Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into op_transpose
xzl
8 years ago
commit
35967e8658
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@ -1,124 +0,0 @@
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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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|
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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
|
||||
distributed under the License is distributed on an "AS IS" BASIS,
|
||||
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
|
||||
limitations under the License. */
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#include "ExpandConvBaseLayer.h"
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#include "paddle/utils/Logging.h"
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namespace paddle {
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bool ExpandConvBaseLayer::init(const LayerMap &layerMap,
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const ParameterMap ¶meterMap) {
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/* Initialize the basic convolutional parent class */
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ConvBaseLayer::init(layerMap, parameterMap);
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int index = 0;
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for (auto &inputConfig : config_.inputs()) {
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const ConvConfig &conf = inputConfig.conv_conf();
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/* Consistent caffe mode for multiple input */
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caffeMode_ = conf.caffe_mode();
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// create a new weight
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size_t height, width;
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height = filterPixels_[index] * filterChannels_[index];
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width = (!isDeconv_) ? numFilters_ : channels_[index];
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CHECK_EQ(parameters_[index]->getSize(), width * height);
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Weight *w = new Weight(height, width, parameters_[index]);
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weights_.emplace_back(w);
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index++;
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}
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if (biasParameter_.get()) {
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if (sharedBiases_) {
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CHECK_EQ((size_t)numFilters_, biasParameter_->getSize());
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biases_ =
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std::unique_ptr<Weight>(new Weight(numFilters_, 1, biasParameter_));
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} else {
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biases_ =
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std::unique_ptr<Weight>(new Weight(getSize(), 1, biasParameter_));
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}
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}
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getOutputSize();
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return true;
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}
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size_t ExpandConvBaseLayer::getOutputSize() {
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CHECK_NE(inputLayers_.size(), 0UL);
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size_t layerSize = ConvBaseLayer::calOutputSize();
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return layerSize;
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}
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void ExpandConvBaseLayer::addSharedBias() {
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size_t mapW = getOutputSize() / numFilters_;
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size_t mapH = getOutputValue()->getElementCnt() / mapW;
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MatrixPtr out =
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Matrix::create(getOutputValue()->getData(), mapH, mapW, false, useGpu_);
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Matrix::resizeOrCreate(transOutValue_, mapW, mapH, false, useGpu_);
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out->transpose(transOutValue_, false); // false means no memory allocation
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transOutValue_->reshape(transOutValue_->getElementCnt() / numFilters_,
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numFilters_);
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MatrixPtr bias = Matrix::create(biases_->getW()->getData(),
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1,
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biases_->getW()->getElementCnt(),
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false,
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useGpu_);
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transOutValue_->addBias(*bias, 1.0f);
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transOutValue_->reshape(mapW, mapH);
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transOutValue_->transpose(out, false); // false means no memory allocation
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out->clear();
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bias->clear();
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}
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void ExpandConvBaseLayer::addUnsharedBias() {
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MatrixPtr outValue = getOutputValue();
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MatrixPtr bias = Matrix::create(biases_->getW()->getData(),
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1,
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biases_->getW()->getElementCnt(),
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false,
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useGpu_);
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outValue->addBias(*bias, 1.0f);
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}
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void ExpandConvBaseLayer::bpropSharedBias(MatrixPtr biases, MatrixPtr v) {
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size_t mapW = getOutputSize() / numFilters_;
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size_t mapH = v->getElementCnt() / mapW;
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MatrixPtr vTmp = Matrix::create(v->getData(), mapH, mapW, false, useGpu_);
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Matrix::resizeOrCreate(transOutValue_, mapW, mapH, false, useGpu_);
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vTmp->transpose(transOutValue_, false); // false means no memory allocation
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transOutValue_->reshape(transOutValue_->getElementCnt() / numFilters_,
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numFilters_);
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biases->collectBias(*transOutValue_, 1.0f);
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}
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void ExpandConvBaseLayer::bpropBiases(MatrixPtr v) {
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MatrixPtr biases = Matrix::create(biases_->getWGrad()->getData(),
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1,
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biases_->getWGrad()->getElementCnt(),
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false,
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useGpu_);
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if (sharedBiases_) {
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bpropSharedBias(biases, v);
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} else {
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biases->collectBias(*v, 1.0f);
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}
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biases->clear();
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}
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} // namespace paddle
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@ -1,57 +0,0 @@
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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|
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Licensed under the Apache License, Version 2.0 (the "License");
|
||||
you may not use this file except in compliance with the License.
