[1/2] Add FP8 Blockscale MoE CUTLASS kernel for Blackwell (#5281)
This commit is contained in:
Elfie Guo
1
sgl-kernel/CMakeLists.txt
Normal file → Executable file
1
sgl-kernel/CMakeLists.txt
Normal file → Executable file
@@ -195,6 +195,7 @@ set(SOURCES
|
||||
"csrc/moe/moe_align_kernel.cu"
|
||||
"csrc/moe/moe_fused_gate.cu"
|
||||
"csrc/moe/moe_topk_softmax_kernels.cu"
|
||||
"csrc/moe/fp8_blockwise_moe_kernel.cu"
|
||||
"csrc/speculative/eagle_utils.cu"
|
||||
"csrc/speculative/speculative_sampling.cu"
|
||||
"csrc/speculative/packbit.cu"
|
||||
|
||||
5
sgl-kernel/csrc/common_extension.cc
Normal file → Executable file
5
sgl-kernel/csrc/common_extension.cc
Normal file → Executable file
@@ -150,6 +150,11 @@ TORCH_LIBRARY_FRAGMENT(sgl_kernel, m) {
|
||||
"n_share_experts_fusion, float routed_scaling_factor) -> "
|
||||
"(Tensor[])");
|
||||
m.impl("moe_fused_gate", torch::kCUDA, &moe_fused_gate);
|
||||
m.def(
|
||||
"fp8_blockwise_scaled_grouped_mm(Tensor output, Tensor a, Tensor b, Tensor scales_a, Tensor scales_b, Tensor "
|
||||
"stride_a, Tensor stride_b, Tensor stride_c, Tensor layout_sfa, Tensor layout_sfb, Tensor problem_sizes, Tensor "
|
||||
"expert_offsets) -> ()");
|
||||
m.impl("fp8_blockwise_scaled_grouped_mm", torch::kCUDA, &fp8_blockwise_scaled_grouped_mm);
|
||||
|
||||
/*
|
||||
* From csrc/speculative
|
||||
|
||||
142
sgl-kernel/csrc/moe/cutlass_moe_helper.cu
Normal file
142
sgl-kernel/csrc/moe/cutlass_moe_helper.cu
Normal file
@@ -0,0 +1,142 @@
|
||||
#pragma once
|
||||
|
||||
#include <c10/cuda/CUDAStream.h>
|
||||
#include <cuda.h>
|
||||
#include <torch/all.h>
|
||||
|
||||
#include "cutlass/bfloat16.h"
|
||||
#include "cutlass/float8.h"
|
||||
|
||||
template <
|
||||
typename ElementAB,
|
||||
typename ElementC,
|
||||
typename ElementAccumulator,
|
||||
typename LayoutSFA,
|
||||
typename LayoutSFB,
|
||||
typename ScaleConfig>
|
||||
__global__ void get_group_gemm_starts(
|
||||
int32_t* expert_offsets,
|
||||
ElementAB** a_offsets,
|
||||
ElementAB** b_offsets,
|
||||
ElementC** out_offsets,
|
||||
ElementAccumulator** a_scales_offsets,
|
||||
ElementAccumulator** b_scales_offsets,
|
||||
ElementAB* a_base_as_int,
|
||||
ElementAB* b_base_as_int,
|
||||
ElementC* out_base_as_int,
|
||||
ElementAccumulator* a_scales_base_as_int,
|
||||
ElementAccumulator* b_scales_base_as_int,
|
||||
LayoutSFA* layout_sfa_base_as_int,
|
||||
LayoutSFB* layout_sfb_base_as_int,
|
||||
int* problem_sizes,
|
||||
int* problem_sizes_transpose,
|
||||
bool transpose = false) {
|
||||
int expert_id = threadIdx.x;
|
||||
|
||||
if (expert_id >= gridDim.x * blockDim.x) {
|
||||
return;
|
||||
}
|
||||
|
||||
int m = problem_sizes[expert_id * 3];
|
||||
int n = problem_sizes[expert_id * 3 + 1];
|
||||
int k = problem_sizes[expert_id * 3 + 2];
|
||||
if (transpose) {
|
||||
problem_sizes_transpose[expert_id * 3] = n;
|
||||
problem_sizes_transpose[expert_id * 3 + 1] = m;
|
||||
problem_sizes_transpose[expert_id * 3 + 2] = k;
|
||||
}
|
||||
|
||||
int32_t expert_offset = expert_offsets[expert_id];
|
||||
int a_stride = 0;
|
||||
int b_stride = 0;
|
||||
int a_scale_stride = 0;
|
||||
int b_scale_stride = 0;
|
||||
if (!transpose) {
|
||||
a_stride = expert_offset * k;
|
||||
b_stride = expert_id * k * n;
|
||||
a_scale_stride = expert_offset * k / 128;
|
||||
b_scale_stride = expert_id * k * n / 128 / 128;
|
||||
} else {
|
||||
a_stride = expert_id * k * n;
|
||||
b_stride = expert_offset * k;
|
||||
a_scale_stride = expert_id * k * n / 128 / 128;
