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131 lines
4.9 KiB
131 lines
4.9 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 "MulOp.h"
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#include "hl_base.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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