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enable bias for mkldnn_addto

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mobile_baidu
tensor-tang 7 years ago
parent 6f43c9361c
commit 93e22e7b67
3 changed files with 99 additions and 15 deletions
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83
paddle/gserver/layers/MKLDNNAddtoLayer.cpp
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@ -62,16 +62,14 @@ void MKLDNNAddtoLayer::resetFwd(std::vector<primitive>& pipeline,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) {
if (biases_) {
LOG(FATAL) << "not implemented yet";
}
resetFwdBuffers(inVals_, out);
resetFwdBuffers(inVals_, bias, out);
in = inVals_[0];
std::shared_ptr<sum::primitive_desc> fwdPD;
resetFwdPD(fwdPD, inVals_, out);
std::shared_ptr<sum::primitive_desc> biasPD;
resetFwdPD(fwdPD, biasPD, inVals_, bias, out);
resetFwdPipeline(pipeline, fwdPD, inVals_, out);
resetFwdPipeline(pipeline, fwdPD, biasPD, inVals_, bias, out);
}
void MKLDNNAddtoLayer::resetBwd(std::vector<primitive>& pipeline,
@ -79,7 +77,7 @@ void MKLDNNAddtoLayer::resetBwd(std::vector<primitive>& pipeline,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) {
resetBwdBuffers(inGrads_, out);
resetBwdBuffers(inGrads_, bias, out);
in = inGrads_[0];
// backward only need share output grad to input grad
@ -89,6 +87,20 @@ void MKLDNNAddtoLayer::resetBwd(std::vector<primitive>& pipeline,
inputLayers_[i]->getOutputGrad()->setData(inGrads_[i]->getData());
}
}
// backward bias
bwdBias_ = nullptr;
if (bias) {
std::vector<double> scales(bs_, 1.0);
std::vector<memory::primitive_desc> srcPDs(bs_, bias->getPrimitiveDesc());
auto biasPD = sum::primitive_desc(bias->getMemoryDesc(), scales, srcPDs);
std::vector<primitive::at> srcs;
for (size_t i = 0; i < grads_.size(); ++i) {
srcs.push_back(*(grads_[i]));
}
bwdBias_.reset(new sum(biasPD, srcs, *bias));
pipeline.push_back(*bwdBias_);
}
}
void MKLDNNAddtoLayer::updateWeights(const UpdateCallback& callback) {
@ -97,7 +109,25 @@ void MKLDNNAddtoLayer::updateWeights(const UpdateCallback& callback) {
}
}
void MKLDNNAddtoLayer::prepareBias(MKLDNNMatrixPtr& bias,
const MatrixPtr& biasMat,
const MKLDNNMatrixPtr& out,
std::vector<MKLDNNMatrixPtr>& outs) {
auto pd = MKLDNNMatrix::createPrimitiveDesc(
{(int)layerSize_}, memory::format::x, engine_);
bias = MKLDNNMatrix::create(pd, biasMat);
outs.clear();
real* data = out->getData();
CHECK_EQ(bs_ * layerSize_, out->getElementCnt());
for (int i = 0; i < bs_; ++i) {
MatrixPtr tmp =
Matrix::create(data + i * layerSize_, 1, layerSize_, false, false);
outs.push_back(MKLDNNMatrix::create(bias->getPrimitiveDesc(), tmp));
}
}
void MKLDNNAddtoLayer::resetFwdBuffers(std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) {
inputs.resize(inputLayers_.size());
for (size_t i = 0; i < inputs.size(); i++) {
@ -110,10 +140,18 @@ void MKLDNNAddtoLayer::resetFwdBuffers(std::vector<MKLDNNMatrixPtr>& inputs,
}
resetOutValue(out, inputs[0]->getPrimitiveDesc());
if (biases_ && biases_->getW()) {
prepareBias(bias, biases_->getW(), out, vals_);
} else {
bias = nullptr;
}
}
void MKLDNNAddtoLayer::resetFwdPD(std::shared_ptr<sum::primitive_desc>& pd,
std::shared_ptr<sum::primitive_desc>& biasPD,
std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr bias,
MKLDNNMatrixPtr out) {
std::vector<double> scales(inputs.size(), 1.0);
std::vector<memory::primitive_desc> srcPDs;
@ -123,12 +161,23 @@ void MKLDNNAddtoLayer::resetFwdPD(std::shared_ptr<sum::primitive_desc>& pd,
CHECK(out);
pd.reset(new sum::primitive_desc(out->getMemoryDesc(), scales, srcPDs));
CHECK_PRIMITIVE_DESC_EQ(out, pd->dst_primitive_desc());
biasPD = nullptr;
if (bias) {
std::vector<double> scales(2, 1.0);
std::vector<memory::primitive_desc> srcPDs(2, bias->getPrimitiveDesc());
biasPD.reset(
new sum::primitive_desc(bias->getMemoryDesc(), scales, srcPDs));
CHECK_PRIMITIVE_DESC_EQ(bias, biasPD->dst_primitive_desc());
}
}
void MKLDNNAddtoLayer::resetFwdPipeline(
std::vector<primitive>& pipeline,
std::shared_ptr<sum::primitive_desc>& pd,
std::shared_ptr<sum::primitive_desc>& biasPD,
std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) {
std::vector<primitive::at> srcs;
for (size_t i = 0; i < inputs.size(); i++) {
@ -136,9 +185,23 @@ void MKLDNNAddtoLayer::resetFwdPipeline(
}
fwd_.reset(new sum(*pd, srcs, *out));
pipeline.push_back(*fwd_);
fwdBias_.clear();
if (biasPD == nullptr || bias == nullptr) {
return;
}
fwdBias_.resize(vals_.size());
for (size_t i = 0; i < vals_.size(); ++i) {
std::vector<primitive::at> srcs;
srcs.push_back(*(vals_[i]));
srcs.push_back(*bias);
fwdBias_[i].reset(new sum(*biasPD, srcs, *vals_[i]));
pipeline.push_back(*fwdBias_[i]);
}
}
void MKLDNNAddtoLayer::resetBwdBuffers(std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) {
CHECK(outVal_);
resetOutGrad(out, outVal_->getPrimitiveDesc());
@ -149,6 +212,12 @@ void MKLDNNAddtoLayer::resetBwdBuffers(std::vector<MKLDNNMatrixPtr>& inputs,
resetInGrad(inputs[i], inVal_->getPrimitiveDesc(), i);
CHECK_PRIMITIVE_DESC_EQ(inputs[i], out->getPrimitiveDesc());
}
if (biases_ && biases_->getWGrad()) {
prepareBias(bias, biases_->getWGrad(), out, grads_);
} else {
bias = nullptr;
}
}
} // namespace paddle