|
||||
b_scale_stride = expert_offset * k / 128;
|
||||
}
|
||||
a_offsets[expert_id] = a_base_as_int + a_stride;
|
||||
b_offsets[expert_id] = b_base_as_int + b_stride;
|
||||
out_offsets[expert_id] = out_base_as_int + expert_offset * n;
|
||||
a_scales_offsets[expert_id] = a_scales_base_as_int + a_scale_stride;
|
||||
b_scales_offsets[expert_id] = b_scales_base_as_int + b_scale_stride;
|
||||
|
||||
LayoutSFA* layout_sfa_ptr = layout_sfa_base_as_int + expert_id;
|
||||
LayoutSFB* layout_sfb_ptr = layout_sfb_base_as_int + expert_id;
|
||||
|
||||
if (!transpose) {
|
||||
*layout_sfa_ptr = ScaleConfig::tile_atom_to_shape_SFA(cute::make_shape(m, n, k, 1));
|
||||
*layout_sfb_ptr = ScaleConfig::tile_atom_to_shape_SFB(cute::make_shape(m, n, k, 1));
|
||||
} else {
|
||||
*layout_sfa_ptr = ScaleConfig::tile_atom_to_shape_SFA(cute::make_shape(n, m, k, 1));
|
||||
*layout_sfb_ptr = ScaleConfig::tile_atom_to_shape_SFB(cute::make_shape(n, m, k, 1));
|
||||
}
|
||||
}
|
||||
|
||||
#define __CALL_GET_STARTS_KERNEL(TENSOR_C_TYPE, C_TYPE, LayoutSFA, LayoutSFB, ScaleConfig) \
|
||||
else if (out_tensors.dtype() == TENSOR_C_TYPE) { \
|
||||
get_group_gemm_starts<cutlass::float_e4m3_t, C_TYPE, float, LayoutSFA, LayoutSFB, ScaleConfig> \
|
||||
<<<1, num_experts, 0, stream>>>( \
|
||||
static_cast<int32_t*>(expert_offsets.data_ptr()), \
|
||||
static_cast<cutlass::float_e4m3_t**>(a_ptrs.data_ptr()), \
|
||||
static_cast<cutlass::float_e4m3_t**>(b_ptrs.data_ptr()), \
|
||||
static_cast<C_TYPE**>(out_ptrs.data_ptr()), \
|
||||
static_cast<float**>(a_scales_ptrs.data_ptr()), \
|
||||
static_cast<float**>(b_scales_ptrs.data_ptr()), \
|
||||
static_cast<cutlass::float_e4m3_t*>(a_tensors.data_ptr()), \
|
||||
static_cast<cutlass::float_e4m3_t*>(b_tensors.data_ptr()), \
|
||||
static_cast<C_TYPE*>(out_tensors.data_ptr()), \
|
||||
static_cast<float*>(a_scales.data_ptr()), \
|
||||
static_cast<float*>(b_scales.data_ptr()), \
|
||||
reinterpret_cast<LayoutSFA*>(layout_sfa.data_ptr()), \
|
||||
reinterpret_cast<LayoutSFB*>(layout_sfb.data_ptr()), \
|
||||
static_cast<int*>(problem_sizes.data_ptr()), \
|
||||
static_cast<int*>(problem_sizes_transpose.data_ptr()), \
|
||||
transpose); \
|
||||
}
|
||||
|
||||
namespace {
|
||||
template <typename LayoutSFA, typename LayoutSFB, typename ScaleConfig>
|
||||
void run_get_group_gemm_starts(
|
||||
torch::Tensor const& expert_offsets,
|
||||
torch::Tensor& a_ptrs,
|
||||
torch::Tensor& b_ptrs,
|
||||
torch::Tensor& out_ptrs,
|
||||
torch::Tensor& a_scales_ptrs,
|
||||
torch::Tensor& b_scales_ptrs,
|
||||
torch::Tensor const& a_tensors,
|
||||
torch::Tensor const& b_tensors,
|
||||
torch::Tensor& out_tensors,
|
||||
torch::Tensor const& a_scales,
|
||||
torch::Tensor const& b_scales,
|
||||
torch::Tensor const& layout_sfa,
|
||||
torch::Tensor const& layout_sfb,
|
||||
torch::Tensor const& problem_sizes,
|
||||
torch::Tensor& problem_sizes_transpose,
|
||||
bool transpose = false) {
|
||||
TORCH_CHECK(a_tensors.dtype() == torch::kFloat8_e4m3fn);
|
||||
TORCH_CHECK(b_tensors.dtype() == torch::kFloat8_e4m3fn);
|
||||
TORCH_CHECK(a_scales.dtype() == torch::kFloat32);
|
||||
TORCH_CHECK(b_scales.dtype() == torch::kFloat32);
|
||||
TORCH_CHECK(out_tensors.size(1) % 128 == 0 or out_tensors.size(0) % 128 == 0);
|
||||
TORCH_CHECK(a_tensors.size(1) % 128 == 0 or a_tensors.size(0) % 128 == 0);
|
||||
|
||||
int num_experts = (int)expert_offsets.size(0);
|
||||
auto stream = at::cuda::getCurrentCUDAStream(a_tensors.device().index());
