[Refactor] Deduplicate kernel helpers and remove unused code (#40197)

python/sglang/kernels/jit/csrc/attention/kda_packed_decode.cuh

@@ -21,6 +21,7 @@
 
 #include <sgl_kernel/type.cuh>   // For bf16_t, fp32_t, device::cast
 #include <sgl_kernel/utils.cuh>  // For LaunchKernel
+#include <sgl_kernel/warp.cuh>
 
 #include <tvm/ffi/container/tensor.h>
 
@@ -48,19 +49,6 @@ struct KdaPackedDecodeParams {
   int32_t use_lower_bound;
 };
 
-__device__ __forceinline__ float warp_allreduce_sum(float v) {
-#if defined(__HIP_PLATFORM_AMD__)
-  constexpr uint64_t kFullMask = 0xffffffffffffffffull;
-#else
-  constexpr uint32_t kFullMask = 0xffffffffu;
-#endif
-#pragma unroll
-  for (int off = 16; off > 0; off >>= 1) {
-    v += __shfl_xor_sync(kFullMask, v, off);
-  }
-  return v;
-}
-
 // K = V = 128 specialization: one lane owns 4 consecutive K-elements (16B).
 template <int kWarps, bool kUsePDL>
 __global__
@@ -104,8 +92,8 @@ __launch_bounds__(kWarps * 32) void kda_packed_decode_kernel(const KdaPackedDeco
     k_sq += k[e] * k[e];
   }
   // tl: q / sqrt(sum(q*q) + 1e-6), then * scale
-  const float q_inv = 1.0f / sqrtf(warp_allreduce_sum(q_sq) + 1e-6f);
-  const float k_inv = 1.0f / sqrtf(warp_allreduce_sum(k_sq) + 1e-6f);
+  const float q_inv = 1.0f / sqrtf(warp::reduce_sum<32>(q_sq) + 1e-6f);
+  const float k_inv = 1.0f / sqrtf(warp::reduce_sum<32>(k_sq) + 1e-6f);
 #pragma unroll
   for (int e = 0; e < kElems; ++e) {
     q[e] = q[e] * q_inv * params.scale;
@@ -143,7 +131,7 @@ __launch_bounds__(kWarps * 32) void kda_packed_decode_kernel(const KdaPackedDeco
       h[e] *= decay[e];
       t += h[e] * k[e];
     }
-    t = warp_allreduce_sum(t);
+    t = warp::reduce_sum<32>(t);
     const float v_new = (cast<fp32_t>(v_ptr[r]) - t) * beta;
     float o_acc = 0.0f;
 #pragma unroll
@@ -151,7 +139,7 @@ __launch_bounds__(kWarps * 32) void kda_packed_decode_kernel(const KdaPackedDeco
       h[e] += v_new * k[e];
       o_acc += h[e] * q[e];
     }
-    o_acc = warp_allreduce_sum(o_acc);
+    o_acc = warp::reduce_sum<32>(o_acc);
     *reinterpret_cast<float4*>(h_base + r * K + e0) = make_float4(h[0], h[1], h[2], h[3]);
     if (lane == 0) {
       o_ptr[r] = cast<bf16_t>(o_acc);

python/sglang/kernels/jit/csrc/inkling/inkling_ar_fused_decode.cuh

@@ -161,7 +161,7 @@ __global__ __launch_bounds__(1024, 1) void inkling_ar_sconv_norm_kernel(const __
   // ---- 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");
+  device::PDLWaitPrimary<true>();
   const auto* in_row = static_cast<const __nv_bfloat16*>(p.in) + t * p.in_stride_t;
   const auto* sh_row =
       p.shared == nullptr ? nullptr : static_cast<const __nv_bfloat16*>(p.shared) + t * p.shared_stride_t;
@@ -393,7 +393,7 @@ __launch_bounds__(1024, 1) void inkling_ar_sconv_norm_verify_kernel(const __grid
   // 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");
+  device::PDLWaitPrimary<true>();
   auto* mc = static_cast<__nv_bfloat16*>(p.mc_stage);
   const auto* in = static_cast<const __nv_bfloat16*>(p.in);
   const auto* sh = static_cast<const __nv_bfloat16*>(p.shared);

python/sglang/kernels/jit/csrc/inkling/inkling_ar_scattered_sconv.cuh

@@ -1028,7 +1028,7 @@ __global__ __launch_bounds__(1024, 1) void inkling_ar_col_decode_kernel(const __
           static_cast<const __nv_bfloat16*>(p.residual_in) + static_cast<int64_t>(t) * p.H + c0[i]);
     }
   }
-  asm volatile("griddepcontrol.wait;" ::: "memory");
+  device::PDLWaitPrimary<true>();
 
   // ---- 1. entry: peers' producer partials visible (block t <-> peer t) ----
   inkling_ar::block_system_barrier<kNumGPU>(p.state, p.flag_ptrs, p.rank);
@@ -1764,7 +1764,7 @@ __launch_bounds__(1024, 1) void inkling_ar_ssconv_norm_decode_kernel(const __gri
   }
 
   // ---- 1. push: this rank's partial row + its window shard ----
-  asm volatile("griddepcontrol.wait;" ::: "memory");
+  device::PDLWaitPrimary<true>();
   const auto* in_row = static_cast<const __nv_bfloat16*>(p.in) + t * p.in_stride_t;
   auto* slot = static_cast<__nv_bfloat16*>(p.mc_stage) + (static_cast<uint64_t>(p.rank) * p.T + t) * p.D;
   auto* wslot = static_cast<__nv_bfloat16*>(p.mc_wstage) + static_cast<uint64_t>(t) * W1 * p.D;

python/sglang/kernels/jit/csrc/moe/moe_align_kernel.cu

@@ -13,10 +13,12 @@ See the License for the specific language governing permissions and
 limitations under the License.
 ==============================================================================*/
 
+#include <sgl_kernel/bits.h>
 #include <sgl_kernel/tensor.h>
 #include <sgl_kernel/utils.h>
 
 #include <sgl_kernel/utils.cuh>
+#include <sgl_kernel/warp.cuh>
 
 #include <tvm/ffi/container/tensor.h>
 
@@ -33,28 +35,8 @@ namespace sglang {
 
 using Vec = int4;
 
-inline uint32_t next_pow2(uint32_t x) noexcept {
-  --x;
-  x |= x >> 1;
-  x |= x >> 2;
-  x |= x >> 4;
-  x |= x >> 8;
-  x |= x >> 16;
-  return x + 1;
-}
-
 namespace moe {
 
-__device__ __forceinline__ int warp_exclusive_scan(int v, unsigned mask = 0xffffffffu) {
-  int original = v;
-#pragma unroll
-  for (int offset = 1; offset < WARP_SIZE; offset <<= 1) {
-    int n = __shfl_up_sync(mask, v, offset);
-    if ((threadIdx.x & (WARP_SIZE - 1)) >= offset) v += n;
-  }
-  return v - original;
-}
-
 template <typename scalar_t>
 __global__ void count_and_sort_expert_tokens_kernel(
     const scalar_t* __restrict__ topk_ids,
@@ -187,14 +169,14 @@ __global__ void moe_align_block_size_kernel(
   const int warp_id = tid / WARP_SIZE;
   const int lane_id = tid & (WARP_SIZE - 1);
   const int num_warps_for_scan = (scan_size + WARP_SIZE - 1) / WARP_SIZE;
-  const int warp_sum = warp_exclusive_scan(padded_count) + padded_count;
+  const int warp_sum = device::warp::inclusive_sum<32>(padded_count);
   if (lane_id == WARP_SIZE - 1) warp_sums[warp_id] = warp_sum;
   __syncthreads();
 
   // warp0 accumulate all the block's prefix sum
   if (tid < WARP_SIZE) {
     int val = (tid < num_warps_for_scan) ? warp_sums[tid] : 0;
-    int incl = warp_exclusive_scan(val) + val;
+    int incl = device::warp::inclusive_sum<32>(val);
     warp_sums[tid] = incl;
   }
   __syncthreads();
@@ -213,13 +195,13 @@ __global__ void moe_align_block_size_kernel(
 
   // Perform 2 level exclusive-prefix-sum to scan_buf
   int v = (tid < scan_size) ? scan_buf[tid] : 0;
-  int pre = warp_exclusive_scan(v);
+  int pre = device::warp::inclusive_sum<32>(v) - v;
   if (lane_id == WARP_SIZE - 1) warp_sums[warp_id] = pre + v;
   __syncthreads();
 
   if (warp_id == 0) {
     int val = (lane_id < num_warps_for_scan) ? warp_sums[lane_id] : 0;
-    warp_sums[lane_id] = warp_exclusive_scan(val);
+    warp_sums[lane_id] = device::warp::inclusive_sum<32>(val) - val;
   }
   __syncthreads();
 
@@ -409,7 +391,7 @@ __global__ void moe_align_block_size_kernel_v2(
   }
 
   // Level 1: intra-warp exclusive scan on thread_sum
-  int32_t warp_prefix = warp_exclusive_scan(thread_sum);
+  int32_t warp_prefix = device::warp::inclusive_sum<32>(thread_sum) - thread_sum;
   int32_t warp_total = warp_prefix + thread_sum;
   if (lane_id == WARP_SIZE - 1) warp_sums[warp_id] = warp_total;
   __syncthreads();
@@ -418,7 +400,7 @@ __global__ void moe_align_block_size_kernel_v2(
   const int num_warps = (blockDim.x + WARP_SIZE - 1) / WARP_SIZE;
   if (tid < WARP_SIZE) {
     int val = (tid < num_warps) ? warp_sums[tid] : 0;
-    warp_sums[tid] = warp_exclusive_scan(val);
+    warp_sums[tid] = device::warp::inclusive_sum<32>(val) - val;
   }
   __syncthreads();
 
@@ -510,7 +492,7 @@ struct MoeAlignBlockSizeKernel {
           pad_sorted_token_ids,
           (int32_t)max_num_tokens_padded);
     } else if (num_experts <= 1024) {
-      const size_t scan_size = next_pow2(num_experts);
+      const size_t scan_size = host::round_up_pow2(static_cast<uint32_t>(num_experts));
       const size_t shared_mem_size = (num_experts + (num_experts + 1) + scan_size + WARP_SIZE) * sizeof(int32_t);
 
       auto align_kernel = moe::moe_align_block_size_kernel<scalar_t>;

python/sglang/kernels/jit/csrc/trtllm_lora_temp/moe_lora_merged_align_kernel.cu

@@ -24,10 +24,12 @@ limitations under the License.
 // using fused scatter for eligible shapes and two kernels otherwise. Larger
 // domains keep the old path through the Python dispatcher.
 
