[feat]: CUTLASS block scaled group gemm for SM100 (#19757)
Signed-off-by: Duncan Moss <djm.moss@gmail.com> Co-authored-by: Duncan Moss <dmoss@nvidia.com>
This commit is contained in:
Duncan Moss
GitHub
@ -259,7 +259,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
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SET(CUTLASS_ENABLE_HEADERS_ONLY ON CACHE BOOL "Enable only the header library")
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# Set CUTLASS_REVISION. Used for FetchContent. Also fixes some bogus messages when building.
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set(CUTLASS_REVISION "v3.9.2" CACHE STRING "CUTLASS revision to use")
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set(CUTLASS_REVISION "v4.0.0" CACHE STRING "CUTLASS revision to use")
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# Use the specified CUTLASS source directory for compilation if VLLM_CUTLASS_SRC_DIR is provided
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if (DEFINED ENV{VLLM_CUTLASS_SRC_DIR})
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@ -615,6 +615,26 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
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"in CUDA target architectures.")
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endif()
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endif()
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cuda_archs_loose_intersection(SCALED_MM_ARCHS "10.0a" "${CUDA_ARCHS}")
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if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8 AND SCALED_MM_ARCHS)
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set(SRCS "csrc/quantization/cutlass_w8a8/moe/blockwise_scaled_group_mm_sm100.cu")
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set_gencode_flags_for_srcs(
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SRCS "${SRCS}"
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CUDA_ARCHS "${SCALED_MM_ARCHS}")
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list(APPEND VLLM_EXT_SRC "${SRCS}")
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list(APPEND VLLM_GPU_FLAGS "-DENABLE_CUTLASS_MOE_SM100=1")
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message(STATUS "Building blockwise_scaled_group_mm_sm100 for archs: ${SCALED_MM_ARCHS}")
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else()
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if (NOT ${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8 AND SCALED_MM_ARCHS)
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message(STATUS "Not building blockwise_scaled_group_mm_sm100 kernels as CUDA Compiler version is "
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"not >= 12.8, we recommend upgrading to CUDA 12.8 or later "
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"if you intend on running FP8 quantized MoE models on Blackwell.")
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else()
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message(STATUS "Not building blockwise_scaled_group_mm_sm100 as no compatible archs found "
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"in CUDA target architectures")
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endif()
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endif()
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#
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# Machete kernels
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@ -45,7 +45,6 @@
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#include "cute/algorithm/functional.hpp"
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#include "cute/atom/mma_atom.hpp"
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#include "cute/algorithm/gemm.hpp"
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#include "cute/tensor_predicate.hpp"
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#include "cute/numeric/arithmetic_tuple.hpp"
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#include "cutlass_extensions/gemm/dispatch_policy.hpp"
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@ -239,6 +239,11 @@ void cutlass_moe_mm(
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torch::Tensor const& b_strides, torch::Tensor const& c_strides,
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bool per_act_token, bool per_out_ch);
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void cutlass_blockwise_scaled_grouped_mm(
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torch::Tensor& output, const torch::Tensor& a, const torch::Tensor& b,
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const torch::Tensor& scales_a, const torch::Tensor& scales_b,
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const torch::Tensor& problem_sizes, const torch::Tensor& expert_offsets);
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void cutlass_fp4_group_mm(
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torch::Tensor& output, const torch::Tensor& a, const torch::Tensor& b,
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const torch::Tensor& a_blockscale, const torch::Tensor& b_blockscales,
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@ -51,7 +51,8 @@ struct cutlass_3x_gemm {
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// These are the minimum alignments needed for the kernels to compile
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static constexpr int AlignmentAB =
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128 / cutlass::sizeof_bits<ElementAB>::value;
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static constexpr int AlignmentCD = 4;
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static constexpr int AlignmentCD =
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128 / cutlass::sizeof_bits<ElementD>::value;
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using CollectiveEpilogue =
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typename cutlass::epilogue::collective::CollectiveBuilder<
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@ -0,0 +1,367 @@
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#include <torch/all.h>
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#include <cutlass/arch/arch.h>
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#include <ATen/cuda/CUDAContext.h>
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#include <c10/cuda/CUDAGuard.h>
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#include <c10/cuda/CUDAStream.h>
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#include "cute/tensor.hpp"
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#include "cutlass/tensor_ref.h"
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#include "cutlass/epilogue/collective/default_epilogue.hpp"
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#include "cutlass/epilogue/thread/linear_combination.h"
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#include "cutlass/gemm/dispatch_policy.hpp"
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#include "cutlass/gemm/group_array_problem_shape.hpp"
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#include "cutlass/gemm/collective/collective_builder.hpp"
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#include "cutlass/epilogue/collective/collective_builder.hpp"
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#include "cutlass/gemm/device/gemm_universal_adapter.h"
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#include "cutlass/gemm/kernel/gemm_universal.hpp"
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#include "cutlass/util/command_line.h"
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#include "cutlass/util/distribution.h"
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#include "cutlass/util/host_tensor.h"
