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// Copyright (c) Microsoft Corporation. All rights reserved.
// Licensed under the MIT License.
#pragma once
#include <ATen/cuda/CUDAContext.h>
#include <ATen/cuda/CUDADataType.h>
#include <ATen/cuda/tunable/TunableOp.h>
#include <ATen/cuda/tunable/GemmCommon.h>
#include <c10/cuda/CUDACachingAllocator.h>
#include <c10/util/StringUtil.h>
#include <fmt/printf.h>
#include <hipblaslt/hipblaslt.h>
#include <hipblaslt/hipblaslt-ext.hpp>
#define TORCH_HIPBLASLT_CHECK(EXPR) \
do { \
hipblasStatus_t __err = EXPR; \
TORCH_CHECK(__err == HIPBLAS_STATUS_SUCCESS, \
"hipblaslt error: ", \
hipblasStatusToString(__err), \
" when calling `" #EXPR "`"); \
} while (0)
namespace at::cuda::tunable {
template <typename T>
constexpr hipDataType HipDataTypeFor();
template <>
constexpr hipDataType HipDataTypeFor<float>() {
return HIP_R_32F;
}
template <>
constexpr hipDataType HipDataTypeFor<Half>() {
return HIP_R_16F;
}
template <>
constexpr hipDataType HipDataTypeFor<BFloat16>() {
return HIP_R_16BF;
}
template <>
constexpr hipDataType HipDataTypeFor<double>() {
return HIP_R_64F;
}
template <>
constexpr hipDataType HipDataTypeFor<c10::Float8_e4m3fnuz>() {
return HIP_R_8F_E4M3_FNUZ;
}
template <>
constexpr hipDataType HipDataTypeFor<c10::Float8_e5m2fnuz>() {
return HIP_R_8F_E5M2_FNUZ;
}
// This code is instantiated regardless of ROCm version.
// Prior to ROCm 6.3, we hard-code the known enum values.
template <>
constexpr hipDataType HipDataTypeFor<c10::Float8_e4m3fn>() {
#if ROCM_VERSION >= 60300
return HIP_R_8F_E4M3;
#else
return static_cast<hipDataType>(28);
#endif
}
template <>
constexpr hipDataType HipDataTypeFor<c10::Float8_e5m2>() {
#if ROCM_VERSION >= 60300
return HIP_R_8F_E5M2;
#else
return static_cast<hipDataType>(29);
#endif
}
// This type is not intended for matrix types but rather a scale factor.
// Return a dummy value to satisfy linker.
template <>
constexpr hipDataType HipDataTypeFor<c10::Float8_e8m0fnu>() {
return static_cast<hipDataType>(500);
}
template <typename T>
int GetBatchFromParams(const GemmParams<T>* params) {
return 1;
}
template <typename T>
int GetBatchFromParams(const GemmAndBiasParams<T>* params) {
return 1;
}
template <typename T>
int GetBatchFromParams(const GemmStridedBatchedParams<T>* params) {
return params->batch;
}
template <typename T>
int GetBatchFromParams(const ScaledGemmParams<T>* params) {
return 1;
}
template <typename T>
int GetStrideAFromParams(const GemmParams<T>* params) {
return 1;
}
template <typename T>
int GetStrideAFromParams(const GemmAndBiasParams<T>* params) {
return 1;
}
template <typename T>
int GetStrideAFromParams(const GemmStridedBatchedParams<T>* params) {
return params->stride_a;
}
template <typename T>
int GetStrideAFromParams(const ScaledGemmParams<T>* params) {
return 1;
}
template <typename T>
int GetStrideBFromParams(const GemmParams<T>* params) {
return 1;
}
template <typename T>
int GetStrideBFromParams(const GemmAndBiasParams<T>* params) {
return 1;
}
template <typename T>
int GetStrideBFromParams(const GemmStridedBatchedParams<T>* params) {
return params->stride_b;
}
template <typename T>
int GetStrideBFromParams(const ScaledGemmParams<T>* params) {
