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[Kernel][Bugfix] Refactor and Fix CUTLASS 2:4 Sparse Kernels (#13198)

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Signed-off-by: Tyler Michael Smith <tyler@neuralmagic.com>
This commit is contained in:
Tyler Michael Smith
2025-02-13 19:01:14 -05:00
committed by GitHub
parent 2344192a55
commit c1e37bf71b
16 changed files with 576 additions and 473 deletions
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165
csrc/sparse/cutlass/sparse_compressor_c3x.cu
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@ -1,165 +0,0 @@
// clang-format will break include orders
// clang-format off
#include <cudaTypedefs.h>
#if defined CUDA_VERSION && CUDA_VERSION >= 12020
#include "sparse_scaled_mm_c3x.cuh"
#include "cutlass/numeric_conversion.h"
#include "cutlass/transform/device/transform_universal_adapter.hpp"
#include "cutlass/transform/kernel/sparse_gemm_compressor.hpp"
#include "cutlass/epilogue/collective/default_epilogue.hpp"
#include "cutlass/util/host_tensor.h"
#include "cutlass/util/packed_stride.hpp"
// clang-format on
using namespace cute;
using namespace vllm;
/// Make A structured sparse by replacing elements with 0 and compress it
template <typename ElementA_, typename ElementAcc_>
bool cutlass_sparse_compress(torch::Tensor& a_nzs, torch::Tensor& a_meta,
torch::Tensor const& a) {
// Checks for conformality
TORCH_CHECK(a.dtype() == torch::kInt8 || a.dtype() == torch::kFloat8_e4m3fn ||
a.dtype() == torch::kFloat16 || a.dtype() == torch::kBFloat16);
TORCH_CHECK(a.dim() == 2)
// Check for strides and alignment
TORCH_CHECK(a.stride(0) % 4 == 0) // Required for semi-structured sparsity
TORCH_CHECK(a.stride(1) == 1)
int m = a.size(0);
int k = a.size(1);
// Sparse kernel setup; this kernel is not used for matmul,
// but just for setting up the compressor utility
// A matrix configuration
using ElementA = ElementA_;
using LayoutTagA = cutlass::layout::RowMajor;
constexpr int AlignmentA = 128 / cutlass::sizeof_bits<ElementA>::value;
// B matrix configuration
using ElementB = ElementA;
using LayoutTagB = cutlass::layout::ColumnMajor;
constexpr int AlignmentB = 128 / cutlass::sizeof_bits<ElementB>::value;
// C/D matrix configuration
using ElementC = float;
using LayoutTagC = cutlass::layout::ColumnMajor;
constexpr int AlignmentC = 128 / cutlass::sizeof_bits<ElementC>::value;
// Core kernel configurations
using ElementAccumulator = ElementAcc_;
using TileShape = Shape<_128, _128, _128>;
using TileShapeRef = Shape<_128, _128, _64>;
using ClusterShape = Shape<_1, _2, _1>;
using KernelSchedule = typename std::conditional<
std::is_same_v<ElementA, cutlass::float_e4m3_t>,
cutlass::gemm::KernelTmaWarpSpecializedFP8FastAccum,
cutlass::gemm::KernelTmaWarpSpecialized>::type;
using EpilogueSchedule = cutlass::epilogue::TmaWarpSpecialized;
using ProblemShape = Shape<int, int, int, int>;
using CollectiveEpilogue =
typename cutlass::epilogue::collective::CollectiveBuilder<
cutlass::arch::Sm90, cutlass::arch::OpClassTensorOp, TileShape,
ClusterShape, cutlass::epilogue::collective::EpilogueTileAuto,
ElementAccumulator, ElementAccumulator, ElementC, LayoutTagC,
AlignmentC, ElementC, LayoutTagC, AlignmentC,
EpilogueSchedule>::CollectiveOp;
using CollectiveMainloop =
typename cutlass::gemm::collective::CollectiveBuilder<
cutlass::arch::Sm90, cutlass::arch::OpClassSparseTensorOp, ElementA,
LayoutTagA, AlignmentA, ElementB, LayoutTagB, AlignmentB,
ElementAccumulator, TileShape, ClusterShape,
cutlass::gemm::collective::StageCountAutoCarveout<static_cast<int>(
sizeof(typename CollectiveEpilogue::SharedStorage))>,
KernelSchedule>::CollectiveOp;
using GemmKernel =
cutlass::gemm::kernel::GemmUniversal<ProblemShape, CollectiveMainloop,
CollectiveEpilogue>;
using Gemm = cutlass::gemm::device::GemmUniversalAdapter<GemmKernel>;
using StrideA = cutlass::gemm::TagToStrideA_t<LayoutTagA>;
using StrideE = StrideA;
using StrideA = Stride<int64_t, Int<1>, int64_t>;
// The n (=1) dimension does not matter for the compressor
typename GemmKernel::ProblemShape prob_shape{m, 1, k, 1};
using LayoutA = typename GemmKernel::CollectiveMainloop::LayoutA;
using LayoutE = typename GemmKernel::CollectiveMainloop::LayoutE;
using ElementE = typename GemmKernel::CollectiveMainloop::ElementE;
using SparseConfig = typename GemmKernel::CollectiveMainloop::SparseConfig;
// Offline compressor kernel
using CompressorUtility =
cutlass::transform::kernel::StructuredSparseCompressorUtility<
ProblemShape, ElementA, LayoutTagA, SparseConfig>;
using CompressorKernel =
cutlass::transform::kernel::StructuredSparseCompressor<
ProblemShape, ElementA, LayoutTagA, SparseConfig,
cutlass::arch::Sm90>;
using Compressor =
cutlass::transform::device::TransformUniversalAdapter<CompressorKernel>;
auto [M, N, K, L] = prob_shape;
StrideA stride_A;
stride_A =
cutlass::make_cute_packed_stride(StrideA{}, cute::make_shape(M, K, L));
CompressorUtility compressor_utility(prob_shape, stride_A);
int ME = compressor_utility.get_metadata_m_physical();
int KE = compressor_utility.get_metadata_k_physical();
int KC = compressor_utility.get_tensorA_k_physical();
auto a_ptr = static_cast<ElementA*>(a.data_ptr());
auto a_nzs_ptr = static_cast<ElementA*>(a_nzs.data_ptr());
auto a_meta_ptr = static_cast<typename Gemm::CollectiveMainloop::ElementE*>(
a_meta.data_ptr());
cutlass::KernelHardwareInfo hw_info;
hw_info.device_id = 0;
hw_info.sm_count =
cutlass::KernelHardwareInfo::query_device_multiprocessor_count(
