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提交 384368f4 编写于 作者: Z zchen0211
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Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into develop

上级 490ca5f1 7bcb1fc3
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Showing with 596 addition and 361 deletion
paddle/gserver/layers/ExpandConvBaseLayer.cpp 已删除 100644 → 0
浏览文件 @ 490ca5f1
/* 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. */
#include "ExpandConvBaseLayer.h"
#include "paddle/utils/Logging.h"
namespace paddle {
bool ExpandConvBaseLayer::init(const LayerMap &layerMap,
const ParameterMap &parameterMap) {
/* Initialize the basic convolutional parent class */
ConvBaseLayer::init(layerMap, parameterMap);
int index = 0;
for (auto &inputConfig : config_.inputs()) {
const ConvConfig &conf = inputConfig.conv_conf();
/* Consistent caffe mode for multiple input */
caffeMode_ = conf.caffe_mode();
// create a new weight
size_t height, width;
height = filterPixels_[index] * filterChannels_[index];
width = (!isDeconv_) ? numFilters_ : channels_[index];
CHECK_EQ(parameters_[index]->getSize(), width * height);
Weight *w = new Weight(height, width, parameters_[index]);
weights_.emplace_back(w);
index++;
}
if (biasParameter_.get()) {
if (sharedBiases_) {
CHECK_EQ((size_t)numFilters_, biasParameter_->getSize());
biases_ =
std::unique_ptr<Weight>(new Weight(numFilters_, 1, biasParameter_));
} else {
biases_ =
std::unique_ptr<Weight>(new Weight(getSize(), 1, biasParameter_));
}
}
getOutputSize();
return true;
}
size_t ExpandConvBaseLayer::getOutputSize() {
CHECK_NE(inputLayers_.size(), 0UL);
size_t layerSize = ConvBaseLayer::calOutputSize();
return layerSize;
}
void ExpandConvBaseLayer::addSharedBias() {
size_t mapW = getOutputSize() / numFilters_;
size_t mapH = getOutputValue()->getElementCnt() / mapW;
MatrixPtr out =
Matrix::create(getOutputValue()->getData(), mapH, mapW, false, useGpu_);
Matrix::resizeOrCreate(transOutValue_, mapW, mapH, false, useGpu_);
out->transpose(transOutValue_, false); // false means no memory allocation
transOutValue_->reshape(transOutValue_->getElementCnt() / numFilters_,
numFilters_);
MatrixPtr bias = Matrix::create(biases_->getW()->getData(),
1,
biases_->getW()->getElementCnt(),
false,
useGpu_);
transOutValue_->addBias(*bias, 1.0f);
transOutValue_->reshape(mapW, mapH);
transOutValue_->transpose(out, false); // false means no memory allocation
out->clear();
bias->clear();
}
void ExpandConvBaseLayer::addUnsharedBias() {
MatrixPtr outValue = getOutputValue();
MatrixPtr bias = Matrix::create(biases_->getW()->getData(),
1,
biases_->getW()->getElementCnt(),
false,
useGpu_);
outValue->addBias(*bias, 1.0f);
}
void ExpandConvBaseLayer::bpropSharedBias(MatrixPtr biases, MatrixPtr v) {
size_t mapW = getOutputSize() / numFilters_;
size_t mapH = v->getElementCnt() / mapW;
MatrixPtr vTmp = Matrix::create(v->getData(), mapH, mapW, false, useGpu_);
Matrix::resizeOrCreate(transOutValue_, mapW, mapH, false, useGpu_);
vTmp->transpose(transOutValue_, false); // false means no memory allocation
transOutValue_->reshape(transOutValue_->getElementCnt() / numFilters_,
numFilters_);
biases->collectBias(*transOutValue_, 1.0f);
}
void ExpandConvBaseLayer::bpropBiases(MatrixPtr v) {
MatrixPtr biases = Matrix::create(biases_->getWGrad()->getData(),
1,
biases_->getWGrad()->getElementCnt(),
false,
useGpu_);
if (sharedBiases_) {
bpropSharedBias(biases, v);
} else {
biases->collectBias(*v, 1.0f);
}
biases->clear();
}
} // namespace paddle
paddle/gserver/layers/ExpandConvBaseLayer.h 已删除 100644 → 0
浏览文件 @ 490ca5f1
/* 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 "ConvBaseLayer.h"
#include "paddle/math/Matrix.h"
namespace paddle {
/**
* @brief A subclass of ConvBaseLayer that is a superclass of both
* ExpandConvLayer and ExpandConvTransLayer
*/
class ExpandConvBaseLayer : public ConvBaseLayer {
protected:
/// The transpose of output, which is an auxiliary matrix.
MatrixPtr transOutValue_;
public:
explicit ExpandConvBaseLayer(const LayerConfig& config)
: ConvBaseLayer(config) {}
~ExpandConvBaseLayer() {}
bool init(const LayerMap& layerMap,
const ParameterMap& parameterMap) override;
size_t getOutputSize();
/**
* Add shared bias.
*/
void addSharedBias();
/**
* Add unshared bias.
*/
void addUnsharedBias();
void bpropSharedBias(MatrixPtr biases, MatrixPtr v);
void bpropBiases(MatrixPtr v);
};
} // namespace paddle
paddle/gserver/layers/ExpandConvLayer.cpp
浏览文件 @ 384368f4
......@@ -36,7 +36,36 @@ inline bool isDepthwiseConv(int channels, int groups) {
bool ExpandConvLayer::init(const LayerMap &layerMap,
const ParameterMap &parameterMap) {
/* Initialize the basic convolutional parent class */
ExpandConvBaseLayer::init(layerMap, parameterMap);
ConvBaseLayer::init(layerMap, parameterMap);
int index = 0;
for (auto &inputConfig : config_.inputs()) {
const ConvConfig &conf = inputConfig.conv_conf();
/* Consistent caffe mode for multiple input */
caffeMode_ = conf.caffe_mode();
// create a new weight
size_t height, width;
height = filterPixels_[index] * filterChannels_[index];
width = (!isDeconv_) ? numFilters_ : channels_[index];
CHECK_EQ(parameters_[index]->getSize(), width * height);
Weight *w = new Weight(height, width, parameters_[index]);
weights_.emplace_back(w);
index++;
}
if (biasParameter_.get()) {
if (sharedBiases_) {
CHECK_EQ((size_t)numFilters_, biasParameter_->getSize());
biases_ = std::unique_ptr<Weight>(
new Weight(1, numFilters_, biasParameter_, 0));
} else {
biases_ =
std::unique_ptr<Weight>(new Weight(1, getSize(), biasParameter_, 0));
}
}
getOutputSize();
size_t numInputs = config_.inputs_size();
inputShape_.resize(numInputs);
......@@ -108,6 +137,12 @@ bool ExpandConvLayer::init(const LayerMap &layerMap,
return true;
}
size_t ExpandConvLayer::getOutputSize() {
CHECK_NE(inputLayers_.size(), 0UL);
size_t layerSize = ConvBaseLayer::calOutputSize();
return layerSize;
}
// i is the index of input layers
#define BACKWARD_INPUT(i, inputs, outputs) \
backward_[2 * i]->calc(inputs, outputs)
......@@ -155,11 +190,7 @@ void ExpandConvLayer::forward(PassType passType) {
/* add the bias-vector */
if (biases_.get()) {
if (sharedBiases_) {
addSharedBias();
} else {
addUnsharedBias();
}
output_.value->addBias(*biases_->getW(), 1.0, sharedBiases_);
}
/* activation */
......@@ -171,7 +202,7 @@ void ExpandConvLayer::backward(const UpdateCallback &callback) {
MatrixPtr outGrad = getOutputGrad();
if (biases_ && biases_->getWGrad()) {
bpropBiases(outGrad);
biases_->getWGrad()->collectBias(*getOutputGrad(), 1, sharedBiases_);
/* Increasing the number of gradient */
biases_->getParameterPtr()->incUpdate(callback);
}
......