return 1;
}
template <typename T>
int GetStrideCFromParams(const GemmParams<T>* params) {
return 1;
}
template <typename T>
int GetStrideCFromParams(const GemmAndBiasParams<T>* params) {
return 1;
}
template <typename T>
int GetStrideCFromParams(const GemmStridedBatchedParams<T>* params) {
return params->stride_c;
}
template <typename T>
int GetStrideCFromParams(const ScaledGemmParams<T>* params) {
return 1;
}
template <typename T>
float GetAlphaFromParams(const GemmParams<T>* params) {
return params->alpha;
}
template <typename T>
float GetAlphaFromParams(const GemmAndBiasParams<T>* params) {
return params->alpha;
}
template <typename T>
float GetAlphaFromParams(const GemmStridedBatchedParams<T>* params) {
return params->alpha;
}
template <typename T>
float GetAlphaFromParams(const ScaledGemmParams<T>* params) {
return 1.0;
}
template <typename T>
float GetBetaFromParams(const GemmParams<T>* params) {
return params->beta;
}
template <typename T>
float GetBetaFromParams(const GemmAndBiasParams<T>* params) {
return 0.0;
}
template <typename T>
float GetBetaFromParams(const GemmStridedBatchedParams<T>* params) {
return params->beta;
}
template <typename T>
float GetBetaFromParams(const ScaledGemmParams<T>* params) {
return 0.0;
}
template <typename T>
bool GetUseRowwiseFromParams(const GemmParams<T>* params) {
return false;
}
template <typename T>
bool GetUseRowwiseFromParams(const GemmAndBiasParams<T>* params) {
return false;
}
template <typename T>
bool GetUseRowwiseFromParams(const GemmStridedBatchedParams<T>* params) {
return false;
}
template <typename T>
bool GetUseRowwiseFromParams(const ScaledGemmParams<T>* params) {
return params->use_rowwise;
}
template <typename T>
const void* GetAScalePointerFromParams(const GemmParams<T>* params) {
return nullptr;
}
template <typename T>
const void* GetAScalePointerFromParams(const GemmAndBiasParams<T>* params) {
return nullptr;
}
template <typename T>
const void* GetAScalePointerFromParams(const GemmStridedBatchedParams<T>* params) {
return nullptr;
}
template <typename T>
const void* GetAScalePointerFromParams(const ScaledGemmParams<T>* params) {
return params->a_scale_ptr;
}
template <typename T>
const void* GetBScalePointerFromParams(const GemmParams<T>* params) {
return nullptr;
}
template <typename T>
const void* GetBScalePointerFromParams(const GemmAndBiasParams<T>* params) {
return nullptr;
}
template <typename T>
const void* GetBScalePointerFromParams(const GemmStridedBatchedParams<T>* params) {
return nullptr;
}
template <typename T>
const void* GetBScalePointerFromParams(const ScaledGemmParams<T>* params) {
return params->b_scale_ptr;
}
template <typename T>
const void* GetDScalePointerFromParams(const GemmParams<T>* params) {
return nullptr;
}
template <typename T>
const void* GetDScalePointerFromParams(const GemmAndBiasParams<T>* params) {
return nullptr;
}
template <typename T>
const void* GetDScalePointerFromParams(const GemmStridedBatchedParams<T>* params) {
return nullptr;
}
template <typename T>
const void* GetDScalePointerFromParams(const ScaledGemmParams<T>* params) {
return params->c_scale_ptr;
}
template <typename T>
