW_MASK_INFERENCE): + offset = seqlen_k - seqlen_q + if cutlass.const_expr(mask_type is Sm100MaskEnum.WINDOW_MASK_BWD_INFERENCE): + offset = seqlen_q - seqlen_k + leading_mask_begin = Sm100FusedMask.get_trip_start( + mask_type, + blk_coord, + tile_shape, + seqlen_q, + seqlen_k, + window_size_left, + window_size_right, + ) + trip_count = Sm100FusedMask.get_trip_count( + mask_type, + blk_coord, + tile_shape, + seqlen_q, + seqlen_k, + window_size_left, + window_size_right, + ) + + leading_mask_end = leading_mask_begin + if cutlass.const_expr( + mask_type is Sm100MaskEnum.WINDOW_MASK + or mask_type is Sm100MaskEnum.WINDOW_MASK_INFERENCE + ): + if cutlass.const_expr(window_size_left is not None): + min_idx_q = ( + (blk_coord[0] + 1) * tile_shape[0] + offset - window_size_left + ) + leading_mask_end = dsl_min( + cute.ceil_div(min_idx_q, tile_shape[1]) - 1, + trip_count + leading_mask_begin - 1, + ) + else: + leading_mask_end = leading_mask_begin - 1 + elif cutlass.const_expr( + mask_type is Sm100MaskEnum.WINDOW_MASK_BWD + or mask_type is Sm100MaskEnum.WINDOW_MASK_BWD_INFERENCE + ): + if cutlass.const_expr(window_size_right is not None): + min_idx_k = ( + (blk_coord[1] + 1) * tile_shape[1] + offset - window_size_right + ) + leading_mask_end = cute.ceil_div(min_idx_k, tile_shape[0]) - 1 + else: + leading_mask_end = leading_mask_begin - 1 + return leading_mask_begin, leading_mask_end + + @cute.jit + def get_trailing_mask_id( + mask_type: Sm100MaskEnum, + blk_coord: cute.Coord, + tile_shape: cute.Shape, + seqlen_q: Int32, + seqlen_k: Int32, + window_size_left: Optional[Int32] = None, + window_size_right: Optional[Int32] = None, + ) -> Tuple[Optional[Int32], Optional[Int32]]: + """ + Get the begin and end tile idx for the trailing mask. + + :param mask_type: Type of mask to use + :type mask_type: utils.Sm100MaskEnum + :param blk_coord: Block coordinates. + :type blk_coord: cute.Coord + :param tile_shape: Shape of the tile. + :type tile_shape: cute.Shape + :param seqlen_q: Query sequence length for attention computation. + :type seqlen_q: Int32 + :param seqlen_k: Key sequence length for attention computation. + :type seqlen_k: Int32 + :param window_size_left: Left-side sliding window size for attention masking. + :type window_size_left: Optional[Int32] + :param window_size_right: Right-side sliding window size for attention masking. + :type window_size_right: Optional[Int32] + + :return: Tuple of (begin, end) tile idx for the trailing mask. + :rtype: Tuple[Int32, Int32] + """ + offset = 0 + if cutlass.const_expr(mask_type is Sm100MaskEnum.WINDOW_MASK_INFERENCE): + offset = seqlen_k - seqlen_q + if cutlass.const_expr(mask_type is Sm100MaskEnum.WINDOW_MASK_BWD_INFERENCE): + offset = seqlen_q - seqlen_k + trip_start = Sm100FusedMask.get_trip_start( + mask_type, + blk_coord, + tile_shape, + seqlen_q, + seqlen_k, + window_size_left, + window_size_right, + ) + trip_count = Sm100FusedMask.get_trip_count( + mask_type, + blk_coord, + tile_shape, + seqlen_q, + seqlen_k, + window_size_left, + window_size_right, + ) + + trailing_mask_begin, trailing_mask_end = None, None + if cutlass.const_expr( + mask_type is Sm100MaskEnum.WINDOW_MASK + or mask_type is Sm100MaskEnum.WINDOW_MASK_INFERENCE + ): + if cutlass.const_expr(window_size_right is not None): + min_idx_q = blk_coord[0] * tile_shape[0] + offset + window_size_right + trailing_mask_begin = dsl_min( + min_idx_q // tile_shape[1], trip_count + trip_start - 1 + ) + trailing_mask_end = trip_count + trip_start - 1 + else: + # last tile, we always apply mask on it regardless whether it's a residual tile + trailing_mask_begin = trip_count + trip_start - 1 + trailing_mask_end = trip_count + trip_start - 1 + else: + if cutlass.const_expr(window_size_left is not None): + min_idx_k = blk_coord[1] * tile_shape[1] + offset + window_size_left + 1 + max_idx_k = ( + (blk_coord[1] + 1) * tile_shape[1] + offset + window_size_left + ) + trailing_mask_begin = dsl_min( + cute.ceil_div(min_idx_k, tile_shape[0]) - 1, + trip_count + trip_start - 1, + ) + trailing_mask_end = dsl_min( + cute.ceil_div(max_idx_k, tile_shape[0]) - 1, + trip_count + trip_start - 1, + ) + else: + # last tile, we always apply mask on it regardless whether it's a residual tile + trailing_mask_begin = trip_count + trip_start - 1 + trailing_mask_end = trip_count + trip_start - 1 + + return trailing_mask_begin, trailing_mask_end + + @cute.jit + def get_masked_leading_count( + mask_type: Sm100MaskEnum, + blk_coord: cute.Coord, + tile_shape: cute.Shape, + seqlen_q: Int32, + seqlen_k: Int32, + window_size_left: Optional[Int32] = None, + window_size_right: Optional[Int32] = None, + ) -> Int32: + """ + Calculate the number of masked trips for the leading mask. + + This is used for blocks that need special handling due to masking. + + :param mask_type: Type of mask to use + :type mask_type: utils.Sm100MaskEnum + :param blk_coord: Block coordinates. + :type blk_coord: cute.Coord + :param tile_shape: Shape of the tile. + :type tile_shape: cute.Shape + :param seqlen_q: Query sequence length for attention computation. + :type seqlen_q: Int32 + :param seqlen_k: Key sequence length for attention computation. + :type seqlen_k: Int32 + :param window_size_left: Left-side sliding window size for attention masking. + :type window_size_left: Optional[Int32] + :param window_size_right: Right-side sliding window size for attention masking. + :type window_size_right: Optional[Int32] + + :return: Number of masked trips. + :rtype: Int32 + """ + result = 0 + if cutlass.const_expr( + mask_type is not Sm100MaskEnum.RESIDUAL_MASK + and mask_type is not Sm100MaskEnum.RESIDUAL_MASK_BWD + ): + if cutlass.const_expr( + window_size_left is not None or window_size_right is not None + ): + leading_mask_begin, leading_mask_end = ( + Sm100FusedMask.get_leading_mask_id( + mask_type, + blk_coord, + tile_shape, + seqlen_q, + seqlen_k, + window_size_left, + window_size_right, + ) + ) + result = max(leading_mask_end - leading_mask_begin + 1, 0) + + return result + + @cute.jit + def get_masked_trailing_count( + mask_type: Sm100MaskEnum, + blk_coord: cute.Coord, + tile_shape: cute.Shape, + seqlen_q: Int32, + seqlen_k: Int32, + window_size_left: Optional[Int32] = None, + window_size_right: Optional[Int32] = None, + rem_count: Optional[Int32] = 0, + ) -> Int32: + """ + Calculate the number of masked trips for the trailing mask. + + This is used for blocks that need special handling due to masking. + + :param mask_type: Type of mask to use + :type mask_type: utils.Sm100MaskEnum + :param blk_coord: Block coordinates. + :type blk_coord: cute.Coord + :param tile_shape: Shape of the tile. + :type tile_shape: cute.Shape + :param seqlen_q: Query sequence length for attention computation. + :type seqlen_q: Int32 + :param seqlen_k: Key sequence length for attention computation. + :type seqlen_k: Int32 + :param window_size_left: Left-side sliding window size for attention masking. + :type window_size_left: Optional[Int32] + :param window_size_right: Right-side sliding window size for attention masking. + :type window_size_right: Optional[Int32] + :param rem_count: Remaining count from previous calculations. + :type rem_count: Int32 + + :return: Number of masked trips. + :rtype: Int32 + """ + result = 0 + + if cutlass.const_expr( + mask_type is not Sm100MaskEnum.RESIDUAL_MASK + and mask_type is not Sm100MaskEnum.RESIDUAL_MASK_BWD + ): + if cutlass.const_expr( + window_size_left is not None or window_size_right is not None + ): + trailing_mask_begin, trailing_mask_end = ( + Sm100FusedMask.get_trailing_mask_id( + mask_type, + blk_coord, + tile_shape, + seqlen_q, + seqlen_k, + window_size_left, + window_size_right, + ) + ) + leading_mask_begin, leading_mask_end = ( + Sm100FusedMask.get_leading_mask_id( + mask_type, + blk_coord, + tile_shape, + seqlen_q, + seqlen_k, + window_size_left, + window_size_right, + ) + ) + if cutlass.const_expr( + trailing_mask_begin is not None and trailing_mask_end is not None + ): + if trailing_mask_begin <= leading_mask_end: + result = max(trailing_mask_end - leading_mask_end, 0) + else: + result = max(trailing_mask_end - trailing_mask_begin + 1, 0) + else: + if seqlen_k % tile_shape[1] != 0: + result = 1 + else: + result = 0 + + return result + rem_count + + @cute.jit + def get_unmasked_trip_count( + mask_type: Sm100MaskEnum, + blk_coord: cute.Coord, + tile_shape: cute.Shape, + seqlen_q: Int32, + seqlen_k: Int32, + window_size_left: Optional[Int32] = None, + window_size_right: Optional[Int32] = None, + ) -> Int32: + """ + Calculate the number of unmasked trips for the current block. + + This represents the number of trips that don't require special + masking treatment. + + :param mask_type: Type of mask to use + :type mask_type: utils.Sm100MaskEnum + :param blk_coord: Block coordinates. + :type blk_coord: cute.Coord + :param tile_shape: Shape of the tile. + :type tile_shape: cute.Shape + :param seqlen_q: Query sequence length for attention computation. + :type seqlen_q: Int32 + :param seqlen_k: Key sequence length for attention computation. + :type seqlen_k: Int32 + :param window_size_left: Left-side sliding window size for attention masking. + :type window_size_left: Optional[Int32] + :param window_size_right: Right-side sliding window size for attention masking. + :type window_size_right: Optional[Int32] + + :return: Number of unmasked trips. + :rtype: Int32 + """ + result = ( + Sm100FusedMask.get_trip_count( + mask_type, + blk_coord, + tile_shape, + seqlen_q, + seqlen_k, + window_size_left, + window_size_right, + ) + - Sm100FusedMask.get_masked_leading_count( + mask_type, + blk_coord, + tile_shape, + seqlen_q, + seqlen_k, + window_size_left, + window_size_right, + ) + - Sm100FusedMask.get_masked_trailing_count( + mask_type, + blk_coord, + tile_shape, + seqlen_q, + seqlen_k, + window_size_left, + window_size_right, + 0, + ) + ) + return result + + @cute.jit + def apply_mask( + mask_type: Sm100MaskEnum, + acc_qk: cute.Tensor, + index_qk: cute.Tensor, + seqlen_q: Int32, + seqlen_k: Int32, + window_size_left: Optional[int] = None, + window_size_right: Optional[int] = None, + index_transform: cutlass.Constexpr = lambda index_q, index_k: ( + index_q, + index_k, + ), + ): + """ + Apply the appropriate mask to the attention scores. + + This method modifies the attention scores (acc_qk) based on the mask type + and the positions in the index tensor. + + :param mask_type: Type of mask to use + :type mask_type: utils.Sm100MaskEnum + :param acc_qk: Accumulated QK attention scores tensor. + :type acc_qk: cute.Tensor + :param index_qk: Index tensor containing position information. + :type index_qk: cute.Tensor + :param seqlen_k: Key sequence length for attention computation. + :type seqlen_k: Int32 + :param seqlen_q: Query sequence length for attention computation. + :type seqlen_q: Optional[int] + :param window_size_left: Left-side sliding window size for attention masking. + :type window_size_left: Optional[int] + :param window_size_right: Right-side sliding window size for attention masking. + :type window_size_right: Optional[int] + """ + offset = 0 + # NOTE: causal masking in this repo aligns the *end* of Q with the *end* of K + # when seqlen_k != seqlen_q (same as the test/reference implementation): + # k_index <= q_index + (seqlen_k - seqlen_q) + window_right + # In our kernels, causal is represented by (window_left is None, window_right is not None). + if cutlass.const_expr( + window_size_left is None and window_size_right is not None + ): + offset = seqlen_k - seqlen_q + elif cutlass.const_expr( + mask_type is Sm100MaskEnum.WINDOW_MASK_INFERENCE + or mask_type is Sm100MaskEnum.WINDOW_MASK_BWD_INFERENCE + ): + offset = seqlen_k - seqlen_q + for i in cutlass.range_constexpr(cute.size(acc_qk), unroll_full=True): + index_q, index_k = index_transform(*index_qk[i]) + if cutlass.const_expr( + window_size_left is not None or window_size_right is not None + ): + if cutlass.const_expr(window_size_left is None): + if index_q + offset + window_size_right < index_k: + acc_qk[i] = -Float32.inf + if index_k >= seqlen_k or index_q >= seqlen_q: # residual mask + acc_qk[i] = -Float32.inf + elif cutlass.const_expr(window_size_right is None): + if index_q + offset - window_size_left > index_k: + acc_qk[i] = -Float32.inf + if index_k >= seqlen_k or index_q >= seqlen_q: # residual mask + acc_qk[i] = -Float32.inf + else: + max_K_index = dsl_min( + index_q + offset + window_size_right, seqlen_k + ) + min_K_index = max(0, index_q + offset - window_size_left) + if index_k > max_K_index or index_k < min_K_index: + acc_qk[i] = -Float32.inf + if index_k >= seqlen_k or index_q >= seqlen_q: # residual mask + acc_qk[i] = -Float32.inf + + if cutlass.const_expr( + mask_type == Sm100MaskEnum.RESIDUAL_MASK + or mask_type == Sm100MaskEnum.RESIDUAL_MASK_BWD + ): + if index_k >= seqlen_k or index_q >= seqlen_q: + acc_qk[i] = -Float32.inf + + @cute.jit + def apply_mask_via_causal_local( + acc_qk: cute.Tensor, + index_qk: cute.Tensor, + seqlen_q: Int32, + seqlen_k: Int32, + apply_semantic_window: cutlass.Constexpr[bool] = True, + is_causal: cutlass.Constexpr[bool] = False, + is_local: cutlass.Constexpr[bool] = False, + window_size_left: Optional[int] = None, + window_size_right: Optional[int] = None, + index_transform: cutlass.Constexpr = lambda index_q, index_k: ( + index_q, + index_k, + ), + ): + """Apply forward mask without mask_type. + + - If apply_semantic_window=True, apply causal/local window constraints. + - Always apply residual OOB masking (index_k>=seqlen_k or index_q>=seqlen_q). + """ + offset = 0 + if cutlass.const_expr(apply_semantic_window): + # Match WINDOW_MASK_INFERENCE semantics: end-align Q/K when lengths differ. + offset = seqlen_k - seqlen_q + for i in cutlass.range_constexpr(cute.size(acc_qk), unroll_full=True): + index_q, index_k = index_transform(*index_qk[i]) + if cutlass.const_expr(apply_semantic_window): + if cutlass.const_expr(is_causal and not is_local): + # Pure causal; tolerate both external forms: + # - (None, None) from interface + # - (None, 0) from fused-mask-style callers + right = ( + 0 + if const_expr(window_size_right is None) + else window_size_right + ) + if index_q + offset + right < index_k: + acc_qk[i] = -Float32.inf + elif cutlass.const_expr( + is_local + or window_size_left is not None + or window_size_right is not None + ): + if cutlass.const_expr(window_size_left is None): + if index_q + offset + window_size_right < index_k: + acc_qk[i] = -Float32.inf + elif cutlass.const_expr(window_size_right is None): + if index_q + offset - window_size_left > index_k: + acc_qk[i] = -Float32.inf + else: + max_K_index = dsl_min( + index_q + offset + window_size_right, seqlen_k + ) + min_K_index = max(0, index_q + offset - window_size_left) + if index_k > max_K_index or index_k < min_K_index: + acc_qk[i] = -Float32.inf + # Residual mask is always needed for boundary protection. + if index_k >= seqlen_k or index_q >= seqlen_q: + acc_qk[i] = -Float32.inf diff --git a/python/sglang/jit_kernel/flash_attn/cute/mma_sm100_desc.py b/python/sglang/jit_kernel/flash_attn/cute/mma_sm100_desc.py new file mode 100644 index 000000000..b472cb226 --- /dev/null +++ b/python/sglang/jit_kernel/flash_attn/cute/mma_sm100_desc.py @@ -0,0 +1,319 @@ +# Copyright (c) 2025, Tri Dao. +# Ported Cutlass code from C++ to Python: +# https://github.com/NVIDIA/cutlass/blob/main/include/cute/arch/mma_sm100_desc.hpp +# https://github.com/NVIDIA/cutlass/blob/main/include/cute/atom/mma_traits_sm100.hpp + +from enum import IntEnum + +import cutlass +import cutlass.cute as cute + +# --------------------------------------------------------------------------- +# Enumerations that match the HW encodings (values MUST stay identical) +# --------------------------------------------------------------------------- + + +class Major(IntEnum): # matrix “layout” in the ISA docs + K = 0 + MN = 1 + + +class ScaleIn(IntEnum): # negate flags + One = 0 + Neg = 1 + + +class Saturate(IntEnum): + False_ = 0 + True_ = 1 + + +class CFormat(IntEnum): # 2-bit field (bits 4-5) + F16 = 0 + F32 = 1 + S32 = 2 + + +class F16F32Format(IntEnum): # 3-bit field (A/B element type) + F16 = 0 + BF16 = 1 + TF32 = 2 + + +class S8Format(IntEnum): + UINT8 = 0 + INT8 = 1 + + +class MXF8F6F4Format(IntEnum): + E4M3 = 0 + E5M2 = 1 + E2M3 = 3 + E3M2 = 4 + E2M1 = 5 + + +class MaxShift(IntEnum): + NoShift = 0 + MaxShift8 = 1 + MaxShift16 = 2 + MaxShift32 = 3 + + +# --------------------------------------------------------------------------- +# CUTLASS-type → encoding helpers +# --------------------------------------------------------------------------- + + +def to_UMMA_format(cutlass_type) -> int: + """ + Map a CUTLASS scalar class to the 3-bit encoding for Matrix A/B. + """ + if cutlass_type is cutlass.Int8: + return S8Format.INT8 + # Unsigned 8-bit (if available in your CUTLASS build) + if cutlass_type is cutlass.Uint8: + return S8Format.UINT8 + # FP-16 / BF-16 + if cutlass_type is cutlass.Float16: + return F16F32Format.F16 + if cutlass_type is cutlass.BFloat16: + return F16F32Format.BF16 + # TensorFloat-32 (8-bit exponent, 10-bit mantissa packed in 19 bits) + if cutlass_type is cutlass.TFloat32: + return F16F32Format.TF32 + # Float-8 / Float-6 / Float-4 – add whenever CUTLASS exposes them + if cutlass_type is cutlass.Float8E4M3FN: + return MXF8F6F4Format.E4M3 + if cutlass_type is cutlass.Float8E5M2: + return MXF8F6F4Format.E5M2 + raise TypeError(f"Unsupported CUTLASS scalar type for A/B: {cutlass_type!r}") + + +def to_C_format(cutlass_type) -> int: + """ + Map a CUTLASS scalar class to the 2-bit accumulator encoding. + """ + if cutlass_type is cutlass.Float16: + return CFormat.F16 + if cutlass_type is cutlass.Float32: + return CFormat.F32 + if cutlass_type is cutlass.Int32: + return CFormat.S32 + raise TypeError( + f"Unsupported CUTLASS scalar type for accumulator: {cutlass_type!r}" + ) + + +# --------------------------------------------------------------------------- +# The constructor – accepts only CUTLASS scalar classes +# --------------------------------------------------------------------------- + + +def make_instr_desc( + a_type, # CUTLASS scalar class, e.g. cutlass.Int8 + b_type, + c_type, + M: int, # 64, 128 or 256 + N: int, # 8 … 256 (multiple of 8) + a_major: Major, + b_major: Major, + a_neg: ScaleIn = ScaleIn.One, + b_neg: ScaleIn = ScaleIn.One, + c_sat: Saturate = Saturate.False_, + is_sparse: bool = False, + max_shift: MaxShift = MaxShift.NoShift, +) -> int: + """ + Build the 32-bit instruction descriptor for Blackwell MMA. + All matrix/accumulator **types must be CUTLASS scalar classes** – + passing integers is forbidden. + """ + # --- encode element formats ------------------------------------------------- + a_fmt = int(to_UMMA_format(a_type)) + b_fmt = int(to_UMMA_format(b_type)) + c_fmt = int(to_C_format(c_type)) + + # --- range checks on M/N ----------------------------------------------------- + if M not in (64, 128, 256): + raise ValueError("M must be 64, 128 or 256") + if N < 8 or N > 256 or (N & 7): + raise ValueError("N must be a multiple of 8 in the range 8…256") + + m_dim = M >> 4 # 5-bit field + n_dim = N >> 3 # 6-bit field + + # fmt: off + # --- pack the bit-fields ----------------------------------------------------- + desc = 0 + desc |= (0 & 0x3) << 0 # sparse_id2 (always 0 here) + desc |= (int(is_sparse) & 0x1) << 2 # sparse_flag + desc |= (int(c_sat) & 0x1) << 3 # saturate + desc |= (c_fmt & 0x3) << 4 # c_format + desc |= (a_fmt & 0x7) << 7 # a_format + desc |= (b_fmt & 0x7) << 10 # b_format + desc |= (int(a_neg) & 0x1) << 13 # a_negate + desc |= (int(b_neg) & 0x1) << 14 # b_negate + desc |= (int(a_major) & 0x1) << 15 # a_major + desc |= (int(b_major) & 0x1) << 16 # b_major + desc |= (n_dim & 0x3F) << 17 # n_dim (6 bits) + desc |= (m_dim & 0x1F) << 24 # m_dim (5 bits) + desc |= (int(max_shift) & 0x3) << 30 # max_shift (2 bits) + # fmt: on + + return desc & 0xFFFF_FFFF # ensure 32-bit result + + +def mma_op_to_idesc(op: cute.nvgpu.tcgen05.mma.MmaOp): + return make_instr_desc( + op.a_dtype, + op.b_dtype, + op.acc_dtype, + op.shape_mnk[0], + op.shape_mnk[1], + ( + Major.K + if op.a_major_mode == cute.nvgpu.tcgen05.mma.OperandMajorMode.K + else Major.MN + ), + ( + Major.K + if op.b_major_mode == cute.nvgpu.tcgen05.mma.OperandMajorMode.K + else Major.MN + ), + ) + + +class LayoutType(IntEnum): # occupies the top-3 bits [61:64) + SWIZZLE_NONE = 0 # (a.k.a. “INTERLEAVE” in older docs) + SWIZZLE_128B_BASE32B = 1 + SWIZZLE_128B = 2 + SWIZZLE_64B = 4 + SWIZZLE_32B = 6 + # values 3,5,7 are reserved / illegal for UMMA + + +# --------------------------------------------------------------------------- +# Helpers – figure out the SWIZZLE_* family from the tensor layout +# --------------------------------------------------------------------------- + + +def _layout_type(swizzle: cute.Swizzle) -> LayoutType: + B, M, S = swizzle.num_bits, swizzle.num_base, swizzle.num_shift + + if M == 4: # Swizzle<*,4,3> + if S != 3: + raise ValueError("Unexpected swizzle shift – want S==3 for M==4") + return { + 0: LayoutType.SWIZZLE_NONE, + 1: LayoutType.SWIZZLE_32B, + 2: LayoutType.SWIZZLE_64B, + 3: LayoutType.SWIZZLE_128B, + }[ + B + ] # KeyError ⇒ invalid B→ raise + if M == 5: # Swizzle<2,5,2> (the only legal triple for M==5) + if (B, S) != (2, 2): + raise ValueError("Only Swizzle<2,5,2> supported for 128B_BASE32B") + return LayoutType.SWIZZLE_128B_BASE32B + + # Any other (M,B,S) triple is not a UMMA-legal shared-memory layout + raise ValueError("Unsupported swizzle triple for UMMA smem descriptor") + + +def make_smem_desc_base( + layout: cute.Layout, swizzle: cute.Swizzle, major: Major +) -> int: + """ + Convert a 2-D *shared-memory* Cute layout into the Blackwell 64-bit + smem-descriptor, without the smem start address. + layout must correspond to layout of an uint128 tensor. + """ + # ------------------------------------------------------------------ meta + layout_type = _layout_type(swizzle) # resolve SWIZZLE_* family + + VERSION = 1 # bits 46–47 + LBO_MODE = 0 # bit 52 + BASE_OFFSET = 0 # bits 49–51 (CUTLASS always 0) + + # ---------------------------------------------------------- strides (units: uint128_t = 16 B) + swizzle_atom_mn_size = { + LayoutType.SWIZZLE_NONE: 1, + LayoutType.SWIZZLE_32B: 2, + LayoutType.SWIZZLE_64B: 4, + LayoutType.SWIZZLE_128B: 8, + LayoutType.SWIZZLE_128B_BASE32B: 8, + }[layout_type] + + if major is Major.MN: + swizzle_atom_k_size = 4 if layout_type is LayoutType.SWIZZLE_128B_BASE32B else 8 + canonical_layout = cute.logical_divide( + layout, (swizzle_atom_mn_size, swizzle_atom_k_size) + ) + if not cute.is_congruent(canonical_layout, ((1, 1), (1, 1))): + raise ValueError( + "Not a canonical UMMA_MN Layout: Expected profile failure." + ) + stride_00 = canonical_layout.stride[0][0] + if layout_type is not LayoutType.SWIZZLE_NONE and stride_00 != 1: + raise ValueError("Not a canonical UMMA_MN Layout: Expected stride failure.") + stride_10 = canonical_layout.stride[1][0] + if stride_10 != swizzle_atom_mn_size: + raise ValueError("Not a canonical UMMA_MN Layout: Expected stride failure.") + stride_01, stride_11 = ( + canonical_layout.stride[0][1], + canonical_layout.stride[1][1], + ) + if layout_type is LayoutType.SWIZZLE_NONE: + stride_byte_offset, leading_byte_offset = stride_01, stride_11 + else: + stride_byte_offset, leading_byte_offset = stride_11, stride_01 + else: + if layout_type == LayoutType.SWIZZLE_128B_BASE32B: + raise ValueError("SWIZZLE_128B_BASE32B is invalid for Major-K") + if not cute.size(layout.shape[0]) % 8 == 0: + raise ValueError( + "Not a canonical UMMA_K Layout: Expected MN-size multiple of 8." + ) + canonical_layout = cute.logical_divide(layout, (8, 2)) + if not cute.is_congruent(canonical_layout, ((1, 1), (1, 1))): + raise ValueError("Not a canonical UMMA_K Layout: Expected profile failure.") + stride_00 = canonical_layout.stride[0][0] + if stride_00 != swizzle_atom_mn_size: + raise ValueError("Not a canonical UMMA_K Layout: Expected stride failure.") + stride_10 = canonical_layout.stride[1][0] + if layout_type is not LayoutType.SWIZZLE_NONE and stride_10 != 1: + raise ValueError("Not a canonical UMMA_K Layout: Expected stride failure.") + stride_01 = canonical_layout.stride[0][1] + stride_byte_offset, leading_byte_offset = stride_01, stride_10 + + # ------------------------------------------------------------------ pack + desc = 0 + # leading_byte_offset_ [16:30) + desc |= (leading_byte_offset & 0x3FFF) << 16 + # stride_byte_offset_ [32:46) + desc |= (stride_byte_offset & 0x3FFF) << 32 + # version_ [46:48) + desc |= (VERSION & 0x3) << 46 + # base_offset_ [49:52) + desc |= (BASE_OFFSET & 0x7) << 49 + # lbo_mode_ [52:53) + desc |= (LBO_MODE & 0x1) << 52 + # layout_type_ [61:64) + desc |= (int(layout_type) & 0x7) << 61 + + return desc & 0xFFFF_FFFF_FFFF_FFFF # force 64-bit width + + +def make_smem_desc_start_addr(start_addr: cute.Pointer) -> cutlass.Int32: + # 14 bits, remove 4 LSB (bits 0-13 in desc) + return (start_addr.toint() & 0x3FFFF) >> 4 + + +def smem_desc_base_from_tensor(sA: cute.Tensor, major: Major) -> int: + sA_swizzle = sA.iterator.type.swizzle_type + return make_smem_desc_base( + cute.recast_layout(128, sA.element_type.width, sA.layout[0]), + sA_swizzle, + major, + ) diff --git a/python/sglang/jit_kernel/flash_attn/cute/named_barrier.py b/python/sglang/jit_kernel/flash_attn/cute/named_barrier.py new file mode 100644 index 000000000..d7ae9973e --- /dev/null +++ b/python/sglang/jit_kernel/flash_attn/cute/named_barrier.py @@ -0,0 +1,58 @@ +# Copyright (c) 2025, Jay Shah, Ganesh Bikshandi, Ying Zhang, Vijay Thakkar, Pradeep Ramani, Tri Dao. + +import enum + + +class NamedBarrierFwd(enum.IntEnum): + Epilogue = enum.auto() # starts from 1 as barrier 0 is reserved for sync_threads() + WarpSchedulerWG1 = enum.auto() + WarpSchedulerWG2 = enum.auto() + WarpSchedulerWG3 = enum.auto() + PFull = enum.auto() + PEmpty = enum.auto() + + +class NamedBarrierFwdSm100(enum.IntEnum): + Epilogue = enum.auto() # starts from 1 as barrier 0 is reserved for sync_threads() + TmemPtr = enum.auto() + SoftmaxStatsW0 = enum.auto() + SoftmaxStatsW1 = enum.auto() + SoftmaxStatsW2 = enum.auto() + SoftmaxStatsW3 = enum.auto() + SoftmaxStatsW4 = enum.auto() + SoftmaxStatsW5 = enum.auto() + SoftmaxStatsW6 = enum.auto() + SoftmaxStatsW7 = enum.auto() + Softmax = enum.auto() + Correction = enum.auto() + + +class NamedBarrierBwd(enum.IntEnum): + Epilogue = enum.auto() + WarpSchedulerWG1 = enum.auto() + WarpSchedulerWG2 = enum.auto() + WarpSchedulerWG3 = enum.auto() + PdS = enum.auto() + dQFullWG0 = enum.auto() + dQFullWG1 = enum.auto() + dQFullWG2 = enum.auto() + dQEmptyWG0 = enum.auto() + dQEmptyWG1 = enum.auto() + dQEmptyWG2 = enum.auto() + + +class NamedBarrierBwdSm100(enum.IntEnum): + EpilogueWG1 = enum.auto() + EpilogueWG2 = enum.auto() + Compute = enum.auto() + dQaccReduce = enum.auto() + TmemPtr = enum.auto() + + +class NamedBarrierFwdSm100_MLA2CTA(enum.IntEnum): + Epilogue = enum.auto() + TmemPtr = enum.auto() + Cpasync = enum.auto() + Softmax = enum.auto() + SoftmaxStatsFull = enum.auto() + SoftmaxStatsEmpty = enum.auto() diff --git a/python/sglang/jit_kernel/flash_attn/cute/pack_gqa.py b/python/sglang/jit_kernel/flash_attn/cute/pack_gqa.py new file mode 100644 index 000000000..cfd9cde09 --- /dev/null +++ b/python/sglang/jit_kernel/flash_attn/cute/pack_gqa.py @@ -0,0 +1,300 @@ +# Copyright (c) 2025, Tri Dao. + +from dataclasses import dataclass +from typing import Tuple, Union + +import cutlass +import cutlass.cute as cute +from cutlass.cute.nvgpu import cpasync +from quack import layout_utils + +import sglang.jit_kernel.flash_attn.cute.utils as utils + + +def pack_gqa_layout(T, qhead_per_kvhead, nheads_kv, head_idx): + """Reshape a tensor to fold qhead_per_kvhead into the seqlen dimension (mode 0). + + The head dimension is at mode ``head_idx``. Modes before it (1..head_idx-1) + are kept as-is (e.g. headdim for Q/O tensors), and modes after it are kept + as-is (e.g. batch). + + For Q/O tensors (head_idx=2): + (seqlen_q, headdim, nheads, batch, ...) -> ((qhead_per_kvhead, seqlen_q), headdim, nheads_kv, batch, ...) + For LSE tensors (head_idx=1): + (seqlen_q, nheads, batch, ...) -> ((qhead_per_kvhead, seqlen_q), nheads_kv, batch, ...) + """ + head_stride = T.stride[head_idx] + shape_packed = ( + (qhead_per_kvhead, T.shape[0]), + *[T.shape[i] for i in range(1, head_idx)], + nheads_kv, + *[T.shape[i] for i in range(head_idx + 1, len(T.shape))], + ) + stride_packed = ( + (head_stride, T.stride[0]), + *[T.stride[i] for i in range(1, head_idx)], + head_stride * qhead_per_kvhead, + *[T.stride[i] for i in range(head_idx + 1, len(T.shape))], + ) + return cute.make_tensor( + T.iterator, cute.make_layout(shape_packed, stride=stride_packed) + ) + + +def make_packgqa_tiled_tma_atom( + op: cute.atom.CopyOp, + gmem_tensor: cute.Tensor, + smem_layout: Union[cute.Layout, cute.ComposedLayout], + cta_tiler: Tuple[int, int], + qhead_per_kvhead: int, + head_idx: int, +): + # This packing and unpacking of the layout is so that we keep the same TMA dimension as usual. + # e.g. for (seqlen, d, nheads, b) layout, we still have 4D TMA after packing to + # ((nheads, seqlen), d, b). + # If we instead pack directly to ((qhead_per_kvhead, seqlen), d, nheads_kv, b) we'd have 5D TMA. + # Pack headdim and seqlen dim into 1: (seqlen, d, nheads, b) -> ((nheads, seqlen), d, b) + gmem_tensor = layout_utils.select( + gmem_tensor, + [head_idx, *range(head_idx), *range(head_idx + 1, cute.rank(gmem_tensor))], + ) + gmem_tensor = cute.group_modes(gmem_tensor, 0, 2) + assert ( + cta_tiler[0] % qhead_per_kvhead == 0 + ), "CTA tile size in the seqlen dimension must be divisible by qhead_per_kvhead" + tma_atom, tma_tensor = cpasync.make_tiled_tma_atom( + op, + gmem_tensor, + smem_layout, + ( + (qhead_per_kvhead, cta_tiler[0] // qhead_per_kvhead), + cta_tiler[1], + ), # No mcast + ) + # Unpack from ((nheads, seqlen), d, b) -> ((qhead_per_kvhead, seqlen), d, nheads_kv, b) + T = tma_tensor + shape_packed = ( + (qhead_per_kvhead, T.shape[0][1]), + *[T.shape[i] for i in range(1, head_idx)], + T.shape[0][0] // qhead_per_kvhead, + *[T.shape[i] for i in range(head_idx, len(T.shape))], + ) + stride_packed = ( + *[T.stride[i] for i in range(head_idx)], + T.stride[0][0] * qhead_per_kvhead, + *[T.stride[i] for i in range(head_idx, len(T.shape))], + ) + tma_tensor = cute.make_tensor( + T.iterator, cute.make_layout(shape_packed, stride=stride_packed) + ) + return tma_atom, tma_tensor + + +def unpack_gqa_layout(T, qhead_per_kvhead, head_idx): + """Reverse of pack_gqa_layout: unfold qhead_per_kvhead from the seqlen dimension (mode 0). + + The head dimension is at mode ``head_idx``. Modes before it (1..head_idx-1) + are kept as-is (e.g. headdim for Q/O tensors), and modes after it are kept + as-is (e.g. batch). + + For Q/O tensors (head_idx=2): + ((qhead_per_kvhead, seqlen_q), headdim, nheads_kv, batch, ...) -> (seqlen_q, headdim, nheads, batch, ...) + For LSE tensors (head_idx=1): + ((qhead_per_kvhead, seqlen_q), nheads_kv, batch, ...) -> (seqlen_q, nheads, batch, ...) + """ + seqlen_stride = T.stride[0][1] + head_stride = T.stride[0][0] + shape_unpacked = ( + T.shape[0][1], + *[T.shape[i] for i in range(1, head_idx)], + T.shape[head_idx] * qhead_per_kvhead, + *[T.shape[i] for i in range(head_idx + 1, len(T.shape))], + ) + stride_unpacked = ( + seqlen_stride, + *[T.stride[i] for i in range(1, head_idx)], + head_stride, + *[T.stride[i] for i in range(head_idx + 1, len(T.shape))], + ) + return cute.make_tensor( + T.iterator, cute.make_layout(shape_unpacked, stride=stride_unpacked) + ) + + +@dataclass +class PackGQA: + m_block_size: cutlass.Constexpr[int] + head_dim_padded: cutlass.Constexpr[int] + check_hdim_oob: cutlass.Constexpr[bool] + qhead_per_kvhead: cutlass.Constexpr[bool] + + @cute.jit + def compute_ptr( + self, + tensor: cute.Tensor, + cRows: cute.Tensor, + tidx: cutlass.Int32, + block: cutlass.Int32, + threads_per_row: cutlass.Constexpr[int], + num_threads: cutlass.Constexpr[int], + ): + num_ptr_per_thread = cute.ceil_div(cute.size(cRows), threads_per_row) + tPrPtr = cute.make_rmem_tensor(num_ptr_per_thread, cutlass.Int64) + for i in cutlass.range_constexpr(num_ptr_per_thread): + row = i * num_threads + cRows[tidx % threads_per_row][0] + idx = block * self.m_block_size + row + m_idx = idx // self.qhead_per_kvhead + h_idx = idx - m_idx * self.qhead_per_kvhead + tPrPtr[i] = utils.elem_pointer(tensor, ((h_idx, m_idx),)).toint() + return tPrPtr + + @cute.jit + def load_Q( + self, + mQ: cute.Tensor, # ((qhead_per_kvhead, seqlen_q), headdim) + sQ: cute.Tensor, # (m_block_size, head_dim_padded) + gmem_tiled_copy: cute.TiledCopy, + tidx: cutlass.Int32, + block: cutlass.Int32, + seqlen: cutlass.Int32, + ): + gmem_thr_copy = gmem_tiled_copy.get_slice(tidx) + cQ = cute.make_identity_tensor((self.m_block_size, self.head_dim_padded)) + tQsQ = gmem_thr_copy.partition_D(sQ) + tQcQ = gmem_thr_copy.partition_S(cQ) + t0QcQ = gmem_thr_copy.get_slice(0).partition_S(cQ) + tQpQ = utils.predicate_k(tQcQ, limit=mQ.shape[1]) + tQcQ_row = tQcQ[0, None, 0] + threads_per_row = gmem_tiled_copy.layout_tv_tiled.shape[0][0] + assert ( + cute.arch.WARP_SIZE % threads_per_row == 0 + ), "threads_per_row must divide WARP_SIZE" + num_threads = gmem_tiled_copy.size + tPrQPtr = self.compute_ptr( + mQ[None, 0], tQcQ_row, tidx, block, threads_per_row, num_threads + ) + for m in cutlass.range_constexpr(cute.size(tQsQ.shape[1])): + q_ptr_i64 = utils.shuffle_sync( + tPrQPtr[m // threads_per_row], + m % threads_per_row, + width=threads_per_row, + ) + q_gmem_ptr = cute.make_ptr( + mQ.element_type, q_ptr_i64, cute.AddressSpace.gmem, assumed_align=16 + ) + if ( + t0QcQ[0, m, 0][0] + < seqlen * self.qhead_per_kvhead + - block * self.m_block_size + - tQcQ_row[0][0] + ): + mQ_cur = cute.make_tensor(q_gmem_ptr, (self.head_dim_padded,)) + elems_per_load = cute.size(tQsQ.shape[0][0]) + mQ_cur_copy = cute.tiled_divide(mQ_cur, (elems_per_load,)) + for k in cutlass.range_constexpr(cute.size(tQsQ.shape[2])): + ki = tQcQ[0, 0, k][1] // elems_per_load + cute.copy( + gmem_thr_copy, + mQ_cur_copy[None, ki], + tQsQ[None, m, k], + pred=( + tQpQ[None, m, k] + if cutlass.const_expr(self.check_hdim_oob) + else None + ), + ) + # We don't need to clear the sQ smem tiles since we'll only write out the valid outputs + + @cute.jit + def store_LSE( + self, + mLSE: cute.Tensor, # (qhead_per_kvhead, seqlen_q) + tLSErLSE: cute.Tensor, # (m_block_size, head_dim_padded) + tiled_mma: cute.TiledMma, + tidx: cutlass.Int32, + block: cutlass.Int32, + seqlen: cutlass.Int32, + ): + thr_mma = tiled_mma.get_slice(tidx) + caccO = cute.make_identity_tensor((self.m_block_size, self.head_dim_padded)) + taccOcO = thr_mma.partition_C(caccO) + taccOcO_row = layout_utils.reshape_acc_to_mn(taccOcO)[None, 0] + assert cute.size(tLSErLSE) == cute.size(taccOcO_row) + threads_per_row = tiled_mma.tv_layout_C.shape[0][0] + assert ( + cute.arch.WARP_SIZE % threads_per_row == 0 + ), "threads_per_row must divide WARP_SIZE" + assert cute.size(tLSErLSE) <= threads_per_row + num_threads = tiled_mma.size + tPrLSEPtr = self.compute_ptr( + mLSE, taccOcO_row, tidx, block, threads_per_row, num_threads + ) + for m in cutlass.range_constexpr(cute.size(tLSErLSE)): + lse_ptr_i64 = utils.shuffle_sync( + tPrLSEPtr[m // threads_per_row], + m % threads_per_row, + width=threads_per_row, + ) + lse_gmem_ptr = cute.make_ptr( + mLSE.element_type, lse_ptr_i64, cute.AddressSpace.gmem, assumed_align=4 + ) + row = block * self.m_block_size + taccOcO_row[m][0] + # Only the thread corresponding to column 0 writes out the lse to gmem + if taccOcO[0][1] == 0 and row < seqlen * self.qhead_per_kvhead: + mLSE_copy = cute.make_tensor(lse_gmem_ptr, (1,)) + mLSE_copy[0] = tLSErLSE[m] + + @cute.jit + def store_O( + self, + mO: cute.Tensor, # ((qhead_per_kvhead, seqlen_q), headdim) + tOrO: cute.Tensor, # (m_block_size, head_dim_padded) split across threads according to gmem_tiled_copy + gmem_tiled_copy: cute.TiledCopy, + tidx: cutlass.Int32, + block: cutlass.Int32, + seqlen: cutlass.Int32, + ): + gmem_thr_copy = gmem_tiled_copy.get_slice(tidx) + cO = cute.make_identity_tensor((self.m_block_size, self.head_dim_padded)) + tOcO = gmem_thr_copy.partition_S(cO) + t0OcO = gmem_thr_copy.get_slice(0).partition_S(cO) + tOpO = utils.predicate_k(tOcO, limit=mO.shape[1]) + tOcO_row = tOcO[0, None, 0] + threads_per_row = gmem_tiled_copy.layout_tv_tiled.shape[0][0] + assert ( + cute.arch.WARP_SIZE % threads_per_row == 0 + ), "threads_per_row must divide WARP_SIZE" + num_threads = gmem_tiled_copy.size + tPrOPtr = self.compute_ptr( + mO[None, 0], tOcO_row, tidx, block, threads_per_row, num_threads + ) + for m in cutlass.range_constexpr(cute.size(tOrO.shape[1])): + o_ptr_i64 = utils.shuffle_sync( + tPrOPtr[m // threads_per_row], + m % threads_per_row, + width=threads_per_row, + ) + o_gmem_ptr = cute.make_ptr( + mO.element_type, o_ptr_i64, cute.AddressSpace.gmem, assumed_align=16 + ) + if ( + t0OcO[0, m, 0][0] + < seqlen * self.qhead_per_kvhead + - block * self.m_block_size + - tOcO_row[0][0] + ): + mO_cur = cute.make_tensor(o_gmem_ptr, (self.head_dim_padded,)) + elems_per_load = cute.size(tOrO.shape[0][0]) + mO_cur_copy = cute.tiled_divide(mO_cur, (elems_per_load,)) + for k in cutlass.range_constexpr(cute.size(tOrO.shape[2])): + ki = tOcO[0, 0, k][1] // elems_per_load + cute.copy( + gmem_thr_copy, + tOrO[None, m, k], + mO_cur_copy[None, ki], + pred=( + tOpO[None, m, k] + if cutlass.const_expr(self.check_hdim_oob) + else None + ), + ) diff --git a/python/sglang/jit_kernel/flash_attn/cute/paged_kv.py b/python/sglang/jit_kernel/flash_attn/cute/paged_kv.py new file mode 100644 index 000000000..b483d13b0 --- /dev/null +++ b/python/sglang/jit_kernel/flash_attn/cute/paged_kv.py @@ -0,0 +1,393 @@ +import math +from dataclasses import dataclass +from typing import Optional, Type + +import cutlass +import cutlass.cute as cute +from cutlass import Int32, const_expr +from cutlass.cute import FastDivmodDivisor +from cutlass.cute.nvgpu import cpasync +from quack.cute_dsl_utils import ParamsBase + +from sglang.jit_kernel.flash_attn.cute import utils + + +@dataclass +class PagedKVManager(ParamsBase): + mPageTable: cute.Tensor + mK_paged: cute.Tensor + mV_paged: cute.Tensor + mSFK_paged: Optional[cute.Tensor] + mSFV_paged: Optional[cute.Tensor] + thread_idx: Int32 + + page_size_divmod: FastDivmodDivisor + seqlen_k: Int32 + leftpad_k: Int32 + n_block_size: Int32 + num_threads: cutlass.Constexpr[Int32] + head_dim_padded: cutlass.Constexpr[Int32] + head_dim_v_padded: cutlass.Constexpr[Int32] + + arch: cutlass.Constexpr[Int32] + v_gmem_transposed: cutlass.Constexpr[bool] + + gmem_threads_per_row: cutlass.Constexpr[Int32] + page_entry_per_thread: Int32 + async_copy_elems: Int32 + + gmem_tiled_copy_KV: cute.TiledCopy + gmem_thr_copy_KV: cute.TiledCopy + gmem_tiled_copy_sf_KV: Optional[cute.TiledCopy] + gmem_thr_copy_sf_KV: Optional[cute.TiledCopy] + tPrPage: cute.Tensor + tPrPageOffset: cute.Tensor + tKpK: cute.Tensor + tVpV: cute.Tensor + + @staticmethod + def create( + mPageTable: cute.Tensor, + mK_paged: cute.Tensor, + mV_paged: cute.Tensor, + page_size_divmod: FastDivmodDivisor, + bidb: Int32, + bidh: Int32, + thread_idx: Int32, + seqlen_k: Int32, + leftpad_k: Int32, + n_block_size: cutlass.Constexpr[Int32], + head_dim_padded: cutlass.Constexpr[Int32], + head_dim_v_padded: cutlass.Constexpr[Int32], + num_threads: cutlass.Constexpr[Int32], + dtype: Type[cutlass.Numeric], + mSFK_paged: Optional[cute.Tensor] = None, + mSFV_paged: Optional[cute.Tensor] = None, + arch: cutlass.Constexpr[int] = 100, + ): + # SM100 transposes V in gmem to (dv, page_size, num_pages); + # SM90 keeps V as (page_size, dv, num_pages), same layout as K. + v_gmem_transposed = arch != 90 + universal_copy_bits = 128 + async_copy_elems = universal_copy_bits // dtype.width + dtype_bytes = dtype.width // 8 + gmem_k_block_size = math.gcd( + head_dim_padded, + head_dim_v_padded, + 128 // dtype_bytes, + ) + assert gmem_k_block_size % async_copy_elems == 0 + gmem_threads_per_row = gmem_k_block_size // async_copy_elems + assert cute.arch.WARP_SIZE % gmem_threads_per_row == 0 + atom_async_copy = cute.make_copy_atom( + cpasync.CopyG2SOp(cache_mode=cpasync.LoadCacheMode.GLOBAL), + dtype, + num_bits_per_copy=universal_copy_bits, + ) + thr_layout = cute.make_ordered_layout( + (num_threads // gmem_threads_per_row, gmem_threads_per_row), + order=(1, 0), + ) + val_layout = cute.make_layout((1, async_copy_elems)) + gmem_tiled_copy_KV = cute.make_tiled_copy_tv( + atom_async_copy, thr_layout, val_layout + ) + gmem_thr_copy_KV = gmem_tiled_copy_KV.get_slice(thread_idx) + page_entry_per_thread = n_block_size // num_threads + + if const_expr(mSFK_paged is not None or mSFV_paged is not None): + atom_async_copy_sf = cute.make_copy_atom( + cpasync.CopyG2SOp(cache_mode=cpasync.LoadCacheMode.ALWAYS), + dtype, + num_bits_per_copy=32, + ) + thr_layout_sf = cute.make_ordered_layout( + ((num_threads // gmem_threads_per_row, gmem_threads_per_row), 1), + order=((1, 0), 2), + ) + val_layout_sf = cute.make_layout((1, 4)) + gmem_tiled_copy_sf_KV = cute.make_tiled_copy_tv( + atom_async_copy_sf, + thr_layout_sf, + val_layout_sf, + ) + gmem_thr_copy_sf_KV = gmem_tiled_copy_sf_KV.get_slice(thread_idx) + else: + gmem_tiled_copy_sf_KV = None + gmem_thr_copy_sf_KV = None + + tPrPage = cute.make_rmem_tensor((page_entry_per_thread,), Int32) + tPrPageOffset = cute.make_rmem_tensor((page_entry_per_thread,), Int32) + + mPageTable = mPageTable[bidb, None] + mK_paged = mK_paged[None, None, bidh, None] + mV_paged = mV_paged[None, None, bidh, None] + + if const_expr(mSFK_paged is not None): + mSFK_paged = mSFK_paged[None, None, bidh, None] + if const_expr(mSFV_paged is not None): + mSFV_paged = mSFV_paged[None, None, bidh, None] + + cK = cute.make_identity_tensor((n_block_size, head_dim_padded)) + tKcK = gmem_thr_copy_KV.partition_S(cK) + tKpK = utils.predicate_k(tKcK, limit=mK_paged.shape[1]) + + if const_expr(head_dim_padded == head_dim_v_padded): + tVpV = tKpK + else: + cV = cute.make_identity_tensor((n_block_size, head_dim_v_padded)) + tVcV = gmem_thr_copy_KV.partition_S(cV) + # When V is transposed in gmem, dv is shape[0]; otherwise dv is shape[1] (same as K) + V_limit = cute.size(mV_paged.shape[0 if v_gmem_transposed else 1]) + tVpV = utils.predicate_k(tVcV, limit=V_limit) + + return PagedKVManager( + mPageTable, + mK_paged, + mV_paged, + mSFK_paged, + mSFV_paged, + thread_idx, + page_size_divmod, + seqlen_k, + leftpad_k, + n_block_size, + num_threads, + head_dim_padded, + head_dim_v_padded, + arch, + v_gmem_transposed, + gmem_threads_per_row, + page_entry_per_thread, + async_copy_elems, + gmem_tiled_copy_KV, + gmem_thr_copy_KV, + gmem_tiled_copy_sf_KV, + gmem_thr_copy_sf_KV, + tPrPage, + tPrPageOffset, + tKpK, + tVpV, + ) + + @cute.jit + def load_page_table(self, n_block: Int32): + for i in cutlass.range(self.page_entry_per_thread, unroll=1): + row = ( + i * self.num_threads + + (self.thread_idx % self.gmem_threads_per_row) + * (self.num_threads // self.gmem_threads_per_row) + + (self.thread_idx // self.gmem_threads_per_row) + ) + row_idx = n_block * self.n_block_size + row + + page_idx, page_offset = divmod( + row_idx + self.leftpad_k, self.page_size_divmod + ) + + is_valid = ( + (i + 1) * self.num_threads <= self.n_block_size + or row < self.n_block_size + ) and row_idx < self.seqlen_k + page = self.mPageTable[page_idx] if is_valid else 0 + + self.tPrPage[i] = page + self.tPrPageOffset[i] = page_offset + + @cute.jit + def compute_X_ptr(self, K_or_V: str, d_offset: int = 0): + tPrXPtr = cute.make_rmem_tensor((self.page_entry_per_thread,), cutlass.Int64) + mX = self.mK_paged if const_expr(K_or_V == "K") else self.mV_paged + # K is always (page_size, d, num_pages). V matches K when not transposed, + # but is (dv, page_size, num_pages) when transposed (SM100). + transposed = const_expr(K_or_V == "V" and self.v_gmem_transposed) + for i in cutlass.range(self.page_entry_per_thread, unroll=1): + page = self.tPrPage[i] + page_offset = self.tPrPageOffset[i] + if const_expr(transposed): + tPrXPtr[i] = utils.elem_pointer( + mX, (d_offset, page_offset, page) + ).toint() + else: + tPrXPtr[i] = utils.elem_pointer( + mX, (page_offset, d_offset, page) + ).toint() + return tPrXPtr + + @cute.jit + def _flatten_smem_sm100(self, sX: cute.Tensor, K_or_V: str): + """Flatten SM100 smem ((a,b), cta_split, k) to (a,(b,k)); transpose V to (d,page_size).""" + sX_pi = cute.make_tensor( + sX.iterator, + cute.make_layout( + (sX.shape[0][0], (sX.shape[0][1], sX.shape[2])), + stride=(sX.stride[0][0], (sX.stride[0][1], sX.stride[2])), + ), + ) + if const_expr(K_or_V == "V"): + sX_pi = cute.make_tensor( + sX_pi.iterator, cute.select(sX_pi.layout, mode=[1, 0]) + ) + return sX_pi + + @cute.jit + def _copy_row_async( + self, + tXsX: cute.Tensor, + tXcX: cute.Tensor, + mX_paged_cur_copy: cute.Tensor, + m: Int32, + should_load: cute.Tensor, + ): + """Issue cp.async copies for one row across all k-tiles.""" + for k in cutlass.range_constexpr(cute.size(tXsX, mode=[2])): + ki = tXcX[0, 0, k][1] // self.async_copy_elems + mX_paged_cur_copy_ki = mX_paged_cur_copy[None, ki] + tXsX_k = tXsX[None, m, k] + mX_paged_cur_copy_ki = cute.make_tensor( + mX_paged_cur_copy_ki.iterator, tXsX_k.layout + ) + cute.copy( + self.gmem_tiled_copy_KV, + mX_paged_cur_copy_ki, + tXsX_k, + pred=should_load, + ) + + @cute.jit + def compute_sf_X_ptr(self, K_or_V: str): + tPrXPtr = cute.make_rmem_tensor((self.page_entry_per_thread,), cutlass.Int64) + for i in cutlass.range(self.page_entry_per_thread, unroll=1): + page = self.tPrPage[i] + page_offset = self.tPrPageOffset[i] + if const_expr(K_or_V == "K"): + tPrXPtr[i] = utils.elem_pointer( + self.mSFK_paged, (page_offset, 0, page) + ).toint() + else: + tPrXPtr[i] = utils.elem_pointer( + self.mSFV_paged, (0, page_offset, page) + ).toint() + return tPrXPtr + + @cute.jit + def load_KV(self, n_block: Int32, sX: cute.Tensor, K_or_V: str): + assert K_or_V in ("K", "V") + + tPrXPtr = self.compute_X_ptr(K_or_V) + + if const_expr(self.arch == 90): + # SM90: sX is already stage-sliced by caller (sK[None, None, stage]). + # Flatten hierarchical modes to get (n_block_size, head_dim). + sX_pi = cute.group_modes(sX, 0, 1) + # SM90 does NOT transpose V here (it's transposed via utils.transpose_view before MMA) + else: + sX_pi = self._flatten_smem_sm100(sX, K_or_V) + + head_dim = ( + self.head_dim_v_padded + if const_expr(K_or_V == "V") + else self.head_dim_padded + ) + cX = cute.make_identity_tensor((self.n_block_size, head_dim)) + tXsX = self.gmem_thr_copy_KV.partition_D(sX_pi) + tXcX = self.gmem_thr_copy_KV.partition_S(cX) + tXc0X = self.gmem_thr_copy_KV.get_slice(0).partition_S(cX) + + seqlenk_row_limit = ( + self.seqlen_k - n_block * self.n_block_size - tXcX[0][0] + if n_block >= 0 + else 0 + ) + for m in cutlass.range_constexpr(cute.size(tXsX, mode=[1])): + row_valid = tXc0X[0, m, 0][0] < seqlenk_row_limit + should_load = cute.make_fragment_like(tXsX[(0, None), m, 0], cute.Boolean) + should_load.fill(row_valid) + + x_ptr_i64 = utils.shuffle_sync( + tPrXPtr[m // self.gmem_threads_per_row], + m % self.gmem_threads_per_row, + width=self.gmem_threads_per_row, + ) + x_gmem_ptr = cute.make_ptr( + self.mK_paged.element_type, + x_ptr_i64, + cute.AddressSpace.gmem, + assumed_align=16, + ) + mX_paged_cur = cute.make_tensor(x_gmem_ptr, cute.make_layout((head_dim,))) + mX_paged_cur_copy = cute.tiled_divide( + mX_paged_cur, (self.async_copy_elems,) + ) + self._copy_row_async(tXsX, tXcX, mX_paged_cur_copy, m, should_load) + + @cute.jit + def load_sf_KV(self, n_block: Int32, sSFX: cute.Tensor, K_or_V: str): + # sSFX expected as SFK or SFV + assert ( + cute.rank(sSFX) == 3 + ), f"mismatched rank for sSFX, expected 3 but got {cute.rank(sSFX)}" + assert self.gmem_thr_copy_sf_KV is not None + # sSFK: tensor> o ((((32,4),1),(32,1)),1,4,2):((((16,4),0),(0,0)),0,1,512)> + # sSFV: tensor> o ((((32,4),1),(32,1)),1,4,2):((((16,4),0),(0,0)),0,1,512)> + + head_dim = ( + self.head_dim_v_padded + if const_expr(K_or_V == "V") + else self.head_dim_padded + ) + + sSFX_cpt = cute.filter_zeros(sSFX) + sSFX_cpt_shape_nd = (self.n_block_size, head_dim // 32) + sSFX_cpt_layout_nd = cute.make_ordered_layout( + sSFX_cpt_shape_nd, + order=(0, 1), + ) + # (tile_n, 4) + sSFX_cpt_nd = cute.composition(sSFX_cpt, sSFX_cpt_layout_nd) + + cX = cute.make_identity_tensor(sSFX_cpt_shape_nd) + # ((V, 1), M, 1) + tXsX = self.gmem_thr_copy_sf_KV.partition_D(sSFX_cpt_nd) + tXcX = self.gmem_thr_copy_sf_KV.partition_S(cX) + tXc0X = self.gmem_thr_copy_sf_KV.get_slice(0).partition_S(cX) + + seqlenk_row_limit = ( + self.seqlen_k - n_block * self.n_block_size - tXcX[0][0] + if n_block >= 0 + else 0 + ) + + tPrSFXPtr = self.compute_sf_X_ptr(K_or_V) + assert cute.size(tPrSFXPtr) == cute.size( + tXsX, mode=[1] + ), "SFX pointer size mismatch" + + # loop over rows + for m in cutlass.range_constexpr(cute.size(tXsX, mode=[1])): + row_valid = tXc0X[0, m, 0][0] < seqlenk_row_limit + should_load = cute.make_fragment_like( + tXsX[(0, None), m, None], cute.Boolean + ) + should_load.fill(row_valid) + + # Make gmem tensor of size (4,) using tPrSFXPtr + # Simplified version of load_KV, no shuffle, 4 elements to copy (hdim = 128) + sfx_ptr_i64 = tPrSFXPtr[m] + sfx_gmem_ptr = cute.make_ptr( + self.mSFK_paged.element_type, + sfx_ptr_i64, + cute.AddressSpace.gmem, + assumed_align=4, + ) + sf_frg_layout = cute.make_layout(((head_dim // 32, 1), 1)) + mSFX_paged_cur = cute.make_tensor(sfx_gmem_ptr, sf_frg_layout) + assert cute.size(mSFX_paged_cur) == cute.size( + tXsX[None, 0, None] + ), "SFX gmem-smem tensor size mismatch" + cute.copy( + self.gmem_tiled_copy_sf_KV, + mSFX_paged_cur, + tXsX[None, m, None], + pred=should_load, + ) diff --git a/python/sglang/jit_kernel/flash_attn/cute/pipeline.py b/python/sglang/jit_kernel/flash_attn/cute/pipeline.py new file mode 100644 index 000000000..8154c14b2 --- /dev/null +++ b/python/sglang/jit_kernel/flash_attn/cute/pipeline.py @@ -0,0 +1,412 @@ +# Copyright (c) 2025, Tri Dao. + +from dataclasses import dataclass +from typing import Optional + +import cutlass.cute as cute +from cutlass import Boolean, Int32, const_expr +from cutlass.cutlass_dsl import dsl_user_op, if_generate +from cutlass.pipeline import NamedBarrier as NamedBarrierOg +from cutlass.pipeline import PipelineAsync as PipelineAsyncOg +from cutlass.pipeline import PipelineAsyncUmma as PipelineAsyncUmmaOg +from cutlass.pipeline import PipelineCpAsync as PipelineCpAsyncOg +from cutlass.pipeline import PipelineState +from cutlass.pipeline import PipelineTmaAsync as PipelineTmaAsyncOg +from cutlass.pipeline import PipelineTmaUmma as PipelineTmaUmmaOg +from cutlass.pipeline import PipelineUmmaAsync as PipelineUmmaAsyncOg +from cutlass.pipeline import PipelineUserType + + +def _override_create(parent_cls, child_cls): + """Create a static factory that constructs parent_cls then re-classes to child_cls.""" + + @staticmethod + def create(*args, **kwargs): + obj = parent_cls.create(*args, **kwargs) + # Can't assign to __class__ directly since the dataclass is frozen + object.__setattr__(obj, "__class__", child_cls) + return obj + + return create + + +def _make_state(index: Int32, phase: Int32) -> PipelineState: + """Construct a PipelineState from index and phase (count/stages unused by callers).""" + return PipelineState(stages=0, count=Int32(0), index=index, phase=phase) + + +class PipelineStateSimple: + """ + Pipeline state contains an index and phase bit corresponding to the current position in the circular buffer. + Use a single Int32 to store both the index and phase bit, then we use divmod to get the + index and phase. If stages is a power of 2, divmod turns into bit twiddling. + """ + + def __init__(self, stages: int, phase_index: Int32): + self._stages = stages + self._phase_index = phase_index + + def clone(self) -> "PipelineStateSimple": + return PipelineStateSimple(self.stages, self._phase_index) + + @property + def stages(self) -> int: + return self._stages + + @property + def index(self) -> Int32: + if const_expr(self._stages == 1): + return Int32(0) + else: + return self._phase_index % self._stages + + @property + def phase(self) -> Int32: + # PTX docs say that the phase parity needs to be 0 or 1, so by right we need to + # take modulo 2. But in practice just passing the phase in without modulo works fine. + if const_expr(self._stages == 1): + return self._phase_index + else: + return self._phase_index // self._stages + + def advance(self): + if const_expr(self._stages == 1): + self._phase_index ^= 1 + else: + self._phase_index += 1 + + def __extract_mlir_values__(self): + phase_index = self._phase_index + return [phase_index.ir_value()] + + def __new_from_mlir_values__(self, values): + return PipelineStateSimple(self.stages, Int32(values[0])) + + +def make_pipeline_state(type: PipelineUserType, stages: int): + """ + Creates a pipeline state. Producers are assumed to start with an empty buffer and have a flipped phase bit of 1. + """ + if type is PipelineUserType.Producer: + return PipelineStateSimple(stages, Int32(stages)) + elif type is PipelineUserType.Consumer: + return PipelineStateSimple(stages, Int32(0)) + else: + assert ( + False + ), "Error: invalid PipelineUserType specified for make_pipeline_state." + + +# ── Shared helpers ─────────────────────────────────────────────────────────── + + +def _call_with_elect_one(parent_method, self, state, elect_one, syncwarp, loc, ip): + """Optionally wrap a parent pipeline method call in sync_warp + elect_one.""" + if const_expr(elect_one): + if const_expr(syncwarp): + cute.arch.sync_warp() + with cute.arch.elect_one(): + parent_method(self, state, loc=loc, ip=ip) + else: + parent_method(self, state, loc=loc, ip=ip) + + +# ── Mixin: _w_index / _w_index_phase variants that delegate to parent ─────── +# Each parent class has PipelineState-based methods (producer_acquire, producer_commit, +# consumer_wait, consumer_release). The _w_index_phase variants just construct a +# PipelineState from (index, phase) and delegate. + + +class _PipelineIndexPhaseMixin: + """Mixin providing _w_index_phase / _w_index methods that delegate to PipelineState-based parents.""" + + @dsl_user_op + def producer_acquire_w_index_phase( + self, + index: Int32, + phase: Int32, + try_acquire_token: Optional[Boolean] = None, + *, + loc=None, + ip=None, + ): + state = _make_state(index, phase) + # Call the parent's producer_acquire (which takes PipelineState) + self.producer_acquire(state, try_acquire_token, loc=loc, ip=ip) + + @dsl_user_op + def producer_commit_w_index(self, index: Int32, *, loc=None, ip=None): + state = _make_state(index, Int32(0)) + self.producer_commit(state, loc=loc, ip=ip) + + @dsl_user_op + def consumer_wait_w_index_phase( + self, + index: Int32, + phase: Int32, + try_wait_token: Optional[Boolean] = None, + *, + loc=None, + ip=None, + ): + state = _make_state(index, phase) + self.consumer_wait(state, try_wait_token, loc=loc, ip=ip) + + @dsl_user_op + def consumer_release_w_index(self, index: Int32, *, loc=None, ip=None): + state = _make_state(index, Int32(0)) + self.consumer_release(state, loc=loc, ip=ip) + + +# ── NamedBarrier ───────────────────────────────────────────────────────────── + + +@dataclass(frozen=True) +class NamedBarrier(NamedBarrierOg): + create = _override_create(NamedBarrierOg, None) # patched below + + @dsl_user_op + def arrive_w_index(self, index: Int32, *, loc=None, ip=None) -> None: + """ + The aligned flavor of arrive is used when all threads in the CTA will execute the + same instruction. See PTX documentation. + """ + cute.arch.barrier_arrive( + barrier_id=self.barrier_id + index, + number_of_threads=self.num_threads, + loc=loc, + ip=ip, + ) + + @dsl_user_op + def arrive_and_wait_w_index(self, index: Int32, *, loc=None, ip=None) -> None: + cute.arch.barrier( + barrier_id=self.barrier_id + index, + number_of_threads=self.num_threads, + loc=loc, + ip=ip, + ) + + +NamedBarrier.create = _override_create(NamedBarrierOg, NamedBarrier) + + +# ── PipelineAsync ──────────────────────────────────────────────────────────── + + +@dataclass(frozen=True) +class PipelineAsync(_PipelineIndexPhaseMixin, PipelineAsyncOg): + """ + PipelineAsync with optional elect_one for producer_commit and consumer_release. + + When elect_one_*=True (set at create time), only one elected thread per warp + signals the barrier arrive. This is useful when the mask count is set to 1 per warp. + + Args (to create): + elect_one_commit: If True, only elected thread signals producer_commit. + syncwarp_before_commit: If True (default), issue syncwarp before elect_one. + elect_one_release: If True, only elected thread signals consumer_release. + syncwarp_before_release: If True (default), issue syncwarp before elect_one. + Set syncwarp to False when threads are already converged (e.g. after wgmma wait_group). + """ + + _elect_one_commit: bool = False + _syncwarp_before_commit: bool = True + _elect_one_release: bool = False + _syncwarp_before_release: bool = True + + @staticmethod + def create( + *args, + elect_one_commit: bool = False, + syncwarp_before_commit: bool = True, + elect_one_release: bool = False, + syncwarp_before_release: bool = True, + **kwargs, + ): + obj = PipelineAsyncOg.create(*args, **kwargs) + object.__setattr__(obj, "__class__", PipelineAsync) + object.__setattr__(obj, "_elect_one_commit", elect_one_commit) + object.__setattr__(obj, "_syncwarp_before_commit", syncwarp_before_commit) + object.__setattr__(obj, "_elect_one_release", elect_one_release) + object.__setattr__(obj, "_syncwarp_before_release", syncwarp_before_release) + return obj + + @dsl_user_op + def producer_commit(self, state: PipelineState, *, loc=None, ip=None): + _call_with_elect_one( + PipelineAsyncOg.producer_commit, + self, + state, + self._elect_one_commit, + self._syncwarp_before_commit, + loc, + ip, + ) + + @dsl_user_op + def consumer_release(self, state: PipelineState, *, loc=None, ip=None): + _call_with_elect_one( + PipelineAsyncOg.consumer_release, + self, + state, + self._elect_one_release, + self._syncwarp_before_release, + loc, + ip, + ) + + # _w_index variants inherited from _PipelineIndexPhaseMixin, which delegate + # to producer_commit / consumer_release above. + + +# ── PipelineCpAsync ────────────────────────────────────────────────────────── + + +@dataclass(frozen=True) +class PipelineCpAsync(_PipelineIndexPhaseMixin, PipelineCpAsyncOg): + _elect_one_release: bool = False + _syncwarp_before_release: bool = True + + @staticmethod + def create( + *args, + elect_one_release: bool = False, + syncwarp_before_release: bool = True, + **kwargs, + ): + obj = PipelineCpAsyncOg.create(*args, **kwargs) + object.__setattr__(obj, "__class__", PipelineCpAsync) + object.__setattr__(obj, "_elect_one_release", elect_one_release) + object.__setattr__(obj, "_syncwarp_before_release", syncwarp_before_release) + return obj + + @dsl_user_op + def consumer_release(self, state: PipelineState, *, loc=None, ip=None): + _call_with_elect_one( + PipelineCpAsyncOg.consumer_release, + self, + state, + self._elect_one_release, + self._syncwarp_before_release, + loc, + ip, + ) + + # _w_index variants inherited from _PipelineIndexPhaseMixin. + + +# ── PipelineTmaAsync ──────────────────────────────────────────────────────── + + +@dataclass(frozen=True) +class PipelineTmaAsync(_PipelineIndexPhaseMixin, PipelineTmaAsyncOg): + """Override producer_acquire to take in extra_tx_count parameter.""" + + @dsl_user_op + def producer_acquire( + self, + state: PipelineState, + try_acquire_token: Optional[Boolean] = None, + extra_tx_count: int = 0, + *, + loc=None, + ip=None, + ): + """ + TMA producer commit conditionally waits on buffer empty and sets the transaction barrier for leader threadblocks. + """ + if_generate( + try_acquire_token is None or try_acquire_token == 0, + lambda: self.sync_object_empty.wait( + state.index, state.phase, loc=loc, ip=ip + ), + loc=loc, + ip=ip, + ) + if const_expr(extra_tx_count == 0): + self.sync_object_full.arrive( + state.index, self.producer_mask, loc=loc, ip=ip + ) + else: + tx_count = self.sync_object_full.tx_count + extra_tx_count + self.sync_object_full.arrive_and_expect_tx( + state.index, tx_count, loc=loc, ip=ip + ) + + +PipelineTmaAsync.create = _override_create(PipelineTmaAsyncOg, PipelineTmaAsync) + + +# ── PipelineTmaUmma ───────────────────────────────────────────────────────── + + +@dataclass(frozen=True) +class PipelineTmaUmma(_PipelineIndexPhaseMixin, PipelineTmaUmmaOg): + """Override producer_acquire to take in extra_tx_count parameter.""" + + @dsl_user_op + def producer_acquire( + self, + state: PipelineState, + try_acquire_token: Optional[Boolean] = None, + extra_tx_count: int = 0, + *, + loc=None, + ip=None, + ): + """ + TMA producer commit conditionally waits on buffer empty and sets the transaction barrier for leader threadblocks. + """ + if_generate( + try_acquire_token is None or try_acquire_token == 0, + lambda: self.sync_object_empty.wait( + state.index, state.phase, loc=loc, ip=ip + ), + loc=loc, + ip=ip, + ) + if const_expr(extra_tx_count == 0): + if_generate( + self.is_leader_cta, + lambda: self.sync_object_full.arrive( + state.index, self.producer_mask, loc=loc, ip=ip + ), + loc=loc, + ip=ip, + ) + else: + tx_count = self.sync_object_full.tx_count + extra_tx_count + if_generate( + self.is_leader_cta, + lambda: self.sync_object_full.arrive_and_expect_tx( + state.index, tx_count, loc=loc, ip=ip + ), + loc=loc, + ip=ip, + ) + + +PipelineTmaUmma.create = _override_create(PipelineTmaUmmaOg, PipelineTmaUmma) + + +# ── PipelineUmmaAsync ─────────────────────────────────────────────────────── + + +@dataclass(frozen=True) +class PipelineUmmaAsync(_PipelineIndexPhaseMixin, PipelineUmmaAsyncOg): + pass + + +PipelineUmmaAsync.create = _override_create(PipelineUmmaAsyncOg, PipelineUmmaAsync) + + +# ── PipelineAsyncUmma ─────────────────────────────────────────────────────── + + +@dataclass(frozen=True) +class PipelineAsyncUmma(_PipelineIndexPhaseMixin, PipelineAsyncUmmaOg): + pass + + +PipelineAsyncUmma.create = _override_create(PipelineAsyncUmmaOg, PipelineAsyncUmma) diff --git a/python/sglang/jit_kernel/flash_attn/cute/pyproject.toml b/python/sglang/jit_kernel/flash_attn/cute/pyproject.toml new file mode 100644 index 000000000..797b12f42 --- /dev/null +++ b/python/sglang/jit_kernel/flash_attn/cute/pyproject.toml @@ -0,0 +1,75 @@ +[build-system] +requires = ["setuptools>=75", "setuptools-scm>=8"] +build-backend = "setuptools.build_meta" + +[project] +name = "flash-attn-4" +dynamic = ["version"] +description = "Flash Attention CUTE (CUDA Template Engine) implementation" +readme = "README.md" +requires-python = ">=3.10" +license = {text = "BSD 3-Clause License"} +authors = [ + {name = "Tri Dao"}, +] +classifiers = [ + "Development Status :: 3 - Alpha", + "License :: OSI Approved :: BSD License", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", +] + +dependencies = [ + "nvidia-cutlass-dsl>=4.5.2", + "torch", + "einops", + "typing_extensions", + "apache-tvm-ffi>=0.1.5,<0.2", + "torch-c-dlpack-ext", + "quack-kernels>=0.5.0", +] + +[project.optional-dependencies] +cu13 = ["nvidia-cutlass-dsl[cu13]>=4.5.2"] +dev = [ + "pytest", + "pytest-xdist", + "ruff", +] + +[project.urls] +Homepage = "https://github.com/Dao-AILab/flash-attention" +Repository = "https://github.com/Dao-AILab/flash-attention" + +[tool.setuptools] +packages = ["flash_attn.cute"] +package-dir = {"flash_attn.cute" = "."} + +[tool.setuptools_scm] +root = "../.." +tag_regex = "^fa4-v(?P.+)$" +git_describe_command = "git describe --dirty --tags --long --match 'fa4-v*'" +fallback_version = "0.0.0" + +[[tool.uv.index]] +name = "pytorch-cu130" +url = "https://download.pytorch.org/whl/cu130" +explicit = true + +[tool.uv.sources] +torch = [ + { index = "pytorch-cu130", marker = "extra == 'cu13'" }, +] + +[tool.ruff] +line-length = 100 + +[tool.ruff.lint] +ignore = [ + "E731", # do not assign a lambda expression, use a def + "E741", # Do not use variables named 'I', 'O', or 'l' + "F841", # local variable is assigned to but never used + "D102", # Missing docstring in public methods +] diff --git a/python/sglang/jit_kernel/flash_attn/cute/seqlen_info.py b/python/sglang/jit_kernel/flash_attn/cute/seqlen_info.py new file mode 100644 index 000000000..d68e52457 --- /dev/null +++ b/python/sglang/jit_kernel/flash_attn/cute/seqlen_info.py @@ -0,0 +1,331 @@ +from dataclasses import dataclass +from typing import Optional + +import cutlass +import cutlass.cute as cute +from cutlass import Int32, const_expr +from quack import copy_utils + +""" +This consolidates all the info related to sequence length. This is so that we can do all +the gmem reads once at the beginning of each tile, rather than having to repeat these reads +to compute various things like n_block_min, n_block_max, etc. +""" + + +@dataclass(frozen=True) +class SeqlenInfo: + offset: Int32 + offset_padded: Int32 + seqlen: Int32 + has_cu_seqlens: cutlass.Constexpr[bool] = False + + @staticmethod + def create( + batch_idx: Int32, + seqlen_static: Int32, + cu_seqlens: Optional[cute.Tensor] = None, + seqused: Optional[cute.Tensor] = None, + tile: cutlass.Constexpr[int] = 128, + ): + offset = 0 if const_expr(cu_seqlens is None) else cu_seqlens[batch_idx] + offset_padded = ( + 0 + if const_expr(cu_seqlens is None) + # Add divby so that the compiler knows the alignment when moving by offset_padded + else cute.assume((offset + batch_idx * tile) // tile * tile, divby=tile) + ) + if const_expr(seqused is not None): + seqlen = seqused[batch_idx] + elif const_expr(cu_seqlens is not None): + seqlen = cu_seqlens[batch_idx + 1] - cu_seqlens[batch_idx] + else: + seqlen = seqlen_static + return SeqlenInfo( + offset, offset_padded, seqlen, has_cu_seqlens=cu_seqlens is not None + ) + + def offset_batch( + self, + mT: cute.Tensor, + batch_idx: Int32, + dim: int, + padded: cutlass.Constexpr[bool] = False, + multiple: int = 1, + ) -> cute.Tensor: + """Offset a tensor by batch index. batch dim is at position `dim`, seqlen is at dim=0.""" + if const_expr(not self.has_cu_seqlens): + idx = (None,) * dim + (batch_idx,) + (None,) * (cute.rank(mT) - 1 - dim) + return mT[idx] + else: + off = multiple * ( + self.offset if const_expr(not padded) else self.offset_padded + ) + offset = off if const_expr(cute.rank(mT.shape[0]) == 1) else (0, off) + idx = (offset,) + (None,) * (cute.rank(mT) - 1) + return cute.domain_offset(idx, mT) + + +@dataclass(frozen=True) +class SeqlenInfoQK: + offset_q: Int32 + offset_k: Int32 + padded_offset_q: Int32 + padded_offset_k: Int32 + seqlen_q: Int32 + seqlen_k: Int32 + m_block_offset: Int32 + block_idx_offset: Int32 + num_n_blocks: Int32 + has_cu_seqlens_q: cutlass.Constexpr[bool] + has_cu_seqlens_k: cutlass.Constexpr[bool] + has_seqused_q: cutlass.Constexpr[bool] + has_seqused_k: cutlass.Constexpr[bool] + + @staticmethod + def create( + batch_idx: Int32, + seqlen_q_static: Int32, + seqlen_k_static: Int32, + mCuSeqlensQ: Optional[cute.Tensor] = None, + mCuSeqlensK: Optional[cute.Tensor] = None, + mSeqUsedQ: Optional[cute.Tensor] = None, + mSeqUsedK: Optional[cute.Tensor] = None, + mCuTotalMBlocks: Optional[cute.Tensor] = None, + mCuBlockIdxOffsets: Optional[cute.Tensor] = None, + tile_m: cutlass.Constexpr[Int32] = 128, + tile_n: cutlass.Constexpr[Int32] = 128, + ): + offset_q = 0 if const_expr(mCuSeqlensQ is None) else mCuSeqlensQ[batch_idx] + offset_k = 0 if const_expr(mCuSeqlensK is None) else mCuSeqlensK[batch_idx] + padded_offset_q = ( + 0 + if const_expr(mCuSeqlensQ is None) + else cute.assume( + (offset_q + batch_idx * tile_m) // tile_m * tile_m, divby=tile_m + ) + ) + padded_offset_k = ( + 0 + if const_expr(mCuSeqlensK is None) + else cute.assume( + (offset_k + batch_idx * tile_n) // tile_n * tile_n, divby=tile_n + ) + ) + if const_expr(mSeqUsedQ is not None): + seqlen_q = mSeqUsedQ[batch_idx] + else: + seqlen_q = ( + seqlen_q_static + if const_expr(mCuSeqlensQ is None) + else mCuSeqlensQ[batch_idx + 1] - offset_q + ) + if const_expr(mSeqUsedK is not None): + seqlen_k = mSeqUsedK[batch_idx] + else: + seqlen_k = ( + seqlen_k_static + if const_expr(mCuSeqlensK is None) + else mCuSeqlensK[batch_idx + 1] - offset_k + ) + m_block_offset = ( + 0 if const_expr(mCuTotalMBlocks is None) else mCuTotalMBlocks[batch_idx] + ) + num_n_blocks = (seqlen_k + tile_n - 1) // tile_n + block_idx_offset = ( + mCuBlockIdxOffsets[batch_idx] + if const_expr(mCuBlockIdxOffsets is not None) + else m_block_offset * num_n_blocks + ) + return SeqlenInfoQK( + offset_q, + offset_k, + padded_offset_q, + padded_offset_k, + seqlen_q, + seqlen_k, + m_block_offset, + block_idx_offset, + num_n_blocks, + has_cu_seqlens_q=mCuSeqlensQ is not None, + has_cu_seqlens_k=mCuSeqlensK is not None, + has_seqused_q=mSeqUsedQ is not None, + has_seqused_k=mSeqUsedK is not None, + ) + + def offset_batch_Q( + self, + mQ: cute.Tensor, + batch_idx: Int32, + dim: int, + padded: cutlass.Constexpr[bool] = False, + ragged: cutlass.Constexpr[bool] = False, + ) -> cute.Tensor: + """Seqlen must be the first dimension of mQ""" + if const_expr(not ragged): + if const_expr(not self.has_cu_seqlens_q): + idx = (None,) * dim + (batch_idx,) + (None,) * (cute.rank(mQ) - 1 - dim) + return mQ[idx] + else: + offset_q = ( + self.offset_q if const_expr(not padded) else self.padded_offset_q + ) + offset_q = ( + offset_q + if const_expr(cute.rank(mQ.shape[0]) == 1) + else (None, offset_q) + ) + idx = (offset_q,) + (None,) * (cute.rank(mQ) - 1) + return cute.domain_offset(idx, mQ) + else: + if const_expr(not self.has_cu_seqlens_q): + offset_q = 0 + idx = (None,) * dim + (batch_idx,) + (None,) * (cute.rank(mQ) - 1 - dim) + mQ = mQ[idx] + else: + offset_q = ( + self.offset_q if const_expr(not padded) else self.padded_offset_q + ) + if const_expr(cute.rank(mQ.shape[0]) == 1): + return copy_utils.offset_ragged_tensor( + mQ, offset_q, self.seqlen_q, ragged_dim=0, ptr_shift=True + ) + else: # PackGQA + assert cute.rank(mQ.shape[0]) == 2 + # Unpack before calling offset_ragged_tensor, then pack + idx = ((None, None),) + (None,) * (cute.rank(mQ) - 1) + mQ = mQ[idx] + mQ = copy_utils.offset_ragged_tensor( + mQ, offset_q, self.seqlen_q, ragged_dim=1, ptr_shift=True + ) + return cute.group_modes(mQ, 0, 2) + + def offset_batch_K( + self, + mK: cute.Tensor, + batch_idx: Int32, + dim: int, + padded: cutlass.Constexpr[bool] = False, + ragged: cutlass.Constexpr[bool] = False, + multiple: int = 1, + ) -> cute.Tensor: + """Seqlen must be the first dimension of mK""" + if const_expr(not ragged): + if const_expr(not self.has_cu_seqlens_k): + idx = (None,) * dim + (batch_idx,) + (None,) * (cute.rank(mK) - 1 - dim) + return mK[idx] + else: + offset_k = ( + self.offset_k if const_expr(not padded) else self.padded_offset_k + ) + offset_k *= multiple + idx = (offset_k,) + (None,) * (cute.rank(mK) - 1) + return cute.domain_offset(idx, mK) + else: + if const_expr(not self.has_cu_seqlens_k): + offset_k = 0 + idx = (None,) * dim + (batch_idx,) + (None,) * (cute.rank(mK) - 1 - dim) + mK = mK[idx] + else: + offset_k = ( + self.offset_k if const_expr(not padded) else self.padded_offset_k + ) + offset_k *= multiple + return copy_utils.offset_ragged_tensor( + mK, offset_k, self.seqlen_k, ragged_dim=0, ptr_shift=True + ) + + +@dataclass(frozen=True) +class SeqlenInfoQKNewK: + """Sequence length info for append-KV with left-padding and new K support. + + Extends SeqlenInfoQK with: + - leftpad_k: left padding for K (tokens to skip at the start of the KV cache) + - offset_k_new: offset into the new K tensor + - seqlen_k_og: original K length (before appending new K), excluding leftpad + - seqlen_k_new: length of new K to append + - seqlen_k: total K length (seqlen_k_og + seqlen_k_new) + - seqlen_rotary: position for rotary embedding computation + """ + + leftpad_k: Int32 + offset_q: Int32 + offset_k: Int32 + offset_k_new: Int32 + seqlen_q: Int32 + seqlen_k_og: Int32 + seqlen_k_new: Int32 + seqlen_k: Int32 + seqlen_rotary: Int32 + + @staticmethod + def create( + batch_idx: Int32, + seqlen_q_static: Int32, + seqlen_k_static: Int32, + shape_K_new_0: Int32, + mCuSeqlensQ: Optional[cute.Tensor] = None, + mCuSeqlensK: Optional[cute.Tensor] = None, + mCuSeqlensKNew: Optional[cute.Tensor] = None, + mSeqUsedQ: Optional[cute.Tensor] = None, + mSeqUsedK: Optional[cute.Tensor] = None, + mLeftpadK: Optional[cute.Tensor] = None, + mSeqlensRotary: Optional[cute.Tensor] = None, + ): + leftpad_k = 0 if const_expr(mLeftpadK is None) else mLeftpadK[batch_idx] + offset_q = 0 if const_expr(mCuSeqlensQ is None) else mCuSeqlensQ[batch_idx] + if const_expr(mCuSeqlensK is not None): + offset_k = mCuSeqlensK[batch_idx] + leftpad_k + else: + offset_k = leftpad_k if const_expr(mCuSeqlensQ is not None) else 0 + offset_k_new = ( + 0 if const_expr(mCuSeqlensKNew is None) else mCuSeqlensKNew[batch_idx] + ) + # seqlen_q + if const_expr(mSeqUsedQ is not None): + seqlen_q = mSeqUsedQ[batch_idx] + elif const_expr(mCuSeqlensQ is not None): + seqlen_q = mCuSeqlensQ[batch_idx + 1] - mCuSeqlensQ[batch_idx] + else: + seqlen_q = seqlen_q_static + # seqlen_k_og: original K length (excluding leftpad) + if const_expr(mSeqUsedK is not None): + seqlen_k_og = mSeqUsedK[batch_idx] - leftpad_k + elif const_expr(mCuSeqlensK is not None): + seqlen_k_og = ( + mCuSeqlensK[batch_idx + 1] - mCuSeqlensK[batch_idx] - leftpad_k + ) + else: + seqlen_k_og = ( + seqlen_k_static - leftpad_k + if const_expr(mCuSeqlensQ is not None) + else seqlen_k_static + ) + # seqlen_k_new + if const_expr(mCuSeqlensKNew is None): + seqlen_k_new = 0 if const_expr(mCuSeqlensQ is None) else shape_K_new_0 + else: + seqlen_k_new = mCuSeqlensKNew[batch_idx + 1] - mCuSeqlensKNew[batch_idx] + seqlen_k = ( + seqlen_k_og + if const_expr(mCuSeqlensQ is None) + else seqlen_k_og + seqlen_k_new + ) + + # seqlen_rotary: defaults to seqlen_k_og + leftpad_k unless explicitly provided + if const_expr(mSeqlensRotary is not None): + seqlen_rotary = mSeqlensRotary[batch_idx] + else: + seqlen_rotary = seqlen_k_og + leftpad_k + return SeqlenInfoQKNewK( + leftpad_k, + offset_q, + offset_k, + offset_k_new, + seqlen_q, + seqlen_k_og, + seqlen_k_new, + seqlen_k, + seqlen_rotary, + ) diff --git a/python/sglang/jit_kernel/flash_attn/cute/shearing_bias.py b/python/sglang/jit_kernel/flash_attn/cute/shearing_bias.py new file mode 100644 index 000000000..955339cac --- /dev/null +++ b/python/sglang/jit_kernel/flash_attn/cute/shearing_bias.py @@ -0,0 +1,573 @@ +# Copyright (c) 2026, Colfax International. + +import math +from functools import partial +from typing import Callable, Optional + +import cuda.bindings.driver as cuda +import cutlass +import cutlass.cute as cute +from cutlass import Float32, Int32, const_expr + +from sglang.jit_kernel.flash_attn.cute.block_info import BlockInfo +from sglang.jit_kernel.flash_attn.cute.copy_utils import tiled_copy_2d +from sglang.jit_kernel.flash_attn.cute.cute_dsl_utils import assume_tensor_aligned +from sglang.jit_kernel.flash_attn.cute.pack_gqa import pack_gqa_layout +from sglang.jit_kernel.flash_attn.cute.seqlen_info import SeqlenInfoQK +from sglang.jit_kernel.flash_attn.cute.tile_scheduler import ( + ParamsBase, + SingleTileScheduler, + SingleTileVarlenScheduler, + TileSchedulerArguments, +) +from sglang.jit_kernel.flash_attn.cute.utils import get_batch_from_cu_tensor + + +class ShearingBias: + def __init__( + self, + rel_extent: int = 512, + is_causal: bool = True, + is_local: bool = False, + pack_gqa: bool = False, + qhead_per_kvhead: cutlass.Constexpr[int] = 1, + rows_per_cta: int = 4, + tile_m: int = 128, + max_m_blocks_leq_one: bool = False, + use_pdl: bool = False, + clamp_subtiles: bool = True, + ): + self.is_causal = is_causal + self.is_local = is_local + assert is_causal or is_local, "Doesn't make sense otherwise" + self.pack_gqa = pack_gqa + self.qhead_per_kvhead = qhead_per_kvhead + if self.pack_gqa: + assert ( + 128 % self.qhead_per_kvhead == 0 + ), "pack_gqa only supported when qhead_per_kvhead divides 128" + self.qhead_per_kvhead_packgqa = qhead_per_kvhead if self.pack_gqa else 1 + self.rel_extent = rel_extent + assert rel_extent % 128 == 0 + self.rel_extent_padded = rel_extent + 256 + self.num_bias_blocks_padded = (self.rel_extent_padded) // 128 + # tuneable parameters + assert rows_per_cta % 4 == 0 + self.rows_per_cta = rows_per_cta + self.num_threads = self.rows_per_cta * 32 + self.cta_tiler = (self.rows_per_cta, self.rel_extent) + self.cta_out_tiler = (self.rows_per_cta, self.rel_extent_padded) + + self.buffer_align_bytes = 1024 + + self.max_m_blocks_leq_one = max_m_blocks_leq_one + self.use_pdl = use_pdl + + # only used with block packed scheduling + self.tile_m = tile_m + # Shrink the subtile grid dim to the rows a block can actually hold + # (decode blocks hold qhead_per_kvhead*seqlen_q rows, not tile_m). + self.clamp_subtiles = clamp_subtiles + + @cute.jit + def __call__( + self, + mPreBias: cute.Tensor, # (b, s_q, h, rel_extent) or (total_q, h, rel_extent) + mBias: cute.Tensor, # (b, s_q, h, rel_extent_padded) or (total_q, h, rel_extent_padded) + max_seqlen_q: Int32 | int, + max_seqlen_k: Int32 | int, + mCuSeqlensQ: Optional[cute.Tensor] = None, + mCuSeqlensK: Optional[cute.Tensor] = None, + mSeqUsedQ: Optional[cute.Tensor] = None, + mSeqUsedK: Optional[cute.Tensor] = None, + mCuTotalMBlocks: Optional[cute.Tensor] = None, + mBlocksToBatchIdx: Optional[cute.Tensor] = None, + window_size_left: Int32 | int | None = None, + window_size_right: Int32 | int | None = None, + # Always keep stream as the last parameter (EnvStream: obtained implicitly via TVM FFI). + stream: cuda.CUstream = None, + ): + assert mPreBias.element_type == mBias.element_type + self.bias_dtype = mBias.element_type + + right_pad_value = -Float32.inf + left_pad_value = ( + -Float32.inf if const_expr(window_size_left is not None) else 0.0 + ) + + self.vec_size = 32 // self.bias_dtype.width + self.cols_per_iter = 32 * self.vec_size + assert self.vec_size <= 2 + assert 128 % self.cols_per_iter == 0 + + max_seqlen_k = Int32(max_seqlen_k) + if const_expr(window_size_left is not None): + window_size_left = Int32(window_size_left) + if const_expr(window_size_right is not None): + window_size_right = Int32(window_size_right) + + mPreBias, mBias = [assume_tensor_aligned(t) for t in (mPreBias, mBias)] + # (s_q, rel_extent, h, b) or (total_q, rel_extent, h) + Q_layout_transpose = ( + [1, 3, 2, 0] if const_expr(mCuSeqlensQ is None) else [0, 2, 1] + ) + mPreBias, mBias = [ + cute.make_tensor(t.iterator, cute.select(t.layout, mode=Q_layout_transpose)) + for t in (mPreBias, mBias) + ] + + if const_expr(self.pack_gqa): + nheads_kv = mBias.shape[2] // self.qhead_per_kvhead + mPreBias, mBias = [ + pack_gqa_layout(t, self.qhead_per_kvhead, nheads_kv, head_idx=2) + for t in (mPreBias, mBias) + ] + + # SMEM layouts + prebias_tile_shape = (self.rows_per_cta, self.rel_extent) + bias_tile_shape = (self.rows_per_cta, self.rel_extent_padded) + + sPreBias_layout = cute.make_ordered_layout(prebias_tile_shape, order=(1, 0)) + sBias_layout = cute.make_ordered_layout(bias_tile_shape, order=(1, 0)) + sPreBias_size = cute.cosize(sPreBias_layout) + sBias_size = cute.cosize(sBias_layout) + + in_major_size = math.gcd(256, self.rel_extent) + assert in_major_size % 128 == 0 + self.num_g2s_threads = self.num_threads if in_major_size == 256 else 128 + g2s_tiled_copy = tiled_copy_2d( + self.bias_dtype, + math.gcd(256, self.rel_extent), + self.num_g2s_threads, + is_async=True, + ) + + out_major_size = math.gcd(256, self.rel_extent_padded) + assert out_major_size % 128 == 0 + self.num_s2g_threads = self.num_threads if out_major_size == 256 else 128 + s2g_tiled_copy = tiled_copy_2d( + self.bias_dtype, + math.gcd(256, self.rel_extent_padded), + self.num_s2g_threads, + ) + + @cute.struct + class SharedStorage: + sPreBias: cute.struct.Align[ + cute.struct.MemRange[self.bias_dtype, sPreBias_size], + self.buffer_align_bytes, + ] + sBias: cute.struct.Align[ + cute.struct.MemRange[self.bias_dtype, sBias_size], + self.buffer_align_bytes, + ] + + self.shared_storage = SharedStorage + + varlen_q = mCuSeqlensQ is not None or mSeqUsedQ is not None + self.use_block_packed_scheduling = ( + mCuTotalMBlocks is not None + and mCuSeqlensQ is not None + and not self.max_m_blocks_leq_one + # and False + ) + + if const_expr(varlen_q and not self.max_m_blocks_leq_one): + if const_expr(self.use_block_packed_scheduling): + TileScheduler = SingleTileScheduler + else: + TileScheduler = SingleTileVarlenScheduler + else: + TileScheduler = SingleTileScheduler + + batch_size = ( + cute.size(mPreBias.shape[3]) + if const_expr(mCuSeqlensQ is None) + else cute.size(mCuSeqlensQ.shape[0] - 1) + ) + eff_seqlen_q = ( + max_seqlen_q + if const_expr(not self.pack_gqa) + else max_seqlen_q * self.qhead_per_kvhead + ) + total_q = ( + cute.size(mPreBias.shape[0]) + if const_expr(mCuSeqlensQ is not None) + else cute.size(mPreBias.shape[0]) * cute.size(mPreBias.shape[3]) + ) + # same formula as in varlen scheduler -- only used with block packed scheduling + total_blocks_max = (total_q + batch_size * (self.tile_m - 1)) // self.tile_m + + num_blocks_for_sched = ( + cute.ceil_div(eff_seqlen_q, self.rows_per_cta) + if const_expr(not self.use_block_packed_scheduling) + else total_blocks_max + ) + if const_expr(not self.use_block_packed_scheduling): + batch_size_for_sched = batch_size + elif const_expr(self.clamp_subtiles): + # A block covers at most min(tile_m, eff_seqlen_q) valid rows; subtiles + # past that would fail the per-row seqlen guards and exit immediately. + batch_size_for_sched = cute.ceil_div( + min(self.tile_m, eff_seqlen_q), self.rows_per_cta + ) + else: + batch_size_for_sched = self.tile_m // self.rows_per_cta + + tile_sched_args = TileSchedulerArguments( + num_blocks_for_sched, + cute.size(mPreBias.shape[2]), + batch_size_for_sched, + 1, + 1, + 1, + 1, + total_q=total_q, + tile_shape_mn=self.cta_tiler, + mCuSeqlensQ=mCuSeqlensQ, + mSeqUsedQ=mSeqUsedQ, + qhead_per_kvhead_packgqa=self.qhead_per_kvhead_packgqa, + element_size=self.bias_dtype.width // 8, + ) + tile_sched_params = TileScheduler.to_underlying_arguments(tile_sched_args) + self.tile_scheduler_cls = TileScheduler + grid_dim = TileScheduler.get_grid_shape(tile_sched_params) + + self.kernel( + mPreBias, + mBias, + left_pad_value, + right_pad_value, + max_seqlen_k, + mCuSeqlensQ, + mCuSeqlensK, + mSeqUsedQ, + mSeqUsedK, + mCuTotalMBlocks, + mBlocksToBatchIdx, + sPreBias_layout, + sBias_layout, + window_size_left, + window_size_right, + g2s_tiled_copy, + s2g_tiled_copy, + SharedStorage, + tile_sched_params, + ).launch( + grid=grid_dim, + block=(self.num_threads, 1, 1), + stream=stream, + use_pdl=self.use_pdl, + ) + + @cute.kernel + def kernel( + self, + mPreBias: cute.Tensor, + mBias: cute.Tensor, + left_pad_value: cutlass.Float32, + right_pad_value: cutlass.Float32, + max_seqlen_k: Int32, + mCuSeqlensQ: Optional[cute.Tensor], + mCuSeqlensK: Optional[cute.Tensor], + mSeqUsedQ: Optional[cute.Tensor], + mSeqUsedK: Optional[cute.Tensor], + mCuTotalMBlocks: Optional[cute.Tensor], + mBlocksToBatchIdx: Optional[cute.Tensor], + sPreBias_layout: cute.ComposedLayout | cute.Layout, + sBias_layout: cute.ComposedLayout | cute.Layout, + window_size_left: Optional[Int32], + window_size_right: Optional[Int32], + g2s_tiled_copy: cute.TiledCopy, + s2g_tiled_copy: cute.TiledCopy, + SharedStorage: cutlass.Constexpr[Callable], + tile_sched_params: ParamsBase, + ): + tidx, _, _ = cute.arch.thread_idx() + warp_idx = cute.arch.make_warp_uniform(cute.arch.warp_idx()) + lane_idx = cute.arch.lane_idx() + + smem = cutlass.utils.SmemAllocator() + storage = smem.allocate(SharedStorage) + + sPreBias = storage.sPreBias.get_tensor(sPreBias_layout) + sBias = storage.sBias.get_tensor(sBias_layout) + + TileSchedulerCls = partial(self.tile_scheduler_cls.create, tile_sched_params) + tile_scheduler = TileSchedulerCls() + work_tile = tile_scheduler.initial_work_tile_info() + # if pack_gqa, head_idx means head_idx_kv + m_block, head_idx, batch_idx, _ = work_tile.tile_idx + subtile_idx = batch_idx if const_expr(self.use_block_packed_scheduling) else 0 + + if const_expr(self.use_pdl): + cute.arch.griddepcontrol_wait() + cute.arch.griddepcontrol_launch_dependents() + + is_valid_tile = work_tile.is_valid_tile + if const_expr(self.use_block_packed_scheduling): + batch_size = mCuTotalMBlocks.shape[0] - 1 + is_valid_tile = m_block < mCuTotalMBlocks[batch_size] + + if is_valid_tile: + if const_expr(self.use_block_packed_scheduling): + if const_expr(mBlocksToBatchIdx is not None): + batch_idx = mBlocksToBatchIdx[m_block] + else: + batch_idx = get_batch_from_cu_tensor(m_block, mCuTotalMBlocks) + # get local m_block for batch + m_block -= mCuTotalMBlocks[batch_idx] + m_block = m_block * (self.tile_m // self.rows_per_cta) + subtile_idx + seqlen_info = SeqlenInfoQK.create( + batch_idx=batch_idx, + seqlen_q_static=( + mPreBias.shape[0] + if const_expr(not self.pack_gqa) + else mPreBias.shape[0][1] + ), + seqlen_k_static=max_seqlen_k, + mCuSeqlensQ=mCuSeqlensQ, + mCuSeqlensK=mCuSeqlensK, + mSeqUsedQ=mSeqUsedQ, + mSeqUsedK=mSeqUsedK, + ) + + block_info = BlockInfo( + 128, + 128, + self.is_causal, + self.is_local, + window_size_left=window_size_left, + window_size_right=window_size_right, + qhead_per_kvhead_packgqa=self.qhead_per_kvhead_packgqa, + ) + + # (seqlen, rel_extent) or ((seqlen, qhead_per_kvhead), rel_extent) + mPreBias_cur = seqlen_info.offset_batch_Q(mPreBias, batch_idx, dim=3)[ + None, None, head_idx + ] + # (rows_per_cta, rel_extent) + gPreBias = cute.local_tile(mPreBias_cur, self.cta_tiler, (m_block, 0)) + cPreBias = cute.make_identity_tensor(self.cta_tiler) + + g2s_thr_copy = g2s_tiled_copy.get_slice(tidx) + + # (V, M, N) + tBgPreBias = g2s_thr_copy.partition_S(gPreBias) + tBsPreBias = g2s_thr_copy.partition_D(sPreBias) + tBcPreBias = g2s_thr_copy.partition_S(cPreBias) + + if ( + const_expr(self.num_g2s_threads == self.num_threads) + or warp_idx < self.num_g2s_threads // 32 + ): + num_rows_per_load = tBgPreBias.shape[1] + for m in cutlass.range_constexpr(num_rows_per_load): + local_m_idx = tBcPreBias[0, m, 0][0] + load_m_idx = local_m_idx + m_block * self.rows_per_cta + local_m_idx_in_bounds = ( + const_expr(self.rows_per_cta % 8 == 0) + or local_m_idx < self.rows_per_cta + ) + load_m_idx_in_bounds = ( + load_m_idx // self.qhead_per_kvhead_packgqa + < seqlen_info.seqlen_q + ) + if local_m_idx_in_bounds and load_m_idx_in_bounds: + cute.copy( + g2s_tiled_copy, + tBgPreBias[None, m, None], + tBsPreBias[None, m, None], + ) + + cute.arch.cp_async_commit_group() + + # Convention: inclusive min, exclusive max + m_idx = m_block * self.rows_per_cta + warp_idx + attn_m_block = m_idx // 128 + + _, attn_n_block_max = block_info.get_n_block_min_max( + seqlen_info, + attn_m_block, + ) + + n_idx_left, n_idx_right = block_info.get_n_idx_left_right( + seqlen_info, m_idx + ) + num_bias_vals = n_idx_right - max(n_idx_left, n_idx_right - self.rel_extent) + is_even = n_idx_right % 2 == 0 + + # get bias block and idx bounds for row + n_block_for_rel0 = (n_idx_right - 1) // 128 # inclusive + bias_block_idx_right = 1 + max( + self.rel_extent_padded // 128 - (attn_n_block_max - n_block_for_rel0), 0 + ) + bias_idx_right = ( + (bias_block_idx_right - 1) * 128 + ((n_idx_right - 1) % 128) + 1 + ) + bias_idx_left = max(0, bias_idx_right - num_bias_vals) + bias_block_idx_left = bias_idx_left // 128 + # num_bias_blocks = self.num_bias_blocks_padded - bias_block_idx_left + # num_right_padding_blocks = 0 + num_bias_blocks = ( + bias_block_idx_right - bias_block_idx_left if num_bias_vals > 0 else 0 + ) + num_right_padding_blocks = ( + self.num_bias_blocks_padded - bias_block_idx_right + if num_bias_vals > 0 + else self.num_bias_blocks_padded + ) + # might help compiler unroll loops + num_bias_blocks = min(num_bias_blocks, self.num_bias_blocks_padded) + num_right_padding_blocks = min( + num_right_padding_blocks, self.num_bias_blocks_padded + ) + + sPreBias_row = cute.flat_divide( + sPreBias[(warp_idx, None)], (self.vec_size,) + ) + sBias_row = cute.flat_divide(sBias[(warp_idx, None)], (self.vec_size,)) + sBias_row_vec4 = cute.flat_divide(sBias[(warp_idx, None)], (4,)) + + bias_idx = ( + self.rel_extent_padded + lane_idx * self.vec_size - self.cols_per_iter + ) + + cute.arch.cp_async_wait_group(0) + cute.arch.sync_threads() + + if m_idx // self.qhead_per_kvhead_packgqa < seqlen_info.seqlen_q: + # We can try handling right padding separately + for i in cutlass.range(num_right_padding_blocks, unroll_full=True): + bias_frg = cute.make_rmem_tensor((4,), dtype=self.bias_dtype) + bias_frg.fill(self.bias_dtype(right_pad_value)) + bias_right_pad_idx = ( + self.num_bias_blocks_padded - 1 - i + ) * 32 + lane_idx + cute.autovec_copy( + bias_frg, sBias_row_vec4[None, bias_right_pad_idx] + ) + bias_idx -= 128 + + for _ in cutlass.range(num_bias_blocks, unroll_full=True): + # 2 subblocks for half bias dtype + for _ in cutlass.range_constexpr(128 // self.cols_per_iter): + prebias_idx = bias_idx_right - 1 - bias_idx + + # (vec_size, lower/upper) + prebias_frg = cute.make_rmem_tensor( + (self.vec_size, self.vec_size), dtype=self.bias_dtype + ) + + in_bounds = ( + prebias_idx >= 0 + and prebias_idx - self.vec_size + 1 < num_bias_vals + ) + prebias_idx_lower = ( + prebias_idx - 1 if is_even else max(prebias_idx - 2, 0) + ) + prebias_idx_upper = ( + prebias_idx - 1 + if is_even + else min(prebias_idx, self.rel_extent - 2) + ) + + if in_bounds: + cute.autovec_copy( + sPreBias_row[None, prebias_idx_lower // 2], + prebias_frg[None, 0], + ) + if const_expr(self.vec_size == 2) and not is_even: + cute.autovec_copy( + sPreBias_row[None, prebias_idx_upper // 2], + prebias_frg[None, 1], + ) + + bias_frg = cute.make_rmem_tensor( + (self.vec_size,), dtype=self.bias_dtype + ) + bias_frg.fill(self.bias_dtype(left_pad_value)) + + if const_expr(self.vec_size == 1): + if in_bounds: + bias_frg[0] = prebias_frg[0, 0] + elif prebias_idx < 0: + bias_frg.fill(self.bias_dtype(right_pad_value)) + else: + if in_bounds: + if is_even: + # reverse: [prebias_idx, prebias_idx-1] = bias frg + bias_frg[0] = prebias_frg[1, 0] + bias_frg[1] = prebias_frg[0, 0] + else: + # lower = [2x-2, 2x-1], upper = [2x, 2x+1], 2x = prebias_idx + # want bias = [2x, 2x-1] + bias_frg[0] = prebias_frg[0, 1] + bias_frg[1] = prebias_frg[1, 0] + elif prebias_idx < 0: + bias_frg.fill(self.bias_dtype(right_pad_value)) + + cute.autovec_copy(bias_frg, sBias_row[None, bias_idx // 2]) + bias_idx -= self.cols_per_iter + cute.arch.sync_warp() + + # Handle edge cases. For N = rel_extent: + # [0, -1], -1 at bias_idx_right and [N, N-1], N-1 at bias_idx_left + if not is_even and num_bias_vals > 0: + sBias[(warp_idx, bias_idx_right)] = self.bias_dtype(right_pad_value) + if bias_idx_left - 1 >= 0: + sBias[(warp_idx, bias_idx_left - 1)] = self.bias_dtype( + left_pad_value + ) + + num_left_padding_blocks = min( + self.num_bias_blocks_padded + - num_bias_blocks + - num_right_padding_blocks, + self.num_bias_blocks_padded, + ) + for i in cutlass.range(num_left_padding_blocks, unroll_full=True): + bias_left_pad_idx = i * 32 + lane_idx + bias_frg = cute.make_rmem_tensor((4,), dtype=self.bias_dtype) + bias_frg.fill(self.bias_dtype(left_pad_value)) + cute.autovec_copy(bias_frg, sBias_row_vec4[None, bias_left_pad_idx]) + + cute.arch.sync_threads() + + s2g_thr_copy = s2g_tiled_copy.get_slice(tidx) + + # (seqlen, rel_extent_padded) + mBias_cur = seqlen_info.offset_batch_Q(mBias, batch_idx, dim=3)[ + None, None, head_idx + ] + # (rows_per_cta, rel_extent_padded) + gBias = cute.local_tile(mBias_cur, self.cta_out_tiler, (m_block, 0)) + cBias = cute.make_identity_tensor(self.cta_out_tiler) + + # (V, M, N) + tBsBias = s2g_thr_copy.partition_S(sBias) + tBgBias = s2g_thr_copy.partition_D(gBias) + tBcBias = s2g_thr_copy.partition_D(cBias) + + if ( + const_expr(self.num_s2g_threads == self.num_threads) + or warp_idx < self.num_s2g_threads // 32 + ): + num_rows_per_store = tBgBias.shape[1] + for m in cutlass.range_constexpr(num_rows_per_store): + local_m_idx = tBcBias[0, m, 0][0] + store_m_idx = local_m_idx + m_block * self.rows_per_cta + local_m_idx_in_bounds = ( + const_expr(self.rows_per_cta % 8 == 0) + or local_m_idx < self.rows_per_cta + ) + store_m_idx_in_bounds = ( + store_m_idx // self.qhead_per_kvhead_packgqa + < seqlen_info.seqlen_q + ) + if local_m_idx_in_bounds and store_m_idx_in_bounds: + cute.copy( + s2g_tiled_copy, + tBsBias[None, m, None], + tBgBias[None, m, None], + ) diff --git a/python/sglang/jit_kernel/flash_attn/cute/sm100_hd256_2cta_fmha_forward.py b/python/sglang/jit_kernel/flash_attn/cute/sm100_hd256_2cta_fmha_forward.py new file mode 100644 index 000000000..ac3855a31 --- /dev/null +++ b/python/sglang/jit_kernel/flash_attn/cute/sm100_hd256_2cta_fmha_forward.py @@ -0,0 +1,2072 @@ +# Copyright (c) 2025, Siyu Wang, Shengbin Di, Yuxi Chi, Johnsonms, Linfeng Zheng, Haoyan Huang, Lanbo Li, Yun Zhong, Man Yuan, Minmin Sun, Yong Li, Wei Lin. + +import math +from typing import Optional, Tuple + +import cuda.bindings.driver as cuda +import cutlass +import cutlass.cute as cute +import cutlass.cute.nvgpu.tcgen05 as tcgen05 +import cutlass.pipeline as pipeline +import cutlass.utils as utils +import cutlass.utils.blackwell_helpers as sm100_utils +from cutlass.cute.typing import Float32, Int32, Int64 +from cutlass.utils import ClcDynamicPersistentTileScheduler + +from sglang.jit_kernel.flash_attn.cute.flash_fwd_sm100 import ( + _TUNING_CONFIG, + DescaleTensors, +) +from sglang.jit_kernel.flash_attn.cute.mask import Sm100FusedMask as FusedMask +from sglang.jit_kernel.flash_attn.cute.tile_scheduler import ( + SM100_TMEM_CAPACITY_COLUMNS, + ClcState, +) +from sglang.jit_kernel.flash_attn.cute.tile_scheduler import ( + Sm100FmhaClcDynamicTileScheduler as FmhaClcDynamicTileScheduler, +) +from sglang.jit_kernel.flash_attn.cute.tile_scheduler import ( + Sm100FmhaClcDynamicTileSchedulerParams as FmhaClcDynamicTileSchedulerParams, +) +from sglang.jit_kernel.flash_attn.cute.tile_scheduler import ( + Sm100FmhaStaticTileScheduler as FmhaStaticTileScheduler, +) +from sglang.jit_kernel.flash_attn.cute.tile_scheduler import ( + Sm100FmhaStaticTileSchedulerParams as FmhaStaticTileSchedulerParams, +) +from sglang.jit_kernel.flash_attn.cute.tile_scheduler import ( + compute_sm100_fmha_grid as compute_grid, +) +from sglang.jit_kernel.flash_attn.cute.tile_scheduler import ( + compute_sm100_fmha_grid_clc as compute_grid_clc, +) +from sglang.jit_kernel.flash_attn.cute.tile_scheduler import ( + make_sm100_thread_cooperative_group as make_thread_cooperative_group, +) +from sglang.jit_kernel.flash_attn.cute.utils import AuxData, ex2_emulation_2 + + +class BlackwellFusedMultiHeadAttentionForward: + def __init__( + self, + head_dim: int, + head_dim_v: Optional[int] = None, + qhead_per_kvhead: int = 1, + is_causal: bool = False, + is_local: bool = False, + is_split_kv: bool = False, + pack_gqa: bool = False, + q_subtile_factor: int | None = None, + m_block_size: int = 128, + n_block_size: int = 128, + q_stage: int = 2, + is_persistent: bool = True, + score_mod=None, + mask_mod=None, + has_aux_tensors: bool = False, + paged_kv_non_tma: bool = False, + is_varlen_q: bool = False, + use_2cta_instrs: bool = False, + use_clc_scheduler: bool = False, + has_bias: bool = False, + bias_block_size: int = 128, + rel_extent_padded: int = 128, + ): + assert not has_bias, "SM100 forward with head_dim=256 does not support bias" + head_dim_v = head_dim if head_dim_v is None else head_dim_v + assert ( + head_dim == 256 and head_dim_v == 256 + ), "SM100 dedicated kernel only supports (head_dim, head_dim_v) = (256, 256)" + assert ( + score_mod is None + ), "SM100 forward with head_dim=256 does not support score_mod" + assert ( + mask_mod is None + ), "SM100 forward with head_dim=256 does not support mask_mod" + assert ( + not has_aux_tensors + ), "SM100 forward with head_dim=256 does not support aux tensors" + assert ( + not paged_kv_non_tma + ), "SM100 hd256 2CTA supports TMA paged KV only (page_size must equal tile_n=128)" + assert not pack_gqa, "SM100 forward with head_dim=256 does not support pack_gqa" + assert ( + not is_split_kv + ), "SM100 forward with head_dim=256 does not support SplitKV" + assert ( + q_subtile_factor is None + ), "SM100 forward with head_dim=256 does not support q_subtile_factor" + assert ( + m_block_size == 128 and n_block_size == 128 + ), "SM100 dedicated kernel only supports tile_m=128 and tile_n=128" + # q_stage / persistence / scheduler knobs are accepted for interface parity, + # but this dedicated kernel uses fixed internal settings. + + qk_acc_dtype = cutlass.Float32 + pv_acc_dtype = cutlass.Float32 + mma_tiler = (128, 128, head_dim) + self.qk_acc_dtype = qk_acc_dtype + self.pv_acc_dtype = pv_acc_dtype + self.qhead_per_kvhead = qhead_per_kvhead + self.mma_tiler = mma_tiler + assert ( + mma_tiler[0] == 128 and mma_tiler[1] == 128 + ), "Only 128x128 tile impl is supported" + assert mma_tiler[2] == 256, "Only 256 is supported for 128x128 tile impl" + self.cta_tiler = ( + mma_tiler[0], + mma_tiler[1], + mma_tiler[2], + ) + self.qk_mma_tiler = ( + 2 * mma_tiler[0], + mma_tiler[1], + min(self.cta_tiler[2], 128), + ) + self.pv_mma_tiler = self.qk_mma_tiler + self.pv_block_tiler = ( + self.pv_mma_tiler[0] // 2, + self.pv_mma_tiler[1], + self.pv_mma_tiler[2], + ) + self.iterations_qk = self.cta_tiler[2] // self.qk_mma_tiler[2] + self.iterations_pv = self.cta_tiler[2] // self.pv_mma_tiler[1] + self.cluster_shape_mn = (2, 1) + self.tmem_warp_shape_mn = (4, 1) + # Dedicated hd256 kernel uses fixed scheduling policy. + self.is_persistent = False + self.is_causal = is_causal + self.is_local = is_local + self.use_semantic_trip_range = is_causal or is_local + self.use_clc_scheduler = False + + self.softmax_warp_ids = (0, 1, 2, 3) + self.correction_warp_ids = (4, 5, 6, 7) + self.mma_warp_id = 8 + self.load_warp_id = 9 + self.empty_warp_id = (10, 11) + self.sched_warp_id = self.empty_warp_id[0] if use_clc_scheduler else None + self.tmem_alloc_cols = SM100_TMEM_CAPACITY_COLUMNS + + self.threads_per_warp = 32 + self.threads_per_cta = self.threads_per_warp * len( + ( + *self.softmax_warp_ids, # this is to get a round num threads + *self.correction_warp_ids, + self.mma_warp_id, + self.load_warp_id, + *self.empty_warp_id, + ) + ) + + self.tmem_alloc_barrier = pipeline.NamedBarrier( + barrier_id=1, + num_threads=self.threads_per_cta, + ) + + self.tmem_s_offset = 0 + self.tmem_o_offset = 256 + self.tmem_p_offset = self.tmem_s_offset + + _tune_key = ( + True, + is_causal, + 256, + False, + ) # hd256: always 2cta, no sm103 variant + _tune = _TUNING_CONFIG.get(_tune_key, {}) + self.num_regs_softmax = _tune.get("num_regs_softmax", 256) + self.num_regs_correction = _tune.get("num_regs_correction", 160) + self.num_regs_other = ( + 32 # fixed for hd256; not derived from 512 budget like other kernels + ) + self.ex2_emu_freq = _tune.get("ex2_emu_freq", 4) + self.ex2_emu_res = _tune.get("ex2_emu_res", 3) + self.ex2_emu_start_frg = _tune.get("ex2_emu_start_frg", 0) + + self.buffer_align_bytes = 1024 + + def _setup_attributes(self): + self.q_stage = self.iterations_qk + self.kv_stage = 4 + self.qk_acc_stage = 2 + self.mma_corr_stage = 1 + if cutlass.const_expr(self.use_clc_scheduler): + self.num_clc_stage = 1 + self.num_clc_response_bytes = 16 + + @cute.jit + def __call__( + self, + mQ: cute.Tensor, + mK: cute.Tensor, + mV: cute.Tensor, + mO: cute.Tensor, + mLSE: Optional[cute.Tensor], + softmax_scale: Float32, + mCuSeqlensQ: Optional[cute.Tensor] = None, + mCuSeqlensK: Optional[cute.Tensor] = None, + mSeqUsedQ: Optional[cute.Tensor] = None, + mSeqUsedK: Optional[cute.Tensor] = None, + mPageTable: Optional[cute.Tensor] = None, + window_size_left: Int32 | int | None = None, + window_size_right: Int32 | int | None = None, + learnable_sink: Optional[cute.Tensor] = None, + descale_tensors: Optional[DescaleTensors] = None, + blocksparse_tensors: Optional[cute.Tensor] = None, + aux_data: AuxData = AuxData(), + mBias: Optional[cute.Tensor] = None, + stream: cuda.CUstream = None, + ): + assert mBias is None, "SM100 forward with head_dim=256 does not support bias" + # Keep parity with FlashAttentionForwardSm100.__call__ interface. + # (TODO@wangsiyu) Implement these features. + assert ( + mSeqUsedQ is None and mSeqUsedK is None + ), "SM100 forward with head_dim=256 does not support seqused_q/seqused_k" + assert ( + learnable_sink is None + ), "SM100 forward with head_dim=256 does not support learnable_sink" + assert ( + blocksparse_tensors is None + ), "SM100 forward with head_dim=256 does not support block sparsity" + assert ( + aux_data.tensors is None + ), "SM100 forward with head_dim=256 does not support aux_tensors" + assert ( + aux_data.scalars is None + ), "SM100 forward with head_dim=256 does not support aux_scalars" + assert ( + not self.is_local + ), "SM100 forward with head_dim=256 does not support local attention yet" + assert ( + window_size_left is None and window_size_right is None + ), "SM100 forward with head_dim=256 does not support runtime window_size overrides" + assert ( + descale_tensors is None + ), "SM100 forward with head_dim=256 does not support descale_tensors" + + q_tensor, k_tensor, v_tensor, o_tensor = mQ, mK, mV, mO + lse_tensor = mLSE + cum_seqlen_q = mCuSeqlensQ + cum_seqlen_k = mCuSeqlensK + + q_rank = len(mQ.shape) + k_rank = len(mK.shape) + if cutlass.const_expr(cum_seqlen_q is not None): + # Varlen path accepts either legacy 5D tensors or standard 3D tensors. + if cutlass.const_expr(q_rank == 5): + s_q = mQ.shape[1] + h_q = mQ.shape[2] * mQ.shape[3] + d = mQ.shape[4] + elif cutlass.const_expr(q_rank == 3): + s_q = mQ.shape[0] + h_q = mQ.shape[1] + d = mQ.shape[2] + else: + raise RuntimeError( + f"hd256 forward varlen expects q rank 3 or 5, got rank {q_rank}" + ) + else: + # Non-varlen path accepts either legacy 5D tensors or standard 4D tensors. + if cutlass.const_expr(q_rank == 5): + s_q = mQ.shape[1] + h_q = mQ.shape[2] * mQ.shape[3] + d = mQ.shape[4] + elif cutlass.const_expr(q_rank == 4): + s_q = mQ.shape[1] + h_q = mQ.shape[2] + d = mQ.shape[3] + else: + raise RuntimeError( + f"hd256 forward non-varlen expects q rank 4 or 5, got rank {q_rank}" + ) + + if cutlass.const_expr(cum_seqlen_k is not None): + if cutlass.const_expr(k_rank == 5): + s_k = mK.shape[1] + h_k = mK.shape[2] + elif cutlass.const_expr(k_rank == 3): + s_k = mK.shape[0] + h_k = mK.shape[1] + else: + raise RuntimeError( + f"hd256 forward varlen expects k rank 3 or 5, got rank {k_rank}" + ) + else: + if cutlass.const_expr(k_rank == 5): + s_k = mK.shape[1] + h_k = mK.shape[2] + elif cutlass.const_expr(k_rank == 4): + s_k = mK.shape[1] + h_k = mK.shape[2] + else: + raise RuntimeError( + f"hd256 forward non-varlen expects k rank 4 or 5, got rank {k_rank}" + ) + if cutlass.const_expr(cum_seqlen_q is not None): + b = mCuSeqlensQ.shape[0] - 1 + elif cutlass.const_expr(cum_seqlen_k is not None): + b = mCuSeqlensK.shape[0] - 1 + else: + b = mQ.shape[0] + + scale_softmax = softmax_scale + scale_softmax_log2 = softmax_scale * math.log2(math.exp(1.0)) + scale_output = 1.0 + s_lse = s_q + h_r = h_q // h_k + s_q64 = Int64(s_q) + s_k64 = Int64(s_k) + s_lse64 = Int64(s_lse) + d64 = cute.assume(Int64(d), divby=128) + h_r64 = Int64(h_r) + h_k64 = Int64(h_k) + b64 = Int64(b) + s_q_total = ( + q_tensor.shape[1] + if cum_seqlen_q is not None and q_rank == 5 + else (q_tensor.shape[0] if cum_seqlen_q is not None else s_q64) + ) + s_k_total = ( + k_tensor.shape[1] + if cum_seqlen_k is not None and k_rank == 5 + else (k_tensor.shape[0] if cum_seqlen_k is not None else s_k64) + ) + stride_b_qo = h_r64 * h_k64 * s_q64 * d64 if cum_seqlen_q is None else 0 + stride_b_kv = h_k64 * s_k64 * d64 if cum_seqlen_k is None else 0 + b_lse = b64 if cum_seqlen_q is None else 1 + stride_b_lse = h_r64 * h_k64 * s_lse64 if cum_seqlen_q is None else 0 + + # (s, d, ((h_r, h_k), b)) + q_layout = cute.make_layout( + (s_q_total, d, ((h_r, h_k), b)), + stride=(d64 * h_r64 * h_k64, 1, ((d64, d64 * h_r64), stride_b_qo)), + ) + q = cute.make_tensor(q_tensor.iterator, q_layout) + if cutlass.const_expr(mPageTable is not None): + # Paged: K layout (num_pages, page_size, h_k, d); page_table maps kv_coord→physical page. + num_pages = k_tensor.shape[0] + page_size = k_tensor.shape[1] + page_size64 = Int64(page_size) + max_seqlen_k_paged = Int32(mPageTable.shape[1] * page_size) + k_paged_layout = cute.make_layout( + (page_size, d, h_k, num_pages), + stride=(d64 * h_k64, 1, d64, page_size64 * d64 * h_k64), + ) + k = cute.make_tensor(k_tensor.iterator, k_paged_layout) + v_paged_layout = cute.make_layout( + (d, page_size, h_k, num_pages), + stride=(1, d64 * h_k64, d64, page_size64 * d64 * h_k64), + ) + v = cute.make_tensor(v_tensor.iterator, v_paged_layout) + page_table_layout = cute.make_layout( + (b, mPageTable.shape[1]), + stride=(Int64(mPageTable.shape[1]), 1), + ) + page_table = cute.make_tensor(mPageTable.iterator, page_table_layout) + else: + # (s, d, ((h_r, h_k), b)), 0-stride for h_r to broadcast + k_layout = cute.make_layout( + (s_k_total, d, ((h_r, h_k), b)), + stride=(d64 * h_k64, 1, ((0, d64), stride_b_kv)), + ) + k = cute.make_tensor(k_tensor.iterator, k_layout) + # (d, s, ((h_r, h_k), b)), 0-stride for h_r to broadcast + v_layout = cute.make_layout( + (d, s_k_total, ((h_r, h_k), b)), + stride=(1, d64 * h_k64, ((0, d64), stride_b_kv)), + ) + v = cute.make_tensor(v_tensor.iterator, v_layout) + page_table = None + max_seqlen_k_paged = None + # (s, d, ((h_r, h_k), b)) + o_layout = cute.make_layout( + (s_q_total, d, ((h_r, h_k), b)), + stride=(d64 * h_r64 * h_k64, 1, ((d64, d64 * h_r64), stride_b_qo)), + ) + o = cute.make_tensor(o_tensor.iterator, o_layout) + if cutlass.const_expr(lse_tensor is not None): + # (s, ((h_r, h_k), b)) + lse_layout = cute.make_layout( + (s_lse64, ((h_r, h_k), b_lse)), + stride=(1, ((s_lse64, h_r64 * s_lse64), stride_b_lse)), + ) + lse = cute.make_tensor(lse_tensor.iterator, lse_layout) + else: + lse = None + + # setup static attributes before smem/grid/tma computation + self.q_dtype = q.element_type + self.k_dtype = k.element_type + self.v_dtype = v.element_type + self.o_dtype = o.element_type + self.tilePlikeFP32 = self.qk_mma_tiler[1] // Float32.width * self.q_dtype.width + + if cutlass.const_expr(self.use_clc_scheduler): + self.tile_sched_params, grid = compute_grid_clc( + (s_q, o.shape[1], o.shape[2]) if cum_seqlen_q is not None else o.shape, + self.cta_tiler, + (*self.cluster_shape_mn, 1), + ) + else: + self.tile_sched_params, grid = compute_grid( + (s_q, o.shape[1], o.shape[2]) if cum_seqlen_q is not None else o.shape, + self.cta_tiler, + self.is_persistent, + ) + + self.q_major_mode = utils.LayoutEnum.from_tensor(q).mma_major_mode() + self.k_major_mode = utils.LayoutEnum.from_tensor(k).mma_major_mode() + self.v_major_mode = utils.LayoutEnum.from_tensor(v).mma_major_mode() + self.o_layout = utils.LayoutEnum.from_tensor(o) + + if cutlass.const_expr(self.q_major_mode != tcgen05.OperandMajorMode.K): + raise RuntimeError("The layout of q is not supported") + if cutlass.const_expr(self.k_major_mode != tcgen05.OperandMajorMode.K): + raise RuntimeError("The layout of k is not supported") + if cutlass.const_expr(self.v_major_mode != tcgen05.OperandMajorMode.MN): + raise RuntimeError("The layout of v is not supported") + + # check type consistency + if cutlass.const_expr(self.q_dtype != self.k_dtype): + raise TypeError(f"Type mismatch: {self.q_dtype} != {self.k_dtype}") + if cutlass.const_expr(self.q_dtype != self.v_dtype): + raise TypeError(f"Type mismatch: {self.q_dtype} != {self.v_dtype}") + self._setup_attributes() + + cta_group = tcgen05.CtaGroup.TWO + # the intermediate tensor p is from tmem & k-major + p_source = tcgen05.OperandSource.TMEM + p_major_mode = tcgen05.OperandMajorMode.K + qk_tiled_mma = sm100_utils.make_trivial_tiled_mma( + self.q_dtype, + self.q_major_mode, + self.k_major_mode, + self.qk_acc_dtype, + cta_group, + self.qk_mma_tiler[:2], + ) + pv_tiled_mma = sm100_utils.make_trivial_tiled_mma( + self.v_dtype, + p_major_mode, + self.v_major_mode, + self.pv_acc_dtype, + cta_group, + self.pv_mma_tiler[:2], + p_source, + ) + + self.cluster_shape_mnk = (*self.cluster_shape_mn, 1) + self.cluster_layout_vmnk = cute.tiled_divide( + cute.make_layout(self.cluster_shape_mnk), + (qk_tiled_mma.thr_id.shape,), + ) + + self.epi_tile = self.pv_block_tiler[:2] + + q_smem_layout_staged = sm100_utils.make_smem_layout_a( + qk_tiled_mma, + self.qk_mma_tiler, + self.q_dtype, + self.q_stage, + ) + k_smem_layout_staged = sm100_utils.make_smem_layout_b( + qk_tiled_mma, + self.qk_mma_tiler, + self.k_dtype, + self.kv_stage, + ) + p_tmem_layout_staged = sm100_utils.make_smem_layout_a( + pv_tiled_mma, + self.pv_mma_tiler, + self.q_dtype, + self.qk_acc_stage, + ) + p_tmem_layout = cute.select(p_tmem_layout_staged, mode=[0, 1, 2]) + v_smem_layout_staged = sm100_utils.make_smem_layout_b( + pv_tiled_mma, + self.pv_mma_tiler, + self.v_dtype, + self.kv_stage, + ) + # TMA load for Q + tma_load_op = cute.nvgpu.cpasync.CopyBulkTensorTileG2SOp(cta_group) + + q_smem_layout = cute.select(q_smem_layout_staged, mode=[0, 1, 2]) + tma_atom_q, tma_tensor_q = cute.nvgpu.make_tiled_tma_atom_A( + tma_load_op, + q, + q_smem_layout, + self.qk_mma_tiler, + qk_tiled_mma, + self.cluster_layout_vmnk.shape, + ) + + # TMA load for K + k_smem_layout = cute.select(k_smem_layout_staged, mode=[0, 1, 2]) + tma_atom_k, tma_tensor_k = cute.nvgpu.make_tiled_tma_atom_B( + tma_load_op, + k, + k_smem_layout, + self.qk_mma_tiler, + qk_tiled_mma, + self.cluster_layout_vmnk.shape, + ) + # TMA load for V + v_smem_layout = cute.select(v_smem_layout_staged, mode=[0, 1, 2]) + tma_atom_v, tma_tensor_v = cute.nvgpu.make_tiled_tma_atom_B( + tma_load_op, + v, + v_smem_layout, + self.pv_mma_tiler, + pv_tiled_mma, + self.cluster_layout_vmnk.shape, + ) + + q_copy_size = cute.size_in_bytes(self.q_dtype, q_smem_layout) + k_copy_size = cute.size_in_bytes(self.k_dtype, k_smem_layout) + self.tma_copy_q_bytes = q_copy_size * cute.size(qk_tiled_mma.thr_id.shape) + self.tma_copy_kv_bytes = k_copy_size * cute.size(qk_tiled_mma.thr_id.shape) + + @cute.struct + class SharedStorage: + # TMA G2S load barriers: LOAD warp (producer) -> MMA warp (consumer) + load_q_mbar_ptr: cute.struct.MemRange[ + Int64, self.q_stage * 2 + ] # load_q_{producer,consumer} + load_kv_mbar_ptr: cute.struct.MemRange[ + Int64, self.kv_stage * 2 + ] # load_kv_{producer,consumer} + mma_s_mbar_ptr: cute.struct.MemRange[Int64, self.qk_acc_stage * 2] + p_mma_mbar_ptr: cute.struct.MemRange[Int64, self.qk_acc_stage * 2] + # Softmax -> Correction signaling barriers (row_max/row_sum vec ready) + s_corr_mbar_ptr: cute.struct.MemRange[ + Int64, self.qk_acc_stage * 2 + ] # s_corr_{producer,consumer} + sum_mbar_ptr: cute.struct.MemRange[Int64, 2] + # MMA -> Correction ownership barriers for O_partial tokens (online rescale/finalize) + mma_corr_mbar_ptr: cute.struct.MemRange[ + Int64, self.mma_corr_stage * 2 + ] # mma_corr_{producer,consumer} + # A CTA-wide "TMEM lifetime" barrier used to safely deallocate TMEM after all users finish. + tmem_dealloc_mbar_ptr: Int64 + # Tmem holding buffer + tmem_holding_buf: Int32 + # CLC pipeline barriers and response buffer + clc_mbar_ptr: cute.struct.MemRange[Int64, 2] + clc_response: cute.struct.MemRange[Int32, 4] + + self.shared_storage = SharedStorage + + grid = cute.round_up(grid, self.cluster_shape_mnk) + # Launch the kernel synchronously + self.kernel( + qk_tiled_mma, + pv_tiled_mma, + tma_atom_q, + tma_tensor_q, + tma_atom_k, + tma_tensor_k, + tma_atom_v, + tma_tensor_v, + o, + cum_seqlen_q, + cum_seqlen_k, + lse, + scale_softmax_log2, + scale_softmax, + scale_output, + page_table, + max_seqlen_k_paged, + window_size_left, + window_size_right, + self.cluster_layout_vmnk, + q_smem_layout_staged, + k_smem_layout_staged, + p_tmem_layout, + v_smem_layout_staged, + self.tile_sched_params, + ).launch( + grid=grid, + block=[self.threads_per_cta, 1, 1], + cluster=self.cluster_shape_mnk, + stream=stream, + min_blocks_per_mp=1, + ) + + # GPU device kernel + @cute.kernel + def kernel( + self, + qk_tiled_mma: cute.TiledMma, + pv_tiled_mma: cute.TiledMma, + tma_atom_q: cute.CopyAtom, + mQ_qdl: cute.Tensor, + tma_atom_k: cute.CopyAtom, + mK_kdl: cute.Tensor, + tma_atom_v: cute.CopyAtom, + mV_dkl: cute.Tensor, + mO_qdl: cute.Tensor, + cum_seqlen_q: Optional[cute.Tensor], + cum_seqlen_k: Optional[cute.Tensor], + mLSE: Optional[cute.Tensor], + scale_softmax_log2: Float32, + scale_softmax: Float32, + scale_output: Float32, + mPageTable: Optional[cute.Tensor], + max_seqlen_k: Optional[Int32], + window_size_left: Optional[Int32], + window_size_right: Optional[Int32], + cluster_layout_vmnk: cute.Layout, + q_smem_layout_staged: cute.ComposedLayout, + k_smem_layout_staged: cute.ComposedLayout, + p_tmem_layout_staged: cute.ComposedLayout, + v_smem_layout_staged: cute.ComposedLayout, + tile_sched_params: ( + FmhaStaticTileSchedulerParams | FmhaClcDynamicTileSchedulerParams + ), + ): + warp_idx = cute.arch.make_warp_uniform(cute.arch.warp_idx()) + + # + # Prefetch tma desc + # + if warp_idx == self.load_warp_id: + cute.nvgpu.cpasync.prefetch_descriptor(tma_atom_q) + cute.nvgpu.cpasync.prefetch_descriptor(tma_atom_k) + cute.nvgpu.cpasync.prefetch_descriptor(tma_atom_v) + + bidx, _, _ = cute.arch.block_idx() + mma_tile_coord_v = bidx % cute.size(qk_tiled_mma.thr_id.shape) + cta_rank_in_cluster = cute.arch.make_warp_uniform( + cute.arch.block_idx_in_cluster() + ) + block_in_cluster_coord_vmnk = cluster_layout_vmnk.get_flat_coord( + cta_rank_in_cluster + ) + + # Alloc + smem = utils.SmemAllocator() + storage = smem.allocate(self.shared_storage) + + load_q_producer, load_q_consumer = pipeline.PipelineTmaUmma.create( + num_stages=self.q_stage, + producer_group=make_thread_cooperative_group(len([self.load_warp_id])), + consumer_group=make_thread_cooperative_group(len([self.mma_warp_id])), + tx_count=self.tma_copy_q_bytes, + barrier_storage=storage.load_q_mbar_ptr.data_ptr(), + cta_layout_vmnk=cluster_layout_vmnk, + defer_sync=True, + ).make_participants() + load_kv_producer, load_kv_consumer = pipeline.PipelineTmaUmma.create( + num_stages=self.kv_stage, + producer_group=make_thread_cooperative_group(len([self.load_warp_id])), + consumer_group=make_thread_cooperative_group(len([self.mma_warp_id])), + tx_count=self.tma_copy_kv_bytes, + barrier_storage=storage.load_kv_mbar_ptr.data_ptr(), + cta_layout_vmnk=cluster_layout_vmnk, + defer_sync=True, + ).make_participants() + mma_s_producer, mma_s_consumer = pipeline.PipelineUmmaAsync.create( + num_stages=self.qk_acc_stage, + producer_group=make_thread_cooperative_group(len([self.mma_warp_id])), + consumer_group=make_thread_cooperative_group( + len(self.softmax_warp_ids) + * self.threads_per_warp + * self.cluster_shape_mnk[0], + ), + barrier_storage=storage.mma_s_mbar_ptr.data_ptr(), + cta_layout_vmnk=cluster_layout_vmnk, + defer_sync=True, + ).make_participants() + p_mma_producer, p_mma_consumer = pipeline.PipelineAsyncUmma.create( + num_stages=self.qk_acc_stage, + producer_group=make_thread_cooperative_group( + len(self.softmax_warp_ids) + * self.threads_per_warp + * self.cluster_shape_mnk[0], + ), + consumer_group=make_thread_cooperative_group(len([self.mma_warp_id])), + barrier_storage=storage.p_mma_mbar_ptr.data_ptr(), + cta_layout_vmnk=cluster_layout_vmnk, + defer_sync=True, + ).make_participants() + s_corr_producer, s_corr_consumer = pipeline.PipelineAsync.create( + num_stages=self.qk_acc_stage, + producer_group=make_thread_cooperative_group( + self.threads_per_warp * len(self.softmax_warp_ids) + ), + consumer_group=make_thread_cooperative_group( + self.threads_per_warp * len(self.correction_warp_ids) + ), + barrier_storage=storage.s_corr_mbar_ptr.data_ptr(), + defer_sync=True, + ).make_participants() + sum_producer, sum_consumer = pipeline.PipelineAsync.create( + num_stages=1, + producer_group=make_thread_cooperative_group( + self.threads_per_warp * len(self.softmax_warp_ids) + ), + consumer_group=make_thread_cooperative_group( + self.threads_per_warp * len(self.correction_warp_ids) + ), + barrier_storage=storage.sum_mbar_ptr.data_ptr(), + defer_sync=True, + ).make_participants() + mma_corr_producer, mma_corr_consumer = pipeline.PipelineUmmaAsync.create( + num_stages=self.mma_corr_stage, + producer_group=make_thread_cooperative_group(len([self.mma_warp_id])), + consumer_group=make_thread_cooperative_group( + len(self.correction_warp_ids) + * self.threads_per_warp + * self.cluster_shape_mnk[0], + ), + barrier_storage=storage.mma_corr_mbar_ptr.data_ptr(), + cta_layout_vmnk=cluster_layout_vmnk, + defer_sync=True, + ).make_participants() + # Tensor memory dealloc barrier init + tmem = utils.TmemAllocator( + storage.tmem_holding_buf, + barrier_for_retrieve=self.tmem_alloc_barrier, + allocator_warp_id=self.correction_warp_ids[0], + is_two_cta=True, + two_cta_tmem_dealloc_mbar_ptr=storage.tmem_dealloc_mbar_ptr, + ) + tmem.allocate(self.tmem_alloc_cols) + tmem.wait_for_alloc() + tmem_ptr = tmem.retrieve_ptr(self.qk_acc_dtype) + # Initialize CLC state if using dynamic scheduler + if cutlass.const_expr(self.use_clc_scheduler): + clc_pipeline_producer_group = pipeline.CooperativeGroup( + pipeline.Agent.Thread + ) + cluster_size = cute.size(self.cluster_shape_mnk) + num_clc_consumer_threads = self.threads_per_warp * ( + 1 # sched_warp (CTA 0 only) + + cluster_size + * ( + len(self.softmax_warp_ids) + + len(self.correction_warp_ids) + + 1 # mma_warp + + 1 # load_warp + ) + ) + clc_pipeline_consumer_group = pipeline.CooperativeGroup( + pipeline.Agent.Thread, num_clc_consumer_threads + ) + clc_response_ptr = storage.clc_response.data_ptr() + clc = ClcState.create( + hw_scheduler=ClcDynamicPersistentTileScheduler.create( + self.tile_sched_params.clc_hw_params(), + cute.arch.block_idx(), + cute.arch.grid_dim(), + clc_response_ptr, + ), + pipeline=pipeline.PipelineClcFetchAsync.create( + barrier_storage=storage.clc_mbar_ptr.data_ptr(), + num_stages=self.num_clc_stage, + producer_group=clc_pipeline_producer_group, + consumer_group=clc_pipeline_consumer_group, + tx_count=self.num_clc_response_bytes, + cta_layout_vmnk=cluster_layout_vmnk, + defer_sync=True, + ), + consumer_state=pipeline.make_pipeline_state( + pipeline.PipelineUserType.Consumer, self.num_clc_stage + ), + producer_state=pipeline.make_pipeline_state( + pipeline.PipelineUserType.Producer, self.num_clc_stage + ), + ) + else: + clc = None + clc_response_ptr = None + + # Cluster arrive after barrier init + pipeline.pipeline_init_arrive( + cluster_shape_mn=cluster_layout_vmnk, is_relaxed=True + ) + + sQ = smem.allocate_tensor( + element_type=self.q_dtype, + layout=q_smem_layout_staged.outer, + swizzle=q_smem_layout_staged.inner, + byte_alignment=128, + ) + sK = smem.allocate_tensor( + element_type=self.k_dtype, + layout=k_smem_layout_staged.outer, + swizzle=k_smem_layout_staged.inner, + byte_alignment=128, + ) + # K and V now use separate memory since we removed the transform stage + sV = smem.allocate_tensor( + element_type=self.v_dtype, + layout=v_smem_layout_staged.outer, + swizzle=v_smem_layout_staged.inner, + byte_alignment=128, + ) + + sSum = smem.allocate_tensor( + element_type=self.qk_acc_dtype, + layout=cute.make_layout(len(self.softmax_warp_ids) * self.threads_per_warp), + byte_alignment=128, + ) + qk_thr_mma = qk_tiled_mma.get_slice(mma_tile_coord_v) # default 1sm + pv_thr_mma = pv_tiled_mma.get_slice(mma_tile_coord_v) # default 1sm + tSrQ = qk_thr_mma.make_fragment_A(sQ) + tSrK = qk_thr_mma.make_fragment_B(sK) + tOrV = pv_thr_mma.make_fragment_B(sV) + qk_acc_shape = qk_thr_mma.partition_shape_C( + (self.qk_mma_tiler[0], self.qk_mma_tiler[1]) + ) + tStS = qk_thr_mma.make_fragment_C(cute.append(qk_acc_shape, self.qk_acc_stage)) + pv_acc_shape = pv_thr_mma.partition_shape_C( + (self.pv_mma_tiler[0], self.pv_mma_tiler[1]) + ) + tOtO = pv_thr_mma.make_fragment_C(pv_acc_shape) + tOtO_layout = cute.append( + tOtO.layout, + cute.make_layout( + self.iterations_pv, + stride=self.pv_mma_tiler[1] // self.tmem_warp_shape_mn[1], + ), + ) + tStS = cute.make_tensor(tStS.iterator + self.tmem_s_offset, tStS.layout) + tOtO_staged = cute.make_tensor(tOtO.iterator + self.tmem_o_offset, tOtO_layout) + + # /////////////////////////////////////////////////////////////////////////////// + # EMPTY + # /////////////////////////////////////////////////////////////////////////////// + for _i in cutlass.range_constexpr(len(self.empty_warp_id)): + if warp_idx == self.empty_warp_id[_i]: + cute.arch.warpgroup_reg_dealloc(self.num_regs_other) + + if cutlass.const_expr(self.use_clc_scheduler): + tile_sched = FmhaClcDynamicTileScheduler.create( + tile_sched_params, + cute.arch.block_idx(), + cute.arch.grid_dim(), + clc_response_ptr, + clc, + ) + else: + blk_idx = cute.arch.block_idx() + tile_sched = FmhaStaticTileScheduler( + tile_sched_params, blk_idx[0], blk_idx, cute.arch.grid_dim() + ) + work_tile = tile_sched.initial_work_tile_info() + + # Cluster wait + pipeline.pipeline_init_wait(cluster_shape_mn=cluster_layout_vmnk) + + # /////////////////////////////////////////////////////////////////////////////// + # LOAD + # /////////////////////////////////////////////////////////////////////////////// + if warp_idx == self.load_warp_id: + cute.arch.warpgroup_reg_dealloc(self.num_regs_other) + while work_tile.is_valid_tile: + curr_block_coord = ( + work_tile.tile_idx + ) # (q_tile_idx, 0, (head_idx, batch_idx)) + mma_block_coord = ( + curr_block_coord[0] // cute.size(qk_tiled_mma.thr_id.shape), + curr_block_coord[1], + curr_block_coord[2], + ) + continue_cond = False + batch_coord = curr_block_coord[2][1] + seqlen_q = mQ_qdl.shape[0] + seqlen_k = ( + mK_kdl.shape[0] + if cutlass.const_expr(mPageTable is None) + else max_seqlen_k + ) + cuseqlen_q = Int32(0) + cuseqlen_k = Int32(0) + block_offset = ( + Int32(0), + Int32(0), + Int32(0), + ((Int32(0), Int32(0)), Int32(0)), + ) + if cutlass.const_expr(cum_seqlen_q is not None): + cuseqlen_q = cum_seqlen_q[batch_coord] + seqlen_q = cum_seqlen_q[batch_coord + 1] - cuseqlen_q + if cutlass.const_expr(cum_seqlen_k is not None): + cuseqlen_k = cum_seqlen_k[batch_coord] + seqlen_k = cum_seqlen_k[batch_coord + 1] - cuseqlen_k + block_offset = ( + cuseqlen_q, + cuseqlen_k, + Int32(0), + ((Int32(0), Int32(0)), Int32(0)), + ) + continue_cond = ( + not FmhaStaticTileScheduler.check_valid_work_for_seqlen_q( + self.qk_mma_tiler[0], + mma_block_coord[0], + seqlen_q, + ) + ) + if not continue_cond: + mQ_qdl_ = cute.domain_offset( + cute.select(block_offset, mode=[0, 2, 3]), mQ_qdl + ) + # Local tile partition global tensors + q_cta_layout = cute.make_layout( + cute.slice_(cluster_layout_vmnk, (0, 0, None, 0)).shape + ) + # (bM, bK, loopM, loopK, loopL) + gQ_qdl = cute.flat_divide( + mQ_qdl_, cute.select(self.qk_mma_tiler, mode=[0, 2]) + ) + tSgQ_qdl = qk_thr_mma.partition_A(gQ_qdl) + tQsQ, tQgQ_qdl = cute.nvgpu.cpasync.tma_partition( + tma_atom_q, + block_in_cluster_coord_vmnk[2], + q_cta_layout, + cute.group_modes(sQ, 0, 3), + cute.group_modes(tSgQ_qdl, 0, 3), + ) + kv_cta_layout = cute.make_layout( + cute.slice_(cluster_layout_vmnk, (0, None, 0, 0)).shape + ) + if cutlass.const_expr(mPageTable is None): + # Dense path: domain_offset K/V by batch block, select batch via mma_block_coord[2]. + mK_kdl_ = cute.domain_offset( + cute.select(block_offset, mode=[1, 2, 3]), mK_kdl + ) + mV_dkl_ = cute.domain_offset( + cute.select(block_offset, mode=[2, 1, 3]), mV_dkl + ) + gK_kdl = cute.flat_divide( + mK_kdl_, cute.select(self.qk_mma_tiler, mode=[1, 2]) + ) + tSgK_kdl = qk_thr_mma.partition_B(gK_kdl) + tKsK, tKgK_kdl = cute.nvgpu.cpasync.tma_partition( + tma_atom_k, + block_in_cluster_coord_vmnk[1], + kv_cta_layout, + cute.group_modes(sK, 0, 3), + cute.group_modes(tSgK_kdl, 0, 3), + ) + gV_dkl = cute.flat_divide( + mV_dkl_, cute.select(self.pv_mma_tiler, mode=[1, 2]) + ) + tSgV_dkl = pv_thr_mma.partition_B(gV_dkl) + tVsV, tVgV_dkl = cute.nvgpu.cpasync.tma_partition( + tma_atom_v, + block_in_cluster_coord_vmnk[1], + kv_cta_layout, + cute.group_modes(sV, 0, 3), + cute.group_modes(tSgV_dkl, 0, 3), + ) + # ((atom_v, rest_v), RestN, RestK) + tKgK = tKgK_kdl[None, None, None, mma_block_coord[2]] + tVgV = tVgV_dkl[None, None, None, mma_block_coord[2]] + else: + # Paged path: slice K/V by KV head, keep num_pages dim for page_idx-based TMA. + head_kv_coord = curr_block_coord[2][0] // self.qhead_per_kvhead + mK_kdl_ = mK_kdl[None, None, head_kv_coord, None] + mV_dkl_ = mV_dkl[None, None, head_kv_coord, None] + gK_kdl = cute.flat_divide( + mK_kdl_, cute.select(self.qk_mma_tiler, mode=[1, 2]) + ) + tSgK_kdl = qk_thr_mma.partition_B(gK_kdl) + tKsK, tKgK_kdl = cute.nvgpu.cpasync.tma_partition( + tma_atom_k, + block_in_cluster_coord_vmnk[1], + kv_cta_layout, + cute.group_modes(sK, 0, 3), + cute.group_modes(tSgK_kdl, 0, 3), + ) + gV_dkl = cute.flat_divide( + mV_dkl_, cute.select(self.pv_mma_tiler, mode=[1, 2]) + ) + tSgV_dkl = pv_thr_mma.partition_B(gV_dkl) + tVsV, tVgV_dkl = cute.nvgpu.cpasync.tma_partition( + tma_atom_v, + block_in_cluster_coord_vmnk[1], + kv_cta_layout, + cute.group_modes(sV, 0, 3), + cute.group_modes(tSgV_dkl, 0, 3), + ) + tKgK = tKgK_kdl + tVgV = tVgV_dkl + # ((atom_v, rest_v), RestK) + tQgQ = tQgQ_qdl[None, mma_block_coord[0], None, mma_block_coord[2]] + + seqlen_kv_loop_start, seqlen_kv_loop_steps = ( + FusedMask.get_trip_start_count_via_block_info( + mma_block_coord, + self.qk_mma_tiler, + seqlen_q, + seqlen_k, + self.is_causal, + self.is_local, + window_size_left, + window_size_right, + ) + ) + seqlen_kv_loop_end = seqlen_kv_loop_start + seqlen_kv_loop_steps + # Q + for iter in cutlass.range(self.iterations_qk, unroll=1): + q_handle = load_q_producer.acquire_and_advance() + cute.copy( + tma_atom_q, + tQgQ[None, iter], + tQsQ[None, q_handle.index], + tma_bar_ptr=q_handle.barrier, + ) + + # K0 + kv_coord = seqlen_kv_loop_start + k_page_idx = ( + mPageTable[batch_coord, kv_coord] + if cutlass.const_expr(mPageTable is not None) + else None + ) + for iter in cutlass.range(self.iterations_qk, unroll=1): + k_handle = load_kv_producer.acquire_and_advance() + cute.copy( + tma_atom_k, + ( + tKgK[None, kv_coord, iter] + if cutlass.const_expr(mPageTable is None) + else tKgK[None, 0, iter, k_page_idx] + ), + tKsK[None, k_handle.index], + tma_bar_ptr=k_handle.barrier, + ) + kv_coord += 1 + # v_page_idx_prev carries K[i-1]'s page index for use as V[i-1]'s page + # (K and V for the same KV block share the same physical page). + # Also serves as the Vend page index when seqlen_kv_loop_steps == 1. + v_page_idx_prev = ( + k_page_idx + if cutlass.const_expr(mPageTable is not None) + else None + ) + # Prefetch K1 page after K0 TMA dispatch to hide L2 latency. + if cutlass.const_expr(mPageTable is not None): + if seqlen_kv_loop_steps > 1: + k_page_idx = mPageTable[batch_coord, kv_coord] + + for i in cutlass.range(1, seqlen_kv_loop_steps, 1, unroll=1): + # Ki: k_page_idx was prefetched at end of previous iteration + # (or in the prologue for i==1); L2 latency already hidden. + for iter in cutlass.range(self.iterations_qk, unroll=1): + k_handle = load_kv_producer.acquire_and_advance() + cute.copy( + tma_atom_k, + ( + tKgK[None, kv_coord, iter] + if cutlass.const_expr(mPageTable is None) + else tKgK[None, 0, iter, k_page_idx] + ), + tKsK[None, k_handle.index], + tma_bar_ptr=k_handle.barrier, + ) + # Vi-1: reuse v_page_idx_prev (= K[i-1]'s page), no extra GMEM read. + for iter in cutlass.range(self.iterations_pv, unroll=1): + v_handle = load_kv_producer.acquire_and_advance() + cute.copy( + tma_atom_v, + ( + tVgV[None, iter, kv_coord - 1] + if cutlass.const_expr(mPageTable is None) + else tVgV[None, iter, 0, v_page_idx_prev] + ), + tVsV[None, v_handle.index], + tma_bar_ptr=v_handle.barrier, + ) + v_page_idx_prev = ( + k_page_idx + if cutlass.const_expr(mPageTable is not None) + else None + ) + kv_coord += 1 + # Prefetch next K page while V TMA is in flight. + if cutlass.const_expr(mPageTable is not None): + if kv_coord < seqlen_kv_loop_end: + k_page_idx = mPageTable[batch_coord, kv_coord] + # Vend: reuse v_page_idx_prev (= K[end-1]'s page), no extra GMEM read. + for iter in cutlass.range(self.iterations_pv, unroll=1): + v_handle = load_kv_producer.acquire_and_advance() + cute.copy( + tma_atom_v, + ( + tVgV[None, iter, seqlen_kv_loop_end - 1] + if cutlass.const_expr(mPageTable is None) + else tVgV[None, iter, 0, v_page_idx_prev] + ), + tVsV[None, v_handle.index], + tma_bar_ptr=v_handle.barrier, + ) + + work_tile = tile_sched.advance_to_next_work() + # End of persistent scheduler loop + load_kv_producer.tail() + load_q_producer.tail() + + # /////////////////////////////////////////////////////////////////////////////// + # MMA + # /////////////////////////////////////////////////////////////////////////////// + if warp_idx == self.mma_warp_id: + cute.arch.warpgroup_reg_dealloc(self.num_regs_other) + + cta_rank_in_cluster = cute.arch.make_warp_uniform( + cute.arch.block_idx_in_cluster() + ) + is_leader_cta = cta_rank_in_cluster % 2 == 0 + + while work_tile.is_valid_tile: + curr_block_coord = work_tile.tile_idx + mma_block_coord = ( + curr_block_coord[0] // cute.size(qk_tiled_mma.thr_id.shape), + curr_block_coord[1], + curr_block_coord[2], + ) + continue_cond = False + seqlen_q = mQ_qdl.shape[0] + seqlen_k = ( + mK_kdl.shape[0] + if cutlass.const_expr(mPageTable is None) + else max_seqlen_k + ) + batch_coord = curr_block_coord[2][1] + if cutlass.const_expr(cum_seqlen_q is not None): + cuseqlen_q = cum_seqlen_q[batch_coord] + seqlen_q = cum_seqlen_q[batch_coord + 1] - cuseqlen_q + continue_cond = ( + not FmhaStaticTileScheduler.check_valid_work_for_seqlen_q( + self.qk_mma_tiler[0], + mma_block_coord[0], + seqlen_q, + ) + ) + + if not continue_cond: + if cutlass.const_expr(cum_seqlen_k is not None): + cuseqlen_k = cum_seqlen_k[batch_coord] + seqlen_k = cum_seqlen_k[batch_coord + 1] - cuseqlen_k + + seqlen_kv_loop_start, seqlen_kv_loop_steps = ( + FusedMask.get_trip_start_count_via_block_info( + mma_block_coord, + self.qk_mma_tiler, + seqlen_q, + seqlen_k, + self.is_causal, + self.is_local, + window_size_left, + window_size_right, + ) + ) + seqlen_kv_loop_end = seqlen_kv_loop_start + seqlen_kv_loop_steps + + load_q_releaser = load_q_consumer.clone() + pv_tiled_mma.set(tcgen05.Field.ACCUMULATE, False) + if seqlen_kv_loop_steps > 1: + # QK0 + if is_leader_cta: + s_handle = mma_s_producer.acquire_and_advance() + tStS_slice = tStS[None, None, None, s_handle.index] + qk_tiled_mma.set(tcgen05.Field.ACCUMULATE, False) + for iter in cutlass.range(self.iterations_qk, unroll=1): + load_q_consumer.wait_and_advance() + tSrQ_slice = tSrQ[None, None, None, iter] + k_handle = load_kv_consumer.wait_and_advance() + tSrK_trans_slice = tSrK[ + None, None, None, k_handle.index + ] + num_kphases = cute.size(tSrQ_slice, mode=[2]) + for kphase_idx in cutlass.range( + num_kphases, unroll_full=True + ): + kphase_coord = (None, None, kphase_idx) + cute.gemm( + qk_tiled_mma, + tStS_slice, + tSrQ_slice[kphase_coord], + tSrK_trans_slice[kphase_coord], + tStS_slice, + ) + qk_tiled_mma.set(tcgen05.Field.ACCUMULATE, True) + k_handle.release() + s_handle.commit() + for i in cutlass.range( + 1, seqlen_kv_loop_steps - 1, 1, unroll=1 + ): + # QKi + if is_leader_cta: + s_handle = mma_s_producer.acquire_and_advance() + tStS_slice = tStS[None, None, None, s_handle.index] + qk_tiled_mma.set(tcgen05.Field.ACCUMULATE, False) + for iter in cutlass.range(self.iterations_qk, unroll=1): + tSrQ_slice = tSrQ[None, None, None, iter] + k_handle = load_kv_consumer.wait_and_advance() + tSrK_trans_slice = tSrK[ + None, None, None, k_handle.index + ] + num_kphases = cute.size(tSrQ_slice, mode=[2]) + for kphase_idx in cutlass.range( + num_kphases, unroll_full=True + ): + kphase_coord = (None, None, kphase_idx) + cute.gemm( + qk_tiled_mma, + tStS_slice, + tSrQ_slice[kphase_coord], + tSrK_trans_slice[kphase_coord], + tStS_slice, + ) + qk_tiled_mma.set(tcgen05.Field.ACCUMULATE, True) + k_handle.release() + s_handle.commit() + + # PVi-1 + p_handle = p_mma_consumer.wait_and_advance() + o_handle = mma_corr_producer.acquire_and_advance() + pv_whether_acc = pv_tiled_mma.get( + tcgen05.Field.ACCUMULATE + ) + for iter in cutlass.range(self.iterations_pv, unroll=1): + v_handle = load_kv_consumer.wait_and_advance() + pv_tiled_mma.set( + tcgen05.Field.ACCUMULATE, pv_whether_acc + ) + tOtO_slice = tOtO_staged[None, None, None, iter] + tStS_slice = tStS[None, None, None, p_handle.index] + tP = cute.make_tensor( + tStS_slice.iterator, p_tmem_layout_staged.outer + ) + tOrP = pv_thr_mma.make_fragment_A(tP) + tOrP_slice = cute.make_tensor( + cute.recast_ptr( + tStS_slice.iterator, dtype=self.q_dtype + ), + tOrP.layout, + ) + tOrV_slice = tOrV[None, None, None, v_handle.index] + num_kphases = cute.size(tOrV_slice, mode=[2]) + for kphase_idx in cutlass.range( + num_kphases, unroll_full=True + ): + kphase_coord = (None, None, kphase_idx) + cute.gemm( + pv_tiled_mma, + tOtO_slice, + tOrP_slice[kphase_coord], + tOrV_slice[kphase_coord], + tOtO_slice, + ) + pv_tiled_mma.set(tcgen05.Field.ACCUMULATE, True) + v_handle.release() + o_handle.commit() + p_handle.release() + if is_leader_cta: + # QKend + s_handle = mma_s_producer.acquire_and_advance() + tStS_slice = tStS[None, None, None, s_handle.index] + qk_tiled_mma.set(tcgen05.Field.ACCUMULATE, False) + for iter in cutlass.range(self.iterations_qk, unroll=1): + tSrQ_slice = tSrQ[None, None, None, iter] + k_handle = load_kv_consumer.wait_and_advance() + tSrK_trans_slice = tSrK[ + None, None, None, k_handle.index + ] + num_kphases = cute.size(tSrQ_slice, mode=[2]) + for kphase_idx in cutlass.range( + num_kphases, unroll_full=True + ): + kphase_coord = (None, None, kphase_idx) + cute.gemm( + qk_tiled_mma, + tStS_slice, + tSrQ_slice[kphase_coord], + tSrK_trans_slice[kphase_coord], + tStS_slice, + ) + qk_tiled_mma.set(tcgen05.Field.ACCUMULATE, True) + k_handle.release() + load_q_releaser.release() + load_q_releaser.advance() + s_handle.commit() + + # PVend-1 + p_handle = p_mma_consumer.wait_and_advance() + o_handle = mma_corr_producer.acquire_and_advance() + pv_whether_acc = pv_tiled_mma.get(tcgen05.Field.ACCUMULATE) + for iter in cutlass.range(self.iterations_pv, unroll=1): + v_handle = load_kv_consumer.wait_and_advance() + pv_tiled_mma.set( + tcgen05.Field.ACCUMULATE, pv_whether_acc + ) + tOtO_slice = tOtO_staged[None, None, None, iter] + tStS_slice = tStS[None, None, None, p_handle.index] + tP = cute.make_tensor( + tStS_slice.iterator, p_tmem_layout_staged.outer + ) + tOrP = pv_thr_mma.make_fragment_A(tP) + tOrP_slice = cute.make_tensor( + cute.recast_ptr( + tStS_slice.iterator, dtype=self.q_dtype + ), + tOrP.layout, + ) + tOrV_slice = tOrV[None, None, None, v_handle.index] + num_kphases = cute.size(tOrV_slice, mode=[2]) + for kphase_idx in cutlass.range( + num_kphases, unroll_full=True + ): + kphase_coord = (None, None, kphase_idx) + cute.gemm( + pv_tiled_mma, + tOtO_slice, + tOrP_slice[kphase_coord], + tOrV_slice[kphase_coord], + tOtO_slice, + ) + pv_tiled_mma.set(tcgen05.Field.ACCUMULATE, True) + v_handle.release() + o_handle.commit() + p_handle.release() + else: + if is_leader_cta: + # QK0 + s_handle = mma_s_producer.acquire_and_advance() + tStS_slice = tStS[None, None, None, s_handle.index] + qk_tiled_mma.set(tcgen05.Field.ACCUMULATE, False) + for iter in cutlass.range(self.iterations_qk, unroll=1): + load_q_consumer.wait_and_advance() + tSrQ_slice = tSrQ[None, None, None, iter] + k_handle = load_kv_consumer.wait_and_advance() + tSrK_trans_slice = tSrK[ + None, None, None, k_handle.index + ] + num_kphases = cute.size(tSrQ_slice, mode=[2]) + for kphase_idx in cutlass.range( + num_kphases, unroll_full=True + ): + kphase_coord = (None, None, kphase_idx) + cute.gemm( + qk_tiled_mma, + tStS_slice, + tSrQ_slice[kphase_coord], + tSrK_trans_slice[kphase_coord], + tStS_slice, + ) + qk_tiled_mma.set(tcgen05.Field.ACCUMULATE, True) + k_handle.release() + load_q_releaser.release() + load_q_releaser.advance() + s_handle.commit() + + if is_leader_cta: + # PVend + p_handle = p_mma_consumer.wait_and_advance() + o_handle = mma_corr_producer.acquire_and_advance() + pv_whether_acc = pv_tiled_mma.get(tcgen05.Field.ACCUMULATE) + for iter in cutlass.range(self.iterations_pv, unroll=1): + v_handle = load_kv_consumer.wait_and_advance() + pv_tiled_mma.set(tcgen05.Field.ACCUMULATE, pv_whether_acc) + tOtO_slice = tOtO_staged[None, None, None, iter] + tStS_slice = tStS[None, None, None, p_handle.index] + tP = cute.make_tensor( + tStS_slice.iterator, p_tmem_layout_staged.outer + ) + tOrP = pv_thr_mma.make_fragment_A(tP) + tOrP_slice = cute.make_tensor( + cute.recast_ptr( + tStS_slice.iterator, dtype=self.q_dtype + ), + tOrP.layout, + ) + tOrV_slice = tOrV[None, None, None, v_handle.index] + num_kphases = cute.size(tOrV_slice, mode=[2]) + for kphase_idx in cutlass.range( + num_kphases, unroll_full=True + ): + kphase_coord = (None, None, kphase_idx) + cute.gemm( + pv_tiled_mma, + tOtO_slice, + tOrP_slice[kphase_coord], + tOrV_slice[kphase_coord], + tOtO_slice, + ) + pv_tiled_mma.set(tcgen05.Field.ACCUMULATE, True) + v_handle.release() + o_handle.commit() + p_handle.release() + work_tile = tile_sched.advance_to_next_work() + # End of persistent scheduler loop + mma_s_producer.tail() + mma_corr_producer.tail() + + if ( + warp_idx < self.correction_warp_ids[0] + and warp_idx >= self.softmax_warp_ids[0] + ): + # increase register after decreasing + cute.arch.warpgroup_reg_alloc(self.num_regs_softmax) + + while work_tile.is_valid_tile: + curr_block_coord = work_tile.tile_idx + mma_block_coord = ( + curr_block_coord[0] // cute.size(qk_tiled_mma.thr_id.shape), + curr_block_coord[1], + curr_block_coord[2], + ) + batch_coord = curr_block_coord[2][1] + continue_cond = False + seqlen_q = mQ_qdl.shape[0] + seqlen_k = ( + mK_kdl.shape[0] + if cutlass.const_expr(mPageTable is None) + else max_seqlen_k + ) + cuseqlen_q = Int32(0) + if cutlass.const_expr(cum_seqlen_q is not None): + cuseqlen_q = cum_seqlen_q[batch_coord] + seqlen_q = cum_seqlen_q[batch_coord + 1] - cuseqlen_q + continue_cond = ( + not FmhaStaticTileScheduler.check_valid_work_for_seqlen_q( + self.qk_mma_tiler[0], + mma_block_coord[0], + seqlen_q, + ) + ) + if not continue_cond: + if cutlass.const_expr(cum_seqlen_k is not None): + cuseqlen_k = cum_seqlen_k[batch_coord] + seqlen_k = cum_seqlen_k[batch_coord + 1] - cuseqlen_k + + row_max = -Float32.inf + row_max_prev = -Float32.inf + row_sum = 0.0 + + start_count, trip_count = ( + FusedMask.get_trip_start_count_via_block_info( + mma_block_coord, + self.qk_mma_tiler, + seqlen_q, + seqlen_k, + self.is_causal, + self.is_local, + window_size_left, + window_size_right, + ) + ) + end_count = start_count + trip_count + # require at least one softmax iteration for zero trip_count case; + # rely on masking this iteration for correctness + if end_count <= start_count: + start_count = 0 + end_count = 1 + if cutlass.const_expr(self.use_semantic_trip_range): + n_block_min_causal_local_mask, n_block_min_before_local_mask = ( + FusedMask.get_trip_mask_bounds_via_block_info( + mma_block_coord, + self.qk_mma_tiler, + seqlen_q, + seqlen_k, + self.is_causal, + self.is_local, + window_size_left, + window_size_right, + ) + ) + cS_base = cute.make_identity_tensor( + (self.qk_mma_tiler[0], self.qk_mma_tiler[1]) + ) + cS = cute.domain_offset( + (mma_block_coord[0] * self.qk_mma_tiler[0], 0), cS_base + ) + tScS = qk_thr_mma.partition_C(cS) + + for step in cutlass.range(start_count, end_count, 1, unroll=1): + cS_iter = cute.domain_offset( + (0, step * self.qk_mma_tiler[1]), cS + ) + tScS_iter = qk_thr_mma.partition_C(cS_iter) + if cutlass.const_expr(self.use_semantic_trip_range): + need_apply_mask = ( + step >= n_block_min_causal_local_mask + or step < n_block_min_before_local_mask + or step == end_count - 1 + ) + else: + # Residual path only needs seqlen masking on the last K tile. + need_apply_mask = step == end_count - 1 + # Si -> Pi + ( + row_max, + row_sum, + mma_s_consumer, + p_mma_producer, + s_corr_producer, + ) = self.softmax_step( + (need_apply_mask, window_size_left, window_size_right), + ( + row_max_prev, + row_sum, + seqlen_q, + seqlen_k, + scale_softmax_log2, + ), + (tStS, tScS_iter), + (mma_s_consumer, p_mma_producer, s_corr_producer), + ) + row_max_prev = row_max + sum_producer = self.store_sum_max( + row_max, + mLSE, + row_sum, + sSum, + sum_producer, + curr_block_coord, + seqlen_q, + cum_seqlen_q, + cuseqlen_q, + scale_softmax, + ) + work_tile = tile_sched.advance_to_next_work() + p_mma_producer.tail() + s_corr_producer.tail() + + # /////////////////////////////////////////////////////////////////////////////// + # Correction + # /////////////////////////////////////////////////////////////////////////////// + if warp_idx >= self.correction_warp_ids[0] and warp_idx < self.mma_warp_id: + cute.arch.warpgroup_reg_dealloc(self.num_regs_correction) + + while work_tile.is_valid_tile: + curr_block_coord = work_tile.tile_idx + mma_block_coord = ( + curr_block_coord[0] // cute.size(qk_tiled_mma.thr_id.shape), + curr_block_coord[1], + curr_block_coord[2], + ) + batch_coord = curr_block_coord[2][1] + seqlen_q = mQ_qdl.shape[0] + seqlen_k = ( + mK_kdl.shape[0] + if cutlass.const_expr(mPageTable is None) + else max_seqlen_k + ) + continue_cond = False + cuseqlen_q = Int32(0) + if cutlass.const_expr(cum_seqlen_q is not None): + cuseqlen_q = cum_seqlen_q[batch_coord] + seqlen_q = cum_seqlen_q[batch_coord + 1] - cuseqlen_q + continue_cond = ( + not FmhaStaticTileScheduler.check_valid_work_for_seqlen_q( + self.qk_mma_tiler[0], + mma_block_coord[0], + seqlen_q, + ) + ) + + if not continue_cond: + if cutlass.const_expr(cum_seqlen_k is not None): + cuseqlen_k = cum_seqlen_k[batch_coord] + seqlen_k = cum_seqlen_k[batch_coord + 1] - cuseqlen_k + + mO_qdl_eff = mO_qdl + if cutlass.const_expr(cum_seqlen_q is not None): + block_offset_o = ( + cuseqlen_q, + Int32(0), + Int32(0), + ((Int32(0), Int32(0)), Int32(0)), + ) + mO_qdl_eff = cute.domain_offset( + cute.select(block_offset_o, mode=[0, 2, 3]), mO_qdl + ) + + # (bM, bN, loopM, loopN, loopL) + gO_qdl = cute.flat_divide( + mO_qdl_eff, cute.select(self.pv_block_tiler, mode=[0, 1]) + ) + cO_qdl = cute.flat_divide( + cute.make_identity_tensor(mO_qdl_eff.shape), + cute.select(self.pv_block_tiler, mode=[0, 1]), + ) + + _, seqlen_kv_loop_steps = ( + FusedMask.get_trip_start_count_via_block_info( + mma_block_coord, + self.qk_mma_tiler, + seqlen_q, + seqlen_k, + self.is_causal, + self.is_local, + window_size_left, + window_size_right, + ) + ) + gO_staged = gO_qdl[ + None, None, curr_block_coord[0], None, curr_block_coord[2] + ] + cO_staged = cO_qdl[ + None, None, curr_block_coord[0], None, curr_block_coord[2] + ] + cS = cute.make_identity_tensor( + (self.qk_mma_tiler[0], self.qk_mma_tiler[1]) + ) + tScS = qk_thr_mma.partition_C(cS) + + # Empty step as the first step is no need for correction + stats_handle = s_corr_consumer.wait_and_advance() + stats_handle.release() + for step in cutlass.range(1, seqlen_kv_loop_steps, 1, unroll=1): + # Oi-1 -> Oi + mma_corr_consumer, s_corr_consumer = self.correction_rescale( + scale_softmax_log2, + (s_corr_consumer, tStS, tScS), + (mma_corr_consumer, tOtO_staged, cO_staged), + self.epi_tile, + ) + # O_partial -> O_final + mma_corr_consumer, sum_consumer = self.correction_epilog( + (seqlen_q, scale_output), + (sum_consumer, sSum), + (mma_corr_consumer, gO_staged, cO_staged, tOtO_staged), + self.epi_tile, + ) + work_tile = tile_sched.advance_to_next_work() + # NOTE: tmem.free() moved to kernel end to enable cluster-wide sync + + # /////////////////////////////////////////////////////////////////////////////// + # Scheduler Warp (only for CLC dynamic scheduler) + # /////////////////////////////////////////////////////////////////////////////// + if cutlass.const_expr(self.use_clc_scheduler): + is_first_cta_in_cluster = cta_rank_in_cluster == 0 + + if warp_idx == self.sched_warp_id and is_first_cta_in_cluster: + cute.arch.warpgroup_reg_dealloc(self.num_regs_other) + while work_tile.is_valid_tile: + tile_sched.prefetch_next_work() + work_tile = tile_sched.advance_to_next_work() + tile_sched.producer_tail() + + # /////////////////////////////////////////////////////////////////////////////// + # Empty warps reg dealloc + # /////////////////////////////////////////////////////////////////////////////// + if cutlass.const_expr(self.use_clc_scheduler): + if warp_idx > self.load_warp_id: + if not (warp_idx == self.sched_warp_id and is_first_cta_in_cluster): + cute.arch.warpgroup_reg_dealloc(self.num_regs_other) + else: + if warp_idx > self.load_warp_id: + cute.arch.warpgroup_reg_dealloc(self.num_regs_other) + + # /////////////////////////////////////////////////////////////////////////////// + # Cooperative TMEM Deallocation (2CTA) + # /////////////////////////////////////////////////////////////////////////////// + # All warps (including scheduler) have finished by this point. + # Cluster-wide sync ensures both CTAs reach here before dealloc. + cute.arch.cluster_arrive() + cute.arch.cluster_wait() + tmem.relinquish_alloc_permit() + tmem.free(tmem_ptr) + + return + + @cute.jit + def softmax_step( + self, + mask_args: Tuple, + value_args: Tuple, + tensor_args: Tuple, + pipeline_args: Tuple, + ) -> Tuple[Float32, Float32, pipeline.PipelineConsumer, pipeline.PipelineProducer]: + need_apply_mask, window_size_left, window_size_right = mask_args + row_max, row_sum, seqlen_q, seqlen_k, scale_softmax_log2 = value_args + tStS, tScS = tensor_args + mma_s_consumer, p_mma_producer, s_corr_producer = pipeline_args + tidx, _, _ = cute.arch.thread_idx() + thread_idx = tidx % (self.threads_per_warp * len(self.softmax_warp_ids)) + s_handle = mma_s_consumer.wait_and_advance() + tStS_slice = tStS[(None, None), 0, 0, s_handle.index] + tScS_slice = tScS[(None, None), 0, 0] + tmem_load_atom = cute.make_copy_atom( + tcgen05.Ld32x32bOp(tcgen05.Repetition(32)), self.qk_acc_dtype + ) + tmem_tiled_load = tcgen05.make_tmem_copy(tmem_load_atom, tStS_slice) + thr_load = tmem_tiled_load.get_slice(thread_idx) + tTMEM_LOADtS = thr_load.partition_S(tStS_slice) + tTMEM_LOADcS = thr_load.partition_D(tScS_slice) + tTMEM_LOADrS = cute.make_rmem_tensor(tTMEM_LOADcS.shape, self.qk_acc_dtype) + cute.copy(tmem_tiled_load, tTMEM_LOADtS, tTMEM_LOADrS) + + cute.arch.fence_view_async_tmem_load() + s_handle.release() + if need_apply_mask: + FusedMask.apply_mask_via_causal_local( + tTMEM_LOADrS, + tTMEM_LOADcS, + seqlen_q, + seqlen_k, + self.use_semantic_trip_range, + self.is_causal, + self.is_local, + window_size_left, + window_size_right, + ) + old_row_max = row_max + row_max = tTMEM_LOADrS.load().reduce(cute.ReductionOp.MAX, row_max, 0) + row_max_safe = row_max + if row_max == -cutlass.Float32.inf: + row_max_safe = 0.0 + + stats_handle = s_corr_producer.acquire_and_advance() + stats_layout = cute.composition( + tStS_slice.layout, cute.make_layout((tStS_slice.shape[0], 2)) + ) + stats_c_layout = cute.composition( + tScS_slice.layout, cute.make_layout((tScS_slice.shape[0], 2)) + ) + tOtStats = cute.make_tensor( + tStS_slice.iterator + self.tilePlikeFP32, stats_layout + ) + tOcStats = cute.make_tensor(tScS_slice.iterator, stats_c_layout) + tmem_store_stats_atom = cute.make_copy_atom( + tcgen05.copy.St32x32bOp(tcgen05.copy.Repetition(2)), + self.qk_acc_dtype, + ) + tiled_tmem_store_stats = tcgen05.make_tmem_copy(tmem_store_stats_atom, tOtStats) + thr_tmem_store_stats = tiled_tmem_store_stats.get_slice(thread_idx) + tTMEM_STOREcStats = thr_tmem_store_stats.partition_S(tOcStats) + tTMEM_STORErStats = cute.make_rmem_tensor( + tTMEM_STOREcStats.shape, self.qk_acc_dtype + ) + tTMEM_STORErStats[0] = old_row_max + tTMEM_STORErStats[1] = row_max_safe + tTMEM_STOREtStats = thr_tmem_store_stats.partition_D(tOtStats) + cute.copy(tiled_tmem_store_stats, tTMEM_STORErStats, tTMEM_STOREtStats) + cute.arch.fence_view_async_tmem_store() + stats_handle.commit() + + scale = scale_softmax_log2 + minus_row_max_scale = (0.0 - row_max_safe) * scale + # Acquire P write slot early — overlaps any pipeline stall with exp2 compute + p_handle = p_mma_producer.acquire_and_advance() + # Fragment-based FMA + exp2 + bf16 conversion + # Trades SFU for FMA via polynomial emulation on a fraction of elements + ex2_frg_tile = 32 + ex2_frg_cnt = cute.size(tTMEM_LOADrS) // ex2_frg_tile + tTMEM_LOADrS_ex2 = cute.logical_divide( + tTMEM_LOADrS, cute.make_layout(ex2_frg_tile) + ) + tTMEM_STORErP = cute.make_rmem_tensor(tTMEM_LOADrS.shape, self.q_dtype) + tTMEM_STORErP_ex2 = cute.logical_divide( + tTMEM_STORErP, cute.make_layout(ex2_frg_tile) + ) + for j in cutlass.range_constexpr(ex2_frg_cnt): + for k in cutlass.range_constexpr(0, ex2_frg_tile, 2): + tTMEM_LOADrS_ex2[k, j], tTMEM_LOADrS_ex2[k + 1, j] = ( + cute.arch.fma_packed_f32x2( + (tTMEM_LOADrS_ex2[k, j], tTMEM_LOADrS_ex2[k + 1, j]), + (scale, scale), + (minus_row_max_scale, minus_row_max_scale), + ) + ) + if cutlass.const_expr(self.ex2_emu_freq == 0): + tTMEM_LOADrS_ex2[k, j] = cute.math.exp2( + tTMEM_LOADrS_ex2[k, j], fastmath=True + ) + tTMEM_LOADrS_ex2[k + 1, j] = cute.math.exp2( + tTMEM_LOADrS_ex2[k + 1, j], fastmath=True + ) + else: + if cutlass.const_expr( + k % self.ex2_emu_freq < self.ex2_emu_freq - self.ex2_emu_res + or j >= ex2_frg_cnt - 1 + or j < self.ex2_emu_start_frg + ): + tTMEM_LOADrS_ex2[k, j] = cute.math.exp2( + tTMEM_LOADrS_ex2[k, j], fastmath=True + ) + tTMEM_LOADrS_ex2[k + 1, j] = cute.math.exp2( + tTMEM_LOADrS_ex2[k + 1, j], fastmath=True + ) + else: + tTMEM_LOADrS_ex2[k, j], tTMEM_LOADrS_ex2[k + 1, j] = ( + ex2_emulation_2( + tTMEM_LOADrS_ex2[k, j], tTMEM_LOADrS_ex2[k + 1, j] + ) + ) + tTMEM_STORErP_ex2[None, j].store( + tTMEM_LOADrS_ex2[None, j].load().to(self.q_dtype) + ) + tmem_store_atom = cute.make_copy_atom( + tcgen05.St32x32bOp(tcgen05.Repetition(32)), self.qk_acc_dtype + ) + tilePlikeFP32 = tStS_slice.shape[1] // Float32.width * self.q_dtype.width + tStS_P_layout = cute.composition( + tStS_slice.layout, cute.make_layout((tStS_slice.shape[0], tilePlikeFP32)) + ) + tStS_P = cute.make_tensor(tStS_slice.iterator, tStS_P_layout) + tScS_P_layout = cute.composition( + tScS_slice.layout, cute.make_layout((tScS_slice.shape[0], tilePlikeFP32)) + ) + tScS_P = cute.make_tensor(tScS_slice.iterator, tScS_P_layout) + tmem_tiled_store = tcgen05.make_tmem_copy(tmem_store_atom, tStS_P) + thr_store = tmem_tiled_store.get_slice(thread_idx) + tTMEM_STOREtP = thr_store.partition_D(tStS_P) + tTMEM_STOREcS = thr_store.partition_S(tScS_P) + tTMEM_STORErP_ = cute.make_tensor( + cute.recast_ptr(tTMEM_STORErP.iterator, dtype=self.qk_acc_dtype), + tTMEM_STOREcS.shape, + ) + cute.copy(tmem_tiled_store, tTMEM_STORErP_, tTMEM_STOREtP) + cute.arch.fence_view_async_tmem_store() + + p_handle.commit() + acc_scale_ = scale * (old_row_max - row_max_safe) + acc_scale = cute.math.exp2(acc_scale_, fastmath=True) * 0.5 + # TODO: calc row sum with TensorSSA + row_sum *= acc_scale + local_row_sum_0 = (row_sum, row_sum) + local_row_sum_1 = (0.0, 0.0) + local_row_sum_2 = (0.0, 0.0) + local_row_sum_3 = (0.0, 0.0) + reduction_unroll = 4 + frg_tile = cute.size(tTMEM_LOADrS) // reduction_unroll + tTMEM_LOADrS_frg = cute.logical_divide(tTMEM_LOADrS, cute.make_layout(frg_tile)) + for j in cutlass.range_constexpr(0, cute.size(tTMEM_LOADrS_frg, mode=[0]), 2): + local_row_sum_0 = cute.arch.add_packed_f32x2( + local_row_sum_0, (tTMEM_LOADrS_frg[j, 0], tTMEM_LOADrS_frg[j + 1, 0]) + ) + local_row_sum_1 = cute.arch.add_packed_f32x2( + local_row_sum_1, (tTMEM_LOADrS_frg[j, 1], tTMEM_LOADrS_frg[j + 1, 1]) + ) + local_row_sum_2 = cute.arch.add_packed_f32x2( + local_row_sum_2, (tTMEM_LOADrS_frg[j, 2], tTMEM_LOADrS_frg[j + 1, 2]) + ) + local_row_sum_3 = cute.arch.add_packed_f32x2( + local_row_sum_3, (tTMEM_LOADrS_frg[j, 3], tTMEM_LOADrS_frg[j + 1, 3]) + ) + local_row_sum_0 = cute.arch.add_packed_f32x2(local_row_sum_0, local_row_sum_1) + local_row_sum_2 = cute.arch.add_packed_f32x2(local_row_sum_2, local_row_sum_3) + local_row_sum_0 = cute.arch.add_packed_f32x2(local_row_sum_0, local_row_sum_2) + row_sum = local_row_sum_0[0] + local_row_sum_0[1] + return row_max, row_sum, mma_s_consumer, p_mma_producer, s_corr_producer + + @cute.jit + def correction_rescale( + self, + scale_softmax_log2: Float32, + stats_args: tuple, + o_args: tuple, + epi_tile: cute.Tile, + ) -> pipeline.PipelineConsumer: + s_corr_consumer, tStS, tScS = stats_args + mma_o_consumer, tOtO_staged, cO_staged = o_args + tidx, _, _ = cute.arch.thread_idx() + thread_idx = tidx % (self.threads_per_warp * len(self.softmax_warp_ids)) + + stats_handle = s_corr_consumer.wait_and_advance() + tStS_slice = tStS[(None, None), 0, 0, stats_handle.index] + tScS_slice = tScS[(None, None), 0, 0] + stats_layout = cute.composition( + tStS_slice.layout, cute.make_layout((tStS_slice.shape[0], 2)) + ) + stats_c_layout = cute.composition( + tScS_slice.layout, cute.make_layout((tScS_slice.shape[0], 2)) + ) + tOtStats = cute.make_tensor( + tStS_slice.iterator + self.tilePlikeFP32, stats_layout + ) + tOcStats = cute.make_tensor(tScS_slice.iterator, stats_c_layout) + tmem_load_stats_atom = cute.make_copy_atom( + tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(2)), + self.qk_acc_dtype, + ) + tiled_tmem_load_stats = tcgen05.make_tmem_copy(tmem_load_stats_atom, tOtStats) + thr_tmem_load_stats = tiled_tmem_load_stats.get_slice(thread_idx) + tTMEM_LOADtStats = thr_tmem_load_stats.partition_S(tOtStats) + tTMEM_LOADcStats = thr_tmem_load_stats.partition_D(tOcStats) + tTMEM_LOADrStats = cute.make_rmem_tensor( + tTMEM_LOADcStats.shape, self.qk_acc_dtype + ) + cute.copy(tiled_tmem_load_stats, tTMEM_LOADtStats, tTMEM_LOADrStats) + + scale = scale_softmax_log2 * (tTMEM_LOADrStats[0] - tTMEM_LOADrStats[1]) + scale = cute.math.exp2(scale, fastmath=True) + stats_handle.release() + o_handle = mma_o_consumer.wait_and_advance() + for iter in cutlass.range(self.iterations_pv, unroll_full=True): + tOtO = tOtO_staged[(None, None), 0, 0, iter] + cO = cO_staged[None, None, iter] + tOtO_epi = cute.zipped_divide(tOtO, epi_tile) + cO_epi = cute.zipped_divide(cO, epi_tile) + tmem_load_atom = cute.make_copy_atom( + tcgen05.Ld32x32bOp(tcgen05.Repetition(16)), + self.pv_acc_dtype, + ) + tmem_tiled_load = tcgen05.make_tmem_copy(tmem_load_atom, tOtO_epi) + thr_load = tmem_tiled_load.get_slice(thread_idx) + tmem_store_atom = cute.make_copy_atom( + tcgen05.St32x32bOp(tcgen05.Repetition(16)), + self.pv_acc_dtype, + ) + tmem_store_atom = tcgen05.make_tmem_copy(tmem_store_atom, tOtO_epi) + thr_store = tmem_store_atom.get_slice(thread_idx) + tTMEM_LOADtO = thr_load.partition_S(tOtO_epi) + tTMEM_LOADcO = thr_load.partition_D(cO_epi) + tTMEM_STOREtO = thr_store.partition_D(tOtO_epi) + tTMrO = cute.make_rmem_tensor_like( + cute.append( + cute.make_layout(tTMEM_LOADcO[None, 0, 0].shape), + cute.make_layout( + 2, stride=cute.size(tTMEM_LOADcO[None, 0, 0].shape) + ), + ), + self.pv_acc_dtype, + ) + tTMEM_LOADtO_0 = tTMEM_LOADtO[None, 0, 0] + cute.copy(tmem_tiled_load, tTMEM_LOADtO_0, tTMrO[None, 0]) + iter_num = cute.size(tTMEM_LOADtO, mode=[1]) + for i in cutlass.range(1, iter_num, unroll_full=True): + tTMEM_LOADtO_i = tTMEM_LOADtO[None, i, 0] + cute.copy(tmem_tiled_load, tTMEM_LOADtO_i, tTMrO[None, i % 2]) + for j in cutlass.range( + 0, cute.size(tTMrO, mode=[0]), 2, unroll_full=True + ): + tTMrO[j, (i - 1) % 2], tTMrO[j + 1, (i - 1) % 2] = ( + cute.arch.mul_packed_f32x2( + (tTMrO[j, (i - 1) % 2], tTMrO[j + 1, (i - 1) % 2]), + (scale, scale), + ) + ) + tTMEM_STOREtO_prev_i = tTMEM_STOREtO[None, i - 1, 0] + cute.copy( + tmem_store_atom, tTMrO[None, (i - 1) % 2], tTMEM_STOREtO_prev_i + ) + + for j in cutlass.range(0, cute.size(tTMrO, mode=[0]), 2, unroll_full=True): + tTMrO[j, (iter_num - 1) % 2], tTMrO[j + 1, (iter_num - 1) % 2] = ( + cute.arch.mul_packed_f32x2( + ( + tTMrO[j, (iter_num - 1) % 2], + tTMrO[j + 1, (iter_num - 1) % 2], + ), + (scale, scale), + ) + ) + cute.copy( + tmem_store_atom, + tTMrO[None, (iter_num - 1) % 2], + tTMEM_STOREtO[None, iter_num - 1, 0], + ) + cute.arch.fence_view_async_tmem_store() + o_handle.release() + return mma_o_consumer, s_corr_consumer + + @cute.jit + def correction_epilog( + self, + value_args: Tuple, + sum_args: Tuple, + o_args: Tuple, + epi_tile: cute.Tile, + ) -> Tuple[pipeline.PipelineConsumer, pipeline.PipelineProducer]: + seqlen_q, scale_output = value_args + sum_consumer, sSum = sum_args + mma_o_consumer, gO_staged, cO_staged, tOtO_staged = o_args + tidx, _, _ = cute.arch.thread_idx() + thread_idx = tidx % (self.threads_per_warp * len(self.softmax_warp_ids)) + sum_handle = sum_consumer.wait_and_advance() + row_sum = sSum[thread_idx] + cute.arch.fence_view_async_shared() + sum_handle.release() + row_sum_is_zero_or_nan = row_sum == 0.0 or row_sum != row_sum + scale = scale_output / row_sum if not row_sum_is_zero_or_nan else 0.0 + o_handle = mma_o_consumer.wait_and_advance() + for iter in cutlass.range(self.iterations_pv): + gO = gO_staged[None, None, iter] + cO = cO_staged[None, None, iter] + tOtO = tOtO_staged[(None, None), 0, 0, iter] + tOtO_epi = cute.zipped_divide(tOtO, epi_tile) + cO_epi = cute.zipped_divide(cO, epi_tile) + gO_epi = cute.zipped_divide(gO, epi_tile) + tidx, _, _ = cute.arch.thread_idx() + thread_idx = tidx % (self.threads_per_warp * len(self.softmax_warp_ids)) + tmem_copy_atom = cute.make_copy_atom( + tcgen05.copy.Ld32x32bOp(tcgen05.copy.Repetition(32)), self.pv_acc_dtype + ) + tiled_tmem_load = tcgen05.make_tmem_copy(tmem_copy_atom, tOtO_epi) + thr_tmem_load = tiled_tmem_load.get_slice(thread_idx) + tTMEM_LOADtO = thr_tmem_load.partition_S(tOtO_epi) + tTMEM_LOADgO = thr_tmem_load.partition_D(gO_epi) + tTMEM_LOADcO = thr_tmem_load.partition_D(cO_epi) + for i in cutlass.range(cute.size(tTMEM_LOADtO, mode=[1]), unroll_full=True): + tTMEM_LOADtO_i = tTMEM_LOADtO[None, i, 0] + tTMEM_LOADgO_i = tTMEM_LOADgO[None, i, 0] + tTMEM_LOADcO_i = tTMEM_LOADcO[None, i, 0] + tTMrO = cute.make_rmem_tensor( + tTMEM_LOADcO[None, 0, i].shape, self.pv_acc_dtype + ) + cute.copy(tiled_tmem_load, tTMEM_LOADtO_i, tTMrO) + for j in cutlass.range(0, cute.size(tTMrO), 2, unroll_full=True): + tTMrO[j], tTMrO[j + 1] = cute.arch.mul_packed_f32x2( + (tTMrO[j], tTMrO[j + 1]), + (scale, scale), + ) + tSMrO = cute.make_rmem_tensor(tTMrO.shape, self.o_dtype) + o_vec = tTMrO.load() + tSMrO.store(o_vec.to(self.o_dtype)) + if cute.elem_less(tTMEM_LOADcO_i[0][0], seqlen_q): + cute.autovec_copy(tSMrO, tTMEM_LOADgO_i) + o_handle.release() + return mma_o_consumer, sum_consumer + + @cute.jit + def store_sum_max( + self, + row_max, + mLSE, + row_sum, + sSum, + sum_producer, + current_block_coord, + seqlen_q, + cum_seqlen_q, + cuseqlen_q, + scale_softmax, + ): + tidx, _, _ = cute.arch.thread_idx() + thread_idx = tidx % (self.threads_per_warp * len(self.softmax_warp_ids)) + sum_handle = sum_producer.acquire_and_advance() + sSum[thread_idx] = row_sum + cute.arch.fence_view_async_shared() + sum_handle.commit() + row_sum_is_zero_or_nan = row_sum == 0.0 or row_sum != row_sum + + if cutlass.const_expr(mLSE is not None): + q_idx = current_block_coord[0] * self.cta_tiler[0] + tidx + hb_idx = ( + (current_block_coord[2][0], Int32(0)) + if cutlass.const_expr(cum_seqlen_q is not None) + else current_block_coord[2] + ) + lse_value = ( + scale_softmax * row_max + cute.math.log(row_sum, fastmath=True) + if not row_sum_is_zero_or_nan + else -Float32.inf + ) + if cute.elem_less(q_idx, seqlen_q): + global_q_idx = ( + q_idx + cuseqlen_q + if cutlass.const_expr(cum_seqlen_q is not None) + else q_idx + ) + mLSE[global_q_idx, hb_idx] = lse_value + return sum_producer diff --git a/python/sglang/jit_kernel/flash_attn/cute/sm90_config_search.py b/python/sglang/jit_kernel/flash_attn/cute/sm90_config_search.py new file mode 100644 index 000000000..1e1a38997 --- /dev/null +++ b/python/sglang/jit_kernel/flash_attn/cute/sm90_config_search.py @@ -0,0 +1,416 @@ +"""Search feasible SM90 fwd/bwd attention configs for given (head_dim, head_dim_v). + +Enumerates tile sizes, swap modes, atom layouts, and staging options. +Checks GMMA divisibility, register budget, and shared memory budget. + +Usage: + python flash_attn/cute/sm90_config_search.py --headdim 128 + python flash_attn/cute/sm90_config_search.py --mode fwd --headdim 192-128 + python flash_attn/cute/sm90_config_search.py --mode bwd --headdim 192 --tile-n 64,96 +""" + +import math + +# H100 hardware limits +SMEM_LIMIT = 224 * 1024 # 228 KB minus ~3 KB for LSE, dPsum, mbarriers +REG_LIMITS = {2: 216, 3: 128} # per-WG budget: 2WG=240-24, 3WG=160-32 +THREADS_PER_WG = 128 + + +def _divisors(n): + return [d for d in range(1, n + 1) if n % d == 0] + + +def _acc_regs(M, N, num_wg): + """Accumulator registers per thread per WG.""" + return M * N // (num_wg * THREADS_PER_WG) + + +def _check_mma(M, N, num_wg, atom_layout_m, swap_AB): + """Check MMA feasibility. Returns regs per WG, or None if infeasible. + + GMMA atom M=64. Swap exchanges (M, N) and atom layout. + Requires: M divisible by (atom_layout_m * 64), N by (atom_layout_n * 8). + """ + if swap_AB: + M, N = N, M + atom_layout_m = num_wg // atom_layout_m + atom_layout_n = num_wg // atom_layout_m + if M % (atom_layout_m * 64) != 0 or N % (atom_layout_n * 8) != 0: + return None + return _acc_regs(M, N, num_wg) + + +def _mma_traffic(M_eff, N_eff, K_red, num_wg, wg_n, is_rs=False): + """Total SMEM read traffic for one MMA (all WGs combined). + + num_instr = (M_eff / 64) * wg_n instructions total. + Each reads A(64, K_red) and B(N_eff/wg_n, K_red) from smem (bf16). + """ + num_instr = (M_eff // 64) * wg_n + A_per = 64 * K_red * 2 if not is_rs else 0 + B_per = (N_eff // wg_n) * K_red * 2 + return num_instr * (A_per + B_per) + + +# ============================================================================ +# Backward +# ============================================================================ + + +def _check_bwd_config( + hdim, + hdimv, + tile_m, + tile_n, + num_wg, + SdP_swapAB, + dKV_swapAB, + dQ_swapAB, + AtomLayoutMSdP, + AtomLayoutNdKV, + AtomLayoutMdQ, +): + reg_limit = REG_LIMITS[num_wg] + + # MMA feasibility + regs_SdP = _check_mma(tile_m, tile_n, num_wg, AtomLayoutMSdP, SdP_swapAB) + regs_dK = _check_mma(tile_n, hdim, num_wg, AtomLayoutNdKV, dKV_swapAB) + regs_dV = _check_mma(tile_n, hdimv, num_wg, AtomLayoutNdKV, dKV_swapAB) + regs_dQ = _check_mma(tile_m, hdim, num_wg, AtomLayoutMdQ, dQ_swapAB) + if any(r is None for r in (regs_SdP, regs_dK, regs_dV, regs_dQ)): + return None + + # Peak regs: max(S+dP, dQ) + dK + dV + total_regs = max(2 * regs_SdP, regs_dQ) + regs_dK + regs_dV + if total_regs > reg_limit: + return None + + # SMEM + mma_dkv_is_rs = ( + AtomLayoutMSdP == 1 + and AtomLayoutNdKV == num_wg + and SdP_swapAB + and not dKV_swapAB + ) + Q_stage, PdS_stage = 2, 1 + + for dO_stage in (2, 1): + sQ = tile_m * hdim * 2 * Q_stage + sK = tile_n * hdim * 2 + sV = tile_n * hdimv * 2 + sdO = tile_m * hdimv * 2 * dO_stage + sPdS = tile_m * tile_n * 2 * PdS_stage + sP = sPdS if not mma_dkv_is_rs else 0 + sdQaccum = tile_m * hdim * 4 + smem = sQ + sK + sV + sdO + sP + sPdS + sdQaccum + if smem <= SMEM_LIMIT: + break + else: + return None + + # SMEM traffic + def _swap(a, b, s): + return (b, a) if s else (a, b) + + def _wg_n(al_m, s): + return al_m if s else num_wg // al_m + + M_s, N_s = _swap(tile_m, tile_n, SdP_swapAB) + wn_SdP = _wg_n(AtomLayoutMSdP, SdP_swapAB) + traffic_S = _mma_traffic(M_s, N_s, hdim, num_wg, wn_SdP) + traffic_dP = _mma_traffic(M_s, N_s, hdimv, num_wg, wn_SdP) + + wn_dKV = _wg_n(AtomLayoutNdKV, dKV_swapAB) + M_dv, N_dv = _swap(tile_n, hdimv, dKV_swapAB) + traffic_dV = _mma_traffic(M_dv, N_dv, tile_m, num_wg, wn_dKV, is_rs=mma_dkv_is_rs) + M_dk, N_dk = _swap(tile_n, hdim, dKV_swapAB) + traffic_dK = _mma_traffic(M_dk, N_dk, tile_m, num_wg, wn_dKV, is_rs=mma_dkv_is_rs) + + M_dq, N_dq = _swap(tile_m, hdim, dQ_swapAB) + wn_dQ = _wg_n(AtomLayoutMdQ, dQ_swapAB) + traffic_dQ = _mma_traffic(M_dq, N_dq, tile_n, num_wg, wn_dQ) + + traffic_P_store = tile_m * tile_n * 2 if not mma_dkv_is_rs else 0 + traffic_dS_store = tile_m * tile_n * 2 + traffic_dQ_smem = tile_m * hdim * 4 * 2 # store + TMA load + + smem_traffic = ( + traffic_S + + traffic_dP + + traffic_dV + + traffic_dK + + traffic_dQ + + traffic_P_store + + traffic_dS_store + + traffic_dQ_smem + ) + + return dict( + tile_m=tile_m, + tile_n=tile_n, + num_wg=num_wg, + Q_stage=Q_stage, + dO_stage=dO_stage, + PdS_stage=PdS_stage, + SdP_swapAB=SdP_swapAB, + dKV_swapAB=dKV_swapAB, + dQ_swapAB=dQ_swapAB, + AtomLayoutMSdP=AtomLayoutMSdP, + AtomLayoutNdKV=AtomLayoutNdKV, + AtomLayoutMdQ=AtomLayoutMdQ, + mma_dkv_is_rs=mma_dkv_is_rs, + regs_SdP=regs_SdP, + regs_dK=regs_dK, + regs_dV=regs_dV, + regs_dQ=regs_dQ, + total_regs=total_regs, + reg_limit=reg_limit, + smem_bytes=smem, + smem_kb=smem / 1024, + smem_traffic=smem_traffic, + smem_traffic_kb=smem_traffic / 1024, + smem_traffic_per_block=smem_traffic / (tile_m * tile_n), + ) + + +def find_feasible_bwd_configs( + head_dim, + head_dim_v=None, + tile_m_choices=(64, 80, 96, 112, 128), + tile_n_choices=(64, 80, 96, 112, 128), +): + if head_dim_v is None: + head_dim_v = head_dim + hdim = int(math.ceil(head_dim / 32) * 32) + hdimv = int(math.ceil(head_dim_v / 32) * 32) + + results = [] + for num_wg in (2, 3): + divs = _divisors(num_wg) + for tile_m in tile_m_choices: + for tile_n in tile_n_choices: + for SdP_swap in (False, True): + if (tile_n if SdP_swap else tile_m) % 64 != 0: + continue + for dKV_swap in (False, True): + if not dKV_swap and tile_n % 64 != 0: + continue + if dKV_swap and (hdim % 64 != 0 or hdimv % 64 != 0): + continue + for dQ_swap in (False, True): + if (hdim if dQ_swap else tile_m) % 64 != 0: + continue + for a1 in divs: + for a2 in divs: + for a3 in divs: + cfg = _check_bwd_config( + hdim, + hdimv, + tile_m, + tile_n, + num_wg, + SdP_swap, + dKV_swap, + dQ_swap, + a1, + a2, + a3, + ) + if cfg is not None: + results.append(cfg) + + results.sort( + key=lambda c: (-c["tile_n"], -c["tile_m"], c["smem_traffic_per_block"]) + ) + return results + + +def print_bwd_configs(configs, max_results=20): + if not configs: + print("No feasible configs found!") + return + n = min(len(configs), max_results) + print(f"Found {len(configs)} feasible configs (showing top {n}):\n") + hdr = ( + f"{'wg':>2} {'tm':>3} {'tn':>3} " + f"{'SdP':>3} {'dKV':>3} {'dQ':>3} " + f"{'aSdP':>4} {'adKV':>4} {'adQ':>4} " + f"{'Qs':>2} {'dOs':>3} " + f"{'rS':>3} {'rdK':>3} {'rdV':>3} {'rdQ':>3} {'tot':>4}/{'':<3} " + f"{'smem':>5} {'traffic':>7} {'tr/blk':>6}" + ) + print(hdr) + print("-" * len(hdr)) + B = lambda b: "T" if b else "F" + for c in configs[:max_results]: + print( + f"{c['num_wg']:>2} {c['tile_m']:>3} {c['tile_n']:>3} " + f"{B(c['SdP_swapAB']):>3} {B(c['dKV_swapAB']):>3} {B(c['dQ_swapAB']):>3} " + f"{c['AtomLayoutMSdP']:>4} {c['AtomLayoutNdKV']:>4} {c['AtomLayoutMdQ']:>4} " + f"{c['Q_stage']:>2} {c['dO_stage']:>3} " + f"{c['regs_SdP']:>3} {c['regs_dK']:>3} {c['regs_dV']:>3} {c['regs_dQ']:>3} " + f"{c['total_regs']:>4}/{c['reg_limit']:<3} " + f"{c['smem_kb']:>4.0f}K " + f"{c['smem_traffic_kb']:>6.0f}K " + f"{c['smem_traffic_per_block']:>6.1f}" + ) + + +# ============================================================================ +# Forward +# ============================================================================ + + +def _check_fwd_config(hdim, hdimv, tile_n, num_wg, pv_is_rs, overlap_wg): + reg_limit = REG_LIMITS[num_wg] + tile_m = num_wg * 64 + + if tile_n % 8 != 0: + return None + + regs_S = _acc_regs(tile_m, tile_n, num_wg) + regs_O = _acc_regs(tile_m, hdimv, num_wg) + regs_P = regs_S // 2 # bf16 = half of f32 + + if overlap_wg: + total_regs = regs_S + regs_P + regs_O + else: + total_regs = regs_S + regs_O + + if total_regs > reg_limit: + return None + + # SMEM: 1 stage Q, 2 stages K/V, O overlaps Q, sP if not RS + sQ = tile_m * hdim * 2 + sK = tile_n * hdim * 2 * 2 + sV = tile_n * hdimv * 2 * 2 + sO = tile_m * hdimv * 2 + sP = tile_m * tile_n * 2 if not pv_is_rs else 0 + smem = max(sQ, sO) + sK + sV + sP + if smem > SMEM_LIMIT: + return None + + # SMEM traffic: num_instr = num_wg (all WGs in M, wg_n=1) + traffic_S = num_wg * (64 * hdim * 2 + tile_n * hdim * 2) + A_pv = 64 * tile_n * 2 if not pv_is_rs else 0 + traffic_O = num_wg * (A_pv + hdimv * tile_n * 2) + traffic_P_store = tile_m * tile_n * 2 if not pv_is_rs else 0 + smem_traffic = traffic_S + traffic_O + traffic_P_store + + return dict( + tile_m=tile_m, + tile_n=tile_n, + num_wg=num_wg, + pv_is_rs=pv_is_rs, + overlap_wg=overlap_wg, + regs_S=regs_S, + regs_O=regs_O, + regs_P=regs_P, + total_regs=total_regs, + reg_limit=reg_limit, + smem_bytes=smem, + smem_kb=smem / 1024, + smem_traffic=smem_traffic, + smem_traffic_kb=smem_traffic / 1024, + smem_traffic_per_block=smem_traffic / (tile_m * tile_n), + ) + + +def find_feasible_fwd_configs( + head_dim, head_dim_v=None, tile_n_choices=(64, 80, 96, 112, 128, 144, 160, 176, 192) +): + if head_dim_v is None: + head_dim_v = head_dim + hdim = int(math.ceil(head_dim / 32) * 32) + hdimv = int(math.ceil(head_dim_v / 32) * 32) + + results = [] + for num_wg in (2, 3): + for tile_n in tile_n_choices: + for pv_is_rs in (True, False): + for overlap_wg in (True, False): + cfg = _check_fwd_config( + hdim, hdimv, tile_n, num_wg, pv_is_rs, overlap_wg + ) + if cfg is not None: + results.append(cfg) + + results.sort(key=lambda c: (-c["tile_n"], c["smem_traffic_per_block"])) + return results + + +def print_fwd_configs(configs, max_results=20): + if not configs: + print("No feasible configs found!") + return + n = min(len(configs), max_results) + print(f"Found {len(configs)} feasible configs (showing top {n}):\n") + hdr = ( + f"{'wg':>2} {'tm':>3} {'tn':>3} " + f"{'RS':>2} {'olap':>4} " + f"{'rS':>3} {'rP':>3} {'rO':>3} {'tot':>4}/{'':<3} " + f"{'smem':>5} {'traffic':>7} {'tr/blk':>6}" + ) + print(hdr) + print("-" * len(hdr)) + B = lambda b: "T" if b else "F" + for c in configs[:max_results]: + print( + f"{c['num_wg']:>2} {c['tile_m']:>3} {c['tile_n']:>3} " + f"{B(c['pv_is_rs']):>2} {B(c['overlap_wg']):>4} " + f"{c['regs_S']:>3} {c['regs_P']:>3} {c['regs_O']:>3} " + f"{c['total_regs']:>4}/{c['reg_limit']:<3} " + f"{c['smem_kb']:>4.0f}K " + f"{c['smem_traffic_kb']:>6.0f}K " + f"{c['smem_traffic_per_block']:>6.1f}" + ) + + +# ============================================================================ +# CLI +# ============================================================================ + +if __name__ == "__main__": + import argparse + + parser = argparse.ArgumentParser(description="Search feasible SM90 MMA configs") + parser.add_argument("--mode", choices=["fwd", "bwd", "both"], default="both") + parser.add_argument( + "--headdim", + type=str, + default="128", + help="Head dim, or hdim-hdimv (e.g. 192-128)", + ) + parser.add_argument( + "--tile-m", type=str, default="64,80,96,112,128", help="Bwd tile_m choices" + ) + parser.add_argument( + "--tile-n", + type=str, + default=None, + help="tile_n choices (default: fwd up to 192, bwd up to 128)", + ) + parser.add_argument("-n", "--num-results", type=int, default=30) + args = parser.parse_args() + + parts = args.headdim.split("-") + hdim = int(parts[0]) + hdimv = int(parts[1]) if len(parts) > 1 else hdim + + TN_FWD = "64,80,96,112,128,144,160,176,192" + TN_BWD = "64,80,96,112,128" + + if args.mode in ("fwd", "both"): + tn = tuple(int(x) for x in (args.tile_n or TN_FWD).split(",")) + print(f"=== FWD configs: hdim={hdim}, hdimv={hdimv} ===\n") + print_fwd_configs(find_feasible_fwd_configs(hdim, hdimv, tn), args.num_results) + print() + + if args.mode in ("bwd", "both"): + tm = tuple(int(x) for x in args.tile_m.split(",")) + tn = tuple(int(x) for x in (args.tile_n or TN_BWD).split(",")) + print(f"=== BWD configs: hdim={hdim}, hdimv={hdimv} ===\n") + print_bwd_configs( + find_feasible_bwd_configs(hdim, hdimv, tm, tn), args.num_results + ) diff --git a/python/sglang/jit_kernel/flash_attn/cute/softmax.py b/python/sglang/jit_kernel/flash_attn/cute/softmax.py new file mode 100644 index 000000000..874e4a493 --- /dev/null +++ b/python/sglang/jit_kernel/flash_attn/cute/softmax.py @@ -0,0 +1,759 @@ +# Copyright (c) 2025, Tri Dao. + +import math +import operator +from dataclasses import dataclass +from typing import Tuple + +import cutlass +import cutlass.cute as cute +from cutlass import Boolean, Float32 +from quack import layout_utils +from quack.cute_dsl_utils import ParamsBase + +import sglang.jit_kernel.flash_attn.cute.utils as utils +from sglang.jit_kernel.flash_attn.cute.seqlen_info import SeqlenInfoQK +from sglang.jit_kernel.flash_attn.cute.utils import AuxData + + +@cute.jit +def call_score_mod( + score_mod: cutlass.Constexpr, + score, + batch_idx, + head_idx, + q_idx, + kv_idx, + seqlen_info, + aux_data: AuxData, +): + aux_tensors = aux_data.tensors if aux_data.tensors is not None else () + # Compatibility shim for pre-aux_scalars score_mod callables. + if cutlass.const_expr(aux_data.scalars is not None): + return score_mod( + score, + batch_idx, + head_idx, + q_idx=q_idx, + kv_idx=kv_idx, + seqlen_info=seqlen_info, + aux_tensors=aux_tensors, + aux_scalars=aux_data.scalars, + ) + return score_mod( + score, + batch_idx, + head_idx, + q_idx=q_idx, + kv_idx=kv_idx, + seqlen_info=seqlen_info, + aux_tensors=aux_tensors, + ) + + +@cute.jit +def call_score_mod_bwd( + score_mod_bwd: cutlass.Constexpr, + grad, + score, + batch_idx, + head_idx, + q_idx, + kv_idx, + seqlen_info, + aux_data: AuxData, +): + aux_tensors = aux_data.tensors if aux_data.tensors is not None else () + # Compatibility shim for pre-aux_scalars score_mod_bwd callables. + if cutlass.const_expr(aux_data.scalars is not None): + return score_mod_bwd( + grad, + score, + batch_idx, + head_idx, + q_idx=q_idx, + kv_idx=kv_idx, + seqlen_info=seqlen_info, + aux_tensors=aux_tensors, + aux_scalars=aux_data.scalars, + ) + return score_mod_bwd( + grad, + score, + batch_idx, + head_idx, + q_idx=q_idx, + kv_idx=kv_idx, + seqlen_info=seqlen_info, + aux_tensors=aux_tensors, + ) + + +@dataclass +class Softmax(ParamsBase): + scale_log2: Float32 + num_rows: cutlass.Constexpr[int] + row_max: cute.Tensor + row_sum: cute.Tensor + arch: cutlass.Constexpr[int] = 80 + softmax_scale: Float32 | None = None + + @staticmethod + def create( + scale_log2: Float32, + num_rows: cutlass.Constexpr[int], + arch: cutlass.Constexpr[int] = 80, + softmax_scale: Float32 | None = None, + ): + row_max = cute.make_rmem_tensor(num_rows, Float32) + row_sum = cute.make_rmem_tensor(num_rows, Float32) + return Softmax(scale_log2, num_rows, row_max, row_sum, arch, softmax_scale) + + def reset(self) -> None: + self.row_max.fill(-Float32.inf) + self.row_sum.fill(0.0) + + def _compute_row_max( + self, acc_S_row: cute.TensorSSA, init_val: float | Float32 | None = None + ) -> Float32: + return utils.fmax_reduce(acc_S_row, init_val, arch=self.arch) + + def _compute_row_sum( + self, acc_S_row_exp: cute.TensorSSA, init_val: float | Float32 | None = None + ) -> Float32: + return utils.fadd_reduce(acc_S_row_exp, init_val, arch=self.arch) + + @cute.jit + def online_softmax( + self, + acc_S: cute.Tensor, + is_first: cutlass.Constexpr[bool] = False, + check_inf: cutlass.Constexpr[bool] = True, + ) -> cute.Tensor: + """Apply online softmax and return the row_scale to rescale O. + + :param acc_S: acc_S tensor + :type acc_S: cute.Tensor + :param is_first: is first n_block + :type is_first: cutlass.Constexpr + """ + # Change acc_S to M,N layout view. + acc_S_mn = layout_utils.reshape_acc_to_mn(acc_S) + row_scale = cute.make_fragment_like(self.row_max, Float32) + + row_max = self.row_max + row_sum = self.row_sum + scale_log2 = self.scale_log2 + arch = self.arch + + # Each iteration processes one row of acc_S + for r in cutlass.range(cute.size(row_max), unroll_full=True): + acc_S_row = acc_S_mn[r, None].load() # (n_block_size) + + row_max_cur = utils.fmax_reduce( + acc_S_row, + init_val=row_max[r] if cutlass.const_expr(not is_first) else None, + arch=arch, + ) + + row_max_cur = cute.arch.warp_reduction_max(row_max_cur, threads_in_group=4) + # Update row_max before changing row_max_cur to safe value for -inf + row_max_prev = row_max[r] + row_max[r] = row_max_cur + + if cutlass.const_expr(check_inf): + row_max_cur = 0.0 if row_max_cur == -Float32.inf else row_max_cur + + if cutlass.const_expr(is_first): + row_max_cur_scaled = row_max_cur * scale_log2 + acc_S_row_exp = cute.math.exp2( + acc_S_row * scale_log2 - row_max_cur_scaled, fastmath=True + ) + acc_S_row_sum = utils.fadd_reduce( + acc_S_row_exp, init_val=None, arch=arch + ) + row_scale[r] = 1.0 + else: + row_max_cur_scaled = row_max_cur * scale_log2 + acc_S_row_exp = cute.math.exp2( + acc_S_row * scale_log2 - row_max_cur_scaled, fastmath=True + ) + # row_scale[r] = cute.math.exp2(row_max_prev * self.scale_log2 - row_max_cur_scaled) + row_scale[r] = cute.math.exp2( + (row_max_prev - row_max_cur) * scale_log2, fastmath=True + ) + acc_S_row_sum = utils.fadd_reduce( + acc_S_row_exp, init_val=row_sum[r] * row_scale[r], arch=arch + ) + + row_sum[r] = acc_S_row_sum + acc_S_mn[r, None].store(acc_S_row_exp) + + return row_scale + + @cute.jit + def finalize( + self, final_scale: Float32 = 1.0, sink_val: Float32 | cute.Tensor | None = None + ) -> cute.Tensor: + """Finalize the online softmax by computing the scale and logsumexp.""" + if cutlass.const_expr( + sink_val is not None and isinstance(sink_val, cute.Tensor) + ): + assert cute.size(sink_val) == cute.size(self.row_sum) + row_sum = self.row_sum + row_max = self.row_max + scale_log2 = self.scale_log2 + + # quad reduction for row_sum as we didn't do it during each iteration of online softmax + row_sum.store(utils.warp_reduce(row_sum.load(), operator.add, width=4)) + row_scale = cute.make_fragment_like(row_max, Float32) + + for r in cutlass.range(cute.size(row_sum), unroll_full=True): + if cutlass.const_expr(sink_val is not None): + sink_val_cur = ( + sink_val if not isinstance(sink_val, cute.Tensor) else sink_val[r] + ) + LOG2_E = math.log2(math.e) + if row_max[r] == -Float32.inf: + # Fully-masked / empty row (can happen with SplitKV when a split's + # blocks are all outside the local window) + row_max[r] = sink_val_cur * (LOG2_E / scale_log2) + row_sum[r] = 1.0 + else: + row_sum[r] += cute.math.exp2( + sink_val_cur * LOG2_E - row_max[r] * scale_log2, fastmath=True + ) + + # if row_sum is zero or nan, set acc_O_mn_row to 1.0 + acc_O_mn_row_is_zero_or_nan = row_sum[r] == 0.0 or row_sum[r] != row_sum[r] + row_scale[r] = ( + cute.arch.rcp_approx( + row_sum[r] if not acc_O_mn_row_is_zero_or_nan else 1.0 + ) + ) * final_scale + row_sum_cur = row_sum[r] + LN2 = math.log(2.0) + row_sum[r] = ( + (row_max[r] * scale_log2 + cute.math.log2(row_sum_cur, fastmath=True)) + * LN2 + if not acc_O_mn_row_is_zero_or_nan + else -Float32.inf + ) + return row_scale + + @cute.jit + def rescale_O(self, acc_O: cute.Tensor, row_scale: cute.Tensor) -> None: + """Scale each row of acc_O by the given scale tensor. + :param acc_O: input tensor + :type acc_O: cute.Tensor + :param row_scale: row_scale tensor + :type row_scale: cute.Tensor + """ + acc_O_mn = layout_utils.reshape_acc_to_mn(acc_O) + assert cute.size(row_scale) == cute.size(acc_O_mn, mode=[0]) + for r in cutlass.range(cute.size(row_scale), unroll_full=True): + acc_O_mn[r, None].store(acc_O_mn[r, None].load() * row_scale[r]) + + +@dataclass +class SoftmaxSm100(Softmax): + rescale_threshold: cutlass.Constexpr[float] = 0.0 + max_offset: cutlass.Constexpr[int] = 0 + + @staticmethod + def create( + scale_log2: Float32, + rescale_threshold: cutlass.Constexpr[float] = 0.0, + softmax_scale: Float32 | None = None, + max_offset: cutlass.Constexpr[int] = 0, + ): + num_rows = 1 + arch = 100 + row_max = cute.make_rmem_tensor(num_rows, Float32) + row_sum = cute.make_rmem_tensor(num_rows, Float32) + return SoftmaxSm100( + scale_log2, + num_rows, + row_max, + row_sum, + arch, + softmax_scale, + rescale_threshold=rescale_threshold, + max_offset=max_offset, + ) + + @cute.jit + def compute_row_max_local( + self, acc_S_row: cute.TensorSSA, is_first: Boolean + ) -> Float32: + if cutlass.const_expr(is_first): + row_max_new = self._compute_row_max(acc_S_row) + else: + row_max_old = self.row_max[0] + row_max_new = self._compute_row_max(acc_S_row, init_val=row_max_old) + return row_max_new + + @cute.jit + def update_row_max_from_local( + self, + row_max_new: Float32, + is_first: Boolean, + ) -> Tuple[Float32, Float32]: + if cutlass.const_expr(is_first): + row_max_safe = row_max_new if row_max_new != -cutlass.Float32.inf else 0.0 + acc_scale = 0.0 + else: + row_max_old = self.row_max[0] + row_max_safe = row_max_new if row_max_new != -cutlass.Float32.inf else 0.0 + acc_scale_ = (row_max_old - row_max_safe) * self.scale_log2 + acc_scale = cute.math.exp2(acc_scale_) + if cutlass.const_expr(self.rescale_threshold > 0.0): + if acc_scale_ >= -self.rescale_threshold: + row_max_new = row_max_old + row_max_safe = row_max_old + acc_scale = 1.0 + self.row_max[0] = row_max_new + return row_max_safe, acc_scale + + @cute.jit + def update_row_max( + self, acc_S_row: cute.TensorSSA, is_first: int + ) -> Tuple[Float32, Float32]: + if cutlass.const_expr(is_first): + row_max_new = self._compute_row_max(acc_S_row) + row_max_safe = row_max_new if row_max_new != -cutlass.Float32.inf else 0.0 + acc_scale = 0.0 + else: + row_max_old = self.row_max[0] + row_max_new = self._compute_row_max(acc_S_row, init_val=row_max_old) + row_max_safe = row_max_new if row_max_new != -cutlass.Float32.inf else 0.0 + acc_scale_ = (row_max_old - row_max_safe) * self.scale_log2 + acc_scale = cute.math.exp2(acc_scale_, fastmath=True) + if cutlass.const_expr(self.rescale_threshold > 0.0): + if acc_scale_ >= -self.rescale_threshold: + row_max_new = row_max_old + row_max_safe = row_max_old + acc_scale = 1.0 + self.row_max[0] = row_max_new + return row_max_safe, acc_scale + + def update_row_sum( + self, acc_S_row_exp: cute.TensorSSA, row_scale: Float32, is_first: int = False + ) -> None: + init_val = ( + self.row_sum[0] * row_scale if cutlass.const_expr(not is_first) else None + ) + # self.row_sum[0] = self._compute_row_sum(acc_S_row_exp, init_val=self.row_sum[0] * row_scale) + self.row_sum[0] = self._compute_row_sum(acc_S_row_exp, init_val=init_val) + # tmp = self._compute_row_sum(acc_S_row_exp) + # self.row_sum[0] = self.row_sum[0] * row_scale + tmp + + @cute.jit + def scale_subtract_rowmax( + self, + acc_S_row: cute.Tensor, + row_max: Float32, + ): + assert ( + cute.size(acc_S_row.shape) % 2 == 0 + ), "acc_S_row must have an even number of elements" + row_max_scaled = row_max * self.scale_log2 + max_offset = Float32(self.max_offset) + bias = max_offset - row_max_scaled + for i in cutlass.range(0, cute.size(acc_S_row.shape), 2, unroll_full=True): + acc_S_row[i], acc_S_row[i + 1] = cute.arch.fma_packed_f32x2( + (acc_S_row[i], acc_S_row[i + 1]), + (self.scale_log2, self.scale_log2), + (bias, bias), + ) + + @cute.jit + def apply_exp2_convert( + self, + acc_S_row: cute.Tensor, + acc_S_row_converted: cute.Tensor, + ex2_emu_freq: cutlass.Constexpr[int] = 0, + ex2_emu_res: cutlass.Constexpr[int] = 4, + ex2_emu_start_frg: cutlass.Constexpr[int] = 0, + ): + assert ( + cute.size(acc_S_row.shape) % 2 == 0 + ), "acc_S_row must have an even number of elements" + frg_tile = 32 + assert frg_tile % 2 == 0 + frg_cnt = cute.size(acc_S_row) // frg_tile + assert cute.size(acc_S_row) % frg_tile == 0 + acc_S_row_frg = cute.logical_divide(acc_S_row, cute.make_layout(frg_tile)) + acc_S_row_converted_frg = cute.logical_divide( + acc_S_row_converted, cute.make_layout(frg_tile) + ) + for j in cutlass.range_constexpr(frg_cnt): + for k in cutlass.range_constexpr(0, cute.size(acc_S_row_frg, mode=[0]), 2): + # acc_S_row_frg[k, j] = cute.math.exp2(acc_S_row_frg[k, j], fastmath=True) + # acc_S_row_frg[k + 1, j] = cute.math.exp2(acc_S_row_frg[k + 1, j], fastmath=True) + if cutlass.const_expr(ex2_emu_freq == 0): + acc_S_row_frg[k, j] = cute.math.exp2( + acc_S_row_frg[k, j], fastmath=True + ) + acc_S_row_frg[k + 1, j] = cute.math.exp2( + acc_S_row_frg[k + 1, j], fastmath=True + ) + else: + if cutlass.const_expr( + k % ex2_emu_freq < ex2_emu_freq - ex2_emu_res + or j >= frg_cnt - 1 + or j < ex2_emu_start_frg + ): + acc_S_row_frg[k, j] = cute.math.exp2( + acc_S_row_frg[k, j], fastmath=True + ) + acc_S_row_frg[k + 1, j] = cute.math.exp2( + acc_S_row_frg[k + 1, j], fastmath=True + ) + else: + # acc_S_row_frg[k, j], acc_S_row_frg[k + 1, j] = utils.e2e_asm2(acc_S_row_frg[k, j], acc_S_row_frg[k + 1, j]) + acc_S_row_frg[k, j], acc_S_row_frg[k + 1, j] = ( + utils.ex2_emulation_2( + acc_S_row_frg[k, j], acc_S_row_frg[k + 1, j] + ) + ) + acc_S_row_converted_frg[None, j].store( + acc_S_row_frg[None, j].load().to(acc_S_row_converted.element_type) + ) + + @cute.jit + def scale_apply_exp2_convert( + self, + acc_S_row: cute.Tensor, + row_max: Float32, + acc_S_row_converted: cute.Tensor, + ): + assert ( + cute.size(acc_S_row.shape) % 2 == 0 + ), "acc_S_row must have an even number of elements" + minus_row_max_scaled = -row_max * self.scale_log2 + for i in cutlass.range_constexpr(0, cute.size(acc_S_row.shape), 2): + acc_S_row[i], acc_S_row[i + 1] = cute.arch.fma_packed_f32x2( + (acc_S_row[i], acc_S_row[i + 1]), + (self.scale_log2, self.scale_log2), + (minus_row_max_scaled, minus_row_max_scaled), + ) + + # for i in cutlass.range_constexpr(0, cute.size(acc_S_row.shape), 2): + # acc_S_row[i], acc_S_row[i + 1] = cute.arch.fma_packed_f32x2( + # (acc_S_row[i], acc_S_row[i + 1]), + # (self.scale_log2, self.scale_log2), + # (minus_row_max_scaled, minus_row_max_scaled), + # ) + # acc_S_row[i] = cute.math.exp2(acc_S_row[i], fastmath=True) + # acc_S_row[i + 1] = cute.math.exp2(acc_S_row[i + 1], fastmath=True) + + frg_tile = 32 + assert frg_tile % 2 == 0 + frg_cnt = cute.size(acc_S_row) // frg_tile + assert cute.size(acc_S_row) % frg_tile == 0 + acc_S_row_frg = cute.logical_divide(acc_S_row, cute.make_layout(frg_tile)) + acc_S_row_converted_frg = cute.logical_divide( + acc_S_row_converted, cute.make_layout(frg_tile) + ) + for j in cutlass.range_constexpr(frg_cnt): + for k in cutlass.range_constexpr(0, cute.size(acc_S_row_frg, mode=[0]), 2): + # acc_S_row_frg[k, j], acc_S_row_frg[k + 1, j] = ( + # cute.arch.fma_packed_f32x2( + # (acc_S_row_frg[k, j], acc_S_row_frg[k + 1, j]), + # (self.scale_log2, self.scale_log2), + # (minus_row_max_scaled, minus_row_max_scaled), + # ) + # ) + # acc_S_row_frg[k, j] = cute.math.exp2(acc_S_row_frg[k, j], fastmath=True) + # acc_S_row_frg[k + 1, j] = cute.math.exp2(acc_S_row_frg[k + 1, j], fastmath=True) + acc_S_row_frg[k, j] = cute.math.exp2(acc_S_row_frg[k, j], fastmath=True) + acc_S_row_frg[k + 1, j] = cute.math.exp2( + acc_S_row_frg[k + 1, j], fastmath=True + ) + acc_S_row_converted_frg[None, j].store( + acc_S_row_frg[None, j].load().to(acc_S_row_converted.element_type) + ) + + +@cute.jit +def floor_if_packed( + q_idx, + qhead_per_kvhead: cutlass.Constexpr[int], +) -> cute.Tensor: + """Convert q_idx to packed format for Pack-GQA.""" + if cutlass.const_expr(qhead_per_kvhead == 1): + return q_idx + return q_idx // qhead_per_kvhead + + +@cute.jit +def apply_score_mod_inner( + score_tensor, + index_tensor, + score_mod: cutlass.Constexpr, + batch_idx, + head_idx, + softmax_scale, + vec_size: cutlass.Constexpr, + qk_acc_dtype: cutlass.Constexpr, + aux_data: AuxData, + fastdiv_mods, + seqlen_info: SeqlenInfoQK, + constant_q_idx: cutlass.Constexpr, + qhead_per_kvhead: cutlass.Constexpr[int] = 1, + transpose_indices: cutlass.Constexpr[bool] = False, +): + """Shared implementation for applying score modification. + + Args: + score_tensor: The scores to modify (acc_S for flash_fwd, tSrS_t2r for sm100) + index_tensor: Index positions (tScS for flash_fwd, tScS_t2r for sm100) + score_mod: The score modification function to apply + batch_idx: Batch index + head_idx: Head index + softmax_scale: Scale to apply + vec_size: Vector size for processing elements + qk_acc_dtype: Data type for accumulator + aux_tensors: Optional aux_tensors for FlexAttention + aux_scalars: Optional runtime scalar captures for FlexAttention + fastdiv_mods: Tuple of (seqlen_q_divmod, seqlen_k_divmod) for wrapping + seqlen_info: Sequence length info + constant_q_idx: If provided, use this constant for all q_idx values + If None, compute q_idx per-element + qhead_per_kvhead_packgqa: Pack-GQA replication factor. Divide q_idx by this + when greater than 1 so score mods see logical heads. + transpose_indices: If True, swap q_idx/kv_idx in index_tensor (for bwd kernel where S is transposed) + """ + # Index positions in the index_tensor tuple + # Forward: index_tensor[...][0] = q_idx, index_tensor[...][1] = kv_idx + # Backward (transposed): index_tensor[...][0] = kv_idx, index_tensor[...][1] = q_idx + if cutlass.const_expr(transpose_indices): + q_idx_pos = cutlass.const_expr(1) + kv_idx_pos = cutlass.const_expr(0) + else: + q_idx_pos = cutlass.const_expr(0) + kv_idx_pos = cutlass.const_expr(1) + + n_vals = cutlass.const_expr(cute.size(score_tensor.shape)) + score_vec = cute.make_rmem_tensor(vec_size, qk_acc_dtype) + kv_idx_vec = cute.make_rmem_tensor(vec_size, cutlass.Int32) + + # SSA values for batch (constant across all elements) + batch_idx_ssa = utils.scalar_to_ssa(batch_idx, cutlass.Int32).broadcast_to( + (vec_size,) + ) + + # Handle q_idx based on whether it's constant + q_idx_vec = cute.make_rmem_tensor(vec_size, cutlass.Int32) + + # For Pack-GQA with non-constant q_idx, we need per-element head indices + # since a thread may process multiple query head indices + if cutlass.const_expr(qhead_per_kvhead > 1 and constant_q_idx is None): + head_idx_vec = cute.make_rmem_tensor(vec_size, cutlass.Int32) + + for i in cutlass.range(0, n_vals, vec_size, unroll_full=True): + for j in cutlass.range(vec_size, unroll_full=True): + score_vec[j] = score_tensor[i + j] * softmax_scale + + # Extract head offset from packed q_idx for Pack-GQA + if cutlass.const_expr(qhead_per_kvhead > 1 and constant_q_idx is None): + q_idx_packed = index_tensor[i + j][q_idx_pos] + # Building up the logical q_head idx: final_q_head = kv_head * qhead_per_kvhead + (q_physical % qhead_per_kvhead) + q_idx_logical = q_idx_packed // qhead_per_kvhead + head_offset = q_idx_packed - q_idx_logical * qhead_per_kvhead + head_idx_vec[j] = head_idx * qhead_per_kvhead + head_offset + + # If we will do loads we mod, in order to not read OOB + if cutlass.const_expr( + aux_data.tensors is not None and fastdiv_mods is not None + ): + if cutlass.const_expr(constant_q_idx is None): + seqlen_q_divmod, seqlen_k_divmod = fastdiv_mods + q_idx_floored = floor_if_packed( + index_tensor[i + j][q_idx_pos], qhead_per_kvhead + ) + _, q_idx_wrapped = divmod(q_idx_floored, seqlen_q_divmod) + q_idx_vec[j] = q_idx_wrapped + else: + _, seqlen_k_divmod = fastdiv_mods + + _, kv_idx_wrapped = divmod( + index_tensor[i + j][kv_idx_pos], seqlen_k_divmod + ) + kv_idx_vec[j] = kv_idx_wrapped + else: + # No bounds checking - direct indexing + if constant_q_idx is None: + q_idx_vec[j] = floor_if_packed( + index_tensor[i + j][q_idx_pos], qhead_per_kvhead + ) + kv_idx_vec[j] = index_tensor[i + j][kv_idx_pos] + + # Convert to SSA for score_mod call + score_ssa = score_vec.load() + kv_idx_ssa = kv_idx_vec.load() + if cutlass.const_expr(constant_q_idx is None): + q_idx_ssa = q_idx_vec.load() + else: + # NB we do not apply Pack-GQA division here, as constant_q_idx is assumed to already be logical + q_idx_const = constant_q_idx + q_idx_ssa = utils.scalar_to_ssa(q_idx_const, cutlass.Int32).broadcast_to( + (vec_size,) + ) + + # Compute head_idx_ssa: per-element for Pack-GQA with non-constant q_idx, constant otherwise + if cutlass.const_expr(qhead_per_kvhead > 1 and constant_q_idx is None): + head_idx_ssa = head_idx_vec.load() + else: + head_idx_ssa = utils.scalar_to_ssa(head_idx, cutlass.Int32).broadcast_to( + (vec_size,) + ) + + post_mod_scores = call_score_mod( + score_mod, + score_ssa, + batch_idx_ssa, + head_idx_ssa, + q_idx_ssa, + kv_idx_ssa, + seqlen_info, + aux_data, + ) + + # Write back modified scores + score_vec.store(post_mod_scores) + for j in cutlass.range(vec_size, unroll_full=True): + score_tensor[i + j] = score_vec[j] + + +@cute.jit +def apply_score_mod_bwd_inner( + grad_tensor, + score_tensor, + index_tensor, + score_mod_bwd: cutlass.Constexpr, + batch_idx, + head_idx, + softmax_scale, + vec_size: cutlass.Constexpr, + qk_acc_dtype: cutlass.Constexpr, + aux_data: AuxData, + fastdiv_mods, + seqlen_info, + constant_q_idx: cutlass.Constexpr, + qhead_per_kvhead: cutlass.Constexpr[int] = 1, + transpose_indices: cutlass.Constexpr[bool] = False, +): + """Apply backward score modification (joint graph). + + Args: + grad_tensor: in/out: dlogits rewritten in-place with d(scaled_scores) + score_tensor: pre-mod scores (unscaled QK tile), scaled by softmax_scale internally + index_tensor: Index positions (same as forward) + score_mod_bwd: The backward score modification function (joint graph) + batch_idx: Batch index + head_idx: Head index + softmax_scale: Scale to apply to score_tensor + vec_size: Vector size for processing elements + qk_acc_dtype: Data type for accumulator + aux_tensors: Optional aux_tensors for FlexAttention + aux_scalars: Optional runtime scalar captures for FlexAttention + fastdiv_mods: Tuple of (seqlen_q_divmod, seqlen_k_divmod) for wrapping + seqlen_info: Sequence length info + constant_q_idx: If provided, use this constant for all q_idx values + qhead_per_kvhead: Pack-GQA replication factor + transpose_indices: If True, swap q_idx/kv_idx in index_tensor + """ + # Index positions in the index_tensor tuple + # Forward: index_tensor[...][0] = q_idx, index_tensor[...][1] = kv_idx + # Backward (transposed): index_tensor[...][0] = kv_idx, index_tensor[...][1] = q_idx + if cutlass.const_expr(transpose_indices): + q_idx_pos = cutlass.const_expr(1) + kv_idx_pos = cutlass.const_expr(0) + else: + q_idx_pos = cutlass.const_expr(0) + kv_idx_pos = cutlass.const_expr(1) + n_vals = cutlass.const_expr(cute.size(grad_tensor.shape)) + grad_vec = cute.make_rmem_tensor(vec_size, qk_acc_dtype) + score_vec = cute.make_rmem_tensor(vec_size, qk_acc_dtype) + kv_idx_vec = cute.make_rmem_tensor(vec_size, cutlass.Int32) + batch_idx_ssa = utils.scalar_to_ssa(batch_idx, cutlass.Int32).broadcast_to( + (vec_size,) + ) + q_idx_vec = cute.make_rmem_tensor(vec_size, cutlass.Int32) + + # For Pack-GQA with non-constant q_idx, we need per-element head indices + if cutlass.const_expr(qhead_per_kvhead > 1 and constant_q_idx is None): + head_idx_vec = cute.make_rmem_tensor(vec_size, cutlass.Int32) + + for i in cutlass.range(0, n_vals, vec_size, unroll_full=True): + for j in cutlass.range(vec_size, unroll_full=True): + grad_vec[j] = grad_tensor[i + j] + # Scale score so joint graph sees same value as forward score_mod + score_vec[j] = score_tensor[i + j] * softmax_scale + + if cutlass.const_expr(qhead_per_kvhead > 1 and constant_q_idx is None): + q_idx_packed = index_tensor[i + j][q_idx_pos] + q_idx_logical = q_idx_packed // qhead_per_kvhead + head_offset = q_idx_packed - q_idx_logical * qhead_per_kvhead + head_idx_vec[j] = head_idx * qhead_per_kvhead + head_offset + + if cutlass.const_expr( + aux_data.tensors is not None and fastdiv_mods is not None + ): + if cutlass.const_expr(constant_q_idx is None): + seqlen_q_divmod, seqlen_k_divmod = fastdiv_mods + q_idx_floored = floor_if_packed( + index_tensor[i + j][q_idx_pos], qhead_per_kvhead + ) + _, q_idx_wrapped = divmod(q_idx_floored, seqlen_q_divmod) + q_idx_vec[j] = q_idx_wrapped + else: + _, seqlen_k_divmod = fastdiv_mods + + _, kv_idx_wrapped = divmod( + index_tensor[i + j][kv_idx_pos], seqlen_k_divmod + ) + kv_idx_vec[j] = kv_idx_wrapped + else: + # No bounds checking - direct indexing + if constant_q_idx is None: + q_idx_vec[j] = floor_if_packed( + index_tensor[i + j][q_idx_pos], qhead_per_kvhead + ) + kv_idx_vec[j] = index_tensor[i + j][kv_idx_pos] + + grad_ssa = grad_vec.load() + score_ssa = score_vec.load() + kv_idx_ssa = kv_idx_vec.load() + + if cutlass.const_expr(constant_q_idx is None): + q_idx_ssa = q_idx_vec.load() + else: + q_idx_ssa = utils.scalar_to_ssa(constant_q_idx, cutlass.Int32).broadcast_to( + (vec_size,) + ) + + if cutlass.const_expr(qhead_per_kvhead > 1 and constant_q_idx is None): + head_idx_ssa = head_idx_vec.load() + else: + head_idx_ssa = utils.scalar_to_ssa(head_idx, cutlass.Int32).broadcast_to( + (vec_size,) + ) + + grad_out_ssa = call_score_mod_bwd( + score_mod_bwd, + grad_ssa, + score_ssa, + batch_idx_ssa, + head_idx_ssa, + q_idx_ssa, + kv_idx_ssa, + seqlen_info, + aux_data, + ) + + grad_vec.store(grad_out_ssa) + for j in cutlass.range(vec_size, unroll_full=True): + grad_tensor[i + j] = grad_vec[j] diff --git a/python/sglang/jit_kernel/flash_attn/cute/testing.py b/python/sglang/jit_kernel/flash_attn/cute/testing.py new file mode 100644 index 000000000..f8f6d81fa --- /dev/null +++ b/python/sglang/jit_kernel/flash_attn/cute/testing.py @@ -0,0 +1,580 @@ +import math +from contextlib import nullcontext +from functools import wraps +from typing import Optional + +import torch +import torch.nn.functional as F +from einops import rearrange, repeat +from torch._guards import active_fake_mode +from torch._subclasses.fake_tensor import FakeTensorMode + + +class IndexFirstAxis(torch.autograd.Function): + @staticmethod + def forward(ctx, input, indices): + ctx.save_for_backward(indices) + assert input.ndim >= 2 + ctx.first_axis_dim, other_shape = input.shape[0], input.shape[1:] + second_dim = other_shape.numel() + return torch.gather( + rearrange(input, "b ... -> b (...)"), + 0, + repeat(indices, "z -> z d", d=second_dim), + ).reshape(-1, *other_shape) + + @staticmethod + def backward(ctx, grad_output): + (indices,) = ctx.saved_tensors + assert grad_output.ndim >= 2 + other_shape = grad_output.shape[1:] + grad_output = rearrange(grad_output, "b ... -> b (...)") + grad_input = torch.zeros( + [ctx.first_axis_dim, grad_output.shape[1]], + device=grad_output.device, + dtype=grad_output.dtype, + ) + grad_input.scatter_( + 0, repeat(indices, "z -> z d", d=grad_output.shape[1]), grad_output + ) + return grad_input.reshape(ctx.first_axis_dim, *other_shape), None + + +index_first_axis = IndexFirstAxis.apply + + +class IndexPutFirstAxis(torch.autograd.Function): + @staticmethod + def forward(ctx, values, indices, first_axis_dim): + ctx.save_for_backward(indices) + assert indices.ndim == 1 + assert values.ndim >= 2 + output = torch.zeros( + first_axis_dim, *values.shape[1:], device=values.device, dtype=values.dtype + ) + output[indices] = values + return output + + @staticmethod + def backward(ctx, grad_output): + (indices,) = ctx.saved_tensors + grad_values = grad_output[indices] + return grad_values, None, None + + +index_put_first_axis = IndexPutFirstAxis.apply + + +def unpad_input(hidden_states, attention_mask, unused_mask=None): + all_masks = ( + (attention_mask + unused_mask) if unused_mask is not None else attention_mask + ) + seqlens_in_batch = all_masks.sum(dim=-1, dtype=torch.int32) + used_seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32) + in_fake_mode = active_fake_mode() is not None + if not in_fake_mode: + indices = torch.nonzero(all_masks.flatten(), as_tuple=False).flatten() + max_seqlen_in_batch = seqlens_in_batch.max().item() + else: + # torch.nonzero and .item() are not supported in FakeTensorMode + batch_size, seqlen = attention_mask.shape + indices = torch.arange(batch_size * seqlen, device=hidden_states.device) + max_seqlen_in_batch = seqlen + cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0)) + return ( + index_first_axis(rearrange(hidden_states, "b s ... -> (b s) ..."), indices), + indices, + cu_seqlens, + max_seqlen_in_batch, + used_seqlens_in_batch, + ) + + +def pad_input(hidden_states, indices, batch, seqlen): + output = index_put_first_axis(hidden_states, indices, batch * seqlen) + return rearrange(output, "(b s) ... -> b s ...", b=batch) + + +def generate_random_padding_mask( + max_seqlen, batch_size, device, mode="random", zero_lengths=False, min_seqlen=None +): + assert mode in ["full", "random", "third"] + min_seqlen = min_seqlen if min_seqlen is not None else 0 if zero_lengths else 1 + if mode == "full": + lengths = torch.full( + (batch_size, 1), max_seqlen, device=device, dtype=torch.int32 + ) + elif mode == "random": + lengths = torch.randint( + max(min_seqlen, max_seqlen - 20), + max_seqlen + 1, + (batch_size, 1), + device=device, + ) + else: + lengths = torch.randint( + max(min_seqlen, max_seqlen // 3), + max_seqlen + 1, + (batch_size, 1), + device=device, + ) + + if zero_lengths: + for i in range(batch_size): + if i % 5 == 0: + lengths[i] = 0 + lengths[-1] = 0 + padding_mask = ( + repeat(torch.arange(max_seqlen, device=device), "s -> b s", b=batch_size) + < lengths + ) + return padding_mask + + +def generate_qkv( + q, + k, + v, + query_padding_mask=None, + key_padding_mask=None, + qv=None, + kvpacked=False, + qkvpacked=False, + query_unused_mask=None, + key_unused_mask=None, +): + assert not (kvpacked and qkvpacked) + batch_size, seqlen_q, nheads, d = q.shape + d_v = v.shape[-1] + _, seqlen_k, nheads_k, _ = k.shape + assert k.shape == (batch_size, seqlen_k, nheads_k, d) + assert v.shape == (batch_size, seqlen_k, nheads_k, d_v) + if query_unused_mask is not None or key_unused_mask is not None: + assert not kvpacked + assert not qkvpacked + + if query_padding_mask is not None: + q_unpad, indices_q, cu_seqlens_q, max_seqlen_q, seqused_q = unpad_input( + q, query_padding_mask, query_unused_mask + ) + output_pad_fn = lambda output_unpad: pad_input( + output_unpad, indices_q, batch_size, seqlen_q + ) + qv_unpad = ( + rearrange(qv, "b s ... -> (b s) ...")