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PaddleDetection
提交 f191c820 编写于 作者: G guosheng
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Merge branch 'develop' of https://github.com/PaddlePaddle/paddle into fix-GRUUnitOp-dev

上级 3e7fff41 9891667b
展开全部 隐藏空白更改
内联 并排
Showing with 2043 addition and 7596 deletion
benchmark/paddle/image/googlenet.py
浏览文件 @ f191c820
...@@ -5,6 +5,7 @@ height = 224 ...@@ -5,6 +5,7 @@ height = 224
width = 224 width = 224
num_class = 1000 num_class = 1000
batch_size = get_config_arg('batch_size', int, 128) batch_size = get_config_arg('batch_size', int, 128)
use_gpu = get_config_arg('use_gpu', bool, True)
args = {'height': height, 'width': width, 'color': True, 'num_class': num_class} args = {'height': height, 'width': width, 'color': True, 'num_class': num_class}
define_py_data_sources2( define_py_data_sources2(
...@@ -16,6 +17,8 @@ settings( ...@@ -16,6 +17,8 @@ settings(
learning_method=MomentumOptimizer(0.9), learning_method=MomentumOptimizer(0.9),
regularization=L2Regularization(0.0005 * batch_size)) regularization=L2Regularization(0.0005 * batch_size))
conv_projection = conv_projection if use_gpu else img_conv_layer
def inception2(name, input, channels, \ def inception2(name, input, channels, \
filter1, filter1,
filter3R, filter3, filter3R, filter3,
...@@ -138,7 +141,7 @@ def inception(name, input, channels, \ ...@@ -138,7 +141,7 @@ def inception(name, input, channels, \
cat = concat_layer( cat = concat_layer(
name=name, name=name,
input=[cov1, cov3, cov5, covprj], input=[cov1, cov3, cov5, covprj],
bias_attr=True, bias_attr=True if use_gpu else False,
act=ReluActivation()) act=ReluActivation())
return cat return cat
......
benchmark/paddle/image/run_mkldnn.sh
浏览文件 @ f191c820
...@@ -40,6 +40,7 @@ fi ...@@ -40,6 +40,7 @@ fi
for use_mkldnn in True False; do for use_mkldnn in True False; do
for batchsize in 64 128 256; do for batchsize in 64 128 256; do
train vgg 19 $batchsize $use_mkldnn train vgg 19 $batchsize $use_mkldnn
train resnet 50 $batchsize $use_mkldnn train resnet 50 $batchsize $use_mkldnn
train googlenet v1 $batchsize $use_mkldnn
done done
done done
paddle/gserver/activations/ActivationFunction.cpp
浏览文件 @ f191c820
...@@ -212,6 +212,37 @@ Error __must_check backward(Argument& act) { ...@@ -212,6 +212,37 @@ Error __must_check backward(Argument& act) {
} }
END_DEFINE_ACTIVATION(sequence_softmax) 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. * @brief Relu Activation.
* forward. y = max(0, z) * forward. y = max(0, z)
......
paddle/gserver/layers/MKLDNNAddtoLayer.cpp
浏览文件 @ f191c820
...@@ -38,12 +38,13 @@ bool MKLDNNAddtoLayer::init(const LayerMap& layerMap, ...@@ -38,12 +38,13 @@ bool MKLDNNAddtoLayer::init(const LayerMap& layerMap,
} }
void MKLDNNAddtoLayer::reshape( 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"; CHECK_EQ(layerSize_, getSize()) << "this layer size can not be changed";
reshapeInput(bs, ih, iw); reshapeInput(bs, ih, iw);
ic = inputLayers_[0]->getSize() / ih / iw; ic = inputLayers_[0]->getSize() / ih / iw;
CHECK_EQ((size_t)ic * ih * iw, inputLayers_[0]->getSize()); 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++) { for (size_t i = 0; i < inputLayers_.size(); i++) {
CHECK_EQ(int64_t(bs), inputLayers_[i]->getOutput().getBatchSize()); CHECK_EQ(int64_t(bs), inputLayers_[i]->getOutput().getBatchSize());
CHECK_EQ(layerSize_, inputLayers_[i]->getSize()); CHECK_EQ(layerSize_, inputLayers_[i]->getSize());
...@@ -57,47 +58,43 @@ void MKLDNNAddtoLayer::reshape( ...@@ -57,47 +58,43 @@ void MKLDNNAddtoLayer::reshape(
} }
void MKLDNNAddtoLayer::resetFwd(std::vector<primitive>& pipeline, void MKLDNNAddtoLayer::resetFwd(std::vector<primitive>& pipeline,
MKLDNNMatrixPtr& in, std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) { MKLDNNMatrixPtr& out) {
resetFwdBuffers(inVals_, bias, out); resetFwdBuffers(inputs, biasVal_, out);
in = inVals_[0];
std::shared_ptr<sum::primitive_desc> fwdPD; std::shared_ptr<sum::primitive_desc> fwdPD;
std::shared_ptr<sum::primitive_desc> biasPD; 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, void MKLDNNAddtoLayer::resetBwd(std::vector<primitive>& pipeline,
MKLDNNMatrixPtr& in, std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) { MKLDNNMatrixPtr& out) {
resetBwdBuffers(inGrads_, bias, out); resetBwdBuffers(inputs, biasGrad_, out);
in = inGrads_[0];
// backward only need share output grad to input grad // backward only need share output grad to input grad
for (size_t i = 0; i < inGrads_.size(); i++) { for (size_t i = 0; i < inputs.size(); i++) {
if (inGrads_[i] != nullptr) { if (inputs[i] != nullptr) {
inGrads_[i] = out; inputs[i] = out;
inputLayers_[i]->getOutputGrad()->setData(inGrads_[i]->getData()); inputLayers_[i]->getOutputGrad()->setData(inputs[i]->getData());
} }
} }
// backward bias // backward bias
bwdBias_ = nullptr; bwdBias_ = nullptr;
if (bias) { if (biasGrad_) {
std::vector<float> scales(bs_, 1.0); std::vector<float> scales(bs_, 1.0);
std::vector<memory::primitive_desc> srcPDs(bs_, bias->getPrimitiveDesc()); std::vector<memory::primitive_desc> srcPDs(bs_,
auto biasPD = sum::primitive_desc(bias->getMemoryDesc(), scales, srcPDs); biasGrad_->getPrimitiveDesc());
auto biasPD =
sum::primitive_desc(biasGrad_->getMemoryDesc(), scales, srcPDs);
std::vector<primitive::at> srcs; std::vector<primitive::at> srcs;
for (size_t i = 0; i < grads_.size(); ++i) { for (size_t i = 0; i < grads_.size(); ++i) {
srcs.push_back(*(grads_[i])); srcs.push_back(*(grads_[i]));
} }
bwdBias_.reset(new sum(biasPD, srcs, *bias)); bwdBias_.reset(new sum(biasPD, srcs, *biasGrad_));
pipeline.push_back(*bwdBias_); pipeline.push_back(*bwdBias_);
} }
} }
...@@ -208,7 +205,7 @@ void MKLDNNAddtoLayer::resetBwdBuffers(std::vector<MKLDNNMatrixPtr>& inputs, ...@@ -208,7 +205,7 @@ void MKLDNNAddtoLayer::resetBwdBuffers(std::vector<MKLDNNMatrixPtr>& inputs,
inputs.resize(inputLayers_.size()); inputs.resize(inputLayers_.size());
for (size_t i = 0; i < inputs.size(); i++) { 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()); CHECK_PRIMITIVE_DESC_EQ(inputs[i], out->getPrimitiveDesc());
} }
......
paddle/gserver/layers/MKLDNNAddtoLayer.h
浏览文件 @ f191c820
...@@ -26,9 +26,6 @@ namespace paddle { ...@@ -26,9 +26,6 @@ namespace paddle {
*/ */
class MKLDNNAddtoLayer : public MKLDNNLayer { class MKLDNNAddtoLayer : public MKLDNNLayer {
protected: protected:
std::vector<MKLDNNMatrixPtr> inVals_;
std::vector<MKLDNNMatrixPtr> inGrads_;
// layer size == ic * ih * iw == oc * oh *ow, and can not be changed // layer size == ic * ih * iw == oc * oh *ow, and can not be changed
size_t layerSize_; size_t layerSize_;
...@@ -50,52 +47,19 @@ public: ...@@ -50,52 +47,19 @@ public:
const ParameterMap& parameterMap) override; const ParameterMap& parameterMap) override;
void reshape( 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, void resetFwd(std::vector<mkldnn::primitive>& pipeline,
MKLDNNMatrixPtr& in, std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) override; MKLDNNMatrixPtr& out) override;
void resetBwd(std::vector<mkldnn::primitive>& pipeline, void resetBwd(std::vector<mkldnn::primitive>& pipeline,
MKLDNNMatrixPtr& in, std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) override; MKLDNNMatrixPtr& out) override;
void updateWeights(const UpdateCallback& callback) 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: protected:
/**
* Forward functions: reset buffers(inputs, output, bias),
* reset primitive descriptor,
* reset pipeline.
*/
void resetFwdBuffers(std::vector<MKLDNNMatrixPtr>& inputs, void resetFwdBuffers(std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& bias, MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out); MKLDNNMatrixPtr& out);
...@@ -110,17 +74,10 @@ protected: ...@@ -110,17 +74,10 @@ protected:
std::vector<MKLDNNMatrixPtr>& inputs, std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& bias, MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out); MKLDNNMatrixPtr& out);
/**
* Backward functions: reset buffers(inputs, output, bias)
*/
void resetBwdBuffers(std::vector<MKLDNNMatrixPtr>& inputs, void resetBwdBuffers(std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& bias, MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out); MKLDNNMatrixPtr& out);
/**
* prepare for bias
*/
void prepareBias(MKLDNNMatrixPtr& bias, void prepareBias(MKLDNNMatrixPtr& bias,
const MatrixPtr& biasMat, const MatrixPtr& biasMat,
const MKLDNNMatrixPtr& out, const MKLDNNMatrixPtr& out,
......
paddle/gserver/layers/MKLDNNBatchNormLayer.cpp
浏览文件 @ f191c820
...@@ -116,21 +116,20 @@ void MKLDNNBatchNormLayer::calMovingMeanAndVar() { ...@@ -116,21 +116,20 @@ void MKLDNNBatchNormLayer::calMovingMeanAndVar() {
} }
void MKLDNNBatchNormLayer::reshape( 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); reshapeInput(bs, ih, iw);
oh = ih; oh = ih;
ow = iw; ow = iw;
// ic_ and oc can not be changed // 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"; << "Input channel can not be changed";
reshapeOutput(oh, ow); reshapeOutput(oh, ow);
resizeOutput(bs, oc * oh * ow); resizeOutput(bs, oc * oh * ow);
} }
void MKLDNNBatchNormLayer::resetFwd(std::vector<primitive>& pipeline, void MKLDNNBatchNormLayer::resetFwd(std::vector<primitive>& pipeline,
MKLDNNMatrixPtr& in, std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) { MKLDNNMatrixPtr& out) {
// In training phase, it will always calculate mean and var, // In training phase, it will always calculate mean and var,
// so useGlobalStats must be false. // so useGlobalStats must be false.
...@@ -140,25 +139,23 @@ void MKLDNNBatchNormLayer::resetFwd(std::vector<primitive>& pipeline, ...@@ -140,25 +139,23 @@ void MKLDNNBatchNormLayer::resetFwd(std::vector<primitive>& pipeline,
useGlobalStats_ = false; 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, void MKLDNNBatchNormLayer::resetBwd(std::vector<primitive>& pipeline,
MKLDNNMatrixPtr& in, std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) { MKLDNNMatrixPtr& out) {
std::shared_ptr<bn_bwd::primitive_desc> pd; 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) { void MKLDNNBatchNormLayer::forward(PassType passType) {
...@@ -260,9 +257,9 @@ void MKLDNNBatchNormLayer::resetFwdPipeline( ...@@ -260,9 +257,9 @@ void MKLDNNBatchNormLayer::resetFwdPipeline(
void MKLDNNBatchNormLayer::resetBwdBuffers(MKLDNNMatrixPtr& in, void MKLDNNBatchNormLayer::resetBwdBuffers(MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt, MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& out) { MKLDNNMatrixPtr& out) {
CHECK(inVal_ && outVal_); CHECK(inVals_[0] && outVal_);
resetOutGrad(out, outVal_->getPrimitiveDesc()); resetOutGrad(out, outVal_->getPrimitiveDesc());
resetInGrad(in, inVal_->getPrimitiveDesc()); resetInGrad(in, inVals_[0]->getPrimitiveDesc());
if (gradScaleShift_) { if (gradScaleShift_) {
CHECK(wgtVal_); CHECK(wgtVal_);
resetWithMatrix(wgt, gradScaleShift_, wgtVal_->getPrimitiveDesc()); resetWithMatrix(wgt, gradScaleShift_, wgtVal_->getPrimitiveDesc());
...@@ -297,11 +294,12 @@ void MKLDNNBatchNormLayer::resetBwdPipeline( ...@@ -297,11 +294,12 @@ void MKLDNNBatchNormLayer::resetBwdPipeline(
if (pd == nullptr) { if (pd == nullptr) {
return; return;
} }
CHECK(inVal_); CHECK(inVals_[0]);
bwdData_.reset( bwdData_.reset(
wgt && wgtVal_ wgt && wgtVal_
? new bn_bwd(*pd, *inVal_, *mean_, *var_, *out, *wgtVal_, *in, *wgt) ? new bn_bwd(
: new bn_bwd(*pd, *inVal_, *mean_, *var_, *out, *in)); *pd, *inVals_[0], *mean_, *var_, *out, *wgtVal_, *in, *wgt)
: new bn_bwd(*pd, *inVals_[0], *mean_, *var_, *out, *in));
pipeline.push_back(*bwdData_); pipeline.push_back(*bwdData_);
} }
......
paddle/gserver/layers/MKLDNNBatchNormLayer.h
浏览文件 @ f191c820
...@@ -73,18 +73,14 @@ public: ...@@ -73,18 +73,14 @@ public:
void forward(PassType passType) override; void forward(PassType passType) override;
void reshape( 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, void resetFwd(std::vector<mkldnn::primitive>& pipeline,
MKLDNNMatrixPtr& in, std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) override; MKLDNNMatrixPtr& out) override;
void resetBwd(std::vector<mkldnn::primitive>& pipeline, void resetBwd(std::vector<mkldnn::primitive>& pipeline,
MKLDNNMatrixPtr& in, std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) override; MKLDNNMatrixPtr& out) override;
void updateWeights(const UpdateCallback& callback) override; void updateWeights(const UpdateCallback& callback) override;
...@@ -98,11 +94,7 @@ protected: ...@@ -98,11 +94,7 @@ protected:
* moving = moving * AvgFraction + local * (1 - AvgFraction) * moving = moving * AvgFraction + local * (1 - AvgFraction)
*/ */
void calMovingMeanAndVar(); void calMovingMeanAndVar();
/**
* Forward functions: reset buffers(input, weight, output),
* reset primitive descriptor,
* reset pipeline.
*/
void resetFwdBuffers(MKLDNNMatrixPtr& in, void resetFwdBuffers(MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt, MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& out); MKLDNNMatrixPtr& out);
...@@ -115,12 +107,6 @@ protected: ...@@ -115,12 +107,6 @@ protected:
MKLDNNMatrixPtr& in, MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt, MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& out); MKLDNNMatrixPtr& out);
/**
* Backward functions: reset buffers(input, weight, output),
* reset primitive descriptor,
* reset pipeline.
*/
void resetBwdBuffers(MKLDNNMatrixPtr& in, void resetBwdBuffers(MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt, MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& out); MKLDNNMatrixPtr& out);
......
paddle/gserver/layers/MKLDNNConcatLayer.cpp
浏览文件 @ f191c820
...@@ -32,17 +32,16 @@ bool MKLDNNConcatLayer::init(const LayerMap& layerMap, ...@@ -32,17 +32,16 @@ bool MKLDNNConcatLayer::init(const LayerMap& layerMap,
} }
void MKLDNNConcatLayer::reshape( 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); reshapeInput(bs, ih, iw);
ic = inputLayers_[0]->getSize() / ih / iw; ic = inputLayers_[0]->getSize() / ih / iw;
CHECK_EQ((size_t)ic * ih * iw, inputLayers_[0]->getSize()); 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); CHECK_GT(inputLayers_.size(), 1UL);
channels_.resize(inputLayers_.size()); channels_.resize(inputLayers_.size());
channels_[0] = ic; channels_[0] = ic;
// need change the output channel, so use oc_ instead oc = ic;
// TODO(TJ): change API, use &oc
oc_ = ic;
for (size_t i = 1; i < inputLayers_.size(); i++) { for (size_t i = 1; i < inputLayers_.size(); i++) {
int batchsize, height, witdh; int batchsize, height, witdh;
reshapeInput(batchsize, height, witdh, i); reshapeInput(batchsize, height, witdh, i);
...@@ -52,37 +51,31 @@ void MKLDNNConcatLayer::reshape( ...@@ -52,37 +51,31 @@ void MKLDNNConcatLayer::reshape(
channels_[i] = inputLayers_[i]->getSize() / height / witdh; channels_[i] = inputLayers_[i]->getSize() / height / witdh;
CHECK_EQ((size_t)channels_[i] * height * witdh, inputLayers_[i]->getSize()); CHECK_EQ((size_t)channels_[i] * height * witdh, inputLayers_[i]->getSize());
oc_ += channels_[i]; oc += channels_[i];
} }
oh = ih; oh = ih;
ow = iw; ow = iw;
reshapeOutput(oh, ow); reshapeOutput(oh, ow);
resizeOutput(bs, oc_ * oh * ow); resizeOutput(bs, oc * oh * ow);
} }
void MKLDNNConcatLayer::resetFwd(std::vector<primitive>& pipeline, void MKLDNNConcatLayer::resetFwd(std::vector<primitive>& pipeline,
MKLDNNMatrixPtr& in, std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) { MKLDNNMatrixPtr& out) {
resetFwdBuffers(inVals_, out); resetFwdBuffers(inputs, out);
in = inVals_[0];
std::shared_ptr<concat::primitive_desc> fwdPD; 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, void MKLDNNConcatLayer::resetBwd(std::vector<primitive>& pipeline,
MKLDNNMatrixPtr& in, std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) { MKLDNNMatrixPtr& out) {
resetBwdBuffers(inGrads_, out); resetBwdBuffers(inputs, out);
in = inGrads_[0];
resetBwdPipeline(pipeline, bwds_, inGrads_, out); resetBwdPipeline(pipeline, bwds_, inputs, out);
} }
void MKLDNNConcatLayer::resetFwdBuffers(std::vector<MKLDNNMatrixPtr>& inputs, void MKLDNNConcatLayer::resetFwdBuffers(std::vector<MKLDNNMatrixPtr>& inputs,
...@@ -90,10 +83,7 @@ void MKLDNNConcatLayer::resetFwdBuffers(std::vector<MKLDNNMatrixPtr>& inputs, ...@@ -90,10 +83,7 @@ void MKLDNNConcatLayer::resetFwdBuffers(std::vector<MKLDNNMatrixPtr>& inputs,
inputs.resize(inputLayers_.size()); inputs.resize(inputLayers_.size());
bool has8c = false, has16c = false, hasnc = false; bool has8c = false, has16c = false, hasnc = false;
for (size_t i = 0; i < inputs.size(); i++) { for (size_t i = 0; i < inputs.size(); i++) {
// resetInValue will use ic_ so temporary change as current input's channel resetInValue(inputs[i], nullptr, i, channels_[i]);
// TODO(TJ): change ic_ as vector then can remove channels_
ic_ = channels_[i];
resetInValue(inputs[i], nullptr, i);
CHECK(inputs[i]); CHECK(inputs[i]);
auto dm = inputs[i]->getDims(); auto dm = inputs[i]->getDims();
// inputs format can be different, but ndims must equal // inputs format can be different, but ndims must equal
...@@ -114,8 +104,6 @@ void MKLDNNConcatLayer::resetFwdBuffers(std::vector<MKLDNNMatrixPtr>& inputs, ...@@ -114,8 +104,6 @@ void MKLDNNConcatLayer::resetFwdBuffers(std::vector<MKLDNNMatrixPtr>& inputs,
has16c = true; has16c = true;
} }
} }
// change back, ic_ always save the input 0 size
ic_ = channels_[0];
format outFmt; format outFmt;
if (has16c && oc_ % 16 == 0) { if (has16c && oc_ % 16 == 0) {
...@@ -168,14 +156,9 @@ void MKLDNNConcatLayer::resetBwdBuffers(std::vector<MKLDNNMatrixPtr>& inputs, ...@@ -168,14 +156,9 @@ void MKLDNNConcatLayer::resetBwdBuffers(std::vector<MKLDNNMatrixPtr>& inputs,
inputs.resize(inputLayers_.size()); inputs.resize(inputLayers_.size());
for (size_t i = 0; i < inputs.size(); i++) { for (size_t i = 0; i < inputs.size(); i++) {
CHECK(inVals_[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); resetInGrad(inputs[i], inVals_[i]->getPrimitiveDesc(), i);
CHECK_PRIMITIVE_DESC_EQ(inputs[i], inVals_[i]->getPrimitiveDesc()); CHECK_PRIMITIVE_DESC_EQ(inputs[i], inVals_[i]->getPrimitiveDesc());
} }
// change back, inVal_ always save the input 0
inVal_ = inVals_[0];
} }
void MKLDNNConcatLayer::resetBwdPipeline( void MKLDNNConcatLayer::resetBwdPipeline(
......
paddle/gserver/layers/MKLDNNConcatLayer.h
浏览文件 @ f191c820
...@@ -26,8 +26,6 @@ namespace paddle { ...@@ -26,8 +26,6 @@ namespace paddle {
*/ */
class MKLDNNConcatLayer : public MKLDNNLayer { class MKLDNNConcatLayer : public MKLDNNLayer {
protected: protected:
std::vector<MKLDNNMatrixPtr> inVals_;
std::vector<MKLDNNMatrixPtr> inGrads_;
std::vector<std::shared_ptr<mkldnn::primitive>> bwds_; std::vector<std::shared_ptr<mkldnn::primitive>> bwds_;
// input channel numbers // input channel numbers
std::vector<int> channels_; std::vector<int> channels_;
...@@ -47,18 +45,14 @@ public: ...@@ -47,18 +45,14 @@ public:
const ParameterMap& parameterMap) override; const ParameterMap& parameterMap) override;
void reshape( 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, void resetFwd(std::vector<mkldnn::primitive>& pipeline,
MKLDNNMatrixPtr& in, std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) override; MKLDNNMatrixPtr& out) override;
void resetBwd(std::vector<mkldnn::primitive>& pipeline, void resetBwd(std::vector<mkldnn::primitive>& pipeline,
MKLDNNMatrixPtr& in, std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) override; MKLDNNMatrixPtr& out) override;
void printSizeInfo() override { void printSizeInfo() override {
...@@ -72,38 +66,16 @@ public: ...@@ -72,38 +66,16 @@ public:
<< ", " << ow_; << ", " << ow_;
} }
void printValueFormat() override { size_t keepCondition() {
for (size_t i = 0; i < inVals_.size(); ++i) { // reset when the total element size of all inputs changed
VLOG(MKLDNN_FMTS) << "Input " << i << ", " << inputLayers_[i]->getName() size_t totalSize = inputLayers_[0]->getOutputValue()->getElementCnt();
<< ": " << inVals_[i]->getFormat() << " >>>"; for (size_t i = 1; i < inputLayers_.size(); ++i) {
} totalSize += inputLayers_[i]->getOutputValue()->getElementCnt();
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() << "<<<";
} }
return totalSize;
} }
protected: protected:
/**
* Forward functions: reset buffers(inputs, output, bias),
* reset primitive descriptor,
* reset pipeline.