const void* GetBiasPointerFromParams(const GemmParams<T>* params) {
return nullptr;
}
template <typename T>
const void* GetBiasPointerFromParams(const GemmAndBiasParams<T>* params) {
return params->bias;
}
template <typename T>
const void* GetBiasPointerFromParams(const GemmStridedBatchedParams<T>* params) {
return nullptr;
}
template <typename T>
const void* GetBiasPointerFromParams(const ScaledGemmParams<T>* params) {
return params->bias_ptr;
}
template <typename T>
hipDataType GetBiasTypeFromParams(const GemmParams<T>* params) {
return HIP_R_32F;
}
template <typename T>
hipDataType GetBiasTypeFromParams(const GemmAndBiasParams<T>* params) {
return HipDataTypeFor<T>();
}
template <typename T>
hipDataType GetBiasTypeFromParams(const GemmStridedBatchedParams<T>* params) {
return HIP_R_32F;
}
template <typename T>
hipDataType GetBiasTypeFromParams(const ScaledGemmParams<T>* params) {
return at::cuda::ScalarTypeToCudaDataType(params->bias_dtype);
}
template <typename T>
at::cuda::blas::GEMMAndBiasActivationEpilogue GetActivationFromParams(const GemmParams<T>* params) {
return at::cuda::blas::GEMMAndBiasActivationEpilogue::None;
}
template <typename T>
at::cuda::blas::GEMMAndBiasActivationEpilogue GetActivationFromParams(const GemmAndBiasParams<T>* params) {
return params->activation;
}
template <typename T>
at::cuda::blas::GEMMAndBiasActivationEpilogue GetActivationFromParams(const GemmStridedBatchedParams<T>* params) {
return at::cuda::blas::GEMMAndBiasActivationEpilogue::None;
}
template <typename T>
at::cuda::blas::GEMMAndBiasActivationEpilogue GetActivationFromParams(const ScaledGemmParams<T>* params) {
return at::cuda::blas::GEMMAndBiasActivationEpilogue::None;
}
static hipblasOperation_t _hipblasOpFromChar(char op) {
switch (op) {
case 'n':
case 'N':
return HIPBLAS_OP_N;
case 't':
case 'T':
return HIPBLAS_OP_T;
case 'c':
case 'C':
return HIPBLAS_OP_C;
}
TORCH_CHECK(false,
"_hipblasOpFromChar input should be 't', 'n' or 'c' but got `", op, "`");
}
static char _charFromhipblasOp(hipblasOperation_t op) {
switch (op) {
case HIPBLAS_OP_N:
return 'N';
case HIPBLAS_OP_T:
return 'T';
case HIPBLAS_OP_C:
return 'C';
}
TORCH_CHECK(false,
"_charFromhipblasOp input should be HIPBLAS_OP_N/T/C but got `", op, "`");
}
static hipblasOperation_t MapLayoutToHipBlasLt(BlasOp layout) {
if (layout == BlasOp::N) {
return HIPBLAS_OP_N;
}
return HIPBLAS_OP_T;
}
static size_t GetHipblasltWorkspaceSize() {
static const auto env = c10::utils::get_env("HIPBLASLT_WORKSPACE_SIZE");
// 256MB is max workspace size allowed for hipblaslt
// hipblaslt-bench uses 32MB
// recommendation from hipblaslt author was 76MB
// TunableOp hipBLASLt workspace size is aligned with
// PyTorch's default in CUDABlas.cpp (_parseChosenWorkspaceSize)
size_t workspace_size = 76*1024;
if (env) {
try {
workspace_size = std::stoi(env.value());
} catch(std::invalid_argument const& e) {
TORCH_WARN("invalid HIPBLASLT_WORKSPACE_SIZE,",
" using default workspace size of ", workspace_size, " KiB.");
} catch(std::out_of_range const& e) {
TORCH_WARN("HIPBLASLT_WORKSPACE_SIZE out of range,",
" using default workspace size of ", workspace_size, " KiB.");
}
}
return workspace_size * 1024;
}
