Xinghai Sun
8 years ago
commit
8e7fe8cae5
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/* Copyright (c) 2017 PaddlePaddle Authors. All Rights Reserve.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#pragma once
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#include "MKLDNNLayer.h"
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#include "mkldnn.hpp"
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namespace paddle {
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typedef mkldnn::pooling_forward pool_fwd;
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typedef mkldnn::pooling_backward pool_bwd;
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/**
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* @brief A subclass of MKLDNNLayer pool layer.
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*
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* The config file api is mkldnn_pool
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*/
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class MKLDNNPoolLayer : public MKLDNNLayer {
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protected:
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// padding height and width
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int ph_, pw_;
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// stride height and width
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int sh_, sw_;
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// filter(kenerl) height and width
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int fh_, fw_;
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// pooling_avg or pooling_max
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mkldnn::algorithm poolAlgo_;
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// MKLDNNMatrixPtr which should be created from CPU Device
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MKLDNNMatrixPtr cpuOutVal_;
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MKLDNNMatrixPtr cpuOutGrad_;
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// convert handle between CPU device and MKLDNN device
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std::shared_ptr<mkldnn::reorder> cvtOutVal_;
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std::shared_ptr<mkldnn::reorder> cvtOutGrad_;
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// save forward primitive_desc, which can be used backward
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std::shared_ptr<pool_fwd::primitive_desc> fwdPD_;
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// according to https://github.com/01org/mkl-dnn/blob/master/tests/gtests/
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// test_pooling_forward.cpp, pool need workspace for backward
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std::shared_ptr<mkldnn::memory> workspace_;
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public:
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explicit MKLDNNPoolLayer(const LayerConfig& config) : MKLDNNLayer(config) {}
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~MKLDNNPoolLayer() {}
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bool init(const LayerMap& layerMap,
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const ParameterMap& parameterMap) override;
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void reshape(
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int& bs, int& ic, int& ih, int& iw, int oc, int& oh, int& ow) override;
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void resetFwd(std::vector<mkldnn::primitive>& pipeline,
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MKLDNNMatrixPtr& in,
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MKLDNNMatrixPtr& wgt,
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MKLDNNMatrixPtr& bias,
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MKLDNNMatrixPtr& out) override;
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void resetBwd(std::vector<mkldnn::primitive>& pipeline,
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MKLDNNMatrixPtr& in,
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MKLDNNMatrixPtr& wgt,
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MKLDNNMatrixPtr& bias,
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MKLDNNMatrixPtr& out) override;
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void updateInputData() override;
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void printSizeInfo() override {
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MKLDNNLayer::printSizeInfo();
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VLOG(MKLDNN_SIZES) << getName() << ": fh: " << fh_ << ", fw: " << fw_
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<< ": ph: " << ph_ << ", pw: " << pw_ << ", sh: " << sh_
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<< ", sw: " << sw_;
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}
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protected:
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/**
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* Forward functions: reset buffers(input, output),
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* reset primitive descriptor,
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* reset pipeline.
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*/
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void resetFwdBuffers(MKLDNNMatrixPtr& in, MKLDNNMatrixPtr& out);
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void resetInValue(MKLDNNMatrixPtr& in);
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void resetOutValue(MKLDNNMatrixPtr& out);
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void resetFwdPD(std::shared_ptr<pool_fwd::primitive_desc>& pd,
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MKLDNNMatrixPtr in,
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MKLDNNMatrixPtr out);
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void resetFwdPipeline(std::vector<mkldnn::primitive>& pipeline,
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std::shared_ptr<pool_fwd::primitive_desc>& pd,
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MKLDNNMatrixPtr& in,
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MKLDNNMatrixPtr& out);
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/**
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* Backward functions: reset buffers(input, output),
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* reset primitive descriptor,
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* reset pipeline.