+#include <sgl_kernel/bits.h>
 #include <sgl_kernel/tensor.h>
 #include <sgl_kernel/utils.h>
 
 #include <sgl_kernel/utils.cuh>
+#include <sgl_kernel/warp.cuh>
 
 #include <tvm/ffi/container/tensor.h>
 
@@ -44,28 +46,8 @@ namespace sglang {
 
 using Vec = int4;
 
-inline uint32_t next_pow2(uint32_t x) noexcept {
-  --x;
-  x |= x >> 1;
-  x |= x >> 2;
-  x |= x >> 4;
-  x |= x >> 8;
-  x |= x >> 16;
-  return x + 1;
-}
-
 namespace moe_lora_merged {
 
-__device__ __forceinline__ int warp_exclusive_scan(int v, unsigned mask = 0xffffffffu) {
-  int original = v;
-#pragma unroll
-  for (int offset = 1; offset < WARP_SIZE; offset <<= 1) {
-    int n = __shfl_up_sync(mask, v, offset);
-    if ((threadIdx.x & (WARP_SIZE - 1)) >= offset) v += n;
-  }
-  return v - original;
-}
-
 // Inline mirror of _fused_virtual_topk_ids_kernel (virtual_experts.py). Returns
 // the merged virtual expert id for flat slot `i` (range [-1, virtual_num_experts);
 // -1 is the dropped/masked sentinel). The caller adds +1 to get the histogram
@@ -232,14 +214,14 @@ __global__ void moe_align_block_size_kernel(
   const int warp_id = tid / WARP_SIZE;
   const int lane_id = tid & (WARP_SIZE - 1);
   const int num_warps_for_scan = (scan_size + WARP_SIZE - 1) / WARP_SIZE;
-  const int warp_sum = warp_exclusive_scan(padded_count) + padded_count;
+  const int warp_sum = device::warp::inclusive_sum<32>(padded_count);
   if (lane_id == WARP_SIZE - 1) warp_sums[warp_id] = warp_sum;
   __syncthreads();
 
   // warp0 accumulate all the block's prefix sum
   if (tid < WARP_SIZE) {
     int val = (tid < num_warps_for_scan) ? warp_sums[tid] : 0;
-    int incl = warp_exclusive_scan(val) + val;
+    int incl = device::warp::inclusive_sum<32>(val);
     warp_sums[tid] = incl;
   }
   __syncthreads();
@@ -258,13 +240,13 @@ __global__ void moe_align_block_size_kernel(
 
   // Perform 2 level exclusive-prefix-sum to scan_buf
   int v = (tid < scan_size) ? scan_buf[tid] : 0;
-  int pre = warp_exclusive_scan(v);
+  int pre = device::warp::inclusive_sum<32>(v) - v;
   if (lane_id == WARP_SIZE - 1) warp_sums[warp_id] = pre + v;
   __syncthreads();
 
   if (warp_id == 0) {
     int val = (lane_id < num_warps_for_scan) ? warp_sums[lane_id] : 0;
-    warp_sums[lane_id] = warp_exclusive_scan(val);
+    warp_sums[lane_id] = device::warp::inclusive_sum<32>(val) - val;
   }
   __syncthreads();
 
@@ -384,12 +366,12 @@ __global__ void fused_align_scatter_kernel(
     padded_count = (count + block_size - 1) / block_size * block_size;
     scan_buf[tid] = padded_count;
   }
-  const int warp_sum = warp_exclusive_scan(padded_count) + padded_count;
+  const int warp_sum = device::warp::inclusive_sum<32>(padded_count);
   if (lane_id == WARP_SIZE - 1) warp_sums[warp_id] = warp_sum;
   __syncthreads();
   if (tid < WARP_SIZE) {
     int val = (tid < num_warps_for_scan) ? warp_sums[tid] : 0;
-    int incl = warp_exclusive_scan(val) + val;
+    int incl = device::warp::inclusive_sum<32>(val);
     warp_sums[tid] = incl;
   }
   __syncthreads();
@@ -402,12 +384,12 @@ __global__ void fused_align_scatter_kernel(
   if (tid >= num_experts && tid < scan_size) scan_buf[tid] = 0;
   __syncthreads();
   int v = (tid < scan_size) ? scan_buf[tid] : 0;
-  int pre = warp_exclusive_scan(v);
+  int pre = device::warp::inclusive_sum<32>(v) - v;
   if (lane_id == WARP_SIZE - 1) warp_sums[warp_id] = pre + v;
   __syncthreads();
   if (warp_id == 0) {
     int val = (lane_id < num_warps_for_scan) ? warp_sums[lane_id] : 0;
-    warp_sums[lane_id] = warp_exclusive_scan(val);
+    warp_sums[lane_id] = device::warp::inclusive_sum<32>(val) - val;
   }
   __syncthreads();
   int off = warp_sums[warp_id];
@@ -500,7 +482,7 @@ struct MoeLoraMergedAlignKernel {
     int32_t* cumsum_buffer_ptr = static_cast<int32_t*>(cumsum_buffer.data_ptr());
     size_t numel = topk_ids.numel();
 
-    const size_t scan_size = next_pow2(num_experts);
+    const size_t scan_size = host::round_up_pow2(static_cast<uint32_t>(num_experts));
 
     if (fuse_scatter) {
       // One block does fill + histogram + scan + expert_ids + scatter. Extra

python/sglang/kernels/jit/utils/__init__.py

@@ -1,6 +1,7 @@
 """Public interface of sglang.kernels.jit.utils."""
 
 from sglang.kernels.jit.utils.arch import (
+    get_activation_cuda_cflags,
     get_jit_cuda_arch,
     is_arch_support_pdl,
     override_jit_cuda_arch,
@@ -27,6 +28,7 @@ __all__ = [
     "make_cpp_args",
     "load_jit",
     "override_jit_cuda_arch",
+    "get_activation_cuda_cflags",
     "get_jit_cuda_arch",
     "is_arch_support_pdl",
     "KERNEL_PATH",

python/sglang/kernels/jit/utils/arch.py

@@ -179,3 +179,11 @@ def is_arch_support_pdl() -> bool:
     if is_hip_runtime() or is_musa_runtime():
         return False
     return get_jit_cuda_arch().major >= 9
+
+
+def get_activation_cuda_cflags() -> list[str]:
+    """Match the AOT activation fast-math policy without changing other kernels."""
+    # Blackwell needs precise expf; HIP clang rejects --use_fast_math.
+    if is_hip_runtime() or get_jit_cuda_arch().major >= 10:
+        return []
+    return ["--use_fast_math"]

python/sglang/kernels/ops/activation/activation.py

@@ -6,9 +6,8 @@ import torch
 
 from sglang.kernels.jit.utils import (
     cache_once,
-    get_jit_cuda_arch,
+    get_activation_cuda_cflags,
     is_arch_support_pdl,
-    is_hip_runtime,
     load_jit,
     make_cpp_args,
 )
@@ -18,19 +17,9 @@ if TYPE_CHECKING:
     from tvm_ffi.module import Module
 
 
-def _fast_math_flags() -> list[str]:
-    # Mirrors sgl-kernel's CMake policy: fast-math on SM90, precise on
-    # SM100+ (Blackwell needs bit-exact expf), off on HIP (clang rejects).
-    if is_hip_runtime():
-        return []
-    if get_jit_cuda_arch().major >= 10:
-        return []
-    return ["--use_fast_math"]
-
-
 @cache_once
 def activation_module(dtype: torch.dtype, *, fast_math: bool = True) -> Module:
-    fast_math_flags = _fast_math_flags()
+    fast_math_flags = get_activation_cuda_cflags()
     if not fast_math and not fast_math_flags:
         return activation_module(dtype)
     args = make_cpp_args(dtype, is_arch_support_pdl())

python/sglang/kernels/ops/attention/dsa/dequant_k_cache.py

@@ -9,49 +9,6 @@ def dequantize_k_cache(quant_k_cache):
     return _dequantize_k_cache_fast_wrapped(quant_k_cache)
 