|
||||
|
||||
if (false) {
|
||||
}
|
||||
__CALL_GET_STARTS_KERNEL(torch::kBFloat16, cutlass::bfloat16_t, LayoutSFA, LayoutSFB, ScaleConfig)
|
||||
__CALL_GET_STARTS_KERNEL(torch::kFloat16, half, LayoutSFA, LayoutSFB, ScaleConfig)
|
||||
else {
|
||||
TORCH_CHECK(false, "Invalid output type (must be float16 or bfloat16)");
|
||||
}
|
||||
}
|
||||
} // namespace
|
||||
386
sgl-kernel/csrc/moe/fp8_blockwise_moe_kernel.cu
Normal file
386
sgl-kernel/csrc/moe/fp8_blockwise_moe_kernel.cu
Normal file
@@ -0,0 +1,386 @@
|
||||
#include <cutlass/arch/arch.h>
|
||||
#include <torch/all.h>
|
||||
|
||||
#include "cute/tensor.hpp"
|
||||
#include "cutlass/cutlass.h"
|
||||
#include "cutlass/epilogue/collective/collective_builder.hpp"
|
||||
#include "cutlass/epilogue/collective/default_epilogue.hpp"
|
||||
#include "cutlass/epilogue/dispatch_policy.hpp"
|
||||
#include "cutlass/epilogue/thread/activation.h"
|
||||
#include "cutlass/epilogue/thread/linear_combination.h"
|
||||
#include "cutlass/gemm/collective/collective_builder.hpp"
|
||||
#include "cutlass/gemm/device/gemm_universal_adapter.h"
|
||||
#include "cutlass/gemm/dispatch_policy.hpp"
|
||||
#include "cutlass/gemm/group_array_problem_shape.hpp"
|
||||
#include "cutlass/gemm/kernel/gemm_universal.hpp"
|
||||
#include "cutlass/gemm/kernel/tile_scheduler_params.h"
|
||||
#include "cutlass/tensor_ref.h"
|
||||
#include "cutlass/util/command_line.h"
|
||||
#include "cutlass/util/distribution.h"
|
||||
#include "cutlass/util/host_tensor.h"
|
||||
#include "cutlass/util/packed_stride.hpp"
|
||||
#include "cutlass/util/reference/device/gemm.h"
|
||||
#include "cutlass/util/reference/device/tensor_compare.h"
|
||||
#include "cutlass/util/tensor_view_io.h"
|
||||
#include "cutlass_moe_helper.cu"
|
||||
#include "utils.h"
|
||||
|
||||
using namespace cute;
|
||||
|
||||
using ProblemShape = cutlass::gemm::GroupProblemShape<Shape<int, int, int>>;
|
||||
template <typename OutType, typename ScheduleConfig, typename LayoutD>
|
||||
void launch_sm100_fp8_blockwise_scaled_group_mm(
|
||||
torch::Tensor& out_ptrs,
|
||||
const torch::Tensor& a_ptrs,
|
||||
const torch::Tensor& b_ptrs,
|
||||
const torch::Tensor& a_scales_ptrs,
|
||||
const torch::Tensor& b_scales_ptrs,
|
||||
const torch::Tensor& stride_a,
|
||||
const torch::Tensor& stride_b,
|
||||
const torch::Tensor& stride_c,
|
||||
const torch::Tensor& layout_sfa,
|
||||
const torch::Tensor& layout_sfb,
|
||||
const torch::Tensor& problem_sizes,
|
||||
const torch::Tensor& expert_offsets,
|
||||
const torch::Tensor& workspace) {
|
||||
using ProblemShape = cutlass::gemm::GroupProblemShape<Shape<int, int, int>>;
|
||||
using ElementA = cutlass::float_e4m3_t;
|
||||
using ElementB = cutlass::float_e4m3_t;
|
||||
using ElementC = OutType;
|
||||
using ElementD = ElementC;
|
||||
using ElementAccumulator = float;
|
||||
// Layout definitions
|
||||
using LayoutA = cutlass::layout::RowMajor;
|
||||
using LayoutB = cutlass::layout::ColumnMajor;
|
||||
using LayoutC = LayoutD;
|
||||
|
||||
// Alignment constraints
|
||||
static constexpr int AlignmentA = 128 / cutlass::sizeof_bits<ElementA>::value;
|
||||
static constexpr int AlignmentB = 128 / cutlass::sizeof_bits<ElementB>::value;
|
||||
static constexpr int AlignmentC = 128 / cutlass::sizeof_bits<ElementC>::value;
|
||||
|
||||
// Architecture definitions
|
||||
using ArchTag = cutlass::arch::Sm100;
|
||||
using OperatorClass = cutlass::arch::OpClassTensorOp;
|
||||
// For fp8 block scale.