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*/
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void resetBwdBuffers(MKLDNNMatrixPtr& in, MKLDNNMatrixPtr& out);
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void resetOutGrad(MKLDNNMatrixPtr& out);
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void resetInGrad(MKLDNNMatrixPtr& in);
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void resetBwdPD(std::shared_ptr<pool_bwd::primitive_desc>& pd,
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MKLDNNMatrixPtr& in,
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MKLDNNMatrixPtr& out);
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void resetBwdPipeline(std::vector<mkldnn::primitive>& pipeline,
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std::shared_ptr<pool_bwd::primitive_desc>& pd,
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MKLDNNMatrixPtr& in,
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MKLDNNMatrixPtr& out);
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/**
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* get padding_r according to
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* https://github.com/01org/mkl-dnn/blob/master/tests/gtests/
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* test_pooling_forward.cpp
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*/
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mkldnn::memory::dims getPaddingR() const {
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mkldnn::memory::dims padR = {ph_, pw_};
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for (int i = 0; i < 2; ++i) {
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if ((ih_ + ph_ + padR[0] - fh_) / sh_ + 1 < oh_) {
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++padR[0];
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}
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if ((iw_ + pw_ + padR[1] - fw_) / sw_ + 1 < ow_) {
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++padR[1];
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}
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}
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return padR;
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}
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};
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} // namespace paddle
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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Licensed under the Apache License, Version 2.0 (the "License");
|
||||
you may not use this file except in compliance with the License.
|
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You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software
|
||||
distributed under the License is distributed on an "AS IS" BASIS,
|
||||
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
See the License for the specific language governing permissions and
|
||||
limitations under the License. */
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#if defined(__ARM_NEON__) || defined(__ARM_NEON)
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#include "NEONFunctions.h"
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#include <arm_neon.h>
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namespace paddle {
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namespace neon {
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// b[i] = a[i] > 0.0f ? a[i] : 0.0f
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void relu(const float* a, float* b, int len) {
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int offset = len % 16;
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float32x4_t ma0, ma1, ma2, ma3;
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float32x4_t mb0, mb1, mb2, mb3;
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float32x4_t zero = vdupq_n_f32(0.f);
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for (int k = 0; k < len / 16; k++, a += 16, b += 16) {
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ma0 = vld1q_f32(a);
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ma1 = vld1q_f32(a + 4);
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ma2 = vld1q_f32(a + 8);
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ma3 = vld1q_f32(a + 12);
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mb0 = vmaxq_f32(ma0, zero);
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mb1 = vmaxq_f32(ma1, zero);
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mb2 = vmaxq_f32(ma2, zero);
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mb3 = vmaxq_f32(ma3, zero);
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vst1q_f32(b, mb0);
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vst1q_f32(b + 4, mb1);
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vst1q_f32(b + 8, mb2);
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vst1q_f32(b + 12, mb3);
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}
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for (int i = 0; i < offset; i++) {
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b[i] = a[i] > 0.0f ? a[i] : 0.0f;
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}
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}
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} // namespace neon
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} // namespace paddle
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#endif
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@ -0,0 +1,23 @@
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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Licensed under the Apache License, Version 2.0 (the "License");
|
||||
you may not use this file except in compliance with the License.
|
||||
You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software
|
||||
distributed under the License is distributed on an "AS IS" BASIS,
|
||||
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
See the License for the specific language governing permissions and
|
||||
limitations under the License. */
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#pragma once
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namespace paddle {
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namespace neon {
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void relu(const float* a, float* b, int len);
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} // namespace neon
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} // namespace paddle
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@ -0,0 +1,197 @@
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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Licensed under the Apache License, Version 2.0 (the "License");
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||||
you may not use this file except in compliance with the License.