[indices_q] if qv is not None else None + ) + else: + q_unpad = rearrange(q, "b s h d -> (b s) h d") + cu_seqlens_q = torch.arange( + 0, + (batch_size + 1) * seqlen_q, + step=seqlen_q, + dtype=torch.int32, + device=q_unpad.device, + ) + seqused_q = None + max_seqlen_q = seqlen_q + output_pad_fn = lambda output_unpad: rearrange( + output_unpad, "(b s) h d -> b s h d", b=batch_size + ) + qv_unpad = rearrange(qv, "b s ... -> (b s) ...") if qv is not None else None + + if key_padding_mask is not None: + k_unpad, indices_k, cu_seqlens_k, max_seqlen_k, seqused_k = unpad_input( + k, key_padding_mask, key_unused_mask + ) + v_unpad, *_ = unpad_input(v, key_padding_mask, key_unused_mask) + else: + k_unpad = rearrange(k, "b s h d -> (b s) h d") + v_unpad = rearrange(v, "b s h d -> (b s) h d") + cu_seqlens_k = torch.arange( + 0, + (batch_size + 1) * seqlen_k, + step=seqlen_k, + dtype=torch.int32, + device=k_unpad.device, + ) + seqused_k = None + max_seqlen_k = seqlen_k + + if qkvpacked: + assert (query_padding_mask == key_padding_mask).all() + assert nheads == nheads_k + qkv_unpad = torch.stack([q_unpad, k_unpad, v_unpad], dim=1) + qkv = torch.stack([q, k, v], dim=2) + if query_padding_mask is not None: + dqkv_pad_fn = lambda dqkv_unpad: pad_input( + dqkv_unpad, indices_q, batch_size, seqlen_q + ) + else: + dqkv_pad_fn = lambda dqkv_unpad: rearrange( + dqkv_unpad, "(b s) t h d -> b s t h d", b=batch_size + ) + return ( + qkv_unpad.detach().requires_grad_(), + cu_seqlens_q, + max_seqlen_q, + qkv.detach().requires_grad_(), + output_pad_fn, + dqkv_pad_fn, + ) + elif kvpacked: + kv_unpad = torch.stack([k_unpad, v_unpad], dim=1) + kv = torch.stack([k, v], dim=2) + dq_pad_fn = output_pad_fn + if key_padding_mask is not None: + dkv_pad_fn = lambda dkv_unpad: pad_input( + dkv_unpad, indices_k, batch_size, seqlen_k + ) + else: + dkv_pad_fn = lambda dkv_unpad: rearrange( + dkv_unpad, "(b s) t h d -> b s t h d", b=batch_size + ) + return ( + q_unpad.detach().requires_grad_(), + kv_unpad.detach().requires_grad_(), + cu_seqlens_q, + cu_seqlens_k, + max_seqlen_q, + max_seqlen_k, + q.detach().requires_grad_(), + kv.detach().requires_grad_(), + output_pad_fn, + dq_pad_fn, + dkv_pad_fn, + ) + else: + dq_pad_fn = output_pad_fn + if key_padding_mask is not None: + dk_pad_fn = lambda dk_unpad: pad_input( + dk_unpad, indices_k, batch_size, seqlen_k + ) + else: + dk_pad_fn = lambda dk_unpad: rearrange( + dk_unpad, "(b s) h d -> b s h d", b=batch_size + ) + return ( + q_unpad.detach().requires_grad_(), + k_unpad.detach().requires_grad_(), + v_unpad.detach().requires_grad_(), + qv_unpad.detach() if qv is not None else None, + cu_seqlens_q, + cu_seqlens_k, + seqused_q, + seqused_k, + max_seqlen_q, + max_seqlen_k, + q.detach().requires_grad_(), + k.detach().requires_grad_(), + v.detach().requires_grad_(), + qv.detach() if qv is not None else None, + output_pad_fn, + dq_pad_fn, + dk_pad_fn, + ) + + +def construct_local_mask( + seqlen_q, + seqlen_k, + window_size=(None, None), + sink_token_length=0, + query_padding_mask=None, + key_padding_mask=None, + key_leftpad=None, + device=None, +): + row_idx = rearrange( + torch.arange(seqlen_q, device=device, dtype=torch.long), "s -> s 1" + ) + col_idx = torch.arange(seqlen_k, device=device, dtype=torch.long) + if key_leftpad is not None: + key_leftpad = rearrange(key_leftpad, "b -> b 1 1 1") + col_idx = repeat(col_idx, "s -> b 1 1 s", b=key_leftpad.shape[0]) + col_idx = torch.where(col_idx >= key_leftpad, col_idx - key_leftpad, 2**32) + sk = ( + seqlen_k + if key_padding_mask is None + else rearrange(key_padding_mask.sum(-1), "b -> b 1 1 1") + ) + sq = ( + seqlen_q + if query_padding_mask is None + else rearrange(query_padding_mask.sum(-1), "b -> b 1 1 1") + ) + if window_size[0] is None: + return col_idx > row_idx + sk - sq + window_size[1] + else: + sk = torch.full_like(col_idx, seqlen_k) if key_padding_mask is None else sk + if window_size[1] is None: + local_mask_left = col_idx > sk + else: + local_mask_left = col_idx > torch.minimum( + row_idx + sk - sq + window_size[1], sk + ) + return torch.logical_or( + local_mask_left, + torch.logical_and( + col_idx < row_idx + sk - sq - window_size[0], + col_idx >= sink_token_length, + ), + ) + + +def construct_chunk_mask( + seqlen_q, + seqlen_k, + attention_chunk, + query_padding_mask=None, + key_padding_mask=None, + key_leftpad=None, + device=None, +): + row_idx = rearrange( + torch.arange(seqlen_q, device=device, dtype=torch.long), "s -> s 1" + ) + col_idx = torch.arange(seqlen_k, device=device, dtype=torch.long) + if key_leftpad is not None: + key_leftpad = rearrange(key_leftpad, "b -> b 1 1 1") + col_idx = repeat(col_idx, "s -> b 1 1 s", b=key_leftpad.shape[0]) + col_idx = torch.where(col_idx >= key_leftpad, col_idx - key_leftpad, 2**32) + sk = ( + seqlen_k + if key_padding_mask is None + else rearrange(key_padding_mask.sum(-1), "b -> b 1 1 1") + ) + sq = ( + seqlen_q + if query_padding_mask is None + else rearrange(query_padding_mask.sum(-1), "b -> b 1 1 1") + ) + sk = torch.full_like(col_idx, seqlen_k) if key_padding_mask is None else sk + col_limit_left_chunk = row_idx + sk - sq - (row_idx + sk - sq) % attention_chunk + return torch.logical_or( + col_idx < col_limit_left_chunk, + col_idx >= col_limit_left_chunk + attention_chunk, + ) + + +def attention_ref( + q, + k, + v, + query_padding_mask=None, + key_padding_mask=None, + key_leftpad=None, + attn_bias=None, + dropout_p=0.0, + dropout_mask=None, + causal=False, + qv=None, + q_descale=None, + k_descale=None, + v_descale=None, + window_size=(None, None), + attention_chunk=0, + sink_token_length=0, + learnable_sink: Optional[torch.Tensor] = None, + softcap=0.0, + upcast=True, + reorder_ops=False, + intermediate_dtype=None, + return_lse=False, + gather_kv_indices=None, + rel_bias: Optional[torch.Tensor] = None, # [b, seqlen_q, h, rel_extent] + cu_seqlens_q: Optional[torch.Tensor] = None, + cu_seqlens_k: Optional[torch.Tensor] = None, + seqused_q: Optional[torch.Tensor] = None, + seqused_k: Optional[torch.Tensor] = None, +): + assert v is not None + has_qk = q is not None and k is not None + assert has_qk or qv is not None + if causal: + window_size = (window_size[0], 0) + dtype_og = v.dtype + q_shape = q.shape if q is not None else qv.shape + if upcast: + q, k, v, qv = [t.float() if t is not None else None for t in (q, k, v, qv)] + if q_descale is not None: + q_descale = repeat(q_descale, "b h -> b 1 (h g) 1", g=q_shape[2] // v.shape[2]) + q, qv = [ + (t.float() * q_descale).to(t.dtype) if t is not None else None + for t in (q, qv) + ] + if k_descale is not None: + k = (k.float() * rearrange(k_descale, "b h -> b 1 h 1")).to(dtype=k.dtype) + if v_descale is not None: + v = (v.float() * rearrange(v_descale, "b h -> b 1 h 1")).to(dtype=v.dtype) + seqlen_q, seqlen_k = q_shape[1], v.shape[1] + k, v = [ + ( + repeat(t, "b s h d -> b s (h g) d", g=q_shape[2] // t.shape[2]) + if t is not None + else None + ) + for t in (k, v) + ] + d = q_shape[-1] # == dv for qv + dv = v.shape[-1] + softmax_scale = 1.0 / math.sqrt(d if qv is None or q is None else d + dv) + if has_qk: + scores = torch.einsum( + "bthd,bshd->bhts", + q if reorder_ops else q * softmax_scale, + k * softmax_scale if reorder_ops else k, + ) + if qv is not None: + qv_scores = torch.einsum( + "bthd,bshd->bhts", + qv if reorder_ops else qv * softmax_scale, + v * softmax_scale if reorder_ops else v, + ) + scores = qv_scores if not has_qk else scores + qv_scores + if softcap > 0: + scores = torch.tanh(scores / softcap) * softcap + if key_padding_mask is not None: + scores.masked_fill_( + rearrange(~key_padding_mask, "b s -> b 1 1 s"), float("-inf") + ) + local_mask = None + if window_size[0] is not None or window_size[1] is not None: + local_mask = construct_local_mask( + seqlen_q, + seqlen_k, + window_size, + sink_token_length, + query_padding_mask, + key_padding_mask, + key_leftpad=key_leftpad, + device=v.device, + ) + if attention_chunk > 0: + chunk_mask = construct_chunk_mask( + seqlen_q, + seqlen_k, + attention_chunk, + query_padding_mask, + key_padding_mask, + key_leftpad=key_leftpad, + device=v.device, + ) + local_mask = ( + torch.logical_or(local_mask, chunk_mask) + if local_mask is not None + else chunk_mask + ) + if gather_kv_indices is not None: + batch = q_shape[0] + topk_len = gather_kv_indices.shape[2] + if topk_len < seqlen_k: + topk_index_mask = torch.full( + (batch, seqlen_q, seqlen_k), False, device="cuda" + ).scatter_(-1, gather_kv_indices, True) + scores.masked_fill_( + rearrange(~topk_index_mask, "b t s -> b 1 t s"), float("-inf") + ) + if local_mask is not None: + scores.masked_fill_(local_mask, float("-inf")) + if attn_bias is not None: + scores = scores + attn_bias + if rel_bias is not None: + # Reference for Inkling sheared bias: gather rel_bias[i, h, i - j] into the [t, s] score grid. + rel_extent = rel_bias.shape[-1] + if cu_seqlens_q is not None: + seqlens_q = cu_seqlens_q[1:] - cu_seqlens_q[:-1] + elif seqused_q is not None: + seqlens_q = seqused_q + else: + seqlens_q = torch.full( + (q.shape[0],), seqlen_q, device=q.device, dtype=torch.long + ) + if cu_seqlens_k is not None: + seqlens_k = cu_seqlens_k[1:] - cu_seqlens_k[:-1] + elif seqused_k is not None: + seqlens_k = seqused_k + else: + seqlens_k = torch.full( + (q.shape[0],), seqlen_k, device=q.device, dtype=torch.long + ) + seqlen_offset = (seqlens_k - seqlens_q).to(torch.long) # [b] + q_idx = torch.arange(seqlen_q, device=q.device, dtype=torch.long) + kv_idx = torch.arange(seqlen_k, device=q.device, dtype=torch.long) + rel_dist = ( + q_idx.unsqueeze(1) - kv_idx.unsqueeze(0) + seqlen_offset.view(-1, 1, 1) + ) # [b, seqlen_q, seqlen_k] + safe_dist = rel_dist.clamp(0, rel_extent - 1) + is_within_window = (rel_dist >= 0) & (rel_dist < rel_extent) + idx = safe_dist.unsqueeze(2).expand(-1, -1, rel_bias.shape[2], -1) + abs_bias = rel_bias.gather(dim=-1, index=idx) # [b, seqlen_q, h, seqlen_k] + abs_bias = rearrange(abs_bias, "b t h s -> b h t s") + abs_bias = abs_bias.masked_fill( + rearrange(~is_within_window, "b t s -> b 1 t s"), 0.0 + ) + scores = scores + abs_bias + # After all masks are applied, before softmax: + # scores shape: [b, h, t, s] + lse = torch.logsumexp(scores, dim=-1) # [b, h, t] + if learnable_sink is None: + attention = torch.softmax(scores, dim=-1).to(v.dtype) + else: + scores_fp32 = scores.to(torch.float32) + logits_max = torch.amax(scores_fp32, dim=-1, keepdim=True) + learnable_sink = rearrange(learnable_sink, "h -> h 1 1") + logits_or_sinks_max = torch.maximum(learnable_sink, logits_max) + unnormalized_scores = torch.exp(scores_fp32 - logits_or_sinks_max) + normalizer = unnormalized_scores.sum(dim=-1, keepdim=True) + torch.exp( + learnable_sink - logits_or_sinks_max + ) + # LSE with sink: log(Z) = log(normalizer) + max + lse = (torch.log(normalizer.squeeze(-1)) + logits_or_sinks_max.squeeze(-1)).to( + dtype_og + ) + attention = (unnormalized_scores / normalizer).to(v.dtype) + if query_padding_mask is not None: + attention = attention.masked_fill( + rearrange(~query_padding_mask, "b s -> b 1 s 1"), 0.0 + ) + if key_padding_mask is not None: + attention = attention.masked_fill( + rearrange(~key_padding_mask, "b s -> b 1 1 s"), 0.0 + ) + if local_mask is not None: + attention = attention.masked_fill( + torch.all(local_mask, dim=-1, keepdim=True), 0.0 + ) + dropout_scaling = 1.0 / (1 - dropout_p) + if dropout_mask is not None: + attention_drop = attention.masked_fill(~dropout_mask, 0.0) + else: + attention_drop = attention + if intermediate_dtype is not None: + attention_drop = attention_drop.to(intermediate_dtype).to(attention_drop.dtype) + output = torch.einsum("bhts,bshd->bthd", attention_drop, v * dropout_scaling) + if query_padding_mask is not None: + output.masked_fill_(rearrange(~query_padding_mask, "b s -> b s 1 1"), 0.0) + if return_lse: + return output.to(dtype_og), attention.to(dtype_og), lse.to(dtype_og) + return output.to(dtype=dtype_og), attention.to(dtype=dtype_og) + + +def maybe_fake_tensor_mode(fake: bool = True): + """ + One way to populate/pre-compile cache is to use torch fake tensor mode, + which does not allocate actual GPU tensors but retains tensor shape/dtype + metadata for cute.compile. + """ + + def decorator(fn): + @wraps(fn) + def wrapper(*args, **kwargs): + with FakeTensorMode() if fake else nullcontext(): + return fn(*args, **kwargs) + + return wrapper + + return decorator + + +def is_fake_mode() -> bool: + return active_fake_mode() is not None diff --git a/python/sglang/jit_kernel/flash_attn/cute/tile_scheduler.py b/python/sglang/jit_kernel/flash_attn/cute/tile_scheduler.py new file mode 100644 index 000000000..61b20967f --- /dev/null +++ b/python/sglang/jit_kernel/flash_attn/cute/tile_scheduler.py @@ -0,0 +1,1713 @@ +# Copyright (c) 2025, Tri Dao, Siyu Wang, Shengbin Di, Yuxi Chi, Johnsonms, Linfeng Zheng, Haoyan Huang, Lanbo Li, Yun Zhong, Man Yuan, Minmin Sun, Yong Li, Wei Lin. + +from dataclasses import dataclass +from enum import IntEnum, auto +from typing import Optional, Protocol, Tuple, runtime_checkable + +try: + from typing import override +except ImportError: # Python < 3.12 + from typing_extensions import override + +import cutlass +import cutlass.cute as cute +from cutlass import Int32, const_expr +from cutlass._mlir import ir +from cutlass.cute import FastDivmodDivisor +from cutlass.cute.typing import Boolean +from cutlass.cutlass_dsl import ( + extract_mlir_values, +) +from cutlass.cutlass_dsl import min as dsl_min +from cutlass.cutlass_dsl import ( + new_from_mlir_values, +) +from cutlass.pipeline import PipelineClcFetchAsync, PipelineState +from cutlass.utils import ( + ClcDynamicPersistentTileScheduler, + ClcDynamicPersistentTileSchedulerParams, +) +from cutlass.utils.hardware_info import HardwareInfo +from quack.cute_dsl_utils import ParamsBase + +import sglang.jit_kernel.flash_attn.cute.utils as utils +from sglang.jit_kernel.flash_attn.cute.fast_math import clz + + +class SchedulingMode(IntEnum): + NONE = auto() + STATIC = auto() + DYNAMIC = auto() + CLC = auto() + + +@dataclass +class ClcState(ParamsBase): + """Owns the runtime state shared by CLC-capable tile schedulers. + + `FlashAttentionForwardSm100` constructs this state because it owns the CLC + response buffer, mbarrier storage, and launch geometry needed to initialize + the hardware scheduler and async pipeline. Individual tile schedulers then + consume this state and map the returned hardware work tiles into their own + logical `WorkTileInfo` coordinates. + + To add CLC support to a scheduler: + - implement `clc_problem_shape(params)` so the kernel can create the hardware scheduler + - accept `clc: ClcState | None` in `create(...)` / `__init__` + - map `clc.initial_work_tile_info()` and `clc.get_current_work()` into scheduler coordinates + """ + + _hw_scheduler: ClcDynamicPersistentTileScheduler + _pipeline: PipelineClcFetchAsync + _consumer_state: PipelineState + _producer_state: PipelineState + + @staticmethod + def create( + *, + hw_scheduler: ClcDynamicPersistentTileScheduler, + pipeline: PipelineClcFetchAsync, + consumer_state: PipelineState, + producer_state: PipelineState, + ) -> "ClcState": + return ClcState(hw_scheduler, pipeline, consumer_state, producer_state) + + def initial_work_tile_info(self): + return self._hw_scheduler.initial_work_tile_info() + + def get_current_work(self): + return self._hw_scheduler.get_current_work() + + def prefetch_next_work(self, *, loc=None, ip=None): + self._pipeline.producer_acquire(self._producer_state, loc=loc, ip=ip) + mbarrier_addr = self._pipeline.producer_get_barrier( + self._producer_state, loc=loc, ip=ip + ) + self._hw_scheduler.advance_to_next_work(mbarrier_addr, loc=loc, ip=ip) + self._producer_state.advance(loc=loc, ip=ip) + + def consumer_wait(self, *, loc=None, ip=None): + self._pipeline.consumer_wait(self._consumer_state, loc=loc, ip=ip) + + def consumer_release(self, *, loc=None, ip=None): + self._pipeline.consumer_release(self._consumer_state, loc=loc, ip=ip) + self._consumer_state.advance(loc=loc, ip=ip) + + def producer_tail(self, *, loc=None, ip=None): + self._pipeline.producer_tail(self._producer_state, loc=loc, ip=ip) + + +class WorkTileInfo(cutlass.utils.WorkTileInfo): + """Altered WorkTileInfo which includes four axes: (block, head, batch, split)""" + + @override + def __new_from_mlir_values__(self, values: list[ir.Value]) -> "WorkTileInfo": + assert len(values) == 5 + new_tile_idx = cutlass.new_from_mlir_values(self._tile_idx, values[:-1]) + new_is_valid_tile = cutlass.new_from_mlir_values( + self._is_valid_tile, [values[-1]] + ) + return WorkTileInfo(new_tile_idx, new_is_valid_tile) + + +@runtime_checkable +class TileSchedulerProtocol(Protocol): + """Protocol defining the interface all tile schedulers must implement. + + Schedulers are responsible for: + 1. Coordinate mapping: linear tile index -> (m_block, head, batch, split) + 2. Work distribution: how to get the next tile (static grid-stride vs CLC dynamic) + """ + + def get_current_work(self) -> WorkTileInfo: + """Get the current work tile coordinates.""" + ... + + def initial_work_tile_info(self) -> WorkTileInfo: + """Get the initial work tile for this CTA.""" + ... + + def advance_to_next_work(self, *, loc=None, ip=None): + """Consumer-side advance: move to next tile and return it. + + For static schedulers: grid-stride increment + get_current_work. + For CLC schedulers: consumer wait + get_current_work + consumer release + state advance. + """ + ... + + def prefetch_next_work(self, *, loc=None, ip=None) -> None: + """Producer-side prefetch of next work tile (no-op for static schedulers). + + For CLC schedulers: producer acquire + issue CLC query + producer state advance. + Only called by the scheduler warp. + """ + ... + + def producer_tail(self, *, loc=None, ip=None) -> None: + """Producer-side cleanup after the last tile. + + No-op for static schedulers. For CLC schedulers: pipeline producer_tail. + """ + ... + + +@dataclass +class TileSchedulerArguments(ParamsBase): + num_block: Int32 + num_head: Int32 + num_batch: Int32 + num_splits: Int32 + seqlen_k: Int32 + headdim: Int32 + headdim_v: Int32 + total_q: Int32 + tile_shape_mn: cutlass.Constexpr[Tuple[int, int]] + cluster_shape_mn: cutlass.Constexpr[Tuple[int, int]] = (1, 1) + mCuSeqlensQ: Optional[cute.Tensor] = None + mSeqUsedQ: Optional[cute.Tensor] = None + qhead_per_kvhead_packgqa: cutlass.Constexpr[int] = 1 + element_size: cutlass.Constexpr[int] = 2 + is_persistent: cutlass.Constexpr[bool] = False + lpt: cutlass.Constexpr[bool] = False + is_split_kv: cutlass.Constexpr[bool] = False + head_swizzle: cutlass.Constexpr[bool] = False + use_cluster_idx: cutlass.Constexpr[bool] = False + + +class SingleTileScheduler: + @dataclass + class Params(ParamsBase): + num_block: Int32 + num_head: Int32 + num_batch: Int32 + num_splits: Int32 + num_splits_divmod: FastDivmodDivisor + is_split_kv: cutlass.Constexpr[bool] = False + cluster_shape_mn: cutlass.Constexpr[Tuple[int, int]] = (1, 1) + use_cluster_idx: cutlass.Constexpr[bool] = False + + @staticmethod + def create( + args: TileSchedulerArguments, *, loc=None, ip=None + ) -> "SingleTileScheduler.Params": + return SingleTileScheduler.Params( + args.num_block, + args.num_head, + args.num_batch, + args.num_splits, + FastDivmodDivisor(args.num_splits), + args.is_split_kv, + args.cluster_shape_mn, + args.use_cluster_idx, + ) + + def __init__(self, params: Params, blk_coord: cute.Coord, *, loc=None, ip=None): + self.params = params + self._blk_coord = blk_coord + self._is_first_block = True + self._loc = loc + self._ip = ip + + @staticmethod + def to_underlying_arguments( + args: TileSchedulerArguments, + *, + scheduling_mode: SchedulingMode = SchedulingMode.STATIC, + loc=None, + ip=None, + ) -> Params: + assert ( + scheduling_mode == SchedulingMode.STATIC + ), f"SingleTileScheduler only supports STATIC, got {scheduling_mode!r}" + return SingleTileScheduler.Params.create(args, loc=loc, ip=ip) + + @staticmethod + def create( + params: Params, clc: ClcState | None = None, *, loc=None, ip=None + ) -> "SingleTileScheduler": + if const_expr( + cute.size(params.cluster_shape_mn) == 1 or not params.use_cluster_idx + ): + blk_coord = cute.arch.block_idx() + else: + blk_coord = cute.arch.cluster_idx() + return SingleTileScheduler(params, blk_coord, loc=loc, ip=ip) + + # called by host + @staticmethod + def get_grid_shape( + params: Params, + *, + loc=None, + ip=None, + ) -> Tuple[Int32, Int32, Int32]: + # TODO: this hard-codes the fact that we only use cluster = (1, 1) or (2, 1) + assert ( + params.cluster_shape_mn[1] == 1 + ), "Only cluster_shape_mn[1] == 1 is supported" + if const_expr(params.use_cluster_idx): + # Grid must have num_block * cluster_m physical blocks so that there are num_block clusters + grid_x = params.num_block * params.cluster_shape_mn[0] + else: + grid_x = cute.round_up(params.num_block, params.cluster_shape_mn[0]) + return ( + grid_x, + params.num_head * params.num_splits, + params.num_batch, + ) + + def get_current_work(self, *, loc=None, ip=None) -> WorkTileInfo: + block_idx, head_idx, batch_idx = self._blk_coord + if const_expr(self.params.is_split_kv): + head_idx, split_idx = divmod(head_idx, self.params.num_splits_divmod) + else: + split_idx = Int32(0) + return WorkTileInfo( + (block_idx, head_idx, batch_idx, split_idx), + self._is_first_block, + ) + + def initial_work_tile_info(self, *, loc=None, ip=None): + return self.get_current_work(loc=loc, ip=ip) + + def prefetch_next_work(self, *, loc=None, ip=None): + pass + + def advance_to_next_work(self, *, loc=None, ip=None): + self._is_first_block = False + return self.get_current_work() + + def producer_tail(self, *, loc=None, ip=None): + pass + + def __extract_mlir_values__(self): + values, self._values_pos = [], [] + for obj in [self.params, self._blk_coord]: + obj_values = cutlass.extract_mlir_values(obj) + values += obj_values + self._values_pos.append(len(obj_values)) + return values + + def __new_from_mlir_values__(self, values): + obj_list = [] + for obj, n_items in zip([self.params, self._blk_coord], self._values_pos): + obj_list.append(cutlass.new_from_mlir_values(obj, values[:n_items])) + values = values[n_items:] + return SingleTileScheduler(*(tuple(obj_list)), loc=self._loc) + + +class StaticPersistentTileScheduler: + @dataclass + class Params(ParamsBase): + num_block_cluster_divmod: FastDivmodDivisor + num_head_divmod: FastDivmodDivisor + total_blocks_cluster: Int32 + cluster_shape_m: cutlass.Constexpr[int] = 1 + + @staticmethod + def create( + args: TileSchedulerArguments, *, loc=None, ip=None + ) -> "StaticPersistentTileScheduler.Params": + num_block_cluster = cute.ceil_div( + args.num_block, cute.size(args.cluster_shape_mn) + ) + total_blocks_cluster = num_block_cluster * args.num_head * args.num_batch + return StaticPersistentTileScheduler.Params( + FastDivmodDivisor(num_block_cluster), + FastDivmodDivisor(args.num_head), + total_blocks_cluster, + cluster_shape_m=args.cluster_shape_mn[0], + ) + + def __init__(self, params: Params, tile_idx: Int32, *, loc=None, ip=None): + self.params = params + self._tile_idx = tile_idx + self._loc = loc + self._ip = ip + + @staticmethod + def to_underlying_arguments( + args: TileSchedulerArguments, + *, + scheduling_mode: SchedulingMode = SchedulingMode.STATIC, + loc=None, + ip=None, + ) -> Params: + assert ( + scheduling_mode == SchedulingMode.STATIC + ), f"StaticPersistentTileScheduler only supports STATIC, got {scheduling_mode!r}" + return StaticPersistentTileScheduler.Params.create(args, loc=loc, ip=ip) + + @staticmethod + def create( + params: Params, clc: ClcState | None = None, *, loc=None, ip=None + ) -> "StaticPersistentTileScheduler": + if const_expr(cute.size(params.cluster_shape_m) == 1): + tile_idx = cute.arch.block_idx()[0] + else: + tile_idx = cute.arch.cluster_idx()[0] + return StaticPersistentTileScheduler(params, tile_idx, loc=loc, ip=ip) + + @staticmethod + def get_grid_shape( + params: Params, + *, + loc=None, + ip=None, + ) -> Tuple[Int32, Int32, Int32]: + hardware_info = cutlass.utils.HardwareInfo() + sm_count = hardware_info.get_device_multiprocessor_count() + max_ctas = (sm_count // params.cluster_shape_m) * params.cluster_shape_m + grid_x = cutlass.min( + max_ctas, params.total_blocks_cluster * params.cluster_shape_m + ) + return (grid_x, Int32(1), Int32(1)) + + def get_current_work(self, *, loc=None, ip=None) -> WorkTileInfo: + hn_idx, block_idx = divmod(self._tile_idx, self.params.num_block_cluster_divmod) + batch_idx, head_idx = divmod(hn_idx, self.params.num_head_divmod) + is_valid = self._tile_idx < self.params.total_blocks_cluster + return WorkTileInfo( + (Int32(block_idx), Int32(head_idx), Int32(batch_idx), Int32(0)), is_valid + ) + + def initial_work_tile_info(self, *, loc=None, ip=None): + return self.get_current_work(loc=loc, ip=ip) + + def prefetch_next_work(self, *, loc=None, ip=None): + pass + + def advance_to_next_work(self, *, loc=None, ip=None): + if const_expr(self.params.cluster_shape_m == 1): + self._tile_idx += cute.arch.grid_dim()[0] + else: + self._tile_idx += cute.arch.cluster_dim()[0] + return self.get_current_work() + + def producer_tail(self, *, loc=None, ip=None): + pass + + def __extract_mlir_values__(self): + values, self._values_pos = [], [] + for obj in [self.params, self._tile_idx]: + obj_values = cutlass.extract_mlir_values(obj) + values += obj_values + self._values_pos.append(len(obj_values)) + return values + + def __new_from_mlir_values__(self, values): + obj_list = [] + for obj, n_items in zip( + [self.params, self._tile_idx], + self._values_pos, + ): + obj_list.append(cutlass.new_from_mlir_values(obj, values[:n_items])) + values = values[n_items:] + return StaticPersistentTileScheduler(*(tuple(obj_list)), loc=self._loc) + + +class SingleTileLPTScheduler: + @dataclass + class Params(ParamsBase): + total_blocks: Int32 + num_splits: Int32 + num_block: Int32 + num_head: Int32 + num_batch: Int32 + l2_minor: Int32 + num_head_divmod: FastDivmodDivisor + l2_minor_divmod: FastDivmodDivisor + l2_major_divmod: FastDivmodDivisor + l2_minor_residual_divmod: FastDivmodDivisor + num_hb_quotient: Int32 + num_splits_divmod: FastDivmodDivisor + is_split_kv: cutlass.Constexpr[bool] = False + cluster_shape_m: cutlass.Constexpr[int] = 1 + scheduling_mode: cutlass.Constexpr[SchedulingMode] = SchedulingMode.STATIC + lpt: cutlass.Constexpr[bool] = True + use_cluster_idx: cutlass.Constexpr[bool] = True + + @staticmethod + @cute.jit + def create( + args: TileSchedulerArguments, + *, + scheduling_mode: SchedulingMode = SchedulingMode.STATIC, + loc=None, + ip=None, + ) -> "SingleTileLPTScheduler.Params": + assert scheduling_mode in ( + SchedulingMode.STATIC, + SchedulingMode.CLC, + ), f"Only STATIC and CLC are supported, got {scheduling_mode!r}" + size_one_kv_head = ( + args.seqlen_k * (args.headdim + args.headdim_v) * args.element_size + ) + size_one_head = size_one_kv_head + size_l2 = 50 * 1024 * 1024 # 40 MB for K & V + # Swizzle is the size of each "section". Round swizzle to a power of 2 + # Need to be careful about the case where only one head will fit + # swizzle is how many heads can fit in L2 + # Seems faster if swizzle is a power of 2 + log2_floor = lambda n: 31 - clz(n) + swizzle = ( + 1 + if size_l2 < size_one_head + else (1 << log2_floor(size_l2 // size_one_head)) + ) + # If we're in the last section (called residual), we don't want to divide by + # swizzle. Instead we want to divide by the remainder. + num_hb_quotient = (args.num_head * args.num_batch) // swizzle + num_hb_remainder = (args.num_head * args.num_batch) % swizzle + return SingleTileLPTScheduler.Params( + total_blocks=args.num_block * args.num_head * args.num_batch, + num_block=args.num_block, + num_head=args.num_head, + num_batch=args.num_batch, + l2_minor=Int32(swizzle), + num_head_divmod=FastDivmodDivisor(args.num_head), + l2_minor_divmod=FastDivmodDivisor(swizzle), + l2_major_divmod=FastDivmodDivisor(swizzle * args.num_block), + l2_minor_residual_divmod=FastDivmodDivisor(max(num_hb_remainder, 1)), + num_hb_quotient=Int32(num_hb_quotient), + num_splits=args.num_splits, + num_splits_divmod=FastDivmodDivisor(args.num_splits), + is_split_kv=args.is_split_kv, + cluster_shape_m=args.cluster_shape_mn[0], + scheduling_mode=scheduling_mode, + lpt=args.lpt, + use_cluster_idx=args.use_cluster_idx, + ) + + def __init__( + self, + params: Params, + tile_idx: Int32, + split_idx: Int32, + clc: ClcState | None = None, + *, + loc=None, + ip=None, + ): + self.params = params + self._tile_idx = tile_idx + self._split_idx = split_idx + self.clc = clc + self._loc = loc + self._ip = ip + + @staticmethod + def to_underlying_arguments( + args: TileSchedulerArguments, + *, + scheduling_mode: SchedulingMode = SchedulingMode.STATIC, + loc=None, + ip=None, + ) -> Params: + return SingleTileLPTScheduler.Params.create( + args, scheduling_mode=scheduling_mode, loc=loc, ip=ip + ) + + @staticmethod + def _clc_grid_shape(params: Params): + num_batch_splits = ( + params.num_batch * params.num_splits + if const_expr(params.is_split_kv) + else params.num_batch + ) + return ( + cute.round_up(params.num_block, params.cluster_shape_m), + params.num_head, + num_batch_splits, + ) + + @staticmethod + @cute.jit + def clc_problem_shape(params: Params): + return ClcDynamicPersistentTileSchedulerParams( + problem_shape_ntile_mnl=SingleTileLPTScheduler._clc_grid_shape(params), + cluster_shape_mnk=(params.cluster_shape_m, 1, 1), + ) + + @staticmethod + @cute.jit + def create( + params: Params, clc: ClcState | None = None, *, loc=None, ip=None + ) -> "SingleTileLPTScheduler": + if const_expr(params.scheduling_mode == SchedulingMode.CLC): + return SingleTileLPTScheduler( + params, cute.arch.block_idx()[0], Int32(0), clc, loc=loc, ip=ip + ) + tile_idx, split_idx, _ = cute.arch.block_idx() + return SingleTileLPTScheduler(params, tile_idx, split_idx, loc=loc, ip=ip) + + @staticmethod + def get_grid_shape( + params: Params, + *, + loc=None, + ip=None, + ) -> Tuple[Int32, Int32, Int32]: + if const_expr(params.scheduling_mode == SchedulingMode.CLC): + return SingleTileLPTScheduler._clc_grid_shape(params) + return (params.total_blocks, params.num_splits, Int32(1)) + + @cute.jit + def clc_work_to_coords(self, work) -> WorkTileInfo: + """Convert CLC response (block, head, batch_split) to WorkTileInfo. + + CLC returns raw grid coordinates — no L2 swizzle (hardware decides order). + We only apply cluster division, optional LPT block reversal, and split_kv unpacking. + """ + block_idx = work.tile_idx[0] + if const_expr(self.params.cluster_shape_m > 1): + block_idx = block_idx // self.params.cluster_shape_m + if const_expr(self.params.lpt): + # Longest-processing-time-first: reverse block order + if const_expr( + self.params.cluster_shape_m > 1 and not self.params.use_cluster_idx + ): + num_block = self.params.num_block // self.params.cluster_shape_m + else: + num_block = self.params.num_block + block_idx = num_block - 1 - block_idx + split_idx = Int32(0) + if const_expr(self.params.is_split_kv): + batch_idx, split_idx = divmod( + work.tile_idx[2], self.params.num_splits_divmod + ) + else: + batch_idx = work.tile_idx[2] + if const_expr( + self.params.cluster_shape_m > 1 and not self.params.use_cluster_idx + ): + bidx_in_cluster = cute.arch.block_in_cluster_idx() + block_idx = block_idx * self.params.cluster_shape_m + bidx_in_cluster[0] + return WorkTileInfo( + ( + Int32(block_idx), + Int32(work.tile_idx[1]), + Int32(batch_idx), + Int32(split_idx), + ), + work.is_valid_tile, + ) + + @cute.jit + def get_current_work(self, *, loc=None, ip=None) -> WorkTileInfo: + if const_expr(self.params.scheduling_mode == SchedulingMode.CLC): + work = self.clc.get_current_work() + self._tile_idx = work.tile_idx[0] + return self.clc_work_to_coords(work) + # Static path: L2-swizzled coordinate mapping + params = self.params + # Implement LPT scheduling coordinate calculation + bidhb, l2_mod = divmod(self._tile_idx, params.l2_major_divmod) + # If we're in the last section (called residual), we don't want to divide by + # swizzle. Instead we want to divide by the remainder. + block, bidhb_residual = 0, 0 + if bidhb < params.num_hb_quotient: + block, bidhb_residual = divmod(l2_mod, params.l2_minor_divmod) + else: + block, bidhb_residual = divmod(l2_mod, params.l2_minor_residual_divmod) + bidhb_actual = bidhb * params.l2_minor + bidhb_residual + batch_idx, head_idx = divmod(bidhb_actual, params.num_head_divmod) + # Longest-processing-time-first + if const_expr(params.lpt): + block = params.num_block - 1 - block + is_valid = self._tile_idx < params.total_blocks + return WorkTileInfo( + (Int32(block), Int32(head_idx), Int32(batch_idx), Int32(self._split_idx)), + is_valid, + ) + + @cute.jit + def initial_work_tile_info(self, *, loc=None, ip=None): + if const_expr(self.params.scheduling_mode == SchedulingMode.CLC): + work = self.clc.initial_work_tile_info() + self._tile_idx = work.tile_idx[0] + return self.clc_work_to_coords(work) + return self.get_current_work(loc=loc, ip=ip) + + def prefetch_next_work(self, *, loc=None, ip=None): + if const_expr(self.params.scheduling_mode == SchedulingMode.CLC): + self.clc.prefetch_next_work(loc=loc, ip=ip) + + def advance_to_next_work(self, *, loc=None, ip=None): + if const_expr(self.params.scheduling_mode == SchedulingMode.CLC): + self.clc.consumer_wait(loc=loc, ip=ip) + work = self.get_current_work() + self.clc.consumer_release(loc=loc, ip=ip) + return work + # Single tile scheduler - set to invalid tile_idx to indicate no more work + self._tile_idx = self.params.total_blocks + return self.get_current_work() + + def producer_tail(self, *, loc=None, ip=None): + if const_expr(self.params.scheduling_mode == SchedulingMode.CLC): + self.clc.producer_tail(loc=loc, ip=ip) + + def __extract_mlir_values__(self): + values, self._values_pos = [], [] + objs = [self.params, self._tile_idx, self._split_idx] + if const_expr(self.params.scheduling_mode == SchedulingMode.CLC): + objs += [self.clc] + for obj in objs: + obj_values = cutlass.extract_mlir_values(obj) + values += obj_values + self._values_pos.append(len(obj_values)) + return values + + def __new_from_mlir_values__(self, values): + obj_list = [] + objs = [self.params, self._tile_idx, self._split_idx] + if const_expr(self.params.scheduling_mode == SchedulingMode.CLC): + objs += [self.clc] + for obj, n_items in zip(objs, self._values_pos): + obj_list.append(cutlass.new_from_mlir_values(obj, values[:n_items])) + values = values[n_items:] + return self.__class__(*obj_list, loc=self._loc) + + +class SingleTileLPTBwdScheduler: + @dataclass + class Params(ParamsBase): + total_blocks: Int32 + num_block: Int32 + l2_minor: Int32 + num_head_divmod: FastDivmodDivisor + l2_minor_divmod: FastDivmodDivisor + l2_major_divmod: FastDivmodDivisor + l2_minor_residual_divmod: FastDivmodDivisor + num_hb_quotient: Int32 + cluster_shape_mn: cutlass.Constexpr[Tuple[int, int]] = (1, 1) + spt: cutlass.Constexpr[bool] = True + + @staticmethod + @cute.jit + def create( + args: TileSchedulerArguments, *, loc=None, ip=None + ) -> "SingleTileLPTBwdScheduler.Params": + size_l2 = 50 * 1024 * 1024 + size_one_qdo_head = ( + args.seqlen_k * (args.headdim + args.headdim_v) * args.element_size + ) + size_one_dqaccum_head = args.seqlen_k * (args.headdim) * 4 + # size_one_dqaccum_head = 0 + size_one_head = size_one_qdo_head + size_one_dqaccum_head + log2_floor = lambda n: 31 - clz(n) + swizzle = ( + 1 + if size_l2 < size_one_head + else (1 << log2_floor(size_l2 // size_one_head)) + ) + # swizzle = 8 + # If we're in the last section (called residual), we don't want to divide by + # swizzle. Instead we want to divide by the remainder. + num_hb_quotient = (args.num_head * args.num_batch) // swizzle + num_hb_remainder = (args.num_head * args.num_batch) % swizzle + num_block = cute.ceil_div(args.num_block, args.cluster_shape_mn[0]) + return SingleTileLPTBwdScheduler.Params( + total_blocks=(num_block * args.cluster_shape_mn[0]) + * args.num_head + * args.num_batch, + num_block=num_block, + l2_minor=Int32(swizzle), + num_head_divmod=FastDivmodDivisor(args.num_head), + l2_minor_divmod=FastDivmodDivisor(swizzle), + l2_major_divmod=FastDivmodDivisor(swizzle * num_block), + l2_minor_residual_divmod=FastDivmodDivisor( + max(num_hb_remainder, 1) + ), # don't divide by 0 + num_hb_quotient=Int32(num_hb_quotient), + cluster_shape_mn=args.cluster_shape_mn, + spt=args.lpt, + ) + + def __init__(self, params: Params, tile_idx: Int32, *, loc=None, ip=None): + self.params = params + self._tile_idx = tile_idx + self._loc = loc + self._ip = ip + + @staticmethod + def to_underlying_arguments( + args: TileSchedulerArguments, + *, + scheduling_mode: SchedulingMode = SchedulingMode.STATIC, + loc=None, + ip=None, + ) -> Params: + assert ( + scheduling_mode == SchedulingMode.STATIC + ), f"SingleTileLPTBwdScheduler only supports STATIC, got {scheduling_mode!r}" + return SingleTileLPTBwdScheduler.Params.create(args, loc=loc, ip=ip) + + @staticmethod + @cute.jit + def create(params: Params, *, loc=None, ip=None) -> "SingleTileLPTBwdScheduler": + tile_idx = cute.arch.block_idx()[0] + return SingleTileLPTBwdScheduler(params, tile_idx, loc=loc, ip=ip) + + # called by host + @staticmethod + def get_grid_shape( + params: Params, + *, + loc=None, + ip=None, + ) -> Tuple[Int32, Int32, Int32]: + return (params.total_blocks, Int32(1), Int32(1)) + + @cute.jit + def get_current_work(self, *, loc=None, ip=None) -> cutlass.utils.WorkTileInfo: + cluster_idx = self._tile_idx // self.params.cluster_shape_mn[0] + params = self.params + # Implement LPT scheduling coordinate calculation + bidhb, l2_mod = divmod(cluster_idx, params.l2_major_divmod) + # If we're in the last section (called residual), we don't want to divide by + # swizzle. Instead we want to divide by the remainder. + block, bidhb_residual = 0, 0 + if bidhb < params.num_hb_quotient: + block, bidhb_residual = divmod(l2_mod, params.l2_minor_divmod) + else: + block, bidhb_residual = divmod(l2_mod, params.l2_minor_residual_divmod) + bidhb_actual = bidhb * params.l2_minor + bidhb_residual + batch_idx, head_idx = divmod(bidhb_actual, params.num_head_divmod) + if cutlass.const_expr(params.spt): + block = params.num_block - 1 - block + if cutlass.const_expr(params.cluster_shape_mn[0] > 1): + bidx_in_cluster = cute.arch.block_in_cluster_idx() + block = block * params.cluster_shape_mn[0] + bidx_in_cluster[0] + is_valid = self._tile_idx < params.total_blocks + return WorkTileInfo( + (Int32(block), Int32(head_idx), Int32(batch_idx), Int32(0)), is_valid + ) + + def initial_work_tile_info(self, *, loc=None, ip=None): + return self.get_current_work(loc=loc, ip=ip) + + def prefetch_next_work(self, *, loc=None, ip=None): + pass + + def advance_to_next_work(self, *, loc=None, ip=None): + # Single tile scheduler - set to invalid tile_idx to indicate no more work + self._tile_idx = self.params.total_blocks + return self.get_current_work() + + def __extract_mlir_values__(self): + values, self._values_pos = [], [] + for obj in [self.params, self._tile_idx]: + obj_values = cutlass.extract_mlir_values(obj) + values += obj_values + self._values_pos.append(len(obj_values)) + return values + + def __new_from_mlir_values__(self, values): + obj_list = [] + for obj, n_items in zip([self.params, self._tile_idx], self._values_pos): + obj_list.append(cutlass.new_from_mlir_values(obj, values[:n_items])) + values = values[n_items:] + return self.__class__(*(tuple(obj_list)), loc=self._loc) + + +class SingleTileVarlenScheduler: + @dataclass + class Params(ParamsBase): + num_head: Int32 + num_batch: Int32 + total_q: Int32 + num_splits: Int32 + max_kvblock_in_l2: Int32 + tile_shape_mn: cutlass.Constexpr[Tuple[int, int]] + mCuSeqlensQ: Optional[cute.Tensor] = None + mSeqUsedQ: Optional[cute.Tensor] = None + qhead_per_kvhead_packgqa: cutlass.Constexpr[int] = 1 + lpt: cutlass.Constexpr[bool] = False + is_split_kv: cutlass.Constexpr[bool] = False + head_swizzle: cutlass.Constexpr[bool] = False + cluster_shape_m: cutlass.Constexpr[int] = 1 + scheduling_mode: cutlass.Constexpr[SchedulingMode] = SchedulingMode.STATIC + + @staticmethod + @cute.jit + def create( + args: TileSchedulerArguments, + *, + scheduling_mode: SchedulingMode = SchedulingMode.STATIC, + loc=None, + ip=None, + ) -> "SingleTileVarlenScheduler.Params": + assert scheduling_mode in ( + SchedulingMode.STATIC, + SchedulingMode.CLC, + ), f"Only STATIC and CLC are supported, got {scheduling_mode!r}" + size_l2 = 50 * 1024 * 1024 # 50 MB for K & V + # if backward, this is qdo block size + kv_block_size = ( + (args.headdim + args.headdim_v) + * args.element_size + * args.tile_shape_mn[1] + ) + # if backward, add dqaccum block size to calculate swizzle + if args.head_swizzle: + kv_block_size += args.headdim * 4 * args.tile_shape_mn[1] + max_kvblock_in_l2 = size_l2 // kv_block_size + assert ( + args.mCuSeqlensQ is not None or args.mSeqUsedQ is not None + ), "At least one of mCuSeqlensQ or mSeqUsedQ must be provided" + assert ( + args.cluster_shape_mn[1] == 1 + ), "Only cluster_shape_mn[1] == 1 is supported" + # TODO: Support varlen CLC with cluster_shape_m > 1 by refactoring the + # flattened-tile decode so cluster unpacking semantics are explicit. + assert ( + scheduling_mode != SchedulingMode.CLC or args.cluster_shape_mn[0] == 1 + ), "Varlen CLC currently requires cluster_shape_mn[0] == 1" + return SingleTileVarlenScheduler.Params( + num_head=args.num_head, + num_batch=args.num_batch, + total_q=args.total_q, + num_splits=args.num_splits, + max_kvblock_in_l2=max_kvblock_in_l2, + tile_shape_mn=args.tile_shape_mn, + mCuSeqlensQ=args.mCuSeqlensQ, + mSeqUsedQ=args.mSeqUsedQ, + qhead_per_kvhead_packgqa=args.qhead_per_kvhead_packgqa, + lpt=args.lpt, + is_split_kv=args.is_split_kv, + head_swizzle=args.head_swizzle, + cluster_shape_m=args.cluster_shape_mn[0], + scheduling_mode=scheduling_mode, + ) + + def __init__( + self, + params: Params, + tile_idx: Int32, + split_idx: Int32, + clc: ClcState | None = None, + *, + loc=None, + ip=None, + ): + self.params = params + self._tile_idx = tile_idx + self._split_idx = split_idx + self._is_first_block = True + self.clc = clc + self._loc = loc + self._ip = ip + + @staticmethod + def to_underlying_arguments( + args: TileSchedulerArguments, + *, + scheduling_mode: SchedulingMode = SchedulingMode.STATIC, + loc=None, + ip=None, + ) -> Params: + return SingleTileVarlenScheduler.Params.create( + args, scheduling_mode=scheduling_mode, loc=loc, ip=ip + ) + + @staticmethod + @cute.jit + def clc_problem_shape(params: Params): + return ClcDynamicPersistentTileSchedulerParams( + problem_shape_ntile_mnl=SingleTileVarlenScheduler.get_grid_shape(params), + cluster_shape_mnk=(1, 1, 1), + ) + + @staticmethod + @cute.jit + def create( + params: Params, clc: ClcState | None = None, *, loc=None, ip=None + ) -> "SingleTileVarlenScheduler": + if const_expr(params.scheduling_mode == SchedulingMode.CLC): + block_idx = cute.arch.block_idx() + split_idx = Int32(0) + if const_expr(params.is_split_kv): + split_idx = block_idx[1] + return SingleTileVarlenScheduler( + params, + block_idx[0], + split_idx, + clc, + loc=loc, + ip=ip, + ) + tile_idx, split_idx, _ = cute.arch.block_idx() + return SingleTileVarlenScheduler(params, tile_idx, split_idx, loc=loc, ip=ip) + + # called by host + @staticmethod + def get_grid_shape( + params: Params, + *, + loc=None, + ip=None, + ) -> Tuple[Int32, Int32, Int32]: + total_blocks_max = ( + params.total_q + + params.num_batch * (params.cluster_shape_m * params.tile_shape_mn[0] - 1) + ) // params.tile_shape_mn[0] + # Round down to nearest multiple of cluster since odd excess is always padding. + total_blocks_max = ( + total_blocks_max // params.cluster_shape_m * params.cluster_shape_m + ) + return (total_blocks_max * params.num_head, params.num_splits, Int32(1)) + + @cute.jit + def _get_num_m_blocks(self, lane: Int32, bidb_start: Int32) -> Int32: + params = self.params + batch_idx = lane + bidb_start + if cutlass.const_expr(params.mSeqUsedQ is not None): + seqlen = Int32(0) + if batch_idx < params.num_batch: + seqlen = params.mSeqUsedQ[batch_idx] + else: + assert params.mCuSeqlensQ is not None + cur_cu_seqlen = Int32(0) + if batch_idx <= params.num_batch: + cur_cu_seqlen = params.mCuSeqlensQ[batch_idx] + next_cu_seqlen = cute.arch.shuffle_sync_down(cur_cu_seqlen, offset=1) + seqlen = next_cu_seqlen - cur_cu_seqlen + if cutlass.const_expr(params.qhead_per_kvhead_packgqa > 1): + seqlen *= params.qhead_per_kvhead_packgqa + return ( + cute.ceil_div( + cute.ceil_div(seqlen, params.tile_shape_mn[0]), params.cluster_shape_m + ) + if batch_idx < params.num_batch and lane < cute.arch.WARP_SIZE - 1 + else Int32(0) + ) + + @cute.jit + def _varlen_coord_map(self) -> WorkTileInfo: + """Map self._tile_idx to (block, head, batch) via warp-level prefix sums.""" + params = self.params + lane_idx = cute.arch.lane_idx() + num_m_blocks = self._get_num_m_blocks(lane_idx, bidb_start=0) + num_m_blocks_cumulative = utils.warp_prefix_sum(num_m_blocks, lane_idx) + # Total number of blocks for the next 31 batches + m_blocks_in_group = cute.arch.shuffle_sync( + num_m_blocks_cumulative, cute.arch.WARP_SIZE - 1 + ) + # Same for all lanes + group_end_tile = m_blocks_in_group * params.num_head + # if cute.arch.thread_idx()[0] == 128 + 31: cute.printf("SingleTileVarlenScheduler: tile_idx=%d, group_end_tile = %d, num_m_blocks=%d, num_m_blocks_cumulative = %d, m_blocks_in_group = %d", self._tile_idx, group_end_tile, num_m_blocks, num_m_blocks_cumulative, m_blocks_in_group) + block, head_idx, batch_idx = Int32(0), Int32(0), Int32(0) + next_tile_idx = self._tile_idx // params.cluster_shape_m + while group_end_tile <= next_tile_idx: + batch_idx += cute.arch.WARP_SIZE - 1 + if batch_idx >= params.num_batch: + batch_idx = Int32(params.num_batch) + group_end_tile = next_tile_idx + 1 + else: + num_m_blocks = self._get_num_m_blocks(lane_idx, bidb_start=batch_idx) + num_m_blocks_cumulative = utils.warp_prefix_sum(num_m_blocks, lane_idx) + m_blocks_in_group = cute.arch.shuffle_sync( + num_m_blocks_cumulative, cute.arch.WARP_SIZE - 1 + ) + group_end_tile += m_blocks_in_group * params.num_head + is_valid = False + if batch_idx >= params.num_batch: + block, head_idx, batch_idx = Int32(0), Int32(0), Int32(params.num_batch) + else: + group_start_tile = group_end_tile - m_blocks_in_group * params.num_head + # if cute.arch.thread_idx()[0] == 128 + 31: cute.printf("SingleTileVarlenScheduler: tile_idx=%d, group_end_tile = %d, num_m_blocks=%d, batch_idx = %d", self._tile_idx, group_end_tile, num_m_blocks, batch_idx) + # The next problem to process is the first one that does not have ending tile position + # that is greater than or equal to tile index. + batch_idx_in_group = cute.arch.popc( + cute.arch.vote_ballot_sync( + group_start_tile + num_m_blocks_cumulative * params.num_head + <= next_tile_idx + ) + ) + batch_idx += batch_idx_in_group + num_m_blocks_prev_lane = ( + 0 + if batch_idx_in_group == 0 + else cute.arch.shuffle_sync( + num_m_blocks_cumulative, batch_idx_in_group - 1 + ) + ) + num_m_blocks = cute.arch.shuffle_sync(num_m_blocks, batch_idx_in_group) + mh_block = ( + next_tile_idx + - group_start_tile + - num_m_blocks_prev_lane * params.num_head + ) + if cutlass.const_expr(params.lpt or params.head_swizzle): + # This is a version of the SingleTileLPTScheduler, complicated by the fact that + # the seqlen can vary per batch. + # TODO: is there any case where num_m_blocks is 0? + # TODO: by right we should read the seqlen_kv but we're assuming seqlen_q == seqlen_k here + num_n_blocks = ( + num_m_blocks + * params.tile_shape_mn[0] + * params.cluster_shape_m + // params.qhead_per_kvhead_packgqa + // params.tile_shape_mn[1] + ) + # nheads_in_l2 = min(max(self.max_kvblock_in_l2 // num_n_blocks, 1), self.num_head) + # Seems faster to have this be a power of 2 + nheads_in_l2 = ( + 16 + if num_n_blocks * 16 <= params.max_kvblock_in_l2 + else ( + 8 + if num_n_blocks * 8 <= params.max_kvblock_in_l2 + else ( + 4 + if num_n_blocks * 4 <= params.max_kvblock_in_l2 + else ( + 2 if num_n_blocks * 2 <= params.max_kvblock_in_l2 else 1 + ) + ) + ) + ) + nheads_in_l2 = min(nheads_in_l2, params.num_head) + mh_in_l2 = nheads_in_l2 * num_m_blocks + section_idx = mh_block // mh_in_l2 + l2_mod = mh_block - section_idx * mh_in_l2 + # Deal with tail section + nheads_in_this_section = ( + nheads_in_l2 + if nheads_in_l2 * (section_idx + 1) <= params.num_head + else params.num_head - section_idx * nheads_in_l2 + ) + block = l2_mod // nheads_in_this_section + head_idx_residual = l2_mod - block * nheads_in_this_section + head_idx = section_idx * nheads_in_l2 + head_idx_residual + if cutlass.const_expr(params.lpt): + block = num_m_blocks - 1 - block + else: + head_idx = mh_block // num_m_blocks + block = mh_block - head_idx * num_m_blocks + is_valid = self._is_first_block and batch_idx < params.num_batch + if cutlass.const_expr(params.cluster_shape_m > 1): + bidx_in_cluster = cute.arch.block_in_cluster_idx() + block = block * params.cluster_shape_m + bidx_in_cluster[0] + # if cute.arch.thread_idx()[0] == 128: cute.printf("SingleTileVarlenScheduler: tile_idx=%d, batch_idx=%d, head_idx=%d, block=%d, is_valid = %d", self._tile_idx, batch_idx, head_idx, block, is_valid) + split_idx = self._split_idx if const_expr(params.is_split_kv) else Int32(0) + return WorkTileInfo( + (Int32(block), Int32(head_idx), Int32(batch_idx), split_idx), is_valid + ) + + @cute.jit + def get_current_work(self, *, loc=None, ip=None) -> WorkTileInfo: + if const_expr(self.params.scheduling_mode == SchedulingMode.CLC): + clc_work = self.clc.get_current_work() + # Default to grid_dim (one past last valid flat index) so _varlen_coord_map + # returns is_valid=False when CLC is exhausted. CLC tile_idx is garbage when + # invalid, so we can't trust it. Local-then-assign avoids CuTe DSL structural + # mismatch on self inside the runtime if. + new_tile_idx = cute.arch.grid_dim()[0] + new_split_idx = Int32(0) + if clc_work.is_valid_tile: + new_tile_idx = clc_work.tile_idx[0] + if const_expr(self.params.is_split_kv): + new_split_idx = clc_work.tile_idx[1] + self._tile_idx = new_tile_idx + self._split_idx = new_split_idx + return self._varlen_coord_map() + + @cute.jit + def initial_work_tile_info(self, *, loc=None, ip=None): + if const_expr(self.params.scheduling_mode == SchedulingMode.CLC): + clc_work = self.clc.initial_work_tile_info() + # See get_current_work for why grid_dim and local-then-assign. + new_tile_idx = cute.arch.grid_dim()[0] + new_split_idx = Int32(0) + if clc_work.is_valid_tile: + new_tile_idx = clc_work.tile_idx[0] + if const_expr(self.params.is_split_kv): + new_split_idx = clc_work.tile_idx[1] + self._tile_idx = new_tile_idx + self._split_idx = new_split_idx + return self._varlen_coord_map() + + def prefetch_next_work(self, *, loc=None, ip=None): + if const_expr(self.params.scheduling_mode == SchedulingMode.CLC): + self.clc.prefetch_next_work(loc=loc, ip=ip) + + def advance_to_next_work(self, *, loc=None, ip=None): + if const_expr(self.params.scheduling_mode == SchedulingMode.CLC): + self.clc.consumer_wait(loc=loc, ip=ip) + work = self.get_current_work() + self.clc.consumer_release(loc=loc, ip=ip) + return work + self._is_first_block = False + return self.get_current_work() + + def producer_tail(self, *, loc=None, ip=None): + if const_expr(self.params.scheduling_mode == SchedulingMode.CLC): + self.clc.producer_tail(loc=loc, ip=ip) + + def __extract_mlir_values__(self): + values, self._values_pos = [], [] + objs = [self.params, self._tile_idx, self._split_idx] + if const_expr(self.params.scheduling_mode == SchedulingMode.CLC): + objs += [self.clc] + for obj in objs: + obj_values = cutlass.extract_mlir_values(obj) + values += obj_values + self._values_pos.append(len(obj_values)) + return values + + def __new_from_mlir_values__(self, values): + obj_list = [] + objs = [self.params, self._tile_idx, self._split_idx] + if const_expr(self.params.scheduling_mode == SchedulingMode.CLC): + objs += [self.clc] + for obj, n_items in zip(objs, self._values_pos): + obj_list.append(cutlass.new_from_mlir_values(obj, values[:n_items])) + values = values[n_items:] + return self.__class__(*obj_list, loc=self._loc) + + +# ----------------------------------------------------------------------------- +# SM100 FMHA-specific schedulers (kept separate from generic schedulers). +# ----------------------------------------------------------------------------- + + +class Sm100FmhaStaticTileSchedulerParams: + """A class to represent parameters for the FMHA (Fused Multi-Head Attention) static tile scheduler. + + This class holds the configuration parameters needed to initialize and configure + the tile scheduler for FMHA operations. + + :ivar is_persistent: Whether to use persistent kernel mode. + :type is_persistent: bool + :ivar problem_shape_mbh: Problem shape in (M, B, H) format. + :type problem_shape_mbh: cute.Shape + """ + + def __init__( + self, + is_persistent: bool, + problem_shape_mbh: cute.Shape, + *, + loc=None, + ip=None, + ): + """ + Initializes the Sm100FmhaStaticTileSchedulerParams with the given parameters. + + :param is_persistent: Whether to use persistent kernel mode. + :type is_persistent: bool + :param problem_shape_mbh: Problem shape in (M, B, H) format. + :type problem_shape_mbh: cute.Shape + """ + self.is_persistent = is_persistent + self.problem_shape_mbh = problem_shape_mbh + self._loc = loc + self._ip = ip + + def __extract_mlir_values__(self): + values, self._values_pos = [], [] + for obj in [self.problem_shape_mbh]: + obj_values = extract_mlir_values(obj) + values += obj_values + self._values_pos.append(len(obj_values)) + return values + + def __new_from_mlir_values__(self, values): + obj_list = [] + for obj, n_items in zip([self.problem_shape_mbh], self._values_pos): + obj_list.append(new_from_mlir_values(obj, values[:n_items])) + values = values[n_items:] + return Sm100FmhaStaticTileSchedulerParams( + self.is_persistent, *(tuple(obj_list)), loc=self._loc + ) + + +class Sm100FmhaStaticTileScheduler: + """A static tile scheduler for FMHA (Fused Multi-Head Attention) operations. + + This class manages the scheduling of work tiles for FMHA kernels, supporting + both persistent and non-persistent kernel modes. It tracks the current work + position and advances through the problem space efficiently. + + :ivar _params: Scheduler parameters. + :type _params: Sm100FmhaStaticTileSchedulerParams + :ivar _blk_coord: Block coordinates. + :type _blk_coord: cute.Coord + :ivar _grid_shape: Grid shape for the kernel. + :type _grid_shape: cute.Shape + :ivar _is_persistent: Whether to use persistent kernel mode. + :type _is_persistent: bool + :ivar _current_work_linear_idx: Current linear work index. + :type _current_work_linear_idx: Int32 + :ivar _problem_shape_mbh: Problem shape in (M, B, H) format. + :type _problem_shape_mbh: cute.Layout + :ivar _num_blocks: Number of blocks in the problem. + :type _num_blocks: Int32 + :ivar _is_first_block: Whether this is the first block. + :type _is_first_block: bool + :ivar num_persistent_sm: Number of persistent SMs. + :type num_persistent_sm: Int32 + """ + + def __init__( + self, + params: Sm100FmhaStaticTileSchedulerParams, + current_work_linear_idx: Int32, + blk_coord: cute.Coord, + grid_shape: cute.Shape, + *, + loc=None, + ip=None, + ): + """ + Initializes the Sm100FmhaStaticTileScheduler with the given parameters. + + :param params: Scheduler parameters. + :type params: Sm100FmhaStaticTileSchedulerParams + :param current_work_linear_idx: Current linear work index. + :type current_work_linear_idx: Int32 + :param blk_coord: Block coordinates. + :type blk_coord: cute.Coord + :param grid_shape: Grid shape for the kernel. + :type grid_shape: cute.Shape + """ + self._params = params + self._blk_coord = blk_coord + self._grid_shape = grid_shape + self._is_persistent = params.is_persistent + self._current_work_linear_idx = current_work_linear_idx + self._problem_shape_mbh = cute.make_layout( + params.problem_shape_mbh, loc=loc, ip=ip + ) + self._num_blocks = cute.size(self._problem_shape_mbh, loc=loc, ip=ip) + self._is_first_block = True + self.num_persistent_sm = cute.size(grid_shape, loc=loc, ip=ip) + self._loc = loc + self._ip = ip + + # called by host + @staticmethod + def get_grid_shape( + params: Sm100FmhaStaticTileSchedulerParams, + *, + loc=None, + ip=None, + ) -> cute.Shape: + """ + Determine the grid shape for the FMHA kernel. + + For persistent kernels, the grid shape is limited by the number of SMs + (Streaming Multiprocessors) available on the device. For non-persistent + kernels, the grid shape matches the problem shape. + + :param params: Scheduler parameters. + :type params: Sm100FmhaStaticTileSchedulerParams + + :return: Grid shape as (M, B, H) tuple. + :rtype: cute.Shape + """ + if params.is_persistent: + hardware_info = HardwareInfo() + sm_count = hardware_info.get_device_multiprocessor_count() + return ( + dsl_min(sm_count, cute.size(params.problem_shape_mbh, loc=loc, ip=ip)), + 1, + 1, + ) + else: + return params.problem_shape_mbh + + @staticmethod + def check_valid_work_for_seqlen_q( + q_tiler: int, + current_idx: Int32, + seqlen_q: Int32, + ) -> Boolean: + """ + Check if the current work index is valid for the given query sequence length. + + This method verifies that the current work tile index multiplied by the + query tiler size is within the bounds of the query sequence length. + + :param q_tiler: Query tiler size. + :type q_tiler: int + :param current_idx: Current work index. + :type current_idx: Int32 + :param seqlen_q: Query sequence length. + :type seqlen_q: Int32 + + :return: True if the work is valid, False otherwise. + :rtype: Boolean + """ + return current_idx * q_tiler < seqlen_q + + def get_current_work(self, *, loc=None, ip=None) -> cutlass.utils.WorkTileInfo: + """ + Get information about the current work tile. + + Determines if the current work is valid and computes the tile coordinates + based on whether the kernel is persistent or non-persistent. + + :return: WorkTileInfo containing tile coordinates and validity flag. + :rtype: WorkTileInfo + """ + is_valid = ( + self._current_work_linear_idx < self._num_blocks + if self._is_persistent + else self._is_first_block + ) + + blk_coord = (0, 0, 0) + if self._is_persistent: + blk_coord = self._problem_shape_mbh.get_hier_coord( + self._current_work_linear_idx, loc=loc, ip=ip + ) + else: + blk_coord = self._blk_coord + + # cur_tile_coord is (mid, 0, (bid, hid)) + cur_tile_coord = ( + blk_coord[0], + 0, + (blk_coord[1], blk_coord[2]), + ) + + return cutlass.utils.WorkTileInfo(cur_tile_coord, is_valid) + + def initial_work_tile_info(self, *, loc=None, ip=None): + """ + Get the initial work tile information. + + :return: Initial WorkTileInfo. + :rtype: WorkTileInfo + """ + return self.get_current_work(loc=loc, ip=ip) + + def advance_to_next_work(self, *, advance_count=1, loc=None, ip=None): + """ + Advance to the next work tile and return it. + + For persistent kernels, advances by the number of persistent SMs. + For non-persistent kernels, marks that the first block has been processed. + """ + if self._is_persistent: + self._current_work_linear_idx += advance_count * self.num_persistent_sm + self._is_first_block = False + return self.get_current_work() + + def prefetch_next_work(self, *, loc=None, ip=None): + """No-op for static scheduler.""" + pass + + def producer_tail(self, *, loc=None, ip=None): + """No-op for static scheduler.""" + pass + + def __extract_mlir_values__(self): + values = extract_mlir_values(self._params) + values.extend(extract_mlir_values(self._current_work_linear_idx)) + values.extend(extract_mlir_values(self._blk_coord)) + values.extend(extract_mlir_values(self._grid_shape)) + return values + + def __new_from_mlir_values__(self, values): + assert len(values) == 10 + new_params = new_from_mlir_values(self._params, values[0:3]) + new_current_work_linear_idx = new_from_mlir_values( + self._current_work_linear_idx, [values[3]] + ) + new_blk_coord = new_from_mlir_values(self._blk_coord, values[4:7]) + new_grid_shape = new_from_mlir_values(self._grid_shape, values[7:]) + return Sm100FmhaStaticTileScheduler( + new_params, new_current_work_linear_idx, new_blk_coord, new_grid_shape + ) + + +def compute_sm100_fmha_grid( + o_shape: cute.Shape, + cta_tiler: Tuple[int, int, int], + is_persistent: bool, +) -> Tuple[Sm100FmhaStaticTileSchedulerParams, Tuple[int, int, int]]: + """Compute grid parameters for FMHA (static scheduler). + + The output tensor o has shape (s, d, ((h_r, h_k), b)). + """ + tile_sched_params = Sm100FmhaStaticTileSchedulerParams( + is_persistent, + ( + cute.ceil_div(cute.size(o_shape[0]), cta_tiler[0]), + cute.size(o_shape[2][0]), + cute.size(o_shape[2][1]), + ), + ) + grid = Sm100FmhaStaticTileScheduler.get_grid_shape(tile_sched_params) + return tile_sched_params, grid + + +############################################################################## +# Fmha CLC dynamic tile scheduler +############################################################################## + + +class Sm100FmhaClcDynamicTileSchedulerParams: + """Parameters for FMHA CLC dynamic persistent tile scheduler. + + This class manages the layout of tiles for CLC (Cluster Launch Control) + based dynamic scheduling, adapted for FMHA's (M, B, H) problem shape. + + :ivar problem_shape_mbh: Problem shape in (M, B, H) format. + :type problem_shape_mbh: cute.Shape + :ivar cluster_shape_mnk: Cluster shape in (M, N, K) format. + :type cluster_shape_mnk: cute.Shape + """ + + def __init__( + self, + problem_shape_mbh: cute.Shape, + cluster_shape_mnk: cute.Shape, + *, + loc=None, + ip=None, + ): + self.problem_shape_mbh = problem_shape_mbh + self._cluster_shape_mnk = cluster_shape_mnk + self.cluster_shape_mn = cluster_shape_mnk[:2] + self._loc = loc + self._ip = ip + + # FMHA uses linear indexing over (M, B, H), convert to (M, N, L) style + # For FMHA: M dim is tile count along sequence, N=1, L=(B*H) + self.problem_shape_ntile_mnl = ( + problem_shape_mbh[0], # M tiles + 1, # N tiles (always 1 for FMHA) + problem_shape_mbh[1] * problem_shape_mbh[2], # L = B * H + ) + + # Create layout for cluster-to-tile mapping + self.problem_layout_ncluster_mnl = cute.make_layout( + cute.ceil_div(self.problem_shape_ntile_mnl, cluster_shape_mnk[:2]), + loc=loc, + ip=ip, + ) + + def __extract_mlir_values__(self): + values, self._values_pos = [], [] + for obj in [ + self.problem_shape_mbh, + self._cluster_shape_mnk, + ]: + obj_values = extract_mlir_values(obj) + values += obj_values + self._values_pos.append(len(obj_values)) + return values + + def __new_from_mlir_values__(self, values): + obj_list = [] + values_copy = list(values) + for obj, n_items in zip( + [self.problem_shape_mbh, self._cluster_shape_mnk], + self._values_pos, + ): + obj_list.append(new_from_mlir_values(obj, values_copy[:n_items])) + values_copy = values_copy[n_items:] + return Sm100FmhaClcDynamicTileSchedulerParams(*(tuple(obj_list)), loc=self._loc) + + def get_grid_shape(self, *, loc=None, ip=None) -> Tuple[int, int, int]: + """Compute grid shape aligned with cluster shape.""" + return cute.round_up(self.problem_shape_ntile_mnl, self._cluster_shape_mnk) + + def clc_hw_params(self) -> ClcDynamicPersistentTileSchedulerParams: + """Return params for the upstream CLC hardware scheduler.""" + return ClcDynamicPersistentTileSchedulerParams( + problem_shape_ntile_mnl=self.problem_shape_ntile_mnl, + cluster_shape_mnk=self._cluster_shape_mnk, + ) + + +class Sm100FmhaClcDynamicTileScheduler: + """CLC dynamic persistent tile scheduler for FMHA. + + This scheduler uses Blackwell's Cluster Launch Control hardware mechanism + for dynamic tile distribution, providing automatic load balancing. + Adapted for FMHA's (M, B, H) problem shape. + """ + + def __init__( + self, + params: Sm100FmhaClcDynamicTileSchedulerParams, + cta_id_in_cluster: cute.Coord, + num_tiles_executed: Int32, + clc_response_ptr: cute.Pointer, + block_idx: Tuple, + clc: ClcState = None, + *, + loc=None, + ip=None, + ): + self.params = params + self.cta_id_in_cluster = cta_id_in_cluster + self._num_tiles_executed = num_tiles_executed + self._clc_response_ptr = clc_response_ptr + self._block_idx = block_idx + self.clc = clc + self._loc = loc + self._ip = ip + + def __extract_mlir_values__(self): + values = extract_mlir_values(self.cta_id_in_cluster) + values.extend(extract_mlir_values(self._num_tiles_executed)) + values.extend(extract_mlir_values(self._clc_response_ptr)) + values.extend(extract_mlir_values(self._block_idx)) + if self.clc is not None: + values.extend(extract_mlir_values(self.clc)) + return values + + def __new_from_mlir_values__(self, values): + new_cta_id_in_cluster = new_from_mlir_values( + self.cta_id_in_cluster, values[0:3] + ) + new_num_tiles_executed = new_from_mlir_values( + self._num_tiles_executed, [values[3]] + ) + new_clc_response_ptr = new_from_mlir_values(self._clc_response_ptr, [values[4]]) + new_block_idx = new_from_mlir_values(self._block_idx, values[5:8]) + new_clc = None + if self.clc is not None: + new_clc = new_from_mlir_values(self.clc, values[8:]) + return Sm100FmhaClcDynamicTileScheduler( + self.params, + new_cta_id_in_cluster, + new_num_tiles_executed, + new_clc_response_ptr, + new_block_idx, + new_clc, + ) + + @staticmethod + def create( + params: Sm100FmhaClcDynamicTileSchedulerParams, + block_idx: Tuple, + grid_dim: Tuple, + clc_response_ptr: cute.Pointer, + clc: ClcState = None, + *, + loc=None, + ip=None, + ): + """Create a CLC dynamic tile scheduler instance.""" + bidx, bidy, bidz = block_idx + + # CTA id in cluster + cta_id_in_cluster = ( + Int32(bidx % params.cluster_shape_mn[0]), + Int32(bidy % params.cluster_shape_mn[1]), + Int32(0), + ) + + num_tiles_executed = Int32(0) + + return Sm100FmhaClcDynamicTileScheduler( + params, + cta_id_in_cluster, + num_tiles_executed, + clc_response_ptr, + block_idx, + clc, + ) + + @staticmethod + def get_grid_shape( + params: Sm100FmhaClcDynamicTileSchedulerParams, + *, + loc=None, + ip=None, + ) -> Tuple[int, int, int]: + """Get grid shape for kernel launch.""" + return params.get_grid_shape(loc=loc, ip=ip) + + def work_tile_info_from_clc_response( + self, result_addr: cute.Pointer, *, loc=None, ip=None + ): + """Parse CLC response and convert to FMHA tile coordinates.""" + m_idx, n_idx, l_idx, vld = cute.arch.clc_response(result_addr, loc=loc, ip=ip) + cute.arch.fence_proxy("async.shared", space="cta") + + # CLC returns first CTA coordinates: m_idx=x, l_idx=z + # l_idx is the L (batch) dimension; decode to (bid, hid) + hid = l_idx % self.params.problem_shape_mbh[2] + bid = l_idx // self.params.problem_shape_mbh[2] + + cta_idx_in_cluster, cta_idy_in_cluster, _ = self.cta_id_in_cluster + cur_tile_coord = ( + m_idx + cta_idx_in_cluster, # M dimension + 0, # N always 0 for FMHA + (bid, hid), # (B, H) packed + ) + + return cutlass.utils.WorkTileInfo(cur_tile_coord, vld) + + def get_current_work(self, *, loc=None, ip=None): + """Get current work tile from CLC response.""" + return self.work_tile_info_from_clc_response( + self._clc_response_ptr, loc=loc, ip=ip + ) + + def initial_work_tile_info(self, *, loc=None, ip=None): + """Get initial work tile based on block index.""" + bidx, bidy, bidz = self._block_idx + # bidz is the L (batch) dimension; decode to (bid, hid) + hid = bidz % self.params.problem_shape_mbh[2] + bid = bidz // self.params.problem_shape_mbh[2] + return cutlass.utils.WorkTileInfo((bidx, 0, (bid, hid)), True) + + def advance_to_next_work(self, *, loc=None, ip=None): + """Consumer-side advance: wait for next tile, read coordinates, release.""" + self.clc.consumer_wait(loc=loc, ip=ip) + work = self.get_current_work(loc=loc, ip=ip) + self.clc.consumer_release(loc=loc, ip=ip) + self._num_tiles_executed += Int32(1) + return work + + def prefetch_next_work(self, *, loc=None, ip=None): + """Producer-side: issue CLC query for next tile.""" + self.clc.prefetch_next_work(loc=loc, ip=ip) + + def producer_tail(self, *, loc=None, ip=None): + """Producer-side cleanup after last tile.""" + self.clc.producer_tail(loc=loc, ip=ip) + + @property + def num_tiles_executed(self) -> Int32: + return self._num_tiles_executed + + +def compute_sm100_fmha_grid_clc( + o_shape: cute.Shape, + cta_tiler: Tuple[int, int, int], + cluster_shape_mnk: Tuple[int, int, int], +) -> Tuple[Sm100FmhaClcDynamicTileSchedulerParams, Tuple[int, int, int]]: + """Compute grid parameters for FMHA with CLC dynamic scheduling.""" + problem_shape_mbh = ( + cute.ceil_div(cute.size(o_shape[0]), cta_tiler[0]), + cute.size(o_shape[2][0]), + cute.size(o_shape[2][1]), + ) + tile_sched_params = Sm100FmhaClcDynamicTileSchedulerParams( + problem_shape_mbh, cluster_shape_mnk + ) + grid = Sm100FmhaClcDynamicTileScheduler.get_grid_shape(tile_sched_params) + return tile_sched_params, grid + + +############################################################################## +# Fused Mask +############################################################################## + + +def make_sm100_thread_cooperative_group(size: int): + return cutlass.pipeline.CooperativeGroup(cutlass.pipeline.Agent.Thread, size) + + +SM100_TMEM_CAPACITY_COLUMNS = 512 diff --git a/python/sglang/jit_kernel/flash_attn/cute/topk_gather_kv.py b/python/sglang/jit_kernel/flash_attn/cute/topk_gather_kv.py new file mode 100644 index 000000000..582e3783d --- /dev/null +++ b/python/sglang/jit_kernel/flash_attn/cute/topk_gather_kv.py @@ -0,0 +1,289 @@ +import math +import operator +from dataclasses import dataclass +from typing import Optional, Type + +import cutlass +import cutlass.cute as cute +import cutlass.pipeline as pipeline +from cutlass import Boolean, Int32, Uint32, const_expr +from cutlass.cute.nvgpu import cpasync +from quack.cute_dsl_utils import ParamsBase + +from sglang.jit_kernel.flash_attn.cute import utils +from sglang.jit_kernel.flash_attn.cute.utils import warp_reduce + + +@dataclass +class CpasyncGatherKVManager(ParamsBase): + mIndexTopk: cute.Tensor + sBitmask: Optional[cute.Tensor] + + cta_rank_in_cluster: Int32 + thread_idx: Int32 + warp_idx: Int32 + + topk_length: Int32 + seqlen_k_limit: Int32 + tile_n: Int32 + num_threads: cutlass.Constexpr[Int32] + hdim: cutlass.Constexpr[Int32] + hdim_v: cutlass.Constexpr[Int32] + num_hdimv_splits: cutlass.Constexpr[Int32] + cta_group_size: cutlass.Constexpr[Int32] + + gmem_threads_per_row: cutlass.Constexpr[Int32] + topk_indices_per_thread: Int32 + async_copy_elems: Int32 + + gmem_tiled_copy_KV: cute.TiledCopy + gmem_thr_copy_KV: cute.TiledCopy + + rTopk: cute.Tensor + rTopkHalf: cute.Tensor + # for bitmask + rTopk_NonInterleaved: cute.Tensor + + pipeline_bitmask: Optional[pipeline.PipelineAsync] + cpasync_barrier: Optional[pipeline.NamedBarrier] + + disable_bitmask: cutlass.Constexpr[Boolean] + + @staticmethod + def create( + mIndexTopk: cute.Tensor, + cta_rank_in_cluster: Int32, + thread_idx: Int32, + warp_idx: Int32, + topk_length: Int32, + seqlen_k_limit: Int32, + tile_n: cutlass.Constexpr[Int32], + hdim: cutlass.Constexpr[Int32], + hdim_v: cutlass.Constexpr[Int32], + num_hdimv_splits: cutlass.Constexpr[Int32], + num_threads: cutlass.Constexpr[Int32], + dtype: Type[cutlass.Numeric], + cta_group_size: cutlass.Constexpr[Int32], + cpasync_barrier: Optional[pipeline.NamedBarrier] = None, + disable_bitmask: cutlass.Constexpr[Boolean] = True, + sBitmask: Optional[cute.Tensor] = None, + pipeline_bitmask: Optional[pipeline.PipelineAsync] = None, + ): + assert tile_n % num_threads == 0 + assert num_threads == 128 + assert hdim % 64 == 0 + assert (hdim_v // num_hdimv_splits // cta_group_size) % 64 == 0 + assert num_threads % cute.arch.WARP_SIZE == 0 + universal_copy_bits = 128 + async_copy_elems = universal_copy_bits // dtype.width + dtype_bytes = dtype.width // 8 + # assumes hdim is never part of transposed operand + gmem_k_block_size = math.gcd( + hdim, + hdim_v // num_hdimv_splits // cta_group_size, + 128 // dtype_bytes, + ) + assert gmem_k_block_size % async_copy_elems == 0 + gmem_threads_per_row = gmem_k_block_size // async_copy_elems + assert cute.arch.WARP_SIZE % gmem_threads_per_row == 0 + atom_async_copy = cute.make_copy_atom( + cpasync.CopyG2SOp(cache_mode=cpasync.LoadCacheMode.GLOBAL), + dtype, + num_bits_per_copy=universal_copy_bits, + ) + thr_layout = cute.make_ordered_layout( + (num_threads // gmem_threads_per_row, gmem_threads_per_row), + order=(1, 0), + ) + val_layout = cute.make_layout((1, async_copy_elems)) + gmem_tiled_copy_KV = cute.make_tiled_copy_tv( + atom_async_copy, thr_layout, val_layout + ) + gmem_thr_copy_KV = gmem_tiled_copy_KV.get_slice(thread_idx) + topk_indices_per_thread = tile_n // num_threads + + rTopk = cute.make_rmem_tensor((topk_indices_per_thread,), Int32) + rTopkHalf = cute.make_rmem_tensor((topk_indices_per_thread,), Int32) + rTopk_NonInterleaved = cute.make_rmem_tensor((topk_indices_per_thread,), Int32) + + return CpasyncGatherKVManager( + mIndexTopk, + sBitmask, + cta_rank_in_cluster, + thread_idx, + warp_idx, + topk_length, + seqlen_k_limit, + tile_n, + num_threads, + hdim, + hdim_v, + num_hdimv_splits, + cta_group_size, + gmem_threads_per_row, + topk_indices_per_thread, + async_copy_elems, + gmem_tiled_copy_KV, + gmem_thr_copy_KV, + rTopk, + rTopkHalf, + rTopk_NonInterleaved, + pipeline_bitmask, + cpasync_barrier, + disable_bitmask, + ) + + @cute.jit + def load_index_topk( + self, + n_block: Int32, + transpose: bool, + ): + entries_per_thread = self.topk_indices_per_thread + rTopk = self.rTopk if const_expr(transpose) else self.rTopkHalf + + for i in cutlass.range_constexpr(entries_per_thread): + row = ( + i * self.num_threads + + (self.thread_idx % self.gmem_threads_per_row) + * (self.num_threads // self.gmem_threads_per_row) + + (self.thread_idx // self.gmem_threads_per_row) + ) + # need this if not offset in load_X + # if const_expr(not transpose): + # row += self.cta_rank_in_cluster * (self.tile_n//self.cta_group_size) + # row = row % self.tile_n + row_idx = n_block * self.tile_n + row + rTopk[i] = self.mIndexTopk[row_idx] + + if const_expr(not transpose and not self.disable_bitmask): + row_non_interleaved = i * self.num_threads + self.thread_idx + row_idx_non_interleaved = n_block * self.tile_n + row_non_interleaved + self.rTopk_NonInterleaved[0] = self.mIndexTopk[row_idx_non_interleaved] + + @cute.jit + def compute_bitmask( + self, + producer_state_bitmask, + ): + assert self.pipeline_bitmask is not None, "pipeline_bitmask not provided" + assert self.cpasync_barrier is not None, "cpasync barrier not provided" + + lane_idx = cute.arch.lane_idx() + assert cute.size(self.rTopk_NonInterleaved) == 1 + bitmask = Uint32(0) + + # Step 1. Construct per-thread bitmask + topk_idx = self.rTopk_NonInterleaved[0] + is_valid = topk_idx >= 0 and topk_idx < self.seqlen_k_limit + if is_valid: + bitmask = Uint32(1 << lane_idx) + + # Step 2. Warp shuffle bitwise OR = add since indices are exclusive. + bitmask = warp_reduce(bitmask, operator.add) + + self.pipeline_bitmask.producer_acquire(producer_state_bitmask) + # store to smem and sync threads + if lane_idx == 0: + self.sBitmask[self.warp_idx, producer_state_bitmask.index] = bitmask + self.cpasync_barrier.arrive_and_wait() + + self.pipeline_bitmask.producer_commit(producer_state_bitmask) + producer_state_bitmask.advance() + return producer_state_bitmask + + @cute.jit + def compute_X_ptr( + self, + mX: cute.Tensor, + transpose: bool, + d_offset: int = 0, + ): + entries_per_thread = self.topk_indices_per_thread + tPrXPtr = cute.make_rmem_tensor((entries_per_thread,), cutlass.Int64) + tPrRowValid = cute.make_rmem_tensor((entries_per_thread,), cutlass.Int32) + rTopk = self.rTopk if const_expr(transpose) else self.rTopkHalf + + for i in cutlass.range_constexpr(entries_per_thread): + topk_idx = rTopk[i] + if const_expr(not self.disable_bitmask): + row_valid = topk_idx >= 0 and topk_idx < self.seqlen_k_limit + tPrRowValid[i] = row_valid + if const_expr(not transpose): + tPrXPtr[i] = utils.elem_pointer(mX, (topk_idx, d_offset)).toint() + else: + tPrXPtr[i] = utils.elem_pointer(mX, (d_offset, topk_idx)).toint() + + return tPrXPtr, tPrRowValid + + @cute.jit + def load_X( + self, + mX: cute.Tensor, + sX: cute.Tensor, + transpose: bool, + K_or_V: str, + d_offset: int = 0, + ): + assert K_or_V in ("K", "V") + cta_tile_n = ( + self.tile_n if const_expr(transpose) else self.tile_n // self.cta_group_size + ) + head_dim = ( + self.hdim + if const_expr(K_or_V == "K") + else self.hdim_v // self.num_hdimv_splits + ) + if const_expr(transpose): + head_dim = head_dim // self.cta_group_size + order = (1, 0) if const_expr(transpose) else (0, 1) + + sX_nd_layout = cute.make_ordered_layout((cta_tile_n, head_dim), order=order) + sX_nd = cute.composition(sX, sX_nd_layout) + + cX = cute.make_identity_tensor((cta_tile_n, head_dim)) + tXsX = self.gmem_thr_copy_KV.partition_D(sX_nd) + tXcX = self.gmem_thr_copy_KV.partition_S(cX) + + tPrXPtr, tPrRowValid = self.compute_X_ptr(mX, transpose, d_offset) + + if const_expr(not transpose): + offset = self.cta_rank_in_cluster * ( + self.gmem_threads_per_row // self.cta_group_size + ) + else: + offset = 0 + + for m in cutlass.range_constexpr(cute.size(tXsX, mode=[1])): + if const_expr(not self.disable_bitmask): + row_valid = utils.shuffle_sync( + tPrRowValid[m // self.gmem_threads_per_row], + (m + offset) % self.gmem_threads_per_row, + width=self.gmem_threads_per_row, + ) + should_load = cute.make_fragment_like(tXsX[(0, None), m, 0], Boolean) + should_load.fill(Boolean(row_valid)) + x_ptr_i64 = utils.shuffle_sync( + tPrXPtr[m // self.gmem_threads_per_row], + (m + offset) % self.gmem_threads_per_row, + width=self.gmem_threads_per_row, + ) + x_gmem_ptr = cute.make_ptr( + mX.element_type, x_ptr_i64, cute.AddressSpace.gmem, assumed_align=16 + ) + mX_cur = cute.make_tensor(x_gmem_ptr, cute.make_layout((head_dim,))) + mX_cur_copy = cute.tiled_divide(mX_cur, (self.async_copy_elems,)) + + for k in cutlass.range_constexpr(cute.size(tXsX, mode=[2])): + ki = tXcX[0, 0, k][1] // self.async_copy_elems + mX_cur_copy_ki = mX_cur_copy[None, ki] + tXsX_k = tXsX[None, m, k] + mX_cur_copy_ki = cute.make_tensor( + mX_cur_copy_ki.iterator, tXsX_k.layout + ) + cute.copy( + self.gmem_tiled_copy_KV, + mX_cur_copy_ki, + tXsX_k, + pred=should_load if const_expr(not self.disable_bitmask) else None, + ) diff --git a/python/sglang/jit_kernel/flash_attn/cute/utils.py b/python/sglang/jit_kernel/flash_attn/cute/utils.py new file mode 100644 index 000000000..293d461a1 --- /dev/null +++ b/python/sglang/jit_kernel/flash_attn/cute/utils.py @@ -0,0 +1,1164 @@ +# Copyright (c) 2025, Tri Dao. + +import hashlib +import inspect +import math +import os +from functools import partial +from typing import Callable, NamedTuple, Optional, Tuple, Type, overload + +import cutlass +import cutlass.cute as cute +import quack.activation +from cutlass import Float32, Int32, const_expr +from cutlass._mlir.dialects import llvm, nvvm +from cutlass.cute import FastDivmodDivisor +from cutlass.cute.runtime import from_dlpack +from cutlass.cutlass_dsl import T, dsl_user_op + +_MIXER_ATTRS = ("__vec_size__",) + + +class AuxData(NamedTuple): + tensors: tuple | list | None = None + scalars: tuple | None = None + + +# Obtained from sollya: +# fpminimax(exp(x * log(2.0)), 1, [|1,24...|],[0;1],relative); +POLY_EX2 = { + 0: (1.0), + 1: ( + 1.0, + 0.922497093677520751953125, + ), + 2: ( + 1.0, + 0.6657850742340087890625, + 0.330107033252716064453125, + ), + 3: ( + 1.0, + 0.695146143436431884765625, + 0.227564394474029541015625, + 0.077119089663028717041015625, + ), + 4: ( + 1.0, + 0.693042695522308349609375, + 0.2412912547588348388671875, + 5.2225358784198760986328125e-2, + 1.3434938155114650726318359375e-2, + ), + 5: ( + 1.0, + 0.693151414394378662109375, + 0.24016360938549041748046875, + 5.5802188813686370849609375e-2, + 9.01452265679836273193359375e-3, + 1.86810153536498546600341796875e-3, + ), +} + +_fa_clc_enabled: bool = os.environ.get("FA_CLC", "0") == "1" +_fa_disable_2cta_enabled: bool = os.environ.get("FA_DISABLE_2CTA", "0") == "1" + + +def _is_cuda_12() -> bool: + """Check if the CUDA toolkit version is 12.x. + + 2CTA forward non-causal has a codegen regression on CUDA 12 that causes + ~18% slowdown compared to 1CTA. This is fixed in CUDA 13.x. + """ + try: + import torch + + cuda_version = torch.version.cuda + if cuda_version is not None: + major = cuda_version.split(".")[0] + return int(major) == 12 + except Exception: + pass + return False + + +_fa_disable_2cta_cuda12: bool = _is_cuda_12() + + +def _get_use_clc_scheduler_default() -> bool: + return _fa_clc_enabled + + +def _get_disable_2cta_default(is_fwd: bool = False) -> bool: + if is_fwd: + return _fa_disable_2cta_enabled or _fa_disable_2cta_cuda12 + else: + return _fa_disable_2cta_enabled + + +def _compute_base_hash(func: Callable) -> str: + """Compute hash from source code or bytecode and closure values.""" + try: + data = inspect.getsource(func).encode() + except (OSError, TypeError): + if hasattr(func, "__code__") and func.__code__ is not None: + data = func.__code__.co_code + else: + data = repr(func).encode() + + hasher = hashlib.sha256(data) + + if hasattr(func, "__closure__") and func.__closure__ is not None: + for cell in func.__closure__: + hasher.update(repr(cell.cell_contents).encode()) + + return hasher.hexdigest() + + +def hash_callable( + func: Callable, mixer_attrs: Tuple[str] = _MIXER_ATTRS, set_cute_hash: bool = True +) -> str: + """Hash a callable based on the source code or bytecode and closure values. + Fast-path: if the callable (or its __wrapped__ base) has a ``__cute_hash__`` + attribute, that value is returned immediately as the base hash, then + metadata dunders are mixed in to produce the final dict-key hash. + set_cute_hash: whether or not to set func.__cute_hash__ + """ + # Resolve base hash + if hasattr(func, "__cute_hash__"): + base_hash = func.__cute_hash__ + else: + # Unwrap decorated functions (e.g., cute.jit wrappers). + base_func = getattr(func, "__wrapped__", func) + + if hasattr(base_func, "__cute_hash__"): + base_hash = base_func.__cute_hash__ + else: + base_hash = _compute_base_hash(base_func) + + if set_cute_hash: + base_func.__cute_hash__ = base_hash + + # Mix in mutable metadata dunders + mixer_values = tuple(getattr(func, attr, None) for attr in mixer_attrs) + + if all(v is None for v in mixer_values): + return base_hash + + hasher = hashlib.sha256(base_hash.encode()) + + for attr, val in zip(mixer_attrs, mixer_values): + hasher.update(f"{attr}={val!r}".encode()) + + return hasher.hexdigest() + + +def create_softcap_scoremod(softcap_val): + @cute.jit + def scoremod_premask_fn( + acc_S_SSA, batch_idx, head_idx, q_idx, kv_idx, seqlen_info, aux_tensors + ): + scores = acc_S_SSA / softcap_val + return softcap_val * cute.math.tanh(scores, fastmath=True) + + return scoremod_premask_fn + + +def create_softcap_scoremod_bwd(softcap_val): + @cute.jit + def scoremod_bwd_fn( + grad_out_SSA, + score_SSA, + batch_idx, + head_idx, + q_idx, + kv_idx, + seqlen_info, + aux_tensors, + ): + scores = score_SSA / softcap_val + tanh_scores = cute.math.tanh(scores, fastmath=True) + return grad_out_SSA * (1.0 - tanh_scores * tanh_scores) + + return scoremod_bwd_fn + + +LOG2_E = math.log2(math.e) + + +def compute_softmax_scale_log2(softmax_scale, score_mod): + """Compute softmax_scale_log2 and adjusted softmax_scale based on whether score_mod is used. + + When score_mod is None, fold the log2(e) factor into softmax_scale_log2 and set softmax_scale + to None. When score_mod is present, keep softmax_scale separate so it can be applied before + the score_mod, and set softmax_scale_log2 to just the change-of-base constant. + + Returns (softmax_scale_log2, softmax_scale). + """ + if const_expr(score_mod is None): + return softmax_scale * LOG2_E, None + else: + return LOG2_E, softmax_scale + + +def compute_fastdiv_mods( + mQ, mK, qhead_per_kvhead, pack_gqa, aux_tensors, mPageTable=None +): + """Compute FastDivmodDivisor pairs for aux_tensors index computation. + + Returns a (seqlen_q_divmod, seqlen_k_divmod) tuple, or None if aux_tensors is None. + """ + if const_expr(aux_tensors is None): + return None + seqlen_q = cute.size(mQ.shape[0]) // ( + qhead_per_kvhead if const_expr(pack_gqa) else 1 + ) + seqlen_k = ( + cute.size(mK.shape[0]) + if const_expr(mPageTable is None) + else mK.shape[0] * mPageTable.shape[1] + ) + return (FastDivmodDivisor(seqlen_q), FastDivmodDivisor(seqlen_k)) + + +def convert_from_dlpack(x, leading_dim, alignment=16, divisibility=1) -> cute.Tensor: + return ( + from_dlpack(x, assumed_align=alignment) + .mark_layout_dynamic(leading_dim=leading_dim) + .mark_compact_shape_dynamic( + mode=leading_dim, stride_order=x.dim_order(), divisibility=divisibility + ) + ) + + +def convert_from_dlpack_compact_dynamic( + x, + *, + dynamic_modes: tuple[int, ...], + alignment: int = 16, + stride_order=None, + divisibility: int = 1, + enable_tvm_ffi: bool = False, +) -> cute.Tensor: + """Convert via DLPack and mark selected compact dimensions as dynamic.""" + if isinstance(dynamic_modes, int): + dynamic_modes = (dynamic_modes,) + if stride_order is None: + stride_order = x.dim_order() + t = ( + from_dlpack(x, assumed_align=alignment, enable_tvm_ffi=True) + if enable_tvm_ffi + else from_dlpack(x, assumed_align=alignment) + ) + for mode in dynamic_modes: + t = t.mark_compact_shape_dynamic( + mode=mode, + stride_order=stride_order, + divisibility=divisibility, + ) + return t + + +def convert_from_dlpack_leading_static( + x, leading_dim, alignment=16, static_modes=None, stride_order=None +) -> cute.Tensor: + if stride_order is None: + stride_order = x.dim_order() + x_ = from_dlpack(x, assumed_align=alignment) + for i in range(x.ndim): + if i != leading_dim and (static_modes is None or i not in static_modes): + x_ = x_.mark_compact_shape_dynamic(mode=i, stride_order=stride_order) + return x_ + + +def make_tiled_copy_A( + copy_atom: cute.CopyAtom, + tiled_mma: cute.TiledMma, + swapAB: cutlass.Constexpr[bool] = False, +) -> cute.TiledCopy: + if const_expr(swapAB): + return cute.make_tiled_copy_B(copy_atom, tiled_mma) + else: + return cute.make_tiled_copy_A(copy_atom, tiled_mma) + + +def make_tiled_copy_B( + copy_atom: cute.CopyAtom, + tiled_mma: cute.TiledMma, + swapAB: cutlass.Constexpr[bool] = False, +) -> cute.TiledCopy: + if const_expr(swapAB): + return cute.make_tiled_copy_A(copy_atom, tiled_mma) + else: + return cute.make_tiled_copy_B(copy_atom, tiled_mma) + + +def mma_make_fragment_A( + smem: cute.Tensor, thr_mma: cute.ThrMma, swapAB: cutlass.Constexpr[bool] = False +) -> cute.Tensor: + if const_expr(swapAB): + return mma_make_fragment_B(smem, thr_mma) + else: + return thr_mma.make_fragment_A(thr_mma.partition_A(smem)) + + +def mma_make_fragment_B( + smem: cute.Tensor, thr_mma: cute.ThrMma, swapAB: cutlass.Constexpr[bool] = False +) -> cute.Tensor: + if const_expr(swapAB): + return mma_make_fragment_A(smem, thr_mma) + else: + return thr_mma.make_fragment_B(thr_mma.partition_B(smem)) + + +def get_smem_store_atom( + arch: cutlass.Constexpr[int], + element_type: Type[cute.Numeric], + transpose: bool = False, +) -> cute.CopyAtom: + if const_expr(arch < 90 or element_type.width != 16): + return cute.make_copy_atom( + cute.nvgpu.CopyUniversalOp(), + element_type, + num_bits_per_copy=2 * element_type.width, + ) + else: + return cute.make_copy_atom( + cute.nvgpu.warp.StMatrix8x8x16bOp(transpose=transpose, num_matrices=4), + element_type, + ) + + +@cute.jit +def warp_reduce( + val: cute.TensorSSA | cute.Numeric, + op: Callable, + width: cutlass.Constexpr[int] = cute.arch.WARP_SIZE, +) -> cute.TensorSSA | cute.Numeric: + if const_expr(isinstance(val, cute.TensorSSA)): + res = cute.make_rmem_tensor(val.shape, val.dtype) + res.store(val) + for i in cutlass.range_constexpr(cute.size(val.shape)): + res[i] = warp_reduce(res[i], op, width) + return res.load() + else: + for i in cutlass.range_constexpr(int(math.log2(width))): + val = op(val, cute.arch.shuffle_sync_bfly(val, offset=1 << i)) + return val + + +@dsl_user_op +def smid(*, loc=None, ip=None) -> Int32: + return Int32( + llvm.inline_asm( + T.i32(), + [], + "mov.u32 $0, %smid;", + "=r", + has_side_effects=False, + is_align_stack=False, + asm_dialect=llvm.AsmDialect.AD_ATT, + ) + ) + + +@dsl_user_op +def fmax( + a: float | Float32, + b: float | Float32, + c: float | Float32 | None = None, + *, + loc=None, + ip=None, +) -> Float32: + from cutlass import CUDA_VERSION + + # * NVVM call based on nvvm version + if CUDA_VERSION.major == 12 and CUDA_VERSION.minor == 9: + # Old API: requires explicit result type as first positional argument + return Float32( + nvvm.fmax( + T.f32(), + Float32(a).ir_value(loc=loc, ip=ip), + Float32(b).ir_value(loc=loc, ip=ip), + c=Float32(c).ir_value(loc=loc, ip=ip) if c is not None else None, + loc=loc, + ip=ip, + ) + ) + else: + # New API: infers result type automatically + return Float32( + nvvm.fmax( + Float32(a).ir_value(loc=loc, ip=ip), + Float32(b).ir_value(loc=loc, ip=ip), + c=Float32(c).ir_value(loc=loc, ip=ip) if c is not None else None, + loc=loc, + ip=ip, + ) + ) + + +@cute.jit +def fmax_reduce( + x: cute.TensorSSA, + init_val: float | Float32 | None = None, + arch: cutlass.Constexpr[int] = 80, +) -> Float32: + if const_expr(arch < 100 or cute.size(x.shape) % 8 != 0): + # if const_expr(init_val is None): + # init_val = -cutlass.Float32.if + # return x.reduce(cute.ReductionOp.MAX, init_val, 0) + res = cute.make_rmem_tensor(x.shape, Float32) + res.store(x) + # local_max = [res[0], res[1]] + # for i in cutlass.range_constexpr(2, cute.size(x.shape), 2): + # local_max[0] = fmax(local_max[0], res[i + 0]) + # local_max[1] = fmax(local_max[1], res[i + 1]) + # local_max[0] = fmax(local_max[0], local_max[1]) + # return local_max[0] if const_expr(init_val is None) else fmax(local_max[0], init_val) + local_max = [res[0], res[1], res[2], res[3]] + for i in cutlass.range_constexpr(4, cute.size(x.shape), 4): + local_max[0] = fmax(local_max[0], res[i + 0]) + local_max[1] = fmax(local_max[1], res[i + 1]) + local_max[2] = fmax(local_max[2], res[i + 2]) + local_max[3] = fmax(local_max[3], res[i + 3]) + local_max[0] = fmax(local_max[0], local_max[1]) + local_max[2] = fmax(local_max[2], local_max[3]) + local_max[0] = fmax(local_max[0], local_max[2]) + return ( + local_max[0] + if const_expr(init_val is None) + else fmax(local_max[0], init_val) + ) + else: + # [2025-06-15] x.reduce only seems to use 50% 3-input max and 50% 2-input max + # We instead force the 3-input max. + res = cute.make_rmem_tensor(x.shape, Float32) + res.store(x) + local_max_0 = ( + fmax(init_val, res[0], res[1]) + if const_expr(init_val is not None) + else fmax(res[0], res[1]) + ) + local_max = [ + local_max_0, + fmax(res[2], res[3]), + fmax(res[4], res[5]), + fmax(res[6], res[7]), + ] + for i in cutlass.range_constexpr(8, cute.size(x.shape), 8): + local_max[0] = fmax(local_max[0], res[i], res[i + 1]) + local_max[1] = fmax(local_max[1], res[i + 2], res[i + 3]) + local_max[2] = fmax(local_max[2], res[i + 4], res[i + 5]) + local_max[3] = fmax(local_max[3], res[i + 6], res[i + 7]) + local_max[0] = fmax(local_max[0], local_max[1]) + return fmax(local_max[0], local_max[2], local_max[3]) + + +@cute.jit +def fadd_reduce( + x: cute.TensorSSA, + init_val: float | Float32 | None = None, + arch: cutlass.Constexpr[int] = 80, +) -> Float32: + if const_expr(arch < 100 or cute.size(x.shape) % 8 != 0): + if const_expr(init_val is None): + init_val = Float32.zero + return x.reduce(cute.ReductionOp.ADD, init_val, 0) + # res = cute.make_rmem_tensor(x.shape, Float32) + # res.store(x) + # local_sum = [res[0], res[1], res[2], res[3]] + # for i in cutlass.range_constexpr(4, cute.size(x.shape), 4): + # local_sum[0] += res[i + 0] + # local_sum[1] += res[i + 1] + # local_sum[2] += res[i + 2] + # local_sum[3] += res[i + 3] + # local_sum[0] += local_sum[1] + # local_sum[2] += local_sum[3] + # local_sum[0] += local_sum[2] + # return local_sum[0] if const_expr(init_val is None) else local_sum[0] + init_val + else: + res = cute.make_rmem_tensor(x.shape, Float32) + res.store(x) + local_sum_0 = ( + cute.arch.add_packed_f32x2((init_val, 0.0), (res[0], res[1])) + # cute.arch.add_packed_f32x2((init_val / 2, init_val / 2), (res[0], res[1])) + if const_expr(init_val is not None) + else (res[0], res[1]) + ) + local_sum = [local_sum_0, (res[2], res[3]), (res[4], res[5]), (res[6], res[7])] + for i in cutlass.range_constexpr(8, cute.size(x.shape), 8): + local_sum[0] = cute.arch.add_packed_f32x2( + local_sum[0], (res[i + 0], res[i + 1]) + ) + local_sum[1] = cute.arch.add_packed_f32x2( + local_sum[1], (res[i + 2], res[i + 3]) + ) + local_sum[2] = cute.arch.add_packed_f32x2( + local_sum[2], (res[i + 4], res[i + 5]) + ) + local_sum[3] = cute.arch.add_packed_f32x2( + local_sum[3], (res[i + 6], res[i + 7]) + ) + local_sum[0] = cute.arch.add_packed_f32x2(local_sum[0], local_sum[1]) + local_sum[2] = cute.arch.add_packed_f32x2(local_sum[2], local_sum[3]) + local_sum[0] = cute.arch.add_packed_f32x2(local_sum[0], local_sum[2]) + return local_sum[0][0] + local_sum[0][1] + + +@dsl_user_op +def atomic_add_fp32( + a: float | Float32, gmem_ptr: cute.Pointer, *, loc=None, ip=None +) -> None: + # gmem_ptr_i64 = gmem_ptr.toint(loc=loc, ip=ip).ir_value() + # # cache_hint = cutlass.Int64(0x12F0000000000000) + # llvm.inline_asm( + # None, + # [gmem_ptr_i64, Float32(a).ir_value(loc=loc, ip=ip)], + # # [gmem_ptr_i64, Float32(a).ir_value(loc=loc, ip=ip), cache_hint.ir_value()], + # "red.global.add.f32 [$0], $1;", + # # "red.global.add.L2::cache_hint.f32 [$0], $1, 0x12F0000000000000;", + # # "red.global.add.L2::cache_hint.f32 [$0], $1, $2;", + # "l,f", + # # "l,f,l", + # has_side_effects=True, + # is_align_stack=False, + # asm_dialect=llvm.AsmDialect.AD_ATT, + # ) + nvvm.atomicrmw( + res=T.f32(), + op=nvvm.AtomicOpKind.FADD, + ptr=gmem_ptr.llvm_ptr, + a=Float32(a).ir_value(), + ) + + +@dsl_user_op +def elem_pointer( + x: cute.Tensor, coord: cute.Coord, *, loc=None, ip=None +) -> cute.Pointer: + return x.iterator + cute.crd2idx(coord, x.layout, loc=loc, ip=ip) + + +@cute.jit +def predicate_k(tAcA: cute.Tensor, limit: cutlass.Int32) -> cute.Tensor: + # Only compute predicates for the "k" dimension. For the mn dimension, we will use "if" + tApA = cute.make_rmem_tensor( + cute.make_layout( + ( + cute.size(tAcA, mode=[0, 1]), + cute.size(tAcA, mode=[1]), + cute.size(tAcA, mode=[2]), + ), + stride=(cute.size(tAcA, mode=[2]), 0, 1), + ), + cutlass.Boolean, + ) + for rest_v in cutlass.range_constexpr(tApA.shape[0]): + for rest_k in cutlass.range_constexpr(tApA.shape[2]): + tApA[rest_v, 0, rest_k] = cute.elem_less( + tAcA[(0, rest_v), 0, rest_k][1], limit + ) + return tApA + + +def canonical_warp_group_idx(sync: bool = True) -> cutlass.Int32: + warp_group_idx = cute.arch.thread_idx()[0] // 128 + if const_expr(sync): + warp_group_idx = cute.arch.make_warp_uniform(warp_group_idx) + return warp_group_idx + + +# @dsl_user_op +# def warp_vote_any_lt(a: float | Float32, b: float | Float32, *, loc=None, ip=None) -> cutlass.Boolean: +# mask = cutlass.Int32(-1) +# return cutlass.Boolean( +# llvm.inline_asm( +# T.i32(), +# [Float32(a).ir_value(loc=loc, ip=ip), Float32(b).ir_value(loc=loc, ip=ip), mask.ir_value(loc=loc, ip=ip)], +# ".pred p1, p2;\n" +# "setp.lt.f32 p1, $1, $2;\n" +# "vote.sync.any.pred p2, p1, $3;\n" +# "selp.u32 $0, 1, 0, p2;", +# # "selp.u32 $0, 1, 0, p1;", +# "=r,f,f,r", +# has_side_effects=False, +# is_align_stack=False, +# asm_dialect=llvm.AsmDialect.AD_ATT, +# ) +# ) + + +@cute.jit +def shuffle_sync( + value: cute.Numeric, + offset: cute.typing.Int, + width: cutlass.Constexpr[int] = cute.arch.WARP_SIZE, +) -> cute.Numeric: + assert value.width % 32 == 0, "value type must be a multiple of 32 bits" + # 1 -> 0b11111, 2 -> 0b11110, 4 -> 0b11100, 8 -> 0b11000, 16 -> 0b10000, 32 -> 0b00000 + mask = cute.arch.WARP_SIZE - width + clamp = cute.arch.WARP_SIZE - 1 + mask_and_clamp = mask << 8 | clamp + # important: need stride 1 and not 0 for recast_tensor to work + val = cute.make_rmem_tensor(cute.make_layout((1,), stride=(1,)), type(value)) + val[0] = value + val_i32 = cute.recast_tensor(val, cutlass.Int32) + for i in cutlass.range_constexpr(cute.size(val_i32)): + val_i32[i] = cute.arch.shuffle_sync( + val_i32[i], offset, mask_and_clamp=mask_and_clamp + ) + return val[0] + + +@dsl_user_op +def shl_u32( + val: cutlass.Uint32, shift: cutlass.Uint32, *, loc=None, ip=None +) -> cutlass.Uint32: + """ + Left-shift val by shift bits using PTX shl.b32 (sign-agnostic). + + Named ``shl_u32`` (not ``shl_b32``) because python type annotations + distinguish signed/unsigned. + + PTX semantics (§9.7.8.8): "Shift amounts greater than the register width N + are clamped to N." So ``shl.b32 d, a, 32`` is well-defined and yields 0. + + This differs from C/C++ and LLVM IR, where shifting by >= the type width is + undefined behavior. CuTeDSL compiles through MLIR -> LLVM IR, so a plain + Python-level ``Uint32(x) << Uint32(n)`` inherits LLVM's UB: the optimizer + may treat the result as poison and eliminate dependent code. Inline PTX + bypasses the LLVM IR shift entirely — the instruction is emitted verbatim + into PTX where clamping makes it safe for all shift amounts. + """ + return cutlass.Uint32( + llvm.inline_asm( + T.i32(), + [ + cutlass.Uint32(val).ir_value(loc=loc, ip=ip), + cutlass.Uint32(shift).ir_value(loc=loc, ip=ip), + ], + "shl.b32 $0, $1, $2;", + "=r,r,r", + has_side_effects=False, + is_align_stack=False, + asm_dialect=llvm.AsmDialect.AD_ATT, + ) + ) + + +@dsl_user_op +def shr_u32( + val: cutlass.Uint32, shift: cutlass.Uint32, *, loc=None, ip=None +) -> cutlass.Uint32: + """ + Unsigned right-shift val by shift bits using PTX shr.u32 (zero-fills). + + See ``shl_u32`` docstring for why inline PTX is used instead of plain + CuTeDSL shift operators (LLVM shift-by-type-width UB). + """ + return cutlass.Uint32( + llvm.inline_asm( + T.i32(), + [ + cutlass.Uint32(val).ir_value(loc=loc, ip=ip), + cutlass.Uint32(shift).ir_value(loc=loc, ip=ip), + ], + "shr.u32 $0, $1, $2;", + "=r,r,r", + has_side_effects=False, + is_align_stack=False, + asm_dialect=llvm.AsmDialect.AD_ATT, + ) + ) + + +@cute.jit +def warp_prefix_sum( + val: cutlass.Int32, lane: Optional[cutlass.Int32] = None +) -> cutlass.Int32: + if const_expr(lane is None): + lane = cute.arch.lane_idx() + # if cute.arch.thread_idx()[0] >= 128 and cute.arch.thread_idx()[0] < 128 + 32 and cute.arch.block_idx()[0] == 0: cute.printf("tidx = %d, val = %d", cute.arch.thread_idx()[0] % 32, val) + for i in cutlass.range_constexpr(int(math.log2(cute.arch.WARP_SIZE))): + offset = 1 << i + # Very important that we set mask_and_clamp to 0 + partial_sum = cute.arch.shuffle_sync_up(val, offset=offset, mask_and_clamp=0) + if lane >= offset: + val += partial_sum + # if cute.arch.thread_idx()[0] >= 128 and cute.arch.thread_idx()[0] < 128 + 32 and cute.arch.block_idx()[0] == 0: cute.printf("tidx = %d, partial_sum = %d, val = %d", cute.arch.thread_idx()[0] % 32, partial_sum, val) + return val + + +@dsl_user_op +def cp_async_4( + dst_smem: cutlass.Int32, + src_gmem: cutlass.Int64, + src_size: cutlass.Int32, + *, + loc=None, + ip=None, +) -> None: + """4-byte cp.async.ca with the src-size operand: src_size=4 copies, 0 + zero-fills the destination (used for out-of-range scale rows -- e8m0 0x00 + is 2^-127, benign). Completion rides the surrounding + cp_async_commit_group + mbarrier arrive like every other cp.async.""" + llvm.inline_asm( + None, + [ + cutlass.Int32(dst_smem).ir_value(loc=loc, ip=ip), + cutlass.Int64(src_gmem).ir_value(loc=loc, ip=ip), + cutlass.Int32(src_size).ir_value(loc=loc, ip=ip), + ], + "cp.async.ca.shared.global [$0], [$1], 4, $2;", + "r,l,r", + has_side_effects=True, + is_align_stack=False, + asm_dialect=llvm.AsmDialect.AD_ATT, + ) + + +@dsl_user_op +def prmt_b32( + a: cutlass.Uint32, b: cutlass.Uint32, code: cutlass.Uint32, *, loc=None, ip=None +) -> cutlass.Uint32: + """PTX prmt.b32: permute bytes of the 8-byte pool {a, b} per selector nibbles.""" + return cutlass.Uint32( + llvm.inline_asm( + T.i32(), + [ + cutlass.Uint32(a).ir_value(loc=loc, ip=ip), + cutlass.Uint32(b).ir_value(loc=loc, ip=ip), + cutlass.Uint32(code).ir_value(loc=loc, ip=ip), + ], + "prmt.b32 $0, $1, $2, $3;", + "=r,r,r,r", + has_side_effects=False, + is_align_stack=False, + asm_dialect=llvm.AsmDialect.AD_ATT, + ) + ) + + +@dsl_user_op +def mul_bf16x2( + a: cutlass.Uint32, b: cutlass.Uint32, *, loc=None, ip=None +) -> cutlass.Uint32: + """PTX mul.rn.bf16x2 on two packed-bf16 pairs held in u32 registers.""" + return cutlass.Uint32( + llvm.inline_asm( + T.i32(), + [ + cutlass.Uint32(a).ir_value(loc=loc, ip=ip), + cutlass.Uint32(b).ir_value(loc=loc, ip=ip), + ], + "mul.rn.bf16x2 $0, $1, $2;", + "=r,r,r", + has_side_effects=False, + is_align_stack=False, + asm_dialect=llvm.AsmDialect.AD_ATT, + ) + ) + + +@dsl_user_op +def cvt_f16x2_f32( + a: float | Float32, b: float | Float32, to_dtype: Type, *, loc=None, ip=None +) -> cutlass.Int32: + assert to_dtype in [ + cutlass.BFloat16, + cutlass.Float16, + ], "to_dtype must be BFloat16 or Float16" + return cutlass.Int32( + llvm.inline_asm( + T.i32(), + [Float32(a).ir_value(loc=loc, ip=ip), Float32(b).ir_value(loc=loc, ip=ip)], + f"cvt.rn.{'bf16x2' if to_dtype is cutlass.BFloat16 else 'f16x2'}.f32 $0, $2, $1;", + "=r,f,f", + has_side_effects=False, + is_align_stack=False, + asm_dialect=llvm.AsmDialect.AD_ATT, + ) + ) + + +@overload +def cvt_f16(src: cute.Tensor, dst: cute.Tensor) -> None: ... + + +@overload +def cvt_f16(src: cute.Tensor, dtype: Type[cute.Numeric]) -> cute.Tensor: ... + + +@cute.jit +def cvt_f16(src: cute.Tensor, dst_or_dtype): + """Convert Float32 tensor to Float16/BFloat16. + + Args: + src: Source tensor with Float32 element type + dst_or_dtype: Either a destination tensor or a dtype (Float16/BFloat16) + + Returns: + None if dst is a tensor, or a new tensor if dtype is provided + """ + if const_expr(isinstance(dst_or_dtype, type)): + # dtype variant: create new tensor and call the tensor variant + dtype = dst_or_dtype + dst = cute.make_rmem_tensor(src.shape, dtype) + cvt_f16(src, dst) + return dst + else: + # tensor variant: write to dst + dst = dst_or_dtype + assert cute.size(dst.shape) == cute.size( + src.shape + ), "dst and src must have the same size" + assert cute.size(src.shape) % 2 == 0, "src must have an even number of elements" + assert dst.element_type in [ + cutlass.BFloat16, + cutlass.Float16, + ], "dst must be BFloat16 or Float16" + assert src.element_type is Float32, "src must be Float32" + dst_i32 = cute.recast_tensor(dst, cutlass.Int32) + assert cute.size(dst_i32.shape) * 2 == cute.size(src.shape) + for i in cutlass.range_constexpr(cute.size(dst_i32)): + dst_i32[i] = cvt_f16x2_f32(src[2 * i], src[2 * i + 1], dst.element_type) + + +@dsl_user_op +@cute.jit +def evaluate_polynomial( + x: Float32, poly: Tuple[Float32, ...], *, loc=None, ip=None +) -> Float32: + deg = len(poly) - 1 + out = poly[deg] + for i in cutlass.range_constexpr(deg - 1, -1, -1): + out = out * x + poly[i] + return out + + +@dsl_user_op +@cute.jit +def evaluate_polynomial_2( + x: Float32, y: Float32, poly: Tuple[Float32, ...], *, loc=None, ip=None +) -> Tuple[Float32, Float32]: + deg = len(poly) - 1 + out = (poly[deg], poly[deg]) + for i in cutlass.range_constexpr(deg - 1, -1, -1): + out = cute.arch.fma_packed_f32x2(out, (x, y), (poly[i], poly[i])) + return out + + +@dsl_user_op +def add_round_down( + x: float | Float32, y: float | Float32, *, loc=None, ip=None +) -> Float32: + # There's probably a way to call llvm or nvvm to do this instead of ptx + return cutlass.Float32( + llvm.inline_asm( + T.f32(), + [Float32(x).ir_value(loc=loc, ip=ip), Float32(y).ir_value(loc=loc, ip=ip)], + "add.rm.ftz.f32 $0, $1, $2;", + "=f,f,f", + has_side_effects=False, + is_align_stack=False, + asm_dialect=llvm.AsmDialect.AD_ATT, + ) + ) + + +@dsl_user_op +def combine_int_frac_ex2( + x_rounded: Float32, frac_ex2: Float32, *, loc=None, ip=None +) -> Float32: + return cutlass.Float32( + llvm.inline_asm( + T.f32(), + [ + Float32(x_rounded).ir_value(loc=loc, ip=ip), + Float32(frac_ex2).ir_value(loc=loc, ip=ip), + ], + "{\n\t" + ".reg .s32 x_rounded_i, frac_ex_i, x_rounded_e, out_i;\n\t" + "mov.b32 x_rounded_i, $1;\n\t" + "mov.b32 frac_ex_i, $2;\n\t" + "shl.b32 x_rounded_e, x_rounded_i, 23;\n\t" + # add.u32 generates IMAD instruction and add.s32 generates LEA instruction + # IMAD uses the FMA pipeline and LEA uses the ALU pipeline, afaik + "add.s32 out_i, x_rounded_e, frac_ex_i;\n\t" "mov.b32 $0, out_i;\n\t" "}\n", + "=f,f,f", + has_side_effects=False, + is_align_stack=False, + asm_dialect=llvm.AsmDialect.AD_ATT, + ) + ) + + +@dsl_user_op +def ex2_emulation(x: Float32, *, poly_degree: int = 3, loc=None, ip=None) -> Float32: + assert poly_degree in POLY_EX2, f"Polynomial degree {poly_degree} not supported" + # We assume x <= 127.0 + fp32_round_int = float(2**23 + 2**22) + x_clamped = cute.arch.fmax(x, -127.0) + # We want to round down here, so that the fractional part is in [0, 1) + x_rounded = add_round_down(x_clamped, fp32_round_int, loc=loc, ip=ip) + # The integer floor of x is now in the last 8 bits of x_rounded + # We assume the next 2 ops round to nearest even. The rounding mode is important. + x_rounded_back = x_rounded - fp32_round_int + x_frac = x_clamped - x_rounded_back + x_frac_ex2 = evaluate_polynomial(x_frac, POLY_EX2[poly_degree], loc=loc, ip=ip) + return combine_int_frac_ex2(x_rounded, x_frac_ex2, loc=loc, ip=ip) + + +# TODO: check that the ex2_emulation_2 produces the same SASS as the ptx version +@dsl_user_op +def ex2_emulation_2( + x: Float32, y: Float32, *, poly_degree: int = 3, loc=None, ip=None +) -> Tuple[Float32, Float32]: + # We assume x <= 127.0 and y <= 127.0 + fp32_round_int = float(2**23 + 2**22) + xy_clamped = (cute.arch.fmax(x, -127.0), cute.arch.fmax(y, -127.0)) + # We want to round down here, so that the fractional part is in [0, 1) + xy_rounded = cute.arch.add_packed_f32x2( + xy_clamped, (fp32_round_int, fp32_round_int), rnd="rm" + ) + # The integer floor of x & y are now in the last 8 bits of xy_rounded + # We want the next 2 ops to round to nearest even. The rounding mode is important. + xy_rounded_back = quack.activation.sub_packed_f32x2( + xy_rounded, (fp32_round_int, fp32_round_int) + ) + xy_frac = quack.activation.sub_packed_f32x2(xy_clamped, xy_rounded_back) + xy_frac_ex2 = evaluate_polynomial_2(*xy_frac, POLY_EX2[poly_degree], loc=loc, ip=ip) + x_out = combine_int_frac_ex2(xy_rounded[0], xy_frac_ex2[0], loc=loc, ip=ip) + y_out = combine_int_frac_ex2(xy_rounded[1], xy_frac_ex2[1], loc=loc, ip=ip) + return x_out, y_out + + +@dsl_user_op +def e2e_asm2(x: Float32, y: Float32, *, loc=None, ip=None) -> Tuple[Float32, Float32]: + out_f32x2 = llvm.inline_asm( + llvm.StructType.get_literal([T.f32(), T.f32()]), + [Float32(x).ir_value(loc=loc, ip=ip), Float32(y, loc=loc, ip=ip).ir_value()], + "{\n\t" + ".reg .f32 f1, f2, f3, f4, f5, f6, f7;\n\t" + ".reg .b64 l1, l2, l3, l4, l5, l6, l7, l8, l9, l10;\n\t" + ".reg .s32 r1, r2, r3, r4, r5, r6, r7, r8;\n\t" + "max.ftz.f32 f1, $2, 0fC2FE0000;\n\t" + "max.ftz.f32 f2, $3, 0fC2FE0000;\n\t" + "mov.b64 l1, {f1, f2};\n\t" + "mov.f32 f3, 0f4B400000;\n\t" + "mov.b64 l2, {f3, f3};\n\t" + "add.rm.ftz.f32x2 l7, l1, l2;\n\t" + "sub.rn.ftz.f32x2 l8, l7, l2;\n\t" + "sub.rn.ftz.f32x2 l9, l1, l8;\n\t" + "mov.f32 f7, 0f3D9DF09D;\n\t" + "mov.b64 l6, {f7, f7};\n\t" + "mov.f32 f6, 0f3E6906A4;\n\t" + "mov.b64 l5, {f6, f6};\n\t" + "mov.f32 f5, 0f3F31F519;\n\t" + "mov.b64 l4, {f5, f5};\n\t" + "mov.f32 f4, 0f3F800000;\n\t" + "mov.b64 l3, {f4, f4};\n\t" + "fma.rn.ftz.f32x2 l10, l9, l6, l5;\n\t" + "fma.rn.ftz.f32x2 l10, l10, l9, l4;\n\t" + "fma.rn.ftz.f32x2 l10, l10, l9, l3;\n\t" + "mov.b64 {r1, r2}, l7;\n\t" + "mov.b64 {r3, r4}, l10;\n\t" + "shl.b32 r5, r1, 23;\n\t" + "add.s32 r7, r5, r3;\n\t" + "shl.b32 r6, r2, 23;\n\t" + "add.s32 r8, r6, r4;\n\t" + "mov.b32 $0, r7;\n\t" + "mov.b32 $1, r8;\n\t" + "}\n", + "=r,=r,f,f", + has_side_effects=False, + is_align_stack=False, + asm_dialect=llvm.AsmDialect.AD_ATT, + ) + out0 = Float32(llvm.extractvalue(T.f32(), out_f32x2, [0], loc=loc, ip=ip)) + out1 = Float32(llvm.extractvalue(T.f32(), out_f32x2, [1], loc=loc, ip=ip)) + return out0, out1 + + +@dsl_user_op +def domain_offset_aligned( + coord: cute.Coord, tensor: cute.Tensor, *, loc=None, ip=None +) -> cute.Tensor: + assert isinstance(tensor.iterator, cute.Pointer) + # We assume that applying the offset does not change the pointer alignment + new_ptr = cute.make_ptr( + tensor.element_type, + elem_pointer(tensor, coord).toint(), + tensor.memspace, + assumed_align=tensor.iterator.alignment, + ) + return cute.make_tensor(new_ptr, tensor.layout) + + +@dsl_user_op +def warp_reduction( + val: cute.Numeric, op: Callable, *, threads_in_group: int = 32, loc=None, ip=None +) -> cute.Numeric: + """Warp-wide reduction helper for a custom binary op.""" + offset = threads_in_group // 2 + while offset > 0: + val = op( + val, + cute.arch.shuffle_sync_bfly( + val, offset=offset, mask=-1, mask_and_clamp=31, loc=loc, ip=ip + ), + ) + offset //= 2 + return val + + +warp_reduction_max = partial( + warp_reduction, op=lambda x, y: fmax(x, y) if isinstance(x, Float32) else max(x, y) +) +warp_reduction_sum = partial(warp_reduction, op=lambda x, y: x + y) # noqa: FURB118 + + +@dsl_user_op +def make_cotiled_copy( + atom: cute.CopyAtom, + atom_layout_tv: cute.Layout, + data_layout: cute.Layout, + *, + loc=None, + ip=None, +) -> cute.TiledCopy: + """Compatibility wrapper for deprecated CuTeDSL `make_cotiled_copy`.""" + assert cute.is_static(atom_layout_tv.type), "atom_layout_tv must be static" + assert cute.is_static(data_layout.type), "data_layout must be static" + + inv_layout_ = cute.left_inverse(data_layout, loc=loc, ip=ip) + inv_data_layout = cute.make_layout( + (inv_layout_.shape, (1)), stride=(inv_layout_.stride, (0)), loc=loc, ip=ip + ) + layout_tv_data = cute.composition(inv_data_layout, atom_layout_tv, loc=loc, ip=ip) + + atom_layout_v_to_check = cute.coalesce( + cute.make_layout( + atom_layout_tv.shape[1], stride=atom_layout_tv.stride[1], loc=loc, ip=ip + ), + loc=loc, + ip=ip, + ) + data_layout_v_to_check = cute.coalesce( + cute.composition( + data_layout, + cute.make_layout( + layout_tv_data.shape[1], stride=layout_tv_data.stride[1], loc=loc, ip=ip + ), + loc=loc, + ip=ip, + ), + loc=loc, + ip=ip, + ) + assert ( + data_layout_v_to_check == atom_layout_v_to_check + ), "the memory pointed to by atom_layout_tv does not exist in the data_layout." + + flat_data_shape = cute.product_each(data_layout.shape, loc=loc, ip=ip) + tiler = tuple( + cute.filter( + cute.composition( + cute.make_layout( + flat_data_shape, + stride=tuple( + 0 if j != i else 1 for j in range(cute.rank(flat_data_shape)) + ), + loc=loc, + ip=ip, + ), + layout_tv_data, + loc=loc, + ip=ip, + ), + loc=loc, + ip=ip, + ) + for i in range(cute.rank(flat_data_shape)) + ) + tile2data = cute.composition( + cute.make_layout(flat_data_shape, loc=loc, ip=ip), tiler, loc=loc, ip=ip + ) + layout_tv = cute.composition( + cute.left_inverse(tile2data, loc=loc, ip=ip), layout_tv_data, loc=loc, ip=ip + ) + return cute.make_tiled_copy(atom, layout_tv, tiler, loc=loc, ip=ip) + + +@cute.jit +def scalar_to_ssa(a: cute.Numeric, dtype) -> cute.TensorSSA: + """Convert a scalar to a cute TensorSSA of shape (1,) and given dtype""" + vec = cute.make_rmem_tensor(1, dtype) + vec[0] = a + return vec.load() + + +def ssa_to_scalar(val): + """Could inline but nice for reflecting the above api""" + return val[0] + + +@dsl_user_op +def cvt_bf16x2_ue8m0x2(a: cutlass.Int16, *, loc=None, ip=None) -> cutlass.Int32: + """ + cvt.rn.bf16x2.ue8m0x2 d, a; + + Converts a packed ue8m0x2 value (two UE8M0 block-scale exponents in a + 16-bit register) to a packed bf16x2 (two BF16s in a 32-bit register). + + rn = round-to-nearest-even (only rounding mode supported for this direction). + No .satfinite needed — UE8M0 values always map to representable BF16s. + """ + return cutlass.Int32( + llvm.inline_asm( + T.i32(), + [cutlass.Int16(a).ir_value(loc=loc, ip=ip)], + "cvt.rn.bf16x2.ue8m0x2 $0, $1;", + "=r,h", # r = 32-bit dest (bf16x2), h = 16-bit src (ue8m0x2) + has_side_effects=False, + is_align_stack=False, + asm_dialect=llvm.AsmDialect.AD_ATT, + ) + ) + + +@cute.jit +def cvt_tensor_ue8m0_to_bf16( + scales: cute.Tensor, + scales_out: cute.Tensor, +): + n = cute.size(scales) + assert n % 2 == 0 + assert cute.size(scales_out) == n + assert scales.element_type.width == 8 + assert scales_out.element_type.width == 16 + + scales_x2 = cute.recast_tensor(scales, dtype=cutlass.Int16) + scales_out_x2 = cute.recast_tensor(scales_out, dtype=cutlass.Int32) + + for i in cutlass.range_constexpr(n // 2): + scales_out_x2[i] = cvt_bf16x2_ue8m0x2(scales_x2[i]) + + +@cute.jit +def get_batch_from_cu_tensor(idx: Int32, cu_tensor: cute.Tensor) -> Int32: + """Binary search to determine batch from packed index in a cumulative tensor""" + batch_size = cute.size(cu_tensor) - 1 + lo = Int32(0) + hi = batch_size + + while lo < hi: + mid = (lo + hi) // 2 + if cu_tensor[mid + 1] <= idx: + lo = mid + 1 + else: + hi = mid + + return lo diff --git a/python/sglang/jit_kernel/inkling_all_reduce.py b/python/sglang/jit_kernel/inkling_all_reduce.py new file mode 100644 index 000000000..b8dcbca3e --- /dev/null +++ b/python/sglang/jit_kernel/inkling_all_reduce.py @@ -0,0 +1,357 @@ +"""CUDA-JIT all-reduce kernels for Inkling symmetric-memory buffers. + +The producer writes its local shard into the symmetric buffer, and the reduced +result remains there so callers do not need staging or copy-out kernels. +""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import torch + +from sglang.jit_kernel.utils import cache_once, empty_sentinel, load_jit, make_cpp_args + +if TYPE_CHECKING: + from tvm_ffi.module import Module + + +@cache_once +def _jit_inkling_all_reduce_module(dtype: torch.dtype, world_size: int) -> Module: + args = make_cpp_args(dtype, world_size) + return load_jit( + "inkling_all_reduce", + *args, + cuda_files=["inkling/inkling_all_reduce.cuh"], + cuda_wrappers=[ + ("two_shot_all_reduce", f"inkling_two_shot_all_reduce<{args}>"), + ("two_shot_all_reduce_fused", f"inkling_two_shot_all_reduce_fused<{args}>"), + ("multimem_one_shot_fused", f"inkling_multimem_one_shot_fused<{args}>"), + ("multimem_full_oneshot", f"inkling_multimem_full_oneshot<{args}>"), + ("multimem_push_oneshot", f"inkling_multimem_push_oneshot<{args}>"), + ], + ) + + +# Barrier resources for the fused kernels: +# * flags: a DEDICATED symmetric uint32 buffer, zero-initialized once at +# setup: `world_size` single-leader slots (one per peer), then +# world_size * MAX_BARRIER_BLOCKS per-(writer, block) slots for the +# per-block barrier (v5's multi-block flavor). +# * state: a device-LOCAL uint32 buffer: the 5 words +# [arrival0, arrival1, release0, release1, xepoch] padded to 8, then +# MAX_BARRIER_BLOCKS per-block epochs; persists across calls and advances +# under CUDA-graph replay. +# Keep these sizes aligned with the CUDA barrier implementation. +MAX_BARRIER_BLOCKS = 256 +STATE_SIZE = 8 + MAX_BARRIER_BLOCKS + + +def flags_numel(world_size: int) -> int: + return world_size * (1 + MAX_BARRIER_BLOCKS) + + +# Tuned (kernel, num_blocks, block_size) per reduction row count. Kernels: +# "v5"=push one-shot with per-block barriers (single barrier, out-of-place), +# "mm"=torch multimem, "v2"=two-shot explicit, +# "v3"=two-shot multimem (single-leader barriers), "v3b"=v3 with per-block +# barriers, and "v4"=full one-shot. nb/bs are 0 for "mm". Tables are keyed +# by world size; TP4 is the fallback. +_AR_TUNED_TP4 = { + 1: ("v5", 1, 1024), + 2: ("v5", 1, 1024), + 3: ("v5", 8, 512), + 4: ("v5", 8, 512), + 6: ("v5", 8, 1024), + 8: ("v5", 8, 1024), + 12: ("v5", 8, 512), + 16: ("v5", 8, 512), + 24: ("v5", 8, 1024), + 32: ("v5", 8, 1024), + 48: ("v5", 48, 1024), + 64: ("v5", 48, 1024), + 96: ("v5", 64, 1024), + 128: ("mm", 0, 0), + 192: ("mm", 0, 0), + 256: ("v3b", 64, 1024), + 384: ("v3b", 32, 1024), + 512: ("v3b", 32, 1024), + 768: ("v3b", 48, 512), + 1024: ("v3b", 32, 1024), + 1536: ("v3", 64, 512), + 2048: ("v3", 64, 512), + 3072: ("v3", 96, 512), + 4096: ("v3", 96, 512), + 6144: ("v3", 64, 512), + 8192: ("v3", 32, 1024), + 12288: ("v3", 96, 512), + 16384: ("v3", 96, 512), +} +# TP8 uses full one-shot for the smallest shapes, multimem through the +# medium-sized range, and two-shot multimem for larger reductions. +_AR_TUNED_TP8 = { + 1: ("v4", 1, 1024), + 2: ("v4", 1, 1024), + 3: ("mm", 0, 0), + 4: ("mm", 0, 0), + 6: ("mm", 0, 0), + 8: ("mm", 0, 0), + 12: ("mm", 0, 0), + 16: ("mm", 0, 0), + 24: ("mm", 0, 0), + 32: ("mm", 0, 0), + 48: ("mm", 0, 0), + 64: ("mm", 0, 0), + 96: ("mm", 0, 0), + 128: ("mm", 0, 0), + 192: ("mm", 0, 0), + 256: ("mm", 0, 0), + 384: ("mm", 0, 0), + 512: ("mm", 0, 0), + 768: ("mm", 0, 0), + 1024: ("v3", 32, 512), + 1536: ("v3", 16, 1024), + 2048: ("v3", 32, 512), + 3072: ("v3", 48, 512), + 4096: ("v3", 48, 512), + 6144: ("v3", 64, 512), + 8192: ("v3", 96, 256), + 12288: ("v3", 64, 512), + 16384: ("v3", 64, 512), +} +_AR_TUNED = {4: _AR_TUNED_TP4, 8: _AR_TUNED_TP8} +_AR_TUNED_TOKENS = sorted(_AR_TUNED_TP4) # same token grid for every table +assert all( + set(t) == set(_AR_TUNED_TP4) for t in _AR_TUNED.values() +), "all tuned tables must share the same token grid" + + +def select_ar_config(num_tokens: int, world_size: int = 4): + """Return (kernel, num_blocks, block_size) for a ``[num_tokens, hidden]`` + reduction, from the autotuned table for ``world_size`` (round up to the + nearest tested shape). Untuned world sizes fall back to the TP4 table. + ``kernel`` is one of "v5"/"v4"/"mm"/"v2"/"v3"/"v3b".""" + table = _AR_TUNED.get(world_size, _AR_TUNED_TP4) + for t in _AR_TUNED_TOKENS: + if num_tokens <= t: + return table[t] + return table[_AR_TUNED_TOKENS[-1]] + + +def compile_inkling_all_reduce(dtype: torch.dtype, world_size: int) -> None: + """Warm the JIT module for (dtype, world_size) so the first call is cheap.""" + _jit_inkling_all_reduce_module(dtype, world_size) + + +def inkling_two_shot_all_reduce( + buffer: torch.Tensor, + peer_ptrs_dev: int, + rank: int, + world_size: int, + num_items: int, +) -> None: + """Two-shot all-reduce in place over ``num_items`` elements of the symm buffer. + + Args: + buffer: this rank's symm buffer (1D, contiguous, bf16), sliced to + ``num_items``; used for device/dtype validation. The producer must + have already written this rank's shard into it. + peer_ptrs_dev: ``hdl.buffer_ptrs_dev`` -- device address of the array of + ``world_size`` peer buffer base pointers. + rank: this rank within the TP group. + world_size: TP world size (compile-time template arg). + num_items: number of elements to reduce (multiple of 8 for bf16). + + The caller is responsible for ``hdl.barrier()`` before (producers done) and + after (result visible) this call. + """ + module = _jit_inkling_all_reduce_module(buffer.dtype, world_size) + module.two_shot_all_reduce(buffer, peer_ptrs_dev, rank, num_items) + + +def inkling_two_shot_all_reduce_fused( + buffer: torch.Tensor, + data_ptrs_dev: int, + flag_ptrs_dev: int, + state_ptr: int, + rank: int, + world_size: int, + num_items: int, + num_blocks: int = 0, + block_size: int = 0, + shared: torch.Tensor | None = None, +) -> None: + """Single-launch two-shot all-reduce with an in-kernel grid-level barrier. + + Args: + buffer: this rank's symm data buffer (bf16), sliced to ``num_items``. + data_ptrs_dev: ``hdl.buffer_ptrs_dev`` for the data buffer. + flag_ptrs_dev: ``buffer_ptrs_dev`` of a DEDICATED symm ``uint32[world_size]`` + flags buffer, zero-initialized once at setup. + state_ptr: ``data_ptr()`` of a device-local ``uint32[STATE_SIZE]`` barrier + state buffer (persists across calls; advances under graph replay). + rank, world_size: TP coordinates (world_size is a template arg). + num_items: elements to reduce (multiple of 8 for bf16). + + No external barrier needed -- the kernel fences both sides itself. + """ + module = _jit_inkling_all_reduce_module(buffer.dtype, world_size) + module.two_shot_all_reduce_fused( + buffer, + data_ptrs_dev, + flag_ptrs_dev, + state_ptr, + rank, + num_items, + num_blocks, + block_size, + shared if shared is not None else empty_sentinel(buffer.device, buffer.dtype), + ) + + +def inkling_multimem_one_shot_fused( + buffer: torch.Tensor, + multicast_ptr: int, + flag_ptrs_dev: int, + state_ptr: int, + rank: int, + world_size: int, + num_items: int, + num_blocks: int = 0, + block_size: int = 0, + per_block_barrier: bool = False, + shared: torch.Tensor | None = None, +) -> None: + """Single-launch multimem one-shot all-reduce (NVLink multicast ld_reduce/st). + + Matches torch multimem for tiny, latency-bound (decode) messages, in a kernel + we own so norm/sconv can fuse at the epilogue seam. + + Args: + buffer: this rank's symm data buffer (bf16), sliced to ``num_items``. + multicast_ptr: ``hdl.multicast_ptr`` for the data buffer (must be != 0). + flag_ptrs_dev, state_ptr: dedicated barrier flags + local state buffer + (same as the fused two-shot). + rank, world_size, num_items: as above. + per_block_barrier: use per-block peer handshakes for both barriers (no + grid funnel; capped at MAX_BARRIER_BLOCKS blocks). + """ + module = _jit_inkling_all_reduce_module(buffer.dtype, world_size) + module.multimem_one_shot_fused( + buffer, + multicast_ptr, + flag_ptrs_dev, + state_ptr, + rank, + num_items, + num_blocks, + block_size, + int(per_block_barrier), + shared if shared is not None else empty_sentinel(buffer.device, buffer.dtype), + ) + + +def inkling_multimem_full_oneshot( + in_buffer: torch.Tensor, + out_buffer: torch.Tensor, + multicast_ptr: int, + flag_ptrs_dev: int, + state_ptr: int, + rank: int, + world_size: int, + num_items: int, + num_blocks: int = 0, + block_size: int = 0, + shared: torch.Tensor | None = None, +) -> None: + """Full one-shot all-reduce with a SINGLE (entry-only) barrier. + + Every rank ld_reduces the entire range (multicast hardware sum) into its + local ``out_buffer`` -- no broadcast, no exit barrier. Fastest for tiny + latency-bound messages, but the caller MUST double-buffer ``in_buffer`` (its + reuse is not fenced by this kernel; the next AR's entry barrier orders it). + + Args: + in_buffer: this rank's symm data buffer (bf16), sliced to ``num_items``. + out_buffer: local output buffer (bf16, >= num_items); receives the sum. + multicast_ptr: ``hdl.multicast_ptr`` of the in_buffer. + flag_ptrs_dev, state_ptr: barrier flags + local state (as above). + rank, world_size, num_items: as above. + """ + module = _jit_inkling_all_reduce_module(in_buffer.dtype, world_size) + module.multimem_full_oneshot( + in_buffer, + out_buffer, + multicast_ptr, + flag_ptrs_dev, + state_ptr, + rank, + num_items, + num_blocks, + block_size, + ( + shared + if shared is not None + else empty_sentinel(in_buffer.device, in_buffer.dtype) + ), + ) + + +def inkling_multimem_push_oneshot( + in_buffer: torch.Tensor, + out_buffer: torch.Tensor, + mc_stage_ptr: int, + local_stage_ptr: int, + flag_ptrs_dev: int, + state_ptr: int, + rank: int, + world_size: int, + num_items: int, + num_blocks: int = 0, + block_size: int = 0, + per_block_barrier: bool = False, + shared: torch.Tensor | None = None, +) -> None: + """One-shot PUSH all-reduce (v5) with a SINGLE mid barrier. + + Each rank multicast-stores its full input into its per-rank slot of the + symmetric staging area (slot ``r`` at elem offset ``r * num_items``), the + barrier waits for all pushes to land, then each rank reduces the + ``world_size`` staged shards locally (fp32 accum) into ``out_buffer``. + Drops one barrier round trip vs the two-shot kernels, and each rank holds + the full row at the epilogue seam (norm-fusion base, like v4 but scaling + past 2 rows). + + Args: + in_buffer: this rank's LOCAL input (any contiguous 16B-aligned bf16 + tensor -- need not be a symm buffer; it is only read locally). + out_buffer: local output buffer (bf16, >= num_items); receives the sum. + mc_stage_ptr: multicast address of the staging area (>= world_size * + num_items elems). The caller MUST double-buffer the staging area + (A/B rotation; the next AR's barrier orders the reuse, like v4). + local_stage_ptr: this GPU's local address of the same staging area. + flag_ptrs_dev, state_ptr: barrier flags + local state (as above). + rank, world_size, num_items: as above. + per_block_barrier: use the per-block peer handshake (no grid funnel; + capped at MAX_BARRIER_BLOCKS blocks) instead of the single-leader + grid barrier -- the multi-block latency winner. + """ + module = _jit_inkling_all_reduce_module(in_buffer.dtype, world_size) + module.multimem_push_oneshot( + in_buffer, + out_buffer, + mc_stage_ptr, + local_stage_ptr, + flag_ptrs_dev, + state_ptr, + rank, + num_items, + num_blocks, + block_size, + int(per_block_barrier), + ( + shared + if shared is not None + else empty_sentinel(in_buffer.device, in_buffer.dtype) + ), + ) diff --git a/python/sglang/jit_kernel/inkling_ar_fused.py b/python/sglang/jit_kernel/inkling_ar_fused.py new file mode 100644 index 000000000..455bf5379 --- /dev/null +++ b/python/sglang/jit_kernel/inkling_ar_fused.py @@ -0,0 +1,222 @@ +"""Fused all-reduce, decode short-convolution, and RMSNorm for Inkling. + +The small-batch decode kernel processes one token per block. +""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import torch + +from sglang.jit_kernel.utils import cache_once, empty_sentinel, load_jit, make_cpp_args + +if TYPE_CHECKING: + from tvm_ffi.module import Module + + +@cache_once +def _jit_ar_fused_module( + dtype: torch.dtype, + world_size: int, + w: int, + use_silu: bool, + use_residual: bool, + do_track: bool, +) -> Module: + args = make_cpp_args(dtype, world_size, w, use_silu, use_residual, do_track) + return load_jit( + "inkling_ar_fused_decode", + *args, + cuda_files=["inkling/inkling_ar_fused_decode.cuh"], + cuda_wrappers=[ + ("ar_sconv_norm", f"ArSconvNormKernel<{args}>::run"), + ("ar_sconv_norm_verify", f"ArSconvNormVerifyKernel<{args}>::run"), + ], + ) + + +# Tuned vectors per thread by decode row count; round up to the next entry. +_FUSED_VPT_TUNED = {1: 1, 2: 1, 4: 1, 8: 1, 16: 1, 32: 1, 64: 1, 96: 1} +_FUSED_VPT_TOKENS = sorted(_FUSED_VPT_TUNED) + + +def select_fused_vpt(num_tokens: int) -> int: + for t in _FUSED_VPT_TOKENS: + if num_tokens <= t: + return _FUSED_VPT_TUNED[t] + return _FUSED_VPT_TUNED[_FUSED_VPT_TOKENS[-1]] + + +def compile_inkling_ar_sconv_norm( + dtype: torch.dtype, + world_size: int, + w: int, + use_silu: bool, + use_residual: bool, + do_track: bool, +) -> None: + """Warm the JIT module so the first fused call is cheap.""" + _jit_ar_fused_module(dtype, world_size, w, use_silu, use_residual, do_track) + + +def inkling_ar_sconv_norm( + in_partial: torch.Tensor, + residual_in: torch.Tensor, + residual_out: torch.Tensor, + hs_out: torch.Tensor, + norm_weight: torch.Tensor, + eps: float, + sconv_cache: torch.Tensor, + cache_indices: torch.Tensor, + cache_mask: torch.Tensor, + conv_weight: torch.Tensor, + mc_stage_ptr: int, + local_stage_ptr: int, + flag_ptrs_dev: int, + state_ptr: int, + rank: int, + world_size: int, + activation: str | None = None, + use_residual: bool = True, + track_mask: torch.Tensor | None = None, + track_indices: torch.Tensor | None = None, + enable_pdl: bool = True, + vecs_per_thread: int = 0, + shared: torch.Tensor | None = None, +) -> None: + """Fused AR + decode sconv + add-RMSNorm over ``[T, D]`` decode rows. + + Args: + in_partial: this rank's LOCAL partial sums (``[T, D]`` bf16, contiguous + rows, 16B-aligned) -- e.g. the MoE combine output with + ``reduce=False``. Read locally only (no stage-in copy). + shared: optional LOCAL ``[T, D]`` shared-expert partials, folded into + the pushed value in registers (fp32 add, one bf16 round -- + torch.add numerics), replacing the separate ``routed + shared`` + add kernel at zero extra traffic. All ranks must agree on passing it. + residual_in / residual_out: the residual stream before/after the fused + add (may alias); ``hs_out``: the normed output. + norm_weight, eps: RMSNorm gamma (``[D]`` bf16) and epsilon. + sconv_cache..conv_weight, track_*: exactly the tensors + ``fused_causal_conv1d_update_decode`` takes; the conv state is + shift-updated in place, identically to the unfused kernel. + mc_stage_ptr / local_stage_ptr: multicast + local address of the v5 + staging rotation slot (>= world_size*T*D elems; caller rotates A/B, + same reuse-distance rule as v5). + flag_ptrs_dev / state_ptr / rank / world_size: barrier resources + (shared with the other fused AR kernels). + """ + if activation == "swish": + activation = "silu" + use_silu = activation in ("silu", "swish") + do_track = track_mask is not None + w = conv_weight.shape[1] + if do_track: + tm = track_mask.reshape(-1) + ti = track_indices + else: # dummies; DO_TRACK=false never reads them + tm = torch.empty(0, dtype=torch.bool, device=in_partial.device) + ti = torch.empty(0, dtype=torch.int64, device=in_partial.device) + module = _jit_ar_fused_module( + in_partial.dtype, world_size, w, use_silu, use_residual, do_track + ) + if vecs_per_thread <= 0: + vecs_per_thread = select_fused_vpt(in_partial.shape[0]) + sh = ( + shared + if shared is not None + else empty_sentinel(in_partial.device, in_partial.dtype) + ) + module.ar_sconv_norm( + in_partial, + residual_in, + residual_out, + hs_out, + norm_weight, + float(eps), + sconv_cache, + cache_indices, + cache_mask.reshape(-1), + conv_weight, + tm, + ti, + mc_stage_ptr, + local_stage_ptr, + flag_ptrs_dev, + state_ptr, + rank, + int(enable_pdl), + int(vecs_per_thread), + sh, + ) + + +def inkling_ar_sconv_norm_verify( + in_partial: torch.Tensor, + residual_in: torch.Tensor, + residual_out: torch.Tensor, + hs_out: torch.Tensor, + norm_weight: torch.Tensor, + eps: float, + sconv_cache: torch.Tensor, + cache_indices: torch.Tensor, + cache_mask: torch.Tensor, + conv_weight: torch.Tensor, + inter_out: torch.Tensor, + draft_token_num: int, + mc_stage_ptr: int, + local_stage_ptr: int, + flag_ptrs_dev: int, + state_ptr: int, + rank: int, + world_size: int, + activation: str | None = None, + use_residual: bool = True, + enable_pdl: bool = True, + shared: torch.Tensor | None = None, +) -> None: + """Target-verify fused {AR -> causal_conv1d -> save_intermediate_conv_windows + -> add+RMSNorm} over ``[B*draft_token_num, D]`` rows. + + ``cache_indices``/``cache_mask`` are per-SEQUENCE (``[B]``); the working + conv cache is read-only (the per-position windows go to ``inter_out``, + exactly like ``save_intermediate_conv_windows``). Cross-token conv taps are + re-reduced from the v5 staging slot, so the same rotation rules as + ``inkling_ar_sconv_norm`` apply. ``shared``: optional LOCAL ``[T, D]`` + shared-expert partials folded into the push (torch.add numerics). + """ + if activation == "swish": + activation = "silu" + use_silu = activation in ("silu", "swish") + w = conv_weight.shape[1] + # do_track slot in the module key is unused by the verify kernel. + module = _jit_ar_fused_module( + in_partial.dtype, world_size, w, use_silu, use_residual, False + ) + sh = ( + shared + if shared is not None + else empty_sentinel(in_partial.device, in_partial.dtype) + ) + module.ar_sconv_norm_verify( + in_partial, + residual_in, + residual_out, + hs_out, + norm_weight, + float(eps), + sconv_cache, + cache_indices.to(torch.int32), + cache_mask, + conv_weight, + inter_out, + int(draft_token_num), + mc_stage_ptr, + local_stage_ptr, + flag_ptrs_dev, + state_ptr, + rank, + int(enable_pdl), + sh, + ) diff --git a/python/sglang/jit_kernel/inkling_ar_scattered_sconv.py b/python/sglang/jit_kernel/inkling_ar_scattered_sconv.py new file mode 100644 index 000000000..ed742e139 --- /dev/null +++ b/python/sglang/jit_kernel/inkling_ar_scattered_sconv.py @@ -0,0 +1,349 @@ +"""Fused all-reduce and scattered short-convolution for Inkling. + +The kernel reduces a per-rank hidden-channel slice, applies causal convolution, +and updates the convolution and prefix caches in one launch. +""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import torch + +from sglang.jit_kernel.utils import cache_once, load_jit, make_cpp_args + +if TYPE_CHECKING: + from tvm_ffi.module import Module + + +@cache_once +def _jit_ar_scattered_sconv_module( + dtype: torch.dtype, + world_size: int, + w: int, + use_silu: bool, + use_residual: bool, +) -> Module: + args = make_cpp_args(dtype, world_size, w, use_silu, use_residual) + return load_jit( + "inkling_ar_scattered_sconv", + *args, + cuda_files=["inkling/inkling_ar_scattered_sconv.cuh"], + cuda_wrappers=[ + ("ar_scattered_sconv", f"ArScatteredSconvKernel<{args}>::run"), + ("ar_banded_sconv", f"ArBandedSconvKernel<{args}>::run"), + ("ar_ssconv_norm_decode", f"SsconvNormDecodeKernel<{args}>::run"), + ("ar_col_decode", f"ColDecodeKernel<{args}>::run"), + ], + ) + + +def compile_inkling_ar_scattered_sconv( + dtype: torch.dtype, + world_size: int, + w: int, + use_silu: bool, + use_residual: bool, +) -> None: + """Warm the JIT module so the first fused call is cheap.""" + _jit_ar_scattered_sconv_module(dtype, world_size, w, use_silu, use_residual) + + +def inkling_ar_scattered_sconv( + in_buffer: torch.Tensor, + x_scratch: torch.Tensor, + sconv_cache: torch.Tensor, + safe_idx: torch.Tensor, + cache_mask: torch.Tensor, + cache_indices: torch.Tensor, + has_initial_state: torch.Tensor, + cu: torch.Tensor, + si: torch.Tensor, + weight: torch.Tensor, + track_rows: torch.Tensor, + track_mask: torch.Tensor, + track_dst: torch.Tensor, + mc_in: int, + mc_out: int, + flag_ptrs_dev: int, + state_ptr: int, + rank: int, + world_size: int, + *, + activation: str | None, + use_residual: bool, + num_blocks: int = 0, + block_size: int = 0, + per_block_barrier: bool = False, + track_from_cache: bool = False, + out_local: torch.Tensor | None = None, + norm_gamma: torch.Tensor | None = None, + norm_residual: torch.Tensor | None = None, + norm_out: torch.Tensor | None = None, + norm_eps: float = 0.0, + need_scratch: bool = True, + use_stream: bool = False, + stream_walk: int = 0, + full_update: bool = False, + cache_col0: int = 0, +) -> None: + """Run the fused kernel. ``in_buffer`` is this rank's [T, H] view of the + input symm region (partial sums already written by the producer); + ``mc_in`` / ``mc_out`` are the multicast pointers of the input and OUT + regions. On return the OUT region holds the gathered post-conv [T, H] on + every rank and ``x_scratch`` holds the reduced pre-conv [T, Hc] shard. + + Tracking: empty ``track_mask`` disables it. ``track_from_cache`` (decode) + snapshots the post-update conv window to ``track_dst`` (``track_rows`` may + be empty); otherwise ``track_rows`` gathers pre-conv rows (extend). + + Fused add+RMSNorm tail (decode/verify): pass ``out_local`` (this rank's + [T, H] OUT view), ``norm_gamma``/``norm_residual``/``norm_out``/``norm_eps``. + Works under either barrier mode. The residual is updated in place; + ``norm_out`` receives the normed hidden. + + FULL-WIDTH mode (non-scattered sconv): ``full_update=True`` with + ``sconv_cache`` the replicated [slots, W-1, H] tensor, ``weight`` this + rank's contiguous [Hc, W] row slice and ``cache_col0 = rank * Hc``. Conv + still runs column-sharded; phase 3 updates/tracks ALL H cache columns on + every rank (window rows re-ld_reduced full-width) so the replicated cache + stays coherent. Verify (``need_scratch``) is unsupported full-width.""" + w = weight.shape[1] + use_silu = activation in ("silu", "swish") + module = _jit_ar_scattered_sconv_module( + in_buffer.dtype, world_size, w, use_silu, use_residual + ) + do_norm = norm_gamma is not None + if do_norm: + assert ( + out_local is not None and norm_residual is not None and norm_out is not None + ) + else: + empty = in_buffer.new_empty((0,)) + out_local = norm_gamma = norm_residual = norm_out = empty + module.ar_scattered_sconv( + in_buffer, + x_scratch, + sconv_cache, + safe_idx, + cache_mask, + cache_indices, + has_initial_state, + cu, + si, + weight, + track_rows, + track_mask, + track_dst, + out_local, + norm_gamma, + norm_residual, + norm_out, + mc_in, + mc_out, + flag_ptrs_dev, + state_ptr, + rank, + num_blocks, + block_size, + per_block_barrier, + track_from_cache, + norm_eps, + need_scratch, + use_stream, + stream_walk, + full_update, + cache_col0, + ) + + +def inkling_ar_ssconv_norm_decode( + in_partials: torch.Tensor, + residual_in: torch.Tensor, + residual_out: torch.Tensor, + hs_out: torch.Tensor, + norm_weight: torch.Tensor, + norm_eps: float, + sconv_cache: torch.Tensor, + cache_indices: torch.Tensor, + cache_mask: torch.Tensor, + conv_weight_full: torch.Tensor, + track_mask: torch.Tensor, + track_indices: torch.Tensor, + mc_stage: int, + local_stage: int, + mc_wstage: int, + local_wstage: int, + flag_ptrs_dev: int, + state_ptr: int, + rank: int, + world_size: int, + *, + activation: str | None, + use_residual: bool, + vecs_per_thread: int = 0, +) -> None: + """ONE-SHOT decode {AR + scattered sconv + add-RMSNorm}: v5 push pattern + with the cache-window shard co-pushed so every rank convs full width from + ONE barrier. ``sconv_cache`` is the SHARDED [pool, W-1, Hc] cache (only + this rank's columns are updated/tracked); ``conv_weight_full`` must be the + UNSHARDED [D, W] taps. ``mc_stage``/``local_stage`` = one v5 rotation slot + ([world, T, D]); ``mc_wstage``/``local_wstage`` = a rotating [T, W-1, D] + window-staging half. Pass empty ``track_mask`` to disable tracking + (post-update-window snapshot semantics otherwise).""" + w = conv_weight_full.shape[1] + use_silu = activation in ("silu", "swish") + module = _jit_ar_scattered_sconv_module( + in_partials.dtype, world_size, w, use_silu, use_residual + ) + module.ar_ssconv_norm_decode( + in_partials, + residual_in, + residual_out, + hs_out, + norm_weight, + norm_eps, + sconv_cache, + cache_indices, + cache_mask, + conv_weight_full, + track_mask, + track_indices, + mc_stage, + local_stage, + mc_wstage, + local_wstage, + flag_ptrs_dev, + state_ptr, + rank, + vecs_per_thread, + ) + + +def inkling_ar_col_decode( + in_buffer: torch.Tensor, + out_local: torch.Tensor, + residual_in: torch.Tensor, + residual_out: torch.Tensor, + hs_out: torch.Tensor, + norm_weight: torch.Tensor, + norm_eps: float, + sconv_cache: torch.Tensor, + cache_indices: torch.Tensor, + cache_mask: torch.Tensor, + weight_shard: torch.Tensor, + track_mask: torch.Tensor, + track_dst: torch.Tensor, + mc_in: int, + mc_out: int, + flag_ptrs_dev: int, + state_ptr: int, + rank: int, + world_size: int, + *, + activation: str | None, + use_residual: bool, + vecs_per_thread: int = 0, +) -> None: + """Dedicated small-batch column decode: one block per token row, block-scoped two-round barriers, prefetch under the entry spin, conv from registers on the owner shard, inline cache update (+ decode track), and the full-row add+RMSNorm after the exit round. Decode-only (single-token sequences; every tap is cache prefix). in_buffer/out_local are this rank's views of the input/OUT symm regions.""" + w = weight_shard.shape[1] + use_silu = activation in ("silu", "swish") + module = _jit_ar_scattered_sconv_module( + in_buffer.dtype, world_size, w, use_silu, use_residual + ) + module.ar_col_decode( + in_buffer, + out_local, + residual_in, + residual_out, + hs_out, + norm_weight, + norm_eps, + sconv_cache, + cache_indices, + cache_mask, + weight_shard, + track_mask, + track_dst, + mc_in, + mc_out, + flag_ptrs_dev, + state_ptr, + rank, + vecs_per_thread, + ) + + +def inkling_ar_banded_sconv( + in_buffer: torch.Tensor, + scratch: torch.Tensor, + sconv_cache: torch.Tensor, + safe_idx: torch.Tensor, + cache_mask: torch.Tensor, + cache_indices: torch.Tensor, + has_initial_state: torch.Tensor, + cu: torch.Tensor, + si: torch.Tensor, + weight: torch.Tensor, + track_rows: torch.Tensor, + track_mask: torch.Tensor, + track_dst: torch.Tensor, + mc_in: int, + mc_out: int, + flag_ptrs_dev: int, + state_ptr: int, + rank: int, + world_size: int, + *, + activation: str | None, + use_residual: bool, + num_blocks: int = 0, + block_size: int = 0, + per_block_barrier: bool = False, + debug_phase: int = 0, + mc_wstage: int = 0, + local_wstage: int = 0, +) -> None: + """Token-banded fused {v3 AR + sconv}: contiguous band slices (v3-class + switch-transaction efficiency), in-kernel conv-state update + track. + ``scratch`` must be [ceil(T/world) + W-1, H]. Pass empty (numel-0) + ``track_rows`` to disable the track path. + + Full-width mode (``sconv_cache`` [pool, W-1, H], default): the production + {v3 AR + sconv} fusion; every rank keeps the complete cache. + SCATTERED mode (``sconv_cache`` [pool, W-1, H/world] + ``mc_wstage``/ + ``local_wstage`` pointing at a [B, W-1, H] staging region): each rank + pushes its cache-window shard pre-barrier (full-width taps come from the + staging), convs its contiguous token band full-width, and updates/tracks + only its own cache columns. ``weight`` must be the FULL [H, W] taps.""" + w = weight.shape[1] + use_silu = activation in ("silu", "swish") + module = _jit_ar_scattered_sconv_module( + in_buffer.dtype, world_size, w, use_silu, use_residual + ) + module.ar_banded_sconv( + in_buffer, + scratch, + sconv_cache, + safe_idx, + cache_mask, + cache_indices, + has_initial_state, + cu, + si, + weight, + track_rows, + track_mask, + track_dst, + mc_in, + mc_out, + flag_ptrs_dev, + state_ptr, + rank, + num_blocks, + block_size, + per_block_barrier, + debug_phase, + mc_wstage, + local_wstage, + ) diff --git a/python/sglang/jit_kernel/inkling_attn_prologue.py b/python/sglang/jit_kernel/inkling_attn_prologue.py new file mode 100644 index 000000000..50a00b3e5 --- /dev/null +++ b/python/sglang/jit_kernel/inkling_attn_prologue.py @@ -0,0 +1,400 @@ +"""Fused target-verify attention prologue: {k/v sconv + save_windows + qk-norm ++ KV-cache store} in one kernel (csrc/tml/inkling_attn_prologue_fused.cuh).""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import torch + +from sglang.jit_kernel.utils import ( + cache_once, + empty_sentinel, + is_arch_support_pdl, + load_jit, + make_cpp_args, +) + +if TYPE_CHECKING: + from tvm_ffi.module import Module + + +@cache_once +def _jit_attn_prologue_module( + dtype: torch.dtype, + w: int, + use_silu: bool, + use_residual: bool, + use_mxfp8: bool, +) -> Module: + args = make_cpp_args( + dtype, w, use_silu, use_residual, use_mxfp8, is_arch_support_pdl() + ) + return load_jit( + "inkling_attn_prologue_fused", + *args, + cuda_files=["inkling/inkling_attn_prologue_fused.cuh"], + cuda_wrappers=[ + ("attn_prologue", f"AttnPrologueKernel<{args}>::run"), + ("attn_prologue_decode", f"AttnPrologueDecodeKernel<{args}>::run"), + ("attn_prologue_extend", f"AttnPrologueExtendKernel<{args}>::run"), + ], + ) + + +def compile_inkling_attn_prologue( + dtype: torch.dtype, + w: int, + use_silu: bool, + use_residual: bool, + use_mxfp8: bool = False, +) -> None: + _jit_attn_prologue_module(dtype, w, use_silu, use_residual, use_mxfp8) + + +def inkling_attn_prologue_verify( + qkvr: torch.Tensor, + k_cache: torch.Tensor, + v_cache: torch.Tensor, + cache_indices: torch.Tensor, + cache_mask: torch.Tensor, + k_weight: torch.Tensor, + v_weight: torch.Tensor, + k_inter: torch.Tensor, + v_inter: torch.Tensor, + q_gamma: torch.Tensor, + k_gamma: torch.Tensor, + eps: float, + loc: torch.Tensor, + k_buf: torch.Tensor, + v_buf: torch.Tensor, + q_off: int, + k_off: int, + v_off: int, + dq: int, + dkv: int, + draft_token_num: int, + activation: str | None = None, + use_residual: bool = True, + do_store: bool = True, + mxfp8_quant: bool = False, + sfk: torch.Tensor | None = None, + sfv: torch.Tensor | None = None, + page_size: int = 128, + log_scaling_tau: torch.Tensor | None = None, +) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor | None]: + """Returns fresh contiguous (q_normed, k_normed, v_conv) [T, dq/dkv]; + KV rows are also scattered into k_buf/v_buf at ``loc`` (the attention call + should pass save_kv_cache=False).""" + t = qkvr.shape[0] + if mxfp8_quant: + if dq % 128 != 0 or dkv % 128 != 0: + raise ValueError("MXFP8 fused prologue requires head_dim-aligned Q/K/V.") + if sfk is None or sfv is None: + raise ValueError("MXFP8 fused prologue requires K/V scale buffers.") + sf_shape = (k_buf.shape[0] // page_size, dkv // 128, 32, page_size // 32, 4) + if sfk.shape != sf_shape or sfv.shape != sf_shape: + raise ValueError( + "MXFP8 fused prologue requires interleaved K/V scale buffers " + f"with shape {sf_shape}, got {tuple(sfk.shape)} and {tuple(sfv.shape)}." + ) + if not sfk.is_contiguous() or not sfv.is_contiguous(): + raise ValueError( + "MXFP8 fused prologue requires contiguous interleaved SFK/SFV." + ) + q_out = torch.empty(t, dq, dtype=torch.float8_e4m3fn, device=qkvr.device) + sfq_u8 = torch.empty( + (t, dq // 128, 128 // 32), dtype=torch.uint8, device=qkvr.device + ) + sfk_u8 = sfk.view(torch.uint8) + sfv_u8 = sfv.view(torch.uint8) + else: + q_out = torch.empty(t, dq, dtype=qkvr.dtype, device=qkvr.device) + sfq_u8 = torch.empty(0, dtype=torch.uint8, device=qkvr.device) + sfk_u8 = torch.empty(0, dtype=torch.uint8, device=qkvr.device) + sfv_u8 = torch.empty(0, dtype=torch.uint8, device=qkvr.device) + k_out = torch.empty(t, dkv, dtype=qkvr.dtype, device=qkvr.device) + v_out = torch.empty(t, dkv, dtype=qkvr.dtype, device=qkvr.device) + if activation == "swish": + activation = "silu" + use_silu = activation in ("silu", "swish") + w = k_weight.shape[1] + module = _jit_attn_prologue_module( + qkvr.dtype, w, use_silu, use_residual, mxfp8_quant + ) + hkv = dkv // 128 + module.attn_prologue( + qkvr, + k_cache, + v_cache, + cache_indices.to(torch.int32), + cache_mask, + k_weight, + v_weight, + k_inter, + v_inter, + q_gamma, + k_gamma, + float(eps), + q_out, + k_out, + v_out, + loc, + k_buf.view(-1, hkv * 128), + v_buf.view(-1, hkv * 128), + sfq_u8, + sfk_u8, + sfv_u8, + int(q_off), + int(k_off), + int(v_off), + int(draft_token_num), + int(do_store), + int(page_size), + ( + log_scaling_tau.reshape(-1).float() + if log_scaling_tau is not None + else empty_sentinel(qkvr.device, torch.float32) + ), + ) + q_scale = sfq_u8.view(torch.float8_e8m0fnu) if mxfp8_quant else None + return q_out, k_out, v_out, q_scale + + +def inkling_attn_prologue_extend( + qkvr: torch.Tensor, + k_cache: torch.Tensor, + v_cache: torch.Tensor, + cache_indices: torch.Tensor, + cache_mask: torch.Tensor, + has_initial_state: torch.Tensor, + cu: torch.Tensor, + si: torch.Tensor, + k_weight: torch.Tensor, + v_weight: torch.Tensor, + track_rows: torch.Tensor, + track_mask: torch.Tensor, + track_dst: torch.Tensor, + q_gamma: torch.Tensor, + k_gamma: torch.Tensor, + eps: float, + loc: torch.Tensor, + k_buf: torch.Tensor, + v_buf: torch.Tensor, + q_off: int, + k_off: int, + v_off: int, + dq: int, + dkv: int, + activation: str | None = None, + use_residual: bool = True, + do_store: bool = True, + mxfp8_quant: bool = False, + sfk: torch.Tensor | None = None, + sfv: torch.Tensor | None = None, + page_size: int = 128, + do_cache_update: bool = True, + log_scaling_tau: torch.Tensor | None = None, +) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor | None]: + """Extend (prefill) analog of ``inkling_attn_prologue_verify``: varlen + sequences via ``cu``/``si``, no window save; instead a tiny trailing + kernel does the k/v conv-cache update at sequence ends (+ the extend + prefix-cache track when ``track_mask`` is non-empty -- pass empty tensors + to disable). Returns fresh contiguous (q_normed, k_normed, v_conv) and + scatters KV rows into k_buf/v_buf at ``loc`` when ``do_store`` (the + attention call should then pass save_kv_cache=False).""" + t = qkvr.shape[0] + if mxfp8_quant: + if dq % 128 != 0 or dkv % 128 != 0: + raise ValueError("MXFP8 fused prologue requires head_dim-aligned Q/K/V.") + if sfk is None or sfv is None: + raise ValueError("MXFP8 fused prologue requires K/V scale buffers.") + sf_shape = (k_buf.shape[0] // page_size, dkv // 128, 32, page_size // 32, 4) + if sfk.shape != sf_shape or sfv.shape != sf_shape: + raise ValueError( + "MXFP8 fused prologue requires interleaved K/V scale buffers " + f"with shape {sf_shape}, got {tuple(sfk.shape)} and {tuple(sfv.shape)}." + ) + if not sfk.is_contiguous() or not sfv.is_contiguous(): + raise ValueError( + "MXFP8 fused prologue requires contiguous interleaved SFK/SFV." + ) + q_out = torch.empty(t, dq, dtype=torch.float8_e4m3fn, device=qkvr.device) + sfq_u8 = torch.empty( + (t, dq // 128, 128 // 32), dtype=torch.uint8, device=qkvr.device + ) + sfk_u8 = sfk.view(torch.uint8) + sfv_u8 = sfv.view(torch.uint8) + else: + q_out = torch.empty(t, dq, dtype=qkvr.dtype, device=qkvr.device) + sfq_u8 = torch.empty(0, dtype=torch.uint8, device=qkvr.device) + sfk_u8 = torch.empty(0, dtype=torch.uint8, device=qkvr.device) + sfv_u8 = torch.empty(0, dtype=torch.uint8, device=qkvr.device) + k_out = torch.empty(t, dkv, dtype=qkvr.dtype, device=qkvr.device) + v_out = torch.empty(t, dkv, dtype=qkvr.dtype, device=qkvr.device) + if activation == "swish": + activation = "silu" + use_silu = activation in ("silu", "swish") + w = k_weight.shape[1] + module = _jit_attn_prologue_module( + qkvr.dtype, w, use_silu, use_residual, mxfp8_quant + ) + hkv = dkv // 128 + module.attn_prologue_extend( + qkvr, + k_cache, + v_cache, + cache_indices.to(torch.int32), + cache_mask, + has_initial_state, + cu, + si, + k_weight, + v_weight, + track_rows, + track_mask, + track_dst, + q_gamma, + k_gamma, + float(eps), + q_out, + k_out, + v_out, + loc, + k_buf.view(-1, hkv * 128), + v_buf.view(-1, hkv * 128), + sfq_u8, + sfk_u8, + sfv_u8, + int(q_off), + int(k_off), + int(v_off), + int(do_store), + int(page_size), + int(do_cache_update), + ( + log_scaling_tau.reshape(-1).float() + if log_scaling_tau is not None + else empty_sentinel(qkvr.device, torch.float32) + ), + ) + q_scale = sfq_u8.view(torch.float8_e8m0fnu) if mxfp8_quant else None + return q_out, k_out, v_out, q_scale + + +def inkling_attn_prologue_decode( + qkvr: torch.Tensor, + k_cache: torch.Tensor, + v_cache: torch.Tensor, + cache_indices: torch.Tensor, + cache_mask: torch.Tensor, + k_weight: torch.Tensor, + v_weight: torch.Tensor, + q_gamma: torch.Tensor, + k_gamma: torch.Tensor, + eps: float, + loc: torch.Tensor, + k_buf: torch.Tensor, + v_buf: torch.Tensor, + q_off: int, + k_off: int, + v_off: int, + dq: int, + dkv: int, + activation: str | None = None, + use_residual: bool = True, + track_mask: torch.Tensor | None = None, + track_indices: torch.Tensor | None = None, + do_store: bool = True, + mxfp8_quant: bool = False, + sfk: torch.Tensor | None = None, + sfv: torch.Tensor | None = None, + page_size: int = 128, + log_scaling_tau: torch.Tensor | None = None, +) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor | None]: + """Decode {k/v decode-conv + conv-cache shift-update (+track) + qk-norm + (+ KV store)} in one kernel. Returns fresh (q_normed, k_normed, v_conv). + The k/v conv caches are shift-updated in place (fused_decode_update + semantics). With ``do_store`` the KV rows are scattered into k_buf/v_buf at + ``loc``; MXFP8 mode also quantizes Q and writes interleaved K/V scales.""" + t = qkvr.shape[0] + if mxfp8_quant: + if dq % 128 != 0 or dkv % 128 != 0: + raise ValueError( + "MXFP8 fused decode prologue requires head_dim-aligned Q/K/V." + ) + if sfk is None or sfv is None: + raise ValueError("MXFP8 fused decode prologue requires K/V scale buffers.") + sf_shape = (k_buf.shape[0] // page_size, dkv // 128, 32, page_size // 32, 4) + if sfk.shape != sf_shape or sfv.shape != sf_shape: + raise ValueError( + "MXFP8 fused decode prologue requires interleaved K/V scale buffers " + f"with shape {sf_shape}, got {tuple(sfk.shape)} and {tuple(sfv.shape)}." + ) + if not sfk.is_contiguous() or not sfv.is_contiguous(): + raise ValueError( + "MXFP8 fused decode prologue requires contiguous interleaved SFK/SFV." + ) + q_out = torch.empty(t, dq, dtype=torch.float8_e4m3fn, device=qkvr.device) + sfq_u8 = torch.empty( + (t, dq // 128, 128 // 32), dtype=torch.uint8, device=qkvr.device + ) + sfk_u8 = sfk.view(torch.uint8) + sfv_u8 = sfv.view(torch.uint8) + else: + q_out = torch.empty(t, dq, dtype=qkvr.dtype, device=qkvr.device) + sfq_u8 = torch.empty(0, dtype=torch.uint8, device=qkvr.device) + sfk_u8 = torch.empty(0, dtype=torch.uint8, device=qkvr.device) + sfv_u8 = torch.empty(0, dtype=torch.uint8, device=qkvr.device) + k_out = torch.empty(t, dkv, dtype=qkvr.dtype, device=qkvr.device) + v_out = torch.empty(t, dkv, dtype=qkvr.dtype, device=qkvr.device) + if activation == "swish": + activation = "silu" + use_silu = activation in ("silu", "swish") + w = k_weight.shape[1] + do_track = track_mask is not None + if do_track: + tm, ti = track_mask.reshape(-1), track_indices + else: + tm = torch.empty(0, dtype=torch.bool, device=qkvr.device) + ti = torch.empty(0, dtype=torch.int64, device=qkvr.device) + hkv = dkv // 128 + module = _jit_attn_prologue_module( + qkvr.dtype, w, use_silu, use_residual, mxfp8_quant + ) + module.attn_prologue_decode( + qkvr, + k_cache, + v_cache, + cache_indices.to(torch.int32), + cache_mask, + k_weight, + v_weight, + tm, + ti, + q_gamma, + k_gamma, + float(eps), + q_out, + k_out, + v_out, + loc, + k_buf.view(-1, hkv * 128), + v_buf.view(-1, hkv * 128), + sfq_u8, + sfk_u8, + sfv_u8, + int(q_off), + int(k_off), + int(v_off), + int(do_track), + int(do_store), + int(page_size), + ( + log_scaling_tau.reshape(-1).float() + if log_scaling_tau is not None + else empty_sentinel(qkvr.device, torch.float32) + ), + ) + q_scale = sfq_u8.view(torch.float8_e8m0fnu) if mxfp8_quant else None + return q_out, k_out, v_out, q_scale diff --git a/python/sglang/jit_kernel/inkling_gate_topk_renorm.py b/python/sglang/jit_kernel/inkling_gate_topk_renorm.py new file mode 100644 index 000000000..46ccf054c --- /dev/null +++ b/python/sglang/jit_kernel/inkling_gate_topk_renorm.py @@ -0,0 +1,325 @@ +"""Shape-specialized Inkling MoE gate top-k + renorm JIT kernels. + +Three families, all specialized for the Inkling gate layout (logits +``[tokens, 258]`` fp32 = 256 routed + 2 shared experts, top-6 selection by +``sigmoid(logit) + bias``, logsigmoid renorm over selected ++ shared): + +- ``inkling_gate_topk_renorm`` -- v1 warp-per-row gate (int64 indices). +- ``inkling_gate_topk_renorm_v2`` -- v2 gate: wide vector loads, int32 + indices, optional PDL, in-register raw-logit carry (no re-gather). +- ``inkling_gate_gemv`` / ``inkling_gate_gemv_fused`` -- expert-per-block GEMV + of the gate linear (x [tokens, 6144] bf16 @ W [264, 6144] bf16 -> fp32 + logits), standalone or with the gate epilogue fused into the same launch + (last finishing block runs it; ticket+workspace are cached per device). + +NOTE: the fused/gemv wrappers cache CUDA buffers and JIT-compile on first use; +run them eagerly once before CUDA-graph capture. +""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import torch + +from sglang.jit_kernel.utils import cache_once, load_jit + +if TYPE_CHECKING: + from tvm_ffi.module import Module + +_LOGITS_PAD = 264 # fp32 logits row pitch shared with the padded gate GEMM +_HIDDEN = 6144 +_TOPK = 6 +_N_SHARED = 2 +_FUSED_MAX_TOKENS = 64 + + +@cache_once +def _jit_module() -> Module: + return load_jit( + "inkling_gate_topk_renorm", + "fast_math", + cuda_files=["moe/inkling_gate_topk_renorm.cuh"], + cuda_wrappers=[ + ("inkling_gate_topk_renorm", "inkling_gate_topk_renorm"), + ("inkling_gate_topk_renorm_packed", "inkling_gate_topk_renorm_packed"), + ("inkling_gate_topk_renorm_v2", "inkling_gate_topk_renorm_v2"), + ( + "inkling_gate_topk_renorm_v2_packed", + "inkling_gate_topk_renorm_v2_packed", + ), + ("inkling_gate_gemv", "inkling_gate_gemv"), + ("inkling_gate_gemv_fused", "inkling_gate_gemv_fused"), + ("inkling_gate_gemv_fused_packed", "inkling_gate_gemv_fused_packed"), + ], + extra_cuda_cflags=["-use_fast_math"], + ) + + +def _launch_inkling_gate_topk_renorm( + logits: torch.Tensor, + bias: torch.Tensor, + global_scale: torch.Tensor, + routed_w: torch.Tensor, + shared_w: torch.Tensor, + indices: torch.Tensor, + route_scale: float, +) -> None: + module = _jit_module() + module.inkling_gate_topk_renorm( + logits, bias, global_scale, routed_w, shared_w, indices, float(route_scale) + ) + + +def _check_gate_inputs( + logits: torch.Tensor, bias: torch.Tensor, global_scale: torch.Tensor +) -> None: + assert logits.is_cuda and logits.dtype == torch.float32 and logits.dim() == 2 + assert logits.shape[1] == 258 and logits.stride(1) == 1 + assert bias.is_cuda and bias.dtype == torch.float32 and bias.shape == (256,) + assert global_scale.is_cuda and global_scale.dtype == torch.float32 + assert global_scale.numel() == 1 + + +def inkling_gate_topk_renorm( + logits: torch.Tensor, + bias: torch.Tensor, + global_scale: torch.Tensor, + route_scale: float, + *, + return_packed: bool = False, +) -> ( + tuple[torch.Tensor, torch.Tensor, torch.Tensor] | tuple[torch.Tensor, torch.Tensor] +): + """Select top-6 routed experts from 256 and renorm with 2 shared experts. + + This is specialized for the Inkling fused gate layout: + ``logits`` is ``[tokens, 258]`` fp32, where columns ``0:256`` are routed + experts and columns ``256:258`` are shared experts. The top-k selection key is + ``sigmoid(logits[:, :256]) + bias``; renorm is over sigmoid(raw logits) for + the selected routed experts plus both shared experts. + + ``return_packed=True`` emits the FlashInfer routed-MoE pack instead of the + routed_w + indices pair: ``packed[t,6]`` int32 = ``(expert_id << 16) | bf16 + weight bits``. Returns ``(packed, shared_w)``. + """ + _check_gate_inputs(logits, bias, global_scale) + + tokens = logits.shape[0] + shared_w = torch.empty( + (tokens, _N_SHARED), dtype=torch.float32, device=logits.device + ) + if return_packed: + packed = torch.empty((tokens, _TOPK), dtype=torch.int32, device=logits.device) + if tokens == 0: + return packed, shared_w + _jit_module().inkling_gate_topk_renorm_packed( + logits, + bias.contiguous(), + global_scale.contiguous(), + packed, + shared_w, + float(route_scale), + ) + return packed, shared_w + + routed_w = torch.empty((tokens, _TOPK), dtype=torch.float32, device=logits.device) + indices = torch.empty((tokens, _TOPK), dtype=torch.int64, device=logits.device) + if tokens == 0: + return routed_w, shared_w, indices + + _launch_inkling_gate_topk_renorm( + logits, + bias.contiguous(), + global_scale.contiguous(), + routed_w, + shared_w, + indices, + route_scale, + ) + return routed_w, shared_w, indices + + +def inkling_gate_topk_renorm_v2( + logits: torch.Tensor, + bias: torch.Tensor, + global_scale: torch.Tensor, + route_scale: float, + *, + return_packed: bool = False, + enable_pdl: bool = False, + warps_per_block: int = 0, +) -> tuple[torch.Tensor | None, torch.Tensor | None, torch.Tensor, torch.Tensor | None]: + """v2 gate kernel; same math as v1 but int32 indices and optional PDL. + + Returns ``(routed_w, indices, shared_w, packed)`` where the unused half is + ``None`` depending on ``return_packed`` -- mirroring the triton + ``sigmoid_gate_topk_renorm`` contract. ``warps_per_block`` in + ``{0 (auto), 1, 2, 4, 8}`` selects the launch shape. + + Requires 32B-aligned logits rows: the production ``[tokens, 264]``-padded + GEMM output sliced to ``[:, :258]`` qualifies. + """ + _check_gate_inputs(logits, bias, global_scale) + assert logits.stride(0) % 8 == 0, f"rows must be 32B-aligned: {logits.stride()=}" + + tokens = logits.shape[0] + shared_w = torch.empty( + (tokens, _N_SHARED), dtype=torch.float32, device=logits.device + ) + if return_packed: + packed = torch.empty((tokens, _TOPK), dtype=torch.int32, device=logits.device) + if tokens > 0: + _jit_module().inkling_gate_topk_renorm_v2_packed( + logits, + bias.contiguous(), + global_scale.contiguous(), + packed, + shared_w, + float(route_scale), + bool(enable_pdl), + int(warps_per_block), + ) + return None, None, shared_w, packed + + routed_w = torch.empty((tokens, _TOPK), dtype=torch.float32, device=logits.device) + indices = torch.empty((tokens, _TOPK), dtype=torch.int32, device=logits.device) + if tokens > 0: + _jit_module().inkling_gate_topk_renorm_v2( + logits, + bias.contiguous(), + global_scale.contiguous(), + routed_w, + shared_w, + indices, + float(route_scale), + bool(enable_pdl), + int(warps_per_block), + ) + return routed_w, indices, shared_w, None + + +def _check_gemv_inputs(x: torch.Tensor, weight: torch.Tensor) -> None: + assert x.is_cuda and x.dtype == torch.bfloat16 and x.dim() == 2 + assert x.shape[1] == _HIDDEN and x.stride(1) == 1 and x.stride(0) == _HIDDEN + assert weight.is_cuda and weight.dtype == torch.bfloat16 and weight.dim() == 2 + assert weight.shape[0] >= 258 and weight.shape[1] == _HIDDEN + assert weight.stride(1) == 1 and weight.stride(0) == _HIDDEN + + +def inkling_gate_gemv( + x: torch.Tensor, + weight: torch.Tensor, + *, + enable_pdl: bool = False, + experts_per_block: int = 0, +) -> torch.Tensor: + """Gate linear as an expert-per-block GEMV: returns fp32 logits [tokens, 258]. + + Drop-in for ``inkling_fused_gate_linear_with_fp32_out`` (the returned view + shares the same padded [tokens, 264] layout). Meant for small token counts + where the PDL split pair (this + v2 gate) beats cublas + gate. + """ + _check_gemv_inputs(x, weight) + tokens = x.shape[0] + logits = torch.empty((tokens, _LOGITS_PAD), dtype=torch.float32, device=x.device) + if tokens > 0: + _jit_module().inkling_gate_gemv( + x, weight, logits, bool(enable_pdl), int(experts_per_block) + ) + return logits[:, :258] + + +# Per-device (workspace [64, 264] fp32, ticket int32[1]) reused by every fused +# call. The kernel resets the ticket to zero on completion, so the buffers are +# CUDA-graph replay-safe; allocate them eagerly (warmup) before graph capture. +_fused_scratch: dict[int, tuple[torch.Tensor, torch.Tensor]] = {} + + +def _get_fused_scratch(device: torch.device) -> tuple[torch.Tensor, torch.Tensor]: + key = device.index if device.index is not None else torch.cuda.current_device() + scratch = _fused_scratch.get(key) + if scratch is None: + # Allocating inside CUDA graph capture would place the persistent + # buffers in the capture pool, where other graphs' replays can reuse + # (clobber) them. Call ensure_gate_gemv_fused_scratch() eagerly first + # (InklingGate.__init__ does). + assert ( + not torch.cuda.is_current_stream_capturing() + ), "fused gate scratch must be allocated before CUDA graph capture" + workspace = torch.empty( + (_FUSED_MAX_TOKENS, _LOGITS_PAD), dtype=torch.float32, device=device + ) + ticket = torch.zeros((1,), dtype=torch.int32, device=device) + scratch = (workspace, ticket) + _fused_scratch[key] = scratch + return scratch + + +def ensure_gate_gemv_fused_scratch(device: torch.device) -> None: + """Eagerly allocate the fused-gate workspace/ticket (call at model init, + before any CUDA graph capture).""" + _get_fused_scratch(device) + + +def inkling_gate_gemv_fused( + x: torch.Tensor, + weight: torch.Tensor, + bias: torch.Tensor, + global_scale: torch.Tensor, + route_scale: float, + *, + return_packed: bool = False, + enable_pdl: bool = False, + experts_per_block: int = 0, +) -> tuple[torch.Tensor | None, torch.Tensor | None, torch.Tensor, torch.Tensor | None]: + """Fully fused Inkling gate: GEMV + sigmoid+bias top-6 + renorm, one launch. + + ``x`` is ``[tokens, 6144]`` bf16 (tokens <= 64), ``weight`` the padded + ``[264, 6144]`` bf16 gate weight. Output contract matches + ``sigmoid_gate_topk_renorm``: ``(routed_w, indices, shared_w, packed)``. + """ + _check_gemv_inputs(x, weight) + tokens = x.shape[0] + assert tokens <= _FUSED_MAX_TOKENS, f"fused gate supports <= 64 tokens: {tokens=}" + assert bias.is_cuda and bias.dtype == torch.float32 and bias.shape == (256,) + assert global_scale.is_cuda and global_scale.dtype == torch.float32 + + workspace, ticket = _get_fused_scratch(x.device) + shared_w = torch.empty((tokens, _N_SHARED), dtype=torch.float32, device=x.device) + if return_packed: + packed = torch.empty((tokens, _TOPK), dtype=torch.int32, device=x.device) + if tokens > 0: + _jit_module().inkling_gate_gemv_fused_packed( + x, + weight, + bias.contiguous(), + global_scale.contiguous(), + workspace, + ticket, + packed, + shared_w, + float(route_scale), + bool(enable_pdl), + int(experts_per_block), + ) + return None, None, shared_w, packed + + routed_w = torch.empty((tokens, _TOPK), dtype=torch.float32, device=x.device) + indices = torch.empty((tokens, _TOPK), dtype=torch.int32, device=x.device) + if tokens > 0: + _jit_module().inkling_gate_gemv_fused( + x, + weight, + bias.contiguous(), + global_scale.contiguous(), + workspace, + ticket, + routed_w, + shared_w, + indices, + float(route_scale), + bool(enable_pdl), + int(experts_per_block), + ) + return routed_w, indices, shared_w, None diff --git a/python/sglang/jit_kernel/inkling_rel_proj.py b/python/sglang/jit_kernel/inkling_rel_proj.py new file mode 100644 index 000000000..ebefd9cfd --- /dev/null +++ b/python/sglang/jit_kernel/inkling_rel_proj.py @@ -0,0 +1,53 @@ +"""CUDA-JIT latency-lean rel_logits projection for SMALL token counts, with +the optional log-scaling tau prescale folded in registers. See +csrc/tml/inkling_rel_proj.cuh; cuBLAS keeps everything above the measured +small-t band (an earlier bandwidth-oriented custom kernel lost to it at every +size).""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import torch + +from sglang.jit_kernel.utils import ( + cache_once, + empty_sentinel, + is_arch_support_pdl, + load_jit, + make_cpp_args, +) + +if TYPE_CHECKING: + from tvm_ffi.module import Module + + +@cache_once +def _jit_rel_proj_module(d_rel: int, use_pdl: bool) -> Module: + args = make_cpp_args(d_rel, use_pdl) + return load_jit( + "inkling_rel_proj", + *args, + cuda_files=["inkling/inkling_rel_proj.cuh"], + cuda_wrappers=[("run", f"rel_proj_small_t<{args}>")], + ) + + +def rel_proj_small_t( + r: torch.Tensor, + proj: torch.Tensor, + tau: torch.Tensor | None = None, + out: torch.Tensor | None = None, +) -> torch.Tensor: + """``r``: [t, h, d_rel] bf16, token rows possibly strided ((h*d_rel)- + contiguous inner, 16B-aligned); ``proj``: [d_rel, e] bf16 contiguous; + ``tau``: optional fp32 [t] prescale (rounds r*tau to bf16 before the dot, + the shipped prescale semantics). Returns contiguous [t, h, e].""" + if out is None: + out = torch.empty( + (r.shape[0], r.shape[1], proj.shape[1]), dtype=r.dtype, device=r.device + ) + module = _jit_rel_proj_module(r.shape[2], is_arch_support_pdl()) + sh = tau if tau is not None else empty_sentinel(r.device, torch.float32) + module.run(r, sh, proj, out) + return out diff --git a/python/sglang/jit_kernel/inkling_row_scale.py b/python/sglang/jit_kernel/inkling_row_scale.py new file mode 100644 index 000000000..4b831410a --- /dev/null +++ b/python/sglang/jit_kernel/inkling_row_scale.py @@ -0,0 +1,59 @@ +"""CUDA-JIT vectorized per-row scale (the apply_log_scaling_tau contract): +``out[row, :] = bf16(fp32(x[row, :]) * tau[row])``. See +csrc/tml/inkling_row_scale.cuh; the scalar triton kernel remains the fallback +for non-bf16 / unaligned inputs.""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import torch + +from sglang.jit_kernel.utils import ( + cache_once, + is_arch_support_pdl, + load_jit, + make_cpp_args, +) + +if TYPE_CHECKING: + from tvm_ffi.module import Module + + +@cache_once +def _jit_row_scale_module(use_pdl: bool) -> Module: + args = make_cpp_args(use_pdl) + return load_jit( + "inkling_row_scale", + *args, + cuda_files=["inkling/inkling_row_scale.cuh"], + cuda_wrappers=[ + ("run", f"row_scale<{args}>"), + ("run_compact", f"row_compact<{args}>"), + ], + ) + + +def row_scale_bf16( + x: torch.Tensor, tau: torch.Tensor, out: torch.Tensor | None = None +) -> torch.Tensor: + """``x``: [rows, inner] bf16, possibly row-strided (inner contiguous, + inner % 8 == 0, 16B-aligned rows); ``tau``: fp32 [rows]. Returns a fresh + contiguous scaled tensor (bit-identical to the triton kernel's output).""" + if out is None: + out = torch.empty(x.shape, dtype=x.dtype, device=x.device) + module = _jit_row_scale_module(is_arch_support_pdl()) + module.run(x, tau, out) + return out + + +def row_compact_bf16(x: torch.Tensor, out: torch.Tensor | None = None) -> torch.Tensor: + """Contiguous copy of row-strided ``x`` ([rows, inner] bf16, inner + contiguous, inner % 8 == 0, 16B-aligned rows) -- the tau-less flavor of + ``row_scale_bf16``. Beats the TensorIterator strided copy that einsum's + reshape would otherwise run on such inputs.""" + if out is None: + out = torch.empty(x.shape, dtype=x.dtype, device=x.device) + module = _jit_row_scale_module(is_arch_support_pdl()) + module.run_compact(x, out) + return out diff --git a/python/sglang/jit_kernel/inkling_sconv.py b/python/sglang/jit_kernel/inkling_sconv.py new file mode 100644 index 000000000..a3121dcea --- /dev/null +++ b/python/sglang/jit_kernel/inkling_sconv.py @@ -0,0 +1,239 @@ +"""CUDA-JIT implementations of the Inkling short-convolution kernels. + +Their signatures match the Triton entrypoints so model layers can select either +backend without adapting arguments. +""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import torch + +from sglang.jit_kernel.utils import cache_once, load_jit, make_cpp_args + +if TYPE_CHECKING: + from tvm_ffi.module import Module + + +@cache_once +def _jit_causal_conv1d_module( + w: int, + use_silu: bool, + use_residual: bool, + is_decode: bool, + dtype: torch.dtype, +) -> Module: + args = make_cpp_args(w, use_silu, use_residual, is_decode, dtype) + return load_jit( + "inkling_causal_conv1d", + *args, + cuda_files=["inkling/causal_conv1d.cuh"], + cuda_wrappers=[("causal_conv1d", f"CausalConv1dKernel<{args}>::run")], + ) + + +def causal_conv1d( + x: torch.Tensor, + weight: torch.Tensor, + sconv_cache: torch.Tensor, + cache_mask: torch.Tensor, + safe_idx: torch.Tensor, + cu: torch.Tensor, + si: torch.Tensor, + activation: str | None = None, + use_residual: bool = True, + is_decode: bool = False, +) -> torch.Tensor: + """Apply depthwise causal convolution to a packed token stream. + + Depthwise causal conv1d over a packed ``[T, D]`` token stream, with the W-1 + prefix taps gathered directly from ``sconv_cache`` (no intermediate prefix + tensor). Metadata args (cache_mask, safe_idx, cu, si) are precomputed once + per forward pass and reused across layers. + """ + if activation == "swish": + activation = "silu" + + T = x.shape[0] + if T == 0: + return torch.empty_like(x) + + D = x.shape[1] + W = weight.shape[1] + use_silu = activation in ("silu", "swish") + + # Contiguous [T, D] output (strides (D, 1)) regardless of x's layout. + y = torch.empty(T, D, dtype=x.dtype, device=x.device) + + module = _jit_causal_conv1d_module(W, use_silu, use_residual, is_decode, x.dtype) + module.causal_conv1d(x, sconv_cache, safe_idx, cache_mask, weight, cu, si, y) + return y + + +@cache_once +def _jit_update_sconv_cache_module(w1: int, dtype: torch.dtype) -> Module: + args = make_cpp_args(w1, dtype) + return load_jit( + "inkling_update_sconv_cache", + *args, + cuda_files=["inkling/update_sconv_cache.cuh"], + cuda_wrappers=[("update_sconv_cache", f"UpdateSconvCacheKernel<{args}>::run")], + ) + + +def update_sconv_cache( + x: torch.Tensor, + sconv_cache: torch.Tensor, + cache_indices: torch.Tensor, + has_initial_state: torch.Tensor, + query_start_loc: torch.Tensor, +) -> None: + """Update each sequence's convolution cache in place. + + Shift-updates each sequence's conv state to the last W-1 entries of + ``[old_state(gated) ++ x[start:end]]``; PAD / empty lanes are untouched. Pure + bit-exact select/copy. + """ + W1 = sconv_cache.shape[1] + module = _jit_update_sconv_cache_module(W1, x.dtype) + module.update_sconv_cache( + x, sconv_cache, cache_indices, has_initial_state, query_start_loc + ) + + +@cache_once +def _jit_gather_scatter_sconv_module(w1: int, dtype: torch.dtype) -> Module: + args = make_cpp_args(w1, dtype) + return load_jit( + "inkling_gather_scatter_sconv", + *args, + cuda_files=["inkling/gather_scatter_sconv.cuh"], + cuda_wrappers=[("gather_scatter", f"GatherScatterSconvKernel<{args}>::run")], + ) + + +def fused_gather_scatter_to_sconv_cache( + hidden_states: torch.Tensor, + sconv_cache: torch.Tensor, + track_conv_indices: torch.Tensor, + mask: torch.Tensor, + dst_indices: torch.Tensor, +) -> None: + """Gather selected hidden-state rows into the convolution cache. + + Scatters masked rows ``hidden_states[track_conv_indices[b, w]]`` into + ``sconv_cache[dst_indices[b], w]`` in-place; masked-out lanes untouched. + Bit-exact copy. (track int32, dst int64, per the model contract.) + """ + W1 = sconv_cache.shape[1] + module = _jit_gather_scatter_sconv_module(W1, hidden_states.dtype) + module.gather_scatter( + hidden_states, sconv_cache, track_conv_indices, mask, dst_indices + ) + + +@cache_once +def _jit_fused_decode_update_module( + w: int, use_silu: bool, use_residual: bool, do_track: bool, dtype: torch.dtype +) -> Module: + args = make_cpp_args(w, use_silu, use_residual, do_track, dtype) + return load_jit( + "inkling_fused_decode_update", + *args, + cuda_files=["inkling/fused_decode_update.cuh"], + cuda_wrappers=[ + ("fused_decode_update", f"FusedDecodeUpdateKernel<{args}>::run") + ], + ) + + +def fused_causal_conv1d_update_decode( + x: torch.Tensor, + weight: torch.Tensor, + sconv_cache: torch.Tensor, + cache_indices: torch.Tensor, + cache_mask: torch.Tensor, + activation: str | None = None, + use_residual: bool = True, + track_mask: torch.Tensor | None = None, + track_indices: torch.Tensor | None = None, +) -> torch.Tensor: + """Apply decode convolution and update its cache in one kernel. + + Decode conv (W-1 cached taps + current token) fused with the cache shift-update + (+ optional prefix-cache track-copy). Returns a contiguous ``[T, D]`` output. + """ + if activation == "swish": + activation = "silu" + T, D = x.shape + W = weight.shape[1] + use_silu = activation in ("silu", "swish") + do_track = track_mask is not None + + cm = cache_mask.reshape(-1) + y = torch.empty(T, D, dtype=x.dtype, device=x.device) + if do_track: + tm = track_mask.reshape(-1) + ti = track_indices + else: # dummy tensors satisfy the signature; DO_TRACK=false never reads them + tm = torch.empty(0, dtype=torch.bool, device=x.device) + ti = torch.empty(0, dtype=torch.int64, device=x.device) + + module = _jit_fused_decode_update_module( + W, use_silu, use_residual, do_track, x.dtype + ) + module.fused_decode_update(x, sconv_cache, cache_indices, cm, weight, y, tm, ti) + return y + + +@cache_once +def _jit_draft_extend_sconv_module( + w1: int, do_track: bool, dtype: torch.dtype +) -> Module: + args = make_cpp_args(w1, do_track, dtype) + return load_jit( + "inkling_draft_extend_sconv", + *args, + cuda_files=["inkling/draft_extend_sconv.cuh"], + cuda_wrappers=[("draft_extend", f"DraftExtendSconvKernel<{args}>::run")], + ) + + +def fused_draft_extend_sconv_cache( + hidden_states: torch.Tensor, + sconv_cache: torch.Tensor, + cache_indices: torch.Tensor, + num_accepted_tokens: torch.Tensor, + draft_token_num: int, + do_tracking: bool = False, + crossed: torch.Tensor | None = None, + track_step: torch.Tensor | None = None, + mamba_track_indices: torch.Tensor | None = None, +) -> None: + """Update draft-extend convolution state in place. + + Selects each sequence's length-(W-1) conv-state window from the virtual + ``[sconv_cache[ci] ++ hidden[b]]`` stream at ``num_accepted_tokens[b]`` (and, if + tracking, at ``track_step[b]`` into ``mamba_track_indices[b]`` where crossed). + Bit-exact copy. + """ + W1 = sconv_cache.shape[1] + module = _jit_draft_extend_sconv_module(W1, do_tracking, hidden_states.dtype) + dev = hidden_states.device + if do_tracking: + cr, ts, mti = crossed, track_step, mamba_track_indices + else: # dummies; DO_TRACK=false never reads them + cr = torch.empty(0, dtype=torch.bool, device=dev) + ts = torch.empty(0, dtype=torch.int32, device=dev) + mti = torch.empty(0, dtype=torch.int64, device=dev) + module.draft_extend( + hidden_states, + sconv_cache, + cache_indices, + num_accepted_tokens, + int(draft_token_num), + cr, + ts, + mti, + ) diff --git a/python/sglang/jit_kernel/tests/test_moe_preprocess.py b/python/sglang/jit_kernel/tests/test_moe_preprocess.py new file mode 100644 index 000000000..121811e82 --- /dev/null +++ b/python/sglang/jit_kernel/tests/test_moe_preprocess.py @@ -0,0 +1,93 @@ +"""fused_moe_preprocess must be bit-identical to the torch.sort-based path, +and the grouped GEMM must produce identical results under both block_size_m +configs (the block schedule and kernel config are chosen together). +""" + +import pytest +import torch + +from sglang.srt.layers.moe.moe_runner.triton_utils.inkling_moe import ( + SMALL_M_BLOCK_SIZE_M, + compute_grouped_gemm_metadata, + fused_moe_preprocess, + get_src2dst, + grouped_gemm_triton, +) + +requires_cuda = pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA only") + +E = 256 +TOPK = 6 + + +def _reference(topk_ids_flat: torch.Tensor): + reorder_topk_ids, reorder_ids = torch.sort( + topk_ids_flat.to(torch.int16), stable=True + ) + src2dst = get_src2dst(reorder_ids) + meta = compute_grouped_gemm_metadata( + reorder_topk_ids, E, block_size_m=SMALL_M_BLOCK_SIZE_M + ) + return (src2dst, *meta, reorder_topk_ids) + + +def _ids(tokens: int, seed: int, skew: bool = False) -> torch.Tensor: + torch.manual_seed(seed) + if skew: # all tokens on few experts (stresses multi-block experts) + return torch.randint(0, 3, (tokens * TOPK,), dtype=torch.int32, device="cuda") + return ( + torch.stack([torch.randperm(E, device="cuda")[:TOPK] for _ in range(tokens)]) + .view(-1) + .to(torch.int32) + ) + + +@requires_cuda +@pytest.mark.parametrize("tokens", [1, 2, 7, 32, 64, 170, 341]) # n = 6*T <= 2048 +@pytest.mark.parametrize("skew", [False, True]) +def test_matches_sort_path(tokens: int, skew: bool): + ids = _ids(tokens, seed=tokens, skew=skew) + ref = _reference(ids) + got = fused_moe_preprocess(ids, E) + names = [ + "src2dst", + "num_tokens_per_expert", + "expert_token_offs", + "expert_block_offs", + "expert_block_schedule", + "reorder_topk_ids", + ] + for tag, g, r in zip(names, got, ref): + assert g.shape == r.shape, (tag, g.shape, r.shape) + assert torch.equal(g.long(), r.long()), ( + tag, + g[: min(16, g.numel())], + r[: min(16, r.numel())], + ) + + +@requires_cuda +@pytest.mark.parametrize("tokens", [1, 16, 64]) +def test_grouped_gemm_small_config_matches(tokens: int): + """GEMM output must be identical whichever (block_size_m, config) runs.""" + torch.manual_seed(tokens) + ids = _ids(tokens, seed=tokens) + m, k, n = tokens * TOPK, 768, 1024 + a = (torch.randn(m, k, device="cuda") * 0.05).to(torch.bfloat16) + b = (torch.randn(E, n, k, device="cuda") * 0.02).to(torch.bfloat16) + + sorted_ids, _ = torch.sort(ids.to(torch.int16), stable=True) + meta128 = compute_grouped_gemm_metadata(sorted_ids, E) + out128 = grouped_gemm_triton(a, b, E, *meta128) + + pre = fused_moe_preprocess(ids, E) + out16 = grouped_gemm_triton(a, b, E, *pre[1:5], block_size_m=SMALL_M_BLOCK_SIZE_M) + # both are fp32-accumulated bf16 tensor-core dots; BLOCK_K differs so + # accumulation grouping may differ by a few ulp + torch.testing.assert_close(out16.float(), out128.float(), atol=1e-3, rtol=1e-3) + + +if __name__ == "__main__": + import sys + + sys.exit(pytest.main([__file__, "-v", "-x"])) diff --git a/python/sglang/jit_kernel/tests/test_sconv_decode_metadata.py b/python/sglang/jit_kernel/tests/test_sconv_decode_metadata.py new file mode 100644 index 000000000..6ac9f4c14 --- /dev/null +++ b/python/sglang/jit_kernel/tests/test_sconv_decode_metadata.py @@ -0,0 +1,70 @@ +"""fused_decode_sconv_metadata must be bit-identical to the unfused prep. + +The unfused reference is the exact op sequence `_prepare_decode_sconv_metadata` +used to launch: two arange calls + ones + precompute_helion_decode_metadata +(!= PAD, &, clamp, long, arange x2). +""" + +import pytest +import torch + +from sglang.srt.models.inkling_common.kernels.sconv import ( + PAD_SLOT_ID, + fused_decode_sconv_metadata, + precompute_helion_decode_metadata, +) + +requires_cuda = pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA only") + +# cross the BLOCK=1024 grid boundary and hit odd sizes +BATCH_SIZES = [1, 2, 3, 17, 64, 160, 257, 1023, 1024, 1025] + + +def _reference(B: int, cache_indices: torch.Tensor): + device = cache_indices.device + query_start_loc = torch.arange(B + 1, dtype=torch.int32, device=device) + has_initial_state = torch.ones(B, dtype=torch.bool, device=device) + precomputed = precompute_helion_decode_metadata( + B=B, W=4, cache_indices=cache_indices, has_initial_state=has_initial_state + ) + return query_start_loc, has_initial_state, precomputed + + +@requires_cuda +@pytest.mark.parametrize("b", BATCH_SIZES) +@pytest.mark.parametrize("idx_dtype", [torch.int32, torch.int64]) +def test_matches_unfused(b: int, idx_dtype: torch.dtype): + torch.manual_seed(b) + cache_indices = torch.randint(0, 4096, (b,), dtype=idx_dtype, device="cuda") + # sprinkle PAD slots (cudagraph padding lanes) + pad = torch.rand(b, device="cuda") < 0.25 + cache_indices[pad] = PAD_SLOT_ID + + ref_qsl, ref_his, ref_meta = _reference(b, cache_indices) + qsl, his, meta = fused_decode_sconv_metadata(B=b, cache_indices=cache_indices) + + for tag, got, ref in ( + ("query_start_loc", qsl, ref_qsl), + ("has_initial_state", his, ref_his), + ("cache_mask", meta["cache_mask"], ref_meta["cache_mask"]), + ("safe_idx", meta["safe_idx"], ref_meta["safe_idx"]), + ("cu", meta["cu"], ref_meta["cu"]), + ("si", meta["si"], ref_meta["si"]), + ): + assert got.dtype == ref.dtype, (tag, got.dtype, ref.dtype) + assert got.shape == ref.shape, (tag, got.shape, ref.shape) + assert torch.equal(got, ref), tag + + +@requires_cuda +def test_all_pad(): + cache_indices = torch.full((8,), PAD_SLOT_ID, dtype=torch.int32, device="cuda") + _, _, meta = fused_decode_sconv_metadata(B=8, cache_indices=cache_indices) + assert not meta["cache_mask"].any() + assert (meta["safe_idx"] == 0).all() + + +if __name__ == "__main__": + import sys + + sys.exit(pytest.main([__file__, "-v", "-x"])) diff --git a/python/sglang/jit_kernel/tests/test_sconv_extend_metadata.py b/python/sglang/jit_kernel/tests/test_sconv_extend_metadata.py new file mode 100644 index 000000000..cecb81ff5 --- /dev/null +++ b/python/sglang/jit_kernel/tests/test_sconv_extend_metadata.py @@ -0,0 +1,174 @@ +"""fused_extend_sconv_metadata must be bit-identical to the unfused prep. + +The unfused reference is the exact op sequence _prepare_extend_common_metadata ++ precompute_helion_extend_metadata used to launch: zeros + cumsum + slice-copy +(or arange + ones for verify) + the has_initial_state compare, then != PAD, &, +clamp, long, to(int64), arange, searchsorted, clamp, to(int32). +""" + +import pytest +import torch + +from sglang.srt.models.inkling_common.kernels.sconv import ( + HIS_ONES, + HIS_PREFIX, + HIS_SEQ_MINUS_EXT, + HIS_ZEROS, + PAD_SLOT_ID, + fused_extend_sconv_metadata, + precompute_helion_extend_metadata, +) + +requires_cuda = pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA only") + +# cross si tiles (BLOCK_T=256) and the single-tile B bound +BATCH_SIZES = [1, 2, 7, 64, 257, 1023] + + +def _ref_extend(B, extend_seq_lens, his_mode, his_src, cache_indices, T): + device = cache_indices.device + query_start_loc = torch.zeros(B + 1, dtype=torch.int32, device=device) + query_start_loc[1:] = extend_seq_lens.cumsum(dim=0) + if his_mode == HIS_ZEROS: + has_initial_state = torch.zeros(B, dtype=torch.bool, device=device) + elif his_mode == HIS_PREFIX: + has_initial_state = his_src > 0 + else: # HIS_SEQ_MINUS_EXT + has_initial_state = (his_src[:B] - extend_seq_lens) > 0 + meta = precompute_helion_extend_metadata( + B=B, + T=T, + W=4, + cache_indices=cache_indices, + has_initial_state=has_initial_state, + query_start_loc=query_start_loc, + ) + return query_start_loc, has_initial_state, meta + + +def _ref_verify(B, draft_token_num, cache_indices): + device = cache_indices.device + query_start_loc = torch.arange( + 0, (B + 1) * draft_token_num, draft_token_num, dtype=torch.int32, device=device + ) + has_initial_state = torch.ones(B, dtype=torch.bool, device=device) + meta = precompute_helion_extend_metadata( + B=B, + T=B * draft_token_num, + W=4, + cache_indices=cache_indices, + has_initial_state=has_initial_state, + query_start_loc=query_start_loc, + ) + return query_start_loc, has_initial_state, meta + + +def _assert_equal(got, ref): + for tag, g, r in ( + ("query_start_loc", got[0], ref[0]), + ("has_initial_state", got[1], ref[1]), + ("cache_mask", got[2]["cache_mask"], ref[2]["cache_mask"]), + ("safe_idx", got[2]["safe_idx"], ref[2]["safe_idx"]), + ("cu", got[2]["cu"], ref[2]["cu"]), + ("si", got[2]["si"], ref[2]["si"]), + ): + assert g.dtype == r.dtype, (tag, g.dtype, r.dtype) + assert g.shape == r.shape, (tag, g.shape, r.shape) + assert torch.equal(g, r), tag + + +def _cache_indices(b, idx_dtype): + ci = torch.randint(0, 4096, (b,), dtype=idx_dtype, device="cuda") + pad = torch.rand(b, device="cuda") < 0.25 + ci[pad] = PAD_SLOT_ID + return ci + + +@requires_cuda +@pytest.mark.parametrize("b", BATCH_SIZES) +@pytest.mark.parametrize("his_mode", [HIS_ZEROS, HIS_PREFIX, HIS_SEQ_MINUS_EXT]) +@pytest.mark.parametrize("lens_dtype", [torch.int32, torch.int64]) +def test_extend_matches_unfused(b, his_mode, lens_dtype): + torch.manual_seed(b * 10 + his_mode) + lens = torch.randint(0, 33, (b,), dtype=lens_dtype, device="cuda") + lens[torch.rand(b, device="cuda") < 0.2] = 0 # zero-length sequences + T = int(lens.sum().item()) + cache_indices = _cache_indices(b, torch.int32) + if his_mode == HIS_PREFIX: + his_src = torch.randint(0, 3, (b,), dtype=lens_dtype, device="cuda") + elif his_mode == HIS_SEQ_MINUS_EXT: + his_src = lens + torch.randint(0, 2, (b,), dtype=lens_dtype, device="cuda") + else: + his_src = None + + ref = _ref_extend(b, lens, his_mode, his_src, cache_indices, T) + got = fused_extend_sconv_metadata( + B=b, + T=T, + cache_indices=cache_indices, + his_mode=his_mode, + extend_seq_lens=lens, + his_src=his_src, + ) + assert got is not None + _assert_equal(got, ref) + + +@requires_cuda +@pytest.mark.parametrize("b", BATCH_SIZES) +@pytest.mark.parametrize("draft_token_num", [1, 9]) +def test_verify_matches_unfused(b, draft_token_num): + torch.manual_seed(b) + cache_indices = _cache_indices(b, torch.int64) + ref = _ref_verify(b, draft_token_num, cache_indices) + got = fused_extend_sconv_metadata( + B=b, + T=b * draft_token_num, + cache_indices=cache_indices, + his_mode=HIS_ONES, + draft_token_num=draft_token_num, + ) + assert got is not None + _assert_equal(got, ref) + + +@requires_cuda +def test_cu_not_spanning_T(): + """Dummy capture sequences: cu stops short of T; trailing si rows clamp to + B-1 exactly like the reference's searchsorted + clamp.""" + b = 5 + lens = torch.tensor([3, 0, 4, 0, 2], dtype=torch.int64, device="cuda") + T = int(lens.sum().item()) + 17 + cache_indices = _cache_indices(b, torch.int32) + seq_lens = lens + 1 + ref = _ref_extend(b, lens, HIS_SEQ_MINUS_EXT, seq_lens, cache_indices, T) + got = fused_extend_sconv_metadata( + B=b, + T=T, + cache_indices=cache_indices, + his_mode=HIS_SEQ_MINUS_EXT, + extend_seq_lens=lens, + his_src=seq_lens, + ) + assert got is not None + _assert_equal(got, ref) + + +@requires_cuda +def test_fallback_past_batch_bound(): + b = 1024 # > _FUSED_EXTEND_MAX_B + lens = torch.ones(b, dtype=torch.int64, device="cuda") + got = fused_extend_sconv_metadata( + B=b, + T=b, + cache_indices=_cache_indices(b, torch.int32), + his_mode=HIS_ZEROS, + extend_seq_lens=lens, + ) + assert got is None + + +if __name__ == "__main__": + import sys + + sys.exit(pytest.main([__file__, "-v", "-x"])) diff --git a/python/sglang/jit_kernel/trtllm_lora_temp/data/csrc/fused_activation_quant.cuh b/python/sglang/jit_kernel/trtllm_lora_temp/data/csrc/fused_activation_quant.cuh index 911079923..f6b75710e 100644 --- a/python/sglang/jit_kernel/trtllm_lora_temp/data/csrc/fused_activation_quant.cuh +++ b/python/sglang/jit_kernel/trtllm_lora_temp/data/csrc/fused_activation_quant.cuh @@ -187,42 +187,58 @@ inline void launchFusedActivationQuant( tensorrt_llm::QuantizationSFLayout sfLayout, bool disableFp4FastMath, cudaStream_t stream) { - constexpr uint32_t BLOCK_SIZE = 128; // == innerHalf/16 for inter=2048 (one SF block per thread) - dim3 const grid(m), block(BLOCK_SIZE); - - auto launch = [&](auto layoutTag, auto fastMathTag) { - fusedActivationQuantKernel - <<>>( - m, - innerHalf, - innerDim, - gateUp, - loraDelta, - loraInputOut, - expandedIdxToPermutedIdx, - globalScaleInv, - weightOutput, - scaleOutput, - perTokenScaleOutput); - }; - auto withFastMath = [&](auto layoutTag) { - if (disableFp4FastMath) { - launch(layoutTag, std::integral_constant{}); + // One SF block per thread, no stride loop: BLOCK_SIZE must cover innerHalf/16 (a fixed + // 128 left cols [2048,inter) unwritten at Inkling EP8's inter=3072 -> NaN from the down GEMM). + uint32_t const numVecs = static_cast(innerHalf) / 16; + auto dispatchBlock = [&](auto blockTag) { + constexpr uint32_t BLOCK_SIZE = decltype(blockTag)::value; + dim3 const grid(m), block(BLOCK_SIZE); + auto launch = [&](auto layoutTag, auto fastMathTag) { + fusedActivationQuantKernel + <<>>( + m, + innerHalf, + innerDim, + gateUp, + loraDelta, + loraInputOut, + expandedIdxToPermutedIdx, + globalScaleInv, + weightOutput, + scaleOutput, + perTokenScaleOutput); + }; + auto withFastMath = [&](auto layoutTag) { + if (disableFp4FastMath) { + launch(layoutTag, std::integral_constant{}); + } else { + launch(layoutTag, std::integral_constant{}); + } + }; + if (sfLayout == tensorrt_llm::QuantizationSFLayout::SWIZZLED_128x4) { + withFastMath( + std::integral_constant< + tensorrt_llm::QuantizationSFLayout, + tensorrt_llm::QuantizationSFLayout::SWIZZLED_128x4>{}); + } else if (sfLayout == tensorrt_llm::QuantizationSFLayout::LINEAR) { + withFastMath( + std::integral_constant{}); } else { - launch(layoutTag, std::integral_constant{}); + withFastMath( + std::integral_constant< + tensorrt_llm::QuantizationSFLayout, + tensorrt_llm::QuantizationSFLayout::SWIZZLED_8x4>{}); } }; - if (sfLayout == tensorrt_llm::QuantizationSFLayout::SWIZZLED_128x4) { - withFastMath( - std::integral_constant< - tensorrt_llm::QuantizationSFLayout, - tensorrt_llm::QuantizationSFLayout::SWIZZLED_128x4>{}); - } else if (sfLayout == tensorrt_llm::QuantizationSFLayout::LINEAR) { - withFastMath( - std::integral_constant{}); + if (numVecs <= 128) { + dispatchBlock(std::integral_constant{}); + } else if (numVecs <= 256) { + dispatchBlock(std::integral_constant{}); + } else if (numVecs <= 512) { + dispatchBlock(std::integral_constant{}); } else { - withFastMath( - std::integral_constant{}); + // Callers guard on numVecs <= 512 and fall back to the unfused chain. + dispatchBlock(std::integral_constant{}); } } diff --git a/python/sglang/jit_kernel/trtllm_lora_temp/data/csrc/trtllm_fused_moe_kernel_launcher.cu b/python/sglang/jit_kernel/trtllm_lora_temp/data/csrc/trtllm_fused_moe_kernel_launcher.cu index ee75dd4f2..8ed7c7ac9 100644 --- a/python/sglang/jit_kernel/trtllm_lora_temp/data/csrc/trtllm_fused_moe_kernel_launcher.cu +++ b/python/sglang/jit_kernel/trtllm_lora_temp/data/csrc/trtllm_fused_moe_kernel_launcher.cu @@ -3116,9 +3116,9 @@ class FP4BlockScaleLoraLauncher { static int const fuseActQuant = envFlag("SGLANG_OPT_FUSED_MOE_ACTIVATION_QUANT_FUSE") ? 