Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into dilate_conv
wanghaoshuang
8 years ago
commit
2c8b3c4430
@ -0,0 +1,107 @@
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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.
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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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#include "Layer.h"
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namespace paddle {
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/**
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* A layer applies a linear transformation to each element in each row of
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* the input matrix. For each element, the layer first re-scale it and then
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* adds a bias to it.
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*
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* \f[
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* y = wx + b
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* \f]
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*
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* Here, w is the scale and b is the bias. Both w and b are trainable scalars.
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*
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*/
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class ScaleShiftLayer : public Layer {
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protected:
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std::unique_ptr<Weight> scale_;
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std::unique_ptr<Weight> offset_;
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public:
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explicit ScaleShiftLayer(const LayerConfig& config) : Layer(config) {}
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bool init(const LayerMap& layerMap,
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const ParameterMap& parameterMap) override;
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void forward(PassType passType) override;
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void backward(const UpdateCallback& callback = nullptr) override;
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};
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REGISTER_LAYER(scale_shift, ScaleShiftLayer);
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bool ScaleShiftLayer::init(const LayerMap& layerMap,
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const ParameterMap& parameterMap) {
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Layer::init(layerMap, parameterMap);
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CHECK_EQ(inputLayers_.size(), 1U);
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scale_.reset(new Weight(1, 1, parameters_[0]));
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if (biasParameter_.get() != NULL) {
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offset_ = std::unique_ptr<Weight>(new Weight(1, 1, biasParameter_));
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}
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return true;
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}
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void ScaleShiftLayer::forward(PassType passType) {
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Layer::forward(passType);
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MatrixPtr inV = getInputValue(0);
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resetOutput(inV->getHeight(), inV->getWidth());
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MatrixPtr outV = getOutputValue();
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real scaleValue = scale_->getW()->getElement(0, 0);
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outV->mulScalar(*inV, scaleValue);
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if (offset_) {
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real offsetValue = offset_->getW()->getElement(0, 0);
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outV->add(offsetValue);
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}
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}
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void ScaleShiftLayer::backward(const UpdateCallback& callback) {
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MatrixPtr inV = getInputValue(0);
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MatrixPtr inG = getInputGrad(0);
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MatrixPtr outV = getOutputValue();
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MatrixPtr outG = getOutputGrad();
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/* Calculate the parameter gradient for the current layer */
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if (scale_->getWGrad()) {
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MatrixPtr rowSumMtx;
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Matrix::resizeOrCreate(rowSumMtx, outG->getHeight(), 1, false, useGpu_);
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// this_i = scaleDest * this_i + scaleSum * \sum_j b_{ij} * c_{ij}
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rowSumMtx->sumOfProducts(
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/* b= */ *inV, /* c= */ *outG, /* scaleSum= */ 1, /* scaleDest= */ 0.);
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// this_i = scaleDest * this_i + scaleSum * \sum_j b_{ji}
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scale_->getWGrad()->sumCols(
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/* b= */ *rowSumMtx, /* scaleSum= */ 1., /* scaleDest= */ 1.);
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scale_->getParameterPtr()->incUpdate(callback);
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}
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if (offset_ && offset_->getWGrad()) {
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MatrixPtr rowSumMtx;
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Matrix::resizeOrCreate(rowSumMtx, outG->getHeight(), 1, false, useGpu_);
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rowSumMtx->sumRows(*outG, 1., 0.);
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offset_->getWGrad()->sumCols(*rowSumMtx, 1., 1.);
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offset_->getParameterPtr()->incUpdate(callback);
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}
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/* Calculate the input layers error */
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if (inG) {
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real scaleValue = scale_->getW()->getElement(0, 0);
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inG->add(*outG, scaleValue);
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}
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}
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} // namespace paddle
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@ -0,0 +1,72 @@
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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.
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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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#include "paddle/operators/gather_op.h"
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#include "paddle/framework/ddim.h"
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namespace paddle {
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namespace operators {
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class GatherOp : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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protected:
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void InferShape(const framework::InferShapeContext &ctx) const override {
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int batch_size = ctx.Input<Tensor>("Index")->dims()[0];
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PADDLE_ENFORCE_GE(batch_size, 0, "Batch size must be >0");
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framework::DDim output_dims(ctx.Input<Tensor>("X")->dims());
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output_dims[0] = batch_size;
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ctx.Output<Tensor>("Out")->Resize(output_dims);
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}
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};
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class GatherGradOp : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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protected:
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void InferShape(const framework::InferShapeContext &ctx) const override {
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auto X_grad = ctx.Output<Tensor>(framework::GradVarName("X"));
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auto X = ctx.Input<Tensor>("X");
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X_grad->Resize(X->dims());
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}
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};
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class GatherOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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GatherOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
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: OpProtoAndCheckerMaker(proto, op_checker) {
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AddInput("X", "The source input of gather op");
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AddInput("Index", "The index input of gather op");
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AddOutput("Out", "The output of add op");
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AddComment(R"DOC(
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Gather Operator by selecting from the first axis,
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Out = X[Index]
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)DOC");
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}
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};
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} // namespace operators
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} // namespace paddle
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namespace ops = paddle::operators;
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REGISTER_OP(gather, ops::GatherOp, ops::GatherOpMaker, gather_grad,
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ops::GatherGradOp);
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REGISTER_OP_CPU_KERNEL(gather,
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ops::GatherOpKernel<paddle::platform::CPUPlace, float>);
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REGISTER_OP_CPU_KERNEL(
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gather_grad,
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ops::GatherGradientOpKernel<paddle::platform::CPUPlace, float>);
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@ -0,0 +1,20 @@
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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.