 
-def _dequantize_k_cache_ref(
-    quant_k_cache: torch.Tensor,  # (num_blocks, block_size, 1, bytes_per_token)
-    dv: int = 512,
-    tile_size: int = 128,
-    d: int = 576,
-) -> torch.Tensor:
-    """
-    De-quantize the k-cache
-    """
-    assert dv % tile_size == 0
-    original_ndim = quant_k_cache.ndim
-    if original_ndim == 3:
-        # set block_size = 1
-        quant_k_cache = quant_k_cache.unsqueeze(1)
-    num_tiles = dv // tile_size
-    num_blocks, block_size, h_k, _ = quant_k_cache.shape
-    assert h_k == 1
-    result = torch.empty(
-        (num_blocks, block_size, d), dtype=torch.bfloat16, device=quant_k_cache.device
-    )
-
-    quant_k_cache = quant_k_cache.view(num_blocks, block_size, -1)
-
-    input_nope = quant_k_cache[..., :dv]
-    input_scale = quant_k_cache[..., dv : dv + num_tiles * 4].view(torch.float32)
-    input_rope = quant_k_cache[..., dv + num_tiles * 4 :].view(torch.bfloat16)
-    result[..., dv:] = input_rope
-
-    for tile_idx in range(0, num_tiles):
-        cur_nope = input_nope[
-            ..., tile_idx * tile_size : (tile_idx + 1) * tile_size
-        ].to(torch.float32)
-        cur_scales = input_scale[..., tile_idx].unsqueeze(-1)
-        result[..., tile_idx * tile_size : (tile_idx + 1) * tile_size] = (
-            cur_nope * cur_scales
-        )
-
-    if original_ndim == 3:
-        return result.view(num_blocks, 1, -1)
-    else:
-        return result.view(num_blocks, block_size, 1, -1)
-
-
 def _dequantize_k_cache_fast_wrapped(
     quant_k_cache: torch.Tensor,
     dv: int = 512,

python/sglang/kernels/ops/attention/dsa/triton_sparse_mla_decode.py

@@ -11,17 +11,19 @@ Two variants:
   2. Split-K: adaptive split-K with fused fast path (adapted from DSv4)
 """
 
-import functools
-
 import torch
 import triton
 import triton.language as tl
 
 from sglang.kernels.ops.attention.dsa.triton_sparse_mla import (
     _PREFERRED_BLOCK_K,
+    _cu_count,
+    _kv_splits_heuristic,
+    _next_pow2,
     _no_async_copy,
     _row_strides,
     _sparse_mla_block_k,
+    _sparse_mla_reduce_kernel,
     _validate_input_dtypes,
 )
 from sglang.kernels.ops.quantization.fp8_kernel import is_fp8_fnuz
@@ -70,44 +72,6 @@ def _get_splitk_bufs(
 LOG2E = 1.4426950408889634
 
 
-@functools.lru_cache(maxsize=1)
-def _cu_count() -> int:
-    return torch.cuda.get_device_properties(
-        torch.cuda.current_device()
-    ).multi_processor_count
-
-
-def _prev_pow2(n: int) -> int:
-    if n < 1:
-        return 1
-    return 1 << (n.bit_length() - 1)
-
-
-def _next_pow2(n: int) -> int:
-    if n < 1:
-        return 1
-    return 1 << (n - 1).bit_length()
-
-
-def _kv_splits_heuristic(
-    T: int,
-    H: int,
-    block_h: int,
-    num_cu: int | None = None,
-    target_wg_per_cu: float = 2.0,
-    max_kv_splits: int = 64,
-) -> int:
-    if num_cu is None:
-        num_cu = _cu_count()
-    target_wg = max(1, int(target_wg_per_cu * num_cu))
-    head_blocks = max(1, (H + block_h - 1) // block_h)
-    base_ctas = max(1, T * head_blocks)
-    if base_ctas >= target_wg:
-        return 1
-    splits_to_fill = max(1, target_wg // base_ctas)
-    return _prev_pow2(min(splits_to_fill, max_kv_splits))
-
-
 @triton.jit
 def _sparse_mla_decode_fused_kernel(
     q_nope_ptr,  # [N, H, D_V]
@@ -512,64 +476,6 @@ def _sparse_mla_decode_split_kernel(
         )
 
 
-@triton.jit
-def _sparse_mla_decode_reduce_kernel(
-    lse_partial_ptr,  # [N, KV_SPLITS, H_padded]  fp32
-    acc_partial_ptr,  # [N, KV_SPLITS, H_padded, D_V]  bf16
-    out_ptr,  # [N, H, D_V]
-    H: tl.constexpr,
-    D_V: tl.constexpr,
-    KV_SPLITS: tl.constexpr,
-    ACTIVE_SPLITS: tl.constexpr,
-    ACTIVE_SPLITS_POW2: tl.constexpr,
-    D_CHUNK: tl.constexpr,
-    BLOCK_K: tl.constexpr,
-):
-    t = tl.program_id(0)
-    h = tl.program_id(1)
-    dc = tl.program_id(2)
-
-    d_offs = dc * D_CHUNK + tl.arange(0, D_CHUNK)
-    # tl.arange needs a power-of-two extent, but ACTIVE_SPLITS is only a power
-    # of two when topk // BLOCK_K is. Iterate over the padded range and mask the
-    # tail: -3.4e38 drives exp2() to 0 without the NaN an -inf would produce.
-    k_offs = tl.arange(0, ACTIVE_SPLITS_POW2)
-    k_mask = k_offs < ACTIVE_SPLITS
-    d_mask = d_offs < D_V
-
-    H_padded = tl.cdiv(H, 16) * 16
-
-    lse_base = t * KV_SPLITS * H_padded
-    lse_p = tl.load(
-        lse_partial_ptr + lse_base + k_offs * H_padded + h,
-        mask=k_mask,
-        other=-3.4e38,
-    )
-
-    ap_base = t * KV_SPLITS * H_padded * D_V
-    a_p = tl.load(
-        acc_partial_ptr
-        + ap_base
-        + k_offs[:, None] * H_padded * D_V
-        + h * D_V
-        + d_offs[None, :],
-        mask=k_mask[:, None] & d_mask[None, :],
-        other=0.0,
-    ).to(tl.float32)
-
-    lse_max = tl.max(lse_p, axis=0)
-    weights = tl.exp2(lse_p - lse_max)
-    w_sum = tl.sum(weights, axis=0)
-    scale = tl.exp2(lse_p - lse_max - tl.log2(tl.maximum(w_sum, 1.0e-30)))
-    out = tl.sum(a_p * scale[:, None], axis=0)
-
-    tl.store(
-        out_ptr + t * H * D_V + h * D_V + d_offs,
-        out.to(tl.bfloat16),
-        mask=d_mask,
-    )
-
-
 def triton_sparse_mla_decode_splitk(
     q_nope: torch.Tensor,
     q_rope: torch.Tensor,
@@ -710,7 +616,7 @@ def triton_sparse_mla_decode_splitk(
 
     D_CHUNK = 64
     grid_reduce = (bs, H, (d_v + D_CHUNK - 1) // D_CHUNK)
-    _sparse_mla_decode_reduce_kernel[grid_reduce](
+    _sparse_mla_reduce_kernel[grid_reduce](
         lse_partial,
         acc_partial,
         out,

python/sglang/kernels/ops/attention/linear/gdn_blackwell/kernel_h.py

@@ -17,6 +17,10 @@ from sglang.kernels.ops.attention.cute_utils import (
     fence_before_tma_store,
     simple_tma_copy,
 )
+from sglang.kernels.ops.attention.linear.tma import (
+    make_chunk_tma_args,
+    make_recurrent_state_tma_args,
+)
 
 
 class Sm100ChunkHKernel:
@@ -53,46 +57,6 @@ class Sm100ChunkHKernel:
         self.num_stages = num_stages
         self.num_warps = 10
 
-    @cute.jit
-    def _make_bf16_tma_args(
-        self,
-        tensor: cute.Tensor,
-        dim: cutlass.Constexpr[int],
-        op: cpasync.TmaCopyOp,
-        stages: cutlass.Constexpr[int],
-    ):
-        swizzle_128B = cute.make_swizzle(3, 4, 3)
-        slayout = cute.make_layout(
-            (self.BT, 1, (64, dim // 64), stages),
-            stride=(64, 0, (1, self.BT * 64), self.BT * dim),
-        )
-        slayout = cute.make_composed_layout(swizzle_128B, 0, slayout)
-        atom, tma_tensor = cpasync.make_tiled_tma_atom(
-            op,
-            cute.logical_divide(tensor, (None, None, 64)),
-            slayout,
-            cta_tiler=(self.BT, 1, dim),
-        )
-        return atom, tma_tensor, slayout
-
-    @cute.jit
-    def _make_h_tma_args(self, tensor: cute.Tensor, op: cpasync.TmaCopyOp):
-        # number of elements to fill 128B
-        num_elems = 128 // (tensor.element_type.width // 8)
-        swizzle_128B = cute.make_swizzle(3, 4, 3)
-        slayout = cute.make_layout(
-            (1, 1, self.V_dim, (num_elems, self.K_dim // num_elems)),
-            stride=(0, 0, num_elems, (1, self.V_dim * num_elems)),
-        )
-        slayout = cute.make_composed_layout(swizzle_128B, 0, slayout)
-        atom, tma_tensor = cpasync.make_tiled_tma_atom(
-            op,
-            cute.logical_divide(tensor, (None, None, None, num_elems)),
-            slayout,
-            cta_tiler=(1, 1, self.V_dim, self.K_dim),
-        )
-        return atom, tma_tensor, slayout
-
     @cute.jit
     def __call__(
         self,
@@ -112,13 +76,13 @@ class Sm100ChunkHKernel:
         tma_g2s = cpasync.CopyBulkTensorTileG2SOp()
         tma_s2g = cpasync.CopyBulkTensorTileS2GOp()
 
