/
blas_impl.hip.h
1586 lines (1495 loc) · 64.6 KB
/
blas_impl.hip.h
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// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#pragma once
#include "paddle/common/flags.h"
#include "paddle/phi/backends/dynload/rocblas.h"
#include "paddle/phi/backends/gpu/gpu_context.h"
#include "paddle/phi/kernels/funcs/math_function.h"
COMMON_DECLARE_bool(enable_cublas_tensor_op_math);
namespace phi {
namespace funcs {
template <typename T>
struct CUBlas;
template <>
struct CUBlas<float> {
template <typename... ARGS>
static void GEMM(ARGS... args) {
PADDLE_ENFORCE_GPU_SUCCESS(phi::dynload::rocblas_sgemm(args...));
}
template <typename... ARGS>
static void AXPY(ARGS... args) {
PADDLE_ENFORCE_GPU_SUCCESS(phi::dynload::rocblas_saxpy(args...));
}
template <typename... ARGS>
static void SCAL(ARGS... args) {
PADDLE_ENFORCE_GPU_SUCCESS(phi::dynload::rocblas_sscal(args...));
}
template <typename... ARGS>
static void VCOPY(ARGS... args) {
PADDLE_ENFORCE_GPU_SUCCESS(phi::dynload::rocblas_scopy(args...));
}
template <typename... ARGS>
static void GEMV(ARGS... args) {
PADDLE_ENFORCE_GPU_SUCCESS(phi::dynload::rocblas_sgemv(args...));
}
template <typename... ARGS>
static void GEMM_STRIDED_BATCH(ARGS... args) {
PADDLE_ENFORCE_GPU_SUCCESS(
phi::dynload::rocblas_sgemm_strided_batched(args...));
}
// HIP not supportted, refer to the doc here:
// https://github.com/ROCm-Developer-Tools/HIP/blob/roc-3.5.x/docs/markdown/CUBLAS_API_supported_by_HIP.md
template <typename... ARGS>
static void GEMM_EX(ARGS... args) {
PADDLE_THROW(phi::errors::Unimplemented(
"cublasSgemmEx is not supported on HIP platform."));
}
template <typename... ARGS>
static void TRSM(ARGS... args) {
PADDLE_ENFORCE_GPU_SUCCESS(phi::dynload::rocblas_strsm(args...));
}
template <typename... ARGS>
static void GETRF_BATCH(ARGS... args) {
PADDLE_THROW(phi::errors::Unimplemented(
"cublasSgetrfBatched is not supported on HIP platform."));
}
template <typename... ARGS>
static void GETRI_BATCH(ARGS... args) {
PADDLE_THROW(phi::errors::Unimplemented(
"cublasSgetriBatched is not supported on HIP platform."));
}
template <typename... ARGS>
static void MATINV_BATCH(ARGS... args) {
PADDLE_THROW(phi::errors::Unimplemented(
"cublasSmatinvBatched is not supported on HIP platform."));
}
template <typename... ARGS>
static void TRSM_BATCH(ARGS... args) {
PADDLE_THROW(phi::errors::Unimplemented(
"cublasStrsmBatched is not supported on HIP platform."));
}
};
template <>
struct CUBlas<double> {
template <typename... ARGS>
static void GEMM(ARGS... args) {
PADDLE_ENFORCE_GPU_SUCCESS(phi::dynload::rocblas_dgemm(args...));
}
template <typename... ARGS>
static void AXPY(ARGS... args) {
PADDLE_ENFORCE_GPU_SUCCESS(phi::dynload::rocblas_daxpy(args...));
}
template <typename... ARGS>
static void SCAL(ARGS... args) {
PADDLE_ENFORCE_GPU_SUCCESS(phi::dynload::rocblas_dscal(args...));
