[Kernel] Add NVFP4 MoE CUTLASS support for SM120 (#29242)

Signed-off-by: mgoin <mgoin64@gmail.com>
Signed-off-by: Michael Goin <mgoin64@gmail.com>

CMakeLists.txt

@@ -604,12 +604,15 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
     set(SRCS
       "csrc/quantization/fp4/nvfp4_quant_kernels.cu"
       "csrc/quantization/fp4/activation_nvfp4_quant_fusion_kernels.cu"
-      "csrc/quantization/fp4/nvfp4_scaled_mm_sm120_kernels.cu")
+      "csrc/quantization/fp4/nvfp4_experts_quant.cu"
+      "csrc/quantization/fp4/nvfp4_scaled_mm_sm120_kernels.cu"
+      "csrc/quantization/fp4/nvfp4_blockwise_moe_kernel.cu")
     set_gencode_flags_for_srcs(
       SRCS "${SRCS}"
       CUDA_ARCHS "${FP4_ARCHS}")
     list(APPEND VLLM_EXT_SRC "${SRCS}")
     list(APPEND VLLM_GPU_FLAGS "-DENABLE_NVFP4_SM120=1")
+    list(APPEND VLLM_GPU_FLAGS "-DENABLE_CUTLASS_MOE_SM120=1")
     message(STATUS "Building NVFP4 for archs: ${FP4_ARCHS}")
   else()
     message(STATUS "Not building NVFP4 as no compatible archs were found.")

csrc/quantization/fp4/nvfp4_blockwise_moe_kernel.cu

@@ -22,6 +22,7 @@
 #include <ATen/cuda/CUDAContext.h>
 #include <c10/cuda/CUDAGuard.h>
 #include <c10/cuda/CUDAStream.h>
+#include "cutlass_extensions/common.hpp"
 
 #include "cute/tensor.hpp"
 #include "cutlass/tensor_ref.h"
@@ -173,7 +174,7 @@ void run_get_group_gemm_starts(
 }
 
 template <typename OutType>
-void run_fp4_blockwise_scaled_group_mm(
+void run_fp4_blockwise_scaled_group_mm_sm100(
     torch::Tensor& output, const torch::Tensor& a, const torch::Tensor& b,
     const torch::Tensor& a_blockscale, const torch::Tensor& b_blockscales,
     const torch::Tensor& alphas, const torch::Tensor& problem_sizes,
@@ -343,17 +344,225 @@ void run_fp4_blockwise_scaled_group_mm(
 
   auto can_implement_status = gemm_op.can_implement(args);
   TORCH_CHECK(can_implement_status == cutlass::Status::kSuccess,
-              "Failed to implement GEMM");
+              "Failed to implement GEMM: status=", (int)can_implement_status);
 
   // Run the GEMM
   auto status = gemm_op.initialize(args, workspace.data_ptr());
-  TORCH_CHECK(status == cutlass::Status::kSuccess, "Failed to initialize GEMM");
+  TORCH_CHECK(status == cutlass::Status::kSuccess,
+              "Failed to initialize GEMM: status=", (int)status,
+              " workspace_size=", workspace_size, " num_experts=", num_experts,
+              " M=", M, " N=", N, " K=", K);
 
   status = gemm_op.run(args, workspace.data_ptr(), stream);
   TORCH_CHECK(status == cutlass::Status::kSuccess, "Failed to run GEMM");
 }
 