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#include "cutlass/util/packed_stride.hpp"
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#include "cutlass/util/tensor_view_io.h"
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#include "cutlass/util/reference/device/gemm.h"
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#include "cutlass/util/reference/device/tensor_compare.h"
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#include "cutlass/util/reference/host/tensor_fill.h"
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#include "cutlass/util/reference/host/gett.hpp"
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#include "cutlass/util/reference/host/tensor_norm.h"
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#include "cutlass/util/reference/host/tensor_compare.h"
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#include <cassert>
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using namespace cute;
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template <typename ElementAB, typename ElementC, typename ElementAccumulator,
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typename LayoutSFA, typename LayoutSFB, typename ScaleConfig>
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__global__ void get_ggemm_starts(
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int32_t* expert_offsets, ElementAB** a_offsets, ElementAB** b_offsets,
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ElementC** out_offsets, ElementAccumulator** a_scale_offsets,
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ElementAccumulator** b_scale_offsets, ElementAB* a_base_as_int,
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ElementAB* b_base_as_int, ElementC* out_base_as_int,
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ElementAccumulator* a_scale_base_as_int,
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ElementAccumulator* b_scale_base_as_int, LayoutSFA* layout_sfa_base_as_int,
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LayoutSFB* layout_sfb_base_as_int, int* problem_sizes) {
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int expert_id = threadIdx.x;
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if (expert_id >= gridDim.x * blockDim.x) {
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return;
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}
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int m = problem_sizes[expert_id * 3];
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int n = problem_sizes[expert_id * 3 + 1];
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int k = problem_sizes[expert_id * 3 + 2];
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int32_t expert_offset = expert_offsets[expert_id];
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int a_stride = expert_offset * k;
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int b_stride = expert_id * k * n;
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int a_scale_stride = expert_offset * k / 128;
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int b_scale_stride = expert_id * k * n / 128 / 128;
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a_offsets[expert_id] = a_base_as_int + a_stride;
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b_offsets[expert_id] = b_base_as_int + b_stride;
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out_offsets[expert_id] = out_base_as_int + expert_offset * n;
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a_scale_offsets[expert_id] = a_scale_base_as_int + a_scale_stride;
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b_scale_offsets[expert_id] = b_scale_base_as_int + b_scale_stride;
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LayoutSFA* layout_sfa_ptr = layout_sfa_base_as_int + expert_id;
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LayoutSFB* layout_sfb_ptr = layout_sfb_base_as_int + expert_id;
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*layout_sfa_ptr =
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ScaleConfig::tile_atom_to_shape_SFA(cute::make_shape(m, n, k, 1));
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*layout_sfb_ptr =
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ScaleConfig::tile_atom_to_shape_SFB(cute::make_shape(m, n, k, 1));
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}
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#define __CALL_GET_STARTS_KERNEL(TENSOR_C_TYPE, C_TYPE, LayoutSFA, LayoutSFB, \
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ScaleConfig) \
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else if (out_tensors.dtype() == TENSOR_C_TYPE) { \
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get_ggemm_starts<cutlass::float_e4m3_t, C_TYPE, float, LayoutSFA, \
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LayoutSFB, ScaleConfig><<<1, num_experts, 0, stream>>>( \
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static_cast<int32_t*>(expert_offsets.data_ptr()), \
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static_cast<cutlass::float_e4m3_t**>(a_ptrs.data_ptr()), \
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static_cast<cutlass::float_e4m3_t**>(b_ptrs.data_ptr()), \
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static_cast<C_TYPE**>(out_ptrs.data_ptr()), \
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static_cast<float**>(a_scales_ptrs.data_ptr()), \
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static_cast<float**>(b_scales_ptrs.data_ptr()), \
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static_cast<cutlass::float_e4m3_t*>(a_tensors.data_ptr()), \
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static_cast<cutlass::float_e4m3_t*>(b_tensors.data_ptr()), \
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static_cast<C_TYPE*>(out_tensors.data_ptr()), \
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static_cast<float*>(a_scales.data_ptr()), \
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static_cast<float*>(b_scales.data_ptr()), \
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reinterpret_cast<LayoutSFA*>(layout_sfa.data_ptr()), \
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reinterpret_cast<LayoutSFB*>(layout_sfb.data_ptr()), \
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static_cast<int*>(problem_sizes.data_ptr())); \
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}
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template <typename LayoutSFA, typename LayoutSFB, typename ScaleConfig>
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void run_get_ggemm_starts(
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torch::Tensor const& expert_offsets, torch::Tensor& a_ptrs,
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torch::Tensor& b_ptrs, torch::Tensor& out_ptrs,
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torch::Tensor& a_scales_ptrs, torch::Tensor& b_scales_ptrs,
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torch::Tensor const& a_tensors, torch::Tensor const& b_tensors,
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torch::Tensor out_tensors, torch::Tensor const& a_scales,
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torch::Tensor const& b_scales, torch::Tensor const& layout_sfa,
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torch::Tensor const& layout_sfb, torch::Tensor const& problem_sizes) {
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TORCH_CHECK(a_tensors.dtype() == torch::kFloat8_e4m3fn);
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TORCH_CHECK(b_tensors.dtype() == torch::kFloat8_e4m3fn);
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TORCH_CHECK(a_scales.dtype() == torch::kFloat32);