hw_info.device_id);
typename Compressor::Arguments arguments{
prob_shape, {a_ptr, stride_A, a_nzs_ptr, a_meta_ptr}, {hw_info}};
Compressor compressor_op;
size_t workspace_size = Compressor::get_workspace_size(arguments);
cutlass::device_memory::allocation<uint8_t> workspace(workspace_size);
CUTLASS_CHECK(compressor_op.can_implement(arguments));
CUTLASS_CHECK(compressor_op.initialize(arguments, workspace.get()));
CUTLASS_CHECK(compressor_op.run());
CUDA_CHECK(cudaDeviceSynchronize());
return true;
}
bool cutlass_sparse_compress_sm90(torch::Tensor& a_nzs, torch::Tensor& a_meta,
torch::Tensor const& a) {
if (a.dtype() == torch::kBFloat16) {
return cutlass_sparse_compress<cutlass::bfloat16_t, float>(a_nzs, a_meta,
a);
} else if (a.dtype() == torch::kFloat16) {
return cutlass_sparse_compress<cutlass::half_t, float>(a_nzs, a_meta, a);
} else if (a.dtype() == torch::kFloat8_e4m3fn) {
return cutlass_sparse_compress<cutlass::float_e4m3_t, float>(a_nzs, a_meta,
a);
} else if (a.dtype() == torch::kInt8) {
return cutlass_sparse_compress<int8_t, int32_t>(a_nzs, a_meta, a);
}
return false;
}
#endif

90
csrc/sparse/cutlass/sparse_compressor_c3x.cuh Normal file
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@ -0,0 +1,90 @@
#pragma once
// clang-format will break include orders
// clang-format off
#include <cudaTypedefs.h>
#if defined CUDA_VERSION && CUDA_VERSION >= 12020
#include "sparse_scaled_mm_c3x.cuh"
#include "cutlass/numeric_conversion.h"
#include "cutlass/transform/device/transform_universal_adapter.hpp"
#include "cutlass/transform/kernel/sparse_gemm_compressor.hpp"
#include "cutlass/epilogue/collective/default_epilogue.hpp"
// clang-format on
using namespace cute;
using namespace vllm;
using CompressorResult = std::tuple<torch::Tensor, torch::Tensor>;
/// Make A structured sparse by replacing elements with 0 and compress it
template <typename Gemm>
CompressorResult cutlass_sparse_compress(torch::Tensor const& a) {
// Checks for conformality
TORCH_CHECK(a.dtype() == torch::kInt8 || a.dtype() == torch::kFloat8_e4m3fn ||
a.dtype() == torch::kFloat16 || a.dtype() == torch::kBFloat16);
TORCH_CHECK(a.dim() == 2)
// Check for strides and alignment
TORCH_CHECK(a.stride(0) % 4 == 0) // Required for semi-structured sparsity
TORCH_CHECK(a.stride(1) == 1)
using GemmKernel = typename Gemm::KernelType;
using ElementA = typename Gemm::ElementAB;
using ElementE = typename GemmKernel::CollectiveMainloop::ElementE;
int m = a.size(0);
int k = a.size(1);
using ProblemShape = typename GemmKernel::ProblemShape;
ProblemShape prob_shape{m, 1, k, 1};
int64_t lda = a.stride(0);
using StrideA = Stride<int64_t, Int<1>, int64_t>;
StrideA a_stride{lda, Int<1>{}, 0};
using CompressorUtility = typename Gemm::CompressorUtility;
CompressorUtility compressor_utility(prob_shape, a_stride);
// Allocate buffers for the metadata E and the compressed matrix A
int ME = compressor_utility.get_metadata_m_physical();
int KE = compressor_utility.get_metadata_k_physical();
int MC = compressor_utility.get_tensorA_m_physical();
int KC = compressor_utility.get_tensorA_k_physical();
auto const a_meta_options =
torch::TensorOptions().dtype(torch::kUInt8).device(a.device());
auto const a_nzs_options =
torch::TensorOptions().dtype(a.dtype()).device(a.device());
auto a_meta = torch::zeros({ME, KE}, a_meta_options);
auto a_nzs = torch::zeros({MC, KC}, a_nzs_options);
auto a_ptr = static_cast<ElementA*>(a.data_ptr());
auto a_nzs_ptr = static_cast<ElementA*>(a_nzs.data_ptr());
auto a_meta_ptr = static_cast<ElementE*>(a_meta.data_ptr());
cutlass::KernelHardwareInfo hw_info;
hw_info.device_id = a.device().index();
hw_info.sm_count =
cutlass::KernelHardwareInfo::query_device_multiprocessor_count(
hw_info.device_id);
using Compressor = typename Gemm::Compressor;
typename Compressor::Arguments arguments{
prob_shape, {a_ptr, a_stride, a_nzs_ptr, a_meta_ptr}, {hw_info}};
Compressor compressor_op;
size_t workspace_size = Compressor::get_workspace_size(arguments);
auto const workspace_options =
torch::TensorOptions().dtype(torch::kUInt8).device(a.device());
auto workspace = torch::empty(workspace_size, workspace_options);
CUTLASS_CHECK(compressor_op.can_implement(arguments));
CUTLASS_CHECK(compressor_op.initialize(arguments, workspace.data_ptr()));
CUTLASS_CHECK(compressor_op.run());
CUDA_CHECK(cudaDeviceSynchronize());
return {a_meta, a_nzs};
}
#endif

42
csrc/sparse/cutlass/sparse_compressor_entry.cu
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@ -1,42 +0,0 @@
#include <cudaTypedefs.h>
#include <c10/cuda/CUDAGuard.h>
#include <torch/all.h>
#include "cutlass_extensions/common.hpp"
#if defined ENABLE_SPARSE_SCALED_MM_C3X && ENABLE_SPARSE_SCALED_MM_C3X
bool cutlass_sparse_compress_sm90(torch::Tensor& a_nzs, torch::Tensor& a_meta,
torch::Tensor const& a);
#endif
bool cutlass_sparse_compress_entry(torch::Tensor& a_nzs, torch::Tensor& a_meta,
torch::Tensor const& a) {
// Checks for conformality
TORCH_CHECK(a.dim() == 2 && a_meta.dim() == 2 && a_nzs.dim() == 2);
TORCH_CHECK(a.size(0) == a_nzs.size(0) && a.size(0) == a_meta.size(0) &&
a_nzs.size(1) * 2 == a.size(1) &&
a_meta.size(1) * 2 * 4 == a.size(1));
// Considering elemsPerMetaElem = 8b / 2b_per_nz = 4
// Check for strides and alignment
TORCH_CHECK(a.stride(1) == 1 && a_nzs.stride(1) == 1 &&
a_meta.stride(1) == 1); // Row-major
TORCH_CHECK(a.stride(0) % 8 == 0); // 8 Byte Alignment for Compression
