Merge pull request #6719 from tensor-tang/mkl_packed
enable MKL Packed Recurrent Layercross_channel_norm
Tao Luo
7 years ago
committed by
GitHub
commit
c10023006b
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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 "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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@ -0,0 +1,58 @@
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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 "MKLPackedWeight.h"
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#include "RecurrentLayer.h"
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DECLARE_bool(rnn_use_batch);
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namespace paddle {
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/**
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* @brief MKLPackedRecurrentLayer is almost the same with RecurrentLayer
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* but is optimized with MKL cblas packed gemm.
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* More details:
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* https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/mkl/mkl_packed.md
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*/
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class MKLPackedRecurrentLayer : public RecurrentLayer {
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public:
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explicit MKLPackedRecurrentLayer(const LayerConfig& config)
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: RecurrentLayer(config) {}
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bool init(const LayerMap& layerMap,
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const ParameterMap& parameterMap) override;
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void backward(const UpdateCallback& callback) override;
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protected:
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void forwardBatch(int batchSize,
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size_t numSequences,
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const int* starts) override;
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void backwardBatch(int batchSize,
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size_t numSequences,
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const int* starts) override;
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protected:
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/// packed_weight_ contains same data with
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/// RecurrentLayer::weight_ but is packed
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std::unique_ptr<MKLPackedWeight> packed_weight_;
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/// packed_weightT_ is the transposition matrix of packed_weight_
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std::unique_ptr<MKLPackedWeight> packed_weightT_;
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};
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} // namespace paddle
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@ -0,0 +1,86 @@
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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 "paddle/math/MathFunctions.h"
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#include "paddle/parameter/Parameter.h"
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#include "paddle/parameter/Weight.h"
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namespace paddle {
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class MKLPackedWeight {
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protected:
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/// The pointer of weight
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real *weight_;
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/// The pointer of cblas packed gemm to weight
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real *packedWeight_;
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size_t height_;
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size_t width_;
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bool transW_;
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public:
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explicit MKLPackedWeight(MatrixPtr weight, bool transW = false) {
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packedWeight_ = nullptr;
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weight_ = weight->getData();
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height_ = weight->getHeight();
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width_ = weight->getWidth();
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transW_ = transW;
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}
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~MKLPackedWeight() { free_(); }
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void pack() { pack_(weight_); }
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void gemm_compute(const MatrixPtr src, MatrixPtr dst) {
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cblas_sgemm_compute(CblasRowMajor,
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CblasNoTrans,
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CblasPacked,
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src->getHeight(),
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transW_ ? height_ : width_,
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transW_ ? width_ : height_,
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src->getData(),
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src->getWidth(),
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packedWeight_,
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width_,
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1.0,
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dst->getData(),
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dst->getWidth());
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}
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protected:
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void pack_(real *src) {
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if (!packedWeight_) {
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packedWeight_ = cblas_sgemm_alloc(CblasBMatrix, 1, width_, height_);
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}
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cblas_sgemm_pack(CblasRowMajor,
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CblasBMatrix,
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transW_ ? CblasTrans : CblasNoTrans,
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1,
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transW_ ? height_ : width_,
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transW_ ? width_ : height_,
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1.0,
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src,
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width_,
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packedWeight_);
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}
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void free_() {
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if (packedWeight_) {
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cblas_sgemm_free(packedWeight_);
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}
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}
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};
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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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#pragma once
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#include <gflags/gflags.h>
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#include "Layer.h"
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#include "SequenceToBatch.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 RecurrentLayer takes 1 input layer. The output size is the same with
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* input layer.
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* For each sequence [start, end] it performs the following computation:
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* \f[
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* out_{i} = act(in_{i}) \ \ \text{for} \ i = start \\
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* out_{i} = act(in_{i} + out_{i-1} * W) \ \ \text{for} \ start < i <= end
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*
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* \f]
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* If reversed is true, the order is reversed:
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* \f[
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* out_{i} = act(in_{i}) \ \ \text{for} \ i = end \\
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* out_{i} = act(in_{i} + out_{i+1} * W) \ \ \text{for} \ start <= i < end
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* \f]
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* There are two methods to calculate rnn. One way is to compute rnn one
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* sequence by one sequence. The other way is to reorganize the input
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* into batches, then compute rnn one batch by one batch. Users can select
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* them by rnn_use_batch flag.
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*/
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class RecurrentLayer : public Layer {
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public:
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explicit RecurrentLayer(const LayerConfig& config) : Layer(config) {}
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bool init(const LayerMap& layerMap,
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const ParameterMap& parameterMap) override;
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void forward(PassType passType) override;
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void backward(const UpdateCallback& callback) override;
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void resetState() override;
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void setState(LayerStatePtr state) override;
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LayerStatePtr getState() override;
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protected:
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/**
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* @brief If user do not set --rnn_use_batch=true, it will
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* compute rnn forward one sequence by one sequence in default.
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* @param batchSize Total words number of all samples in this batch.
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* @param numSequences The sample number.
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* @param starts Each start position of each samples.
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*/
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void forwardSequence(int batchSize, size_t numSequences, const int* starts);
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/**
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* @brief Compute rnn forward by one sequence.
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* @param start The start position of this sequence (or sample).
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* @param length The length of this sequence (or sample), namely the words
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* number of this sequence.
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*/
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void forwardOneSequence(int start, int length);
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/**
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* @brief Compute rnn backward one sequence by onesequence.
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* @param batchSize Total words number of all samples in this batch.
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* @param numSequences The sample number.
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* @param starts Each start position of each samples.
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*/
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void backwardSequence(int batchSize, size_t numSequences, const int* starts);
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/**
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* @brief Compute rnn backward by one sequence.
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* @param start The start position of this sequence (or sample).
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* @param length The length of this sequence (or sample), namely the words
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* number of this sequence.
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*/
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void backwardOneSequence(int start, int length);
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/**
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* @brief Reorganize input into batches and compute rnn forward batch
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* by batch. It will convert batch shape to sequence after finishing forward.
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* The batch info can refer to SequenceToBatch class.
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* @param batchSize Total words number of all samples in this batch.
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* @param numSequences The sample number.
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* @param starts Each start position of each samples.
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*/
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virtual void forwardBatch(int batchSize,
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size_t numSequences,
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const int* starts);
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/**
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* @brief Reorganize input into batches and compute rnn forward batch
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* by batch.
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* @param batchSize Total words number of all samples in this batch.
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* @param numSequences The sample number.
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* @param starts Each start position of each samples.
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*/
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virtual void backwardBatch(int batchSize,
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size_t numSequences,
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const int* starts);
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protected:
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std::unique_ptr<Weight> weight_;
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std::unique_ptr<Weight> bias_;
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/// frameOutput_[i] is used to hold the i-th sample of output_
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std::vector<Argument> frameOutput_;
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MatrixPtr prevOutput_;
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/// Whether compute rnn by reverse.
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bool reversed_;
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/// If compute batch by batch, batchValue_ will be used to save the
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/// reorganized input value.
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std::unique_ptr<SequenceToBatch> batchValue_;
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/// If compute batch by batch, batchGrad_ will be used to save the
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/// gradient with respect to reorganized input value.
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std::unique_ptr<SequenceToBatch> batchGrad_;
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};
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} // namespace paddle
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