|
||||
You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software
|
||||
distributed under the License is distributed on an "AS IS" BASIS,
|
||||
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
See the License for the specific language governing permissions and
|
||||
limitations under the License. */
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#pragma once
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#include <vector>
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#include "ConvBaseLayer.h"
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#include "paddle/math/Matrix.h"
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namespace paddle {
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/**
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* @brief A subclass of ConvBaseLayer that is a superclass of both
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* ExpandConvLayer and ExpandConvTransLayer
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*/
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class ExpandConvBaseLayer : public ConvBaseLayer {
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protected:
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/// The transpose of output, which is an auxiliary matrix.
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MatrixPtr transOutValue_;
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public:
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explicit ExpandConvBaseLayer(const LayerConfig& config)
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: ConvBaseLayer(config) {}
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~ExpandConvBaseLayer() {}
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bool init(const LayerMap& layerMap,
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const ParameterMap& parameterMap) override;
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size_t getOutputSize();
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/**
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* Add shared bias.
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*/
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void addSharedBias();
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/**
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* Add unshared bias.
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*/
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void addUnsharedBias();
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void bpropSharedBias(MatrixPtr biases, MatrixPtr v);
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void bpropBiases(MatrixPtr v);
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};
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} // namespace paddle
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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
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
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|
||||
Unless required by applicable law or agreed to in writing, software
|
||||
distributed under the License is distributed on an "AS IS" BASIS,
|
||||
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
See the License for the specific language governing permissions and
|
||||
limitations under the License. */
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#if defined(__ARM_NEON__) || defined(__ARM_NEON)
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#include "NEONFunctions.h"
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#include <arm_neon.h>
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namespace paddle {
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namespace neon {
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// b[i] = a[i] > 0.0f ? a[i] : 0.0f
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void relu(const float* a, float* b, int len) {
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int offset = len % 16;
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float32x4_t ma0, ma1, ma2, ma3;
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float32x4_t mb0, mb1, mb2, mb3;
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float32x4_t zero = vdupq_n_f32(0.f);
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for (int k = 0; k < len / 16; k++, a += 16, b += 16) {
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ma0 = vld1q_f32(a);
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ma1 = vld1q_f32(a + 4);
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ma2 = vld1q_f32(a + 8);
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ma3 = vld1q_f32(a + 12);
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mb0 = vmaxq_f32(ma0, zero);
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mb1 = vmaxq_f32(ma1, zero);
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mb2 = vmaxq_f32(ma2, zero);
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mb3 = vmaxq_f32(ma3, zero);
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vst1q_f32(b, mb0);
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vst1q_f32(b + 4, mb1);
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vst1q_f32(b + 8, mb2);
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vst1q_f32(b + 12, mb3);
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}
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for (int i = 0; i < offset; i++) {
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b[i] = a[i] > 0.0f ? a[i] : 0.0f;
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}
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}
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} // namespace neon
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} // namespace paddle
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#endif
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@ -0,0 +1,23 @@
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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|
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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.
|
||||
You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software
|
||||
distributed under the License is distributed on an "AS IS" BASIS,
|
||||
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
See the License for the specific language governing permissions and
|
||||
limitations under the License. */
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#pragma once
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namespace paddle {
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namespace neon {
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void relu(const float* a, float* b, int len);
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} // namespace neon
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} // namespace paddle
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@ -0,0 +1,229 @@
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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|
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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.
