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add cutlass support for blackwell fp8 gemm (#13798)

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This commit is contained in:
kushanam
2025-03-04 07:55:07 -08:00
committed by GitHub
parent b3cf368d79
commit f89978ad7c
11 changed files with 272 additions and 65 deletions
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32
csrc/quantization/cutlass_w8a8/c3x/cutlass_gemm_caller.cuh
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@@ -16,6 +16,7 @@
#include "cutlass/gemm/kernel/gemm_universal.hpp"
#include "cutlass/epilogue/collective/collective_builder.hpp"
#include "cutlass/gemm/collective/collective_builder.hpp"
#include "cutlass/util/packed_stride.hpp"
#include "core/math.hpp"
#include "cutlass_extensions/common.hpp"
@@ -64,22 +65,28 @@ void cutlass_gemm_caller(torch::Tensor& out, torch::Tensor const& a,
torch::Tensor const& b,
EpilogueArgs&&... epilogue_params) {
using ElementAB = typename Gemm::ElementAB;
using ElementC = typename Gemm::ElementC;
using ElementD = typename Gemm::ElementD;
using GemmKernel = typename Gemm::GemmKernel;
int64_t lda = a.stride(0);
int64_t ldb = b.stride(1);
int64_t ldc = out.stride(0);
using StrideA = cute::Stride<int64_t, cute::Int<1>, int64_t>;
using StrideB = cute::Stride<int64_t, cute::Int<1>, int64_t>;
using StrideC = typename Gemm::StrideC;
StrideA a_stride{lda, cute::Int<1>{}, 0};
StrideB b_stride{ldb, cute::Int<1>{}, 0};
StrideC c_stride{ldc, cute::Int<1>{}, cute::Int<0>{}};
using StrideA = typename Gemm::GemmKernel::StrideA;
using StrideB = typename Gemm::GemmKernel::StrideB;
using StrideC = typename Gemm::GemmKernel::StrideC;
using StrideD = StrideC;
using StrideAux = StrideC;
typename GemmKernel::ProblemShape prob_shape = get_problem_shape(a, b);
auto [M, N, K, L] = prob_shape;
StrideA a_stride =
cutlass::make_cute_packed_stride(StrideA{}, cute::make_shape(M, K, L));
StrideB b_stride =
cutlass::make_cute_packed_stride(StrideB{}, cute::make_shape(N, K, L));
StrideC c_stride =
cutlass::make_cute_packed_stride(StrideC{}, cute::make_shape(M, N, L));
StrideD d_stride =
cutlass::make_cute_packed_stride(StrideD{}, cute::make_shape(M, N, L));
StrideAux aux_stride = d_stride;
auto a_ptr = static_cast<ElementAB*>(a.data_ptr());
auto b_ptr = static_cast<ElementAB*>(b.data_ptr());
@@ -87,10 +94,11 @@ void cutlass_gemm_caller(torch::Tensor& out, torch::Tensor const& a,
b_stride};
auto c_ptr = static_cast<ElementD*>(out.data_ptr());
// auto d_ptr = static_cast<ElementC*>(out.data_ptr());
typename GemmKernel::EpilogueArguments epilogue_args{
Gemm::Epilogue::prepare_args(
std::forward<EpilogueArgs>(epilogue_params)...),
c_ptr, c_stride, c_ptr, c_stride};
c_ptr, c_stride, c_ptr, d_stride};
cutlass_gemm_caller<GemmKernel>(a.device(), prob_shape, mainloop_args,
epilogue_args);

