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139 lines
3.9 KiB
139 lines
3.9 KiB
/* Copyright (c) 2016 Baidu, Inc. 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/utils/Logging.h"
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#include "Layer.h"
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#include "paddle/math/Matrix.h"
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#include "paddle/utils/Stat.h"
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namespace paddle {
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/**
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* @brief A layer for computing the outer product of two vectors
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* @note used in NEURAL TURING MACHINE
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* Input1: vector (batchSize * dim1)
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* Input2: vector (batchSize * dim2)
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* Output: a matrix: (batchSize * (dim1*dim2))
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*/
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class OuterProdLayer : public Layer {
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protected:
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MatrixPtr tmpMtx0;
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MatrixPtr tmpRow0;
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MatrixPtr tmpRow1;
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public:
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explicit OuterProdLayer(const LayerConfig& config) : Layer(config) {}
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~OuterProdLayer() {}
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bool init(const LayerMap& layerMap, const ParameterMap& parameterMap);
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void forward(PassType passType);
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void backward(const UpdateCallback& callback = nullptr);
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};
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REGISTER_LAYER(out_prod, OuterProdLayer);
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bool OuterProdLayer::init(const LayerMap& layerMap,
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const ParameterMap& parameterMap) {
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Layer::init(layerMap, parameterMap);
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CHECK_EQ(inputLayers_.size(), 2U);
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size_t dim0 = inputLayers_[0]->getSize();
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size_t dim1 = inputLayers_[1]->getSize();
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CHECK_EQ(dim0 * dim1, getSize()) << "Dimension mismatch";
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tmpRow0 = Matrix::create(nullptr, /* height= */ 1, dim0, /* trans= */ false,
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useGpu_);
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tmpRow1 = Matrix::create(nullptr, /* height= */ 1, dim1, /* trans= */ false,
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useGpu_);
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tmpMtx0 = Matrix::create(nullptr, /* height= */ dim0, dim1,
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/* trans= */ false, useGpu_);
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return true;
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}
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void OuterProdLayer::forward(PassType passType) {
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Layer::forward(passType);
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MatrixPtr inV0 = getInputValue(0);
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MatrixPtr inV1 = getInputValue(1);
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size_t batchSize = inV0->getHeight();
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size_t dim0 = inV0->getWidth();
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size_t dim1 = inV1->getWidth();
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CHECK_EQ(dim0 * dim1, getSize());
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CHECK_EQ(inV1->getHeight(), batchSize);
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{
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REGISTER_TIMER_INFO("FwResetTimer", getName().c_str());
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reserveOutput(batchSize, dim0 * dim1);
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}
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MatrixPtr outV = getOutputValue();
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{
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REGISTER_TIMER_INFO("FwOutProdTimer", getName().c_str());
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for (size_t i = 0; i < batchSize; i++) {
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tmpMtx0->setData(outV->getData() + i * dim0 * dim1);
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tmpRow0->setData(inV0->getData() + i * dim0);
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tmpRow1->setData(inV1->getData() + i * dim1);
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tmpMtx0->mul(tmpRow0->getTranspose(), tmpRow1);
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}
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}
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}
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void OuterProdLayer::backward(const UpdateCallback& callback) {
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MatrixPtr inV0 = getInputValue(0);
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MatrixPtr inV1 = getInputValue(1);
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MatrixPtr outG = getOutputGrad();
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MatrixPtr inG0 = getInputGrad(0);
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MatrixPtr inG1 = getInputGrad(1);
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size_t batchSize = inV0->getHeight();
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size_t dim0 = inV0->getWidth();
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size_t dim1 = inV1->getWidth();
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{
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REGISTER_TIMER_INFO("BwOutProdTimer", getName().c_str());
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if (inG0) {
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for (size_t i = 0; i < batchSize; i++) {
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tmpMtx0->setData(outG->getData() + i * dim0 * dim1);
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tmpRow0->setData(inG0->getData() + i * dim0);
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tmpRow1->setData(inV1->getData() + i * dim1);
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tmpRow0->mul(tmpRow1, tmpMtx0->getTranspose(), 1, 1);
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}
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}
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if (inG1) {
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for (size_t i = 0; i < batchSize; i++) {
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tmpMtx0->setData(outG->getData() + i * dim0 * dim1);
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tmpRow0->setData(inV0->getData() + i * dim0);
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tmpRow1->setData(inG1->getData() + i * dim1);
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tmpRow1->mul(tmpRow0, tmpMtx0, 1, 1);
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}
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}
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}
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}
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} // namespace paddle
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