+void run_fp4_blockwise_scaled_group_mm_sm120(
+    torch::Tensor& output, const torch::Tensor& a, const torch::Tensor& b,
+    const torch::Tensor& a_blockscale, const torch::Tensor& b_blockscales,
+    const torch::Tensor& alphas, const torch::Tensor& problem_sizes,
+    const torch::Tensor& expert_offsets, const torch::Tensor& sf_offsets, int M,
+    int N, int K) {
+  using ProblemShape =
+      cutlass::gemm::GroupProblemShape<Shape<int32_t, int32_t, int32_t>>;
+  using ElementType = cutlass::float_e2m1_t;
+  using ElementSFType = cutlass::float_ue4m3_t;
+  using ElementA = cutlass::nv_float4_t<cutlass::float_e2m1_t>;
+  using ElementB = cutlass::nv_float4_t<cutlass::float_e2m1_t>;
+
+  // NOTE: For SM120 it seems templating the output type is not supported and
+  // we need to hardcode the output type to bfloat16
+  using ElementC = cutlass::bfloat16_t;
+  using ElementD = ElementC;
+  using ElementAccumulator = float;
+  // Layout definitions
+  using LayoutA = cutlass::layout::RowMajor;
+  using LayoutB = cutlass::layout::ColumnMajor;
+  using LayoutC = cutlass::layout::RowMajor;
+  using LayoutD = LayoutC;
+
+  // Alignment constraints
+  static constexpr int AlignmentA = 32;
+  static constexpr int AlignmentB = 32;
+  static constexpr int AlignmentC = 128 / cutlass::sizeof_bits<ElementC>::value;
+  static constexpr int AlignmentD = 128 / cutlass::sizeof_bits<ElementD>::value;
+
+  // Architecture definitions
+  using ArchTag = cutlass::arch::Sm120;
+  using OperatorClass = cutlass::arch::OpClassBlockScaledTensorOp;
+
+  using ClusterShape = Shape<_1, _1, _1>;
+  using MmaTileShape = Shape<_128, _128, _128>;
+
+  using FusionOperation = cutlass::epilogue::fusion::LinearCombination<
+      ElementD, ElementAccumulator, ElementC, ElementAccumulator>;
+
+  using CollectiveEpilogue =
+      typename cutlass::epilogue::collective::CollectiveBuilder<
+          ArchTag, OperatorClass, MmaTileShape, ClusterShape,
+          cutlass::epilogue::collective::EpilogueTileAuto, ElementAccumulator,
+          ElementAccumulator, ElementC, LayoutC*, AlignmentC, ElementD,
+          LayoutD*, AlignmentD,
+          cutlass::epilogue::collective::EpilogueScheduleAuto,
+          FusionOperation>::CollectiveOp;
+
+  using CollectiveMainloop =
+      typename cutlass::gemm::collective::CollectiveBuilder<
+          ArchTag, OperatorClass, ElementA, LayoutA*, AlignmentA, ElementB,
+          LayoutB*, AlignmentB, ElementAccumulator, MmaTileShape, ClusterShape,
+          cutlass::gemm::collective::StageCountAutoCarveout<static_cast<int>(
+              sizeof(typename CollectiveEpilogue::SharedStorage))>,
+          cutlass::gemm::collective::KernelScheduleAuto>::CollectiveOp;
+
+  using GemmKernel =
+      cutlass::gemm::kernel::GemmUniversal<ProblemShape, CollectiveMainloop,
+                                           CollectiveEpilogue>;
+
+  using Gemm = cutlass::gemm::device::GemmUniversalAdapter<GemmKernel>;
+  using StrideA = typename Gemm::GemmKernel::InternalStrideA;
+  using StrideB = typename Gemm::GemmKernel::InternalStrideB;
+  using StrideC = typename Gemm::GemmKernel::InternalStrideC;
+  using StrideD = typename Gemm::GemmKernel::InternalStrideD;
+
+  using LayoutSFA =
+      typename Gemm::GemmKernel::CollectiveMainloop::InternalLayoutSFA;
+  using LayoutSFB =
+      typename Gemm::GemmKernel::CollectiveMainloop::InternalLayoutSFB;
+  using ScaleConfig =
+      typename Gemm::GemmKernel::CollectiveMainloop::Sm1xxBlkScaledConfig;
+
+  using UnderlyingProblemShape = ProblemShape::UnderlyingProblemShape;
+  int num_experts = static_cast<int>(expert_offsets.size(0));
+  auto options_int =
+      torch::TensorOptions().dtype(torch::kInt64).device(a.device());