}
template <typename... ARGS>
static void VCOPY(ARGS... args) {
PADDLE_ENFORCE_GPU_SUCCESS(phi::dynload::rocblas_dcopy(args...));
}
template <typename... ARGS>
static void GEMV(ARGS... args) {
PADDLE_ENFORCE_GPU_SUCCESS(phi::dynload::rocblas_dgemv(args...));
}
template <typename... ARGS>
static void GEMM_STRIDED_BATCH(ARGS... args) {
PADDLE_ENFORCE_GPU_SUCCESS(
phi::dynload::rocblas_dgemm_strided_batched(args...));
}
template <typename... ARGS>
static void GEMM_EX(ARGS... args) {
PADDLE_THROW(
phi::errors::Unimplemented("Currently there are not cublasDgemmEx."));
}
template <typename... ARGS>
static void TRSM(ARGS... args) {
PADDLE_ENFORCE_GPU_SUCCESS(phi::dynload::rocblas_dtrsm(args...));
}
template <typename... ARGS>
static void GETRF_BATCH(ARGS... args) {
PADDLE_THROW(phi::errors::Unimplemented(
"cublasDgetrfBatched is not supported on HIP platform."));
}
template <typename... ARGS>
static void GETRI_BATCH(ARGS... args) {
PADDLE_THROW(phi::errors::Unimplemented(
"cublasDgetriBatched is not supported on HIP platform."));
}
template <typename... ARGS>
static void MATINV_BATCH(ARGS... args) {
PADDLE_THROW(phi::errors::Unimplemented(
"cublasDmatinvBatched is not supported on HIP platform."));
}
template <typename... ARGS>
static void TRSM_BATCH(ARGS... args) {
PADDLE_THROW(phi::errors::Unimplemented(
"cublasDtrsmBatched is not supported on HIP platform."));
}
};
template <>
struct CUBlas<phi::dtype::float16> {
using float16 = phi::dtype::float16;
static void GEMM(rocblas_handle handle,
rocblas_operation transa,
rocblas_operation transb,
int m,
int n,
int k,
const float16 *alpha,
const float16 *A,
int lda,
const float16 *B,
int ldb,
const float16 *beta,
float16 *C,
int ldc) {
PADDLE_ENFORCE_GPU_SUCCESS(phi::dynload::rocblas_hgemm(
handle,
transa,
transb,
m,
n,
k,
reinterpret_cast<const rocblas_half *>(alpha),
reinterpret_cast<const rocblas_half *>(A),
lda,
reinterpret_cast<const rocblas_half *>(B),
ldb,
reinterpret_cast<const rocblas_half *>(beta),
reinterpret_cast<rocblas_half *>(C),
ldc));
}
static void GEMM_STRIDED_BATCH(rocblas_handle handle,
rocblas_operation transa,
rocblas_operation transb,
int m,
int n,
int k,
const float16 *alpha,
const float16 *A,
int lda,
long long int strideA, // NOLINT
const float16 *B, // NOLINT
int ldb,
long long int strideB, // NOLINT
const float16 *beta,
float16 *C,
int ldc,
long long int strideC, // NOLINT
int batchCount) {
PADDLE_ENFORCE_GPU_SUCCESS(phi::dynload::rocblas_hgemm_strided_batched(
handle,
transa,
transb,
m,
n,
k,
reinterpret_cast<const rocblas_half *>(alpha),
reinterpret_cast<const rocblas_half *>(A),
lda,
strideA,
reinterpret_cast<const rocblas_half *>(B),
ldb,
strideB,
reinterpret_cast<const rocblas_half *>(beta),
reinterpret_cast<rocblas_half *>(C),
ldc,
strideC,
batchCount));
}
// NOTES: GEMM_EX can use Tensor Core to accelerate matrix multiply.
