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提交 36dd770a 编写于 作者: W wanghaox
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add roi operator unittest

上级 79609288 53bd51e3
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Showing with 2441 addition and 7522 deletion
benchmark/IntelOptimizedPaddle.md
浏览文件 @ 36dd770a
......@@ -12,11 +12,11 @@ Machine:
System: CentOS release 6.3 (Final), Docker 1.12.1.
PaddlePaddle: paddlepaddle/paddle:latest (TODO: will rerun after 0.11.0)
- MKL-DNN tag v0.10
- MKLML 2018.0.20170720
PaddlePaddle: paddlepaddle/paddle:latest (for MKLML and MKL-DNN), paddlepaddle/paddle:latest-openblas (for OpenBLAS)
- MKL-DNN tag v0.11
- MKLML 2018.0.1.20171007
- OpenBLAS v0.2.20
(TODO: will rerun after 0.11.0)
On each machine, we will test and compare the performance of training on single node using MKL-DNN / MKLML / OpenBLAS respectively.
......@@ -31,15 +31,26 @@ Input image size - 3 * 224 * 224, Time: images/second
| BatchSize | 64 | 128 | 256 |
|--------------|-------| -----| --------|
| OpenBLAS | 7.82 | 8.62 | 10.34 |
| MKLML | 11.02 | 12.86 | 15.33 |
| MKL-DNN | 27.69 | 28.8 | 29.27 |
| OpenBLAS | 7.80 | 9.00 | 10.80 |
| MKLML | 12.12 | 13.70 | 16.18 |
| MKL-DNN | 28.46 | 29.83 | 30.44 |
chart on batch size 128
TBD
- ResNet-50
| BatchSize | 64 | 128 | 256 |
|--------------|-------| ------| -------|
| OpenBLAS | 25.22 | 25.68 | 27.12 |
| MKLML | 32.52 | 31.89 | 33.12 |
| MKL-DNN | 81.69 | 82.35 | 84.08 |
chart on batch size 128
TBD
- ResNet
- GoogLeNet
### Laptop
......
paddle/gserver/activations/ActivationFunction.cpp
浏览文件 @ 36dd770a
......@@ -212,6 +212,37 @@ Error __must_check backward(Argument& act) {
}
END_DEFINE_ACTIVATION(sequence_softmax)
/*
* @brief SoftSign Activation.
* \f[
* f(z) = \frac{z}{1 + |z|}
* \f]
*/
BEGIN_DEFINE_ACTIVATION(softsign)
private:
MatrixPtr denominator_;
Error __must_check forward(Argument& act) {
size_t height = act.value->getHeight();
size_t width = act.value->getWidth();
Matrix::resizeOrCreate(
denominator_, height, width, false, useGpu(act.deviceId));
denominator_->assign(*act.value);
denominator_->abs2();
denominator_->add(1.);
act.value->dotDiv(*act.value, *denominator_);
return Error();
}
Error __must_check backward(Argument& act) {
denominator_->square2();
denominator_->scalarDiv(*denominator_, 1.);
act.grad->dotMul(*act.grad, *denominator_);
return Error();
}
END_DEFINE_ACTIVATION(softsign)
/**
* @brief Relu Activation.
* forward. y = max(0, z)
......
paddle/gserver/layers/MKLDNNAddtoLayer.cpp
浏览文件 @ 36dd770a
......@@ -38,12 +38,13 @@ bool MKLDNNAddtoLayer::init(const LayerMap& layerMap,
}
void MKLDNNAddtoLayer::reshape(
int& bs, int& ic, int& ih, int& iw, int oc, int& oh, int& ow) {
int& bs, int& ic, int& ih, int& iw, int& oc, int& oh, int& ow) {
CHECK_EQ(layerSize_, getSize()) << "this layer size can not be changed";
reshapeInput(bs, ih, iw);
ic = inputLayers_[0]->getSize() / ih / iw;
CHECK_EQ((size_t)ic * ih * iw, inputLayers_[0]->getSize());
CHECK_EQ(inputElemenCnt_, (size_t)bs * ic * ih * iw);
CHECK_EQ(inputLayers_[0]->getOutputValue()->getElementCnt(),
(size_t)bs * ic * ih * iw);
for (size_t i = 0; i < inputLayers_.size(); i++) {
CHECK_EQ(int64_t(bs), inputLayers_[i]->getOutput().getBatchSize());
CHECK_EQ(layerSize_, inputLayers_[i]->getSize());
......@@ -57,47 +58,43 @@ void MKLDNNAddtoLayer::reshape(
}
void MKLDNNAddtoLayer::resetFwd(std::vector<primitive>& pipeline,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& out) {
resetFwdBuffers(inVals_, bias, out);
in = inVals_[0];
resetFwdBuffers(inputs, biasVal_, out);
std::shared_ptr<sum::primitive_desc> fwdPD;
std::shared_ptr<sum::primitive_desc> biasPD;
resetFwdPD(fwdPD, biasPD, inVals_, bias, out);
resetFwdPD(fwdPD, biasPD, inputs, biasVal_, out);
resetFwdPipeline(pipeline, fwdPD, biasPD, inVals_, bias, out);
resetFwdPipeline(pipeline, fwdPD, biasPD, inputs, biasVal_, out);
}
void MKLDNNAddtoLayer::resetBwd(std::vector<primitive>& pipeline,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& out) {
resetBwdBuffers(inGrads_, bias, out);
in = inGrads_[0];
resetBwdBuffers(inputs, biasGrad_, out);
// backward only need share output grad to input grad
for (size_t i = 0; i < inGrads_.size(); i++) {
if (inGrads_[i] != nullptr) {
inGrads_[i] = out;
inputLayers_[i]->getOutputGrad()->setData(inGrads_[i]->getData());
for (size_t i = 0; i < inputs.size(); i++) {
if (inputs[i] != nullptr) {
inputs[i] = out;
inputLayers_[i]->getOutputGrad()->setData(inputs[i]->getData());
}
}
// backward bias
bwdBias_ = nullptr;
if (bias) {
if (biasGrad_) {
std::vector<float> scales(bs_, 1.0);
std::vector<memory::primitive_desc> srcPDs(bs_, bias->getPrimitiveDesc());
auto biasPD = sum::primitive_desc(bias->getMemoryDesc(), scales, srcPDs);
std::vector<memory::primitive_desc> srcPDs(bs_,
biasGrad_->getPrimitiveDesc());
auto biasPD =
sum::primitive_desc(biasGrad_->getMemoryDesc(), scales, srcPDs);
std::vector<primitive::at> srcs;
for (size_t i = 0; i < grads_.size(); ++i) {
srcs.push_back(*(grads_[i]));
}
bwdBias_.reset(new sum(biasPD, srcs, *bias));
bwdBias_.reset(new sum(biasPD, srcs, *biasGrad_));
pipeline.push_back(*bwdBias_);
}
}
......@@ -208,7 +205,7 @@ void MKLDNNAddtoLayer::resetBwdBuffers(std::vector<MKLDNNMatrixPtr>& inputs,
inputs.resize(inputLayers_.size());
for (size_t i = 0; i < inputs.size(); i++) {
resetInGrad(inputs[i], inVal_->getPrimitiveDesc(), i);
resetInGrad(inputs[i], inVals_[i]->getPrimitiveDesc(), i);
CHECK_PRIMITIVE_DESC_EQ(inputs[i], out->getPrimitiveDesc());
}
......
paddle/gserver/layers/MKLDNNAddtoLayer.h
浏览文件 @ 36dd770a
......@@ -26,9 +26,6 @@ namespace paddle {
*/
class MKLDNNAddtoLayer : public MKLDNNLayer {
protected:
std::vector<MKLDNNMatrixPtr> inVals_;
std::vector<MKLDNNMatrixPtr> inGrads_;
// layer size == ic * ih * iw == oc * oh *ow, and can not be changed
size_t layerSize_;
......@@ -50,52 +47,19 @@ public:
const ParameterMap& parameterMap) override;
void reshape(
int& bs, int& ic, int& ih, int& iw, int oc, int& oh, int& ow) override;
int& bs, int& ic, int& ih, int& iw, int& oc, int& oh, int& ow) override;
void resetFwd(std::vector<mkldnn::primitive>& pipeline,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& out) override;
void resetBwd(std::vector<mkldnn::primitive>& pipeline,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& out) override;
void updateWeights(const UpdateCallback& callback) override;
void printValueFormat() override {
for (size_t i = 0; i < inVals_.size(); ++i) {
VLOG(MKLDNN_FMTS) << i << " input: " << inVals_[i]->getFormat() << " >>>";
}
if (outVal_) {
VLOG(MKLDNN_FMTS) << outVal_->getFormat() << " >>> ";
}
if (extOutVal_) {
VLOG(MKLDNN_FMTS) << extOutVal_->getFormat();
}
}
void printGradFormat() override {
if (extOutGrad_) {
VLOG(MKLDNN_FMTS) << extOutGrad_->getFormat();
}
if (outGrad_) {
VLOG(MKLDNN_FMTS) << outGrad_->getFormat() << " <<< ";
}
for (size_t i = 0; i < inGrads_.size(); ++i) {
VLOG(MKLDNN_FMTS) << i << " input: " << inGrads_[i]->getFormat() << "<<<";
}
}
protected:
/**
* Forward functions: reset buffers(inputs, output, bias),
* reset primitive descriptor,
* reset pipeline.
*/
void resetFwdBuffers(std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out);
......@@ -110,17 +74,10 @@ protected:
std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out);
/**
* Backward functions: reset buffers(inputs, output, bias)
*/
void resetBwdBuffers(std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out);
/**
* prepare for bias
*/
void prepareBias(MKLDNNMatrixPtr& bias,
const MatrixPtr& biasMat,
const MKLDNNMatrixPtr& out,
......
paddle/gserver/layers/MKLDNNBatchNormLayer.cpp
浏览文件 @ 36dd770a
......@@ -116,21 +116,20 @@ void MKLDNNBatchNormLayer::calMovingMeanAndVar() {
}
void MKLDNNBatchNormLayer::reshape(
int& bs, int& ic, int& ih, int& iw, int oc, int& oh, int& ow) {
int& bs, int& ic, int& ih, int& iw, int& oc, int& oh, int& ow) {
reshapeInput(bs, ih, iw);
oh = ih;
ow = iw;
// ic_ and oc can not be changed
CHECK_EQ(inputElemenCnt_ / bs / ih / iw, (size_t)ic)
CHECK_EQ((size_t)ic,
inputLayers_[0]->getOutputValue()->getElementCnt() / bs / ih / iw)
<< "Input channel can not be changed";
reshapeOutput(oh, ow);
resizeOutput(bs, oc * oh * ow);
}
void MKLDNNBatchNormLayer::resetFwd(std::vector<primitive>& pipeline,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& out) {
// In training phase, it will always calculate mean and var,
// so useGlobalStats must be false.
......@@ -140,25 +139,23 @@ void MKLDNNBatchNormLayer::resetFwd(std::vector<primitive>& pipeline,
useGlobalStats_ = false;
}
resetFwdBuffers(in, wgt, out);
resetFwdBuffers(inputs[0], wgtVal_, out);
resetFwdPD(fwdPD_, in, wgt, out);
resetFwdPD(fwdPD_, inputs[0], wgtVal_, out);
resetFwdPipeline(pipeline, fwdPD_, in, wgt, out);
resetFwdPipeline(pipeline, fwdPD_, inputs[0], wgtVal_, out);
}
void MKLDNNBatchNormLayer::resetBwd(std::vector<primitive>& pipeline,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& out) {
std::shared_ptr<bn_bwd::primitive_desc> pd;
resetBwdBuffers(in, wgt, out);
resetBwdBuffers(inputs[0], wgtGrad_, out);
resetBwdPD(pd, in, wgt, out);
resetBwdPD(pd, inputs[0], wgtGrad_, out);
resetBwdPipeline(pipeline, pd, in, wgt, out);
resetBwdPipeline(pipeline, pd, inputs[0], wgtGrad_, out);
}
void MKLDNNBatchNormLayer::forward(PassType passType) {
......@@ -260,9 +257,9 @@ void MKLDNNBatchNormLayer::resetFwdPipeline(
void MKLDNNBatchNormLayer::resetBwdBuffers(MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& out) {
CHECK(inVal_ && outVal_);
CHECK(inVals_[0] && outVal_);
resetOutGrad(out, outVal_->getPrimitiveDesc());
resetInGrad(in, inVal_->getPrimitiveDesc());
resetInGrad(in, inVals_[0]->getPrimitiveDesc());
if (gradScaleShift_) {
CHECK(wgtVal_);
resetWithMatrix(wgt, gradScaleShift_, wgtVal_->getPrimitiveDesc());
......@@ -297,11 +294,12 @@ void MKLDNNBatchNormLayer::resetBwdPipeline(
if (pd == nullptr) {
return;
}
CHECK(inVal_);
CHECK(inVals_[0]);
bwdData_.reset(
wgt && wgtVal_
? new bn_bwd(*pd, *inVal_, *mean_, *var_, *out, *wgtVal_, *in, *wgt)
: new bn_bwd(*pd, *inVal_, *mean_, *var_, *out, *in));
? new bn_bwd(
*pd, *inVals_[0], *mean_, *var_, *out, *wgtVal_, *in, *wgt)
: new bn_bwd(*pd, *inVals_[0], *mean_, *var_, *out, *in));
pipeline.push_back(*bwdData_);
}
......
paddle/gserver/layers/MKLDNNBatchNormLayer.h
浏览文件 @ 36dd770a
......@@ -73,18 +73,14 @@ public:
void forward(PassType passType) override;
void reshape(
int& bs, int& ic, int& ih, int& iw, int oc, int& oh, int& ow) override;
int& bs, int& ic, int& ih, int& iw, int& oc, int& oh, int& ow) override;
void resetFwd(std::vector<mkldnn::primitive>& pipeline,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& out) override;
void resetBwd(std::vector<mkldnn::primitive>& pipeline,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& out) override;
void updateWeights(const UpdateCallback& callback) override;
......@@ -98,11 +94,7 @@ protected:
* moving = moving * AvgFraction + local * (1 - AvgFraction)
*/
void calMovingMeanAndVar();
/**
* Forward functions: reset buffers(input, weight, output),
* reset primitive descriptor,
* reset pipeline.
*/
void resetFwdBuffers(MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& out);
......@@ -115,12 +107,6 @@ protected:
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& out);
/**
* Backward functions: reset buffers(input, weight, output),
* reset primitive descriptor,
* reset pipeline.
*/
void resetBwdBuffers(MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& out);
......
paddle/gserver/layers/MKLDNNConcatLayer.cpp
浏览文件 @ 36dd770a
......@@ -32,17 +32,16 @@ bool MKLDNNConcatLayer::init(const LayerMap& layerMap,
}
void MKLDNNConcatLayer::reshape(
int& bs, int& ic, int& ih, int& iw, int oc, int& oh, int& ow) {
int& bs, int& ic, int& ih, int& iw, int& oc, int& oh, int& ow) {
reshapeInput(bs, ih, iw);
ic = inputLayers_[0]->getSize() / ih / iw;
CHECK_EQ((size_t)ic * ih * iw, inputLayers_[0]->getSize());
CHECK_EQ(inputElemenCnt_, (size_t)bs * ic * ih * iw);
CHECK_EQ(inputLayers_[0]->getOutputValue()->getElementCnt(),
(size_t)bs * ic * ih * iw);
CHECK_GT(inputLayers_.size(), 1UL);
channels_.resize(inputLayers_.size());
channels_[0] = ic;
// need change the output channel, so use oc_ instead
// TODO(TJ): change API, use &oc
oc_ = ic;
oc = ic;
for (size_t i = 1; i < inputLayers_.size(); i++) {
int batchsize, height, witdh;
reshapeInput(batchsize, height, witdh, i);
......@@ -52,37 +51,31 @@ void MKLDNNConcatLayer::reshape(
channels_[i] = inputLayers_[i]->getSize() / height / witdh;
CHECK_EQ((size_t)channels_[i] * height * witdh, inputLayers_[i]->getSize());
oc_ += channels_[i];
oc += channels_[i];
}
oh = ih;
ow = iw;
reshapeOutput(oh, ow);
resizeOutput(bs, oc_ * oh * ow);
resizeOutput(bs, oc * oh * ow);
}
void MKLDNNConcatLayer::resetFwd(std::vector<primitive>& pipeline,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& out) {
resetFwdBuffers(inVals_, out);
in = inVals_[0];
resetFwdBuffers(inputs, out);
std::shared_ptr<concat::primitive_desc> fwdPD;
resetFwdPD(fwdPD, inVals_, out);
resetFwdPD(fwdPD, inputs, out);
resetFwdPipeline(pipeline, fwdPD, inVals_, out);
resetFwdPipeline(pipeline, fwdPD, inputs, out);
}
void MKLDNNConcatLayer::resetBwd(std::vector<primitive>& pipeline,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& out) {
resetBwdBuffers(inGrads_, out);
in = inGrads_[0];
resetBwdBuffers(inputs, out);
resetBwdPipeline(pipeline, bwds_, inGrads_, out);
resetBwdPipeline(pipeline, bwds_, inputs, out);
}
void MKLDNNConcatLayer::resetFwdBuffers(std::vector<MKLDNNMatrixPtr>& inputs,
......@@ -90,10 +83,7 @@ void MKLDNNConcatLayer::resetFwdBuffers(std::vector<MKLDNNMatrixPtr>& inputs,
inputs.resize(inputLayers_.size());
bool has8c = false, has16c = false, hasnc = false;
for (size_t i = 0; i < inputs.size(); i++) {
// resetInValue will use ic_ so temporary change as current input's channel
// TODO(TJ): change ic_ as vector then can remove channels_
ic_ = channels_[i];
resetInValue(inputs[i], nullptr, i);
resetInValue(inputs[i], nullptr, i, channels_[i]);
CHECK(inputs[i]);
auto dm = inputs[i]->getDims();
// inputs format can be different, but ndims must equal
......@@ -114,8 +104,6 @@ void MKLDNNConcatLayer::resetFwdBuffers(std::vector<MKLDNNMatrixPtr>& inputs,
has16c = true;
}
}
// change back, ic_ always save the input 0 size
ic_ = channels_[0];
format outFmt;
if (has16c && oc_ % 16 == 0) {
......@@ -168,14 +156,9 @@ void MKLDNNConcatLayer::resetBwdBuffers(std::vector<MKLDNNMatrixPtr>& inputs,
inputs.resize(inputLayers_.size());
for (size_t i = 0; i < inputs.size(); i++) {
CHECK(inVals_[i]);
// resetInGrad will use inVal_
// TODO(TJ): change move inVals_ to MKLDNNLayer ans remove inVal_
inVal_ = inVals_[i];
resetInGrad(inputs[i], inVals_[i]->getPrimitiveDesc(), i);
CHECK_PRIMITIVE_DESC_EQ(inputs[i], inVals_[i]->getPrimitiveDesc());
}
// change back, inVal_ always save the input 0
inVal_ = inVals_[0];
}
void MKLDNNConcatLayer::resetBwdPipeline(
......
paddle/gserver/layers/MKLDNNConcatLayer.h
浏览文件 @ 36dd770a
......@@ -26,8 +26,6 @@ namespace paddle {
*/
class MKLDNNConcatLayer : public MKLDNNLayer {
protected:
std::vector<MKLDNNMatrixPtr> inVals_;
std::vector<MKLDNNMatrixPtr> inGrads_;
std::vector<std::shared_ptr<mkldnn::primitive>> bwds_;
// input channel numbers
std::vector<int> channels_;
......@@ -47,18 +45,14 @@ public:
const ParameterMap& parameterMap) override;
void reshape(
int& bs, int& ic, int& ih, int& iw, int oc, int& oh, int& ow) override;
int& bs, int& ic, int& ih, int& iw, int& oc, int& oh, int& ow) override;
void resetFwd(std::vector<mkldnn::primitive>& pipeline,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& out) override;
void resetBwd(std::vector<mkldnn::primitive>& pipeline,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& out) override;
void printSizeInfo() override {
......@@ -72,38 +66,16 @@ public:
<< ", " << ow_;
}
void printValueFormat() override {
for (size_t i = 0; i < inVals_.size(); ++i) {
VLOG(MKLDNN_FMTS) << "Input " << i << ", " << inputLayers_[i]->getName()
<< ": " << inVals_[i]->getFormat() << " >>>";
}
if (outVal_) {
VLOG(MKLDNN_FMTS) << outVal_->getFormat() << " >>> ";
}
if (extOutVal_) {
VLOG(MKLDNN_FMTS) << extOutVal_->getFormat();
}
}
void printGradFormat() override {
if (extOutGrad_) {
VLOG(MKLDNN_FMTS) << extOutGrad_->getFormat();
}
if (outGrad_) {
VLOG(MKLDNN_FMTS) << outGrad_->getFormat() << " <<< ";
}
for (size_t i = 0; i < inGrads_.size(); ++i) {
VLOG(MKLDNN_FMTS) << "Input " << i << ", " << inputLayers_[i]->getName()
<< ": " << inGrads_[i]->getFormat() << "<<<";
size_t keepCondition() {
// reset when the total element size of all inputs changed
size_t totalSize = inputLayers_[0]->getOutputValue()->getElementCnt();
for (size_t i = 1; i < inputLayers_.size(); ++i) {
totalSize += inputLayers_[i]->getOutputValue()->getElementCnt();
}
return totalSize;
}
protected:
/**
* Forward functions: reset buffers(inputs, output, bias),
* reset primitive descriptor,
* reset pipeline.