+
+  torch::Tensor a_ptrs = torch::empty(num_experts, options_int);
+  torch::Tensor b_ptrs = torch::empty(num_experts, options_int);
+  torch::Tensor out_ptrs = torch::empty(num_experts, options_int);
+  torch::Tensor a_scales_ptrs = torch::empty(num_experts, options_int);
+  torch::Tensor b_scales_ptrs = torch::empty(num_experts, options_int);
+  torch::Tensor alpha_ptrs = torch::empty(num_experts, options_int);
+  torch::Tensor layout_sfa = torch::empty({num_experts, 5}, options_int);
+  torch::Tensor layout_sfb = torch::empty({num_experts, 5}, options_int);
+  torch::Tensor c_strides1 =
+      torch::full({num_experts}, output.stride(0), options_int);
+  torch::Tensor a_strides1 =
+      torch::full({num_experts}, a.stride(0) * 2, options_int);
+  torch::Tensor b_strides1 =
+      torch::full({num_experts}, b.stride(1) * 2, options_int);
+
+  run_get_group_gemm_starts<LayoutSFA, LayoutSFB, ScaleConfig>(
+      a_ptrs, b_ptrs, out_ptrs, a_scales_ptrs, b_scales_ptrs, alpha_ptrs,
+      layout_sfa, layout_sfb, a, b, output, a_blockscale, b_blockscales, alphas,
+      expert_offsets, sf_offsets, problem_sizes, M, N, K);
+
+  // Create an instance of the GEMM
+  Gemm gemm_op;
+
+  // Initialize problem_sizes_as_shapes correctly
+  UnderlyingProblemShape* problem_sizes_as_shapes =
+      static_cast<UnderlyingProblemShape*>(problem_sizes.data_ptr());
+
+  // Set the Scheduler info
+  cutlass::KernelHardwareInfo hw_info;
+  using RasterOrderOptions = cutlass::gemm::kernel::detail::RasterOrderOptions;
+  typename Gemm::GemmKernel::TileSchedulerArguments scheduler;
+  scheduler.raster_order = RasterOrderOptions::AlongM;
+  hw_info.device_id = a.get_device();
+  static std::unordered_map<int, int> cached_sm_counts;
+  if (cached_sm_counts.find(hw_info.device_id) == cached_sm_counts.end()) {
+    cached_sm_counts[hw_info.device_id] =
+        cutlass::KernelHardwareInfo::query_device_multiprocessor_count(
+            hw_info.device_id);
+  }
+  hw_info.sm_count = min(cached_sm_counts[hw_info.device_id], INT_MAX);
+
+  // Mainloop Arguments
+  typename GemmKernel::MainloopArguments mainloop_args{
+      static_cast<const ElementType**>(a_ptrs.data_ptr()),
+      static_cast<StrideA*>(a_strides1.data_ptr()),
+      static_cast<const ElementType**>(b_ptrs.data_ptr()),
+      static_cast<StrideB*>(b_strides1.data_ptr()),
+      static_cast<const ElementSFType**>(a_scales_ptrs.data_ptr()),
+      reinterpret_cast<LayoutSFA*>(layout_sfa.data_ptr()),
+      static_cast<const ElementSFType**>(b_scales_ptrs.data_ptr()),
+      reinterpret_cast<LayoutSFB*>(layout_sfb.data_ptr())};
+
+  // Epilogue Arguments
+  typename GemmKernel::EpilogueArguments epilogue_args{
+      {},  // epilogue.thread
+      nullptr,
+      static_cast<StrideC*>(c_strides1.data_ptr()),
+      static_cast<ElementD**>(out_ptrs.data_ptr()),
+      static_cast<StrideC*>(c_strides1.data_ptr())};
+  auto& fusion_args = epilogue_args.thread;
+  fusion_args.alpha_ptr_array =
+      reinterpret_cast<float**>(alpha_ptrs.data_ptr());
+  fusion_args.dAlpha = {_0{}, _0{}, 1};
+  fusion_args.beta = 0.0f;
+
+  // Gemm Arguments
+  typename GemmKernel::Arguments args{
+      cutlass::gemm::GemmUniversalMode::kGrouped,
+      {num_experts, problem_sizes_as_shapes, nullptr},
+      mainloop_args,
+      epilogue_args,
+      hw_info,
+      scheduler};
+
+  size_t workspace_size = Gemm::get_workspace_size(args);
+  auto const workspace_options =
+      torch::TensorOptions().dtype(torch::kUInt8).device(a.device());
+  auto workspace = torch::empty(workspace_size, workspace_options);
+  const cudaStream_t stream = at::cuda::getCurrentCUDAStream(a.get_device());
+
+  auto can_implement_status = gemm_op.can_implement(args);
