Merge pull request #1147 from tianbingsz/paddle_func_sparse
Matrix::MUL operators using and test Daoyuan's Paddle Function, SparseMatrixArg and Function Testavx_docs
tianbingsz
8 years ago
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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");
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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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#pragma once
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#include "Function.h"
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#include "paddle/math/Matrix.h"
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#include "paddle/math/SparseMatrix.h"
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namespace paddle {
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/// CPU, dense matrix (+)= dense matrix * dense matrix
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template <DeviceType DType>
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void MulOp(CpuMatrix& out,
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const CpuMatrix& a,
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const CpuMatrix& b,
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real scaleAB,
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real scaleT,
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bool aTrans,
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bool bTrans);
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/// CPU, dense matrix (+)= sparse matrix * dense matrix
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template <DeviceType DType>
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void MulOp(CpuMatrix& out,
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const CpuSparseMatrix& a,
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const CpuMatrix& b,
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real scaleAB,
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real scaleT,
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bool aTrans,
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bool bTrans);
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/// CPU, dense matrix (+)= dense matrix * sparse matrix
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template <DeviceType DType>
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void MulOp(CpuMatrix& out,
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const CpuMatrix& a,
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const CpuSparseMatrix& b,
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real scaleAB,
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real scaleT,
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bool aTrans,
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bool bTrans);
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/// CPU, sparse matrix (+)= dense matrix * dense matrix
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template <DeviceType DType>
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void MulOp(CpuSparseMatrix& out,
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const CpuMatrix& a,
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const CpuMatrix& b,
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real scaleAB,
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real scaleT,
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bool aTrans,
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bool bTrans);
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/// GPU, dense matrix (+)= dense matrix * dense matrix
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template <DeviceType DType>
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void MulOp(GpuMatrix& out,
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const GpuMatrix& a,
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const GpuMatrix& b,
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real scaleAB,
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real scaleT,
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bool aTrans,
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bool bTrans);
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/// GPU, dense matrix (+)= sparse matrix * dense matrix
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template <DeviceType DType>
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void MulOp(GpuMatrix& out,
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const GpuSparseMatrix& a,
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const GpuMatrix& b,
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real scaleAB,
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real scaleT,
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bool aTrans,
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bool bTrans);
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/// GPU, dense matrix (+)= dense matrix * sparse matrix
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template <DeviceType DType>
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void MulOp(GpuMatrix& out,
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const GpuMatrix& a,
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const GpuSparseMatrix& b,
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real scaleAB,
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real scaleT,
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bool aTrans,
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bool bTrans);
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/// GPU, sparse matrix (+)= dense matrix * dense matrix
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template <DeviceType DType>
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void MulOp(GpuSparseMatrix& out,
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const GpuMatrix& a,
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const GpuMatrix& b,
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real scaleAB,
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real scaleT,
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bool aTrans,
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bool bTrans);
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} // namespace paddle
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@ -0,0 +1,130 @@
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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");
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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 "hl_base.h"
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#include "MulOp.h"
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#include "paddle/math/Matrix.h"
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#include "paddle/math/SparseMatrix.h"
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namespace paddle {
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/// dense matrix (+)= dense matrix * dense matrix
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template <>
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void MulOp<DEVICE_TYPE_GPU>(GpuMatrix& out,
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const GpuMatrix& a,
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const GpuMatrix& b,
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real scaleAB,
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real scaleT,
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bool aTrans,
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bool bTrans) {
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CHECK(a.useGpu_ && b.useGpu_) << "matrix device type not match";
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hl_matrix_mul(const_cast<real*>(a.getData()),
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!aTrans ? HPPL_OP_N : HPPL_OP_T,
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const_cast<real*>(b.getData()),
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!bTrans ? HPPL_OP_N : HPPL_OP_T,
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const_cast<real*>(out.getData()),
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out.getHeight(),
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out.getWidth(),
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!aTrans ? a.getWidth() : a.getHeight(),
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scaleAB,
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scaleT,
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a.getStride(),
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b.getStride(),
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out.getStride());
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}
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/// dense matrix (+)= sparse matrix * dense matrix
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template <>
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void MulOp<DEVICE_TYPE_GPU>(GpuMatrix& out,
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const GpuSparseMatrix& a,
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const GpuMatrix& b,
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real scaleAB,
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real scaleT,
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bool aTrans,
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bool bTrans) {
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CHECK(out.isContiguous());
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CHECK(b.isContiguous());
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CHECK(a.useGpu_ && b.useGpu_) << "matrix device type not match";
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hl_matrix_csr_mul_dense(a.sMatrix_.get(),
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aTrans ? HPPL_OP_T : HPPL_OP_N,
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const_cast<real*>(b.getData()),
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HPPL_OP_N,
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const_cast<real*>(out.getData()),