*/
void resetFwdBuffers(std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& out);
void resetFwdPD(std::shared_ptr<mkldnn::concat::primitive_desc>& pd,
......@@ -113,11 +85,6 @@ protected:
std::shared_ptr<mkldnn::concat::primitive_desc>& pd,
std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& out);
/**
* Backward functions: reset buffers(inputs, output, bias)
* reset primitives and pipeline
*/
void resetBwdBuffers(std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& out);
void resetBwdPipeline(std::vector<mkldnn::primitive>& pipeline,
......
paddle/gserver/layers/MKLDNNConvLayer.cpp
浏览文件 @ 36dd770a
......@@ -90,7 +90,7 @@ void MKLDNNConvLayer::convertWeightsToPaddle() {
}
void MKLDNNConvLayer::reshape(
int& bs, int& ic, int& ih, int& iw, int oc, int& oh, int& ow) {
int& bs, int& ic, int& ih, int& iw, int& oc, int& oh, int& ow) {
reshapeInput(bs, ih, iw);
// cal output sizes
......@@ -105,21 +105,17 @@ void MKLDNNConvLayer::reshape(
}
void MKLDNNConvLayer::resetFwd(std::vector<primitive>& pipeline,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& out) {
resetFwdPD(fwdPD_);
resetFwdBuffers(fwdPD_, in, wgt, bias, out);
resetFwdBuffers(fwdPD_, inputs[0], wgtVal_, biasVal_, out);
resetFwdPipeline(pipeline, fwdPD_, in, wgt, bias, out);
resetFwdPipeline(pipeline, fwdPD_, inputs[0], wgtVal_, biasVal_, out);
}
void MKLDNNConvLayer::resetBwd(std::vector<primitive>& pipeline,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& out) {
std::shared_ptr<conv_bwdWgt::primitive_desc> bwdWgtPD;
std::shared_ptr<conv_bwdData::primitive_desc> bwdDataPD;
......@@ -128,9 +124,10 @@ void MKLDNNConvLayer::resetBwd(std::vector<primitive>& pipeline,
resetBwdDataPD(bwdDataPD);
resetBwdBuffers(bwdWgtPD, bwdDataPD, in, wgt, bias, out);
resetBwdBuffers(bwdWgtPD, bwdDataPD, inputs[0], wgtGrad_, biasGrad_, out);
resetBwdPipeline(pipeline, bwdWgtPD, bwdDataPD, in, wgt, bias, out);
resetBwdPipeline(
pipeline, bwdWgtPD, bwdDataPD, inputs[0], wgtGrad_, biasGrad_, out);
}
void MKLDNNConvLayer::updateWeights(const UpdateCallback& callback) {
......@@ -236,14 +233,14 @@ void MKLDNNConvLayer::resetBwdWgtPD(
loadConvSettings(wgtDims, biasDims, strides, dilations, padL, padR);
// create backward weight using input, output and weight value memory desc
CHECK(inVal_) << "Should have internal input value";
CHECK(inVals_[0]) << "Should have internal input value";
CHECK(outVal_) << "Should have internal output value";
CHECK(wgtVal_) << "Should have weight value";
algorithm algo = algorithm::convolution_direct;
padding_kind padKind = padding_kind::zero;
auto bwdWgtDesc = biasVal_ != nullptr
? conv_bwdWgt::desc(algo,
inVal_->getMemoryDesc(),
inVals_[0]->getMemoryDesc(),
wgtVal_->getMemoryDesc(),
biasVal_->getMemoryDesc(),
outVal_->getMemoryDesc(),
......@@ -252,7 +249,7 @@ void MKLDNNConvLayer::resetBwdWgtPD(
padR,
padKind)
: conv_bwdWgt::desc(algo,
inVal_->getMemoryDesc(),
inVals_[0]->getMemoryDesc(),
wgtVal_->getMemoryDesc(),
outVal_->getMemoryDesc(),
strides,
......@@ -260,7 +257,7 @@ void MKLDNNConvLayer::resetBwdWgtPD(
padR,
padKind);
pd.reset(new conv_bwdWgt::primitive_desc(bwdWgtDesc, engine_, *fwdPD_));
CHECK_PRIMITIVE_DESC_EQ(inVal_, pd->src_primitive_desc());
CHECK_PRIMITIVE_DESC_EQ(inVals_[0], pd->src_primitive_desc());
CHECK_PRIMITIVE_DESC_EQ(
outVal_,
pd->diff_dst_primitive_desc(),
......@@ -280,12 +277,12 @@ void MKLDNNConvLayer::resetBwdDataPD(
memory::dims wgtDims, biasDims, strides, dilations, padL, padR;
loadConvSettings(wgtDims, biasDims, strides, dilations, padL, padR);
CHECK(inVal_) << "Should have internal input value";
CHECK(inVals_[0]) << "Should have internal input value";
CHECK(outVal_) << "Should have internal output value";
// create backward data using input and output value memory desc
// but using weight memory desc with any format
auto bwdDataDesc = conv_bwdData::desc(algorithm::convolution_direct,
inVal_->getMemoryDesc(),
inVals_[0]->getMemoryDesc(),
MKLDNNMatrix::createMemoryDesc(wgtDims),
outVal_->getMemoryDesc(),
strides,
......@@ -294,7 +291,7 @@ void MKLDNNConvLayer::resetBwdDataPD(
padding_kind::zero);
pd.reset(new conv_bwdData::primitive_desc(bwdDataDesc, engine_, *fwdPD_));
CHECK_PRIMITIVE_DESC_EQ(
inVal_,
inVals_[0],
pd->diff_src_primitive_desc(),
"primitive desc of in value and grad should be equal");
CHECK_PRIMITIVE_DESC_EQ(
......@@ -346,12 +343,12 @@ void MKLDNNConvLayer::resetBwdPipeline(
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) {
CHECK(inVal_);
CHECK(inVals_[0]);
// add bwdWgt handle
if (bias) {
bwdWgt_.reset(new conv_bwdWgt(*wgtPD, *inVal_, *out, *wgt, *bias));
bwdWgt_.reset(new conv_bwdWgt(*wgtPD, *inVals_[0], *out, *wgt, *bias));
} else {
bwdWgt_.reset(new conv_bwdWgt(*wgtPD, *inVal_, *out, *wgt));
bwdWgt_.reset(new conv_bwdWgt(*wgtPD, *inVals_[0], *out, *wgt));
}
pipeline.push_back(*bwdWgt_);
......
paddle/gserver/layers/MKLDNNConvLayer.h
浏览文件 @ 36dd770a
......@@ -69,18 +69,14 @@ public:
const ParameterMap& parameterMap) override;
void reshape(
int& bs, int& ic, int& ih, int& iw, int oc, int& oh, int& ow) override;
int& bs, int& ic, int& ih, int& iw, int& oc, int& oh, int& ow) override;
void resetFwd(std::vector<mkldnn::primitive>& pipeline,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& out) override;
void resetBwd(std::vector<mkldnn::primitive>& pipeline,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& out) override;
void updateWeights(const UpdateCallback& callback) override;
......@@ -107,48 +103,26 @@ protected:
mkldnn::memory::dims& padL,
mkldnn::memory::dims& padR);
/**
* reset the forward primitive descriptor.
*/
void resetFwdPD(std::shared_ptr<conv_fwd::primitive_desc>& pd);
/**
* reset the MKLDNNMatrix buffers used in forward.
*/
void resetFwdBuffers(std::shared_ptr<conv_fwd::primitive_desc>& pd,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out);
/**
* reset the forward pipeline.
*/
void resetFwdPipeline(std::vector<mkldnn::primitive>& pipeline,
std::shared_ptr<conv_fwd::primitive_desc>& pd,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out);
/**
* reset the backward weight primitive descriptor.
*/
void resetBwdWgtPD(std::shared_ptr<conv_bwdWgt::primitive_desc>& pd);
/**
* reset the backward data primitive descriptor.
*/
void resetBwdDataPD(std::shared_ptr<conv_bwdData::primitive_desc>& pd);
/**
* reset the MKLDNNMatrix buffers used in backward.
*/
void resetBwdBuffers(std::shared_ptr<conv_bwdWgt::primitive_desc>& wgtPD,
std::shared_ptr<conv_bwdData::primitive_desc>& dataPD,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out);
/**
* reset the backward pipeline.
*/
void resetBwdPipeline(std::vector<mkldnn::primitive>& pipeline,
std::shared_ptr<conv_bwdWgt::primitive_desc>& wgtPD,
std::shared_ptr<conv_bwdData::primitive_desc>& dataPD,
......
paddle/gserver/layers/MKLDNNFcLayer.cpp
浏览文件 @ 36dd770a
......@@ -74,7 +74,7 @@ void MKLDNNFcLayer::convertWeightsToPaddle() {
}
void MKLDNNFcLayer::reshape(
int& bs, int& ic, int& ih, int& iw, int oc, int& oh, int& ow) {
int& bs, int& ic, int& ih, int& iw, int& oc, int& oh, int& ow) {
reshapeInput(bs, ih, iw);
CHECK_EQ(iLayerSize_, inputLayers_[0]->getSize());
......@@ -87,32 +87,29 @@ void MKLDNNFcLayer::reshape(
}
void MKLDNNFcLayer::resetFwd(std::vector<primitive>& pipeline,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& out) {
resetFwdBuffers(in, wgt, bias, out);
resetFwdBuffers(inputs[0], wgtVal_, biasVal_, out);
resetFwdPD(fwdPD_, in, wgt, bias, out);
resetFwdPD(fwdPD_, inputs[0], wgtVal_, biasVal_, out);
resetFwdPipeline(pipeline, fwdPD_, in, wgt, bias, out);
resetFwdPipeline(pipeline, fwdPD_, inputs[0], wgtVal_, biasVal_, out);
}
void MKLDNNFcLayer::resetBwd(std::vector<primitive>& pipeline,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& out) {
std::shared_ptr<fc_bwdWgt::primitive_desc> bwdWgtPD;
std::shared_ptr<fc_bwdData::primitive_desc> bwdDataPD;
resetBwdBuffers(in, wgt, bias, out);
resetBwdBuffers(inputs[0], wgtGrad_, biasGrad_, out);
resetBwdWgtPD(bwdWgtPD, wgt, bias, out);
resetBwdWgtPD(bwdWgtPD, wgtGrad_, biasGrad_, out);
resetBwdDataPD(bwdDataPD, in, out);
resetBwdDataPD(bwdDataPD, inputs[0], out);
resetBwdPipeline(pipeline, bwdWgtPD, bwdDataPD, in, wgt, bias, out);
resetBwdPipeline(
pipeline, bwdWgtPD, bwdDataPD, inputs[0], wgtGrad_, biasGrad_, out);
}
void MKLDNNFcLayer::updateWeights(const UpdateCallback& callback) {
......@@ -193,9 +190,9 @@ void MKLDNNFcLayer::resetBwdBuffers(MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) {
CHECK(inVal_ && outVal_);
CHECK(inVals_[0] && outVal_);
resetOutGrad(out, outVal_->getPrimitiveDesc());
resetInGrad(in, inVal_->getPrimitiveDesc());
resetInGrad(in, inVals_[0]->getPrimitiveDesc());
CHECK(wgtVal_);
resetWithMatrix(wgt, weight_->getWGrad(), wgtVal_->getPrimitiveDesc());
......@@ -212,14 +209,15 @@ void MKLDNNFcLayer::resetBwdWgtPD(
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());
CHECK(inVals_[0]);
fc_bwdWgt::desc bwdWgtDesc =
bias ? fc_bwdWgt::desc(inVals_[0]->getMemoryDesc(),
wgt->getMemoryDesc(),
bias->getMemoryDesc(),
out->getMemoryDesc())
: fc_bwdWgt::desc(inVals_[0]->getMemoryDesc(),
wgt->getMemoryDesc(),
out->getMemoryDesc());
pd.reset(new fc_bwdWgt::primitive_desc(bwdWgtDesc, engine_, *fwdPD_));
}
......@@ -245,11 +243,11 @@ void MKLDNNFcLayer::resetBwdPipeline(
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) {
CHECK(inVal_);
CHECK(inVals_[0]);
if (bias) {
bwdWgt_.reset(new fc_bwdWgt(*bwdWgtPD, *inVal_, *out, *wgt, *bias));
bwdWgt_.reset(new fc_bwdWgt(*bwdWgtPD, *inVals_[0], *out, *wgt, *bias));
} else {
bwdWgt_.reset(new fc_bwdWgt(*bwdWgtPD, *inVal_, *out, *wgt));
bwdWgt_.reset(new fc_bwdWgt(*bwdWgtPD, *inVals_[0], *out, *wgt));
}
pipeline.push_back(*bwdWgt_);
......
paddle/gserver/layers/MKLDNNFcLayer.h
浏览文件 @ 36dd770a
......@@ -52,18 +52,14 @@ public:
const ParameterMap& parameterMap) override;
void reshape(
int& bs, int& ic, int& ih, int& iw, int oc, int& oh, int& ow) override;
int& bs, int& ic, int& ih, int& iw, int& oc, int& oh, int& ow) override;
void resetFwd(std::vector<mkldnn::primitive>& pipeline,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& out) override;
void resetBwd(std::vector<mkldnn::primitive>& pipeline,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& out) override;
void updateWeights(const UpdateCallback& callback) override;
......@@ -73,11 +69,6 @@ public:
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,
......@@ -93,13 +84,6 @@ protected:
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,
......
paddle/gserver/layers/MKLDNNLayer.cpp
浏览文件 @ 36dd770a
......@@ -48,31 +48,20 @@ void MKLDNNLayer::forward(PassType passType) {
REGISTER_TIMER_INFO("mkldnn_FwdTimer", getName().c_str());
CHECK(!inputLayers_.empty());
copySeqInfoToOutputs();
size_t elemenCnt = inputLayers_[0]->getOutputValue()->getElementCnt();
if (inputElemenCnt_ != elemenCnt) {
if (condition_ != keepCondition()) {
VLOG(MKLDNN_BASE) << getName() << " reset mkldnn forward";
// reset when input total sizes changed, not only the batchsize
inputElemenCnt_ = elemenCnt;
pipelineFwd_.clear();
condition_ = keepCondition();
reshape(bs_, ic_, ih_, iw_, oc_, oh_, ow_);
// all cpu device output grad or value share output's
printSizeInfo();
// the output_.value and output_.grad are shared with CPU device
shareCPUDevice();
resetFwd(pipelineFwd_, inVal_, wgtVal_, biasVal_, outVal_);
// MKLDNNLayer output value should be MKLDNNMatrix
// so external output value is necessary.
// Then external input value is not necessary,
// since input may be mkldnn internal buffer.
CHECK(extOutVal_) << "external output value is necessary";
output_.value = std::dynamic_pointer_cast<Matrix>(extOutVal_);
CHECK(inVal_ && outVal_) << "internal memories are necessary";
if (cvtInVal_) {
pipelineFwd_.insert(pipelineFwd_.begin(), *cvtInVal_);
}
if (cvtOutVal_) {
pipelineFwd_.push_back(*cvtOutVal_);
}
pipelineFwd_.clear();
inVals_.resize(inputLayers_.size(), nullptr);
extInVals_.resize(inputLayers_.size(), nullptr);
cvtInVals_.resize(inputLayers_.size(), nullptr);
resetFwd(pipelineFwd_, inVals_, outVal_);
prepareValueConversions(pipelineFwd_);
convertWeightsFromPaddle();
printSizeInfo();
printValueFormat();
needResetBwd_ = true;
}
......@@ -80,8 +69,8 @@ void MKLDNNLayer::forward(PassType passType) {
if (inputLayers_[0]->getType() == "data" && inputLayers_.size() == 1) {
// Update input value data when input layer is "data" type,
// since the input value data address might be changed.
CHECK(extInVal_);
extInVal_->setData(getInputValue(0, CPU_DEVICE)->getData());
CHECK(extInVals_[0]);
extInVals_[0]->setData(getInputValue(0, CPU_DEVICE)->getData());
}
if (!outputOnlyMKLDNN_) {
......@@ -99,22 +88,13 @@ void MKLDNNLayer::backward(const UpdateCallback& callback) {
if (needResetBwd_) {
VLOG(MKLDNN_BASE) << getName() << " reset mkldnn backward";
pipelineBwd_.clear();
inGrads_.resize(inputLayers_.size(), nullptr);
extInGrads_.resize(inputLayers_.size(), nullptr);
cvtInGrads_.resize(inputLayers_.size(), nullptr);
pipelineMergeGrad_.clear();
mergeGrad_ = nullptr;
resetBwd(pipelineBwd_, inGrad_, wgtGrad_, biasGrad_, outGrad_);
// external output grad is not necessary
// since output may be mkldnn internal buffer or merge them directly.
CHECK(outGrad_) << "internal output grad is necessary";
if (extOutGrad_) {
CHECK_EQ(extOutGrad_->getData(), output_.grad->getData())
<< "the external buffer should share the same data with output_.grad";
}
if (cvtOutGrad_) {
pipelineBwd_.insert(pipelineBwd_.begin(), *cvtOutGrad_);
}
if (cvtInGrad_) {
pipelineBwd_.push_back(*cvtInGrad_);
}
resetBwd(pipelineBwd_, inGrads_, outGrad_);
prepareGradConversions(pipelineBwd_);
printGradFormat();
needResetBwd_ = false;
}
......@@ -141,8 +121,8 @@ void MKLDNNLayer::backward(const UpdateCallback& callback) {
void MKLDNNLayer::reshapeInput(int& batchsize,
int& height,
int& width,
size_t inputIdx) {
const Argument& input = inputLayers_[inputIdx]->getOutput();
size_t idx) {
const Argument& input = inputLayers_[idx]->getOutput();
batchsize = input.getBatchSize();
int h = input.getFrameHeight();
int w = input.getFrameWidth();
......@@ -176,27 +156,30 @@ void MKLDNNLayer::resetWithMatrix(MKLDNNMatrixPtr& dnn,
void MKLDNNLayer::resetInValue(
MKLDNNMatrixPtr& in,
const std::shared_ptr<memory::primitive_desc>& intPD,
size_t inputIdx) {
cvtInVal_ = nullptr;
extInVal_ = nullptr;
size_t idx,
int inputChannel) {
cvtInVals_[idx] = nullptr;
extInVals_[idx] = nullptr;
in = nullptr;
CHECK_GT(bs_ * ic_ * ih_ * iw_, 0);
inputChannel = inputChannel == 0 ? ic_ : inputChannel;
CHECK_GT(bs_ * inputChannel * ih_ * iw_, 0);
auto extPD = MKLDNNMatrix::createPrimitiveDesc(
{bs_, ic_, ih_, iw_}, format::nchw, engine_);
const MatrixPtr& inMat = inputLayers_[inputIdx]->getOutputValue();
extInVal_ = std::dynamic_pointer_cast<MKLDNNMatrix>(inMat);
CHECK_EQ(inputIsOnlyMKLDNN(), extInVal_ != nullptr);
if (extInVal_ == nullptr || extInVal_->getFormat() == format::nc) {
extInVal_ = MKLDNNMatrix::create(extPD, inMat);
{bs_, inputChannel, ih_, iw_}, format::nchw, engine_);
const MatrixPtr& inMat = inputLayers_[idx]->getOutputValue();
extInVals_[idx] = std::dynamic_pointer_cast<MKLDNNMatrix>(inMat);
CHECK_EQ(inputIsOnlyMKLDNN(), extInVals_[idx] != nullptr);
if (extInVals_[idx] == nullptr ||
extInVals_[idx]->getFormat() == format::nc) {
extInVals_[idx] = MKLDNNMatrix::create(extPD, inMat);
}
in = extInVal_;
in = extInVals_[idx];
if (nullptr == intPD || in->getPrimitiveDesc() == *intPD) {
return;
}
// need create reorder
in = MKLDNNMatrix::create(*intPD);
cvtInVal_ = MKLDNNMatrix::createReorder(extInVal_, in);
CHECK(cvtInVal_) << "should not be emptry";
cvtInVals_[idx] = MKLDNNMatrix::createReorder(extInVals_[idx], in);
CHECK(cvtInVals_[idx]) << "should not be emptry";
}
void MKLDNNLayer::resetOutValue(MKLDNNMatrixPtr& out,
......@@ -218,11 +201,11 @@ void MKLDNNLayer::resetOutValue(MKLDNNMatrixPtr& out,
void MKLDNNLayer::resetInGrad(MKLDNNMatrixPtr& in,
memory::primitive_desc intPD,
size_t inputIdx) {
cvtInGrad_ = nullptr;
extInGrad_ = nullptr;
size_t idx) {
cvtInGrads_[idx] = nullptr;
extInGrads_[idx] = nullptr;
in = nullptr;
LayerPtr& input = inputLayers_[inputIdx];
LayerPtr& input = inputLayers_[idx];
if (input->getOutputGrad() == nullptr) {
// no need input grad
return;
......@@ -237,23 +220,25 @@ void MKLDNNLayer::resetInGrad(MKLDNNMatrixPtr& in,
in = MKLDNNMatrix::create(intPD, inMat);
Argument& arg = input->getOutput(this->getName());
arg.grad = std::dynamic_pointer_cast<Matrix>(in);
CHECK_PRIMITIVE_DESC_EQ(inVal_, intPD);
CHECK_PRIMITIVE_DESC_EQ(inVals_[idx], intPD);
if (inputIsOnlyMKLDNN()) {
return;
}
extInGrad_ = in;
if (isPaddleFormat(extInGrad_->getFormat())) {
extInGrads_[idx] = in;
if (isPaddleFormat(extInGrads_[idx]->getFormat())) {
return;
}
// need create reorder
CHECK(extInVal_ != nullptr && isPaddleFormat(extInVal_->getFormat()))
CHECK(extInVals_[idx] != nullptr &&
isPaddleFormat(extInVals_[idx]->getFormat()))
<< "should have external input value and the format must be nchw(nc)";
extInGrad_ = MKLDNNMatrix::create(extInVal_->getPrimitiveDesc(), inMat);
CHECK_PRIMITIVE_DESC_EQ(inVal_, intPD);
extInGrads_[idx] =
MKLDNNMatrix::create(extInVals_[idx]->getPrimitiveDesc(), inMat);
CHECK_PRIMITIVE_DESC_EQ(inVals_[idx], intPD);
in = MKLDNNMatrix::create(intPD);
cvtInGrad_ = MKLDNNMatrix::createReorder(in, extInGrad_);
CHECK(cvtInGrad_);
cvtInGrads_[idx] = MKLDNNMatrix::createReorder(in, extInGrads_[idx]);
CHECK(cvtInGrads_[idx]);
}
void MKLDNNLayer::resetOutGrad(MKLDNNMatrixPtr& out,
......@@ -309,22 +294,8 @@ void MKLDNNLayer::resetMergeGrad(MKLDNNMatrixPtr& out) {
srcs.push_back(*src);
}
// TODO(TJ): remove me when mkldnn sum support different formats
for (size_t i = 1; i < srcPDs.size(); ++i) {
CHECK(srcPDs[0] == srcPDs[i]);
}
tmpOutGrad_ = out;
tmpCvt_ = nullptr;
if (out->getPrimitiveDesc() != srcPDs[0]) {
tmpOutGrad_ = MKLDNNMatrix::create(srcPDs[0]);
tmpCvt_ = MKLDNNMatrix::createReorder(tmpOutGrad_, out);
CHECK(tmpCvt_);
pipelineMergeGrad_.push_back(*tmpCvt_);
}
auto sumPD =
sum::primitive_desc(tmpOutGrad_->getMemoryDesc(), scales, srcPDs);
mergeGrad_.reset(new sum(sumPD, srcs, *tmpOutGrad_));
auto sumPD = sum::primitive_desc(out->getMemoryDesc(), scales, srcPDs);
mergeGrad_.reset(new sum(sumPD, srcs, *out));
pipelineMergeGrad_.insert(pipelineMergeGrad_.begin(), *mergeGrad_);
}
......