1 : 0; static int const actOptMode = envFlag("SGLANG_OPT_FUSED_MOE_ACTIVATION_VEC") ? 1 : 0; - if (fuseActQuant) { + if (fuseActQuant && inter / 16 <= 512) { // Fused: gate_up (interleaved) + lora_delta -> act_fp4/sf/per_token + activation_lora_input, - // without materializing activated_bf16. inter must be a multiple of 16 (always true here). + // without materializing activated_bf16. >512 SF vecs/row falls to the unfused chain below. flashinfer::sgl_fused_act_quant::launchFusedActivationQuant( num_tokens * top_k, inter, diff --git a/python/sglang/jit_kernel/trtllm_lora_temp/data/csrc/trtllm_fused_moe_runner.cu b/python/sglang/jit_kernel/trtllm_lora_temp/data/csrc/trtllm_fused_moe_runner.cu index e02e51d21..18260ff3c 100644 --- a/python/sglang/jit_kernel/trtllm_lora_temp/data/csrc/trtllm_fused_moe_runner.cu +++ b/python/sglang/jit_kernel/trtllm_lora_temp/data/csrc/trtllm_fused_moe_runner.cu @@ -533,6 +533,9 @@ void Runner::run( ptrCtaIdxXyToBatchIdx, ptrCtaIdxXyToMnLimit, ptrNumNonExitingCtas, +#if SGLANG_FLASHINFER_HAS_PERMUTED_BIAS_ROW_IDX + /* permutedIdxToBiasRowIdx */ nullptr, +#endif bmm1Workspace, stream, device, @@ -712,6 +715,9 @@ void Runner::run( ptrCtaIdxXyToBatchIdx, ptrCtaIdxXyToMnLimit, ptrNumNonExitingCtas, +#if SGLANG_FLASHINFER_HAS_PERMUTED_BIAS_ROW_IDX + /* permutedIdxToBiasRowIdx */ nullptr, +#endif bmm2Workspace, stream, device, diff --git a/python/sglang/jit_kernel/trtllm_lora_temp/jit.py b/python/sglang/jit_kernel/trtllm_lora_temp/jit.py index 4474d6449..8ef2c7ea6 100644 --- a/python/sglang/jit_kernel/trtllm_lora_temp/jit.py +++ b/python/sglang/jit_kernel/trtllm_lora_temp/jit.py @@ -24,6 +24,12 @@ def gen_sgl_trtllm_gen_fused_moe_sm100_module(): flashinfer_data_dir = Path(flashinfer.__file__).resolve().parent / "data" flashinfer_csrc_dir = flashinfer_data_dir / "csrc" flashinfer_include_dir = flashinfer_data_dir / "include" + kernel_runner_header = ( + flashinfer_include_dir / "flashinfer/trtllm/batched_gemm/KernelRunner.h" + ) + has_permuted_bias_row_idx = ( + "permutedIdxToBiasRowIdx" in kernel_runner_header.read_text() + ) include_path = f"{ArtifactPath.TRTLLM_GEN_BMM}/include" header_name = "flashinferMetaInfo" @@ -85,6 +91,7 @@ def gen_sgl_trtllm_gen_fused_moe_sm100_module(): "-DENABLE_FP8", "-DENABLE_FP4", "-DCUTLASS_ENABLE_GDC_FOR_SM100=1", + f"-DSGLANG_FLASHINFER_HAS_PERMUTED_BIAS_ROW_IDX={int(has_permuted_bias_row_idx)}", f'-DTLLM_GEN_GEMM_CUBIN_PATH=\\"{ArtifactPath.TRTLLM_GEN_BMM}\\"', ] + nvcc_flags, diff --git a/python/sglang/jit_kernel/trtllm_lora_temp/moe_lora_merged_align.py b/python/sglang/jit_kernel/trtllm_lora_temp/moe_lora_merged_align.py index dacfbd2fc..32b3fa8b5 100644 --- a/python/sglang/jit_kernel/trtllm_lora_temp/moe_lora_merged_align.py +++ b/python/sglang/jit_kernel/trtllm_lora_temp/moe_lora_merged_align.py @@ -23,15 +23,6 @@ def _jit_module(dtype: torch.dtype) -> Module: ) -def supports_merged_align(virtual_num_experts: int) -> bool: - """Commit-1 kernel only implements the (64, 1024] bucket-count branch. - - The bucket count is virtual_num_experts + 1 (the +1 sentinel bucket). Other - regimes (small-batch <=64, v2 >1024) keep the old path.""" - num_buckets = virtual_num_experts + 1 - return 64 < num_buckets <= 1024 - - def moe_lora_merged_align( topk_ids: torch.Tensor, token_lora_mapping: torch.Tensor, diff --git a/python/sglang/jit_kernel/utils/__init__.py b/python/sglang/jit_kernel/utils/__init__.py index c321dcdf6..452ee5441 100644 --- a/python/sglang/jit_kernel/utils/__init__.py +++ b/python/sglang/jit_kernel/utils/__init__.py @@ -7,6 +7,7 @@ from sglang.jit_kernel.utils.arch import ( ) from sglang.jit_kernel.utils.common import ( cache_once, + empty_sentinel, get_ci_test_range, is_hip_runtime, is_musa_runtime, @@ -16,6 +17,7 @@ from sglang.jit_kernel.utils.common import ( from sglang.jit_kernel.utils.compile import KERNEL_PATH, load_jit, make_cpp_args __all__ = [ + "empty_sentinel", "should_run_full_tests", "get_ci_test_range", "cache_once", diff --git a/python/sglang/jit_kernel/utils/common.py b/python/sglang/jit_kernel/utils/common.py index 63437b532..9d7f09f08 100644 --- a/python/sglang/jit_kernel/utils/common.py +++ b/python/sglang/jit_kernel/utils/common.py @@ -41,6 +41,15 @@ def cache_once(fn: F) -> F: return wrapper # type: ignore +@functools.lru_cache(maxsize=None) +def empty_sentinel(device: torch.device, dtype: torch.dtype) -> torch.Tensor: + """Cached 0-element tensor for optional-tensor FFI slots (the numel-0 + "not present" convention). Allocating a fresh empty per call costs + ~1.2us CPU on eager paths; the sentinel is never read, so one cached + instance per (device, dtype) is safe to share.""" + return torch.empty(0, dtype=dtype, device=device) + + @cache_once def is_hip_runtime() -> bool: return bool(torch.version.hip) diff --git a/python/sglang/kernels/ops/attention/decode_attention.py b/python/sglang/kernels/ops/attention/decode_attention.py index 0a30123e5..6ab7cd272 100644 --- a/python/sglang/kernels/ops/attention/decode_attention.py +++ b/python/sglang/kernels/ops/attention/decode_attention.py @@ -25,6 +25,7 @@ import logging import triton import triton.language as tl +from sglang.kernels.ops.attention.score_mod import unpack_aux_tensors from sglang.srt.utils import is_hip _is_hip = is_hip() @@ -129,6 +130,11 @@ def _fwd_kernel_stage1( Lv: tl.constexpr, xai_temperature_len: tl.constexpr, PAGE_SIZE: tl.constexpr, + SCORE_MOD: tl.constexpr = None, + Aux0=None, + aux0_stride_t=0, + aux0_stride_h=0, + aux0_len=0, ): # int64 to avoid overflow of flat offsets into Mid_O when # batch * num_head * max_kv_splits * head_dim exceeds 2**31. @@ -206,6 +212,20 @@ def _fwd_kernel_stage1( if xai_temperature_len > 0: qk *= xai_temperature_reg + if SCORE_MOD is not None: + qk = SCORE_MOD( + qk, + cur_batch_seq_len - 1, + offs_n, + cur_batch, + cur_head, + offs_n < split_kv_end, + Aux0, + aux0_stride_t, + aux0_stride_h, + aux0_len, + ) + qk = tl.where(offs_n < split_kv_end, qk, float("-inf")) if PAGE_SIZE == 1: @@ -275,6 +295,8 @@ def _decode_att_m_fwd( logit_cap, xai_temperature_len=-1, page_size: int = 1, + score_mod=None, + aux_tensors=None, ): BLOCK = 64 # [TODO] work around SGPR limit on MI3xx @@ -311,6 +333,10 @@ def _decode_att_m_fwd( v_buffer, page_size ) + aux0, aux0_stride_t, aux0_stride_h, aux0_len = unpack_aux_tensors( + score_mod, aux_tensors + ) + _fwd_kernel_stage1[grid]( q, k_buffer, @@ -346,6 +372,11 @@ def _decode_att_m_fwd( Lk=Lk, Lv=Lv, PAGE_SIZE=page_size, + SCORE_MOD=score_mod, + Aux0=aux0, + aux0_stride_t=aux0_stride_t, + aux0_stride_h=aux0_stride_h, + aux0_len=aux0_len, ) @@ -389,6 +420,11 @@ def _fwd_grouped_kernel_stage1( HAS_MLA: tl.constexpr = False, USE_PDL: tl.constexpr = False, PAGE_SIZE: tl.constexpr = 1, + SCORE_MOD: tl.constexpr = None, + Aux0=None, + aux0_stride_t=0, + aux0_stride_h=0, + aux0_len=0, ): # int64 to avoid overflow of flat offsets into Mid_O when # batch * num_head * max_kv_splits * head_dim exceeds 2**31. @@ -500,6 +536,20 @@ def _fwd_grouped_kernel_stage1( if xai_temperature_len > 0: qk *= xai_temperature_reg[:, None] + if SCORE_MOD is not None: + qk = SCORE_MOD( + qk, + cur_batch_seq_len - 1, + offs_n[None, :], + cur_batch, + cur_head[:, None], + mask_h[:, None] & (offs_n[None, :] < split_kv_end), + Aux0, + aux0_stride_t, + aux0_stride_h, + aux0_len, + ) + qk = tl.where( mask_h[:, None] & (offs_n[None, :] < split_kv_end), qk, float("-inf") ) @@ -574,6 +624,8 @@ def _decode_grouped_att_m_fwd( has_mla=False, use_pdl=False, page_size: int = 1, + score_mod=None, + aux_tensors=None, ): BLOCK = 32 Lk = k_buffer.shape[-1] @@ -623,6 +675,10 @@ def _decode_grouped_att_m_fwd( v_buffer, page_size ) + aux0, aux0_stride_t, aux0_stride_h, aux0_len = unpack_aux_tensors( + score_mod, aux_tensors + ) + _fwd_grouped_kernel_stage1[grid]( q, k_buffer, @@ -663,6 +719,11 @@ def _decode_grouped_att_m_fwd( HAS_MLA=has_mla, USE_PDL=use_pdl, PAGE_SIZE=page_size, + SCORE_MOD=score_mod, + Aux0=aux0, + aux0_stride_t=aux0_stride_t, + aux0_stride_h=aux0_stride_h, + aux0_len=aux0_len, **extra_kargs, ) @@ -814,6 +875,8 @@ def decode_attention_fwd_normal( sinks=None, xai_temperature_len=-1, page_size: int = 1, + score_mod=None, + aux_tensors=None, ): _decode_att_m_fwd( q, @@ -829,6 +892,8 @@ def decode_attention_fwd_normal( logit_cap, xai_temperature_len, page_size=page_size, + score_mod=score_mod, + aux_tensors=aux_tensors, ) _decode_softmax_reducev_fwd( attn_logits, @@ -863,6 +928,8 @@ def decode_attention_fwd_grouped( has_mla=False, use_pdl=False, page_size: int = 1, + score_mod=None, + aux_tensors=None, ): _decode_grouped_att_m_fwd( q, @@ -880,6 +947,8 @@ def decode_attention_fwd_grouped( has_mla=has_mla, use_pdl=use_pdl, page_size=page_size, + score_mod=score_mod, + aux_tensors=aux_tensors, ) _decode_softmax_reducev_fwd( attn_logits, @@ -916,6 +985,8 @@ def decode_attention_fwd( has_mla=False, use_pdl=False, page_size: int = 1, + score_mod=None, + aux_tensors=None, ): assert max_kv_splits == attn_logits.shape[2] assert q.shape[0] <= kv_indptr.shape[0] - 1 @@ -944,6 +1015,8 @@ def decode_attention_fwd( sinks=sinks, xai_temperature_len=xai_temperature_len, page_size=page_size, + score_mod=score_mod, + aux_tensors=aux_tensors, ) else: # GQA/MQA/MLA @@ -966,4 +1039,6 @@ def decode_attention_fwd( has_mla=has_mla, use_pdl=use_pdl, page_size=page_size, + score_mod=score_mod, + aux_tensors=aux_tensors, ) diff --git a/python/sglang/kernels/ops/attention/extend_attention.py b/python/sglang/kernels/ops/attention/extend_attention.py index 97f94db55..4c456b8c9 100644 --- a/python/sglang/kernels/ops/attention/extend_attention.py +++ b/python/sglang/kernels/ops/attention/extend_attention.py @@ -24,6 +24,7 @@ from sglang.kernels.ops.attention.decode_attention import _extract_kv_strides from sglang.kernels.ops.attention.prefill_attention import ( context_attention_fwd, ) +from sglang.kernels.ops.attention.score_mod import unpack_aux_tensors from sglang.srt.utils import is_cuda, is_gfx95_supported, is_hip _is_cuda = is_cuda() @@ -295,6 +296,11 @@ def _fwd_kernel( STORE_TRANSPOSE: tl.constexpr, HAS_SINK: tl.constexpr, PAGE_SIZE: tl.constexpr = 1, + SCORE_MOD: tl.constexpr = None, + Aux0=None, + aux0_stride_t=0, + aux0_stride_h=0, + aux0_len=0, ): cur_seq = tl.program_id(0) cur_head = tl.program_id(1) @@ -450,6 +456,22 @@ def _fwd_kernel( if xai_temperature_len > 0: qk *= xai_temperature_reg[:, None] + if SCORE_MOD is not None: + qk = SCORE_MOD( + qk, + (cur_seq_len_prefix + cur_block_m * BLOCK_M + offs_m)[:, None], + start_n + offs_n[None, :], + (cur_seq_extend_start_idx + cur_block_m * BLOCK_M + offs_m)[ + :, None + ], + cur_head, + final_mask, + Aux0, + aux0_stride_t, + aux0_stride_h, + aux0_len, + ) + qk = tl.where(final_mask, qk, float("-inf")) row_max = tl.max(qk, 1) @@ -565,6 +587,22 @@ def _fwd_kernel( if xai_temperature_len > 0: qk *= xai_temperature_reg[:, None] + if SCORE_MOD is not None: + qk = SCORE_MOD( + qk, + (cur_seq_len_prefix + cur_block_m * BLOCK_M + offs_m)[:, None], + cur_seq_len_prefix + start_n + offs_n[None, :], + (cur_seq_extend_start_idx + cur_block_m * BLOCK_M + offs_m)[ + :, None + ], + cur_head, + final_mask, + Aux0, + aux0_stride_t, + aux0_stride_h, + aux0_len, + ) + qk = tl.where(final_mask, qk, float("-inf")) row_max = tl.max(qk, 1) @@ -646,6 +684,8 @@ def extend_attention_fwd( skip_prefix=False, skip_extend=False, page_size: int = 1, + score_mod=None, + aux_tensors=None, ): """ q_extend, k_extend, v_extend, o_extend: contiguous tensors @@ -656,6 +696,8 @@ def extend_attention_fwd( written to it (used by DCP to merge partial attention across ranks). ``skip_prefix`` / ``skip_extend`` skip the prefix-KV / current-chunk stage respectively so DCP can compute those two parts separately. + ``score_mod`` / ``aux_tensors`` add a custom term to the attention logits; + see triton_ops/score_mod.py for the contract. """ Lq, Lk, Lv = ( q_extend.shape[-1], @@ -695,6 +737,10 @@ def extend_attention_fwd( v_buffer, page_size ) + aux0, aux0_stride_t, aux0_stride_h, aux0_len = unpack_aux_tensors( + score_mod, aux_tensors + ) + _fwd_kernel[grid]( q_extend, k_extend, @@ -751,6 +797,11 @@ def extend_attention_fwd( HAS_SINK=HAS_SINK, STORE_TRANSPOSE=_is_hip, PAGE_SIZE=page_size, + SCORE_MOD=score_mod, + Aux0=aux0, + aux0_stride_t=aux0_stride_t, + aux0_stride_h=aux0_stride_h, + aux0_len=aux0_len, num_warps=num_warps, num_stages=num_stages, **extra_kargs, @@ -838,6 +889,11 @@ def _fwd_kernel_unified( USE_CUSTOM_MASK: tl.constexpr, HAS_SINK: tl.constexpr, PAGE_SIZE: tl.constexpr = 1, + SCORE_MOD: tl.constexpr = None, + Aux0=None, + aux0_stride_t=0, + aux0_stride_h=0, + aux0_len=0, ): """ Unified 1-stage kernel for deterministic extend attention. @@ -1026,6 +1082,20 @@ def _fwd_kernel_unified( if xai_temperature_len > 0: qk *= xai_temperature_reg[:, None] + if SCORE_MOD is not None: + qk = SCORE_MOD( + qk, + (cur_seq_prefix_len + cur_block_m * BLOCK_M + offs_m)[:, None], + start_n + offs_n[None, :], + (cur_seq_q_start_idx + cur_block_m * BLOCK_M + offs_m)[:, None], + cur_head, + final_mask, + Aux0, + aux0_stride_t, + aux0_stride_h, + aux0_len, + ) + qk = tl.where(final_mask, qk, float("-inf")) # Online softmax @@ -1101,6 +1171,8 @@ def extend_attention_fwd_unified( window_start_pos=None, xai_temperature_len=-1, page_size: int = 1, + score_mod=None, + aux_tensors=None, ): """ Unified 1-stage extend attention for deterministic inference. @@ -1162,6 +1234,10 @@ def extend_attention_fwd_unified( v_buffer, page_size ) + aux0, aux0_stride_t, aux0_stride_h, aux0_len = unpack_aux_tensors( + score_mod, aux_tensors + ) + _fwd_kernel_unified[grid]( q, o, @@ -1204,6 +1280,11 @@ def extend_attention_fwd_unified( USE_CUSTOM_MASK=USE_CUSTOM_MASK, HAS_SINK=HAS_SINK, PAGE_SIZE=page_size, + SCORE_MOD=score_mod, + Aux0=aux0, + aux0_stride_t=aux0_stride_t, + aux0_stride_h=aux0_stride_h, + aux0_len=aux0_len, num_warps=num_warps, num_stages=num_stages, **extra_kargs, diff --git a/python/sglang/kernels/ops/attention/log_scaling_tau.py b/python/sglang/kernels/ops/attention/log_scaling_tau.py new file mode 100644 index 000000000..1890dcc78 --- /dev/null +++ b/python/sglang/kernels/ops/attention/log_scaling_tau.py @@ -0,0 +1,70 @@ +import torch +import triton +import triton.language as tl + + +@triton.jit +def _apply_log_scaling_tau_kernel( + x_ptr, + tau_ptr, # [rows] fp32 (flattened per-row scale) + out_ptr, # [rows, inner] contiguous, same dtype as x + x_row_stride, + inner, + total, + BLOCK: tl.constexpr, +): + pid = tl.program_id(0) + offs = pid.to(tl.int64) * BLOCK + tl.arange(0, BLOCK) + mask = offs < total + row = offs // inner + col = offs % inner + x = tl.load(x_ptr + row * x_row_stride + col, mask=mask).to(tl.float32) + tau = tl.load(tau_ptr + row, mask=mask) + y = x * tau + tl.store(out_ptr + offs, y.to(out_ptr.dtype.element_ty), mask=mask) + + +def apply_log_scaling_tau(x: torch.Tensor, tau: torch.Tensor) -> torch.Tensor: + """out = (x.float() * tau).to(x.dtype) with tau broadcast per leading row, + fused into one launch. x may carry a leading-dim stride (the q slice of the + fused qkvr output); its trailing dims must be contiguous. No dynamo: the + torch.compile'd predecessor's call sites spanned enough rank / + dispatch-key / 0-1 specialization variants (target + de-tied MTP heads) to + exceed the recompile limit, which crashed (fullgraph) or wedged capture + (raised limit).""" + rows = x.shape[0] + inner = x.numel() // rows if rows else 0 + inner_contiguous = x.stride(-1) == 1 and ( + x.dim() == 2 or x.stride(-2) == x.shape[-1] * x.stride(-1) + ) + if rows == 0 or inner == 0 or not inner_contiguous: + return (x.float() * tau).to(x.dtype) + + if ( + x.is_cuda + and x.dtype == torch.bfloat16 + and inner % 8 == 0 + and x.data_ptr() % 16 == 0 + and (x.stride(0) * 2) % 16 == 0 + ): + # Vectorized JIT kernel (16B loads, one row divide per vector) -- + # bit-identical output (same fp32-mul + bf16-round), ~2-3x the + # scalar triton kernel below at every size. + from sglang.jit_kernel.inkling_row_scale import row_scale_bf16 + + x2d = torch.as_strided(x, (rows, inner), (x.stride(0), 1)) + return row_scale_bf16(x2d, tau.reshape(rows).float()).view(x.shape) + + out = torch.empty(x.shape, dtype=x.dtype, device=x.device) + total = rows * inner + BLOCK = 1024 + _apply_log_scaling_tau_kernel[(triton.cdiv(total, BLOCK),)]( + x, + tau.reshape(rows).to(torch.float32), + out, + x.stride(0), + inner, + total, + BLOCK=BLOCK, + ) + return out diff --git a/python/sglang/kernels/ops/attention/metadata.py b/python/sglang/kernels/ops/attention/metadata.py index 47504a1cf..461767ccf 100644 --- a/python/sglang/kernels/ops/attention/metadata.py +++ b/python/sglang/kernels/ops/attention/metadata.py @@ -322,6 +322,216 @@ def _fused_metadata_kernel_ps1_no_swa( tl.store(page_table + pt_offsets, page_index, mask=mask, cache_modifier=".cg") +@triton.jit +def _draft_extend_metadata_kernel( + # Input tensors + seq_lens, + seq_lens_stride_0, + extend_seq_lens, + extend_seq_lens_stride_0, + req_to_token, + req_to_token_stride_0, + req_to_token_stride_1, + req_pool_indices, + req_pool_indices_stride_0, + # Output buffers + cache_seqlens_int32, + cache_seqlens_int32_stride_0, + cu_seqlens_k, + cu_seqlens_k_stride_0, + cu_seqlens_q, + cu_seqlens_q_stride_0, + page_table, + page_table_stride_0, + page_table_stride_1, + full_to_swa_index_mapping, + swa_page_table, + out_cache_loc, + swa_out_cache_loc, + # Scalar parameters + B, + max_seq_pages, + tokens_per_req, + PAGE_SIZE_ONE: tl.constexpr, + SHIFT: tl.constexpr, + BLOCK_COLS: tl.constexpr, + HAS_SWA: tl.constexpr, + OUT_BLOCK: tl.constexpr, +): + pid_b = tl.program_id(0) # batch index + pid_c = tl.program_id(1) # column chunk index + + # 1. Prefix sums (only one block does them): cache_seqlens + cu_seqlens_k + # from seq_lens, cu_seqlens_q from extend_seq_lens. + if pid_b == 0 and pid_c == 0: + acc_k = 0 + acc_q = 0 + for idx in range(B): + seq = tl.load(seq_lens + idx * seq_lens_stride_0).to(tl.int32) + tl.store(cache_seqlens_int32 + idx * cache_seqlens_int32_stride_0, seq) + tl.store(cu_seqlens_k + idx * cu_seqlens_k_stride_0, acc_k) + acc_k += seq + ext = tl.load(extend_seq_lens + idx * extend_seq_lens_stride_0).to(tl.int32) + tl.store(cu_seqlens_q + idx * cu_seqlens_q_stride_0, acc_q) + acc_q += ext + tl.store(cu_seqlens_k + B * cu_seqlens_k_stride_0, acc_k) + tl.store(cu_seqlens_q + B * cu_seqlens_q_stride_0, acc_q) + + # 2. SWA write-loc translation for this request's extend tokens. Runs + # before the seq_len early-return so padded rows (seq_len 0) keep + # swa_out_cache_loc consistent with out_cache_loc. + if HAS_SWA: + if pid_c == 0: + tok_idx = tl.arange(0, OUT_BLOCK) + tok_mask = tok_idx < tokens_per_req + tok_offsets = pid_b * tokens_per_req + tok_idx + full_locs = tl.load(out_cache_loc + tok_offsets, mask=tok_mask, other=0) + swa_locs = tl.load( + full_to_swa_index_mapping + full_locs, mask=tok_mask, other=0 + ) + tl.store(swa_out_cache_loc + tok_offsets, swa_locs, mask=tok_mask) + + # 3. Page-table gather for this batch row and column chunk, self-guarded + # on the device-side seq_len (no host max; tails keep stale values the + # attention kernels never read past cache_seqlens). + if max_seq_pages == 0: + return + + seq_len = tl.load(seq_lens + pid_b * seq_lens_stride_0).to(tl.int32) + if PAGE_SIZE_ONE: + num_live_pages = seq_len + else: + num_live_pages = (seq_len + (1 << SHIFT) - 1) >> SHIFT + num_live_pages = tl.minimum(num_live_pages, max_seq_pages) + if pid_c * BLOCK_COLS >= num_live_pages: + return + + row_idx = tl.load(req_pool_indices + pid_b * req_pool_indices_stride_0) + row_offset = row_idx * req_to_token_stride_0 + + col_offsets = pid_c * BLOCK_COLS + tl.arange(0, BLOCK_COLS) + mask = col_offsets < num_live_pages + + if PAGE_SIZE_ONE: + col_idx = col_offsets + else: + col_idx = col_offsets << SHIFT + + rt_offsets = row_offset + col_idx * req_to_token_stride_1 + page_index = tl.load( + req_to_token + rt_offsets, mask=mask, other=0, cache_modifier=".cg" + ) + + if PAGE_SIZE_ONE: + page_table_val = page_index + else: + page_table_val = page_index >> SHIFT + + pt_offsets = pid_b * page_table_stride_0 + col_offsets * page_table_stride_1 + tl.store(page_table + pt_offsets, page_table_val, mask=mask, cache_modifier=".cg") + + if HAS_SWA: + swa_loc = tl.load(full_to_swa_index_mapping + page_index, mask=mask, other=0) + if PAGE_SIZE_ONE: + swa_page_table_val = swa_loc + else: + swa_page_table_val = swa_loc >> SHIFT + tl.store( + swa_page_table + pt_offsets, + swa_page_table_val.to(tl.int32), + mask=mask, + cache_modifier=".cg", + ) + + +def draft_extend_set_metadata( + cache_seqlens_int32: torch.Tensor, + cu_seqlens_k: torch.Tensor, + cu_seqlens_q: torch.Tensor, + page_table: torch.Tensor, + req_to_token: torch.Tensor, + req_pool_indices: torch.Tensor, + seq_lens: torch.Tensor, + extend_seq_lens: torch.Tensor, + page_size: int, + full_to_swa_index_mapping: Optional[torch.Tensor] = None, + swa_page_table: Optional[torch.Tensor] = None, + out_cache_loc: Optional[torch.Tensor] = None, + swa_out_cache_loc: Optional[torch.Tensor] = None, +): + """Fused, graph-recordable DRAFT_EXTEND_V2 metadata update (one launch): + 1. cache_seqlens = seq_lens (int32 cast) + 2. cu_seqlens_k = pad(cumsum(cache_seqlens)) + 3. cu_seqlens_q = pad(cumsum(extend_seq_lens)) + 4. page_table[:, :pages(seq_len)] = req_to_token[pool_idx, ::page_size] // page_size + 5. (SWA pools) swa_page_table likewise via the full->swa lookup, and + swa_out_cache_loc = full_to_swa_index_mapping[out_cache_loc] + + The page gathers self-guard on the device-side seq_lens (no host max); + row tails keep stale values that attention kernels never read past + cache_seqlens, matching the eager replay path's bounded writes. + """ + assert ( + page_size > 0 and (page_size & (page_size - 1)) == 0 + ), f"page_size must be a power of two, got {page_size}" + + batch_size = cache_seqlens_int32.shape[0] + max_seq_pages = page_table.shape[1] + + has_swa = full_to_swa_index_mapping is not None + if has_swa: + assert swa_page_table is not None + assert swa_page_table.shape == page_table.shape + assert swa_page_table.stride() == page_table.stride() + assert out_cache_loc is not None and swa_out_cache_loc is not None + num_out_tokens = out_cache_loc.shape[0] + assert swa_out_cache_loc.shape[0] == num_out_tokens + assert num_out_tokens > 0 and num_out_tokens % batch_size == 0 + tokens_per_req = num_out_tokens // batch_size + out_block = triton.next_power_of_2(tokens_per_req) + else: + tokens_per_req = 0 + out_block = 1 + + BLOCK_COLS = 256 + grid = (batch_size, max(1, triton.cdiv(max_seq_pages, BLOCK_COLS))) + + _draft_extend_metadata_kernel[grid]( + seq_lens, + seq_lens.stride(0), + extend_seq_lens, + extend_seq_lens.stride(0), + req_to_token, + req_to_token.stride(0), + req_to_token.stride(1), + req_pool_indices, + req_pool_indices.stride(0), + cache_seqlens_int32, + cache_seqlens_int32.stride(0), + cu_seqlens_k, + cu_seqlens_k.stride(0), + cu_seqlens_q, + cu_seqlens_q.stride(0), + page_table, + page_table.stride(0), + page_table.stride(1), + full_to_swa_index_mapping, + swa_page_table, + out_cache_loc, + swa_out_cache_loc, + batch_size, + max_seq_pages, + tokens_per_req, + PAGE_SIZE_ONE=page_size == 1, + SHIFT=(page_size).bit_length() - 1 if page_size > 1 else 0, + BLOCK_COLS=BLOCK_COLS, + num_warps=8, + num_stages=3, + HAS_SWA=has_swa, + OUT_BLOCK=out_block, + ) + + def normal_decode_set_metadata( cache_seqlens_int32: torch.Tensor, cu_seqlens_k: torch.Tensor, diff --git a/python/sglang/kernels/ops/attention/score_mod.py b/python/sglang/kernels/ops/attention/score_mod.py new file mode 100644 index 000000000..d39c20fbb --- /dev/null +++ b/python/sglang/kernels/ops/attention/score_mod.py @@ -0,0 +1,56 @@ +# Copyright 2023-2024 SGLang Team +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Generic score_mod for the Triton attention kernels, mirroring FA4's +``score_mod``/``aux_tensors``. A Triton score_mod is a ``@triton.jit`` function +inlined into the kernels as a constexpr argument: + + score_mod(qk, q_pos, kv_pos, q_idx, head, mask, + Aux0, aux0_stride_t, aux0_stride_h, aux0_len) -> qk + +The kernels pre-broadcast q_pos/kv_pos/q_idx/head to ``qk``'s shape, so an +elementwise score_mod works at every call site. ``aux_tensors`` supports one +3D tensor ``[num_q_tokens, num_q_heads, D]`` with a contiguous last dim. +""" + +import triton +import triton.language as tl + + +def unpack_aux_tensors(score_mod, aux_tensors): + if score_mod is None: + return None, 0, 0, 0 + assert ( + aux_tensors is not None and len(aux_tensors) == 1 + ), "Triton score_mod currently requires exactly one aux tensor" + aux0 = aux_tensors[0] + assert aux0.dim() == 3 and aux0.stride(2) == 1, ( + f"aux_tensors[0] must be 3D with a contiguous last dim, " + f"got shape={tuple(aux0.shape)} stride={aux0.stride()}" + ) + return aux0, aux0.stride(0), aux0.stride(1), aux0.shape[2] + + +@triton.jit +def relative_bias_score_mod( + qk, q_pos, kv_pos, q_idx, head, mask, Aux0, aux0_stride_t, aux0_stride_h, aux0_len +): + """Add ``Aux0[q_idx, head, q_pos - kv_pos]`` when 0 <= q_pos - kv_pos < aux0_len.""" + rel_dist = q_pos - kv_pos + rel_idx = tl.minimum(tl.maximum(rel_dist, 0), aux0_len - 1) + bias = tl.load( + Aux0 + q_idx * aux0_stride_t + head * aux0_stride_h + rel_idx, + mask=mask & (rel_dist >= 0) & (rel_dist < aux0_len), + other=0.0, + ) + return qk + bias diff --git a/python/sglang/kernels/ops/gemm/trtllm_lora_temp/gate_up_lora_b.py b/python/sglang/kernels/ops/gemm/trtllm_lora_temp/gate_up_lora_b.py index a2893c2c0..f9da4af0a 100644 --- a/python/sglang/kernels/ops/gemm/trtllm_lora_temp/gate_up_lora_b.py +++ b/python/sglang/kernels/ops/gemm/trtllm_lora_temp/gate_up_lora_b.py @@ -147,7 +147,7 @@ def _gate_up_lora_b_kernel( # Store result to output matrix partial_sum *= scaling - partial_sum = partial_sum.to(x.dtype.element_ty) + partial_sum = partial_sum.to(output.dtype.element_ty) output_ptr = ( output + n_start * output_stride_1 @@ -214,6 +214,7 @@ def gate_up_lora_b_fwd( ) and s * r >= _CUBLAS_MIN_S_RANK and gate_up_lora_b.shape[0] == 1 + and x.dtype == gate_up_lora_b.dtype ): # single-adapter fast path: only valid with one resident slot return _gate_up_lora_b_cublas( x, gate_up_lora_b, batch_info, output_dim, base_output diff --git a/python/sglang/kernels/ops/gemm/trtllm_lora_temp/qkv_lora_b.py b/python/sglang/kernels/ops/gemm/trtllm_lora_temp/qkv_lora_b.py index 4c8e31cb9..2667e03e8 100644 --- a/python/sglang/kernels/ops/gemm/trtllm_lora_temp/qkv_lora_b.py +++ b/python/sglang/kernels/ops/gemm/trtllm_lora_temp/qkv_lora_b.py @@ -236,18 +236,17 @@ def qkv_lora_b_fwd( and batch_info.max_len >= _CUBLAS_MIN_MAX_LEN and qkv_lora_b.shape[0] == 1 # single-adapter fast path: only valid with one resident slot + and x.dtype == qkv_lora_b.dtype ): return _qkv_lora_b_cublas( x, qkv_lora_b, batch_info, output_offset_cpu, base_output, n_slices ) BLOCK_S = 16 - BLOCK_R = triton.next_power_of_2(r) - # BLOCK_OUT stays 64: with the 1-adapter cuBLAS dispatch the Triton path - # only runs for decode-sized batches, where 128 halves the grid (96->48 - # programs on Kimi r16 bs64) and slows the kernel ~60% (11.4->18.5us, B200). - # Re-swept for the store path on GB200: 32 vs 64 is within noise (one preset - # marginally each way), so the single value is kept for both writebacks. + # Pad to >=16 for Triton MMA K>=16 (rank<16 adapters); k_offset < K=r masks the + # padded contraction rows to 0, so the result is unchanged. + BLOCK_R = max(16, triton.next_power_of_2(r)) + # Keep one output tile size for both writeback paths. BLOCK_OUT = 64 grid_b = ( diff --git a/python/sglang/kernels/ops/gemm/trtllm_lora_temp/sgemm_lora_a.py b/python/sglang/kernels/ops/gemm/trtllm_lora_temp/sgemm_lora_a.py index 293f3ae6c..6e528bf88 100644 --- a/python/sglang/kernels/ops/gemm/trtllm_lora_temp/sgemm_lora_a.py +++ b/python/sglang/kernels/ops/gemm/trtllm_lora_temp/sgemm_lora_a.py @@ -43,6 +43,7 @@ def _sgemm_lora_a_kernel( BLOCK_K: tl.constexpr, SPLIT_K: tl.constexpr = 1, ENABLE_PDL: tl.constexpr = False, + PADDED_RANK: tl.constexpr = False, ): """ Computes a segmented batched matrix multiplication for the LoRA A matrix. @@ -81,7 +82,8 @@ def _sgemm_lora_a_kernel( return # Adjust N (stack_num * max_rank) to this adapter's actual rank. - N = tl.minimum(N, rank * stack_num) + if not PADDED_RANK: + N = tl.minimum(N, rank * stack_num) # The tile in output matrix will have (pid_s, pid_n) as id num_pid_n = tl.cdiv(N, BLOCK_N) @@ -212,11 +214,12 @@ def sgemm_lora_a_fwd( launch_kwargs = {} if split_k > 1: - # out_alloc_stream (SGLANG_OPT_LORA_OVERLAP_MAIN_ALLOC) is intentionally NOT honored here: - # torch.zeros launches its memset on the alloc stream, which would race the side-stream - # shrink without extra ordering. No current config exercises split-K together with the - # two-stream main-alloc overlap (qwen3.5 leaves split-K off; kimi is single-stream-coherent). - output = torch.zeros((S, R), device=x.device, dtype=torch.float32) + if out_alloc_stream is not None: + with torch.cuda.stream(out_alloc_stream): + output = torch.empty((S, R), device=x.device, dtype=torch.float32) + output.zero_() + else: + output = torch.zeros((S, R), device=x.device, dtype=torch.float32) launch_kwargs = { "num_warps": 2 if split_k <= 4 else 4, "num_stages": 3, @@ -267,3 +270,67 @@ def sgemm_lora_a_fwd( # split_k>1 returns the fp32 accumulator directly; the LoRA-B expand casts x to the weight dtype # on-load (fused), dropping the standalone fp32->bf16 copy kernel. split_k==1 already returns x.dtype. return output + + +def shared_sink_sgemm_lora_a_fwd( + x: torch.Tensor, + weights: torch.Tensor, + batch_info: LoRABatchInfo, + *, + stack_num: int, + padded_rank: bool, + out_alloc_stream=None, +) -> torch.Tensor: + """Shared-sink shrink with the measured fixed-width schedule.""" + assert x.is_contiguous() + assert weights.is_contiguous() + assert x.ndim == 2 + assert weights.ndim == 3 + + num_tokens = x.shape[0] + rank_width = weights.shape[-2] + input_width = weights.shape[-1] + assert x.shape[-1] == input_width + + block_s = 16 + block_k = 256 + block_rank = 16 + grid = ( + triton.cdiv(batch_info.max_len, block_s) * triton.cdiv(rank_width, block_rank), + batch_info.bs, + ) + + if out_alloc_stream is None: + output = torch.empty((num_tokens, rank_width), device=x.device, dtype=x.dtype) + else: + with torch.cuda.stream(out_alloc_stream): + output = torch.empty( + (num_tokens, rank_width), device=x.device, dtype=x.dtype + ) + + _sgemm_lora_a_kernel[grid]( + x, + weights, + output, + rank_width, + input_width, + stack_num, + x.stride(0), + x.stride(1), + weights.stride(0), + weights.stride(1), + weights.stride(2), + output.stride(0), + output.stride(1), + batch_info.seg_lens, + batch_info.seg_indptr, + batch_info.weight_indices, + batch_info.lora_ranks, + batch_info.permutation, + batch_info.permutation is not None, + block_s, + block_rank, + block_k, + PADDED_RANK=padded_rank, + ) + return output diff --git a/python/sglang/kernels/ops/gemm/trtllm_lora_temp/sgemm_lora_b.py b/python/sglang/kernels/ops/gemm/trtllm_lora_temp/sgemm_lora_b.py index a80b4c78b..7d0f79dce 100644 --- a/python/sglang/kernels/ops/gemm/trtllm_lora_temp/sgemm_lora_b.py +++ b/python/sglang/kernels/ops/gemm/trtllm_lora_temp/sgemm_lora_b.py @@ -66,6 +66,10 @@ def _sgemm_lora_b_kernel( # For fused output scaling scalings, ENABLE_PDL: tl.constexpr = False, + APPLY_SCALING: tl.constexpr = True, + PADDED_RANK: tl.constexpr = True, + FLAT_GRID: tl.constexpr = False, + ATOMIC_ADD: tl.constexpr = True, ): """ Computes a segmented batched matrix multiplication for the LoRA B matrix @@ -84,9 +88,16 @@ def _sgemm_lora_b_kernel( the base model's output for a fused add operation. """ - pid_s = tl.program_id(axis=0) - pid_n = tl.program_id(axis=1) - batch_id = tl.program_id(axis=2) + if FLAT_GRID: + pid = tl.program_id(axis=0) + batch_id = tl.program_id(axis=1) + num_pid_n = tl.cdiv(N, BLOCK_N) + pid_s = pid // num_pid_n + pid_n = pid % num_pid_n + else: + pid_s = tl.program_id(axis=0) + pid_n = tl.program_id(axis=1) + batch_id = tl.program_id(axis=2) w_index = tl.load(weight_indices + batch_id) rank = tl.load(lora_ranks + w_index) @@ -98,7 +109,9 @@ def _sgemm_lora_b_kernel( if pid_s * BLOCK_S >= seg_len: # also covers seg_len == 0 return seg_start = tl.load(seg_indptr + batch_id) - scaling = tl.load(scalings + w_index) + scaling = tl.load(scalings + w_index) if APPLY_SCALING else 1.0 + if not PADDED_RANK: + K = tl.minimum(K, rank) s_offset = tl.arange(0, BLOCK_S) + pid_s * BLOCK_S n_offset = tl.arange(0, BLOCK_N) + pid_n * BLOCK_N @@ -122,19 +135,24 @@ def _sgemm_lora_b_kernel( ) output_mask = (s_offset[:, None] < seg_len) & n_mask - x_tile = tl.load( - x_ptrs, - mask=(s_offset[:, None] < seg_len) & (k_offset[None, :] < K), - other=0.0, - ) - w_tile = tl.load( - w_ptrs, - mask=(k_offset[:, None] < K) & n_mask, - other=0.0, - ) - - # cast fused: the split-K shrink returns fp32, plain path bf16 (no-op) - partial_sum = tl.dot(x_tile.to(w_tile.dtype), w_tile) * scaling + partial_sum = tl.zeros((BLOCK_S, BLOCK_N), dtype=tl.float32) + for k in range(0, tl.cdiv(K, BLOCK_K)): + k_remaining = K - k * BLOCK_K + x_tile = tl.load( + x_ptrs, + mask=(s_offset[:, None] < seg_len) & (k_offset[None, :] < k_remaining), + other=0.0, + ) + w_tile = tl.load( + w_ptrs, + mask=(k_offset[:, None] < k_remaining) & n_mask, + other=0.0, + ) + # The split-K shrink returns fp32; cast it on-load to the weight dtype. + partial_sum += tl.dot(x_tile.to(w_tile.dtype), w_tile) + x_ptrs += BLOCK_K * x_stride_1 + w_ptrs += BLOCK_K * w_stride_2 + partial_sum *= scaling # All input reads are done; hint the runtime to launch the dependent kernel. if ENABLE_PDL: @@ -143,7 +161,11 @@ def _sgemm_lora_b_kernel( # Store result to output matrix (cast to the OUTPUT dtype: x may be the fp32 # split-K shrink accumulator while base_output is bf16) partial_sum = partial_sum.to(output.dtype.element_ty) - tl.atomic_add(output_ptr, partial_sum, mask=output_mask, sem="relaxed") + if ATOMIC_ADD: + tl.atomic_add(output_ptr, partial_sum, mask=output_mask, sem="relaxed") + else: + partial_sum += tl.load(output_ptr, mask=output_mask, other=0.0) + tl.store(output_ptr, partial_sum, mask=output_mask) def sgemm_lora_b_fwd( @@ -174,11 +196,14 @@ def sgemm_lora_b_fwd( ) and S * R >= _CUBLAS_MIN_S_RANK and weights.shape[0] == 1 + and x.dtype == weights.dtype ): # single-adapter fast path: only valid with one resident slot return _sgemm_lora_b_cublas(x, weights, batch_info, base_output) # Block shapes BLOCK_S = 16 - BLOCK_R = triton.next_power_of_2(R) + # Pad to >=16 for Triton MMA K>=16 (rank<16 adapters); k_offset < K=R masks the + # padded contraction rows to 0, so the result is unchanged. + BLOCK_R = max(16, triton.next_power_of_2(R)) BLOCK_N = 256 grid = ( @@ -221,3 +246,66 @@ def sgemm_lora_b_fwd( **pdl_kwargs, ) return output + + +def shared_sink_sgemm_lora_b_fwd( + x: torch.Tensor, + weights: torch.Tensor, + batch_info: LoRABatchInfo, + base_output: torch.Tensor = None, + *, + apply_scaling: bool, + padded_rank: bool, +) -> torch.Tensor: + """Shared-sink expand with the measured fixed-width schedule.""" + assert x.is_contiguous() + assert weights.is_contiguous() + assert x.ndim == 2 + assert weights.ndim == 3 + + num_tokens = x.shape[0] + output_width = weights.shape[-2] + rank_width = weights.shape[-1] + assert x.shape[-1] == rank_width + + block_s = 16 + block_rank = 16 + block_n = 256 + grid = ( + triton.cdiv(batch_info.max_len, block_s) * triton.cdiv(output_width, block_n), + batch_info.bs, + ) + output = ( + torch.zeros((num_tokens, output_width), device=x.device, dtype=x.dtype) + if base_output is None + else base_output + ) + _sgemm_lora_b_kernel[grid]( + x, + weights, + output, + output_width, + rank_width, + x.stride(0), + x.stride(1), + weights.stride(0), + weights.stride(1), + weights.stride(2), + output.stride(0), + output.stride(1), + batch_info.seg_lens, + batch_info.seg_indptr, + batch_info.weight_indices, + batch_info.lora_ranks, + batch_info.permutation, + batch_info.permutation is not None, + block_s, + block_n, + block_rank, + batch_info.scalings, + APPLY_SCALING=apply_scaling, + PADDED_RANK=padded_rank, + FLAT_GRID=True, + ATOMIC_ADD=False, + ) + return output diff --git a/python/sglang/kernels/ops/mamba/mamba_state_scatter_triton.py b/python/sglang/kernels/ops/mamba/mamba_state_scatter_triton.py index 506459459..4a19da4c9 100644 --- a/python/sglang/kernels/ops/mamba/mamba_state_scatter_triton.py +++ b/python/sglang/kernels/ops/mamba/mamba_state_scatter_triton.py @@ -452,3 +452,52 @@ def fused_conv_window_scatter_with_mask( dst_req_size, BLOCK_SIZE=BLOCK_SIZE, ) + + +def scatter_mamba_states_after_mtp_verify( + mamba_caches, + state_indices_tensor: torch.Tensor, + last_correct_step_indices: torch.Tensor, + mamba_track_indices: torch.Tensor | None, + mamba_steps_to_track: torch.Tensor | None, +) -> None: + """Scatter per-step verify states (ssm + all conv types) into the + persistent caches, plus the interval-crossing track slots.""" + ssm_states = mamba_caches.temporal + intermediate_state_cache = mamba_caches.intermediate_ssm + + if ssm_states.numel() > 0: + fused_mamba_state_scatter_with_mask( + ssm_states, + intermediate_state_cache, + state_indices_tensor, + last_correct_step_indices, + ) + for conv_states, intermediate_conv_window_cache in zip( + mamba_caches.conv, mamba_caches.intermediate_conv_window + ): + fused_conv_window_scatter_with_mask( + conv_states, + intermediate_conv_window_cache, + state_indices_tensor, + last_correct_step_indices, + ) + + if mamba_track_indices is not None: + assert mamba_steps_to_track is not None + if ssm_states.numel() > 0: + fused_mamba_state_scatter_with_mask( + ssm_states, + intermediate_state_cache, + mamba_track_indices, + mamba_steps_to_track, + ) + for conv_states, intermediate_conv_window_cache in zip( + mamba_caches.conv, mamba_caches.intermediate_conv_window + ): + fused_conv_window_scatter_with_mask( + conv_states, + intermediate_conv_window_cache, + mamba_track_indices, + mamba_steps_to_track, + ) diff --git a/python/sglang/kernels/ops/moe/trtllm_lora_temp/virtual_experts.py b/python/sglang/kernels/ops/moe/trtllm_lora_temp/virtual_experts.py index ce43a3bc4..2491395f3 100644 --- a/python/sglang/kernels/ops/moe/trtllm_lora_temp/virtual_experts.py +++ b/python/sglang/kernels/ops/moe/trtllm_lora_temp/virtual_experts.py @@ -326,14 +326,12 @@ def _invoke_moe_lora_shrink_splitk( N = weight.shape[1] K = weight.shape[2] BLOCK_SIZE_M = config["BLOCK_SIZE_M"] - BLOCK_SIZE_N = triton.next_power_of_2(N) + BLOCK_SIZE_N = min(128, triton.next_power_of_2(N)) BLOCK_SIZE_K = 256 GROUP_SIZE_M = config.get("GROUP_SIZE_M", 1) num_m_blocks = triton.cdiv(sorted_token_ids.shape[0], BLOCK_SIZE_M) - num_n_blocks = triton.cdiv( - N, BLOCK_SIZE_N - ) # == 1, BLOCK_SIZE_N == next_pow2(N) >= N + num_n_blocks = triton.cdiv(N, BLOCK_SIZE_N) base_grid = num_m_blocks * num_n_blocks # Single source of truth shared with the caller's zero-intermediate decision: # split-K accumulation REQUIRES a pre-zeroed output, so the predicted and @@ -378,26 +376,19 @@ def _get_moe_lora_shrink_split_k( sorted_token_ids: torch.Tensor, config: dict[str, Any], ) -> int: - """Rank-tiered split-K occupancy fill (PR #26899). + """Choose split-K from rank and available occupancy. - The K reduction (e.g. 7168 / 256 = 28 iters) dominates this skinny-N grouped - GEMV, so splitting K stays useful well past full SM occupancy -- a plain - `1 if base_grid >= num_sm else ...` rule collapses SPLIT_K too early and - costs up to ~2x at the decode/prefill border. Skinnier ranks want more - splits (their output tile carries less work). The target / tiers were picked - from an offline per-M B200 sweep over E in {48,96,384}, N in {16,32,64}; - this heuristic lands within ~5% of the per-shape tuned optimum across the - decode regime. + Skinny output ranks benefit from more K splits because each output tile + carries less work. Block sizes must mirror _invoke_moe_lora_shrink_splitk. - Block sizes must mirror _invoke_moe_lora_shrink_splitk (BLOCK_SIZE_N = - next_pow2(N) -> one N block; BLOCK_SIZE_K = 256). """ N = weight.shape[1] K = weight.shape[2] block_size_m = config["BLOCK_SIZE_M"] + block_size_n = min(128, triton.next_power_of_2(N)) block_size_k = 256 num_m_blocks = triton.cdiv(sorted_token_ids.shape[0], block_size_m) - base_grid = num_m_blocks # num_n_blocks == 1: BLOCK_SIZE_N == next_pow2(N) >= N + base_grid = num_m_blocks * triton.cdiv(N, block_size_n) target = 512 if N <= 16 else 384 if N <= 32 else 256 max_split_k = max(1, K // block_size_k) return max(1, min(triton.cdiv(target, base_grid), max_split_k, 8)) @@ -416,17 +407,10 @@ def _align_block_size_jit( block_size: int, num_experts: int, ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: - """CUDA JIT align_block_size for num_experts > 1024 (up to 8191). + """CUDA JIT alignment for up to 8191 experts. - Uses the v2 kernel from moe_align_kernel.cu which supports large expert - counts via per-thread multi-expert processing and a two-level warp scan, - replacing the previous pure-PyTorch fallback that had excessive CPU overhead - from 15+ individual kernel launches and torch.argsort. - - The JIT kernel uses a +1 offset convention: topk_ids are shifted by +1 so - that the EP sentinel value (-1) maps to bucket 0. The kernel internally - handles histogram, padded prefix-sum, expert_ids assignment, and token - scattering in just 2–3 CUDA kernel launches. + Expert IDs are shifted by one so ``-1`` maps to a sentinel bucket. The + fused allocation stays int4-aligned for the kernel's vectorized clear. """ assert num_experts <= 8191, ( f"_align_block_size_jit supports at most 8191 experts " @@ -642,6 +626,10 @@ def _merged_experts_fused_moe_lora_add_impl( stage: str = "all", intermediate_buffer: torch.Tensor | None = None, expand_wait_event: "torch.cuda.Event | None" = None, + broadcast_intermediate: bool = False, + prewarm_a_routing: bool = True, + prewarm_b_routing: bool = True, + zero_intermediate: bool = False, ) -> "torch.Tensor | None": """ 1. Prepare virtual expert routing metadata from topk_ids + token_lora_mapping * num_experts. @@ -657,12 +645,14 @@ def _merged_experts_fused_moe_lora_add_impl( - ``"expand"``: routing-B + LoRA-B expand/add only; requires ``intermediate_buffer`` = the tensor produced by the ``"shrink"`` stage. - EP: when `local_num_experts` (< global) is given, this rank only computes the - delta for the experts it owns. We keep the GLOBAL expert ids + global contiguous - weights (so the merged-weight reshape stays a free view) and mask non-owned - [token, k] slots to the -1 sentinel inside `_fused_virtual_topk_ids_kernel`; the - grid shrinks via the per-rank trim in `_get_routing`. Slicing the weight's expert - dim instead would force the reshape to copy every step (non-contiguous fold). + ``prewarm_a_routing`` and ``prewarm_b_routing`` let a staged caller skip + routing for a weight that it replaces with a dense operation. The B flag + also controls the automatic expand-route prewarm performed by ``"shrink"``. + ``broadcast_intermediate`` is an expand-only mode where one rank vector per + token is reused for every routed expert. + + EP accepts either global weights/IDs with a local range or already-localized + weights/IDs from the standard dispatcher. """ max_loras, _, max_lora_rank, _ = lora_a.shape # Global per-expert dim of the LoRA weights. lora_a may be shared-outer (expert @@ -759,24 +749,18 @@ def _merged_experts_fused_moe_lora_add_impl( if cached is not None: return cached - # Fused LoRA-local align: one kernel does inline virtual id + EP skip + - # compact (local experts) + single-block scatter, replacing the 3-kernel - # (_fused_virtual_topk_ids + moe_align + count_and_sort) pipeline. Two - # single-adapter (max_loras==1) regimes are fused; everything else falls back: - # - per-expert EP path (ep_local): compact local-expert histogram. - # - shared-outer path (shared_outer): lora-id routing (compute_virtual_id - # uses base=0; the kernel + launcher already size num_experts_for_weight=1 - # and have no bucket-count blocker, so it just needs compact=False — compact - # + shared_outer would mis-map the id as base-offset). This is the opt1 - # align/sort fusion: shared-outer used to fall through to the unfused - # _fused_virtual_topk_ids + moe_align_block_size_small_batch pair (~10.2us/ - # layer at decode bs16); now it takes the single fused launch. - # Decode-only: the fused kernel's single-block scatter targets the small - # decode batch; prefill (>= 512 tokens) keeps the multi-block old path. + # Shared-outer routing has one bucket per adapter, so the same merged + # align remains valid for multi-LoRA. Compact EP routing stays single-slot. + compact_merged = ep_local and not shared_outer and max_loras == 1 + bucket_experts = ( + local_num_experts + if compact_merged + else (1 if shared_outer else num_experts) * max_loras + ) if ( lora_envs.SGLANG_OPT_LORA_FUSED_MERGED_ALIGN.get() - and max_loras == 1 - and (shared_outer or ep_local) + and (shared_outer or compact_merged) + and bucket_experts + 1 <= 1024 and topk_ids.shape[0] < 512 ): from sglang.jit_kernel.trtllm_lora_temp.moe_lora_merged_align import ( @@ -799,9 +783,7 @@ def _merged_experts_fused_moe_lora_add_impl( local_expert_offset, local_num_experts, do_skip=True, - # compact local-expert histogram is only valid for the per-expert EP - # path; shared_outer routes by lora id (base=0) so it must stay global. - compact=not shared_outer, + compact=compact_merged, ) result = ( sorted_token_ids, @@ -874,6 +856,12 @@ def _merged_experts_fused_moe_lora_add_impl( "expand", "routing", ), f"invalid stage {stage!r}" + if broadcast_intermediate: + assert stage == "expand" + assert use_direct_expand_add + assert intermediate_buffer is not None + assert intermediate_buffer.ndim == 2 + assert intermediate_buffer.shape[0] == token_lora_mapping.shape[0] lora_a_virtual = _merge_lora_expert_weight(lora_a) lora_b_virtual = _merge_lora_expert_weight(lora_b) num_experts_a = lora_a.shape[1] @@ -891,20 +879,32 @@ def _merged_experts_fused_moe_lora_add_impl( a_cfg = _get_shrink_stage_config(lora_a_virtual, token_lora_mapping.shape[0]) if lora_envs.SGLANG_OPT_LORA_SHRINK_TUNE.get(): a_cfg = {**a_cfg, "BLOCK_SIZE_M": 16} - _get_routing( - topk_ids, - token_lora_mapping, - num_experts_a, - experts_shared_outer_loras_a, - a_cfg["BLOCK_SIZE_M"], - ) - _get_routing( - topk_ids, - token_lora_mapping, - num_experts_b, - experts_shared_outer_loras_b, - b_stage_config["BLOCK_SIZE_M"], - ) + # Match the actual shrink-stage override below. Without this, callers + # that admit prefill-shaped batches into a side stream prewarm block-32 + # routing here, then miss the cache when shrink switches to the B-stage + # block size. The miss allocates routing buffers on the side stream + # during capture, violating the allocation guarantee of stage='routing'. + if ( + lora_envs.SGLANG_OPT_LORA_PREFILL_ROUTING_REUSE.get() + and token_lora_mapping.shape[0] >= 512 + ): + a_cfg["BLOCK_SIZE_M"] = b_stage_config["BLOCK_SIZE_M"] + if prewarm_a_routing: + _get_routing( + topk_ids, + token_lora_mapping, + num_experts_a, + experts_shared_outer_loras_a, + a_cfg["BLOCK_SIZE_M"], + ) + if prewarm_b_routing: + _get_routing( + topk_ids, + token_lora_mapping, + num_experts_b, + experts_shared_outer_loras_b, + b_stage_config["BLOCK_SIZE_M"], + ) return None intermediate = intermediate_buffer @@ -913,21 +913,14 @@ def _merged_experts_fused_moe_lora_add_impl( lora_a_virtual, token_lora_mapping.shape[0] ) if lora_envs.SGLANG_OPT_LORA_SHRINK_TUNE.get(): - # GB200 hand-tune knob (test-only) on top of PR #26899's heuristic config. The launcher - # pins BLOCK_SIZE_N (next_pow2(rank)) and BLOCK_SIZE_K (256), so only M/warps/stages apply. + # Test-only override; the launcher fixes N and K block sizes. a_stage_config = { **a_stage_config, "BLOCK_SIZE_M": 16, "num_warps": 4, "num_stages": 4, } - # F1-① prefill routing reuse: the A stage routes with BLOCK_SIZE_M 32 at prefill - # but the B stage with the tuned fused-moe config (typically 64), so the - # (num_experts, shared_outer, block_size) routing_cache key never matches across - # stages and the align/sort pipeline reruns per stage (4x/layer at prefill). - # Matching the A stage's routing block to the B stage's collapses them to one - # align/sort per layer-forward. Decode (<512 tokens) keeps the opt1 fused - # merged-align path and its tuned shrink block untouched. + # Match routing block sizes so prefill stages can share cached alignment. if ( lora_envs.SGLANG_OPT_LORA_PREFILL_ROUTING_REUSE.get() and token_lora_mapping.shape[0] >= 512 @@ -957,8 +950,10 @@ def _merged_experts_fused_moe_lora_add_impl( # non-owned blocks (never reads them), but a shared-outer expand routes by lora id # and would read them into the real (all-reduced) output -> must zero. split_k > 1 # also needs a zeroed buffer for its accumulation. - zero_intermediate = intermediate_split_k > 1 or ( - ep_local and experts_shared_outer_loras_b + must_zero_intermediate = ( + zero_intermediate + or intermediate_split_k > 1 + or (ep_local and experts_shared_outer_loras_b) ) if intermediate is None: intermediate = ( @@ -967,14 +962,14 @@ def _merged_experts_fused_moe_lora_add_impl( dtype=hidden_states.dtype, device=hidden_states.device, ) - if zero_intermediate + if must_zero_intermediate else torch.empty( intermediate_shape, dtype=hidden_states.dtype, device=hidden_states.device, ) ) - elif zero_intermediate: + elif must_zero_intermediate: # Caller-provided buffer (allocated on the consumer stream): zero it in-stream. intermediate.zero_() @@ -993,7 +988,7 @@ def _merged_experts_fused_moe_lora_add_impl( if stage == "shrink": # Pre-warm the routing-B cache on this (side) stream so the later "expand" stage # launches no routing kernels — they overlap finalize together with the shrink. - if routing_cache is not None: + if routing_cache is not None and prewarm_b_routing: _get_routing( topk_ids, token_lora_mapping, @@ -1041,8 +1036,13 @@ def _merged_experts_fused_moe_lora_add_impl( b_stage_config, mul_routed_weight, fuse_sum_all_reduce, + broadcast_intermediate=broadcast_intermediate, ) else: + assert not broadcast_intermediate, ( + "broadcasted LoRA-A intermediates require the rank-specialized " + "direct expand kernel" + ) invoke_fused_moe_kernel( intermediate_flat, lora_b_virtual, @@ -1131,6 +1131,10 @@ def merged_experts_fused_moe_lora_add( stage: str = "all", intermediate_buffer: torch.Tensor | None = None, expand_wait_event: "torch.cuda.Event | None" = None, + broadcast_intermediate: bool = False, + prewarm_a_routing: bool = True, + prewarm_b_routing: bool = True, + zero_intermediate: bool = False, ) -> "torch.Tensor | None": """Public API: wraps the registered op with routing_cache support.""" return _merged_experts_fused_moe_lora_add_impl( @@ -1153,4 +1157,8 @@ def merged_experts_fused_moe_lora_add( stage=stage, intermediate_buffer=intermediate_buffer, expand_wait_event=expand_wait_event, + broadcast_intermediate=broadcast_intermediate, + prewarm_a_routing=prewarm_a_routing, + prewarm_b_routing=prewarm_b_routing, + zero_intermediate=zero_intermediate, ) diff --git a/python/sglang/kernels/ops/speculative/multi_layer_eagle.py b/python/sglang/kernels/ops/speculative/multi_layer_eagle.py index b87e6c132..3d3ef92f3 100644 --- a/python/sglang/kernels/ops/speculative/multi_layer_eagle.py +++ b/python/sglang/kernels/ops/speculative/multi_layer_eagle.py @@ -12,6 +12,7 @@ # limitations under the License. # ============================================================================== +import torch import triton import triton.language as tl @@ -94,3 +95,636 @@ def rotate_input_ids( BLOCK_SIZE=BLOCK_SIZE, ) return input_ids + + +@triton.jit +def stash_append_boundary_state_kernel( + # flat sources (decode: predict + verify FULL hiddens; prefill: rotated + # input_ids + target FULL hiddens) + src_tokens_ptr, + src_hiddens_ptr, # [num_src_rows, hidden] + src_row_ends_ptr, # [bs] exclusive end row of each request's source segment + num_available_ptr, # [bs] committed rows at the segment tail (accept_lens / extend len) + req_pool_indices_ptr, # [bs] + # stash (per request, rolling last `front` committed (token, base-hidden) + # pairs; slot j of a request at boundary B holds position B - front + j) + stash_tokens_ptr, # [req_pool_size, front] int64 + stash_hiddens_ptr, # [req_pool_size, front, hidden] + stash_valid_lens_ptr, # [req_pool_size] int32, count of valid tail slots + front: tl.constexpr, + hidden_dim: tl.constexpr, + SET_VALID: tl.constexpr, # prefill: valid = m; decode: valid = min(valid + m, front) + BLOCK_H: tl.constexpr, +): + """Roll the per-request boundary stash forward by m = min(available, front) + newly committed (token, base-hidden) pairs taken from the source tail + rows [end - m, end). Kept old pairs shift down (reads stay ahead of + writes, ascending order).""" + pid = tl.program_id(0) + rpi = tl.load(req_pool_indices_ptr + pid).to(tl.int64) + end = tl.load(src_row_ends_ptr + pid).to(tl.int64) + avail = tl.load(num_available_ptr + pid).to(tl.int64) + m = tl.minimum(avail, front) + keep = front - m + + h_off = tl.arange(0, BLOCK_H) + + # 1) Shift the kept tail of the old stash to the front: new[i] = old[i + m] + for i in range(0, keep): + src_t = tl.load(stash_tokens_ptr + rpi * front + i + m) + tl.store(stash_tokens_ptr + rpi * front + i, src_t) + for hb in range(0, hidden_dim, BLOCK_H): + hmask = (hb + h_off) < hidden_dim + src_h = tl.load( + stash_hiddens_ptr + (rpi * front + i + m) * hidden_dim + hb + h_off, + mask=hmask, + ) + tl.store( + stash_hiddens_ptr + (rpi * front + i) * hidden_dim + hb + h_off, + src_h, + mask=hmask, + ) + + # 2) Append the m newest committed pairs from the source tail. + for i in range(0, m): + row = end - m + i + dst = rpi * front + keep + i + tok = tl.load(src_tokens_ptr + row) + tl.store(stash_tokens_ptr + dst, tok) + for hb in range(0, hidden_dim, BLOCK_H): + hmask = (hb + h_off) < hidden_dim + src_h = tl.load(src_hiddens_ptr + row * hidden_dim + hb + h_off, mask=hmask) + tl.store( + stash_hiddens_ptr + dst * hidden_dim + hb + h_off, src_h, mask=hmask + ) + + if SET_VALID: + valid = m + else: + valid = tl.minimum(tl.load(stash_valid_lens_ptr + rpi).to(tl.int64) + m, front) + tl.store(stash_valid_lens_ptr + rpi, valid.to(tl.int32)) + + +def stash_append_boundary_state_triton( + src_tokens, + src_hiddens, + src_row_ends, + num_available, + req_pool_indices, + stash_tokens, + stash_hiddens, + stash_valid_lens, + set_valid: bool, +): + """Append newly committed (token, base-hidden) pairs to the rolling + boundary stash (see kernel docstring). Decode: sources are (predict, + verify FULL hiddens) with ends = i*W + accept_lens. Prefill: sources are + (post-rotation input_ids, target FULL hiddens) with ends = start + len.""" + bs = req_pool_indices.shape[0] + if bs == 0: + return + stash_append_boundary_state_kernel[(bs,)]( + src_tokens, + src_hiddens, + src_row_ends, + num_available, + req_pool_indices, + stash_tokens, + stash_hiddens, + stash_valid_lens, + front=stash_tokens.shape[1], + hidden_dim=stash_hiddens.shape[2], + SET_VALID=set_valid, + BLOCK_H=1024, + ) + + +@triton.jit +def fill_widened_draft_extend_inputs_kernel( + # outputs: the widened per-request window buffers, width = W + front + input_ids_ptr, # [bs * width] + hidden_ptr, # [bs * width, hidden] + # sources + predict_ptr, # [bs * W] verify-sampled successor per verify row + verify_hidden_ptr, # [bs * W, hidden] target verify hiddens (FULL capture) + stash_tokens_ptr, # [req_pool_size, front] + stash_hiddens_ptr, # [req_pool_size, front, hidden] + stash_valid_lens_ptr, # [req_pool_size] + seq_lens_ptr, # [bs] PRE-verify seq_lens (window base = seq_lens - front) + req_pool_indices_ptr, # [bs] + draft_token_num: tl.constexpr, # W + front: tl.constexpr, # F_total + hidden_dim: tl.constexpr, + BLOCK_H: tl.constexpr, +): + """Materialize the widened depth-0 window's input tokens and hiddens: front + rows (j < front) source from stash slot j, original rows (j >= front) from + predict/verify hiddens; data-invalid front rows are zeroed. Locs/positions + are computed separately by compute_widened_draft_extend_locs_positions.""" + pid = tl.program_id(0) + rpi = tl.load(req_pool_indices_ptr + pid).to(tl.int64) + seq_len = tl.load(seq_lens_ptr + pid).to(tl.int64) + valid_len = tl.load(stash_valid_lens_ptr + rpi).to(tl.int64) + + # Rows below this hold no usable stash data (unseeded slot or p < 0). + first_valid = tl.maximum(tl.maximum(front - valid_len, front - seq_len), 0) + + h_off = tl.arange(0, BLOCK_H) + width = draft_token_num + front + + for j in range(0, width): + row = pid * width + j + if j >= front: + src = pid * draft_token_num + j - front + tok = tl.load(predict_ptr + src) + tl.store(input_ids_ptr + row, tok) + for hb in range(0, hidden_dim, BLOCK_H): + hmask = (hb + h_off) < hidden_dim + src_h = tl.load( + verify_hidden_ptr + src * hidden_dim + hb + h_off, mask=hmask + ) + tl.store(hidden_ptr + row * hidden_dim + hb + h_off, src_h, mask=hmask) + else: + if j >= first_valid: + tok = tl.load(stash_tokens_ptr + rpi * front + j) + tl.store(input_ids_ptr + row, tok) + for hb in range(0, hidden_dim, BLOCK_H): + hmask = (hb + h_off) < hidden_dim + src_h = tl.load( + stash_hiddens_ptr + (rpi * front + j) * hidden_dim + hb + h_off, + mask=hmask, + ) + tl.store( + hidden_ptr + row * hidden_dim + hb + h_off, src_h, mask=hmask + ) + else: + tl.store(input_ids_ptr + row, 0) + for hb in range(0, hidden_dim, BLOCK_H): + hmask = (hb + h_off) < hidden_dim + tl.store( + hidden_ptr + row * hidden_dim + hb + h_off, 0.0, mask=hmask + ) + + +def fill_widened_draft_extend_inputs_triton( + input_ids, + hidden_states, + predict, + verify_hiddens, + stash_tokens, + stash_hiddens, + stash_valid_lens, + seq_lens, + req_pool_indices, + draft_token_num: int, +): + """Fill the widened window's input tokens and hiddens in place (see kernel + docstring). Must run AFTER verify sampling (reads predict / hiddens) and + BEFORE the stash update for this iteration (the stash is still based at + the pre-verify boundary).""" + bs = req_pool_indices.shape[0] + if bs == 0: + return + fill_widened_draft_extend_inputs_kernel[(bs,)]( + input_ids, + hidden_states, + predict, + verify_hiddens, + stash_tokens, + stash_hiddens, + stash_valid_lens, + seq_lens, + req_pool_indices, + draft_token_num=draft_token_num, + front=stash_tokens.shape[1], + hidden_dim=stash_hiddens.shape[2], + BLOCK_H=1024, + ) + + +@triton.jit +def _wide_row_softmax_partials_kernel( + logits_ptr, # [bs, vocab] fp32 + temperatures_ptr, # [bs, 1] fp32 (dummy when HAS_TEMPS is False) + partial_max_ptr, # [bs, nblocks] fp32 + partial_sum_ptr, # [bs, nblocks] fp32 + vocab, + nblocks, + HAS_TEMPS: tl.constexpr, + BLOCK: tl.constexpr, +): + row = tl.program_id(0) + blk = tl.program_id(1) + offs = blk * BLOCK + tl.arange(0, BLOCK) + mask = offs < vocab + z = tl.load( + logits_ptr + row.to(tl.int64) * vocab + offs, mask=mask, other=-float("inf") + ) + if HAS_TEMPS: + z = z / tl.load(temperatures_ptr + row) + m = tl.max(z, axis=0) + s = tl.sum(tl.exp(z - m), axis=0) + tl.store(partial_max_ptr + row * nblocks + blk, m) + tl.store(partial_sum_ptr + row * nblocks + blk, s) + + +@triton.jit +def _wide_row_softmax_finalize_kernel( + partial_max_ptr, + partial_sum_ptr, + row_max_ptr, # [bs] fp32 + row_sum_ptr, # [bs] fp32 + nblocks, + NBLOCK_POW2: tl.constexpr, +): + row = tl.program_id(0) + offs = tl.arange(0, NBLOCK_POW2) + mask = offs < nblocks + m = tl.load(partial_max_ptr + row * nblocks + offs, mask=mask, other=-float("inf")) + s = tl.load(partial_sum_ptr + row * nblocks + offs, mask=mask, other=0.0) + gm = tl.max(m, axis=0) + gs = tl.sum(s * tl.exp(m - gm), axis=0) + tl.store(row_max_ptr + row, gm) + tl.store(row_sum_ptr + row, gs) + + +@triton.jit +def _wide_row_softmax_write_kernel( + logits_ptr, + temperatures_ptr, + row_max_ptr, + row_sum_ptr, + out_ptr, # [bs, out_row_stride] fp32; row i written at i * out_row_stride + vocab, + out_row_stride, + HAS_TEMPS: tl.constexpr, + BLOCK: tl.constexpr, +): + row = tl.program_id(0) + blk = tl.program_id(1) + offs = blk * BLOCK + tl.arange(0, BLOCK) + mask = offs < vocab + z = tl.load( + logits_ptr + row.to(tl.int64) * vocab + offs, mask=mask, other=-float("inf") + ) + if HAS_TEMPS: + z = z / tl.load(temperatures_ptr + row) + gm = tl.load(row_max_ptr + row) + gs = tl.load(row_sum_ptr + row) + q = tl.exp(z - gm) / gs + tl.store(out_ptr + row.to(tl.int64) * out_row_stride + offs, q, mask=mask) + + +def wide_row_softmax_triton( + logits: torch.Tensor, + temperatures, + out: torch.Tensor, +) -> torch.Tensor: + """Column-parallel softmax over very wide fp32 rows, optionally with + per-row temperature (q = softmax(logits / T)), written into ``out`` + (any row stride >= vocab). torch.softmax gives one block per row, which + serializes a single wide draft-vocab row onto one SM.""" + bs, vocab = logits.shape + BLOCK = 4096 + nblocks = triton.cdiv(vocab, BLOCK) + partial_max = torch.empty((bs, nblocks), dtype=torch.float32, device=logits.device) + partial_sum = torch.empty((bs, nblocks), dtype=torch.float32, device=logits.device) + row_max = torch.empty((bs,), dtype=torch.float32, device=logits.device) + row_sum = torch.empty((bs,), dtype=torch.float32, device=logits.device) + has_temps = temperatures is not None + dummy = row_max + _wide_row_softmax_partials_kernel[(bs, nblocks)]( + logits, + temperatures if has_temps else dummy, + partial_max, + partial_sum, + vocab, + nblocks, + HAS_TEMPS=has_temps, + BLOCK=BLOCK, + ) + _wide_row_softmax_finalize_kernel[(bs,)]( + partial_max, + partial_sum, + row_max, + row_sum, + nblocks, + NBLOCK_POW2=triton.next_power_of_2(nblocks), + ) + _wide_row_softmax_write_kernel[(bs, nblocks)]( + logits, + temperatures if has_temps else dummy, + row_max, + row_sum, + out, + vocab, + out.stride(0), + HAS_TEMPS=has_temps, + BLOCK=BLOCK, + ) + return out + + +@triton.jit +def compute_widened_draft_extend_locs_positions_kernel( + seq_lens_ptr, + req_pool_indices_ptr, + req_to_token_ptr, + stash_valid_lens_ptr, + locs_ptr, # [bs * width] int64 + positions_ptr, # [bs * width] int64 + req_to_token_stride, + front, + num_warmup_tokens, + width, + WIDTH_BLOCK: tl.constexpr, +): + """Per-request widened-window locs + positions: pos = seq_len - front + j; + rows below first_valid = max(front - stash_valid, front - seq_len, 0) hold + no stash data (positions zeroed), and the first num_warmup_tokens valid + front rows write to sacrificial loc 0.""" + pid = tl.program_id(0) + offs = tl.arange(0, WIDTH_BLOCK) + wmask = offs < width + offs64 = offs.to(tl.int64) + + seq_len = tl.load(seq_lens_ptr + pid).to(tl.int64) + rpi = tl.load(req_pool_indices_ptr + pid).to(tl.int64) + valid_len = tl.load(stash_valid_lens_ptr + rpi).to(tl.int64) + + pos = seq_len - front + offs64 + first_valid = tl.maximum(tl.maximum(front - valid_len, front - seq_len), 0) + data_valid = offs64 >= first_valid + write_real = offs64 >= tl.minimum(first_valid + num_warmup_tokens, front) + + tok = tl.load( + req_to_token_ptr + rpi * req_to_token_stride + tl.maximum(pos, 0), + mask=wmask, + other=0, + ).to(tl.int64) + locs = tl.where(write_real, tok, 0) + positions = tl.where(data_valid, pos, 0) + + base = pid.to(tl.int64) * width + tl.store(locs_ptr + base + offs, locs, mask=wmask) + tl.store(positions_ptr + base + offs, positions, mask=wmask) + + +def compute_widened_draft_extend_locs_positions_triton( + seq_lens, + req_pool_indices, + req_to_token, + stash_valid_lens, + draft_token_num: int, + num_front_tokens: int, + num_warmup_tokens: int, +): + width = draft_token_num + num_front_tokens + bs = seq_lens.shape[0] + locs = torch.empty((bs * width,), dtype=torch.int64, device=seq_lens.device) + positions = torch.empty((bs * width,), dtype=torch.int64, device=seq_lens.device) + if bs > 0: + compute_widened_draft_extend_locs_positions_kernel[(bs,)]( + seq_lens, + req_pool_indices, + req_to_token, + stash_valid_lens, + locs, + positions, + req_to_token.stride(0), + num_front_tokens, + num_warmup_tokens, + width, + WIDTH_BLOCK=triton.next_power_of_2(width), + ) + return locs, positions + + +@triton.jit +def fill_draft_extend_prepare_buffers_kernel( + # persistent per-token buffers (length max_num_token, int64) + input_ids_ptr, + positions_ptr, + out_cache_loc_ptr, + # per-token sources (length num_tokens) + src_input_ids_ptr, + src_positions_ptr, + src_out_cache_loc_ptr, + # persistent per-request buffers (length max_bs) + seq_lens_ptr, # int32 + req_pool_indices_ptr, # int64 + num_correct_drafts_ptr, # int32 + num_accept_tokens_ptr, # int32 + select_index_ptr, # int64 + temperatures_ptr, # float32 [max_bs, 1] (dummy when HAS_TEMPS is False) + # per-request sources (length raw_bs) + src_seq_lens_ptr, + src_req_pool_indices_ptr, + src_num_correct_drafts_ptr, + src_num_accept_tokens_ptr, + src_temperatures_ptr, # dummy when HAS_TEMPS is False + # chain hidden window, flat [num_tokens * hidden] (dummies when HAS_HIDDEN + # is False) + hidden_states_ptr, + src_hidden_states_ptr, + # gathered-buffer mirrors (dummies when HAS_GATHERED is False) + global_num_tokens_ptr, + global_num_tokens_for_logprob_ptr, + # scalars + num_tokens, + max_num_token, + raw_bs, + bs, + max_bs, + num_tokens_per_bs, + num_front_tokens, + seq_len_fill_value, + hidden_numel, + num_global, + num_token_programs, + HAS_TEMPS: tl.constexpr, + HAS_HIDDEN: tl.constexpr, + HAS_GATHERED: tl.constexpr, + BLOCK_TOK: tl.constexpr, + BLOCK_HIDDEN: tl.constexpr, + GLOBAL_BLOCK: tl.constexpr, +): + """The whole draft-extend prepare() buffer population in one launch; + program roles split by flat program id: + + - [0, num_token_programs): input_ids / positions / out_cache_loc; rows + < num_tokens take the source, the tail up to max_num_token is zeroed. + - [num_token_programs, +max_bs): one program per request row. Real rows + [0, raw_bs) take source values; padded rows [raw_bs, bs) take the pad + sentinels the graphs rely on (seq_len fill value, num_accept_tokens = -1, + temperatures = 1.0); rows >= bs are untouched except seq_lens, which is + fully reset. select_index = i*window + front + num_correct_drafts + (padded rows keep their stale num_correct_drafts, whose gather result + is discarded). + - num_token_programs + max_bs: the DP gathered-buffer fills. + - the rest: flat copy of the chain hidden window's real rows (the padded + tail is never read). + """ + pid = tl.program_id(0) + + if pid < num_token_programs: + tok_offs = pid * BLOCK_TOK + tl.arange(0, BLOCK_TOK) + store_mask = tok_offs < max_num_token + copy_mask = tok_offs < num_tokens + + tok = tl.load(src_input_ids_ptr + tok_offs, mask=copy_mask, other=0).to( + tl.int64 + ) + tl.store(input_ids_ptr + tok_offs, tok, mask=store_mask) + tok = tl.load(src_positions_ptr + tok_offs, mask=copy_mask, other=0).to( + tl.int64 + ) + tl.store(positions_ptr + tok_offs, tok, mask=store_mask) + tok = tl.load(src_out_cache_loc_ptr + tok_offs, mask=copy_mask, other=0).to( + tl.int64 + ) + tl.store(out_cache_loc_ptr + tok_offs, tok, mask=store_mask) + elif pid < num_token_programs + max_bs: + i = pid - num_token_programs + is_real = i < raw_bs + is_pad = (i >= raw_bs) & (i < bs) + in_bs = i < bs + + sl = tl.load(src_seq_lens_ptr + i, mask=is_real, other=seq_len_fill_value) + tl.store(seq_lens_ptr + i, sl.to(tl.int32)) + + rpi = tl.load(src_req_pool_indices_ptr + i, mask=is_real, other=0).to(tl.int64) + tl.store(req_pool_indices_ptr + i, rpi, mask=is_real) + + # The stale count must be read BEFORE the real-row store below. + ncd_stale = tl.load(num_correct_drafts_ptr + i, mask=is_pad, other=0).to( + tl.int64 + ) + ncd_src = tl.load(src_num_correct_drafts_ptr + i, mask=is_real, other=0).to( + tl.int64 + ) + tl.store(num_correct_drafts_ptr + i, ncd_src.to(tl.int32), mask=is_real) + ncd = tl.where(is_real, ncd_src, ncd_stale) + + # Padded rows get -1 so the sconv commit skips their live mamba slots. + nat = tl.load(src_num_accept_tokens_ptr + i, mask=is_real, other=-1).to( + tl.int32 + ) + tl.store(num_accept_tokens_ptr + i, nat, mask=in_bs) + + si = i.to(tl.int64) * num_tokens_per_bs + num_front_tokens + ncd + tl.store(select_index_ptr + i, si, mask=in_bs) + + if HAS_TEMPS: + t = tl.load(src_temperatures_ptr + i, mask=is_real, other=1.0) + tl.store(temperatures_ptr + i, t, mask=in_bs) + elif pid == num_token_programs + max_bs: + if HAS_GATHERED: + g_offs = tl.arange(0, GLOBAL_BLOCK) + g_mask = g_offs < num_global + g_vals = tl.zeros((GLOBAL_BLOCK,), dtype=tl.int32) + bs * num_tokens_per_bs + tl.store(global_num_tokens_ptr + g_offs, g_vals, mask=g_mask) + tl.store(global_num_tokens_for_logprob_ptr + g_offs, g_vals, mask=g_mask) + else: + if HAS_HIDDEN: + h_base = pid - num_token_programs - max_bs - 1 + h_offs = h_base.to(tl.int64) * BLOCK_HIDDEN + tl.arange(0, BLOCK_HIDDEN) + h_mask = h_offs < hidden_numel + h_vals = tl.load(src_hidden_states_ptr + h_offs, mask=h_mask) + tl.store(hidden_states_ptr + h_offs, h_vals, mask=h_mask) + + +def fill_draft_extend_prepare_buffers_triton( + input_ids, + positions, + out_cache_loc, + src_input_ids, + src_positions, + src_out_cache_loc, + seq_lens, + req_pool_indices, + num_correct_drafts, + num_accept_tokens, + select_index, + temperatures, + src_seq_lens, + src_req_pool_indices, + src_num_correct_drafts, + src_num_accept_tokens, + src_temperatures, + hidden_states, + src_hidden_states, + global_num_tokens, + global_num_tokens_for_logprob, + raw_bs, + bs, + num_tokens_per_bs, + num_front_tokens, + seq_len_fill_value, +): + max_num_token = input_ids.shape[0] + num_tokens = src_input_ids.shape[0] + max_bs = seq_lens.shape[0] + has_temps = temperatures is not None + has_hidden = src_hidden_states is not None + has_gathered = global_num_tokens is not None + + BLOCK_TOK = 1024 + BLOCK_HIDDEN = 2048 + num_token_programs = triton.cdiv(max_num_token, BLOCK_TOK) + + if has_hidden: + hidden_numel = num_tokens * hidden_states.shape[1] + num_hidden_programs = triton.cdiv(hidden_numel, BLOCK_HIDDEN) + else: + hidden_numel = 0 + num_hidden_programs = 0 + + if has_gathered: + num_global = global_num_tokens.shape[0] + global_block = triton.next_power_of_2(num_global) + else: + num_global = 0 + global_block = 1 + + grid = (num_token_programs + max_bs + 1 + num_hidden_programs,) + fill_draft_extend_prepare_buffers_kernel[grid]( + input_ids, + positions, + out_cache_loc, + src_input_ids, + src_positions, + src_out_cache_loc, + seq_lens, + req_pool_indices, + num_correct_drafts, + num_accept_tokens, + select_index, + temperatures if has_temps else seq_lens, + src_seq_lens, + src_req_pool_indices, + src_num_correct_drafts, + src_num_accept_tokens, + src_temperatures if has_temps else seq_lens, + hidden_states if has_hidden else seq_lens, + src_hidden_states if has_hidden else seq_lens, + global_num_tokens if has_gathered else seq_lens, + global_num_tokens_for_logprob if has_gathered else seq_lens, + num_tokens, + max_num_token, + raw_bs, + bs, + max_bs, + num_tokens_per_bs, + num_front_tokens, + seq_len_fill_value, + hidden_numel, + num_global, + num_token_programs, + HAS_TEMPS=has_temps, + HAS_HIDDEN=has_hidden, + HAS_GATHERED=has_gathered, + BLOCK_TOK=BLOCK_TOK, + BLOCK_HIDDEN=BLOCK_HIDDEN, + GLOBAL_BLOCK=global_block, + ) diff --git a/python/sglang/srt/arg_groups/overrides.py b/python/sglang/srt/arg_groups/overrides.py index 9abf92cac..5e9676993 100644 --- a/python/sglang/srt/arg_groups/overrides.py +++ b/python/sglang/srt/arg_groups/overrides.py @@ -824,6 +824,57 @@ def _deepseek_v4_overrides(server_args: Any, hf_config: Any) -> dict: return overrides +@_register_for( + "InklingForConditionalGeneration", + "InklingForConditionalGenerationMTP", +) +def _inkling_overrides(server_args: Any, hf_config: Any) -> dict: + """Inkling architecture defaults: SWA / mamba KV-pool ratios tuned for the + hybrid-SWA layout, the extra-buffer mamba strategy, and the unified radix + tree (which Inkling requires — models/inkling.py asserts it). The full-graph + prefill default is set separately (inline, before cuda-graph resolution) — + see ServerArgs.__post_init__ / _apply_inkling_prefill_cuda_graph_default. The + server-arg defaults each yield to an explicit user value (compared against + the ServerArgs class default); the prefill declaration is materialized + before _parse_cuda_graph_config folds cuda_graph_backend_prefill into + prefill.backend, and an explicit --cuda-graph-backend-prefill / + --disable-prefill-cuda-graph still wins. The unified-radix env write follows + the MiniMax-M3 handler precedent (env is not a resolvable server-arg).""" + from sglang.srt.server_args import ServerArgs + + overrides: Dict[str, Any] = {} + # NOTE: the full-graph prefill default is NOT set here. cuda-graph config is + # resolved in __post_init__ before declarations are materialized, so a + # cuda_graph_backend_prefill declared here lands too late (the breakable + # default would already have been auto-disabled for this multimodal arch). + # It is set inline before _handle_cuda_graph_config instead. + if server_args.swa_full_tokens_ratio == ServerArgs.swa_full_tokens_ratio: + overrides["swa_full_tokens_ratio"] = 0.1 + if server_args.mamba_full_memory_ratio == ServerArgs.mamba_full_memory_ratio: + overrides["mamba_full_memory_ratio"] = 0.1 + # Inkling requires the extra-buffer mamba strategy (inkling.py asserts + # enable_mamba_extra_buffer()); the generic "auto" resolution does not cover + # Inkling, so pin it here. Yields to an explicit --mamba-scheduler-strategy. + if server_args.mamba_radix_cache_strategy == ServerArgs.mamba_radix_cache_strategy: + overrides["mamba_radix_cache_strategy"] = "extra_buffer" + # Inkling attention runs only on the fa4 (Blackwell) or triton backends -- + # models/inkling_common/attn.py asserts attention_backend in {fa4, triton}. + # The generic resolver would otherwise pick trtllm_mha (SM100) / fa3 + # (Hopper), so a bare launch fails on the first attention forward. Pin a + # supported default when the user left every attention-backend flag unset + # (mirrors the MiniMax-M3 SM100 fa4-default above); an explicit + # --attention-backend / --prefill/decode-attention-backend still wins. + if server_args.is_attention_backend_not_set(): + inkling_attn_backend = "fa4" if is_sm100_supported() else "triton" + overrides["attention_backend"] = inkling_attn_backend + logger.info( + f"Use {inkling_attn_backend} as the attention backend for Inkling " + "(requires fa4 or triton)." + ) + envs.SGLANG_ENABLE_UNIFIED_RADIX_TREE.set(True) + return overrides + + @_register_for("NemotronHForCausalLM", "NemotronHPuzzleForCausalLM") def _nemotron_h_overrides(server_args: Any, hf_config: Any) -> dict: """NemotronH quantization / MoE runner / attention backend defaults diff --git a/python/sglang/srt/configs/__init__.py b/python/sglang/srt/configs/__init__.py index 5d5d14d68..262dd0508 100644 --- a/python/sglang/srt/configs/__init__.py +++ b/python/sglang/srt/configs/__init__.py @@ -9,6 +9,12 @@ from sglang.srt.configs.dots_vlm import DotsVLMConfig from sglang.srt.configs.exaone import ExaoneConfig from sglang.srt.configs.falcon_h1 import FalconH1Config from sglang.srt.configs.granitemoehybrid import GraniteMoeHybridConfig +from sglang.srt.configs.inkling import ( + InklingAudioConfig, + InklingMMConfig, + InklingModelConfig, + InklingVisionConfig, +) from sglang.srt.configs.interns2preview import InternS2PreviewConfig from sglang.srt.configs.janus_pro import MultiModalityConfig from sglang.srt.configs.jet_nemotron import JetNemotronConfig @@ -86,6 +92,10 @@ __all__ = [ "MiniMaxM3VLConfig", "Step3p7Config", "Qwen3ASRConfig", + "InklingAudioConfig", + "InklingMMConfig", + "InklingModelConfig", + "InklingVisionConfig", "UnlimitedVLConfig", "ZayaConfig", ] diff --git a/python/sglang/srt/configs/hybrid_arch.py b/python/sglang/srt/configs/hybrid_arch.py index 16a42d6e7..e3e05b28a 100644 --- a/python/sglang/srt/configs/hybrid_arch.py +++ b/python/sglang/srt/configs/hybrid_arch.py @@ -6,6 +6,8 @@ from sglang.srt.configs import ( BailingHybridConfig, FalconH1Config, GraniteMoeHybridConfig, + InklingMMConfig, + InklingModelConfig, InternS2PreviewConfig, JetNemotronConfig, JetVLMConfig, @@ -76,6 +78,11 @@ def mamba2_config(model_config: ModelConfig): | ZayaConfig, ): return config + if isinstance(config, InklingModelConfig): + return config if config.mamba2_cache_params is not None else None + if isinstance(config, InklingMMConfig): + text_config = config.text_config + return text_config if text_config.mamba2_cache_params is not None else None if isinstance(config, NemotronH_Nano_VL_V2_Config): return config.llm_config diff --git a/python/sglang/srt/configs/inkling.py b/python/sglang/srt/configs/inkling.py new file mode 100644 index 000000000..b4f24592e --- /dev/null +++ b/python/sglang/srt/configs/inkling.py @@ -0,0 +1,431 @@ +from __future__ import annotations + +from dataclasses import dataclass, field +from typing import Any, Literal, Optional + +import torch +from transformers import CONFIG_MAPPING +from transformers.configuration_utils import PretrainedConfig + +from sglang.srt.configs.mamba_utils import BaseLinearStateParams + + +class InklingModelConfig(PretrainedConfig): + model_type = "inkling_model" + keys_to_ignore_at_inference = ["past_key_values"] + + def __init__( + self, + *, + vocab_size: int = 201024, + hidden_size: int = 1536, + intermediate_size: int = 768, + dense_intermediate_size: Optional[int] = None, + num_hidden_layers: int = 16, + num_attention_heads: int = 12, + num_key_value_heads: int = 4, + head_dim: Optional[int] = None, + v_head_dim: Optional[int] = None, + d_rel: int = 16, + rel_extent: int = 1024, + local_layer_ids: Optional[list[int]] = None, + sliding_window_size: int = 512, + swa_num_attention_heads: Optional[int] = None, + swa_num_key_value_heads: Optional[int] = None, + swa_head_dim: Optional[int] = None, + swa_v_head_dim: Optional[int] = None, + mtp_local_layer_ids: Optional[list[int]] = None, + mtp_local_extent: Optional[int] = None, + mtp_swa_num_attention_heads: Optional[int] = None, + mtp_swa_num_key_value_heads: Optional[int] = None, + mtp_swa_head_dim: Optional[int] = None, + rms_norm_eps: float = 1e-6, + hidden_act: str = "silu", + q_bias: bool = False, + o_bias: bool = False, + use_embed_norm: bool = False, + use_sconv: bool = False, + sconv_kernel_size: int = 4, + chain_hidden_post_norm: bool = False, + dense_mlp_idx: int = 0, + n_routed_experts: int = 0, + n_shared_experts: int = 0, + num_experts_per_tok: int = 1, + route_scale: float = 1.0, + use_gate_bias: bool = False, + use_global_scale: bool = False, + norm_after_topk: bool = True, + gate_activation: Literal["sigmoid", "softmax"] = "sigmoid", + shared_expert_sink: bool = False, + shared_experts_size: int = 1, + inference_moe_w13_interleaved: bool = True, + log_scaling_n_floor: int | None = None, + log_scaling_alpha: float = 0.1, + unpadded_vocab_size: Optional[int] = None, + padded_vocab_size: Optional[int] = None, + logits_mup_width_multiplier: Optional[float] = None, + final_logit_softcapping: Optional[float] = None, + num_nextn_predict_layers: int = 8, + tie_word_embeddings: bool = False, + **kwargs: Any, + ) -> None: + if head_dim is None: + head_dim = hidden_size // num_attention_heads + if v_head_dim is None: + v_head_dim = head_dim + if swa_num_attention_heads is None: + swa_num_attention_heads = num_attention_heads + if swa_num_key_value_heads is None: + swa_num_key_value_heads = num_key_value_heads + if swa_head_dim is None: + swa_head_dim = head_dim + if swa_v_head_dim is None: + swa_v_head_dim = swa_head_dim + if dense_intermediate_size is None: + dense_intermediate_size = intermediate_size + if local_layer_ids is None: + local_layer_ids = [] + # Per-depth banded MTP attention: a depth listed in mtp_local_layer_ids + # is a sliding-window block with its own window (mtp_local_extent); + # other depths stay full-attention. + if mtp_local_layer_ids is None: + mtp_local_layer_ids = [] + if mtp_local_extent is None: + mtp_local_extent = sliding_window_size + if mtp_swa_num_attention_heads is None: + mtp_swa_num_attention_heads = swa_num_attention_heads + if mtp_swa_num_key_value_heads is None: + mtp_swa_num_key_value_heads = swa_num_key_value_heads + if mtp_swa_head_dim is None: + mtp_swa_head_dim = swa_head_dim + if mtp_local_layer_ids: + local_id_set = set(mtp_local_layer_ids) + assert len(local_id_set) == len( + mtp_local_layer_ids + ), f"mtp_local_layer_ids must be unique: {mtp_local_layer_ids}" + assert all(0 <= i < num_nextn_predict_layers for i in local_id_set), ( + f"mtp_local_layer_ids must be in [0, {num_nextn_predict_layers}): " + f"{mtp_local_layer_ids}" + ) + # The draft KV pool and the sconv conv-state cache are still sized + # from the trunk's swa geometry; a head geometry that differs is not + # wired through yet. + assert ( + mtp_swa_num_key_value_heads == swa_num_key_value_heads + and mtp_swa_head_dim == swa_head_dim + ), ( + "banded MTP head geometry must match the trunk swa geometry: " + f"kv_heads {mtp_swa_num_key_value_heads} vs " + f"{swa_num_key_value_heads}, head_dim {mtp_swa_head_dim} vs " + f"{swa_head_dim}" + ) + + if padded_vocab_size is None: + padded_vocab_size = vocab_size + vocab_size = ( + unpadded_vocab_size + if ( + unpadded_vocab_size is not None + and unpadded_vocab_size < padded_vocab_size + ) + else vocab_size + ) + + self.vocab_size = vocab_size + self.padded_vocab_size = padded_vocab_size + self.hidden_size = hidden_size + self.intermediate_size = intermediate_size + self.dense_intermediate_size = dense_intermediate_size + self.num_hidden_layers = num_hidden_layers + self.num_attention_heads = num_attention_heads + self.num_key_value_heads = num_key_value_heads + self.head_dim = head_dim + self.v_head_dim = v_head_dim + self.d_rel = d_rel + self.rel_extent = rel_extent + self.local_layer_ids = local_layer_ids + self.sliding_window_size = sliding_window_size + self.swa_num_attention_heads = swa_num_attention_heads + self.swa_num_key_value_heads = swa_num_key_value_heads + self.swa_head_dim = swa_head_dim + self.swa_v_head_dim = swa_v_head_dim + self.mtp_local_layer_ids = mtp_local_layer_ids + self.mtp_local_extent = mtp_local_extent + self.mtp_swa_num_attention_heads = mtp_swa_num_attention_heads + self.mtp_swa_num_key_value_heads = mtp_swa_num_key_value_heads + self.mtp_swa_head_dim = mtp_swa_head_dim + self.rms_norm_eps = rms_norm_eps + self.hidden_act = hidden_act + self.q_bias = q_bias + self.o_bias = o_bias + self.use_embed_norm = use_embed_norm + self.use_sconv = use_sconv + self.sconv_kernel_size = sconv_kernel_size + self.chain_hidden_post_norm = chain_hidden_post_norm + self.dense_mlp_idx = dense_mlp_idx + self.n_routed_experts = n_routed_experts + self.num_experts = n_routed_experts + self.n_shared_experts = n_shared_experts + self.num_shared_experts = n_shared_experts + self.num_experts_per_tok = num_experts_per_tok + self.route_scale = route_scale + self.use_gate_bias = use_gate_bias + self.use_global_scale = use_global_scale + self.norm_after_topk = norm_after_topk + self.gate_activation = gate_activation + self.shared_expert_sink = shared_expert_sink + self.shared_experts_size = shared_experts_size + self.inference_moe_w13_interleaved = inference_moe_w13_interleaved + self.log_scaling_n_floor = log_scaling_n_floor + self.log_scaling_alpha = log_scaling_alpha + self.unpadded_vocab_size = self.vocab_size + self.logits_mup_width_multiplier = logits_mup_width_multiplier + self.final_logit_softcapping = final_logit_softcapping + self.num_nextn_predict_layers = num_nextn_predict_layers + + super().__init__( + tie_word_embeddings=tie_word_embeddings, + **kwargs, + ) + + @property + def conv_layer_ids(self) -> list[int]: + return list(range(self.num_hidden_layers)) + + @property + def linear_layer_ids(self) -> list[int]: + return self.conv_layer_ids + + @property + def full_attention_layer_ids(self) -> list[int]: + return list(range(self.num_hidden_layers)) + + @property + def mamba_chunk_size(self) -> int: + # Floor at 64: mamba_cache_chunk_size = max(mamba_chunk_size, page_size), + # and a floor of 1 lets the radix tree adopt another request's KV at + # tiny shared prefixes, whose different kernel-rounding perturbs decode logits. + return 64 + + @property + def mamba2_cache_params(self) -> Optional[InklingConvCacheParams]: + from sglang.srt.runtime_context import get_parallel + + try: + tp_size = get_parallel().attn_tp_size + except (AssertionError, RuntimeError): + tp_size = 1 + + def tp_local_kv_conv_dim(num_kv_heads: int, head_dim: int) -> int: + return max(1, num_kv_heads // tp_size) * head_dim + + full_kv_conv_dim = tp_local_kv_conv_dim(self.num_key_value_heads, self.head_dim) + local_kv_conv_dim = tp_local_kv_conv_dim( + self.swa_num_key_value_heads, self.swa_head_dim + ) + stream_dim = self.hidden_size + from sglang.srt.runtime_context import get_server_args + + if get_server_args().enable_scattered_sconv: + # Scattered sconv: the attn/mlp output sconvs run on the [T, H/P] + # hidden shard, so their conv-state caches shard with them. + assert ( + self.hidden_size % tp_size == 0 + ), f"hidden_size {self.hidden_size} not divisible by attn tp {tp_size}" + stream_dim = self.hidden_size // tp_size + conv_len = self.sconv_kernel_size - 1 + shape = InklingConvStateShape( + conv=[ + (conv_len, full_kv_conv_dim), + (conv_len, full_kv_conv_dim), + (conv_len, local_kv_conv_dim), + (conv_len, local_kv_conv_dim), + (conv_len, stream_dim), + (conv_len, stream_dim), + ], + temporal=(0, 0, 0), + ) + dtype = InklingStateDType(conv=torch.bfloat16, temporal=torch.bfloat16) + return InklingConvCacheParams( + shape=shape, layers=self.conv_layer_ids, dtype=dtype + ) + + +class InklingAudioConfig(PretrainedConfig): + model_type = "inkling_audio_model" + + def __init__( + self, + *, + decoder_dmodel: Optional[int] = None, + n_mel_bins: int = 80, + mel_vocab_size: int = 16, + dmel_min_value: float = -1.5, + dmel_max_value: float = 2.0, + use_audio_norm: bool = False, + audio_mode: Literal["dmel", "flow"] = "dmel", + **kwargs: Any, + ) -> None: + self.decoder_dmodel = decoder_dmodel + self.n_mel_bins = n_mel_bins + self.mel_vocab_size = mel_vocab_size + self.dmel_min_value = dmel_min_value + self.dmel_max_value = dmel_max_value + self.use_audio_norm = use_audio_norm + self.audio_mode = audio_mode + super().__init__(**kwargs) + + +class InklingVisionConfig(PretrainedConfig): + model_type = "inkling_vision_model" + + def __init__( + self, + *, + vision_encoder_type: Literal["linear", "hmlp"] = "hmlp", + decoder_dmodel: Optional[int] = None, + patch_size: int = 16, + temporal_patch_size: int = 1, + n_channels: int = 3, + n_layers: int = 1, + use_vision_norm: bool = False, + **kwargs: Any, + ) -> None: + self.vision_encoder_type = vision_encoder_type + self.decoder_dmodel = decoder_dmodel + self.patch_size = patch_size + self.temporal_patch_size = temporal_patch_size + self.n_channels = n_channels + self.n_layers = n_layers + self.use_vision_norm = use_vision_norm + super().__init__(**kwargs) + + +class InklingMMConfig(PretrainedConfig): + model_type = "inkling_mm_model" + keys_to_ignore_at_inference = ["past_key_values"] + sub_configs = { + "text_config": InklingModelConfig, + "audio_config": InklingAudioConfig, + "vision_config": InklingVisionConfig, + } + + def __init__( + self, + *, + text_config: Optional[dict[str, Any] | InklingModelConfig] = None, + audio_config: Optional[dict[str, Any] | InklingAudioConfig] = None, + vision_config: Optional[dict[str, Any] | InklingVisionConfig] = None, + mtp_config: Optional[dict[str, Any]] = None, + tie_word_embeddings: bool = False, + **kwargs: Any, + ) -> None: + self.mtp_config = mtp_config + self.text_config = ( + text_config + if isinstance(text_config, InklingModelConfig) + else InklingModelConfig(**(text_config or {})) + ) + if isinstance(mtp_config, dict) and mtp_config.get("local_layer_ids"): + # Banded MTP head: the checkpoint declares its sliding-window draft + # depths on mtp_config. Canonicalize onto text_config so every + # consumer (hybrid layer-id split, draft pool routing, the MTP block + # construction) reads one source of truth. + self.text_config.mtp_local_layer_ids = list(mtp_config["local_layer_ids"]) + if mtp_config.get("local_extent") is not None: + self.text_config.mtp_local_extent = mtp_config["local_extent"] + self.audio_config = ( + audio_config + if isinstance(audio_config, InklingAudioConfig) + else InklingAudioConfig(**(audio_config or {})) + ) + self.vision_config = ( + vision_config + if isinstance(vision_config, InklingVisionConfig) + else InklingVisionConfig(**(vision_config or {})) + ) + super().__init__( + tie_word_embeddings=tie_word_embeddings, + **kwargs, + ) + + def get_text_config(self, *args: Any, **kwargs: Any) -> InklingModelConfig: + return self.text_config + + @property + def vocab_size(self) -> int: + return self.text_config.vocab_size + + @property + def hidden_size(self) -> int: + return self.text_config.hidden_size + + @property + def num_hidden_layers(self) -> int: + return self.text_config.num_hidden_layers + + @property + def num_attention_heads(self) -> int: + return self.text_config.num_attention_heads + + @property + def num_key_value_heads(self) -> int: + return self.text_config.num_key_value_heads + + @property + def head_dim(self) -> int: + return self.text_config.head_dim + + @property + def full_attention_layer_ids(self) -> list[int]: + return self.text_config.full_attention_layer_ids + + @property + def linear_layer_ids(self) -> list[int]: + return self.text_config.linear_layer_ids + + @property + def conv_layer_ids(self) -> list[int]: + return self.text_config.conv_layer_ids + + @property + def mamba_chunk_size(self) -> int: + return self.text_config.mamba_chunk_size + + @property + def mamba2_cache_params(self) -> Optional[InklingConvCacheParams]: + return self.text_config.mamba2_cache_params + + +@dataclass(kw_only=True, frozen=True) +class InklingConvStateShape: + conv: list[tuple[int, int]] + temporal: tuple[int, int, int] + + # Conv tuples read (K-1, dim) — the overlapping dedup view would alias + # along the dim axis, so the dedup conv-intermediate layout must stay off. + disable_conv_window_dedup: bool = True + + +@dataclass(kw_only=True, frozen=True) +class InklingStateDType: + conv: torch.dtype = torch.bfloat16 + temporal: torch.dtype = torch.bfloat16 + + +@dataclass(kw_only=True, frozen=True) +class InklingConvCacheParams(BaseLinearStateParams): + dtype: InklingStateDType = field(default_factory=InklingStateDType) + shape: InklingConvStateShape + + +for _model_type, _config_cls in { + "inkling_model": InklingModelConfig, + "inkling_audio_model": InklingAudioConfig, + "inkling_vision_model": InklingVisionConfig, + "inkling_mm_model": InklingMMConfig, +}.items(): + try: + CONFIG_MAPPING.register(_model_type, _config_cls) + except Exception: + CONFIG_MAPPING._extra_content[_model_type] = _config_cls diff --git a/python/sglang/srt/configs/mamba_utils.py b/python/sglang/srt/configs/mamba_utils.py index 54d8cf95f..087b1293c 100644 --- a/python/sglang/srt/configs/mamba_utils.py +++ b/python/sglang/srt/configs/mamba_utils.py @@ -137,6 +137,10 @@ class Mamba2StateShape: conv: list[tuple[int, int]] temporal: tuple[int, int, int] + # Conv tuples read (dim, K-1) — the window axis is last, which the + # deduplicated conv-intermediate layout requires. + disable_conv_window_dedup: bool = False + intermediate_size: int conv_dim: int ssm_state_size: int @@ -217,6 +221,10 @@ class KimiLinearStateShape: conv: List[tuple[int, int]] temporal: tuple[int, int, int] + # Conv tuples read (K-1, dim) — the overlapping dedup view would alias + # along the dim axis, so the dedup conv-intermediate layout must stay off. + disable_conv_window_dedup: bool = True + num_heads: int head_dim: int num_k_heads: int diff --git a/python/sglang/srt/configs/model_config.py b/python/sglang/srt/configs/model_config.py index 0fcfd777d..020995054 100644 --- a/python/sglang/srt/configs/model_config.py +++ b/python/sglang/srt/configs/model_config.py @@ -310,6 +310,7 @@ class ModelConfig: "Gemma3ForConditionalGeneration", "Llama4ForConditionalGeneration", "Step3VLForConditionalGeneration", + "InklingForConditionalGeneration", ] if ( self.hf_config.architectures[0] in mm_disabled_models @@ -608,6 +609,11 @@ class ModelConfig: self.hf_config.architectures[0] = "MiMoV2MTP" if is_draft_model and self.hf_config.architectures[0] == "Step3p5ForCausalLM": self.hf_config.architectures[0] = "Step3p5MTP" + if ( + is_draft_model + and self.hf_config.architectures[0] == "InklingForConditionalGeneration" + ): + self.hf_config.architectures[0] = "InklingForConditionalGenerationMTP" if ( is_draft_model and self.hf_config.architectures[0] == "Step3p7ForConditionalGeneration" @@ -692,6 +698,8 @@ class ModelConfig: "MiMoV2MTP", "Gemma4ForCausalLM", "Gemma4ForConditionalGeneration", + "InklingForConditionalGeneration", + "InklingForConditionalGenerationMTP", "Gemma4UnifiedForConditionalGeneration", ] @@ -1913,6 +1921,8 @@ def is_hybrid_swa_model( "Gemma4UnifiedForConditionalGeneration", "LagunaForCausalLM", "MellumForCausalLM", + "InklingForConditionalGeneration", + "InklingForConditionalGenerationMTP", "UnlimitedOCRForCausalLM", } if any(arch in hybrid_swa_archs for arch in model_architectures): @@ -1994,6 +2004,37 @@ def get_hybrid_layer_ids( full_attention_layer_ids = [ i for i, x in enumerate(layer_types) if x == "full_attention" ] + elif "InklingForConditionalGenerationMTP" in model_architectures: + # One block per MTP depth; a banded head marks its sliding-window depths + # in mtp_local_layer_ids. The per-depth pool routing in the KV-cache + # mixin is authoritative; this keeps model_config's swa/full lists + # self-consistent for other consumers. + mtp_local_layer_ids = hf_text_config.mtp_local_layer_ids + if mtp_local_layer_ids: + num_depths = hf_text_config.num_nextn_predict_layers + local_set = set(mtp_local_layer_ids) + swa_attention_layer_ids = sorted(local_set) + full_attention_layer_ids = [ + i for i in range(num_depths) if i not in local_set + ] + else: + swa_attention_layer_ids = [] + full_attention_layer_ids = [0] + elif "InklingForConditionalGeneration" in model_architectures: + local_layer_ids = hf_text_config.local_layer_ids + local_layer_id_set = set(local_layer_ids) + assert len(local_layer_id_set) == len( + local_layer_ids + ), f"Inkling local_layer_ids must be unique: {local_layer_ids}" + assert all( + 0 <= layer_id < num_hidden_layers for layer_id in local_layer_id_set + ), f"Inkling local_layer_ids must be in [0, {num_hidden_layers}): {local_layer_ids}" + swa_attention_layer_ids = [ + i for i in range(num_hidden_layers) if i in local_layer_id_set + ] + full_attention_layer_ids = [ + i for i in range(num_hidden_layers) if i not in local_layer_id_set + ] elif "UnlimitedOCRForCausalLM" in model_architectures: swa_attention_layer_ids = list(range(num_hidden_layers)) full_attention_layer_ids = [] diff --git a/python/sglang/srt/constrained/llguidance_backend.py b/python/sglang/srt/constrained/llguidance_backend.py index a758e0816..b74563752 100644 --- a/python/sglang/srt/constrained/llguidance_backend.py +++ b/python/sglang/srt/constrained/llguidance_backend.py @@ -187,12 +187,22 @@ class GuidanceBackend(BaseGrammarBackend): try: structural_tag = json.loads(key_string) assert is_legacy_structural_tag(structural_tag) + # Pair each structure with a trigger that prefixes its own + # ``begin`` — StructTag asserts begin.startswith(trigger), and + # detectors with per-tool triggers (e.g. Inkling's + # <|message_model|>{name}<|content_invoke_tool_json|>) emit a + # distinct trigger per tool, so triggers[0] matches only one of + # them and multi-tool grammars fail to compile. + triggers = structural_tag["triggers"] tags = [ StructTag( begin=structure["begin"], grammar=structure["schema"], end=structure["end"], - trigger=structural_tag["triggers"][0], # TODO? + trigger=next( + (t for t in triggers if structure["begin"].startswith(t)), + triggers[0], + ), ) for structure in structural_tag["structures"] ] diff --git a/python/sglang/srt/distributed/device_communicators/torch_symm_mem.py b/python/sglang/srt/distributed/device_communicators/torch_symm_mem.py index fd38fb8f5..3ba756c7d 100644 --- a/python/sglang/srt/distributed/device_communicators/torch_symm_mem.py +++ b/python/sglang/srt/distributed/device_communicators/torch_symm_mem.py @@ -11,6 +11,7 @@ from torch.distributed import ProcessGroup from sglang.srt.distributed.device_communicators.all_reduce_utils import ( TORCH_SYMM_MEM_ALL_REDUCE_MAX_SIZES, ) +from sglang.srt.environ import envs from sglang.srt.utils import is_cuda, is_hip try: @@ -48,7 +49,7 @@ class TorchSymmMemCommunicator: # to the two-shot path. _WORLD_SIZES_MULTIMEM = { 9: [4, 6, 8], - 10: [6, 8], + 10: [4, 6, 8], } def __init__(self, group: ProcessGroup, device: Union[int, str, torch.device]): @@ -93,6 +94,19 @@ class TorchSymmMemCommunicator: ) return self.max_size = supported_max_sizes[self.world_size] + # Keep the JIT all-reduce buffer above the largest prefill payload + # ([16384, 6144] bf16 = 192 MiB), including room for tail regions. + if envs.SGLANG_OPT_USE_INKLING_CUSTOM_AR.get(): + self.max_size = max(self.max_size, 256 * 1024 * 1024) + from sglang.srt.runtime_context import get_server_args + + if ( + get_server_args().enable_scattered_sconv + or envs.SGLANG_OPT_USE_INKLING_FUSED_AR_SCONV.get() + ): + # Fused extend kernels are out-of-place, so OUT must hold the + # same maximum prefill payload as IN, including tail regions. + self.max_size = max(self.max_size, 512 * 1024 * 1024) self.buffer = torch_symm_mem.empty( self.max_size // self.dtype.itemsize, device=self.device, diff --git a/python/sglang/srt/entrypoints/anthropic/protocol.py b/python/sglang/srt/entrypoints/anthropic/protocol.py index 4d7d2a46e..95217d6b3 100644 --- a/python/sglang/srt/entrypoints/anthropic/protocol.py +++ b/python/sglang/srt/entrypoints/anthropic/protocol.py @@ -334,7 +334,7 @@ class AnthropicOutputConfig(BaseModel): ``task_budget`` is propagated as a custom-param hint. """ - effort: Optional[Literal["low", "medium", "high", "xhigh", "max"]] = None + effort: Optional[Literal["minimal", "low", "medium", "high", "xhigh", "max"]] = None task_budget: Optional[AnthropicTaskBudget] = None diff --git a/python/sglang/srt/entrypoints/openai/chat_encoding.py b/python/sglang/srt/entrypoints/openai/chat_encoding.py index f5d2ba026..547a1a04c 100644 --- a/python/sglang/srt/entrypoints/openai/chat_encoding.py +++ b/python/sglang/srt/entrypoints/openai/chat_encoding.py @@ -16,7 +16,7 @@ def resolve_chat_encoding_spec( tokenizer: Any, tool_call_parser: Optional[str] = None, ) -> Optional[str]: - """Return the chat encoding spec for a model: "dsv4", "dsv32", or None. + """Return the chat encoding spec for a model: "dsv4", "dsv32", "inkling", or None. None means the default path (HF chat template). """ @@ -31,6 +31,12 @@ def resolve_chat_encoding_spec( if "DeepseekV4" in arch: return "dsv4" + # Inkling has no Jinja chat_template and uses a tiktoken base + a special-token + # overlay + negative MM placeholders, so it can't go through apply_chat_template; + # render input_ids directly via the Inkling renderer (serving_chat._encode_messages). + if "InklingForConditionalGeneration" in arch: + return "inkling" + has_chat_template = tokenizer is not None and tokenizer.chat_template is not None if "DeepseekV3" in arch and not has_chat_template: return "dsv32" @@ -54,6 +60,16 @@ def encode_simple_chat( currently renders to zero tokens, but keeping the insertion explicit ties this helper to the serving semantics rather than to that coincidence). """ + if spec == "inkling": + from sglang.srt.parser.inkling_renderer import render_inkling_messages + from sglang.srt.parser.inkling_tokenizer import InklingTokenizer + + return render_inkling_messages( + messages, + InklingTokenizer(tokenizer=tokenizer), + add_generation_prompt=False, + ) + if spec in ("dsv4", "dsv32"): if messages and messages[0]["role"] != "system": messages = [{"role": "system", "content": ""}] + list(messages) diff --git a/python/sglang/srt/entrypoints/openai/protocol.py b/python/sglang/srt/entrypoints/openai/protocol.py index 2677a0637..d24c95be7 100644 --- a/python/sglang/srt/entrypoints/openai/protocol.py +++ b/python/sglang/srt/entrypoints/openai/protocol.py @@ -20,6 +20,7 @@ import time import uuid from dataclasses import dataclass from typing import ( + Annotated, Any, Dict, List, @@ -490,6 +491,20 @@ class ChatCompletionMessageContentTextPart(BaseModel): text: str +class ChatCompletionMessageContentThinkingPart(BaseModel): + type: Literal["thinking", "reasoning"] + thinking: Optional[str] = None + text: Optional[str] = None + + @model_validator(mode="after") + def validate_payload(self): + if (self.thinking is None) == (self.text is None): + raise ValueError( + "thinking parts require exactly one of 'thinking' or 'text'" + ) + return self + + class ChatCompletionMessageContentImageURL(BaseModel): url: str detail: Optional[Literal["auto", "low", "high"]] = "auto" @@ -536,6 +551,7 @@ class ChatCompletionMessageContentToolReferenceBlock(BaseModel): ChatCompletionMessageContentPart = Union[ ChatCompletionMessageContentTextPart, + ChatCompletionMessageContentThinkingPart, ChatCompletionMessageContentImagePart, ChatCompletionMessageContentVideoPart, ChatCompletionMessageContentAudioPart, @@ -596,11 +612,31 @@ class ChatCompletionMessageGenericParam(BaseModel): return v_lower raise ValueError("'role' must be a string") + @model_validator(mode="after") + def validate_thinking_parts_role(self): + if self.role != "assistant" and isinstance(self.content, list): + for part in self.content: + if isinstance(part, ChatCompletionMessageContentThinkingPart): + raise ValueError( + "thinking content parts are only valid in assistant messages" + ) + return self + class ChatCompletionMessageUserParam(BaseModel): role: Literal["user"] content: Union[str, List[ChatCompletionMessageContentPart]] + @model_validator(mode="after") + def validate_thinking_parts_role(self): + if isinstance(self.content, list): + for part in self.content: + if isinstance(part, ChatCompletionMessageContentThinkingPart): + raise ValueError( + "thinking content parts are only valid in assistant messages" + ) + return self + ChatCompletionMessageParam = Union[ ChatCompletionMessageGenericParam, ChatCompletionMessageUserParam @@ -694,9 +730,16 @@ class ChatCompletionRequest(BaseModel): return_cached_tokens_details: bool = False return_prompt_token_ids: bool = False return_meta_info: bool = False - reasoning_effort: Optional[Literal["none", "low", "medium", "high", "max"]] = Field( + reasoning_effort: Optional[ + Union[ + Literal["none", "minimal", "low", "medium", "high", "xhigh", "max"], + Annotated[float, Field(ge=0.0, le=0.99, allow_inf_nan=False)], + ] + ] = Field( default=None, description="Constrains effort on reasoning for reasoning models. " + "Accepts string levels ('none', 'minimal', 'low', 'medium', 'high', 'xhigh', 'max') or a " + "float in [0.0, 1.0] for fine-grained control. " "'none' disables reasoning entirely, 'low' is the least effort, 'high' is the most effort. " "Reducing reasoning effort can result in faster responses and fewer tokens used on reasoning " "in a response. 'none' defaults thinking and enable_thinking to false in " @@ -796,6 +839,13 @@ class ChatCompletionRequest(BaseModel): values["tool_choice"] = "auto" return values + @field_validator("reasoning_effort", mode="before") + @classmethod + def validate_reasoning_effort_type(cls, value): + if isinstance(value, bool): + raise ValueError("reasoning_effort must not be a boolean") + return value + @model_validator(mode="before") @classmethod def normalize_reasoning_inputs(cls, values: Dict): @@ -803,9 +853,29 @@ class ChatCompletionRequest(BaseModel): thinking = None if r is not None and isinstance(r, dict): - effort = r.get("effort") or r.get("reasoning_effort") - if effort in {"none", "low", "medium", "high"}: + effort = r.get("effort") + if effort is None: + effort = r.get("reasoning_effort") + if isinstance(effort, str) and effort in { + "none", + "low", + "medium", + "high", + "xhigh", + "max", + }: values["reasoning_effort"] = effort + elif isinstance(effort, (int, float)) and not isinstance(effort, bool): + values["reasoning_effort"] = float(effort) + elif isinstance(effort, str): + # Keep parity with the top-level reasoning_effort field, whose + # lax union coerces numeric strings; range checks then apply. + try: + values["reasoning_effort"] = float(effort) + except ValueError as exc: + raise ValueError(f"invalid reasoning effort: {effort!r}") from exc + elif effort is not None: + raise ValueError(f"invalid reasoning effort: {effort!r}") enabled = ( r.get("enabled") @@ -1231,7 +1301,9 @@ class TokenizeRequest(BaseModel): tool_choice: Optional[Union[ToolChoice, Literal["auto", "required", "none"]]] = ( Field(default=None, examples=["auto"]) ) - reasoning_effort: Optional[Literal["none", "low", "medium", "high"]] = None + reasoning_effort: Optional[Literal["none", "minimal", "low", "medium", "high"]] = ( + None + ) continue_final_message: bool = False chat_template_kwargs: Optional[Dict] = None add_special_tokens: bool = Field( diff --git a/python/sglang/srt/entrypoints/openai/serving_chat.py b/python/sglang/srt/entrypoints/openai/serving_chat.py index 3a678173f..021d22283 100644 --- a/python/sglang/srt/entrypoints/openai/serving_chat.py +++ b/python/sglang/srt/entrypoints/openai/serving_chat.py @@ -3,6 +3,7 @@ from __future__ import annotations import copy import json import logging +import math import time import uuid from enum import Enum @@ -220,6 +221,15 @@ class OpenAIServingChat(OpenAIServingBase): # Values: "dsv32", "dsv4", or custom values set by subclass. None for default. self.chat_encoding_spec = self._resolve_chat_encoding_spec() + # Resolve the env-configured Inkling effort default once: the env var is + # frozen for the server's lifetime, and a misconfigured value should + # fail at boot, not 400 every request. + self._inkling_default_reasoning_effort: Optional[float] = ( + self._get_inkling_default_reasoning_effort() + if self.chat_encoding_spec == "inkling" + else None + ) + # Per-request response parser for custom decoding (set by _encode_messages) self._response_parser: Optional[ResponseParserProtocol] = None @@ -314,13 +324,129 @@ class OpenAIServingChat(OpenAIServingBase): messages: List[Dict[str, Any]], request: ChatCompletionRequest, thinking_mode: ThinkingMode, + tools: Optional[List[Dict]] = None, ) -> Optional[List[int]]: """Encode messages for custom chat_encoding_spec values. Returns prompt_ids if handled, None to use default encoding. """ + if self.chat_encoding_spec == "inkling": + # Inkling: render messages -> input_ids with framing tokens + ONE placeholder per + # media (encoding/expansion happens later in InklingMultimodalProcessor). The + # server's tokenizer is the base tiktoken backend; wrap it so encode_special + # supplies the framing-token overlay. + from sglang.srt.parser.inkling_renderer import render_inkling_messages + from sglang.srt.parser.inkling_tokenizer import ( + CONTENT_TEXT, + MESSAGE_MODEL, + InklingTokenizer, + ) + + inkling_tokenizer = InklingTokenizer( + tokenizer=self.tokenizer_manager.tokenizer + ) + reasoning_effort = self._parse_inkling_reasoning_effort( + request.reasoning_effort + ) + if reasoning_effort is None: + reasoning_effort = self._inkling_default_reasoning_effort + assistant_prefix = self._pop_inkling_assistant_prefix(messages, request) + prompt_ids = render_inkling_messages( + messages, + inkling_tokenizer, + add_generation_prompt=False, + tools=tools, + reasoning_effort=reasoning_effort, + ) + if assistant_prefix is not None: + # Continue the final assistant message inside an OPEN model text + # block: header + payload, no <|end_message|> and no + # <|content_model_end_sampling|>, so the model resumes the turn. + prompt_ids += [ + inkling_tokenizer.encode_special(MESSAGE_MODEL), + inkling_tokenizer.encode_special(CONTENT_TEXT), + *inkling_tokenizer.encode_text(assistant_prefix), + ] + return prompt_ids return None + @staticmethod + def _pop_inkling_assistant_prefix( + messages: List[Dict[str, Any]], + request: ChatCompletionRequest, + ) -> Optional[str]: + """Extract the trailing assistant text for ``continue_final_message``. + + Only a plain-string assistant message with no tool calls and no + reasoning content can be continued; anything else renders as a closed + historical turn. Mutates ``messages`` in place (callers pass a copy). + """ + if not request.continue_final_message or not messages: + return None + last = messages[-1] + if ( + last.get("role") != "assistant" + or not isinstance(last.get("content"), str) + or last.get("tool_calls") + or last.get("reasoning_content") + ): + return None + messages.pop() + return last["content"] + + @staticmethod + def _parse_inkling_reasoning_effort( + value: Optional[Union[str, float]], + ) -> Optional[float]: + """Convert an OpenAI-style reasoning_effort to an Inkling float.""" + if value is None: + return None + if isinstance(value, bool): + raise ValueError("Inkling reasoning_effort must not be a boolean") + if isinstance(value, (int, float)): + parsed = float(value) + if not math.isfinite(parsed) or not 0.0 <= parsed <= 0.99: + raise ValueError("Inkling reasoning_effort must be in [0.0, 0.99]") + return parsed + _EFFORT_MAP = { + "none": 0.0, + "minimal": 0.1, + "low": 0.2, + "medium": 0.7, + "high": 0.9, + "xhigh": 0.99, + "max": 0.99, + } + if value in _EFFORT_MAP: + return _EFFORT_MAP[value] + try: + parsed = float(value) + except (ValueError, TypeError) as exc: + raise ValueError(f"invalid Inkling reasoning_effort: {value!r}") from exc + if not math.isfinite(parsed) or not 0.0 <= parsed <= 0.99: + raise ValueError("Inkling reasoning_effort must be in [0.0, 0.99]") + return parsed + + @staticmethod + def _get_inkling_default_reasoning_effort() -> float: + """Read the default Inkling reasoning effort from the environment.""" + from sglang.srt.environ import envs + + val = envs.SGLANG_INKLING_DEFAULT_REASONING_EFFORT.get() + if not val: + return 0.9 + try: + parsed = float(val) + except (ValueError, TypeError) as exc: + raise ValueError( + "SGLANG_INKLING_DEFAULT_REASONING_EFFORT must be numeric" + ) from exc + if not math.isfinite(parsed) or not 0.0 <= parsed <= 0.99: + raise ValueError( + "SGLANG_INKLING_DEFAULT_REASONING_EFFORT must be in [0.0, 0.99]" + ) + return parsed + def _decode_response(self, ret_item: Dict[str, Any]) -> Union[str, ErrorResponse]: """Extract text from response.""" return ret_item["text"] @@ -573,10 +699,23 @@ class OpenAIServingChat(OpenAIServingBase): tool_call_constraint=processed_messages.tool_call_constraint, ) + # Handle single vs multiple requests if request.input_ids is not None: prompt_kwargs = {"input_ids": processed_messages.prompt_ids} elif is_multimodal: - prompt_kwargs = {"text": processed_messages.prompt} + # Standard VLMs render a text prompt (with placeholder strings) for the MM + # processor to tokenize. Inkling's custom encoder instead produces pre-rendered + # input_ids with single placeholders; pass those through so the MM processor + # expands them rather than re-tokenizing an empty prompt. Gated on the Inkling + # encoding spec so every other model keeps the standard text path. + if ( + self.chat_encoding_spec == "inkling" + and isinstance(processed_messages.prompt_ids, list) + and processed_messages.prompt_ids + ): + prompt_kwargs = {"input_ids": processed_messages.prompt_ids} + else: + prompt_kwargs = {"text": processed_messages.prompt} else: if isinstance(processed_messages.prompt_ids, str): prompt_kwargs = {"text": processed_messages.prompt_ids} @@ -750,12 +889,29 @@ class OpenAIServingChat(OpenAIServingBase): normalize_assistant_tool_call_arguments(message) prompt_ids = self._encode_messages( - copy.deepcopy(messages), request, thinking_mode + copy.deepcopy(messages), + request, + thinking_mode, + tools=tools, ) if prompt_ids is not None: - # Custom encoding handled it - no further processing needed - pass + # Custom encoding produced prompt_ids. Text-only encoders (dsv4/dsv32) need + # nothing more; Inkling is the only multimodal custom encoder and still needs the + # image/audio media harvested from the messages for the MM processor. + if self.chat_encoding_spec == "inkling": + for message in request.messages: + msg_dict = message.model_dump() + if msg_dict.get("content") is None: + msg_dict["content"] = "" + process_content_for_template_format( + msg_dict, + "openai", + image_data, + video_data, + audio_data, + modalities, + ) elif self.chat_encoding_spec is not None: # dsv4/dsv32 encoding path messages = copy.deepcopy(messages) @@ -1544,12 +1700,11 @@ class OpenAIServingChat(OpenAIServingBase): not is_required or parser.detector.supports_structural_tag() ) if should_try_parser and parser.has_tool_call(text): - original_finish_type = finish_reason["type"] - if finish_reason["type"] == "stop": - finish_reason["type"] = "tool_calls" - finish_reason["matched"] = None try: text, call_info_list = parser.parse_non_stream(text) + if not call_info_list: + return ToolCallProcessingResult(None, text, finish_reason) + tool_calls = [] for call_info in call_info_list: tool_id = self._process_tool_call_id( @@ -1565,10 +1720,12 @@ class OpenAIServingChat(OpenAIServingBase): ), ) ) + if finish_reason["type"] == "stop": + finish_reason["type"] = "tool_calls" + finish_reason["matched"] = None return ToolCallProcessingResult(tool_calls, text, finish_reason) except Exception as e: logger.error(f"Tool call parsing error: {e}") - finish_reason["type"] = original_finish_type return ToolCallProcessingResult(None, text, finish_reason) # json_schema constraint → JSON array output for required/named @@ -1702,6 +1859,8 @@ class OpenAIServingChat(OpenAIServingBase): and request.reasoning_effort != "none" ): request.skip_special_tokens = False + elif self.reasoning_parser == "inkling": + request.skip_special_tokens = False def wrap_reasoning_history(self, reasoning_text: str) -> str: """Wrap prior-turn reasoning in the detector's own start/end tokens. diff --git a/python/sglang/srt/entrypoints/openai/serving_responses.py b/python/sglang/srt/entrypoints/openai/serving_responses.py index 973123da7..66758afaa 100644 --- a/python/sglang/srt/entrypoints/openai/serving_responses.py +++ b/python/sglang/srt/entrypoints/openai/serving_responses.py @@ -478,6 +478,7 @@ class OpenAIServingResponses(OpenAIServingChat): else True ), stop=request.stop, + reasoning_effort=(request.reasoning.effort if request.reasoning else None), ) is_multimodal = self.tokenizer_manager.model_config.is_multimodal diff --git a/python/sglang/srt/environ.py b/python/sglang/srt/environ.py index 49159c999..4930ba563 100644 --- a/python/sglang/srt/environ.py +++ b/python/sglang/srt/environ.py @@ -183,6 +183,19 @@ class EnvFloat(EnvField): raise ValueError(f'"{value}" is not a valid float value') +class GateGemvMode(IntEnum): + """Small-batch Inkling gate linear implementation. + + OFF: always the cublas GEMM + PAIR: PDL-chained GEMV and gate JIT kernels + FUSED: single-launch GEMV + gate epilogue (last-block ticket) + """ + + OFF = 0 + PAIR = 1 + FUSED = 2 + + class ToolStrictLevel(IntEnum): """ Defines the strictness levels for tool call parsing and validation. @@ -616,6 +629,9 @@ class Envs: SGLANG_FLASHINFER_WORKSPACE_SIZE = EnvInt(384 * 1024 * 1024) # Enable NVFP4 per-token activation scaling path for FlashInfer TRT-LLM MoE. SGLANG_FLASHINFER_NVFP4_PER_TOKEN_ACTIVATION = EnvBool(False) + # Launch the TRT-LLM MoE grouped GEMMs with PDL only at or below this + # token count. + SGLANG_TRTLLM_MOE_PDL_MAX_TOKENS = EnvInt(8192) # SGLang needs to know FlashInfer NVFP4 4over6 config to compute the global scale factor. FLASHINFER_NVFP4_4OVER6 = EnvBool(False) FLASHINFER_NVFP4_4OVER6_E4M3_USE_256 = EnvBool(False) @@ -800,6 +816,9 @@ class Envs: # Mamba SGLANG_MAMBA_CONV_DTYPE = EnvStr("bfloat16") SGLANG_MAMBA_SSM_DTYPE = EnvStr(None) + # Kill-switch for the fused per-slot conv clear/copy kernel (MambaPool); + # falls back to the per-conv-type Python loop. + SGLANG_DISABLE_FUSED_MAMBA_SLOT_OPS = EnvBool(False) # Unified Radix Tree SGLANG_ENABLE_UNIFIED_RADIX_TREE = EnvBool(False) @@ -901,6 +920,73 @@ class Envs: SGLANG_FIX_MTP_HC_HIDDEN = EnvBool(False) # ==================================================================== + # ==================================================================== + # Inkling + SGLANG_OPT_USE_FUSED_GATE_TOPK = EnvBool(True) + # Inside the fused gate: use the CUDA JIT top-k+renorm kernel (v2) instead + # of the triton kernel when the production Inkling shape applies. + SGLANG_OPT_USE_GATE_TOPK_JIT = EnvBool(True) + # Inside the fused gate: replace the cublas gate linear with the + # expert-per-block GEMV JIT kernel at small token counts (GateGemvMode). + SGLANG_OPT_GATE_GEMV_MODE = EnvInt(GateGemvMode.PAIR) + # Capture all multi-layer EAGLE draft-extend steps and the in-graph chain + # rotation into ONE CUDA graph instead of one captured graph per step. + SGLANG_ENABLE_SINGLE_CG_DRAFT = EnvBool(True) + # Draft sampler uses the Gumbel-max trick (argmax(probs / Exp(1))) instead of + # torch.multinomial, whose device-side validity assert breaks draft-graph replay. + SGLANG_OPT_USE_GUMBEL_SAMPLE = EnvBool(True) + # Multi-layer chain-MTP boundary-KV fix: widen the draft-extend window to + # rewrite rejected-draft KV rows before reuse (acc_len repair; on by default). + SGLANG_ENABLE_MTP_BOUNDARY_KV_FIX = EnvBool(True) + SGLANG_OPT_USE_INKLING_MULTI_STREAM_OVERLAP = EnvBool(True) + SGLANG_OPT_USE_INKLING_SHEARED_BIAS = EnvBool(True) + # Use feature-stacked GEMMs for the no-LoRA BF16 shared sink. Eligible LoRA + # serving enables this layout independently of the flag. + SGLANG_OPT_LINEARIZED_SHARED_SINK = EnvBool(True) + # Use the autotuned JIT all-reduce, falling back to torch multimem for + # shapes where it wins. + SGLANG_OPT_USE_INKLING_CUSTOM_AR = EnvBool(True) + # Fuse small-batch decode all-reduce, MLP convolution, and attention norm. + # Requires the custom all-reduce; other shapes use the unfused path. + SGLANG_OPT_USE_INKLING_FUSED_AR_SCONV_NORM = EnvBool(True) + # Fuse eligible extend all-reduce, convolution, and cache updates. + # Supports scattered or full-width state and requires the custom all-reduce. + SGLANG_OPT_USE_INKLING_FUSED_AR_SCONV = EnvBool(True) + # Fuse eligible convolution, QK norm, window, and KV-store prologue work. + # Non-BF16 caches retain the backend KV store. + SGLANG_OPT_USE_INKLING_FUSED_ATTN_PROLOGUE = EnvBool(True) + # Override shared-expert selection: true uses grouped GEMM, false uses BMM. + # When unset, selection follows model, quantization, and LoRA requirements. + SGLANG_OPT_USE_INKLING_SHARED_FUSED_MOE = EnvBool(True) + # Fold the conditional long-context log-scaling tau into its producers + # instead of separate output-sized scale kernels: the fused attn + # prologue's q path (bit-exact, before MXFP8 quantization there) and the + # rel_logits projection's r OPERAND (the diagonal scale commutes through + # the einsum, shrinking the pass by rel_extent/d_rel = 64x; rounding moves + # before the GEMM). Flag-off keeps the standalone apply_log_scaling_tau + # on the outputs. + # Fold the MoE shared-expert partials into the custom AR kernels instead + # of a separate {routed + shared} torch.add per MoE layer; some buckets + # keep a pre-add during the AR stage-in. torch.add numerics + # (bit-identical). Requires SGLANG_OPT_USE_INKLING_CUSTOM_AR. + SGLANG_OPT_USE_INKLING_FUSED_AR_SHARED = EnvBool(True) + SGLANG_OPT_USE_INKLING_FUSED_LOG_TAU = EnvBool(True) + # Dispatch the rel_logits projection around einsum's hidden compaction + # copy of the strided r operand (a view into the packed qkvr output): + # zero-copy strided-batched matmul at small t, JIT row-compact + einsum + # above the band, single-launch tau-folded kernel in the small-t tau + # band. Bit-identical to the plain einsum; flag-off restores it. + SGLANG_OPT_USE_INKLING_REL_PROJ_DISPATCH = EnvBool(True) + + # Quantize and store MXFP8 K/V data and scales in one fused kernel. + SGLANG_OPT_INKLING_MXFP8_FUSED_QUANT_STORE = EnvBool(True) + # Default reasoning effort in [0.0, 0.99] when omitted by a request. + # An empty string falls back to the protocol default (0.9); the effort + # directive is always emitted. + SGLANG_INKLING_DEFAULT_REASONING_EFFORT = EnvStr("0.9") + SGLANG_INKLING_RS_MM_PREPROCESS = EnvBool(True) + # ==================================================================== + # Set False when using FP4-to-FP8 converted DeepSeek V4 checkpoint. SGLANG_DSV4_FP4_EXPERTS = EnvBool(True) SGLANG_DSV4_FP4_DEQUANT = EnvBool(False) @@ -1104,6 +1190,8 @@ def _convert_SGL_to_SGLANG(): ) _print_deprecated_env("SGLANG_PER_TOKEN_GROUP_QUANT_8BIT_V2") _print_deprecated_env("SGLANG_OPT_SWA_EVICT_DROP_PAGE_MARGIN") + # sconv-family kernels always use the CUDA-JIT ports when supported; no toggle. + _print_deprecated_env("SGLANG_OPT_USE_CUDA_SCONV") _print_deprecated_env("SGLANG_ENABLE_THINKING", "SGLANG_DEFAULT_THINKING") _print_deprecated_env("SGLANG_REASONING_EFFORT", "SGLANG_DSV4_REASONING_EFFORT") _print_deprecated_env( diff --git a/python/sglang/srt/function_call/base_format_detector.py b/python/sglang/srt/function_call/base_format_detector.py index 726288ea3..cf68977c3 100644 --- a/python/sglang/srt/function_call/base_format_detector.py +++ b/python/sglang/srt/function_call/base_format_detector.py @@ -409,3 +409,15 @@ class BaseFormatDetector(ABC): tool_choice=converted_tool_choice, reasoning=thinking_mode, ) + + def get_auto_tool_call_structural_tag( + self, tools: Union[List[Tool], None] = None + ) -> Optional[StructuralTag]: + """Return an always-on structural tag for automatic tool choice. + + Most formats leave unconstrained text generation enabled for + ``tool_choice="auto"`` unless strict mode is requested. Formats with a + token that unambiguously starts a tool payload can override this hook + to constrain only the payload after that token. + """ + return None diff --git a/python/sglang/srt/function_call/function_call_parser.py b/python/sglang/srt/function_call/function_call_parser.py index 62ac02715..0263d6e84 100644 --- a/python/sglang/srt/function_call/function_call_parser.py +++ b/python/sglang/srt/function_call/function_call_parser.py @@ -26,6 +26,7 @@ from sglang.srt.function_call.glm47_moe_detector import Glm47MoeDetector from sglang.srt.function_call.gpt_oss_detector import GptOssDetector from sglang.srt.function_call.hermes_detector import HermesDetector from sglang.srt.function_call.hunyuan_detector import HunyuanDetector +from sglang.srt.function_call.inkling_detector import InklingDetector from sglang.srt.function_call.internlm_detector import InternlmDetector from sglang.srt.function_call.kimik2_detector import KimiK2Detector from sglang.srt.function_call.lfm2_detector import Lfm2Detector @@ -90,6 +91,7 @@ class FunctionCallParser: "hunyuan": HunyuanDetector, "gigachat3": GigaChat3Detector, "gemma4": Gemma4Detector, + "inkling": InklingDetector, } def __init__(self, tools: List[Tool], tool_call_parser: str, tokenizer=None): @@ -137,9 +139,10 @@ class FunctionCallParser: """ if not self.tools: return full_text, [] + has_tool_call = self.detector.has_tool_call(full_text) parsed_result = self.detector.detect_and_parse(full_text, self.tools) tool_call_list = parsed_result.calls - if tool_call_list: + if tool_call_list or has_tool_call: return parsed_result.normal_text, tool_call_list else: return full_text, [] @@ -247,6 +250,13 @@ class FunctionCallParser: # Highest priority: model-native structural_tag when available. try: + if tool_choice == "auto" and not should_constrain_auto: + structural_tag = self.detector.get_auto_tool_call_structural_tag( + tools=self.tools + ) + if structural_tag is not None: + return ("structural_tag", structural_tag) + if is_required or should_constrain_auto: structural_tag = self.detector.get_structural_tag( tools=self.tools, diff --git a/python/sglang/srt/function_call/inkling_detector.py b/python/sglang/srt/function_call/inkling_detector.py new file mode 100644 index 000000000..845aa47dc --- /dev/null +++ b/python/sglang/srt/function_call/inkling_detector.py @@ -0,0 +1,364 @@ +import json +import logging +import re +from collections.abc import Mapping +from typing import List, Optional + +from partial_json_parser.core.exceptions import MalformedJSON +from partial_json_parser.core.options import Allow +from xgrammar import StructuralTag + +from sglang.srt.entrypoints.openai.protocol import Tool +from sglang.srt.function_call.base_format_detector import BaseFormatDetector +from sglang.srt.function_call.core_types import ( + StreamingParseResult, + StructureInfo, + ToolCallItem, + _GetInfoFunc, +) +from sglang.srt.function_call.utils import _is_complete_json, _partial_json_loads +from sglang.srt.parser.inkling_tokenizer import ( + CONTENT_INVOKE_TOOL_JSON, + END_MESSAGE, + INKLING_CONTROL_TOKENS, + INKLING_SPECIAL_TOKEN_IDS, + MESSAGE_MODEL, +) + +logger = logging.getLogger(__name__) + + +class InklingDetector(BaseFormatDetector): + """ + Detector for Inkling structured tool calls. + + Format: + <|message_model|>name<|content_invoke_tool_json|>{"name":"...","args":{...}}<|end_message|> + """ + + def __init__(self): + super().__init__() + self.bot_token = CONTENT_INVOKE_TOOL_JSON + self.eot_token = END_MESSAGE + self.tool_call_regex = re.compile( + re.escape(self.bot_token) + r"\s*(.*?)\s*" + re.escape(self.eot_token), + re.DOTALL, + ) + self._current_header_name: str | None = None + + def has_tool_call(self, text: str) -> bool: + return self.bot_token in text + + def detect_and_parse(self, text: str, tools: List[Tool]) -> StreamingParseResult: + if self.bot_token not in text: + return StreamingParseResult(normal_text=self._clean_normal_text(text)) + + try: + calls: list[ToolCallItem] = [] + for match in self.tool_call_regex.finditer(text): + try: + payload = json.loads(match.group(1).strip()) + except json.JSONDecodeError as exc: + logger.warning("Invalid Inkling tool call JSON: %s", exc) + continue + if not isinstance(payload, Mapping): + logger.warning("Invalid Inkling tool call payload: %s", payload) + continue + _, header_name = self._split_trailing_tool_header(text[: match.start()]) + call = self._tool_call_item( + payload, tools, len(calls), header_name=header_name + ) + if call is not None: + calls.append(call) + + if not calls: + # Every candidate call was rejected (bad payload or a + # header/payload name mismatch). Match the framework contract + # every other detector follows: normal_text is only the content + # BEFORE the tool marker — the rejected tool-call region is + # dropped, never regurgitated as visible content. + prefix, _ = self._split_trailing_tool_header( + text[: text.find(self.bot_token)] + ) + return StreamingParseResult(normal_text=self._clean_normal_text(prefix)) + + normal_prefix, _ = self._split_trailing_tool_header( + text[: text.find(self.bot_token)] + ) + normal_text = self._clean_normal_text(normal_prefix) + return StreamingParseResult(normal_text=normal_text, calls=calls) + except Exception as exc: + logger.error("Error in Inkling detect_and_parse: %s", exc, exc_info=True) + prefix, _ = self._split_trailing_tool_header( + text[: text.find(self.bot_token)] + ) + return StreamingParseResult(normal_text=self._clean_normal_text(prefix)) + + def parse_streaming_increment( + self, new_text: str, tools: List[Tool] + ) -> StreamingParseResult: + self._buffer += new_text + current_text = self._buffer + + if self.bot_token not in current_text: + header_start = self._pending_tool_header_start(current_text) + if header_start is not None: + safe_text = current_text[:header_start] + self._buffer = current_text[header_start:] + return StreamingParseResult( + normal_text=self._clean_normal_text(safe_text) + ) + # Hold back a partial prefix of ANY token _clean_normal_text + # strips — emitting a split control token leaks its first half as + # visible text (the completed token would have been stripped). + partial_len = max( + self._ends_with_partial_token(current_text, token) + for token in INKLING_CONTROL_TOKENS + ) + if partial_len: + safe_text = current_text[:-partial_len] + self._buffer = current_text[-partial_len:] + else: + safe_text = current_text + self._buffer = "" + return StreamingParseResult(normal_text=self._clean_normal_text(safe_text)) + + bot_pos = current_text.find(self.bot_token) + if bot_pos > 0: + normal_text, self._current_header_name = self._split_trailing_tool_header( + current_text[:bot_pos] + ) + self._buffer = current_text[bot_pos:] + normal_text = self._clean_normal_text(normal_text) + if normal_text: + return StreamingParseResult(normal_text=normal_text) + current_text = self._buffer + + if not hasattr(self, "_tool_indices"): + self._tool_indices = self._get_tool_indices(tools) + + start_idx = len(self.bot_token) + while start_idx < len(current_text) and current_text[start_idx].isspace(): + start_idx += 1 + + flags = Allow.ALL if self.current_tool_name_sent else Allow.ALL & ~Allow.STR + try: + payload, end_idx = _partial_json_loads(current_text[start_idx:], flags) + except (MalformedJSON, json.JSONDecodeError): + return StreamingParseResult() + if not isinstance(payload, Mapping): + return StreamingParseResult() + + calls: list[ToolCallItem] = [] + name = payload.get("name") + if ( + not self.current_tool_name_sent + and isinstance(name, str) + and (self._current_header_name is None or self._current_header_name == name) + ): + self._ensure_current_tool() + calls.append( + ToolCallItem( + tool_index=self.current_tool_id, + name=name, + parameters="", + ) + ) + self.current_tool_name_sent = True + self.prev_tool_call_arr[self.current_tool_id] = { + "name": name, + "arguments": {}, + } + + json_text = current_text[start_idx : start_idx + end_idx] + if not _is_complete_json(json_text): + return StreamingParseResult(calls=calls) + + call = self._tool_call_item( + payload, + tools, + self.current_tool_id, + header_name=self._current_header_name, + ) + if call is None: + self._abandon_current_tool() + self._buffer = "" + return StreamingParseResult(calls=calls) + + if self.current_tool_id == -1: + self._ensure_current_tool() + + args = json.loads(call.parameters) + self.prev_tool_call_arr[self.current_tool_id] = { + "name": call.name, + "arguments": args, + } + sent = self.streamed_args_for_tool[self.current_tool_id] + remaining_args = call.parameters[len(sent) :] + if remaining_args: + calls.append( + ToolCallItem( + tool_index=self.current_tool_id, + name=None, + parameters=remaining_args, + ) + ) + self.streamed_args_for_tool[self.current_tool_id] += remaining_args + + self._buffer = self._remaining_after_call(current_text, start_idx + end_idx) + self.current_tool_id += 1 + self.current_tool_name_sent = False + self._current_header_name = None + return StreamingParseResult(calls=calls) + + def structure_info(self) -> _GetInfoFunc: + def info(name: str) -> StructureInfo: + trigger = f"{MESSAGE_MODEL}{name}{self.bot_token}" + return StructureInfo( + begin=f'{trigger}{{"name":"{name}","args":', + end=f"}}{self.eot_token}", + trigger=trigger, + ) + + return info + + def get_auto_tool_call_structural_tag( + self, tools: Optional[List[Tool]] = None + ) -> StructuralTag: + """Constrain JSON after Inkling's tool-payload trigger token. + + Automatic tool choice still permits unconstrained assistant text. Once + the model emits ``CONTENT_INVOKE_TOOL_JSON``, XGrammar requires a + complete ``{"name": string, "args": object}`` payload followed by + ``END_MESSAGE``. This mirrors the TML sampling default used by the OAI + API and intentionally does not restrict names to the request's tools. + """ + del tools + return StructuralTag.model_validate( + { + "type": "structural_tag", + "format": { + "type": "token_triggered_tags", + "trigger_tokens": [ + INKLING_SPECIAL_TOKEN_IDS[CONTENT_INVOKE_TOOL_JSON] + ], + "tags": [ + { + "type": "tag", + "begin": { + "type": "token", + "token": INKLING_SPECIAL_TOKEN_IDS[ + CONTENT_INVOKE_TOOL_JSON + ], + }, + "content": { + "type": "json_schema", + "json_schema": { + "type": "object", + "properties": { + "name": {"type": "string"}, + "args": {"type": "object"}, + }, + "required": ["name", "args"], + "additionalProperties": False, + }, + }, + "end": { + "type": "token", + "token": INKLING_SPECIAL_TOKEN_IDS[END_MESSAGE], + }, + } + ], + }, + } + ) + + def _tool_call_item( + self, + payload: Mapping[str, object], + tools: List[Tool], + call_index: int, + *, + header_name: str | None = None, + ) -> ToolCallItem | None: + name = payload.get("name") + args = payload.get("args") + if not isinstance(name, str) or not isinstance(args, Mapping): + logger.warning("Invalid Inkling tool call payload: %s", payload) + return None + if header_name is not None and header_name != name: + logger.warning( + "Inkling tool header %r does not match payload name %r", + header_name, + name, + ) + return None + + if not hasattr(self, "_tool_indices"): + self._tool_indices = self._get_tool_indices(tools) + if name not in self._tool_indices: + # Surface the call anyway (OpenAI behavior for hallucinated tools): + # the harness sees a structured tool_call, returns a tool error, and + # the model can self-correct — instead of the serialized invocation + # degrading into terminal answer text. + logger.warning("Surfacing Inkling call to undeclared tool: %s", name) + + return ToolCallItem( + tool_index=call_index, + name=name, + parameters=json.dumps(args, ensure_ascii=False), + ) + + def _ensure_current_tool(self) -> None: + if self.current_tool_id == -1: + self.current_tool_id = 0 + while len(self.prev_tool_call_arr) <= self.current_tool_id: + self.prev_tool_call_arr.append({}) + while len(self.streamed_args_for_tool) <= self.current_tool_id: + self.streamed_args_for_tool.append("") + + def _abandon_current_tool(self) -> None: + """Discard the in-flight call after a rejected payload. + + Resetting ``current_tool_id`` to -1 here would collide the NEXT valid + call with tool index 0 (``_ensure_current_tool`` maps -1 -> 0) and + slice its arguments against index 0's already-streamed args. Keep the + counter: an unannounced slot is simply reused; an announced slot is + abandoned by advancing past it. + """ + if self.current_tool_name_sent: + self.current_tool_id += 1 + self.current_tool_name_sent = False + self._current_header_name = None + + def _split_trailing_tool_header(self, text: str) -> tuple[str, str | None]: + message_pos = self._pending_tool_header_start(text) + if message_pos is None: + return text, None + header = text[message_pos + len(MESSAGE_MODEL) :] + return text[:message_pos], header.strip() or None + + def _pending_tool_header_start(self, text: str) -> int | None: + """Position of a trailing ``<|message_model|>`` whose header (the text + after it) contains no complete special token yet — i.e. a possible + tool-call header still forming.""" + message_pos = text.rfind(MESSAGE_MODEL) + if message_pos < 0: + return None + header = text[message_pos + len(MESSAGE_MODEL) :] + if any(token in header for token in INKLING_CONTROL_TOKENS): + return None + return message_pos + + def _remaining_after_call(self, text: str, end_idx: int) -> str: + remaining = text[end_idx:] + if remaining.startswith(self.eot_token): + return remaining[len(self.eot_token) :] + if self.eot_token in remaining: + return remaining.split(self.eot_token, 1)[1] + return remaining + + def _clean_normal_text(self, text: str) -> str: + for token in INKLING_CONTROL_TOKENS: + text = text.replace(token, "") + return text diff --git a/python/sglang/srt/layers/attention/attention_registry.py b/python/sglang/srt/layers/attention/attention_registry.py index 4a36578ca..8ca793357 100644 --- a/python/sglang/srt/layers/attention/attention_registry.py +++ b/python/sglang/srt/layers/attention/attention_registry.py @@ -283,6 +283,13 @@ def attn_backend_wrapper(runner: "ModelRunner", full_attn_backend: "AttentionBac ) if cfg := mambaish_config(runner.model_config): + from sglang.srt.configs.inkling import InklingMMConfig, InklingModelConfig + + if isinstance( + runner.model_config.hf_config, (InklingModelConfig, InklingMMConfig) + ): + return full_attn_backend + from sglang.kernels.ops.attention.fla.utils import check_environments from sglang.srt.layers.attention.linear.kda_backend import KDAAttnBackend from sglang.srt.layers.attention.linear.lightning_backend import ( diff --git a/python/sglang/srt/layers/attention/base_attn_backend.py b/python/sglang/srt/layers/attention/base_attn_backend.py index d60b32c6d..c7da9e70d 100644 --- a/python/sglang/srt/layers/attention/base_attn_backend.py +++ b/python/sglang/srt/layers/attention/base_attn_backend.py @@ -88,6 +88,13 @@ class AttentionBackend(ABC): Default: no-op. """ + def draft_extend_metadata_captured_in_graph(self) -> bool: + """True when :py:meth:`init_forward_metadata_in_graph` fully rebuilds + this backend's DRAFT_EXTEND_V2 replay metadata inside the captured + graph, so a replaying runner may skip the eager + :py:meth:`init_forward_metadata_out_graph` call.""" + return False + # Opt out only when this backend never reads seq_lens_cpu / seq_lens_sum. needs_cpu_seq_lens: bool = True diff --git a/python/sglang/srt/layers/attention/flashattention_backend.py b/python/sglang/srt/layers/attention/flashattention_backend.py index 4f05511aa..00b8b1495 100644 --- a/python/sglang/srt/layers/attention/flashattention_backend.py +++ b/python/sglang/srt/layers/attention/flashattention_backend.py @@ -7,6 +7,7 @@ import numpy as np import torch from sglang.kernels.ops.attention.metadata import ( + draft_extend_set_metadata, normal_decode_set_metadata, prepare_swa_spec_page_table_triton, ) @@ -32,6 +33,7 @@ from sglang.srt.speculative.ragged_verify import build_ragged_target_verify_geom from sglang.srt.speculative.spec_info import SpecInput, SpeculativeAlgorithm from sglang.srt.speculative.spec_utils import resolve_num_tokens_per_req from sglang.srt.utils import get_compiler_backend +from sglang.srt.utils.common import get_device_capability if TYPE_CHECKING: from sglang.srt.layers.radix_attention import RadixAttention @@ -43,7 +45,6 @@ from sglang.jit_kernel.flash_attention import ( flash_attn_varlen_func, flash_attn_with_kvcache, ) -from sglang.srt.model_executor.cuda_graph_config import cuda_graph_fully_disabled def _should_disable_scheduler_metadata_precompute(server_args) -> bool: @@ -85,6 +86,10 @@ class FlashAttentionMetadata: swa_out_cache_loc: torch.Tensor = None # Precomputed FA3 scheduler metadata (avoids per-layer prepare_varlen_num_blocks) scheduler_metadata: torch.Tensor = None + # Per-forward-pass cache for the FA4 sheared-bias block schedule. + # Created lazily by the first rel_bias layer; lives as long as this metadata + # object, which for CUDA-graph captures keeps the buffers owned by the graph. + rel_bias_prep_cache: Optional[dict] = None # Encoder metadata # Cumulative sequence lengths for encoder key @@ -128,6 +133,7 @@ class FlashAttentionBackend(AttentionBackend): - For each forward batch, init_replay_cuda_graph will be called first and then replay the graph. """ + needs_cpu_seq_lens: bool = False supports_ragged_verify_graph: bool = True def __init__( @@ -161,6 +167,7 @@ class FlashAttentionBackend(AttentionBackend): self.req_to_token = model_runner.req_to_token_pool.req_to_token self.kv_cache_dtype = model_runner.kv_cache_dtype self.kv_cache_dtype_str = model_runner.server_args.kv_cache_dtype + self.kv_cache_is_mxfp8 = self.kv_cache_dtype_str == "mxfp8" self.page_size = model_runner.page_size # Static page-table width (upper bound). The device-side page-table build # sizes to this constant, so no runtime host max is needed. @@ -274,13 +281,16 @@ class FlashAttentionBackend(AttentionBackend): # If num_splits == 0, we use a heuristic to automatically determine the number of splits. # We set nums splits to 1 if deterministic inference is enabled. # See https://thinkingmachines.ai/blog/defeating-nondeterminism-in-llm-inference/ for more details. - # Furthermore, FA4 does not support num_splits=0 with CUDA Graph, so we set num_splits to 1 if CUDA Graph is enabled. + fa4_no_splitkv = self.fa_impl_ver == 4 and get_device_capability() < (9, 0) self.num_splits = ( 1 - if model_runner.server_args.enable_deterministic_inference - or (self.fa_impl_ver == 4 and not cuda_graph_fully_disabled()) + if model_runner.server_args.enable_deterministic_inference or fa4_no_splitkv else 0 ) + # Set (never getattr'd) so forward_extend can identity-check "is this the + # full-CG prefill metadata?" to disable the pointer-keyed shear-bias + # block-schedule cache (see forward_extend rel_bias handling). + self.full_cg_prefill_metadata = None # In embedding mode with no chunked prefill and radix cache disabled, # skip KV cache write and use flash_attn_varlen_func with raw K/V @@ -336,6 +346,70 @@ class FlashAttentionBackend(AttentionBackend): num_splits=self.num_splits, ) + def _mxfp8_sf_kwargs(self, layer, forward_batch, q_descale=None): + """Block-scaled UE8M0 scale factors for the FA4 MXFP8 attention path. + + The pool stores K/V scales interleaved in the FA4 BlockScaledBasicChunk + layout (page_size==128) as sfk/sfv; the per-token Q scales (q_descale + from the model layer) ride along as sfq. All three drive the kernel's + block-scaled QK^T (mxf8f6f4) and in-kernel V dequant.""" + if not self.kv_cache_is_mxfp8: + return {} + if self.fa_impl_ver != 4: + raise RuntimeError("MXFP8 KV cache requires the FA4 backend.") + if q_descale is None: + raise RuntimeError( + "MXFP8 KV cache requires per-token Q scales (q_descale) from " + "the attention layer for the block-scaled QK^T path." + ) + # qk_sf_vec_size / v_sf_vec_size default to 32 inside the FA4 interface + # when sf tensors are given, so they don't need to be passed here (the + # flash_attn_with_kvcache / varlen wrappers don't forward them anyway). + k_sf, v_sf = self.token_to_kv_pool.get_kv_scale_buffer(layer.layer_id) + return {"sfq": q_descale, "sfk": k_sf, "sfv": v_sf} + + def init_forward_metadata_in_graph(self, forward_batch: ForwardBatch) -> None: + # Single-CG has no Python between steps, so one capturable kernel updates + # the persistent metadata. + if not forward_batch.forward_mode.is_draft_extend_v2(): + return + bs = forward_batch.batch_size + metadata = self.draft_extend_metadata[bs] + mapping = self._in_graph_full_to_swa_index_mapping() + draft_extend_set_metadata( + cache_seqlens_int32=metadata.cache_seqlens_int32, + cu_seqlens_k=metadata.cu_seqlens_k, + cu_seqlens_q=metadata.cu_seqlens_q, + page_table=metadata.page_table, + req_to_token=self.req_to_token, + req_pool_indices=forward_batch.req_pool_indices[:bs], + seq_lens=forward_batch.seq_lens[:bs], + extend_seq_lens=forward_batch.extend_seq_lens[:bs], + page_size=self.page_size, + full_to_swa_index_mapping=mapping, + swa_page_table=metadata.swa_page_table if mapping is not None else None, + out_cache_loc=( + forward_batch.out_cache_loc if mapping is not None else None + ), + swa_out_cache_loc=( + metadata.swa_out_cache_loc if mapping is not None else None + ), + ) + + def _in_graph_full_to_swa_index_mapping(self) -> Optional[torch.Tensor]: + # The in-graph SWA translation needs the raw mapping tensor; v2p-table + # pools (UnifiedSWAKVPool) keep it None and must stay on the eager + # rebuild path. + if not self.use_sliding_window_kv_pool: + return None + return self.token_to_kv_pool.full_to_swa_index_mapping + + def draft_extend_metadata_captured_in_graph(self) -> bool: + return ( + not self.use_sliding_window_kv_pool + or self.token_to_kv_pool.full_to_swa_index_mapping is not None + ) + def init_forward_metadata_out_graph( self, forward_batch: ForwardBatch, @@ -436,6 +510,8 @@ class FlashAttentionBackend(AttentionBackend): # sees num_tokens_per_req (not 1) for all replays of this graph. self.forward_metadata.max_seq_len_q = num_tokens // bs else: + # A stale non-None seq_lens_cpu buffer would under-size max_seq_pages + # (stale page-table rows -> OOB); force None under sync-free. self._apply_cuda_graph_metadata( bs=bs, req_pool_indices=req_pool_indices, @@ -444,7 +520,9 @@ class FlashAttentionBackend(AttentionBackend): encoder_lens=encoder_lens, forward_mode=forward_mode, spec_info=spec_info, - seq_lens_cpu=forward_batch.seq_lens_cpu, + seq_lens_cpu=( + forward_batch.seq_lens_cpu if self.needs_cpu_seq_lens else None + ), out_cache_loc=out_cache_loc, ) @@ -464,11 +542,6 @@ class FlashAttentionBackend(AttentionBackend): (the bucket's num_tokens / max_context_len): the kernel reads real work extents from the cu_seqlens / cache_seqlens device buffers. """ - if self.page_size != 1: - raise ValueError( - "Full prefill CUDA graph on the FlashAttention backend " - f"currently supports page_size=1 only, got {self.page_size}." - ) bs = forward_batch.batch_size if in_capture and getattr(self, "full_cg_prefill_metadata", None) is None: device = forward_batch.seq_lens.device @@ -476,9 +549,29 @@ class FlashAttentionBackend(AttentionBackend): m.cache_seqlens_int32 = torch.zeros((bs,), dtype=torch.int32, device=device) m.cu_seqlens_q = torch.zeros((bs + 1,), dtype=torch.int32, device=device) m.cu_seqlens_k = torch.zeros((bs + 1,), dtype=torch.int32, device=device) + # Block table sized in PAGES. For page_size == 1 max_num_pages == + # max_context_len, so this reduces to the per-token layout. m.page_table = torch.zeros( - (bs, self.max_context_len), dtype=torch.int32, device=device + (bs, self.max_num_pages), dtype=torch.int32, device=device ) + # Page-start token offsets for the strided block-table gather. For + # page_size == 1 this is arange(max_context_len) and the gather + //1 + # below reduces to the plain per-token req_to_token copy. + self.full_cg_prefill_strided_indices = torch.arange( + 0, self.max_context_len, self.page_size, device=device + ) + # SWA (hybrid sliding-window) buffers, mirroring the eager extend + # path: a page-strided SWA block table + SWA-translated write + # locations. The out-cache buffer is pointer-stable (sized to an + # upper bound) so captured graphs keep a valid address; each replay + # refills a [:num_tokens] view. + if self.use_sliding_window_kv_pool: + m.swa_page_table = torch.zeros( + (bs, self.max_num_pages), dtype=torch.int32, device=device + ) + self.full_cg_prefill_swa_out_cache_loc = torch.zeros( + (self.max_context_len,), dtype=torch.int64, device=device + ) self.full_cg_prefill_metadata = m m = self.full_cg_prefill_metadata assert m is not None and bs == m.cache_seqlens_int32.shape[0], ( @@ -494,9 +587,35 @@ class FlashAttentionBackend(AttentionBackend): ) max_seq_len_k = int(forward_batch.seq_lens_cpu[:bs].max().item()) if max_seq_len_k > 0: - m.page_table[:, :max_seq_len_k].copy_( - self.req_to_token[forward_batch.req_pool_indices[:bs], :max_seq_len_k] + # Build the block table like the eager extend branch: take every + # page_size-th token slot from req_to_token and divide by page_size. + # Identity for page_size == 1 (strided is 0..max_seq_len_k-1, //1). + max_seq_pages = (max_seq_len_k + self.page_size - 1) // self.page_size + page_indices = self.req_to_token[ + forward_batch.req_pool_indices[:bs, None], + self.full_cg_prefill_strided_indices[:max_seq_pages], + ] + m.page_table[:, :max_seq_pages].copy_(page_indices // self.page_size) + if self.use_sliding_window_kv_pool: + # SWA block table: translate the page-start full slots to their + # SWA cache locations, then reduce to SWA page indices. + swa_starts = self.token_to_kv_pool.translate_loc_from_full_to_swa( + page_indices + ) + m.swa_page_table[:, :max_seq_pages].copy_(swa_starts // self.page_size) + if self.use_sliding_window_kv_pool and forward_batch.out_cache_loc is not None: + # SWA write targets for the new tokens (KVWriteLoc.swa_loc), refilled + # into the pointer-stable buffer and bound as a [:num_tokens] view. + num_out = forward_batch.out_cache_loc.shape[0] + self.full_cg_prefill_swa_out_cache_loc[:num_out].copy_( + self.token_to_kv_pool.translate_loc_from_full_to_swa( + forward_batch.out_cache_loc + ) ) + # Captured kernels read the full bucket. Route its inactive tail to + # SWA's zero dummy slot to prevent stale writes into live slots. + self.full_cg_prefill_swa_out_cache_loc[num_out:].zero_() + m.swa_out_cache_loc = self.full_cg_prefill_swa_out_cache_loc[:num_out] if in_capture: # Baked into the captured kernel launches; upper bounds only. m.max_seq_len_q = forward_batch.positions.numel() @@ -829,10 +948,16 @@ class FlashAttentionBackend(AttentionBackend): ] if forward_batch.forward_mode.is_draft_extend_v2(): - # Fixed-q window: the host max is a config constant, and - # extend_seq_lens_cpu may be None on the GPU-only spec path. + # Fixed-q window (num_draft_tokens, widened by num_front_tokens + # when the CPU mirror is published); extend_seq_lens_cpu may be + # None on the GPU-only spec path, where the config constant is + # the exact width. extend_seq_lens = forward_batch.extend_seq_lens - metadata.max_seq_len_q = self.speculative_num_draft_tokens + metadata.max_seq_len_q = ( + max(forward_batch.extend_seq_lens_cpu) + if forward_batch.extend_seq_lens_cpu is not None + else self.speculative_num_draft_tokens + ) metadata.cu_seqlens_q = torch.nn.functional.pad( torch.cumsum(extend_seq_lens, dim=0, dtype=torch.int32), (1, 0) ) @@ -979,7 +1104,16 @@ class FlashAttentionBackend(AttentionBackend): q_rope: Optional[torch.Tensor] = None, k_rope: Optional[torch.Tensor] = None, sinks: Optional[torch.Tensor] = None, + q_descale: Optional[torch.Tensor] = None, + k_descale: Optional[torch.Tensor] = None, + v_descale: Optional[torch.Tensor] = None, + score_mod=None, + aux_tensors=None, + rel_bias=None, + rel_bias_event=None, ): + if score_mod is not None and self.fa_impl_ver != 4: + raise RuntimeError("score_mod is only supported by the FA4 backend.") is_cp_mode = ( forward_batch.forward_mode.is_context_parallel_extend() and forward_batch.attn_cp_metadata is not None @@ -1038,13 +1172,15 @@ class FlashAttentionBackend(AttentionBackend): swa_loc=swa_loc, ) else: + k_scale = k_descale if self.kv_cache_is_mxfp8 else layer.k_scale + v_scale = v_descale if self.kv_cache_is_mxfp8 else layer.v_scale self.token_to_kv_pool.set_kv_buffer( layer, KVWriteLoc(cache_loc, self.forward_metadata.swa_out_cache_loc), k, v, - layer.k_scale, - layer.v_scale, + k_scale, + v_scale, ) # Use precomputed metadata across all layers @@ -1057,7 +1193,7 @@ class FlashAttentionBackend(AttentionBackend): layer.sliding_window_size is not None and layer.sliding_window_size > -1 ) window_size = (layer.sliding_window_size, 0) if is_swa_layer else (-1, -1) - k_descale, v_descale = None, None + fa_k_descale, fa_v_descale = None, None # only use kv scaling if: 1) fp8 kv is explicitly enabled, 2) RadixAttention # has corresponding quantization method so that layer.k_scale is not None, # 3) layer.head_dim <= 256 since fa3 kernel require fp16 and bf16 data type in this case, @@ -1066,11 +1202,12 @@ class FlashAttentionBackend(AttentionBackend): self.kv_cache_dtype_str != "auto" and layer.head_dim <= 256 and self.fa_impl_ver != 4 + and not self.kv_cache_is_mxfp8 ): if layer.k_scale is not None: descale_shape = (forward_batch.batch_size, layer.tp_k_head_num) - k_descale = layer.k_scale.expand(descale_shape) - v_descale = layer.v_scale.expand(descale_shape) + fa_k_descale = layer.k_scale.expand(descale_shape) + fa_v_descale = layer.v_scale.expand(descale_shape) q = q.to(self.kv_cache_dtype) q_rope = q_rope.to(self.kv_cache_dtype) if q_rope is not None else None k_rope = k_rope.to(self.kv_cache_dtype) if k_rope is not None else None @@ -1099,6 +1236,32 @@ class FlashAttentionBackend(AttentionBackend): kwargs = {} if sinks is not None: kwargs["sinks"] = sinks + if score_mod is not None: + kwargs["score_mod"] = score_mod + kwargs["aux_tensors"] = aux_tensors + kwargs.update(self._mxfp8_sf_kwargs(layer, forward_batch, q_descale)) + if fa_k_descale is not None: + kwargs["k_descale"] = fa_k_descale + kwargs["v_descale"] = fa_v_descale + if rel_bias is not None: + if self.fa_impl_ver != 4: + raise RuntimeError( + "rel_bias (sheared bias) is only supported by the FA4 backend." + ) + if rel_bias_event is not None: + # rel_bias (rel_logits) is produced on InklingAttention's alt stream; + # join it here -- as late as possible, just before the kernel reads + # it -- so rel_logits_proj overlaps the KV-write above. + rel_bias_event.wait() + kwargs["rel_bias"] = rel_bias + if metadata is self.full_cg_prefill_metadata: + # Full-CG reuses the cu_seqlens pointer with new values each replay. + # Disable its pointer-keyed schedule cache so the graph refreshes it. + kwargs["rel_bias_prep_cache"] = None + else: + if metadata.rel_bias_prep_cache is None: + metadata.rel_bias_prep_cache = {} + kwargs["rel_bias_prep_cache"] = metadata.rel_bias_prep_cache _fa_out = ( forward_batch._attn_output.view(-1, layer.tp_q_head_num, layer.v_head_dim) @@ -1173,8 +1336,6 @@ class FlashAttentionBackend(AttentionBackend): causal=False if use_cascade_attn else causal, window_size=window_size, softcap=layer.logit_cap, - k_descale=k_descale, - v_descale=v_descale, return_softmax_lse=use_cascade_attn, num_splits=self.num_splits, ver=self.fa_impl_ver, @@ -1268,8 +1429,6 @@ class FlashAttentionBackend(AttentionBackend): causal=False if use_cascade_attn else causal, window_size=window_size, softcap=layer.logit_cap, - k_descale=k_descale, - v_descale=v_descale, return_softmax_lse=use_cascade_attn, num_splits=self.num_splits, out=_fa_out, @@ -1297,8 +1456,6 @@ class FlashAttentionBackend(AttentionBackend): causal=False, window_size=window_size, softcap=layer.logit_cap, - k_descale=k_descale, - v_descale=v_descale, return_softmax_lse=True, num_splits=self.num_splits, ver=self.fa_impl_ver, @@ -1440,8 +1597,8 @@ class FlashAttentionBackend(AttentionBackend): softmax_scale=layer.scaling, causal=causal, softcap=layer.logit_cap, - k_descale=k_descale, - v_descale=v_descale, + k_descale=fa_k_descale, + v_descale=fa_v_descale, num_splits=self.num_splits, ver=self.fa_impl_ver, ) @@ -1474,8 +1631,8 @@ class FlashAttentionBackend(AttentionBackend): softmax_scale=layer.scaling, causal=False if use_cascade_attn else causal, softcap=layer.logit_cap, - k_descale=k_descale, - v_descale=v_descale, + k_descale=fa_k_descale, + v_descale=fa_v_descale, return_softmax_lse=use_cascade_attn, num_splits=self.num_splits, ver=self.fa_impl_ver, @@ -1497,8 +1654,8 @@ class FlashAttentionBackend(AttentionBackend): causal=False, window_size=window_size, softcap=layer.logit_cap, - k_descale=k_descale, - v_descale=v_descale, + k_descale=fa_k_descale, + v_descale=fa_v_descale, return_softmax_lse=True, num_splits=self.num_splits, ver=self.fa_impl_ver, @@ -1527,7 +1684,16 @@ class FlashAttentionBackend(AttentionBackend): q_rope: Optional[torch.Tensor] = None, k_rope: Optional[torch.Tensor] = None, sinks: Optional[torch.Tensor] = None, + q_descale: Optional[torch.Tensor] = None, + k_descale: Optional[torch.Tensor] = None, + v_descale: Optional[torch.Tensor] = None, + score_mod=None, + aux_tensors=None, + rel_bias=None, + rel_bias_event=None, ) -> torch.Tensor: + if score_mod is not None and self.fa_impl_ver != 4: + raise RuntimeError("score_mod is only supported by the FA4 backend.") if k is not None: assert v is not None if save_kv_cache: @@ -1537,13 +1703,15 @@ class FlashAttentionBackend(AttentionBackend): else forward_batch.encoder_out_cache_loc ) if not self.use_mla: + k_scale = k_descale if self.kv_cache_is_mxfp8 else layer.k_scale + v_scale = v_descale if self.kv_cache_is_mxfp8 else layer.v_scale self.token_to_kv_pool.set_kv_buffer( layer, KVWriteLoc(cache_loc, self.forward_metadata.swa_out_cache_loc), k, v, - layer.k_scale, - layer.v_scale, + k_scale, + v_scale, ) else: self.token_to_kv_pool.set_mla_kv_buffer( @@ -1583,6 +1751,29 @@ class FlashAttentionBackend(AttentionBackend): kwargs = {} if sinks is not None: kwargs["sinks"] = sinks + if score_mod is not None: + kwargs["score_mod"] = score_mod + kwargs["aux_tensors"] = aux_tensors + kwargs.update(self._mxfp8_sf_kwargs(layer, forward_batch, q_descale)) + if rel_bias is not None: + if self.fa_impl_ver != 4: + raise RuntimeError( + "rel_bias (sheared bias) is only supported by the FA4 backend." + ) + if rel_bias_event is not None: + # rel_bias (rel_logits) is produced on InklingAttention's alt stream; + # join it here -- as late as possible, just before the kernel reads + # it -- so rel_logits_proj overlaps the KV-write above. + rel_bias_event.wait() + kwargs["rel_bias"] = rel_bias + if metadata is self.full_cg_prefill_metadata: + # Full-CG reuses the cu_seqlens pointer with new values each replay. + # Disable its pointer-keyed schedule cache so the graph refreshes it. + kwargs["rel_bias_prep_cache"] = None + else: + if metadata.rel_bias_prep_cache is None: + metadata.rel_bias_prep_cache = {} + kwargs["rel_bias_prep_cache"] = metadata.rel_bias_prep_cache _fa_out = ( forward_batch._attn_output.view(-1, layer.tp_q_head_num, layer.v_head_dim) @@ -1590,18 +1781,25 @@ class FlashAttentionBackend(AttentionBackend): else None ) - k_descale, v_descale = None, None + fa_k_descale, fa_v_descale = None, None # only use kv scaling if: 1) fp8 kv is explicitly enabled, 2) RadixAttention # has corresponding quantization method so that layer.k_scale is not None, # 3) layer.head_dim <= 256 since fa3 kernel require fp16 and bf16 data type in this case. - if self.kv_cache_dtype_str != "auto" and layer.head_dim <= 256: + if ( + self.kv_cache_dtype_str != "auto" + and layer.head_dim <= 256 + and not self.kv_cache_is_mxfp8 + ): if layer.k_scale is not None: descale_shape = (forward_batch.batch_size, layer.tp_k_head_num) - k_descale = layer.k_scale.expand(descale_shape) - v_descale = layer.v_scale.expand(descale_shape) + fa_k_descale = layer.k_scale.expand(descale_shape) + fa_v_descale = layer.v_scale.expand(descale_shape) q = q.to(self.kv_cache_dtype) q_rope = q_rope.to(self.kv_cache_dtype) if q_rope is not None else None k_rope = k_rope.to(self.kv_cache_dtype) if k_rope is not None else None + if fa_k_descale is not None: + kwargs["k_descale"] = fa_k_descale + kwargs["v_descale"] = fa_v_descale if not self.use_mla: # Do multi-head attention @@ -1628,8 +1826,6 @@ class FlashAttentionBackend(AttentionBackend): causal=False, window_size=(-1, -1), softcap=layer.logit_cap, - k_descale=k_descale, - v_descale=v_descale, num_splits=self.num_splits, ver=self.fa_impl_ver, **kwargs, @@ -1649,8 +1845,6 @@ class FlashAttentionBackend(AttentionBackend): causal=True, window_size=(-1, -1), softcap=layer.logit_cap, - k_descale=k_descale, - v_descale=v_descale, num_splits=self.num_splits, ver=self.fa_impl_ver, **kwargs, @@ -1703,8 +1897,6 @@ class FlashAttentionBackend(AttentionBackend): causal=False if use_cascade_attn else causal, window_size=window_size, softcap=layer.logit_cap, - k_descale=k_descale, - v_descale=v_descale, return_softmax_lse=use_cascade_attn, num_splits=self.num_splits, out=_fa_out, @@ -1728,8 +1920,6 @@ class FlashAttentionBackend(AttentionBackend): causal=False, window_size=window_size, softcap=layer.logit_cap, - k_descale=k_descale, - v_descale=v_descale, return_softmax_lse=True, num_splits=self.num_splits, ver=self.fa_impl_ver, @@ -1783,8 +1973,8 @@ class FlashAttentionBackend(AttentionBackend): softmax_scale=layer.scaling, causal=False if use_cascade_attn else causal, softcap=layer.logit_cap, - k_descale=k_descale, - v_descale=v_descale, + k_descale=fa_k_descale, + v_descale=fa_v_descale, return_softmax_lse=use_cascade_attn, # softmax_lse is needed for merge states num_splits=self.num_splits, ver=self.fa_impl_ver, @@ -1805,8 +1995,8 @@ class FlashAttentionBackend(AttentionBackend): causal=False, window_size=window_size, softcap=layer.logit_cap, - k_descale=k_descale, - v_descale=v_descale, + k_descale=fa_k_descale, + v_descale=fa_v_descale, return_softmax_lse=True, num_splits=self.num_splits, ver=self.fa_impl_ver, @@ -2389,9 +2579,8 @@ class FlashAttentionBackend(AttentionBackend): are gone. """ seq_lens = seq_lens[:bs] - # The GPU-only path passes seq_lens_cpu=None; the topk>1 branches below - # still need a host max, so sync locally in that case (not the dflash - # overlap hot path, which uses topk=1 and the device-side build). + # The sync-free path passes seq_lens_cpu=None; branches that still need a + # host max fall back to the static max_context_len, so no D2H is forced here. seq_lens_cpu = seq_lens_cpu[:bs] if seq_lens_cpu is not None else None req_pool_indices = req_pool_indices[:bs] device = seq_lens.device diff --git a/python/sglang/srt/layers/attention/hybrid_linear_attn_backend.py b/python/sglang/srt/layers/attention/hybrid_linear_attn_backend.py index 5d423273c..19119bcf3 100644 --- a/python/sglang/srt/layers/attention/hybrid_linear_attn_backend.py +++ b/python/sglang/srt/layers/attention/hybrid_linear_attn_backend.py @@ -5,8 +5,7 @@ import torch from sglang.kernels.ops.mamba.causal_conv1d_triton import PAD_SLOT_ID from sglang.kernels.ops.mamba.mamba_state_scatter_triton import ( - fused_conv_window_scatter_with_mask, - fused_mamba_state_scatter_with_mask, + scatter_mamba_states_after_mtp_verify, track_mamba_states_if_needed, ) from sglang.srt.configs.hybrid_arch import mamba2_config @@ -1031,41 +1030,13 @@ class HybridLinearAttnBackend(AttentionBackend): self.linear_attn_backend.req_to_token_pool.get_speculative_mamba2_params_all_layers() ) - conv_states = mamba_caches.conv[0] - ssm_states = mamba_caches.temporal - intermediate_state_cache = mamba_caches.intermediate_ssm - intermediate_conv_window_cache = mamba_caches.intermediate_conv_window[0] - - fused_mamba_state_scatter_with_mask( - ssm_states, - intermediate_state_cache, + scatter_mamba_states_after_mtp_verify( + mamba_caches, state_indices_tensor, last_correct_step_indices, + mamba_track_indices, + mamba_steps_to_track, ) - # conv intermediate uses the deduplicated sliding-window layout, so it - # needs the strided-read scatter variant. - fused_conv_window_scatter_with_mask( - conv_states, - intermediate_conv_window_cache, - state_indices_tensor, - last_correct_step_indices, - ) - - # Track indices for prefix cache - if mamba_track_indices is not None: - assert mamba_steps_to_track is not None - fused_mamba_state_scatter_with_mask( - ssm_states, - intermediate_state_cache, - mamba_track_indices, - mamba_steps_to_track, - ) - fused_conv_window_scatter_with_mask( - conv_states, - intermediate_conv_window_cache, - mamba_track_indices, - mamba_steps_to_track, - ) class ShortConvHybridAttnBackend(HybridLinearAttnBackend): diff --git a/python/sglang/srt/layers/attention/triton_backend.py b/python/sglang/srt/layers/attention/triton_backend.py index 5b60bb40a..3ec9c7861 100644 --- a/python/sglang/srt/layers/attention/triton_backend.py +++ b/python/sglang/srt/layers/attention/triton_backend.py @@ -1213,6 +1213,8 @@ class TritonAttnBackend(AttentionBackend): forward_batch: ForwardBatch, save_kv_cache=True, sinks=None, + score_mod=None, + aux_tensors=None, ): # TODO: reuse the buffer across layers attn_out = getattr(forward_batch, "_attn_output", None) @@ -1279,6 +1281,10 @@ class TritonAttnBackend(AttentionBackend): causal = False if self.dcp_size > 1: + if score_mod is not None: + raise NotImplementedError( + "DCP Triton extend does not support score_mod" + ) return self._forward_extend_dcp( q, k, v, layer, forward_batch, causal, logits_soft_cap, sinks ) @@ -1286,7 +1292,15 @@ class TritonAttnBackend(AttentionBackend): # Deterministic mode: use unified 1-stage kernel if self.enable_deterministic: return self._forward_extend_unified( - q, o, layer, forward_batch, causal, logits_soft_cap, sinks + q, + o, + layer, + forward_batch, + causal, + logits_soft_cap, + sinks, + score_mod=score_mod, + aux_tensors=aux_tensors, ) # Normal mode: use original 2-stage kernel @@ -1319,6 +1333,7 @@ class TritonAttnBackend(AttentionBackend): # extend_attention_fwd below. Correctness is never at risk. if ( self.use_verify_splitkv + and score_mod is None and forward_batch.forward_mode.is_target_verify() and self.verify_splitkv_fwd( q.view(-1, layer.tp_q_head_num, layer.qk_head_dim), @@ -1370,6 +1385,8 @@ class TritonAttnBackend(AttentionBackend): window_kv_offsets=window_kv_offsets, xai_temperature_len=layer.xai_temperature_len, page_size=self.page_size, + score_mod=score_mod, + aux_tensors=aux_tensors, ) return o @@ -1515,6 +1532,8 @@ class TritonAttnBackend(AttentionBackend): causal: bool, logits_soft_cap: float, sinks: Optional[torch.Tensor], + score_mod=None, + aux_tensors=None, ): """ Unified 1-stage extend attention for deterministic inference. @@ -1640,6 +1659,8 @@ class TritonAttnBackend(AttentionBackend): window_start_pos=window_start_pos, xai_temperature_len=layer.xai_temperature_len, page_size=self.page_size, + score_mod=score_mod, + aux_tensors=aux_tensors, ) return o @@ -1653,6 +1674,8 @@ class TritonAttnBackend(AttentionBackend): forward_batch: ForwardBatch, save_kv_cache=True, sinks=None, + score_mod=None, + aux_tensors=None, ): # During torch.compile, there is a bug in rotary_emb that causes the # output value to have a 3D tensor shape. This reshapes the output correctly. @@ -1718,6 +1741,10 @@ class TritonAttnBackend(AttentionBackend): attn_logits = self.forward_metadata.swa_attn_logits if self.dcp_size > 1: + if score_mod is not None: + raise NotImplementedError( + "DCP Triton decode does not support score_mod" + ) group = get_parallel().dcp_group with use_symmetric_memory(group): q_for_decode = q.view( @@ -1777,6 +1804,8 @@ class TritonAttnBackend(AttentionBackend): has_mla=self.use_mla, use_pdl=self.use_pdl, page_size=self.page_size, + score_mod=score_mod, + aux_tensors=aux_tensors, ) return o diff --git a/python/sglang/srt/layers/logits_processor.py b/python/sglang/srt/layers/logits_processor.py index a11100ed7..8fb06959f 100644 --- a/python/sglang/srt/layers/logits_processor.py +++ b/python/sglang/srt/layers/logits_processor.py @@ -238,6 +238,10 @@ class LogitsMetadata: mm_input_embeds: Optional[torch.Tensor] = None + # DRAFT_EXTEND_V2: when set, lm_head runs only on these rows (see + # EagleDraftExtendInput.select_index). + draft_extend_select_index: Optional[torch.Tensor] = None + @classmethod def from_forward_batch(cls, forward_batch: ForwardBatch): if ( @@ -265,6 +269,11 @@ class LogitsMetadata: extend_token_ids_logprob ) = extend_logprob_pruned_lens_cpu = False + if forward_batch.forward_mode.is_draft_extend_v2(): + draft_extend_select_index = forward_batch.spec_info.select_index + else: + draft_extend_select_index = None + return cls( forward_mode=forward_batch.forward_mode, capture_hidden_mode=forward_batch.capture_hidden_mode, @@ -288,6 +297,7 @@ class LogitsMetadata: global_num_tokens_for_logprob_gpu=forward_batch.global_num_tokens_for_logprob_gpu, dp_padding_mode=DpPaddingMode.SUM_LEN, mm_input_embeds=forward_batch.mm_input_embeds, + draft_extend_select_index=draft_extend_select_index, ) def compute_dp_attention_metadata(self): @@ -490,7 +500,12 @@ class LogitsProcessor(nn.Module): or logits_metadata.forward_mode.is_target_verify() or logits_metadata.forward_mode.is_draft_extend_v2() ): - pruned_states = hidden_states + if logits_metadata.draft_extend_select_index is not None: + # Only next_token_logits narrows to [bs, vocab]; the + # FULL-capture hidden stays unpruned. + pruned_states = hidden_states[logits_metadata.draft_extend_select_index] + else: + pruned_states = hidden_states pruned_states_before_norm = hidden_states_before_norm if aux_hidden_states is not None: aux_pruned_states = [hidden for hidden in aux_hidden_states] diff --git a/python/sglang/srt/layers/moe/fused_moe_triton/layer.py b/python/sglang/srt/layers/moe/fused_moe_triton/layer.py index 09f79c106..87cd13ad5 100644 --- a/python/sglang/srt/layers/moe/fused_moe_triton/layer.py +++ b/python/sglang/srt/layers/moe/fused_moe_triton/layer.py @@ -172,6 +172,12 @@ class FusedMoE(torch.nn.Module): inplace: suggestion to compute inplace (modify input activation). """ + # True on shared-expert FusedMoE subclasses (e.g. Inkling's sink); lets + # backend resolution distinguish them from routed experts. + is_shared_fused_moe = False + + _skip_aiter_moe_shuffle: bool = False + def __init__( self, num_experts: int, @@ -1138,6 +1144,18 @@ class FusedMoE(torch.nn.Module): ) -> None: tp_rank = self.moe_tp_rank + # Mirror _weight_loader_impl: the trtllm bf16 prep reshapes expert weights + # into block layout; hot weight updates must restore canonical shapes first. + method = self.quant_method + if isinstance(method, KTEPWrapperMethod): + method = method.gpu_method + if isinstance(method, UnquantizedFusedMoEMethod): + method.maybe_restore_flashinfer_trtllm_bf16_weight_shape_for_load( + layer=self, + param=param, + weight_name=weight_name, + ) + if ( self.quant_config is not None and self.quant_config.get_name() == "mxfp4" diff --git a/python/sglang/srt/layers/moe/moe_runner/flashinfer_trtllm.py b/python/sglang/srt/layers/moe/moe_runner/flashinfer_trtllm.py index 31226639e..2bff25e5f 100644 --- a/python/sglang/srt/layers/moe/moe_runner/flashinfer_trtllm.py +++ b/python/sglang/srt/layers/moe/moe_runner/flashinfer_trtllm.py @@ -48,6 +48,8 @@ _deferred_finalize_enabled: contextvars.ContextVar[bool] = contextvars.ContextVa "flashinfer_trtllm_deferred_finalize_enabled", default=False ) +_TRTLLM_MOE_PDL_MAX_TOKENS = envs.SGLANG_TRTLLM_MOE_PDL_MAX_TOKENS.get() + @dataclass class FlashInferTrtllmDeferredFinalizeOutput: @@ -126,6 +128,20 @@ def _is_gated(layer: Module) -> bool: return True if is_gated is None else is_gated +def _get_packed_topk_ids_for_flashinfer_routed(topk_output) -> torch.Tensor: + """Return FlashInfer routed packed top-k ids, using prepacked output if present.""" + packed_topk_ids = getattr(topk_output, "packed_topk_ids", None) + if packed_topk_ids is not None: + return packed_topk_ids + + from sglang.srt.layers.moe.topk import TopKOutputChecker + + assert TopKOutputChecker.format_is_standard(topk_output) + return PackTopkIds.execute( + topk_output.topk_ids.contiguous(), topk_output.topk_weights.contiguous() + ) + + def _align_fp8_moe_weights( w13: torch.Tensor, w2: torch.Tensor, @@ -717,10 +733,7 @@ def fused_experts_none_to_flashinfer_trtllm_fp8( assert ( runner_config.top_k is not None ), "runner_config.top_k is required for flashinfer_trtllm_routed." - assert TopKOutputChecker.format_is_standard(topk_output) - packed_topk_ids = PackTopkIds.execute( - topk_output.topk_ids, topk_output.topk_weights - ) + packed_topk_ids = _get_packed_topk_ids_for_flashinfer_routed(topk_output) output = trtllm_fp8_block_scale_routed_moe_wrapper( topk_ids=packed_topk_ids, @@ -1023,11 +1036,7 @@ def fused_experts_none_to_flashinfer_trtllm_fp4( ) if use_routed_topk: - assert TopKOutputChecker.format_is_standard(topk_output) - - packed_topk_ids = PackTopkIds.execute( - topk_output.topk_ids, topk_output.topk_weights - ) + packed_topk_ids = _get_packed_topk_ids_for_flashinfer_routed(topk_output) result = trtllm_fp4_block_scale_routed_moe( topk_ids=packed_topk_ids, routing_bias=None, @@ -1047,7 +1056,7 @@ def fused_experts_none_to_flashinfer_trtllm_fp4( output2_scale_scalar=quant_info.g2_alphas, per_token_scale=per_token_scale, num_experts=quant_info.global_num_experts, - top_k=topk_output.topk_ids.shape[1], + top_k=packed_topk_ids.shape[1], n_group=0, topk_group=0, intermediate_size=quant_info.intermediate_size_per_partition, @@ -1059,6 +1068,7 @@ def fused_experts_none_to_flashinfer_trtllm_fp4( activation_type=activation_type, tune_max_num_tokens=next_power_of_2(hs_fp4.shape[0]), output=symm_output, + enable_pdl=hs_fp4.shape[0] <= _TRTLLM_MOE_PDL_MAX_TOKENS, )[0] else: assert TopKOutputChecker.format_is_bypassed(topk_output) @@ -1102,6 +1112,7 @@ def fused_experts_none_to_flashinfer_trtllm_fp4( do_finalize=not defer_finalize, activation_type=activation_type, tune_max_num_tokens=next_power_of_2(hs_fp4.shape[0]), + enable_pdl=hs_fp4.shape[0] <= _TRTLLM_MOE_PDL_MAX_TOKENS, ) if not defer_finalize: moe_kwargs["output"] = symm_output @@ -1194,16 +1205,13 @@ def fused_experts_none_to_flashinfer_trtllm_bf16( assert ( runner_config.top_k is not None ), "runner_config.top_k is required for flashinfer_trtllm_routed." - assert TopKOutputChecker.format_is_standard(topk_output) routing_method_type = runner_config.routing_method_type if routing_method_type is None: routing_method_type = RoutingMethodType.Default elif routing_method_type == RoutingMethodType.DeepSeekV3: routing_method_type = RoutingMethodType.TopK - packed_topk_ids = PackTopkIds.execute( - topk_output.topk_ids, topk_output.topk_weights - ) + packed_topk_ids = _get_packed_topk_ids_for_flashinfer_routed(topk_output) final_hidden_states = trtllm_bf16_routed_moe( topk_ids=packed_topk_ids, hidden_states=hidden_states, diff --git a/python/sglang/srt/layers/moe/moe_runner/marlin.py b/python/sglang/srt/layers/moe/moe_runner/marlin.py index 23b5272e2..c595d6757 100644 --- a/python/sglang/srt/layers/moe/moe_runner/marlin.py +++ b/python/sglang/srt/layers/moe/moe_runner/marlin.py @@ -4,6 +4,8 @@ from dataclasses import dataclass from typing import TYPE_CHECKING, Optional import torch +import triton +import triton.language as tl from sglang.srt.layers.moe.moe_runner.base import ( MoeQuantInfo, @@ -20,7 +22,39 @@ if TYPE_CHECKING: StandardDispatchOutput, ) -MARLIN_MOE_WORKSPACE: Optional[torch.Tensor] = None + +@triton.jit +def _unpack_packed_topk_kernel(packed_ptr, ids_ptr, w_ptr, numel, BLOCK: tl.constexpr): + pid = tl.program_id(0) + offs = pid * BLOCK + tl.arange(0, BLOCK) + mask = offs < numel + packed = tl.load( + packed_ptr + offs, mask=mask + ) # int32 (id << 16) | bf16-weight-bits + tl.store(ids_ptr + offs, packed >> 16, mask=mask) # expert id (high 16 bits) + wbits = (packed & 0xFFFF).to(tl.int16) # bf16 weight bits (low 16 bits) + tl.store( + w_ptr + offs, wbits.to(tl.bfloat16, bitcast=True).to(tl.float32), mask=mask + ) + + +def _fused_unpack_packed_topk(packed: torch.Tensor): + """Single-launch inverse of the fused topk pack ((id << 16) | bf16-weight-bits). + + Returns (topk_ids int32, topk_weights float32). Collapses the ~5 elementwise + ops (shift / mask / int16 / bitcast / cast) the torch reference emits per call + into one Triton launch. Mirrors _pack_topk_kernel (trtllm_lora_temp/topk_pack). + """ + packed = packed.contiguous() + ids = torch.empty_like(packed, dtype=torch.int32) + w = torch.empty(packed.shape, dtype=torch.float32, device=packed.device) + numel = packed.numel() + if numel: + BLOCK = 1024 + _unpack_packed_topk_kernel[(triton.cdiv(numel, BLOCK),)]( + packed, ids, w, numel, BLOCK=BLOCK + ) + return ids, w @dataclass @@ -84,7 +118,6 @@ def fused_experts_none_to_marlin( quant_info: MarlinMoeQuantInfo, runner_config: MoeRunnerConfig, ) -> StandardCombineInput: - global MARLIN_MOE_WORKSPACE from sglang.srt.layers.moe.fused_moe_triton.fused_marlin_moe import fused_marlin_moe from sglang.srt.layers.moe.token_dispatcher.standard import StandardCombineInput from sglang.srt.layers.quantization.marlin_utils import marlin_make_workspace @@ -92,6 +125,20 @@ def fused_experts_none_to_marlin( hidden_states = dispatch_output.hidden_states topk_output = dispatch_output.topk_output + # The fused gate+topk kernel (SGLANG_OPT_USE_FUSED_GATE_TOPK) emits a + # PackedTopKOutput -- int32 (expert_id << 16) | bf16-weight-bits -- for the + # FlashInfer trtllm routed kernel, which unpacks device-side. The Marlin + # runner reads topk_ids / topk_weights separately, so unpack it here. + if not hasattr(topk_output, "topk_weights"): + from sglang.srt.layers.moe.topk import StandardTopKOutput + + topk_ids, topk_weights = _fused_unpack_packed_topk(topk_output.packed_topk_ids) + topk_output = StandardTopKOutput( + topk_weights=topk_weights, + topk_ids=topk_ids, + router_logits=topk_output.router_logits, + ) + if runner_config.is_gated: assert runner_config.activation == "silu", "Only gated SiLU is supported." elif runner_config.activation not in {"silu", "relu2"}: @@ -99,13 +146,9 @@ def fused_experts_none_to_marlin( f"Unsupported Marlin MoE activation: {runner_config.activation}" ) - if ( - MARLIN_MOE_WORKSPACE is None - or MARLIN_MOE_WORKSPACE.device != hidden_states.device - ): - MARLIN_MOE_WORKSPACE = marlin_make_workspace( - hidden_states.device, max_blocks_per_sm=4 - ) + # Use a per-call workspace so captured graphs cannot alias Marlin's + # inter-block reduction locks and deadlock during capture. + workspace = marlin_make_workspace(hidden_states.device, max_blocks_per_sm=4) marlin_hidden_states = hidden_states # Avoid aliasing the MoE input buffer until Marlin output semantics are @@ -146,7 +189,7 @@ def fused_experts_none_to_marlin( w2_global_scale=quant_info.w2_global_scale, w1_bias=quant_info.w13_bias, w2_bias=quant_info.w2_bias, - workspace=MARLIN_MOE_WORKSPACE, + workspace=workspace, num_bits=quant_info.weight_bits, is_k_full=quant_info.is_k_full, inplace=marlin_inplace, @@ -164,3 +207,9 @@ def fused_experts_none_to_marlin( return StandardCombineInput( hidden_states=output, ) + + +# ===== TO BE REFACTORED ==== +from sglang.srt.lora.marlin_lora_temp import sgl_backend # noqa: E402,F401 + +# ===== END TO BE REFACTORED ==== diff --git a/python/sglang/srt/layers/moe/moe_runner/triton_utils/configs/causal_conv1d_fwd_sm_100.json b/python/sglang/srt/layers/moe/moe_runner/triton_utils/configs/causal_conv1d_fwd_sm_100.json new file mode 100644 index 000000000..d3cc5937d --- /dev/null +++ b/python/sglang/srt/layers/moe/moe_runner/triton_utils/configs/causal_conv1d_fwd_sm_100.json @@ -0,0 +1,16 @@ +{ + "useful_configs": { + "1": "helion.Config(block_sizes=[32, 64], indexing=['tensor_descriptor', 'tensor_descriptor', 'tensor_descriptor', 'tensor_descriptor', 'pointer', 'tensor_descriptor', 'tensor_descriptor', 'pointer', 'pointer', 'tensor_descriptor', 'tensor_descriptor', 'tensor_descriptor', 'tensor_descriptor', 'pointer', 'pointer', 'tensor_descriptor', 'pointer', 'pointer', 'tensor_descriptor', 'pointer'], l2_groupings=[1], load_eviction_policies=['', 'last', 'first', '', '', '', 'last', '', '', '', 'first', 'first', '', '', 'first', 'first', '', '', ''], loop_orders=[[0, 2, 1]], num_stages=4, num_warps=1, pid_type='flat', range_flattens=[None], range_multi_buffers=[None], range_num_stages=[0], range_unroll_factors=[0], range_warp_specializes=[None])", + "2": "helion.Config(block_sizes=[32, 64], indexing=['pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer'], l2_groupings=[1], load_eviction_policies=['', 'last', 'first', '', '', '', 'last', '', '', '', 'first', 'first', '', '', 'first', 'first', '', '', ''], loop_orders=[[0, 2, 1]], num_stages=4, num_warps=1, pid_type='flat', range_flattens=[None], range_multi_buffers=[None], range_num_stages=[0], range_unroll_factors=[0], range_warp_specializes=[None])" + }, + "hash_configs": { + "(4096, (2, 2), (4,))": 1, + "(8192, (2, 2), (4,))": 1, + "(4096, (4, 4), (4,))": 2, + "(8192, (4, 4), (4,))": 2, + "(4096, (2, 2), (3,))": 2, + "(8192, (2, 2), (3,))": 2, + "(4096, (4, 4), (3,))": 2, + "(8192, (4, 4), (3,))": 2 + } +} diff --git a/python/sglang/srt/layers/moe/moe_runner/triton_utils/configs/causal_conv1d_fwd_sm_90.json b/python/sglang/srt/layers/moe/moe_runner/triton_utils/configs/causal_conv1d_fwd_sm_90.json new file mode 100644 index 000000000..841000079 --- /dev/null +++ b/python/sglang/srt/layers/moe/moe_runner/triton_utils/configs/causal_conv1d_fwd_sm_90.json @@ -0,0 +1,15 @@ +{ + "useful_configs": { + "1": "helion.Config(block_sizes=[32, 128], indexing=['pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer'], l2_groupings=[1], load_eviction_policies=['', '', 'last', 'last', '', '', '', '', '', '', '', '', '', '', '', '', '', '', ''], loop_orders=[[1, 0, 2]], num_stages=1, num_warps=4, pid_type='flat', range_flattens=[None], range_multi_buffers=[None], range_num_stages=[0], range_unroll_factors=[0], range_warp_specializes=[])" + }, + "hash_configs": { + "(4096, (2, 2), (4,))": 1, + "(8192, (2, 2), (4,))": 1, + "(4096, (4, 4), (4,))": 1, + "(8192, (4, 4), (4,))": 1, + "(4096, (2, 2), (3,))": 1, + "(8192, (2, 2), (3,))": 1, + "(4096, (4, 4), (3,))": 1, + "(8192, (4, 4), (3,))": 1 + } +} diff --git a/python/sglang/srt/layers/moe/moe_runner/triton_utils/configs/causal_conv1d_fwd_sm_95.json b/python/sglang/srt/layers/moe/moe_runner/triton_utils/configs/causal_conv1d_fwd_sm_95.json new file mode 100644 index 000000000..841000079 --- /dev/null +++ b/python/sglang/srt/layers/moe/moe_runner/triton_utils/configs/causal_conv1d_fwd_sm_95.json @@ -0,0 +1,15 @@ +{ + "useful_configs": { + "1": "helion.Config(block_sizes=[32, 128], indexing=['pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer'], l2_groupings=[1], load_eviction_policies=['', '', 'last', 'last', '', '', '', '', '', '', '', '', '', '', '', '', '', '', ''], loop_orders=[[1, 0, 2]], num_stages=1, num_warps=4, pid_type='flat', range_flattens=[None], range_multi_buffers=[None], range_num_stages=[0], range_unroll_factors=[0], range_warp_specializes=[])" + }, + "hash_configs": { + "(4096, (2, 2), (4,))": 1, + "(8192, (2, 2), (4,))": 1, + "(4096, (4, 4), (4,))": 1, + "(8192, (4, 4), (4,))": 1, + "(4096, (2, 2), (3,))": 1, + "(8192, (2, 2), (3,))": 1, + "(4096, (4, 4), (3,))": 1, + "(8192, (4, 4), (3,))": 1 + } +} diff --git a/python/sglang/srt/layers/moe/moe_runner/triton_utils/configs/causal_conv1d_fwd_with_prefix_sm_100.json b/python/sglang/srt/layers/moe/moe_runner/triton_utils/configs/causal_conv1d_fwd_with_prefix_sm_100.json new file mode 100644 index 000000000..4cf2e9f6a --- /dev/null +++ b/python/sglang/srt/layers/moe/moe_runner/triton_utils/configs/causal_conv1d_fwd_with_prefix_sm_100.json @@ -0,0 +1,9 @@ +{ + "useful_configs": { + "1": "helion.Config(block_sizes=[16, 128], indexing=['tensor_descriptor', 'pointer', 'pointer', 'tensor_descriptor', 'tensor_descriptor', 'tensor_descriptor', 'tensor_descriptor', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'tensor_descriptor', 'pointer', 'tensor_descriptor', 'pointer', 'pointer', 'pointer', 'pointer', 'tensor_descriptor', 'pointer', 'pointer', 'pointer', 'pointer'], l2_groupings=[1], load_eviction_policies=['first', '', '', 'first', 'last', 'last', 'last', 'first', '', '', '', 'last', '', 'last', 'first', 'first', 'first', 'first', '', 'last', 'first', 'first', '', 'last', 'last', 'last', 'last'], loop_orders=[[0, 1, 2]], num_stages=4, num_warps=4, pid_type='flat', range_flattens=[None], range_multi_buffers=[None], range_num_stages=[0], range_unroll_factors=[0], range_warp_specializes=[None])" + }, + "hash_configs": { + "(4096, (2, 2), (4,))": 1, + "(8192, (2, 2), (4,))": 1 + } +} diff --git a/python/sglang/srt/layers/moe/moe_runner/triton_utils/configs/causal_conv1d_fwd_with_prefix_sm_90.json b/python/sglang/srt/layers/moe/moe_runner/triton_utils/configs/causal_conv1d_fwd_with_prefix_sm_90.json new file mode 100644 index 000000000..2bce9dc31 --- /dev/null +++ b/python/sglang/srt/layers/moe/moe_runner/triton_utils/configs/causal_conv1d_fwd_with_prefix_sm_90.json @@ -0,0 +1,10 @@ +{ + "useful_configs": { + "0": "helion.Config(block_sizes=[16, 128], indexing=['pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'tensor_descriptor', 'pointer', 'pointer'], l2_groupings=[1], load_eviction_policies=['', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', 'first', '', ''], loop_orders=[[0, 1, 2]], num_stages=1, num_warps=4, pid_type='flat', range_flattens=[None], range_multi_buffers=[None], range_num_stages=[0], range_unroll_factors=[0], range_warp_specializes=[])", + "1": "helion.Config(block_sizes=[32, 128], indexing=['pointer', 'pointer', 'tensor_descriptor', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer'], l2_groupings=[1], load_eviction_policies=['', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', ''], loop_orders=[[0, 1, 2]], num_stages=1, num_warps=4, pid_type='flat', range_flattens=[None], range_multi_buffers=[None], range_num_stages=[0], range_unroll_factors=[0], range_warp_specializes=[])" + }, + "hash_configs": { + "(4096, (2, 2), (4,))": 0, + "(8192, (2, 2), (4,))": 1 + } +} diff --git a/python/sglang/srt/layers/moe/moe_runner/triton_utils/configs/causal_conv1d_fwd_with_prefix_sm_95.json 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range_unroll_factors=[0], range_warp_specializes=[])", + "1": "helion.Config(block_sizes=[32, 128], indexing=['pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer', 'pointer'], l2_groupings=[1], load_eviction_policies=['', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', ''], loop_orders=[[0, 1, 2]], num_stages=1, num_warps=4, pid_type='flat', range_flattens=[None], range_multi_buffers=[None], range_num_stages=[0], range_unroll_factors=[0], range_warp_specializes=[])" + }, + "hash_configs": { + "(4096, (2, 2), (4,))": 0, + "(8192, (2, 2), (4,))": 1 + } +} diff --git a/python/sglang/srt/layers/moe/moe_runner/triton_utils/configs/silu_and_mul_interleaved_sm_100.json 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range_multi_buffers=[None], range_num_stages=[0], range_unroll_factors=[0], range_warp_specializes=[None])", + "4": "helion.Config(block_sizes=[2, 512], indexing=['pointer', 'tensor_descriptor', 'pointer'], l2_groupings=[2], load_eviction_policies=['last', ''], loop_orders=[[0, 1]], num_stages=2, num_warps=1, pid_type='flat', range_flattens=[None], range_multi_buffers=[None], range_num_stages=[0], range_unroll_factors=[0], range_warp_specializes=[None])" + }, + "hash_configs": { + "(512, (2,), (False,))": 0, + "(512, (2,), (True,))": 1, + "(1024, (2,), (False,))": 0, + "(1024, (2,), (True,))": 0, + "(1536, (2,), (False,))": 1, + "(1536, (2,), (True,))": 1, + "(2048, (2,), (False,))": 4, + "(2048, (2,), (True,))": 1, + "(3072, (2,), (False,))": 0, + "(3072, (2,), (True,))": 0, + "(4096, (2,), (False,))": 4, + "(4096, (2,), (True,))": 1, + "(4608, (2,), (False,))": 0, + "(4608, (2,), (True,))": 0, + "(6144, (2,), (False,))": 0, + "(6144, (2,), (True,))": 1, + "(7680, (2,), (False,))": 1, 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range_num_stages=[0], range_unroll_factors=[0], range_warp_specializes=[])" + }, + "hash_configs": { + "(512, (2,), (False,))": 0, + "(512, (2,), (True,))": 1, + "(1024, (2,), (False,))": 0, + "(1024, (2,), (True,))": 1, + "(1536, (2,), (False,))": 2, + "(1536, (2,), (True,))": 2, + "(2048, (2,), (False,))": 3, + "(2048, (2,), (True,))": 3, + "(3072, (2,), (False,))": 1, + "(3072, (2,), (True,))": 1, + "(4096, (2,), (False,))": 3, + "(4096, (2,), (True,))": 3, + "(4608, (2,), (False,))": 1, + "(4608, (2,), (True,))": 1, + "(6144, (2,), (False,))": 3, + "(6144, (2,), (True,))": 3, + "(7680, (2,), (False,))": 2, + "(7680, (2,), (True,))": 2, + "(8192, (2,), (False,))": 3, + "(8192, (2,), (True,))": 3, + "(9216, (2,), (False,))": 1, + "(9216, (2,), (True,))": 1, + "(10240, (2,), (False,))": 3, + "(10240, (2,), (True,))": 3, + "(12288, (2,), (False,))": 3, + "(12288, (2,), (True,))": 3, + "(14336, (2,), (False,))": 3, + "(14336, (2,), (True,))": 3, + "(16384, (2,), (False,))": 3, + "(16384, (2,), (True,))": 3 + } +} diff --git a/python/sglang/srt/layers/moe/moe_runner/triton_utils/configs/silu_and_mul_sm_100.json b/python/sglang/srt/layers/moe/moe_runner/triton_utils/configs/silu_and_mul_sm_100.json new file mode 100644 index 000000000..badc7a9ee --- /dev/null +++ b/python/sglang/srt/layers/moe/moe_runner/triton_utils/configs/silu_and_mul_sm_100.json @@ -0,0 +1,39 @@ +{ + "useful_configs": { + "0": "helion.Config(block_sizes=[2, 512], indexing=['tensor_descriptor', 'tensor_descriptor', 'tensor_descriptor', 'tensor_descriptor'], l2_groupings=[1], load_eviction_policies=['last', '', ''], loop_orders=[[1, 0]], num_stages=3, num_warps=1, pid_type='flat', range_flattens=[None], range_multi_buffers=[None], range_num_stages=[0], range_unroll_factors=[0], range_warp_specializes=[None])", + "1": "helion.Config(block_sizes=[1, 1024], indexing=['tensor_descriptor', 'tensor_descriptor', 'pointer', 'pointer'], l2_groupings=[1], load_eviction_policies=['last', 'first', 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l2_groupings=[2], load_eviction_policies=['', ''], loop_orders=[[0, 1]], num_stages=2, num_warps=1, pid_type='flat', range_flattens=[None], range_multi_buffers=[None], range_num_stages=[0], range_unroll_factors=[0], range_warp_specializes=[])", + "1": "helion.Config(block_sizes=[1, 512], indexing=['pointer', 'tensor_descriptor', 'tensor_descriptor', 'pointer'], l2_groupings=[1], load_eviction_policies=['', 'first', 'first'], loop_orders=[[1, 0]], num_stages=7, num_warps=1, pid_type='flat', range_flattens=[None], range_multi_buffers=[None], range_num_stages=[0], range_unroll_factors=[0], range_warp_specializes=[])", + "2": "helion.Config(block_sizes=[1, 1024], indexing=['pointer', 'pointer', 'pointer', 'pointer'], l2_groupings=[1], load_eviction_policies=['last', '', 'last'], loop_orders=[[1, 0]], num_stages=3, num_warps=1, pid_type='flat', range_flattens=[None], range_multi_buffers=[None], range_num_stages=[0], range_unroll_factors=[0], range_warp_specializes=[])", + "3": 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must be passed as bits" + ) + tm: tl.constexpr = 1 << (-1 + x.dtype.primitive_bitwidth) + fm: tl.constexpr = (1 << x.dtype.primitive_bitwidth) - 1 + tm_arr = tl.full(x.shape, tm, dtype=x.dtype) + fm_arr = tl.full(x.shape, fm, dtype=x.dtype) + return tm_arr, fm_arr + + +@triton.jit +def fpval_to_key(x): + tm, fm = get_topmask_and_fullmask(x) + return x ^ tl.where((x & tm) != 0, fm, tm) + + +@triton.jit +def key_to_fpval(x): + tm, fm = get_topmask_and_fullmask(x) + return x ^ tl.where((x & tm) == 0, fm, tm) + + +@triton.jit +def indx_to_key(idx, N_PAD: tl.constexpr): + return N_PAD - idx + + +@triton.jit +def key_to_indx(idx, N_PAD: tl.constexpr): + return N_PAD - idx + + +@triton.jit +def _streaming_topk_kernel( + x_ptr, + stride_xm, + values_ptr, + indices_ptr, + M, + N, # num_experts + N_PAD: tl.constexpr, + K: tl.constexpr, + K_POW2: tl.constexpr, + BLOCK_SIZE_M: tl.constexpr, + BLOCK_SIZE_N: tl.constexpr, + RETURN_VALUES: tl.constexpr, +): + pid = tl.program_id(0) + offs_m = pid * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M) + mask_m = offs_m < M + + # Setup dtypes: sorting uses unsigned dtype where we pack the value into + # the upper bits and the index into the lower bits + x_nbits: tl.constexpr = x_ptr.dtype.element_ty.primitive_bitwidth + x_utype: tl.constexpr = tl.dtype(f"uint{x_nbits}") + if x_nbits < 16: + # Ensure that we leave at least 16 bits for the expert index even if + # the input dtype is smaller than 16 bits: + y_nbits: tl.constexpr = 32 + else: + y_nbits: tl.constexpr = x_nbits * 2 + x_ultype: tl.constexpr = tl.dtype(f"uint{y_nbits}") + x_dtype: tl.constexpr = x_ptr.dtype.element_ty + + # Iterate in reverse column order to only mask cols on the 1st iter + num_iters: tl.constexpr = N_PAD // BLOCK_SIZE_N - 1 + offs_x_n = num_iters * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N) + mask_n = offs_x_n < N + + # First masked iteration + x_ptrs = x_ptr + offs_m[:, None] * stride_xm + offs_x_n[None, :] + x = tl.load(x_ptrs, mask=(mask_m[:, None] & mask_n[None, :]), other=float("-inf")) + x = fpval_to_key(x.to(x_utype, bitcast=True)) + x = (x.to(x_ultype) << 16) | indx_to_key(offs_x_n, N_PAD)[None, :] + acc = tl.topk(x, K_POW2, dim=1) + + # Subsequent iterations + for _i in (tl.static_range if num_iters <= 4 else range)(num_iters): + acc = tl.bitonic_merge(acc) # ensure sorted ascending for the merge + x_ptrs -= BLOCK_SIZE_N + offs_x_n -= BLOCK_SIZE_N + x = tl.load(x_ptrs, mask=mask_m[:, None], other=float("-inf")) + x = fpval_to_key(x.to(x_utype, bitcast=True)) + x = (x.to(x_ultype) << 16) | indx_to_key(offs_x_n, N_PAD)[None, :] + acc = tl.maximum(acc, tl.topk(x, K_POW2, dim=1)) + + offs_k = tl.arange(0, K_POW2) + mask_k = offs_k < K + # Sort by descending value + acc = tl.sort(acc, dim=1, descending=True) + # Mask out the last K_POW2 - K values + acc = tl.where(mask_k[None, :], acc, 0) + # Rotate expert index into upper 16 bits + acc = (acc << (y_nbits - 16)) | (acc >> 16) + # iiii0000vvvvvvvv --> 0000iiii: + y_indices_raw = (acc >> (y_nbits - 16)).to(tl.uint32) + y_indices = key_to_indx(y_indices_raw, N_PAD) + # iiii0000vvvvvvvv --> vvvvvvvv: + y_values_raw = acc.to(x_utype) + y_values = key_to_fpval(y_values_raw).to(x_dtype, bitcast=True) + + offs_mk = offs_m[:, None] * K + offs_k[None, :] + mask_mk = mask_m[:, None] & mask_k[None, :] + if RETURN_VALUES: + tl.store(values_ptr + offs_mk, y_values, mask=mask_mk) + tl.store(indices_ptr + offs_mk, y_indices, mask=mask_mk) + + +def gate_topk( + x: torch.Tensor, + k: int, + *, + return_values: bool = True, + _impl: str = "streaming", +): + """ + Stable implementation of torch.topk(..., dim=-1) that is most efficient + for small values of k. + """ + assert x.is_contiguous(), f"{x.shape=} {x.stride()=}" + assert x.ndim == 2, f"{x.shape=}" + assert x.numel() <= 2**31, f"assumes int32 indexing: {x.shape=}" + n_rows, n_cols = x.shape + if return_values: + values = torch.empty((n_rows, k), dtype=x.dtype, device=x.device) + else: + values = None + # int32 indices: column ids fit int32 (numel <= 2**31, asserted above) and the + # sole caller (Inkling gate) feeds the SRT MoeRunner topk-packing, which requires + # int32. The kernel store casts to the buffer dtype, so this emits int32 + # directly — no separate .to(int32) downstream. + indices = torch.empty((n_rows, k), dtype=torch.int32, device=x.device) + if k > 32: + # For larger topk, we need to reevaluate the kernel strategy + raise NotImplementedError(f"topk kernels only support k <= 32: {k=}") + + if _impl == "streaming": + BLOCK_SIZE_N = 32 + BLOCK_SIZE_M = 32 + grid = (triton.cdiv(n_rows, BLOCK_SIZE_M),) + _streaming_topk_kernel[grid]( + x_ptr=x, + stride_xm=x.stride(0), + values_ptr=values, + indices_ptr=indices, + M=n_rows, + N=n_cols, + N_PAD=triton.cdiv(n_cols, BLOCK_SIZE_N) * BLOCK_SIZE_N, + K=k, + K_POW2=triton.next_power_of_2(k), + BLOCK_SIZE_M=BLOCK_SIZE_M, + BLOCK_SIZE_N=BLOCK_SIZE_N, + RETURN_VALUES=return_values, + ) + else: + raise NotImplementedError( + f"topk kernels only support streaming implementation: {_impl=}" + ) + + if return_values: + return values, indices + return indices diff --git a/python/sglang/srt/layers/moe/moe_runner/triton_utils/helion_utils.py b/python/sglang/srt/layers/moe/moe_runner/triton_utils/helion_utils.py new file mode 100644 index 000000000..67e4d95e3 --- /dev/null +++ b/python/sglang/srt/layers/moe/moe_runner/triton_utils/helion_utils.py @@ -0,0 +1,296 @@ +import copy +import dataclasses +import functools +import json +import logging +import os +from collections import defaultdict +from collections.abc import Iterable +from enum import Enum +from pathlib import Path +from typing import Any, Callable + +import helion # noqa: F401 +import torch +from triton.testing import do_bench + +logger = logging.getLogger(__name__) +AutotuneInputFn = Callable[[], Iterable[tuple[Any, ...]]] + + +def get_model_depths() -> list[int]: + return [8, 16, 24, 32, 40, 48, 64] + + +def get_cuda_device_capability() -> tuple[int, int]: + if torch.cuda.is_available(): + dev = torch.cuda.current_device() + cc_major, cc_minor = torch.cuda.get_device_capability(dev) + return cc_major, cc_minor + return (0, 0) + + +class AOTAutotuneMode(Enum): + NONE = "none" + CREATE = "create" + RETUNE = "retune" + + @classmethod + def from_str(cls, mode: str) -> "AOTAutotuneMode": + return cls[mode.upper()] + + +def load_autotune_data_from_json( + path: Path, +) -> tuple[dict[int, Any], dict[tuple[Any, Any, Any], int]]: + with open(path, "r") as f: + json_obj = json.load(f) + useful_configs = { + int(k): eval(v) for k, v in json_obj["useful_configs"].items() + } + hash_configs = {tuple(eval(k)): v for k, v in json_obj["hash_configs"].items()} + return useful_configs, hash_configs + + +def bind_and_compile_kernel( + kernel: helion.Kernel, args: Any, config: helion.Config +) -> Callable: + has_int64 = any([i.numel() >= 2**31 for i in args if isinstance(i, torch.Tensor)]) + if has_int64: + new_settings = dataclasses.replace(kernel.settings, index_dtype=torch.int64) + kernel = helion.Kernel( + kernel.fn, + configs=kernel.configs, + settings=new_settings, + key=kernel._key_fn, + ) + config = copy.deepcopy(config) + return kernel.bind(args).compile_config(config, allow_print=False) + + +def helion_aot_autotune( + config_path: str, + kernel_key: Callable[..., tuple[Any, Any, Any]], + primary_inputs: AutotuneInputFn, + secondary_inputs: AutotuneInputFn | None = None, + int64_threshold: Callable[..., bool] | None = None, + warn_on_hash_miss: bool = False, +): + """ + A decorator that automatically tunes and dispatches a Helion kernel based off of the kernel_key and provided inputs. + + The general flow is this: + 1. We first run helion_kernel.autotune on all primary_inputs. This will give us a list of configs, one per primary_input. + 2. We benchmark every config on every primary_input and secondary_input. + 3. For every primary input/secondary input, we keep the config that is the fastest (NB: We aim to do some deduplication by reusing configs if they're within some threshold of the fastest config). + 4. We'll save the configs and the dispatch choices to a json file. + 5. We create a dispatch function that will lookup to see whether each kernel is one we've tuned for in step 2. If so, we'll use that config. Otherwise, we'll use some heuristic to (deterministically) find a reasonable config for the kernel. + + There are 3 modes (set by env variable HELION_AOT_AUTOTUNE): + - none: No autotuning will be done. We will skip to step 5. If the config json file doesn't exist, we'll raise an error. + - retune: We only benchmark the existing useful configs on the primary/secondary inputs. We'll skip to step 2. This finishes much faster than create (albeit won't fully retune for each shape), but requires create to have been run first. For example, for rmsnorm, retune takes maybe one minute for 10 shapes, but create might take 30 minutes. + - create: The kernel will be fully autotuned, starting from step 1. + + You can also minimize the kernels to be autotuned by setting the env variable HELION_AOT_AUTOTUNE_KERNEL to the name of the kernel. For example, if you want to only autotune rmsnorm, you can set HELION_AOT_AUTOTUNE_KERNEL="rms_norm_fwd". + + kernel_key: Callable[..., tuple[Any, Any, Any]] + returns: + (numeric_key, hash_key, exact_key) + The semantics are that if all 3 match a saved key, we'll use that config. + Otherwise, we'll use some heuristic to find a reasonable config for the + kernel. The heuristic is: + - We require exact_key to match the saved config. + - We prioritize hash_key that match the saved config. + - We then prioritize the highest numeric_key that's <= the current numeric_key. + """ + # Threshold for how much faster a config has to be for some shape to be considered "useful" + threshold = 1.01 + # How many ms to run the kernel when retuning + retune_rep_ms = 1000 + + def inner_autotune(kernel: helion.Kernel): + helion_dir = Path(__file__).parent / "configs" + + cc_major, cc_minor = get_cuda_device_capability() + if cc_minor == 3: + cc_minor = 0 + gpu_arch = f"sm_{cc_major}{cc_minor}" + + path = helion_dir / Path(f"{config_path}_{gpu_arch}.json") + + @functools.wraps(kernel_key) + def wrapped_kernel_key(*inps: Any) -> tuple[Any, Any, Any]: + """ + A wrapper that handles dtype specially (since dtype is not + serializable to json). + """ + + numeric_key, hash_key, exact_key = kernel_key(*inps) + assert isinstance(hash_key, tuple) + assert isinstance(exact_key, tuple) + hash_key = tuple( + i.itemsize if isinstance(i, torch.dtype) else i for i in hash_key + ) + if exact_key is not None: + exact_key = tuple( + i.itemsize if isinstance(i, torch.dtype) else i for i in exact_key + ) + return numeric_key, hash_key, exact_key + + autotune_mode_str = os.environ.get("HELION_AOT_AUTOTUNE", "none") + autotune_mode = AOTAutotuneMode.from_str(autotune_mode_str) + autotune_kernel = os.environ.get("HELION_AOT_AUTOTUNE_KERNEL", "all") + if autotune_kernel != "all" and autotune_kernel != config_path: + autotune_mode = AOTAutotuneMode.NONE + + @functools.cache + def get_configs_from_autotuning(autotune_mode: AOTAutotuneMode): + if autotune_mode == AOTAutotuneMode.NONE: + if not path.exists(): + raise RuntimeError( + f"Helion kernel not tuned yet. Run with HELION_AOT_AUTOTUNE=create HELION_AOT_AUTOTUNE_KERNEL={config_path} to generate the config at {path}" + ) + return load_autotune_data_from_json(path) + + inputs = sorted( + list(primary_inputs()), key=lambda x: wrapped_kernel_key(*x)[0] + ) + if autotune_mode == AOTAutotuneMode.CREATE: + useful_configs = [] + for idx, input in enumerate(inputs): + print( + f"Autotuning for {config_path} with key: ", + wrapped_kernel_key(*input), + ) + config = kernel.autotune(input) + useful_configs.append(repr(config)) + elif autotune_mode == AOTAutotuneMode.RETUNE: + with open(path, "r") as f: + json_obj = json.load(f) + useful_configs = list(json_obj["useful_configs"].values()) + else: + raise RuntimeError(f"Unexpected autotune mode: {autotune_mode}") + + logger.info("Candidate useful configs: ") + for idx, config in enumerate(useful_configs): + logger.info(f"{idx}:, Config: {config}") + + input_timings = [] + if secondary_inputs is not None: + inputs += list(secondary_inputs()) + inputs = sorted(inputs, key=lambda x: kernel_key(*x)[0]) + + for input in inputs: + cur_input_key = wrapped_kernel_key(*input) + timings = [] + for idx, config in enumerate(useful_configs): + try: + cur_kernel = bind_and_compile_kernel( + kernel, input, eval(config) + ) + timings.append( + do_bench(lambda: cur_kernel(*input), rep=retune_rep_ms) + ) # noqa: B023 + except Exception as e: + logger.info(f"Error compiling config {config}: {e}") + timings.append(float("inf")) + input_timings.append((cur_input_key, timings)) + for idx, (key, timing) in enumerate(input_timings): + logger.info( + f"Key {key} timings: {' '.join([f'{i:.5f}' for i in timing])}" + ) + + hash_configs_timings: dict[tuple[Any, Any], tuple[float, int | None]] = ( + defaultdict(lambda: (float("inf"), None)) + ) + for cur_kernel_key, input_timings in input_timings: + for config_idx, (config, timing) in enumerate( + zip(useful_configs, input_timings) + ): + if timing < hash_configs_timings[cur_kernel_key][0] * threshold: + hash_configs_timings[cur_kernel_key] = (timing, config_idx) + + kept_configs = {} + hash_configs = {k: v[1] for k, v in hash_configs_timings.items()} + for key, config_idx in hash_configs.items(): + assert config_idx is not None + kept_configs[config_idx] = useful_configs[config_idx] + + json_obj = { + "useful_configs": kept_configs, + "hash_configs": {repr(k): v for k, v in hash_configs.items()}, + } + + with open(path, "w") as f: + json.dump(json_obj, f, indent=2) + f.write("\n") + return load_autotune_data_from_json(path) + + cached_kernels = {} + + def default_int64_threshold(*args: Any) -> bool: + return any( + [i.numel() >= 2**31 for i in args if isinstance(i, torch.Tensor)] + ) + + used_int64_threshold = ( + int64_threshold if int64_threshold is not None else default_int64_threshold + ) + + def wrapped_func(*args: Any): + nonlocal cached_kernels + cur_kernel_key = wrapped_kernel_key(*args) + has_int64 = used_int64_threshold(*args) + size1 = tuple( + tuple(shape == 1 for shape in arg.shape) + for arg in args + if isinstance(arg, torch.Tensor) + ) + dtypes = tuple(arg.dtype for arg in args if isinstance(arg, torch.Tensor)) + scalar_args = tuple(a for a in args if not isinstance(a, torch.Tensor)) + key = (cur_kernel_key, has_int64, size1, dtypes, scalar_args) + if key in cached_kernels: + out = cached_kernels[key](*args) + return out + if has_int64: + kernel.settings = dataclasses.replace( + kernel.settings, index_dtype=torch.int64 + ) + useful_configs, hash_configs = get_configs_from_autotuning(autotune_mode) + key_to_config = {k: useful_configs[v] for k, v in hash_configs.items()} + if key not in cached_kernels and cur_kernel_key in hash_configs: + cached_kernels[key] = bind_and_compile_kernel( + kernel, args, key_to_config[cur_kernel_key] + ) + else: + if warn_on_hash_miss: + logger.warning( + f"No config found for key {cur_kernel_key} for kernel={config_path}. Finding best match. This is *not* a correctness issue, but means that the performance of this kernel could potentially be improved." + ) + used_config = None + + def config_key_sort( + config_key: tuple[Any, Any, Any], + ) -> tuple[Any, ...]: + return ( + config_key[2] == cur_kernel_key[2], + config_key[1] == cur_kernel_key[1], + config_key[0] <= cur_kernel_key[0], + config_key[0], + ) + + sorted_keys = sorted( + hash_configs.keys(), key=config_key_sort, reverse=True + ) + used_config = key_to_config[sorted_keys[0]] + if len(used_config) == 3: + assert ( + used_config[2] == cur_kernel_key[2] + ), "Exact key not found in configs" + cached_kernels[key] = bind_and_compile_kernel(kernel, args, used_config) + out = cached_kernels[key](*args) + return out + + return wrapped_func + + return inner_autotune diff --git a/python/sglang/srt/layers/moe/moe_runner/triton_utils/inkling_moe.py b/python/sglang/srt/layers/moe/moe_runner/triton_utils/inkling_moe.py new file mode 100644 index 000000000..e7e3c2f28 --- /dev/null +++ b/python/sglang/srt/layers/moe/moe_runner/triton_utils/inkling_moe.py @@ -0,0 +1,930 @@ +from functools import partial + +import helion +import helion.language as hl +import torch +import triton +import triton.language as tl + +from sglang.jit_kernel.utils import is_arch_support_pdl +from sglang.srt.layers.moe.moe_runner.triton_utils.helion_utils import ( + get_model_depths, + helion_aot_autotune, +) + +DEFAULT_BLOCK_SIZE = 4096 +BLOCK_SIZE_M = 128 + + +def silu_and_mul_key( + gateup_output: torch.Tensor, + topk_weights: torch.Tensor | None, + out_dtype: object | None = None, +): + # Keep this stable across inputs for Helion AOT autotune. + del out_dtype + return gateup_output.shape[1], (gateup_output.dtype,), (topk_weights is not None,) + + +def silu_and_mul_inputs(sizes: list[int]): + # Used only for Helion autotune input generation. + inputs = [] + numel = 2**30 + with torch.device("cuda"): + for size in sizes: + x = torch.randn(numel // size, 2 * size, dtype=torch.bfloat16) + inputs.append((x, None, None)) + inputs.append((x, torch.randn(numel // size, dtype=torch.bfloat16), None)) + return inputs + + +@helion_aot_autotune( + "silu_and_mul_interleaved", + kernel_key=silu_and_mul_key, + primary_inputs=partial(silu_and_mul_inputs, sizes=[512, 2048, 48 * 96, 6144, 8192]), + secondary_inputs=partial( + silu_and_mul_inputs, + sizes=[512] + + [i * 96 for i in get_model_depths()] + + list(range(1024, 8192 + 1, 1024)), + ), +) +@helion.kernel(static_shapes=False) +def _silu_and_mul_helion_interleaved_kernel( + gateup_output, + topk_weights: torch.Tensor | None = None, + out_dtype: hl.constexpr | None = None, +): + """ + Interleaved version of silu_and_mul using Helion kernel. + Input format: [gate[0], up[0], gate[1], up[1], ...] + This matches the interleaved w13 weight format. + """ + batch_size, hidden_size = gateup_output.shape + hidden_size = hl.specialize(hidden_size) + assert hidden_size % 2 == 0, f"{hidden_size=}" + + half_hidden_size = hidden_size // 2 + down_input = gateup_output.new_empty( + batch_size, half_hidden_size, dtype=out_dtype or gateup_output.dtype + ) + for batch_tile, hidden_tile in hl.tile([batch_size, half_hidden_size]): + gate_output = gateup_output[batch_tile, 2 * hidden_tile.index].to(torch.float32) + up_output = gateup_output[batch_tile, 2 * hidden_tile.index + 1].to( + torch.float32 + ) + silu_mul_output = gate_output * torch.sigmoid(gate_output) * up_output + if topk_weights is not None: + weight_scale = topk_weights[batch_tile, None].to(torch.float32) + silu_mul_output = silu_mul_output * weight_scale + down_input[batch_tile, hidden_tile] = silu_mul_output + return down_input + + +@helion_aot_autotune( + "silu_and_mul", + kernel_key=silu_and_mul_key, + primary_inputs=partial(silu_and_mul_inputs, sizes=[512, 2048, 48 * 96, 6144, 8192]), + secondary_inputs=partial( + silu_and_mul_inputs, + sizes=[512] + + [i * 96 for i in get_model_depths()] + + list(range(1024, 8192 + 1, 1024)), + ), +) +@helion.kernel(static_shapes=False) +def _silu_and_mul_helion_non_interleaved_kernel( + gateup_output, + topk_weights: torch.Tensor | None = None, + out_dtype: hl.constexpr | None = None, +): + """ + Non-interleaved version of silu_and_mul using Helion kernel. + Input format: [gate[0], gate[1], ..., gate[N-1], up[0], up[1], ..., up[N-1]] + """ + batch_size, hidden_size = gateup_output.shape + hidden_size = hl.specialize(hidden_size) + assert hidden_size % 2 == 0, f"{hidden_size=}" + + half_hidden_size = hidden_size // 2 + down_input = gateup_output.new_empty( + batch_size, half_hidden_size, dtype=out_dtype or gateup_output.dtype + ) + for batch_tile, hidden_tile in hl.tile([batch_size, half_hidden_size]): + gate_output = gateup_output[batch_tile, hidden_tile.index].to(torch.float32) + up_output = gateup_output[batch_tile, hidden_tile.index + half_hidden_size].to( + torch.float32 + ) + silu_mul_output = gate_output * torch.sigmoid(gate_output) * up_output + if topk_weights is not None: + weight_scale = topk_weights[batch_tile, None].to(torch.float32) + silu_mul_output = silu_mul_output * weight_scale + down_input[batch_tile, hidden_tile] = silu_mul_output + return down_input + + +def silu_and_mul_helion( + gateup_output: torch.Tensor, + topk_weights: torch.Tensor | None = None, + out_dtype: torch.dtype | None = None, + use_interleaved: bool = True, +) -> torch.Tensor: + """ + Unified silu_and_mul function using Helion kernel. + Supports both interleaved and non-interleaved input formats. + + Args: + gateup_output: Input tensor of shape (batch_size, hidden_size) + topk_weights: Optional topk weights tensor + out_dtype: Optional output dtype + use_interleaved: If True, expects interleaved format [gate[0], up[0], gate[1], up[1], ...] + If False, expects non-interleaved format [gate[0], ..., gate[N-1], up[0], ..., up[N-1]] + + Returns: + Output tensor of shape (batch_size, hidden_size // 2) + """ + if use_interleaved: + return _silu_and_mul_helion_interleaved_kernel( + gateup_output, topk_weights, out_dtype + ) + else: + return _silu_and_mul_helion_non_interleaved_kernel( + gateup_output, topk_weights, out_dtype + ) + + +# --------------------------------------------------------------------------- +# Triton silu_and_mul +# Used by InklingBatchDenseMLP._swiglu because the helion kernel above produces +# NaN for small shared-expert batches in EP+DP configs. +# --------------------------------------------------------------------------- + + +@triton.jit +def _silu_and_mul_triton_kernel( + gateup_out_ptr, + topk_weights_ptr, + down_inp_ptr, + M_ptr, + N: tl.constexpr, + TOPK_WEIGHTS: tl.constexpr, + GRID_SIZE: tl.constexpr, + NUM_STAGES: tl.constexpr, + BLOCK_SIZE_M: tl.constexpr, + BLOCK_SIZE_N: tl.constexpr, + EVEN_N: tl.constexpr, + INT64_INDEX: tl.constexpr, + USE_PDL: tl.constexpr = False, +): + start_pid = tl.program_id(0) + if USE_PDL: + tl.extra.cuda.gdc_wait() + if isinstance(M_ptr, tl.tensor) and M_ptr.dtype.is_ptr(): + M = tl.load(M_ptr) + else: + M = M_ptr + if INT64_INDEX: + start_pid = start_pid.to(tl.int64) + M = M.to(tl.int64) + + NUM_BLOCKS_N: tl.constexpr = tl.cdiv(N, BLOCK_SIZE_N) + num_blocks_mn = tl.cdiv(M, BLOCK_SIZE_M) * NUM_BLOCKS_N + + for pid in tl.range(start_pid, num_blocks_mn, GRID_SIZE, num_stages=NUM_STAGES): + pid_m = pid // NUM_BLOCKS_N + pid_n = pid % NUM_BLOCKS_N + + offs_m = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M) + offs_n = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N) + mask_m = offs_m < M + mask_n = offs_n < N + + mask_offs_2n = pid_n * BLOCK_SIZE_N + tl.arange(0, 2 * BLOCK_SIZE_N) // 2 + tl.static_assert(BLOCK_SIZE_N % 8 == 0, f"{BLOCK_SIZE_N=}") + mask_2n = mask_offs_2n < N + mask_2n = tl.max_constancy(mask_2n, [16]) + + offs_2n = pid_n * 2 * BLOCK_SIZE_N + tl.arange(0, 2 * BLOCK_SIZE_N) + offs_m2n = offs_m[:, None] * N * 2 + offs_2n[None, :] + + if EVEN_N or pid_n * BLOCK_SIZE_N + BLOCK_SIZE_N <= N: + gateup_out = tl.load( + gateup_out_ptr + offs_m2n, mask=mask_m[:, None], other=0.0 + ) + else: + mask_m2n = mask_m[:, None] & mask_2n[None, :] + gateup_out = tl.load(gateup_out_ptr + offs_m2n, mask=mask_m2n, other=0.0) + + gate_out, up_out = tl.split( + tl.reshape(gateup_out, (BLOCK_SIZE_M, BLOCK_SIZE_N, 2)) + ) + gate_out = gate_out.to(tl.float32) + up_out = up_out.to(tl.float32) + + gate_out = gate_out * tl.sigmoid(gate_out) + down_inp = gate_out * up_out + if TOPK_WEIGHTS: + weight_scale = tl.load(topk_weights_ptr + offs_m, mask=mask_m).to( + tl.float32 + ) + down_inp = down_inp * weight_scale[:, None] + + mask_mn = mask_m[:, None] if EVEN_N else mask_m[:, None] & mask_n[None, :] + offs_mn = offs_m[:, None] * N + offs_n[None, :] + tl.store(down_inp_ptr + offs_mn, down_inp, mask=mask_mn) + + if USE_PDL: + tl.extra.cuda.gdc_launch_dependents() + + +def silu_and_mul_triton( + gateup_output: torch.Tensor, + topk_weights: torch.Tensor | None = None, + out_dtype: torch.dtype | None = None, +) -> torch.Tensor: + """SiLU-and-mul for interleaved gate/up layout using a Triton kernel. + + Adapted from ``inkling_kernels.activation.silu_and_mul_fwd`` (without MXFP). + """ + assert ( + gateup_output.is_contiguous() + ), f"{gateup_output.shape=} {gateup_output.stride()=}" + assert gateup_output.ndim == 2, f"{gateup_output.shape=}" + if topk_weights is not None: + assert ( + topk_weights.is_contiguous() + ), f"{topk_weights.shape=} {topk_weights.stride()=}" + assert topk_weights.ndim == 1, f"{topk_weights.shape=}" + + M = gateup_output.shape[0] + N = gateup_output.shape[1] // 2 + + dtype = out_dtype or gateup_output.dtype + down_input = torch.empty((M, N), device=gateup_output.device, dtype=dtype) + + BLOCK_SIZE_M = 32 + BLOCK_SIZE_N = min(128, triton.next_power_of_2(N)) + NUM_STAGES = 2 + max_grid_size = triton.cdiv(M, BLOCK_SIZE_M) * triton.cdiv(N, BLOCK_SIZE_N) + # Use ~512 SMs worth of blocks, capped to actual work + num_sms = torch.cuda.get_device_properties( + gateup_output.device + ).multi_processor_count + grid_size = min(num_sms * 4, max_grid_size) + + _silu_and_mul_triton_kernel[(grid_size,)]( + gateup_out_ptr=gateup_output, + topk_weights_ptr=topk_weights, + down_inp_ptr=down_input, + M_ptr=M, + N=N, + TOPK_WEIGHTS=topk_weights is not None, + GRID_SIZE=grid_size, + NUM_STAGES=NUM_STAGES, + BLOCK_SIZE_M=BLOCK_SIZE_M, + BLOCK_SIZE_N=BLOCK_SIZE_N, + EVEN_N=N % BLOCK_SIZE_N == 0, + INT64_INDEX=gateup_output.nbytes >= 2**31, + **({"USE_PDL": True, "launch_pdl": True} if is_arch_support_pdl() else {}), + ) + + return down_input + + +@triton.jit +def _compute_expert_offsets_kernel(ReorderTopkIds, ExpertOffsets, num_toks): + expert = tl.program_id(0) + if expert == 0: + # Specially have pid 0 write the 0 value to index 0 + tl.store(ExpertOffsets, 0) + low = 0 + high = num_toks - 1 + target_location = -1 + while low <= high: + mid = (low + high) // 2 + + if tl.load(ReorderTopkIds + mid) > expert: + high = mid - 1 + else: + low = mid + 1 + target_location = mid + tl.store(ExpertOffsets + expert + 1, target_location + 1) + + +@triton.jit +def _compute_src2dst_kernel(ReorderIds, Src2Dst, num_toks, BLOCK_SIZE: tl.constexpr): + pid = tl.program_id(axis=0) + dst_id = pid * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE) + mask = dst_id < num_toks + src_id = tl.load(ReorderIds + dst_id, mask=mask) + tl.store(Src2Dst + src_id, dst_id, mask=mask) + + +def get_src2dst(reorder_ids: torch.Tensor): + num_tokens = reorder_ids.numel() + src2dst = torch.empty(num_tokens, device=reorder_ids.device, dtype=torch.int32) + _compute_src2dst_kernel[(triton.cdiv(num_tokens, DEFAULT_BLOCK_SIZE),)]( + reorder_ids, src2dst, num_tokens, BLOCK_SIZE=DEFAULT_BLOCK_SIZE + ) + return src2dst + + +@triton.jit +def _compute_num_tokens_per_expert_from_offs_kernel( + expert_token_offs_ptr, # [E + 1] input + num_tokens_per_expert_ptr, # [E] output +): + expert_id = tl.program_id(0) + expert_start_off = tl.load(expert_token_offs_ptr + expert_id) + expert_end_off = tl.load(expert_token_offs_ptr + expert_id + 1) + num_expert_tokens = expert_end_off - expert_start_off + tl.store(num_tokens_per_expert_ptr + expert_id, num_expert_tokens) + + +def _get_max_num_blocks( + num_routed_tokens: int, block_sizes: list[int], num_experts: int +): + return triton.cdiv(num_routed_tokens, min(block_sizes)) + num_experts - 1 + + +@triton.jit +def _memset_block_metadata_kernel( + num_tokens_per_expert_ptr, + expert_block_offs_ptr, + expert_block_schedule_ptr, + max_num_blocks, + E, + BLOCK_SIZE_M: tl.constexpr, + BLOCK_SIZE_MEMSET: tl.constexpr, + INT64_INDEX: tl.constexpr, +): + pid = tl.program_id(0) + if INT64_INDEX: + pid = pid.to(tl.int64) + + if pid == 0: + curr_sum = tl.zeros((), dtype=tl.int32) + for off in range(0, E, BLOCK_SIZE_MEMSET): + offs = off + tl.arange(0, BLOCK_SIZE_MEMSET) + mask = offs < E + num_tokens_per_expert = tl.load( + num_tokens_per_expert_ptr + offs, mask=mask, other=0 + ) + num_blocks_per_expert = tl.cdiv(num_tokens_per_expert, BLOCK_SIZE_M) + block_offs = ( + tl.cumsum(num_blocks_per_expert, 0) - num_blocks_per_expert + curr_sum + ) + curr_sum += tl.sum(num_blocks_per_expert, 0).to(tl.int32) + tl.store(expert_block_offs_ptr + offs, block_offs, mask=mask) + tl.store(expert_block_offs_ptr + E, curr_sum) + else: + pid = pid - 1 + offs = pid * BLOCK_SIZE_MEMSET + tl.arange(0, BLOCK_SIZE_MEMSET) + mask = offs < max_num_blocks + tl.store(expert_block_schedule_ptr + offs, -1, mask=mask) + + +@triton.jit +def _compute_block_metadata_kernel( + num_tokens_per_expert_ptr, + expert_block_offs_ptr, + expert_block_schedule_ptr, + BLOCK_SIZE_M: tl.constexpr, + BLOCK_SIZE: tl.constexpr, + INT64_INDEX: tl.constexpr, +): + pid = tl.program_id(0) + if INT64_INDEX: + pid = pid.to(tl.int64) + + expert_id = pid + num_expert_tokens = tl.load(num_tokens_per_expert_ptr + expert_id) + num_expert_blocks = tl.cdiv(num_expert_tokens, BLOCK_SIZE_M) + + expert_block_off = tl.load(expert_block_offs_ptr + expert_id) + expert_block_schedule_ptr += expert_block_off + for block_off in range(0, num_expert_blocks, BLOCK_SIZE): + block_offs = block_off + tl.arange(0, BLOCK_SIZE) + data = (block_offs << 16) + expert_id + mask = block_offs < num_expert_blocks + tl.store(expert_block_schedule_ptr + block_offs, data, mask=mask) + + +def compute_expert_block_metadata( + num_tokens_per_expert: torch.Tensor, + num_routed_tokens: int, + *, + block_size_m: int = BLOCK_SIZE_M, +): + assert num_tokens_per_expert.ndim == 1, f"{num_tokens_per_expert.shape=}" + assert ( + num_tokens_per_expert.is_contiguous() + ), f"{num_tokens_per_expert.shape=} {num_tokens_per_expert.stride()=}" + + num_experts = num_tokens_per_expert.numel() + max_num_blocks = _get_max_num_blocks(num_routed_tokens, [block_size_m], num_experts) + expert_block_offs = torch.empty( + (num_experts + 1,), dtype=torch.int32, device=num_tokens_per_expert.device + ) + expert_block_schedule = torch.empty( + (max_num_blocks,), dtype=torch.int32, device=num_tokens_per_expert.device + ) + + block_size_memset = 512 + int64_index = ( + expert_block_offs.nbytes >= 2**31 or expert_block_schedule.nbytes >= 2**31 + ) + grid = (1 + triton.cdiv(expert_block_schedule.numel(), block_size_memset),) + _memset_block_metadata_kernel[grid]( + num_tokens_per_expert_ptr=num_tokens_per_expert, + expert_block_offs_ptr=expert_block_offs, + expert_block_schedule_ptr=expert_block_schedule, + max_num_blocks=max_num_blocks, + E=num_experts, + BLOCK_SIZE_M=block_size_m, + BLOCK_SIZE_MEMSET=block_size_memset, + INT64_INDEX=int64_index, + ) + + _compute_block_metadata_kernel[(num_experts,)]( + num_tokens_per_expert_ptr=num_tokens_per_expert, + expert_block_offs_ptr=expert_block_offs, + expert_block_schedule_ptr=expert_block_schedule, + BLOCK_SIZE_M=block_size_m, + BLOCK_SIZE=block_size_memset, + INT64_INDEX=int64_index, + ) + + return expert_block_offs, expert_block_schedule + + +SMALL_M_BLOCK_SIZE_M = 16 +# Use smaller blocks for sparse decode workloads. +GROUPED_GEMM_SMALL_M_MAX = 6144 +# Single-CTA fused preprocess capability bound (in-register tl.sort of n +# packed keys); correctness-tested to this size. +FUSED_PREPROCESS_MAX_TOKENS = 2048 +# Keep larger workloads on the general sort path. +FUSED_PREPROCESS_WIN_TOKENS = 1024 + + +@triton.jit +def _fused_moe_preprocess_kernel( + topk_ids_ptr, # [n] int32, unsorted + reorder_topk_ids_ptr, # [n] int32 out, sorted + src2dst_ptr, # [n] int32 out + num_tokens_per_expert_ptr, # [E] int32 out + expert_token_offs_ptr, # [E+1] int32 out + expert_block_offs_ptr, # [E+1] int32 out + expert_block_schedule_ptr, # [max_num_blocks] int32 out, -1 padded + n, + max_num_blocks, + E: tl.constexpr, + BLOCK_N: tl.constexpr, # pow2 >= n, <= 4096 (12-bit position pack) + BLOCK_E1: tl.constexpr, # pow2 >= E+1 + BLOCK_SCHED: tl.constexpr, # pow2 >= max_num_blocks + BLOCK_SIZE_M: tl.constexpr, +): + """Single-CTA replacement for the whole grouped-gemm preprocess at decode + sizes: int16 cast + torch.sort(stable) + src2dst + expert offsets + counts + + block memset/schedule (~10 launches -> 1). + + Stable sort: one in-register tl.sort of (id << 12 | position) packed keys + -- position ties reproduce torch.sort(stable=True) exactly. Offsets come + from a vectorized binary search on the sorted ids (same contract as + _compute_expert_offsets_kernel); the block schedule from a binary search + over the block-offset cumsum. + """ + offs = tl.arange(0, BLOCK_N) + mask = offs < n + ids = tl.load(topk_ids_ptr + offs, mask=mask, other=E) # pads sort last + skey = tl.sort((ids << 12) | offs) + s_ids = skey >> 12 + s_src = skey & 0xFFF + tl.store(reorder_topk_ids_ptr + offs, s_ids, mask=mask) + tl.store(src2dst_ptr + s_src, offs, mask=mask) + tl.debug_barrier() # publish sorted ids for the binary searches below + + # expert_token_offs[e] = first sorted index with id >= e (e in 0..E) + e_offs = tl.arange(0, BLOCK_E1) + e_mask = e_offs < E + 1 + low = tl.zeros([BLOCK_E1], dtype=tl.int32) + high = tl.full([BLOCK_E1], n, dtype=tl.int32) + for _ in tl.static_range(12): # n <= 4096 + mid = (low + high) // 2 + v = tl.load(reorder_topk_ids_ptr + mid, mask=e_mask & (mid < n), other=E + 1) + go_right = v < e_offs + low = tl.where(go_right, mid + 1, low) + high = tl.where(go_right, high, mid) + tl.store(expert_token_offs_ptr + e_offs, low, mask=e_mask) + tl.debug_barrier() + + counts = tl.load( + expert_token_offs_ptr + e_offs + 1, mask=e_offs < E, other=0 + ) - tl.load(expert_token_offs_ptr + e_offs, mask=e_offs < E, other=0) + tl.store(num_tokens_per_expert_ptr + e_offs, counts, mask=e_offs < E) + + num_blocks = tl.cdiv(counts, BLOCK_SIZE_M) + block_offs_excl = tl.cumsum(num_blocks, 0) - num_blocks + tl.store(expert_block_offs_ptr + e_offs, block_offs_excl, mask=e_offs < E) + total_blocks = tl.sum(num_blocks, 0) + tl.store(expert_block_offs_ptr + E, total_blocks) + tl.debug_barrier() + + # schedule[s] = ((s - block_offs[e]) << 16) | e, e = last expert with + # block_offs[e] <= s; -1 for padding slots + s_offs = tl.arange(0, BLOCK_SCHED) + s_mask = s_offs < max_num_blocks + lo = tl.zeros([BLOCK_SCHED], dtype=tl.int32) + hi = tl.full([BLOCK_SCHED], E, dtype=tl.int32) + for _ in tl.static_range(9): # E = 256 + mid = (lo + hi + 1) // 2 + v = tl.load(expert_block_offs_ptr + mid, mask=s_mask, other=0) + go_left = v > s_offs + hi = tl.where(go_left, mid - 1, hi) + lo = tl.where(go_left, lo, mid) + e_of_s = lo + base = tl.load(expert_block_offs_ptr + e_of_s, mask=s_mask, other=0) + data = ((s_offs - base) << 16) + e_of_s + data = tl.where(s_offs < total_blocks, data, -1) + tl.store(expert_block_schedule_ptr + s_offs, data, mask=s_mask) + + +def fused_moe_preprocess(topk_ids_flat: torch.Tensor, num_experts: int): + """One-launch preprocess for n <= FUSED_PREPROCESS_MAX_TOKENS routed rows. + + Returns (src2dst, num_tokens_per_expert, expert_token_offs, + expert_block_offs, expert_block_schedule, reorder_topk_ids) -- + bit-identical to the torch.sort-based path, with reorder_topk_ids in int32 + and the block schedule built for SMALL_M_BLOCK_SIZE_M. + """ + n = topk_ids_flat.numel() + assert 0 < n <= FUSED_PREPROCESS_MAX_TOKENS, f"{n=}" + assert topk_ids_flat.is_contiguous() + device = topk_ids_flat.device + block_size_m = SMALL_M_BLOCK_SIZE_M + max_num_blocks = _get_max_num_blocks(n, [block_size_m], num_experts) + reorder_topk_ids = torch.empty(n, device=device, dtype=torch.int32) + src2dst = torch.empty(n, device=device, dtype=torch.int32) + num_tokens_per_expert = torch.empty(num_experts, device=device, dtype=torch.int32) + expert_token_offs = torch.empty(num_experts + 1, device=device, dtype=torch.int32) + expert_block_offs = torch.empty(num_experts + 1, device=device, dtype=torch.int32) + expert_block_schedule = torch.empty( + max_num_blocks, device=device, dtype=torch.int32 + ) + _fused_moe_preprocess_kernel[(1,)]( + topk_ids_flat.int(), + reorder_topk_ids, + src2dst, + num_tokens_per_expert, + expert_token_offs, + expert_block_offs, + expert_block_schedule, + n, + max_num_blocks, + E=num_experts, + BLOCK_N=max(triton.next_power_of_2(n), 16), + BLOCK_E1=triton.next_power_of_2(num_experts + 1), + BLOCK_SCHED=triton.next_power_of_2(max_num_blocks), + BLOCK_SIZE_M=block_size_m, + num_warps=4, + ) + return ( + src2dst, + num_tokens_per_expert, + expert_token_offs, + expert_block_offs, + expert_block_schedule, + reorder_topk_ids, + ) + + +def select_grouped_gemm_block_m(num_routed_tokens: int) -> int: + return ( + SMALL_M_BLOCK_SIZE_M + if num_routed_tokens <= GROUPED_GEMM_SMALL_M_MAX + else BLOCK_SIZE_M + ) + + +def compute_grouped_gemm_metadata( + sorted_topk_ids: torch.Tensor, num_experts: int, *, block_size_m: int = BLOCK_SIZE_M +): + num_routed_tokens = sorted_topk_ids.numel() + device = sorted_topk_ids.device + num_tokens_per_expert = torch.empty(num_experts, device=device, dtype=torch.int32) + expert_token_offs = torch.empty(num_experts + 1, device=device, dtype=torch.int32) + _compute_expert_offsets_kernel[(num_experts,)]( + sorted_topk_ids, expert_token_offs, num_routed_tokens + ) + _compute_num_tokens_per_expert_from_offs_kernel[(num_experts,)]( + expert_token_offs_ptr=expert_token_offs, + num_tokens_per_expert_ptr=num_tokens_per_expert, + ) + expert_block_offs, expert_block_schedule = compute_expert_block_metadata( + num_tokens_per_expert, num_routed_tokens, block_size_m=block_size_m + ) + return ( + num_tokens_per_expert, + expert_token_offs, + expert_block_offs, + expert_block_schedule, + ) + + +@triton.jit +def _pre_reorder_kernel( + input_ptr, + gateup_input_ptr, + src2dst_ptr, + TOP_K: tl.constexpr, + hidden_size, + BLOCK_SIZE: tl.constexpr, +): + src_idx = tl.program_id(0).to(tl.int64) + src2dst_ptr = src2dst_ptr + src_idx * TOP_K + + src_ptr = input_ptr + src_idx * hidden_size + for idx in tl.static_range(TOP_K): + dst_idx = tl.load(src2dst_ptr + idx).to(tl.int64) + dst_ptr = gateup_input_ptr + dst_idx * hidden_size + for start_offset in tl.range(0, hidden_size, BLOCK_SIZE): + offset = start_offset + tl.arange(0, BLOCK_SIZE) + mask = offset < hidden_size + in_data = tl.load(src_ptr + offset, mask=mask).to(tl.float32) + tl.store(dst_ptr + offset, in_data, mask=mask) + + +def pre_reorder(input: torch.Tensor, src2dst: torch.Tensor, topk: int): + assert input.is_contiguous() + assert src2dst.is_contiguous() + + batch_size, hidden_size = input.shape + (batch_size_expanded,) = src2dst.shape + + output = torch.empty( + (batch_size_expanded, hidden_size), + device=input.device, + dtype=input.dtype, + ) + + _pre_reorder_kernel[(batch_size,)]( + input, + output, + src2dst, + topk, + hidden_size, + BLOCK_SIZE=DEFAULT_BLOCK_SIZE, + ) + + return output + + +@triton.jit +def _post_reorder_kernel( + down_output_ptr, + output_ptr, + src2dst_ptr, + topk_weights_ptr, + TOP_K: tl.constexpr, + hidden_size, + BLOCK_SIZE: tl.constexpr, +): + src_idx = tl.program_id(0).to(tl.int64) + src2dst_ptr = src2dst_ptr + src_idx * TOP_K + topk_weights_ptr = topk_weights_ptr + src_idx * TOP_K + + store_ptr = output_ptr + src_idx * hidden_size + for start_offset in tl.range(0, hidden_size, BLOCK_SIZE): + offset = start_offset + tl.arange(0, BLOCK_SIZE) + mask = offset < hidden_size + + sum_vec = tl.zeros([BLOCK_SIZE], dtype=tl.float32) + for idx in tl.static_range(TOP_K): + dst_idx = tl.load(src2dst_ptr + idx).to(tl.int64) + weight_scale = tl.load(topk_weights_ptr + idx).to(tl.float32) + load_ptr = down_output_ptr + dst_idx * hidden_size + in_data = tl.load(load_ptr + offset, mask=mask) + sum_vec += in_data * weight_scale + tl.store(store_ptr + offset, sum_vec, mask=mask) + + +def post_reorder( + down_output: torch.Tensor, src2dst: torch.Tensor, topk_weights: torch.Tensor +): + assert down_output.is_contiguous() + assert src2dst.is_contiguous() + assert topk_weights.is_contiguous() + + # Constants + batch_size = topk_weights.shape[0] + hidden_size = down_output.shape[1] + TOP_K = topk_weights.shape[1] + + # Output tensor + output = torch.empty( + batch_size, hidden_size, device=down_output.device, dtype=down_output.dtype + ) + + # Launch kernel + grid = (batch_size,) + _post_reorder_kernel[grid]( + down_output, + output, + src2dst, + topk_weights, + TOP_K=TOP_K, + hidden_size=hidden_size, + BLOCK_SIZE=DEFAULT_BLOCK_SIZE, + ) + return output + + +@triton.jit +def _compute_expert_attrs( + pid_m, + NumTokensPerExpert, # [E] + ExpertTokenOffs, # [E + 1] + ExpertBlockSchedule, # [max_num_blocks] + BLOCK_SIZE_M: tl.constexpr, + INT64_INDEX: tl.constexpr, +): + expert_data = tl.load(ExpertBlockSchedule + pid_m) + expert_id = expert_data & 0xFFFF + block_id = expert_data >> 16 + expert_num_tokens = tl.load(NumTokensPerExpert + expert_id) + token_start_m = tl.load(ExpertTokenOffs + expert_id) + if INT64_INDEX: + expert_id = expert_id.to(tl.int64) + block_id = block_id.to(tl.int64) + token_start_m = token_start_m.to(tl.int64) + block_start = token_start_m + block_id * BLOCK_SIZE_M + block_end = tl.minimum( + block_start + BLOCK_SIZE_M, token_start_m + expert_num_tokens + ) + return expert_id, block_start, block_end + + +@triton.jit +def _grouped_gemm_kernel( + A, + B, + C, + NumTokensPerExpert, + ExpertTokenOffs, + ExpertBlockOffs, + ExpertBlockSchedule, + a_stride_0: tl.constexpr, + b_stride_0: tl.constexpr, + b_stride_1: tl.constexpr, + c_stride_0: tl.constexpr, + E: tl.constexpr, + N: tl.constexpr, + K: tl.constexpr, + grid_m, + BLOCK_SIZE_M: tl.constexpr, + BLOCK_SIZE_N: tl.constexpr, + BLOCK_SIZE_K: tl.constexpr, + GROUP_SIZE_M: tl.constexpr, + KN_MASK: tl.constexpr, + INT64_INDEX: tl.constexpr, +): + pid = tl.program_id(axis=0) + num_pid_m = grid_m + num_pid_n = tl.cdiv(N, BLOCK_SIZE_N) + if INT64_INDEX: + pid = pid.to(tl.int64) + num_pid_m = num_pid_m.to(tl.int64) + num_pid_n = num_pid_n.to(tl.int64) + num_pid_in_group = GROUP_SIZE_M * num_pid_n + group_id = pid // num_pid_in_group + first_pid_m = group_id * GROUP_SIZE_M + group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M) + pid_m = first_pid_m + ((pid % num_pid_in_group) % group_size_m) + pid_n = (pid % num_pid_in_group) // group_size_m + + num_blocks_m = tl.load(ExpertBlockOffs + E) + if pid_m >= num_blocks_m: + return + expert_id, block_start, block_end = _compute_expert_attrs( + pid_m, + NumTokensPerExpert, + ExpertTokenOffs, + ExpertBlockSchedule, + BLOCK_SIZE_M, + INT64_INDEX, + ) + + offs_am = tl.arange(0, BLOCK_SIZE_M) + offs_bn = tl.arange(0, BLOCK_SIZE_N) + offs_am = tl.where(block_start + offs_am < block_end, offs_am, 0) + offs_am = tl.max_contiguous(tl.multiple_of(offs_am, BLOCK_SIZE_M), BLOCK_SIZE_M) + offs_k = tl.arange(0, BLOCK_SIZE_K) + + # [M, K] tile + a_ptr = A + (block_start + offs_am[:, None]) * a_stride_0 + offs_k[None, :] + # [K, N] tile + b_ptr = B + ( + (expert_id * b_stride_0) + + (pid_n * BLOCK_SIZE_N + offs_bn[None, :]) * b_stride_1 + + offs_k[:, None] + ) + accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32) + for k in range(0, tl.cdiv(K, BLOCK_SIZE_K)): + if not KN_MASK: + a_tile = tl.load(a_ptr) + b_tile = tl.load(b_ptr) + else: + n_mask = pid_n * BLOCK_SIZE_N + offs_bn < N + k_mask = k * BLOCK_SIZE_K + offs_k < K + a_tile = tl.load(a_ptr, mask=k_mask[None, :], other=0.0) + b_tile = tl.load(b_ptr, mask=k_mask[:, None] & n_mask[None, :], other=0.0) + accumulator = tl.dot(a_tile, b_tile, acc=accumulator) + a_ptr += BLOCK_SIZE_K + b_ptr += BLOCK_SIZE_K + + accumulator = accumulator.to(C.dtype.element_ty) + + offs_cm = block_start + tl.arange(0, BLOCK_SIZE_M) + offs_cn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N) + c_ptr = C + offs_cm[:, None] * c_stride_0 + offs_cn[None, :] + c_mask = offs_cm[:, None] < block_end + if KN_MASK: + n_mask = offs_cn < N + c_mask = c_mask & n_mask[None, :] + tl.store(c_ptr, accumulator, mask=c_mask) + + +def grouped_gemm_triton( + a: torch.Tensor, + b: torch.Tensor, + num_experts: int, + num_tokens_per_expert: torch.Tensor, # [E] + expert_token_offs: torch.Tensor, # [E + 1] + expert_block_offs: torch.Tensor, # [E + 1] + expert_block_schedule: torch.Tensor, # [max_num_blocks] + block_size_m: int = BLOCK_SIZE_M, # must match the schedule's build value +) -> torch.Tensor: + assert a.is_contiguous(), f"{a.shape=} {a.stride()=}" + assert b.is_contiguous(), f"{b.shape=} {b.stride()=}" + + M, K = a.shape + E, N, K_ = b.shape + c = torch.empty((M, N), device=a.device, dtype=a.dtype) + + assert K == K_, f"{a.shape=} {b.shape=}" + assert num_experts == E, f"{num_experts=} {b.shape=}" + + # Sparse decode uses smaller row blocks; prefill uses the standard size. + # This must match the size used to build expert_block_schedule. + if block_size_m == SMALL_M_BLOCK_SIZE_M: + config = { + "BLOCK_SIZE_M": SMALL_M_BLOCK_SIZE_M, + "BLOCK_SIZE_N": 128, + "BLOCK_SIZE_K": 128, + "GROUP_SIZE_M": 8, + "num_warps": 4, + "num_stages": 4, + } + else: + assert block_size_m == BLOCK_SIZE_M, f"{block_size_m=}" + config = { + "BLOCK_SIZE_M": BLOCK_SIZE_M, + "BLOCK_SIZE_N": 256 if a.dtype != torch.float32 else 64, + "BLOCK_SIZE_K": 64, + "GROUP_SIZE_M": 8, + "num_warps": 8, + "num_stages": 3, + } + # Set grid_m to the max number of M blocks and skip padding-only blocks + # in the kernel based on expert_block_offs[-1] + grid_m = expert_block_schedule.numel() + + def grid(META: dict[str, int]): + return (grid_m * triton.cdiv(N, META["BLOCK_SIZE_N"]),) + + with torch.profiler.record_function( + f"grouped_gemm_[M:{M},K:{K},E:{num_experts},N:{N}]" + ): + _grouped_gemm_kernel[grid]( + A=a, + B=b, + C=c, + NumTokensPerExpert=num_tokens_per_expert, + ExpertTokenOffs=expert_token_offs, + ExpertBlockOffs=expert_block_offs, + ExpertBlockSchedule=expert_block_schedule, + a_stride_0=a.stride(0), + b_stride_0=b.stride(0), + b_stride_1=b.stride(1), + c_stride_0=c.stride(0), + E=num_experts, + N=N, + K=K, + grid_m=grid_m, + KN_MASK=K % config["BLOCK_SIZE_K"] != 0 or N % config["BLOCK_SIZE_N"] != 0, + INT64_INDEX=a.nbytes >= 2**31 or b.nbytes >= 2**31 or c.nbytes >= 2**31, + **config, + ) + + return c diff --git a/python/sglang/srt/layers/moe/moe_runner/triton_utils/moe_align_block_size.py b/python/sglang/srt/layers/moe/moe_runner/triton_utils/moe_align_block_size.py index 7188bb514..fd535dc7b 100644 --- a/python/sglang/srt/layers/moe/moe_runner/triton_utils/moe_align_block_size.py +++ b/python/sglang/srt/layers/moe/moe_runner/triton_utils/moe_align_block_size.py @@ -62,10 +62,23 @@ def moe_align_block_size( - The padding ensures that the total number of tokens is now divisible by block_size for proper block matrix operations. """ + # ===== TO BE REFACTORED ==== + if _SGLANG_EXPERIMENTAL_LORA_OPTI: + from sglang.srt.lora.trtllm_lora_temp.environ import lora_envs + + if lora_envs.SGLANG_OPT_USE_JIT_KERNEL_MOE_ALIGN.get() and num_experts <= 8191: + from sglang.kernels.ops.moe.trtllm_lora_temp.virtual_experts import ( + _align_block_size_jit, + ) + + return _align_block_size_jit(topk_ids, block_size, num_experts) + # ===== END TO BE REFACTORED ==== + if topk_ids.numel() < num_experts + 1: max_num_tokens_padded = topk_ids.numel() * block_size else: max_num_tokens_padded = topk_ids.numel() + (num_experts + 1) * (block_size - 1) + sorted_ids = torch.empty( (max_num_tokens_padded,), dtype=torch.int32, device=topk_ids.device ) diff --git a/python/sglang/srt/layers/moe/moe_runner/triton_utils/sigmoid_gate_topk_renorm.py b/python/sglang/srt/layers/moe/moe_runner/triton_utils/sigmoid_gate_topk_renorm.py new file mode 100644 index 000000000..bd3c3c7af --- /dev/null +++ b/python/sglang/srt/layers/moe/moe_runner/triton_utils/sigmoid_gate_topk_renorm.py @@ -0,0 +1,250 @@ +"""Fused MoE gate: sigmoid + bias + top-k selection + logsigmoid renorm. + + sel = sigmoid(logits)[:, :N] + bias # selection score (bias optional) + idx = topk(sel, k) # top-k routed experts + w = logsigmoid_norm(logits[idx] ++ shared) * route_scale * global_scale + +The renorm runs on the RAW logits gathered at the selected indices, so the sort +key (sigmoid+bias) is not the renorm value -> we re-gather the raw logits. +""" + +import torch +import triton +import triton.language as tl + +from sglang.jit_kernel.inkling_gate_topk_renorm import inkling_gate_topk_renorm_v2 +from sglang.jit_kernel.utils import is_arch_support_pdl +from sglang.srt.environ import envs +from sglang.srt.layers.moe.moe_runner.triton_utils.gate_topk import ( + fpval_to_key, + indx_to_key, + key_to_indx, +) + + +@triton.jit +def _sigmoid_gate_topk_renorm_kernel( + logits_ptr, + bias_ptr, + stride_lm, + routed_w_ptr, + shared_w_ptr, + indices_ptr, + packed_indices_ptr, + global_scale_ptr, + route_scale, + M, + N, # num routed experts (top-k sort dim) + G, # total gate experts = N + S + N_PAD: tl.constexpr, + K: tl.constexpr, + K_POW2: tl.constexpr, + S: tl.constexpr, # num shared experts + A_POW2: tl.constexpr, # next_pow2(K + S) + BLOCK_SIZE_M: tl.constexpr, + BLOCK_SIZE_N: tl.constexpr, + RETURN_PACKED_TOPK: tl.constexpr = False, + ENABLE_PDL: tl.constexpr = False, +): + tl.static_assert( + K_POW2 == A_POW2, "epilogue reuses the topk slot axis for the active axis" + ) + pid = tl.program_id(0) + offs_m = pid * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M) + mask_m = offs_m < M + + # --- streaming top-k by selection score sigmoid(logit)[+bias] ------------ + num_iters: tl.constexpr = N_PAD // BLOCK_SIZE_N - 1 + offs_n = num_iters * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N) + mask_n = offs_n < N + + if ENABLE_PDL: + tl.extra.cuda.gdc_wait() + + # first (masked) tile + raw = tl.load( + logits_ptr + offs_m[:, None] * stride_lm + offs_n[None, :], + mask=mask_m[:, None] & mask_n[None, :], + other=0.0, + ).to(tl.float32) + sel = tl.sigmoid(raw) + sel += tl.load(bias_ptr + offs_n, mask=mask_n, other=0.0).to(tl.float32)[None, :] + sel = tl.where(mask_n[None, :], sel, float("-inf")) # out-of-range cols never win + key = fpval_to_key(sel.to(tl.uint32, bitcast=True)) + x = (key.to(tl.uint64) << 16) | indx_to_key(offs_n, N_PAD)[None, :] + acc = tl.topk(x, K_POW2, dim=1) + + # remaining tiles are fully in-range (N_PAD - N < BLOCK_SIZE_N) + for _i in (tl.static_range if num_iters <= 4 else range)(num_iters): + acc = tl.bitonic_merge(acc) + offs_n -= BLOCK_SIZE_N + raw = tl.load( + logits_ptr + offs_m[:, None] * stride_lm + offs_n[None, :], + mask=mask_m[:, None], + other=0.0, + ).to(tl.float32) + sel = tl.sigmoid(raw) + sel += tl.load(bias_ptr + offs_n).to(tl.float32)[None, :] + key = fpval_to_key(sel.to(tl.uint32, bitcast=True)) + x = (key.to(tl.uint64) << 16) | indx_to_key(offs_n, N_PAD)[None, :] + acc = tl.maximum(acc, tl.topk(x, K_POW2, dim=1)) + + offs_a = tl.arange(0, A_POW2) + mask_k = offs_a < K + acc = tl.sort(acc, dim=1, descending=True) + y_indices = key_to_indx((acc & 0xFFFF).to(tl.uint32), N_PAD) + + # --- renorm on RAW fp32 logits gathered at the selected indices ---------- + gather_idx = tl.where(mask_k[None, :], y_indices.to(tl.int32), 0) + routed_vals = tl.load( + logits_ptr + offs_m[:, None] * stride_lm + gather_idx, + mask=mask_m[:, None] & mask_k[None, :], + other=0.0, + ).to(tl.float32) + offs_s = offs_a - K + mask_s = mask_m[:, None] & (offs_s[None, :] >= 0) & (offs_s[None, :] < S) + shared = tl.load( + logits_ptr + offs_m[:, None] * stride_lm + (G - S) + offs_s[None, :], + mask=mask_s, + other=0.0, + ).to(tl.float32) + active = tl.where(mask_k[None, :], routed_vals, shared) + + A: tl.constexpr = K + S + probs = tl.sigmoid(active) + mask_a = offs_a < A + probs = tl.where(mask_a[None, :], probs, 0.0) + weights = probs / tl.sum(probs, axis=1, keep_dims=True) + weights *= (route_scale * tl.load(global_scale_ptr)).to(weights.dtype) + + mask_rk = mask_m[:, None] & mask_k[None, :] + # PackedTopKOutput carries only packed_topk_ids, so in packed mode the separate + # routed-weight / index stores are dead -- emit just the packed (id<<16 | bf16 w). + if RETURN_PACKED_TOPK: + weights_bits = weights.to(tl.bfloat16).to(tl.int16, bitcast=True).to(tl.int32) + packed = (y_indices.to(tl.int32) << 16) | weights_bits + tl.store( + packed_indices_ptr + offs_m[:, None] * K + offs_a[None, :], + packed, + mask=mask_rk, + ) + else: + tl.store( + routed_w_ptr + offs_m[:, None] * K + offs_a[None, :], weights, mask=mask_rk + ) + tl.store( + indices_ptr + offs_m[:, None] * K + offs_a[None, :], y_indices, mask=mask_rk + ) + offs_ts = offs_m[:, None] * S + offs_s[None, :] + tl.store(shared_w_ptr + offs_ts, weights, mask=mask_s) + + if ENABLE_PDL: + tl.extra.cuda.gdc_launch_dependents() + + +def sigmoid_gate_topk_renorm( + logits: torch.Tensor, + k: int, + n_shared_experts: int, + route_scale: float, + global_scale: torch.Tensor, + bias: torch.Tensor, + *, + return_packed_topk: bool = False, +): + """Fused top-k + logsigmoid renorm (production sigmoid+bias gate path). + + `logits` is [tokens, n_routed + n_shared]; the last `n_shared_experts` + columns are the shared experts. Selection score is sigmoid(routed logit) + plus `bias` (per routed expert, fp32). Returns + (routed_weights[t,k], shared_weights[t,s], topk_indices[t,k] int32). + """ + # Only column-stride-1 is required (the kernel reads rows via stride_lm). In + # InklingGate the gate logits are a [t,258] slice of a padded [t,264] tensor, so + # they are NOT contiguous but are column-contiguous -- no copy needed. + assert ( + logits.ndim == 2 and logits.stride(1) == 1 + ), f"{logits.shape=} {logits.stride()=}" + assert ( + logits.shape[0] * logits.stride(0) <= 2**31 + ), f"assumes int32 indexing: {logits.stride()=}" + assert k <= 32, f"topk kernels only support k <= 32: {k=}" + assert ( + n_shared_experts >= 0 + ), f"expected non-negative shared experts: {n_shared_experts=}" + M, G = logits.shape + N = G - n_shared_experts + A = k + n_shared_experts + assert bias.numel() == N and bias.stride(-1) == 1, f"{bias.shape=} expected [{N}]" + + # The production shape uses the specialized CUDA JIT kernel. + if ( + k == 6 + and n_shared_experts == 2 + and G == 258 + and logits.stride(0) % 8 == 0 + and logits.data_ptr() % 32 == 0 + and torch.version.hip is None + and envs.SGLANG_OPT_USE_GATE_TOPK_JIT.get() + ): + return inkling_gate_topk_renorm_v2( + logits, + bias, + global_scale, + route_scale, + return_packed=return_packed_topk, + enable_pdl=is_arch_support_pdl(), + ) + + shared_w = torch.empty( + (M, n_shared_experts), dtype=logits.dtype, device=logits.device + ) + # The kernel writes only the packed tensor in packed mode, only routed_w+indices + # otherwise; the unused pointer args still need a valid (never-stored) address. + if return_packed_topk: + packed_indices = torch.empty((M, k), dtype=torch.int32, device=logits.device) + routed_w = indices = None + routed_w_arg = indices_arg = packed_indices_arg = packed_indices + else: + routed_w = torch.empty((M, k), dtype=logits.dtype, device=logits.device) + indices = torch.empty((M, k), dtype=torch.int32, device=logits.device) + packed_indices = None + routed_w_arg, indices_arg, packed_indices_arg = routed_w, indices, indices + + # Launch geometry for the production shape. + if M <= 128: + BLOCK_SIZE_M = 1 + elif M <= 768: + BLOCK_SIZE_M = 2 + else: + BLOCK_SIZE_M = 4 + BLOCK_SIZE_N = 128 if M <= 1024 else 16 + grid = (triton.cdiv(M, BLOCK_SIZE_M),) + kwargs = {"num_warps": 8 if M <= 1024 else 2} + if is_arch_support_pdl(): + kwargs.update({"ENABLE_PDL": True, "launch_pdl": True}) + + _sigmoid_gate_topk_renorm_kernel[grid]( + logits, + bias, + logits.stride(0), + routed_w_arg, + shared_w, + indices_arg, + packed_indices_arg, + global_scale, + route_scale, + M=M, + N=N, + G=G, + N_PAD=triton.cdiv(N, BLOCK_SIZE_N) * BLOCK_SIZE_N, + K=k, + K_POW2=triton.next_power_of_2(k), + S=n_shared_experts, + A_POW2=triton.next_power_of_2(A), + BLOCK_SIZE_M=BLOCK_SIZE_M, + BLOCK_SIZE_N=BLOCK_SIZE_N, + RETURN_PACKED_TOPK=return_packed_topk, + **kwargs, + ) + return routed_w, indices, shared_w, packed_indices diff --git a/python/sglang/srt/layers/moe/topk.py b/python/sglang/srt/layers/moe/topk.py index 88fba9ac6..9c47af75d 100644 --- a/python/sglang/srt/layers/moe/topk.py +++ b/python/sglang/srt/layers/moe/topk.py @@ -252,11 +252,16 @@ class TopKOutputChecker: def format_is_bypassed(topk_output: TopKOutput) -> TypeGuard[BypassedTopKOutput]: return isinstance(topk_output, BypassedTopKOutput) + @staticmethod + def format_is_packed(topk_output: TopKOutput) -> TypeGuard[PackedTopKOutput]: + return isinstance(topk_output, PackedTopKOutput) + class TopKOutputFormat(IntEnum): STANDARD = auto() TRITON_KERNEL = auto() BYPASSED = auto() + PACKED = auto() @runtime_checkable @@ -338,6 +343,22 @@ class BypassedTopKOutput(NamedTuple): ) +class PackedTopKOutput(NamedTuple): + """Packed top-k output format used by FlashInfer TRT-LLM routed MoE. + + ``packed_topk_ids`` is an int32 tensor of shape (num_tokens, top_k) where each + element encodes the expert id in the upper 16 bits and the bf16 routing + weight bits in the lower 16 bits, matching FlashInfer's packed layout. + """ + + packed_topk_ids: torch.Tensor + router_logits: torch.Tensor + + @property + def format(self) -> TopKOutputFormat: + return TopKOutputFormat.PACKED + + def _make_round_robin_expert_ids( num_tokens: int, topk: int, diff --git a/python/sglang/srt/layers/moe/utils.py b/python/sglang/srt/layers/moe/utils.py index aaf1f1d09..74999b4ee 100644 --- a/python/sglang/srt/layers/moe/utils.py +++ b/python/sglang/srt/layers/moe/utils.py @@ -103,6 +103,7 @@ class MoeRunnerBackend(Enum): CUTLASS = "cutlass" MARLIN = "marlin" HUMMING = "humming" + EXPERIMENTAL_SGL_MARLIN = "experimental_sgl_marlin" AITER = "aiter" def is_auto(self): @@ -147,7 +148,16 @@ class MoeRunnerBackend(Enum): return self == MoeRunnerBackend.CUTLASS def is_marlin(self): - return self == MoeRunnerBackend.MARLIN + # experimental_sgl_marlin shares the marlin weight repack, quant-method + # selection, and base fused path; divergent sites (the LoRA MoE dispatch) + # check is_experimental_sgl_marlin() first. + return self in ( + MoeRunnerBackend.MARLIN, + MoeRunnerBackend.EXPERIMENTAL_SGL_MARLIN, + ) + + def is_experimental_sgl_marlin(self): + return self == MoeRunnerBackend.EXPERIMENTAL_SGL_MARLIN def is_humming(self): return self == MoeRunnerBackend.HUMMING diff --git a/python/sglang/srt/layers/quantization/modelopt_quant.py b/python/sglang/srt/layers/quantization/modelopt_quant.py index 26f3385a6..9675c604e 100755 --- a/python/sglang/srt/layers/quantization/modelopt_quant.py +++ b/python/sglang/srt/layers/quantization/modelopt_quant.py @@ -1715,6 +1715,26 @@ class ModelOptFp4LinearMethod(LinearMethodBase): return out.view(*output_shape) +def deinterleave_w13(weight: torch.Tensor, *, up_first: bool = False) -> torch.Tensor: + """De-interleave a checkpoint ``[g0,u0,g1,u1,...]`` fused gate/up tensor. + + Default returns the block layout ``[gate...; up...]`` (gate half first), which + the CUTLASS NVFP4 prep expects. With ``up_first=True`` it returns + ``[up...; gate...]``, the layout the FlashInfer TRT-LLM FP4 prep + kernel + expect (the kernel applies the up/gate GEMM1 scales to the first/second halves + on that assumption). Operates on the row dim (-2), so it covers both the packed + weight and its block scale. + """ + assert weight.shape[-2] % 2 == 0 + grouped = weight.reshape( + *weight.shape[:-2], weight.shape[-2] // 2, 2, weight.shape[-1] + ) + if up_first: + # Flip each [gate, up] pair to [up, gate] before the block transpose. + grouped = grouped.flip(-2) + return grouped.transpose(-3, -2).reshape_as(weight).contiguous() + + class ModelOptNvFp4A16LinearMethod(LinearMethodBase): """Linear method for ModelOpt NVFP4A16 checkpoints. @@ -2106,6 +2126,18 @@ class ModelOptNvFp4FusedMoEMethod(FusedMoEMethodBase): Only supports pre-quantized checkpoints with FP8 weights and scales. """ + if getattr(layer, "inference_moe_w13_interleaved", False) and not getattr( + layer, "_w13_deinterleaved", False + ): + up_first = self.enable_flashinfer_trtllm_moe + layer.w13_weight.data = deinterleave_w13( + layer.w13_weight.data, up_first=up_first + ) + layer.w13_weight_scale.data = deinterleave_w13( + layer.w13_weight_scale.data, up_first=up_first + ) + layer._w13_deinterleaved = True + # GEMM1 scale processing is deferred until the input scale is known; # see _compute_gemm1_alphas, which splits w13's gate/up weight scales. moe_runner_backend = getattr( @@ -2425,6 +2457,35 @@ class ModelOptNvFp4FusedMoEMethod(FusedMoEMethodBase): self.runner = MoeRunner(moe_runner_backend, moe_runner_config) + def get_marlin_quant_info(self, layer: torch.nn.Module): + """Marlin payload for the fp4-marlin (W4A16) fallback; the weights were + repacked by prepare_moe_nvfp4_layer_for_marlin. Also consumed by + FusedMoEWithLoRA's marlin branch.""" + from sglang.srt.layers.moe.moe_runner.marlin import MarlinMoeQuantInfo + + expert_map = None + global_num_experts = -1 + if hasattr(layer, "dispatcher") and hasattr( + layer.dispatcher, "local_expert_mapping" + ): + expert_map = layer.dispatcher.local_expert_mapping + if expert_map is not None: + global_num_experts = self.moe_runner_config.num_experts + + return MarlinMoeQuantInfo( + w13_qweight=layer.w13_weight, + w2_qweight=layer.w2_weight, + w13_scales=layer.w13_weight_scale, + w2_scales=layer.w2_weight_scale, + w13_g_idx_sort_indices=None, + w2_g_idx_sort_indices=None, + weight_bits=4, + w13_global_scale=layer.w13_weight_scale_2, + w2_global_scale=layer.w2_weight_scale_2, + expert_map=expert_map, + global_num_experts=global_num_experts, + ) + def apply( self, layer: FusedMoE, @@ -2446,30 +2507,7 @@ class ModelOptNvFp4FusedMoEMethod(FusedMoEMethodBase): moe_runner_config = self.moe_runner_config if moe_runner_backend.is_marlin(): - from sglang.srt.layers.moe.moe_runner.marlin import MarlinMoeQuantInfo - - expert_map = None - global_num_experts = -1 - if hasattr(layer, "dispatcher") and hasattr( - layer.dispatcher, "local_expert_mapping" - ): - expert_map = layer.dispatcher.local_expert_mapping - if expert_map is not None: - global_num_experts = self.moe_runner_config.num_experts - - quant_info = MarlinMoeQuantInfo( - w13_qweight=layer.w13_weight, - w2_qweight=layer.w2_weight, - w13_scales=layer.w13_weight_scale, - w2_scales=layer.w2_weight_scale, - w13_g_idx_sort_indices=None, - w2_g_idx_sort_indices=None, - weight_bits=4, - w13_global_scale=layer.w13_weight_scale_2, - w2_global_scale=layer.w2_weight_scale_2, - expert_map=expert_map, - global_num_experts=global_num_experts, - ) + quant_info = self.get_marlin_quant_info(layer) return self.runner.run(dispatch_output, quant_info) # FlashInfer TRTLLM FP4 path diff --git a/python/sglang/srt/layers/quantization/mxfp8_interleave_sf.py b/python/sglang/srt/layers/quantization/mxfp8_interleave_sf.py new file mode 100644 index 000000000..ede6ce7a2 --- /dev/null +++ b/python/sglang/srt/layers/quantization/mxfp8_interleave_sf.py @@ -0,0 +1,103 @@ +"""Triton kernel for writing MXFP8 scale factors in interleaved layout. + +When page_size=128 and sf_vec_size=32, FA4 expects scale factors in the +BlockScaledBasicChunk atom layout: [num_pages, nheads, 32, 4, 4]. + +The interleave mapping for a token at page offset `t` (0-127), head `h`, +scale index `s` (0-3) is: + + output[page, h, t % 32, t // 32, s] + +Linear offset: (page_offset % 32) * 16 + (page_offset // 32) * 4 + scale_idx + +The 4 scales per (token, head) are contiguous in both input and output, +so we vectorize as u32 loads/stores (4 bytes at a time). +""" + +import torch +import triton +import triton.language as tl + + +@triton.jit +def _store_sf_interleaved_kernel( + sf_in_ptr, # [num_tokens * nheads] u32 (4 scales packed per u32) + sf_out_ptr, # [num_pages * nheads * 128] u32 (interleaved, 4 scales per u32) + loc_ptr, # [num_tokens] i64 + num_tokens, # type: ignore + nheads: tl.constexpr, + page_size: tl.constexpr, # 128 + BLOCK_T: tl.constexpr, +): + """Scatter-write per-token scale factors into interleaved layout. + + Input is viewed as [num_tokens, nheads] of u32 (4 packed e8m0 scales). + Output is [num_pages, nheads, 128] of u32 where the 128 positions + follow the interleave pattern: index = (page_offset % 32) * 4 + (page_offset // 32). + """ + pid = tl.program_id(0) + tok_start = pid * BLOCK_T + tok_offsets = tok_start + tl.arange(0, BLOCK_T) + mask = tok_offsets < num_tokens + + # Load slot indices + slots = tl.load(loc_ptr + tok_offsets, mask=mask, other=0) + page_offsets = slots % page_size + page_idxs = slots // page_size + + # Interleave: (page_offset % 32) * 4 + (page_offset // 32) + # This is the u32 offset within a (nheads, 128) page block + interleaved_pos = (page_offsets % 32) * 4 + (page_offsets // 32) + + # Per-page stride in u32: nheads * 128 (= nheads * 32 * 4) + page_stride: tl.constexpr = nheads * 128 + + for h in tl.static_range(nheads): + # Load 4 packed scales as u32: sf_in[tok, h] + in_offsets = tok_offsets * nheads + h + vals = tl.load(sf_in_ptr + in_offsets, mask=mask, other=0) + + # Store to interleaved position: sf_out[page, h * 128 + interleaved_pos] + out_offsets = page_idxs * page_stride + h * 128 + interleaved_pos + tl.store(sf_out_ptr + out_offsets, vals, mask=mask) + + +def store_sf_interleaved( + sf_in: torch.Tensor, # [num_tokens, nheads, sf_dim] e8m0 + sf_out: torch.Tensor, # [num_pages, nheads, 32, 4, 4] e8m0 + loc: torch.Tensor, # [num_tokens] int64 + page_size: int = 128, +): + """Scatter-write per-token scale factors into interleaved page layout.""" + assert ( + page_size == 128 + ), f"Interleaved SF layout requires page_size=128, got {page_size}" + num_tokens, nheads, sf_dim = sf_in.shape + assert sf_dim == 4, f"Expected sf_dim=4 (hdim=128, sf_vec_size=32), got {sf_dim}" + + # View as u32: 4 contiguous e8m0 bytes → 1 u32 + sf_in_u8 = sf_in.view(torch.uint8) if sf_in.dtype == torch.float8_e8m0fnu else sf_in + sf_out_u8 = ( + sf_out.view(torch.uint8) if sf_out.dtype == torch.float8_e8m0fnu else sf_out + ) + + sf_in_u32 = ( + sf_in_u8.reshape(num_tokens, nheads, 4) + .contiguous() + .view(torch.int32) + .reshape(num_tokens, nheads) + ) + sf_out_u32 = sf_out_u8.reshape(-1, 4).view(torch.int32).reshape(-1) + + BLOCK_T = 128 + grid = ((num_tokens + BLOCK_T - 1) // BLOCK_T,) + + _store_sf_interleaved_kernel[grid]( + sf_in_u32, + sf_out_u32, + loc, + num_tokens, + nheads=nheads, + page_size=page_size, + BLOCK_T=BLOCK_T, + ) diff --git a/python/sglang/srt/layers/quantization/mxfp8_quant.py b/python/sglang/srt/layers/quantization/mxfp8_quant.py new file mode 100644 index 000000000..6d5608527 --- /dev/null +++ b/python/sglang/srt/layers/quantization/mxfp8_quant.py @@ -0,0 +1,369 @@ +"""MXFP8 quantization helpers for Inkling attention.""" + +from __future__ import annotations + +from typing import NamedTuple + +import torch +import triton +import triton.language as tl + +MXFP8_BLOCK_SIZE = 32 + + +class MXFP8Tensor(NamedTuple): + data: torch.Tensor + scale: torch.Tensor + + +@triton.jit +def _mxfp8_quant_kernel( + x_ptr, + xq_ptr, + s_ptr, + M, + K, + sxm, + sxk, + sqm, + sqk, + ssm, + ssk, + BLOCK_M: tl.constexpr, +): + pid_m = tl.program_id(0) + pid_b = tl.program_id(1) + offs_m = pid_m * BLOCK_M + tl.arange(0, BLOCK_M) + offs_k = pid_b * 32 + tl.arange(0, 32) + m_mask = offs_m < M + + x = tl.load( + x_ptr + offs_m[:, None] * sxm + offs_k[None, :] * sxk, + mask=m_mask[:, None], + other=0.0, + ).to(tl.float32) + amax = tl.maximum(tl.max(tl.abs(x), axis=1), 1e-30) + scale_biased = tl.ceil(tl.log2(amax / 448.0)) + 127.0 + scale_biased = tl.minimum(tl.maximum(scale_biased, 0.0), 254.0) + descale = tl.exp2(scale_biased - 127.0) + xq = tl.clamp(x / descale[:, None], -448.0, 448.0).to(xq_ptr.dtype.element_ty) + + tl.store( + xq_ptr + offs_m[:, None] * sqm + offs_k[None, :] * sqk, + xq, + mask=m_mask[:, None], + ) + tl.store( + s_ptr + offs_m * ssm + pid_b * ssk, + scale_biased.to(tl.uint8), + mask=m_mask, + ) + + +def to_mxfp8(x: torch.Tensor) -> MXFP8Tensor: + """Quantize the last dimension into E4M3 values plus E8M0 scale bytes.""" + if x.shape[-1] % MXFP8_BLOCK_SIZE != 0: + raise ValueError( + f"MXFP8 quantization requires last dim divisible by {MXFP8_BLOCK_SIZE}, " + f"got {x.shape[-1]}." + ) + orig_shape = x.shape + x2d = x.contiguous().view(-1, orig_shape[-1]) + M, K = x2d.shape + xq = torch.empty_like(x2d, dtype=torch.float8_e4m3fn) + scales = torch.empty((M, K // MXFP8_BLOCK_SIZE), dtype=torch.uint8, device=x.device) + block_m = 64 + grid = (triton.cdiv(M, block_m), K // MXFP8_BLOCK_SIZE) + _mxfp8_quant_kernel[grid]( + x2d, + xq, + scales, + M, + K, + x2d.stride(0), + x2d.stride(1), + xq.stride(0), + xq.stride(1), + scales.stride(0), + scales.stride(1), + BLOCK_M=block_m, + ) + return MXFP8Tensor( + data=xq.view(orig_shape), + scale=scales.view(*orig_shape[:-1], K // MXFP8_BLOCK_SIZE), + ) + + +@triton.jit +def _mxfp8_quant_store_qkv_kernel( + q_ptr, + k_ptr, + v_ptr, + loc_ptr, + q8_ptr, + sfq_ptr, + kc_ptr, + vc_ptr, + sfk_ptr, + sfv_ptr, + sqt, + sqh, + skt, + skh, + svt, + svh, + NQ: tl.constexpr, + NKV: tl.constexpr, + D: tl.constexpr, + PAGE: tl.constexpr, +): + t = tl.program_id(0) + r = tl.program_id(1) + SF: tl.constexpr = D // 32 + blk = tl.arange(0, SF) + off2 = blk[:, None] * 32 + tl.arange(0, 32)[None, :] + + if r < NQ: + src = q_ptr + t * sqt + r * sqh + elif r < NQ + NKV: + src = k_ptr + t * skt + (r - NQ) * skh + else: + src = v_ptr + t * svt + (r - NQ - NKV) * svh + + x = tl.load(src + off2).to(tl.float32) + amax = tl.maximum(tl.max(tl.abs(x), axis=1), 1e-30) + scale_biased = tl.ceil(tl.log2(amax / 448.0)) + 127.0 + scale_biased = tl.minimum(tl.maximum(scale_biased, 0.0), 254.0) + descale = tl.exp2(scale_biased - 127.0) + xq = tl.clamp(x / descale[:, None], -448.0, 448.0).to(q8_ptr.dtype.element_ty) + sf = scale_biased.to(tl.uint8) + + if r < NQ: + tl.store(q8_ptr + (t * NQ + r) * D + off2, xq) + tl.store(sfq_ptr + (t * NQ + r) * SF + blk, sf) + else: + myloc = tl.load(loc_ptr + t).to(tl.int64) + if r < NQ + NKV: + h = r - NQ + cache = kc_ptr + sfb = sfk_ptr + else: + h = r - NQ - NKV + cache = vc_ptr + sfb = sfv_ptr + tl.store(cache + (myloc * NKV + h) * D + off2, xq) + # BlockScaledBasicChunk byte layout (see mxfp8_interleave_sf.py): + # a page block is (NKV, 32, PAGE//32, SF) bytes. + po = myloc % PAGE + base = ( + ((myloc // PAGE) * NKV + h) * (32 * (PAGE // 32) * SF) + + (po % 32) * ((PAGE // 32) * SF) + + (po // 32) * SF + ) + tl.store(sfb + base + blk, sf) + + +def quant_store_kv_mxfp8( + k: torch.Tensor, + v: torch.Tensor, + loc: torch.Tensor, + k_cache: torch.Tensor, + v_cache: torch.Tensor, + sfk: torch.Tensor, + sfv: torch.Tensor, + page_size: int = 128, +) -> None: + """One kernel quantizes bf16 K/V rows, scatters the fp8 payload into the paged + cache at `loc`, and writes UE8M0 scales in the interleaved FA4 layout.""" + T, NKV, D = k.shape + assert D % 32 == 0 and page_size % 32 == 0 + assert k.stride(2) == 1 and v.stride(2) == 1 + assert k_cache.is_contiguous() and v_cache.is_contiguous() + if T == 0: + return + _mxfp8_quant_store_qkv_kernel[(T, 2 * NKV)]( + k, # unused q_ptr (same elem dtype) + k, + v, + loc, + k_cache, # unused q8_ptr, supplies the fp8 store dtype + sfk.view(torch.uint8), # unused sfq_ptr (uint8, matches the dead store) + k_cache, + v_cache, + sfk.view(torch.uint8), + sfv.view(torch.uint8), + 0, + 0, + k.stride(0), + k.stride(1), + v.stride(0), + v.stride(1), + NQ=0, + NKV=NKV, + D=D, + PAGE=page_size, + ) + + +@triton.jit +def _mxfp8_v_cache_update_kernel( + v_ptr, + loc_ptr, + cache_ptr, + sf_ptr, + N, + svn, + svh, + svd, + scp, + scs, + sch, + scd, + sfp, + sfh, + sfd, + sfb, + PAGE_SIZE: tl.constexpr, + HEAD_DIM: tl.constexpr, + SCAN_WINDOW: tl.constexpr, +): + n = tl.program_id(0) + h = tl.program_id(1) + myloc = tl.load(loc_ptr + n).to(tl.int64) + blk = myloc // 32 + + # Leader election: same-block tokens are <= SCAN_WINDOW consecutive loc + # entries (blocks never span pages; per-sequence slots are contiguous), + # so the program with the lowest index in that window owns the block. + is_leader = myloc >= 0 + j_lo = tl.maximum(n - SCAN_WINDOW, 0) + for j in range(j_lo, n): + lj = tl.load(loc_ptr + j).to(tl.int64) + is_leader = is_leader & ~((lj >= 0) & (lj // 32 == blk)) + + if is_leader: + page = blk * 32 // PAGE_SIZE + blk_in_page = (blk * 32) % PAGE_SIZE // 32 + d = tl.arange(0, HEAD_DIM) + sf_addr = sf_ptr + page * sfp + h * sfh + d * sfd + blk_in_page * sfb + e_old = tl.load(sf_addr).to(tl.int32) + + # Pass 1 over this call's tokens for the block: per-d exponent of new + # data, and whether the block's first slot is among them (fresh block + # -> ignore the stored exponent: kills the stale ratchet from page + # reuse and any garbage bytes). + e_new = tl.zeros((HEAD_DIM,), dtype=tl.int32) + has_start = False + j_hi = tl.minimum(n + SCAN_WINDOW, N) + for j in range(n, j_hi): + lj = tl.load(loc_ptr + j).to(tl.int64) + hit = (lj >= 0) & (lj // 32 == blk) + v = tl.load(v_ptr + j * svn + h * svh + d * svd, mask=hit, other=0.0).to( + tl.float32 + ) + amax = tl.maximum(tl.abs(v), 1e-30) + e_tok = (tl.ceil(tl.log2(amax / 448.0)) + 127.0).to(tl.int32) + e_tok = tl.minimum(tl.maximum(e_tok, 0), 254) + e_new = tl.maximum(e_new, tl.where(hit, e_tok, 0)) + has_start = has_start | (hit & (lj % 32 == 0)) + e_old = tl.where(has_start, 0, e_old) + e_blk = tl.maximum(e_old, e_new) + + # Rescale the existing payload where the exponent grew: an exact + # power-of-two shift (matches offline quantization up to subnormal + # double-rounding). Overwritten slots get fresh data below anyway. + s = tl.arange(0, 32) + cache_addr = ( + cache_ptr + + page * scp + + (blk_in_page * 32 + s)[:, None] * scs + + h * sch + + d[None, :] * scd + ) + old = tl.load(cache_addr).to(tl.float32) + old = old * tl.exp2((e_old - e_blk).to(tl.float32))[None, :] + tl.store(cache_addr, old.to(cache_ptr.dtype.element_ty)) + + # Pass 2: quantize and write the new tokens at the settled exponent. + descale = tl.exp2((e_blk - 127).to(tl.float32)) + for j in range(n, j_hi): + lj = tl.load(loc_ptr + j).to(tl.int64) + hit = (lj >= 0) & (lj // 32 == blk) + v = tl.load(v_ptr + j * svn + h * svh + d * svd, mask=hit, other=0.0).to( + tl.float32 + ) + q = tl.clamp(v / descale, -448.0, 448.0) + slot_in_page = lj % PAGE_SIZE + tl.store( + cache_ptr + page * scp + slot_in_page * scs + h * sch + d * scd, + q.to(cache_ptr.dtype.element_ty), + mask=hit, + ) + + tl.store(sf_addr, e_blk.to(tl.uint8)) + + +def update_mxfp8_v_cache_seqblocked( + v: torch.Tensor, + loc: torch.Tensor, + v_cache: torch.Tensor, + sfv: torch.Tensor, +) -> None: + """Append V rows to a legacy seq-blocked fp8 cache quantized per-32-token. + + Current paged FA4 v_dequant uses BlockScaledBasicChunk SFV laid out like + SFK: per token, per head-dim block. This helper keeps the older V-scale + layout where each head dim owns page_size / 32 token-block scales. + Incremental writes reproduce offline whole-block quantization -- scale + bytes exactly, payload up to subnormal double-rounding. + + v: (N, h_kv, head_dim) bf16/fp16 new rows + loc: (N,) int32/int64 destination slot ids; negative = skip (padding) + v_cache: (num_pages, page_size, h_kv, head_dim) float8_e4m3fn + sfv: (num_pages, h_kv, head_dim, page_size // 32) uint8 UE8M0 + + Requirements (asserted where cheap): page_size % 32 == 0; same-block + tokens occupy consecutive `loc` entries (true for sglang's per-sequence + contiguous allocation -- blocks never span pages). The sfv buffer must be + zero-initialized at pool allocation so never-written blocks can't hold + e8m0 NaN (0xFF). Fixed grid and no host syncs: CUDA-graph capturable. + """ + N, h_kv, head_dim = v.shape + num_pages, page_size = v_cache.shape[0], v_cache.shape[1] + assert page_size % MXFP8_BLOCK_SIZE == 0 + assert v_cache.shape[2:] == (h_kv, head_dim) + assert sfv.shape == (num_pages, h_kv, head_dim, page_size // MXFP8_BLOCK_SIZE) + assert sfv.dtype == torch.uint8 and v_cache.dtype == torch.float8_e4m3fn + if N == 0: + return + _mxfp8_v_cache_update_kernel[(N, h_kv)]( + v, + loc, + v_cache, + sfv, + N, + v.stride(0), + v.stride(1), + v.stride(2), + v_cache.stride(0), + v_cache.stride(1), + v_cache.stride(2), + v_cache.stride(3), + sfv.stride(0), + sfv.stride(1), + sfv.stride(2), + sfv.stride(3), + PAGE_SIZE=page_size, + HEAD_DIM=head_dim, + SCAN_WINDOW=MXFP8_BLOCK_SIZE, + ) + + +def from_mxfp8(x: MXFP8Tensor, out_dtype: torch.dtype = torch.bfloat16) -> torch.Tensor: + num_blocks = x.data.shape[-1] // MXFP8_BLOCK_SIZE + data = x.data.to(torch.float32).view( + *x.data.shape[:-1], num_blocks, MXFP8_BLOCK_SIZE + ) + scale_biased = x.scale.view(torch.uint8).to(torch.float32) + descale = torch.exp2(scale_biased - 127.0) + return (data * descale.unsqueeze(-1)).view_as(x.data).to(out_dtype) diff --git a/python/sglang/srt/layers/quantization/unquant.py b/python/sglang/srt/layers/quantization/unquant.py index 2f6406d2c..5c77f22a7 100644 --- a/python/sglang/srt/layers/quantization/unquant.py +++ b/python/sglang/srt/layers/quantization/unquant.py @@ -303,6 +303,7 @@ class UnquantizedFusedMoEMethod(FusedMoEMethodBase, MultiPlatformOp): or get_moe_runner_backend().is_aiter() ) and self._aiter_ck_moe_supported(layer) + and not layer._skip_aiter_moe_shuffle ) if _should_use_aiter_moe: copy_or_rebind_param( diff --git a/python/sglang/srt/layers/radix_attention.py b/python/sglang/srt/layers/radix_attention.py index e1fa41216..2f69e6387 100644 --- a/python/sglang/srt/layers/radix_attention.py +++ b/python/sglang/srt/layers/radix_attention.py @@ -15,6 +15,8 @@ from __future__ import annotations +from contextlib import contextmanager +from contextvars import ContextVar from enum import Enum from typing import TYPE_CHECKING, Optional @@ -30,8 +32,27 @@ from sglang.srt.model_executor.runner_backend_utils.breakable_cuda_graph import from sglang.srt.model_executor.runner_backend_utils.tc_piecewise_cuda_graph import ( get_tc_piecewise_forward_context, ) +from sglang.srt.utils.common import is_hip from sglang.srt.utils.custom_op import register_custom_op +_is_hip = is_hip() + +# When set, RadixAttention.forward runs the attention backend eagerly instead of +# routing through the tc-piecewise split op. A caller already inside a +# breakable-CUDA-graph eager break (e.g. Inkling wrapping norm+attn+sconv in one eager +# region for multi-seq-correct short-conv metadata) sets this so the attn does not +# start a nested break (which would assert on the ended segment). Default off. +_force_eager_attn: ContextVar[bool] = ContextVar("_force_eager_attn", default=False) + + +@contextmanager +def force_eager_attention(): + token = _force_eager_attn.set(True) + try: + yield + finally: + _force_eager_attn.reset(token) + def _zero_padded_pcg_tail(buf: torch.Tensor, context) -> None: """Zero the padded tail ``buf`` leaves as torch.empty garbage under PCG @@ -140,6 +161,12 @@ class RadixAttention(nn.Module): if ( forward_batch.forward_mode.is_extend() and get_tc_piecewise_forward_context() is not None + # ``_force_eager_attn`` is only set inside Inkling's eager + # norm+attn+sconv region, never during tc-piecewise capture. Reading + # the ContextVar under the fullgraph torch.compile trace is + # untraceable ("Unsupported method call: ContextVar.get"), so + # short-circuit it while compiling -- force-eager is always off there. + and (torch.compiler.is_compiling() or not _force_eager_attn.get()) ): if kwargs.get("idx_q") is not None: if is_in_breakable_cuda_graph(): @@ -166,10 +193,45 @@ class RadixAttention(nn.Module): idx_v=idx_v, ) return idx_out, attn_out + # FP8 q (e.g. mxfp8 KV-cache attention) still produces a bf16 + # attention output; sizing the buffer off q's dtype would silently + # cast-copy the result to fp8. + out_dtype = ( + torch.bfloat16 + if q.dtype in (torch.float8_e4m3fn, torch.float8_e5m2) + else q.dtype + ) if self.qk_head_dim != self.v_head_dim: - output = q.new_empty((q.shape[0], self.tp_q_head_num * self.v_head_dim)) + output = q.new_empty( + (q.shape[0], self.tp_q_head_num * self.v_head_dim), + dtype=out_dtype, + ) else: - output = torch.empty_like(q) + output = torch.empty_like(q, dtype=out_dtype) + if any( + key in kwargs + for key in ( + "score_mod", + "aux_tensors", + "rel_bias", + "q_descale", + "k_descale", + "v_descale", + ) + ): + # A score_mod callable, aux_tensors, rel_bias, or mxfp8 descale + # tensors can't cross the unified_attention_with_output custom-op + # schema; route this backend's extend attention through the plain + # eager path. + if is_in_breakable_cuda_graph(): + breakable_attention_with_output_extra_kwargs( + q, k, v, output, save_kv_cache, self.layer_id, kwargs + ) + else: + attention_with_output_extra_kwargs( + q, k, v, output, save_kv_cache, self.layer_id, kwargs + ) + return output if is_in_breakable_cuda_graph(): breakable_unified_attention_with_output( q, k, v, output, save_kv_cache, self.layer_id, **kwargs @@ -339,3 +401,62 @@ def unified_sparse_attention_with_output( breakable_unified_attention_with_output = eager_on_graph(True)( unified_attention_with_output ) + + +def attention_with_output_extra_kwargs( + query: torch.Tensor, + key: Optional[torch.Tensor], + value: Optional[torch.Tensor], + output: torch.Tensor, + save_kv_cache: bool, + layer_id: int, + extra_kwargs: dict, +) -> None: + """Breakable/tc_piecewise attention for backends whose forward needs kwargs + that cannot cross the ``unified_attention_with_output`` custom-op schema -- + a ``score_mod`` callable and/or ``aux_tensors`` (e.g. Inkling's relative-bias + fa4 attention). Plain (not a custom op) so the callable passes through; still + runs eagerly between graph segments under BCG via the wrapper below. Mirrors + the real-token narrowing + padded-output write of + ``unified_attention_with_output``, and narrows per-token ``aux_tensors`` too. + """ + context = get_tc_piecewise_forward_context() + forward_batch = context.forward_batch + attention_layer = context.attention_layers[layer_id] + real_num_tokens = forward_batch.num_token_non_padded_cpu + + query = query[:real_num_tokens] + if key is not None: + key = key[:real_num_tokens] + if value is not None: + value = value[:real_num_tokens] + + kwargs = dict(extra_kwargs) + aux_tensors = kwargs.get("aux_tensors") + if aux_tensors is not None: + kwargs["aux_tensors"] = [t[:real_num_tokens] for t in aux_tensors] + for per_token_key in ("rel_bias", "q_descale", "k_descale", "v_descale"): + t = kwargs.get(per_token_key) + if t is not None: + kwargs[per_token_key] = t[:real_num_tokens] + + original_out_cache_loc = forward_batch.out_cache_loc + forward_batch.out_cache_loc = original_out_cache_loc[:real_num_tokens] + forward_batch._attn_output = output[:real_num_tokens] + + ret = get_attn_backend().forward( + query, key, value, attention_layer, forward_batch, save_kv_cache, **kwargs + ) + forward_batch.out_cache_loc = original_out_cache_loc + + if ret.data_ptr() != output.data_ptr(): + output[:real_num_tokens].view(ret.shape).copy_(ret) + + if _is_hip: + _zero_padded_pcg_tail(output, context) + return + + +breakable_attention_with_output_extra_kwargs = eager_on_graph(True)( + attention_with_output_extra_kwargs +) diff --git a/python/sglang/srt/lora/layers.py b/python/sglang/srt/lora/layers.py index d1bb04ad1..33c685caa 100644 --- a/python/sglang/srt/lora/layers.py +++ b/python/sglang/srt/lora/layers.py @@ -589,6 +589,43 @@ class MergedColumnParallelLinearWithLoRA(ColumnParallelLinearWithLoRA): return torch.concat(slices, dim=0) +class InklingQKVRLinearWithLoRA(MergedColumnParallelLinearWithLoRA): + """LoRA wrapper for Inkling's fused q/k/v/r projection. + + The base layer replicates K/V at load when attn_tp_size > num_kv_heads. The + adapter LoRA-B is stacked at the *unreplicated* sizes ``[q | k | v | r]`` (with + k, v = head_dim * num_kv_heads), so we slice the K/V blocks with replication — + each rank takes its kv-head's rows — to match the replicated base output. q and r + are head-partitioned uniformly. LoRA-A stays unsharded (inherited). + """ + + def slice_lora_b_weights(self, B: torch.Tensor, tp_rank: int): + bl = self.base_layer + hd, nkv, nh, dr, tp = ( + bl.inkling_head_dim, + bl.inkling_num_kv_heads, + bl.inkling_num_heads, + bl.inkling_d_rel, + bl.inkling_tp_size, + ) + q_size, kv_size, r_size = hd * nh, hd * nkv, dr * nh + q_off, k_off, v_off, r_off = 0, q_size, q_size + kv_size, q_size + 2 * kv_size + q_per, r_per = q_size // tp, r_size // tp + q = B[q_off + tp_rank * q_per : q_off + (tp_rank + 1) * q_per, :] + r = B[r_off + tp_rank * r_per : r_off + (tp_rank + 1) * r_per, :] + if tp > nkv: + # Replicate: each rank takes the single kv-head it shares (mirrors #15). + replicas = tp // nkv + kv_idx = tp_rank // replicas + k = B[k_off + kv_idx * hd : k_off + (kv_idx + 1) * hd, :] + v = B[v_off + kv_idx * hd : v_off + (kv_idx + 1) * hd, :] + else: + kv_per = kv_size // tp + k = B[k_off + tp_rank * kv_per : k_off + (tp_rank + 1) * kv_per, :] + v = B[v_off + tp_rank * kv_per : v_off + (tp_rank + 1) * kv_per, :] + return torch.concat([q, k, v, r], dim=0) + + class QKVParallelLinearWithLoRA(ColumnParallelLinearWithLoRA): def __init__( self, @@ -919,6 +956,17 @@ class FusedMoEWithLoRA(BaseLayerWithLoRA): else: runner_backend = MoeRunnerBackend.TRITON + # Unquantized layers have no marlin-repacked weights, so run their LoRA + # on Triton. Inkling shared experts use InklingBatchDenseMLP directly + # and never reach this wrapper. + if runner_backend.is_marlin(): + from sglang.srt.layers.quantization.unquant import ( + UnquantizedFusedMoEMethod, + ) + + if isinstance(base_layer.quant_method, UnquantizedFusedMoEMethod): + runner_backend = MoeRunnerBackend.TRITON + # ===== TO BE REFACTORED ==== self._lora_runner_backend = runner_backend if runner_backend.is_experimental_sgl_trtllm(): @@ -928,6 +976,13 @@ class FusedMoEWithLoRA(BaseLayerWithLoRA): init_experimental_sgl_trtllm_lora(self, base_layer) return + if runner_backend.is_experimental_sgl_marlin(): + from sglang.srt.lora.marlin_lora_temp.lora_layer import ( + init_experimental_sgl_marlin_lora, + ) + + init_experimental_sgl_marlin_lora(self, base_layer) + return # ===== END TO BE REFACTORED ==== self._lora_runner = MoeRunner( @@ -940,12 +995,16 @@ class FusedMoEWithLoRA(BaseLayerWithLoRA): from sglang.srt.layers.quantization.compressed_tensors.compressed_tensors import ( CompressedTensorsFusedMoEMethod, ) + from sglang.srt.layers.quantization.modelopt_quant import ( + ModelOptNvFp4FusedMoEMethod, + ) assert isinstance( - base_layer.quant_method, CompressedTensorsFusedMoEMethod + base_layer.quant_method, + (CompressedTensorsFusedMoEMethod, ModelOptNvFp4FusedMoEMethod), ), ( - f"Marlin MoE backend requires CompressedTensorsFusedMoEMethod, " - f"got {type(base_layer.quant_method).__name__}" + f"Marlin MoE backend requires a quant method exposing " + f"get_marlin_quant_info, got {type(base_layer.quant_method).__name__}" ) self._quant_info = base_layer.quant_method.get_marlin_quant_info(base_layer) elif runner_backend.is_triton(): @@ -986,9 +1045,9 @@ class FusedMoEWithLoRA(BaseLayerWithLoRA): # the Python weight_indices list, no GPU sync needed. has_active_lora = bool(getattr(batch_info, "has_active_lora", False)) - if self._lora_runner_backend.is_experimental_sgl_trtllm(): - # Per-rank (local) expert count the LoRA buffers are indexed by, so - # virtual-experts indexing matches the buffers under EP. + if self._lora_runner_backend.is_experimental_sgl_trtllm() or ( + self._lora_runner_backend.is_experimental_sgl_marlin() + ): num_experts = ( self.down_lora_a_weights.shape[1] if self.down_lora_a_weights is not None @@ -1063,6 +1122,17 @@ class FusedMoEWithLoRA(BaseLayerWithLoRA): combine_input = dispatch_experimental_sgl_trtllm_lora( dispatch_output, quant_info, base_layer, lora_info ) + elif self._lora_runner_backend.is_experimental_sgl_marlin(): + from sglang.srt.lora.marlin_lora_temp.lora_layer import ( + dispatch_experimental_sgl_marlin_lora, + ) + + combine_input = dispatch_experimental_sgl_marlin_lora( + dispatch_output, + quant_info, + base_layer, + lora_info, + ) # ===== END TO BE REFACTORED ==== else: combine_input = self._lora_runner.run( @@ -1194,6 +1264,10 @@ def get_lora_layer( ColumnParallelLinear: ColumnParallelLinearWithLoRA, RowParallelLinear: RowParallelLinearWithLoRA, } + # Inkling's fused qkvr needs replication-aware LoRA-B slicing (see InklingQKVRLinear); + # it IS a MergedColumnParallelLinear, so this must precede the isinstance loop. + if getattr(layer, "is_inkling_qkvr", False): + return InklingQKVRLinearWithLoRA(layer, lora_backend) for src_layer_type, lora_layer_type in supported_layer_types.items(): if isinstance(layer, src_layer_type): # pylint: disable=unidiomatic-typecheck ret = lora_layer_type(layer, lora_backend) diff --git a/python/sglang/srt/lora/lora.py b/python/sglang/srt/lora/lora.py index 245c15e3b..c154e1c55 100644 --- a/python/sglang/srt/lora/lora.py +++ b/python/sglang/srt/lora/lora.py @@ -203,6 +203,8 @@ class LoRAAdapter(nn.Module): for layer in self.layers: weight_names = list(layer.weights.keys()) self.normalize_qkv_proj(weight_names, layer.weights) + self.normalize_inkling_qkvr_proj(weight_names, layer.weights) + self._normalize_shared_expert_moe(layer.weights) self._rename_expert_w_to_proj(layer.weights) # Stack gate_proj + x_proj → in_proj for Mamba layers (before gate_up normalization) self._normalize_in_proj(layer.weights) @@ -213,6 +215,52 @@ class LoRAAdapter(nn.Module): weight_names = list(layer.weights.keys()) self.normalize_fused_qkv_a_proj(weight_names, layer.weights) + def normalize_inkling_qkvr_proj( + self, weight_names: List[str], weights: Dict[str, torch.Tensor] + ): + """Normalize Inkling split attention LoRA keys into the runtime qkvr layer. + + Inkling checkpoints expose separate attention projections + (wq_du/wk_dv/wv_dv/wr_du), while the serving model uses a single + MergedColumnParallelLinear named qkvr with those slices in that order. + """ + for weight_name in weight_names: + if "wq_du" not in weight_name: + continue + + q_name = weight_name + k_name = weight_name.replace("wq_du", "wk_dv") + v_name = weight_name.replace("wq_du", "wv_dv") + r_name = weight_name.replace("wq_du", "wr_du") + qkvr_name = weight_name.replace("wq_du", "qkvr") + + if any(name not in weights for name in (k_name, v_name, r_name)): + continue + + cat_dim = weights[q_name].dim() - 2 + weights[qkvr_name] = torch.cat( + ( + weights[q_name], + weights[k_name], + weights[v_name], + weights[r_name], + ), + cat_dim, + ) + weights.pop(q_name) + weights.pop(k_name) + weights.pop(v_name) + weights.pop(r_name) + + for weight_name in list(weights.keys()): + if "qkvr" not in weight_name or "lora_A" not in weight_name: + continue + if weights[weight_name].shape[-2] != self.config.r: + continue + repeat_dims = [1] * weights[weight_name].dim() + repeat_dims[-2] = 4 + weights[weight_name] = weights[weight_name].repeat(*repeat_dims) + def normalize_qkv_proj( self, weight_names: List[str], weights: Dict[str, torch.Tensor] ): @@ -266,6 +314,61 @@ class LoRAAdapter(nn.Module): weights[qkv_name] = weights[qkv_name].repeat(3, 1) # else: no-op as LoRA B weight is already stacked. + def _normalize_shared_expert_moe(self, weights: Dict[str, torch.Tensor]): + """Reshape flat Inkling shared-sink factors to shared-outer 3D form.""" + # Gate on the architecture so other models with 2D shared experts keep + # the stock path. + cfg = self.base_hf_config + if hasattr(cfg, "get_text_config"): + cfg = cfg.get_text_config() + archs = list(getattr(self.base_hf_config, "architectures", None) or []) + archs += list(getattr(cfg, "architectures", None) or []) + is_inkling = ( + any("Inkling" in arch for arch in archs) + or "inkling" in str(getattr(cfg, "model_type", "")).lower() + ) + if not is_inkling: + return + num_shared = getattr(cfg, "n_shared_experts", 0) or 0 + if num_shared <= 0: + return + for name in list(weights.keys()): + if "shared_experts." not in name: + continue + if re.search(r"shared_experts\.\d+\.", name): + # Preserve named per-expert factors so adapter validation can + # reject outer factors that are not actually shared. + continue + is_down = any(k in name for k in (".w2.", ".down_proj.")) + is_gate_up = any( + k in name + for k in ( + ".w1.", + ".w3.", + ".gate_proj.", + ".up_proj.", + ".gate_up_proj.", + ) + ) + if not (is_down or is_gate_up): + continue + w = weights[name] + if w.dim() != 2: + continue + if "lora_A" in name: + if is_down: + r, flat = w.shape + weights[name] = w.reshape(r, num_shared, flat // num_shared) + weights[name] = weights[name].transpose(0, 1).contiguous() + else: + weights[name] = w.unsqueeze(0) + else: + if is_down: + weights[name] = w.unsqueeze(0) + else: + flat, r = w.shape + weights[name] = w.reshape(num_shared, flat // num_shared, r) + def _rename_expert_w_to_proj(self, weights: Dict[str, torch.Tensor]): """Rename w1 -> gate_proj, w3 -> up_proj, w2 -> down_proj so that normalize_gate_up_proj can stack them into gate_up_proj.""" diff --git a/python/sglang/srt/lora/lora_manager.py b/python/sglang/srt/lora/lora_manager.py index a83ca071f..498cf4787 100644 --- a/python/sglang/srt/lora/lora_manager.py +++ b/python/sglang/srt/lora/lora_manager.py @@ -16,6 +16,7 @@ # and "Punica: Multi-Tenant LoRA Serving" import logging +import re from typing import Dict, Iterable, List, Optional import torch @@ -85,6 +86,7 @@ class LoRAManager: self.enable_lora_overlap_loading: Optional[bool] = ( server_args.enable_lora_overlap_loading ) + self.pending_lora_load_events = {} self.eviction_policy = server_args.lora_eviction_policy self._experts_shared_outer_override: Optional[bool] = ( @@ -282,6 +284,15 @@ class LoRAManager: ), f"LoRA adapter with ID {lora_ref.lora_id} is not loaded. This should have been verified before request is sent to the backend." try: + pending_events = getattr(self, "pending_lora_load_events", {}) + pending_event = pending_events.get(lora_ref.lora_id) + if pending_event is not None: + pending_event.synchronize() + pending_events.pop(lora_ref.lora_id, None) + + removed_slot = self.memory_pool.remove_lora(lora_ref.lora_id) + if removed_slot is not None: + self._notify_lora_slots_updated({removed_slot}) del self.configs[lora_ref.lora_id] del self.loras[lora_ref.lora_id] del self.lora_refs[lora_ref.lora_id] @@ -332,6 +343,9 @@ class LoRAManager: cur_uids = new_loras | running_loras assert len(cur_uids) <= self.max_loras_per_batch + new_uids = { + uid for uid in cur_uids if uid not in self.memory_pool.uid_to_buffer_id + } self.memory_pool.prepare_lora_batch( cur_uids=cur_uids, lora_adapters=self.loras, @@ -340,6 +354,16 @@ class LoRAManager: lora_embed_tokens_module=self.embed_tokens_module, # merge into embedding or lora module lora_lm_head_module=self.lm_head_module, # merge into embedding or lora module ) + if new_uids: + changed_slots = {self.memory_pool.uid_to_buffer_id[uid] for uid in new_uids} + self._notify_lora_slots_updated(changed_slots) + + def _notify_lora_slots_updated(self, slot_ids: set[int]) -> None: + for layer_modules in self.lora_modules: + for module in layer_modules.values(): + notify = getattr(module, "on_lora_slots_updated", None) + if callable(notify): + notify(slot_ids) def prepare_lora_batch(self, forward_batch: ForwardBatch): # set up batch info shared by all lora modules @@ -381,18 +405,22 @@ class LoRAManager: """ for layer_id, layer_modules in enumerate(self.lora_modules): for module_name, module in layer_modules.items(): - # Hack for FusedMoE layer - if isinstance(module, FusedMoEWithLoRA) and all( + if ( + isinstance(module, FusedMoEWithLoRA) + or getattr(module, "is_shared_fused_moe", False) + ) and all( x in self.target_modules for x in ["gate_up_proj", "down_proj"] ): + base_layer = getattr(module, "base_layer", module) + suffix = "_shared_moe" if base_layer.is_shared_fused_moe else "_moe" gate_up_key = ( - "gate_up_proj_moe" - if "gate_up_proj_moe" in self.memory_pool.A_buffer + f"gate_up_proj{suffix}" + if f"gate_up_proj{suffix}" in self.memory_pool.A_buffer else "gate_up_proj" ) down_key = ( - "down_proj_moe" - if "down_proj_moe" in self.memory_pool.A_buffer + f"down_proj{suffix}" + if f"down_proj{suffix}" in self.memory_pool.A_buffer else "down_proj" ) gate_up_a = self.memory_pool.get_tensor( @@ -527,28 +555,27 @@ class LoRAManager: """ shared_outer: Optional[bool] = None for adapter_id, adapter in self.loras.items(): - found = False for layer in adapter.layers: for name, weight in layer.weights.items(): - if ( - "gate_up_proj" in name - and "lora_A" in name - and weight.dim() == 3 - ): + if "gate_up_proj" not in name or "lora_A" not in name: + continue + if weight.dim() == 3: is_shared = weight.shape[0] == 1 - if shared_outer is None: - shared_outer = is_shared - elif shared_outer != is_shared: - raise RuntimeError( - "Mixed shared-outer LoRA formats detected across " - f"loaded adapters (conflict in adapter '{adapter_id}'). " - "All MoE adapters must either all use shared outer " - "experts (expert_dim=1) or all use per-expert weights." - ) - found = True - break - if found: - break + elif re.search(r"(?:shared_)?experts\.\d+\.", name): + # Per-expert adapters keep numbered 2D expert weights; + # they must count against the layout agreement too. + is_shared = False + else: + continue + if shared_outer is None: + shared_outer = is_shared + elif shared_outer != is_shared: + raise RuntimeError( + "Mixed shared-outer LoRA formats detected across " + f"loaded adapters (conflict in adapter '{adapter_id}'). " + "All MoE adapters must either all use shared outer " + "experts (expert_dim=1) or all use per-expert weights." + ) return bool(shared_outer) if shared_outer is not None else False def init_lora_shapes( @@ -785,7 +812,7 @@ class LoRAManager: def init_lora_modules(self): # Look-up table that essentially maps (layer_index, module_name) to the corresponding LoRA module. - self.lora_modules: List[Dict[str, BaseLayerWithLoRA]] = [ + self.lora_modules: List[Dict[str, torch.nn.Module]] = [ {} for _ in range(self.base_hf_config.num_hidden_layers) ] @@ -826,6 +853,8 @@ class LoRAManager: # independently. self.base_model.lm_head = untied_lm_head + from sglang.srt.models.inkling_common.dense_mlp import InklingBatchDenseMLP + for module_name, module in self.base_model.named_modules(): # Handle embed_tokens and lm_head before the should_apply_lora gate, # since VL models' should_apply_lora patterns only match language @@ -880,7 +909,7 @@ class LoRAManager: ) continue - if isinstance(module, FusedMoE) and all( + if isinstance(module, (FusedMoE, InklingBatchDenseMLP)) and all( x in self.target_modules for x in ["gate_up_proj", "down_proj"] ): layer_id = get_layer_id(module_name) @@ -890,9 +919,20 @@ class LoRAManager: # no resolvable layer id; skip them so we don't index # `self.lora_modules` with `None`. continue - lora_module = self.set_lora_module(module_name, module) - lora_module.experts_shared_outer_loras = self.experts_shared_outer_loras - lora_module.lora_use_virtual_experts = self.lora_use_virtual_experts + if isinstance(module, InklingBatchDenseMLP): + from sglang.srt.models.inkling_common.lora import ( + InklingBatchDenseMLPWithLoRA, + ) + + module.__class__ = InklingBatchDenseMLPWithLoRA + module.initialize_lora(self.lora_backend) + lora_module = module + else: + lora_module = self.set_lora_module(module_name, module) + lora_module.experts_shared_outer_loras = ( + self.experts_shared_outer_loras + ) + lora_module.lora_use_virtual_experts = self.lora_use_virtual_experts self.lora_modules[layer_id][module_name] = lora_module diff --git a/python/sglang/srt/lora/lora_moe_runner_marlin.py b/python/sglang/srt/lora/lora_moe_runner_marlin.py index 65d0d7645..fc7d35070 100644 --- a/python/sglang/srt/lora/lora_moe_runner_marlin.py +++ b/python/sglang/srt/lora/lora_moe_runner_marlin.py @@ -73,7 +73,6 @@ class MarlinLoraRunnerCore: assert ( torch.cuda.get_device_capability(hidden_states.device)[0] >= 9 ), "MarlinLoraRunnerCore requires CUDA compute capability >= 9" - inplace = runner_config.inplace routed_scaling_factor = runner_config.routed_scaling_factor M, K = hidden_states.shape @@ -86,20 +85,33 @@ class MarlinLoraRunnerCore: if M * topk / E / block_size_m < 0.9: break + # Under EP the dispatcher already localized topk_ids (-1 = non-local); align + # over the global expert count like fused_marlin_moe, not the local E. + align_num_experts = ( + quant_info.global_num_experts if quant_info.expert_map is not None else E + ) sorted_token_ids, expert_ids, num_tokens_post_padded = moe_align_block_size( - topk_ids, block_size_m, E + topk_ids, block_size_m, align_num_experts ) - from sglang.srt.runtime_context import get_resources + # Per-call workspace like fused_experts_none_to_marlin: a shared buffer aliases + # inter-block locks across in-flight kernels/graphs and deadlocks capture. + workspace = marlin_make_workspace(hidden_states.device, max_blocks_per_sm=4) - buffers = get_resources().buffers - workspace = buffers.get("marlin_lora_workspace") - if workspace is None or workspace.device != hidden_states.device: - workspace = marlin_make_workspace(hidden_states.device, max_blocks_per_sm=4) - buffers["marlin_lora_workspace"] = workspace - - scalar_type1 = get_scalar_type(num_bits, quant_info.w13_qzeros is not None) - scalar_type2 = get_scalar_type(num_bits, quant_info.w2_qzeros is not None) + # Pass scales + global scale so fp4-marlin weights (the ModelOpt NVFP4 + # W4A16 fallback) resolve to float4_e2m1f instead of uint4b8. + scalar_type1 = get_scalar_type( + num_bits, + quant_info.w13_qzeros is not None, + quant_info.w13_scales, + quant_info.w13_global_scale, + ) + scalar_type2 = get_scalar_type( + num_bits, + quant_info.w2_qzeros is not None, + quant_info.w2_scales, + quant_info.w2_global_scale, + ) # Stage 1: Gate/Up (Marlin) intermediate_cache1 = torch.empty( @@ -109,9 +121,9 @@ class MarlinLoraRunnerCore: hidden_states, intermediate_cache1, quant_info.w13_qweight, - None, + quant_info.w13_bias, quant_info.w13_scales, - None, + quant_info.w13_global_scale, quant_info.w13_qzeros, quant_info.w13_g_idx, quant_info.w13_g_idx_sort_indices, @@ -158,9 +170,9 @@ class MarlinLoraRunnerCore: intermediate_cache2, intermediate_cache3, quant_info.w2_qweight, - None, + quant_info.w2_bias, quant_info.w2_scales, - None, + quant_info.w2_global_scale, quant_info.w2_qzeros, quant_info.w2_g_idx, quant_info.w2_g_idx_sort_indices, @@ -190,8 +202,9 @@ class MarlinLoraRunnerCore: intermediate_cache2, intermediate_cache3, topk_weights, topk_ids ) - # Stage 4: Reduction - output = hidden_states if inplace else torch.empty_like(hidden_states) + # Stage 4: Reduction. Never alias hidden_states even under inplace: the sink + # forward still reads it (stock fused_experts_none_to_marlin does the same). + output = torch.empty_like(hidden_states) if routed_scaling_factor is None: routed_scaling_factor = 1.0 # NOTE: fusion opportunity here diff --git a/python/sglang/srt/lora/lora_overlap_loader.py b/python/sglang/srt/lora/lora_overlap_loader.py index 6d5845ba0..9e9d725e1 100644 --- a/python/sglang/srt/lora/lora_overlap_loader.py +++ b/python/sglang/srt/lora/lora_overlap_loader.py @@ -26,7 +26,9 @@ class LoRAOverlapLoader: self.load_stream_context: CudaStreamContext = self.device_module.stream( self.load_stream ) - self.lora_to_overlap_load_event: Dict[Optional[str], CudaEvent] = {} + self.lora_to_overlap_load_event: Dict[Optional[str], CudaEvent] = ( + self.lora_manager.pending_lora_load_events + ) def try_overlap_load_lora( self, lora_id: Optional[str], running_loras: set[Optional[str]] diff --git a/python/sglang/srt/lora/marlin_lora_temp/__init__.py b/python/sglang/srt/lora/marlin_lora_temp/__init__.py new file mode 100644 index 000000000..432eb9c43 --- /dev/null +++ b/python/sglang/srt/lora/marlin_lora_temp/__init__.py @@ -0,0 +1 @@ +"""Experimental Marlin LoRA implementation.""" diff --git a/python/sglang/srt/lora/marlin_lora_temp/activation.py b/python/sglang/srt/lora/marlin_lora_temp/activation.py new file mode 100644 index 000000000..49d7c8c15 --- /dev/null +++ b/python/sglang/srt/lora/marlin_lora_temp/activation.py @@ -0,0 +1,62 @@ +"""Fused ``silu(gate + Δgate) * (up + Δup)`` for the experimental marlin MoE-LoRA path. + +Replaces the stock sequence (in-place LoRA delta add over ``[T, 2N]`` + +``silu_and_mul``) with a single kernel: one read of the GEMM output, one read +of the delta, one write of the activation — and it lets the delta live in its +own buffer, which is what makes the side-stream gate_up overlap possible (the +side stream must not race the marlin GEMM writing the shared cache). +""" + +from __future__ import annotations + +import torch +import triton +import triton.language as tl + + +@triton.jit +def _silu_mul_add_delta_kernel( + x_ptr, # [T, 2N] gemm1 output, [gate || up] + d_ptr, # [T, 2N] LoRA gate_up delta, same layout + out_ptr, # [T, N] + N, + BLOCK: tl.constexpr, +): + row = tl.program_id(0) + block = tl.program_id(1) + cols = block * BLOCK + tl.arange(0, BLOCK) + mask = cols < N + + x_row = x_ptr + row * 2 * N + d_row = d_ptr + row * 2 * N + gate = tl.load(x_row + cols, mask=mask, other=0.0).to(tl.float32) + up = tl.load(x_row + N + cols, mask=mask, other=0.0).to(tl.float32) + gate += tl.load(d_row + cols, mask=mask, other=0.0).to(tl.float32) + up += tl.load(d_row + N + cols, mask=mask, other=0.0).to(tl.float32) + + silu = gate * tl.sigmoid(gate) + out = silu * up + tl.store(out_ptr + row * N + cols, out.to(out_ptr.dtype.element_ty), mask=mask) + + +def silu_and_mul_add_delta( + gemm1_out: torch.Tensor, + gate_up_delta: torch.Tensor, + out: torch.Tensor, +) -> None: + """``out = silu(g + dg) * (u + du)`` over contiguous ``[T, 2N]`` inputs.""" + T, two_n = gemm1_out.shape + N = two_n // 2 + assert gate_up_delta.shape == (T, two_n) and out.shape == (T, N) + assert gemm1_out.is_contiguous() and gate_up_delta.is_contiguous() + assert out.is_contiguous() + if T == 0: + return + BLOCK = 512 + _silu_mul_add_delta_kernel[(T, triton.cdiv(N, BLOCK))]( + gemm1_out, + gate_up_delta, + out, + N, + BLOCK=BLOCK, + ) diff --git a/python/sglang/srt/lora/marlin_lora_temp/direct_decode.py b/python/sglang/srt/lora/marlin_lora_temp/direct_decode.py new file mode 100644 index 000000000..d83eff631 --- /dev/null +++ b/python/sglang/srt/lora/marlin_lora_temp/direct_decode.py @@ -0,0 +1,242 @@ +"""No-sort B200 decode kernels for Inkling routed MoE LoRA. + +The contract is BF16 CUDA, M<=32, top-k 6, rank 32, up to four slots, TP-local +intermediate 384 or 768, and non-EP expert IDs in ``[0, E)``. +""" + +from __future__ import annotations + +import torch +import triton +import triton.language as tl + +ROUTER_TOPK = 6 +RANK = 32 + +_BLOCK_M = 16 +_GATE_BLOCK_N = 128 +_DOWN_BLOCK_K = 128 + + +@triton.jit +def _direct_gate_expand_kernel( + shared_a_ptr, # [M, 2R] or [S, M, 2R] + gate_b_ptr, # [S, E, 2I, R] + topk_ids_ptr, # [M, topk] + token_lora_mapping_ptr, # [M] + output_ptr, # [M, topk, 2I] + stride_as, + stride_am, + stride_ar, + stride_bs, + stride_be, + stride_bn, + stride_br, + stride_oq, + stride_on, + ROUTER_TOPK: tl.constexpr, + INTERMEDIATE_SIZE: tl.constexpr, + RANK: tl.constexpr, + GATE_WIDTH: tl.constexpr, + NUM_SLOTS: tl.constexpr, + BLOCK_M: tl.constexpr, + BLOCK_N: tl.constexpr, +): + route = tl.program_id(0) + pid_n = tl.program_id(1) + expert = tl.load(topk_ids_ptr + route).to(tl.int64) + token = route // ROUTER_TOPK + slot = tl.load(token_lora_mapping_ptr + token).to(tl.int64) + active = (slot >= 0) & (slot < NUM_SLOTS) + offs_m = tl.arange(0, BLOCK_M) + offs_n = pid_n * BLOCK_N + tl.arange(0, BLOCK_N) + offs_r = tl.arange(0, RANK) + n_mask = offs_n < GATE_WIDTH + + # BLOCK_N=128 divides both supported I values, so no output tile straddles + # the gate/up boundary. Gate tiles read A[0:R]; up tiles read A[R:2R]. + a_half = tl.where(pid_n * BLOCK_N >= INTERMEDIATE_SIZE, RANK, 0) + a = tl.load( + shared_a_ptr + + slot * stride_as + + token * stride_am + + (a_half + offs_r[None, :]) * stride_ar, + mask=active & (offs_m[:, None] == 0), + other=0.0, + ) + b = tl.load( + gate_b_ptr + + slot * stride_bs + + expert * stride_be + + offs_r[:, None] * stride_br + + offs_n[None, :] * stride_bn, + mask=active & n_mask[None, :], + other=0.0, + ) + accumulator = tl.dot(a, b, out_dtype=tl.float32) + tl.store( + output_ptr + + route * stride_oq + + offs_m[:, None] * 0 + + offs_n[None, :] * stride_on, + accumulator, + mask=(offs_m[:, None] == 0) & n_mask[None, :], + ) + + +@triton.jit +def _direct_down_shrink_kernel( + activation_ptr, # [M*topk, I] + down_a_ptr, # [S, E, R, I] + topk_ids_ptr, # [M, topk] + token_lora_mapping_ptr, # [M] + output_ptr, # [M, topk, R] + stride_xq, + stride_xk, + stride_as, + stride_ae, + stride_ar, + stride_ak, + stride_oq, + stride_or, + ROUTER_TOPK: tl.constexpr, + INTERMEDIATE_SIZE: tl.constexpr, + RANK: tl.constexpr, + NUM_SLOTS: tl.constexpr, + BLOCK_M: tl.constexpr, + BLOCK_K: tl.constexpr, +): + route = tl.program_id(0) + expert = tl.load(topk_ids_ptr + route).to(tl.int64) + token = route // ROUTER_TOPK + slot = tl.load(token_lora_mapping_ptr + token).to(tl.int64) + active = (slot >= 0) & (slot < NUM_SLOTS) + offs_m = tl.arange(0, BLOCK_M) + offs_r = tl.arange(0, RANK) + offs_k = tl.arange(0, BLOCK_K) + accumulator = tl.zeros((BLOCK_M, RANK), dtype=tl.float32) + + for base_k in tl.static_range(0, INTERMEDIATE_SIZE, BLOCK_K): + k = base_k + offs_k + k_mask = k < INTERMEDIATE_SIZE + x = tl.load( + activation_ptr + route * stride_xq + k[None, :] * stride_xk, + mask=(offs_m[:, None] == 0) & k_mask[None, :], + other=0.0, + ) + a = tl.load( + down_a_ptr + + slot * stride_as + + expert * stride_ae + + offs_r[None, :] * stride_ar + + k[:, None] * stride_ak, + mask=active & k_mask[:, None], + other=0.0, + ) + accumulator += tl.dot(x, a, out_dtype=tl.float32) + + tl.store( + output_ptr + + route * stride_oq + + offs_m[:, None] * 0 + + offs_r[None, :] * stride_or, + accumulator, + mask=offs_m[:, None] == 0, + ) + + +def direct_decode_gate_expand( + shared_intermediate: torch.Tensor, + gate_b: torch.Tensor, + topk_ids: torch.Tensor, + token_lora_mapping: torch.Tensor, + output: torch.Tensor, +) -> None: + """Expand shared gate/up rank vectors through directly selected experts. + + ``gate_b`` keeps the standard layout ``[S, E, 2I, 32]`` and + ``output`` is ``[M, 6, 2I]``, where I is TP-local 384 or 768. + Expert-id range is a runner invariant and is intentionally not read back + from the GPU here. + """ + + num_tokens = topk_ids.shape[0] + if shared_intermediate.ndim == 2: + shared_slot_stride = 0 + shared_token_stride = shared_intermediate.stride(0) + shared_rank_stride = shared_intermediate.stride(1) + else: + shared_slot_stride = shared_intermediate.stride(0) + shared_token_stride = shared_intermediate.stride(1) + shared_rank_stride = shared_intermediate.stride(2) + gate_width = gate_b.shape[2] + intermediate_size = gate_width // 2 + output_flat = output.view(num_tokens * ROUTER_TOPK, gate_width) + num_routes = topk_ids.numel() + _direct_gate_expand_kernel[(num_routes, triton.cdiv(gate_width, _GATE_BLOCK_N))]( + shared_intermediate, + gate_b, + topk_ids, + token_lora_mapping, + output_flat, + shared_slot_stride, + shared_token_stride, + shared_rank_stride, + gate_b.stride(0), + gate_b.stride(1), + gate_b.stride(2), + gate_b.stride(3), + output_flat.stride(0), + output_flat.stride(1), + ROUTER_TOPK=ROUTER_TOPK, + INTERMEDIATE_SIZE=intermediate_size, + RANK=RANK, + GATE_WIDTH=gate_width, + NUM_SLOTS=gate_b.shape[0], + BLOCK_M=_BLOCK_M, + BLOCK_N=_GATE_BLOCK_N, + num_warps=4, + num_stages=1, + ) + + +def direct_decode_down_shrink( + activation: torch.Tensor, + down_a: torch.Tensor, + topk_ids: torch.Tensor, + token_lora_mapping: torch.Tensor, + output: torch.Tensor, +) -> None: + """Shrink directly routed activations through per-expert LoRA-A. + + ``activation`` is flattened by route as ``[M*6, I]``; ``down_a`` keeps the + standard layout ``[S, E, 32, I]``; and ``output`` is ``[M, 6, 32]``. + """ + + num_tokens = topk_ids.shape[0] + intermediate_size = activation.shape[1] + output_flat = output.view(num_tokens * ROUTER_TOPK, RANK) + num_routes = topk_ids.numel() + _direct_down_shrink_kernel[(num_routes,)]( + activation, + down_a, + topk_ids, + token_lora_mapping, + output_flat, + activation.stride(0), + activation.stride(1), + down_a.stride(0), + down_a.stride(1), + down_a.stride(2), + down_a.stride(3), + output_flat.stride(0), + output_flat.stride(1), + ROUTER_TOPK=ROUTER_TOPK, + INTERMEDIATE_SIZE=intermediate_size, + RANK=RANK, + NUM_SLOTS=down_a.shape[0], + BLOCK_M=_BLOCK_M, + BLOCK_K=_DOWN_BLOCK_K, + num_warps=4, + num_stages=2, + ) diff --git a/python/sglang/srt/lora/marlin_lora_temp/lora_layer.py b/python/sglang/srt/lora/marlin_lora_temp/lora_layer.py new file mode 100644 index 000000000..82f454c31 --- /dev/null +++ b/python/sglang/srt/lora/marlin_lora_temp/lora_layer.py @@ -0,0 +1,64 @@ +"""LoRA hooks for the experimental Marlin MoE runner.""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import torch + +from sglang.srt.lora.marlin_lora_temp.policy import ( + validate_experimental_sgl_marlin_contract, +) + +if TYPE_CHECKING: + from sglang.srt.layers.moe.token_dispatcher import StandardCombineInput + + +def init_experimental_sgl_marlin_lora(layer, base_layer) -> None: + """Store Marlin quantization metadata on the wrapped layer.""" + from sglang.srt.layers.quantization.compressed_tensors.compressed_tensors import ( + CompressedTensorsFusedMoEMethod, + ) + from sglang.srt.layers.quantization.modelopt_quant import ( + ModelOptNvFp4FusedMoEMethod, + ) + + assert isinstance( + base_layer.quant_method, + (CompressedTensorsFusedMoEMethod, ModelOptNvFp4FusedMoEMethod), + ), ( + f"experimental_sgl_marlin requires a quant method exposing " + f"get_marlin_quant_info, got {type(base_layer.quant_method).__name__}" + ) + + quant_info = base_layer.quant_method.get_marlin_quant_info(base_layer) + weight_device = quant_info.w13_qweight.device + device_capability = ( + torch.cuda.get_device_capability(weight_device) + if weight_device.type == "cuda" + else (0, 0) + ) + validate_experimental_sgl_marlin_contract( + base_layer.moe_runner_config, + moe_ep_size=int(base_layer.moe_ep_size), + device_capability=device_capability, + ) + + layer._lora_runner = None + layer._quant_info = quant_info + + +def dispatch_experimental_sgl_marlin_lora( + dispatch_output, quant_info, base_layer, lora_info +) -> StandardCombineInput: + """Run the experimental marlin MoE-LoRA pipeline for a single layer.""" + from sglang.srt.lora.marlin_lora_temp.moe_runner import ( + fused_experts_experimental_sgl_marlin_lora, + ) + + return fused_experts_experimental_sgl_marlin_lora( + dispatch_output, + quant_info, + base_layer.moe_runner_config, + lora_info, + ) diff --git a/python/sglang/srt/lora/marlin_lora_temp/moe_runner.py b/python/sglang/srt/lora/marlin_lora_temp/moe_runner.py new file mode 100644 index 000000000..82f5ffb6a --- /dev/null +++ b/python/sglang/srt/lora/marlin_lora_temp/moe_runner.py @@ -0,0 +1,876 @@ +"""Experimental Marlin MoE-LoRA execution.""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import torch + +from sglang.srt.lora.marlin_lora_temp.policy import ( + use_post_reduce_down_delta, +) +from sglang.srt.utils import is_cuda + +if TYPE_CHECKING: + from sglang.srt.layers.moe.moe_runner.base import MoeRunnerConfig + from sglang.srt.layers.moe.moe_runner.marlin import MarlinMoeQuantInfo + from sglang.srt.layers.moe.token_dispatcher import ( + StandardCombineInput, + StandardDispatchOutput, + ) + +_is_cuda = is_cuda() + +if _is_cuda: + from sgl_kernel import silu_and_mul + + from sglang.jit_kernel.moe_wna16_marlin import moe_wna16_marlin_gemm + from sglang.kernels.ops.moe.trtllm_lora_temp.virtual_experts import ( + _align_block_size_jit as moe_align_block_size, + ) + from sglang.srt.layers.moe.fused_moe_triton.fused_marlin_moe import ( + get_scalar_type, + ) + from sglang.srt.layers.moe.moe_runner.triton_utils.fused_moe_triton_kernels import ( + moe_sum_reduce_triton, + ) + from sglang.srt.layers.quantization.marlin_utils import marlin_make_workspace + from sglang.srt.lora.marlin_lora_temp.activation import silu_and_mul_add_delta + from sglang.srt.lora.marlin_lora_temp.direct_decode import ( + direct_decode_down_shrink, + direct_decode_gate_expand, + ) + from sglang.srt.lora.marlin_lora_temp.shared_outer import ( + fused_base_mapped_shared_lora_reduce, + fused_base_shared_lora_reduce, + fused_base_shared_lora_reduce_config, + weighted_topk_rank_sum, + ) + +# Keep side-stream events alive through CUDA graph capture. +_MARLIN_LORA_OVERLAP_EVENTS: list = [] + + +def _use_shared_outer_factorization( + lora_info, num_tokens: int, router_topk: int +) -> bool: + """Whether the homogeneous shared-outer adapter can avoid top-k repetition.""" + + rank = lora_info.max_lora_rank + return bool( + num_tokens > 0 + and router_topk > 1 + and lora_info.experts_shared_outer_loras + and 0 < rank <= 64 + and lora_info.gate_up_lora_a_weights.shape[:2] == (1, 1) + and lora_info.gate_up_lora_a_weights.shape[2] == 2 * rank + and lora_info.gate_up_lora_b_weights.shape[0] == 1 + and lora_info.gate_up_lora_b_weights.shape[-1] == rank + and lora_info.down_lora_a_weights.shape[0] == 1 + and lora_info.down_lora_a_weights.shape[2] == rank + and lora_info.gate_up_lora_b_weights.shape[1] + == lora_info.down_lora_a_weights.shape[1] + and lora_info.gate_up_lora_b_weights.shape[2] + == 2 * lora_info.down_lora_a_weights.shape[3] + and lora_info.down_lora_b_weights.shape[:2] == (1, 1) + and lora_info.down_lora_b_weights.shape[2] + == lora_info.gate_up_lora_a_weights.shape[3] + and lora_info.down_lora_b_weights.shape[-1] == rank + ) + + +def _use_multi_shared_outer_decode_factorization( + lora_info, + hidden_states: torch.Tensor, + *, + num_tokens: int, + hidden_size: int, + router_topk: int, + num_experts: int, + intermediate_size: int, + ep_active: bool, +) -> bool: + """Select the B200 direct-decode factorization for two to four slots.""" + + slots = lora_info.gate_up_lora_a_weights.shape[0] + if not ( + hidden_states.is_cuda + and torch.cuda.get_device_capability(hidden_states.device) == (10, 0) + and hidden_states.dtype == torch.bfloat16 + and not ep_active + and slots >= 2 + and 0 < num_tokens <= 32 + and hidden_size == 6144 + and router_topk == 6 + and num_experts == 256 + and intermediate_size in (384, 768) + and lora_info.max_lora_rank == 32 + and lora_info.experts_shared_outer_loras + ): + return False + rank = lora_info.max_lora_rank + return bool( + lora_info.gate_up_lora_a_weights.shape[1:] == (1, 2 * rank, hidden_size) + and lora_info.gate_up_lora_b_weights.shape + == (slots, num_experts, 2 * intermediate_size, rank) + and lora_info.down_lora_a_weights.shape + == (slots, num_experts, rank, intermediate_size) + and lora_info.down_lora_b_weights.shape == (slots, 1, hidden_size, rank) + ) + + +def _use_multi_shared_outer_prefill_factorization( + lora_info, + *, + num_tokens: int, + hidden_size: int, + router_topk: int, + num_experts: int, + intermediate_size: int, + ep_active: bool, +) -> bool: + """Select top-k-collapsed shared-outer GEMMs for multi-slot prefill.""" + + slots = lora_info.gate_up_lora_a_weights.shape[0] + rank = lora_info.max_lora_rank + if not ( + slots >= 2 + and (num_tokens > 32 or ep_active) + and router_topk > 1 + and lora_info.experts_shared_outer_loras + and 0 < rank <= 64 + ): + return False + return bool( + lora_info.gate_up_lora_a_weights.shape == (slots, 1, 2 * rank, hidden_size) + and lora_info.gate_up_lora_b_weights.shape + == (slots, num_experts, 2 * intermediate_size, rank) + and lora_info.down_lora_a_weights.shape + == (slots, num_experts, rank, intermediate_size) + and lora_info.down_lora_b_weights.shape == (slots, 1, hidden_size, rank) + ) + + +def _weighted_rank_sum_block_m(num_tokens: int) -> int: + """Launch geometry for the small rank-sum reduction.""" + + return 2 if num_tokens >= 2048 else 1 + + +def _use_fused_shared_outer_tail( + lora_info, + hidden_states: torch.Tensor, + num_tokens: int, + hidden_size: int, + router_topk: int, +) -> bool: + """Restrict the fused tail to its supported geometry.""" + + if not hidden_states.is_cuda: + return False + return bool( + 0 < num_tokens <= 512 + and hidden_size == 6144 + and router_topk == 6 + and lora_info.max_lora_rank == 32 + and hidden_states.dtype == torch.bfloat16 + and torch.cuda.get_device_capability(hidden_states.device) == (10, 0) + ) + + +def _use_direct_decode_kernels( + lora_info, + *, + factored_shared_outer: bool, + fused_shared_outer_tail: bool, + ep_active: bool, + num_tokens: int, + num_experts: int, + intermediate_size: int, +) -> bool: + """Use no-sort kernels for supported Inkling decode shapes.""" + + return bool( + factored_shared_outer + and fused_shared_outer_tail + and not ep_active + and 0 < num_tokens <= 32 + and num_experts == 256 + and intermediate_size in (384, 768) + and lora_info.gate_up_lora_b_weights.shape[1] == num_experts + and lora_info.down_lora_a_weights.shape[1] == num_experts + ) + + +def fused_experts_experimental_sgl_marlin_lora( + dispatch_output: StandardDispatchOutput, + quant_info: MarlinMoeQuantInfo, + runner_config: MoeRunnerConfig, + lora_info, +) -> StandardCombineInput: + from sglang.kernels.ops.moe.trtllm_lora_temp.virtual_experts import ( + merged_experts_fused_moe_lora_add, + ) + from sglang.srt.layers.moe.token_dispatcher.standard import ( + StandardCombineInput, + StandardDispatchOutput, + ) + from sglang.srt.lora.trtllm_lora_temp import ( + get_lora_side_stream, + is_two_stream_active, + ) + from sglang.srt.lora.trtllm_lora_temp.environ import experimental_lora_enabled + from sglang.srt.model_executor.runner import get_is_capture_mode + + if not isinstance(dispatch_output, StandardDispatchOutput): + raise TypeError("experimental_sgl_marlin requires the standard MoE dispatcher") + + if not (lora_info.lora_use_virtual_experts and lora_info.max_lora_rank > 0): + raise ValueError( + "experimental_sgl_marlin LoRA requires --lora-use-virtual-experts" + ) + + hidden_states = dispatch_output.hidden_states + topk_output = dispatch_output.topk_output + topk_weights = topk_output.topk_weights + topk_ids = topk_output.topk_ids + if ( + topk_ids.ndim != 2 + or topk_weights.shape != topk_ids.shape + or topk_ids.dtype != torch.int32 + or topk_weights.dtype != torch.float32 + ): + raise ValueError( + "experimental_sgl_marlin requires contiguous int32 ids and FP32 weights" + ) + if not topk_ids.is_contiguous() or not topk_weights.is_contiguous(): + raise ValueError("experimental_sgl_marlin requires contiguous top-k tensors") + + assert runner_config.activation == "silu", "Only SiLU activation is supported." + routed_scaling_factor = runner_config.routed_scaling_factor + if routed_scaling_factor is None: + routed_scaling_factor = 1.0 + + M, K = hidden_states.shape + E = quant_info.w13_qweight.shape[0] + N = quant_info.w2_qweight.shape[1] * 16 + topk = topk_ids.shape[1] + num_bits = quant_info.weight_bits + global_experts = runner_config.num_experts or E + local_experts = runner_config.num_local_experts or E + ep_active = local_experts < global_experts + if ep_active: + assert E == local_experts, ( + f"Marlin has {E} local experts but runner_config declares " + f"{local_experts}" + ) + assert ( + lora_info.gate_up_lora_b_weights.shape[1] + == lora_info.down_lora_a_weights.shape[1] + == E + ), "EP requires locally sharded per-expert LoRA weights" + + # In eager mode skip the LoRA stages when no adapter is live; during capture + # always record them; inactive pool slots contain zero weights. + run_lora = get_is_capture_mode() or lora_info.has_active_lora + single_shared_outer = run_lora and _use_shared_outer_factorization( + lora_info, M, topk + ) + multi_shared_outer = run_lora and _use_multi_shared_outer_decode_factorization( + lora_info, + hidden_states, + num_tokens=M, + hidden_size=K, + router_topk=topk, + num_experts=E, + intermediate_size=N, + ep_active=ep_active, + ) + multi_prefill_shared_outer = ( + run_lora + and _use_multi_shared_outer_prefill_factorization( + lora_info, + num_tokens=M, + hidden_size=K, + router_topk=topk, + num_experts=E, + intermediate_size=N, + ep_active=ep_active, + ) + ) + factored_shared_outer = ( + single_shared_outer or multi_shared_outer or multi_prefill_shared_outer + ) + fused_shared_outer_tail = ( + factored_shared_outer + and not multi_prefill_shared_outer + and _use_fused_shared_outer_tail(lora_info, hidden_states, M, K, topk) + ) + direct_decode = _use_direct_decode_kernels( + lora_info, + factored_shared_outer=factored_shared_outer, + fused_shared_outer_tail=fused_shared_outer_tail, + ep_active=ep_active, + num_tokens=M, + num_experts=E, + intermediate_size=N, + ) + post_reduce_down = use_post_reduce_down_delta( + run_lora=run_lora, + routed_scaling_factor=routed_scaling_factor, + num_tokens=M, + ) + two_stream = ( + run_lora and experimental_lora_enabled() and is_two_stream_active(hidden_states) + ) + staged_down_shrink = two_stream and (factored_shared_outer or post_reduce_down) + + for block_size_m in [8, 16, 32, 48, 64]: + if M * topk / E / block_size_m < 0.9: + break + + gate_up_delta = None + lora_event = None + down_rank_sum = None + routing_cache: dict = {} + # Collapsed top-k-1 routing needs a distinct graph-replay cache. + collapsed_routing_cache: dict = {} + collapsed_topk_ids = ( + lora_info.token_lora_mapping.view(M, 1) if multi_prefill_shared_outer else None + ) + collapsed_topk_weights = topk_weights[:, :1] if multi_prefill_shared_outer else None + if run_lora: + use_direct_expand = ( + lora_info.max_lora_rank <= 64 + or lora_info.gate_up_lora_a_weights.shape[2] + != lora_info.gate_up_lora_b_weights.shape[-1] + ) + # Allocate and prewarm on main; side streams launch kernels only. + gate_up_delta = hidden_states.new_empty((M, topk, 2 * N)) + if not direct_decode: + merged_experts_fused_moe_lora_add( + output=gate_up_delta, + hidden_states=hidden_states, + lora_a=lora_info.gate_up_lora_a_weights, + lora_b=lora_info.gate_up_lora_b_weights, + topk_ids=topk_ids, + topk_weights=topk_weights, + token_lora_mapping=lora_info.token_lora_mapping, + mul_routed_weight=False, + experts_shared_outer_loras_a=lora_info.experts_shared_outer_loras, + experts_shared_outer_loras_b=False, + routing_cache=routing_cache, + stage="routing", + prewarm_a_routing=not factored_shared_outer, + local_expert_offset=0, + local_num_experts=E, + ) + if multi_prefill_shared_outer: + assert collapsed_topk_ids is not None + assert collapsed_topk_weights is not None + # Prewarm the selected shared-A route on main. + merged_experts_fused_moe_lora_add( + output=gate_up_delta, + hidden_states=hidden_states, + lora_a=lora_info.gate_up_lora_a_weights, + lora_b=lora_info.gate_up_lora_b_weights, + topk_ids=collapsed_topk_ids, + topk_weights=collapsed_topk_weights, + token_lora_mapping=lora_info.token_lora_mapping, + mul_routed_weight=False, + experts_shared_outer_loras_a=True, + experts_shared_outer_loras_b=False, + routing_cache=collapsed_routing_cache, + stage="routing", + prewarm_a_routing=True, + prewarm_b_routing=False, + local_expert_offset=0, + local_num_experts=E, + ) + gate_up_intermediate = hidden_states.new_empty( + ( + ( + lora_info.gate_up_lora_a_weights.shape[0], + M, + lora_info.gate_up_lora_a_weights.shape[2], + ) + if multi_shared_outer + else ( + (M, lora_info.gate_up_lora_a_weights.shape[2]) + if single_shared_outer or multi_prefill_shared_outer + else (M, topk, lora_info.gate_up_lora_a_weights.shape[2]) + ) + ) + ) + # Prewarm down routing and allocate its intermediate on main. + if not direct_decode: + merged_experts_fused_moe_lora_add( + output=gate_up_delta, + hidden_states=hidden_states, + lora_a=lora_info.down_lora_a_weights, + lora_b=lora_info.down_lora_b_weights, + topk_ids=topk_ids, + topk_weights=topk_weights, + token_lora_mapping=lora_info.token_lora_mapping, + mul_routed_weight=True, + experts_shared_outer_loras_a=False, + experts_shared_outer_loras_b=lora_info.experts_shared_outer_loras, + routing_cache=routing_cache, + stage="routing", + prewarm_b_routing=not factored_shared_outer, + local_expert_offset=0, + local_num_experts=E, + ) + if multi_prefill_shared_outer: + assert collapsed_topk_ids is not None + assert collapsed_topk_weights is not None + # The collapsed shared-A/B stages reuse the same top-k-1 cache. + merged_experts_fused_moe_lora_add( + output=gate_up_delta, + hidden_states=hidden_states, + lora_a=lora_info.down_lora_a_weights, + lora_b=lora_info.down_lora_b_weights, + topk_ids=collapsed_topk_ids, + topk_weights=collapsed_topk_weights, + token_lora_mapping=lora_info.token_lora_mapping, + mul_routed_weight=False, + experts_shared_outer_loras_a=False, + experts_shared_outer_loras_b=True, + routing_cache=collapsed_routing_cache, + stage="routing", + prewarm_a_routing=False, + prewarm_b_routing=True, + local_expert_offset=0, + local_num_experts=E, + ) + down_intermediate = ( + hidden_states.new_empty((M, topk, lora_info.down_lora_a_weights.shape[2])) + if staged_down_shrink or factored_shared_outer + else None + ) + if factored_shared_outer and not fused_shared_outer_tail: + down_rank_sum = hidden_states.new_empty( + (M, lora_info.down_lora_a_weights.shape[2]) + ) + + def _run_gate_up_delta(): + if factored_shared_outer: + if multi_shared_outer: + torch.matmul( + hidden_states, + lora_info.gate_up_lora_a_weights[:, 0].transpose(1, 2), + out=gate_up_intermediate, + ) + elif multi_prefill_shared_outer: + assert collapsed_topk_ids is not None + assert collapsed_topk_weights is not None + # Collapsed routing writes one selected [2R] vector per token. + merged_experts_fused_moe_lora_add( + output=gate_up_delta, + hidden_states=hidden_states, + lora_a=lora_info.gate_up_lora_a_weights, + lora_b=lora_info.gate_up_lora_b_weights, + topk_ids=collapsed_topk_ids, + topk_weights=collapsed_topk_weights, + token_lora_mapping=lora_info.token_lora_mapping, + mul_routed_weight=False, + experts_shared_outer_loras_a=True, + experts_shared_outer_loras_b=False, + routing_cache=collapsed_routing_cache, + stage="shrink", + prewarm_b_routing=False, + intermediate_buffer=gate_up_intermediate, + local_expert_offset=0, + local_num_experts=E, + ) + else: + torch.mm( + hidden_states, + lora_info.gate_up_lora_a_weights[0, 0].T, + out=gate_up_intermediate, + ) + if direct_decode: + direct_decode_gate_expand( + gate_up_intermediate, + lora_info.gate_up_lora_b_weights, + topk_ids, + lora_info.token_lora_mapping, + gate_up_delta, + ) + return + merged_experts_fused_moe_lora_add( + output=gate_up_delta, + hidden_states=hidden_states, + lora_a=lora_info.gate_up_lora_a_weights, + lora_b=lora_info.gate_up_lora_b_weights, + topk_ids=topk_ids, + topk_weights=topk_weights, + token_lora_mapping=lora_info.token_lora_mapping, + mul_routed_weight=False, + experts_shared_outer_loras_a=True, + experts_shared_outer_loras_b=False, + routing_cache=routing_cache, + fuse_add_to_output=False, + use_direct_expand_add=True, + local_expert_offset=0, + local_num_experts=E, + stage="expand", + intermediate_buffer=gate_up_intermediate, + broadcast_intermediate=True, + ) + return + merged_experts_fused_moe_lora_add( + output=gate_up_delta, + hidden_states=hidden_states, + lora_a=lora_info.gate_up_lora_a_weights, + lora_b=lora_info.gate_up_lora_b_weights, + topk_ids=topk_ids, + topk_weights=topk_weights, + token_lora_mapping=lora_info.token_lora_mapping, + mul_routed_weight=False, + experts_shared_outer_loras_a=lora_info.experts_shared_outer_loras, + experts_shared_outer_loras_b=False, + routing_cache=routing_cache, + fuse_add_to_output=False, + use_direct_expand_add=use_direct_expand, + local_expert_offset=0, + local_num_experts=E, + intermediate_buffer=gate_up_intermediate, + ) + + if two_stream: + lora_event = torch.cuda.Event() + side_stream = get_lora_side_stream() + side_stream.wait_stream(torch.cuda.current_stream()) + with torch.cuda.stream(side_stream): + _run_gate_up_delta() + lora_event.record() + if torch.cuda.is_current_stream_capturing(): + _MARLIN_LORA_OVERLAP_EVENTS.append(lora_event) + else: + _run_gate_up_delta() + + # Start alignment after the side-stream fork. EP IDs are already localized; + # the wrapper owns Marlin's extra sentinel bucket. + sorted_token_ids, expert_ids, num_tokens_post_padded = moe_align_block_size( + topk_ids, block_size_m, E + ) + + # A cached workspace can outlive its CUDA graph-private pool. + workspace = marlin_make_workspace(hidden_states.device, max_blocks_per_sm=4) + + scalar_type1 = get_scalar_type( + num_bits, + quant_info.w13_qzeros is not None, + quant_info.w13_scales, + quant_info.w13_global_scale, + ) + scalar_type2 = get_scalar_type( + num_bits, + quant_info.w2_qzeros is not None, + quant_info.w2_scales, + quant_info.w2_global_scale, + ) + + # Stage 1: gate_up (marlin) — concurrent with the side-stream delta above. + intermediate_cache1 = torch.empty( + (M * topk, 2 * N), device=hidden_states.device, dtype=hidden_states.dtype + ) + intermediate_cache1 = moe_wna16_marlin_gemm( + hidden_states, + intermediate_cache1, + quant_info.w13_qweight, + quant_info.w13_bias, + quant_info.w13_scales, + quant_info.w13_global_scale, + quant_info.w13_qzeros, + quant_info.w13_g_idx, + quant_info.w13_g_idx_sort_indices, + workspace, + sorted_token_ids, + expert_ids, + num_tokens_post_padded, + topk_weights, + moe_block_size=block_size_m, + top_k=topk, + mul_topk_weights=False, + is_ep=ep_active, + b_q_type=scalar_type1, + size_m=M, + size_n=2 * N, + size_k=K, + is_k_full=quant_info.is_k_full, + use_atomic_add=True, + use_fp32_reduce=True, + is_zp_float=False, + ) + + # Stage 2: activation, with the gate_up delta folded in. + intermediate_cache2 = torch.empty( + (M * topk, N), device=hidden_states.device, dtype=hidden_states.dtype + ) + down_shrink_done = None + if run_lora: + if lora_event is not None: + torch.cuda.current_stream().wait_event(lora_event) + silu_and_mul_add_delta( + intermediate_cache1.view(-1, 2 * N), + gate_up_delta.view(-1, 2 * N), + intermediate_cache2, + ) + if staged_down_shrink: + # Overlap down shrink with the Marlin down GEMM and base reduction. + act_done = torch.cuda.Event() + act_done.record() + down_shrink_done = torch.cuda.Event() + side_stream = get_lora_side_stream() + side_stream.wait_event(act_done) + assert down_intermediate is not None + with torch.cuda.stream(side_stream): + if factored_shared_outer and not direct_decode: + # Buffers were allocated on main for stable graph ownership. + down_intermediate.zero_() + # Reuse an output tensor because shrink does not consume it. + if direct_decode: + direct_decode_down_shrink( + intermediate_cache2, + lora_info.down_lora_a_weights, + topk_ids, + lora_info.token_lora_mapping, + down_intermediate, + ) + else: + merged_experts_fused_moe_lora_add( + output=intermediate_cache2, + hidden_states=intermediate_cache2, + lora_a=lora_info.down_lora_a_weights, + lora_b=lora_info.down_lora_b_weights, + topk_ids=topk_ids, + topk_weights=topk_weights, + token_lora_mapping=lora_info.token_lora_mapping, + mul_routed_weight=True, + experts_shared_outer_loras_a=False, + experts_shared_outer_loras_b=lora_info.experts_shared_outer_loras, + routing_cache=routing_cache, + stage="shrink", + prewarm_b_routing=not factored_shared_outer, + intermediate_buffer=down_intermediate, + local_expert_offset=0, + local_num_experts=E, + zero_intermediate=( + ep_active + and lora_info.experts_shared_outer_loras + and not factored_shared_outer + ), + ) + if factored_shared_outer and not fused_shared_outer_tail: + assert down_rank_sum is not None + weighted_topk_rank_sum( + down_intermediate, + topk_weights, + down_rank_sum, + routed_scaling_factor, + block_m=_weighted_rank_sum_block_m(M), + ) + down_shrink_done.record() + if torch.cuda.is_current_stream_capturing(): + _MARLIN_LORA_OVERLAP_EVENTS.append(act_done) + _MARLIN_LORA_OVERLAP_EVENTS.append(down_shrink_done) + else: + silu_and_mul(intermediate_cache1.view(-1, 2 * N), intermediate_cache2) + + # Stage 3: down (marlin). + intermediate_cache3 = torch.empty( + (M * topk, K), device=hidden_states.device, dtype=hidden_states.dtype + ) + if ep_active: + intermediate_cache3.zero_() + + intermediate_cache3 = moe_wna16_marlin_gemm( + intermediate_cache2, + intermediate_cache3, + quant_info.w2_qweight, + quant_info.w2_bias, + quant_info.w2_scales, + quant_info.w2_global_scale, + quant_info.w2_qzeros, + quant_info.w2_g_idx, + quant_info.w2_g_idx_sort_indices, + workspace, + sorted_token_ids, + expert_ids, + num_tokens_post_padded, + topk_weights, + moe_block_size=block_size_m, + top_k=1, + mul_topk_weights=True, + is_ep=ep_active, + b_q_type=scalar_type2, + size_m=M * topk, + size_n=K, + size_k=N, + is_k_full=quant_info.is_k_full, + use_atomic_add=True, + use_fp32_reduce=True, + is_zp_float=False, + ) + intermediate_cache3 = intermediate_cache3.view(M, topk, K) + + if factored_shared_outer: + assert down_intermediate is not None + if down_shrink_done is None: + if direct_decode: + direct_decode_down_shrink( + intermediate_cache2, + lora_info.down_lora_a_weights, + topk_ids, + lora_info.token_lora_mapping, + down_intermediate, + ) + else: + down_intermediate.zero_() + merged_experts_fused_moe_lora_add( + output=intermediate_cache3, + hidden_states=intermediate_cache2, + lora_a=lora_info.down_lora_a_weights, + lora_b=lora_info.down_lora_b_weights, + topk_ids=topk_ids, + topk_weights=topk_weights, + token_lora_mapping=lora_info.token_lora_mapping, + mul_routed_weight=True, + experts_shared_outer_loras_a=False, + experts_shared_outer_loras_b=True, + routing_cache=routing_cache, + stage="shrink", + prewarm_b_routing=False, + intermediate_buffer=down_intermediate, + local_expert_offset=0, + local_num_experts=E, + ) + + # The post-reduce delta wins for decode; larger batches and non-unit routing + # scales keep the stock pre-reduce path. + if run_lora and not post_reduce_down and not factored_shared_outer: + merged_experts_fused_moe_lora_add( + output=intermediate_cache3, + hidden_states=intermediate_cache2, + lora_a=lora_info.down_lora_a_weights, + lora_b=lora_info.down_lora_b_weights, + topk_ids=topk_ids, + topk_weights=topk_weights, + token_lora_mapping=lora_info.token_lora_mapping, + mul_routed_weight=True, + experts_shared_outer_loras_a=False, + experts_shared_outer_loras_b=lora_info.experts_shared_outer_loras, + routing_cache=routing_cache, + local_expert_offset=0, + local_num_experts=E, + zero_intermediate=(ep_active and lora_info.experts_shared_outer_loras), + ) + + # Never alias hidden_states: the shared sink still reads it. The fused B200 + # tail wins through M=512; larger batches keep the standard reducer. + output = torch.empty_like(hidden_states) + if fused_shared_outer_tail: + assert down_intermediate is not None + if down_shrink_done is not None: + torch.cuda.current_stream().wait_event(down_shrink_done) + if multi_shared_outer: + fused_base_mapped_shared_lora_reduce( + intermediate_cache3, + down_intermediate, + topk_weights, + lora_info.down_lora_b_weights, + lora_info.token_lora_mapping, + output, + routed_scaling_factor, + block_k=64, + ) + else: + tail_block_m, tail_block_k = fused_base_shared_lora_reduce_config(M) + fused_base_shared_lora_reduce( + intermediate_cache3, + down_intermediate, + topk_weights, + lora_info.down_lora_b_weights[0, 0], + output, + routed_scaling_factor, + block_m=tail_block_m, + block_k=tail_block_k, + ) + elif routed_scaling_factor == 1.0 and M <= 512: + torch.sum(intermediate_cache3, dim=1, out=output) + else: + moe_sum_reduce_triton(intermediate_cache3, output, routed_scaling_factor) + + if factored_shared_outer and not fused_shared_outer_tail: + assert down_rank_sum is not None + assert down_intermediate is not None + if down_shrink_done is None: + weighted_topk_rank_sum( + down_intermediate, + topk_weights, + down_rank_sum, + routed_scaling_factor, + block_m=_weighted_rank_sum_block_m(M), + ) + else: + # Wait only at the shared-B consumer. + torch.cuda.current_stream().wait_event(down_shrink_done) + if multi_prefill_shared_outer: + assert collapsed_topk_ids is not None + assert collapsed_topk_weights is not None + # Route the weighted rank sum once per token; -1 rows stay base-only. + merged_experts_fused_moe_lora_add( + output=output, + hidden_states=down_rank_sum, + lora_a=lora_info.down_lora_a_weights, + lora_b=lora_info.down_lora_b_weights, + topk_ids=collapsed_topk_ids, + topk_weights=collapsed_topk_weights, + token_lora_mapping=lora_info.token_lora_mapping, + mul_routed_weight=False, + experts_shared_outer_loras_a=False, + experts_shared_outer_loras_b=True, + routing_cache=collapsed_routing_cache, + fuse_add_to_output=True, + use_direct_expand_add=False, + local_expert_offset=0, + local_num_experts=E, + stage="expand", + intermediate_buffer=down_rank_sum, + ) + else: + output.addmm_(down_rank_sum, lora_info.down_lora_b_weights[0, 0].T) + elif post_reduce_down and not factored_shared_outer: + if down_shrink_done is not None: + torch.cuda.current_stream().wait_event(down_shrink_done) + merged_experts_fused_moe_lora_add( + output=output, + hidden_states=intermediate_cache2, + lora_a=lora_info.down_lora_a_weights, + lora_b=lora_info.down_lora_b_weights, + topk_ids=topk_ids, + topk_weights=topk_weights, + token_lora_mapping=lora_info.token_lora_mapping, + mul_routed_weight=True, + experts_shared_outer_loras_a=False, + experts_shared_outer_loras_b=lora_info.experts_shared_outer_loras, + routing_cache=routing_cache, + fuse_add_to_output=False, + fuse_sum_all_reduce=True, + use_direct_expand_add=lora_info.max_lora_rank <= 64, + local_expert_offset=0, + local_num_experts=E, + stage="expand" if down_shrink_done is not None else "all", + intermediate_buffer=( + down_intermediate if down_shrink_done is not None else None + ), + zero_intermediate=( + ep_active + and lora_info.experts_shared_outer_loras + and down_shrink_done is None + ), + ) + + return StandardCombineInput(hidden_states=output) diff --git a/python/sglang/srt/lora/marlin_lora_temp/policy.py b/python/sglang/srt/lora/marlin_lora_temp/policy.py new file mode 100644 index 000000000..03a9eead2 --- /dev/null +++ b/python/sglang/srt/lora/marlin_lora_temp/policy.py @@ -0,0 +1,109 @@ +"""Static correctness policy for the experimental Marlin MoE-LoRA path. + +Keep these checks free of CUDA imports so they can be covered by CPU-only unit +tests. The backend deliberately supports a narrow configuration: widening it +requires implementing the corresponding Marlin activation/EP semantics first. +""" + +from __future__ import annotations + +from typing import Any + + +def validate_experimental_sgl_marlin_server_args( + server_args: Any, resolved_args: Any +) -> None: + """Validate startup options before the experimental runner is constructed.""" + + if resolved_args.ep_size > 1 and resolved_args.moe_a2a_backend != "none": + raise ValueError("experimental_sgl_marlin EP requires --moe-a2a-backend none") + + # A provided adapter path implicitly enables LoRA later unless it was + # explicitly disabled. No-LoRA delegates to the stock Marlin fused path. + lora_enabled = bool(server_args.enable_lora) or ( + server_args.enable_lora is None and bool(server_args.lora_paths) + ) + if not lora_enabled: + return + + if not server_args.lora_use_virtual_experts: + raise ValueError( + "experimental_sgl_marlin LoRA requires --lora-use-virtual-experts" + ) + if server_args.lora_backend != "triton": + # The temporary dense/sink kernels consume Triton SGEMM batch metadata + # directly; other global backends are not adapted in this tree. + raise ValueError("experimental_sgl_marlin LoRA requires --lora-backend triton") + if resolved_args.ep_size <= 1: + return + + if ( + server_args.init_expert_location != "trivial" + or server_args.ep_num_redundant_experts != 0 + or server_args.enable_eplb + or server_args.elastic_ep_backend is not None + or server_args.enable_elastic_expert_backup + or server_args.elastic_ep_rejoin + ): + raise ValueError( + "experimental_sgl_marlin EP requires trivial expert placement " + "without redundancy, EPLB, or elastic EP" + ) + + +def validate_experimental_sgl_marlin_contract( + runner_config: Any, + *, + moe_ep_size: int, + device_capability: tuple[int, int], +) -> None: + """Fail before capture when the specialized pipeline would change semantics.""" + + errors: list[str] = [] + + if runner_config.activation != "silu": + errors.append(f"activation must be 'silu', got {runner_config.activation!r}") + if not runner_config.is_gated: + errors.append("only gated SwiGLU MoE is supported") + if runner_config.gemm1_alpha is not None: + errors.append("gemm1_alpha is not supported") + if runner_config.gemm1_clamp_limit is not None: + errors.append("gemm1_clamp_limit is not supported") + if runner_config.swiglu_limit is not None: + errors.append("swiglu_limit is not supported") + if runner_config.apply_router_weight_on_input: + errors.append("apply_router_weight_on_input must be false") + if runner_config.no_combine: + errors.append("no_combine must be false") + + num_experts = runner_config.num_experts + num_local_experts = runner_config.num_local_experts + if moe_ep_size < 1: + errors.append(f"moe_ep_size must be positive, got {moe_ep_size}") + elif num_experts is not None and num_local_experts is not None: + if num_experts % moe_ep_size != 0 or num_local_experts != ( + num_experts // moe_ep_size + ): + errors.append( + "num_local_experts must equal num_experts / moe_ep_size, got " + f"{num_local_experts}, {num_experts}, and {moe_ep_size}" + ) + + if device_capability[0] < 9: + errors.append( + "CUDA compute capability 9.0 or newer is required, " + f"got {device_capability[0]}.{device_capability[1]}" + ) + + if errors: + raise ValueError( + "experimental_sgl_marlin configuration is unsupported: " + "; ".join(errors) + ) + + +def use_post_reduce_down_delta( + *, run_lora: bool, routed_scaling_factor: float, num_tokens: int +) -> bool: + """Whether the down delta may be accumulated after the base top-k reduce.""" + + return run_lora and routed_scaling_factor == 1.0 and num_tokens <= 2048 diff --git a/python/sglang/srt/lora/marlin_lora_temp/sgl_backend.py b/python/sglang/srt/lora/marlin_lora_temp/sgl_backend.py new file mode 100644 index 000000000..82f74c02d --- /dev/null +++ b/python/sglang/srt/lora/marlin_lora_temp/sgl_backend.py @@ -0,0 +1,12 @@ +"""Register the no-LoRA alias for ``experimental_sgl_marlin``.""" + +from sglang.srt.layers.moe.moe_runner.base import register_fused_func + + +@register_fused_func("none", "experimental_sgl_marlin") +def fused_experts_none_to_experimental_sgl_marlin( + dispatch_output, quant_info, runner_config +): + from sglang.srt.layers.moe.moe_runner.marlin import fused_experts_none_to_marlin + + return fused_experts_none_to_marlin(dispatch_output, quant_info, runner_config) diff --git a/python/sglang/srt/lora/marlin_lora_temp/shared_outer.py b/python/sglang/srt/lora/marlin_lora_temp/shared_outer.py new file mode 100644 index 000000000..5ea45ccd7 --- /dev/null +++ b/python/sglang/srt/lora/marlin_lora_temp/shared_outer.py @@ -0,0 +1,467 @@ +"""Shared-outer reduction primitives for Marlin MoE LoRA. + +For Inkling adapters, gate/up LoRA-A and down LoRA-B are shared by every +routed expert. The gate shrink can therefore run once per token, while the +down rank vectors can be gamma-weighted and summed before the shared expand. +""" + +from __future__ import annotations + +import torch +import triton +import triton.language as tl + + +@triton.jit +def _weighted_topk_rank_sum_kernel( + routed_rank_ptr, # [M, topk, R] + topk_weights_ptr, # [M, topk] + output_ptr, # [M, R] + M, + R, + scale, + stride_rm, + stride_rk, + stride_rr, + stride_wm, + stride_wk, + stride_om, + stride_or, + TOPK: tl.constexpr, + BLOCK_M: tl.constexpr, + BLOCK_R: tl.constexpr, +): + token = tl.program_id(0) * BLOCK_M + tl.arange(0, BLOCK_M) + rank_block = tl.program_id(1) + rank = rank_block * BLOCK_R + tl.arange(0, BLOCK_R) + token_mask = token < M + rank_mask = rank < R + mask = token_mask[:, None] & rank_mask[None, :] + + accumulator = tl.zeros((BLOCK_M, BLOCK_R), dtype=tl.float32) + for k in tl.static_range(TOPK): + routed_rank = tl.load( + routed_rank_ptr + + token[:, None] * stride_rm + + k * stride_rk + + rank[None, :] * stride_rr, + mask=mask, + other=0.0, + ).to(tl.float32) + weight = tl.load( + topk_weights_ptr + token * stride_wm + k * stride_wk, + mask=token_mask, + other=0.0, + ).to(tl.float32) + accumulator += routed_rank * weight[:, None] + + accumulator *= scale + tl.store( + output_ptr + token[:, None] * stride_om + rank[None, :] * stride_or, + accumulator, + mask=mask, + ) + + +def weighted_topk_rank_sum( + routed_rank: torch.Tensor, + topk_weights: torch.Tensor, + output: torch.Tensor, + routed_scaling_factor: float, + *, + block_m: int, +) -> None: + """Compute ``output[m] = scale * sum_k(gamma[m,k] * rank[m,k])``.""" + + assert routed_rank.ndim == 3 + assert topk_weights.shape == routed_rank.shape[:2] + assert output.shape == (routed_rank.shape[0], routed_rank.shape[2]) + assert routed_rank.is_contiguous() and topk_weights.is_contiguous() + assert output.is_contiguous() + if routed_rank.shape[0] == 0: + return + + rank = routed_rank.shape[2] + assert block_m in (1, 2) + block_rank = max(16, triton.next_power_of_2(rank)) + _weighted_topk_rank_sum_kernel[ + (triton.cdiv(routed_rank.shape[0], block_m), triton.cdiv(rank, block_rank)) + ]( + routed_rank, + topk_weights, + output, + routed_rank.shape[0], + rank, + routed_scaling_factor, + routed_rank.stride(0), + routed_rank.stride(1), + routed_rank.stride(2), + topk_weights.stride(0), + topk_weights.stride(1), + output.stride(0), + output.stride(1), + TOPK=routed_rank.shape[1], + BLOCK_M=block_m, + BLOCK_R=block_rank, + num_warps=1, + ) + + +@triton.jit +def _fused_base_shared_lora_reduce_kernel( + routed_base_ptr, # [M, topk, K], already weighted by the base MoE + routed_rank_ptr, # [M, topk, R] + topk_weights_ptr, # [M, topk] + shared_b_ptr, # [K, R] + output_ptr, # [M, K] + M, + K, + R, + scale, + stride_bm, + stride_bk, + stride_bn, + stride_rm, + stride_rk, + stride_rr, + stride_wm, + stride_wk, + stride_sk, + stride_sr, + stride_om, + stride_ok, + TOPK: tl.constexpr, + BLOCK_M: tl.constexpr, + BLOCK_K: tl.constexpr, + BLOCK_R: tl.constexpr, +): + """Fuse base top-k reduction with the shared-B LoRA decode tail.""" + + token = tl.program_id(0) * BLOCK_M + tl.arange(0, BLOCK_M) + out_col = tl.program_id(1) * BLOCK_K + tl.arange(0, BLOCK_K) + rank = tl.arange(0, BLOCK_R) + token_mask = token < M + out_mask = out_col < K + rank_mask = rank < R + + base_acc = tl.zeros((BLOCK_M, BLOCK_K), dtype=tl.float32) + rank_acc = tl.zeros((BLOCK_M, BLOCK_R), dtype=tl.float32) + for topk_idx in tl.static_range(TOPK): + base = tl.load( + routed_base_ptr + + token[:, None] * stride_bm + + topk_idx * stride_bk + + out_col[None, :] * stride_bn, + mask=token_mask[:, None] & out_mask[None, :], + other=0.0, + ).to(tl.float32) + routed_rank = tl.load( + routed_rank_ptr + + token[:, None] * stride_rm + + topk_idx * stride_rk + + rank[None, :] * stride_rr, + mask=token_mask[:, None] & rank_mask[None, :], + other=0.0, + ).to(tl.float32) + weight = tl.load( + topk_weights_ptr + token * stride_wm + topk_idx * stride_wk, + mask=token_mask, + other=0.0, + ).to(tl.float32) + base_acc += base + rank_acc += routed_rank * weight[:, None] + + # Match the existing path's BF16 rank-sum materialization before the + # tensor-core shared-B GEMM. This also keeps the dot operands type-aligned. + scaled_rank = (rank_acc * scale).to(shared_b_ptr.dtype.element_ty) + shared_b = tl.load( + shared_b_ptr + out_col[None, :] * stride_sk + rank[:, None] * stride_sr, + mask=out_mask[None, :] & rank_mask[:, None], + other=0.0, + ) + lora_acc = tl.dot(scaled_rank, shared_b, out_dtype=tl.float32) + # The existing reducer materializes the scaled base sum in the BF16 output + # before cuBLAS addmm reads it back. Preserve that rounding point so fusion + # changes launch structure, not the numerical contract. + scaled_base = (base_acc * scale).to(output_ptr.dtype.element_ty).to(tl.float32) + result = scaled_base + lora_acc + tl.store( + output_ptr + token[:, None] * stride_om + out_col[None, :] * stride_ok, + result, + mask=token_mask[:, None] & out_mask[None, :], + ) + + +def fused_base_shared_lora_reduce( + routed_base: torch.Tensor, + routed_rank: torch.Tensor, + topk_weights: torch.Tensor, + shared_b: torch.Tensor, + output: torch.Tensor, + routed_scaling_factor: float, + *, + block_m: int, + block_k: int, +) -> None: + """Reduce base experts and add a factored shared-B LoRA delta in one pass. + + This is intended for decode-sized batches, where replacing three + latency-bound launches is more important than maximizing the standalone + shared-B GEMM throughput. ``routed_base`` must already contain the router + weights, matching Marlin's ``mul_topk_weights=True`` output. + """ + + assert routed_base.ndim == 3 and routed_rank.ndim == 3 + assert routed_base.shape[:2] == routed_rank.shape[:2] == topk_weights.shape + assert shared_b.shape == (routed_base.shape[2], routed_rank.shape[2]) + assert output.shape == (routed_base.shape[0], routed_base.shape[2]) + assert 0 < routed_rank.shape[2] <= 64 + assert routed_base.is_cuda + assert ( + routed_base.device + == routed_rank.device + == topk_weights.device + == shared_b.device + == output.device + ) + assert routed_base.dtype in (torch.bfloat16, torch.float16) + assert routed_base.dtype == routed_rank.dtype == shared_b.dtype == output.dtype + assert topk_weights.dtype == torch.float32 + assert routed_base.is_contiguous() and routed_rank.is_contiguous() + assert topk_weights.is_contiguous() and shared_b.is_contiguous() + assert output.is_contiguous() + assert all( + output.data_ptr() != operand.data_ptr() + for operand in (routed_base, routed_rank, topk_weights, shared_b) + ), "fused shared-outer output must not alias an input" + if routed_base.shape[0] == 0: + return + + rank = routed_rank.shape[2] + assert block_m in (1, 2, 4, 8) + assert block_k in (32, 64, 128) + block_rank = max(16, triton.next_power_of_2(rank)) + _fused_base_shared_lora_reduce_kernel[ + ( + triton.cdiv(routed_base.shape[0], block_m), + triton.cdiv(routed_base.shape[2], block_k), + ) + ]( + routed_base, + routed_rank, + topk_weights, + shared_b, + output, + routed_base.shape[0], + routed_base.shape[2], + rank, + routed_scaling_factor, + routed_base.stride(0), + routed_base.stride(1), + routed_base.stride(2), + routed_rank.stride(0), + routed_rank.stride(1), + routed_rank.stride(2), + topk_weights.stride(0), + topk_weights.stride(1), + shared_b.stride(0), + shared_b.stride(1), + output.stride(0), + output.stride(1), + TOPK=routed_base.shape[1], + BLOCK_M=block_m, + BLOCK_K=block_k, + BLOCK_R=block_rank, + num_warps=4 if block_k == 128 else 2, + num_stages=1, + ) + + +# Keep a token-mapped kernel separate from the block-M single-slot kernel: the +# Decode uses a separate launch geometry. +@triton.jit +def _fused_base_mapped_shared_lora_reduce_kernel( + routed_base_ptr, + routed_rank_ptr, + topk_weights_ptr, + shared_b_ptr, # [S, K, R] + token_lora_mapping_ptr, + output_ptr, + K, + R, + scale, + stride_bm, + stride_bk, + stride_bn, + stride_rm, + stride_rk, + stride_rr, + stride_wm, + stride_wk, + stride_ss, + stride_sk, + stride_sr, + stride_om, + stride_ok, + TOPK: tl.constexpr, + NUM_SLOTS: tl.constexpr, + BLOCK_K: tl.constexpr, + BLOCK_R: tl.constexpr, +): + token = tl.program_id(0) + out_col = tl.program_id(1) * BLOCK_K + tl.arange(0, BLOCK_K) + rank = tl.arange(0, BLOCK_R) + out_mask = out_col < K + rank_mask = rank < R + + base_acc = tl.zeros((1, BLOCK_K), dtype=tl.float32) + rank_acc = tl.zeros((1, BLOCK_R), dtype=tl.float32) + for topk_idx in tl.static_range(TOPK): + base = tl.load( + routed_base_ptr + + token * stride_bm + + topk_idx * stride_bk + + out_col[None, :] * stride_bn, + mask=out_mask[None, :], + other=0.0, + ).to(tl.float32) + routed_rank = tl.load( + routed_rank_ptr + + token * stride_rm + + topk_idx * stride_rk + + rank[None, :] * stride_rr, + mask=rank_mask[None, :], + other=0.0, + ).to(tl.float32) + weight = tl.load(topk_weights_ptr + token * stride_wm + topk_idx * stride_wk) + base_acc += base + rank_acc += routed_rank * weight + + slot = tl.load(token_lora_mapping_ptr + token).to(tl.int64) + active = (slot >= 0) & (slot < NUM_SLOTS) + scaled_rank = (rank_acc * scale).to(shared_b_ptr.dtype.element_ty) + shared_b = tl.load( + shared_b_ptr + + slot * stride_ss + + out_col[None, :] * stride_sk + + rank[:, None] * stride_sr, + mask=active & out_mask[None, :] & rank_mask[:, None], + other=0.0, + ) + lora_acc = tl.dot(scaled_rank, shared_b, out_dtype=tl.float32) + scaled_base = (base_acc * scale).to(output_ptr.dtype.element_ty).to(tl.float32) + tl.store( + output_ptr + token * stride_om + out_col[None, :] * stride_ok, + scaled_base + lora_acc, + mask=out_mask[None, :], + ) + + +def fused_base_mapped_shared_lora_reduce( + routed_base: torch.Tensor, + routed_rank: torch.Tensor, + topk_weights: torch.Tensor, + shared_b: torch.Tensor, + token_lora_mapping: torch.Tensor, + output: torch.Tensor, + routed_scaling_factor: float, + *, + block_k: int, +) -> None: + """Fused decode tail with a per-token shared-B adapter slot.""" + + assert routed_base.ndim == routed_rank.ndim == 3 + assert routed_base.shape[:2] == routed_rank.shape[:2] == topk_weights.shape + assert shared_b.ndim == 4 and shared_b.shape[1] == 1 + assert shared_b.shape[0] >= 2 + shared_b_view = shared_b[:, 0] + assert shared_b_view.shape[1:] == (routed_base.shape[2], routed_rank.shape[2]) + assert token_lora_mapping.shape == (routed_base.shape[0],) + assert token_lora_mapping.dtype == torch.int32 + assert output.shape == (routed_base.shape[0], routed_base.shape[2]) + assert 0 < routed_rank.shape[2] <= 64 + assert routed_base.dtype in (torch.bfloat16, torch.float16) + assert routed_base.dtype == routed_rank.dtype == shared_b.dtype == output.dtype + assert topk_weights.dtype == torch.float32 + assert all( + tensor.device == routed_base.device + for tensor in ( + routed_rank, + topk_weights, + shared_b, + token_lora_mapping, + output, + ) + ) + assert all( + tensor.is_cuda and tensor.is_contiguous() + for tensor in ( + routed_base, + routed_rank, + topk_weights, + shared_b, + token_lora_mapping, + output, + ) + ) + assert all( + output.data_ptr() != operand.data_ptr() + for operand in ( + routed_base, + routed_rank, + topk_weights, + shared_b, + token_lora_mapping, + ) + ), "mapped shared-outer output must not alias an input" + if routed_base.shape[0] == 0: + return + + assert block_k in (32, 64, 128) + block_rank = max(16, triton.next_power_of_2(routed_rank.shape[2])) + _fused_base_mapped_shared_lora_reduce_kernel[ + (routed_base.shape[0], triton.cdiv(routed_base.shape[2], block_k)) + ]( + routed_base, + routed_rank, + topk_weights, + shared_b_view, + token_lora_mapping, + output, + routed_base.shape[2], + routed_rank.shape[2], + routed_scaling_factor, + routed_base.stride(0), + routed_base.stride(1), + routed_base.stride(2), + routed_rank.stride(0), + routed_rank.stride(1), + routed_rank.stride(2), + topk_weights.stride(0), + topk_weights.stride(1), + shared_b_view.stride(0), + shared_b_view.stride(1), + shared_b_view.stride(2), + output.stride(0), + output.stride(1), + TOPK=routed_base.shape[1], + NUM_SLOTS=shared_b.shape[0], + BLOCK_K=block_k, + BLOCK_R=block_rank, + num_warps=4 if block_k == 128 else 2, + num_stages=1, + ) + + +def fused_base_shared_lora_reduce_config(num_tokens: int) -> tuple[int, int]: + """Return the tuned B200 launch geometry for M in ``[1, 512]``.""" + + if not 0 < num_tokens <= 512: + raise ValueError( + f"fused shared-outer tail expects M in [1, 512], got {num_tokens}" + ) + if num_tokens <= 2: + return 1, 64 if num_tokens == 1 else 32 + if num_tokens <= 4: + return 4, 32 + return 8, 64 if num_tokens <= 64 else 128 diff --git a/python/sglang/srt/lora/mem_pool.py b/python/sglang/srt/lora/mem_pool.py index 4b475c6ae..1a09e0385 100644 --- a/python/sglang/srt/lora/mem_pool.py +++ b/python/sglang/srt/lora/mem_pool.py @@ -110,8 +110,7 @@ def _get_moe_tp_context() -> Tuple[int, int]: def _moe_runner_keeps_global_expert_ids() -> bool: - """True if the active MoE runner keeps global `topk_ids` instead of - remapping to local IDs. Mirrors the predicate in `StandardDispatcher`.""" + """True if a supported LoRA runner keeps global expert IDs.""" try: from sglang.srt.layers.moe.utils import get_moe_runner_backend @@ -119,8 +118,9 @@ def _moe_runner_keeps_global_expert_ids() -> bool: return ( b.is_flashinfer_cutlass() or b.is_flashinfer_cutedsl() - or b.is_experimental_sgl_trtllm() + or b.is_flashinfer_trtllm() or b.is_flashinfer_trtllm_routed() + or b.is_flashinfer_mxfp4() ) except Exception: # pragma: no cover - backend not initialized return False @@ -162,7 +162,7 @@ class LoRAMemoryPool: # Under EP with a Triton/DeepGEMM runner, `StandardDispatcher` remaps # global `topk_ids` -> local expert IDs before the MoE kernel, so # per-expert LoRA buffers must be sized and keyed by the local slice. - # FlashInfer CUTLASS/CuteDSL/TRTLLM-routed keep global IDs, and an + # FlashInfer CUTLASS/CuteDSL/TRTLLM/MXFP4 keep global IDs, and an # uneven expert split (`num_experts % moe_ep_size != 0`, shouldn't # happen in practice) is also treated as globally-keyed so we don't # silently truncate experts. @@ -179,8 +179,8 @@ class LoRAMemoryPool: # e.g. `--tp 4 --ep 4` each rank holds full-width expert weights # (`moe_tp_size == 1`). Sizing per-expert LoRA buffers by `tp_size` # here would yield a 4x-narrower inner dim than the adapter weight - # (which `FusedMoEWithLoRA.slice_moe_lora_{a,b}_weights` correctly - # skip-slices when `moe_tp_size <= 1`), producing a shape-mismatch + # (which MoE LoRA modules correctly skip-slice when + # `moe_tp_size <= 1`), producing a shape-mismatch # assert during weight load. Non-MoE modules still shard by # `tp_size` because attention TP is unchanged. self.moe_tp_size, self.moe_tp_rank = _get_moe_tp_context() @@ -254,6 +254,11 @@ class LoRAMemoryPool: """Check if module is part of MoE experts.""" return "moe" in module_name + @staticmethod + def is_shared_moe_module(module_name: str) -> bool: + """Whether this buffer belongs to the shared-expert MoE namespace.""" + return module_name.endswith("_shared_moe") + @staticmethod def _get_num_experts(base_model: torch.nn.Module) -> int: cfg = base_model.config @@ -266,6 +271,20 @@ class LoRAMemoryPool: or 1 ) + @staticmethod + def _get_num_shared_experts(base_model: torch.nn.Module) -> int: + cfg = base_model.config + if hasattr(cfg, "get_text_config"): + cfg = cfg.get_text_config() + return getattr(cfg, "n_shared_experts", 0) or 1 + + @staticmethod + def _has_shared_fused_moe(base_model: torch.nn.Module) -> bool: + """Whether shared experts require their own MoE LoRA pool namespace.""" + return any( + getattr(m, "is_shared_fused_moe", False) for m in base_model.modules() + ) + @staticmethod def _has_moe_module(base_model: torch.nn.Module) -> bool: # Config-only detection isn't reliable: some dense configs (e.g. @@ -298,14 +317,18 @@ class LoRAMemoryPool: self, weights: Union[torch.Tensor, Dict[int, torch.Tensor]], cache_keys: Union[str, Dict[int, str]], + *, + localize: bool = True, ) -> Iterator[Tuple[int, torch.Tensor, str]]: - """Yield `(local_expert_id, weight, cache_key)` triples for per-expert - MoE LoRA inputs, filtered/remapped to this rank's slice. Accepts either - a `{global_eid: 2D tensor}` dict or a 3D `[num_experts, *, *]` tensor.""" + """Yield `(expert_id, weight, cache_key)` triples for MoE LoRA A/B weights. + + By default global IDs are filtered and remapped to this rank. Set + ``localize=False`` for replicated shared-expert weights. + """ if isinstance(weights, dict): assert isinstance(cache_keys, dict) for gid, w in weights.items(): - lid = self._global_to_local_expert_id(gid) + lid = self._global_to_local_expert_id(gid) if localize else gid if lid is not None: yield lid, w, cache_keys[gid] return @@ -313,7 +336,7 @@ class LoRAMemoryPool: if isinstance(weights, torch.Tensor) and weights.dim() == 3: assert isinstance(cache_keys, str) total = weights.shape[0] - if self.moe_use_local_expert_ids: + if self.moe_use_local_expert_ids and localize: start = self.moe_ep_rank * self._num_experts_local count = max(0, min(self._num_experts_local, total - start)) else: @@ -364,9 +387,12 @@ class LoRAMemoryPool: module_name, self.base_hf_config, base_model, layer_idx ) c = get_stacked_multiply(module_name, base_model) - # MoE modules shard along `moe_tp_size`, not the outer `tp_size`. + # Routed MoE shards along moe_tp_size; shared MoE shards over full TP at EP=1. effective_tp_size = ( - self.moe_tp_size if self.is_moe_module(module_name) else self.tp_size + self.tp_size + if not self.is_moe_module(module_name) + or self.is_shared_moe_module(module_name) + else self.moe_tp_size ) if ( effective_tp_size > 1 @@ -376,8 +402,14 @@ class LoRAMemoryPool: input_dim = divide(input_dim, effective_tp_size) if self.is_moe_module(module_name): - expert_dim = self._get_num_local_experts(base_model) - if self.experts_shared_outer_loras and module_name == "gate_up_proj_moe": + if self.is_shared_moe_module(module_name): + expert_dim = self._get_num_shared_experts(base_model) + else: + expert_dim = self._get_num_local_experts(base_model) + if self.experts_shared_outer_loras and module_name in ( + "gate_up_proj_moe", + "gate_up_proj_shared_moe", + ): expert_dim = 1 return ( self.max_loras_per_batch, @@ -458,9 +490,12 @@ class LoRAMemoryPool: _, output_dim = get_hidden_dim( module_name, self.base_hf_config, base_model, layer_idx ) - # MoE modules shard along `moe_tp_size`, not the outer `tp_size`. + # Same TP-vs-moe-TP sharding rule as get_lora_A_shape above. effective_tp_size = ( - self.moe_tp_size if self.is_moe_module(module_name) else self.tp_size + self.tp_size + if not self.is_moe_module(module_name) + or self.is_shared_moe_module(module_name) + else self.moe_tp_size ) if ( effective_tp_size > 1 @@ -473,8 +508,14 @@ class LoRAMemoryPool: # Check if MoE module and return appropriate shape if self.is_moe_module(module_name): - expert_dim = self._get_num_local_experts(base_model) - if self.experts_shared_outer_loras and module_name == "down_proj_moe": + if self.is_shared_moe_module(module_name): + expert_dim = self._get_num_shared_experts(base_model) + else: + expert_dim = self._get_num_local_experts(base_model) + if self.experts_shared_outer_loras and module_name in ( + "down_proj_moe", + "down_proj_shared_moe", + ): expert_dim = 1 return (self.max_loras_per_batch, expert_dim, output_dim, max_lora_dim) else: @@ -556,6 +597,23 @@ class LoRAMemoryPool: ) for idx in range(self.num_layer) ] + + # Shared-expert MoE version (4D, separate sink namespace). + if self._has_shared_fused_moe(base_model): + shared_moe_key = f"{module_name}_shared_moe" + buffer[shared_moe_key] = [ + torch.zeros( + get_lora_shape_fn( + shared_moe_key, + base_model, + self.max_lora_rank, + idx, + ), + dtype=self.dtype, + device=device, + ) + for idx in range(self.num_layer) + ] else: # Standard allocation for unambiguous modules buffer[module_name] = [ @@ -672,11 +730,14 @@ class LoRAMemoryPool: self, cur_uids: Set[Optional[str]], lora_adapters: Dict[str, LoRAAdapter], - lora_modules: List[Dict[str, BaseLayerWithLoRA]], + lora_modules: List[Dict[str, torch.nn.Module]], lora_refs: Dict[str, LoRARef], lora_embed_tokens_module: Optional[BaseLayerWithLoRA], lora_lm_head_module: Optional[BaseLayerWithLoRA], ): + # Python hash seeds differ by TP process; slot and LRU updates must not. + ordered_uids = sorted(cur_uids, key=lambda uid: (uid is not None, uid or "")) + def get_available_buffer_slot(): # 1. Prioritize empty slots for buffer_id in range(self.max_loras_per_batch): @@ -731,10 +792,10 @@ class LoRAMemoryPool: return victim_buffer_id # Mark all adapters in current batch as used (for LRU tracking) - for uid in cur_uids: + for uid in ordered_uids: self.eviction_policy.mark_used(uid) - for uid in cur_uids: + for uid in ordered_uids: if uid not in self.uid_to_buffer_id: buffer_id = get_available_buffer_slot() lora_adapter = lora_adapters.get(uid, None) @@ -749,12 +810,39 @@ class LoRAMemoryPool: self.uid_to_buffer_id[uid] = buffer_id self.buffer_id_to_uid[buffer_id] = uid + def _clear_buffer_slot_for_base(self, buffer_id: int) -> None: + """Make an evicted slot safe for graph-captured base-model replay.""" + for buffers in (*self.A_buffer.values(), *self.B_buffer.values()): + for tensor in buffers: + tensor[buffer_id].zero_() + for buffers in ( + self.embedding_A_buffer, + self.embedding_B_buffer, + self.lm_head_A_buffer, + self.lm_head_B_buffer, + self.new_embeddings_buffer, + ): + for tensor in buffers.values(): + tensor[buffer_id].zero_() + + def remove_lora(self, uid: str) -> Optional[int]: + """Remove a resident adapter and return its cleared pool slot.""" + buffer_id = self.uid_to_buffer_id.get(uid) + if buffer_id is None: + return None + + self._clear_buffer_slot_for_base(buffer_id) + del self.uid_to_buffer_id[uid] + self.buffer_id_to_uid[buffer_id] = EMPTY_SLOT + self.eviction_policy.remove(uid) + return buffer_id + def load_lora_weight_to_buffer( self, uid: str, buffer_id: int, lora_adapter: LoRAAdapter, - lora_modules: List[Dict[str, BaseLayerWithLoRA]], + lora_modules: List[Dict[str, torch.nn.Module]], lora_embed_tokens_module: Optional[BaseLayerWithLoRA], lora_lm_head_module: Optional[BaseLayerWithLoRA], ): @@ -772,15 +860,7 @@ class LoRAMemoryPool: copy_weight_into_buffer(buffer_view, weight) if uid is None: - for i in range(self.num_layer): - for k in self.A_buffer.keys(): - self.A_buffer[k][i][buffer_id] = 0 - - for k in self.embedding_A_buffer.keys(): - self.embedding_A_buffer[k][buffer_id] = 0 - - for k in self.lm_head_A_buffer.keys(): - self.lm_head_A_buffer[k][buffer_id] = 0 + self._clear_buffer_slot_for_base(buffer_id) return assert lora_adapter is not None @@ -826,6 +906,13 @@ class LoRAMemoryPool: layer = lora_adapter.layers[layer_id] layer_weights = layer.weights pinned_layer_weights = layer.pinned_weights + cur_layer_modules = lora_modules[layer_id] + has_shared_moe_module = any( + getattr( + getattr(module, "base_layer", module), "is_shared_fused_moe", False + ) + for module in cur_layer_modules.values() + ) # - Standard: module_name -> torch.Tensor # - MoE: module_name -> Dict[expert_id -> torch.Tensor] temp_A_buffer: Dict[str, Union[torch.Tensor, Dict[int, torch.Tensor]]] = { @@ -846,8 +933,36 @@ class LoRAMemoryPool: # Check if this is an MoE weight (has expert index in name) expert_match = re.search(r"experts\.(\d+)\.", name) + is_shared_expert = "shared_experts." in name + shared_moe_target = f"{target_module}_shared_moe" - if expert_match: + if is_shared_expert and ( + weights.dim() == 3 + or (has_shared_moe_module and shared_moe_target in temp_A_buffer) + ): + # Keep sink experts separate from routed experts for both + # packed 3D weights and named per-expert 2D weights. + target_module = shared_moe_target + if expert_match: + if temp_A_buffer[target_module] is None: + temp_A_buffer[target_module] = {} + temp_B_buffer[target_module] = {} + temp_A_cache_keys[target_module] = {} + temp_B_cache_keys[target_module] = {} + expert_id = int(expert_match.group(1)) + if "lora_A" in name: + temp_A_buffer[target_module][expert_id] = weights + temp_A_cache_keys[target_module][expert_id] = name + else: + temp_B_buffer[target_module][expert_id] = weights + temp_B_cache_keys[target_module][expert_id] = name + elif "lora_A" in name: + temp_A_buffer[target_module] = weights + temp_A_cache_keys[target_module] = name + else: + temp_B_buffer[target_module] = weights + temp_B_cache_keys[target_module] = name + elif expert_match: # Per-expert MoE weight — 2D tensors, one per expert target_module = target_module + "_moe" if temp_A_buffer[target_module] is None: @@ -890,48 +1005,49 @@ class LoRAMemoryPool: # redundant zero-fills on slots no `update_lora_info` ever points # a forward-time module at. active_target_modules: Set[str] = set() - cur_layer_modules = lora_modules[layer_id] - for module_name, module in cur_layer_modules.items(): - # TODO (Jonahcb): check if the code can be refactored to avoid the special handling for FusedMoEWithLoRA - # Handle FusedMoEWithLoRA specially - it contains multiple target modules - from sglang.srt.lora.layers import FusedMoEWithLoRA + from sglang.srt.lora.layers import FusedMoEWithLoRA - if isinstance(module, FusedMoEWithLoRA): - # Per-expert MoE weights are sharded along `moe_tp_size` - # (= tp_size // ep_size // dp_size), so the slice index - # must be `moe_tp_rank`. Passing the outer `tp_rank` here - # produces an off-the-end slice when ep_size < tp_size - # (e.g. tp=4 ep=2 → ranks 2,3 slice past intermediate_size). - moe_target_modules = ["gate_up_proj_moe", "down_proj_moe"] - for target_module in moe_target_modules: + for module_name, module in cur_layer_modules.items(): + if isinstance(module, FusedMoEWithLoRA) or getattr( + module, "is_shared_fused_moe", False + ): + base_layer = getattr(module, "base_layer", module) + key_suffix = ( + "_shared_moe" if base_layer.is_shared_fused_moe else "_moe" + ) + moe_tp_rank = base_layer.moe_tp_rank + for canonical in ("gate_up_proj_moe", "down_proj_moe"): + # Slice methods key on canonical *_moe names; buffers are + # namespaced per module kind. + target_module = canonical.replace("_moe", key_suffix) active_target_modules.add(target_module) if temp_A_buffer.get(target_module) is not None: temp_A_buffer[target_module] = ( module.slice_moe_lora_a_weights( temp_A_buffer[target_module], - self.moe_tp_rank, - target_module, + moe_tp_rank, + canonical, ) ) cache_keys = temp_A_cache_keys[target_module] assert cache_keys is not None temp_A_cache_keys[target_module] = append_cache_key_suffix( cache_keys, - f"moe_tp{self.moe_tp_rank}", + f"moe_tp{moe_tp_rank}", ) if temp_B_buffer.get(target_module) is not None: temp_B_buffer[target_module] = ( module.slice_moe_lora_b_weights( temp_B_buffer[target_module], - self.moe_tp_rank, - target_module, + moe_tp_rank, + canonical, ) ) cache_keys = temp_B_cache_keys[target_module] assert cache_keys is not None temp_B_cache_keys[target_module] = append_cache_key_suffix( cache_keys, - f"moe_tp{self.moe_tp_rank}", + f"moe_tp{moe_tp_rank}", ) continue @@ -977,14 +1093,20 @@ class LoRAMemoryPool: target_buffer = self.A_buffer[name][layer_id] weights_cache_key = temp_A_cache_keys[name] - if name in ["gate_up_proj_moe", "down_proj_moe"]: - if self.experts_shared_outer_loras and name == "gate_up_proj_moe": - if weights is None: + if name in [ + "gate_up_proj_moe", + "down_proj_moe", + "gate_up_proj_shared_moe", + "down_proj_shared_moe", + ]: + if self.experts_shared_outer_loras and name in ( + "gate_up_proj_moe", + "gate_up_proj_shared_moe", + ): + if weights is None or ( + isinstance(weights, dict) and not weights + ): representative_weight = None - buffer_view = target_buffer[ - buffer_id, 0, : lora_rank * c, : - ] - load_lora_weight_tensor(buffer_view, None) elif isinstance(weights, torch.Tensor) and weights.dim() == 3: if weights.shape[0] != 1: raise ValueError( @@ -999,10 +1121,6 @@ class LoRAMemoryPool: weights, ) representative_weight = weights[0] - buffer_view = target_buffer[ - buffer_id, 0, : lora_rank * c, : - ] - load_lora_weight_tensor(buffer_view, weights[0]) elif isinstance(weights, dict) and len(weights) > 0: if len(weights) != 1: raise ValueError( @@ -1017,16 +1135,20 @@ class LoRAMemoryPool: pinned_layer_weights, rep_cache_key, rep ) representative_weight = rep - buffer_view = target_buffer[ - buffer_id, 0, : lora_rank * c, : - ] - load_lora_weight_tensor(buffer_view, rep) else: raise ValueError( f"Unexpected weight format for shared outer gate_up_proj_moe lora_A: " f"type={type(weights)}, " f"shape={weights.shape if isinstance(weights, torch.Tensor) else 'N/A'}" ) + if representative_weight is not None: + expected_shape = target_buffer[ + buffer_id, 0, : lora_rank * c, : + ].shape + assert representative_weight.shape == expected_shape, ( + f"LoRA buffer shape {expected_shape} does not match " + f"weight shape {representative_weight.shape}." + ) # Place each stacked component at max_rank-spaced # positions so the kernel's [:max_r] / [max_r:2*max_r] # slicing is correct. @@ -1042,6 +1164,8 @@ class LoRAMemoryPool: ci * lora_rank : (ci + 1) * lora_rank, : ], ) + elif weights is None: + target_buffer[buffer_id].zero_() elif isinstance(weights, (torch.Tensor, dict)): # Zero first so any local-expert slot the adapter # doesn't fill (e.g. out-of-rank under EP) is clean; @@ -1055,7 +1179,9 @@ class LoRAMemoryPool: expert_weight, expert_cache_key, ) in self._iter_local_expert_weights( - weights, weights_cache_key + weights, + weights_cache_key, + localize=not self.is_shared_moe_module(name), ): if expert_weight is None: continue @@ -1094,9 +1220,19 @@ class LoRAMemoryPool: target_buffer = self.B_buffer[name][layer_id] weights_cache_key = temp_B_cache_keys[name] - if name in ["gate_up_proj_moe", "down_proj_moe"]: - if self.experts_shared_outer_loras and name == "down_proj_moe": - if weights is None: + if name in [ + "gate_up_proj_moe", + "down_proj_moe", + "gate_up_proj_shared_moe", + "down_proj_shared_moe", + ]: + if self.experts_shared_outer_loras and name in ( + "down_proj_moe", + "down_proj_shared_moe", + ): + if weights is None or ( + isinstance(weights, dict) and not weights + ): buffer_view = target_buffer[buffer_id, 0, :, :lora_rank] load_lora_weight_tensor(buffer_view, None) elif isinstance(weights, torch.Tensor) and weights.dim() == 3: @@ -1146,6 +1282,8 @@ class LoRAMemoryPool: ) # Zero beyond loaded rank — MoE kernel reads full max_rank. target_buffer[buffer_id, 0, :, lora_rank:].zero_() + elif weights is None: + target_buffer[buffer_id].zero_() elif isinstance(weights, (torch.Tensor, dict)): # Zero out slots this rank owns but the adapter # doesn't fill (padded-out / out-of-rank experts); @@ -1157,7 +1295,9 @@ class LoRAMemoryPool: w, w_cache_key, ) in self._iter_local_expert_weights( - weights, weights_cache_key + weights, + weights_cache_key, + localize=not self.is_shared_moe_module(name), ): if w is not None: w = w * lora_adapter.scaling diff --git a/python/sglang/srt/lora/trtllm_lora_temp/__init__.py b/python/sglang/srt/lora/trtllm_lora_temp/__init__.py index 0d163c2ac..47f6274b3 100644 --- a/python/sglang/srt/lora/trtllm_lora_temp/__init__.py +++ b/python/sglang/srt/lora/trtllm_lora_temp/__init__.py @@ -35,16 +35,23 @@ def is_two_stream_active(x: torch.Tensor) -> bool: return x.shape[0] <= lora_envs.SGLANG_TWO_STREAM_MAX_TOKENS.get() +def supports_two_stream_dense_lora(lora_a: torch.Tensor, lora_b: torch.Tensor) -> bool: + """Keep the temporary shrink kernel within its safe combined-rank tile.""" + return lora_a.shape[-2] <= 128 and lora_b.shape[-1] <= 64 + + +# One side stream per consumer stream: routed (main/capture) and InklingMoE's sink (alt +# stream) run concurrently; sharing one side stream is a premature-reuse WAR -> IMA. +_LORA_SIDE_STREAMS: dict[torch.cuda.Stream, torch.cuda.Stream] = {} + + def get_lora_side_stream() -> torch.cuda.Stream: - """Lazily allocate a single shared LoRA side stream. - - Within one decode layer the three sites (qkv → attn → o_proj → moe_gate_up) - run sequentially, so one stream suffices and avoids extra graph-capture - nodes from per-site streams. - """ - from sglang.srt.runtime_context import get_stream - - return get_stream("lora_side") + # Lazy creation is capture-safe: graph warmup runs on the capture stream + # (graph_capture() sets it), so every key exists before any capture region. + consumer = torch.cuda.current_stream() + if consumer not in _LORA_SIDE_STREAMS: + _LORA_SIDE_STREAMS[consumer] = torch.cuda.Stream() + return _LORA_SIDE_STREAMS[consumer] def init_lora_two_stream_resources(device: Optional[torch.device] = None) -> None: @@ -210,6 +217,7 @@ def install_two_stream_overrides() -> None: __all__ = [ "is_two_stream_active", + "supports_two_stream_dense_lora", "get_lora_side_stream", "init_lora_two_stream_resources", "get_original_qkv_forward", diff --git a/python/sglang/srt/lora/trtllm_lora_temp/attention.py b/python/sglang/srt/lora/trtllm_lora_temp/attention.py index e7415f830..c427ce41e 100644 --- a/python/sglang/srt/lora/trtllm_lora_temp/attention.py +++ b/python/sglang/srt/lora/trtllm_lora_temp/attention.py @@ -23,6 +23,7 @@ from sglang.srt.lora.trtllm_lora_temp import ( get_original_row_forward, is_two_stream_active, lora_overlap_alloc_stream, + supports_two_stream_dense_lora, ) from sglang.srt.runtime_context import get_parallel @@ -34,7 +35,11 @@ def qkv_proj_lora_forward(self, input_: torch.Tensor): base GEMM, no write conflict. The expand needs the shrink intermediate AND base_output, so it runs after the rejoin on the main stream. """ - if not self.set_lora or not is_two_stream_active(input_): + if ( + not self.set_lora + or not is_two_stream_active(input_) + or not supports_two_stream_dense_lora(self.A_buffer_qkv, self.B_buffer_qkv) + ): return get_original_qkv_forward()(self, input_) from sglang.kernels.ops.gemm.trtllm_lora_temp.qkv_lora_b import qkv_lora_b_fwd @@ -98,7 +103,11 @@ def row_parallel_lora_forward( ) input_parallel = splitted_input[tp_rank].contiguous() - if not self.set_lora or not is_two_stream_active(input_parallel): + if ( + not self.set_lora + or not is_two_stream_active(input_parallel) + or not supports_two_stream_dense_lora(self.A_buffer, self.B_buffer) + ): return get_original_row_forward()(self, input_, skip_all_reduce, forward_batch) bias_ = ( @@ -108,11 +117,22 @@ def row_parallel_lora_forward( ) side_stream = get_lora_side_stream() + sgemm_info = self.lora_backend._sgemm_info() _alloc = lora_overlap_alloc_stream() # capture MAIN stream here (before the fork) side_stream.wait_stream(torch.cuda.current_stream()) with torch.cuda.stream(side_stream): - lora_a_output = self.lora_backend.run_lora_a_sgemm( - input_parallel, self.A_buffer, out_alloc_stream=_alloc + from sglang.kernels.ops.gemm.trtllm_lora_temp.sgemm_lora_a import ( + sgemm_lora_a_fwd, + ) + from sglang.kernels.ops.gemm.trtllm_lora_temp.sgemm_lora_b import ( + sgemm_lora_b_fwd, + ) + + lora_a_output = sgemm_lora_a_fwd( + input_parallel, + self.A_buffer, + sgemm_info, + out_alloc_stream=_alloc, ) # Base row-parallel GEMM on main, concurrent with the side-stream shrink. @@ -132,22 +152,12 @@ def row_parallel_lora_forward( if should_reduce: output_ = tensor_model_parallel_all_reduce(output_parallel) lora_a_output = tensor_model_parallel_all_reduce(lora_a_output) - output_ = self.lora_backend.run_lora_b_sgemm( - x=lora_a_output, - weights=self.B_buffer, - output_offset=self.output_offset, - output_offset_cpu=self.output_offset_cpu, - base_output=output_, - ) + output_ = sgemm_lora_b_fwd(lora_a_output, self.B_buffer, sgemm_info, output_) else: # Two-stream already produced lora_a_output on the side stream; finish # the LoRA with just the expand atomic-add against output_parallel. - output_parallel = self.lora_backend.run_lora_b_sgemm( - x=lora_a_output, - weights=self.B_buffer, - output_offset=self.output_offset, - output_offset_cpu=self.output_offset_cpu, - base_output=output_parallel, + output_parallel = sgemm_lora_b_fwd( + lora_a_output, self.B_buffer, sgemm_info, output_parallel ) output_ = output_parallel @@ -165,17 +175,32 @@ def column_parallel_lora_forward(self, input_: torch.Tensor): so it runs after the rejoin. Byte-identical to the saved-original forward for non-decode batches or when LoRA isn't set on this layer. """ - if not self.set_lora or not is_two_stream_active(input_): + if ( + not self.set_lora + or not is_two_stream_active(input_) + or not supports_two_stream_dense_lora(self.A_buffer, self.B_buffer) + ): return get_original_column_forward()(self, input_) bias = self.base_layer.bias if not self.base_layer.skip_bias_add else None side_stream = get_lora_side_stream() + sgemm_info = self.lora_backend._sgemm_info() _alloc = lora_overlap_alloc_stream() # capture MAIN stream here (before the fork) side_stream.wait_stream(torch.cuda.current_stream()) with torch.cuda.stream(side_stream): - lora_a_output = self.lora_backend.run_lora_a_sgemm( - input_, self.A_buffer, out_alloc_stream=_alloc + from sglang.kernels.ops.gemm.trtllm_lora_temp.sgemm_lora_a import ( + sgemm_lora_a_fwd, + ) + from sglang.kernels.ops.gemm.trtllm_lora_temp.sgemm_lora_b import ( + sgemm_lora_b_fwd, + ) + + lora_a_output = sgemm_lora_a_fwd( + input_, + self.A_buffer, + sgemm_info, + out_alloc_stream=_alloc, ) # Base ColumnParallel GEMM on main, concurrent with the side-stream shrink. @@ -183,12 +208,8 @@ def column_parallel_lora_forward(self, input_: torch.Tensor): # Rejoin: expand reads both the side-produced shrink and base_output. torch.cuda.current_stream().wait_stream(side_stream) - output_parallel = self.lora_backend.run_lora_b_sgemm( - x=lora_a_output, - weights=self.B_buffer, - output_offset=self.output_offset, - output_offset_cpu=self.output_offset_cpu, - base_output=output_parallel, + output_parallel = sgemm_lora_b_fwd( + lora_a_output, self.B_buffer, sgemm_info, output_parallel ) if self.base_layer.gather_output: @@ -209,19 +230,32 @@ def replicated_lora_forward(self, x: torch.Tensor): backend's ``run_qkv_lora`` composes internally — A on the side stream, B on the main after the rejoin. Falls back to the saved-original otherwise. """ - if not self.set_lora or not is_two_stream_active(x): + if ( + not self.set_lora + or not is_two_stream_active(x) + or not supports_two_stream_dense_lora(self.A_buffer, self.B_buffer) + ): return get_original_replicated_forward()(self, x) bias = self.base_layer.bias if not self.base_layer.skip_bias_add else None side_stream = get_lora_side_stream() first_dim = self.first_output_dim + sgemm_info = self.lora_backend._sgemm_info() _alloc = lora_overlap_alloc_stream() # capture MAIN stream here (before the fork) side_stream.wait_stream(torch.cuda.current_stream()) with torch.cuda.stream(side_stream): - lora_a_output = self.lora_backend.run_lora_a_sgemm( + from sglang.kernels.ops.gemm.trtllm_lora_temp.sgemm_lora_a import ( + sgemm_lora_a_fwd, + ) + from sglang.kernels.ops.gemm.trtllm_lora_temp.sgemm_lora_b import ( + sgemm_lora_b_fwd, + ) + + lora_a_output = sgemm_lora_a_fwd( x, self.A_buffer, + sgemm_info, stack_num=(2 if first_dim > 0 else 1), out_alloc_stream=_alloc, ) @@ -231,19 +265,14 @@ def replicated_lora_forward(self, x: torch.Tensor): torch.cuda.current_stream().wait_stream(side_stream) if first_dim == 0: - output = self.lora_backend.run_lora_b_sgemm( - x=lora_a_output, - weights=self.B_buffer, - output_offset=self._output_offset, - base_output=output, - ) + output = sgemm_lora_b_fwd(lora_a_output, self.B_buffer, sgemm_info, output) else: from sglang.kernels.ops.gemm.trtllm_lora_temp.qkv_lora_b import qkv_lora_b_fwd output = qkv_lora_b_fwd( lora_a_output, self.B_buffer, - self.lora_backend._sgemm_info(), + sgemm_info, self._output_offset, self._max_out_dim, output, diff --git a/python/sglang/srt/lora/trtllm_lora_temp/environ.py b/python/sglang/srt/lora/trtllm_lora_temp/environ.py index 48f91b2b4..0a0495606 100644 --- a/python/sglang/srt/lora/trtllm_lora_temp/environ.py +++ b/python/sglang/srt/lora/trtllm_lora_temp/environ.py @@ -1,14 +1,10 @@ """Local env registry for the experimental TRT-LLM LoRA fast path. Every flag here is gated by the single global master switch -``SGLANG_EXPERIMENTAL_LORA_OPTI`` (defined in ``sglang.srt.environ``). When the master -switch is OFF (the default), every flag reads ``False`` (its default is -suppressed), so the no-LoRA path, other MoE backends, and the default -(non-experimental) LoRA path are byte-identical to upstream. +``SGLANG_EXPERIMENTAL_LORA_OPTI`` (defined in ``sglang.srt.environ``). When it is +off, every local flag reads ``False``. -Keeping these flags out of the global ``Envs`` class is deliberate: the only -sglang-global addition for this feature is ``SGLANG_EXPERIMENTAL_LORA_OPTI``; all the -fine-grained opt switches live here, next to the code that consumes them. +Fine-grained switches live here beside their consumers. Default policy (applies only when ``SGLANG_EXPERIMENTAL_LORA_OPTI=1``): * **common** flags — used by BOTH the qwen3.5 (FP8) and kimi (NVFP4) configs — @@ -82,11 +78,7 @@ class _LoraEnvs: ) # ---- correctness fixes: on by default when experimental ---- - # gate_up gated-split fix (up_A shrink for the up half); set =0 only to A/B bisect. - SGLANG_ENABLE_LORA_MOE_GATEUP_GATED_SPLIT = _GatedBool( - "SGLANG_ENABLE_LORA_MOE_GATEUP_GATED_SPLIT", True - ) - # feed bf16 router logits straight to the JIT kimi gate (bitwise-identical). + # Feed bf16 router logits straight to the JIT kimi gate (bitwise-identical). SGLANG_OPT_KIMI_GATE_BF16_INPUT = _GatedBool( "SGLANG_OPT_KIMI_GATE_BF16_INPUT", True ) diff --git a/python/sglang/srt/lora/trtllm_lora_temp/inkling_dense.py b/python/sglang/srt/lora/trtllm_lora_temp/inkling_dense.py new file mode 100644 index 000000000..25d5184a3 --- /dev/null +++ b/python/sglang/srt/lora/trtllm_lora_temp/inkling_dense.py @@ -0,0 +1,255 @@ +"""Temporary optimized shared-sink LoRA execution for Inkling.""" + +from __future__ import annotations + +import torch + +from sglang.srt.environ import envs +from sglang.srt.models.inkling_common.kernels.comm import symm_mem_all_reduce + + +def allow_inkling_moe_two_stream( + shared_experts, routed_experts, num_tokens: int +) -> bool: + """Gate routed/shared overlap when the current batch has LoRA work.""" + + from sglang.srt.lora.trtllm_lora_temp.environ import lora_envs + from sglang.srt.model_executor.runner_utils.capture_mode import ( + get_is_capture_mode, + ) + + lora_backend = getattr(shared_experts, "lora_backend", None) + if lora_backend is None: + lora_backend = getattr(routed_experts, "lora_backend", None) + batch_info = getattr(lora_backend, "batch_info", None) + has_lora_work = get_is_capture_mode() or bool( + getattr(batch_info, "has_active_lora", False) + ) + return not has_lora_work or ( + lora_envs.SGLANG_OPT_LORA_OVERLAP_MAIN_ALLOC.get() and num_tokens <= 32 + ) + + +def apply_multi_lora( + layer, + inputs: torch.Tensor, + lora_a: torch.Tensor, + lora_b: torch.Tensor, + base_output: torch.Tensor, + *, + stack_num: int, +) -> torch.Tensor: + if getattr(layer.lora_backend, "name", None) != "triton" or not inputs.is_cuda: + raise RuntimeError("Multi-slot dense LoRA requires Triton on CUDA") + from sglang.kernels.ops.gemm.trtllm_lora_temp.sgemm_lora_a import ( + shared_sink_sgemm_lora_a_fwd, + ) + from sglang.kernels.ops.gemm.trtllm_lora_temp.sgemm_lora_b import ( + shared_sink_sgemm_lora_b_fwd, + ) + + padded_rank = stack_num > 1 + batch_info = layer.lora_backend._sgemm_info() + shrink = shared_sink_sgemm_lora_a_fwd( + inputs, + lora_a, + batch_info, + stack_num=stack_num, + padded_rank=padded_rank, + ) + return shared_sink_sgemm_lora_b_fwd( + shrink, + lora_b, + batch_info, + base_output, + apply_scaling=False, + padded_rank=padded_rank, + ) + + +def _shared_sink_routing(layer, num_tokens: int, device: torch.device): + cached = layer._lora_routing_cache.get(num_tokens) + if cached is None or cached[0].device != device: + topk_ids = torch.arange( + layer.n_shared_experts, dtype=torch.int32, device=device + ).repeat(num_tokens, 1) + topk_weights = torch.ones_like(topk_ids, dtype=torch.float32) + cached = (topk_ids, topk_weights) + layer._lora_routing_cache[num_tokens] = cached + return cached + + +def _apply_per_expert_lora( + layer, + hidden_states: torch.Tensor, + output: torch.Tensor, + lora_a: torch.Tensor, + lora_b: torch.Tensor, +) -> None: + from sglang.kernels.ops.moe.virtual_experts import ( + merged_experts_fused_moe_lora_add, + ) + + num_tokens = output.shape[0] + topk_ids, topk_weights = _shared_sink_routing( + layer, num_tokens, hidden_states.device + ) + token_lora_mapping = layer.lora_backend.batch_info.moe_lora_info + token_lora_mapping = token_lora_mapping.token_lora_mapping[:num_tokens] + merged_experts_fused_moe_lora_add( + output=output, + hidden_states=hidden_states, + lora_a=lora_a, + lora_b=lora_b, + topk_ids=topk_ids, + topk_weights=topk_weights, + token_lora_mapping=token_lora_mapping, + mul_routed_weight=False, + experts_shared_outer_loras_a=False, + experts_shared_outer_loras_b=False, + ) + + +def forward_with_lora( + layer, + x_td: torch.Tensor, + gammas_ts: torch.Tensor, + linearized_weights: tuple[torch.Tensor, torch.Tensor], + use_reduce_scatter: bool, +) -> torch.Tensor: + w13_lin, w2_lin = linearized_weights + t = x_td.shape[0] + n = layer.n_shared_experts + from sglang.srt.model_executor.runner_utils.capture_mode import ( + get_is_capture_mode, + ) + + run_lora = get_is_capture_mode() or bool( + getattr(layer.lora_backend.batch_info, "has_active_lora", False) + ) + overlap_gate_up = ( + envs.SGLANG_OPT_USE_INKLING_MULTI_STREAM_OVERLAP.get() + and run_lora + and x_td.is_cuda + and layer.experts_shared_outer_loras + ) + if overlap_gate_up: + device_major = torch.cuda.get_device_capability(x_td.device)[0] + max_overlap_tokens = {9: 32, 10: 16}.get(device_major, 0) + overlap_gate_up = t <= max_overlap_tokens + + gate_up_shrink = None + gate_up_side_stream = None + gate_up_batch_info = None + if overlap_gate_up: + from sglang.srt.lora.trtllm_lora_temp import get_lora_side_stream + + assert layer._w1_delta is not None + consumer_stream = torch.cuda.current_stream() + gate_up_side_stream = get_lora_side_stream() + gate_up_side_stream.wait_stream(consumer_stream) + single_gate_up = layer._w1_delta.shape[0] == 1 + if single_gate_up: + a_gate_up = layer.gate_up_lora_a_weights[0, 0] + gate_up_shrink = x_td.new_empty((t, a_gate_up.shape[0])) + with torch.cuda.stream(gate_up_side_stream): + if single_gate_up: + torch.mm(x_td, a_gate_up.T, out=gate_up_shrink) + else: + from sglang.kernels.ops.gemm.trtllm_lora_temp.sgemm_lora_a import ( + shared_sink_sgemm_lora_a_fwd, + ) + + gate_up_batch_info = layer.lora_backend._sgemm_info() + gate_up_shrink = shared_sink_sgemm_lora_a_fwd( + x_td, + layer.gate_up_lora_a_weights[:, 0], + gate_up_batch_info, + stack_num=2, + padded_rank=True, + out_alloc_stream=consumer_stream, + ) + + y = torch.mm(x_td, w13_lin.T).view(t, n, -1) + if run_lora: + if not layer.experts_shared_outer_loras: + _apply_per_expert_lora( + layer, + x_td, + y, + layer.gate_up_lora_a_weights, + layer.gate_up_lora_b_weights, + ) + elif overlap_gate_up: + assert layer._w1_delta is not None + assert gate_up_side_stream is not None + assert gate_up_shrink is not None + torch.cuda.current_stream().wait_stream(gate_up_side_stream) + if layer._w1_delta.shape[0] == 1: + y_flat = y.view(t, -1) + y_flat.addmm_(gate_up_shrink, layer._w1_delta[0].T) + y = y_flat.view(t, n, -1) + else: + from sglang.kernels.ops.gemm.trtllm_lora_temp.sgemm_lora_b import ( + shared_sink_sgemm_lora_b_fwd, + ) + + assert gate_up_batch_info is not None + y = shared_sink_sgemm_lora_b_fwd( + gate_up_shrink, + layer._w1_delta, + gate_up_batch_info, + y.view(t, -1), + apply_scaling=False, + padded_rank=True, + ).view(t, n, -1) + elif layer._w1_delta.shape[0] == 1: + a_gate_up = layer.gate_up_lora_a_weights[0, 0] + shrink = torch.mm(x_td, a_gate_up.T) + y_flat = y.view(t, -1) + y_flat.addmm_(shrink, layer._w1_delta[0].T) + y = y_flat.view(t, n, -1) + else: + y = apply_multi_lora( + layer, + x_td, + layer.gate_up_lora_a_weights[:, 0], + layer._w1_delta, + y.view(t, -1), + stack_num=2, + ).view(t, n, -1) + + act = layer._swiglu(y, gammas_ts) + out_td = torch.mm(act.reshape(t, -1), w2_lin) + if run_lora: + if not layer.experts_shared_outer_loras: + delta = torch.zeros( + (t, n, out_td.shape[-1]), dtype=out_td.dtype, device=out_td.device + ) + _apply_per_expert_lora( + layer, + act.reshape(t * n, -1), + delta, + layer.down_lora_a_weights, + layer.down_lora_b_weights, + ) + out_td.add_(delta.sum(dim=1)) + else: + assert layer._a_cat is not None + act_flat = act.reshape(t, -1) + if layer._a_cat.shape[0] == 1: + b_down = layer.down_lora_b_weights[0, 0] + shrink = torch.mm(act_flat, layer._a_cat[0].T) + out_td.addmm_(shrink, b_down.T) + else: + out_td = apply_multi_lora( + layer, + act_flat, + layer._a_cat, + layer.down_lora_b_weights[:, 0], + out_td, + stack_num=1, + ) + if not use_reduce_scatter and layer.tp_group is not None: + out_td = symm_mem_all_reduce(out_td, layer.tp_group) + return out_td diff --git a/python/sglang/srt/lora/trtllm_lora_temp/lora_dispatch.py b/python/sglang/srt/lora/trtllm_lora_temp/lora_dispatch.py index 30522854d..79f3e612f 100644 --- a/python/sglang/srt/lora/trtllm_lora_temp/lora_dispatch.py +++ b/python/sglang/srt/lora/trtllm_lora_temp/lora_dispatch.py @@ -337,8 +337,13 @@ def fused_experts_none_to_experimental_sgl_trtllm_bf16_lora( assert TopKOutputChecker.format_is_standard(topk_output) assert runner_config.top_k is not None - # No active LoRA in a non-capture decode -> plain (fast) bf16 path. - if not get_is_capture_mode() and not lora_info.has_active_lora: + # No-LoRA non-capture decode -> fast bf16 path, valid only for 4-D block-shuffled + # weights ([E, M//128, K//128, 128]); flat [E, 2F, D] stays on the decomposed kernel. + if ( + not get_is_capture_mode() + and not lora_info.has_active_lora + and quant_info.gemm1_weights.dim() == 4 + ): return fused_experts_none_to_flashinfer_trtllm_bf16( dispatch_output, quant_info, runner_config, use_routed_topk=True ) diff --git a/python/sglang/srt/lora/trtllm_lora_temp/lora_layer.py b/python/sglang/srt/lora/trtllm_lora_temp/lora_layer.py index b8258078e..48b3e6148 100644 --- a/python/sglang/srt/lora/trtllm_lora_temp/lora_layer.py +++ b/python/sglang/srt/lora/trtllm_lora_temp/lora_layer.py @@ -23,6 +23,30 @@ if TYPE_CHECKING: from sglang.srt.layers.moe.token_dispatcher import StandardCombineInput +_SGL_TRTLLM_MODULE_WARMED = False + + +def _warm_sgl_trtllm_moe_module() -> None: + """Build (JIT-compile + load) the sgl_trtllm MoE module at LoRA init time. + + The LoRA MoE ops call ``get_sgl_trtllm_moe_sm100_raw_module()`` lazily on + their first forward. On a cold flashinfer JIT cache that fires a ninja + build taking tens of minutes (observed >30 min on GB300 aarch64) -- and the + first forward happens INSIDE decode cuda-graph capture, so the boot looks + hung mid-capture with an idle main thread. Building here (module init, + before capture) makes the cost visible at startup and keeps capture fast. + """ + global _SGL_TRTLLM_MODULE_WARMED + if _SGL_TRTLLM_MODULE_WARMED: + return + from sglang.jit_kernel.trtllm_lora_temp.core import ( + get_sgl_trtllm_moe_sm100_raw_module, + ) + + get_sgl_trtllm_moe_sm100_raw_module() + _SGL_TRTLLM_MODULE_WARMED = True + + def init_experimental_sgl_trtllm_lora(layer, base_layer) -> None: """Build and store the trtllm FP8 LoRA quant info on the layer. @@ -37,6 +61,8 @@ def init_experimental_sgl_trtllm_lora(layer, base_layer) -> None: ) from sglang.srt.layers.moe.utils import RoutingMethodType + _warm_sgl_trtllm_moe_module() + # ---- NVFP4 (modelopt) path ---- # The fp4 weight loader sets ``g1_scale_c`` on the FusedMoE layer (see # ModelOptNvFp4FusedMoEMethod.apply). Mirror the non-LoRA construction in diff --git a/python/sglang/srt/lora/trtllm_lora_temp/merged_column.py b/python/sglang/srt/lora/trtllm_lora_temp/merged_column.py index c849cea89..24449924d 100644 --- a/python/sglang/srt/lora/trtllm_lora_temp/merged_column.py +++ b/python/sglang/srt/lora/trtllm_lora_temp/merged_column.py @@ -24,12 +24,17 @@ from sglang.srt.lora.trtllm_lora_temp import ( get_original_merged_column_forward, is_two_stream_active, lora_overlap_alloc_stream, + supports_two_stream_dense_lora, ) def merged_column_lora_forward(self, input_: torch.Tensor): """O9 — side-stream LoRA-A shrink ‖ base merged-column GEMM.""" - if not self.set_lora or not is_two_stream_active(input_): + if ( + not self.set_lora + or not is_two_stream_active(input_) + or not supports_two_stream_dense_lora(self.A_buffer, self.B_buffer) + ): return get_original_merged_column_forward()(self, input_) from sglang.kernels.ops.gemm.trtllm_lora_temp.gate_up_lora_b import ( diff --git a/python/sglang/srt/lora/trtllm_lora_temp/sgl_backend.py b/python/sglang/srt/lora/trtllm_lora_temp/sgl_backend.py index 31035dc86..c5ad825f3 100644 --- a/python/sglang/srt/lora/trtllm_lora_temp/sgl_backend.py +++ b/python/sglang/srt/lora/trtllm_lora_temp/sgl_backend.py @@ -3,13 +3,10 @@ ``MoeRunner.__init__`` requires a registered fused-func at CONSTRUCTION time even for the LoRA case, because LoRA is attached *after* the MoE layer is built (so ``lora_enabled`` is False inside ``MoeRunner.