// https://docs.nvidia.com/cuda/cublas/index.html#cublassetmathmode
template <typename... ARGS>
static void GEMM_EX(phi::GPUContext *dev_ctx,
rocblas_operation transa,
rocblas_operation transb,
int m,
int n,
int k,
const void *alpha,
const void *A,
rocblas_datatype Atype,
int lda,
const void *B,
rocblas_datatype Btype,
int ldb,
const void *beta,
void *C,
rocblas_datatype Ctype,
int ldc,
rocblas_datatype computeType) {
rocblas_gemm_algo algo = rocblas_gemm_algo_standard;
dev_ctx->TensorCoreCublasCallIfAvailable([&](rocblas_handle handle) {
PADDLE_ENFORCE_GPU_SUCCESS(phi::dynload::rocblas_gemm_ex(handle,
transa,
transb,
m,
n,
k,
alpha,
A,
Atype,
lda,
B,
Btype,
ldb,
beta,
C,
Ctype,
ldc,
C,
Ctype,
ldc,
computeType,
algo,
0,
0));
});
}
};
template <>
struct CUBlas<phi::dtype::complex<float>> {
static void GEMV(rocblas_handle handle,
rocblas_operation transa,
int m,
int n,
const phi::dtype::complex<float> *alpha,
const phi::dtype::complex<float> *A,
int lda,
const phi::dtype::complex<float> *B,
int ldb,
const phi::dtype::complex<float> *beta,
phi::dtype::complex<float> *C,
int ldc) {
PADDLE_ENFORCE_GPU_SUCCESS(phi::dynload::rocblas_cgemv(
handle,
transa,
m,
n,
reinterpret_cast<const rocblas_float_complex *>(alpha),
reinterpret_cast<const rocblas_float_complex *>(A),
lda,
reinterpret_cast<const rocblas_float_complex *>(B),
ldb,
reinterpret_cast<const rocblas_float_complex *>(beta),
reinterpret_cast<rocblas_float_complex *>(C),
ldc));
}
static void AXPY(rocblas_handle handle,
int n,
const phi::dtype::complex<float> *alpha,
const phi::dtype::complex<float> *X,
const int incX,
phi::dtype::complex<float> *Y,
const int incY) {
PADDLE_ENFORCE_GPU_SUCCESS(phi::dynload::rocblas_caxpy(
handle,
n,
reinterpret_cast<const rocblas_float_complex *>(alpha),
reinterpret_cast<const rocblas_float_complex *>(X),
incX,
reinterpret_cast<rocblas_float_complex *>(Y),
incY));
}
static void GEMM_STRIDED_BATCH(rocblas_handle handle,
rocblas_operation transa,
rocblas_operation transb,
int m,
int n,
int k,
const phi::dtype::complex<float> *alpha,
const phi::dtype::complex<float> *A,
int lda,
long long int strideA, // NOLINT
const phi::dtype::complex<float> *B, // NOLINT
int ldb,
long long int strideB, // NOLINT
const phi::dtype::complex<float> *beta,
phi::dtype::complex<float> *C,
int ldc,
long long int strideC, // NOLINT
int batchCount) {
PADDLE_ENFORCE_GPU_SUCCESS(phi::dynload::rocblas_cgemm_strided_batched(
handle,
transa,
transb,
m,
n,
k,
reinterpret_cast<const rocblas_float_complex *>(alpha),
reinterpret_cast<const rocblas_float_complex *>(A),
lda,
strideA,
reinterpret_cast<const rocblas_float_complex *>(B),
ldb,
strideB,
reinterpret_cast<const rocblas_float_complex *>(beta),
reinterpret_cast<rocblas_float_complex *>(C),
ldc,
strideC,
batchCount));
}
static void GEMM(rocblas_handle handle,
rocblas_operation transa,
rocblas_operation transb,
int m,
int n,
int k,
const phi::dtype::complex<float> *alpha,
const phi::dtype::complex<float> *A,
int lda,
const phi::dtype::complex<float> *B,
int ldb,
const phi::dtype::complex<float> *beta,
phi::dtype::complex<float> *C,
int ldc) {
PADDLE_ENFORCE_GPU_SUCCESS(phi::dynload::rocblas_cgemm(
handle,
transa,
transb,
m,
n,
k,
reinterpret_cast<const rocblas_float_complex *>(alpha),
reinterpret_cast<const rocblas_float_complex *>(A),
lda,
reinterpret_cast<const rocblas_float_complex *>(B),
ldb,
reinterpret_cast<const rocblas_float_complex *>(beta),
reinterpret_cast<rocblas_float_complex *>(C),
ldc));
}
// NOTES: GEMM_EX can use Tensor Core to accelerate matrix multiply.