template <typename T, cublasStatus_t (*destructor)(T*)>
struct HipBlasLtDeleter {
void operator()(T* x) {
if (x != nullptr) {
TORCH_CUDABLAS_CHECK(destructor(x));
}
}
};
template <typename T, hipblasStatus_t (*destructor)(T*)>
class HipBlasLtDescriptor {
public:
T* descriptor() const {
return descriptor_.get();
}
T* descriptor() {
return descriptor_.get();
}
protected:
std::unique_ptr<T, HipBlasLtDeleter<T, destructor>> descriptor_;
};
class HipBlasLtMatmulDescriptor : public HipBlasLtDescriptor<
hipblasLtMatmulDescOpaque_t,
&hipblasLtMatmulDescDestroy> {
public:
HipBlasLtMatmulDescriptor(
hipblasComputeType_t compute_type,
hipDataType scale_type) {
hipblasLtMatmulDesc_t raw_descriptor = nullptr;
TORCH_HIPBLASLT_CHECK(
hipblasLtMatmulDescCreate(&raw_descriptor, compute_type, scale_type));
descriptor_.reset(raw_descriptor);
}
template <typename T>
inline void setAttribute(hipblasLtMatmulDescAttributes_t attr, const T value) {
TORCH_HIPBLASLT_CHECK(::hipblasLtMatmulDescSetAttribute(descriptor(), attr, &value, sizeof(T)));
}
};
template <typename AT, typename BT, typename CT, BlasOp ALayout, BlasOp BLayout, typename ParamsT>
class HipblasltGemmOp : public Callable<ParamsT> {
public:
HipblasltGemmOp(hipblasLtMatmulAlgo_t algo) : algo_{algo} {}
TuningStatus Call(const ParamsT* params) override {
hipblasOperation_t transa_outer = MapLayoutToHipBlasLt(ALayout);
hipblasOperation_t transb_outer = MapLayoutToHipBlasLt(BLayout);
auto a_datatype = HipDataTypeFor<AT>();
auto b_datatype = HipDataTypeFor<BT>();
auto in_out_datatype = HipDataTypeFor<CT>();
auto opa = _hipblasOpFromChar(params->transa);
auto opb = _hipblasOpFromChar(params->transb);
TORCH_CHECK(transa_outer == opa && transb_outer == opb, "trans mismatch, shouldn't happen");
float alpha = GetAlphaFromParams<CT>(params);
float beta = GetBetaFromParams<CT>(params);
hipblasLtMatrixLayout_t mat_a, mat_b, mat_c;
if (opa == HIPBLAS_OP_N) {
TORCH_HIPBLASLT_CHECK(hipblasLtMatrixLayoutCreate(&mat_a, a_datatype, params->m, params->k, params->lda));
}
else {
TORCH_HIPBLASLT_CHECK(hipblasLtMatrixLayoutCreate(&mat_a, a_datatype, params->k, params->m, params->lda));
}
if (opb == HIPBLAS_OP_N) {
TORCH_HIPBLASLT_CHECK(hipblasLtMatrixLayoutCreate(&mat_b, b_datatype, params->k, params->n, params->ldb));
}
else {
TORCH_HIPBLASLT_CHECK(hipblasLtMatrixLayoutCreate(&mat_b, b_datatype, params->n, params->k, params->ldb));
}
TORCH_HIPBLASLT_CHECK(hipblasLtMatrixLayoutCreate(&mat_c, in_out_datatype, params->m, params->n, params->ldc));
// specific to batched gemmm
int batch = GetBatchFromParams<CT>(params);
if (batch > 1) {
int64_t stride_a = GetStrideAFromParams<CT>(params);
int64_t stride_b = GetStrideBFromParams<CT>(params);
int64_t stride_c = GetStrideCFromParams<CT>(params);
TORCH_HIPBLASLT_CHECK(hipblasLtMatrixLayoutSetAttribute(
mat_a, HIPBLASLT_MATRIX_LAYOUT_BATCH_COUNT, &batch, sizeof(batch)));
TORCH_HIPBLASLT_CHECK(hipblasLtMatrixLayoutSetAttribute(
mat_a, HIPBLASLT_MATRIX_LAYOUT_STRIDED_BATCH_OFFSET, &stride_a, sizeof(stride_a)));
TORCH_HIPBLASLT_CHECK(hipblasLtMatrixLayoutSetAttribute(
mat_b, HIPBLASLT_MATRIX_LAYOUT_BATCH_COUNT, &batch, sizeof(batch)));
TORCH_HIPBLASLT_CHECK(hipblasLtMatrixLayoutSetAttribute(