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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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#define EIGEN_USE_GPU
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#include "paddle/operators/gather_op.h"
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namespace ops = paddle::operators;
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REGISTER_OP_GPU_KERNEL(gather,
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ops::GatherOpKernel<paddle::platform::GPUPlace, float>);
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@ -0,0 +1,53 @@
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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.
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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 "gather.h"
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#include "paddle/framework/eigen.h"
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#include "paddle/framework/op_registry.h"
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#include "scatter.h"
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namespace paddle {
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namespace operators {
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using Tensor = framework::Tensor;
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template <typename Place, typename T>
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class GatherOpKernel : public framework::OpKernel {
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public:
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void Compute(const framework::ExecutionContext &ctx) const override {
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auto *X = ctx.Input<Tensor>("X");
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auto *Index = ctx.Input<Tensor>("Index");
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auto *Y = ctx.Output<Tensor>("Out");
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Y->mutable_data<T>(ctx.GetPlace());
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Gather<T>(ctx.GetPlace(), X, Index, Y);
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}
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};
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template <typename Place, typename T>
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class GatherGradientOpKernel : public framework::OpKernel {
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public:
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void Compute(const framework::ExecutionContext &ctx) const override {
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auto *Index = ctx.Input<Tensor>("Index");
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auto *dX = ctx.Output<Tensor>(framework::GradVarName("X"));
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auto *dO = ctx.Input<Tensor>(framework::GradVarName("Out"));
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dX->mutable_data<T>(ctx.GetPlace());
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ScatterUpdate<T>(ctx.GetPlace(), dO, Index, dX);
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}
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};
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} // namespace operators
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} // namespace paddle
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@ -1,53 +1,65 @@
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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.
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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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#include <memory>
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#include <random>
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#include "paddle/platform/dynload/curand.h"
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#include "paddle/platform/gpu_info.h"
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#include <thrust/device_ptr.h>
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#include <thrust/iterator/counting_iterator.h>
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#include <thrust/random.h>
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#include <thrust/transform.h>
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#include "paddle/framework/op_registry.h"
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#include "paddle/framework/operator.h"
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namespace paddle {
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namespace operators {
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template <typename T>
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class GaussianRandomKernel : public framework::OpKernel {
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struct GaussianGenerator {
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T mean_, std_;
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unsigned int seed_;
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__host__ __device__ GaussianGenerator(T mean, T std, int seed)
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: mean_(mean), std_(std), seed_(seed) {}
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__host__ __device__ T operator()(const unsigned int n) const {
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thrust::minstd_rand rng;
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rng.seed(seed_);
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thrust::normal_distribution<T> dist(mean_, std_);
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rng.discard(n);
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return dist(rng);
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}
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};
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template <typename T>
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class GPUGaussianRandomKernel : public framework::OpKernel {
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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float mean = context.op_.GetAttr<float>("mean");
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float std = context.op_.GetAttr<float>("std");
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auto* tensor = context.Output<framework::Tensor>(0);
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auto* tensor = context.Output<framework::Tensor>("Out");
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T* data = tensor->mutable_data<T>(context.GetPlace());
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int seed = context.op_.GetAttr<int>("seed");
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unsigned int seed =
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static_cast<unsigned int>(context.op_.GetAttr<int>("seed"));
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if (seed == 0) {
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std::random_device rd;
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seed = rd();
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}
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curandGenerator_t g;
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PADDLE_ENFORCE(platform::dynload::curandCreateGenerator(
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&g, CURAND_RNG_PSEUDO_DEFAULT));
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PADDLE_ENFORCE(
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platform::dynload::curandSetPseudoRandomGeneratorSeed(g, seed));
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platform::dynload::curandGenerateNormal(
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g, data, framework::product(tensor->dims()), mean, std);
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T mean = static_cast<T>(context.op_.GetAttr<float>("mean"));
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T std = static_cast<T>(context.op_.GetAttr<float>("std"));
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thrust::counting_iterator<unsigned int> index_sequence_begin(0);
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ssize_t N = framework::product(tensor->dims());
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thrust::transform(index_sequence_begin, index_sequence_begin + N,
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thrust::device_ptr<T>(data),
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GaussianGenerator<T>(mean, std, seed));
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}
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};
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} // namespace operators
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} // namespace paddle
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namespace ops = paddle::operators;
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REGISTER_OP_GPU_KERNEL(gaussian_random, ops::GaussianRandomKernel<float>);
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REGISTER_OP_GPU_KERNEL(gaussian_random,
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paddle::operators::GPUGaussianRandomKernel<float>);
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