paddle/gserver/layers/MKLDNNLayer.h
浏览文件 @ 36dd770a
......@@ -34,15 +34,16 @@ typedef std::shared_ptr<MKLDNNLayer> MKLDNNLayerPtr;
*/
class MKLDNNLayer : public Layer {
protected:
// input value element count
size_t inputElemenCnt_;
// batch size
int bs_;
// their sizes are always from the first input layer
// input image channel, height and width
int ic_, ih_, iw_;
// output image channel, height and width
int oc_, oh_, ow_;
// the condition that forward need be reset
size_t condition_;
// backward also need reset after reset forward handle
bool needResetBwd_;
......@@ -67,18 +68,18 @@ protected:
* When all layers are mkldnn layers, they could save internal data.
*/
// below MKLDNNMatrix buffers are all internal buffers
MKLDNNMatrixPtr inVal_;
MKLDNNMatrixPtr inGrad_;
std::vector<MKLDNNMatrixPtr> inVals_;
std::vector<MKLDNNMatrixPtr> inGrads_;
MKLDNNMatrixPtr outVal_;
MKLDNNMatrixPtr outGrad_;
// below are external value and grad
MKLDNNMatrixPtr extInVal_;
MKLDNNMatrixPtr extInGrad_;
std::vector<MKLDNNMatrixPtr> extInVals_;
std::vector<MKLDNNMatrixPtr> extInGrads_;
MKLDNNMatrixPtr extOutVal_;
MKLDNNMatrixPtr extOutGrad_;
// convert handle between external and internal buffers
std::shared_ptr<mkldnn::reorder> cvtInVal_;
std::shared_ptr<mkldnn::reorder> cvtInGrad_;
std::vector<std::shared_ptr<mkldnn::reorder>> cvtInVals_;
std::vector<std::shared_ptr<mkldnn::reorder>> cvtInGrads_;
std::shared_ptr<mkldnn::reorder> cvtOutVal_;
std::shared_ptr<mkldnn::reorder> cvtOutGrad_;
......@@ -93,23 +94,11 @@ protected:
std::vector<mkldnn::primitive> pipelineMergeGrad_;
// tmp input argument to save input grad, only used to merge grad
Argument tmpInArg_;
// since mkldnn sum do not support different formats:
// can refer to https://github.com/01org/mkl-dnn/issues/134
// so need create reorder manually and save tmp MKLDNNMatrix
MKLDNNMatrixPtr tmpOutGrad_;
std::shared_ptr<mkldnn::primitive> tmpCvt_;
public:
explicit MKLDNNLayer(const LayerConfig& config)
: Layer(config),
inputElemenCnt_(0),
bs_(0),
ic_(0),
ih_(0),
iw_(0),
oc_(0),
oh_(0),
ow_(0),
condition_(0),
needResetBwd_(true),
outputOnlyMKLDNN_(false),
engine_(mkldnn::engine::cpu, 0),
......@@ -125,31 +114,28 @@ public:
virtual void backward(const UpdateCallback& callback);
/**
* reshape the input image sizes
* and reset output image and buffer size
* output channel can not be changed
* reshape the input and output channels and image sizes
* and reset output buffer size
*/
virtual void reshape(
int& bs, int& ic, int& ih, int& iw, int oc, int& oh, int& ow) = 0;
int& bs, int& ic, int& ih, int& iw, int& oc, int& oh, int& ow) = 0;
/**
* reset the mkldnn forward primitve and memories
* only would be called when input size changes
* weight and bias buffers should be coverd by child class itself
*/
virtual void resetFwd(std::vector<mkldnn::primitive>& pipeline,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& out) = 0;
/**
* reset the mkldnn backward primitve and memories
* only would be called when needed
* weight and bias buffers should be coverd by child class itself
*/
virtual void resetBwd(std::vector<mkldnn::primitive>& pipeline,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& out) = 0;
/**
......@@ -175,13 +161,19 @@ public:
void addOutputArgument(int deviceId) { Layer::addOutputArgument(deviceId); }
protected:
/**
* Some layers may have different condition to reset the forward.
* The function returns the condition that do not need reset forward.
*/
inline virtual size_t keepCondition() {
// reset when the first input element size changed, not only the batchsize
return inputLayers_[0]->getOutputValue()->getElementCnt();
}
/**
* reshape the input image sizes and input batchsize
*/
void reshapeInput(int& batchsize,
int& height,
int& width,
size_t inputIdx = 0);
void reshapeInput(int& batchsize, int& height, int& width, size_t idx = 0);
/**
* reshape output image sizes
......@@ -199,11 +191,13 @@ protected:
/**
* reset input value from input MKLDNNMatrix and internal primitive desc.
* reset both internal and external buffer and create reorder if necessary.
* input channel may be different in concat.
*/
void resetInValue(
MKLDNNMatrixPtr& in,
const std::shared_ptr<mkldnn::memory::primitive_desc>& intPD = nullptr,
size_t inputIdx = 0);
size_t idx = 0,
int inputChannel = 0);
/**
* reset output value from internal primitive desc.
......@@ -218,7 +212,7 @@ protected:
*/
void resetInGrad(MKLDNNMatrixPtr& in,
mkldnn::memory::primitive_desc intPD,
size_t inputIdx = 0);
size_t idx = 0);
/**
* reset output grad from internal primitive desc.
......@@ -296,17 +290,19 @@ protected:
* print the mkldnn memory format of value
*/
virtual void printValueFormat() {
if (extInVal_) {
VLOG(MKLDNN_FMTS) << extInVal_->getFormat() << " >>> ";
}
if (inVal_) {
VLOG(MKLDNN_FMTS) << inVal_->getFormat() << " >>>";
for (size_t i = 0; i < inVals_.size(); ++i) {
if (!inVals_[i]) {
continue;
}
VLOG(MKLDNN_FMTS) << "Input " << i << ", " << inputLayers_[i]->getName()
<< ": " << (extInVals_[i] ? extInVals_[i]->getFormat()
: inVals_[i]->getFormat())
<< " >>> " << inVals_[i]->getFormat() << " >>>";
}
if (outVal_) {
VLOG(MKLDNN_FMTS) << outVal_->getFormat() << " >>> ";
}
if (extOutVal_) {
VLOG(MKLDNN_FMTS) << extOutVal_->getFormat();
VLOG(MKLDNN_FMTS) << outVal_->getFormat() << " >>> "
<< (extOutVal_ ? extOutVal_->getFormat()
: outVal_->getFormat());
}
if (wgtVal_) {
VLOG(MKLDNN_FMTS) << "Weight value format: " << wgtVal_->getFormat();
......@@ -320,17 +316,19 @@ protected:
* print the mkldnn memory format of grad
*/
virtual void printGradFormat() {
if (extOutGrad_) {
VLOG(MKLDNN_FMTS) << extOutGrad_->getFormat();
}
if (outGrad_) {
VLOG(MKLDNN_FMTS) << outGrad_->getFormat() << " <<< ";
VLOG(MKLDNN_FMTS) << outGrad_->getFormat() << " <<< "
<< (extOutGrad_ ? extOutGrad_->getFormat()
: outGrad_->getFormat());
}
if (inGrad_) {
VLOG(MKLDNN_FMTS) << inGrad_->getFormat() << " <<<";
}
if (extInGrad_) {
VLOG(MKLDNN_FMTS) << extInGrad_->getFormat() << " <<< ";
for (size_t i = 0; i < inGrads_.size(); ++i) {
if (!inGrads_[i]) {
continue;
}
VLOG(MKLDNN_FMTS) << "Input " << i << ", " << inputLayers_[i]->getName()
<< ": " << (extInGrads_[i] ? extInGrads_[i]->getFormat()
: inGrads_[i]->getFormat())
<< " <<< " << inGrads_[i]->getFormat() << " <<<";
}
if (wgtGrad_) {
VLOG(MKLDNN_FMTS) << "Weight grad format: " << wgtGrad_->getFormat();
......@@ -437,6 +435,41 @@ private:
outputOtherDevice_[i].cpuSequenceDims = output_.cpuSequenceDims;
}
}
void prepareValueConversions(std::vector<mkldnn::primitive>& pipeline) {
// MKLDNNLayer output value should be MKLDNNMatrix
// so external output value is necessary.
// Then external input value is not necessary,
// since input may be mkldnn internal buffer.
CHECK(extOutVal_) << "external output value is necessary";
output_.value = std::dynamic_pointer_cast<Matrix>(extOutVal_);
CHECK(inVals_[0] && outVal_) << "internal memories are necessary";
for (size_t i = 0; i < cvtInVals_.size(); ++i) {
if (cvtInVals_[i]) {
pipeline.insert(pipeline.begin(), *cvtInVals_[i]);
}
}
if (cvtOutVal_) {
pipeline.push_back(*cvtOutVal_);
}
}
void prepareGradConversions(std::vector<mkldnn::primitive>& pipeline) {
// external output grad is not necessary
// since output may be mkldnn internal buffer or merge them directly.
CHECK(outGrad_) << "internal output grad is necessary";
if (extOutGrad_) {
CHECK_EQ(extOutGrad_->getData(), output_.grad->getData())
<< "the external buffer should share the same data with output_.grad";
}
if (cvtOutGrad_) {
pipeline.insert(pipeline.begin(), *cvtOutGrad_);
}
for (size_t i = 0; i < cvtInGrads_.size(); ++i) {
if (cvtInGrads_[i]) {
pipeline.push_back(*cvtInGrads_[i]);
}
}
}
};
} // namespace paddle
paddle/gserver/layers/MKLDNNPoolLayer.cpp
浏览文件 @ 36dd770a
......@@ -58,10 +58,11 @@ bool MKLDNNPoolLayer::init(const LayerMap& layerMap,
}
void MKLDNNPoolLayer::reshape(
int& bs, int& ic, int& ih, int& iw, int oc, int& oh, int& ow) {
int& bs, int& ic, int& ih, int& iw, int& oc, int& oh, int& ow) {
reshapeInput(bs, ih, iw);
// ic_ and oc can not be changed
CHECK_EQ(inputElemenCnt_ / bs / ih / iw, (size_t)ic)
CHECK_EQ((size_t)ic,
inputLayers_[0]->getOutputValue()->getElementCnt() / bs / ih / iw)
<< "Input channel can not be changed";
// cal output sizes
......@@ -74,29 +75,25 @@ void MKLDNNPoolLayer::reshape(
}
void MKLDNNPoolLayer::resetFwd(std::vector<primitive>& pipeline,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& out) {
resetFwdBuffers(in, out);
resetFwdBuffers(inputs[0], out);
resetFwdPD(fwdPD_, in, out);
resetFwdPD(fwdPD_, inputs[0], out);
resetFwdPipeline(pipeline, fwdPD_, in, out);
resetFwdPipeline(pipeline, fwdPD_, inputs[0], out);
}
void MKLDNNPoolLayer::resetBwd(std::vector<primitive>& pipeline,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& out) {
std::shared_ptr<pool_bwd::primitive_desc> pd;
resetBwdBuffers(in, out);
resetBwdBuffers(inputs[0], out);
resetBwdPD(pd, in, out);
resetBwdPD(pd, inputs[0], out);
resetBwdPipeline(pipeline, pd, in, out);
resetBwdPipeline(pipeline, pd, inputs[0], out);
}
void MKLDNNPoolLayer::resetFwdBuffers(MKLDNNMatrixPtr& in,
......@@ -151,9 +148,9 @@ void MKLDNNPoolLayer::resetFwdPipeline(
void MKLDNNPoolLayer::resetBwdBuffers(MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& out) {
CHECK(inVal_ && outVal_);
CHECK(inVals_[0] && outVal_);
resetOutGrad(out, outVal_->getPrimitiveDesc());
resetInGrad(in, inVal_->getPrimitiveDesc());
resetInGrad(in, inVals_[0]->getPrimitiveDesc());
}
void MKLDNNPoolLayer::resetBwdPD(std::shared_ptr<pool_bwd::primitive_desc>& pd,
......
paddle/gserver/layers/MKLDNNPoolLayer.h
浏览文件 @ 36dd770a
......@@ -53,18 +53,14 @@ public:
const ParameterMap& parameterMap) override;
void reshape(
int& bs, int& ic, int& ih, int& iw, int oc, int& oh, int& ow) override;
int& bs, int& ic, int& ih, int& iw, int& oc, int& oh, int& ow) override;
void resetFwd(std::vector<mkldnn::primitive>& pipeline,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& out) override;
void resetBwd(std::vector<mkldnn::primitive>& pipeline,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& out) override;
void printSizeInfo() override {
......@@ -75,11 +71,6 @@ public:
}
protected:
/**
* Forward functions: reset buffers(input, output),
* reset primitive descriptor,
* reset pipeline.
*/
void resetFwdBuffers(MKLDNNMatrixPtr& in, MKLDNNMatrixPtr& out);
void resetFwdPD(std::shared_ptr<pool_fwd::primitive_desc>& pd,
MKLDNNMatrixPtr in,
......@@ -88,12 +79,6 @@ protected:
std::shared_ptr<pool_fwd::primitive_desc>& pd,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& out);
/**
* Backward functions: reset buffers(input, output),
* reset primitive descriptor,
* reset pipeline.
*/
void resetBwdBuffers(MKLDNNMatrixPtr& in, MKLDNNMatrixPtr& out);
void resetBwdPD(std::shared_ptr<pool_bwd::primitive_desc>& pd,
MKLDNNMatrixPtr& in,
......
paddle/gserver/tests/test_MKLDNN.cpp
浏览文件 @ 36dd770a
......@@ -315,7 +315,7 @@ TEST(MKLDNNLayer, AddtoLayer) {
static void getMKLDNNConcatConfig(TestConfig& cfg,
const std::vector<testImageDesc>& inputs) {
CHECK_GE(inputs.size(), 2) << "at least two inputs";
CHECK_GE(inputs.size(), 2UL) << "at least two inputs";
int oc = inputs[0].ic;
for (size_t i = 1; i < inputs.size(); ++i) {
CHECK_EQ(inputs[i].bs, inputs[0].bs);
......
paddle/operators/CMakeLists.txt
浏览文件 @ 36dd770a
......@@ -184,6 +184,7 @@ set(DEPS_OPS
sequence_softmax_op
sum_op
pool_op
maxout_op
pool_with_index_op
conv_op
conv_transpose_op
......@@ -210,6 +211,7 @@ op_library(sgd_op DEPS selected_rows_functor)
op_library(adagrad_op DEPS selected_rows_functor)
op_library(conv_op DEPS vol2col)
op_library(pool_op DEPS pooling)
op_library(maxout_op DEPS maxouting)
op_library(pool_with_index_op DEPS pooling)
op_library(lod_rank_table_op SRCS lod_rank_table_op.cc DEPS lod_rank_table)
op_library(lod_tensor_to_array_op SRCS lod_tensor_to_array_op.cc DEPS lod_rank_table_op)
......
paddle/operators/activation_op.cc
浏览文件 @ 36dd770a
......@@ -98,7 +98,6 @@ $y = \max(x, 0)$
}
};
template <typename AttrType>
class LeakyReluOpMaker : public framework::OpProtoAndCheckerMaker {
public:
LeakyReluOpMaker(framework::OpProto *proto,
......@@ -106,8 +105,7 @@ class LeakyReluOpMaker : public framework::OpProtoAndCheckerMaker {
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of LeakyRelu operator");
AddOutput("Y", "Output of LeakyRelu operator");
AddAttr<AttrType>("alpha", "The small negative slope")
.SetDefault(static_cast<AttrType>(0.02f));
AddAttr<float>("alpha", "The small negative slope").SetDefault(0.02f);
AddComment(R"DOC(
LeakyRelu Activation Operator.
......@@ -117,7 +115,6 @@ $y = \max(x, \alpha * x)$
}
};
template <typename AttrType>
class SoftShrinkOpMaker : public framework::OpProtoAndCheckerMaker {
public:
SoftShrinkOpMaker(framework::OpProto *proto,
......@@ -125,8 +122,7 @@ class SoftShrinkOpMaker : public framework::OpProtoAndCheckerMaker {
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of Softshrink operator");
AddOutput("Y", "Output of Softshrink operator");
AddAttr<AttrType>("lambda", "non-negative offset")
.SetDefault(static_cast<AttrType>(0.5f));
AddAttr<float>("lambda", "non-negative offset").SetDefault(0.5f);
AddComment(R"DOC(
Softshrink Activation Operator.
......@@ -173,7 +169,6 @@ $$y = x - \frac{e^{x} - e^{-x}}{e^{x} + e^{-x}}$$
}
};
template <typename AttrType>
class HardShrinkOpMaker : public framework::OpProtoAndCheckerMaker {
public:
HardShrinkOpMaker(framework::OpProto *proto,
......@@ -181,8 +176,8 @@ class HardShrinkOpMaker : public framework::OpProtoAndCheckerMaker {
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of HardShrink operator");
AddOutput("Y", "Output of HardShrink operator");
AddAttr<AttrType>("threshold", "The value of threshold for HardShrink")
.SetDefault(static_cast<AttrType>(0.5));
AddAttr<float>("threshold", "The value of threshold for HardShrink")
.SetDefault(0.5f);
AddComment(R"DOC(
HardShrink Activation Operator.
......@@ -308,17 +303,16 @@ $$y = \frac{x}{1 + |x|}$$
}
};
template <typename AttrType>
class BReluOpMaker : public framework::OpProtoAndCheckerMaker {
public:
BReluOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of BRelu operator");
AddOutput("Y", "Output of BRelu operator");
AddAttr<AttrType>("t_min", "The min marginal value of BRelu")
.SetDefault(static_cast<AttrType>(0));
AddAttr<AttrType>("t_max", "The max marginal value of BRelu")
.SetDefault(static_cast<AttrType>(24));
AddAttr<float>("t_min", "The min marginal value of BRelu")
.SetDefault(static_cast<float>(0));
AddAttr<float>("t_max", "The max marginal value of BRelu")
.SetDefault(static_cast<float>(24));
AddComment(R"DOC(
BRelu Activation Operator.
......@@ -328,7 +322,6 @@ $y = \max(\min(x, t_{min}), t_{max})$
}
};
template <typename AttrType>
class SoftReluOpMaker : public framework::OpProtoAndCheckerMaker {
public:
SoftReluOpMaker(framework::OpProto *proto,
......@@ -336,8 +329,8 @@ class SoftReluOpMaker : public framework::OpProtoAndCheckerMaker {
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of SoftRelu operator");
AddOutput("Y", "Output of SoftRelu operator");
AddAttr<AttrType>("threshold", "The threshold value of SoftRelu")
.SetDefault(static_cast<AttrType>(40));
AddAttr<float>("threshold", "The threshold value of SoftRelu")
.SetDefault(40.0f);
AddComment(R"DOC(
SoftRelu Activation Operator.