-        K_args = self._make_bf16_tma_args(K, self.K_dim, tma_g2s, self.num_stages)
-        V_args = self._make_bf16_tma_args(V, self.V_dim, tma_g2s, self.num_stages)
-        W_args = self._make_bf16_tma_args(W, self.K_dim, tma_g2s, self.num_stages)
-        V_new_args = self._make_bf16_tma_args(V_new, self.V_dim, tma_s2g, 1)
-        H0_args = self._make_h_tma_args(h0, tma_g2s)
-        HT_args = self._make_h_tma_args(ht, tma_s2g)
-        H_args = self._make_h_tma_args(h, tma_s2g)
+        K_args = make_chunk_tma_args(K, self.K_dim, tma_g2s, self.num_stages, self.BT)
+        V_args = make_chunk_tma_args(V, self.V_dim, tma_g2s, self.num_stages, self.BT)
+        W_args = make_chunk_tma_args(W, self.K_dim, tma_g2s, self.num_stages, self.BT)
+        V_new_args = make_chunk_tma_args(V_new, self.V_dim, tma_s2g, 1, self.BT)
+        H0_args = make_recurrent_state_tma_args(h0, tma_g2s, self.K_dim, self.V_dim)
+        HT_args = make_recurrent_state_tma_args(ht, tma_s2g, self.K_dim, self.V_dim)
+        H_args = make_recurrent_state_tma_args(h, tma_s2g, self.K_dim, self.V_dim)
 
         # h0/ht may be the full state pool ([num_slots, ...]) rather than a
         # per-sequence gather, so the sequence count comes from cu_seqlens and

python/sglang/kernels/ops/attention/linear/gdn_blackwell/kernel_kkt_inv_uw.py

@@ -18,6 +18,9 @@ from sglang.kernels.ops.attention.cute_utils import (
     mma_bf16,
     simple_tma_copy,
 )
+from sglang.kernels.ops.attention.linear.tma import (
+    make_chunk_tma_args,
+)
 
 
 class Sm100ChunkUWKernel:
@@ -50,33 +53,6 @@ class Sm100ChunkUWKernel:
         self.BT = 64
         self.num_warps = 2 + 4 + 4
 
-    @cute.jit
-    def _make_tma_args(
-        self,
-        tensor: cute.Tensor,
-        dim: cutlass.Constexpr[int],
-        num_stages: int,
-        op: cpasync.TmaCopyOp,
-    ):
-        # logical layout: [BT, dim]
-        # permute for TMA: [dim/64, BT, 64] with swizzling
-        swizzle_128B = cute.make_swizzle(3, 4, 3)
-        slayout = cute.make_layout(
-            (self.BT, 1, (64, dim // 64), num_stages),
-            stride=(64, 0, (1, self.BT * 64), self.BT * dim),
-        )
-        slayout = cute.make_composed_layout(swizzle_128B, 0, slayout)
-
-        # we need to convert gmem layout to (T, H, (64, D/64)) for make_tiled_tma_atom()
-        # to emit a single 4D TMA. otherwise, it will emit (D/64)x 3D TMA.
-        atom, tma_tensor = cpasync.make_tiled_tma_atom(
-            op,
-            cute.logical_divide(tensor, (None, None, 64)),
-            slayout,
-            cta_tiler=(self.BT, 1, dim),
-        )
-        return atom, tma_tensor, slayout
-
     @cute.jit
     def __call__(
         self,
@@ -96,10 +72,10 @@ class Sm100ChunkUWKernel:
         tma_g2s = cpasync.CopyBulkTensorTileG2SOp()
         tma_s2g = cpasync.CopyBulkTensorTileS2GOp()
 
-        K_args = self._make_tma_args(K, self.K_dim, self.num_stages, tma_g2s)
-        V_args = self._make_tma_args(V, self.V_dim, self.num_stages, tma_g2s)
-        U_args = self._make_tma_args(U, self.V_dim, 1, tma_s2g)
-        W_args = self._make_tma_args(W, self.K_dim, 1, tma_s2g)
+        K_args = make_chunk_tma_args(K, self.K_dim, tma_g2s, self.num_stages, self.BT)
+        V_args = make_chunk_tma_args(V, self.V_dim, tma_g2s, self.num_stages, self.BT)
+        U_args = make_chunk_tma_args(U, self.V_dim, tma_s2g, 1, self.BT)
+        W_args = make_chunk_tma_args(W, self.K_dim, tma_s2g, 1, self.BT)
 
         grid = (num_sms // self.Hv, self.Hv, 1)
         block = (self.num_warps * 32, 1, 1)

python/sglang/kernels/ops/attention/linear/gdn_blackwell/kernel_o.py

@@ -17,6 +17,10 @@ from sglang.kernels.ops.attention.cute_utils import (
     fence_before_tma_store,
     simple_tma_copy,
 )
+from sglang.kernels.ops.attention.linear.tma import (
+    make_chunk_tma_args,
+    make_output_state_tma_args,
+)
 
 
 class Sm100ChunkOKernel:
@@ -48,50 +52,6 @@ class Sm100ChunkOKernel:
         self.num_stages = num_stages
         self.num_warps = 10
 
-    @cute.jit
-    def _make_bf16_tma_args(
-        self,
-        tensor: cute.Tensor,
-        dim: cutlass.Constexpr[int],
-        op: cpasync.TmaCopyOp,
-        stages: cutlass.Constexpr[int],
-    ):
-        swizzle_128B = cute.make_swizzle(3, 4, 3)
-        slayout = cute.make_layout(
-            (self.BT, 1, (64, dim // 64), stages),
-            stride=(64, 0, (1, self.BT * 64), self.BT * dim),
-        )
-        slayout = cute.make_composed_layout(swizzle_128B, 0, slayout)
-        atom, tma_tensor = cpasync.make_tiled_tma_atom(
-            op,
-            cute.logical_divide(tensor, (None, None, 64)),
-            slayout,
-            cta_tiler=(self.BT, 1, dim),
-        )
-        return atom, tma_tensor, slayout
-
-    @cute.jit
-    def _make_h_tma_args(
-        self,
-        tensor: cute.Tensor,
-        op: cpasync.TmaCopyOp,
-        stages: cutlass.Constexpr[int],
-    ):
-        num_elems = 128 // (tensor.element_type.width // 8)
-        swizzle_128B = cute.make_swizzle(3, 4, 3)
-        slayout = cute.make_layout(
-            (1, self.V_dim, (num_elems, self.K_dim // num_elems), stages),
-            stride=(0, num_elems, (1, self.V_dim * num_elems), self.V_dim * self.K_dim),
-        )
-        slayout = cute.make_composed_layout(swizzle_128B, 0, slayout)
-        atom, tma_tensor = cpasync.make_tiled_tma_atom(
-            op,
-            cute.logical_divide(tensor, (None, None, num_elems)),
-            slayout,
-            cta_tiler=(1, self.V_dim, self.K_dim),
-        )
-        return atom, tma_tensor, slayout
-
     @cute.jit
     def __call__(
         self,
@@ -112,13 +72,15 @@ class Sm100ChunkOKernel:
         block = (self.num_warps * 32, 1, 1)
         tma_g2s = cpasync.CopyBulkTensorTileG2SOp()
         tma_s2g = cpasync.CopyBulkTensorTileS2GOp()
-        Q_args = self._make_bf16_tma_args(q, self.K_dim, tma_g2s, self.num_stages)
-        K_args = self._make_bf16_tma_args(k, self.K_dim, tma_g2s, self.num_stages)
-        V_args = self._make_bf16_tma_args(
-            v_new_chunks, self.V_dim, tma_g2s, self.num_stages
+        Q_args = make_chunk_tma_args(q, self.K_dim, tma_g2s, self.num_stages, self.BT)
+        K_args = make_chunk_tma_args(k, self.K_dim, tma_g2s, self.num_stages, self.BT)
+        V_args = make_chunk_tma_args(
+            v_new_chunks, self.V_dim, tma_g2s, self.num_stages, self.BT
         )
-        H_args = self._make_h_tma_args(h, tma_g2s, self.num_stages)
-        O_args = self._make_bf16_tma_args(o, self.V_dim, tma_s2g, 1)
+        H_args = make_output_state_tma_args(
+            h, tma_g2s, self.num_stages, self.K_dim, self.V_dim
+        )
+        O_args = make_chunk_tma_args(o, self.V_dim, tma_s2g, 1, self.BT)
         self.kernel(
             Q_args,
             K_args,

python/sglang/kernels/ops/attention/linear/kda_blackwell/kernel_h.py

@@ -33,6 +33,10 @@ from sglang.kernels.ops.attention.cute_utils import (
     fence_before_tma_store,
     simple_tma_copy,
 )
+from sglang.kernels.ops.attention.linear.tma import (
+    make_chunk_tma_args,
+    make_recurrent_state_tma_args,
+)
 
 
 class Sm100KdaChunkHKernel:
@@ -60,45 +64,6 @@ class Sm100KdaChunkHKernel:
         self.num_stages = num_stages
         self.num_warps = 10
 