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TORCH_CHECK(b_scales.dtype() == torch::kFloat32);
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TORCH_CHECK(out_tensors.size(1) % 128 == 0 or out_tensors.size(0) % 128 == 0);
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TORCH_CHECK(a_tensors.size(1) % 128 == 0 or a_tensors.size(0) % 128 == 0);
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int num_experts = (int)expert_offsets.size(0);
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auto stream = at::cuda::getCurrentCUDAStream(a_tensors.device().index());
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if (false) {
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}
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__CALL_GET_STARTS_KERNEL(torch::kBFloat16, cutlass::bfloat16_t, LayoutSFA,
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LayoutSFB, ScaleConfig)
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__CALL_GET_STARTS_KERNEL(torch::kFloat16, cutlass::half_t, LayoutSFA,
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LayoutSFB, ScaleConfig)
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else {
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TORCH_CHECK(false, "Unsupported output tensor type");
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}
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}
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template <typename OutType, typename ScheduleConfig, typename LayoutD>
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void run_blockwise_scaled_group_mm(
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torch::Tensor& out_ptrs, const torch::Tensor& a_ptrs,
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const torch::Tensor& b_ptrs, const torch::Tensor& a_scales_ptrs,
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const torch::Tensor& b_scales_ptrs, const torch::Tensor& stride_a,
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const torch::Tensor& stride_b, const torch::Tensor& stride_c,
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const torch::Tensor& layout_sfa, const torch::Tensor& layout_sfb,
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const torch::Tensor& problem_sizes, const torch::Tensor& expert_offsets) {
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using ProblemShape = cutlass::gemm::GroupProblemShape<Shape<int, int, int>>;
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// Types
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using ElementA = cutlass::float_e4m3_t;
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using ElementB = cutlass::float_e4m3_t;
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using ElementC = OutType;
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using ElementD = ElementC;
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using ElementAccumulator = float;
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using LayoutA = cutlass::layout::RowMajor;
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using LayoutB = cutlass::layout::ColumnMajor;
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using LayoutC = LayoutD;
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// Alignments
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static constexpr int AlignmentA = 128 / cutlass::sizeof_bits<ElementA>::value;
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static constexpr int AlignmentB = 128 / cutlass::sizeof_bits<ElementB>::value;
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static constexpr int AlignmentC = 128 / cutlass::sizeof_bits<ElementC>::value;
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using ArchTag = cutlass::arch::Sm100;
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using OperatorClass = cutlass::arch::OpClassTensorOp;
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using CollectiveEpilogue =
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typename cutlass::epilogue::collective::CollectiveBuilder<
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ArchTag, OperatorClass, typename ScheduleConfig::MmaTileShape,
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typename ScheduleConfig::ClusterShape,
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cutlass::epilogue::collective::EpilogueTileAuto, ElementAccumulator,
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ElementAccumulator, void, LayoutC*, AlignmentC, ElementD, LayoutC*,
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AlignmentC, typename ScheduleConfig::EpilogueSchedule>::CollectiveOp;
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using CollectiveMainloop =
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typename cutlass::gemm::collective::CollectiveBuilder<
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ArchTag, OperatorClass, ElementA,
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cute::tuple<LayoutA*, typename ScheduleConfig::LayoutSFA*>,
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AlignmentA, ElementB,
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cute::tuple<LayoutB*, typename ScheduleConfig::LayoutSFB*>,
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AlignmentB, ElementAccumulator, typename ScheduleConfig::MmaTileShape,
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typename ScheduleConfig::ClusterShape,
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cutlass::gemm::collective::StageCountAutoCarveout<static_cast<int>(
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sizeof(typename CollectiveEpilogue::SharedStorage))>,
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typename ScheduleConfig::KernelSchedule>::CollectiveOp;
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using GemmKernel =
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cutlass::gemm::kernel::GemmUniversal<ProblemShape, CollectiveMainloop,
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CollectiveEpilogue, void>;
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using Gemm = cutlass::gemm::device::GemmUniversalAdapter<GemmKernel>;
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using StrideA = typename Gemm::GemmKernel::InternalStrideA;
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using StrideB = typename Gemm::GemmKernel::InternalStrideB;
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using StrideC = typename Gemm::GemmKernel::InternalStrideC;
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using StrideD = typename Gemm::GemmKernel::InternalStrideD;
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using UnderlyingProblemShape = ProblemShape::UnderlyingProblemShape;
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int num_experts = (int)expert_offsets.size(0);
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Gemm gemm_op;
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// Mainloop Arguments
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typename GemmKernel::MainloopArguments mainloop_args{
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static_cast<const ElementA**>(a_ptrs.data_ptr()),
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static_cast<StrideA*>(stride_a.data_ptr()),
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static_cast<const ElementB**>(b_ptrs.data_ptr()),
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static_cast<StrideB*>(stride_b.data_ptr()),
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static_cast<const ElementAccumulator**>(a_scales_ptrs.data_ptr()),
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reinterpret_cast<typename ScheduleConfig::LayoutSFA*>(
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layout_sfa.data_ptr()),