mat_b, HIPBLASLT_MATRIX_LAYOUT_STRIDED_BATCH_OFFSET, &stride_b, sizeof(stride_b)));
TORCH_HIPBLASLT_CHECK(hipblasLtMatrixLayoutSetAttribute(
mat_c, HIPBLASLT_MATRIX_LAYOUT_BATCH_COUNT, &batch, sizeof(batch)));
TORCH_HIPBLASLT_CHECK(hipblasLtMatrixLayoutSetAttribute(
mat_c, HIPBLASLT_MATRIX_LAYOUT_STRIDED_BATCH_OFFSET, &stride_c, sizeof(stride_c)));
}
hipblasComputeType_t computeType = HIPBLAS_COMPUTE_32F;
if (at::globalContext().allowTF32CuBLAS()) {
computeType = HIPBLAS_COMPUTE_32F_FAST_TF32;
}
HipBlasLtMatmulDescriptor matmul(computeType, HIP_R_32F);
matmul.setAttribute(HIPBLASLT_MATMUL_DESC_TRANSA, opa);
matmul.setAttribute(HIPBLASLT_MATMUL_DESC_TRANSB, opb);
// specific to scaled gemm
const void* mat1_scale_ptr = GetAScalePointerFromParams<CT>(params);
const void* mat2_scale_ptr = GetBScalePointerFromParams<CT>(params);
const void* result_scale_ptr = GetDScalePointerFromParams<CT>(params);
if (mat1_scale_ptr && mat2_scale_ptr) {
#ifdef HIPBLASLT_VEC_EXT
if (GetUseRowwiseFromParams<CT>(params)) {
matmul.setAttribute(HIPBLASLT_MATMUL_DESC_A_SCALE_POINTER_VEC_EXT, mat1_scale_ptr);
matmul.setAttribute(HIPBLASLT_MATMUL_DESC_B_SCALE_POINTER_VEC_EXT, mat2_scale_ptr);
}
else
#endif
{
matmul.setAttribute(HIPBLASLT_MATMUL_DESC_A_SCALE_POINTER, mat1_scale_ptr);
matmul.setAttribute(HIPBLASLT_MATMUL_DESC_B_SCALE_POINTER, mat2_scale_ptr);
}
#ifdef HIPBLASLT_OUTER_VEC
if (GetUseRowwiseFromParams<CT>(params)) {
matmul.setAttribute(HIPBLASLT_MATMUL_DESC_A_SCALE_MODE, HIPBLASLT_MATMUL_MATRIX_SCALE_OUTER_VEC_32F);
matmul.setAttribute(HIPBLASLT_MATMUL_DESC_B_SCALE_MODE, HIPBLASLT_MATMUL_MATRIX_SCALE_OUTER_VEC_32F);
}
#endif
}
if (result_scale_ptr) {
matmul.setAttribute(HIPBLASLT_MATMUL_DESC_D_SCALE_POINTER, result_scale_ptr);
}
const void* bias_ptr = GetBiasPointerFromParams<CT>(params);
auto bias_datatype = GetBiasTypeFromParams<CT>(params);
if (bias_ptr) {
matmul.setAttribute(HIPBLASLT_MATMUL_DESC_BIAS_POINTER, bias_ptr);
matmul.setAttribute(HIPBLASLT_MATMUL_DESC_BIAS_DATA_TYPE, bias_datatype);
auto activation = GetActivationFromParams<CT>(params);
if (activation == at::cuda::blas::GEMMAndBiasActivationEpilogue::RELU) {
matmul.setAttribute(HIPBLASLT_MATMUL_DESC_EPILOGUE, HIPBLASLT_EPILOGUE_RELU_BIAS);
}
else if (activation == at::cuda::blas::GEMMAndBiasActivationEpilogue::GELU) {
matmul.setAttribute(HIPBLASLT_MATMUL_DESC_EPILOGUE, HIPBLASLT_EPILOGUE_GELU_BIAS);
}
else {
matmul.setAttribute(HIPBLASLT_MATMUL_DESC_EPILOGUE, HIPBLASLT_EPILOGUE_BIAS);
}
}
size_t workspace_size = GetHipblasltWorkspaceSize();
auto op_handle = at::cuda::getCurrentCUDABlasLtHandle();
size_t ret_workspace_size = 0;
auto status = hipblaslt_ext::matmulIsAlgoSupported(op_handle,
matmul.descriptor(),
&alpha,
mat_a,
mat_b,
&beta,
mat_c,
mat_c,
algo_,
ret_workspace_size);
if (status == HIPBLAS_STATUS_SUCCESS) {
if (ret_workspace_size >= workspace_size) {
return FAIL;
}
}
else {
return FAIL;
}
void* workspace_buffer = nullptr;
if (workspace_size > 0) {
workspace_buffer = c10::cuda::CUDACachingAllocator::raw_alloc(workspace_size);
}
TORCH_HIPBLASLT_CHECK(hipblasLtMatmul(op_handle,
matmul.descriptor(),
&alpha,
params->a,
mat_a,
params->b,
mat_b,
&beta,
params->c,
mat_c,
params->c,
