Add a CUDA kernel for fusing mapping and weighted sum for MoE.

python/sglang/srt/layers/moe/cutlass_moe.py

@@ -15,6 +15,7 @@
 if _is_cuda:
     import sgl_kernel
     from sgl_kernel import (
+        apply_shuffle_mul_sum,
         cutlass_fp4_group_mm,
         fp8_blockwise_scaled_grouped_mm,
         prepare_moe_input,
@@ -151,8 +152,8 @@ def cutlass_fused_experts_fp8(
         k,
     )
 
-    rep_a_q = a_q.view(dtype=torch.uint8)[a_map].view(dtype=a_q.dtype)
-    rep_a1_scales = a1_scale[a_map]
+    rep_a_q = shuffle_rows(a_q, a_map, (m * topk, k))
+    rep_a1_scales = shuffle_rows(a1_scale, a_map, (m * topk, int(k / 128)))
 
     c1 = torch.empty((m * topk, n * 2), device=device, dtype=out_dtype)
     c2 = torch.empty((m * topk, k), device=device, dtype=out_dtype)
@@ -206,9 +207,9 @@ def cutlass_fused_experts_fp8(
         expert_offsets[:-1],
         workspace,
     )
-    return (
-        c2[c_map].view(m, topk, k) * topk_weights.view(m, topk, 1).to(out_dtype)
-    ).sum(dim=1)
+
+    result = torch.empty((m, k), device=device, dtype=out_dtype)
+    return apply_shuffle_mul_sum(c2, result, c_map, topk_weights)
 
 
 FLOAT4_E2M1_MAX = 6.0

sgl-kernel/csrc/common_extension.cc

@@ -195,7 +195,8 @@ TORCH_LIBRARY_FRAGMENT(sgl_kernel, m) {
 
   m.def("shuffle_rows(Tensor input, Tensor dst2src_map, Tensor output) -> ()");
   m.impl("shuffle_rows", torch::kCUDA, &shuffle_rows);
-
+  m.def("apply_shuffle_mul_sum(Tensor input, Tensor output, Tensor permutation, Tensor? factors) -> ()");
+  m.impl("apply_shuffle_mul_sum", torch::kCUDA, &apply_shuffle_mul_sum);
   /*
    * From csrc/speculative
    */

sgl-kernel/csrc/moe/fp8_blockwise_moe_kernel.cu

@@ -174,10 +174,10 @@ void sm100_fp8_blockwise_group_mm_dispatch_shape(
   // bool use_small_config = a[0].size(0) <= 128;
   struct MmaConfig1 {
     using ElementA = cutlass::float_e4m3_t;
-    using MmaTileShape = Shape<_128, _32, _128>;
-    using ClusterShape = Shape<_1, _1, _1>;  // Layout type for SFB matrix operand
-    using KernelSchedule = cutlass::gemm::KernelPtrArrayTmaWarpSpecializedBlockwise1SmSm100;
-    using EpilogueSchedule = cutlass::epilogue::PtrArrayTmaWarpSpecialized1Sm;
+    using MmaTileShape = Shape<_256, _32, _128>;
+    using ClusterShape = Shape<_2, _1, _1>;  // Layout type for SFB matrix operand
+    using KernelSchedule = cutlass::gemm::KernelPtrArrayTmaWarpSpecializedBlockwise2SmSm100;
+    using EpilogueSchedule = cutlass::epilogue::PtrArrayTmaWarpSpecialized2Sm;
     using ScaleConfig =
         cutlass::detail::Sm100BlockwiseScaleConfig<128, 1, 128, cute::UMMA::Major::K, cute::UMMA::Major::K>;
     using LayoutSFA = decltype(ScaleConfig::deduce_layoutSFA());
@@ -214,7 +214,7 @@ void sm100_fp8_blockwise_group_mm_dispatch_shape(
   torch::Tensor scales_a_t = scales_a.t();
   torch::Tensor scales_b_t = scales_b.transpose(1, 2);
 