at::cuda::OptionalCUDAGuard const device_guard(device_of(a));
int32_t version_num = get_sm_version_num();
// Guard against compilation issues for sm90 kernels
#if defined ENABLE_SPARSE_SCALED_MM_C3X && ENABLE_SPARSE_SCALED_MM_C3X
if (version_num >= 90) {
return cutlass_sparse_compress_sm90(a_nzs, a_meta, a);
}
#endif
TORCH_CHECK_NOT_IMPLEMENTED(
false,
"No compiled cutlass_scaled_sparse_mm for a compute capability less than "
"CUDA device capability: ",
version_num);
}

264
csrc/sparse/cutlass/sparse_scaled_mm_c3x.cu
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@ -9,17 +9,30 @@
using namespace cute;
using namespace vllm;
struct GemmCallerTraits {
using return_type = void;
template <typename GemmConfig, typename... Args>
static return_type invoke(Args&&... args) {
return cutlass_sparse_gemm_caller<GemmConfig>(std::forward<Args>(args)...);
}
};
struct GemmCompressorTraits {
using return_type = CompressorResult;
template <typename GemmConfig, typename... Args>
static return_type invoke(Args&&... args) {
return cutlass_sparse_compress<GemmConfig>(std::forward<Args>(args)...);
}
};
template <typename InType, typename OutType,
template <typename, typename, typename> typename Epilogue,
typename... EpilogueArgs>
void cutlass_gemm_sm90_fp8_dispatch(torch::Tensor& out, torch::Tensor const& a,
torch::Tensor const& bt_nzs,
torch::Tensor const& bt_meta,
EpilogueArgs&&... args) {
static_assert(std::is_same<InType, cutlass::float_e4m3_t>());
TORCH_CHECK(a.dtype() == torch::kFloat8_e4m3fn);
TORCH_CHECK(bt_meta.dtype() == torch::kUInt8);
TORCH_CHECK(bt_nzs.dtype() == torch::kFloat8_e4m3fn);
typename DispatchFunc, typename... Args>
typename DispatchFunc::return_type cutlass_gemm_sm90_fp8_dispatch(
uint32_t m, uint32_t n, Args&&... args) {
static_assert(std::is_same_v<InType, cutlass::float_e4m3_t>);
using Cutlass3xGemmDefault =
typename sm90_config_default<InType, OutType, Epilogue>::Cutlass3xGemm;
@ -49,122 +62,87 @@ void cutlass_gemm_sm90_fp8_dispatch(torch::Tensor& out, torch::Tensor const& a,
using Cutlass3xGemm8 =
typename sm90_fp8_config_8<InType, OutType, Epilogue>::Cutlass3xGemm;
uint32_t const n = bt_nzs.size(0);
uint32_t const m = a.size(0); // Batch size
uint32_t const mp2 =
std::max(static_cast<uint32_t>(64), next_pow_2(m)); // next power of 2
if (mp2 <= 64) {
if (n == 28672) {
return cutlass_sparse_gemm_caller<Cutlass3xGemm2>(
out, a, bt_nzs, bt_meta, std::forward<EpilogueArgs>(args)...);
return DispatchFunc::template invoke<Cutlass3xGemm2>(
std::forward<Args>(args)...);
} else if (n == 4096 || n == 6144) {
return cutlass_sparse_gemm_caller<Cutlass3xGemm1>(
out, a, bt_nzs, bt_meta, std::forward<EpilogueArgs>(args)...);
return DispatchFunc::template invoke<Cutlass3xGemm1>(
std::forward<Args>(args)...);
}
} else if (mp2 <= 128) {
if (n == 4096) {
return cutlass_sparse_gemm_caller<Cutlass3xGemm3>(
out, a, bt_nzs, bt_meta, std::forward<EpilogueArgs>(args)...);
return DispatchFunc::template invoke<Cutlass3xGemm3>(
std::forward<Args>(args)...);
} else if (n == 28672) {
return cutlass_sparse_gemm_caller<Cutlass3xGemm5>(
out, a, bt_nzs, bt_meta, std::forward<EpilogueArgs>(args)...);
return DispatchFunc::template invoke<Cutlass3xGemm5>(
std::forward<Args>(args)...);
} else if (n == 6144) {
return cutlass_sparse_gemm_caller<Cutlass3xGemm4>(
out, a, bt_nzs, bt_meta, std::forward<EpilogueArgs>(args)...);
return DispatchFunc::template invoke<Cutlass3xGemm4>(
std::forward<Args>(args)...);
}
} else if (mp2 <= 256) {
if (n == 4096) {
return cutlass_sparse_gemm_caller<Cutlass3xGemm6>(
out, a, bt_nzs, bt_meta, std::forward<EpilogueArgs>(args)...);
return DispatchFunc::template invoke<Cutlass3xGemm6>(
std::forward<Args>(args)...);
} else if (n == 28672) {
return cutlass_sparse_gemm_caller<Cutlass3xGemm8>(
out, a, bt_nzs, bt_meta, std::forward<EpilogueArgs>(args)...);
return DispatchFunc::template invoke<Cutlass3xGemm8>(
std::forward<Args>(args)...);
} else if (n == 6144) {
return cutlass_sparse_gemm_caller<Cutlass3xGemm7>(
out, a, bt_nzs, bt_meta, std::forward<EpilogueArgs>(args)...);
return DispatchFunc::template invoke<Cutlass3xGemm7>(
std::forward<Args>(args)...);
}
} else {
if (n == 6144 || n == 28672) {
return cutlass_sparse_gemm_caller<Cutlass3xGemm8>(
out, a, bt_nzs, bt_meta, std::forward<EpilogueArgs>(args)...);
return DispatchFunc::template invoke<Cutlass3xGemm8>(
std::forward<Args>(args)...);
} else if (n == 4096) {
return cutlass_sparse_gemm_caller<Cutlass3xGemm7>(
out, a, bt_nzs, bt_meta, std::forward<EpilogueArgs>(args)...);
return DispatchFunc::template invoke<Cutlass3xGemm7>(
std::forward<Args>(args)...);
}
}
// Otherwise the default heuristic
if (mp2 <= 64) {
// n in [1, 64]
return cutlass_sparse_gemm_caller<Cutlass3xGemmM64>(
out, a, bt_nzs, bt_meta, std::forward<EpilogueArgs>(args)...);
return DispatchFunc::template invoke<Cutlass3xGemmM64>(
std::forward<Args>(args)...);
} else if (mp2 <= 128) {
// n in (64, 128]
return cutlass_sparse_gemm_caller<Cutlass3xGemmM128>(
out, a, bt_nzs, bt_meta, std::forward<EpilogueArgs>(args)...);
return DispatchFunc::template invoke<Cutlass3xGemmM128>(
std::forward<Args>(args)...);
} else if (mp2 <= 256) {
// n in (128, 256]
return cutlass_sparse_gemm_caller<Cutlass3xGemmM256>(
out, a, bt_nzs, bt_meta, std::forward<EpilogueArgs>(args)...);
return DispatchFunc::template invoke<Cutlass3xGemmM256>(
std::forward<Args>(args)...);
} else {
// n in (256, inf)
return cutlass_sparse_gemm_caller<Cutlass3xGemmM512>(