paddle/gserver/layers/ExpandConvLayer.h
浏览文件 @ 384368f4
......@@ -15,7 +15,7 @@ limitations under the License. */
#pragma once
#include <vector>
#include "ExpandConvBaseLayer.h"
#include "ConvBaseLayer.h"
#include "paddle/math/Matrix.h"
namespace paddle {
......@@ -28,10 +28,9 @@ namespace paddle {
* The config file api is img_conv_layer.
*/
class ExpandConvLayer : public ExpandConvBaseLayer {
class ExpandConvLayer : public ConvBaseLayer {
public:
explicit ExpandConvLayer(const LayerConfig& config)
: ExpandConvBaseLayer(config) {}
explicit ExpandConvLayer(const LayerConfig& config) : ConvBaseLayer(config) {}
~ExpandConvLayer() {}
......@@ -41,6 +40,8 @@ public:
void forward(PassType passType) override;
void backward(const UpdateCallback& callback) override;
size_t getOutputSize();
protected:
std::vector<TensorShape> inputShape_;
std::vector<TensorShape> filterShape_;
......
paddle/gserver/layers/MKLDNNConvLayer.cpp
浏览文件 @ 384368f4
......@@ -285,10 +285,9 @@ void MKLDNNConvLayer::resetWgtBiasValue(
wgt = MKLDNNMatrix::create(weight_->getW(), pd->weights_primitive_desc());
VLOG(MKLDNN_FMTS) << "Weight value format: " << wgt->getFormat();
bias = nullptr;
if (biases_ && biases_->getW()) {
bias = MKLDNNMatrix::create(biases_->getW(), pd->bias_primitive_desc());
}
bias = (biases_ && biases_->getW())
? MKLDNNMatrix::create(biases_->getW(), pd->bias_primitive_desc())
: nullptr;
}
void MKLDNNConvLayer::resetOutValue(
......@@ -356,6 +355,7 @@ void MKLDNNConvLayer::resetBwdWgtPD(
void MKLDNNConvLayer::resetBwdDataPD(
std::shared_ptr<conv_bwdData::primitive_desc>& pd) {
pd = nullptr;
if (inputLayers_[0]->getOutput().grad == nullptr) {
return;
}
......@@ -476,6 +476,7 @@ void MKLDNNConvLayer::resetWgtBiasGrad(
<< "primitive desc of weight grad and value should be equal";
VLOG(MKLDNN_FMTS) << "weight grad format: " << wgt->getFormat();
bias = nullptr;
if (biasVal_ == nullptr) {
return;
}
......
paddle/gserver/layers/MKLDNNFcLayer.cpp
浏览文件 @ 384368f4
......@@ -17,9 +17,6 @@ limitations under the License. */
using namespace mkldnn; // NOLINT
typedef memory::format format;
typedef inner_product_forward fc_fwd;
typedef inner_product_backward_weights fc_bwdWgt;
typedef inner_product_backward_data fc_bwdData;
namespace paddle {
......@@ -93,35 +90,88 @@ void MKLDNNFcLayer::reshape(
printSizeInfo();
}
void MKLDNNFcLayer::resetFwd(std::vector<mkldnn::primitive>& pipeline,
void MKLDNNFcLayer::resetFwd(std::vector<primitive>& pipeline,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) {
pipeline.clear();
bool hasBias = biases_ && biases_->getW();
const MatrixPtr& wgtVal = weight_->getW();
const MatrixPtr& biasVal = hasBias ? biases_->getW() : nullptr;
const MatrixPtr& outVal = output_.value;
resetFwdBuffers(in, wgt, bias, out);
resetFwdPD(fwdPD_, in, wgt, bias, out);
resetFwdPipeline(pipeline, fwdPD_, in, wgt, bias, out);
printValueFormatFlow();
}
void MKLDNNFcLayer::resetBwd(std::vector<primitive>& pipeline,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) {
std::shared_ptr<fc_bwdWgt::primitive_desc> bwdWgtPD;
std::shared_ptr<fc_bwdData::primitive_desc> bwdDataPD;
resetBwdBuffers(in, wgt, bias, out);
resetBwdWgtPD(bwdWgtPD, wgt, bias, out);
resetBwdDataPD(bwdDataPD, in, out);
resetBwdPipeline(pipeline, bwdWgtPD, bwdDataPD, in, wgt, bias, out);
printGradFormatFlow();
}
void MKLDNNFcLayer::updateInputData() {
inVal_->setData(getInputValue(0, CPU_DEVICE)->getData());
}
void MKLDNNFcLayer::updateWeights(const UpdateCallback& callback) {
weight_->getParameterPtr()->incUpdate(callback);
if (biases_ && biases_->getWGrad()) {
biases_->getParameterPtr()->incUpdate(callback);
}
}
void MKLDNNFcLayer::resetFwdBuffers(MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) {
resetInValue(in);
resetWgtBiasValue(wgt, bias);
resetOutValue(out);
}
void MKLDNNFcLayer::resetInValue(MKLDNNMatrixPtr& in) {
if (inputIsOnlyMKLDNN()) {
const MatrixPtr& inVal = getInputValue(0);
in = std::dynamic_pointer_cast<MKLDNNMatrix>(inVal);
const MatrixPtr& dnnIn = getInputValue(0);
in = std::dynamic_pointer_cast<MKLDNNMatrix>(dnnIn);
CHECK(in) << "Input should be MKLDNNMatrix";
} else {
CHECK_EQ(getPrev(0)->getDeviceId(), CPU_DEVICE) << "Only support CPU yet";
const MatrixPtr& inVal = getInputValue(0, CPU_DEVICE);
const MatrixPtr& cpuIn = getInputValue(0, CPU_DEVICE);
in = MKLDNNMatrix::create(
inVal, memory::dims{bs_, ic_, ih_, iw_}, format::nchw, engine_);
cpuIn, {bs_, ic_, ih_, iw_}, format::nchw, engine_);
}
in->downSpatial();
}
void MKLDNNFcLayer::resetWgtBiasValue(MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias) {
wgt = MKLDNNMatrix::create(
wgtVal, memory::dims{oc_, ic_, ih_, iw_}, format::oihw, engine_);
weight_->getW(), {oc_, ic_, ih_, iw_}, format::oihw, engine_);
wgt->downSpatial();
bias = hasBias ? MKLDNNMatrix::create(biasVal, {oc_}, format::x, engine_)
: nullptr;
out = MKLDNNMatrix::create(outVal, {bs_, oc_}, format::nc, engine_);
bias = (biases_ && biases_->getW())
? MKLDNNMatrix::create(biases_->getW(), {oc_}, format::x, engine_)
: nullptr;
}
void MKLDNNFcLayer::resetOutValue(MKLDNNMatrixPtr& out) {