68
csrc/quantization/cutlass_w8a8/c3x/scaled_mm.cuh
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@@ -40,12 +40,7 @@ struct cutlass_3x_gemm {
typename std::conditional<std::is_same_v<ElementAB, int8_t>, int32_t,
float>::type;
using EpilogueDescriptor =
cutlass::epilogue::collective::detail::EpilogueDescriptor<
TileShape, cutlass::epilogue::collective::EpilogueTileAuto, ElementD,
ElementD, EpilogueSchedule>;
using Epilogue = Epilogue_<ElementAcc, ElementD, EpilogueDescriptor>;
using Epilogue = Epilogue_<ElementAcc, ElementD, TileShape>;
using StrideD = Stride<int64_t, Int<1>, Int<0>>;
using ElementC = void;
@@ -88,4 +83,65 @@ struct cutlass_3x_gemm {
struct GemmKernel : public KernelType {};
};
template <typename ElementAB_, typename ElementD_,
template <typename, typename, typename> typename Epilogue_,
typename TileShape, typename ClusterShape, typename KernelSchedule,
typename EpilogueSchedule>
struct cutlass_3x_gemm_sm100 {
using ElementAB = ElementAB_;
using LayoutA = cutlass::layout::RowMajor;
static constexpr int AlignmentA =
128 / cutlass::sizeof_bits<ElementAB>::value;
using LayoutB = cutlass::layout::ColumnMajor;
static constexpr int AlignmentB =
128 / cutlass::sizeof_bits<ElementAB>::value;
using ElementC = void;
using LayoutC = cutlass::layout::RowMajor;
static constexpr int AlignmentC =
128 / cutlass::sizeof_bits<ElementD_>::value;
using ElementD = ElementD_;
using LayoutD = cutlass::layout::RowMajor;
static constexpr int AlignmentD = AlignmentC;
using ElementAcc =
typename std::conditional<std::is_same_v<ElementAB, int8_t>, int32_t,
float>::type;
using Epilogue = Epilogue_<ElementAcc, ElementD, TileShape>;
// MMA type
using ElementAccumulator = float;
// Epilogue types
using ElementBias = cutlass::half_t;
using ElementCompute = float;
using ElementAux = ElementD;
using LayoutAux = LayoutD;
using ElementAmax = float;
using EVTCompute = typename Epilogue::EVTCompute;
using CollectiveEpilogue =
typename cutlass::epilogue::collective::CollectiveBuilder<
cutlass::arch::Sm100, cutlass::arch::OpClassTensorOp, TileShape,
ClusterShape, cutlass::epilogue::collective::EpilogueTileAuto,
ElementAccumulator, ElementCompute, ElementC, LayoutC, AlignmentC,
ElementD, LayoutD, AlignmentD, EpilogueSchedule,
EVTCompute>::CollectiveOp;
using CollectiveMainloop =
typename cutlass::gemm::collective::CollectiveBuilder<
cutlass::arch::Sm100, cutlass::arch::OpClassTensorOp, ElementAB,
LayoutA, AlignmentA, ElementAB, LayoutB, AlignmentB,
ElementAccumulator, TileShape, ClusterShape,
cutlass::gemm::collective::StageCountAutoCarveout<static_cast<int>(
sizeof(typename CollectiveEpilogue::SharedStorage))>,
KernelSchedule>::CollectiveOp;
using GemmKernel = cutlass::gemm::kernel::GemmUniversal<
Shape<int, int, int, int>, CollectiveMainloop, CollectiveEpilogue, void>;
};
} // namespace vllm

6
csrc/quantization/cutlass_w8a8/c3x/scaled_mm_kernels.hpp
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@@ -30,4 +30,10 @@ void cutlass_scaled_mm_blockwise_sm90_fp8(torch::Tensor& out,
torch::Tensor const& a_scales,
torch::Tensor const& b_scales);
void cutlass_scaled_mm_sm100_fp8(torch::Tensor& out, torch::Tensor const& a,
torch::Tensor const& b,
torch::Tensor const& a_scales,
torch::Tensor const& b_scales,
std::optional<torch::Tensor> const& bias);
} // namespace vllm