|
||||
You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software
|
||||
distributed under the License is distributed on an "AS IS" BASIS,
|
||||
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
|
||||
limitations under the License. */
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#include "paddle/operators/cond_op.h"
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#include <cstring>
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#include <sstream>
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#include "paddle/framework/op_registry.h"
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#include "paddle/operators/gather.h"
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#include "paddle/operators/net_op.h"
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#include "paddle/operators/scatter.h"
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namespace paddle {
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namespace operators {
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using Scope = framework::Scope;
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using Variable = framework::Variable;
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using Tensor = framework::Tensor;
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using LoDTensor = framework::LoDTensor;
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using DDim = framework::DDim;
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void CondOp::CreateScope(const Scope& scope) const {
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auto sub_scopes_var = scope.FindVar("SubScopes");
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PADDLE_ENFORCE_NOT_NULL(sub_scopes_var,
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"Output(SubScopes) of CondOp should not be null.");
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auto sub_scopes = sub_scopes_var->GetMutable<std::vector<Scope*>>();
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auto& sub_scope = scope.NewScope();
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sub_scopes->push_back(&sub_scope);
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}
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void CondOp::CreateIndexTensor(const Scope& scope) const {
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auto index_tensors_var = scope.FindVar("IndexTensors");
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PADDLE_ENFORCE_NOT_NULL(index_tensors_var,
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"Output(IndexTensors) of CondOp should not be null.");
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auto& index_tensors =
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*index_tensors_var->GetMutable<std::vector<LoDTensor>>();
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index_tensors.push_back(LoDTensor());
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}
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void CondOp::InferShape(const Scope& scope) const {
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auto sub_scopes_var = scope.FindVar("SubScopes");
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PADDLE_ENFORCE_NOT_NULL(sub_scopes_var,
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"Output(SubScopes) of CondOp should not be null.");
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auto& sub_scopes = *sub_scopes_var->GetMutable<std::vector<Scope*>>();
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for (int i = 0; i < 2; ++i) {
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// Create two sub scopes for true and false branches
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// sub_scopes[0] for the true branch and sub_scopes[1] for the false
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// branch
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CreateScope(scope);
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// Create two tensors for true and false indices
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// index_tensors[0] for the true branch and index_tensors[1] for the false
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// branch
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CreateIndexTensor(scope);
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PADDLE_ENFORCE(!Inputs("Xs").empty(),
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"Inputs(Xs) of CondOp can't be empty.");
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for (auto& input : Inputs("Xs")) {
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// Create a new tensor in sub-scope for input-type tensor
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Variable* v = sub_scopes[i]->NewVar(input);
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LoDTensor* sub_input = v->GetMutable<LoDTensor>();
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sub_input->Resize(scope.FindVar(input)->GetMutable<LoDTensor>()->dims());
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}
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for (auto& output : (*sub_net_op_[i]).Outputs()) {
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for (auto& var_name : output.second) {
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sub_scopes[i]->NewVar(var_name);
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}
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}
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// each net calls InferShape
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sub_net_op_[i]->InferShape(*sub_scopes[i]);
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}
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for (auto& output : Outputs("Outs")) {
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LoDTensor* tensor_t_out =
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sub_scopes[0]->FindVar(output)->GetMutable<LoDTensor>();
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PADDLE_ENFORCE_NOT_NULL(tensor_t_out, "True output should not be NULL");
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LoDTensor* tensor_f_out =
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sub_scopes[1]->FindVar(output)->GetMutable<LoDTensor>();
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PADDLE_ENFORCE_NOT_NULL(tensor_f_out, "False output should not be NULL");
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auto* tensor_out_var = scope.FindVar(output);
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PADDLE_ENFORCE_NOT_NULL(tensor_out_var, "Output not found");