......@@ -347,15 +340,13 @@ $y = \ln(1 + \exp(\max(\min(x, threshold), threshold))$
}
};
template <typename AttrType>
class ELUOpMaker : public framework::OpProtoAndCheckerMaker {
public:
ELUOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of ELU operator");
AddOutput("Y", "Output of ELU operator");
AddAttr<AttrType>("alpha", "The alpha value of ELU")
.SetDefault(static_cast<AttrType>(1.0f));
AddAttr<float>("alpha", "The alpha value of ELU").SetDefault(1.0f);
AddComment(R"DOC(
ELU Activation Operator.
......@@ -368,15 +359,14 @@ $y = \max(0, x) + \min(0, \alpha * (e^x - 1))$
}
};
template <typename AttrType>
class Relu6OpMaker : public framework::OpProtoAndCheckerMaker {
public:
Relu6OpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of Relu6 operator");
AddOutput("Y", "Output of Relu6 operator");
AddAttr<AttrType>("threshold", "The threshold value of Relu6")
.SetDefault(static_cast<AttrType>(6));
AddAttr<float>("threshold", "The threshold value of Relu6")
.SetDefault(6.0f);
AddComment(R"DOC(
Relu6 Activation Operator.
......@@ -386,15 +376,13 @@ $y = \min(\max(0, x), 6)$
}
};
template <typename AttrType>
class PowOpMaker : public framework::OpProtoAndCheckerMaker {
public:
PowOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of Pow operator");
AddOutput("Y", "Output of Pow operator");
AddAttr<AttrType>("factor", "The exponential factor of Pow")
.SetDefault(static_cast<AttrType>(1));
AddAttr<float>("factor", "The exponential factor of Pow").SetDefault(1.0f);
AddComment(R"DOC(
Pow Activation Operator.
......@@ -404,17 +392,16 @@ $y = x^{factor}$
}
};
template <typename AttrType>
class STanhOpMaker : public framework::OpProtoAndCheckerMaker {
public:
STanhOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of STanh operator");
AddOutput("Y", "Output of STanh operator");
AddAttr<AttrType>("scale_a", "The scale parameter of a for the input")
.SetDefault(static_cast<AttrType>(2 / 3));
AddAttr<AttrType>("scale_b", "The scale parameter of b for the input")
.SetDefault(static_cast<AttrType>(1.7159));
AddAttr<float>("scale_a", "The scale parameter of a for the input")
.SetDefault(2.0f / 3.0f);
AddAttr<float>("scale_b", "The scale parameter of b for the input")
.SetDefault(1.7159f);
AddComment(R"DOC(
STanh Activation Operator.
......@@ -424,7 +411,6 @@ $$y = b * \frac{e^{a * x} - e^{-a * x}}{e^{a * x} + e^{-a * x}}$$
}
};
template <typename AttrType>
class ThresholdedReluOpMaker : public framework::OpProtoAndCheckerMaker {
public:
ThresholdedReluOpMaker(framework::OpProto *proto,
......@@ -432,8 +418,8 @@ class ThresholdedReluOpMaker : public framework::OpProtoAndCheckerMaker {
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of ThresholdedRelu operator");
AddOutput("Y", "Output of ThresholdedRelu operator");
AddAttr<AttrType>("threshold", "The threshold location of activation")
.SetDefault(static_cast<AttrType>(1.0));
AddAttr<float>("threshold", "The threshold location of activation")
.SetDefault(1.0f);
AddComment(R"DOC(
ThresholdedRelu Activation Operator.
......@@ -448,7 +434,6 @@ $$
}
};
template <typename AttrType>
class HardSigmoidOpMaker : public framework::OpProtoAndCheckerMaker {
public:
HardSigmoidOpMaker(framework::OpProto *proto,
......@@ -456,10 +441,10 @@ class HardSigmoidOpMaker : public framework::OpProtoAndCheckerMaker {
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of HardSigmoid operator");
AddOutput("Y", "Output of HardSigmoid operator");
AddAttr<AttrType>("slope", "Slope for linear approximation of sigmoid")
.SetDefault(static_cast<AttrType>(0.2));
AddAttr<AttrType>("offset", "Offset for linear approximation of sigmoid")
.SetDefault(static_cast<AttrType>(0.5));
AddAttr<float>("slope", "Slope for linear approximation of sigmoid")
.SetDefault(0.2f);
AddAttr<float>("offset", "Offset for linear approximation of sigmoid")
.SetDefault(0.5f);
AddComment(R"DOC(
HardSigmoid Activation Operator.
......@@ -499,7 +484,7 @@ REGISTER_OP(tanh, ops::ActivationOp, ops::TanhOpMaker, tanh_grad,
REGISTER_OP(tanh_shrink, ops::ActivationOp, ops::TanhShrinkOpMaker,
tanh_shrink_grad, ops::ActivationOpGrad);
REGISTER_OP(softshrink, ops::ActivationOp, ops::SoftShrinkOpMaker<float>,
REGISTER_OP(softshrink, ops::ActivationOp, ops::SoftShrinkOpMaker,
softshrink_grad, ops::ActivationOpGrad);
REGISTER_OP(sqrt, ops::ActivationOp, ops::SqrtOpMaker, sqrt_grad,
......@@ -523,35 +508,34 @@ REGISTER_OP(softplus, ops::ActivationOp, ops::SoftplusOpMaker, softplus_grad,
REGISTER_OP(softsign, ops::ActivationOp, ops::SoftsignOpMaker, softsign_grad,
ops::ActivationOpGrad);
REGISTER_OP(brelu, ops::ActivationOp, ops::BReluOpMaker<float>, brelu_grad,
REGISTER_OP(brelu, ops::ActivationOp, ops::BReluOpMaker, brelu_grad,
ops::ActivationOpGrad);
REGISTER_OP(leaky_relu, ops::ActivationOp, ops::LeakyReluOpMaker<float>,
REGISTER_OP(leaky_relu, ops::ActivationOp, ops::LeakyReluOpMaker,
leaky_relu_grad, ops::ActivationOpGrad);
REGISTER_OP(soft_relu, ops::ActivationOp, ops::SoftReluOpMaker<float>,
soft_relu_grad, ops::ActivationOpGrad);
REGISTER_OP(soft_relu, ops::ActivationOp, ops::SoftReluOpMaker, soft_relu_grad,
ops::ActivationOpGrad);
REGISTER_OP(elu, ops::ActivationOp, ops::ELUOpMaker<float>, elu_grad,
REGISTER_OP(elu, ops::ActivationOp, ops::ELUOpMaker, elu_grad,
ops::ActivationOpGrad);
REGISTER_OP(relu6, ops::ActivationOp, ops::Relu6OpMaker<float>, relu6_grad,
REGISTER_OP(relu6, ops::ActivationOp, ops::Relu6OpMaker, relu6_grad,
ops::ActivationOpGrad);
REGISTER_OP(pow, ops::ActivationOp, ops::PowOpMaker<float>, pow_grad,
REGISTER_OP(pow, ops::ActivationOp, ops::PowOpMaker, pow_grad,
ops::ActivationOpGrad);
REGISTER_OP(stanh, ops::ActivationOp, ops::STanhOpMaker<float>, stanh_grad,
REGISTER_OP(stanh, ops::ActivationOp, ops::STanhOpMaker, stanh_grad,
ops::ActivationOpGrad);
REGISTER_OP(hard_shrink, ops::ActivationOp, ops::HardShrinkOpMaker<float>,
REGISTER_OP(hard_shrink, ops::ActivationOp, ops::HardShrinkOpMaker,
hard_shrink_grad, ops::ActivationOpGrad);
REGISTER_OP(thresholded_relu, ops::ActivationOp,
ops::ThresholdedReluOpMaker<float>, thresholded_relu_grad,
ops::ActivationOpGrad);
REGISTER_OP(thresholded_relu, ops::ActivationOp, ops::ThresholdedReluOpMaker,
thresholded_relu_grad, ops::ActivationOpGrad);
REGISTER_OP(hard_sigmoid, ops::ActivationOp, ops::HardSigmoidOpMaker<float>,
REGISTER_OP(hard_sigmoid, ops::ActivationOp, ops::HardSigmoidOpMaker,
hard_sigmoid_grad, ops::ActivationOpGrad);
#define REGISTER_ACTIVATION_CPU_KERNEL(act_type, functor, grad_functor) \
......
paddle/operators/adadelta_op.cc
浏览文件 @ 36dd770a
......@@ -109,4 +109,5 @@ paramOut = param + paramUpdate$$
namespace ops = paddle::operators;
REGISTER_OP_WITHOUT_GRADIENT(adadelta, ops::AdadeltaOp, ops::AdadeltaOpMaker);
REGISTER_OP_CPU_KERNEL(
adadelta, ops::AdadeltaOpKernel<paddle::platform::CPUPlace, float>);
adadelta, ops::AdadeltaOpKernel<paddle::platform::CPUPlace, float>,
ops::AdadeltaOpKernel<paddle::platform::CPUPlace, double>);
paddle/operators/adadelta_op.cu
浏览文件 @ 36dd770a
......@@ -17,4 +17,5 @@
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(
adadelta, ops::AdadeltaOpKernel<paddle::platform::GPUPlace, float>);
adadelta, ops::AdadeltaOpKernel<paddle::platform::GPUPlace, float>,
ops::AdadeltaOpKernel<paddle::platform::GPUPlace, double>);
paddle/operators/adadelta_op.h
浏览文件 @ 36dd770a
......@@ -33,8 +33,8 @@ class AdadeltaOpKernel : public framework::OpKernel<T> {
avg_squared_grad_out_tensor->mutable_data<T>(ctx.GetPlace());
avg_squared_update_out_tensor->mutable_data<T>(ctx.GetPlace());
float rho = ctx.Attr<float>("rho");
float epsilon = ctx.Attr<float>("epsilon");
T rho = static_cast<T>(ctx.Attr<float>("rho"));
T epsilon = static_cast<T>(ctx.Attr<float>("epsilon"));
auto param = framework::EigenVector<T>::Flatten(
*ctx.Input<framework::Tensor>("Param"));
......
paddle/operators/adagrad_op.cu
浏览文件 @ 36dd770a
......@@ -14,8 +14,8 @@
#define EIGEN_USE_GPU
#include "paddle/operators/adagrad_op.h"
#include "paddle/operators/math/selected_rows_functor.h"
#include "paddle/operators/math/math_function.h"
#include "paddle/operators/math/selected_rows_functor.h"
#include "paddle/platform/cuda_helper.h"
namespace paddle {
......@@ -134,8 +134,8 @@ struct SparseAdagradFunctor<platform::GPUPlace, T> {
T, 256><<<grid2, threads, 0,
reinterpret_cast<const platform::CUDADeviceContext&>(context)
.stream()>>>(grad_merge_data, grad_merge->rows().data(),
lr, param_data,
moment_data, grad_width, epsilon);
lr, param_data, moment_data, grad_width,
epsilon);
}
};
......
paddle/operators/adam_op.cc
浏览文件 @ 36dd770a
......@@ -127,4 +127,5 @@ paramOut = param - learningRate * moment_1/ ($\sqrt{(moment_2)} + \epsilon)$$
namespace ops = paddle::operators;
REGISTER_OP_WITHOUT_GRADIENT(adam, ops::AdamOp, ops::AdamOpMaker);
REGISTER_OP_CPU_KERNEL(adam,
ops::AdamOpKernel<paddle::platform::CPUPlace, float>);
ops::AdamOpKernel<paddle::platform::CPUPlace, float>,
ops::AdamOpKernel<paddle::platform::CPUPlace, double>);
paddle/operators/adam_op.cu
浏览文件 @ 36dd770a
......@@ -17,4 +17,5 @@
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(adam,
ops::AdamOpKernel<paddle::platform::GPUPlace, float>);
ops::AdamOpKernel<paddle::platform::GPUPlace, float>,
ops::AdamOpKernel<paddle::platform::GPUPlace, double>);
paddle/operators/adam_op.h
浏览文件 @ 36dd770a
......@@ -31,9 +31,9 @@ class AdamOpKernel : public framework::OpKernel<T> {
moment1_out_tensor->mutable_data<T>(ctx.GetPlace());
moment2_out_tensor->mutable_data<T>(ctx.GetPlace());
float beta1 = ctx.Attr<float>("beta1");
float beta2 = ctx.Attr<float>("beta2");
float epsilon = ctx.Attr<float>("epsilon");
T beta1 = static_cast<T>(ctx.Attr<float>("beta1"));
T beta2 = static_cast<T>(ctx.Attr<float>("beta2"));
T epsilon = static_cast<T>(ctx.Attr<float>("epsilon"));
auto param = framework::EigenVector<T>::Flatten(
*ctx.Input<framework::Tensor>("Param"));
......
paddle/operators/adamax_op.cc
浏览文件 @ 36dd770a
......@@ -126,4 +126,5 @@ division by 0 error.
namespace ops = paddle::operators;
REGISTER_OP_WITHOUT_GRADIENT(adamax, ops::AdamaxOp, ops::AdamaxOpMaker);
REGISTER_OP_CPU_KERNEL(adamax,
ops::AdamaxOpKernel<paddle::platform::CPUPlace, float>);
ops::AdamaxOpKernel<paddle::platform::CPUPlace, float>,
ops::AdamaxOpKernel<paddle::platform::CPUPlace, double>);
paddle/operators/adamax_op.cu
浏览文件 @ 36dd770a
......@@ -17,4 +17,5 @@
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(adamax,
ops::AdamaxOpKernel<paddle::platform::GPUPlace, float>);
ops::AdamaxOpKernel<paddle::platform::GPUPlace, float>,
ops::AdamaxOpKernel<paddle::platform::GPUPlace, double>);
paddle/operators/adamax_op.h
浏览文件 @ 36dd770a
......@@ -31,9 +31,9 @@ class AdamaxOpKernel : public framework::OpKernel<T> {
moment_out_tensor->mutable_data<T>(ctx.GetPlace());
inf_norm_out_tensor->mutable_data<T>(ctx.GetPlace());
float beta1 = ctx.Attr<float>("beta1");
float beta2 = ctx.Attr<float>("beta2");
float epsilon = ctx.Attr<float>("epsilon");
T beta1 = static_cast<T>(ctx.Attr<float>("beta1"));
T beta2 = static_cast<T>(ctx.Attr<float>("beta2"));
T epsilon = static_cast<T>(ctx.Attr<float>("epsilon"));
auto param = framework::EigenVector<T>::Flatten(
*ctx.Input<framework::Tensor>("Param"));
......
paddle/operators/beam_search_op.cc
浏览文件 @ 36dd770a
......@@ -139,7 +139,7 @@ bool BeamSearch::NextItemSet(std::vector<BeamSearch::Item> *items) {
items->reserve(framework::product(ids.dims()));
for (size_t offset = abs_lod[lod_level_][sent_offset_];
offset < abs_lod[lod_level_][sent_offset_ + 1]; offset++) {
for (int d = 0; d < instance_dim; d++) {
for (size_t d = 0; d < instance_dim; d++) {
const size_t dim_offset = offset * instance_dim + d;
items->emplace_back(offset, ids_data[dim_offset],
scores_data[dim_offset]);
......
paddle/operators/gru_unit_op.cc
浏览文件 @ 36dd770a
......@@ -114,18 +114,19 @@ class GRUUnitOpMaker : public framework::OpProtoAndCheckerMaker {
.SetDefault(sigmoid)
.InEnum({identity, sigmoid, tanh, relu});
AddComment(R"DOC(
GRUUnit Operator.
This operator implements partial calculations of the GRU unit as follows:
GRUUnit Operator implements partial calculations of the GRU unit as following:
$$
update \ gate: u_t = actGate(xu_t + W_u * hidden_{prev} + bias_u) \\
reset \ gate: r_t = actGate(xr_t + W_r * hidden_{prev} + bias_r) \\
output \ candidate: {h}_t = actNode({xc}_t + W_c * dot(r_t, hidden_{prev}) + bias_c) \\
output: h_t = dot((1-u_t), {h}_t) + dot(u_t, hidden_{prev})
update \ gate: u_t = actGate(xu_t + W_u * h_{t-1} + b_u) \\
reset \ gate: r_t = actGate(xr_t + W_r * h_{t-1} + b_r) \\
output \ candidate: {h}_t = actNode(xc_t + W_c * dot(r_t, h_{t-1}) + b_c) \\
output: h_t = dot((1 - u_t), h_{t-1}) + dot(u_t, {h}_t)
$$
The rest of GRU unit can be completed by using FCOp's output as the input of GRUUnitOp.
which is same as one time step of GRU Operator.
@note To implement the complete GRU unit, fully-connected operator must be
used before to feed xu, xr and xc as the Input of GRUUnit operator.
)DOC");
}
......@@ -150,12 +151,6 @@ class GRUUnitGradOp : public framework::OperatorWithKernel {
"ResetHiddenPrev");
PADDLE_ENFORCE(ctx->HasInput("Hidden"),
"Input(%s) of GRUUnitGradOp should not be null.", "Hidden");
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Gate")),
"Input(%s@GRAD) of GRUUnitGradOp should not be null.",
"Gate");
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("ResetHiddenPrev")),
"Input(%s@GRAD) of GRUUnitGradOp should not be null.",
"ResetHiddenPrev");
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Hidden")),
"Input(%s@GRAD) of GRUUnitGradOp should not be null.",
"Hidden");
......
paddle/operators/gru_unit_op.h
浏览文件 @ 36dd770a
......@@ -110,7 +110,7 @@ class GRUUnitKernel : public framework::OpKernel<T> {
auto c = g.slice(c_offsets, extents); // output candidate
// calculate final output
h.device(place) = u * (h_p - c) + c;
h.device(place) = u * (c - h_p) + h_p;
}
};
......@@ -146,35 +146,27 @@ class GRUUnitGradKernel : public framework::OpKernel<T> {
auto* weight_grad =
context.Output<Tensor>(framework::GradVarName("Weight"));
auto* bias_grad = context.Output<Tensor>(framework::GradVarName("Bias"));
input_grad->mutable_data<T>(context.GetPlace());
hidden_prev_grad->mutable_data<T>(context.GetPlace());
weight_grad->mutable_data<T>(context.GetPlace());
Tensor gate_grad;
gate_grad.mutable_data<T>(input->dims(), context.GetPlace());
Tensor reset_hidden_prev_grad;
reset_hidden_prev_grad.mutable_data<T>(reset_hidden_prev->dims(),
context.GetPlace());
int batch_size = input->dims()[0];
int frame_size = hidden_prev->dims()[1];
const T* hidden_prev_data = hidden_prev->data<T>();
T* hidden_prev_grad_data = hidden_prev_grad->data<T>();
const T* weight_data = weight->data<T>();
T* weight_grad_data = weight_grad->data<T>();
T* gate_grad_data = gate_grad.data<T>();
T* gate_grad_data =
gate_grad.mutable_data<T>(input->dims(), context.GetPlace());
const T* reset_hidden_prev_data = reset_hidden_prev->data<T>();
T* reset_hidden_prev_grad_data = reset_hidden_prev_grad.data<T>();
T* reset_hidden_prev_grad_data = reset_hidden_prev_grad.mutable_data<T>(
reset_hidden_prev->dims(), context.GetPlace());
auto h_p = EigenMatrix<T>::From(*hidden_prev);
auto g = EigenMatrix<T>::From(*gate);
auto d_h = EigenMatrix<T>::From(*hidden_grad);
auto d_x = EigenMatrix<T>::From(*input_grad);
auto d_h_p = EigenMatrix<T>::From(*hidden_prev_grad);
auto d_g = EigenMatrix<T>::From(gate_grad);
auto d_r_h_p = EigenMatrix<T>::From(reset_hidden_prev_grad);
auto place = context.GetEigenDevice<Place>();
int batch_size = input->dims()[0];
int frame_size = hidden_prev->dims()[1];
Eigen::array<int, 2> extents({{batch_size, frame_size}});
Eigen::array<int, 2> u_offsets({{0, 0}});
auto u = g.slice(u_offsets, extents); // update gate
......@@ -185,38 +177,52 @@ class GRUUnitGradKernel : public framework::OpKernel<T> {
// backward for unactivated update gate
ActGradCompute(context.Attr<int>("gate_activation"), place, u, u,
d_g.slice(u_offsets, extents), d_h * (h_p - c));
d_g.slice(u_offsets, extents), d_h * (c - h_p));
// backward for unactivated output candidate
ActGradCompute(context.Attr<int>("activation"), place, c, c,
d_g.slice(c_offsets, extents), d_h * (u.constant(T(1)) - u));
d_g.slice(c_offsets, extents), d_h * u);
// backward for reset_hidden_prev
math::gemm<Place, T>(context.device_context(), false, true, batch_size,
frame_size, frame_size, 1,
gate_grad_data + frame_size * 2, frame_size * 3,
weight_data + frame_size * frame_size * 2, frame_size,
0, reset_hidden_prev_grad_data, frame_size);
// backward for state_weight
math::gemm<Place, T>(
context.device_context(), true, false, frame_size, frame_size,
batch_size, 1, reset_hidden_prev_data, frame_size,
gate_grad_data + frame_size * 2, frame_size * 3, 0,
weight_grad_data + frame_size * frame_size * 2, frame_size);
// backward for unactivated reset gate
ActGradCompute(context.Attr<int>("gate_activation"), place, r, r,
d_g.slice(r_offsets, extents), d_r_h_p * h_p);
// backward for update_gate_weight and reset_gate_weight
math::gemm<Place, T>(context.device_context(), true, false, frame_size,
frame_size * 2, batch_size, 1, hidden_prev_data,
frame_size, gate_grad_data, frame_size * 3, 0,
weight_grad_data, frame_size * 2);
// backward for weight
if (weight_grad) {
T* weight_grad_data = weight_grad->mutable_data<T>(context.GetPlace());
// backward for state_weight
math::gemm<Place, T>(
context.device_context(), true, false, frame_size, frame_size,
batch_size, 1, reset_hidden_prev_data, frame_size,
gate_grad_data + frame_size * 2, frame_size * 3, 0,
weight_grad_data + frame_size * frame_size * 2, frame_size);
// backward for update_gate_weight and reset_gate_weight
math::gemm<Place, T>(context.device_context(), true, false, frame_size,
frame_size * 2, batch_size, 1, hidden_prev_data,
frame_size, gate_grad_data, frame_size * 3, 0,
weight_grad_data, frame_size * 2);
}
// backward for hidden_prev
d_h_p.device(place) = d_r_h_p * r + d_h * u;
math::gemm<Place, T>(context.device_context(), false, true, batch_size,
frame_size, frame_size * 2, 1, gate_grad_data,
frame_size * 3, weight_data, frame_size * 2, 1,
hidden_prev_grad_data, frame_size);
if (hidden_prev_grad) {
T* hidden_prev_grad_data =
hidden_prev_grad->mutable_data<T>(context.GetPlace());
auto d_h_p = EigenMatrix<T>::From(*hidden_prev_grad);
d_h_p.device(place) = d_r_h_p * r + d_h * (u.constant(T(1)) - u);
math::gemm<Place, T>(context.device_context(), false, true, batch_size,
frame_size, frame_size * 2, 1, gate_grad_data,
frame_size * 3, weight_data, frame_size * 2, 1,
hidden_prev_grad_data, frame_size);
}
// backward for input
d_x.device(place) = d_g;
if (input_grad) {
input_grad->mutable_data<T>(context.GetPlace());
auto d_x = EigenMatrix<T>::From(*input_grad);
d_x.device(place) = d_g;
}
// backward for bias
if (bias_grad) {
bias_grad->mutable_data<T>(context.GetPlace());
......