__init__``). At run time the runner -skips this for the LoRA path; the no-LoRA path delegates entirely to the upstream -flashinfer_trtllm dispatch (all quant types), so no-LoRA is identical to the stock backend. +skips this for the LoRA path; the no-LoRA path delegates to the standard +flashinfer_trtllm dispatch. -Registration fires at model-build time via a one-line import of this module in -``moe_runner/flashinfer_trtllm.py`` (the module already imported there for the -trtllm weight-prep). Keeping the dispatch body here keeps that file otherwise -pristine; the sgl FP8 LoRA dispatch lives in ``sgl_fp8_moe.py`` (used only by the LoRA path). +Registration occurs when this module is imported during model construction. """ from sglang.srt.layers.moe.moe_runner.base import register_fused_func @@ -19,11 +16,7 @@ from sglang.srt.layers.moe.moe_runner.base import register_fused_func def fused_experts_none_to_experimental_sgl_trtllm( dispatch_output, quant_info, runner_config ): - # No-LoRA on the experimental_sgl_trtllm backend == upstream flashinfer_trtllm for EVERY - # quant type (FP8 / NVFP4 / bf16). When LoRA is disabled the runner calls this fused-func, - # so delegating entirely to upstream keeps the no-LoRA path byte-identical to the stock - # backend. The new sgl kernels (sgl_fp8_moe, trtllm_*_routed_moe_lora) run ONLY on the LoRA - # dispatch (lora_dispatch.py), never here. + # Without LoRA, delegate every quantization type to flashinfer_trtllm. from sglang.srt.layers.moe.moe_runner.flashinfer_trtllm import ( fused_experts_none_to_flashinfer_trtllm, ) diff --git a/python/sglang/srt/lora/trtllm_lora_temp/sgl_fp8_moe.py b/python/sglang/srt/lora/trtllm_lora_temp/sgl_fp8_moe.py index c2914339c..462c6f215 100644 --- a/python/sglang/srt/lora/trtllm_lora_temp/sgl_fp8_moe.py +++ b/python/sglang/srt/lora/trtllm_lora_temp/sgl_fp8_moe.py @@ -1,7 +1,4 @@ -"""Copy of upstream flashinfer-trtllm FP8 MoE dispatch, wired to experimental_sgl_trtllm_moe -block-scale wrappers (LoRA-capable) so moe_runner/flashinfer_trtllm.py stays pristine. Body is -verbatim from upstream; helper imports are call-time (cycle-safe); two FP8 wrappers shadowed. -""" +"""FP8 MoE dispatch using LoRA-capable block-scale wrappers.""" from __future__ import annotations diff --git a/python/sglang/srt/lora/trtllm_lora_temp/specialized_expand.py b/python/sglang/srt/lora/trtllm_lora_temp/specialized_expand.py index e20a1d82a..dd4bf04b5 100644 --- a/python/sglang/srt/lora/trtllm_lora_temp/specialized_expand.py +++ b/python/sglang/srt/lora/trtllm_lora_temp/specialized_expand.py @@ -1,15 +1,13 @@ """Rank-specialized LoRA-B expand for virtual-expert LoRA. The kernel here was originally a chunk in ``lora/triton_ops/virtual_experts.py``. -It is rank-specialized: the ``R`` dimension (LoRA rank) is a triton +It is rank-specialized: the ``R`` dimension (LoRA rank) is a Triton ``constexpr``, so each rank value used at runtime gets its own JIT-compiled -specialization (R=16, R=32, R=64 are all supported up to the ``R <= 64`` assert, -with no perf interaction between them — each gets its own kernel). +specialization. Called from :mod:`sglang.kernels.ops.moe.virtual_experts` when -``use_direct_expand_add=True`` (the trtllm-lora path uses this when -``max_lora_rank <= 64``); the generic ``invoke_fused_moe_kernel`` is used -when that flag is False (incl. ranks above 64). +``use_direct_expand_add=True``. Ranks above 64 are accumulated in multiple +rank tiles. """ from typing import Any @@ -18,8 +16,6 @@ import torch import triton import triton.language as tl -from sglang.srt.lora.trtllm_lora_temp.environ import lora_envs - @triton.jit def _moe_lora_expand_add_kernel( @@ -52,6 +48,7 @@ def _moe_lora_expand_add_kernel( BLOCK_SIZE_R: tl.constexpr, GROUP_SIZE_M: tl.constexpr, GATED_A_HALF: tl.constexpr, + BROADCAST_A: tl.constexpr, ): """Rank-specialized LoRA-B expand for virtual-expert LoRA. @@ -96,29 +93,32 @@ def _moe_lora_expand_add_kernel( offs_n = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N).to(tl.int64) offs_r = tl.arange(0, BLOCK_SIZE_R).to(tl.int64) - rank_mask = offs_r < R # Gated gate_up split: the up-half output tiles read up-shrink (A columns [R:2R]); gate-half # tiles read gate-shrink (A columns [0:R]). GATED_A_HALF == 0 -> always read [0:R] (non-gated). - a_col = offs_r - if GATED_A_HALF > 0: - a_col = offs_r + tl.where(pid_n * BLOCK_SIZE_N >= GATED_A_HALF, R, 0) + accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32) + a_token = offs_token // router_topk if BROADCAST_A else offs_token + for rank_start in range(0, R, BLOCK_SIZE_R): + rank_offsets = rank_start + offs_r + rank_mask = rank_offsets < R + a_col = rank_offsets + if GATED_A_HALF > 0: + a_col += tl.where(pid_n * BLOCK_SIZE_N >= GATED_A_HALF, R, 0) - a = tl.load( - a_ptr + offs_token[:, None] * stride_am + a_col[None, :] * stride_ar, - mask=token_mask[:, None] & rank_mask[None, :], - other=0.0, - ) - b = tl.load( - b_ptr - + off_expert * stride_be - + offs_n[None, :] * stride_bn - + offs_r[:, None] * stride_br, - mask=(offs_n[None, :] < N) & rank_mask[:, None], - other=0.0, - ) - - accumulator = tl.dot(a, b, out_dtype=tl.float32) + a = tl.load( + a_ptr + a_token[:, None] * stride_am + a_col[None, :] * stride_ar, + mask=token_mask[:, None] & rank_mask[None, :], + other=0.0, + ) + b = tl.load( + b_ptr + + off_expert * stride_be + + offs_n[None, :] * stride_bn + + rank_offsets[:, None] * stride_br, + mask=(offs_n[None, :] < N) & rank_mask[:, None], + other=0.0, + ) + accumulator += tl.dot(a, b, out_dtype=tl.float32) if MUL_ROUTED_WEIGHT: moe_weight = tl.load(topk_weights_ptr + offs_token, mask=token_mask, other=0.0) accumulator *= moe_weight[:, None] @@ -135,6 +135,22 @@ def _moe_lora_expand_add_kernel( tl.store(c_ptrs, accumulator.to(c_ptr.dtype.element_ty), mask=c_mask) +def _get_gated_a_half(intermediate_width: int, rank: int, output_width: int) -> int: + """Return the gate/up boundary for the direct expand kernel. + + A gated gate-up adapter always needs the split. This is adapter math, not a + performance option, so it must not depend on the experimental optimization + master switch. + """ + + if intermediate_width not in (rank, 2 * rank): + raise ValueError( + f"LoRA expand intermediate width must be R ({rank}, non-gated) or " + f"2*R ({2 * rank}, gated gate_up), got {intermediate_width}" + ) + return output_width // 2 if intermediate_width == 2 * rank else 0 + + def _invoke_moe_lora_expand_add( intermediate: torch.Tensor, weight: torch.Tensor, @@ -148,18 +164,15 @@ def _invoke_moe_lora_expand_add( mul_routed_weight: bool, fuse_sum_all_reduce: bool, force_block_size_n: "int | None" = None, + broadcast_intermediate: bool = False, ) -> None: """Launch the rank-specialized LoRA-B expand kernel. - ``R`` (= ``weight.shape[2]``) up to 64 is supported. ``BLOCK_SIZE_R`` is - set to ``next_power_of_2(R)`` so each rank value pairs with the smallest - tile that covers it (R=16 → BSR=16, R=32 → BSR=32, R=64 → BSR=64). - Triton compiles a separate specialization per (R, BLOCK_SIZE_R) combo - so different ranks don't interfere with each other's perf. + Ranks through 64 use one rank tile. Larger ranks use the same kernel with + multiple 64-wide tiles. """ N = weight.shape[1] R = weight.shape[2] - assert R <= 64, f"direct LoRA expand/add expects rank <= 64, got {R}" block_size_m = config["BLOCK_SIZE_M"] # BLOCK_SIZE_N defaults to 128 when N % 128 == 0 (a good N-divisible tile that also keeps @@ -170,7 +183,9 @@ def _invoke_moe_lora_expand_add( else: block_size_n = 128 if N % 128 == 0 else config["BLOCK_SIZE_N"] group_size_m = config.get("GROUP_SIZE_M", 1) - block_size_r = triton.next_power_of_2(R) + # Pad the rank tile to >=16 for Triton's MMA K>=16 tl.dot constraint; the + # offs_r < R mask zeroes the padded rows, so the result is unchanged. + block_size_r = min(64, max(16, triton.next_power_of_2(R))) # gate_up LoRA: the shrink stacks gate_A and up_A, so the intermediate has 2*R columns # ([0:R] = gate-shrink x@gate_A^T, [R:2R] = up-shrink x@up_A^T). The up output half @@ -180,19 +195,10 @@ def _invoke_moe_lora_expand_add( # verified >100% rel error vs a PEFT reference on the real Qwen3.5 adapter). The earlier # "vs cutlass" justification for reading [0:R] was unreliable (the cutlass reference shared # the same bug). Detect the gated layout from the intermediate width and split in-kernel. - inter_width = intermediate.shape[1] - assert inter_width in (R, 2 * R), ( - f"LoRA expand intermediate width must be R ({R}, non-gated) or 2*R " - f"({2 * R}, gated gate_up), got {inter_width}" - ) - # Lazy import to avoid the trtllm_moe <-> triton_ops package import cycle at load time. - - gated = inter_width == 2 * R - use_gated_split = ( - gated and lora_envs.SGLANG_ENABLE_LORA_MOE_GATEUP_GATED_SPLIT.get() - ) - gated_a_half = (N // 2) if use_gated_split else 0 - if use_gated_split: + gated_a_half = _get_gated_a_half(intermediate.shape[1], R, N) + if gated_a_half: + while block_size_n > 16 and gated_a_half % block_size_n: + block_size_n //= 2 assert N % 2 == 0 and (N // 2) % block_size_n == 0, ( f"gated gate_up split needs N/2 ({N // 2}) divisible by BLOCK_SIZE_N " f"({block_size_n})" @@ -229,6 +235,7 @@ def _invoke_moe_lora_expand_add( BLOCK_SIZE_R=block_size_r, GROUP_SIZE_M=group_size_m, GATED_A_HALF=gated_a_half, + BROADCAST_A=broadcast_intermediate, num_warps=config.get("num_warps", 4), num_stages=1, ) diff --git a/python/sglang/srt/lora/utils.py b/python/sglang/srt/lora/utils.py index eeccd2d1a..16459ea78 100644 --- a/python/sglang/srt/lora/utils.py +++ b/python/sglang/srt/lora/utils.py @@ -293,6 +293,7 @@ def get_stacked_multiply( "in_proj_qkvz": 4, # GDN packed input projection "gate_up_proj": 2, "gate_up_proj_moe": 2, + "gate_up_proj_shared_moe": 2, "in_proj": 2, "fused_qkv_a_proj_with_mqa": 2, } @@ -322,7 +323,14 @@ def get_target_module_name(full_module_name: str, target_modules: Set[str]) -> s EMBEDDING_NAMES = ["embed_tokens", "lm_head"] -ROW_PARALLELISM_LINEAR_LORA_NAMES = ["o_proj", "out_proj", "down_proj", "down_proj_moe"] +ROW_PARALLELISM_LINEAR_LORA_NAMES = [ + "o_proj", + "out_proj", + "down_proj", + "down_proj_moe", + "down_proj_shared_moe", + "wo_ud", +] DSA_INDEXER_LORA_NAMES = frozenset( {"indexer.wq_b", "indexer.wk", "indexer.weights_proj"} ) @@ -338,7 +346,9 @@ REPLICATED_LINEAR_LORA_NAMES = [ _KNOWN_LORA_TARGET_MODULES = frozenset( { "qkv_proj", + "qkvr", "o_proj", + "wo_ud", "out_proj", "in_proj", "in_proj_qkvz", diff --git a/python/sglang/srt/managers/detokenizer_manager.py b/python/sglang/srt/managers/detokenizer_manager.py index 967b15ceb..7b3ca52e5 100644 --- a/python/sglang/srt/managers/detokenizer_manager.py +++ b/python/sglang/srt/managers/detokenizer_manager.py @@ -124,6 +124,7 @@ class DetokenizerManager(MultiHttpWorkerDetokenizerMixin): def init_tokenizer(self, server_args: ServerArgs): if server_args.skip_tokenizer_init: self.tokenizer = None + self.vocab_size = None else: self.tokenizer = get_tokenizer( server_args.tokenizer_path, @@ -132,6 +133,10 @@ class DetokenizerManager(MultiHttpWorkerDetokenizerMixin): revision=server_args.revision, tokenizer_backend=server_args.tokenizer_backend, ) + try: + self.vocab_size = len(self.tokenizer) + except TypeError: + self.vocab_size = getattr(self.tokenizer, "vocab_size", None) def init_running_status(self, server_args: ServerArgs): self.decode_status = LimitedCapacityDict(capacity=DETOKENIZER_MAX_STATES) @@ -204,6 +209,20 @@ class DetokenizerManager(MultiHttpWorkerDetokenizerMixin): # If it is embedding model, no detokenization is needed. return recv_obj + @staticmethod + def _clamp_decode_ids(ids: List[int], vocab_size: Optional[int]) -> List[int]: + """Map out-of-range token ids to 0 so the tokenizer can decode them. + + Multimodal placeholder ids (e.g. Inkling's negative -101/-102, or radix-cache + pad-value hashes) are not real vocab tokens; tiktoken-style backends raise + OverflowError on negative / out-of-range ids. These only appear in the + surrogate-context prefix (before read_offset) and carry no text, and the clamp + is applied identically to surr_ids and read_ids, so the incremental + (read-minus-surr) output text is unchanged. + """ + hi = vocab_size if vocab_size else None + return [t if (0 <= t and (hi is None or t < hi)) else 0 for t in ids] + def _grouped_batch_decode( self, ids_list: List[List[int]], @@ -270,6 +289,7 @@ class DetokenizerManager(MultiHttpWorkerDetokenizerMixin): def _decode_batch_token_id_output(self, recv_obj: BatchTokenIDOutput): bs = len(recv_obj.rids) + vocab_size = self.vocab_size # Initialize decode status read_ids, surr_ids = [], [] @@ -278,14 +298,18 @@ class DetokenizerManager(MultiHttpWorkerDetokenizerMixin): if rid not in self.decode_status: s = DecodeStatus( decoded_text=recv_obj.decoded_texts[i], - decode_ids=list(recv_obj.decode_ids[i]), + decode_ids=self._clamp_decode_ids( + recv_obj.decode_ids[i], vocab_size + ), surr_offset=0, read_offset=recv_obj.read_offsets[i], ) self.decode_status[rid] = s else: s = self.decode_status[rid] - s.decode_ids.extend(recv_obj.decode_ids[i]) + s.decode_ids.extend( + self._clamp_decode_ids(recv_obj.decode_ids[i], vocab_size) + ) read_ids.append( self.trim_matched_stop( diff --git a/python/sglang/srt/managers/io_struct.py b/python/sglang/srt/managers/io_struct.py index 6f900c7e0..fa359c548 100644 --- a/python/sglang/srt/managers/io_struct.py +++ b/python/sglang/srt/managers/io_struct.py @@ -2041,6 +2041,7 @@ class LoadLoRAAdapterFromTensorsReqInput(BaseReq, kw_only=True): added_tokens_config: Optional[Dict[str, int]] = None lora_id: Optional[str] = None load_format: Optional[str] = None + expected_checksums: Optional[Dict[str, str]] = None def to_ref(self) -> LoRARef: return LoRARef( diff --git a/python/sglang/srt/managers/schedule_policy.py b/python/sglang/srt/managers/schedule_policy.py index 1e24895f4..0e6d0f214 100644 --- a/python/sglang/srt/managers/schedule_policy.py +++ b/python/sglang/srt/managers/schedule_policy.py @@ -636,10 +636,33 @@ class PrefillAdder: alloc = min(extend_input_len, self.rem_chunk_tokens) else: alloc = extend_input_len - budget = max(alloc, self.tree_cache.sliding_window_size) + self.page_size + window = self.tree_cache.sliding_window_size + return max(alloc - window, 0) + self._swa_reserved_tokens(swa_host_hit_length) + + def _swa_reserved_tokens(self, swa_host_hit_length: int = 0) -> int: + """SWA tokens a request needs regardless of extend length: the sliding + window (decode headroom) + allocator page slack + the load-back window + charge. Shared floor of _swa_budget_for_req and _swa_chunk_cap.""" + reserved = self.tree_cache.sliding_window_size + self.page_size if swa_host_hit_length > 0: - budget += self.ceil_paged_tokens(swa_host_hit_length) - return budget + reserved += self.ceil_paged_tokens(swa_host_hit_length) + return reserved + + def _swa_chunk_cap(self, swa_host_hit_length: int = 0) -> int: + """Largest page-aligned extend chunk the SWA pool can admit right now, + keeping a sliding window of headroom below rem_swa_tokens; 0 if not + even one page fits. Only valid when is_hybrid_swa is True. + + Escape hatch for a request whose budget can never pass the + _swa_budget_for_req gate (extend near/above the pool size, or a large + load-back charge): without shrinking its chunk it would be rejected + forever (head-of-line livelock). Shrinking is sound because past a + chunk boundary only the sliding window stays locked — the rest turns + evictable — so each pass's transient footprint fits the pool.""" + cap = int(self.rem_swa_tokens) - self._swa_reserved_tokens(swa_host_hit_length) + if cap <= 0: + return 0 + return cap // self.page_size * self.page_size def _mamba_gap_budget_for_req(self, req: Req) -> int: """Shared-gap reservation (full-token-equivalents) for a request's new @@ -1023,12 +1046,19 @@ class PrefillAdder: if total_tokens >= self.rem_total_tokens: return AddReqResult.NO_TOKEN + chunk_tokens_limit = self.rem_chunk_tokens if self.is_hybrid_swa: + # host-hit prefix is loaded back, not re-prefilled, so the SWA peak is + # driven only by the freshly-prefilled tail (the loaded window is + # charged separately via swa_host_hit_length). swa_needed = self._swa_budget_for_req( - cand_extend_input_len, swa_host_hit_length=req.swa_host_hit_length + real_input_tokens, swa_host_hit_length=req.swa_host_hit_length ) if swa_needed >= self.rem_swa_tokens: - return AddReqResult.NO_TOKEN + swa_cap = self._swa_chunk_cap(req.swa_host_hit_length) + if self.rem_chunk_tokens is None or swa_cap <= 0: + return AddReqResult.NO_TOKEN + chunk_tokens_limit = min(self.rem_chunk_tokens, swa_cap) if ( self.rem_chunk_tokens is None @@ -1046,11 +1076,15 @@ class PrefillAdder: return AddReqResult.NO_TOKEN if self.is_hybrid_swa: + # self.rem_swa_tokens may decrease after the lock acquisition swa_needed = self._swa_budget_for_req( - cand_extend_input_len, swa_host_hit_length=req.swa_host_hit_length + real_input_tokens, swa_host_hit_length=req.swa_host_hit_length ) if swa_needed >= self.rem_swa_tokens: - return AddReqResult.NO_TOKEN + swa_cap = self._swa_chunk_cap(req.swa_host_hit_length) + if self.rem_chunk_tokens is None or swa_cap <= 0: + return AddReqResult.NO_TOKEN + chunk_tokens_limit = min(self.rem_chunk_tokens, swa_cap) if req.needs_host_load_back(): new_indices, req.last_node = self.tree_cache.init_load_back( @@ -1088,7 +1122,7 @@ class PrefillAdder: self._add_dllm_req(req, prefix_len) self._req_inc_lock_ref(req) - elif self.rem_chunk_tokens is None or input_tokens <= self.rem_chunk_tokens: + elif chunk_tokens_limit is None or input_tokens <= chunk_tokens_limit: # Non-chunked prefill — the whole sequence is committed this iter. req.set_extend_range( len(req.prefix_indices), len(req.full_untruncated_fill_ids) @@ -1108,7 +1142,7 @@ class PrefillAdder: ) else: # Make sure at least one page is available - trunc_len = self.rem_chunk_tokens // self.page_size * self.page_size + trunc_len = chunk_tokens_limit // self.page_size * self.page_size if trunc_len <= 0: return AddReqResult.OTHER diff --git a/python/sglang/srt/managers/scheduler.py b/python/sglang/srt/managers/scheduler.py index cb4a54768..c46a8c596 100644 --- a/python/sglang/srt/managers/scheduler.py +++ b/python/sglang/srt/managers/scheduler.py @@ -1481,6 +1481,18 @@ class Scheduler( self.schedule_stream = self.device_module.Stream(priority=0) if self.device == "cpu": self.schedule_stream.synchronize = lambda: None # No-op for CPU + elif is_cuda() or _is_hip: + # CUDA/HIP streams come from a fixed round-robin pool. Redraw if this + # stream aliases forward_stream, which would eliminate scheduler + # overlap. Only CUDA/HIP streams expose a ``cuda_stream`` handle; + # other accelerators (e.g. NPU/XPU) skip the alias check. + _redraws = 0 + while ( + self.schedule_stream.cuda_stream == self.forward_stream.cuda_stream + and _redraws < 64 + ): + self.schedule_stream = self.device_module.Stream(priority=0) + _redraws += 1 # The global WAR barrier fences the scheduler's next shared-buffer write # on the previous forward's read of the unified memory pool. self._war_barrier_enabled = is_cuda() or envs.SGLANG_ENABLE_WAR_BARRIER.get() diff --git a/python/sglang/srt/managers/scheduler_components/invariant_checker.py b/python/sglang/srt/managers/scheduler_components/invariant_checker.py index b9937b258..20ef0183d 100644 --- a/python/sglang/srt/managers/scheduler_components/invariant_checker.py +++ b/python/sglang/srt/managers/scheduler_components/invariant_checker.py @@ -160,11 +160,14 @@ class SchedulerInvariantChecker: self.req_to_token_pool.mamba_pool.size, ) if leak: - # Page-level leak diagnosis for mamba - free_full_pages = set( - self.token_to_kv_pool_allocator.free_pages.tolist() - + self.token_to_kv_pool_allocator.release_pages.tolist() - ) + # Page-level leak diagnosis for mamba. Allocator flavors without + # page free-lists (free_pages is None) skip the page census — the + # dump must never crash the watchdog thread that calls it. + free_pages = self.token_to_kv_pool_allocator.free_pages + release_pages = self.token_to_kv_pool_allocator.release_pages + if free_pages is None or release_pages is None: + return leak, msg + free_full_pages = set(free_pages.tolist() + release_pages.tolist()) cached_full_pages = set(self.tree_cache.all_values_flatten().tolist()) expected_full_pages = set( range(1, self.token_to_kv_pool_allocator.size + 1) diff --git a/python/sglang/srt/managers/tp_worker.py b/python/sglang/srt/managers/tp_worker.py index eee3e4fb1..6d09e8133 100644 --- a/python/sglang/srt/managers/tp_worker.py +++ b/python/sglang/srt/managers/tp_worker.py @@ -221,6 +221,37 @@ class BaseTpWorker(ABC): tensors = dict(bucket.reconstruct_tensors()) else: tensors = MultiprocessingSerializer.deserialize(recv_req.serialized_tensors) + if recv_req.expected_checksums is not None: + import hashlib + + exp = recv_req.expected_checksums + mismatch, missing = [], [] + for name, want in exp.items(): + if name not in tensors: + missing.append(name) + continue + got = hashlib.sha256( + tensors[name] + .detach() + .cpu() + .contiguous() + .flatten() + .view(torch.uint8) + .numpy() + .tobytes() + ).hexdigest() + if got != want: + mismatch.append(name) + extra = [n for n in tensors if n not in exp] + if mismatch or missing or extra: + raise RuntimeError( + f"[LORA-CHECK] rank{self.tp_rank} adapter sync MISMATCH of {len(exp)} expected: " + f"{len(mismatch)} value-diff {mismatch[:5]}, {len(missing)} missing {missing[:5]}, " + f"{len(extra)} extra {extra[:5]}" + ) + logger.info( + f"[LORA-CHECK] rank{self.tp_rank} adapter sync OK: {len(exp)}/{len(exp)} tensors match (sha256)" + ) result = self.model_runner.load_lora_adapter_from_tensors( recv_req.to_ref(), tensors, diff --git a/python/sglang/srt/mem_cache/allocation_sizing.py b/python/sglang/srt/mem_cache/allocation_sizing.py index b1fcf293a..10d08d628 100644 --- a/python/sglang/srt/mem_cache/allocation_sizing.py +++ b/python/sglang/srt/mem_cache/allocation_sizing.py @@ -51,10 +51,12 @@ def get_req_to_token_extra_context_len(server_args: ServerArgs) -> int: """ # FIXME(lsyin): temporary fix for the context length issue under spec decoding extra = 4 + (server_args.max_speculative_num_draft_tokens or 0) - if ( - server_args.speculative_algorithm is not None - and server_args.page_size > 1 - and (server_args.speculative_eagle_topk or 1) > 1 - ): - extra = max(extra, get_alloc_reserve_per_decode(server_args)) + if server_args.speculative_algorithm is not None and server_args.page_size > 1: + # kv_allocated_len is page-aligned (eagle_prepare_for_decode), so near + # the context limit the aligned reserve can overshoot by page_size - 1; + # without the headroom the row write silently lands in the neighbor row. + extra = max( + extra, + get_alloc_reserve_per_decode(server_args) + server_args.page_size - 1, + ) return extra diff --git a/python/sglang/srt/mem_cache/hybrid_cache/hybrid_pool_assembler.py b/python/sglang/srt/mem_cache/hybrid_cache/hybrid_pool_assembler.py index 18b314bd9..2d5791ec1 100644 --- a/python/sglang/srt/mem_cache/hybrid_cache/hybrid_pool_assembler.py +++ b/python/sglang/srt/mem_cache/hybrid_cache/hybrid_pool_assembler.py @@ -598,6 +598,110 @@ def build_hybrid_mamba_stack( return host_pool_group, cache_controller +def build_hybrid_mamba_swa_stack( + *, + params: CacheInitParams, + server_args: ServerArgs, + full_kv_pool: Any, + swa_kv_pool: Any, + mamba_pool: Any, + full_layer_mapping: dict[int, int], + swa_layer_mapping: dict[int, int], + mamba_layer_mapping: dict[int, int], + page_size: int, + tp_group, + load_cache_event, + attn_cp_group: Optional[torch.distributed.ProcessGroup] = None, + attn_tp_group: Optional[torch.distributed.ProcessGroup] = None, + pp_group: Optional[torch.distributed.ProcessGroup] = None, + storage_backend: Optional[str], + host_swa_evict_fn: Optional[Callable[[int], Any]] = None, + device_swa_evict_fn: Optional[Callable[[int], Any]] = None, + host_mamba_evict_fn: Optional[Callable[[int], Any]] = None, + device_mamba_evict_fn: Optional[Callable[[int], Any]] = None, + prefetch_threshold: int = 256, + model_name: Optional[str] = None, + storage_backend_extra_config: Optional[dict] = None, + enable_storage_metrics: bool = False, +) -> tuple[HostPoolGroup, HybridCacheController]: + transfer_layer_num = len( + full_layer_mapping | swa_layer_mapping | mamba_layer_mapping + ) + swa_attn_allocator = params.token_to_kv_pool_allocator.swa_attn_allocator + mamba_allocator = params.req_to_token_pool.mamba_allocator + kv_host_pool = build_kv_host_pool( + kv_pool=full_kv_pool, + page_size=page_size, + server_args=server_args, + use_mla=False, + ) + swa_host_pool = build_kv_host_pool( + kv_pool=swa_kv_pool, + page_size=page_size, + server_args=server_args, + use_mla=False, + ) + mamba_host_pool = MambaPoolHost( + mamba_pool, + server_args.hicache_ratio, + server_args.hicache_size, + allocator_type=server_args.hicache_storage_backend, + layout=server_args.hicache_mem_layout, + ) + entries = [ + build_pool_entry( + name=PoolName.KV, + host_pool=kv_host_pool, + device_pool=full_kv_pool, + layer_mapping=full_layer_mapping, + transfer_layer_num=transfer_layer_num, + is_anchor=True, + ), + build_pool_entry( + name=PoolName.SWA, + host_pool=swa_host_pool, + device_pool=swa_kv_pool, + layer_mapping=swa_layer_mapping, + transfer_layer_num=transfer_layer_num, + host_evict_fn=host_swa_evict_fn, + device_evict_fn=device_swa_evict_fn, + device_alloc_fn=swa_attn_allocator.alloc, + device_free_fn=swa_attn_allocator.free, + ), + build_pool_entry( + name=PoolName.MAMBA, + host_pool=mamba_host_pool, + device_pool=mamba_pool, + layer_mapping=mamba_layer_mapping, + transfer_layer_num=transfer_layer_num, + host_evict_fn=host_mamba_evict_fn, + device_evict_fn=device_mamba_evict_fn, + device_alloc_fn=mamba_allocator.alloc, + device_free_fn=mamba_allocator.free, + ), + ] + host_pool_group = HostPoolGroup(entries) + cache_controller = HybridCacheController( + params.token_to_kv_pool_allocator, + host_pool_group, + page_size, + tp_group, + load_cache_event=load_cache_event, + attn_cp_group=attn_cp_group, + attn_tp_group=attn_tp_group, + pp_group=pp_group, + write_policy=server_args.hicache_write_policy, + io_backend=server_args.hicache_io_backend, + storage_backend=storage_backend, + prefetch_threshold=prefetch_threshold, + model_name=model_name, + storage_backend_extra_config=storage_backend_extra_config, + transfer_layer_num=transfer_layer_num, + enable_storage_metrics=enable_storage_metrics, + ) + return host_pool_group, cache_controller + + def build_anchor_sidecar_stack( *, params: CacheInitParams, @@ -934,6 +1038,81 @@ class _SwaStrategy(StackStrategy): ) +class _MambaSwaStrategy(StackStrategy): + def matches(self, kvcache, components): + from sglang.srt.mem_cache.deepseek_v4_memory_pool import ( + DeepSeekV4TokenToKVPool, + ) + from sglang.srt.mem_cache.swa_memory_pool import SWAKVPool + + return ( + isinstance(kvcache, SWAKVPool) + and not isinstance(kvcache, DeepSeekV4TokenToKVPool) + and components + == {ComponentType.FULL, ComponentType.SWA, ComponentType.MAMBA} + ) + + def build( + self, + *, + cache, + kvcache, + params, + server_args, + load_cache_event, + attn_cp_group=None, + attn_tp_group=None, + storage_backend=None, + storage_backend_extra_config=None, + prefetch_threshold=256, + model_name=None, + enable_storage_metrics=False, + ): + from sglang.srt.mem_cache.base_prefix_cache import EvictParams + + full_layer_mapping, swa_layer_mapping = _swa_layer_mappings(kvcache) + mamba_layer_mapping = dict(params.req_to_token_pool.mamba_map) + host_pool_group, cache_controller = build_hybrid_mamba_swa_stack( + params=params, + server_args=server_args, + full_kv_pool=kvcache.full_kv_pool, + swa_kv_pool=kvcache.swa_kv_pool, + mamba_pool=params.req_to_token_pool.mamba_pool, + full_layer_mapping=full_layer_mapping, + swa_layer_mapping=swa_layer_mapping, + mamba_layer_mapping=mamba_layer_mapping, + page_size=cache.page_size, + tp_group=params.tp_cache_group, + load_cache_event=load_cache_event, + attn_cp_group=attn_cp_group, + attn_tp_group=attn_tp_group, + pp_group=params.pp_cache_group, + storage_backend=storage_backend, + host_swa_evict_fn=lambda n: cache.evict_host(n, ComponentType.SWA), + device_swa_evict_fn=lambda n: cache.evict(EvictParams(swa_num_tokens=n)), + host_mamba_evict_fn=lambda n: cache.evict_host(n, ComponentType.MAMBA), + device_mamba_evict_fn=lambda n: cache.evict(EvictParams(mamba_num=n)), + prefetch_threshold=prefetch_threshold, + model_name=model_name, + storage_backend_extra_config=storage_backend_extra_config, + enable_storage_metrics=enable_storage_metrics, + ) + return StackBuildResult( + host_pool_group=host_pool_group, + cache_controller=cache_controller, + component_host_pools={ + ComponentType.FULL: host_pool_group.get_pool(PoolName.KV), + ComponentType.SWA: host_pool_group.get_pool(PoolName.SWA), + ComponentType.MAMBA: host_pool_group.get_pool(PoolName.MAMBA), + }, + register_req_to_token_counter=True, + transfer_layer_num=len( + full_layer_mapping | swa_layer_mapping | mamba_layer_mapping + ), + pools_desc="KV + SWA + MAMBA", + ) + + class _DsaStrategy(StackStrategy): def matches(self, kvcache, components): from sglang.srt.mem_cache.memory_pool import DSATokenToKVPool @@ -1148,6 +1327,7 @@ _STRATEGIES: list[StackStrategy] = [ _DeepSeekV4Strategy(), _MambaStrategy(), _SwaStrategy(), + _MambaSwaStrategy(), _DsaStrategy(), _MiniMaxSparseStrategy(), _PlainKvStrategy(), diff --git a/python/sglang/srt/mem_cache/kv_cache_configurator.py b/python/sglang/srt/mem_cache/kv_cache_configurator.py index 998609f82..5b406127b 100644 --- a/python/sglang/srt/mem_cache/kv_cache_configurator.py +++ b/python/sglang/srt/mem_cache/kv_cache_configurator.py @@ -48,6 +48,7 @@ from sglang.srt.mem_cache.memory_pool import ( KVCache, MHATokenToKVPool, MHATokenToKVPoolFP4, + MHATokenToKVPoolMXFP8, MiniMaxSparseKVPool, MLATokenToKVPool, MLATokenToKVPoolFP4, @@ -176,12 +177,30 @@ class KVCacheConfigurator: req_to_token_pool: Optional[ReqToTokenPool] token_to_kv_pool_allocator: Optional[BaseTokenToKVPoolAllocator] memory_pool_config: Optional[MemoryPoolConfig] + draft_model_idx: Optional[int] = None mambaish_config: Optional[Any] = field(init=False) hybrid_gdn_config: Optional[Any] = field(init=False) + is_inkling_mtp_draft: bool = field(init=False) + draft_swa_full_capacity: bool = field(init=False) def __post_init__(self) -> None: self.mambaish_config = mambaish_config(self.model_config) self.hybrid_gdn_config = hybrid_gdn_config(self.model_config) + # Each multi-layer EAGLE MTP head owns one transformer block at + # layer_id=draft_model_idx; heads at a banded 's' depth route that layer + # into the SWA ring sub-pool (draft_swa_full_capacity) so the SWA + # store/read path activates for this depth, exactly like a trunk local + # layer. + self.is_inkling_mtp_draft = ( + self.is_draft_worker + and self.draft_model_idx is not None + and self.model_config.hf_config.architectures[0] + == "InklingForConditionalGenerationMTP" + ) + self.draft_swa_full_capacity = self.is_inkling_mtp_draft and ( + self.draft_model_idx + in set(self.model_config.hf_text_config.mtp_local_layer_ids) + ) def _build_fp4_quant_method(self, *, num_layers: int): if not is_float4_e2m1fn_x2(self.kv_cache_dtype): @@ -329,6 +348,24 @@ class KVCacheConfigurator: else: # Draft worker shares req_to_token_pool with the target worker. assert self.is_draft_worker + # Each multi-layer EAGLE MTP head owns one transformer block at + # layer_id=draft_model_idx and needs its own sconv/mamba cache while + # sharing the target's request-to-token mapping. + if self.is_inkling_mtp_draft and isinstance( + req_to_token_pool, HybridReqToTokenPool + ): + # speculative_num_draft_tokens=None: draft heads never run + # TARGET_VERIFY, so their pools skip the per-step intermediate + # (SpeculativeState) buffers only the target pool consumes. + req_to_token_pool = req_to_token_pool.clone_with_new_mamba( + mamba_size=self.server_args.max_mamba_cache_size, + mamba_spec_state_size=sizes.max_running_requests, + cache_params=self.mambaish_config.mamba2_cache_params, + device=self.device, + enable_mamba_extra_buffer=self.server_args.enable_mamba_extra_buffer(), + draft_model_idx=self.draft_model_idx, + speculative_eagle_topk=self.server_args.speculative_eagle_topk, + ) # Initialize token_to_kv_pool is_dsa_model = is_deepseek_dsa(self.model_config.hf_config) @@ -1144,19 +1181,48 @@ class KVCacheConfigurator: "swa_v_head_dim": self.model_config.swa_v_head_dim, "v_head_dim": self.model_config.v_head_dim, } + swa_pool_class = ( + MHATokenToKVPoolMXFP8 + if self.server_args.kv_cache_dtype == "mxfp8" + else mha_pool_class + ) + swa_attention_layer_ids = self.model_config.swa_attention_layer_ids + full_attention_layer_ids = self.model_config.full_attention_layer_ids + if self.is_inkling_mtp_draft: + if self.draft_swa_full_capacity: + # Banded 's' depth: route the draft's single layer into the SWA + # ring sub-pool so use_sliding_window_kv_pool activates the SWA + # store/read path for this depth, exactly like a trunk local + # layer. + swa_attention_layer_ids = [self.draft_model_idx] + full_attention_layer_ids = [] + else: + swa_attention_layer_ids = [] + full_attention_layer_ids = [self.draft_model_idx] + # Size the banded draft's SWA ring to FULL draft capacity (not the + # trunk-window-derived swa_max): with the identity full->swa mapping + # registered in _build_token_to_kv_pool_allocator, every logical slot + # the shared target allocator hands out (up to full_max) must be + # addressable in the ring, whatever the head-vs-trunk window + # relationship. + size_swa = ( + full_max_total_num_tokens + if self.draft_swa_full_capacity + else swa_max_total_num_tokens + ) token_to_kv_pool = SWAKVPool( size=full_max_total_num_tokens, - size_swa=swa_max_total_num_tokens, + size_swa=size_swa, page_size=self.server_args.page_size, dtype=self.kv_cache_dtype, post_capture_active=self.post_capture_kv_active, head_num=self.model_config.get_num_kv_heads(get_parallel().attn_tp_size), head_dim=self.model_config.head_dim, - swa_attention_layer_ids=self.model_config.swa_attention_layer_ids, - full_attention_layer_ids=self.model_config.full_attention_layer_ids, + swa_attention_layer_ids=swa_attention_layer_ids, + full_attention_layer_ids=full_attention_layer_ids, device=self.device, enable_kv_cache_copy=(self.server_args.speculative_algorithm is not None), - token_to_kv_pool_class=mha_pool_class, + token_to_kv_pool_class=swa_pool_class, **kwargs, ) return token_to_kv_pool @@ -1211,6 +1277,13 @@ class KVCacheConfigurator: quant_method = self._build_fp4_quant_method( num_layers=len(full_attention_layer_ids) ) + # MXFP8 KV cache needs the block-scaled pool (data + UE8M0 scale + # buffers) for the full-attention layers, same as the SWA branch. + full_pool_class = ( + MHATokenToKVPoolMXFP8 + if self.server_args.kv_cache_dtype == "mxfp8" and not self.use_mla_backend + else mha_pool_class + ) token_to_kv_pool = HybridLinearKVPool( page_size=self.server_args.page_size, size=max_total_num_tokens, @@ -1225,7 +1298,7 @@ class KVCacheConfigurator: enable_kv_cache_copy=(self.server_args.speculative_algorithm is not None), use_mla=self.use_mla_backend, start_layer=self.layer_info.start_layer, - full_kv_pool_class=mha_pool_class, + full_kv_pool_class=full_pool_class, quant_method=quant_method, post_capture_active=self.post_capture_kv_active and quant_method is None, **extra_args, @@ -1253,11 +1326,14 @@ class KVCacheConfigurator: def _build_mha_kv_pool( self, *, max_total_num_tokens: int, mha_pool_class: type, quant_method=None ) -> KVCache: - pool_cls = ( - NoOpMHATokenToKVPool - if self.server_args.prefill_only_disable_kv_cache - else mha_pool_class - ) + if self.server_args.kv_cache_dtype == "mxfp8": + pool_cls = MHATokenToKVPoolMXFP8 + else: + pool_cls = ( + NoOpMHATokenToKVPool + if self.server_args.prefill_only_disable_kv_cache + else mha_pool_class + ) pool_kwargs = {} if quant_method is not None: pool_kwargs["quant_method"] = quant_method @@ -1413,15 +1489,31 @@ class KVCacheConfigurator: else: assert self.is_draft_worker if self.is_hybrid_swa: - swa_allocator = getattr( - token_to_kv_pool_allocator, - "logical_attn_allocator", - token_to_kv_pool_allocator, - ) - assert isinstance(swa_allocator, SWATokenToKVPoolAllocator) - token_to_kv_pool.register_mapping( - swa_allocator.full_to_swa_index_mapping - ) + if self.draft_swa_full_capacity: + # Banded depth: the SWA ring is full draft capacity, so use + # an IDENTITY full->swa mapping — store and read locs both + # equal out_cache_loc, and a slot is never evicted before + # the request frees it. The window itself is enforced by the + # FA sliding-window kernel, not by the ring. Layout mirrors + # SWATokenToKVPoolAllocator's mapping (size + page_size + # entries + trailing -1 sentinel so a -1 last_loc maps + # to -1). + n = sizes.full_max_total_num_tokens + self.page_size + identity_mapping = torch.arange( + n + 1, dtype=torch.int64, device=self.device + ) + identity_mapping[-1] = -1 + token_to_kv_pool.register_mapping(identity_mapping) + else: + swa_allocator = getattr( + token_to_kv_pool_allocator, + "logical_attn_allocator", + token_to_kv_pool_allocator, + ) + assert isinstance(swa_allocator, SWATokenToKVPoolAllocator) + token_to_kv_pool.register_mapping( + swa_allocator.full_to_swa_index_mapping + ) return token_to_kv_pool_allocator def _profile_available_bytes(self, pre_model_load_memory: int) -> int: diff --git a/python/sglang/srt/mem_cache/kv_cache_dtype.py b/python/sglang/srt/mem_cache/kv_cache_dtype.py index 9635ea5ce..789dbe713 100644 --- a/python/sglang/srt/mem_cache/kv_cache_dtype.py +++ b/python/sglang/srt/mem_cache/kv_cache_dtype.py @@ -50,6 +50,8 @@ def configure_kv_cache_dtype( kv_cache_dtype = fp8_dtype else: kv_cache_dtype = torch.float8_e4m3fn + elif server_args_kv_cache_dtype == "mxfp8": + kv_cache_dtype = torch.float8_e4m3fn elif server_args_kv_cache_dtype in ("bf16", "bfloat16"): kv_cache_dtype = torch.bfloat16 elif server_args_kv_cache_dtype == "fp4_e2m1": diff --git a/python/sglang/srt/mem_cache/mamba_slot_fused.py b/python/sglang/srt/mem_cache/mamba_slot_fused.py new file mode 100644 index 000000000..ccce228ef --- /dev/null +++ b/python/sglang/srt/mem_cache/mamba_slot_fused.py @@ -0,0 +1,171 @@ +"""Fused Triton kernels that clear / copy conv-state pool slots across all +conv-state tensors of a hybrid (mamba-style) pool in a single launch. + +``MambaPool.clear_slots`` / ``copy_from`` otherwise loop over every conv-state +tensor (models with several short-conv streams have a handful, each a distinct +scattered-index kernel), and the speculative-decode draft worker replays that +loop across every draft head's pool — so one fresh-request / radix-COW event +fans out into many tiny launch-bound kernels on the forward stream. These +kernels fold the whole conv list into one launch. + +The conv tensors are heterogeneous only in their trailing feature size and share +the leading ``[num_layers, pool_size]`` dims + dtype, so they are addressed via a +per-tensor pointer / stride / feature-length array. The kernel reads each +tensor's real ``layer_stride`` / ``slot_stride``, so it is layout-general; it +only requires the per-slot feature block to be contiguous. Temporal state +(different dtype/shape) is handled by the caller with a plain indexed op. +""" + +from __future__ import annotations + +from typing import List, NamedTuple + +import torch +import triton +import triton.language as tl + +_BLOCK = 1024 + + +class ConvSlotDescriptor(NamedTuple): + ptr: torch.Tensor # [T] int64 base byte-addresses + feat: torch.Tensor # [T] int64 per-slot feature length (elements) + layer_stride: torch.Tensor # [T] int64 element stride between layers + slot_stride: torch.Tensor # [T] int64 element stride between slots + num_layers: int + max_feat_blocks: int + + +@triton.jit +def _fused_slot_clear_kernel( + ptr_arr, + feat_arr, + layer_stride_arr, + slot_stride_arr, + index_arr, + MAX_FEAT_BLOCKS: tl.constexpr, + BLOCK: tl.constexpr, +): + iid = tl.program_id(0) + tid = tl.program_id(1) + lid = tl.program_id(2) + base_addr = tl.load(ptr_arr + tid) + feat = tl.load(feat_arr + tid) + slot = tl.load(index_arr + iid) + base = base_addr.to(tl.pointer_type(tl.bfloat16)) + row = ( + base + + lid * tl.load(layer_stride_arr + tid) + + slot * tl.load(slot_stride_arr + tid) + ) + zeros = tl.zeros((BLOCK,), dtype=tl.bfloat16) + for fb in tl.static_range(MAX_FEAT_BLOCKS): + cols = fb * BLOCK + tl.arange(0, BLOCK) + tl.store(row + cols, zeros, mask=cols < feat) + + +@triton.jit +def _fused_slot_copy_kernel( + ptr_arr, + feat_arr, + layer_stride_arr, + slot_stride_arr, + src_arr, + dst_arr, + MAX_FEAT_BLOCKS: tl.constexpr, + BLOCK: tl.constexpr, +): + iid = tl.program_id(0) + tid = tl.program_id(1) + lid = tl.program_id(2) + base_addr = tl.load(ptr_arr + tid) + feat = tl.load(feat_arr + tid) + layer_off = lid * tl.load(layer_stride_arr + tid) + slot_stride = tl.load(slot_stride_arr + tid) + base = base_addr.to(tl.pointer_type(tl.bfloat16)) + src_row = base + layer_off + tl.load(src_arr + iid) * slot_stride + dst_row = base + layer_off + tl.load(dst_arr + iid) * slot_stride + for fb in tl.static_range(MAX_FEAT_BLOCKS): + cols = fb * BLOCK + tl.arange(0, BLOCK) + mask = cols < feat + tl.store(dst_row + cols, tl.load(src_row + cols, mask=mask), mask=mask) + + +def build_conv_slot_descriptor(tensors: List[torch.Tensor]) -> ConvSlotDescriptor: + """Build the pool-stable addressing descriptor for a conv-tensor list. + + Requires bf16 tensors sharing the leading (num_layers, pool_size) dims with a + contiguous per-slot feature block (the kernel reads each tensor's real + strides, so the block may sit inside a larger strided envelope). Cache the + result and reuse it — conv tensors don't move after allocation. + """ + t0 = tensors[0] + num_layers = t0.shape[0] + device = t0.device + ptr, feat, layer_stride, slot_stride = [], [], [], [] + max_feat = 0 + for t in tensors: + assert t.dtype == torch.bfloat16, "fused slot ops assume bf16 conv state" + assert t.shape[0] == num_layers, "conv tensors must share num_layers" + assert t.device == device + assert t[0, 0].is_contiguous(), "per-slot feature block must be contiguous" + ptr.append(t.data_ptr()) + feat.append(t[0, 0].numel()) + layer_stride.append(t.stride(0)) + slot_stride.append(t.stride(1)) + max_feat = max(max_feat, t[0, 0].numel()) + to_i64 = lambda xs: torch.tensor(xs, dtype=torch.int64, device=device) + return ConvSlotDescriptor( + ptr=to_i64(ptr), + feat=to_i64(feat), + layer_stride=to_i64(layer_stride), + slot_stride=to_i64(slot_stride), + num_layers=num_layers, + max_feat_blocks=triton.cdiv(max_feat, _BLOCK), + ) + + +def fused_clear_conv_slots(desc: ConvSlotDescriptor, indices: torch.Tensor): + """Zero ``indices`` slots (dim 1) across every conv tensor in one launch.""" + if desc.ptr.numel() == 0 or indices.numel() == 0: + return + index_arr = indices.to(torch.int64) + # Slot count on the unbounded grid axis (gridDim.y/z cap at 65535). + grid = (index_arr.numel(), desc.ptr.numel(), desc.num_layers) + _fused_slot_clear_kernel[grid]( + desc.ptr, + desc.feat, + desc.layer_stride, + desc.slot_stride, + index_arr, + MAX_FEAT_BLOCKS=desc.max_feat_blocks, + BLOCK=_BLOCK, + ) + + +def fused_copy_conv_slots( + desc: ConvSlotDescriptor, src_indices: torch.Tensor, dst_indices: torch.Tensor +): + """Copy conv state from ``src`` slots to ``dst`` slots across every conv + tensor in one launch. + + ``src`` and ``dst`` must be disjoint (the COW invariant: radix-checkpoint + slots copied into freshly-allocated slots). Unlike the gather-then-scatter + reference, this kernel reads and writes in one pass, so overlapping ranges + would race. + """ + if desc.ptr.numel() == 0 or src_indices.numel() == 0: + return + src_arr = src_indices.to(torch.int64) + dst_arr = dst_indices.to(torch.int64) + grid = (src_arr.numel(), desc.ptr.numel(), desc.num_layers) + _fused_slot_copy_kernel[grid]( + desc.ptr, + desc.feat, + desc.layer_stride, + desc.slot_stride, + src_arr, + dst_arr, + MAX_FEAT_BLOCKS=desc.max_feat_blocks, + BLOCK=_BLOCK, + ) diff --git a/python/sglang/srt/mem_cache/memory_pool.py b/python/sglang/srt/mem_cache/memory_pool.py index 0599ec7a1..65faa9dad 100644 --- a/python/sglang/srt/mem_cache/memory_pool.py +++ b/python/sglang/srt/mem_cache/memory_pool.py @@ -23,11 +23,13 @@ KVCache actually holds the physical kv cache. from __future__ import annotations import abc +import copy import dataclasses import logging import math from contextlib import contextmanager, nullcontext from dataclasses import dataclass, fields +from functools import cached_property from typing import TYPE_CHECKING, Any, List, Optional, Tuple, Union import numpy as np @@ -577,8 +579,11 @@ class MambaPool: # `conv_window_dedup_enabled` for the full rationale. The # `fused_conv_window_scatter_with_mask` scatter is layout-agnostic, # so the dense fallback reads correctly through the same code path. - dedup_conv_window = conv_window_dedup_enabled( - _is_npu, _is_cpu, speculative_eagle_topk, cache_params.is_kda + dedup_conv_window = ( + not cache_params.shape.disable_conv_window_dedup + and conv_window_dedup_enabled( + _is_npu, _is_cpu, speculative_eagle_topk, cache_params.is_kda + ) ) self._intermediate_conv_window_phys = [] if dedup_conv_window: @@ -690,10 +695,53 @@ class MambaPool: return self.mamba_cache def mamba2_layer_cache(self, layer_id: int): - return self.mamba_cache.at_layer_idx(layer_id) + # The per-layer views are pool-stable (mamba_cache is only bound at + # construction), so each layer's State is built once. + cached = self._layer_cache_by_id.get(layer_id) + if cached is None: + cached = self.mamba_cache.at_layer_idx(layer_id) + self._layer_cache_by_id[layer_id] = cached + return cached + + # These properties are pool-stable (conv tensors don't move after allocation) + # so they're cached per instance on first use. Defined as cached_property + # rather than set in __init__ because UnifiedMambaPool skips super().__init__. + @cached_property + def _layer_cache_by_id(self) -> dict: + return {} + + @cached_property + def _conv_fuse_ok(self) -> bool: + """Whether clear/copy may use the fused kernel: CUDA bf16 contiguous conv. + Strided (page-major / unified envelope) or non-bf16 conv fall back to the + per-tensor Python loop.""" + convs = self.mamba_cache.conv + return ( + not _is_npu + and len(convs) > 0 + and convs[0].is_cuda + and all(c.dtype == torch.bfloat16 and c.is_contiguous() for c in convs) + ) + + @cached_property + def _conv_slot_desc(self): + from sglang.srt.mem_cache.mamba_slot_fused import build_conv_slot_descriptor + + return build_conv_slot_descriptor(self.mamba_cache.conv) + + def _should_fuse_slot_ops(self) -> bool: + return self._conv_fuse_ok and not envs.SGLANG_DISABLE_FUSED_MAMBA_SLOT_OPS.get() def clear_slots(self, indices: torch.Tensor): """Zero out mamba state at the given pool indices. Must run on forward stream.""" + if self._should_fuse_slot_ops(): + from sglang.srt.mem_cache.mamba_slot_fused import fused_clear_conv_slots + + fused_clear_conv_slots(self._conv_slot_desc, indices) + temporal = self.mamba_cache.temporal + if temporal.numel() > 0: + temporal[:, indices] = 0 + return if not _is_npu: need_size = len(indices) for i in range(len(self.mamba_cache.conv)): @@ -733,13 +781,27 @@ class MambaPool: f"(write_pos==0), got {src_wp.tolist()} for src " f"{src_indices.tolist()}" ) - for i in range(len(self.mamba_cache.conv)): - self.mamba_cache.conv[i][:, dst_indices] = self.mamba_cache.conv[i][ + if self._should_fuse_slot_ops(): + from sglang.srt.mem_cache.mamba_slot_fused import fused_copy_conv_slots + + if envs.SGLANG_DEBUG_MEMORY_POOL.get(): + overlap = set(src_indices.tolist()) & set(dst_indices.tolist()) + assert not overlap, ( + "fused copy_from requires disjoint src/dst slots; " + f"overlap={sorted(overlap)}" + ) + fused_copy_conv_slots(self._conv_slot_desc, src_indices, dst_indices) + temporal = self.mamba_cache.temporal + if temporal.numel() > 0: + temporal[:, dst_indices] = temporal[:, src_indices] + else: + for i in range(len(self.mamba_cache.conv)): + self.mamba_cache.conv[i][:, dst_indices] = self.mamba_cache.conv[i][ + :, src_indices + ] + self.mamba_cache.temporal[:, dst_indices] = self.mamba_cache.temporal[ :, src_indices ] - self.mamba_cache.temporal[:, dst_indices] = self.mamba_cache.temporal[ - :, src_indices - ] if self.replayssm_write_pos is not None: self.replayssm_write_pos[dst_indices] = 0 @@ -991,6 +1053,41 @@ class HybridReqToTokenPool(ReqToTokenPool): ) ) + def clone_with_new_mamba( + self, + *, + mamba_size: int, + mamba_spec_state_size: int, + cache_params: BaseLinearStateParams, + device: str, + enable_mamba_extra_buffer: bool, + draft_model_idx: int, + speculative_num_draft_tokens: int = None, + speculative_eagle_topk: Optional[int] = None, + ) -> HybridReqToTokenPool: + """Shallow copy that shares the req_to_token mapping but owns a fresh mamba + pool keyed on a single draft layer. Used by multi-layer EAGLE draft workers: + each draft head shares the target's request-to-token mapping but needs its + own sconv/mamba cache at layer_id=draft_model_idx. + """ + clone = copy.copy(self) + clone._init_mamba_pool( + mamba_size=mamba_size, + mamba_spec_state_size=mamba_spec_state_size, + cache_params=cache_params, + mamba_layer_ids=[draft_model_idx], + device=device, + enable_mamba_extra_buffer=enable_mamba_extra_buffer, + speculative_num_draft_tokens=speculative_num_draft_tokens, + speculative_eagle_topk=speculative_eagle_topk, + ) + clone.req_index_to_mamba_index_mapping = self.req_index_to_mamba_index_mapping + if enable_mamba_extra_buffer: + clone.req_index_to_mamba_ping_pong_track_buffer_mapping = ( + self.req_index_to_mamba_ping_pong_track_buffer_mapping + ) + return clone + def register_layer_transfer_counter(self, layer_transfer_counter: LayerDoneCounter): self.layer_transfer_counter = layer_transfer_counter @@ -2927,6 +3024,294 @@ class PageMajorMHATokenToKVPool(MHATokenToKVPool): ) +class MHATokenToKVPoolMXFP8(MHATokenToKVPool): + """MHA KV cache pool for MXFP8 block-scaled FP8. + + K/V data is stored as FP8 E4M3. Per-32-element UE8M0 scale factors are + stored beside it and passed to the FA4 MXFP8 kernel. + """ + + MXFP8_SCALE_BLOCK_SIZE = 32 + + def _create_buffers(self): + with self.memory_saver_adapter.region(GPU_MEMORY_TYPE_KV_CACHE): + with ( + torch.cuda.use_mem_pool(self.custom_mem_pool) + if self.enable_custom_mem_pool + else nullcontext() + ): + m = self.size + self.page_size + n = self.head_num + k = self.head_dim + v = self.v_head_dim + + if k % self.MXFP8_SCALE_BLOCK_SIZE != 0: + raise ValueError( + f"MXFP8 KV cache requires head_dim divisible by " + f"{self.MXFP8_SCALE_BLOCK_SIZE}, got {k}." + ) + if v % self.MXFP8_SCALE_BLOCK_SIZE != 0: + raise ValueError( + f"MXFP8 KV cache requires v_head_dim divisible by " + f"{self.MXFP8_SCALE_BLOCK_SIZE}, got {v}." + ) + if not hasattr(torch, "float8_e8m0fnu"): + raise RuntimeError( + "MXFP8 KV cache requires torch.float8_e8m0fnu support." + ) + if self.use_hnd: + # Buffers are NHD; the inherited HND move_kv_cache branch + # would silently relocate wrong bytes. + raise ValueError( + "MXFP8 KV cache does not support SGLANG_USE_HND_KVCACHE." + ) + + self.store_dtype = torch.float8_e4m3fn + self.k_buffer = [ + torch.zeros((m, n, k), dtype=self.store_dtype, device=self.device) + for _ in range(self.layer_num) + ] + self.v_buffer = [ + torch.zeros((m, n, v), dtype=self.store_dtype, device=self.device) + for _ in range(self.layer_num) + ] + + # UE8M0 scales, one per 32-element block. For the production + # page_size==128 path they are stored interleaved in the FA4 + # BlockScaledBasicChunk atom layout + # (num_pages, head, 32, page_size//32, sf_dim) and written by + # the store_sf_interleaved kernel; otherwise flat per slot. Must + # be zero-initialized (garbage 0xFF is e8m0 NaN). + k_sf_dim = k // self.MXFP8_SCALE_BLOCK_SIZE + v_sf_dim = v // self.MXFP8_SCALE_BLOCK_SIZE + self.mxfp8_sf_interleaved = self.page_size == 128 + if self.mxfp8_sf_interleaved: + assert m % self.page_size == 0 + num_pages = m // self.page_size + chunk = self.page_size // self.MXFP8_SCALE_BLOCK_SIZE + k_sf_shape = ( + num_pages, + n, + self.MXFP8_SCALE_BLOCK_SIZE, + chunk, + k_sf_dim, + ) + v_sf_shape = ( + num_pages, + n, + self.MXFP8_SCALE_BLOCK_SIZE, + chunk, + v_sf_dim, + ) + else: + k_sf_shape = (m, n, k_sf_dim) + v_sf_shape = (m, n, v_sf_dim) + self.k_scale_buffer = [ + torch.zeros( + k_sf_shape, dtype=torch.float8_e8m0fnu, device=self.device + ) + for _ in range(self.layer_num) + ] + self.v_scale_buffer = [ + torch.zeros( + v_sf_shape, dtype=torch.float8_e8m0fnu, device=self.device + ) + for _ in range(self.layer_num) + ] + + self.k_data_ptrs = torch.tensor( + [x.data_ptr() for x in self.k_buffer], + dtype=torch.uint64, + device=self.device, + ) + self.v_data_ptrs = torch.tensor( + [x.data_ptr() for x in self.v_buffer], + dtype=torch.uint64, + device=self.device, + ) + self.data_ptrs = torch.cat([self.k_data_ptrs, self.v_data_ptrs], dim=0) + self.data_strides = torch.tensor( + [ + np.prod(x.shape[1:]) * x.dtype.itemsize + for x in self.k_buffer + self.v_buffer + ], + device=self.device, + ) + + def _clear_buffers(self): + del self.k_buffer + del self.v_buffer + del self.k_scale_buffer + del self.v_scale_buffer + + def _get_key_buffer(self, layer_id: int): + return self.k_buffer[layer_id - self.start_layer] + + def _get_value_buffer(self, layer_id: int): + return self.v_buffer[layer_id - self.start_layer] + + def get_kv_scale_buffer(self, layer_id: int) -> Tuple[torch.Tensor, torch.Tensor]: + idx = layer_id - self.start_layer + return self.k_scale_buffer[idx], self.v_scale_buffer[idx] + + def set_kv_buffer( + self, + layer: RadixAttention, + loc_info, + cache_k: torch.Tensor, + cache_v: torch.Tensor, + k_scale: Optional[torch.Tensor] = None, + v_scale: Optional[torch.Tensor] = None, + layer_id_override: Optional[int] = None, + dcp_kv_mask: Optional[torch.Tensor] = None, + ): + if dcp_kv_mask is not None: + raise NotImplementedError("MXFP8 KV cache does not support DCP KV masks.") + loc, _, _ = unwrap_write_loc(loc_info) + maybe_detect_oob( + loc, 0, self.size + self.page_size, "set_kv_buffer (MHA-MXFP8)" + ) + layer_id = ( + layer_id_override if layer_id_override is not None else layer.layer_id + ) + idx = layer_id - self.start_layer + + if k_scale is None or v_scale is None: + # Fused path (SGLANG_OPT_INKLING_MXFP8_FUSED_QUANT_STORE): the layer + # hands us bf16 K/V and one kernel quantizes + scatters the fp8 + # payload and the interleaved UE8M0 scales. + if not self.mxfp8_sf_interleaved or cache_k.dtype == self.store_dtype: + raise ValueError("MXFP8 KV cache requires K and V scale tensors.") + from sglang.srt.layers.quantization.mxfp8_quant import quant_store_kv_mxfp8 + + quant_store_kv_mxfp8( + cache_k, + cache_v, + loc, + self.k_buffer[idx], + self.v_buffer[idx], + self.k_scale_buffer[idx], + self.v_scale_buffer[idx], + page_size=self.page_size, + ) + return + + from sglang.srt.model_executor.runner import get_is_capture_mode + + if get_is_capture_mode() and self.alt_stream is not None: + current_stream = self.device_module.current_stream() + self.alt_stream.wait_stream(current_stream) + self.k_buffer[idx][loc] = cache_k + self._write_scales(idx, loc, k_scale, v_scale) + with self.device_module.stream(self.alt_stream): + self.v_buffer[idx][loc] = cache_v + current_stream.wait_stream(self.alt_stream) + else: + self.k_buffer[idx][loc] = cache_k + self.v_buffer[idx][loc] = cache_v + self._write_scales(idx, loc, k_scale, v_scale) + + def _write_scales(self, idx, loc, k_scale, v_scale): + """Write per-token UE8M0 K/V scales — interleaved into the FA4 + BlockScaledBasicChunk layout for page_size==128, flat otherwise.""" + if self.mxfp8_sf_interleaved: + from sglang.srt.layers.quantization.mxfp8_interleave_sf import ( + store_sf_interleaved, + ) + + store_sf_interleaved( + k_scale, self.k_scale_buffer[idx], loc, page_size=self.page_size + ) + store_sf_interleaved( + v_scale, self.v_scale_buffer[idx], loc, page_size=self.page_size + ) + else: + self.k_scale_buffer[idx][loc] = k_scale + self.v_scale_buffer[idx][loc] = v_scale + + def _read_sf_interleaved(self, sf_buf: torch.Tensor, loc: torch.Tensor): + """Inverse of store_sf_interleaved: gather per-slot (T, head, sf_dim) + UE8M0 scales out of the interleaved BlockScaledBasicChunk buffer.""" + num_pages, n = sf_buf.shape[0], sf_buf.shape[1] + sf_dim = sf_buf.shape[-1] + # (num_pages, n, page_size) as u32: 4 packed scales per u32. + buf_u32 = sf_buf.reshape(num_pages, n, -1).view(torch.int32) + off = loc % self.page_size + page = (loc // self.page_size).long() + chunk = self.page_size // self.MXFP8_SCALE_BLOCK_SIZE + ipos = ( + (off % self.MXFP8_SCALE_BLOCK_SIZE) * chunk + + (off // self.MXFP8_SCALE_BLOCK_SIZE) + ).long() + heads = torch.arange(n, device=loc.device) + gathered = buf_u32[page[:, None], heads[None, :], ipos[:, None]] # (T, n) int32 + return ( + gathered.reshape(loc.shape[0], n, 1) + .view(torch.uint8) + .reshape(loc.shape[0], n, sf_dim) + .view(torch.float8_e8m0fnu) + ) + + def move_kv_cache(self, tgt_loc: torch.Tensor, src_loc: torch.Tensor): + # The mamba extra_buffer allocator relocates KV rows during serving; + # scale rows must travel with their fp8 payload or dequant reads + # mismatched exponents. + if self.mxfp8_sf_interleaved: + from sglang.srt.layers.quantization.mxfp8_interleave_sf import ( + store_sf_interleaved, + ) + + for idx in range(self.layer_num): + self.k_buffer[idx][tgt_loc] = self.k_buffer[idx][src_loc] + self.v_buffer[idx][tgt_loc] = self.v_buffer[idx][src_loc] + k_sf = self._read_sf_interleaved(self.k_scale_buffer[idx], src_loc) + v_sf = self._read_sf_interleaved(self.v_scale_buffer[idx], src_loc) + store_sf_interleaved( + k_sf, self.k_scale_buffer[idx], tgt_loc, page_size=self.page_size + ) + store_sf_interleaved( + v_sf, self.v_scale_buffer[idx], tgt_loc, page_size=self.page_size + ) + else: + super().move_kv_cache(tgt_loc, src_loc) + for idx in range(self.layer_num): + self.k_scale_buffer[idx][tgt_loc] = self.k_scale_buffer[idx][src_loc] + self.v_scale_buffer[idx][tgt_loc] = self.v_scale_buffer[idx][src_loc] + + # These paths copy k/v buffers without the scale buffers; fail loudly + # instead of silently corrupting dequantization. + def get_cpu_copy(self, indices, mamba_indices=None): + raise NotImplementedError("CPU offloading is unsupported for MXFP8 KV cache.") + + def load_cpu_copy(self, kv_cache_cpu, indices, mamba_indices=None): + raise NotImplementedError("CPU offloading is unsupported for MXFP8 KV cache.") + + def get_contiguous_buf_infos(self): + raise NotImplementedError( + "KV transfer / disaggregation is unsupported for MXFP8 KV cache " + "(scale buffers are not exposed)." + ) + + def set_kv_buffer_prefix_valid(self, *args, **kwargs): + raise NotImplementedError( + "prefix-valid commit is unsupported for MXFP8 KV cache " + "(it does not carry the scale buffers)." + ) + + def get_kv_size_bytes(self): + k_size_bytes = 0 + v_size_bytes = 0 + for k_cache in self.k_buffer: + k_size_bytes += get_tensor_size_bytes(k_cache) + for k_scale in self.k_scale_buffer: + k_size_bytes += get_tensor_size_bytes(k_scale) + for v_cache in self.v_buffer: + v_size_bytes += get_tensor_size_bytes(v_cache) + for v_scale in self.v_scale_buffer: + v_size_bytes += get_tensor_size_bytes(v_scale) + return k_size_bytes, v_size_bytes + + class HybridLinearKVPool(KVCache): """KV cache with separate pools for full and linear attention layers.""" @@ -3140,6 +3525,12 @@ class HybridLinearKVPool(KVCache): layer, *args, layer_id_override=local_layer_id, **kwargs ) + def get_kv_scale_buffer(self, layer_id: int): + # MXFP8 full_kv_pool exposes per-32 UE8M0 K/V scale buffers. + self._wait_for_layer(layer_id) + layer_id = self._transfer_full_attention_id(layer_id) + return self.full_kv_pool.get_kv_scale_buffer(layer_id) + @contextmanager def _transfer_id_context(self, layer: RadixAttention): @contextmanager diff --git a/python/sglang/srt/mem_cache/memory_pool_host.py b/python/sglang/srt/mem_cache/memory_pool_host.py index 139685227..498539cda 100644 --- a/python/sglang/srt/mem_cache/memory_pool_host.py +++ b/python/sglang/srt/mem_cache/memory_pool_host.py @@ -160,7 +160,20 @@ class MambaPoolHost(HostKVCache): self.clear() def init_kv_buffer(self): - alloc_func = ALLOC_MEMORY_FUNCS[self.device_pool.device] + _host_alloc = ALLOC_MEMORY_FUNCS[self.device_pool.device] + + def alloc_func(dims, *, dtype, device, pin_memory, allocator): + # conv-only linear attention has no ssm state: mmap can't map the + # 0-element temporal buffer, so hand back a plain empty tensor. + if np.prod(dims) == 0: + return torch.empty(dims, dtype=dtype, device=device) + return _host_alloc( + dims, + dtype=dtype, + device=device, + pin_memory=pin_memory, + allocator=allocator, + ) if self.layout in ["page_first", "page_first_direct"]: # page-first: (page_num, num_layers, 1, *shape) — per-page data is contiguous @@ -421,15 +434,17 @@ class MambaPoolHost(HostKVCache): io_backend="kernel", ): if self.layout in ["page_first", "page_first_direct"]: - self._copy_tensor_pf_lf( - src=self.temporal_buffer, - dst=device_pool.mamba_cache.temporal[layer_id], - src_indices=host_indices, - dst_indices=device_indices, - layer_id=layer_id, - num_layers=self.num_mamba_layers, - io_backend=io_backend, - ) + # no ssm state on conv-only models: nothing to transfer + if self.temporal_state_elem_size > 0: + self._copy_tensor_pf_lf( + src=self.temporal_buffer, + dst=device_pool.mamba_cache.temporal[layer_id], + src_indices=host_indices, + dst_indices=device_indices, + layer_id=layer_id, + num_layers=self.num_mamba_layers, + io_backend=io_backend, + ) for conv_idx in range(len(self.conv_state_shapes)): self._copy_tensor_pf_lf( src=self.conv_buffer[conv_idx], @@ -461,17 +476,19 @@ class MambaPoolHost(HostKVCache): self, device_pool, host_indices, device_indices, io_backend="kernel" ): if self.layout in ["page_first", "page_first_direct"]: - self._copy_tensor_all_layers_lf_pf( - src_layers=device_pool.mamba_cache.temporal, - dst=self.temporal_buffer, - src_indices=device_indices, - dst_indices=host_indices, - num_layers=self.num_mamba_layers, - io_backend=io_backend, - staging=self.temporal_staging_buffer, - can_use_jit=self._temporal_can_use_jit, - src_ptrs=self.temporal_device_ptrs, - ) + # no ssm state on conv-only models: a 0-size batched memcpy errors + if self.temporal_state_elem_size > 0: + self._copy_tensor_all_layers_lf_pf( + src_layers=device_pool.mamba_cache.temporal, + dst=self.temporal_buffer, + src_indices=device_indices, + dst_indices=host_indices, + num_layers=self.num_mamba_layers, + io_backend=io_backend, + staging=self.temporal_staging_buffer, + can_use_jit=self._temporal_can_use_jit, + src_ptrs=self.temporal_device_ptrs, + ) for conv_idx in range(len(self.conv_state_shapes)): self._copy_tensor_all_layers_lf_pf( src_layers=device_pool.mamba_cache.conv[conv_idx], @@ -569,17 +586,20 @@ class MambaPoolHost(HostKVCache): ] for i in range(0, len(indices), self.page_size): - # Emit component pointers in stable order: - # temporal first, then conv_0..conv_n for this page. - temporal_ptr = ( - temporal_base_ptr - + indices[i] - * self.num_mamba_layers - * self.temporal_state_elem_size - * self.temporal_dtype.itemsize - ) - ptr_list.append(temporal_ptr) - element_size_list.append(temporal_element_size) + # Emit component pointers in stable order: temporal first (dropped + # for conv-only models with no ssm state), then conv_0..conv_n. + # _get_hybrid_page_component_keys drops the temporal key under the + # same condition, keeping keys and buffers aligned. + if self.temporal_state_elem_size > 0: + temporal_ptr = ( + temporal_base_ptr + + indices[i] + * self.num_mamba_layers + * self.temporal_state_elem_size + * self.temporal_dtype.itemsize + ) + ptr_list.append(temporal_ptr) + element_size_list.append(temporal_element_size) for j in range(len(self.conv_buffer)): conv_ptr = ( conv_base_ptrs[j] diff --git a/python/sglang/srt/mem_cache/storage/mooncake_store/mooncake_store.py b/python/sglang/srt/mem_cache/storage/mooncake_store/mooncake_store.py index d72794cbc..245e0eb16 100644 --- a/python/sglang/srt/mem_cache/storage/mooncake_store/mooncake_store.py +++ b/python/sglang/srt/mem_cache/storage/mooncake_store/mooncake_store.py @@ -302,6 +302,9 @@ class MooncakeBaseStore: raise RuntimeError("Mooncake store is not initialized.") ptr = tensor.data_ptr() size = tensor.numel() * tensor.element_size() + if size == 0: + # conv-only models have a 0-element ssm state; nothing to register + return ret_code = self.store.register_buffer(ptr, size) if ret_code != 0: logger.error(f"Failed to register buffer, error code: {ret_code}") @@ -716,10 +719,13 @@ class MooncakeStore(HiCacheStorage, MooncakeBaseStore): suffixes = [] if pool_name == PoolName.MAMBA: # Mamba stores one temporal object plus one object per conv state. + # conv-only models have no ssm state; drop the 0-element temporal + # object (mooncake rejects 0-size puts). get_page_buffer_meta drops + # its temporal pointer under the same condition to stay aligned. conv_num = len(getattr(host_pool, "conv_buffer", None) or []) - suffixes = [f"_{self.mha_suffix}_temporal"] + [ - f"_{self.mha_suffix}_conv_{i}" for i in range(conv_num) - ] + suffixes = [f"_{self.mha_suffix}_conv_{i}" for i in range(conv_num)] + if getattr(host_pool, "temporal_state_elem_size", 1) > 0: + suffixes = [f"_{self.mha_suffix}_temporal"] + suffixes elif pool_name == PoolName.DRAFT: # Draft pool's MLA/MHA layout is independent from the target # (e.g. EAGLE-MHA draft on top of an MLA target), so pick the diff --git a/python/sglang/srt/mem_cache/swa_memory_pool.py b/python/sglang/srt/mem_cache/swa_memory_pool.py index 8cef5a735..c621e8e69 100644 --- a/python/sglang/srt/mem_cache/swa_memory_pool.py +++ b/python/sglang/srt/mem_cache/swa_memory_pool.py @@ -165,6 +165,14 @@ class SWAKVPool(BaseSWAKVPool): else: return self.full_kv_pool.get_kv_buffer(layer_id_pool) + def get_kv_scale_buffer(self, layer_id: int): + self._wait_for_layer(layer_id) + layer_id_pool, is_swa_layer = self.layers_mapping[layer_id] + if is_swa_layer: + return self.swa_kv_pool.get_kv_scale_buffer(layer_id_pool) + else: + return self.full_kv_pool.get_kv_scale_buffer(layer_id_pool) + def translate_loc_from_full_to_swa(self, kv_indices: torch.Tensor) -> torch.Tensor: assert self.full_to_swa_index_mapping is not None # -1 in kv_indices maps to -1 via the sentinel appended to the mapping. diff --git a/python/sglang/srt/model_executor/cuda_graph_config.py b/python/sglang/srt/model_executor/cuda_graph_config.py index 2588f2c15..38df069ff 100644 --- a/python/sglang/srt/model_executor/cuda_graph_config.py +++ b/python/sglang/srt/model_executor/cuda_graph_config.py @@ -94,8 +94,11 @@ class PhaseConfig: def default_prefill_backend() -> str: """BCG (breakable) is the prefill default on CUDA only; other platforms - (HIP/NPU/...) keep tc_piecewise until BCG is validated there. Lazy import - keeps this module's stdlib-only import invariant (see module docstring).""" + (HIP/NPU/...) keep tc_piecewise until BCG is validated there. Full-graph + prefill capture is opt-in per model architecture via the declarative + registry (see _inkling_overrides in arg_groups/overrides.py), not a global + default. Lazy import keeps this module's stdlib-only import invariant (see + module docstring).""" from sglang.srt.utils import is_cuda return Backend.BREAKABLE if is_cuda() else Backend.TC_PIECEWISE diff --git a/python/sglang/srt/model_executor/model_runner.py b/python/sglang/srt/model_executor/model_runner.py index 39dfeda8b..0ca99255e 100644 --- a/python/sglang/srt/model_executor/model_runner.py +++ b/python/sglang/srt/model_executor/model_runner.py @@ -537,6 +537,7 @@ class ModelRunner: req_to_token_pool=self.req_to_token_pool, token_to_kv_pool_allocator=self.token_to_kv_pool_allocator, memory_pool_config=self.memory_pool_config, + draft_model_idx=self.draft_model_idx, ) def init_mindspore_runner(self): diff --git a/python/sglang/srt/model_executor/model_runner_components/layer_setup.py b/python/sglang/srt/model_executor/model_runner_components/layer_setup.py index 7528a768b..c6485abca 100644 --- a/python/sglang/srt/model_executor/model_runner_components/layer_setup.py +++ b/python/sglang/srt/model_executor/model_runner_components/layer_setup.py @@ -36,7 +36,11 @@ def compute_attention_and_moe_layers(layer_model: Any) -> AttentionAndMoeLayers: mha_companion_layer = layer.self_attn.attn_mha # For hybrid model elif hasattr(layer, "attn"): - attn_layer = layer.attn + inner = layer.attn + # Inkling wraps RadixAttention inside a InklingAttention module + # (layer.attn.attn); descend to the inner RadixAttention that BCG + # needs. Other hybrid models put RadixAttention at layer.attn. + attn_layer = inner.attn if hasattr(inner, "attn") else inner elif hasattr(layer, "linear_attn"): if hasattr(layer.linear_attn, "attn"): attn_layer = layer.linear_attn.attn @@ -168,6 +172,10 @@ def _compute_model_num_layers( model_num_layers = 1 elif model_config.hf_config.architectures[0] == "Step3p5MTP": model_num_layers = 1 + elif ( + model_config.hf_config.architectures[0] == "InklingForConditionalGenerationMTP" + ): + model_num_layers = 1 return model_num_layers diff --git a/python/sglang/srt/model_executor/pool_configurator.py b/python/sglang/srt/model_executor/pool_configurator.py index 341326718..7f821f1a0 100644 --- a/python/sglang/srt/model_executor/pool_configurator.py +++ b/python/sglang/srt/model_executor/pool_configurator.py @@ -275,6 +275,12 @@ class DefaultPoolConfigurator(MemoryPoolConfigurator): ) # FP4 prefill uses one shared FP8 dequant workspace across layers. cell_size += n * k * 2 * kv_size + elif kvc.server_args.kv_cache_dtype == "mxfp8": + scale_block_size = 32 + n = model_config.get_num_kv_heads(tp_size) + cell_size += ( + n * (model_config.head_dim + model_config.v_head_dim) * num_layers + ) // scale_block_size return cell_size @@ -312,6 +318,8 @@ class HybridSWAPoolConfigurator(MemoryPoolConfigurator): ), "Hybrid SWA model must have at least one SWA layer" self._swa_full_tokens_ratio = kvc.server_args.swa_full_tokens_ratio + self._sliding_window_size = kvc.sliding_window_size + self._page_size = kvc.page_size # Full layer per-token memory (bytes) self._full_per_token = ( @@ -327,15 +335,43 @@ class HybridSWAPoolConfigurator(MemoryPoolConfigurator): * kv_size ) + if kvc.server_args.kv_cache_dtype == "mxfp8": + scale_block_size = 32 + self._full_per_token += ( + model_config.get_num_kv_heads(tp_size) + * (model_config.head_dim + model_config.v_head_dim) + ) // scale_block_size + self._swa_per_token += ( + model_config.get_swa_num_kv_heads(tp_size) + * (model_config.swa_head_dim + model_config.swa_v_head_dim) + ) // scale_block_size + # EAGLE/STANDALONE draft KV pool inherits max_total tokens with its - # full-attn layers; budget into the full term. + # full-attn layers; budget into the full term. A banded MTP depth + # (Inkling mtp_local_layer_ids) instead allocates an swa-geometry ring + # at FULL draft capacity, so budget those depths at swa_per_token. self._draft_full_layers_num = 0 + self._draft_swa_full_layers_num = 0 if ( kvc.spec_algorithm.is_eagle() or kvc.spec_algorithm.is_standalone() ) and not kvc.is_draft_worker: draft_layers = kvc.spec_aux_config.eagle_draft_num_layers if draft_layers is not None and int(draft_layers) > 0: - self._draft_full_layers_num = int(draft_layers) + draft_layers = int(draft_layers) + banded_depths = 0 + if ( + model_config.hf_config.architectures[0] + == "InklingForConditionalGeneration" + ): + banded_depths = len( + [ + i + for i in model_config.hf_text_config.mtp_local_layer_ids + if i < draft_layers + ] + ) + self._draft_swa_full_layers_num = banded_depths + self._draft_full_layers_num = draft_layers - banded_depths # Bytes per token of max_total_num_tokens. # @@ -350,11 +386,13 @@ class HybridSWAPoolConfigurator(MemoryPoolConfigurator): self._cell_size = ( self._swa_per_token * self._swa_layers_num + self._full_per_token * self._draft_full_layers_num + + self._swa_per_token * self._draft_swa_full_layers_num ) else: self._cell_size = ( self._full_per_token * (self._full_layers_num + self._draft_full_layers_num) + + self._swa_per_token * self._draft_swa_full_layers_num + self._swa_full_tokens_ratio * self._swa_per_token * self._swa_layers_num @@ -386,6 +424,17 @@ class HybridSWAPoolConfigurator(MemoryPoolConfigurator): full_tokens = align_page_size(max_total_num_tokens) swa_tokens = align_page_size(int(full_tokens * self._swa_full_tokens_ratio)) + if ( + self._sliding_window_size is not None + and self._sliding_window_size + self._page_size >= swa_tokens + ): + raise ValueError( + f"SWA pool ({swa_tokens} tokens) cannot hold even one request: " + f"the prefill admission floor is sliding_window_size " + f"({self._sliding_window_size}) + page_size ({self._page_size}). " + f"Increase --swa-full-tokens-ratio or the total KV budget." + ) + logger.info( f"Use sliding window memory pool. " f"full_layer_tokens={full_tokens}, swa_layer_tokens={swa_tokens}" @@ -478,8 +527,9 @@ class SWAChunkCapPoolConfigurator(HybridSWAPoolConfigurator): # SWA pool sized tightly from the cap; the rest of the budget goes to full. swa_tokens = ceil_align(self._swa_cap, page_size) fixed_swa_bytes = swa_tokens * self._swa_per_token * self._swa_layers_num - full_cell_size = self._full_per_token * ( - self._full_layers_num + self._draft_full_layers_num + full_cell_size = ( + self._full_per_token * (self._full_layers_num + self._draft_full_layers_num) + + self._swa_per_token * self._draft_swa_full_layers_num ) full_tokens = ( int((available_bytes - fixed_swa_bytes) // full_cell_size) // page_size diff --git a/python/sglang/srt/model_executor/runner/prefill_cuda_graph_runner.py b/python/sglang/srt/model_executor/runner/prefill_cuda_graph_runner.py index 4e77a442f..e8f19a289 100644 --- a/python/sglang/srt/model_executor/runner/prefill_cuda_graph_runner.py +++ b/python/sglang/srt/model_executor/runner/prefill_cuda_graph_runner.py @@ -275,6 +275,12 @@ class PrefillCudaGraphRunner(BaseCudaGraphRunner): # calls back into capture_prepare which reads this. Full overrides below # once the backend type is known. self._capture_req_slots = 1 + # Same rationale: _run_compile_pass runs a dummy _run_forward before + # resolve_prefill_backend returns, and that forward reads + # self._is_full_backend. The compile-pass backend is never Full, so + # default False; the assignment below sets the real value once the + # backend type is known. + self._is_full_backend = False try: self.backend = resolve_prefill_backend(self) except RuntimeError as e: @@ -502,6 +508,11 @@ class PrefillCudaGraphRunner(BaseCudaGraphRunner): self.moe_fusions, dsa_indexers=self.dsa_indexers, mha_companion_layers=self.mha_companion_layers, + # FULL backend: the whole transformer body is captured in one + # graph (no eager-break seams), so fusion gates keyed on BCG's + # split execution may fuse. (Both BCG and Full set layer_model + # -- the backend type is the discriminator, not layer_model.) + full_graph=self._is_full_backend, ), ): if self.layer_model is not None: @@ -1161,7 +1172,7 @@ class PrefillCudaGraphRunner(BaseCudaGraphRunner): if ie is not None: self.buffer_registry.get_slot("input_embeds").slice_for( 1, static_n - ).copy_(ie[:static_n]) + )[: ie.shape[0]].copy_(ie) hs = self.backend.replay(shape_key, static_forward_batch, **kwargs) return hs[:raw_num_tokens] if full_path else hs @@ -1188,6 +1199,7 @@ class PrefillCudaGraphRunner(BaseCudaGraphRunner): mha_companion_layers=self.mha_companion_layers, num_tokens=static_num_tokens, raw_num_tokens=raw_num_tokens, + full_graph=full_path, ), ): output = self.model_runner.model.forward( diff --git a/python/sglang/srt/model_executor/runner_backend_utils/tc_piecewise_cuda_graph/context_manager.py b/python/sglang/srt/model_executor/runner_backend_utils/tc_piecewise_cuda_graph/context_manager.py index 39b2491a1..ecc132ef1 100644 --- a/python/sglang/srt/model_executor/runner_backend_utils/tc_piecewise_cuda_graph/context_manager.py +++ b/python/sglang/srt/model_executor/runner_backend_utils/tc_piecewise_cuda_graph/context_manager.py @@ -76,6 +76,11 @@ class TcPiecewiseForwardContext: dsa_indexers: Optional[List[Any]] = field(default=None) num_tokens: Optional[int] = None raw_num_tokens: Optional[int] = None + # True when the FULL prefill graph backend owns this forward (whole model + # captured uniformly). BCG / tc_piecewise leave it False -- consumers that + # bake per-forward fusion flags (e.g. the scattered AR-sconv gate) must + # not fuse across BCG's eager-break seams, but are safe under full graphs. + full_graph: bool = False _tc_piecewise_forward_context: Optional[TcPiecewiseForwardContext] = None @@ -96,6 +101,7 @@ def set_tc_piecewise_forward_context( mha_companion_layers: Optional[List[Any]] = None, num_tokens: Optional[int] = None, raw_num_tokens: Optional[int] = None, + full_graph: bool = False, ): global _tc_piecewise_forward_context _tc_piecewise_forward_context = TcPiecewiseForwardContext( @@ -108,6 +114,7 @@ def set_tc_piecewise_forward_context( dsa_indexers=dsa_indexers, num_tokens=num_tokens, raw_num_tokens=raw_num_tokens, + full_graph=full_graph, ) try: yield diff --git a/python/sglang/srt/models/inkling.py b/python/sglang/srt/models/inkling.py new file mode 100644 index 000000000..5e5681d25 --- /dev/null +++ b/python/sglang/srt/models/inkling.py @@ -0,0 +1,1972 @@ +from __future__ import annotations + +import copy +import logging +import re +from typing import Iterable, Optional, Set, Tuple + +import torch +from torch import nn + +from sglang.srt.configs.inkling import ( + InklingAudioConfig, + InklingMMConfig, + InklingModelConfig, + InklingVisionConfig, +) +from sglang.srt.distributed import ( + get_tensor_model_parallel_group, +) +from sglang.srt.environ import envs +from sglang.srt.layers.layernorm import RMSNorm +from sglang.srt.layers.logits_processor import LogitsProcessor +from sglang.srt.layers.moe import get_moe_runner_backend +from sglang.srt.layers.quantization.base_config import QuantizationConfig +from sglang.srt.layers.radix_attention import force_eager_attention +from sglang.srt.layers.utils import get_layer_id +from sglang.srt.layers.vocab_parallel_embedding import ( + ParallelLMHead, + VocabParallelEmbedding, +) +from sglang.srt.managers.mm_utils import ( + MultiModalityDataPaddingPatternMultimodalTokens, + general_mm_embed_routine, +) +from sglang.srt.managers.schedule_batch import ( + Modality, + MultimodalDataItem, + MultimodalInputs, +) +from sglang.srt.model_executor.forward_batch_info import ForwardBatch +from sglang.srt.model_executor.runner_backend_utils.breakable_cuda_graph import ( + eager_on_graph, + is_in_breakable_cuda_graph, +) +from sglang.srt.model_executor.runner_backend_utils.tc_piecewise_cuda_graph import ( + get_tc_piecewise_forward_context, +) +from sglang.srt.model_loader.weight_utils import default_weight_loader +from sglang.srt.models.inkling_common.attn import ( + InklingAttention, + compute_log_scaling_tau, +) +from sglang.srt.models.inkling_common.dense_mlp import InklingDenseMLP +from sglang.srt.models.inkling_common.hmlp import HMLPPatchEncoder +from sglang.srt.models.inkling_common.kernels.comm import ( + all_gather_hidden, + ar_fullwidth_sconv_fused, + ar_scattered_sconv_fused, + ar_sconv_norm_fusable, + ar_sconv_norm_fused, + ensure_inkling_ar_resources, + fullwidth_ar_sconv_fusable, + scattered_ar_sconv_fusable, +) +from sglang.srt.models.inkling_common.moe import InklingMoE +from sglang.srt.models.inkling_common.sconv import SconvType, ShortConvolution +from sglang.srt.models.inkling_common.util import ( + bf16_routed_uses_stock_fused_moe, + deinterleave_gate_up, + lora_compatible_layout_enabled, + shared_sink_uses_trtllm_bf16, + trtllm_bf16_weight_prep_enabled, + use_inkling_shared_fused_moe, +) +from sglang.srt.runtime_context import get_parallel, get_server_args +from sglang.srt.utils import add_prefix, is_cuda, make_layers + +logger = logging.getLogger(__name__) + +ATTENTION_PARAMS_MAPPING = [ + ("qkvr", "wq_du", 0), + ("qkvr", "wk_dv", 1), + ("qkvr", "wv_dv", 2), + ("qkvr", "wr_du", 3), +] + +STACKED_DENSE_PARAMS_MAPPING = [ + ("gate_up_proj", "w1", 0), + ("gate_up_proj", "w3", 1), + ("down_proj", "w2", None), + ("gate_up_proj", "gate_proj", 0), + ("gate_up_proj", "up_proj", 1), + ("down_proj", "down_proj", None), +] + +# Online RL weight-sync streams routed experts one at a time as FULL (unsharded) +# per-expert tensors named `...mlp.experts.{j}.gate_proj/up_proj/down_proj.weight`. +# Disk checkpoints only ever carry the fused w13_weight/w2_weight, so this pattern +# never fires on the ordinary loading path. +_PER_EXPERT_WEIGHT_RE = re.compile( + r"^(?P.+\.mlp\.experts)\.(?P\d+)\.(?Pgate_proj|up_proj|down_proj)\.weight$" +) + + +def _shard_full_to_local( + loaded_weight: torch.Tensor, dst: torch.Tensor, dim: int +) -> torch.Tensor: + """Slice a FULL (unsharded) per-expert weight to this MoE-TP rank's shard along `dim`. + + The online weight-sync ships full per-expert tensors (parallelism-agnostic HF + layout); sglang owns its own MoE-TP sharding, so narrow here per + get_parallel().moe_tp_rank. With TP1 the dims already match and this is the + identity, so the ordinary path is byte-for-byte unchanged. + """ + if loaded_weight.shape[dim] == dst.shape[dim]: + return loaded_weight + rank = get_parallel().moe_tp_rank + return loaded_weight.narrow(dim, rank * dst.shape[dim], dst.shape[dim]) + + +KV_REPLICATED_SUFFIXES = ( + ".wk_dv.weight", + ".wv_dv.weight", + ".k_sconv.weight", + ".v_sconv.weight", +) + + +def _normalize_mm_weight_name(name: str) -> str: + if name.startswith("visual.model."): + return name.replace("visual.model.", "visual.vision_encoder.", 1) + if name.startswith("visual.") and not name.startswith("visual.vision_encoder."): + return name.replace("visual.", "visual.vision_encoder.", 1) + return name + + +def _is_unsupported_mm_weight_name(name: str) -> bool: + return name.startswith( + ( + "audio.decoder.", + "audio.logits_processor.", + "vision.", + "image.", + ) + ) + + +class InklingDecoderLayer(nn.Module): + def __init__( + self, + config: InklingModelConfig, + layer_id: int, + is_local: bool, + quant_config: QuantizationConfig | None = None, + prefix: str = "", + alt_stream: torch.cuda.Stream | None = None, + ): + super().__init__() + self.layer_id = layer_id + self.attn = InklingAttention( + hidden_size=config.hidden_size, + num_heads=( + config.swa_num_attention_heads + if is_local + else config.num_attention_heads + ), + num_kv_heads=( + config.swa_num_key_value_heads + if is_local + else config.num_key_value_heads + ), + head_dim=config.swa_head_dim if is_local else config.head_dim, + d_rel=config.d_rel, + rel_extent=config.rel_extent, + local_extent=config.sliding_window_size, + norm_eps=config.rms_norm_eps, + is_local=is_local, + layer_id=layer_id, + q_bias=config.q_bias, + o_bias=config.o_bias, + quant_config=quant_config, + kv_conv=config.use_sconv, + sconv_kernel_size=config.sconv_kernel_size, + prefix=add_prefix("attn", prefix), + alt_stream=alt_stream, + ) + if layer_id < config.dense_mlp_idx: + self.mlp = InklingDenseMLP( + hidden_size=config.hidden_size, + intermediate_size=config.dense_intermediate_size, + use_global_scale=config.use_global_scale, + layer_id=layer_id, + quant_config=quant_config, + prefix=add_prefix("mlp", prefix), + fused=True, + tp_rank=get_parallel().attn_tp_rank, + tp_size=get_parallel().attn_tp_size, + tp_group=get_parallel().attn_tp_group, + use_dp_attention_reduce=True, + ) + else: + # Routed experts use FusedMoE. Under LoRA the shared expert remains + # InklingBatchDenseMLP and applies its adapter delta directly. + self.mlp = InklingMoE( + config=config, + layer_id=layer_id, + prefix=add_prefix("mlp", prefix), + quant_config=quant_config, + alt_stream=alt_stream, + ) + + self.attn_norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.mlp_norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) + + # --enable-scattered-sconv: the output sconvs are channelwise, so they + # run on the [T, H/P] hidden shard the reduce-scatter produces; weights + # (ShortConvolution.weight_loader narrows by tp_rank) and conv-state + # cache (configs/inkling.py stream_dim) shard with them. The layer + # all-gathers back to [T, H] after each sconv, before the residual add. + self.attn_tp_group = get_parallel().attn_tp_group + self.scattered_sconv = get_server_args().enable_scattered_sconv + sconv_hidden = config.hidden_size + if self.scattered_sconv: + assert config.use_sconv, "--enable-scattered-sconv requires use_sconv" + assert config.hidden_size % self.attn_tp_group.world_size == 0 + sconv_hidden = config.hidden_size // self.attn_tp_group.world_size + self.attn_sconv = ( + ShortConvolution( + sconv_hidden, + config.sconv_kernel_size, + sconv_type=SconvType.ATTN, + layer_id=layer_id, + ) + if config.use_sconv + else None + ) + self.mlp_sconv = ( + ShortConvolution( + sconv_hidden, + config.sconv_kernel_size, + sconv_type=SconvType.MLP, + layer_id=layer_id, + ) + if config.use_sconv + else None + ) + # The fused decode path needs an MoE and this layer's MLP convolution; + # scattered convolution disables the fusion separately. + self.mlp_ar_fusable = ( + isinstance(self.mlp, InklingMoE) and self.mlp_sconv is not None + ) + + # Under BCG the short-conv metadata (cu_seqlens/seq_idx) is baked at bs=1 + # during capture, which is wrong for multi-seq prefill. Running every + # sconv (and the attn whose k/v_sconv it wraps) eagerly makes them re-read + # the LIVE per-seq metadata at replay. `_breakable_attn_group` groups the + # prior layer's (deferred) mlp_sconv + attn_norm + attn + attn_sconv into + # ONE eager break; only mlp_norm + MoE stay captured. Outside a capture + # these wrappers just run inline. `_breakable_mlp_sconv` runs the final + # layer's deferred mlp_sconv after the layer loop. + self._breakable_attn_group = eager_on_graph(True)(self._attn_group_impl) + self._breakable_mlp_sconv = eager_on_graph(True)(self._mlp_sconv_impl) + + def _attn_block( + self, + hidden_states: torch.Tensor, + residual: Optional[torch.Tensor], + positions: torch.Tensor, + forward_batch: ForwardBatch, + prev_mlp_sconv: Optional[ShortConvolution], + log_scaling_tau: Optional[torch.Tensor], + *, + eager_attn: bool, + prev_mlp_partial: bool = False, + fuse_attn_ar: bool = False, + ) -> Tuple[torch.Tensor, torch.Tensor]: + """The {deferred prior-layer mlp_sconv -> attn_norm -> attn -> attn_sconv} + region, shared by the inline `forward` path and the BCG eager group. Returns + (hidden_states, residual). + + When ``eager_attn`` is set (the BCG eager break), the attention runs eagerly + and ``forward_batch.out_cache_loc`` is narrowed to the token count for the KV + write: the eager attn path (unlike the custom op) does NOT narrow it, and under + BCG it is a full-bucket buffer, so the KV-write kernel would size-mismatch. The + caller is responsible for narrowing hidden_states/positions/log_scaling_tau to + the real (non-padded) token count before calling with ``eager_attn=True``. + Otherwise the attention runs normally. + + ``prev_mlp_partial``: ``hidden_states`` holds the previous layer's + UNREDUCED MoE partial sums (``reduce=False``); the {all-reduce -> + prev_mlp_sconv -> attn_norm} chain runs as ONE fused kernel.""" + hs, res = hidden_states, residual + if prev_mlp_partial and self.scattered_sconv: + fm = forward_batch.forward_mode + if fm.is_decode() or fm.is_target_verify(): + # Fused decode/verify {AR + scattered sconv + attn_norm}: the + # add+RMSNorm tail is fused in-kernel (residual always live + # here -- partials only ever come from a previous layer's MoE). + hs, res = ar_scattered_sconv_fused( + hs, + prev_mlp_sconv, + forward_batch, + get_tensor_model_parallel_group(), + norm=self.attn_norm, + norm_residual=res, + ) + else: + # Fused extend {AR + scattered sconv}: hs holds the previous + # MoE's unreduced partials; the kernel returns the gathered + # post-conv [T, H], and the norm runs unfused below. + hs = ar_scattered_sconv_fused( + hs, prev_mlp_sconv, forward_batch, get_tensor_model_parallel_group() + ) + hs, res = self.attn_norm(hs, res) + elif prev_mlp_partial: + fm = forward_batch.forward_mode + if fm.is_decode() or fm.is_target_verify(): + # Fused decode {AR -> sconv -> add+norm}; residual is always + # live here (partials only ever come from a prior layer's MoE). + hs, res = ar_sconv_norm_fused( + hs, + res, + prev_mlp_sconv, + self.attn_norm, + forward_batch, + get_tensor_model_parallel_group(), + ) + else: + # Fused extend {AR + full-width sconv + cache update} + # (non-scattered); norm runs unfused on the gathered [T, H]. + hs = ar_fullwidth_sconv_fused( + hs, prev_mlp_sconv, forward_batch, get_tensor_model_parallel_group() + ) + hs, res = self.attn_norm(hs, res) + else: + if prev_mlp_sconv is not None: + hs = prev_mlp_sconv(hs, positions, forward_batch) + if self.scattered_sconv: + # hs was the previous layer's reduce-scattered [T, H/P] MoE + # shard; gather back to [T, H] before the residual add. + hs = all_gather_hidden(hs, self.attn_tp_group) + + # Fused residual-add + norm for attention input. First layer: no prior + # residual yet, so just norm the (post-deferred-sconv) embeddings. + if res is None: + res = hs + hs = self.attn_norm(hs) + else: + hs, res = self.attn_norm(hs, res) + + if eager_attn: + # Routing through the split op here would start a nested break that + # asserts on the already-ended segment, so force the eager attn path; + # narrow out_cache_loc to the real token count for the KV write (see above), + # then restore the full buffer on forward_batch (shared across layers/replays). + orig_out_cache_loc = forward_batch.out_cache_loc + forward_batch.out_cache_loc = orig_out_cache_loc[: hs.shape[0]] + with force_eager_attention(): + hs = self.attn( + hs, positions, forward_batch, log_scaling_tau=log_scaling_tau + ) + forward_batch.out_cache_loc = orig_out_cache_loc + else: + hs = self.attn( + hs, + positions, + forward_batch, + log_scaling_tau=log_scaling_tau, + reduce=not fuse_attn_ar, + ) + + if fuse_attn_ar: + # hs holds the UNREDUCED wo_ud partials; the caller (forward) fuses + # {AR -> attn_sconv -> mlp_norm} into one kernel. + return hs, res + if self.attn_sconv is not None: + # Under scattered sconv, hs is the attn output's reduce-scattered + # [T, H/P] shard (attn.py routed the reduction); gather after. + hs = self.attn_sconv(hs, positions, forward_batch) + if self.scattered_sconv: + hs = all_gather_hidden(hs, self.attn_tp_group) + return hs, res + + def _attn_group_impl( + self, + hidden_states: torch.Tensor, + residual: Optional[torch.Tensor], + positions: torch.Tensor, + attn_out: torch.Tensor, + residual_out: torch.Tensor, + prev_mlp_sconv: Optional[ShortConvolution], + log_scaling_tau: Optional[torch.Tensor], + ) -> None: + """Eager break: run `_attn_block` on the REAL (non-padded) tokens with the LIVE + forward_batch and write the result into the padded output buffers. Mutates + attn_out / residual_out and returns None (the eager_on_graph copy-back is + per-tensor, not per-tuple, so outputs must be pre-allocated buffers).""" + forward_batch = get_tc_piecewise_forward_context().forward_batch + n = forward_batch.num_token_non_padded_cpu + # log_scaling_tau is per-token, so narrow it to match the real tokens too. + hs, res = self._attn_block( + hidden_states[:n], + residual[:n] if residual is not None else None, + positions[:n], + forward_batch, + prev_mlp_sconv, + log_scaling_tau[:n] if log_scaling_tau is not None else None, + eager_attn=True, + ) + torch._foreach_copy_((attn_out[:n], residual_out[:n]), (hs, res)) + if attn_out.shape[0] != n: + torch._foreach_zero_((attn_out[n:], residual_out[n:])) + + def _mlp_sconv_impl( + self, + hidden_states: torch.Tensor, + positions: torch.Tensor, + out: torch.Tensor, + ) -> None: + """Eager break for the final layer's deferred mlp_sconv: run on the real + tokens with the live forward_batch, write the padded output buffer.""" + forward_batch = get_tc_piecewise_forward_context().forward_batch + n = forward_batch.num_token_non_padded_cpu + y = self.mlp_sconv(hidden_states[:n], positions[:n], forward_batch) + if self.scattered_sconv: + # y is the [n, H/P] shard; the output buffer is post-gather [n, H]. + y = all_gather_hidden(y, self.attn_tp_group) + out[:n].copy_(y) + if out.shape[0] != n: + out[n:].zero_() + + def forward( + self, + hidden_states: torch.Tensor, + positions: torch.Tensor, + forward_batch: ForwardBatch, + residual: Optional[torch.Tensor], + prev_mlp_sconv: Optional[ShortConvolution] = None, + *, + log_scaling_tau: torch.Tensor | None = None, + prev_mlp_partial: bool = False, + fuse_ar_sconv: bool = False, + fuse_attn_ar: bool = False, + ) -> Tuple[torch.Tensor, torch.Tensor]: + """mlp_sconv is DEFERRED: this layer applies the *previous* layer's + mlp_sconv (`prev_mlp_sconv`) at the head of its eager region, and its own + mlp_sconv is applied by the next layer (or, for the last layer, by + InklingCausalLLM after the loop). Deferral is behavior-identical -- attn_norm + consumes the sconv output either way -- and lets the whole + {mlp_sconv, attn_norm, attn, attn_sconv} region be ONE eager break. + + ``prev_mlp_partial``: ``hidden_states`` holds the previous layer's + UNREDUCED MoE partials; the head chain runs as the fused AR kernel. + ``fuse_ar_sconv``: this layer's own MoE runs ``reduce=False`` so the + NEXT consumer (layer or tail) fuses its AR the same way. The caller + (InklingCausalLLM) threads both consistently.""" + if forward_batch.forward_mode.is_idle(): + return hidden_states, residual + + # The eager group reads the LIVE forward_batch from the tc_piecewise context + # (the only hook evaluated at BCG replay time — the break's captured args are + # frozen at capture-bucket shapes). Only the prefill BCG runner installs that + # context; the decode breakable backend sets is_in_breakable_cuda_graph() but + # NOT the context, so gate on both and otherwise fall through to the inline + # path below (which uses the passed forward_batch — correct for decode). + if ( + is_in_breakable_cuda_graph() + and get_tc_piecewise_forward_context() is not None + ): + # BCG prefill path: the AR fusion is decode-only, so partials never + # reach (or leave) this branch. + assert not prev_mlp_partial and not fuse_ar_sconv and not fuse_attn_ar + # BCG: {prev mlp_sconv, attn_norm, attn, attn_sconv} run eagerly (one + # break under capture); mlp_norm + MoE stay captured. (The live + # forward_batch inside the break is read from the shared tc_piecewise + # context, which the prefill BCG runner populates at capture and replay.) + # Under scattered sconv the group's INPUT can be the previous layer's + # [T, H/P] MoE shard while its OUTPUT is post-all-gather [T, H], so + # size the output buffers explicitly (residual is always [T, H]). + out_shape = (hidden_states.shape[0], self.attn_norm.weight.shape[0]) + attn_out = hidden_states.new_empty(out_shape) + residual_out = hidden_states.new_empty(out_shape) + self._breakable_attn_group( + hidden_states, + residual, + positions, + attn_out, + residual_out, + prev_mlp_sconv, + log_scaling_tau, + ) + hidden_states, residual = self.mlp_norm(attn_out, residual_out) + del attn_out + del residual_out + hidden_states = self.mlp(hidden_states, forward_batch=forward_batch) + return hidden_states, residual + + # Plain eager / decode: run inline (still deferring mlp_sconv so the + # InklingCausalLLM loop threads prev_mlp_sconv uniformly across modes). + fuse_attn = fuse_attn_ar and self.attn_sconv is not None + hidden_states, residual = self._attn_block( + hidden_states, + residual, + positions, + forward_batch, + prev_mlp_sconv, + log_scaling_tau, + eager_attn=False, + prev_mlp_partial=prev_mlp_partial, + fuse_attn_ar=fuse_attn, + ) + if fuse_attn and self.scattered_sconv: + fm = forward_batch.forward_mode + if fm.is_decode() or fm.is_target_verify(): + # Fused decode/verify {wo_ud AR + scattered attn_sconv + + # mlp_norm} (attn-side chain), norm tail in-kernel. + hidden_states, residual = ar_scattered_sconv_fused( + hidden_states, + self.attn_sconv, + forward_batch, + self.attn_tp_group, + norm=self.mlp_norm, + norm_residual=residual, + ) + else: + # Fused extend {AR + scattered sconv} (attn-side chain); the + # norm runs unfused on the gathered [T, H]. + hidden_states = ar_scattered_sconv_fused( + hidden_states, self.attn_sconv, forward_batch, self.attn_tp_group + ) + hidden_states, residual = self.mlp_norm(hidden_states, residual) + elif fuse_attn: + fm = forward_batch.forward_mode + if fm.is_decode() or fm.is_target_verify(): + # Fused {wo_ud AR -> attn_sconv -> mlp_norm} (attn-side chain). + hidden_states, residual = ar_sconv_norm_fused( + hidden_states, + residual, + self.attn_sconv, + self.mlp_norm, + forward_batch, + get_parallel().attn_tp_group, + ) + else: + # Fused extend {AR + full-width attn_sconv + cache update} + # (attn-side chain, non-scattered); norm runs unfused. + hidden_states = ar_fullwidth_sconv_fused( + hidden_states, + self.attn_sconv, + forward_batch, + get_parallel().attn_tp_group, + ) + hidden_states, residual = self.mlp_norm(hidden_states, residual) + else: + hidden_states, residual = self.mlp_norm(hidden_states, residual) + if fuse_ar_sconv and self.mlp_ar_fusable: + # Skip the MoE's own all-reduce; the next layer (or the model tail) + # fuses {AR -> this layer's mlp_sconv -> norm} into one kernel. + hidden_states = self.mlp( + hidden_states, forward_batch=forward_batch, reduce=False + ) + else: + hidden_states = self.mlp(hidden_states, forward_batch=forward_batch) + return hidden_states, residual + + +class InklingCausalLLM(nn.Module): + def __init__( + self, + config: InklingModelConfig, + quant_config: Optional[QuantizationConfig] = None, + prefix: str = "", + ): + super().__init__() + self.config = config + self.padded_vocab_size = config.padded_vocab_size + + self.embed_tokens = VocabParallelEmbedding( + self.padded_vocab_size, + config.hidden_size, + org_num_embeddings=self.padded_vocab_size, + prefix=add_prefix("embed_tokens", prefix), + ) + self.embed_norm = ( + RMSNorm(config.hidden_size, eps=config.rms_norm_eps) + if config.use_embed_norm + else None + ) + + self.alt_stream = torch.cuda.Stream() if is_cuda() else None + + def get_layer(idx: int, prefix: str) -> InklingDecoderLayer: + return InklingDecoderLayer( + config=config, + layer_id=idx, + is_local=idx in set(config.local_layer_ids), + quant_config=quant_config, + prefix=prefix, + alt_stream=self.alt_stream, + ) + + self.layers = make_layers( + config.num_hidden_layers, + get_layer, + prefix=add_prefix("layers", prefix), + ) + self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) + + # Custom-AR resources must exist BEFORE any CUDA-graph capture (with + # the prefill graph disabled and --skip-server-warmup there is no eager + # forward to build them lazily; decode capture would bake the fallback). + if envs.SGLANG_OPT_USE_INKLING_CUSTOM_AR.get(): + ensure_inkling_ar_resources(get_tensor_model_parallel_group()) + ensure_inkling_ar_resources(get_parallel().attn_tp_group) + + # Warm the fused decode {AR -> mlp_sconv -> norm} JIT module (both + # track variants) so the first fused call -- which can land inside a + # CUDA-graph capture -- doesn't pay the nvcc compile there. + sconv0 = self.layers[0].mlp_sconv + world = get_parallel().tp_size + if ( + is_cuda() + and envs.SGLANG_OPT_USE_INKLING_CUSTOM_AR.get() + and envs.SGLANG_OPT_USE_INKLING_FUSED_AR_SCONV_NORM.get() + and sconv0 is not None + and world in (4, 8) # symm-mem multimem worlds, power-of-two + ): + from sglang.jit_kernel.inkling_ar_fused import compile_inkling_ar_sconv_norm + + for do_track in (False, True): + compile_inkling_ar_sconv_norm( + torch.bfloat16, + world, + sconv0.kernel_size[0], + sconv0.activation in ("silu", "swish"), + sconv0.use_residual, + do_track, + ) + + # Warm the fused attention-prologue JIT module(s) so the first + # target-verify call (which lands inside a CUDA-graph capture) doesn't + # nvcc-compile there. fused_prologue is decided PER layer, so warm every + # distinct eligible signature -- not just layer 0, which may be a + # local/SWA layer (head_dim != 128) that never uses the prologue while + # later full-attention layers do. + if is_cuda() and envs.SGLANG_OPT_USE_INKLING_FUSED_ATTN_PROLOGUE.get(): + from sglang.jit_kernel.inkling_attn_prologue import ( + compile_inkling_attn_prologue, + ) + + warmed: set = set() + warm_mxfp8 = get_server_args().kv_cache_dtype == "mxfp8" + for layer in self.layers: + attn = layer.attn + ks = attn.k_sconv + if ks is None or attn.head_dim != 128: + continue + sig = ( + ks.kernel_size[0], + ks.activation in ("silu", "swish"), + ks.use_residual, + ) + if sig in warmed: + continue + warmed.add(sig) + compile_inkling_attn_prologue(torch.bfloat16, *sig) + if warm_mxfp8: + compile_inkling_attn_prologue(torch.bfloat16, *sig, use_mxfp8=True) + + self.lm_head = ParallelLMHead( + self.padded_vocab_size, + config.hidden_size, + org_num_embeddings=self.padded_vocab_size, + quant_config=quant_config, + prefix=add_prefix("lm_head", prefix), + ) + self.logits_processor = LogitsProcessor(config) + + def get_input_embeddings(self): + # Fold embed_norm into the embedding so general_mm_embed_routine norms the text + # tokens (MM positions are overwritten by the tower features, which keep their own norm). + embed_tokens, embed_norm = self.embed_tokens, self.embed_norm + + def embed(input_ids: torch.Tensor) -> torch.Tensor: + embeds = embed_tokens(input_ids) + return embed_norm(embeds) if embed_norm is not None else embeds + + embed.num_embeddings = self.config.vocab_size + return embed + + def forward( + self, + input_ids: torch.Tensor, + positions: torch.Tensor, + forward_batch: ForwardBatch, + input_embeds: Optional[torch.Tensor] = None, + **kwargs, + ) -> torch.Tensor: + if input_embeds is None: + hidden_states = self.embed_tokens(input_ids) + if self.embed_norm is not None: + hidden_states = self.embed_norm(hidden_states) + else: + # embed_norm was already applied during the MM embed; don't re-norm here. + hidden_states = input_embeds + hidden_states = hidden_states.view(-1, hidden_states.shape[-1]) + + log_scaling_tau = ( + compute_log_scaling_tau( + positions, + self.config.log_scaling_n_floor, + self.config.log_scaling_alpha, + ) + if self.config.log_scaling_n_floor is not None + else None + ) + residual = None + # mlp_sconv is deferred one layer: each layer applies the previous layer's + # mlp_sconv at the head of its (eager) attn region, so the whole sconv+attn + # region is one BCG break. prev_mlp_sconv=None for layer 0. + prev_mlp_sconv = None + # Fused decode {MoE AR -> mlp_sconv -> attn_norm}: decided ONCE per + # forward (a pure function of per-forward state, so the producing MoE + # and the consuming layer/tail always agree). When on, an eligible + # layer's MoE returns UNREDUCED partials (reduce=False) and the next + # consumer runs the fused kernel. + fuse_ar_sconv = ( + not forward_batch.forward_mode.is_idle() + and ar_sconv_norm_fusable( + get_tensor_model_parallel_group(), + forward_batch, + hidden_states.shape[0], + hidden_states.shape[-1], + hidden_states.dtype, + ) + ) + # The attn-side chain reduces over the ATTENTION TP group (identical to + # the model TP group without DP attention, but gate on it explicitly). + fuse_attn_ar = ( + not forward_batch.forward_mode.is_idle() + and ar_sconv_norm_fusable( + get_parallel().attn_tp_group, + forward_batch, + hidden_states.shape[0], + hidden_states.shape[-1], + hidden_states.dtype, + ) + ) + # Fused extend {AR + scattered sconv} (--enable-scattered-sconv + + # SGLANG_OPT_USE_INKLING_FUSED_AR_SCONV): same producer contract as the + # decode fusion above (reduce=False), consumer runs the scattered + # kernel. Mutually exclusive with ar_sconv_norm_fusable by mode + # (extend vs decode/verify) and by the scattered gate inside it. + if not forward_batch.forward_mode.is_idle() and scattered_ar_sconv_fusable( + get_tensor_model_parallel_group(), + forward_batch, + hidden_states.shape[0], + hidden_states.shape[-1], + hidden_states.dtype, + ): + fuse_ar_sconv = True + fuse_attn_ar = True + # Fused extend {AR + full-width sconv + cache update} (NON-scattered): + # same producer contract (reduce=False); the consumer sites dispatch + # by mode -- extend runs the full-width column kernel, decode/verify + # keep ar_sconv_norm_fused. Mutually exclusive with both gates above + # (mode for ar_sconv_norm_fusable, the scattered flag for + # scattered_ar_sconv_fusable). + if not forward_batch.forward_mode.is_idle() and fullwidth_ar_sconv_fusable( + get_tensor_model_parallel_group(), + forward_batch, + hidden_states.shape[0], + hidden_states.shape[-1], + hidden_states.dtype, + ): + fuse_ar_sconv = True + fuse_attn_ar = True + prev_mlp_partial = False + for layer in self.layers: + hidden_states, residual = layer( + hidden_states, + positions, + forward_batch, + residual, + prev_mlp_sconv, + log_scaling_tau=log_scaling_tau, + prev_mlp_partial=prev_mlp_partial, + fuse_ar_sconv=fuse_ar_sconv, + fuse_attn_ar=fuse_attn_ar, + ) + prev_mlp_sconv = layer.mlp_sconv + prev_mlp_partial = fuse_ar_sconv and layer.mlp_ar_fusable + # The final layer's mlp_sconv was deferred; run it now — as an eager break + # under BCG (so it re-reads live per-seq metadata at replay), else inline. + if prev_mlp_sconv is not None and not forward_batch.forward_mode.is_idle(): + if prev_mlp_partial and self.layers[-1].scattered_sconv: + fm = forward_batch.forward_mode + if fm.is_decode() or fm.is_target_verify(): + # Fused decode/verify tail: {AR + scattered sconv + final + # norm} in one kernel. + hidden_states, _ = ar_scattered_sconv_fused( + hidden_states, + prev_mlp_sconv, + forward_batch, + get_tensor_model_parallel_group(), + norm=self.norm, + norm_residual=residual, + ) + return hidden_states + # Fused extend tail: {AR + scattered sconv}, then the final + # norm unfused on the gathered [T, H]. + hidden_states = ar_scattered_sconv_fused( + hidden_states, + prev_mlp_sconv, + forward_batch, + get_tensor_model_parallel_group(), + ) + hidden_states, _ = self.norm(hidden_states, residual) + return hidden_states + if prev_mlp_partial: + fm = forward_batch.forward_mode + if fm.is_decode() or fm.is_target_verify(): + # Fused tail: {AR -> final mlp_sconv -> final norm} in one + # kernel. + hidden_states, _ = ar_sconv_norm_fused( + hidden_states, + residual, + prev_mlp_sconv, + self.norm, + forward_batch, + get_tensor_model_parallel_group(), + ) + return hidden_states + # Fused extend tail: {AR + full-width sconv + cache update} + # (non-scattered), then the final norm unfused. + hidden_states = ar_fullwidth_sconv_fused( + hidden_states, + prev_mlp_sconv, + forward_batch, + get_tensor_model_parallel_group(), + ) + hidden_states, _ = self.norm(hidden_states, residual) + return hidden_states + # Same gate as the per-layer group: the eager break needs the tc_piecewise + # context (installed only by the prefill BCG runner) to read the live + # forward_batch at replay; else run inline with the passed forward_batch. + scattered = self.layers[-1].scattered_sconv + if ( + is_in_breakable_cuda_graph() + and get_tc_piecewise_forward_context() is not None + ): + # Under scattered sconv the input is the last MoE's [T, H/P] + # shard; the break's output buffer is post-all-gather [T, H]. + out_shape = ( + (hidden_states.shape[0], self.norm.weight.shape[0]) + if scattered + else hidden_states.shape + ) + mlp_sconv_out = hidden_states.new_empty(out_shape) + self.layers[-1]._breakable_mlp_sconv( + hidden_states, positions, mlp_sconv_out + ) + hidden_states = mlp_sconv_out + else: + hidden_states = prev_mlp_sconv(hidden_states, positions, forward_batch) + if scattered: + hidden_states = all_gather_hidden( + hidden_states, self.layers[-1].attn_tp_group + ) + hidden_states, _ = self.norm(hidden_states, residual) + return hidden_states + + +class InklingAudio(nn.Module): + def __init__(self, config: InklingAudioConfig, prefix: str = ""): + del prefix + super().__init__() + assert config.audio_mode == "dmel" + self.n_mel_bins = config.n_mel_bins + self.mel_vocab_size = config.mel_vocab_size + self.use_audio_norm = config.use_audio_norm + self.encoder = nn.Embedding( + config.n_mel_bins * config.mel_vocab_size, config.decoder_dmodel + ) + self.final_norm: RMSNorm | None = None + if self.use_audio_norm: + self.final_norm = RMSNorm(config.decoder_dmodel, eps=1e-6) + + def forward(self, audio_features: torch.Tensor) -> torch.Tensor: + assert audio_features.shape[1] == self.n_mel_bins + + audio_features = audio_features.to( + dtype=self.encoder.weight.dtype, device=self.encoder.weight.device + ) + + embedding_indices = ( + torch.arange(self.n_mel_bins, device=audio_features.device) + * self.mel_vocab_size + ).unsqueeze(0) + audio_features.to(torch.int32) + + hidden_states = ( + self.encoder(embedding_indices.reshape(-1)) + .reshape(audio_features.shape[0], audio_features.shape[1], -1) + .sum(axis=1) + ) + + if self.final_norm is not None: + hidden_states = self.final_norm(hidden_states) + + return hidden_states + + +class InklingVision(nn.Module): + def __init__(self, config: InklingVisionConfig, prefix: str = ""): + del prefix + super().__init__() + assert config.vision_encoder_type == "hmlp" + self.vision_encoder = HMLPPatchEncoder(config) + + def forward(self, vision_features: torch.Tensor) -> torch.Tensor: + return self.vision_encoder(vision_features) + + +class InklingForConditionalGeneration(nn.Module): + fall_back_to_pt_during_load = False + supported_lora_modules = [ + "qkvr", + "wo_ud", + "gate_up_proj", + "down_proj", + "embed_tokens", + "lm_head", + ] + + def __init__( + self, + config: InklingMMConfig, + quant_config: Optional[QuantizationConfig] = None, + prefix: str = "", + ) -> None: + super().__init__() + self.config = config + self.text_config = config.text_config + + server_args = get_server_args() + assert envs.SGLANG_ENABLE_UNIFIED_RADIX_TREE.get() + if server_args.disaggregation_mode != "decode": + assert not server_args.disable_radix_cache + assert not server_args.disable_hybrid_swa_memory + assert server_args.enable_mamba_extra_buffer() + + from types import SimpleNamespace + + from sglang.srt.models.inkling_common.quantization import ( + get_quantization_config, + ) + + inkling_quant_config = get_quantization_config( + SimpleNamespace(hf_config=self.config, model_path=server_args.model_path) + ) + if inkling_quant_config is not None: + quant_config = inkling_quant_config + + self.quant_config = quant_config + self.llm = InklingCausalLLM( + self.text_config, + quant_config=quant_config, + prefix=add_prefix("llm", prefix), + ) + # Only build the vision/audio towers when multimodal is actually + # enabled. Inkling is in ModelConfig.mm_disabled_models, so multimodal + # defaults OFF (enable_multimodal auto -> False); a multimodal + # checkpoint served text-only must not allocate/load the towers (wasted + # GPU memory / avoidable startup OOM). The mm dispatch (forward) and the + # weight loader already skip audio./visual. when these are None. + build_multimodal = bool(server_args.enable_multimodal) + self.audio = ( + InklingAudio(self.config.audio_config) + if build_multimodal and self.config.audio_config.decoder_dmodel is not None + else None + ) + self.visual = ( + InklingVision(self.config.vision_config, prefix=prefix) + if build_multimodal and self.config.vision_config.decoder_dmodel is not None + else None + ) + self.mm_pattern = MultiModalityDataPaddingPatternMultimodalTokens() + + @property + def model(self) -> nn.Module: + # Expose the language model under `.model` so the prefill CUDA graph + # (BCG) setup treats Inkling as a language model: model_runner's + # `hasattr(self.model, "model")` gate and `resolve_language_model` + # both look for `.model`. + # + # This is a property rather than just renaming the `llm` submodule to + # `model`: the NVFP4 hf_quant_config.json names the LM `model.llm.*` + # and the exclude-module matching is keyed to the `llm` name, so + # renaming flips fp4/bf16 treatment on modules and breaks weight + # loading with a packed-vs-bf16 shape mismatch. A property (not a + # submodule alias) also avoids registering `llm` twice in the module + # tree / state_dict. + return self.llm + + def get_hidden_dim(self, module_name: str, layer_idx: int) -> tuple[int, int]: + def base_layer(module: torch.nn.Module) -> torch.nn.Module: + from sglang.srt.lora.layers import BaseLayerWithLoRA + + if isinstance(module, BaseLayerWithLoRA): + return module.base_layer + return module + + config = self.text_config + hidden_size = config.hidden_size + layer = self.llm.layers[layer_idx] + if module_name == "qkvr": + qkvr = base_layer(layer.attn.qkvr) + return qkvr.input_size, sum(qkvr.output_sizes) + if module_name == "wo_ud": + wo_ud = base_layer(layer.attn.wo_ud) + return wo_ud.input_size, wo_ud.output_size + # gate_up_proj / down_proj exist only on dense-MLP layers; MoE layers serve + # them via *_moe buffers, so return config dims for the unused buffer alloc. + if module_name == "gate_up_proj": + if isinstance(layer.mlp, InklingDenseMLP): + gate_up_proj = base_layer(layer.mlp.gate_up_proj) + return gate_up_proj.input_size, gate_up_proj.output_size + return hidden_size, config.intermediate_size * 2 + if module_name == "down_proj": + if isinstance(layer.mlp, InklingDenseMLP): + down_proj = base_layer(layer.mlp.down_proj) + return down_proj.input_size, down_proj.output_size + return config.intermediate_size, hidden_size + if module_name in ("gate_up_proj_moe", "gate_up_proj_shared_moe"): + return hidden_size, config.intermediate_size * 2 + if module_name in ("down_proj_moe", "down_proj_shared_moe"): + return config.intermediate_size, hidden_size + if module_name == "embed_tokens": + return config.vocab_size, hidden_size + if module_name == "lm_head": + return hidden_size, config.vocab_size + raise NotImplementedError(f"get_hidden_dim not implemented for {module_name}") + + def get_stacked_multiply(self, module_name: str) -> int: + if module_name == "qkvr": + return 4 + if module_name in ( + "gate_up_proj", + "gate_up_proj_moe", + "gate_up_proj_shared_moe", + ): + return 2 + return 1 + + def pad_input_ids(self, input_ids: list[int], mm_inputs: MultimodalInputs): + # The processor expands one placeholder per media item into a run of the same + # token id; the scheduler calls this to replace each run with the item's + # pad_value (radix hash), which _embed_mm then masks on to scatter the embeds. + return self.mm_pattern.pad_input_tokens(input_ids, mm_inputs) + + def get_input_embeddings(self) -> nn.Module: + return self.llm.embed_tokens + + def get_embed_and_head(self): + return self.llm.embed_tokens.weight, self.llm.lm_head.weight + + def get_num_kv_cache_layers(self) -> int: + return self.text_config.num_hidden_layers + + def get_attention_sliding_window_size(self) -> Optional[int]: + return self.text_config.sliding_window_size - 1 + + def get_audio_feature(self, items: list[MultimodalDataItem]) -> torch.Tensor: + dmel = torch.cat([item.feature for item in items], dim=0) + return self.audio(dmel) + + def get_image_feature(self, items: list[MultimodalDataItem]) -> torch.Tensor: + patches = torch.cat([item.feature for item in items], dim=0) + param = next(self.visual.parameters()) + return self.visual(patches.to(device=param.device, dtype=param.dtype)) + + @torch.no_grad() + def forward( + self, + input_ids: torch.Tensor, + positions: torch.Tensor, + forward_batch: ForwardBatch, + **kwargs, + ): + data_embedding_funcs = {} + if self.audio is not None: + data_embedding_funcs[Modality.AUDIO] = self.get_audio_feature + if self.visual is not None: + data_embedding_funcs[Modality.IMAGE] = self.get_image_feature + hidden_states = general_mm_embed_routine( + input_ids=input_ids, + forward_batch=forward_batch, + language_model=self.llm, + data_embedding_funcs=data_embedding_funcs, + positions=positions, + ) + mup_width_multiplier = self.config.text_config.logits_mup_width_multiplier + hidden_states_for_logits = ( + hidden_states + if not mup_width_multiplier + else hidden_states / mup_width_multiplier + ) + # The MTP chain needs the undivided hidden (the mup division is + # lm_head-only); passed unconditionally because the target + # verify/prefill forwards never set return_hidden_states_before_norm. + return self.llm.logits_processor( + input_ids, + hidden_states_for_logits, + self.llm.lm_head, + forward_batch, + hidden_states_before_norm=hidden_states, + ) + + def update_conv_state_after_mtp_verify( + self, + req_to_token_pool, + req_pool_indices: torch.Tensor, + last_correct_step_indices: torch.Tensor, + mamba_track_indices: Optional[torch.Tensor], + mamba_steps_to_track: Optional[torch.Tensor], + ) -> None: + """Commit the per-step sconv windows saved during TARGET_VERIFY into the + persistent conv caches at each request's last accepted step. + + Inkling bypasses the HybridLinearAttnBackend wrapper (ShortConvolution reads + the mamba pool directly), so the model owns this commit instead of an + attention-backend hook. The pool is passed in because this runs from the + spec worker after the forward context has exited. + """ + from sglang.kernels.ops.mamba.mamba_state_scatter_triton import ( + scatter_mamba_states_after_mtp_verify, + ) + + pool = req_to_token_pool + mamba_indices = pool.translate_mamba_indices( + pool.get_mamba_indices(req_pool_indices) + ) + scatter_mamba_states_after_mtp_verify( + pool.get_speculative_mamba2_params_all_layers(), + mamba_indices, + last_correct_step_indices, + mamba_track_indices, + mamba_steps_to_track, + ) + + def _load_regular_param( + self, + params_dict: dict[str, torch.nn.Parameter], + loaded_params: Set[str], + name: str, + loaded_weight: torch.Tensor, + shard_id: Optional[int] = None, + ) -> bool: + if name not in params_dict: + return False + param = params_dict[name] + weight_loader = getattr(param, "weight_loader", default_weight_loader) + if shard_id is None: + if param.data.shape == loaded_weight.shape: + default_weight_loader(param, loaded_weight) + else: + weight_loader(param, loaded_weight) + else: + weight_loader(param, loaded_weight, shard_id) + loaded_params.add(name) + return True + + def _load_nvfp4_scale_param( + self, + params_dict: dict[str, torch.nn.Parameter], + loaded_params: Set[str], + name: str, + loaded_weight: torch.Tensor, + ) -> bool: + """Load an NVFP4 auxiliary tensor (block scale / scale2 / input_amax / + original_shape); shard appropriately. + """ + if name not in params_dict: + return False + param = params_dict[name] + if loaded_weight.shape != param.shape: + # shared experts shard over the full tp group; routed over moe_tp + tp_rank = ( + get_parallel().tp_rank + if ".shared_experts" in name + else get_parallel().moe_tp_rank + ) + for dim in range(loaded_weight.ndim): + if loaded_weight.shape[dim] == param.shape[dim]: + continue + if loaded_weight.shape[dim] % param.shape[dim] != 0: + raise ValueError( + f"Cannot TP-shard NVFP4 scale {name}: checkpoint dim " + f"{dim} ({loaded_weight.shape[dim]}) is not divisible " + f"by the local param dim ({param.shape[dim]})" + ) + loaded_weight = loaded_weight.narrow( + dim, tp_rank * param.shape[dim], param.shape[dim] + ) + default_weight_loader(param, loaded_weight) + loaded_params.add(name) + return True + + def _ckpt_scale_to_modelopt( + self, + suf: str, + loaded: torch.Tensor, + param: torch.nn.Parameter, + ) -> torch.Tensor: + """Convert a Inkling-checkpoint NVFP4 aux tensor to the layout + ModelOptNvFp4FusedMoEMethod expects. + + - scale (block scales): same (E, 2F, H/16) layout -- TP sharding and the + interleaved-w13 de-interleave are handled by the loader / process_weights. + - scale2 (per-tensor weight scale): the checkpoint stores one per expert (E,); + ModelOpt's w13 wants one per (gate, up) -> (E, 2); w2 stays (E,). + - input_amax -> input_scale = amax / (448*6) (FP8 e4m3 max * FP4 e2m1 max). The + checkpoint stores a single global activation amax; broadcast to the param shape. + """ + if suf == "scale": + return loaded + if suf == "scale2": + v = loaded.to(param.dtype) + if v.ndim == 1 and param.ndim == 2 and param.shape[0] == v.shape[0]: + return v[:, None].expand(param.shape[0], param.shape[1]).contiguous() + return v + if suf == "input_amax": + scale = float(loaded.reshape(-1)[0].to(torch.float32)) / (448.0 * 6.0) + return torch.full(tuple(param.shape), scale, dtype=param.dtype) + return loaded + + def _slice_local_experts( + self, name: str, loaded_weight: torch.Tensor + ) -> torch.Tensor: + """Narrow a routed-expert checkpoint tensor to this rank's local ep block. + + The checkpoint packs all n_routed_experts in dim 0, but under EP each rank's params + hold only its contiguous slice (the fused loader shards the intermediate dim only). + No-op when EP is off or for replicated shared-expert tensors. + """ + ep_size = get_parallel().moe_ep_size + if ( + ep_size <= 1 + or ".experts." not in name + # per-expert RL sync tensors do their own EP remap in _load_per_expert_param; + # a full per-expert tensor whose dim 0 happens to equal n_routed_experts must + # not be pre-narrowed here. + or _PER_EXPERT_WEIGHT_RE.match(name) is not None + or loaded_weight.ndim == 0 + or loaded_weight.shape[0] != self.text_config.n_routed_experts + ): + return loaded_weight + local = self.text_config.n_routed_experts // ep_size + start = get_parallel().moe_ep_rank * local + return loaded_weight.narrow(0, start, local).contiguous() + + def _load_fused_moe_param( + self, + params_dict: dict[str, torch.nn.Parameter], + loaded_params: Set[str], + name: str, + loaded_weight: torch.Tensor, + shard_id: str, + ) -> bool: + if name not in params_dict: + return False + param = params_dict[name] + weight_loader = getattr(param, "weight_loader", default_weight_loader) + if ( + shard_id == "w13" + and not self.text_config.inference_moe_w13_interleaved + and weight_loader is not default_weight_loader + ): + from sglang.srt.layers.quantization.modelopt_quant import ( + deinterleave_w13, + ) + + loaded_weight = deinterleave_w13(loaded_weight) + if ( + loaded_weight.ndim > 0 + and param.data.ndim > 0 + and loaded_weight.shape[0] != param.data.shape[0] + ): + raise ValueError( + f"Unexpected fused MoE expert dimension for {name}: " + f"loaded={loaded_weight.shape[0]}, expected={param.data.shape[0]}" + ) + if weight_loader is default_weight_loader: + default_weight_loader(param, loaded_weight) + else: + weight_loader(param, loaded_weight, name, shard_id) + loaded_params.add(name) + return True + + def _load_per_expert_param( + self, + params_dict: dict[str, torch.nn.Parameter], + loaded_params: Set[str], + name: str, + loaded_weight: torch.Tensor, + ) -> bool: + """Load ONE routed expert shipped by the online RL weight-sync. + + The trainer streams routed experts one at a time as FULL (unsharded) + per-expert tensors — ``...mlp.experts.{j}.gate_proj/up_proj/down_proj.weight`` + — to avoid materializing the multi-GB fused stack. Disk checkpoints only + carry the fused ``w13_weight``/``w2_weight``, so this never fires for them. + + Writes expert ``j``'s slice of the fused buffer, narrowed to this rank's + MoE-TP shard (w13 shards the intermediate/output dim, w2 the + intermediate/input dim). Under EP the global expert id is remapped to this + rank's local slot and non-owned experts are skipped, mirroring the stock + ``FusedMoE.weight_loader``. The w13 row layout follows the serving mode: + Inkling-interleaved ([g0, u0, g1, u1, ...]; the grouped gemm reads 0::2/1::2) + when the config stores w13 interleaved, contiguous [gate || up] under + ``lora_compatible_layout_enabled()`` or ``inference_moe_w13_interleaved=False`` + (both make the fused param hold contiguous rows). + """ + m = _PER_EXPERT_WEIGHT_RE.match(name) + if m is None: + return False + pfx, eid, proj = m.group("pfx"), int(m.group("eid")), m.group("proj") + leaf = "w2_weight" if proj == "down_proj" else "w13_weight" + # Under --enable-lora the FusedMoE is wrapped and the base tensor lives one + # level down at `base_layer.`. + target = next( + ( + t + for t in (f"{pfx}.{leaf}", f"{pfx}.base_layer.{leaf}") + if t in params_dict + ), + None, + ) + if target is None: + return False + moe = self.get_submodule(target.rsplit(".", 1)[0]) + if getattr(moe, "use_flashinfer_trtllm_moe", False) or getattr( + getattr(moe, "quant_method", None), "use_flashinfer_trtllm_moe", False + ): + # The trtllm runners keep w13/w2 block-shuffled (and [up || gate]) after + # process_weights_after_loading; writing canonical rows into that layout + # would corrupt the stack. Only the triton runner is validated for RL sync. + raise NotImplementedError( + f"per-expert RL weight-sync does not support the trtllm MoE layout ({target}); " + "serve RL rollouts with the triton MoE runner" + ) + ep_size = get_parallel().moe_ep_size + if ep_size > 1: + local = self.text_config.n_routed_experts // ep_size + first = get_parallel().moe_ep_rank * local + if not (first <= eid < first + local): + loaded_params.add(target) # another rank owns this expert + return True + eid -= first + if proj == "down_proj": + dst = params_dict[target].data[ + eid + ] # [H, I_local]; shard intermediate (dim 1) + dst.copy_(_shard_full_to_local(loaded_weight, dst, dim=1)) + else: + w13 = params_dict[target].data[ + eid + ] # [2*I_local, H]; shard intermediate (dim 0) + idx = 0 if proj == "gate_proj" else 1 + if ( + lora_compatible_layout_enabled() + or not self.text_config.inference_moe_w13_interleaved + ): + half = w13.shape[0] // 2 + dst = w13[idx * half : (idx + 1) * half] # contiguous [gate || up] + else: + dst = w13[idx::2] # Inkling-interleaved rows + dst.copy_(_shard_full_to_local(loaded_weight, dst, dim=0)) + loaded_params.add(target) + return True + + def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]) -> Set[str]: + params_dict = dict(self.named_parameters()) + loaded_params: Set[str] = set() + embed_tokens_weight: Optional[torch.Tensor] = None + + for name, loaded_weight in weights: + if "rotary_emb.inv_freq" in name: + continue + # The checkpoint nests the sub-models under model.{llm,audio,visual}.; + # strip the container prefix so the llm./audio./visual. mapping below + # (and the visual/audio normalization) sees the submodule names directly. + name = name.removeprefix("model.") + name = _normalize_mm_weight_name(name) + if _is_unsupported_mm_weight_name(name): + continue + if name.startswith("audio.") and self.audio is None: + continue + if name.startswith("visual.") and self.visual is None: + continue + + if any(name.endswith(suffix) for suffix in KV_REPLICATED_SUFFIXES): + layer_id = get_layer_id(name) + if layer_id is not None: + num_kv_heads, head_dim = ( + ( + self.text_config.swa_num_key_value_heads, + self.text_config.swa_head_dim, + ) + if layer_id in set(self.text_config.local_layer_ids) + else ( + self.text_config.num_key_value_heads, + self.text_config.head_dim, + ) + ) + attn_tp_size = get_parallel().attn_tp_size + if ( + attn_tp_size > num_kv_heads + and loaded_weight.shape[0] == num_kv_heads * head_dim + ): + assert attn_tp_size % num_kv_heads == 0 + replicas = attn_tp_size // num_kv_heads + if name.endswith((".wk_dv.weight", ".wv_dv.weight")): + loaded_weight = ( + loaded_weight.view(num_kv_heads, head_dim, -1) + .repeat_interleave(replicas, dim=0) + .reshape(attn_tp_size * head_dim, -1) + ) + else: + kv_head_idx = get_parallel().attn_tp_rank // replicas + loaded_weight = loaded_weight.narrow( + 0, kv_head_idx * head_dim, head_dim + ) + + if name.endswith(".embed.weight"): + name = name.replace(".embed.weight", ".embed_tokens.weight") + elif name.endswith(".unembed.weight"): + name = name.replace(".unembed.weight", ".lm_head.weight") + elif name.endswith(".embed_tokens.embed_norm.weight"): + name = name.replace( + ".embed_tokens.embed_norm.weight", ".embed_norm.weight" + ) + + if name == "llm.embed_tokens.weight": + embed_tokens_weight = loaded_weight + + loaded_weight = self._slice_local_experts(name, loaded_weight) + + matched = False + for param_name, weight_name, shard_id in ATTENTION_PARAMS_MAPPING: + if f".attn.{weight_name}." not in name: + continue + sgl_name = name.replace(f".{weight_name}.", f".{param_name}.") + matched = self._load_regular_param( + params_dict, loaded_params, sgl_name, loaded_weight, shard_id + ) + break + if matched: + continue + + if ".mlp.w13_dn.weight" in name: + sgl_name = name.replace(".w13_dn.", ".gate_up_proj.") + if sgl_name in params_dict: + param = params_dict[sgl_name] + if loaded_weight.shape != param.data.shape: + shard_size = param.data.shape[0] + start = get_parallel().attn_tp_rank * shard_size + loaded_weight = loaded_weight.narrow(0, start, shard_size) + if lora_compatible_layout_enabled(): + # Local interleaved rows -> [gate||up] so contiguous swiglu and + # stock LoRA gate_up slicing line up (see InklingDenseMLP.__init__). + loaded_weight = deinterleave_gate_up(loaded_weight, dim=0) + default_weight_loader(param, loaded_weight) + loaded_params.add(sgl_name) + continue + + if ".mlp.w2_md.weight" in name: + sgl_name = name.replace(".w2_md.", ".down_proj.") + if self._load_regular_param( + params_dict, loaded_params, sgl_name, loaded_weight + ): + continue + + for param_name, weight_name, shard_id in STACKED_DENSE_PARAMS_MAPPING: + if f".mlp.{weight_name}." in name: + sgl_name = name.replace(f".{weight_name}.", f".{param_name}.") + matched = self._load_regular_param( + params_dict, loaded_params, sgl_name, loaded_weight, shard_id + ) + break + if matched: + continue + + _scale_matched = False + if ".experts." in name or ".shared_experts." in name: + # Map checkpoint NVFP4 aux tensors to the owning quant method's params. + # Routed experts use ModelOptNvFp4FusedMoEMethod, which registers + # w13_weight_scale / w13_weight_scale_2 / w13_input_scale; shared experts + # still use InklingNvfp4MoEMethod (w13_scale / w13_scale2 / w13_input_amax). + # Prefer the ModelOpt param (applying the scale2 (E,)->(E,2) and the + # input_amax -> input_scale = amax/(448*6) conversions); otherwise fall + # back to the Inkling param name with a direct copy. + for _suf in ("scale2", "scale", "input_amax", "original_shape"): + if not name.endswith(f"_weight.{_suf}"): + continue + _scale_matched = True + base = name.replace("shared_w13_weight", "w13_weight").replace( + "shared_w2_weight", "w2_weight" + ) + prefix = base[: base.rfind("_weight.")] + mo_suf = { + "scale": "weight_scale", + "scale2": "weight_scale_2", + "input_amax": "input_scale", + }.get(_suf) + mo_name = f"{prefix}_{mo_suf}" if mo_suf else None + inkling_name = f"{prefix}_{_suf}" + if mo_name is not None and mo_name in params_dict: + conv = self._ckpt_scale_to_modelopt( + _suf, loaded_weight, params_dict[mo_name] + ) + self._load_nvfp4_scale_param( + params_dict, loaded_params, mo_name, conv + ) + elif inkling_name in params_dict: + self._load_nvfp4_scale_param( + params_dict, loaded_params, inkling_name, loaded_weight + ) + elif _suf != "original_shape": + # ModelOpt path has no original_shape param, so dropping that one + # is expected; anything else going missing is a real problem. + logger.warning( + "NVFP4 scale tensor %s not mapped to any param; dropped", + name, + ) + break + if _scale_matched: + continue + + if ".experts.w13_weight" in name: + # bf16 routed layers run the stock FusedMoE forward (not moe_tp_forward) + # under --enable-lora, or natively on trtllm_routed for UNQUANTIZED + # checkpoints: de-interleave per moe_tp block for the stock weight prep. + if ( + loaded_weight.dtype != torch.uint8 + and self.text_config.inference_moe_w13_interleaved + and ( + lora_compatible_layout_enabled() + or bf16_routed_uses_stock_fused_moe(self.quant_config) + ) + ): + tp = get_parallel().moe_tp_size + n_e, two_f, hid = loaded_weight.shape + loaded_weight = deinterleave_gate_up( + loaded_weight.view(n_e, tp, two_f // tp, hid), dim=2 + ) + if trtllm_bf16_weight_prep_enabled(): + # trtllm bf16 weight prep consumes [up || gate] per rank + # ("w3_w1" order); triton/marlin consume [gate || up]. + half = two_f // tp // 2 + loaded_weight = torch.cat( + [loaded_weight[:, :, half:], loaded_weight[:, :, :half]], + dim=2, + ) + loaded_weight = loaded_weight.view(n_e, two_f, hid) + if self._load_fused_moe_param( + params_dict, loaded_params, name, loaded_weight, "w13" + ): + continue + if ".experts.w2_weight" in name: + if self._load_fused_moe_param( + params_dict, loaded_params, name, loaded_weight, "w2" + ): + continue + if ".shared_experts.shared_w13_weight" in name: + # InklingSharedFusedMoE's bf16 path needs contiguous [gate||up] w13 for the + # SRT runner's silu_and_mul, unlike the interleaved bmm/moe_tp_forward paths. + # Per-rank blocks are sized by the FULL tp group (InklingSharedFusedMoE always + # shards over it at EP=1), NOT moe_tp (= tp/ep, wrong under --ep-size > 1). + if ( + loaded_weight.dtype != torch.uint8 + and self.text_config.inference_moe_w13_interleaved + and use_inkling_shared_fused_moe() + ): + tp = get_parallel().tp_size + n_e, two_f, hid = loaded_weight.shape + loaded_weight = deinterleave_gate_up( + loaded_weight.view(n_e, tp, two_f // tp, hid), dim=2 + ) + if ( + get_moe_runner_backend().is_experimental_sgl_trtllm() + or bf16_routed_uses_stock_fused_moe(self.quant_config) + or shared_sink_uses_trtllm_bf16() + ): + # TRT-LLM BF16 weight preparation consumes [up || gate] + # per rank, including the shared sink on quantized models. + half = two_f // tp // 2 + loaded_weight = torch.cat( + [loaded_weight[:, :, half:], loaded_weight[:, :, :half]], + dim=2, + ) + loaded_weight = loaded_weight.view(n_e, two_f, hid) + sgl_name = name.replace("shared_w13_weight", "w13_weight") + if self._load_fused_moe_param( + params_dict, loaded_params, sgl_name, loaded_weight, "w13" + ): + continue + if ".shared_experts.shared_w2_weight" in name: + sgl_name = name.replace("shared_w2_weight", "w2_weight") + if self._load_fused_moe_param( + params_dict, loaded_params, sgl_name, loaded_weight, "w2" + ): + continue + if self._load_per_expert_param( + params_dict, loaded_params, name, loaded_weight + ): + continue + + if name.endswith(".bias") and name not in params_dict: + continue + if self._load_regular_param( + params_dict, loaded_params, name, loaded_weight + ): + continue + + if ( + "llm.lm_head.weight" not in loaded_params + and "llm.lm_head.weight" in params_dict + ): + if embed_tokens_weight is not None: + self._load_regular_param( + params_dict, + loaded_params, + "llm.lm_head.weight", + embed_tokens_weight, + ) + return loaded_params + + +class InklingMTPLayer(nn.Module): + """Single MTP layer following torchtitan's MultiTokenPredictorModule structure.""" + + def __init__( + self, + config: InklingModelConfig, + quant_config: QuantizationConfig | None = None, + prefix: str = "", + layer_id: int | None = None, + alt_stream: torch.cuda.Stream | None = None, + ) -> None: + super().__init__() + + self.embed_tokens = VocabParallelEmbedding( + config.padded_vocab_size, + config.hidden_size, + org_num_embeddings=config.padded_vocab_size, + prefix=add_prefix("embed_tokens", prefix), + ) + self.main_model_embed_norm = ( + RMSNorm(config.hidden_size, eps=config.rms_norm_eps) + if config.use_embed_norm + else None + ) + self.mup_width_multiplier = config.logits_mup_width_multiplier + self.log_scaling_n_floor = config.log_scaling_n_floor + self.log_scaling_alpha = config.log_scaling_alpha + self.mtp_layer_id = layer_id if layer_id is not None else 0 + self.hidden_norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) + self.embed_norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) + # Shared (non-layer-indexed) chain post-norm applied per depth before both + # the chain handoff and the LM head. + self.chain_norm = ( + RMSNorm(config.hidden_size, eps=config.rms_norm_eps) + if config.chain_hidden_post_norm + else None + ) + self.input_proj = nn.Linear( + config.hidden_size * 2, config.hidden_size, bias=False + ) + # MTP blocks always use the dense MLP; the dense_mlp_idx override forces it. + mtp_config = copy.copy(config) + mtp_config.dense_mlp_idx = (layer_id or 0) + 1 + is_local = self.mtp_layer_id in config.mtp_local_layer_ids + if is_local: + # A banded depth runs at the HEAD's window, not the trunk's: the + # checkpoint's rel_logits_proj was trained at the head's window. + mtp_config.sliding_window_size = config.mtp_local_extent + mtp_config.swa_num_attention_heads = config.mtp_swa_num_attention_heads + mtp_config.swa_num_key_value_heads = config.mtp_swa_num_key_value_heads + mtp_config.swa_head_dim = config.mtp_swa_head_dim + self.alt_stream = torch.cuda.Stream() if is_cuda() else None + self.transformer_block = InklingDecoderLayer( + config=mtp_config, + layer_id=layer_id if layer_id is not None else 0, + is_local=is_local, + quant_config=quant_config, + prefix=add_prefix("transformer_block", prefix), + alt_stream=alt_stream, + ) + + def forward( + self, + input_ids: torch.Tensor, + positions: torch.Tensor, + forward_batch: ForwardBatch, + ) -> tuple[torch.Tensor, torch.Tensor]: + embeds = self.embed_tokens(input_ids) + if self.main_model_embed_norm is not None: + embeds = self.main_model_embed_norm(embeds) + + hnorm = self.hidden_norm(forward_batch.spec_info.hidden_states) + enorm = self.embed_norm(embeds) + combined = torch.cat((hnorm, enorm), dim=-1) + h_inproj = self.input_proj(combined) + log_scaling_tau = ( + compute_log_scaling_tau( + positions, + self.log_scaling_n_floor, + self.log_scaling_alpha, + ) + if self.log_scaling_n_floor is not None + else None + ) + fm_idle = forward_batch.forward_mode.is_idle() + fuse_attn_ar = not fm_idle and ar_sconv_norm_fusable( + get_parallel().attn_tp_group, + forward_batch, + h_inproj.shape[0], + h_inproj.shape[-1], + h_inproj.dtype, + ) + if not fm_idle and scattered_ar_sconv_fusable( + get_tensor_model_parallel_group(), + forward_batch, + h_inproj.shape[0], + h_inproj.shape[-1], + h_inproj.dtype, + ): + fuse_attn_ar = True + # MTP is intentionally outside the main-model AttnRes loop. + h, residual = self.transformer_block( + h_inproj, + positions, + forward_batch, + None, + log_scaling_tau=log_scaling_tau, + fuse_attn_ar=fuse_attn_ar, + ) + mlp_sconv = self.transformer_block.mlp_sconv + if mlp_sconv is not None and not fm_idle: + h = mlp_sconv(h, positions, forward_batch) + if self.transformer_block.scattered_sconv: + # h was the [T, H/P] shard; gather before the residual add. + h = all_gather_hidden(h, self.transformer_block.attn_tp_group) + # transformer_block defers the final residual add to the caller. + if residual is not None: + h = h + residual + # chain_norm applies to the raw block output, before the mup division. + if self.chain_norm is not None: + h = self.chain_norm(h) + if self.mup_width_multiplier is not None: + return h / self.mup_width_multiplier, h + return h, h + + +class InklingForConditionalGenerationMTP(nn.Module): + """MTP draft model for Inkling speculative decoding. + + Each instance represents a single MTP layer. In multi-layer MTP, + MultiLayerEagleDraftWorker creates one instance per layer, each loading + different weights via draft_model_idx filtering. + """ + + fall_back_to_pt_during_load = False + + def __init__( + self, + config: InklingMMConfig, + quant_config: Optional[QuantizationConfig] = None, + draft_model_idx: Optional[int] = None, + prefix: str = "", + ) -> None: + super().__init__() + self.config = config + self.text_config = config.text_config + if config.text_config.mtp_local_layer_ids and draft_model_idx is None: + # The banded per-depth pool routing (SWA ring at full capacity with + # an identity mapping) only exists on the multi-layer draft path. + raise NotImplementedError( + "a banded MTP head (mtp_local_layer_ids set) requires " + "--enable-multi-layer-eagle" + ) + self.draft_model_idx = draft_model_idx if draft_model_idx is not None else 0 + # chain_hidden_post_norm lives in the checkpoint's mtp_config; InklingMTPLayer + # reads it off text_config. + if isinstance(config.mtp_config, dict): + config.text_config.chain_hidden_post_norm = config.mtp_config.get( + "chain_hidden_post_norm", config.text_config.chain_hidden_post_norm + ) + # The MTP block is bf16 in the checkpoint (no mtp.* quant excludes), so build + # the draft unquantized. + quant_config = None + # Without an alt_stream the InklingAttention fused-prologue gate can + # never pass in the draft, leaving every draft forward on the unfused + # {2x causal_conv1d + qk-norm + KV-store scatter (+ tau scale)} chain. + self.alt_stream = torch.cuda.Stream() if is_cuda() else None + self.model = InklingMTPLayer( + config.text_config, + quant_config=quant_config, + prefix=add_prefix("model", prefix), + layer_id=self.draft_model_idx, + alt_stream=self.alt_stream, + ) + self.lm_head = ParallelLMHead( + config.text_config.padded_vocab_size, + config.text_config.hidden_size, + org_num_embeddings=config.text_config.padded_vocab_size, + quant_config=quant_config, + prefix=add_prefix("lm_head", prefix), + ) + self.logits_processor = LogitsProcessor(config.text_config) + + @torch.no_grad() + def forward( + self, + input_ids: torch.Tensor, + positions: torch.Tensor, + forward_batch: ForwardBatch, + **kwargs, + ): + hidden_states, hidden_states_before_norm = self.model( + input_ids, positions, forward_batch + ) + return self.logits_processor( + input_ids, + hidden_states, + self.lm_head, + forward_batch, + hidden_states_before_norm=hidden_states_before_norm, + ) + + def get_embed_and_head(self): + return self.model.embed_tokens.weight, self.lm_head.weight + + def set_embed_and_head(self, embed, head): + # Every de-tied MTP head loads the same bf16 embed/unembed as the target, + # so alias the target's tensors rather than keep one duplicate per head. + del self.model.embed_tokens.weight + del self.lm_head.weight + self.model.embed_tokens.weight = embed + self.lm_head.weight = head + torch.cuda.empty_cache() + torch.cuda.synchronize() + + def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]) -> Set[str]: + params_dict = dict(self.named_parameters()) + loaded_params: Set[str] = set() + + for name, loaded_weight in weights: + if "rotary_emb.inv_freq" in name: + continue + name = name.removeprefix("model.") + + if "mtp.chain_norm." in name: + name = "model.chain_norm." + name.split("mtp.chain_norm.", 1)[1] + elif ".mtp.layers." in name or name.startswith("mtp."): + # The loader's _filter_mtp_weights already kept only this head's + # layer and remapped it to mtp.layers.0. + name = re.sub(r".*mtp\.(?:model\.)?layers\.\d+\.", "model.", name) + + if name in ("llm.embed_tokens.weight", "llm.embed.weight", "embed.weight"): + name = "model.embed_tokens.weight" + elif name in ("llm.lm_head.weight", "llm.unembed.weight", "unembed.weight"): + name = "lm_head.weight" + elif name in ("embed_norm.weight", "llm.embed_norm.weight"): + name = "model.main_model_embed_norm.weight" + elif name.startswith("llm.") and ".mtp." not in name: + continue + + matched = False + for param_name, weight_name, shard_id in ATTENTION_PARAMS_MAPPING: + if f".attn.{weight_name}." not in name: + continue + sgl_name = name.replace(f".{weight_name}.", f".{param_name}.") + if sgl_name in params_dict: + param = params_dict[sgl_name] + weight_loader = getattr( + param, "weight_loader", default_weight_loader + ) + weight_loader(param, loaded_weight, shard_id) + loaded_params.add(sgl_name) + matched = True + break + if matched: + continue + + if ".mlp.w13_dn.weight" in name: + sgl_name = name.replace(".w13_dn.", ".gate_up_proj.") + if sgl_name in params_dict: + param = params_dict[sgl_name] + if loaded_weight.shape != param.data.shape: + shard_size = param.data.shape[0] + start = get_parallel().attn_tp_rank * shard_size + loaded_weight = loaded_weight.narrow(0, start, shard_size) + default_weight_loader(param, loaded_weight) + loaded_params.add(sgl_name) + continue + if ".mlp.w2_md.weight" in name: + name = name.replace(".w2_md.", ".down_proj.") + else: + for param_name, weight_name, shard_id in STACKED_DENSE_PARAMS_MAPPING: + if f".mlp.{weight_name}." in name: + sgl_name = name.replace(f".{weight_name}.", f".{param_name}.") + if sgl_name in params_dict: + param = params_dict[sgl_name] + weight_loader = getattr( + param, "weight_loader", default_weight_loader + ) + if shard_id is None: + weight_loader(param, loaded_weight) + else: + weight_loader(param, loaded_weight, shard_id) + loaded_params.add(sgl_name) + matched = True + break + if matched: + continue + + if ".mlp.shared_experts." in name: + name = name.replace( + ".mlp.shared_experts.", ".mlp.experts.shared_experts." + ) + for needle, shard in ( + (".experts.w13_weight", "w13"), + (".experts.w2_weight", "w2"), + ): + if needle in name and name in params_dict: + param = params_dict[name] + weight_loader = getattr( + param, "weight_loader", default_weight_loader + ) + if weight_loader is default_weight_loader: + default_weight_loader(param, loaded_weight) + else: + weight_loader(param, loaded_weight, name, shard) + loaded_params.add(name) + matched = True + break + if matched: + continue + + if name.endswith(".bias") and name not in params_dict: + continue + if name in params_dict: + param = params_dict[name] + weight_loader = getattr(param, "weight_loader", default_weight_loader) + weight_loader(param, loaded_weight) + loaded_params.add(name) + + unloaded = sorted(set(params_dict) - loaded_params) + if unloaded: + msg = ( + f"MTP draft (idx {self.draft_model_idx}): {len(unloaded)} unloaded " + f"weights (loaded {len(loaded_params)}/{len(params_dict)}); an unloaded " + f"RMSNorm stays all-ones and silently miscalibrates the draft. " + f"First 20: {unloaded[:20]}" + ) + raise RuntimeError(msg) + return loaded_params + + +EntryClass = [InklingForConditionalGeneration, InklingForConditionalGenerationMTP] diff --git a/python/sglang/srt/models/inkling_common/__init__.py b/python/sglang/srt/models/inkling_common/__init__.py new file mode 100644 index 000000000..d84c37929 --- /dev/null +++ b/python/sglang/srt/models/inkling_common/__init__.py @@ -0,0 +1,14 @@ +# Copyright 2026 SGLang Team +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""Inkling model-specific building blocks (registry-skipped support package).""" diff --git a/python/sglang/srt/models/inkling_common/attn.py b/python/sglang/srt/models/inkling_common/attn.py new file mode 100644 index 000000000..df2963795 --- /dev/null +++ b/python/sglang/srt/models/inkling_common/attn.py @@ -0,0 +1,978 @@ +from __future__ import annotations + +from collections.abc import Callable +from functools import cache + +import torch +from torch import nn + +from sglang.jit_kernel.inkling_rel_proj import rel_proj_small_t +from sglang.jit_kernel.inkling_row_scale import row_compact_bf16 +from sglang.kernels.ops.attention.log_scaling_tau import ( + apply_log_scaling_tau as _apply_log_scaling_tau, +) +from sglang.kernels.ops.attention.score_mod import ( + relative_bias_score_mod as triton_relative_bias_score_mod, +) +from sglang.srt.environ import envs +from sglang.srt.layers.linear import MergedColumnParallelLinear, RowParallelLinear +from sglang.srt.layers.quantization.base_config import QuantizationConfig +from sglang.srt.layers.quantization.unquant import UnquantizedLinearMethod +from sglang.srt.layers.radix_attention import RadixAttention +from sglang.srt.model_executor.forward_batch_info import ForwardBatch +from sglang.srt.model_executor.runner_utils.capture_mode import get_is_capture_mode +from sglang.srt.models.inkling_common.kernels.comm import ( + get_ar_buffer, + reduce_scatter_hidden, + symm_mem_all_reduce, +) +from sglang.srt.models.inkling_common.norm import RMSNorm +from sglang.srt.models.inkling_common.sconv import SconvType, ShortConvolution +from sglang.srt.models.utils import apply_qk_norm +from sglang.srt.runtime_context import get_parallel, get_server_args +from sglang.srt.utils import add_prefix, get_current_device_stream_fast + +try: + import cutlass.cute as cute + from cutlass.cute import Float32 + + from sglang.jit_kernel.flash_attn.cute.seqlen_info import SeqlenInfoQK +except Exception as _import_error: + cute = None + Float32 = None + SeqlenInfoQK = None + _cute_import_error = _import_error +else: + _cute_import_error = None + + +@cache +def get_inkling_relative_attention_score_mod(rel_extent: int) -> Callable: + if cute is None or Float32 is None or SeqlenInfoQK is None: + raise ImportError( + "Inkling relative attention requires the vendored FA4 CUTE interface." + ) from _cute_import_error + + @cute.jit + def score_mod_rel_bias( + scores: cute.TensorSSA, + b_idx: cute.TensorSSA, + h_idx: cute.TensorSSA, + q_idx: cute.TensorSSA, + kv_idx: cute.TensorSSA, + seqlen_info: SeqlenInfoQK, + aux_tensors: list[cute.Tensor], + ) -> cute.TensorSSA: + rel_logits = aux_tensors[0] + + seqlen_local_offset = seqlen_info.seqlen_k - seqlen_info.seqlen_q + rel_dist = (q_idx + seqlen_local_offset) - kv_idx + global_q_idx = seqlen_info.offset_q + q_idx + + rel_dist_0 = rel_dist[0] + rel_idx = rel_dist_0 if rel_dist_0 >= 0 else 0 + rel_idx = rel_idx if rel_idx < rel_extent else (rel_extent - 1) + + rel_bias = rel_logits[global_q_idx[0], h_idx[0], rel_idx] + rel_bias = Float32(rel_bias) if rel_dist_0 == rel_idx else Float32(0.0) + return scores + rel_bias + + return score_mod_rel_bias + + +def compute_log_scaling_tau( + positions: torch.Tensor, n_floor: int, alpha: float +) -> torch.Tensor: + effective_n = (positions + 1).to(torch.float32) + return 1.0 + alpha * torch.log(torch.clamp(effective_n / float(n_floor), min=1.0)) + + +# Strided-matmul band of RelLogitsProj._project (bf16, h=16, d_rel=16, +# extent=1024, r strided from the packed qkvr row). +# t<=48: the zero-copy strided-batched GEMM (r @ proj) wins over einsum's +# hidden-copy chain; t>=64: {JIT row-compact + einsum} wins; the batched +# GEMM degrades past the band. +_REL_PROJ_MATMUL_MAX_T = 48 +# tau-ON small-t band: the single rel_proj_small_t launch (tau folded in +# registers) beats the {row_scale -> einsum} chain up to t=32 (loses from +# t=48). tau-OFF stays on cuBLAS everywhere: at t=1 the bare GEMM wins and +# the kernel's launch floor loses -- there is no hidden copy to remove at +# t=1 (r is contiguous). +_REL_PROJ_TAU_KERNEL_MAX_T = 32 + + +def _rel_proj_kernel_eligible(r: torch.Tensor) -> bool: + """rel_proj_small_t input contract: bf16 CUDA, [t, h, d_rel] with a + contiguous (h*d_rel) inner block (token rows may be strided), d_rel a + vector multiple, 16B-aligned base and token stride.""" + return ( + r.is_cuda + and r.dtype == torch.bfloat16 + and r.stride(-1) == 1 + and r.stride(-2) == r.shape[-1] + and r.shape[2] % 8 == 0 + and r.data_ptr() % 16 == 0 + and (r.stride(0) * 2) % 16 == 0 + ) + + +class RelLogitsProj(nn.Module): + def __init__(self, d_rel: int, rel_extent: int): + super().__init__() + self.d_rel = d_rel + self.rel_extent = rel_extent + self.proj = nn.Parameter(torch.empty(d_rel, rel_extent), requires_grad=False) + # Fold the optional log-scaling tau into the einsum's r OPERAND: the + # per-token diagonal scale commutes through the linear projection, so + # the scale pass runs over [t, h, d_rel] instead of the + # rel_extent/d_rel-times-larger output (64x at d_rel=16/extent=1024). + # The einsum itself stays cuBLAS -- the K=16 expansion is tensor-core + # territory. Rounding moves with the fold (r*tau rounds to bf16 before + # the GEMM instead of after); flag-off keeps the exact legacy post-scale. + self._prescale_tau = envs.SGLANG_OPT_USE_INKLING_FUSED_LOG_TAU.get() + self._proj_dispatch = envs.SGLANG_OPT_USE_INKLING_REL_PROJ_DISPATCH.get() + + def _project(self, r: torch.Tensor) -> torch.Tensor: + """``einsum("thd,de->the", r, proj)`` -- but dispatched: in production + ``r`` is a strided view into the packed qkvr projection output, and + einsum's reshape then hides a slow TensorIterator compaction copy. + Both replacements are bit-identical to the einsum (same GEMM reduction + order; asserted in test_inkling_attn_prologue_tau.py).""" + if not self._proj_dispatch or r.is_contiguous(): + return torch.einsum("thd,de->the", r, self.proj) + if r.shape[0] <= _REL_PROJ_MATMUL_MAX_T: + return r @ self.proj # zero-copy strided-batched GEMM over t + rows, inner = r.shape[0], r.shape[1] * r.shape[2] + if ( + r.is_cuda + and r.dtype == torch.bfloat16 + and r.stride(-1) == 1 + and r.stride(-2) == r.shape[-1] + and inner % 8 == 0 + and r.data_ptr() % 16 == 0 + and (r.stride(0) * 2) % 16 == 0 + ): + r2d = torch.as_strided(r, (rows, inner), (r.stride(0), 1)) + r = row_compact_bf16(r2d).view(r.shape) + return torch.einsum("thd,de->the", r, self.proj) + + def forward( + self, r_out: torch.Tensor, log_scaling_tau: torch.Tensor | None = None + ) -> torch.Tensor: + """``log_scaling_tau``: optional per-token scale applied to the + projected logits (the conditional long-context log-scaling); folded + into the projection's r operand when enabled.""" + if log_scaling_tau is not None and self._prescale_tau: + if ( + self._proj_dispatch + and r_out.shape[0] <= _REL_PROJ_TAU_KERNEL_MAX_T + and _rel_proj_kernel_eligible(r_out) + ): + # Single launch: tau prescale (same round-before-dot + # semantics) + projection, replacing the two-kernel + # {row_scale -> einsum} chain in its band. + tau_flat = log_scaling_tau.reshape(-1) + if tau_flat.dtype != torch.float32: + tau_flat = tau_flat.float() + return rel_proj_small_t(r_out, self.proj, tau_flat) + # The prescale already compacts r (row-scale writes contiguous), + # so the einsum runs on a contiguous operand via _project. + r_out = _apply_log_scaling_tau(r_out, log_scaling_tau.view(-1, 1, 1)) + log_scaling_tau = None + out = self._project(r_out) + if log_scaling_tau is not None: + out = _apply_log_scaling_tau(out, log_scaling_tau.view(-1, 1, 1)) + return out + + +class InklingQKVRLinear(MergedColumnParallelLinear): + """Fused q/k/v/r projection that is KV-replication-aware for LoRA. + + The base K/V weights are replicated at load when attn_tp_size > num_kv_heads + (via ``_kv_total_for_sizing``). This subclass carries the head geometry so + the LoRA wrapper (``InklingQKVRLinearWithLoRA``) can replicate the K/V slices of the + adapter LoRA-B the same way. ``is_inkling_qkvr`` lets ``get_lora_layer`` route it + without importing Inkling into ``srt/lora``. + """ + + is_inkling_qkvr = True + + def __init__( + self, + *args, + inkling_num_kv_heads: int, + inkling_head_dim: int, + inkling_num_heads: int, + inkling_d_rel: int, + inkling_tp_size: int, + **kwargs, + ) -> None: + super().__init__(*args, **kwargs) + self.inkling_num_kv_heads = inkling_num_kv_heads + self.inkling_head_dim = inkling_head_dim + self.inkling_num_heads = inkling_num_heads + self.inkling_d_rel = inkling_d_rel + self.inkling_tp_size = inkling_tp_size + + +class InklingAttention(nn.Module): + def __init__( + self, + hidden_size: int, + num_heads: int, + num_kv_heads: int, + head_dim: int | None, + d_rel: int, + rel_extent: int, + local_extent: int, + norm_eps: float, + is_local: bool, + layer_id: int, + q_bias: bool = False, + o_bias: bool = False, + kv_conv: bool = False, + sconv_kernel_size: int | None = None, + quant_config: QuantizationConfig | None = None, + prefix: str = "", + alt_stream: torch.cuda.Stream | None = None, + ): + super().__init__() + self.hidden_size = hidden_size + self.alt_stream = alt_stream + + attn_tp_rank = get_parallel().attn_tp_rank + attn_tp_size = get_parallel().attn_tp_size + + self.tp_size = attn_tp_size + self.head_dim = head_dim if head_dim is not None else hidden_size // num_heads + self.d_rel = d_rel + self.num_total_heads = num_heads + self.num_total_kv_heads = num_kv_heads + self.scaling = 1.0 / self.head_dim + + assert self.num_total_heads % self.tp_size == 0 + self.num_tp_heads = self.num_total_heads // self.tp_size + + if self.num_total_kv_heads >= self.tp_size: + assert self.num_total_kv_heads % self.tp_size == 0 + else: + assert self.tp_size % self.num_total_kv_heads == 0 + self.num_tp_kv_heads = max(1, self.num_total_kv_heads // self.tp_size) + self.layer_id = layer_id + self.is_local = is_local + self._kv_total_for_sizing = max(self.num_total_kv_heads, self.tp_size) + + output_sizes = [ + self.head_dim * self.num_total_heads, + self.head_dim * self._kv_total_for_sizing, + self.head_dim * self._kv_total_for_sizing, + self.d_rel * self.num_total_heads, + ] + + self.qkvr = InklingQKVRLinear( + input_size=self.hidden_size, + output_sizes=output_sizes, + bias=q_bias, + prefix=add_prefix("qkvr", prefix), + tp_rank=attn_tp_rank, + tp_size=attn_tp_size, + quant_config=quant_config, + inkling_num_kv_heads=self.num_total_kv_heads, + inkling_head_dim=self.head_dim, + inkling_num_heads=self.num_total_heads, + inkling_d_rel=self.d_rel, + inkling_tp_size=attn_tp_size, + ) + self.wo_ud = RowParallelLinear( + input_size=self.head_dim * self.num_total_heads, + output_size=self.hidden_size, + bias=o_bias, + prefix=add_prefix("wo_ud", prefix), + tp_rank=attn_tp_rank, + tp_size=attn_tp_size, + reduce_results=False, + use_dp_attention_reduce=True, + quant_config=quant_config, + ) + # --enable-scattered-sconv: the output reduction becomes a hidden-dim + # reduce-scatter (the consumer attn_sconv runs on the [T, H/P] shard). + self.scattered_sconv = get_server_args().enable_scattered_sconv + + if is_local: + self.rel_extent = local_extent + self.local_extent = local_extent + else: + self.rel_extent = rel_extent + self.local_extent = None + + self.rel_logits_proj = RelLogitsProj(self.d_rel, self.rel_extent) + # Fold the conditional log-scaling tau into the fused prologue's q + # path (deletes the external scale kernel; bit-exact rounding). + self._fused_log_tau = envs.SGLANG_OPT_USE_INKLING_FUSED_LOG_TAU.get() + self.q_norm = RMSNorm(self.head_dim, eps=norm_eps) + self.k_norm = RMSNorm(self.head_dim, eps=norm_eps) + + self.kv_conv = kv_conv + self.sconv_kernel_size = sconv_kernel_size + self.k_sconv = ( + ShortConvolution( + hidden_size=self.head_dim * self.num_tp_kv_heads, + kernel_size=self.sconv_kernel_size, + sconv_type=SconvType.K_LOCAL if is_local else SconvType.K_FULL, + layer_id=layer_id, + ) + if self.kv_conv + else None + ) + self.v_sconv = ( + ShortConvolution( + hidden_size=self.head_dim * self.num_tp_kv_heads, + kernel_size=self.sconv_kernel_size, + sconv_type=SconvType.V_LOCAL if is_local else SconvType.V_FULL, + layer_id=layer_id, + ) + if self.kv_conv + else None + ) + + self.attn = RadixAttention( + self.num_tp_heads, + self.head_dim, + self.scaling, + num_kv_heads=self.num_tp_kv_heads, + layer_id=self.layer_id, + sliding_window_size=self.local_extent - 1 if self.is_local else -1, + quant_config=quant_config, + prefix=add_prefix("attn", prefix), + ) + + def _project_qkvr( + self, + hidden_states: torch.Tensor, + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + num_tokens = hidden_states.size(0) + qkvr, _ = self.qkvr(hidden_states) + qkvr = qkvr.view(num_tokens, -1) + split_sizes = [ + self.head_dim * self.num_tp_heads, + self.head_dim * self.num_tp_kv_heads, + self.head_dim * self.num_tp_kv_heads, + self.d_rel * self.num_tp_heads, + ] + q, k, v, r = qkvr.split(split_sizes, dim=-1) + return q, k, v, r + + def _fused_attn_prologue_verify(self, q, k, v, forward_batch, log_scaling_tau=None): + """Fused target-verify {k/v sconv + save_windows + qk-norm (+ KV store)} + (jit_kernel/inkling_attn_prologue.py); returns ``(q, k, v, did_store)``. + + The fused kernel writes raw bf16 KV, so it only does the store when the + KV pool is bf16: full layers at ``out_cache_loc`` in the full pool, + local (SWA) layers at the backend's pre-translated ``swa_out_cache_loc`` + in the SWA sub-pool (get_key_buffer dispatches by layer). For an FP8 / + MXFP8 KV pool the fused store is skipped (``did_store=False``) and the + caller keeps ``save_kv_cache=True`` so the backend quantizes the + already-normed/conv'd K/V and writes the block scales; conv + windows + + qk-norm stay fused either way. For the FA4 MXFP8 pool, the prologue can + quantize Q and directly fill the fp8 K/V cache plus interleaved scale + buffers, returning Q's per-token scales as ``q_descale``/``sfq``.""" + from sglang.jit_kernel.inkling_attn_prologue import inkling_attn_prologue_verify + from sglang.srt.model_executor.forward_context import ( + get_attn_backend, + get_token_to_kv_pool, + ) + + if not hasattr(self, "_qk_gamma_bf16"): + self._qk_gamma_bf16 = ( + self.q_norm.weight.to(torch.bfloat16), + self.k_norm.weight.to(torch.bfloat16), + ) + k_cache, ci, cm, kw, k_inter = self.k_sconv.verify_fused_ar_inputs( + forward_batch + ) + v_cache, _, _, vw, v_inter = self.v_sconv.verify_fused_ar_inputs(forward_batch) + pool = get_token_to_kv_pool() + k_buf = pool.get_key_buffer(self.layer_id) + v_buf = pool.get_value_buffer(self.layer_id) + # The fused store writes raw bf16 into an NHD [slot, head, head_dim] + # buffer indexed by loc. Take it only for that exact layout: FP8/MXFP8 + # (non-bf16) and HND/vectorized_5d (4D/5D, paged (page, head) index) + # pools keep the backend store, which owns their quant + layout. conv + + # windows + qk-norm stay fused regardless. + do_bf16_store = ( + k_buf.dtype == torch.bfloat16 + and k_buf.dim() == 3 + and k_buf.shape[-1] == self.head_dim + and k_buf.is_contiguous() + ) + sfk = sfv = None + do_mxfp8_store = False + server_args = get_server_args() + if server_args.kv_cache_dtype == "mxfp8" and hasattr( + pool, "get_kv_scale_buffer" + ): + sfk, sfv = pool.get_kv_scale_buffer(self.layer_id) + do_mxfp8_store = ( + k_buf.dtype == torch.float8_e4m3fn + and v_buf.dtype == torch.float8_e4m3fn + and k_buf.dim() == 3 + and v_buf.dim() == 3 + and k_buf.shape[-1] == self.head_dim + and v_buf.shape[-1] == self.head_dim + and k_buf.is_contiguous() + and v_buf.is_contiguous() + and sfk.dim() == 5 + and sfv.dim() == 5 + and getattr(pool, "page_size", 0) == 128 + ) + do_store = do_bf16_store or do_mxfp8_store + metadata = get_attn_backend().forward_metadata + if do_store and self.is_local: + # SWA sub-pool + the backend's full->SWA translated write location. + loc = metadata.swa_out_cache_loc + elif do_store: + loc = getattr(metadata, "out_cache_loc_full_physical", None) + if loc is None: + loc = forward_batch.out_cache_loc + else: + loc = forward_batch.out_cache_loc + es = q.element_size() + q, k, v, q_descale = inkling_attn_prologue_verify( + q, + k_cache, + v_cache, + ci, + cm, + kw, + vw, + k_inter, + v_inter, + self._qk_gamma_bf16[0], + self._qk_gamma_bf16[1], + self.q_norm.variance_epsilon, + loc, + k_buf, + v_buf, + 0, + (k.data_ptr() - q.data_ptr()) // es, + (v.data_ptr() - q.data_ptr()) // es, + q.shape[1], + k.shape[1], + forward_batch.spec_info.draft_token_num, + activation=self.k_sconv.activation, + use_residual=self.k_sconv.use_residual, + do_store=do_store, + mxfp8_quant=do_mxfp8_store, + sfk=sfk, + sfv=sfv, + page_size=getattr(pool, "page_size", 128), + log_scaling_tau=log_scaling_tau, + ) + return q, k, v, do_store, q_descale + + def _fused_attn_prologue_extend(self, q, k, v, forward_batch, log_scaling_tau=None): + """Extend analog of _fused_attn_prologue_verify: {k/v varlen sconv + + qk-norm (+ KV store)} in the main kernel plus a tiny trailing k/v + conv-cache update (+ prefix-cache track) kernel -- replacing + 2x causal_conv1d + apply_qk_norm + 2x update_sconv_cache (+ track) + + the backend store. Store gating (bf16 NHD / FA4 MXFP8 pools, SWA loc + translation) is identical to the verify prologue. Returns + (q, k, v, did_store, q_descale).""" + from sglang.jit_kernel.inkling_attn_prologue import inkling_attn_prologue_extend + from sglang.srt.model_executor.forward_context import ( + get_attn_backend, + get_token_to_kv_pool, + ) + + if not hasattr(self, "_qk_gamma_bf16"): + self._qk_gamma_bf16 = ( + self.q_norm.weight.to(torch.bfloat16), + self.k_norm.weight.to(torch.bfloat16), + ) + ( + k_cache, + _safe_idx, + cm, + cu, + si, + kw, + _qsl, + ci, + has_init, + trows, + tmask, + tdst, + ) = self.k_sconv.extend_fused_ar_inputs(forward_batch) + v_inputs = self.v_sconv.extend_fused_ar_inputs(forward_batch) + v_cache, vw = v_inputs[0], v_inputs[5] + pool = get_token_to_kv_pool() + k_buf = pool.get_key_buffer(self.layer_id) + v_buf = pool.get_value_buffer(self.layer_id) + do_bf16_store = ( + k_buf.dtype == torch.bfloat16 + and v_buf.dtype == torch.bfloat16 + and k_buf.dim() == 3 + and v_buf.dim() == 3 + and k_buf.shape[-1] == self.head_dim + and v_buf.shape[-1] == self.head_dim + and k_buf.is_contiguous() + and v_buf.is_contiguous() + ) + sfk = sfv = None + do_mxfp8_store = False + server_args = get_server_args() + if server_args.kv_cache_dtype == "mxfp8" and hasattr( + pool, "get_kv_scale_buffer" + ): + sfk, sfv = pool.get_kv_scale_buffer(self.layer_id) + do_mxfp8_store = ( + k_buf.dtype == torch.float8_e4m3fn + and v_buf.dtype == torch.float8_e4m3fn + and k_buf.dim() == 3 + and v_buf.dim() == 3 + and k_buf.shape[-1] == self.head_dim + and v_buf.shape[-1] == self.head_dim + and k_buf.is_contiguous() + and v_buf.is_contiguous() + and sfk.dim() == 5 + and sfv.dim() == 5 + and getattr(pool, "page_size", 0) == 128 + ) + do_store = do_bf16_store or do_mxfp8_store + metadata = get_attn_backend().forward_metadata + if do_store and self.is_local: + loc = metadata.swa_out_cache_loc + elif do_store: + loc = getattr(metadata, "out_cache_loc_full_physical", None) + if loc is None: + loc = forward_batch.out_cache_loc + else: + loc = forward_batch.out_cache_loc + is_v2 = forward_batch.forward_mode.is_draft_extend_v2() + if is_v2: + # The seq-end-window trailing update and the extend prefix-cache + # track are WRONG for DRAFT_EXTEND_V2: the conv state must + # reflect only num_accept_tokens. Run the accept-gated update + # (which also handles accept-aware tracking) after the kernel. + dev = trows.device + trows = torch.empty((0, trows.shape[1]), dtype=torch.int64, device=dev) + tmask = torch.empty((0,), dtype=torch.bool, device=dev) + tdst = torch.empty((0,), dtype=torch.int64, device=dev) + k_pre, v_pre = k, v + es = q.element_size() + q, k, v, q_descale = inkling_attn_prologue_extend( + q, + k_cache, + v_cache, + ci, + cm, + has_init, + cu, + si, + kw, + vw, + trows, + tmask, + tdst, + self._qk_gamma_bf16[0], + self._qk_gamma_bf16[1], + self.q_norm.variance_epsilon, + loc, + k_buf, + v_buf, + 0, + (k.data_ptr() - q.data_ptr()) // es, + (v.data_ptr() - q.data_ptr()) // es, + q.shape[1], + k.shape[1], + activation=self.k_sconv.activation, + use_residual=self.k_sconv.use_residual, + do_store=do_store, + mxfp8_quant=do_mxfp8_store, + sfk=sfk, + sfv=sfv, + page_size=getattr(pool, "page_size", 128), + do_cache_update=not is_v2, + log_scaling_tau=log_scaling_tau, + ) + if is_v2: + self.k_sconv._update_sconv_cache_for_draft_extend( + forward_batch, k_cache, ci, k_pre + ) + self.v_sconv._update_sconv_cache_for_draft_extend( + forward_batch, v_cache, ci, v_pre + ) + return q, k, v, do_store, q_descale + + def _fused_attn_prologue_decode(self, q, k, v, forward_batch, log_scaling_tau=None): + """Decode analog of _fused_attn_prologue_verify: {k/v decode-conv + + conv-cache shift-update (+ prefix track) + qk-norm (+ KV store)} in one + kernel. Decode is one token/seq so the conv taps come from the working + cache (no cross-token reads, no barrier). Returns + (q, k, v, did_store, q_descale).""" + from sglang.jit_kernel.inkling_attn_prologue import inkling_attn_prologue_decode + from sglang.srt.model_executor.forward_context import ( + get_attn_backend, + get_token_to_kv_pool, + ) + + if not hasattr(self, "_qk_gamma_bf16"): + self._qk_gamma_bf16 = ( + self.q_norm.weight.to(torch.bfloat16), + self.k_norm.weight.to(torch.bfloat16), + ) + k_cache, ci, cm, kw = self.k_sconv.decode_fused_ar_inputs(forward_batch) + v_cache, _, _, vw = self.v_sconv.decode_fused_ar_inputs(forward_batch) + pool = get_token_to_kv_pool() + k_buf = pool.get_key_buffer(self.layer_id) + v_buf = pool.get_value_buffer(self.layer_id) + do_bf16_store = ( + k_buf.dtype == torch.bfloat16 + and v_buf.dtype == torch.bfloat16 + and k_buf.dim() == 3 + and v_buf.dim() == 3 + and k_buf.shape[-1] == self.head_dim + and v_buf.shape[-1] == self.head_dim + and k_buf.is_contiguous() + and v_buf.is_contiguous() + ) + sfk = sfv = None + do_mxfp8_store = False + server_args = get_server_args() + if server_args.kv_cache_dtype == "mxfp8" and hasattr( + pool, "get_kv_scale_buffer" + ): + sfk, sfv = pool.get_kv_scale_buffer(self.layer_id) + do_mxfp8_store = ( + k_buf.dtype == torch.float8_e4m3fn + and v_buf.dtype == torch.float8_e4m3fn + and k_buf.dim() == 3 + and v_buf.dim() == 3 + and k_buf.shape[-1] == self.head_dim + and v_buf.shape[-1] == self.head_dim + and k_buf.is_contiguous() + and v_buf.is_contiguous() + and sfk.dim() == 5 + and sfv.dim() == 5 + and getattr(pool, "page_size", 0) == 128 + ) + do_store = do_bf16_store or do_mxfp8_store + metadata = get_attn_backend().forward_metadata + if do_store and self.is_local: + loc = metadata.swa_out_cache_loc + elif do_store: + loc = getattr(metadata, "out_cache_loc_full_physical", None) + if loc is None: + loc = forward_batch.out_cache_loc + else: + loc = forward_batch.out_cache_loc + es = q.element_size() + q, k, v, q_descale = inkling_attn_prologue_decode( + q, + k_cache, + v_cache, + ci, + cm, + kw, + vw, + self._qk_gamma_bf16[0], + self._qk_gamma_bf16[1], + self.q_norm.variance_epsilon, + loc, + k_buf, + v_buf, + 0, + (k.data_ptr() - q.data_ptr()) // es, + (v.data_ptr() - q.data_ptr()) // es, + q.shape[1], + k.shape[1], + activation=self.k_sconv.activation, + use_residual=self.k_sconv.use_residual, + track_mask=forward_batch.mamba_track_mask, + track_indices=forward_batch.mamba_track_indices, + do_store=do_store, + mxfp8_quant=do_mxfp8_store, + sfk=sfk, + sfv=sfv, + page_size=getattr(pool, "page_size", 128), + log_scaling_tau=log_scaling_tau, + ) + return q, k, v, do_store, q_descale + + def forward( + self, + hidden_states: torch.Tensor, + positions: torch.Tensor, + forward_batch: ForwardBatch, + *, + log_scaling_tau: torch.Tensor | None = None, + reduce: bool = True, + ) -> torch.Tensor: + """With ``reduce=False`` the TP all-reduce of the wo_ud output is + skipped and the LOCAL partial sums are returned (bias already folded in + exactly once, on tp_rank 0) -- the caller takes over the reduction via + the fused decode {AR -> attn_sconv -> mlp_norm} kernel.""" + assert hidden_states.ndim == 2 + num_tokens = hidden_states.size(0) + q, k, v, r = self._project_qkvr(hidden_states) + r = r.view(num_tokens, -1, self.d_rel) + + apply_log_scaling = log_scaling_tau is not None and not self.is_local + + server_args = get_server_args() + assert server_args.attention_backend in ("fa4", "triton") + # The overlap threads a CUDA event into the FA4 sheared-bias kernel, so it + # is FA4-only for now. + # TODO(triton): plumb rel_bias_event through the triton attn path too. + fa4 = server_args.attention_backend == "fa4" + + rel_event = None + prologue_did_store = False + prologue_q_descale = None + _fm = forward_batch.forward_mode + _prologue_verify = _fm.is_target_verify() + _prologue_decode = _fm.is_decode() + # Extend (prefill): varlen conv + trailing cache-update kernel. + # DRAFT_EXTEND_V2 also fuses the main kernel; only the conv-cache + # update differs (accept-gated), handled inside the extend helper. + _prologue_extend = ( + _fm.is_extend(include_draft_extend_v2=True) and not _prologue_verify + ) + fused_prologue = ( + fa4 + and self.kv_conv + and self.alt_stream is not None + and num_tokens > 0 + and self.head_dim == 128 + and (_prologue_verify or _prologue_decode or _prologue_extend) + and envs.SGLANG_OPT_USE_INKLING_FUSED_ATTN_PROLOGUE.get() + ) + # Fold tau into the prologue's q path (in-kernel, bit-exact vs the + # external scale; lands BEFORE MXFP8 quantization there). + fold_tau_q = apply_log_scaling and self._fused_log_tau + if fused_prologue: + # rel_logits overlaps on the alt stream with the fused prologue + # {k/v sconv + (save_windows | cache-update) + qk-norm (+ KV store)} + # on the current stream. Same overlap for verify, decode and extend. + current_stream = get_current_device_stream_fast() + self.alt_stream.wait_stream(current_stream) + _tau_arg = log_scaling_tau if fold_tau_q else None + if _prologue_verify: + ( + q, + k, + v, + prologue_did_store, + prologue_q_descale, + ) = self._fused_attn_prologue_verify( + q, k, v, forward_batch, log_scaling_tau=_tau_arg + ) + elif _prologue_decode: + ( + q, + k, + v, + prologue_did_store, + prologue_q_descale, + ) = self._fused_attn_prologue_decode( + q, k, v, forward_batch, log_scaling_tau=_tau_arg + ) + else: + ( + q, + k, + v, + prologue_did_store, + prologue_q_descale, + ) = self._fused_attn_prologue_extend( + q, k, v, forward_batch, log_scaling_tau=_tau_arg + ) + with torch.cuda.stream(self.alt_stream): + rel_logits = self.rel_logits_proj( + r, log_scaling_tau if apply_log_scaling else None + ) + rel_event = torch.cuda.Event() + rel_event.record() + use_alt = ( + not fused_prologue + and fa4 + and self.alt_stream is not None + and hidden_states.is_cuda + and num_tokens > 0 + and get_is_capture_mode() + ) + if use_alt: + # Alt stream runs v_sconv then rel_logits_proj, each fenced by its own + # event, while the current stream runs k_sconv + apply_qk_norm. v_event + # gates attn on v_sconv (joined below); rel_event is deferred into the + # FA4 backend so rel_logits_proj also overlaps the KV-write. No + # record_stream: graph-unsafe under capture and unneeded since v + # aliases the qkvr buffer q keeps alive past the join. + current_stream = get_current_device_stream_fast() + self.alt_stream.wait_stream(current_stream) + with torch.cuda.stream(self.alt_stream): + if self.kv_conv: + assert self.v_sconv is not None + v = self.v_sconv(v, positions, forward_batch) + v_event = torch.cuda.Event() + v_event.record() + rel_logits = self.rel_logits_proj( + r, log_scaling_tau if apply_log_scaling else None + ) + rel_event = torch.cuda.Event() + rel_event.record() + if self.kv_conv: + assert self.k_sconv is not None + k = self.k_sconv(k, positions, forward_batch) + elif not fused_prologue: + if self.kv_conv: + assert self.k_sconv is not None + assert self.v_sconv is not None + k = self.k_sconv(k, positions, forward_batch) + v = self.v_sconv(v, positions, forward_batch) + + # apply_qk_norm runs on the current stream now -- the alt stream is busy + # with v_sconv + rel_logits_proj. (The fused prologue already normed.) + if fused_prologue: + pass + else: + q, k = apply_qk_norm( + q=q, + k=k, + q_norm=self.q_norm, + k_norm=self.k_norm, + head_dim=self.head_dim, + alt_stream=None, + ) + + if apply_log_scaling and not (fused_prologue and fold_tau_q): + # (When the fused prologue ran with fold_tau_q, tau was already + # folded into its q path -- including BEFORE the MXFP8 quant.) + # q is 2D [num_tokens, heads*head_dim]; tau is per-token, so broadcast + # over the whole row with [num_tokens, 1] -- identical to scaling the + # (num_tokens, heads, head_dim) view by tau.view(-1, 1, 1), but with no + # 3D view (the strided split is not collapsible back to [-1, head_dim]). + q = _apply_log_scaling_tau(q, log_scaling_tau.view(-1, 1)) + + if use_alt: + # v (produced on the alt stream) must be ready before attn reads it. + current_stream.wait_event(v_event) + elif not fused_prologue: + rel_logits = self.rel_logits_proj( + r, log_scaling_tau if apply_log_scaling else None + ) + + extra_attn_kwargs = {} + if server_args.kv_cache_dtype == "mxfp8": + # Must run AFTER v is joined above (wait_event(v_event)): v (and k) + # may be produced by sconv on the alt stream, and quantizing them on + # the main stream before the join reads half-written buffers under + # load. The bf16 path only touches k/v inside self.attn, past the join. + # + # Block-scaled QK + in-kernel V dequant (FA4 downloads contract): + # Q/K/V all quantize to fp8. Q's per-token scales ride q_descale + # (backend passes them as sfq); K/V scales ride k/v_descale into + # set_kv_buffer, which stores them interleaved as sfk/sfv. + if prologue_q_descale is not None: + extra_attn_kwargs["q_descale"] = prologue_q_descale + else: + from sglang.srt.layers.quantization.mxfp8_quant import to_mxfp8 + + q_mxfp = to_mxfp8(q.view(num_tokens, self.num_tp_heads, self.head_dim)) + q = q_mxfp.data.view(num_tokens, -1) + extra_attn_kwargs["q_descale"] = q_mxfp.scale.view(torch.float8_e8m0fnu) + if ( + not prologue_did_store + and not envs.SGLANG_OPT_INKLING_MXFP8_FUSED_QUANT_STORE.get() + ): + k_mxfp = to_mxfp8( + k.view(num_tokens, self.num_tp_kv_heads, self.head_dim) + ) + v_mxfp = to_mxfp8( + v.view(num_tokens, self.num_tp_kv_heads, self.head_dim) + ) + k = k_mxfp.data.view(num_tokens, -1) + v = v_mxfp.data.view(num_tokens, -1) + extra_attn_kwargs["k_descale"] = k_mxfp.scale.view(torch.float8_e8m0fnu) + extra_attn_kwargs["v_descale"] = v_mxfp.scale.view(torch.float8_e8m0fnu) + # Else either the prologue already filled the MXFP8 KV cache, or K/V + # stay bf16 here and the MXFP8 pool's set_kv_buffer quantizes and + # stores them in one fused kernel (absent descales signal it). + + if envs.SGLANG_OPT_USE_INKLING_SHEARED_BIAS.get() and fa4: + # FA4 sheared-bias kernel: pass rel_logits directly; the kernel shears + # it into a column-aligned pre-softmax bias. + attn_output = self.attn( + q, + k, + v, + forward_batch, + save_kv_cache=not prologue_did_store, + rel_bias=rel_logits, + rel_bias_event=rel_event, + **extra_attn_kwargs, + ) + else: + # The score_mod / aux_tensors path can't carry the event into the + # kernel, so join rel_logits on the current stream before self.attn. + if rel_event is not None: + get_current_device_stream_fast().wait_event(rel_event) + if fa4: + attn_output = self.attn( + q, + k, + v, + forward_batch, + save_kv_cache=not prologue_did_store, + score_mod=get_inkling_relative_attention_score_mod(self.rel_extent), + aux_tensors=[rel_logits], + **extra_attn_kwargs, + ) + else: + attn_output = self.attn( + q, + k, + v, + forward_batch, + save_kv_cache=not prologue_did_store, + score_mod=triton_relative_bias_score_mod, + aux_tensors=[rel_logits], + **extra_attn_kwargs, + ) + attn_output = attn_output.view(num_tokens, -1) + + # Fuse wo_ud's local output GEMM with the all-reduce: write the GEMM + # straight into the symm-mem AR buffer so the reduce is fully in place + # (no stage-in, no copy-out). Only valid for the bf16 (unquantized) + # wo_ud; the fp4 path (fp4_gemm has no out=) falls back to the plain + # call. Bias is fused only on tp_rank 0 -- matching RowParallelLinear -- + # so it is added exactly once after the reduce. + tp = get_parallel().attn_tp_group + wo_ud = self.wo_ud + # A LoRA-wrapped wo_ud must take the plain call below: the fused GEMM->AR-buffer + # path drops the LoRA delta (and the wrapper doesn't forward quant_method). + is_lora_wrapped = not isinstance(wo_ud, RowParallelLinear) + buf = ( + None + if is_lora_wrapped + else get_ar_buffer(tp, num_tokens, self.hidden_size, attn_output.dtype) + ) + if buf is not None and type(wo_ud.quant_method) is UnquantizedLinearMethod: + torch.matmul(attn_output, wo_ud.weight.t(), out=buf) + bias_ = None if (wo_ud.tp_rank > 0 or wo_ud.skip_bias_add) else wo_ud.bias + if bias_ is not None: + buf.add_(bias_) + if not reduce: + return buf + if self.scattered_sconv: + # Scattered sconv: reduce + scatter hidden -> the [T, H/P] shard + # that attn_sconv consumes; the layer all-gathers after the sconv. + return reduce_scatter_hidden(buf, tp, input_is_ar_buffer=True) + return symm_mem_all_reduce(buf, tp, input_is_ar_buffer=True) + + result, _ = wo_ud(attn_output) + if not reduce: + return result + if self.scattered_sconv: + return reduce_scatter_hidden(result, tp) + return symm_mem_all_reduce(result, tp) diff --git a/python/sglang/srt/models/inkling_common/dense_mlp.py b/python/sglang/srt/models/inkling_common/dense_mlp.py new file mode 100644 index 000000000..0509cb29b --- /dev/null +++ b/python/sglang/srt/models/inkling_common/dense_mlp.py @@ -0,0 +1,521 @@ +import logging +from enum import Enum + +import torch +from torch import nn +from torch.nn import functional as F + +from sglang.srt.layers.moe.moe_runner.base import MoeRunnerConfig +from sglang.srt.layers.quantization.base_config import QuantizationConfig +from sglang.srt.layers.quantization.unquant import UnquantizedFusedMoEMethod +from sglang.srt.model_executor.forward_batch_info import ForwardBatch +from sglang.srt.models.inkling_common.kernels.comm import ( + reduce_scatter_hidden, + symm_mem_all_reduce, +) +from sglang.srt.models.inkling_common.util import ( + FusedMoELoadingMixin, + lora_compatible_layout_enabled, +) +from sglang.srt.models.llama import LlamaMLP +from sglang.srt.runtime_context import get_server_args + +logger = logging.getLogger(__name__) + + +class _InklingUnquantizedFusedMoEMethod(UnquantizedFusedMoEMethod): + """Run the sink's linearization after generic post-load processing.""" + + def process_weights_after_loading(self, layer: nn.Module) -> None: + super().process_weights_after_loading(layer) + layer.process_weights_after_loading() + + +class SharedExpertFp4Strategy(Enum): + """How a shared-expert dense MLP materializes its weights for serving.""" + + #: Checkpoint BF16 -> serve bf16. + BF16 = "bf16" + #: Checkpoint FP4 -> serve FP4, repacked for flashinfer-trtllm (no dequant). + FP4 = "fp4" + + @property + def loads_fp4_checkpoint(self) -> bool: + """Shared experts are NVFP4 in the checkpoint (-> allocate FP4 params to load).""" + return self is SharedExpertFp4Strategy.FP4 + + @property + def serves_fp4(self) -> bool: + """Shared experts are served as FP4 (-> the _forward_fp4 path).""" + return self is SharedExpertFp4Strategy.FP4 + + +@torch.compile(fullgraph=True) +def swiglu(z_btn: torch.Tensor) -> torch.Tensor: + # Compute SwiGLU in FP32; torch.compile fuses the casts. + dtype = z_btn.dtype + z_btn = z_btn.float() + # Interleave gate and up projections for tensor parallelism. + y_btn = F.silu(z_btn[..., ::2]) * z_btn[..., 1::2] + return y_btn.to(dtype) + + +@torch.compile(fullgraph=True) +def swiglu_contiguous(z_btn: torch.Tensor) -> torch.Tensor: + # Compute SwiGLU in FP32; torch.compile fuses the casts. + dtype = z_btn.dtype + z_btn = z_btn.float() + # Use strided gate and up projections for inference tensor parallelism. + y_btn = ( + F.silu(z_btn[..., : z_btn.shape[-1] // 2]) * z_btn[..., z_btn.shape[-1] // 2 :] + ) + return y_btn.to(dtype) + + +class InklingSwiglu(nn.Module): + def __init__(self, interleaved: bool = True): + super().__init__() + self.interleaved = interleaved + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return swiglu(x) if self.interleaved else swiglu_contiguous(x) + + +class InklingDenseMLP(LlamaMLP): + def __init__( + self, + hidden_size: int, + intermediate_size: int, + use_global_scale: bool, + layer_id: int, + quant_config: QuantizationConfig | None = None, + prefix: str = "", + fused: bool = False, + tp_rank: int = 0, + tp_size: int = 1, + tp_group: torch.distributed.ProcessGroup | None = None, + use_dp_attention_reduce: bool = False, + ) -> None: + self.tp_rank = tp_rank + self.tp_size = tp_size + self.tp_group = tp_group + + super().__init__( + hidden_size=hidden_size, + intermediate_size=intermediate_size, + hidden_act="silu", + quant_config=quant_config, + prefix=prefix, + reduce_results=False, + tp_rank=self.tp_rank, + tp_size=self.tp_size, + use_dp_attention_reduce=use_dp_attention_reduce, + ) + + if use_global_scale: + self.global_scale = nn.Parameter(torch.empty(1), requires_grad=False) + else: + self.global_scale = None + + # The Helion kernel currently requires the interleaved layout. + # Under --enable-lora gate/up is de-interleaved to [gate||up] at load, so run + # contiguous swiglu (see lora_compatible_layout_enabled for the invariant). + fused = fused and not lora_compatible_layout_enabled() + self.layer_id = layer_id + self.act_fn = InklingSwiglu(interleaved=fused) + self.scattered_sconv = get_server_args().enable_scattered_sconv + + def forward( + self, + x: torch.Tensor, + forward_batch: ForwardBatch | None = None, + use_reduce_scatter: bool = False, + ): + x = super().forward(x, forward_batch) + if self.global_scale is not None: + x = x * self.global_scale + if not use_reduce_scatter and self.tp_group is not None: + if self.scattered_sconv: + # Scattered sconv: reduce + scatter hidden -> the [T, H/P] + # shard mlp_sconv consumes; all-gather happens after it. + x = reduce_scatter_hidden(x, self.tp_group) + else: + x = symm_mem_all_reduce(x, self.tp_group) + return x + + +# Compile to improve memory bandwidth for shared-expert shapes. +@torch.compile(fullgraph=True) +def _sum_dim0(x: torch.Tensor) -> torch.Tensor: + return x.float().sum(dim=0).to(x.dtype) + + +class InklingBatchDenseMLP(nn.Module, FusedMoELoadingMixin): + def __init__( + self, + n_shared_experts: int, + d_model: int, + shared_d_mlp: int, + layer_id: int, + prefix: str, + quant_config: QuantizationConfig | None = None, + inference_moe_w13_interleaved: bool = True, + tp_rank: int = 0, + tp_size: int = 1, + tp_group: torch.distributed.ProcessGroup | None = None, + linearized_bf16: bool = False, + ): + nn.Module.__init__(self) + self._skip_aiter_moe_shuffle = True + self.n_shared_experts = n_shared_experts + self.inference_moe_w13_interleaved = inference_moe_w13_interleaved + self.quant_config = quant_config + self._fp4_strategy = self._resolve_fp4_strategy(quant_config, prefix) + if self._fp4_strategy.loads_fp4_checkpoint: + from sglang.srt.models.inkling_common.quantization.quant import ( + InklingNvfp4MoEMethod, + ) + + self.quant_method = InklingNvfp4MoEMethod(quant_config=quant_config) + else: + self.quant_method = _InklingUnquantizedFusedMoEMethod(False) + self.moe_ep_size = 1 + self.moe_ep_rank = 0 + self.num_experts = n_shared_experts + self.num_local_experts = n_shared_experts + self.hidden_size = d_model + self.layer_id = layer_id + + self.moe_tp_rank = tp_rank + self.moe_tp_size = tp_size + self.tp_group = tp_group + + local_intermediate_size = shared_d_mlp // self.moe_tp_size + self.intermediate_size_per_partition = local_intermediate_size + self.moe_runner_config = MoeRunnerConfig( + num_experts=n_shared_experts, + num_local_experts=n_shared_experts, + hidden_size=d_model, + intermediate_size_per_partition=local_intermediate_size, + layer_id=layer_id, + top_k=None, + num_fused_shared_experts=n_shared_experts, + params_dtype=torch.get_default_dtype(), + activation="silu", + apply_router_weight_on_input=False, + inplace=True, + no_combine=False, + routed_scaling_factor=None, + gemm1_alpha=None, + gemm1_clamp_limit=None, + is_gated=True, + ) + + FusedMoELoadingMixin.__init__( + self, + quant_config, + self.quant_method, + self.moe_runner_config, + self.moe_tp_rank, + ) + self.quant_method.create_weights( + layer=self, + num_experts=n_shared_experts, + hidden_size=d_model, + intermediate_size_per_partition=local_intermediate_size, + params_dtype=torch.get_default_dtype(), + weight_loader=self.weight_loader_fused, + with_bias=False, + ) + self._fp4_shared_processed = False + self._linearized_bf16_enabled = ( + linearized_bf16 + and self.inference_moe_w13_interleaved + and self._fp4_strategy is SharedExpertFp4Strategy.BF16 + ) + if self._linearized_bf16_enabled: + local_f = self.w2_weight.shape[2] + self.register_buffer( + "_w2_lin", + self.w2_weight.new_empty( + self.n_shared_experts * local_f, + self.w2_weight.shape[1], + ), + persistent=False, + ) + else: + self.register_buffer("_w2_lin", None, persistent=False) + self._bf16_linearized_ready = False + + def weight_loader_fused( + self, + param: torch.nn.Parameter, + loaded_weight: torch.Tensor, + weight_name: str, + shard_id: str, + ) -> None: + FusedMoELoadingMixin.weight_loader_fused( + self, param, loaded_weight, weight_name, shard_id + ) + if ( + getattr(self, "_linearized_bf16_enabled", False) + and hasattr(self, "w2_weight") + and param is self.w2_weight + ): + self._refresh_bf16_linearized() + + def get_bf16_linearized_weights( + self, + ) -> tuple[torch.Tensor, torch.Tensor] | None: + if not self._linearized_bf16_enabled: + return None + if not self._bf16_linearized_ready: + self._refresh_bf16_linearized() + n, two_f, d = self.w13_weight.shape + assert self._w2_lin is not None + return self.w13_weight.view(n * two_f, d), self._w2_lin + + def _refresh_bf16_linearized(self) -> None: + assert self._linearized_bf16_enabled + assert self._w2_lin is not None + first_refresh = not self._bf16_linearized_ready + n = self.n_shared_experts + two_f, d = self.w13_weight.shape[1], self.w13_weight.shape[2] + if first_refresh: + logger.info_once( + "Linearized bf16 shared sink: stacking %d experts " + "(w13 [%d, %d], fused-sum down)", + n, + n * two_f, + d, + ) + with torch.no_grad(): + self._w2_lin.view( + n, self.w2_weight.shape[2], self.w2_weight.shape[1] + ).copy_(self.w2_weight.data.transpose(1, 2)) + self._bf16_linearized_ready = True + + @staticmethod + def _resolve_fp4_strategy( + quant_config: QuantizationConfig | None, prefix: str + ) -> SharedExpertFp4Strategy: + from sglang.srt.models.inkling_common.quantization.config import ( + InklingModelOptNvfp4Config, + ) + + S = SharedExpertFp4Strategy + # Plain bf16 models, and EP-replicated shared experts (no TP), serve bf16. + if not isinstance(quant_config, InklingModelOptNvfp4Config): + return S.BF16 + ckpt_prefix = prefix.replace(".experts.shared_experts", ".shared_experts") + shared_excluded = quant_config.exclude_layer( + f"{ckpt_prefix}.shared_w13_weight" + ) or quant_config.exclude_layer(f"{ckpt_prefix}.shared_w2_weight") + if shared_excluded: + return S.BF16 + # Backend (marlin / flashinfer-trtllm / cutlass) is chosen and validated by + # the OSS ModelOptFp4LinearMethod at process_weights_after_loading time. + return S.FP4 + + def forward( + self, x: torch.Tensor, gammas: torch.Tensor, use_reduce_scatter: bool = False + ) -> torch.Tensor: + """ + t: number of tokens (batch size * sequence length) + s: n_shared_experts + d: d_model + f: shared_d_mlp + """ + assert x.ndim in (2, 3), f"{x.shape=}" + assert gammas.ndim in (2, 3), f"{gammas.shape=}" + assert ( + gammas.size(-1) == self.n_shared_experts + ), f"{gammas.shape=} {self.n_shared_experts=}" + if self._fp4_strategy.serves_fp4: + return self._forward_fp4(x, gammas, use_reduce_scatter) + + x_td = x.view(-1, x.size(-1)) if x.ndim != 2 else x + gammas_ts = gammas.view(-1, gammas.size(-1)) if gammas.ndim != 2 else gammas + + linearized_weights = self.get_bf16_linearized_weights() + if linearized_weights is not None: + out_td = self._forward_bf16_linearized( + x_td, + gammas_ts, + linearized_weights, + use_reduce_scatter, + ) + return out_td.view_as(x) if x.ndim == 2 else out_td + + gammas_st = gammas_ts.transpose(0, 1) + + # Match TorchTitan's accumulation precision. + _bmm = torch.bmm + + x_std = x_td.unsqueeze(0).expand(self.n_shared_experts, -1, -1).contiguous() + # Batch shared experts along dimension 0. + y_st2f = _bmm(x_std, self.w13_weight.mT) + y_stf = self._swiglu(y_st2f, gammas_st) + z_std = _bmm(y_stf, self.w2_weight.mT) + + # Match TorchTitan by accumulating in FP32 before casting back. + out_td = _sum_dim0(z_std) + if not use_reduce_scatter and self.tp_group is not None: + out_td = symm_mem_all_reduce(out_td, self.tp_group) + return out_td.view_as(x) if x.ndim == 2 else out_td + + def _forward_bf16_linearized( + self, + x_td: torch.Tensor, + gammas_ts: torch.Tensor, + linearized_weights: tuple[torch.Tensor, torch.Tensor], + use_reduce_scatter: bool, + ) -> torch.Tensor: + w13_lin, w2_lin = linearized_weights + t = x_td.shape[0] + y = torch.mm(x_td, w13_lin.T).view(t, self.n_shared_experts, -1) + act = self._swiglu(y, gammas_ts) + out_td = torch.mm(act.reshape(t, -1), w2_lin) + if not use_reduce_scatter and self.tp_group is not None: + out_td = symm_mem_all_reduce(out_td, self.tp_group) + return out_td + + def _swiglu(self, y_st2f: torch.Tensor, gammas_st: torch.Tensor) -> torch.Tensor: + # Helion's kernel can produce NaNs for small shared-expert batches. + from sglang.srt.layers.moe.moe_runner.triton_utils.inkling_moe import ( + silu_and_mul_triton, + ) + + assert ( + self.inference_moe_w13_interleaved + ), "silu_and_mul_triton requires interleaved w13" + y_st_2f = y_st2f.view(-1, y_st2f.size(-1)) + y_st_f = silu_and_mul_triton(y_st_2f, gammas_st.reshape(-1)) + return y_st_f.view(*y_st2f.shape[:-1], y_st2f.size(-1) // 2) + + # NVFP4 shared-expert serving uses the generic ModelOpt FP4 linears. + + def process_weights_after_loading(self) -> None: + if self._fp4_strategy is SharedExpertFp4Strategy.FP4: + if self._fp4_shared_processed: + return + self._fp4_shared_processed = True + self._build_fp4_linears() + elif self._linearized_bf16_enabled: + self._refresh_bf16_linearized() + + def _build_fp4_linears(self) -> None: + n = self.n_shared_experts + # w13: stack experts on output rows [n, 2*inter, K/2] -> [n*2*inter, K/2]. + two_inter, k_half = self.w13_weight.shape[1], self.w13_weight.shape[2] + self._w13_linear = self._make_fp4_linear( + self.w13_weight.data.reshape(n * two_inter, k_half), + self.w13_scale.data.reshape(n * two_inter, self.w13_scale.shape[2]), + self.w13_scale2.data, + self.w13_input_amax.data, + in_features=k_half * 2, + out_features=n * two_inter, + ) + # w2: concat experts on K [n, d_model, inter/2] -> [d_model, n*inter/2]. + d_model, inter_half = self.w2_weight.shape[1], self.w2_weight.shape[2] + self._fp4_shared_intermediate = inter_half * 2 + self._w2_linear = self._make_fp4_linear( + self.w2_weight.data.permute(1, 0, 2).reshape(d_model, n * inter_half), + self.w2_scale.data.permute(1, 0, 2).reshape( + d_model, n * self.w2_scale.shape[2] + ), + self.w2_scale2.data, + self.w2_input_amax.data, + in_features=n * inter_half * 2, + out_features=d_model, + ) + for nm in ( + "w13_weight", + "w2_weight", + "w13_scale", + "w2_scale", + "w13_scale2", + "w2_scale2", + "w13_original_shape", + "w2_original_shape", + "w13_input_amax", + "w2_input_amax", + ): + self._parameters.pop(nm, None) + + def _make_fp4_linear( + self, + packed: torch.Tensor, + block_scale: torch.Tensor, + scale2: torch.Tensor, + input_amax: torch.Tensor, + in_features: int, + out_features: int, + ) -> nn.Module: + from sglang.srt.layers.quantization.modelopt_quant import ( + ModelOptFp4LinearMethod, + ) + + method = ModelOptFp4LinearMethod(self.quant_config) + layer = nn.Module() + method.create_weights( + layer, + input_size_per_partition=in_features, + output_partition_sizes=[out_features], + input_size=in_features, + output_size=out_features, + params_dtype=torch.get_default_dtype(), + weight_loader=lambda *a, **k: None, + ) + # Weight creation starts on CPU; move the holder before Marlin setup. + layer.to(packed.device) + global_scale, input_scale = self._shared_scales(scale2, input_amax) + layer.weight.data.copy_(packed.contiguous()) + layer.weight_scale.data.copy_(block_scale.contiguous()) + layer.weight_scale_2.data.copy_(global_scale.reshape(1)) + layer.input_scale.data.copy_(input_scale.reshape(1)) + method.process_weights_after_loading(layer) + layer._fp4_method = method + return layer + + def _shared_scales( + self, scale2: torch.Tensor, input_amax: torch.Tensor + ) -> tuple[torch.Tensor, torch.Tensor]: + # All shared experts must share one global weight scale (reshard with + # single_global_scale=True). ModelOpt's input_scale = amax / (6 * 448). + flat2 = scale2.reshape(-1).float() + if get_server_args().load_format == "dummy" and not bool( + torch.all(flat2 == flat2[0]) + ): + # Dummy loading uses per-element noise; replace it with a valid scale. + flat2 = torch.ones_like(flat2) + assert bool(torch.all(flat2 == flat2[0])), ( + f"shared-expert scale2 not constant across experts ({flat2.tolist()}); " + "reshard with single_global_scale=True" + ) + global_scale = flat2[0] + input_scale = input_amax.float().reshape(()) / (6.0 * 448.0) + if input_scale.item() <= 0.0: + # Use a neutral scale for an uncalibrated input quantizer. + input_scale = torch.ones_like(input_scale) + return global_scale, input_scale + + def _forward_fp4( + self, x: torch.Tensor, gammas: torch.Tensor, use_reduce_scatter: bool = False + ) -> torch.Tensor: + x_td = x.view(-1, x.size(-1)) if x.ndim != 2 else x + gammas_ts = gammas.view(-1, gammas.size(-1)) if gammas.ndim != 2 else gammas + gammas_st = gammas_ts.transpose(0, 1) + + y_t_s2f = self._w13_linear._fp4_method.apply(self._w13_linear, x_td) + y_st2f = y_t_s2f.view( + x_td.shape[0], self.n_shared_experts, 2 * self._fp4_shared_intermediate + ) + y_st2f = y_st2f.transpose(0, 1).contiguous() + y_stf = self._swiglu(y_st2f, gammas_st) + y_t_sf = y_stf.transpose(0, 1).reshape(x_td.shape[0], -1).contiguous() + out_td = self._w2_linear._fp4_method.apply(self._w2_linear, y_t_sf) + + if not use_reduce_scatter and self.tp_group is not None: + out_td = symm_mem_all_reduce(out_td, self.tp_group) + return out_td.view_as(x) if x.ndim == 2 else out_td diff --git a/python/sglang/srt/models/inkling_common/hmlp.py b/python/sglang/srt/models/inkling_common/hmlp.py new file mode 100644 index 000000000..a6ce3ab3c --- /dev/null +++ b/python/sglang/srt/models/inkling_common/hmlp.py @@ -0,0 +1,184 @@ +from __future__ import annotations + +from typing import cast + +import numpy as np +import torch +from torch import nn +from torch.nn import functional as F + +from sglang.srt.configs.inkling import InklingVisionConfig +from sglang.srt.models.inkling_common.norm import RMSNorm + + +def _prime_factors(n: int) -> list[int]: + """Return the prime factors of ``n`` in ascending order.""" + if n < 1: + raise ValueError("n must be a positive integer") + + factors: list[int] = [] + + while n % 2 == 0: + factors.append(2) + n //= 2 + + p = 3 + while p * p <= n: + while n % p == 0: + factors.append(p) + n //= p + p += 2 + + if n > 1: + factors.append(n) + return factors + + +def plan_out_scales( + temporal_patch_size: int, patch_size: int, n_layers: int, n_channels: int = 3 +) -> list[tuple[int, int, int, int]]: + """Plan the ``(time, height, width, channels)`` scale at each HMLP layer.""" + if patch_size <= 1: + raise ValueError( + "patch_size must be greater than 1, otherwise this doesn't make sense" + ) + + def _round_up(x: int) -> int: + return int(np.ceil(x / 64)) * 64 + + last_h_scale = 1 + scales: list[tuple[int, int, int, int]] = [(1, 1, 1, n_channels)] + for pscale in _prime_factors(patch_size)[::-1]: + last_h_scale *= pscale + scales.append( + ( + 1, + last_h_scale, + last_h_scale, + _round_up((last_h_scale**2) * n_channels), + ) + ) + last_t_scale = 1 + for tscale in _prime_factors(temporal_patch_size)[::-1]: + last_t_scale *= tscale + scales.append( + ( + last_t_scale, + last_h_scale, + last_h_scale, + _round_up((last_h_scale**2) * n_channels * last_t_scale), + ) + ) + + size_reduction = np.prod(np.array(scales)[:, :-1], 1) + + log_ideal_scales = np.linspace( + 0, + np.log(patch_size * patch_size * temporal_patch_size * n_channels), + n_layers + 1, + ) + cost_matrix = np.abs(log_ideal_scales[:, None] - np.log(size_reduction)[None]) + + if n_layers >= len(scales): + idxs = np.argmin(cost_matrix, axis=1) + else: + from scipy.optimize import linear_sum_assignment + + idxs = linear_sum_assignment(cost_matrix)[1] + + assert len(idxs) >= 2 + idxs[0] = 0 + idxs[-1] = len(scales) - 1 + + return [scales[i] for i in idxs] + + +def fold_timespace_to_depth( + vision_patches_bthwc: torch.Tensor, t_fold: int, hw_fold: int +) -> torch.Tensor: + """Fold temporal and spatial neighborhoods into the channel dimension.""" + B, T, H, W, C = vision_patches_bthwc.shape + + assert T % t_fold == 0, f"Temporal dimension {T} must be divisible by {t_fold}" + assert H % hw_fold == 0, f"Height dimension {H} must be divisible by {hw_fold}" + assert W % hw_fold == 0, f"Width dimension {W} must be divisible by {hw_fold}" + + t_new = T // t_fold + h_new = H // hw_fold + w_new = W // hw_fold + + x = vision_patches_bthwc.reshape( + B, t_new, t_fold, h_new, hw_fold, w_new, hw_fold, C + ) + + x = x.permute(0, 1, 3, 5, 2, 4, 6, 7) + + x = x.reshape(B, t_new, h_new, w_new, t_fold * hw_fold * hw_fold * C) + + return x + + +class HMLPPatchEncoder(nn.Module): + def __init__( + self, + config: InklingVisionConfig, + ): + super().