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LoDTensor* tensor_out = tensor_out_var->GetMutable<LoDTensor>();
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PADDLE_ENFORCE_NOT_NULL(tensor_t_out,
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"True output tensor should not be NULL");
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// check output size should be same
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PADDLE_ENFORCE_EQ(tensor_t_out->dims(), tensor_f_out->dims(),
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"Outputs not of the same shape");
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tensor_out->Resize(tensor_t_out->dims());
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// tensor_out->mutable_data<float>(tensor_out->dims(),
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// platform::CPUPlace());
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tensor_out->mutable_data<float>(platform::CPUPlace());
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}
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}
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|
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void CondOp::Run(const Scope& scope,
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const platform::DeviceContext& dev_ctx) const {
|
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auto* sub_scopes_var = scope.FindVar("SubScopes");
|
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PADDLE_ENFORCE_NOT_NULL(sub_scopes_var,
|
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"Output(SubScopes) of CondOp should not be null.");
|
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auto sub_scopes = sub_scopes_var->Get<std::vector<Scope*>>();
|
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auto* index_tensors_var = scope.FindVar("IndexTensors");
|
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PADDLE_ENFORCE_NOT_NULL(index_tensors_var,
|
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"Output(IndexTensors) of CondOp should not be null.");
|
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auto index_tensors = index_tensors_var->Get<std::vector<LoDTensor>>();
|
||||
|
||||
std::string cond_name = Input("Cond");
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Variable* cond_var = scope.FindVar(cond_name);
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PADDLE_ENFORCE_NOT_NULL(cond_var,
|
||||
"Input(Cond) of CondOp should not be null.");
|
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const LoDTensor* cond = cond_var->GetMutable<LoDTensor>();
|
||||
|
||||
// Step 1: get the true/false index at runtime
|
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// index_[0]: vector<int>, contains all index for cond[i] == true
|
||||
// index_[1]: vector<int>, contains all index for cond[i] == false
|
||||
for (int i = 0; i < 2; ++i) index_[i].clear();
|
||||
|
||||
const int* cond_data = cond->data<int>();
|
||||
for (int i = 0; i < cond->dims()[0]; ++i) {
|
||||
if (cond_data[i])
|
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index_[0].push_back(i);
|
||||
else
|
||||
index_[1].push_back(i);
|
||||
}
|
||||
|
||||
// put index_[0] and index_[1] into two tensors:
|
||||
// index_tensor_[0] and index_tensor_[1]
|
||||
DDim dim = paddle::framework::make_ddim({0});
|
||||
for (int i = 0; i < 2; ++i) {
|
||||
dim[0] = index_[i].size();
|
||||
int* tmp_ptr =
|
||||
index_tensors[i].mutable_data<int>(dim, platform::CPUPlace());
|
||||
index_tensors[i].Resize(dim);
|
||||
memcpy(tmp_ptr, index_[i].data(), dim[0] * sizeof(int));
|
||||
}
|
||||
|
||||
// Step 2: collect data by calling gather
|
||||
for (int i = 0; i < 2; ++i) {
|
||||
// i= 0/i for True and False branches respectively
|
||||
for (auto& input : Inputs("Xs")) {
|
||||
// find Tensor
|
||||
Variable* v = scope.FindVar(input);
|
||||
PADDLE_ENFORCE_NOT_NULL(v);
|
||||
LoDTensor* tensor_parent = v->GetMutable<LoDTensor>();
|
||||
|
||||
v = sub_scopes[i]->FindVar(input);
|
||||
PADDLE_ENFORCE_NOT_NULL(v);
|
||||
LoDTensor* tensor_child = v->GetMutable<LoDTensor>();
|
||||
|
||||
// Resize child
|
||||
DDim dim = tensor_child->dims();
|
||||
dim[0] = index_[i].size();
|
||||
tensor_child->Resize(dim);
|
||||
tensor_child->mutable_data<float>(dim, platform::CPUPlace());
|
||||
|
||||
Gather<float>(dev_ctx.GetPlace(), tensor_parent, &index_tensors[i],
|
||||
tensor_child);
|
||||
}
|
||||
}
|
||||
|
||||
// Step 3: run
|
||||
for (int i = 0; i < 2; ++i) {
|
||||
sub_net_op_[i]->Run(*sub_scopes[i], dev_ctx);
|
||||
}
|
||||
|
||||
// Step 4: merge output results
|
||||
PADDLE_ENFORCE(!Outputs("Outs").empty(),
|
||||
"Outputs(Outs) of CondOp can't be empty.");
|
||||
for (int i = 0; i < 2; ++i) {
|
||||
// i= 0/i for True and False branches respectively
|
||||
for (auto& output : Outputs("Outs")) {
|
||||
// find Tensor
|
||||
Variable* v = scope.FindVar(output);
|
||||
PADDLE_ENFORCE_NOT_NULL(v);
|
||||
LoDTensor* tensor_parent = v->GetMutable<LoDTensor>();
|
||||
|
||||
v = sub_scopes[i]->FindVar(output);
|
||||
PADDLE_ENFORCE_NOT_NULL(v);
|
||||
LoDTensor* tensor_child = v->GetMutable<LoDTensor>();
|
||||
|
||||
ScatterUpdate<float>(dev_ctx.GetPlace(), tensor_child, &index_tensors[i],
|
||||
tensor_parent);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
class CondOpProtoAndCheckerMaker : public framework::OpProtoAndCheckerMaker {
|
||||
public:
|
||||
CondOpProtoAndCheckerMaker(framework::OpProto* proto,
|
||||
framework::OpAttrChecker* op_checker)
|
||||
: OpProtoAndCheckerMaker(proto, op_checker) {
|
||||
AddInput("Cond", "The condition, which is a bool vector");
|
||||
AddInput("Xs", "Inputs of Subnets").AsDuplicable();
|
||||
AddOutput("Outs", "Outputs of Cond_Op after merge").AsDuplicable();
|
||||
|
||||
AddOutput("SubScopes", "sub scopes for true and false branches");
|
||||
AddOutput("IndexTensors", "Index Tensors contains indices for true/false");
|
||||
|
||||
AddComment(R"DOC(
|
||||
Sample dependent Cond Operator:
|
||||
Given Cond[i] as a 1/0 vector to indicate true/false
|
||||
The equation is:
|
||||
Out[i] = subnet_t[i], if Cond[i] == true
|
||||
Out[i] = subnet_t[i], if Cond[i] == false
|
||||
)DOC");
|
||||
}
|
||||
};
|
||||
|
||||
} // namespace operators
|
||||
} // namespace paddle
|
||||
|
||||
REGISTER_OP_WITHOUT_GRADIENT(cond, paddle::operators::CondOp,
|
||||
paddle::operators::CondOpProtoAndCheckerMaker);
|
||||
@ -0,0 +1,91 @@
|
||||
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License");
|
||||
you may not use this file except in compliance with the License.
