[Feature][JIT Kernel] JIT activation and update skills (by codex) (#21766)

Co-authored-by: weiminc <tnwilly@gmail.com>
2026-04-03 23:28:54 +08:00
parent 030fb1c4b1
commit 44e5d35703
16 changed files with 489 additions and 64 deletions
@@ -0,0 +1,137 @@
+#include <sgl_kernel/tensor.h>
+#include <sgl_kernel/utils.h>
+
+#include <sgl_kernel/runtime.cuh>
+#include <sgl_kernel/type.cuh>
+#include <sgl_kernel/utils.cuh>
+#include <sgl_kernel/vec.cuh>
+
+#include <tvm/ffi/container/tensor.h>
+
+#include <cmath>
+#include <cstdint>
+#include <limits>
+#include <string>
+
+namespace {
+
+enum class ActivationKind : uint32_t {
+  kSiLU,
+  kGELU,
+  kGELUTanh,
+};
+
+template <typename T, ActivationKind kAct>
+SGL_DEVICE T apply_activation(T x) {
+  const float x_f32 = device::cast<fp32_t>(x);
+  float y_f32 = 0.0f;
+
+  if constexpr (kAct == ActivationKind::kSiLU) {
+    y_f32 = x_f32 / (1.0f + expf(-x_f32));
+  } else if constexpr (kAct == ActivationKind::kGELU) {
+    constexpr auto kSqrt1Over2 = 0.7071067811865475f;
+    y_f32 = x_f32 * (0.5f * (1.0f + erff(x_f32 * kSqrt1Over2)));
+  } else if constexpr (kAct == ActivationKind::kGELUTanh) {
+    constexpr auto kGeluTanhAlpha = 0.044715f;
+    constexpr auto kGeluTanhBeta = 0.7978845608028654f;
+    const float x_cube = x_f32 * x_f32 * x_f32;
+    const float cdf = 0.5f * (1.0f + tanhf(kGeluTanhBeta * (x_f32 + kGeluTanhAlpha * x_cube)));
+    y_f32 = x_f32 * cdf;
+  } else {
+    static_assert(host::dependent_false_v<T>, "unsupported activation kind");
+  }
+
+  return device::cast<T>(y_f32);
+}
+
+struct ActivationParams {
+  const void* __restrict__ input;
+  void* __restrict__ out;
+  uint32_t hidden_dim;
+  uint32_t num_tokens;
+};
+
+template <typename T, ActivationKind kAct, bool kUsePDL>
+__global__ void act_and_mul_kernel(const __grid_constant__ ActivationParams params) {
+  using namespace device;
+  constexpr auto kVecSize = kMaxVecBytes / sizeof(T);
+  using vec_t = AlignedVector<T, kMaxVecBytes / sizeof(T)>;
+  const auto num_vecs = params.hidden_dim / kVecSize;  // per token
+  const auto tid = blockIdx.x * blockDim.x + threadIdx.x;
+  const auto token_id = tid / num_vecs;
+
+  if (token_id >= params.num_tokens) return;
+  const auto offset = tid % num_vecs;
+  const auto input_offset = token_id * (num_vecs * 2) + offset;
+  const auto output_offset = tid;
+  PDLWaitPrimary<kUsePDL>();
+  const auto gate = device::load_as<vec_t>(params.input, input_offset);
+  const auto up = device::load_as<vec_t>(params.input, input_offset + num_vecs);
+  vec_t out;
+#pragma unroll
+  for (int i = 0; i < kVecSize; ++i) {
+    out[i] = apply_activation<T, kAct>(gate[i]) * up[i];
+  }
+  device::store_as<vec_t>(params.out, out, output_offset);
+  PDLTriggerSecondary<kUsePDL>();
+}
+
+template <typename T, bool kUsePDL>
+struct ActivationKernel {
+  static constexpr auto kVecSize = device::kMaxVecBytes / sizeof(T);
+  static constexpr auto kBlockSize = 256u;
+
+  template <ActivationKind kAct>
+  static constexpr auto activation_kernel = act_and_mul_kernel<T, kAct, kUsePDL>;
+
+  static_assert(device::kMaxVecBytes % sizeof(T) == 0, "unsupported data type");
+  static void run_activation(const tvm::ffi::TensorView input, const tvm::ffi::TensorView out, std::string type) {
+    using namespace host;
+
+    auto N = SymbolicSize{"num_tokens"};
+    auto D_in = SymbolicSize{"input_width"};
+    auto D_out = SymbolicSize{"output_width"};
+    auto device_ = SymbolicDevice{};
+    device_.set_options<kDLCUDA>();
+
+    TensorMatcher({N, D_out})  //
+        .with_dtype<T>()
+        .with_device(device_)
+        .verify(out);
+    TensorMatcher({N, D_in})  //
+        .with_dtype<T>()
+        .with_device(device_)
+        .verify(input);
+
+    const auto hidden_size = D_out.unwrap();
+    const auto num_tokens = static_cast<uint32_t>(N.unwrap());
+    const auto device = device_.unwrap();
+    RuntimeCheck(hidden_size * 2 == D_in.unwrap(), "invalid activation dimension");
+    RuntimeCheck(hidden_size % kVecSize == 0, "hidden size must be divisible by vector size");
+    const auto kernel = [&]() -> decltype(activation_kernel<ActivationKind::kSiLU>) {
+      if (type == "silu") {
+        return activation_kernel<ActivationKind::kSiLU>;
+      } else if (type == "gelu") {
+        return activation_kernel<ActivationKind::kGELU>;
+      } else if (type == "gelu_tanh") {
+        return activation_kernel<ActivationKind::kGELUTanh>;
+      } else {
+        Panic("unsupported activation type: ", type);
+      }
+      return nullptr;
+    }();
+    // only get once to avoid overhead
+    const auto num_total_items = num_tokens * (hidden_size / kVecSize);
+    RuntimeCheck(num_total_items <= std::numeric_limits<uint32_t>::max(), "too many items for 32-bit indexing");
+    const auto num_blocks = div_ceil(static_cast<uint32_t>(num_total_items), kBlockSize);
+    const auto params = ActivationParams{
+        .input = input.data_ptr(),
+        .out = out.data_ptr(),
+        .hidden_dim = hidden_size,
+        .num_tokens = num_tokens,
+    };
+    LaunchKernel(num_blocks, kBlockSize, device).enable_pdl(kUsePDL)(kernel, params);
+  }
+};
+
+}  // namespace