|
||||
// using ScaleConfig = cutlass::detail::Sm100BlockwiseScaleConfig<ScaleGranularityM, ScaleGranularityN,
|
||||
// ScaleGranularityK, cute::UMMA::Major::K, cute::UMMA::Major::K>; using LayoutSFA =
|
||||
// decltype(ScaleConfig::deduce_layoutSFA()); using LayoutSFB = decltype(ScaleConfig::deduce_layoutSFB());
|
||||
using CollectiveEpilogue = typename cutlass::epilogue::collective::CollectiveBuilder<
|
||||
ArchTag,
|
||||
OperatorClass,
|
||||
typename ScheduleConfig::MmaTileShape,
|
||||
typename ScheduleConfig::ClusterShape,
|
||||
cutlass::epilogue::collective::EpilogueTileAuto,
|
||||
ElementAccumulator,
|
||||
ElementAccumulator,
|
||||
void,
|
||||
LayoutC*,
|
||||
AlignmentC,
|
||||
ElementD,
|
||||
LayoutC*,
|
||||
AlignmentC,
|
||||
typename ScheduleConfig::EpilogueSchedule>::CollectiveOp;
|
||||
|
||||
using CollectiveMainloop = typename cutlass::gemm::collective::CollectiveBuilder<
|
||||
ArchTag,
|
||||
OperatorClass,
|
||||
ElementA,
|
||||
cute::tuple<LayoutA*, typename ScheduleConfig::LayoutSFA*>,
|
||||
AlignmentA,
|
||||
ElementB,
|
||||
cute::tuple<LayoutB*, typename ScheduleConfig::LayoutSFB*>,
|
||||
AlignmentB,
|
||||
ElementAccumulator,
|
||||
typename ScheduleConfig::MmaTileShape,
|
||||
typename ScheduleConfig::ClusterShape,
|
||||
cutlass::gemm::collective::StageCountAutoCarveout<static_cast<int>(
|
||||
sizeof(typename CollectiveEpilogue::SharedStorage))>,
|
||||
typename ScheduleConfig::KernelSchedule>::CollectiveOp;
|
||||
|
||||
using GemmKernel = cutlass::gemm::kernel::GemmUniversal<ProblemShape, CollectiveMainloop, CollectiveEpilogue, void>;
|
||||
|
||||
using Gemm = cutlass::gemm::device::GemmUniversalAdapter<GemmKernel>;
|
||||
using UnderlyingProblemShape = ProblemShape::UnderlyingProblemShape;
|
||||
using StrideA = typename Gemm::GemmKernel::InternalStrideA;
|
||||
using StrideB = typename Gemm::GemmKernel::InternalStrideB;
|
||||
using StrideC = typename Gemm::GemmKernel::InternalStrideC;
|
||||
using StrideD = typename Gemm::GemmKernel::InternalStrideD;
|
||||
|
||||
int num_experts = (int)expert_offsets.size(0);
|
||||
// Create an instance of the GEMM
|
||||
Gemm gemm_op;
|
||||
|
||||
typename GemmKernel::MainloopArguments mainloop_args{
|
||||
static_cast<const ElementA**>(a_ptrs.data_ptr()),
|
||||
static_cast<StrideA*>(stride_a.data_ptr()),
|
||||
static_cast<const ElementB**>(b_ptrs.data_ptr()),
|
||||
static_cast<StrideB*>(stride_b.data_ptr()),
|
||||
static_cast<const ElementAccumulator**>(a_scales_ptrs.data_ptr()),
|
||||
reinterpret_cast<typename ScheduleConfig::LayoutSFA*>(layout_sfa.data_ptr()),
|
||||
static_cast<const ElementAccumulator**>(b_scales_ptrs.data_ptr()),
|
||||
reinterpret_cast<typename ScheduleConfig::LayoutSFB*>(layout_sfb.data_ptr())};
|
||||
|
||||
cutlass::KernelHardwareInfo hw_info;
|
||||
|
||||
hw_info.device_id = 0;
|
||||
hw_info.sm_count = 1;
|
||||
// Currently, we are only able to do broadcast on either all or none a_scales
|
||||
// and on either all or none b_scales
|
||||
typename GemmKernel::EpilogueArguments epilogue_args{
|
||||
{},
|
||||
nullptr,
|
||||
static_cast<StrideC*>(stride_c.data_ptr()),
|
||||
static_cast<ElementD**>(out_ptrs.data_ptr()),
|
||||
static_cast<StrideC*>(stride_c.data_ptr())};
|
||||
|
||||
// Initialize problem_sizes_as_shapes correctly
|
||||
UnderlyingProblemShape* problem_sizes_as_shapes = static_cast<UnderlyingProblemShape*>(problem_sizes.data_ptr());
|
||||
// Use prob_shape in the GEMM arguments
|
||||
typename GemmKernel::Arguments args{
|
||||
cutlass::gemm::GemmUniversalMode::kGrouped,
|
||||
{num_experts, problem_sizes_as_shapes, nullptr},
|
||||
mainloop_args,
|
||||
epilogue_args,
|
||||
hw_info};
|
||||
|
||||
auto can_implement_status = gemm_op.can_implement(args);
|
||||
TORCH_CHECK(can_implement_status == cutlass::Status::kSuccess, "Failed to implement GEMM");
|
||||
|
||||
// Run the GEMM
|
||||
auto status = gemm_op.initialize(args, workspace.data_ptr());
|
||||
|
||||
TORCH_CHECK(status == cutlass::Status::kSuccess, "Failed to initialize GEMM");
|
||||
|
||||
status = gemm_op.run();
|
||||
