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133 lines
4.2 KiB
133 lines
4.2 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
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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 "MKLPackedRecurrentLayer.h"
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namespace paddle {
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REGISTER_LAYER(mkl_packed_recurrent, MKLPackedRecurrentLayer);
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bool MKLPackedRecurrentLayer::init(const LayerMap& layerMap,
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const ParameterMap& parameterMap) {
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if (!RecurrentLayer::init(layerMap, parameterMap)) return false;
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packed_weight_.reset(new MKLPackedWeight(weight_->getW()));
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packed_weight_->pack();
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if (needGradient_) {
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packed_weightT_.reset(new MKLPackedWeight(weight_->getW(), true));
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packed_weightT_->pack();
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}
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return true;
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}
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void MKLPackedRecurrentLayer::backward(const UpdateCallback& callback) {
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RecurrentLayer::backward(callback);
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packed_weight_->pack();
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if (needGradient_) {
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packed_weightT_->pack();
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}
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}
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void MKLPackedRecurrentLayer::forwardBatch(int batchSize,
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size_t numSequences,
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const int* starts) {
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if (!batchValue_) {
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batchValue_.reset(new SequenceToBatch(useGpu_));
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}
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batchValue_->resizeOrCreateBatch(batchSize, numSequences, starts, reversed_);
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batchValue_->copyFromSeq(*output_.value);
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{
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REGISTER_TIMER_INFO("RecurrentFwBatch", getName().c_str());
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/* forward one batch */
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for (size_t n = 0; n < batchValue_->getNumBatch(); n++) {
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MatrixPtr batchValue = batchValue_->getBatchValue(n);
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if (n != 0) {
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MatrixPtr preBatchValue =
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batchValue_->getBatchValue(n - 1, batchValue->getHeight());
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packed_weight_->gemm_compute(preBatchValue, batchValue);
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}
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Argument arg;
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arg.value = batchValue;
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activation_->forward(arg).check();
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}
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}
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batchValue_->copyBackSeq(*output_.value);
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}
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void MKLPackedRecurrentLayer::backwardBatch(int batchSize,
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size_t numSequences,
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const int* starts) {
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if (!batchGrad_) {
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batchGrad_.reset(new SequenceToBatch(useGpu_));
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}
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batchGrad_->shareIndexWith(*batchValue_);
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size_t numBatch = batchGrad_->getNumBatch();
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bool backwardByBatch = numBatch < numSequences;
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batchGrad_->copyFromSeq(*output_.grad);
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{
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REGISTER_TIMER_INFO("RecurrentBwData", getName().c_str());
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/* backward one batch */
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for (int n = (int)numBatch - 1; n >= 0; n--) {
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MatrixPtr batchGrad = batchGrad_->getBatchValue(n);
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MatrixPtr batchValue =
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batchValue_->getBatchValue(n, batchGrad->getHeight());
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Argument arg;
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arg.value = batchValue;
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arg.grad = batchGrad;
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activation_->backward(arg).check();
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if (n != 0) {
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batchValue = batchGrad_->getBatchValue(n - 1, batchGrad->getHeight());
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packed_weightT_->gemm_compute(batchGrad, batchValue);
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}
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if (backwardByBatch && weight_->getWGrad()) {
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if (n != 0) {
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/* backward weight */
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batchValue =
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batchValue_->getBatchValue(n - 1, batchGrad->getHeight());
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weight_->getWGrad()->mul(
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*batchValue->getTranspose(), *batchGrad, 1, 1);
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}
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}
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}
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}
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batchGrad_->copyBackSeq(*output_.grad);
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if (!backwardByBatch && weight_->getWGrad()) {
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REGISTER_TIMER_INFO("RecurrentBwWeight", getName().c_str());
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for (size_t seq = 0; seq < numSequences; ++seq) {
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int len = starts[seq + 1] - starts[seq];
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weight_->getWGrad()->mul(
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*output_.value
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->subMatrix(reversed_ ? starts[seq] + 1 : starts[seq], len - 1)
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->getTranspose(),
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*output_.grad->subMatrix(reversed_ ? starts[seq] : starts[seq] + 1,
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len - 1),
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1,
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1);
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}
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}
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}
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} // namespace paddle
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