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static_cast<const ElementAccumulator**>(b_scales_ptrs.data_ptr()),
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reinterpret_cast<typename ScheduleConfig::LayoutSFB*>(
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layout_sfb.data_ptr())};
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cutlass::KernelHardwareInfo hw_info;
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hw_info.device_id = a_ptrs.get_device();
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hw_info.sm_count =
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cutlass::KernelHardwareInfo::query_device_multiprocessor_count(
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hw_info.device_id);
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// Epilogue Arguments
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typename GemmKernel::EpilogueArguments epilogue_args{
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{}, // epilogue.thread
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nullptr,
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static_cast<StrideC*>(stride_c.data_ptr()),
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static_cast<ElementD**>(out_ptrs.data_ptr()),
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static_cast<StrideC*>(stride_c.data_ptr())};
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UnderlyingProblemShape* problem_sizes_as_shapes =
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static_cast<UnderlyingProblemShape*>(problem_sizes.data_ptr());
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// Gemm Arguments
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typename GemmKernel::Arguments args{
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cutlass::gemm::GemmUniversalMode::kGrouped,
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{num_experts, problem_sizes_as_shapes, nullptr},
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mainloop_args,
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epilogue_args,
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hw_info};
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at::cuda::CUDAGuard device_guard{(char)a_ptrs.device().index()};
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const cudaStream_t stream =
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at::cuda::getCurrentCUDAStream(a_ptrs.get_device());
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auto can_implement_status = gemm_op.can_implement(args);
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TORCH_CHECK(can_implement_status == cutlass::Status::kSuccess,
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"Failed to implement GEMM");
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size_t workspace_size = gemm_op.get_workspace_size(args);
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auto const workspace_options =
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torch::TensorOptions().dtype(torch::kUInt8).device(a_ptrs.device());
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auto workspace = torch::empty(workspace_size, workspace_options);
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auto status = gemm_op.initialize(args, workspace.data_ptr(), stream);
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TORCH_CHECK(status == cutlass::Status::kSuccess, "Failed to initialize GEMM");
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status = gemm_op.run(stream);
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TORCH_CHECK(status == cutlass::Status::kSuccess, "Failed to run GEMM");
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}
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template <typename OutType>
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void blockwise_scaled_group_mm_dispatch_shape(
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torch::Tensor& output, const torch::Tensor& a, const torch::Tensor& b,
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const torch::Tensor& scales_a, const torch::Tensor& scales_b,
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const torch::Tensor& problem_sizes, const torch::Tensor& expert_offsets) {
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struct MmaConfig {
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using ElementA = cutlass::float_e4m3_t;
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using KernelSchedule =
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cutlass::gemm::KernelPtrArrayTmaWarpSpecializedBlockwise1SmSm100;
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using EpilogueSchedule = cutlass::epilogue::PtrArrayTmaWarpSpecialized1Sm;
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using ScaleConfig = cutlass::detail::Sm100BlockwiseScaleConfig<
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1, 128, 128, cute::UMMA::Major::K, cute::UMMA::Major::K>;
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using LayoutSFA = decltype(ScaleConfig::deduce_layoutSFA());
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using LayoutSFB = decltype(ScaleConfig::deduce_layoutSFB());
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using LayoutC = cutlass::layout::RowMajor;
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using MmaTileShape = Shape<_128, _128, _128>;
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using ClusterShape = Shape<_1, _1, _1>;
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};
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int num_experts = (int)expert_offsets.size(0);
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auto a_ptrs = torch::empty(
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{num_experts},
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torch::TensorOptions().dtype(torch::kInt64).device(a.device()));
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auto b_ptrs = torch::empty(
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{num_experts},
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torch::TensorOptions().dtype(torch::kInt64).device(a.device()));
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auto out_ptrs = torch::empty(
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{num_experts},
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torch::TensorOptions().dtype(torch::kInt64).device(a.device()));
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auto a_scales_ptrs = torch::empty(
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{num_experts},
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torch::TensorOptions().dtype(torch::kInt64).device(a.device()));
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auto b_scales_ptrs = torch::empty(
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{num_experts},
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torch::TensorOptions().dtype(torch::kInt64).device(a.device()));
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auto layout_sfa = torch::empty(
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{num_experts, 5},
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torch::TensorOptions().dtype(torch::kInt32).device(a.device()));
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auto layout_sfb = torch::empty(
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{num_experts, 5},
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torch::TensorOptions().dtype(torch::kInt32).device(a.device()));
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auto stride_a = torch::full(
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{num_experts}, a.size(1),
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torch::TensorOptions().dtype(torch::kInt64).device(a.device()));