paddle/operators/linear_chain_crf_op.h
浏览文件 @ 36dd770a
......@@ -271,7 +271,7 @@ class LinearChainCRFOpKernel : public framework::OpKernel<T> {
ll -= std::log(sum);
// Now ll is equal to -log(Z).
const int* lbl = label.data<int>();
const int64_t* lbl = label.data<int64_t>();
PADDLE_ENFORCE_LT(
static_cast<size_t>(*std::max_element(lbl, lbl + seq_length)), tag_num,
"An invalid tag label that execesses the largest tag number.");
......@@ -449,7 +449,7 @@ class LinearChainCRFGradOpKernel : public framework::OpKernel<T> {
Tensor* emission_grad) const {
const T* w_exps = transition_exps.data<T>();
const T* x_exps = emission_exps.data<T>();
const int* label_value = label.data<int>();
const int64_t* label_value = label.data<int64_t>();
T* beta_value = beta->data<T>();
auto x_dims = emission_exps.dims();
......
paddle/operators/math/CMakeLists.txt
浏览文件 @ 36dd770a
......@@ -14,6 +14,7 @@ if(WITH_GPU)
nv_library(sequence2batch SRCS sequence2batch.cc sequence2batch.cu DEPS device_context)
nv_library(lstm_compute SRCS lstm_compute.cc lstm_compute.cu DEPS device_context activation_functions)
nv_library(gru_compute SRCS gru_compute.cc gru_compute.cu DEPS device_context activation_functions math_function)
nv_library(maxouting SRCS maxouting.cc maxouting.cu DEPS device_context)
else()
cc_library(math_function SRCS math_function.cc im2col.cc DEPS cblas device_context framework_proto)
cc_library(selected_rows_functor SRCS selected_rows_functor.cc DEPS selected_rows math_function)
......@@ -26,6 +27,7 @@ else()
cc_library(sequence2batch SRCS sequence2batch.cc DEPS device_context)
cc_library(lstm_compute SRCS lstm_compute.cc DEPS device_context activation_functions)
cc_library(gru_compute SRCS gru_compute.cc DEPS device_context activation_functions math_function)
cc_library(maxouting SRCS maxouting.cc DEPS device_context)
endif()
cc_test(math_function_test SRCS math_function_test.cc DEPS math_function tensor)
......
paddle/operators/math/maxouting.cc 0 → 100644
浏览文件 @ 36dd770a
/* 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 "paddle/operators/math/maxouting.h"
namespace paddle {
namespace operators {
namespace math {
// All tensors are in NCHW format, and the groups must be greater than 1
template <typename T>
class MaxOutFunctor<platform::CPUPlace, T> {
public:
void operator()(const platform::DeviceContext& context,
const framework::Tensor& input,
framework::Tensor * output,
int groups) {
const int batch_size = input.dims()[0];
const int input_height = input.dims()[2];
const int input_width = input.dims()[3];
const int output_channels = output->dims()[1];
int fea_size = input_height * input_width;
// c_size means the output size of each sample
int c_size = fea_size * output_channels;
const T* input_data = input.data<T>();
T* output_data = output->mutable_data<T>(context.GetPlace());
for (int i = 0; i < batch_size; ++i) {
int new_bindex = c_size * i;
for (int c = 0; c < output_channels; ++c) {
int new_cindex = fea_size * c;
for (int f = 0; f < fea_size; ++f) {
T ele = static_cast<T>(-FLT_MAX);
for (int ph = 0; ph < groups; ++ph) {
T x = input_data[(new_bindex + new_cindex) * groups
+ ph * fea_size + f];
ele = ele > x ? ele : x;
}
output_data[(new_bindex+new_cindex+f)] = ele;
}
}
}
}
};
template <class T>
class MaxOutGradFunctor<platform::CPUPlace, T> {
public:
void operator()(const platform::DeviceContext& context,
const framework::Tensor& input,
framework::Tensor * input_grad,
const framework::Tensor& output,
const framework::Tensor& output_grad,
int groups) {
const int batch_size = input.dims()[0];
const int input_height = input.dims()[2];
const int input_width = input.dims()[3];
const int output_channels = output.dims()[1];
int fea_size = input_height * input_width;
const T* input_data = input.data<T>();
const T* output_data = output.data<T>();
const T* output_grad_data = output_grad.data<T>();
T* input_grad_data = input_grad->mutable_data<T>(context.GetPlace());
for (int i = 0; i < batch_size; ++i) {
int blen = fea_size * output_channels * i;
for (int c = 0; c < output_channels; ++c) {
int clen = fea_size * c;
for (int f = 0; f < fea_size; ++f) {
int input_idx0 = (blen + clen) * groups + f;
bool continue_match = true;
int output_idx = blen + clen + f;
for (int g = 0; g < groups && continue_match; ++g) {
int input_idx = input_idx0 + fea_size * g;
if (input_data[input_idx] == output_data[output_idx]) {
input_grad_data[input_idx] += output_grad_data[output_idx];
continue_match = false;
}
}
}
}
}
}
};
template class MaxOutGradFunctor<platform::CPUPlace, float>;
template class MaxOutGradFunctor<platform::CPUPlace, double>;
template class MaxOutFunctor<platform::CPUPlace, float>;
template class MaxOutFunctor<platform::CPUPlace, double>;
} // namespace math
} // namespace operators
} // namespace paddle
paddle/operators/math/maxouting.cu 0 → 100644
浏览文件 @ 36dd770a
/* 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 "paddle/operators/math/maxouting.h"
#include "paddle/platform/cuda_helper.h"
namespace paddle {
namespace operators {
namespace math {
template <typename T>
__global__ void KernelMaxOut(const int nthreads, const T* input_data,
const int channels,
const int input_height, const int input_width,
int groups, T* output_data ) {
const int size = input_height * input_width * channels / groups;
const int feat_len = input_height * input_width;
int index = blockIdx.x * blockDim.x + threadIdx.x;
int offset = blockDim.x * gridDim.x;
for (int i = index; i < nthreads; i += offset) {
int batch_idx = i / size;
int batch_offset = i % size;
int channel_idx = batch_offset / feat_len;
int feat_idx = batch_offset % feat_len;
int data_idx =
(batch_idx * size + channel_idx * feat_len) * groups + feat_idx;
T ele = static_cast<T>(-FLT_MAX);
for (int g = 0; g < groups; ++g) {
T x = input_data[data_idx + g * feat_len];
ele = ele > x ? ele : x;
}
output_data[i] = ele;
}
}
template <typename T>
__global__ void KernelMaxoutGrad(
const int nthreads, const T* input_data, const T* output_data,
const T* output_grad, T* input_grad, const int channels,
const int input_height, const int input_width, int groups) {
const int size = input_height * input_width * channels / groups;
const int feat_len = input_height * input_width;
int index = blockIdx.x * blockDim.x + threadIdx.x;
int offset = blockDim.x * gridDim.x;
for (int i = index; i < nthreads; i += offset) {
int batch_idx = i / size;
int batch_offset = i % size;
int channel_idx = batch_offset / feat_len;
int feat_idx = batch_offset % feat_len;
int data_idx =
(batch_idx * size + channel_idx * feat_len) * groups + feat_idx;
int max_index = -1;
bool continue_match = true;
for (int g = 0; g < groups && continue_match; ++g) {
if (input_data[data_idx + g * feat_len] == output_data[i]) {
max_index = data_idx + g * feat_len;
continue_match = false;
break;
}
}
if (max_index != -1) {
input_grad[max_index] += output_grad[index];
}
}
}
/*
* All tensors are in NCHW format.
*/
template <typename T>
class MaxOutFunctor<platform::GPUPlace, T> {
public:
void operator()(const platform::DeviceContext& context,
const framework::Tensor& input, framework::Tensor * output,
int groups) {
const int batch_size = input.dims()[0];
const int input_channels = input.dims()[1];
const int input_height = input.dims()[2];
const int input_width = input.dims()[3];
const int output_channels = output->dims()[1];
const int output_height = output->dims()[2];
const int output_width = output->dims()[3];
const T* input_data = input.data<T>();
T* output_data = output->mutable_data<T>(context.GetPlace());
int nthreads = output->numel();
int blocks = (nthreads + 1024 - 1) / 1024;
dim3 threads(1024, 1);
dim3 grid(blocks, 1);
KernelMaxOut<
T><<<grid, threads, 0,
reinterpret_cast<const platform::CUDADeviceContext&>(context)
.stream()>>>(nthreads, input_data, input_channels,
input_height, input_width, groups,
output_data);
}
};
/*
* All tensors are in NCHW format.
*/
template <typename T>
class MaxOutGradFunctor<platform::GPUPlace, T> {
public:
void operator()(const platform::DeviceContext& context,
const framework::Tensor& input,
framework::Tensor * input_grad,
const framework::Tensor& output,
const framework::Tensor& output_grad,
int groups) {
const int batch_size = input.dims()[0];
const int input_channels = input.dims()[1];
const int input_height = input.dims()[2];
const int input_width = input.dims()[3];
const int output_channels = output.dims()[1];
const int output_height = output.dims()[2];
const int output_width = output.dims()[3];
const T* input_data = input.data<T>();
const T* output_data = output.data<T>();
const T* output_grad_data = output_grad.data<T>();
T* input_grad_data = input_grad->mutable_data<T>(context.GetPlace());
int nthreads = output.numel();
int blocks = (nthreads + 1024 - 1) / 1024;
dim3 threads(1024, 1);
dim3 grid(blocks, 1);
KernelMaxoutGrad<
T><<<grid, threads, 0,
reinterpret_cast<const platform::CUDADeviceContext&>(context)
.stream()>>>(
nthreads, input_data, output_data, output_grad_data, input_grad_data,
input_channels, input_height, input_width, groups);
}
};
template class MaxOutGradFunctor<platform::GPUPlace, float>;
template class MaxOutGradFunctor<platform::GPUPlace, double>;
template class MaxOutFunctor<platform::GPUPlace, float>;
template class MaxOutFunctor<platform::GPUPlace, double>;
} // namespace math
} // namespace operators
} // namespace paddle
paddle/operators/math/maxouting.h 0 → 100644
浏览文件 @ 36dd770a
/* 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 "paddle/framework/tensor.h"
#include "paddle/platform/device_context.h"
#include "paddle/platform/hostdevice.h"
namespace paddle {
namespace operators {
namespace math {
#define FLT_MAX \
__FLT_MAX__
template <typename Place, typename T>
class MaxOutFunctor {
public:
void operator()(const platform::DeviceContext& context,
const framework::Tensor& input, framework::Tensor * output,
int groups);
};
template <typename Place, class T>
class MaxOutGradFunctor {
public:
void operator()(const platform::DeviceContext& context,
const framework::Tensor& input,
framework::Tensor * input_grad,
const framework::Tensor& output,
const framework::Tensor& output_grad, int groups);
};
} // namespace math
} // namespace operators
} // namespace paddle
paddle/operators/maxout_op.cc 0 → 100644
浏览文件 @ 36dd770a
/* 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 "paddle/operators/maxout_op.h"
namespace paddle {
namespace operators {
using framework::Tensor;
class MaxOutOpMaker : public framework::OpProtoAndCheckerMaker {
public:
MaxOutOpMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X",
"(Tensor) The input tensor of maxout operator. "
"The format of input tensor is NCHW. Where N is batch size, C is the "
"number of channels, H and W is the height and width of feature.");
AddOutput("Out",
"(Tensor) The output tensor of maxout operator."
"The format of output tensor is also NCHW."
"Where N is batch size, C is "
"the number of channels, H and W is the height and "
"width of feature.");
AddAttr<int>(
"groups",
R"DOC("Specifies how many groups the input tensor will be split"
"in the channel dimension. And the number of output channel is "
"the number of channels divided by groups.."
)DOC");
AddComment(R"DOC(
Assumed the input shape is (N, Ci, H, W).
The output shape is (N, Co, H, W). Then `Co = Ci / groups`.
math:
y_{si+j} = \max_k x_{gsi + sk + j}
g = groups
s = input.size / num_channels
0 \le i < num_channels / groups
0 \le j < s
0 \le k < groups
Please refer to Paper:
- Maxout Networks: http://www.jmlr.org/proceedings/papers/v28/goodfellow13.pdf
- Multi-digit Number Recognition from Street View \
Imagery using Deep Convolutional Neural Networks: \
https://arxiv.org/pdf/1312.6082v4.pdf
)DOC");
}
};
class MaxOutOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) of MaxoutOp"
"should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of MaxoutOp should not be null.");
auto in_x_dims = ctx->GetInputDim("X");
int groups = ctx->Attrs().Get<int>("groups");
// check groups > 1
PADDLE_ENFORCE_GT(
groups, 1,
"groups should be larger than 1 in maxoutop");
std::vector<int64_t> output_shape({in_x_dims[0], in_x_dims[1] / groups});
output_shape.push_back(in_x_dims[2]);
output_shape.push_back(in_x_dims[3]);
ctx->SetOutputDim("Out", framework::make_ddim(output_shape));
}
};
class MaxOutOpGrad : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) must not be null.");
PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("X")),
"Input(X@GRAD) should not be null.");
ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP(maxout, ops::MaxOutOp, ops::MaxOutOpMaker, maxout_grad,
ops::MaxOutOpGrad);
REGISTER_OP_CPU_KERNEL(maxout, ops::MaxOutKernel<paddle::platform::CPUPlace,
float>);
REGISTER_OP_CPU_KERNEL(maxout_grad,
ops::MaxOutGradKernel<paddle::platform::CPUPlace,
float>);
paddle/operators/maxout_op.cu.cc 0 → 100644
浏览文件 @ 36dd770a
/* 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 "paddle/operators/maxout_op.h"
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(maxout,
ops::MaxOutKernel<paddle::platform::GPUPlace, float>,
ops::MaxOutKernel<paddle::platform::GPUPlace, double>);
REGISTER_OP_GPU_KERNEL(maxout_grad,
ops::MaxOutGradKernel<paddle::platform::GPUPlace,
float>,
ops::MaxOutGradKernel<paddle::platform::GPUPlace,
double>);
paddle/operators/maxout_op.h 0 → 100644
浏览文件 @ 36dd770a
/* 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 "paddle/framework/op_registry.h"
#include "paddle/operators/math/math_function.h"
#include "paddle/operators/math/maxouting.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
template <typename Place, typename T>
class MaxOutKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
const Tensor* in_x = context.Input<Tensor>("X");
Tensor* out = context.Output<Tensor>("Out");
int groups = context.template Attr<int>("groups");
math::MaxOutFunctor<Place, T> maxout_forward;
maxout_forward(context.device_context(), *in_x, out, groups);
}
};
template <typename Place, typename T>
class MaxOutGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
const Tensor* in_x = context.Input<Tensor>("X");
const Tensor* out = context.Input<Tensor>("Out");
const Tensor* out_grad =
context.Input<Tensor>(framework::GradVarName("Out"));
Tensor* in_x_grad = context.Output<Tensor>(framework::GradVarName("X"));
int groups = context.template Attr<int>("groups");
auto& device_ctx = context.device_context();
math::SetConstant<Place, T> zero;
if (in_x_grad) {
in_x_grad->mutable_data<T>(context.GetPlace());
zero(device_ctx, in_x_grad, static_cast<T>(0.0));
math::MaxOutGradFunctor<Place, T> maxout_backward;
maxout_backward(context.device_context(), *in_x, in_x_grad, *out,
*out_grad, groups);
}
}
};
} // namespace operators
} // namespace paddle
paddle/operators/sequence_conv_op.cc
浏览文件 @ 36dd770a
......@@ -179,7 +179,9 @@ REGISTER_OP(sequence_conv, ops::SequenceConvOp, ops::SequenceConvOpMaker,
sequence_conv_grad, ops::SequenceConvGradOp);
REGISTER_OP_CPU_KERNEL(
sequence_conv, ops::SequenceConvKernel<paddle::platform::CPUPlace, float>);
sequence_conv, ops::SequenceConvKernel<paddle::platform::CPUPlace, float>,
ops::SequenceConvKernel<paddle::platform::CPUPlace, double>);
REGISTER_OP_CPU_KERNEL(
sequence_conv_grad,
ops::SequenceConvGradKernel<paddle::platform::CPUPlace, float>);
ops::SequenceConvGradKernel<paddle::platform::CPUPlace, float>,
ops::SequenceConvGradKernel<paddle::platform::CPUPlace, double>);
paddle/operators/sequence_conv_op.cu.cc
浏览文件 @ 36dd770a
......@@ -16,7 +16,9 @@
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(
sequence_conv, ops::SequenceConvKernel<paddle::platform::GPUPlace, float>);
sequence_conv, ops::SequenceConvKernel<paddle::platform::GPUPlace, float>,
ops::SequenceConvKernel<paddle::platform::GPUPlace, double>);
REGISTER_OP_GPU_KERNEL(
sequence_conv_grad,
ops::SequenceConvGradKernel<paddle::platform::GPUPlace, float>);
ops::SequenceConvGradKernel<paddle::platform::GPUPlace, float>,
ops::SequenceConvGradKernel<paddle::platform::GPUPlace, double>);
paddle/scripts/docker/build.sh
浏览文件 @ 36dd770a
......@@ -144,7 +144,7 @@ function gen_dockerfile() {
DOCKERFILE_GPU_ENV=""
DOCKERFILE_CUDNN_DSO=""
if [[ ${WITH_GPU:-OFF} == 'ON' ]]; then
DOCKERFILE_GPU_ENV="ENV LD_LIBRARY_PATH /usr/lib/x86_64-linux-gnu:${LD_LIBRARY_PATH}"
DOCKERFILE_GPU_ENV="ENV LD_LIBRARY_PATH /usr/lib/x86_64-linux-gnu:\${LD_LIBRARY_PATH}"
DOCKERFILE_CUDNN_DSO="RUN ln -s /usr/lib/x86_64-linux-gnu/libcudnn.so.5 /usr/lib/x86_64-linux-gnu/libcudnn.so"
fi
......
paddle/trainer/Trainer.cpp
浏览文件 @ 36dd770a
......@@ -138,7 +138,7 @@ void Trainer::init(const std::shared_ptr<TrainerConfigHelper>& config,
}
if (FLAGS_use_mkldnn) {
CHECK_EQ(FLAGS_trainer_count, 1UL) << "MKLDNN only need 1 trainer";
CHECK_EQ(FLAGS_trainer_count, 1) << "MKLDNN only need 1 trainer";
}
if (testing) {
......
paddle/trainer/tests/CMakeLists.txt
浏览文件 @ 36dd770a
......@@ -11,7 +11,6 @@ add_unittest_without_exec(test_Trainer
test_Trainer.cpp)
add_test(NAME test_Trainer
COMMAND ${PADDLE_SOURCE_DIR}/paddle/.set_python_path.sh -d ${PADDLE_SOURCE_DIR}/python/
${PYTHON_EXECUTABLE} ${PADDLE_SOURCE_DIR}/paddle/trainer/tests/gen_proto_data.py &&
${PADDLE_SOURCE_DIR}/paddle/.set_python_path.sh -d ${PADDLE_SOURCE_DIR}/python/
${CMAKE_CURRENT_BINARY_DIR}/test_Trainer
WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle/)
......
paddle/trainer/tests/chunking.conf 已删除 100644 → 0
浏览文件 @ 79609288
#edit-mode: -*- python -*-
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# 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.
#Todo(luotao02) This config is only used for unitest. It is out of date now, and will be updated later.