// https://docs.nvidia.com/cuda/cublas/index.html#cublassetmathmode
template <typename... ARGS>
static void GEMM_EX(phi::GPUContext *dev_ctx,
rocblas_operation transa,
rocblas_operation transb,
int m,
int n,
int k,
const void *alpha,
const void *A,
rocblas_datatype Atype,
int lda,
const void *B,
rocblas_datatype Btype,
int ldb,
const void *beta,
void *C,
rocblas_datatype Ctype,
int ldc,
rocblas_datatype computeType) {
rocblas_gemm_algo algo = rocblas_gemm_algo_standard;
dev_ctx->TensorCoreCublasCallIfAvailable([&](rocblas_handle handle) {
PADDLE_ENFORCE_GPU_SUCCESS(phi::dynload::rocblas_gemm_ex(handle,
transa,
transb,
m,
n,
k,
alpha,
A,
Atype,
lda,
B,
Btype,
ldb,
beta,
C,
Ctype,
ldc,
C,
Ctype,
ldc,
computeType,
algo,
0,
0));
});
}
};
template <>
struct CUBlas<phi::dtype::complex<double>> {
static void GEMV(rocblas_handle handle,
rocblas_operation transa,
int m,
int n,
const phi::dtype::complex<double> *alpha,
const phi::dtype::complex<double> *A,
int lda,
const phi::dtype::complex<double> *B,
int ldb,
const phi::dtype::complex<double> *beta,
phi::dtype::complex<double> *C,
int ldc) {
PADDLE_ENFORCE_GPU_SUCCESS(phi::dynload::rocblas_zgemv(
handle,
transa,
m,
n,
reinterpret_cast<const rocblas_double_complex *>(alpha),
reinterpret_cast<const rocblas_double_complex *>(A),
lda,
reinterpret_cast<const rocblas_double_complex *>(B),
ldb,
reinterpret_cast<const rocblas_double_complex *>(beta),
reinterpret_cast<rocblas_double_complex *>(C),
ldc));
}
static void AXPY(rocblas_handle handle,
int n,
const phi::dtype::complex<double> *alpha,
const phi::dtype::complex<double> *X,
const int incX,
phi::dtype::complex<double> *Y,
const int incY) {
PADDLE_ENFORCE_GPU_SUCCESS(phi::dynload::rocblas_zaxpy(
handle,
n,
reinterpret_cast<const rocblas_double_complex *>(alpha),
reinterpret_cast<const rocblas_double_complex *>(X),
incX,
reinterpret_cast<rocblas_double_complex *>(Y),
incY));
}
static void GEMM_STRIDED_BATCH(
rocblas_handle handle,
rocblas_operation transa,
rocblas_operation transb,
int m,
int n,
int k,
const phi::dtype::complex<double> *alpha,
const phi::dtype::complex<double> *A,
int lda,
long long int strideA, // NOLINT
const phi::dtype::complex<double> *B, // NOLINT
int ldb,
long long int strideB, // NOLINT
const phi::dtype::complex<double> *beta,
phi::dtype::complex<double> *C,
int ldc,
long long int strideC, // NOLINT
int batchCount) {
PADDLE_ENFORCE_GPU_SUCCESS(phi::dynload::rocblas_zgemm_strided_batched(
handle,
transa,
transb,
m,
n,
k,
reinterpret_cast<const rocblas_double_complex *>(alpha),
reinterpret_cast<const rocblas_double_complex *>(A),
lda,
strideA,
reinterpret_cast<const rocblas_double_complex *>(B),
ldb,
strideB,
reinterpret_cast<const rocblas_double_complex *>(beta),
reinterpret_cast<rocblas_double_complex *>(C),
ldc,
strideC,
batchCount));
}
static void GEMM(rocblas_handle handle,
rocblas_operation transa,
rocblas_operation transb,
int m,
int n,
int k,
const phi::dtype::complex<double> *alpha,
const phi::dtype::complex<double> *A,
int lda,
const phi::dtype::complex<double> *B,
int ldb,
const phi::dtype::complex<double> *beta,
phi::dtype::complex<double> *C,
int ldc) {
PADDLE_ENFORCE_GPU_SUCCESS(phi::dynload::rocblas_zgemm(
handle,
transa,
transb,
m,
n,
k,
reinterpret_cast<const rocblas_double_complex *>(alpha),
reinterpret_cast<const rocblas_double_complex *>(A),
lda,
reinterpret_cast<const rocblas_double_complex *>(B),
ldb,
reinterpret_cast<const rocblas_double_complex *>(beta),
reinterpret_cast<rocblas_double_complex *>(C),
ldc));
}
// NOTES: GEMM_EX can use Tensor Core to accelerate matrix multiply.