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out.getHeight(),
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out.getWidth(),
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b.getHeight(),
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scaleAB,
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scaleT);
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}
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/// dense matrix (+)= dense matrix * sparse matrix
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template <>
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void MulOp<DEVICE_TYPE_GPU>(GpuMatrix& out,
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const GpuMatrix& a,
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const GpuSparseMatrix& b,
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real scaleAB,
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real scaleT,
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bool aTrans,
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bool bTrans) {
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CHECK(out.isContiguous());
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CHECK(a.isContiguous());
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CHECK(a.useGpu_ && b.useGpu_) << "matrix device type not match";
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if (b.format_ == SPARSE_CSC) {
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hl_matrix_dense_mul_csc(const_cast<real*>(a.getData()),
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HPPL_OP_N,
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b.sMatrix_.get(),
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bTrans ? HPPL_OP_T : HPPL_OP_N,
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const_cast<real*>(out.getData()),
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out.getHeight(),
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out.getWidth(),
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a.getWidth(),
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scaleAB,
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scaleT);
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} else {
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hl_matrix_dense_mul_csr(const_cast<real*>(a.getData()),
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HPPL_OP_N,
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b.sMatrix_.get(),
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bTrans ? HPPL_OP_T : HPPL_OP_N,
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const_cast<real*>(out.getData()),
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out.getHeight(),
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out.getWidth(),
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a.getWidth(),
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scaleAB,
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scaleT);
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}
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}
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/// sparse matrix (+)= dense matrix * dense matrix
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template <>
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void MulOp<DEVICE_TYPE_GPU>(GpuSparseMatrix& out,
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const GpuMatrix& a,
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const GpuMatrix& b,
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real scaleAB,
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real scaleT,
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bool aTrans,
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bool bTrans) {
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CHECK(a.useGpu_ && b.useGpu_) << "matrix device type not match";
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hl_sparse_matrix_mul(const_cast<real*>(a.getData()),
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aTrans ? HPPL_OP_T : HPPL_OP_N,
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const_cast<real*>(b.getData()),
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bTrans ? HPPL_OP_T : HPPL_OP_N,
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out.sMatrix_.get(),
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out.getHeight(),
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out.getWidth(),
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!bTrans ? b.getHeight() : b.getWidth(),
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scaleAB,
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scaleT);
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}
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} // namespace paddle
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@ -0,0 +1,212 @@
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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");
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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 <gtest/gtest.h>
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#include "FunctionTest.h"
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#include "paddle/math/Matrix.h"
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#include "paddle/math/SparseMatrix.h"
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#include "paddle/math/tests/test_matrixUtil.h"
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#include "paddle/testing/TestUtil.h"
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using namespace paddle; // NOLINT
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/**
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* C += A * B, A, B, C dense matrix
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* dense = dense * dense
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*/
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void testFuncDDDMatrix(
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bool transa, bool transb, size_t dimM, size_t dimN, size_t dimK) {
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real scaleT = 1.0;
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size_t heightA = (transa == false) ? dimM : dimK;
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size_t widthA = (transa == false) ? dimK : dimM;
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size_t heightB = (transb == false) ? dimK : dimN;
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size_t widthB = (transb == false) ? dimN : dimK;
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size_t heightC = dimM;
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size_t widthC = dimN;
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// init Test object
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FunctionCompare test(
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"MulOp", FuncConfig().set("aTrans", transa).set("bTrans", transb));
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// prepare input arguments
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/// matrix A : HA * WA
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test.addInputs(BufferArg(VALUE_TYPE_FLOAT, TensorShape{heightA, widthA}));
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/// matrix B: HB * WB
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test.addInputs(BufferArg(VALUE_TYPE_FLOAT, TensorShape{heightB, widthB}));
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/// output matrix C: HC * WC
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test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, TensorShape{heightC, widthC}),
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scaleT == 1.0 ? ADD_TO : ASSIGN_TO);
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// run Function
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test.run();
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}
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TEST(MulOp, DDDMatrixMul) {
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LOG(INFO) << "function test for dense = dense * dense matrix";
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for (const auto transa : {false, true}) {
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for (const auto transb : {false, true}) {
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for (const auto dimM : {1, 10, 100}) {
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for (const auto dimN : {1, 10}) {
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for (const auto dimK : {8}) {
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if (transa && transb) {
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continue;
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}
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VLOG(3) << setiosflags(std::ios::left) << std::setfill(' ')
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<< " transa=" << transa << " transb=" << transb
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<< " dimM=" << std::setw(5) << dimM
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<< " dimN=" << std::setw(5) << dimN
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<< " dimK=" << std::setw(5) << dimK;
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testFuncDDDMatrix(transa, transb, dimM, dimN, dimK);
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}
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}
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}
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}
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}
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}
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/**
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* C += A * B, B, C dense, A sparse
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* dense = sparse * dense
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*/
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void testFuncDSparseDMatrix(
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size_t dimM, size_t dimN, size_t dimK, size_t nnz, SparseFormat FORMAT) {