*/
void resetFwdBuffers(std::vector<MKLDNNMatrixPtr>& inputs, void resetFwdBuffers(std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& out); MKLDNNMatrixPtr& out);
void resetFwdPD(std::shared_ptr<mkldnn::concat::primitive_desc>& pd, void resetFwdPD(std::shared_ptr<mkldnn::concat::primitive_desc>& pd,
...@@ -113,11 +85,6 @@ protected: ...@@ -113,11 +85,6 @@ protected:
std::shared_ptr<mkldnn::concat::primitive_desc>& pd, std::shared_ptr<mkldnn::concat::primitive_desc>& pd,
std::vector<MKLDNNMatrixPtr>& inputs, std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& out); MKLDNNMatrixPtr& out);
/**
* Backward functions: reset buffers(inputs, output, bias)
* reset primitives and pipeline
*/
void resetBwdBuffers(std::vector<MKLDNNMatrixPtr>& inputs, void resetBwdBuffers(std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& out); MKLDNNMatrixPtr& out);
void resetBwdPipeline(std::vector<mkldnn::primitive>& pipeline, void resetBwdPipeline(std::vector<mkldnn::primitive>& pipeline,
......
paddle/gserver/layers/MKLDNNConvLayer.cpp
浏览文件 @ f191c820
...@@ -90,7 +90,7 @@ void MKLDNNConvLayer::convertWeightsToPaddle() { ...@@ -90,7 +90,7 @@ void MKLDNNConvLayer::convertWeightsToPaddle() {
} }
void MKLDNNConvLayer::reshape( 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); reshapeInput(bs, ih, iw);
// cal output sizes // cal output sizes
...@@ -105,21 +105,17 @@ void MKLDNNConvLayer::reshape( ...@@ -105,21 +105,17 @@ void MKLDNNConvLayer::reshape(
} }
void MKLDNNConvLayer::resetFwd(std::vector<primitive>& pipeline, void MKLDNNConvLayer::resetFwd(std::vector<primitive>& pipeline,
MKLDNNMatrixPtr& in, std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) { MKLDNNMatrixPtr& out) {
resetFwdPD(fwdPD_); 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, void MKLDNNConvLayer::resetBwd(std::vector<primitive>& pipeline,
MKLDNNMatrixPtr& in, std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) { MKLDNNMatrixPtr& out) {
std::shared_ptr<conv_bwdWgt::primitive_desc> bwdWgtPD; std::shared_ptr<conv_bwdWgt::primitive_desc> bwdWgtPD;
std::shared_ptr<conv_bwdData::primitive_desc> bwdDataPD; std::shared_ptr<conv_bwdData::primitive_desc> bwdDataPD;
...@@ -128,9 +124,10 @@ void MKLDNNConvLayer::resetBwd(std::vector<primitive>& pipeline, ...@@ -128,9 +124,10 @@ void MKLDNNConvLayer::resetBwd(std::vector<primitive>& pipeline,
resetBwdDataPD(bwdDataPD); 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) { void MKLDNNConvLayer::updateWeights(const UpdateCallback& callback) {
...@@ -236,14 +233,14 @@ void MKLDNNConvLayer::resetBwdWgtPD( ...@@ -236,14 +233,14 @@ void MKLDNNConvLayer::resetBwdWgtPD(
loadConvSettings(wgtDims, biasDims, strides, dilations, padL, padR); loadConvSettings(wgtDims, biasDims, strides, dilations, padL, padR);
// create backward weight using input, output and weight value memory desc // 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(outVal_) << "Should have internal output value";
CHECK(wgtVal_) << "Should have weight value"; CHECK(wgtVal_) << "Should have weight value";
algorithm algo = algorithm::convolution_direct; algorithm algo = algorithm::convolution_direct;
padding_kind padKind = padding_kind::zero; padding_kind padKind = padding_kind::zero;
auto bwdWgtDesc = biasVal_ != nullptr auto bwdWgtDesc = biasVal_ != nullptr
? conv_bwdWgt::desc(algo, ? conv_bwdWgt::desc(algo,
inVal_->getMemoryDesc(), inVals_[0]->getMemoryDesc(),
wgtVal_->getMemoryDesc(), wgtVal_->getMemoryDesc(),
biasVal_->getMemoryDesc(), biasVal_->getMemoryDesc(),
outVal_->getMemoryDesc(), outVal_->getMemoryDesc(),
...@@ -252,7 +249,7 @@ void MKLDNNConvLayer::resetBwdWgtPD( ...@@ -252,7 +249,7 @@ void MKLDNNConvLayer::resetBwdWgtPD(
padR, padR,
padKind) padKind)
: conv_bwdWgt::desc(algo, : conv_bwdWgt::desc(algo,
inVal_->getMemoryDesc(), inVals_[0]->getMemoryDesc(),
wgtVal_->getMemoryDesc(), wgtVal_->getMemoryDesc(),
outVal_->getMemoryDesc(), outVal_->getMemoryDesc(),
strides, strides,
...@@ -260,7 +257,7 @@ void MKLDNNConvLayer::resetBwdWgtPD( ...@@ -260,7 +257,7 @@ void MKLDNNConvLayer::resetBwdWgtPD(
padR, padR,
padKind); padKind);
pd.reset(new conv_bwdWgt::primitive_desc(bwdWgtDesc, engine_, *fwdPD_)); 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( CHECK_PRIMITIVE_DESC_EQ(
outVal_, outVal_,
pd->diff_dst_primitive_desc(), pd->diff_dst_primitive_desc(),
...@@ -280,12 +277,12 @@ void MKLDNNConvLayer::resetBwdDataPD( ...@@ -280,12 +277,12 @@ void MKLDNNConvLayer::resetBwdDataPD(
memory::dims wgtDims, biasDims, strides, dilations, padL, padR; memory::dims wgtDims, biasDims, strides, dilations, padL, padR;
loadConvSettings(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"; CHECK(outVal_) << "Should have internal output value";
// create backward data using input and output value memory desc // create backward data using input and output value memory desc
// but using weight memory desc with any format // but using weight memory desc with any format
auto bwdDataDesc = conv_bwdData::desc(algorithm::convolution_direct, auto bwdDataDesc = conv_bwdData::desc(algorithm::convolution_direct,
inVal_->getMemoryDesc(), inVals_[0]->getMemoryDesc(),
MKLDNNMatrix::createMemoryDesc(wgtDims), MKLDNNMatrix::createMemoryDesc(wgtDims),
outVal_->getMemoryDesc(), outVal_->getMemoryDesc(),
strides, strides,
...@@ -294,7 +291,7 @@ void MKLDNNConvLayer::resetBwdDataPD( ...@@ -294,7 +291,7 @@ void MKLDNNConvLayer::resetBwdDataPD(
padding_kind::zero); padding_kind::zero);
pd.reset(new conv_bwdData::primitive_desc(bwdDataDesc, engine_, *fwdPD_)); pd.reset(new conv_bwdData::primitive_desc(bwdDataDesc, engine_, *fwdPD_));
CHECK_PRIMITIVE_DESC_EQ( CHECK_PRIMITIVE_DESC_EQ(
inVal_, inVals_[0],
pd->diff_src_primitive_desc(), pd->diff_src_primitive_desc(),
"primitive desc of in value and grad should be equal"); "primitive desc of in value and grad should be equal");
CHECK_PRIMITIVE_DESC_EQ( CHECK_PRIMITIVE_DESC_EQ(
...@@ -346,12 +343,12 @@ void MKLDNNConvLayer::resetBwdPipeline( ...@@ -346,12 +343,12 @@ void MKLDNNConvLayer::resetBwdPipeline(
MKLDNNMatrixPtr& wgt, MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias, MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) { MKLDNNMatrixPtr& out) {
CHECK(inVal_); CHECK(inVals_[0]);
// add bwdWgt handle // add bwdWgt handle
if (bias) { if (bias) {
bwdWgt_.reset(new conv_bwdWgt(*wgtPD, *inVal_, *out, *wgt, *bias)); bwdWgt_.reset(new conv_bwdWgt(*wgtPD, *inVals_[0], *out, *wgt, *bias));
} else { } else {
bwdWgt_.reset(new conv_bwdWgt(*wgtPD, *inVal_, *out, *wgt)); bwdWgt_.reset(new conv_bwdWgt(*wgtPD, *inVals_[0], *out, *wgt));
} }
pipeline.push_back(*bwdWgt_); pipeline.push_back(*bwdWgt_);
......
paddle/gserver/layers/MKLDNNConvLayer.h
浏览文件 @ f191c820
...@@ -69,18 +69,14 @@ public: ...@@ -69,18 +69,14 @@ public:
const ParameterMap& parameterMap) override; const ParameterMap& parameterMap) override;
void reshape( 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, void resetFwd(std::vector<mkldnn::primitive>& pipeline,
MKLDNNMatrixPtr& in, std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) override; MKLDNNMatrixPtr& out) override;
void resetBwd(std::vector<mkldnn::primitive>& pipeline, void resetBwd(std::vector<mkldnn::primitive>& pipeline,
MKLDNNMatrixPtr& in, std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) override; MKLDNNMatrixPtr& out) override;
void updateWeights(const UpdateCallback& callback) override; void updateWeights(const UpdateCallback& callback) override;
...@@ -107,48 +103,26 @@ protected: ...@@ -107,48 +103,26 @@ protected:
mkldnn::memory::dims& padL, mkldnn::memory::dims& padL,
mkldnn::memory::dims& padR); mkldnn::memory::dims& padR);
/**
* reset the forward primitive descriptor.
*/
void resetFwdPD(std::shared_ptr<conv_fwd::primitive_desc>& pd); 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, void resetFwdBuffers(std::shared_ptr<conv_fwd::primitive_desc>& pd,
MKLDNNMatrixPtr& in, MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt, MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias, MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out); MKLDNNMatrixPtr& out);
/**
* reset the forward pipeline.
*/
void resetFwdPipeline(std::vector<mkldnn::primitive>& pipeline, void resetFwdPipeline(std::vector<mkldnn::primitive>& pipeline,
std::shared_ptr<conv_fwd::primitive_desc>& pd, std::shared_ptr<conv_fwd::primitive_desc>& pd,
MKLDNNMatrixPtr& in, MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt, MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias, MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out); MKLDNNMatrixPtr& out);
/**
* reset the backward weight primitive descriptor.
*/
void resetBwdWgtPD(std::shared_ptr<conv_bwdWgt::primitive_desc>& pd); 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); 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, void resetBwdBuffers(std::shared_ptr<conv_bwdWgt::primitive_desc>& wgtPD,
std::shared_ptr<conv_bwdData::primitive_desc>& dataPD, std::shared_ptr<conv_bwdData::primitive_desc>& dataPD,
MKLDNNMatrixPtr& in, MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt, MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias, MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out); MKLDNNMatrixPtr& out);
/**
* reset the backward pipeline.
*/
void resetBwdPipeline(std::vector<mkldnn::primitive>& pipeline, void resetBwdPipeline(std::vector<mkldnn::primitive>& pipeline,
std::shared_ptr<conv_bwdWgt::primitive_desc>& wgtPD, std::shared_ptr<conv_bwdWgt::primitive_desc>& wgtPD,
std::shared_ptr<conv_bwdData::primitive_desc>& dataPD, std::shared_ptr<conv_bwdData::primitive_desc>& dataPD,
......
paddle/gserver/layers/MKLDNNFcLayer.cpp
浏览文件 @ f191c820
...@@ -74,7 +74,7 @@ void MKLDNNFcLayer::convertWeightsToPaddle() { ...@@ -74,7 +74,7 @@ void MKLDNNFcLayer::convertWeightsToPaddle() {
} }
void MKLDNNFcLayer::reshape( 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); reshapeInput(bs, ih, iw);
CHECK_EQ(iLayerSize_, inputLayers_[0]->getSize()); CHECK_EQ(iLayerSize_, inputLayers_[0]->getSize());
...@@ -87,32 +87,29 @@ void MKLDNNFcLayer::reshape( ...@@ -87,32 +87,29 @@ void MKLDNNFcLayer::reshape(
} }
void MKLDNNFcLayer::resetFwd(std::vector<primitive>& pipeline, void MKLDNNFcLayer::resetFwd(std::vector<primitive>& pipeline,
MKLDNNMatrixPtr& in, std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) { 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, void MKLDNNFcLayer::resetBwd(std::vector<primitive>& pipeline,
MKLDNNMatrixPtr& in, std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) { MKLDNNMatrixPtr& out) {
std::shared_ptr<fc_bwdWgt::primitive_desc> bwdWgtPD; std::shared_ptr<fc_bwdWgt::primitive_desc> bwdWgtPD;
std::shared_ptr<fc_bwdData::primitive_desc> bwdDataPD; 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) { void MKLDNNFcLayer::updateWeights(const UpdateCallback& callback) {
...@@ -193,9 +190,9 @@ void MKLDNNFcLayer::resetBwdBuffers(MKLDNNMatrixPtr& in, ...@@ -193,9 +190,9 @@ void MKLDNNFcLayer::resetBwdBuffers(MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt, MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias, MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) { MKLDNNMatrixPtr& out) {
CHECK(inVal_ && outVal_); CHECK(inVals_[0] && outVal_);
resetOutGrad(out, outVal_->getPrimitiveDesc()); resetOutGrad(out, outVal_->getPrimitiveDesc());
resetInGrad(in, inVal_->getPrimitiveDesc()); resetInGrad(in, inVals_[0]->getPrimitiveDesc());
CHECK(wgtVal_); CHECK(wgtVal_);
resetWithMatrix(wgt, weight_->getWGrad(), wgtVal_->getPrimitiveDesc()); resetWithMatrix(wgt, weight_->getWGrad(), wgtVal_->getPrimitiveDesc());
...@@ -212,14 +209,15 @@ void MKLDNNFcLayer::resetBwdWgtPD( ...@@ -212,14 +209,15 @@ void MKLDNNFcLayer::resetBwdWgtPD(
MKLDNNMatrixPtr& wgt, MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias, MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) { MKLDNNMatrixPtr& out) {
CHECK(inVal_); CHECK(inVals_[0]);
fc_bwdWgt::desc bwdWgtDesc = bias ? fc_bwdWgt::desc(inVal_->getMemoryDesc(), fc_bwdWgt::desc bwdWgtDesc =
wgt->getMemoryDesc(), bias ? fc_bwdWgt::desc(inVals_[0]->getMemoryDesc(),
bias->getMemoryDesc(), wgt->getMemoryDesc(),
out->getMemoryDesc()) bias->getMemoryDesc(),
: fc_bwdWgt::desc(inVal_->getMemoryDesc(), out->getMemoryDesc())
wgt->getMemoryDesc(), : fc_bwdWgt::desc(inVals_[0]->getMemoryDesc(),
out->getMemoryDesc()); wgt->getMemoryDesc(),
out->getMemoryDesc());
pd.reset(new fc_bwdWgt::primitive_desc(bwdWgtDesc, engine_, *fwdPD_)); pd.reset(new fc_bwdWgt::primitive_desc(bwdWgtDesc, engine_, *fwdPD_));
} }
...@@ -245,11 +243,11 @@ void MKLDNNFcLayer::resetBwdPipeline( ...@@ -245,11 +243,11 @@ void MKLDNNFcLayer::resetBwdPipeline(
MKLDNNMatrixPtr& wgt, MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias, MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) { MKLDNNMatrixPtr& out) {
CHECK(inVal_); CHECK(inVals_[0]);
if (bias) { if (bias) {
bwdWgt_.reset(new fc_bwdWgt(*bwdWgtPD, *inVal_, *out, *wgt, *bias)); bwdWgt_.reset(new fc_bwdWgt(*bwdWgtPD, *inVals_[0], *out, *wgt, *bias));
} else { } else {
bwdWgt_.reset(new fc_bwdWgt(*bwdWgtPD, *inVal_, *out, *wgt)); bwdWgt_.reset(new fc_bwdWgt(*bwdWgtPD, *inVals_[0], *out, *wgt));
} }
pipeline.push_back(*bwdWgt_); pipeline.push_back(*bwdWgt_);
......
paddle/gserver/layers/MKLDNNFcLayer.h
浏览文件 @ f191c820
...@@ -52,18 +52,14 @@ public: ...@@ -52,18 +52,14 @@ public:
const ParameterMap& parameterMap) override; const ParameterMap& parameterMap) override;
void reshape( 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, void resetFwd(std::vector<mkldnn::primitive>& pipeline,
MKLDNNMatrixPtr& in, std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) override; MKLDNNMatrixPtr& out) override;
void resetBwd(std::vector<mkldnn::primitive>& pipeline, void resetBwd(std::vector<mkldnn::primitive>& pipeline,
MKLDNNMatrixPtr& in, std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) override; MKLDNNMatrixPtr& out) override;
void updateWeights(const UpdateCallback& callback) override; void updateWeights(const UpdateCallback& callback) override;
...@@ -73,11 +69,6 @@ public: ...@@ -73,11 +69,6 @@ public:
void convertWeightsToPaddle() override; void convertWeightsToPaddle() override;
protected: protected:
/**
* Forward functions: reset buffers(input, output, weight and bias),
* reset primitive descriptor,
* reset pipeline.