// https://docs.nvidia.com/cuda/cublas/index.html#cublassetmathmode
template <typename... ARGS>
static void GEMM_EX(phi::GPUContext *dev_ctx,
rocblas_operation transa,
rocblas_operation transb,
int m,
int n,
int k,
const void *alpha,
const void *A,
rocblas_datatype Atype,
int lda,
const void *B,
rocblas_datatype Btype,
int ldb,
const void *beta,
void *C,
rocblas_datatype Ctype,
int ldc,
rocblas_datatype computeType) {
rocblas_gemm_algo algo = rocblas_gemm_algo_standard;
dev_ctx->TensorCoreCublasCallIfAvailable([&](rocblas_handle handle) {
PADDLE_ENFORCE_GPU_SUCCESS(phi::dynload::rocblas_gemm_ex(handle,
transa,
transb,
m,
n,
k,
alpha,
A,
Atype,
lda,
B,
Btype,
ldb,
beta,
C,
Ctype,
ldc,
C,
Ctype,
ldc,
computeType,
algo,
0,
0));
});
}
};
template <>
template <typename T>
void Blas<phi::GPUContext>::GEMM(CBLAS_TRANSPOSE transA,
CBLAS_TRANSPOSE transB,
int M,
int N,
int K,
T alpha,
const T *A,
const T *B,
T beta,
T *C) const {
// Note that cublas follows fortran order, so the order is different from
// the cblas convention.
int lda = (transA == CblasNoTrans) ? K : M;
int ldb = (transB == CblasNoTrans) ? N : K;
rocblas_operation cuTransA = (transA == CblasNoTrans)
? rocblas_operation_none
: rocblas_operation_transpose;
rocblas_operation cuTransB = (transB == CblasNoTrans)
? rocblas_operation_none
: rocblas_operation_transpose;
context_.CublasCall([&](rocblas_handle handle) {
CUBlas<T>::GEMM(handle,
cuTransB,
cuTransA,
N,
M,
K,
&alpha,
B,
ldb,
A,
lda,
&beta,
C,
N);
});
}
template <>
template <>
inline void Blas<phi::GPUContext>::GEMM(CBLAS_TRANSPOSE transA,
CBLAS_TRANSPOSE transB,
int M,
int N,
int K,
phi::dtype::float16 alpha,
const phi::dtype::float16 *A,
const phi::dtype::float16 *B,
phi::dtype::float16 beta,
phi::dtype::float16 *C) const {
// Note that cublas follows fortran order, so the order is different from
// the cblas convention.
int lda = (transA == CblasNoTrans) ? K : M;
int ldb = (transB == CblasNoTrans) ? N : K;
rocblas_operation cuTransA = (transA == CblasNoTrans)
? rocblas_operation_none
: rocblas_operation_transpose;
rocblas_operation cuTransB = (transB == CblasNoTrans)
? rocblas_operation_none
: rocblas_operation_transpose;
// TODO(kexinzhao): add processing code for compute capability < 53 case
PADDLE_ENFORCE_GE(
context_.GetComputeCapability(),
53,
phi::errors::InvalidArgument(
"cublas fp16 gemm requires GPU compute capability >= 53,"
"but received %d",
context_.GetComputeCapability()));
float h_alpha = static_cast<float>(alpha);
float h_beta = static_cast<float>(beta);
auto &cuda_ctx = const_cast<phi::GPUContext &>(context_);
CUBlas<phi::dtype::float16>::GEMM_EX(&cuda_ctx,
cuTransB,
cuTransA,
N,
M,
K,
&h_alpha,
B,
rocblas_datatype_f16_r,
ldb,
A,
rocblas_datatype_f16_r,
lda,
&h_beta,
C,
rocblas_datatype_f16_r,
N,
rocblas_datatype_f32_r);
}
template <>
template <>
inline void Blas<phi::GPUContext>::GEMM(CBLAS_TRANSPOSE transA,
CBLAS_TRANSPOSE transB,
int M,
int N,
int K,
phi::dtype::bfloat16 alpha,
const phi::dtype::bfloat16 *A,
const phi::dtype::bfloat16 *B,
phi::dtype::bfloat16 beta,
phi::dtype::bfloat16 *C) const {
// Note that cublas follows fortran order, so the order is different from
// the cblas convention.