-  if (a.size(0) <= 512 && a.size(1) >= 2048) {
+  if (a.size(0) <= 2048 && a.size(1) >= 2048) {
     run_get_group_gemm_starts<MmaConfig1::LayoutSFA, MmaConfig1::LayoutSFB, MmaConfig1::ScaleConfig>(
         expert_offsets,
         a_ptrs,
@@ -247,7 +247,7 @@ void sm100_fp8_blockwise_group_mm_dispatch_shape(
         expert_offsets,
         workspace);
     output = output_t.t();
-  } else if (a.size(0) > 512 && a.size(1) >= 2048) {
+  } else if (a.size(0) > 2048 && a.size(1) >= 2048) {
     run_get_group_gemm_starts<MmaConfig2::LayoutSFA, MmaConfig2::LayoutSFB, MmaConfig2::ScaleConfig>(
         expert_offsets,
         a_ptrs,

sgl-kernel/csrc/moe/prepare_moe_input.cu

@@ -252,3 +252,117 @@ void shuffle_rows(const torch::Tensor& input_tensor, const torch::Tensor& dst2sr
   shuffle_rows_caller(input_tensor, dst2src_map, output_tensor);
   return;
 }
+
+template <typename scalar_t>
+__global__ void apply_shuffle_mul_sum_kernel(
+    const scalar_t* __restrict__ input_tensor,  // [m * topk, row_stride]
+    scalar_t* __restrict__ output_tensor,       // [m, row_stride]
+    const int32_t* __restrict__ permutation,    // [m * topk]
+    int m,
+    int topk,
+    int row_stride,
+    const scalar_t* __restrict__ factors)  // [m * topk] or nullptr
+{
+  int i = blockIdx.x;   // [0, m * topk)
+  int d = threadIdx.x;  // [0, row_stride)
+
+  if (i >= m || d >= row_stride) return;
+
+  scalar_t sum_val = 0.0;
+
+  for (int j = 0; j < topk; ++j) {
+    int index_2d = i * topk + j;
+    int src_row = permutation[index_2d];
+    if (src_row >= m) continue;
+
+    scalar_t val = input_tensor[src_row * row_stride + d];
+
+    scalar_t factor = 1.0;
+    if (factors != nullptr) {
+      factor = factors[index_2d];
+    }
+
+    sum_val += factor * val;
+  }
+
+  output_tensor[i * row_stride + d] = sum_val;
+}
+
+void get_apply_shuffle_mul_sum_caller(
+    const torch::Tensor& input_tensor,                // [m * topk, row_stride], bf16/f16
+    torch::Tensor& output_tensor,                     // [m, row_stride], bf16/f16
+    const torch::Tensor& permutation,                 // [m * topk], int32
+    const std::optional<torch::Tensor>& factors_opt)  // optional [m * topk], bf16/f16
+{
+  TORCH_CHECK(input_tensor.dim() == 2, "input_tensor must be 2D [m * topk, row_stride]");
+  TORCH_CHECK(output_tensor.dim() == 2, "output_tensor must be 2D [m, row_stride]");
+  TORCH_CHECK(permutation.dim() == 1, "permutation must be 1D [m * topk]");
+
+  int m = output_tensor.size(0);
+  int topk = int(permutation.size(0) / m);
+  int row_stride = output_tensor.size(1);
+
+  TORCH_CHECK(permutation.size(0) == m * topk, "permutation size must match m * topk");
+
+  dim3 block(std::min(256, row_stride));
+  dim3 grid(m);  // blockIdx.x = j, blockIdx.y = i
+  auto stream = at::cuda::getCurrentCUDAStream(input_tensor.device().index());
+
+  const int32_t* perm_ptr = permutation.data_ptr<int32_t>();
+
+  void* factors_ptr = nullptr;
+  if (factors_opt.has_value()) {
+    TORCH_CHECK(factors_opt->dtype() == output_tensor.dtype(), "Factors must match output dtype");
+    TORCH_CHECK(factors_opt->numel() == m * topk, "Factors must have shape [m * topk]");
+    factors_ptr = factors_opt->data_ptr();
+  }
+
+  if (output_tensor.scalar_type() == at::ScalarType::Half) {
+    const at::Half* factor_data = static_cast<const at::Half*>(factors_ptr);
+    apply_shuffle_mul_sum_kernel<at::Half><<<grid, block, 0, stream>>>(
+        input_tensor.data_ptr<at::Half>(),
+        output_tensor.data_ptr<at::Half>(),
+        perm_ptr,
+        m,
+        topk,
+        row_stride,
+        static_cast<const at::Half*>(factors_ptr));
+  } else if (output_tensor.scalar_type() == at::ScalarType::BFloat16) {
+    const c10::BFloat16* factor_data = static_cast<const c10::BFloat16*>(factors_ptr);
+    apply_shuffle_mul_sum_kernel<c10::BFloat16><<<grid, block, 0, stream>>>(
+        input_tensor.data_ptr<c10::BFloat16>(),
+        output_tensor.data_ptr<c10::BFloat16>(),
+        perm_ptr,
+        m,
+        topk,
+        row_stride,
+        static_cast<const c10::BFloat16*>(factors_ptr));
+  } else {
+    TORCH_CHECK(false, "Unsupported output dtype for cast+mul kernel: ", output_tensor.scalar_type());
+  }
+}
+
+/**
+ * @brief Applies a permutation-based shuffle, element-wise multiplication, and reduction over the second dimension.
+ *
+ * This function performs the equivalent of the following PyTorch expression:
+ *
+ *     (c2[c_map].view(m, topk, k) * topk_weights.view(m, topk, 1).to(out_dtype)).sum(dim=1)
+ *
+ * Specifically:
+ * - `input` is shuffled using the `permutation` tensor.
+ * - The shuffled tensor is reshaped and multiplied element-wise with `factors` (e.g., top-k weights).
+ * - The result is summed along dimension 1 (the top-k dimension), and stored in `output`.
+ *
+ * @param input        Input tensor of shape (m * topk, k), representing c2.
+ * @param output       Output tensor of shape (m, k), where the final reduced results are stored.
+ * @param permutation  Index tensor (e.g., c_map) that maps positions in `input` to shuffled layout.
+ * @param factors      Optional scaling factors (e.g., top-k weights), shape (m * topk) or (m, topk).
+ */
+void apply_shuffle_mul_sum(
+    const torch::Tensor& input,
+    torch::Tensor& output,
+    const torch::Tensor& permutation,
+    const std::optional<torch::Tensor>& factors) {
+  get_apply_shuffle_mul_sum_caller(input, output, permutation, factors);
+}

