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161 lines
7.1 KiB
161 lines
7.1 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#include "paddle/operators/math/math_function.h"
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namespace paddle {
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namespace operators {
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namespace math {
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template <>
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void gemm<platform::GPUPlace, float>(const platform::DeviceContext& context,
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const CBLAS_TRANSPOSE transA,
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const CBLAS_TRANSPOSE transB, const int M,
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const int N, const int K,
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const float alpha, const float* A,
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const float* B, const float beta,
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float* C) {
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// Note that cublas follows fortran order, so the order is different from
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// the cblas convention.
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int lda = (transA == CblasNoTrans) ? K : M;
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int ldb = (transB == CblasNoTrans) ? N : K;
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cublasOperation_t cuTransA =
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(transA == CblasNoTrans) ? CUBLAS_OP_N : CUBLAS_OP_T;
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cublasOperation_t cuTransB =
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(transB == CblasNoTrans) ? CUBLAS_OP_N : CUBLAS_OP_T;
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PADDLE_ENFORCE(platform::dynload::cublasSgemm(
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reinterpret_cast<const platform::CUDADeviceContext&>(context)
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.cublas_handle(),
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cuTransB, cuTransA, N, M, K, &alpha, B, ldb, A, lda, &beta, C, N));
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}
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template <>
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void gemm<platform::GPUPlace, double>(const platform::DeviceContext& context,
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const CBLAS_TRANSPOSE transA,
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const CBLAS_TRANSPOSE transB, const int M,
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const int N, const int K,
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const double alpha, const double* A,
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const double* B, const double beta,
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double* C) {
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// Note that cublas follows fortran order, so the order is different from
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// the cblas convention.
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int lda = (transA == CblasNoTrans) ? K : M;
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int ldb = (transB == CblasNoTrans) ? N : K;
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cublasOperation_t cuTransA =
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(transA == CblasNoTrans) ? CUBLAS_OP_N : CUBLAS_OP_T;
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cublasOperation_t cuTransB =
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(transB == CblasNoTrans) ? CUBLAS_OP_N : CUBLAS_OP_T;
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PADDLE_ENFORCE(platform::dynload::cublasDgemm(
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reinterpret_cast<const platform::CUDADeviceContext&>(context)
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.cublas_handle(),
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cuTransB, cuTransA, N, M, K, &alpha, B, ldb, A, lda, &beta, C, N));
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}
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template <>
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void gemm<platform::GPUPlace, float>(const platform::DeviceContext& context,
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const bool transA, const bool transB,
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const int M, const int N, const int K,
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const float alpha, const float* A,
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const int lda, const float* B,
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const int ldb, const float beta, float* C,
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const int ldc) {
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// Note that cublas follows fortran order, so the order is different from
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// the cblas convention.
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cublasOperation_t cuTransA = transA == false ? CUBLAS_OP_N : CUBLAS_OP_T;
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cublasOperation_t cuTransB = transB == false ? CUBLAS_OP_N : CUBLAS_OP_T;
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PADDLE_ENFORCE(platform::dynload::cublasSgemm(
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reinterpret_cast<const platform::CUDADeviceContext&>(context)
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.cublas_handle(),
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cuTransB, cuTransA, N, M, K, &alpha, B, ldb, A, lda, &beta, C, ldc));
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}
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template <>
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void gemm<platform::GPUPlace, double>(const platform::DeviceContext& context,
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const bool transA, const bool transB,
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const int M, const int N, const int K,
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const double alpha, const double* A,
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const int lda, const double* B,
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const int ldb, const double beta,
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double* C, const int ldc) {
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// Note that cublas follows fortran order, so the order is different from
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// the cblas convention.
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cublasOperation_t cuTransA = transA == false ? CUBLAS_OP_N : CUBLAS_OP_T;
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cublasOperation_t cuTransB = transB == false ? CUBLAS_OP_N : CUBLAS_OP_T;
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PADDLE_ENFORCE(platform::dynload::cublasDgemm(
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reinterpret_cast<const platform::CUDADeviceContext&>(context)
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.cublas_handle(),
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cuTransB, cuTransA, N, M, K, &alpha, B, ldb, A, lda, &beta, C, ldc));
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}
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template <>
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void matmul<platform::GPUPlace, float>(
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const platform::DeviceContext& context, const framework::Tensor& matrix_a,
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bool trans_a, const framework::Tensor& matrix_b, bool trans_b, float alpha,
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framework::Tensor* matrix_out, float beta) {
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auto dim_a = matrix_a.dims();
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auto dim_b = matrix_b.dims();
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auto dim_out = matrix_out->dims();
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PADDLE_ENFORCE(dim_a.size() == 2 && dim_b.size() == 2 && dim_out.size() == 2,
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"The input and output of matmul be matrix");
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PADDLE_ENFORCE(platform::is_gpu_place(matrix_a.place()) &&
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platform::is_gpu_place(matrix_b.place()) &&
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platform::is_gpu_place(matrix_out->place()),
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"Matrix must all be in GPUPlace");
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int M = dim_out[0];
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int N = dim_out[1];
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int K = (trans_a == false) ? dim_a[1] : dim_a[0];
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CBLAS_TRANSPOSE transA = (trans_a == false) ? CblasNoTrans : CblasTrans;
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CBLAS_TRANSPOSE transB = (trans_b == false) ? CblasNoTrans : CblasTrans;
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gemm<platform::GPUPlace, float>(
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context, transA, transB, M, N, K, alpha, matrix_a.data<float>(),
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matrix_b.data<float>(), beta, matrix_out->data<float>());
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}
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template <>
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void matmul<platform::GPUPlace, double>(
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const platform::DeviceContext& context, const framework::Tensor& matrix_a,
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bool trans_a, const framework::Tensor& matrix_b, bool trans_b, double alpha,
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framework::Tensor* matrix_out, double beta) {
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auto dim_a = matrix_a.dims();
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auto dim_b = matrix_b.dims();
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auto dim_out = matrix_out->dims();
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PADDLE_ENFORCE(dim_a.size() == 2 && dim_b.size() == 2 && dim_out.size() == 2,
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"The input and output of matmul be matrix");
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PADDLE_ENFORCE(platform::is_gpu_place(matrix_a.place()) &&
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platform::is_gpu_place(matrix_b.place()) &&
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platform::is_gpu_place(matrix_out->place()),
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"Matrix must all be in GPUPlace");
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int M = dim_out[0];
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int N = dim_out[1];
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int K = (trans_a == false) ? dim_a[1] : dim_a[0];
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CBLAS_TRANSPOSE transA = (trans_a == false) ? CblasNoTrans : CblasTrans;
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CBLAS_TRANSPOSE transB = (trans_b == false) ? CblasNoTrans : CblasTrans;
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gemm<platform::GPUPlace, double>(
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context, transA, transB, M, N, K, alpha, matrix_a.data<double>(),
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matrix_b.data<double>(), beta, matrix_out->data<double>());
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}
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} // namespace math
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} // namespace operators
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} // namespace paddle
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