int lda = (transA == CblasNoTrans) ? K : M;
int ldb = (transB == CblasNoTrans) ? N : K;
rocblas_operation cuTransA = (transA == CblasNoTrans)
? rocblas_operation_none
: rocblas_operation_transpose;
rocblas_operation cuTransB = (transB == CblasNoTrans)
? rocblas_operation_none
: rocblas_operation_transpose;
// TODO(zhiqiu): 80 has the same meaning for rocm and cuda?
PADDLE_ENFORCE_GE(
context_.GetComputeCapability(),
80,
phi::errors::InvalidArgument(
"rocblas bf16 gemm requires GPU compute capability >= 80,"
"but received %d",
context_.GetComputeCapability()));
float h_alpha = static_cast<float>(alpha);
float h_beta = static_cast<float>(beta);
rocblas_gemm_algo algo = rocblas_gemm_algo_standard;
context_.TensorCoreCublasCallIfAvailable([&](rocblas_handle handle) {
PADDLE_ENFORCE_GPU_SUCCESS(
phi::dynload::rocblas_gemm_ex(handle,
cuTransB,
cuTransA,
N,
M,
K,
&h_alpha,
B,
rocblas_datatype_bf16_r,
ldb,
A,
rocblas_datatype_bf16_r,
lda,
&h_beta,
C,
rocblas_datatype_bf16_r,
N,
C,
rocblas_datatype_bf16_r,
N,
rocblas_datatype_f32_r,
algo,
0,
0));
});
}
template <>
template <>
inline void Blas<phi::GPUContext>::GEMM(CBLAS_TRANSPOSE transA,
CBLAS_TRANSPOSE transB,
int M,
int N,
int K,
phi::dtype::complex<float> alpha,
const phi::dtype::complex<float> *A,
const phi::dtype::complex<float> *B,
phi::dtype::complex<float> beta,
phi::dtype::complex<float> *C) const {
// Note that cublas follows fortran order, so the order is different from
// the cblas convention.
int lda = (transA == CblasNoTrans) ? K : M;
int ldb = (transB == CblasNoTrans) ? N : K;
rocblas_operation cuTransA = (transA == CblasNoTrans)
? rocblas_operation_none
: rocblas_operation_transpose;
rocblas_operation cuTransB = (transB == CblasNoTrans)
? rocblas_operation_none
: rocblas_operation_transpose;
// TODO(kexinzhao): add processing code for compute capability < 53 case
PADDLE_ENFORCE_GE(
context_.GetComputeCapability(),
53,
phi::errors::InvalidArgument(
"cublas complex64 gemm requires GPU compute capability >= 53,"
"but received %d",
context_.GetComputeCapability()));
thrust::complex<float> c_alpha =
thrust::complex<float>(alpha.real, alpha.imag);
thrust::complex<float> c_beta = thrust::complex<float>(beta.real, beta.imag);
auto &cuda_ctx = const_cast<phi::GPUContext &>(context_);
CUBlas<phi::dtype::complex<float>>::GEMM_EX(&cuda_ctx,
cuTransB,
cuTransA,
N,
M,
K,
&c_alpha,
B,
rocblas_datatype_f32_c,
ldb,
A,
rocblas_datatype_f32_c,
lda,
&c_beta,
C,
rocblas_datatype_f32_c,
N,
rocblas_datatype_f32_c);
}
template <>
template <>
inline void Blas<phi::GPUContext>::GEMM(CBLAS_TRANSPOSE transA,
CBLAS_TRANSPOSE transB,
int M,
int N,
int K,
phi::dtype::complex<double> alpha,
const phi::dtype::complex<double> *A,
const phi::dtype::complex<double> *B,
phi::dtype::complex<double> beta,
phi::dtype::complex<double> *C) const {
// Note that cublas follows fortran order, so the order is different from
// the cblas convention.
