Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into public_to_protected
qiaolongfei
8 years ago
commit
2f74e60865
@ -0,0 +1,13 @@
|
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if(WITH_MKLML)
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set(BLAS_LIB mklml)
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else()
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set(BLAS_LIB cblas)
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endif()
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if(WITH_GPU)
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nv_library(math_function SRCS math_function.cc math_function.cu DEPS ${BLAS_LIB} device_context)
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else()
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cc_library(math_function SRCS math_function.cc DEPS ${BLAS_LIB} device_context)
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endif()
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nv_test(math_function_test SRCS math_function_test.cc DEPS math_function tensor)
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@ -0,0 +1,114 @@
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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|
||||
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. */
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||||
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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::CPUPlace, float>(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, float* C,
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platform::DeviceContext* context) {
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int lda = K;
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int ldb = N;
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int ldc = N;
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cblas_sgemm(CblasRowMajor, transA, transB, M, N, K, alpha, A, lda, B, ldb,
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beta, C, ldc);
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}
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template <>
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void gemm<platform::CPUPlace, double>(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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platform::DeviceContext* context) {
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int lda = K;
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int ldb = N;
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int ldc = N;
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cblas_dgemm(CblasRowMajor, transA, transB, M, N, K, alpha, A, lda, B, ldb,
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beta, C, ldc);
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}
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template <>
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void matmul<platform::CPUPlace, float>(const framework::Tensor& matrix_a,
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bool trans_a,
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const framework::Tensor& matrix_b,
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bool trans_b, float alpha,
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framework::Tensor* matrix_out,
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float beta,
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platform::DeviceContext* context) {
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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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|
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PADDLE_ENFORCE(platform::is_cpu_place(matrix_a.place()) &&
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platform::is_cpu_place(matrix_b.place()) &&
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platform::is_cpu_place(matrix_out->place()),
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"Matrix must all be in CPUPlace");
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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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|
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gemm<platform::CPUPlace, float>(
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transA, transB, M, N, K, alpha, matrix_a.data<float>(),
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matrix_b.data<float>(), beta, matrix_out->data<float>(), context);
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}
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template <>
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void matmul<platform::CPUPlace, double>(const framework::Tensor& matrix_a,
|
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bool trans_a,
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const framework::Tensor& matrix_b,
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bool trans_b, double alpha,
|
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framework::Tensor* matrix_out,
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double beta,
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platform::DeviceContext* context) {
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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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|
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PADDLE_ENFORCE(platform::is_cpu_place(matrix_a.place()) &&
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platform::is_cpu_place(matrix_b.place()) &&
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platform::is_cpu_place(matrix_out->place()),
|
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"Matrix must all be in CPUPlace");
|
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|
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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::CPUPlace, double>(
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transA, transB, M, N, K, alpha, matrix_a.data<double>(),
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matrix_b.data<double>(), beta, matrix_out->data<double>(), context);
|
||||
}
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||||
|
||||
} // namespace math
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||||
} // namespace operators
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} // namespace paddle
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||||
@ -0,0 +1,127 @@
|
||||
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
|
||||
|
||||
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. */
|
||||
|
||||
#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 <>
|
||||
void gemm<platform::GPUPlace, float>(const CBLAS_TRANSPOSE transA,
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const CBLAS_TRANSPOSE transB, const int M,
|
||||
const int N, const int K,
|
||||
const float alpha, const float* A,
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const float* B, const float beta, float* C,
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||||
platform::DeviceContext* context) {
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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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||||
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PADDLE_ENFORCE(platform::dynload::cublasSgemm(
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reinterpret_cast<platform::CUDADeviceContext*>(context)->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 <>
|
||||
void gemm<platform::GPUPlace, double>(const CBLAS_TRANSPOSE transA,
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||||
const CBLAS_TRANSPOSE transB, const int M,
|
||||
const int N, const int K,
|
||||
const double alpha, const double* A,
|
||||
const double* B, const double beta,
|
||||
double* C,
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||||
platform::DeviceContext* context) {
|
||||
// Note that cublas follows fortran order, so the order is different from
|
||||
// the cblas convention.