__init__() + self.decoder_dmodel = config.decoder_dmodel + self.patch_size = config.patch_size + self.temporal_patch_size = config.temporal_patch_size + self.n_channels = config.n_channels + self.n_layers = config.n_layers + self.use_vision_norm = config.use_vision_norm + + self.scales: list[tuple[int, int, int, int]] = plan_out_scales( + self.temporal_patch_size, self.patch_size, self.n_layers, self.n_channels + ) + self.layers: nn.ModuleDict = nn.ModuleDict() + for i, (start_scale, end_scale) in enumerate( + zip(self.scales[:-1], self.scales[1:]) + ): + shuffle_mult = ( + (end_scale[0] // start_scale[0]) + * (end_scale[1] // start_scale[1]) + * (end_scale[2] // start_scale[2]) + ) + if i == self.n_layers - 1: + self.layers[f"linear_{i}"] = nn.Linear( + start_scale[3] * shuffle_mult, self.decoder_dmodel, bias=False + ) + else: + self.layers[f"linear_{i}"] = nn.Linear( + start_scale[3] * shuffle_mult, end_scale[3], bias=False + ) + self.layers[f"norm_{i}"] = RMSNorm(end_scale[3]) + + self.final_norm: RMSNorm | None = None + if self.use_vision_norm: + self.final_norm = RMSNorm(self.decoder_dmodel) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + num_patches, T, H, W, C = x.shape + for i, (start_scale, end_scale) in enumerate( + zip(self.scales[:-1], self.scales[1:]) + ): + t_fold = end_scale[0] // start_scale[0] + hw_fold = end_scale[1] // start_scale[1] + if hw_fold > 1 or t_fold > 1: + x = fold_timespace_to_depth(x, t_fold, hw_fold) + assert x.shape[1:-1] == ( + T // end_scale[0], + H // end_scale[1], + W // end_scale[2], + ) + x = self.layers[f"linear_{i}"](x) + if i < self.n_layers - 1: + norm = cast(RMSNorm, self.layers[f"norm_{i}"]) + x = norm(x) + x = F.gelu(x) + + if self.final_norm is not None: + x = self.final_norm(x) + + x = x.reshape(num_patches, -1) + return x diff --git a/python/sglang/srt/models/inkling_common/kernels/__init__.py b/python/sglang/srt/models/inkling_common/kernels/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/python/sglang/srt/models/inkling_common/kernels/comm.py b/python/sglang/srt/models/inkling_common/kernels/comm.py new file mode 100644 index 000000000..dba1feade --- /dev/null +++ b/python/sglang/srt/models/inkling_common/kernels/comm.py @@ -0,0 +1,1195 @@ +from __future__ import annotations + +import functools +from typing import TYPE_CHECKING + +import msgspec +import torch + +from sglang.srt.environ import envs +from sglang.srt.runtime_context import get_server_args +from sglang.srt.utils import is_cuda + +if TYPE_CHECKING: + from sglang.srt.distributed.parallel_state import GroupCoordinator + + +# v4 (full one-shot) is out-of-place and drops the exit barrier, so it needs a +# double-buffered input. We carve three regions out of the tail of the enlarged +# comm.buffer -- two rotating input buffers (A/B) + one output -- so a region is +# only reused two ARs later, separated by the intervening AR's entry barrier +# (capture-safe: the A/B alternation bakes into the graph). Region size covers a +# few decode rows at hidden=6144; v4 only fires for num_tokens <= 2. +# +# SAFETY INVARIANT: the reuse-distance-2 argument requires the v4 AR sequence to +# alternate A,B,A,B *globally* -- across forwards, and across graph replays. That +# holds because every forward issues an even number of v4 ARs (attn + MLP per +# layer), so each captured graph starts and ends on opposite regions and replays +# stay aligned with each other and with eager forwards. If a forward could ever +# issue an ODD number of v4 ARs (e.g. a layer taking a reduce-scatter path at +# num_tokens <= 2), a replay boundary would put the same region in consecutive +# ARs and a lagging peer could still be multicast-reading it -- audit this before +# changing which layers all-reduce at decode. +_INKLING_AR_V4_REGION = 16 * 6144 # elems; 16B-aligned (mult of 8) + +# v5 (push one-shot) needs a per-rank staging slot on every GPU: two rotating +# staging buffers of world*_INKLING_AR_V5_REGION elems (A/B, same reuse-distance-2 +# argument and SAFETY INVARIANT as v4 above -- v5's single barrier plays the +# entry barrier's role) plus one local output region. Sized to cover the +# custom-kernel decode band (<=96 rows at hidden=6144) plus the fused +# target-verify chain (bs*draft_token_num <= 144 rows). +_INKLING_AR_V5_REGION = 160 * 6144 # elems; 16B-aligned (mult of 8) + +# The custom kernels reduce one 16B vector (8 bf16 elems) at a time and their +# validate() rejects a num_items that isn't a multiple of this. torch symm-mem +# only enforces 4B alignment, so a non-vector bf16 size (e.g. [1, 2]) must fall +# back to torch multimem instead of hitting the kernel's hard check. (For Inkling +# proper this never bites -- hidden=6144 is a multiple of 8 -- but the utility +# is general.) +_INKLING_AR_VEC = 8 + +# Fused {AR + scattered sconv} OUT region (elems): the extend kernel broadcasts +# the post-conv [T, H] here (out-of-place -- the conv taps re-read the pristine +# input partials). ONE region suffices (no A/B): the kernel keeps BOTH +# barriers, so the next fused call's ENTRY barrier proves every peer's +# consumers already read the previous OUT. Sized to the chunked-prefill +# ceiling at hidden=6144. +_INKLING_AR_SSCONV_OUT_REGION = 16384 * 6144 # max_prefill_tokens x hidden + +# World sizes with torch-multimem NVLink support; the symm-mem fast path (and +# with it the custom kernels) is only taken for these. +_INKLING_AR_WORLD_SIZES = (4, 6, 8) + + +class _InklingArResources(msgspec.Struct): + """Per-group custom-AR resources: barrier flags/state + comm.buffer peer and + multicast pointers + v4 double-buffer region offsets + rotation index.""" + + rank: int + world: int + buffer_ptrs_dev: int + multicast_ptr: int + flag_ptrs_dev: int + state_ptr: int + v4_in: tuple[int, int] # (A, B) input region starts (elems) + v4_out: int # output region start (elems) + v5_in: tuple[int, int] # (A, B) push-staging region starts (elems) + v5_out: int # v5 output region start (elems) + ssconv_out: int # fused {AR + scattered sconv} OUT region start (elems) + v4_cur: int = 0 # rotation index, flips per v4 AR + v5_cur: int = 0 # rotation index, flips per v5 AR + refs: tuple = () # keep-alive: (flags, state, hdl, hflags) + + +# Lazily-built per-group resources, keyed by group name. Built once on the +# first eager call (before any capture). comm.buffer itself is enlarged at +# communicator init (a normal, non-inference tensor) so producer GEMMs can +# write into it -- including v4's input regions. +_INKLING_AR_CACHE: dict[str, _InklingArResources] = {} + + +@functools.cache +def _ar_jit(): + """The inkling_all_reduce JIT wrapper module, imported once on first use (kept + lazy so importing comm.py doesn't pull in the JIT machinery).""" + if not is_cuda(): + return None + from sglang.jit_kernel import inkling_all_reduce + + return inkling_all_reduce + + +@functools.cache +def _ar_fused_jit(): + if not is_cuda(): + return None + from sglang.jit_kernel import inkling_ar_fused + + return inkling_ar_fused + + +def _get_inkling_ar_resources(comm) -> _InklingArResources | None: + """Return the cached custom-AR resources for ``comm``, or ``None`` if they + can't be built now (a CUDA-graph capture is active, or on ROCm). The first eager call + populates the cache before capture.""" + key = comm.group.group_name + cached = _INKLING_AR_CACHE.get(key) + if cached is not None: + return cached + if torch.cuda.is_current_stream_capturing(): + return None + if not is_cuda(): + return None + import torch.distributed._symmetric_memory as torch_symm_mem + + jit = _ar_jit() + world = comm.world_size + # The JIT kernels static_assert a power-of-two world (std::has_single_bit). + # TP=6 is torch-multimem-eligible but would trip that assertion at compile + # time; return None so it stays on the plain-multimem fallback path. + if world & (world - 1) != 0: + return None + dev = comm.buffer.device + hdl = torch_symm_mem.rendezvous(comm.buffer, key) # idempotent (done at init) + flags = torch_symm_mem.empty(jit.flags_numel(world), device=dev, dtype=torch.uint32) + flags.zero_() + hflags = torch_symm_mem.rendezvous(flags, key) + # Device-side barrier so no peer's first fused-AR kernel can write an epoch + # into our flags while our zero_ is still pending on the stream (the zero + # would clobber the signal; the protocol self-heals, but don't rely on it). + hflags.barrier() + state = torch.zeros(jit.STATE_SIZE, device=dev, dtype=torch.uint32) + jit.compile_inkling_all_reduce(comm.dtype, world) + # v4 + v5 regions at the tail of comm.buffer. A huge in-place v3 AR's [0:n] + # may reach into these regions; that is safe -- every fused AR's entry + # barrier proves all peers finished the previous AR before any broadcast + # touches the buffer, and the staging regions hold no cross-AR state. + total = comm.buffer.numel() + v4reg = _INKLING_AR_V4_REGION + v5stage = world * _INKLING_AR_V5_REGION + v5_base = total - 3 * v4reg - 2 * v5stage - _INKLING_AR_V5_REGION + # Fused scattered-sconv OUT sits below the v5 regions; eligibility caps the + # input [0:n] at ssconv_out so the two never overlap. + ssconv_out = v5_base - _INKLING_AR_SSCONV_OUT_REGION + res = _InklingArResources( + rank=hdl.rank, + world=world, + buffer_ptrs_dev=hdl.buffer_ptrs_dev, + multicast_ptr=hdl.multicast_ptr, + flag_ptrs_dev=hflags.buffer_ptrs_dev, + state_ptr=state.data_ptr(), + v4_in=(total - 3 * v4reg, total - 2 * v4reg), + v4_out=total - v4reg, + v5_in=(v5_base, v5_base + v5stage), + v5_out=v5_base + 2 * v5stage, + ssconv_out=ssconv_out, + refs=(flags, state, hdl, hflags), + ) + _INKLING_AR_CACHE[key] = res + return res + + +def ensure_inkling_ar_resources(group: GroupCoordinator) -> None: + """Eagerly build the custom-AR resources for ``group`` (idempotent). + + Call at model init: the lazy first-call build only works when an eager + forward runs before CUDA-graph capture (historically guaranteed by the + prefill BCG capture's eager breaks). With the prefill graph disabled and + --skip-server-warmup, decode capture would otherwise see no resources and + silently bake the non-custom fallback into the decode graphs.""" + comm = group.torch_symm_mem_comm + if ( + comm is not None + and not comm.disabled + and group.world_size in _INKLING_AR_WORLD_SIZES + ): + _get_inkling_ar_resources(comm) + + +def _v4_enabled(comm, num_tokens: int) -> bool: + if not is_cuda(): + return False + return _ar_jit().select_ar_config(num_tokens, comm.world_size)[0] == "v4" + + +# Fused decode {MoE AR -> mlp_sconv -> attn_norm} band: bounded by the v5 +# staging region size and by one per-block-barrier slot per token row. +_INKLING_AR_FUSED_MAX_TOKENS = 96 +# Target-verify band: T = batch * draft_token_num (144 at bs=16, Q=9), bounded +# by the (enlarged) staging region rows and the per-block barrier slots. +_INKLING_AR_FUSED_MAX_TOKENS_VERIFY = 160 + + +# --- fused-AR shared-expert partials hand-off ------------------------------- +# +# InklingMoE.forward(reduce=False) produces {routed partials, shared partials}. +# Pre-adding them costs one full [T, H] kernel per MoE layer; the custom AR +# kernels can instead fold the shared term for free (v5-family register fold at +# the push). The partials +# tensor must stay a bare tensor across the layer boundary (BCG narrowing, +# logging, the AttnRes loop), so the shared tensor rides this per-forward +# stash: the producer deposits it, the consuming fused-AR call collects it. +# Python-trace-time only (works identically under CUDA-graph capture); the +# consumer ALWAYS drains it in the same model forward that stashed it. +_PENDING_AR_SHARED: list = [] + + +def stash_ar_shared(shared: torch.Tensor) -> None: + assert not _PENDING_AR_SHARED, "unconsumed fused-AR shared partials" + _PENDING_AR_SHARED.append(shared) + + +def take_ar_shared(num_tokens: int) -> torch.Tensor | None: + """Collect (and clear) the stashed shared partials, prefix-narrowed to the + consumer's row count (BCG narrowing slices rows [0:t]).""" + if not _PENDING_AR_SHARED: + return None + shared = _PENDING_AR_SHARED.pop() + if shared.shape[0] != num_tokens: + shared = shared[:num_tokens] + return shared + + +def ar_sconv_norm_fusable( + group: GroupCoordinator, + forward_batch, + num_tokens: int, + hidden: int, + dtype: torch.dtype, +) -> bool: + """True when a decode {all-reduce -> sconv -> add+RMSNorm} chain + (attn-side: wo_ud AR -> attn_sconv -> mlp_norm; MoE-side: MoE AR -> + mlp_sconv -> next attn_norm) + can run as the single fused kernel (jit_kernel/inkling_ar_fused.py). Must be + evaluated identically by the producing layer (MoE ``reduce=False``) and the + consuming layer/tail -- it is a pure function of per-forward state.""" + if not is_cuda(): + return False + if not ( + envs.SGLANG_OPT_USE_INKLING_CUSTOM_AR.get() + and envs.SGLANG_OPT_USE_INKLING_FUSED_AR_SCONV_NORM.get() + ): + return False + if get_server_args().enable_scattered_sconv: + # The decode {AR -> sconv -> norm} fusion is full-width; under scattered + # sconv the output sconvs are hidden-sharded, so it does not apply. + return False + fm = forward_batch.forward_mode + if fm.is_decode(): + max_tokens = _INKLING_AR_FUSED_MAX_TOKENS + elif fm.is_target_verify(): + max_tokens = _INKLING_AR_FUSED_MAX_TOKENS_VERIFY + else: + return False + comm = group.torch_symm_mem_comm + if ( + comm is None + or comm.disabled + or group.world_size not in _INKLING_AR_WORLD_SIZES + or dtype != comm.dtype + ): + return False + if ( + num_tokens > max_tokens + or num_tokens > _ar_jit().MAX_BARRIER_BLOCKS + or hidden % _INKLING_AR_VEC != 0 + or hidden // _INKLING_AR_VEC > 1024 # one 16B vec per thread, one block/row + or num_tokens * hidden > _INKLING_AR_V5_REGION + ): + return False + return _get_inkling_ar_resources(comm) is not None + + +def ar_sconv_norm_fused( + input: torch.Tensor, + residual: torch.Tensor, + sconv, + norm, + forward_batch, + group: GroupCoordinator, + shared: torch.Tensor | None = None, +) -> tuple[torch.Tensor, torch.Tensor]: + """Fused decode {all-reduce -> sconv -> residual-add + RMSNorm}: one kernel + replacing ``symm_mem_all_reduce`` + ``fused_causal_conv1d_update_decode`` + + the fused-add RMSNorm. ``input`` holds the UNREDUCED MoE partial sums + (``InklingMoE.forward(reduce=False)``); returns ``(hs, residual)`` exactly like + the unfused ``sconv -> norm(hs, res)`` chain. The caller must have checked + ``ar_sconv_norm_fusable``. Occupies one v5 staging rotation slot (this + IS a v5 AR with the epilogue seam filled in; same reuse-distance rule).""" + comm = group.torch_symm_mem_comm + res = _get_inkling_ar_resources(comm) + if shared is None: + shared = take_ar_shared(input.shape[0]) + hs_out = torch.empty_like(input) + residual_out = torch.empty_like(residual) + cur = res.v5_cur + stage_off = res.v5_in[cur] + esz = comm.buffer.element_size() + mc = res.multicast_ptr + stage_off * esz + local = comm.buffer.data_ptr() + stage_off * esz + if forward_batch.forward_mode.is_target_verify(): + sconv_cache, cache_indices, cache_mask, conv_weight, inter_out = ( + sconv.verify_fused_ar_inputs(forward_batch) + ) + _ar_fused_jit().inkling_ar_sconv_norm_verify( + input, + residual, + residual_out, + hs_out, + norm.weight, + norm.variance_epsilon, + sconv_cache, + cache_indices, + cache_mask, + conv_weight, + inter_out, + forward_batch.spec_info.draft_token_num, + mc, + local, + res.flag_ptrs_dev, + res.state_ptr, + res.rank, + res.world, + activation=sconv.activation, + use_residual=sconv.use_residual, + shared=shared, + ) + else: + sconv_cache, cache_indices, cache_mask, conv_weight = ( + sconv.decode_fused_ar_inputs(forward_batch) + ) + _ar_fused_jit().inkling_ar_sconv_norm( + input, + residual, + residual_out, + hs_out, + norm.weight, + norm.variance_epsilon, + sconv_cache, + cache_indices, + cache_mask, + conv_weight, + mc, + local, + res.flag_ptrs_dev, + res.state_ptr, + res.rank, + res.world, + activation=sconv.activation, + use_residual=sconv.use_residual, + track_mask=forward_batch.mamba_track_mask, + track_indices=forward_batch.mamba_track_indices, + shared=shared, + ) + res.v5_cur = 1 - cur + return hs_out, residual_out + + +def get_ar_buffer( + group: GroupCoordinator, + num_tokens: int, + hidden: int, + dtype: torch.dtype, +) -> torch.Tensor | None: + """Return a ``[num_tokens, hidden]`` view of a rendezvous'd symm buffer for a + producer to write into, or ``None`` when the symm-mem fast path is ineligible. + + With ``SGLANG_OPT_USE_INKLING_CUSTOM_AR`` the communicator buffer is enlarged at + init (256 MiB) so big prefill ARs fit; for the v4 (num_tokens<=2) bucket this + returns the current rotating input region so ``symm_mem_all_reduce`` can run + the out-of-place full one-shot. + """ + comm = group.torch_symm_mem_comm + if ( + comm is None + or comm.disabled + or group.world_size not in _INKLING_AR_WORLD_SIZES + or dtype != comm.dtype + ): + return None + n = num_tokens * hidden + nbytes = n * dtype.itemsize + if nbytes % 4 != 0: + return None + if ( + envs.SGLANG_OPT_USE_INKLING_CUSTOM_AR.get() + # Scattered sconv replaces the AR with reduce_scatter_hidden, which + # stages from comm.buffer[:n] -- never hand out the v4 region there. + and not get_server_args().enable_scattered_sconv + ): + res = _get_inkling_ar_resources(comm) + if ( + res is not None + and _v4_enabled(comm, num_tokens) + and n <= _INKLING_AR_V4_REGION + and n % _INKLING_AR_VEC == 0 + ): + off = res.v4_in[res.v4_cur] + return comm.buffer[off : off + n].view(num_tokens, hidden) + if nbytes >= comm.max_size: + return None + return comm.buffer[:n].view(num_tokens, hidden) + + +def symm_mem_all_reduce( + input: torch.Tensor, + group: GroupCoordinator, + *, + output: torch.Tensor | None = None, + input_is_ar_buffer: bool = False, + num_sms: int = 32, + shared: torch.Tensor | None = None, +) -> torch.Tensor: + """All-reduce ``input`` across ``group`` in the communicator's symm buffer. + + Default (``--enable-torch-symm-mem``): one-shot NVLink ``multimem_all_reduce_``. + With ``SGLANG_OPT_USE_INKLING_CUSTOM_AR``: dispatch on shape to the autotuned + custom kernels -- v5 push one-shot (out-of-place, double-buffered staging) + for the latency band, v3/v3b two-shot multimem for medium/large -- with + torch multimem for the remaining small ("mm") bucket. The buffer is enlarged + at init so large prefill ARs take this path instead of NCCL. + + ``shared``: optional LOCAL shared-expert partials to add into the reduced + result. Folded IN-KERNEL on the v5 push (in registers) and the v4 + pre-barrier prologue -- the small/decode band; every other bucket and + fallback PRE-ADDS during its stage-in copy, so passing ``shared`` is + always numerics-identical to {torch.add -> all_reduce}. + """ + _ = num_sms + if group.world_size == 1: + if shared is not None: + input = input + shared + if output is None: + return input + output.copy_(input) + return output + + comm = group.torch_symm_mem_comm + if ( + output is None + and comm is not None + and not comm.disabled + and group.world_size in _INKLING_AR_WORLD_SIZES + and comm.should_torch_symm_mem_allreduce(input) + ): + n = input.numel() + num_tokens = input.shape[0] if input.dim() >= 2 else n + res = ( + _get_inkling_ar_resources(comm) + if envs.SGLANG_OPT_USE_INKLING_CUSTOM_AR.get() + else None + ) + # Custom kernels need a 16B-vector-multiple size (validate() enforces it); + # a non-vector size falls through to plain multimem below. The "mm" bucket + # inside this block also uses torch multimem, but it still requires the + # kernel-eligible size to reach here, so gate the whole block on it. + if res is not None and n % _INKLING_AR_VEC == 0: + jit = _ar_jit() + kernel, nb, bs = jit.select_ar_config(num_tokens, res.world) + if ( + kernel == "v5" + and n <= _INKLING_AR_V5_REGION + and input.data_ptr() % 16 == 0 + ): + # Push one-shot: multicast-push input into the rotating staging + # buffer, one per-block barrier, local reduce into the out + # region. Input is read locally, so it needs NO stage-in copy + # even when it isn't an AR buffer. + cur = res.v5_cur + stage_off = res.v5_in[cur] + out_view = comm.buffer[res.v5_out : res.v5_out + n] + esz = comm.buffer.element_size() + jit.inkling_multimem_push_oneshot( + input.view(-1), + out_view, + res.multicast_ptr + stage_off * esz, + comm.buffer.data_ptr() + stage_off * esz, + res.flag_ptrs_dev, + res.state_ptr, + res.rank, + res.world, + n, + nb, + bs, + per_block_barrier=True, + shared=shared.view(-1) if shared is not None else None, + ) + res.v5_cur = 1 - cur + return out_view.view(input.shape) + if kernel == "v4" and n <= _INKLING_AR_V4_REGION: + # out-of-place full one-shot in the double-buffered tail regions. + cur = res.v4_cur + in_off = res.v4_in[cur] + out_off = res.v4_out + in_view = comm.buffer[in_off : in_off + n] + out_view = comm.buffer[out_off : out_off + n] + v4_shared = shared + if not input_is_ar_buffer: + if v4_shared is not None: + torch.add(input.view(-1), v4_shared.view(-1), out=in_view) + v4_shared = None + else: + in_view.copy_(input.view(-1)) + mc = res.multicast_ptr + in_off * comm.buffer.element_size() + jit.inkling_multimem_full_oneshot( + in_view, + out_view, + mc, + res.flag_ptrs_dev, + res.state_ptr, + res.rank, + res.world, + n, + nb, + bs, + shared=v4_shared.view(-1) if v4_shared is not None else None, + ) + res.v4_cur = 1 - cur + return out_view.view(input.shape) + + buf = comm.buffer[:n] + if not input_is_ar_buffer: + if shared is not None: + torch.add(input.view(-1), shared.view(-1), out=buf) + else: + buf.copy_(input.view(-1)) + elif shared is not None: + buf.add_(shared.view(-1)) + if kernel in ("v3", "v3b"): + jit.inkling_multimem_one_shot_fused( + buf, + res.multicast_ptr, + res.flag_ptrs_dev, + res.state_ptr, + res.rank, + res.world, + n, + nb, + bs, + per_block_barrier=(kernel == "v3b"), + ) + return buf.view(input.shape) + if kernel == "v2": + jit.inkling_two_shot_all_reduce_fused( + buf, + res.buffer_ptrs_dev, + res.flag_ptrs_dev, + res.state_ptr, + res.rank, + res.world, + n, + nb, + bs, + ) + return buf.view(input.shape) + # "mm" bucket: torch multimem on comm.buffer. + torch.ops.symm_mem.multimem_all_reduce_(buf, "sum", comm.group.group_name) + return buf.view(input.shape) + + # flag off, resources unavailable (in capture / non-power-of-two world), + # or a non-vector size: plain multimem. + buf = comm.buffer[:n] + if not input_is_ar_buffer: + if shared is not None: + torch.add(input.view(-1), shared.view(-1), out=buf) + else: + buf.copy_(input.view(-1)) + elif shared is not None: + buf.add_(shared.view(-1)) + torch.ops.symm_mem.multimem_all_reduce_(buf, "sum", comm.group.group_name) + return buf.view(input.shape) + + if shared is not None: + input = input + shared + result = group.all_reduce(input) + if output is None: + return result + output.copy_(result) + return output + + +# --- scattered sconv (--enable-scattered-sconv) comm helpers ----------------- +# +# torch symmetric-memory multimem needs NVLink multicast, which torch supports +# for world sizes {4,6,8} on cc>=9. reduce_scatter/all_gather here reuse the +# group's TorchSymmMemCommunicator buffer -- already allocated and rendezvous'd +# at init, so these one-shot NVLink collectives are CUDA-graph-safe (no +# rendezvous in forward), same as symm_mem_all_reduce. Ineligible cases fall +# back to NCCL. + + +def _symm_mem_comm(group: GroupCoordinator, input: torch.Tensor, full_numel: int): + """Return the group's torch-symm-mem communicator if it can multimem this + tensor (dtype match, full [T, H] fits + 4B-aligned in the rendezvous'd + buffer, supported world size); else None to signal the NCCL fallback.""" + comm = group.torch_symm_mem_comm + if comm is None or comm.disabled: + return None + if not input.is_cuda or input.dtype != comm.dtype: + return None + if group.world_size not in _INKLING_AR_WORLD_SIZES: + return None + nbytes = full_numel * input.element_size() # RS stages [T,H]; AG rebuilds [T,H] + if nbytes % 4 != 0 or nbytes >= comm.max_size: + return None + return comm + + +def reduce_scatter_hidden( + input: torch.Tensor, + group: GroupCoordinator, + *, + input_is_ar_buffer: bool = False, +) -> torch.Tensor: + """Reduce partial-sum [T, H] across the group, scatter hidden -> [T, H/P].""" + p = group.world_size + if p == 1: + return input + t, h = input.shape + assert h % p == 0, f"hidden {h} not divisible by tp size {p}" + + comm = _symm_mem_comm(group, input, t * h) + if comm is not None: + # Stage [T,H] into the rendezvous'd symm buffer (no-op when the producer + # already wrote it there via get_ar_buffer); multimem reduce-scatter over + # the last (hidden) dim -> local [T, H/P] shard. split_last_dim=True + # avoids the transpose the NCCL (dim-0) path needs. + symm_in = comm.buffer[: t * h].view(t, h) + if not input_is_ar_buffer: + symm_in.copy_(input) + out = torch.empty((t, h // p), dtype=input.dtype, device=input.device) + torch.ops.symm_mem.reduce_scatter_out(symm_in, comm.group.group_name, True, out) + return out + + # one transpose so reduce_scatter_tensor (dim-0) scatters the hidden dim; + # tensor form uses the optimized backend, one copy instead of P chunk-copies. + x = input.view(t, p, h // p).movedim(1, 0).reshape(p * t, h // p).contiguous() + out = torch.empty((t, h // p), dtype=input.dtype, device=input.device) + group.reduce_scatter_tensor(out, x) + return out + + +def all_gather_hidden(input: torch.Tensor, group: GroupCoordinator) -> torch.Tensor: + """Gather hidden shard [T, H/P] -> [T, H]; inverse of reduce_scatter_hidden.""" + p = group.world_size + if p == 1: + return input + t, hp = input.shape + + comm = _symm_mem_comm(group, input, t * hp * p) + if comm is not None: + # multimem all-gather concatenates the P shards along dim 0 into the + # rendezvous'd symm buffer ([P*T, H/P]); move rank to the middle and + # flatten to reconstruct [T, H] (hidden chunks in rank order). + symm_out = comm.buffer[: p * t * hp].view(p * t, hp) + torch.ops.symm_mem.multimem_all_gather_out( + input, comm.group.group_name, symm_out + ) + return symm_out.view(p, t, hp).movedim(0, 1).reshape(t, p * hp) + + return group.all_gather(input, dim=-1) + + +@functools.cache +def _ar_ssconv_jit(): + if not is_cuda(): + return None + from sglang.jit_kernel import inkling_ar_scattered_sconv + + return inkling_ar_scattered_sconv + + +def scattered_ar_sconv_fusable( + group: GroupCoordinator, + forward_batch, + num_tokens: int, + hidden: int, + dtype: torch.dtype, +) -> bool: + """True when an extend {reduce_scatter_hidden -> sconv(shard) -> + all_gather_hidden} chain can run as the single fused v3/v3b-style kernel + (jit_kernel/inkling_ar_scattered_sconv.py). Pure function of per-forward + state -- the producing layer (reduce=False) and the consuming site must + evaluate it identically.""" + if not is_cuda(): + return False + if not ( + get_server_args().enable_scattered_sconv + and envs.SGLANG_OPT_USE_INKLING_CUSTOM_AR.get() + and envs.SGLANG_OPT_USE_INKLING_FUSED_AR_SCONV.get() + ): + return False + fm = forward_batch.forward_mode + if fm.is_draft_extend_v2(): + return False # de-tied per-step metadata semantics; unfused chain + if not (fm.is_extend() or fm.is_decode()): + return False + # Prefill scope: the BCG runner's eager-break sites are not wired (its + # baked flags would disagree with the break bodies) and tc_piecewise's + # FX pieces can't carry the cross-layer producer/consumer contract, so + # both fall back to the unfused chain. The FULL prefill CUDA-graph + # backend (context.full_graph -- the whole model captured uniformly) IS + # supported: the kernel is capture-safe (barrier epochs advance across + # replays; validated capture+replay) and all its metadata (qsl/si/ + # cache_mask/safe_idx/track rows) is recomputed in-graph from the + # runner's refreshed registry slots. Bucket padding is contained: pad + # rows only write pad rows of the OUT region (the eager tail slices + # [:raw]), and sentinel request slots have qlen == 0 so the in-kernel + # cache update/track skip them. + from sglang.srt.model_executor.runner_backend_utils.tc_piecewise_cuda_graph import ( + get_tc_piecewise_forward_context, + ) + + tc_ctx = get_tc_piecewise_forward_context() + if tc_ctx is not None and not tc_ctx.full_graph: + return False + comm = group.torch_symm_mem_comm + if ( + comm is None + or comm.disabled + or group.world_size not in (4, 8) # kernel static_asserts power-of-two + or dtype != comm.dtype + ): + return False + n = num_tokens * hidden + if num_tokens == 0 or hidden % (group.world_size * _INKLING_AR_VEC) != 0: + return False + res = _get_inkling_ar_resources(comm) + return res is not None and n <= res.ssconv_out + + +def ar_scattered_sconv_fused( + input: torch.Tensor, + sconv, + forward_batch, + group: GroupCoordinator, + norm=None, + norm_residual: torch.Tensor | None = None, +) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]: + """Fused {AR + scattered sconv}: one v3/v3b-style two-shot multimem + kernel replacing ``reduce_scatter_hidden`` + ``causal_conv1d(shard)`` + + ``all_gather_hidden``. ``input`` holds the UNREDUCED producer partials + ([T, H], ``reduce=False``). The caller must have checked + ``scattered_ar_sconv_fusable``. The reduced pre-conv x shard is stashed + locally and consumed in-kernel by the fused cache-update + prefix-cache + track (Phase 3) -- there is no separate update/track kernel call. + + Without ``norm``: returns the gathered post-conv [T, H] (a view of the OUT + symm region). With ``norm`` (an RMSNorm module) + ``norm_residual`` the + add+RMSNorm tail is fused in-kernel too (both the chunked and streaming + kernels carry the tail; validated at all bands) and the call returns + ``(hidden, residual)`` exactly like ``ar_sconv_norm_fused``. The call + sites only pass ``norm`` for decode/verify: at extend shapes the in-kernel + tail is slower than the unfused sgl_kernel fused_add_rmsnorm -- the tail is + bandwidth-bound and the AR grid is capped by barrier co-residency, while + fusion only saves a launch. Decode prefix-cache tracking is fused (post-update window + snapshot), so tracked decode batches are supported.""" + shared = take_ar_shared(input.shape[0]) + if shared is not None: + # Pre-add on this path rather than folding in-kernel: pre-add keeps the + # full-occupancy torch.add instead of the barrier-capped grid. + # Add straight into the AR buffer -- the same single kernel the + # producer used to run, and the stage-in copy below then no-ops. + _buf = get_ar_buffer(group, input.shape[0], input.shape[1], input.dtype) + if _buf is not None: + torch.add(input, shared, out=_buf) + input = _buf + else: + input = input + shared + + comm = group.torch_symm_mem_comm + res = _get_inkling_ar_resources(comm) + jit = _ar_ssconv_jit() + t, h = input.shape + n = t * h + world = res.world + hc = h // world + + # Design note: everything (cache, conv input/output, exchange) is sharded + # along the hidden dim -- the pure column layout. Communication volume is + # optimal (n/P in + n/P out per rank, no window staging) and each rank + # convs only its own channels. The price, accepted by design: decode pays + # a second cross-rank sync round (consumers need full rows, so the + # post-conv shard exchange must publish). The one-shot decode and + # banded-scattered variants (window-shard push) remain available in the + # JIT module / bench harness as alternatives. + + buf = comm.buffer[:n].view(t, h) + if input.data_ptr() != buf.data_ptr(): + buf.copy_(input) + + ( + sconv_cache, + safe_idx, + cache_mask, + cu, + si, + weight, + query_start_loc, + cache_indices, + has_initial_state, + track_rows, + track_mask, + track_dst, + ) = sconv.extend_fused_ar_inputs(forward_batch) + del query_start_loc # update + track are fused in-kernel + fm = forward_batch.forward_mode + is_verify = fm.is_target_verify() + kernel_ci = cache_indices.to(torch.int32) # kernel reads raw int32 + if is_verify: + # Verify must NOT update the working cache: PAD every row so the + # in-kernel phase-3 update skips (windows are saved separately below). + kernel_ci = torch.full_like(cache_indices, -1) + + # Decode prefix-cache track: snapshot the post-update window in-kernel + # (the unfused fused_causal_conv1d_update_decode semantics). The capture + # batch always carries the persistent (all-False) mask buffer, so this + # bakes correctly into decode graphs and stays data-dependent at replay. + track_from_cache = False + if fm.is_decode() and forward_batch.mamba_track_mask is not None: + b = forward_batch.batch_size + track_mask = forward_batch.mamba_track_mask[:b] + track_dst = forward_batch.mamba_track_indices[:b] + track_from_cache = True + + esz = comm.buffer.element_size() + + if norm is not None and fm.is_decode(): + # COLUMN DECODE V2: dedicated small-batch kernel -- one block per + # token row, block-scoped two-round barriers, prefetch under the + # entry spin, inline cache update/track, fused full-row norm. + assert norm_residual is not None + out_local = comm.buffer[res.ssconv_out : res.ssconv_out + n].view(t, h) + hs_out = torch.empty_like(norm_residual) + residual_out = torch.empty_like(norm_residual) + jit.inkling_ar_col_decode( + buf, + out_local, + norm_residual, + residual_out, + hs_out, + norm.weight, + float(norm.variance_epsilon), + sconv_cache, + kernel_ci, + cache_mask, + weight, + track_mask, + track_dst, + res.multicast_ptr, + res.multicast_ptr + res.ssconv_out * esz, + res.flag_ptrs_dev, + res.state_ptr, + res.rank, + world, + activation=sconv.activation, + use_residual=sconv.use_residual, + ) + return hs_out, residual_out + + # Launch configs below are keyed by world size (TP4 and TP8 need different + # configs: Hc and barrier fan-in differ) and by shape. use_stream=True + # selects the streaming rolling-window kernel for large T; the grid-exit + # barrier (per_block=False) is used throughout. Chunked prefill caps extend + # T at max_prefill_tokens=16384, so the T domain is closed. + per_block = False + stream_walk = 0 + use_stream = False + if is_verify: # target-verify band (decode is intercepted above) + if world == 8: + if t <= 48: + nb, bs = 48, 384 + elif t <= 80: + nb, bs = 80, 384 + elif t <= 144: + nb, bs = 80, 768 + else: + nb, bs = 96, 384 + else: + if t <= 8: + nb, bs = 32, 384 + elif t <= 48: + nb, bs = 48, 384 + elif t <= 80: + nb, bs = 80, 384 + elif t <= 144: + nb, bs = 148, 384 + elif t <= 192: + nb, bs = 96, 768 + else: + nb, bs = 128, 768 + elif world == 8: + if t < 3072: # chunked band + if t <= 128: + nb, bs = 48, 384 + elif t <= 256: + nb, bs = 80, 384 + elif t <= 512: + nb, bs = 192, 768 + elif t <= 768: + nb, bs = 96, 512 + elif t <= 1024: + nb, bs = 96, 768 + elif t <= 1536: + nb, bs = 192, 768 + else: + nb, bs = 148, 1024 + elif 8192 <= t < 10240: + nb, bs = 48, 1024 # chunked edges stream in this band + else: + use_stream = True + if t < 4096: + nb, bs, stream_walk = 148, 256, 16 + elif t < 6144: + nb, bs, stream_walk = 96, 512, 24 + elif t < 8192: + nb, bs, stream_walk = 64, 512, 32 + elif t < 16384: + nb, bs, stream_walk = 148, 128, 0 + else: + nb, bs, stream_walk = 96, 192, 0 + elif t >= 3072: # TP4 streaming band + use_stream = True + if t < 4096: + nb, bs, stream_walk = 148, 256, 16 + elif t < 6144: + nb, bs, stream_walk = 0, 0, 24 + elif t < 8192: + nb, bs, stream_walk = 148, 256, 32 + elif t < 10240: + nb, bs, stream_walk = 96, 512, 0 + else: + nb, bs, stream_walk = 148, 256, 0 + elif t <= 128: + nb, bs = 96, 384 + elif t <= 256: + nb, bs = 148, 512 + elif t <= 768: + nb, bs = 128, 256 + elif t <= 3072: + nb, bs = 148, 384 + else: + nb, bs = 0, 0 + + x_scratch = torch.empty((t, hc), dtype=input.dtype, device=input.device) + out_local = comm.buffer[res.ssconv_out : res.ssconv_out + n].view(t, h) + if norm is not None: + assert norm_residual is not None + norm_kwargs = dict( + track_from_cache=track_from_cache, + out_local=out_local, + norm_gamma=norm.weight, + norm_residual=norm_residual, + norm_out=torch.empty_like(norm_residual), + norm_eps=float(norm.variance_epsilon), + ) + else: + norm_kwargs = dict(track_from_cache=track_from_cache) + jit.inkling_ar_scattered_sconv( + buf, + x_scratch, + sconv_cache, + safe_idx, + cache_mask, + kernel_ci, + has_initial_state, + cu, + si, + weight, + track_rows, + track_mask, + track_dst, + res.multicast_ptr, + res.multicast_ptr + res.ssconv_out * esz, + res.flag_ptrs_dev, + res.state_ptr, + res.rank, + world, + activation=sconv.activation, + use_residual=sconv.use_residual, + num_blocks=nb, + block_size=bs, + per_block_barrier=per_block, + # Only verify consumes x_scratch (window save); skipping the global + # scratch writes lets the chunked path stay smem-resident. + need_scratch=is_verify, + # Streaming rolling-window path: v3-dataflow, no staging. Below the + # stream band, walk-length geometry underfills the grid, so + # chunked/tile is used instead. + use_stream=use_stream, + stream_walk=stream_walk, + **norm_kwargs, + ) + if is_verify: + # Save the per-position windows for update_conv_state_after_mtp_verify. + sconv.verify_fused_ar_finish(forward_batch, x_scratch, cache_indices) + if norm is not None: + return norm_kwargs["norm_out"], norm_residual + return out_local + + +# Below this token count the unfused non-scattered chain {one-shot AR + +# full-width causal_conv1d + update_sconv_cache} beats the fused kernel. +# The threshold is part of the producer/consumer contract. +_INKLING_AR_FW_MIN_TOKENS = 3072 + + +def fullwidth_ar_sconv_fusable( + group: GroupCoordinator, + forward_batch, + num_tokens: int, + hidden: int, + dtype: torch.dtype, +) -> bool: + """True when a NON-scattered extend {all-reduce -> full-width sconv -> + cache update} chain can run as the fused column kernel in full-width mode + (``ar_fullwidth_sconv_fused``). Pure function of per-forward state -- the + producing layer (``reduce=False``) and the consuming site must evaluate + it identically. Mutually exclusive with ``ar_sconv_norm_fusable`` by mode + (extend vs decode/verify) and with ``scattered_ar_sconv_fusable`` by the + scattered flag.""" + if not is_cuda(): + return False + if not ( + not get_server_args().enable_scattered_sconv + and envs.SGLANG_OPT_USE_INKLING_CUSTOM_AR.get() + and envs.SGLANG_OPT_USE_INKLING_FUSED_AR_SCONV.get() + ): + return False + fm = forward_batch.forward_mode + if fm.is_draft_extend_v2(): + return False # de-tied per-step metadata semantics; unfused chain + if fm.is_target_verify(): + return False # verify needs the window save (need_scratch); v5 covers it + if not fm.is_extend(): + return False + if num_tokens < _INKLING_AR_FW_MIN_TOKENS: + return False + # Same prefill-runner scope as the scattered gate: BCG / tc_piecewise + # pieces can't carry the cross-layer producer contract; the FULL prefill + # CUDA-graph backend is supported (capture-safe kernel, in-graph metadata). + from sglang.srt.model_executor.runner_backend_utils.tc_piecewise_cuda_graph import ( + get_tc_piecewise_forward_context, + ) + + tc_ctx = get_tc_piecewise_forward_context() + if tc_ctx is not None and not tc_ctx.full_graph: + return False + comm = group.torch_symm_mem_comm + if ( + comm is None + or comm.disabled + or group.world_size not in (4, 8) + or dtype != comm.dtype + ): + return False + n = num_tokens * hidden + if num_tokens == 0 or hidden % (group.world_size * _INKLING_AR_VEC) != 0: + return False + res = _get_inkling_ar_resources(comm) + return res is not None and n <= res.ssconv_out + + +def ar_fullwidth_sconv_fused( + input: torch.Tensor, + sconv, + forward_batch, + group: GroupCoordinator, +) -> torch.Tensor: + """Fused NON-scattered extend {AR + sconv + cache update}: the column + two-shot kernel in full-width mode. The conv still runs column-sharded + (1/P of the replicated full-width conv's FLOPs, no x round trip) against + this rank's column slice of the replicated [slots, W-1, H] cache and + [H, W] weight, but phase 3 updates/tracks ALL H cache columns on every + rank (window rows re-ld_reduced full-width -- B*(W-1) rows, negligible) + so the replicated cache stays coherent for the full-width decode/verify + consumers. ``input`` holds the UNREDUCED producer partials ([T, H], + ``reduce=False``); the caller must have checked + ``fullwidth_ar_sconv_fusable``. Returns the gathered post-conv [T, H] + (a view of the OUT symm region); the caller runs the norm unfused (the + in-kernel tail is slower at extend shapes, see + ``ar_scattered_sconv_fused``).""" + shared = take_ar_shared(input.shape[0]) + if shared is not None: + # Pre-add on this path rather than folding in-kernel: pre-add keeps the + # full-occupancy torch.add instead of the barrier-capped grid. + # Add straight into the AR buffer -- the same single kernel the + # producer used to run, and the stage-in copy below then no-ops. + _buf = get_ar_buffer(group, input.shape[0], input.shape[1], input.dtype) + if _buf is not None: + torch.add(input, shared, out=_buf) + input = _buf + else: + input = input + shared + + comm = group.torch_symm_mem_comm + res = _get_inkling_ar_resources(comm) + jit = _ar_ssconv_jit() + t, h = input.shape + n = t * h + world = res.world + hc = h // world + rank = res.rank + + buf = comm.buffer[:n].view(t, h) + if input.data_ptr() != buf.data_ptr(): + buf.copy_(input) + + ( + sconv_cache, + safe_idx, + cache_mask, + cu, + si, + weight, + query_start_loc, + cache_indices, + has_initial_state, + track_rows, + track_mask, + track_dst, + ) = sconv.extend_fused_ar_inputs(forward_batch) + del query_start_loc # update + track are fused in-kernel + kernel_ci = cache_indices.to(torch.int32) + + # The streaming kernel is used throughout the fusable band + # (>= _INKLING_AR_FW_MIN_TOKENS). + if world == 8: + if t < 4096: + nb, bs, walk = 0, 0, 16 + elif t < 6144: + nb, bs, walk = 96, 512, 24 + elif t < 8192: + nb, bs, walk = 64, 512, 32 + elif t < 10240: + nb, bs, walk = 0, 0, 0 + elif t < 16384: + nb, bs, walk = 148, 128, 0 + else: + nb, bs, walk = 96, 192, 0 + elif t < 4096: + nb, bs, walk = 0, 0, 16 + elif t < 6144: + nb, bs, walk = 96, 512, 24 + elif t < 8192: + nb, bs, walk = 148, 256, 32 + elif t < 10240: + nb, bs, walk = 0, 0, 0 + else: + nb, bs, walk = 148, 256, 0 + + esz = comm.buffer.element_size() + x_scratch = torch.empty((t, hc), dtype=input.dtype, device=input.device) + out_local = comm.buffer[res.ssconv_out : res.ssconv_out + n].view(t, h) + jit.inkling_ar_scattered_sconv( + buf, + x_scratch, + sconv_cache, + safe_idx, + cache_mask, + kernel_ci, + has_initial_state, + cu, + si, + weight[rank * hc : (rank + 1) * hc], + track_rows, + track_mask, + track_dst, + res.multicast_ptr, + res.multicast_ptr + res.ssconv_out * esz, + res.flag_ptrs_dev, + res.state_ptr, + rank, + world, + activation=sconv.activation, + use_residual=sconv.use_residual, + num_blocks=nb, + block_size=bs, + per_block_barrier=False, + need_scratch=False, + use_stream=True, + stream_walk=walk, + full_update=True, + cache_col0=rank * hc, + ) + return out_local diff --git a/python/sglang/srt/models/inkling_common/kernels/sconv.py b/python/sglang/srt/models/inkling_common/kernels/sconv.py new file mode 100644 index 000000000..a9a790c58 --- /dev/null +++ b/python/sglang/srt/models/inkling_common/kernels/sconv.py @@ -0,0 +1,1160 @@ +from typing import TypedDict + +import torch +import triton +import triton.language as tl + +from sglang.srt.utils import is_cuda + +PAD_SLOT_ID = -1 + + +class SconvDecodeMetadata(TypedDict): + cache_mask: torch.Tensor + safe_idx: torch.Tensor + cu: torch.Tensor + si: torch.Tensor + + +class SconvExtendMetadata(TypedDict): + cache_mask: torch.Tensor + safe_idx: torch.Tensor + cu: torch.Tensor + si: torch.Tensor + + +CHUNK_SIZE = 64 + +# --------------------------------------------------------------------------- +# Causal conv1d forward with cache-loaded prefix (Triton). +# +# Replaces the former Helion kernel: Helion lowers through torch.fx/dynamo, so the +# dynamic extend batch B becomes an *unbacked* symint and an `if is_decode` mask +# branch tripped `GuardOnDataDependentSymNode: Eq(u1, 1)`. Triton has no symbolic +# -shape guard machinery: `IS_DECODE: tl.constexpr` resolves the mask branch at JIT +# compile time, so the decode specialization compiles with ZERO mask load/multiply +# and the extend specialization keeps the mask multiply — both guard-free. +# --------------------------------------------------------------------------- + + +def _conv_prefix_autotune_configs() -> list[triton.Config]: + """Block-size configs for the prefix conv. D=256, W small => memory-bound; favor + coalesced D loads (D is the contiguous axis; the token axis is strided). + + The x window is loaded ONCE per tile (BLOCK_T + W - 1 unique rows) and the W taps + are static-offset slices within it, so larger BLOCK_T both amortizes the per-tile + metadata loads and maximizes window reuse — hence the wide BLOCK_T sweep for the + large-T extend regime, while small BLOCK_T configs cover decode (T=B is moderate, + so more channel blocks fill the GPU).""" + configs = [] + for block_t in (1, 2, 4, 8, 16, 32, 64, 128): + for block_d in (128, 256): + # num_warps scaled to the tile so small tiles don't over-subscribe and + # large tiles get enough parallelism; a couple of num_stages each. + tile = block_t * block_d + if tile <= 256: + warps_opts = (2, 4) + elif tile <= 4096: + warps_opts = (4, 8) + else: + warps_opts = (8,) + for num_warps in warps_opts: + for num_stages in (2, 3, 4): + configs.append( + triton.Config( + {"BLOCK_T": block_t, "BLOCK_D": block_d}, + num_warps=num_warps, + num_stages=num_stages, + ) + ) + return configs + + +@triton.autotune(configs=_conv_prefix_autotune_configs(), key=["D", "W", "t_bucket"]) +@triton.jit +def _causal_conv1d_fwd_with_prefix_kernel( + x, # [T, D] + sconv_cache, # [max_slots, W-1, D] + safe_idx, # [num_seqs] int64 — cache slot per sequence + cache_mask, # [num_seqs, 1, 1] RAW metadata (bool 0/1); read only when not IS_DECODE + weight, # [D, W] + cu_seqlens, # [num_seqs + 1] int64 — packed sequence start offsets + seq_idx, # [T] int32 — which sequence each packed token belongs to + y, # [T, D] — contiguous output + t_bucket, # AUTOTUNE-KEY ONLY (coarse token-count regime); never read in the body. + stride_x_t, + stride_x_d, + stride_cache_slot, + stride_cache_w, + stride_cache_d, + stride_cm, # cache_mask dim-0 (per-sequence) stride + stride_weight_d, + stride_weight_w, + stride_y_t, + stride_y_d, + T, + D, + USE_SILU: tl.constexpr, + USE_RESIDUAL: tl.constexpr, + IS_DECODE: tl.constexpr, + W: tl.constexpr, + BLOCK_T: tl.constexpr, + BLOCK_D: tl.constexpr, +): + """Depthwise causal conv1d over a packed [T, D] token stream, with the W-1 prefix + taps gathered directly from sconv_cache (no intermediate prefix tensor). + + For packed token t in sequence s (bos = cu_seqlens[s], slot = safe_idx[s]) and + tap iw in 0..W-1: shifted = t - (W-1) + iw + shifted >= bos (and < T) -> tap = x[shifted, d] + shifted < bos, pp = shifted-bos+(W-1) in [0, W-1) + -> tap = sconv_cache[slot, pp, d] + (* cache_mask[s] when not IS_DECODE) + else -> tap = 0 + out[t, d] = act(sum_iw tap*weight[d, iw]) [+ x[t, d] if residual], fp32 accumulate. + + MEMORY-BOUND: this conv is dominated by HBM x traffic. The W taps for a token tile + read overlapping x rows (positions t0-(W-1) .. t0+BLOCK_T-1). We warm the whole + window with one streaming load and pin it in L2 across the W taps via + `eviction_policy="evict_last"` on the per-tap x loads, so the W overlapping reads + hit L2 instead of HBM. The current-token x (tap iw=W-1) is loaded once into + `x_cur` and reused for both that tap and the residual add (no duplicate load). + Only the boundary prefix taps touch sconv_cache. + + IS_DECODE (constexpr): when True, the prefix-mask multiply is omitted entirely + (the decode mask is all-ones, so the multiply was a bit-exact no-op). + """ + t_off = tl.program_id(0) * BLOCK_T + tl.arange(0, BLOCK_T) + d_off = tl.program_id(1) * BLOCK_D + tl.arange(0, BLOCK_D) + t_mask = t_off < T + d_mask = d_off < D + td_mask = t_mask[:, None] & d_mask[None, :] + + # Per-token sequence id, sequence start, and cache slot. + si = tl.load(seq_idx + t_off, mask=t_mask, other=0).to(tl.int64) + bos = tl.load(cu_seqlens + si, mask=t_mask, other=0).to(tl.int64) + slot = tl.load(safe_idx + si, mask=t_mask, other=0).to(tl.int64) + + # Per-token prefix mask (extend only). Loaded from the RAW [num_seqs,1,1] bool + # metadata (indexed on dim0 by si) and cast IN-KERNEL to x's dtype — bool 0/1 -> + # bf16 0.0/1.0, bit-identical to the old host-side `cache_mask.to(x.dtype)` cast, + # but with one fewer kernel launch. Under IS_DECODE this load is constexpr-pruned. + if not IS_DECODE: + m_val = tl.load(cache_mask + si * stride_cm, mask=t_mask, other=0).to( + x.dtype.element_ty + ) + + x_row_base = x + d_off[None, :] * stride_x_d # [1, BLOCK_D] partial + weight_base = weight + d_off * stride_weight_d # [BLOCK_D] + acc = tl.zeros([BLOCK_T, BLOCK_D], dtype=tl.float32) + + # Current-token x (tap iw = W-1, always in-sequence for valid t): load ONCE, + # reused for the last tap and the residual add. + x_cur = tl.load( + x_row_base + t_off[:, None] * stride_x_t, + mask=td_mask, + other=0, + ) + + for iw in tl.static_range(W): + shifted = t_off - (W - 1) + iw # [BLOCK_T] + + if iw == W - 1: + # Current token: in_x is always true for valid t (shifted == t_off). + x_val = x_cur + else: + # Earlier in-sequence x rows. These overlap heavily across the tile and + # across taps; evict_last keeps them resident in L2 so the W-1 history + # taps coalesce onto cached lines instead of re-streaming from HBM. + in_x = (shifted >= bos) & (shifted < T) + x_val = tl.load( + x_row_base + shifted[:, None] * stride_x_t, + mask=in_x[:, None] & d_mask[None, :], + other=0, + eviction_policy="evict_last", + ) + + # prefix tap (fused gather): positions before bos, pp in [0, W-1). + # (The last tap, iw=W-1, never hits the prefix: shifted==t_off>=bos.) + if iw == W - 1: + tap = x_val.to(tl.float32) + else: + prefix_pos = shifted - bos + (W - 1) + in_prefix = (shifted < bos) & (prefix_pos >= 0) + p_val = tl.load( + sconv_cache + + slot[:, None] * stride_cache_slot + + prefix_pos[:, None] * stride_cache_w + + d_off[None, :] * stride_cache_d, + mask=in_prefix[:, None] & d_mask[None, :], + other=0, + ) + if not IS_DECODE: + # bf16 mul by the 0/1 mask == pre-kernel `sconv_cache[safe_idx]*cache_mask`. + p_val = p_val * m_val[:, None] + # bf16 add (in_x / in_prefix mutually exclusive => one operand is 0), then + # cast to fp32 — bit-identical to the v3 Helion `(x_val + p_val).to(f32)`. + tap = (x_val + p_val).to(tl.float32) + + w_val = tl.load(weight_base + iw * stride_weight_w, mask=d_mask, other=0).to( + tl.float32 + ) + acc += tap * w_val[None, :] + + if USE_SILU: + acc = acc * tl.sigmoid(acc) + + if USE_RESIDUAL: + acc += x_cur.to(tl.float32) # reuse the once-loaded current-token x + + tl.store( + y + t_off[:, None] * stride_y_t + d_off[None, :] * stride_y_d, + acc.to(y.dtype.element_ty), + mask=td_mask, + ) + + +# todo(horace): Shift this to be precomputed data +def _seq_idx_from_cu_seqlens(cu_seqlens: torch.Tensor, T: int) -> torch.Tensor: + """Compute seq_idx from cu_seqlens: for each position, which sequence it belongs to.""" + t = torch.arange(T, dtype=torch.int64, device=cu_seqlens.device) + # Clamp to [0, num_seqs-1] to prevent OOB when cu_seqlens doesn't span all T + # tokens (e.g. during CUDA graph capture warmup with dummy zero-length sequences). + num_seqs = cu_seqlens.shape[0] - 1 + return ( + (torch.searchsorted(cu_seqlens, t, side="right") - 1) + .clamp(max=num_seqs - 1) + .to(torch.int32) + ) + + +@triton.jit +def _fused_decode_metadata_kernel( + cache_indices_ptr, # [B] int + query_start_loc_ptr, # [B+1] int32 out + has_initial_state_ptr, # [B] bool out + cache_mask_ptr, # [B] bool out (callers view it [B,1,1]) + safe_idx_ptr, # [B] int64 out + cu_ptr, # [B+1] int64 out + si_ptr, # [B] int32 out + B, + BLOCK: tl.constexpr, +): + """All decode sconv metadata in one launch (see fused_decode_sconv_metadata). + + Decode invariants baked in: every token is its own length-1 sequence + (query_start_loc = cu = arange, si = arange) and always has initial state + (has_initial_state = ones), so cache_mask reduces to cache_indices != PAD. + """ + offs = tl.program_id(0) * BLOCK + tl.arange(0, BLOCK) + mask_b1 = offs < B + 1 + mask_b = offs < B + tl.store(query_start_loc_ptr + offs, offs.to(tl.int32), mask=mask_b1) + tl.store(cu_ptr + offs, offs.to(tl.int64), mask=mask_b1) + tl.store(si_ptr + offs, offs.to(tl.int32), mask=mask_b) + ones = tl.full([BLOCK], 1, tl.int1) + tl.store(has_initial_state_ptr + offs, ones, mask=mask_b) + ci = tl.load(cache_indices_ptr + offs, mask=mask_b, other=-1) + tl.store(cache_mask_ptr + offs, ci != -1, mask=mask_b) # PAD_SLOT_ID = -1 + tl.store(safe_idx_ptr + offs, tl.maximum(ci, 0).to(tl.int64), mask=mask_b) + + +def fused_decode_sconv_metadata( + B: int, cache_indices: torch.Tensor +) -> tuple[torch.Tensor, torch.Tensor, SconvDecodeMetadata]: + """Single-launch replacement for the decode metadata prep: the two arange calls, + ones, `!= PAD`, `&`, `clamp` and `.long()` that + ``precompute_helion_decode_metadata`` (+ its callers) issued as ~7 tiny + elementwise kernels. Returns + ``(query_start_loc, has_initial_state, SconvDecodeMetadata)`` with tensors + bit-identical to the unfused path. + """ + assert cache_indices.shape[0] == B and cache_indices.stride(0) == 1 + device = cache_indices.device + query_start_loc = torch.empty(B + 1, dtype=torch.int32, device=device) + has_initial_state = torch.empty(B, dtype=torch.bool, device=device) + cache_mask = torch.empty((B, 1, 1), dtype=torch.bool, device=device) + safe_idx = torch.empty(B, dtype=torch.int64, device=device) + cu = torch.empty(B + 1, dtype=torch.int64, device=device) + si = torch.empty(B, dtype=torch.int32, device=device) + BLOCK = 1024 + _fused_decode_metadata_kernel[(triton.cdiv(B + 1, BLOCK),)]( + cache_indices, + query_start_loc, + has_initial_state, + cache_mask, + safe_idx, + cu, + si, + B, + BLOCK=BLOCK, + ) + return ( + query_start_loc, + has_initial_state, + SconvDecodeMetadata(cache_mask=cache_mask, safe_idx=safe_idx, cu=cu, si=si), + ) + + +def precompute_helion_decode_metadata( + B: int, + W: int, + cache_indices: torch.Tensor, + has_initial_state: torch.Tensor, +) -> SconvDecodeMetadata: + """Precompute metadata for the helion decode path. Call once, reuse across layers.""" + device = cache_indices.device + valid = cache_indices != PAD_SLOT_ID + cache_mask = (has_initial_state & valid)[:, None, None] # [B, 1, 1] + safe_idx = cache_indices.clamp(min=0).long() + # Each sequence has exactly 1 token in the packed [1, B, D] layout + cu = torch.arange(B + 1, dtype=torch.int64, device=device) + si = torch.arange(B, dtype=torch.int32, device=device) + return SconvDecodeMetadata( + cache_mask=cache_mask, + safe_idx=safe_idx, + cu=cu, + si=si, + ) + + +# has_initial_state variants for the fused extend metadata kernel. +HIS_ZEROS = 0 # boundary-KV draft extend: force conv to run fresh +HIS_PREFIX = 1 # extend_prefix_lens > 0 +HIS_SEQ_MINUS_EXT = 2 # (seq_lens[:B] - extend_seq_lens) > 0 (draft_extend_v2 capture) +HIS_ONES = 3 # target_verify: always has initial state + +# The single-tile local cumsum bounds the fused path; larger batches fall back +# to the unfused op sequence. +_FUSED_EXTEND_MAX_B = 1023 + + +@triton.jit +def _fused_extend_metadata_kernel( + cache_indices_ptr, # [B] int + extend_seq_lens_ptr, # [B]; unused when IS_VERIFY + his_src_ptr, # [>=B]: prefix_lens (HIS_PREFIX) / seq_lens (HIS_SEQ_MINUS_EXT) + query_start_loc_ptr, # [B+1] int32 out + has_initial_state_ptr, # [B] bool out + cache_mask_ptr, # [B] bool out (callers view it [B,1,1]) + safe_idx_ptr, # [B] int64 out + cu_ptr, # [B+1] int64 out + si_ptr, # [T] int32 out + B, + T, + draft_token_num, # verify only + IS_VERIFY: tl.constexpr, + HIS_MODE: tl.constexpr, + BLOCK_B: tl.constexpr, # pow2 >= B+1 + BLOCK_T: tl.constexpr, +): + """All extend sconv metadata in one launch (see fused_extend_sconv_metadata). + + Grid is (1 + cdiv(T, BLOCK_T),): program 0 writes the [B]-sized outputs, + programs 1.. fill their si tile. There is no cross-program dependency: + every program rebuilds cu from extend_seq_lens with a local single-tile + cumsum (B is small), so no barrier or second launch is needed. + """ + pid = tl.program_id(0) + offs_b = tl.arange(0, BLOCK_B) + mask_b = offs_b < B + mask_b1 = offs_b < B + 1 + + if IS_VERIFY: + # Uniform draft_token_num tokens per request: cu is a strided arange. + cu_local = offs_b.to(tl.int64) * draft_token_num + else: + # cu_local[i] = sum(extend_seq_lens[:i]); inclusive cumsum of the + # one-right-shifted lens gives the exclusive prefix sum with cu[0]=0. + lens = tl.load( + extend_seq_lens_ptr + offs_b - 1, + mask=mask_b1 & (offs_b > 0), + other=0, + ).to(tl.int64) + cu_local = tl.cumsum(lens, axis=0) + + if pid == 0: + tl.store(query_start_loc_ptr + offs_b, cu_local.to(tl.int32), mask=mask_b1) + tl.store(cu_ptr + offs_b, cu_local, mask=mask_b1) + if HIS_MODE == 0: # HIS_ZEROS + his = offs_b < 0 + elif HIS_MODE == 1: # HIS_PREFIX + his = tl.load(his_src_ptr + offs_b, mask=mask_b, other=0).to(tl.int64) > 0 + elif HIS_MODE == 2: # HIS_SEQ_MINUS_EXT + seq = tl.load(his_src_ptr + offs_b, mask=mask_b, other=0).to(tl.int64) + ext = tl.load(extend_seq_lens_ptr + offs_b, mask=mask_b, other=0).to( + tl.int64 + ) + his = (seq - ext) > 0 + else: # HIS_ONES + his = offs_b >= 0 + tl.store(has_initial_state_ptr + offs_b, his, mask=mask_b) + ci = tl.load(cache_indices_ptr + offs_b, mask=mask_b, other=-1) + tl.store(cache_mask_ptr + offs_b, his & (ci != -1), mask=mask_b) # PAD = -1 + tl.store(safe_idx_ptr + offs_b, tl.maximum(ci, 0).to(tl.int64), mask=mask_b) + else: + offs_t = (pid - 1) * BLOCK_T + tl.arange(0, BLOCK_T) + mask_t = offs_t < T + if IS_VERIFY: + si = tl.minimum(offs_t // draft_token_num, B - 1) + else: + # si[t] = #{s in 1..B : cu[s] <= t}, clamped to B-1 -- identical to + # searchsorted(cu, t, right) - 1 then clamp (cu[0] = 0 <= t always), + # including the last-index tie-break for zero-length sequences and + # the clamp when cu does not span T (dummy capture sequences). + bounds = tl.where(mask_b1 & (offs_b > 0), cu_local, 9223372036854775807) + cnt = tl.sum( + (offs_t[:, None].to(tl.int64) >= bounds[None, :]).to(tl.int32), axis=1 + ) + si = tl.minimum(cnt, B - 1) + tl.store(si_ptr + offs_t, si.to(tl.int32), mask=mask_t) + + +def fused_extend_sconv_metadata( + *, + B: int, + T: int, + cache_indices: torch.Tensor, + his_mode: int, + extend_seq_lens: torch.Tensor | None = None, + his_src: torch.Tensor | None = None, + draft_token_num: int | None = None, +) -> tuple[torch.Tensor, torch.Tensor, SconvExtendMetadata] | None: + """Single-launch replacement for the extend metadata prep: the + zeros + cumsum(+scan-init) + slice-copy + compare chain of + ``_prepare_extend_common_metadata`` plus the != PAD, &, clamp, long, to, + arange, searchsorted, clamp, int32 chain of + ``precompute_helion_extend_metadata`` (~10-14 tiny kernels, re-issued per + owning sconv instance -- and per de-tied draft step under draft_extend_v2). + Returns ``(query_start_loc, has_initial_state, SconvExtendMetadata)`` with + tensors bit-identical to the unfused path, or None when the shape falls + outside the fused kernel's single-tile bound (caller runs unfused). + + ``his_mode`` selects the has_initial_state source: HIS_ZEROS (boundary-KV + draft extend), HIS_PREFIX (``his_src`` = extend_prefix_lens), HIS_SEQ_MINUS_EXT + (``his_src`` = seq_lens), HIS_ONES (target_verify; ``draft_token_num`` set, + ``extend_seq_lens`` unused). + """ + if B > _FUSED_EXTEND_MAX_B or not cache_indices.is_cuda: + return None + assert cache_indices.shape[0] >= B and cache_indices.stride(0) == 1 + is_verify = his_mode == HIS_ONES + if is_verify: + assert draft_token_num is not None + else: + assert extend_seq_lens is not None and extend_seq_lens.stride(0) == 1 + device = cache_indices.device + query_start_loc = torch.empty(B + 1, dtype=torch.int32, device=device) + has_initial_state = torch.empty(B, dtype=torch.bool, device=device) + cache_mask = torch.empty((B, 1, 1), dtype=torch.bool, device=device) + safe_idx = torch.empty(B, dtype=torch.int64, device=device) + cu = torch.empty(B + 1, dtype=torch.int64, device=device) + si = torch.empty(T, dtype=torch.int32, device=device) + BLOCK_T = 256 + dummy = cache_indices # never dereferenced thanks to masks/constexpr + _fused_extend_metadata_kernel[(1 + triton.cdiv(T, BLOCK_T),)]( + cache_indices, + extend_seq_lens if extend_seq_lens is not None else dummy, + his_src if his_src is not None else dummy, + query_start_loc, + has_initial_state, + cache_mask, + safe_idx, + cu, + si, + B, + T, + draft_token_num if draft_token_num is not None else 1, + IS_VERIFY=is_verify, + HIS_MODE=his_mode, + BLOCK_B=triton.next_power_of_2(B + 1), + BLOCK_T=BLOCK_T, + ) + return ( + query_start_loc, + has_initial_state, + SconvExtendMetadata(cache_mask=cache_mask, safe_idx=safe_idx, cu=cu, si=si), + ) + + +def precompute_helion_extend_metadata( + B: int, + T: int, + W: int, + cache_indices: torch.Tensor, + has_initial_state: torch.Tensor, + query_start_loc: torch.Tensor, +) -> SconvExtendMetadata: + """Precompute metadata for the helion extend path. Call once, reuse across layers.""" + device = cache_indices.device + + valid = cache_indices != PAD_SLOT_ID + cache_mask = (has_initial_state & valid)[:, None, None] # [B, 1, 1] + safe_idx = cache_indices.clamp(min=0).long() + + cu = query_start_loc.to(torch.int64) + si = _seq_idx_from_cu_seqlens(cu, T) + + return SconvExtendMetadata( + cache_mask=cache_mask, + safe_idx=safe_idx, + cu=cu, + si=si, + ) + + +def causal_conv1d( + x: torch.Tensor, + weight: torch.Tensor, + sconv_cache: torch.Tensor, + cache_mask: torch.Tensor, + safe_idx: torch.Tensor, + cu: torch.Tensor, + si: torch.Tensor, + activation: str | None = None, + use_residual: bool = True, + is_decode: bool = False, +) -> torch.Tensor: + """Inference sconv with prefix loaded directly from cache. + + Metadata args (cache_mask, safe_idx, cu, si) should be precomputed once + per forward pass via precompute_helion_{decode,extend}_metadata and reused + across layers. + """ + if activation == "swish": + activation = "silu" + + T = x.shape[0] + + if T == 0: + return torch.empty_like(x) + + D = x.shape[1] + W = weight.shape[1] + use_silu = activation in ("silu", "swish") + + if ( + is_cuda() + and not is_decode + and x.dtype == torch.bfloat16 + and D % 2 == 0 + and x.stride(1) == 1 + ): + from sglang.jit_kernel.inkling_sconv import causal_conv1d as _cuda_causal_conv1d + + return _cuda_causal_conv1d( + x, + weight, + sconv_cache, + cache_mask, + safe_idx, + cu, + si, + activation=activation, + use_residual=use_residual, + is_decode=is_decode, + ) + + # The heavy prefix gather (sconv_cache[safe_idx], a [B, W-1, D] op) is folded INTO + # the Triton kernel, which reads sconv_cache + safe_idx directly — no intermediate + # [B, W-1, D] prefix tensor is materialised. + # + # The cache-mask multiply is handled per-path via the IS_DECODE constexpr, reading + # the RAW [B,1,1] bool `cache_mask` metadata directly (no host-side `.to(x.dtype)` + # cast launch, no placeholder): + # - extend (is_decode=False): the kernel loads cache_mask[si] and casts bool->x + # dtype in-kernel (0/1), multiplying the prefix tap by it — reproducing + # `prefix = sconv_cache[safe_idx] * cache_mask` (incl. has_initial_state=False / + # PAD slots whose mask is 0 => zeroed prefix). + # - decode (is_decode=True): the mask load+multiply is constexpr-pruned at JIT + # compile time; cache_mask is passed but never read. + + # Contiguous [T, D] output (strides (D, 1)) regardless of x's layout. + y = torch.empty(T, D, dtype=x.dtype, device=x.device) + + # Coarse token-count regime for the autotune key. D and W are CONSTANT across all + # model calls, so keying autotune on (D, W) alone tunes the kernel exactly ONCE at + # whatever shape the first call (warmup) happens to have, then reuses that single + # config for every T — a small-T warmup config is poison at large T. A coarse + # t_bucket in the key gives each token-count regime its own tuned config: + # small (<=8192), medium (<=65536), large (>65536). + t_bucket = 0 if T <= 8192 else (1 if T <= 65536 else 2) + + grid = lambda meta: ( + triton.cdiv(T, meta["BLOCK_T"]), + triton.cdiv(D, meta["BLOCK_D"]), + ) + _causal_conv1d_fwd_with_prefix_kernel[grid]( + x, + sconv_cache, + safe_idx, + cache_mask, + weight, + cu, + si, + y, + t_bucket, + x.stride(0), + x.stride(1), + sconv_cache.stride(0), + sconv_cache.stride(1), # stride_cache_w + sconv_cache.stride(2), # stride_cache_d + cache_mask.stride(0), + weight.stride(0), + weight.stride(1), + y.stride(0), + y.stride(1), + T, + D, + USE_SILU=use_silu, + USE_RESIDUAL=use_residual, + IS_DECODE=is_decode, + W=W, + ) + return y + + +@triton.jit +def _update_sconv_cache_kernel( + x, # [T, D] + sconv_cache, # [max_slots, W-1, D] + cache_indices, # [B] int32 + has_initial_state, # [B] bool + query_start_loc, # [B+1] int32 + stride_x_t, + stride_x_d, + stride_cache_slot, + stride_cache_w, + stride_cache_d, + B, + D, + W_MINUS_1: tl.constexpr, + BLOCK_B: tl.constexpr, + BLOCK_D: tl.constexpr, +): + """General update_sconv_cache: handles both decode (query_len=1) and extend (query_len>=1). + + The new conv state for a sequence is the last W-1 entries of the virtual stream + [ old_state (W-1, gated by has_state) ++ x_seq (query_len) ]. For output position w: + - x token (query_len >= W_MINUS_1 - w): x[end - W_MINUS_1 + w] + - shifted state (query_len < W_MINUS_1 - w): old_cache[w + query_len] * has_state + Positions are left untouched when the slot is PAD or the sequence is empty. + + The shift source old_cache[w + query_len] is selected with a static inner loop over + src_w (no data-dependent subscript on the W dim). RAW-safe: this writes positions in + increasing w, and the selected source w+query_len is always > w (query_len > 0), so a + position is only ever read before it is overwritten — matching the prior Helion kernel. + Triton replaces Helion to drop the AOT-autotune dependency (cf. causal_conv1d). + """ + pid_b = tl.program_id(0) + pid_d = tl.program_id(1) + b_off = pid_b * BLOCK_B + tl.arange(0, BLOCK_B) + d_off = pid_d * BLOCK_D + tl.arange(0, BLOCK_D) + b_mask = b_off < B + d_mask = d_off < D + bd_mask = b_mask[:, None] & d_mask[None, :] + + ci_raw = tl.load(cache_indices + b_off, mask=b_mask, other=-1) + ci = tl.maximum(ci_raw, 0).to(tl.int64) + end = tl.load(query_start_loc + b_off + 1, mask=b_mask, other=0).to(tl.int64) + start = tl.load(query_start_loc + b_off, mask=b_mask, other=0).to(tl.int64) + query_len = end - start + has_state = tl.load(has_initial_state + b_off, mask=b_mask, other=0) != 0 + # PAD_SLOT_ID = -1 (can't reference Python global in @jit) + valid = (ci_raw != -1) & (query_len > 0) + + cache_base = ( + sconv_cache + ci[:, None] * stride_cache_slot + d_off[None, :] * stride_cache_d + ) + # Only valid lanes (real, non-empty slots) write. Invalid lanes (PAD or + # query_len==0) write nothing — their clamped index aliases slot 0, and + # storing there would race a real lane's update. Distinct real lanes have + # distinct working slots, so valid writes never collide. + write_mask = bd_mask & valid[:, None] + + for w in tl.static_range(W_MINUS_1): + # Token from x (address clamped >= 0; only selected when gets_x, but the + # clamped index is always in [0, T) so the load is in-bounds regardless). + gets_x = query_len >= (W_MINUS_1 - w) + x_idx = tl.maximum(end - W_MINUS_1 + w, 0) + x_val = tl.load( + x + x_idx[:, None] * stride_x_t + d_off[None, :] * stride_x_d, + mask=bd_mask, + other=0, + ) + + # Shifted cache value: select old_cache[w + query_len] via static loop. + shift_val = tl.zeros([BLOCK_B, BLOCK_D], dtype=sconv_cache.dtype.element_ty) + for src_w in tl.static_range(W_MINUS_1): + match = query_len == (src_w - w) + src_val = tl.load( + cache_base + src_w * stride_cache_w, mask=bd_mask, other=0 + ) + shift_val = tl.where(match[:, None], src_val, shift_val) + shift_val = tl.where(has_state[:, None], shift_val, 0) + + new_val = tl.where(gets_x[:, None], x_val, shift_val) + tl.store(cache_base + w * stride_cache_w, new_val, mask=write_mask) + + +def update_sconv_cache( + x: torch.Tensor, + sconv_cache: torch.Tensor, + cache_indices: torch.Tensor, + has_initial_state: torch.Tensor, + query_start_loc: torch.Tensor, +) -> None: + B = cache_indices.shape[0] + D = x.shape[-1] + W_minus_1 = sconv_cache.shape[1] + + if ( + is_cuda() + and x.dtype == torch.bfloat16 + and D % 2 == 0 + and x.stride(-1) == 1 + and sconv_cache.stride(2) == 1 + ): + from sglang.jit_kernel.inkling_sconv import ( + update_sconv_cache as _cuda_update_sconv_cache, + ) + + _cuda_update_sconv_cache( + x, + sconv_cache, + cache_indices.to(torch.int32), + has_initial_state, + query_start_loc.to(torch.int32), + ) + return + + BLOCK_D = min(triton.next_power_of_2(D), 1024) + BLOCK_B = 1 # B is the (small) sequence count; one program per sequence + num_warps = 4 if BLOCK_D >= 512 else (2 if BLOCK_D >= 128 else 1) + grid = (triton.cdiv(B, BLOCK_B), triton.cdiv(D, BLOCK_D)) + _update_sconv_cache_kernel[grid]( + x, + sconv_cache, + cache_indices, + has_initial_state, + query_start_loc, + x.stride(0), + x.stride(1), + sconv_cache.stride(0), + sconv_cache.stride(1), # stride_cache_w + sconv_cache.stride(2), # stride_cache_d + B, + D, + W_MINUS_1=W_minus_1, + BLOCK_B=BLOCK_B, + BLOCK_D=BLOCK_D, + num_warps=num_warps, + ) + + +# --------------------------------------------------------------------------- +# Fused decode kernel: causal_conv1d + update_sconv_cache in one launch +# --------------------------------------------------------------------------- + + +@triton.jit +def _fused_causal_conv1d_update_decode_kernel( + x, # [T, D] + sconv_cache, # [max_slots, W-1, D] + cache_indices, # [B] int32 + cache_mask, # [B] bool (cache_indices != PAD_SLOT_ID) + weight, # [D, W] + y, # [T, D] – always contiguous + track_mask, # [B] bool – prefix-cache track mask (dummy if not DO_TRACK) + track_indices, # [B] int – persistent ping-pong slots (dummy if not DO_TRACK) + stride_x_t, + stride_x_d, + stride_y_t, + stride_y_d, + stride_cache_slot, + stride_cache_d, + stride_cache_w, + stride_weight_d, + stride_weight_w, + stride_track_idx, + T, + D, + USE_SILU: tl.constexpr, + USE_RESIDUAL: tl.constexpr, + BLOCK_T: tl.constexpr, + BLOCK_D: tl.constexpr, + W: tl.constexpr, + DO_TRACK: tl.constexpr, +): + """Fused depthwise causal conv1d + cache shift-update (+ optional track-copy) for decode. + + Decode invariant: each token t belongs to sequence t, with bos=t. + - iw = 0..W-2: always reads from sconv_cache (the conv state history) + - iw = W-1: always reads from x (the current token) + + General for any W. Uses tl.static_range for both conv and update. + Cache values are re-read for the update shift (trades W-1 extra loads + for generality and lower register pressure vs manual unroll). + + Track-copy fusion (DO_TRACK): for prefix caching, the post-update conv + window of sequence b must also be snapshotted into a persistent ping-pong + slot track_indices[b] when track_mask[b] is set. The post-update window is + already produced in-register by the shift below, so it is written to BOTH + the working slot cache_indices[b] and track_indices[b] in the same pass — + no separate copy_if_needed launch and no re-read of the row. + + This is race-free because working slots (cache_indices) and ping-pong track + slots (track_indices) are independent allocations from the same mamba pool, + hence pairwise-distinct across the batch: every slot written here is unique, + and the only slot read (cache_indices[b], for the conv taps) is written by + no other program. track_mask / track_indices are sized to the real batch and + read only where ci != -1 (the non-pad, real-token lanes) so cudagraph padding + lanes never index out of bounds. + """ + t_off = tl.program_id(0) * BLOCK_T + tl.arange(0, BLOCK_T) + d_off = tl.program_id(1) * BLOCK_D + tl.arange(0, BLOCK_D) + t_mask = t_off < T + d_mask = d_off < D + td_mask = t_mask[:, None] & d_mask[None, :] + + ci = tl.load(cache_indices + t_off, mask=t_mask, other=-1) + safe_idx = tl.maximum(ci, 0).to(tl.int64) + valid = ci != -1 # PAD_SLOT_ID = -1 (can't reference Python global in @jit) + cm = tl.load(cache_mask + t_off, mask=t_mask, other=0).to( + sconv_cache.dtype.element_ty + ) + + cache_base = ( + sconv_cache + + safe_idx[:, None] * stride_cache_slot + + d_off[None, :] * stride_cache_d + ) + weight_base = weight + d_off * stride_weight_d + + if DO_TRACK: + # Real (non-pad) lanes only: track tensors are sized to the real batch, + # while t_off may extend into cudagraph padding (ci == -1 there). + real = t_mask & valid + do_track = tl.load(track_mask + t_off, mask=real, other=0) != 0 + track_slot = tl.load( + track_indices + t_off * stride_track_idx, mask=real, other=0 + ).to(tl.int64) + track_base = ( + sconv_cache + + track_slot[:, None] * stride_cache_slot + + d_off[None, :] * stride_cache_d + ) + track_write_mask = td_mask & (valid & do_track)[:, None] + + # ---- CONV ---- + acc = tl.zeros([BLOCK_T, BLOCK_D], dtype=tl.float32) + + # Cache taps: iw = 0..W-2 + for iw in tl.static_range(W - 1): + pv = tl.load( + cache_base + iw * stride_cache_w, + mask=td_mask, + other=0, + eviction_policy="evict_last", + ) + w = tl.load(weight_base + iw * stride_weight_w, mask=d_mask, other=0).to( + tl.float32 + ) + acc += (pv * cm[:, None]).to(tl.float32) * w[None, :] + + # Current token: iw = W-1 + xv = tl.load( + x + t_off[:, None] * stride_x_t + d_off[None, :] * stride_x_d, + mask=td_mask, + other=0, + ) + xv_f32 = xv.to(tl.float32) + w_last = tl.load(weight_base + (W - 1) * stride_weight_w, mask=d_mask, other=0).to( + tl.float32 + ) + acc += xv_f32 * w_last[None, :] + + if USE_SILU: + acc = acc * tl.sigmoid(acc) + + if USE_RESIDUAL: + acc += xv_f32 + + tl.store( + y + t_off[:, None] * stride_y_t + d_off[None, :] * stride_y_d, + acc.to(xv.dtype), + mask=td_mask, + ) + + # ---- UPDATE: shift cache left, write new token ---- + # cache[slot, w, d] = cache[slot, w+1, d] * cm for w = 0..W-3 + # cache[slot, W-2, d] = xv + # When DO_TRACK, the same post-update window is also snapshotted into the + # persistent ping-pong slot track_indices[b] (prefix caching), in-register + # with no re-read and no separate copy_if_needed launch. + write_mask = td_mask & valid[:, None] + for iw in tl.static_range(W - 2): + # Re-read cache[slot, d, iw+1] for the shift + shifted = tl.load(cache_base + (iw + 1) * stride_cache_w, mask=td_mask, other=0) + new_val = shifted * cm[:, None] + tl.store(cache_base + iw * stride_cache_w, new_val, mask=write_mask) + if DO_TRACK: + tl.store(track_base + iw * stride_cache_w, new_val, mask=track_write_mask) + # Last position gets the new token + tl.store(cache_base + (W - 2) * stride_cache_w, xv, mask=write_mask) + if DO_TRACK: + tl.store(track_base + (W - 2) * stride_cache_w, xv, mask=track_write_mask) + + +def _select_fused_decode_config(T: int, D: int) -> tuple[int, int, int, int]: + """Select (BLOCK_T, BLOCK_D, num_warps, num_stages) for the fused decode kernel. + + Heuristic: keep BLOCK_T small (1-2) for decode since T=B is moderate. + Scale BLOCK_D so that grid has enough blocks to fill the GPU. + """ + if T <= 2048: + block_t = 2 + else: + # Round down to power of 2; Triton requires tl.arange size to be power of 2. + raw = min(T // 1024, 8) + block_t = 1 << (raw.bit_length() - 1) + + target_blocks = 1024 + t_blocks = max(T // block_t, 1) + needed_d_blocks = max(target_blocks // t_blocks, 1) + block_d = max(D // needed_d_blocks, 64) + block_d = 1 << max(min((block_d).bit_length() - 1, 9), 6) + + tile_elems = block_t * block_d + if tile_elems <= 128: + num_warps = 1 + elif tile_elems <= 512: + num_warps = 2 + else: + num_warps = 4 + + return block_t, block_d, num_warps, 3 + + +def fused_causal_conv1d_update_decode( + x: torch.Tensor, + weight: torch.Tensor, + sconv_cache: torch.Tensor, + cache_indices: torch.Tensor, + cache_mask: torch.Tensor, + activation: str | None = None, + use_residual: bool = True, + track_mask: torch.Tensor | None = None, + track_indices: torch.Tensor | None = None, +) -> torch.Tensor: + """Fused causal_conv1d + update_sconv_cache (+ optional prefix-cache track) for decode. + + Replaces the sequence: prefix construction -> conv -> cache update + (-> copy_if_needed) with a single kernel launch. + + When track_mask / track_indices are provided (prefix caching with the mamba + extra buffer), the post-update conv window is also snapshotted into the + persistent ping-pong slot track_indices[b] wherever track_mask[b] is set — + folding in the former separate `copy_if_needed` (`_track_conv_state_decode`) + launch. This is race-free: working slots and ping-pong track slots are + independent allocations from the same mamba pool and therefore pairwise + distinct across the batch (see kernel docstring). + """ + T, D = x.shape + W = weight.shape[1] + + if ( + is_cuda() + and x.dtype == torch.bfloat16 + and D % 2 == 0 + and x.stride(1) == 1 + and sconv_cache.stride(2) == 1 + ): + from sglang.jit_kernel.inkling_sconv import ( + fused_causal_conv1d_update_decode as _cuda_fused_decode, + ) + + _ti = track_indices.to(torch.int64) if track_indices is not None else None + return _cuda_fused_decode( + x, + weight, + sconv_cache, + cache_indices.to(torch.int32), + cache_mask, + activation=activation, + use_residual=use_residual, + track_mask=track_mask, + track_indices=_ti, + ) + # Always allocate contiguous output so callers receive a [T, D] tensor + # with strides (D, 1) regardless of whether x is a non-contiguous view. + y = torch.empty(T, D, dtype=x.dtype, device=x.device) + cm = cache_mask.view(-1) + use_silu = activation in ("silu", "swish") + + do_track = track_mask is not None + if do_track: + track_mask = track_mask.view(-1) + # Sentinel never dereferenced for the no-track path. + stride_track_idx = track_indices.stride(0) + else: + # Dummy tensors satisfy the kernel signature; never dereferenced (DO_TRACK=False). + track_mask = torch.empty(0, dtype=torch.bool, device=x.device) + track_indices = torch.empty(0, dtype=torch.int64, device=x.device) + stride_track_idx = 1 + + bt, bd, nw, ns = _select_fused_decode_config(T, D) + + grid = (triton.cdiv(T, bt), triton.cdiv(D, bd)) + _fused_causal_conv1d_update_decode_kernel[grid]( + x, + sconv_cache, + cache_indices, + cm, + weight, + y, + track_mask, + track_indices, + x.stride(0), + x.stride(1), + y.stride(0), + y.stride(1), + sconv_cache.stride(0), + sconv_cache.stride(2), # stride_cache_d + sconv_cache.stride(1), # stride_cache_w + weight.stride(0), + weight.stride(1), + stride_track_idx, + T, + D, + USE_SILU=use_silu, + USE_RESIDUAL=use_residual, + BLOCK_T=bt, + BLOCK_D=bd, + W=W, + DO_TRACK=do_track, + num_warps=nw, + num_stages=ns, + ) + return y + + +@triton.jit +def _save_intermediate_conv_windows_kernel( + sconv_cache_ptr, # [cache_size, W-1, D] + hidden_states_ptr, # [B, T_max, D] + cache_indices_ptr, # [B] int32 + out_ptr, # [max_bs, T, W-1, D] + cache_slot_stride, + cache_pos_stride, + hidden_b_stride, + hidden_t_stride, + out_b_stride, + out_t_stride, + out_w_stride, + D, + W_MINUS_1: tl.constexpr, + BLOCK_D: tl.constexpr, + PAD_SLOT_ID: tl.constexpr, +): + pid_b = tl.program_id(0) + pid_t = tl.program_id(1) + pid_d = tl.program_id(2) + + cache_idx = tl.load(cache_indices_ptr + pid_b).to(tl.int64) + + # PAD_SLOT_ID guard: skip padded batch slots. Mirrors + # fused_mamba_state_scatter_with_mask's early-exit. Avoids the OOB + # negative-stride load that would result from `cache_idx == PAD_SLOT_ID`. + if cache_idx == PAD_SLOT_ID: + return + + d_off = pid_d * BLOCK_D + tl.arange(0, BLOCK_D) + d_mask = d_off < D + + for w in tl.static_range(W_MINUS_1): + position = pid_t + 1 + w + if position < W_MINUS_1: + src_offset = ( + cache_idx * cache_slot_stride + position * cache_pos_stride + d_off + ) + val = tl.load(sconv_cache_ptr + src_offset, mask=d_mask, other=0.0) + else: + t_in_hidden = position - W_MINUS_1 + src_offset = ( + pid_b.to(tl.int64) * hidden_b_stride + + t_in_hidden.to(tl.int64) * hidden_t_stride + + d_off + ) + val = tl.load(hidden_states_ptr + src_offset, mask=d_mask, other=0.0) + + dst_offset = ( + pid_b.to(tl.int64) * out_b_stride + + pid_t.to(tl.int64) * out_t_stride + + w * out_w_stride + + d_off + ) + tl.store(out_ptr + dst_offset, val, mask=d_mask) + + +def save_intermediate_conv_windows( + sconv_cache: torch.Tensor, # [cache_size, W-1, D] + hidden_states: torch.Tensor, # [B, T_max, D] or [B*T_max, D] + cache_indices: torch.Tensor, # [B], int32 or int64 + intermediate_out: torch.Tensor, # [max_bs, T, W-1, D] + batch_size: int, + draft_token_num: int, +) -> None: + """Fused unfold-and-write into intermediate_out[:batch_size]. + + Equivalent to: + initial = sconv_cache[cache_indices[:batch_size]] + padded = torch.cat([initial, hidden_states[:batch_size, :draft_token_num]], dim=1) + windows = padded.unfold(1, W-1, 1)[:, 1:draft_token_num+1].transpose(-2,-1).contiguous() + intermediate_out[:batch_size] = windows + """ + if batch_size == 0 or draft_token_num == 0: + return + + W_minus_1, D = sconv_cache.shape[1], sconv_cache.shape[2] + if W_minus_1 == 0: + return + + if hidden_states.dim() == 2: + hidden_states = hidden_states.view(batch_size, -1, hidden_states.shape[-1]) + assert ( + hidden_states.dim() == 3 + ), f"unexpected hidden_states shape {hidden_states.shape}" + assert hidden_states.shape[0] == batch_size + assert hidden_states.shape[2] == D + assert intermediate_out.shape[1] == draft_token_num + assert intermediate_out.shape[2] == W_minus_1 + assert intermediate_out.shape[3] == D + + # kernel assumption + assert sconv_cache.stride(-1) == 1, "sconv_cache must be D-contiguous" + assert hidden_states.stride(-1) == 1, "hidden_states must be D-contiguous" + assert intermediate_out.stride(-1) == 1, "intermediate_out must be D-contiguous" + + cache_indices = cache_indices[:batch_size].to(torch.int32).contiguous() + + BLOCK_D = min(triton.next_power_of_2(D), 1024) + grid = (batch_size, draft_token_num, triton.cdiv(D, BLOCK_D)) + + _save_intermediate_conv_windows_kernel[grid]( + sconv_cache, + hidden_states, + cache_indices, + intermediate_out, + sconv_cache.stride(0), + sconv_cache.stride(1), + hidden_states.stride(0), + hidden_states.stride(1), + intermediate_out.stride(0), + intermediate_out.stride(1), + intermediate_out.stride(2), + D, + W_MINUS_1=W_minus_1, + BLOCK_D=BLOCK_D, + PAD_SLOT_ID=PAD_SLOT_ID, + ) diff --git a/python/sglang/srt/models/inkling_common/lora.py b/python/sglang/srt/models/inkling_common/lora.py new file mode 100644 index 000000000..92d9cea58 --- /dev/null +++ b/python/sglang/srt/models/inkling_common/lora.py @@ -0,0 +1,243 @@ +from __future__ import annotations + +import torch + +from sglang.srt.lora.backend.base_backend import BaseLoRABackend +from sglang.srt.models.inkling_common.dense_mlp import InklingBatchDenseMLP + + +class InklingBatchDenseMLPWithLoRA(InklingBatchDenseMLP): + """LoRA layer for Inkling's dense shared-expert sink.""" + + is_shared_fused_moe = True + + def initialize_lora(self, lora_backend: BaseLoRABackend) -> None: + problems = [] + if ( + lora_backend.max_loras_per_batch > 1 + and getattr(lora_backend, "name", None) != "triton" + ): + problems.append("multi-slot dense LoRA requires the Triton backend") + if not self._linearized_bf16_enabled: + problems.append("the shared sink does not use linearized BF16 weights") + if problems: + raise ValueError( + "InklingBatchDenseMLPWithLoRA is ineligible: " + "; ".join(problems) + ) + + self.lora_backend = lora_backend + self.set_lora = False + self.experts_shared_outer_loras = False + self.register_buffer("_w1_delta", None, persistent=False) + self.register_buffer("_a_cat", None, persistent=False) + self._lora_routing_cache = {} + lora_backend.is_moe_lora = True + + def set_lora_info( + self, + gate_up_lora_a_weights: torch.Tensor, + gate_up_lora_b_weights: torch.Tensor, + down_lora_a_weights: torch.Tensor, + down_lora_b_weights: torch.Tensor, + ) -> None: + tensors = ( + gate_up_lora_a_weights, + gate_up_lora_b_weights, + down_lora_a_weights, + down_lora_b_weights, + ) + if any(weight.ndim != 4 for weight in tensors): + raise ValueError("Inkling shared-sink LoRA requires four 4D MoE buffers") + gate_outer = gate_up_lora_a_weights.shape[1] + down_outer = down_lora_b_weights.shape[1] + valid_outer_dims = (1, self.n_shared_experts) + if gate_outer not in valid_outer_dims or down_outer not in valid_outer_dims: + raise ValueError( + "Inkling shared-sink LoRA outer factors must have expert dimension " + f"1 or {self.n_shared_experts}" + ) + if gate_outer != down_outer: + raise ValueError( + "Inkling shared-sink gate-up A and down B must use the same " + "expert layout" + ) + if ( + gate_up_lora_b_weights.shape[1] != self.n_shared_experts + or down_lora_a_weights.shape[1] != self.n_shared_experts + ): + raise ValueError("Inkling shared-sink LoRA expert count does not match") + + max_rank = gate_up_lora_b_weights.shape[-1] + if ( + gate_up_lora_a_weights.shape[2] != 2 * max_rank + or down_lora_a_weights.shape[2] != max_rank + or down_lora_b_weights.shape[-1] != max_rank + ): + raise ValueError("Inkling shared-sink LoRA rank dimensions do not match") + + self.set_lora = True + self.gate_up_lora_a_weights = gate_up_lora_a_weights + self.gate_up_lora_b_weights = gate_up_lora_b_weights + self.down_lora_a_weights = down_lora_a_weights + self.down_lora_b_weights = down_lora_b_weights + self.experts_shared_outer_loras = gate_outer == 1 + self._allocate_lora_operands() + self._refresh_lora_operands() + + def _allocate_lora_operands(self) -> None: + if not self.experts_shared_outer_loras: + self._w1_delta = None + self._a_cat = None + return + slots, n, two_f, rank = self.gate_up_lora_b_weights.shape + _, _, _, f = self.down_lora_a_weights.shape + expected_gate_up = (slots, n * two_f, 2 * rank) + expected_down = (slots, rank, n * f) + if self._w1_delta is None: + self._w1_delta = self.gate_up_lora_b_weights.new_empty(expected_gate_up) + self._a_cat = self.down_lora_a_weights.new_empty(expected_down) + return + if ( + tuple(self._w1_delta.shape) != expected_gate_up + or tuple(self._a_cat.shape) != expected_down + ): + raise RuntimeError( + "Shared-sink LoRA pool shape changed after initialization: " + f"gate-up {tuple(self._w1_delta.shape)} -> {expected_gate_up}, " + f"down-A {tuple(self._a_cat.shape)} -> {expected_down}" + ) + + def on_lora_slots_updated(self, slot_ids: set[int] | None) -> None: + self._refresh_lora_operands(slot_ids) + + def _refresh_lora_operands(self, slot_ids: set[int] | None = None) -> None: + if not self.set_lora or self._w1_delta is None or self._a_cat is None: + return + b_gate_up = self.gate_up_lora_b_weights + a_down = self.down_lora_a_weights + slots, n, two_f, rank = b_gate_up.shape + f = two_f // 2 + if slot_ids is None: + slot_ids = set(range(slots)) + elif any(slot < 0 or slot >= slots for slot in slot_ids): + raise IndexError(f"Shared-sink LoRA slot out of range: {sorted(slot_ids)}") + with torch.no_grad(): + gate_up = self._w1_delta.view(slots, n, f, 2, 2 * rank) + a_cat = self._a_cat.view(slots, rank, n, a_down.shape[3]) + for slot in slot_ids: + gate_up[slot].zero_() + gate_up[slot, :, :, 0, :rank].copy_(b_gate_up[slot, :, :f, :]) + gate_up[slot, :, :, 1, rank:].copy_(b_gate_up[slot, :, f:, :]) + a_cat[slot].copy_(a_down[slot].permute(1, 0, 2)) + + def slice_moe_lora_a_weights( + self, + weights: torch.Tensor | dict[int, torch.Tensor], + tp_rank: int, + target_module: str, + ) -> torch.Tensor | dict[int, torch.Tensor]: + if isinstance(weights, torch.Tensor) and weights.dim() == 2: + if target_module == "gate_up_proj_moe": + weights = weights.unsqueeze(0) + else: + rank, flat_intermediate = weights.shape + if flat_intermediate % self.n_shared_experts != 0: + raise ValueError( + "Shared-sink down LoRA-A width must be divisible by " + f"{self.n_shared_experts}, got {flat_intermediate}" + ) + weights = ( + weights.view( + rank, + self.n_shared_experts, + flat_intermediate // self.n_shared_experts, + ) + .transpose(0, 1) + .contiguous() + ) + if self.moe_tp_size <= 1 or target_module != "down_proj_moe": + return weights + if isinstance(weights, dict): + return { + expert_id: self._slice_down_lora_a(weight, tp_rank) + for expert_id, weight in weights.items() + } + return self._slice_down_lora_a(weights, tp_rank) + + def _slice_down_lora_a(self, weights: torch.Tensor, tp_rank: int) -> torch.Tensor: + start = tp_rank * self.intermediate_size_per_partition + end = start + self.intermediate_size_per_partition + return weights[..., start:end].contiguous() + + def slice_moe_lora_b_weights( + self, + weights: torch.Tensor | dict[int, torch.Tensor], + tp_rank: int, + target_module: str, + ) -> torch.Tensor | dict[int, torch.Tensor]: + if isinstance(weights, torch.Tensor) and weights.dim() == 2: + if target_module == "down_proj_moe": + weights = weights.unsqueeze(0) + else: + flat_intermediate, rank = weights.shape + if flat_intermediate % self.n_shared_experts != 0: + raise ValueError( + "Shared-sink gate/up LoRA-B height must be divisible by " + f"{self.n_shared_experts}, got {flat_intermediate}" + ) + weights = weights.view( + self.n_shared_experts, + flat_intermediate // self.n_shared_experts, + rank, + ) + if self.moe_tp_size <= 1 or target_module != "gate_up_proj_moe": + return weights + if isinstance(weights, dict): + return { + expert_id: self._slice_gate_up_lora_b(weight, tp_rank) + for expert_id, weight in weights.items() + } + if weights.dim() == 3: + return torch.stack( + [ + self._slice_gate_up_lora_b(weights[i], tp_rank) + for i in range(weights.shape[0]) + ] + ) + return self._slice_gate_up_lora_b(weights, tp_rank) + + def _slice_gate_up_lora_b( + self, weights: torch.Tensor, tp_rank: int + ) -> torch.Tensor: + shard = self.intermediate_size_per_partition + start = tp_rank * shard + end = start + shard + full_intermediate = weights.shape[0] // 2 + gate = weights[start:end] + up = weights[full_intermediate + start : full_intermediate + end] + return torch.cat([gate, up], dim=0).contiguous() + + def _forward_bf16_linearized( + self, + x_td: torch.Tensor, + gammas_ts: torch.Tensor, + linearized_weights: tuple[torch.Tensor, torch.Tensor], + use_reduce_scatter: bool, + ) -> torch.Tensor: + if not self.set_lora: + return super()._forward_bf16_linearized( + x_td, + gammas_ts, + linearized_weights, + use_reduce_scatter, + ) + + from sglang.srt.lora.trtllm_lora_temp.inkling_dense import forward_with_lora + + return forward_with_lora( + self, + x_td, + gammas_ts, + linearized_weights, + use_reduce_scatter, + ) diff --git a/python/sglang/srt/models/inkling_common/moe.py b/python/sglang/srt/models/inkling_common/moe.py new file mode 100644 index 000000000..9746d9a5c --- /dev/null +++ b/python/sglang/srt/models/inkling_common/moe.py @@ -0,0 +1,1098 @@ +from __future__ import annotations + +from typing import Literal, cast + +import torch +import torch.nn.functional as F +import triton +import triton.language as tl +from torch import nn +from triton.language.extra import libdevice + +from sglang.jit_kernel.inkling_gate_topk_renorm import ( + ensure_gate_gemv_fused_scratch, + inkling_gate_gemv, + inkling_gate_gemv_fused, +) +from sglang.jit_kernel.utils import is_arch_support_pdl +from sglang.srt.configs.inkling import InklingModelConfig +from sglang.srt.distributed import ( + get_tensor_model_parallel_group, +) +from sglang.srt.environ import GateGemvMode, envs +from sglang.srt.layers.moe import get_moe_runner_backend +from sglang.srt.layers.moe.fused_moe_triton.layer import FusedMoE +from sglang.srt.layers.moe.moe_runner.triton_utils.gate_topk import gate_topk +from sglang.srt.layers.moe.moe_runner.triton_utils.inkling_moe import ( + FUSED_PREPROCESS_WIN_TOKENS, + compute_grouped_gemm_metadata, + fused_moe_preprocess, + get_src2dst, + grouped_gemm_triton, + post_reorder, + pre_reorder, + select_grouped_gemm_block_m, + silu_and_mul_helion, +) +from sglang.srt.layers.moe.moe_runner.triton_utils.sigmoid_gate_topk_renorm import ( + sigmoid_gate_topk_renorm, +) +from sglang.srt.layers.moe.topk import PackedTopKOutput, StandardTopKOutput +from sglang.srt.layers.moe.utils import RoutingMethodType +from sglang.srt.layers.quantization.base_config import QuantizationConfig +from sglang.srt.layers.quantization.fp4_utils import get_fp4_gemm_runner_backend +from sglang.srt.layers.quantization.unquant import UnquantizedFusedMoEMethod +from sglang.srt.model_executor.forward_batch_info import ForwardBatch +from sglang.srt.model_executor.runner_utils.capture_mode import get_is_capture_mode +from sglang.srt.models.inkling_common.dense_mlp import ( + InklingBatchDenseMLP, + InklingDenseMLP, +) +from sglang.srt.models.inkling_common.kernels.comm import ( + get_ar_buffer, + reduce_scatter_hidden, + stash_ar_shared, + symm_mem_all_reduce, +) +from sglang.srt.models.inkling_common.util import ( + bf16_routed_uses_stock_fused_moe, + lora_compatible_layout_enabled, + use_inkling_shared_fused_moe, +) +from sglang.srt.runtime_context import get_parallel +from sglang.srt.state_capturer.routed_experts import get_global_experts_capturer +from sglang.srt.utils import add_prefix, is_cuda, is_hip + +_FP32_GEMM_UPCAST = is_hip() + + +def _mm_fp32(a: torch.Tensor, b: torch.Tensor) -> torch.Tensor: + if _FP32_GEMM_UPCAST: + return torch.mm(a.float(), b.float()) + return torch.mm(a, b, out_dtype=torch.float32) + + +def _addmm_fp32(bias: torch.Tensor, a: torch.Tensor, b: torch.Tensor) -> torch.Tensor: + if _FP32_GEMM_UPCAST: + return torch.addmm(bias.float(), a.float(), b.float()) + return torch.addmm(bias, a, b, out_dtype=torch.float32) + + +_INKLING_FUSED_GATE_OUT_FEATURES = 258 +# Pad the expert dimension for aligned cuBLAS access. The loader initializes +# the padding once, avoiding a per-forward copy. +_INKLING_FUSED_GATE_OUT_FEATURES_PADDED = ( + (_INKLING_FUSED_GATE_OUT_FEATURES + 7) // 8 * 8 +) + +# Use the specialized GEMV kernel only for small batches at this hidden size. +_INKLING_GATE_GEMV_HIDDEN = 6144 +_GATE_GEMV_MAX_TOKENS = 4 + + +def _load_gate_weight_padded(param: torch.Tensor, loaded_weight: torch.Tensor) -> None: + """Copy the checkpoint rows into the pre-padded gate weight; zero the tail. + + Reached only when shapes differ (param padded, checkpoint not); see + InklingForConditionalGeneration._load_regular_param. + """ + n = loaded_weight.shape[0] + param.data[:n].copy_(loaded_weight) + param.data[n:].zero_() + + +def inkling_fused_gate_linear_with_fp32_out( + input: torch.Tensor, weight: torch.Tensor +) -> torch.Tensor: + assert weight.ndim == 2, f"{weight.shape=}" + assert weight.size(0) == _INKLING_FUSED_GATE_OUT_FEATURES_PADDED, f"{weight.shape=}" + assert input.ndim == 2, f"{input.shape=}" + return _mm_fp32(input, weight.T)[:, :_INKLING_FUSED_GATE_OUT_FEATURES] + + +def linear_with_fp32_out( + input: torch.Tensor, weight: torch.Tensor, bias: torch.Tensor | None +) -> torch.Tensor: + leading_dims = list(input.shape[:-1]) + flat_input = input.flatten(0, -2) + + if bias is None: + out = _mm_fp32(flat_input, weight.T) + else: + out = _addmm_fp32(bias, flat_input, weight.T) + + out = out.view(*leading_dims, weight.shape[0]) + return out + + +def linear_with_pad(x: torch.Tensor, w: torch.Tensor, bias: torch.Tensor | None): + assert w.ndim == 2, f"{w.shape=}" + w_size0 = w.size(0) + pad_size = 8 - (w_size0 % 8) if w_size0 % 8 != 0 else 0 + if pad_size > 0: + w = torch.cat([w, w.new_zeros((pad_size, w.size(1)))], dim=0) + y = linear_with_fp32_out(x, w, bias) + if pad_size > 0: + y = y[..., :-pad_size] + return y + + +def _logsigmoid_normalize(logits: torch.Tensor) -> torch.Tensor: + log_probs = F.logsigmoid(logits) + return torch.exp(log_probs - torch.logsumexp(log_probs, dim=-1, keepdim=True)) + + +def _renorm_topk_logits( + logits_TG: torch.Tensor, + topk_indices_TK: torch.Tensor, + n_shared_experts: int, + gate_activation: str, +) -> torch.Tensor: + routed_logits = ( + logits_TG[..., :-n_shared_experts] if n_shared_experts > 0 else logits_TG + ) + # gate_topk now emits int32 ids (for the SRT MoE path); torch.gather requires + # int64, so widen here on this (CPU/non-sigmoid) fallback path. + topk_logits = routed_logits.gather(-1, topk_indices_TK.long()) + if n_shared_experts > 0: + shared_logits = logits_TG[..., -n_shared_experts:] + topk_logits = torch.cat([topk_logits, shared_logits], dim=-1) + if gate_activation == "sigmoid": + return _logsigmoid_normalize(topk_logits) + return topk_logits.softmax(dim=-1, dtype=torch.float32) + + +@triton.jit +def _inkling_compute_logsigmoid_norm(logits, mask_a): + abs_logits = tl.abs(logits) + min_logits = tl.minimum(logits, 0.0) + log_probs = min_logits - libdevice.log1p(libdevice.exp(-abs_logits)) + + max_log_probs = tl.max(log_probs, axis=1)[:, None] + exp_shifted = libdevice.exp(log_probs - max_log_probs) + sum_exp = tl.sum( + tl.where(mask_a[None, :], exp_shifted, 0.0), axis=1, keep_dims=True + ) + logsumexp = max_log_probs + libdevice.log(sum_exp) + return libdevice.exp(log_probs - logsumexp) + + +@triton.jit(do_not_specialize=["T", "route_scale"]) +def _renorm_topk_logits_fwd_kernel( + logits_ptr, + indices_ptr, + routed_weights_ptr, + shared_weights_ptr, + global_scale_ptr, + route_scale, + T, + G: tl.constexpr, + stride_logits_0, + K: tl.constexpr, + S: tl.constexpr, + A_POW2: tl.constexpr, + BLOCK_SIZE_T: tl.constexpr, +): + A: tl.constexpr = K + S + pid = tl.program_id(0).to(tl.int64) + + offs_t = pid * BLOCK_SIZE_T + tl.arange(0, BLOCK_SIZE_T) + mask_t = offs_t < T + offs_a = tl.arange(0, A_POW2) + mask_a = offs_a < A + + mask_k = mask_t[:, None] & (offs_a < K)[None, :] + indices = tl.load( + indices_ptr + offs_t[:, None] * K + offs_a[None, :], mask=mask_k, other=0 + ) + routed_logits = tl.load( + logits_ptr + offs_t[:, None] * stride_logits_0 + indices, + mask=mask_k, + other=float("-inf"), + ).to(tl.float32) + + if S > 0: + offs_s = offs_a - K + mask_s = mask_t[:, None] & (offs_s[None, :] >= 0) & (offs_s[None, :] < S) + shared_logits = tl.load( + logits_ptr + offs_t[:, None] * stride_logits_0 + (G - S) + offs_s[None, :], + mask=mask_s, + other=float("-inf"), + ).to(tl.float32) + active_logits = tl.where((offs_a < K)[None, :], routed_logits, shared_logits) + else: + active_logits = routed_logits + + weights = _inkling_compute_logsigmoid_norm(active_logits, mask_a) + weights *= route_scale + weights *= tl.load(global_scale_ptr).to(weights.dtype) + + offs_tk = offs_t[:, None] * K + offs_a[None, :] + mask_tk = mask_t[:, None] & (offs_a < K)[None, :] + tl.store(routed_weights_ptr + offs_tk, weights, mask=mask_tk) + + if S > 0: + offs_s = offs_a - K + mask_ts = mask_t[:, None] & (offs_s[None, :] >= 0) & (offs_s[None, :] < S) + offs_ts = offs_t[:, None] * S + offs_s[None, :] + tl.store(shared_weights_ptr + offs_ts, weights, mask=mask_ts) + + +def renorm_topk_logits_scaled( + logits: torch.Tensor, + topk_indices: torch.Tensor, + n_shared_experts: int, + route_scale: float, + global_scale: torch.Tensor, +) -> tuple[torch.Tensor, torch.Tensor | None]: + if not logits.is_cuda: + weights = _renorm_topk_logits(logits, topk_indices, n_shared_experts, "sigmoid") + weights = weights * route_scale + weights = weights * global_scale + topk = topk_indices.shape[-1] + shared = weights[..., topk:].contiguous() if n_shared_experts > 0 else None + return weights[..., :topk].contiguous(), shared + + logits = logits.contiguous() + topk_indices = topk_indices.contiguous() + tokens, gate_experts = logits.shape + topk = topk_indices.shape[-1] + active = topk + n_shared_experts + active_pow2 = triton.next_power_of_2(active) + block_size_t = max(1, 1024 // active_pow2) + routed_weights = torch.empty( + (tokens, topk), dtype=logits.dtype, device=logits.device + ) + shared_weights = ( + torch.empty( + (tokens, n_shared_experts), dtype=logits.dtype, device=logits.device + ) + if n_shared_experts > 0 + else None + ) + _renorm_topk_logits_fwd_kernel[(triton.cdiv(tokens, block_size_t),)]( + logits, + topk_indices, + routed_weights, + shared_weights, + global_scale, + route_scale, + tokens, + gate_experts, + logits.stride(0), + topk, + n_shared_experts, + active_pow2, + block_size_t, + ) + return routed_weights, shared_weights + + +class InklingGate(nn.Module): + def __init__( + self, + d_model: int, + n_routed_experts: int, + n_shared_experts: int, + experts_per_token: int, + route_scale: float, + layer_id: int, + prefix: str = "", + norm_after_topk: bool = True, + use_global_scale: bool = False, + use_gate_bias: bool = False, + gate_activation: Literal["sigmoid", "softmax"] = "sigmoid", + shared_expert_sink: bool = False, + ): + super().__init__() + self.n_routed_experts = n_routed_experts + self.n_shared_experts = n_shared_experts + self.n_total_experts = n_routed_experts + n_shared_experts + self.topk = experts_per_token + self.layer_id = layer_id + self.prefix = prefix + self.norm_after_topk = norm_after_topk + self.gate_activation = gate_activation + self.shared_expert_sink = shared_expert_sink + # The fused gate emits pre-packed topk only when the routed experts consume it + # (the SRT MoeRunner apply path). The unquantized forward_moe path needs standard + # topk tensors, so the owning InklingMoE flips this off for unquantized experts. + self.emit_packed_topk = True + + if use_global_scale: + self.global_scale = nn.Parameter( + torch.empty(1, dtype=torch.float32), requires_grad=False + ) + else: + self.global_scale = None + self.route_scale = route_scale + # Rows pre-padded to a multiple of 8 (see _INKLING_FUSED_GATE_OUT_FEATURES_PADDED); + # the loader fills the real rows. + padded_experts = (self.n_total_experts + 7) // 8 * 8 + self.weight = nn.Parameter( + torch.zeros(padded_experts, d_model), requires_grad=False + ) + self.weight.weight_loader = _load_gate_weight_padded + if use_gate_bias: + self.bias = nn.Parameter( + torch.empty(self.n_routed_experts, dtype=torch.float32), + requires_grad=False, + ) + else: + self.bias = None + # The FUSED gate mode uses persistent scratch buffers; they must exist + # before CUDA graph capture (with --skip-server-warmup nothing runs + # eagerly first, so allocating lazily would land in the capture pool). + if ( + envs.SGLANG_OPT_GATE_GEMV_MODE.get() >= GateGemvMode.FUSED + and torch.cuda.is_available() + and torch.version.hip is None + ): + ensure_gate_gemv_fused_scratch(torch.device("cuda")) + + def forward_fused( + self, x: torch.Tensor + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor | None, torch.Tensor | None]: + # Small decode batches use the specialized GEMV path. FUSED also runs + # the gate epilogue in the same launch. + # sigmoid_gate_topk_renorm sees the identical [tokens, 264]-padded slice + # either way, so the top-k path below is oblivious to the choice. + gemv_mode = envs.SGLANG_OPT_GATE_GEMV_MODE.get() + if ( + gemv_mode != GateGemvMode.OFF + and x.shape[0] <= _GATE_GEMV_MAX_TOKENS + and x.dtype == torch.bfloat16 + and x.is_contiguous() + and x.shape[-1] == _INKLING_GATE_GEMV_HIDDEN + and torch.version.hip is None + ): + if gemv_mode >= GateGemvMode.FUSED: + # Single launch: outputs are bitwise-identical to the pair + # (shared GEMV + epilogue code paths, asserted in tests). + return inkling_gate_gemv_fused( + x, + self.weight, + self.bias, + self.global_scale, + self.route_scale, + return_packed=self.emit_packed_topk, + enable_pdl=is_arch_support_pdl(), + ) + logits = inkling_gate_gemv(x, self.weight, enable_pdl=is_arch_support_pdl()) + else: + logits = inkling_fused_gate_linear_with_fp32_out(x, self.weight) + # Fused sigmoid[+bias] select-top-k + logsigmoid-renorm in one launch. + # Pre-packed topk is consumed only by the SRT MoeRunner apply path (quantized + # experts). Unquantized experts use forward_moe, which needs standard topk + # tensors; packed mode returns None for routed_weights/topk_indices and would + # crash there. InklingMoE sets emit_packed_topk from the experts' quant method. + return_packed_topk = self.emit_packed_topk + gate_output = sigmoid_gate_topk_renorm( + logits, + self.topk, + self.n_shared_experts, + self.route_scale, + self.global_scale, + self.bias, + return_packed_topk=return_packed_topk, + ) + routed_weights, topk_indices, shared_gammas, packed_topk_ids = gate_output + return routed_weights, topk_indices, shared_gammas, packed_topk_ids + + def forward( + self, + x: torch.Tensor, + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor | None, torch.Tensor | None]: + # R3 (rollout routing replay) needs the plain [T, K] topk indices captured on + # the standard path; the fused kernel's packed output never exposes them, so + # bypass the fused shortcut whenever an experts capturer is active. + if ( + get_global_experts_capturer() is None + and envs.SGLANG_OPT_USE_FUSED_GATE_TOPK.get() + and self.n_total_experts == _INKLING_FUSED_GATE_OUT_FEATURES + and self.gate_activation == "sigmoid" + and self.norm_after_topk + and self.global_scale is not None + and self.bias is not None + ): + return self.forward_fused(x) + + # self.weight is pre-padded; pass the real rows so scores stay [tokens, experts]. + scores = linear_with_pad(x, self.weight[: self.n_total_experts], None) + assert scores.ndim == 2, f"{scores.shape=} should be TE" + + logits = scores + if self.gate_activation == "sigmoid": + scores = scores.sigmoid() + else: + scores = scores.softmax(dim=-1, dtype=torch.float32) + routed_scores = ( + scores[..., : -self.n_shared_experts] + if self.n_shared_experts > 0 + else scores + ) + bias_for_topk = self.bias + routed_scores_for_topk = ( + routed_scores + bias_for_topk + if bias_for_topk is not None + else routed_scores + ) + _, topk_indices = gate_topk(routed_scores_for_topk, self.topk) + # R3 (rollout routing replay): feed the routed-expert selection to sglang's global + # experts capturer so --use-rollout-routing-replay can ship it back to the trainer, + # which replays the exact same routing during training. Inkling's gate uses its own + # gate_topk (not srt/layers/moe/topk.py), so the standard capture call is re-added here. + # NOTE: this inlines cap.capture() rather than going through topk.py's + # capture_routed_experts_if_allowed, so disable_routed_experts_capture_for_draft + # (which only rewires TopK modules) cannot opt a InklingGate out. Moot while Inkling MTP + # draft blocks are forced dense; give the gate an allow_capture flag if a draft + # ever carries MoE. + if (cap := get_global_experts_capturer()) is not None: + cap.capture(layer_id=self.layer_id, topk_indices=topk_indices) + if self.norm_after_topk: + if self.gate_activation == "sigmoid" and self.global_scale is not None: + routed_weights, shared_gammas = renorm_topk_logits_scaled( + logits, + topk_indices, + self.n_shared_experts, + self.route_scale, + self.global_scale, + ) + else: + routed_weights = _renorm_topk_logits( + logits, topk_indices, self.n_shared_experts, self.gate_activation + ) + if self.global_scale is not None: + routed_weights = ( + routed_weights * self.route_scale * self.global_scale + ) + else: + routed_weights = routed_weights * self.route_scale + if self.shared_expert_sink and self.n_shared_experts > 0: + shared_gammas = routed_weights[ + ..., -self.n_shared_experts : + ].contiguous() + routed_weights = routed_weights[..., : self.topk].contiguous() + else: + shared_gammas = None + else: + # int32 topk ids (from gate_topk) -> int64 for torch.gather. + routed_weights = routed_scores.gather(dim=-1, index=topk_indices.long()) + if self.global_scale is not None: + routed_weights = routed_weights * self.route_scale * self.global_scale + else: + routed_weights = routed_weights * self.route_scale + shared_gammas = None + + return routed_weights, topk_indices, shared_gammas, None + + +def make_forward_inputs_2d( + hidden_states: torch.Tensor, + topk_weights: torch.Tensor, + topk_ids: torch.Tensor, + w2_weight: torch.Tensor, +): + *outer, hidden_size = hidden_states.shape + *_outer, top_k = topk_ids.shape + *__outer, _top_k = topk_weights.shape + assert _outer == __outer, f"{topk_ids.shape=} {topk_weights.shape=}" + assert top_k == _top_k, f"{topk_ids.shape=} {topk_weights.shape=}" + + hidden_states = hidden_states.view(-1, hidden_size) + topk_weights = topk_weights.view(-1, top_k) + topk_ids = topk_ids.view(-1, top_k) + + assert hidden_states.is_contiguous() + assert topk_weights.is_contiguous() + assert topk_ids.is_contiguous() + num_experts, _, intermediate_size = w2_weight.shape + del outer, intermediate_size + + return hidden_states, topk_weights, topk_ids, top_k, num_experts + + +def run_moe_preprocess(topk_ids: torch.Tensor, num_experts: int) -> tuple[ + torch.Tensor, + torch.Tensor, + torch.Tensor, + torch.Tensor, + torch.Tensor, + torch.Tensor, + int, +]: + n = topk_ids.numel() + # Decode/verify band: the whole preprocess (cast + stable sort + src2dst + + # offsets + counts + block schedule, ~10 launches) collapses into one + # single-CTA kernel; outputs are bit-identical (see test_moe_preprocess). + if 0 < n <= FUSED_PREPROCESS_WIN_TOKENS: + outs = fused_moe_preprocess(topk_ids.view(-1), num_experts) + return (*outs, select_grouped_gemm_block_m(n)) + block_size_m = select_grouped_gemm_block_m(n) + topk_ids = topk_ids.to(torch.int16) + reorder_topk_ids, reorder_ids = torch.sort(topk_ids.view(-1), stable=True) + src2dst = get_src2dst(reorder_ids) + ( + num_tokens_per_expert, + expert_token_offs, + expert_block_offs, + expert_block_schedule, + ) = compute_grouped_gemm_metadata( + reorder_topk_ids, num_experts, block_size_m=block_size_m + ) + return ( + src2dst, + num_tokens_per_expert, + expert_token_offs, + expert_block_offs, + expert_block_schedule, + reorder_topk_ids, + block_size_m, + ) + + +def apply_grouped_bias(x: torch.Tensor, bias: torch.Tensor, reorder_ids: torch.Tensor): + return x + bias.index_select(index=reorder_ids.int(), dim=0) + + +def activation( + activation_type: str, + gateup_output: torch.Tensor, + topk_weights: torch.Tensor | None = None, + use_interleaved: bool = True, +): + if activation_type == "silu_and_mul": + assert ( + gateup_output.is_contiguous() + ), f"{gateup_output.shape=} {gateup_output.stride()=}" + assert gateup_output.ndim == 2, f"{gateup_output.shape=}" + out_dtype = None + if gateup_output.numel() == 0: + return gateup_output.new_zeros( + *gateup_output.shape[:-1], gateup_output.shape[-1] // 2, dtype=out_dtype + ) + + return silu_and_mul_helion( + gateup_output, topk_weights, out_dtype, use_interleaved=use_interleaved + ) + raise ValueError(f"Unsupported activation: {activation_type}") + + +def moe_tp_forward( + hidden_states: torch.Tensor, + topk_weights: torch.Tensor, + topk_ids: torch.Tensor, + w13_weight_E_2f_D: torch.Tensor, + w2_weight_EDf: torch.Tensor, + w13_bias_E_2f: torch.Tensor | None = None, + w2_bias_ED: torch.Tensor | None = None, + activation_type: str = "silu_and_mul", + use_interleaved: bool = True, +) -> torch.Tensor: + orig_shape: torch.Size = hidden_states.shape + hidden_states_TD, topk_weights_TK, topk_ids_TK, top_k, num_experts = ( + make_forward_inputs_2d(hidden_states, topk_weights, topk_ids, w2_weight_EDf) + ) + del hidden_states, topk_weights, topk_ids + + ( + src2dst, + num_tokens_per_expert, + expert_token_offs, + expert_block_offs, + expert_block_schedule, + reorder_topk_ids, + block_size_m, + ) = run_moe_preprocess(topk_ids_TK, num_experts) + + gateup_input_TK_D = pre_reorder(hidden_states_TD, src2dst, top_k) + + gateup_output_TK_2f = grouped_gemm_triton( + gateup_input_TK_D, + w13_weight_E_2f_D, + num_experts, + num_tokens_per_expert, + expert_token_offs, + expert_block_offs, + expert_block_schedule, + block_size_m=block_size_m, + ) + + if w13_bias_E_2f is not None: + gateup_output_TK_2f = apply_grouped_bias( + gateup_output_TK_2f, w13_bias_E_2f, reorder_topk_ids + ) + + down_input_TK_f = activation( + activation_type, gateup_output_TK_2f, use_interleaved=use_interleaved + ) + + down_output_TK_D = grouped_gemm_triton( + down_input_TK_f, + w2_weight_EDf, + num_experts, + num_tokens_per_expert, + expert_token_offs, + expert_block_offs, + expert_block_schedule, + block_size_m=block_size_m, + ) + + if w2_bias_ED is not None: + down_output_TK_D = apply_grouped_bias( + down_output_TK_D, w2_bias_ED, reorder_topk_ids + ) + + return post_reorder(down_output_TK_D, src2dst, topk_weights_TK).view(orig_shape) + + +class InklingSharedFusedMoE(FusedMoE): + """Sink shared experts (E = n_shared, top_k = n_shared) as a stock FusedMoE. + + Every token routes to all sink experts weighted by the gate's gammas, so + the fused kernel computes sum_j gamma_j * expert_j(x) -- the same math as + the bmm-based InklingBatchDenseMLP it replaces. Always shards over the full TP + group at EP=1 (see __init__), independent of the routed experts' EP setting. + """ + + # Duck-typed marker: srt/lora keys this module's buffers under *_shared_moe so + # a layer can carry both a routed and a sink FusedMoE without collision. + is_shared_fused_moe = True + + def __init__( + self, + n_shared_experts: int, + hidden_size: int, + intermediate_size: int, + layer_id: int, + prefix: str, + quant_config: QuantizationConfig | None, + inference_moe_w13_interleaved: bool, + ) -> None: + # FusedMoE.__init__ reads get_parallel() once and caches it on self, so + # scoping the override to just this call is sufficient for the module's lifetime. + with get_parallel().override( + moe_ep_size=1, + moe_ep_rank=0, + moe_tp_size=get_parallel().tp_size, + moe_tp_rank=get_parallel().tp_rank, + ): + super().__init__( + num_experts=n_shared_experts, + hidden_size=hidden_size, + intermediate_size=intermediate_size, + layer_id=layer_id, + top_k=n_shared_experts, + num_fused_shared_experts=0, + reduce_results=False, + quant_config=quant_config, + prefix=prefix, + activation="silu", + # TopK takes the gate's gamma-weighted topk verbatim, no re-routing. + routing_method_type=RoutingMethodType.TopK, + is_gated=True, + use_weight_loader_fused=True, + with_bias=False, + # InklingMoE feeds the same hidden_states to routed experts AND sink; an + # inplace runner (stock triton under --enable-lora bf16) corrupts the sink. + inplace=False, + ) + # Arms the fp4 w13 de-interleave in ModelOpt; bf16 is de-interleaved + # separately at load time in models/inkling.py. + self.inference_moe_w13_interleaved = inference_moe_w13_interleaved + + +def _build_inkling_shared_experts( + *, + n_shared_experts: int, + shared_expert_sink: bool, + shared_experts_size: int, + inference_moe_w13_interleaved: bool, + hidden_size: int, + intermediate_size: int, + layer_id: int, + prefix: str, + moe_tp_rank: int, + moe_tp_size: int, + quant_config: QuantizationConfig | None = None, +) -> nn.Module | None: + if n_shared_experts <= 0: + return None + if shared_expert_sink: + shared_prefix = add_prefix("shared_experts", prefix) + shared_sink_serves_fp4 = InklingBatchDenseMLP._resolve_fp4_strategy( + quant_config, shared_prefix + ).serves_fp4 + use_fused_shared = use_inkling_shared_fused_moe( + inference_moe_w13_interleaved=inference_moe_w13_interleaved, + shared_sink_serves_fp4=shared_sink_serves_fp4, + ) + if use_fused_shared: + # moe_tp_rank/moe_tp_size are derived internally; kept as args only + # for the legacy and non-sink InklingDenseMLP paths below. + return InklingSharedFusedMoE( + n_shared_experts=n_shared_experts, + hidden_size=hidden_size, + intermediate_size=intermediate_size, + layer_id=layer_id, + prefix=shared_prefix, + quant_config=quant_config, + inference_moe_w13_interleaved=inference_moe_w13_interleaved, + ) + dense_kwargs = dict( + n_shared_experts=n_shared_experts, + d_model=hidden_size, + shared_d_mlp=intermediate_size, + layer_id=layer_id, + prefix=shared_prefix, + quant_config=quant_config, + inference_moe_w13_interleaved=inference_moe_w13_interleaved, + tp_rank=moe_tp_rank, + tp_size=moe_tp_size, + tp_group=get_tensor_model_parallel_group(), + ) + return InklingBatchDenseMLP( + **dense_kwargs, + linearized_bf16=( + lora_compatible_layout_enabled() + or envs.SGLANG_OPT_LINEARIZED_SHARED_SINK.get() + ), + ) + return InklingDenseMLP( + hidden_size=hidden_size, + intermediate_size=n_shared_experts * shared_experts_size * intermediate_size, + use_global_scale=False, + fused=True, + layer_id=layer_id, + prefix=add_prefix("shared_experts", prefix), + quant_config=quant_config, + tp_rank=moe_tp_rank, + tp_size=moe_tp_size, + tp_group=get_tensor_model_parallel_group(), + ) + + +class InklingMoE(nn.Module): + def __init__( + self, + config: InklingModelConfig, + layer_id: int, + prefix: str = "", + quant_config: QuantizationConfig | None = None, + alt_stream: torch.cuda.Stream | None = None, + ): + super().__init__() + self.quant_config = quant_config + self.layer_id = layer_id + self.prefix = prefix + hidden_size = config.hidden_size + self.n_shared_experts = config.n_shared_experts + self.shared_expert_sink = config.shared_expert_sink + self.shared_experts_size = config.shared_experts_size + self.intermediate_dim = config.intermediate_size + self.n_routed_experts = config.n_routed_experts + self.experts_per_token = config.num_experts_per_tok + self.route_scale = config.route_scale + self.norm_after_topk = config.norm_after_topk + self.use_global_scale = config.use_global_scale + self.use_gate_bias = config.use_gate_bias + self.gate_activation = config.gate_activation + self.inference_moe_w13_interleaved = config.inference_moe_w13_interleaved + self.moe_ep_size = get_parallel().moe_ep_size + + self.gate = InklingGate( + d_model=hidden_size, + n_routed_experts=self.n_routed_experts, + n_shared_experts=self.n_shared_experts if self.shared_expert_sink else 0, + experts_per_token=self.experts_per_token, + route_scale=self.route_scale, + layer_id=layer_id, + prefix=prefix, + norm_after_topk=self.norm_after_topk, + use_global_scale=self.use_global_scale, + use_gate_bias=self.use_gate_bias, + gate_activation=self.gate_activation, + shared_expert_sink=self.shared_expert_sink, + ) + + # Routed experts: the standard SGLang FusedMoE (no shared experts here). The Inkling + # gate pre-routes (produces topk), so routing_method_type=TopK makes the runner + # honor the supplied topk rather than re-route. reduce_results=False because the + # single all_reduce in forward() covers routed + shared together. + self.experts = FusedMoE( + num_experts=self.n_routed_experts, + hidden_size=hidden_size, + intermediate_size=self.intermediate_dim, + layer_id=layer_id, + top_k=self.experts_per_token, + num_fused_shared_experts=0, + reduce_results=False, + quant_config=self.quant_config, + prefix=add_prefix("experts", prefix), + activation="silu", + routing_method_type=RoutingMethodType.TopK, + is_gated=True, + use_weight_loader_fused=True, + with_bias=False, + # See InklingSharedFusedMoE above: hidden_states is shared with the sink; + # inplace runners corrupt it. + inplace=False, + ) + # ModelOptNvFp4FusedMoEMethod.process_weights_after_loading de-interleaves the + # Inkling interleaved-w13 layout only when this attr is truthy; the stock FusedMoE + # never sets it, so set it explicitly (else w13 is not de-interleaved). + self.experts.inference_moe_w13_interleaved = ( + config.inference_moe_w13_interleaved + ) + + if ( + is_hip() + and ( + get_moe_runner_backend().is_aiter() + or get_moe_runner_backend().is_auto() + ) + and isinstance(self.experts.quant_method, UnquantizedFusedMoEMethod) + and not lora_compatible_layout_enabled() + and not bf16_routed_uses_stock_fused_moe(self.quant_config) + ): + self.experts._skip_aiter_moe_shuffle = True + + # Inkling shared expert is a separate dense MLP (gammas + sink) — NOT FusedMoE's + # num_fused_shared_experts mechanism. Owned here; it runs with reduce_scatter so + # it does not self-all-reduce (the final all_reduce below covers routed+shared). + self.shared_experts = _build_inkling_shared_experts( + n_shared_experts=self.n_shared_experts, + shared_expert_sink=self.shared_expert_sink, + shared_experts_size=self.shared_experts_size, + inference_moe_w13_interleaved=config.inference_moe_w13_interleaved, + hidden_size=hidden_size, + intermediate_size=self.intermediate_dim, + layer_id=layer_id, + prefix=prefix, + # Shared expert is a replicated dense MLP: shard over the full tp group, not + # moe_tp (the single full-tp all_reduce in forward() reconstructs it). + moe_tp_rank=get_parallel().tp_rank, + moe_tp_size=get_parallel().tp_size, + quant_config=self.quant_config, + ) + if isinstance(self.shared_experts, InklingSharedFusedMoE): + # Static "route to every sink expert" ids; int32 for the trtllm topk + # packer. Expanded per token in _forward_shared. Built lazily at first + # forward rather than as a registered buffer: sglang's own + # release/resume stashes and restores named_buffers, but RL + # trainer-side flows that rebuild or diff engine state re-ship only + # *parameters*, so a buffer allocated in the memory-saver pool comes + # back as uninitialized memory there, silently corrupting every + # shared-expert gather. A runtime allocation lives outside the pool + # in both flows; the hot path is a plain attribute read. + self._shared_topk_ids = None + self.alt_stream = ( + alt_stream if is_cuda() and self.shared_experts is not None else None + ) + # --enable-scattered-sconv: the output reduction becomes a hidden-dim + # reduce-scatter (the consumer mlp_sconv runs on the [T, H/P] shard). + from sglang.srt.runtime_context import get_server_args + + self.scattered_sconv = get_server_args().enable_scattered_sconv + # Fold the shared-expert partials into the custom AR kernels (or their + # stage-in copies) instead of a separate torch.add per MoE layer. + self._fused_ar_shared = envs.SGLANG_OPT_USE_INKLING_FUSED_AR_SHARED.get() + # The alt-stream fused sink races with the marlin routed GEMM on the shared + # input under allocator churn (NaN in MTP draft extend); clone breaks the + # shared storage. Scoped to the confirmed combo, others stay zero-copy. + lora_enabled = lora_compatible_layout_enabled() + self._clone_fused_sink_input = ( + self.shared_expert_sink + and self.shared_experts is not None + and ( + not isinstance(self.shared_experts, InklingBatchDenseMLP) + or lora_enabled + ) + and ( + get_fp4_gemm_runner_backend().is_marlin() + or get_moe_runner_backend().is_marlin() + ) + ) + + # Packed topk is for the stock SRT apply path: quantized layers, or bf16 on the + # trtllm_routed runner (unquantized ckpts only — a quantized ckpt's excluded + # bf16 layers resolve to the triton runner, which needs standard topk). + # moe_tp_forward and MoE-LoRA also need standard topk (LoRA packs internally). + self.gate.emit_packed_topk = not lora_compatible_layout_enabled() and ( + not isinstance(self.experts.quant_method, UnquantizedFusedMoEMethod) + or bf16_routed_uses_stock_fused_moe(self.quant_config) + ) + + def _forward_routed( + self, + hidden_states: torch.Tensor, + topk_weights: torch.Tensor, + topk_ids: torch.Tensor, + packed_topk_ids: torch.Tensor | None, + ) -> torch.Tensor: + if ( + isinstance(self.experts.quant_method, UnquantizedFusedMoEMethod) + and not lora_compatible_layout_enabled() + and not bf16_routed_uses_stock_fused_moe(self.quant_config) + ): + # Preserve the Inkling triton grouped-GEMM for the unquantized (bf16) path; the + # FusedMoE still owns the w13/w2 weights created by the unquantized method. + # Skipped under --enable-lora (moe_tp_forward would drop the routed LoRA + # delta) and for unquantized ckpts on trtllm_routed (its prep made w13/w2 + # 4-D block-shuffled): those run the stock forward (w13 [up||gate] at load). + if self.moe_ep_size > 1: + # moe_tp_forward has no local_expert_offset: remap the gate's global topk + # ids to this rank's local experts and zero the non-local weights. + local = self.n_routed_experts // self.moe_ep_size + lo = get_parallel().moe_ep_rank * local + is_local = (topk_ids >= lo) & (topk_ids < lo + local) + topk_weights = topk_weights * is_local.to(topk_weights.dtype) + topk_ids = torch.where( + is_local, topk_ids - lo, torch.zeros_like(topk_ids) + ) + return moe_tp_forward( + hidden_states, + topk_weights, + topk_ids, + cast(torch.Tensor, self.experts.w13_weight), + cast(torch.Tensor, self.experts.w2_weight), + None, + None, + "silu_and_mul", + use_interleaved=self.inference_moe_w13_interleaved, + ) + # NVFP4 routed experts: feed the gate's topk straight into FusedMoE.forward. For + # flashinfer_trtllm_routed the dispatcher is a pass-through, so a PackedTopKOutput + # reaches the same trtllm_fp4_block_scale_routed_moe kernel as before. + router_logits = hidden_states.new_empty((hidden_states.shape[0], 0)) + if packed_topk_ids is not None: + topk_output = PackedTopKOutput( + packed_topk_ids=packed_topk_ids, router_logits=router_logits + ) + else: + topk_output = StandardTopKOutput( + topk_weights=topk_weights, + topk_ids=topk_ids, + router_logits=router_logits, + ) + return self.experts(hidden_states, topk_output) + + def _forward_shared( + self, + hidden_states: torch.Tensor, + shared_gammas: torch.Tensor | None, + ) -> torch.Tensor | None: + if self.shared_experts is None: + return None + if self.shared_expert_sink: + if isinstance(self.shared_experts, InklingBatchDenseMLP): + assert shared_gammas is not None + return self.shared_experts( + hidden_states, gammas=shared_gammas, use_reduce_scatter=True + ) + assert shared_gammas is not None + # Every token selects all E=n_shared experts, weighted by the gate's + # gammas; ids/weights must be contiguous int32/fp32 (trtllm packer asserts both). + num_tokens = hidden_states.shape[0] + if ( + self._shared_topk_ids is None + or self._shared_topk_ids.device != hidden_states.device + ): + self._shared_topk_ids = torch.arange( + self.n_shared_experts, + dtype=torch.int32, + device=hidden_states.device, + ) + topk_output = StandardTopKOutput( + topk_weights=shared_gammas.to(torch.float32), + topk_ids=self._shared_topk_ids.unsqueeze(0) + .expand(num_tokens, -1) + .contiguous(), + router_logits=hidden_states.new_empty((num_tokens, 0)), + ) + return self.shared_experts(hidden_states, topk_output) + return self.shared_experts(hidden_states, use_reduce_scatter=True) + + def forward( + self, + x: torch.Tensor, + forward_batch: ForwardBatch | None = None, + reduce: bool = True, + ) -> torch.Tensor: + """Return local routed and shared partial sums when ``reduce=False``. + + The caller is then responsible for tensor-parallel reduction. + """ + del forward_batch + topk_weights, topk_ids, shared_gammas, packed_topk_ids = self.gate(x) + + allow_lora_overlap = True + if lora_compatible_layout_enabled(): + # ===== TO BE REFACTORED ==== + from sglang.srt.lora.trtllm_lora_temp.inkling_dense import ( + allow_inkling_moe_two_stream, + ) + + # ===== END TO BE REFACTORED ==== + + allow_lora_overlap = allow_inkling_moe_two_stream( + self.shared_experts, self.experts, x.shape[0] + ) + + use_two_stream = ( + self.alt_stream is not None + and x.is_cuda + and x.shape[0] > 0 + and envs.SGLANG_OPT_USE_INKLING_MULTI_STREAM_OVERLAP.get() + and get_is_capture_mode() + and allow_lora_overlap + ) + if use_two_stream: + current_stream = torch.cuda.current_stream() + self.alt_stream.wait_stream(current_stream) + with torch.cuda.stream(self.alt_stream): + sink_x = x.clone() if self._clone_fused_sink_input else x + shared_out = self._forward_shared(sink_x, shared_gammas) + out = self._forward_routed(x, topk_weights, topk_ids, packed_topk_ids) + current_stream.wait_stream(self.alt_stream) + else: + out = self._forward_routed(x, topk_weights, topk_ids, packed_topk_ids) + shared_out = self._forward_shared(x, shared_gammas) + + if not reduce: + if shared_out is not None: + if self._fused_ar_shared: + # Hand the shared partials to the consuming fused-AR call + # (register fold in the decode/verify kernels; pre-add in + # the scattered/extend consumers) -- deletes the separate + # {routed + shared} add on the fold paths. + stash_ar_shared(shared_out) + return out + tp = get_tensor_model_parallel_group() + buf = get_ar_buffer(tp, out.shape[0], out.shape[1], out.dtype) + if buf is not None: + torch.add(out, shared_out, out=buf) + return buf + return out + shared_out + return out + + tp = get_tensor_model_parallel_group() + if shared_out is not None: + if self._fused_ar_shared and not self.scattered_sconv: + # The AR dispatch folds in-kernel on the fold paths and pre-adds + # during its stage-in otherwise -- never worse than the explicit + # add below. + return symm_mem_all_reduce(out, tp, shared=shared_out) + buf = get_ar_buffer(tp, out.shape[0], out.shape[1], out.dtype) + if buf is not None: + torch.add(out, shared_out, out=buf) + if self.scattered_sconv: + # Scattered sconv: reduce + scatter hidden -> the [T, H/P] + # shard mlp_sconv consumes; all-gather happens after it. + return reduce_scatter_hidden(buf, tp, input_is_ar_buffer=True) + return symm_mem_all_reduce(buf, tp, input_is_ar_buffer=True) + out = out + shared_out + + if self.scattered_sconv: + return reduce_scatter_hidden(out, tp) + return symm_mem_all_reduce(out, tp) diff --git a/python/sglang/srt/models/inkling_common/norm.py b/python/sglang/srt/models/inkling_common/norm.py new file mode 100644 index 000000000..0fe41032c --- /dev/null +++ b/python/sglang/srt/models/inkling_common/norm.py @@ -0,0 +1,40 @@ +from __future__ import annotations + +import torch +import torch.nn.functional as F +from torch import nn + +try: + from sgl_kernel import rmsnorm +except ImportError: + rmsnorm = None + + +class RMSNorm(nn.Module): + def __init__(self, hidden_size: int, eps: float = 1e-6) -> None: + super().__init__() + self.weight = nn.Parameter(torch.ones(hidden_size)) + self.variance_epsilon = eps + self.hidden_size = hidden_size + + def forward(self, x: torch.Tensor) -> torch.Tensor: + if x.numel() == 0: + return x + if not x.is_cuda or rmsnorm is None: + return F.rms_norm( + x, (self.hidden_size,), self.weight, self.variance_epsilon + ) + + original_shape = x.shape + if original_shape[-1] != self.hidden_size: + raise RuntimeError( + f"RMSNorm expected hidden size {self.hidden_size}, got {original_shape[-1]}" + ) + x_2d = x.reshape(-1, self.hidden_size) + try: + y = rmsnorm(x_2d, self.weight.to(x_2d.dtype), self.variance_epsilon) + except (AttributeError, RuntimeError): + return F.rms_norm( + x, (self.hidden_size,), self.weight, self.variance_epsilon + ) + return y.view(original_shape) diff --git a/python/sglang/srt/models/inkling_common/quantization/__init__.py b/python/sglang/srt/models/inkling_common/quantization/__init__.py new file mode 100644 index 000000000..a65a34018 --- /dev/null +++ b/python/sglang/srt/models/inkling_common/quantization/__init__.py @@ -0,0 +1,17 @@ +from sglang.srt.models.inkling_common.quantization.config import ( + InklingModelOptNvfp4Config, + InklingQuantizationConfigBase, + get_quantization_config, +) +from sglang.srt.models.inkling_common.quantization.quant import ( + InklingMoEMethodBase, + InklingNvfp4MoEMethod, +) + +__all__ = [ + "InklingModelOptNvfp4Config", + "InklingQuantizationConfigBase", + "get_quantization_config", + "InklingMoEMethodBase", + "InklingNvfp4MoEMethod", +] diff --git a/python/sglang/srt/models/inkling_common/quantization/config.py b/python/sglang/srt/models/inkling_common/quantization/config.py new file mode 100644 index 000000000..bdd9542d7 --- /dev/null +++ b/python/sglang/srt/models/inkling_common/quantization/config.py @@ -0,0 +1,268 @@ +from __future__ import annotations + +import json +import logging +import os +from typing import Any + +import huggingface_hub +import torch +from huggingface_hub import snapshot_download + +from sglang.srt.configs.model_config import ModelConfig +from sglang.srt.layers.quantization.base_config import ( + QuantizationConfig, + QuantizeMethodBase, +) +from sglang.srt.layers.quantization.modelopt_quant import ( + ModelOptFp4Config, + ModelOptFp4LinearMethod, +) + +logger = logging.getLogger(__name__) + + +def _get_raw_quant_config( + model_config: ModelConfig, +) -> dict[str, Any] | None: + """ + pared-down version of `model_loader.weight_utils.get_quant_config` + + Returns just the loaded quant config. + """ + hf_quant_config = getattr(model_config.hf_config, "quantization_config", None) + # some vision model may keep quantization_config in their text_config + hf_text_config = getattr(model_config.hf_config, "text_config", None) + if hf_quant_config is None and hf_text_config is not None: + hf_quant_config = getattr(hf_text_config, "quantization_config", None) + if hf_quant_config is None: + # compressed-tensors uses a compressions_config + hf_quant_config = getattr(model_config.hf_config, "compression_config", None) + if hf_quant_config is not None: + return hf_quant_config + + model_name_or_path = model_config.model_path + + # A local path holds hf_quant_config.json directly; a remote HF repo id must + # first resolve its JSON configs from the hub (mirrors weight_utils.get_quant_config). + if os.path.isdir(model_name_or_path): + hf_folder = model_name_or_path + else: + hf_folder = snapshot_download( + model_name_or_path, + allow_patterns="*.json", + local_files_only=huggingface_hub.constants.HF_HUB_OFFLINE, + ) + + quant_config_file = os.path.join(hf_folder, "hf_quant_config.json") + if not os.path.exists(quant_config_file): + return None + with open(quant_config_file) as f: + config = json.load(f) + return config + + +def _map_exclude_modules(exclude_modules: list[str]) -> list[str]: + """Map checkpoint exclude_modules names to SGLang model prefixes.""" + new_exclude_modules = set() + + for module in exclude_modules: + if "audio" in module or "visual" in module: + new_exclude_modules.add(module) + continue + module = module.removeprefix("model.") + # Dense (non-MoE) MLP linears and the unembedding use different names in + # the sglang module tree; translate so exclusion matches their prefixes. + module = module.replace(".mlp.w13_dn", ".mlp.gate_up_proj").replace( + ".mlp.w2_md", ".mlp.down_proj" + ) + if module == "unembed": + module = "lm_head" + new_exclude_modules.add(module) + + return list(new_exclude_modules) + + +class InklingQuantizationConfigBase: + exclude_modules: list[str] + + @classmethod + def maybe_from_model_config( + cls, model_config: ModelConfig + ) -> InklingQuantizationConfigBase | None: + raise NotImplementedError() + + @staticmethod + def is_nvfp4(config: dict[str, Any]) -> bool: + weight_quant_cfg = config["modelopt_quant_config"]["quant_cfg"][ + "*weight_quantizer" + ] + return tuple(weight_quant_cfg["num_bits"]) == (2, 1) and tuple( + weight_quant_cfg["block_sizes"].get("scale_bits", []) + ) == (4, 3) + + def exclude_layer(self, prefix: str) -> bool: + if len(self.exclude_modules) == 0: + return False + return any( + module in prefix + or ( + prefix.startswith("language_model.") + and module in prefix.removeprefix("language_model.") + ) + for module in self.exclude_modules + ) + + +class InklingModelOptNvfp4Config(ModelOptFp4Config, InklingQuantizationConfigBase): + moe_ep_size: int + nvfp4_moe_backend: str + + def __init__( + self, + is_checkpoint_nvfp4_serialized: bool = False, + kv_cache_quant_algo: str | None = None, + group_size: int | None = None, + exclude_modules: list[str] | None = None, + packed_modules_mapping: dict[str, list[str]] | None = None, + # New Inkling args + scales_2d: bool = False, + moe_ep_size: int = 1, + nvfp4_moe_backend: str = "trtllm-routed", + ) -> None: + # unfortunately parent types are completely incorrect + super().__init__( + is_checkpoint_nvfp4_serialized=is_checkpoint_nvfp4_serialized, + kv_cache_quant_algo=kv_cache_quant_algo, # type: ignore[reportArgumentType] + group_size=group_size, # type: ignore[reportArgumentType] + exclude_modules=exclude_modules, # type: ignore[reportArgumentType] + packed_modules_mapping=packed_modules_mapping, + ) + if group_size != 16: + raise ValueError("Inkling only supports group size 16 for NVFP4") + if scales_2d: + self.dim1_group_size = group_size + else: + self.dim1_group_size = 1 + self.dim2_group_size = group_size + self.moe_ep_size = moe_ep_size + self.nvfp4_moe_backend = nvfp4_moe_backend + + @classmethod + def get_name(cls) -> str: + return "inkling_nvfp4" + + def get_quant_method( + self, layer: torch.nn.Module, prefix: str + ) -> QuantizeMethodBase | None: + """Map layers to Inkling-compatible quant methods.""" + + # hidden to avoid circular imports + from sglang.srt.layers.linear import LinearBase + from sglang.srt.layers.moe.fused_moe_triton.layer import FusedMoE + from sglang.srt.layers.quantization.modelopt_quant import ( + ModelOptNvFp4FusedMoEMethod, + ) + from sglang.srt.layers.quantization.unquant import ( + UnquantizedFusedMoEMethod, + UnquantizedLinearMethod, + ) + + if isinstance(layer, LinearBase): + if self.exclude_layer(prefix): + logger.debug(f"excluded linear layer for quantization: {prefix}") + return UnquantizedLinearMethod() + logger.debug(f"quantizing linear layer: {prefix}") + return ModelOptFp4LinearMethod(self) + elif isinstance(layer, FusedMoE): + if self.exclude_layer(prefix): + logger.debug(f"excluded fused MoE layer for quantization: {prefix}") + from sglang.srt.models.inkling_common.util import ( + shared_sink_uses_trtllm_bf16, + ) + + if layer.is_shared_fused_moe and shared_sink_uses_trtllm_bf16(): + # bf16 shared-expert sink on flashinfer_trtllm_routed: arm the + # stock trtllm bf16 path (this instance flag drives the trtllm + # weight prep, the FLASHINFER_TRTLLM_ROUTED runner, and the + # forward branch in UnquantizedFusedMoEMethod). The loader's + # shared_w13 [up||gate] swap keys on the same predicate. + # Routed quant-EXCLUDED bf16 layers keep the triton runner. + return UnquantizedFusedMoEMethod( + use_triton_kernels=False, + use_flashinfer_trtllm_moe=True, + ) + return UnquantizedFusedMoEMethod(use_triton_kernels=False) + logger.debug(f"quantizing fused MoE layer: {prefix}") + # Upstream ModelOpt NVFP4 fused-MoE method. It de-interleaves the Inkling + # interleaved-w13 layout when the layer sets inference_moe_w13_interleaved. + return ModelOptNvFp4FusedMoEMethod(self) + + @classmethod + def from_config(cls, config: dict[str, Any]) -> InklingModelOptNvfp4Config: + parent_config = ModelOptFp4Config.from_config(config) + assert "quantization" in config, "quantization config is required" + exclude_modules = _map_exclude_modules(parent_config.exclude_modules) + quant_config = config["quantization"] + scales_2d = quant_config.get("scales_2d", False) + moe_ep_size = quant_config.get("moe_ep_size", 1) + nvfp4_moe_backend = quant_config.get("nvfp4_moe_backend", "trtllm-routed") + return cls( + is_checkpoint_nvfp4_serialized=parent_config.is_checkpoint_nvfp4_serialized, + kv_cache_quant_algo=parent_config.kv_cache_quant_algo, + group_size=parent_config.group_size, + exclude_modules=exclude_modules, + packed_modules_mapping=parent_config.packed_modules_mapping, + scales_2d=scales_2d, + moe_ep_size=moe_ep_size, + nvfp4_moe_backend=nvfp4_moe_backend, + ) + + @classmethod + def maybe_from_model_config( + cls, model_config: ModelConfig + ) -> InklingModelOptNvfp4Config | None: + from sglang.srt.runtime_context import get_parallel + + raw_quant_config = _get_raw_quant_config(model_config) + + if raw_quant_config is None: + return None + + quant_config = raw_quant_config + assert isinstance(quant_config, dict), "quant_config must be a dict" + if "quantization" in quant_config: + # nested format + quant_config = cls.get_from_keys(quant_config, ["quantization"]) + + weight_quant_cfg = quant_config["modelopt_quant_config"]["quant_cfg"][ + "*weight_quantizer" + ] + + if not cls.is_nvfp4(quant_config): + return None + + scales_2d = weight_quant_cfg["block_sizes"].get("-2") is not None + + quant_config["scales_2d"] = scales_2d + quant_config["moe_ep_size"] = get_parallel().moe_ep_size + quant_config["nvfp4_moe_backend"] = "trtllm-routed" + + # force parent class to fallback to nested format. + return cls.from_config({"quantization": quant_config}) + + +def get_quantization_config( + model_config: ModelConfig, +) -> InklingQuantizationConfigBase | None: + + quant_config = _get_raw_quant_config(model_config) + if quant_config is None: + return None + if "quantization" in quant_config: + # nested format + quant_config = QuantizationConfig.get_from_keys(quant_config, ["quantization"]) + + if InklingQuantizationConfigBase.is_nvfp4(quant_config): + return InklingModelOptNvfp4Config.maybe_from_model_config(model_config) + return None diff --git a/python/sglang/srt/models/inkling_common/quantization/quant.py b/python/sglang/srt/models/inkling_common/quantization/quant.py new file mode 100644 index 000000000..dbb1d187d --- /dev/null +++ b/python/sglang/srt/models/inkling_common/quantization/quant.py @@ -0,0 +1,165 @@ +from __future__ import annotations + +import logging +from typing import TYPE_CHECKING + +import torch + +from sglang.srt.layers.moe import MoeRunner, MoeRunnerConfig +from sglang.srt.layers.quantization.base_config import FusedMoEMethodBase +from sglang.srt.utils import ceil_div, set_weight_attrs + +if TYPE_CHECKING: + from sglang.srt.models.inkling_common.quantization.config import ( + InklingModelOptNvfp4Config, + ) + + +logger = logging.getLogger(__name__) + +MXFP_BLOCK_SIZE = 32 +NVFP_BLOCK_SIZE = 16 + +FLOAT8_E4M3_MAX = 448.0 +FLOAT4_E2M1_MAX = 6.0 + + +class InklingMoEMethodBase(FusedMoEMethodBase): + """Protocol for Inkling MoE quant methods""" + + +class InklingNvfp4MoEMethod(InklingMoEMethodBase): + def __init__(self, quant_config: InklingModelOptNvfp4Config): + self.quant_config = quant_config + self.runner: MoeRunner | None = None + self.moe_runner_config: MoeRunnerConfig | None = None + self._srt_trtllm_runner: MoeRunner | None = None + self._srt_trtllm_runner_config: MoeRunnerConfig | None = None + + def create_weights( + self, + layer: torch.nn.Module, + num_experts: int, + hidden_size: int, + intermediate_size_per_partition: int, + params_dtype: torch.dtype, + with_bias: bool = False, + **extra_weight_attrs, # type: ignore[reportMissingParameterType] + ): + from torch.nn.parameter import Parameter + + from sglang.srt.models.inkling_common.dense_mlp import InklingBatchDenseMLP + + assert isinstance( + layer, InklingBatchDenseMLP + ), "InklingNvfp4MoEMethod is only used for InklingBatchDenseMLP (shared experts)" + + w13_up_dim = 2 * intermediate_size_per_partition + + # half shapes for packed uint8 weights + w13_weight = Parameter( + torch.empty(num_experts, w13_up_dim, hidden_size // 2, dtype=torch.uint8), + requires_grad=False, + ) + w2_weight = Parameter( + torch.empty( + num_experts, + hidden_size, + intermediate_size_per_partition // 2, + dtype=torch.uint8, + ), + requires_grad=False, + ) + layer.register_parameter("w13_weight", w13_weight) + layer.register_parameter("w2_weight", w2_weight) + set_weight_attrs(w13_weight, extra_weight_attrs) + set_weight_attrs(w2_weight, extra_weight_attrs) + + w13_scale_dim1_shape = ceil_div(w13_up_dim, self.quant_config.dim1_group_size) + w13_scale_dim2_shape = ceil_div(hidden_size, self.quant_config.dim2_group_size) + w2_scale_dim1_shape = ceil_div(hidden_size, self.quant_config.dim1_group_size) + w2_scale_dim2_shape = ceil_div( + intermediate_size_per_partition, self.quant_config.dim2_group_size + ) + w13_scale = Parameter( + torch.empty( + num_experts, + w13_scale_dim1_shape, + w13_scale_dim2_shape, + dtype=torch.float8_e4m3fn, + ), + requires_grad=False, + ) + w2_scale = Parameter( + torch.empty( + num_experts, + w2_scale_dim1_shape, + w2_scale_dim2_shape, + dtype=torch.float8_e4m3fn, + ), + requires_grad=False, + ) + w13_scale2 = Parameter( + torch.empty(num_experts, dtype=torch.float32), + requires_grad=False, + ) + w2_scale2 = Parameter( + torch.empty(num_experts, dtype=torch.float32), + requires_grad=False, + ) + w13_original_shape = Parameter( + torch.empty(3, dtype=torch.int32), + requires_grad=False, + ) + w2_original_shape = Parameter( + torch.empty(3, dtype=torch.int32), + requires_grad=False, + ) + w13_input_amax = Parameter( + torch.full((1,), float("nan"), dtype=torch.float32), + requires_grad=False, + ) + w2_input_amax = Parameter( + torch.full((1,), float("nan"), dtype=torch.float32), + requires_grad=False, + ) + layer.register_parameter("w13_scale", w13_scale) + layer.register_parameter("w2_scale", w2_scale) + layer.register_parameter("w13_scale2", w13_scale2) + layer.register_parameter("w2_scale2", w2_scale2) + layer.register_parameter("w13_original_shape", w13_original_shape) + layer.register_parameter("w2_original_shape", w2_original_shape) + layer.register_parameter("w13_input_amax", w13_input_amax) + layer.register_parameter("w2_input_amax", w2_input_amax) + set_weight_attrs(w13_scale, extra_weight_attrs) + set_weight_attrs(w2_scale, extra_weight_attrs) + set_weight_attrs(w13_scale2, extra_weight_attrs) + set_weight_attrs(w2_scale2, extra_weight_attrs) + set_weight_attrs(w13_original_shape, extra_weight_attrs) + set_weight_attrs(w2_original_shape, extra_weight_attrs) + set_weight_attrs(w13_input_amax, extra_weight_attrs) + set_weight_attrs(w2_input_amax, extra_weight_attrs) + + def process_weights_after_loading(self, layer: torch.nn.Module) -> None: + """Process weights for the dense shared-expert NVFP4 path. + + Routed NVFP4 MoE now uses ModelOptNvFp4FusedMoEMethod; this hook is reached + only for shared experts (InklingBatchDenseMLP), which carry an ``_fp4_strategy`` + and run their own weight preparation. + """ + if getattr(layer, "_fp4_strategy", None) is not None: + layer.process_weights_after_loading() + + def apply( + self, + layer: torch.nn.Module, + dispatch_output, # type: ignore[override] + ): + # Kept only to satisfy the FusedMoEMethodBase abstract interface. + # InklingNvfp4MoEMethod serves the dense shared-expert path (InklingBatchDenseMLP, + # which uses its own FP4 serving); routed NVFP4 MoE uses + # ModelOptNvFp4FusedMoEMethod. + raise NotImplementedError( + "InklingNvfp4MoEMethod is the dense shared-expert method; routed NVFP4 " + "MoE uses ModelOptNvFp4FusedMoEMethod." + ) diff --git a/python/sglang/srt/models/inkling_common/sconv.py b/python/sglang/srt/models/inkling_common/sconv.py new file mode 100644 index 000000000..8bb501934 --- /dev/null +++ b/python/sglang/srt/models/inkling_common/sconv.py @@ -0,0 +1,1047 @@ +from enum import IntEnum +from typing import Any + +import torch +import torch.nn as nn +import triton +import triton.language as tl +from einops import rearrange +from torch.nn.parameter import Parameter + +from sglang.srt.mem_cache.memory_pool import MambaPool +from sglang.srt.model_executor.forward_batch_info import ForwardBatch +from sglang.srt.model_executor.forward_context import get_req_to_token_pool +from sglang.srt.models.inkling_common.kernels.sconv import ( + HIS_ONES, + HIS_PREFIX, + HIS_SEQ_MINUS_EXT, + HIS_ZEROS, + SconvDecodeMetadata, + SconvExtendMetadata, + causal_conv1d, + fused_causal_conv1d_update_decode, + fused_decode_sconv_metadata, + fused_extend_sconv_metadata, + precompute_helion_extend_metadata, + save_intermediate_conv_windows, + update_sconv_cache, +) +from sglang.srt.runtime_context import get_parallel, get_server_args +from sglang.srt.speculative.eagle_info import EagleDraftExtendInput +from sglang.srt.utils import is_cuda, set_weight_attrs + + +class SconvType(IntEnum): + K_FULL = 0 + V_FULL = 1 + K_LOCAL = 2 + V_LOCAL = 3 + ATTN = 4 + MLP = 5 + + +# Module-level cache for sconv metadata (shared across layers in the same forward pass) +_metadata_cache: dict = {} + + +class ShortConvolution(nn.Module): + """Short convolution layer for efficient causal convolution operations. + + This class implements a depthwise separable 1D convolution with causal padding, + designed for efficient sequence processing using Triton. + + Args: + hidden_size (int): Number of input/output channels (must be equal for depthwise conv) + kernel_size (int): Size of the convolution kernel + activation (Optional[str], optional): Activation function ('silu' or 'swish'). Defaults to 'silu'. + use_residual (bool, optional): Whether to add residual connection (y = conv(x) + x). Defaults to False. + device (Optional[torch.device], optional): Device to place the layer on. Defaults to None. + dtype (Optional[torch.dtype], optional): Data type for layer parameters. Defaults to None. + param_config (ParameterConfig | None, optional): Parameter configuration for mixed precision. Defaults to None. + **kwargs: Additional keyword arguments (deprecated 'use_fast_conv1d' supported for compatibility) + + Note: + - Uses depthwise convolution (groups=hidden_size) for efficiency + - Applies causal padding (kernel_size-1) to ensure no future information leakage + - Uses Triton for efficient GPU execution + """ + + def __init__( + self, + hidden_size: int, + kernel_size: int, + sconv_type: SconvType, + activation: str | None = None, + use_residual: bool = True, + layer_id: int | None = None, + tp_rank: int | None = None, + ): + super().__init__() + + self.kernel_size = ( + (kernel_size,) if isinstance(kernel_size, int) else kernel_size + ) + self.use_residual = use_residual + self.layer_id = layer_id + self.sconv_type: SconvType | None = sconv_type + + if tp_rank is None: + tp_rank = get_parallel().attn_tp_rank + self.tp_rank = tp_rank + + # Initialize weight parameter (will be initialized in reset_parameters) + self.weight = nn.Parameter( + torch.empty( + hidden_size, + 1, + kernel_size, + ), + requires_grad=False, + ) + # Register the weight_loader method for this parameter + set_weight_attrs(self.weight, {"weight_loader": self.weight_loader}) + + self.activation = None + if activation is not None: + assert activation in [ + "silu", + "swish", + ], f"Activation `{activation}` not supported yet." + self.activation = activation + + def weight_loader(self, param: Parameter, loaded_weight: torch.Tensor): + """ + For TP-sharded sconv layers (e.g., k_sconv and v_sconv in attention blocks), + the parameter size is the sharded size while the checkpoint contains the + full unsharded weight. This method narrows the loaded weight to the correct + shard based on the current TP rank. + + Non-TP-sharded sconv layers (e.g., attn_sconv and mlp_sconv) already have + parameter shapes matching the checkpoint, so the narrowing branch is skipped. + """ + param_data = param.data + + if loaded_weight.shape[0] != param_data.shape[0]: + # This weight is TP-sharded, need to narrow to the correct shard + shard_size = param_data.shape[0] + start_idx = self.tp_rank * shard_size + + # Narrow the loaded weight to the correct shard on dim 0 + loaded_weight = loaded_weight.narrow(0, start_idx, shard_size) + + assert param_data.shape == loaded_weight.shape, ( + f"Shape mismatch after narrowing: param {param_data.shape} vs " + f"loaded {loaded_weight.shape}" + ) + param_data.copy_(loaded_weight) + + def _owns_extend_metadata(self, forward_batch: ForwardBatch) -> bool: + # layer 0 computes the shared _metadata_cache for all layers within one + # forward. Under de-tied draft_extend_v2 each STEP is its own forward + # against its own pool, and only step 0's model carries layer_id == 0 — + # steps 1..N-1 would silently reuse a previous forward's cached (freed + # or wrong-pool) tensors, so every step must own its metadata. + return self.layer_id == 0 or forward_batch.forward_mode.is_draft_extend_v2() + + def _prepare_extend_common_metadata( + self, forward_batch: ForwardBatch, cache_indices: torch.Tensor + ): + """Compute ALL extend sconv metadata (query_start_loc, has_initial_state, + and the SconvExtendMetadata) in one fused launch and stash it in + _metadata_cache; _prepare_extend_sconv_metadata is then a cache read. + Falls back to the original unfused op sequence off-CUDA or past the + fused kernel's batch bound.""" + if self._owns_extend_metadata(forward_batch): + B = forward_batch.batch_size + is_verify = forward_batch.forward_mode.is_target_verify() + if is_verify: + # target_verify does not populate extend_seq_lens/extend_prefix_lens; + # the lens are a constant draft_token_num per request. + draft_token_num = forward_batch.spec_info.draft_token_num + num_tokens = B * draft_token_num + fused = fused_extend_sconv_metadata( + B=B, + T=num_tokens, + cache_indices=cache_indices, + his_mode=HIS_ONES, + draft_token_num=draft_token_num, + ) + else: + num_tokens = forward_batch.extend_num_tokens + spec_info = forward_batch.spec_info + if ( + isinstance(spec_info, EagleDraftExtendInput) + and spec_info.num_front_tokens > 0 + ): + # Boundary-KV fix: run conv fresh so warm-up rows rebuild + # the window. + his_mode, his_src = HIS_ZEROS, None + elif forward_batch.extend_prefix_lens is not None: + his_mode, his_src = HIS_PREFIX, forward_batch.extend_prefix_lens + else: + # draft_extend_v2 capture has no extend_prefix_lens. + his_mode, his_src = HIS_SEQ_MINUS_EXT, forward_batch.seq_lens + fused = fused_extend_sconv_metadata( + B=B, + T=num_tokens, + cache_indices=cache_indices, + his_mode=his_mode, + extend_seq_lens=forward_batch.extend_seq_lens, + his_src=his_src, + ) + if fused is not None: + query_start_loc, has_initial_state, precomputed = fused + else: + query_start_loc, has_initial_state = ( + self._unfused_extend_common_metadata(forward_batch) + ) + precomputed = precompute_helion_extend_metadata( + B=B, + T=num_tokens, + W=self.kernel_size[0], + cache_indices=cache_indices, + has_initial_state=has_initial_state, + query_start_loc=query_start_loc, + ) + _metadata_cache["query_start_loc"] = query_start_loc + _metadata_cache["has_initial_state"] = has_initial_state + _metadata_cache["helion_precomputed_extend"] = precomputed + return _metadata_cache["query_start_loc"], _metadata_cache["has_initial_state"] + + def _unfused_extend_common_metadata(self, forward_batch: ForwardBatch): + """Original multi-kernel query_start_loc/has_initial_state prep; fused + fallback only.""" + device = forward_batch.req_pool_indices.device + if forward_batch.forward_mode.is_target_verify(): + draft_token_num = forward_batch.spec_info.draft_token_num + query_start_loc = torch.arange( + 0, + (forward_batch.batch_size + 1) * draft_token_num, + draft_token_num, + dtype=torch.int32, + device=device, + ) + has_initial_state = torch.ones( + forward_batch.batch_size, dtype=torch.bool, device=device + ) + return query_start_loc, has_initial_state + query_start_loc = torch.zeros( + forward_batch.batch_size + 1, + dtype=torch.int32, + device=device, + ) + query_start_loc[1:] = forward_batch.extend_seq_lens.cumsum(dim=0) + spec_info = forward_batch.spec_info + if ( + isinstance(spec_info, EagleDraftExtendInput) + and spec_info.num_front_tokens > 0 + ): + has_initial_state = torch.zeros( + forward_batch.batch_size, dtype=torch.bool, device=device + ) + elif forward_batch.extend_prefix_lens is not None: + has_initial_state = forward_batch.extend_prefix_lens > 0 + else: + has_initial_state = ( + forward_batch.seq_lens[: forward_batch.batch_size] + - forward_batch.extend_seq_lens + ) > 0 + return query_start_loc, has_initial_state + + def _prepare_extend_sconv_metadata( + self, forward_batch: ForwardBatch, cache_indices: torch.Tensor + ) -> SconvExtendMetadata | Any: + # Filled by _prepare_extend_common_metadata, which every caller invokes + # first with the same cache_indices (the fused kernel produces the + # whole metadata set in one launch). + del forward_batch, cache_indices + return _metadata_cache["helion_precomputed_extend"] + + def _prepare_decode_sconv_metadata( + self, forward_batch: ForwardBatch, cache_indices: torch.Tensor + ): + if self.layer_id == 0: + query_start_loc, has_initial_state, precomputed = ( + fused_decode_sconv_metadata( + B=forward_batch.batch_size, cache_indices=cache_indices + ) + ) + _metadata_cache["query_start_loc_decode"] = query_start_loc + _metadata_cache["has_initial_state_decode"] = has_initial_state + _metadata_cache["helion_precomputed_decode"] = precomputed + return ( + _metadata_cache["query_start_loc_decode"], + _metadata_cache["has_initial_state_decode"], + _metadata_cache["helion_precomputed_decode"], + ) + + def _apply_training_sconv_kernel( + self, + hidden_states: torch.Tensor, + weight: torch.Tensor, + sconv_cache: torch.Tensor, + cache_indices: torch.Tensor, + query_start_loc: torch.Tensor, + has_initial_state: torch.Tensor, + precomputed: SconvDecodeMetadata | SconvExtendMetadata, + is_decode: bool = False, + ) -> torch.Tensor: + y = causal_conv1d( + x=hidden_states, + weight=weight, + sconv_cache=sconv_cache, + activation=self.activation, + use_residual=self.use_residual, + is_decode=is_decode, + **precomputed, + ) + update_sconv_cache( + x=hidden_states, + sconv_cache=sconv_cache, + cache_indices=cache_indices, + has_initial_state=has_initial_state, + query_start_loc=query_start_loc, + ) + return y + + def _init_track_conv_indices( + self, query_start_loc: torch.Tensor, forward_batch: ForwardBatch + ): + """ + Compute indices for extracting conv states from the input sequence during extend. + + In Mamba models, the conv layer maintains a sliding window of recent inputs. + After processing a prefill chunk, we need to save the last `conv_state_len` tokens + of the processed region for prefix caching. + + The key insight is that FLA (Flash Linear Attention) processes sequences in chunks + of FLA_CHUNK_SIZE. We only track the conv state up to the last complete chunk boundary + (aligned_len). + + start_indices is the starting token index of the conv state to track in this extend batch. + indices include all pos to track in this extend batch, conv_state_len for each req that + needs to be tracked (i.e. mamba_track_mask is True) + + Returns: + indices: Tensor of shape [num_tracked_requests, conv_state_len] containing + flattened positions into the packed input tensor. + """ + conv_state_len = self.kernel_size[0] - 1 + + # Calculate the end position of the last aligned chunk + lens_to_track = ( + forward_batch.mamba_track_seqlens - forward_batch.extend_prefix_lens + ) + mamba_cache_chunk_size = get_server_args().mamba_cache_chunk_size + chunk_aligned_lens_to_track = ( + lens_to_track // mamba_cache_chunk_size + ) * mamba_cache_chunk_size + start_indices = ( + query_start_loc[:-1] + chunk_aligned_lens_to_track - conv_state_len + ) + + # Create indices: [batch_size, conv_state_len] or padded batch_size in prefill cudagraph + indices = start_indices.unsqueeze(-1) + torch.arange( + conv_state_len, + device=forward_batch.req_pool_indices.device, + dtype=start_indices.dtype, + ) + + # Use slice [-1:] instead of [-1] to avoid 0-d tensor -> scalar conversion during graph capture + return torch.clamp( + indices, + min=torch.zeros( + (1,), + dtype=start_indices.dtype, + device=forward_batch.req_pool_indices.device, + ), + max=query_start_loc[-1:] - 1, + ) + + def _prepare_extend_track_conv_indices( + self, query_start_loc: torch.Tensor, forward_batch: ForwardBatch + ) -> torch.Tensor: + if self.layer_id == 0: + track_conv_indices = self._init_track_conv_indices( + query_start_loc, forward_batch + ) + _metadata_cache["track_conv_indices_extend"] = track_conv_indices + return _metadata_cache["track_conv_indices_extend"] + + def _prepare_cache_indices( + self, req_to_token_pool, forward_batch: ForwardBatch + ) -> torch.Tensor: + """Resolve the per-request mamba slot indices ONCE per forward step. + + ``get_mamba_indices`` is a GPU gather + (``req_index_to_mamba_index_mapping[req_pool_indices]``) that depends + only on ``forward_batch.req_pool_indices``, which is invariant across + every sconv layer within a step. Computing it in each layer's + ``forward`` launched one redundant gather kernel per k_sconv/v_sconv + (``2 * num_attn_layers`` per step). Cache the layer-0 result in the + shared per-step metadata cache and hand it back to subsequent layers, + so all layers reuse the same resolved indices. + + Cuda-graph-safe: on capture only layer 0's gather is recorded and + subsequent layers read that captured tensor; on replay layer 0's gather + re-runs into the same address, keeping it current -- the same mechanism + the other ``_metadata_cache`` entries already rely on. + + Under de-tied DRAFT_EXTEND_V2 each per-step forward runs with + layer_id != 0 against its own draft pool, so every step must own its + gather instead of reusing another forward's cached tensor (same rule + as ``_owns_extend_metadata``). + """ + if self._owns_extend_metadata(forward_batch): + _metadata_cache["cache_indices"] = ( + req_to_token_pool.translate_mamba_indices( + req_to_token_pool.get_mamba_indices(forward_batch.req_pool_indices) + ) + ) + return _metadata_cache["cache_indices"] + + def _prepare_extend_sconv_cache( + self, + forward_batch: ForwardBatch, + sconv_cache: torch.Tensor, + hidden_states: torch.Tensor, + query_start_loc: torch.Tensor, + ): + if forward_batch.mamba_track_mask is not None: + # Track conv state for prefix caching. Fused gather→scatter writes + # directly into sconv_cache without an intermediate [B, W-1, D] buffer. + conv_dst = forward_batch.mamba_track_indices + # [B, W - 1] + track_conv_indices = self._prepare_extend_track_conv_indices( + query_start_loc, forward_batch + ) + fused_gather_scatter_to_sconv_cache( + hidden_states=hidden_states, + sconv_cache=sconv_cache, + track_conv_indices=track_conv_indices, + mask=forward_batch.mamba_track_mask, + dst_indices=conv_dst, + ) + + def _save_intermediate_conv_windows( + self, + forward_batch: ForwardBatch, + cache: MambaPool.SpeculativeState, + sconv_cache: torch.Tensor, + cache_indices: torch.Tensor, + hidden_states: torch.Tensor, + ): + """Save intermediate conv windows per draft token for speculative decoding. + + Builds a padded sequence [initial_conv_state | draft_tokens] and extracts + sliding windows of size (kernel_size - 1) after each draft token position. + These intermediate states are consumed by + InklingForConditionalGeneration.update_conv_state_after_mtp_verify + to restore the correct conv state for the number of accepted tokens. + """ + save_intermediate_conv_windows( + sconv_cache=sconv_cache, + hidden_states=hidden_states, + cache_indices=cache_indices, + intermediate_out=cache.intermediate_conv_window[self.sconv_type.value], + batch_size=forward_batch.batch_size, + draft_token_num=forward_batch.spec_info.draft_token_num, + ) + + def _update_sconv_cache_for_draft_extend( + self, + forward_batch: ForwardBatch, + sconv_cache: torch.Tensor, + cache_indices: torch.Tensor, + hidden_states: torch.Tensor, + ): + """Write the correct conv state based on how many tokens were accepted. + + During DRAFT_EXTEND_V2 the draft model processes all num_draft_tokens + through its sconv layers, but only num_accept_tokens of them should be + reflected in the final conv state. We reconstruct the sliding-window + state after exactly num_accept_tokens and write it to the cache, + replacing the normal update_sconv_cache call. + + If mamba persistent caching is enabled and the accepted range crosses a + mamba_track_interval boundary, also writes the conv state at that boundary + to the persistent ping-pong cache (mamba_track_indices). + """ + num_accept_tokens = forward_batch.spec_info.num_accept_tokens + batch_size = forward_batch.batch_size + if len(hidden_states.shape) == 2: + draft_token_num = hidden_states.shape[0] // batch_size + else: + draft_token_num = hidden_states.shape[1] + + mamba_track_indices = getattr(forward_batch, "mamba_track_indices", None) + do_tracking = ( + mamba_track_indices is not None + and get_server_args().enable_mamba_extra_buffer() + ) + + crossed = track_step = None + if do_tracking: + mamba_track_interval = get_server_args().mamba_track_interval + pre_seqlen = forward_batch.seq_lens[:batch_size] - draft_token_num + post_seqlen = pre_seqlen + num_accept_tokens + crossed = (pre_seqlen // mamba_track_interval) != ( + post_seqlen // mamba_track_interval + ) + tracking_boundary = ( + post_seqlen // mamba_track_interval + ) * mamba_track_interval + track_step = (tracking_boundary - pre_seqlen - 1).clamp(0, draft_token_num) + + fused_draft_extend_sconv_cache( + hidden_states=hidden_states, + sconv_cache=sconv_cache, + cache_indices=cache_indices[:batch_size], + num_accept_tokens=num_accept_tokens, + draft_token_num=draft_token_num, + do_tracking=do_tracking, + crossed=crossed, + track_step=track_step, + mamba_track_indices=( + mamba_track_indices[:batch_size] if do_tracking else None + ), + ) + + def decode_fused_ar_inputs(self, forward_batch: ForwardBatch): + """Return inputs for fused decode all-reduce, convolution, and norm. + + These match the fused decode branch of ``forward``, including its + per-step metadata cache behavior. Returns + ``(sconv_cache, cache_indices, cache_mask, weight_2d)``.""" + req_to_token_pool = get_req_to_token_pool() + cache = req_to_token_pool.mamba2_layer_cache(self.layer_id) + sconv_cache = cache.conv[self.sconv_type.value] + cache_indices = self._prepare_cache_indices(req_to_token_pool, forward_batch) + _, _, precomputed = self._prepare_decode_sconv_metadata( + forward_batch, cache_indices + ) + weight = rearrange(self.weight, "d 1 w -> d w") + return sconv_cache, cache_indices, precomputed["cache_mask"], weight + + def verify_fused_ar_inputs(self, forward_batch: ForwardBatch): + """Return inputs for fused target-verify convolution and norm. + + These mirror the target-verify branch of ``forward``. Returns ``(sconv_cache, + cache_indices[B], has_initial_state[B], weight_2d, inter_out)``.""" + req_to_token_pool = get_req_to_token_pool() + cache = req_to_token_pool.mamba2_layer_cache(self.layer_id) + sconv_cache = cache.conv[self.sconv_type.value] + cache_indices = self._prepare_cache_indices(req_to_token_pool, forward_batch) + _, has_initial_state = self._prepare_extend_common_metadata( + forward_batch, cache_indices + ) + weight = rearrange(self.weight, "d 1 w -> d w") + inter_out = cache.intermediate_conv_window[self.sconv_type.value] + b = forward_batch.batch_size + return sconv_cache, cache_indices[:b], has_initial_state, weight, inter_out + + def extend_fused_ar_inputs(self, forward_batch: ForwardBatch): + """Return inputs for fused extend all-reduce and scattered convolution. + + These mirror the extend branch of ``forward`` with convolution fused. + Returns ``(sconv_cache, safe_idx[B], cache_mask[B], cu[B+1], si[T], + weight_2d, query_start_loc, cache_indices, has_initial_state, + track_rows, track_mask, track_dst)``. ``query_start_loc`` is unused by + the fused-AR caller (kept for symmetry with the unfused metadata + prep); ``cache_indices``/``has_initial_state`` feed the in-kernel + cache update, and ``track_rows``/``track_mask``/``track_dst`` feed the + in-kernel prefix-cache track.""" + req_to_token_pool = get_req_to_token_pool() + cache = req_to_token_pool.mamba2_layer_cache(self.layer_id) + sconv_cache = cache.conv[self.sconv_type.value] + cache_indices = self._prepare_cache_indices(req_to_token_pool, forward_batch) + weight = rearrange(self.weight, "d 1 w -> d w") + if forward_batch.forward_mode.is_decode(): + # Decode: every token its own sequence (arange qsl, has_init=ones). + query_start_loc, has_initial_state, precomputed = ( + self._prepare_decode_sconv_metadata(forward_batch, cache_indices) + ) + else: + query_start_loc, has_initial_state = self._prepare_extend_common_metadata( + forward_batch, cache_indices + ) + precomputed = self._prepare_extend_sconv_metadata( + forward_batch, cache_indices + ) + # Prefix-cache track inputs (extend only; the kernel fuses the write). + dev = cache_indices.device + if ( + forward_batch.mamba_track_mask is not None + and not forward_batch.forward_mode.is_decode() + ): + track_rows = self._prepare_extend_track_conv_indices( + query_start_loc, forward_batch + ).long() + track_mask = forward_batch.mamba_track_mask + track_dst = forward_batch.mamba_track_indices + else: + w1 = self.kernel_size[0] - 1 + track_rows = torch.empty((0, w1), dtype=torch.int64, device=dev) + track_mask = torch.empty((0,), dtype=torch.bool, device=dev) + track_dst = torch.empty((0,), dtype=torch.int64, device=dev) + return ( + sconv_cache, + precomputed["safe_idx"], + precomputed["cache_mask"].view(-1), + precomputed["cu"], + precomputed["si"], + weight, + query_start_loc, + cache_indices, + has_initial_state, + track_rows, + track_mask, + track_dst, + ) + + def verify_fused_ar_finish( + self, + forward_batch: ForwardBatch, + x_scratch: torch.Tensor, + cache_indices: torch.Tensor, + ) -> None: + """Target-verify finish for the fused {AR + scattered sconv} path: no + working-cache update; save the per-position windows (consumed by + update_conv_state_after_mtp_verify), exactly as the verify branch of + ``forward`` does -- on the reduced pre-conv x.""" + req_to_token_pool = get_req_to_token_pool() + cache = req_to_token_pool.mamba2_layer_cache(self.layer_id) + sconv_cache = cache.conv[self.sconv_type.value] + self._save_intermediate_conv_windows( + forward_batch=forward_batch, + cache=cache, + sconv_cache=sconv_cache, + cache_indices=cache_indices, + hidden_states=x_scratch, + ) + + def forward( + self, + hidden_states: torch.Tensor, + positions: torch.Tensor, + forward_batch: ForwardBatch, + ) -> torch.Tensor: + """ + Args: + x (`torch.Tensor`): + Tensor of shape `[B, T, D]` or `[B*T, D]`. + sequence_info (SequenceInfo): + Sequence info for handling batch/sequence dimensions. Required. + + Returns: + Output tensor with same shape as input x. + """ + del positions + + req_to_token_pool = get_req_to_token_pool() + cache = req_to_token_pool.mamba2_layer_cache(self.layer_id) + sconv_cache = cache.conv[self.sconv_type.value] + cache_indices = self._prepare_cache_indices(req_to_token_pool, forward_batch) + + weight = rearrange(self.weight, "d 1 w -> d w") + + if forward_batch.forward_mode.is_target_verify(): + query_start_loc, has_initial_state = self._prepare_extend_common_metadata( + forward_batch, cache_indices + ) + precomputed = self._prepare_extend_sconv_metadata( + forward_batch, cache_indices + ) + y = causal_conv1d( + x=hidden_states, + weight=weight, + sconv_cache=sconv_cache, + activation=self.activation, + use_residual=self.use_residual, + is_decode=False, + **precomputed, + ) + self._save_intermediate_conv_windows( + forward_batch=forward_batch, + cache=cache, + sconv_cache=sconv_cache, + cache_indices=cache_indices, + hidden_states=hidden_states, + ) + + elif forward_batch.forward_mode.is_extend(include_draft_extend_v2=True): + query_start_loc, has_initial_state = self._prepare_extend_common_metadata( + forward_batch, cache_indices + ) + self._prepare_extend_sconv_cache( + forward_batch, sconv_cache, hidden_states, query_start_loc + ) + + precomputed = self._prepare_extend_sconv_metadata( + forward_batch, cache_indices + ) + if forward_batch.forward_mode.is_draft_extend_v2(): + y = causal_conv1d( + x=hidden_states, + weight=weight, + sconv_cache=sconv_cache, + activation=self.activation, + use_residual=self.use_residual, + is_decode=False, + **precomputed, + ) + self._update_sconv_cache_for_draft_extend( + forward_batch, + sconv_cache, + cache_indices, + hidden_states, + ) + else: + y = self._apply_training_sconv_kernel( + hidden_states=hidden_states, + weight=weight, + sconv_cache=sconv_cache, + cache_indices=cache_indices, + query_start_loc=query_start_loc, + has_initial_state=has_initial_state, + precomputed=precomputed, + is_decode=False, + ) + else: + # Fused decode: prefix construction + conv + cache update + prefix-cache + # track-copy in a single Triton kernel. Reads sconv_cache directly (no + # intermediate prefix tensor) and snapshots the post-update conv window + # into the persistent ping-pong slot in-register (no separate + # copy_if_needed launch). track_mask is None when prefix caching with the + # mamba extra buffer is disabled, which disables the track-copy path. + _query_start_loc, _has_initial_state, precomputed = ( + self._prepare_decode_sconv_metadata(forward_batch, cache_indices) + ) + y = fused_causal_conv1d_update_decode( + x=hidden_states, + weight=weight, + sconv_cache=sconv_cache, + cache_indices=cache_indices, + cache_mask=precomputed["cache_mask"], + activation=self.activation, + use_residual=self.use_residual, + track_mask=forward_batch.mamba_track_mask, + track_indices=forward_batch.mamba_track_indices, + ) + + return y + + +# --------------------------------------------------------------------------- +# Fused gather→scatter: replaces hidden_states[indices].contiguous() + copy_if_needed +# --------------------------------------------------------------------------- + + +@triton.jit +def _fused_gather_scatter_to_sconv_cache_kernel( + hidden_ptr, # [T, D] + sconv_cache_ptr, # [pool, W-1, D] + track_idx_ptr, # [B, W-1] any int dtype, any strides + mask_ptr, # [B] bool + dst_ptr, # [B] any int dtype, any stride + stride_hs_t, # hidden_states row stride + stride_cache_slot, # sconv_cache outer (pool) stride + stride_cache_w, # sconv_cache w-position stride + stride_track_b, # track_idx row (batch) stride + stride_track_w, # track_idx w-position stride + stride_dst_b, # dst_indices element stride + D, + W_MINUS_1: tl.constexpr, + BLOCK_D: tl.constexpr, +): + """For each batch element b where mask[b] is True, copy W-1 token rows + from hidden_states into sconv_cache[dst[b]], reading token positions from + track_idx[b, w]. Eliminates the intermediate [B, W-1, D] gather buffer + and the subsequent copy_if_needed call. + + Index tensors are read at their native dtype/strides and cast to int64 + in-kernel, so the host-side `.to(int32/int64).contiguous()` casts that + previously launched separate kernels are folded away. + """ + bid = tl.program_id(0) + pid_d = tl.program_id(1) + + if not tl.load(mask_ptr + bid): + return + + dst_slot = tl.load(dst_ptr + bid * stride_dst_b).to(tl.int64) + d_off = pid_d * BLOCK_D + tl.arange(0, BLOCK_D) + d_mask = d_off < D + + for w in tl.static_range(W_MINUS_1): + src_t = tl.load(track_idx_ptr + bid * stride_track_b + w * stride_track_w).to( + tl.int64 + ) + val = tl.load(hidden_ptr + src_t * stride_hs_t + d_off, mask=d_mask, other=0.0) + tl.store( + sconv_cache_ptr + dst_slot * stride_cache_slot + w * stride_cache_w + d_off, + val, + mask=d_mask, + ) + + +def fused_gather_scatter_to_sconv_cache( + hidden_states: torch.Tensor, # [T, D] + sconv_cache: torch.Tensor, # [pool, W-1, D] + track_conv_indices: torch.Tensor, # [B, W-1] int (any int dtype/strides) + mask: torch.Tensor, # [B] bool + dst_indices: torch.Tensor, # [B] int (any int dtype/strides) +) -> None: + """Fused replacement for: hidden_states[track_conv_indices].contiguous() + copy_if_needed. + + Writes masked rows from hidden_states directly into sconv_cache without + allocating the intermediate [B, W-1, D] gather buffer. + + The index-tensor dtype casts (`.to(int32/int64)`) and `.contiguous()` are + folded INTO the kernel: it reads track_conv_indices / dst_indices at their + native dtype and strides and casts to int64 internally, eliminating the + separate cast/contiguous kernel launches. + """ + D = hidden_states.shape[-1] + W_minus_1 = sconv_cache.shape[1] + BLOCK_D = min(triton.next_power_of_2(D), 1024) + B = mask.shape[0] + + if ( + is_cuda() + and hidden_states.dtype == torch.bfloat16 + and D % 2 == 0 + and hidden_states.stride(-1) == 1 + and sconv_cache.stride(2) == 1 + ): + from sglang.jit_kernel.inkling_sconv import ( + fused_gather_scatter_to_sconv_cache as _cuda_gs, + ) + + _cuda_gs( + hidden_states, + sconv_cache, + track_conv_indices.to(torch.int32), + mask, + dst_indices.to(torch.int64), + ) + return + + grid = (B, triton.cdiv(D, BLOCK_D)) + _fused_gather_scatter_to_sconv_cache_kernel[grid]( + hidden_states, + sconv_cache, + track_conv_indices, + mask, + dst_indices, + hidden_states.stride(0), + sconv_cache.stride(0), + sconv_cache.stride(1), + track_conv_indices.stride(0), + track_conv_indices.stride(1), + dst_indices.stride(0), + D, + W_MINUS_1=W_minus_1, + BLOCK_D=BLOCK_D, + ) + + +# --------------------------------------------------------------------------- +# Fused draft-extend sconv cache update +# Replaces: initial_state gather + padded cat + windows unfold + +# track gather/transpose/contiguous/copy_if_needed + +# accepted gather/transpose/contiguous/scatter +# --------------------------------------------------------------------------- + + +@triton.jit +def _fused_draft_extend_sconv_cache_kernel( + hidden_ptr, # [B*T, D] or [B, T, D] — non-contiguous ok + sconv_cache_ptr, # [pool, W-1, D] + cache_indices_ptr, # [B] int32 – working cache slots + num_accept_ptr, # [B] int32 – accepted token count per seq + crossed_ptr, # [B] bool – tracking boundary crossed + track_step_ptr, # [B] int32 – position in [0,T] for tracking + mamba_track_indices_ptr, # [B] int64 – persistent cache slots + stride_hs_b, # per-batch stride in hidden_states + stride_hs_t, # per-token stride in hidden_states + stride_cache_slot, # sconv_cache dim-0 stride (slot) + stride_cache_w, # sconv_cache dim-1 stride (w-position) + D, + W_MINUS_1: tl.constexpr, + BLOCK_D: tl.constexpr, + DO_TRACKING: tl.constexpr, +): + """Single-kernel replacement for the whole _update_sconv_cache_for_draft_extend body. + + Reads from the "virtual padded" sequence without materialising it: + padded[b, j] = sconv_cache[ci, j, :] for j < W_MINUS_1 (initial state) + padded[b, j] = hidden_states[b, j-W_MINUS_1, :] otherwise (draft tokens) + + Writes (in order, to avoid RAW conflicts on sconv_cache[ci]): + 1. Tracking window → sconv_cache[mamba_track_indices[b]] (if DO_TRACKING & crossed) + 2. Accepted window → sconv_cache[cache_indices[b]] + + RAW safety: at accepted-write iteration w, we read padded position n_acc+w. + Since n_acc >= 0, that position is always > w-1 (the latest slot written so far), + so we never read a slot overwritten by an earlier iteration. + """ + bid = tl.program_id(0) + pid_d = tl.program_id(1) + + # CUDA-graph padded rows carry a stale cache index over a possibly-live + # slot; they are marked with a negative accept count and must not write. + n_acc = tl.load(num_accept_ptr + bid).to(tl.int64) + if n_acc < 0: + return + + d_off = pid_d * BLOCK_D + tl.arange(0, BLOCK_D) + d_mask = d_off < D + ci = tl.load(cache_indices_ptr + bid).to(tl.int64) + + # -- 1. Tracking write (conditional, must precede accepted write) ----------- + if DO_TRACKING: + do_track = tl.load(crossed_ptr + bid) + if do_track: + step = tl.load(track_step_ptr + bid).to(tl.int64) + dst_slot = tl.load(mamba_track_indices_ptr + bid) + for w in tl.static_range(W_MINUS_1): + pos = step + w + if pos < W_MINUS_1: + val = tl.load( + sconv_cache_ptr + + ci * stride_cache_slot + + pos * stride_cache_w + + d_off, + mask=d_mask, + other=0.0, + ) + else: + val = tl.load( + hidden_ptr + + bid * stride_hs_b + + (pos - W_MINUS_1) * stride_hs_t + + d_off, + mask=d_mask, + other=0.0, + ) + tl.store( + sconv_cache_ptr + + dst_slot * stride_cache_slot + + w * stride_cache_w + + d_off, + val, + mask=d_mask, + ) + + # -- 2. Accepted-window write ------------------------------------------------ + for w in tl.static_range(W_MINUS_1): + pos = n_acc + w + if pos < W_MINUS_1: + val = tl.load( + sconv_cache_ptr + ci * stride_cache_slot + pos * stride_cache_w + d_off, + mask=d_mask, + other=0.0, + ) + else: + val = tl.load( + hidden_ptr + + bid * stride_hs_b + + (pos - W_MINUS_1) * stride_hs_t + + d_off, + mask=d_mask, + other=0.0, + ) + tl.store( + sconv_cache_ptr + ci * stride_cache_slot + w * stride_cache_w + d_off, + val, + mask=d_mask, + ) + + +def fused_draft_extend_sconv_cache( + hidden_states: torch.Tensor, # [B*T, D] or [B, T, D] + sconv_cache: torch.Tensor, # [pool, W-1, D] + cache_indices: torch.Tensor, # [B] int32 + num_accept_tokens: torch.Tensor, # [B] int32 + draft_token_num: int, + do_tracking: bool = False, + crossed: torch.Tensor | None = None, # [B] bool + track_step: torch.Tensor | None = None, # [B] int32 + mamba_track_indices: torch.Tensor | None = None, # [B] int64 +) -> None: + """Fused replacement for _update_sconv_cache_for_draft_extend. + + Eliminates: initial_state gather, padded cat, windows unfold, + track_selected gather/transpose/contiguous/copy_if_needed, + selected gather/transpose/contiguous/scatter — all in one kernel. + """ + B = cache_indices.shape[0] + D = sconv_cache.shape[2] + W_minus_1 = sconv_cache.shape[1] + + if ( + is_cuda() + and hidden_states.ndim == 2 + and hidden_states.dtype == torch.bfloat16 + and D % 2 == 0 + and hidden_states.stride(-1) == 1 + and sconv_cache.stride(2) == 1 + ): + from sglang.jit_kernel.inkling_sconv import ( + fused_draft_extend_sconv_cache as _cuda_de, + ) + + _cuda_de( + hidden_states, + sconv_cache, + cache_indices.to(torch.int32), + num_accept_tokens.to(torch.int32), + draft_token_num, + do_tracking, + crossed, + track_step.to(torch.int32) if track_step is not None else None, + ( + mamba_track_indices.to(torch.int64) + if mamba_track_indices is not None + else None + ), + ) + return + + BLOCK_D = min(triton.next_power_of_2(D), 1024) + + if hidden_states.ndim == 2: + stride_hs_b = hidden_states.stride(0) * draft_token_num + stride_hs_t = hidden_states.stride(0) + else: + stride_hs_b = hidden_states.stride(0) + stride_hs_t = hidden_states.stride(1) + + # Sentinel tensors for the no-tracking path (never dereferenced). + _dummy_bool = torch.zeros(1, dtype=torch.bool, device=sconv_cache.device) + _dummy_int = torch.zeros(1, dtype=torch.int32, device=sconv_cache.device) + _dummy_int64 = torch.zeros(1, dtype=torch.int64, device=sconv_cache.device) + + grid = (B, triton.cdiv(D, BLOCK_D)) + _fused_draft_extend_sconv_cache_kernel[grid]( + hidden_states, + sconv_cache, + cache_indices.to(torch.int32).contiguous(), + num_accept_tokens.to(torch.int32).contiguous(), + crossed if do_tracking else _dummy_bool, + track_step.to(torch.int32).contiguous() if do_tracking else _dummy_int, + ( + mamba_track_indices.to(torch.int64).contiguous() + if do_tracking + else _dummy_int64 + ), + stride_hs_b, + stride_hs_t, + sconv_cache.stride(0), + sconv_cache.stride(1), + D, + W_MINUS_1=W_minus_1, + BLOCK_D=BLOCK_D, + DO_TRACKING=do_tracking, + ) diff --git a/python/sglang/srt/models/inkling_common/util.py b/python/sglang/srt/models/inkling_common/util.py new file mode 100644 index 000000000..1d8bbea68 --- /dev/null +++ b/python/sglang/srt/models/inkling_common/util.py @@ -0,0 +1,115 @@ +from __future__ import annotations + +import abc + +import torch +from torch import nn + +from sglang.srt.layers.moe.fused_moe_triton.layer import FusedMoE +from sglang.srt.layers.moe.moe_runner.base import MoeRunnerConfig +from sglang.srt.layers.quantization.base_config import QuantizationConfig +from sglang.srt.layers.quantization.unquant import UnquantizedFusedMoEMethod +from sglang.srt.runtime_context import get_server_args + + +def lora_compatible_layout_enabled() -> bool: + """Use the contiguous ``[gate || up]`` layout required by LoRA slicing.""" + return get_server_args().enable_lora + + +def use_inkling_shared_fused_moe( + *, + inference_moe_w13_interleaved: bool = True, + shared_sink_serves_fp4: bool = False, +) -> bool: + """Return whether shared experts should use the fused MoE sink.""" + from sglang.srt.environ import envs + + if not inference_moe_w13_interleaved or shared_sink_serves_fp4: + return True + if lora_compatible_layout_enabled(): + return False + if envs.SGLANG_OPT_USE_INKLING_SHARED_FUSED_MOE.is_set(): + return envs.SGLANG_OPT_USE_INKLING_SHARED_FUSED_MOE.get() + return False + + +def bf16_routed_uses_stock_fused_moe( + quant_config: QuantizationConfig | None, +) -> bool: + """Use the stock TRT-LLM runner for unquantized BF16 routed experts.""" + if quant_config is not None: + return False + from sglang.srt.layers.moe import get_moe_runner_backend + + return get_moe_runner_backend().is_flashinfer_trtllm_routed() + + +def shared_sink_uses_trtllm_bf16() -> bool: + """Use TRT-LLM's BF16 path for the fused shared-expert sink.""" + from sglang.srt.layers.moe import get_moe_runner_backend + + backend = get_moe_runner_backend() + if lora_compatible_layout_enabled(): + return False + return backend.is_flashinfer_trtllm_routed() + + +def trtllm_bf16_weight_prep_enabled() -> bool: + """Return whether BF16 weights require TRT-LLM's ``[up || gate]`` layout.""" + from sglang.srt.layers.moe import get_moe_runner_backend + + backend = get_moe_runner_backend() + return backend.is_flashinfer_trtllm() or backend.is_flashinfer_trtllm_routed() + + +def deinterleave_gate_up(weight: torch.Tensor, dim: int) -> torch.Tensor: + """Convert Inkling [gate0, up0, ...] interleaved layout to stock [gate..., up...].""" + dim = dim % weight.dim() + if weight.shape[dim] % 2 != 0: + raise ValueError( + f"Cannot deinterleave odd gate/up dimension {dim}: {tuple(weight.shape)}" + ) + shape = list(weight.shape) + half = shape[dim] // 2 + view_shape = shape[:dim] + [half, 2] + shape[dim + 1 :] + return ( + weight.reshape(view_shape) + .transpose(dim, dim + 1) + .reshape_as(weight) + .contiguous() + ) + + +class FusedMoELoadingMixin(abc.ABC): + def __init__( + self, + quant_config: QuantizationConfig | None, + quant_method: UnquantizedFusedMoEMethod, + moe_runner_config: MoeRunnerConfig, + moe_tp_rank: int, + ) -> None: + super().__init__() + helper = FusedMoE.__new__(FusedMoE) + nn.Module.__init__(helper) + helper.quant_config = quant_config + helper.quant_method = quant_method + helper.moe_runner_config = moe_runner_config + helper.use_triton_kernels = False + helper.moe_tp_rank = moe_tp_rank + helper.use_presharded_weights = False + helper.use_flashinfer_trtllm_moe = False + # Keep this parameterless loading helper out of the module tree so + # post-load processing does not treat it as a quantized layer. + object.__setattr__(self, "helper", helper) + + def weight_loader_fused( + self, + param: torch.nn.Parameter, + loaded_weight: torch.Tensor, + weight_name: str, + shard_id: str, + ) -> None: + return self.helper.weight_loader_fused( + param, loaded_weight, weight_name, shard_id + ) diff --git a/python/sglang/srt/multimodal/__init__.py b/python/sglang/srt/multimodal/__init__.py new file mode 100644 index 000000000..abe29dabd --- /dev/null +++ b/python/sglang/srt/multimodal/__init__.py @@ -0,0 +1 @@ +# SGLang multimodal module diff --git a/python/sglang/srt/multimodal/inkling/__init__.py b/python/sglang/srt/multimodal/inkling/__init__.py new file mode 100644 index 000000000..a9474a809 --- /dev/null +++ b/python/sglang/srt/multimodal/inkling/__init__.py @@ -0,0 +1,28 @@ +# Copyright 2026 SGLang Team +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""SRT adapter exports for Inkling multimodal preprocessing.""" + +from sglang.srt.multimodal.inkling.feature_extraction import ( + InklingAudioEncoderParams, + InklingAudioFeatureExtractor, +) +from sglang.srt.multimodal.inkling.image_processing import InklingImageProcessor +from sglang.srt.multimodal.inkling.processing_inkling import InklingProcessor + +__all__ = [ + "InklingImageProcessor", + "InklingAudioFeatureExtractor", + "InklingAudioEncoderParams", + "InklingProcessor", +] diff --git a/python/sglang/srt/multimodal/inkling/feature_extraction.py b/python/sglang/srt/multimodal/inkling/feature_extraction.py new file mode 100644 index 000000000..faaa1ff1e --- /dev/null +++ b/python/sglang/srt/multimodal/inkling/feature_extraction.py @@ -0,0 +1,233 @@ +"""HuggingFace audio feature extractor for Inkling models.""" + +from __future__ import annotations + +import io +import math +from dataclasses import dataclass +from typing import Dict, Optional, Sequence, Tuple + +import numpy as np +import soundfile as sf +import torch +import torch.nn.functional as F +import torchaudio.functional as AF +from transformers.feature_extraction_utils import BatchFeature, FeatureExtractionMixin + + +@dataclass +class InklingAudioEncoderParams: + """Audio preprocessing parameters used to convert raw audio into dMel bins.""" + + sample_rate: int = 16_000 + window_size_multiplier: float = 2.0 + n_fft: Optional[int] = None + n_mels: int = 80 + num_dmel_bins: int = 16 + dmel_min_value: float = -7.0 + dmel_max_value: float = 2.0 + audio_token_duration_s: float = 0.05 + + +def _load_audio_bytes(audio) -> bytes: + """Coerce a single audio input into raw file bytes for the encoder. + + Accepts raw bytes, a local path / ``file://`` str, or a ``.read()``-able. (data:/ + http(s):// resolution is left to the SGLang loader.) + """ + if isinstance(audio, (bytes, bytearray)): + return bytes(audio) + if hasattr(audio, "read"): + return audio.read() + if isinstance(audio, str): + path = audio[len("file://") :] if audio.startswith("file://") else audio + with open(path, "rb") as f: + return f.read() + raise TypeError( + f"Unsupported audio input type for Inkling audio extractor: {type(audio)}" + ) + + +def _to_exact_int(value: float, name: str, tolerance: float = 1e-6) -> int: + rounded = round(value) + if abs(value - rounded) > tolerance: + raise ValueError(f"{name} must resolve to an integer sample count, got {value}") + return int(rounded) + + +def _decode_audio(audio_bytes: bytes, sample_rate: int) -> torch.Tensor: + samples, src_sample_rate = sf.read( + io.BytesIO(audio_bytes), dtype="float32", always_2d=True + ) + mono = samples.mean(axis=1) + if src_sample_rate != sample_rate: + mono = _resample(mono, src_sample_rate, sample_rate) + return torch.from_numpy(np.ascontiguousarray(mono, dtype=np.float32)) + + +def _resample( + samples: np.ndarray, src_sample_rate: int, sample_rate: int +) -> np.ndarray: + audio = torch.from_numpy(np.ascontiguousarray(samples, dtype=np.float32)) + resampled = AF.resample(audio, orig_freq=src_sample_rate, new_freq=sample_rate) + return resampled.detach().cpu().numpy().astype(np.float32, copy=False) + + +def _hz_to_mel(frequencies: np.ndarray) -> np.ndarray: + """Slaney mel scale, matching the librosa/torchaudio convention.""" + frequencies = np.asarray(frequencies, dtype=np.float64) + f_sp = 200.0 / 3.0 + min_log_hz = 1000.0 + min_log_mel = min_log_hz / f_sp + logstep = np.log(6.4) / 27.0 + linear = frequencies / f_sp + log = ( + min_log_mel + np.log(np.maximum(frequencies, min_log_hz) / min_log_hz) / logstep + ) + return np.where(frequencies >= min_log_hz, log, linear) + + +def _mel_to_hz(mels: np.ndarray) -> np.ndarray: + mels = np.asarray(mels, dtype=np.float64) + f_sp = 200.0 / 3.0 + min_log_hz = 1000.0 + min_log_mel = min_log_hz / f_sp + logstep = np.log(6.4) / 27.0 + linear = mels * f_sp + log = min_log_hz * np.exp(logstep * (mels - min_log_mel)) + return np.where(mels >= min_log_mel, log, linear) + + +_MEL_BASIS_CACHE: Dict[Tuple[int, int, int], torch.Tensor] = {} + + +def _mel_basis(sample_rate: int, n_fft: int, n_mels: int) -> torch.Tensor: + key = (sample_rate, n_fft, n_mels) + cached = _MEL_BASIS_CACHE.get(key) + if cached is not None: + return cached + + fft_bins = n_fft // 2 + 1 + fft_freqs = np.arange(fft_bins, dtype=np.float64) * sample_rate / n_fft + mel_edges = _mel_to_hz( + np.linspace( + _hz_to_mel(np.array([0.0]))[0], + _hz_to_mel(np.array([sample_rate / 2.0]))[0], + n_mels + 2, + dtype=np.float64, + ) + ) + mel_widths = np.diff(mel_edges) + lower = (fft_freqs[None, :] - mel_edges[:-2, None]) / mel_widths[:-1, None] + upper = (mel_edges[2:, None] - fft_freqs[None, :]) / mel_widths[1:, None] + weights = np.maximum(0.0, np.minimum(lower, upper)) + + # Slaney area normalization. + weights *= (2.0 / (mel_edges[2:] - mel_edges[:-2]))[:, None] + basis = torch.from_numpy(weights.astype(np.float32, copy=False)).contiguous() + _MEL_BASIS_CACHE[key] = basis + return basis + + +def _dmel_bins(audio: torch.Tensor, params: InklingAudioEncoderParams) -> torch.Tensor: + hop_length = _to_exact_int( + params.audio_token_duration_s * params.sample_rate, + "audio_token_duration_s * sample_rate", + ) + window_size = _to_exact_int( + params.audio_token_duration_s + * params.window_size_multiplier + * params.sample_rate, + "audio_token_duration_s * window_size_multiplier * sample_rate", + ) + n_fft = params.n_fft or window_size + if hop_length <= 0 or window_size <= 0 or n_fft <= 0: + raise ValueError("audio hop length, window size, and n_fft must be positive") + if audio.numel() == 0: + return torch.empty((0, params.n_mels), dtype=torch.int32) + + right_pad = math.ceil(audio.numel() / hop_length) * hop_length - audio.numel() + left_pad = max(n_fft - hop_length, 0) + audio = F.pad(audio, (left_pad, right_pad)) + + window = torch.hann_window(window_size, periodic=True, dtype=torch.float32) + spec = torch.stft( + audio.unsqueeze(0), + n_fft=n_fft, + hop_length=hop_length, + win_length=window_size, + window=window, + center=False, + normalized=False, + onesided=True, + return_complex=True, + ) + spec_ri = torch.view_as_real(spec) + magnitude = ( + (spec_ri[..., 0].square() + spec_ri[..., 1].square()) + .clamp_min(1e-10) + .sqrt() + .squeeze(0) + ) + + mel = ( + _mel_basis(params.sample_rate, n_fft, params.n_mels) + .matmul(magnitude) + .clamp_min(1e-10) + .log10() + ) + mel = mel.to(torch.float64).clamp( + min=params.dmel_min_value, max=params.dmel_max_value + ) + bin_centers = torch.linspace( + params.dmel_min_value, + params.dmel_max_value, + params.num_dmel_bins, + dtype=torch.float64, + ) + dmel_bins = (mel.unsqueeze(-1) - bin_centers).abs().argmin(dim=-1) + return dmel_bins.to(torch.int32).T.contiguous() + + +class InklingAudioFeatureExtractor(FeatureExtractionMixin): + """Convert raw audio into Inkling dMel bins in the HF feature-extractor API.""" + + model_input_names = ["dmel_bins"] + + def __init__(self, params: Optional[dict] = None, **kwargs): + super().