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92 lines
2.7 KiB
92 lines
2.7 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 <memory>
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#include <random>
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#include "Layer.h"
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namespace paddle {
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/**
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* @brief A layer for sampling id from multinomial distribution from the
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* input layer. Sampling one id for one sample. The result is stored in
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* output_.ids.
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*
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* The config file api is sampling_id_layer.
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*/
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class SamplingIdLayer : public Layer {
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/// Produces random floating-point values, uniformly distributed on [0, 1).
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std::uniform_real_distribution<double> rand1_;
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std::vector<Argument> tmpCpuInput_;
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public:
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explicit SamplingIdLayer(const LayerConfig& config)
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: Layer(config), rand1_(0, 1) {}
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virtual bool init(const LayerMap& layerMap,
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const ParameterMap& parameterMap) {
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bool ret = Layer::init(layerMap, parameterMap);
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CHECK_EQ(1UL, inputLayers_.size());
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if (useGpu_) {
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tmpCpuInput_.reserve(inputLayers_.size());
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for (size_t i = 0; i < inputLayers_.size(); i++) {
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tmpCpuInput_.push_back(Argument());
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}
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}
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return ret;
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}
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void forward(PassType passType) {
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Layer::forward(passType);
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if (useGpu_) {
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for (size_t i = 0; i < inputLayers_.size(); i++) {
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tmpCpuInput_[i].resizeAndCopyFrom(
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getInput(i), false, HPPL_STREAM_DEFAULT);
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}
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hl_stream_synchronize(HPPL_STREAM_DEFAULT);
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forwardImp(tmpCpuInput_[0]);
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} else {
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forwardImp(getInput(0));
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}
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}
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void forwardImp(const Argument& input) {
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size_t batchSize = input.getBatchSize();
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IVector::resizeOrCreate(output_.ids, batchSize, useGpu_);
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real* buf = input.value->getData();
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int dim = input.value->getWidth();
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std::vector<int> ids(batchSize);
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auto& reng = ThreadLocalRandomEngine::get();
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for (size_t i = 0; i < batchSize; ++i) {
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double r = rand1_(reng);
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int id = dim - 1;
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for (int j = 0; j < dim; ++j) {
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if ((r -= buf[i * dim + j]) < 0) {
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id = j;
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break;
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}
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}
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ids[i] = id;
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}
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output_.ids->copyFrom(ids.data(), batchSize);
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}
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virtual void backward(const UpdateCallback& callback) {}
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};
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REGISTER_LAYER(sampling_id, SamplingIdLayer);
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} // namespace paddle
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