out = MKLDNNMatrix::create(output_.value, {bs_, oc_}, format::nc, engine_);
// change original output value to mkldnn output value
output_.value = std::dynamic_pointer_cast<Matrix>(out);
if (!outputIsOnlyMKLDNN()) {
......@@ -129,46 +179,59 @@ void MKLDNNFcLayer::resetFwd(std::vector<mkldnn::primitive>& pipeline,
// just share point
getOutput(CPU_DEVICE).value->setData(output_.value->getData());
}
}
// create forward handle
void MKLDNNFcLayer::resetFwdPD(std::shared_ptr<fc_fwd::primitive_desc>& pd,
MKLDNNMatrixPtr in,
MKLDNNMatrixPtr wgt,
MKLDNNMatrixPtr bias,
MKLDNNMatrixPtr out) {
CHECK(in);
CHECK(wgt);
CHECK(out);
prop_kind pk = prop_kind::forward;
fc_fwd::desc fwdDesc = hasBias ? fc_fwd::desc(pk,
in->getMemoryDesc(),
wgt->getMemoryDesc(),
bias->getMemoryDesc(),
out->getMemoryDesc())
: fc_fwd::desc(pk,
in->getMemoryDesc(),
wgt->getMemoryDesc(),
out->getMemoryDesc());
fc_fwd::primitive_desc fwdPD = fc_fwd::primitive_desc(fwdDesc, engine_);
if (hasBias) {
fwd_.reset(new fc_fwd(fwdPD, *in, *wgt, *bias, *out));
fc_fwd::desc fwdDesc = bias != nullptr ? fc_fwd::desc(pk,
in->getMemoryDesc(),
wgt->getMemoryDesc(),
bias->getMemoryDesc(),
out->getMemoryDesc())
: fc_fwd::desc(pk,
in->getMemoryDesc(),
wgt->getMemoryDesc(),
out->getMemoryDesc());
pd.reset(new fc_fwd::primitive_desc(fwdDesc, engine_));
}
void MKLDNNFcLayer::resetFwdPipeline(
std::vector<primitive>& pipeline,
std::shared_ptr<fc_fwd::primitive_desc>& pd,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) {
pipeline.clear();
if (bias) {
fwd_.reset(new fc_fwd(*pd, *in, *wgt, *bias, *out));
} else {
fwd_.reset(new fc_fwd(fwdPD, *in, *wgt, *out));
fwd_.reset(new fc_fwd(*pd, *in, *wgt, *out));
}
printValueFormatFlow();
pipeline.push_back(*fwd_);
}
void MKLDNNFcLayer::resetBwd(std::vector<mkldnn::primitive>& pipeline,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) {
pipeline.clear();
if (!needResetBwd_) {
return;
}
needResetBwd_ = false;
bool hasBias = biases_ && biases_->getWGrad();
void MKLDNNFcLayer::resetBwdBuffers(MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) {
resetOutGrad(out);
resetWgtBiasGrad(wgt, bias);
/// backward weight
CHECK(inVal_) << "Should have input value";
const MatrixPtr& wgtGrad = weight_->getWGrad();
const MatrixPtr& biasGrad = hasBias ? biases_->getWGrad() : nullptr;
resetInGrad(in);
}
void MKLDNNFcLayer::resetOutGrad(MKLDNNMatrixPtr& out) {
// TODO(TJ): merge outgrad
int device = outputIsOnlyMKLDNN() ? MKLDNN_DEVICE : CPU_DEVICE;
// for MKLDNN device:
......@@ -178,66 +241,88 @@ void MKLDNNFcLayer::resetBwd(std::vector<mkldnn::primitive>& pipeline,
// for CPU device:
// fc do not need to convert from cpu device since output is always nc format
// only need create from cpu device
const MatrixPtr& outGrad = getOutput(device).grad;
out = MKLDNNMatrix::create(outGrad, outVal_->getPrimitiveDesc());
wgt = MKLDNNMatrix::create(wgtGrad, wgtVal_->getPrimitiveDesc());
bias = hasBias ? MKLDNNMatrix::create(biasGrad, biasVal_->getPrimitiveDesc())
: nullptr;
// create memory primitive desc
fc_fwd::desc fwdDesc = fc_fwd::desc(prop_kind::forward,
inVal_->getMemoryDesc(),
wgt->getMemoryDesc(),
out->getMemoryDesc());
fc_fwd::primitive_desc fwdPD = fc_fwd::primitive_desc(fwdDesc, engine_);
fc_bwdWgt::desc bwdWgtDesc = hasBias
? fc_bwdWgt::desc(inVal_->getMemoryDesc(),
wgt->getMemoryDesc(),
bias->getMemoryDesc(),
out->getMemoryDesc())
: fc_bwdWgt::desc(inVal_->getMemoryDesc(),
wgt->getMemoryDesc(),
out->getMemoryDesc());
fc_bwdWgt::primitive_desc bwdWgtPD =
fc_bwdWgt::primitive_desc(bwdWgtDesc, engine_, fwdPD);
if (hasBias) {
bwdWgt_.reset(new fc_bwdWgt(bwdWgtPD, *inVal_, *out, *wgt, *bias));
} else {
bwdWgt_.reset(new fc_bwdWgt(bwdWgtPD, *inVal_, *out, *wgt));
CHECK(outVal_);
out =
MKLDNNMatrix::create(getOutput(device).grad, outVal_->getPrimitiveDesc());
}
void MKLDNNFcLayer::resetWgtBiasGrad(MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias) {
CHECK(wgtVal_);
wgt = MKLDNNMatrix::create(weight_->getWGrad(), wgtVal_->getPrimitiveDesc());
bias = nullptr;
if (biasVal_ == nullptr) {
return;
}
pipeline.push_back(*bwdWgt_);
bias =
MKLDNNMatrix::create(biases_->getWGrad(), biasVal_->getPrimitiveDesc());
}
/// backward data
void MKLDNNFcLayer::resetInGrad(MKLDNNMatrixPtr& in) {
in = nullptr;
const MatrixPtr& inGrad = inputLayers_[0]->getOutput().grad;
if (inGrad == nullptr) {
return;
}
if (getInput(0, MKLDNN_DEVICE).getAllCount() > 1) {
// TODO(TJ): use outputMaps_ ways to get the inGrad_ when merge outgrad done
} else {
in = MKLDNNMatrix::create(inGrad, inVal_->getPrimitiveDesc());
}
fc_bwdData::desc bwdDataDesc = fc_bwdData::desc(
inVal_->getMemoryDesc(), wgt->getMemoryDesc(), out->getMemoryDesc());
fc_bwdData::primitive_desc bwdDataPD =
fc_bwdData::primitive_desc(bwdDataDesc, engine_, fwdPD);
// TODO(TJ): use outputMaps_ ways to get the inGrad_ when merge outgrad done
CHECK(inVal_);
in = MKLDNNMatrix::create(inGrad, inVal_->getPrimitiveDesc());
}
CHECK(wgtVal_) << "Should have weight memory";
bwdData_.reset(new fc_bwdData(bwdDataPD, *out, *wgtVal_, *in));
printGradFormatFlow();