*/
void resetFwdBuffers(MKLDNNMatrixPtr& in, void resetFwdBuffers(MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt, MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias, MKLDNNMatrixPtr& bias,
...@@ -93,13 +84,6 @@ protected: ...@@ -93,13 +84,6 @@ protected:
MKLDNNMatrixPtr& wgt, MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias, MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out); 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, void resetBwdBuffers(MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt, MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias, MKLDNNMatrixPtr& bias,
......
paddle/gserver/layers/MKLDNNLayer.cpp
浏览文件 @ f191c820
...@@ -48,31 +48,20 @@ void MKLDNNLayer::forward(PassType passType) { ...@@ -48,31 +48,20 @@ void MKLDNNLayer::forward(PassType passType) {
REGISTER_TIMER_INFO("mkldnn_FwdTimer", getName().c_str()); REGISTER_TIMER_INFO("mkldnn_FwdTimer", getName().c_str());
CHECK(!inputLayers_.empty()); CHECK(!inputLayers_.empty());
copySeqInfoToOutputs(); copySeqInfoToOutputs();
size_t elemenCnt = inputLayers_[0]->getOutputValue()->getElementCnt(); if (condition_ != keepCondition()) {
if (inputElemenCnt_ != elemenCnt) {
VLOG(MKLDNN_BASE) << getName() << " reset mkldnn forward"; VLOG(MKLDNN_BASE) << getName() << " reset mkldnn forward";
// reset when input total sizes changed, not only the batchsize condition_ = keepCondition();
inputElemenCnt_ = elemenCnt;
pipelineFwd_.clear();
reshape(bs_, ic_, ih_, iw_, oc_, oh_, ow_); 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(); shareCPUDevice();
resetFwd(pipelineFwd_, inVal_, wgtVal_, biasVal_, outVal_); pipelineFwd_.clear();
// MKLDNNLayer output value should be MKLDNNMatrix inVals_.resize(inputLayers_.size(), nullptr);
// so external output value is necessary. extInVals_.resize(inputLayers_.size(), nullptr);
// Then external input value is not necessary, cvtInVals_.resize(inputLayers_.size(), nullptr);
// since input may be mkldnn internal buffer. resetFwd(pipelineFwd_, inVals_, outVal_);
CHECK(extOutVal_) << "external output value is necessary"; prepareValueConversions(pipelineFwd_);
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_);
}
convertWeightsFromPaddle(); convertWeightsFromPaddle();
printSizeInfo();
printValueFormat(); printValueFormat();
needResetBwd_ = true; needResetBwd_ = true;
} }
...@@ -80,8 +69,8 @@ void MKLDNNLayer::forward(PassType passType) { ...@@ -80,8 +69,8 @@ void MKLDNNLayer::forward(PassType passType) {
if (inputLayers_[0]->getType() == "data" && inputLayers_.size() == 1) { if (inputLayers_[0]->getType() == "data" && inputLayers_.size() == 1) {
// Update input value data when input layer is "data" type, // Update input value data when input layer is "data" type,
// since the input value data address might be changed. // since the input value data address might be changed.
CHECK(extInVal_); CHECK(extInVals_[0]);
extInVal_->setData(getInputValue(0, CPU_DEVICE)->getData()); extInVals_[0]->setData(getInputValue(0, CPU_DEVICE)->getData());
} }
if (!outputOnlyMKLDNN_) { if (!outputOnlyMKLDNN_) {
...@@ -99,22 +88,13 @@ void MKLDNNLayer::backward(const UpdateCallback& callback) { ...@@ -99,22 +88,13 @@ void MKLDNNLayer::backward(const UpdateCallback& callback) {
if (needResetBwd_) { if (needResetBwd_) {
VLOG(MKLDNN_BASE) << getName() << " reset mkldnn backward"; VLOG(MKLDNN_BASE) << getName() << " reset mkldnn backward";
pipelineBwd_.clear(); pipelineBwd_.clear();
inGrads_.resize(inputLayers_.size(), nullptr);
extInGrads_.resize(inputLayers_.size(), nullptr);
cvtInGrads_.resize(inputLayers_.size(), nullptr);
pipelineMergeGrad_.clear(); pipelineMergeGrad_.clear();
mergeGrad_ = nullptr; mergeGrad_ = nullptr;
resetBwd(pipelineBwd_, inGrad_, wgtGrad_, biasGrad_, outGrad_); resetBwd(pipelineBwd_, inGrads_, outGrad_);
// external output grad is not necessary prepareGradConversions(pipelineBwd_);
// 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_);
}
printGradFormat(); printGradFormat();
needResetBwd_ = false; needResetBwd_ = false;
} }
...@@ -141,8 +121,8 @@ void MKLDNNLayer::backward(const UpdateCallback& callback) { ...@@ -141,8 +121,8 @@ void MKLDNNLayer::backward(const UpdateCallback& callback) {
void MKLDNNLayer::reshapeInput(int& batchsize, void MKLDNNLayer::reshapeInput(int& batchsize,
int& height, int& height,
int& width, int& width,
size_t inputIdx) { size_t idx) {
const Argument& input = inputLayers_[inputIdx]->getOutput(); const Argument& input = inputLayers_[idx]->getOutput();
batchsize = input.getBatchSize(); batchsize = input.getBatchSize();
int h = input.getFrameHeight(); int h = input.getFrameHeight();
int w = input.getFrameWidth(); int w = input.getFrameWidth();
...@@ -176,27 +156,30 @@ void MKLDNNLayer::resetWithMatrix(MKLDNNMatrixPtr& dnn, ...@@ -176,27 +156,30 @@ void MKLDNNLayer::resetWithMatrix(MKLDNNMatrixPtr& dnn,
void MKLDNNLayer::resetInValue( void MKLDNNLayer::resetInValue(
MKLDNNMatrixPtr& in, MKLDNNMatrixPtr& in,
const std::shared_ptr<memory::primitive_desc>& intPD, const std::shared_ptr<memory::primitive_desc>& intPD,
size_t inputIdx) { size_t idx,
cvtInVal_ = nullptr; int inputChannel) {
extInVal_ = nullptr; cvtInVals_[idx] = nullptr;
extInVals_[idx] = nullptr;
in = 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( auto extPD = MKLDNNMatrix::createPrimitiveDesc(
{bs_, ic_, ih_, iw_}, format::nchw, engine_); {bs_, inputChannel, ih_, iw_}, format::nchw, engine_);
const MatrixPtr& inMat = inputLayers_[inputIdx]->getOutputValue(); const MatrixPtr& inMat = inputLayers_[idx]->getOutputValue();
extInVal_ = std::dynamic_pointer_cast<MKLDNNMatrix>(inMat); extInVals_[idx] = std::dynamic_pointer_cast<MKLDNNMatrix>(inMat);
CHECK_EQ(inputIsOnlyMKLDNN(), extInVal_ != nullptr); CHECK_EQ(inputIsOnlyMKLDNN(), extInVals_[idx] != nullptr);
if (extInVal_ == nullptr || extInVal_->getFormat() == format::nc) { if (extInVals_[idx] == nullptr ||
extInVal_ = MKLDNNMatrix::create(extPD, inMat); extInVals_[idx]->getFormat() == format::nc) {
extInVals_[idx] = MKLDNNMatrix::create(extPD, inMat);
} }
in = extInVal_; in = extInVals_[idx];
if (nullptr == intPD || in->getPrimitiveDesc() == *intPD) { if (nullptr == intPD || in->getPrimitiveDesc() == *intPD) {
return; return;
} }
// need create reorder // need create reorder
in = MKLDNNMatrix::create(*intPD); in = MKLDNNMatrix::create(*intPD);
cvtInVal_ = MKLDNNMatrix::createReorder(extInVal_, in); cvtInVals_[idx] = MKLDNNMatrix::createReorder(extInVals_[idx], in);
CHECK(cvtInVal_) << "should not be emptry"; CHECK(cvtInVals_[idx]) << "should not be emptry";
} }
void MKLDNNLayer::resetOutValue(MKLDNNMatrixPtr& out, void MKLDNNLayer::resetOutValue(MKLDNNMatrixPtr& out,
...@@ -218,11 +201,11 @@ void MKLDNNLayer::resetOutValue(MKLDNNMatrixPtr& out, ...@@ -218,11 +201,11 @@ void MKLDNNLayer::resetOutValue(MKLDNNMatrixPtr& out,
void MKLDNNLayer::resetInGrad(MKLDNNMatrixPtr& in, void MKLDNNLayer::resetInGrad(MKLDNNMatrixPtr& in,
memory::primitive_desc intPD, memory::primitive_desc intPD,
size_t inputIdx) { size_t idx) {
cvtInGrad_ = nullptr; cvtInGrads_[idx] = nullptr;
extInGrad_ = nullptr; extInGrads_[idx] = nullptr;
in = nullptr; in = nullptr;
LayerPtr& input = inputLayers_[inputIdx]; LayerPtr& input = inputLayers_[idx];
if (input->getOutputGrad() == nullptr) { if (input->getOutputGrad() == nullptr) {
// no need input grad // no need input grad
return; return;
...@@ -237,23 +220,25 @@ void MKLDNNLayer::resetInGrad(MKLDNNMatrixPtr& in, ...@@ -237,23 +220,25 @@ void MKLDNNLayer::resetInGrad(MKLDNNMatrixPtr& in,
in = MKLDNNMatrix::create(intPD, inMat); in = MKLDNNMatrix::create(intPD, inMat);
Argument& arg = input->getOutput(this->getName()); Argument& arg = input->getOutput(this->getName());
arg.grad = std::dynamic_pointer_cast<Matrix>(in); arg.grad = std::dynamic_pointer_cast<Matrix>(in);
CHECK_PRIMITIVE_DESC_EQ(inVal_, intPD); CHECK_PRIMITIVE_DESC_EQ(inVals_[idx], intPD);
if (inputIsOnlyMKLDNN()) { if (inputIsOnlyMKLDNN()) {
return; return;
} }
extInGrad_ = in; extInGrads_[idx] = in;
if (isPaddleFormat(extInGrad_->getFormat())) { if (isPaddleFormat(extInGrads_[idx]->getFormat())) {
return; return;
} }
// need create reorder // 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)"; << "should have external input value and the format must be nchw(nc)";
extInGrad_ = MKLDNNMatrix::create(extInVal_->getPrimitiveDesc(), inMat); extInGrads_[idx] =
CHECK_PRIMITIVE_DESC_EQ(inVal_, intPD); MKLDNNMatrix::create(extInVals_[idx]->getPrimitiveDesc(), inMat);
CHECK_PRIMITIVE_DESC_EQ(inVals_[idx], intPD);
in = MKLDNNMatrix::create(intPD); in = MKLDNNMatrix::create(intPD);
cvtInGrad_ = MKLDNNMatrix::createReorder(in, extInGrad_); cvtInGrads_[idx] = MKLDNNMatrix::createReorder(in, extInGrads_[idx]);
CHECK(cvtInGrad_); CHECK(cvtInGrads_[idx]);
} }
void MKLDNNLayer::resetOutGrad(MKLDNNMatrixPtr& out, void MKLDNNLayer::resetOutGrad(MKLDNNMatrixPtr& out,
......
paddle/gserver/layers/MKLDNNLayer.h
浏览文件 @ f191c820
...@@ -34,15 +34,16 @@ typedef std::shared_ptr<MKLDNNLayer> MKLDNNLayerPtr; ...@@ -34,15 +34,16 @@ typedef std::shared_ptr<MKLDNNLayer> MKLDNNLayerPtr;
*/ */
class MKLDNNLayer : public Layer { class MKLDNNLayer : public Layer {
protected: protected:
// input value element count
size_t inputElemenCnt_;
// batch size // batch size
int bs_; int bs_;
// they sizes are always from the first input layer
// input image channel, height and width // input image channel, height and width
int ic_, ih_, iw_; int ic_, ih_, iw_;
// output image channel, height and width // output image channel, height and width
int oc_, oh_, ow_; int oc_, oh_, ow_;
// the condition that forward need be reset
size_t condition_;
// backward also need reset after reset forward handle // backward also need reset after reset forward handle
bool needResetBwd_; bool needResetBwd_;
...@@ -67,18 +68,18 @@ protected: ...@@ -67,18 +68,18 @@ protected:
* When all layers are mkldnn layers, they could save internal data. * When all layers are mkldnn layers, they could save internal data.
*/ */
// below MKLDNNMatrix buffers are all internal buffers // below MKLDNNMatrix buffers are all internal buffers
MKLDNNMatrixPtr inVal_; std::vector<MKLDNNMatrixPtr> inVals_;
MKLDNNMatrixPtr inGrad_; std::vector<MKLDNNMatrixPtr> inGrads_;
MKLDNNMatrixPtr outVal_; MKLDNNMatrixPtr outVal_;
MKLDNNMatrixPtr outGrad_; MKLDNNMatrixPtr outGrad_;
// below are external value and grad // below are external value and grad
MKLDNNMatrixPtr extInVal_; std::vector<MKLDNNMatrixPtr> extInVals_;
MKLDNNMatrixPtr extInGrad_; std::vector<MKLDNNMatrixPtr> extInGrads_;
MKLDNNMatrixPtr extOutVal_; MKLDNNMatrixPtr extOutVal_;
MKLDNNMatrixPtr extOutGrad_; MKLDNNMatrixPtr extOutGrad_;
// convert handle between external and internal buffers // convert handle between external and internal buffers
std::shared_ptr<mkldnn::reorder> cvtInVal_; std::vector<std::shared_ptr<mkldnn::reorder>> cvtInVals_;
std::shared_ptr<mkldnn::reorder> cvtInGrad_; std::vector<std::shared_ptr<mkldnn::reorder>> cvtInGrads_;
std::shared_ptr<mkldnn::reorder> cvtOutVal_; std::shared_ptr<mkldnn::reorder> cvtOutVal_;
std::shared_ptr<mkldnn::reorder> cvtOutGrad_; std::shared_ptr<mkldnn::reorder> cvtOutGrad_;
...@@ -102,14 +103,7 @@ protected: ...@@ -102,14 +103,7 @@ protected:
public: public:
explicit MKLDNNLayer(const LayerConfig& config) explicit MKLDNNLayer(const LayerConfig& config)
: Layer(config), : Layer(config),
inputElemenCnt_(0), condition_(0),
bs_(0),
ic_(0),
ih_(0),
iw_(0),
oc_(0),
oh_(0),
ow_(0),
needResetBwd_(true), needResetBwd_(true),
outputOnlyMKLDNN_(false), outputOnlyMKLDNN_(false),
engine_(mkldnn::engine::cpu, 0), engine_(mkldnn::engine::cpu, 0),
...@@ -125,31 +119,28 @@ public: ...@@ -125,31 +119,28 @@ public:
virtual void backward(const UpdateCallback& callback); virtual void backward(const UpdateCallback& callback);
/** /**
* reshape the input image sizes * reshape the input and output channels and image sizes
* and reset output image and buffer size * and reset output buffer size
* output channel can not be changed
*/ */
virtual void reshape( 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 * reset the mkldnn forward primitve and memories
* only would be called when input size changes * 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, virtual void resetFwd(std::vector<mkldnn::primitive>& pipeline,
MKLDNNMatrixPtr& in, std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) = 0; MKLDNNMatrixPtr& out) = 0;
/** /**
* reset the mkldnn backward primitve and memories * reset the mkldnn backward primitve and memories
* only would be called when needed * 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, virtual void resetBwd(std::vector<mkldnn::primitive>& pipeline,
MKLDNNMatrixPtr& in, std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) = 0; MKLDNNMatrixPtr& out) = 0;
/** /**
...@@ -175,13 +166,19 @@ public: ...@@ -175,13 +166,19 @@ public:
void addOutputArgument(int deviceId) { Layer::addOutputArgument(deviceId); } void addOutputArgument(int deviceId) { Layer::addOutputArgument(deviceId); }
protected: 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 * reshape the input image sizes and input batchsize
*/ */
void reshapeInput(int& batchsize, void reshapeInput(int& batchsize, int& height, int& width, size_t idx = 0);
int& height,
int& width,
size_t inputIdx = 0);
/** /**
* reshape output image sizes * reshape output image sizes
...@@ -199,11 +196,13 @@ protected: ...@@ -199,11 +196,13 @@ protected:
/** /**
* reset input value from input MKLDNNMatrix and internal primitive desc. * reset input value from input MKLDNNMatrix and internal primitive desc.
* reset both internal and external buffer and create reorder if necessary. * reset both internal and external buffer and create reorder if necessary.
* input channel may be different in concat.
*/ */
void resetInValue( void resetInValue(
MKLDNNMatrixPtr& in, MKLDNNMatrixPtr& in,
const std::shared_ptr<mkldnn::memory::primitive_desc>& intPD = nullptr, 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. * reset output value from internal primitive desc.
...@@ -218,7 +217,7 @@ protected: ...@@ -218,7 +217,7 @@ protected:
*/ */
void resetInGrad(MKLDNNMatrixPtr& in, void resetInGrad(MKLDNNMatrixPtr& in,
mkldnn::memory::primitive_desc intPD, mkldnn::memory::primitive_desc intPD,
size_t inputIdx = 0); size_t idx = 0);
/** /**
* reset output grad from internal primitive desc. * reset output grad from internal primitive desc.
...@@ -296,17 +295,19 @@ protected: ...@@ -296,17 +295,19 @@ protected:
* print the mkldnn memory format of value * print the mkldnn memory format of value
*/ */
virtual void printValueFormat() { virtual void printValueFormat() {
if (extInVal_) { for (size_t i = 0; i < inVals_.size(); ++i) {
VLOG(MKLDNN_FMTS) << extInVal_->getFormat() << " >>> "; if (!inVals_[i]) {
} continue;
if (inVal_) { }
VLOG(MKLDNN_FMTS) << inVal_->getFormat() << " >>>"; VLOG(MKLDNN_FMTS) << "Input " << i << ", " << inputLayers_[i]->getName()
<< ": " << (extInVals_[i] ? extInVals_[i]->getFormat()
: inVals_[i]->getFormat())
<< " >>> " << inVals_[i]->getFormat() << " >>>";
} }
if (outVal_) { if (outVal_) {
VLOG(MKLDNN_FMTS) << outVal_->getFormat() << " >>> "; VLOG(MKLDNN_FMTS) << outVal_->getFormat() << " >>> "
} << (extOutVal_ ? extOutVal_->getFormat()
if (extOutVal_) { : outVal_->getFormat());
VLOG(MKLDNN_FMTS) << extOutVal_->getFormat();
} }
if (wgtVal_) { if (wgtVal_) {
VLOG(MKLDNN_FMTS) << "Weight value format: " << wgtVal_->getFormat(); VLOG(MKLDNN_FMTS) << "Weight value format: " << wgtVal_->getFormat();
...@@ -320,17 +321,19 @@ protected: ...@@ -320,17 +321,19 @@ protected:
* print the mkldnn memory format of grad * print the mkldnn memory format of grad
*/ */
virtual void printGradFormat() { virtual void printGradFormat() {
if (extOutGrad_) {
VLOG(MKLDNN_FMTS) << extOutGrad_->getFormat();
}
if (outGrad_) { if (outGrad_) {
VLOG(MKLDNN_FMTS) << outGrad_->getFormat() << " <<< "; VLOG(MKLDNN_FMTS) << outGrad_->getFormat() << " <<< "
<< (extOutGrad_ ? extOutGrad_->getFormat()
: outGrad_->getFormat());
} }
if (inGrad_) { for (size_t i = 0; i < inGrads_.size(); ++i) {
VLOG(MKLDNN_FMTS) << inGrad_->getFormat() << " <<<"; if (!inGrads_[i]) {
} continue;
if (extInGrad_) { }
VLOG(MKLDNN_FMTS) << extInGrad_->getFormat() << " <<< "; VLOG(MKLDNN_FMTS) << "Input " << i << ", " << inputLayers_[i]->getName()
<< ": " << (extInGrads_[i] ? extInGrads_[i]->getFormat()
: inGrads_[i]->getFormat())
<< " <<< " << inGrads_[i]->getFormat() << " <<<";
} }
if (wgtGrad_) { if (wgtGrad_) {
VLOG(MKLDNN_FMTS) << "Weight grad format: " << wgtGrad_->getFormat(); VLOG(MKLDNN_FMTS) << "Weight grad format: " << wgtGrad_->getFormat();
...@@ -437,6 +440,41 @@ private: ...@@ -437,6 +440,41 @@ private:
outputOtherDevice_[i].cpuSequenceDims = output_.cpuSequenceDims; 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 } // namespace paddle
paddle/gserver/layers/MKLDNNPoolLayer.cpp
浏览文件 @ f191c820
...@@ -58,10 +58,11 @@ bool MKLDNNPoolLayer::init(const LayerMap& layerMap, ...@@ -58,10 +58,11 @@ bool MKLDNNPoolLayer::init(const LayerMap& layerMap,
} }
void MKLDNNPoolLayer::reshape( 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); reshapeInput(bs, ih, iw);
// ic_ and oc can not be changed // 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"; << "Input channel can not be changed";
// cal output sizes // cal output sizes
...@@ -74,29 +75,25 @@ void MKLDNNPoolLayer::reshape( ...@@ -74,29 +75,25 @@ void MKLDNNPoolLayer::reshape(
} }
void MKLDNNPoolLayer::resetFwd(std::vector<primitive>& pipeline, void MKLDNNPoolLayer::resetFwd(std::vector<primitive>& pipeline,
MKLDNNMatrixPtr& in, std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) { 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, void MKLDNNPoolLayer::resetBwd(std::vector<primitive>& pipeline,
MKLDNNMatrixPtr& in, std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) { MKLDNNMatrixPtr& out) {
std::shared_ptr<pool_bwd::primitive_desc> pd; 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, void MKLDNNPoolLayer::resetFwdBuffers(MKLDNNMatrixPtr& in,
...@@ -151,9 +148,9 @@ void MKLDNNPoolLayer::resetFwdPipeline( ...@@ -151,9 +148,9 @@ void MKLDNNPoolLayer::resetFwdPipeline(
void MKLDNNPoolLayer::resetBwdBuffers(MKLDNNMatrixPtr& in, void MKLDNNPoolLayer::resetBwdBuffers(MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& out) { MKLDNNMatrixPtr& out) {
CHECK(inVal_ && outVal_); CHECK(inVals_[0] && outVal_);
resetOutGrad(out, outVal_->getPrimitiveDesc()); resetOutGrad(out, outVal_->getPrimitiveDesc());
resetInGrad(in, inVal_->getPrimitiveDesc()); resetInGrad(in, inVals_[0]->getPrimitiveDesc());
} }
void MKLDNNPoolLayer::resetBwdPD(std::shared_ptr<pool_bwd::primitive_desc>& pd, void MKLDNNPoolLayer::resetBwdPD(std::shared_ptr<pool_bwd::primitive_desc>& pd,
......
paddle/gserver/layers/MKLDNNPoolLayer.h
浏览文件 @ f191c820
...@@ -53,18 +53,14 @@ public: ...@@ -53,18 +53,14 @@ public:
const ParameterMap& parameterMap) override; const ParameterMap& parameterMap) override;
void reshape( 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, void resetFwd(std::vector<mkldnn::primitive>& pipeline,
MKLDNNMatrixPtr& in, std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) override; MKLDNNMatrixPtr& out) override;
void resetBwd(std::vector<mkldnn::primitive>& pipeline, void resetBwd(std::vector<mkldnn::primitive>& pipeline,
MKLDNNMatrixPtr& in, std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) override; MKLDNNMatrixPtr& out) override;
void printSizeInfo() override { void printSizeInfo() override {
...@@ -75,11 +71,6 @@ public: ...@@ -75,11 +71,6 @@ public:
} }
protected: protected:
/**
* Forward functions: reset buffers(input, output),
* reset primitive descriptor,
* reset pipeline.