+  TORCH_CHECK(can_implement_status == cutlass::Status::kSuccess,
+              "Failed to implement GEMM: status=", (int)can_implement_status);
+
+  // Run the GEMM
+  auto status = gemm_op.initialize(args, workspace.data_ptr());
+  TORCH_CHECK(status == cutlass::Status::kSuccess,
+              "Failed to initialize GEMM: status=", (int)status,
+              " workspace_size=", workspace_size, " num_experts=", num_experts,
+              " M=", M, " N=", N, " K=", K);
+
+  status = gemm_op.run(args, workspace.data_ptr(), stream);
+  TORCH_CHECK(status == cutlass::Status::kSuccess, "Failed to run GEMM");
+}
+
+template <typename OutType>
+void run_fp4_blockwise_scaled_group_mm(
+    torch::Tensor& output, const torch::Tensor& a, const torch::Tensor& b,
+    const torch::Tensor& a_blockscale, const torch::Tensor& b_blockscales,
+    const torch::Tensor& alphas, const torch::Tensor& problem_sizes,
+    const torch::Tensor& expert_offsets, const torch::Tensor& sf_offsets, int M,
+    int N, int K) {
+  int32_t version_num = get_sm_version_num();
+#if defined ENABLE_NVFP4_SM120 && ENABLE_NVFP4_SM120
+  if (version_num >= 120 && version_num < 130) {
+    run_fp4_blockwise_scaled_group_mm_sm120(
+        output, a, b, a_blockscale, b_blockscales, alphas, problem_sizes,
+        expert_offsets, sf_offsets, M, N, K);
+    return;
+  }
+#endif
 #if defined ENABLE_NVFP4_SM100 && ENABLE_NVFP4_SM100
+  if (version_num >= 100 && version_num < 120) {
+    run_fp4_blockwise_scaled_group_mm_sm100<OutType>(
+        output, a, b, a_blockscale, b_blockscales, alphas, problem_sizes,
+        expert_offsets, sf_offsets, M, N, K);
+    return;
+  }
+#endif
+  TORCH_CHECK_NOT_IMPLEMENTED(
+      false,
+      "No compiled cutlass_fp4_group_mm kernel for CUDA device capability: ",
+      version_num, ". Required capability: 100 or 120");
+}
+
+#if (defined ENABLE_NVFP4_SM100 && ENABLE_NVFP4_SM100) || \
+    (defined ENABLE_NVFP4_SM120 && ENABLE_NVFP4_SM120)
 constexpr auto FLOAT4_E2M1X2 = at::ScalarType::Byte;
 constexpr auto SF_DTYPE = at::ScalarType::Float8_e4m3fn;
 #endif
@@ -374,7 +583,8 @@ void cutlass_fp4_group_mm(
     const torch::Tensor& a_blockscale, const torch::Tensor& b_blockscales,
     const torch::Tensor& alphas, const torch::Tensor& problem_sizes,
     const torch::Tensor& expert_offsets, const torch::Tensor& sf_offsets) {
-#if defined ENABLE_NVFP4_SM100 && ENABLE_NVFP4_SM100
+#if (defined ENABLE_NVFP4_SM100 && ENABLE_NVFP4_SM100) || \
+    (defined ENABLE_NVFP4_SM120 && ENABLE_NVFP4_SM120)
   // Input validation
   CHECK_INPUT(a, FLOAT4_E2M1X2, "a");
   CHECK_INPUT(b, FLOAT4_E2M1X2, "b");
@@ -408,6 +618,14 @@ void cutlass_fp4_group_mm(
         output, a, b, a_blockscale, b_blockscales, alphas, problem_sizes,
         expert_offsets, sf_offsets, M, N, K);
   } else {
+  #if defined ENABLE_NVFP4_SM120 && ENABLE_NVFP4_SM120
+    int32_t version_num = get_sm_version_num();
+    if (version_num >= 120 && version_num < 130) {
+      TORCH_CHECK_NOT_IMPLEMENTED(
+          false, "SM120 NVFP4 MOE only supports bfloat16 output, got: ",
+          output.scalar_type());
+    }
+  #endif
     run_fp4_blockwise_scaled_group_mm<cutlass::half_t>(
         output, a, b, a_blockscale, b_blockscales, alphas, problem_sizes,
         expert_offsets, sf_offsets, M, N, K);
@@ -416,8 +634,8 @@ void cutlass_fp4_group_mm(
   TORCH_CHECK_NOT_IMPLEMENTED(
       false,
       "No compiled cutlass_fp4_group_mm kernel, vLLM must "
-      "be compiled with ENABLE_NVFP4_SM100 for SM100+ and CUDA "
+      "be compiled with ENABLE_NVFP4_SM100 or ENABLE_NVFP4_SM120 for SM100/120 "
-      "12.8 or above.");
+      "and CUDA 12.8 or above.");
 #endif
 }
 