TORCH_CHECK(status == cutlass::Status::kSuccess, "Failed to run GEMM");
|
||||
}
|
||||
|
||||
template <typename OutType>
|
||||
void sm100_fp8_blockwise_group_mm_dispatch_shape(
|
||||
torch::Tensor& output,
|
||||
const torch::Tensor& a,
|
||||
const torch::Tensor& b,
|
||||
const torch::Tensor& scales_a,
|
||||
const torch::Tensor& scales_b,
|
||||
const torch::Tensor& stride_a,
|
||||
const torch::Tensor& stride_b,
|
||||
const torch::Tensor& stride_c,
|
||||
const torch::Tensor& layout_sfa,
|
||||
const torch::Tensor& layout_sfb,
|
||||
const torch::Tensor& problem_sizes,
|
||||
const torch::Tensor& expert_offsets) {
|
||||
// Check the first matrix size to decide on the configuration
|
||||
// Assuming all matrices in the group have similar size characteristics
|
||||
// bool use_small_config = a[0].size(0) <= 128;
|
||||
struct MMALargeConfig {
|
||||
using ElementA = cutlass::float_e4m3_t;
|
||||
using MmaTileShape = Shape<_128, _128, _128>;
|
||||
using ClusterShape = Shape<_1, _1, _1>; // Layout type for SFB matrix operand
|
||||
using KernelSchedule = cutlass::gemm::KernelPtrArrayTmaWarpSpecializedBlockwise1SmSm100;
|
||||
using EpilogueSchedule = cutlass::epilogue::PtrArrayTmaWarpSpecialized1Sm;
|
||||
using ScaleConfig =
|
||||
cutlass::detail::Sm100BlockwiseScaleConfig<1, 128, 128, cute::UMMA::Major::K, cute::UMMA::Major::K>;
|
||||
using LayoutSFA = decltype(ScaleConfig::deduce_layoutSFA());
|
||||
using LayoutSFB = decltype(ScaleConfig::deduce_layoutSFB());
|
||||
};
|
||||
|
||||
struct MMASmallConfig {
|
||||
using ElementA = cutlass::float_e4m3_t;
|
||||
using MmaTileShape = Shape<_128, _16, _128>;
|
||||
using ClusterShape = Shape<_1, _1, _1>; // Layout type for SFB matrix operand
|
||||
using KernelSchedule = cutlass::gemm::KernelPtrArrayTmaWarpSpecializedBlockwise1SmSm100;
|
||||
using EpilogueSchedule = cutlass::epilogue::PtrArrayTmaWarpSpecialized1Sm;
|
||||
using ScaleConfig =
|
||||
cutlass::detail::Sm100BlockwiseScaleConfig<128, 1, 128, cute::UMMA::Major::K, cute::UMMA::Major::K>;
|
||||
using LayoutSFA = decltype(ScaleConfig::deduce_layoutSFA());
|
||||
using LayoutSFB = decltype(ScaleConfig::deduce_layoutSFB());
|
||||
};
|
||||
int num_experts = (int)expert_offsets.size(0);
|
||||
torch::TensorOptions 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 problem_sizes_transpose = torch::empty(num_experts * 3, options_int);
|
||||
torch::Tensor workspace = torch::empty(100, options_int);
|
||||
torch::Tensor output_t = output.t();
|
||||
torch::Tensor a_t = a.t();
|
||||
torch::Tensor b_t = b.transpose(1, 2);
|
||||
torch::Tensor scales_a_t = scales_a.t();
|
||||
torch::Tensor scales_b_t = scales_b.transpose(1, 2);
|
||||
|
||||
if (a.size(0) <= 512) {
|
||||
run_get_group_gemm_starts<MMASmallConfig::LayoutSFA, MMASmallConfig::LayoutSFB, MMASmallConfig::ScaleConfig>(
|
||||
expert_offsets,
|
||||
a_ptrs,
|
||||
b_ptrs,
|
||||
out_ptrs,
|
||||
a_scales_ptrs,
|
||||
b_scales_ptrs,
|
||||
b_t,
|
||||
a_t,
|
||||
output_t,
|
||||
scales_b_t,
|
||||
scales_a_t,
|
||||
layout_sfa,
|
||||
layout_sfb,
|
||||
problem_sizes,
|
||||
problem_sizes_transpose,
|
||||
true);
|
||||
launch_sm100_fp8_blockwise_scaled_group_mm<OutType, MMASmallConfig, cutlass::layout::ColumnMajor>(
|
||||
out_ptrs,
|
||||
a_ptrs,
|
||||
b_ptrs,
|
||||
a_scales_ptrs,
|
||||
b_scales_ptrs,
|
||||
stride_a,
|
||||
stride_b,
|
||||
stride_c,
|
||||
layout_sfa,
|
||||
layout_sfb,
|
||||
problem_sizes_transpose,
|
||||
expert_offsets,
|
||||
workspace);
|
||||
output = output_t.t();
|
||||
} else {
|
||||
run_get_group_gemm_starts<MMALargeConfig::LayoutSFA, MMALargeConfig::LayoutSFB, MMALargeConfig::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,
|
||||
problem_sizes_transpose);
|
||||
launch_sm100_fp8_blockwise_scaled_group_mm<OutType, MMALargeConfig, cutlass::layout::RowMajor>(
|
||||
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,
|
||||
workspace);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* @brief Performs blockwise grouped matrix multiplication on FP8 quantized inputs,
|
||||
* with per-block scaling.
