From 29f25fbe033e97f74123f2380d6e384ba840d0da Mon Sep 17 00:00:00 2001 From: wanghaoshuang Date: Mon, 10 Jul 2017 12:26:35 +0800 Subject: [PATCH 0001/2018] Add pixel softmax layer for FCN model 1. Add switch function for switching image dimensions order 2. Add CpuMatrix::backwardSoftmax function 3. Add pixel softmax layer, python wrapper and grad_test --- paddle/function/CMakeLists.txt | 1 + paddle/function/SwitchOp.cpp | 132 ++++++++++++++++++ paddle/function/SwitchOp.h | 62 ++++++++ paddle/function/SwitchOpGpu.cu | 80 +++++++++++ paddle/function/SwitchOpTest.cpp | 44 ++++++ paddle/gserver/layers/PixelSoftmaxLayer.cpp | 89 ++++++++++++ paddle/gserver/layers/PixelSoftmaxLayer.h | 44 ++++++ paddle/gserver/tests/test_LayerGrad.cpp | 19 +++ paddle/math/Matrix.cpp | 21 +++ paddle/math/Matrix.h | 5 + python/paddle/trainer/config_parser.py | 16 +++ .../paddle/trainer_config_helpers/layers.py | 38 +++++ 12 files changed, 551 insertions(+) create mode 100644 paddle/function/SwitchOp.cpp create mode 100644 paddle/function/SwitchOp.h create mode 100644 paddle/function/SwitchOpGpu.cu create mode 100644 paddle/function/SwitchOpTest.cpp create mode 100644 paddle/gserver/layers/PixelSoftmaxLayer.cpp create mode 100644 paddle/gserver/layers/PixelSoftmaxLayer.h diff --git a/paddle/function/CMakeLists.txt b/paddle/function/CMakeLists.txt index 1518a8a65..138f7dcf1 100644 --- a/paddle/function/CMakeLists.txt +++ b/paddle/function/CMakeLists.txt @@ -37,6 +37,7 @@ if(WITH_GPU) add_simple_unittest(MulOpTest) add_simple_unittest(CosSimOpTest) add_simple_unittest(RowConvOpTest) + add_simple_unittest(SwitchOpTest) endif() add_simple_unittest(ConvOpTest) diff --git a/paddle/function/SwitchOp.cpp b/paddle/function/SwitchOp.cpp new file mode 100644 index 000000000..4667c4e01 --- /dev/null +++ b/paddle/function/SwitchOp.cpp @@ -0,0 +1,132 @@ +/* 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 "SwitchOp.h" +#include "paddle/math/Vector.h" + +namespace paddle { + +template <> +void NCHW2NHWC(real* outputs, + const real* inputs, + const int num, + const int inC, + const int inH, + const int inW) { + for (int n = 0; n < num; ++n) { + for (int c = 0; c < inC; ++c) { + for (int h = 0; h < inH; ++h) { + for (int w = 0; w < inW; ++w) { + outputs[((n * inH + h) * inW + w) * inC + c] = *(inputs++); + } + } + } + } +} + +template <> +void NHWC2NCHW(real* outputs, + const real* inputs, + const int num, + const int inH, + const int inW, + const int inC) { + for (int n = 0; n < num; ++n) { + for (int h = 0; h < inH; ++h) { + for (int w = 0; w < inW; ++w) { + for (int c = 0; c < inC; ++c) { + outputs[((n * inC + c) * inH + h) * inW + w] = *(inputs++); + } + } + } + } +} + +/** + * \brief Padding zeros to input according to the specify dimension. + * The struct pad_ contains the padding size in each dimension. + * The input and output is a 4D tensor. In PadFunc, we only + * pad zeros to the 2nd to 4th dimension. + * + * Argument in this Function: + * \param pad_ A struct object contains the padding size in each dimension. + * It has six integers. The channelStart and channelEnd indicate + * how many zeros to add before and after the input in channel + * dimension. And the heightStart and heightEnd indicate padding + * in height dimension. The widthStart and widthEnd indicate the + * padding in width dimension. + * \param inputs A 4D tensor, only one input. + * \param outputs A 4D tensor, the output value after padding. + * + */ + +template +class NCHW2NHWCFunc : public FunctionBase { +public: + void init(const FuncConfig& config) override {} + + void calc(const BufferArgs& inputs, const BufferArgs& outputs) override { + CHECK_EQ(1UL, inputs.size()); + CHECK_EQ(1UL, outputs.size()); + + size_t num = inputs[0].shape()[0]; + size_t inC = inputs[0].shape()[1]; + size_t inH = inputs[0].shape()[2]; + size_t inW = inputs[0].shape()[3]; + typename Tensor::Vector vec(outputs[0].shape().getElements(), + outputs[0].data()); + vec.zero(); + + NCHW2NHWC( + outputs[0].data(), inputs[0].data(), num, inC, inH, inW); + } +}; + +/** + * \brief The backward propagation of padding Function. Remove the elements + * in the padding positions of forward. + * + * Argument in this Function: + * \param pad_ The same meaning as it in PadFunc. + * \param inputs The gradient with respect to the output value of PadFunc. + * \param outputs The gradient with respect to the input value of PadFunc. + */ + +template +class NHWC2NCHWFunc : public FunctionBase { +public: + void init(const FuncConfig& config) override {} + + void calc(const BufferArgs& inputs, const BufferArgs& outputs) override { + CHECK_EQ(1UL, inputs.size()); + CHECK_EQ(1UL, outputs.size()); + + size_t num = inputs[0].shape()[0]; + size_t inH = inputs[0].shape()[1]; + size_t inW = inputs[0].shape()[2]; + size_t inC = inputs[0].shape()[3]; + + NHWC2NCHW( + outputs[0].data(), inputs[0].data(), num, inH, inW, inC); + } +}; + +REGISTER_TYPED_FUNC(NCHW2NHWC, CPU, NCHW2NHWCFunc); +REGISTER_TYPED_FUNC(NHWC2NCHW, CPU, NHWC2NCHWFunc); +#ifndef PADDLE_ONLY_CPU +REGISTER_TYPED_FUNC(NCHW2NHWC, GPU, NCHW2NHWCFunc); +REGISTER_TYPED_FUNC(NHWC2NCHW, GPU, NHWC2NCHWFunc); +#endif + +} // namespace paddle diff --git a/paddle/function/SwitchOp.h b/paddle/function/SwitchOp.h new file mode 100644 index 000000000..5a2418a70 --- /dev/null +++ b/paddle/function/SwitchOp.h @@ -0,0 +1,62 @@ +/* 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 "Function.h" + +namespace paddle { + +/** + * \brief This funtion switch dimension order of image input. + * The input and output is a 4D tensor. Switch order 'batch_size, + *channels, height, width' to + * order 'batch_size, height, width, channels'. + * + * \param[out] outputs save results. + * \param[in] inputs input data. + * \param[in] num batch size of input data. + * \param[in] inC channel number of input data. + * \param[in] inH height of input data. + * \param[in] inH with of input data. + */ +template +void NCHW2NHWC(real* outputs, + const real* inputs, + const int num, + const int inC, + const int inH, + const int inW); + +/** + * \brief This funtion switch dimension order of image input. + * The input and output is a 4D tensor. Switch order 'batch_size, + *height, width, channels' to + * order 'batch_size, channels, height, width'. + * + * \param[out] inGrad gradients of previous layer. + * \param[in] outGrad output gradients. + * \param[in] num batch size of input data. + * \param[in] inH height of input data. + * \param[in] inW with of input data. + * \param[in] inC channel number of input data. + */ +template +void NHWC2NCHW(real* inGrad, + const real* outGrad, + const int num, + const int inH, + const int inW, + const int inC); +} // namespace paddle diff --git a/paddle/function/SwitchOpGpu.cu b/paddle/function/SwitchOpGpu.cu new file mode 100644 index 000000000..c2020cb2a --- /dev/null +++ b/paddle/function/SwitchOpGpu.cu @@ -0,0 +1,80 @@ +/* Copyright (c) 2016 Paddle + +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 "hl_base.h" +#include "SwitchOp.h" + +namespace paddle { + +__global__ void KeNCHW2NHWC(real* outputs, const real* inputs, + int inC, int inH, int inW, + int nthreads) { + const int idx = threadIdx.x + blockIdx.x * blockDim.x; + if (idx < nthreads) { + const int w = idx % inW; + const int h = (idx / inW) % inH; + const int c = (idx / inW / inH) % inC; + const int n = idx / inW / inH / inC; + + const int off = ((n * inH + h) * inW + w) * inC +c; + outputs[off] = inputs[idx]; + } +} + +template <> +void NCHW2NHWC(real* outputs, + const real* inputs, + const int num, + const int inC, + const int inH, + const int inW) { + size_t nth = num * inC * inH * inW; + int blockSize = 1024; + int gridSize = (nth + 1024 - 1) / 1024; + KeNCHW2NHWC<<>> + (outputs, inputs, inC, inH, inW, nth); + CHECK_SYNC("NCHW2NHWC"); +} + +__global__ void KeNHWC2NCHW(real* outputs, const real* inputs, + int inH, int inW, int inC, + int nthreads) { + const int idx = threadIdx.x + blockIdx.x * blockDim.x; + if (idx < nthreads) { + const int c = idx % inC; + const int w = (idx / inC) % inW; + const int h = (idx / inC / inW) % inH; + const int n = idx / inW / inH / inC; + + const int off = ((n * inC + c) * inH + h) * inW + w; + outputs[off] = inputs[idx]; + } +} + +template <> +void NHWC2NCHW(real* outputs, + const real* inputs, + const int num, + const int inH, + const int inW, + const int inC) { + int nth = num * inC * inH * inW; + int blockSize = 1024; + int gridSize = (nth + 1024 - 1) / 1024; + KeNHWC2NCHW<<>> + (outputs, inputs, inH, inW, inC, nth); + CHECK_SYNC("NHWC2NCHW"); +} + +} // namespace paddle diff --git a/paddle/function/SwitchOpTest.cpp b/paddle/function/SwitchOpTest.cpp new file mode 100644 index 000000000..03b0dd66d --- /dev/null +++ b/paddle/function/SwitchOpTest.cpp @@ -0,0 +1,44 @@ +/* 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 +#include "FunctionTest.h" + +namespace paddle { + +TEST(Pad, real) { + for (size_t numSamples : {1, 4, 8, 16}) { + for (size_t channels : {1, 4, 8, 16}) { + for (size_t imgSizeH : {1, 4, 8, 16}) { + for (size_t imgSizeW : {1, 4, 8, 16}) { + VLOG(3) << " numSamples=" << numSamples << " channels=" << channels + << " imgSizeH=" << imgSizeH << " imgSizeW=" << imgSizeW; + for (bool test_grad : {true, false}) { + CpuGpuFuncCompare compare(test_grad ? "NHWC2NCHW" : "NCHW2NHWC", + FuncConfig()); + TensorShape inDims{numSamples, channels, imgSizeH, imgSizeW}; + TensorShape outDims{numSamples, imgSizeH, imgSizeW, channels}; + compare.addInputs( + BufferArg(VALUE_TYPE_FLOAT, test_grad ? outDims : inDims)); + compare.addOutputs(BufferArg( + VALUE_TYPE_FLOAT, test_grad ? inDims : outDims, ASSIGN_TO)); + compare.run(); + } + } + } + } + } +} + +} // namespace paddle diff --git a/paddle/gserver/layers/PixelSoftmaxLayer.cpp b/paddle/gserver/layers/PixelSoftmaxLayer.cpp new file mode 100644 index 000000000..6da84a630 --- /dev/null +++ b/paddle/gserver/layers/PixelSoftmaxLayer.cpp @@ -0,0 +1,89 @@ +/* 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 "PixelSoftmaxLayer.h" +#include "paddle/utils/Stat.h" + +namespace paddle { + +REGISTER_LAYER(pixel_softmax, PixelSoftmaxLayer); + +bool PixelSoftmaxLayer::init(const LayerMap& layerMap, + const ParameterMap& parameterMap) { + /* Initialize the basic parent class */ + Layer::init(layerMap, parameterMap); + auto& img_conf = config_.inputs(0).image_conf(); + inH_ = + img_conf.has_img_size_y() ? img_conf.img_size_y() : img_conf.img_size(); + inW_ = img_conf.img_size(); + inC_ = img_conf.channels(); + createFunction(forward_, "NCHW2NHWC", FuncConfig()); + createFunction(backward_, "NHWC2NCHW", FuncConfig()); + inDims_ = TensorShape({0, inH_, inW_, inC_}); + outDims_ = TensorShape({0, inC_, inH_, inW_}); + return true; +} + +void PixelSoftmaxLayer::forward(PassType passType) { + Layer::forward(passType); + MatrixPtr input = inputLayers_[0]->getOutputValue(); + size_t batchSize = input->getHeight(); + // cout<<"useGpu:"<zeroMem(); + resetOutput(batchSize, inH_ * inW_ * inC_); + inDims_.setDim(0, batchSize); + outDims_.setDim(0, batchSize); + + // switch NCHW to NHWC + BufferArgs inputs; + BufferArgs outputs; + inputs.addArg(*getInputValue(0), inDims_); + outputs.addArg(*tmpInput_, outDims_); + forward_[0]->calc(inputs, outputs); + // softmax forward and save softmax result into tmpMatrix_ + tmpInput_->softmax(*tmpOutput_); + + // switch NHWC to NCHW + BufferArgs inputs_1; + BufferArgs outputs_1; + inputs_1.addArg(*tmpOutput_, outDims_); + outputs_1.addArg(*getOutputValue(), inDims_); + backward_[0]->calc(inputs_1, outputs_1); +} + +void PixelSoftmaxLayer::backward(const UpdateCallback& callback) { + (void)callback; + REGISTER_TIMER_INFO("PixelSoftmaxBackward", getName().c_str()); + + // switch NCHW to NHWC + BufferArgs inputs; + BufferArgs outputs; + inputs.addArg(*getOutputGrad(), inDims_); + outputs.addArg(*tmpInput_, outDims_); + forward_[0]->calc(inputs, outputs); + // softmax backward and save grad result into tmpOutput_ + tmpInput_->softmaxBackward(*tmpOutput_); + + // switch NHWC to NCHW + BufferArgs inputs_1; + BufferArgs outputs_1; + inputs_1.addArg(*tmpInput_, outDims_); + outputs_1.addArg(*getInputGrad(0), inDims_); + backward_[0]->calc(inputs_1, outputs_1); +} +} // namespace paddle diff --git a/paddle/gserver/layers/PixelSoftmaxLayer.h b/paddle/gserver/layers/PixelSoftmaxLayer.h new file mode 100644 index 000000000..80a4ddad5 --- /dev/null +++ b/paddle/gserver/layers/PixelSoftmaxLayer.h @@ -0,0 +1,44 @@ +/* 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 "Layer.h" + +namespace paddle { + +/** + * \brief This layer calculate softmax in image channel dimension. + */ +class PixelSoftmaxLayer : public Layer { +public: + explicit PixelSoftmaxLayer(const LayerConfig& config) : Layer(config) {} + + ~PixelSoftmaxLayer() {} + + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override; + void forward(PassType passType) override; + void backward(const UpdateCallback& callback = nullptr) override; + +protected: + uint32_t inC_; + uint32_t inH_; + uint32_t inW_; + TensorShape inDims_; + TensorShape outDims_; + MatrixPtr tmpInput_; + MatrixPtr tmpOutput_; +}; +} // namespace paddle diff --git a/paddle/gserver/tests/test_LayerGrad.cpp b/paddle/gserver/tests/test_LayerGrad.cpp index 59d1e9273..8a9904087 100644 --- a/paddle/gserver/tests/test_LayerGrad.cpp +++ b/paddle/gserver/tests/test_LayerGrad.cpp @@ -1792,6 +1792,25 @@ TEST(Layer, RowConvLayer) { } } +TEST(Layer, PixelSoftmaxLayer) { + TestConfig config; + // config input_0 + config.inputDefs.push_back({INPUT_DATA, "layer_0", 1024, 0}); + LayerInputConfig* input = config.layerConfig.add_inputs(); + ImageConfig* img = input->mutable_image_conf(); + img->set_channels(4); + img->set_img_size(16); + img->set_img_size_y(16); + + // config softmax layer + config.layerConfig.set_type("pixel_softmax"); + config.layerConfig.set_name("pixelSofrmaxLayer"); + + for (auto useGpu : {false, true}) { + testLayerGrad(config, "pixel_softmax", 100, false, useGpu, true, 2); + } +} + int main(int argc, char** argv) { testing::InitGoogleTest(&argc, argv); initMain(argc, argv); diff --git a/paddle/math/Matrix.cpp b/paddle/math/Matrix.cpp index 4431d613f..2c18df373 100644 --- a/paddle/math/Matrix.cpp +++ b/paddle/math/Matrix.cpp @@ -3385,6 +3385,27 @@ void CpuMatrix::oneHotCrossEntropyWithSelfNormBp(Matrix& output, real* out = output.getData(); \ for (size_t i = 0; i < numSamples; ++i, grad += dim, out += dim) +void CpuMatrix::softmaxBackward(Matrix& outputV) { + CHECK(!outputV.useGpu()) << "Matrix type are not equal"; + size_t height = getHeight(); + size_t width = getWidth(); + CHECK(height == outputV.getHeight() && width == outputV.getWidth()) + << "Matrix dimensions are not equal"; + Matrix::resizeOrCreate(sftmaxDot_, + height_, + width_, + /* trans */ false, + useGpu_); + Matrix::resizeOrCreate(sftmaxSum_, + height_, + 1, + /* trans */ false, + useGpu_); + sftmaxDot_->dotMul(*this, outputV); + sftmaxSum_->colMerge(*sftmaxDot_); + softmaxDerivative(outputV, *sftmaxSum_); +} + void CpuMatrix::softmax(Matrix& output) { CHECK(!output.useGpu()); diff --git a/paddle/math/Matrix.h b/paddle/math/Matrix.h index 7dfd59322..dcb63a2d3 100644 --- a/paddle/math/Matrix.h +++ b/paddle/math/Matrix.h @@ -1456,6 +1456,10 @@ public: }; class CpuMatrix : public Matrix { +private: + MatrixPtr sftmaxSum_; + MatrixPtr sftmaxDot_; + public: CpuMatrix(size_t height, size_t width, bool trans = false); CpuMatrix(real* data, size_t height, size_t width, bool trans = false) @@ -1728,6 +1732,7 @@ public: Matrix& prevGrad2); void softmax(Matrix& output); + void softmaxBackward(Matrix& outputV); void sequenceSoftmax(Matrix& output, const IVector& index); void softmaxDerivative(Matrix& output, Matrix& sftmaxSum); diff --git a/python/paddle/trainer/config_parser.py b/python/paddle/trainer/config_parser.py index 370529ed9..dc9c503e0 100644 --- a/python/paddle/trainer/config_parser.py +++ b/python/paddle/trainer/config_parser.py @@ -3171,6 +3171,22 @@ class RecurrentLayerGroup(LayerBase): name, 'recurrent_layer_group', 0, inputs=[], device=device) +@config_layer('pixel_softmax') +class PixelSoftmaxLayer(LayerBase): + def __init__(self, input, name, **xargs): + super(PixelSoftmaxLayer, self).__init__( + name, 'pixel_softmax', 0, inputs=inputs, **xargs) + + input_layer = self.get_input_layer(0) + image_conf = self.config.inputs[0].image_conf + image_conf.img_size = input_layer.width + image_conf.img_size_y = input_layer.height + image_conf.channels = input_layer.size / (input_layer.width * + input_layer.height) + self.set_cnn_layer(name, image_conf.img_size_y, image_conf.img_size, + image_conf.channels) + + # Deprecated, use a new layer specific class instead @config_func def Layer(name, type, **xargs): diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py index 206de1f8e..fdac5984b 100755 --- a/python/paddle/trainer_config_helpers/layers.py +++ b/python/paddle/trainer_config_helpers/layers.py @@ -217,6 +217,7 @@ class LayerType(object): SMOOTH_L1 = 'smooth_l1' PRELU = 'prelu' + PIXEL_SOFTMAX_LAYER = 'pixel_softmax' @staticmethod def is_layer_type(type_name): @@ -5853,3 +5854,40 @@ def prelu_layer(input, layer_type=LayerType.PRELU, parents=input, size=l.config.size) + + +@layer_support() +@wrap_name_default('pixel_softmax') +def pixel_softmax_layer(input, name=None, layer_attr=None): + """ + This layer calculate softmax in image channel dimension + + The example usage is: + + .. code-block:: python + + prelu = pixel_softmax(input=layer, name='softmax') + + :param name: Name of this layer. + :type name: basestring + :param input: The input layer. + :type input: LayerOutput + :return: LayerOutput object. + :rtype: LayerOutput + """ + if isinstance(input, LayerOutput): + input = [input] + elif isinstance(input, Projection): + input = [input] + else: + assert isinstance(input, collections.Sequence) + l = Layer( + inputs=[x.name for x in input], + name=name, + type=LayerType.PIXEL_SOFTMAX_LAYER, + **ExtraLayerAttribute.to_kwargs(layer_attr)) + return LayerOutput( + name=name, + layer_type=LayerType.PIXEL_SOFTMAX_LAYER, + parents=input, + size=l.config.size) -- GitLab From 0152d97e6344fbf866d75bf24f6f6034a81f5e81 Mon Sep 17 00:00:00 2001 From: wanghaoshuang Date: Tue, 11 Jul 2017 10:23:29 +0800 Subject: [PATCH 0002/2018] fix pixel softmax python wrapper bug --- python/paddle/trainer/config_parser.py | 2 +- python/paddle/trainer_config_helpers/layers.py | 3 ++- 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/python/paddle/trainer/config_parser.py b/python/paddle/trainer/config_parser.py index c24af47c4..261e834e1 100644 --- a/python/paddle/trainer/config_parser.py +++ b/python/paddle/trainer/config_parser.py @@ -3176,7 +3176,7 @@ class RecurrentLayerGroup(LayerBase): @config_layer('pixel_softmax') class PixelSoftmaxLayer(LayerBase): - def __init__(self, input, name, **xargs): + def __init__(self, name, inputs, **xargs): super(PixelSoftmaxLayer, self).__init__( name, 'pixel_softmax', 0, inputs=inputs, **xargs) diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py index d8cc52d40..2f8b0d100 100755 --- a/python/paddle/trainer_config_helpers/layers.py +++ b/python/paddle/trainer_config_helpers/layers.py @@ -126,6 +126,7 @@ __all__ = [ 'row_conv_layer', 'dropout_layer', 'prelu_layer', + 'pixel_softmax_layer', ] @@ -5905,8 +5906,8 @@ def pixel_softmax_layer(input, name=None, layer_attr=None): else: assert isinstance(input, collections.Sequence) l = Layer( - inputs=[x.name for x in input], name=name, + inputs=[x.name for x in input], type=LayerType.PIXEL_SOFTMAX_LAYER, **ExtraLayerAttribute.to_kwargs(layer_attr)) return LayerOutput( -- GitLab From 56a722a1d01eb49bfbe5120065c615ecf1e16fe5 Mon Sep 17 00:00:00 2001 From: caoying03 Date: Mon, 10 Jul 2017 14:22:18 +0800 Subject: [PATCH 0003/2018] output all beam search results in layer group. --- .../RecurrentGradientMachine.cpp | 104 ++++++++++++------ .../RecurrentGradientMachine.h | 7 +- paddle/parameter/Argument.cpp | 36 +++--- paddle/parameter/Argument.h | 1 + .../paddle/trainer_config_helpers/networks.py | 13 +-- 5 files changed, 102 insertions(+), 59 deletions(-) diff --git a/paddle/gserver/gradientmachines/RecurrentGradientMachine.cpp b/paddle/gserver/gradientmachines/RecurrentGradientMachine.cpp index 41e092995..4cb5b8ec2 100644 --- a/paddle/gserver/gradientmachines/RecurrentGradientMachine.cpp +++ b/paddle/gserver/gradientmachines/RecurrentGradientMachine.cpp @@ -1012,11 +1012,6 @@ void RecurrentGradientMachine::generateSequence() { /* width */ resultNum, false, /* useGpu */ false); - Matrix::resizeOrCreate(generator_.outArg.value, - /* height */ maxGenWordCount, - /* width */ 1, - false, - /* useGpu */ false); } ICpuGpuVector::resizeOrCreate(generator_.outArg.sequenceStartPositions, numSequences + 1, @@ -1026,7 +1021,7 @@ void RecurrentGradientMachine::generateSequence() { } else { oneWaySearch(numSequences); } - if (dataArgsSize_) createDataOutlink(batchMachineIdVec_); + if (dataArgsSize_) createDataOutlink(); size_t size = generator_.ids.size(); generator_.outArg.ids->resize(size); @@ -1106,6 +1101,7 @@ void RecurrentGradientMachine::oneWaySearch(size_t batchSize) { } batchMachineIdVec_.clear(); + batchMachineStartPos_.clear(); int* starts = generator_.outArg.sequenceStartPositions->getMutableData(false); starts[0] = 0; generator_.ids.clear(); @@ -1312,13 +1308,20 @@ void RecurrentGradientMachine::fillGenOutputs() { finalPaths_[i].resize(minFinalPathsSize); } - batchMachineIdVec_.clear(); generator_.ids.clear(); int* starts = generator_.outArg.sequenceStartPositions->getMutableData(false); starts[0] = 0; if (numResults > 1) { - real* probs = generator_.outArg.in->getData(); + int idsProbSaveSize = 0; + for (auto inSeq : finalPaths_) { + for (auto path : inSeq) idsProbSaveSize += path.ids.size(); + idsProbSaveSize += inSeq.size(); + } + Matrix::resizeOrCreate( + generator_.outArg.value, idsProbSaveSize, 1, false, false); real* idsProb = generator_.outArg.value->getData(); + + real* probs = generator_.outArg.in->getData(); size_t curPos = 0; for (size_t i = 0; i < finalPaths_.size(); ++i) { for (size_t j = 0; j < finalPaths_[i].size(); ++j) { @@ -1333,24 +1336,16 @@ void RecurrentGradientMachine::fillGenOutputs() { curPos += genLen; idsProb[curPos++] = -1.0; probs[i * numResults + j] = path.logProb; - - if (!j && dataArgsSize_) { - // in beam search, here only reserved the top 1 generated result - // for out_links that are not the generated word indices. - batchMachineIdVec_.insert(batchMachineIdVec_.end(), - path.machineIdVec.begin(), - path.machineIdVec.end()); - } } starts[i + 1] = generator_.ids.size(); } } else { for (size_t i = 0; i < finalPaths_.size(); ++i) { CHECK(!finalPaths_[i].empty()); - generator_.ids.insert(generator_.ids.begin(), - finalPaths_[i][0].ids.begin(), - finalPaths_[i][0].ids.end()); - starts[i + 1] = starts[i] + finalPaths_[i][0].ids.size(); + Path& path = finalPaths_[i][0]; + generator_.ids.insert( + generator_.ids.begin(), path.ids.begin(), path.ids.end()); + starts[i + 1] = starts[i] + path.ids.size(); } } } @@ -1364,25 +1359,70 @@ void RecurrentGradientMachine::copyDataOutlinkFrame(size_t machineCur) { } } -void RecurrentGradientMachine::createDataOutlink( - std::vector& machineIdVec) { - size_t seqNum = - getBeamSize() > 1UL ? finalPaths_.size() : finalPaths_[0].size(); - std::vector starts(seqNum + 1, 0); - for (size_t i = 0; i < seqNum; ++i) { - size_t seqLen = getBeamSize() > 1UL ? finalPaths_[i][0].ids.size() - : finalPaths_[0][i].ids.size(); - starts[i + 1] = starts[i] + seqLen; +void RecurrentGradientMachine::createDataOutlinkSelRowsInfo( + bool isSeq, std::vector& outArgs) { + batchMachineIdVec_.clear(); + + size_t seqIdx = 0; + for (size_t i = 0; i < finalPaths_.size(); ++i) { + for (size_t j = 0; j < finalPaths_[i].size(); ++j) { + std::vector& machineIdVec = finalPaths_[i][j].machineIdVec; + if (isSeq) { + for (size_t i = 0; i < machineIdVec.size(); ++i) { + size_t rowId = machineIdVec[i]; + int* seqPos = + outArgs[i].sequenceStartPositions->getMutableData(false); + batchMachineIdVec_.push_back(seqPos[rowId]); + } + } else { + batchMachineIdVec_.insert( + batchMachineIdVec_.end(), machineIdVec.begin(), machineIdVec.end()); + } + seqIdx++; + } + } +} + +void RecurrentGradientMachine::createDataOutlinkCopySizeInfo( + bool isSeq, std::vector& outArgs, std::vector& copySize) { + size_t totalSeqNum = std::accumulate( + finalPaths_.begin(), + finalPaths_.end(), + 0UL, + [](size_t a, const std::vector& b) { return a + b.size(); }); + copySize.resize(totalSeqNum, 1); + + batchMachineStartPos_.resize(totalSeqNum + 1, 0); + if (isSeq) { + ICpuGpuVectorPtr inputSeqStartPos = outArgs[0].sequenceStartPositions; + CHECK_EQ(inputSeqStartPos->getSize() - 1, finalPaths_.size()); + int* starts = inputSeqStartPos->getMutableData(false); + int seqId = 0; + for (int i = 0; i < finalPaths_.size(); ++i) { + for (int j = 0; j < finalPaths_[i].size(); ++j) { + copySize[seqId] = starts[i + 1] - starts[i]; + batchMachineStartPos_[seqId + 1] = + batchMachineStartPos_[seqId] + finalPaths_[i][j].ids.size(); + seqId++; + } + } } +} +void RecurrentGradientMachine::createDataOutlink() { for (size_t i = 0; i < dataArgsSize_; i++) { + bool isSeq = dataArgsFrame_[i][0].hasSeq(); + std::vector copySize; + createDataOutlinkCopySizeInfo(isSeq, dataArgsFrame_[i], copySize); + createDataOutlinkSelRowsInfo(isSeq, dataArgsFrame_[i]); + dataArgs_[i].concat(dataArgsFrame_[i], - machineIdVec, - starts, + batchMachineIdVec_, + batchMachineStartPos_, + copySize, useGpu_, HPPL_STREAM_1, PASS_TEST); - auto dataAgent = dynamic_cast(outFrameLines_[i + 1].agentLayer.get()); CHECK_NOTNULL(dataAgent); diff --git a/paddle/gserver/gradientmachines/RecurrentGradientMachine.h b/paddle/gserver/gradientmachines/RecurrentGradientMachine.h index fb3fc5877..bd096770b 100644 --- a/paddle/gserver/gradientmachines/RecurrentGradientMachine.h +++ b/paddle/gserver/gradientmachines/RecurrentGradientMachine.h @@ -480,7 +480,11 @@ private: * @param machineIdVec : select a row of output matrix in each frame * that the generation process expanded. */ - void createDataOutlink(std::vector& machineIdVec); + void createDataOutlink(); + void createDataOutlinkCopySizeInfo(bool isSeq, + std::vector& outArgs, + std::vector& copySize); + void createDataOutlinkSelRowsInfo(bool isSeq, std::vector& outArgs); /* * @brief used in beam search, connect previous frame to form recurrent link @@ -543,6 +547,7 @@ private: std::vector topIds_; std::vector seqIds_; std::vector batchMachineIdVec_; + std::vector batchMachineStartPos_; std::vector> finalPaths_; std::vector minFinalPathLogProb_; BeamSearchControlCallbacks* beamSearchCtrlCallbacks_; diff --git a/paddle/parameter/Argument.cpp b/paddle/parameter/Argument.cpp index ef72b973c..e7522def0 100644 --- a/paddle/parameter/Argument.cpp +++ b/paddle/parameter/Argument.cpp @@ -276,17 +276,21 @@ int32_t Argument::resizeAndCopyFrom(const Argument& src, void Argument::concat(const std::vector& args, const std::vector& selectRows, const std::vector& seqStartPos, + const std::vector& copySize, bool useGpu, hl_stream_t stream, PassType passType) { CHECK(!subSequenceStartPositions) << "undefined behavior for subsequence positions"; - size_t batchSize = selectRows.size(); + size_t batchSize = 0; + for (size_t i = 0; i < copySize.size(); ++i) + batchSize += copySize[i] * (seqStartPos[i + 1] - seqStartPos[i]); + auto copyArg = [batchSize, stream](MatrixPtr& dst, MatrixPtr src, - int startRow, - int pos, + int desStartRow, + int srcStartRow, int size, bool useGpu) { if (!src) { @@ -300,8 +304,8 @@ void Argument::concat(const std::vector& args, dst->resize(batchSize, width); } - MatrixPtr tmpMatrix = dst->subMatrix(startRow, size); - tmpMatrix->copyFrom(*src->subMatrix(pos, size), stream); + MatrixPtr tmpMatrix = dst->subMatrix(desStartRow, size); + tmpMatrix->copyFrom(*src->subMatrix(srcStartRow, size), stream); }; auto copyIds = [batchSize, stream](IVectorPtr& dst, @@ -339,24 +343,24 @@ void Argument::concat(const std::vector& args, dataId = args[0].dataId; CHECK_NE(seqStartPos.size(), 0UL); - size_t sampleNum = seqStartPos.size() - 1; - for (size_t i = 0; i < sampleNum; ++i) { + int desStartRow = 0; + for (size_t i = 0; i < copySize.size(); ++i) { int startPos = seqStartPos[i]; int endPos = seqStartPos[i + 1]; CHECK_GE(args.size(), static_cast(endPos - startPos)); for (int j = startPos; j < endPos; ++j) { const Argument& arg = args[j - startPos]; - CHECK_EQ(arg.dataId, dataId) << "Arguments in concat should have" - << " same dataId"; - const int copySize = 1; - const int rowIdx = selectRows[j]; - copyArg(in, arg.in, j, rowIdx, copySize, useGpu); - copyArg(value, arg.value, j, rowIdx, copySize, useGpu); + CHECK_EQ(arg.dataId, dataId) << "Arguments in concat should have the " + << "same dataId"; + const int srcStartRow = selectRows[j]; + copyArg(in, arg.in, desStartRow, srcStartRow, copySize[i], useGpu); + copyArg(value, arg.value, desStartRow, srcStartRow, copySize[i], useGpu); if (passType != PASS_TEST) { - copyArg(grad, arg.grad, j, rowIdx, copySize, useGpu); + copyArg(grad, arg.grad, desStartRow, srcStartRow, copySize[i], useGpu); } - copyIds(ids, arg.ids, j, rowIdx, copySize, useGpu); - copyStrs(strs, arg.strs, j, rowIdx, copySize, useGpu); + copyIds(ids, arg.ids, desStartRow, srcStartRow, copySize[i], useGpu); + copyStrs(strs, arg.strs, desStartRow, srcStartRow, copySize[i], useGpu); + desStartRow += copySize[i]; } } ICpuGpuVector::resizeOrCreate( diff --git a/paddle/parameter/Argument.h b/paddle/parameter/Argument.h index 0ccdef802..be8717565 100644 --- a/paddle/parameter/Argument.h +++ b/paddle/parameter/Argument.h @@ -240,6 +240,7 @@ struct Argument { void concat(const std::vector& args, const std::vector& selectRows, const std::vector& seqStartPos, + const std::vector& copySize, bool useGpu, hl_stream_t stream, PassType passType); diff --git a/python/paddle/trainer_config_helpers/networks.py b/python/paddle/trainer_config_helpers/networks.py index b77932ce5..c0b2ced23 100755 --- a/python/paddle/trainer_config_helpers/networks.py +++ b/python/paddle/trainer_config_helpers/networks.py @@ -1370,14 +1370,7 @@ def simple_attention(encoded_sequence, param_attr=softmax_param_attr, name="%s_softmax" % name, bias_attr=False) - - scaled = scaling_layer( - weight=attention_weight, - input=encoded_sequence, - name='%s_scaling' % name) - - return pooling_layer( - input=scaled, pooling_type=SumPooling(), name="%s_pooling" % name) + return attention_weight def inputs(layers, *args): @@ -1395,7 +1388,7 @@ def inputs(layers, *args): if len(args) != 0: layers.extend(args) - Inputs(* [l.name for l in layers]) + Inputs(*[l.name for l in layers]) def outputs(layers, *args): @@ -1438,7 +1431,7 @@ def outputs(layers, *args): assert len(layers) > 0 if HasInputsSet(): # input already set - Outputs(* [l.name for l in layers]) + Outputs(*[l.name for l in layers]) return # just return outputs. if len(layers) != 1: -- GitLab From 4c134c7c7d201a9f28449974d489111b51c6f6fb Mon Sep 17 00:00:00 2001 From: caoying03 Date: Fri, 14 Jul 2017 17:21:36 +0800 Subject: [PATCH 0004/2018] add comments. --- .../RecurrentGradientMachine.h | 38 ++++++++++++++++--- paddle/parameter/Argument.cpp | 4 +- .../paddle/trainer_config_helpers/networks.py | 4 +- 3 files changed, 36 insertions(+), 10 deletions(-) diff --git a/paddle/gserver/gradientmachines/RecurrentGradientMachine.h b/paddle/gserver/gradientmachines/RecurrentGradientMachine.h index a3d04b207..cc0eda9f1 100644 --- a/paddle/gserver/gradientmachines/RecurrentGradientMachine.h +++ b/paddle/gserver/gradientmachines/RecurrentGradientMachine.h @@ -190,7 +190,7 @@ public: std::vector ids; /** - * @brief idsProb, log probability of each generated words. + * @brief idsProb, log probability of each generated word. */ std::vector idsProb; @@ -472,16 +472,42 @@ private: void copyDataOutlinkFrame(size_t machineCur); /* - * @brief In generation, if the layer group has more than 1 outlink, outlinks - * except the first one are data outlinks. This function creates the data - * outlinks. - * @note In beam search, only one generated sequence with the hightest log - * probabilites are retained. + * @brief In generation, if the layer group has more than 1 outlink, outlink + * except the first one is a data outlink. In RecurrentLayerGroup, each time + * step is a separate Network, outputs of a layer inside the + * RecurrentLayerGroup are stored in separate Arguments. If one layer is + * specified as an outlink of RecurrentLayerGroup. This function will + * collect outputs in each time step of each generated sequence which are + * dispersed in separate Arguments to form a new single Argument as output of + * RecurrentLayerGroup. */ void createDataOutlink(); + + /* + * @brief decide to select how many rows from the Matrix stored the forward + * pass results from a start position. + * + * @param isSeq: a flag indicating whetehr the layer to be output of the + * RecurrentGradientMachine is a sequence or not + * @param outArgs: all of the the returned Arguments of the forward pass + * during the generation process. + * @param copySize: the returned result, number of rows to select from the + * Matrix stored the forward pass results from a start position. + */ void createDataOutlinkCopySizeInfo(bool isSeq, std::vector& outArgs, std::vector& copySize); + + /* + * @brief decide index of the start row for each time step of a generated + * sequence in Matrix stored the entire beam search batch's forward pass + * results. + * + * @param isSeq: a flag indicating whetehr the layer to be output of the + * RecurrentGradientMachine is a sequence or not + * @param outArgs: all of the the returned Arguments of the forward pass + * during the generation process. + */ void createDataOutlinkSelRowsInfo(bool isSeq, std::vector& outArgs); /* diff --git a/paddle/parameter/Argument.cpp b/paddle/parameter/Argument.cpp index f45a51d7b..9a9092af9 100644 --- a/paddle/parameter/Argument.cpp +++ b/paddle/parameter/Argument.cpp @@ -352,8 +352,8 @@ void Argument::concat(const std::vector& args, CHECK_GE(args.size(), static_cast(endPos - startPos)); for (int j = startPos; j < endPos; ++j) { const