csrc/quantization/fp4/nvfp4_experts_quant.cu

@@ -307,7 +307,7 @@ constexpr auto FLOAT = at::ScalarType::Float;
 constexpr auto INT = at::ScalarType::Int;
 constexpr auto UINT8 = at::ScalarType::Byte;
 
-void scaled_fp4_experts_quant_sm100a(
+void scaled_fp4_experts_quant_sm1xxa(
     torch::Tensor& output, torch::Tensor& output_scale,
     torch::Tensor const& input, torch::Tensor const& input_global_scale,
     torch::Tensor const& input_offset_by_experts,

csrc/quantization/fp4/nvfp4_quant_entry.cu

@@ -24,8 +24,9 @@ void scaled_fp4_quant_sm1xxa(torch::Tensor const& output,
                              torch::Tensor const& input_sf);
 #endif
 
-#if defined ENABLE_NVFP4_SM100 && ENABLE_NVFP4_SM100
+#if (defined(ENABLE_NVFP4_SM100) && ENABLE_NVFP4_SM100) || \
-void scaled_fp4_experts_quant_sm100a(
+    (defined(ENABLE_NVFP4_SM120) && ENABLE_NVFP4_SM120)
+void scaled_fp4_experts_quant_sm1xxa(
     torch::Tensor& output, torch::Tensor& output_scale,
     torch::Tensor const& input, torch::Tensor const& input_global_scale,
     torch::Tensor const& input_offset_by_experts,
@@ -54,8 +55,9 @@ void scaled_fp4_experts_quant(
     torch::Tensor const& input, torch::Tensor const& input_global_scale,
     torch::Tensor const& input_offset_by_experts,
     torch::Tensor const& output_scale_offset_by_experts) {
-#if defined ENABLE_NVFP4_SM100 && ENABLE_NVFP4_SM100
+#if (defined(ENABLE_NVFP4_SM100) && ENABLE_NVFP4_SM100) || \
-  return scaled_fp4_experts_quant_sm100a(
+    (defined(ENABLE_NVFP4_SM120) && ENABLE_NVFP4_SM120)
+  return scaled_fp4_experts_quant_sm1xxa(
       output, output_scale, input, input_global_scale, input_offset_by_experts,
       output_scale_offset_by_experts);
 #endif

csrc/quantization/w8a8/cutlass/scaled_mm_entry.cu

@@ -67,9 +67,9 @@ void cutlass_scaled_mm_sm100(torch::Tensor& c, torch::Tensor const& a,
                              std::optional<torch::Tensor> const& bias);
 #endif
 
-#if defined(ENABLE_SCALED_MM_SM90) && ENABLE_SCALED_MM_SM90 ||   \
+#if (defined(ENABLE_CUTLASS_MOE_SM90) && ENABLE_CUTLASS_MOE_SM90) ||   \
-    defined(ENABLE_SCALED_MM_SM100) && ENABLE_SCALED_MM_SM100 || \
+    (defined(ENABLE_CUTLASS_MOE_SM100) && ENABLE_CUTLASS_MOE_SM100) || \
-    defined(ENABLE_SCALED_MM_SM120) && ENABLE_SCALED_MM_SM120
+    (defined(ENABLE_CUTLASS_MOE_SM120) && ENABLE_CUTLASS_MOE_SM120)
 void get_cutlass_moe_mm_data_caller(
     const torch::Tensor& topk_ids, torch::Tensor& expert_offsets,
     torch::Tensor& problem_sizes1, torch::Tensor& problem_sizes2,
@@ -284,8 +284,9 @@ void get_cutlass_moe_mm_data(
   // This function currently gets compiled only if we have a valid cutlass moe
   // mm to run it for.
   int32_t version_num = get_sm_version_num();
-#if (defined ENABLE_CUTLASS_MOE_SM90 && ENABLE_CUTLASS_MOE_SM90) || \
+#if (defined ENABLE_CUTLASS_MOE_SM90 && ENABLE_CUTLASS_MOE_SM90) ||   \
-    (defined ENABLE_CUTLASS_MOE_SM100 && ENABLE_CUTLASS_MOE_SM100)
+    (defined ENABLE_CUTLASS_MOE_SM100 && ENABLE_CUTLASS_MOE_SM100) || \
+    (defined ENABLE_CUTLASS_MOE_SM120 && ENABLE_CUTLASS_MOE_SM120)
   get_cutlass_moe_mm_data_caller(topk_ids, expert_offsets, problem_sizes1,
                                  problem_sizes2, input_permutation,
                                  output_permutation, num_experts, n, k,
@@ -296,7 +297,7 @@ void get_cutlass_moe_mm_data(
       false,
       "No compiled get_cutlass_moe_mm_data: no cutlass_scaled_mm kernel for "
       "CUDA device capability: ",
-      version_num, ". Required capability: 90 or 100");
+      version_num, ". Required capability: 90, 100, or 120");
 }
 