|
||||
*
|
||||
* This function dispatches to hardware-specific implementations (e.g., SM100 FP8)
|
||||
* to compute:
|
||||
* C_i = scale_a[i] * A_i * scale_b[i] * B_i
|
||||
* for each expert group `i`, using input `problem_sizes` and `expert_offsets`
|
||||
* to describe the individual matrix dimensions and their offsets.
|
||||
*
|
||||
* Input tensors A and B must be quantized to 8-bit formats and dequantized before multiplication.
|
||||
* The output tensor is written with bfloat16 or half precision.
|
||||
*
|
||||
* @param output Output tensor (must be of type bfloat16 or half).
|
||||
* @param a Input tensor A (must be kFloat8_e4m3fn).
|
||||
* @param b Input tensor B (must be kFloat8_e4m3fn).
|
||||
* @param scales_a Scaling factors for tensor A, float32 per expert group.
|
||||
* @param scales_b Scaling factors for tensor B, float32 per expert group.
|
||||
* @param stride_a Stride information for tensor A (int32).
|
||||
* @param stride_b Stride information for tensor B (int32).
|
||||
* @param stride_c Stride information for output tensor C (int32).
|
||||
* @param layout_sfa Layout descriptor for A (int32), e.g., row-major/column-major.
|
||||
* @param layout_sfb Layout descriptor for B (int32).
|
||||
* @param problem_sizes 2D int32 tensor of shape (num_experts, 3), specifying (M, N, K)
|
||||
* for each grouped matrix multiplication problem.
|
||||
* @param expert_offsets 1D int32 tensor of size (num_experts), used to index into
|
||||
* the grouped input tensors for dispatch.
|
||||
* @note Performance Optimization:
|
||||
* If the batch size (a.size(0)) is smaller than 512, the implementation
|
||||
* will internally transpose input matrices to align with the optimal memory access
|
||||
* pattern for better GPU efficiency. This transformation is done within the kernel.
|
||||
*/
|
||||
void fp8_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& stride_a,
|
||||
const torch::Tensor& stride_b,
|
||||
const torch::Tensor& stride_c,
|
||||
const torch::Tensor& layout_sfa,
|
||||
const torch::Tensor& layout_sfb,
|
||||
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(stride_a.scalar_type() == torch::kInt64, "stride_a must be int64");
|
||||
TORCH_CHECK(stride_b.scalar_type() == torch::kInt64, "stride_b must be int64");
|
||||
TORCH_CHECK(stride_c.scalar_type() == torch::kInt64, "stride_c must be int64");
|
||||
TORCH_CHECK(layout_sfa.scalar_type() == torch::kInt32, "layout_sfa must be int32");
|
||||
TORCH_CHECK(layout_sfb.scalar_type() == torch::kInt32, "layout_sfb must be int32");
|
||||
TORCH_CHECK(expert_offsets.scalar_type() == torch::kInt32, "expert_offsets must be int32");
|
||||
|
||||
bool can_implement = false;
|
||||
auto sm_version = getSMVersion();
|
||||
|
||||
#if defined(CUTLASS_ARCH_MMA_SM100A_SUPPORTED) || defined(CUTLASS_ARCH_MMA_SM100_SUPPORTED)
|
||||
#if defined CUDA_VERSION && CUDA_VERSION >= 12080
|
||||
if (sm_version == 100) {
|
||||
if (output.scalar_type() == torch::kBFloat16) {
|
||||
sm100_fp8_blockwise_group_mm_dispatch_shape<cutlass::bfloat16_t>(
|
||||
output,
|
||||
a,
|
||||
b,
|
||||
scales_a,
|
||||
scales_b,
|
||||
stride_a,
|
||||
stride_b,
|
||||
stride_c,
|
||||
layout_sfa,
|
||||
layout_sfb,
|
||||
problem_sizes,
|
||||
expert_offsets);
|
||||
} else {
|
||||
sm100_fp8_blockwise_group_mm_dispatch_shape<cutlass::half_t>(
|
||||
output,
|
||||
a,
|
||||
b,
|
||||
scales_a,
|
||||
scales_b,
|
||||
stride_a,
|
||||
stride_b,
|
||||
stride_c,
|
||||
layout_sfa,
|
||||
layout_sfb,
|
||||
problem_sizes,
|
||||
expert_offsets);
|
||||
}
|
||||
can_implement = true;
|
||||
}
|
||||
#endif
|
||||
#endif
|
||||
TORCH_CHECK_NOT_IMPLEMENTED(
|
||||
can_implement, "No implemented fp8_blockwise_scaled_mm for current compute capability: ", sm_version);