*/
void resetFwdBuffers(MKLDNNMatrixPtr& in, MKLDNNMatrixPtr& out); void resetFwdBuffers(MKLDNNMatrixPtr& in, MKLDNNMatrixPtr& out);
void resetFwdPD(std::shared_ptr<pool_fwd::primitive_desc>& pd, void resetFwdPD(std::shared_ptr<pool_fwd::primitive_desc>& pd,
MKLDNNMatrixPtr in, MKLDNNMatrixPtr in,
...@@ -88,12 +79,6 @@ protected: ...@@ -88,12 +79,6 @@ protected:
std::shared_ptr<pool_fwd::primitive_desc>& pd, std::shared_ptr<pool_fwd::primitive_desc>& pd,
MKLDNNMatrixPtr& in, MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& out); MKLDNNMatrixPtr& out);
/**
* Backward functions: reset buffers(input, output),
* reset primitive descriptor,
* reset pipeline.
*/
void resetBwdBuffers(MKLDNNMatrixPtr& in, MKLDNNMatrixPtr& out); void resetBwdBuffers(MKLDNNMatrixPtr& in, MKLDNNMatrixPtr& out);
void resetBwdPD(std::shared_ptr<pool_bwd::primitive_desc>& pd, void resetBwdPD(std::shared_ptr<pool_bwd::primitive_desc>& pd,
MKLDNNMatrixPtr& in, MKLDNNMatrixPtr& in,
......
paddle/operators/CMakeLists.txt
浏览文件 @ f191c820
...@@ -184,6 +184,7 @@ set(DEPS_OPS ...@@ -184,6 +184,7 @@ set(DEPS_OPS
sequence_softmax_op sequence_softmax_op
sum_op sum_op
pool_op pool_op
maxout_op
pool_with_index_op pool_with_index_op
conv_op conv_op
conv_transpose_op conv_transpose_op
...@@ -210,6 +211,7 @@ op_library(sgd_op DEPS selected_rows_functor) ...@@ -210,6 +211,7 @@ op_library(sgd_op DEPS selected_rows_functor)
op_library(adagrad_op DEPS selected_rows_functor) op_library(adagrad_op DEPS selected_rows_functor)
op_library(conv_op DEPS vol2col) op_library(conv_op DEPS vol2col)
op_library(pool_op DEPS pooling) op_library(pool_op DEPS pooling)
op_library(maxout_op DEPS maxouting)
op_library(pool_with_index_op DEPS pooling) 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_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) op_library(lod_tensor_to_array_op SRCS lod_tensor_to_array_op.cc DEPS lod_rank_table_op)
......
paddle/operators/conv_cudnn_op.cc
浏览文件 @ f191c820
...@@ -40,7 +40,8 @@ REGISTER_OP(conv_cudnn, ops::ConvOp, ops::CudnnConvOpMaker, conv_cudnn_grad, ...@@ -40,7 +40,8 @@ REGISTER_OP(conv_cudnn, ops::ConvOp, ops::CudnnConvOpMaker, conv_cudnn_grad,
ops::ConvOpGrad); ops::ConvOpGrad);
REGISTER_OP_CPU_KERNEL(conv_cudnn, REGISTER_OP_CPU_KERNEL(conv_cudnn,
ops::GemmConvKernel<paddle::platform::CPUPlace, float>); ops::GemmConvKernel<paddle::platform::CPUPlace, float>,
ops::GemmConvKernel<paddle::platform::CPUPlace, double>);
REGISTER_OP_CPU_KERNEL( REGISTER_OP_CPU_KERNEL(
conv_cudnn_grad, conv_cudnn_grad, ops::GemmConvGradKernel<paddle::platform::CPUPlace, float>,
ops::GemmConvGradKernel<paddle::platform::CPUPlace, float>); ops::GemmConvGradKernel<paddle::platform::CPUPlace, double>);
paddle/operators/conv_cudnn_op.cu.cc
浏览文件 @ f191c820
...@@ -259,6 +259,8 @@ class CudnnConvGradOpKernel : public framework::OpKernel<T> { ...@@ -259,6 +259,8 @@ class CudnnConvGradOpKernel : public framework::OpKernel<T> {
} // namespace operators } // namespace operators
} // namespace paddle } // namespace paddle
REGISTER_OP_GPU_KERNEL(conv_cudnn, paddle::operators::CudnnConvOpKernel<float>); REGISTER_OP_GPU_KERNEL(conv_cudnn, paddle::operators::CudnnConvOpKernel<float>,
paddle::operators::CudnnConvOpKernel<double>);
REGISTER_OP_GPU_KERNEL(conv_cudnn_grad, REGISTER_OP_GPU_KERNEL(conv_cudnn_grad,
paddle::operators::CudnnConvGradOpKernel<float>); paddle::operators::CudnnConvGradOpKernel<float>,
paddle::operators::CudnnConvGradOpKernel<double>);
paddle/operators/conv_transpose_cudnn_op.cc
浏览文件 @ f191c820
...@@ -61,10 +61,12 @@ REGISTER_OP(conv2d_transpose_cudnn, ops::ConvTransposeOp, ...@@ -61,10 +61,12 @@ REGISTER_OP(conv2d_transpose_cudnn, ops::ConvTransposeOp,
REGISTER_OP_CPU_KERNEL( REGISTER_OP_CPU_KERNEL(
conv2d_transpose_cudnn, conv2d_transpose_cudnn,
ops::GemmConvTransposeKernel<paddle::platform::CPUPlace, float>); ops::GemmConvTransposeKernel<paddle::platform::CPUPlace, float>,
ops::GemmConvTransposeKernel<paddle::platform::CPUPlace, double>);
REGISTER_OP_CPU_KERNEL( REGISTER_OP_CPU_KERNEL(
conv2d_transpose_cudnn_grad, conv2d_transpose_cudnn_grad,
ops::GemmConvTransposeGradKernel<paddle::platform::CPUPlace, float>); ops::GemmConvTransposeGradKernel<paddle::platform::CPUPlace, float>,
ops::GemmConvTransposeGradKernel<paddle::platform::CPUPlace, double>);
REGISTER_OP(conv3d_transpose_cudnn, ops::ConvTransposeOp, REGISTER_OP(conv3d_transpose_cudnn, ops::ConvTransposeOp,
ops::CudnnConv3DTransposeOpMaker, conv3d_transpose_cudnn_grad, ops::CudnnConv3DTransposeOpMaker, conv3d_transpose_cudnn_grad,
...@@ -72,7 +74,9 @@ REGISTER_OP(conv3d_transpose_cudnn, ops::ConvTransposeOp, ...@@ -72,7 +74,9 @@ REGISTER_OP(conv3d_transpose_cudnn, ops::ConvTransposeOp,
REGISTER_OP_CPU_KERNEL( REGISTER_OP_CPU_KERNEL(
conv3d_transpose_cudnn, conv3d_transpose_cudnn,
ops::GemmConvTransposeKernel<paddle::platform::CPUPlace, float>); ops::GemmConvTransposeKernel<paddle::platform::CPUPlace, float>,
ops::GemmConvTransposeKernel<paddle::platform::CPUPlace, double>);
REGISTER_OP_CPU_KERNEL( REGISTER_OP_CPU_KERNEL(
conv3d_transpose_cudnn_grad, conv3d_transpose_cudnn_grad,
ops::GemmConvTransposeGradKernel<paddle::platform::CPUPlace, float>); ops::GemmConvTransposeGradKernel<paddle::platform::CPUPlace, float>,
ops::GemmConvTransposeGradKernel<paddle::platform::CPUPlace, double>);
paddle/operators/conv_transpose_cudnn_op.cu.cc
浏览文件 @ f191c820
...@@ -235,11 +235,15 @@ class CudnnConvTransposeGradOpKernel : public framework::OpKernel<T> { ...@@ -235,11 +235,15 @@ class CudnnConvTransposeGradOpKernel : public framework::OpKernel<T> {
namespace ops = paddle::operators; namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(conv2d_transpose_cudnn, REGISTER_OP_GPU_KERNEL(conv2d_transpose_cudnn,
ops::CudnnConvTransposeOpKernel<float>); ops::CudnnConvTransposeOpKernel<float>,
ops::CudnnConvTransposeOpKernel<double>);
REGISTER_OP_GPU_KERNEL(conv2d_transpose_cudnn_grad, REGISTER_OP_GPU_KERNEL(conv2d_transpose_cudnn_grad,
ops::CudnnConvTransposeGradOpKernel<float>); ops::CudnnConvTransposeGradOpKernel<float>,
ops::CudnnConvTransposeGradOpKernel<double>);
REGISTER_OP_GPU_KERNEL(conv3d_transpose_cudnn, REGISTER_OP_GPU_KERNEL(conv3d_transpose_cudnn,
ops::CudnnConvTransposeOpKernel<float>); ops::CudnnConvTransposeOpKernel<float>,
ops::CudnnConvTransposeOpKernel<double>);
REGISTER_OP_GPU_KERNEL(conv3d_transpose_cudnn_grad, REGISTER_OP_GPU_KERNEL(conv3d_transpose_cudnn_grad,
ops::CudnnConvTransposeGradOpKernel<float>); ops::CudnnConvTransposeGradOpKernel<float>,
ops::CudnnConvTransposeGradOpKernel<double>);
paddle/operators/math/CMakeLists.txt
浏览文件 @ f191c820
...@@ -14,6 +14,7 @@ if(WITH_GPU) ...@@ -14,6 +14,7 @@ if(WITH_GPU)
nv_library(sequence2batch SRCS sequence2batch.cc sequence2batch.cu DEPS device_context) 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(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(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() else()
cc_library(math_function SRCS math_function.cc im2col.cc DEPS cblas device_context framework_proto) 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) cc_library(selected_rows_functor SRCS selected_rows_functor.cc DEPS selected_rows math_function)
...@@ -26,6 +27,7 @@ else() ...@@ -26,6 +27,7 @@ else()
cc_library(sequence2batch SRCS sequence2batch.cc DEPS device_context) cc_library(sequence2batch SRCS sequence2batch.cc DEPS device_context)
cc_library(lstm_compute SRCS lstm_compute.cc DEPS device_context activation_functions) 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(gru_compute SRCS gru_compute.cc DEPS device_context activation_functions math_function)
cc_library(maxouting SRCS maxouting.cc DEPS device_context)
endif() endif()
cc_test(math_function_test SRCS math_function_test.cc DEPS math_function tensor) 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 @ f191c820
/* 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 @ f191c820
/* 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 @ f191c820
/* 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/math/pooling.cc
浏览文件 @ f191c820
...@@ -498,8 +498,8 @@ template class Pool3dGradFunctor< ...@@ -498,8 +498,8 @@ template class Pool3dGradFunctor<
* Ksize, strides, paddings are two elements. These two elements represent * Ksize, strides, paddings are two elements. These two elements represent
* height and width, respectively. * height and width, respectively.
*/ */
template <typename T> template <typename T1, typename T2>
class MaxPool2dWithIndexFunctor<platform::CPUPlace, T> { class MaxPool2dWithIndexFunctor<platform::CPUPlace, T1, T2> {
public: public:
void operator()(const platform::DeviceContext& context, void operator()(const platform::DeviceContext& context,
const framework::Tensor& input, std::vector<int>& ksize, const framework::Tensor& input, std::vector<int>& ksize,
...@@ -520,9 +520,9 @@ class MaxPool2dWithIndexFunctor<platform::CPUPlace, T> { ...@@ -520,9 +520,9 @@ class MaxPool2dWithIndexFunctor<platform::CPUPlace, T> {
const int input_stride = input_height * input_width; const int input_stride = input_height * input_width;
const int output_stride = output_height * output_width; const int output_stride = output_height * output_width;
const T* input_data = input.data<T>(); const T1* input_data = input.data<T1>();
T* output_data = output->mutable_data<T>(context.GetPlace()); T1* output_data = output->mutable_data<T1>(context.GetPlace());
T* mask_data = mask->mutable_data<T>(context.GetPlace()); T2* mask_data = mask->mutable_data<T2>(context.GetPlace());
for (int i = 0; i < batch_size; i++) { for (int i = 0; i < batch_size; i++) {
for (int c = 0; c < output_channels; ++c) { for (int c = 0; c < output_channels; ++c) {
...@@ -535,7 +535,7 @@ class MaxPool2dWithIndexFunctor<platform::CPUPlace, T> { ...@@ -535,7 +535,7 @@ class MaxPool2dWithIndexFunctor<platform::CPUPlace, T> {
int wend = std::min(wstart + ksize_width, input_width); int wend = std::min(wstart + ksize_width, input_width);
wstart = std::max(wstart, 0); wstart = std::max(wstart, 0);
T ele = static_cast<T>(-FLT_MAX); T1 ele = static_cast<T1>(-FLT_MAX);
int index = -1; int index = -1;
for (int h = hstart; h < hend; ++h) { for (int h = hstart; h < hend; ++h) {
for (int w = wstart; w < wend; ++w) { for (int w = wstart; w < wend; ++w) {
...@@ -563,8 +563,8 @@ class MaxPool2dWithIndexFunctor<platform::CPUPlace, T> { ...@@ -563,8 +563,8 @@ class MaxPool2dWithIndexFunctor<platform::CPUPlace, T> {
* Ksize, strides, paddings are two elements. These two elements represent * Ksize, strides, paddings are two elements. These two elements represent
* height and width, respectively. * height and width, respectively.
*/ */
template <typename T> template <typename T1, typename T2>
class MaxPool2dWithIndexGradFunctor<platform::CPUPlace, T> { class MaxPool2dWithIndexGradFunctor<platform::CPUPlace, T1, T2> {
public: public:
void operator()(const platform::DeviceContext& context, void operator()(const platform::DeviceContext& context,
const framework::Tensor& output_grad, const framework::Tensor& output_grad,
...@@ -580,9 +580,9 @@ class MaxPool2dWithIndexGradFunctor<platform::CPUPlace, T> { ...@@ -580,9 +580,9 @@ class MaxPool2dWithIndexGradFunctor<platform::CPUPlace, T> {
const int input_stride = input_height * input_width; const int input_stride = input_height * input_width;
const int output_stride = output_height * output_width; const int output_stride = output_height * output_width;
const T* mask_data = mask.data<T>(); const T2* mask_data = mask.data<T2>();
const T* output_grad_data = output_grad.data<T>(); const T1* output_grad_data = output_grad.data<T1>();
T* input_grad_data = input_grad->mutable_data<T>(context.GetPlace()); T1* input_grad_data = input_grad->mutable_data<T1>(context.GetPlace());
for (int n = 0; n < batch_size; ++n) { for (int n = 0; n < batch_size; ++n) {
for (int c = 0; c < output_channels; ++c) { for (int c = 0; c < output_channels; ++c) {
...@@ -602,18 +602,18 @@ class MaxPool2dWithIndexGradFunctor<platform::CPUPlace, T> { ...@@ -602,18 +602,18 @@ class MaxPool2dWithIndexGradFunctor<platform::CPUPlace, T> {
} }
}; };
template class MaxPool2dWithIndexFunctor<platform::CPUPlace, float>; template class MaxPool2dWithIndexFunctor<platform::CPUPlace, float, int>;
template class MaxPool2dWithIndexGradFunctor<platform::CPUPlace, float>; template class MaxPool2dWithIndexGradFunctor<platform::CPUPlace, float, int>;
template class MaxPool2dWithIndexFunctor<platform::CPUPlace, double>; template class MaxPool2dWithIndexFunctor<platform::CPUPlace, double, int>;
template class MaxPool2dWithIndexGradFunctor<platform::CPUPlace, double>; template class MaxPool2dWithIndexGradFunctor<platform::CPUPlace, double, int>;
/* /*
* All tensors are in NCDHW format. * All tensors are in NCDHW format.
* Ksize, strides, paddings are three elements. These three elements represent * Ksize, strides, paddings are three elements. These three elements represent
* depth, height and width, respectively. * depth, height and width, respectively.
*/ */
template <typename T> template <typename T1, typename T2>
class MaxPool3dWithIndexFunctor<platform::CPUPlace, T> { class MaxPool3dWithIndexFunctor<platform::CPUPlace, T1, T2> {
public: public:
void operator()(const platform::DeviceContext& context, void operator()(const platform::DeviceContext& context,
const framework::Tensor& input, std::vector<int>& ksize, const framework::Tensor& input, std::vector<int>& ksize,
...@@ -639,9 +639,9 @@ class MaxPool3dWithIndexFunctor<platform::CPUPlace, T> { ...@@ -639,9 +639,9 @@ class MaxPool3dWithIndexFunctor<platform::CPUPlace, T> {
const int input_stride = input_depth * input_height * input_width; const int input_stride = input_depth * input_height * input_width;
const int output_stride = output_depth * output_height * output_width; const int output_stride = output_depth * output_height * output_width;
const T* input_data = input.data<T>(); const T1* input_data = input.data<T1>();
T* output_data = output->mutable_data<T>(context.GetPlace()); T1* output_data = output->mutable_data<T1>(context.GetPlace());
T* mask_data = mask->mutable_data<T>(context.GetPlace()); T2* mask_data = mask->mutable_data<T2>(context.GetPlace());
for (int i = 0; i < batch_size; i++) { for (int i = 0; i < batch_size; i++) {
for (int c = 0; c < output_channels; ++c) { for (int c = 0; c < output_channels; ++c) {
...@@ -659,7 +659,7 @@ class MaxPool3dWithIndexFunctor<platform::CPUPlace, T> { ...@@ -659,7 +659,7 @@ class MaxPool3dWithIndexFunctor<platform::CPUPlace, T> {
wstart = std::max(wstart, 0); wstart = std::max(wstart, 0);
int output_idx = (pd * output_height + ph) * output_width + pw; int output_idx = (pd * output_height + ph) * output_width + pw;
T ele = static_cast<T>(-FLT_MAX); T1 ele = static_cast<T1>(-FLT_MAX);
int index = -1; int index = -1;
for (int d = dstart; d < dend; ++d) { for (int d = dstart; d < dend; ++d) {
for (int h = hstart; h < hend; ++h) { for (int h = hstart; h < hend; ++h) {
...@@ -691,8 +691,8 @@ class MaxPool3dWithIndexFunctor<platform::CPUPlace, T> { ...@@ -691,8 +691,8 @@ class MaxPool3dWithIndexFunctor<platform::CPUPlace, T> {
* Ksize, strides, paddings are three elements. These three elements represent * Ksize, strides, paddings are three elements. These three elements represent
* depth, height and width, respectively. * depth, height and width, respectively.