pipeline.push_back(*bwdData_);
void MKLDNNFcLayer::resetBwdWgtPD(
std::shared_ptr<fc_bwdWgt::primitive_desc>& pd,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) {
CHECK(inVal_);
fc_bwdWgt::desc bwdWgtDesc = bias ? fc_bwdWgt::desc(inVal_->getMemoryDesc(),
wgt->getMemoryDesc(),
bias->getMemoryDesc(),
out->getMemoryDesc())
: fc_bwdWgt::desc(inVal_->getMemoryDesc(),
wgt->getMemoryDesc(),
out->getMemoryDesc());
pd.reset(new fc_bwdWgt::primitive_desc(bwdWgtDesc, engine_, *fwdPD_));
}
void MKLDNNFcLayer::updateInputData() {
inVal_->setData(getInputValue(0, CPU_DEVICE)->getData());
void MKLDNNFcLayer::resetBwdDataPD(
std::shared_ptr<fc_bwdData::primitive_desc>& pd,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& out) {
pd = nullptr;
if (in == nullptr) {
return;
}
CHECK(wgtVal_);
fc_bwdData::desc bwdDataDesc = fc_bwdData::desc(
in->getMemoryDesc(), wgtVal_->getMemoryDesc(), out->getMemoryDesc());
pd.reset(new fc_bwdData::primitive_desc(bwdDataDesc, engine_, *fwdPD_));
}
void MKLDNNFcLayer::updateWeights(const UpdateCallback& callback) {
weight_->getParameterPtr()->incUpdate(callback);
if (biases_ && biases_->getWGrad()) {
biases_->getParameterPtr()->incUpdate(callback);
void MKLDNNFcLayer::resetBwdPipeline(
std::vector<primitive>& pipeline,
std::shared_ptr<fc_bwdWgt::primitive_desc>& bwdWgtPD,
std::shared_ptr<fc_bwdData::primitive_desc>& bwdDataPD,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) {
pipeline.clear();
CHECK(inVal_);
if (bias) {
bwdWgt_.reset(new fc_bwdWgt(*bwdWgtPD, *inVal_, *out, *wgt, *bias));
} else {
bwdWgt_.reset(new fc_bwdWgt(*bwdWgtPD, *inVal_, *out, *wgt));
}
pipeline.push_back(*bwdWgt_);
if (bwdDataPD == nullptr) {
return;
}
CHECK(wgtVal_) << "Should have weight memory";
bwdData_.reset(new fc_bwdData(*bwdDataPD, *out, *wgtVal_, *in));
pipeline.push_back(*bwdData_);
}
} // namespace paddle
paddle/gserver/layers/MKLDNNFcLayer.h
浏览文件 @ 384368f4
......@@ -18,6 +18,9 @@ limitations under the License. */
#include "mkldnn.hpp"
namespace paddle {
typedef mkldnn::inner_product_forward fc_fwd;
typedef mkldnn::inner_product_backward_weights fc_bwdWgt;
typedef mkldnn::inner_product_backward_data fc_bwdData;
/**
* @brief A subclass of MKLDNNLayer fc layer.
......@@ -32,6 +35,9 @@ protected:
// if has already init the weight
bool hasInitedWgt_;
// save forward primitive_desc, which can be used backward
std::shared_ptr<fc_fwd::primitive_desc> fwdPD_;
// fc weight and bias
std::unique_ptr<Weight> weight_;
std::unique_ptr<Weight> biases_;
......@@ -67,6 +73,59 @@ public:
void convertWeightsFromPaddle() override;
void convertWeightsToPaddle() override;
protected:
/**
* Forward functions: reset buffers(input, output, weight and bias),
* reset primitive descriptor,
* reset pipeline.
*/
void resetFwdBuffers(MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out);
void resetInValue(MKLDNNMatrixPtr& in);
void resetWgtBiasValue(MKLDNNMatrixPtr& wgt, MKLDNNMatrixPtr& bias);
void resetOutValue(MKLDNNMatrixPtr& out);
void resetFwdPD(std::shared_ptr<fc_fwd::primitive_desc>& pd,
MKLDNNMatrixPtr in,
MKLDNNMatrixPtr wgt,
MKLDNNMatrixPtr bias,
MKLDNNMatrixPtr out);
void resetFwdPipeline(std::vector<mkldnn::primitive>& pipeline,
std::shared_ptr<fc_fwd::primitive_desc>& pd,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out);
/**
* Backward functions: reset buffers(input, output, weight and bias),
* reset primitive descriptor for backward weight,
* reset primitive descriptor for backward data,
* reset pipeline.
*/
void resetBwdBuffers(MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out);
void resetOutGrad(MKLDNNMatrixPtr& out);
void resetWgtBiasGrad(MKLDNNMatrixPtr& wgt, MKLDNNMatrixPtr& bias);
void resetInGrad(MKLDNNMatrixPtr& in);
void resetBwdWgtPD(std::shared_ptr<fc_bwdWgt::primitive_desc>& pd,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out);
void resetBwdDataPD(std::shared_ptr<fc_bwdData::primitive_desc>& pd,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& out);
void resetBwdPipeline(std::vector<mkldnn::primitive>& pipeline,
std::shared_ptr<fc_bwdWgt::primitive_desc>& bwdWgtPD,
std::shared_ptr<fc_bwdData::primitive_desc>& bwdDataPD,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out);
};
} // namespace paddle
paddle/math/MKLDNNMatrix.h
浏览文件 @ 384368f4
......@@ -66,11 +66,12 @@ public:
/**
* Create reorder primitive.
* Create a mkldnn::reorder handle for converting src MKLDNNMatrix to dst.
* checkData: for whether to check the data handle of src and dst is the same.
* if true, means check it and do not want support inplace reorder;
* otherwise do not check data which means the created reorder
* maybe inplace buffer and do not guarantee the logical is correct
* since not all format or conversion support inplace.
* checkData: whether to check the data handle of src and dst.
* if true, it will check the data and do not allow them equal;
* otherwise, it will not check them, then the reorder created
* may have inplace buffer.
* Do not set false, if you can not guarantee the inplace logical
* would work with your reorder.