out, a, bt_nzs, bt_meta, std::forward<EpilogueArgs>(args)...);
return DispatchFunc::template invoke<Cutlass3xGemmM512>(
std::forward<Args>(args)...);
}
}
template <typename InType, typename OutType,
template <typename, typename, typename> typename Epilogue,
typename... EpilogueArgs>
void cutlass_gemm_sm90_fp16_dispatch(torch::Tensor& out, torch::Tensor const& a,
torch::Tensor const& bt_nzs,
torch::Tensor const& bt_meta,
EpilogueArgs&&... args) {
static_assert(std::is_same<InType, cutlass::half_t>());
TORCH_CHECK(a.dtype() == torch::kFloat16);
TORCH_CHECK(bt_meta.dtype() == torch::kUInt8);
TORCH_CHECK(bt_nzs.dtype() == torch::kFloat16);
typename DispatchFunc, typename... Args>
typename DispatchFunc::return_type cutlass_gemm_sm90_16bit_dispatch(
uint32_t m, uint32_t n, Args&&... args) {
using Cutlass3xGemmDefault =
typename sm90_config_default<InType, OutType, Epilogue>::Cutlass3xGemm;
// m in (128, inf)
return cutlass_sparse_gemm_caller<Cutlass3xGemmDefault>(
out, a, bt_nzs, bt_meta, std::forward<EpilogueArgs>(args)...);
return DispatchFunc::template invoke<Cutlass3xGemmDefault>(
std::forward<Args>(args)...);
}
template <typename InType, typename OutType,
template <typename, typename, typename> typename Epilogue,
typename... EpilogueArgs>
void cutlass_gemm_sm90_bf16_dispatch(torch::Tensor& out, torch::Tensor const& a,
torch::Tensor const& bt_nzs,
torch::Tensor const& bt_meta,
EpilogueArgs&&... args) {
static_assert(std::is_same<InType, cutlass::bfloat16_t>());
TORCH_CHECK(a.dtype() == torch::kBFloat16);
TORCH_CHECK(bt_meta.dtype() == torch::kUInt8);
TORCH_CHECK(bt_nzs.dtype() == torch::kBFloat16);
using Cutlass3xGemmDefault =
typename sm90_config_default<InType, OutType, Epilogue>::Cutlass3xGemm;
// m in (128, inf)
return cutlass_sparse_gemm_caller<Cutlass3xGemmDefault>(
out, a, bt_nzs, bt_meta, std::forward<EpilogueArgs>(args)...);
}
template <typename InType, typename OutType,
template <typename, typename, typename> typename Epilogue,
typename... EpilogueArgs>
void cutlass_gemm_sm90_int8_dispatch(torch::Tensor& out, torch::Tensor const& a,
torch::Tensor const& bt_nzs,
torch::Tensor const& bt_meta,
EpilogueArgs&&... args) {
static_assert(std::is_same<InType, int8_t>());
TORCH_CHECK(a.dtype() == torch::kInt8);
TORCH_CHECK(bt_meta.dtype() == torch::kUInt8);
TORCH_CHECK(bt_nzs.dtype() == torch::kInt8);
typename DispatchFunc, typename... Args>
typename DispatchFunc::return_type cutlass_gemm_sm90_int8_dispatch(
uint32_t m, uint32_t n, Args&&... args) {
static_assert(std::is_same_v<InType, int8_t>);
using Cutlass3xGemmDefault =
typename sm90_config_default<InType, OutType, Epilogue>::Cutlass3xGemm;
@ -179,37 +157,35 @@ void cutlass_gemm_sm90_int8_dispatch(torch::Tensor& out, torch::Tensor const& a,
typename sm90_int8_config_M32_NSmall<InType, OutType,
Epilogue>::Cutlass3xGemm;
uint32_t const n = out.size(1);
bool const is_small_n = n < 8192;
uint32_t const m = a.size(0);
uint32_t const mp2 =
std::max(static_cast<uint32_t>(32), next_pow_2(m)); // next power of 2
if (mp2 <= 32) {
// m in [1, 32]
if (is_small_n) {
return cutlass_sparse_gemm_caller<Cutlass3xGemmM32NSmall>(
out, a, bt_nzs, bt_meta, std::forward<EpilogueArgs>(args)...);
return DispatchFunc::template invoke<Cutlass3xGemmM32NSmall>(
std::forward<Args>(args)...);
} else {
return cutlass_sparse_gemm_caller<Cutlass3xGemmM32NBig>(
out, a, bt_nzs, bt_meta, std::forward<EpilogueArgs>(args)...);
return DispatchFunc::template invoke<Cutlass3xGemmM32NBig>(
std::forward<Args>(args)...);
}
} else if (mp2 <= 64) {
// m in (32, 64]
return cutlass_sparse_gemm_caller<Cutlass3xGemmM64>(
out, a, bt_nzs, bt_meta, std::forward<EpilogueArgs>(args)...);
return DispatchFunc::template invoke<Cutlass3xGemmM64>(
std::forward<Args>(args)...);
} else if (mp2 <= 128) {
// m in (64, 128]
return cutlass_sparse_gemm_caller<Cutlass3xGemmM128>(
out, a, bt_nzs, bt_meta, std::forward<EpilogueArgs>(args)...);
return DispatchFunc::template invoke<Cutlass3xGemmM128>(
std::forward<Args>(args)...);
} else {
// m in (128, inf)
return cutlass_sparse_gemm_caller<Cutlass3xGemmDefault>(
out, a, bt_nzs, bt_meta, std::forward<EpilogueArgs>(args)...);
return DispatchFunc::template invoke<Cutlass3xGemmDefault>(
std::forward<Args>(args)...);
}
}
// Dispatch to GEMM implementations based on element types
template <template <typename, typename, typename> typename Epilogue,
typename... EpilogueArgs>
void cutlass_scaled_sparse_mm_sm90_epilogue(torch::Tensor& out,
@ -217,19 +193,24 @@ void cutlass_scaled_sparse_mm_sm90_epilogue(torch::Tensor& out,
torch::Tensor const& bt_nzs,
torch::Tensor const& bt_meta,
EpilogueArgs&&... epilogue_args) {
uint32_t const m = out.size(0);
uint32_t const n = out.size(1);
// TODO: add dispatch functions to all of these
TORCH_CHECK(bt_meta.dtype() == torch::kUInt8);
if (a.dtype() == torch::kInt8) {
TORCH_CHECK(bt_nzs.dtype() == torch::kInt8);
if (out.dtype() == torch::kBFloat16) {
return cutlass_gemm_sm90_int8_dispatch<int8_t, cutlass::bfloat16_t,
Epilogue>(
out, a, bt_nzs, bt_meta,
Epilogue, GemmCallerTraits>(
m, n, out, a, bt_nzs, bt_meta,
std::forward<EpilogueArgs>(epilogue_args)...);
} else {
TORCH_CHECK(out.dtype() == torch::kFloat16);
return cutlass_gemm_sm90_int8_dispatch<int8_t, cutlass::half_t, Epilogue>(
out, a, bt_nzs, bt_meta,
return cutlass_gemm_sm90_int8_dispatch<int8_t, cutlass::half_t, Epilogue,
GemmCallerTraits>(
m, n, out, a, bt_nzs, bt_meta,
std::forward<EpilogueArgs>(epilogue_args)...);
}