|
||||
You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software
|
||||
distributed under the License is distributed on an "AS IS" BASIS,
|
||||
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
See the License for the specific language governing permissions and
|
||||
limitations under the License. */
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#include "paddle/framework/op_registry.h"
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#include "paddle/operators/net_op.h"
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namespace paddle {
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namespace operators {
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class FCOp : public NetOp {
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public:
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FCOp(const std::string &type, const framework::VariableNameMap &inputs,
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const framework::VariableNameMap &outputs,
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const framework::AttributeMap &attrs)
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: NetOp(type, inputs, outputs, attrs) {
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PADDLE_ENFORCE(!Inputs("X").empty(),
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"Inputs(X) of FCOp should not be null.");
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PADDLE_ENFORCE(!Inputs("W").empty(),
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"Inputs(W) of FCOp should not be null.");
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PADDLE_ENFORCE(!Outputs("MulOut").empty(),
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"Outputs(MulOut) of FCOp should not be null.");
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PADDLE_ENFORCE_NE(Output("Out"), framework::kEmptyVarName,
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"Output(Out) of FCOp should not be null.");
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auto x = Inputs("X");
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auto w = Inputs("W");
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auto mul_out = Outputs("MulOut");
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PADDLE_ENFORCE_EQ(
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x.size(), w.size(),
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"The size of inputs X(%d) should be the same as that of weights W(%d).",
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x.size(), w.size());
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PADDLE_ENFORCE_EQ(mul_out.size(), x.size(),
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"The size of intermediate mul_out(%d) should be the same "
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"as that of inputs X(%d).",
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mul_out.size(), x.size());
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size_t n = x.size();
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PADDLE_ENFORCE_GE(n, static_cast<size_t>(1),
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"The size of inputs X(%d) should be no less than 1.", n);
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auto x_num_col_dims = Attr<std::vector<int>>("xNumColDims");
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|
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// Set all values or set no values (use the default value)
|
||||
if (!x_num_col_dims.empty()) {
|
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PADDLE_ENFORCE_EQ(x_num_col_dims.size(), n,
|
||||
"The size of attribute xNumColDims(%d) should be the "
|
||||
"same as that of inputs X(%d).",
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x_num_col_dims.size(), n);
|
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} else {
|
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x_num_col_dims.resize(n);
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for (size_t i = 0; i < n; i++) {
|
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x_num_col_dims[i] = 1;
|
||||
}
|
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}
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// mul_out[i] = X[i] * W[i]
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for (size_t i = 0; i < n; i++) {
|
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framework::AttributeMap mul_attr;
|
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mul_attr["x_num_col_dims"] = static_cast<int>(x_num_col_dims[i]);
|
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mul_attr["y_num_col_dims"] = static_cast<int>(1);
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AppendOp(
|
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framework::OpRegistry::CreateOp("mul", {{"X", {x[i]}}, {"Y", {w[i]}}},
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{{"Out", {mul_out[i]}}}, mul_attr));
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}
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|
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// sum_out = X[0] * W[0] + ... + X[n-1] * W[n-1]
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auto sum_out = mul_out[0];
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if (n > 1) {
|
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PADDLE_ENFORCE_NE(Output("SumOut"), framework::kEmptyVarName,
|
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"Output(SumOut) of FCOp should not be null when the "
|
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"size of Inputs(X) > 1.");
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|
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sum_out = Output("SumOut");
|
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AppendOp(framework::OpRegistry::CreateOp("sum", {{"X", {mul_out}}},
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{{"Out", {sum_out}}}, {}));
|
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} else {
|
||||
if (Output("SumOut") != framework::kEmptyVarName) {
|
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this->Rename(Output("SumOut"), framework::kEmptyVarName);
|
||||
}
|
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}
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// add_out = sum_out + b
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auto b = Input("B");
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auto add_out = sum_out;
|
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if (b != framework::kEmptyVarName) {
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PADDLE_ENFORCE_NE(
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Output("AddOut"), framework::kEmptyVarName,
|
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"Output(AddOut) of FCOp should not be null when Input(B) is set.");
|
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add_out = Output("AddOut");
|
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AppendOp(framework::OpRegistry::CreateOp(
|
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"rowwise_add", {{"X", {sum_out}}, {"b", {Input("B")}}},
|
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{{"Out", {add_out}}}, {}));
|
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} else {
|
||||
if (Output("AddOut") != framework::kEmptyVarName) {
|
||||
this->Rename(Output("AddOut"), framework::kEmptyVarName);
|
||||
}
|
||||
}
|
||||
|
||||
auto activation = Attr<std::string>("activation");
|
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AppendOp(framework::OpRegistry::CreateOp(activation, {{"X", {add_out}}},
|
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{{"Y", {Output("Out")}}}, {}));
|
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CompleteAddOp(false);
|
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}
|
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};
|
||||
|
||||
class FCOpMaker : public framework::OpProtoAndCheckerMaker {
|
||||
public:
|
||||
FCOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
|
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: OpProtoAndCheckerMaker(proto, op_checker) {
|
||||
AddInput("X",
|
||||
"(A vector of Tensors) each input Tensor can be of arbitrary "
|
||||
"dimension, and will be reshaped to a 2-D matrix of size "
|
||||
"(minibatch, number_of_input_features) according to attribute "
|
||||
"xNumColDims.")