|
||||
You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software
|
||||
distributed under the License is distributed on an "AS IS" BASIS,
|
||||
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
See the License for the specific language governing permissions and
|
||||
limitations under the License. */
|
||||
|
||||
#pragma once
|
||||
#include <vector>
|
||||
#include "glog/logging.h"
|
||||
#include "paddle/framework/ddim.h"
|
||||
#include "paddle/framework/eigen.h"
|
||||
#include "paddle/framework/operator.h"
|
||||
#include "paddle/framework/tensor.h"
|
||||
#include "paddle/operators/net_op.h"
|
||||
|
||||
namespace paddle {
|
||||
namespace operators {
|
||||
|
||||
/*
|
||||
* @brief CondOp is a dynamic if-else Operator
|
||||
*
|
||||
* It has a input tensor named cond indicating which netop each instance will
|
||||
* run.
|
||||
*
|
||||
* if cond == 1, it will run true_net, which is a NetOp.
|
||||
*
|
||||
* if cond == 0, it will run false_net, which is another NetOp.
|
||||
*/
|
||||
class CondOp : public framework::OperatorBase {
|
||||
public:
|
||||
CondOp(const std::string& type, const framework::VariableNameMap& inputs,
|
||||
const framework::VariableNameMap& outputs,
|
||||
const framework::AttributeMap& attrs)
|
||||
: OperatorBase(type, inputs, outputs, attrs) {
|
||||
index_.resize(2);
|
||||
sub_net_op_.resize(2);
|
||||
}
|
||||
|
||||
CondOp(const CondOp& o)
|
||||
: framework::OperatorBase(
|
||||
static_cast<const framework::OperatorBase&>(o)) {
|
||||
// TODO(yuyang18): Implement copy ctor well.
|
||||
PADDLE_THROW("Not implemented");
|
||||
}
|
||||
|
||||
void CreateScope(const framework::Scope& scope) const;
|
||||
|
||||
void CreateIndexTensor(const framework::Scope& scope) const;
|
||||
|
||||
/*
|
||||
* InferShape must be called before Run.
|
||||
*/
|
||||
void InferShape(const framework::Scope& scope) const override;
|
||||
|
||||
/*
|
||||
* Set True Block
|
||||
*/
|
||||
void set_truenet(std::unique_ptr<OperatorBase>&& net) {
|
||||
sub_net_op_[0] = std::move(net);
|
||||
}
|
||||
|
||||
/*
|
||||
* Set False Block
|
||||
*/
|
||||
void set_falsenet(std::unique_ptr<OperatorBase>&& net) {
|
||||
sub_net_op_[1] = std::move(net);
|
||||
}
|
||||
|
||||
void Run(const framework::Scope& scope,
|
||||
const platform::DeviceContext& dev_ctx) const override;
|
||||
|
||||
private:
|
||||
// sub_net_op_[0]: subnet_t
|
||||
// sub_net_op_[1]: subnet_f
|
||||
std::vector<std::unique_ptr<framework::OperatorBase>> sub_net_op_;
|
||||
|
||||
// index_[0]: True_index;
|
||||
// index_[1]: False_index;
|
||||
mutable std::vector<std::vector<int>> index_;
|
||||
};
|
||||
|
||||
} // namespace operators
|
||||
} // namespace paddle
|
||||
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Reference in new issue