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||||
int lda = (transA == CblasNoTrans) ? K : M;
|
||||
int ldb = (transB == CblasNoTrans) ? N : K;
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||||
cublasOperation_t cuTransA =
|
||||
(transA == CblasNoTrans) ? CUBLAS_OP_N : CUBLAS_OP_T;
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||||
cublasOperation_t cuTransB =
|
||||
(transB == CblasNoTrans) ? CUBLAS_OP_N : CUBLAS_OP_T;
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||||
PADDLE_ENFORCE(platform::dynload::cublasDgemm(
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||||
reinterpret_cast<platform::CUDADeviceContext*>(context)->cublas_handle(),
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||||
cuTransB, cuTransA, N, M, K, &alpha, B, ldb, A, lda, &beta, C, N));
|
||||
}
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||||
|
||||
template <>
|
||||
void matmul<platform::GPUPlace, float>(const framework::Tensor& matrix_a,
|
||||
bool trans_a,
|
||||
const framework::Tensor& matrix_b,
|
||||
bool trans_b, float alpha,
|
||||
framework::Tensor* matrix_out,
|
||||
float beta,
|
||||
platform::DeviceContext* context) {
|
||||
auto dim_a = matrix_a.dims();
|
||||
auto dim_b = matrix_b.dims();
|
||||
auto dim_out = matrix_out->dims();
|
||||
PADDLE_ENFORCE(dim_a.size() == 2 && dim_b.size() == 2 && dim_out.size() == 2,
|
||||
"The input and output of matmul be matrix");
|
||||
|
||||
PADDLE_ENFORCE(platform::is_gpu_place(matrix_a.place()) &&
|
||||
platform::is_gpu_place(matrix_b.place()) &&
|
||||
platform::is_gpu_place(matrix_out->place()),
|
||||
"Matrix must all be in GPUPlace");
|
||||
|
||||
int M = dim_out[0];
|
||||
int N = dim_out[1];
|
||||
int K = (trans_a == false) ? dim_a[1] : dim_a[0];
|
||||
|
||||
CBLAS_TRANSPOSE transA = (trans_a == false) ? CblasNoTrans : CblasTrans;
|
||||
CBLAS_TRANSPOSE transB = (trans_b == false) ? CblasNoTrans : CblasTrans;
|
||||
|
||||
gemm<platform::GPUPlace, float>(
|
||||
transA, transB, M, N, K, alpha, matrix_a.data<float>(),
|
||||
matrix_b.data<float>(), beta, matrix_out->data<float>(), context);
|
||||
}
|
||||
|
||||
template <>
|
||||
void matmul<platform::GPUPlace, double>(const framework::Tensor& matrix_a,
|
||||
bool trans_a,
|
||||
const framework::Tensor& matrix_b,
|
||||
bool trans_b, double alpha,
|
||||
framework::Tensor* matrix_out,
|
||||
double beta,
|
||||
platform::DeviceContext* context) {
|
||||
auto dim_a = matrix_a.dims();
|
||||
auto dim_b = matrix_b.dims();
|
||||
auto dim_out = matrix_out->dims();
|
||||
PADDLE_ENFORCE(dim_a.size() == 2 && dim_b.size() == 2 && dim_out.size() == 2,
|
||||
"The input and output of matmul be matrix");
|
||||
|
||||
PADDLE_ENFORCE(platform::is_gpu_place(matrix_a.place()) &&
|
||||
platform::is_gpu_place(matrix_b.place()) &&
|
||||
platform::is_gpu_place(matrix_out->place()),
|
||||
"Matrix must all be in GPUPlace");
|
||||
|
||||