*/ */
template <typename T> template <typename T1, typename T2>
class MaxPool3dWithIndexGradFunctor<platform::CPUPlace, T> { class MaxPool3dWithIndexGradFunctor<platform::CPUPlace, T1, T2> {
public: public:
void operator()(const platform::DeviceContext& context, void operator()(const platform::DeviceContext& context,
const framework::Tensor& output_grad, const framework::Tensor& output_grad,
...@@ -710,9 +710,9 @@ class MaxPool3dWithIndexGradFunctor<platform::CPUPlace, T> { ...@@ -710,9 +710,9 @@ class MaxPool3dWithIndexGradFunctor<platform::CPUPlace, T> {
const int input_stride = input_depth * input_height * input_width; const int input_stride = input_depth * input_height * input_width;
const int output_stride = output_depth * output_height * output_width; const int output_stride = output_depth * output_height * output_width;
const T* mask_data = mask.data<T>(); const T2* mask_data = mask.data<T2>();
const T* output_grad_data = output_grad.data<T>(); const T1* output_grad_data = output_grad.data<T1>();
T* input_grad_data = input_grad->mutable_data<T>(context.GetPlace()); T1* input_grad_data = input_grad->mutable_data<T1>(context.GetPlace());
for (int n = 0; n < batch_size; ++n) { for (int n = 0; n < batch_size; ++n) {
for (int c = 0; c < output_channels; ++c) { for (int c = 0; c < output_channels; ++c) {
...@@ -735,10 +735,10 @@ class MaxPool3dWithIndexGradFunctor<platform::CPUPlace, T> { ...@@ -735,10 +735,10 @@ class MaxPool3dWithIndexGradFunctor<platform::CPUPlace, T> {
} }
}; };
template class MaxPool3dWithIndexFunctor<platform::CPUPlace, float>; template class MaxPool3dWithIndexFunctor<platform::CPUPlace, float, int>;
template class MaxPool3dWithIndexGradFunctor<platform::CPUPlace, float>; template class MaxPool3dWithIndexGradFunctor<platform::CPUPlace, float, int>;
template class MaxPool3dWithIndexFunctor<platform::CPUPlace, double>; template class MaxPool3dWithIndexFunctor<platform::CPUPlace, double, int>;
template class MaxPool3dWithIndexGradFunctor<platform::CPUPlace, double>; template class MaxPool3dWithIndexGradFunctor<platform::CPUPlace, double, int>;
} // namespace math } // namespace math
} // namespace operators } // namespace operators
} // namespace paddle } // namespace paddle
paddle/operators/math/pooling.cu
浏览文件 @ f191c820
...@@ -658,13 +658,13 @@ template class Pool3dGradFunctor< ...@@ -658,13 +658,13 @@ template class Pool3dGradFunctor<
template class Pool3dGradFunctor< template class Pool3dGradFunctor<
platform::GPUPlace, paddle::operators::math::AvgPoolGrad<double>, double>; platform::GPUPlace, paddle::operators::math::AvgPoolGrad<double>, double>;
template <typename T> template <typename T1, typename T2>
__global__ void KernelMaxPool2dWithIdx( __global__ void KernelMaxPool2dWithIdx(
const int nthreads, const T* input_data, const int channels, const int nthreads, const T1* input_data, const int channels,
const int input_height, const int input_width, const int output_height, const int input_height, const int input_width, const int output_height,
const int output_width, const int ksize_height, const int ksize_width, const int output_width, const int ksize_height, const int ksize_width,
const int stride_height, const int stride_width, const int padding_height, const int stride_height, const int stride_width, const int padding_height,
const int padding_width, T* output_data, T* mask_data) { const int padding_width, T1* output_data, T2* mask_data) {
for (int index = blockIdx.x * blockDim.x + threadIdx.x; index < nthreads; for (int index = blockIdx.x * blockDim.x + threadIdx.x; index < nthreads;
index += blockDim.x * gridDim.x) { index += blockDim.x * gridDim.x) {
int pw = index % output_width; int pw = index % output_width;
...@@ -681,7 +681,7 @@ __global__ void KernelMaxPool2dWithIdx( ...@@ -681,7 +681,7 @@ __global__ void KernelMaxPool2dWithIdx(
wstart = max(wstart, 0); wstart = max(wstart, 0);
input_data += (batch_idx * channels + c) * input_height * input_width; input_data += (batch_idx * channels + c) * input_height * input_width;
T ele = -FLT_MAX; T1 ele = -FLT_MAX;
int max_index = -1; int max_index = -1;
for (int h = hstart; h < hend; ++h) { for (int h = hstart; h < hend; ++h) {
for (int w = wstart; w < wend; ++w) { for (int w = wstart; w < wend; ++w) {
...@@ -697,13 +697,13 @@ __global__ void KernelMaxPool2dWithIdx( ...@@ -697,13 +697,13 @@ __global__ void KernelMaxPool2dWithIdx(
} }
} }
template <typename T> template <typename T1, typename T2>
__global__ void KernelMaxPool2DWithIdxGrad( __global__ void KernelMaxPool2DWithIdxGrad(
const int nthreads, const T* output_grad, const T* mask_data, const int nthreads, const T1* output_grad, const T2* mask_data,
const int channels, const int input_height, const int input_width, const int channels, const int input_height, const int input_width,
const int output_height, const int output_width, const int ksize_height, const int output_height, const int output_width, const int ksize_height,
const int ksize_width, const int stride_height, const int stride_width, const int ksize_width, const int stride_height, const int stride_width,
const int padding_height, const int padding_width, T* input_grad) { const int padding_height, const int padding_width, T1* input_grad) {
for (int index = blockIdx.x * blockDim.x + threadIdx.x; index < nthreads; for (int index = blockIdx.x * blockDim.x + threadIdx.x; index < nthreads;
index += blockDim.x * gridDim.x) { index += blockDim.x * gridDim.x) {
int w_offset = index % input_width; int w_offset = index % input_width;
...@@ -724,7 +724,7 @@ __global__ void KernelMaxPool2DWithIdxGrad( ...@@ -724,7 +724,7 @@ __global__ void KernelMaxPool2DWithIdxGrad(
int pw_end = int pw_end =
min((w_offset + padding_width) / stride_width + 1, output_width); min((w_offset + padding_width) / stride_width + 1, output_width);
T gradient = 0; T1 gradient = 0;
int input_current_featuremap_idx = h_offset * input_width + w_offset; int input_current_featuremap_idx = h_offset * input_width + w_offset;
int output_idx = int output_idx =
(batch_idx * channels + c_offset) * output_height * output_width; (batch_idx * channels + c_offset) * output_height * output_width;
...@@ -746,8 +746,8 @@ __global__ void KernelMaxPool2DWithIdxGrad( ...@@ -746,8 +746,8 @@ __global__ void KernelMaxPool2DWithIdxGrad(
* Ksize, strides, paddings are two elements. These two elements represent * Ksize, strides, paddings are two elements. These two elements represent
* height and width, respectively. * height and width, respectively.
*/ */
template <typename T> template <typename T1, typename T2>
class MaxPool2dWithIndexFunctor<platform::GPUPlace, T> { class MaxPool2dWithIndexFunctor<platform::GPUPlace, T1, T2> {
public: public:
void operator()(const platform::DeviceContext& context, void operator()(const platform::DeviceContext& context,
const framework::Tensor& input, std::vector<int>& ksize, const framework::Tensor& input, std::vector<int>& ksize,
...@@ -767,9 +767,9 @@ class MaxPool2dWithIndexFunctor<platform::GPUPlace, T> { ...@@ -767,9 +767,9 @@ class MaxPool2dWithIndexFunctor<platform::GPUPlace, T> {
const int padding_height = paddings[0]; const int padding_height = paddings[0];
const int padding_width = paddings[1]; const int padding_width = paddings[1];
const T* input_data = input.data<T>(); const T1* input_data = input.data<T1>();
T* output_data = output->mutable_data<T>(context.GetPlace()); T1* output_data = output->mutable_data<T1>(context.GetPlace());
T* mask_data = mask->mutable_data<T>(context.GetPlace()); T2* mask_data = mask->mutable_data<T2>(context.GetPlace());
int nthreads = batch_size * output_channels * output_height * output_width; int nthreads = batch_size * output_channels * output_height * output_width;
int blocks = (nthreads + 1024 - 1) / 1024; int blocks = (nthreads + 1024 - 1) / 1024;
...@@ -777,9 +777,9 @@ class MaxPool2dWithIndexFunctor<platform::GPUPlace, T> { ...@@ -777,9 +777,9 @@ class MaxPool2dWithIndexFunctor<platform::GPUPlace, T> {
dim3 grid(blocks, 1); dim3 grid(blocks, 1);
KernelMaxPool2dWithIdx< KernelMaxPool2dWithIdx<
T><<<grid, threads, 0, T1, T2><<<grid, threads, 0,
reinterpret_cast<const platform::CUDADeviceContext&>(context) reinterpret_cast<const platform::CUDADeviceContext&>(context)
.stream()>>>( .stream()>>>(
nthreads, input_data, input_channels, input_height, input_width, nthreads, input_data, input_channels, input_height, input_width,
output_height, output_width, ksize_height, ksize_width, stride_height, output_height, output_width, ksize_height, ksize_width, stride_height,
stride_width, padding_height, padding_width, output_data, mask_data); stride_width, padding_height, padding_width, output_data, mask_data);
...@@ -791,8 +791,8 @@ class MaxPool2dWithIndexFunctor<platform::GPUPlace, T> { ...@@ -791,8 +791,8 @@ class MaxPool2dWithIndexFunctor<platform::GPUPlace, T> {
* Ksize, strides, paddings are two elements. These two elements represent * Ksize, strides, paddings are two elements. These two elements represent
* height and width, respectively. * height and width, respectively.
*/ */
template <typename T> template <typename T1, typename T2>
class MaxPool2dWithIndexGradFunctor<platform::GPUPlace, T> { class MaxPool2dWithIndexGradFunctor<platform::GPUPlace, T1, T2> {
public: public:
void operator()(const platform::DeviceContext& context, void operator()(const platform::DeviceContext& context,
const framework::Tensor& output_grad, const framework::Tensor& output_grad,
...@@ -812,9 +812,9 @@ class MaxPool2dWithIndexGradFunctor<platform::GPUPlace, T> { ...@@ -812,9 +812,9 @@ class MaxPool2dWithIndexGradFunctor<platform::GPUPlace, T> {
const int padding_height = paddings[0]; const int padding_height = paddings[0];
const int padding_width = paddings[1]; const int padding_width = paddings[1];
const T* mask_data = mask.data<T>(); const T2* mask_data = mask.data<T2>();
const T* output_grad_data = output_grad.data<T>(); const T1* output_grad_data = output_grad.data<T1>();
T* input_grad_data = input_grad->mutable_data<T>(context.GetPlace()); T1* input_grad_data = input_grad->mutable_data<T1>(context.GetPlace());
int nthreads = batch_size * input_channels * input_height * input_width; int nthreads = batch_size * input_channels * input_height * input_width;
int blocks = (nthreads + 1024 - 1) / 1024; int blocks = (nthreads + 1024 - 1) / 1024;
...@@ -822,30 +822,30 @@ class MaxPool2dWithIndexGradFunctor<platform::GPUPlace, T> { ...@@ -822,30 +822,30 @@ class MaxPool2dWithIndexGradFunctor<platform::GPUPlace, T> {
dim3 grid(blocks, 1); dim3 grid(blocks, 1);
KernelMaxPool2DWithIdxGrad< KernelMaxPool2DWithIdxGrad<
T><<<grid, threads, 0, T1, T2><<<grid, threads, 0,
reinterpret_cast<const platform::CUDADeviceContext&>(context) reinterpret_cast<const platform::CUDADeviceContext&>(context)
.stream()>>>(nthreads, output_grad_data, mask_data, .stream()>>>(
input_channels, input_height, input_width, nthreads, output_grad_data, mask_data, input_channels, input_height,
output_height, output_width, ksize_height, input_width, output_height, output_width, ksize_height, ksize_width,
ksize_width, stride_height, stride_width, stride_height, stride_width, padding_height, padding_width,
padding_height, padding_width, input_grad_data); input_grad_data);
} }
}; };
template class MaxPool2dWithIndexFunctor<platform::GPUPlace, float>; template class MaxPool2dWithIndexFunctor<platform::GPUPlace, float, int>;
template class MaxPool2dWithIndexGradFunctor<platform::GPUPlace, float>; template class MaxPool2dWithIndexGradFunctor<platform::GPUPlace, float, int>;
template class MaxPool2dWithIndexFunctor<platform::GPUPlace, double>; template class MaxPool2dWithIndexFunctor<platform::GPUPlace, double, int>;
template class MaxPool2dWithIndexGradFunctor<platform::GPUPlace, double>; template class MaxPool2dWithIndexGradFunctor<platform::GPUPlace, double, int>;
template <typename T> template <typename T1, typename T2>
__global__ void KernelMaxPool3DWithIdx( __global__ void KernelMaxPool3DWithIdx(
const int nthreads, const T* input_data, const int channels, const int nthreads, const T1* input_data, const int channels,
const int input_depth, const int input_height, const int input_width, const int input_depth, const int input_height, const int input_width,
const int output_depth, const int output_height, const int output_width, const int output_depth, const int output_height, const int output_width,
const int ksize_depth, const int ksize_height, const int ksize_width, const int ksize_depth, const int ksize_height, const int ksize_width,
const int stride_depth, const int stride_height, const int stride_width, const int stride_depth, const int stride_height, const int stride_width,
const int padding_depth, const int padding_height, const int padding_width, const int padding_depth, const int padding_height, const int padding_width,
T* output_data, T* mask_data) { T1* output_data, T2* mask_data) {
for (int index = blockIdx.x * blockDim.x + threadIdx.x; index < nthreads; for (int index = blockIdx.x * blockDim.x + threadIdx.x; index < nthreads;
index += blockDim.x * gridDim.x) { index += blockDim.x * gridDim.x) {
int pw = index % output_width; int pw = index % output_width;
...@@ -865,7 +865,7 @@ __global__ void KernelMaxPool3DWithIdx( ...@@ -865,7 +865,7 @@ __global__ void KernelMaxPool3DWithIdx(
hstart = max(hstart, 0); hstart = max(hstart, 0);
wstart = max(wstart, 0); wstart = max(wstart, 0);
T ele = -FLT_MAX; T1 ele = -FLT_MAX;
int max_index = -1; int max_index = -1;
input_data += input_data +=
(batch_idx * channels + c) * input_depth * input_height * input_width; (batch_idx * channels + c) * input_depth * input_height * input_width;
...@@ -885,15 +885,15 @@ __global__ void KernelMaxPool3DWithIdx( ...@@ -885,15 +885,15 @@ __global__ void KernelMaxPool3DWithIdx(
} }
} }
template <typename T> template <typename T1, typename T2>
__global__ void KernelMaxPool3DWithIdxGrad( __global__ void KernelMaxPool3DWithIdxGrad(
const int nthreads, const T* output_grad, const T* mask, const int channels, const int nthreads, const T1* output_grad, const T2* mask,
const int input_depth, const int input_height, const int input_width, const int channels, const int input_depth, const int input_height,
const int output_depth, const int output_height, const int output_width, const int input_width, const int output_depth, const int output_height,
const int ksize_depth, const int ksize_height, const int ksize_width, const int output_width, const int ksize_depth, const int ksize_height,
const int stride_depth, const int stride_height, const int stride_width, const int ksize_width, const int stride_depth, const int stride_height,
const int padding_depth, const int padding_height, const int padding_width, const int stride_width, const int padding_depth, const int padding_height,
T* input_grad) { const int padding_width, T1* input_grad) {
for (int index = blockIdx.x * blockDim.x + threadIdx.x; index < nthreads; for (int index = blockIdx.x * blockDim.x + threadIdx.x; index < nthreads;
index += blockDim.x * gridDim.x) { index += blockDim.x * gridDim.x) {
int w_offset = index % input_width; int w_offset = index % input_width;
...@@ -922,7 +922,7 @@ __global__ void KernelMaxPool3DWithIdxGrad( ...@@ -922,7 +922,7 @@ __global__ void KernelMaxPool3DWithIdxGrad(
int pw_end = int pw_end =
min((w_offset + padding_width) / stride_width + 1, output_width); min((w_offset + padding_width) / stride_width + 1, output_width);
T gradient = 0; T1 gradient = 0;
int input_current_feature_map_idx = int input_current_feature_map_idx =
(d_offset * input_height + h_offset) * input_width + w_offset; (d_offset * input_height + h_offset) * input_width + w_offset;
int output_idx = (batch_idx * channels + c_offset) * output_depth * int output_idx = (batch_idx * channels + c_offset) * output_depth *
...@@ -949,8 +949,8 @@ __global__ void KernelMaxPool3DWithIdxGrad( ...@@ -949,8 +949,8 @@ __global__ void KernelMaxPool3DWithIdxGrad(
* Ksize, strides, paddings are three elements. These three elements represent * Ksize, strides, paddings are three elements. These three elements represent
* depth, height and width, respectively. * depth, height and width, respectively.
*/ */
template <typename T> template <typename T1, typename T2>
class MaxPool3dWithIndexFunctor<platform::GPUPlace, T> { class MaxPool3dWithIndexFunctor<platform::GPUPlace, T1, T2> {
public: public:
void operator()(const platform::DeviceContext& context, void operator()(const platform::DeviceContext& context,
const framework::Tensor& input, std::vector<int>& ksize, const framework::Tensor& input, std::vector<int>& ksize,
...@@ -975,9 +975,9 @@ class MaxPool3dWithIndexFunctor<platform::GPUPlace, T> { ...@@ -975,9 +975,9 @@ class MaxPool3dWithIndexFunctor<platform::GPUPlace, T> {
const int padding_height = paddings[1]; const int padding_height = paddings[1];
const int padding_width = paddings[2]; const int padding_width = paddings[2];
const T* input_data = input.data<T>(); const T1* input_data = input.data<T1>();
T* output_data = output->mutable_data<T>(context.GetPlace()); T1* output_data = output->mutable_data<T1>(context.GetPlace());
T* mask_data = mask->mutable_data<T>(context.GetPlace()); T2* mask_data = mask->mutable_data<T2>(context.GetPlace());
int nthreads = batch_size * output_channels * output_depth * output_height * int nthreads = batch_size * output_channels * output_depth * output_height *
output_width; output_width;
...@@ -986,9 +986,9 @@ class MaxPool3dWithIndexFunctor<platform::GPUPlace, T> { ...@@ -986,9 +986,9 @@ class MaxPool3dWithIndexFunctor<platform::GPUPlace, T> {
dim3 grid(blocks, 1); dim3 grid(blocks, 1);
KernelMaxPool3DWithIdx< KernelMaxPool3DWithIdx<
T><<<grid, threads, 0, T1, T2><<<grid, threads, 0,
reinterpret_cast<const platform::CUDADeviceContext&>(context) reinterpret_cast<const platform::CUDADeviceContext&>(context)
.stream()>>>( .stream()>>>(
nthreads, input_data, input_channels, input_depth, input_height, nthreads, input_data, input_channels, input_depth, input_height,
input_width, output_depth, output_height, output_width, ksize_depth, input_width, output_depth, output_height, output_width, ksize_depth,
ksize_height, ksize_width, stride_depth, stride_height, stride_width, ksize_height, ksize_width, stride_depth, stride_height, stride_width,
...@@ -1001,8 +1001,8 @@ class MaxPool3dWithIndexFunctor<platform::GPUPlace, T> { ...@@ -1001,8 +1001,8 @@ class MaxPool3dWithIndexFunctor<platform::GPUPlace, T> {
* Ksize, strides, paddings are three elements. These three elements represent * Ksize, strides, paddings are three elements. These three elements represent
* depth, height and width, respectively. * depth, height and width, respectively.