|
||||
.AsDuplicable();
|
||||
AddInput("W",
|
||||
"(A vector of Tensors) the weights of FC operator, a "
|
||||
"vector of 2-D matrix of size "
|
||||
"(number_of_input_features, number_of_neurons).")
|
||||
.AsDuplicable();
|
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AddInput("B",
|
||||
"(Tensor) the bias of FC operator, a 1-D vector of size "
|
||||
"number_of_neurons.");
|
||||
|
||||
AddOutput("Out",
|
||||
"(Tensor) the activated output matrix of FC operator, a 2-D "
|
||||
"matrix of size (minibatch, number_of_neurons).");
|
||||
AddOutput("MulOut",
|
||||
"(A vector of Tensors) the intermediate outputs of FC operator, "
|
||||
"each Tensor saving the product of X_i * W_i.")
|
||||
.AsIntermediate()
|
||||
.AsDuplicable();
|
||||
AddOutput(
|
||||
"SumOut",
|
||||
"(Tensor) the intermediate output of FC operator, "
|
||||
"saving the sum of the products of X and W, that is sum{X_i * W_i}.")
|
||||
.AsIntermediate();
|
||||
AddOutput("AddOut",
|
||||
"(Tensor) the non-actived output of FC operator, "
|
||||
"saving sum{X_i * W_i} + B.")
|
||||
.AsIntermediate();
|
||||
AddAttr<std::string>(
|
||||
"activation",
|
||||
"(string, default identity) the activation type of FC operator.")
|
||||
.SetDefault("identity")
|
||||
.InEnum({"identity", "sigmoid", "softmax"});
|
||||
AddAttr<std::vector<int>>(
|
||||
"xNumColDims",
|
||||
"(std::vector<int>) The inputs Tensors of FC operator can be of "
|
||||
"more than 2 dimensions. In that case, each input Tensor `X_i` will be "
|
||||
"reshaped to a 2-D matrix. The matrix's first dimension "
|
||||
"(the length of column) will be the product of `X_i`'s last "
|
||||
"`xNumColDims_i` dimensions, that is "
|
||||
"`X_i.dims[0] x ... x X_i.dims[xNumColDims_i - 1]`. "
|
||||
"The matrix's second dimension (the length of row) will be the product "
|
||||
"of `X_i`'s first `rank - xNumColDims_i` dimensions, that is "
|
||||
"`X_i.dims[xNumColDims_i] x ... x X_i.dims[rank - 1]`)")
|
||||
.SetDefault(std::vector<int>{});
|
||||
|
||||
AddComment(R"DOC(
|
||||
Fully Connected Operator, known as Fully Connected Layer or Inner Product Layer
|
||||
in Convolutional Neural Networks. Neurons in a fully connected layer have
|
||||
full connections to all activations in the previous layer.
|
||||
It computes an inner product of a set of
|
||||
learned weights with a matrix multiplication followed by a bias offset
|
||||
(optionally).
|
||||
|
||||
Equation:
|
||||
Out = Act(sum_n{X_i * W_i} + B)
|
||||
|
||||
where X_i is Tensor that will be reshaped to a 2-D matrix of size (M x K),
|
||||
usually M is the minibatch size and K is the number of input features.
|
||||
W_i is a 2-D matrix of size (K x N), where N means the number of neurons
|
||||
in the fully connected layer. B is a 1-D vector of size N.
|
||||
Thus, the output Out is a 2-D matrix of size (M x N).
|
||||
Activation type can be set to `identity` (default), `sigmoid` or `softmax`.
|
||||
)DOC");
|
||||
}
|
||||
};
|
||||
|
||||
} // namespace operators
|
||||
} // namespace paddle
|
||||
|
||||
namespace ops = paddle::operators;
|
||||
REGISTER_OP_WITHOUT_GRADIENT(fc, ops::FCOp, ops::FCOpMaker);
|
||||
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