int M = dim_out[0];
|
||||
int N = dim_out[1];
|
||||
int K = (trans_a == false) ? dim_a[1] : dim_a[0];
|
||||
|
||||
CBLAS_TRANSPOSE transA = (trans_a == false) ? CblasNoTrans : CblasTrans;
|
||||
CBLAS_TRANSPOSE transB = (trans_b == false) ? CblasNoTrans : CblasTrans;
|
||||
|
||||
gemm<platform::GPUPlace, double>(
|
||||
transA, transB, M, N, K, alpha, matrix_a.data<double>(),
|
||||
matrix_b.data<double>(), beta, matrix_out->data<double>(), context);
|
||||
}
|
||||
|
||||
} // namespace math
|
||||
} // namespace operators
|
||||
} // namespace paddle
|
||||
@ -0,0 +1,82 @@
|
||||
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
|
||||
|
||||
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
|
||||
#ifdef PADDLE_USE_MKLML
|
||||
#include <mkl_cblas.h>
|
||||
#include <mkl_lapacke.h>
|
||||
#include <mkl_vml_functions.h>
|
||||
#endif
|
||||
|
||||
#ifdef PADDLE_USE_MKL
|
||||
#include <mkl.h>
|
||||
#include <mkl_lapacke.h>
|
||||
#endif
|
||||
|
||||
#ifdef PADDLE_USE_ATLAS
|
||||
extern "C" {
|
||||
#include <cblas.h>
|
||||
#include <clapack.h>
|
||||
}
|
||||
#endif
|
||||
|
||||
#ifdef PADDLE_USE_OPENBLAS
|
||||
#include <cblas.h>
|
||||
#include <lapacke.h>
|
||||
#endif
|
||||
|
||||
#ifndef LAPACK_FOUND
|
||||
extern "C" {
|
||||
#include <cblas.h>
|
||||
int LAPACKE_sgetrf(int matrix_layout, int m, int n, float* a, int lda,
|
||||
int* ipiv);
|
||||
int LAPACKE_dgetrf(int matrix_layout, int m, int n, double* a, int lda,
|
||||
int* ipiv);
|
||||
int LAPACKE_sgetri(int matrix_layout, int n, float* a, int lda,
|
||||
const int* ipiv);
|
||||
int LAPACKE_dgetri(int matrix_layout, int n, double* a, int lda,
|
||||
const int* ipiv);
|
||||
}
|
||||
#endif
|
||||
|
||||
#include <cmath>
|
||||
|
||||
#include "paddle/framework/tensor.h"
|
||||
#include "paddle/platform/device_context.h"
|
||||
#include "paddle/platform/enforce.h"
|
||||
|
||||
namespace paddle {
|
||||
namespace operators {
|
||||
namespace math {
|
||||
|
||||
// Support continuous memory now
|
||||
// If transA = N, and transB = N
|
||||
// Then matrixA: M * K, matrixB: K * N matrixC : M * N
|
||||
// For more detailed info, please refer to
|
||||
// http://www.netlib.org/lapack/explore-html/d4/de2/sgemm_8f.html
|
||||
template <typename Place, typename T>
|
||||
void gemm(const CBLAS_TRANSPOSE transA, const CBLAS_TRANSPOSE transB,
|
||||
const int M, const int N, const int K, const T alpha, const T* A,
|
||||
const T* B, const T beta, T* C, platform::DeviceContext* context);
|
||||
|
||||
// matrix multiply with continuous memory
|
||||
template <typename Place, typename T>
|
||||
void matmul(const framework::Tensor& matrix_a, bool trans_a,
|
||||
const framework::Tensor& matrix_b, bool trans_b, T alpha,
|
||||
framework::Tensor* matrix_out, T beta,
|
||||
platform::DeviceContext* context);
|
||||
|
||||
} // namespace math
|
||||
} // namespace operators
|
||||
} // namespace paddle