*/
static std::shared_ptr<mkldnn::reorder> createReorder(
const MKLDNNMatrixPtr& src,
......
paddle/operators/accuracy_op.cc
浏览文件 @ 384368f4
......@@ -23,10 +23,15 @@ class AccuracyOp : public framework::OperatorWithKernel {
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Inference"),
"Input of Inference must be initialized.");
PADDLE_ENFORCE_NOT_NULL(
ctx.InputVar("Inference"),
"Input(Inference) of AccuracyOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Label"),
"Input of Inference must be initialized.");
"Input(Label) of AccuracyOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(
ctx.OutputVar("Accuracy"),
"Output(Accuracy) of AccuracyOp should not be null.");
auto *inference = ctx.Input<framework::Tensor>("Inference");
auto *label = ctx.Input<framework::Tensor>("Label");
......
paddle/operators/add_op.cc
浏览文件 @ 384368f4
......@@ -23,6 +23,13 @@ class AddOp : public framework::OperatorWithKernel {
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"),
"Input(X) of AddOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Y"),
"Input(Y) of AddOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Out"),
"Output(Out) of AddOp should not be null.");
PADDLE_ENFORCE_EQ(ctx.Input<Tensor>("X")->dims(),
ctx.Input<Tensor>("Y")->dims(),
"Two input of Add Op's dimension must be same.");
......
paddle/operators/concat_op.cc
浏览文件 @ 384368f4
......@@ -25,6 +25,9 @@ class ConcatOp : public framework::OperatorWithKernel {
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Out"),
"Output(Out) of ConcatOp should not be null.");
auto ins = ctx.MultiInput<framework::Tensor>("X");
auto *out = ctx.Output<framework::LoDTensor>("Out");
size_t axis = static_cast<size_t>(ctx.Attr<int>("axis"));
......
paddle/operators/cond_op.cc
浏览文件 @ 384368f4
......@@ -33,7 +33,8 @@ using DDim = framework::DDim;
void CondOp::CreateScope(const Scope& scope) const {
auto sub_scopes_var = scope.FindVar("SubScopes");
PADDLE_ENFORCE(sub_scopes_var != nullptr, "");
PADDLE_ENFORCE_NOT_NULL(sub_scopes_var,
"Output(SubScopes) of CondOp should not be null.");
auto sub_scopes = sub_scopes_var->GetMutable<std::vector<Scope*>>();
auto& sub_scope = scope.NewScope();
sub_scopes->push_back(&sub_scope);
......@@ -41,7 +42,8 @@ void CondOp::CreateScope(const Scope& scope) const {
void CondOp::CreateIndexTensor(const Scope& scope) const {
auto index_tensors_var = scope.FindVar("IndexTensors");
PADDLE_ENFORCE(index_tensors_var != nullptr, "");
PADDLE_ENFORCE_NOT_NULL(index_tensors_var,
"Output(IndexTensors) of CondOp should not be null.");
auto& index_tensors =
*index_tensors_var->GetMutable<std::vector<LoDTensor>>();
index_tensors.push_back(LoDTensor());
......@@ -49,7 +51,8 @@ void CondOp::CreateIndexTensor(const Scope& scope) const {
void CondOp::InferShape(const Scope& scope) const {
auto sub_scopes_var = scope.FindVar("SubScopes");
PADDLE_ENFORCE_NOT_NULL(sub_scopes_var);
PADDLE_ENFORCE_NOT_NULL(sub_scopes_var,
"Output(SubScopes) of CondOp should not be null.");
auto& sub_scopes = *sub_scopes_var->GetMutable<std::vector<Scope*>>();
for (int i = 0; i < 2; ++i) {
......@@ -63,7 +66,8 @@ void CondOp::InferShape(const Scope& scope) const {
// branch
CreateIndexTensor(scope);
PADDLE_ENFORCE(!Inputs("Xs").empty(), "Inputs can't be empty");
PADDLE_ENFORCE(!Inputs("Xs").empty(),
"Inputs(Xs) of CondOp can't be empty.");
for (auto& input : Inputs("Xs")) {
// Create a new tensor in sub-scope for input-type tensor
Variable* v = sub_scopes[i]->NewVar(input);
......@@ -108,13 +112,18 @@ void CondOp::InferShape(const Scope& scope) const {
void CondOp::Run(const Scope& scope,
const platform::DeviceContext& dev_ctx) const {
auto* sub_scopes_var = scope.FindVar("SubScopes");
PADDLE_ENFORCE_NOT_NULL(sub_scopes_var,
"Output(SubScopes) of CondOp should not be null.");
auto sub_scopes = sub_scopes_var->Get<std::vector<Scope*>>();
auto* index_tensors_var = scope.FindVar("IndexTensors");
PADDLE_ENFORCE_NOT_NULL(index_tensors_var,
"Output(IndexTensors) of CondOp should not be null.");
auto index_tensors = index_tensors_var->Get<std::vector<LoDTensor>>();
std::string cond_name = Input("Cond");
Variable* cond_var = scope.FindVar(cond_name);
PADDLE_ENFORCE_NOT_NULL(cond_var);
PADDLE_ENFORCE_NOT_NULL(cond_var,
"Input(Cond) of CondOp should not be null.");
const LoDTensor* cond = cond_var->GetMutable<LoDTensor>();
// Step 1: get the true/false index at runtime
......@@ -171,6 +180,8 @@ void CondOp::Run(const Scope& scope,
}
// 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")) {
......
paddle/operators/cos_sim_op.cc
浏览文件 @ 384368f4
......@@ -26,8 +26,16 @@ class CosSimOp : public framework::OperatorWithKernel {
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
// notnull check
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) must not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Y"), "Input(Y) must not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"),
"Input(X) of CosSimOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Y"),
"Input(Y) of CosSimOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Out"),
"Output(Out) of CosSimOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("XNorm"),
"Output(XNorm) of CosSimOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("YNorm"),
"Output(YNorm) of CosSimOp should not be null.");
// shape check
auto x_dims = ctx.Input<Tensor>("X")->dims();
......
paddle/operators/elementwise_mul_op.cc
浏览文件 @ 384368f4
......@@ -25,8 +25,14 @@ class ElementWiseMulOp : public framework::OperatorWithKernel {
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) should not be null");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Y"), "Input(Y) should not be null");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"),
"Input(X) of ElementWiseMulOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Y"),
"Input(Y) of ElementWiseMulOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(
ctx.OutputVar("Out"),
"Output(Out) of ElementWiseMulOp should not be null.");
auto x_dim = ctx.Input<Tensor>("X")->dims();
auto y_dim = ctx.Input<Tensor>("Y")->dims();
PADDLE_ENFORCE_GE(x_dim.size(), y_dim.size(),
......
paddle/operators/elementwise_mul_op.h
浏览文件 @ 384368f4
......@@ -13,10 +13,8 @@
limitations under the License. */
#pragma once
#include <iostream>
#include "paddle/framework/eigen.h"
#include "paddle/framework/op_registry.h"
#include "paddle/operators/math/math_function.h"
namespace paddle {
namespace operators {
......
paddle/operators/fill_zeros_like_op.cc
浏览文件 @ 384368f4
......@@ -23,6 +23,13 @@ class FillZerosLikeOp : public framework::OperatorWithKernel {
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(
ctx.InputVar("Src"),
"Input(Src) of FillZerosLikeOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(
ctx.OutputVar("Dst"),
"Output(Dst) of FillZerosLikeOp should not be null.");
ctx.Output<framework::LoDTensor>("Dst")->Resize(
ctx.Input<framework::Tensor>("Src")->dims());
}
......