mat_c,
&algo_,
workspace_buffer,
workspace_size,
at::cuda::getCurrentCUDAStream()));
//TORCH_HIPBLASLT_CHECK(hipblasLtMatmulDescDestroy(matmul));
TORCH_HIPBLASLT_CHECK(hipblasLtMatrixLayoutDestroy(mat_a));
TORCH_HIPBLASLT_CHECK(hipblasLtMatrixLayoutDestroy(mat_b));
TORCH_HIPBLASLT_CHECK(hipblasLtMatrixLayoutDestroy(mat_c));
if (workspace_size > 0) {
c10::cuda::CUDACachingAllocator::raw_delete(workspace_buffer);
}
return OK;
}
private:
hipblasLtMatmulAlgo_t algo_;
};
template <typename AT, typename BT, typename CT, BlasOp ALayout, BlasOp BLayout, typename ParamsT>
auto GetHipBlasLtTypeStringAndOps() {
hipblasOperation_t transa_outer = MapLayoutToHipBlasLt(ALayout);
hipblasOperation_t transb_outer = MapLayoutToHipBlasLt(BLayout);
auto a_datatype = HipDataTypeFor<AT>();
auto b_datatype = HipDataTypeFor<BT>();
auto in_out_datatype = HipDataTypeFor<CT>();
std::vector<hipblasLtMatmulHeuristicResult_t> heuristic_result;
#if ROCM_VERSION == 60400
// hipblaslt TT fp32 regression on ROCm 6.4, cannot use
if ((a_datatype == HIP_R_32F || b_datatype == HIP_R_32F || in_out_datatype == HIP_R_32F)
&& (transa_outer == HIPBLAS_OP_T && transb_outer == HIPBLAS_OP_T)) {
std::vector<std::pair<std::string, std::unique_ptr<Callable<ParamsT>>>> ignore;
return ignore;
}
#endif
hipblasComputeType_t computeType = HIPBLAS_COMPUTE_32F;
if (at::globalContext().allowTF32CuBLAS()) {
computeType = HIPBLAS_COMPUTE_32F_FAST_TF32;
}
hipblasLtHandle_t handle;
TORCH_HIPBLASLT_CHECK(hipblasLtCreate(&handle));
TORCH_HIPBLASLT_CHECK(hipblaslt_ext::getAllAlgos(handle,
hipblaslt_ext::GemmType::HIPBLASLT_GEMM,
transa_outer,
transb_outer,
a_datatype,
b_datatype,
in_out_datatype,
in_out_datatype,
computeType,
heuristic_result));
TORCH_HIPBLASLT_CHECK(hipblasLtDestroy(handle));
int returned_algo_count = heuristic_result.size();
std::vector<std::pair<std::string, std::unique_ptr<Callable<ParamsT>>>> ret;
for (int i = 0; i < returned_algo_count; i++) {
auto algo = heuristic_result[i].algo;
int algo_index = hipblaslt_ext::getIndexFromAlgo(algo);
auto callable = std::make_unique<HipblasltGemmOp<AT, BT, CT, ALayout, BLayout, ParamsT>>(algo);
std::string type_string = fmt::sprintf("Gemm_Hipblaslt_%d", algo_index);
ret.emplace_back(type_string, std::move(callable));
}
return ret;
}
template <typename T, BlasOp ALayout, BlasOp BLayout>
auto GetHipBlasLtGemmTypeStringAndOps() {
return GetHipBlasLtTypeStringAndOps<T, T, T, ALayout, BLayout, GemmParams<T>>();
}
template <typename T, BlasOp ALayout, BlasOp BLayout>
auto GetHipBlasLtGemmAndBiasTypeStringAndOps() {
return GetHipBlasLtTypeStringAndOps<T, T, T, ALayout, BLayout, GemmAndBiasParams<T>>();
}
template <typename T, BlasOp ALayout, BlasOp BLayout>
auto GetHipBlasLtGemmStridedBatchedTypeStringAndOps() {
return GetHipBlasLtTypeStringAndOps<T, T, T, ALayout, BLayout, GemmStridedBatchedParams<T>>();
}
template <typename AT, typename BT, typename CT, BlasOp ALayout, BlasOp BLayout>
auto GetHipBlasLtScaledGemmTypeStringAndOps() {
return GetHipBlasLtTypeStringAndOps<AT, BT, CT, ALayout, BLayout, ScaledGemmParams<CT>>();
}
#undef TORCH_HIPBLASLT_CHECK
} // namespace at::cuda::tunable
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