-    @cute.jit
-    def _make_bf16_tma_args(
-        self,
-        tensor: cute.Tensor,
-        dim: cutlass.Constexpr[int],
-        op: cpasync.TmaCopyOp,
-        stages: cutlass.Constexpr[int],
-    ):
-        swizzle_128B = cute.make_swizzle(3, 4, 3)
-        slayout = cute.make_layout(
-            (self.BT, 1, (64, dim // 64), stages),
-            stride=(64, 0, (1, self.BT * 64), self.BT * dim),
-        )
-        slayout = cute.make_composed_layout(swizzle_128B, 0, slayout)
-        atom, tma_tensor = cpasync.make_tiled_tma_atom(
-            op,
-            cute.logical_divide(tensor, (None, None, 64)),
-            slayout,
-            cta_tiler=(self.BT, 1, dim),
-        )
-        return atom, tma_tensor, slayout
-
-    @cute.jit
-    def _make_h_tma_args(self, tensor: cute.Tensor, op: cpasync.TmaCopyOp):
-        num_elems = 128 // (tensor.element_type.width // 8)
-        swizzle_128B = cute.make_swizzle(3, 4, 3)
-        slayout = cute.make_layout(
-            (1, 1, self.V_dim, (num_elems, self.K_dim // num_elems)),
-            stride=(0, 0, num_elems, (1, self.V_dim * num_elems)),
-        )
-        slayout = cute.make_composed_layout(swizzle_128B, 0, slayout)
-        atom, tma_tensor = cpasync.make_tiled_tma_atom(
-            op,
-            cute.logical_divide(tensor, (None, None, None, num_elems)),
-            slayout,
-            cta_tiler=(1, 1, self.V_dim, self.K_dim),
-        )
-        return atom, tma_tensor, slayout
-
     @cute.jit
     def __call__(
         self,
@@ -118,13 +83,13 @@ class Sm100KdaChunkHKernel:
         tma_g2s = cpasync.CopyBulkTensorTileG2SOp()
         tma_s2g = cpasync.CopyBulkTensorTileS2GOp()
 
-        K_args = self._make_bf16_tma_args(K, self.K_dim, tma_g2s, self.num_stages)
-        V_args = self._make_bf16_tma_args(V, self.V_dim, tma_g2s, self.num_stages)
-        W_args = self._make_bf16_tma_args(W, self.K_dim, tma_g2s, self.num_stages)
-        V_new_args = self._make_bf16_tma_args(V_new, self.V_dim, tma_s2g, 1)
-        H0_args = self._make_h_tma_args(h0, tma_g2s)
-        HT_args = self._make_h_tma_args(ht, tma_s2g)
-        H_args = self._make_h_tma_args(h, tma_s2g)
+        K_args = make_chunk_tma_args(K, self.K_dim, tma_g2s, self.num_stages, self.BT)
+        V_args = make_chunk_tma_args(V, self.V_dim, tma_g2s, self.num_stages, self.BT)
+        W_args = make_chunk_tma_args(W, self.K_dim, tma_g2s, self.num_stages, self.BT)
+        V_new_args = make_chunk_tma_args(V_new, self.V_dim, tma_s2g, 1, self.BT)
+        H0_args = make_recurrent_state_tma_args(h0, tma_g2s, self.K_dim, self.V_dim)
+        HT_args = make_recurrent_state_tma_args(ht, tma_s2g, self.K_dim, self.V_dim)
+        H_args = make_recurrent_state_tma_args(h, tma_s2g, self.K_dim, self.V_dim)
 
         # h0/ht may be the full state pool ([num_slots, ...]) rather than a
         # per-sequence gather, so the sequence count comes from cu_seqlens and

python/sglang/kernels/ops/attention/linear/kda_blackwell/kernel_kkt_inv_uw.py

@@ -33,6 +33,9 @@ from sglang.kernels.ops.attention.cute_utils import (
     mma_bf16,
     simple_tma_copy,
 )
+from sglang.kernels.ops.attention.linear.tma import (
+    make_chunk_tma_args,
+)
 
 
 class Sm100KdaChunkUWKernel:
@@ -57,28 +60,6 @@ class Sm100KdaChunkUWKernel:
         self.BT = 64
         self.num_warps = 2 + 4 + 4
 
-    @cute.jit
-    def _make_tma_args(
-        self,
-        tensor: cute.Tensor,
-        dim: cutlass.Constexpr[int],
-        num_stages: int,
-        op: cpasync.TmaCopyOp,
-    ):
-        swizzle_128B = cute.make_swizzle(3, 4, 3)
-        slayout = cute.make_layout(
-            (self.BT, 1, (64, dim // 64), num_stages),
-            stride=(64, 0, (1, self.BT * 64), self.BT * dim),
-        )
-        slayout = cute.make_composed_layout(swizzle_128B, 0, slayout)
-        atom, tma_tensor = cpasync.make_tiled_tma_atom(
-            op,
-            cute.logical_divide(tensor, (None, None, 64)),
-            slayout,
-            cta_tiler=(self.BT, 1, dim),
-        )
-        return atom, tma_tensor, slayout
-
     @cute.jit
     def __call__(
         self,
@@ -98,12 +79,12 @@ class Sm100KdaChunkUWKernel:
         tma_g2s = cpasync.CopyBulkTensorTileG2SOp()
         tma_s2g = cpasync.CopyBulkTensorTileS2GOp()
 
-        KL_args = self._make_tma_args(KL, self.K_dim, self.num_stages, tma_g2s)
-        KR_args = self._make_tma_args(KR, self.K_dim, self.num_stages, tma_g2s)
-        KG_args = self._make_tma_args(KG, self.K_dim, self.num_stages, tma_g2s)
-        V_args = self._make_tma_args(V, self.V_dim, self.num_stages, tma_g2s)
-        U_args = self._make_tma_args(U, self.V_dim, 1, tma_s2g)
-        W_args = self._make_tma_args(W, self.K_dim, 1, tma_s2g)
+        KL_args = make_chunk_tma_args(KL, self.K_dim, tma_g2s, self.num_stages, self.BT)
+        KR_args = make_chunk_tma_args(KR, self.K_dim, tma_g2s, self.num_stages, self.BT)
+        KG_args = make_chunk_tma_args(KG, self.K_dim, tma_g2s, self.num_stages, self.BT)
+        V_args = make_chunk_tma_args(V, self.V_dim, tma_g2s, self.num_stages, self.BT)
+        U_args = make_chunk_tma_args(U, self.V_dim, tma_s2g, 1, self.BT)
+        W_args = make_chunk_tma_args(W, self.K_dim, tma_s2g, 1, self.BT)
 
         grid = (num_sms // self.Hv, self.Hv, 1)
         block = (self.num_warps * 32, 1, 1)

python/sglang/kernels/ops/attention/linear/kda_blackwell/kernel_o.py

@@ -32,6 +32,10 @@ from sglang.kernels.ops.attention.cute_utils import (
     fence_before_tma_store,
     simple_tma_copy,
 )
+from sglang.kernels.ops.attention.linear.tma import (
+    make_chunk_tma_args,
+    make_output_state_tma_args,
+)
 
 
 class Sm100KdaChunkOKernel:
@@ -58,50 +62,6 @@ class Sm100KdaChunkOKernel:
         self.num_stages = num_stages
         self.num_warps = 10
 
-    @cute.jit
-    def _make_bf16_tma_args(
-        self,
-        tensor: cute.Tensor,
-        dim: cutlass.Constexpr[int],
-        op: cpasync.TmaCopyOp,
-        stages: cutlass.Constexpr[int],
-    ):
-        swizzle_128B = cute.make_swizzle(3, 4, 3)
-        slayout = cute.make_layout(
-            (self.BT, 1, (64, dim // 64), stages),
-            stride=(64, 0, (1, self.BT * 64), self.BT * dim),
-        )
-        slayout = cute.make_composed_layout(swizzle_128B, 0, slayout)
-        atom, tma_tensor = cpasync.make_tiled_tma_atom(
-            op,
-            cute.logical_divide(tensor, (None, None, 64)),
-            slayout,
-            cta_tiler=(self.BT, 1, dim),
-        )
-        return atom, tma_tensor, slayout
-
-    @cute.jit
-    def _make_h_tma_args(
-        self,
-        tensor: cute.Tensor,
-        op: cpasync.TmaCopyOp,
-        stages: cutlass.Constexpr[int],
-    ):
-        num_elems = 128 // (tensor.element_type.width // 8)
-        swizzle_128B = cute.make_swizzle(3, 4, 3)
-        slayout = cute.make_layout(
-            (1, self.V_dim, (num_elems, self.K_dim // num_elems), stages),
-            stride=(0, num_elems, (1, self.V_dim * num_elems), self.V_dim * self.K_dim),
-        )
-        slayout = cute.make_composed_layout(swizzle_128B, 0, slayout)
-        atom, tma_tensor = cpasync.make_tiled_tma_atom(
-            op,
-            cute.logical_divide(tensor, (None, None, num_elems)),
-            slayout,
-            cta_tiler=(1, self.V_dim, self.K_dim),
-        )
-        return atom, tma_tensor, slayout
-
     @cute.jit
     def __call__(
         self,
@@ -121,14 +81,16 @@ class Sm100KdaChunkOKernel:
         block = (self.num_warps * 32, 1, 1)
         tma_g2s = cpasync.CopyBulkTensorTileG2SOp()
         tma_s2g = cpasync.CopyBulkTensorTileS2GOp()
-        Q_args = self._make_bf16_tma_args(qg2, self.K_dim, tma_g2s, self.num_stages)
-        Q2_args = self._make_bf16_tma_args(qg, self.K_dim, tma_g2s, self.num_stages)
-        K_args = self._make_bf16_tma_args(kg, self.K_dim, tma_g2s, self.num_stages)
-        V_args = self._make_bf16_tma_args(
-            v_new_chunks, self.V_dim, tma_g2s, self.num_stages
+        Q_args = make_chunk_tma_args(qg2, self.K_dim, tma_g2s, self.num_stages, self.BT)
+        Q2_args = make_chunk_tma_args(qg, self.K_dim, tma_g2s, self.num_stages, self.BT)
+        K_args = make_chunk_tma_args(kg, self.K_dim, tma_g2s, self.num_stages, self.BT)
+        V_args = make_chunk_tma_args(
+            v_new_chunks, self.V_dim, tma_g2s, self.num_stages, self.BT
         )
-        H_args = self._make_h_tma_args(h, tma_g2s, self.num_stages)
-        O_args = self._make_bf16_tma_args(o, self.V_dim, tma_s2g, 1)
+        H_args = make_output_state_tma_args(
+            h, tma_g2s, self.num_stages, self.K_dim, self.V_dim
+        )
+        O_args = make_chunk_tma_args(o, self.V_dim, tma_s2g, 1, self.BT)
         self.kernel(
             Q_args,
             Q2_args,