} else if (a.dtype() == torch::kFloat8_e4m3fn) {
@ -237,47 +218,34 @@ void cutlass_scaled_sparse_mm_sm90_epilogue(torch::Tensor& out,
if (out.dtype() == torch::kBFloat16) {
return cutlass_gemm_sm90_fp8_dispatch<cutlass::float_e4m3_t,
cutlass::bfloat16_t, Epilogue>(
out, a, bt_nzs, bt_meta,
cutlass::bfloat16_t, Epilogue,
GemmCallerTraits>(
m, n, out, a, bt_nzs, bt_meta,
std::forward<EpilogueArgs>(epilogue_args)...);
} else {
TORCH_CHECK(out.dtype() == torch::kFloat16);
return cutlass_gemm_sm90_fp8_dispatch<cutlass::float_e4m3_t,
cutlass::half_t, Epilogue>(
out, a, bt_nzs, bt_meta,
return cutlass_gemm_sm90_fp8_dispatch<
cutlass::float_e4m3_t, cutlass::half_t, Epilogue, GemmCallerTraits>(
m, n, out, a, bt_nzs, bt_meta,
std::forward<EpilogueArgs>(epilogue_args)...);
}
} else if (a.dtype() == torch::kFloat16) {
TORCH_CHECK(bt_nzs.dtype() == torch::kFloat16);
TORCH_CHECK(out.dtype() == torch::kFloat16);
if (out.dtype() == torch::kBFloat16) {
return cutlass_gemm_sm90_fp16_dispatch<cutlass::half_t,
cutlass::bfloat16_t, Epilogue>(
out, a, bt_nzs, bt_meta,
std::forward<EpilogueArgs>(epilogue_args)...);
} else {
TORCH_CHECK(out.dtype() == torch::kFloat16);
return cutlass_gemm_sm90_fp16_dispatch<cutlass::half_t, cutlass::half_t,
Epilogue>(
out, a, bt_nzs, bt_meta,
std::forward<EpilogueArgs>(epilogue_args)...);
}
return cutlass_gemm_sm90_16bit_dispatch<cutlass::half_t, cutlass::half_t,
Epilogue, GemmCallerTraits>(
m, n, out, a, bt_nzs, bt_meta,
std::forward<EpilogueArgs>(epilogue_args)...);
} else { // a.dtype() == torch::kBFloat16
TORCH_CHECK(a.dtype() == torch::kBFloat16);
TORCH_CHECK(bt_nzs.dtype() == torch::kBFloat16);
TORCH_CHECK(out.dtype() == torch::kBFloat16);
if (out.dtype() == torch::kBFloat16) {
return cutlass_gemm_sm90_bf16_dispatch<cutlass::bfloat16_t,
cutlass::bfloat16_t, Epilogue>(
out, a, bt_nzs, bt_meta,
std::forward<EpilogueArgs>(epilogue_args)...);
} else {
TORCH_CHECK(out.dtype() == torch::kFloat16);
return cutlass_gemm_sm90_bf16_dispatch<cutlass::bfloat16_t,
cutlass::half_t, Epilogue>(
out, a, bt_nzs, bt_meta,
std::forward<EpilogueArgs>(epilogue_args)...);
}
return cutlass_gemm_sm90_16bit_dispatch<
cutlass::bfloat16_t, cutlass::bfloat16_t, Epilogue, GemmCallerTraits>(
m, n, out, a, bt_nzs, bt_meta,
std::forward<EpilogueArgs>(epilogue_args)...);
}
}
@ -287,17 +255,53 @@ void cutlass_scaled_sparse_mm_sm90(torch::Tensor& out, torch::Tensor const& a,
torch::Tensor const& a_scales,
torch::Tensor const& b_scales,
std::optional<torch::Tensor> const& bias) {
TORCH_CHECK(bt_meta.dtype() == torch::kUInt8);
TORCH_CHECK(a_scales.dtype() == torch::kFloat32);
TORCH_CHECK(b_scales.dtype() == torch::kFloat32);
if (bias) {
TORCH_CHECK(bias->dtype() == out.dtype(),
"currently bias dtype must match output dtype ", out.dtype());
return cutlass_scaled_sparse_mm_sm90_epilogue<c3x::ScaledEpilogueBias>(
out, a, bt_nzs, bt_meta, b_scales, a_scales, *bias);
"CUTLASS scaled_mm bias dtype must match output dtype ",
out.dtype());
return cutlass_scaled_sparse_mm_sm90_epilogue<
c3x::ScaledEpilogueColumnBias>(out, a, bt_nzs, bt_meta, b_scales,
a_scales, *bias);
} else {
return cutlass_scaled_sparse_mm_sm90_epilogue<c3x::ScaledEpilogue>(
out, a, bt_nzs, bt_meta, b_scales, a_scales);
}
}
CompressorResult cutlass_sparse_compress_sm90(torch::Tensor const& a) {
// These m and n variables are fordispatching to different GEMM algorithms.
uint32_t const m = 1; // Set M to 1 for compression
uint32_t const n = a.size(1);
// Note: For correctess, the compressed format must be invariant in:
// - M, the flattened number of tokens
// - Whether output dtype is fp16 or bf16
// - CUTLASS epilogues
if (a.dtype() == torch::kInt8) {
return cutlass_gemm_sm90_int8_dispatch<int8_t, cutlass::bfloat16_t,
c3x::TrivialEpilogue,
GemmCompressorTraits>(m, n, a);
} else if (a.dtype() == torch::kFloat8_e4m3fn) {
return cutlass_gemm_sm90_fp8_dispatch<
cutlass::float_e4m3_t, cutlass::bfloat16_t, c3x::TrivialEpilogue,
GemmCompressorTraits>(m, n, a);
} else if (a.dtype() == torch::kFloat16) {
return cutlass_gemm_sm90_16bit_dispatch<
cutlass::bfloat16_t, cutlass::bfloat16_t, c3x::TrivialEpilogue,
GemmCompressorTraits>(m, n, a);
} else {
TORCH_CHECK(a.dtype() == torch::kBFloat16,
"cutlass_sparse_compress only supports int8, fp8_e4m3, fp16, "
"and bf16 datatypes");
return cutlass_gemm_sm90_16bit_dispatch<cutlass::half_t, cutlass::half_t,
c3x::TrivialEpilogue,
GemmCompressorTraits>(m, n, a);
}
}
#endif

232
csrc/sparse/cutlass/sparse_scaled_mm_c3x.cuh
View File

@ -1,3 +1,5 @@
#pragma once
// clang-format will break include orders
// clang-format off
#include <cudaTypedefs.h>
@ -12,6 +14,9 @@
#include "cutlass/epilogue/collective/collective_builder.hpp"
#include "cutlass/gemm/collective/collective_builder.hpp"
#include "cutlass/transform/device/transform_universal_adapter.hpp"
#include "cutlass/transform/kernel/sparse_gemm_compressor.hpp"
#include "core/math.hpp"
#include "cutlass_extensions/cute_utils.cuh"
#include "cutlass_extensions/epilogue/scaled_mm_epilogues_c3x.hpp"
@ -22,7 +27,7 @@
using namespace cute;
/*
This file defines sparse quantized GEMM operations using the CUTLASS 3.x API,
This file defines 2:4 sparse GEMM operations using the CUTLASS 3.x API,
for NVIDIA GPUs with sm90a (Hopper) or later.
*/
@ -45,17 +50,20 @@ struct enable_sm90_or_later : Kernel {
using GemmUniversalMode = cutlass::gemm::GemmUniversalMode;
/*
* cutlass_sparse_3x_gemm defines a 2:4 sparse GEMM kernel via CUTLASS
* for SM90 Hopper systems.