|
||||
@ -0,0 +1,75 @@
|
||||
#include "paddle/operators/math/math_function.h"
|
||||
#include "gtest/gtest.h"
|
||||
|
||||
#ifndef PADDLE_ONLY_CPU
|
||||
TEST(math_function, notrans_mul_trans) {
|
||||
paddle::framework::Tensor input1;
|
||||
paddle::framework::Tensor input1_gpu;
|
||||
paddle::framework::Tensor input2_gpu;
|
||||
paddle::framework::Tensor out_gpu;
|
||||
paddle::framework::Tensor out;
|
||||
|
||||
auto* cpu_place = new paddle::platform::CPUPlace();
|
||||
float* input1_ptr = input1.mutable_data<float>({2, 3}, *cpu_place);
|
||||
float arr[6] = {0, 1, 2, 3, 4, 5};
|
||||
memcpy(input1_ptr, arr, 6 * sizeof(float));
|
||||
|
||||
auto* gpu_place = new paddle::platform::GPUPlace(0);
|
||||
paddle::platform::DeviceContext* context =
|
||||
new paddle::platform::CUDADeviceContext(*gpu_place);
|
||||
|
||||
input1_gpu.CopyFrom<float>(input1, *gpu_place);
|
||||
input2_gpu.CopyFrom<float>(input1, *gpu_place);
|
||||
|
||||
out_gpu.mutable_data<float>({2, 2}, *gpu_place);
|
||||
|
||||
paddle::operators::math::matmul<paddle::platform::GPUPlace, float>(
|
||||
input1_gpu, false, input2_gpu, true, 1, &out_gpu, 0, context);
|
||||
|
||||
out.CopyFrom<float>(out_gpu, *cpu_place);
|
||||
|
||||
float* out_ptr = out.data<float>();
|
||||
EXPECT_EQ(out_ptr[0], 5);
|
||||
EXPECT_EQ(out_ptr[1], 14);
|
||||
EXPECT_EQ(out_ptr[2], 14);
|
||||
EXPECT_EQ(out_ptr[3], 50);
|
||||
}
|
||||
|
||||
TEST(math_function, trans_mul_notrans) {
|
||||
paddle::framework::Tensor input1;
|
||||
paddle::framework::Tensor input1_gpu;
|
||||
paddle::framework::Tensor input2_gpu;
|
||||
paddle::framework::Tensor out_gpu;
|
||||
paddle::framework::Tensor out;
|
||||
|
||||
auto* cpu_place = new paddle::platform::CPUPlace();
|
||||
float* input1_ptr = input1.mutable_data<float>({2, 3}, *cpu_place);
|
||||
float arr[6] = {0, 1, 2, 3, 4, 5};
|
||||
memcpy(input1_ptr, arr, 6 * sizeof(float));
|
||||
|
||||
auto* gpu_place = new paddle::platform::GPUPlace(0);
|
||||
paddle::platform::DeviceContext* context =
|
||||
new paddle::platform::CUDADeviceContext(*gpu_place);
|
||||
|
||||
input1_gpu.CopyFrom<float>(input1, *gpu_place);
|
||||
input2_gpu.CopyFrom<float>(input1, *gpu_place);
|
||||
|
||||
out_gpu.mutable_data<float>({3, 3}, *gpu_place);
|
||||
|
||||
paddle::operators::math::matmul<paddle::platform::GPUPlace, float>(
|
||||
input1_gpu, true, input2_gpu, false, 1, &out_gpu, 0, context);
|
||||
|
||||
out.CopyFrom<float>(out_gpu, *cpu_place);
|
||||
|
||||
float* out_ptr = out.data<float>();
|
||||
EXPECT_EQ(out_ptr[0], 9);
|
||||
EXPECT_EQ(out_ptr[1], 12);
|
||||
EXPECT_EQ(out_ptr[2], 15);
|
||||
EXPECT_EQ(out_ptr[3], 12);
|
||||
EXPECT_EQ(out_ptr[4], 17);
|
||||
EXPECT_EQ(out_ptr[5], 22);
|
||||
EXPECT_EQ(out_ptr[6], 15);
|
||||
EXPECT_EQ(out_ptr[7], 22);
|
||||
EXPECT_EQ(out_ptr[8], 29);
|
||||
}
|
||||
#endif
|
||||
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