Argument& arg = args[j - startPos]; - CHECK_EQ(arg.dataId, dataId) << "Arguments in concat should have the " - << "same dataId"; + CHECK_EQ(arg.dataId, dataId) << "Arguments to concatenate should have " + << "the same dataId."; const int srcStartRow = selectRows[j]; copyArg(in, arg.in, desStartRow, srcStartRow, copySize[i], useGpu); copyArg(value, arg.value, desStartRow, srcStartRow, copySize[i], useGpu); diff --git a/python/paddle/trainer_config_helpers/networks.py b/python/paddle/trainer_config_helpers/networks.py index 30c826ffc..810bea913 100755 --- a/python/paddle/trainer_config_helpers/networks.py +++ b/python/paddle/trainer_config_helpers/networks.py @@ -1375,9 +1375,9 @@ def simple_attention(encoded_sequence, weight=attention_weight, input=encoded_sequence, name='%s_scaling' % name) + return pooling_layer( - input=scaled, pooling_type=SumPooling(), - name="%s_pooling" % name), attention_weight + input=scaled, pooling_type=SumPooling(), name="%s_pooling" % name) def inputs(layers, *args): -- GitLab From 1cdf149b6fccf4fba030f0bb847965500960fa9b Mon Sep 17 00:00:00 2001 From: wanghaoshuang Date: Wed, 19 Jul 2017 12:50:45 +0800 Subject: [PATCH 0005/2018] 1. delete PixelSoftmaxLayer and add SwitchOrderLayer 2. Make SwitchOrderLayer support for softmax activation 3. Fix bugs --- CMakeLists.txt | 2 +- paddle/function/SwitchOp.cpp | 72 ++++++----- paddle/function/SwitchOp.h | 8 +- paddle/function/SwitchOpGpu.cu | 26 ++-- paddle/gserver/layers/PixelSoftmaxLayer.cpp | 89 -------------- paddle/gserver/layers/SwitchOrderLayer.cpp | 112 ++++++++++++++++++ ...PixelSoftmaxLayer.h => SwitchOrderLayer.h} | 19 +-- paddle/gserver/tests/test_LayerGrad.cpp | 14 ++- paddle/math/Matrix.cpp | 21 ---- paddle/math/Matrix.h | 1 - proto/ModelConfig.proto | 8 ++ python/paddle/trainer/config_parser.py | 21 ++-- .../paddle/trainer_config_helpers/layers.py | 36 +++--- 13 files changed, 231 insertions(+), 198 deletions(-) delete mode 100644 paddle/gserver/layers/PixelSoftmaxLayer.cpp create mode 100644 paddle/gserver/layers/SwitchOrderLayer.cpp rename paddle/gserver/layers/{PixelSoftmaxLayer.h => SwitchOrderLayer.h} (71%) diff --git a/CMakeLists.txt b/CMakeLists.txt index 15a7c6b07..fdc62b315 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -13,7 +13,7 @@ # limitations under the License cmake_minimum_required(VERSION 3.0) - +SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -ldl -lpthread") set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} "${CMAKE_CURRENT_SOURCE_DIR}/cmake") set(PROJ_ROOT ${CMAKE_CURRENT_SOURCE_DIR}) set(PROJ_BINARY_ROOT ${CMAKE_CURRENT_BINARY_DIR}) diff --git a/paddle/function/SwitchOp.cpp b/paddle/function/SwitchOp.cpp index 4667c4e01..01e252a8d 100644 --- a/paddle/function/SwitchOp.cpp +++ b/paddle/function/SwitchOp.cpp @@ -23,12 +23,17 @@ void NCHW2NHWC(real* outputs, const int num, const int inC, const int inH, - const int inW) { + const int inW, + const int argType) { for (int n = 0; n < num; ++n) { for (int c = 0; c < inC; ++c) { for (int h = 0; h < inH; ++h) { for (int w = 0; w < inW; ++w) { - outputs[((n * inH + h) * inW + w) * inC + c] = *(inputs++); + if (argType == ADD_TO) { + outputs[((n * inH + h) * inW + w) * inC + c] += *(inputs++); + } else { + outputs[((n * inH + h) * inW + w) * inC + c] = *(inputs++); + } } } } @@ -41,12 +46,17 @@ void NHWC2NCHW(real* outputs, const int num, const int inH, const int inW, - const int inC) { + const int inC, + const int argType) { for (int n = 0; n < num; ++n) { for (int h = 0; h < inH; ++h) { for (int w = 0; w < inW; ++w) { for (int c = 0; c < inC; ++c) { - outputs[((n * inC + c) * inH + h) * inW + w] = *(inputs++); + if (argType == ADD_TO) { + outputs[((n * inC + c) * inH + h) * inW + w] += *(inputs++); + } else { + outputs[((n * inC + c) * inH + h) * inW + w] = *(inputs++); + } } } } @@ -54,23 +64,15 @@ void NHWC2NCHW(real* outputs, } /** - * \brief Padding zeros to input according to the specify dimension. - * The struct pad_ contains the padding size in each dimension. - * The input and output is a 4D tensor. In PadFunc, we only - * pad zeros to the 2nd to 4th dimension. + * \brief Switch dimension order of image input. + * The input and output is a 4D tensor. Switch order + * 'batch_size,channels, height, width' to + * order 'batch_size, height, width, channels'. * * Argument in this Function: - * \param pad_ A struct object contains the padding size in each dimension. - * It has six integers. The channelStart and channelEnd indicate - * how many zeros to add before and after the input in channel - * dimension. And the heightStart and heightEnd indicate padding - * in height dimension. The widthStart and widthEnd indicate the - * padding in width dimension. - * \param inputs A 4D tensor, only one input. - * \param outputs A 4D tensor, the output value after padding. - * + * \param inputs input data with order 'batch_size,channels, height, width'. + * \param outputs output data with order 'batch_size, height, width, channels'. */ - template class NCHW2NHWCFunc : public FunctionBase { public: @@ -84,25 +86,26 @@ public: size_t inC = inputs[0].shape()[1]; size_t inH = inputs[0].shape()[2]; size_t inW = inputs[0].shape()[3]; - typename Tensor::Vector vec(outputs[0].shape().getElements(), - outputs[0].data()); - vec.zero(); - - NCHW2NHWC( - outputs[0].data(), inputs[0].data(), num, inC, inH, inW); + NCHW2NHWC(outputs[0].data(), + inputs[0].data(), + num, + inC, + inH, + inW, + outputs[0].getArgType()); } }; /** - * \brief The backward propagation of padding Function. Remove the elements - * in the padding positions of forward. + * \brief Switch dimension order of image input. + * The input and output is a 4D tensor. Switch order + * 'batch_size, height, width, channels' to + * order 'batch_size, channels, height, width'. * * Argument in this Function: - * \param pad_ The same meaning as it in PadFunc. - * \param inputs The gradient with respect to the output value of PadFunc. - * \param outputs The gradient with respect to the input value of PadFunc. + * \param inputs input data with order 'batch_size, height, width, channels'. + * \param outputs output data with order 'batch_size, channels, height, width'. */ - template class NHWC2NCHWFunc : public FunctionBase { public: @@ -117,8 +120,13 @@ public: size_t inW = inputs[0].shape()[2]; size_t inC = inputs[0].shape()[3]; - NHWC2NCHW( - outputs[0].data(), inputs[0].data(), num, inH, inW, inC); + NHWC2NCHW(outputs[0].data(), + inputs[0].data(), + num, + inH, + inW, + inC, + outputs[0].getArgType()); } }; diff --git a/paddle/function/SwitchOp.h b/paddle/function/SwitchOp.h index 5a2418a70..e4c1c3ac9 100644 --- a/paddle/function/SwitchOp.h +++ b/paddle/function/SwitchOp.h @@ -30,6 +30,7 @@ namespace paddle { * \param[in] inC channel number of input data. * \param[in] inH height of input data. * \param[in] inH with of input data. + * \param[in] argType type of output argument. */ template void NCHW2NHWC(real* outputs, @@ -37,7 +38,8 @@ void NCHW2NHWC(real* outputs, const int num, const int inC, const int inH, - const int inW); + const int inW, + const int argtype); /** * \brief This funtion switch dimension order of image input. @@ -51,6 +53,7 @@ void NCHW2NHWC(real* outputs, * \param[in] inH height of input data. * \param[in] inW with of input data. * \param[in] inC channel number of input data. + * \param[in] argType type of output argument. */ template void NHWC2NCHW(real* inGrad, @@ -58,5 +61,6 @@ void NHWC2NCHW(real* inGrad, const int num, const int inH, const int inW, - const int inC); + const int inC, + const int argType); } // namespace paddle diff --git a/paddle/function/SwitchOpGpu.cu b/paddle/function/SwitchOpGpu.cu index c2020cb2a..0b9401dea 100644 --- a/paddle/function/SwitchOpGpu.cu +++ b/paddle/function/SwitchOpGpu.cu @@ -19,7 +19,7 @@ namespace paddle { __global__ void KeNCHW2NHWC(real* outputs, const real* inputs, int inC, int inH, int inW, - int nthreads) { + int nthreads, int argType) { const int idx = threadIdx.x + blockIdx.x * blockDim.x; if (idx < nthreads) { const int w = idx % inW; @@ -28,7 +28,11 @@ __global__ void KeNCHW2NHWC(real* outputs, const real* inputs, const int n = idx / inW / inH / inC; const int off = ((n * inH + h) * inW + w) * inC +c; - outputs[off] = inputs[idx]; + if (argType == ADD_TO) { + outputs[off] += inputs[idx]; + } else { + outputs[off] = inputs[idx]; + } } } @@ -38,18 +42,19 @@ void NCHW2NHWC(real* outputs, const int num, const int inC, const int inH, - const int inW) { + const int inW, + const int argType) { size_t nth = num * inC * inH * inW; int blockSize = 1024; int gridSize = (nth + 1024 - 1) / 1024; KeNCHW2NHWC<<>> - (outputs, inputs, inC, inH, inW, nth); + (outputs, inputs, inC, inH, inW, nth, argType); CHECK_SYNC("NCHW2NHWC"); } __global__ void KeNHWC2NCHW(real* outputs, const real* inputs, int inH, int inW, int inC, - int nthreads) { + int nthreads, int argType) { const int idx = threadIdx.x + blockIdx.x * blockDim.x; if (idx < nthreads) { const int c = idx % inC; @@ -58,7 +63,11 @@ __global__ void KeNHWC2NCHW(real* outputs, const real* inputs, const int n = idx / inW / inH / inC; const int off = ((n * inC + c) * inH + h) * inW + w; - outputs[off] = inputs[idx]; + if (argType == ADD_TO) { + outputs[off] += inputs[idx]; + } else { + outputs[off] = inputs[idx]; + } } } @@ -68,12 +77,13 @@ void NHWC2NCHW(real* outputs, const int num, const int inH, const int inW, - const int inC) { + const int inC, + const int argType) { int nth = num * inC * inH * inW; int blockSize = 1024; int gridSize = (nth + 1024 - 1) / 1024; KeNHWC2NCHW<<>> - (outputs, inputs, inH, inW, inC, nth); + (outputs, inputs, inH, inW, inC, nth, argType); CHECK_SYNC("NHWC2NCHW"); } diff --git a/paddle/gserver/layers/PixelSoftmaxLayer.cpp b/paddle/gserver/layers/PixelSoftmaxLayer.cpp deleted file mode 100644 index 6da84a630..000000000 --- a/paddle/gserver/layers/PixelSoftmaxLayer.cpp +++ /dev/null @@ -1,89 +0,0 @@ -/* 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 "PixelSoftmaxLayer.h" -#include "paddle/utils/Stat.h" - -namespace paddle { - -REGISTER_LAYER(pixel_softmax, PixelSoftmaxLayer); - -bool PixelSoftmaxLayer::init(const LayerMap& layerMap, - const ParameterMap& parameterMap) { - /* Initialize the basic parent class */ - Layer::init(layerMap, parameterMap); - auto& img_conf = config_.inputs(0).image_conf(); - inH_ = - img_conf.has_img_size_y() ? img_conf.img_size_y() : img_conf.img_size(); - inW_ = img_conf.img_size(); - inC_ = img_conf.channels(); - createFunction(forward_, "NCHW2NHWC", FuncConfig()); - createFunction(backward_, "NHWC2NCHW", FuncConfig()); - inDims_ = TensorShape({0, inH_, inW_, inC_}); - outDims_ = TensorShape({0, inC_, inH_, inW_}); - return true; -} - -void PixelSoftmaxLayer::forward(PassType passType) { - Layer::forward(passType); - MatrixPtr input = inputLayers_[0]->getOutputValue(); - size_t batchSize = input->getHeight(); - // cout<<"useGpu:"<zeroMem(); - resetOutput(batchSize, inH_ * inW_ * inC_); - inDims_.setDim(0, batchSize); - outDims_.setDim(0, batchSize); - - // switch NCHW to NHWC - BufferArgs inputs; - BufferArgs outputs; - inputs.addArg(*getInputValue(0), inDims_); - outputs.addArg(*tmpInput_, outDims_); - forward_[0]->calc(inputs, outputs); - // softmax forward and save softmax result into tmpMatrix_ - tmpInput_->softmax(*tmpOutput_); - - // switch NHWC to NCHW - BufferArgs inputs_1; - BufferArgs outputs_1; - inputs_1.addArg(*tmpOutput_, outDims_); - outputs_1.addArg(*getOutputValue(), inDims_); - backward_[0]->calc(inputs_1, outputs_1); -} - -void PixelSoftmaxLayer::backward(const UpdateCallback& callback) { - (void)callback; - REGISTER_TIMER_INFO("PixelSoftmaxBackward", getName().c_str()); - - // switch NCHW to NHWC - BufferArgs inputs; - BufferArgs outputs; - inputs.addArg(*getOutputGrad(), inDims_); - outputs.addArg(*tmpInput_, outDims_); - forward_[0]->calc(inputs, outputs); - // softmax backward and save grad result into tmpOutput_ - tmpInput_->softmaxBackward(*tmpOutput_); - - // switch NHWC to NCHW - BufferArgs inputs_1; - BufferArgs outputs_1; - inputs_1.addArg(*tmpInput_, outDims_); - outputs_1.addArg(*getInputGrad(0), inDims_); - backward_[0]->calc(inputs_1, outputs_1); -} -} // namespace paddle diff --git a/paddle/gserver/layers/SwitchOrderLayer.cpp b/paddle/gserver/layers/SwitchOrderLayer.cpp new file mode 100644 index 000000000..2a8a9500f --- /dev/null +++ b/paddle/gserver/layers/SwitchOrderLayer.cpp @@ -0,0 +1,112 @@ +/* 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 "SwitchOrderLayer.h" +#include "paddle/utils/Stat.h" + +namespace paddle { + +REGISTER_LAYER(switch_order, SwitchOrderLayer); + +bool SwitchOrderLayer::init(const LayerMap& layerMap, + const ParameterMap& parameterMap) { + /* Initialize the basic parent class */ + Layer::init(layerMap, parameterMap); + auto& img_conf = config_.inputs(0).image_conf(); + size_t inH = + img_conf.has_img_size_y() ? img_conf.img_size_y() : img_conf.img_size(); + size_t inW = img_conf.img_size(); + size_t inC = img_conf.channels(); + inDims_ = TensorShape({0, inC, inH, inW}); + outDims_ = TensorShape(4); + + auto& reshape_conf = config_.reshape_conf(); + for (size_t i = 0; i < reshape_conf.heightaxis_size(); i++) { + LOG(INFO) << "reshape height axis: " << reshape_conf.heightaxis(i); + heightAxis_.push_back(reshape_conf.heightaxis(i)); + } + for (size_t i = 0; i < reshape_conf.widthaxis_size(); i++) { + LOG(INFO) << "reshape width axis: " << reshape_conf.widthaxis(i); + widthAxis_.push_back(reshape_conf.widthaxis(i)); + } + createFunction(nchw2nhwc_, "NCHW2NHWC", FuncConfig()); + createFunction(nhwc2nchw_, "NHWC2NCHW", FuncConfig()); + return true; +} + +void SwitchOrderLayer::setOutDims() { + outDims_.setDim(0, inDims_[0]); + outDims_.setDim(1, inDims_[2]); + outDims_.setDim(2, inDims_[3]); + outDims_.setDim(3, inDims_[1]); + reshapeHeight_ = 1; + for (size_t i = 0; i < heightAxis_.size(); i++) { + reshapeHeight_ *= outDims_[heightAxis_[i]]; + } + output_.setFrameHeight(reshapeHeight_); + reshapeWidth_ = 1; + for (size_t i = 0; i < widthAxis_.size(); i++) { + reshapeWidth_ *= outDims_[widthAxis_[i]]; + } + output_.setFrameWidth(reshapeWidth_); + LOG(INFO) << "outDims: " << outDims_[0] << "; " << outDims_[1] << ";" + << outDims_[2] << ";" << outDims_[3]; +} + +void SwitchOrderLayer::setInDims() { + MatrixPtr input = inputLayers_[0]->getOutputValue(); + size_t batchSize = input->getHeight(); + inDims_.setDim(0, batchSize); + + int h = inputLayers_[0]->getOutput().getFrameHeight(); + if (h != 0) inDims_.setDim(2, h); + int w = inputLayers_[0]->getOutput().getFrameWidth(); + if (w != 0) inDims_.setDim(3, w); + int totalCount = input->getElementCnt(); + int channels = totalCount / (inDims_[0] * inDims_[2] * inDims_[3]); + if (channels != 0) inDims_.setDim(1, channels); + LOG(INFO) << "inDims: " << inDims_[0] << "; " << inDims_[1] << ";" + << inDims_[2] << ";" << inDims_[3]; +} + +void SwitchOrderLayer::forward(PassType passType) { + Layer::forward(passType); + setInDims(); + setOutDims(); + resetOutput(outDims_[0], outDims_[1] * outDims_[2] * outDims_[3]); + if (heightAxis_.size() > 0) { + getOutputValue()->reshape(reshapeHeight_, reshapeWidth_); + } + + // switch NCHW to NHWC + BufferArgs inputs; + BufferArgs outputs; + inputs.addArg(*getInputValue(0), inDims_); + outputs.addArg(*getOutputValue(), outDims_); + nchw2nhwc_[0]->calc(inputs, outputs); + // forwardActivation(); +} + +void SwitchOrderLayer::backward(const UpdateCallback& callback) { + (void)callback; + // backwardActivation(); + + // switch NHWC to NCHW + BufferArgs inputs; + BufferArgs outputs; + inputs.addArg(*getOutputGrad(), outDims_); + outputs.addArg(*getInputGrad(0), inDims_, ADD_TO); + nhwc2nchw_[0]->calc(inputs, outputs); +} +} // namespace paddle diff --git a/paddle/gserver/layers/PixelSoftmaxLayer.h b/paddle/gserver/layers/SwitchOrderLayer.h similarity index 71% rename from paddle/gserver/layers/PixelSoftmaxLayer.h rename to paddle/gserver/layers/SwitchOrderLayer.h index 80a4ddad5..47b1f7f73 100644 --- a/paddle/gserver/layers/PixelSoftmaxLayer.h +++ b/paddle/gserver/layers/SwitchOrderLayer.h @@ -21,24 +21,27 @@ namespace paddle { /** * \brief This layer calculate softmax in image channel dimension. */ -class PixelSoftmaxLayer : public Layer { +class SwitchOrderLayer : public Layer { public: - explicit PixelSoftmaxLayer(const LayerConfig& config) : Layer(config) {} + explicit SwitchOrderLayer(const LayerConfig& config) : Layer(config) {} - ~PixelSoftmaxLayer() {} + ~SwitchOrderLayer() {} bool init(const LayerMap& layerMap, const ParameterMap& parameterMap) override; void forward(PassType passType) override; void backward(const UpdateCallback& callback = nullptr) override; + void setInDims(); + void setOutDims(); protected: - uint32_t inC_; - uint32_t inH_; - uint32_t inW_; + std::vector> nchw2nhwc_; + std::vector> nhwc2nchw_; TensorShape inDims_; TensorShape outDims_; - MatrixPtr tmpInput_; - MatrixPtr tmpOutput_; + std::vector heightAxis_; + std::vector widthAxis_; + size_t reshapeHeight_; + size_t reshapeWidth_; }; } // namespace paddle diff --git a/paddle/gserver/tests/test_LayerGrad.cpp b/paddle/gserver/tests/test_LayerGrad.cpp index 98c9cbe9f..42c23f022 100644 --- a/paddle/gserver/tests/test_LayerGrad.cpp +++ b/paddle/gserver/tests/test_LayerGrad.cpp @@ -1802,7 +1802,7 @@ TEST(Layer, RowConvLayer) { } } -TEST(Layer, PixelSoftmaxLayer) { +TEST(Layer, SwitchOrderLayer) { TestConfig config; // config input_0 config.inputDefs.push_back({INPUT_DATA, "layer_0", 1024, 0}); @@ -1812,12 +1812,18 @@ TEST(Layer, PixelSoftmaxLayer) { img->set_img_size(16); img->set_img_size_y(16); + ReshapeConfig* reshape = config.layerConfig.mutable_reshape_conf(); + reshape->add_heightaxis(0); + reshape->add_heightaxis(1); + reshape->add_heightaxis(2); + reshape->add_widthaxis(3); + // config softmax layer - config.layerConfig.set_type("pixel_softmax"); - config.layerConfig.set_name("pixelSofrmaxLayer"); + config.layerConfig.set_type("switch_order"); + config.layerConfig.set_name("switchOrderLayer"); for (auto useGpu : {false, true}) { - testLayerGrad(config, "pixel_softmax", 100, false, useGpu, true, 2); + testLayerGrad(config, "switch_order", 100, false, useGpu, true); } } diff --git a/paddle/math/Matrix.cpp b/paddle/math/Matrix.cpp index 2c18df373..4431d613f 100644 --- a/paddle/math/Matrix.cpp +++ b/paddle/math/Matrix.cpp @@ -3385,27 +3385,6 @@ void CpuMatrix::oneHotCrossEntropyWithSelfNormBp(Matrix& output, real* out = output.getData(); \ for (size_t i = 0; i < numSamples; ++i, grad += dim, out += dim) -void CpuMatrix::softmaxBackward(Matrix& outputV) { - CHECK(!outputV.useGpu()) << "Matrix type are not equal"; - size_t height = getHeight(); - size_t width = getWidth(); - CHECK(height == outputV.getHeight() && width == outputV.getWidth()) - << "Matrix dimensions are not equal"; - Matrix::resizeOrCreate(sftmaxDot_, - height_, - width_, - /* trans */ false, - useGpu_); - Matrix::resizeOrCreate(sftmaxSum_, - height_, - 1, - /* trans */ false, - useGpu_); - sftmaxDot_->dotMul(*this, outputV); - sftmaxSum_->colMerge(*sftmaxDot_); - softmaxDerivative(outputV, *sftmaxSum_); -} - void CpuMatrix::softmax(Matrix& output) { CHECK(!output.useGpu()); diff --git a/paddle/math/Matrix.h b/paddle/math/Matrix.h index dcb63a2d3..20f97a506 100644 --- a/paddle/math/Matrix.h +++ b/paddle/math/Matrix.h @@ -1732,7 +1732,6 @@ public: Matrix& prevGrad2); void softmax(Matrix& output); - void softmaxBackward(Matrix& outputV); void sequenceSoftmax(Matrix& output, const IVector& index); void softmaxDerivative(Matrix& output, Matrix& sftmaxSum); diff --git a/proto/ModelConfig.proto b/proto/ModelConfig.proto index 37cd16c79..9fd017b23 100644 --- a/proto/ModelConfig.proto +++ b/proto/ModelConfig.proto @@ -266,6 +266,11 @@ message PadConfig { repeated uint32 pad_w = 4; } +message ReshapeConfig { + repeated uint32 heightAxis = 1; + repeated uint32 widthAxis = 2; +} + message MultiBoxLossConfig { required uint32 num_classes = 1; required float overlap_threshold = 2; @@ -476,6 +481,9 @@ message LayerConfig { // controls the scope of pooling operation. can be set > 0. // leave empty or set to -1 to disable this stride pooling. optional int32 seq_pool_stride = 53 [default = -1]; + + // for switch order layer + optional ReshapeConfig reshape_conf = 54; } message EvaluatorConfig { diff --git a/python/paddle/trainer/config_parser.py b/python/paddle/trainer/config_parser.py index 261e834e1..fe06dd812 100644 --- a/python/paddle/trainer/config_parser.py +++ b/python/paddle/trainer/config_parser.py @@ -3174,20 +3174,13 @@ class RecurrentLayerGroup(LayerBase): name, 'recurrent_layer_group', 0, inputs=[], device=device) -@config_layer('pixel_softmax') -class PixelSoftmaxLayer(LayerBase): - def __init__(self, name, inputs, **xargs): - super(PixelSoftmaxLayer, self).__init__( - name, 'pixel_softmax', 0, inputs=inputs, **xargs) - - input_layer = self.get_input_layer(0) - image_conf = self.config.inputs[0].image_conf - image_conf.img_size = input_layer.width - image_conf.img_size_y = input_layer.height - image_conf.channels = input_layer.size / (input_layer.width * - input_layer.height) - self.set_cnn_layer(name, image_conf.img_size_y, image_conf.img_size, - image_conf.channels) +@config_layer('switch_order') +class SwitchOrderLayer(LayerBase): + def __init__(self, name, inputs, reshape, **xargs): + super(SwitchOrderLayer, self).__init__( + name, 'switch_order', 0, inputs=inputs, **xargs) + self.conf.reshape_conf.heightAxis_ = reshape['height'] + self.conf.reshape_conf.widthAxis_ = reshape['width'] # Deprecated, use a new layer specific class instead diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py index 2f8b0d100..6980a3167 100755 --- a/python/paddle/trainer_config_helpers/layers.py +++ b/python/paddle/trainer_config_helpers/layers.py @@ -126,7 +126,7 @@ __all__ = [ 'row_conv_layer', 'dropout_layer', 'prelu_layer', - 'pixel_softmax_layer', + 'switch_order_layer', ] @@ -218,7 +218,7 @@ class LayerType(object): SMOOTH_L1 = 'smooth_l1' PRELU = 'prelu' - PIXEL_SOFTMAX_LAYER = 'pixel_softmax' + SWITCH_ORDER_LAYER = 'switch_order' @staticmethod def is_layer_type(type_name): @@ -5881,37 +5881,37 @@ def prelu_layer(input, @layer_support() -@wrap_name_default('pixel_softmax') -def pixel_softmax_layer(input, name=None, layer_attr=None): +@wrap_name_default('switch_order') +def switch_order_layer(input, name=None, reshape=None, layer_attr=None): """ - This layer calculate softmax in image channel dimension + This layer switch dimension order of image input. + From order "batchSize, channels, height, width" + to order "batchSize, height, width, channels". The example usage is: .. code-block:: python + reshape = {'height':[ 0, 1, 2], 'width':[3]} + switch = switch_order(input=layer, name='switch', reshape=reshape) - prelu = pixel_softmax(input=layer, name='softmax') - - :param name: Name of this layer. - :type name: basestring :param input: The input layer. :type input: LayerOutput + :param name: Name of this layer. + :type name: basestring + :param reshape: reshape matrix by axises. + :type reshape: Dict :return: LayerOutput object. :rtype: LayerOutput """ - if isinstance(input, LayerOutput): - input = [input] - elif isinstance(input, Projection): - input = [input] - else: - assert isinstance(input, collections.Sequence) + assert isinstance(input, LayerOutput) l = Layer( name=name, - inputs=[x.name for x in input], - type=LayerType.PIXEL_SOFTMAX_LAYER, + inputs=input, + reshape=reshape, + type=LayerType.SWITCH_ORDER_LAYER, **ExtraLayerAttribute.to_kwargs(layer_attr)) return LayerOutput( name=name, - layer_type=LayerType.PIXEL_SOFTMAX_LAYER, + layer_type=LayerType.SWITCH_ORDER_LAYER, parents=input, size=l.config.size) -- GitLab From fa02963659239fbbd61594b61073802cc9ab4513 Mon Sep 17 00:00:00 2001 From: wanghaoshuang Date: Wed, 19 Jul 2017 13:15:03 +0800 Subject: [PATCH 0006/2018] Delete debug log --- paddle/gserver/layers/SwitchOrderLayer.cpp | 10 ++-------- 1 file changed, 2 insertions(+), 8 deletions(-) diff --git a/paddle/gserver/layers/SwitchOrderLayer.cpp b/paddle/gserver/layers/SwitchOrderLayer.cpp index 2a8a9500f..8d337611b 100644 --- a/paddle/gserver/layers/SwitchOrderLayer.cpp +++ b/paddle/gserver/layers/SwitchOrderLayer.cpp @@ -33,11 +33,9 @@ bool SwitchOrderLayer::init(const LayerMap& layerMap, auto& reshape_conf = config_.reshape_conf(); for (size_t i = 0; i < reshape_conf.heightaxis_size(); i++) { - LOG(INFO) << "reshape height axis: " << reshape_conf.heightaxis(i); heightAxis_.push_back(reshape_conf.heightaxis(i)); } for (size_t i = 0; i < reshape_conf.widthaxis_size(); i++) { - LOG(INFO) << "reshape width axis: " << reshape_conf.widthaxis(i); widthAxis_.push_back(reshape_conf.widthaxis(i)); } createFunction(nchw2nhwc_, "NCHW2NHWC", FuncConfig()); @@ -60,8 +58,6 @@ void SwitchOrderLayer::setOutDims() { reshapeWidth_ *= outDims_[widthAxis_[i]]; } output_.setFrameWidth(reshapeWidth_); - LOG(INFO) << "outDims: " << outDims_[0] << "; " << outDims_[1] << ";" - << outDims_[2] << ";" << outDims_[3]; } void SwitchOrderLayer::setInDims() { @@ -76,8 +72,6 @@ void SwitchOrderLayer::setInDims() { int totalCount = input->getElementCnt(); int channels = totalCount / (inDims_[0] * inDims_[2] * inDims_[3]); if (channels != 0) inDims_.setDim(1, channels); - LOG(INFO) << "inDims: " << inDims_[0] << "; " << inDims_[1] << ";" - << inDims_[2] << ";" << inDims_[3]; } void SwitchOrderLayer::forward(PassType passType) { @@ -95,12 +89,12 @@ void SwitchOrderLayer::forward(PassType passType) { inputs.addArg(*getInputValue(0), inDims_); outputs.addArg(*getOutputValue(), outDims_); nchw2nhwc_[0]->calc(inputs, outputs); - // forwardActivation(); + forwardActivation(); } void SwitchOrderLayer::backward(const UpdateCallback& callback) { (void)callback; - // backwardActivation(); + backwardActivation(); // switch NHWC to NCHW BufferArgs inputs; -- GitLab From e23acb4e6f7b12f1b61faf3cf8d74872b7df5b39 Mon Sep 17 00:00:00 2001 From: wanghaoshuang Date: Wed, 19 Jul 2017 14:09:32 +0800 Subject: [PATCH 0007/2018] fix cmake --- CMakeLists.txt | 1 - 1 file changed, 1 deletion(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 9a8522484..2a6b0a20e 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -13,7 +13,6 @@ # limitations under the License cmake_minimum_required(VERSION 3.0) -SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -ldl -lpthread") set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} "${CMAKE_CURRENT_SOURCE_DIR}/cmake") set(PROJ_ROOT ${CMAKE_CURRENT_SOURCE_DIR}) set(PROJ_BINARY_ROOT ${CMAKE_CURRENT_BINARY_DIR}) -- GitLab From a6c53fc2fcef380784829cfb29764e1a6458827d Mon Sep 17 00:00:00 2001 From: wanghaoshuang Date: Wed, 19 Jul 2017 17:32:05 +0800 Subject: [PATCH 0008/2018] fix python wrapper bugs --- python/paddle/trainer/config_parser.py | 4 ++-- python/paddle/trainer_config_helpers/layers.py | 9 +++++++-- 2 files changed, 9 insertions(+), 4 deletions(-) diff --git a/python/paddle/trainer/config_parser.py b/python/paddle/trainer/config_parser.py index 6e2f21823..0a466380a 100644 --- a/python/paddle/trainer/config_parser.py +++ b/python/paddle/trainer/config_parser.py @@ -3187,8 +3187,8 @@ class SwitchOrderLayer(LayerBase): def __init__(self, name, inputs, reshape, **xargs): super(SwitchOrderLayer, self).__init__( name, 'switch_order', 0, inputs=inputs, **xargs) - self.conf.reshape_conf.heightAxis_ = reshape['height'] - self.conf.reshape_conf.widthAxis_ = reshape['width'] + self.config.reshape_conf.heightAxis.extend(reshape['height']) + self.config.reshape_conf.widthAxis.extend(reshape['width']) # Deprecated, use a new layer specific class instead diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py index 1f5b9e999..0bcfbe1e0 100755 --- a/python/paddle/trainer_config_helpers/layers.py +++ b/python/paddle/trainer_config_helpers/layers.py @@ -5976,7 +5976,11 @@ def gated_unit_layer(input, @layer_support() @wrap_name_default('switch_order') -def switch_order_layer(input, name=None, reshape=None, layer_attr=None): +def switch_order_layer(input, + name=None, + reshape=None, + act=None, + layer_attr=None): """ This layer switch dimension order of image input. From order "batchSize, channels, height, width" @@ -6000,9 +6004,10 @@ def switch_order_layer(input, name=None, reshape=None, layer_attr=None): assert isinstance(input, LayerOutput) l = Layer( name=name, - inputs=input, + inputs=input.name, reshape=reshape, type=LayerType.SWITCH_ORDER_LAYER, + active_type=act.name, **ExtraLayerAttribute.to_kwargs(layer_attr)) return LayerOutput( name=name, -- GitLab From baae8447ac936b29fb2b14981851bb502f5193cd Mon Sep 17 00:00:00 2001 From: wanghaoshuang Date: Wed, 19 Jul 2017 18:53:32 +0800 Subject: [PATCH 0009/2018] Fix SwitchOrderLayer grad bugs by reshape output.grad --- paddle/gserver/layers/SwitchOrderLayer.cpp | 1 + 1 file changed, 1 insertion(+) diff --git a/paddle/gserver/layers/SwitchOrderLayer.cpp b/paddle/gserver/layers/SwitchOrderLayer.cpp index 8d337611b..6a91042f6 100644 --- a/paddle/gserver/layers/SwitchOrderLayer.cpp +++ b/paddle/gserver/layers/SwitchOrderLayer.cpp @@ -81,6 +81,7 @@ void SwitchOrderLayer::forward(PassType passType) { resetOutput(outDims_[0], outDims_[1] * outDims_[2] * outDims_[3]); if (heightAxis_.size() > 0) { getOutputValue()->reshape(reshapeHeight_, reshapeWidth_); + getOutputGrad()->reshape(reshapeHeight_, reshapeWidth_); } // switch NCHW to NHWC -- GitLab From 264b644718c14da348114bb9a44afddcd7166f11 Mon Sep 17 00:00:00 2001 From: dongzhihong Date: Wed, 2 Aug 2017 21:26:29 +0800 Subject: [PATCH 0010/2018] "add rowwise add backward op" --- paddle/operators/rowwise_add_op.cc | 15 +++++++++++++++ paddle/operators/rowwise_add_op.h | 19 +++++++++++++++++++ 2 files changed, 34 insertions(+) diff --git a/paddle/operators/rowwise_add_op.cc b/paddle/operators/rowwise_add_op.cc index 2ad2b66c8..cc763a8cf 100644 --- a/paddle/operators/rowwise_add_op.cc +++ b/paddle/operators/rowwise_add_op.cc @@ -46,6 +46,17 @@ for i in xrange(X.shape[0]): )DOC"); } }; +class RowWiseAddGradOp : public OperatorWithKernel { +protected: + void InferShape(const InferShapeContext &ctx) const override { + PADDLE_ENFORCE(ctx.InputSize() == 4UL, + "RowWiseAddGrad inputs is I, O, OG, size must be 4"); + PADDLE_ENFORCE(ctx.OutputSize() == 2, + "RowWiseAddGrad output is IG, size must be 2"); + ctx.Output(0)->Resize(ctx.Input(0)->dims()); + ctx.Output(1)->Resize(ctx.Input(1)->dims()); + } +}; } // namespace operators } // namespace paddle @@ -53,3 +64,7 @@ for i in xrange(X.shape[0]): REGISTER_OP(rowwise_add, ops::RowWiseAddOp, ops::RowWiseAddOpMaker); REGISTER_OP_CPU_KERNEL(rowwise_add, ops::RowWiseAddKernel); + +REGISTER_GRADIENT_OP(rowwise_add, rowwise_add_grad, ops::RowWiseAddGradOp); +REGISTER_OP_CPU_KERNEL(rowwise_add_grad, + ops::RowWiseAddGradKernel); diff --git a/paddle/operators/rowwise_add_op.h b/paddle/operators/rowwise_add_op.h index b86dd5463..940459e0f 100644 --- a/paddle/operators/rowwise_add_op.h +++ b/paddle/operators/rowwise_add_op.h @@ -38,5 +38,24 @@ public: } }; +template +class RowWiseAddGradKernel : public OpKernel { +public: + void Compute(const ExecutionContext& context) const override { + auto XGrad = context.Output(0); + auto bGrad = context.Output(1); + XGrad->mutable_data(context.GetPlace()); + bGrad->mutable_data(context.GetPlace()); + + // I, O, OG => [X, b], [Out], [OutGrad] + auto OutGrad = EigenMatrix::From(*context.Input(3)); + EigenMatrix::From(*XGrad).device(*(context.GetEigenDevice())) = + OutGrad; + // const int dimension = bGrad.dimension(0); + // https://eigen.tuxfamily.org/dox/unsupported/TensorBase_8h_source.html + EigenVector::Flatten(*bGrad).device(*(context.GetEigenDevice())) = + OutGrad.cumsum(1); // colwise add + } +}; } // namespace operators } // namespace paddle -- GitLab From 8ff3590eda2a6488f4b06f5ce6ffe553ae42d0a6 Mon Sep 17 00:00:00 2001 From: dongzhihong Date: Fri, 4 Aug 2017 01:15:56 +0800 Subject: [PATCH 0011/2018] fix op name --- paddle/operators/rowwise_add_op.cc | 20 ++++++++++---------- paddle/operators/rowwise_add_op.cu | 2 +- paddle/operators/rowwise_add_op.h | 6 +++--- 3 files changed, 14 insertions(+), 14 deletions(-) diff --git a/paddle/operators/rowwise_add_op.cc b/paddle/operators/rowwise_add_op.cc index cc763a8cf..178ea3c61 100644 --- a/paddle/operators/rowwise_add_op.cc +++ b/paddle/operators/rowwise_add_op.cc @@ -16,7 +16,7 @@ namespace paddle { namespace operators { -class RowWiseAddOp : public OperatorWithKernel { +class RowwiseAddOp : public OperatorWithKernel { protected: void InferShape(const InferShapeContext &ctx) const override { PADDLE_ENFORCE(ctx.InputSize() == 2UL, @@ -32,9 +32,9 @@ protected: } }; -class RowWiseAddOpMaker : public OpProtoAndCheckerMaker { +class RowwiseAddOpMaker : public OpProtoAndCheckerMaker { public: - RowWiseAddOpMaker(OpProto *proto, OpAttrChecker *op_checker) + RowwiseAddOpMaker(OpProto *proto, OpAttrChecker *op_checker) : OpProtoAndCheckerMaker(proto, op_checker) { AddInput("X", "The left input of row-wise add op, must be matrix"); AddInput("b", "The right input of row-wise add op, must be vector"); @@ -46,13 +46,13 @@ for i in xrange(X.shape[0]): )DOC"); } }; -class RowWiseAddGradOp : public OperatorWithKernel { +class RowwiseAddGradOp : public OperatorWithKernel { protected: void InferShape(const InferShapeContext &ctx) const override { PADDLE_ENFORCE(ctx.InputSize() == 4UL, - "RowWiseAddGrad inputs is I, O, OG, size must be 4"); + "RowwiseAddGrad inputs is I, O, OG, size must be 4"); PADDLE_ENFORCE(ctx.OutputSize() == 2, - "RowWiseAddGrad output is IG, size must be 2"); + "RowwiseAddGrad output is IG, size must be 2"); ctx.Output(0)->Resize(ctx.Input(0)->dims()); ctx.Output(1)->Resize(ctx.Input(1)->dims()); } @@ -61,10 +61,10 @@ protected: } // namespace operators } // namespace paddle -REGISTER_OP(rowwise_add, ops::RowWiseAddOp, ops::RowWiseAddOpMaker); +REGISTER_OP(rowwise_add, ops::RowwiseAddOp, ops::RowwiseAddOpMaker); REGISTER_OP_CPU_KERNEL(rowwise_add, - ops::RowWiseAddKernel); + ops::RowwiseAddKernel); -REGISTER_GRADIENT_OP(rowwise_add, rowwise_add_grad, ops::RowWiseAddGradOp); +REGISTER_GRADIENT_OP(rowwise_add, rowwise_add_grad, ops::RowwiseAddGradOp); REGISTER_OP_CPU_KERNEL(rowwise_add_grad, - ops::RowWiseAddGradKernel); + ops::RowwiseAddGradKernel); diff --git a/paddle/operators/rowwise_add_op.cu b/paddle/operators/rowwise_add_op.cu index 4b33e38eb..f48dfeb6f 100644 --- a/paddle/operators/rowwise_add_op.cu +++ b/paddle/operators/rowwise_add_op.cu @@ -1,4 +1,4 @@ #include "paddle/operators/rowwise_add_op.h" REGISTER_OP_GPU_KERNEL(rowwise_add, - ops::RowWiseAddKernel); + ops::RowwiseAddKernel); diff --git a/paddle/operators/rowwise_add_op.h b/paddle/operators/rowwise_add_op.h index 940459e0f..321f51e61 100644 --- a/paddle/operators/rowwise_add_op.h +++ b/paddle/operators/rowwise_add_op.h @@ -19,7 +19,7 @@ namespace paddle { namespace operators { template -class RowWiseAddKernel : public OpKernel { +class RowwiseAddKernel : public OpKernel { public: void Compute(const ExecutionContext& context) const override { auto out = context.Output(0); @@ -39,7 +39,7 @@ public: }; template -class RowWiseAddGradKernel : public OpKernel { +class RowwiseAddGradKernel : public OpKernel { public: void Compute(const ExecutionContext& context) const override { auto XGrad = context.Output(0); @@ -51,7 +51,7 @@ public: auto OutGrad = EigenMatrix::From(*context.Input(3)); EigenMatrix::From(*XGrad).device(*(context.GetEigenDevice())) = OutGrad; - // const int dimension = bGrad.dimension(0); + // https://eigen.tuxfamily.org/dox/unsupported/TensorBase_8h_source.html EigenVector::Flatten(*bGrad).device(*(context.GetEigenDevice())) = OutGrad.cumsum(1); // colwise add -- GitLab From 9d569c5a38582cbf9022578c046f89a88697c493 Mon Sep 17 00:00:00 2001 From: fengjiayi Date: Thu, 3 Aug 2017 17:57:00 -0700 Subject: [PATCH 0012/2018] Update Backward.md Add the "Backward Operator Registry" section --- paddle/framework/backward.md | 24 ++++++++++++++++++++++-- 1 file changed, 22 insertions(+), 2 deletions(-) diff --git a/paddle/framework/backward.md b/paddle/framework/backward.md index 74c001b06..61f308b46 100644 --- a/paddle/framework/backward.md +++ b/paddle/framework/backward.md @@ -1,8 +1,28 @@ -## Operator/expression 's Backward +# Operator/expression 's Backward -### Motivation +## Motivation In Neural Network, the backpropagation algorithm follows the chain rule, so we need to compound the fundmental gradient operators/expressions together with chain rule . Every forward network need a backward network to construct the full computation lineage, the operator/ expression's Backward feature will generate the backward pass respect to forward pass. + +## Backward Operator Registry + +A backward network is built up with several backward operators. Backward operators take forward operators' inputs, outputs and output gradients, and then calculate its input gradients. In most cases, there is a one-to-one correspondence between forward and backward operators. We use registry mechanism to save these correspondences, which is quite similar with operator registry itself. + +For example, we have got a `add_two_op`, and is registered by the following code: + +```cpp +REGISTER_OP(add_two, AddTwoOp, AddTwoOpMaker); +``` + +`add_two` is the operator's type. `AddTwoOp` and `AddTwoOpMaker` are the operator class and the operator maker class respectively. + +Assume that we have also got the backward operator of `add_two_op`, which calculating the gradients of `add_two_op`'s inputs. Then we register it by the following way: + +```cpp +REGISTER_GRADIENT_OP(add_two, add_two_grad, AddTwoGradOp); +``` + +`add_two_grad` is the type of backward operator, and `AddTwoGradOp` is its class name. ### Implement : gradient operator registry -- GitLab From 84627bb934ed6b4c7213eeebc0fe59e5fbe7a84b Mon Sep 17 00:00:00 2001 From: caoying03 Date: Mon, 7 Aug 2017 14:03:13 +0800 Subject: [PATCH 0013/2018] add config helper for sequence slice layer. --- doc/api/v2/config/layer.rst | 5 ++ python/paddle/trainer/config_parser.py | 45 +++++++++++ .../paddle/trainer_config_helpers/layers.py | 68 ++++++++++++++++ .../tests/configs/file_list.sh | 3 +- .../protostr/test_seq_slice_layer.protostr | 79 +++++++++++++++++++ .../tests/configs/test_seq_slice_layer.py | 13 +++ 6 files changed, 212 insertions(+), 1 deletion(-) create mode 100644 python/paddle/trainer_config_helpers/tests/configs/protostr/test_seq_slice_layer.protostr create mode 100644 python/paddle/trainer_config_helpers/tests/configs/test_seq_slice_layer.py diff --git a/doc/api/v2/config/layer.rst b/doc/api/v2/config/layer.rst index 372272a53..232ea6b49 100644 --- a/doc/api/v2/config/layer.rst +++ b/doc/api/v2/config/layer.rst @@ -257,6 +257,11 @@ seq_concat .. autoclass:: paddle.v2.layer.seq_concat :noindex: +seq_slice +--------- +.. autoclass:: paddle.v2.layer.seq_slice + :noindex: + Reshaping Layers ================ diff --git a/python/paddle/trainer/config_parser.py b/python/paddle/trainer/config_parser.py index 9ea69fc5e..11e54ba42 100644 --- a/python/paddle/trainer/config_parser.py +++ b/python/paddle/trainer/config_parser.py @@ -2657,6 +2657,51 @@ class SubSequenceLayer(LayerBase): self.create_bias_parameter(bias, size) +@config_layer('seq_slice') +class SeqSliceLayer(LayerBase): + def __init__(self, name, inputs, starts, ends, bias=False, **xargs): + if isinstance(inputs, list): + assert len(inputs) == 1, ('the first input of sequence slice layer ' + 'is a single sequence input.') + else: + inputs = [inputs] + + if starts is not None: + if isinstance(starts, list): + assert len(starts) == 1, ( + 'the start indices for sequence slice layer cannot ' + 'be a list having more than one element.') + starts = starts[0] + inputs.append(starts) + + if ends is not None: + if isinstance(ends, list): + assert len(ends) == 1, ( + 'the end indices for sequence slice layer cannot ' + 'be a list having more than one element.') + ends = ends[0] + inputs.append(ends) + assert len(inputs) >= 2, ( + 'the sequence slice layer has at least two inputs.') + + super(SeqSliceLayer, self).__init__( + name, 'seq_slice', 0, inputs=inputs, **xargs) + input_layer0 = self.get_input_layer(0) + size = input_layer0.size + self.set_layer_size(size) + + if len(inputs) == 3: + assert ( + self.get_input_layer(1).size == self.get_input_layer(2).size), ( + 'If start and end indices are both given to' + 'sequence slice layer, they should have the same width.') + elif len(inputs) == 2: + if starts is not None: + self.config.select_first = True + else: + self.config.select_first = False + + @config_layer('out_prod') class OuterProdLayer(LayerBase): def __init__(self, name, inputs, device=None): diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py index ea5fdcc50..15636b144 100755 --- a/python/paddle/trainer_config_helpers/layers.py +++ b/python/paddle/trainer_config_helpers/layers.py @@ -131,6 +131,7 @@ __all__ = [ 'crop_layer', 'clip_layer', 'slice_projection', + 'seq_slice_layer', ] @@ -225,6 +226,7 @@ class LayerType(object): PRELU = 'prelu' CROP_LAYER = 'crop' CLIP_LAYER = 'clip' + SEQ_SLICE = 'seq_slice' @staticmethod def is_layer_type(type_name): @@ -6119,3 +6121,69 @@ def clip_layer(input, min, max, name=None): max=max) return LayerOutput( name, LayerType.CLIP_LAYER, parents=[input], size=input.size) + + +@wrap_name_default() +def seq_slice_layer(input, starts, ends, name=None): + """ + seq_slice_layer will return one or several sub-sequences from the + input sequence layer given start and end indices. + + - If only start indices are given, and end indices are set to None, + this layer slices the input sequence from the given start indices + to its end. + - If only end indices are given, and start indices are set to None, + this layer slices the input sequence from its beginning to the + given end indices. + - If start and end indices are both given, they should have the same + number of elements. + + If start or end indices contains more than one elements, the input sequence + will be sliced for multiple times. + + + .. code-block:: python + + seq_silce = seq_slice_layer(input=input_seq, + starts=start_pos, ends=end_pos) + + :param name: name of this layer. + :type name: basestring + :param input: input for this layer, it should be a sequence. + :type input: LayerOutput + :param starts: start indices to slice the input sequence. + :type starts: LayerOutput|None + :param ends: end indices to slice the input sequence. + :type ends: LayerOutput|None + :return: LayerOutput object. + :rtype: LayerOutput + + """ + + assert isinstance(input, LayerOutput), ( + 'The first input of seq_slice layer must be a PaddlePaddle layer.') + + if starts is not None: + assert isinstance(starts, LayerOutput), ( + 'The start indices for seq_slice layer ' + 'must be a PaddlePaddle layer.') + if ends is not None: + assert isinstance(ends, LayerOutput), ( + 'The end indices for seq_slice layer must be a PaddlePaddle layer.') + assert starts is not None or ends is not None, ( + 'start and end indices ' + 'cannot be set to None at the same time, at least one of ' + 'them should be given.') + if starts is not None and ends is not None: + assert starts.size == ends.size, ( + 'If start and end indices are both given to seq_slice_layer, ' + 'they should have the same width.') + + Layer( + name=name, + type=LayerType.SEQ_SLICE, + inputs=input.name, + starts=starts.name if starts is not None else None, + ends=ends.name if ends is not None else None) + return LayerOutput( + name, LayerType.SEQ_SLICE, parents=[input], size=input.size) diff --git a/python/paddle/trainer_config_helpers/tests/configs/file_list.sh b/python/paddle/trainer_config_helpers/tests/configs/file_list.sh index 0ffa58bc1..1ce865cea 100755 --- a/python/paddle/trainer_config_helpers/tests/configs/file_list.sh +++ b/python/paddle/trainer_config_helpers/tests/configs/file_list.sh @@ -7,6 +7,7 @@ test_rnn_group shared_fc shared_lstm shared_gru test_cost_layers_with_weight test_spp_layer test_bilinear_interp test_maxout test_bi_grumemory math_ops test_seq_concat_reshape test_pad test_smooth_l1 test_multiplex_layer test_prelu_layer test_row_conv test_detection_output_layer test_multibox_loss_layer -test_recursive_topology test_gated_unit_layer test_clip_layer test_row_l2_norm_layer) +test_recursive_topology test_gated_unit_layer test_clip_layer test_row_l2_norm_layer +test_seq_slice_layer) export whole_configs=(test_split_datasource) diff --git a/python/paddle/trainer_config_helpers/tests/configs/protostr/test_seq_slice_layer.protostr b/python/paddle/trainer_config_helpers/tests/configs/protostr/test_seq_slice_layer.protostr new file mode 100644 index 000000000..5b73d614f --- /dev/null +++ b/python/paddle/trainer_config_helpers/tests/configs/protostr/test_seq_slice_layer.protostr @@ -0,0 +1,79 @@ +type: "nn" +layers { + name: "word" + type: "data" + size: 128 + active_type: "" +} +layers { + name: "starts" + type: "data" + size: 5 + active_type: "" +} +layers { + name: "ends" + type: "data" + size: 5 + active_type: "" +} +layers { + name: "__seq_slice_layer_0__" + type: "seq_slice" + size: 128 + active_type: "" + inputs { + input_layer_name: "word" + } + inputs { + input_layer_name: "starts" + } + inputs { + input_layer_name: "ends" + } +} +layers { + name: "__seq_slice_layer_1__" + type: "seq_slice" + size: 128 + active_type: "" + inputs { + input_layer_name: "word" + } + inputs { + input_layer_name: "starts" + } + select_first: true +} +layers { + name: "__seq_slice_layer_2__" + type: "seq_slice" + size: 128 + active_type: "" + inputs { + input_layer_name: "word" + } + inputs { + input_layer_name: "ends" + } + select_first: false +} +input_layer_names: "word" +output_layer_names: "__seq_slice_layer_0__" +output_layer_names: "__seq_slice_layer_1__" +output_layer_names: "__seq_slice_layer_2__" +sub_models { + name: "root" + layer_names: "word" + layer_names: "starts" + layer_names: "ends" + layer_names: "__seq_slice_layer_0__" + layer_names: "__seq_slice_layer_1__" + layer_names: "__seq_slice_layer_2__" + input_layer_names: "word" + output_layer_names: "__seq_slice_layer_0__" + output_layer_names: "__seq_slice_layer_1__" + output_layer_names: "__seq_slice_layer_2__" + is_recurrent_layer_group: false +} + diff --git a/python/paddle/trainer_config_helpers/tests/configs/test_seq_slice_layer.py b/python/paddle/trainer_config_helpers/tests/configs/test_seq_slice_layer.py new file mode 100644 index 000000000..510ad3220 --- /dev/null +++ b/python/paddle/trainer_config_helpers/tests/configs/test_seq_slice_layer.py @@ -0,0 +1,13 @@ +#!/usr/bin/env python +#coding=utf-8 +from paddle.trainer_config_helpers import * + +input_seq = data_layer("word", size=128) +starts = data_layer("starts", size=5) +ends = data_layer("ends", size=5) + +seq_slice1 = seq_slice_layer(input=input_seq, starts=starts, ends=ends) +seq_slice2 = seq_slice_layer(input=input_seq, starts=starts, ends=None) +seq_slice3 = seq_slice_layer(input=input_seq, starts=None, ends=ends) + +outputs(seq_slice1, seq_slice2, seq_slice3) -- GitLab From 2988a58ef01a56e84cff02463972e0150bc6ab13 Mon Sep 17 00:00:00 2001 From: caoying03 Date: Tue, 8 Aug 2017 08:52:05 +0800 Subject: [PATCH 0014/2018] add unittest. --- paddle/gserver/tests/CMakeLists.txt | 6 + .../gserver/tests/test_SeqSliceLayerGrad.cpp | 214 ++++++++++++++++++ 2 files changed, 220 insertions(+) create mode 100644 paddle/gserver/tests/test_SeqSliceLayerGrad.cpp diff --git a/paddle/gserver/tests/CMakeLists.txt b/paddle/gserver/tests/CMakeLists.txt index 4546d12a9..9fdb14886 100644 --- a/paddle/gserver/tests/CMakeLists.txt +++ b/paddle/gserver/tests/CMakeLists.txt @@ -30,6 +30,12 @@ add_unittest_without_exec(test_CRFLayerGrad add_test(NAME test_CRFLayerGrad COMMAND test_CRFLayerGrad) +################ test_SeqSliceLayerGrad #################### +add_unittest_without_exec(test_SeqSliceLayerGrad + test_SeqSliceLayerGrad.cpp + LayerGradUtil.cpp) +add_test(NAME test_SeqSliceLayerGrad + COMMAND test_SeqSliceLayerGrad) add_unittest_without_exec(test_ActivationGrad test_ActivationGrad.cpp diff --git a/paddle/gserver/tests/test_SeqSliceLayerGrad.cpp b/paddle/gserver/tests/test_SeqSliceLayerGrad.cpp new file mode 100644 index 000000000..e456dd5db --- /dev/null +++ b/paddle/gserver/tests/test_SeqSliceLayerGrad.cpp @@ -0,0 +1,214 @@ +/* Copyright (c) 2016 Baidu, Inc. 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 +#include "ModelConfig.pb.h" +#include "paddle/gserver/layers/DataLayer.h" +#include "paddle/trainer/Trainer.h" + +#include "LayerGradUtil.h" +#include "paddle/testing/TestUtil.h" + +using namespace paddle; // NOLINT +using namespace std; // NOLINT + +DECLARE_int32(gpu_id); +DECLARE_bool(thread_local_rand_use_global_seed); + +const int MAX_SEQ_NUM = 5; +const int MAX_SEQ_LEN = 5; +const int MAX_BEAM_SIZE = 3; + +vector randSampling(real range, int n) { + CHECK_GE(range, n); + vector num(range); + iota(begin(num), end(num), 0.); + if (range == n) return num; + + random_shuffle(begin(num), end(num)); + num.resize(n); + sort(begin(num), end(num)); + return num; +} + +void genSeqInfo(vector& seqStartPos, vector& subSeqStartPos) { + seqStartPos.resize(1, 0); + subSeqStartPos.resize(1, 0); + + // srand((size_t)(time(NULL))); + srand(1); + int seqNum = 1 + (rand() % MAX_SEQ_NUM); + for (int i = 0; i < seqNum; ++i) { + int subSeqNum = 1 + (rand() % MAX_SEQ_NUM); + for (int j = 0; j < subSeqNum; ++j) + subSeqStartPos.push_back(subSeqStartPos.back() + + (1 + (rand() % MAX_SEQ_LEN))); + seqStartPos.push_back(subSeqStartPos.back()); + } +} + +/* + generate start indices according to sequence start positions. + */ +void genStarts(vector& seqStartPos, + vector>& starts, + size_t beamSize) { + starts.clear(); + starts.resize(seqStartPos.size() - 1, vector(beamSize, -1.)); + + for (size_t i = 0; i < seqStartPos.size() - 1; ++i) { + int seqLen = seqStartPos[i + 1] - seqStartPos[i]; + vector randStarts = + randSampling(seqLen, min(seqLen, static_cast(beamSize))); + copy(begin(randStarts), end(randStarts), begin(starts[i])); + } +} + +/* + generate end indices according to sequence start positions and start indices. + */ +void genEnds(vector& seqStartPos, + vector>& starts, + vector>& ends, + size_t beamSize) { + CHECK_EQ(seqStartPos.size() - 1, starts.size()); + ends.clear(); + ends.resize(seqStartPos.size() - 1, vector(beamSize, -1.)); + + for (size_t i = 0; i < starts.size(); ++i) { + for (size_t j = 0; j < starts[i].size(); ++j) { + int seqLen = seqStartPos[i + 1] - seqStartPos[i]; + CHECK_GE(seqLen - 1, starts[i][j]); + if (starts[i][j] == -1.) break; + if (starts[i][j] == (seqLen - 1)) { + ends[i][j] = starts[i][j]; + } else { + ends[i][j] = starts[i][j] + randSampling(seqLen - starts[i][j], 1)[0]; + } + } + } +} + +void genTestData(vector& seqStartPos, + vector& subSeqStartPos, + vector>& starts, + vector>& ends, + bool hasSubseq) { + size_t beamSize = MAX_BEAM_SIZE; + genSeqInfo(seqStartPos, subSeqStartPos); + + genStarts(hasSubseq ? subSeqStartPos : seqStartPos, starts, beamSize); + genEnds(hasSubseq ? subSeqStartPos : seqStartPos, starts, ends, beamSize); +} + +template +void flatten2dVector(vector>& inVec, vector& outVec) { + size_t totalSize{0}; + for (auto const& items : inVec) totalSize += items.size(); + outVec.reserve(totalSize); + + for (auto& items : inVec) + move(items.begin(), items.end(), back_inserter(outVec)); +} + +void testSeqSliceLayer(bool hasSubseq, + bool useGpu, + vector& seqStartPos, + vector& subSeqStartPos, + vector>& starts, + vector>& ends) { + // layer size is not crutial for this layer, + // so here use a small layer size in the unittest. + const size_t layerSize{4}; + TestConfig config; + config.layerConfig.set_type("seq_slice"); + config.layerConfig.set_size(layerSize); + + // add the first input + MatrixPtr seqInputPtr = + Matrix::create(hasSubseq ? subSeqStartPos.back() : seqStartPos.back(), + layerSize, + false, + false); + seqInputPtr->randomizeUniform(); + + if (hasSubseq) { + config.inputDefs.push_back({INPUT_SELF_DEFINE_DATA, + "seq_input", + seqInputPtr, + seqStartPos, + subSeqStartPos}); + } else { + config.inputDefs.push_back( + {INPUT_SELF_DEFINE_DATA, "seq_input", seqInputPtr, seqStartPos}); + } + config.layerConfig.add_inputs(); + + // add start indices + if (starts.size()) { + vector startsToVec; + flatten2dVector(starts, startsToVec); + + MatrixPtr startMatrixPtr = + Matrix::create(starts.size(), starts[0].size(), false, false); + startMatrixPtr->copyFrom(startsToVec.data(), startsToVec.size()); + + config.inputDefs.push_back( + {INPUT_SELF_DEFINE_DATA, "starts", startMatrixPtr}); + config.layerConfig.add_inputs(); + } + + // add end indices + if (ends.size()) { + vector endsToVec; + flatten2dVector(ends, endsToVec); + MatrixPtr endMatrixPtr = + Matrix::create(ends.size(), ends[0].size(), false, false); + config.inputDefs.push_back({INPUT_SELF_DEFINE_DATA, "ends", endMatrixPtr}); + config.layerConfig.add_inputs(); + } + + testLayerGrad(config, "seq_slice", /*batchSize*/ 100, false, useGpu, false); +} + +TEST(Layer, SeqSliceLayer) { + vector seqStartPos; + vector subSeqStartPos; + vector> starts; + vector> ends; + + genSeqInfo(seqStartPos, subSeqStartPos); + for (bool hasSubseq : {false, true}) { + genTestData(seqStartPos, subSeqStartPos, starts, ends, hasSubseq); + for (bool useGpu : {false, true}) { + vector> tmp; + testSeqSliceLayer( + hasSubseq, useGpu, seqStartPos, subSeqStartPos, tmp, ends); + testSeqSliceLayer( + hasSubseq, useGpu, seqStartPos, subSeqStartPos, starts, tmp); + testSeqSliceLayer( + hasSubseq, useGpu, seqStartPos, subSeqStartPos, starts, ends); + } + } +} + +int main(int argc, char** argv) { + initMain(argc, argv); + hl_start(); + hl_init(FLAGS_gpu_id); + FLAGS_thread_local_rand_use_global_seed = true; + srand(1); + testing::InitGoogleTest(&argc, argv); + return RUN_ALL_TESTS(); +} -- GitLab From b7ee1e7d9c7f01844b23c54a3c5a2584e0a6a410 Mon Sep 17 00:00:00 2001 From: dongzhihong Date: Wed, 9 Aug 2017 00:12:09 +0800 Subject: [PATCH 0015/2018] "backward check todo" --- paddle/operators/rowwise_add_op.h | 8 ++++---- python/paddle/v2/framework/tests/test_rowwise_add_op.py | 2 ++ 2 files changed, 6 insertions(+), 4 deletions(-) diff --git a/paddle/operators/rowwise_add_op.h b/paddle/operators/rowwise_add_op.h index 06af88a99..965c0df53 100644 --- a/paddle/operators/rowwise_add_op.h +++ b/paddle/operators/rowwise_add_op.h @@ -42,18 +42,18 @@ template class RowwiseAddGradKernel : public OpKernel { public: void Compute(const ExecutionContext& context) const override { - auto XGrad = context.Output(0); - auto bGrad = context.Output(1); + auto* XGrad = context.Output(0); + auto* bGrad = context.Output(1); XGrad->mutable_data(context.GetPlace()); bGrad->mutable_data(context.GetPlace()); // I, O, OG => [X, b], [Out], [OutGrad] auto OutGrad = EigenMatrix::From(*context.Input(3)); - EigenMatrix::From(*XGrad).device(*(context.GetEigenDevice())) = + EigenMatrix::From(*XGrad).device(context.GetEigenDevice()) = OutGrad; // https://eigen.tuxfamily.org/dox/unsupported/TensorBase_8h_source.html - EigenVector::Flatten(*bGrad).device(*(context.GetEigenDevice())) = + EigenVector::Flatten(*bGrad).device(context.GetEigenDevice()) = OutGrad.cumsum(1); // colwise add } }; diff --git a/python/paddle/v2/framework/tests/test_rowwise_add_op.py b/python/paddle/v2/framework/tests/test_rowwise_add_op.py index f8521eb51..e957dd6b3 100644 --- a/python/paddle/v2/framework/tests/test_rowwise_add_op.py +++ b/python/paddle/v2/framework/tests/test_rowwise_add_op.py @@ -15,5 +15,7 @@ class TestRowwiseAddOp(unittest.TestCase): self.outputs = {'Out': np.add(self.inputs['X'], self.inputs['b'])} +#TODO(dzh): rowwise_grad check + if __name__ == '__main__': unittest.main() -- GitLab From 7304006b7121c844d071227a6c2d24245a06e32e Mon Sep 17 00:00:00 2001 From: fengjiayi Date: Tue, 8 Aug 2017 16:38:27 -0700 Subject: [PATCH 0016/2018] Update backward.md --- paddle/framework/backward.md | 29 ++++++++++++++++++++--------- 1 file changed, 20 insertions(+), 9 deletions(-) diff --git a/paddle/framework/backward.md b/paddle/framework/backward.md index 61f308b46..c717c2f30 100644 --- a/paddle/framework/backward.md +++ b/paddle/framework/backward.md @@ -24,20 +24,31 @@ REGISTER_GRADIENT_OP(add_two, add_two_grad, AddTwoGradOp); `add_two_grad` is the type of backward operator, and `AddTwoGradOp` is its class name. -### Implement : gradient operator registry +## Backward Opeartor Creating -| | forward operator | backward operator | -| ---------------------- | ---------------- | -------------------------------- | -| **Operator::inputs_** | Inputs | Inputs, Outputs, OutputGradients | -| **Operator::outputs_** | Outputs | InputGradients | +### Usage -Inputs/Outputs means the input/output of the operator, InputGradients/OutputGradients is the gradient respect to forward opeartor. Forward operator and Backward operator are isomorphic, save their corresponding needs into member attribute. +Given a certain forward operator, we can get its corresponding backward opeartor by calling: -We use a global hash map record the gradient operators available, follow the philosophy of minimum core, make operator pluggable unit. Each gradient is an operator and it needs to regist itself. +```cpp +OperatorBase* bwd_op = BuildGradOp(const OperatorBase* fwd_op); +``` + +The function `BuildGradOp` will sequentially execute following processes: + +1. Getting the `type_` of given forward operator, and then creating the corresponding backward operator. + +2. Copying all the attributes of forward operator expect `input_format` and `output_format`(if it has), for their elements differ between forward and backward operators. + +3. Copying forward operator's `inputs_` and `outputs_` to backward operator's `inputs_`. And adding forward inputs' gradient variables into backward `output_`, adding forward outputs' gradient variables into backward `input_`. + +4. Building backward operator's `input_format`, `output_format` (if necessary) and `in_out_idxs_` according to its `inputs_` and `outputs_` just created. + +## Backward Network Building -grad_op_builder(fengjiayi) +A backward network is a series of backward operators. The main idea of building a backward network is creating backward operators in the inverted sequence and put them together. -### Implement : Backward network +In our design, the network itself is also a kind of operator. So the operators contained by a big network may be some small network. given a forward network, it generates the backward network. We only care about the Gradients—`OutputGradients`,`InputGradients`. -- GitLab From 200e3e2c6b7b3c1be47204f0e76ab79696f46efb Mon Sep 17 00:00:00 2001 From: dongzhihong Date: Wed, 9 Aug 2017 10:14:20 +0800 Subject: [PATCH 0017/2018] "change namespace prefix" --- paddle/operators/rowwise_add_op.cc | 6 +++--- paddle/operators/rowwise_add_op.h | 2 +- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/paddle/operators/rowwise_add_op.cc b/paddle/operators/rowwise_add_op.cc index c192da04d..a012ab0be 100644 --- a/paddle/operators/rowwise_add_op.cc +++ b/paddle/operators/rowwise_add_op.cc @@ -35,7 +35,7 @@ class RowwiseAddOp : public framework::OperatorWithKernel { class RowwiseAddOpMaker : public framework::OpProtoAndCheckerMaker { public: - RowWiseAddOpMaker(framework::OpProto *proto, + RowwiseAddOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker) : OpProtoAndCheckerMaker(proto, op_checker) { AddInput("X", "The left input of row-wise add op, must be matrix"); @@ -48,9 +48,9 @@ for i in xrange(X.shape[0]): )DOC"); } }; -class RowwiseAddGradOp : public OperatorWithKernel { +class RowwiseAddGradOp : public framework::OperatorWithKernel { protected: - void InferShape(const InferShapeContext &ctx) const override { + void InferShape(const framework::InferShapeContext &ctx) const override { PADDLE_ENFORCE(ctx.InputSize() == 4UL, "RowwiseAddGrad inputs is I, O, OG, size must be 4"); PADDLE_ENFORCE(ctx.OutputSize() == 2, diff --git a/paddle/operators/rowwise_add_op.h b/paddle/operators/rowwise_add_op.h index ad43e753e..27d7a33e8 100644 --- a/paddle/operators/rowwise_add_op.h +++ b/paddle/operators/rowwise_add_op.h @@ -28,7 +28,7 @@ template ; template -class RowwiseAddKernel : public OpKernel { +class RowwiseAddKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& context) const override { auto out = context.Output(0); -- GitLab From 68bfc3ff963474e12c8af1c3575128b0acac90ed Mon Sep 17 00:00:00 2001 From: dongzhihong Date: Wed, 9 Aug 2017 21:02:51 +0800 Subject: [PATCH 0018/2018] "add python test" --- .../v2/framework/tests/test_rowwise_add_op.py | 13 ++++++++++++- 1 file changed, 12 insertions(+), 1 deletion(-) diff --git a/python/paddle/v2/framework/tests/test_rowwise_add_op.py b/python/paddle/v2/framework/tests/test_rowwise_add_op.py index e957dd6b3..1b27f54f1 100644 --- a/python/paddle/v2/framework/tests/test_rowwise_add_op.py +++ b/python/paddle/v2/framework/tests/test_rowwise_add_op.py @@ -1,6 +1,7 @@ import unittest -from op_test_util import OpTestMeta import numpy as np +from op_test_util import OpTestMeta +from gradient_checker import GradientChecker, create_op class TestRowwiseAddOp(unittest.TestCase): @@ -15,6 +16,16 @@ class TestRowwiseAddOp(unittest.TestCase): self.outputs = {'Out': np.add(self.inputs['X'], self.inputs['b'])} +class RowwiseAddGradOpTest(GradientChecker): + def test_rowwise_add(self): + op = create_op("rowwise_add") + inputs = { + "X": np.random.uniform(0.1, 1, [10, 10]).astype("float32"), + "b": np.random.uniform(0.1, 1, [10, 1]).astype("float32") + } + self.check_grad(op, inputs, set("X", "b"), "Out") + + #TODO(dzh): rowwise_grad check if __name__ == '__main__': -- GitLab From 26bc5b12596c945956f7a6b003712805e579a36d Mon Sep 17 00:00:00 2001 From: caoying03 Date: Tue, 8 Aug 2017 18:48:11 +0800 Subject: [PATCH 0019/2018] add implementations. --- paddle/gserver/layers/KmaxSeqScoreLayer.cpp | 5 + paddle/gserver/layers/SequenceSliceLayer.cpp | 228 ++++++++++++++++++ .../gserver/layers/SubNestedSequenceLayer.cpp | 16 +- .../gserver/tests/test_SeqSliceLayerGrad.cpp | 25 +- paddle/parameter/Argument.cpp | 27 ++- 5 files changed, 278 insertions(+), 23 deletions(-) create mode 100644 paddle/gserver/layers/SequenceSliceLayer.cpp diff --git a/paddle/gserver/layers/KmaxSeqScoreLayer.cpp b/paddle/gserver/layers/KmaxSeqScoreLayer.cpp index 8ce591d47..e96fd61fc 100644 --- a/paddle/gserver/layers/KmaxSeqScoreLayer.cpp +++ b/paddle/gserver/layers/KmaxSeqScoreLayer.cpp @@ -97,6 +97,11 @@ void KmaxSeqScoreLayer::forward(PassType passType) { scores_ = inputScore; } + // TODO(caoying) + // Here selSubSeqIdx is automatically converted from real to int + // This is very dangerous if user fill this matrix himself, invalid data may + // occur. The selected indices should be stored in + // CpuSparseMatrix with SparseValueType set to NO_VALUE. Matrix::resizeOrCreate( output_.value, input.hasSubseq() ? input.getNumSubSequences() : input.getNumSequences(), diff --git a/paddle/gserver/layers/SequenceSliceLayer.cpp b/paddle/gserver/layers/SequenceSliceLayer.cpp new file mode 100644 index 000000000..410aba663 --- /dev/null +++ b/paddle/gserver/layers/SequenceSliceLayer.cpp @@ -0,0 +1,228 @@ +/* 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 "Layer.h" +#include "paddle/math/Matrix.h" +#include "paddle/math/Vector.h" +#include "paddle/utils/Logging.h" +#include "paddle/utils/Stat.h" + +namespace paddle { + +class SequenceSliceLayer : public Layer { +public: + explicit SequenceSliceLayer(const LayerConfig& config) : Layer(config) {} + + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override; + + void forward(PassType passType) override; + void backward(const UpdateCallback& callback = nullptr) override; + +private: + // TODO(caoying) + // Here selSubSeqIdx is automatically converted from real to int + // This is very dangerous if user fill this matrix himself, invalid data + // may occur. The selected indices should be stored in CpuSparseMatrix + // with SparseValueType set to NO_VALUE. + MatrixPtr startIdsOnCpu_; + MatrixPtr endIdsOnCpu_; + + std::vector selectedRows_; + IVectorPtr rowIndice_; + std::vector> inputSeqInfoVec_; + std::vector outSubSeqStartPos_; + std::vector outSeqStartPos_; + + void checkInputs(); + void copySliceIdsToCpu(); + void calSelectedRows(const MatrixPtr starts, const MatrixPtr ends); +}; + +REGISTER_LAYER(seq_slice, SequenceSliceLayer); + +bool SequenceSliceLayer::init(const LayerMap& layerMap, + const ParameterMap& parameterMap) { + /* Initialize the basic parent class */ + Layer::init(layerMap, parameterMap); + CHECK_GE(inputLayers_.size(), 2U); + CHECK_LE(inputLayers_.size(), 3U); + + setNeedSequenceInfo(false); + return true; +} + +void SequenceSliceLayer::checkInputs() { + const Argument& inputSeq = getInput(0); + CHECK(inputSeq.hasSeq()) << "The first input of sequence slic layer " + << "must be a sequence."; + // Check inputs + const MatrixPtr indices1 = getInputValue(1); + CHECK_EQ(indices1->getHeight(), + inputSeq.hasSubseq() ? inputSeq.getNumSubSequences() + : inputSeq.getNumSequences()) + << "Height of the second input should be equal to number of sequence " + << "in the first input."; + if (inputLayers_.size() == 3) { + const MatrixPtr indices2 = getInputValue(2); + CHECK_EQ(indices2->getHeight(), indices1->getHeight()) + << "start indices and end indices should have the same height."; + CHECK_EQ(indices2->getWidth(), indices1->getWidth()) + << "start indices and end indices should have the same Width."; + } +} + +void SequenceSliceLayer::copySliceIdsToCpu() { + if (!useGpu_) { + if (inputLayers_.size() == 2U) { + if (config_.select_first()) { + startIdsOnCpu_ = getInputValue(1); + endIdsOnCpu_ = nullptr; + } else { + startIdsOnCpu_ = nullptr; + endIdsOnCpu_ = getInputValue(1); + } + } else if (inputLayers_.size() == 3U) { + startIdsOnCpu_ = getInputValue(1); + endIdsOnCpu_ = getInputValue(2); + } + return; + } + + const MatrixPtr indices1 = getInputValue(1); + if (inputLayers_.size() == 2U) { + if (config_.select_first()) { + Matrix::resizeOrCreate(startIdsOnCpu_, + indices1->getHeight(), + indices1->getWidth(), + false /* trans */, + false /* useGpu */); + startIdsOnCpu_->copyFrom(*indices1); + endIdsOnCpu_ = nullptr; + } else { + Matrix::resizeOrCreate(endIdsOnCpu_, + indices1->getHeight(), + indices1->getWidth(), + false /* trans */, + false /* useGpu */); + endIdsOnCpu_->copyFrom(*indices1); + startIdsOnCpu_ = nullptr; + } + } else if (inputLayers_.size() == 3U) { + Matrix::resizeOrCreate(startIdsOnCpu_, + indices1->getHeight(), + indices1->getWidth(), + false /* trans */, + false /* useGpu */); + startIdsOnCpu_->copyFrom(*indices1); + + const MatrixPtr indices2 = getInputValue(2); + Matrix::resizeOrCreate(endIdsOnCpu_, + indices2->getHeight(), + indices2->getWidth(), + false /* trans */, + false /* useGpu */); + endIdsOnCpu_->copyFrom(*indices2); + } +} + +void SequenceSliceLayer::calSelectedRows(const MatrixPtr starts, + const MatrixPtr ends) { + outSeqStartPos_.resize(1, 0); + outSubSeqStartPos_.resize(1, 0); + selectedRows_.clear(); + + size_t beamSize = starts ? starts->getWidth() : ends->getWidth(); + // iterate over sequence + size_t rowIdx = 0; + for (size_t i = 0; i < inputSeqInfoVec_.size(); ++i) { + // iterate over sub-sequence in a sequence + for (size_t j = 0; j < inputSeqInfoVec_[i].size() - 1; ++j) { + // iterate over each index for slicing. + for (size_t k = 0; k < beamSize; ++k) { + if (starts) { + if (starts->getElement(rowIdx, k) == -1.) break; + } else if (ends->getElement(rowIdx, k) == -1.) + break; + + int begPos = inputSeqInfoVec_[i][j]; + if (starts) begPos += starts->getElement(rowIdx, k); + + int endPos = inputSeqInfoVec_[i][j + 1] - 1; + if (ends) endPos = inputSeqInfoVec_[i][j] + ends->getElement(rowIdx, k); + + int seqLen = endPos - begPos + 1; + CHECK(seqLen); + for (int m = begPos; m <= endPos; ++m) selectedRows_.push_back(m); + inputSeqInfoVec_.size() > 1 + ? outSubSeqStartPos_.push_back(outSubSeqStartPos_.back() + seqLen) + : outSeqStartPos_.push_back(outSeqStartPos_.back() + seqLen); + } + rowIdx++; + } + if (inputSeqInfoVec_.size() > 1) + outSeqStartPos_.push_back(outSubSeqStartPos_.back()); + } + + if (useGpu_) { + rowIndice_ = IVector::create(selectedRows_.size(), useGpu_); + rowIndice_->copyFrom(selectedRows_.data(), selectedRows_.size()); + } else { + rowIndice_ = + IVector::create(selectedRows_.data(), selectedRows_.size(), useGpu_); + } + + // create the sequence information for the output. + ICpuGpuVector::resizeOrCreate( + output_.sequenceStartPositions, outSeqStartPos_.size(), false); + output_.sequenceStartPositions->copyFrom( + outSeqStartPos_.data(), outSeqStartPos_.size(), false); + + if (inputSeqInfoVec_.size() > 1) { + ICpuGpuVector::resizeOrCreate( + output_.subSequenceStartPositions, outSubSeqStartPos_.size(), false); + output_.subSequenceStartPositions->copyFrom( + outSubSeqStartPos_.data(), outSubSeqStartPos_.size(), false); + } +} + +void SequenceSliceLayer::forward(PassType passType) { + Layer::forward(passType); + checkInputs(); + + const Argument& inputSeq = getInput(0); + inputSeqInfoVec_.clear(); + Argument::reorganizeSeqInfo(inputSeq.sequenceStartPositions, + inputSeq.subSequenceStartPositions, + inputSeqInfoVec_); + copySliceIdsToCpu(); + + // calculate the selected row indices in a batch, + // and build the output sequence information. + calSelectedRows(startIdsOnCpu_ ? startIdsOnCpu_ : nullptr, + endIdsOnCpu_ ? endIdsOnCpu_ : nullptr); + + resetOutput(selectedRows_.size(), getSize()); + + getOutputValue()->selectRows(*getInputValue(0), *rowIndice_); +} + +void SequenceSliceLayer::backward(const UpdateCallback& callback) { + MatrixPtr inputSeqGrad = getInputGrad(0); + MatrixPtr outputGrad = getOutputGrad(); + + outputGrad->addToRows(*inputSeqGrad, *rowIndice_); +} + +} // namespace paddle diff --git a/paddle/gserver/layers/SubNestedSequenceLayer.cpp b/paddle/gserver/layers/SubNestedSequenceLayer.cpp index 76f587fff..0db030027 100644 --- a/paddle/gserver/layers/SubNestedSequenceLayer.cpp +++ b/paddle/gserver/layers/SubNestedSequenceLayer.cpp @@ -52,11 +52,10 @@ private: * ] * * ths output is saved to private member rowIndice_; - * [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15, - * 16,17,18,19,20,21,22,23,24,25,26,27] + * [0,1,2,3,4,5,6,7,8,9,15,16,17,18,19,20,21,23,24,25,26,27] */ - void calSelectedCols(const MatrixPtr selectedIndices, + void calSelectedRows(const MatrixPtr selectedIndices, const std::vector>& inputSeqInfo); // if the second input of this layer is on GPU memory, copy it to CPU memory. @@ -67,7 +66,7 @@ private: std::vector> inputSeqInfoVec_; // the final selected row indices in a batch, - // rowIdx_ and selectedRows_ actually share a same memory. + // rowIndice_ and selectedRows_ actually share a same memory. IVectorPtr rowIndice_; std::vector selectedRows_; }; @@ -83,7 +82,7 @@ bool SubNestedSequenceLayer::init(const LayerMap& layerMap, return true; } -void SubNestedSequenceLayer::calSelectedCols( +void SubNestedSequenceLayer::calSelectedRows( const MatrixPtr selectedIndices, const std::vector>& inputSeqInfo) { selectedRows_.clear(); @@ -96,6 +95,11 @@ void SubNestedSequenceLayer::calSelectedCols( for (size_t i = 0; i < seqNum; ++i) { for (size_t j = 0; j < beamSize; ++j) { if (selectedIndices->getElement(i, j) == -1.) break; + // TODO(caoying) + // Here selSubSeqIdx is automatically converted from real to int + // This is very dangerous if user fill this matrix himself, invalid data + // may occur. The selected indices should be stored in + // CpuSparseMatrix with SparseValueType set to NO_VALUE. int selSubSeqIdx = selectedIndices->getElement(i, j); CHECK_GT(inputSeqInfoVec_[i].size() - 1, selSubSeqIdx); @@ -160,7 +164,7 @@ void SubNestedSequenceLayer::forward(PassType passType) { Argument::reorganizeSeqInfo(inputSeq.sequenceStartPositions, inputSeq.subSequenceStartPositions, inputSeqInfoVec_); - calSelectedCols(selIdsCpu_, inputSeqInfoVec_); + calSelectedRows(selIdsCpu_, inputSeqInfoVec_); resetOutput(selectedRows_.size(), getSize()); getOutputValue()->selectRows(*getInputValue(0), *rowIndice_); diff --git a/paddle/gserver/tests/test_SeqSliceLayerGrad.cpp b/paddle/gserver/tests/test_SeqSliceLayerGrad.cpp index e456dd5db..d560ca650 100644 --- a/paddle/gserver/tests/test_SeqSliceLayerGrad.cpp +++ b/paddle/gserver/tests/test_SeqSliceLayerGrad.cpp @@ -26,9 +26,9 @@ using namespace std; // NOLINT DECLARE_int32(gpu_id); DECLARE_bool(thread_local_rand_use_global_seed); -const int MAX_SEQ_NUM = 5; -const int MAX_SEQ_LEN = 5; -const int MAX_BEAM_SIZE = 3; +const int MAX_SEQ_NUM = 17; +const int MAX_SEQ_LEN = 23; +const int MAX_BEAM_SIZE = 13; vector randSampling(real range, int n) { CHECK_GE(range, n); @@ -46,8 +46,7 @@ void genSeqInfo(vector& seqStartPos, vector& subSeqStartPos) { seqStartPos.resize(1, 0); subSeqStartPos.resize(1, 0); - // srand((size_t)(time(NULL))); - srand(1); + srand((size_t)(time(NULL))); int seqNum = 1 + (rand() % MAX_SEQ_NUM); for (int i = 0; i < seqNum; ++i) { int subSeqNum = 1 + (rand() % MAX_SEQ_NUM); @@ -105,7 +104,7 @@ void genTestData(vector& seqStartPos, vector>& starts, vector>& ends, bool hasSubseq) { - size_t beamSize = MAX_BEAM_SIZE; + size_t beamSize = 1 + (rand() % MAX_BEAM_SIZE); genSeqInfo(seqStartPos, subSeqStartPos); genStarts(hasSubseq ? subSeqStartPos : seqStartPos, starts, beamSize); @@ -167,16 +166,21 @@ void testSeqSliceLayer(bool hasSubseq, config.inputDefs.push_back( {INPUT_SELF_DEFINE_DATA, "starts", startMatrixPtr}); config.layerConfig.add_inputs(); + config.layerConfig.set_select_first(true); } // add end indices if (ends.size()) { vector endsToVec; flatten2dVector(ends, endsToVec); + MatrixPtr endMatrixPtr = Matrix::create(ends.size(), ends[0].size(), false, false); + endMatrixPtr->copyFrom(endsToVec.data(), endsToVec.size()); + config.inputDefs.push_back({INPUT_SELF_DEFINE_DATA, "ends", endMatrixPtr}); config.layerConfig.add_inputs(); + config.layerConfig.set_select_first(false); } testLayerGrad(config, "seq_slice", /*batchSize*/ 100, false, useGpu, false); @@ -188,10 +192,15 @@ TEST(Layer, SeqSliceLayer) { vector> starts; vector> ends; + std::vector mode = {false}; +#ifndef PADDLE_ONLY_CPU + mode.push_back(true); +#endif genSeqInfo(seqStartPos, subSeqStartPos); - for (bool hasSubseq : {false, true}) { + for (bool hasSubseq : {true, false}) { + LOG(INFO) << "hasSubSeq : " << hasSubseq; genTestData(seqStartPos, subSeqStartPos, starts, ends, hasSubseq); - for (bool useGpu : {false, true}) { + for (bool useGpu : mode) { vector> tmp; testSeqSliceLayer( hasSubseq, useGpu, seqStartPos, subSeqStartPos, tmp, ends); diff --git a/paddle/parameter/Argument.cpp b/paddle/parameter/Argument.cpp index 0547ac93c..06f7e5245 100644 --- a/paddle/parameter/Argument.cpp +++ b/paddle/parameter/Argument.cpp @@ -670,19 +670,28 @@ void Argument::reorganizeSeqInfo( const ICpuGpuVectorPtr seqStartPos, const ICpuGpuVectorPtr subSeqStartPos, std::vector>& reorganizedSeqInfo) { - int* seqStarts = seqStartPos->getMutableData(false); - int* subSeqStarts = subSeqStartPos->getMutableData(false); + CHECK(seqStartPos); int seqNum = seqStartPos->getSize() - 1; - reorganizedSeqInfo.resize(seqNum, std::vector()); - int seqIdx = 0; - for (size_t i = 0; i < subSeqStartPos->getSize(); ++i) { - reorganizedSeqInfo[seqIdx].push_back(subSeqStarts[i]); - if (subSeqStarts[i] == seqStarts[seqIdx + 1]) { - seqIdx++; - if (seqIdx == seqNum) return; + int* seqStarts = seqStartPos->getMutableData(false); + + if (subSeqStartPos) { + int* subSeqStarts = subSeqStartPos->getMutableData(false); + reorganizedSeqInfo.resize(seqNum, std::vector()); + int seqIdx = 0; + for (size_t i = 0; i < subSeqStartPos->getSize(); ++i) { reorganizedSeqInfo[seqIdx].push_back(subSeqStarts[i]); + if (subSeqStarts[i] == seqStarts[seqIdx + 1]) { + seqIdx++; + if (seqIdx == seqNum) return; + reorganizedSeqInfo[seqIdx].push_back(subSeqStarts[i]); + } } + } else { + reorganizedSeqInfo.resize(1, std::vector(seqNum + 1, 0)); + memcpy(reorganizedSeqInfo[0].data(), + seqStarts, + sizeof(int) * seqStartPos->getSize()); } } -- GitLab From 7c0cb0c7901093e7b2aa57100f086f737ab39739 Mon Sep 17 00:00:00 2001 From: dongzhihong Date: Wed, 9 Aug 2017 23:51:46 +0800 Subject: [PATCH 0020/2018] "fix ci launch" --- python/paddle/v2/framework/tests/test_rowwise_add_op.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/python/paddle/v2/framework/tests/test_rowwise_add_op.py b/python/paddle/v2/framework/tests/test_rowwise_add_op.py index 1b27f54f1..8118d2d74 100644 --- a/python/paddle/v2/framework/tests/test_rowwise_add_op.py +++ b/python/paddle/v2/framework/tests/test_rowwise_add_op.py @@ -23,7 +23,7 @@ class RowwiseAddGradOpTest(GradientChecker): "X": np.random.uniform(0.1, 1, [10, 10]).astype("float32"), "b": np.random.uniform(0.1, 1, [10, 1]).astype("float32") } - self.check_grad(op, inputs, set("X", "b"), "Out") + self.check_grad(op, inputs, set(["X", "b"]), "Out") #TODO(dzh): rowwise_grad check -- GitLab From 2ddb11222adef0545a2691d73281516026b9de10 Mon Sep 17 00:00:00 2001 From: dongzhihong Date: Thu, 10 Aug 2017 11:31:08 +0800 Subject: [PATCH 0021/2018] "on hold" --- paddle/operators/mul_op.cc | 27 +++++++++++++++--- paddle/operators/mul_op.cu | 3 +- paddle/operators/mul_op.h | 28 +++++++++++++++++++ .../paddle/v2/framework/tests/test_mul_op.py | 2 ++ 4 files changed, 55 insertions(+), 5 deletions(-) diff --git a/paddle/operators/mul_op.cc b/paddle/operators/mul_op.cc index db81fd555..fb79796f3 100644 --- a/paddle/operators/mul_op.cc +++ b/paddle/operators/mul_op.cc @@ -54,10 +54,27 @@ The equation is: Out = X * Y class MulOpGrad : public framework::OperatorWithKernel { protected: - void InferShape(const framework::InferShapeContext &ctx) const override {} - std::string DebugString() const override { - LOG(INFO) << "MulGrad"; - return ""; + void InferShape(const framework::InferShapeContext &ctx) const override { + PADDLE_ENFORCE_EQ(ctx.InputSize(), 3UL, + "Input of MulOpGrad should be 3, X, Y, Out@GRAD"); + PADDLE_ENFORCE_EQ(ctx.OutputSize(), 2UL, + "Output of MulOpGrad should be 2, X@GRAD, Y@GRAD"); + PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) should not be null"); + PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Y"), "Input(Y) should not be null"); + PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")), + "Input(Out@GRAD) should not be null"); + auto *x_grad = ctx.Output(framework::GradVarName("X")); + auto *y_grad = ctx.Output(framework::GradVarName("Y")); + auto dim0 = ctx.Input(0)->dims(); + auto dim1 = ctx.Input(1)->dims(); + auto out_dims = ctx.Input(2)->dims(); + PADDLE_ENFORCE(dim0[0] * dim1[0] == out_dims[0], + "Out@GRAD[0] must equal to X[0] * Y[0]"); + PADDLE_ENFORCE(dim0[1] * dim1[1] == out_dims[1], + "Out@GRAD shape must equal to X[1] * Y[1]"); + + x_grad->Resize(dim1); + y_grad->Resize(dim0); } }; @@ -69,3 +86,5 @@ REGISTER_OP(mul, ops::MulOp, ops::MulOpMaker); REGISTER_GRADIENT_OP(mul, mul_grad, ops::MulOpGrad); REGISTER_OP_CPU_KERNEL(mul, ops::MulKernel); +REGISTER_OP_CPU_KERNEL(mul_grad, + ops::MulGradKernel); diff --git a/paddle/operators/mul_op.cu b/paddle/operators/mul_op.cu index 43debbc21..a81444dbe 100644 --- a/paddle/operators/mul_op.cu +++ b/paddle/operators/mul_op.cu @@ -16,5 +16,6 @@ #include "paddle/operators/mul_op.h" namespace ops = paddle::operators; - REGISTER_OP_GPU_KERNEL(mul, ops::MulKernel); +REGISTER_OP_GPU_KERNEL(mul_grad, + ops::MulGradKernel); diff --git a/paddle/operators/mul_op.h b/paddle/operators/mul_op.h index ab12631c0..2032a2add 100644 --- a/paddle/operators/mul_op.h +++ b/paddle/operators/mul_op.h @@ -46,5 +46,33 @@ class MulKernel : public framework::OpKernel { } }; +template +class MulGradKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& ctx) const override { + auto* input0 = ctx.Input("X"); + auto* input1 = ctx.Input("Y"); + auto* input2 = ctx.Input(framework::GradVarName("Out")); + + auto* output0 = ctx.Output(0); + auto* output1 = ctx.Output(1); + output0->mutable_data(ctx.GetPlace()); + output1->mutable_data(ctx.GetPlace()); + + auto X = EigenMatrix::From(*input0); + auto Y = EigenMatrix::From(*input1); + auto dOut = EigenMatrix::From(*input2); + auto dX = EigenMatrix::From(*output0); + auto dY = EigenMatrix::From(*output1); + + // dX = Out@G * Y' + // dY = X' * Out@G + auto place = ctx.GetEigenDevice(); + // TODO(dzh,qijun) : need transpose feature of blas library + // Eigen Tensor does not support it very well + // dX.device(place) = dOut.contract(dOut, transpose) + } +}; + } // namespace operators } // namespace paddle diff --git a/python/paddle/v2/framework/tests/test_mul_op.py b/python/paddle/v2/framework/tests/test_mul_op.py index ec0ac9915..126a7f398 100644 --- a/python/paddle/v2/framework/tests/test_mul_op.py +++ b/python/paddle/v2/framework/tests/test_mul_op.py @@ -15,5 +15,7 @@ class TestMulOp(unittest.TestCase): self.outputs = {'Out': np.dot(self.inputs['X'], self.inputs['Y'])} +# TODO(dzh,qijun) : mulgrad test case need transpose feature of blas library + if __name__ == '__main__': unittest.main() -- GitLab From b97f020f9c34da04e093deb4691f6286f4017e62 Mon Sep 17 00:00:00 2001 From: caoying03 Date: Thu, 10 Aug 2017 10:37:07 +0800 Subject: [PATCH 0022/2018] fix unittest error. --- paddle/gserver/layers/SequenceSliceLayer.cpp | 3 +-- python/paddle/trainer_config_helpers/layers.py | 1 + .../protostr/test_kmax_seq_socre_layer.protostr | 17 +++++------------ .../tests/configs/test_kmax_seq_socre_layer.py | 4 +--- 4 files changed, 8 insertions(+), 17 deletions(-) diff --git a/paddle/gserver/layers/SequenceSliceLayer.cpp b/paddle/gserver/layers/SequenceSliceLayer.cpp index 424f89855..165ee6311 100644 --- a/paddle/gserver/layers/SequenceSliceLayer.cpp +++ b/paddle/gserver/layers/SequenceSliceLayer.cpp @@ -70,9 +70,8 @@ void SequenceSliceLayer::checkInputs() { const Argument& inputSeq = getInput(0); CHECK(inputSeq.hasSeq()) << "The first input of sequence slic layer " << "must be a sequence."; - // Check inputs const MatrixPtr indices1 = getInputValue(1); - CHECK_EQ(indices1->getHeight(), + CHECK_EQ(static_cast(indices1->getHeight()), inputSeq.hasSubseq() ? inputSeq.getNumSubSequences() : inputSeq.getNumSequences()) << "Height of the second input should be equal to number of sequence " diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py index e51332da0..79d24cfe5 100755 --- a/python/paddle/trainer_config_helpers/layers.py +++ b/python/paddle/trainer_config_helpers/layers.py @@ -6242,6 +6242,7 @@ def seq_slice_layer(input, starts, ends, name=None): name, LayerType.SEQ_SLICE, parents=[input], size=input.size) +@wrap_name_default() @layer_support() def kmax_sequence_score_layer(input, name=None, beam_size=1): """ diff --git a/python/paddle/trainer_config_helpers/tests/configs/protostr/test_kmax_seq_socre_layer.protostr b/python/paddle/trainer_config_helpers/tests/configs/protostr/test_kmax_seq_socre_layer.protostr index 81bd71f68..3d32220bf 100644 --- a/python/paddle/trainer_config_helpers/tests/configs/protostr/test_kmax_seq_socre_layer.protostr +++ b/python/paddle/trainer_config_helpers/tests/configs/protostr/test_kmax_seq_socre_layer.protostr @@ -1,12 +1,6 @@ type: "nn" layers { - name: "input" - type: "data" - size: 300 - active_type: "" -} -layers { - name: "data" + name: "input_seq" type: "data" size: 128 active_type: "" @@ -17,7 +11,7 @@ layers { size: 1 active_type: "exponential" inputs { - input_layer_name: "data" + input_layer_name: "input_seq" input_parameter_name: "___fc_layer_0__.w0" } bias_parameter_name: "___fc_layer_0__.wbias" @@ -51,15 +45,14 @@ parameters { initial_strategy: 0 initial_smart: false } -input_layer_names: "data" +input_layer_names: "input_seq" output_layer_names: "__kmax_sequence_score_layer_0__" sub_models { name: "root" - layer_names: "input" - layer_names: "data" + layer_names: "input_seq" layer_names: "__fc_layer_0__" layer_names: "__kmax_sequence_score_layer_0__" - input_layer_names: "data" + input_layer_names: "input_seq" output_layer_names: "__kmax_sequence_score_layer_0__" is_recurrent_layer_group: false } diff --git a/python/paddle/trainer_config_helpers/tests/configs/test_kmax_seq_socre_layer.py b/python/paddle/trainer_config_helpers/tests/configs/test_kmax_seq_socre_layer.py index d245c5a41..48d0cd55d 100644 --- a/python/paddle/trainer_config_helpers/tests/configs/test_kmax_seq_socre_layer.py +++ b/python/paddle/trainer_config_helpers/tests/configs/test_kmax_seq_socre_layer.py @@ -2,9 +2,7 @@ #coding=utf-8 from paddle.trainer_config_helpers import * -data = data_layer(name='input', size=300) - -data = data_layer(name="data", size=128) +data = data_layer(name="input_seq", size=128) scores = fc_layer(input=data, size=1, act=ExpActivation()) kmax_seq_id = kmax_sequence_score_layer(input=scores, beam_size=5) -- GitLab From cfb86c4e23d424328066fe8d2fbbacb9c9ead6c1 Mon Sep 17 00:00:00 2001 From: chengduoZH Date: Sun, 13 Aug 2017 09:30:41 +0800 Subject: [PATCH 0023/2018] Add vol2col and col2vol cuda kernel --- paddle/cuda/include/hl_matrix.h | 58 ++++++++++ paddle/cuda/include/stub/hl_matrix_stub.h | 15 +++ paddle/cuda/src/hl_cuda_matrix.cu | 135 ++++++++++++++++++++++ 3 files changed, 208 insertions(+) diff --git a/paddle/cuda/include/hl_matrix.h b/paddle/cuda/include/hl_matrix.h index eb454c59c..da2ed8cab 100644 --- a/paddle/cuda/include/hl_matrix.h +++ b/paddle/cuda/include/hl_matrix.h @@ -224,4 +224,62 @@ extern void hl_matrix_collect_shared_bias(real* B_d, extern void hl_matrix_rotate( real* mat, real* matRot, int dimM, int dimN, bool clockWise); +/** + * @brief Matrix vol2Col: Convert 3D volume into col matrix + * + * @param[in] matSrc input matrix. + * @param[in] channel channel of matSrc. + * @param[in] depth depth of matSrc. + * @param[in] height height of matSrc. + * @param[in] width width of matSrc. + * @param[in] filterD depth of filter. + * @param[in] filterH height of filter. + * @param[in] filterW width of filter. + * @param[in] strideD stride in the depth. + * @param[in] strideH stride in the height. + * @param[in] strideW stride in the width. + * @param[in] paddingD padding in the depth. + * @param[in] paddingH padding in the height. + * @param[in] paddingW padding in the width. + * @param[out] matDst output matrix. + * + */ +extern void hl_matrix_vol2Col(real* matSrc, + int channel, int depth, int height, int width, + int filterD, int filterH, int filterW, + int strideD, int strideH, int strideW, + int paddingD, int paddingH, int paddingW, + real* matDst); + +/** + * @brief Matrix col2Vol: Convert col matrix into 3D volume + * + * @param[out] matDst output matrix. + * @param[in] channel channel of matDst. + * @param[in] depth depth of matDst. + * @param[in] height height of matDst. + * @param[in] width width of matDst. + * @param[in] filterD depth of filter. + * @param[in] filterH height of filter. + * @param[in] filterW width of filter. + * @param[in] strideD stride in the depth. + * @param[in] strideH stride in the height. + * @param[in] strideW stride in the width. + * @param[in] paddingD padding in the depth. + * @param[in] paddingH padding in the height. + * @param[in] paddingW padding in the width. + * @param[in] matSrc input matrix. + * @param[in] beta input + * @param[in] alpha input + * + */ +extern void hl_matrix_col2Vol(real* matDst, + int channels, int depth, int height, int width, + int filterD, int filterH, int filterW, + int strideD, int strideH, int strideW, + int paddingD, int paddingH, int paddingW, + real* matSrc, + real alpha, real beta); + + #endif /* HL_MATRIX_H_ */ diff --git a/paddle/cuda/include/stub/hl_matrix_stub.h b/paddle/cuda/include/stub/hl_matrix_stub.h index 127cb7e27..0b7377781 100644 --- a/paddle/cuda/include/stub/hl_matrix_stub.h +++ b/paddle/cuda/include/stub/hl_matrix_stub.h @@ -99,4 +99,19 @@ inline void hl_matrix_collect_shared_bias(real* B_d, inline void hl_matrix_rotate( real* mat, real* matRot, int dimM, int dimN, bool clockWise) {} +inline void hl_matrix_vol2Col(real* data, + int channels, int depth, int height, int width, + int filterD, int filterH, int filterW, + int strideD, int strideH, int strideW, + int paddingD, int paddingH, int paddingW, + real* data_col) {} + +inline void hl_matrix_col2Vol(real* data, + int channels, int depth, int height, int width, + int filterD, int filterH, int filterW, + int strideD, int strideH, int strideW, + int paddingD, int paddingH, int paddingW, + real* data_Im, + real alpha, real beta) {} + #endif // HL_MATRIX_STUB_H_ diff --git a/paddle/cuda/src/hl_cuda_matrix.cu b/paddle/cuda/src/hl_cuda_matrix.cu index 39272456c..f626c07a0 100644 --- a/paddle/cuda/src/hl_cuda_matrix.cu +++ b/paddle/cuda/src/hl_cuda_matrix.cu @@ -592,3 +592,138 @@ void hl_matrix_rotate( mat, matRot, dimM, dimN, clockWise); CHECK_SYNC("hl_matrix_rotate failed"); } + + +__global__ void keMatrixVol2Col( + int num_kernels, real*dataSrc, real* dataDst, + int depth, int height, int width, + int filterD, int filterH, int filterW, + int strideD, int strideH, int strideW, + int paddingD, int paddingH, int paddingW, + int depth_col, int height_col, int width_col){ + + for (int index = blockIdx.x * blockDim.x + threadIdx.x; + index < num_kernels; + index += blockDim.x * gridDim.x){ + + int w_out = index % width_col; + int h_out = (index / width_col ) % height_col; + int d_out = (index / width_col / height_col) % depth_col; + int channel_in = index / width_col / height_col / depth_col; + int channel_out = channel_in * filterD * filterH * filterW; + int w_in = w_out * strideW - paddingW; + int h_in = h_out * strideH - paddingH; + int d_in = d_out * strideD - paddingD; + + dataDst += ((channel_out * depth_col + d_out) * height_col + h_out) * width_col + w_out; + dataSrc += ((channel_in * depth + d_in) * height + h_in) * width + w_in; + for (int k = 0; k < filterD; ++k) { + for (int i = 0; i < filterH; ++i) { + for (int j = 0; j < filterW; ++j) { + int d = d_in + k; + int h = h_in + i; + int w = w_in + j; + *dataDst = (d >= 0 && d < depth && h >= 0 && h < height && w >= 0 && w < width ) ? + dataSrc[(k * height + i) * width + j] : 0; + dataDst += depth_col * height_col * width_col; + } + } + } + } +} + +void hl_matrix_vol2Col(real* dataSrc, + int channels, int depth, int height, int width, + int filterD, int filterH, int filterW, + int strideD, int strideH, int strideW, + int paddingD, int paddingH, int paddingW, real* dataDst){ + + int depth_col = (depth + 2 * paddingD - filterD) / strideD + 1; + int height_col = (height + 2 * paddingH - filterH) / strideH + 1; + int width_col = (width + 2 * paddingW - filterW) / strideW + 1; + int num_kernels = channels * depth_col * height_col * width_col; + + const int threads = 512; + const int blocks = DIVUP(num_kernels, threads); + + keMatrixVol2Col<<< blocks, threads >>>( + num_kernels, dataSrc, dataDst, + depth, height, width, + filterD, filterH, filterW, + strideD, strideH, strideW, + paddingD, paddingH, paddingW, + depth_col, height_col, width_col); + CHECK_SYNC("hl_matrix_vol2Col failed"); +} + +__global__ void keMatrixCol2Vol( + int num_kernels, real*dataDst, real* dataSrc, + int depth, int height, int width, + int filterD, int filterH, int filterW, + int strideD, int strideH, int strideW, + int paddingD, int paddingH, int paddingW, + int depth_col, int height_col, int width_col, + real alpha, real beta){ + + for (int index = blockIdx.x * blockDim.x + threadIdx.x; + index < num_kernels; + index += blockDim.x * gridDim.x) { + + real val = 0; + int w = index % width + paddingW; + int h = (index / width) % height + paddingH; + int d = (index / width / height) % depth + paddingD; + int c = index / (width * height * depth); + // compute the start and end of the output + int w_col_start = (w < filterW) ? 0 : (w - filterW) / strideW + 1; + int w_col_end = min(w / strideW + 1, width_col); + int h_col_start = (h < filterH) ? 0 : (h - filterH) / strideH + 1; + int h_col_end = min(h / strideH + 1, height_col); + int d_col_start = (d < filterD) ? 0 : (d - filterD) / strideD + 1; + int d_col_end = min(d / strideD + 1, depth_col); + + int offset = (c * filterD * filterW * filterH + \ + d * filterW * filterH + h * filterW + w) * depth_col * height_col * width_col; + + int coeff_d_col = (1 - strideD * filterW * filterH * depth_col) * height_col * width_col; + int coeff_h_col = (1 - strideH * filterW * depth_col * height_col) * width_col; + int coeff_w_col = (1 - strideW * depth_col * height_col * width_col); + + for (int d_col = d_col_start; d_col < d_col_end; ++d_col) { + for (int h_col = h_col_start; h_col < h_col_end; ++h_col) { + for (int w_col = w_col_start; w_col < w_col_end; ++w_col) { + val += dataSrc[offset + d_col * coeff_d_col + h_col * coeff_h_col + w_col * coeff_w_col]; + } + } + } + dataDst[index] = val; + } +} + +void hl_matrix_col2Vol(real* dataDst, + int channels, int depth, int height, int width, + int filterD, int filterH, int filterW, + int strideD, int strideH, int strideW, + int paddingD, int paddingH, int paddingW, + real* dataSrc, + real alpha, real beta){ + + int depth_col = (depth + 2 * paddingD - filterD) / strideD + 1; + int height_col = (height + 2 * paddingH - filterH) / strideH + 1; + int width_col = (width + 2 * paddingW - filterW) / strideW + 1; + int num_kernels = channels * depth * height * width; + + const int threads = 512; + const int blocks = DIVUP(num_kernels, threads); + + keMatrixCol2Vol<<< blocks, threads >>>( + num_kernels, dataDst, dataSrc, + depth, height, width, + filterD, filterH, filterW, + strideD, strideH, strideW, + paddingD, paddingH, paddingW, + depth_col, height_col, width_col, + alpha, beta); + + CHECK_SYNC("hl_matrix_col2Vol failed"); +} -- GitLab From 8cc0eb9c5d564b71452e65d1bac3f9f19f5bf89e Mon Sep 17 00:00:00 2001 From: chengduoZH Date: Sun, 13 Aug 2017 09:38:02 +0800 Subject: [PATCH 0024/2018] Modify ConvConfig, Add depth dimension --- proto/ModelConfig.proto | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/proto/ModelConfig.proto b/proto/ModelConfig.proto index 4f3d5bf3f..043ae502b 100644 --- a/proto/ModelConfig.proto +++ b/proto/ModelConfig.proto @@ -82,6 +82,12 @@ message ConvConfig { // if not set, use img_size optional uint32 img_size_y = 14; + + optional uint32 filter_size_z = 15 [ default = 1 ]; + optional uint32 padding_z = 16 [ default = 1 ]; + optional uint32 stride_z = 17 [ default = 1 ]; + optional uint32 output_z = 18 [ default = 1 ]; + optional uint32 img_size_z = 19 [ default = 1 ]; } message PoolConfig { @@ -631,4 +637,4 @@ message ModelConfig { // For External Machine, defining how to split a neural network // into multiple parts. optional ExternalConfig external_config = 9; -}; +}; \ No newline at end of file -- GitLab From 5d7f6dde52af781e15953c041374b5671bdf918d Mon Sep 17 00:00:00 2001 From: chengduoZH Date: Sun, 13 Aug 2017 09:42:48 +0800 Subject: [PATCH 0025/2018] Add depth dimension information to ConvBaseLayer --- paddle/gserver/layers/ConvBaseLayer.cpp | 17 +++++++++++++---- paddle/gserver/layers/ConvBaseLayer.h | 8 ++++++++ 2 files changed, 21 insertions(+), 4 deletions(-) diff --git a/paddle/gserver/layers/ConvBaseLayer.cpp b/paddle/gserver/layers/ConvBaseLayer.cpp index e161d89c3..e437b0b86 100644 --- a/paddle/gserver/layers/ConvBaseLayer.cpp +++ b/paddle/gserver/layers/ConvBaseLayer.cpp @@ -21,9 +21,11 @@ bool ConvBaseLayer::init(const LayerMap& layerMap, const ParameterMap& parameterMap) { /* Initialize the basic parent class */ Layer::init(layerMap, parameterMap); - isDeconv_ = (config_.type() == "exconv" || config_.type() == "cudnn_conv") - ? false - : true; + isDeconv_ = (config_.type() == "exconv" || + config_.type() == "cudnn_conv" || + config_.type() == "conv3d" || + config_.type() == "deconv3d" ) + ? false : true; /* Initialize the convolutional layer parameter */ numFilters_ = config_.num_filters(); @@ -36,7 +38,6 @@ bool ConvBaseLayer::init(const LayerMap& layerMap, paddingY_.push_back(conf.padding_y()); strideY_.push_back(conf.stride_y()); filterSizeY_.push_back(conf.filter_size_y()); - filterPixels_.push_back(filterSize_.back() * filterSizeY_.back()); channels_.push_back(conf.channels()); imgSizeH_.push_back(conf.has_img_size_y() ? conf.img_size_y() : conf.img_size()); @@ -45,6 +46,14 @@ bool ConvBaseLayer::init(const LayerMap& layerMap, filterChannels_.push_back(conf.filter_channels()); outputH_.push_back(conf.has_output_y() ? conf.output_y() : conf.output_x()); outputW_.push_back(conf.output_x()); + + paddingZ_.push_back(conf.padding_z()); + strideZ_.push_back(conf.stride_z()); + filterSizeZ_.push_back(conf.filter_size_z()); + imgSizeD_.push_back(conf.img_size_z()); + outputD_.push_back(conf.output_z()); + filterPixels_.push_back( + filterSize_.back() * filterSizeY_.back() * filterSizeZ_.back()); } CHECK(inputLayers_.size() == parameters_.size()); diff --git a/paddle/gserver/layers/ConvBaseLayer.h b/paddle/gserver/layers/ConvBaseLayer.h index e9d15d94f..8d1fd989e 100644 --- a/paddle/gserver/layers/ConvBaseLayer.h +++ b/paddle/gserver/layers/ConvBaseLayer.h @@ -23,6 +23,7 @@ namespace paddle { * with learned filters and (optionally) adds biases. */ + class ConvBaseLayer : public Layer { protected: typedef std::vector IntV; @@ -58,6 +59,13 @@ protected: IntV outputH_; /// The spatial dimensions of output feature map width. IntV outputW_; + + IntV outputD_; + IntV imgSizeD_; + IntV filterSizeZ_; + IntV strideZ_; + IntV paddingZ_; + /// Group size, refer to grouped convolution in /// Alex Krizhevsky's paper: when group=2, the first half of the /// filters are only connected to the first half of the input channels, -- GitLab From 11975b4f9185907b5f2518722e5311d744361887 Mon Sep 17 00:00:00 2001 From: chengduoZH Date: Sun, 13 Aug 2017 09:47:37 +0800 Subject: [PATCH 0026/2018] Add Conv3DLayer --- paddle/gserver/layers/Conv3DLayer.cpp | 225 ++++++++++++++++++++++++++ paddle/gserver/layers/Conv3DLayer.h | 57 +++++++ 2 files changed, 282 insertions(+) create mode 100644 paddle/gserver/layers/Conv3DLayer.cpp create mode 100644 paddle/gserver/layers/Conv3DLayer.h diff --git a/paddle/gserver/layers/Conv3DLayer.cpp b/paddle/gserver/layers/Conv3DLayer.cpp new file mode 100644 index 000000000..0fa9c5f9f --- /dev/null +++ b/paddle/gserver/layers/Conv3DLayer.cpp @@ -0,0 +1,225 @@ +/* Copyright (c) 2016 Baidu, Inc. 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/utils/Logging.h" +#include "paddle/utils/Stat.h" +#include "Conv3DLayer.h" + +namespace paddle { + +REGISTER_LAYER(conv3d, Conv3DLayer); + +bool Conv3DLayer::init(const LayerMap &layerMap, + const ParameterMap ¶meterMap) { + if (!ConvBaseLayer::init(layerMap, parameterMap)) + return false; + int index = 0; + for (auto &inputConfig : config_.inputs()) { + const ConvConfig &conf = inputConfig.conv_conf(); + M_.push_back(numFilters_ / conf.groups()); + K_.push_back( + conf.filter_channels() * conf.filter_size_z() * \ + conf.filter_size_y() * conf.filter_size()); + weights_[index]->getW()->reshape( + weights_[index]->getW()->getWidth(), + weights_[index]->getW()->getHeight()); + weights_[index]->getWGrad()->reshape( + weights_[index]->getWGrad()->getWidth(), + weights_[index]->getWGrad()->getHeight()); + ++index; + } + biases_->getWGrad()->reshape( + biases_->getWGrad()->width_, biases_->getWGrad()->height_); + biases_->getW()->reshape( + biases_->getW()->width_, biases_->getW()->height_); + CHECK(inputLayers_.size() == parameters_.size()); + return true; +} + + +size_t Conv3DLayer::getSize() { + CHECK_NE(inputLayers_.size(), 0UL); + // imgSizeH_.clear(); + // imgSizeW_.clear(); + // imgSizeD_.clear(); + outputH_.clear(); + outputW_.clear(); + outputD_.clear(); + N_.clear(); + size_t layerSize = 0; + for (size_t i = 0; i < inputLayers_.size(); ++i) { + // imgSizeH_.push_back(inputLayers_[i]->getOutput().getFrameHeight()); + // imgSizeW_.push_back(inputLayers_[i]->getOutput().getFrameWidth()); + // imgSizeD_.push_back(inputLayers_[i]->getOutput().getFrameDepth()); + outputW_.push_back(outputSize( + imgSizeW_[i], filterSize_[i], + padding_[i], stride_[i], true)); + outputH_.push_back(outputSize( + imgSizeH_[i], filterSizeY_[i], + paddingY_[i], strideY_[i], true)); + outputD_.push_back(outputSize( + imgSizeD_[i], filterSizeZ_[i], + paddingZ_[i], strideZ_[i], true)); + + N_.push_back(outputD_[i] * outputH_[i] * outputW_[i]); + CHECK(layerSize == 0 || N_[i] * size_t(numFilters_) == layerSize); + layerSize += N_[i] * numFilters_; + } + getOutput().setFrameHeight(outputH_[0]); + getOutput().setFrameWidth(outputW_[0]); + getOutput().setFrameDepth(outputD_[0]); + return layerSize; +} + +void Conv3DLayer::forward(PassType passType) { + Layer::forward(passType); + + int batchSize = inputLayers_[0]->getOutputValue()->getHeight(); + int outWidth = getSize(); + resetOutput(batchSize, outWidth); + const MatrixPtr outMat = getOutputValue(); + + for (size_t i = 0; i != inputLayers_.size(); ++i) { + REGISTER_TIMER_INFO("FwdConv3D", getName().c_str()); + const MatrixPtr& inMat = getInputValue(i); + int width = inMat->getWidth(); + int M = M_[i]; + int N = N_[i]; + int K = K_[i]; + Matrix::resizeOrCreate(colBuf_, K * groups_[i], N, false, useGpu_); + MatrixPtr wMat = weights_[i]->getW(); + for (int n = 0; n < batchSize; ++n) { + colBuf_->vol2Col(inMat->getData() + n * width, channels_[i], + imgSizeD_[i], imgSizeH_[i], imgSizeW_[i], + filterSizeZ_[i], filterSizeY_[i], filterSize_[i], + strideZ_[i], strideY_[i], stride_[i], + paddingZ_[i], paddingY_[i], padding_[i]); + + real *outData = outMat->getData() + n * outWidth; + MatrixPtr outMatSub = + Matrix::create(outData, groups_[i] * M, N, false, useGpu_); + for (int g = 0; g < groups_[i]; g++) { + MatrixPtr wMatSub = wMat->subMatrix(g * M, M); + MatrixPtr in = colBuf_->subMatrix(g * K, K); + MatrixPtr out = outMatSub->subMatrix(g * M, M); + out->mul(*wMatSub, *in, 1.0, 0.0); + } + } + } + if (nullptr != this->biasParameter_) { + REGISTER_TIMER_INFO("FwBiasTimer", getName().c_str()); + this->addBias(); + } + forwardActivation(); +} + +void Conv3DLayer::backward(const UpdateCallback &callback) { + backwardActivation(); + + if (biases_ && biases_->getWGrad()) { + bpropBiases(); + biases_->getParameterPtr()->incUpdate(callback); + } + + for (size_t i = 0; i != inputLayers_.size(); ++i) { + REGISTER_TIMER_INFO("BwdConv3D", getName().c_str()); + if (weights_[i]->getWGrad()) { + bpropWeights(i); + } + if (this->needGradient_) { + bpropData(i); + } + REGISTER_TIMER_INFO("WeightUpdate", getName().c_str()); + weights_[i]->getParameterPtr()->incUpdate(callback); + } +} + +void Conv3DLayer::bpropWeights(int i) { + int M = M_[i]; + int N = N_[i]; + int K = K_[i]; + const MatrixPtr& inMat = getInputValue(i); + int width = inMat->getWidth(); + Matrix::resizeOrCreate(colBuf_, K * groups_[i], N, false, useGpu_); + MatrixPtr wGradMat = weights_[i]->getWGrad(); + real* outGradData = getOutputGrad()->getData(); + int batchSize = inputLayers_[0]->getOutputValue()->getHeight(); + + for (int n = 0; n < batchSize; ++n) { + colBuf_->vol2Col(inMat->getData() + n * width, channels_[i], + imgSizeD_[i], imgSizeH_[i], imgSizeW_[i], + filterSizeZ_[i], filterSizeY_[i], filterSize_[i], + strideZ_[i], strideY_[i], stride_[i], + paddingZ_[i], paddingY_[i], padding_[i]); + outGradData += n * getOutputGrad()->getWidth(); + MatrixPtr outGradSub = + Matrix::create(outGradData, groups_[i] * M, N, false, useGpu_); + for (int g = 0; g < groups_[i]; ++g) { + MatrixPtr inMatSub = colBuf_->subMatrix(g * K, K); + MatrixPtr outG = outGradSub->subMatrix(g * M, M); + MatrixPtr wGradSub = wGradMat->subMatrix(g * M, M); + wGradSub->mul(*outG, *(inMatSub->getTranspose()), 1.0, 1.0); + } + } +} + +void Conv3DLayer::bpropData(int i) { + int M = M_[i]; + int N = N_[i]; + int K = K_[i]; + Matrix::resizeOrCreate(colBuf_, K * groups_[i], N, false, useGpu_); + MatrixPtr wMat = weights_[i]->getW(); + real* outGradData = getOutputGrad()->getData(); + real* preGradData = getInputGrad(i)->getData(); + int batchSize = inputLayers_[0]->getOutputValue()->getHeight(); + for (int n = 0; n < batchSize; ++n) { + outGradData += n * getOutputGrad()->getWidth(); + preGradData += n * getInputGrad(i)->getWidth(); + MatrixPtr outGradSub = + Matrix::create(outGradData, M * groups_[i], N, false, useGpu_); + for (int g = 0; g < groups_[i]; ++g) { + MatrixPtr wMatSub = wMat->subMatrix(g * M, M); + MatrixPtr outG = outGradSub->subMatrix(g * M, M); + MatrixPtr inGradMatSub = colBuf_->subMatrix(g * K, K); + inGradMatSub->mul(*(wMatSub->getTranspose()), *outG, 1.0, 0.0); + } + colBuf_->col2Vol(preGradData, channels_[i], + imgSizeD_[i], imgSizeH_[i], imgSizeW_[i], + filterSizeZ_[i], filterSizeY_[i], filterSize_[i], + strideZ_[i], strideY_[i], stride_[i], + paddingZ_[i], paddingY_[i], padding_[i], + 1.0, 1.0); + } +} + +void Conv3DLayer::bpropBiases() { + MatrixPtr outGradMat = getOutputGrad(); + if (this->sharedBiases_) { + biases_->getWGrad()->collectSharedBias(*outGradMat, 1.0f); + } else { + biases_->getWGrad()->collectBias(*outGradMat, 1.0f); + } +} + +void Conv3DLayer::addBias() { + MatrixPtr outMat = getOutputValue(); + + if (this->sharedBiases_) { + outMat->addSharedBias(*(biases_->getW()), 1.0f); + } else { + outMat->addBias(*(biases_->getW()), 1.0f); + } +} + +} // namespace paddle diff --git a/paddle/gserver/layers/Conv3DLayer.h b/paddle/gserver/layers/Conv3DLayer.h new file mode 100644 index 000000000..703671e5d --- /dev/null +++ b/paddle/gserver/layers/Conv3DLayer.h @@ -0,0 +1,57 @@ +/* Copyright (c) 2016 Baidu, Inc. 