paddle/operators/gather_op.cc
浏览文件 @ 384368f4
......@@ -24,6 +24,13 @@ class GatherOp : public framework::OperatorWithKernel {
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"),
"Input(X) of GatherOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Index"),
"Input(Index) of GatherOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Out"),
"Output(Out) of GatherOp should not be null.");
int batch_size = ctx.Input<Tensor>("Index")->dims()[0];
PADDLE_ENFORCE_GE(batch_size, 0, "Batch size must be >0");
framework::DDim output_dims(ctx.Input<Tensor>("X")->dims());
......
paddle/operators/gaussian_random_op.cc
浏览文件 @ 384368f4
......@@ -43,8 +43,12 @@ class GaussianRandomOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext& context) const override {
auto* tensor = context.Output<framework::LoDTensor>("Out");
void InferShape(const framework::InferShapeContext& ctx) const override {
PADDLE_ENFORCE_NOT_NULL(
ctx.OutputVar("Out"),
"Output(Out) of GaussianRandomOp should not be null.");
auto* tensor = ctx.Output<framework::LoDTensor>("Out");
auto dims = Attr<std::vector<int>>("dims");
std::vector<int64_t> temp;
temp.reserve(dims.size());
......
paddle/operators/identity_op.cc
浏览文件 @ 384368f4
......@@ -42,6 +42,11 @@ class IdentityOp : public NetOp {
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: NetOp(type, inputs, outputs, attrs) {
PADDLE_ENFORCE_NE(Input("X"), framework::kEmptyVarName,
"Input(X) of IdentityOp should not be null.");
PADDLE_ENFORCE_NE(Output("Out"), framework::kEmptyVarName,
"Output(Out) of IdentityOp should not be null.");
AppendOp(framework::OpRegistry::CreateOp(
"scale", {{"X", {Input("X")}}}, {{"Out", {Output("Out")}}},
{{"scale", static_cast<AttrType>(1)}}));
......
paddle/operators/lookup_table_op.cc
浏览文件 @ 384368f4
......@@ -22,10 +22,17 @@ class LookupTableOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &context) const override {
auto table_t = context.Input<Tensor>("W");
auto ids_t = context.Input<Tensor>("Ids");
auto output_t = context.Output<framework::LoDTensor>("Out");
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("W"),
"Input(W) of LookupTableOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Ids"),
"Input(Ids) of LookupTableOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Out"),
"Output(Out) of LookupTableOp should not be null.");
auto table_t = ctx.Input<Tensor>("W");
auto ids_t = ctx.Input<Tensor>("Ids");
auto output_t = ctx.Output<framework::LoDTensor>("Out");
output_t->Resize({ids_t->dims()[0], table_t->dims()[1]});
}
......
paddle/operators/mean_op.cc
浏览文件 @ 384368f4
......@@ -24,7 +24,9 @@ class MeanOp : public framework::OperatorWithKernel {
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"),
"Input of MeanOp must be initialized.");
"Input(X) of MeanOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Out"),
"Output(Out) of MeanOp should not be null.");
ctx.Output<framework::LoDTensor>("Out")->Resize({1});
}
};
......
paddle/operators/minus_op.cc
浏览文件 @ 384368f4
......@@ -27,6 +27,13 @@ class MinusOp : public framework::OperatorWithKernel {
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"),
"Input(X) of MinusOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Y"),
"Input(Y) of MinusOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Out"),
"Output(Out) of MinusOp should not be null.");
auto *left_tensor = ctx.Input<framework::Tensor>("X");
auto *right_tensor = ctx.Input<framework::Tensor>("Y");
......@@ -77,8 +84,6 @@ class MinusGradOp : public NetOp {
} // namespace operators
} // namespace paddle
USE_OP(scale);
USE_NO_KERNEL_OP(identity);
namespace ops = paddle::operators;
REGISTER_OP(minus, ops::MinusOp, ops::MinusOpMaker, minus_grad,
ops::MinusGradOp<float>);
......
paddle/operators/mul_op.cc
浏览文件 @ 384368f4
......@@ -26,6 +26,13 @@ class MulOp : public framework::OperatorWithKernel {
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"),
"Input(X) of MulOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Y"),
"Input(Y) of MulOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Out"),
"Output(Out) of MulOp should not be null.");
auto x_dims = ctx.Input<Tensor>("X")->dims();
auto y_dims = ctx.Input<Tensor>("Y")->dims();
int x_num_col_dims = Attr<int>("x_num_col_dims");
......
paddle/operators/onehot_cross_entropy_op.cc
浏览文件 @ 384368f4
......@@ -23,6 +23,16 @@ class OnehotCrossEntropyOp : public framework::OperatorWithKernel {
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(
ctx.InputVar("X"),
"Input(X) of OnehotCrossEntropyOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(
ctx.InputVar("label"),
"Input(label) of OnehotCrossEntropyOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(
ctx.OutputVar("Y"),
"Output(Y) of OnehotCrossEntropyOp should not be null.");
auto *X = ctx.Input<Tensor>("X");
auto *label = ctx.Input<Tensor>("label");
......
paddle/operators/pad_op.cc
浏览文件 @ 384368f4
......@@ -25,6 +25,11 @@ class PadOp : public framework::OperatorWithKernel {
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"),
"Input(X) of PadOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Out"),
"Output(Out) of PadOp should not be null.");
auto x_dim = ctx.Input<Tensor>("X")->dims();
auto paddings = Attr<std::vector<int>>("paddings");
PADDLE_ENFORCE_EQ(x_dim.size() * 2, int64_t(paddings.size()),
......
paddle/operators/reshape_op.cc
浏览文件 @ 384368f4
......@@ -28,7 +28,11 @@ class ReshapeOp : public framework::OperatorWithKernel {
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
// input check
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) shouldn't be null");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"),
"Input(X) of ReshapeOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Out"),
"Output(Out) of ReshapeOp should not be null.");
auto shape = ctx.Attr<std::vector<int>>("shape");
PADDLE_ENFORCE(shape.size() > 0, "Attr(shape) shouldn't be empty.");
for (auto dim : shape) {
......
paddle/operators/rowwise_add_op.cc
浏览文件 @ 384368f4
......@@ -25,6 +25,13 @@ class RowwiseAddOp : public framework::OperatorWithKernel {
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"),
"Input(X) of RowwiseAddOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("b"),
"Input(b) of RowwiseAddOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Out"),
"Output(Out) of RowwiseAddOp should not be null.");
auto x_dims = ctx.Input<Tensor>("X")->dims();
auto b_dims = ctx.Input<Tensor>("b")->dims();
PADDLE_ENFORCE_GT(
......
paddle/operators/scale_op.cc
浏览文件 @ 384368f4
......@@ -27,6 +27,11 @@ class ScaleOp : public framework::OperatorWithKernel {
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"),
"Input(X) of ScaleOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Out"),
"Output(Out) of ScaleOp should not be null.");
auto *in = ctx.Input<framework::Tensor>("X");
auto *out = ctx.Output<framework::LoDTensor>("Out");
out->Resize(in->dims());
......