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auto stride_b = torch::full(
|
||||
{num_experts}, a.size(1),
|
||||
torch::TensorOptions().dtype(torch::kInt64).device(a.device()));
|
||||
auto stride_c = torch::full(
|
||||
{num_experts}, output.size(1),
|
||||
torch::TensorOptions().dtype(torch::kInt64).device(a.device()));
|
||||
|
||||
torch::TensorOptions options_int =
|
||||
torch::TensorOptions().dtype(torch::kInt64).device(a.device());
|
||||
|
||||
run_get_ggemm_starts<typename MmaConfig::LayoutSFA,
|
||||
typename MmaConfig::LayoutSFB,
|
||||
typename MmaConfig::ScaleConfig>(
|
||||
expert_offsets, a_ptrs, b_ptrs, out_ptrs, a_scales_ptrs, b_scales_ptrs, a,
|
||||
b, output, scales_a, scales_b, layout_sfa, layout_sfb, problem_sizes);
|
||||
|
||||
run_blockwise_scaled_group_mm<OutType, MmaConfig,
|
||||
typename MmaConfig::LayoutC>(
|
||||
out_ptrs, a_ptrs, b_ptrs, a_scales_ptrs, b_scales_ptrs, stride_a,
|
||||
stride_b, stride_c, layout_sfa, layout_sfb, problem_sizes,
|
||||
expert_offsets);
|
||||
}
|
||||
|
||||
void cutlass_blockwise_scaled_grouped_mm(
|
||||
torch::Tensor& output, const torch::Tensor& a, const torch::Tensor& b,
|
||||
const torch::Tensor& scales_a, const torch::Tensor& scales_b,
|
||||
const torch::Tensor& problem_sizes, const torch::Tensor& expert_offsets) {
|
||||
TORCH_CHECK(problem_sizes.dim() == 2, "problem_sizes must be 2D tensor");
|
||||
TORCH_CHECK(problem_sizes.size(1) == 3,
|
||||
"problem_sizes must have shape (num_experts, 3)");
|
||||
TORCH_CHECK(problem_sizes.size(0) == expert_offsets.size(0),
|
||||
"Number of experts in problem_sizes must match expert_offsets");
|
||||
TORCH_CHECK(problem_sizes.dtype() == torch::kInt32,
|
||||
"problem_sizes must be int32");
|
||||
TORCH_CHECK(a.scalar_type() == torch::kFloat8_e4m3fn,
|
||||
"a must be kFloat8_e4m3fn");
|
||||
TORCH_CHECK(b.scalar_type() == torch::kFloat8_e4m3fn,
|
||||
"b must be kFloat8_e4m3fn");
|
||||
TORCH_CHECK(output.scalar_type() == torch::kBFloat16 ||
|
||||
output.scalar_type() == torch::kHalf,
|
||||
"output must be bfloat16 or half");
|
||||
TORCH_CHECK(scales_a.scalar_type() == torch::kFloat32,
|
||||
"scales_a must be float32");
|
||||
TORCH_CHECK(scales_b.scalar_type() == torch::kFloat32,
|
||||
"scales_b must be float32");
|
||||
TORCH_CHECK(expert_offsets.scalar_type() == torch::kInt32,
|
||||
"expert_offsets must be int32");
|
||||
|
||||
TORCH_CHECK(output.dim() == 2, "output must be 2D tensor");
|
||||
TORCH_CHECK(a.dim() == 2, "a must be 2D tensor");
|
||||
TORCH_CHECK(b.dim() == 3, "b must be 3D tensor");
|
||||
TORCH_CHECK(scales_a.dim() == 2, "scales_a must be 2D tensor");
|
||||
TORCH_CHECK(scales_b.dim() == 3, "scales_b must be 3D tensor");
|
||||
TORCH_CHECK(problem_sizes.dim() == 2, "problem_sizes must be 2D tensor");
|
||||
TORCH_CHECK(problem_sizes.size(1) == 3,
|
||||
"problem_sizes must have shape (num_experts, 3)");
|
||||
TORCH_CHECK(problem_sizes.size(0) == expert_offsets.size(0),
|
||||
"Number of experts in problem_sizes must match expert_offsets");
|
||||
TORCH_CHECK(problem_sizes.dtype() == torch::kInt32,
|
||||
"problem_sizes must be int32");
|
||||
TORCH_CHECK(expert_offsets.dim() == 1, "expert_offsets must be 1D tensor");
|
||||
|
||||
#if defined(ENABLE_CUTLASS_MOE_SM100) && ENABLE_CUTLASS_MOE_SM100
|
||||
if (output.scalar_type() == torch::kBFloat16) {
|
||||
blockwise_scaled_group_mm_dispatch_shape<cutlass::bfloat16_t>(
|
||||
output, a, b, scales_a, scales_b, problem_sizes, expert_offsets);
|
||||
} else if (output.scalar_type() == torch::kFloat16) {
|
||||
blockwise_scaled_group_mm_dispatch_shape<cutlass::half_t>(
|
||||
output, a, b, scales_a, scales_b, problem_sizes, expert_offsets);
|
||||
} else {
|
||||
TORCH_CHECK(false, "Unsupported output tensor type");
|
||||
}
|
||||
#endif
|
||||
}
|
||||
@ -38,7 +38,6 @@
|
||||
#include "cute/atom/mma_atom.hpp"
|
||||
#include "cute/atom/copy_traits_sm90_tma.hpp"
|
||||
#include "cute/algorithm/gemm.hpp"
|
||||
#include "cute/tensor_predicate.hpp"
|
||||
#include "cute/numeric/arithmetic_tuple.hpp"
|
||||
#include "cutlass/pipeline/pipeline.hpp"
|
||||
#include "cutlass/transform/collective/sm90_wgmma_transpose.hpp"
|
||||
|
||||
@ -79,7 +79,8 @@ struct cutlass_sparse_3x_gemm {
|
||||
// These are the minimum alignments needed for the kernels to compile
|
||||
static constexpr int AlignmentAB =
|
||||
128 / cutlass::sizeof_bits<ElementAB>::value;
|
||||
static constexpr int AlignmentCD = 4;
|
||||
static constexpr int AlignmentCD =
|
||||
128 / cutlass::sizeof_bits<ElementD>::value;
|
||||
|
||||
using CollectiveEpilogue =
|
||||
typename cutlass::epilogue::collective::CollectiveBuilder<
|
||||
|
||||
@ -393,6 +393,15 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
|
||||
{stride_tag});
|
||||
ops.impl("cutlass_scaled_fp4_mm", torch::kCUDA, &cutlass_scaled_fp4_mm);
|
||||
|
||||
// cutlass blockwise scaledgroup GEMM
|
||||
ops.def(
|
||||
"cutlass_blockwise_scaled_grouped_mm(Tensor! output, Tensor a, Tensor b, "
|
||||
"Tensor scales_a, Tensor scales_b, "
|
||||
"Tensor problem_sizes, Tensor expert_offsets) -> ()",
|
||||
{stride_tag});
|
||||
ops.impl("cutlass_blockwise_scaled_grouped_mm", torch::kCUDA,
|
||||
&cutlass_blockwise_scaled_grouped_mm);
|
||||
|
||||
// cutlass nvfp4 block scaled group GEMM
|
||||
ops.def(
|
||||
"cutlass_fp4_group_mm(Tensor! out, Tensor a, Tensor b,"
|
||||
|
||||
115
tests/kernels/moe/test_cutlass_grouped_gemm.py
Normal file
115
tests/kernels/moe/test_cutlass_grouped_gemm.py
Normal file
@ -0,0 +1,115 @@
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
|
||||
# DeepGEMM Style Cutlass Grouped GEMM Test
|
||||
# See https://github.com/deepseek-ai/DeepGEMM/blob/main/tests/test_core.py
|
||||
|
||||
import random
|
||||
|
||||
import pytest
|
||||
import torch
|
||||
|
||||
from tests.kernels.utils import baseline_scaled_mm
|
||||
from vllm import _custom_ops as ops
|
||||
from vllm.platforms import current_platform
|
||||
|
||||
|
||||
def cdiv(a, b):
|
||||
return (a + b - 1) // b
|
||||
|
||||
|
||||
def per_token_cast_to_fp8(
|
||||
x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
|
||||
assert x.dim() == 2
|
||||
m, n = x.shape
|
||||
pad_size = (128 - (n % 128)) % 128
|
||||
x = torch.nn.functional.pad(x,
|
||||
(0, pad_size), value=0) if pad_size > 0 else x
|
||||
x_view = x.view(m, -1, 128)
|
||||
x_amax = x_view.abs().float().amax(dim=2).view(m, -1).clamp(1e-4)
|
||||
fp8_data = (x_view *
|
||||
(448.0 / x_amax.unsqueeze(2))).to(dtype=torch.float8_e4m3fn)
|
||||
return fp8_data.view(m, n + pad_size)[:, :n], (x_amax / 448.0).view(m, -1)
|
||||
|
||||
|
||||
def per_block_cast_to_fp8(
|
||||
x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
|
||||
assert x.dim() == 2
|
||||
m, n = x.shape
|
||||
x_padded = torch.zeros((cdiv(m, 128) * 128, cdiv(n, 128) * 128),
|
||||
device=x.device,
|
||||
dtype=x.dtype)
|
||||
x_padded[:m, :n] = x
|
||||
x_view = x_padded.view(-1, 128, x_padded.size(1) // 128, 128)
|
||||
x_amax = x_view.abs().float().amax(dim=(1, 3), keepdim=True).clamp(1e-4)
|
||||
x_scaled = (x_view * (448.0 / x_amax)).to(dtype=torch.float8_e4m3fn)
|
||||
return x_scaled.view_as(x_padded)[:m, :n].contiguous(), (
|
||||
x_amax / 448.0).view(x_view.size(0), x_view.size(2))
|
||||
|
||||
|
||||
@pytest.mark.parametrize("num_groups, expected_m_per_group, k, n", [
|
||||
(4, 8192, 7168, 4096),
|
||||
(4, 8192, 2048, 7168),
|
||||
(8, 4096, 7168, 4096),
|
||||
(8, 4096, 2048, 7168),
|
||||
(32, 1024, 7168, 4096),
|
||||
(32, 1024, 2048, 7168),
|
||||
])
|
||||
@pytest.mark.parametrize("out_dtype", [torch.float16])
|
||||
@pytest.mark.skipif(
|
||||
(lambda x: x is None or x.to_int() != 100)(
|
||||
current_platform.get_device_capability()),
|
||||
reason="Block Scaled Grouped GEMM is only supported on SM100.")