TrainData(ProtoData(
files = 'trainer/tests/train_files.txt',
usage_ratio = 1.0,
))
TestData(ProtoData(
files = 'trainer/tests/test_files.txt'
))
default_initial_std(1)
default_decay_rate(4e-4)
default_device(0)
Inputs("features", "word", "pos", "chunk")
Outputs("crf")
Layer(
name = "features",
type = "data",
size = 4339,
)
Layer(
name = "word",
type = "data",
size = 478,
)
Layer(
name = "pos",
type = "data",
size = 45
)
Layer(
name = "chunk",
type = "data",
size = 23
)
Layer(
name = "output",
type = "mixed",
size = 23,
bias = False,
device = -1,
inputs = [
FullMatrixProjection("features", parameter_name="feature_weights"),
# TableProjection("word"),
# TableProjection("pos"),
],
)
Layer(
name = "crf",
type = "crf",
size = 23,
device = -1,
inputs = [
Input("output", parameter_name="crfw"),
"chunk"
]
)
Layer(
name = "crf_decoding",
type = "crf_decoding",
size = 23,
device = -1,
inputs = [
Input("output", parameter_name="crfw"),
"chunk"
]
)
Evaluator(
name = "error",
type = "sum",
inputs = "crf_decoding",
)
'''
# chuck evaluator cannot be used for GPU training
Evaluator(
name = "chunk_f1",
type = "chunk",
inputs = ["crf_decoding", "chunk"],
chunk_scheme = "IOB",
num_chunk_types = 11,
)
'''
Settings(
algorithm = 'sgd',
batch_size = 100,
average_window = 0.5,
max_average_window = 2500,
learning_rate = 1e-1,
learning_rate_decay_a = 5e-7,
learning_rate_decay_b = 0.75,
l1weight = 0,
l2weight = 1,
c1 = 0.0001,
backoff = 0.5,
owlqn_steps = 100,
max_backoff = 5,
)
paddle/trainer/tests/data_bin_part 已删除 100644 → 0
浏览文件 @ 79609288
因为 它太大了无法显示 source diff 。你可以改为 查看blob。
paddle/trainer/tests/gen_proto_data.py 已删除 100644 → 0
浏览文件 @ 79609288
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# 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.
from cStringIO import StringIO
import paddle.proto.DataFormat_pb2 as DataFormat
from google.protobuf.internal.encoder import _EncodeVarint
import logging
import pprint
logging.basicConfig(
format='[%(levelname)s %(asctime)s %(filename)s:%(lineno)s] %(message)s', )
logger = logging.getLogger('paddle')
logger.setLevel(logging.INFO)
OOV_POLICY_IGNORE = 0
OOV_POLICY_USE = 1
OOV_POLICY_ERROR = 2
num_original_columns = 3
# Feature combination patterns.
# [[-1,0], [0,0]] means previous token at column 0 and current token at
# column 0 are combined as one feature.
patterns = [
[[-2, 0]],
[[-1, 0]],
[[0, 0]],
[[1, 0]],
[[2, 0]],
[[-1, 0], [0, 0]],
[[0, 0], [1, 0]],
[[-2, 1]],
[[-1, 1]],
[[0, 1]],
[[1, 1]],
[[2, 1]],
[[-2, 1], [-1, 1]],
[[-1, 1], [0, 1]],
[[0, 1], [1, 1]],
[[1, 1], [2, 1]],
[[-2, 1], [-1, 1], [0, 1]],
[[-1, 1], [0, 1], [1, 1]],
[[0, 1], [1, 1], [2, 1]],
]
def make_features(sequence):
length = len(sequence)
num_features = len(sequence[0])
def get_features(pos):
if pos < 0:
return ['#B%s' % -pos] * num_features
if pos >= length:
return ['#E%s' % (pos - length + 1)] * num_features
return sequence[pos]
for i in xrange(length):
for pattern in patterns:
fname = '/'.join([get_features(i + pos)[f] for pos, f in pattern])
sequence[i].append(fname)
'''
Source file format:
Each line is for one timestep. The features are separated by space.
An empty line indicates end of a sequence.
cutoff: a list of numbers. If count of a feature is smaller than this,
it will be ignored.
if oov_policy[i] is OOV_POLICY_USE, id 0 is reserved for OOV features of
i-th column.
return a list of dict for each column
'''
def create_dictionaries(filename, cutoff, oov_policy):
def add_to_dict(sequence, dicts):
num_features = len(dicts)
for features in sequence:
l = len(features)
assert l == num_features, "Wrong number of features " + line
for i in xrange(l):
if features[i] in dicts[i]:
dicts[i][features[i]] += 1
else:
dicts[i][features[i]] = 1
num_features = len(cutoff)
dicts = []
for i in xrange(num_features):
dicts.append(dict())
f = open(filename, 'rb')
sequence = []
for line in f:
line = line.strip()
if not line:
make_features(sequence)
add_to_dict(sequence, dicts)
sequence = []
continue
features = line.split(' ')
sequence.append(features)
for i in xrange(num_features):
dct = dicts[i]
n = 1 if oov_policy[i] == OOV_POLICY_USE else 0
todo = []
for k, v in dct.iteritems():
if v < cutoff[i]:
todo.append(k)
else:
dct[k] = n
n += 1
if oov_policy[i] == OOV_POLICY_USE:
# placeholder so that len(dct) will be the number of features
# including OOV
dct['#OOV#'] = 0
logger.info('column %d dict size=%d, ignored %d' % (i, n, len(todo)))
for k in todo:
del dct[k]
f.close()
return dicts
def encode_varint(v):
out = StringIO()
_EncodeVarint(out.write, v)
return out.getvalue()
def write_proto(file, message):
s = message.SerializeToString()
packed_len = encode_varint(len(s))
file.write(packed_len + s)
'''
if oov_policy[i] == OOV_POLICY_USE, features in i-th column which are not
existed in dicts[i] will be assigned to id 0.
if oov_policy[i] == OOV_POLICY_ERROR, all features in i-th column MUST exist
in dicts[i].
'''
def gen_proto_file(input_file, dicts, oov_policy, output_file):
def write_sequence(out, sequence):
num_features = len(dicts)
is_beginning = True
for features in sequence:
assert len(features) == num_features, \
"Wrong number of features: " + line
sample = DataFormat.DataSample()
for i in xrange(num_original_columns):
id = dicts[i].get(features[i], -1)
if id != -1:
sample.id_slots.append(id)
elif oov_policy[i] == OOV_POLICY_IGNORE:
sample.id_slots.append(0xffffffff)
elif oov_policy[i] == OOV_POLICY_ERROR:
logger.fatal("Unknown token: %s" % features[i])
else:
sample.id_slots.append(0)
if patterns:
dim = 0
vec = sample.vector_slots.add()
for i in xrange(num_original_columns, num_features):
id = dicts[i].get(features[i], -1)
if id != -1:
vec.ids.append(dim + id)
elif oov_policy[i] == OOV_POLICY_IGNORE:
pass
elif oov_policy[i] == OOV_POLICY_ERROR:
logger.fatal("Unknown token: %s" % features[i])
else:
vec.ids.append(dim + 0)
dim += len(dicts[i])
sample.is_beginning = is_beginning
is_beginning = False
write_proto(out, sample)
num_features = len(dicts)
f = open(input_file, 'rb')
out = open(output_file, 'wb')
header = DataFormat.DataHeader()
if patterns:
slot_def = header.slot_defs.add()
slot_def.type = DataFormat.SlotDef.VECTOR_SPARSE_NON_VALUE
slot_def.dim = sum(
[len(dicts[i]) for i in xrange(num_original_columns, len(dicts))])
logger.info("feature_dim=%s" % slot_def.dim)
for i in xrange(num_original_columns):
slot_def = header.slot_defs.add()
slot_def.type = DataFormat.SlotDef.INDEX
slot_def.dim = len(dicts[i])
write_proto(out, header)
num_sequences = 0
sequence = []
for line in f:
line = line.strip()
if not line:
make_features(sequence)
write_sequence(out, sequence)
sequence = []
num_sequences += 1
continue
features = line.split(' ')
sequence.append(features)
f.close()
out.close()
logger.info("num_sequences=%s" % num_sequences)
dict2 = {
'B-ADJP': 0,
'I-ADJP': 1,
'B-ADVP': 2,
'I-ADVP': 3,
'B-CONJP': 4,
'I-CONJP': 5,
'B-INTJ': 6,
'I-INTJ': 7,
'B-LST': 8,
'I-LST': 9,
'B-NP': 10,
'I-NP': 11,
'B-PP': 12,
'I-PP': 13,
'B-PRT': 14,
'I-PRT': 15,
'B-SBAR': 16,
'I-SBAR': 17,
'B-UCP': 18,
'I-UCP': 19,
'B-VP': 20,
'I-VP': 21,
'O': 22
}
if __name__ == '__main__':
cutoff = [3, 1, 0]
cutoff += [3] * len(patterns)
oov_policy = [OOV_POLICY_IGNORE, OOV_POLICY_ERROR, OOV_POLICY_ERROR]
oov_policy += [OOV_POLICY_IGNORE] * len(patterns)
dicts = create_dictionaries('trainer/tests/train.txt', cutoff, oov_policy)
dicts[2] = dict2
gen_proto_file('trainer/tests/train.txt', dicts, oov_policy,
'trainer/tests/train_proto.bin')
gen_proto_file('trainer/tests/test.txt', dicts, oov_policy,
'trainer/tests/test_proto.bin')
paddle/trainer/tests/test.txt 已删除 100644 → 0
浏览文件 @ 79609288
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the DT B-NP
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is VBZ B-VP
widely RB I-VP
expected VBN I-VP
to TO I-VP
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from IN B-PP
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and CC I-NP
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near-record JJ I-NP
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the DT B-NP
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restated VBN I-NP
commitment NN I-NP
to TO B-PP
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has VBZ B-VP
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to TO I-VP
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for IN B-PP
sterling NN B-NP
has VBZ B-VP
been VBN I-VP
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by IN B-PP
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to TO B-VP
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to TO I-VP
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16 CD B-NP
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from IN B-PP
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pound NN I-NP
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say VBP B-VP
. . O
`` `` O
The DT B-NP
risks NNS I-NP
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senior JJ B-NP
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. . O
`` `` O
If IN B-SBAR
there EX B-NP
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, , O
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be VB I-VP
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, , O
'' '' O
noted VBD B-VP
Simon NNP B-NP
Briscoe NNP I-NP
, , O
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for IN B-PP
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Montagu NNP I-NP
, , O
a DT B-NP
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. . O
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for IN B-PP
the DT B-NP
trade NN I-NP
figures NNS I-NP
range VBP B-VP
widely RB B-ADVP
, , O
but CC O
few JJ B-NP
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expect VBP B-VP
the DT B-NP
data NNS I-NP
to TO B-VP
show VB I-VP
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from IN B-PP
the DT O
# # O
2 CD O
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for IN B-PP
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. . O
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and CC O
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in IN B-PP
July NNP B-NP
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only RB B-ADVP
by IN B-PP
the DT B-NP
# # I-NP
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of IN B-PP
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1988 CD I-NP
. . O
Sanjay NNP B-NP
Joshi NNP I-NP
, , O
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economist NN I-NP
at IN B-PP
Baring NNP B-NP
Brothers NNPS I-NP
& CC I-NP
Co. NNP I-NP
, , O
said VBD B-VP
there EX B-NP
is VBZ B-VP
no DT B-NP
sign NN I-NP
that IN B-SBAR
Britain NNP B-NP
's POS B-NP
manufacturing NN I-NP
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is VBZ B-VP
transforming VBG I-VP
itself PRP B-NP
to TO B-VP
boost VB I-VP
exports NNS B-NP
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the DT B-NP
same JJ I-NP
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, , O
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remains VBZ B-VP
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about IN B-PP
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outlook NN I-NP
for IN B-PP
imports NNS B-NP
, , O
given VBN B-PP
continued VBD B-NP
high JJ I-NP
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and CC I-NP
capital NN I-NP
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inflows NNS I-NP
. . O
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current JJ I-NP
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to TO B-PP
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# # I-NP
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in IN B-PP
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. . O
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, , O
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he PRP B-NP
believes VBZ B-VP
that IN B-SBAR
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in IN B-PP
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by IN B-PP
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lead VB I-VP
to TO B-PP
a DT B-NP
sharp JJ I-NP
drop NN I-NP
in IN B-PP
imports NNS B-NP
. . O
Combined VBN B-PP
with IN B-PP
at IN B-ADVP
least JJS I-ADVP
some DT B-NP
rebound NN I-NP
in IN B-PP
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after IN B-PP
August NNP B-NP
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unexpected JJ I-NP
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, , O
the DT B-NP
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could MD B-VP
narrow VB I-VP
to TO B-PP
as RB B-NP
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as IN I-NP
# # I-NP
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billion CD I-NP
. . O
Mr. NNP B-NP
Briscoe NNP I-NP
, , O
who WP B-NP
also RB B-ADVP
forecasts VBZ B-VP
a DT B-NP
# # I-NP
1.3 CD I-NP
billion CD I-NP
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account NN I-NP
gap NN I-NP
, , O
warns VBZ B-VP
that IN B-SBAR
even RB B-SBAR
if IN I-SBAR
the DT B-NP
trade NN I-NP
figures NNS I-NP
are VBP B-VP
bullish JJ B-ADJP
for IN B-PP
sterling NN B-NP
, , O
the DT B-NP
currency NN I-NP
wo MD B-VP
n't RB I-VP
advance VB I-VP
much JJ B-NP
because IN B-SBAR
investors NNS B-NP
will MD B-VP
want VB I-VP
to TO I-VP
see VB I-VP
further JJ B-NP
evidence NN I-NP
of IN B-PP
the DT B-NP
turnaround NN I-NP
before IN B-PP
adjusting VBG B-VP
positions NNS B-NP
. . O
Nevertheless RB B-ADVP
, , O
he PRP B-NP
noted VBD B-VP
, , O
`` `` O
No DT B-NP
one PRP I-NP
will MD B-VP
want VB I-VP
to TO I-VP
go VB I-VP
into IN B-PP
the DT B-NP
trade NN I-NP
figures NNS I-NP
without IN B-PP
a DT B-NP
flat JJ I-NP
position NN I-NP
'' '' O
in IN B-PP
the DT B-NP
pound NN I-NP
. . O
Meanwhile RB B-ADVP
, , O
overall JJ B-NP
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on IN B-PP
the DT B-NP
economy NN I-NP
remains VBZ B-VP
fairly RB B-ADJP
clouded VBN I-ADJP
. . O
In IN B-PP
his PRP$ B-NP
Mansion NNP I-NP
House NNP I-NP
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, , O
Mr. NNP B-NP
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warned VBD B-VP
that IN B-SBAR
a DT B-NP
further JJ I-NP
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can MD B-VP
be VB I-VP
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as IN B-SBAR
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impact NN I-NP
of IN B-PP
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last JJ I-NP
rise NN I-NP
in IN B-PP
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earlier RBR B-NP
this DT I-NP
month NN I-NP
takes VBZ B-VP
effect NN B-NP
. . O
U.K. JJ B-NP
base NN I-NP
rates NNS I-NP
are VBP B-VP
at IN B-PP
their PRP$ B-NP
highest JJS I-NP
level NN I-NP
in IN B-PP
eight CD B-NP
years NNS I-NP
. . O
But CC O
consumer NN B-NP
expenditure NN I-NP
data NNS I-NP
released VBD B-VP
Friday NNP B-NP
do VBP B-VP
n't RB I-VP
suggest VB I-VP
that IN B-SBAR
the DT B-NP
U.K. NNP I-NP
economy NN I-NP
is VBZ B-VP
slowing VBG I-VP
that DT B-ADVP
quickly RB I-ADVP
. . O
The DT B-NP
figures NNS I-NP
show VBP B-VP
that DT O
spending NN B-NP
rose VBD B-VP
0.1 CD B-NP
% NN I-NP
in IN B-PP
the DT B-NP
third JJ I-NP
quarter NN I-NP
from IN B-PP
the DT B-NP
second JJ I-NP
quarter NN I-NP
and CC O
was VBD B-VP
up IN B-ADVP
3.8 CD B-NP
% NN I-NP
from IN B-PP
a DT B-NP
year NN I-NP
ago RB B-ADVP
. . O
This DT B-NP
compares VBZ B-VP
with IN B-PP
a DT B-NP
1.6 CD I-NP
% NN I-NP
rise NN I-NP
in IN B-PP
the DT B-NP
second NN I-NP
from IN B-PP
the DT B-NP
first JJ I-NP
quarter NN I-NP
and CC O
a DT B-NP
5.4 CD I-NP
% NN I-NP
increase NN I-NP
from IN B-PP
the DT B-NP
second JJ I-NP
quarter NN I-NP
of IN B-PP
1988 CD B-NP
. . O
Mr. NNP B-NP
Dillow NNP I-NP
said VBD B-VP
the DT B-NP
data NNS I-NP
show VBP B-VP
the DT B-NP
economy NN I-NP
`` `` O
is VBZ B-VP
still RB B-ADVP
quite RB B-ADJP
strong JJ I-ADJP
, , O
'' '' O
but CC O
suggestions NNS B-NP
that IN B-SBAR
much NN B-NP
of IN B-PP
the DT B-NP
spending NN I-NP
went VBD B-VP
on IN B-PP
services NNS B-NP
rather RB B-PP
than IN I-PP
consumer NN B-NP
goods NNS I-NP
should MD B-VP
reduce VB I-VP
fears NNS B-NP
of IN B-PP
more JJR B-NP
import NN I-NP
rises NNS I-NP
. . O
Certainly RB B-ADVP
, , O
the DT B-NP
chancellor NN I-NP
has VBZ B-VP
made VBN I-VP
it PRP B-NP
clear JJ B-ADJP
that IN B-SBAR
he PRP B-NP
is VBZ B-VP
prepared VBN I-VP
to TO I-VP
increase VB I-VP
interest NN B-NP
rates NNS I-NP
again RB B-ADVP
if IN B-SBAR
necessary JJ B-ADJP
to TO B-VP
both DT I-VP
ensure VB I-VP
that IN B-SBAR
a DT B-NP
substantial JJ I-NP
slowdown NN I-NP
does VBZ B-VP
take VB I-VP
place NN B-NP
and CC O
that DT O
sterling NN B-NP
does VBZ B-VP
n't RB I-VP
decline VB I-VP
further JJ B-ADVP
. . O
Thursday NNP B-NP
, , O
he PRP B-NP
reminded VBD B-VP
his PRP$ B-NP
audience NN I-NP
that IN B-SBAR
the DT B-NP
government NN I-NP
`` `` O
can MD B-VP
not RB I-VP
allow VB I-VP
the DT B-NP
necessary JJ I-NP
rigor NN I-NP
of IN B-PP
monetary JJ B-NP
policy NN I-NP
to TO B-VP
be VB I-VP
undermined VBN I-VP
by IN B-PP
exchange NN B-NP
rate NN I-NP
weakness NN I-NP
. . O
'' '' O
Analysts NNS B-NP
agree VBP B-VP
there EX B-NP
is VBZ B-VP
little JJ B-NP
holding NN B-VP
sterling NN B-NP
firm NN B-ADJP
at IN B-PP
the DT B-NP
moment NN I-NP
other JJ B-ADJP
than IN B-PP
Mr. NNP B-NP
Lawson NNP I-NP
's POS B-NP
promise NN I-NP
that IN B-SBAR
rates NNS B-NP
will MD B-VP
be VB I-VP
pushed VBN I-VP
higher JJR B-ADJP
if IN B-SBAR
necessary JJ B-ADJP
. . O
And CC O
, , O
they PRP B-NP
warn VBP B-VP
, , O
any DT B-NP
further JJ I-NP
drop NN I-NP
in IN B-PP
the DT B-NP
government NN I-NP
's POS B-NP
popularity NN I-NP
could MD B-VP
swiftly RB I-VP
make VB I-VP
this DT B-NP
promise NN I-NP
sound NN B-VP
hollow JJ B-ADJP
. . O
Sterling NNP B-NP
was VBD B-VP
already RB I-VP
showing VBG I-VP
some DT B-NP
signs NNS I-NP
of IN B-PP
a DT B-NP
lack NN I-NP
of IN B-PP
confidence NN B-NP
in IN B-PP
Mr. NNP B-NP
Lawson NNP I-NP
's POS B-NP
promise NN I-NP
Friday NNP B-NP
. . O
In IN B-PP
European JJ B-NP
trading NN I-NP
it PRP B-NP
declined VBD B-VP
to TO B-PP
$ $ B-NP
1.5890 CD I-NP
and CC O
2.9495 CD B-NP
marks NNS I-NP
from IN B-PP
$ $ B-NP
1.5940 CD I-NP
and CC O
2.9429 CD B-NP
marks NNS I-NP
late JJ B-NP
Thursday NNP I-NP
. . O
Economists NNS B-NP
suggested VBD B-VP
that IN B-SBAR
if IN B-SBAR
the DT B-NP
pound NN I-NP
falls VBZ B-VP
much JJ B-NP
below IN B-PP
2.90 CD B-NP
marks NNS I-NP
, , O
the DT B-NP
government NN I-NP
will MD B-VP
be VB I-VP
forced VBN I-VP
to TO I-VP
increase VB I-VP
rates NNS B-NP
to TO B-PP
16 CD B-NP
% NN I-NP
, , O
both DT B-VP
to TO I-VP
halt VB B-VP
any DT B-NP
further JJ I-NP
decline NN I-NP
and CC O
ensure VB B-VP
that IN B-SBAR
the DT B-NP
balance NN I-NP
of IN B-PP
monetary JJ B-NP
policy NN I-NP
remains VBZ B-VP
unchanged JJ B-ADJP
. . O
Friday NNP B-NP
's POS B-NP
Market NNP I-NP
Activity NN I-NP
The DT B-NP
dollar NN I-NP
posted VBD B-VP
gains NNS B-NP
in IN B-PP
quiet JJ B-NP
trading NN I-NP
as IN B-SBAR
concerns NNS B-NP
about IN B-PP
equities NNS B-NP
abated VBN B-VP
. . O
Foreign JJ B-NP
exchange NN I-NP
dealers NNS I-NP
said VBD B-VP
that IN B-SBAR
the DT B-NP
currency NN I-NP
market NN I-NP
has VBZ B-VP
begun VBN I-VP
to TO I-VP
distance VB I-VP
itself PRP B-NP
from IN B-PP
the DT B-NP
volatile JJ I-NP
stock NN I-NP
exchange NN I-NP
, , O
which WDT B-NP
has VBZ B-VP
preoccupied VBN I-VP
the DT B-NP
market NN I-NP
since IN B-PP
Oct. NNP B-NP
13 CD I-NP
, , O
when WRB B-ADVP
the DT B-NP
Dow NNP I-NP
Jones NNP I-NP
Industrial NNP I-NP
Average NNP I-NP
plunged VBD B-VP
more JJR B-NP
than IN I-NP
190 CD I-NP
points NNS I-NP
. . O
Currency NN B-NP
analysts NNS I-NP
predict VBP B-VP
that IN B-SBAR
in IN B-PP
the DT B-NP
coming VBG I-NP
week NN I-NP
the DT B-NP
foreign JJ I-NP
exchange NN I-NP
market NN I-NP
will MD B-VP
shift VB I-VP
its PRP$ B-NP
focus NN I-NP
back RB B-ADVP
to TO B-PP
economic JJ B-NP
fundamentals NNS I-NP
, , O
keeping VBG B-VP
a DT B-NP
close NN I-NP
eye NN I-NP
out IN B-ADVP
for IN B-PP
any DT B-NP
signs NNS I-NP
of IN B-PP
monetary JJ B-NP
easing NN I-NP
by IN B-PP
U.S. NNP B-NP
Federal NNP I-NP
Reserve NNP I-NP
. . O
Late RB B-ADVP
in IN B-PP
the DT B-NP
New NNP I-NP
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trading NN I-NP
day NN I-NP
, , O
the DT B-NP
dollar NN I-NP
was VBD B-VP
quoted VBN I-VP
at IN B-PP
1.8578 CD B-NP
marks NNS I-NP
, , O
up IN B-ADVP
from IN B-PP
1.8470 CD B-NP
marks NNS I-NP
late JJ B-NP
Thursday NNP I-NP
in IN B-PP
New NNP B-NP
York NNP I-NP
. . O
The DT B-NP
U.S. NNP I-NP
currency NN I-NP
was VBD B-VP
also RB I-VP
changing VBG I-VP
hands NNS B-NP
at IN B-PP
142.43 CD B-NP
yen NN I-NP
, , O
up IN B-ADVP
from IN B-PP
141.70 CD B-NP
yen NN I-NP
in IN B-PP
New NNP B-NP
York NNP I-NP
late JJ B-NP
Thursday NNP I-NP
. . O
In IN B-PP
Tokyo NNP B-NP
on IN B-PP
Monday NNP B-NP
, , O
the DT B-NP
U.S. NNP I-NP
currency NN I-NP
opened VBD B-VP
for IN B-PP
trading NN B-NP
at IN B-PP
141.95 CD B-NP
yen NN I-NP
, , O
up IN B-ADVP
from IN B-PP
Friday NNP B-NP
's POS B-NP
Tokyo NNP I-NP
paddle/trainer/tests/test_Trainer.cpp
浏览文件 @ 36dd770a
......@@ -24,7 +24,6 @@ using namespace std; // NOLINT
static const string& configFile1 = "trainer/tests/sample_trainer_config.conf";
static const string& configFile2 =
"trainer/tests/sample_trainer_config_hsigmoid.conf";
static const string& configFile3 = "trainer/tests/chunking.conf";
static const string& configFile4 =
"trainer/tests/sample_trainer_config_parallel.conf";
......@@ -95,13 +94,6 @@ TEST(checkGradient, multi) {
TEST(checkGradient, hsigmoid) { checkGradientTest(configFile2, false, false); }
TEST(checkGradient, chunk) {
checkGradientTest(configFile3, false, false);
#ifdef PADDLE_WITH_CUDA
checkGradientTest(configFile3, true, true);
#endif
}
TEST(checkGradient, non_parallel) {
checkGradientTest(configFile4, false, false);
}
......