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 "ConvBaseLayer.h" +#include "paddle/math/Matrix.h" +#include "paddle/math/MathUtils.h" +#include + +namespace paddle { + +/** + * @brief A subclass of convolution layer. + * This layer expands input and use matrix multiplication to + * calculate convolution operation. + */ +class Conv3DLayer : public ConvBaseLayer { +public: + explicit Conv3DLayer(const LayerConfig& config) : ConvBaseLayer(config) {} + + ~Conv3DLayer() {} + + bool init(const LayerMap &layerMap, const ParameterMap ¶meterMap); + + size_t getSize(); + + void forward(PassType passType); + void addBias(); + + void backward(const UpdateCallback& callback); + + void bpropBiases(); + void bpropData(int i); + void bpropWeights(int i); + +protected: + // Figure out the dimensions for individual gemms. + IntV M_; /// numFilters_ / filter_group_; + IntV N_; /// channels_ * filterSizeZ_ * filterSize_ * filterSizeY_ + IntV K_; /// outputD_ * outputH_ * outputW_ + MatrixPtr colBuf_; +}; + +} // namespace paddle -- GitLab From 23cf0c61e066f54b360efc4e17576a056868b050 Mon Sep 17 00:00:00 2001 From: chengduoZH Date: Sun, 13 Aug 2017 09:48:59 +0800 Subject: [PATCH 0027/2018] Add DeConv3DLayer --- paddle/gserver/layers/DeConv3DLayer.cpp | 211 ++++++++++++++++++++++++ paddle/gserver/layers/DeConv3DLayer.h | 58 +++++++ 2 files changed, 269 insertions(+) create mode 100644 paddle/gserver/layers/DeConv3DLayer.cpp create mode 100644 paddle/gserver/layers/DeConv3DLayer.h diff --git a/paddle/gserver/layers/DeConv3DLayer.cpp b/paddle/gserver/layers/DeConv3DLayer.cpp new file mode 100644 index 000000000..8de40b681 --- /dev/null +++ b/paddle/gserver/layers/DeConv3DLayer.cpp @@ -0,0 +1,211 @@ +/* Copyright (c) 2016 Baidu, Inc. 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/utils/Logging.h" +#include "paddle/utils/Stat.h" +#include "DeConv3DLayer.h" + +namespace paddle { + +REGISTER_LAYER(deconv3d, DeConv3DLayer); + +#define DECONV_OUTPUT_SIZE(IN_SIZE, STRID, PAD, KSIZE) \ + (((IN_SIZE) - 1) * (STRID) - 2 * (PAD) + (KSIZE)) + +bool DeConv3DLayer::init(const LayerMap &layerMap, + const ParameterMap ¶meterMap) { + if (!ConvBaseLayer::init(layerMap, parameterMap)) return false; + // for Deconv, the dimension of Kernel is + // channel * output * depth * height * weigth + // Matrix storage format: (output * depth * height * weigth) x channel + for (int index = 0; index < config_.inputs().size(); ++index) { + M_.push_back(filterChannels_[index]); + K_.push_back( + filterPixels_[index] * (numFilters_/groups_[index])); + weights_[index]->getW()->reshape( + filterPixels_[index] * numFilters_, + filterChannels_[index]); + weights_[index]->getWGrad()->reshape( + filterPixels_[index] * numFilters_, + filterChannels_[index]); + } + biases_->getWGrad()->reshape( + biases_->getWGrad()->width_, biases_->getWGrad()->height_); + biases_->getW()->reshape( + biases_->getW()->width_, biases_->getW()->height_); + CHECK(inputLayers_.size() == parameters_.size()); + return true; +} + + +size_t DeConv3DLayer::getSize() { + CHECK_NE(inputLayers_.size(), 0UL); + // imgSizeH_.clear(); + // imgSizeW_.clear(); + // imgSizeD_.clear(); + outputH_.clear(); + outputW_.clear(); + outputD_.clear(); + N_.clear(); + No_.clear(); + size_t layerSize = 0; + for (size_t i = 0; i < inputLayers_.size(); ++i) { + // imgSizeH_.push_back(inputLayers_[i]->getOutput().getFrameHeight()); + // imgSizeW_.push_back(inputLayers_[i]->getOutput().getFrameWidth()); + // imgSizeD_.push_back(inputLayers_[i]->getOutput().getFrameDepth()); + outputW_.push_back( + DECONV_OUTPUT_SIZE( + imgSizeW_[i], stride_[i], + padding_[i], filterSize_[i])); + outputH_.push_back( + DECONV_OUTPUT_SIZE( + imgSizeH_[i], strideY_[i], + paddingY_[i], filterSizeY_[i])); + outputD_.push_back( + DECONV_OUTPUT_SIZE( + imgSizeD_[i], strideZ_[i], + paddingZ_[i], filterSizeZ_[i])); + No_.push_back(outputD_[i] * outputH_[i] * outputW_[i]); + N_.push_back(imgSizeD_[i] * imgSizeH_[i] * imgSizeW_[i]); + CHECK(layerSize == 0 || N_[i] * size_t(numFilters_) == layerSize); + layerSize += No_[i] * numFilters_; + } + getOutput().setFrameHeight(outputH_[0]); + getOutput().setFrameWidth(outputW_[0]); + getOutput().setFrameDepth(outputD_[0]); + return layerSize; +} + +void DeConv3DLayer::forward(PassType passType) { + Layer::forward(passType); + int batchSize = inputLayers_[0]->getOutputValue()->getHeight(); + int outWidth = getSize(); + resetOutput(batchSize, outWidth); + const MatrixPtr outMat = getOutputValue(); + + for (size_t i = 0; i != inputLayers_.size(); ++i) { + REGISTER_TIMER_INFO("FwdDeConv3D", getName().c_str()); + const MatrixPtr& inMat = getInputValue(i); + int width = inMat->getWidth(); + int M = M_[i]; + int N = N_[i]; + int K = K_[i]; + MatrixPtr wMat = weights_[i]->getW(); + Matrix::resizeOrCreate(colBuf_, K * groups_[i] , N, false, useGpu_); + + for (int n = 0; n < batchSize; ++n) { + real *inData = inMat->getData() + n * width; + real *colBufData = colBuf_->getData(); + for (int g = 0; g < groups_[i]; g++) { + MatrixPtr wMatSub = wMat->subMatrix(g * K, K); + MatrixPtr inMatSub = + Matrix::create(inData, M, N, false, useGpu_); + MatrixPtr colBufDataSub = + Matrix::create(colBufData, K, N, false, useGpu_); + colBufDataSub->mul(*wMatSub, *inMatSub, 1.0, 0.0); + colBufData += K * N; + inData += M * N; + } + colBuf_->col2Vol(outMat->getData()+ n * outMat->getWidth(), + numFilters_, outputD_[i], outputH_[i], outputW_[i], + filterSizeZ_[i], filterSizeY_[i], filterSize_[i], + strideZ_[i], strideY_[i], stride_[i], + paddingZ_[i], paddingY_[i], padding_[i], 1.0, 1.0); + } + } + if (nullptr != this->biasParameter_) { + REGISTER_TIMER_INFO("FwBiasTimer", getName().c_str()); + this->addBias(); + } + forwardActivation(); +} + +void DeConv3DLayer::backward(const UpdateCallback &callback) { + backwardActivation(); + int batchSize = getOutputGrad()->getHeight(); + int outputWidth = getOutputGrad()->getWidth(); + if (biases_ && biases_->getWGrad()) { + bpropBiases(); + biases_->getParameterPtr()->incUpdate(callback); + } + for (size_t i =0; i < inputLayers_.size(); ++i) { + int M = M_[i]; + int N = N_[i]; + int K = K_[i]; + Matrix::resizeOrCreate(colBuf_, K * groups_[i], N, false, useGpu_); + const MatrixPtr& inMat = getInputValue(i); + for (int n = 0; n < batchSize; ++n) { + REGISTER_TIMER_INFO("BwdDeConv3D", getName().c_str()); + if (weights_[i]->getWGrad() || this->needGradient_) { + colBuf_->vol2Col(getOutputGrad()->getData() + n * outputWidth, + numFilters_, outputD_[i], outputH_[i], outputW_[i], + filterSizeZ_[i], filterSizeY_[i], filterSize_[i], + strideZ_[i], strideY_[i], stride_[i], + paddingZ_[i], paddingY_[i], padding_[i]); + } + if (weights_[i]->getWGrad()) { + real *inData = inMat->getData() + n * inMat->getWidth();; + real *wGradData = weights_[i]->getWGrad()->getData(); + for (int g = 0; g < groups_[i]; g++) { + MatrixPtr colBufDataSub = colBuf_->subMatrix(g * K, K); + MatrixPtr inMatSub = Matrix::create( + inData, M, N, false, useGpu_); + MatrixPtr wGradMatSub = Matrix::create( + wGradData, K, M, false, useGpu_); + wGradMatSub->mul(*colBufDataSub, + *(inMatSub->getTranspose()), 1.0, 1.0); + wGradData += K * M; + inData += M * N; + } + weights_[i]->getParameterPtr()->incUpdate(callback); + } + if (this->needGradient_) { + real* preGrad = getInputGrad(i)->getData(); + for (int g = 0; g < groups_[i]; ++g) { + MatrixPtr w = weights_[i]->getW()->subMatrix(g * K, K); + MatrixPtr outGradMat = colBuf_->subMatrix(g * K, K); + MatrixPtr inGradMatSub = Matrix::create( + preGrad, M, N, false, useGpu_); + inGradMatSub->mul(*(w->getTranspose()), *outGradMat, 1.0, 0.0); + preGrad += M * N; + } + } + REGISTER_TIMER_INFO("WeightUpdate", getName().c_str()); + } + } +} + +void DeConv3DLayer::bpropWeights(int i) { } +void DeConv3DLayer::bpropData(int i) { } + +void DeConv3DLayer::bpropBiases() { + MatrixPtr outGradMat = getOutputGrad(); + + if (this->sharedBiases_) { + biases_->getWGrad()->collectSharedBias(*outGradMat, 1.0f); + } else { + biases_->getWGrad()->collectBias(*outGradMat, 1.0f); + } +} + +void DeConv3DLayer::addBias() { + MatrixPtr outMat = getOutputValue(); + if (this->sharedBiases_) { + outMat->addSharedBias(*(biases_->getW()), 1.0f); + } else { + outMat->addBias(*(biases_->getW()), 1.0f); + } +} + +} // namespace paddle diff --git a/paddle/gserver/layers/DeConv3DLayer.h b/paddle/gserver/layers/DeConv3DLayer.h new file mode 100644 index 000000000..435807fe5 --- /dev/null +++ b/paddle/gserver/layers/DeConv3DLayer.h @@ -0,0 +1,58 @@ +/* Copyright (c) 2016 Baidu, Inc. 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 "ConvBaseLayer.h" +#include "paddle/math/Matrix.h" +#include "paddle/math/MathUtils.h" +#include + +namespace paddle { + +/** + * @brief A subclass of deconvolution3D layer. + * This layer expands input and use matrix multiplication to + * calculate deconvolution3D operation. + */ +class DeConv3DLayer : public ConvBaseLayer { +public: + explicit DeConv3DLayer(const LayerConfig& config) : ConvBaseLayer(config) {} + + ~DeConv3DLayer() {} + + bool init(const LayerMap &layerMap, const ParameterMap ¶meterMap); + + size_t getSize(); + + void forward(PassType passType); + void addBias(); + + void backward(const UpdateCallback& callback); + + void bpropBiases(); + void bpropData(int i); + void bpropWeights(int i); + +protected: + // Figure out the dimensions for individual gemms. + IntV M_; /// numFilters_ / filter_group_; + IntV N_; /// channels_ * filterSizeZ_ * filterSize_ * filterSizeY_ + IntV K_; /// outputD_ * outputH_ * outputW_ + IntV No_; + MatrixPtr colBuf_; +}; + +} // namespace paddle -- GitLab From 52ceeedba5ca1371302414a0ad11ff93d9ed7d9a Mon Sep 17 00:00:00 2001 From: chengduoZH Date: Sun, 13 Aug 2017 09:51:39 +0800 Subject: [PATCH 0028/2018] Add col2vol and vol2col CPU funtion --- paddle/math/Matrix.cpp | 135 +++++++++++++++++++++++++++++++++++++++++ paddle/math/Matrix.h | 64 +++++++++++++++++++ 2 files changed, 199 insertions(+) diff --git a/paddle/math/Matrix.cpp b/paddle/math/Matrix.cpp index 27f7d95b7..66868e73b 100644 --- a/paddle/math/Matrix.cpp +++ b/paddle/math/Matrix.cpp @@ -1389,6 +1389,52 @@ void GpuMatrix::multiBinaryLabelCrossEntropyBp(Matrix& output, Matrix& label) { output_d, grad_d, mat_d, height_, width_); } +void GpuMatrix::vol2Col(real* data, + int channels, + int depth, + int height, + int width, + int filterD, + int filterH, + int filterW, + int strideD, + int strideH, + int strideW, + int paddingD, + int paddingH, + int paddingW) { + hl_matrix_vol2Col(data, + channels, depth, height, width, + filterD, filterH, filterW, + strideD, strideH, strideW, + paddingD, paddingH, paddingW, getData()); +} + +void GpuMatrix::col2Vol(real* trg, + int channels, + int depth, + int height, + int width, + int filterD, + int filterH, + int filterW, + int strideD, + int strideH, + int strideW, + int paddingD, + int paddingH, + int paddingW, + real alpha, + real beta) { + hl_matrix_col2Vol(trg, + channels, depth, height, width, + filterD, filterH, filterW, + strideD, strideH, strideW, + paddingD, paddingH, paddingW, + getData(), + alpha, beta); + } + /** * CpuMatrix */ @@ -3975,6 +4021,95 @@ void CpuMatrix::bilinearBackward(const Matrix& out, } } +void CpuMatrix::vol2Col(real* data, + int channels, + int depth, + int height, + int width, + int filterD, + int filterH, + int filterW, + int strideD, + int strideH, + int strideW, + int paddingD, + int paddingH, + int paddingW) { + real* outData = getData(); + int outHeight = (height + 2 * paddingH - filterH) / strideH + 1; + int outWidth = (width + 2 * paddingW - filterW) / strideW + 1; + int outDepth = (depth + 2 * paddingD - filterD) / strideD + 1; + + int channelsCol = channels * filterD * filterH * filterW; + for (int c = 0; c < channelsCol; ++c) { + int wOffset = c % filterW; + int hOffset = (c / filterW) % filterH; + int dOffset = (c / filterW / filterH) % filterD; + int cIn = c / filterW / filterH / filterD; + for (int d = 0; d < outDepth; ++d) { + for (int h = 0; h < outHeight; ++h) { + for (int w = 0; w < outWidth; ++w) { + int dPad = d * strideD - paddingD + dOffset; + int hPad = h * strideH - paddingH + hOffset; + int wPad = w * strideW - paddingW + wOffset; + + if (hPad >= 0 && hPad < height && wPad >= 0 && wPad < width && + dPad >= 0 && dPad < depth) + outData[((c * outDepth + d) * outHeight + h) * outWidth + w] = + data[((cIn * depth + dPad) * height + hPad) * width + wPad]; + else + outData[((c * outDepth + d) * outHeight + h) * outWidth + w] = 0; + } + } + } + } +} + +void CpuMatrix::col2Vol(real* trg, + int channels, + int depth, + int height, + int width, + int filterD, + int filterH, + int filterW, + int strideD, + int strideH, + int strideW, + int paddingD, + int paddingH, + int paddingW, + real alpha, + real beta) { + real* src = getData(); + int outDepth = (depth + 2 * paddingH - filterD) / strideD + 1; + int outHeight = (height + 2 * paddingH - filterH) / strideH + 1; + int outWidth = (width + 2 * paddingW - filterW) / strideW + 1; + int channelsCol = channels * filterD * filterH * filterW; + for (int c = 0; c < channelsCol; ++c) { + int wOffset = c % filterW; + int hOffset = (c / filterW) % filterH; + int dOffset = (c / filterW / filterH) % filterD; + int cIm = c / filterW / filterH / filterD; + for (int d = 0; d < outDepth; ++d) { + for (int h = 0; h < outHeight; ++h) { + for (int w = 0; w < outWidth; ++w) { + int dPad = d * strideD - paddingD + dOffset; + int hPad = h * strideH - paddingH + hOffset; + int wPad = w * strideW - paddingW + wOffset; + if (hPad >= 0 && hPad < height && wPad >= 0 && wPad < width && + dPad >= 0 && dPad < depth) + trg[((cIm * depth + dPad) * height + hPad) * width + wPad] = + alpha * + src[((c * outDepth + d) * outHeight + h) * outWidth + w] + + beta * + trg[((cIm * depth + dPad) * height + hPad) * width + wPad]; + } + } + } + } +} + //////////////////////////////////////////////////////////////// // functions executed via cpu // //////////////////////////////////////////////////////////////// diff --git a/paddle/math/Matrix.h b/paddle/math/Matrix.h index bb802bbb2..4354996ce 100644 --- a/paddle/math/Matrix.h +++ b/paddle/math/Matrix.h @@ -1039,6 +1039,42 @@ public: LOG(FATAL) << "Not implemented"; } + virtual void vol2Col(real* data, + int channels, + int depth, + int height, + int width, + int filterD, + int filterH, + int filterW, + int strideD, + int strideH, + int strideW, + int paddingD, + int paddingH, + int paddingW) { + LOG(FATAL) << "Not implemeted"; + } + + virtual void col2Vol(real* trg, + int channels, + int depth, + int height, + int width, + int filterD, + int filterH, + int filterW, + int strideD, + int strideH, + int strideW, + int paddingD, + int paddingH, + int paddingW, + real alpha, + real beta) { + LOG(FATAL) << "Not implemeted"; + } + virtual void bilinearForward(const Matrix& in, const size_t inImgH, const size_t inImgW, @@ -1374,6 +1410,20 @@ public: const real ratioH, const real ratioW); + void vol2Col(real* data, + int channels, + int depth, int height, int width, + int filterD, int filterH, int filterW, + int strideD, int strideH, int strideW, + int paddingD, int paddingH, int paddingW); + + void col2Vol(real* trg, + int channels, int depth, int height, int width, + int filterD, int filterH, int filterW, + int strideD, int strideH, int strideW, + int paddingD, int paddingH, int paddingW, + real alpha, real beta); + void multiBinaryLabelCrossEntropy(Matrix& output, Matrix& label); void multiBinaryLabelCrossEntropyBp(Matrix& output, Matrix& label); @@ -1715,6 +1765,20 @@ public: const real ratioH, const real ratioW); + void vol2Col(real* data, + int channels, + int depth, int height, int width, + int filterD, int filterH, int filterW, + int strideD, int strideH, int strideW, + int paddingD, int paddingH, int paddingW); + + void col2Vol(real* trg, + int channels, int depth, int height, int width, + int filterD, int filterH, int filterW, + int strideD, int strideH, int strideW, + int paddingD, int paddingH, int paddingW, + real alpha, real beta); + template void operator=(const ExpressionType& expr) { TensorCpuApply(*this, expr); -- GitLab From 9b3d6acdbfc2fd6bc26185ddb9c38dfb90632324 Mon Sep 17 00:00:00 2001 From: chengduoZH Date: Sun, 13 Aug 2017 09:54:10 +0800 Subject: [PATCH 0029/2018] Add depth dimension information to Argument --- paddle/parameter/Argument.cpp | 2 ++ paddle/parameter/Argument.h | 8 +++++--- 2 files changed, 7 insertions(+), 3 deletions(-) diff --git a/paddle/parameter/Argument.cpp b/paddle/parameter/Argument.cpp index 0547ac93c..77fd0c589 100644 --- a/paddle/parameter/Argument.cpp +++ b/paddle/parameter/Argument.cpp @@ -186,6 +186,7 @@ void Argument::resizeAndCopyFrom(const Argument& src, resizeAndCopy(strs, src.strs, useGpu, stream); frameWidth = src.frameWidth; frameHeight = src.frameHeight; + frameDepth = src.frameDepth; } int32_t Argument::resizeAndCopyFrom(const Argument& src, @@ -206,6 +207,7 @@ int32_t Argument::resizeAndCopyFrom(const Argument& src, dataId = src.dataId; frameWidth = src.frameWidth; frameHeight = src.frameHeight; + frameDepth = src.frameDepth; if (!src.sequenceStartPositions) { // non-sequence input, copy samples directly diff --git a/paddle/parameter/Argument.h b/paddle/parameter/Argument.h index d8d7a4398..ba3ad2fd4 100644 --- a/paddle/parameter/Argument.h +++ b/paddle/parameter/Argument.h @@ -1,11 +1,8 @@ /* 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. @@ -35,6 +32,7 @@ struct Argument { strs(nullptr), frameHeight(0), frameWidth(0), + frameDepth(0), sequenceStartPositions(nullptr), subSequenceStartPositions(nullptr), cpuSequenceDims(nullptr), @@ -64,6 +62,7 @@ struct Argument { allCount = argument.allCount; frameHeight = argument.frameHeight; frameWidth = argument.frameWidth; + frameDepth = argument.frameDepth; dataId = argument.dataId; } @@ -76,6 +75,7 @@ struct Argument { // A dataBatch includes batchSize frames, one frame maybe not only vector size_t frameHeight; size_t frameWidth; + size_t frameDepth; // If NULL, each position is treated independently. // Otherwise, its size should be #NumberOfSequences + 1. @@ -136,8 +136,10 @@ struct Argument { } size_t getFrameHeight() const { return frameHeight; } size_t getFrameWidth() const { return frameWidth; } + size_t getFrameDepth() const { return frameDepth; } void setFrameHeight(size_t h) { frameHeight = h; } void setFrameWidth(size_t w) { frameWidth = w; } + void setFrameDepth(size_t d) { frameDepth = d; } int64_t getNumSequences() const { return sequenceStartPositions ? sequenceStartPositions->getSize() - 1 -- GitLab From 44ae44da49f206af56d02816aff8e9b2920d0bf8 Mon Sep 17 00:00:00 2001 From: caoying03 Date: Mon, 14 Aug 2017 09:01:22 +0800 Subject: [PATCH 0030/2018] add configuratioin helpers. --- python/paddle/trainer/config_parser.py | 16 ++ .../paddle/trainer_config_helpers/layers.py | 34 ++- .../tests/configs/file_list.sh | 2 +- .../test_cross_entropy_over_beam.protostr | 208 ++++++++++++++++++ .../configs/test_cross_entropy_over_beam.py | 39 ++++ 5 files changed, 295 insertions(+), 4 deletions(-) create mode 100644 python/paddle/trainer_config_helpers/tests/configs/protostr/test_cross_entropy_over_beam.protostr create mode 100644 python/paddle/trainer_config_helpers/tests/configs/test_cross_entropy_over_beam.py diff --git a/python/paddle/trainer/config_parser.py b/python/paddle/trainer/config_parser.py index da99e5bd5..a24299787 100644 --- a/python/paddle/trainer/config_parser.py +++ b/python/paddle/trainer/config_parser.py @@ -1602,6 +1602,21 @@ class MultiClassCrossEntropySelfNormCostLayer(LayerBase): self.config.softmax_selfnorm_alpha = softmax_selfnorm_alpha +@config_layer('cross_entropy_over_beam') +class CrossEntropyOverBeamLayer(LayerBase): + def __init__(self, name, inputs, **xargs): + config_assert(len(inputs) % 3 == 0, "Error input numbers.") + super(CrossEntropyOverBeamLayer, self).__init__( + name, 'cross_entropy_over_beam', 0, inputs, **xargs) + input_num = len(inputs) / 3 + for i in range(input_num): + input_layer = self.get_input_layer(i * 2) + config_assert( + input_layer.size == 1, "Inputs for this layer are made up of " + "several pairs and the first one in a pair is scores for " + "all the candidates, so its size should be equal to 1.") + + @config_layer('fc') class FCLayer(LayerBase): layer_type = 'fc' @@ -2249,6 +2264,7 @@ def define_cost(class_name, cost_type): define_cost('MultiClassCrossEntropy', 'multi-class-cross-entropy') +define_cost('CrossEntropyOverBeamCostLayer', 'cross_entropy_over_beam') define_cost('RankingCost', 'rank-cost') define_cost('AucValidation', 'auc-validation') define_cost('PnpairValidation', 'pnpair-validation') diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py index 1bc55c869..2b01b6ad4 100755 --- a/python/paddle/trainer_config_helpers/layers.py +++ b/python/paddle/trainer_config_helpers/layers.py @@ -11,7 +11,6 @@ # 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. - import functools import collections import inspect @@ -104,6 +103,7 @@ __all__ = [ 'nce_layer', 'cross_entropy_with_selfnorm', 'cross_entropy', + 'cross_entropy_over_beam', 'multi_binary_label_cross_entropy', 'sum_cost', 'rank_cost', @@ -219,6 +219,7 @@ class LayerType(object): HUBER = 'huber' CROSS_ENTROPY = 'multi-class-cross-entropy' CROSS_ENTROPY_WITH_SELFNORM = 'multi_class_cross_entropy_with_selfnorm' + CROSS_ENTROPY_OVER_BEAM = 'cross_entropy_over_beam' SOFT_BIN_CLASS_CROSS_ENTROPY = 'soft_binary_class_cross_entropy' MULTI_BIN_LABEL_CROSS_ENTROPY = 'multi_binary_label_cross_entropy' SUM_COST = 'sum_cost' @@ -4028,8 +4029,12 @@ def __cost_input__(input, label, weight=None): """ inputs and parents for cost layers. """ - ipts = [Input(input.name), Input(label.name)] - parents = [input, label] + if isinstance(input, LayerOutput): + input = [input] + if isinstance(label, LayerOutput): + label = [label] + ipts = [Input(ipt.name) for ipt in (input + label)] + parents = [ipt for ipt in (input + label)] if weight is not None: assert weight.size == 1 ipts.append(Input(weight.name)) @@ -5692,6 +5697,29 @@ def multi_binary_label_cross_entropy(input, size=1) +@wrap_name_default() +@layer_support() +def cross_entropy_over_beam(input, label, name=None, coeff=1.0, weight=None): + """ + TODO(caoying) add comments. + """ + + assert len(input) / 2 == len(label), "Error input numbers." + for i in range(0, len(input), 2): + assert (input[i].size == 1), ( + "Inputs for this layer are made up of " + "several pairs and the first one in a pair is scores for " + "all the candidates, so its size should be equal to 1.") + + ipts, parents = __cost_input__(input, label, weight) + Layer( + name=name, + type=LayerType.CROSS_ENTROPY_OVER_BEAM, + inputs=ipts, + coeff=coeff) + return LayerOutput(name, LayerType.CROSS_ENTROPY, parents=parents, size=1) + + @wrap_name_default() @layer_support() def smooth_l1_cost(input, label, name=None, coeff=1.0, layer_attr=None): diff --git a/python/paddle/trainer_config_helpers/tests/configs/file_list.sh b/python/paddle/trainer_config_helpers/tests/configs/file_list.sh index a61beb871..130e6332a 100755 --- a/python/paddle/trainer_config_helpers/tests/configs/file_list.sh +++ b/python/paddle/trainer_config_helpers/tests/configs/file_list.sh @@ -8,6 +8,6 @@ test_spp_layer test_bilinear_interp test_maxout test_bi_grumemory math_ops test_seq_concat_reshape test_pad test_smooth_l1 test_multiplex_layer test_prelu_layer test_row_conv test_detection_output_layer test_multibox_loss_layer test_recursive_topology test_gated_unit_layer test_clip_layer test_row_l2_norm_layer -test_kmax_seq_socre_layer test_seq_select_layers) +test_kmax_seq_socre_layer test_seq_select_layers test_cross_entropy_over_beam) export whole_configs=(test_split_datasource) diff --git a/python/paddle/trainer_config_helpers/tests/configs/protostr/test_cross_entropy_over_beam.protostr b/python/paddle/trainer_config_helpers/tests/configs/protostr/test_cross_entropy_over_beam.protostr new file mode 100644 index 000000000..e44478ec2 --- /dev/null +++ b/python/paddle/trainer_config_helpers/tests/configs/protostr/test_cross_entropy_over_beam.protostr @@ -0,0 +1,208 @@ +type: "nn" +layers { + name: "sentence_states" + type: "data" + size: 32 + active_type: "" +} +layers { + name: "sentence_scores" + type: "data" + size: 1 + active_type: "" +} +layers { + name: "__kmax_sequence_score_layer_0__" + type: "kmax_seq_score" + active_type: "" + inputs { + input_layer_name: "sentence_scores" + } + beam_size: 5 +} +layers { + name: "__sub_nested_seq_layer_0__" + type: "sub_nested_seq" + size: 32 + active_type: "" + inputs { + input_layer_name: "sentence_states" + } + inputs { + input_layer_name: "__kmax_sequence_score_layer_0__" + } +} +layers { + name: "__fc_layer_0__" + type: "fc" + size: 1 + active_type: "" + inputs { + input_layer_name: "__sub_nested_seq_layer_0__" + input_parameter_name: "___fc_layer_0__.w0" + } + bias_parameter_name: "___fc_layer_0__.wbias" +} +layers { + name: "__kmax_sequence_score_layer_1__" + type: "kmax_seq_score" + active_type: "" + inputs { + input_layer_name: "sentence_scores" + } + beam_size: 5 +} +layers { + name: "__seq_slice_layer_0__" + type: "seq_slice" + size: 32 + active_type: "" + inputs { + input_layer_name: "__sub_nested_seq_layer_0__" + } + inputs { + input_layer_name: "__kmax_sequence_score_layer_1__" + } + select_first: true +} +layers { + name: "__fc_layer_1__" + type: "fc" + size: 1 + active_type: "" + inputs { + input_layer_name: "__seq_slice_layer_0__" + input_parameter_name: "___fc_layer_1__.w0" + } + bias_parameter_name: "___fc_layer_1__.wbias" +} +layers { + name: "__kmax_sequence_score_layer_2__" + type: "kmax_seq_score" + active_type: "" + inputs { + input_layer_name: "__fc_layer_1__" + } + beam_size: 5 +} +layers { + name: "sentences_ids" + type: "data" + size: 1 + active_type: "" +} +layers { + name: "start_ids" + type: "data" + size: 1 + active_type: "" +} +layers { + name: "end_ids" + type: "data" + size: 1 + active_type: "" +} +layers { + name: "__cross_entropy_over_beam_0__" + type: "cross_entropy_over_beam" + active_type: "" + inputs { + input_layer_name: "sentence_scores" + } + inputs { + input_layer_name: "__kmax_sequence_score_layer_0__" + } + inputs { + input_layer_name: "__fc_layer_0__" + } + inputs { + input_layer_name: "__kmax_sequence_score_layer_1__" + } + inputs { + input_layer_name: "__fc_layer_1__" + } + inputs { + input_layer_name: "__kmax_sequence_score_layer_2__" + } + inputs { + input_layer_name: "sentences_ids" + } + inputs { + input_layer_name: "start_ids" + } + inputs { + input_layer_name: "end_ids" + } + coeff: 1.0 +} +parameters { + name: "___fc_layer_0__.w0" + size: 32 + initial_mean: 0.0 + initial_std: 0.176776695297 + dims: 32 + dims: 1 + initial_strategy: 0 + initial_smart: true +} +parameters { + name: "___fc_layer_0__.wbias" + size: 1 + initial_mean: 0.0 + initial_std: 0.0 + dims: 1 + dims: 1 + initial_strategy: 0 + initial_smart: false +} +parameters { + name: "___fc_layer_1__.w0" + size: 32 + initial_mean: 0.0 + initial_std: 0.176776695297 + dims: 32 + dims: 1 + initial_strategy: 0 + initial_smart: true +} +parameters { + name: "___fc_layer_1__.wbias" + size: 1 + initial_mean: 0.0 + initial_std: 0.0 + dims: 1 + dims: 1 + initial_strategy: 0 + initial_smart: false +} +input_layer_names: "sentence_scores" +input_layer_names: "sentence_states" +input_layer_names: "sentences_ids" +input_layer_names: "start_ids" +input_layer_names: "end_ids" +output_layer_names: "__cross_entropy_over_beam_0__" +sub_models { + name: "root" + layer_names: "sentence_states" + layer_names: "sentence_scores" + layer_names: "__kmax_sequence_score_layer_0__" + layer_names: "__sub_nested_seq_layer_0__" + layer_names: "__fc_layer_0__" + layer_names: "__kmax_sequence_score_layer_1__" + layer_names: "__seq_slice_layer_0__" + layer_names: "__fc_layer_1__" + layer_names: "__kmax_sequence_score_layer_2__" + layer_names: "sentences_ids" + layer_names: "start_ids" + layer_names: "end_ids" + layer_names: "__cross_entropy_over_beam_0__" + input_layer_names: "sentence_scores" + input_layer_names: "sentence_states" + input_layer_names: "sentences_ids" + input_layer_names: "start_ids" + input_layer_names: "end_ids" + output_layer_names: "__cross_entropy_over_beam_0__" + is_recurrent_layer_group: false +} + diff --git a/python/paddle/trainer_config_helpers/tests/configs/test_cross_entropy_over_beam.py b/python/paddle/trainer_config_helpers/tests/configs/test_cross_entropy_over_beam.py new file mode 100644 index 000000000..edc2d32fc --- /dev/null +++ b/python/paddle/trainer_config_helpers/tests/configs/test_cross_entropy_over_beam.py @@ -0,0 +1,39 @@ +#!