int lda = (transA == CblasNoTrans) ? K : M;
int ldb = (transB == CblasNoTrans) ? N : K;
rocblas_operation cuTransA = (transA == CblasNoTrans)
? rocblas_operation_none
: rocblas_operation_transpose;
rocblas_operation cuTransB = (transB == CblasNoTrans)
? rocblas_operation_none
: rocblas_operation_transpose;
// TODO(kexinzhao): add processing code for compute capability < 53 case
PADDLE_ENFORCE_GE(
context_.GetComputeCapability(),
53,
phi::errors::InvalidArgument(
"cublas complex128 gemm requires GPU compute capability >= 53,"
"but received %d",
context_.GetComputeCapability()));
thrust::complex<double> c_alpha =
thrust::complex<double>(alpha.real, alpha.imag);
thrust::complex<double> c_beta =
thrust::complex<double>(beta.real, beta.imag);
auto &cuda_ctx = const_cast<phi::GPUContext &>(context_);
CUBlas<phi::dtype::complex<double>>::GEMM_EX(&cuda_ctx,
cuTransB,
cuTransA,
N,
M,
K,
&c_alpha,
B,
rocblas_datatype_f64_c,
ldb,
A,
rocblas_datatype_f64_c,
lda,
&c_beta,
C,
rocblas_datatype_f64_c,
N,
rocblas_datatype_f64_c);
}
template <>
template <typename T>
void Blas<phi::GPUContext>::GEMM(bool transA,
bool transB,
int M,
int N,
int K,
T alpha,
const T *A,
int lda,
const T *B,
int ldb,
T beta,
T *C,
int ldc) const {
// Note that cublas follows fortran order, so the order is different from
// the cblas convention.
rocblas_operation cuTransA =
transA ? rocblas_operation_transpose : rocblas_operation_none;
rocblas_operation cuTransB =
transB ? rocblas_operation_transpose : rocblas_operation_none;
context_.CublasCall([&](rocblas_handle handle) {
CUBlas<T>::GEMM(handle,
cuTransB,
cuTransA,
N,
M,
K,
&alpha,
B,
ldb,
A,
lda,
&beta,
C,
ldc);
});
}
template <>
template <>
inline void Blas<phi::GPUContext>::GEMM(bool transA,
bool transB,
int M,
int N,
int K,
phi::dtype::float16 alpha,
const phi::dtype::float16 *A,
int lda,
const phi::dtype::float16 *B,
int ldb,
phi::dtype::float16 beta,
phi::dtype::float16 *C,
int ldc) const {
// Note that cublas follows fortran order, so the order is different from
// the cblas convention.
rocblas_operation cuTransA =
transA ? rocblas_operation_transpose : rocblas_operation_none;
rocblas_operation cuTransB =
transB ? rocblas_operation_transpose : rocblas_operation_none;
context_.CublasCall([&](rocblas_handle handle) {
CUBlas<phi::dtype::float16>::GEMM(handle,
cuTransB,
cuTransA,
N,
M,
K,
&alpha,
B,
ldb,
A,
lda,
&beta,
C,
ldc);
});
}
template <>
template <>
inline void Blas<phi::GPUContext>::GEMM(bool transA,
bool transB,
int M,
int N,
int K,
phi::dtype::bfloat16 alpha,
const phi::dtype::bfloat16 *A,
int lda,
const phi::dtype::bfloat16 *B,
int ldb,
phi::dtype::bfloat16 beta,
phi::dtype::bfloat16 *C,
int ldc) const {
// Note that cublas follows fortran order, so the order is different from