python/sglang/kernels/ops/attention/linear/kda_nvidia_prefill/chunk_fwd.py

@@ -341,111 +341,6 @@ def _get_padded_input_buffers(
     return e
 
 
-# Multi-seq varlen repack cache for the Phase 2.2 path. Keyed by id(cu_seqlens).
-# Stores: (orig_seq_lens, padded_seq_lens, new_cu_seqlens_tensor,
-#          new_chunk_indices_tensor, new_T_total, padded_input_buffers).
-# All tensors are GPU-side and pre-allocated at cache build time. Per-call the
-# kernel reads from / writes to these buffers; we copy caller's input slices in
-# and output slices back (only the valid prefix of each seq).
-_multiseq_repack_cache = {}
-
-_caller_layout_O_cache = {}
-
-
-def _get_caller_layout_O_buffer(multiseq_info, dtype, V_dim, device):
-    """Per-shape cached output buffer at caller's contiguous layout (sum of
-    seq_lens, no padding gaps). Filled by per-seq copies from K4's padded O."""
-    caller_T = multiseq_info["caller_T"]
-    H_x_V = multiseq_info.get("_H_V")  # not strictly needed since we fix B=1 H known
-    key = (caller_T, V_dim, dtype, device.index if device.index is not None else 0)
-    e = _caller_layout_O_cache.get(key)
-    if e is None:
-        # B=1 enforced upstream when multiseq_info is built.
-        e = torch.empty(
-            1,
-            caller_T,
-            multiseq_info["q_pad"].shape[2],
-            V_dim,
-            dtype=dtype,
-            device=device,
-        )
-        _caller_layout_O_cache[key] = e
-    return e
-
-
-def _get_multiseq_repack_info(cu_seqlens, q, k, v, g, beta, BT, device):
-    """Build (and cache) the padded layout for multi-seq varlen with non-aligned
-    seqs. Returns None if all seqs are already 64-aligned (caller can use the
-    existing varlen_pure path)."""
-    import weakref
-
-    key = id(cu_seqlens)
-    cached = _multiseq_repack_cache.get(key)
-    if cached is not None:
-        wref, e = cached
-        if wref() is cu_seqlens:
-            return e
-        # id collision after GC: rebuild
-        del _multiseq_repack_cache[key]
-    cu_cpu = cu_seqlens.cpu().tolist()
-    seq_lens = [cu_cpu[i + 1] - cu_cpu[i] for i in range(len(cu_cpu) - 1)]
-    if all(sl % BT == 0 for sl in seq_lens):
-        _multiseq_repack_cache[key] = (weakref.ref(cu_seqlens), None)
-        return None
-    padded_lens = [((sl + BT - 1) // BT) * BT for sl in seq_lens]
-    new_cu = [0]
-    for pl in padded_lens:
-        new_cu.append(new_cu[-1] + pl)
-    new_T_total = new_cu[-1]
-    B = q.shape[0]
-    H = q.shape[2]
-    K = q.shape[3]
-    # Pre-allocated padded input buffers. q/k/v/beta tail = 0 (zero MMA), g
-    # tail = -1e3 sentinel (zero gate activation). The PER-SEQ tail regions
-    # are between (new_cu[i] + seq_lens[i], new_cu[i+1]) — pre-fill once.
-    q_pad = torch.zeros(B, new_T_total, H, K, dtype=q.dtype, device=device)
-    k_pad = torch.zeros_like(q_pad)
-    v_pad = torch.zeros(B, new_T_total, H, v.shape[3], dtype=v.dtype, device=device)
-    beta_pad = torch.zeros(B, new_T_total, H, dtype=beta.dtype, device=device)
-    g_pad = torch.zeros(B, new_T_total, H, K, dtype=g.dtype, device=device)
-    for i, (sl, pl) in enumerate(zip(seq_lens, padded_lens)):
-        if sl < pl:
-            tail_start = new_cu[i] + sl
-            tail_end = new_cu[i + 1]
-            g_pad[:, tail_start:tail_end] = -1000.0
-    new_cu_tensor = torch.tensor(new_cu, dtype=cu_seqlens.dtype, device=device)
-    new_chunk_indices = prepare_chunk_indices(new_cu_tensor, BT)
-    # Build index map: dst_indices[i] = position in padded layout where orig
-    # row i lives. Used by index_copy_ to do the scatter in one op (instead of
-    # N_seqs × 5 separate slice copies, which cost ~5us each in Python).
-    T_total_orig = cu_cpu[-1]
-    dst_indices_list = []
-    for i, sl in enumerate(seq_lens):
-        for j in range(sl):
-            dst_indices_list.append(new_cu[i] + j)
-    dst_indices = torch.tensor(dst_indices_list, dtype=torch.long, device=device)
-    # Mark this cu_seqlens as VARLEN_PURE eligible — every seq in the new
-    # layout is 64-aligned by construction.
-    _varlen_pure_cache[id(new_cu_tensor)] = True
-    e = {
-        "seq_lens": seq_lens,
-        "padded_lens": padded_lens,
-        "new_cu": new_cu,
-        "new_T_total": new_T_total,
-        "new_cu_tensor": new_cu_tensor,
-        "new_chunk_indices": new_chunk_indices,
-        "q_pad": q_pad,
-        "k_pad": k_pad,
-        "v_pad": v_pad,
-        "g_pad": g_pad,
-        "beta_pad": beta_pad,
-        "dst_indices": dst_indices,
-        "T_total_orig": T_total_orig,
-    }
-    _multiseq_repack_cache[key] = (weakref.ref(cu_seqlens), e)
-    return e
-
-
 def _get_buffers(dev, dtype_k, B, T, H, K_dim, V_dim, NT, N_seqs, BT):
     """All beta fusion lives in akk_inv kernel epilogue (post-inv column-scale)."""
     key = (dev.index or 0, B, T, H, K_dim, V_dim, NT, N_seqs)

python/sglang/kernels/ops/attention/linear/tma.py

@@ -0,0 +1,75 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Shared TMA layouts for the Blackwell GDN and KDA chunk kernels."""
+
+import cutlass
+from cutlass import cute
+from cutlass.cute.nvgpu import cpasync
+
+
+@cute.jit
+def make_chunk_tma_args(
+    tensor: cute.Tensor,
+    dim: cutlass.Constexpr[int],
+    op: cpasync.TmaCopyOp,
+    stages: cutlass.Constexpr[int],
+    chunk_size: cutlass.Constexpr[int],
+):
+    # Divide the contiguous dimension so the descriptor issues one 4D TMA.
+    slayout = cute.make_layout(
+        (chunk_size, 1, (64, dim // 64), stages),
+        stride=(64, 0, (1, chunk_size * 64), chunk_size * dim),
+    )
+    slayout = cute.make_composed_layout(cute.make_swizzle(3, 4, 3), 0, slayout)
+    atom, tma_tensor = cpasync.make_tiled_tma_atom(
+        op,
+        cute.logical_divide(tensor, (None, None, 64)),
+        slayout,
+        cta_tiler=(chunk_size, 1, dim),
+    )
+    return atom, tma_tensor, slayout
+
+
+@cute.jit
+def make_recurrent_state_tma_args(
+    tensor: cute.Tensor,
+    op: cpasync.TmaCopyOp,
+    key_dim: cutlass.Constexpr[int],
+    value_dim: cutlass.Constexpr[int],
+):
+    num_elems = 128 // (tensor.element_type.width // 8)
+    slayout = cute.make_layout(
+        (1, 1, value_dim, (num_elems, key_dim // num_elems)),
+        stride=(0, 0, num_elems, (1, value_dim * num_elems)),
+    )
+    slayout = cute.make_composed_layout(cute.make_swizzle(3, 4, 3), 0, slayout)
+    atom, tma_tensor = cpasync.make_tiled_tma_atom(
+        op,
+        cute.logical_divide(tensor, (None, None, None, num_elems)),
+        slayout,
+        cta_tiler=(1, 1, value_dim, key_dim),
+    )
+    return atom, tma_tensor, slayout
+
+
+@cute.jit
+def make_output_state_tma_args(
+    tensor: cute.Tensor,
+    op: cpasync.TmaCopyOp,
+    stages: cutlass.Constexpr[int],
+    key_dim: cutlass.Constexpr[int],
+    value_dim: cutlass.Constexpr[int],
+):
+    num_elems = 128 // (tensor.element_type.width // 8)
+    slayout = cute.make_layout(
+        (1, value_dim, (num_elems, key_dim // num_elems), stages),
+        stride=(0, num_elems, (1, value_dim * num_elems), value_dim * key_dim),
+    )
+    slayout = cute.make_composed_layout(cute.make_swizzle(3, 4, 3), 0, slayout)
+    atom, tma_tensor = cpasync.make_tiled_tma_atom(
+        op,
+        cute.logical_divide(tensor, (None, None, num_elems)),
+        slayout,
+        cta_tiler=(1, value_dim, key_dim),
+    )
+    return atom, tma_tensor, slayout