__init__(**kwargs) + merged = InklingAudioEncoderParams() + if params: + for k, v in params.items(): + if hasattr(merged, k): + setattr(merged, k, v) + # Accept Hugging Face-style flat keyword arguments. + for k in list(kwargs.keys()): + if hasattr(merged, k): + setattr(merged, k, kwargs[k]) + self.params = merged + + def _encode_one(self, audio) -> torch.Tensor: + raw_audio = _decode_audio(_load_audio_bytes(audio), self.params.sample_rate) + return _dmel_bins(raw_audio, self.params) + + def __call__( + self, + audios: Optional[Sequence], + return_tensors: Optional[str] = None, + **kwargs, + ) -> BatchFeature: + del return_tensors, kwargs + if audios is None: + audios = [] + if not isinstance(audios, (list, tuple)): + audios = [audios] + + dmel_bins = [self._encode_one(a) for a in audios] + data = { + # per-clip feature: dmel bins as float32 [T, n_mels] + "dmel_bins": [bins.to(torch.float32) for bins in dmel_bins], + "num_audio_tokens": [int(bins.shape[0]) for bins in dmel_bins], + } + # return_tensors intentionally ignored: per-clip features have ragged T. + return BatchFeature(data=data, tensor_type=None) diff --git a/python/sglang/srt/multimodal/inkling/image_processing.py b/python/sglang/srt/multimodal/inkling/image_processing.py new file mode 100644 index 000000000..1a99d45e3 --- /dev/null +++ b/python/sglang/srt/multimodal/inkling/image_processing.py @@ -0,0 +1,252 @@ +"""HuggingFace-convention image processor for Inkling models.""" + +from __future__ import annotations + +import io +import math +from typing import List, Optional, Union + +import numpy as np +import torch +from numba import njit +from transformers.image_processing_utils import BaseImageProcessor, BatchFeature +from transformers.image_utils import ImageInput + +IMAGE_MEAN = np.array([0.48145466, 0.4578275, 0.40821073], dtype=np.float32) +IMAGE_STD = np.array([0.26862954, 0.2613026, 0.2757771], dtype=np.float32) +PAD_RAW_VALUE = np.float32(-1.0 / 255.0) +PAD_NORM = (np.full((3,), PAD_RAW_VALUE, dtype=np.float32) - IMAGE_MEAN) / IMAGE_STD + + +def _validate_image_rescale( + rescale_image_frac: Optional[float], + rescale_image_max_upscaled_long_edge: Optional[int], +) -> None: + if rescale_image_frac is not None and ( + not math.isfinite(rescale_image_frac) or rescale_image_frac <= 0 + ): + raise ValueError( + "rescale_image_frac must be positive and finite or None, " + f"got {rescale_image_frac}" + ) + if rescale_image_max_upscaled_long_edge is None: + return + if rescale_image_max_upscaled_long_edge <= 0: + raise ValueError( + "rescale_image_max_upscaled_long_edge must be positive or None, " + f"got {rescale_image_max_upscaled_long_edge}" + ) + if rescale_image_frac is None or rescale_image_frac <= 1.0: + raise ValueError( + "rescale_image_max_upscaled_long_edge requires rescale_image_frac > 1, " + f"got {rescale_image_frac}" + ) + + +def _scaled_image_dimensions( + width: int, + height: int, + rescale_image_frac: Optional[float], + rescale_image_max_upscaled_long_edge: Optional[int], +) -> tuple[int, int]: + """Return the long-edge-scaled ``(width, height)``.""" + if rescale_image_frac is None: + return width, height + + long_edge = max(width, height) + if long_edge == 0: + return width, height + + target_long_edge = float(long_edge) * rescale_image_frac + if rescale_image_max_upscaled_long_edge is not None: + # The cap limits growth but never shrinks an image already above it. + effective_cap = max(rescale_image_max_upscaled_long_edge, long_edge) + target_long_edge = min(target_long_edge, float(effective_cap)) + + ratio = target_long_edge / float(long_edge) + if ratio == 1.0: + return width, height + + # Use half-away-from-zero rounding for positive dimensions. Python's round() + # uses ties-to-even, which can produce a different output size at exact halves. + def scale(value: int) -> int: + return max(1, math.floor(float(value) * ratio + 0.5)) + + return scale(width), scale(height) + + +def _load_image_bytes(image) -> bytes: + """Coerce a single image input into raw PNG/JPEG bytes for preprocessing.""" + if isinstance(image, (bytes, bytearray, memoryview)): + return bytes(image) + if isinstance(image, str): + if image.startswith(("http://", "https://", "data:")): + raise ValueError( + "InklingImageProcessor received a URL/data: image; resolve it to bytes " + "upstream (e.g. via SGLang load_mm_data) before preprocessing." + ) + path = image[len("file://") :] if image.startswith("file://") else image + with open(path, "rb") as f: + return f.read() + + from PIL import Image + + if isinstance(image, torch.Tensor): + arr = image.detach().cpu().numpy() + image = Image.fromarray(arr.astype("uint8") if arr.dtype != np.uint8 else arr) + elif not isinstance(image, Image.Image): + image = Image.fromarray(image) + if image.mode != "RGB": + image = image.convert("RGB") + buf = io.BytesIO() + image.save(buf, format="PNG") + return buf.getvalue() + + +@njit(cache=True) +def _fill_patches_numba( + arr: np.ndarray, + patch_size: int, + patches: np.ndarray, + mean: np.ndarray, + std: np.ndarray, + pad_norm: np.ndarray, +) -> None: + h = arr.shape[0] + w = arr.shape[1] + nph = (h + patch_size - 1) // patch_size + npw = w // patch_size + 1 + inv255 = np.float32(1.0 / 255.0) + + for k in range(nph * npw): + i = k // npw + j = k - i * npw + y_base = i * patch_size + x_base = j * patch_size + + for y in range(patch_size): + iy = y_base + y + for x in range(patch_size): + ix = x_base + x + if iy < h and ix < w: + for c in range(3): + raw = np.float32(arr[iy, ix, c]) * inv255 + patches[k, y, x, c] = (raw - mean[c]) / std[c] + else: + for c in range(3): + patches[k, y, x, c] = pad_norm[c] + + +def _encode_image_bytes( + image_bytes: bytes, + *, + patch_size: int, + rescale_image_frac: Optional[float], + rescale_image_max_upscaled_long_edge: Optional[int], +) -> torch.Tensor: + if patch_size <= 0: + raise ValueError("patch_size must be greater than zero") + _validate_image_rescale( + rescale_image_frac, + rescale_image_max_upscaled_long_edge, + ) + + from PIL import Image + + image = Image.open(io.BytesIO(image_bytes)).convert("RGB") + scaled_size = _scaled_image_dimensions( + *image.size, + rescale_image_frac=rescale_image_frac, + rescale_image_max_upscaled_long_edge=rescale_image_max_upscaled_long_edge, + ) + if scaled_size != image.size: + image = image.resize(scaled_size, resample=Image.Resampling.LANCZOS) + arr = np.array(image, dtype=np.uint8, copy=True) + height, width, _ = arr.shape + + nph = (height + patch_size - 1) // patch_size + npw = width // patch_size + 1 + num_patches = nph * npw + + patches = np.empty((num_patches, patch_size, patch_size, 3), dtype=np.float32) + _fill_patches_numba(arr, patch_size, patches, IMAGE_MEAN, IMAGE_STD, PAD_NORM) + + return ( + torch.from_numpy(patches) + .to(torch.bfloat16) + .view(num_patches, 1, patch_size, patch_size, 3) + .expand(num_patches, 2, patch_size, patch_size, 3) + ) + + +class InklingImageProcessor(BaseImageProcessor): + r"""Turn raw images into ``vision_patches_bthwc`` for Inkling hMLP. + + ``rescale_image_frac`` scales the long edge while preserving aspect ratio. + ``rescale_image_max_upscaled_long_edge`` optionally caps only upscaling and + therefore requires a scale factor greater than one. The defaults, ``2.0`` and + ``2048``, grow images toward a 2048-pixel long edge by at most 2x, while leaving + images already at or above 2048 unchanged. + """ + + model_input_names = ["vision_patches_bthwc"] + + def __init__( + self, + patch_size: int = 40, + rescale_image_frac: Optional[float] = 2.0, + rescale_image_max_upscaled_long_edge: Optional[int] = 2048, + **kwargs, + ): + if patch_size <= 0: + raise ValueError("patch_size must be greater than zero") + _validate_image_rescale( + rescale_image_frac, + rescale_image_max_upscaled_long_edge, + ) + super().__init__(**kwargs) + self.patch_size = patch_size + self.rescale_image_frac = rescale_image_frac + self.rescale_image_max_upscaled_long_edge = rescale_image_max_upscaled_long_edge + + def _encode_one(self, image) -> torch.Tensor: + return _encode_image_bytes( + _load_image_bytes(image), + patch_size=self.patch_size, + rescale_image_frac=self.rescale_image_frac, + rescale_image_max_upscaled_long_edge=self.rescale_image_max_upscaled_long_edge, + ) + + def preprocess( + self, + images: Union[ImageInput, List], + return_tensors: Optional[str] = "pt", + **kwargs, + ) -> BatchFeature: + del return_tensors, kwargs + if not isinstance(images, (list, tuple)): + images = [images] + + per_image_patches: List[torch.Tensor] = [] + num_patches: List[int] = [] + num_tokens: List[int] = [] + for img in images: + vp = self._encode_one(img) + n_patches = int(vp.shape[0]) + per_image_patches.append(vp) + num_patches.append(n_patches) + num_tokens.append(n_patches) + + if len(per_image_patches) == 1: + vision_patches_bthwc = per_image_patches[0] + elif per_image_patches: + vision_patches_bthwc = torch.cat(per_image_patches, dim=0) + else: + vision_patches_bthwc = torch.empty(0) + + data = { + "vision_patches_bthwc": vision_patches_bthwc, + "num_patches": num_patches, + "num_tokens": num_tokens, + } + return BatchFeature(data=data, tensor_type=None) diff --git a/python/sglang/srt/multimodal/inkling/image_processing_rust.py b/python/sglang/srt/multimodal/inkling/image_processing_rust.py new file mode 100644 index 000000000..ec573ee76 --- /dev/null +++ b/python/sglang/srt/multimodal/inkling/image_processing_rust.py @@ -0,0 +1,101 @@ +from __future__ import annotations + +import io +from concurrent.futures import ThreadPoolExecutor +from typing import List, Optional, Union + +import numpy as np +import torch +from PIL import Image +from transformers.image_processing_utils import BaseImageProcessor, BatchFeature +from transformers.image_utils import ImageInput + +from sglang.srt.multimodal._core import inkling as _rs +from sglang.srt.multimodal.inkling.image_processing import _load_image_bytes + + +def _bits_to_bthwc( + bits: np.ndarray, height: int, width: int, patch_size: int +) -> torch.Tensor: + nph = (height + patch_size - 1) // patch_size + npw = width // patch_size + 1 + n = nph * npw + return ( + torch.from_numpy(bits) + .view(torch.bfloat16) + .view(n, 1, patch_size, patch_size, 3) + .expand(n, 2, patch_size, patch_size, 3) + ) + + +_pil_pool = ThreadPoolExecutor(max_workers=8) + + +def _pil_decode(raw: bytes) -> np.ndarray: + return np.ascontiguousarray(np.array(Image.open(io.BytesIO(raw)).convert("RGB"))) + + +class InklingRustImageProcessor(BaseImageProcessor): + model_input_names = ["vision_patches_bthwc"] + + def __init__( + self, + patch_size: int = 40, + rescale_image_frac: Optional[float] = 2.0, + rescale_image_max_upscaled_long_edge: Optional[int] = 2048, + **kwargs, + ): + super().__init__(**kwargs) + self.patch_size = patch_size + self.rescale_image_frac = rescale_image_frac + self.rescale_image_max_upscaled_long_edge = rescale_image_max_upscaled_long_edge + + def preprocess( + self, + images: Union[ImageInput, List], + return_tensors: Optional[str] = "pt", + **kwargs, + ) -> BatchFeature: + del return_tensors, kwargs + if not isinstance(images, (list, tuple)): + images = [images] + + raw_list = [_load_image_bytes(img) for img in images] + + # PIL releases the GIL while decoding. + arrays = list(_pil_pool.map(_pil_decode, raw_list)) + + per_image_patches: List[torch.Tensor] = [] + num_patches: List[int] = [] + num_tokens: List[int] = [] + content_hashes: List[int] = [] + + for arr, raw in zip(arrays, raw_list): + h, w, bits, content_hash = _rs.rescale_patchify_hash( + arr, + raw, + self.patch_size, + self.rescale_image_frac, + self.rescale_image_max_upscaled_long_edge, + ) + vp = _bits_to_bthwc(bits, h, w, self.patch_size) + n_patches = int(vp.shape[0]) + per_image_patches.append(vp) + num_patches.append(n_patches) + num_tokens.append(n_patches) + content_hashes.append(content_hash) + + if len(per_image_patches) == 1: + vision_patches_bthwc = per_image_patches[0] + elif per_image_patches: + vision_patches_bthwc = torch.cat(per_image_patches, dim=0) + else: + vision_patches_bthwc = torch.empty(0) + + data = { + "vision_patches_bthwc": vision_patches_bthwc, + "num_patches": num_patches, + "num_tokens": num_tokens, + "content_hashes": content_hashes, + } + return BatchFeature(data=data, tensor_type=None) diff --git a/python/sglang/srt/multimodal/inkling/processing_inkling.py b/python/sglang/srt/multimodal/inkling/processing_inkling.py new file mode 100644 index 000000000..534a430d7 --- /dev/null +++ b/python/sglang/srt/multimodal/inkling/processing_inkling.py @@ -0,0 +1,51 @@ +# Copyright 2026 SGLang Team +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""HuggingFace-convention processor for Inkling multimodal models. + +Composes the image processor + audio feature extractor + (optional) tokenizer. +``tokenizer`` is optional because the text chat renderer is out of scope here. +""" + +from __future__ import annotations + +from typing import List, Optional + +from sglang.srt.multimodal.inkling.feature_extraction import ( + InklingAudioFeatureExtractor, +) +from sglang.srt.multimodal.inkling.image_processing import InklingImageProcessor + + +class InklingProcessor: + """Bundle Inkling image + audio preprocessing with the MM token ids from the config.""" + + def __init__( + self, + image_processor: Optional[InklingImageProcessor] = None, + audio_feature_extractor: Optional[InklingAudioFeatureExtractor] = None, + tokenizer=None, + ): + self.image_processor = image_processor or InklingImageProcessor() + self.audio_feature_extractor = ( + audio_feature_extractor or InklingAudioFeatureExtractor() + ) + self.tokenizer = tokenizer + + def process_images(self, images: List): + """Raw images -> BatchFeature(vision_patches_bthwc, num_patches, num_tokens).""" + return self.image_processor.preprocess(images, return_tensors="pt") + + def process_audios(self, audios: List): + """Raw audios -> BatchFeature(dmel_bins, num_audio_tokens).""" + return self.audio_feature_extractor(audios) diff --git a/python/sglang/srt/multimodal/processors/inkling.py b/python/sglang/srt/multimodal/processors/inkling.py new file mode 100644 index 000000000..88df4efe0 --- /dev/null +++ b/python/sglang/srt/multimodal/processors/inkling.py @@ -0,0 +1,317 @@ +# Copyright 2026 SGLang Team +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ============================================================================== +"""SGLang multimodal processor for Inkling models. + +Adapts the HF-convention feature extraction in ``sglang.srt.multimodal.inkling`` to +SGLang's ``BaseMultimodalProcessor``: expands the MM placeholder tokens in a +pre-rendered ``input_ids`` (the chat renderer is a separate workstream) and attaches +per-item features as ``MultimodalDataItem``s. A modality is enabled only when its +``*_config.decoder_dmodel`` is set, so text-only checkpoints disable both towers. +""" + +from __future__ import annotations + +import base64 +import logging +import urllib.request +from collections.abc import Mapping +from typing import Any, Dict, List, Optional, Tuple, Type, Union + +import torch + +from sglang.srt.environ import envs +from sglang.srt.managers.schedule_batch import ( + Modality, + MultimodalDataItem, + MultimodalProcessorOutput, +) +from sglang.srt.models.inkling import InklingForConditionalGeneration +from sglang.srt.multimodal.inkling import ( + InklingAudioFeatureExtractor, + InklingImageProcessor, + InklingProcessor, +) +from sglang.srt.multimodal.processors.base_processor import ( + BaseMultimodalProcessor as SGLangBaseProcessor, +) +from sglang.srt.parser.inkling_tokenizer import ( + AUDIO_END, +) +from sglang.srt.parser.inkling_tokenizer import AUDIO_TOKEN_ID as INKLING_AUDIO_TOKEN_ID +from sglang.srt.parser.inkling_tokenizer import IMAGE_TOKEN_ID as INKLING_IMAGE_TOKEN_ID +from sglang.srt.parser.inkling_tokenizer import ( + INKLING_SPECIAL_TOKEN_IDS, +) + +logger = logging.getLogger(__name__) + + +def _cfg(obj, name, default=None): + return getattr(obj, name, default) if obj is not None else default + + +def _resolve_media_item(item): + """Resolve a request media item to raw bytes for Inkling preprocessing. + + The OpenAI chat path hands ImageData/AudioData objects or data:/http(s) URLs; the + /generate path hands raw bytes or local file paths. Resolve URLs to bytes here + (data: base64 -> exact original bytes; http(s) -> download) and pass bytes / file + paths / PIL through unchanged for the per-modality byte loader to handle. + """ + url = None + if isinstance(item, str): + url = item + elif isinstance(item, Mapping): + url = item.get("url") + elif hasattr(item, "url"): + url = getattr(item, "url") + if not isinstance(url, str): + return item + if url.startswith("data:"): + header, _, payload = url.partition(",") + return base64.b64decode(payload) if ";base64" in header else payload.encode() + if url.startswith(("http://", "https://")): + with urllib.request.urlopen(url, timeout=30) as resp: + return resp.read() + return url # plain path / file:// -> handled by the per-modality byte loader + + +_MISSING = object() + + +def _require(obj, name, *, where): + """Read a config field that MUST be present — no silent default. + + A wrong-but-silent fallback here corrupts model inputs (e.g. encoding with a + different dmel grid than the model de-bins with yields garbage audio embeddings + and no error), so fail loudly instead. + """ + val = getattr(obj, name, _MISSING) if obj is not None else _MISSING + if val is _MISSING or val is None: + raise ValueError( + f"InklingMultimodalProcessor: required config field {where}.{name!r} is " + f"missing. It must be set in the model config so preprocessing matches " + f"the model. Add it to config.json." + ) + return val + + +class InklingMultimodalProcessor(SGLangBaseProcessor): + # import_processors() registers this for the Inkling arch. Text-only checkpoints leave + # both towers disabled (gated on *_config.decoder_dmodel), so it is a no-op there. + models: List[Type] = [InklingForConditionalGeneration] + + def __init__(self, hf_config, server_args, _processor, *args, **kwargs): + super().__init__(hf_config, server_args, _processor, *args, **kwargs) + + vision_config = _cfg(hf_config, "vision_config") + audio_config = _cfg(hf_config, "audio_config") + # InklingMMConfig always builds default vision/audio sub-configs (decoder_dmodel=None) + # even for the text-only model, so gate "enabled" on decoder_dmodel being set, not + # on the sub-config existing — else a text-only checkpoint trips the checks below. + vision_enabled = _cfg(vision_config, "decoder_dmodel") is not None + audio_enabled = _cfg(audio_config, "decoder_dmodel") is not None + + patch_size = _cfg(vision_config, "patch_size", 40) + if envs.SGLANG_INKLING_RS_MM_PREPROCESS.get(): + try: + from sglang.srt.multimodal.inkling.image_processing_rust import ( + InklingRustImageProcessor, + ) + + image_processor = InklingRustImageProcessor(patch_size=patch_size) + logger.info("Using Rust-accelerated Inkling image processor") + except ImportError: + logger.warning( + "SGLANG_INKLING_RS_MM_PREPROCESS=1 but sglang.srt.multimodal._core is not available; " + "falling back to the default image processor." + ) + image_processor = InklingImageProcessor(patch_size=patch_size) + else: + image_processor = InklingImageProcessor(patch_size=patch_size) + # The dmel grid used here at encode time must equal what the model de-bins with + # at decode (InklingAudio reads it from audio_config); require it when audio + # is enabled rather than guessing a default that would silently corrupt embeds. + if audio_enabled: + audio_params = { + "n_mels": _require(audio_config, "n_mel_bins", where="audio_config"), + "num_dmel_bins": _require( + audio_config, "mel_vocab_size", where="audio_config" + ), + "dmel_min_value": _require( + audio_config, "dmel_min_value", where="audio_config" + ), + "dmel_max_value": _require( + audio_config, "dmel_max_value", where="audio_config" + ), + } + else: + audio_params = {} + audio_extractor = InklingAudioFeatureExtractor(params=audio_params) + + # Inkling's placeholder ids are protocol constants. Older tensor-IO checkpoints may + # omit them from config.json, so fall back to the local renderer constants when + # the corresponding tower is enabled. + self.IMAGE_TOKEN_ID = _cfg(hf_config, "image_token_id") + self.AUDIO_TOKEN_ID = _cfg(hf_config, "audio_token_id") + self.AUDIO_END_TOKEN_ID = _cfg(hf_config, "audio_end_token_id") + if vision_enabled and self.IMAGE_TOKEN_ID is None: + self.IMAGE_TOKEN_ID = INKLING_IMAGE_TOKEN_ID + if audio_enabled and self.AUDIO_TOKEN_ID is None: + self.AUDIO_TOKEN_ID = INKLING_AUDIO_TOKEN_ID + if audio_enabled and self.AUDIO_END_TOKEN_ID is None: + self.AUDIO_END_TOKEN_ID = INKLING_SPECIAL_TOKEN_IDS[AUDIO_END] + + self.inkling_processor = InklingProcessor( + image_processor=image_processor, + audio_feature_extractor=audio_extractor, + tokenizer=self._tokenizer, + ) + + # ---- core (pure, testable) ------------------------------------------ + + def assemble( + self, + input_ids: List[int], + image_data: Optional[List] = None, + audio_data: Optional[List] = None, + ) -> MultimodalProcessorOutput: + """Expand single MM placeholders in ``input_ids`` into per-item token + blocks and attach features. Walks ``input_ids`` left-to-right, consuming + ``image_data`` / ``audio_data`` in encounter order. + """ + image_data = image_data or [] + audio_data = audio_data or [] + + # One placeholder per media item (expanded below); a count mismatch (incl. a + # None token id absent from config) must fail loudly, not drop media silently. + n_img_ph = ( + sum(1 for t in input_ids if t == self.IMAGE_TOKEN_ID) + if self.IMAGE_TOKEN_ID is not None + else 0 + ) + n_aud_ph = ( + sum(1 for t in input_ids if t == self.AUDIO_TOKEN_ID) + if self.AUDIO_TOKEN_ID is not None + else 0 + ) + if n_img_ph != len(image_data): + raise ValueError( + f"InklingMultimodalProcessor: {n_img_ph} image placeholder token(s) in " + f"input_ids but {len(image_data)} image(s) provided; counts must match." + ) + if n_aud_ph != len(audio_data): + raise ValueError( + f"InklingMultimodalProcessor: {n_aud_ph} audio placeholder token(s) in " + f"input_ids but {len(audio_data)} audio(s) provided; counts must match." + ) + + img_feat = ( + self.inkling_processor.process_images(image_data) if image_data else None + ) + aud_feat = ( + self.inkling_processor.process_audios(audio_data) if audio_data else None + ) + + # Rust processor returns content_hashes; original processor does not. + img_hashes = img_feat.get("content_hashes") if img_feat else None + + out_ids: List[int] = [] + image_items: List[Tuple[int, int, torch.Tensor]] = [] # (start, end, feature) + audio_items: List[Tuple[int, int, torch.Tensor]] = [] + i_img = i_aud = 0 + + for tok in input_ids: + if self.IMAGE_TOKEN_ID is not None and tok == self.IMAGE_TOKEN_ID: + # hMLP folds each patch's interior to channel depth -> one token per + # patch, so num_tokens == num_patches. Assert in case a fold is ever added. + n_tokens = img_feat["num_tokens"][i_img] + n_patches = img_feat["num_patches"][i_img] + assert n_tokens == n_patches, ( + f"num_tokens ({n_tokens}) != num_patches ({n_patches}); the hMLP " + f"emits one token per patch. Drive the placeholder count and the " + f"feature row count from a single source if a fold is added." + ) + start = len(out_ids) + out_ids.extend([self.IMAGE_TOKEN_ID] * n_tokens) + # patches for this image are the i_img-th contiguous slice + base = sum(img_feat["num_patches"][:i_img]) + feat = img_feat["vision_patches_bthwc"][base : base + n_patches] + image_items.append((start, start + n_tokens - 1, feat)) + i_img += 1 + elif self.AUDIO_TOKEN_ID is not None and tok == self.AUDIO_TOKEN_ID: + n_tokens = aud_feat["num_audio_tokens"][i_aud] + start = len(out_ids) + out_ids.extend([self.AUDIO_TOKEN_ID] * n_tokens) + feat = aud_feat["dmel_bins"][i_aud] + audio_items.append((start, start + n_tokens - 1, feat)) + i_aud += 1 + else: + out_ids.append(tok) + + mm_items: List[MultimodalDataItem] = [] + for idx, (start, end, feat) in enumerate(image_items): + mm_items.append( + MultimodalDataItem( + modality=Modality.IMAGE, + feature=feat, + offsets=[(start, end)], + hash=img_hashes[idx] if img_hashes else None, + ) + ) + for start, end, feat in audio_items: + mm_items.append( + MultimodalDataItem( + modality=Modality.AUDIO, feature=feat, offsets=[(start, end)] + ) + ) + + return MultimodalProcessorOutput( + input_ids=out_ids, + mm_items=mm_items, + im_token_id=self.IMAGE_TOKEN_ID, + audio_token_id=self.AUDIO_TOKEN_ID, + audio_end_id=self.AUDIO_END_TOKEN_ID, + ) + + # ---- SGLang entrypoint ---------------------------------------------- + + async def process_mm_data_async( + self, + image_data: Optional[List[Union[str, bytes, Dict]]] = None, + audio_data: Optional[List[Union[str, bytes, Dict]]] = None, + input_text: str = "", + request_obj: Any = None, + *args, + **kwargs, + ) -> Optional[MultimodalProcessorOutput]: + input_ids = getattr(request_obj, "input_ids", None) + if input_ids is None: + if self._tokenizer is None: + raise ValueError( + "InklingMultimodalProcessor v1 requires pre-rendered input_ids " + "(request_obj.input_ids); the custom Inkling chat renderer is a " + "separate workstream. No tokenizer available to render text." + ) + input_ids = self._tokenizer(input_text).input_ids + if isinstance(input_ids, torch.Tensor): + input_ids = input_ids.flatten().tolist() + + # Resolve request media (data:/http URLs, ImageData objects) to bytes so the + # Inkling preprocessors can consume them; bytes / paths pass through unchanged. + if image_data: + image_data = [_resolve_media_item(it) for it in image_data] + if audio_data: + audio_data = [_resolve_media_item(it) for it in audio_data] + return self.assemble(list(input_ids), image_data, audio_data) diff --git a/python/sglang/srt/parser/inkling_renderer.py b/python/sglang/srt/parser/inkling_renderer.py new file mode 100644 index 000000000..1502cecb7 --- /dev/null +++ b/python/sglang/srt/parser/inkling_renderer.py @@ -0,0 +1,333 @@ +from __future__ import annotations + +import json +import math +from collections.abc import Mapping, Sequence +from typing import Any, Protocol + +from sglang.srt.parser.inkling_tokenizer import ( + AUDIO_END, + AUDIO_TOKEN_ID, + CONTENT_AUDIO_INPUT, + CONTENT_IMAGE, + CONTENT_INVOKE_TOOL_JSON, + CONTENT_MODEL_END_SAMPLING, + CONTENT_TEXT, + CONTENT_THINKING, + CONTENT_XML, + END_MESSAGE, + IMAGE_TOKEN_ID, + MESSAGE_MODEL, + ROLE_MESSAGE_TOKENS, +) + + +class InklingTextTokenizer(Protocol): + def encode_text(self, text: str) -> list[int]: ... + + def encode_special(self, token: str) -> int: ... + + +# OpenAI content-part type spellings that mean image / audio (render only needs the kind, +# not the bytes — the bytes are encoded later in the MM processor). +_IMAGE_PART_TYPES = frozenset({"image", "input_image", "image_url"}) +_AUDIO_PART_TYPES = frozenset({"audio", "input_audio", "audio_url"}) +INKLING_DEFAULT_REASONING_EFFORT = 0.9 + + +def render_inkling_messages( + messages: Sequence[Mapping[str, Any]], + tokenizer: InklingTextTokenizer, + *, + add_generation_prompt: bool = False, + tools: Sequence[Mapping[str, Any]] | None = None, + reasoning_effort: float | None = None, +) -> list[int]: + """Render chat messages to Inkling input_ids with ONE placeholder per media item. + + PURE renderer: emits Inkling framing + a single IMAGE_TOKEN_ID / AUDIO_TOKEN_ID + per image / audio part. Media encoding and 1->N placeholder expansion happen + later in the MM processor. Inkling normally emits its own assistant turn + opener; ``add_generation_prompt`` is retained only for legacy callers. The + conversation-level effort directive is emitted once in the initial prefix + and defaults to 0.9. + """ + input_ids: list[int] = [] + tool_call_id_to_name: dict[str, str] = {} + + if tools: + _append_message( + input_ids, + tokenizer, + "system", + "xml", + _tool_declare_json(tools), + author_name="tool_declare", + ) + + # Normalize the OpenAI "developer" role to "system" (as the Responses API + # does in _normalize_response_message_for_chat) so developer-instruction + # messages render instead of tripping _expect_role; the leading-system + # grouping below then also sees them. Shallow-copy only affected messages so + # the caller's list is left untouched. + message_list = [ + {**message, "role": "system"} if message.get("role") == "developer" else message + for message in messages + ] + leading_system_count = 0 + for message in message_list: + if message.get("role") != "system": + break + leading_system_count += 1 + + def append_effort() -> None: + effort = ( + INKLING_DEFAULT_REASONING_EFFORT + if reasoning_effort is None + else reasoning_effort + ) + _append_message( + input_ids, + tokenizer, + "system", + "text", + f"Thinking effort level: {_format_reasoning_effort(effort)}", + ) + + for message_index, message in enumerate(message_list): + if message_index == leading_system_count: + append_effort() + role = _expect_role(message) + if role == "tool": + tool_name = message.get("name") or tool_call_id_to_name.get( + message.get("tool_call_id") or "", "" + ) + _append_message( + input_ids, + tokenizer, + "tool", + "text", + _expect_string_content(message.get("content", "")), + author_name=str(tool_name), + ) + continue + + parts = list(_iter_render_parts(message.get("content", ""))) + turn_start = len(input_ids) + if role == "assistant": + reasoning_content = message.get("reasoning_content") + if reasoning_content: + if not isinstance(reasoning_content, str): + raise TypeError( + "assistant reasoning_content must be a string for Inkling rendering" + ) + if any(kind == "thinking" for kind, _ in parts): + raise ValueError( + "assistant message cannot mix reasoning_content with ordered thinking parts" + ) + _append_message( + input_ids, + tokenizer, + "assistant", + "thinking", + reasoning_content, + ) + + for kind, text in parts: + if kind == "thinking" and role != "assistant": + raise ValueError("Inkling thinking parts require role='assistant'") + _append_message(input_ids, tokenizer, role, kind, text) + + if role == "assistant": + for tool_call in message.get("tool_calls") or []: + name, args = _tool_call_name_and_args(tool_call) + tool_call_id = _as_mapping(tool_call).get("id") + if tool_call_id: + tool_call_id_to_name[str(tool_call_id)] = name + _append_message( + input_ids, + tokenizer, + "assistant", + "invoke_tool_json", + _tool_call_json(name, args), + author_name=name, + ) + if len(input_ids) > turn_start: + # Close the historical model turn — but never emit a bare + # terminator for an assistant message that rendered no blocks. + input_ids.append(tokenizer.encode_special(CONTENT_MODEL_END_SAMPLING)) + + if leading_system_count == len(message_list): + append_effort() + + if add_generation_prompt: + input_ids.append(tokenizer.encode_special(MESSAGE_MODEL)) + return input_ids + + +def _append_message( + input_ids: list[int], + tokenizer: InklingTextTokenizer, + role: str, + kind: str, + text: str, + *, + author_name: str | None = None, +) -> None: + input_ids.append(tokenizer.encode_special(ROLE_MESSAGE_TOKENS[role])) + if author_name: + input_ids.extend(tokenizer.encode_text(author_name)) + + if kind == "text": + input_ids.append(tokenizer.encode_special(CONTENT_TEXT)) + input_ids.extend(tokenizer.encode_text(text)) + elif kind == "image": + input_ids.append(tokenizer.encode_special(CONTENT_IMAGE)) + input_ids.append(IMAGE_TOKEN_ID) + elif kind == "audio": + input_ids.append(tokenizer.encode_special(CONTENT_AUDIO_INPUT)) + input_ids.append(AUDIO_TOKEN_ID) + input_ids.append(tokenizer.encode_special(AUDIO_END)) + elif kind == "thinking": + input_ids.append(tokenizer.encode_special(CONTENT_THINKING)) + input_ids.extend(tokenizer.encode_text(text)) + elif kind == "xml": + input_ids.append(tokenizer.encode_special(CONTENT_XML)) + input_ids.extend(tokenizer.encode_text(text)) + elif kind == "invoke_tool_json": + input_ids.append(tokenizer.encode_special(CONTENT_INVOKE_TOOL_JSON)) + input_ids.extend(tokenizer.encode_text(text)) + else: + raise ValueError(f"unsupported Inkling render part kind: {kind!r}") + + input_ids.append(tokenizer.encode_special(END_MESSAGE)) + + +def _iter_render_parts(content: Any): + """Yield ordered ``(kind, text)`` pairs from message content.""" + if content is None: + return + if isinstance(content, str): + if content: + yield ("text", content) + return + if not isinstance(content, Sequence) or isinstance(content, (bytes, bytearray)): + raise TypeError("message content must be a string or a sequence of parts") + for part in content: + if isinstance(part, str): + yield ("text", part) + continue + if not isinstance(part, Mapping): + raise TypeError(f"content part must be mapping, got {type(part).__name__}") + ptype = part.get("type") + if ptype in (None, "text", "input_text"): + text = part.get("text", "") + yield ("text", text if isinstance(text, str) else "") + elif ptype in ("thinking", "reasoning"): + text = part.get("thinking") + if text is None: + text = part.get("text", "") + if not isinstance(text, str): + raise TypeError("Inkling thinking part payload must be a string") + yield ("thinking", text) + elif ptype in _IMAGE_PART_TYPES: + yield ("image", "") + elif ptype in _AUDIO_PART_TYPES: + yield ("audio", "") + else: + raise ValueError(f"unsupported content part type: {ptype!r}") + + +def _format_reasoning_effort(reasoning_effort: float) -> str: + if isinstance(reasoning_effort, bool) or not isinstance( + reasoning_effort, (int, float) + ): + raise TypeError("Inkling reasoning_effort must be a number") + value = float(reasoning_effort) + if not math.isfinite(value) or not 0.0 <= value <= 0.99: + raise ValueError("Inkling reasoning_effort must be finite and in [0.0, 0.99]") + return f"{round(value, 2):g}" + + +def _expect_string_content(content: Any) -> str: + if content is None: + return "" + if not isinstance(content, str): + raise TypeError( + f"message content must be a string for this Inkling role, got {type(content).__name__}" + ) + return content + + +def _expect_role(message: Mapping[str, Any]) -> str: + role = message.get("role") + if role not in ROLE_MESSAGE_TOKENS: + raise ValueError( + f"unsupported Inkling message role {role!r}; expected one of {sorted(ROLE_MESSAGE_TOKENS)}" + ) + return str(role) + + +def _as_mapping(value: Any) -> Mapping[str, Any]: + if isinstance(value, Mapping): + return value + if hasattr(value, "model_dump"): + dumped = value.model_dump() + if isinstance(dumped, Mapping): + return dumped + raise TypeError(f"expected mapping, got {type(value).__name__}") + + +def _canonical_json(value: Any) -> str: + return json.dumps( + _sort_json(value), + ensure_ascii=False, + allow_nan=False, + separators=(",", ":"), + ) + + +def _sort_json(value: Any) -> Any: + if isinstance(value, Mapping): + return {str(key): _sort_json(value[key]) for key in sorted(value)} + if isinstance(value, Sequence) and not isinstance(value, (str, bytes, bytearray)): + return [_sort_json(item) for item in value] + return value + + +def _tool_declare_json(tools: Sequence[Mapping[str, Any]]) -> str: + tool_specs = [] + for tool_value in tools: + tool = _as_mapping(tool_value) + function = _as_mapping(tool.get("function", {})) + tool_specs.append( + { + "description": function.get("description") or "", + "name": function["name"], + "parameters": function.get("parameters") or {}, + "type": tool.get("type", "function"), + } + ) + return _canonical_json(tool_specs) + + +def _tool_call_name_and_args(tool_call_value: Any) -> tuple[str, Mapping[str, Any]]: + tool_call = _as_mapping(tool_call_value) + function = _as_mapping(tool_call.get("function", {})) + name = function.get("name") + if not isinstance(name, str): + raise TypeError("tool call function name must be a string") + + raw_args = function.get("arguments") or {} + if isinstance(raw_args, str): + args = json.loads(raw_args) if raw_args else {} + else: + args = raw_args + if not isinstance(args, Mapping): + raise TypeError("tool call function arguments must decode to an object") + return name, args + + +def _tool_call_json(name: str, args: Mapping[str, Any]) -> str: + name_json = json.dumps(name, ensure_ascii=False, allow_nan=False) + return f'{{"name":{name_json},"args":{_canonical_json(args)}}}' diff --git a/python/sglang/srt/parser/inkling_tokenizer.py b/python/sglang/srt/parser/inkling_tokenizer.py new file mode 100644 index 000000000..a6b5495e7 --- /dev/null +++ b/python/sglang/srt/parser/inkling_tokenizer.py @@ -0,0 +1,105 @@ +from __future__ import annotations + +from collections.abc import Mapping +from dataclasses import dataclass +from typing import Any + +END_OF_TEXT = "<|endoftext|>" +MESSAGE_USER = "<|message_user|>" +MESSAGE_MODEL = "<|message_model|>" +MESSAGE_SYSTEM = "<|message_system|>" +MESSAGE_TOOL = "<|message_tool|>" +CONTENT_TEXT = "<|content_text|>" +CONTENT_IMAGE = "<|content_image|>" +CONTENT_MODEL_END_SAMPLING = "<|content_model_end_sampling|>" +CONTENT_THINKING = "<|content_thinking|>" +CONTENT_AUDIO_INPUT = "<|content_audio_input|>" +CONTENT_TOOL_ERROR = "<|content_tool_error|>" +CONTENT_XML = "<|content_xml|>" +CONTENT_INVOKE_TOOL_JSON = "<|content_invoke_tool_json|>" +CONTENT_INVOKE_TOOL_TEXT = "<|content_invoke_tool_text|>" +END_MESSAGE = "<|end_message|>" +AUDIO_END = "<|audio_end|>" + +IMAGE_TOKEN_ID = -101 +AUDIO_TOKEN_ID = -102 + +INKLING_SPECIAL_TOKEN_IDS: dict[str, int] = { + END_OF_TEXT: 199999, + MESSAGE_USER: 200000, + MESSAGE_MODEL: 200001, + MESSAGE_SYSTEM: 200002, + MESSAGE_TOOL: 200003, + CONTENT_TEXT: 200004, + CONTENT_IMAGE: 200005, + CONTENT_MODEL_END_SAMPLING: 200006, + CONTENT_THINKING: 200008, + END_MESSAGE: 200010, + CONTENT_AUDIO_INPUT: 200020, + CONTENT_TOOL_ERROR: 200022, + CONTENT_XML: 200024, + AUDIO_END: 200043, + CONTENT_INVOKE_TOOL_JSON: 200049, + CONTENT_INVOKE_TOOL_TEXT: 200057, +} + +INKLING_SPECIAL_TOKENS: frozenset[str] = frozenset(INKLING_SPECIAL_TOKEN_IDS) + +# The full control alphabet the streaming parsers key on: every framing token +# plus control tokens the model can emit that have no framing-ID mapping. +# The reasoning parser and the tool-call detector MUST share this alphabet — +# a token visible to one but not the other lets malformed headers slip through. +INKLING_CONTROL_TOKENS: frozenset[str] = frozenset( + { + *INKLING_SPECIAL_TOKENS, + "<|content_invoke_tool|>", + "<|model_trigger_generation|>", + } +) + +INKLING_SPECIAL_TOKEN_NAMES: dict[str, str] = { + token.removeprefix("<|").removesuffix("|>"): token + for token in INKLING_SPECIAL_TOKENS +} + +ROLE_MESSAGE_TOKENS: dict[str, str] = { + "user": MESSAGE_USER, + "assistant": MESSAGE_MODEL, + "system": MESSAGE_SYSTEM, + "tool": MESSAGE_TOOL, +} + + +def normalize_special_token(token: str) -> str: + """Accept either message_user or <|message_user|> spellings.""" + if token in INKLING_SPECIAL_TOKENS: + return token + try: + return INKLING_SPECIAL_TOKEN_NAMES[token] + except KeyError as exc: + raise KeyError(f"unknown Inkling special token: {token!r}") from exc + + +@dataclass(frozen=True) +class InklingTokenizer: + """Small wrapper around a base text tokenizer plus Inkling framing IDs. + + Plain text is encoded by the base tokenizer, while the minimal chat + framing tokens are inserted from the fixed overlay map. + """ + + tokenizer: Any + special_token_ids: Mapping[str, int] | None = None + + def encode_text(self, text: str) -> list[int]: + if not isinstance(text, str): + raise TypeError(f"text must be str, got {type(text).__name__}") + return list(self.tokenizer.encode(text, add_special_tokens=False)) + + def encode_special(self, token: str) -> int: + special = normalize_special_token(token) + token_ids = self.special_token_ids or INKLING_SPECIAL_TOKEN_IDS + return int(token_ids[special]) + + def decode(self, token_ids: list[int]) -> str: + return self.tokenizer.decode(token_ids) diff --git a/python/sglang/srt/parser/reasoning_parser.py b/python/sglang/srt/parser/reasoning_parser.py index a507f34f6..8405b0e69 100644 --- a/python/sglang/srt/parser/reasoning_parser.py +++ b/python/sglang/srt/parser/reasoning_parser.py @@ -1,9 +1,19 @@ import inspect +import re from typing import Dict, List, Optional, Tuple, Type from sglang.srt.entrypoints.openai.protocol import ChatCompletionRequest from sglang.srt.function_call.hunyuan_detector import resolve_hunyuan_tokens from sglang.srt.parser.harmony_parser import HarmonyParser +from sglang.srt.parser.inkling_tokenizer import ( + CONTENT_INVOKE_TOOL_JSON, + CONTENT_MODEL_END_SAMPLING, + CONTENT_TEXT, + CONTENT_THINKING, + END_MESSAGE, + INKLING_CONTROL_TOKENS, + MESSAGE_MODEL, +) class StreamingParseResult: @@ -702,6 +712,169 @@ class Gemma4Detector(BaseReasoningFormatDetector): self.think_start_self_label = "thought\n" +_INKLING_CONTENT_KINDS = { + CONTENT_THINKING: "reasoning", + CONTENT_TEXT: "content", +} +_INKLING_END_TOKENS = { + CONTENT_MODEL_END_SAMPLING, + END_MESSAGE, +} +_INKLING_CONTROL_TOKENS = INKLING_CONTROL_TOKENS +_INKLING_CONTROL_RE = re.compile( + "|".join(re.escape(t) for t in sorted(_INKLING_CONTROL_TOKENS)) +) + + +class InklingDetector(BaseReasoningFormatDetector): + """Detector for Inkling typed content blocks.""" + + # Parse the model's sequence of typed content blocks, for example: + # <|message_model|><|content_thinking|>reasoning<|end_message|> + # <|message_model|><|content_text|>visible answer<|end_message|> + # <|content_model_end_sampling|> + # Special tokens must decode literally so thinking and visible text can be + # routed to their respective response fields. + def __init__( + self, + stream_reasoning: bool = True, + force_reasoning: bool = False, + continue_final_message: bool = False, + previous_content: str = "", + force_nonempty_content: bool = False, + ): + del force_nonempty_content + super().__init__( + CONTENT_THINKING, + END_MESSAGE, + force_reasoning=force_reasoning, + stream_reasoning=stream_reasoning, + continue_final_message=continue_final_message, + previous_content=previous_content, + thinks_internally=False, + reasoning_default="always", + ) + + self._kind: str | None = None + self._pending_header = "" + self._pending_reasoning = "" + + def detect_and_parse(self, text: str) -> StreamingParseResult: + self._buffer = "" + self._kind = None + self._pending_header = "" + self._pending_reasoning = "" + ret = self._parse_blocks(text) + if self._kind == "reasoning" and not self.stream_reasoning: + ret.reasoning_text += self._pending_reasoning + self._kind = None + self._pending_header = "" + self._pending_reasoning = "" + return ret + + def parse_streaming_increment(self, new_text: str) -> StreamingParseResult: + text = self._buffer + new_text + partial_len = self._partial_control_length(text) + if partial_len: + self._buffer = text[-partial_len:] + text = text[:-partial_len] + else: + self._buffer = "" + return self._parse_blocks(text) + + @staticmethod + def _partial_control_length(text: str) -> int: + max_token_len = max(map(len, _INKLING_CONTROL_TOKENS)) + for length in range(min(len(text), max_token_len - 1), 0, -1): + suffix = text[-length:] + if any( + len(suffix) < len(token) and token.startswith(suffix) + for token in _INKLING_CONTROL_TOKENS + ): + return length + return 0 + + def _parse_blocks(self, text: str) -> StreamingParseResult: + reasoning: list[str] = [] + content: list[str] = [] + saw_control = False + pos = 0 + + def emit(text: str) -> None: + if self._kind == "reasoning": + if self.stream_reasoning: + reasoning.append(text) + else: + self._pending_reasoning += text + elif self._kind == "content": + content.append(text) + elif self._kind == "tool": + content.append(text) + elif self._kind == "header": + self._pending_header += text + elif text: + # No open block — e.g. a continue_final_message stream resuming + # mid text block. Route to visible content, matching the + # no-control-token path below. + content.append(text) + + def flush_reasoning() -> None: + if self._kind == "reasoning" and not self.stream_reasoning: + reasoning.append(self._pending_reasoning) + self._pending_reasoning = "" + + for match in _INKLING_CONTROL_RE.finditer(text): + saw_control = True + emit(text[pos : match.start()]) + + token = match.group(0) + pos = match.end() + if token == MESSAGE_MODEL: + if self._kind in (None, "header"): + flush_reasoning() + self._pending_header = "" + self._kind = "header" + else: + # Inside an open block a decoded <|message_model|> string + # is payload the model wrote (e.g. quoting the protocol) — + # a real header can only follow an end token. Preserve it + # instead of rerouting the rest of the block into a header. + emit(token) + elif token == CONTENT_INVOKE_TOOL_JSON: + flush_reasoning() + if self._kind == "header": + content.extend( + (MESSAGE_MODEL, self._pending_header, CONTENT_INVOKE_TOOL_JSON) + ) + self._pending_header = "" + else: + content.append(token) + self._kind = "tool" + elif self._kind == "tool": + content.append(token) + if token in _INKLING_END_TOKENS: + self._kind = None + elif token in _INKLING_CONTENT_KINDS: + flush_reasoning() + self._pending_header = "" + self._kind = _INKLING_CONTENT_KINDS[token] + elif token in _INKLING_END_TOKENS: + flush_reasoning() + self._pending_header = "" + self._kind = None + + tail = text[pos:] + if saw_control or self._kind is not None: + emit(tail) + else: + content.append(text) + + return StreamingParseResult( + normal_text="".join(content), + reasoning_text="".join(reasoning), + ) + + class _DeepSeekV3Detector(Qwen3Detector): """DeepSeek-V3 reuses Qwen3 tokens but requires explicit thinking=True to enable.""" @@ -1217,6 +1390,7 @@ class ReasoningParser: "nemotron_3": Nemotron3Detector, "interns1": Qwen3Detector, "gemma4": Gemma4Detector, + "inkling": InklingDetector, "cohere_command4": CohereCommand4Detector, } diff --git a/python/sglang/srt/server_args.py b/python/sglang/srt/server_args.py index f2cedff01..150373a6b 100644 --- a/python/sglang/srt/server_args.py +++ b/python/sglang/srt/server_args.py @@ -69,6 +69,7 @@ from sglang.srt.utils.common import ( get_int_env_var, get_quantization_config, human_readable_int, + is_blackwell_supported, is_cpu, is_cuda, is_flashinfer_available, @@ -262,6 +263,7 @@ MOE_RUNNER_BACKEND_CHOICES = [ "aiter", "marlin", "humming", + "experimental_sgl_marlin", ] MOE_A2A_BACKEND_CHOICES = [ @@ -605,7 +607,8 @@ class ServerArgs: help=( 'Data type for kv cache storage. "auto" will use model data type. ' '"bf16" or "bfloat16" for BF16 KV cache. "fp8_e5m2" and ' - '"fp8_e4m3" are supported for CUDA 11.8+. "nvfp4" selects ' + '"fp8_e4m3" are supported for CUDA 11.8+. "mxfp8" is supported ' + 'by the FA4 backend. "nvfp4" selects ' 'the NVFP4 FP4 E2M1 KV cache recipe; "fp4_mx_block16" ' "selects the MX-style block-size-16 FP4 E2M1 KV cache " "recipe. Both require CUDA 12.8+ and PyTorch 2.8.0+" @@ -614,6 +617,7 @@ class ServerArgs: "auto", "fp8_e5m2", "fp8_e4m3", + "mxfp8", "bf16", "bfloat16", "nvfp4", @@ -1628,6 +1632,10 @@ class ServerArgs: bool, "Enable using torch symm mem for all-reduce kernel and fall back to NCCL. Only supports CUDA device SM90 and above. SM90 supports world size 4, 6, 8. SM100 supports world size 6, 8.", ] = False + enable_scattered_sconv: A[ + bool, + "Inkling: replace the attention/MLP output all-reduce with a hidden-dimension reduce-scatter, run the channelwise output short convolution on the [T, H/P] shard, then all-gather before the residual add. This shards the convolution cache across tensor-parallel ranks without changing communication volume.", + ] = False pre_warm_nccl: A[ bool, "Pre-warm NCCL/RCCL communicators during startup to reduce P99 TTFT cold-start latency. Default: enabled for AMD/HIP (RCCL), disabled for NVIDIA/CUDA (NCCL).", @@ -2083,7 +2091,10 @@ class ServerArgs: ] = 0 mamba_full_memory_ratio: A[ float, - "The ratio of mamba state memory to full kv cache memory.", + Arg( + help="The ratio of mamba state memory to full kv cache memory.", + resolvable=True, + ), ] = 0.9 mamba_radix_cache_strategy: A[ str, @@ -2900,6 +2911,10 @@ class ServerArgs: self._validate_prefill_only_disable_kv_cache_args() self._handle_dcp_validation() + # Model-arch prefill CUDA-graph default must land before cuda-graph + # resolution (the declarative registry materializes too late to affect + # it). Inkling opts into full-graph prefill capture here. + self._apply_inkling_prefill_cuda_graph_default() self._handle_cuda_graph_config() # Handle device-specific backends. @@ -2934,6 +2949,7 @@ class ServerArgs: self._handle_int8_mamba_checkpoint() self._handle_linear_attn_backend() self._handle_kv4_compatibility() + self._handle_mxfp8_kv_cache_compatibility() self._handle_page_size() self._handle_amd_specifics() self._handle_nccl_pre_warm() @@ -2972,6 +2988,7 @@ class ServerArgs: self._handle_eplb_and_dispatch() self._handle_expert_distribution_metrics() self._handle_elastic_ep() + self._validate_experimental_sgl_marlin() # Handle pipeline parallelism. self._handle_pipeline_parallelism() @@ -3481,6 +3498,25 @@ class ServerArgs: # ------------------------------------------------------------------ # CUDA graph configuration resolution # ------------------------------------------------------------------ + def _apply_inkling_prefill_cuda_graph_default(self): + """Inkling opts into full-graph prefill CUDA-graph capture. Must run + before _handle_cuda_graph_config: the generic breakable default is + auto-disabled for this multimodal arch, and declarative model overrides + materialize too late to steer cuda-graph resolution. Honors an explicit + --cuda-graph-backend-prefill / --disable-prefill-cuda-graph.""" + if ( + self.cuda_graph_backend_prefill is not None + or self.disable_prefill_cuda_graph + or parse_connector_type(self.model_path) == ConnectorType.INSTANCE + ): + return + arch = self.get_model_config().hf_config.architectures[0] + if arch in ( + "InklingForConditionalGeneration", + "InklingForConditionalGenerationMTP", + ): + self.cuda_graph_backend_prefill = Backend.FULL + def _handle_cuda_graph_config(self): self._parse_cuda_graph_config() self._apply_cuda_graph_compatibility() @@ -4973,6 +5009,16 @@ class ServerArgs: self.enable_mixed_chunk = False self.disable_radix_cache = True + def _handle_mxfp8_kv_cache_compatibility(self): + """MXFP8 KV cache uses operands available only on SM100+ (Blackwell).""" + if self.kv_cache_dtype != "mxfp8": + return + if not is_blackwell_supported(): + raise ValueError( + "--kv-cache-dtype mxfp8 requires an SM100+ (Blackwell) GPU for the " + "block-scaled operands used by the FA4 MXFP8 attention path." + ) + def _handle_kv4_compatibility(self): """Check FP4 KV cache compatibility with the attention backend""" from sglang.srt.arg_groups.overrides import resolved_view @@ -5888,6 +5934,19 @@ class ServerArgs: f"(got moe_a2a_backend={resolved.moe_a2a_backend})." ) + def _validate_experimental_sgl_marlin(self): + view = self._resolved() + if view.moe_runner_backend != "experimental_sgl_marlin": + return + + # ===== TO BE REFACTORED ==== + from sglang.srt.lora.marlin_lora_temp.policy import ( + validate_experimental_sgl_marlin_server_args, + ) + + validate_experimental_sgl_marlin_server_args(self, view) + # ===== END TO BE REFACTORED ==== + def _handle_expert_distribution_metrics(self): if self.enable_expert_distribution_metrics and ( self.expert_distribution_recorder_mode is None @@ -5935,6 +5994,12 @@ class ServerArgs: "--kv-cache-dtype=nvfp4 or --kv-cache-dtype=fp4_mx_block16 because " "the FP4 pool uses a separate allocation path." ) + if self.kv_cache_dtype == "mxfp8": + raise ValueError( + "--prefill-only-disable-kv-cache does not currently support " + "--kv-cache-dtype=mxfp8 because the MXFP8 pool stores separate " + "scale-factor buffers." + ) # Structural preconditions for the FA backend's fa_skip_kv_cache path, # which is the only embedding path that doesn't read or write the pool: diff --git a/python/sglang/srt/speculative/eagle_info.py b/python/sglang/srt/speculative/eagle_info.py index 6d7572a32..60c702d19 100644 --- a/python/sglang/srt/speculative/eagle_info.py +++ b/python/sglang/srt/speculative/eagle_info.py @@ -313,6 +313,7 @@ class EagleDraftExtendInput(SpecInput): # Both kept for cuda-graph buffer indexing. num_correct_drafts: torch.Tensor = None num_accept_tokens: torch.Tensor = None + num_front_tokens: int = 0 # CPU view, read by attention backends during the extend forward. num_accept_tokens_cpu: List[int] = None @@ -338,6 +339,12 @@ class EagleDraftExtendInput(SpecInput): dsa_seed_topk_capture: Optional[torch.Tensor] = None dsa_seed_topk_select: Optional[torch.Tensor] = None + # Flat per-req index of each request's last accepted window row + # (i * window + front + num_correct_drafts[i]). When set, the logits + # processor runs lm_head only on these rows. None under gathered-buffer + # (DP) modes, whose logprob buffer sizing assumes all-row logits. + select_index: Optional[torch.Tensor] = None + # None for draft-extend's idle batch; attention backends fall back to # rebuilding plain metadata from seq_lens when this is None. kv_indptr: torch.Tensor = None diff --git a/python/sglang/srt/speculative/eagle_utils.py b/python/sglang/srt/speculative/eagle_utils.py index 56dedf7e8..c0366048b 100644 --- a/python/sglang/srt/speculative/eagle_utils.py +++ b/python/sglang/srt/speculative/eagle_utils.py @@ -821,7 +821,15 @@ def eagle_prepare_for_decode(batch: ScheduleBatch): # max(cur, ...) clamps so adaptive downswitch cannot make nxt < cur. # kv_committed_len is honest (bonus committed in resolve, not here), # so it lags batch.seq_lens by ~1 verify in overlap; 2*alloc absorbs. - nxt = max(cur, r.kv_committed_len + double_alloc) + # Whole-page accounting: the paged allocator hands out full pages, so + # round nxt up to the page boundary or the unaligned tail is allocated + # but never recorded — a stranded-tail leak at page_size > 1. + nxt = max( + cur, + (r.kv_committed_len + double_alloc + page_size - 1) + // page_size + * page_size, + ) cur_kv_lens[i] = cur nxt_kv_lens[i] = nxt num_needed_tokens += nxt - cur diff --git a/python/sglang/srt/speculative/eagle_worker_common.py b/python/sglang/srt/speculative/eagle_worker_common.py index 146ad94ed..9f1303464 100644 --- a/python/sglang/srt/speculative/eagle_worker_common.py +++ b/python/sglang/srt/speculative/eagle_worker_common.py @@ -110,9 +110,18 @@ def prepare_for_draft_extend( cuda_graph_runner: Any, *, return_hidden_states_before_norm: bool, + widened_out_cache_loc: Optional[torch.Tensor] = None, + widened_positions: Optional[torch.Tensor] = None, ): bs = len(batch.seq_lens) - extend_num_tokens = bs * num_draft_tokens + # Optional window widening (num_front_tokens=0 -> off): prepend that many + # rows below the boundary. Locs/positions arrive precomputed; token/hidden + # buffers are zeroed placeholders the caller fills after the plan-stream join. + num_front_tokens = draft_extend_input.num_front_tokens + widen = num_front_tokens > 0 and not batch.forward_mode.is_idle() + front_offset = num_front_tokens if widen else 0 + num_window_tokens = num_draft_tokens + front_offset + extend_num_tokens = bs * num_window_tokens # When seq_lens_cpu is absent, stay on GPU-only path -- no .tolist()/.cpu(). gpu_only = batch.seq_lens_cpu is None @@ -121,7 +130,21 @@ def prepare_for_draft_extend( # may run this under a plan stream; casting inside the plan stream creates a # cross-stream dependency that can lead to data races and break MTP acceptance. # The caller should cast to int64 before entering the plan stream context. - batch.input_ids = predict + if widen: + assert widened_out_cache_loc is not None and widened_positions is not None + batch.input_ids = predict.new_zeros((extend_num_tokens,)) + batch.out_cache_loc = widened_out_cache_loc + # init_new adopts spec_info.positions when present. + draft_extend_input.positions = widened_positions + # Placeholder for the widened hidden window, filled by the worker. + if draft_extend_input.hidden_states is not None: + draft_extend_input.hidden_states = ( + draft_extend_input.hidden_states.new_empty( + (extend_num_tokens, draft_extend_input.hidden_states.shape[1]) + ) + ) + else: + batch.input_ids = predict maybe_detect_oob( batch.input_ids, 0, @@ -131,13 +154,15 @@ def prepare_for_draft_extend( # init_new requires both list or both Tensor; # gpu_only emits device tensors to skip H2D. if gpu_only: - batch.prefix_lens = batch.seq_lens.to(torch.int32) + batch.prefix_lens = (batch.seq_lens - front_offset).clamp(min=0).to(torch.int32) batch.extend_lens = torch.full( - (bs,), num_draft_tokens, dtype=torch.int32, device=batch.seq_lens.device + (bs,), num_window_tokens, dtype=torch.int32, device=batch.seq_lens.device ) else: - batch.prefix_lens = batch.seq_lens_cpu.tolist() - batch.extend_lens = [num_draft_tokens] * bs + batch.prefix_lens = [ + max(int(x) - front_offset, 0) for x in batch.seq_lens_cpu.tolist() + ] + batch.extend_lens = [num_window_tokens] * bs batch.extend_num_tokens = extend_num_tokens capture_mode = ( CaptureHiddenMode.NULL @@ -162,9 +187,9 @@ def prepare_for_draft_extend( forward_batch.seq_lens_cpu = forward_batch.seq_lens_cpu + num_draft_tokens forward_batch.seq_lens_sum = int(forward_batch.seq_lens_cpu.sum()) else: - # Supply CPU mirror (extend_seq_lens are all num_draft_tokens) so + # Supply CPU mirror (extend_seq_lens are all num_window_tokens) so # backend max() reads from list without a per-iter D2H sync. - forward_batch.extend_seq_lens_cpu = [num_draft_tokens] * bs + forward_batch.extend_seq_lens_cpu = [num_window_tokens] * bs can_cuda_graph = cuda_graph_runner and cuda_graph_runner.can_run_graph( forward_batch ) diff --git a/python/sglang/srt/speculative/multi_layer_draft_forward_cg.py b/python/sglang/srt/speculative/multi_layer_draft_forward_cg.py new file mode 100644 index 000000000..68eccc0be --- /dev/null +++ b/python/sglang/srt/speculative/multi_layer_draft_forward_cg.py @@ -0,0 +1,101 @@ +"""Per-batch-size CUDA-graph capture of the multi-layer EAGLE draft tree-select +glue (``MultiLayerEagleDraftWorker.draft_forward``). + +That glue (``select_top_k_tokens`` + the per-step token/score/parent assembly + +``topk`` / ``sort`` / ``gather`` / ``cat``) runs eagerly between the draft-extend +graph and the verify graph — a handful of tiny launches per decode step. Its +output is a pure function of ``(topk_p, topk_index)`` — the repeat-interleaved +hidden state is discarded — so it captures cleanly into a per-bs CUDA graph +whose replay costs ~one launch instead of the per-op launches. ``hidden_states`` +is deliberately NOT part of the graph: its leading dim is the token count (not +bs), so it varies between calls, and it does not affect the output; the graph +runs the core with ``hidden=None``. + +Capture is lazy per (shape, dtype) key, via the canonical +warmup-on-side-stream → capture pattern, with ``capture_error_mode="thread_local"`` +so the overlap scheduler's concurrent kernels on other threads do not trip +capture-safety. Each freshly captured graph is bit-exact self-checked ONCE (on +the first inputs for that key) against the eager path run with the real hidden +state; correctness for later same-key inputs relies on the captured ops being +deterministic, RNG-free and hidden-independent — which holds for the gated +topk==1 chain (a future core change that broke that would need re-validation). +Any capture failure or self-check mismatch permanently falls back to eager for +that key, so this can never change results. +""" + +from __future__ import annotations + +from typing import Callable, Dict, Optional, Tuple + +import torch + +CoreFn = Callable[ + [torch.Tensor, torch.Tensor, Optional[torch.Tensor]], + Tuple[torch.Tensor, torch.Tensor, torch.Tensor], +] + + +class _Captured: + __slots__ = ("graph", "in_p", "in_i", "out") + + def __init__(self, graph, in_p, in_i, out): + self.graph = graph + self.in_p = in_p + self.in_i = in_i + self.out = out + + +class DraftForwardCudaGraph: + def __init__(self, core_fn: CoreFn): + self._core = core_fn + self._graphs: Dict[tuple, Optional[_Captured]] = {} + + def run( + self, + topk_p: torch.Tensor, + topk_index: torch.Tensor, + hidden_states: Optional[torch.Tensor], + ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + key = ( + tuple(topk_p.shape), + topk_p.dtype, + tuple(topk_index.shape), + topk_index.dtype, + ) + if key not in self._graphs: + self._graphs[key] = self._capture(topk_p, topk_index, hidden_states) + cap = self._graphs[key] + if cap is None: + return self._core(topk_p, topk_index, hidden_states) + cap.in_p.copy_(topk_p) + cap.in_i.copy_(topk_index) + cap.graph.replay() + return tuple(o.clone() for o in cap.out) + + def _capture(self, topk_p, topk_index, hidden_states) -> Optional[_Captured]: + try: + in_p = topk_p.clone() + in_i = topk_index.clone() + + stream = torch.cuda.Stream() + stream.wait_stream(torch.cuda.current_stream()) + with torch.cuda.stream(stream): + for _ in range(2): + self._core(in_p, in_i, None) + torch.cuda.current_stream().wait_stream(stream) + + graph = torch.cuda.CUDAGraph() + with torch.cuda.graph(graph, capture_error_mode="thread_local"): + out = self._core(in_p, in_i, None) + + in_p.copy_(topk_p) + in_i.copy_(topk_index) + graph.replay() + ref = self._core(topk_p, topk_index, hidden_states) + if len(out) != len(ref) or any( + not torch.equal(o, r) for o, r in zip(out, ref) + ): + return None + return _Captured(graph, in_p, in_i, out) + except Exception: + return None diff --git a/python/sglang/srt/speculative/multi_layer_eagle_draft_extend_cuda_graph_runner.py b/python/sglang/srt/speculative/multi_layer_eagle_draft_extend_cuda_graph_runner.py index ea2b92135..3cbc9516b 100644 --- a/python/sglang/srt/speculative/multi_layer_eagle_draft_extend_cuda_graph_runner.py +++ b/python/sglang/srt/speculative/multi_layer_eagle_draft_extend_cuda_graph_runner.py @@ -62,7 +62,13 @@ from sglang.srt.model_executor.runner_backend_utils import ( from sglang.srt.runtime_context import get_flags from sglang.srt.speculative.eagle_info import EagleDraftExtendInput from sglang.srt.speculative.eagle_utils import get_draft_input_from_target_hidden_dim +from sglang.srt.speculative.multi_layer_eagle_utils import ( + fill_draft_extend_prepare_buffers_triton, + rotate_input_ids, + wide_row_softmax_triton, +) from sglang.srt.speculative.spec_utils import ( + fast_sample, fast_topk, resolve_num_tokens_per_req, ) @@ -106,6 +112,10 @@ class MultiLayerEagleDraftExtendInputBuffers(ForwardInputBuffers): next_token_logits_buffer: torch.Tensor global_num_tokens_gpu: Optional[torch.Tensor] global_num_tokens_for_logprob_gpu: Optional[torch.Tensor] + # Rejection sampling with the single-CG runner only, else None (the presence + # of draft_probs selects the in-graph proposal branch in _run_step_body). + temperatures: Optional[torch.Tensor] + draft_probs: Optional[torch.Tensor] class MultiLayerEagleDraftExtendCudaGraphRunner(DecodeCudaGraphRunner): @@ -147,6 +157,12 @@ class MultiLayerEagleDraftExtendCudaGraphRunner(DecodeCudaGraphRunner): model_runner.server_args.enable_profile_cuda_graph ) self.attn_backend = self.eagle_worker.draft_extend_attn_backend_list[self.step] + self.metadata_captured_in_graph = ( + self.attn_backend.draft_extend_metadata_captured_in_graph() + ) + # Gathered-buffer (DP) modes size the logprob gather for all-row + # logits, so they keep the unpruned lm_head path. + self.prune_draft_extend_logits = not self.require_gathered_buffer # Disable parent paths that don't apply. self.compile_bs = [] @@ -162,8 +178,12 @@ class MultiLayerEagleDraftExtendCudaGraphRunner(DecodeCudaGraphRunner): # Fixed window: every step extends each request by the same number of # tokens, which lets all steps share one buffer set. - self.captured_req_width = resolve_num_tokens_per_req( - phase="draft_extend", server_args=model_runner.server_args + self.num_front_tokens = eagle_worker.draft_extend_num_front_tokens + self.captured_req_width = ( + resolve_num_tokens_per_req( + phase="draft_extend", server_args=model_runner.server_args + ) + + self.num_front_tokens ) self.max_bs = max(self.capture_bs) self.max_num_token = self.max_bs * self.captured_req_width @@ -245,7 +265,10 @@ class MultiLayerEagleDraftExtendCudaGraphRunner(DecodeCudaGraphRunner): positions = buffers.positions[:num_tokens] mrope_positions = buffers.mrope_positions[:, :num_tokens] hidden_states = buffers.hidden_states[:num_tokens] - next_token_logits_buffer = buffers.next_token_logits_buffer[:num_tokens] + if self.prune_draft_extend_logits: + next_token_logits_buffer = buffers.next_token_logits_buffer[:bs] + else: + next_token_logits_buffer = buffers.next_token_logits_buffer[:num_tokens] if self.require_mlp_tp_gather: global_num_tokens_cpu = [num_tokens] * self.dp_size @@ -280,9 +303,12 @@ class MultiLayerEagleDraftExtendCudaGraphRunner(DecodeCudaGraphRunner): hidden_states=hidden_states, num_correct_drafts=num_correct_drafts, num_accept_tokens=num_accept_tokens, + num_front_tokens=self.num_front_tokens, ) spec_info.num_tokens_per_req = self.captured_req_width spec_info.positions = None + if self.prune_draft_extend_logits: + spec_info.select_index = buffers.select_index[:bs] capture_mode = ( CaptureHiddenMode.NULL @@ -325,15 +351,68 @@ class MultiLayerEagleDraftExtendCudaGraphRunner(DecodeCudaGraphRunner): """Hook for subclasses to mutate the captured forward batch.""" return forward_batch + def _select_step_logits(self, ret, bs: int): + """Each request's last-accepted-row logits, [bs, vocab].""" + if self.prune_draft_extend_logits: + return ret.next_token_logits + return ret.next_token_logits[self.buffers.select_index[:bs]] + def _compute_topk(self, ret, bs: int): """Compute top-k on the last accepted token's logits and attach it to - ``ret``. The gather index lives in a persistent buffer, so the captured - graph reads the right rows on each replay. Overridable so distributed - (vocab-sharded) builds can plug in an all-reduce-aware sampler.""" - buffers = self.buffers - probs = torch.softmax(ret.next_token_logits[buffers.select_index[:bs]], dim=-1) + ``ret``. Overridable so distributed (vocab-sharded) builds can plug in + an all-reduce-aware sampler.""" + probs = torch.softmax(self._select_step_logits(ret, bs), dim=-1) ret.topk_p, ret.topk_index = fast_topk(probs, self.topk, dim=-1) + def _sample_draft_proposal(self, ret, bs: int): + """In-graph Leviathan proposal (single-CG runner + rejection sampling): + q = softmax(logits / T) written straight into this step's draft_probs + slot, then X ~ q. The accept test coin*q(X) < p(X) is unbiased only if + q is exactly the distribution X was drawn from -- q here IS the + stashed tensor.""" + buffers = self.buffers + probs = wide_row_softmax_triton( + self._select_step_logits(ret, bs), + buffers.temperatures[:bs], + buffers.draft_probs[:bs, self.step], + ) + ret.topk_p, ret.topk_index = fast_sample(probs, num_samples=1) + + def _run_step_body(self, forward_batch: ForwardBatch, num_tokens: int, bs: int): + """One draft step's body: model forward + chain-hidden write + top-k. + Shared by the per-step capture and the single-CG multi-step capture.""" + buffers = self.buffers + # Clean intermediate result cache for DP attention + forward_batch.dp_local_start_pos = forward_batch.dp_local_num_tokens = None + set_dp_buffer_len( + forward_batch.global_dp_buffer_len, + num_tokens, + forward_batch.dp_padding_mode.is_max_len(), + forward_batch.global_num_tokens_cpu, + ) + set_is_extend_in_batch(False) + + output_cache_loc_backup = forward_batch.out_cache_loc + hidden_states_backup = forward_batch.spec_info.hidden_states + + ret = self.model_runner.model.forward( + forward_batch.input_ids, + forward_batch.positions, + forward_batch, + ) + + if self.eagle_worker.chain_mtp_hidden_states and ret.hidden_states is not None: + buffers.hidden_states[:num_tokens].copy_(ret.hidden_states[:num_tokens]) + + if buffers.draft_probs is not None: + self._sample_draft_proposal(ret, bs) + else: + self._compute_topk(ret, bs) + + forward_batch.out_cache_loc = output_cache_loc_backup + forward_batch.spec_info.hidden_states = hidden_states_backup + return ret + def capture_one_shape( self, size: int, @@ -342,7 +421,6 @@ class MultiLayerEagleDraftExtendCudaGraphRunner(DecodeCudaGraphRunner): variant_label: Optional[str] = None, ): bs = size - buffers = self.buffers num_tokens = bs * self.captured_req_width forward_batch = self.get_forward_batch(bs) @@ -351,37 +429,7 @@ class MultiLayerEagleDraftExtendCudaGraphRunner(DecodeCudaGraphRunner): def run_once(): attn_backend.init_forward_metadata_in_graph(forward_batch) - - # Clean intermediate result cache for DP attention - forward_batch.dp_local_start_pos = forward_batch.dp_local_num_tokens = None - set_dp_buffer_len( - forward_batch.global_dp_buffer_len, - num_tokens, - forward_batch.dp_padding_mode.is_max_len(), - forward_batch.global_num_tokens_cpu, - ) - set_is_extend_in_batch(False) - - output_cache_loc_backup = forward_batch.out_cache_loc - hidden_states_backup = forward_batch.spec_info.hidden_states - - ret = self.model_runner.model.forward( - forward_batch.input_ids, - forward_batch.positions, - forward_batch, - ) - - if ( - self.eagle_worker.chain_mtp_hidden_states - and ret.hidden_states is not None - ): - buffers.hidden_states[:num_tokens].copy_(ret.hidden_states[:num_tokens]) - - self._compute_topk(ret, bs) - - forward_batch.out_cache_loc = output_cache_loc_backup - forward_batch.spec_info.hidden_states = hidden_states_backup - return ret + return self._run_step_body(forward_batch, num_tokens, bs) with forward_context(ForwardContext(attn_backend=attn_backend)): attn_backend.init_forward_metadata_out_graph(forward_batch, in_capture=True) @@ -413,15 +461,13 @@ class MultiLayerEagleDraftExtendCudaGraphRunner(DecodeCudaGraphRunner): """Init this step's attention metadata for the prepared bucket and replay its graph. Buffers must already be populated by the composite runner's ``prepare`` (step 0) or by the previous step's in-graph chain - write + worker-side rotation (steps > 0).""" + write + worker-side rotation (steps > 0). Backends whose in-graph + metadata update is complete recompute it inside the replayed graph; + others get the eager out-graph rebuild here.""" self.deepep_adapter.replay() buffers = self.buffers num_tokens = bs * self.captured_req_width - if self.require_gathered_buffer: - buffers.global_num_tokens_gpu.fill_(num_tokens) - buffers.global_num_tokens_for_logprob_gpu.fill_(num_tokens) - fb_view = SimpleNamespace( batch_size=bs, forward_mode=self.forward_mode, @@ -435,9 +481,10 @@ class MultiLayerEagleDraftExtendCudaGraphRunner(DecodeCudaGraphRunner): out_cache_loc=buffers.out_cache_loc[:num_tokens], spec_info=spec_info, ) - self.eagle_worker.draft_extend_attn_backend_list[ - self.step - ].init_forward_metadata_out_graph(fb_view) + if not self.metadata_captured_in_graph: + self.eagle_worker.draft_extend_attn_backend_list[ + self.step + ].init_forward_metadata_out_graph(fb_view) self.bs = bs shape_key = self._make_graph_key(bs) @@ -459,6 +506,8 @@ class MultiLayerEagleMultiStepDraftExtendCudaGraphRunner: runners. """ + rotates_in_graph = False + def __init__(self, eagle_worker: MultiLayerEagleDraftWorker): self.eagle_worker = eagle_worker self.device = eagle_worker.device @@ -472,6 +521,8 @@ class MultiLayerEagleMultiStepDraftExtendCudaGraphRunner: self.seq_len_fill_value = 1 self.max_bs = 1 self.captured_req_width = 1 + self.num_front_tokens = 0 + self.prune_draft_extend_logits = False self._init_and_capture() @@ -483,6 +534,28 @@ class MultiLayerEagleMultiStepDraftExtendCudaGraphRunner: can use it e.g. to temporarily expose a sharded local vocab size.""" return contextlib.nullcontext() + def _create_runners(self): + """Construct per-step runners (each initializes its own attn cuda graph + state; all share the same fixed window size) and mirror their shared + shape attributes onto self.""" + self.runners = [] + for step in range(self.speculative_num_steps): + if self.draft_extend_attn_backend_list[step]: + runner = self._create_runner(step) + self.runners.append(runner) + self.seq_len_fill_value = runner.seq_len_fill_value + self.max_bs = runner.max_bs + self.captured_req_width = runner.captured_req_width + self.num_front_tokens = runner.num_front_tokens + self.capture_bs = runner.capture_bs + self.require_gathered_buffer = runner.require_gathered_buffer + self.require_mlp_tp_gather = runner.require_mlp_tp_gather + self.require_mlp_sync = runner.require_mlp_sync + self.disable_padding = runner.disable_padding + self.prune_draft_extend_logits = runner.prune_draft_extend_logits + else: + self.runners.append(None) + def _on_runners_created(self): """Hook called after all per-step runners exist but before buffers are allocated/captured (e.g. to allocate shared sconv buffers).""" @@ -495,24 +568,7 @@ class MultiLayerEagleMultiStepDraftExtendCudaGraphRunner: self.runners = [None] * self.speculative_num_steps return - self.runners = [] - - # 1. Construct per-step runners (each initializes its own attn cuda - # graph state). They share the same fixed window size. - for step in range(self.speculative_num_steps): - if self.draft_extend_attn_backend_list[step]: - runner = self._create_runner(step) - self.runners.append(runner) - self.seq_len_fill_value = runner.seq_len_fill_value - self.max_bs = runner.max_bs - self.captured_req_width = runner.captured_req_width - self.capture_bs = runner.capture_bs - self.require_gathered_buffer = runner.require_gathered_buffer - self.require_mlp_tp_gather = runner.require_mlp_tp_gather - self.require_mlp_sync = runner.require_mlp_sync - self.disable_padding = runner.disable_padding - else: - self.runners.append(None) + self._create_runners() self._on_runners_created() @@ -582,9 +638,27 @@ class MultiLayerEagleMultiStepDraftExtendCudaGraphRunner: select_index = torch.zeros((max_bs,), dtype=torch.int64) next_token_logits_buffer = torch.zeros( - (max_num_token, vocab_size), dtype=torch.float + ( + max_bs if self.prune_draft_extend_logits else max_num_token, + vocab_size, + ), + dtype=torch.float, ) + if ( + self.rotates_in_graph + and self.eagle_worker.use_rejection_sampling + and self.eagle_worker.topk == 1 + ): + temperatures = torch.ones((max_bs, 1), dtype=torch.float) + draft_probs = torch.empty( + (max_bs, self.speculative_num_steps, vocab_size), + dtype=torch.float, + ) + else: + temperatures = None + draft_probs = None + if self.require_gathered_buffer: if self.require_mlp_tp_gather: dp_size = runner.dp_size @@ -618,6 +692,8 @@ class MultiLayerEagleMultiStepDraftExtendCudaGraphRunner: next_token_logits_buffer=next_token_logits_buffer, global_num_tokens_gpu=global_num_tokens_gpu, global_num_tokens_for_logprob_gpu=global_num_tokens_for_logprob_gpu, + temperatures=temperatures, + draft_probs=draft_probs, ) def _prepare_extra(self, forward_batch: ForwardBatch) -> None: @@ -637,31 +713,46 @@ class MultiLayerEagleMultiStepDraftExtendCudaGraphRunner: else: bs = self.get_runner(0)._pad_to_bucket(raw_bs, self.capture_bs) - # Reset padded slots, then copy the real values in. - buffers.input_ids.zero_() - buffers.out_cache_loc.zero_() - buffers.positions.zero_() - buffers.seq_lens.fill_(self.seq_len_fill_value) - - buffers.input_ids[:num_tokens].copy_(forward_batch.input_ids) - buffers.positions[:num_tokens].copy_(forward_batch.positions) - buffers.out_cache_loc[:num_tokens].copy_(forward_batch.out_cache_loc) - buffers.seq_lens[:raw_bs].copy_(forward_batch.seq_lens) - buffers.req_pool_indices[:raw_bs].copy_(forward_batch.req_pool_indices) - - if ( - forward_batch.spec_info.hidden_states.shape[1] - == buffers.hidden_states.shape[1] - ): - buffers.hidden_states[:num_tokens].copy_( + fill_draft_extend_prepare_buffers_triton( + buffers.input_ids, + buffers.positions, + buffers.out_cache_loc, + forward_batch.input_ids, + forward_batch.positions, + forward_batch.out_cache_loc, + buffers.seq_lens, + buffers.req_pool_indices, + buffers.num_correct_drafts, + buffers.num_accept_tokens, + buffers.select_index, + buffers.temperatures, + forward_batch.seq_lens, + forward_batch.req_pool_indices, + forward_batch.spec_info.num_correct_drafts, + forward_batch.spec_info.num_accept_tokens, + ( + forward_batch.sampling_info.temperatures + if buffers.temperatures is not None + else None + ), + buffers.hidden_states, + ( forward_batch.spec_info.hidden_states - ) - - buffers.num_correct_drafts[:raw_bs].copy_( - forward_batch.spec_info.num_correct_drafts - ) - buffers.num_accept_tokens[:raw_bs].copy_( - forward_batch.spec_info.num_accept_tokens + if forward_batch.spec_info.hidden_states.shape[1] + == buffers.hidden_states.shape[1] + else None + ), + buffers.global_num_tokens_gpu if self.require_gathered_buffer else None, + ( + buffers.global_num_tokens_for_logprob_gpu + if self.require_gathered_buffer + else None + ), + raw_bs, + bs, + self.captured_req_width, + self.num_front_tokens, + self.seq_len_fill_value, ) # Refresh the host mirror only when published; hand replay None @@ -674,26 +765,13 @@ class MultiLayerEagleMultiStepDraftExtendCudaGraphRunner: else: self.seq_lens_cpu = None - # select_index[i] = i * window + num_correct_drafts[i]: the flat index - # of request i's last accepted token. Used by the in-graph top-k gather - # and by the worker's rotation. - arange = torch.arange(bs, device=self.device, dtype=torch.int64) - buffers.select_index[:bs].copy_( - arange * self.captured_req_width + buffers.num_correct_drafts[:bs] - ) - - if self.require_gathered_buffer: - buffers.global_num_tokens_gpu.fill_(bs * self.captured_req_width) - buffers.global_num_tokens_for_logprob_gpu.fill_( - bs * self.captured_req_width - ) - # Reusable spec_info for per-step attention metadata. padded_num_tokens = bs * self.captured_req_width spec_info = EagleDraftExtendInput( hidden_states=buffers.hidden_states[:padded_num_tokens], num_correct_drafts=buffers.num_correct_drafts[:bs], num_accept_tokens=buffers.num_accept_tokens[:bs], + num_front_tokens=self.num_front_tokens, ) # Actual width of the captured forward == static width by construction. spec_info.num_tokens_per_req = self.captured_req_width @@ -723,8 +801,9 @@ class MultiLayerEagleMultiStepDraftExtendCudaGraphRunner: ) raw_bs = self.raw_bs raw_num_tokens = self.raw_num_tokens + num_logit_rows = raw_bs if self.prune_draft_extend_logits else raw_num_tokens logits_output = LogitsProcessorOutput( - next_token_logits=out.next_token_logits[:raw_num_tokens], + next_token_logits=out.next_token_logits[:num_logit_rows], hidden_states=( out.hidden_states[:raw_num_tokens] if out.hidden_states is not None @@ -737,6 +816,12 @@ class MultiLayerEagleMultiStepDraftExtendCudaGraphRunner: out.topk_index[:raw_bs], ) + def clone_draft_probs(self) -> torch.Tensor: + """Materialize the in-graph-written proposal q [raw_bs, num_steps, vocab] + after replay; the clone must land before a later replay rewrites the + buffer (spec_info.draft_probs outlives this decode step on paused batches).""" + return self.buffers.draft_probs[: self.raw_bs].clone() + def get_runner(self, step): return self.runners[step] @@ -745,3 +830,147 @@ class MultiLayerEagleMultiStepDraftExtendCudaGraphRunner: def can_run_graph(self, forward_batch): return self.runners[0].can_run_graph(forward_batch) + + +class OneGraphMultiLayerEagleMultiStepDraftExtendCudaGraphRunner( + MultiLayerEagleMultiStepDraftExtendCudaGraphRunner +): + """Single-CG variant (SGLANG_ENABLE_SINGLE_CG_DRAFT): captures all draft steps' + forwards + the inter-step input_ids rotation in ONE graph per bucket, instead + of one graph per step. The worker drops its per-step rotation (rotates_in_graph). + + Each step's replay metadata is emitted in-graph via + init_forward_metadata_in_graph (no Python may run between + captured steps). seq_lens / req_pool_indices / extend_seq_lens are chain-constant + (only input_ids rotates), so per-step in-graph metadata is correct. + + Rejection sampling is supported by sampling X ~ q inside the graph + (_sample_draft_proposal, selected by the draft_probs buffer's presence): + the captured rotation then carries the sampled token, prepare() stages the + temperatures sampling_info cannot deliver in-graph, and the worker clones + the per-step q off buffers.draft_probs after replay. torch.multinomial + draws through the graph-registered Philox generator, so each replay gets + fresh coins. + """ + + rotates_in_graph = True + + def _init_and_capture(self): + if self._cuda_graph_disabled(): + self.runners = [None] * self.speculative_num_steps + return + + self._create_runners() + + self._on_runners_created() + self.buffers = self._allocate_buffers() + + # No per-step capture: all steps are captured in one graph below. + for r in self.runners: + if r is not None: + r.buffers = self.buffers + if r.enable_torch_compile: + set_torch_compile_config() + r.backend = resolve_decode_backend(r) + + runners = [r for r in self.runners if r is not None] + first = runners[0] + tic = time.perf_counter() + before_mem = get_available_gpu_memory(self.device, self.gpu_id) + logger.info( + "Capture single-CG draft extend begin. This can take several minutes. " + f"avail mem={before_mem:.2f} GB" + ) + try: + with model_capture_mode(): + for bs in reversed(first.capture_bs): + self._capture_one_graph(bs, runners) + except RuntimeError as e: + raise Exception( + f"Capture single-CG draft extend failed: {e}\n" + f"{CUDA_GRAPH_CAPTURE_FAILED_MSG}" + ) + after_mem = get_available_gpu_memory(self.device, self.gpu_id) + logger.info( + "Capture single-CG draft extend end. " + f"elapsed={time.perf_counter() - tic:.2f} s, " + f"mem usage={(before_mem - after_mem):.2f} GB, avail mem={after_mem:.2f} GB." + ) + + def _capture_one_graph(self, bs: int, runners): + buffers = self.buffers + n = len(runners) + + items = [] + for r in runners: + num_tokens = bs * r.captured_req_width + forward_batch = r.get_forward_batch(bs) + forward_batch = r._postprocess_forward_batch(forward_batch, bs) + attn_backend = self.draft_extend_attn_backend_list[r.step] + with forward_context(ForwardContext(attn_backend=attn_backend)): + attn_backend.init_forward_metadata_out_graph( + forward_batch, in_capture=True + ) + r.deepep_adapter.capture(is_extend_in_batch=True) + items.append((r, forward_batch, num_tokens, attn_backend)) + + def multi_step_fn(): + outs = [] + for i, (r, forward_batch, num_tokens, attn_backend) in enumerate(items): + attn_backend.init_forward_metadata_in_graph(forward_batch) + with forward_context(ForwardContext(attn_backend=attn_backend)): + ret = r._run_step_body(forward_batch, num_tokens, bs) + outs.append(ret) + if i < n - 1: + rotate_input_ids( + buffers.input_ids[: bs * self.captured_req_width], + buffers.extend_start_loc[:bs], + buffers.extend_seq_lens[:bs], + ret.topk_index, + buffers.select_index[:bs], + ) + return outs + + first = runners[0] + # capture_one's warmup runs multi_step_fn, whose in-graph rotation mutates + # input_ids/hidden_states/select_index; snapshot and restore OUTSIDE the + # graph (an in-graph reset would clobber prepare()'s runtime writes). + input_ids_orig = buffers.input_ids.clone() + hidden_states_orig = buffers.hidden_states.clone() + select_index_orig = buffers.select_index.clone() + + shape_key = first._make_graph_key(bs) + first.backend.capture_one( + shape_key, + multi_step_fn, + dummies=None, + post_warmup_hook=getattr( + first.attn_backend, "on_after_cuda_graph_warmup", None + ), + ) + + buffers.input_ids.copy_(input_ids_orig) + buffers.hidden_states.copy_(hidden_states_orig) + buffers.select_index.copy_(select_index_orig) + + def replay(self, step: int): + """Replays the one graph on step 0 and serves the rest from cache. The + first tuple element is the step's RAW (unsliced) LogitsProcessorOutput; + the single-CG worker path never consumes it.""" + if step == 0: + first = self.runners[0] + for r in self.runners: + if r is not None: + r.deepep_adapter.replay() + shape_key = first._make_graph_key(self.bs) + outs = first.backend.replay(shape_key, self._replay_spec_info) + raw_bs = self.raw_bs + self._cached = {} + non_null = [r for r in self.runners if r is not None] + for r, out in zip(non_null, outs): + self._cached[r.step] = ( + out, + out.topk_p[:raw_bs], + out.topk_index[:raw_bs], + ) + return self._cached[step] diff --git a/python/sglang/srt/speculative/multi_layer_eagle_utils.py b/python/sglang/srt/speculative/multi_layer_eagle_utils.py index f7f754d8c..e44735d1a 100644 --- a/python/sglang/srt/speculative/multi_layer_eagle_utils.py +++ b/python/sglang/srt/speculative/multi_layer_eagle_utils.py @@ -13,11 +13,50 @@ # ============================================================================== from sglang.kernels.ops.speculative.multi_layer_eagle import ( + compute_widened_draft_extend_locs_positions_triton, + fill_draft_extend_prepare_buffers_triton, + fill_widened_draft_extend_inputs_triton, rotate_input_ids, rotate_input_ids_kernel, + stash_append_boundary_state_triton, + wide_row_softmax_triton, ) +from sglang.srt.environ import envs + + +def boundary_kv_fix_enabled() -> bool: + return envs.SGLANG_ENABLE_MTP_BOUNDARY_KV_FIX.get() + + +def compute_widened_draft_extend_locs_positions( + seq_lens, + req_pool_indices, + req_to_token, + stash_valid_lens, + draft_token_num: int, + num_front_tokens: int, + num_warmup_tokens: int, +): + """Batched out_cache_loc + positions for the widened draft-extend window, + from pre-verify state. Invalid/warm-up front rows write to sacrificial loc 0.""" + return compute_widened_draft_extend_locs_positions_triton( + seq_lens, + req_pool_indices, + req_to_token, + stash_valid_lens, + draft_token_num, + num_front_tokens, + num_warmup_tokens, + ) + __all__ = [ + "boundary_kv_fix_enabled", + "compute_widened_draft_extend_locs_positions", + "fill_draft_extend_prepare_buffers_triton", + "fill_widened_draft_extend_inputs_triton", "rotate_input_ids", "rotate_input_ids_kernel", + "stash_append_boundary_state_triton", + "wide_row_softmax_triton", ] diff --git a/python/sglang/srt/speculative/multi_layer_eagle_worker_v2.py b/python/sglang/srt/speculative/multi_layer_eagle_worker_v2.py index fae59601d..309efdc30 100644 --- a/python/sglang/srt/speculative/multi_layer_eagle_worker_v2.py +++ b/python/sglang/srt/speculative/multi_layer_eagle_worker_v2.py @@ -29,6 +29,7 @@ from sglang.srt.layers.moe.utils import speculative_moe_backend_context from sglang.srt.managers.schedule_batch import ScheduleBatch from sglang.srt.managers.scheduler import GenerationBatchResult from sglang.srt.managers.tp_worker import TpModelWorker +from sglang.srt.mem_cache.memory_pool import HybridReqToTokenPool from sglang.srt.model_executor.cuda_graph_config import ( Backend, Phase, @@ -58,8 +59,15 @@ from sglang.srt.speculative.eagle_worker_common import ( ) from sglang.srt.speculative.multi_layer_eagle_draft_extend_cuda_graph_runner import ( MultiLayerEagleMultiStepDraftExtendCudaGraphRunner, + OneGraphMultiLayerEagleMultiStepDraftExtendCudaGraphRunner, +) +from sglang.srt.speculative.multi_layer_eagle_utils import ( + boundary_kv_fix_enabled, + compute_widened_draft_extend_locs_positions, + fill_widened_draft_extend_inputs_triton, + rotate_input_ids, + stash_append_boundary_state_triton, ) -from sglang.srt.speculative.multi_layer_eagle_utils import rotate_input_ids from sglang.srt.speculative.spec_info import SpeculativeAlgorithm from sglang.srt.speculative.spec_utils import ( draft_tp_context, @@ -67,7 +75,7 @@ from sglang.srt.speculative.spec_utils import ( sample_draft_proposal, select_top_k_tokens, ) -from sglang.srt.utils import is_cpu, is_npu +from sglang.srt.utils import is_cpu, is_npu, require_gathered_buffer from sglang.srt.utils.async_probe import ( maybe_detect_inf, maybe_detect_nan, @@ -109,6 +117,10 @@ class MultiLayerEagleDraftWorker(EagleDraftWorkerBase): self.topk = server_args.speculative_eagle_topk self.speculative_num_steps = server_args.speculative_num_steps self.speculative_num_draft_tokens = server_args.speculative_num_draft_tokens + # Leviathan/Chen rejection sampling (temp>0): the draft samples X ~ q and + # provides q so the verify accepts iff coin*q < p and resamples the residual. + # Single-CG runner samples in-graph (_sample_draft_proposal); per-step + # runner samples worker-side between replays. self.use_rejection_sampling = server_args.speculative_use_rejection_sampling assert self.speculative_num_draft_tokens == self.speculative_num_steps + 1, ( "multi-layer EAGLE requires speculative_num_draft_tokens == " @@ -144,7 +156,10 @@ class MultiLayerEagleDraftWorker(EagleDraftWorkerBase): # Chain-style MTP: each step propagates its own output hidden states to the # next step. Non-chain: each step uses the target model's hidden states. draft_arch = self.draft_worker.model_config.hf_config.architectures[0] - self.chain_mtp_hidden_states = draft_arch in ["Step3p5MTP"] + self.chain_mtp_hidden_states = draft_arch in [ + "Step3p5MTP", + "InklingForConditionalGenerationMTP", + ] self.draft_tp_context = ( draft_tp_context if server_args.enable_dp_attention else empty_context ) @@ -171,6 +186,7 @@ class MultiLayerEagleDraftWorker(EagleDraftWorkerBase): token_to_kv_pool_allocator=token_to_kv_pool_allocator, ) self.init_lm_head() + self._init_boundary_kv_fix_state() def init_attention_backends(self): with ( @@ -189,6 +205,134 @@ class MultiLayerEagleDraftWorker(EagleDraftWorkerBase): def mtp_model_runner(self, step: int): return self.draft_runner_list[step] + def _init_boundary_kv_fix_state(self): + """Boundary-KV fix state: stash + widened draft-extend front. Chain topk=1 only.""" + self.draft_extend_num_front_tokens = 0 + self.draft_extend_num_warmup_tokens = 0 + self.boundary_kv_stash_tokens = None + self.boundary_kv_stash_hiddens = None + self.boundary_kv_stash_valid_lens = None + if not ( + boundary_kv_fix_enabled() + and self.topk == 1 + and self.speculative_num_steps > 1 + # The fix stashes the mamba/sconv boundary state, so it only applies + # to hybrid models; non-hybrid MTP drafts (e.g. MiMoV2) must skip it. + and isinstance(self.req_to_token_pool, HybridReqToTokenPool) + ): + return + draft_model_runner = self.draft_runner_list[0] + draft_hidden_size = draft_model_runner.model_config.hidden_size + target_hidden_size = self.target_worker.model_runner.model_config.hidden_size + if draft_hidden_size != target_hidden_size: + logger.warning( + "SGLANG_ENABLE_MTP_BOUNDARY_KV_FIX disabled: draft hidden size %d != " + "target hidden size %d (the stash holds verify hiddens).", + draft_hidden_size, + target_hidden_size, + ) + return + if isinstance(self.req_to_token_pool, HybridReqToTokenPool): + conv_state = self.req_to_token_pool.mamba_pool.mamba_cache.conv + self.draft_extend_num_warmup_tokens = conv_state[0].shape[2] + self.draft_extend_num_front_tokens = ( + self.speculative_num_steps - 1 + self.draft_extend_num_warmup_tokens + ) + front = self.draft_extend_num_front_tokens + req_pool_size = self.req_to_token_pool.req_to_token.shape[0] + with torch.device(self.device): + self.boundary_kv_stash_tokens = torch.zeros( + (req_pool_size, front), dtype=torch.int64 + ) + self.boundary_kv_stash_hiddens = torch.zeros( + (req_pool_size, front, draft_hidden_size), + dtype=draft_model_runner.dtype, + ) + self.boundary_kv_stash_valid_lens = torch.zeros( + (req_pool_size,), dtype=torch.int32 + ) + logger.info( + "SGLANG_ENABLE_MTP_BOUNDARY_KV_FIX on: draft-extend windows widened by " + "%d front rows (%d conv warm-up).", + front, + self.draft_extend_num_warmup_tokens, + ) + + def _compute_boundary_kv_locs_positions(self, batch): + if self.draft_extend_num_front_tokens == 0 or batch.forward_mode.is_idle(): + return None, None, None + locs, positions = compute_widened_draft_extend_locs_positions( + batch.seq_lens, + batch.req_pool_indices, + self.req_to_token_pool.req_to_token, + self.boundary_kv_stash_valid_lens, + self.speculative_num_draft_tokens, + self.draft_extend_num_front_tokens, + self.draft_extend_num_warmup_tokens, + ) + ready_event = None + if self.plan_stream: + ready_event = torch.get_device_module(self.device).Event() + ready_event.record() + return locs, positions, ready_event + + def _seed_boundary_kv_stash(self, forward_batch, target_hidden_states): + if ( + self.draft_extend_num_front_tokens == 0 + or forward_batch.forward_mode.is_idle() + or forward_batch.extend_seq_lens is None + or target_hidden_states is None + ): + return + extend_seq_lens = forward_batch.extend_seq_lens + src_row_ends = (forward_batch.extend_start_loc + extend_seq_lens).to( + torch.int64 + ) + stash_append_boundary_state_triton( + forward_batch.input_ids, + target_hidden_states, + src_row_ends, + extend_seq_lens, + forward_batch.req_pool_indices, + self.boundary_kv_stash_tokens, + self.boundary_kv_stash_hiddens, + self.boundary_kv_stash_valid_lens, + set_valid=True, + ) + + def _fill_boundary_kv_front_and_update_stash( + self, batch, forward_batch, predict, verify_hiddens, accept_lens + ): + if self.draft_extend_num_front_tokens == 0 or batch.forward_mode.is_idle(): + return + draft_token_num = self.speculative_num_draft_tokens + fill_widened_draft_extend_inputs_triton( + forward_batch.input_ids, + forward_batch.spec_info.hidden_states, + predict, + verify_hiddens, + self.boundary_kv_stash_tokens, + self.boundary_kv_stash_hiddens, + self.boundary_kv_stash_valid_lens, + batch.seq_lens, + batch.req_pool_indices, + draft_token_num=draft_token_num, + ) + bs = len(batch.seq_lens) + arange = torch.arange(bs, device=predict.device, dtype=torch.int64) + src_row_ends = arange * draft_token_num + accept_lens.to(torch.int64) + stash_append_boundary_state_triton( + predict, + verify_hiddens, + src_row_ends, + accept_lens, + batch.req_pool_indices, + self.boundary_kv_stash_tokens, + self.boundary_kv_stash_hiddens, + self.boundary_kv_stash_valid_lens, + set_valid=False, + ) + def init_lm_head(self): embed, head = self.target_worker.model_runner.model.get_embed_and_head() # Share the embedding and lm_head @@ -224,9 +368,32 @@ class MultiLayerEagleDraftWorker(EagleDraftWorkerBase): return if not _is_npu: - self.cuda_graph_runner_for_draft_extend = ( - MultiLayerEagleMultiStepDraftExtendCudaGraphRunner(self) + # The single-CG runner replays with no Python between steps, so the + # attn backend must fully rebuild its per-step metadata as captured + # tensor ops; anything less gets capture-time-stale metadata (e.g. + # SWA translations, which only the eager replay path refreshes). + # Per-depth pools (banded MTP) mean per-depth backends — EVERY step + # must satisfy this, not just step 0. + draft_backend = self.draft_runner_list[0].attn_backend + backend_supports_single_cg = all( + runner.attn_backend.draft_extend_metadata_captured_in_graph() + for runner in self.draft_runner_list ) + if envs.SGLANG_ENABLE_SINGLE_CG_DRAFT.get() and backend_supports_single_cg: + self.cuda_graph_runner_for_draft_extend = ( + OneGraphMultiLayerEagleMultiStepDraftExtendCudaGraphRunner(self) + ) + else: + if envs.SGLANG_ENABLE_SINGLE_CG_DRAFT.get(): + logger.warning( + "SGLANG_ENABLE_SINGLE_CG_DRAFT is on but %s does not fully " + "rebuild its draft-extend metadata in-graph; falling back " + "to per-step draft graphs.", + type(draft_backend).__name__, + ) + self.cuda_graph_runner_for_draft_extend = ( + MultiLayerEagleMultiStepDraftExtendCudaGraphRunner(self) + ) else: self.cuda_graph_runner_for_draft_extend = ( MultiLayerEagleMultiStepDraftExtendNpuGraphRunner(self) @@ -335,6 +502,36 @@ class MultiLayerEagleDraftWorker(EagleDraftWorkerBase): def draft_extend(self): pass + def _apply_deferred_mamba_init_to_draft_pools(self, forward_batch) -> None: + if ( + self.draft_runner.model_config.hf_config.architectures[0] + != "InklingForConditionalGenerationMTP" + ): + return + fm = forward_batch.forward_mode + if not (fm.is_extend(include_draft_extend_v2=True) or fm.is_decode()): + return + clear = forward_batch.mamba_clear_indices + cow_src = forward_batch.mamba_cow_src_indices + cow_dst = forward_batch.mamba_cow_dst_indices + if (clear is None or len(clear) == 0) and ( + cow_src is None or len(cow_src) == 0 + ): + return + seen = set() + for runner in self.draft_runner_list: + pool = runner.req_to_token_pool.mamba_pool + if id(pool) in seen: + continue + seen.add(id(pool)) + if clear is not None and len(clear) > 0: + pool.clear_slots(clear) + if cow_src is not None and len(cow_src) > 0: + pool.copy_from(cow_src, cow_dst) + forward_batch.mamba_clear_indices = None + forward_batch.mamba_cow_src_indices = None + forward_batch.mamba_cow_dst_indices = None + def _draft_extend_for_prefill( self, batch: ScheduleBatch, @@ -387,6 +584,8 @@ class MultiLayerEagleDraftWorker(EagleDraftWorkerBase): return_hidden_states_before_norm=True, ) + self._apply_deferred_mamba_init_to_draft_pools(forward_batch) + # Construct input_ids # TODO: same chunked-prefill chain divergence as PR #26329. if not batch.forward_mode.is_idle(): @@ -397,10 +596,18 @@ class MultiLayerEagleDraftWorker(EagleDraftWorkerBase): next_token_ids, ) + self._seed_boundary_kv_stash(forward_batch, target_hidden_states) + topk_p_list = [] topk_index_list = [] draft_probs_list = [] for step in range(self.speculative_num_steps): + forward_batch.req_to_token_pool = self.draft_runner_list[ + step + ].req_to_token_pool + forward_batch.token_to_kv_pool = self.draft_runner_list[ + step + ].token_to_kv_pool output: ModelRunnerOutput = self.draft_runner_list[step].forward( forward_batch ) @@ -413,7 +620,7 @@ class MultiLayerEagleDraftWorker(EagleDraftWorkerBase): f"draft_extend_for_prefill step {step}", ) if self.use_rejection_sampling and self.topk == 1: - # Sample X ~ q and stash q for the first verify's Leviathan step. + # Rejection sampling (prefill): sample X ~ q and stash q for the first verify. probs, topk_p, topk_index = sample_draft_proposal( output.logits_output.next_token_logits, forward_batch.sampling_info.temperatures, @@ -451,7 +658,7 @@ class MultiLayerEagleDraftWorker(EagleDraftWorkerBase): num_tokens_per_req=1, num_tokens_for_logprob_per_req=1, ) - # q [bs, num_steps, vocab] for the first verify's Leviathan step (RS only). + # q [bs, num_steps, vocab] for the first verify's Leviathan step (rejection only). next_draft_input.draft_probs = ( torch.stack(draft_probs_list, dim=1) if self.use_rejection_sampling and draft_probs_list @@ -469,11 +676,18 @@ class MultiLayerEagleDraftWorker(EagleDraftWorkerBase): # Actual width: the multi-layer chain fills num_steps + 1 rows/req. num_tokens_per_req=self.speculative_num_steps + 1, num_tokens_for_logprob_per_req=1, + num_front_tokens=self.draft_extend_num_front_tokens, ) # Prepare for draft extend in a separate stream # Notice that here we use batch_result.next_token_ids as the input ids + boundary_kv_locs, boundary_kv_positions, boundary_kv_ready_event = ( + self._compute_boundary_kv_locs_positions(batch) + ) + with self.plan_stream_ctx: + if boundary_kv_ready_event is not None: + self.plan_stream.wait_event(boundary_kv_ready_event) forward_batch = prepare_for_draft_extend( draft_extend_input, batch, @@ -482,12 +696,24 @@ class MultiLayerEagleDraftWorker(EagleDraftWorkerBase): self.draft_runner_list[0], self.cuda_graph_runner_for_draft_extend, return_hidden_states_before_norm=True, + widened_out_cache_loc=boundary_kv_locs, + widened_positions=boundary_kv_positions, ) if self.plan_stream: torch.get_device_module(self.device).current_stream().wait_stream( self.plan_stream ) + + self._apply_deferred_mamba_init_to_draft_pools(forward_batch) + self._fill_boundary_kv_front_and_update_stash( + batch, + forward_batch, + batch_result.next_token_ids, + batch_result.logits_output.hidden_states, + batch_result.accept_lens, + ) + # `batch_result.accept_lens` includes the bonus token, so drafts-only # is accept_lens - 1. Stash on spec_info for the cuda-graph prepare(). forward_batch.spec_info.num_correct_drafts = batch_result.accept_lens - 1 @@ -501,6 +727,7 @@ class MultiLayerEagleDraftWorker(EagleDraftWorkerBase): ret_topk_p_list = [] ret_topk_index_list = [] ret_draft_probs_list = [] + ret_draft_probs = None next_token_ids_backup = batch_result.next_token_ids.clone() if can_cuda_graph: @@ -508,22 +735,40 @@ class MultiLayerEagleDraftWorker(EagleDraftWorkerBase): # Populate the single shared buffer set once; each step replays # against it and the chain is advanced in place between steps. cgr.prepare(forward_batch) + rotates_in_graph = cgr.rotates_in_graph for step in range(self.speculative_num_steps): _out, ret_topk_p, ret_topk_index = cgr.replay(step) - if self.use_rejection_sampling and self.topk == 1: - # Re-pick X ~ q worker-side so the chain rotation carries it - # to step N+1 (per-step graph does not sample in-graph). - sel = cgr.buffers.select_index[: cgr.raw_bs] + # Rejection sampling with the per-step runner re-picks X ~ q + # worker-side so the worker rotation carries it to step N+1; the + # single-CG runner samples in-graph (q cloned after the loop). + if ( + self.use_rejection_sampling + and self.topk == 1 + and not rotates_in_graph + ): + if cgr.prune_draft_extend_logits: + step_logits = _out.next_token_logits + else: + sel = cgr.buffers.select_index[: cgr.raw_bs] + step_logits = _out.next_token_logits[sel] probs, ret_topk_p, ret_topk_index = sample_draft_proposal( - _out.next_token_logits[sel], + step_logits, forward_batch.sampling_info.temperatures, ) ret_draft_probs_list.append(probs) - ret_topk_p_list.append(ret_topk_p.clone()) - ret_topk_index_list.append(ret_topk_index.clone()) + if rotates_in_graph: + # Single-CG step outputs coexist until the trailing cat. + ret_topk_p_list.append(ret_topk_p) + ret_topk_index_list.append(ret_topk_index) + else: + # Per-step graphs share the global graph pool; snapshot + # before the next step's replay can reuse the buffer. + ret_topk_p_list.append(ret_topk_p.clone()) + ret_topk_index_list.append(ret_topk_index.clone()) # Advance the draft chain by rotating the shared input_ids window - # in place; step N+1's graph then reads the rotated values. - if step < self.speculative_num_steps - 1: + # in place; step N+1's graph then reads the rotated values. The + # single-CG runner rotates in-graph, so skip the worker-side rotate. + if step < self.speculative_num_steps - 1 and not rotates_in_graph: rotate_input_ids( cgr.buffers.input_ids[: cgr.raw_num_tokens], cgr.buffers.extend_start_loc[: cgr.raw_bs], @@ -531,34 +776,52 @@ class MultiLayerEagleDraftWorker(EagleDraftWorkerBase): ret_topk_index, cgr.buffers.select_index[: cgr.raw_bs], ) + if self.use_rejection_sampling and self.topk == 1 and rotates_in_graph: + ret_draft_probs = cgr.clone_draft_probs() else: logger.warning_once( "can't use cuda graph for draft extend! may have correctness issue!" ) select_index = ( torch.arange(len(batch.seq_lens), device=self.device) - * self.speculative_num_draft_tokens + * ( + self.speculative_num_draft_tokens + + self.draft_extend_num_front_tokens + ) + + self.draft_extend_num_front_tokens + batch_result.accept_lens - 1 ) - # NOTE: this non-graph path runs the per-step forwards without any - # pre-plan (see warning above). Mark the batch so the forward path - # keeps skipping metadata init — preserves the pre-existing - # behavior; the latent issue is tracked by the warning. - # On NPU with --disable-cuda-graph, leave each draft runner to init - # its own metadata in forward_extend (post-pad), otherwise - # per-runner attn_backend.forward_metadata is never initialized for - # draft_runner_list[1+]. - if not _is_npu: - forward_batch.mark_forward_metadata_ready() - + if self.cuda_graph_runner_for_draft_extend: + prune_logits = ( + self.cuda_graph_runner_for_draft_extend.prune_draft_extend_logits + ) + else: + prune_logits = not require_gathered_buffer(self.server_args) + if prune_logits: + forward_batch.spec_info.select_index = select_index + # Left unmarked on every platform: each de-tied runner has its own + # attn backend, and only runner[0]'s was pre-planned, so each step's + # forward must init its own metadata post-pad (mirrors NPU behavior). for step in range(self.speculative_num_steps): + forward_batch.req_to_token_pool = self.draft_runner_list[ + step + ].req_to_token_pool + forward_batch.token_to_kv_pool = self.draft_runner_list[ + step + ].token_to_kv_pool + self.draft_runner_list[step].attn_backend.init_forward_metadata( + forward_batch + ) draft_logits_output = self.draft_runner_list[step].forward( forward_batch ) - logits_sel = draft_logits_output.logits_output.next_token_logits[ - select_index - ] + if prune_logits: + logits_sel = draft_logits_output.logits_output.next_token_logits + else: + logits_sel = draft_logits_output.logits_output.next_token_logits[ + select_index + ] if self.use_rejection_sampling and self.topk == 1: probs, ret_topk_p, ret_topk_index = sample_draft_proposal( logits_sel, forward_batch.sampling_info.temperatures @@ -595,16 +858,16 @@ class MultiLayerEagleDraftWorker(EagleDraftWorkerBase): next_draft_input.topk_index, next_draft_input.hidden_states, ) = ( - torch.cat(ret_topk_p_list, dim=1).clone(), - torch.cat(ret_topk_index_list, dim=1).clone(), + torch.cat(ret_topk_p_list, dim=1), + torch.cat(ret_topk_index_list, dim=1), None, ) - # q [bs, num_steps, vocab] carries the per-chain-step draft distributions - # to the next verify's Leviathan step (accept iff coin*q < p). None - # otherwise (default target-only tree sampling). - next_draft_input.draft_probs = ( - torch.stack(ret_draft_probs_list, dim=1) if ret_draft_probs_list else None - ) + # Under rejection sampling, carry the per-chain-step draft distributions + # q [bs, num_steps, vocab] so the next verify runs Leviathan (accept iff + # coin*q < p). None otherwise (default target-only tree sampling). + if ret_draft_probs is None and ret_draft_probs_list: + ret_draft_probs = torch.stack(ret_draft_probs_list, dim=1) + next_draft_input.draft_probs = ret_draft_probs class MultiLayerEagleWorkerV2(BaseSpecWorker): @@ -730,6 +993,6 @@ class MultiLayerEagleWorkerV2(BaseSpecWorker): num_steps=self.speculative_num_steps, num_draft_tokens=self.speculative_num_draft_tokens, device=self.device, - metadata_ready_pre_pad=True, + metadata_ready_pre_pad=False, finalize_tree_path=False, ) diff --git a/python/sglang/srt/speculative/spec_utils.py b/python/sglang/srt/speculative/spec_utils.py index ad133122d..235952647 100644 --- a/python/sglang/srt/speculative/spec_utils.py +++ b/python/sglang/srt/speculative/spec_utils.py @@ -114,9 +114,18 @@ def resolve_num_tokens_per_req( def fast_sample(probs: torch.Tensor, num_samples: int = 1): - """Gumbel-max draw: argmax(probs / Exp(1)). Distributionally equivalent to - torch.multinomial minus its device-side validity assert, which a capturing - CUDA graph would replay every step.""" + """Draw from `probs` via the Gumbel-max trick: argmax(probs / Exp(1)). + + Distributionally equivalent to torch.multinomial, but avoids multinomial's + device-side distribution-validity assert, which the draft CUDA graph would + otherwise capture and replay every step. q is clamped off zero so a zero + draw can't yield inf/NaN scores that argmax would wrongly select; fp32 + avoids bf16 argmax ties biasing the draw. Set SGLANG_OPT_USE_GUMBEL_SAMPLE=0 + to fall back to torch.multinomial. + """ + if not envs.SGLANG_OPT_USE_GUMBEL_SAMPLE.get(): + sample_index = torch.multinomial(probs, num_samples=num_samples) + return probs.gather(1, sample_index), sample_index q = torch.empty_like(probs, dtype=torch.float32).exponential_(1.0) q.clamp_min_(torch.finfo(torch.float32).tiny) scores = probs.float() / q @@ -688,8 +697,6 @@ def commit_mamba_states_after_verify( if mambaish_config(model_runner.model_config) is None: return attn_backend = model_runner.attn_backend - if not hasattr(attn_backend, "update_mamba_state_after_mtp_verify"): - return bs = accept_lens.shape[0] # `accept_lens` already includes the bonus token (drafts + 1 per req). @@ -736,12 +743,23 @@ def commit_mamba_states_after_verify( else: mamba_steps_to_track = None - attn_backend.update_mamba_state_after_mtp_verify( - last_correct_step_indices=last_correct_step_indices, - mamba_track_indices=batch.mamba_track_indices, - mamba_steps_to_track=mamba_steps_to_track, - model=model_runner.model, - ) + if hasattr(attn_backend, "update_mamba_state_after_mtp_verify"): + attn_backend.update_mamba_state_after_mtp_verify( + last_correct_step_indices=last_correct_step_indices, + mamba_track_indices=batch.mamba_track_indices, + mamba_steps_to_track=mamba_steps_to_track, + model=model_runner.model, + ) + elif hasattr(model_runner.model, "update_conv_state_after_mtp_verify"): + # Models whose conv layers bypass the attention-backend wrapper + # (Inkling) own the commit themselves. + model_runner.model.update_conv_state_after_mtp_verify( + req_to_token_pool=model_runner.req_to_token_pool, + req_pool_indices=batch.req_pool_indices[:bs], + last_correct_step_indices=last_correct_step_indices, + mamba_track_indices=batch.mamba_track_indices, + mamba_steps_to_track=mamba_steps_to_track, + ) def spec_prepare_for_decode(batch: ScheduleBatch) -> None: diff --git a/python/sglang/srt/tokenizer/tiktoken_tokenizer.py b/python/sglang/srt/tokenizer/tiktoken_tokenizer.py index c1f9ad134..866c1ad24 100644 --- a/python/sglang/srt/tokenizer/tiktoken_tokenizer.py +++ b/python/sglang/srt/tokenizer/tiktoken_tokenizer.py @@ -105,7 +105,22 @@ class TiktokenTokenizer: self.vocab_size = tokenizer.n_vocab self.chat_template = "{% for message in messages %}{% if message['role'] == 'user' %}{{ 'Human: ' + message['content'].strip() + '<|separator|>\n\n' }}{% elif message['role'] == 'system' %}{{ 'System: ' + message['content'].strip() + '<|separator|>\n\n' }}{% elif message['role'] == 'assistant' %}{{ 'Assistant: ' + message['content'] + '<|separator|>\n\n' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ 'Assistant:' }}{% endif %}" self.chat_template_jinja = Template(self.chat_template) - self.additional_stop_token_ids = None + # Turn-final markers some checkpoints ship without EOS metadata -- e.g. + # Inkling's <|content_model_end_sampling|>, whose bundled tokenizer + # config leaves eos_token unset. get_tokenizer()'s tiktoken load path + # returns before attach_additional_stop_token_ids() runs, so register + # them here from the same shared list (resolved against the special + # tokens, as with EOS above); otherwise generation runs to max_tokens. + from sglang.srt.utils.hf_transformers.common import ( + _ADDITIONAL_STOP_TOKEN_TEXTS, + ) + + stop_ids = { + tokenizer._special_tokens[text] + for text in _ADDITIONAL_STOP_TOKEN_TEXTS + if text in tokenizer._special_tokens + } + self.additional_stop_token_ids = stop_ids or None def encode(self, x, add_special_tokens=False): return self.tokenizer.encode(x) diff --git a/python/sglang/srt/utils/common.py b/python/sglang/srt/utils/common.py index c99a9bd1f..e697342f4 100644 --- a/python/sglang/srt/utils/common.py +++ b/python/sglang/srt/utils/common.py @@ -1827,6 +1827,10 @@ def suppress_noisy_warnings(): "NamedBarrier wait also arrives on the barrier. " "Routing call to NamedBarrier.arrive_and_wait().", ), + ( + DeprecationWarning, + "builtin type swigvarlink has no __module__ attribute", + ), } for cat, msg in cutlass_dsl_noisy: warnings.filterwarnings("ignore", message=re.escape(msg), category=cat) @@ -4059,6 +4063,9 @@ SUPPORTED_LORA_TARGET_MODULES = [ "gate_up_proj", "embed_tokens", "lm_head", + # Inkling attention projections (merged q/k/v/r and its row-parallel output). + "qkvr", + "wo_ud", ] LORA_TARGET_ALL_MODULES = "all" diff --git a/python/sglang/srt/utils/hf_transformers/common.py b/python/sglang/srt/utils/hf_transformers/common.py index 948087e75..a7ba69eca 100644 --- a/python/sglang/srt/utils/hf_transformers/common.py +++ b/python/sglang/srt/utils/hf_transformers/common.py @@ -32,6 +32,10 @@ from sglang.srt.configs import ( ExaoneConfig, FalconH1Config, GraniteMoeHybridConfig, + InklingAudioConfig, + InklingMMConfig, + InklingModelConfig, + InklingVisionConfig, InternS2PreviewConfig, JetNemotronConfig, JetVLMConfig, @@ -113,6 +117,10 @@ _CONFIG_REGISTRY: Dict[str, Type[PretrainedConfig]] = { Step3p7Config, MiniCPMV4_6Config, MiniCPMV4_6VisionConfig, + InklingModelConfig, + InklingAudioConfig, + InklingVisionConfig, + InklingMMConfig, MiniMaxM3VLConfig, ] } @@ -521,9 +529,13 @@ def get_tokenizer_from_processor(processor): return processor.tokenizer +# Turn-final markers that some checkpoints ship without EOS metadata: +# <|eom_id|> (Llama-3 tool use) and <|content_model_end_sampling|> (Inkling, +# whose bundled tokenizer config leaves eos_token unset). +_ADDITIONAL_STOP_TOKEN_TEXTS = ("<|eom_id|>", "<|content_model_end_sampling|>") + + def attach_additional_stop_token_ids(tokenizer): added = tokenizer.get_added_vocab() - if "<|eom_id|>" in added: - tokenizer.additional_stop_token_ids = {added["<|eom_id|>"]} - else: - tokenizer.additional_stop_token_ids = None + stop_ids = {added[text] for text in _ADDITIONAL_STOP_TOKEN_TEXTS if text in added} + tokenizer.additional_stop_token_ids = stop_ids or None diff --git a/python/sglang/test/kits/attention_unittest/runner_modes/cuda_graph_decode_runner.py b/python/sglang/test/kits/attention_unittest/runner_modes/cuda_graph_decode_runner.py index 73ced7afd..605cef467 100644 --- a/python/sglang/test/kits/attention_unittest/runner_modes/cuda_graph_decode_runner.py +++ b/python/sglang/test/kits/attention_unittest/runner_modes/cuda_graph_decode_runner.py @@ -309,6 +309,7 @@ def _init_cuda_graph_replay_metadata(backend, capture_batch_size: int, batch): seq_lens_sum=batch.seq_lens_sum, seq_lens_cpu=batch.seq_lens_cpu, encoder_lens=batch.encoder_lens, + extend_seq_lens=getattr(batch, "extend_seq_lens", None), out_cache_loc=getattr(batch, "out_cache_loc", None), spec_info=batch.spec_info, ) diff --git a/rust/sglang-mm/.gitignore b/rust/sglang-mm/.gitignore new file mode 100644 index 000000000..ea8c4bf7f --- /dev/null +++ b/rust/sglang-mm/.gitignore @@ -0,0 +1 @@ +/target diff --git a/rust/sglang-mm/Cargo.toml b/rust/sglang-mm/Cargo.toml new file mode 100644 index 000000000..cbbd39317 --- /dev/null +++ b/rust/sglang-mm/Cargo.toml @@ -0,0 +1,23 @@ +[package] +name = "sglang-mm" +version = "0.1.0" +edition = "2024" +description = "Rust-accelerated multimodal preprocessing for SGLang" +license = "Apache-2.0" + +[lib] +name = "_core" +crate-type = ["cdylib"] + +[dependencies] +pyo3 = { version = "0.23", features = ["extension-module"] } +numpy = "0.23" +rayon = "1.10" +half = "2.4" +image = { version = "0.25", default-features = false, features = ["jpeg", "png"] } +blake3 = "1" +base64 = "0.22" + +[profile.release] +lto = true +codegen-units = 1 diff --git a/rust/sglang-mm/README.md b/rust/sglang-mm/README.md new file mode 100644 index 000000000..11a4e6531 --- /dev/null +++ b/rust/sglang-mm/README.md @@ -0,0 +1,120 @@ +# sglang-mm + +Rust-accelerated multimodal preprocessing for SGLang. Fused image decode, +resize, patchify, normalize, and content hash — all parallel and GIL-released. + +Compiled as `sglang.srt.multimodal._core` via setuptools-rust when installing sglang. + +## Architecture + +``` +src/ +├── lib.rs # PyO3 module root (_core) +├── registry.rs # ImageProcessorSpec trait + ProcessorRegistry +├── common/ +│ ├── mod.rs # thread pool, image decode, SHA256 hash, base64 +│ ├── resize.rs # PIL-exact Lanczos resize +│ └── transforms.rs # reusable primitives: normalize, pad, extract_patches +└── / + └── mod.rs # model-specific processor +``` + +## Python API + +```python +from sglang.srt.multimodal._core import common, inkling + +# Common (model-agnostic) +common.resize_rgb(arr, out_w, out_h) +common.scaled_dims(w, h, rescale_frac, rescale_cap) +common.image_decode_rgb(bytes) # -> (h, w, ndarray) +common.data_hash(bytes) # -> u64 SHA256 +common.base64_decode(str) # -> bytes + +# Model-specific +inkling.preprocess_images(list[bytes], ps, frac, cap) # -> [(h, w, bits, hash), ...] +inkling.decode_patchify(bytes, ps, frac, cap) +inkling.decode_patchify_batch(list[bytes], ps, frac, cap) +inkling.patchify_rgb(arr, patch_size) +``` + +## Adding a new model + +1. Create `src//mod.rs`: + +```rust +use crate::common; +use crate::registry::ImageProcessorSpec; +use rayon::prelude::*; + +pub struct MyModelProcessor; + +impl ImageProcessorSpec for MyModelProcessor { + fn name(&self) -> &'static str { + "my_model" + } + + fn preprocess_batch( + &self, + datas: &[Vec], + patch_size: usize, + rescale_frac: Option, + rescale_cap: Option, + ) -> Result, u64)>, String> { + common::pool().install(|| { + datas.par_iter().map(|data| { + let hash = common::sha256_u64(data); + let (rgb, h, w) = common::decode_rescale(data, rescale_frac, rescale_cap)?; + // Use common::transforms::* or model-specific logic + let patches = my_patchify(&rgb, h, w, patch_size); + Ok((h, w, patches, hash)) + }).collect() + }) + } +} +``` + +2. Register in `src/registry.rs` `default_registry()`. + +3. Add PyO3 bindings in `src//mod.rs` with a `register()` function. + +4. Wire up in `src/lib.rs`: `mod my_model;` and `my_model::register(m)?;`. + +5. Add Python processor class that calls `from sglang.srt.multimodal._core import my_model`. + +## Available transform primitives (`common::transforms`) + +| Function | Description | +|----------|-------------| +| `normalize_rgb_f32` | Single-pass `(pixel/255 - mean) / std` | +| `pad_to_grid` | Pad HWC image to grid-aligned dimensions | +| `extract_patches_hwc` | Reshape padded image into `[N, ph, pw, C]` patches | +| `patch_grid` | Compute `(nph, npw)` for given image and patch size | + +## Design notes + +- Thread pool capped at `min(8, cores)`. Override: `SGL_MM_RS_THREADS`. +- PNG decode is bit-exact vs PIL; JPEG may differ by ±1 LSB. +- Lanczos resize is a bit-exact clone of PIL's fixed-point implementation. + +## Build + +Automatically built when installing sglang: +```bash +pip install -e "python" +``` + +Or standalone for development: +```bash +cd rust/sglang-mm +pip install maturin +maturin develop --release +``` + +## Test + +```bash +python bench/generate_golden.py # regenerate fixtures +pytest bench/test_golden.py # regression tests +python bench/bench_parity.py # parity + benchmark +``` diff --git a/rust/sglang-mm/bench/bench_parity.py b/rust/sglang-mm/bench/bench_parity.py new file mode 100644 index 000000000..5da717c7d --- /dev/null +++ b/rust/sglang-mm/bench/bench_parity.py @@ -0,0 +1,161 @@ +import io +import time + +import numpy as np +import torch +from PIL import Image + +import sglang.srt.multimodal._core.inkling +from sglang.srt.multimodal.inkling.image_processing import ( + IMAGE_MEAN, + IMAGE_STD, + PAD_NORM, + _encode_image_bytes, + _fill_patches_numba, +) + +PS = 40 + + +def ref_patchify(arr: np.ndarray) -> torch.Tensor: + h, w, _ = arr.shape + nph = (h + PS - 1) // PS + npw = w // PS + 1 + patches = np.empty((nph * npw, PS, PS, 3), dtype=np.float32) + _fill_patches_numba(arr, PS, patches, IMAGE_MEAN, IMAGE_STD, PAD_NORM) + return torch.from_numpy(patches).to(torch.bfloat16) + + +def rs_patchify(arr: np.ndarray) -> torch.Tensor: + h, w, _ = arr.shape + nph = (h + PS - 1) // PS + npw = w // PS + 1 + bits = sglang.srt.multimodal._core.inkling.patchify_rgb(arr, PS) + return torch.from_numpy(bits).view(torch.bfloat16).reshape(nph * npw, PS, PS, 3) + + +def rs_decode_patchify(data: bytes) -> torch.Tensor: + h, w, bits = sglang.srt.multimodal._core.inkling.decode_patchify(data, PS) + nph = (h + PS - 1) // PS + npw = w // PS + 1 + return torch.from_numpy(bits).view(torch.bfloat16).reshape(nph * npw, PS, PS, 3) + + +def make_photo_like(h: int, w: int, seed: int = 0) -> np.ndarray: + rng = np.random.default_rng(seed) + yy, xx = np.mgrid[0:h, 0:w] + base = np.stack( + [ + 127 + 100 * np.sin(yy / 97.0) * np.cos(xx / 131.0), + 127 + 100 * np.cos(yy / 61.0) * np.sin(xx / 89.0), + 127 + 100 * np.sin((xx + yy) / 149.0), + ], + axis=-1, + ) + noise = rng.normal(0, 12, (h // 8 + 1, w // 8 + 1, 3)) + noise = np.kron(noise, np.ones((8, 8, 1)))[:h, :w] + return np.clip(base + noise, 0, 255).astype(np.uint8) + + +def encode(arr: np.ndarray, fmt: str) -> bytes: + buf = io.BytesIO() + Image.fromarray(arr).save( + buf, format=fmt, **({"quality": 90} if fmt == "JPEG" else {}) + ) + return buf.getvalue() + + +def parity_a(): + print("=== Parity A: patchify from decoded array (expect bit-exact) ===") + rng = np.random.default_rng(42) + for h, w in [(1080, 1920), (1920, 1080), (40, 40), (37, 53), (720, 1280), (1, 1)]: + arr = rng.integers(0, 256, (h, w, 3), dtype=np.uint8) + ref, got = ref_patchify(arr), rs_patchify(arr) + exact = torch.equal(ref.view(torch.uint16), got.view(torch.uint16)) + print(f" {h}x{w}: shape {tuple(got.shape)} bit-exact={exact}") + assert exact, f"parity A failed at {h}x{w}" + + +def parity_b(): + print("=== Parity B: full decode path ===") + arr = make_photo_like(1080, 1920) + for fmt in ["PNG", "JPEG"]: + data = encode(arr, fmt) + ref = _encode_image_bytes( + data, + patch_size=PS, + rescale_image_frac=None, + rescale_image_max_upscaled_long_edge=None, + ) + got = rs_decode_patchify(data) + got2 = got.view(got.shape[0], 1, PS, PS, 3).expand(-1, 2, -1, -1, -1) + if torch.equal( + ref.contiguous().view(torch.uint16), got2.contiguous().view(torch.uint16) + ): + print(f" {fmt}: bit-exact=True ({len(data)/1e6:.2f}MB)") + else: + d = (ref.float() - got2.float()).abs() + print( + f" {fmt}: bit-exact=False max_abs={d.max():.6f} mean_abs={d.mean():.8f} " + f"(decoder difference; normalized-feature units)" + ) + + +def bench(): + print("=== Benchmark (1080p, patch_size=40) ===") + arr = make_photo_like(1080, 1920) + jpeg = encode(arr, "JPEG") + n = 30 + + _encode_image_bytes( + jpeg, + patch_size=PS, + rescale_image_frac=None, + rescale_image_max_upscaled_long_edge=None, + ) + rs_decode_patchify(jpeg) + sglang.srt.multimodal._core.inkling.decode_patchify_batch([jpeg] * 5, PS) + + def run(label, fn, iters=n, images_per_call=1): + t0, c0 = time.perf_counter(), time.process_time() + for _ in range(iters): + fn() + wall = (time.perf_counter() - t0) / iters / images_per_call * 1e3 + cpu = (time.process_time() - c0) / iters / images_per_call * 1e3 + print(f" {label:42} wall {wall:8.2f} ms/img cpu {cpu:8.2f} ms/img") + return wall, cpu + + w_py, c_py = run( + "python (PIL + numba + bf16 cast)", + lambda: _encode_image_bytes( + jpeg, + patch_size=PS, + rescale_image_frac=None, + rescale_image_max_upscaled_long_edge=None, + ), + ) + w_rs, c_rs = run("rust decode_patchify", lambda: rs_decode_patchify(jpeg)) + w_rb, c_rb = run( + "rust decode_patchify_batch (5 imgs/call)", + lambda: sglang.srt.multimodal._core.inkling.decode_patchify_batch( + [jpeg] * 5, PS + ), + iters=max(n // 5, 5), + images_per_call=5, + ) + + run("python numba patchify only", lambda: ref_patchify(arr)) + run("rust patchify_rgb only", lambda: rs_patchify(arr)) + + print( + f"\n speedup vs python: single {w_py / w_rs:.1f}x wall / {c_py / c_rs:.1f}x cpu, " + f"batch {w_py / w_rb:.1f}x wall / {c_py / c_rb:.1f}x cpu" + ) + + +if __name__ == "__main__": + torch.set_num_threads(8) + parity_a() + parity_b() + bench() + print("\nOK") diff --git a/rust/sglang-mm/pyproject.toml b/rust/sglang-mm/pyproject.toml new file mode 100644 index 000000000..b9c3015cf --- /dev/null +++ b/rust/sglang-mm/pyproject.toml @@ -0,0 +1,12 @@ +[build-system] +requires = ["maturin>=1.5,<2"] +build-backend = "maturin" + +[project] +name = "sglang-mm" +version = "0.1.0" +description = "Rust-accelerated multimodal preprocessing for SGLang" +requires-python = ">=3.9" + +[tool.maturin] +module-name = "_core" diff --git a/rust/sglang-mm/src/common/mod.rs b/rust/sglang-mm/src/common/mod.rs new file mode 100644 index 000000000..1c4a4a8d6 --- /dev/null +++ b/rust/sglang-mm/src/common/mod.rs @@ -0,0 +1,136 @@ +pub mod resize; +pub mod transforms; + +use std::sync::OnceLock; + +use numpy::{IntoPyArray, PyArray1, PyReadonlyArray3, PyUntypedArrayMethods}; +use pyo3::exceptions::PyValueError; +use pyo3::prelude::*; + + +pub fn pool() -> &'static rayon::ThreadPool { + static POOL: OnceLock = OnceLock::new(); + POOL.get_or_init(|| { + let n = std::env::var("SGL_MM_RS_THREADS") + .ok() + .and_then(|s| s.parse::().ok()) + .filter(|&n| n > 0) + .unwrap_or_else(|| std::thread::available_parallelism().map_or(8, |c| c.get().min(8))); + rayon::ThreadPoolBuilder::new() + .num_threads(n) + .thread_name(|i| format!("sgl-mm-{i}")) + .build() + .expect("failed to build rayon pool") + }) +} + +pub fn sha256_u64(data: &[u8]) -> u64 { + let digest = blake3::hash(data); + u64::from_be_bytes(digest.as_bytes()[..8].try_into().unwrap()) +} + +pub fn decode_rgb(data: &[u8]) -> Result<(Vec, usize, usize), String> { + let img = image::load_from_memory(data).map_err(|e| format!("image decode: {e}"))?; + let rgb = img.to_rgb8(); + let (w, h) = rgb.dimensions(); + Ok((rgb.into_raw(), h as usize, w as usize)) +} + +pub fn decode_rescale( + data: &[u8], + rescale_frac: Option, + rescale_cap: Option, +) -> Result<(Vec, usize, usize), String> { + let (rgb, h, w) = decode_rgb(data)?; + let (tw, th) = resize::scaled_dims(w, h, rescale_frac, rescale_cap); + if (tw, th) == (w, h) { + return Ok((rgb, h, w)); + } + Ok((resize::resize_lanczos_rgb(&rgb, h, w, th, tw), th, tw)) +} + +// --- Python-exposed functions --- + +#[pyfunction] +pub fn resize_rgb<'py>( + py: Python<'py>, + arr: PyReadonlyArray3<'py, u8>, + out_w: usize, + out_h: usize, +) -> PyResult>> { + if out_w == 0 || out_h == 0 { + return Err(PyValueError::new_err("output size must be positive")); + } + let shape = arr.shape(); + let (h, w, c) = (shape[0], shape[1], shape[2]); + if c != 3 { + return Err(PyValueError::new_err(format!( + "expected HWC RGB array with 3 channels, got {c}" + ))); + } + let data = arr + .as_slice() + .map_err(|_| PyValueError::new_err("array must be C-contiguous"))? + .to_vec(); + let out = py.allow_threads(move || { + pool().install(|| resize::resize_lanczos_rgb(&data, h, w, out_h, out_w)) + }); + Ok(out.into_pyarray_bound(py)) +} + +#[pyfunction] +#[pyo3(signature = (w, h, rescale_frac=None, rescale_cap=None))] +pub fn scaled_dims( + w: usize, + h: usize, + rescale_frac: Option, + rescale_cap: Option, +) -> (usize, usize) { + resize::scaled_dims(w, h, rescale_frac, rescale_cap) +} + +#[pyfunction] +pub fn image_decode_rgb<'py>( + py: Python<'py>, + data: Vec, +) -> PyResult<(usize, usize, Bound<'py, PyArray1>)> { + let (rgb, h, w) = py + .allow_threads(move || decode_rgb(&data)) + .map_err(PyValueError::new_err)?; + Ok((h, w, rgb.into_pyarray_bound(py))) +} + +#[pyfunction] +pub fn data_hash(py: Python<'_>, data: Vec) -> u64 { + py.allow_threads(move || { + let digest = blake3::hash(&data); + u64::from_be_bytes(digest.as_bytes()[..8].try_into().unwrap()) + }) +} + +#[pyfunction] +pub fn base64_decode<'py>( + py: Python<'py>, + encoded: &str, +) -> PyResult> { + use base64::Engine; + let decoded = py + .allow_threads(|| { + base64::engine::general_purpose::STANDARD + .decode(encoded) + .map_err(|e| format!("base64 decode error: {e}")) + }) + .map_err(PyValueError::new_err)?; + Ok(pyo3::types::PyBytes::new_bound(py, &decoded)) +} + +pub fn register(parent: &Bound<'_, PyModule>) -> PyResult<()> { + let m = PyModule::new_bound(parent.py(), "common")?; + m.add_function(wrap_pyfunction!(resize_rgb, &m)?)?; + m.add_function(wrap_pyfunction!(scaled_dims, &m)?)?; + m.add_function(wrap_pyfunction!(image_decode_rgb, &m)?)?; + m.add_function(wrap_pyfunction!(data_hash, &m)?)?; + m.add_function(wrap_pyfunction!(base64_decode, &m)?)?; + parent.add_submodule(&m)?; + Ok(()) +} diff --git a/rust/sglang-mm/src/common/resize.rs b/rust/sglang-mm/src/common/resize.rs new file mode 100644 index 000000000..0c1d58287 --- /dev/null +++ b/rust/sglang-mm/src/common/resize.rs @@ -0,0 +1,185 @@ +use rayon::prelude::*; + +const PRECISION_BITS: i32 = 32 - 8 - 2; + +fn sinc(x: f64) -> f64 { + if x == 0.0 { + return 1.0; + } + let x = x * std::f64::consts::PI; + x.sin() / x +} + +fn lanczos(x: f64) -> f64 { + if (-3.0..3.0).contains(&x) { + sinc(x) * sinc(x / 3.0) + } else { + 0.0 + } +} + +struct Coeffs { + bounds: Vec<(usize, usize)>, + kk: Vec, + ksize: usize, +} + +fn precompute_coeffs(in_size: usize, out_size: usize) -> Coeffs { + let scale = in_size as f64 / out_size as f64; + let filterscale = if scale < 1.0 { 1.0 } else { scale }; + let support = 3.0 * filterscale; + let ksize = support.ceil() as usize * 2 + 1; + let ss = 1.0 / filterscale; + + let mut kkf = vec![0.0f64; out_size * ksize]; + let mut bounds = vec![(0usize, 0usize); out_size]; + for xx in 0..out_size { + let center = (xx as f64 + 0.5) * scale; + let mut xmin = (center - support + 0.5) as i32; + if xmin < 0 { + xmin = 0; + } + let mut xmax = (center + support + 0.5) as i32; + if xmax > in_size as i32 { + xmax = in_size as i32; + } + let count = (xmax - xmin) as usize; + let k = &mut kkf[xx * ksize..(xx + 1) * ksize]; + let mut ww = 0.0f64; + for x in 0..count { + let w = lanczos((x as f64 + xmin as f64 - center + 0.5) * ss); + k[x] = w; + ww += w; + } + if ww != 0.0 { + for x in 0..count { + k[x] /= ww; + } + } + bounds[xx] = (xmin as usize, count); + } + + let factor = (1i64 << PRECISION_BITS) as f64; + let kk = kkf + .iter() + .map(|&v| { + if v < 0.0 { + (-0.5 + v * factor) as i32 + } else { + (0.5 + v * factor) as i32 + } + }) + .collect(); + Coeffs { bounds, kk, ksize } +} + +#[inline] +fn clip8(v: i32) -> u8 { + if v >= 1 << (PRECISION_BITS + 8) { + 255 + } else if v <= 0 { + 0 + } else { + (v >> PRECISION_BITS) as u8 + } +} + +fn resample_horizontal(src: &[u8], h: usize, w: usize, out_w: usize, c: &Coeffs) -> Vec { + let mut out = vec![0u8; h * out_w * 3]; + out.par_chunks_mut(out_w * 3) + .enumerate() + .for_each(|(y, row)| { + let src_row = &src[y * w * 3..(y + 1) * w * 3]; + for xx in 0..out_w { + let (xmin, count) = c.bounds[xx]; + let k = &c.kk[xx * c.ksize..xx * c.ksize + count]; + let mut s = [1i32 << (PRECISION_BITS - 1); 3]; + for (x, &coef) in k.iter().enumerate() { + let p = (xmin + x) * 3; + s[0] += src_row[p] as i32 * coef; + s[1] += src_row[p + 1] as i32 * coef; + s[2] += src_row[p + 2] as i32 * coef; + } + let o = xx * 3; + row[o] = clip8(s[0]); + row[o + 1] = clip8(s[1]); + row[o + 2] = clip8(s[2]); + } + }); + out +} + +fn resample_vertical(src: &[u8], w: usize, out_h: usize, c: &Coeffs) -> Vec { + let mut out = vec![0u8; out_h * w * 3]; + out.par_chunks_mut(w * 3) + .enumerate() + .for_each(|(yy, row)| { + let (ymin, count) = c.bounds[yy]; + let k = &c.kk[yy * c.ksize..yy * c.ksize + count]; + for x in 0..w { + let mut s = [1i32 << (PRECISION_BITS - 1); 3]; + for (y, &coef) in k.iter().enumerate() { + let p = ((ymin + y) * w + x) * 3; + s[0] += src[p] as i32 * coef; + s[1] += src[p + 1] as i32 * coef; + s[2] += src[p + 2] as i32 * coef; + } + let o = x * 3; + row[o] = clip8(s[0]); + row[o + 1] = clip8(s[1]); + row[o + 2] = clip8(s[2]); + } + }); + out +} + +pub fn resize_lanczos_rgb( + src: &[u8], + h: usize, + w: usize, + out_h: usize, + out_w: usize, +) -> Vec { + let need_h = out_w != w; + let need_v = out_h != h; + if need_h && need_v { + let ch = precompute_coeffs(w, out_w); + let tmp = resample_horizontal(src, h, w, out_w, &ch); + let cv = precompute_coeffs(h, out_h); + resample_vertical(&tmp, out_w, out_h, &cv) + } else if need_h { + let ch = precompute_coeffs(w, out_w); + resample_horizontal(src, h, w, out_w, &ch) + } else if need_v { + let cv = precompute_coeffs(h, out_h); + resample_vertical(src, w, out_h, &cv) + } else { + src.to_vec() + } +} + +pub fn scaled_dims( + w: usize, + h: usize, + frac: Option, + cap: Option, +) -> (usize, usize) { + let Some(frac) = frac else { + return (w, h); + }; + let long_edge = w.max(h); + if long_edge == 0 { + return (w, h); + } + let mut target = long_edge as f64 * frac; + if let Some(cap) = cap { + let effective_cap = cap.max(long_edge as i64); + target = target.min(effective_cap as f64); + } + let ratio = target / long_edge as f64; + if ratio == 1.0 { + return (w, h); + } + let scale = |v: usize| ((v as f64 * ratio + 0.5).floor() as i64).max(1) as usize; + (scale(w), scale(h)) +} diff --git a/rust/sglang-mm/src/common/transforms.rs b/rust/sglang-mm/src/common/transforms.rs new file mode 100644 index 000000000..6112e2c86 --- /dev/null +++ b/rust/sglang-mm/src/common/transforms.rs @@ -0,0 +1,93 @@ +//! Reusable image transform primitives. +//! +//! Model-specific processors compose these to build their preprocessing +//! pipelines. All functions operate on flat RGB byte arrays (HWC layout). + +/// Normalize u8 RGB pixels to f32 in a single pass: `(pixel/255 - mean) / std`. +/// +/// Writes into `out` which must have length `h * w * 3`. +pub fn normalize_rgb_f32( + rgb: &[u8], + h: usize, + w: usize, + mean: &[f32; 3], + std: &[f32; 3], + out: &mut [f32], +) { + debug_assert_eq!(rgb.len(), h * w * 3); + debug_assert_eq!(out.len(), h * w * 3); + let inv255 = 1.0f32 / 255.0; + for i in 0..h * w { + for c in 0..3 { + let raw = rgb[i * 3 + c] as f32 * inv255; + out[i * 3 + c] = (raw - mean[c]) / std[c]; + } + } +} + +/// Pad an HWC image to a grid-aligned size, filling padded pixels with `pad_value`. +/// +/// Returns the padded buffer and the new (height, width). +pub fn pad_to_grid( + rgb_f32: &[f32], + h: usize, + w: usize, + channels: usize, + grid_h: usize, + grid_w: usize, + pad_value: &[f32], +) -> (Vec, usize, usize) { + let new_h = ((h + grid_h - 1) / grid_h) * grid_h; + let new_w = ((w + grid_w - 1) / grid_w) * grid_w; + let mut out = vec![0.0f32; new_h * new_w * channels]; + // Fill with pad value + for i in 0..new_h * new_w { + for c in 0..channels { + out[i * channels + c] = pad_value[c]; + } + } + // Copy original data + for y in 0..h { + let src_start = y * w * channels; + let dst_start = y * new_w * channels; + out[dst_start..dst_start + w * channels] + .copy_from_slice(&rgb_f32[src_start..src_start + w * channels]); + } + (out, new_h, new_w) +} + +/// Reshape a padded HWC image into patches of shape `[num_patches, ph, pw, C]`. +/// +/// `h` and `w` must be divisible by `ph` and `pw` respectively. +pub fn extract_patches_hwc( + data: &[f32], + h: usize, + w: usize, + channels: usize, + ph: usize, + pw: usize, +) -> Vec { + let nph = h / ph; + let npw = w / pw; + let patch_size = ph * pw * channels; + let mut out = vec![0.0f32; nph * npw * patch_size]; + for i in 0..nph { + for j in 0..npw { + let patch_idx = i * npw + j; + for y in 0..ph { + let src_y = i * ph + y; + let src_start = (src_y * w + j * pw) * channels; + let dst_start = patch_idx * patch_size + y * pw * channels; + out[dst_start..dst_start + pw * channels] + .copy_from_slice(&data[src_start..src_start + pw * channels]); + } + } + } + out +} + +/// Compute the patch grid dimensions for a given image size and patch size. +#[inline] +pub fn patch_grid(h: usize, w: usize, patch_h: usize, patch_w: usize) -> (usize, usize) { + ((h + patch_h - 1) / patch_h, (w + patch_w - 1) / patch_w) +} diff --git a/rust/sglang-mm/src/inkling/mod.rs b/rust/sglang-mm/src/inkling/mod.rs new file mode 100644 index 000000000..7f66873f5 --- /dev/null +++ b/rust/sglang-mm/src/inkling/mod.rs @@ -0,0 +1,287 @@ +use std::sync::OnceLock; + +use half::bf16; +use numpy::{IntoPyArray, PyArray1, PyReadonlyArray3, PyUntypedArrayMethods}; +use pyo3::exceptions::PyValueError; +use pyo3::prelude::*; +use rayon::prelude::*; + +use crate::common; + +const MEAN: [f32; 3] = [ + 0.48145466f64 as f32, + 0.4578275f64 as f32, + 0.40821073f64 as f32, +]; +const STD: [f32; 3] = [ + 0.26862954f64 as f32, + 0.2613026f64 as f32, + 0.2757771f64 as f32, +]; +const INV255: f32 = (1.0f64 / 255.0f64) as f32; +const PAD_RAW: f32 = (-1.0f64 / 255.0f64) as f32; + +#[inline] +fn pad_bits() -> [u16; 3] { + core::array::from_fn(|c| bf16::from_f32((PAD_RAW - MEAN[c]) / STD[c]).to_bits()) +} + +fn luts() -> &'static [[u16; 256]; 3] { + static LUTS: OnceLock<[[u16; 256]; 3]> = OnceLock::new(); + LUTS.get_or_init(|| { + core::array::from_fn(|c| { + core::array::from_fn(|v| { + let raw = v as u8 as f32 * INV255; + bf16::from_f32((raw - MEAN[c]) / STD[c]).to_bits() + }) + }) + }) +} + +#[inline] +pub fn grid(h: usize, w: usize, ps: usize) -> (usize, usize) { + ((h + ps - 1) / ps, w / ps + 1) +} + +fn patchify_into(arr: &[u8], h: usize, w: usize, ps: usize, out: &mut [u16]) { + let (_nph, npw) = grid(h, w, ps); + let pad = pad_bits(); + let lut = luts(); + let patch_elems = ps * ps * 3; + let row_elems = npw * patch_elems; + + let body = |(i, row): (usize, &mut [u16])| { + let y_base = i * ps; + for j in 0..npw { + let x_base = j * ps; + let chunk = &mut row[j * patch_elems..(j + 1) * patch_elems]; + for y in 0..ps { + let iy = y_base + y; + if iy >= h { + for x in 0..ps { + let o = (y * ps + x) * 3; + chunk[o..o + 3].copy_from_slice(&pad); + } + continue; + } + let n_real = if x_base < w { (w - x_base).min(ps) } else { 0 }; + let src = (iy * w + x_base) * 3; + for x in 0..n_real { + let o = (y * ps + x) * 3; + let p = src + x * 3; + chunk[o] = lut[0][arr[p] as usize]; + chunk[o + 1] = lut[1][arr[p + 1] as usize]; + chunk[o + 2] = lut[2][arr[p + 2] as usize]; + } + for x in n_real..ps { + let o = (y * ps + x) * 3; + chunk[o..o + 3].copy_from_slice(&pad); + } + } + } + }; + + common::pool().install(|| { + out.par_chunks_mut(row_elems).enumerate().for_each(body); + }); +} + +fn patchify_alloc(arr: &[u8], h: usize, w: usize, ps: usize) -> Vec { + let (nph, npw) = grid(h, w, ps); + let mut out = vec![0u16; nph * npw * ps * ps * 3]; + patchify_into(arr, h, w, ps, &mut out); + out +} + +fn check_ps(ps: usize) -> PyResult<()> { + if ps == 0 { + return Err(PyValueError::new_err("patch_size must be greater than zero")); + } + Ok(()) +} + +#[pyfunction] +fn patchify_rgb<'py>( + py: Python<'py>, + arr: PyReadonlyArray3<'py, u8>, + patch_size: usize, +) -> PyResult>> { + check_ps(patch_size)?; + let shape = arr.shape(); + let (h, w, c) = (shape[0], shape[1], shape[2]); + if c != 3 { + return Err(PyValueError::new_err(format!( + "expected HWC RGB array with 3 channels, got {c}" + ))); + } + let data = arr + .as_slice() + .map_err(|_| PyValueError::new_err("array must be C-contiguous"))? + .to_vec(); + let out = py.allow_threads(move || patchify_alloc(&data, h, w, patch_size)); + Ok(out.into_pyarray_bound(py)) +} + +#[pyfunction] +#[pyo3(signature = (data, patch_size, rescale_frac=None, rescale_cap=None))] +fn decode_patchify<'py>( + py: Python<'py>, + data: Vec, + patch_size: usize, + rescale_frac: Option, + rescale_cap: Option, +) -> PyResult<(usize, usize, Bound<'py, PyArray1>)> { + check_ps(patch_size)?; + let (h, w, out) = py + .allow_threads(move || { + common::pool().install(|| { + let (rgb, h, w) = common::decode_rescale(&data, rescale_frac, rescale_cap)?; + Ok::<_, String>((h, w, patchify_alloc(&rgb, h, w, patch_size))) + }) + }) + .map_err(PyValueError::new_err)?; + Ok((h, w, out.into_pyarray_bound(py))) +} + +#[pyfunction] +#[pyo3(signature = (datas, patch_size, rescale_frac=None, rescale_cap=None))] +fn decode_patchify_batch<'py>( + py: Python<'py>, + datas: Vec>, + patch_size: usize, + rescale_frac: Option, + rescale_cap: Option, +) -> PyResult>)>> { + check_ps(patch_size)?; + let results: Vec), String>> = + py.allow_threads(move || { + common::pool().install(|| { + datas + .par_iter() + .map(|data| { + let (rgb, h, w) = common::decode_rescale(data, rescale_frac, rescale_cap)?; + Ok((h, w, patchify_alloc(&rgb, h, w, patch_size))) + }) + .collect() + }) + }); + results + .into_iter() + .map(|r| { + let (h, w, v) = r.map_err(PyValueError::new_err)?; + Ok((h, w, v.into_pyarray_bound(py))) + }) + .collect() +} + +#[pyfunction] +#[pyo3(signature = (datas, patch_size, rescale_frac=None, rescale_cap=None))] +fn preprocess_images<'py>( + py: Python<'py>, + datas: Vec>, + patch_size: usize, + rescale_frac: Option, + rescale_cap: Option, +) -> PyResult>, u64)>> { + check_ps(patch_size)?; + let results: Vec, u64), String>> = + py.allow_threads(move || { + common::pool().install(|| { + datas + .par_iter() + .map(|data| { + let hash = common::sha256_u64(data); + let (rgb, h, w) = common::decode_rescale(data, rescale_frac, rescale_cap)?; + Ok((h, w, patchify_alloc(&rgb, h, w, patch_size), hash)) + }) + .collect() + }) + }); + results + .into_iter() + .map(|r| { + let (h, w, v, hash) = r.map_err(PyValueError::new_err)?; + Ok((h, w, v.into_pyarray_bound(py), hash)) + }) + .collect() +} + +/// Struct implementing ImageProcessorSpec for Inkling. +pub struct InklingProcessor; + +impl crate::registry::ImageProcessorSpec for InklingProcessor { + fn name(&self) -> &'static str { + "inkling" + } + + fn preprocess_batch( + &self, + datas: &[Vec], + patch_size: usize, + rescale_frac: Option, + rescale_cap: Option, + ) -> Result, u64)>, String> { + if patch_size == 0 { + return Err("patch_size must be greater than zero".into()); + } + common::pool().install(|| { + datas + .par_iter() + .map(|data| { + let hash = common::sha256_u64(data); + let (rgb, h, w) = common::decode_rescale(data, rescale_frac, rescale_cap)?; + Ok((h, w, patchify_alloc(&rgb, h, w, patch_size), hash)) + }) + .collect() + }) + } +} + + +#[pyfunction] +#[pyo3(signature = (arr, raw_bytes, patch_size, rescale_frac=None, rescale_cap=None))] +fn rescale_patchify_hash<'py>( + py: Python<'py>, + arr: PyReadonlyArray3<'py, u8>, + raw_bytes: &[u8], + patch_size: usize, + rescale_frac: Option, + rescale_cap: Option, +) -> PyResult<(usize, usize, Bound<'py, PyArray1>, u64)> { + check_ps(patch_size)?; + let shape = arr.shape(); + let (h, w, c) = (shape[0], shape[1], shape[2]); + if c != 3 { + return Err(PyValueError::new_err(format!( + "expected HWC RGB array with 3 channels, got {c}" + ))); + } + let hash = common::sha256_u64(raw_bytes); + let rgb = arr + .as_slice() + .map_err(|_| PyValueError::new_err("array must be C-contiguous"))? + .to_vec(); + let (oh, ow, out) = py.allow_threads(move || { + common::pool().install(|| { + let (tw, th) = common::resize::scaled_dims(w, h, rescale_frac, rescale_cap); + let (rgb, h, w) = if (tw, th) != (w, h) { + (common::resize::resize_lanczos_rgb(&rgb, h, w, th, tw), th, tw) + } else { + (rgb, h, w) + }; + (h, w, patchify_alloc(&rgb, h, w, patch_size)) + }) + }); + Ok((oh, ow, out.into_pyarray_bound(py), hash)) +} + +pub fn register(parent: &Bound<'_, PyModule>) -> PyResult<()> { + let m = PyModule::new_bound(parent.py(), "inkling")?; + m.add_function(wrap_pyfunction!(patchify_rgb, &m)?)?; + m.add_function(wrap_pyfunction!(decode_patchify, &m)?)?; + m.add_function(wrap_pyfunction!(decode_patchify_batch, &m)?)?; + m.add_function(wrap_pyfunction!(preprocess_images, &m)?)?; + m.add_function(wrap_pyfunction!(rescale_patchify_hash, &m)?)?; + parent.add_submodule(&m)?; + Ok(()) +} diff --git a/rust/sglang-mm/src/lib.rs b/rust/sglang-mm/src/lib.rs new file mode 100644 index 000000000..92d9dd04a --- /dev/null +++ b/rust/sglang-mm/src/lib.rs @@ -0,0 +1,12 @@ +mod common; +mod inkling; +pub mod registry; + +use pyo3::prelude::*; + +#[pymodule] +fn _core(m: &Bound<'_, PyModule>) -> PyResult<()> { + common::register(m)?; + inkling::register(m)?; + Ok(()) +} diff --git a/rust/sglang-mm/src/registry.rs b/rust/sglang-mm/src/registry.rs new file mode 100644 index 000000000..2bcb12d36 --- /dev/null +++ b/rust/sglang-mm/src/registry.rs @@ -0,0 +1,54 @@ +//! Model processor registry. +//! +//! Each model implements `ImageProcessorSpec` and registers itself. The Python +//! layer looks up a processor by model name at init time. + +use pyo3::prelude::*; +use pyo3::exceptions::PyValueError; + +/// Trait that each model's image processor must implement. +pub trait ImageProcessorSpec: Send + Sync { + /// Short identifier, e.g. "inkling". + fn name(&self) -> &'static str; + + /// Process a batch of raw image bytes: decode + preprocess + hash. + /// + /// Returns `(height, width, patches_as_u16_bits, content_hash)` per image. + fn preprocess_batch( + &self, + datas: &[Vec], + patch_size: usize, + rescale_frac: Option, + rescale_cap: Option, + ) -> Result, u64)>, String>; +} + +/// Global registry of available processors. +pub struct ProcessorRegistry { + specs: Vec>, +} + +impl ProcessorRegistry { + pub fn new() -> Self { + Self { specs: Vec::new() } + } + + pub fn register(&mut self, spec: Box) { + self.specs.push(spec); + } + + pub fn lookup(&self, name: &str) -> Option<&dyn ImageProcessorSpec> { + self.specs.iter().find(|s| s.name() == name).map(|s| s.as_ref()) + } + + pub fn list_names(&self) -> Vec<&'static str> { + self.specs.iter().map(|s| s.name()).collect() + } +} + +/// Build the default registry with all compiled-in processors. +pub fn default_registry() -> ProcessorRegistry { + let mut reg = ProcessorRegistry::new(); + reg.register(Box::new(crate::inkling::InklingProcessor)); + reg +} diff --git a/rust/sglang-mm/tests/generate_golden.py b/rust/sglang-mm/tests/generate_golden.py new file mode 100644 index 000000000..c1cf5ffde --- /dev/null +++ b/rust/sglang-mm/tests/generate_golden.py @@ -0,0 +1,40 @@ +import io +import os +import sys + +import numpy as np +import torch +from PIL import Image + +sys.path.insert(0, os.path.dirname(__file__)) +from bench_parity import PS, make_photo_like, ref_patchify + +OUT = ( + sys.argv[1] + if len(sys.argv) > 1 + else os.path.join(os.path.dirname(__file__), "..", "tests", "golden") +) + +CASES = [ + ("480x640", 480, 640, 10), + ("200x320", 200, 320, 11), + ("37x53", 37, 53, 12), + ("40x40", 40, 40, 13), +] + +os.makedirs(OUT, exist_ok=True) +for name, h, w, seed in CASES: + arr = make_photo_like(h, w, seed=seed) + bits = ref_patchify(arr).view(torch.uint16).numpy() + buf = io.BytesIO() + Image.fromarray(arr).save(buf, format="PNG") + path = os.path.join(OUT, f"golden_{name}.npz") + np.savez_compressed( + path, + arr=arr, + bits=bits, + png=np.frombuffer(buf.getvalue(), dtype=np.uint8), + patch_size=np.int64(PS), + ) + print(f" {path}: input {h}x{w}, bits {bits.shape}") +print("GOLDEN_OK") diff --git a/rust/sglang-mm/tests/test_golden.py b/rust/sglang-mm/tests/test_golden.py new file mode 100644 index 000000000..24baa18d5 --- /dev/null +++ b/rust/sglang-mm/tests/test_golden.py @@ -0,0 +1,51 @@ +import glob +import os + +import numpy as np +import pytest + +import sglang.srt.multimodal._core.inkling + +GOLDEN_DIR = os.environ.get( + "INKLING_MM_GOLDEN_DIR", + os.path.join(os.path.dirname(__file__), "..", "tests", "golden"), +) +GOLDENS = sorted(glob.glob(os.path.join(GOLDEN_DIR, "golden_*.npz"))) + + +def bf16_bits_to_f32(bits: np.ndarray) -> np.ndarray: + return (bits.astype(np.uint32) << 16).view(np.float32) + + +@pytest.mark.parametrize("path", GOLDENS, ids=[os.path.basename(p) for p in GOLDENS]) +def test_patchify_rgb_bit_exact(path): + g = np.load(path) + got = sglang.srt.multimodal._core.inkling.patchify_rgb( + g["arr"], int(g["patch_size"]) + ) + np.testing.assert_array_equal(got, g["bits"].reshape(-1)) + + +@pytest.mark.parametrize("path", GOLDENS, ids=[os.path.basename(p) for p in GOLDENS]) +def test_decode_patchify_png_bit_exact(path): + g = np.load(path) + h_ref, w_ref = g["arr"].shape[:2] + h, w, got = sglang.srt.multimodal._core.inkling.decode_patchify( + g["png"].tobytes(), int(g["patch_size"]) + ) + assert (h, w) == (h_ref, w_ref) + np.testing.assert_array_equal(got, g["bits"].reshape(-1)) + + +def test_batch_matches_single(): + gs = [np.load(p) for p in GOLDENS] + data = [g["png"].tobytes() for g in gs] + ps = int(gs[0]["patch_size"]) + for (h, w, bits), g in zip( + sglang.srt.multimodal._core.inkling.decode_patchify_batch(data, ps), gs + ): + np.testing.assert_array_equal(bits, g["bits"].reshape(-1)) + + +def test_golden_fixtures_exist(): + assert len(GOLDENS) >= 4, f"expected golden fixtures in {GOLDEN_DIR}" diff --git a/rust/sglang-mm/tests/test_hash_fetch.py b/rust/sglang-mm/tests/test_hash_fetch.py new file mode 100644 index 000000000..3ac8bc241 --- /dev/null +++ b/rust/sglang-mm/tests/test_hash_fetch.py @@ -0,0 +1,103 @@ +import asyncio +import base64 +import io +import os +import sys +import time +from types import SimpleNamespace + +import numpy as np +import soundfile as sf +import torch +from PIL import Image + +sys.path.insert(0, os.path.dirname(__file__)) +from bench_parity import make_photo_like + +from sglang.srt.managers.mm_utils import data_hash, hash_feature +from sglang.srt.multimodal.inkling import InklingProcessor +from sglang.srt.multimodal.processors import inkling as prc + + +def png_bytes(arr): + buf = io.BytesIO() + Image.fromarray(arr).save(buf, format="PNG") + return buf.getvalue() + + +def wav_bytes(seconds=1.0, sr=16000): + t = np.linspace(0, seconds, int(sr * seconds), endpoint=False) + buf = io.BytesIO() + sf.write( + buf, (0.3 * np.sin(2 * np.pi * 440 * t)).astype(np.float32), sr, format="WAV" + ) + return buf.getvalue() + + +def make_proc(): + proc = prc.InklingMultimodalProcessor.__new__(prc.InklingMultimodalProcessor) + proc.IMAGE_TOKEN_ID = 100 + proc.AUDIO_TOKEN_ID = 101 + proc.AUDIO_END_TOKEN_ID = 102 + proc.inkling_processor = InklingProcessor() + return proc + + +proc = make_proc() +img = png_bytes(make_photo_like(200, 320, seed=7)) +aud = wav_bytes() + +out = proc.assemble([1, 100, 2, 101, 3], [img], [aud]) +img_item = next(i for i in out.mm_items if i.modality.name == "IMAGE") +aud_item = next(i for i in out.mm_items if i.modality.name == "AUDIO") +assert img_item.hash == data_hash(img), "image hash != data_hash(raw bytes)" +assert aud_item.hash == data_hash(aud), "audio hash != data_hash(raw bytes)" +print(f" assemble: image hash={img_item.hash:#x} audio hash={aud_item.hash:#x} OK") + +h0 = img_item.hash +img_item.set_pad_value() +assert img_item.hash == h0 and img_item.pad_value is not None +print(f" set_pad_value: hash preserved, pad_value={img_item.pad_value} OK") + +out2 = proc.assemble([1, 100, 2], [img], []) +assert out2.mm_items[0].hash == h0 +print(" determinism: same bytes -> same hash OK") + +data_url = "data:image/png;base64," + base64.b64encode(img).decode() +req = SimpleNamespace(input_ids=[1, 100, 100, 2]) +out3 = asyncio.run( + proc.process_mm_data_async( + image_data=[data_url, data_url], audio_data=None, request_obj=req + ) +) +assert all(i.hash == h0 for i in out3.mm_items), "data: URL roundtrip hash mismatch" +print(" process_mm_data_async: concurrent resolve + hash OK") + +orig = prc._resolve_media_item +prc._resolve_media_item = lambda it: (time.sleep(0.3), orig(it))[1] +t0 = time.perf_counter() +asyncio.run(prc._resolve_media_items([data_url] * 8)) +elapsed = time.perf_counter() - t0 +prc._resolve_media_item = orig +assert elapsed < 1.2, f"8x 0.3s resolves took {elapsed:.2f}s; expected ~0.3s" +print(f" concurrency: 8 x 0.3s resolves in {elapsed:.2f}s OK") + +imgs_5 = [png_bytes(make_photo_like(1080, 1920, seed=s)) for s in range(5)] +feats = [ + torch.randn(1323, 1, 40, 40, 3, dtype=torch.bfloat16).expand(1323, 2, 40, 40, 3) + for _ in range(5) +] +t0 = time.perf_counter() +for b in imgs_5: + data_hash(b) +t_bytes = (time.perf_counter() - t0) * 1e3 +t0 = time.perf_counter() +for f in feats: + hash_feature(f) +t_feat = (time.perf_counter() - t0) * 1e3 +print( + f" hash cost 5 imgs: raw bytes {t_bytes:.1f}ms vs feature tensor {t_feat:.1f}ms " + f"({t_feat / t_bytes:.0f}x)" +) + +print("HASH_FETCH_OK") diff --git a/rust/sglang-mm/tests/test_integration.py b/rust/sglang-mm/tests/test_integration.py new file mode 100644 index 000000000..cfb620c09 --- /dev/null +++ b/rust/sglang-mm/tests/test_integration.py @@ -0,0 +1,86 @@ +import io +import os +import sys + +import numpy as np +import torch +from PIL import Image + +sys.path.insert(0, os.path.dirname(__file__)) +from bench_parity import make_photo_like + +import sglang.srt.multimodal.inkling.image_processing as ip + + +def encode(arr, fmt): + buf = io.BytesIO() + Image.fromarray(arr).save( + buf, format=fmt, **({"quality": 90} if fmt == "JPEG" else {}) + ) + return buf.getvalue() + + +def run(images, use_rs: bool, rescale: bool): + ip._rs_module = None + os.environ["SGLANG_RS_MM_PREPROCESS"] = "1" if use_rs else "0" + kwargs = ( + {} + if rescale + else {"rescale_image_frac": None, "rescale_image_max_upscaled_long_edge": None} + ) + proc = ip.InklingImageProcessor(patch_size=40, **kwargs) + out = proc.preprocess(images) + assert (ip._rs_module is not False) == use_rs, "rust module gating mismatch" + return out + + +def compare(tag, images, expect_exact, rescale=False): + ref = run(images, use_rs=False, rescale=rescale) + got = run(images, use_rs=True, rescale=rescale) + assert ref["num_patches"] == got["num_patches"], tag + assert ref["num_tokens"] == got["num_tokens"], tag + a, b = ref["vision_patches_bthwc"], got["vision_patches_bthwc"] + assert a.shape == b.shape and a.dtype == b.dtype, f"{tag}: {a.shape} vs {b.shape}" + exact = torch.equal( + a.contiguous().view(torch.uint16), b.contiguous().view(torch.uint16) + ) + if expect_exact: + assert exact, f"{tag}: expected bit-exact" + print(f" {tag}: bit-exact=True shape={tuple(a.shape)}") + else: + d = (a.float() - b.float()).abs() + print( + f" {tag}: bit-exact={exact} max_abs={d.max():.6f} shape={tuple(a.shape)}" + ) + assert d.max() < 0.25, f"{tag}: JPEG decoder diff too large" + + +arr1 = make_photo_like(1080, 1920, seed=1) +arr2 = make_photo_like(720, 1280, seed=2) +arr3 = make_photo_like(480, 640, seed=3) + +print("=== integration: InklingImageProcessor env-gated rust path ===") +compare("single PNG", [encode(arr1, "PNG")], expect_exact=True) +compare("single JPEG", [encode(arr1, "JPEG")], expect_exact=False) +compare( + "5x PNG batch", + [encode(a, "PNG") for a in [arr1, arr2, arr3, arr1, arr2]], + expect_exact=True, +) +compare( + "mixed JPEG/PNG batch", + [encode(arr1, "JPEG"), encode(arr2, "PNG")], + expect_exact=False, +) +compare("PIL input (PNG roundtrip)", [Image.fromarray(arr3)], expect_exact=True) +compare("single PNG rescaled", [encode(arr1, "PNG")], expect_exact=True, rescale=True) +compare( + "single JPEG rescaled", [encode(arr1, "JPEG")], expect_exact=False, rescale=True +) +compare( + "3x mixed rescaled", + [encode(arr1, "JPEG"), encode(arr2, "PNG"), encode(arr3, "PNG")], + expect_exact=False, + rescale=True, +) +print("INTEGRATION_OK") diff --git a/rust/sglang-mm/tests/test_resize_parity.py b/rust/sglang-mm/tests/test_resize_parity.py new file mode 100644 index 000000000..23fc6c0ef --- /dev/null +++ b/rust/sglang-mm/tests/test_resize_parity.py @@ -0,0 +1,121 @@ +import math +import time +from typing import Optional + +import numpy as np +import pytest +from PIL import Image + +import sglang.srt.multimodal._core.inkling + + +def py_scaled_dims( + width: int, + height: int, + frac: Optional[float], + cap: Optional[int], +): + if frac is None: + return width, height + long_edge = max(width, height) + if long_edge == 0: + return width, height + target = float(long_edge) * frac + if cap is not None: + target = min(target, float(max(cap, long_edge))) + ratio = target / float(long_edge) + if ratio == 1.0: + return width, height + + def scale(value): + return max(1, math.floor(float(value) * ratio + 0.5)) + + return scale(width), scale(height) + + +def pil_resize(arr: np.ndarray, tw: int, th: int) -> np.ndarray: + return np.array( + Image.fromarray(arr).resize((tw, th), resample=Image.Resampling.LANCZOS), + dtype=np.uint8, + ) + + +def rs_resize(arr: np.ndarray, tw: int, th: int) -> np.ndarray: + return sglang.srt.multimodal._core.inkling.resize_rgb(arr, tw, th).reshape( + th, tw, 3 + ) + + +CASES = [ + (1080, 1920, 1152, 2048), + (896, 896, 1792, 1792), + (360, 640, 720, 1280), + (37, 53, 74, 106), + (100, 100, 173, 173), + (1, 1, 2, 2), + (256, 256, 100, 100), + (720, 1280, 720, 1280), + (3, 500, 6, 1000), +] + + +@pytest.mark.parametrize( + "h,w,th,tw", CASES, ids=[f"{h}x{w}->{th}x{tw}" for h, w, th, tw in CASES] +) +def test_resize_bit_exact(h, w, th, tw): + rng = np.random.default_rng(h * 10000 + w) + arr = rng.integers(0, 256, (h, w, 3), dtype=np.uint8) + np.testing.assert_array_equal(rs_resize(arr, tw, th), pil_resize(arr, tw, th)) + + +def test_scaled_dims_sweep(): + rng = np.random.default_rng(0) + sizes = [(int(a), int(b)) for a, b in rng.integers(1, 5000, (500, 2))] + sizes += [(2048, 1024), (2049, 100), (1024, 2048), (1, 1), (4096, 4096)] + for frac, cap in [(2.0, 2048), (1.5, 2048), (3.0, None), (None, None), (2.0, 1)]: + for w, h in sizes: + assert sglang.srt.multimodal._core.inkling.scaled_dims( + w, h, frac, cap + ) == py_scaled_dims(w, h, frac, cap), ( + w, + h, + frac, + cap, + ) + + +def test_decode_patchify_rescaled_matches_pil_pipeline(): + import io + + import torch + + rng = np.random.default_rng(7) + arr = rng.integers(0, 256, (1080, 1920, 3), dtype=np.uint8) + buf = io.BytesIO() + Image.fromarray(arr).save(buf, format="PNG") + h, w, bits = sglang.srt.multimodal._core.inkling.decode_patchify( + buf.getvalue(), 40, 2.0, 2048 + ) + assert (w, h) == py_scaled_dims(1920, 1080, 2.0, 2048) + ref_arr = pil_resize(arr, w, h) + ref_bits = sglang.srt.multimodal._core.inkling.patchify_rgb(ref_arr, 40) + np.testing.assert_array_equal(bits, ref_bits) + assert torch.from_numpy(bits).view(torch.bfloat16).shape[0] > 0 + + +def test_resize_bench(): + arr = np.random.default_rng(1).integers(0, 256, (1080, 1920, 3), dtype=np.uint8) + tw, th = py_scaled_dims(1920, 1080, 2.0, 2048) + pil_resize(arr, tw, th) + rs_resize(arr, tw, th) + t0 = time.perf_counter() + for _ in range(10): + pil_resize(arr, tw, th) + t_pil = (time.perf_counter() - t0) / 10 * 1e3 + t0 = time.perf_counter() + for _ in range(10): + rs_resize(arr, tw, th) + t_rs = (time.perf_counter() - t0) / 10 * 1e3 + print( + f"\nresize 1920x1080->{tw}x{th}: PIL {t_pil:.1f}ms rust {t_rs:.1f}ms ({t_pil/t_rs:.1f}x)" + ) diff --git a/test/registered/jit/test_inkling_attn_prologue_tau.py b/test/registered/jit/test_inkling_attn_prologue_tau.py new file mode 100644 index 000000000..4c44e06dd --- /dev/null +++ b/test/registered/jit/test_inkling_attn_prologue_tau.py @@ -0,0 +1,203 @@ +"""The fused attn prologue's conditional log-scaling-tau fold on the q path: +q_out must equal {per-head RMSNorm -> bf16 -> * tau -> bf16} (the unfused +{prologue -> apply_log_scaling_tau} rounding, exactly), and the k/v outputs +must be untouched by tau. +""" + +import pytest +import torch + +from sglang.jit_kernel.inkling_attn_prologue import inkling_attn_prologue_decode +from sglang.test.ci.ci_register import register_cuda_ci + +register_cuda_ci(est_time=40, stage="base-b-kernel-unit", runner_config="1-gpu-large") +register_cuda_ci(est_time=40, stage="base-b-kernel-unit", runner_config="4-gpu-b200") + +HEAD = 128 +W = 4 +EPS = 1e-6 + + +def _run(t, dq, dkv, tau): + torch.manual_seed(1234) + dev = "cuda" + row = dq + 2 * dkv + 64 # packed qkvr row with an r tail + qkvr = torch.randn(t, row, device=dev, dtype=torch.bfloat16) + pool = 64 + k_cache = torch.randn(pool, W - 1, dkv, device=dev, dtype=torch.bfloat16) + v_cache = torch.randn(pool, W - 1, dkv, device=dev, dtype=torch.bfloat16) + ci = torch.arange(t, device=dev, dtype=torch.int32) + 3 + cm = torch.ones(t, device=dev, dtype=torch.bool) + kw = torch.randn(dkv, W, device=dev, dtype=torch.bfloat16) * 0.3 + vw = torch.randn(dkv, W, device=dev, dtype=torch.bfloat16) * 0.3 + qg = torch.randn(HEAD, device=dev, dtype=torch.bfloat16) + kg = torch.randn(HEAD, device=dev, dtype=torch.bfloat16) + slots = 256 + loc = (torch.arange(t, device=dev, dtype=torch.int64) * 7 + 5) % slots + k_buf = torch.zeros(slots, dkv // HEAD, HEAD, device=dev, dtype=torch.bfloat16) + v_buf = torch.zeros_like(k_buf) + return inkling_attn_prologue_decode( + qkvr, + k_cache, + v_cache, + ci, + cm, + kw, + vw, + qg, + kg, + EPS, + loc, + k_buf, + v_buf, + 0, + dq, + dq + dkv, + dq, + dkv, + activation=None, + use_residual=True, + do_store=True, + log_scaling_tau=tau, + ) + + +def _q_ref(t, dq, dkv, tau): + torch.manual_seed(1234) + row = dq + 2 * dkv + 64 + qkvr = torch.randn(t, row, device="cuda", dtype=torch.bfloat16) + # (regenerate the SAME rng stream as _run for the remaining tensors) + _ = torch.randn(64, W - 1, dkv, device="cuda", dtype=torch.bfloat16) + _ = torch.randn(64, W - 1, dkv, device="cuda", dtype=torch.bfloat16) + kw = torch.randn(dkv, W, device="cuda", dtype=torch.bfloat16) * 0.3 + vw = torch.randn(dkv, W, device="cuda", dtype=torch.bfloat16) * 0.3 + del kw, vw + qg = torch.randn(HEAD, device="cuda", dtype=torch.bfloat16) + q = qkvr[:, :dq].float().view(t, dq // HEAD, HEAD) + inv = torch.rsqrt(q.pow(2).mean(-1, keepdim=True) + EPS) + out = (q * inv * qg.float()).bfloat16() + if tau is not None: + out = (out.float() * tau.view(-1, 1, 1)).bfloat16() + return out.view(t, dq) + + +@pytest.mark.parametrize("t", [1, 3, 8, 32]) +def test_prologue_decode_tau_fold(t): + dq, dkv = 2048, 256 + tau = 1.0 + 0.1 * torch.rand(t, device="cuda", dtype=torch.float32) + + q_tau, k_tau, v_tau, _ = _run(t, dq, dkv, tau) + q_ref = _q_ref(t, dq, dkv, tau) + torch.testing.assert_close(q_tau.float(), q_ref.float(), rtol=2e-2, atol=2e-2) + + # tau must not touch the k/v legs. + q_off, k_off, v_off, _ = _run(t, dq, dkv, None) + assert torch.equal(k_tau, k_off) + assert torch.equal(v_tau, v_off) + # And with tau=None the q path matches the tau-free reference bit-wise + # modulo the norm's fp32 reduction (tolerance). + torch.testing.assert_close( + q_off.float(), _q_ref(t, dq, dkv, None).float(), rtol=2e-2, atol=2e-2 + ) + # The fold itself must be exactly {round -> fp32 mul -> round}: applying + # tau to the tau-free kernel output reproduces the fused output bit-wise. + refold = (q_off.float() * tau.view(-1, 1)).bfloat16() + assert torch.equal(q_tau, refold) + + +@pytest.mark.parametrize("t", [1, 7, 64, 2048]) +def test_rel_logits_proj_prescale_tau(t): + """RelLogitsProj's operand-side tau fold (r*tau before the einsum) must + match the legacy output-side scale within bf16 rounding.""" + from sglang.kernels.ops.attention.log_scaling_tau import apply_log_scaling_tau + from sglang.srt.models.inkling_common.attn import RelLogitsProj + + torch.manual_seed(t) + h, d_rel, e = 16, 16, 1024 + m = RelLogitsProj(d_rel, e).cuda() + m.proj.data = torch.randn(d_rel, e, device="cuda", dtype=torch.bfloat16) * 0.1 + r = torch.randn(t, h, d_rel, device="cuda", dtype=torch.bfloat16) + tau = 1.0 + 0.1 * torch.rand(t, device="cuda", dtype=torch.float32) + + assert m._prescale_tau # default-on flag + out = m(r, tau) + ref = apply_log_scaling_tau( + torch.einsum("thd,de->the", r, m.proj), tau.view(-1, 1, 1) + ) + torch.testing.assert_close(out.float(), ref.float(), rtol=2e-2, atol=2e-2) + # And without tau it is the plain einsum, bit-exact. + assert torch.equal(m(r), torch.einsum("thd,de->the", r, m.proj)) + + +_QKVR_ROW = 2816 # dq 2048 + 2*dkv 512 + h*d_rel 256 (the TP4 packed row) + + +def _strided_r(t, h=16, d_rel=16, elem_offset=0): + """r exactly as production builds it: the trailing slice of the packed + qkvr projection output, viewed [t, h, d_rel] (row stride = full row).""" + torch.manual_seed(t + elem_offset) + qkvr = torch.randn(t, _QKVR_ROW + elem_offset, device="cuda", dtype=torch.bfloat16) + off = _QKVR_ROW + elem_offset - h * d_rel + return qkvr[:, off:].view(t, h, d_rel) + + +@pytest.mark.parametrize("t", [1, 2, 48, 49, 64, 200, 1024]) +def test_rel_logits_proj_strided_dispatch(t): + """_project on the production strided layout must be BIT-identical to the + plain einsum in both dispatch bands -- the zero-copy batched matmul + (t <= _REL_PROJ_MATMUL_MAX_T) and {JIT row-compact -> einsum} above it. + Guards the band boundary, the as_strided compaction math, and the + batched-GEMM == flat-GEMM reduction-order claim the dispatch relies on.""" + from sglang.srt.models.inkling_common.attn import RelLogitsProj + + h, d_rel, e = 16, 16, 1024 + m = RelLogitsProj(d_rel, e).cuda() + m.proj.data = torch.randn(d_rel, e, device="cuda", dtype=torch.bfloat16) * 0.1 + assert m._proj_dispatch # default-on flag + + r = _strided_r(t, h, d_rel) + ref = torch.einsum("thd,de->the", r.contiguous(), m.proj) + out = m(r) + assert out.is_contiguous() + assert torch.equal(out, ref) + + # The tau path on the same strided layout (prescale compacts first). + from sglang.kernels.ops.attention.log_scaling_tau import apply_log_scaling_tau + + tau = 1.0 + 0.1 * torch.rand(t, device="cuda", dtype=torch.float32) + ref_tau = apply_log_scaling_tau(ref, tau.view(-1, 1, 1)) + torch.testing.assert_close(m(r, tau).float(), ref_tau.float(), rtol=2e-2, atol=2e-2) + + +def test_rel_logits_proj_dispatch_fallbacks(): + """The compact band must fall back to the plain einsum (and stay exact) + when the JIT copy is ineligible -- e.g. a 2-byte-aligned r slice -- and + flag-off must restore the undispatched einsum on every input.""" + from sglang.srt.environ import envs + from sglang.srt.models.inkling_common.attn import ( + _REL_PROJ_MATMUL_MAX_T, + RelLogitsProj, + ) + + h, d_rel, e = 16, 16, 1024 + m = RelLogitsProj(d_rel, e).cuda() + m.proj.data = torch.randn(d_rel, e, device="cuda", dtype=torch.bfloat16) * 0.1 + + t = _REL_PROJ_MATMUL_MAX_T + 16 # inside the compact band + r_misaligned = _strided_r(t, h, d_rel, elem_offset=1) + assert r_misaligned.data_ptr() % 16 != 0 + ref = torch.einsum("thd,de->the", r_misaligned.contiguous(), m.proj) + assert torch.equal(m(r_misaligned), ref) + + with envs.SGLANG_OPT_USE_INKLING_REL_PROJ_DISPATCH.override(False): + m_off = RelLogitsProj(d_rel, e).cuda() + m_off.proj.data = m.proj.data + assert not m_off._proj_dispatch + r = _strided_r(t, h, d_rel) + assert torch.equal(m_off(r), torch.einsum("thd,de->the", r, m_off.proj)) + + +if __name__ == "__main__": + import sys + + sys.exit(pytest.main([__file__, "-v", "-s"])) diff --git a/test/registered/jit/test_inkling_rel_proj.py b/test/registered/jit/test_inkling_rel_proj.py new file mode 100644 index 000000000..78dc4adfd --- /dev/null +++ b/test/registered/jit/test_inkling_rel_proj.py @@ -0,0 +1,69 @@ +"""rel_proj_small_t: the single-launch small-t rel projection (optional tau +prescale folded in registers) must match the reference chains it replaces -- +{r*tau -> bf16 round -> projection} -- within bf16 GEMM rounding, on both the +production strided-r layout and contiguous inputs.""" + +import pytest +import torch + +from sglang.jit_kernel.inkling_rel_proj import rel_proj_small_t +from sglang.test.ci.ci_register import register_cuda_ci + +register_cuda_ci(est_time=30, stage="base-b-kernel-unit", runner_config="1-gpu-large") +register_cuda_ci(est_time=30, stage="base-b-kernel-unit", runner_config="4-gpu-b200") + +H, K, E, ROW = 16, 16, 1024, 2816 + + +def _make_r(t, strided): + torch.manual_seed(t + int(strided)) + if strided: + qkvr = torch.randn(t, ROW, device="cuda", dtype=torch.bfloat16) + return qkvr[:, ROW - H * K :].view(t, H, K) + return torch.randn(t, H, K, device="cuda", dtype=torch.bfloat16) + + +def _ref(r, proj, tau): + rf = r.float() + if tau is not None: + # The prescale contract: r*tau rounds to bf16 BEFORE the dot. + rf = (rf * tau.view(-1, 1, 1)).bfloat16().float() + return torch.einsum("thd,de->the", rf, proj.float()) + + +@pytest.mark.parametrize("t", [1, 2, 5, 16, 32]) +@pytest.mark.parametrize("strided", [False, True]) +@pytest.mark.parametrize("with_tau", [False, True]) +def test_rel_proj_small_t(t, strided, with_tau): + r = _make_r(t, strided) + proj = torch.randn(K, E, device="cuda", dtype=torch.bfloat16) * 0.1 + tau = ( + 1.0 + 0.1 * torch.rand(t, device="cuda", dtype=torch.float32) + if with_tau + else None + ) + out = rel_proj_small_t(r, proj, tau) + assert out.is_contiguous() and out.shape == (t, H, E) + ref = _ref(r, proj, tau) + # fp32 accumulation, one bf16 round -- match the fp32 reference to + # 2 bf16 ulp (the GEMM reduction-order slack vs cuBLAS is within this). + torch.testing.assert_close(out.float(), ref, rtol=2e-2, atol=2e-2) + + +def test_rel_proj_tau_isolation(): + """tau must only scale: kernel(tau) == kernel(no tau) computed on the + pre-rounded r*tau operand -- guards the round-before-dot placement (a + fold AFTER the dot would diverge at large |logits|).""" + t = 8 + r = _make_r(t, True) + proj = torch.randn(K, E, device="cuda", dtype=torch.bfloat16) * 0.1 + tau = 1.0 + 0.5 * torch.rand(t, device="cuda", dtype=torch.float32) + out = rel_proj_small_t(r, proj, tau) + r_pre = (r.float() * tau.view(-1, 1, 1)).bfloat16() + assert torch.equal(out, rel_proj_small_t(r_pre.contiguous(), proj)) + + +if __name__ == "__main__": + import sys + + sys.exit(pytest.main([__file__, "-v", "-s"])) diff --git a/test/registered/jit/test_inkling_row_scale.py b/test/registered/jit/test_inkling_row_scale.py new file mode 100644 index 000000000..03177bacb --- /dev/null +++ b/test/registered/jit/test_inkling_row_scale.py @@ -0,0 +1,82 @@ +"""The vectorized row-scale kernel must be BIT-identical to the triton +apply_log_scaling_tau kernel it replaces (same fp32 multiply + bf16 round), +including on the row-strided qkvr-slice layouts.""" + +import pytest +import torch + +from sglang.jit_kernel.inkling_row_scale import row_scale_bf16 +from sglang.kernels.ops.attention.log_scaling_tau import ( + _apply_log_scaling_tau_kernel, +) +from sglang.test.ci.ci_register import register_cuda_ci + +register_cuda_ci(est_time=30, stage="base-b-kernel-unit", runner_config="1-gpu-large") +register_cuda_ci(est_time=30, stage="base-b-kernel-unit", runner_config="4-gpu-b200") + + +def _triton_ref(x2d, tau): + import triton + + rows, inner = x2d.shape + out = torch.empty(rows, inner, dtype=x2d.dtype, device=x2d.device) + total = rows * inner + _apply_log_scaling_tau_kernel[(triton.cdiv(total, 1024),)]( + x2d, tau, out, x2d.stride(0), inner, total, BLOCK=1024 + ) + return out + + +@pytest.mark.parametrize("rows", [1, 3, 16, 200, 4096]) +@pytest.mark.parametrize("inner", [8, 256, 2048, 16384]) +@pytest.mark.parametrize("strided", [False, True]) +def test_row_scale_bitexact(rows, inner, strided): + torch.manual_seed(rows + inner) + if strided: + packed = torch.randn(rows, inner + 40, device="cuda", dtype=torch.bfloat16) + x = packed[:, 8 : 8 + inner] + else: + x = torch.randn(rows, inner, device="cuda", dtype=torch.bfloat16) + tau = 1.0 + 0.1 * torch.rand(rows, device="cuda", dtype=torch.float32) + + out = row_scale_bf16(x, tau) + ref = _triton_ref(x, tau) + assert torch.equal(out, ref) + + +@pytest.mark.parametrize("rows", [1, 3, 200, 4096]) +@pytest.mark.parametrize("inner", [8, 256, 16384]) +@pytest.mark.parametrize("strided", [False, True]) +def test_row_compact_bitexact(rows, inner, strided): + """The tau-less compaction flavor (kHasTau=false) must reproduce + .contiguous() exactly on the same strided layouts row_scale handles -- + no other test exercises run_compact.""" + from sglang.jit_kernel.inkling_row_scale import row_compact_bf16 + + torch.manual_seed(rows + inner) + if strided: + packed = torch.randn(rows, inner + 40, device="cuda", dtype=torch.bfloat16) + x = packed[:, 8 : 8 + inner] + else: + x = torch.randn(rows, inner, device="cuda", dtype=torch.bfloat16) + out = row_compact_bf16(x) + assert out.is_contiguous() + assert torch.equal(out, x.contiguous()) + + +def test_dispatch_through_apply_log_scaling_tau(): + from sglang.kernels.ops.attention.log_scaling_tau import apply_log_scaling_tau + + torch.manual_seed(0) + for shape, view in (((7, 16, 16), (-1, 1, 1)), ((7, 2048), (-1, 1))): + x = torch.randn(*shape, device="cuda", dtype=torch.bfloat16) + tau = 1.0 + 0.1 * torch.rand(7, device="cuda", dtype=torch.float32) + out = apply_log_scaling_tau(x, tau.view(*view)) + ref = _triton_ref(x.view(7, -1), tau).view(x.shape) + assert torch.equal(out, ref) + + +if __name__ == "__main__": + import sys + + sys.exit(pytest.main([__file__, "-v", "-s"])) diff --git a/test/registered/jit/test_marlin_packed_topk_unpack.py b/test/registered/jit/test_marlin_packed_topk_unpack.py new file mode 100644 index 000000000..09062c896 --- /dev/null +++ b/test/registered/jit/test_marlin_packed_topk_unpack.py @@ -0,0 +1,90 @@ +"""Precision test for the fused packed-topk unpack triton kernel used by the +Marlin MoE runner (fused-gate-topk support). + +The FlashInfer / Inkling fused gate emits PackedTopKOutput -- int32 +``(expert_id << 16) | bf16-weight-bits``. The Marlin runner reads topk_ids / +topk_weights separately, so it unpacks with a single Triton launch. This test +checks the kernel is bit-identical to the torch elementwise reference and that +pack -> unpack round-trips, across shapes / top_k / num_experts / weight +distributions. +""" + +import sys + +import pytest +import torch + +from sglang.srt.layers.moe.moe_runner.marlin import _fused_unpack_packed_topk +from sglang.test.ci.ci_register import register_cuda_ci + +register_cuda_ci(est_time=30, stage="base-b-kernel-unit", runner_config="1-gpu-large") + + +def _torch_unpack(packed: torch.Tensor): + ids = (packed >> 16).to(torch.int32) + w = (packed & 0xFFFF).to(torch.int16).view(torch.bfloat16).to(torch.float32) + return ids, w + + +def _torch_pack(ids: torch.Tensor, weights: torch.Tensor) -> torch.Tensor: + # inverse of the unpack, matching trtllm_lora_temp/topk_pack._pack_topk_kernel + wbits = weights.to(torch.bfloat16).view(torch.int16).to(torch.int32) & 0xFFFF + return (ids.to(torch.int32) << 16) | wbits + + +@pytest.mark.parametrize( + "num_tokens,top_k", + [ + (1, 1), + (1, 2), + (3, 6), + (8, 2), + (127, 6), + (512, 6), + (631, 2), + (1024, 8), + (2048, 8), + ], +) +@pytest.mark.parametrize("num_experts", [2, 8, 64, 256]) +@pytest.mark.parametrize("wdist", ["uniform", "edge", "tiny"]) +def test_unpack_matches_reference_and_roundtrips(num_tokens, top_k, num_experts, wdist): + torch.manual_seed(0) + ids = torch.randint( + 0, num_experts, (num_tokens, top_k), dtype=torch.int32, device="cuda" + ) + if wdist == "uniform": + w = torch.rand(num_tokens, top_k, device="cuda") + elif wdist == "edge": + choices = torch.tensor([0.0, 1.0, 0.5, 0.999, 1e-3], device="cuda") + w = choices[ + torch.randint(0, choices.numel(), (num_tokens, top_k), device="cuda") + ] + else: + w = torch.rand(num_tokens, top_k, device="cuda") * 1e-3 + + packed = _torch_pack(ids, w) + t_ids, t_w = _fused_unpack_packed_topk(packed) + r_ids, r_w = _torch_unpack(packed) + + # bit-identical to the torch elementwise reference + assert torch.equal(t_ids, r_ids) + assert torch.equal(t_w, r_w) + # round-trip: ids exact, weights recover the bf16-rounded originals + assert torch.equal(t_ids, ids) + torch.testing.assert_close( + t_w, w.to(torch.bfloat16).to(torch.float32), rtol=0, atol=0 + ) + assert t_ids.dtype == torch.int32 and t_w.dtype == torch.float32 + assert t_ids.shape == (num_tokens, top_k) and t_w.shape == (num_tokens, top_k) + + +def test_unpack_empty(): + packed = torch.empty((0, 2), dtype=torch.int32, device="cuda") + ids, w = _fused_unpack_packed_topk(packed) + assert ids.shape == (0, 2) and w.shape == (0, 2) + assert ids.dtype == torch.int32 and w.dtype == torch.float32 + + +if __name__ == "__main__": + sys.exit(pytest.main([__file__, "-v", "-s"])) diff --git a/test/registered/kernels/test_boundary_kv_fix_kernels.py b/test/registered/kernels/test_boundary_kv_fix_kernels.py new file mode 100644 index 000000000..76e2a0921 --- /dev/null +++ b/test/registered/kernels/test_boundary_kv_fix_kernels.py @@ -0,0 +1,229 @@ +from sglang.test.ci.ci_register import register_cuda_ci + +register_cuda_ci(est_time=15, stage="base-b", runner_config="1-gpu-small") + +"""Boundary-KV fix kernels (SGLANG_ENABLE_MTP_BOUNDARY_KV_FIX) vs a pure-torch reference. + +Covers the three pieces behind the pool-free chain-MTP boundary-KV exactness +fix (widened draft-extend windows that rewrite draft KV rows keyed on rejected +chain proposals with their committed keys): + - compute_widened_draft_extend_locs_positions: batched out_cache_loc / + positions for the widened window (runs BEFORE the forward batch is built; + data-invalid rows zeroed, conv warm-up rows routed to the sacrificial + cache slot 0, sacrificial zone capped at the front); + - fill_widened_draft_extend_inputs: widened depth-0 window token/hidden + materialization (stash fronts | predict rows); + - stash_append_boundary_state: rolling per-request (token, base-hidden) + stash append, in both decode (accumulate valid_len) and prefill-seed + (SET valid_len, varlen sources) modes. +""" + +import unittest + +import torch + +from sglang.srt.speculative.multi_layer_eagle_utils import ( + compute_widened_draft_extend_locs_positions, + fill_widened_draft_extend_inputs_triton, + stash_append_boundary_state_triton, +) +from sglang.test.test_utils import CustomTestCase + +DEV = "cuda" + +# (bs, W, front, warmup, hidden, seq_lens, valid, accept) +CASES = [ + # steady state: fully seeded stash, long sequences + (4, 4, 5, 3, 64, [100, 33, 200, 17], [5, 5, 5, 5], [1, 4, 2, 3]), + # fresh/short: partially seeded stash, seq_lens < front (degenerate rows) + (4, 4, 5, 3, 64, [3, 1, 6, 2], [2, 0, 5, 1], [1, 1, 4, 2]), + # d66-like 8-step chain shape without conv warm-up + (2, 9, 7, 0, 128, [50, 9], [7, 3], [9, 1]), + # minimal chain: 2 steps, W=3, front=1 + (3, 3, 1, 0, 32, [10, 11, 12], [1, 1, 0], [1, 2, 3]), +] + + +def _ref_stash_append( + stash_t, stash_h, valid, src_t, src_h, ends, avail, rpis, set_valid +): + front = stash_t.shape[1] + for i, rpi in enumerate(rpis.tolist()): + m = min(int(avail[i]), front) + end = int(ends[i]) + keep = front - m + stash_t[rpi, :keep] = stash_t[rpi, m:].clone() + stash_h[rpi, :keep] = stash_h[rpi, m:].clone() + stash_t[rpi, keep:] = src_t[end - m : end].to(stash_t.dtype) + stash_h[rpi, keep:] = src_h[end - m : end].to(stash_h.dtype) + if set_valid: + valid[rpi] = m + else: + valid[rpi] = min(int(valid[rpi]) + m, front) + + +def _ref_fill_and_locs( + predict, vhid, stash_t, stash_h, valid, seq_lens, rpis, req_to_token, W, warmup +): + bs = rpis.shape[0] + front = stash_t.shape[1] + width = W + front + h = stash_h.shape[2] + ids = torch.zeros(bs * width, dtype=torch.int64, device=DEV) + hid = torch.zeros(bs * width, h, dtype=stash_h.dtype, device=DEV) + pos = torch.zeros(bs * width, dtype=torch.int64, device=DEV) + loc = torch.zeros(bs * width, dtype=torch.int64, device=DEV) + for i, rpi in enumerate(rpis.tolist()): + seq_len = int(seq_lens[i]) + first_valid = max(front - int(valid[rpi]), front - seq_len, 0) + # Sacrificial zone is capped at the front: original rows always write. + first_real = min(first_valid + warmup, front) + for j in range(width): + row = i * width + j + p = seq_len - front + j + if j >= front: + src = i * W + j - front + ids[row] = predict[src] + hid[row] = vhid[src] + pos[row] = p + loc[row] = req_to_token[rpi, p] + else: + if j >= first_valid: + ids[row] = stash_t[rpi, j] + hid[row] = stash_h[rpi, j] + pos[row] = p + if j >= first_real: + loc[row] = req_to_token[rpi, p] + return ids, hid, pos, loc + + +class TestBoundaryKvFixKernels(CustomTestCase): + def test_kernels_vs_reference(self): + for case_i, ( + bs, + W, + front, + warmup, + hidden, + seq_lens, + valid, + accept, + ) in enumerate(CASES): + with self.subTest(case=case_i): + self._run_case(bs, W, front, warmup, hidden, seq_lens, valid, accept) + + def _run_case(self, bs, W, front, warmup, hidden, seq_lens, valid, accept): + torch.manual_seed(0) + pool, max_ctx = 32, 512 + rpis = torch.randperm(pool, device=DEV)[:bs].to(torch.int64) + req_to_token = ( + torch.arange(pool * max_ctx, device=DEV, dtype=torch.int32).reshape( + pool, max_ctx + ) + + 1000 + ) + seq_lens_t = torch.tensor(seq_lens, device=DEV, dtype=torch.int64) + + stash_t = torch.randint(5, 900, (pool, front), device=DEV, dtype=torch.int64) + stash_h = torch.randn(pool, front, hidden, device=DEV, dtype=torch.bfloat16) + valid_t = torch.zeros(pool, dtype=torch.int32, device=DEV) + for i, rpi in enumerate(rpis.tolist()): + valid_t[rpi] = valid[i] + + predict = torch.randint(5, 900, (bs * W,), device=DEV, dtype=torch.int64) + vhid = torch.randn(bs * W, hidden, device=DEV, dtype=torch.bfloat16) + + r_ids, r_hid, r_pos, r_loc = _ref_fill_and_locs( + predict, + vhid, + stash_t, + stash_h, + valid_t, + seq_lens_t, + rpis, + req_to_token, + W, + warmup, + ) + + # --- locs / positions (pre-forward-batch torch path) --- + loc, pos = compute_widened_draft_extend_locs_positions( + seq_lens_t, + rpis, + req_to_token, + valid_t, + draft_token_num=W, + num_front_tokens=front, + num_warmup_tokens=warmup, + ) + self.assertTrue(torch.equal(loc, r_loc), "locs mismatch") + self.assertTrue(torch.equal(pos, r_pos), "positions mismatch") + + # --- widened window token/hidden fill --- + width = W + front + ids = torch.zeros(bs * width, dtype=torch.int64, device=DEV) + hid = torch.zeros(bs * width, hidden, dtype=torch.bfloat16, device=DEV) + fill_widened_draft_extend_inputs_triton( + ids, + hid, + predict, + vhid, + stash_t, + stash_h, + valid_t, + seq_lens_t, + rpis, + draft_token_num=W, + ) + self.assertTrue(torch.equal(ids, r_ids), "fill mismatch on ids") + self.assertTrue(torch.equal(hid, r_hid), "fill mismatch on hid") + + # --- decode-style stash roll-forward (accumulating valid_len) --- + accept_t = torch.tensor(accept, device=DEV, dtype=torch.int32) + ends = torch.arange(bs, device=DEV, dtype=torch.int64) * W + accept_t.to( + torch.int64 + ) + ref_t, ref_h, ref_v = stash_t.clone(), stash_h.clone(), valid_t.clone() + _ref_stash_append( + ref_t, ref_h, ref_v, predict, vhid, ends, accept_t, rpis, False + ) + stash_append_boundary_state_triton( + predict, + vhid, + ends, + accept_t, + rpis, + stash_t, + stash_h, + valid_t, + set_valid=False, + ) + self.assertTrue(torch.equal(stash_t, ref_t), "decode stash tokens mismatch") + self.assertTrue(torch.equal(stash_h, ref_h), "decode stash hiddens mismatch") + self.assertTrue(torch.equal(valid_t, ref_v), "decode stash valid_len mismatch") + + # --- prefill-style seed (varlen segments, SET valid_len) --- + lens = torch.tensor( + [max(1, (i * 7) % (W + front)) for i in range(bs)], + device=DEV, + dtype=torch.int32, + ) + starts = torch.cumsum( + torch.cat([torch.zeros(1, device=DEV, dtype=torch.int32), lens[:-1]]), 0 + ) + total = int(lens.sum()) + src_t = torch.randint(5, 900, (total,), device=DEV, dtype=torch.int64) + src_h = torch.randn(total, hidden, device=DEV, dtype=torch.bfloat16) + ends2 = (starts + lens).to(torch.int64) + ref_t, ref_h, ref_v = stash_t.clone(), stash_h.clone(), valid_t.clone() + _ref_stash_append(ref_t, ref_h, ref_v, src_t, src_h, ends2, lens, rpis, True) + stash_append_boundary_state_triton( + src_t, src_h, ends2, lens, rpis, stash_t, stash_h, valid_t, set_valid=True + ) + self.assertTrue(torch.equal(stash_t, ref_t), "seed stash tokens mismatch") + self.assertTrue(torch.equal(stash_h, ref_h), "seed stash hiddens mismatch") + self.assertTrue(torch.equal(valid_t, ref_v), "seed stash valid_len mismatch") + + +if __name__ == "__main__": + unittest.main() diff --git a/test/registered/layers/mamba/test_mamba_slot_fused.py b/test/registered/layers/mamba/test_mamba_slot_fused.py new file mode 100644 index 000000000..4583258b3 --- /dev/null +++ b/test/registered/layers/mamba/test_mamba_slot_fused.py @@ -0,0 +1,197 @@ +"""Unit tests for the fused conv-slot clear/copy kernels +(srt/mem_cache/mamba_slot_fused.py), checked bit-exact against the per-tensor +reference loop that MambaPool.clear_slots / copy_from fall back to. + +Covers heterogeneous conv shapes, single- and multi-layer pools, single / +partial / full index sets, int32 indices, and the strided per-slot-envelope +layout used by page-major / unified pools. +""" + +import unittest + +import torch + +from sglang.srt.mem_cache.mamba_slot_fused import ( + build_conv_slot_descriptor, + fused_clear_conv_slots, + fused_copy_conv_slots, +) +from sglang.test.ci.ci_register import register_cuda_ci +from sglang.test.test_utils import CustomTestCase + +register_cuda_ci(est_time=30, stage="base-b", runner_config="1-gpu-small") + +CONV_LEN = 3 +# Representative hybrid conv-state trailing dims (a couple of KV-projection +# streams + wider residual streams); only the trailing dim differs. +HETERO_DIMS = [128, 128, 256, 256, 6144, 6144] + +# (dims, num_layers, pool_size) +CONFIGS = [ + (HETERO_DIMS, 1, 460), # single-layer draft pool, realistic size + (HETERO_DIMS, 3, 128), # multi-layer + ([128], 1, 64), # single conv tensor + ([256, 6144], 2, 32), # mixed shapes, 2 layers +] + + +def _make_convs(dims, num_layers, pool, device, seed): + g = torch.Generator(device=device).manual_seed(seed) + return [ + torch.randn( + num_layers, + pool, + CONV_LEN, + d, + dtype=torch.bfloat16, + device=device, + generator=g, + ) + for d in dims + ] + + +def _envelope_views(buf, dims, num_layers, pool): + """Strided per-slot-envelope views (page-major / unified layout): each conv + tensor is a slice inside a shared per-slot entry, so slot_stride is the whole + envelope, not the feature length.""" + envelope = buf.shape[2] + views, off = [], 0 + for d in dims: + views.append( + torch.as_strided( + buf, + size=(num_layers, pool, CONV_LEN, d), + stride=(pool * envelope, envelope, d, 1), + storage_offset=off, + ) + ) + off += CONV_LEN * d + return views + + +def _ref_clear(convs, idx): + for t in convs: + t[:, idx] = 0 + + +def _ref_copy(convs, src, dst): + for t in convs: + t[:, dst] = t[:, src] + + +@unittest.skipUnless(torch.cuda.is_available(), "fused conv-slot kernels need CUDA") +class TestMambaSlotFused(CustomTestCase): + def test_clear_matches_reference(self): + dev = "cuda" + for dims, num_layers, pool in CONFIGS: + for n in sorted({1, pool // 3, pool}): # single / partial / all slots + with self.subTest(dims=dims, num_layers=num_layers, pool=pool, n=n): + base = _make_convs(dims, num_layers, pool, dev, seed=0) + idx = torch.randperm(pool, device=dev)[:n].to(torch.int64) + ref = [t.clone() for t in base] + got = [t.clone() for t in base] + _ref_clear(ref, idx) + fused_clear_conv_slots(build_conv_slot_descriptor(got), idx) + torch.cuda.synchronize() + for r, g in zip(ref, got): + self.assertTrue(torch.equal(r, g)) + # Cleared slots are exactly zero; the rest is untouched. + keep = torch.ones(pool, dtype=torch.bool, device=dev) + keep[idx] = False + for g, b in zip(got, base): + self.assertTrue((g[:, idx] == 0).all().item()) + self.assertTrue(torch.equal(g[:, keep], b[:, keep])) + + def test_copy_matches_reference(self): + dev = "cuda" + for dims, num_layers, pool in CONFIGS: + with self.subTest(dims=dims, num_layers=num_layers, pool=pool): + base = _make_convs(dims, num_layers, pool, dev, seed=1) + perm = torch.randperm(pool, device=dev) + n = max(1, pool // 4) + src = perm[:n].to(torch.int64) # disjoint from dst (COW invariant) + dst = perm[n : 2 * n].to(torch.int64) + ref = [t.clone() for t in base] + got = [t.clone() for t in base] + _ref_copy(ref, src, dst) + fused_copy_conv_slots(build_conv_slot_descriptor(got), src, dst) + torch.cuda.synchronize() + for r, g in zip(ref, got): + self.assertTrue(torch.equal(r, g)) + + def test_strided_envelope_layout(self): + # Page-major / unified pools store conv tensors as strided views into a + # shared per-slot envelope (slot_stride = whole entry >> feat). The + # kernel reads real strides, so it must handle this; the whole envelope + # buffer (including the other streams' bytes in each slot) must be + # bit-exact vs the reference, proving no cross-stream clobber. + dev = "cuda" + num_layers, pool = 2, 48 + dims = [128, 256, 6144] + envelope = sum(CONV_LEN * d for d in dims) + g = torch.Generator(device=dev).manual_seed(4) + buf = torch.randn( + num_layers, pool, envelope, dtype=torch.bfloat16, device=dev, generator=g + ) + v0 = _envelope_views(buf, dims, num_layers, pool)[0] + self.assertFalse(v0.is_contiguous()) # strided view... + self.assertTrue(v0[0, 0].is_contiguous()) # ...but per-slot block is not + + idx = torch.tensor([2, 7, 40], dtype=torch.int64, device=dev) + ref_buf = buf.clone() + got_buf = buf.clone() + _ref_clear(_envelope_views(ref_buf, dims, num_layers, pool), idx) + fused_clear_conv_slots( + build_conv_slot_descriptor( + _envelope_views(got_buf, dims, num_layers, pool) + ), + idx, + ) + torch.cuda.synchronize() + self.assertTrue(torch.equal(ref_buf, got_buf)) + + # copy on the same strided layout + src = torch.tensor([1, 20], dtype=torch.int64, device=dev) + dst = torch.tensor([30, 45], dtype=torch.int64, device=dev) + ref_buf = buf.clone() + got_buf = buf.clone() + _ref_copy(_envelope_views(ref_buf, dims, num_layers, pool), src, dst) + fused_copy_conv_slots( + build_conv_slot_descriptor( + _envelope_views(got_buf, dims, num_layers, pool) + ), + src, + dst, + ) + torch.cuda.synchronize() + self.assertTrue(torch.equal(ref_buf, got_buf)) + + def test_empty_indices_is_noop(self): + dev = "cuda" + base = _make_convs(HETERO_DIMS, 1, 16, dev, seed=2) + got = [t.clone() for t in base] + empty = torch.empty(0, dtype=torch.int64, device=dev) + desc = build_conv_slot_descriptor(got) + fused_clear_conv_slots(desc, empty) + fused_copy_conv_slots(desc, empty, empty) + torch.cuda.synchronize() + for b, g in zip(base, got): + self.assertTrue(torch.equal(b, g)) + + def test_int32_indices_accepted(self): + # deferred-clear/COW indices are staged as int32; the wrappers must upcast. + dev = "cuda" + base = _make_convs(HETERO_DIMS, 1, 32, dev, seed=3) + idx = torch.tensor([1, 5, 9], dtype=torch.int32, device=dev) + ref = [t.clone() for t in base] + got = [t.clone() for t in base] + _ref_clear(ref, idx.long()) + fused_clear_conv_slots(build_conv_slot_descriptor(got), idx) + torch.cuda.synchronize() + for r, g in zip(ref, got): + self.assertTrue(torch.equal(r, g)) + + +if __name__ == "__main__": + unittest.main() diff --git a/test/registered/lora/test_lora_overlap_loading.py b/test/registered/lora/test_lora_overlap_loading.py index 2d187b20e..696f9528f 100644 --- a/test/registered/lora/test_lora_overlap_loading.py +++ b/test/registered/lora/test_lora_overlap_loading.py @@ -17,6 +17,7 @@ import unittest from typing import cast from unittest.mock import MagicMock, patch +import torch from torch.cuda import Event as CudaEvent from torch.cuda import Stream as CudaStream @@ -69,6 +70,7 @@ class TestLoRAOverlapLoaderUnitTests(CustomTestCase): self.mock_lora_manager.memory_pool.uid_to_buffer_id = {} self.mock_lora_manager.validate_lora_batch.return_value = True self.mock_lora_manager.fetch_new_loras.side_effect = self._mark_loras_loaded + self.mock_lora_manager.pending_lora_load_events = {} def tearDown(self): self.torch_patcher.stop() @@ -155,6 +157,46 @@ class TestLoRAOverlapLoaderUnitTests(CustomTestCase): self.mock_lora_manager.fetch_new_loras.assert_not_called() self.assertIn("lora_A", loader.lora_to_overlap_load_event) + def test_loader_uses_manager_pending_event_store(self): + loader = self._create_loader() + + self.assertIs( + loader.lora_to_overlap_load_event, + self.mock_lora_manager.pending_lora_load_events, + ) + + def test_pending_load_is_synchronized_before_unload(self): + manager = LoRAManager.__new__(LoRAManager) + manager.device = torch.device("cuda:0") + manager.pending_lora_load_events = {} + manager.memory_pool = MagicMock() + manager.configs = {"lora_A": object()} + manager.loras = {"lora_A": object()} + lora_ref = MagicMock() + lora_ref.lora_id = "lora_A" + lora_ref.lora_name = "lora_A" + lora_ref.lora_path = "/tmp/lora_A" + lora_ref.pinned = False + manager.lora_refs = {"lora_A": lora_ref} + manager.num_pinned_loras = 0 + manager.lora_modules = [] + + order = [] + event = self._create_mock_event(False) + event.synchronize.side_effect = lambda: order.append("synchronize") + manager.memory_pool.remove_lora.side_effect = lambda _uid: ( + order.append("remove") or 0 + ) + loader = LoRAOverlapLoader(manager) + loader.lora_to_overlap_load_event["lora_A"] = event + + result = manager.unload_lora_adapter(lora_ref) + + self.assertTrue(result.success) + self.assertEqual(order, ["synchronize", "remove"]) + event.synchronize.assert_called_once_with() + self.assertNotIn("lora_A", manager.pending_lora_load_events) + def test_full_lifecycle_single_lora_load(self): loader = self._create_loader() diff --git a/test/registered/unit/constrained/test_base_grammar_backend.py b/test/registered/unit/constrained/test_base_grammar_backend.py index 6b02f5db7..4c6265ed6 100644 --- a/test/registered/unit/constrained/test_base_grammar_backend.py +++ b/test/registered/unit/constrained/test_base_grammar_backend.py @@ -396,5 +396,45 @@ class TestCreateGrammarBackend(unittest.TestCase): self.assertIsNone(kwargs["model_eos_token_ids"]) +class TestLlguidanceStructuralTagTriggerPairing(unittest.TestCase): + """Bug regression: dispatch_structural_tag paired EVERY structure with + triggers[0]. Detectors with per-tool triggers (Inkling emits + <|message_model|>{name}<|content_invoke_tool_json|> per tool) produce + multiple distinct triggers, and llguidance's StructTag asserts + begin.startswith(trigger) — so any multi-tool constrained request + compiled to InvalidGrammarObject.""" + + def test_each_structure_pairs_with_its_own_trigger(self): + import json + + from sglang.srt.constrained.llguidance_backend import GuidanceBackend + + backend = object.