*/ */
template <typename T> template <typename T1, typename T2>
class MaxPool3dWithIndexGradFunctor<platform::GPUPlace, T> { class MaxPool3dWithIndexGradFunctor<platform::GPUPlace, T1, T2> {
public: public:
void operator()(const platform::DeviceContext& context, void operator()(const platform::DeviceContext& context,
const framework::Tensor& output_grad, const framework::Tensor& output_grad,
...@@ -1027,9 +1027,9 @@ class MaxPool3dWithIndexGradFunctor<platform::GPUPlace, T> { ...@@ -1027,9 +1027,9 @@ class MaxPool3dWithIndexGradFunctor<platform::GPUPlace, T> {
const int padding_height = paddings[1]; const int padding_height = paddings[1];
const int padding_width = paddings[2]; const int padding_width = paddings[2];
const T* output_grad_data = output_grad.data<T>(); const T1* output_grad_data = output_grad.data<T1>();
const T* mask_data = mask.data<T>(); const T2* mask_data = mask.data<T2>();
T* input_grad_data = input_grad->mutable_data<T>(context.GetPlace()); T1* input_grad_data = input_grad->mutable_data<T1>(context.GetPlace());
int nthreads = int nthreads =
batch_size * input_channels * input_depth * input_height * input_width; batch_size * input_channels * input_depth * input_height * input_width;
...@@ -1038,9 +1038,9 @@ class MaxPool3dWithIndexGradFunctor<platform::GPUPlace, T> { ...@@ -1038,9 +1038,9 @@ class MaxPool3dWithIndexGradFunctor<platform::GPUPlace, T> {
dim3 grid(blocks, 1); dim3 grid(blocks, 1);
KernelMaxPool3DWithIdxGrad< KernelMaxPool3DWithIdxGrad<
T><<<grid, threads, 0, T1, T2><<<grid, threads, 0,
reinterpret_cast<const platform::CUDADeviceContext&>(context) reinterpret_cast<const platform::CUDADeviceContext&>(context)
.stream()>>>( .stream()>>>(
nthreads, output_grad_data, mask_data, input_channels, input_depth, nthreads, output_grad_data, mask_data, input_channels, input_depth,
input_height, input_width, output_depth, output_height, output_width, input_height, input_width, output_depth, output_height, output_width,
ksize_depth, ksize_height, ksize_width, stride_depth, stride_height, ksize_depth, ksize_height, ksize_width, stride_depth, stride_height,
...@@ -1049,10 +1049,10 @@ class MaxPool3dWithIndexGradFunctor<platform::GPUPlace, T> { ...@@ -1049,10 +1049,10 @@ class MaxPool3dWithIndexGradFunctor<platform::GPUPlace, T> {
} }
}; };
template class MaxPool3dWithIndexFunctor<platform::GPUPlace, float>; template class MaxPool3dWithIndexFunctor<platform::GPUPlace, float, int>;
template class MaxPool3dWithIndexGradFunctor<platform::GPUPlace, float>; template class MaxPool3dWithIndexGradFunctor<platform::GPUPlace, float, int>;
template class MaxPool3dWithIndexFunctor<platform::GPUPlace, double>; template class MaxPool3dWithIndexFunctor<platform::GPUPlace, double, int>;
template class MaxPool3dWithIndexGradFunctor<platform::GPUPlace, double>; template class MaxPool3dWithIndexGradFunctor<platform::GPUPlace, double, int>;
} // namespace math } // namespace math
} // namespace operators } // namespace operators
......
paddle/operators/math/pooling.h
浏览文件 @ f191c820
...@@ -153,7 +153,7 @@ class MaxPool3dGradFunctor { ...@@ -153,7 +153,7 @@ class MaxPool3dGradFunctor {
* In pool2d, all tensors are in NCHW format. In pool3d, all tensors are in * In pool2d, all tensors are in NCHW format. In pool3d, all tensors are in
* NCDHW format. * NCDHW format.
*/ */
template <typename Place, typename T> template <typename Place, typename T1, typename T2>
class MaxPool2dWithIndexFunctor { class MaxPool2dWithIndexFunctor {
public: public:
void operator()(const platform::DeviceContext& context, void operator()(const platform::DeviceContext& context,
...@@ -162,7 +162,7 @@ class MaxPool2dWithIndexFunctor { ...@@ -162,7 +162,7 @@ class MaxPool2dWithIndexFunctor {
framework::Tensor* output, framework::Tensor* mask); framework::Tensor* output, framework::Tensor* mask);
}; };
template <typename Place, typename T> template <typename Place, typename T1, typename T2>
class MaxPool2dWithIndexGradFunctor { class MaxPool2dWithIndexGradFunctor {
public: public:
void operator()(const platform::DeviceContext& context, void operator()(const platform::DeviceContext& context,
...@@ -172,7 +172,7 @@ class MaxPool2dWithIndexGradFunctor { ...@@ -172,7 +172,7 @@ class MaxPool2dWithIndexGradFunctor {
framework::Tensor* input_grad); framework::Tensor* input_grad);
}; };
template <typename Place, typename T> template <typename Place, typename T1, typename T2>
class MaxPool3dWithIndexFunctor { class MaxPool3dWithIndexFunctor {
public: public:
void operator()(const platform::DeviceContext& context, void operator()(const platform::DeviceContext& context,
...@@ -181,7 +181,7 @@ class MaxPool3dWithIndexFunctor { ...@@ -181,7 +181,7 @@ class MaxPool3dWithIndexFunctor {
framework::Tensor* output, framework::Tensor* mask); framework::Tensor* output, framework::Tensor* mask);
}; };
template <typename Place, typename T> template <typename Place, typename T1, typename T2>
class MaxPool3dWithIndexGradFunctor { class MaxPool3dWithIndexGradFunctor {
public: public:
void operator()(const platform::DeviceContext& context, void operator()(const platform::DeviceContext& context,
......
paddle/operators/maxout_op.cc 0 → 100644
浏览文件 @ f191c820
/* 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 @ f191c820
/* 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 @ f191c820
/* 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/pool_cudnn_op.cc
浏览文件 @ f191c820
...@@ -20,6 +20,18 @@ REGISTER_OP(pool2d_cudnn, ops::PoolOp, ops::Pool2dOpMaker, pool2d_cudnn_grad, ...@@ -20,6 +20,18 @@ REGISTER_OP(pool2d_cudnn, ops::PoolOp, ops::Pool2dOpMaker, pool2d_cudnn_grad,
ops::PoolOpGrad); ops::PoolOpGrad);
REGISTER_OP_CPU_KERNEL(pool2d_cudnn, REGISTER_OP_CPU_KERNEL(pool2d_cudnn,
ops::PoolKernel<paddle::platform::CPUPlace, float>); ops::PoolKernel<paddle::platform::CPUPlace, float>,
ops::PoolKernel<paddle::platform::CPUPlace, double>);
REGISTER_OP_CPU_KERNEL(pool2d_cudnn_grad, REGISTER_OP_CPU_KERNEL(pool2d_cudnn_grad,
ops::PoolGradKernel<paddle::platform::CPUPlace, float>) ops::PoolGradKernel<paddle::platform::CPUPlace, float>,
ops::PoolGradKernel<paddle::platform::CPUPlace, double>)
REGISTER_OP(pool3d_cudnn, ops::PoolOp, ops::Pool3dOpMaker, pool3d_cudnn_grad,
ops::PoolOpGrad);
REGISTER_OP_CPU_KERNEL(pool3d_cudnn,
ops::PoolKernel<paddle::platform::CPUPlace, float>,
ops::PoolKernel<paddle::platform::CPUPlace, double>);
REGISTER_OP_CPU_KERNEL(pool3d_cudnn_grad,
ops::PoolGradKernel<paddle::platform::CPUPlace, float>,
ops::PoolGradKernel<paddle::platform::CPUPlace, double>)
paddle/operators/pool_cudnn_op.cu.cc
浏览文件 @ f191c820
...@@ -52,7 +52,13 @@ class PoolCudnnOpKernel : public framework::OpKernel<T> { ...@@ -52,7 +52,13 @@ class PoolCudnnOpKernel : public framework::OpKernel<T> {
ScopedTensorDescriptor input_desc; ScopedTensorDescriptor input_desc;
ScopedTensorDescriptor output_desc; ScopedTensorDescriptor output_desc;
ScopedPoolingDescriptor pool_desc; ScopedPoolingDescriptor pool_desc;
DataLayout layout = DataLayout::kNCHW; DataLayout layout;
if (strides.size() == 2U) {
layout = DataLayout::kNCHW;
} else {
layout = DataLayout::kNCDHW;
}
cudnnTensorDescriptor_t cudnn_input_desc = input_desc.descriptor<T>( cudnnTensorDescriptor_t cudnn_input_desc = input_desc.descriptor<T>(
layout, framework::vectorize2int(input->dims())); layout, framework::vectorize2int(input->dims()));
...@@ -112,7 +118,13 @@ class PoolCudnnGradOpKernel : public framework::OpKernel<T> { ...@@ -112,7 +118,13 @@ class PoolCudnnGradOpKernel : public framework::OpKernel<T> {
ScopedTensorDescriptor input_desc; ScopedTensorDescriptor input_desc;
ScopedTensorDescriptor output_desc; ScopedTensorDescriptor output_desc;
ScopedPoolingDescriptor pool_desc; ScopedPoolingDescriptor pool_desc;
DataLayout layout = DataLayout::kNCHW; DataLayout layout;
if (strides.size() == 2U) {
layout = DataLayout::kNCHW;
} else {
layout = DataLayout::kNCDHW;
}
cudnnTensorDescriptor_t cudnn_input_desc = input_desc.descriptor<T>( cudnnTensorDescriptor_t cudnn_input_desc = input_desc.descriptor<T>(
layout, framework::vectorize2int(input->dims())); layout, framework::vectorize2int(input->dims()));
...@@ -150,5 +162,12 @@ class PoolCudnnGradOpKernel : public framework::OpKernel<T> { ...@@ -150,5 +162,12 @@ class PoolCudnnGradOpKernel : public framework::OpKernel<T> {
namespace ops = paddle::operators; namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(pool2d_cudnn, ops::PoolCudnnOpKernel<float>); REGISTER_OP_GPU_KERNEL(pool2d_cudnn, ops::PoolCudnnOpKernel<float>,
REGISTER_OP_GPU_KERNEL(pool2d_cudnn_grad, ops::PoolCudnnGradOpKernel<float>); ops::PoolCudnnOpKernel<double>);
REGISTER_OP_GPU_KERNEL(pool2d_cudnn_grad, ops::PoolCudnnGradOpKernel<float>,
ops::PoolCudnnGradOpKernel<double>);
REGISTER_OP_GPU_KERNEL(pool3d_cudnn, ops::PoolCudnnOpKernel<float>,
ops::PoolCudnnOpKernel<double>);
REGISTER_OP_GPU_KERNEL(pool3d_cudnn_grad, ops::PoolCudnnGradOpKernel<float>,
ops::PoolCudnnGradOpKernel<double>);
paddle/operators/pool_op.cc
浏览文件 @ f191c820
...@@ -217,14 +217,18 @@ REGISTER_OP(pool2d, ops::PoolOp, ops::Pool2dOpMaker, pool2d_grad, ...@@ -217,14 +217,18 @@ REGISTER_OP(pool2d, ops::PoolOp, ops::Pool2dOpMaker, pool2d_grad,
ops::PoolOpGrad); ops::PoolOpGrad);
REGISTER_OP_CPU_KERNEL(pool2d, REGISTER_OP_CPU_KERNEL(pool2d,
ops::PoolKernel<paddle::platform::CPUPlace, float>); ops::PoolKernel<paddle::platform::CPUPlace, float>,
ops::PoolKernel<paddle::platform::CPUPlace, double>);
REGISTER_OP_CPU_KERNEL(pool2d_grad, REGISTER_OP_CPU_KERNEL(pool2d_grad,
ops::PoolGradKernel<paddle::platform::CPUPlace, float>) ops::PoolGradKernel<paddle::platform::CPUPlace, float>,
ops::PoolGradKernel<paddle::platform::CPUPlace, double>)
REGISTER_OP(pool3d, ops::PoolOp, ops::Pool3dOpMaker, pool3d_grad, REGISTER_OP(pool3d, ops::PoolOp, ops::Pool3dOpMaker, pool3d_grad,
ops::PoolOpGrad); ops::PoolOpGrad);
REGISTER_OP_CPU_KERNEL(pool3d, REGISTER_OP_CPU_KERNEL(pool3d,
ops::PoolKernel<paddle::platform::CPUPlace, float>); ops::PoolKernel<paddle::platform::CPUPlace, float>,
ops::PoolKernel<paddle::platform::CPUPlace, double>);
REGISTER_OP_CPU_KERNEL(pool3d_grad, REGISTER_OP_CPU_KERNEL(pool3d_grad,
ops::PoolGradKernel<paddle::platform::CPUPlace, float>); ops::PoolGradKernel<paddle::platform::CPUPlace, float>,
ops::PoolGradKernel<paddle::platform::CPUPlace, double>);
paddle/operators/pool_op.cu.cc
浏览文件 @ f191c820
...@@ -17,11 +17,15 @@ limitations under the License. */ ...@@ -17,11 +17,15 @@ limitations under the License. */
namespace ops = paddle::operators; namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(pool2d, REGISTER_OP_GPU_KERNEL(pool2d,
ops::PoolKernel<paddle::platform::GPUPlace, float>); ops::PoolKernel<paddle::platform::GPUPlace, float>,
ops::PoolKernel<paddle::platform::GPUPlace, double>);
REGISTER_OP_GPU_KERNEL(pool2d_grad, REGISTER_OP_GPU_KERNEL(pool2d_grad,
ops::PoolGradKernel<paddle::platform::GPUPlace, float>); ops::PoolGradKernel<paddle::platform::GPUPlace, float>,
ops::PoolGradKernel<paddle::platform::GPUPlace, double>);
REGISTER_OP_GPU_KERNEL(pool3d, REGISTER_OP_GPU_KERNEL(pool3d,
ops::PoolKernel<paddle::platform::GPUPlace, float>); ops::PoolKernel<paddle::platform::GPUPlace, float>,
ops::PoolKernel<paddle::platform::GPUPlace, double>);
REGISTER_OP_GPU_KERNEL(pool3d_grad, REGISTER_OP_GPU_KERNEL(pool3d_grad,
ops::PoolGradKernel<paddle::platform::GPUPlace, float>); ops::PoolGradKernel<paddle::platform::GPUPlace, float>,
ops::PoolGradKernel<paddle::platform::GPUPlace, double>);
paddle/operators/pool_with_index_op.cc
浏览文件 @ f191c820
...@@ -29,11 +29,11 @@ class MaxPoolWithIndexOp : public framework::OperatorWithKernel { ...@@ -29,11 +29,11 @@ class MaxPoolWithIndexOp : public framework::OperatorWithKernel {
void InferShape(framework::InferShapeContext *ctx) const override { void InferShape(framework::InferShapeContext *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), PADDLE_ENFORCE(ctx->HasInput("X"),
"X(Input) of Pooling should not be null."); "Input(X) of Pooling should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"), PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Out(Output) of Pooling should not be null."); "Output(Out) of Pooling should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Mask"), PADDLE_ENFORCE(ctx->HasOutput("Mask"),
"Mask(Output) of Pooling should not be null."); "Output(Mask) of Pooling should not be null.");
auto in_x_dims = ctx->GetInputDim("X"); auto in_x_dims = ctx->GetInputDim("X");
...@@ -67,6 +67,14 @@ class MaxPoolWithIndexOp : public framework::OperatorWithKernel { ...@@ -67,6 +67,14 @@ class MaxPoolWithIndexOp : public framework::OperatorWithKernel {
ctx->SetOutputDim("Out", framework::make_ddim(output_shape)); ctx->SetOutputDim("Out", framework::make_ddim(output_shape));
ctx->SetOutputDim("Mask", framework::make_ddim(output_shape)); ctx->SetOutputDim("Mask", framework::make_ddim(output_shape));
} }
protected:
framework::OpKernelType GetKernelType(
const framework::ExecutionContext &ctx) const override {
return framework::OpKernelType(
framework::ToDataType(ctx.Input<framework::Tensor>("X")->type()),
ctx.device_context());
}
}; };
class MaxPoolWithIndexOpGrad : public framework::OperatorWithKernel { class MaxPoolWithIndexOpGrad : public framework::OperatorWithKernel {
...@@ -80,6 +88,14 @@ class MaxPoolWithIndexOpGrad : public framework::OperatorWithKernel { ...@@ -80,6 +88,14 @@ class MaxPoolWithIndexOpGrad : public framework::OperatorWithKernel {
"Input(X@GRAD) should not be null."); "Input(X@GRAD) should not be null.");
ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X")); ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
} }
protected:
framework::OpKernelType GetKernelType(
const framework::ExecutionContext &ctx) const override {
return framework::OpKernelType(
framework::ToDataType(ctx.Input<framework::Tensor>("X")->type()),
ctx.device_context());
}
}; };
class MaxPool2dWithIndexOpMaker : public framework::OpProtoAndCheckerMaker { class MaxPool2dWithIndexOpMaker : public framework::OpProtoAndCheckerMaker {
...@@ -116,7 +132,7 @@ class MaxPool2dWithIndexOpMaker : public framework::OpProtoAndCheckerMaker { ...@@ -116,7 +132,7 @@ class MaxPool2dWithIndexOpMaker : public framework::OpProtoAndCheckerMaker {
// TypedAttrChecker don't support vector type.) // TypedAttrChecker don't support vector type.)
AddAttr<bool>( AddAttr<bool>(
"global_pooling", "global_pooling",
"(bool, default false) Whether to use the global pooling. " "(bool, default:false) Whether to use the global pooling. "
"If global_pooling = true, ksize and paddings will be ignored.") "If global_pooling = true, ksize and paddings will be ignored.")
.SetDefault(false); .SetDefault(false);
AddAttr<std::vector<int>>("strides", AddAttr<std::vector<int>>("strides",
...@@ -126,7 +142,7 @@ class MaxPool2dWithIndexOpMaker : public framework::OpProtoAndCheckerMaker { ...@@ -126,7 +142,7 @@ class MaxPool2dWithIndexOpMaker : public framework::OpProtoAndCheckerMaker {
// TypedAttrChecker don't support vector type.) // TypedAttrChecker don't support vector type.)
AddAttr<std::vector<int>>( AddAttr<std::vector<int>>(
"paddings", "paddings",
"(vector<int>, defalut {0, 0}), paddings(height, width) of pooling " "(vector<int>, defalut:{0, 0}), paddings(height, width) of pooling "
"operator. " "operator. "
"If global_pooling = true, paddings and will be ignored.") "If global_pooling = true, paddings and will be ignored.")