paddle/operators/scatter_op.cc
浏览文件 @ 384368f4
......@@ -24,6 +24,15 @@ class ScatterOp : public framework::OperatorWithKernel {
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Ref"),
"Input(Ref) of ScatterOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Index"),
"Input(Index) of ScatterOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Updates"),
"Input(Updates) of ScatterOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Out"),
"Output(Out) of ScatterOp should not be null.");
PADDLE_ENFORCE_EQ(ctx.Input<Tensor>("Index")->dims().size(), 1,
"Update Index should be 1-D.");
PADDLE_ENFORCE_EQ(ctx.Input<Tensor>("Ref")->dims().size(),
......
paddle/operators/sequence_avg_pool_op.cc
浏览文件 @ 384368f4
......@@ -23,9 +23,12 @@ class SequenceAvgPoolOp : public framework::OperatorWithKernel {
protected:
void InferShape(const framework::InferShapeContext& ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"),
"Input of SequenceAvgPoolOp"
"must be initialized.");
PADDLE_ENFORCE_NOT_NULL(
ctx.InputVar("X"), "Input(X) of SequenceAvgPoolOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(
ctx.OutputVar("Out"),
"Output(Out) of SequenceAvgPoolOp should not be null.");
auto* x = ctx.Input<framework::LoDTensor>("X");
auto dims = x->dims();
auto lod = x->lod();
......@@ -60,7 +63,9 @@ class SequenceAvgPoolGradOp : public framework::OperatorWithKernel {
protected:
void InferShape(const framework::InferShapeContext& ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
"Gradient of Out should not be null");
"Gradient of Out should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"),
"The input X should not be null.");
auto og_dims =
ctx.Input<framework::LoDTensor>(framework::GradVarName("Out"))->dims();
auto x_dims = ctx.Input<framework::LoDTensor>("X")->dims();
......
paddle/operators/sequence_avg_pool_op.h
浏览文件 @ 384368f4
......@@ -21,6 +21,9 @@ namespace operators {
using Tensor = framework::Tensor;
using LoDTensor = framework::LoDTensor;
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
......@@ -43,8 +46,8 @@ class SequenceAvgPoolKernel : public framework::OpKernel {
static_cast<int>(lod[0][i + 1]));
Tensor out_t = out->Slice<T>(i, i + 1);
int64_t h = static_cast<int64_t>(lod[0][i + 1] - lod[0][i]);
auto in_e = EigenMatrix<T>::From(in_t, {h, w});
auto out_e = EigenMatrix<T>::From(out_t, {h, w});
auto in_e = EigenMatrix<T>::From(in_t, framework::make_ddim({h, w}));
auto out_e = EigenVector<T>::Flatten(out_t);
out_e.device(place) = in_e.mean(Eigen::array<int, 1>({{0}}));
}
}
......@@ -54,9 +57,9 @@ template <typename Place, typename T>
class SequenceAvgPoolGradKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto* in = context.Output<LoDTensor>("X");
auto* in_g = context.Output<LoDTensor>(framework::GradVarName("X"));
auto* in = context.Input<LoDTensor>("X");
auto* out_g = context.Input<LoDTensor>(framework::GradVarName("Out"));
auto* in_g = context.Output<LoDTensor>(framework::GradVarName("X"));
auto dims = in->dims();
auto lod = in->lod();
......@@ -71,7 +74,7 @@ class SequenceAvgPoolGradKernel : public framework::OpKernel {
int64_t h = static_cast<int64_t>(lod[0][i + 1] - lod[0][i]);
auto in_g_e = EigenMatrix<T>::From(in_g_t, {h, w});
auto out_g_e = EigenMatrix<T>::From(out_g_t, {1, w});
Eigen::DSizes<int, 2> bcast(h, w);
Eigen::DSizes<int, 2> bcast(h, 1);
in_g_e.device(place) = (out_g_e / static_cast<T>(h)).broadcast(bcast);
}
}
......
paddle/operators/sgd_op.cc
浏览文件 @ 384368f4
......@@ -23,6 +23,13 @@ class SGDOp : public framework::OperatorWithKernel {
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("param"),
"Input(param) of SGDOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("grad"),
"Input(grad) of SGDOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("param_out"),
"Output(param_out) of SGDOp should not be null.");
PADDLE_ENFORCE_EQ(ctx.Input<Tensor>("param")->dims(),
ctx.Input<Tensor>("grad")->dims(),
"Two input of SGD Op's dimension must be same.");
......
paddle/operators/sigmoid_op.cc
浏览文件 @ 384368f4
......@@ -23,6 +23,11 @@ class SigmoidOp : public framework::OperatorWithKernel {
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"),
"Input(X) of SigmoidOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Y"),
"Output(Y) of SigmoidOp should not be null.");
ctx.Output<framework::LoDTensor>("Y")->Resize(
ctx.Input<Tensor>("X")->dims());
}
......
paddle/operators/softmax_op.cc
浏览文件 @ 384368f4
......@@ -23,6 +23,11 @@ class SoftmaxOp : public framework::OperatorWithKernel {
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"),
"Input(X) of SoftmaxOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Y"),
"Output(Y) of SoftmaxOp should not be null.");
PADDLE_ENFORCE(ctx.Input<Tensor>("X")->dims().size() == 2UL,
"The input of softmax op must be a matrix.");
ctx.Output<framework::LoDTensor>("Y")->Resize(
......
paddle/operators/squared_l2_distance_op.cc
浏览文件 @ 384368f4
......@@ -23,12 +23,18 @@ class SquaredL2DistanceOp : public framework::OperatorWithKernel {
protected:
void InferShape(const framework::InferShapeContext& ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"),
"Input of SquaredL2DistanceOp "
"must be initialized.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Y"),
"Target of SquaredL2DistanceOp "
"must be initialized.");
PADDLE_ENFORCE_NOT_NULL(
ctx.InputVar("X"),
"Input(X) of SquaredL2DistanceOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(
ctx.InputVar("Y"),
"Input(Y) of SquaredL2DistanceOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(
ctx.OutputVar("sub_result"),
"Output(sub_result) of SquaredL2DistanceOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(
ctx.OutputVar("Out"),
"Output(Out) of SquaredL2DistanceOp should not be null.");
auto* x = ctx.Input<Tensor>("X");
auto x_dims = x->dims();
......
paddle/operators/sum_op.cc
浏览文件 @ 384368f4
......@@ -22,6 +22,11 @@ class SumOp : public framework::OperatorWithKernel {
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE(!ctx.MultiInputVar("X").empty(),
"Input(X) of SumOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Out"),
"Output(Out) of SumOp should not be null.");
auto ins = ctx.MultiInput<framework::Tensor>("X");
auto *out = ctx.Output<framework::LoDTensor>("Out");
int N = ins.size();
......
paddle/operators/top_k_op.cc
浏览文件 @ 384368f4
......@@ -24,7 +24,12 @@ class TopkOp : public framework::OperatorWithKernel {
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"),
"Input of TopkOP must be initialized.");
"Input(X) of TopkOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Out"),
"Output(Out) of TopkOp should not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Indices"),
"Output(Indices) of TopkOp should not be null.");
auto *input = ctx.Input<framework::Tensor>("X");
const int k = static_cast<int>(ctx.Attr<int>("k"));
......