24
csrc/quantization/cutlass_w8a8/c3x/scaled_mm_sm100_fp8.cu Normal file
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@@ -0,0 +1,24 @@
#include "scaled_mm_kernels.hpp"
#include "scaled_mm_sm100_fp8_dispatch.cuh"
#include "cutlass_extensions/epilogue/scaled_mm_epilogues_c3x.hpp"
namespace vllm {
void cutlass_scaled_mm_sm100_fp8(torch::Tensor& out, torch::Tensor const& a,
torch::Tensor const& b,
torch::Tensor const& a_scales,
torch::Tensor const& b_scales,
std::optional<torch::Tensor> const& bias) {
TORCH_CHECK(a_scales.is_contiguous() && b_scales.is_contiguous());
if (bias) {
TORCH_CHECK(bias->dtype() == out.dtype(),
"currently bias dtype must match output dtype ", out.dtype());
return cutlass_scaled_mm_sm100_fp8_epilogue<c3x::ScaledEpilogueBias>(
out, a, b, a_scales, b_scales, *bias);
} else {
return cutlass_scaled_mm_sm100_fp8_epilogue<c3x::ScaledEpilogue>(
out, a, b, a_scales, b_scales);
}
}
} // namespace vllm

67
csrc/quantization/cutlass_w8a8/c3x/scaled_mm_sm100_fp8_dispatch.cuh Normal file
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@@ -0,0 +1,67 @@
#pragma once
#include "scaled_mm.cuh"
#include "cutlass_gemm_caller.cuh"
/**
* This file defines Gemm kernel configurations for SM100 (fp8) based on the
* Gemm shape.
*/
namespace vllm {
using c3x::cutlass_gemm_caller;
template <typename InType, typename OutType,
template <typename, typename, typename> typename Epilogue>
struct sm100_fp8_config_default {
static_assert(std::is_same<InType, cutlass::float_e4m3_t>());
using KernelSchedule = cutlass::gemm::collective::KernelScheduleAuto;
using EpilogueSchedule = cutlass::epilogue::collective::EpilogueScheduleAuto;
using TileShape = Shape<_256, _128, _64>;
using ClusterShape = Shape<_2, _2, _1>;
using Cutlass3xGemm =
cutlass_3x_gemm_sm100<InType, OutType, Epilogue, TileShape, ClusterShape,
KernelSchedule, EpilogueSchedule>;
};
template <typename InType, typename OutType,
template <typename, typename, typename> typename Epilogue,
typename... EpilogueArgs>
inline void cutlass_gemm_sm100_fp8_dispatch(torch::Tensor& out,
torch::Tensor const& a,
torch::Tensor const& b,
EpilogueArgs&&... args) {
static_assert(std::is_same<InType, cutlass::float_e4m3_t>());
TORCH_CHECK(a.dtype() == torch::kFloat8_e4m3fn);
TORCH_CHECK(b.dtype() == torch::kFloat8_e4m3fn);
using Cutlass3xGemmDefault =
typename sm100_fp8_config_default<InType, OutType,
Epilogue>::Cutlass3xGemm;
return cutlass_gemm_caller<Cutlass3xGemmDefault>(
out, a, b, std::forward<EpilogueArgs>(args)...);
}
template <template <typename, typename, typename> typename Epilogue,
typename... EpilogueArgs>
void cutlass_scaled_mm_sm100_fp8_epilogue(torch::Tensor& out,
torch::Tensor const& a,
torch::Tensor const& b,
EpilogueArgs&&... epilogue_args) {
TORCH_CHECK(a.dtype() == torch::kFloat8_e4m3fn);
TORCH_CHECK(b.dtype() == torch::kFloat8_e4m3fn);
if (out.dtype() == torch::kBFloat16) {
return cutlass_gemm_sm100_fp8_dispatch<cutlass::float_e4m3_t,
cutlass::bfloat16_t, Epilogue>(
out, a, b, std::forward<EpilogueArgs>(epilogue_args)...);
} else {
TORCH_CHECK(out.dtype() == torch::kFloat16);
return cutlass_gemm_sm100_fp8_dispatch<cutlass::float_e4m3_t,
cutlass::half_t, Epilogue>(
out, a, b, std::forward<EpilogueArgs>(epilogue_args)...);
}
}
} // namespace vllm