.SetDefault({0, 0}); // TODO(Chengduo): Add checker. (Currently, .SetDefault({0, 0}); // TODO(Chengduo): Add checker. (Currently,
...@@ -250,10 +266,12 @@ REGISTER_OP(max_pool2d_with_index, ops::MaxPoolWithIndexOp, ...@@ -250,10 +266,12 @@ REGISTER_OP(max_pool2d_with_index, ops::MaxPoolWithIndexOp,
REGISTER_OP_CPU_KERNEL( REGISTER_OP_CPU_KERNEL(
max_pool2d_with_index, max_pool2d_with_index,
ops::MaxPoolWithIndexKernel<paddle::platform::CPUPlace, float>); ops::MaxPoolWithIndexKernel<paddle::platform::CPUPlace, float, int>,
ops::MaxPoolWithIndexKernel<paddle::platform::CPUPlace, double, int>);
REGISTER_OP_CPU_KERNEL( REGISTER_OP_CPU_KERNEL(
max_pool2d_with_index_grad, max_pool2d_with_index_grad,
ops::MaxPoolWithIndexGradKernel<paddle::platform::CPUPlace, float>) ops::MaxPoolWithIndexGradKernel<paddle::platform::CPUPlace, float, int>,
ops::MaxPoolWithIndexGradKernel<paddle::platform::CPUPlace, double, int>)
REGISTER_OP(max_pool3d_with_index, ops::MaxPoolWithIndexOp, REGISTER_OP(max_pool3d_with_index, ops::MaxPoolWithIndexOp,
ops::MaxPool3dWithIndexOpMaker, max_pool3d_with_index_grad, ops::MaxPool3dWithIndexOpMaker, max_pool3d_with_index_grad,
...@@ -261,7 +279,9 @@ REGISTER_OP(max_pool3d_with_index, ops::MaxPoolWithIndexOp, ...@@ -261,7 +279,9 @@ REGISTER_OP(max_pool3d_with_index, ops::MaxPoolWithIndexOp,
REGISTER_OP_CPU_KERNEL( REGISTER_OP_CPU_KERNEL(
max_pool3d_with_index, max_pool3d_with_index,
ops::MaxPoolWithIndexKernel<paddle::platform::CPUPlace, float>); ops::MaxPoolWithIndexKernel<paddle::platform::CPUPlace, float, int>,
ops::MaxPoolWithIndexKernel<paddle::platform::CPUPlace, double, int>);
REGISTER_OP_CPU_KERNEL( REGISTER_OP_CPU_KERNEL(
max_pool3d_with_index_grad, max_pool3d_with_index_grad,
ops::MaxPoolWithIndexGradKernel<paddle::platform::CPUPlace, float>) ops::MaxPoolWithIndexGradKernel<paddle::platform::CPUPlace, float, int>,
ops::MaxPoolWithIndexGradKernel<paddle::platform::CPUPlace, double, int>)
paddle/operators/pool_with_index_op.cu.cc
浏览文件 @ f191c820
...@@ -18,14 +18,18 @@ namespace ops = paddle::operators; ...@@ -18,14 +18,18 @@ namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL( REGISTER_OP_GPU_KERNEL(
max_pool2d_with_index, max_pool2d_with_index,
ops::MaxPoolWithIndexKernel<paddle::platform::GPUPlace, float>); ops::MaxPoolWithIndexKernel<paddle::platform::GPUPlace, float, int>,
ops::MaxPoolWithIndexKernel<paddle::platform::GPUPlace, double, int>);
REGISTER_OP_GPU_KERNEL( REGISTER_OP_GPU_KERNEL(
max_pool2d_with_index_grad, max_pool2d_with_index_grad,
ops::MaxPoolWithIndexGradKernel<paddle::platform::GPUPlace, float>) ops::MaxPoolWithIndexGradKernel<paddle::platform::GPUPlace, float, int>,
ops::MaxPoolWithIndexGradKernel<paddle::platform::GPUPlace, double, int>)
REGISTER_OP_GPU_KERNEL( REGISTER_OP_GPU_KERNEL(
max_pool3d_with_index, max_pool3d_with_index,
ops::MaxPoolWithIndexKernel<paddle::platform::GPUPlace, float>); ops::MaxPoolWithIndexKernel<paddle::platform::GPUPlace, float, int>,
ops::MaxPoolWithIndexKernel<paddle::platform::GPUPlace, double, int>);
REGISTER_OP_GPU_KERNEL( REGISTER_OP_GPU_KERNEL(
max_pool3d_with_index_grad, max_pool3d_with_index_grad,
ops::MaxPoolWithIndexGradKernel<paddle::platform::GPUPlace, float>) ops::MaxPoolWithIndexGradKernel<paddle::platform::GPUPlace, float, int>,
ops::MaxPoolWithIndexGradKernel<paddle::platform::GPUPlace, double, int>)
paddle/operators/pool_with_index_op.h
浏览文件 @ f191c820
...@@ -24,8 +24,8 @@ namespace operators { ...@@ -24,8 +24,8 @@ namespace operators {
using Tensor = framework::Tensor; using Tensor = framework::Tensor;
template <typename Place, typename T> template <typename Place, typename T1, typename T2>
class MaxPoolWithIndexKernel : public framework::OpKernel<T> { class MaxPoolWithIndexKernel : public framework::OpKernel<T1> {
public: public:
void Compute(const framework::ExecutionContext& context) const override { void Compute(const framework::ExecutionContext& context) const override {
const Tensor* in_x = context.Input<Tensor>("X"); const Tensor* in_x = context.Input<Tensor>("X");
...@@ -44,13 +44,13 @@ class MaxPoolWithIndexKernel : public framework::OpKernel<T> { ...@@ -44,13 +44,13 @@ class MaxPoolWithIndexKernel : public framework::OpKernel<T> {
switch (ksize.size()) { switch (ksize.size()) {
case 2: { case 2: {
paddle::operators::math::MaxPool2dWithIndexFunctor<Place, T> paddle::operators::math::MaxPool2dWithIndexFunctor<Place, T1, T2>
pool2d_forward; pool2d_forward;
pool2d_forward(context.device_context(), *in_x, ksize, strides, pool2d_forward(context.device_context(), *in_x, ksize, strides,
paddings, out, mask); paddings, out, mask);
} break; } break;
case 3: { case 3: {
paddle::operators::math::MaxPool3dWithIndexFunctor<Place, T> paddle::operators::math::MaxPool3dWithIndexFunctor<Place, T1, T2>
pool3d_forward; pool3d_forward;
pool3d_forward(context.device_context(), *in_x, ksize, strides, pool3d_forward(context.device_context(), *in_x, ksize, strides,
paddings, out, mask); paddings, out, mask);
...@@ -60,8 +60,8 @@ class MaxPoolWithIndexKernel : public framework::OpKernel<T> { ...@@ -60,8 +60,8 @@ class MaxPoolWithIndexKernel : public framework::OpKernel<T> {
} }
}; };
template <typename Place, typename T> template <typename Place, typename T1, typename T2>
class MaxPoolWithIndexGradKernel : public framework::OpKernel<T> { class MaxPoolWithIndexGradKernel : public framework::OpKernel<T1> {
public: public:
void Compute(const framework::ExecutionContext& context) const override { void Compute(const framework::ExecutionContext& context) const override {
const Tensor* mask = context.Input<Tensor>("Mask"); const Tensor* mask = context.Input<Tensor>("Mask");
...@@ -80,19 +80,19 @@ class MaxPoolWithIndexGradKernel : public framework::OpKernel<T> { ...@@ -80,19 +80,19 @@ class MaxPoolWithIndexGradKernel : public framework::OpKernel<T> {
} }
if (in_x_grad) { if (in_x_grad) {
in_x_grad->mutable_data<T>(context.GetPlace()); in_x_grad->mutable_data<T1>(context.GetPlace());
auto& device_ctx = context.device_context(); auto& device_ctx = context.device_context();
math::set_constant(device_ctx, in_x_grad, 0); math::set_constant(device_ctx, in_x_grad, 0);
switch (ksize.size()) { switch (ksize.size()) {
case 2: { case 2: {
paddle::operators::math::MaxPool2dWithIndexGradFunctor<Place, T> paddle::operators::math::MaxPool2dWithIndexGradFunctor<Place, T1, T2>
pool2d_backward; pool2d_backward;
pool2d_backward(device_ctx, *out_grad, *mask, ksize, strides, pool2d_backward(device_ctx, *out_grad, *mask, ksize, strides,
paddings, in_x_grad); paddings, in_x_grad);
} break; } break;
case 3: { case 3: {
paddle::operators::math::MaxPool3dWithIndexGradFunctor<Place, T> paddle::operators::math::MaxPool3dWithIndexGradFunctor<Place, T1, T2>
pool3d_backward; pool3d_backward;
pool3d_backward(device_ctx, *out_grad, *mask, ksize, strides, pool3d_backward(device_ctx, *out_grad, *mask, ksize, strides,
paddings, in_x_grad); paddings, in_x_grad);
......
paddle/operators/sequence_slice_op.cc 0 → 100755
浏览文件 @ f191c820
/* 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/sequence_slice_op.h"
namespace paddle {
namespace operators {
class SequenceSliceOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of SequenceSliceOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Offset"),
"Input(Offset) of SequenceSliceOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Length"),
"Input(Length) of SequenceSliceOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of SequenceSliceOp should not be null.");
auto input_dims = ctx->GetInputDim("X");
auto offset_dim = ctx->GetInputDim("Offset");
auto length_dim = ctx->GetInputDim("Length");
PADDLE_ENFORCE_EQ(
offset_dim.size(), 2UL,
"Only support one level sequence now, The rank of offset must be 2.");
PADDLE_ENFORCE_EQ(
length_dim.size(), 2UL,
"Only support one level sequence now, The rank of Length must be 2.");
// Initialize the output's dims to maximum,
// and re-set to real dims by the value of Offset and Length at kernel
ctx->SetOutputDim("Out", input_dims);
}
protected:
framework::OpKernelType GetKernelType(
const framework::ExecutionContext& ctx) const override {
return framework::OpKernelType(
framework::ToDataType(ctx.Input<framework::LoDTensor>("X")->type()),
ctx.device_context());
}
};
class SequenceSliceGradOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
"The gradient of Out should not be null.");
PADDLE_ENFORCE(ctx->HasOutputs(framework::GradVarName("X")),
"The gradient of X should not be null.");
ctx->SetOutputsDim(framework::GradVarName("X"), ctx->GetInputsDim("X"));
}
protected:
framework::OpKernelType GetKernelType(
const framework::ExecutionContext& ctx) const override {
return framework::OpKernelType(
framework::ToDataType(ctx.Input<framework::LoDTensor>("X")->type()),
ctx.device_context());
}
};
class SequenceSliceOpMaker : public framework::OpProtoAndCheckerMaker {
public:
SequenceSliceOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X",
"(LoDTensor), "
"the input of SequenceSliceOp.");
AddInput("Offset",
"(Tensor), "
"a vector<int> to describe the offset of every input sequence for "
"sub sequence item.");
AddInput("Length",
"(Tensor), "
"a vector<int> to describe the length of every input sequence for "
"sub sequence item.");
AddOutput("Out",
"(LoDTensor), the output of SequenceSliceOp.");
AddComment(R"DOC(
Sequence slice operator
The operator crops a subsequence from given sequence with given start offset and subsequence length.
It only supports sequence (LoD Tensor with level number is 1).
- Case:
X = [[a1, a2;
b1, b2;
c1, c2]
[d1, d2;
e1, e2]]
LoD(X) = {{0, 3, 5}}; Dims(X) = (5, 2)
Offset = [[0], [1]]; Length = [[2], [1]]
Out = [[a1, a2;
b1, b2]
[e1, e2]]
LoD(Out) = {{0, 2, 3}}; Dims(Out) = (3, 2)
NOTE: The first dimension size of input, the size of offset and Length, should be equal. The offset start from 0.
)DOC");
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP(sequence_slice, ops::SequenceSliceOp, ops::SequenceSliceOpMaker,
sequence_slice_grad, ops::SequenceSliceGradOp);
REGISTER_OP_CPU_KERNEL(
sequence_slice,
ops::SequenceSliceOpKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(
sequence_slice_grad,
ops::SequenceSliceGradOpKernel<paddle::platform::CPUPlace, float>);
paddle/operators/sequence_slice_op.cu 0 → 100755
浏览文件 @ f191c820
/* 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/sequence_slice_op.h"
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(
sequence_slice,
ops::SequenceSliceOpKernel<paddle::platform::GPUPlace, float>);
REGISTER_OP_GPU_KERNEL(
sequence_slice_grad,
ops::SequenceSliceGradOpKernel<paddle::platform::GPUPlace, float>);
paddle/operators/sequence_slice_op.h 0 → 100755
浏览文件 @ f191c820
/* 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/strided_memcpy.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
using LoDTensor = framework::LoDTensor;
using LoD = framework::LoD;
template <typename T>
inline LoD SequenceSliceLoD(const T& in, const int64_t* offset_data,
const int64_t* length_data) {
auto out_lod = in.lod();
size_t lod_offset = 0;
auto n = in.lod()[0].size() - 1;
out_lod[0][0] = 0;
for (size_t i = 0; i < n; ++i) {
lod_offset += length_data[i];
out_lod[0][i+1] = lod_offset;
}
return out_lod;
}
template <typename Place, typename T>
class SequenceSliceOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* in = ctx.Input<LoDTensor>("X");
auto* offset = ctx.Input<Tensor>("Offset");
auto* length = ctx.Input<Tensor>("Length");
auto* out = ctx.Output<LoDTensor>("Out");
auto lod = in->lod();
auto n = lod[0].size() - 1;
PADDLE_ENFORCE_EQ(lod.size(), 1UL,
"Only support one level sequence now.");
PADDLE_ENFORCE_EQ(
n, static_cast<size_t>(length->dims()[0]),
"The size of input-sequence and length-array should be the same")
PADDLE_ENFORCE_EQ(
n, static_cast<size_t>(offset->dims()[0]),
"The size of input-sequence and offset-array should be the same")
const int64_t* offset_data = offset->data<int64_t>();
const int64_t* length_data = length->data<int64_t>();
framework::Tensor offset_cpu;
framework::Tensor length_cpu;
if (platform::is_gpu_place(ctx.GetPlace())) {
offset_cpu.mutable_data<T>(offset->dims(), platform::CPUPlace());
offset_cpu.CopyFrom(*offset, platform::CPUPlace(), ctx.device_context());
offset_data = offset_cpu.data<int64_t>();
length_cpu.mutable_data<T>(length->dims(), platform::CPUPlace());
length_cpu.CopyFrom(*length, platform::CPUPlace(), ctx.device_context());
length_data = length_cpu.data<int64_t>();
}
for (size_t i = 0; i < n; ++i) {
PADDLE_ENFORCE_LT(0, offset_data[i],
"The offset[%d] must greater than zero.", i)
PADDLE_ENFORCE_LT(0, length_data[i],
"The length[%d] must greater than zero.", i)
PADDLE_ENFORCE_LT(
lod[0][i] + offset_data[i] + length_data[i],
lod[0][i + 1],
"The target tensor's length overflow.")
}
out->mutable_data<T>(ctx.GetPlace());
auto out_lod = SequenceSliceLoD(*in, offset_data, length_data);
auto out_dims = in->dims();
out_dims[0] = out_lod[0][out_lod[0].size() - 1];
out->Resize(out_dims);
out->set_lod(out_lod);
auto in_stride = framework::stride(in->dims());
auto out_stride = framework::stride(out->dims());
size_t out_offset = 0;
for (size_t i = 0; i < n; ++i) {
Tensor in_t =
in->Slice(static_cast<int>(lod[0][i] + offset_data[i]),
static_cast<int>(lod[0][i] + offset_data[i] +
length_data[i]));
StridedMemcpy<T>(ctx.device_context(), in_t.data<T>(),
in_stride, in_t.dims(), out_stride,
out->data<T>() + out_offset);
out_offset += length_data[i] * in_stride[0];
}
}
};
template <typename Place, typename T>
class SequenceSliceGradOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* in = ctx.Input<LoDTensor>("X");
auto* offset = ctx.Input<Tensor>("Offset");
auto* length = ctx.Input<Tensor>("Length");
auto* out_grad =
ctx.Input<framework::LoDTensor>(framework::GradVarName("Out"));
auto* x_grad =
ctx.Output<framework::LoDTensor>(framework::GradVarName("X"));
const int64_t* offset_data = offset->data<int64_t>();
const int64_t* length_data = length->data<int64_t>();
framework::Tensor offset_cpu;
framework::Tensor length_cpu;
if (platform::is_gpu_place(ctx.GetPlace())) {
offset_cpu.mutable_data<T>(offset->dims(), platform::CPUPlace());
offset_cpu.CopyFrom(*offset, platform::CPUPlace(), ctx.device_context());
offset_data = offset_cpu.data<int64_t>();
length_cpu.mutable_data<T>(length->dims(), platform::CPUPlace());
length_cpu.CopyFrom(*length, platform::CPUPlace(), ctx.device_context());
length_data = length_cpu.data<int64_t>();
}
auto lod = in->lod();
auto out_lod = out_grad->lod();
if (x_grad) {
x_grad->mutable_data<T>(ctx.GetPlace());
x_grad->set_lod(in->lod());
math::SetConstant<Place, T> set_zero;
set_zero(ctx.device_context(), x_grad, static_cast<T>(0));
auto out_grad_stride = framework::stride(out_grad->dims());
for (size_t i = 0; i < out_lod[0].size() - 1; ++i) {
Tensor out_grad_t =
out_grad->Slice(static_cast<int>(out_lod[0][i]),
static_cast<int>(out_lod[0][i + 1]));
auto out_grad_stride = framework::stride(out_grad_t.dims());
auto x_grad_stride = framework::stride(x_grad->dims());
Tensor x_grad_t = x_grad->Slice(
static_cast<int>(lod[0][i] + offset_data[i]),
static_cast<int>(lod[0][i] + offset_data[i] + length_data[i]));
StridedMemcpy<T>(ctx.device_context(), out_grad_t.data<T>(),
out_grad_stride, out_grad_t.dims(), x_grad_stride,
x_grad_t.data<T>());
}
}
}
};
} // namespace operators
} // namespace paddle
paddle/platform/cudnn_helper.h
浏览文件 @ f191c820
...@@ -224,13 +224,15 @@ class ScopedConvolutionDescriptor { ...@@ -224,13 +224,15 @@ class ScopedConvolutionDescriptor {
PADDLE_ENFORCE_EQ(pads.size(), strides.size()); PADDLE_ENFORCE_EQ(pads.size(), strides.size());
PADDLE_ENFORCE_EQ(pads.size(), dilations.size()); PADDLE_ENFORCE_EQ(pads.size(), dilations.size());
#if CUDNN_VERSION < 6000 #if !CUDNN_VERSION_MIN(6, 0, 0)
// cudnn v5 does not support dilation conv, the argument is called upscale // cudnn v5 does not support dilation conv, the argument is called upscale
// instead of dilations and it is must be one. // instead of dilations and it is must be one.
for (size_t i = 0; i < dilations.size(); ++i) { for (size_t i = 0; i < dilations.size(); ++i) {
PADDLE_ENFORCE_EQ( PADDLE_ENFORCE_EQ(
dilations[i], 1, dilations[i], 1,
"Dilations conv is not supported in this cuDNN version"); "Dilations conv is not supported in this cuDNN version(%d.%d.%d).",
CUDNN_VERSION / 1000, CUDNN_VERSION % 1000 / 100,
CUDNN_VERSION % 100);
} }
#endif #endif
......
paddle/platform/cudnn_helper_test.cc
浏览文件 @ f191c820
...@@ -38,6 +38,26 @@ TEST(CudnnHelper, ScopedTensorDescriptor) { ...@@ -38,6 +38,26 @@ TEST(CudnnHelper, ScopedTensorDescriptor) {
EXPECT_EQ(strides[2], 6); EXPECT_EQ(strides[2], 6);
EXPECT_EQ(strides[1], 36); EXPECT_EQ(strides[1], 36);
EXPECT_EQ(strides[0], 144); EXPECT_EQ(strides[0], 144);
// test tensor5d: ScopedTensorDescriptor
ScopedTensorDescriptor tensor5d_desc;
std::vector<int> shape_5d = {2, 4, 6, 6, 6};
auto desc_5d = tensor5d_desc.descriptor<float>(DataLayout::kNCDHW, shape_5d);
std::vector<int> dims_5d(5);
std::vector<int> strides_5d(5);
paddle::platform::dynload::cudnnGetTensorNdDescriptor(
desc_5d, 5, &type, &nd, dims_5d.data(), strides_5d.data());
EXPECT_EQ(nd, 5);
for (size_t i = 0; i < dims_5d.size(); ++i) {
EXPECT_EQ(dims_5d[i], shape_5d[i]);
}
EXPECT_EQ(strides_5d[4], 1);
EXPECT_EQ(strides_5d[3], 6);
EXPECT_EQ(strides_5d[2], 36);
EXPECT_EQ(strides_5d[1], 216);
EXPECT_EQ(strides_5d[0], 864);
} }
TEST(CudnnHelper, ScopedFilterDescriptor) { TEST(CudnnHelper, ScopedFilterDescriptor) {
...@@ -60,6 +80,20 @@ TEST(CudnnHelper, ScopedFilterDescriptor) { ...@@ -60,6 +80,20 @@ TEST(CudnnHelper, ScopedFilterDescriptor) {
for (size_t i = 0; i < shape.size(); ++i) { for (size_t i = 0; i < shape.size(); ++i) {
EXPECT_EQ(kernel[i], shape[i]); EXPECT_EQ(kernel[i], shape[i]);
} }
ScopedFilterDescriptor filter_desc_4d;
std::vector<int> shape_4d = {2, 3, 3, 3};
auto desc_4d = filter_desc.descriptor<float>(DataLayout::kNCDHW, shape_4d);
std::vector<int> kernel_4d(4);
paddle::platform::dynload::cudnnGetFilterNdDescriptor(
desc_4d, 4, &type, &format, &nd, kernel_4d.data());
EXPECT_EQ(GetCudnnTensorFormat(DataLayout::kNCHW), format);
EXPECT_EQ(nd, 4);
for (size_t i = 0; i < shape_4d.size(); ++i) {
EXPECT_EQ(kernel_4d[i], shape_4d[i]);
}
} }
TEST(CudnnHelper, ScopedConvolutionDescriptor) { TEST(CudnnHelper, ScopedConvolutionDescriptor) {
......
paddle/scripts/docker/build.sh
浏览文件 @ f191c820
...@@ -144,7 +144,7 @@ function gen_dockerfile() { ...@@ -144,7 +144,7 @@ function gen_dockerfile() {
DOCKERFILE_GPU_ENV="" DOCKERFILE_GPU_ENV=""
DOCKERFILE_CUDNN_DSO="" DOCKERFILE_CUDNN_DSO=""
if [[ ${WITH_GPU:-OFF} == 'ON' ]]; then 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" DOCKERFILE_CUDNN_DSO="RUN ln -s /usr/lib/x86_64-linux-gnu/libcudnn.so.5 /usr/lib/x86_64-linux-gnu/libcudnn.so"
fi fi
......