paddle/operators/uniform_random_op.cc
浏览文件 @ 384368f4
......@@ -48,6 +48,10 @@ class UniformRandomOp : public framework::OperatorWithKernel {
protected:
void InferShape(const framework::InferShapeContext& ctx) const override {
PADDLE_ENFORCE_NOT_NULL(
ctx.OutputVar("Out"),
"Output(Out) of UniformRandomOp should not be null.");
PADDLE_ENFORCE(Attr<float>("min") < Attr<float>("max"),
"uniform_random's min must less then max");
auto* tensor = ctx.Output<framework::LoDTensor>("Out");
......
python/paddle/v2/framework/tests/op_test.py
浏览文件 @ 384368f4
......@@ -47,17 +47,24 @@ def set_input(scope, op, inputs, place):
if in_name in inputs:
if in_dup:
sub_in = inputs[in_name]
for sub_in_name, sub_in_array in sub_in:
for sub_in_name, sub_in_val in sub_in:
var = scope.find_var(sub_in_name)
tensor = var.get_tensor()
sub_in_array = sub_in_val[0] \
if isinstance(sub_in_val, tuple) else sub_in_val
tensor.set_dims(sub_in_array.shape)
tensor.set(sub_in_array, place)
if isinstance(sub_in_val, tuple):
tensor.set_lod(sub_in_val[1])
else:
var = scope.find_var(in_name)
tensor = var.get_tensor()
arr = inputs[in_name]
tensor.set_dims(arr.shape)
tensor.set(arr, place)
in_val = inputs[in_name]
in_array = in_val[0] if isinstance(in_val, tuple) else in_val
tensor.set_dims(in_array.shape)
tensor.set(in_array, place)
if isinstance(in_val, tuple):
tensor.set_lod(in_val[1])
def set_output_grad(scope, op, outputs, place):
......
python/paddle/v2/framework/tests/test_gaussian_random_op.py
浏览文件 @ 384368f4
......@@ -4,7 +4,7 @@ from paddle.v2.framework.op import Operator
import numpy
class GaussianRandomTest(unittest.TestCase):
class TestGaussianRandomOp(unittest.TestCase):
def test_cpu(self):
self.gaussian_random_test(place=core.CPUPlace())
......
python/paddle/v2/framework/tests/test_identity_op.py 0 → 100644
浏览文件 @ 384368f4
import unittest
import numpy as np
from op_test import OpTest
class TestIdentityOp(OpTest):
def setUp(self):
self.op_type = "identity"
self.inputs = {'X': np.random.random((10, 10)).astype("float32")}
self.outputs = {'Out': self.inputs['X']}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Out')
if __name__ == "__main__":
unittest.main()
python/paddle/v2/framework/tests/test_minus_op.py
浏览文件 @ 384368f4
......@@ -3,7 +3,7 @@ import numpy as np
from op_test import OpTest
class MinusOpTest(OpTest):
class TestMinusOp(OpTest):
def setUp(self):
self.op_type = "minus"
self.inputs = {
......
python/paddle/v2/framework/tests/test_cross_entropy_op.py → python/paddle/v2/framework/tests/test_onehot_cross_entropy_op.py
浏览文件 @ 384368f4
......@@ -3,7 +3,7 @@ import numpy
from op_test import OpTest
class TestCrossEntropy(OpTest):
class TestOnehotCrossEntropyOp(OpTest):
def setUp(self):
self.op_type = "onehot_cross_entropy"
batch_size = 30
......
python/paddle/v2/framework/tests/test_scale_and_identity_op.py → python/paddle/v2/framework/tests/test_scale_op.py
浏览文件 @ 384368f4
......@@ -3,20 +3,7 @@ import numpy as np
from op_test import OpTest
class IdentityTest(OpTest):
def setUp(self):
self.op_type = "identity"
self.inputs = {'X': np.random.random((10, 10)).astype("float32")}
self.outputs = {'Out': self.inputs['X']}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Out')
class ScaleTest(OpTest):
class TestScaleOp(OpTest):
def setUp(self):
self.op_type = "scale"
self.inputs = {'X': np.random.random((10, 10)).astype("float32")}
......
python/paddle/v2/framework/tests/test_seq_pool.py 0 → 100644
浏览文件 @ 384368f4
import unittest
import numpy as np
from op_test import OpTest
class TestSeqAvgPool1D(OpTest):
def setUp(self):
self.op_type = 'sequence_avg_pool'
# one level, batch size is 4
x = np.random.uniform(0.1, 1, [11, 23]).astype('float32')
lod = [[0, 4, 5, 8, 11]]
out = np.zeros((4, 23)).astype('float32')
for i in range(4):
sub_x = x[lod[0][i]:lod[0][i + 1], :]
out[i] = sub_x.mean(axis=0)
self.inputs = {'X': (x, lod)}
self.outputs = {'Out': out}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(["X"], "Out")
class TestSeqAvgPool2D(OpTest):
def setUp(self):
self.op_type = 'sequence_avg_pool'
# one level, batch size is 4
x = np.random.uniform(0.1, 1, [13, 3, 17]).astype('float32')
lod = [[0, 4, 5, 8, 13]]
out = np.zeros((4, 3, 17)).astype('float32')
for i in range(4):
sub_x = np.reshape(x[lod[0][i]:lod[0][i + 1], :], (-1, 3 * 17))
out[i] = np.reshape(sub_x.mean(axis=0), (3, 17))
self.inputs = {'X': (x, lod)}
self.outputs = {'Out': out}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(["X"], "Out")
if __name__ == '__main__':
unittest.main()
python/paddle/v2/framework/tests/test_sgd_op.py
浏览文件 @ 384368f4
......@@ -3,7 +3,7 @@ import numpy as np
from op_test import OpTest
class TestSGD(OpTest):
class TestSGDOp(OpTest):
def setUp(self):
self.op_type = "sgd"
w = np.random.random((102, 105)).astype("float32")
......
python/paddle/v2/framework/tests/test_sigmoid_op.py
浏览文件 @ 384368f4
......@@ -3,7 +3,7 @@ import numpy as np
from op_test import OpTest
class TestSigmoid(OpTest):
class TestSigmoidOp(OpTest):
def setUp(self):
self.op_type = "sigmoid"
self.inputs = {
......
python/paddle/v2/framework/tests/test_top_k_op.py
浏览文件 @ 384368f4
......@@ -21,6 +21,9 @@ class TestTopkOp(OpTest):
self.outputs = {'Out': output, 'Indices': indices}
def test_check_output(self):
self.check_output()
class TestTopkOp3d(OpTest):
def setUp(self):
......@@ -42,6 +45,9 @@ class TestTopkOp3d(OpTest):
self.outputs = {'Out': output, 'Indices': indices}
def test_check_output(self):
self.check_output()
if __name__ == "__main__":
unittest.main()
python/paddle/v2/framework/tests/test_uniform_random_op.py
浏览文件 @ 384368f4
......@@ -4,7 +4,7 @@ import paddle.v2.framework.core as core
import numpy
class UniformRandomTest(unittest.TestCase):
class TestUniformRandomOp(unittest.TestCase):
def test_uniform_random_cpu(self):
self.uniform_random_test(place=core.CPUPlace())
......
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