|
||||
}
|
||||
14
sgl-kernel/include/sgl_kernel_ops.h
Normal file → Executable file
14
sgl-kernel/include/sgl_kernel_ops.h
Normal file → Executable file
@@ -209,6 +209,20 @@ std::vector<at::Tensor> moe_fused_gate(
|
||||
int64_t n_share_experts_fusion,
|
||||
double routed_scaling_factor);
|
||||
|
||||
void fp8_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& stride_a,
|
||||
const torch::Tensor& stride_b,
|
||||
const torch::Tensor& stride_c,
|
||||
const torch::Tensor& layout_sfa,
|
||||
const torch::Tensor& layout_sfb,
|
||||
const torch::Tensor& problem_sizes,
|
||||
const torch::Tensor& expert_offsets);
|
||||
|
||||
/*
|
||||
* From csrc/speculative
|
||||
*/
|
||||
|
||||
7
sgl-kernel/python/sgl_kernel/__init__.py
Normal file → Executable file
7
sgl-kernel/python/sgl_kernel/__init__.py
Normal file → Executable file
@@ -41,7 +41,12 @@ from sgl_kernel.gemm import (
|
||||
sgl_per_token_group_quant_int8,
|
||||
sgl_per_token_quant_fp8,
|
||||
)
|
||||
from sgl_kernel.moe import moe_align_block_size, moe_fused_gate, topk_softmax
|
||||
from sgl_kernel.moe import (
|
||||
fp8_blockwise_scaled_grouped_mm,
|
||||
moe_align_block_size,
|
||||
moe_fused_gate,
|
||||
topk_softmax,
|
||||
)
|
||||
from sgl_kernel.sampling import (
|
||||
min_p_sampling_from_probs,
|
||||
top_k_renorm_prob,
|
||||
|
||||
30
sgl-kernel/python/sgl_kernel/moe.py
Normal file → Executable file
30
sgl-kernel/python/sgl_kernel/moe.py
Normal file → Executable file
@@ -60,3 +60,33 @@ def moe_fused_gate(
|
||||
n_share_experts_fusion,
|
||||
routed_scaling_factor,
|
||||
)
|
||||
|
||||
|
||||
def fp8_blockwise_scaled_grouped_mm(
|
||||
output,
|
||||
a,
|
||||
b,
|
||||
scales_a,
|
||||
scales_b,
|
||||
stride_a,
|
||||
stride_b,
|
||||
stride_c,
|
||||
layout_sfa,
|
||||
layout_sfb,
|
||||
problem_sizes,
|
||||
expert_offsets,
|
||||
):
|
||||
torch.ops.sgl_kernel.fp8_blockwise_scaled_grouped_mm.default(
|
||||
output,
|
||||
a,
|
||||
b,
|
||||
scales_a,
|
||||
scales_b,
|
||||
stride_a,
|
||||
stride_b,
|
||||
stride_c,
|
||||
layout_sfa,
|
||||
layout_sfb,
|
||||
problem_sizes,
|
||||
expert_offsets,
|
||||
)
|
||||
|
||||
148
sgl-kernel/tests/test_fp8_blockwise_moe.py
Executable file
148
sgl-kernel/tests/test_fp8_blockwise_moe.py
Executable file
@@ -0,0 +1,148 @@
|
||||
import random
|
||||
|
||||
import pytest
|
||||
import torch
|
||||
from sgl_kernel import fp8_blockwise_scaled_grouped_mm
|
||||
|
||||
|
||||
def cdiv(a: int, b: int) -> int:
|
||||
return -(a // -b)
|
||||
|
||||
|
||||
def scale_shape(shape, group_shape):
|
||||
return tuple(cdiv(shape[i], group_shape[i]) for i in range(len(group_shape)))
|
||||
|
||||
|
||||
def to_fp8(tensor: torch.Tensor) -> torch.Tensor:
|
||||
finfo = torch.finfo(torch.float8_e4m3fn)
|
||||
return torch.round(tensor.clamp(min=finfo.min, max=finfo.max)).to(
|
||||
dtype=torch.float8_e4m3fn
|
||||
)
|
||||
|
||||
|
||||
def baseline_scaled_mm(
|
||||
a: torch.Tensor,
|
||||
b: torch.Tensor,
|
||||
scale_a: torch.Tensor,
|
||||
scale_b: torch.Tensor,
|
||||
out_dtype: type[torch.dtype],
|
||||
) -> torch.Tensor:
|
||||
|
||||
def group_broadcast(t, shape):
|
||||
for i, s in enumerate(shape):
|
||||
if t.shape[i] != s and t.shape[i] != 1:
|
||||
assert s % t.shape[i] == 0
|
||||
t = (
|
||||
t.unsqueeze(i + 1)
|
||||
.expand(*t.shape[: i + 1], s // t.shape[i], *t.shape[i + 1 :])
|
||||
.flatten(i, i + 1)
|
||||
)
|
||||
return t
|
||||
|
||||
scale_a = group_broadcast(scale_a, a.shape)
|
||||
scale_b = group_broadcast(scale_b, b.shape)
|
||||
|
||||
return torch.mm(
|
||||
(scale_a * a.to(dtype=torch.float32)), (scale_b * b.to(dtype=torch.float32))
|
||||
).to(out_dtype)
|
||||
|
||||
|
||||