 void get_cutlass_moe_mm_problem_sizes(
@@ -304,8 +305,9 @@ void get_cutlass_moe_mm_problem_sizes(
     torch::Tensor& problem_sizes2, const int64_t num_experts, const int64_t n,
     const int64_t k, const std::optional<torch::Tensor>& blockscale_offsets) {
   int32_t version_num = get_sm_version_num();
-#if (defined ENABLE_CUTLASS_MOE_SM90 && ENABLE_CUTLASS_MOE_SM90) || \
+#if (defined ENABLE_CUTLASS_MOE_SM90 && ENABLE_CUTLASS_MOE_SM90) ||   \
-    (defined ENABLE_CUTLASS_MOE_SM100 && ENABLE_CUTLASS_MOE_SM100)
+    (defined ENABLE_CUTLASS_MOE_SM100 && ENABLE_CUTLASS_MOE_SM100) || \
+    (defined ENABLE_CUTLASS_MOE_SM120 && ENABLE_CUTLASS_MOE_SM120)
   get_cutlass_moe_mm_problem_sizes_caller(topk_ids, problem_sizes1,
                                           problem_sizes2, num_experts, n, k,
                                           blockscale_offsets);
@@ -315,7 +317,7 @@ void get_cutlass_moe_mm_problem_sizes(
       false,
       "No compiled get_cutlass_moe_mm_problem_sizes: no cutlass_scaled_mm "
       "kernel for CUDA device capability: ",
-      version_num, ". Required capability: 90 or 100");
+      version_num, ". Required capability: 90, 100, or 120");
 }
 
 void get_cutlass_pplx_moe_mm_data(torch::Tensor& expert_offsets,
@@ -328,8 +330,9 @@ void get_cutlass_pplx_moe_mm_data(torch::Tensor& expert_offsets,
   // This function currently gets compiled only if we have a valid cutlass moe
   // mm to run it for.
   int32_t version_num = get_sm_version_num();
-#if (defined ENABLE_CUTLASS_MOE_SM90 && ENABLE_CUTLASS_MOE_SM90) || \
+#if (defined ENABLE_CUTLASS_MOE_SM90 && ENABLE_CUTLASS_MOE_SM90) ||   \
-    (defined ENABLE_CUTLASS_MOE_SM100 && ENABLE_CUTLASS_MOE_SM100)
+    (defined ENABLE_CUTLASS_MOE_SM100 && ENABLE_CUTLASS_MOE_SM100) || \
+    (defined ENABLE_CUTLASS_MOE_SM120 && ENABLE_CUTLASS_MOE_SM120)
   get_cutlass_pplx_moe_mm_data_caller(expert_offsets, problem_sizes1,
                                       problem_sizes2, expert_num_tokens,
                                       num_local_experts, padded_m, n, k);
@@ -339,7 +342,7 @@ void get_cutlass_pplx_moe_mm_data(torch::Tensor& expert_offsets,
       false,
       "No compiled get_cutlass_pplx_moe_mm_data: no cutlass_scaled_mm kernel "
       "for CUDA device capability: ",
-      version_num, ". Required capability: 90 or 100");
+      version_num, ". Required capability: 90, 100, or 120");
 }
 
 void cutlass_scaled_mm_azp(torch::Tensor& c, torch::Tensor const& a,

docs/design/moe_kernel_features.md

@@ -60,7 +60,7 @@ Modular kernels are supported by the following `FusedMoEMethodBase` classes.
 