paddle/trainer/tests/test_config.conf
浏览文件 @ 36dd770a
......@@ -15,12 +15,7 @@
from paddle.trainer_config_helpers import *
TrainData(ProtoData(
files = "dummy_list",
constant_slots = [1.0],
async_load_data = True))
TestData(SimpleData(
TrainData(SimpleData(
files = "trainer/tests/sample_filelist.txt",
feat_dim = 3,
context_len = 0,
......
paddle/trainer/tests/train.txt 已删除 100644 → 0
浏览文件 @ 79609288
此差异已折叠。
python/paddle/trainer/config_parser.py
浏览文件 @ 36dd770a
......@@ -1116,35 +1116,6 @@ def PyData(files=None,
return data_config
@config_func
def ProtoData(files=None,
type=None,
file_group_queue_capacity=None,
load_file_count=None,
constant_slots=None,
load_thread_num=None,
**xargs):
data_config = create_data_config_proto(**xargs)
if type is None:
data_config.type = 'proto'
else:
data_config.type = type
data_config.files = files
# When type="proto_group", one data provider contains at most
# load_file_count files, and there are at most
# (queue_capacity + load_thread_num + 1) data providers in memory
if file_group_queue_capacity is not None:
data_config.file_group_conf.queue_capacity = file_group_queue_capacity
if load_file_count is not None:
data_config.file_group_conf.load_file_count = load_file_count
if load_thread_num is not None:
data_config.file_group_conf.load_thread_num = load_thread_num
if constant_slots:
data_config.constant_slots.extend(constant_slots)
return data_config
#real data for training is actually provided by "sub_data" data providers.
@config_func
def MultiData(sub_data=[]):
......@@ -2066,13 +2037,20 @@ class ParameterReluLayer(LayerBase):
def __init__(self, name, inputs, partial_sum=1, **args):
super(ParameterReluLayer, self).__init__(
name, self.layer_type, 0, inputs=inputs, **args)
input_layer = self.get_input_layer(0)
config_assert(len(self.inputs) == 1, "prelu layer has only one input.")
config_assert(input_layer.size % partial_sum == 0,
"a wrong setting for partial_sum")
dims = [1, input_layer.size / partial_sum]
self.set_layer_size(input_layer.size)
self.config.partial_sum = partial_sum
self.create_input_parameter(0, input_layer.size / partial_sum)
self.create_input_parameter(0, input_layer.size / partial_sum, dims)
self.set_layer_height_width(self.get_input_layer(0).height, \
self.get_input_layer(0).width)
self.set_layer_depth(self.get_input_layer(0).depth)
@config_layer('conv')
......@@ -2714,7 +2692,7 @@ Usage:
max_sort_size = -1, inputs = ["output", "score"])
Input data: Samples of the same query should be loaded as a sequence,
by ProtoDataProvider or PyDataProvider etc.. User should provide
by PyDataProvider etc.. User should provide
scores for each sample. The score slot should be the 2nd
input of lambdaRank layer.
......
python/paddle/trainer_config_helpers/activations.py
浏览文件 @ 36dd770a
......@@ -17,7 +17,8 @@ __all__ = [
"IdentityActivation", "LinearActivation", 'SequenceSoftmaxActivation',
'ExpActivation', "ReluActivation", "BReluActivation", "SoftReluActivation",
"STanhActivation", "AbsActivation", "SquareActivation", "BaseActivation",
"LogActivation", "SqrtActivation", "ReciprocalActivation"
"LogActivation", "SqrtActivation", "ReciprocalActivation",
"SoftSignActivation"
]
......@@ -243,8 +244,20 @@ class ReciprocalActivation(BaseActivation):
Reciprocal Activation.
.. math::
f(z) = 1/z
f(z)=\\frac{1}{z}
"""
def __init__(self):
BaseActivation.__init__(self, 'reciprocal', False)
class SoftSignActivation(BaseActivation):
"""
SoftSign Activation.
.. math::
f(z)=\\frac{z}{1 + |z|}
"""
def __init__(self):
BaseActivation.__init__(self, 'softsign', False)
python/paddle/trainer_config_helpers/evaluators.py
浏览文件 @ 36dd770a
......@@ -297,7 +297,7 @@ def auc_evaluator(
def pnpair_evaluator(
input,
label,
info,
query_id,
weight=None,
name=None, ):
"""
......@@ -308,16 +308,20 @@ def pnpair_evaluator(
.. code-block:: python
eval = pnpair_evaluator(input, label, info)
eval = pnpair_evaluator(input, label, query_id)
:param input: Input Layer name. The output prediction of network.
:type input: LayerOutput
:param label: Label layer name.
:type label: LayerOutput
:param info: Info layer name. (TODO, explaination)
:type info: LayerOutput
:param query_id: Query_id layer name. Query_id indicates that which query
each sample belongs to. Its shape should be
the same as output of Label layer.
:type query_id: LayerOutput
:param weight: Weight Layer name. It should be a matrix with size
[sample_num, 1]. (TODO, explaination)
[sample_num, 1] which indicates the weight of each sample.
The default weight of sample is 1 if the weight layer is None.
And the pair weight is the mean of the two samples' weight.
:type weight: LayerOutput
:param name: Evaluator name.
:type name: None|basestring
......@@ -326,8 +330,8 @@ def pnpair_evaluator(
input = [input]
if label:
input.append(label)
if info:
input.append(info)
if query_id:
input.append(query_id)
evaluator_base(
input=input,
type="pnpair",
......
python/paddle/trainer_config_helpers/layers.py
浏览文件 @ 36dd770a
......@@ -2507,12 +2507,12 @@ def img_conv_layer(input,
input is raw pixels of image(mono or RGB), or it may be the previous layer's
num_filters * num_group.
There are several group of filter in PaddlePaddle implementation.
Each group will process some channel of the inputs. For example, if an input
There are several groups of filters in PaddlePaddle implementation.
Each group will process some channels of the input. For example, if
num_channel = 256, group = 4, num_filter=32, the PaddlePaddle will create
32*4 = 128 filters to process inputs. The channels will be split into 4
pieces. First 256/4 = 64 channels will process by first 32 filters. The
rest channels will be processed by rest group of filters.
32*4 = 128 filters to process the input. The channels will be split into 4
pieces. First 256/4 = 64 channels will be processed by first 32 filters. The
rest channels will be processed by the rest groups of filters.
The example usage is:
......@@ -2528,53 +2528,68 @@ def img_conv_layer(input,
:type name: basestring
:param input: The input of this layer.
:type input: LayerOutput
:param filter_size: The x dimension of a filter kernel. Or input a tuple for
two image dimension.
:param filter_size: The dimensions of the filter kernel. If the parameter is
set to one integer, the two dimensions on x and y axises
will be same when filter_size_y is not set. If it is set
to a list, the first element indicates the dimension on
the x axis, and the second is used to specify the dimension
on the y axis when filter_size_y is not provided.
:type filter_size: int | tuple | list
:param filter_size_y: The y dimension of a filter kernel. Since PaddlePaddle
currently supports rectangular filters, the filter's
shape will be (filter_size, filter_size_y).
:type filter_size_y: int | None
:param filter_size_y: The dimension of the filter kernel on the y axis. If the parameter
is not set, it will be set automatically according to filter_size.
:type filter_size_y: int
:param num_filters: Each filter group's number of filter
:param act: Activation type. ReluActivation is the default activation.
:type act: BaseActivation
:param groups: Group size of filters.
:param groups: The group number. 1 is the default group number.
:type groups: int
:param stride: The x dimension of the stride. Or input a tuple for two image
dimension.
:param stride: The strides. If the parameter is set to one integer, the strides
on x and y axises will be same when stride_y is not set. If it is
set to a list, the first element indicates the stride on the x axis,
and the second is used to specify the stride on the y axis when
stride_y is not provided. 1 is the default value.
:type stride: int | tuple | list
:param stride_y: The y dimension of the stride.
:param stride_y: The stride on the y axis.
:type stride_y: int
:param padding: The x dimension of the padding. Or input a tuple for two
image dimension
:param padding: The padding sizes. If the parameter is set to one integer, the padding
sizes on x and y axises will be same when padding_y is not set. If it
is set to a list, the first element indicates the padding size on the
x axis, and the second is used to specify the padding size on the y axis
when padding_y is not provided. 0 is the default padding size.
:type padding: int | tuple | list
:param padding_y: The y dimension of the padding.
:param padding_y: The padding size on the y axis.
:type padding_y: int
:param dilation: The x dimension of the dilation. Or input a tuple for two
image dimension
:param dilation: The dimensions of the dilation. If the parameter is set to one integer,
the two dimensions on x and y axises will be same when dilation_y is not
set. If it is set to a list, the first element indicates the dimension
on the x axis, and the second is used to specify the dimension on the y
axis when dilation_y is not provided. 1 is the default dimension.
:type dilation: int | tuple | list
:param dilation_y: The y dimension of the dilation.
:param dilation_y: The dimension of the dilation on the y axis.
:type dilation_y: int
:param bias_attr: The bias attribute. If the parameter is set to False or an object
whose type is not ParameterAttribute, no bias is defined. If the
parameter is set to True, the bias is initialized to zero.
:type bias_attr: ParameterAttribute | None | bool | Any
:param num_channels: number of input channels. If None will be set
automatically from previous output.
:param num_channels: The number of input channels. If the parameter is not set or
set to None, its actual value will be automatically set to
the channel number of the input.
:type num_channels: int
:param param_attr: Convolution param attribute. None means default attribute
:param param_attr: The parameter attribute. See ParameterAttribute for
details.
:type param_attr: ParameterAttribute
:param shared_biases: Is biases will be shared between filters or not.
:param shared_biases: Whether biases will be shared between filters or not.
:type shared_biases: bool
:param layer_attr: Layer Extra Attribute.
:param layer_attr: The extra layer attributes. See ExtraLayerAttribute for
details.
:type layer_attr: ExtraLayerAttribute
:param trans: true if it is a convTransLayer, false if it is a convLayer
:param trans: True if it is a convTransLayer, False if it is a convLayer
:type trans: bool
:param layer_type: specify the layer_type, default is None. If trans=True,
layer_type has to be "exconvt" or "cudnn_convt",
otherwise layer_type has to be either "exconv" or
"cudnn_conv"
:type layer_type: String
:param layer_type: Specify the layer type. If the dilation's dimension on one axis is
larger than 1, layer_type has to be "cudnn_conv" or "cudnn_convt".
If trans=True, layer_type has to be "exconvt" or "cudnn_convt",
otherwise layer_type has to be either "exconv" or "cudnn_conv".
:type layer_type: basestring
:return: LayerOutput object.
:rtype: LayerOutput
"""
......@@ -2679,7 +2694,7 @@ def img_pool_layer(input,
"""
Image pooling Layer.
The details of pooling layer, please refer ufldl's pooling_ .
The details of pooling layer, please refer to ufldl's pooling_ .
.. _pooling: http://ufldl.stanford.edu/tutorial/supervised/Pooling/
......@@ -2711,32 +2726,37 @@ def img_pool_layer(input,
padding_y=2,
pool_type=MaxPooling())
:param padding: pooling padding width.
:param padding: The padding size on the x axis. 0 is the default padding size.
:type padding: int
:param padding_y: pooling padding height. It's equal to padding by default.
:type padding_y: int | None
:param name: name of pooling layer
:type name: basestring.
:param padding_y: The padding size on the y axis. If the parameter is not set
or set to None, it will be set to 'padding' automatically.
:param name: The name of this layer. It is optional.
:type name: basestring
:param input: The input of this layer.
:type input: LayerOutput
:param pool_size: pooling window width
:param pool_size: The pooling window length on the x axis.
:type pool_size: int
:param pool_size_y: pooling window height. It's eaqual to pool_size by default.
:type pool_size_y: int | None
:param num_channels: number of input channel.
:param pool_size_y: The pooling window length on the y axis. If the parameter is
not set or set to None, its actual value will be automatically
set to pool_size.
:type pool_size_y: int
:param num_channels: The number of input channels. If the parameter is not set or
set to None, its actual value will be automatically set to
the channels number of the input.
:type num_channels: int
:param pool_type: pooling type. MaxPooling or AvgPooling. Default is
MaxPooling.
:param pool_type: Pooling type. MaxPooling is the default pooling.
:type pool_type: BasePoolingType
:param stride: stride width of pooling.
:param stride: The stride on the x axis. 1 is the default value.
:type stride: int
:param stride_y: stride height of pooling. It is equal to stride by default.
:type stride_y: int | None
:param layer_attr: Extra Layer attribute.
:param stride_y: The stride on the y axis. If the parameter is not set or set to
None, its actual value will be automatically set to 'stride'.
:type stride_y: int
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
details.
:type layer_attr: ExtraLayerAttribute
:param ceil_mode: Wether to use ceil mode to calculate output height and with.
Defalut is True. If set false, Otherwise use floor.
:param ceil_mode: Wether to use the ceil function to calculate output height and width.
True is the default. If it is set to False, the floor function will
be used.
:type ceil_mode: bool
:return: LayerOutput object.
:rtype: LayerOutput
......@@ -2842,24 +2862,32 @@ def img_pool3d_layer(input,
:param padding: pooling padding width.
:type padding: int | tuple | list
:param name: name of pooling layer
:param name: The name of this layer. It is optional.
:type name: basestring.
:param input: The input of this layer.
:type input: LayerOutput
:param pool_size: pooling window width
:param pool_size: The pooling window lengths along three axises. If the parameter
is set to one integer, the three lengths will be same.
:type pool_size: int | tuple | list
:param num_channels: number of input channel.
:param num_channels: The number of input channels. If the parameter is not set or
set to None, its actual value will be automatically set to
the channels number of the input.
:type num_channels: int
:param pool_type: pooling type. MaxPooling or AvgPooling. Default is
MaxPooling.
:param pool_type: Pooling type. MaxPooling is the default pooling.
:type pool_type: BasePoolingType
:param stride: stride width of pooling.
:param stride: The strides of the pooling along three axises. If the parameter
is set to one integer, the three strides will be same. 1 is the
default value.
:type stride: int | tuple | list
:param layer_attr: Extra Layer attribute.
:param padding: The sizes of padding along three axises. If the parameter is set to
one integer, they will be same. 0 is the default padding size.
:type padding: int | tuple | list
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
details.
:type layer_attr: ExtraLayerAttribute
:param ceil_mode: Wether to use ceil mode to calculate output height and with.
Defalut is True. If set false, Otherwise use floor.
:param ceil_mode: Wether to use the ceil function to calculate output height and width.
True is the default. If it is set to False, the floor function will
be used.
:type ceil_mode: bool
:return: LayerOutput object.
:rtype: LayerOutput
......@@ -2938,9 +2966,11 @@ def spp_layer(input,
pyramid_height=None,
layer_attr=None):
"""
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition.
The details please refer to
`Kaiming He's paper <https://arxiv.org/abs/1406.4729>`_.
A layer performs spatial pyramid pooling.
Reference:
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
https://arxiv.org/abs/1406.4729
The example usage is:
......@@ -2955,13 +2985,16 @@ def spp_layer(input,
:type name: basestring
:param input: The input of this layer.
:type input: LayerOutput
:param num_channels: number of input channel.
:param num_channels: The number of input channels. If the parameter is not set or
set to None, its actual value will be automatically set to
the channels number of the input.
:type num_channels: int
:param pool_type: Pooling type. MaxPooling or AveragePooling. Default is MaxPooling.
:param pool_type: Pooling type. MaxPooling is the default pooling.
:type scale: BasePoolingType
:param pyramid_height: pyramid height.
:param pyramid_height: The pyramid height of this pooling.
:type pyramid_height: int
:param layer_attr: Extra Layer Attribute.
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
details.
:type layer_attr: ExtraLayerAttribute
:return: LayerOutput object.
:rtype: LayerOutput
......@@ -4694,7 +4727,7 @@ def conv_projection(input,
will be same when filter_size_y is not set. If it is set
to a list, the first element indicates the dimension on
the x axis, and the second is used to specify the dimension
on the y axis when filter_size is not provided.
on the y axis when filter_size_y is not provided.
:type filter_size: int | tuple | list
:param filter_size_y: The dimension of the filter kernel on the y axis. If the parameter
is not set, it will be set automatically according to filter_size.
......@@ -6571,10 +6604,11 @@ def row_conv_layer(input,
@layer_support()
@wrap_name_default()
@wrap_param_attr_default()
def prelu_layer(input,
name=None,
partial_sum=1,
channel_shared=None,
num_channels=None,
param_attr=None,
layer_attr=None):
"""
......@@ -6605,6 +6639,14 @@ def prelu_layer(input,
- partial_sum = number of outputs, indicates all elements share the same weight.
:type partial_sum: int
:param channel_shared: whether or not the parameter are shared across channels.