|
||||
def test_cutlass_grouped_gemm(
|
||||
num_groups: int,
|
||||
expected_m_per_group: int,
|
||||
k: int,
|
||||
n: int,
|
||||
out_dtype: torch.dtype,
|
||||
):
|
||||
device = "cuda"
|
||||
alignment = 128
|
||||
group_ms = [
|
||||
int(expected_m_per_group * random.uniform(0.7, 1.3))
|
||||
for _ in range(num_groups)
|
||||
]
|
||||
m = sum([cdiv(m, alignment) * alignment for m in group_ms])
|
||||
|
||||
x = torch.randn((m, k), device=device, dtype=out_dtype)
|
||||
y = torch.randn((num_groups, n, k), device=device, dtype=out_dtype)
|
||||
out = torch.empty((m, n), device=device, dtype=out_dtype)
|
||||
ref_out = torch.randn((m, n), device=device, dtype=out_dtype)
|
||||
|
||||
ep_offset = [0] + [sum(group_ms[:i]) for i in range(1, num_groups)] + [m]
|
||||
pb_size = []
|
||||
for i in range(num_groups):
|
||||
pb_size.append([ep_offset[i + 1] - ep_offset[i], n, k])
|
||||
problem_sizes = torch.tensor(pb_size, device=device, dtype=torch.int32)
|
||||
expert_offsets = torch.tensor(ep_offset, device=device, dtype=torch.int32)
|
||||
|
||||
x_fp8 = per_token_cast_to_fp8(x)
|
||||
y_fp8 = (torch.empty_like(y, dtype=torch.float8_e4m3fn),
|
||||
torch.empty((num_groups, cdiv(n, 128), k // 128),
|
||||
device=device,
|
||||
dtype=torch.float))
|
||||
for i in range(num_groups):
|
||||
y_fp8[0][i], y_fp8[1][i] = per_block_cast_to_fp8(y[i])
|
||||
|
||||
for i in range(num_groups):
|
||||
a = x_fp8[0][ep_offset[i]:ep_offset[i + 1]]
|
||||
a_scale = x_fp8[1][ep_offset[i]:ep_offset[i + 1]]
|
||||
b = y_fp8[0][i].t()
|
||||
b_scale = y_fp8[1][i].t()
|
||||
baseline = baseline_scaled_mm(a, b, a_scale, b_scale, out_dtype)
|
||||
ref_out[ep_offset[i]:ep_offset[i + 1]] = baseline
|
||||
|
||||
ops.cutlass_blockwise_scaled_grouped_mm(
|
||||
out,
|
||||
x_fp8[0],
|
||||
y_fp8[0],
|
||||
x_fp8[1],
|
||||
y_fp8[1],
|
||||
problem_sizes,
|
||||
expert_offsets[:-1],
|
||||
)
|
||||
|
||||
torch.testing.assert_close(ref_out, out, atol=5e-1, rtol=1e-3)
|
||||
@ -646,6 +646,20 @@ def cutlass_scaled_mm_supports_fp4(cuda_device_capability: int) -> bool:
|
||||
return torch.ops._C.cutlass_scaled_mm_supports_fp4(cuda_device_capability)
|
||||
|
||||
|
||||
def cutlass_blockwise_scaled_grouped_mm(
|
||||
output: torch.Tensor,
|
||||
a: torch.Tensor,
|
||||
b: torch.Tensor,
|
||||
scales_a: torch.Tensor,
|
||||
scales_b: torch.Tensor,
|
||||
problem_sizes: torch.Tensor,
|
||||
expert_offsets: torch.Tensor,
|
||||
):
|
||||
torch.ops._C.cutlass_blockwise_scaled_grouped_mm(output, a, b, scales_a,
|
||||
scales_b, problem_sizes,
|
||||
expert_offsets)
|
||||
|
||||
|
||||
def cutlass_scaled_fp4_mm(a: torch.Tensor, b: torch.Tensor,
|
||||
block_scale_a: torch.Tensor,
|
||||
block_scale_b: torch.Tensor, alpha: torch.Tensor,
|
||||
|
||||
@ -7,12 +7,17 @@ import torch
|
||||
|
||||
import vllm.model_executor.layers.fused_moe.modular_kernel as mk
|
||||
from vllm import _custom_ops as ops
|
||||
from vllm.logger import init_logger
|
||||
from vllm.model_executor.layers.fused_moe.config import FusedMoEQuantConfig
|
||||
from vllm.model_executor.layers.fused_moe.prepare_finalize import (
|
||||
MoEPrepareAndFinalizeNoEP)
|
||||
from vllm.model_executor.layers.fused_moe.utils import _fp8_perm, _resize_cache
|
||||
from vllm.model_executor.layers.fused_moe.utils import (_fp8_perm,
|
||||
_fp8_quantize,
|
||||
_resize_cache)
|
||||
from vllm.scalar_type import scalar_types
|
||||
|
||||
logger = init_logger(__name__)
|
||||
|
||||
|
||||
def run_cutlass_moe_fp8(
|
||||
output: torch.Tensor,
|
||||
@ -508,3 +513,130 @@ def cutlass_moe_fp4(a: torch.Tensor, a1_gscale: torch.Tensor,
|
||||
out = (c2.view(m, num_topk, k) *
|
||||
topk_weights.view(m, num_topk, 1).half()).sum(dim=1)
|
||||
return out.to(dtype=out_dtype)
|
||||
|
||||
|
||||
def _valid_cutlass_block_scaled_grouped_gemm(hidden_states: torch.Tensor,
|
||||
w1: torch.Tensor,
|
||||
w2: torch.Tensor) -> bool:
|
||||
|
||||
def _valid_cutlass_block_scaled_grouped_gemm_shape(M: int, N: int, K: int):
|
||||
return M >= 128 and N % 128 == 0 and K % 128 == 0
|
||||
|
||||
m = hidden_states.size(0)
|
||||
_, K, N = w2.size()
|
||||
if not _valid_cutlass_block_scaled_grouped_gemm_shape(m, N, K):
|
||||
logger.debug(
|
||||
"CutlassBlockScaledGroupedGemm disabled: unalinged problem size.")