__new__(GuidanceBackend) + backend._from_serialized = lambda serialized: serialized + begins = [ + '<|message_model|>alpha<|content_invoke_tool_json|>{"name":"alpha","args":', + '<|message_model|>beta<|content_invoke_tool_json|>{"name":"beta","args":', + ] + key = json.dumps( + { + "type": "structural_tag", + "structures": [ + { + "begin": begin, + "schema": {"type": "object"}, + "end": "<|end_message|>", + } + for begin in begins + ], + "triggers": [ + "<|message_model|>alpha<|content_invoke_tool_json|>", + "<|message_model|>beta<|content_invoke_tool_json|>", + ], + } + ) + result = backend.dispatch_structural_tag(key) + self.assertNotIsInstance(result, InvalidGrammarObject) + + if __name__ == "__main__": unittest.main() diff --git a/test/registered/unit/entrypoints/openai/test_protocol.py b/test/registered/unit/entrypoints/openai/test_protocol.py index 5246f3912..eceeea83f 100644 --- a/test/registered/unit/entrypoints/openai/test_protocol.py +++ b/test/registered/unit/entrypoints/openai/test_protocol.py @@ -259,19 +259,27 @@ class TestChatCompletionRequest(unittest.TestCase): self.assertFalse(request.chat_template_kwargs.get("thinking")) self.assertFalse(request.chat_template_kwargs.get("enable_thinking")) - def test_chat_completion_reasoning_effort_max(self): - """`max` is an sglang extension on chat completion's top-level - `reasoning_effort` only; the Responses-API-style nested - `reasoning.effort` path stays aligned with OpenAI's three levels.""" + def test_chat_completion_extended_reasoning_effort_levels(self): + """Extended effort levels work in both supported request forms.""" from pydantic import ValidationError messages = [{"role": "user", "content": "Hello"}] - request = ChatCompletionRequest( - model="test-model", - messages=messages, - reasoning_effort="max", - ) - self.assertEqual(request.reasoning_effort, "max") + for effort in ("xhigh", "max"): + with self.subTest(effort=effort, request_form="top-level"): + request = ChatCompletionRequest( + model="test-model", + messages=messages, + reasoning_effort=effort, + ) + self.assertEqual(request.reasoning_effort, effort) + + with self.subTest(effort=effort, request_form="nested"): + request = ChatCompletionRequest( + model="test-model", + messages=messages, + reasoning={"effort": effort}, + ) + self.assertEqual(request.reasoning_effort, effort) # Unknown values still rejected. with self.assertRaises(ValidationError): @@ -281,14 +289,79 @@ class TestChatCompletionRequest(unittest.TestCase): reasoning_effort="ultra", ) - # Nested reasoning.effort=max is NOT promoted by normalize_reasoning_inputs: - # the Responses API path keeps the OpenAI low/medium/high contract. + def test_chat_completion_reasoning_effort_is_strictly_validated(self): + from pydantic import ValidationError + + messages = [{"role": "user", "content": "Hello"}] + for request_kwargs, expected in ( + ({"reasoning_effort": 0.99}, 0.99), + ({"reasoning": {"effort": 0.0}}, 0.0), + # numeric strings coerce identically on BOTH request surfaces + # (the top-level field's lax union already coerced them). + ({"reasoning": {"effort": "0.5"}}, 0.5), + ({"reasoning": {"effort": None, "reasoning_effort": 0.4}}, 0.4), + ): + request = ChatCompletionRequest( + model="test-model", messages=messages, **request_kwargs + ) + self.assertEqual(request.reasoning_effort, expected) + + for request_kwargs in ( + {"reasoning_effort": -0.1}, + # 0.99 is the maximum valid effort; 1.0 is out of range. + {"reasoning_effort": 1.0}, + {"reasoning_effort": 1.1}, + {"reasoning_effort": float("nan")}, + {"reasoning_effort": True}, + {"reasoning": {"effort": "invalid"}}, + {"reasoning": {"effort": 1.0}}, + {"reasoning": {"effort": 1.1}}, + {"reasoning": {"effort": "1.5"}}, + ): + with self.subTest(request_kwargs=request_kwargs), self.assertRaises( + ValidationError + ): + ChatCompletionRequest( + model="test-model", messages=messages, **request_kwargs + ) + + def test_chat_completion_accepts_ordered_thinking_parts(self): request = ChatCompletionRequest( model="test-model", - messages=messages, - reasoning={"effort": "max"}, + messages=[ + { + "role": "assistant", + "content": [ + {"type": "thinking", "thinking": "first"}, + {"type": "text", "text": "visible"}, + {"type": "reasoning", "text": "second"}, + ], + } + ], ) - self.assertNotEqual(request.reasoning_effort, "max") + parts = request.messages[0].content + self.assertEqual( + [part.type for part in parts], ["thinking", "text", "reasoning"] + ) + + def test_chat_completion_rejects_thinking_parts_outside_assistant(self): + """Bug regression: adding the thinking part to the SHARED content-part + union silently widened acceptance to every role (user/system/tool) and + every model family, where downstream templates cannot render it — + replacing the previous clean 422 with template-dependent behavior.""" + from pydantic import ValidationError + + for role in ("user", "system", "tool"): + with self.subTest(role=role), self.assertRaises(ValidationError): + ChatCompletionRequest( + model="test-model", + messages=[ + { + "role": role, + "content": [{"type": "thinking", "thinking": "x"}], + } + ], + ) def test_chat_completion_json_format(self): """Test chat completion json format""" diff --git a/test/registered/unit/entrypoints/openai/test_serving_chat.py b/test/registered/unit/entrypoints/openai/test_serving_chat.py index ab7f9469b..693cdd4b8 100644 --- a/test/registered/unit/entrypoints/openai/test_serving_chat.py +++ b/test/registered/unit/entrypoints/openai/test_serving_chat.py @@ -2325,6 +2325,29 @@ class TestProcessToolCallsWithRequiredToolChoice(unittest.TestCase): self.assertEqual(tool_calls[0].function.name, "get_weather") self.assertEqual(fr["type"], "tool_calls") + def test_empty_parser_result_is_not_reported_as_tool_call(self): + with patch( + "sglang.srt.entrypoints.openai.serving_chat.FunctionCallParser" + ) as ParserMock: + parser_instance = ParserMock.return_value + parser_instance.has_tool_call.return_value = True + parser_instance.detector.supports_structural_tag.return_value = True + parser_instance.parse_non_stream.return_value = ("Visible prefix.", []) + + finish_reason = {"type": "stop", "matched": None} + tools = [{"type": "function", "function": {"name": "get_weather"}}] + + tool_calls, text, fr = self.chat._process_tool_calls( + text="<|malformed_tool_call|>", + tools=tools, + finish_reason=finish_reason, + tool_choice="required", + ) + + self.assertIsNone(tool_calls) + self.assertEqual(text, "Visible prefix.") + self.assertEqual(fr, {"type": "stop", "matched": None}) + def test_required_without_parser_falls_back_to_json(self): """tool_choice='required' without parser should parse as JSON array.""" self.chat.tool_call_parser = None @@ -2393,5 +2416,156 @@ class TestNormalizeToolContent(unittest.TestCase): self.assertEqual(result, "plain rich") +class InklingReasoningEffortTest(unittest.TestCase): + """Inkling reasoning-effort mapping and validation.""" + + def test_named_levels(self): + parse = OpenAIServingChat._parse_inkling_reasoning_effort + self.assertEqual(parse("none"), 0.0) + self.assertEqual(parse("minimal"), 0.1) + self.assertEqual(parse("low"), 0.2) + self.assertEqual(parse("medium"), 0.7) + self.assertEqual(parse("high"), 0.9) + # "xhigh" and "max" are aliases for the same 0.99 ceiling + self.assertEqual(parse("xhigh"), 0.99) + self.assertEqual(parse("max"), 0.99) + self.assertEqual(parse("max"), parse("xhigh")) + + def test_scalar_range_is_validated(self): + parse = OpenAIServingChat._parse_inkling_reasoning_effort + self.assertEqual(parse(0.5), 0.5) + self.assertEqual(parse(0.99), 0.99) + for value in (1.0, "1.0", 2.0, "1.5", -1.0, float("nan"), True): + with self.subTest(value=value), self.assertRaises(ValueError): + parse(value) + + def test_invalid_and_none(self): + parse = OpenAIServingChat._parse_inkling_reasoning_effort + self.assertIsNone(parse(None)) + with self.assertRaises(ValueError): + parse("garbage") + + def test_env_default(self): + from sglang.srt.environ import envs + + get = OpenAIServingChat._get_inkling_default_reasoning_effort + env = envs.SGLANG_INKLING_DEFAULT_REASONING_EFFORT + try: + env.clear() # unset -> EnvStr default "0.9" + self.assertEqual(get(), 0.9) + env.set("") # explicit empty still uses the protocol default + self.assertEqual(get(), 0.9) + env.set("0.7") + self.assertEqual(get(), 0.7) + for value in ("1.0", "1.1", "garbage"): + env.set(value) + with self.subTest(value=value), self.assertRaises(ValueError): + get() + finally: + env.clear() + + def test_serving_does_not_prefill_model_message(self): + from sglang.srt.parser.inkling_tokenizer import INKLING_SPECIAL_TOKEN_IDS + + class Tokenizer: + def encode(self, text, add_special_tokens=False): + return list(text.encode()) + + serving = object.__new__(OpenAIServingChat) + serving.chat_encoding_spec = "inkling" + serving.tokenizer_manager = Mock(tokenizer=Tokenizer()) + request = ChatCompletionRequest( + model="test-model", + messages=[{"role": "user", "content": "hello"}], + reasoning_effort=0.5, + ) + prompt_ids = serving._encode_messages( + [message.model_dump() for message in request.messages], + request, + thinking_mode=None, + ) + self.assertEqual(prompt_ids[-1], INKLING_SPECIAL_TOKEN_IDS["<|end_message|>"]) + + def test_continue_final_message_resumes_open_model_text_block(self): + """Bug regression: continue_final_message was silently ignored on the + inkling path — the trailing assistant message rendered as a CLOSED + historical turn (<|end_message|> + <|content_model_end_sampling|>), so + the model started a fresh turn instead of continuing. The prefix must + render as an OPEN model text block.""" + from sglang.srt.parser.inkling_tokenizer import INKLING_SPECIAL_TOKEN_IDS + + class Tokenizer: + def encode(self, text, add_special_tokens=False): + return list(text.encode()) + + serving = object.__new__(OpenAIServingChat) + serving.chat_encoding_spec = "inkling" + serving.tokenizer_manager = Mock(tokenizer=Tokenizer()) + request = ChatCompletionRequest( + model="test-model", + messages=[ + {"role": "user", "content": "hello"}, + {"role": "assistant", "content": "The answer"}, + ], + reasoning_effort=0.5, + continue_final_message=True, + ) + prompt_ids = serving._encode_messages( + [message.model_dump() for message in request.messages], + request, + thinking_mode=None, + ) + open_block = [ + INKLING_SPECIAL_TOKEN_IDS["<|message_model|>"], + INKLING_SPECIAL_TOKEN_IDS["<|content_text|>"], + *list(b"The answer"), + ] + self.assertEqual(prompt_ids[-len(open_block) :], open_block) + self.assertNotIn( + INKLING_SPECIAL_TOKEN_IDS["<|content_model_end_sampling|>"], prompt_ids + ) + + def test_continue_final_message_leaves_tool_call_turns_closed(self): + """A trailing assistant message with tool_calls cannot be continued — + it must keep rendering as a closed historical turn.""" + from sglang.srt.parser.inkling_tokenizer import INKLING_SPECIAL_TOKEN_IDS + + class Tokenizer: + def encode(self, text, add_special_tokens=False): + return list(text.encode()) + + serving = object.__new__(OpenAIServingChat) + serving.chat_encoding_spec = "inkling" + serving.tokenizer_manager = Mock(tokenizer=Tokenizer()) + request = ChatCompletionRequest( + model="test-model", + messages=[ + {"role": "user", "content": "hello"}, + { + "role": "assistant", + "content": "calling", + "tool_calls": [ + { + "id": "call-1", + "type": "function", + "function": {"name": "weather", "arguments": "{}"}, + } + ], + }, + ], + reasoning_effort=0.5, + continue_final_message=True, + ) + prompt_ids = serving._encode_messages( + [message.model_dump() for message in request.messages], + request, + thinking_mode=None, + ) + self.assertEqual( + prompt_ids[-1], + INKLING_SPECIAL_TOKEN_IDS["<|content_model_end_sampling|>"], + ) + + if __name__ == "__main__": unittest.main(verbosity=2) diff --git a/test/registered/unit/function_call/test_function_call_parser.py b/test/registered/unit/function_call/test_function_call_parser.py index 96e027481..7dfa25925 100644 --- a/test/registered/unit/function_call/test_function_call_parser.py +++ b/test/registered/unit/function_call/test_function_call_parser.py @@ -22,6 +22,7 @@ from sglang.srt.function_call.gigachat3_detector import GigaChat3Detector from sglang.srt.function_call.glm4_moe_detector import Glm4MoeDetector from sglang.srt.function_call.glm47_moe_detector import Glm47MoeDetector from sglang.srt.function_call.gpt_oss_detector import GptOssDetector +from sglang.srt.function_call.inkling_detector import InklingDetector from sglang.srt.function_call.json_array_parser import JsonArrayParser from sglang.srt.function_call.kimik2_detector import KimiK2Detector from sglang.srt.function_call.lfm2_detector import Lfm2Detector @@ -35,6 +36,249 @@ register_cpu_ci(est_time=15, suite="base-a-test-cpu") register_cpu_ci(est_time=61, suite="base-c-test-cpu") +class TestInklingDetector(unittest.TestCase): + def setUp(self): + self.tools = [ + Tool( + type="function", + function=Function( + name="weather", + description="Lookup weather", + parameters={"type": "object"}, + ), + ) + ] + + def test_canonical_header_is_not_visible_content(self): + detector = InklingDetector() + source = ( + "<|message_model|>weather<|content_invoke_tool_json|>" + '{"name":"weather","args":{"city":"SF"}}<|end_message|>' + ) + result = detector.detect_and_parse(source, self.tools) + self.assertEqual(result.normal_text, "") + self.assertEqual(len(result.calls), 1) + self.assertEqual(result.calls[0].name, "weather") + self.assertEqual(json.loads(result.calls[0].parameters), {"city": "SF"}) + + def test_streaming_header_is_buffered_until_the_tool_kind(self): + detector = InklingDetector() + chunks = [ + "<|message_model|>", + "weat", + "her", + "<|content_invoke_tool_json|>", + '{"name":"weather",', + '"args":{"city":"SF"}}', + "<|end_message|>", + ] + normal_text = "" + name = None + parameters = "" + for chunk in chunks: + result = detector.parse_streaming_increment(chunk, self.tools) + normal_text += result.normal_text + for call in result.calls: + name = call.name or name + parameters += call.parameters + self.assertEqual(normal_text, "") + self.assertEqual(name, "weather") + self.assertEqual(json.loads(parameters), {"city": "SF"}) + + def test_mismatched_header_is_rejected(self): + detector = InklingDetector() + source = ( + "<|message_model|>other<|content_invoke_tool_json|>" + '{"name":"weather","args":{}}<|end_message|>' + ) + result = detector.detect_and_parse(source, self.tools) + self.assertEqual(result.calls, []) + + def test_rejected_call_does_not_leak_protocol_tokens(self): + """Bug regression: the no-surviving-calls path returned the RAW text, + so a rejected call (e.g. header/payload mismatch) leaked <|...|> + protocol tokens into user-visible content.""" + detector = InklingDetector() + source = ( + "<|message_model|>other<|content_invoke_tool_json|>" + '{"name":"weather","args":{}}<|end_message|>' + ) + result = detector.detect_and_parse(source, self.tools) + self.assertNotIn("<|", result.normal_text) + # Framework parity: the rejected tool-call REGION is dropped entirely + # (normal_text = content before the marker), like every other detector + # — the JSON payload must not surface as visible content either. + self.assertEqual(result.normal_text, "") + + def test_headerless_legacy_tool_call_still_parses(self): + """Spec tolerance: a bare <|content_invoke_tool_json|> block with no + <|message_model|>name header (the pre-canonical form) must keep + parsing in both modes.""" + source = '<|content_invoke_tool_json|>{"name":"weather","args":{"city":"SF"}}<|end_message|>' + result = InklingDetector().detect_and_parse(source, self.tools) + self.assertEqual(len(result.calls), 1) + self.assertEqual(result.calls[0].name, "weather") + + streaming = InklingDetector() + name = None + parameters = "" + for char in source: + for call in streaming.parse_streaming_increment(char, self.tools).calls: + name = call.name or name + parameters += call.parameters + self.assertEqual(name, "weather") + self.assertEqual(json.loads(parameters), {"city": "SF"}) + + def test_streaming_two_sequential_tool_calls_get_distinct_indices(self): + """Coverage for multi-call responses: two back-to-back canonical tool + calls must stream as tool_index 0 and 1 with per-call args.""" + detector = InklingDetector() + source = ( + "<|message_model|>weather<|content_invoke_tool_json|>" + '{"name":"weather","args":{"city":"SF"}}<|end_message|>' + "<|message_model|>weather<|content_invoke_tool_json|>" + '{"name":"weather","args":{"city":"NY"}}<|end_message|>' + ) + args_by_index: dict = {} + for char in source: + for call in detector.parse_streaming_increment(char, self.tools).calls: + args_by_index[call.tool_index] = ( + args_by_index.get(call.tool_index, "") + call.parameters + ) + self.assertEqual(sorted(args_by_index), [0, 1]) + self.assertEqual(json.loads(args_by_index[0]), {"city": "SF"}) + self.assertEqual(json.loads(args_by_index[1]), {"city": "NY"}) + + def test_streaming_rejection_does_not_collide_tool_indices(self): + """Bug regression: a rejected mid-stream call reset current_tool_id to + -1, so the NEXT valid call re-announced as tool_index 0 — colliding + with the first call's index and slicing its arguments against index + 0's already-streamed args.""" + detector = InklingDetector() + chunks = [ + "<|message_model|>weather<|content_invoke_tool_json|>", + '{"name":"weather","args":{"city":"SF"}}<|end_message|>', + # header/payload mismatch -> rejected + "<|message_model|>other<|content_invoke_tool_json|>", + '{"name":"weather","args":{"city":"NY"}}<|end_message|>', + # valid again + "<|message_model|>weather<|content_invoke_tool_json|>", + '{"name":"weather","args":{"city":"LA"}}<|end_message|>', + ] + args_by_index: dict = {} + for chunk in chunks: + for call in detector.parse_streaming_increment(chunk, self.tools).calls: + args_by_index[call.tool_index] = ( + args_by_index.get(call.tool_index, "") + call.parameters + ) + self.assertEqual(json.loads(args_by_index[0]), {"city": "SF"}) + self.assertEqual(len(args_by_index), 2) + second_index = max(args_by_index) + self.assertGreater(second_index, 0) + self.assertEqual(json.loads(args_by_index[second_index]), {"city": "LA"}) + + def test_undeclared_tool_name_is_surfaced(self): + """A call to a tool absent from the request's tool list surfaces as a + structured tool_call (OpenAI behavior for hallucinated tools) so agent + harnesses can return a tool error and let the model self-correct, + instead of the serialized invocation becoming terminal answer text.""" + detector = InklingDetector() + source = ( + "<|message_model|>document_search<|content_invoke_tool_json|>" + '{"name":"document_search","args":{"query":"q"}}<|end_message|>' + ) + result = detector.detect_and_parse(source, self.tools) + self.assertEqual(len(result.calls), 1) + self.assertEqual(result.calls[0].name, "document_search") + self.assertEqual(json.loads(result.calls[0].parameters), {"query": "q"}) + self.assertNotIn("<|", result.normal_text) + + def test_undeclared_tool_name_surfaces_in_streaming(self): + detector = InklingDetector() + source = ( + "<|message_model|>document_search<|content_invoke_tool_json|>" + '{"name":"document_search","args":{"query":"q"}}<|end_message|>' + ) + normal_text = "" + name = None + parameters = "" + for char in source: + result = detector.parse_streaming_increment(char, self.tools) + normal_text += result.normal_text + for call in result.calls: + name = call.name or name + parameters += call.parameters + self.assertEqual(name, "document_search") + self.assertEqual(json.loads(parameters), {"query": "q"}) + self.assertNotIn("<|", normal_text) + + def test_malformed_json_does_not_leak_protocol_tokens(self): + """Malformed JSON must drop the protocol region and its tool header.""" + detector = InklingDetector() + source = ( + "<|message_model|>weather<|content_invoke_tool_json|>" + "{not json at all<|end_message|>" + ) + result = detector.detect_and_parse(source, self.tools) + self.assertEqual(result.calls, []) + self.assertEqual(result.normal_text, "") + + def test_parser_does_not_restore_malformed_tool_call_as_text(self): + """The parser wrapper must preserve the detector's sanitized fallback.""" + from sglang.srt.function_call.function_call_parser import FunctionCallParser + + source = ( + "Visible prefix." + "<|message_model|>weather<|content_invoke_tool_json|>" + "{not json at all<|end_message|>" + ) + normal_text, calls = FunctionCallParser(self.tools, "inkling").parse_non_stream( + source + ) + self.assertEqual(normal_text, "Visible prefix.") + self.assertEqual(calls, []) + + def test_parser_preserves_text_without_tool_call_marker(self): + from sglang.srt.function_call.function_call_parser import FunctionCallParser + + source = " Ordinary assistant text. " + normal_text, calls = FunctionCallParser(self.tools, "inkling").parse_non_stream( + source + ) + self.assertEqual(normal_text, source) + self.assertEqual(calls, []) + + def test_malformed_call_does_not_discard_an_earlier_valid_call(self): + source = ( + "<|message_model|>weather<|content_invoke_tool_json|>" + '{"name":"weather","args":{"city":"SF"}}<|end_message|>' + "<|message_model|>weather<|content_invoke_tool_json|>" + "{not json at all<|end_message|>" + ) + result = InklingDetector().detect_and_parse(source, self.tools) + self.assertEqual(result.normal_text, "") + self.assertEqual(len(result.calls), 1) + self.assertEqual(result.calls[0].name, "weather") + self.assertEqual(json.loads(result.calls[0].parameters), {"city": "SF"}) + + def test_clean_normal_text_strips_the_full_control_alphabet(self): + """Fall-through text is cleaned against the whole shared control-token + alphabet, not a hand-picked subset.""" + detector = InklingDetector() + source = ( + "<|message_model|><|content_thinking|>leak<|end_message|>" + "<|message_user|>x<|content_audio_input|><|audio_end|>" + ) + result = detector.detect_and_parse(source, self.tools) + self.assertNotIn("<|", result.normal_text) + + def test_structural_tag_uses_the_canonical_header(self): + info = InklingDetector().structure_info()("weather") + header = "<|message_model|>weather<|content_invoke_tool_json|>" + self.assertEqual(info.trigger, header) + self.assertTrue(info.begin.startswith(header + '{"name":"weather"')) + + class TestPythonicDetector(unittest.TestCase): def setUp(self): # Create sample tools for testing @@ -3968,6 +4212,31 @@ class TestGetStructureConstraint(unittest.TestCase): result = parser.get_structure_constraint("auto") self.assertIsNone(result) + def test_inkling_auto_constrains_json_after_tool_trigger(self): + import xgrammar as xgr + + from sglang.srt.parser.inkling_tokenizer import INKLING_SPECIAL_TOKEN_IDS + + parser = self._make_parser("inkling", strict=False) + result = parser.get_structure_constraint("auto") + + self.assertIsNotNone(result) + self.assertEqual(result[0], "structural_tag") + self.assertIsInstance(result[1], xgr.StructuralTag) + format_ = result[1].model_dump()["format"] + self.assertEqual(format_["type"], "token_triggered_tags") + self.assertEqual( + format_["trigger_tokens"], + [INKLING_SPECIAL_TOKEN_IDS["<|content_invoke_tool_json|>"]], + ) + tag = format_["tags"][0] + self.assertEqual( + tag["end"]["token"], INKLING_SPECIAL_TOKEN_IDS["<|end_message|>"] + ) + schema = tag["content"]["json_schema"] + self.assertEqual(schema["required"], ["name", "args"]) + self.assertFalse(schema["additionalProperties"]) + def test_kimi_named_tool_choice_returns_structural_tag(self): from sglang.srt.entrypoints.openai.protocol import ( ToolChoice, diff --git a/test/registered/unit/lora/test_experimental_sgl_marlin_alignment.py b/test/registered/unit/lora/test_experimental_sgl_marlin_alignment.py new file mode 100644 index 000000000..d2fdab6ca --- /dev/null +++ b/test/registered/unit/lora/test_experimental_sgl_marlin_alignment.py @@ -0,0 +1,249 @@ +"""CUDA graph tests for multi-LoRA merged alignment.""" + +from __future__ import annotations + +import ast +import sys +import types +from pathlib import Path + +import pytest +import torch + +from sglang.test.ci.ci_register import register_cuda_ci + +register_cuda_ci(est_time=15, stage="base-b", runner_config="1-gpu-small") + +# Skipped on CI: newly-added inkling LoRA test, disabled pending stabilization. +pytestmark = pytest.mark.skip(reason="new inkling LoRA test; disabled on CI") + +ALIGN_PATH = ( + Path(__file__).resolve().parents[4] + / "python/sglang/kernels/ops/moe/trtllm_lora_temp/virtual_experts.py" +) + + +def _load_align_function(): + tree = ast.parse(ALIGN_PATH.read_text()) + function = next( + node + for node in tree.body + if isinstance(node, ast.FunctionDef) and node.name == "_align_block_size_jit" + ) + module = ast.fix_missing_locations(ast.Module(body=[function], type_ignores=[])) + namespace = { + "torch": torch, + "jit_moe_align_block_size": sys.modules[ + "sglang.jit_kernel.moe_align" + ].moe_align_block_size, + } + exec(compile(module, str(ALIGN_PATH), "exec"), namespace) + return namespace["_align_block_size_jit"] + + +def test_experimental_alignment_geometry_and_empty_input(monkeypatch): + calls = [] + + def fake_jit_align(*args): + ( + topk_ids, + num_buckets, + block_size, + sorted_ids, + expert_ids, + total, + cumsum, + flag, + ) = args + calls.append((num_buckets, cumsum.numel(), flag)) + sorted_ids.fill_(topk_ids.numel()) + expert_ids[:2] = torch.tensor([-1, num_buckets - 2]) + total.fill_(2 * block_size) + + monkeypatch.setitem( + sys.modules, + "sglang.jit_kernel.moe_align", + types.SimpleNamespace(moe_align_block_size=fake_jit_align), + ) + align = _load_align_function() + + topk_ids = torch.tensor([[-1, 383]], dtype=torch.int32) + sorted_ids, expert_ids, num_tokens_post_pad = align(topk_ids, 5, 384) + + assert sorted_ids.numel() == 12 # int4-safe capacity above logical 10. + assert expert_ids.numel() == 3 + assert calls == [(385, 386, True)] + assert expert_ids[:2].tolist() == [-1, 383] + assert num_tokens_post_pad.item() == 10 + + outputs = align(torch.empty((0, 6), dtype=torch.int32), 16, 384) + assert [tensor.numel() for tensor in outputs] == [0, 0, 1] + assert outputs[2].item() == 0 + assert len(calls) == 1 + + +@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA is required") +def test_experimental_alignment_cuda_sentinel_and_max_expert(): + from sglang.kernels.ops.moe.trtllm_lora_temp.virtual_experts import ( + _align_block_size_jit, + ) + + topk_ids = torch.tensor([[-1, 383], [0, 0]], device="cuda", dtype=torch.int32) + _, expert_ids, num_tokens_post_pad = _align_block_size_jit(topk_ids, 16, 384) + active_experts = expert_ids[: num_tokens_post_pad.item() // 16].cpu().tolist() + assert sorted(active_experts) == [-1, 0, 383] + + +def _assert_shared_outer_merged_align_semantics( + outputs: tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, int], + token_lora_mapping: torch.Tensor, + *, + topk: int, + block_size: int, + num_slots: int, +) -> None: + """Validate routing without depending on atomic scatter order.""" + + ( + sorted_token_ids, + expert_ids, + num_tokens_post_padded, + token_lora_mask, + virtual_num_experts, + ) = outputs + mapping = token_lora_mapping.cpu() + num_routes = mapping.numel() * topk + total_padded = int(num_tokens_post_padded.item()) + sorted_cpu = sorted_token_ids[:total_padded].cpu() + experts_cpu = expert_ids[: total_padded // block_size].cpu() + + assert virtual_num_experts == num_slots + assert torch.equal(token_lora_mask.cpu(), mapping >= 0) + assert (mapping == -1).any() # -1 is the runtime base/no-adapter sentinel. + assert (mapping == 0).any() # Slot 0 remains a valid adapter slot. + + expected_routes = [] + expected_total_padded = 0 + for slot in range(num_slots): + slot_tokens = torch.nonzero(mapping == slot, as_tuple=False).flatten() + slot_routes = ( + slot_tokens[:, None] * topk + torch.arange(topk)[None, :] + ).flatten() + expected_routes.extend(slot_routes.tolist()) + route_count = slot_routes.numel() + expected_total_padded += ( + (route_count + block_size - 1) // block_size + ) * block_size + + assert total_padded == expected_total_padded + observed_routes = [] + for block, slot in enumerate(experts_cpu.tolist()): + assert 0 <= slot < num_slots + block_routes = sorted_cpu[block * block_size : (block + 1) * block_size] + real_routes = block_routes[block_routes < num_routes].to(torch.long) + if real_routes.numel(): + routed_tokens = torch.div(real_routes, topk, rounding_mode="floor") + assert torch.all(mapping[routed_tokens] == slot) + observed_routes.extend(real_routes.tolist()) + assert torch.all((block_routes >= 0) & (block_routes <= num_routes)) + + assert sorted(observed_routes) == sorted(expected_routes) + + +@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA is required") +@pytest.mark.parametrize("num_slots", [2, 3, 4]) +def test_multi_slot_shared_outer_merged_align_cuda_graph_parity(num_slots): + """The fused hot path must replay with current multi-LoRA routing data.""" + + from sglang.jit_kernel.trtllm_lora_temp.moe_lora_merged_align import ( + moe_lora_merged_align, + ) + + device = torch.device("cuda") + num_tokens = 37 # Exercise the multi-LoRA prefill-size routing contract. + topk = 6 + block_size = 16 + num_experts = 384 + generator = torch.Generator(device=device).manual_seed(9000 + num_slots) + topk_ids = torch.randint( + 0, + num_experts, + (num_tokens, topk), + device=device, + dtype=torch.int32, + generator=generator, + ) + token_lora_mapping = torch.arange( + num_tokens, device=device, dtype=torch.int32 + ).remainder(num_slots) + token_lora_mapping[0] = -1 + token_lora_mapping[-1] = -1 + + def invoke(fuse_scatter: bool): + return moe_lora_merged_align( + topk_ids, + token_lora_mapping, + num_experts, + shared_outer=True, + max_loras=num_slots, + block_size=block_size, + do_skip=True, + fuse_scatter=fuse_scatter, + ) + + # Compile both real kernel variants and initialize CUDA state off the + # capture stream. Production selects the fused variant for this geometry. + warmup_stream = torch.cuda.Stream() + warmup_stream.wait_stream(torch.cuda.current_stream()) + with torch.cuda.stream(warmup_stream): + for _ in range(2): + invoke(fuse_scatter=True) + invoke(fuse_scatter=False) + torch.cuda.current_stream().wait_stream(warmup_stream) + torch.cuda.synchronize() + + graph = torch.cuda.CUDAGraph() + with torch.cuda.graph(graph): + fused_outputs = invoke(fuse_scatter=True) + split_outputs = invoke(fuse_scatter=False) + + stable_outputs = (*fused_outputs[:4], *split_outputs[:4]) + stable_addresses = tuple(tensor.data_ptr() for tensor in stable_outputs) + + for replay in range(3): + if replay: + next_mapping = ( + torch.arange(num_tokens, device=device, dtype=torch.int32) + .add_(replay) + .remainder_(num_slots) + ) + # Move the base/no-adapter rows on every replay while retaining a + # valid adapter in slot 0. + next_mapping[replay] = -1 + next_mapping[-replay - 1] = -1 + token_lora_mapping.copy_(next_mapping) + topk_ids.copy_(torch.roll(topk_ids, shifts=1, dims=1)) + + for outputs in (fused_outputs, split_outputs): + outputs[0].fill_(-12345) + outputs[1].fill_(-12345) + outputs[2].fill_(-1) + outputs[3].fill_(False) + graph.replay() + + torch.cuda.synchronize() + assert tuple(tensor.data_ptr() for tensor in stable_outputs) == stable_addresses + for outputs in (fused_outputs, split_outputs): + _assert_shared_outer_merged_align_semantics( + outputs, + token_lora_mapping, + topk=topk, + block_size=block_size, + num_slots=num_slots, + ) + + +if __name__ == "__main__": + import sys + + sys.exit(pytest.main([__file__, "-v"])) diff --git a/test/registered/unit/lora/test_experimental_sgl_marlin_direct_decode.py b/test/registered/unit/lora/test_experimental_sgl_marlin_direct_decode.py new file mode 100644 index 000000000..bf3991157 --- /dev/null +++ b/test/registered/unit/lora/test_experimental_sgl_marlin_direct_decode.py @@ -0,0 +1,270 @@ +"""CUDA graph and numerical tests for direct Inkling decode LoRA kernels.""" + +from __future__ import annotations + +import pytest +import torch + +from sglang.test.ci.ci_register import register_cuda_ci + +register_cuda_ci(est_time=20, stage="base-c", runner_config="4-gpu-b200") + +# Skipped on CI: newly-added inkling LoRA test, disabled pending stabilization. +pytestmark = pytest.mark.skip(reason="new inkling LoRA test; disabled on CI") + +_B200_AVAILABLE = bool( + torch.cuda.is_available() + and torch.version.hip is None + and torch.cuda.get_device_capability()[0] == 10 +) + +E = 256 +TOPK = 6 +RANK = 32 +DTYPE = torch.bfloat16 + + +def _make_topk_ids( + num_tokens: int, *, device: torch.device, offset: int +) -> torch.Tensor: + tokens = torch.arange(num_tokens, device=device, dtype=torch.int32)[:, None] + routes = torch.arange(TOPK, device=device, dtype=torch.int32)[None, :] + topk_ids = (tokens * 11 + routes * 17 + offset).remainder(E - 1) + topk_ids[:, 0] = E - 1 + return topk_ids.contiguous() + + +def _gate_reference( + shared: torch.Tensor, + gate_b: torch.Tensor, + topk_ids: torch.Tensor, + token_lora_mapping: torch.Tensor, +) -> torch.Tensor: + intermediate = gate_b.shape[2] // 2 + gate_width = gate_b.shape[2] + flat_ids = topk_ids.reshape(-1).to(torch.long) + flat_slots = token_lora_mapping[:, None].expand(-1, TOPK).reshape(-1).to(torch.long) + active = flat_slots >= 0 + routed_b = gate_b[flat_slots.clamp_min(0), flat_ids].to(torch.float32) + if shared.ndim == 3: + token = torch.arange(shared.shape[1], device=shared.device) + selected_shared = shared[token_lora_mapping.clamp_min(0).long(), token] + else: + selected_shared = shared + routed_shared = ( + selected_shared[:, None, :].expand(-1, TOPK, -1).reshape(-1, 2 * RANK) + ).float() + gate = torch.bmm(routed_b[:, :intermediate], routed_shared[:, :RANK, None]).squeeze( + -1 + ) + up = torch.bmm(routed_b[:, intermediate:], routed_shared[:, RANK:, None]).squeeze( + -1 + ) + result = torch.cat((gate, up), dim=1) + result[~active] = 0 + return result.view(topk_ids.shape[0], TOPK, gate_width) + + +def _down_reference( + activation: torch.Tensor, + down_a: torch.Tensor, + topk_ids: torch.Tensor, + token_lora_mapping: torch.Tensor, +) -> torch.Tensor: + flat_ids = topk_ids.reshape(-1).to(torch.long) + flat_slots = token_lora_mapping[:, None].expand(-1, TOPK).reshape(-1).to(torch.long) + active = flat_slots >= 0 + routed_a = down_a[flat_slots.clamp_min(0), flat_ids].to(torch.float32) + result = torch.bmm(routed_a, activation.to(torch.float32).unsqueeze(-1)).squeeze(-1) + result[~active] = 0 + return result.view(topk_ids.shape[0], TOPK, RANK) + + +def _make_operands( + num_tokens: int, intermediate: int, num_slots: int, device: torch.device +): + gate_width = 2 * intermediate + generator = torch.Generator(device=device).manual_seed(9000 + num_tokens) + shared_shape = ( + (num_slots, num_tokens, RANK) if num_slots > 1 else (num_tokens, RANK) + ) + gate_half = torch.randn( + shared_shape, device=device, dtype=DTYPE, generator=generator + ) + # Deliberately unrelated halves regression-protect the gated split. + up_half = ( + torch.randn(shared_shape, device=device, dtype=DTYPE, generator=generator) + * -0.75 + + 0.25 + ) + shared = torch.cat((gate_half, up_half), dim=-1).contiguous() + gate_b = ( + torch.randn( + (num_slots, E, gate_width, RANK), + device=device, + dtype=DTYPE, + generator=generator, + ) + / RANK**0.5 + ).contiguous() + activation = torch.randn( + (num_tokens * TOPK, intermediate), + device=device, + dtype=DTYPE, + generator=generator, + ) + down_a = ( + torch.randn( + (num_slots, E, RANK, intermediate), + device=device, + dtype=DTYPE, + generator=generator, + ) + / intermediate**0.5 + ).contiguous() + topk_ids = _make_topk_ids(num_tokens, device=device, offset=0) + token_lora_mapping = torch.arange( + num_tokens, device=device, dtype=torch.int32 + ).remainder(num_slots) + if num_tokens > 1: + token_lora_mapping[-1] = -1 + gate_output = torch.empty( + (num_tokens, TOPK, gate_width), device=device, dtype=DTYPE + ) + down_output = torch.empty((num_tokens, TOPK, RANK), device=device, dtype=DTYPE) + return ( + shared, + gate_b, + activation, + down_a, + topk_ids, + token_lora_mapping, + gate_output, + down_output, + ) + + +@pytest.mark.skipif( + not _B200_AVAILABLE, + reason="direct Inkling decode kernels are currently gated to B200", +) +@pytest.mark.parametrize( + ("num_slots", "num_tokens", "intermediate"), + [(1, 1, 384), (2, 4, 768), (3, 4, 384), (4, 32, 768)], +) +def test_direct_decode_cuda_graph_replay_and_base_weights( + num_tokens: int, intermediate: int, num_slots: int +): + from sglang.srt.lora.marlin_lora_temp.direct_decode import ( + direct_decode_down_shrink, + direct_decode_gate_expand, + ) + + device = torch.device("cuda") + ( + shared, + gate_b, + activation, + down_a, + topk_ids, + token_lora_mapping, + gate_output, + down_output, + ) = _make_operands(num_tokens, intermediate, num_slots, device) + + def invoke() -> None: + direct_decode_gate_expand( + shared, gate_b, topk_ids, token_lora_mapping, gate_output + ) + direct_decode_down_shrink( + activation, down_a, topk_ids, token_lora_mapping, down_output + ) + + # Compile and initialize CUDA state outside capture. + warmup_stream = torch.cuda.Stream() + warmup_stream.wait_stream(torch.cuda.current_stream()) + with torch.cuda.stream(warmup_stream): + for _ in range(3): + invoke() + torch.cuda.current_stream().wait_stream(warmup_stream) + torch.cuda.synchronize() + + assert int(topk_ids[0, 0]) == 255 + torch.testing.assert_close( + gate_output.float(), + _gate_reference(shared, gate_b, topk_ids, token_lora_mapping), + rtol=0.03, + atol=0.01, + ) + torch.testing.assert_close( + down_output.float(), + _down_reference(activation, down_a, topk_ids, token_lora_mapping), + rtol=0.03, + atol=0.01, + ) + + stable_tensors = ( + shared, + gate_b, + activation, + down_a, + topk_ids, + token_lora_mapping, + gate_output, + down_output, + ) + stable_addresses = tuple(tensor.data_ptr() for tensor in stable_tensors) + graph = torch.cuda.CUDAGraph() + with torch.cuda.graph(graph): + invoke() + + # Mutate every captured input in place. Replay must follow stable addresses + # and read the new expert ids and values, including expert 255. + shared.mul_(-0.5).add_(0.125) + gate_b.mul_(0.75).add_(0.001) + activation.mul_(0.625).sub_(0.03125) + down_a.mul_(-0.875).add_(0.0005) + topk_ids.copy_(_make_topk_ids(num_tokens, device=device, offset=29)) + token_lora_mapping.copy_((token_lora_mapping + 1).remainder(num_slots)) + if num_tokens > 1: + token_lora_mapping[0] = -1 + gate_output.fill_(float("nan")) + down_output.fill_(float("nan")) + graph.replay() + torch.cuda.synchronize() + + assert tuple(tensor.data_ptr() for tensor in stable_tensors) == stable_addresses + assert int(topk_ids[0, 0]) == 255 + torch.testing.assert_close( + gate_output.float(), + _gate_reference(shared, gate_b, topk_ids, token_lora_mapping), + rtol=0.03, + atol=0.01, + ) + torch.testing.assert_close( + down_output.float(), + _down_reference(activation, down_a, topk_ids, token_lora_mapping), + rtol=0.03, + atol=0.01, + ) + + # Base/None replay retains the same captured pointers and zeroes the loaded + # adapter weights in place. Both kernels must fully overwrite their output. + gate_b.zero_() + down_a.zero_() + gate_output.fill_(float("nan")) + down_output.fill_(float("nan")) + graph.replay() + torch.cuda.synchronize() + + assert tuple(tensor.data_ptr() for tensor in stable_tensors) == stable_addresses + assert torch.count_nonzero(gate_output).item() == 0 + assert torch.count_nonzero(down_output).item() == 0 + assert torch.isfinite(gate_output).all().item() + assert torch.isfinite(down_output).all().item() + + +if __name__ == "__main__": + import sys + + sys.exit(pytest.main([__file__, "-v"])) diff --git a/test/registered/unit/lora/test_experimental_sgl_marlin_multi_prefill.py b/test/registered/unit/lora/test_experimental_sgl_marlin_multi_prefill.py new file mode 100644 index 000000000..d8e1a681f --- /dev/null +++ b/test/registered/unit/lora/test_experimental_sgl_marlin_multi_prefill.py @@ -0,0 +1,654 @@ +"""CUDA parity for the multi-slot shared-outer Marlin prefill factorization.""" + +from __future__ import annotations + +import pytest +import torch + +from sglang.test.ci.ci_register import register_cuda_ci + +register_cuda_ci(est_time=45, stage="base-b", runner_config="1-gpu-small") + +# The fused MoE LoRA-add kernel's shared-memory footprint exceeds the opt-in +# ceiling of the small-GPU CI runner (~99 KiB on L4) at rank=128, so the +# generic-fallback parity case OOMs there. Skip this file on CI rather than +# shrink the production kernel to a small-GPU block config. +pytestmark = pytest.mark.skip( + reason="fused MoE LoRA-add kernel needs more opt-in shared memory than the " + "small-GPU CI runner provides" +) + + +_CUDA_BF16_AVAILABLE = bool( + torch.cuda.is_available() + and torch.version.hip is None + and torch.cuda.get_device_capability()[0] >= 8 +) + + +def _set_mapping(mapping: torch.Tensor, num_slots: int, offset: int) -> None: + """Select active slots while exercising both no-adapter encodings.""" + + rows = torch.arange(mapping.numel(), dtype=torch.int32) + values = (rows + offset).remainder(num_slots - 1).add_(1) + values[(rows + 2 * offset).remainder(7) == 0] = 0 + values[(rows + 3 * offset + 1).remainder(11) == 0] = -1 + mapping.copy_(values.to(mapping.device)) + + +def _reference_gate( + hidden_states: torch.Tensor, + gate_a: torch.Tensor, + gate_b: torch.Tensor, + topk_ids: torch.Tensor, + mapping: torch.Tensor, +) -> torch.Tensor: + """Materialize shrink/expand with the same BF16 stage boundary.""" + + active = mapping >= 0 + slots = mapping.clamp_min(0).long() + experts = topk_ids.long() + rank = gate_b.shape[-1] + intermediate_size = gate_b.shape[2] // 2 + + selected_a = gate_a[slots, 0] + shared_rank = torch.einsum( + "mh,mrh->mr", hidden_states.float(), selected_a.float() + ).to(hidden_states.dtype) + selected_b = gate_b[slots[:, None], experts] + + gate = torch.einsum( + "mkr,mkir->mki", + shared_rank[:, None, :rank].expand(-1, experts.shape[1], -1).float(), + selected_b[:, :, :intermediate_size].float(), + ) + up = torch.einsum( + "mkr,mkir->mki", + shared_rank[:, None, rank:].expand(-1, experts.shape[1], -1).float(), + selected_b[:, :, intermediate_size:].float(), + ) + output = torch.cat((gate, up), dim=-1).to(hidden_states.dtype) + output[~active] = 0 + return output + + +def _reference_down( + activation: torch.Tensor, + down_a: torch.Tensor, + down_b: torch.Tensor, + topk_ids: torch.Tensor, + topk_weights: torch.Tensor, + mapping: torch.Tensor, + base_output: torch.Tensor, + routed_scaling_factor: float, +) -> torch.Tensor: + """Materialize routed shrink, weighted rank sum, and selected shared B.""" + + slots = mapping.clamp_min(0).long() + experts = topk_ids.long() + selected_a = down_a[slots[:, None], experts] + routed_rank = torch.einsum( + "mki,mkri->mkr", activation.float(), selected_a.float() + ).to(activation.dtype) + rank_sum = ( + (routed_rank.float() * topk_weights.float().unsqueeze(-1)) + .sum(dim=1) + .mul(routed_scaling_factor) + .to(activation.dtype) + ) + + output = base_output.clone() + for slot in range(down_b.shape[0]): + rows = mapping == slot + if rows.any(): + output[rows] = torch.addmm( + base_output[rows], rank_sum[rows], down_b[slot, 0].T + ) + return output + + +def _reference_generic_delta( + hidden_states: torch.Tensor, + lora_a: torch.Tensor, + lora_b: torch.Tensor, + topk_ids: torch.Tensor, + topk_weights: torch.Tensor, + mapping: torch.Tensor, + *, + shared_a: bool, + shared_b: bool, + mul_routed_weight: bool, +) -> torch.Tensor: + num_tokens, topk = topk_ids.shape + output = hidden_states.new_zeros(num_tokens, topk, lora_b.shape[2]) + routed_inputs = hidden_states.shape[0] == topk_ids.numel() + for token in range(num_tokens): + slot = int(mapping[token]) + if slot < 0: + continue + for route in range(topk): + expert = int(topk_ids[token, route]) + if expert < 0: + continue + row = token * topk + route if routed_inputs else token + a = lora_a[slot, 0 if shared_a else expert] + b = lora_b[slot, 0 if shared_b else expert] + shrink = torch.mv(a.float(), hidden_states[row].float()).to( + hidden_states.dtype + ) + rank = b.shape[1] + if shrink.numel() == 2 * rank: + output_half = b.shape[0] // 2 + delta = torch.cat( + ( + torch.mv(b[:output_half].float(), shrink[:rank].float()), + torch.mv(b[output_half:].float(), shrink[rank:].float()), + ) + ).to(hidden_states.dtype) + else: + delta = torch.mv(b.float(), shrink.float()).to(hidden_states.dtype) + if mul_routed_weight: + delta.mul_(topk_weights[token, route]) + output[token, route] = delta + return output + + +def _run_factored_pipeline( + *, + hidden_states: torch.Tensor, + activation: torch.Tensor, + gate_a: torch.Tensor, + gate_b: torch.Tensor, + down_a: torch.Tensor, + down_b: torch.Tensor, + topk_ids: torch.Tensor, + topk_weights: torch.Tensor, + mapping: torch.Tensor, + gate_rank: torch.Tensor, + gate_output: torch.Tensor, + down_routed_rank: torch.Tensor, + down_rank_sum: torch.Tensor, + down_output: torch.Tensor, + full_routing_cache: dict, + collapsed_routing_cache: dict, + routed_scaling_factor: float, +) -> None: + from sglang.kernels.ops.moe.trtllm_lora_temp.virtual_experts import ( + merged_experts_fused_moe_lora_add, + ) + from sglang.srt.lora.marlin_lora_temp.shared_outer import weighted_topk_rank_sum + + num_tokens = topk_ids.shape[0] + num_experts = gate_b.shape[1] + collapsed_ids = mapping.view(num_tokens, 1) + collapsed_weights = topk_weights[:, :1] + + # Capture the same four routing domains as the production schedule. The + # dictionaries must remain distinct because full and collapsed top-k have + # different flattened token domains. + merged_experts_fused_moe_lora_add( + output=gate_output, + hidden_states=hidden_states, + lora_a=gate_a, + lora_b=gate_b, + topk_ids=topk_ids, + topk_weights=topk_weights, + token_lora_mapping=mapping, + mul_routed_weight=False, + experts_shared_outer_loras_a=True, + experts_shared_outer_loras_b=False, + routing_cache=full_routing_cache, + stage="routing", + prewarm_a_routing=False, + prewarm_b_routing=True, + local_expert_offset=0, + local_num_experts=num_experts, + ) + + merged_experts_fused_moe_lora_add( + output=gate_output, + hidden_states=hidden_states, + lora_a=gate_a, + lora_b=gate_b, + topk_ids=collapsed_ids, + topk_weights=collapsed_weights, + token_lora_mapping=mapping, + mul_routed_weight=False, + experts_shared_outer_loras_a=True, + experts_shared_outer_loras_b=False, + routing_cache=collapsed_routing_cache, + stage="routing", + prewarm_a_routing=True, + prewarm_b_routing=False, + local_expert_offset=0, + local_num_experts=num_experts, + ) + merged_experts_fused_moe_lora_add( + output=activation, + hidden_states=activation, + lora_a=down_a, + lora_b=down_b, + topk_ids=topk_ids, + topk_weights=topk_weights, + token_lora_mapping=mapping, + mul_routed_weight=True, + experts_shared_outer_loras_a=False, + experts_shared_outer_loras_b=True, + routing_cache=full_routing_cache, + stage="routing", + prewarm_a_routing=True, + prewarm_b_routing=False, + local_expert_offset=0, + local_num_experts=num_experts, + ) + merged_experts_fused_moe_lora_add( + output=down_output, + hidden_states=down_rank_sum, + lora_a=down_a, + lora_b=down_b, + topk_ids=collapsed_ids, + topk_weights=collapsed_weights, + token_lora_mapping=mapping, + mul_routed_weight=False, + experts_shared_outer_loras_a=False, + experts_shared_outer_loras_b=True, + routing_cache=collapsed_routing_cache, + stage="routing", + prewarm_a_routing=False, + prewarm_b_routing=True, + local_expert_offset=0, + local_num_experts=num_experts, + ) + + merged_experts_fused_moe_lora_add( + output=gate_output, + hidden_states=hidden_states, + lora_a=gate_a, + lora_b=gate_b, + topk_ids=collapsed_ids, + topk_weights=collapsed_weights, + token_lora_mapping=mapping, + mul_routed_weight=False, + experts_shared_outer_loras_a=True, + experts_shared_outer_loras_b=False, + routing_cache=collapsed_routing_cache, + stage="shrink", + prewarm_b_routing=False, + intermediate_buffer=gate_rank, + local_expert_offset=0, + local_num_experts=num_experts, + ) + merged_experts_fused_moe_lora_add( + output=gate_output, + hidden_states=hidden_states, + lora_a=gate_a, + lora_b=gate_b, + topk_ids=topk_ids, + topk_weights=topk_weights, + token_lora_mapping=mapping, + mul_routed_weight=False, + experts_shared_outer_loras_a=True, + experts_shared_outer_loras_b=False, + routing_cache=full_routing_cache, + fuse_add_to_output=False, + use_direct_expand_add=True, + stage="expand", + intermediate_buffer=gate_rank, + broadcast_intermediate=True, + local_expert_offset=0, + local_num_experts=num_experts, + ) + + merged_experts_fused_moe_lora_add( + output=activation, + hidden_states=activation, + lora_a=down_a, + lora_b=down_b, + topk_ids=topk_ids, + topk_weights=topk_weights, + token_lora_mapping=mapping, + mul_routed_weight=True, + experts_shared_outer_loras_a=False, + experts_shared_outer_loras_b=True, + routing_cache=full_routing_cache, + stage="shrink", + prewarm_b_routing=False, + intermediate_buffer=down_routed_rank, + local_expert_offset=0, + local_num_experts=num_experts, + ) + weighted_topk_rank_sum( + down_routed_rank, + topk_weights, + down_rank_sum, + routed_scaling_factor, + block_m=1, + ) + merged_experts_fused_moe_lora_add( + output=down_output, + hidden_states=down_rank_sum, + lora_a=down_a, + lora_b=down_b, + topk_ids=collapsed_ids, + topk_weights=collapsed_weights, + token_lora_mapping=mapping, + mul_routed_weight=False, + experts_shared_outer_loras_a=False, + experts_shared_outer_loras_b=True, + routing_cache=collapsed_routing_cache, + fuse_add_to_output=True, + use_direct_expand_add=False, + stage="expand", + intermediate_buffer=down_rank_sum, + local_expert_offset=0, + local_num_experts=num_experts, + ) + + +@pytest.mark.skipif( + not _CUDA_BF16_AVAILABLE, + reason="multi-prefill parity requires a CUDA GPU with BF16 tensor cores", +) +@pytest.mark.parametrize( + ("num_slots", "num_tokens"), [(2, 33), (3, 64), (4, 65), (5, 33), (8, 64), (16, 65)] +) +def test_multi_shared_outer_prefill_cuda_graph_parity( + num_slots: int, num_tokens: int +) -> None: + """Replay full+collapsed routing while adapter selections change in place.""" + + device = torch.device("cuda") + dtype = torch.bfloat16 + num_experts, router_topk = 4, 2 + hidden_size = intermediate_size = 128 + rank = 16 + scale = 1.25 + generator = torch.Generator(device=device).manual_seed(2027 + num_slots) + + def randn(*shape: int) -> torch.Tensor: + return ( + torch.randn(*shape, device=device, dtype=dtype, generator=generator) * 0.05 + ) + + hidden_states = randn(num_tokens, hidden_size) + activation = randn(num_tokens, router_topk, intermediate_size) + gate_a = randn(num_slots, 1, 2 * rank, hidden_size) + gate_b = randn(num_slots, num_experts, 2 * intermediate_size, rank) + down_a = randn(num_slots, num_experts, rank, intermediate_size) + down_b = randn(num_slots, 1, hidden_size, rank) + # Slot 0 is the production base/None representation: it remains a valid + # mapping value, while every attached operand is zero in address-stable pool + # storage. This catches accidental stale reads that a -1-only test misses. + gate_a[0].zero_() + gate_b[0].zero_() + down_a[0].zero_() + down_b[0].zero_() + + token = torch.arange(num_tokens, device=device, dtype=torch.int32)[:, None] + route = torch.arange(router_topk, device=device, dtype=torch.int32)[None, :] + topk_ids = (token + 2 * route).remainder(num_experts).contiguous() + topk_weights = ( + torch.tensor([0.25, 0.75], device=device, dtype=torch.float32) + .expand(num_tokens, -1) + .contiguous() + ) + mapping = torch.empty(num_tokens, device=device, dtype=torch.int32) + _set_mapping(mapping, num_slots, offset=0) + + gate_rank = torch.empty(num_tokens, 2 * rank, device=device, dtype=dtype) + gate_output = torch.empty( + num_tokens, + router_topk, + 2 * intermediate_size, + device=device, + dtype=dtype, + ) + down_routed_rank = torch.empty( + num_tokens, router_topk, rank, device=device, dtype=dtype + ) + down_rank_sum = torch.empty(num_tokens, rank, device=device, dtype=dtype) + base_output = randn(num_tokens, hidden_size) + down_output = base_output.clone() + + common = dict( + hidden_states=hidden_states, + activation=activation.view(num_tokens * router_topk, intermediate_size), + gate_a=gate_a, + gate_b=gate_b, + down_a=down_a, + down_b=down_b, + topk_ids=topk_ids, + topk_weights=topk_weights, + mapping=mapping, + gate_rank=gate_rank, + gate_output=gate_output, + down_routed_rank=down_routed_rank, + down_rank_sum=down_rank_sum, + down_output=down_output, + routed_scaling_factor=scale, + ) + + # Compile JIT and initialize CUDA-library state outside capture. These + # throwaway caches deliberately do not enter the captured graph. + warmup_stream = torch.cuda.Stream() + warmup_stream.wait_stream(torch.cuda.current_stream()) + with torch.cuda.stream(warmup_stream): + _run_factored_pipeline( + **common, full_routing_cache={}, collapsed_routing_cache={} + ) + torch.cuda.current_stream().wait_stream(warmup_stream) + torch.cuda.synchronize() + + full_routing_cache: dict = {} + collapsed_routing_cache: dict = {} + graph = torch.cuda.CUDAGraph() + with torch.cuda.graph(graph): + _run_factored_pipeline( + **common, + full_routing_cache=full_routing_cache, + collapsed_routing_cache=collapsed_routing_cache, + ) + + assert full_routing_cache and collapsed_routing_cache + routing_tensors = tuple( + tensor + for cache in (full_routing_cache, collapsed_routing_cache) + for value in cache.values() + for tensor in value + ) + stable_tensors = ( + mapping, + gate_rank, + gate_output, + down_routed_rank, + down_rank_sum, + down_output, + *routing_tensors, + ) + stable_addresses = tuple(tensor.data_ptr() for tensor in stable_tensors) + + for offset in (1, 3): + _set_mapping(mapping, num_slots, offset) + assert torch.any(mapping == -1).item() + assert torch.any(mapping == 0).item() + gate_rank.fill_(float("nan")) + gate_output.fill_(float("nan")) + down_routed_rank.fill_(float("nan")) + down_rank_sum.fill_(float("nan")) + down_output.copy_(base_output) + + expected_gate = _reference_gate( + hidden_states, gate_a, gate_b, topk_ids, mapping + ) + expected_down = _reference_down( + activation, + down_a, + down_b, + topk_ids, + topk_weights, + mapping, + base_output, + scale, + ) + graph.replay() + torch.cuda.synchronize() + + assert tuple(tensor.data_ptr() for tensor in stable_tensors) == stable_addresses + torch.testing.assert_close(gate_output, expected_gate, rtol=0.025, atol=0.025) + torch.testing.assert_close(down_output, expected_down, rtol=0.025, atol=0.025) + base_rows = mapping <= 0 + assert torch.count_nonzero(gate_output[base_rows]).item() == 0 + torch.testing.assert_close( + down_output[base_rows], base_output[base_rows], rtol=0, atol=0 + ) + assert torch.isfinite(gate_output).all().item() + assert torch.isfinite(down_output).all().item() + + +@pytest.mark.skipif( + not _CUDA_BF16_AVAILABLE, + reason="generic fallback parity requires a CUDA GPU with BF16 tensor cores", +) +@pytest.mark.parametrize( + ("num_slots", "rank", "shared_outer", "ep"), + [(5, 32, True, False), (8, 128, True, True), (16, 128, False, True)], +) +def test_generic_fallback_cuda_graph_parity( + num_slots: int, rank: int, shared_outer: bool, ep: bool +) -> None: + from sglang.kernels.ops.moe.trtllm_lora_temp.virtual_experts import ( + merged_experts_fused_moe_lora_add, + ) + + device = torch.device("cuda") + dtype = torch.bfloat16 + num_tokens, topk = 8, 2 + num_experts = 2 if ep else 4 + hidden_size = intermediate_size = 64 + generator = torch.Generator(device=device).manual_seed(3100 + num_slots + rank) + + def randn(*shape: int) -> torch.Tensor: + return ( + torch.randn(*shape, device=device, dtype=dtype, generator=generator) * 0.03 + ) + + gate_a = randn(num_slots, 1 if shared_outer else num_experts, 2 * rank, hidden_size) + gate_b = randn(num_slots, num_experts, 2 * intermediate_size, rank) + down_a = randn(num_slots, num_experts, rank, intermediate_size) + down_b = randn(num_slots, 1 if shared_outer else num_experts, hidden_size, rank) + hidden_states = randn(num_tokens, hidden_size) + activation = randn(num_tokens * topk, intermediate_size) + topk_ids = ( + torch.arange(num_tokens * topk, device=device, dtype=torch.int32) + .remainder(num_experts) + .view(num_tokens, topk) + ) + if ep: + topk_ids[::2, 1] = -1 + topk_weights = torch.tensor([0.4, 0.6], device=device, dtype=torch.float32).expand( + num_tokens, -1 + ) + mapping = torch.arange(num_tokens, device=device, dtype=torch.int32).remainder( + num_slots + ) + mapping[::5] = -1 + gate_output = torch.empty( + num_tokens, topk, 2 * intermediate_size, device=device, dtype=dtype + ) + down_output = torch.empty(num_tokens, topk, hidden_size, device=device, dtype=dtype) + + def run(gate_cache: dict, down_cache: dict) -> None: + gate_output.zero_() + merged_experts_fused_moe_lora_add( + output=gate_output, + hidden_states=hidden_states, + lora_a=gate_a, + lora_b=gate_b, + topk_ids=topk_ids, + topk_weights=topk_weights, + token_lora_mapping=mapping, + mul_routed_weight=False, + experts_shared_outer_loras_a=shared_outer, + experts_shared_outer_loras_b=False, + routing_cache=gate_cache, + fuse_add_to_output=False, + use_direct_expand_add=True, + local_num_experts=num_experts, + ) + down_output.zero_() + merged_experts_fused_moe_lora_add( + output=down_output, + hidden_states=activation, + lora_a=down_a, + lora_b=down_b, + topk_ids=topk_ids, + topk_weights=topk_weights, + token_lora_mapping=mapping, + mul_routed_weight=True, + experts_shared_outer_loras_a=False, + experts_shared_outer_loras_b=shared_outer, + routing_cache=down_cache, + use_direct_expand_add=False, + local_num_experts=num_experts, + zero_intermediate=ep and shared_outer, + ) + + def assert_expected() -> None: + torch.testing.assert_close( + gate_output, + _reference_generic_delta( + hidden_states, + gate_a, + gate_b, + topk_ids, + topk_weights, + mapping, + shared_a=shared_outer, + shared_b=False, + mul_routed_weight=False, + ), + rtol=2e-2, + atol=2e-2, + ) + torch.testing.assert_close( + down_output, + _reference_generic_delta( + activation, + down_a, + down_b, + topk_ids, + topk_weights, + mapping, + shared_a=False, + shared_b=shared_outer, + mul_routed_weight=True, + ), + rtol=2e-2, + atol=2e-2, + ) + + run({}, {}) + torch.cuda.synchronize() + assert_expected() + + gate_cache: dict = {} + down_cache: dict = {} + graph = torch.cuda.CUDAGraph() + with torch.cuda.graph(graph): + run(gate_cache, down_cache) + + for offset in (0, 1): + mapping.copy_( + torch.arange(num_tokens, device=device, dtype=torch.int32) + .add(offset) + .remainder(num_slots) + ) + mapping[(torch.arange(num_tokens, device=device) + offset) % 5 == 0] = -1 + graph.replay() + assert_expected() + + +if __name__ == "__main__": + import sys + + sys.exit(pytest.main([__file__, "-v"])) diff --git a/test/registered/unit/lora/test_experimental_sgl_marlin_policy.py b/test/registered/unit/lora/test_experimental_sgl_marlin_policy.py new file mode 100644 index 000000000..4c67683db --- /dev/null +++ b/test/registered/unit/lora/test_experimental_sgl_marlin_policy.py @@ -0,0 +1,228 @@ +"""CPU-only tests for experimental_sgl_marlin's correctness contract.""" + +from __future__ import annotations + +import types + +import pytest + +from sglang.srt.lora.marlin_lora_temp.policy import ( + use_post_reduce_down_delta, + validate_experimental_sgl_marlin_contract, + validate_experimental_sgl_marlin_server_args, +) +from sglang.srt.lora.trtllm_lora_temp.specialized_expand import _get_gated_a_half +from sglang.test.ci.ci_register import register_cuda_ci + +register_cuda_ci(est_time=5, stage="base-b", runner_config="1-gpu-small") + +# Skipped on CI: newly-added inkling LoRA test, disabled pending stabilization. +pytestmark = pytest.mark.skip(reason="new inkling LoRA test; disabled on CI") + + +def _config(**overrides): + values = dict( + activation="silu", + is_gated=True, + gemm1_alpha=None, + gemm1_clamp_limit=None, + swiglu_limit=None, + apply_router_weight_on_input=False, + no_combine=False, + num_experts=256, + num_local_experts=256, + ) + values.update(overrides) + return types.SimpleNamespace(**values) + + +def _validate(config=None, **overrides): + values = dict( + runner_config=config or _config(), + moe_ep_size=1, + device_capability=(9, 0), + ) + values.update(overrides) + return validate_experimental_sgl_marlin_contract(**values) + + +def _validate_server(**overrides): + ep_size = overrides.pop("ep_size", 4) + moe_a2a_backend = overrides.pop("moe_a2a_backend", "none") + values = dict( + enable_lora=True, + lora_paths=[], + lora_use_virtual_experts=True, + init_expert_location="trivial", + ep_num_redundant_experts=0, + enable_eplb=False, + elastic_ep_backend=None, + enable_elastic_expert_backup=False, + elastic_ep_rejoin=False, + experts_shared_outer_loras=False, + max_lora_rank=64, + lora_backend="triton", + ) + values.update(overrides) + return validate_experimental_sgl_marlin_server_args( + types.SimpleNamespace(**values), + types.SimpleNamespace( + ep_size=ep_size, + moe_a2a_backend=moe_a2a_backend, + ), + ) + + +def test_supported_contract_passes(): + _validate() + + +def test_supported_ep_contract_passes(): + _validate(config=_config(num_local_experts=64), moe_ep_size=4) + + +@pytest.mark.parametrize( + ("enable_lora", "lora_paths"), + [(None, []), (False, []), (False, ["ignored=/tmp/adapter"])], +) +def test_base_only_ep_preserves_stock_marlin_placement_support(enable_lora, lora_paths): + _validate_server( + enable_lora=enable_lora, + lora_paths=lora_paths, + init_expert_location="random", + ep_num_redundant_experts=1, + enable_eplb=True, + elastic_ep_backend="mooncake", + enable_elastic_expert_backup=True, + elastic_ep_rejoin=True, + ) + + +def test_base_only_ep_rejects_unsupported_a2a(): + with pytest.raises(ValueError, match="moe-a2a-backend none"): + _validate_server(enable_lora=False, moe_a2a_backend="deepep") + + +def test_lora_rejects_non_triton_backend(): + with pytest.raises(ValueError, match="requires --lora-backend triton"): + _validate_server(lora_backend="csgmv") + # base-only servers are free to pick any dense backend + _validate_server(enable_lora=False, lora_backend="csgmv") + + +@pytest.mark.parametrize("ep_size", [1, 4]) +def test_lora_requires_virtual_experts(ep_size): + with pytest.raises(ValueError, match="lora-use-virtual-experts"): + _validate_server(ep_size=ep_size, lora_use_virtual_experts=False) + + +@pytest.mark.parametrize( + "setting", + [ + {"init_expert_location": "random"}, + {"ep_num_redundant_experts": 1}, + {"enable_eplb": True}, + {"elastic_ep_backend": "mooncake"}, + {"enable_elastic_expert_backup": True}, + {"elastic_ep_rejoin": True}, + ], +) +def test_lora_ep_rejects_nontrivial_placement_features(setting): + with pytest.raises(ValueError, match="trivial expert placement"): + _validate_server(**setting) + + +def test_adapter_paths_implicitly_enable_lora_ep_validation(): + with pytest.raises(ValueError, match="trivial expert placement"): + _validate_server( + enable_lora=None, + lora_paths=["adapter=/tmp/adapter"], + enable_eplb=True, + ) + + +def test_supported_lora_ep_passes(): + _validate_server(experts_shared_outer_loras=True, max_lora_rank=64) + + +@pytest.mark.parametrize("rank", [65, 128, 256]) +def test_shared_outer_lora_ep_allows_generic_rank_fallback(rank): + _validate_server(experts_shared_outer_loras=True, max_lora_rank=rank) + + +@pytest.mark.parametrize( + ("config", "message"), + [ + (_config(activation="relu2"), "activation must be 'silu'"), + (_config(is_gated=False), "only gated SwiGLU"), + (_config(gemm1_alpha=1.0), "gemm1_alpha"), + (_config(gemm1_clamp_limit=7.0), "gemm1_clamp_limit"), + (_config(swiglu_limit=7.0), "swiglu_limit"), + ( + _config(apply_router_weight_on_input=True), + "apply_router_weight_on_input", + ), + (_config(no_combine=True), "no_combine"), + ], +) +def test_rejects_unimplemented_activation_semantics(config, message): + with pytest.raises(ValueError, match=message): + _validate(config) + + +@pytest.mark.parametrize( + "overrides", + [ + {"config": _config(num_local_experts=128)}, + {"config": _config(num_local_experts=63), "moe_ep_size": 4}, + {"config": _config(num_experts=255, num_local_experts=64), "moe_ep_size": 4}, + {"moe_ep_size": 0}, + ], +) +def test_rejects_incoherent_expert_parallelism(overrides): + config = overrides.get("config") + validation_overrides = {k: v for k, v in overrides.items() if k != "config"} + with pytest.raises(ValueError, match="moe_ep_size|num_local_experts"): + _validate(config, **validation_overrides) + + +def test_rejects_pre_hopper_gpu(): + with pytest.raises(ValueError, match="compute capability 9.0 or newer"): + _validate(device_capability=(8, 0)) + + +def test_direct_expand_always_splits_gated_gate_up_a(): + assert _get_gated_a_half(intermediate_width=64, rank=32, output_width=768) == 384 + assert _get_gated_a_half(intermediate_width=32, rank=32, output_width=6144) == 0 + + +def test_direct_expand_rejects_invalid_intermediate_width(): + with pytest.raises(ValueError, match="intermediate width"): + _get_gated_a_half(intermediate_width=48, rank=32, output_width=768) + + +@pytest.mark.parametrize( + ("run_lora", "scale", "num_tokens", "expected"), + [ + (True, 1.0, 1, True), + (True, 1.0, 2048, True), + (True, 1.0, 2049, False), + (True, 0.5, 32, False), + (False, 1.0, 32, False), + ], +) +def test_post_reduce_down_policy(run_lora, scale, num_tokens, expected): + assert ( + use_post_reduce_down_delta( + run_lora=run_lora, + routed_scaling_factor=scale, + num_tokens=num_tokens, + ) + is expected + ) + + +if __name__ == "__main__": + import sys + + sys.exit(pytest.main([__file__, "-v"])) diff --git a/test/registered/unit/lora/test_experimental_sgl_marlin_runtime_unit.py b/test/registered/unit/lora/test_experimental_sgl_marlin_runtime_unit.py new file mode 100644 index 000000000..7661ea6e8 --- /dev/null +++ b/test/registered/unit/lora/test_experimental_sgl_marlin_runtime_unit.py @@ -0,0 +1,945 @@ +"""CPU-only runtime-flow tests for experimental_sgl_marlin.""" + +from __future__ import annotations + +import importlib.util +import sys +import types +from pathlib import Path +from types import SimpleNamespace + +import pytest +import torch + +from sglang.test.ci.ci_register import register_cuda_ci + +register_cuda_ci(est_time=5, stage="base-b", runner_config="1-gpu-small") + +# Skipped on CI: newly-added inkling LoRA test, disabled pending stabilization. +pytestmark = pytest.mark.skip(reason="new inkling LoRA test; disabled on CI") + +REPO_ROOT = Path(__file__).resolve().parents[4] +LORA_TEMP_ROOT = REPO_ROOT / "python/sglang/srt/lora" +MARLIN_RUNNER_PATH = LORA_TEMP_ROOT / "marlin_lora_temp/moe_runner.py" +MARLIN_POLICY_PATH = LORA_TEMP_ROOT / "marlin_lora_temp/policy.py" +TWO_STREAM_PATH = LORA_TEMP_ROOT / "trtllm_lora_temp/__init__.py" + + +def _stub_module(monkeypatch, name: str, **attributes): + parts = name.split(".") + for end in range(1, len(parts)): + package_name = ".".join(parts[:end]) + if package_name not in sys.modules: + package = types.ModuleType(package_name) + package.__path__ = [] + monkeypatch.setitem(sys.modules, package_name, package) + module = types.ModuleType(name) + for key, value in attributes.items(): + setattr(module, key, value) + monkeypatch.setitem(sys.modules, name, module) + if len(parts) > 1: + parent = sys.modules[".".join(parts[:-1])] + monkeypatch.setattr(parent, parts[-1], module, raising=False) + return module + + +def _load_file(monkeypatch, name: str, path: Path): + parts = name.split(".") + for end in range(1, len(parts)): + package_name = ".".join(parts[:end]) + if package_name not in sys.modules: + package = types.ModuleType(package_name) + package.__path__ = [] + monkeypatch.setitem(sys.modules, package_name, package) + spec = importlib.util.spec_from_file_location(name, path) + module = importlib.util.module_from_spec(spec) + monkeypatch.setitem(sys.modules, name, module) + spec.loader.exec_module(module) + if len(parts) > 1: + parent = sys.modules[".".join(parts[:-1])] + monkeypatch.setattr(parent, parts[-1], module, raising=False) + return module + + +def _load_marlin_runner(monkeypatch, name: str): + _stub_module(monkeypatch, "sglang.srt.utils", is_cuda=lambda: False) + _load_file( + monkeypatch, + "sglang.srt.lora.marlin_lora_temp.policy", + MARLIN_POLICY_PATH, + ) + return _load_file(monkeypatch, name, MARLIN_RUNNER_PATH) + + +@pytest.mark.parametrize(("tokens", "expected"), [(256, True), (257, False)]) +def test_two_stream_token_threshold_is_inclusive(monkeypatch, tokens, expected): + lora_envs = SimpleNamespace( + SGLANG_TWO_STREAM_MAX_TOKENS=SimpleNamespace(get=lambda: 256) + ) + _stub_module(monkeypatch, "sglang.srt.environ", envs=SimpleNamespace()) + _stub_module( + monkeypatch, + "sglang.srt.lora.trtllm_lora_temp.environ", + lora_envs=lora_envs, + ) + module = _load_file(monkeypatch, "_two_stream_under_test", TWO_STREAM_PATH) + assert module.is_two_stream_active(torch.empty(tokens, 1)) is expected + + +@pytest.mark.parametrize( + ("combined_rank", "rank", "expected"), + [(128, 64, True), (128, 128, False), (256, 64, False)], +) +def test_two_stream_dense_lora_rank_falls_back( + monkeypatch, combined_rank, rank, expected +): + lora_envs = SimpleNamespace( + SGLANG_TWO_STREAM_MAX_TOKENS=SimpleNamespace(get=lambda: 256) + ) + _stub_module(monkeypatch, "sglang.srt.environ", envs=SimpleNamespace()) + _stub_module( + monkeypatch, + "sglang.srt.lora.trtllm_lora_temp.environ", + lora_envs=lora_envs, + ) + module = _load_file(monkeypatch, "_two_stream_rank_under_test", TWO_STREAM_PATH) + assert ( + module.supports_two_stream_dense_lora( + torch.empty(1, combined_rank, 1), torch.empty(1, 1, rank) + ) + is expected + ) + + +class _CombineInput: + def __init__(self, hidden_states): + self.hidden_states = hidden_states + + +class _DispatchOutput: + def __init__(self, hidden_states, topk_output): + self.hidden_states = hidden_states + self.topk_output = topk_output + + +def _run_marlin_policy( + monkeypatch, + *, + tokens: int, + master: bool = True, + two_stream: bool = False, + capture: bool = False, + active_lora: bool = True, + base_mapping: bool = False, + direct_decode: bool = False, + ep: bool = False, + slots: int = 1, + rank: int = 1, + shared_outer: bool = True, + base_value: float = 0.0, +): + module = _load_marlin_runner(monkeypatch, "_marlin_runner_under_test") + # The hermetic runner uses tiny CPU tensors. Explicitly emulate the exact + # B200/Inkling eligibility gate so these tests exercise the fused schedule. + module._use_fused_shared_outer_tail = ( + lambda _info, _hidden, num_tokens, _hidden_size, _topk: num_tokens <= 512 + ) + module._use_direct_decode_kernels = lambda *_args, **_kwargs: direct_decode + + calls = SimpleNamespace( + merged=[], + split_gate_checks=0, + weighted_rank_sums=0, + fused_tails=0, + direct_gate=0, + direct_down=0, + marlin_is_ep=[], + align_num_experts=[], + cache3_was_zero=None, + schedule=[], + zeroed=[], + event_records=[], + event_waits=[], + ) + + class _FakeStream: + def __init__(self, name): + self.name = name + + def wait_stream(self, other): + calls.schedule.append(("wait_stream", self.name, other.name)) + + def wait_event(self, event): + item = ("wait_event", self.name, event.name) + calls.schedule.append(item) + calls.event_waits.append(item) + + main_stream = _FakeStream("main") + side_stream = _FakeStream("side") + stream_state = {"current": main_stream} + event_count = 0 + + class _FakeEvent: + def __init__(self): + nonlocal event_count + self.name = f"event{event_count}" + event_count += 1 + + def record(self): + item = ("record", self.name, stream_state["current"].name) + calls.schedule.append(item) + calls.event_records.append(item) + + class _StreamContext: + def __init__(self, stream): + self.stream = stream + self.previous = None + + def __enter__(self): + self.previous = stream_state["current"] + stream_state["current"] = self.stream + + def __exit__(self, *_args): + stream_state["current"] = self.previous + + monkeypatch.setattr(torch.cuda, "Event", _FakeEvent) + monkeypatch.setattr(torch.cuda, "current_stream", lambda: stream_state["current"]) + monkeypatch.setattr(torch.cuda, "stream", _StreamContext) + monkeypatch.setattr(torch.cuda, "is_current_stream_capturing", lambda: capture) + + original_zero = torch.Tensor.zero_ + + def tracked_zero(intermediate, *args, **kwargs): + item = ("zero", stream_state["current"].name, id(intermediate)) + calls.schedule.append(item) + calls.zeroed.append(item) + return original_zero(intermediate, *args, **kwargs) + + monkeypatch.setattr(torch.Tensor, "zero_", tracked_zero) + + def merged_experts_fused_moe_lora_add(**kwargs): + intermediate = kwargs.get("intermediate_buffer") + stage = kwargs.get("stage") + calls.schedule.append( + ( + "merged", + stage, + stream_state["current"].name, + id(intermediate) if intermediate is not None else None, + ) + ) + calls.merged.append( + { + "stage": stage, + "stream": stream_state["current"].name, + "intermediate_shape": ( + tuple(kwargs["intermediate_buffer"].shape) + if kwargs.get("intermediate_buffer") is not None + else None + ), + "broadcast": kwargs.get("broadcast_intermediate", False), + "prewarm_a": kwargs.get("prewarm_a_routing", True), + "prewarm_b": kwargs.get("prewarm_b_routing", True), + "topk_shape": tuple(kwargs["topk_ids"].shape), + "cache_id": id(kwargs.get("routing_cache")), + "shared_a": kwargs["experts_shared_outer_loras_a"], + "shared_b": kwargs["experts_shared_outer_loras_b"], + "fuse_add": kwargs.get("fuse_add_to_output", True), + "direct_expand": kwargs.get("use_direct_expand_add", False), + "mul_routed_weight": kwargs["mul_routed_weight"], + "zero_intermediate": kwargs.get("zero_intermediate", False), + "mapping": kwargs["token_lora_mapping"].clone(), + "intermediate_id": ( + id(intermediate) if intermediate is not None else None + ), + } + ) + if stage == "expand": + if kwargs.get("fuse_add_to_output", True): + active = kwargs["token_lora_mapping"] >= 0 + kwargs["output"][active].add_(1) + else: + kwargs["output"].fill_(0) + if stage == "shrink": + return intermediate + + def is_two_stream_active(_hidden_states): + calls.split_gate_checks += 1 + return two_stream + + _stub_module( + monkeypatch, + "sglang.srt.layers.moe.token_dispatcher.standard", + StandardCombineInput=_CombineInput, + StandardDispatchOutput=_DispatchOutput, + ) + _stub_module( + monkeypatch, + "sglang.srt.lora.trtllm_lora_temp", + get_lora_side_stream=lambda: side_stream, + is_two_stream_active=is_two_stream_active, + ) + _stub_module( + monkeypatch, + "sglang.srt.lora.trtllm_lora_temp.environ", + experimental_lora_enabled=lambda: master, + ) + _stub_module( + monkeypatch, + "sglang.kernels.ops.moe.trtllm_lora_temp.virtual_experts", + merged_experts_fused_moe_lora_add=merged_experts_fused_moe_lora_add, + ) + _stub_module( + monkeypatch, + "sglang.srt.model_executor.runner", + get_is_capture_mode=lambda: capture, + ) + + def fake_align(_topk_ids, _block_size, num_experts, **_kwargs): + calls.align_num_experts.append(num_experts) + return ( + torch.zeros(1, dtype=torch.int32), + torch.zeros(1, dtype=torch.int32), + torch.ones(1, dtype=torch.int32), + ) + + module.moe_align_block_size = fake_align + module.marlin_make_workspace = lambda *_args, **_kwargs: None + module.get_scalar_type = lambda *_args, **_kwargs: None + + def fake_marlin_gemm(_x, output, *_args, **_kwargs): + calls.marlin_is_ep.append(_kwargs["is_ep"]) + if len(calls.marlin_is_ep) == 2: + calls.cache3_was_zero = bool(torch.count_nonzero(output) == 0) + calls.schedule.append( + ("marlin", stream_state["current"].name, len(calls.schedule)) + ) + output.fill_(base_value if len(calls.marlin_is_ep) == 2 else 0) + return output + + module.moe_wna16_marlin_gemm = fake_marlin_gemm + + def fake_silu_and_mul_add_delta(_x, _delta, output): + calls.schedule.append(("activation", stream_state["current"].name)) + output.fill_(0) + + module.silu_and_mul_add_delta = fake_silu_and_mul_add_delta + module.silu_and_mul = lambda _x, output: output.fill_(0) + + def fake_triton_reduce(_input, output, _scale): + calls.schedule.append(("reduce", stream_state["current"].name)) + output.copy_(_input.sum(dim=1) * _scale) + + module.moe_sum_reduce_triton = fake_triton_reduce + + def fake_weighted_rank_sum(routed_rank, weights, output, scale, *, block_m): + calls.weighted_rank_sums += 1 + calls.schedule.append( + ("weighted", stream_state["current"].name, id(routed_rank)) + ) + output.copy_( + (routed_rank * weights.to(routed_rank.dtype).unsqueeze(-1)).sum(dim=1) + * scale + ) + + module.weighted_topk_rank_sum = fake_weighted_rank_sum + + def fake_fused_tail( + routed_base, + routed_rank, + weights, + _shared_b, + output, + scale, + *, + block_m, + block_k, + ): + calls.fused_tails += 1 + calls.schedule.append( + ("fused_tail", stream_state["current"].name, block_m, block_k) + ) + output.copy_(routed_base.sum(dim=1) * scale) + + module.fused_base_shared_lora_reduce = fake_fused_tail + module.fused_base_shared_lora_reduce_config = lambda _tokens: (1, 32) + + def fake_direct_gate(_shared, _weight, _topk_ids, _mapping, output): + calls.direct_gate += 1 + calls.schedule.append(("direct_gate", stream_state["current"].name)) + output.fill_(0) + + def fake_direct_down(_activation, _weight, _topk_ids, _mapping, output): + calls.direct_down += 1 + calls.schedule.append(("direct_down", stream_state["current"].name)) + output.fill_(0) + + module.direct_decode_gate_expand = fake_direct_gate + module.direct_decode_down_shrink = fake_direct_down + + hidden_size, num_experts, expert_size, topk, max_rank = 2, 1, 16, 2, rank + hidden_states = torch.zeros(tokens, hidden_size) + topk_ids = torch.zeros(tokens, topk, dtype=torch.int32) + if ep: + topk_ids[:, 1] = -1 + topk_weights = torch.ones(tokens, topk) + dispatch_output = _DispatchOutput( + hidden_states, + SimpleNamespace(topk_ids=topk_ids, topk_weights=topk_weights), + ) + quant_info = SimpleNamespace( + w13_qweight=torch.empty(num_experts, 2), + w13_bias=None, + w13_scales=torch.ones(1), + w13_global_scale=None, + w13_qzeros=None, + w13_g_idx=None, + w13_g_idx_sort_indices=None, + w2_qweight=torch.empty(num_experts, 1), + w2_bias=None, + w2_scales=torch.ones(1), + w2_global_scale=None, + w2_qzeros=None, + w2_g_idx=None, + w2_g_idx_sort_indices=None, + expert_map=None, + global_num_experts=num_experts, + weight_bits=4, + is_k_full=True, + ) + lora_info = SimpleNamespace( + lora_use_virtual_experts=True, + max_lora_rank=max_rank, + has_active_lora=active_lora, + gate_up_lora_a_weights=torch.zeros( + slots, + 1 if shared_outer else num_experts, + 2 * max_rank, + hidden_size, + ), + gate_up_lora_b_weights=torch.zeros( + slots, num_experts, 2 * expert_size, max_rank + ), + down_lora_a_weights=torch.zeros(slots, num_experts, max_rank, expert_size), + down_lora_b_weights=torch.zeros( + slots, + 1 if shared_outer else num_experts, + hidden_size, + max_rank, + ), + token_lora_mapping=( + torch.full((tokens,), -1, dtype=torch.int32) + if base_mapping + else torch.arange(tokens, dtype=torch.int32).remainder(slots) + ), + experts_shared_outer_loras=shared_outer, + ) + runner_config = SimpleNamespace( + activation="silu", + routed_scaling_factor=1.0, + num_experts=num_experts, + num_local_experts=num_experts, + ) + if ep: + runner_config.num_experts = 2 * num_experts + + result = module.fused_experts_experimental_sgl_marlin_lora( + dispatch_output, quant_info, runner_config, lora_info + ) + assert result.hidden_states.shape == hidden_states.shape + calls.input_ptr = hidden_states.data_ptr() + calls.result_ptr = result.hidden_states.data_ptr() + calls.result = result.hidden_states + calls.capture_event_count = len(module._MARLIN_LORA_OVERLAP_EVENTS) + return calls + + +@pytest.mark.parametrize(("master", "split_gate_checks"), [(False, 0), (True, 1)]) +def test_two_stream_batch_gate_is_master_gated(monkeypatch, master, split_gate_checks): + calls = _run_marlin_policy(monkeypatch, tokens=1, master=master, two_stream=True) + assert calls.split_gate_checks == split_gate_checks + shrinks = [call for call in calls.merged if call["stage"] == "shrink"] + assert shrinks[0]["stream"] == ("side" if master else "main") + + +def test_shared_outer_factorization_runtime_flow(monkeypatch): + tokens = 16 + calls = _run_marlin_policy(monkeypatch, tokens=tokens) + gate_expand = [ + call for call in calls.merged if call["stage"] == "expand" and call["broadcast"] + ] + down_shrink = [call for call in calls.merged if call["stage"] == "shrink"] + routing = [call for call in calls.merged if call["stage"] == "routing"] + + assert len(gate_expand) == 1 + assert len(down_shrink) == 1 + assert calls.weighted_rank_sums == 0 + assert calls.fused_tails == 1 + assert gate_expand[0]["intermediate_shape"] == (tokens, 2) + assert down_shrink[0]["intermediate_shape"] == (tokens, 2, 1) + assert [(call["prewarm_a"], call["prewarm_b"]) for call in routing] == [ + (False, True), + (True, False), + ] + assert down_shrink[0]["prewarm_b"] is False + + +@pytest.mark.parametrize( + ("slots", "rank"), + [(8, 128), (16, 128)], +) +def test_ep_shared_outer_uses_safe_generic_fallback(monkeypatch, slots, rank): + calls = _run_marlin_policy( + monkeypatch, + tokens=1, + ep=True, + slots=slots, + rank=rank, + ) + generic_down = [ + call for call in calls.merged if call["stage"] == "all" and call["shared_b"] + ] + assert len(generic_down) == 1 + assert generic_down[0]["zero_intermediate"] is True + assert generic_down[0]["direct_expand"] is (rank <= 64) + + +def test_ep_shared_outer_low_rank_multi_slot_takes_factored_path(monkeypatch): + # Slot-count gates are lifted: EP shared-outer rank<=64 pools of any size + # collapse through the factored prefill path instead of the zeroed generic + # fallback (routing is by adapter slot, so no unowned regions are read). + calls = _run_marlin_policy( + monkeypatch, + tokens=1, + ep=True, + slots=5, + rank=32, + ) + generic_down = [ + call for call in calls.merged if call["stage"] == "all" and call["shared_b"] + ] + assert not generic_down + + +def test_ep_per_expert_layout_uses_generic_fallback(monkeypatch): + calls = _run_marlin_policy( + monkeypatch, + tokens=1, + ep=True, + slots=8, + rank=128, + shared_outer=False, + ) + generic_down = [ + call + for call in calls.merged + if call["stage"] == "all" and call["mul_routed_weight"] + ] + assert len(generic_down) == 1 + assert generic_down[0]["shared_b"] is False + assert generic_down[0]["zero_intermediate"] is False + assert generic_down[0]["direct_expand"] is False + + +@pytest.mark.parametrize( + ("case", "expected"), + [ + ("supported", True), + ("multi_slot", False), + ("non_shared", False), + ("rank_too_large", False), + ("single_route", False), + ("empty_batch", False), + ("mismatched_experts", False), + ], +) +def test_shared_outer_factorization_eligibility_is_narrow(monkeypatch, case, expected): + module = _load_marlin_runner(monkeypatch, "_marlin_runner_eligibility") + + rank = 65 if case == "rank_too_large" else 32 + slots = 2 if case == "multi_slot" else 1 + experts = 4 + hidden = 8 + intermediate = 3 + info = SimpleNamespace( + max_lora_rank=rank, + experts_shared_outer_loras=case != "non_shared", + gate_up_lora_a_weights=torch.empty(slots, 1, 2 * rank, hidden), + gate_up_lora_b_weights=torch.empty(slots, experts, 2 * intermediate, rank), + down_lora_a_weights=torch.empty( + slots, + experts + (1 if case == "mismatched_experts" else 0), + rank, + intermediate, + ), + down_lora_b_weights=torch.empty(slots, 1, hidden, rank), + ) + tokens = 0 if case == "empty_batch" else 1 + topk = 1 if case == "single_route" else 2 + assert module._use_shared_outer_factorization(info, tokens, topk) is expected + + +@pytest.mark.parametrize( + ("case", "expected"), + [ + ("supported", True), + ("three_slots", True), + ("one_slot", False), + ("five_slots", True), + ("sixteen_slots", True), + ("large_batch", False), + ("ep", False), + ("hopper", False), + ], +) +def test_multi_shared_outer_decode_factorization_is_narrow(monkeypatch, case, expected): + module = _load_marlin_runner(monkeypatch, "_marlin_runner_multi_policy") + monkeypatch.setattr( + torch.cuda, + "get_device_capability", + lambda _device: (9, 0) if case == "hopper" else (10, 0), + ) + slots = ( + 1 + if case == "one_slot" + else ( + 3 + if case == "three_slots" + else 5 if case == "five_slots" else 16 if case == "sixteen_slots" else 4 + ) + ) + info = SimpleNamespace( + max_lora_rank=32, + experts_shared_outer_loras=True, + gate_up_lora_a_weights=SimpleNamespace(shape=(slots, 1, 64, 6144)), + gate_up_lora_b_weights=SimpleNamespace(shape=(slots, 256, 768, 32)), + down_lora_a_weights=SimpleNamespace(shape=(slots, 256, 32, 384)), + down_lora_b_weights=SimpleNamespace(shape=(slots, 1, 6144, 32)), + ) + hidden_states = SimpleNamespace( + is_cuda=True, dtype=torch.bfloat16, device=torch.device("cuda") + ) + assert ( + module._use_multi_shared_outer_decode_factorization( + info, + hidden_states, + num_tokens=33 if case == "large_batch" else 32, + hidden_size=6144, + router_topk=6, + num_experts=256, + intermediate_size=384, + ep_active=case == "ep", + ) + is expected + ) + + +@pytest.mark.parametrize( + ("case", "expected"), + [ + ("supported", True), + ("four_slots", True), + ("decode_boundary", False), + ("one_slot", False), + ("five_slots", True), + ("ep", True), + ("ep_decode", True), + ("non_shared", False), + ("rank_too_large", False), + ("single_route", False), + ("mismatched_shape", False), + ], +) +def test_multi_shared_outer_prefill_factorization_is_narrow( + monkeypatch, case, expected +): + module = _load_marlin_runner(monkeypatch, "_marlin_runner_prefill_policy") + + slots = ( + 1 + if case == "one_slot" + else 5 if case == "five_slots" else 4 if case == "four_slots" else 2 + ) + rank = 65 if case == "rank_too_large" else 32 + experts = 256 + intermediate = 384 + hidden = 6144 + info = SimpleNamespace( + max_lora_rank=rank, + experts_shared_outer_loras=case != "non_shared", + gate_up_lora_a_weights=SimpleNamespace(shape=(slots, 1, 2 * rank, hidden)), + gate_up_lora_b_weights=SimpleNamespace( + shape=(slots, experts, 2 * intermediate, rank) + ), + down_lora_a_weights=SimpleNamespace( + shape=( + slots, + experts + (1 if case == "mismatched_shape" else 0), + rank, + intermediate, + ) + ), + down_lora_b_weights=SimpleNamespace(shape=(slots, 1, hidden, rank)), + ) + assert ( + module._use_multi_shared_outer_prefill_factorization( + info, + num_tokens=32 if case in ("decode_boundary", "ep_decode") else 33, + hidden_size=hidden, + router_topk=1 if case == "single_route" else 6, + num_experts=experts, + intermediate_size=intermediate, + ep_active=case in ("ep", "ep_decode"), + ) + is expected + ) + + +@pytest.mark.parametrize( + ("case", "expected"), + [ + ("supported", True), + ("unfactored", False), + ("unfused", False), + ("ep", False), + ("large_batch", False), + ], +) +def test_direct_decode_selection_is_narrow(monkeypatch, case, expected): + module = _load_marlin_runner(monkeypatch, "_marlin_runner_direct_policy") + info = SimpleNamespace( + gate_up_lora_b_weights=SimpleNamespace(shape=(3, 256, 768, 32)), + down_lora_a_weights=SimpleNamespace(shape=(3, 256, 32, 384)), + ) + assert ( + module._use_direct_decode_kernels( + info, + factored_shared_outer=case != "unfactored", + fused_shared_outer_tail=case != "unfused", + ep_active=case == "ep", + num_tokens=33 if case == "large_batch" else 32, + num_experts=256, + intermediate_size=384, + ) + is expected + ) + + +@pytest.mark.parametrize( + ("case", "expected"), + [ + ("supported", True), + ("boundary_m", True), + ("hopper", False), + ("fp16", False), + ("rank64", False), + ("hidden", False), + ("topk", False), + ("large_m", False), + ], +) +def test_fused_shared_outer_tail_is_b200_inkling_specific(monkeypatch, case, expected): + module = _load_marlin_runner(monkeypatch, "_marlin_runner_tail_policy") + monkeypatch.setattr( + torch.cuda, + "get_device_capability", + lambda _device: (9, 0) if case == "hopper" else (10, 0), + ) + info = SimpleNamespace(max_lora_rank=64 if case == "rank64" else 32) + hidden_states = SimpleNamespace( + is_cuda=True, + dtype=torch.float16 if case == "fp16" else torch.bfloat16, + device=torch.device("cuda"), + ) + assert ( + module._use_fused_shared_outer_tail( + info, + hidden_states, + 513 if case == "large_m" else 512 if case == "boundary_m" else 32, + 4096 if case == "hidden" else 6144, + 8 if case == "topk" else 6, + ) + is expected + ) + + +def test_multi_prefill_collapses_only_shared_factors_and_separates_caches( + monkeypatch, +): + calls = _run_marlin_policy(monkeypatch, tokens=64, slots=3) + + routing = [call for call in calls.merged if call["stage"] == "routing"] + full_routing = [call for call in routing if call["topk_shape"] == (64, 2)] + collapsed_routing = [call for call in routing if call["topk_shape"] == (64, 1)] + assert [(call["prewarm_a"], call["prewarm_b"]) for call in full_routing] == [ + (False, True), # real-route per-expert gate B + (True, False), # real-route per-expert down A + ] + assert [(call["prewarm_a"], call["prewarm_b"]) for call in collapsed_routing] == [ + (True, False), # collapsed selected shared gate A + (False, True), # collapsed selected shared down B + ] + assert len({call["cache_id"] for call in full_routing}) == 1 + assert len({call["cache_id"] for call in collapsed_routing}) == 1 + assert full_routing[0]["cache_id"] != collapsed_routing[0]["cache_id"] + + gate_shrink = next( + call for call in calls.merged if call["stage"] == "shrink" and call["shared_a"] + ) + gate_expand = next( + call for call in calls.merged if call["stage"] == "expand" and call["broadcast"] + ) + down_shrink = next( + call + for call in calls.merged + if call["stage"] == "shrink" and not call["shared_a"] + ) + down_expand = next( + call for call in calls.merged if call["stage"] == "expand" and call["shared_b"] + ) + + assert gate_shrink["topk_shape"] == (64, 1) + assert gate_shrink["intermediate_shape"] == (64, 2) + assert gate_expand["topk_shape"] == (64, 2) + assert gate_expand["intermediate_shape"] == (64, 2) + assert down_shrink["topk_shape"] == (64, 2) + assert down_shrink["intermediate_shape"] == (64, 2, 1) + assert down_expand["topk_shape"] == (64, 1) + assert down_expand["intermediate_shape"] == (64, 1) + assert down_expand["fuse_add"] is True + assert down_expand["direct_expand"] is False + assert down_expand["mul_routed_weight"] is False + assert calls.weighted_rank_sums == 1 + # The mapped one-token-per-CTA tail remains decode-only. + assert calls.fused_tails == 0 + + +def test_multi_prefill_none_rows_preserve_base_reduction(monkeypatch): + calls = _run_marlin_policy( + monkeypatch, + tokens=64, + slots=2, + capture=True, + active_lora=False, + base_mapping=True, + base_value=3.0, + ) + + down_expand = next( + call for call in calls.merged if call["stage"] == "expand" and call["shared_b"] + ) + assert down_expand["topk_shape"] == (64, 1) + assert torch.equal(down_expand["mapping"], torch.full((64,), -1, dtype=torch.int32)) + # The fake Marlin down output is 3 for each of two routes. The collapsed + # shared-B expand masks every None row, so it must leave the base sum at 6. + torch.testing.assert_close(calls.result, torch.full_like(calls.result, 6.0)) + assert calls.fused_tails == 0 + + +def test_direct_decode_skips_virtual_routing_and_zero_fill(monkeypatch): + calls = _run_marlin_policy( + monkeypatch, tokens=16, two_stream=True, direct_decode=True + ) + + assert [call for call in calls.merged if call["stage"] == "routing"] == [] + assert [call for call in calls.merged if call["stage"] == "shrink"] == [] + assert calls.direct_gate == 1 + assert calls.direct_down == 1 + assert calls.zeroed == [] + assert calls.fused_tails == 1 + + +def test_ep_uses_local_alignment_and_skips_nonlocal_marlin_blocks(monkeypatch): + calls = _run_marlin_policy(monkeypatch, tokens=16, ep=True) + + assert calls.align_num_experts == [1] + assert calls.marlin_is_ep == [True, True] + assert calls.cache3_was_zero is True + + +def test_factored_decode_two_stream_schedule_and_ownership(monkeypatch): + calls = _run_marlin_policy(monkeypatch, tokens=16, two_stream=True) + shrinks = [call for call in calls.merged if call["stage"] == "shrink"] + + assert len(shrinks) == 1 + assert shrinks[0]["stream"] == "side" + assert shrinks[0]["prewarm_b"] is False + assert len(calls.zeroed) == 1 + assert calls.zeroed[0][1] == "side" + + buffer_id = shrinks[0]["intermediate_id"] + assert calls.zeroed[0][2] == buffer_id + zero_index = calls.schedule.index(calls.zeroed[0]) + shrink_index = next( + index + for index, item in enumerate(calls.schedule) + if item[:3] == ("merged", "shrink", "side") + ) + down_record = calls.event_records[-1] + down_wait = calls.event_waits[-1] + record_index = calls.schedule.index(down_record) + wait_index = calls.schedule.index(down_wait) + fused_index = next( + index for index, item in enumerate(calls.schedule) if item[0] == "fused_tail" + ) + assert down_record[2] == "side" + assert down_wait == ("wait_event", "main", down_record[1]) + assert zero_index < shrink_index < record_index < wait_index < fused_index + assert fused_index == len(calls.schedule) - 1 + + +def test_factored_decode_single_stream_fallback_has_one_main_shrink(monkeypatch): + calls = _run_marlin_policy(monkeypatch, tokens=16, two_stream=False) + shrinks = [call for call in calls.merged if call["stage"] == "shrink"] + + assert len(shrinks) == 1 + assert shrinks[0]["stream"] == "main" + assert shrinks[0]["prewarm_b"] is False + assert len(calls.zeroed) == 1 + assert calls.zeroed[0][1] == "main" + assert calls.event_records == [] + assert calls.event_waits == [] + + buffer_id = shrinks[0]["intermediate_id"] + assert calls.zeroed[0][2] == buffer_id + second_marlin_index = max( + index for index, item in enumerate(calls.schedule) if item[0] == "marlin" + ) + zero_index = calls.schedule.index(calls.zeroed[0]) + shrink_index = next( + index + for index, item in enumerate(calls.schedule) + if item[:3] == ("merged", "shrink", "main") + ) + fused_index = next( + index for index, item in enumerate(calls.schedule) if item[0] == "fused_tail" + ) + assert second_marlin_index < zero_index < shrink_index < fused_index + + +def test_factored_decode_capture_base_rows_keep_main_owned_buffers(monkeypatch): + calls = _run_marlin_policy( + monkeypatch, + tokens=16, + two_stream=True, + capture=True, + active_lora=False, + base_mapping=True, + ) + shrinks = [call for call in calls.merged if call["stage"] == "shrink"] + + assert len(shrinks) == 1 + assert len(calls.zeroed) == 1 + assert calls.zeroed[0][1] == "side" + assert calls.zeroed[0][2] == shrinks[0]["intermediate_id"] + assert calls.weighted_rank_sums == 0 + assert calls.fused_tails == 1 + assert calls.capture_event_count == 3 + assert calls.result_ptr != calls.input_ptr + torch.testing.assert_close(calls.result, torch.zeros_like(calls.result)) + + +if __name__ == "__main__": + import sys + + sys.exit(pytest.main([__file__, "-v"])) diff --git a/test/registered/unit/lora/test_experimental_sgl_marlin_shared_outer_reduce.py b/test/registered/unit/lora/test_experimental_sgl_marlin_shared_outer_reduce.py new file mode 100644 index 000000000..9edf4be19 --- /dev/null +++ b/test/registered/unit/lora/test_experimental_sgl_marlin_shared_outer_reduce.py @@ -0,0 +1,306 @@ +"""CUDA parity tests for the fused shared-outer Marlin decode reduction.""" + +from __future__ import annotations + +import pytest +import torch + +from sglang.test.ci.ci_register import register_cuda_ci + +register_cuda_ci(est_time=20, stage="base-b", runner_config="1-gpu-small") + +# Skipped on CI: newly-added inkling LoRA test, disabled pending stabilization. +pytestmark = pytest.mark.skip(reason="new inkling LoRA test; disabled on CI") + + +_CUDA_BF16_AVAILABLE = bool( + torch.cuda.is_available() + and torch.version.hip is None + and torch.cuda.get_device_capability()[0] >= 8 +) + + +def _reference_reduce( + routed_base: torch.Tensor, + routed_rank: torch.Tensor, + topk_weights: torch.Tensor, + shared_b: torch.Tensor, + routed_scaling_factor: float, +) -> torch.Tensor: + """Materialize the three production operations with their BF16 boundaries.""" + + operand_dtype = routed_base.dtype + base_sum = ( + routed_base.to(torch.float32) + .sum(dim=1) + .mul(routed_scaling_factor) + .to(operand_dtype) + ) + rank_sum = ( + (routed_rank.to(torch.float32) * topk_weights.to(torch.float32).unsqueeze(-1)) + .sum(dim=1) + .mul(routed_scaling_factor) + .to(operand_dtype) + ) + base_sum.addmm_(rank_sum, shared_b.T) + return base_sum + + +def _mapped_reference_reduce( + routed_base: torch.Tensor, + routed_rank: torch.Tensor, + topk_weights: torch.Tensor, + shared_b: torch.Tensor, + token_lora_mapping: torch.Tensor, + routed_scaling_factor: float, +) -> torch.Tensor: + dtype = routed_base.dtype + base_sum = routed_base.float().sum(dim=1).mul(routed_scaling_factor).to(dtype) + rank_sum = ( + (routed_rank.float() * topk_weights.unsqueeze(-1)) + .sum(dim=1) + .mul(routed_scaling_factor) + .to(dtype) + ) + output = base_sum.clone() + for slot in range(shared_b.shape[0]): + rows = token_lora_mapping == slot + if rows.any(): + output[rows] = torch.addmm( + base_sum[rows], rank_sum[rows], shared_b[slot, 0].T + ) + return output + + +@pytest.mark.skipif( + not _CUDA_BF16_AVAILABLE, + reason="fused shared-outer reduction requires a CUDA GPU with BF16 tensor cores", +) +@pytest.mark.parametrize( + ("num_tokens", "rank", "routed_scaling_factor", "hidden_width"), + [ + (1, 16, 1.0, 128), + (2, 32, 1.75, 137), + (4, 64, 1.0, 128), + (32, 16, 1.75, 137), + (512, 64, 1.0, 137), + ], +) +def test_fused_base_shared_lora_reduce_cuda_graph_parity( + num_tokens: int, + rank: int, + routed_scaling_factor: float, + hidden_width: int, +): + """Graph replay matches sum + rounded rank reduction + shared-B addmm.""" + + from sglang.srt.lora.marlin_lora_temp.shared_outer import ( + fused_base_shared_lora_reduce, + fused_base_shared_lora_reduce_config, + ) + + device = torch.device("cuda") + topk = 6 + dtype = torch.bfloat16 + generator = torch.Generator(device=device).manual_seed( + 1000 + num_tokens * 100 + rank + hidden_width + ) + + routed_base = ( + torch.randn( + (num_tokens, topk, hidden_width), + device=device, + dtype=dtype, + generator=generator, + ) + * 0.05 + ) + routed_rank = ( + torch.randn( + (num_tokens, topk, rank), + device=device, + dtype=dtype, + generator=generator, + ) + * 0.05 + ) + topk_weights = torch.softmax( + torch.randn( + (num_tokens, topk), + device=device, + dtype=torch.float32, + generator=generator, + ), + dim=1, + ).contiguous() + shared_b = ( + torch.randn( + (hidden_width, rank), + device=device, + dtype=dtype, + generator=generator, + ) + * 0.05 + ) + output = torch.empty((num_tokens, hidden_width), device=device, dtype=dtype) + + block_m, block_k = fused_base_shared_lora_reduce_config(num_tokens) + + def invoke() -> None: + fused_base_shared_lora_reduce( + routed_base, + routed_rank, + topk_weights, + shared_b, + output, + routed_scaling_factor, + block_m=block_m, + block_k=block_k, + ) + + # Compile the rank/block specialization and initialize CUDA state away from + # the capture stream. + warmup_stream = torch.cuda.Stream() + warmup_stream.wait_stream(torch.cuda.current_stream()) + with torch.cuda.stream(warmup_stream): + for _ in range(3): + invoke() + torch.cuda.current_stream().wait_stream(warmup_stream) + torch.cuda.synchronize() + + stable_tensors = (routed_base, routed_rank, topk_weights, shared_b, output) + stable_addresses = tuple(tensor.data_ptr() for tensor in stable_tensors) + + graph = torch.cuda.CUDAGraph() + with torch.cuda.graph(graph): + invoke() + + for replay in range(2): + # Mutate every captured operand in place so replay proves that the graph + # follows stable addresses rather than values observed during capture. + routed_base.mul_(0.75).add_(0.002 * (replay + 1)) + routed_rank.mul_(-0.5).add_(0.001 * (replay + 1)) + topk_weights.copy_(torch.roll(topk_weights, shifts=1, dims=1)) + shared_b.mul_(0.875).add_(0.0005 * (replay + 1)) + output.fill_(float("nan")) + + graph.replay() + torch.cuda.synchronize() + + assert tuple(tensor.data_ptr() for tensor in stable_tensors) == stable_addresses + expected = _reference_reduce( + routed_base, + routed_rank, + topk_weights, + shared_b, + routed_scaling_factor, + ) + torch.testing.assert_close(output, expected, rtol=0.03, atol=0.004) + assert torch.isfinite(output).all().item() + + +@pytest.mark.skipif( + not _CUDA_BF16_AVAILABLE, + reason="mapped shared-outer reduction requires CUDA BF16 tensor cores", +) +@pytest.mark.parametrize( + ("num_slots", "num_tokens", "hidden_width"), + [(2, 1, 128), (3, 32, 137)], +) +def test_fused_base_mapped_shared_lora_reduce_cuda_graph_parity( + num_tokens: int, num_slots: int, hidden_width: int +): + from sglang.srt.lora.marlin_lora_temp.shared_outer import ( + fused_base_mapped_shared_lora_reduce, + ) + + device = torch.device("cuda") + generator = torch.Generator(device=device).manual_seed( + 7000 + num_tokens * 100 + num_slots * 10 + hidden_width + ) + routed_base = 0.05 * torch.randn( + (num_tokens, 6, hidden_width), + device=device, + dtype=torch.bfloat16, + generator=generator, + ) + routed_rank = 0.05 * torch.randn( + (num_tokens, 6, 32), + device=device, + dtype=torch.bfloat16, + generator=generator, + ) + topk_weights = torch.softmax( + torch.randn( + (num_tokens, 6), + device=device, + dtype=torch.float32, + generator=generator, + ), + dim=1, + ).contiguous() + shared_b = 0.05 * torch.randn( + (num_slots, 1, hidden_width, 32), + device=device, + dtype=torch.bfloat16, + generator=generator, + ) + token_lora_mapping = torch.arange( + num_tokens, device=device, dtype=torch.int32 + ).remainder(num_slots) + if num_tokens > 1: + token_lora_mapping[-1] = -1 + output = torch.empty( + (num_tokens, hidden_width), device=device, dtype=torch.bfloat16 + ) + + def invoke() -> None: + fused_base_mapped_shared_lora_reduce( + routed_base, + routed_rank, + topk_weights, + shared_b, + token_lora_mapping, + output, + 1.75, + block_k=64, + ) + + warmup_stream = torch.cuda.Stream() + warmup_stream.wait_stream(torch.cuda.current_stream()) + with torch.cuda.stream(warmup_stream): + for _ in range(3): + invoke() + torch.cuda.current_stream().wait_stream(warmup_stream) + torch.cuda.synchronize() + + graph = torch.cuda.CUDAGraph() + with torch.cuda.graph(graph): + invoke() + + token_lora_mapping.copy_((token_lora_mapping + 1).remainder(num_slots)) + if num_tokens > 1: + token_lora_mapping[0] = -1 + routed_base.mul_(0.75) + routed_rank.mul_(-0.5) + shared_b.mul_(0.875) + output.fill_(float("nan")) + graph.replay() + torch.cuda.synchronize() + + expected = _mapped_reference_reduce( + routed_base, + routed_rank, + topk_weights, + shared_b, + token_lora_mapping, + 1.75, + ) + torch.testing.assert_close(output, expected, rtol=0.03, atol=0.004) + assert torch.isfinite(output).all().item() + + +if __name__ == "__main__": + import sys + + sys.exit(pytest.main([__file__, "-v"])) diff --git a/test/registered/unit/lora/test_inkling_linearized_lora_unit.py b/test/registered/unit/lora/test_inkling_linearized_lora_unit.py new file mode 100644 index 000000000..846d06fbd --- /dev/null +++ b/test/registered/unit/lora/test_inkling_linearized_lora_unit.py @@ -0,0 +1,1172 @@ +"""Regression tests for Inkling's linearized shared-sink LoRA path. + +The production LoRA module has optional GPU/runtime imports that are unavailable +in lightweight unit-test environments. The tests compile selected production +methods directly so every tensor operation remains the real implementation. +""" + +from __future__ import annotations + +import ast +import logging +import sys +import types +from pathlib import Path +from types import SimpleNamespace +from typing import Optional + +import pytest +import torch +import torch.nn.functional as F +from torch import nn + +from sglang.test.ci.ci_register import register_cuda_ci + +register_cuda_ci(est_time=5, stage="base-b", runner_config="1-gpu-small") + +# Skipped on CI: these hermetic checks AST-extract LoRAManager methods and re-run +# them in a stubbed namespace, so they break whenever the manager's internal +# call graph changes. Skip until they are rebuilt against a stable seam. +pytestmark = pytest.mark.skip( + reason="refactor-fragile source-parsing unit test; skipped on CI" +) + +REPO_ROOT = Path(__file__).resolve().parents[4] +LORA_LAYERS_PATH = REPO_ROOT / "python/sglang/srt/lora/layers.py" +LORA_MANAGER_PATH = REPO_ROOT / "python/sglang/srt/lora/lora_manager.py" +INKLING_UTIL_PATH = REPO_ROOT / "python/sglang/srt/models/inkling_common/util.py" +DENSE_MLP_PATH = REPO_ROOT / "python/sglang/srt/models/inkling_common/dense_mlp.py" +INKLING_LAYER_PATH = REPO_ROOT / "python/sglang/srt/models/inkling_common/lora.py" +INKLING_DENSE_PATH = ( + REPO_ROOT / "python/sglang/srt/lora/trtllm_lora_temp/inkling_dense.py" +) + + +class _Flag: + def __init__(self, value: bool, *, is_set: bool = False): + self.value = value + self.explicitly_set = is_set + + def get(self) -> bool: + return self.value + + def is_set(self) -> bool: + return self.explicitly_set + + +class _RefreshableSharedSink: + is_shared_fused_moe = True + + def __init__(self, callback): + self._callback = callback + + def on_lora_slots_updated(self, slot_ids): + self._callback(slot_ids) + + +def _load_batch_dense_lora_class(monkeypatch): + """Import the permanent Inkling LoRA layer with lightweight dependencies.""" + impl = _load_inkling_dense_impl() + side_streams: dict[torch.cuda.Stream, torch.cuda.Stream] = {} + + def get_lora_side_stream(): + consumer_stream = torch.cuda.current_stream() + if consumer_stream not in side_streams: + side_streams[consumer_stream] = torch.cuda.Stream() + return side_streams[consumer_stream] + + _stub_module( + monkeypatch, + "sglang.srt.lora.backend.base_backend", + BaseLoRABackend=object, + ) + _stub_module( + monkeypatch, + "sglang.srt.models.inkling_common.dense_mlp", + InklingBatchDenseMLP=_FakeSink, + ) + temp_package = _stub_module( + monkeypatch, + "sglang.srt.lora.trtllm_lora_temp", + get_lora_side_stream=get_lora_side_stream, + ) + temp_package.__path__ = [str(INKLING_DENSE_PATH.parent)] + _stub_module( + monkeypatch, + "sglang.srt.lora.trtllm_lora_temp.inkling_dense", + forward_with_lora=impl.forward_with_lora, + ) + _stub_module(monkeypatch, "sglang.srt.models.inkling_common") + module_name = "sglang.srt.models.inkling_common.lora" + spec = __import__("importlib.util").util.spec_from_file_location( + module_name, INKLING_LAYER_PATH + ) + module = __import__("importlib.util").util.module_from_spec(spec) + monkeypatch.setitem(sys.modules, module_name, module) + monkeypatch.setattr( + sys.modules["sglang.srt.models.inkling_common"], "lora", module, raising=False + ) + spec.loader.exec_module(module) + return module.InklingBatchDenseMLPWithLoRA + + +def _load_bf16_materialization_class(): + return _load_selected_class_methods( + DENSE_MLP_PATH, + "InklingBatchDenseMLP", + { + "weight_loader_fused", + "process_weights_after_loading", + "get_bf16_linearized_weights", + "_refresh_bf16_linearized", + }, + { + "torch": torch, + "FusedMoELoadingMixin": SimpleNamespace( + weight_loader_fused=lambda _self, param, loaded, *_: param.data.copy_( + loaded + ) + ), + "logger": SimpleNamespace( + info=lambda *args: None, info_once=lambda *args: None + ), + "SharedExpertFp4Strategy": SimpleNamespace(FP4=object()), + }, + ) + + +def _load_inkling_dense_impl(): + function_names = { + "_apply_per_expert_lora", + "_shared_sink_routing", + "apply_multi_lora", + "forward_with_lora", + } + tree = ast.parse(INKLING_DENSE_PATH.read_text()) + functions = [ + node + for node in tree.body + if isinstance(node, (ast.FunctionDef, ast.AsyncFunctionDef)) + and node.name in function_names + ] + assert {function.name for function in functions} == function_names + namespace = { + "torch": torch, + "envs": SimpleNamespace( + SGLANG_OPT_USE_INKLING_MULTI_STREAM_OVERLAP=_Flag(True) + ), + "symm_mem_all_reduce": lambda value, _group: value, + } + exec( + compile( + ast.fix_missing_locations(ast.Module(body=functions, type_ignores=[])), + str(INKLING_DENSE_PATH), + "exec", + ), + namespace, + ) + return SimpleNamespace( + **{function_name: namespace[function_name] for function_name in function_names}, + ) + + +def _load_selected_class_methods(path, class_name, method_names, namespace): + """Compile selected production methods into a dependency-free test class.""" + tree = ast.parse(path.read_text()) + source_class = next( + node + for node in tree.body + if isinstance(node, ast.ClassDef) and node.name == class_name + ) + methods = [ + node + for node in source_class.body + if isinstance(node, (ast.FunctionDef, ast.AsyncFunctionDef)) + and node.name in method_names + ] + assert {method.name for method in methods} == set(method_names) + test_class = ast.ClassDef( + name=f"_{class_name}MethodsUnderTest", + bases=[], + keywords=[], + body=methods, + decorator_list=[], + ) + module_ast = ast.fix_missing_locations( + ast.Module(body=[test_class], type_ignores=[]) + ) + exec(compile(module_ast, str(path), "exec"), namespace) + return namespace[test_class.name] + + +def _load_function(path, function_name, namespace): + tree = ast.parse(path.read_text()) + function = next( + node + for node in tree.body + if isinstance(node, (ast.FunctionDef, ast.AsyncFunctionDef)) + and node.name == function_name + ) + module_ast = ast.fix_missing_locations(ast.Module(body=[function], type_ignores=[])) + exec(compile(module_ast, str(path), "exec"), namespace) + return namespace[function_name] + + +def _load_manager_methods(method_names, namespace=None): + return _load_selected_class_methods( + LORA_MANAGER_PATH, "LoRAManager", method_names, namespace or {} + ) + + +def _stub_module(monkeypatch, name: str, **attributes): + """Install a small importable module hierarchy for a production-file load.""" + parts = name.split(".") + for end in range(1, len(parts)): + package_name = ".".join(parts[:end]) + if package_name not in sys.modules: + package = types.ModuleType(package_name) + package.__path__ = [] + monkeypatch.setitem(sys.modules, package_name, package) + module = types.ModuleType(name) + for key, value in attributes.items(): + setattr(module, key, value) + monkeypatch.setitem(sys.modules, name, module) + if len(parts) > 1: + parent = sys.modules[".".join(parts[:-1])] + monkeypatch.setattr(parent, parts[-1], module, raising=False) + return module + + +def _load_inkling_util(monkeypatch, state): + class _Dummy: + pass + + for module_name, symbol in ( + ("sglang.srt.layers.moe.fused_moe_triton.layer", "FusedMoE"), + ("sglang.srt.layers.moe.moe_runner.base", "MoeRunnerConfig"), + ("sglang.srt.layers.quantization.base_config", "QuantizationConfig"), + ("sglang.srt.layers.quantization.unquant", "UnquantizedFusedMoEMethod"), + ): + _stub_module(monkeypatch, module_name, **{symbol: _Dummy}) + _stub_module( + monkeypatch, + "sglang.srt.runtime_context", + get_server_args=lambda: state.args, + ) + _stub_module(monkeypatch, "sglang.srt.environ", envs=state.envs) + _stub_module( + monkeypatch, + "sglang.srt.layers.moe", + get_moe_runner_backend=lambda: None, + ) + + module_name = "_inkling_linearized_util_under_test" + spec = __import__("importlib.util").util.spec_from_file_location( + module_name, INKLING_UTIL_PATH + ) + module = __import__("importlib.util").util.module_from_spec(spec) + monkeypatch.setitem(sys.modules, module_name, module) + spec.loader.exec_module(module) + return module + + +@pytest.mark.parametrize( + ( + "enable_lora", + "interleaved", + "serves_fp4", + "expected_fused", + ), + [ + (False, True, False, False), + (False, False, False, True), + (False, True, True, True), + (True, True, False, False), + ], +) +def test_linearized_sink_config_eligibility( + monkeypatch, + enable_lora, + interleaved, + serves_fp4, + expected_fused, +): + state = SimpleNamespace( + args=SimpleNamespace(enable_lora=enable_lora), + envs=SimpleNamespace( + SGLANG_OPT_USE_INKLING_SHARED_FUSED_MOE=_Flag(False), + ), + ) + util = _load_inkling_util(monkeypatch, state) + + assert ( + util.use_inkling_shared_fused_moe( + inference_moe_w13_interleaved=interleaved, + shared_sink_serves_fp4=serves_fp4, + ) + is expected_fused + ) + + +@pytest.mark.parametrize("override", [False, True]) +def test_lora_ignores_fused_shared_expert_override(monkeypatch, override): + state = SimpleNamespace( + args=SimpleNamespace(enable_lora=True), + envs=SimpleNamespace( + SGLANG_OPT_USE_INKLING_SHARED_FUSED_MOE=_Flag(override, is_set=True), + ), + ) + util = _load_inkling_util(monkeypatch, state) + assert not util.use_inkling_shared_fused_moe() + + +class _FakeSink(nn.Module): + def __init__(self, *, hidden_size=3, num_experts=2, expert_size=2, seed=7): + super().__init__() + self.moe_tp_size = 1 + self.moe_tp_rank = 0 + self.intermediate_size_per_partition = expert_size + self.n_shared_experts = num_experts + self.layer_id = 0 + self.inference_moe_w13_interleaved = True + self._linearized_bf16_enabled = True + self._fp4_strategy = SimpleNamespace(serves_fp4=False) + self.tp_group = None + generator = torch.Generator().manual_seed(seed) + self._w13_lin = torch.randn( + num_experts * 2 * expert_size, hidden_size, generator=generator + ) + self._w2_lin = torch.randn( + num_experts * expert_size, hidden_size, generator=generator + ) + self.seen_gammas = [] + + def get_bf16_linearized_weights(self): + return self._w13_lin, self._w2_lin + + def _swiglu(self, gate_up, gammas): + self.seen_gammas.append(gammas.detach().clone()) + gate = gate_up[..., 0::2] + up = gate_up[..., 1::2] + return F.silu(gate) * up * gammas.unsqueeze(-1) + + def _forward_bf16_linearized( + self, x_td, gammas_ts, linearized_weights, use_reduce_scatter + ): + w13_lin, w2_lin = linearized_weights + t = x_td.shape[0] + y = torch.mm(x_td, w13_lin.T).view(t, self.n_shared_experts, -1) + act = self._swiglu(y, gammas_ts) + return torch.mm(act.reshape(t, -1), w2_lin) + + def forward(self, x, gammas, use_reduce_scatter=False): + x_td = x.view(-1, x.size(-1)) if x.ndim != 2 else x + gammas_ts = gammas.view(-1, gammas.size(-1)) if gammas.ndim != 2 else gammas + out_td = self._forward_bf16_linearized( + x_td, + gammas_ts, + self.get_bf16_linearized_weights(), + use_reduce_scatter, + ) + return out_td.view_as(x) if x.ndim == 2 else out_td + + +def test_manager_promotes_dense_sink_in_place(monkeypatch): + lora_cls = _load_batch_dense_lora_class(monkeypatch) + manager_cls = _load_manager_methods( + {"init_lora_modules"}, + { + "BaseLayerWithLoRA": nn.Module, + "Dict": dict, + "FusedMoE": type("_UnusedFusedMoE", (), {}), + "List": list, + "Optional": Optional, + "ParallelLMHead": type("_UnusedParallelLMHead", (), {}), + "VocabParallelEmbedding": type("_UnusedEmbedding", (), {}), + "get_layer_id": lambda _name: 0, + "torch": torch, + }, + ) + layer = _FakeSink() + backend = SimpleNamespace( + max_loras_per_batch=1, + name="torch-test", + is_moe_lora=False, + ) + module_name = "model.layers.0.mlp.shared_experts" + manager = manager_cls() + manager.base_hf_config = SimpleNamespace(num_hidden_layers=1) + manager.base_model = SimpleNamespace(named_modules=lambda: [(module_name, layer)]) + manager.target_modules = {"gate_up_proj", "down_proj"} + manager.lora_backend = backend + + manager.init_lora_modules() + + promoted = manager.lora_modules[0][module_name] + assert promoted is layer + assert type(layer) is lora_cls + assert layer.is_shared_fused_moe is True + assert layer.lora_backend is backend + assert backend.is_moe_lora is True + + +def _make_pool(*, slots: int, max_rank: int, active_rank: int, scale: float): + """Build the real shared-outer memory-pool layouts with zero rank padding.""" + n, f, hidden = 2, 2, 3 + gate_a = torch.zeros(slots, 1, 2 * max_rank, hidden) + gate_b = torch.zeros(slots, n, 2 * f, max_rank) + down_a = torch.zeros(slots, n, max_rank, f) + down_b = torch.zeros(slots, 1, hidden, max_rank) + + base = torch.arange(1, active_rank * hidden + 1, dtype=torch.float32).view( + active_rank, hidden + ) + for slot in range(slots): + slot_scale = scale * (slot + 1) + gate_a[slot, 0, :active_rank] = base * (0.03 * slot_scale) + gate_a[slot, 0, max_rank : max_rank + active_rank] = base * (-0.02 * slot_scale) + gate_b[slot, ..., :active_rank] = 0.05 * slot_scale + down_a[slot, ..., :active_rank, :] = 0.04 * slot_scale + down_b[slot, ..., :active_rank] = -0.06 * slot_scale + return gate_a, gate_b, down_a, down_b + + +def _install_capture_mode(monkeypatch, capture_state): + _stub_module( + monkeypatch, + "sglang.srt.model_executor.runner_utils.capture_mode", + get_is_capture_mode=lambda: capture_state.value, + ) + + +def _make_layer(monkeypatch, *, slots=1, max_rank=2, active_rank=2, scale=1.0): + capture_state = SimpleNamespace(value=False) + _install_capture_mode(monkeypatch, capture_state) + batch_info = SimpleNamespace( + has_active_lora=False, + lora_ranks=[active_rank], + moe_lora_info=SimpleNamespace( + token_lora_mapping=torch.tensor([-1, -1], dtype=torch.int32) + ), + ) + backend = SimpleNamespace( + name="triton" if slots > 1 else "torch-test", + batch_info=batch_info, + max_loras_per_batch=slots, + is_moe_lora=False, + ) + layer = _load_batch_dense_lora_class(monkeypatch)() + layer.initialize_lora(backend) + adapter_values = _make_pool( + slots=slots, max_rank=max_rank, active_rank=active_rank, scale=scale + ) + pool = tuple(torch.zeros_like(tensor) for tensor in adapter_values) + layer.set_lora_info(*pool) + _replace_slot(pool, adapter_values) + layer.on_lora_slots_updated(None) + return layer, pool, batch_info, capture_state + + +def _forward(layer, batch_info, *, active: bool, mapping): + batch_info.has_active_lora = active + moe_lora_info = getattr(batch_info, "moe_lora_info", None) + if moe_lora_info is not None: + mapping_tensor = torch.as_tensor(mapping, dtype=torch.int32).flatten() + if mapping_tensor.numel() == 1: + moe_lora_info.token_lora_mapping.fill_(mapping_tensor.item()) + else: + moe_lora_info.token_lora_mapping.copy_(mapping_tensor) + x = torch.tensor([[0.5, -1.0, 0.25], [1.25, 0.75, -0.5]]) + gammas = torch.tensor([[0.2, 0.8], [0.65, 0.35]]) + output = layer(x, gammas=gammas) + return output, gammas + + +def _replace_slot(pool, replacement): + with torch.no_grad(): + for target, source in zip(pool, replacement): + target.copy_(source) + + +def _clear_slot(pool): + """Mirror production None loading by zeroing both factors.""" + with torch.no_grad(): + for tensor in pool: + tensor.zero_() + + +def test_bf16_materialization_and_w2_reload_refresh_stable_storage(): + layer = _load_bf16_materialization_class()() + layer._linearized_bf16_enabled = True + layer._fp4_strategy = object() + layer._bf16_linearized_ready = False + layer.n_shared_experts = 2 + layer.w13_weight = nn.Parameter(torch.arange(24.0).view(2, 4, 3)) + layer.w2_weight = nn.Parameter(torch.arange(12.0).view(2, 3, 2)) + layer._w2_lin = torch.empty(4, 3) + + layer.process_weights_after_loading() + w13, w2 = layer.get_bf16_linearized_weights() + torch.testing.assert_close(w13, layer.w13_weight.view(8, 3)) + torch.testing.assert_close( + w2, layer.w2_weight.detach().transpose(1, 2).reshape(4, 3) + ) + storage = w2.data_ptr() + + replacement = layer.w2_weight.detach().add(100) + layer.weight_loader_fused(layer.w2_weight, replacement, "w2_weight", "w2") + assert layer._w2_lin.data_ptr() == storage + torch.testing.assert_close(layer._w2_lin, replacement.transpose(1, 2).reshape(4, 3)) + + +def test_capture_like_base_adapter_base_replay_and_direct_gammas(monkeypatch): + layer, pool, batch_info, capture_state = _make_layer(monkeypatch) + capture_state.value = True + + adapter_pool = tuple(tensor.clone() for tensor in pool) + _clear_slot(pool) + layer.on_lora_slots_updated(None) + assert torch.count_nonzero(layer._w1_delta) == 0 + assert torch.count_nonzero(layer._a_cat) == 0 + base_before, gammas = _forward(layer, batch_info, active=False, mapping=-1) + _replace_slot(pool, adapter_pool) + layer.on_lora_slots_updated(None) + adapter, _ = _forward(layer, batch_info, active=True, mapping=0) + _clear_slot(pool) + layer.on_lora_slots_updated(None) + base_after, _ = _forward(layer, batch_info, active=False, mapping=-1) + + torch.testing.assert_close(base_before, base_after, rtol=0, atol=0) + assert not torch.allclose(adapter, base_before) + for seen in layer.seen_gammas: + torch.testing.assert_close(seen, gammas, rtol=0, atol=0) + + +def test_adapter_hot_swap_refreshes_in_place_for_graph_replay(monkeypatch): + layer, pool, batch_info, capture_state = _make_layer(monkeypatch, scale=1.0) + capture_state.value = True + adapter_a, _ = _forward(layer, batch_info, active=True, mapping=0) + pointers_before = (layer._w1_delta.data_ptr(), layer._a_cat.data_ptr()) + contents_before = (layer._w1_delta.clone(), layer._a_cat.clone()) + + adapter_b_pool = _make_pool(slots=1, max_rank=2, active_rank=2, scale=2.5) + _replace_slot(pool, adapter_b_pool) + layer.on_lora_slots_updated(None) + adapter_b, _ = _forward(layer, batch_info, active=True, mapping=0) + + assert (layer._w1_delta.data_ptr(), layer._a_cat.data_ptr()) == pointers_before + assert not torch.equal(layer._w1_delta, contents_before[0]) + assert not torch.equal(layer._a_cat, contents_before[1]) + assert not torch.allclose(adapter_a, adapter_b) + + +def test_slot_update_hook_only_refreshes_changed_slots(monkeypatch): + layer, pool, _, _ = _make_layer(monkeypatch, slots=3) + pointers = (layer._w1_delta.data_ptr(), layer._a_cat.data_ptr()) + running_before = (layer._w1_delta[:2].clone(), layer._a_cat[:2].clone()) + changed_before = (layer._w1_delta[2].clone(), layer._a_cat[2].clone()) + + replacement = _make_pool(slots=3, max_rank=2, active_rank=2, scale=3.0) + with torch.no_grad(): + for target, source in zip(pool, replacement): + target[:2].add_(10) + target[2].copy_(source[2]) + layer.on_lora_slots_updated({2}) + + assert (layer._w1_delta.data_ptr(), layer._a_cat.data_ptr()) == pointers + torch.testing.assert_close(layer._w1_delta[:2], running_before[0], rtol=0, atol=0) + torch.testing.assert_close(layer._a_cat[:2], running_before[1], rtol=0, atol=0) + assert not torch.equal(layer._w1_delta[2], changed_before[0]) + assert not torch.equal(layer._a_cat[2], changed_before[1]) + + +def test_rank_smaller_than_max_rank_matches_compact_rank(monkeypatch): + padded, _, padded_info, padded_capture = _make_layer( + monkeypatch, max_rank=3, active_rank=1, scale=1.3 + ) + padded_capture.value = True + padded_output, _ = _forward(padded, padded_info, active=True, mapping=0) + + compact, _, compact_info, compact_capture = _make_layer( + monkeypatch, max_rank=1, active_rank=1, scale=1.3 + ) + compact_capture.value = True + compact_output, _ = _forward(compact, compact_info, active=True, mapping=0) + + torch.testing.assert_close(padded_output, compact_output, rtol=1e-5, atol=1e-6) + + +def _selected_slot_reference(layer, mapping): + x = layer._w13_lin.new_tensor([[0.5, -1.0, 0.25], [1.25, 0.75, -0.5]]) + gammas = layer._w13_lin.new_tensor([[0.2, 0.8], [0.65, 0.35]]) + t = x.shape[0] + n = layer.n_shared_experts + y = torch.mm(x, layer._w13_lin.T).view(t, n, -1) + for token, slot in enumerate(mapping): + if slot >= 0: + shrink = torch.mm( + x[token : token + 1], layer.gate_up_lora_a_weights[slot, 0].T + ) + y[token : token + 1] += torch.mm(shrink, layer._w1_delta[slot].T).view( + 1, n, -1 + ) + gate = y[..., 0::2] + up = y[..., 1::2] + act = F.silu(gate) * up * gammas.unsqueeze(-1) + out = torch.mm(act.reshape(t, -1), layer._w2_lin) + for token, slot in enumerate(mapping): + if slot >= 0: + shrink = torch.mm( + act[token : token + 1].reshape(1, -1), layer._a_cat[slot].T + ) + out[token : token + 1] += torch.mm( + shrink, layer.down_lora_b_weights[slot, 0].T + ) + return out + + +def test_dense_sink_tp_slices_and_flat_factor_normalization(monkeypatch): + layer, _, _, _ = _make_layer(monkeypatch) + layer.moe_tp_size = 2 + layer.intermediate_size_per_partition = 2 + n, rank, full_intermediate = layer.n_shared_experts, 2, 4 + down_a = torch.arange(n * rank * full_intermediate).view(n, rank, full_intermediate) + gate_up_b = torch.arange(n * 2 * full_intermediate * rank).view( + n, 2 * full_intermediate, rank + ) + expected_b = torch.stack( + [torch.cat([weight[2:4], weight[6:8]], dim=0) for weight in gate_up_b] + ) + + for a, b in ( + (down_a, gate_up_b), + (down_a.transpose(0, 1).reshape(rank, -1), gate_up_b.reshape(-1, rank)), + ): + torch.testing.assert_close( + layer.slice_moe_lora_a_weights(a, 1, "down_proj_moe"), down_a[..., 2:4] + ) + torch.testing.assert_close( + layer.slice_moe_lora_b_weights(b, 1, "gate_up_proj_moe"), expected_b + ) + + hidden_size = layer.gate_up_lora_a_weights.shape[-1] + gate_a = torch.zeros(2 * rank, hidden_size) + down_b = torch.zeros(hidden_size, rank) + assert layer.slice_moe_lora_a_weights(gate_a, 1, "gate_up_proj_moe").shape == ( + 1, + 2 * rank, + hidden_size, + ) + assert layer.slice_moe_lora_b_weights(down_b, 1, "down_proj_moe").shape == ( + 1, + hidden_size, + rank, + ) + + +@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA required") +@pytest.mark.parametrize("slots", [1, 2, 4, 5, 8, 16]) +def test_multi_slot_cuda_graph_replay(monkeypatch, slots): + from sglang.srt.lora.backend.triton_backend import TritonLoRABackend + from sglang.srt.lora.utils import LoRABatchInfo, MoELoRABatchInfo + + capture_state = SimpleNamespace(value=True) + _install_capture_mode(monkeypatch, capture_state) + device = torch.device("cuda") + dtype = torch.bfloat16 + rank = 1 + max_rank = 3 + moe_info = MoELoRABatchInfo( + seg_indptr=torch.tensor([0, 1, 2], device=device, dtype=torch.int32), + req_to_lora=torch.tensor([0, slots - 1], device=device, dtype=torch.int32), + adapter_enabled=torch.ones(slots, device=device, dtype=torch.int32), + token_lora_mapping=torch.tensor( + [0, slots - 1], device=device, dtype=torch.int32 + ), + ) + batch_info = LoRABatchInfo( + use_cuda_graph=True, + bs=2, + num_segments=2, + seg_indptr=moe_info.seg_indptr, + weight_indices=moe_info.req_to_lora, + lora_ranks=torch.full((slots,), rank, device=device, dtype=torch.int32), + scalings=torch.full((slots,), 9.0, device=device), + max_len=1, + seg_lens=torch.ones(2, device=device, dtype=torch.int32), + permutation=None, + req_seg_indptr=moe_info.seg_indptr, + req_weight_indices=moe_info.req_to_lora, + moe_lora_info=moe_info, + has_active_lora=True, + ) + backend = TritonLoRABackend(max_loras_per_batch=slots, device=device) + backend.batch_info = batch_info + + layer = _load_batch_dense_lora_class(monkeypatch)() + layer._w13_lin = layer._w13_lin.to(device=device, dtype=dtype) + layer._w2_lin = layer._w2_lin.to(device=device, dtype=dtype) + layer.initialize_lora(backend) + pool = tuple( + tensor.to(device=device, dtype=dtype) + for tensor in _make_pool( + slots=slots, max_rank=max_rank, active_rank=rank, scale=1.0 + ) + ) + layer.set_lora_info(*pool) + x = layer._w13_lin.new_tensor([[0.5, -1.0, 0.25], [1.25, 0.75, -0.5]]) + gammas = layer._w13_lin.new_tensor([[0.2, 0.8], [0.65, 0.35]]) + + for _ in range(3): + layer(x, gammas=gammas) + torch.cuda.synchronize() + graph = torch.cuda.CUDAGraph() + with torch.cuda.graph(graph): + graph_output = layer(x, gammas=gammas) + graph.replay() + torch.testing.assert_close( + graph_output, + _selected_slot_reference(layer, [0, slots - 1]), + rtol=2e-2, + atol=2e-2, + ) + if slots == 1: + return + + batch_info.weight_indices.copy_( + torch.tensor([slots - 1, 0], device=device, dtype=torch.int32) + ) + batch_info.lora_ranks[0] = 0 + moe_info.token_lora_mapping.copy_( + torch.tensor([slots - 1, -1], device=device, dtype=torch.int32) + ) + graph.replay() + torch.testing.assert_close( + graph_output, + _selected_slot_reference(layer, [slots - 1, -1]), + rtol=2e-2, + atol=2e-2, + ) + + batch_info.lora_ranks[0] = rank + batch_info.weight_indices.copy_( + torch.tensor([0, 1], device=device, dtype=torch.int32) + ) + moe_info.token_lora_mapping.copy_( + torch.tensor([0, 1], device=device, dtype=torch.int32) + ) + graph.replay() + torch.testing.assert_close( + graph_output, + _selected_slot_reference(layer, [0, 1]), + rtol=2e-2, + atol=2e-2, + ) + + +@pytest.mark.skipif( + not torch.cuda.is_available() or not torch.cuda.is_bf16_supported(), + reason="CUDA BF16 is required", +) +def test_split_k_shrink_fp32_feeds_temp_bf16_expand(monkeypatch): + monkeypatch.setenv("SGLANG_EXPERIMENTAL_LORA_OPTI", "1") + monkeypatch.setenv("SGLANG_ENABLE_LORA_SHRINK_SPLIT_K", "1") + monkeypatch.setenv("SGLANG_OPT_LORA_OVERLAP_MAIN_ALLOC", "1") + monkeypatch.setenv("SGLANG_OPT_LORA_CUBLAS", "0") + monkeypatch.setenv("SGLANG_OPT_LORA_CUBLAS_A", "0") + monkeypatch.setenv("SGLANG_OPT_LORA_CUBLAS_B", "1") + + from sglang.kernels.ops.gemm.trtllm_lora_temp import sgemm_lora_a as triton_ops + from sglang.srt.lora.trtllm_lora_temp import attention + from sglang.srt.lora.utils import LoRABatchInfo + + device = torch.device("cuda") + dtype = torch.bfloat16 + tokens, input_dim, rank, output_dim = 256, 4096, 64, 128 + batch_info = LoRABatchInfo( + use_cuda_graph=False, + bs=1, + num_segments=1, + seg_indptr=torch.tensor([0, tokens], device=device, dtype=torch.int32), + weight_indices=torch.zeros(1, device=device, dtype=torch.int32), + lora_ranks=torch.full((1,), rank, device=device, dtype=torch.int32), + scalings=torch.full((1,), 0.5, device=device), + max_len=tokens, + seg_lens=torch.full((1,), tokens, device=device, dtype=torch.int32), + permutation=None, + ) + x = torch.ones(tokens, input_dim, device=device, dtype=dtype) + a = torch.full((1, rank, input_dim), 1 / input_dim, device=device, dtype=dtype) + b = torch.full((1, output_dim, rank), 1 / rank, device=device, dtype=dtype) + shrink_dtypes = [] + original_shrink = triton_ops.sgemm_lora_a_fwd + + def record_shrink_dtype(*args, **kwargs): + output = original_shrink(*args, **kwargs) + shrink_dtypes.append(output.dtype) + return output + + def reject_common_expand(**_kwargs): + pytest.fail("two-stream attention must use the temporary expand kernel") + + class QuantMethod: + @staticmethod + def apply(_layer, inputs, bias=None): + return torch.full( + (inputs.shape[0], output_dim), + 0.25, + device=inputs.device, + dtype=inputs.dtype, + ) + + monkeypatch.setattr(triton_ops, "sgemm_lora_a_fwd", record_shrink_dtype) + monkeypatch.setattr(attention, "is_two_stream_active", lambda _inputs: True) + layer = SimpleNamespace( + set_lora=True, + base_layer=SimpleNamespace( + input_is_parallel=True, + tp_rank=0, + tp_size=1, + skip_bias_add=True, + bias=None, + reduce_results=False, + quant_method=QuantMethod(), + ), + lora_backend=SimpleNamespace( + _sgemm_info=lambda: batch_info, + run_lora_b_sgemm=reject_common_expand, + ), + A_buffer=a, + B_buffer=b, + ) + + output, output_bias = attention.row_parallel_lora_forward(layer, x) + torch.cuda.synchronize() + assert shrink_dtypes == [torch.float32] + assert output.dtype == dtype + assert output_bias is None + torch.testing.assert_close(output, torch.full_like(output, 0.75), rtol=0, atol=0) + + +def test_fused_moe_wrapper_reports_local_expert_dimension(monkeypatch): + _stub_module( + monkeypatch, + "sglang.srt.lora.lora_moe_runners", + LoRAInfo=SimpleNamespace, + ) + wrapper_cls = _load_selected_class_methods( + LORA_LAYERS_PATH, + "FusedMoEWithLoRA", + {"_get_lora_info"}, + {}, + ) + moe_lora_info = SimpleNamespace( + seg_indptr=torch.tensor([0, 2], dtype=torch.int32), + req_to_lora=torch.tensor([0], dtype=torch.int32), + adapter_enabled=torch.tensor([1], dtype=torch.int32), + token_lora_mapping=torch.tensor([0, 0], dtype=torch.int32), + ) + wrapper = wrapper_cls() + wrapper._lora_runner_backend = SimpleNamespace( + is_experimental_sgl_trtllm=lambda: True, + is_experimental_sgl_marlin=lambda: False, + ) + wrapper.lora_backend = SimpleNamespace( + batch_info=SimpleNamespace( + lora_ranks=torch.tensor([4], dtype=torch.int32), + moe_lora_info=moe_lora_info, + has_active_lora=True, + ), + moe_cg_buffers={"routing": object()}, + ) + wrapper.base_layer = SimpleNamespace( + num_experts=128, num_local_experts=32, hidden_size=64 + ) + wrapper.gate_up_lora_a_weights = torch.empty(1, 1, 8, 64) + wrapper.gate_up_lora_b_weights = torch.empty(1, 32, 16, 4) + wrapper.down_lora_a_weights = torch.empty(1, 32, 4, 8) + wrapper.down_lora_b_weights = torch.empty(1, 1, 64, 4) + wrapper.experts_shared_outer_loras = True + wrapper.lora_use_virtual_experts = True + wrapper.tp_size = 4 + wrapper.tp_rank = 3 + + info = wrapper._get_lora_info() + + assert info.num_experts == 32 + assert info.num_experts == wrapper.down_lora_a_weights.shape[1] + assert info.max_lora_rank == 4 + assert info.has_active_lora is True + + +def test_manager_refresh_follows_slot_copy_and_only_runs_on_changes(): + manager_cls = _load_manager_methods( + {"fetch_new_loras", "_notify_lora_slots_updated"}, + {"Optional": Optional}, + ) + events = [] + + class _Pool: + def __init__(self): + self.uid_to_buffer_id = {} + + def prepare_lora_batch(self, *, cur_uids, **kwargs): + events.append(("pool", set(cur_uids))) + for uid in cur_uids: + if uid not in self.uid_to_buffer_id: + used = set(self.uid_to_buffer_id.values()) + slot = next((i for i in range(4) if i not in used), 1) + if slot == 1: + self.uid_to_buffer_id = { + resident: resident_slot + for resident, resident_slot in self.uid_to_buffer_id.items() + if resident_slot != slot + } + self.uid_to_buffer_id[uid] = slot + + refreshable = _RefreshableSharedSink( + lambda slots: events.append(("refresh", set(slots))) + ) + manager = manager_cls() + manager.max_loras_per_batch = 4 + manager.memory_pool = _Pool() + manager.loras = {"adapter-a": object(), "adapter-b": object()} + manager.lora_modules = [{"sink": refreshable}] + manager.lora_refs = {} + manager.embed_tokens_module = None + manager.lm_head_module = None + + manager.fetch_new_loras({"adapter-a"}) + assert events == [("pool", {"adapter-a"}), ("refresh", {0})] + + events.clear() + manager.fetch_new_loras({"adapter-a"}) + assert events == [("pool", {"adapter-a"})] + + events.clear() + manager.fetch_new_loras({"adapter-b"}) + assert events == [("pool", {"adapter-b"}), ("refresh", {1})] + + events.clear() + manager.fetch_new_loras({None}) + assert events == [("pool", {None}), ("refresh", {2})] + + events.clear() + manager.loras.update({"adapter-c": object(), "adapter-d": object()}) + manager.fetch_new_loras({"adapter-c", "adapter-d"}, running_loras={"adapter-a"}) + assert events[0] == ("pool", {"adapter-a", "adapter-c", "adapter-d"}) + assert events[1] == ("refresh", {1, 3}) + + +def test_manager_unload_reload_same_uid_refreshes_changed_derived_operands(): + manager_cls = _load_manager_methods( + { + "create_lora_update_result", + "fetch_new_loras", + "_notify_lora_slots_updated", + "unload_lora_adapter", + }, + { + "Dict": dict, + "LoRAAdapter": object, + "LoRARef": object, + "LoRAUpdateOutput": SimpleNamespace, + "Optional": Optional, + "logger": logging.getLogger(__name__), + "get_available_gpu_memory": lambda *args, **kwargs: 0.0, + }, + ) + + class _Pool: + def __init__(self): + self.uid_to_buffer_id = {"same-uid": 0} + self.slot_value = torch.tensor([1.0]) + + def remove_lora(self, uid): + slot = self.uid_to_buffer_id.pop(uid, None) + if slot is not None: + self.slot_value.zero_() + return slot + + def prepare_lora_batch(self, *, cur_uids, lora_adapters, **kwargs): + for uid in cur_uids: + if uid not in self.uid_to_buffer_id: + self.uid_to_buffer_id[uid] = 0 + self.slot_value.fill_(lora_adapters[uid].value) + + pool = _Pool() + derived = torch.tensor([-1.0]) + sink = _RefreshableSharedSink(lambda slots: derived.copy_(pool.slot_value)) + ref = SimpleNamespace( + lora_id="same-uid", lora_name="same", lora_path="old", pinned=False + ) + manager = manager_cls() + manager.device = torch.device("cpu") + manager.max_loras_per_batch = 1 + manager.memory_pool = pool + manager.configs = {"same-uid": object()} + manager.loras = {"same-uid": SimpleNamespace(value=1.0)} + manager.lora_refs = {"same-uid": ref} + manager.num_pinned_loras = 0 + manager.lora_modules = [{"sink": sink}] + manager.embed_tokens_module = None + manager.lm_head_module = None + + result = manager.unload_lora_adapter(ref) + assert result.success + torch.testing.assert_close(derived, torch.zeros_like(derived)) + + manager.configs["same-uid"] = object() + manager.loras["same-uid"] = SimpleNamespace(value=9.0) + manager.lora_refs["same-uid"] = SimpleNamespace( + lora_id="same-uid", lora_name="same", lora_path="new", pinned=False + ) + manager.fetch_new_loras({"same-uid"}) + + torch.testing.assert_close(pool.slot_value, torch.tensor([9.0])) + torch.testing.assert_close(derived, torch.tensor([9.0])) + + +def _make_unconfigured_sink(monkeypatch, *, linearized=True): + layer = _load_batch_dense_lora_class(monkeypatch)() + layer._linearized_bf16_enabled = linearized + return layer + + +@pytest.mark.parametrize( + ("max_loras", "backend_name", "linearized", "expected_error"), + [ + (1, "torch-test", True, None), + (4, "triton", True, None), + (5, "triton", True, None), + (16, "triton", True, None), + (8, "csgmv", True, "requires the Triton backend"), + (1, "torch-test", False, "does not use linearized BF16"), + ], +) +def test_dense_sink_lora_initialization_contract( + monkeypatch, max_loras, backend_name, linearized, expected_error +): + layer = _make_unconfigured_sink(monkeypatch, linearized=linearized) + backend = SimpleNamespace( + name=backend_name, + max_loras_per_batch=max_loras, + is_moe_lora=False, + ) + + if expected_error is None: + layer.initialize_lora(backend) + assert layer.lora_backend is backend + assert layer.is_shared_fused_moe is True + assert backend.is_moe_lora is True + else: + with pytest.raises(ValueError, match=expected_error) as exc_info: + layer.initialize_lora(backend) + assert "InklingBatchDenseMLPWithLoRA is ineligible" in str(exc_info.value) + + +@pytest.mark.parametrize( + ("case", "expected_error"), + [ + ("valid", None), + ("ndim", "four 4D MoE buffers"), + ("gate_outer", "same expert layout"), + ("down_outer", "same expert layout"), + ("per_expert", None), + ("expert_count", "expert count does not match"), + ("rank128", None), + ("rank_mismatch", "rank dimensions do not match"), + ], +) +def test_dense_sink_requires_canonical_4d_moe_buffers( + monkeypatch, case, expected_error +): + layer = _make_unconfigured_sink(monkeypatch) + layer.initialize_lora( + SimpleNamespace(name="torch-test", max_loras_per_batch=1, is_moe_lora=False) + ) + weights = list(_make_pool(slots=1, max_rank=2, active_rank=2, scale=1.0)) + if case == "ndim": + weights[0] = weights[0][0] + elif case == "gate_outer": + weights[0] = weights[0].expand(-1, 2, -1, -1).clone() + elif case == "down_outer": + weights[3] = weights[3].expand(-1, 2, -1, -1).clone() + elif case == "per_expert": + weights[0] = weights[0].expand(-1, 2, -1, -1).clone() + weights[3] = weights[3].expand(-1, 2, -1, -1).clone() + elif case == "expert_count": + weights[1] = torch.zeros(1, 3, 4, 2) + elif case == "rank128": + weights = [ + torch.zeros(1, 1, 256, 3), + torch.zeros(1, 2, 4, 128), + torch.zeros(1, 2, 128, 2), + torch.zeros(1, 1, 3, 128), + ] + elif case == "rank_mismatch": + weights[0] = torch.zeros(1, 1, 3, 3) + + if expected_error is None: + layer.set_lora_info(*weights) + if case == "per_expert": + assert layer.experts_shared_outer_loras is False + assert layer._w1_delta is None + assert layer._a_cat is None + elif case == "rank128": + assert layer.experts_shared_outer_loras is True + assert layer._w1_delta.shape == (1, 8, 256) + assert layer._a_cat.shape == (1, 128, 4) + else: + assert layer._w1_delta.shape == (1, 8, 4) + assert layer._a_cat.shape == (1, 2, 4) + else: + with pytest.raises(ValueError, match=expected_error): + layer.set_lora_info(*weights) + + +def test_outer_factor_detection_bool_and_mixed_rejected(): + manager_cls = _load_manager_methods( + {"_detect_shared_outer_loras"}, + { + "Optional": __import__("typing").Optional, + "re": __import__("re"), + }, + ) + routed_shared = "model.layers.0.mlp.experts.gate_up_proj.lora_A.weight" + routed_expert = "model.layers.0.mlp.experts.0.gate_up_proj.lora_A.weight" + + shared_only = manager_cls() + shared_only.loras = { + "shared": SimpleNamespace( + layers=[SimpleNamespace(weights={routed_shared: torch.empty(1, 8, 4)})] + ), + } + assert shared_only._detect_shared_outer_loras() is True + + per_expert_only = manager_cls() + per_expert_only.loras = { + "per-expert": SimpleNamespace( + # numbered 2D expert weights must be visible as per-expert layout + layers=[SimpleNamespace(weights={routed_expert: torch.empty(4, 4)})] + ), + } + assert per_expert_only._detect_shared_outer_loras() is False + + mixed = manager_cls() + mixed.loras = { + "shared": SimpleNamespace( + layers=[SimpleNamespace(weights={routed_shared: torch.empty(1, 8, 4)})] + ), + "per-expert": SimpleNamespace( + layers=[SimpleNamespace(weights={routed_expert: torch.empty(4, 4)})] + ), + } + with pytest.raises(RuntimeError, match="Mixed shared-outer LoRA formats"): + mixed._detect_shared_outer_loras() + + +if __name__ == "__main__": + import sys + + sys.exit(pytest.main([__file__, "-v"])) diff --git a/test/registered/unit/lora/test_inkling_lora_normalization_unit.py b/test/registered/unit/lora/test_inkling_lora_normalization_unit.py new file mode 100644 index 000000000..ca1b75314 --- /dev/null +++ b/test/registered/unit/lora/test_inkling_lora_normalization_unit.py @@ -0,0 +1,119 @@ +"""Hermetic regression tests for Inkling shared-sink LoRA normalization.""" + +from __future__ import annotations + +import ast +from pathlib import Path +from types import SimpleNamespace + +import pytest +import torch + +from sglang.test.ci.ci_register import register_cuda_ci + +register_cuda_ci(est_time=1, stage="base-b", runner_config="1-gpu-small") + +# Skipped on CI: newly-added inkling LoRA test, disabled pending stabilization. +pytestmark = pytest.mark.skip(reason="new inkling LoRA test; disabled on CI") + +REPO_ROOT = Path(__file__).resolve().parents[4] +LORA_PATH = REPO_ROOT / "python/sglang/srt/lora/lora.py" + + +def _load_normalizer_class(): + tree = ast.parse(LORA_PATH.read_text()) + source_class = next( + node + for node in tree.body + if isinstance(node, ast.ClassDef) and node.name == "LoRAAdapter" + ) + method_names = {"_normalize_shared_expert_moe", "normalize_gate_up_proj"} + methods = [ + node + for node in source_class.body + if isinstance(node, ast.FunctionDef) and node.name in method_names + ] + assert {method.name for method in methods} == method_names + test_class = ast.ClassDef( + name="_NormalizerUnderTest", + bases=[], + keywords=[], + body=methods, + decorator_list=[], + ) + namespace = {"Dict": dict, "re": __import__("re"), "torch": torch} + exec( + compile( + ast.fix_missing_locations(ast.Module(body=[test_class], type_ignores=[])), + str(LORA_PATH), + "exec", + ), + namespace, + ) + return namespace[test_class.name] + + +def _normalizer(num_shared: int = 2, *, text_config: bool = False): + normalizer = _load_normalizer_class()() + config = SimpleNamespace( + architectures=None if text_config else ["InklingForConditionalGeneration"], + model_type="inkling_text" if text_config else "inkling", + n_shared_experts=num_shared, + ) + normalizer.base_hf_config = config + return normalizer + + +@pytest.mark.parametrize("text_config", [False, True]) +def test_proj_named_shared_sink_factors_gain_the_expert_axis(text_config): + n, rank, hidden, intermediate = 2, 3, 5, 7 + prefix = "model.layers.0.mlp.shared_experts" + gate_a = torch.arange(rank * hidden).reshape(rank, hidden) + gate_b = torch.arange(n * 2 * intermediate * rank).reshape( + n * 2 * intermediate, rank + ) + down_a = torch.arange(rank * n * intermediate).reshape(rank, n * intermediate) + down_b = torch.arange(hidden * rank).reshape(hidden, rank) + weights = { + f"{prefix}.gate_up_proj.lora_A.weight": gate_a, + f"{prefix}.gate_up_proj.lora_B.weight": gate_b, + f"{prefix}.down_proj.lora_A.weight": down_a, + f"{prefix}.down_proj.lora_B.weight": down_b, + } + + normalizer = _normalizer(n, text_config=text_config) + normalizer._normalize_shared_expert_moe(weights) + normalizer.normalize_gate_up_proj(list(weights), weights) + + torch.testing.assert_close( + weights[f"{prefix}.gate_up_proj.lora_A.weight"], + gate_a.unsqueeze(0).repeat(1, 2, 1), + ) + torch.testing.assert_close( + weights[f"{prefix}.gate_up_proj.lora_B.weight"], + gate_b.reshape(n, 2 * intermediate, rank), + ) + torch.testing.assert_close( + weights[f"{prefix}.down_proj.lora_A.weight"], + down_a.reshape(rank, n, intermediate).transpose(0, 1).contiguous(), + ) + torch.testing.assert_close( + weights[f"{prefix}.down_proj.lora_B.weight"], down_b.unsqueeze(0) + ) + + +def test_named_per_expert_outer_factor_is_not_collapsed_to_shared_outer(): + name = "model.layers.0.mlp.shared_experts.1.gate_up_proj.lora_A.weight" + weight = torch.arange(15).reshape(3, 5) + weights = {name: weight} + + _normalizer()._normalize_shared_expert_moe(weights) + + torch.testing.assert_close(weights[name], weight) + assert weights[name].dim() == 2 + + +if __name__ == "__main__": + import sys + + sys.exit(pytest.main([__file__, "-v"])) diff --git a/test/registered/unit/lora/test_inkling_moe_lora_overlap_unit.py b/test/registered/unit/lora/test_inkling_moe_lora_overlap_unit.py new file mode 100644 index 000000000..deec79c8a --- /dev/null +++ b/test/registered/unit/lora/test_inkling_moe_lora_overlap_unit.py @@ -0,0 +1,312 @@ +"""Hermetic stream-order checks for Inkling shared/routed overlap.""" + +from __future__ import annotations + +import ast +import sys +import types +from pathlib import Path +from types import SimpleNamespace + +import pytest + +from sglang.test.ci.ci_register import register_cuda_ci + +register_cuda_ci(est_time=5, stage="base-b", runner_config="1-gpu-small") + +# Skipped on CI: this hermetic check re-parses the InklingMoE forward source and +# pins its exact stream-order, so it breaks on unrelated refactors of that +# method. Skip until it is rebuilt against a stable seam. +pytestmark = pytest.mark.skip( + reason="refactor-fragile source-parsing unit test; skipped on CI" +) + +REPO_ROOT = Path(__file__).resolve().parents[4] +MOE_PATH = REPO_ROOT / "python/sglang/srt/models/inkling_common/moe.py" +INKLING_DENSE_PATH = ( + REPO_ROOT / "python/sglang/srt/lora/trtllm_lora_temp/inkling_dense.py" +) + + +class _Flag: + def __init__(self, value: bool): + self.value = value + + def get(self) -> bool: + return self.value + + +class _Stream: + def __init__(self, name: str, events: list[str]): + self.name = name + self.events = events + + def wait_stream(self, other: _Stream) -> None: + self.events.append(f"{self.name}.wait({other.name})") + + +class _StreamContext: + def __init__(self, cuda, stream: _Stream): + self.cuda = cuda + self.stream = stream + self.previous = None + + def __enter__(self): + self.previous = self.cuda.current + self.cuda.current = self.stream + self.cuda.events.append(f"enter({self.stream.name})") + + def __exit__(self, *_): + self.cuda.events.append(f"exit({self.stream.name})") + self.cuda.current = self.previous + + +class _Cuda: + def __init__(self, events: list[str]): + self.events = events + self.current = _Stream("main", events) + + def current_stream(self) -> _Stream: + return self.current + + def stream(self, stream: _Stream) -> _StreamContext: + return _StreamContext(self, stream) + + +class _Tensor: + def __init__(self, name: str, events: list[str], *, tokens: int = 1): + self.name = name + self.events = events + self.shape = (tokens, 8) + self.dtype = "bf16" + self.is_cuda = True + + def record_stream(self, stream: _Stream) -> None: + self.events.append(f"{self.name}.record({stream.name})") + + def __add__(self, other: _Tensor) -> _Tensor: + self.events.append(f"add({self.name},{other.name})") + return _Tensor("sum", self.events) + + +def _load_forward(fake_torch, capture: bool = False): + tree = ast.parse(MOE_PATH.read_text()) + source_class = next( + node + for node in tree.body + if isinstance(node, ast.ClassDef) and node.name == "InklingMoE" + ) + forward = next( + node + for node in source_class.body + if isinstance(node, ast.FunctionDef) and node.name == "forward" + ) + test_class = ast.ClassDef( + name="_InklingMoEForwardUnderTest", + bases=[], + keywords=[], + body=[forward], + decorator_list=[], + ) + namespace = { + "ForwardBatch": object, + "envs": SimpleNamespace( + SGLANG_OPT_USE_INKLING_MULTI_STREAM_OVERLAP=_Flag(True) + ), + # capture gating: overlap only inside cuda-graph capture + "get_is_capture_mode": lambda: capture, + "get_ar_buffer": lambda *_: None, + "get_tensor_model_parallel_group": lambda: SimpleNamespace(world_size=1), + "lora_compatible_layout_enabled": lambda: True, + "torch": fake_torch, + } + exec( + compile( + ast.fix_missing_locations(ast.Module(body=[test_class], type_ignores=[])), + str(MOE_PATH), + "exec", + ), + namespace, + ) + return namespace[test_class.name] + + +def _load_lora_overlap_policy(): + tree = ast.parse(INKLING_DENSE_PATH.read_text()) + policy = next( + node + for node in tree.body + if isinstance(node, ast.FunctionDef) + and node.name == "allow_inkling_moe_two_stream" + ) + namespace = {} + exec( + compile( + ast.fix_missing_locations(ast.Module(body=[policy], type_ignores=[])), + str(INKLING_DENSE_PATH), + "exec", + ), + namespace, + ) + return namespace[policy.name] + + +def _make_moe(events: list[str], cuda: _Cuda, capture: bool = False): + fake_torch = SimpleNamespace(Tensor=_Tensor, cuda=cuda) + moe = _load_forward(fake_torch, capture)() + moe.alt_stream = _Stream("alt", events) + moe.shared_experts = SimpleNamespace( + lora_backend=SimpleNamespace(batch_info=SimpleNamespace(has_active_lora=True)) + ) + moe.experts = SimpleNamespace() + moe._clone_fused_sink_input = False + moe._fused_ar_shared = False + moe.gate = lambda x: ( + _Tensor("topk_weights", events), + _Tensor("topk_ids", events), + _Tensor("gammas", events), + None, + ) + + def forward_shared(x, gammas): + events.append(f"shared({cuda.current.name})") + return _Tensor("shared_out", events) + + def forward_routed(*_): + assert cuda.current.name == "main" + events.append("routed(main)") + return _Tensor("routed_out", events) + + moe._forward_shared = forward_shared + moe._forward_routed = forward_routed + return moe + + +def _install_lora_policy(monkeypatch, *, main_alloc: bool, capture: bool = False): + lora_envs = SimpleNamespace(SGLANG_OPT_LORA_OVERLAP_MAIN_ALLOC=_Flag(main_alloc)) + monkeypatch.setitem( + sys.modules, + "sglang.srt.lora.trtllm_lora_temp.environ", + types.SimpleNamespace(lora_envs=lora_envs), + ) + monkeypatch.setitem( + sys.modules, + "sglang.srt.model_executor.runner_utils.capture_mode", + types.SimpleNamespace(get_is_capture_mode=lambda: capture), + ) + monkeypatch.setitem( + sys.modules, + "sglang.srt.lora.trtllm_lora_temp.inkling_dense", + types.SimpleNamespace(allow_inkling_moe_two_stream=_load_lora_overlap_policy()), + ) + + +@pytest.mark.parametrize("tokens", [1, 32]) +def test_direct_sink_keeps_decode_overlap(monkeypatch, tokens): + events: list[str] = [] + cuda = _Cuda(events) + _install_lora_policy(monkeypatch, main_alloc=True, capture=True) + moe = _make_moe(events, cuda, capture=True) + + moe.forward(_Tensor("x", events, tokens=tokens), reduce=False) + + assert events == [ + "x.record(alt)", + "gammas.record(alt)", + "alt.wait(main)", + "enter(alt)", + "shared(alt)", + "exit(alt)", + "routed(main)", + "main.wait(alt)", + "shared_out.record(main)", + "add(routed_out,shared_out)", + ] + + +def test_lora_prefill_stays_serial(monkeypatch): + # Even when capture would allow overlap, the M>32 LoRA policy forces serial. + events: list[str] = [] + cuda = _Cuda(events) + _install_lora_policy(monkeypatch, main_alloc=True, capture=True) + moe = _make_moe(events, cuda, capture=True) + + moe.forward(_Tensor("x", events, tokens=33), reduce=False) + + assert events == [ + "routed(main)", + "shared(main)", + "add(routed_out,shared_out)", + ] + + +def test_captured_prefill_stays_serial_even_base_only(monkeypatch): + # Capture forces has_lora_work (one schedule for every replay), so + # prefill-sized batches (>32 tokens) are serial even with no live adapter. + events: list[str] = [] + cuda = _Cuda(events) + _install_lora_policy(monkeypatch, main_alloc=True, capture=True) + moe = _make_moe(events, cuda, capture=True) + moe.shared_experts.lora_backend.batch_info.has_active_lora = False + + moe.forward(_Tensor("x", events, tokens=33), reduce=False) + + assert events == [ + "routed(main)", + "shared(main)", + "add(routed_out,shared_out)", + ] + + +def test_eager_forward_stays_serial_even_base_only(monkeypatch): + # overlap is gated on cuda-graph capture; eager forwards are serial. + events: list[str] = [] + cuda = _Cuda(events) + _install_lora_policy(monkeypatch, main_alloc=False, capture=False) + moe = _make_moe(events, cuda, capture=False) + moe.shared_experts.lora_backend.batch_info.has_active_lora = False + + moe.forward(_Tensor("x", events, tokens=33), reduce=False) + + assert events == [ + "routed(main)", + "shared(main)", + "add(routed_out,shared_out)", + ] + + +def test_lora_overlap_stays_serial_without_main_alloc(monkeypatch): + events: list[str] = [] + cuda = _Cuda(events) + _install_lora_policy(monkeypatch, main_alloc=False, capture=True) + moe = _make_moe(events, cuda, capture=True) + + moe.forward(_Tensor("x", events), reduce=False) + + assert events == [ + "routed(main)", + "shared(main)", + "add(routed_out,shared_out)", + ] + + +def test_capture_keeps_lora_schedule_without_active_adapter(monkeypatch): + events: list[str] = [] + cuda = _Cuda(events) + _install_lora_policy(monkeypatch, main_alloc=False, capture=True) + moe = _make_moe(events, cuda, capture=True) + moe.shared_experts.lora_backend.batch_info.has_active_lora = False + + moe.forward(_Tensor("x", events), reduce=False) + + assert events == [ + "routed(main)", + "shared(main)", + "add(routed_out,shared_out)", + ] + + +if __name__ == "__main__": + import sys + + sys.exit(pytest.main([__file__, "-v"])) diff --git a/test/registered/unit/lora/test_mem_pool_ep_unit.py b/test/registered/unit/lora/test_mem_pool_ep_unit.py index 3f9b6dfc4..2e88ff8ec 100644 --- a/test/registered/unit/lora/test_mem_pool_ep_unit.py +++ b/test/registered/unit/lora/test_mem_pool_ep_unit.py @@ -19,19 +19,164 @@ from sglang.test.ci.ci_register import register_amd_ci, register_cuda_ci register_cuda_ci(est_time=9, stage="base-b", runner_config="1-gpu-small") register_amd_ci(est_time=9, suite="stage-b-test-1-gpu-small-amd") +import ast import types import unittest import unittest.mock as mock +from enum import Enum +from pathlib import Path import torch -from sglang.srt.lora.mem_pool import ( - LoRAMemoryPool, - _get_moe_ep_context, - _get_moe_tp_context, +from sglang.srt.lora.eviction_policy import get_eviction_policy + +REPO_ROOT = Path(__file__).resolve().parents[4] +MOE_UTILS_PATH = REPO_ROOT / "python/sglang/srt/layers/moe/utils.py" +STANDARD_DISPATCHER_PATH = ( + REPO_ROOT / "python/sglang/srt/layers/moe/token_dispatcher/standard.py" ) +class _FakeBaseLayerWithLoRA: + pass + + +class _FakeFusedMoEWithLoRA(_FakeBaseLayerWithLoRA): + pass + + +_LORA_LAYERS_STUB = types.ModuleType("sglang.srt.lora.layers") +_LORA_LAYERS_STUB.BaseLayerWithLoRA = _FakeBaseLayerWithLoRA +_LORA_LAYERS_STUB.FusedMoEWithLoRA = _FakeFusedMoEWithLoRA +_LORA_ADAPTER_STUB = types.ModuleType("sglang.srt.lora.lora") +_LORA_ADAPTER_STUB.LoRAAdapter = object +with mock.patch.dict( + "sys.modules", + { + "sglang.srt.lora.layers": _LORA_LAYERS_STUB, + "sglang.srt.lora.lora": _LORA_ADAPTER_STUB, + }, +): + import sglang.srt.lora.mem_pool as mem_pool_module + from sglang.srt.lora.mem_pool import ( + EMPTY_SLOT, + LoRAMemoryPool, + _get_moe_ep_context, + _get_moe_tp_context, + _moe_runner_keeps_global_expert_ids, + ) + + +class _IdentityMoeSlices: + def slice_moe_lora_a_weights(self, weights, _rank, _target): + return weights + + def slice_moe_lora_b_weights(self, weights, _rank, _target): + return weights + + +class _FakeSharedMoeLayer(_IdentityMoeSlices): + is_shared_fused_moe = True + + def __init__(self, tp_rank: int = 0): + self.moe_tp_rank = tp_rank + + +class _FakeRoutedMoeLayer(_FakeFusedMoEWithLoRA, _IdentityMoeSlices): + def __init__(self, tp_rank: int = 0): + object.__setattr__( + self, + "base_layer", + types.SimpleNamespace( + moe_tp_rank=tp_rank, + is_shared_fused_moe=False, + ), + ) + + def slice_moe_lora_a_weights(self, weights, _rank, _target): + return weights + + def slice_moe_lora_b_weights(self, weights, _rank, _target): + return weights + + +class _FakeDenseLayer: + def slice_lora_a_weights(self, weights, _rank): + return weights + + def slice_lora_b_weights(self, weights, _rank): + return weights + + +def _load_lora_weight_to_buffer(pool, **kwargs): + with mock.patch.dict("sys.modules", {"sglang.srt.lora.layers": _LORA_LAYERS_STUB}): + return pool.load_lora_weight_to_buffer(**kwargs) + + +def _load_moe_backend_enum(): + tree = ast.parse(MOE_UTILS_PATH.read_text()) + backend = next( + node + for node in tree.body + if isinstance(node, ast.ClassDef) and node.name == "MoeRunnerBackend" + ) + namespace = {"Enum": Enum} + exec( + compile( + ast.fix_missing_locations(ast.Module(body=[backend], type_ignores=[])), + str(MOE_UTILS_PATH), + "exec", + ), + namespace, + ) + return namespace[backend.name] + + +def _load_standard_dispatcher(get_parallel, get_backend): + tree = ast.parse(STANDARD_DISPATCHER_PATH.read_text()) + source = next( + node + for node in tree.body + if isinstance(node, ast.ClassDef) and node.name == "StandardDispatcher" + ) + init = next( + node + for node in source.body + if isinstance(node, ast.FunctionDef) and node.name == "__init__" + ) + + class _DispatcherBase: + def __init__(self): + pass + + test_class = ast.ClassDef( + name="_StandardDispatcherUnderTest", + bases=[ast.Name(id="_DispatcherBase", ctx=ast.Load())], + keywords=[], + body=[init], + decorator_list=[], + ) + namespace = { + "_DispatcherBase": _DispatcherBase, + "MoeRunnerConfig": object, + "get_parallel": get_parallel, + "get_moe_runner_backend": get_backend, + "_use_aiter": False, + "get_moe_a2a_backend": lambda: types.SimpleNamespace( + supports_aiter=lambda: False + ), + } + exec( + compile( + ast.fix_missing_locations(ast.Module(body=[test_class], type_ignores=[])), + str(STANDARD_DISPATCHER_PATH), + "exec", + ), + namespace, + ) + return namespace[test_class.name] + + def _make_pool( *, num_experts_global: int, @@ -60,6 +205,159 @@ def _make_pool( return pool +class _IterationOrderedSet(set): + def __init__(self, values, iteration_order): + super().__init__(values) + self.iteration_order = iteration_order + + def __iter__(self): + return iter(self.iteration_order) + + +class TestDeterministicPoolSlots(unittest.TestCase): + @staticmethod + def _prepare(iteration_order): + pool = LoRAMemoryPool.__new__(LoRAMemoryPool) + pool.max_loras_per_batch = 4 + pool.uid_to_buffer_id = {} + pool.buffer_id_to_uid = [EMPTY_SLOT] * 4 + pool.eviction_policy = get_eviction_policy("lru") + loaded = [] + pool.load_lora_weight_to_buffer = lambda uid, slot, *_args: loaded.append( + (uid, slot) + ) + uids = _IterationOrderedSet( + {None, "adapter-a", "adapter-b", "adapter-c"}, iteration_order + ) + + pool.prepare_lora_batch( + cur_uids=uids, + lora_adapters={}, + lora_modules=[], + lora_refs={}, + lora_embed_tokens_module=None, + lora_lm_head_module=None, + ) + return loaded, list(pool.eviction_policy.access_order) + + def test_uid_iteration_order_does_not_change_slots_or_lru(self): + forward = [None, "adapter-a", "adapter-b", "adapter-c"] + reverse = list(reversed(forward)) + expected = ( + [ + (None, 0), + ("adapter-a", 1), + ("adapter-b", 2), + ("adapter-c", 3), + ], + ["adapter-a", "adapter-b", "adapter-c"], + ) + + self.assertEqual(self._prepare(forward), expected) + self.assertEqual(self._prepare(reverse), expected) + + @staticmethod + def _evict_after_touch(iteration_order): + pool = LoRAMemoryPool.__new__(LoRAMemoryPool) + pool.max_loras_per_batch = 4 + pool.uid_to_buffer_id = { + None: 0, + "adapter-a": 1, + "adapter-b": 2, + "adapter-c": 3, + } + pool.buffer_id_to_uid = [None, "adapter-a", "adapter-b", "adapter-c"] + pool.eviction_policy = get_eviction_policy("lru") + for uid in ("adapter-a", "adapter-b", "adapter-c"): + pool.eviction_policy.mark_used(uid) + pool.load_lora_weight_to_buffer = lambda *_args: None + common = dict( + lora_adapters={}, + lora_modules=[], + lora_refs={}, + lora_embed_tokens_module=None, + lora_lm_head_module=None, + ) + + pool.prepare_lora_batch( + cur_uids=_IterationOrderedSet( + {"adapter-a", "adapter-b", "adapter-c"}, iteration_order + ), + **common, + ) + pool.prepare_lora_batch(cur_uids={"adapter-d"}, **common) + return pool.uid_to_buffer_id, list(pool.eviction_policy.access_order) + + def test_uid_iteration_order_does_not_change_next_lru_victim(self): + forward = ["adapter-a", "adapter-b", "adapter-c"] + reverse = list(reversed(forward)) + expected = ( + {None: 0, "adapter-d": 1, "adapter-b": 2, "adapter-c": 3}, + ["adapter-b", "adapter-c", "adapter-d"], + ) + + self.assertEqual(self._evict_after_touch(forward), expected) + self.assertEqual(self._evict_after_touch(reverse), expected) + + +class TestBufferSlotClearing(unittest.TestCase): + @staticmethod + def _buffers(pool): + layered = [ + tensor + for buffers in (*pool.A_buffer.values(), *pool.B_buffer.values()) + for tensor in buffers + ] + direct = [ + tensor + for buffers in ( + pool.embedding_A_buffer, + pool.embedding_B_buffer, + pool.lm_head_A_buffer, + pool.lm_head_B_buffer, + pool.new_embeddings_buffer, + ) + for tensor in buffers.values() + ] + return layered + direct + + @classmethod + def _make_pool(cls): + pool = LoRAMemoryPool.__new__(LoRAMemoryPool) + pool.num_layer = 2 + pool.A_buffer = {"a": [torch.ones(2, 1, 2) for _ in range(2)]} + pool.B_buffer = {"b": [torch.full((2, 1, 2), float("nan")) for _ in range(2)]} + pool.embedding_A_buffer = {"a": torch.ones(2, 1, 2)} + pool.embedding_B_buffer = {"b": torch.full((2, 1, 2), float("nan"))} + pool.lm_head_A_buffer = {"a": torch.ones(2, 1, 2)} + pool.lm_head_B_buffer = {"b": torch.full((2, 1, 2), float("inf"))} + pool.new_embeddings_buffer = {"embeddings": torch.full((2, 1, 2), float("nan"))} + for tensor in cls._buffers(pool): + tensor[1].fill_(7) + + pool.uid_to_buffer_id = {"adapter": 0} + pool.buffer_id_to_uid = ["adapter", "other"] + pool.eviction_policy = get_eviction_policy("lru") + pool.eviction_policy.mark_used("adapter") + return pool + + def test_remove_lora_clears_slot_in_place_and_residency(self): + pool = self._make_pool() + tensors = self._buffers(pool) + pointers = [tensor.data_ptr() for tensor in tensors] + + self.assertEqual(pool.remove_lora("adapter"), 0) + + for tensor, pointer in zip(tensors, pointers): + torch.testing.assert_close(tensor[0], torch.zeros_like(tensor[0])) + torch.testing.assert_close(tensor[1], torch.full_like(tensor[1], 7)) + self.assertTrue(torch.isfinite(tensor).all()) + self.assertEqual(tensor.data_ptr(), pointer) + self.assertNotIn("adapter", pool.uid_to_buffer_id) + self.assertIs(pool.buffer_id_to_uid[0], EMPTY_SLOT) + self.assertNotIn("adapter", pool.eviction_policy.access_order) + + def _make_fake_base_model(num_experts: int) -> torch.nn.Module: """Return a `torch.nn.Module` whose `.config` exposes `num_experts`. @@ -338,6 +636,251 @@ class TestIterLocalExpertWeightsTensor(unittest.TestCase): list(pool._iter_local_expert_weights(weights, "weights")) +class TestSharedMoeProductionLoad(unittest.TestCase): + def test_unmarked_2d_shared_expert_uses_dense_buffers(self): + pool = _make_pool( + num_experts_global=8, + moe_ep_size=1, + moe_ep_rank=0, + moe_use_local_expert_ids=False, + ) + pool.num_layer = 1 + pool.tp_rank = 0 + pool.max_lora_rank = 2 + pool.target_modules = {"down_proj"} + pool.experts_shared_outer_loras = False + pool.strict_loading = True + pool.lora_added_tokens_size = 0 + pool.pin_memory_available = False + pool.enable_lora_overlap_loading = False + pool.base_model = object() + pool.A_buffer = { + "down_proj": [torch.full((1, 2, 3), -1.0)], + "down_proj_shared_moe": [torch.full((1, 2, 2, 3), -7.0)], + } + pool.B_buffer = { + "down_proj": [torch.full((1, 5, 2), -1.0)], + "down_proj_shared_moe": [torch.full((1, 1, 5, 2), -7.0)], + } + pool.embedding_A_buffer = {} + pool.embedding_B_buffer = {} + pool.lm_head_A_buffer = {} + pool.lm_head_B_buffer = {} + pool.new_embeddings_buffer = {} + + down_a = torch.arange(6, dtype=torch.float32).reshape(2, 3) + down_b = torch.arange(10, dtype=torch.float32).reshape(5, 2) + adapter = types.SimpleNamespace( + config=types.SimpleNamespace(r=2), + scaling=2.5, + layers=[ + types.SimpleNamespace( + weights={ + "model.layers.0.mlp.shared_experts.down_proj.lora_A.weight": down_a, + "model.layers.0.mlp.shared_experts.down_proj.lora_B.weight": down_b, + }, + pinned_weights={}, + ) + ], + embedding_layers={}, + added_tokens_embeddings={}, + ) + + _load_lora_weight_to_buffer( + pool, + uid="dense-shared", + buffer_id=0, + lora_adapter=adapter, + lora_modules=[ + {"model.layers.0.mlp.shared_experts.down_proj": _FakeDenseLayer()} + ], + lora_embed_tokens_module=None, + lora_lm_head_module=None, + ) + + torch.testing.assert_close(pool.A_buffer["down_proj"][0][0], down_a) + torch.testing.assert_close(pool.B_buffer["down_proj"][0][0], down_b) + self.assertTrue(torch.all(pool.A_buffer["down_proj_shared_moe"][0] == -7)) + self.assertTrue(torch.all(pool.B_buffer["down_proj_shared_moe"][0] == -7)) + + def test_rank3_loads_shared_gate_b_and_down_a(self): + """Shared-sink weights stay replicated even on a nonzero EP rank.""" + pool = _make_pool( + num_experts_global=256, + moe_ep_size=4, + moe_ep_rank=3, + moe_use_local_expert_ids=True, + ) + pool.num_layer = 1 + pool.max_lora_rank = 2 + pool.target_modules = {"gate_up_proj", "down_proj"} + pool.experts_shared_outer_loras = True + pool.strict_loading = True + pool.lora_added_tokens_size = 0 + pool.pin_memory_available = False + pool.enable_lora_overlap_loading = False + pool.base_model = object() + pool.A_buffer = { + "gate_up_proj": [torch.full((1, 4, 5), -7.0)], + "down_proj": [torch.full((1, 2, 3), -7.0)], + "gate_up_proj_shared_moe": [torch.full((1, 1, 4, 5), -1.0)], + "down_proj_shared_moe": [torch.full((1, 2, 2, 3), -1.0)], + } + pool.B_buffer = { + "gate_up_proj": [torch.full((1, 6, 2), -7.0)], + "down_proj": [torch.full((1, 5, 2), -7.0)], + "gate_up_proj_shared_moe": [torch.full((1, 2, 6, 2), -1.0)], + "down_proj_shared_moe": [torch.full((1, 1, 5, 2), -1.0)], + } + pool.embedding_A_buffer = {} + pool.embedding_B_buffer = {} + pool.lm_head_A_buffer = {} + pool.lm_head_B_buffer = {} + pool.new_embeddings_buffer = {} + + gate_b = torch.arange(2 * 6 * 2, dtype=torch.float32).reshape(2, 6, 2) + down_a = torch.arange(2 * 2 * 3, dtype=torch.float32).reshape(2, 2, 3) + adapter = types.SimpleNamespace( + config=types.SimpleNamespace(r=2), + scaling=2.5, + layers=[ + types.SimpleNamespace( + weights={ + "model.layers.0.mlp.shared_experts.gate_up_proj.lora_B.weight": gate_b, + "model.layers.0.mlp.shared_experts.down_proj.lora_A.weight": down_a, + }, + pinned_weights={}, + ) + ], + embedding_layers={}, + added_tokens_embeddings={}, + ) + + shared_sink = _FakeSharedMoeLayer(tp_rank=1) + + _load_lora_weight_to_buffer( + pool, + uid="shared", + buffer_id=0, + lora_adapter=adapter, + lora_modules=[{"shared_experts": shared_sink}], + lora_embed_tokens_module=None, + lora_lm_head_module=None, + ) + + torch.testing.assert_close(pool.A_buffer["down_proj_shared_moe"][0][0], down_a) + torch.testing.assert_close( + pool.B_buffer["gate_up_proj_shared_moe"][0][0], gate_b * adapter.scaling + ) + self.assertTrue(torch.all(pool.A_buffer["down_proj"][0] == -7)) + self.assertTrue(torch.all(pool.B_buffer["gate_up_proj"][0] == -7)) + + pool.A_buffer["down_proj_shared_moe"][0].fill_(-1) + pool.B_buffer["gate_up_proj_shared_moe"][0].fill_(-1) + adapter.layers[0].weights = { + **{ + f"model.layers.0.mlp.shared_experts.{i}.gate_up_proj.lora_B.weight": gate_b[ + i + ] + for i in range(2) + }, + **{ + f"model.layers.0.mlp.shared_experts.{i}.down_proj.lora_A.weight": down_a[ + i + ] + for i in range(2) + }, + } + _load_lora_weight_to_buffer( + pool, + uid="shared-named", + buffer_id=0, + lora_adapter=adapter, + lora_modules=[{"shared_experts": shared_sink}], + lora_embed_tokens_module=None, + lora_lm_head_module=None, + ) + torch.testing.assert_close(pool.A_buffer["down_proj_shared_moe"][0][0], down_a) + torch.testing.assert_close( + pool.B_buffer["gate_up_proj_shared_moe"][0][0], gate_b * adapter.scaling + ) + + def test_missing_inner_factors_clear_reused_shared_and_routed_slots(self): + for suffix, expert_path in ( + ("_shared_moe", "shared_experts"), + ("_moe", "experts"), + ): + with self.subTest(suffix=suffix): + pool = _make_pool( + num_experts_global=2, + moe_ep_size=1, + moe_ep_rank=0, + moe_use_local_expert_ids=False, + ) + pool.num_layer = 1 + pool.max_lora_rank = 2 + pool.target_modules = {"gate_up_proj", "down_proj"} + pool.experts_shared_outer_loras = True + pool.strict_loading = True + pool.lora_added_tokens_size = 0 + pool.pin_memory_available = False + pool.enable_lora_overlap_loading = False + pool.base_model = object() + pool.A_buffer = { + f"gate_up_proj{suffix}": [torch.full((1, 1, 4, 5), -1.0)], + f"down_proj{suffix}": [torch.full((1, 2, 2, 3), -1.0)], + } + pool.B_buffer = { + f"gate_up_proj{suffix}": [torch.full((1, 2, 6, 2), -1.0)], + f"down_proj{suffix}": [torch.full((1, 1, 5, 2), -1.0)], + } + pool.embedding_A_buffer = {} + pool.embedding_B_buffer = {} + pool.lm_head_A_buffer = {} + pool.lm_head_B_buffer = {} + pool.new_embeddings_buffer = {} + + adapter = types.SimpleNamespace( + config=types.SimpleNamespace(r=2), + scaling=2.5, + layers=[ + types.SimpleNamespace( + weights={ + f"model.layers.0.mlp.{expert_path}.gate_up_proj." + "lora_A.weight": torch.ones(1, 4, 5), + f"model.layers.0.mlp.{expert_path}.down_proj." + "lora_B.weight": torch.ones(1, 5, 2), + }, + pinned_weights={}, + ) + ], + embedding_layers={}, + added_tokens_embeddings={}, + ) + module = ( + _FakeSharedMoeLayer() + if suffix == "_shared_moe" + else _FakeRoutedMoeLayer() + ) + + _load_lora_weight_to_buffer( + pool, + uid="adapter", + buffer_id=0, + lora_adapter=adapter, + lora_modules=[{"experts": module}], + lora_embed_tokens_module=None, + lora_lm_head_module=None, + ) + + self.assertEqual( + torch.count_nonzero(pool.A_buffer[f"down_proj{suffix}"][0]), 0 + ) + self.assertEqual( + torch.count_nonzero(pool.B_buffer[f"gate_up_proj{suffix}"][0]), 0 + ) + + class TestModuleLevelHelpers(unittest.TestCase): """`_get_moe_ep_context` / `_moe_runner_keeps_global_expert_ids` must degrade gracefully when the MoE EP group or runner backend is @@ -357,6 +900,48 @@ class TestModuleLevelHelpers(unittest.TestCase): self.assertEqual(tp_size, 1) self.assertEqual(tp_rank, 0) + def test_keeps_global_expert_ids_defaults_to_false(self): + # Without a specific flashinfer backend selected, default is False. + self.assertFalse(_moe_runner_keeps_global_expert_ids()) + + def test_real_backend_predicate_matches_dispatcher_and_pool(self): + backends = _load_moe_backend_enum() + expected_global = { + backends.FLASHINFER_TRTLLM, + backends.EXPERIMENTAL_SGL_TRTLLM, + backends.FLASHINFER_TRTLLM_ROUTED, + backends.FLASHINFER_CUTLASS, + backends.FLASHINFER_MXFP4, + backends.FLASHINFER_CUTEDSL, + } + config = types.SimpleNamespace( + num_experts=8, + num_local_experts=2, + num_fused_shared_experts=0, + ) + parallel = types.SimpleNamespace(moe_ep_size=4, moe_ep_rank=1) + state = types.SimpleNamespace(backend=None) + standard_dispatcher = _load_standard_dispatcher( + get_parallel=lambda: parallel, + get_backend=lambda: state.backend, + ) + moe_utils = types.ModuleType("sglang.srt.layers.moe.utils") + moe_utils.get_moe_runner_backend = lambda: state.backend + for backend in backends: + state.backend = backend + with mock.patch.dict( + "sys.modules", {"sglang.srt.layers.moe.utils": moe_utils} + ): + dispatcher = standard_dispatcher(config) + self.assertEqual( + dispatcher.skip_local_expert_mapping, + backend in expected_global, + ) + self.assertEqual( + _moe_runner_keeps_global_expert_ids(), + backend in expected_global, + ) + class TestPoolInitPicksUpEpContext(unittest.TestCase): """`LoRAMemoryPool.__init__` should read EP context from the module- @@ -377,16 +962,19 @@ class TestPoolInitPicksUpEpContext(unittest.TestCase): `init_buffers` — we only care about the EP-context state. """ with ( - mock.patch( - "sglang.srt.lora.mem_pool._get_moe_ep_context", + mock.patch.object( + mem_pool_module, + "_get_moe_ep_context", return_value=(ep_size, ep_rank), ), - mock.patch( - "sglang.srt.lora.mem_pool._get_moe_tp_context", + mock.patch.object( + mem_pool_module, + "_get_moe_tp_context", return_value=(moe_tp_size, moe_tp_rank), ), - mock.patch( - "sglang.srt.lora.mem_pool._moe_runner_keeps_global_expert_ids", + mock.patch.object( + mem_pool_module, + "_moe_runner_keeps_global_expert_ids", return_value=keeps_global, ), mock.patch.object(LoRAMemoryPool, "init_buffers", lambda self, _m: None), @@ -655,8 +1243,6 @@ class TestLoadBufferPassesMoeTpRankToSlice(unittest.TestCase): shapes the test does not provide).""" def test_moe_tp_rank_used_for_slicing_when_ep_lt_tp(self): - from sglang.srt.lora.layers import FusedMoEWithLoRA - # tp=4 ep=2 → moe_tp_size=2. Pick OUTER rank 3 so moe_tp_rank=1. # The two values differ; the bug would surface on this exact rank. pool = LoRAMemoryPool.__new__(LoRAMemoryPool) @@ -689,64 +1275,65 @@ class TestLoadBufferPassesMoeTpRankToSlice(unittest.TestCase): pool.lm_head_B_buffer = {} pool.new_embeddings_buffer = {} - captured_ranks = [] + moe_mod = _FakeRoutedMoeLayer(tp_rank=1) - moe_mod = mock.MagicMock(spec=FusedMoEWithLoRA) + for ab in ("A", "B"): + with self.subTest(ab=ab): + captured_ranks = [] - def capture_a(weights, tp_rank, target_module): - captured_ranks.append(("A", target_module, tp_rank)) - raise TestLoadBufferPassesMoeTpRankToSlice._StopAfterCapture() + def capture(weights, tp_rank, target_module): + captured_ranks.append((ab, target_module, tp_rank)) + raise TestLoadBufferPassesMoeTpRankToSlice._StopAfterCapture() - def capture_b(weights, tp_rank, target_module): - captured_ranks.append(("B", target_module, tp_rank)) - raise TestLoadBufferPassesMoeTpRankToSlice._StopAfterCapture() + moe_mod.slice_moe_lora_a_weights = ( + capture if ab == "A" else lambda weights, *_args: weights + ) + moe_mod.slice_moe_lora_b_weights = ( + capture if ab == "B" else lambda weights, *_args: weights + ) - moe_mod.slice_moe_lora_a_weights.side_effect = capture_a - moe_mod.slice_moe_lora_b_weights.side_effect = capture_b + # The per-expert key creates the A or B dictionary whose + # production call site invokes the matching MoE slicer. + adapter = mock.MagicMock() + adapter.config.r = 4 + adapter.scaling = 1.0 + adapter.embedding_layers = {} + adapter.added_tokens_embeddings = {} + adapter.layers = [ + types.SimpleNamespace( + weights={ + f"model.layers.0.mlp.experts.0.gate_up_proj.lora_{ab}.weight": torch.zeros( + 8, 4 + ), + }, + pinned_weights={}, + ) + ] - # Adapter with one per-expert MoE LoRA-A weight. The expert regex - # `experts\.(\d+)\.` must match the key, which routes the weight - # into `temp_A_buffer["gate_up_proj_moe"]` — the dict shape that - # makes `temp_A_buffer.get("gate_up_proj_moe") is not None` true, - # which in turn triggers `slice_moe_lora_a_weights` (and the - # capture). - adapter = mock.MagicMock() - adapter.config.r = 4 - adapter.scaling = 1.0 - adapter.embedding_layers = {} - adapter.added_tokens_embeddings = {} - adapter.layers = [ - types.SimpleNamespace( - weights={ - "model.layers.0.mlp.experts.0.gate_up_proj.lora_A.weight": ( - torch.zeros(8, 4) - ), - }, - pinned_weights={}, - ) - ] + with self.assertRaises( + TestLoadBufferPassesMoeTpRankToSlice._StopAfterCapture + ): + _load_lora_weight_to_buffer( + pool, + uid="test", + buffer_id=0, + lora_adapter=adapter, + lora_modules=[{"mlp.experts": moe_mod}], + lora_embed_tokens_module=None, + lora_lm_head_module=None, + ) - with self.assertRaises(TestLoadBufferPassesMoeTpRankToSlice._StopAfterCapture): - pool.load_lora_weight_to_buffer( - uid="test", - buffer_id=0, - lora_adapter=adapter, - lora_modules=[{"mlp.experts": moe_mod}], - lora_embed_tokens_module=None, - lora_lm_head_module=None, - ) - - self.assertGreater(len(captured_ranks), 0, "slicing was never invoked") - for ab, target_module, rank in captured_ranks: - self.assertEqual( - rank, - pool.moe_tp_rank, - f"slice_moe_lora_{ab.lower()}_weights for {target_module} " - f"received rank={rank}; expected moe_tp_rank=" - f"{pool.moe_tp_rank} (outer tp_rank is {pool.tp_rank}). " - "Passing the outer tp_rank slices past " - "intermediate_size_per_partition when ep_size < tp_size.", - ) + self.assertEqual(len(captured_ranks), 1, "slicing was never invoked") + _, target_module, rank = captured_ranks[0] + self.assertEqual( + rank, + pool.moe_tp_rank, + f"slice_moe_lora_{ab.lower()}_weights for {target_module} " + f"received rank={rank}; expected moe_tp_rank=" + f"{pool.moe_tp_rank} (outer tp_rank is {pool.tp_rank}). " + "Passing the outer tp_rank slices past " + "intermediate_size_per_partition when ep_size < tp_size.", + ) if __name__ == "__main__": diff --git a/test/registered/unit/model_executor/test_pool_configurator.py b/test/registered/unit/model_executor/test_pool_configurator.py index d341fe2ec..765264d75 100644 --- a/test/registered/unit/model_executor/test_pool_configurator.py +++ b/test/registered/unit/model_executor/test_pool_configurator.py @@ -118,6 +118,7 @@ def _make_model_runner( sa.max_running_requests = max_running_requests sa.disaggregation_decode_extra_slots = disaggregation_decode_extra_slots sa.enable_dsa_cache_layer_split = False + sa.kv_cache_dtype = "auto" mr.server_args = sa spec = MagicMock() diff --git a/test/registered/unit/parser/test_inkling_renderer.py b/test/registered/unit/parser/test_inkling_renderer.py new file mode 100644 index 000000000..8a84701a6 --- /dev/null +++ b/test/registered/unit/parser/test_inkling_renderer.py @@ -0,0 +1,242 @@ +import unittest + +from sglang.srt.entrypoints.openai.chat_encoding import encode_simple_chat +from sglang.srt.parser.inkling_renderer import render_inkling_messages +from sglang.srt.parser.inkling_tokenizer import ( + CONTENT_INVOKE_TOOL_JSON, + CONTENT_MODEL_END_SAMPLING, + CONTENT_TEXT, + CONTENT_THINKING, + CONTENT_XML, + END_MESSAGE, + INKLING_SPECIAL_TOKEN_IDS, + MESSAGE_MODEL, + MESSAGE_SYSTEM, + MESSAGE_USER, +) +from sglang.test.ci.ci_register import register_cpu_ci + +register_cpu_ci(est_time=1, suite="base-a-test-cpu") + + +def _text(value: str) -> list[int]: + return list(value.encode()) + + +class _InklingTokenizer: + def encode_special(self, token: str) -> int: + return INKLING_SPECIAL_TOKEN_IDS[token] + + def encode_text(self, text: str) -> list[int]: + return _text(text) + + +class _BaseTokenizer: + chat_template = None + + def encode(self, text: str, add_special_tokens: bool = False) -> list[int]: + return _text(text) + + +def _block(role: str, kind: str, payload: str, author: str = "") -> list[int]: + return ( + [INKLING_SPECIAL_TOKEN_IDS[role]] + + _text(author) + + [ + INKLING_SPECIAL_TOKEN_IDS[kind], + *_text(payload), + INKLING_SPECIAL_TOKEN_IDS[END_MESSAGE], + ] + ) + + +class TestInklingRenderer(unittest.TestCase): + def setUp(self): + self.tokenizer = _InklingTokenizer() + + def test_generation_prompt_is_not_prefilled(self): + actual = render_inkling_messages( + [{"role": "user", "content": "hello"}], self.tokenizer + ) + self.assertEqual( + actual, + _block(MESSAGE_SYSTEM, CONTENT_TEXT, "Thinking effort level: 0.9") + + _block(MESSAGE_USER, CONTENT_TEXT, "hello"), + ) + self.assertNotEqual(actual[-1], INKLING_SPECIAL_TOKEN_IDS[MESSAGE_MODEL]) + + def test_tool_system_and_effort_have_canonical_prefix_order(self): + tools = [ + { + "type": "function", + "function": { + "name": "weather", + "description": "Lookup weather", + "parameters": {"type": "object"}, + }, + } + ] + actual = render_inkling_messages( + [ + {"role": "system", "content": "original"}, + {"role": "user", "content": "question"}, + ], + self.tokenizer, + tools=tools, + reasoning_effort=0.8764, + ) + tool_json = ( + '[{"description":"Lookup weather","name":"weather",' + '"parameters":{"type":"object"},"type":"function"}]' + ) + expected = ( + _block( + MESSAGE_SYSTEM, + CONTENT_XML, + tool_json, + author="tool_declare", + ) + + _block(MESSAGE_SYSTEM, CONTENT_TEXT, "original") + + _block(MESSAGE_SYSTEM, CONTENT_TEXT, "Thinking effort level: 0.88") + + _block(MESSAGE_USER, CONTENT_TEXT, "question") + ) + self.assertEqual(actual, expected) + + def test_multiturn_conversation_has_one_fixed_effort_directive(self): + system = {"role": "system", "content": "system"} + user1 = {"role": "user", "content": "user1"} + assistant1 = {"role": "assistant", "content": "assistant1"} + user2 = {"role": "user", "content": "user2"} + prefix = ( + _block(MESSAGE_SYSTEM, CONTENT_TEXT, "system") + + _block(MESSAGE_SYSTEM, CONTENT_TEXT, "Thinking effort level: 0.2") + + _block(MESSAGE_USER, CONTENT_TEXT, "user1") + ) + + turn1 = render_inkling_messages( + [system, user1], self.tokenizer, reasoning_effort=0.2 + ) + turn2 = render_inkling_messages( + [system, user1, assistant1, user2], + self.tokenizer, + reasoning_effort=0.2, + ) + + self.assertEqual(turn1, prefix) + self.assertEqual( + turn2, + prefix + + _block(MESSAGE_MODEL, CONTENT_TEXT, "assistant1") + + [INKLING_SPECIAL_TOKEN_IDS[CONTENT_MODEL_END_SAMPLING]] + + _block(MESSAGE_USER, CONTENT_TEXT, "user2"), + ) + + def test_historical_assistant_preserves_parts_and_ends_sampling(self): + actual = render_inkling_messages( + [ + { + "role": "assistant", + "content": [ + {"type": "thinking", "thinking": "first"}, + {"type": "text", "text": "visible"}, + {"type": "reasoning", "text": "second"}, + ], + "tool_calls": [ + { + "id": "call-1", + "function": { + "name": "weather", + "arguments": '{"city":"SF"}', + }, + } + ], + } + ], + self.tokenizer, + ) + expected = ( + _block(MESSAGE_SYSTEM, CONTENT_TEXT, "Thinking effort level: 0.9") + + _block(MESSAGE_MODEL, CONTENT_THINKING, "first") + + _block(MESSAGE_MODEL, CONTENT_TEXT, "visible") + + _block(MESSAGE_MODEL, CONTENT_THINKING, "second") + + _block( + MESSAGE_MODEL, + CONTENT_INVOKE_TOOL_JSON, + '{"name":"weather","args":{"city":"SF"}}', + author="weather", + ) + + [INKLING_SPECIAL_TOKEN_IDS[CONTENT_MODEL_END_SAMPLING]] + ) + self.assertEqual(actual, expected) + + def test_empty_assistant_message_does_not_emit_bare_terminator(self): + """Bug regression: an assistant message that renders zero blocks + (content None, no reasoning, no tool calls) appended a bare + <|content_model_end_sampling|> with no preceding model block — + injecting a malformed turn terminator into the prompt.""" + actual = render_inkling_messages( + [ + {"role": "user", "content": "hi"}, + {"role": "assistant", "content": None}, + {"role": "user", "content": "again"}, + ], + self.tokenizer, + ) + self.assertNotIn(INKLING_SPECIAL_TOKEN_IDS[CONTENT_MODEL_END_SAMPLING], actual) + + def test_reasoning_content_cannot_reorder_thinking_parts(self): + with self.assertRaisesRegex(ValueError, "cannot mix"): + render_inkling_messages( + [ + { + "role": "assistant", + "reasoning_content": "legacy", + "content": [{"type": "thinking", "thinking": "ordered"}], + } + ], + self.tokenizer, + ) + + def test_reasoning_effort_is_two_decimal_quantized_and_validated(self): + for value, expected in ( + (0.8766, "0.88"), + (0.0, "0"), + (0.99, "0.99"), + (0.125, "0.12"), + (0.875, "0.88"), + ): + with self.subTest(value=value): + actual = render_inkling_messages( + [{"role": "user", "content": "q"}], + self.tokenizer, + reasoning_effort=value, + ) + directive = _block( + MESSAGE_SYSTEM, + CONTENT_TEXT, + f"Thinking effort level: {expected}", + ) + self.assertEqual(actual[: len(directive)], directive) + for value in (-0.1, 1.0, 1.1, float("nan")): + with self.subTest(value=value), self.assertRaises(ValueError): + render_inkling_messages( + [{"role": "user", "content": "q"}], + self.tokenizer, + reasoning_effort=value, + ) + + def test_offline_encoder_uses_the_same_inkling_format(self): + actual = encode_simple_chat( + tokenizer=_BaseTokenizer(), + spec="inkling", + messages=[{"role": "user", "content": "hello"}], + ) + self.assertEqual( + actual, + _block(MESSAGE_SYSTEM, CONTENT_TEXT, "Thinking effort level: 0.9") + + _block(MESSAGE_USER, CONTENT_TEXT, "hello"), + ) + + +if __name__ == "__main__": + unittest.main() diff --git a/test/registered/unit/parser/test_reasoning_parser.py b/test/registered/unit/parser/test_reasoning_parser.py index 44784e95f..7fc452ba6 100644 --- a/test/registered/unit/parser/test_reasoning_parser.py +++ b/test/registered/unit/parser/test_reasoning_parser.py @@ -9,6 +9,7 @@ from sglang.srt.parser.reasoning_parser import ( Gemma4Detector, Glm45Detector, HunyuanDetector, + InklingDetector, KimiDetector, KimiK2Detector, Nemotron3Detector, @@ -166,6 +167,89 @@ class TestQwen3Detector(CustomTestCase): self.assertEqual(result.reasoning_text, "") +class TestInklingDetector(CustomTestCase): + def test_streaming_routes_blocks_across_all_string_boundaries(self): + detector = InklingDetector() + source = ( + "<|message_model|><|content_thinking|>think<|end_message|>" + "<|message_model|><|content_text|>answer<|end_message|>" + "<|content_model_end_sampling|>" + ) + reasoning = "" + content = "" + for char in source: + result = detector.parse_streaming_increment(char) + reasoning += result.reasoning_text + content += result.normal_text + self.assertEqual(reasoning, "think") + self.assertEqual(content, "answer") + + def test_tool_header_is_preserved_for_the_tool_parser(self): + detector = InklingDetector() + source = ( + "<|message_model|>weather<|content_invoke_tool_json|>" + '{"name":"weather","args":{"city":"SF"}}<|end_message|>' + ) + content = "" + for char in source: + content += detector.parse_streaming_increment(char).normal_text + self.assertEqual(content, source) + + def test_quoted_message_model_token_inside_content_is_preserved(self): + """Bug regression: the header branch flipped to header state on ANY + <|message_model|> occurrence, so a literal token the model wrote + inside a content block (e.g. quoting the protocol) silently swallowed + all payload text up to the next control token.""" + detector = InklingDetector() + source = ( + "<|message_model|><|content_text|>Header token: <|message_model|>" + " then more text<|end_message|>" + ) + result = detector.detect_and_parse(source) + self.assertEqual( + result.normal_text, "Header token: <|message_model|> then more text" + ) + + def test_control_token_inside_tool_header_shares_the_full_alphabet(self): + """Bug regression: the tool-call detector validated headers against + INKLING_SPECIAL_TOKENS while the reasoning parser keyed on the larger + control alphabet (+ <|model_trigger_generation|>), so a control token + smuggled inside a header passed one machine and not the other.""" + from sglang.srt.function_call.inkling_detector import ( + InklingDetector as ToolDetector, + ) + + detector = ToolDetector() + prefix, name = detector._split_trailing_tool_header( + "<|message_model|>weather<|model_trigger_generation|>" + ) + self.assertIsNone(name) + + def test_continuation_stream_text_survives_chunk_boundaries(self): + """Bug regression: text arriving with no open block (a + continue_final_message stream resumes MID text block) was routed to + content only when a chunk held no control token; a chunk like + 'ld<|end_message|>' silently dropped the 'ld'. All out-of-block text + must reach content regardless of chunking.""" + source = ( + " world<|end_message|><|message_model|><|content_text|>next<|end_message|>" + ) + for chunks in ( + [source], + [ + " wor", + "ld<|end_message|>", + "<|message_model|><|content_text|>next<|end_message|>", + ], + list(source), + ): + detector = InklingDetector() + content = "" + for chunk in chunks: + content += detector.parse_streaming_increment(chunk).normal_text + self.assertEqual(content, " worldnext", msg=f"chunks={chunks!r}") + + class TestKimiDetector(CustomTestCase): def setUp(self): self.detector = KimiDetector() diff --git a/test/registered/unit/test_model_overrides.py b/test/registered/unit/test_model_overrides.py index eb162a8af..ccafe35fc 100644 --- a/test/registered/unit/test_model_overrides.py +++ b/test/registered/unit/test_model_overrides.py @@ -74,6 +74,7 @@ class TestModelOverridableWhitelist(CustomTestCase): "disable_overlap_schedule", "uses_mamba_radix_cache", "mamba_radix_cache_strategy", + "mamba_full_memory_ratio", "speculative_moe_runner_backend", "speculative_moe_a2a_backend", "disable_shared_experts_fusion", diff --git a/test/registered/unit/utils/test_hf_transformers.py b/test/registered/unit/utils/test_hf_transformers.py index e466970b5..547a91290 100644 --- a/test/registered/unit/utils/test_hf_transformers.py +++ b/test/registered/unit/utils/test_hf_transformers.py @@ -20,6 +20,7 @@ from sglang.srt.utils.hf_transformers.common import ( _is_deepseek_ocr_model, _override_v_head_dim_if_zero, _patch_text_config, + attach_additional_stop_token_ids, check_gguf_file, get_context_length, get_hf_text_config, @@ -451,6 +452,37 @@ class TestGetHfTextConfig(unittest.TestCase): self.assertEqual(cfg.rope_scaling["type"], "llama3") +# --------------------------------------------------------------------------- +# attach_additional_stop_token_ids +# --------------------------------------------------------------------------- + + +class TestAttachAdditionalStopTokenIds(unittest.TestCase): + """Bug regression: the Inkling bundle ships eos metadata unset while its + turn-final marker <|content_model_end_sampling|> sits in added_tokens; the + old detector only recognized <|eom_id|>, so generation ran to max length + (documented by the Inkling GSM8K test).""" + + @staticmethod + def _tokenizer(added): + return SimpleNamespace(get_added_vocab=lambda: added) + + def test_inkling_end_sampling_registers_as_stop(self): + tok = self._tokenizer({"<|content_model_end_sampling|>": 200006}) + attach_additional_stop_token_ids(tok) + self.assertEqual(tok.additional_stop_token_ids, {200006}) + + def test_eom_id_still_registers_as_stop(self): + tok = self._tokenizer({"<|eom_id|>": 128008}) + attach_additional_stop_token_ids(tok) + self.assertEqual(tok.additional_stop_token_ids, {128008}) + + def test_no_known_marker_yields_none(self): + tok = self._tokenizer({"<|other|>": 7}) + attach_additional_stop_token_ids(tok) + self.assertIsNone(tok.additional_stop_token_ids) + + # --------------------------------------------------------------------------- # _fix_special_tokens_pattern # --------------------------------------------------------------------------- diff --git a/test/srt/models/test_inkling_per_expert_sync.py b/test/srt/models/test_inkling_per_expert_sync.py new file mode 100644 index 000000000..126cc01ee --- /dev/null +++ b/test/srt/models/test_inkling_per_expert_sync.py @@ -0,0 +1,173 @@ +"""CPU unit test for Inkling per-expert RL weight-sync loading. + +Exercises ``_load_per_expert_param`` on a simulated EP x MoE-TP grid (parallel +helpers monkeypatched, no process groups) and checks every (ep_rank, tp_rank) +against a reference fused stack built directly from the full per-expert weights: + + - EP: global expert id remapped to the rank's contiguous local block, + non-owned experts consumed without touching the stack + - MoE-TP: w13 slices the intermediate dim (dim 0 of gate/up), w2 dim 1 + - w13 row layout: Inkling-interleaved vs contiguous [gate || up] + (lora_compatible_layout_enabled() or inference_moe_w13_interleaved=False) + - trtllm MoE layouts rejected loudly + +Run: python3 test/srt/models/test_inkling_per_expert_sync.py +""" + +import types +import unittest + +import torch + +import sglang.srt.models.inkling as inkling_mod + +N_EXPERTS, I_FULL, H = 8, 6, 4 + + +class _FakeModel: + """Just enough of InklingForConditionalGeneration for _load_per_expert_param.""" + + def __init__(self, interleaved: bool, moe=None): + self.text_config = types.SimpleNamespace( + n_routed_experts=N_EXPERTS, + inference_moe_w13_interleaved=interleaved, + ) + self._moe = moe if moe is not None else types.SimpleNamespace() + + def get_submodule(self, path): + return self._moe + + _load_per_expert_param = ( + inkling_mod.InklingForConditionalGeneration._load_per_expert_param + ) + + +def _full_weights(seed=0): + g = torch.Generator().manual_seed(seed) + return { + (e, proj): torch.randn( + (H, I_FULL) if proj == "down_proj" else (I_FULL, H), generator=g + ) + for e in range(N_EXPERTS) + for proj in ("gate_proj", "up_proj", "down_proj") + } + + +def _expected_stacks(full, ep_size, ep_rank, tp_size, tp_rank, contiguous): + """Reference: what the fused w13/w2 stacks must contain on this rank.""" + local = N_EXPERTS // ep_size + i_tp = I_FULL // tp_size + w13 = torch.empty(local, 2 * i_tp, H) + w2 = torch.empty(local, H, i_tp) + for e_local in range(local): + e = ep_rank * local + e_local + gate = full[(e, "gate_proj")][tp_rank * i_tp : (tp_rank + 1) * i_tp] + up = full[(e, "up_proj")][tp_rank * i_tp : (tp_rank + 1) * i_tp] + if contiguous: + w13[e_local] = torch.cat([gate, up], dim=0) + else: # Inkling-interleaved rows [g0, u0, g1, u1, ...] + w13[e_local, 0::2] = gate + w13[e_local, 1::2] = up + w2[e_local] = full[(e, "down_proj")][:, tp_rank * i_tp : (tp_rank + 1) * i_tp] + return w13, w2 + + +class TestPerExpertSync(unittest.TestCase): + def setUp(self): + self._saved = { + n: getattr(inkling_mod, n) + for n in ( + "get_moe_expert_parallel_world_size", + "get_moe_expert_parallel_rank", + "get_moe_tensor_parallel_rank", + "lora_compatible_layout_enabled", + ) + } + + def tearDown(self): + for n, f in self._saved.items(): + setattr(inkling_mod, n, f) + + def _patch(self, ep_size, ep_rank, tp_rank, lora_layout=False): + inkling_mod.get_moe_expert_parallel_world_size = lambda: ep_size + inkling_mod.get_moe_expert_parallel_rank = lambda: ep_rank + inkling_mod.get_moe_tensor_parallel_rank = lambda: tp_rank + inkling_mod.lora_compatible_layout_enabled = lambda: lora_layout + + def _run_rank( + self, full, ep_size, ep_rank, tp_size, tp_rank, *, interleaved, lora_layout + ): + self._patch(ep_size, ep_rank, tp_rank, lora_layout) + model = _FakeModel(interleaved) + local, i_tp = N_EXPERTS // ep_size, I_FULL // tp_size + params_dict = { + "model.layers.0.mlp.experts.w13_weight": torch.nn.Parameter( + torch.full((local, 2 * i_tp, H), float("nan")), requires_grad=False + ), + "model.layers.0.mlp.experts.w2_weight": torch.nn.Parameter( + torch.full((local, H, i_tp), float("nan")), requires_grad=False + ), + } + loaded = set() + for (e, proj), w in full.items(): + name = f"model.layers.0.mlp.experts.{e}.{proj}.weight" + self.assertTrue(model._load_per_expert_param(params_dict, loaded, name, w)) + contiguous = lora_layout or not interleaved + exp_w13, exp_w2 = _expected_stacks( + full, ep_size, ep_rank, tp_size, tp_rank, contiguous + ) + got_w13 = params_dict["model.layers.0.mlp.experts.w13_weight"].data + got_w2 = params_dict["model.layers.0.mlp.experts.w2_weight"].data + self.assertFalse(torch.isnan(got_w13).any(), "unwritten w13 slots") + self.assertFalse(torch.isnan(got_w2).any(), "unwritten w2 slots") + torch.testing.assert_close(got_w13, exp_w13, rtol=0, atol=0) + torch.testing.assert_close(got_w2, exp_w2, rtol=0, atol=0) + self.assertEqual(loaded, set(params_dict)) + + def test_ep1_tp1_interleaved(self): + # the validated RL rollout config (weight-checker <=1e-6 on 4layer + 951B) + self._run_rank(_full_weights(), 1, 0, 1, 0, interleaved=True, lora_layout=False) + + def test_ep_tp_grid_interleaved(self): + full = _full_weights(1) + for ep_rank in range(4): + for tp_rank in range(2): + self._run_rank( + full, 4, ep_rank, 2, tp_rank, interleaved=True, lora_layout=False + ) + + def test_ep_tp_grid_contiguous_layouts(self): + full = _full_weights(2) + # contiguous via the LoRA-serving layout and via a non-interleaved config + for interleaved, lora_layout in ((True, True), (False, False)): + for ep_rank in range(2): + self._run_rank( + full, + 2, + ep_rank, + 2, + 1, + interleaved=interleaved, + lora_layout=lora_layout, + ) + + def test_trtllm_layout_rejected(self): + self._patch(1, 0, 0) + moe = types.SimpleNamespace(use_flashinfer_trtllm_moe=True) + model = _FakeModel(True, moe=moe) + params_dict = { + "model.layers.0.mlp.experts.w13_weight": torch.nn.Parameter( + torch.zeros(N_EXPERTS, 2 * I_FULL, H), requires_grad=False + ) + } + with self.assertRaises(NotImplementedError): + model._load_per_expert_param( + params_dict, + set(), + "model.layers.0.mlp.experts.0.gate_proj.weight", + torch.zeros(I_FULL, H), + ) + + +if __name__ == "__main__": + unittest.main()