python/sglang/kernels/ops/gemm/trtllm_lora_temp/kernel_utils.py

@@ -1,7 +1,7 @@
-import triton
-import triton.language as tl
-
 from sglang.kernels.jit.utils import is_arch_support_pdl
+from sglang.kernels.ops.gemm.kernel_utils import (
+    _resolve_token_positions as _resolve_token_positions,
+)
 
 
 def get_pdl_launch_metadata() -> tuple[bool, dict]:
@@ -12,20 +12,3 @@ def get_pdl_launch_metadata() -> tuple[bool, dict]:
     """
     enable_pdl = is_arch_support_pdl()
     return enable_pdl, ({"launch_pdl": True} if enable_pdl else {})
-
-
-@triton.jit
-def _resolve_token_positions(
-    sorted_token_ids, seg_start, s_offset, seg_len, SORTED_BY_ADAPTER: tl.constexpr
-):
-    """Map logical segment offsets to physical token positions.
-
-    When SORTED_BY_ADAPTER is True, segments are grouped by adapter and
-    sorted_token_ids provides the indirection to the original token rows.
-    When False, tokens are already contiguous starting at seg_start.
-    """
-    if SORTED_BY_ADAPTER:
-        return tl.load(
-            sorted_token_ids + seg_start + s_offset, mask=s_offset < seg_len
-        ).to(tl.int64)
-    return (seg_start + s_offset).to(tl.int64)

python/sglang/kernels/ops/kimi_k3/activation.py

@@ -6,9 +6,8 @@ import torch
 
 from sglang.kernels.jit.utils import (
     cache_once,
-    get_jit_cuda_arch,
+    get_activation_cuda_cflags,
     is_arch_support_pdl,
-    is_hip_runtime,
     load_jit,
     make_cpp_args,
 )
@@ -21,16 +20,6 @@ def _make_name(*args):
     return "kimi_k3_" + "_".join(str(a) for a in args)
 
 
-def _fast_math_flags() -> list[str]:
-    # Mirrors sgl-kernel's CMake policy: fast-math on SM90, precise on
-    # SM100+ (Blackwell needs bit-exact expf), off on HIP (clang rejects).
-    if is_hip_runtime():
-        return []
-    if get_jit_cuda_arch().major >= 10:
-        return []
-    return ["--use_fast_math"]
-
-
 @cache_once
 def _jit_situ_and_mul_module(in_dtype: torch.dtype, out_dtype: torch.dtype) -> Module:
     """Compile and cache the JIT SiTU-and-mul module for an (in, out) dtype pair."""
@@ -40,7 +29,7 @@ def _jit_situ_and_mul_module(in_dtype: torch.dtype, out_dtype: torch.dtype) -> M
         *args,
         cuda_files=["kimi_k3/situ_and_mul.cuh"],
         cuda_wrappers=[("run", f"SituAndMulKernel<{args}>::run")],
-        extra_cuda_cflags=_fast_math_flags(),
+        extra_cuda_cflags=get_activation_cuda_cflags(),
     )
 
 

python/sglang/kernels/ops/moe/trtllm_lora_temp/virtual_experts.py

@@ -12,9 +12,7 @@ import triton.language as tl
 from sglang.kernels.ops.gemm.trtllm_lora_temp.kernel_utils import (
     get_pdl_launch_metadata,
 )
-from sglang.kernels.ops.moe.moe_align import (
-    moe_align_block_size as jit_moe_align_block_size,
-)
+from sglang.kernels.ops.moe.virtual_experts import _align_block_size_large
 from sglang.srt.lora.trtllm_lora_temp.environ import lora_envs
 
 
@@ -404,195 +402,6 @@ from sglang.srt.lora.trtllm_lora_temp.specialized_expand import (  # noqa: E402,
 )
 
 
-def _align_block_size_jit(
-    topk_ids: torch.Tensor,
-    block_size: int,
-    num_experts: int,
-) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-    """CUDA JIT alignment for up to 8191 experts.
-
-    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 "
-        f"(num_moe_experts * max_loras), got {num_experts}"
-    )
-
-    device = topk_ids.device
-    flat_topk_ids = topk_ids.reshape(-1)
-    if flat_topk_ids.dtype == torch.int64:
-        flat_topk_ids = flat_topk_ids.to(torch.int32)
-    num_total_tokens = flat_topk_ids.numel()
-
-    if num_total_tokens == 0:
-        empty = torch.empty(0, dtype=torch.int32, device=device)
-        return empty, empty, torch.zeros(1, dtype=torch.int32, device=device)
-
-    # JIT kernel uses +1 offset convention: -1 -> bucket 0 (sentinel),
-    # expert i -> bucket i+1. So pass num_experts + 1 as the bucket count.
-    jit_num_experts = num_experts + 1
-
-    if num_total_tokens < jit_num_experts:
-        max_num_tokens_padded = num_total_tokens * block_size
-    else:
-        max_num_tokens_padded = num_total_tokens + jit_num_experts * (block_size - 1)
-
-    # Align every sub-buffer offset to a multiple of 4 (VEC_SIZE). The CUDA
-    # kernel fills sorted_token_ids with vectorized int4 writes whose last
-    # store can spill up to 3 int32s past the logical end. With a fused
-    # allocation the spill would corrupt the adjacent sub-buffer.
-    _A4 = lambda n: (n + 3) & ~3  # noqa: E731
-    max_num_tokens_padded = _A4(max_num_tokens_padded)
-    max_num_m_blocks = (max_num_tokens_padded + block_size - 1) // block_size
-    max_num_m_blocks_padded = _A4(max_num_m_blocks)
-    num_post_pad_size = _A4(1)  # 1 element, padded to 4
-    cumsum_size = _A4(jit_num_experts + 1)
-
-    # Single allocation sliced into 4 views (zero-copy) to avoid
-    # per-call Python overhead of 4 separate torch.empty calls.
-    total_buf = (
-        max_num_tokens_padded
-        + max_num_m_blocks_padded
-        + num_post_pad_size
-        + cumsum_size
-    )
-    buf = torch.empty(total_buf, dtype=torch.int32, device=device)
-    off = 0
-    sorted_token_ids = buf[off : off + max_num_tokens_padded]
-    off += max_num_tokens_padded
-    expert_ids = buf[off : off + max_num_m_blocks]
-    off += max_num_m_blocks_padded
-    num_tokens_post_padded = buf[off : off + 1]
-    off += num_post_pad_size
-    cumsum_buffer = buf[off : off + jit_num_experts + 1]
-
-    jit_moe_align_block_size(
-        flat_topk_ids,
-        jit_num_experts,
-        block_size,
-        sorted_token_ids,
-        expert_ids,
-        num_tokens_post_padded,
-        cumsum_buffer,
-        True,  # pad_sorted_token_ids
-    )
-
-    return sorted_token_ids, expert_ids, num_tokens_post_padded
-
-
-@torch.compile(dynamic=True)
-def _align_block_size_torch(
-    topk_ids: torch.Tensor,
-    block_size: int,
-    num_experts: int,
-) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-    """Pure-PyTorch align_block_size for num_experts > 1024, compiled via torch.compile.
-
-    Fallback for platforms where the CUDA JIT kernel is unavailable (e.g. AMD/ROCm).
-
-    Out-of-range topk_ids (negative sentinels left by EP dispatch, or virtual-
-    expert IDs >= num_experts produced when those sentinels are combined with
-    a per-adapter offset) are routed into a dedicated sentinel bucket. Without
-    this, indexing ``padded_offsets[sorted_expert_ids]`` would wrap (-1) or
-    OOB-read, and the bad expert ids would propagate into the downstream LoRA
-    GEMM as real expert slots.
-    """
-    device = topk_ids.device
-    flat_topk_ids = topk_ids.reshape(-1).to(torch.int64)
-    num_total_tokens = flat_topk_ids.numel()
-
-    sentinel = num_experts
-    valid_mask = (flat_topk_ids >= 0) & (flat_topk_ids < num_experts)
-    safe_topk_ids = torch.where(
-        valid_mask,
-        flat_topk_ids,
-        torch.full_like(flat_topk_ids, sentinel),
-    )
-
-    bucket_count = num_experts + 1
-    max_total_padded_tokens = (
-        (num_total_tokens + bucket_count * (block_size - 1) + block_size - 1)
-        // block_size
-    ) * block_size
-    max_num_blocks = max_total_padded_tokens // block_size
-
-    sorted_token_ids = torch.full(
-        (max_total_padded_tokens,),
-        num_total_tokens,
-        dtype=torch.int32,
-        device=device,
-    )
-    expert_ids = torch.full(
-        (max_num_blocks,),
-        -1,
-        dtype=torch.int32,
-        device=device,
-    )
-
-    if num_total_tokens == 0:
-        num_tokens_post_padded = torch.zeros((1,), dtype=torch.int32, device=device)
-        return sorted_token_ids, expert_ids, num_tokens_post_padded
-
-    sorted_order = torch.argsort(safe_topk_ids)
-    sorted_expert_ids = safe_topk_ids[sorted_order]
-    expert_range = torch.arange(bucket_count, device=device, dtype=torch.int64)
-    counts_offsets = torch.searchsorted(sorted_expert_ids, expert_range, right=False)
-    counts_end = torch.searchsorted(sorted_expert_ids, expert_range, right=True)
-    counts = counts_end - counts_offsets
-    padded_counts = ((counts + block_size - 1) // block_size) * block_size
-    total_padded_tokens = padded_counts.sum().to(torch.int32).reshape(1)
-    padded_offsets = torch.cumsum(padded_counts, dim=0) - padded_counts
-
-    token_ranks = (
-        torch.arange(num_total_tokens, device=device, dtype=torch.int64)
-        - counts_offsets[sorted_expert_ids]
-    )
-    output_positions = padded_offsets[sorted_expert_ids] + token_ranks
-    sorted_token_ids.scatter_(
-        0,
-        output_positions.to(torch.int64),
-        sorted_order.to(torch.int32),
-    )
-
-    block_counts = padded_counts // block_size
-    real_block_counts = block_counts.clone()
-    real_block_counts[sentinel] = 0
-    actual_num_blocks = real_block_counts.sum()
-
-    if max_num_blocks <= 0:
-        return sorted_token_ids, expert_ids, total_padded_tokens
-
-    block_offsets = torch.cumsum(real_block_counts, dim=0)
-    all_block_positions = torch.arange(max_num_blocks, device=device, dtype=torch.int64)
-    assigned_experts = torch.searchsorted(
-        block_offsets, all_block_positions, right=True
-    ).to(torch.int32)
-    expert_ids.copy_(
-        torch.where(
-            all_block_positions < actual_num_blocks,
-            assigned_experts,
-            torch.full_like(assigned_experts, -1),
-        )
-    )
-
-    return sorted_token_ids, expert_ids, total_padded_tokens
-
-
-def _align_block_size_large(
-    topk_ids: torch.Tensor,
-    block_size: int,
-    num_experts: int,
-) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-    """Dispatch to the CUDA JIT kernel when available, otherwise fall back to
-    the pure-PyTorch torch.compile path (needed on AMD/ROCm or when the JIT
-    module fails to load)."""
-    try:
-        return _align_block_size_jit(topk_ids, block_size, num_experts)
-    except Exception:
-        return _align_block_size_torch(topk_ids, block_size, num_experts)
-
-
 def _merged_experts_fused_moe_lora_add_fake(
     output: torch.Tensor,
     hidden_states: torch.Tensor,