*/
template <typename ElementAB_, typename ElementD_,
template <typename, typename, typename> typename Epilogue_,
typename TileShape, typename ClusterShape, typename KernelSchedule,
typename EpilogueSchedule, typename AccType,
typename TileSchedule = cutlass::gemm::PersistentScheduler,
GemmUniversalMode Mode_ = GemmUniversalMode::kGemm>
typename EpilogueSchedule>
struct cutlass_sparse_3x_gemm {
static const GemmUniversalMode Mode = Mode_;
using ElementAB = ElementAB_;
using ElementD = ElementD_;
using ElementAcc = AccType;
using ElementAcc =
typename std::conditional<std::is_same_v<ElementAB, int8_t>, int32_t,
float>::type;
using EpilogueDescriptor =
cutlass::epilogue::collective::detail::EpilogueDescriptor<
@ -66,30 +74,22 @@ struct cutlass_sparse_3x_gemm {
using ElementC = void;
using LayoutC = cutlass::layout::RowMajor;
using LayoutD = LayoutC;
using StrideC = cutlass::detail::TagToStrideA_t<LayoutC>;
using StrideD = cutlass::detail::TagToStrideA_t<LayoutD>;
using LayoutC_Transpose =
typename cutlass::layout::LayoutTranspose<LayoutC>::type;
using LayoutD_Transpose =
typename cutlass::layout::LayoutTranspose<LayoutD>::type;
using EVTCompute = typename Epilogue::EVTCompute;
static constexpr int AlignmentA =
// These are the minimum alignments needed for the kernels to compile
static constexpr int AlignmentAB =
128 / cutlass::sizeof_bits<ElementAB>::value;
static constexpr int AlignmentB =
128 / cutlass::sizeof_bits<ElementAB>::value;
static constexpr int AlignmentCD =
128 / cutlass::sizeof_bits<ElementD>::value;
static constexpr int AlignmentCD = 4;
using CollectiveEpilogue =
typename cutlass::epilogue::collective::CollectiveBuilder<
cutlass::arch::Sm90, cutlass::arch::OpClassTensorOp, TileShape,
ClusterShape, cutlass::epilogue::collective::EpilogueTileAuto,
ElementAcc, ElementAcc, ElementC, LayoutC_Transpose, AlignmentCD,
ElementD, LayoutD_Transpose, AlignmentCD, EpilogueSchedule,
ElementAcc, float, ElementC, LayoutC_Transpose, AlignmentCD, ElementD,
LayoutC_Transpose, AlignmentCD, EpilogueSchedule,
EVTCompute>::CollectiveOp;
static constexpr size_t CEStorageSize =
@ -101,8 +101,8 @@ struct cutlass_sparse_3x_gemm {
using CollectiveMainloop =
typename cutlass::gemm::collective::CollectiveBuilder<
cutlass::arch::Sm90, cutlass::arch::OpClassSparseTensorOp,
ElementAB, cutlass::layout::RowMajor, AlignmentA,
ElementAB, cutlass::layout::ColumnMajor, AlignmentB,
ElementAB, cutlass::layout::RowMajor, AlignmentAB,
ElementAB, cutlass::layout::ColumnMajor, AlignmentAB,
ElementAcc, TileShape, ClusterShape,
Stages,
KernelSchedule>::CollectiveOp;
@ -110,11 +110,100 @@ struct cutlass_sparse_3x_gemm {
using KernelType = enable_sm90_or_later<cutlass::gemm::kernel::GemmUniversal<
cute::Shape<int, int, int, int>, CollectiveMainloop, CollectiveEpilogue,
TileSchedule>>;
cutlass::gemm::PersistentScheduler>>;
struct GemmKernel : public KernelType {};
// Sparse compressor definitions
using SparseConfig = typename GemmKernel::CollectiveMainloop::SparseConfig;
using LayoutTagA = cutlass::layout::RowMajor;
using CompressorUtility =
cutlass::transform::kernel::StructuredSparseCompressorUtility<
typename GemmKernel::ProblemShape, ElementAB, LayoutTagA,
SparseConfig>;
using CompressorKernel =
cutlass::transform::kernel::StructuredSparseCompressor<
typename GemmKernel::ProblemShape, ElementAB, LayoutTagA,
SparseConfig, cutlass::arch::Sm90>;
using Compressor =
cutlass::transform::device::TransformUniversalAdapter<CompressorKernel>;
};
/*
* This class defines kernel to compress a 2:4 sparse matrix.
* The particular format is defined by the Gemm template parameter,
* which is a cutlass_sparse_3x_gemm.
*/
using CompressorResult = std::tuple<torch::Tensor, torch::Tensor>;
/// Make A structured sparse by replacing elements with 0 and compress it
template <typename Gemm>
CompressorResult cutlass_sparse_compress(torch::Tensor const& a) {
// Checks for conformality
TORCH_CHECK(a.dtype() == torch::kInt8 || a.dtype() == torch::kFloat8_e4m3fn ||
a.dtype() == torch::kFloat16 || a.dtype() == torch::kBFloat16);
TORCH_CHECK(a.dim() == 2)
// Check for strides and alignment
TORCH_CHECK(a.stride(0) % 4 == 0) // Required for semi-structured sparsity
TORCH_CHECK(a.stride(1) == 1)
using GemmKernel = typename Gemm::KernelType;
using ElementA = typename Gemm::ElementAB;
using ElementE = typename GemmKernel::CollectiveMainloop::ElementE;
int m = a.size(0);
int k = a.size(1);
using ProblemShape = typename GemmKernel::ProblemShape;
ProblemShape prob_shape{m, 1, k, 1};
int64_t lda = a.stride(0);
using StrideA = Stride<int64_t, Int<1>, int64_t>;
StrideA a_stride{lda, Int<1>{}, 0};
using CompressorUtility = typename Gemm::CompressorUtility;
CompressorUtility compressor_utility(prob_shape, a_stride);
// Allocate buffers for the metadata E and the compressed matrix A
int ME = compressor_utility.get_metadata_m_physical();
int KE = compressor_utility.get_metadata_k_physical();
int MC = compressor_utility.get_tensorA_m_physical();
int KC = compressor_utility.get_tensorA_k_physical();
auto const a_meta_options =
torch::TensorOptions().dtype(torch::kUInt8).device(a.device());
auto const a_nzs_options =
torch::TensorOptions().dtype(a.dtype()).device(a.device());
auto a_meta = torch::zeros({ME, KE}, a_meta_options);
auto a_nzs = torch::zeros({MC, KC}, a_nzs_options);
auto a_ptr = static_cast<ElementA*>(a.data_ptr());
auto a_nzs_ptr = static_cast<ElementA*>(a_nzs.data_ptr());
auto a_meta_ptr = static_cast<ElementE*>(a_meta.data_ptr());
cutlass::KernelHardwareInfo hw_info;
hw_info.device_id = a.device().index();
hw_info.sm_count =
cutlass::KernelHardwareInfo::query_device_multiprocessor_count(
hw_info.device_id);
using Compressor = typename Gemm::Compressor;
typename Compressor::Arguments arguments{
prob_shape, {a_ptr, a_stride, a_nzs_ptr, a_meta_ptr}, {hw_info}};
Compressor compressor_op;
size_t workspace_size = Compressor::get_workspace_size(arguments);
auto const workspace_options =
torch::TensorOptions().dtype(torch::kUInt8).device(a.device());
auto workspace = torch::empty(workspace_size, workspace_options);
CUTLASS_CHECK(compressor_op.can_implement(arguments));