|
||||
return False
|
||||
|
||||
if (w1.dtype != torch.float8_e4m3fn or w2.dtype != torch.float8_e4m3fn):
|
||||
logger.debug(
|
||||
"CutlassBlockScaledGroupedGemm disabled: invalid weight dtype(s).")
|
||||
return False
|
||||
|
||||
return True
|
||||
|
||||
|
||||
def run_cutlass_block_scaled_fused_experts(
|
||||
a: torch.Tensor,
|
||||
w1: torch.Tensor,
|
||||
w2: torch.Tensor,
|
||||
w1_scale: torch.Tensor,
|
||||
w2_scale: torch.Tensor,
|
||||
topk_weights: torch.Tensor,
|
||||
topk_ids: torch.Tensor,
|
||||
) -> torch.Tensor:
|
||||
w1_q = w1.transpose(1, 2)
|
||||
w2_q = w2.transpose(1, 2)
|
||||
w1_scale = w1_scale.transpose(1, 2)
|
||||
w2_scale = w2_scale.transpose(1, 2)
|
||||
|
||||
assert topk_weights.shape == topk_ids.shape, "topk shape mismatch"
|
||||
assert a.shape[0] == topk_ids.shape[
|
||||
0], "a and topk_ids must have the same batch size"
|
||||
assert w1_q.dtype == torch.float8_e4m3fn, "w1_q must be float8_e4m3fn"
|
||||
assert w2_q.dtype == torch.float8_e4m3fn, "w2_q must be float8_e4m3fn"
|
||||
assert a.shape[1] == w1_q.shape[1], "Hidden size mismatch w1"
|
||||
assert w1_q.shape[2] == w2_q.shape[1] * 2, "Hidden size mismatch w2"
|
||||
assert w1_q.shape[0] == w2_q.shape[0], "Expert number mismatch"
|
||||
assert w1_q.shape[0] == w1_scale.shape[
|
||||
0], "w1_scale expert number mismatch"
|
||||
assert w1_q.shape[0] == w2_scale.shape[
|
||||
0], "w2_scale expert number mismatch"
|
||||
assert a.dtype in [torch.half, torch.bfloat16], "Invalid output dtype"
|
||||
|
||||
out_dtype = a.dtype
|
||||
num_experts = w1_q.size(0)
|
||||
m = a.size(0)
|
||||
k = w1_q.size(1)
|
||||
n = w2_q.size(1)
|
||||
|
||||
expert_offsets = torch.empty((num_experts + 1, ),
|
||||
dtype=torch.int32,
|
||||
device="cuda")
|
||||
problem_sizes1 = torch.empty((num_experts, 3),
|
||||
dtype=torch.int32,
|
||||
device="cuda")
|
||||
problem_sizes2 = torch.empty((num_experts, 3),
|
||||
dtype=torch.int32,
|
||||
device="cuda")
|
||||
|
||||
topk = topk_ids.size(1)
|
||||
|
||||
a_q, a1_scale = _fp8_quantize(a,
|
||||
A_scale=None,
|
||||
per_act_token=False,
|
||||
block_shape=[128, 128])
|
||||
device = a_q.device
|
||||
|
||||
a_map = torch.empty((topk_ids.numel()), dtype=torch.int32, device=device)
|
||||
c_map = torch.empty((topk_ids.numel()), dtype=torch.int32, device=device)
|
||||
|
||||
ops.get_cutlass_moe_mm_data(
|
||||
topk_ids,
|
||||
expert_offsets,
|
||||
problem_sizes1,
|
||||
problem_sizes2,
|
||||
a_map,
|
||||
c_map,
|
||||
num_experts,
|
||||
n,
|
||||
k,
|
||||
)
|
||||
|
||||
rep_a_q = a_q.view(dtype=torch.uint8)[a_map].view(dtype=a_q.dtype)
|
||||
rep_a1_scales = a1_scale[a_map]
|
||||
|
||||
c1 = torch.empty((m * topk, n * 2), dtype=out_dtype, device=device)
|
||||
c2 = torch.empty((m * topk, k), dtype=out_dtype, device=device)
|
||||
|
||||
ops.cutlass_blockwise_scaled_grouped_mm(
|
||||
c1,
|
||||
rep_a_q,
|
||||
w1_q,
|
||||
rep_a1_scales,
|
||||
w1_scale,
|
||||
problem_sizes1,
|
||||
expert_offsets[:-1],
|
||||
)
|
||||
|
||||
intermediate = torch.empty((m * topk, n), dtype=out_dtype, device=device)
|
||||
torch.ops._C.silu_and_mul(intermediate, c1)
|
||||
|
||||
intermediate_q, a2_scale = _fp8_quantize(intermediate,
|
||||
A_scale=None,
|
||||
per_act_token=False,
|
||||
block_shape=[128, 128])
|
||||
|
||||
ops.cutlass_blockwise_scaled_grouped_mm(
|
||||
c2,
|
||||
intermediate_q,
|
||||
w2_q,
|
||||
a2_scale,
|
||||
w2_scale,
|
||||
problem_sizes2,
|
||||
expert_offsets[:-1],
|
||||
)
|
||||
|
||||
return (c2[c_map].view(m, topk, k) *
|
||||
topk_weights.view(m, topk, 1).to(out_dtype)).sum(dim=1)
|
||||
|
||||
@ -15,6 +15,9 @@ from vllm.logger import init_logger
|
||||
# yapf: disable
|
||||
from vllm.model_executor.layers.fused_moe.config import (
|
||||
FusedMoEQuantConfig, get_config_quant_dtype)
|
||||
from vllm.model_executor.layers.fused_moe.cutlass_moe import (
|
||||
_valid_cutlass_block_scaled_grouped_gemm,
|
||||
run_cutlass_block_scaled_fused_experts)
|
||||
# yapf: enable
|
||||
from vllm.model_executor.layers.fused_moe.deep_gemm_moe import (
|
||||
_valid_deep_gemm, deep_gemm_moe_fp8)
|
||||
@ -1129,29 +1132,31 @@ def dispatch_fused_experts_func(inplace: bool) -> Callable[..., torch.Tensor]:
|
||||
|
||||
# TODO (bnell): replace this with modular op. Can get rid of inplace/outplace
|
||||
# torch ops.
|
||||
def fused_experts(hidden_states: torch.Tensor,
|
||||
w1: torch.Tensor,
|
||||
w2: torch.Tensor,
|
||||
topk_weights: torch.Tensor,
|
||||
topk_ids: torch.Tensor,
|
||||
inplace: bool = False,
|
||||
activation: str = "silu",
|
||||
apply_router_weight_on_input: bool = False,
|
||||
use_fp8_w8a8: bool = False,
|
||||
use_int8_w8a8: bool = False,
|
||||
use_int8_w8a16: bool = False,
|
||||
use_int4_w4a16: bool = False,
|
||||
per_channel_quant: bool = False,
|
||||
global_num_experts: int = -1,
|
||||
expert_map: Optional[torch.Tensor] = None,
|
||||
w1_scale: Optional[torch.Tensor] = None,
|
||||
w2_scale: Optional[torch.Tensor] = None,
|
||||
w1_zp: Optional[torch.Tensor] = None,
|
||||
w2_zp: Optional[torch.Tensor] = None,
|
||||
a1_scale: Optional[torch.Tensor] = None,
|
||||
a2_scale: Optional[torch.Tensor] = None,
|
||||
block_shape: Optional[list[int]] = None,
|
||||
allow_deep_gemm: bool = False) -> torch.Tensor:
|
||||
def fused_experts(
|
||||
hidden_states: torch.Tensor,
|
||||
w1: torch.Tensor,
|
||||
w2: torch.Tensor,
|
||||
topk_weights: torch.Tensor,
|
||||
topk_ids: torch.Tensor,
|
||||
inplace: bool = False,
|
||||
activation: str = "silu",
|
||||
apply_router_weight_on_input: bool = False,
|
||||
use_fp8_w8a8: bool = False,
|
||||
use_int8_w8a8: bool = False,
|
||||
use_int8_w8a16: bool = False,
|
||||
use_int4_w4a16: bool = False,
|
||||
per_channel_quant: bool = False,
|
||||
global_num_experts: int = -1,
|
||||
expert_map: Optional[torch.Tensor] = None,
|
||||
w1_scale: Optional[torch.Tensor] = None,
|
||||
w2_scale: Optional[torch.Tensor] = None,
|
||||
w1_zp: Optional[torch.Tensor] = None,
|
||||
w2_zp: Optional[torch.Tensor] = None,
|
||||
a1_scale: Optional[torch.Tensor] = None,
|
||||
a2_scale: Optional[torch.Tensor] = None,
|
||||
block_shape: Optional[list[int]] = None,
|
||||
allow_deep_gemm: bool = False,
|
||||
allow_cutlass_block_scaled_grouped_gemm: bool = False) -> torch.Tensor:
|
||||
# For now, disable DeepGemm for small N (<= 512) until better
|
||||
# permute/unpermute ops are available.
|
||||
N = w1.size(1)
|
||||
@ -1174,6 +1179,17 @@ def fused_experts(hidden_states: torch.Tensor,
|
||||
a2_scale=a2_scale,
|
||||
apply_router_weight_on_input=apply_router_weight_on_input,
|
||||
)
|
||||
elif (allow_cutlass_block_scaled_grouped_gemm and use_fp8_w8a8
|
||||
and _valid_cutlass_block_scaled_grouped_gemm(hidden_states, w1, w2)):
|
||||
assert apply_router_weight_on_input is False
|
||||
return run_cutlass_block_scaled_fused_experts(
|
||||
a=hidden_states,
|
||||
w1=w1,
|
||||
w2=w2,
|
||||
w1_scale=w1_scale,
|
||||
w2_scale=w2_scale,
|
||||
topk_weights=topk_weights,
|
||||
topk_ids=topk_ids)
|
||||
else:
|
||||
return dispatch_fused_experts_func(inplace)(
|
||||
hidden_states=hidden_states,
|
||||
|
||||
@ -473,12 +473,30 @@ class Fp8MoEMethod(FusedMoEMethodBase):
|
||||
logger.warning_once(
|
||||
"DeepGemm not supported on the current platform.")
|
||||
|
||||
# Check for CutlassBlockScaledGroupedGemm support.
|
||||
self.allow_cutlass_block_scaled_grouped_gemm = False
|
||||
if not self.block_quant:
|
||||
logger.warning_once("Model is not block quantized. Not using "
|
||||
"CutlassBlockScaledGroupedGemm kernels")
|
||||
elif (current_platform.is_cuda()
|
||||
and current_platform.has_device_capability(100)):
|
||||
logger.info_once(
|
||||
"Using CutlassBlockScaledGroupedGemm kernels for Fp8MoEMethod."
|
||||
)
|
||||
self.allow_cutlass_block_scaled_grouped_gemm = True
|
||||
else:
|
||||
logger.warning_once(
|
||||
"CutlassBlockScaledGroupedGemm not supported on the current "
|
||||
"platform.")
|
||||
|
||||
self.topk_indices_dtype = None
|
||||
self.fused_experts = functools.partial( # type: ignore
|
||||
fused_experts,
|
||||
use_fp8_w8a8=True,
|
||||
block_shape=self.quant_config.weight_block_size,
|
||||
allow_deep_gemm=self.allow_deep_gemm)
|
||||
allow_deep_gemm=self.allow_deep_gemm,
|
||||
allow_cutlass_block_scaled_grouped_gemm=(
|
||||
self.allow_cutlass_block_scaled_grouped_gemm))
|
||||
|
||||
def create_weights(self, layer: Module, num_experts: int, hidden_size: int,
|
||||
intermediate_size_per_partition: int,
|
||||
|
||||
Reference in New Issue
Block a user