python/sglang/srt/hardware_backend/npu/moe/topk.py

@@ -16,20 +16,6 @@ if TYPE_CHECKING:
     from sglang.srt.layers.moe.topk import TopKConfig, TopKOutput
 
 
-def _apply_routed_scaling_after_renorm(
-    topk_weights: torch.Tensor,
-    topk_config: "TopKConfig",
-) -> torch.Tensor:
-    """Mirror GPU post-renorm scaling when apply_routed_scaling_factor_on_output is set."""
-    if (
-        topk_config.renormalize
-        and topk_config.apply_routed_scaling_factor_on_output
-        and topk_config.routed_scaling_factor is not None
-    ):
-        return topk_weights * topk_config.routed_scaling_factor
-    return topk_weights
-
-
 def fused_topk_npu(
     hidden_states: torch.Tensor,
     router_logits: torch.Tensor,
@@ -38,7 +24,6 @@ def fused_topk_npu(
     expert_location_dispatch_info: Optional["ExpertLocationDispatchInfo"] = None,
     layer_id: Optional[int] = None,
 ) -> "TopKOutput":
-
     use_grouped_topk = topk_config.use_grouped_topk
     renormalize = topk_config.renormalize
     correction_bias = topk_config.correction_bias

python/sglang/srt/layers/attention/dsv4/compressor_v2.py

@@ -50,83 +50,6 @@ def _extract_positions_from_plan(
     return positions
 
 
-def _compress_forward_c128_fallback(
-    kv_score_buffer: torch.Tensor,
-    kv_score_input: torch.Tensor,
-    ape: torch.Tensor,
-    plan: Union[CompressorDecodePlan, CompressorPrefillPlan],
-    head_dim: int,
-) -> torch.Tensor:
-    """PyTorch fallback for C128 compress_forward on HIP (wave64).
-
-    Fully vectorized, compatible with CUDA graph capture.
-    kv_score_buffer: [num_pages, 128, head_dim * 2]
-    ape: [128, head_dim]
-
-    IMPORTANT: This also performs the write to state buffer (like the JIT kernel).
-    The JIT kernel does: (1) write kv_score_input to buffer, (2) compress from buffer.
-    """
-    num_total_slots = kv_score_buffer.shape[0] * kv_score_buffer.shape[1]
-    num_pages = kv_score_buffer.shape[0]
-    last_dim = kv_score_buffer.shape[-1]
-
-    # Step 1: WRITE kv_score_input to state buffer
-    if num_total_slots > 0:
-        buf_flat = kv_score_buffer.view(-1, last_dim)
-        if plan.is_decode:
-            # Decode: plan_d has write_loc per batch item
-            plan_raw = plan[1].view(torch.int32)  # [bs, 4]
-            write_locs = plan_raw[:, 1].long()
-            # Only write valid locations (>= 0 and < buffer size)
-            valid_write = (write_locs >= 0) & (write_locs < num_total_slots)
-            if valid_write.any():
-                buf_flat[write_locs[valid_write]] = kv_score_input[valid_write]
-        else:
-            # Prefill: plan_w has {ragged_id, write_loc} per write entry
-            plan_w = plan[2]  # [num_w, 8] uint8 = WritePlan
-            if plan_w.shape[0] > 0:
-                plan_w_raw = plan_w.view(torch.int32)  # [num_w, 2]
-                ragged_ids = plan_w_raw[:, 0].long() & 0xFFFF
-                write_locs = plan_w_raw[:, 1].long()
-                valid_write = (write_locs >= 0) & (write_locs < num_total_slots)
-                ragged_ids_safe = ragged_ids.clamp(
-                    min=0, max=kv_score_input.shape[0] - 1
-                )
-                if valid_write.any():
-                    buf_flat[write_locs[valid_write]] = kv_score_input[
-                        ragged_ids_safe[valid_write]
-                    ]
-
-    # Step 2: COMPRESS (read from buffer page and do softmax-pool)
-    plan_c = plan[1]  # plan_d for decode, plan_c for prefill
-    num_tokens = plan_c.shape[0]
-    if num_pages == 0 or num_tokens == 0:
-        return kv_score_input.new_zeros(num_tokens, head_dim)
-
-    plan_c_raw = plan_c.view(torch.int32)  # [N, 4]
-    read_page_0 = plan_c_raw[:, 2].long()
-    # Use torch.where instead of clamp to handle -1 (invalid) gracefully
-    valid_read = (read_page_0 >= 0) & (read_page_0 < num_pages)
-    read_page_0_safe = torch.where(
-        valid_read, read_page_0, torch.zeros_like(read_page_0)
-    )
-
-    gathered = kv_score_buffer[read_page_0_safe]  # [N, 128, head_dim*2]
-    kv = gathered[:, :, :head_dim].float()
-    score = gathered[:, :, head_dim:].float() + ape.float().unsqueeze(0)
-    weights = score.softmax(dim=1)
-    out = (weights * kv).sum(dim=1)
-
-    # For decode: zero out non-boundary tokens (seq_len % 128 != 0)
-    # so they don't corrupt kvcache location 0 when stored.
-    if plan.is_decode:
-        seq_lens = plan_c_raw[:, 0].to(torch.int32)
-        is_boundary = (seq_lens % 128 == 0).unsqueeze(-1)  # [N, 1]
-        out = torch.where(is_boundary, out, torch.zeros_like(out))
-
-    return out.to(kv_score_input.dtype)
-
-
 class CompressorBackendMixin:
     def __init__(self):
         super().__init__()

python/sglang/srt/lora/marlin_lora_temp/moe_runner.py

@@ -28,7 +28,7 @@ if _is_cuda:
         moe_sum_reduce_triton,
     )
     from sglang.kernels.ops.moe.moe_wna16_marlin import moe_wna16_marlin_gemm
-    from sglang.kernels.ops.moe.trtllm_lora_temp.virtual_experts import (
+    from sglang.kernels.ops.moe.virtual_experts import (
         _align_block_size_jit as moe_align_block_size,
     )
     from sglang.srt.layers.moe.fused_moe_triton.fused_marlin_moe import (

python/sglang/srt/multimodal/processors/mimo_v2.py

@@ -3,11 +3,9 @@
 import asyncio
 import base64
 import copy
-import json
 import math
 import os
 import re
-import subprocess
 from concurrent.futures import ThreadPoolExecutor, as_completed
 from dataclasses import dataclass, field
 from io import BytesIO
@@ -231,39 +229,6 @@ def _decode_frames_and_timestamps(vdw, ele):
     return video_tensor, timestamps
 
 
-def _ffprobe_has_audio(src, stdin=None, label=None) -> bool:
-    # Header-only audio-stream probe for HTTP URLs; avoids full download.
-    try:
-        r = subprocess.run(
-            [
-                "ffprobe",
-                "-v",
-                "quiet",
-                "-print_format",
-                "json",
-                "-show_streams",
-                "-select_streams",
-                "a",
-                src,
-            ],
-            input=stdin,
-            capture_output=True,
-            timeout=30,
-        )
-        if r.returncode != 0:
-            stderr = r.stderr.decode("utf-8", errors="replace")
-            raise RuntimeError(f"ffprobe failed for {label}: {stderr}")
-        return bool(json.loads(r.stdout).get("streams"))
-    except subprocess.TimeoutExpired:
-        logger.error("ffprobe timed out for %s", label)
-        raise
-    except FileNotFoundError as e:
-        raise RuntimeError("ffprobe not found; install ffmpeg") from e
-    except json.JSONDecodeError:
-        logger.error("ffprobe returned invalid JSON for %s", label)
-        raise
-
-
 class MiMoProcessor:
     def __init__(
         self,
@@ -690,7 +655,6 @@ class MiMoProcessor:
     def process_video(
         self, video_input: VideoInput | VideoAudioInput, temporal_padding_factor=None
     ):
-
         def smart_resize_video(
             num_total_frames, min_pixels, max_pixels, total_max_pixels, **kwargs
         ):