22
paddle/gserver/layers/MKLDNNAddtoLayer.h
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@ -32,9 +32,15 @@ protected:
// layer size == ic * ih * iw == oc * oh *ow, and can not be changed
size_t layerSize_;
// TODO(TJ): this part has not been optimized by MKL-DNN
std::unique_ptr<Weight> biases_;
// buffers for adding bias
std::vector<MKLDNNMatrixPtr> vals_;
std::vector<MKLDNNMatrixPtr> grads_;
// primitives for adding bias
std::vector<std::shared_ptr<mkldnn::primitive>> fwdBias_;
std::shared_ptr<mkldnn::primitive> bwdBias_;
public:
explicit MKLDNNAddtoLayer(const LayerConfig& config) : MKLDNNLayer(config) {}
@ -91,20 +97,34 @@ protected:
* reset pipeline.
*/
void resetFwdBuffers(std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out);
void resetFwdPD(std::shared_ptr<mkldnn::sum::primitive_desc>& pd,
std::shared_ptr<mkldnn::sum::primitive_desc>& biasPD,
std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr bias,
MKLDNNMatrixPtr out);
void resetFwdPipeline(std::vector<mkldnn::primitive>& pipeline,
std::shared_ptr<mkldnn::sum::primitive_desc>& pd,
std::shared_ptr<mkldnn::sum::primitive_desc>& biasPD,
std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out);
/**
* Backward functions: reset buffers(inputs, output, bias)
*/
void resetBwdBuffers(std::vector<MKLDNNMatrixPtr>& inputs,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out);
/**
* prepare for bias
*/
void prepareBias(MKLDNNMatrixPtr& bias,
const MatrixPtr& biasMat,
const MKLDNNMatrixPtr& out,
std::vector<MKLDNNMatrixPtr>& outs);
};
} // namespace paddle

9
paddle/gserver/tests/test_MKLDNN.cpp
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@ -300,13 +300,8 @@ void testAddtoLayer(const testImageDesc& pm, const size_t nInputs) {
TestConfig dnnConfig;
getAddtoConfig(dnnConfig, pm, nInputs);
dnnConfig.layerConfig.set_type("mkldnn_addto");
// TODO(TJ): test with bias
for (auto withBias : {false}) {
if (withBias) {
dnnConfig.biasSize = pm.ic * pm.ih * pm.iw;
} else {
dnnConfig.biasSize = 0;
}
for (auto withBias : {false, true}) {
dnnConfig.biasSize = withBias ? pm.ic * pm.ih * pm.iw : 0;
RUN_MKLDNN_TEST_LAYER(dnnConfig, "addto", pm)
}
}

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