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real scaleT = 1.0;
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// init Test object
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FunctionCompare test("MulOp",
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FuncConfig().set("aTrans", false).set("bTrans", false));
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// prepare input arguments
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/// sparse matrix A : M * K
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test.addInputs(SparseMatrixArg(
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VALUE_TYPE_FLOAT, TensorShape{dimM, dimK}, nnz, FORMAT, FLOAT_VALUE));
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/// matrix B: K * N
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test.addInputs(BufferArg(VALUE_TYPE_FLOAT, TensorShape{dimK, dimN}));
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/// output matrix C: M * N
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test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, TensorShape{dimM, dimN}),
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scaleT == 1.0 ? ADD_TO : ASSIGN_TO);
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// run Function
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test.run();
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}
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TEST(MuLOp, DSparseDMul) {
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LOG(INFO) << "function test for dense = sparse * dense matrix";
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for (const auto dimM : {10, 100, 1000}) {
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for (const auto dimN : {10, 100}) {
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for (const auto dimK : {3, 10}) {
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for (const auto nnz : {3, 10}) {
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for (const auto FORMAT : {SPARSE_CSR}) {
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VLOG(3) << setiosflags(std::ios::left) << std::setfill(' ')
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<< " dimM=" << std::setw(5) << dimM
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<< " dimN=" << std::setw(5) << dimN
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<< " dimK=" << std::setw(5) << dimK
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<< " nnz=" << std::setw(5) << nnz
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<< " format=" << std::setw(5) << FORMAT;
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testFuncDSparseDMatrix(dimM, dimN, dimK, nnz, FORMAT);
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}
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}
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}
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}
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}
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}
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/**
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* C += A * B, A, C dense, B sparse
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* dense = dense * sparse
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*/
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void testFuncDDSparseMatrix(
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size_t dimM, size_t dimN, size_t dimK, size_t nnz, SparseFormat FORMAT) {
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real scaleT = 1.0;
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// init Test object
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FunctionCompare test("MulOp",
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FuncConfig().set("aTrans", false).set("bTrans", false));
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// prepare input arguments
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/// matrix A : M * K
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test.addInputs(BufferArg(VALUE_TYPE_FLOAT, TensorShape{dimM, dimK}));
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/// matrix B: K * N
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test.addInputs(SparseMatrixArg(
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VALUE_TYPE_FLOAT, TensorShape{dimK, dimN}, nnz, FORMAT, FLOAT_VALUE));
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/// output matrix C: M * N
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test.addOutputs(BufferArg(VALUE_TYPE_FLOAT, TensorShape{dimM, dimN}),
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scaleT == 1.0 ? ADD_TO : ASSIGN_TO);
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// run Function
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test.run();
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}
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TEST(MulOp, DDSparseMul) {
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LOG(INFO) << "function test for dense = dense * sparse matrix";
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for (const auto dimM : {10, 100, 1000}) {
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for (const auto dimN : {10, 100}) {
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for (const auto dimK : {3, 10}) {
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for (const auto nnz : {3, 10}) {
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for (const auto FORMAT : {SPARSE_CSR, SPARSE_CSC}) {
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VLOG(3) << setiosflags(std::ios::left) << std::setfill(' ')
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<< " dimM=" << std::setw(5) << dimM
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<< " dimN=" << std::setw(5) << dimN
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<< " dimK=" << std::setw(5) << dimK
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<< " nnz=" << std::setw(5) << nnz
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<< " format=" << std::setw(5) << FORMAT;
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testFuncDDSparseMatrix(dimM, dimN, dimK, nnz, FORMAT);
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}
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}
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}
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}
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}
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}
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/**
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* C += A * B, A sparse, B, C dense
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* sparse = dense * dense
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*/
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void testFuncSparseDDMatrix(
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size_t dimM, size_t dimN, size_t dimK, size_t nnz, SparseFormat FORMAT) {
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real scaleT = 1.0;
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// init Test object
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FunctionCompare test("MulOp",
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FuncConfig().set("aTrans", false).set("bTrans", false));
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// prepare input arguments
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/// matrix A : M * K
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test.addInputs(BufferArg(VALUE_TYPE_FLOAT, TensorShape{dimM, dimK}));
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/// matrix B: K * N
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test.addInputs(BufferArg(VALUE_TYPE_FLOAT, TensorShape{dimK, dimN}));
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/// output sparse matrix C: M * N
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test.addOutputs(
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SparseMatrixArg(
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VALUE_TYPE_FLOAT, TensorShape{dimM, dimN}, nnz, FORMAT, FLOAT_VALUE),
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scaleT == 1.0 ? ADD_TO : ASSIGN_TO);
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// run Function
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test.run();
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}
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TEST(MulOp, SparseDDMul) {
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LOG(INFO) << "function test for sparse = dense * dense matrix";
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for (const auto dimM : {10, 100, 1000}) {
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for (const auto dimN : {10, 100}) {
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for (const auto dimK : {3, 10}) {
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for (const auto nnz : {3, 10}) {
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for (const auto FORMAT : {SPARSE_CSC, SPARSE_CSR}) {
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VLOG(3) << setiosflags(std::ios::left) << std::setfill(' ')
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<< " dimM=" << std::setw(5) << dimM
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<< " dimN=" << std::setw(5) << dimN
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<< " dimK=" << std::setw(5) << dimK
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<< " nnz=" << std::setw(5) << nnz
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<< " format=" << std::setw(5) << FORMAT;
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testFuncSparseDDMatrix(dimM, dimN, dimK, nnz, FORMAT);
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}
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}
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}
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}
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}
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}
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