sgl-kernel/include/sgl_kernel_ops.h

@@ -276,6 +276,12 @@ void ep_moe_post_reorder(
 
 void shuffle_rows(const torch::Tensor& input_tensor, const torch::Tensor& dst2src_map, torch::Tensor& output_tensor);
 
+void apply_shuffle_mul_sum(
+    const torch::Tensor& input,
+    torch::Tensor& output,
+    const torch::Tensor& permutation,
+    const std::optional<torch::Tensor>& factors);
+
 void cutlass_fp4_group_mm(
     torch::Tensor& output,
     const torch::Tensor& a,

sgl-kernel/python/sgl_kernel/__init__.py

@@ -48,6 +48,7 @@
 )
 from sgl_kernel.grammar import apply_token_bitmask_inplace_cuda
 from sgl_kernel.moe import (
+    apply_shuffle_mul_sum,
     cutlass_fp4_group_mm,
     ep_moe_post_reorder,
     ep_moe_pre_reorder,

sgl-kernel/python/sgl_kernel/moe.py

@@ -178,6 +178,17 @@ def prepare_moe_input(
     )
 
 
+def apply_shuffle_mul_sum(
+    input,
+    output,
+    permutation,
+    factors,
+):
+    torch.ops.sgl_kernel.apply_shuffle_mul_sum.default(
+        input, output, permutation, factors
+    )
+
+
 def cutlass_fp4_group_mm(
     a_fp4,
     b_fp4,