@pytest.mark.parametrize("num_experts", [8, 16])
|
||||
@pytest.mark.parametrize("out_dtype", [torch.half, torch.bfloat16])
|
||||
def test_fp8_blockwise_scaled_grouped_mm(num_experts, out_dtype):
|
||||
device = "cuda"
|
||||
alignment = 16
|
||||
n_g = alignment * random.randint(1, 5) * 128
|
||||
k_g = alignment * random.randint(1, 5) * 128
|
||||
|
||||
scale_a_group_shape = (1, 128)
|
||||
scale_b_group_shape = (128, 128)
|
||||
|
||||
expert_offsets = torch.zeros((num_experts + 1), device=device, dtype=torch.int32)
|
||||
problem_sizes = torch.zeros((num_experts, 3), device=device, dtype=torch.int32)
|
||||
layout_sfa = torch.zeros((num_experts, 5), device=device, dtype=torch.int32)
|
||||
layout_sfb = torch.zeros((num_experts, 5), device=device, dtype=torch.int32)
|
||||
|
||||
a_tensors = []
|
||||
b_tensors = []
|
||||
a_scales_tensors = []
|
||||
b_scales_tensors = []
|
||||
baseline_tensors = []
|
||||
|
||||
for g in range(num_experts):
|
||||
m_g = alignment * random.randint(1, 64)
|
||||
expert_offsets[g + 1] = expert_offsets[g] + m_g
|
||||
problem_sizes[g][:] = torch.tensor([m_g, n_g, k_g], device=device)
|
||||
|
||||
a_g = to_fp8(torch.randn((m_g, k_g), device=device))
|
||||
b_g = to_fp8(torch.randn((n_g, k_g), device=device).t())
|
||||
a_tensors.append(a_g)
|
||||
b_tensors.append(b_g)
|
||||
|
||||
scale_a_shape = scale_shape(a_g.shape, scale_a_group_shape)
|
||||
scale_b_shape = scale_shape(b_g.shape, scale_b_group_shape)
|
||||
|
||||
a_scales_tensors.append(torch.randn(scale_a_shape, device=device) * 0.001)
|
||||
b_scales_tensors.append(torch.randn(scale_b_shape, device=device) * 0.001)
|
||||
|
||||
baseline = baseline_scaled_mm(
|
||||
a_g, b_g, a_scales_tensors[-1], b_scales_tensors[-1], out_dtype
|
||||
)
|
||||
baseline_tensors.append(baseline)
|
||||
|
||||
a_stack = torch.empty(
|
||||
(expert_offsets[-1], k_g), device=device, dtype=torch.float8_e4m3fn
|
||||
)
|
||||
b_stack = torch.empty(
|
||||
(num_experts, n_g, k_g), device=device, dtype=torch.float8_e4m3fn
|
||||
)
|
||||
|
||||
for g in range(num_experts):
|
||||
a_stack[expert_offsets[g] : expert_offsets[g + 1]] = a_tensors[g]
|
||||
b_stack[g] = b_tensors[g].t()
|
||||
b_stack = b_stack.transpose(1, 2)
|
||||
|
||||
a_scale_stack = torch.empty(
|
||||
(expert_offsets[-1], k_g // 128), device=device, dtype=torch.float32
|
||||
)
|
||||
b_scale_stack = torch.empty(
|
||||
(num_experts, n_g // 128, k_g // 128), device=device, dtype=torch.float32
|
||||
)
|
||||
|
||||
for g in range(num_experts):
|
||||
a_scale_stack[expert_offsets[g] : expert_offsets[g + 1]] = a_scales_tensors[g]
|
||||
b_scale_stack[g] = b_scales_tensors[g].t()
|
||||
b_scale_stack = b_scale_stack.transpose(1, 2)
|
||||
|
||||
c_out = torch.empty((expert_offsets[-1], n_g), device=device, dtype=out_dtype)
|
||||
a_strides = torch.full(
|
||||
(num_experts,), a_stack.stride(0), device=device, dtype=torch.int64
|
||||
)
|
||||
c_strides = torch.full(
|
||||
(num_experts,), c_out.stride(0), device=device, dtype=torch.int64
|
||||
)
|
||||
|
||||
fp8_blockwise_scaled_grouped_mm(
|
||||
c_out,
|
||||
a_stack,
|
||||
b_stack,
|
||||
a_scale_stack,
|
||||
b_scale_stack,
|
||||
a_strides,
|
||||
a_strides,
|
||||
c_strides,
|
||||
layout_sfa,
|
||||
layout_sfb,
|
||||
problem_sizes,
|
||||
expert_offsets[:-1],
|
||||
)
|
||||
|
||||
for g in range(num_experts):
|
||||
baseline = baseline_tensors[g]
|
||||
actual = c_out[expert_offsets[g] : expert_offsets[g + 1]]
|
||||
torch.testing.assert_close(actual, baseline, rtol=1e-2, atol=5e-4)
|
||||
print(f"num_experts={num_experts}, out_dtype={out_dtype}: OK")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
pytest.main([__file__])
|
||||
Reference in New Issue
Block a user