CUTLASS_CHECK(compressor_op.initialize(arguments, workspace.data_ptr()));
CUTLASS_CHECK(compressor_op.run());
CUDA_CHECK(cudaDeviceSynchronize());
return {a_meta, a_nzs};
}
template <typename Gemm, typename... EpilogueArgs>
void cutlass_sparse_gemm_caller(torch::Tensor& out, torch::Tensor const& a,
torch::Tensor const& bt_nzs,
@ -126,27 +215,25 @@ void cutlass_sparse_gemm_caller(torch::Tensor& out, torch::Tensor const& a,
// Interface stride expected from the argument a (will get transposed)
// We compute C^T = B^T * A^T, but we assume B is transposed before
// compression and hence the bt_* naming
using LayoutA = cutlass::layout::RowMajor;
using LayoutB = typename Gemm::GemmKernel::CollectiveMainloop::LayoutA;
using LayoutE = typename Gemm::GemmKernel::CollectiveMainloop::LayoutE;
using LayoutD = cutlass::layout::RowMajor;
using StrideA = cutlass::detail::TagToStrideA_t<LayoutA>;
using StrideD = cutlass::detail::TagToStrideA_t<LayoutD>;
// M, N, K after transposition
int32_t m = out.size(1);
int32_t n = out.size(0);
int32_t k = a.size(1);
auto layout_A = make_cute_layout<StrideA>(a, "A");
auto layout_D = make_cute_layout<StrideD>(out, "D");
int64_t lda = a.stride(0);
int64_t ldc = out.stride(0);
// Transpose A and D
// A doesn't need to be transposed since cutlass expects a NxK matrix
// for B (which is At)
auto stride_At = layout_A.stride();
auto stride_Dt = permute_layout<1, 0, 2>(layout_D).stride();
using StrideA = Stride<int64_t, Int<1>, int64_t>;
using StrideC = Stride<Int<1>, int64_t, int64_t>;
StrideA a_stride{lda, Int<1>{}, Int<0>{}};
StrideC c_stride{Int<1>{}, ldc, Int<0>{}};
using GemmKernel = typename Gemm::GemmKernel;
typename GemmKernel::ProblemShape prob_shape{
static_cast<int>(bt_nzs.size(0)), static_cast<int>(size<0>(layout_A)),
static_cast<int>(size<1>(layout_A)), 1};
typename GemmKernel::ProblemShape prob_shape{m, n, k, 1};
using ElementE = typename GemmKernel::CollectiveMainloop::ElementE;
using SparseConfig = typename GemmKernel::CollectiveMainloop::SparseConfig;
@ -158,13 +245,13 @@ void cutlass_sparse_gemm_caller(torch::Tensor& out, torch::Tensor const& a,
auto b_ptr = static_cast<ElementAB*>(bt_nzs.data_ptr());
auto e_ptr = static_cast<ElementE*>(bt_meta.data_ptr());
typename GemmKernel::MainloopArguments mainloop_args{
b_ptr, b_layout, a_ptr, stride_At, e_ptr, e_layout};
b_ptr, b_layout, a_ptr, a_stride, e_ptr, e_layout};
auto c_ptr = static_cast<ElementD*>(out.data_ptr());
typename GemmKernel::EpilogueArguments epilogue_args{
Gemm::Epilogue::prepare_args(
std::forward<EpilogueArgs>(epilogue_params)...),
c_ptr, stride_Dt, c_ptr, stride_Dt};
c_ptr, c_stride, c_ptr, c_stride};
typename GemmKernel::Arguments args{cutlass::gemm::GemmUniversalMode::kGemm,
prob_shape, mainloop_args, epilogue_args};
@ -185,6 +272,10 @@ void cutlass_sparse_gemm_caller(torch::Tensor& out, torch::Tensor const& a,
CUTLASS_CHECK(status);
}
//////////////////////////////////////////////////
// Gemm Configs are defined below
//////////////////////////////////////////////////
template <typename InType, typename OutType,
template <typename, typename, typename> typename Epilogue>
struct sm90_config_default {};
@ -192,28 +283,25 @@ struct sm90_config_default {};
template <typename OutType,
template <typename, typename, typename> typename Epilogue>
struct sm90_config_default<half_t, OutType, Epilogue> {
// M in (128, inf)
using KernelSchedule = cutlass::gemm::KernelTmaWarpSpecializedPingpong;
using KernelSchedule = cutlass::gemm::KernelTmaWarpSpecialized;
using EpilogueSchedule = typename cutlass::epilogue::TmaWarpSpecialized;
using TileShape = Shape<_128, _128, _128>;
using ClusterShape = Shape<_2, _1, _1>;
using ClusterShape = Shape<_1, _1, _1>;
using Cutlass3xGemm =
cutlass_sparse_3x_gemm<half_t, OutType, Epilogue, TileShape, ClusterShape,
KernelSchedule, EpilogueSchedule, float>;
KernelSchedule, EpilogueSchedule>;
};
template <typename OutType,
template <typename, typename, typename> typename Epilogue>
struct sm90_config_default<cutlass::bfloat16_t, OutType, Epilogue> {
// M in (128, inf)
using KernelSchedule = cutlass::gemm::KernelTmaWarpSpecializedPingpong;
using KernelSchedule = cutlass::gemm::KernelTmaWarpSpecialized;
using EpilogueSchedule = typename cutlass::epilogue::TmaWarpSpecialized;
using TileShape = Shape<_128, _128, _128>;
using ClusterShape = Shape<_2, _1, _1>;
using ClusterShape = Shape<_1, _1, _1>;
using Cutlass3xGemm =
cutlass_sparse_3x_gemm<cutlass::bfloat16_t, OutType, Epilogue, TileShape,
ClusterShape, KernelSchedule, EpilogueSchedule,
float>;
ClusterShape, KernelSchedule, EpilogueSchedule>;
};
//////////////////////// Cherry-Picking Kernels ////////////////////////
@ -227,7 +315,7 @@ struct sm90_fp8_config_1 {
using ClusterShape = Shape<_8, _1, _1>;
using Cutlass3xGemm =
cutlass_sparse_3x_gemm<InType, OutType, Epilogue, TileShape, ClusterShape,
KernelSchedule, EpilogueSchedule, float>;
KernelSchedule, EpilogueSchedule>;
};
template <typename InType, typename OutType,
@ -242,7 +330,7 @@ struct sm90_fp8_config_2 {
using ClusterShape = Shape<_8, _1, _1>;
using Cutlass3xGemm =
cutlass_sparse_3x_gemm<InType, OutType, Epilogue, TileShape, ClusterShape,
KernelSchedule, EpilogueSchedule, float>;
KernelSchedule, EpilogueSchedule>;
};
template <typename InType, typename OutType,
@ -255,7 +343,7 @@ struct sm90_fp8_config_3 {
using ClusterShape = Shape<_1, _2, _1>;
using Cutlass3xGemm =
cutlass_sparse_3x_gemm<InType, OutType, Epilogue, TileShape, ClusterShape,
KernelSchedule, EpilogueSchedule, float>;
KernelSchedule, EpilogueSchedule>;
};
template <typename InType, typename OutType,
@ -269,7 +357,7 @@ struct sm90_fp8_config_4 {
using ClusterShape = Shape<_8, _1, _1>;
using Cutlass3xGemm =
cutlass_sparse_3x_gemm<InType, OutType, Epilogue, TileShape, ClusterShape,
KernelSchedule, EpilogueSchedule, float>;
KernelSchedule, EpilogueSchedule>;
};
template <typename InType, typename OutType,
@ -283,7 +371,7 @@ struct sm90_fp8_config_5 {
using ClusterShape = Shape<_8, _1, _1>;
using Cutlass3xGemm =
cutlass_sparse_3x_gemm<InType, OutType, Epilogue, TileShape, ClusterShape,