- channel_shared = True, we set the partial_sum to the number of outputs.
- channel_shared = False, we set the partial_sum to the number of elements in one channel.
:type channel_shared: bool
:param num_channels: number of input channel.
:type num_channels: int
:param param_attr: The parameter attribute. See ParameterAttribute for details.
:type param_attr: ParameterAttribute
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
......@@ -6615,7 +6657,25 @@ def prelu_layer(input,
"""
assert isinstance(input, LayerOutput), 'prelu_layer accepts only one input.'
assert isinstance(param_attr, ParameterAttribute)
if not param_attr:
param_attr = ParamAttr(initial_mean=0.25, initial_std=0.0)
else:
assert isinstance(param_attr, ParameterAttribute)
if num_channels is None:
assert input.num_filters is not None, \
'the input channel cannot be detected, please specify the num_channels parameter'
num_channels = input.num_filters
if channel_shared is not None:
assert isinstance(channel_shared, bool)
assert (input.height != 0 and input.width != 0), \
'input height and widht must be setted'
if channel_shared:
partial_sum = input.height * input.width * num_channels
else:
partial_sum = input.height * input.width
l = Layer(
name=name,
......@@ -6627,6 +6687,7 @@ def prelu_layer(input,
name=name,
layer_type=LayerType.PRELU,
parents=input,
num_filters=num_channels,
size=l.config.size)
......@@ -7076,7 +7137,7 @@ def img_conv3d_layer(input,
:type layer_attr: ExtraLayerAttribute
:param trans: True if it is a convTransLayer, False if it is a convLayer
:type trans: bool
:param layer_type: Specify the layer_type. If the parameter is set, it must be "deconv3d"
:param layer_type: Specify the layer type. If the parameter is set, it must be "deconv3d"
when trans=True. If not set, it will be automatically set to "deconv3d"
when trans=True and "conv3d" when trans=False.
:type layer_type: basestring
......
python/paddle/trainer_config_helpers/tests/configs/protostr/test_prelu_layer.protostr
浏览文件 @ 36dd770a
......@@ -4,6 +4,8 @@ layers {
type: "data"
size: 300
active_type: ""
height: 10
width: 10
}
layers {
name: "__prelu_layer_0__"
......@@ -15,6 +17,9 @@ layers {
input_parameter_name: "___prelu_layer_0__.w0"
}
partial_sum: 1
height: 10
width: 10
depth: 1
}
layers {
name: "__prelu_layer_1__"
......@@ -26,6 +31,9 @@ layers {
input_parameter_name: "___prelu_layer_1__.w0"
}
partial_sum: 1
height: 10
width: 10
depth: 1
}
layers {
name: "__prelu_layer_2__"
......@@ -37,41 +45,100 @@ layers {
input_parameter_name: "___prelu_layer_2__.w0"
}
partial_sum: 5
height: 10
width: 10
depth: 1
}
layers {
name: "__prelu_layer_3__"
type: "prelu"
size: 300
active_type: ""
inputs {
input_layer_name: "input"
input_parameter_name: "___prelu_layer_3__.w0"
}
partial_sum: 300
height: 10
width: 10
depth: 1
}
layers {
name: "__prelu_layer_4__"
type: "prelu"
size: 300
active_type: ""
inputs {
input_layer_name: "input"
input_parameter_name: "___prelu_layer_4__.w0"
}
partial_sum: 100
height: 10
width: 10
depth: 1
}
parameters {
name: "___prelu_layer_0__.w0"
size: 300
initial_mean: 0.0
initial_std: 0.057735026919
initial_mean: 0.25
initial_std: 0.0
dims: 1
dims: 300
initial_strategy: 0
initial_smart: true
initial_smart: false
}
parameters {
name: "___prelu_layer_1__.w0"
size: 300
initial_mean: 0.0
initial_std: 0.057735026919
initial_mean: 0.25
initial_std: 0.0
dims: 1
dims: 300
initial_strategy: 0
initial_smart: true
initial_smart: false
}
parameters {
name: "___prelu_layer_2__.w0"
size: 60
initial_mean: 0.0
initial_std: 0.129099444874
initial_mean: 0.25
initial_std: 0.0
dims: 1
dims: 60
initial_strategy: 0
initial_smart: false
}
parameters {
name: "___prelu_layer_3__.w0"
size: 1
initial_mean: 0.25
initial_std: 0.0
dims: 1
dims: 1
initial_strategy: 0
initial_smart: false
}
parameters {
name: "___prelu_layer_4__.w0"
size: 3
initial_mean: 0.25
initial_std: 0.0
dims: 1
dims: 3
initial_strategy: 0
initial_smart: true
initial_smart: false
}
input_layer_names: "input"
output_layer_names: "__prelu_layer_2__"
output_layer_names: "__prelu_layer_4__"
sub_models {
name: "root"
layer_names: "input"
layer_names: "__prelu_layer_0__"
layer_names: "__prelu_layer_1__"
layer_names: "__prelu_layer_2__"
layer_names: "__prelu_layer_3__"
layer_names: "__prelu_layer_4__"
input_layer_names: "input"
output_layer_names: "__prelu_layer_2__"
output_layer_names: "__prelu_layer_4__"
is_recurrent_layer_group: false
}
python/paddle/trainer_config_helpers/tests/configs/test_prelu_layer.py
浏览文件 @ 36dd770a
from paddle.trainer_config_helpers import *
data = data_layer(name='input', size=300)
prelu = prelu_layer(input=data)
prelu = prelu_layer(input=data, partial_sum=1)
prelu = prelu_layer(input=data, partial_sum=5)
data = data_layer(name='input', size=300, height=10, width=10)
prelu = prelu_layer(input=data, num_channels=3)
prelu = prelu_layer(input=data, partial_sum=1, num_channels=3)
prelu = prelu_layer(input=data, partial_sum=5, num_channels=3)
prelu = prelu_layer(input=data, channel_shared=True, num_channels=3)
prelu = prelu_layer(input=data, channel_shared=False, num_channels=3)
outputs(prelu)
python/paddle/v2/__init__.py
浏览文件 @ 36dd770a
......@@ -62,21 +62,15 @@ __all__ = [
cp.begin_parse()
def init(**kwargs):
import py_paddle.swig_paddle as api
args = []
args_dict = {}
# NOTE: append arguments if they are in ENV
for ek, ev in os.environ.iteritems():
if ek.startswith("PADDLE_INIT_"):
args_dict[ek.replace("PADDLE_INIT_", "").lower()] = str(ev)
def set_omp_mkl_env_vars(trainer_count):
'''Auto set CPU environment if have not set before.
export KMP_AFFINITY, OMP_DYNAMIC according to the Hyper Threading status.
export OMP_NUM_THREADS, MKL_NUM_THREADS according to trainer_count.
'''
import platform
if not platform.system() in ['Linux', 'Darwin']:
return
args_dict.update(kwargs)
# NOTE: overwrite arguments from ENV if it is in kwargs
for key in args_dict.keys():
args.append('--%s=%s' % (key, str(args_dict[key])))
# auto set cpu environment
def set_env(key, value):
'''If the key has not been set in the environment, set it with value.'''
assert isinstance(key, str)
......@@ -85,22 +79,59 @@ def init(**kwargs):
if envset is None:
os.environ[key] = value
ht = os.popen("lscpu |grep \"per core\"|awk -F':' '{print $2}'|xargs")
ht = int(ht.read())
if ht == 1: # ht is off
set_env("OMP_DYNAMIC", "false")
set_env("KMP_AFFINITY", "granularity=fine,compact,0,0")
else:
def num_physical_cores():
'''Get the number of physical cores'''
if platform.system() == "Linux":
num_sockets = int(
os.popen("lscpu |grep \"Socket\" |awk -F':' '{print $2}'|xargs")
.read())
num_cores_per_socket = int(
os.popen(
"lscpu |grep \"per socket\" |awk -F':' '{print $2}'|xargs")
.read())
return num_sockets * num_cores_per_socket
else:
cmds = {"Darwin": "sysctl hw.physicalcpu"}
return int(os.popen(cmds.get(platform.system(), "expr 1")).read())
def num_logical_processors():
'''Get the number of logical processors'''
cmds = {
"Linux": "grep \"processor\" /proc/cpuinfo|sort -u|wc -l",
"Darwin": "sysctl hw.logicalcpu"
}
return int(os.popen(cmds.get(platform.system(), "expr 1")).read())
num_cores = num_physical_cores()
num_processors = num_logical_processors()
if num_processors > num_cores: # Hyper Threading is enabled
set_env("OMP_DYNAMIC", "true")
set_env("KMP_AFFINITY", "granularity=fine,compact,1,0")
processors = os.popen("grep \"processor\" /proc/cpuinfo|sort -u|wc -l")
processors = int(processors.read())
trainers = kwargs.get('trainer_count', 1)
threads = processors / trainers
else:
set_env("OMP_DYNAMIC", "false")
set_env("KMP_AFFINITY", "granularity=fine,compact,0,0")
threads = num_processors / trainer_count
threads = '1' if threads < 1 else str(threads)
set_env("OMP_NUM_THREADS", threads)
set_env("MKL_NUM_THREADS", threads)
def init(**kwargs):
import py_paddle.swig_paddle as api
args = []
args_dict = {}
# NOTE: append arguments if they are in ENV
for ek, ev in os.environ.iteritems():
if ek.startswith("PADDLE_INIT_"):
args_dict[ek.replace("PADDLE_INIT_", "").lower()] = str(ev)
args_dict.update(kwargs)
# NOTE: overwrite arguments from ENV if it is in kwargs
for key in args_dict.keys():
args.append('--%s=%s' % (key, str(args_dict[key])))
set_omp_mkl_env_vars(kwargs.get('trainer_count', 1))
if 'use_gpu' in kwargs:
cp.g_command_config_args['use_gpu'] = kwargs['use_gpu']
if 'use_mkldnn' in kwargs:
......
python/paddle/v2/fluid/framework.py
浏览文件 @ 36dd770a
......@@ -15,6 +15,37 @@ def unique_name(prefix):
return "_".join([prefix, str(uid)])
def convert_np_dtype_to_dtype_(np_dtype):
dtype = np.dtype(np_dtype)
if dtype == np.float32:
return core.DataType.FP32
elif dtype == np.float64:
return core.DataType.FP64
elif dtype == np.float16:
return core.DataType.FP16
elif dtype == np.int32:
return core.DataType.INT32
elif dtype == np.int16:
return core.DataType.INT16
elif dtype == np.int64:
return core.DataType.INT64
elif dtype == np.bool:
return core.DataType.BOOL
else:
raise ValueError("Not supported numpy dtype " + str(dtype))
def dtype_is_floating(dtype):
if not isinstance(dtype, core.DataType):
dtype = convert_np_dtype_to_dtype_(dtype)
if (dtype == core.DataType.FP16 or dtype == core.DataType.FP32 or
dtype == core.DataType.FP64):
return True
else:
return False
def _debug_string_(proto, throw_on_error=True):
error_fields = list()
if not proto.IsInitialized(error_fields) and throw_on_error:
......@@ -66,7 +97,7 @@ class Variable(object):
"matched.".format(self.name, old_shape, shape))
if dtype is not None:
if not isinstance(dtype, core.DataType):
dtype = Variable._convert_np_dtype_to_dtype_(dtype)
dtype = convert_np_dtype_to_dtype_(dtype)
if is_new_var:
self.desc.set_data_type(dtype)
else:
......@@ -148,26 +179,6 @@ class Variable(object):
uid = core.unique_integer(prefix) # unique during whole process.
return "_".join([prefix, str(uid)])
@staticmethod
def _convert_np_dtype_to_dtype_(np_dtype):
dtype = np.dtype(np_dtype)
if dtype == np.float32:
return core.DataType.FP32
elif dtype == np.float64:
return core.DataType.FP64
elif dtype == np.float16:
return core.DataType.FP16
elif dtype == np.int32:
return core.DataType.INT32
elif dtype == np.int16:
return core.DataType.INT16
elif dtype == np.int64:
return core.DataType.INT64
elif dtype == np.bool:
return core.DataType.BOOL
else:
raise ValueError("Not supported numpy dtype " + str(dtype))
def get_all_op_protos():
"""
......
python/paddle/v2/fluid/initializer.py
浏览文件 @ 36dd770a
......@@ -285,3 +285,86 @@ class XavierInitializer(Initializer):
})
var.op = op
return op
class MSRAInitializer(Initializer):
"""Implements the MSRA initializer a.k.a. Kaiming Initializer
This class implements the weight initialization from the paper
Delving Deep into Rectifiers: Surpassing Human-Level Performance on
ImageNet Classification[1] by Kaiming He, Xiangyu Zhang, Shaoqing Ren
and Jian Sun. This is a robust initialization method that particularly
considers the rectifier nonlinearities. In case of Uniform distribution,
the range is [-x, x], where x = sqrt(6 / fan_in). In case of Normal
distribution, the mean is 0 and the standard deviation
is sqrt(2/ fan_in).
References:
[1] Delving Deep into Rectifiers: Surpassing Human-Level Performance
on ImageNet Classification
(https://arxiv.org/abs/1502.01852)
"""
def __init__(self, uniform=True, fan_in=None, seed=0):
"""Constructor for MSRAInitializer
Args:
uniform: whether to use uniform or normal distribution
fan_in: fan_in for MSRAInitializer. If None, it is
inferred from the variable.
seed: random seed
Note: It is recommended to set fan_in to None for most cases.
"""
assert uniform is not None
assert seed is not None
super(MSRAInitializer, self).__init__()
self._uniform = uniform
self._fan_in = fan_in
self._seed = seed
def __call__(self, var, block):
"""Add MSRA initialization ops for a variable
Args:
var: Variable that needs to be initialized
block: The block in which initialization ops
should be added
Returns:
the initialization op
"""
assert isinstance(var, framework.Variable)
assert isinstance(block, framework.Block)
f_in, f_out = self._compute_fans(var)
# If fan_in is passed, use it
fan_in = f_in if self._fan_in is None else self._fan_in
if self._uniform:
limit = np.sqrt(6.0 / float(fan_in))
op = block.prepend_op(
type="uniform_random",
outputs={"Out": var},
attrs={
"shape": var.shape,
"data_type": int(var.data_type),
"min": -limit,
"max": limit,
"seed": self._seed
})
else:
std = np.sqrt(2.0 / float(fan_in))
op = block.prepend_op(
type="gaussian_random",
outputs={"Out": var},
attrs={
"shape": var.shape,
"data_type": int(var.data_type),
"mean": 0.0,
"std": std,
"seed": self._seed
})
var.op = op
return op
python/paddle/v2/fluid/layer_helper.py
浏览文件 @ 36dd770a
......@@ -2,7 +2,7 @@ import copy
import itertools
from paddle.v2.fluid.framework import Variable, g_main_program, \
g_startup_program, unique_name, Program
g_startup_program, unique_name, Program, dtype_is_floating
from paddle.v2.fluid.initializer import ConstantInitializer, \
UniformInitializer, XavierInitializer
......@@ -61,7 +61,7 @@ class LayerHelper(object):
@property
def param_attr(self):
default = {'name': None, 'initializer': XavierInitializer()}
default = {'name': None}
actual = self.kwargs.get('param_attr', None)
if actual is None:
actual = default
......@@ -72,7 +72,7 @@ class LayerHelper(object):
@property
def bias_attr(self):
default = {'name': None, 'initializer': ConstantInitializer()}
default = {'name': None}
bias_attr = self.kwargs.get('bias_attr', None)
if bias_attr is None:
bias_attr = default
......@@ -119,12 +119,17 @@ class LayerHelper(object):
attr_copy = copy.deepcopy(attr)
if initializer is not None:
attr_copy['initializer'] = initializer
else:
attr_copy['initializer'] = self._get_default_initializer(dtype)
if attr_copy['name'] is None:
attr_copy['name'] = unique_name(".".join([self.name, suffix]))
self.startup_program.global_block().create_parameter(
dtype=dtype, shape=shape, **attr_copy)
return self.main_program.global_block().create_parameter(
name=attr_copy['name'], dtype=dtype, shape=shape)
name=attr_copy['name'],
dtype=dtype,
shape=shape,
trainable=attr_copy.get('trainable', True))
def create_tmp_variable(self, dtype):
return self.main_program.current_block().create_var(
......@@ -149,13 +154,19 @@ class LayerHelper(object):
persistable=True,
initializer=initializer)
def append_bias_op(self, input_var, dim_start=1, dim_end=None):
def append_bias_op(self,
input_var,
bias_initializer,
dim_start=1,
dim_end=None):
"""
Append bias operator and return its output. If the user does not set
bias_attr, append_bias_op will return input_var
:param input_var: the input variable. The len(input_var.shape) is larger
or equal than 2.
:param input_var: the input variable. The len(input_var.shape) is
larger or equal than 2.
:bias_initializer: an instance of a subclass of Initializer used to
initialize the bias
:param dim_start:
:param dim_end: the shape of the bias will be
input_var.shape[dim_start:dim_end]. The bias is broadcasted to other
......@@ -167,7 +178,11 @@ class LayerHelper(object):
return input_var
b = self.create_parameter(
attr=bias_attr, shape=size, dtype=input_var.data_type, suffix='b')
attr=bias_attr,
shape=size,
dtype=input_var.data_type,
suffix='b',
initializer=bias_initializer)
tmp = self.create_tmp_variable(dtype=input_var.data_type)
self.append_op(
type='elementwise_add',
......@@ -191,3 +206,10 @@ class LayerHelper(object):
outputs={"Y": [tmp]},
attrs=act)
return tmp
def _get_default_initializer(self, dtype):
if dtype is None or dtype_is_floating(dtype) is True:
return XavierInitializer()
else:
# For integer and boolean types, initialize with all zeros
return ConstantInitializer()
python/paddle/v2/fluid/layers.py
浏览文件 @ 36dd770a
此差异已折叠。
python/paddle/v2/fluid/optimizer.py
浏览文件 @ 36dd770a
......@@ -170,7 +170,8 @@ class Optimizer(object):
optimize_ops = []
for param_and_grad in parameters_and_grads:
if param_and_grad[1] is not None:
if param_and_grad[0].trainable is True and param_and_grad[
1] is not None:
optimize_op = self._append_optimize_op(loss.block,
param_and_grad)
optimize_ops.append(optimize_op)
......
python/paddle/v2/fluid/tests/book/test_image_classification_train.py
浏览文件 @ 36dd770a
......@@ -4,6 +4,7 @@ import paddle.v2.fluid.core as core
import paddle.v2.fluid.framework as framework
import paddle.v2.fluid.layers as layers
import paddle.v2.fluid.nets as nets
import paddle.v2.fluid.evaluator as evaluator
from paddle.v2.fluid.executor import Executor
from paddle.v2.fluid.initializer import XavierInitializer
from paddle.v2.fluid.optimizer import AdamOptimizer
......@@ -103,12 +104,13 @@ net = vgg16_bn_drop(images)
predict = layers.fc(input=net, size=classdim, act='softmax')
cost = layers.cross_entropy(input=predict, label=label)
avg_cost = layers.mean(x=cost)
accuracy = layers.accuracy(input=predict, label=label)
# optimizer = SGDOptimizer(learning_rate=0.001)
optimizer = AdamOptimizer(learning_rate=0.001)
opts = optimizer.minimize(avg_cost)
accuracy, acc_out = evaluator.accuracy(input=predict, label=label)
BATCH_SIZE = 128
PASS_NUM = 1
......@@ -124,6 +126,7 @@ exe.run(framework.default_startup_program())
for pass_id in range(PASS_NUM):
batch_id = 0
accuracy.reset(exe)
for data in train_reader():
img_data = np.array(map(lambda x: x[0].reshape(data_shape),
data)).astype("float32")
......@@ -141,12 +144,14 @@ for pass_id in range(PASS_NUM):
outs = exe.run(framework.default_main_program(),
feed={"pixel": tensor_img,
"label": tensor_y},
fetch_list=[avg_cost, accuracy])
fetch_list=[avg_cost, acc_out])
loss = np.array(outs[0])
acc = np.array(outs[1])
pass_acc = accuracy.eval(exe)
print("pass_id:" + str(pass_id) + " batch_id:" + str(batch_id) +
" loss:" + str(loss) + " acc:" + str(acc))
" loss:" + str(loss) + " acc:" + str(acc) + " pass_acc:" + str(
pass_acc))
batch_id = batch_id + 1
if batch_id > 1:
......
python/paddle/v2/fluid/tests/test_linear_chain_crf_op.py
浏览文件 @ 36dd770a
......@@ -104,7 +104,7 @@ class TestLinearChainCrfOp(OpTest):
transition_exps = np.exp(transition)
labels = np.random.randint(
low=0, high=TAG_NUM, size=(lod[-1][-1], 1), dtype="int32")
low=0, high=TAG_NUM, size=(lod[-1][-1], 1), dtype="int64")
self.inputs = {
"Emission": (emission, lod),
......
python/paddle/v2/fluid/tests/test_maxout_op.py 0 → 100644
浏览文件 @ 36dd770a
此差异已折叠。
python/paddle/v2/fluid/tests/test_mnist_if_else_op.py 0 → 100644
浏览文件 @ 36dd770a
此差异已折叠。
python/paddle/v2/fluid/tests/test_roi_pool_op.py 0 → 100644
浏览文件 @ 36dd770a
此差异已折叠。
python/paddle/v2/fluid/tests/test_sequence_slice_op.py 100755 → 100644
浏览文件 @ 36dd770a
此差异已折叠。
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