 - [`ModelOptFp8MoEMethod`][vllm.model_executor.layers.quantization.modelopt.ModelOptFp8MoEMethod]
 - [`Fp8MoEMethod`][vllm.model_executor.layers.quantization.fp8.Fp8MoEMethod]
-- [`CompressedTensorsW4A4MoeMethod`][vllm.model_executor.layers.quantization.compressed_tensors.compressed_tensors_moe.CompressedTensorsW4A4MoeMethod]
+- [`CompressedTensorsW4A4Nvfp4MoeMethod`][vllm.model_executor.layers.quantization.compressed_tensors.compressed_tensors_moe.CompressedTensorsW4A4Nvfp4MoeMethod]
 - [`CompressedTensorsW8A8Fp8MoEMethod`][vllm.model_executor.layers.quantization.compressed_tensors.compressed_tensors_moe.CompressedTensorsW8A8Fp8MoEMethod]
 - [`Mxfp4MoEMethod`][vllm.model_executor.layers.quantization.mxfp4.Mxfp4MoEMethod]
 - [`UnquantizedFusedMoEMethod`][vllm.model_executor.layers.fused_moe.layer.UnquantizedFusedMoEMethod]

vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe.py

@@ -103,7 +103,7 @@ __all__ = [
     "CompressedTensorsW8A8Int8MoEMethod",
     "CompressedTensorsWNA16MarlinMoEMethod",
     "CompressedTensorsWNA16MoEMethod",
-    "CompressedTensorsW4A4MoeMethod",
+    "CompressedTensorsW4A4Nvfp4MoeMethod",
     "CompressedTensorsW4A8Int8MoEMethod",
 ]
 
@@ -171,7 +171,7 @@ class CompressedTensorsMoEMethod(FusedMoEMethodBase):
                     quant_config, layer.moe_config
                 )
         elif quant_config._is_fp4a4_nvfp4(weight_quant, input_quant):
-            return CompressedTensorsW4A4MoeMethod(layer.moe_config)
+            return CompressedTensorsW4A4Nvfp4MoeMethod(layer.moe_config)
         elif (
             quant_config._is_fp8_w8a8_sm90(weight_quant, input_quant)
             or quant_config._is_fp8_w8a8_sm100(weight_quant, input_quant)
@@ -188,7 +188,7 @@ class CompressedTensorsMoEMethod(FusedMoEMethodBase):
             )
 
 
-class CompressedTensorsW4A4MoeMethod(CompressedTensorsMoEMethod):
+class CompressedTensorsW4A4Nvfp4MoeMethod(CompressedTensorsMoEMethod):
     def __init__(self, moe: FusedMoEConfig):
         from vllm.model_executor.layers.quantization.utils.nvfp4_moe_support import (  # noqa: E501
             detect_nvfp4_moe_support,
@@ -205,8 +205,12 @@ class CompressedTensorsW4A4MoeMethod(CompressedTensorsMoEMethod):
             self.flashinfer_moe_backend = get_flashinfer_moe_backend()
             logger.info_once(
                 f"Using FlashInfer {self.flashinfer_moe_backend.value} kernels"
-                " for CompressedTensorsW4A4MoeMethod."
+                " for CompressedTensorsW4A4Nvfp4MoeMethod."
             )
+        elif self.use_marlin:
+            logger.info_once("Using Marlin for CompressedTensorsW4A4Nvfp4MoeMethod.")
+        else:
+            logger.info_once("Using Cutlass for CompressedTensorsW4A4Nvfp4MoeMethod.")
 
     def create_weights(
         self,
@@ -612,7 +616,7 @@ class CompressedTensorsW4A4MoeMethod(CompressedTensorsMoEMethod):
             assert expert_map is None, (
                 "Expert Parallelism / expert_map "
                 "is currently not supported for "
-                "CompressedTensorsW4A4MoeMethod."
+                "CompressedTensorsW4A4Nvfp4MoeMethod."
             )
             assert self.moe_quant_config is not None
 

vllm/model_executor/layers/quantization/modelopt.py

@@ -1132,6 +1132,10 @@ class ModelOptNvFp4FusedMoE(FusedMoEMethodBase):
                 f"Using FlashInfer {self.flashinfer_moe_backend.value} kernels"
                 " for ModelOptNvFp4FusedMoE."
             )
+        elif self.use_marlin:
+            logger.info_once("Using Marlin for ModelOptNvFp4FusedMoE.")
+        else:
+            logger.info_once("Using Cutlass for ModelOptNvFp4FusedMoE.")
 
     def maybe_make_prepare_finalize(
         self,