KernelSchedule, EpilogueSchedule, float>;
KernelSchedule, EpilogueSchedule>;
};
template <typename InType, typename OutType,
@ -296,7 +384,7 @@ struct sm90_fp8_config_6 {
using ClusterShape = Shape<_1, _2, _1>;
using Cutlass3xGemm =
cutlass_sparse_3x_gemm<InType, OutType, Epilogue, TileShape, ClusterShape,
KernelSchedule, EpilogueSchedule, float>;
KernelSchedule, EpilogueSchedule>;
};
template <typename InType, typename OutType,
@ -311,7 +399,7 @@ struct sm90_fp8_config_7 {
using ClusterShape = Shape<_1, _1, _1>;
using Cutlass3xGemm =
cutlass_sparse_3x_gemm<InType, OutType, Epilogue, TileShape, ClusterShape,
KernelSchedule, EpilogueSchedule, float>;
KernelSchedule, EpilogueSchedule>;
};
template <typename InType, typename OutType,
@ -326,7 +414,7 @@ struct sm90_fp8_config_8 {
using ClusterShape = Shape<_8, _1, _1>;
using Cutlass3xGemm =
cutlass_sparse_3x_gemm<InType, OutType, Epilogue, TileShape, ClusterShape,
KernelSchedule, EpilogueSchedule, float>;
KernelSchedule, EpilogueSchedule>;
};
////////////////////////////////////////////////////////////////////////
@ -341,7 +429,7 @@ struct sm90_config_default<cutlass::float_e4m3_t, OutType, Epilogue> {
using Cutlass3xGemm =
cutlass_sparse_3x_gemm<cutlass::float_e4m3_t, OutType, Epilogue,
TileShape, ClusterShape, KernelSchedule,
EpilogueSchedule, float>;
EpilogueSchedule>;
};
template <typename InType, typename OutType,
@ -355,12 +443,9 @@ struct sm90_fp8_config_M64 {
using TileShape = Shape<_64, _64, _256>;
using ClusterShape = Shape<_1, _1, _1>;
using TileSchedule = cutlass::gemm::PersistentScheduler;
using Cutlass3xGemm =
cutlass_sparse_3x_gemm<InType, OutType, Epilogue, TileShape, ClusterShape,
KernelSchedule, EpilogueSchedule, float,
TileSchedule>;
KernelSchedule, EpilogueSchedule>;
};
template <typename InType, typename OutType,
@ -374,12 +459,9 @@ struct sm90_fp8_config_M128 {
using TileShape = Shape<_64, _128, _256>;
using ClusterShape = Shape<_1, _1, _1>;
using TileSchedule = cutlass::gemm::PersistentScheduler;
using Cutlass3xGemm =
cutlass_sparse_3x_gemm<InType, OutType, Epilogue, TileShape, ClusterShape,
KernelSchedule, EpilogueSchedule, float,
TileSchedule>;
KernelSchedule, EpilogueSchedule>;
};
template <typename InType, typename OutType,
@ -394,12 +476,9 @@ struct sm90_fp8_config_M256 {
using TileShape = Shape<_128, _128, _256>;
using ClusterShape = Shape<_1, _1, _1>;
using TileSchedule = cutlass::gemm::PersistentScheduler;
using Cutlass3xGemm =
cutlass_sparse_3x_gemm<InType, OutType, Epilogue, TileShape, ClusterShape,
KernelSchedule, EpilogueSchedule, float,
TileSchedule>;
KernelSchedule, EpilogueSchedule>;
};
template <typename InType, typename OutType,
@ -414,12 +493,9 @@ struct sm90_fp8_config_M512 {
using TileShape = Shape<_128, _128, _256>;
using ClusterShape = Shape<_1, _1, _1>;
using TileSchedule = cutlass::gemm::PersistentScheduler;
using Cutlass3xGemm =
cutlass_sparse_3x_gemm<InType, OutType, Epilogue, TileShape, ClusterShape,
KernelSchedule, EpilogueSchedule, float,
TileSchedule>;
KernelSchedule, EpilogueSchedule>;
};
template <typename OutType,
@ -433,7 +509,7 @@ struct sm90_config_default<int8_t, OutType, Epilogue> {
using ClusterShape = Shape<_2, _1, _1>;
using Cutlass3xGemm =
cutlass_sparse_3x_gemm<int8_t, OutType, Epilogue, TileShape, ClusterShape,
KernelSchedule, EpilogueSchedule, int32_t>;
KernelSchedule, EpilogueSchedule>;
};
template <typename InType, typename OutType,
@ -448,7 +524,7 @@ struct sm90_int8_config_M128 {
using ClusterShape = Shape<_2, _1, _1>;
using Cutlass3xGemm =
cutlass_sparse_3x_gemm<InType, OutType, Epilogue, TileShape, ClusterShape,
KernelSchedule, EpilogueSchedule, int32_t>;
KernelSchedule, EpilogueSchedule>;
};
template <typename InType, typename OutType,
@ -462,7 +538,7 @@ struct sm90_int8_config_M64 {
using ClusterShape = Shape<_1, _1, _1>;
using Cutlass3xGemm =
cutlass_sparse_3x_gemm<InType, OutType, Epilogue, TileShape, ClusterShape,
KernelSchedule, EpilogueSchedule, int32_t>;
KernelSchedule, EpilogueSchedule>;
};
template <typename InType, typename OutType,
@ -476,7 +552,7 @@ struct sm90_int8_config_M32_NBig {
using ClusterShape = Shape<_1, _4, _1>;
using Cutlass3xGemm =
cutlass_sparse_3x_gemm<InType, OutType, Epilogue, TileShape, ClusterShape,
KernelSchedule, EpilogueSchedule, int32_t>;
KernelSchedule, EpilogueSchedule>;
};
template <typename InType, typename OutType,
@ -490,7 +566,7 @@ struct sm90_int8_config_M32_NSmall {
using ClusterShape = Shape<_1, _8, _1>;
using Cutlass3xGemm =
cutlass_sparse_3x_gemm<InType, OutType, Epilogue, TileShape, ClusterShape,
KernelSchedule, EpilogueSchedule, int32_t>;
KernelSchedule, EpilogueSchedule>;
};
} // namespace
} // namespace

30
csrc/sparse/cutlass/sparse_scaled_mm_entry.cu
View File

@ -23,6 +23,9 @@ void cutlass_scaled_sparse_mm_sm90(torch::Tensor& c, torch::Tensor const& a,
torch::Tensor const& a_scales,
torch::Tensor const& b_scales,
std::optional<torch::Tensor> const& bias);
using CompressorResult = std::tuple<torch::Tensor, torch::Tensor>;
CompressorResult cutlass_sparse_compress_sm90(torch::Tensor const& a);
#endif
void cutlass_scaled_sparse_mm(torch::Tensor& c, torch::Tensor const& a,
@ -68,3 +71,30 @@ void cutlass_scaled_sparse_mm(torch::Tensor& c, torch::Tensor const& a,
"CUDA device capability: ",
version_num);
}
std::vector<torch::Tensor> cutlass_sparse_compress(torch::Tensor const& a) {
// Check for strides and alignment
TORCH_CHECK(a.stride(1) == 1); // Row-major
TORCH_CHECK(a.stride(0) % 8 == 0); // 8 Byte Alignment for Compression
at::cuda::OptionalCUDAGuard const device_guard(device_of(a));
int32_t version_num = get_sm_version_num();
// Guard against compilation issues for sm90 kernels
#if defined ENABLE_SPARSE_SCALED_MM_C3X && ENABLE_SPARSE_SCALED_MM_C3X
if (version_num >= 90) {
std::vector<torch::Tensor> result_tensors;
auto [a_meta, a_nzs] = cutlass_sparse_compress_sm90(a);
result_tensors.push_back(std::move(a_nzs));
result_tensors.push_back(std::move(a_meta));
return result_tensors;
}
#endif
TORCH_CHECK_NOT_IMPLEMENTED(
false,
"No compiled cutlass_sparse_compress for a compute capability less than "
"CUDA device capability: ",
version_num);
}