paddle/trainer/tests/CMakeLists.txt
浏览文件 @ f191c820
...@@ -11,7 +11,6 @@ add_unittest_without_exec(test_Trainer ...@@ -11,7 +11,6 @@ add_unittest_without_exec(test_Trainer
test_Trainer.cpp) test_Trainer.cpp)
add_test(NAME test_Trainer add_test(NAME test_Trainer
COMMAND ${PADDLE_SOURCE_DIR}/paddle/.set_python_path.sh -d ${PADDLE_SOURCE_DIR}/python/ 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/ ${PADDLE_SOURCE_DIR}/paddle/.set_python_path.sh -d ${PADDLE_SOURCE_DIR}/python/
${CMAKE_CURRENT_BINARY_DIR}/test_Trainer ${CMAKE_CURRENT_BINARY_DIR}/test_Trainer
WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle/) WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle/)
......
paddle/trainer/tests/chunking.conf 已删除 100644 → 0
浏览文件 @ 3e7fff41
#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 @ 3e7fff41
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 @ 3e7fff41
# 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 @ 3e7fff41
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Lawson NNP I-NP
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promise NN I-NP
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warn VBP B-VP
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swiftly RB I-VP
make VB I-VP
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was VBD B-VP
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lack NN I-NP
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in IN B-PP
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promise NN I-NP
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and CC O
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marks NNS I-NP
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marks NNS I-NP
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the DT B-NP
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increase VB I-VP
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when WRB B-ADVP
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Dow NNP I-NP
Jones NNP I-NP
Industrial NNP I-NP
Average NNP I-NP
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than IN I-NP
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analysts NNS I-NP
predict VBP B-VP
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exchange NN I-NP
market NN I-NP
will MD B-VP
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focus NN I-NP
back RB B-ADVP
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economic JJ B-NP
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signs NNS I-NP
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In IN B-PP
Tokyo NNP B-NP
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paddle/trainer/tests/test_Trainer.cpp
浏览文件 @ f191c820
...@@ -24,7 +24,6 @@ using namespace std; // NOLINT ...@@ -24,7 +24,6 @@ using namespace std; // NOLINT
static const string& configFile1 = "trainer/tests/sample_trainer_config.conf"; static const string& configFile1 = "trainer/tests/sample_trainer_config.conf";
static const string& configFile2 = static const string& configFile2 =
"trainer/tests/sample_trainer_config_hsigmoid.conf"; "trainer/tests/sample_trainer_config_hsigmoid.conf";
static const string& configFile3 = "trainer/tests/chunking.conf";
static const string& configFile4 = static const string& configFile4 =
"trainer/tests/sample_trainer_config_parallel.conf"; "trainer/tests/sample_trainer_config_parallel.conf";
...@@ -95,13 +94,6 @@ TEST(checkGradient, multi) { ...@@ -95,13 +94,6 @@ TEST(checkGradient, multi) {
TEST(checkGradient, hsigmoid) { checkGradientTest(configFile2, false, false); } 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) { TEST(checkGradient, non_parallel) {
checkGradientTest(configFile4, false, false); checkGradientTest(configFile4, false, false);
} }
......
paddle/trainer/tests/test_config.conf
浏览文件 @ f191c820
...@@ -15,12 +15,7 @@ ...@@ -15,12 +15,7 @@
from paddle.trainer_config_helpers import * from paddle.trainer_config_helpers import *
TrainData(ProtoData( TrainData(SimpleData(
files = "dummy_list",
constant_slots = [1.0],
async_load_data = True))
TestData(SimpleData(
files = "trainer/tests/sample_filelist.txt", files = "trainer/tests/sample_filelist.txt",
feat_dim = 3, feat_dim = 3,
context_len = 0, context_len = 0,
......
paddle/trainer/tests/train.txt 已删除 100644 → 0
浏览文件 @ 3e7fff41
此差异已折叠。
python/paddle/trainer/config_parser.py
浏览文件 @ f191c820
...@@ -1116,35 +1116,6 @@ def PyData(files=None, ...@@ -1116,35 +1116,6 @@ def PyData(files=None,
return data_config 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. #real data for training is actually provided by "sub_data" data providers.
@config_func @config_func
def MultiData(sub_data=[]): def MultiData(sub_data=[]):
...@@ -2714,7 +2685,7 @@ Usage: ...@@ -2714,7 +2685,7 @@ Usage:
max_sort_size = -1, inputs = ["output", "score"]) max_sort_size = -1, inputs = ["output", "score"])
Input data: Samples of the same query should be loaded as a sequence, 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 scores for each sample. The score slot should be the 2nd
input of lambdaRank layer. input of lambdaRank layer.
......
python/paddle/trainer_config_helpers/activations.py
浏览文件 @ f191c820
...@@ -17,7 +17,8 @@ __all__ = [ ...@@ -17,7 +17,8 @@ __all__ = [
"IdentityActivation", "LinearActivation", 'SequenceSoftmaxActivation', "IdentityActivation", "LinearActivation", 'SequenceSoftmaxActivation',
'ExpActivation', "ReluActivation", "BReluActivation", "SoftReluActivation", 'ExpActivation', "ReluActivation", "BReluActivation", "SoftReluActivation",
"STanhActivation", "AbsActivation", "SquareActivation", "BaseActivation", "STanhActivation", "AbsActivation", "SquareActivation", "BaseActivation",
"LogActivation", "SqrtActivation", "ReciprocalActivation" "LogActivation", "SqrtActivation", "ReciprocalActivation",
"SoftSignActivation"
] ]
...@@ -243,8 +244,20 @@ class ReciprocalActivation(BaseActivation): ...@@ -243,8 +244,20 @@ class ReciprocalActivation(BaseActivation):
Reciprocal Activation. Reciprocal Activation.
.. math:: .. math::
f(z) = 1/z f(z)=\\frac{1}{z}
""" """
def __init__(self): def __init__(self):
BaseActivation.__init__(self, 'reciprocal', False) 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/layers.py
浏览文件 @ f191c820
...@@ -2507,12 +2507,12 @@ def img_conv_layer(input, ...@@ -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 input is raw pixels of image(mono or RGB), or it may be the previous layer's
num_filters * num_group. num_filters * num_group.
There are several group of filter in PaddlePaddle implementation. There are several groups of filters in PaddlePaddle implementation.
Each group will process some channel of the inputs. For example, if an input Each group will process some channels of the input. For example, if
num_channel = 256, group = 4, num_filter=32, the PaddlePaddle will create 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 32*4 = 128 filters to process the input. The channels will be split into 4
pieces. First 256/4 = 64 channels will process by first 32 filters. The pieces. First 256/4 = 64 channels will be processed by first 32 filters. The
rest channels will be processed by rest group of filters. rest channels will be processed by the rest groups of filters.
The example usage is: The example usage is:
...@@ -2528,53 +2528,68 @@ def img_conv_layer(input, ...@@ -2528,53 +2528,68 @@ def img_conv_layer(input,
:type name: basestring :type name: basestring
:param input: The input of this layer. :param input: The input of this layer.
:type input: LayerOutput :type input: LayerOutput
:param filter_size: The x dimension of a filter kernel. Or input a tuple for :param filter_size: The dimensions of the filter kernel. If the parameter is
two image dimension. 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 :type filter_size: int | tuple | list
:param filter_size_y: The y dimension of a filter kernel. Since PaddlePaddle :param filter_size_y: The dimension of the filter kernel on the y axis. If the parameter
currently supports rectangular filters, the filter's is not set, it will be set automatically according to filter_size.
shape will be (filter_size, filter_size_y). :type filter_size_y: int
:type filter_size_y: int | None
:param num_filters: Each filter group's number of filter :param num_filters: Each filter group's number of filter
:param act: Activation type. ReluActivation is the default activation. :param act: Activation type. ReluActivation is the default activation.
:type act: BaseActivation :type act: BaseActivation
:param groups: Group size of filters. :param groups: The group number. 1 is the default group number.
:type groups: int :type groups: int
:param stride: The x dimension of the stride. Or input a tuple for two image :param stride: The strides. If the parameter is set to one integer, the strides
dimension. 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 :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 :type stride_y: int
:param padding: The x dimension of the padding. Or input a tuple for two :param padding: The padding sizes. If the parameter is set to one integer, the padding
image dimension 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 :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 :type padding_y: int
:param dilation: The x dimension of the dilation. Or input a tuple for two :param dilation: The dimensions of the dilation. If the parameter is set to one integer,
image dimension 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 :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 :type dilation_y: int
:param bias_attr: The bias attribute. If the parameter is set to False or an object :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 whose type is not ParameterAttribute, no bias is defined. If the
parameter is set to True, the bias is initialized to zero. parameter is set to True, the bias is initialized to zero.
:type bias_attr: ParameterAttribute | None | bool | Any :type bias_attr: ParameterAttribute | None | bool | Any
:param num_channels: number of input channels. If None will be set :param num_channels: The number of input channels. If the parameter is not set or
automatically from previous output. set to None, its actual value will be automatically set to
the channel number of the input.
:type num_channels: int :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 :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 :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 :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 :type trans: bool
:param layer_type: specify the layer_type, default is None. If trans=True, :param layer_type: Specify the layer type. If the dilation's dimension on one axis is
layer_type has to be "exconvt" or "cudnn_convt", larger than 1, layer_type has to be "cudnn_conv" or "cudnn_convt".
otherwise layer_type has to be either "exconv" or If trans=True, layer_type has to be "exconvt" or "cudnn_convt",
"cudnn_conv" otherwise layer_type has to be either "exconv" or "cudnn_conv".
:type layer_type: String :type layer_type: basestring
:return: LayerOutput object. :return: LayerOutput object.
:rtype: LayerOutput :rtype: LayerOutput
""" """
...@@ -2679,7 +2694,7 @@ def img_pool_layer(input, ...@@ -2679,7 +2694,7 @@ def img_pool_layer(input,
""" """
Image pooling Layer. 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/ .. _pooling: http://ufldl.stanford.edu/tutorial/supervised/Pooling/
...@@ -2711,32 +2726,37 @@ def img_pool_layer(input, ...@@ -2711,32 +2726,37 @@ def img_pool_layer(input,
padding_y=2, padding_y=2,
pool_type=MaxPooling()) 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 :type padding: int
:param padding_y: pooling padding height. It's equal to padding by default. :param padding_y: The padding size on the y axis. If the parameter is not set
:type padding_y: int | None or set to None, it will be set to 'padding' automatically.
:param name: name of pooling layer :param name: The name of this layer. It is optional.
:type name: basestring. :type name: basestring
:param input: The input of this layer. :param input: The input of this layer.
:type input: LayerOutput :type input: LayerOutput
:param pool_size: pooling window width :param pool_size: The pooling window length on the x axis.
:type pool_size: int :type pool_size: int
:param pool_size_y: pooling window height. It's eaqual to pool_size by default. :param pool_size_y: The pooling window length on the y axis. If the parameter is
:type pool_size_y: int | None not set or set to None, its actual value will be automatically
:param num_channels: number of input channel. 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 :type num_channels: int
:param pool_type: pooling type. MaxPooling or AvgPooling. Default is :param pool_type: Pooling type. MaxPooling is the default pooling.
MaxPooling.
:type pool_type: BasePoolingType :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 :type stride: int
:param stride_y: stride height of pooling. It is equal to stride by default. :param stride_y: The stride on the y axis. If the parameter is not set or set to
:type stride_y: int | None None, its actual value will be automatically set to 'stride'.
:param layer_attr: Extra Layer attribute. :type stride_y: int
:param layer_attr: The extra layer attribute. See ExtraLayerAttribute for
details.
:type layer_attr: ExtraLayerAttribute :type layer_attr: ExtraLayerAttribute
:param ceil_mode: Wether to use ceil mode to calculate output height and with. :param ceil_mode: Wether to use the ceil function to calculate output height and width.
Defalut is True. If set false, Otherwise use floor. True is the default. If it is set to False, the floor function will
be used.
:type ceil_mode: bool :type ceil_mode: bool
:return: LayerOutput object. :return: LayerOutput object.
:rtype: LayerOutput :rtype: LayerOutput
...@@ -2842,24 +2862,32 @@ def img_pool3d_layer(input, ...@@ -2842,24 +2862,32 @@ def img_pool3d_layer(input,
:param padding: pooling padding width. :param padding: pooling padding width.
:type padding: int | tuple | list :type padding: int | tuple | list
:param name: name of pooling layer :param name: The name of this layer. It is optional.
:type name: basestring. :type name: basestring.
:param input: The input of this layer. :param input: The input of this layer.
:type input: LayerOutput :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 :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 :type num_channels: int
:param pool_type: pooling type. MaxPooling or AvgPooling. Default is :param pool_type: Pooling type. MaxPooling is the default pooling.
MaxPooling.
:type pool_type: BasePoolingType :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 :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 :type layer_attr: ExtraLayerAttribute
:param ceil_mode: Wether to use ceil mode to calculate output height and with. :param ceil_mode: Wether to use the ceil function to calculate output height and width.
Defalut is True. If set false, Otherwise use floor. True is the default. If it is set to False, the floor function will
be used.
:type ceil_mode: bool :type ceil_mode: bool
:return: LayerOutput object. :return: LayerOutput object.
:rtype: LayerOutput :rtype: LayerOutput
...@@ -2938,9 +2966,11 @@ def spp_layer(input, ...@@ -2938,9 +2966,11 @@ def spp_layer(input,
pyramid_height=None, pyramid_height=None,
layer_attr=None): layer_attr=None):
""" """
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition. A layer performs spatial pyramid pooling.
The details please refer to
`Kaiming He's paper <https://arxiv.org/abs/1406.4729>`_. Reference:
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
https://arxiv.org/abs/1406.4729
The example usage is: The example usage is:
...@@ -2955,13 +2985,16 @@ def spp_layer(input, ...@@ -2955,13 +2985,16 @@ def spp_layer(input,
:type name: basestring :type name: basestring
:param input: The input of this layer. :param input: The input of this layer.
:type input: LayerOutput :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 :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 :type scale: BasePoolingType
:param pyramid_height: pyramid height. :param pyramid_height: The pyramid height of this pooling.
:type pyramid_height: int :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 :type layer_attr: ExtraLayerAttribute
:return: LayerOutput object. :return: LayerOutput object.
:rtype: LayerOutput :rtype: LayerOutput
...@@ -4694,7 +4727,7 @@ def conv_projection(input, ...@@ -4694,7 +4727,7 @@ def conv_projection(input,
will be same when filter_size_y is not set. If it is set will be same when filter_size_y is not set. If it is set
to a list, the first element indicates the dimension on to a list, the first element indicates the dimension on
the x axis, and the second is used to specify the dimension 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 :type filter_size: int | tuple | list
:param filter_size_y: The dimension of the filter kernel on the y axis. If the parameter :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. is not set, it will be set automatically according to filter_size.
...@@ -7076,7 +7109,7 @@ def img_conv3d_layer(input, ...@@ -7076,7 +7109,7 @@ def img_conv3d_layer(input,
:type layer_attr: ExtraLayerAttribute :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 :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. If not set, it will be automatically set to "deconv3d"
when trans=True and "conv3d" when trans=False. when trans=True and "conv3d" when trans=False.
:type layer_type: basestring :type layer_type: basestring
......
python/paddle/trainer_config_helpers/tests/configs/file_list.sh
浏览文件 @ f191c820
...@@ -11,7 +11,6 @@ test_recursive_topology test_gated_unit_layer test_clip_layer test_row_l2_norm_l ...@@ -11,7 +11,6 @@ test_recursive_topology test_gated_unit_layer test_clip_layer test_row_l2_norm_l
test_kmax_seq_socre_layer test_sub_nested_seq_select_layer test_scale_shift_layer test_kmax_seq_socre_layer test_sub_nested_seq_select_layer test_scale_shift_layer
test_seq_slice_layer test_cross_entropy_over_beam test_roi_pool_layer test_pooling3D_layer test_seq_slice_layer test_cross_entropy_over_beam test_roi_pool_layer test_pooling3D_layer
test_conv3d_layer test_deconv3d_layer test_BatchNorm3D test_resize_layer test_conv3d_layer test_deconv3d_layer test_BatchNorm3D test_resize_layer
test_conv3d_layer test_deconv3d_layer test_BatchNorm3D test_resize_layer
test_scale_sub_region_layer test_dot_prod_layer test_l2_distance_layer) test_scale_sub_region_layer test_dot_prod_layer test_l2_distance_layer)
export whole_configs=(test_split_datasource) export whole_configs=(test_split_datasource)
python/paddle/v2/fluid/framework.py
浏览文件 @ f191c820
...@@ -15,6 +15,37 @@ def unique_name(prefix): ...@@ -15,6 +15,37 @@ def unique_name(prefix):
return "_".join([prefix, str(uid)]) 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): def _debug_string_(proto, throw_on_error=True):
error_fields = list() error_fields = list()
if not proto.IsInitialized(error_fields) and throw_on_error: if not proto.IsInitialized(error_fields) and throw_on_error:
...@@ -66,7 +97,7 @@ class Variable(object): ...@@ -66,7 +97,7 @@ class Variable(object):
"matched.".format(self.name, old_shape, shape)) "matched.".format(self.name, old_shape, shape))
if dtype is not None: if dtype is not None:
if not isinstance(dtype, core.DataType): 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: if is_new_var:
self.desc.set_data_type(dtype) self.desc.set_data_type(dtype)
else: else:
...@@ -148,26 +179,6 @@ class Variable(object): ...@@ -148,26 +179,6 @@ class Variable(object):
uid = core.unique_integer(prefix) # unique during whole process. uid = core.unique_integer(prefix) # unique during whole process.
return "_".join([prefix, str(uid)]) 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(): def get_all_op_protos():
""" """
......
python/paddle/v2/fluid/layers.py
浏览文件 @ f191c820
此差异已折叠。
python/paddle/v2/fluid/tests/book/test_image_classification_train.py
浏览文件 @ f191c820
...@@ -4,6 +4,7 @@ import paddle.v2.fluid.core as core ...@@ -4,6 +4,7 @@ import paddle.v2.fluid.core as core
import paddle.v2.fluid.framework as framework import paddle.v2.fluid.framework as framework
import paddle.v2.fluid.layers as layers import paddle.v2.fluid.layers as layers
import paddle.v2.fluid.nets as nets 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.executor import Executor
from paddle.v2.fluid.initializer import XavierInitializer from paddle.v2.fluid.initializer import XavierInitializer
from paddle.v2.fluid.optimizer import AdamOptimizer from paddle.v2.fluid.optimizer import AdamOptimizer
...@@ -103,12 +104,13 @@ net = vgg16_bn_drop(images) ...@@ -103,12 +104,13 @@ net = vgg16_bn_drop(images)
predict = layers.fc(input=net, size=classdim, act='softmax') predict = layers.fc(input=net, size=classdim, act='softmax')
cost = layers.cross_entropy(input=predict, label=label) cost = layers.cross_entropy(input=predict, label=label)
avg_cost = layers.mean(x=cost) avg_cost = layers.mean(x=cost)
accuracy = layers.accuracy(input=predict, label=label)
# optimizer = SGDOptimizer(learning_rate=0.001) # optimizer = SGDOptimizer(learning_rate=0.001)
optimizer = AdamOptimizer(learning_rate=0.001) optimizer = AdamOptimizer(learning_rate=0.001)
opts = optimizer.minimize(avg_cost) opts = optimizer.minimize(avg_cost)
accuracy, acc_out = evaluator.accuracy(input=predict, label=label)
BATCH_SIZE = 128 BATCH_SIZE = 128
PASS_NUM = 1 PASS_NUM = 1
...@@ -124,6 +126,7 @@ exe.run(framework.default_startup_program()) ...@@ -124,6 +126,7 @@ exe.run(framework.default_startup_program())
for pass_id in range(PASS_NUM): for pass_id in range(PASS_NUM):
batch_id = 0 batch_id = 0
accuracy.reset(exe)
for data in train_reader(): for data in train_reader():
img_data = np.array(map(lambda x: x[0].reshape(data_shape), img_data = np.array(map(lambda x: x[0].reshape(data_shape),
data)).astype("float32") data)).astype("float32")
...@@ -141,12 +144,14 @@ for pass_id in range(PASS_NUM): ...@@ -141,12 +144,14 @@ for pass_id in range(PASS_NUM):
outs = exe.run(framework.default_main_program(), outs = exe.run(framework.default_main_program(),
feed={"pixel": tensor_img, feed={"pixel": tensor_img,
"label": tensor_y}, "label": tensor_y},
fetch_list=[avg_cost, accuracy]) fetch_list=[avg_cost, acc_out])
loss = np.array(outs[0]) loss = np.array(outs[0])
acc = np.array(outs[1]) acc = np.array(outs[1])
pass_acc = accuracy.eval(exe)
print("pass_id:" + str(pass_id) + " batch_id:" + str(batch_id) + 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 batch_id = batch_id + 1
if batch_id > 1: if batch_id > 1:
......
python/paddle/v2/fluid/tests/test_maxout_op.py 0 → 100644
浏览文件 @ f191c820
此差异已折叠。
python/paddle/v2/fluid/tests/test_sequence_slice_op.py 0 → 100644
浏览文件 @ f191c820
此差异已折叠。
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