/usr/bin/env python +#coding=utf-8 + +from paddle.trainer_config_helpers import * +beam_size = 5 + +# the first beam expansion. +sentence_states = data_layer(name="sentence_states", size=32) +sentence_scores = data_layer(name="sentence_scores", size=1) +topk_sentence_ids = kmax_sequence_score_layer( + input=sentence_scores, beam_size=beam_size) + +# the second beam expansion. +topk_sen = sub_nested_seq_layer( + input=sentence_states, selected_indices=topk_sentence_ids) +start_pos_scores = fc_layer(input=topk_sen, size=1, act=LinearActivation()) +topk_start_pos_ids = kmax_sequence_score_layer( + input=sentence_scores, beam_size=beam_size) + +# the final beam expansion. +topk_start_spans = seq_slice_layer( + input=topk_sen, starts=topk_start_pos_ids, ends=None) +end_pos_scores = fc_layer( + input=topk_start_spans, size=1, act=LinearActivation()) +topk_end_pos_ids = kmax_sequence_score_layer( + input=end_pos_scores, beam_size=beam_size) + +# define the cost +sentence_idx = data_layer(name="sentences_ids", size=1) +start_idx = data_layer(name="start_ids", size=1) +end_idx = data_layer(name="end_ids", size=1) +cost = cross_entropy_over_beam( + input=[ + sentence_scores, topk_sentence_ids, start_pos_scores, + topk_start_pos_ids, end_pos_scores, topk_end_pos_ids + ], + label=[sentence_idx, start_idx, end_idx]) + +outputs(cost) -- GitLab From b63e1c6d8a3e44b68263399f9720165703deccfd Mon Sep 17 00:00:00 2001 From: dongzhihong Date: Mon, 14 Aug 2017 11:49:21 +0800 Subject: [PATCH 0031/2018] "op name" --- paddle/operators/name_convention.md | 11 +++++++++++ 1 file changed, 11 insertions(+) create mode 100644 paddle/operators/name_convention.md diff --git a/paddle/operators/name_convention.md b/paddle/operators/name_convention.md new file mode 100644 index 000000000..da5bcb748 --- /dev/null +++ b/paddle/operators/name_convention.md @@ -0,0 +1,11 @@ +## Operator Name Convention + +To make the operator document itself more clear. we recommend operator names observe the listing conventions. + +### Input/Output names + +Variable name is uppercase. e.g. `X`, `Y` + +Tensor name is lowercase. e.g. `tensor` + +if only have one output, use `Out` -- GitLab From 05e8a26b4bb093f9dccb9aeb533a5851aaed09b8 Mon Sep 17 00:00:00 2001 From: caoying03 Date: Mon, 14 Aug 2017 10:33:28 +0800 Subject: [PATCH 0032/2018] add unittest. --- .../gserver/layers/CrossEntropyOverBeam.cpp | 35 +++++++ paddle/gserver/layers/CrossEntropyOverBeam.h | 31 ++++++ paddle/gserver/tests/CMakeLists.txt | 6 ++ paddle/gserver/tests/LayerGradUtil.cpp | 25 +++-- paddle/gserver/tests/LayerGradUtil.h | 18 ++++ .../tests/test_CrossEntropyOverBeamGrad.cpp | 94 +++++++++++++++++++ 6 files changed, 201 insertions(+), 8 deletions(-) create mode 100644 paddle/gserver/layers/CrossEntropyOverBeam.cpp create mode 100644 paddle/gserver/layers/CrossEntropyOverBeam.h create mode 100644 paddle/gserver/tests/test_CrossEntropyOverBeamGrad.cpp diff --git a/paddle/gserver/layers/CrossEntropyOverBeam.cpp b/paddle/gserver/layers/CrossEntropyOverBeam.cpp new file mode 100644 index 000000000..8b6223ec6 --- /dev/null +++ b/paddle/gserver/layers/CrossEntropyOverBeam.cpp @@ -0,0 +1,35 @@ +/* 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 "CrossEntropyOverBeam.h" + +namespace paddle { + +REGISTER_LAYER(cross_entropy_over_beam, CrossEntropyOverBeam); + +bool CrossEntropyOverBeam::init(const LayerMap& layerMap, + const ParameterMap& parameterMap) { + /* Initialize the basic parent class */ + Layer::init(layerMap, parameterMap); + + setNeedSequenceInfo(false); + + return true; +} + +void CrossEntropyOverBeam::forward(PassType passType) {} + +void CrossEntropyOverBeam::backward(const UpdateCallback& callback) {} + +} // namespace paddle diff --git a/paddle/gserver/layers/CrossEntropyOverBeam.h b/paddle/gserver/layers/CrossEntropyOverBeam.h new file mode 100644 index 000000000..3106f9858 --- /dev/null +++ b/paddle/gserver/layers/CrossEntropyOverBeam.h @@ -0,0 +1,31 @@ +/* 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 "CrossEntropyOverBeam.h" +#include "Layer.h" + +namespace paddle { + +class CrossEntropyOverBeam : public Layer { +public: + explicit CrossEntropyOverBeam(const LayerConfig& config) : Layer(config) {} + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override; + void forward(PassType passType) override; + void backward(const UpdateCallback& callback) override; +}; + +} // namespace paddle diff --git a/paddle/gserver/tests/CMakeLists.txt b/paddle/gserver/tests/CMakeLists.txt index c2a299362..24df7e722 100644 --- a/paddle/gserver/tests/CMakeLists.txt +++ b/paddle/gserver/tests/CMakeLists.txt @@ -34,6 +34,12 @@ add_unittest_without_exec(test_CRFLayerGrad add_test(NAME test_CRFLayerGrad COMMAND test_CRFLayerGrad) +################ test_CrossEntropyOverBeam #################### +add_unittest_without_exec(test_CrossEntropyOverBeam + test_CrossEntropyOverBeamGrad.cpp + LayerGradUtil.cpp) +add_test(NAME test_CrossEntropyOverBeam + COMMAND test_CrossEntropyOverBeam) add_unittest_without_exec(test_ActivationGrad test_ActivationGrad.cpp diff --git a/paddle/gserver/tests/LayerGradUtil.cpp b/paddle/gserver/tests/LayerGradUtil.cpp index fd9cfa1dc..a38880e14 100644 --- a/paddle/gserver/tests/LayerGradUtil.cpp +++ b/paddle/gserver/tests/LayerGradUtil.cpp @@ -388,14 +388,23 @@ void initDataLayer(TestConfig testConf, data.grad->zeroMem(); break; case INPUT_SELF_DEFINE_DATA: { - size_t height = testConf.inputDefs[i].selfDefinedData->getHeight(); - size_t width = testConf.inputDefs[i].selfDefinedData->getWidth(); - CHECK_GT(static_cast(height), 0); - CHECK_GT(static_cast(width), 0); - data.value = Matrix::create(height, width, false, useGpu); - data.grad = Matrix::create(height, width, false, useGpu); - data.value->copyFrom(*testConf.inputDefs[i].selfDefinedData); - data.grad->zeroMem(); + if (testConf.inputDefs[i].ids.size()) { + data.ids = IVector::create(testConf.inputDefs[i].ids.size(), useGpu); + data.ids->copyFrom(testConf.inputDefs[i].ids.data(), + testConf.inputDefs[i].ids.size()); + } else if (testConf.inputDefs[i].selfDefinedData) { + size_t height = testConf.inputDefs[i].selfDefinedData->getHeight(); + size_t width = testConf.inputDefs[i].selfDefinedData->getWidth(); + CHECK_GT(static_cast(height), 0); + CHECK_GT(static_cast(width), 0); + data.value = Matrix::create(height, width, false, useGpu); + data.grad = Matrix::create(height, width, false, useGpu); + data.value->copyFrom(*testConf.inputDefs[i].selfDefinedData); + data.grad->zeroMem(); + } else { + LOG(FATAL) << "No self-defined data are given."; + return; + } const std::vector& labelSeqStartPositions = testConf.inputDefs[i].labelSeqStartPositions; diff --git a/paddle/gserver/tests/LayerGradUtil.h b/paddle/gserver/tests/LayerGradUtil.h index 5debedf5e..a35edd2b5 100644 --- a/paddle/gserver/tests/LayerGradUtil.h +++ b/paddle/gserver/tests/LayerGradUtil.h @@ -68,6 +68,7 @@ struct InputDef { std::vector labelInitValue; std::vector labelSeqStartPositions; std::vector labelSubSeqStartPositions; + std::vector ids; MatrixPtr selfDefinedData; InputDef(InputType type, string nameIn, size_t dimIn, size_t sizeIn) { @@ -95,6 +96,23 @@ struct InputDef { isStatic = false; } + InputDef(InputType type, + string nameIn, + std::vector ids, + std::vector selfDefinedSeqStartPos = {}, + std::vector selfDefinedSubSeqStartPos = {}) + : labelSeqStartPositions(selfDefinedSeqStartPos), + labelSubSeqStartPositions(selfDefinedSubSeqStartPos), + ids(ids) { + selfDefinedData = nullptr; + inputType = type; + name = nameIn; + dim = 0; + sparse = {""}; + paraSize = 0; + isStatic = false; + } + InputDef(InputType type, string nameIn, size_t dimIn, diff --git a/paddle/gserver/tests/test_CrossEntropyOverBeamGrad.cpp b/paddle/gserver/tests/test_CrossEntropyOverBeamGrad.cpp new file mode 100644 index 000000000..54daba365 --- /dev/null +++ b/paddle/gserver/tests/test_CrossEntropyOverBeamGrad.cpp @@ -0,0 +1,94 @@ +/* Copyright (c) 2016 Baidu, Inc. 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 + +#include +#include "ModelConfig.pb.h" +#include "paddle/gserver/layers/DataLayer.h" +#include "paddle/trainer/Trainer.h" + +#include "LayerGradUtil.h" +#include "paddle/testing/TestUtil.h" + +using namespace paddle; // NOLINT + +DECLARE_int32(gpu_id); +DECLARE_bool(thread_local_rand_use_global_seed); + +struct SingleBeamExpansion { + vector seqStartPos; + vector subSeqStartPos; + + vector candidateScores; + // TODO(caoying): store this into Argument.ids + vector selectedIndices; + vector groundTruth; +}; + +void genRandomBeamExpansion(size_t expansionCount, + vector& beamExpansions) { + beamExpansions.clear(); +} + +void testCrossEntropyOverBeam() { + const size_t expansionCount = 3; + vector beams; + genRandomBeamExpansion(expansionCount, beams); + + for (size_t i = 0; i < beams.size(); ++i) { + const SingleBeamExpansion& beam = beams[i]; + // create scores for all the candidates + MatrixPtr candidateScorePtr = + Matrix::create(beam.candidateScores.size(), 1, false, false); + candidateScorePtr->copyFrom(candidateScores.data(), candidateScores.size()); + + ostringstream paramName; + paramName << "candidate_scores_" << i; + beam.subSeqStartPos.size() + ? config.inputDefs.push_back({INPUT_SELF_DEFINE_DATA, + ostr.str(), + candidateScorePtr, + beam.seqStartPos, + beam.subSeqStartPos}) + : config.inputDefs.push_back({INPUT_SELF_DEFINE_DATA, + ostr.str(), + candidateScorePtr, + beam.seqStartPos}); + // create indices for the selected candidates + + // create the ground truth + } +} + +TestConfig config; +config.layerConfig.set_type("cross_entropy_over_beam"); + +// testLayerGrad( +// config, "cross_entropy_over_beam", seqNum, false, useGpu, false); +} + +TEST(Layer, CrossEntropyOverBeam) { + for (bool useGpu : {false, true}) testCrossEntropyOverBeam(useGpu); +} + +int main(int argc, char** argv) { + initMain(argc, argv); + hl_start(); + hl_init(FLAGS_gpu_id); + FLAGS_thread_local_rand_use_global_seed = true; + srand(1); + testing::InitGoogleTest(&argc, argv); + return RUN_ALL_TESTS(); +} -- GitLab From e6db484d154c041c1cf6650743bcf27dd2549b77 Mon Sep 17 00:00:00 2001 From: Luo Tao Date: Mon, 14 Aug 2017 15:51:00 +0800 Subject: [PATCH 0033/2018] make clear that current huber_cost is for two-classification --- paddle/gserver/layers/CostLayer.cpp | 29 ++++++++++--------- paddle/gserver/layers/CostLayer.h | 18 +++++------- paddle/gserver/tests/test_LayerGrad.cpp | 2 +- python/paddle/trainer/config_parser.py | 2 +- .../paddle/trainer_config_helpers/layers.py | 27 ++++++++++++----- .../protostr/test_cost_layers.protostr | 10 +++---- .../tests/configs/test_cost_layers.py | 2 +- 7 files changed, 50 insertions(+), 40 deletions(-) diff --git a/paddle/gserver/layers/CostLayer.cpp b/paddle/gserver/layers/CostLayer.cpp index 6bfdea3c6..138c86a6d 100644 --- a/paddle/gserver/layers/CostLayer.cpp +++ b/paddle/gserver/layers/CostLayer.cpp @@ -575,10 +575,10 @@ void MultiBinaryLabelCrossEntropy::backwardImp(Matrix& output, // // Huber loss for robust 2-classes classification // -REGISTER_LAYER(huber, HuberTwoClass); +REGISTER_LAYER(huber, HuberTwoClassification); -bool HuberTwoClass::init(const LayerMap& layerMap, - const ParameterMap& parameterMap) { +bool HuberTwoClassification::init(const LayerMap& layerMap, + const ParameterMap& parameterMap) { CostLayer::init(layerMap, parameterMap); if (useGpu_) { tmpCpuInput_.reserve(inputLayers_.size()); @@ -589,7 +589,9 @@ bool HuberTwoClass::init(const LayerMap& layerMap, return true; } -void HuberTwoClass::forwardImp(Matrix& output, Argument& label, Matrix& cost) { +void HuberTwoClassification::forwardImp(Matrix& output, + Argument& label, + Matrix& cost) { if (useGpu_) { for (size_t i = 0; i < inputLayers_.size(); i++) { tmpCpuInput_[i].resizeAndCopyFrom( @@ -600,10 +602,11 @@ void HuberTwoClass::forwardImp(Matrix& output, Argument& label, Matrix& cost) { forwardImpIn(output, label, cost); } -void HuberTwoClass::forwardImpIn(Matrix& output, - Argument& label, - Matrix& target) { +void HuberTwoClassification::forwardImpIn(Matrix& output, + Argument& label, + Matrix& target) { size_t numSamples = target.getHeight(); + CHECK(label.ids); CHECK_EQ((*label.ids).getSize(), numSamples); CHECK_EQ(output.getHeight(), numSamples); CHECK_EQ(output.getWidth(), (size_t)1); @@ -624,9 +627,9 @@ void HuberTwoClass::forwardImpIn(Matrix& output, target.copyFrom(cost.data(), numSamples); } -void HuberTwoClass::backwardImp(Matrix& outputValue, - Argument& label, - Matrix& outputGrad) { +void HuberTwoClassification::backwardImp(Matrix& outputValue, + Argument& label, + Matrix& outputGrad) { if (useGpu_) { backwardImpIn( *tmpCpuInput_[0].value, tmpCpuInput_[1], *tmpCpuInput_[0].grad); @@ -636,9 +639,9 @@ void HuberTwoClass::backwardImp(Matrix& outputValue, } } -void HuberTwoClass::backwardImpIn(Matrix& output, - Argument& label, - Matrix& outputG) { +void HuberTwoClassification::backwardImpIn(Matrix& output, + Argument& label, + Matrix& outputG) { size_t numSamples = output.getHeight(); real* out = output.getData(); real* grad = outputG.getData(); diff --git a/paddle/gserver/layers/CostLayer.h b/paddle/gserver/layers/CostLayer.h index 14c0b33ec..77427b7a0 100644 --- a/paddle/gserver/layers/CostLayer.h +++ b/paddle/gserver/layers/CostLayer.h @@ -307,21 +307,17 @@ public: /** * Huber loss for robust 2-classes classification. * - * For label={0, 1}, let y=2*label-1. Given output f, the loss is: - * \f[ - * Loss = - * \left\{\begin{matrix} - * 4 * y * f & \textit{if} \ \ y* f < -1 \\ - * (1 - y * f)^2 & \textit{if} \ \ -1 < y * f < 1 \\ - * 0 & \textit{otherwise} - * \end{matrix}\right. - * \f] + * For label={0, 1}, let y=2*label-1. Given output f(x), the loss is: + * Loss = 4 * y * f, if y* f < -1 \\ + * Loss = (1 - y * f)^2, if -1 < y * f < 1 \\ + * Loss = 0, otherwise */ -class HuberTwoClass : public CostLayer { +class HuberTwoClassification : public CostLayer { std::vector tmpCpuInput_; public: - explicit HuberTwoClass(const LayerConfig& config) : CostLayer(config) {} + explicit HuberTwoClassification(const LayerConfig& config) + : CostLayer(config) {} bool init(const LayerMap& layerMap, const ParameterMap& parameterMap) override; diff --git a/paddle/gserver/tests/test_LayerGrad.cpp b/paddle/gserver/tests/test_LayerGrad.cpp index 0f312b6ca..6d60250f6 100644 --- a/paddle/gserver/tests/test_LayerGrad.cpp +++ b/paddle/gserver/tests/test_LayerGrad.cpp @@ -830,7 +830,7 @@ TEST(Layer, square_error_weighted) { TEST(Layer, huber_two_class) { TestConfig config; - config.layerConfig.set_type("huber"); + config.layerConfig.set_type("huber_classification"); config.biasSize = 0; config.inputDefs.push_back({INPUT_DATA, "layer_0", 1, 0}); diff --git a/python/paddle/trainer/config_parser.py b/python/paddle/trainer/config_parser.py index da99e5bd5..248da9417 100644 --- a/python/paddle/trainer/config_parser.py +++ b/python/paddle/trainer/config_parser.py @@ -2255,7 +2255,7 @@ define_cost('PnpairValidation', 'pnpair-validation') define_cost('SumOfSquaresCostLayer', 'square_error') define_cost('MultiBinaryLabelCrossEntropy', 'multi_binary_label_cross_entropy') define_cost('SoftBinaryClassCrossEntropy', 'soft_binary_class_cross_entropy') -define_cost('HuberTwoClass', 'huber') +define_cost('HuberTwoClassification', 'huber_classification') define_cost('SumCost', 'sum_cost') define_cost('SmoothL1Cost', 'smooth_l1') diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py index 1bc55c869..20d96efe1 100755 --- a/python/paddle/trainer_config_helpers/layers.py +++ b/python/paddle/trainer_config_helpers/layers.py @@ -108,7 +108,7 @@ __all__ = [ 'sum_cost', 'rank_cost', 'lambda_cost', - 'huber_cost', + 'huber_classification_cost', 'block_expand_layer', 'maxout_layer', 'out_prod_layer', @@ -216,7 +216,7 @@ class LayerType(object): RANK_COST = 'rank-cost' LAMBDA_COST = 'lambda_cost' - HUBER = 'huber' + HUBER_CLASSIFICATION = 'huber_classification' CROSS_ENTROPY = 'multi-class-cross-entropy' CROSS_ENTROPY_WITH_SELFNORM = 'multi_class_cross_entropy_with_selfnorm' SOFT_BIN_CLASS_CROSS_ENTROPY = 'soft_binary_class_cross_entropy' @@ -5605,16 +5605,26 @@ def sum_cost(input, name=None, layer_attr=None): @wrap_name_default() @layer_support() -def huber_cost(input, label, name=None, coeff=1.0, layer_attr=None): +def huber_classification_cost(input, + label, + name=None, + coeff=1.0, + layer_attr=None): """ - A loss layer for huber loss. + For classification purposes, a variant of the Huber loss called modified Huber + is sometimes used. Given a prediction f(x) (a real-valued classifier score) and + a true binary class label :math:`y\in \left \{-1, 1 \right \}`, the modified Huber + loss is defined as: + + .. math: + loss = \max \left ( 0, 1-yf(x) \right )^2, yf(x)\geq 1 + loss = -4yf(x), \text{otherwise} The example usage is: .. code-block:: python - cost = huber_cost(input=input_layer, - label=label_layer) + cost = huber_classification_cost(input=input_layer, label=label_layer) :param input: The first input layer. :type input: LayerOutput. @@ -5634,11 +5644,12 @@ def huber_cost(input, label, name=None, coeff=1.0, layer_attr=None): assert input.size == 1 Layer( name=name, - type=LayerType.HUBER, + type=LayerType.HUBER_CLASSIFICATION, inputs=[input.name, label.name], coeff=coeff, **ExtraLayerAttribute.to_kwargs(layer_attr)) - return LayerOutput(name, LayerType.HUBER, parents=[input, label], size=1) + return LayerOutput( + name, LayerType.HUBER_CLASSIFICATION, parents=[input, label], size=1) @wrap_name_default() diff --git a/python/paddle/trainer_config_helpers/tests/configs/protostr/test_cost_layers.protostr b/python/paddle/trainer_config_helpers/tests/configs/protostr/test_cost_layers.protostr index 05847344b..a64e5ea0d 100644 --- a/python/paddle/trainer_config_helpers/tests/configs/protostr/test_cost_layers.protostr +++ b/python/paddle/trainer_config_helpers/tests/configs/protostr/test_cost_layers.protostr @@ -180,8 +180,8 @@ layers { active_type: "" } layers { - name: "__huber_cost_0__" - type: "huber" + name: "__huber_classification_cost_0__" + type: "huber_classification" size: 1 active_type: "" inputs { @@ -300,7 +300,7 @@ output_layer_names: "__rank_cost_0__" output_layer_names: "__lambda_cost_0__" output_layer_names: "__cross_entropy_0__" output_layer_names: "__cross_entropy_with_selfnorm_0__" -output_layer_names: "__huber_cost_0__" +output_layer_names: "__huber_classification_cost_0__" output_layer_names: "__multi_binary_label_cross_entropy_0__" output_layer_names: "__sum_cost_0__" output_layer_names: "__nce_layer_0__" @@ -326,7 +326,7 @@ sub_models { layer_names: "__cross_entropy_with_selfnorm_0__" layer_names: "huber_probs" layer_names: "huber_label" - layer_names: "__huber_cost_0__" + layer_names: "__huber_classification_cost_0__" layer_names: "__multi_binary_label_cross_entropy_0__" layer_names: "__sum_cost_0__" layer_names: "__nce_layer_0__" @@ -349,7 +349,7 @@ sub_models { output_layer_names: "__lambda_cost_0__" output_layer_names: "__cross_entropy_0__" output_layer_names: "__cross_entropy_with_selfnorm_0__" - output_layer_names: "__huber_cost_0__" + output_layer_names: "__huber_classification_cost_0__" output_layer_names: "__multi_binary_label_cross_entropy_0__" output_layer_names: "__sum_cost_0__" output_layer_names: "__nce_layer_0__" diff --git a/python/paddle/trainer_config_helpers/tests/configs/test_cost_layers.py b/python/paddle/trainer_config_helpers/tests/configs/test_cost_layers.py index d2a3b702a..98bf026d6 100644 --- a/python/paddle/trainer_config_helpers/tests/configs/test_cost_layers.py +++ b/python/paddle/trainer_config_helpers/tests/configs/test_cost_layers.py @@ -33,7 +33,7 @@ outputs( input=probs, label=xe_label), cross_entropy_with_selfnorm( input=probs, label=xe_label), - huber_cost( + huber_classification_cost( input=data_layer( name='huber_probs', size=1), label=data_layer( -- GitLab From 632b320e9dc11c6991d95187631c311cae7f7162 Mon Sep 17 00:00:00 2001 From: dongzhihong Date: Mon, 14 Aug 2017 17:19:15 +0800 Subject: [PATCH 0034/2018] "refine argument with new style " --- paddle/operators/math/math_function.h | 9 +++ paddle/operators/mul_op.cc | 20 ++++--- paddle/operators/mul_op.h | 60 +++++++++++-------- .../paddle/v2/framework/tests/test_mul_op.py | 13 +++- 4 files changed, 66 insertions(+), 36 deletions(-) diff --git a/paddle/operators/math/math_function.h b/paddle/operators/math/math_function.h index 155589fad..c7c603929 100644 --- a/paddle/operators/math/math_function.h +++ b/paddle/operators/math/math_function.h @@ -77,6 +77,15 @@ void matmul(const framework::Tensor& matrix_a, bool trans_a, framework::Tensor* matrix_out, T beta, platform::DeviceContext* context); +// // matrix multiply with continuous memory +// template +// void matmul(const framework::Tensor& matrix_a, bool trans_a, +// const framework::Tensor& matrix_b, bool trans_b, +// framework::Tensor* matrix_out, +// platform::DeviceContext* context) { +// matmul(matrix_a, matrix_b, trans_a, trans_b, 1, matrix_out, 0, context); +// } + } // namespace math } // namespace operators } // namespace paddle diff --git a/paddle/operators/mul_op.cc b/paddle/operators/mul_op.cc index a1ca66a24..d77c0607a 100644 --- a/paddle/operators/mul_op.cc +++ b/paddle/operators/mul_op.cc @@ -18,6 +18,8 @@ namespace paddle { namespace operators { +using framework::Tensor; + class MulOp : public framework::OperatorWithKernel { public: using framework::OperatorWithKernel::OperatorWithKernel; @@ -60,19 +62,19 @@ class MulOpGrad : public framework::OperatorWithKernel { protected: void InferShape(const framework::InferShapeContext &ctx) const override { - PADDLE_ENFORCE_EQ(ctx.InputSize(), 3UL, - "Input of MulOpGrad should be 3, X, Y, Out@GRAD"); - PADDLE_ENFORCE_EQ(ctx.OutputSize(), 2UL, - "Output of MulOpGrad should be 2, X@GRAD, Y@GRAD"); + // PADDLE_ENFORCE_EQ(ctx.InputSize(), 3UL, + // "Input of MulOpGrad should be 3, X, Y, Out@GRAD"); + // PADDLE_ENFORCE_EQ(ctx.OutputSize(), 2UL, + // "Output of MulOpGrad should be 2, X@GRAD, Y@GRAD"); PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) should not be null"); PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Y"), "Input(Y) should not be null"); PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")), "Input(Out@GRAD) should not be null"); - auto *x_grad = ctx.Output(framework::GradVarName("X")); - auto *y_grad = ctx.Output(framework::GradVarName("Y")); - auto dim0 = ctx.Input(0)->dims(); - auto dim1 = ctx.Input(1)->dims(); - auto out_dims = ctx.Input(2)->dims(); + auto *x_grad = ctx.Output(framework::GradVarName("X")); + auto *y_grad = ctx.Output(framework::GradVarName("Y")); + auto dim0 = ctx.Input(framework::GradVarName("X"))->dims(); + auto dim1 = ctx.Input(framework::GradVarName("Y"))->dims(); + auto out_dims = ctx.Input(framework::GradVarName("Out"))->dims(); PADDLE_ENFORCE(dim0[0] * dim1[0] == out_dims[0], "Out@GRAD[0] must equal to X[0] * Y[0]"); PADDLE_ENFORCE(dim0[1] * dim1[1] == out_dims[1], diff --git a/paddle/operators/mul_op.h b/paddle/operators/mul_op.h index ad40e3cf1..279454c7f 100644 --- a/paddle/operators/mul_op.h +++ b/paddle/operators/mul_op.h @@ -31,18 +31,22 @@ template class MulKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& context) const override { - Eigen::array, 1> dim_pair = { - {Eigen::IndexPair(1, 0)}}; - auto* input0 = context.Input("X"); - auto* input1 = context.Input("Y"); - auto* output = context.Output("Out"); - output->mutable_data(context.GetPlace()); - auto X = EigenMatrix::From(*input0); - auto Y = EigenMatrix::From(*input1); - auto Z = EigenMatrix::From(*output); - auto& place = context.GetEigenDevice(); - - Z.device(place) = X.contract(Y, dim_pair); + // Eigen::array, 1> dim_pair = { + // {Eigen::IndexPair(1, 0)}}; + auto* X = context.Input("X"); + auto* Y = context.Input("Y"); + auto* Z = context.Output("Out"); + Z->mutable_data(context.GetPlace()); + auto* device_context = + const_cast(context.device_context_); + math::matmul(*X, false, *Y, false, 1, Z, 0, device_context); + + // auto X = EigenMatrix::From(*input0); + // auto Y = EigenMatrix::From(*input1); + // auto Z = EigenMatrix::From(*output); + // auto& place = context.GetEigenDevice(); + + // Z.device(place) = X.contract(Y, dim_pair); } }; @@ -50,27 +54,31 @@ template class MulGradKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& ctx) const override { - auto* input0 = ctx.Input("X"); - auto* input1 = ctx.Input("Y"); - auto* input2 = ctx.Input(framework::GradVarName("Out")); + auto* X = ctx.Input("X"); + auto* Y = ctx.Input("Y"); + auto* dOut = ctx.Input(framework::GradVarName("Out")); - auto* output0 = ctx.Output(0); - auto* output1 = ctx.Output(1); - output0->mutable_data(ctx.GetPlace()); - output1->mutable_data(ctx.GetPlace()); + auto* dX = ctx.Output(framework::GradVarName("X")); + auto* dY = ctx.Output(framework::GradVarName("Y")); + // auto* dXdata = dX->template mutable_data(ctx.GetPlace()); + // auto* dYdata = dY->template mutable_data(ctx.GetPlace()); + auto* device_context = + const_cast(ctx.device_context_); + math::matmul(*dOut, false, *Y, true, 1, dX, 0, device_context); + math::matmul(*X, true, *dOut, false, 1, dY, 0, device_context); - auto X = EigenMatrix::From(*input0); - auto Y = EigenMatrix::From(*input1); - auto dOut = EigenMatrix::From(*input2); - auto dX = EigenMatrix::From(*output0); - auto dY = EigenMatrix::From(*output1); + // auto X = EigenMatrix::From(*input0); + // auto Y = EigenMatrix::From(*input1); + // auto dOut = EigenMatrix::From(*input2); + // auto dX = EigenMatrix::From(*output0); + // auto dY = EigenMatrix::From(*output1); // dX = Out@G * Y' // dY = X' * Out@G - auto place = ctx.GetEigenDevice(); + // auto place = ctx.GetEigenDevice(); // TODO(dzh,qijun) : need transpose feature of blas library // Eigen Tensor does not support it very well - // dX.device(place) = dOut.contract(dOut, transpose) + // dX.device(place) = matmul(input2, ) } }; diff --git a/python/paddle/v2/framework/tests/test_mul_op.py b/python/paddle/v2/framework/tests/test_mul_op.py index 126a7f398..eef5a4f96 100644 --- a/python/paddle/v2/framework/tests/test_mul_op.py +++ b/python/paddle/v2/framework/tests/test_mul_op.py @@ -1,6 +1,7 @@ import unittest -from op_test_util import OpTestMeta import numpy as np +from gradient_checker import GradientChecker, create_op +from op_test_util import OpTestMeta class TestMulOp(unittest.TestCase): @@ -15,6 +16,16 @@ class TestMulOp(unittest.TestCase): self.outputs = {'Out': np.dot(self.inputs['X'], self.inputs['Y'])} +class MulGradOpTest(GradientChecker): + def test_mul(self): + op = create_op("mul") + inputs = { + 'X': np.random.random((32, 84)).astype("float32"), + 'Y': np.random.random((84, 100)).astype("float32") + } + self.check_grad(op, inputs, set(["X", "Y"]), "Out") + + # TODO(dzh,qijun) : mulgrad test case need transpose feature of blas library if __name__ == '__main__': -- GitLab From e0395a53e93ff1631dff39582ec4754e4f5acdf0 Mon Sep 17 00:00:00 2001 From: dongzhihong Date: Mon, 14 Aug 2017 17:57:22 +0800 Subject: [PATCH 0035/2018] "remove unused commented code" --- paddle/operators/mul_op.cc | 4 ---- paddle/operators/mul_op.h | 24 ------------------------ 2 files changed, 28 deletions(-) diff --git a/paddle/operators/mul_op.cc b/paddle/operators/mul_op.cc index d77c0607a..95b495b87 100644 --- a/paddle/operators/mul_op.cc +++ b/paddle/operators/mul_op.cc @@ -62,10 +62,6 @@ class MulOpGrad : public framework::OperatorWithKernel { protected: void InferShape(const framework::InferShapeContext &ctx) const override { - // PADDLE_ENFORCE_EQ(ctx.InputSize(), 3UL, - // "Input of MulOpGrad should be 3, X, Y, Out@GRAD"); - // PADDLE_ENFORCE_EQ(ctx.OutputSize(), 2UL, - // "Output of MulOpGrad should be 2, X@GRAD, Y@GRAD"); PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) should not be null"); PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Y"), "Input(Y) should not be null"); PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")), diff --git a/paddle/operators/mul_op.h b/paddle/operators/mul_op.h index 279454c7f..2afed8184 100644 --- a/paddle/operators/mul_op.h +++ b/paddle/operators/mul_op.h @@ -31,8 +31,6 @@ template class MulKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& context) const override { - // Eigen::array, 1> dim_pair = { - // {Eigen::IndexPair(1, 0)}}; auto* X = context.Input("X"); auto* Y = context.Input("Y"); auto* Z = context.Output("Out"); @@ -40,13 +38,6 @@ class MulKernel : public framework::OpKernel { auto* device_context = const_cast(context.device_context_); math::matmul(*X, false, *Y, false, 1, Z, 0, device_context); - - // auto X = EigenMatrix::From(*input0); - // auto Y = EigenMatrix::From(*input1); - // auto Z = EigenMatrix::From(*output); - // auto& place = context.GetEigenDevice(); - - // Z.device(place) = X.contract(Y, dim_pair); } }; @@ -60,25 +51,10 @@ class MulGradKernel : public framework::OpKernel { auto* dX = ctx.Output(framework::GradVarName("X")); auto* dY = ctx.Output(framework::GradVarName("Y")); - // auto* dXdata = dX->template mutable_data(ctx.GetPlace()); - // auto* dYdata = dY->template mutable_data(ctx.GetPlace()); auto* device_context = const_cast(ctx.device_context_); math::matmul(*dOut, false, *Y, true, 1, dX, 0, device_context); math::matmul(*X, true, *dOut, false, 1, dY, 0, device_context); - - // auto X = EigenMatrix::From(*input0); - // auto Y = EigenMatrix::From(*input1); - // auto dOut = EigenMatrix::From(*input2); - // auto dX = EigenMatrix::From(*output0); - // auto dY = EigenMatrix::From(*output1); - - // dX = Out@G * Y' - // dY = X' * Out@G - // auto place = ctx.GetEigenDevice(); - // TODO(dzh,qijun) : need transpose feature of blas library - // Eigen Tensor does not support it very well - // dX.device(place) = matmul(input2, ) } }; -- GitLab From 12ee5014857e751fb429e0d3ebcfd41dcd5da29d Mon Sep 17 00:00:00 2001 From: dongzhihong Date: Mon, 14 Aug 2017 20:57:46 +0800 Subject: [PATCH 0036/2018] "fix operator grad config" --- paddle/operators/rowwise_add_op.cc | 23 +++++++++++++++++------ paddle/operators/rowwise_add_op.h | 21 +++++++++++---------- 2 files changed, 28 insertions(+), 16 deletions(-) diff --git a/paddle/operators/rowwise_add_op.cc b/paddle/operators/rowwise_add_op.cc index 0c6ae64d0..60e5d7749 100644 --- a/paddle/operators/rowwise_add_op.cc +++ b/paddle/operators/rowwise_add_op.cc @@ -17,6 +17,8 @@ namespace paddle { namespace operators { +using framework::Tensor; + class RowwiseAddOp : public framework::OperatorWithKernel { public: using framework::OperatorWithKernel::OperatorWithKernel; @@ -50,14 +52,23 @@ for i in xrange(X.shape[0]): } }; class RowwiseAddGradOp : public framework::OperatorWithKernel { + public: + using framework::OperatorWithKernel::OperatorWithKernel; + protected: void InferShape(const framework::InferShapeContext &ctx) const override { - PADDLE_ENFORCE(ctx.InputSize() == 4UL, - "RowwiseAddGrad inputs is I, O, OG, size must be 4"); - PADDLE_ENFORCE(ctx.OutputSize() == 2, - "RowwiseAddGrad output is IG, size must be 2"); - ctx.Output(0)->Resize(ctx.Input(0)->dims()); - ctx.Output(1)->Resize(ctx.Input(1)->dims()); + // PADDLE_ENFORCE(ctx.InputSize() == 4UL, + // "RowwiseAddGrad inputs is I, O, OG, size must be 4"); + // PADDLE_ENFORCE(ctx.OutputSize() == 2, + // "RowwiseAddGrad output is IG, size must be 2"); + PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "X should not be null"); + PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("b"), "b should not be null"); + PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")), + "Input(Out@GRAD) should not be null"); + auto dims0 = ctx.Input("X")->dims(); + auto dims1 = ctx.Input("b")->dims(); + ctx.Output(framework::GradVarName("X"))->Resize(dims0); + ctx.Output(framework::GradVarName("b"))->Resize(dims1); } }; diff --git a/paddle/operators/rowwise_add_op.h b/paddle/operators/rowwise_add_op.h index 3ad60172c..6593d811e 100644 --- a/paddle/operators/rowwise_add_op.h +++ b/paddle/operators/rowwise_add_op.h @@ -51,19 +51,20 @@ template class RowwiseAddGradKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& context) const override { - auto* XGrad = context.Output(0); - auto* bGrad = context.Output(1); - XGrad->mutable_data(context.GetPlace()); - bGrad->mutable_data(context.GetPlace()); + auto* dX = context.Output(framework::GradVarName("X")); + auto* db = context.Output(framework::GradVarName("b")); + auto* dOut = context.Output(framework::GradVarName("Out")); + dX->mutable_data(context.GetPlace()); + db->mutable_data(context.GetPlace()); - // I, O, OG => [X, b], [Out], [OutGrad] - auto OutGrad = EigenMatrix::From(*context.Input(3)); - EigenMatrix::From(*XGrad).device(context.GetEigenDevice()) = - OutGrad; + auto OutGrad = EigenMatrix::From(*dOut); + auto place = context.GetEigenDevice(); + EigenMatrix::From(*dX).device(place) = OutGrad; // https://eigen.tuxfamily.org/dox/unsupported/TensorBase_8h_source.html - EigenVector::Flatten(*bGrad).device(context.GetEigenDevice()) = - OutGrad.cumsum(1); // colwise add + // colwise add + Eigen::array dims{{1}}; /* dimension to reduce */ + EigenVector::Flatten(*db).device(place) = OutGrad.sum(dims); } }; } // namespace operators -- GitLab From 84d6434d53dbef47b5aa817c5ff25d236a59a83c Mon Sep 17 00:00:00 2001 From: dangqingqing Date: Mon, 14 Aug 2017 20:58:57 +0800 Subject: [PATCH 0037/2018] Compare the gradient consistency between GPU and CPU calculations. --- paddle/operators/sigmoid_op.cc | 3 +- .../paddle/v2/framework/tests/CMakeLists.txt | 1 + .../v2/framework/tests/gradient_checker.py | 173 ++++++++---------- .../v2/framework/tests/test_sigmoid_op.py | 22 ++- 4 files changed, 98 insertions(+), 101 deletions(-) diff --git a/paddle/operators/sigmoid_op.cc b/paddle/operators/sigmoid_op.cc index a7dfb624e..84601bd73 100644 --- a/paddle/operators/sigmoid_op.cc +++ b/paddle/operators/sigmoid_op.cc @@ -44,7 +44,8 @@ class SigmoidOpGrad : public framework::OperatorWithKernel { protected: void InferShape(const framework::InferShapeContext &ctx) const override { - ctx.Output(0)->Resize(ctx.Input(0)->dims()); + ctx.Output(framework::GradVarName("X")) + ->Resize(ctx.Input("Y")->dims()); } }; diff --git a/python/paddle/v2/framework/tests/CMakeLists.txt b/python/paddle/v2/framework/tests/CMakeLists.txt index 96fad9b42..4c088e761 100644 --- a/python/paddle/v2/framework/tests/CMakeLists.txt +++ b/python/paddle/v2/framework/tests/CMakeLists.txt @@ -25,3 +25,4 @@ py_test(test_operator SRCS test_operator.py) # py_test(test_gaussian_random_op SRCS test_gaussian_random_op.py) py_test(test_uniform_random_op SRCS test_uniform_random_op.py) py_test(test_recurrent_op SRCS test_recurrent_op.py) +py_test(test_gradient_checker SRCS test_gradient_checker.py) diff --git a/python/paddle/v2/framework/tests/gradient_checker.py b/python/paddle/v2/framework/tests/gradient_checker.py index 501cf6110..5f9e54837 100644 --- a/python/paddle/v2/framework/tests/gradient_checker.py +++ b/python/paddle/v2/framework/tests/gradient_checker.py @@ -1,6 +1,7 @@ import unittest import numpy +import itertools import paddle.v2.framework.core as core from paddle.v2.framework.op import Operator @@ -8,6 +9,7 @@ __all__ = ['get_numeric_gradient'] def create_op(op_type): + # TODO need to set attrs kwargs = dict() for in_name in Operator.get_op_input_names(op_type): kwargs[in_name] = in_name @@ -66,7 +68,6 @@ def get_numeric_gradient(op, local_scope.find_var(output).get_tensor().alloc_float(core.CPUPlace( )) - # TODO(yuyang18): Only CPU is support now. cpu_ctx = core.DeviceContext.create(core.CPUPlace()) def get_output(): @@ -109,12 +110,71 @@ def get_numeric_gradient(op, class GradientChecker(unittest.TestCase): - def assert_is_close(self, numeric_grads, scope, max_relative_error, - msg_prefix): - for name in numeric_grads: - b = numpy.array(scope.find_var(grad_var_name(name)).get_tensor()) - a = numeric_grads[name] + def get_grad(self, forward_op, backward_op, input_vars, grad_names, place): + scope = core.Scope() + ctx = core.DeviceContext.create(place) + inputs = forward_op.inputs() + in_names = [item for k in inputs for item in inputs[k]] + outputs = forward_op.outputs() + out_names = [item for k in outputs for item in outputs[k]] + + # create input var and set value + for name, value in input_vars.iteritems(): + if name not in in_names: + raise ValueError(name + "does not exist in Op's inputs.") + var = scope.new_var(name).get_tensor() + var.set_dims(value.shape) + var.set(value, place) + + # run forward op + for out_name in out_names: + scope.new_var(out_name) + forward_op.infer_shape(scope) + forward_op.run(scope, ctx) + + # set output var's shape + # set output grad to ones + for name in out_names: + out_tensor = scope.find_var(name).get_tensor() + grad_tensor = scope.new_var(grad_var_name(name)).get_tensor() + grad_tensor.set_dims(out_tensor.shape()) + data = numpy.ones(out_tensor.shape(), dtype=numpy.float32) + grad_tensor.set(data, place) + + # run backward op + for name in backward_op.outputs(): + scope.new_var(name) + backward_op.infer_shape(scope) + backward_op.run(scope, ctx) + + outs = [ + numpy.array(scope.find_var(name).get_tensor()) + for name in grad_names + ] + return outs + + def compare_grad(self, forward_op, inputs): + backward_op = core.Operator.backward(forward_op, set()) + if not (core.is_compile_gpu() and backward_op.support_gpu()): + return + + outputs = backward_op.outputs() + out_names = [item for k in outputs for item in outputs[k]] + cpu_grads = self.get_grad(forward_op, backward_op, inputs, out_names, + core.CPUPlace()) + gpu_grads = self.get_grad(forward_op, backward_op, inputs, out_names, + core.GPUPlace(0)) + + for c_grad, g_grad, name in itertools.izip(cpu_grads, gpu_grads, + out_names): + self.assertTrue( + numpy.allclose(c_grad, g_grad), + "output name: " + name + " has diff") + + def assert_is_close(self, numeric_grads, analytic_grads, names, + max_relative_error, msg_prefix): + for a, b, name in itertools.izip(numeric_grads, analytic_grads, names): abs_a = numpy.abs(a) # if abs_a is nearly zero, then use abs error for a, not relative # error. @@ -159,106 +219,27 @@ class GradientChecker(unittest.TestCase): inputs = forward_op.inputs() in_names = [item for k in inputs for item in inputs[k]] - outputs = forward_op.outputs() - out_names = [item for k in outputs for item in outputs[k]] - for no_grad in no_grad_set: if no_grad not in in_names: raise ValueError("no_grad should be in in_names") backward_op = core.Operator.backward(forward_op, no_grad_set) - bwd_outputs = backward_op.outputs() - bwd_out_names = [item for k in bwd_outputs for item in bwd_outputs[k]] - places = [core.CPUPlace()] if not only_cpu and core.is_compile_gpu() and backward_op.support_gpu(): places.append(core.GPUPlace(0)) - numeric_grad = dict() - # get numeric gradient - for check_name in inputs_to_check: - numeric_grad[check_name] = \ - get_numeric_gradient(forward_op, input_vars, output_name, - check_name) + # get numerical gradients + numeric_grads = [ + get_numeric_gradient(forward_op, input_vars, output_name, name) + for name in inputs_to_check + ] - # get operator gradient according to different device + check_names = [grad_var_name(name) for name in inputs_to_check] for place in places: - scope = core.Scope() - ctx = core.DeviceContext.create(place) - - # create input var and set value - for name, value in input_vars.iteritems(): - if name not in in_names: - raise ValueError(name + " not in op.inputs_") - var = scope.new_var(name).get_tensor() - var.set_dims(value.shape) - var.set(value, place) - - # create output var - for out_name in out_names: - scope.new_var(out_name).get_tensor() - - # infer the shape of output var and compute/set value of output var - forward_op.infer_shape(scope) - forward_op.run(scope, ctx) - - # create output grad var - # set shape as the output var - # set value of this grad to ones - for name in out_names: - out_tensor = scope.find_var(name).get_tensor() - grad_tensor = scope.new_var(grad_var_name(name)).get_tensor() - grad_tensor.set_dims(out_tensor.shape()) - data = 1.0 * numpy.ones(out_tensor.shape()) - grad_tensor.set(data, place) - - # create input grad var - for name in bwd_out_names: - scope.new_var(name).get_tensor() - - # infer the shape of input gradient var and compute/set it's value - # with backward op - backward_op.infer_shape(scope) - backward_op.run(scope, ctx) - - self.assert_is_close(numeric_grad, scope, max_relative_error, + # get analytical gradients according to different device + analytic_grads = self.get_grad(forward_op, backward_op, input_vars, + check_grad_names, place) + self.assert_is_close(numeric_grads, analytic_grads, check_names, + max_relative_error, "Gradient Check On %s" % str(place)) - - -if __name__ == '__main__': - - class GetNumericGradientTest(unittest.TestCase): - def test_add_op(self): - add_op = Operator('add_two', X="X", Y="Y", Out="Z") - x = numpy.random.random((10, 1)).astype("float32") - y = numpy.random.random((10, 1)).astype("float32") - - arr = get_numeric_gradient(add_op, {'X': x, "Y": y}, 'Z', 'X') - self.assertAlmostEqual(arr.mean(), 1.0, delta=1e-2) - - def test_softmax_op(self): - def stable_softmax(x): - """Compute the softmax of vector x in a numerically stable way.""" - shiftx = x - numpy.max(x) - exps = numpy.exp(shiftx) - return exps / numpy.sum(exps) - - def label_softmax_grad(Y, dY): - dX = Y * 0.0 - for i in range(Y.shape[0]): - d = numpy.dot(Y[i, :], dY[i, :]) - dX[i, :] = Y[i, :] * (dY[i, :] - d) - return dX - - softmax_op = Operator("softmax", X="X", Y="Y") - - X = numpy.random.random((2, 2)).astype("float32") - Y = numpy.apply_along_axis(stable_softmax, 1, X) - dY = numpy.ones(Y.shape) - dX = label_softmax_grad(Y, dY) - - arr = get_numeric_gradient(softmax_op, {"X": X}, 'Y', 'X') - numpy.testing.assert_almost_equal(arr, dX, decimal=1e-2) - - unittest.main() diff --git a/python/paddle/v2/framework/tests/test_sigmoid_op.py b/python/paddle/v2/framework/tests/test_sigmoid_op.py index 2a57a41ed..1a6d395be 100644 --- a/python/paddle/v2/framework/tests/test_sigmoid_op.py +++ b/python/paddle/v2/framework/tests/test_sigmoid_op.py @@ -1,6 +1,7 @@ import unittest -from op_test_util import OpTestMeta import numpy as np +from op_test_util import OpTestMeta +from gradient_checker import GradientChecker, create_op class TestSigmoidOp(unittest.TestCase): @@ -8,12 +9,25 @@ class TestSigmoidOp(unittest.TestCase): def setUp(self): self.type = "sigmoid" - self.inputs = {'X': np.random.random((32, 100)).astype("float32")} + self.inputs = {'X': np.random.random((15, 31)).astype("float32")} self.outputs = {'Y': 1 / (1 + np.exp(-self.inputs['X']))} -#class TestSigmoidGradOp(unittest.TestCase): -#TODO(qingqing) add unit test +class TestSigmoidGradOp(GradientChecker): + def test_compare_grad(self): + op = create_op("sigmoid") + inputs = {"X": np.random.random((11, 17)).astype("float32")} + + # compare gpu and cpu results for backward op + self.compare_grad(op, inputs) + + def test_check_grad(self): + op = create_op("sigmoid") + inputs = {"X": np.random.random((11, 17)).astype("float32")} + + # check gradients + self.check_grad(op, inputs, set("X"), "Y") + if __name__ == '__main__': unittest.main() -- GitLab From 01d9134067852a1f9dfecf75f730f9fba14434e0 Mon Sep 17 00:00:00 2001 From: dangqingqing Date: Mon, 14 Aug 2017 21:01:24 +0800 Subject: [PATCH 0038/2018] Add test_gradient_checker.py --- .../framework/tests/test_gradient_checker.py | 43 +++++++++++++++++++ 1 file changed, 43 insertions(+) create mode 100644 python/paddle/v2/framework/tests/test_gradient_checker.py diff --git a/python/paddle/v2/framework/tests/test_gradient_checker.py b/python/paddle/v2/framework/tests/test_gradient_checker.py new file mode 100644 index 000000000..e0b315120 --- /dev/null +++ b/python/paddle/v2/framework/tests/test_gradient_checker.py @@ -0,0 +1,43 @@ +import unittest +import numpy +from paddle.v2.framework.op import Operator +from gradient_checker import GradientChecker +from gradient_checker import get_numeric_gradient + + +class GetNumericGradientTest(unittest.TestCase): + def test_add_op(self): + add_op = Operator('add_two', X="X", Y="Y", Out="Z") + x = numpy.random.random((10, 1)).astype("float32") + y = numpy.random.random((10, 1)).astype("float32") + + arr = get_numeric_gradient(add_op, {'X': x, "Y": y}, 'Z', 'X') + self.assertAlmostEqual(arr.mean(), 1.0, delta=1e-4) + + def test_softmax_op(self): + def stable_softmax(x): + """Compute the softmax of vector x in a numerically stable way.""" + shiftx = x - numpy.max(x) + exps = numpy.exp(shiftx) + return exps / numpy.sum(exps) + + def label_softmax_grad(Y, dY): + dX = Y * 0.0 + for i in range(Y.shape[0]): + d = numpy.dot(Y[i, :], dY[i, :]) + dX[i, :] = Y[i, :] * (dY[i, :] - d) + return dX + + softmax_op = Operator("softmax", X="X", Y="Y") + + X = numpy.random.random((2, 2)).astype("float32") + Y = numpy.apply_along_axis(stable_softmax, 1, X) + dY = numpy.ones(Y.shape) + dX = label_softmax_grad(Y, dY) + + arr = get_numeric_gradient(softmax_op, {"X": X}, 'Y', 'X') + numpy.testing.assert_almost_equal(arr, dX, decimal=1e-2) + + +if __name__ == '__main__': + unittest.main() -- GitLab From 9a0eedf5d4d32e0aaa80e554f608c56e6d36a798 Mon Sep 17 00:00:00 2001 From: dangqingqing Date: Mon, 14 Aug 2017 21:27:17 +0800 Subject: [PATCH 0039/2018] fix bug. --- python/paddle/v2/framework/tests/gradient_checker.py | 3 ++- python/paddle/v2/framework/tests/test_sigmoid_op.py | 11 +++-------- 2 files changed, 5 insertions(+), 9 deletions(-) diff --git a/python/paddle/v2/framework/tests/gradient_checker.py b/python/paddle/v2/framework/tests/gradient_checker.py index 5f9e54837..d251f14b9 100644 --- a/python/paddle/v2/framework/tests/gradient_checker.py +++ b/python/paddle/v2/framework/tests/gradient_checker.py @@ -156,6 +156,7 @@ class GradientChecker(unittest.TestCase): def compare_grad(self, forward_op, inputs): backward_op = core.Operator.backward(forward_op, set()) + # return if not compile with GPU or not implementing GPU kernel if not (core.is_compile_gpu() and backward_op.support_gpu()): return @@ -239,7 +240,7 @@ class GradientChecker(unittest.TestCase): for place in places: # get analytical gradients according to different device analytic_grads = self.get_grad(forward_op, backward_op, input_vars, - check_grad_names, place) + check_names, place) self.assert_is_close(numeric_grads, analytic_grads, check_names, max_relative_error, "Gradient Check On %s" % str(place)) diff --git a/python/paddle/v2/framework/tests/test_sigmoid_op.py b/python/paddle/v2/framework/tests/test_sigmoid_op.py index 1a6d395be..c3bd79f5d 100644 --- a/python/paddle/v2/framework/tests/test_sigmoid_op.py +++ b/python/paddle/v2/framework/tests/test_sigmoid_op.py @@ -17,15 +17,10 @@ class TestSigmoidGradOp(GradientChecker): def test_compare_grad(self): op = create_op("sigmoid") inputs = {"X": np.random.random((11, 17)).astype("float32")} - - # compare gpu and cpu results for backward op + # compare gpu and cpu results for backward op. + # skip this test if only compiling CPU version. self.compare_grad(op, inputs) - - def test_check_grad(self): - op = create_op("sigmoid") - inputs = {"X": np.random.random((11, 17)).astype("float32")} - - # check gradients + # check gradients self.check_grad(op, inputs, set("X"), "Y") -- GitLab From e9eee6f78559d6318e554b7b5ab021b271d8ddb6 Mon Sep 17 00:00:00 2001 From: dongzhihong Date: Tue, 15 Aug 2017 09:57:40 +0800 Subject: [PATCH 0040/2018] "polish words" --- paddle/operators/name_convention.md | 10 +++++++--- 1 file changed, 7 insertions(+), 3 deletions(-) diff --git a/paddle/operators/name_convention.md b/paddle/operators/name_convention.md index da5bcb748..2260bf566 100644 --- a/paddle/operators/name_convention.md +++ b/paddle/operators/name_convention.md @@ -4,8 +4,12 @@ To make the operator document itself more clear. we recommend operator names obs ### Input/Output names -Variable name is uppercase. e.g. `X`, `Y` +* Variable name is prefer uppercase. e.g. `X`, `Y`. But when the variable is tensor, its name should lowercase. e.g. `matrix`, to discriminate with otherone. -Tensor name is lowercase. e.g. `tensor` +* element wise operator, math operator or similar op, please obey common name convention. if the operator only have one output, use `Out`. -if only have one output, use `Out` +* we prefer more meaningful input/output name. + +### Best Practice +e.g. `rowwise_add`, inputs : `X`, `Y`, outputs : `Out` +e.g. `cosine` , inputs : `X`, `axis`, outputs : `Out` -- GitLab From af1eb31afc92ae3ac59869a6a5b0e890e009c44b Mon Sep 17 00:00:00 2001 From: zchen0211 Date: Fri, 11 Aug 2017 11:55:56 -0700 Subject: [PATCH 0041/2018] add as an operator --- paddle/operators/CMakeLists.txt | 2 ++ paddle/operators/gather_op.cc | 64 +++++++++++++++++++++++++++++++++ paddle/operators/gather_op.h | 52 +++++++++++++++++++++++++++ 3 files changed, 118 insertions(+) create mode 100644 paddle/operators/gather_op.cc create mode 100644 paddle/operators/gather_op.h diff --git a/paddle/operators/CMakeLists.txt b/paddle/operators/CMakeLists.txt index a7c89787e..5ac898a8d 100644 --- a/paddle/operators/CMakeLists.txt +++ b/paddle/operators/CMakeLists.txt @@ -43,6 +43,8 @@ endfunction() add_subdirectory(math) cc_test(gather_test SRCS gather_test.cc DEPS tensor) +cc_library(gather_op SRCS gather_op.cc DEPS op_registry) +# cc_test(gather_op_test SRCS gather_op_test.cc DEPS gather_op) cc_test(scatter_test SRCS scatter_test.cc DEPS tensor) diff --git a/paddle/operators/gather_op.cc b/paddle/operators/gather_op.cc new file mode 100644 index 000000000..1008a57a8 --- /dev/null +++ b/paddle/operators/gather_op.cc @@ -0,0 +1,64 @@ +/* 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/gather_op.h" +#include "paddle/framework/ddim.h" + +namespace paddle { +namespace operators { + +class GatherOp : public framework::OperatorWithKernel { + protected: + void InferShape(const framework::InferShapeContext &ctx) const override { + PADDLE_ENFORCE(ctx.InputSize() == 2, ""); + PADDLE_ENFORCE(ctx.OutputSize() == 1, ""); + int batch_size = ctx.Input(1)->dims()[0]; + PADDLE_ENFORCE(batch_size > 0); + } +}; + +class GatherOpMaker : public framework::OpProtoAndCheckerMaker { + public: + GatherOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker) + : OpProtoAndCheckerMaker(proto, op_checker) { + AddInput("X", "The source input of gather op"); + AddInput("Index", "The index input of gather op"); + AddOutput("Y", "The output of add op"); + AddComment(R"DOC( +Gather Operator by selecting from the first axis, + +Y = X[Index] +)DOC"); + } +}; + +class GatherGradOp : public framework::OperatorWithKernel { + protected: + void InferShape(const framework::InferShapeContext &ctx) const override { + // ctx.Output("X" + framework::kGradVarSuffix) + // ->Resize(ctx.Input("X")->dims()); + } +}; + +} // namespace operators +} // namespace paddle + +namespace ops = paddle::operators; +REGISTER_OP(gather, ops::GatherOp, ops::GatherOpMaker); +REGISTER_OP_CPU_KERNEL(gather, + ops::GatherOpKernel); +REGISTER_GRADIENT_OP(gather, gather_grad, ops::GatherGradOp); +REGISTER_OP_CPU_KERNEL( + gather_grad, + ops::GatherGradientOpKernel); diff --git a/paddle/operators/gather_op.h b/paddle/operators/gather_op.h new file mode 100644 index 000000000..13e4c9b05 --- /dev/null +++ b/paddle/operators/gather_op.h @@ -0,0 +1,52 @@ +/* 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 "gather.h" +#include "paddle/framework/eigen.h" +#include "paddle/framework/op_registry.h" +#include "scatter.h" + +namespace paddle { +namespace operators { + +using Tensor = framework::Tensor; + +template +class GatherOpKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& ctx) const override { + auto X = ctx.Input("X"); + auto Index = ctx.Input("Index"); + auto Y = ctx.Output("Y"); + + Y->mutable_data(ctx.GetPlace()); + Gather(ctx.GetPlace(), X, Index, Y); + } +}; + +template +class GatherGradientOpKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& ctx) const override { + auto Index = ctx.Input("Index"); + auto dX = ctx.Output(framework::GradVarName("X")); + auto dY = ctx.Input(framework::GradVarName("Y")); + + ScatterUpdate(ctx.GetPlace(), dY, Index, dX); + } +}; + +} // namespace operators +} // namespace paddle -- GitLab From caaa5f86b91beda67daf8ae295cf99fa4dce12ba Mon Sep 17 00:00:00 2001 From: zchen0211 Date: Fri, 11 Aug 2017 15:09:04 -0700 Subject: [PATCH 0042/2018] gather op added --- paddle/framework/CMakeLists.txt | 2 ++ paddle/framework/empty_test.cc | 56 +++++++++++++++++++++++++++++++++ paddle/operators/gather_op.cc | 2 ++ 3 files changed, 60 insertions(+) create mode 100644 paddle/framework/empty_test.cc diff --git a/paddle/framework/CMakeLists.txt b/paddle/framework/CMakeLists.txt index 039852602..9e306c865 100644 --- a/paddle/framework/CMakeLists.txt +++ b/paddle/framework/CMakeLists.txt @@ -10,6 +10,8 @@ cc_test(eigen_test SRCS eigen_test.cc DEPS tensor) cc_library(lod_tensor SRCS lod_tensor.cc DEPS ddim place tensor) cc_test(lod_tensor_test SRCS lod_tensor_test.cc DEPS lod_tensor) +cc_test(empty_test SRCS empty_test.cc DEPS tensor) + cc_test(variable_test SRCS variable_test.cc) cc_library(scope SRCS scope.cc) diff --git a/paddle/framework/empty_test.cc b/paddle/framework/empty_test.cc new file mode 100644 index 000000000..2237f8ce0 --- /dev/null +++ b/paddle/framework/empty_test.cc @@ -0,0 +1,56 @@ +/* + 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 +#include +#include "paddle/framework/tensor.h" + +TEST(Empty, Dims) { + using namespace paddle::framework; + using namespace paddle::platform; + Tensor tt; + tt.Resize(make_ddim({0, 3, 4})); + DDim dims = tt.dims(); + ASSERT_EQ(arity(dims), 3); + EXPECT_EQ(0, dims[0]); + EXPECT_EQ(3, dims[1]); + EXPECT_EQ(4, dims[2]); +} + +TEST(Empty, MutableData) { + using namespace paddle::framework; + using namespace paddle::platform; + { + Tensor src_tensor; + float* p1 = nullptr; + // initialization + p1 = src_tensor.mutable_data(make_ddim({0, 2, 3}), CPUPlace()); + EXPECT_NE(p1, nullptr); + } + +#ifndef PADDLE_ONLY_CPU + { + Tensor src_tensor; + float* p1 = nullptr; + float* p2 = nullptr; + // initialization + p1 = src_tensor.mutable_data(make_ddim({0, 2, 3}), GPUPlace()); + EXPECT_NE(p1, nullptr); + // set src_tensor a new dim with large size + // momery is supposed to be re-allocated + p2 = src_tensor.mutable_data(make_ddim({0, 4}), GPUPlace()); + EXPECT_NE(p2, nullptr); + // EXPECT_NE(p1, p2); + } +#endif +} diff --git a/paddle/operators/gather_op.cc b/paddle/operators/gather_op.cc index 1008a57a8..3414a3c26 100644 --- a/paddle/operators/gather_op.cc +++ b/paddle/operators/gather_op.cc @@ -23,6 +23,8 @@ class GatherOp : public framework::OperatorWithKernel { void InferShape(const framework::InferShapeContext &ctx) const override { PADDLE_ENFORCE(ctx.InputSize() == 2, ""); PADDLE_ENFORCE(ctx.OutputSize() == 1, ""); + PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(0), + "Inputs of GatherOp must all be set"); int batch_size = ctx.Input(1)->dims()[0]; PADDLE_ENFORCE(batch_size > 0); } -- GitLab From 2a42a73db1ff32f7d1b9dfc772070fc7827aa4e7 Mon Sep 17 00:00:00 2001 From: zchen0211 Date: Fri, 11 Aug 2017 15:10:59 -0700 Subject: [PATCH 0043/2018] modify gather_op with test --- paddle/operators/gather_op.cc | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/paddle/operators/gather_op.cc b/paddle/operators/gather_op.cc index 3414a3c26..5a4f889f3 100644 --- a/paddle/operators/gather_op.cc +++ b/paddle/operators/gather_op.cc @@ -27,6 +27,9 @@ class GatherOp : public framework::OperatorWithKernel { "Inputs of GatherOp must all be set"); int batch_size = ctx.Input(1)->dims()[0]; PADDLE_ENFORCE(batch_size > 0); + paddle::framework::DDim output_dims(ctx.Input(0)->dims()); + output_dims[0] = batch_size; + ctx.Output(0)->Resize(output_dims); } }; @@ -48,8 +51,8 @@ Y = X[Index] class GatherGradOp : public framework::OperatorWithKernel { protected: void InferShape(const framework::InferShapeContext &ctx) const override { - // ctx.Output("X" + framework::kGradVarSuffix) - // ->Resize(ctx.Input("X")->dims()); + ctx.Output("X" + framework::kGradVarSuffix) + ->Resize(ctx.Input("X")->dims()); } }; -- GitLab From f6bffd4e1ff506319fa1a3338038d61d3f653181 Mon Sep 17 00:00:00 2001 From: zchen0211 Date: Fri, 11 Aug 2017 15:40:23 -0700 Subject: [PATCH 0044/2018] gather_op modified --- paddle/operators/gather_op.cc | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/paddle/operators/gather_op.cc b/paddle/operators/gather_op.cc index 5a4f889f3..05ba52ce0 100644 --- a/paddle/operators/gather_op.cc +++ b/paddle/operators/gather_op.cc @@ -51,8 +51,10 @@ Y = X[Index] class GatherGradOp : public framework::OperatorWithKernel { protected: void InferShape(const framework::InferShapeContext &ctx) const override { - ctx.Output("X" + framework::kGradVarSuffix) - ->Resize(ctx.Input("X")->dims()); + auto X_grad = ctx.Output(framework::GradVarName("X")); + auto X = ctx.Input("X"); + + X_grad->Resize(X->dims()); } }; -- GitLab From 4ab36a71c4cdc2319d0566ddef355ad11dcddd7b Mon Sep 17 00:00:00 2001 From: dongzhihong Date: Tue, 15 Aug 2017 13:42:19 +0800 Subject: [PATCH 0045/2018] "fix error" --- paddle/operators/mul_op.cc | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/paddle/operators/mul_op.cc b/paddle/operators/mul_op.cc index 903ca7b18..9a57e6b68 100644 --- a/paddle/operators/mul_op.cc +++ b/paddle/operators/mul_op.cc @@ -68,8 +68,8 @@ class MulOpGrad : public framework::OperatorWithKernel { "Input(Out@GRAD) should not be null"); auto *x_grad = ctx.Output(framework::GradVarName("X")); auto *y_grad = ctx.Output(framework::GradVarName("Y")); - auto dim0 = ctx.Input(framework::GradVarName("X"))->dims(); - auto dim1 = ctx.Input(framework::GradVarName("Y"))->dims(); + auto dim0 = ctx.Output(framework::GradVarName("X"))->dims(); + auto dim1 = ctx.Output(framework::GradVarName("Y"))->dims(); auto out_dims = ctx.Input(framework::GradVarName("Out"))->dims(); PADDLE_ENFORCE(dim0[0] * dim1[0] == out_dims[0], "Out@GRAD[0] must equal to X[0] * Y[0]"); -- GitLab From 95fe318e3ee19004419eb5aff09bca7ddaacad46 Mon Sep 17 00:00:00 2001 From: qijun Date: Tue, 15 Aug 2017 14:08:20 +0800 Subject: [PATCH 0046/2018] init --- Dockerfile | 14 ------ cmake/flags.cmake | 7 --- paddle/platform/CMakeLists.txt | 2 +- paddle/platform/device_context.cc | 79 +++++++++++++++++++++++++------ paddle/platform/device_context.h | 12 +++-- 5 files changed, 74 insertions(+), 40 deletions(-) diff --git a/Dockerfile b/Dockerfile index da0047102..98f61ba58 100644 --- a/Dockerfile +++ b/Dockerfile @@ -71,20 +71,6 @@ RUN pip install -r /root/requirements.txt RUN apt-get install -y libssl-dev libffi-dev RUN pip install certifi urllib3[secure] -# TODO(qijun) The template library Eigen doesn't work well with GCC 5 -# coming with the default Docker image, so we switch to use GCC 4.8 -# by default. And I will check Eigen library later. - -RUN ln -sf gcc-4.8 /usr/bin/gcc && \ - ln -sf gcc-ar-4.8 /usr/bin/gcc-ar && \ - ln -sf gcc-nm-4.8 /usr/bin/gcc-nm && \ - ln -sf gcc-ranlib-4.8 /usr/bin/gcc-ranlib && \ - ln -sf gcc-4.8 /usr/bin/x86_64-linux-gnu-gcc && \ - ln -sf gcc-ar-4.8 /usr/bin/x86_64-linux-gnu-gcc-ar && \ - ln -sf gcc-nm-4.8 /usr/bin/x86_64-linux-gnu-gcc-nm && \ - ln -sf gcc-ranlib-4.8 /usr/bin/x86_64-linux-gnu-gcc-ranlib && \ - ln -sf g++-4.8 /usr/bin/g++ && \ - ln -sf g++-4.8 /usr/bin/x86_64-linux-gnu-g++ # Install woboq_codebrowser to /woboq RUN git clone https://github.com/woboq/woboq_codebrowser /woboq && \ diff --git a/cmake/flags.cmake b/cmake/flags.cmake index b27eb7155..47bb83b00 100644 --- a/cmake/flags.cmake +++ b/cmake/flags.cmake @@ -9,13 +9,6 @@ function(CheckCompilerCXX11Flag) if(${CMAKE_CXX_COMPILER_VERSION} VERSION_LESS 4.8) message(FATAL_ERROR "Unsupported GCC version. GCC >= 4.8 required.") endif() - if(NOT ANDROID) - # TODO(qijun) gcc 4.9 or later versions raise SEGV due to the optimization problem. - # Use Debug mode instead for now. - if(CMAKE_CXX_COMPILER_VERSION VERSION_GREATER 4.9 OR CMAKE_CXX_COMPILER_VERSION VERSION_EQUAL 4.9) - set(CMAKE_BUILD_TYPE "Debug" CACHE STRING "" FORCE) - endif() - endif() elseif(CMAKE_CXX_COMPILER_ID STREQUAL "AppleClang" OR CMAKE_CXX_COMPILER_ID STREQUAL "Clang") # cmake >= 3.0 compiler id "AppleClang" on Mac OS X, otherwise "Clang" # Apple Clang is a different compiler than upstream Clang which havs different version numbers. diff --git a/paddle/platform/CMakeLists.txt b/paddle/platform/CMakeLists.txt index 4154aad15..c1ad60d16 100644 --- a/paddle/platform/CMakeLists.txt +++ b/paddle/platform/CMakeLists.txt @@ -16,5 +16,5 @@ ELSE() set(GPU_CTX_DEPS) ENDIF() -cc_library(device_context SRCS device_context.cc DEPS place eigen3 ${GPU_CTX_DEPS}) +cc_library(device_context SRCS device_context.cc DEPS memory place eigen3 ${GPU_CTX_DEPS}) nv_test(device_context_test SRCS device_context_test.cc DEPS device_context gpu_info) diff --git a/paddle/platform/device_context.cc b/paddle/platform/device_context.cc index a928e0977..dc345bdd5 100644 --- a/paddle/platform/device_context.cc +++ b/paddle/platform/device_context.cc @@ -10,6 +10,7 @@ See the License for the specific language governing permissions and limitations under the License. */ #include "paddle/platform/device_context.h" +#include "paddle/memory/memory.h" namespace paddle { namespace platform { @@ -36,6 +37,59 @@ Place CPUDeviceContext::GetPlace() const { return CPUPlace(); } #ifndef PADDLE_ONLY_CPU +class EigenCudaStreamDevice : public Eigen::StreamInterface { + public: + EigenCudaStreamDevice() : scratch_(nullptr), semaphore_(nullptr) { + Eigen::initializeDeviceProp(); + } + ~EigenCudaStreamDevice() override {} + + void Reinitialize(const cudaStream_t* cuda_stream, GPUPlace place) { + stream_ = cuda_stream; + place_ = place; + device_prop_ = &Eigen::m_deviceProperties[place.device]; + } + + const cudaStream_t& stream() const override { return *stream_; } + + const cudaDeviceProp& deviceProperties() const override { + return *device_prop_; + } + + void* allocate(size_t num_bytes) const override { + paddle::memory::Alloc(place_, num_bytes); + } + + void deallocate(void* buffer) const override { + paddle::memory::Free(place_, buffer); + } + + void* scratchpad() const override { + if (scratch_ == NULL) { + scratch_ = allocate(Eigen::kCudaScratchSize + sizeof(unsigned int)); + } + return scratch_; + } + + unsigned int* semaphore() const override { + if (semaphore_ == NULL) { + char* scratch = + static_cast(scratchpad()) + Eigen::kCudaScratchSize; + semaphore_ = reinterpret_cast(scratch); + PADDLE_ENFORCE( + cudaMemsetAsync(semaphore_, 0, sizeof(unsigned int), *stream_)); + } + return semaphore_; + } + + private: + GPUPlace place_; + const cudaStream_t* stream_; // not owned; + const cudaDeviceProp* device_prop_; // not owned; + mutable char* scratch_; + mutable unsigned int* semaphore_; +}; + template <> Eigen::GpuDevice* DeviceContext::get_eigen_device() const { return reinterpret_cast(this)->eigen_device(); @@ -43,19 +97,9 @@ Eigen::GpuDevice* DeviceContext::get_eigen_device() const { CUDADeviceContext::CUDADeviceContext(GPUPlace place) : place_(place) { SetDeviceId(place_.device); - // TODO(qijun) Pass a created cuda stream to Eigen::CudaStreamDevice directly - // here will cause segment fault. We must implement a class derived from - // Eigen::StreamInterface, and reinitialize it with a cuda stream and a gpu id - // later. Please refer to the implementation of class EigenCudaStreamDevice - // in TensorFlow. - // - // We find that CUDA 7 introduces a new option, the per-thread default stream, - // that has two effects. Please refer to https://devblogs.nvidia.com/ - // parallelforall/gpu-pro-tip-cuda-7-streams-simplify-concurrency/ - // - // So, we decide to use default stream and add –default-stream per-thread nvcc - // flag. Than, two threads with two CUDADeviceContexts will run parallelly. - eigen_stream_.reset(new Eigen::CudaStreamDevice()); + PADDLE_ENFORCE(cudaStreamCreate(&stream_)); + eigen_stream_.reset(new EigenCudaStreamDevice()); + eigen_stream_->Reinitialize(&stream_, place); eigen_device_.reset(new Eigen::GpuDevice(eigen_stream_.get())); } @@ -75,12 +119,13 @@ CUDADeviceContext::~CUDADeviceContext() { } eigen_stream_.reset(); eigen_device_.reset(); + PADDLE_ENFORCE(cudaStreamDestroy(stream_)); } Place CUDADeviceContext::GetPlace() const { return place_; } void CUDADeviceContext::Wait() const { - PADDLE_ENFORCE(cudaStreamSynchronize(0)); + PADDLE_ENFORCE(cudaStreamSynchronize(stream_)); } Eigen::GpuDevice* CUDADeviceContext::eigen_device() const { @@ -91,6 +136,7 @@ cublasHandle_t CUDADeviceContext::cublas_handle() { if (!cublas_handle_) { SetDeviceId(place_.device); PADDLE_ENFORCE(dynload::cublasCreate(&cublas_handle_)); + PADDLE_ENFORCE(dynload::cublasSetStream(cublas_handle_, stream_)); } return cublas_handle_; } @@ -99,10 +145,13 @@ cudnnHandle_t CUDADeviceContext::cudnn_handle() { if (!cudnn_handle_) { SetDeviceId(place_.device); PADDLE_ENFORCE(dynload::cudnnCreate(&cudnn_handle_)); + PADDLE_ENFORCE(dynload::cudnnSetStream(cudnnHandle_t, stream_)); } return cudnn_handle_; } +cudaStream_t CUDADeviceContext::stream() { return stream_; } + curandGenerator_t CUDADeviceContext::curand_generator() { if (!curand_generator_) { SetDeviceId(place_.device); @@ -110,6 +159,8 @@ curandGenerator_t CUDADeviceContext::curand_generator() { CURAND_RNG_PSEUDO_DEFAULT)); PADDLE_ENFORCE( dynload::curandSetPseudoRandomGeneratorSeed(curand_generator_, seed_)); + + PADDLE_ENFORCE(dynload::curandSetStream(curandGenerator_t, stream_)); } return curand_generator_; } diff --git a/paddle/platform/device_context.h b/paddle/platform/device_context.h index 08b5b2cff..b68e177c0 100644 --- a/paddle/platform/device_context.h +++ b/paddle/platform/device_context.h @@ -76,6 +76,9 @@ class CUDADeviceContext : public DeviceContext { /*! \brief Return curand handle in the device context. */ curandGenerator_t curand_generator(); + + /*! \brief Return cuda stream in the device context. */ + cudaStream_t stream(); // clang-format on private: @@ -83,15 +86,16 @@ class CUDADeviceContext : public DeviceContext { private: std::unique_ptr eigen_device_; - std::unique_ptr eigen_stream_; + std::unique_ptr eigen_stream_; private: uint64_t seed_; // clang-format off - cudnnHandle_t cudnn_handle_ = nullptr; - cublasHandle_t cublas_handle_ = nullptr; - curandGenerator_t curand_generator_ = nullptr; + cudaStream_t stream_{nullptr} + cudnnHandle_t cudnn_handle_{nullptr}; + cublasHandle_t cublas_handle_{nullptr}; + curandGenerator_t curand_generator_{nullptr}; // clang-format on }; -- GitLab From f168843e47df6cee8a81a30408ba4c2d092893fa Mon Sep 17 00:00:00 2001 From: qijun Date: Tue, 15 Aug 2017 06:59:05 +0000 Subject: [PATCH 0047/2018] fix gpu build error --- paddle/memory/CMakeLists.txt | 2 +- paddle/platform/CMakeLists.txt | 5 ++++- paddle/platform/device_context.cc | 8 ++++---- paddle/platform/device_context.h | 3 ++- 4 files changed, 11 insertions(+), 7 deletions(-) diff --git a/paddle/memory/CMakeLists.txt b/paddle/memory/CMakeLists.txt index 8035d93bf..9cc4233e4 100644 --- a/paddle/memory/CMakeLists.txt +++ b/paddle/memory/CMakeLists.txt @@ -1,7 +1,7 @@ add_subdirectory(detail) cc_library(memory SRCS memory.cc) -cc_library(memcpy SRCS memcpy.cc DEPS device_context) +cc_library(memcpy SRCS memcpy.cc) cc_library(paddle_memory DEPS diff --git a/paddle/platform/CMakeLists.txt b/paddle/platform/CMakeLists.txt index c1ad60d16..acfc06397 100644 --- a/paddle/platform/CMakeLists.txt +++ b/paddle/platform/CMakeLists.txt @@ -16,5 +16,8 @@ ELSE() set(GPU_CTX_DEPS) ENDIF() -cc_library(device_context SRCS device_context.cc DEPS memory place eigen3 ${GPU_CTX_DEPS}) +# memcpy deoends on device_context, here add deps individually for +# avoiding cycle dependencies +cc_library(device_context SRCS device_context.cc DEPS memory buddy_allocator + system_allocator memory_block meta_data meta_cache place eigen3 ${GPU_CTX_DEPS}) nv_test(device_context_test SRCS device_context_test.cc DEPS device_context gpu_info) diff --git a/paddle/platform/device_context.cc b/paddle/platform/device_context.cc index dc345bdd5..f92c15ae4 100644 --- a/paddle/platform/device_context.cc +++ b/paddle/platform/device_context.cc @@ -57,7 +57,7 @@ class EigenCudaStreamDevice : public Eigen::StreamInterface { } void* allocate(size_t num_bytes) const override { - paddle::memory::Alloc(place_, num_bytes); + return paddle::memory::Alloc(place_, num_bytes); } void deallocate(void* buffer) const override { @@ -86,7 +86,7 @@ class EigenCudaStreamDevice : public Eigen::StreamInterface { GPUPlace place_; const cudaStream_t* stream_; // not owned; const cudaDeviceProp* device_prop_; // not owned; - mutable char* scratch_; + mutable void* scratch_; mutable unsigned int* semaphore_; }; @@ -145,7 +145,7 @@ cudnnHandle_t CUDADeviceContext::cudnn_handle() { if (!cudnn_handle_) { SetDeviceId(place_.device); PADDLE_ENFORCE(dynload::cudnnCreate(&cudnn_handle_)); - PADDLE_ENFORCE(dynload::cudnnSetStream(cudnnHandle_t, stream_)); + PADDLE_ENFORCE(dynload::cudnnSetStream(cudnn_handle_, stream_)); } return cudnn_handle_; } @@ -160,7 +160,7 @@ curandGenerator_t CUDADeviceContext::curand_generator() { PADDLE_ENFORCE( dynload::curandSetPseudoRandomGeneratorSeed(curand_generator_, seed_)); - PADDLE_ENFORCE(dynload::curandSetStream(curandGenerator_t, stream_)); + PADDLE_ENFORCE(dynload::curandSetStream(curand_generator_, stream_)); } return curand_generator_; } diff --git a/paddle/platform/device_context.h b/paddle/platform/device_context.h index b68e177c0..c5042ae33 100644 --- a/paddle/platform/device_context.h +++ b/paddle/platform/device_context.h @@ -52,6 +52,7 @@ class CPUDeviceContext : public DeviceContext { }; #ifndef PADDLE_ONLY_CPU +class EigenCudaStreamDevice; class CUDADeviceContext : public DeviceContext { public: @@ -92,7 +93,7 @@ class CUDADeviceContext : public DeviceContext { uint64_t seed_; // clang-format off - cudaStream_t stream_{nullptr} + cudaStream_t stream_{nullptr}; cudnnHandle_t cudnn_handle_{nullptr}; cublasHandle_t cublas_handle_{nullptr}; curandGenerator_t curand_generator_{nullptr}; -- GitLab From 2403045cbd57eb837d5ab82e2acc66767c1d3224 Mon Sep 17 00:00:00 2001 From: qijun Date: Tue, 15 Aug 2017 07:03:54 +0000 Subject: [PATCH 0048/2018] refine device_context_test --- paddle/platform/device_context_test.cc | 1 + 1 file changed, 1 insertion(+) diff --git a/paddle/platform/device_context_test.cc b/paddle/platform/device_context_test.cc index 65345c433..8b764bdcd 100644 --- a/paddle/platform/device_context_test.cc +++ b/paddle/platform/device_context_test.cc @@ -45,6 +45,7 @@ TEST(Device, CUDADeviceContext) { ASSERT_NE(nullptr, cublas_handle); curandGenerator_t curand_handle = device_context->curand_generator(); ASSERT_NE(nullptr, curand_handle); + ASSERT_NE(nullptr, device_context->stream()); delete device_context; } } -- GitLab From 881c5d00d0e44cd957eeea2532c556a0eef8117a Mon Sep 17 00:00:00 2001 From: qijun Date: Tue, 15 Aug 2017 16:08:11 +0800 Subject: [PATCH 0049/2018] remove device_context deps in memcpy --- paddle/memory/memcpy.cc | 2 -- 1 file changed, 2 deletions(-) diff --git a/paddle/memory/memcpy.cc b/paddle/memory/memcpy.cc index aaab1142c..a19a3e367 100644 --- a/paddle/memory/memcpy.cc +++ b/paddle/memory/memcpy.cc @@ -16,8 +16,6 @@ limitations under the License. */ #include // for memcpy -#include "paddle/platform/device_context.h" - namespace paddle { namespace memory { -- GitLab From e256bfaf28a0984a15d594110ad1e868380a3e25 Mon Sep 17 00:00:00 2001 From: dongzhihong Date: Tue, 15 Aug 2017 17:12:35 +0800 Subject: [PATCH 0050/2018] "update paddle enforce" --- paddle/operators/mul_op.cc | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/paddle/operators/mul_op.cc b/paddle/operators/mul_op.cc index 9a57e6b68..5645df667 100644 --- a/paddle/operators/mul_op.cc +++ b/paddle/operators/mul_op.cc @@ -68,16 +68,16 @@ class MulOpGrad : public framework::OperatorWithKernel { "Input(Out@GRAD) should not be null"); auto *x_grad = ctx.Output(framework::GradVarName("X")); auto *y_grad = ctx.Output(framework::GradVarName("Y")); - auto dim0 = ctx.Output(framework::GradVarName("X"))->dims(); - auto dim1 = ctx.Output(framework::GradVarName("Y"))->dims(); + auto x_dims = ctx.Output(framework::GradVarName("X"))->dims(); + auto y_dims = ctx.Output(framework::GradVarName("Y"))->dims(); auto out_dims = ctx.Input(framework::GradVarName("Out"))->dims(); - PADDLE_ENFORCE(dim0[0] * dim1[0] == out_dims[0], - "Out@GRAD[0] must equal to X[0] * Y[0]"); - PADDLE_ENFORCE(dim0[1] * dim1[1] == out_dims[1], - "Out@GRAD shape must equal to X[1] * Y[1]"); + PADDLE_ENFORCE(x_dims[0] == out_dims[0], + "Out@GRAD M X N must equal to X dims 0, M "); + PADDLE_ENFORCE(y_dims[1] == out_dims[1], + "Out@GRAD M X N must equal to Y dims 1, N "); - x_grad->Resize(dim1); - y_grad->Resize(dim0); + x_grad->Resize(x_dims); + y_grad->Resize(y_dims); } }; -- GitLab From 49aa2c042cbae87ada74e7e63590f7b43239c596 Mon Sep 17 00:00:00 2001 From: dangqingqing Date: Tue, 15 Aug 2017 17:40:26 +0800 Subject: [PATCH 0051/2018] Implement GPU kernel for cross entropy operator. --- paddle/framework/pybind.cc | 2 +- paddle/operators/cross_entropy_op.cc | 15 +-- paddle/operators/cross_entropy_op.cu | 108 +++++++++++++++++- paddle/operators/cross_entropy_op.h | 11 +- .../framework/tests/test_cross_entropy_op.py | 2 +- 5 files changed, 120 insertions(+), 18 deletions(-) diff --git a/paddle/framework/pybind.cc b/paddle/framework/pybind.cc index fe0c87bc5..2b3e7fba4 100644 --- a/paddle/framework/pybind.cc +++ b/paddle/framework/pybind.cc @@ -31,7 +31,7 @@ limitations under the License. */ namespace py = pybind11; USE_OP(add_two); -USE_CPU_ONLY_OP(onehot_cross_entropy); +USE_OP(onehot_cross_entropy); USE_OP(sgd); USE_OP(mul); USE_OP(mean); diff --git a/paddle/operators/cross_entropy_op.cc b/paddle/operators/cross_entropy_op.cc index a623c551e..ab1e1c101 100644 --- a/paddle/operators/cross_entropy_op.cc +++ b/paddle/operators/cross_entropy_op.cc @@ -39,11 +39,10 @@ class OnehotCrossEntropyGradientOp : public framework::OperatorWithKernel { protected: void InferShape(const framework::InferShapeContext &ctx) const override { - auto X_grad = ctx.Output(framework::GradVarName("X")); + auto dX = ctx.Output(framework::GradVarName("X")); auto X = ctx.Input("X"); - // TODO(superjom) add enforce here after helper functions ready - X_grad->Resize(X->dims()); + dX->Resize(X->dims()); } }; @@ -70,9 +69,7 @@ namespace ops = paddle::operators; REGISTER_OP(onehot_cross_entropy, ops::OnehotCrossEntropyOp, ops::OnehotCrossEntropyOpMaker, onehot_cross_entropy_grad, ops::OnehotCrossEntropyGradientOp); -REGISTER_OP_CPU_KERNEL( - onehot_cross_entropy, - ops::OnehotCrossEntropyOpKernel); -REGISTER_OP_CPU_KERNEL( - onehot_cross_entropy_grad, - ops::OnehotCrossEntropyGradientOpKernel); +REGISTER_OP_CPU_KERNEL(onehot_cross_entropy, + ops::OnehotCrossEntropyOpKernel); +REGISTER_OP_CPU_KERNEL(onehot_cross_entropy_grad, + ops::OnehotCrossEntropyGradientOpKernel); diff --git a/paddle/operators/cross_entropy_op.cu b/paddle/operators/cross_entropy_op.cu index 4bbc8f093..2392c3d5e 100644 --- a/paddle/operators/cross_entropy_op.cu +++ b/paddle/operators/cross_entropy_op.cu @@ -12,10 +12,108 @@ See the License for the specific language governing permissions and limitations under the License. */ -#define EIGEN_USE_GPU -#include "paddle/operators/cross_entropy_op.h" +#include "paddle/framework/op_registry.h" +#include "paddle/platform/assert.h" + +namespace paddle { +namespace operators { + +using Tensor = framework::Tensor; + +template +__global__ void CrossEntropyKernel(T* Y, const T* X, const int* label, + const int N, const int D) { + // TOOD(qingqing) define CUDA_1D_KERNEL_LOOP macro in a common file. + // CUDA_1D_KERNEL_LOOP(i, N) { + for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < N; + i += blockDim.x * gridDim.x) { + PADDLE_ASSERT(label[i] >= 0 && label[i] < D); + Y[i] = -log(X[i * D + label[i]]); + } +} + +template +__global__ void zero(T* X, const int N) { + for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < N; + i += blockDim.x * gridDim.x) { + X[i] = 0.0; + } +} + +template +__global__ void CrossEntropyGradientKernel(T* dX, const T* dY, const T* X, + const int* label, const int N, + const int D) { + // TOOD(qingqing) define CUDA_1D_KERNEL_LOOP macro in a common file. + // CUDA_1D_KERNEL_LOOP(i, N) { + for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < N; + i += blockDim.x * gridDim.x) { + int idx = i * D + label[i]; + dX[idx] = -dY[i] / X[idx]; + } +} + +template +class OnehotCrossEntropyOpCUDAKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& ctx) const override { + PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()), + "It must use GPUPlace."); + + auto X = ctx.Input("X"); + const T* Xdata = X->data(); + const int* label_data = ctx.Input("label")->data(); + auto Y = ctx.Output("Y"); + Y->mutable_data(ctx.GetPlace()); + T* Ydata = Y->data(); + + int N = X->dims()[0]; + int D = X->dims()[1]; + int block = 512; + int grid = (N + block - 1) / block; + // TODO(qingqing) launch kernel on specified stream + // base on ExecutionContext. + CrossEntropyKernel<<>>(Ydata, Xdata, label_data, N, D); + } +}; + +template +class OnehotCrossEntropyGradientOpCUDAKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& ctx) const override { + PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()), + "It must use GPUPlace."); + + auto X = ctx.Input("X"); + auto dX = ctx.Output(framework::GradVarName("X")); + auto dY = ctx.Input(framework::GradVarName("Y")); + auto label = ctx.Input("label"); + + auto* dXdata = dX->template mutable_data(ctx.GetPlace()); + auto* dYdata = dY->template data(); + auto* Xdata = X->template data(); + auto* label_data = label->data(); + + int N = X->dims()[0]; + int D = X->dims()[1]; + int block = 512; + int grid = (N * D + block - 1) / block; + // TODO(qingqing): make zero an common function. + zero<<>>(dXdata, N * D); + + grid = (N + block - 1) / block; + // TODO(qingqing): launch kernel on specified stream + // base on ExecutionContext. + CrossEntropyGradientKernel<<>>(dXdata, dYdata, Xdata, + label_data, N, D); + } +}; + +} // namespace operators +} // namespace paddle namespace ops = paddle::operators; -REGISTER_OP_GPU_KERNEL( - onehot_cross_entropy, - ops::OnehotCrossEntropyOpKernel); +REGISTER_OP_GPU_KERNEL(onehot_cross_entropy, + ops::OnehotCrossEntropyOpCUDAKernel); +REGISTER_OP_GPU_KERNEL(onehot_cross_entropy_grad, + ops::OnehotCrossEntropyGradientOpCUDAKernel); diff --git a/paddle/operators/cross_entropy_op.h b/paddle/operators/cross_entropy_op.h index b7df92c9a..261cbe2d4 100644 --- a/paddle/operators/cross_entropy_op.h +++ b/paddle/operators/cross_entropy_op.h @@ -39,10 +39,13 @@ T tolerable_value(T x) { return x; } -template +template class OnehotCrossEntropyOpKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& ctx) const override { + PADDLE_ENFORCE(platform::is_cpu_place(ctx.GetPlace()), + "It must use CPUPlace."); + auto X = ctx.Input("X"); const T* Xdata = X->data(); const int* label_data = ctx.Input("label")->data(); @@ -62,10 +65,13 @@ class OnehotCrossEntropyOpKernel : public framework::OpKernel { } }; -template +template class OnehotCrossEntropyGradientOpKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& ctx) const override { + PADDLE_ENFORCE(platform::is_cpu_place(ctx.GetPlace()), + "It must use CPUPlace."); + auto X = ctx.Input("X"); auto dX = ctx.Output(framework::GradVarName("X")); auto dY = ctx.Input(framework::GradVarName("Y")); @@ -79,6 +85,7 @@ class OnehotCrossEntropyGradientOpKernel : public framework::OpKernel { const int batch_size = X->dims()[0]; const int class_num = X->dims()[1]; + memset(dXdata, 0, sizeof(T) * batch_size * class_num); for (int i = 0; i < batch_size; ++i) { int index = i * class_num + label_data[i]; dXdata[index] = -tolerable_value(dYdata[i] / Xdata[index]); diff --git a/python/paddle/v2/framework/tests/test_cross_entropy_op.py b/python/paddle/v2/framework/tests/test_cross_entropy_op.py index 4815192e2..5557e0d35 100644 --- a/python/paddle/v2/framework/tests/test_cross_entropy_op.py +++ b/python/paddle/v2/framework/tests/test_cross_entropy_op.py @@ -22,7 +22,7 @@ class TestCrossEntropy(unittest.TestCase): class CrossEntropyGradOpTest(GradientChecker): - def test_softmax_grad(self): + def test_check_grad(self): op = create_op("onehot_cross_entropy") batch_size = 100 class_num = 10 -- GitLab From 9eaef75397926819294edda04dbed34aa069f5f4 Mon Sep 17 00:00:00 2001 From: Yan Chunwei Date: Tue, 15 Aug 2017 17:44:08 +0800 Subject: [PATCH 0052/2018] RNN backward create (#3490) * insert rnn's backward into Backward() * add device_context into backward_test --- paddle/framework/CMakeLists.txt | 2 +- paddle/framework/backward.cc | 17 +++++++++++++++++ paddle/operators/recurrent_op.h | 4 ++-- 3 files changed, 20 insertions(+), 3 deletions(-) diff --git a/paddle/framework/CMakeLists.txt b/paddle/framework/CMakeLists.txt index 039852602..68304c9fc 100644 --- a/paddle/framework/CMakeLists.txt +++ b/paddle/framework/CMakeLists.txt @@ -38,7 +38,7 @@ add_custom_command(TARGET framework_py_proto POST_BUILD WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}) cc_library(backward SRCS backward.cc DEPS net_op) -cc_test(backward_test SRCS backward_test.cc DEPS backward) +cc_test(backward_test SRCS backward_test.cc DEPS backward recurrent_op device_context) if(WITH_PYTHON) cc_library(paddle_pybind SHARED diff --git a/paddle/framework/backward.cc b/paddle/framework/backward.cc index 83b7e4cda..c226e4e3d 100644 --- a/paddle/framework/backward.cc +++ b/paddle/framework/backward.cc @@ -17,6 +17,7 @@ #include #include "paddle/framework/op_registry.h" #include "paddle/operators/net_op.h" +#include "paddle/operators/recurrent_op.h" namespace paddle { namespace framework { @@ -178,6 +179,22 @@ std::shared_ptr BackwardRecursive( return false; }); + // process recurrent gradient op as a special operator. + if (forwardOp.Type() == "recurrent_op") { + // NOTE clean up cycle call somewhere (RNN's stepnet constains itself), or + // this will result in infinite loop. + const auto& rnnop = + *static_cast(&forwardOp); + auto rnn_grad_op = + static_cast(grad_op.get()); + const auto& stepnet_op = + *static_cast(&rnnop.stepnet()); + // create stepnet's gradient op + auto grad_stepnet = BackwardRecursive(stepnet_op, no_grad_names, uniq_id); + rnn_grad_op->set_stepnet( + std::static_pointer_cast(grad_stepnet)); + } + if (net->ops_.empty()) { // Current no aux op is added to network return grad_op; } diff --git a/paddle/operators/recurrent_op.h b/paddle/operators/recurrent_op.h index caca644c9..171a0bd2a 100644 --- a/paddle/operators/recurrent_op.h +++ b/paddle/operators/recurrent_op.h @@ -127,7 +127,7 @@ class RecurrentOp final : public framework::OperatorBase { } void set_stepnet(std::shared_ptr net) { stepnet_ = net; } - const NetOp* stepnet() const { return stepnet_.get(); } + const NetOp& stepnet() const { return *stepnet_; } static const rnn::ArgumentName kArgName; @@ -158,7 +158,7 @@ class RecurrentGradientOp final : public framework::OperatorBase { static const rnn::ArgumentName kArgName; void set_stepnet(const std::shared_ptr& net) { stepnet_ = net; } - const NetOp* stepnet() const { return stepnet_.get(); } + const NetOp& stepnet() const { return *stepnet_; } private: RecurrentGradientAlgorithm alg_; -- GitLab From 79a336b78f707ba04076e3130e7b7c0d87a484a7 Mon Sep 17 00:00:00 2001 From: Luo Tao Date: Tue, 15 Aug 2017 18:39:28 +0800 Subject: [PATCH 0053/2018] add shared warpctc lib in whl --- python/setup.py.in | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/python/setup.py.in b/python/setup.py.in index 287442e01..db15f6d95 100644 --- a/python/setup.py.in +++ b/python/setup.py.in @@ -31,7 +31,9 @@ paddle_bins = ['${PADDLE_BINARY_DIR}/paddle/scripts/paddle_usage', '${PADDLE_BINARY_DIR}/paddle/pserver/paddle_pserver_main'] paddle_rt_lib_dir = 'local/lib' -paddle_rt_libs = [] if '${MKL_SHARED_LIBS}'== '' else '${MKL_SHARED_LIBS}'.split(';') +paddle_rt_libs = ['${WARPCTC_LIBRARIES}'] +if '${MKL_SHARED_LIBS}'!= '': + paddle_rt_libs += '${MKL_SHARED_LIBS}'.split(';') setup(name='paddlepaddle', version='${PADDLE_VERSION}', -- GitLab From 26475cd9ba4539a74cd2d36e8697fba4fbc52ddb Mon Sep 17 00:00:00 2001 From: dangqingqing Date: Tue, 15 Aug 2017 19:25:16 +0800 Subject: [PATCH 0054/2018] Use clipping log in cuda kernel, making it same with CPU. --- paddle/operators/cross_entropy_op.cu | 19 +++++++++++++++++-- paddle/operators/cross_entropy_op.h | 3 ++- .../paddle/v2/framework/tests/op_test_util.py | 3 ++- .../framework/tests/test_cross_entropy_op.py | 5 ++--- 4 files changed, 23 insertions(+), 7 deletions(-) diff --git a/paddle/operators/cross_entropy_op.cu b/paddle/operators/cross_entropy_op.cu index 2392c3d5e..5f5d26926 100644 --- a/paddle/operators/cross_entropy_op.cu +++ b/paddle/operators/cross_entropy_op.cu @@ -20,6 +20,21 @@ namespace operators { using Tensor = framework::Tensor; +template +struct clipping_log { + __host__ __device__ T operator()(const T x) { + PADDLE_ASSERT(std::is_floating_point::value); + const T kApproInf = 1e20; + if (x == INFINITY) { + return kApproInf; + } + if (x == -INFINITY) { + return -kApproInf; + } + return x; + } +}; + template __global__ void CrossEntropyKernel(T* Y, const T* X, const int* label, const int N, const int D) { @@ -28,10 +43,11 @@ __global__ void CrossEntropyKernel(T* Y, const T* X, const int* label, for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < N; i += blockDim.x * gridDim.x) { PADDLE_ASSERT(label[i] >= 0 && label[i] < D); - Y[i] = -log(X[i * D + label[i]]); + Y[i] = -clipping_log()(X[i * D + label[i]]); } } +// TODO(qingqing): make zero setting an common function. template __global__ void zero(T* X, const int N) { for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < N; @@ -98,7 +114,6 @@ class OnehotCrossEntropyGradientOpCUDAKernel : public framework::OpKernel { int D = X->dims()[1]; int block = 512; int grid = (N * D + block - 1) / block; - // TODO(qingqing): make zero an common function. zero<<>>(dXdata, N * D); grid = (N + block - 1) / block; diff --git a/paddle/operators/cross_entropy_op.h b/paddle/operators/cross_entropy_op.h index 261cbe2d4..e95f5e116 100644 --- a/paddle/operators/cross_entropy_op.h +++ b/paddle/operators/cross_entropy_op.h @@ -21,7 +21,7 @@ namespace operators { using Tensor = framework::Tensor; template -T tolerable_value(T x) { +T tolerable_value(const T x) { static_assert(std::is_floating_point::value, "tolerable_value works only on float, " "double and double double."); @@ -85,6 +85,7 @@ class OnehotCrossEntropyGradientOpKernel : public framework::OpKernel { const int batch_size = X->dims()[0]; const int class_num = X->dims()[1]; + // TODO(qingqing): make zero setting an common function. memset(dXdata, 0, sizeof(T) * batch_size * class_num); for (int i = 0; i < batch_size; ++i) { int index = i * class_num + label_data[i]; diff --git a/python/paddle/v2/framework/tests/op_test_util.py b/python/paddle/v2/framework/tests/op_test_util.py index dd65e0f2d..ae23108df 100644 --- a/python/paddle/v2/framework/tests/op_test_util.py +++ b/python/paddle/v2/framework/tests/op_test_util.py @@ -64,7 +64,8 @@ class OpTestMeta(type): actual = numpy.array(scope.find_var(out_name).get_tensor()) expect = self.outputs[out_name] self.assertTrue( - numpy.allclose(actual, expect), + numpy.allclose( + actual, expect, atol=1e-04), "output name: " + out_name + "has diff") obj.test_all = test_all diff --git a/python/paddle/v2/framework/tests/test_cross_entropy_op.py b/python/paddle/v2/framework/tests/test_cross_entropy_op.py index 5557e0d35..d4277f2a4 100644 --- a/python/paddle/v2/framework/tests/test_cross_entropy_op.py +++ b/python/paddle/v2/framework/tests/test_cross_entropy_op.py @@ -8,9 +8,8 @@ class TestCrossEntropy(unittest.TestCase): __metaclass__ = OpTestMeta def setUp(self): - # TODO this unit test is not passed self.type = "onehot_cross_entropy" - batch_size = 100 + batch_size = 30 class_num = 10 X = numpy.random.random((batch_size, class_num)).astype("float32") label = 5 * numpy.ones(batch_size).astype("int32") @@ -24,7 +23,7 @@ class TestCrossEntropy(unittest.TestCase): class CrossEntropyGradOpTest(GradientChecker): def test_check_grad(self): op = create_op("onehot_cross_entropy") - batch_size = 100 + batch_size = 30 class_num = 10 inputs = { "X": numpy.random.uniform( -- GitLab From 7bc60b02737ba3695997086ac96d6915b1acb3f9 Mon Sep 17 00:00:00 2001 From: fengjiayi Date: Tue, 15 Aug 2017 14:21:35 -0700 Subject: [PATCH 0055/2018] Move OpRegistry functions to .cc file and move OpMaker to Op module --- paddle/framework/op_registry.cc | 46 ++++++++++- paddle/framework/op_registry.h | 138 ++------------------------------ paddle/framework/operator.cc | 38 +++++++++ paddle/framework/operator.h | 68 ++++++++++++++++ 4 files changed, 156 insertions(+), 134 deletions(-) diff --git a/paddle/framework/op_registry.cc b/paddle/framework/op_registry.cc index 1caa02a2a..f801f970f 100644 --- a/paddle/framework/op_registry.cc +++ b/paddle/framework/op_registry.cc @@ -17,5 +17,49 @@ limitations under the License. */ #include namespace paddle { -namespace framework {} // namespace framework +namespace framework { + +std::shared_ptr OpRegistry::CreateOp(const std::string& type, + const VarNameMap& inputs, + const VarNameMap& outputs, + AttributeMap attrs) { + auto it = op_info_map().find(type); + PADDLE_ENFORCE(it != op_info_map().end(), + "Operator '%s' has not been registered.", type); + it->second.checker_->Check(attrs); + auto op = it->second.creator_(type, inputs, outputs, attrs); + return std::shared_ptr(op); +} + +std::shared_ptr OpRegistry::CreateOp(const OpDesc& op_desc) { + VarNameMap inputs = ConvertOpDescVarsToVarNameMap(op_desc.inputs()); + VarNameMap outputs = ConvertOpDescVarsToVarNameMap(op_desc.outputs()); + AttributeMap attrs; + for (auto& attr : op_desc.attrs()) { + attrs[attr.name()] = GetAttrValue(attr); + } + + return CreateOp(op_desc.type(), inputs, outputs, attrs); +} + +OperatorBase::VarNameMap OpRegistry::ConvertOpDescVarsToVarNameMap( + const google::protobuf::RepeatedPtrField& op_desc_vars) { + VarNameMap ret_val; + for (auto& var : op_desc_vars) { + auto& var_names = ret_val[var.parameter()]; + auto& var_names_in_proto = var.arguments(); + var_names.reserve(static_cast(var_names_in_proto.size())); + std::copy(var_names_in_proto.begin(), var_names_in_proto.end(), + std::back_inserter(var_names)); + } + return ret_val; +} + +std::shared_ptr OpRegistry::CreateGradOp(const OperatorBase& op) { + PADDLE_ENFORCE(!op.IsNetOp(), "Use framework::Backward to get backward ops"); + std::shared_ptr grad_op(BuildGradOp(&op)); + return grad_op; +} + +} // namespace framework } // namespace paddle diff --git a/paddle/framework/op_registry.h b/paddle/framework/op_registry.h index 120f4ede6..cc2234d50 100644 --- a/paddle/framework/op_registry.h +++ b/paddle/framework/op_registry.h @@ -29,103 +29,6 @@ limitations under the License. */ namespace paddle { namespace framework { -// this class not only make proto but also init attribute checkers. -class OpProtoAndCheckerMaker { - public: - OpProtoAndCheckerMaker(OpProto* proto, OpAttrChecker* op_checker) - : proto_(proto), op_checker_(op_checker) {} - - ~OpProtoAndCheckerMaker() { - PADDLE_ENFORCE(validated_, "should call Validate after build"); - } - - void Validate() { - validated_ = true; - CheckNoDuplicatedInOutAttrs(); - } - - protected: - struct VariableBuilder { - OpProto::Var* var_; - - VariableBuilder& AsDuplicable() { - var_->set_duplicable(true); - return *this; - } - - VariableBuilder& AsIntermediate() { - var_->set_intermediate(true); - return *this; - } - - // TODO(FengJiayi, yuyang18): `AsNoGradient` is a very bad name, because it - // means that input/output is not needed when calculate gradient. It does - // not mean no gradient when backward. It should be changed soon. - VariableBuilder& AsNoGradient() { - var_->set_no_gradient(true); - return *this; - } - }; - - VariableBuilder AddInput(const std::string& name, - const std::string& comment) { - auto* input = proto_->add_inputs(); - input->set_name(name); - input->set_comment(comment); - return VariableBuilder{input}; - } - - VariableBuilder AddOutput(const std::string& name, - const std::string& comment) { - auto* output = proto_->add_outputs(); - output->set_name(name); - output->set_comment(comment); - return VariableBuilder{output}; - } - - template - TypedAttrChecker& AddAttr(const std::string& name, - const std::string& comment, - bool generated = false) { - auto* attr = proto_->add_attrs(); - attr->set_name(name); - attr->set_comment(comment); - attr->set_generated(generated); - attr->set_type(AttrTypeID()); - return op_checker_->AddAttrChecker(name); - } - - void AddComment(const std::string& comment) { proto_->set_comment(comment); } - - private: - void CheckNoDuplicatedInOutAttrs() { - std::unordered_set names; - auto checker = [&](const std::string& name) { - PADDLE_ENFORCE(!names.count(name), "[%s] is duplicated", name); - names.insert(name); - }; - for (auto& attr : proto_->attrs()) { - checker(attr.name()); - } - for (auto& input : proto_->inputs()) { - checker(input.name()); - } - for (auto& output : proto_->outputs()) { - checker(output.name()); - } - } - - OpProto* proto_; - OpAttrChecker* op_checker_; - bool validated_{false}; -}; - -class NOPMaker : public OpProtoAndCheckerMaker { - public: - NOPMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker) - : OpProtoAndCheckerMaker(proto, op_checker) {} -}; - class OpRegistry { using VarNameMap = OperatorBase::VarNameMap; using OpCreator = std::function CreateOp(const std::string& type, const VarNameMap& inputs, const VarNameMap& outputs, - AttributeMap attrs) { - auto it = op_info_map().find(type); - PADDLE_ENFORCE(it != op_info_map().end(), - "Operator '%s' has not been registered.", type); - it->second.checker_->Check(attrs); - auto op = it->second.creator_(type, inputs, outputs, attrs); - return std::shared_ptr(op); - } - - static VarNameMap ConvertOpDescVarsToVarNameMap( - const google::protobuf::RepeatedPtrField& op_desc_vars) { - VarNameMap ret_val; - for (auto& var : op_desc_vars) { - auto& var_names = ret_val[var.parameter()]; - auto& var_names_in_proto = var.arguments(); - var_names.reserve(static_cast(var_names_in_proto.size())); - std::copy(var_names_in_proto.begin(), var_names_in_proto.end(), - std::back_inserter(var_names)); - } - return ret_val; - } + AttributeMap attrs); - static std::shared_ptr CreateOp(const OpDesc& op_desc) { - VarNameMap inputs = ConvertOpDescVarsToVarNameMap(op_desc.inputs()); - VarNameMap outputs = ConvertOpDescVarsToVarNameMap(op_desc.outputs()); - AttributeMap attrs; - for (auto& attr : op_desc.attrs()) { - attrs[attr.name()] = GetAttrValue(attr); - } + static std::shared_ptr CreateOp(const OpDesc& op_desc); - return CreateOp(op_desc.type(), inputs, outputs, attrs); - } + static VarNameMap ConvertOpDescVarsToVarNameMap( + const google::protobuf::RepeatedPtrField& op_desc_vars); - static std::shared_ptr CreateGradOp(const OperatorBase& op) { - PADDLE_ENFORCE(!op.IsNetOp(), - "Use framework::Backward to get backward ops"); - std::shared_ptr grad_op(BuildGradOp(&op)); - return grad_op; - } + static std::shared_ptr CreateGradOp(const OperatorBase& op); static std::unordered_map& op_info_map() { static std::unordered_map op_info_map_; diff --git a/paddle/framework/operator.cc b/paddle/framework/operator.cc index 0daf12e7f..eadd8f331 100644 --- a/paddle/framework/operator.cc +++ b/paddle/framework/operator.cc @@ -164,5 +164,43 @@ std::vector OperatorBase::OutputVars(bool has_intermediate) const { return ret_val; } +void OpProtoAndCheckerMaker::Validate() { + validated_ = true; + CheckNoDuplicatedInOutAttrs(); +} + +OpProtoAndCheckerMaker::VariableBuilder OpProtoAndCheckerMaker::AddInput( + const std::string& name, const std::string& comment) { + auto* input = proto_->add_inputs(); + input->set_name(name); + input->set_comment(comment); + return OpProtoAndCheckerMaker::VariableBuilder{input}; +} + +OpProtoAndCheckerMaker::VariableBuilder OpProtoAndCheckerMaker::AddOutput( + const std::string& name, const std::string& comment) { + auto* output = proto_->add_outputs(); + output->set_name(name); + output->set_comment(comment); + return OpProtoAndCheckerMaker::VariableBuilder{output}; +} + +void OpProtoAndCheckerMaker::CheckNoDuplicatedInOutAttrs() { + std::unordered_set names; + auto checker = [&](const std::string& name) { + PADDLE_ENFORCE(!names.count(name), "[%s] is duplicated", name); + names.insert(name); + }; + for (auto& attr : proto_->attrs()) { + checker(attr.name()); + } + for (auto& input : proto_->inputs()) { + checker(input.name()); + } + for (auto& output : proto_->outputs()) { + checker(output.name()); + } +} + } // namespace framework } // namespace paddle diff --git a/paddle/framework/operator.h b/paddle/framework/operator.h index 60d4f06c7..2c8620a7c 100644 --- a/paddle/framework/operator.h +++ b/paddle/framework/operator.h @@ -138,6 +138,74 @@ class NOP : public OperatorBase { const platform::DeviceContext& dev_ctx) const override {} }; +// this class not only make proto but also init attribute checkers. +class OpProtoAndCheckerMaker { + public: + OpProtoAndCheckerMaker(OpProto* proto, OpAttrChecker* op_checker) + : proto_(proto), op_checker_(op_checker) {} + + ~OpProtoAndCheckerMaker() { + PADDLE_ENFORCE(validated_, "should call Validate after build"); + } + + void Validate(); + + protected: + struct VariableBuilder { + OpProto::Var* var_; + + VariableBuilder& AsDuplicable() { + var_->set_duplicable(true); + return *this; + } + + VariableBuilder& AsIntermediate() { + var_->set_intermediate(true); + return *this; + } + + // TODO(FengJiayi, yuyang18): `AsNoGradient` is a very bad name, because it + // means that input/output is not needed when calculate gradient. It does + // not mean no gradient when backward. It should be changed soon. + VariableBuilder& AsNoGradient() { + var_->set_no_gradient(true); + return *this; + } + }; + + VariableBuilder AddInput(const std::string& name, const std::string& comment); + + VariableBuilder AddOutput(const std::string& name, + const std::string& comment); + + template + TypedAttrChecker& AddAttr(const std::string& name, + const std::string& comment, + bool generated = false) { + auto* attr = proto_->add_attrs(); + attr->set_name(name); + attr->set_comment(comment); + attr->set_generated(generated); + attr->set_type(AttrTypeID()); + return op_checker_->AddAttrChecker(name); + } + + void AddComment(const std::string& comment) { proto_->set_comment(comment); } + + private: + void CheckNoDuplicatedInOutAttrs(); + + OpProto* proto_; + OpAttrChecker* op_checker_; + bool validated_{false}; +}; + +class NOPMaker : public OpProtoAndCheckerMaker { + public: + NOPMaker(framework::OpProto* proto, framework::OpAttrChecker* op_checker) + : OpProtoAndCheckerMaker(proto, op_checker) {} +}; + class InferShapeContext { public: InferShapeContext(const OperatorBase& op, const Scope& scope) -- GitLab From 323d4233f3cb0f72ddac36977941e84880a7eedc Mon Sep 17 00:00:00 2001 From: zchen0211 Date: Tue, 15 Aug 2017 23:50:56 +0000 Subject: [PATCH 0056/2018] gather op added with python unittest --- paddle/operators/gather_op.cu | 20 ++++++++++++++++ .../v2/framework/tests/test_gather_op.py | 23 +++++++++++++++++++ 2 files changed, 43 insertions(+) create mode 100644 paddle/operators/gather_op.cu create mode 100644 python/paddle/v2/framework/tests/test_gather_op.py diff --git a/paddle/operators/gather_op.cu b/paddle/operators/gather_op.cu new file mode 100644 index 000000000..3f04a7b3f --- /dev/null +++ b/paddle/operators/gather_op.cu @@ -0,0 +1,20 @@ +/* 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. */ + +#define EIGEN_USE_GPU +#include "paddle/operators/gather_op.h" + +namespace ops = paddle::operators; +REGISTER_OP_GPU_KERNEL(gather, + ops::GatherOpKernel); diff --git a/python/paddle/v2/framework/tests/test_gather_op.py b/python/paddle/v2/framework/tests/test_gather_op.py new file mode 100644 index 000000000..2ffbf1723 --- /dev/null +++ b/python/paddle/v2/framework/tests/test_gather_op.py @@ -0,0 +1,23 @@ +import unittest + +import numpy +import paddle.v2.framework.core as core +from paddle.v2.framework.op import Operator + +from op_test_util import OpTestMeta + + +class TestGatherOp(unittest.TestCase): + __metaclass__ = OpTestMeta + + def setUp(self): + self.type = "gather" + self.inputs = { + 'X': numpy.random.random((10, 20)).astype("float32"), + 'Index': numpy.array([1, 3, 5]).astype("int") + } + self.outputs = {'Y': self.input['X'][self.input['Index']]} + + +if __name__ == "__main__": + unittest.main() -- GitLab From 4d2adab772e3c0789e9696533da61ee3583363d1 Mon Sep 17 00:00:00 2001 From: zchen0211 Date: Tue, 15 Aug 2017 23:54:16 +0000 Subject: [PATCH 0057/2018] gather op added with python unittest --- paddle/framework/CMakeLists.txt | 1 + paddle/framework/pybind.cc | 1 + paddle/operators/CMakeLists.txt | 3 +- paddle/operators/gather_op.cc | 43 +++++++++++-------- .../paddle/v2/framework/tests/CMakeLists.txt | 1 + 5 files changed, 29 insertions(+), 20 deletions(-) diff --git a/paddle/framework/CMakeLists.txt b/paddle/framework/CMakeLists.txt index 9e306c865..30313780a 100644 --- a/paddle/framework/CMakeLists.txt +++ b/paddle/framework/CMakeLists.txt @@ -47,6 +47,7 @@ cc_library(paddle_pybind SHARED SRCS pybind.cc DEPS pybind python backward sgd_op + gather_op add_op mul_op rowwise_add_op diff --git a/paddle/framework/pybind.cc b/paddle/framework/pybind.cc index fe0c87bc5..90311e0dc 100644 --- a/paddle/framework/pybind.cc +++ b/paddle/framework/pybind.cc @@ -42,6 +42,7 @@ USE_OP(fill_zeros_like); USE_OP_ITSELF(recurrent_op); USE_OP(gaussian_random); USE_OP(uniform_random); +USE_CPU_ONLY_OP(gather); namespace paddle { namespace framework { diff --git a/paddle/operators/CMakeLists.txt b/paddle/operators/CMakeLists.txt index 5ac898a8d..6849e39cb 100644 --- a/paddle/operators/CMakeLists.txt +++ b/paddle/operators/CMakeLists.txt @@ -43,7 +43,8 @@ endfunction() add_subdirectory(math) cc_test(gather_test SRCS gather_test.cc DEPS tensor) -cc_library(gather_op SRCS gather_op.cc DEPS op_registry) +op_library(gather_op SRCS gather_op.cc gather_op.cu) +# DEPS op_registry) # cc_test(gather_op_test SRCS gather_op_test.cc DEPS gather_op) cc_test(scatter_test SRCS scatter_test.cc DEPS tensor) diff --git a/paddle/operators/gather_op.cc b/paddle/operators/gather_op.cc index 05ba52ce0..2e08ba8dc 100644 --- a/paddle/operators/gather_op.cc +++ b/paddle/operators/gather_op.cc @@ -19,17 +19,33 @@ namespace paddle { namespace operators { class GatherOp : public framework::OperatorWithKernel { + public: + using framework::OperatorWithKernel::OperatorWithKernel; + protected: void InferShape(const framework::InferShapeContext &ctx) const override { - PADDLE_ENFORCE(ctx.InputSize() == 2, ""); - PADDLE_ENFORCE(ctx.OutputSize() == 1, ""); - PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(0), - "Inputs of GatherOp must all be set"); - int batch_size = ctx.Input(1)->dims()[0]; + // PADDLE_ENFORCE(ctx.InputSize() == 2, ""); + // PADDLE_ENFORCE(ctx.OutputSize() == 1, ""); + // PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(0), + // "Inputs of GatherOp must all be set"); + int batch_size = ctx.Input("Index")->dims()[0]; PADDLE_ENFORCE(batch_size > 0); paddle::framework::DDim output_dims(ctx.Input(0)->dims()); output_dims[0] = batch_size; - ctx.Output(0)->Resize(output_dims); + ctx.Output("Y")->Resize(output_dims); + } +}; + +class GatherGradOp : public framework::OperatorWithKernel { + public: + using framework::OperatorWithKernel::OperatorWithKernel; + + protected: + void InferShape(const framework::InferShapeContext &ctx) const override { + auto X_grad = ctx.Output(framework::GradVarName("X")); + auto X = ctx.Input("X"); + + X_grad->Resize(X->dims()); } }; @@ -47,25 +63,14 @@ Y = X[Index] )DOC"); } }; - -class GatherGradOp : public framework::OperatorWithKernel { - protected: - void InferShape(const framework::InferShapeContext &ctx) const override { - auto X_grad = ctx.Output(framework::GradVarName("X")); - auto X = ctx.Input("X"); - - X_grad->Resize(X->dims()); - } -}; - } // namespace operators } // namespace paddle namespace ops = paddle::operators; -REGISTER_OP(gather, ops::GatherOp, ops::GatherOpMaker); +REGISTER_OP(gather, ops::GatherOp, ops::GatherOpMaker, gather_grad, + ops::GatherGradOp); REGISTER_OP_CPU_KERNEL(gather, ops::GatherOpKernel); -REGISTER_GRADIENT_OP(gather, gather_grad, ops::GatherGradOp); REGISTER_OP_CPU_KERNEL( gather_grad, ops::GatherGradientOpKernel); diff --git a/python/paddle/v2/framework/tests/CMakeLists.txt b/python/paddle/v2/framework/tests/CMakeLists.txt index 96fad9b42..1032743a1 100644 --- a/python/paddle/v2/framework/tests/CMakeLists.txt +++ b/python/paddle/v2/framework/tests/CMakeLists.txt @@ -13,6 +13,7 @@ py_test(test_add_two_op SRCS test_add_two_op.py) py_test(test_sigmoid_op SRCS test_sigmoid_op.py) py_test(test_softmax_op SRCS test_softmax_op.py) py_test(test_cross_entropy_op SRCS test_cross_entropy_op.py) +py_test(test_gather_op SRCS test_gather_op.py) py_test(test_fill_zeros_like_op SRCS test_fill_zeros_like_op.py) py_test(gradient_checker SRCS gradient_checker.py) -- GitLab From c307ee303b982c97ee66f91981f81c606c62ec63 Mon Sep 17 00:00:00 2001 From: Yan Chunwei Date: Wed, 16 Aug 2017 11:31:21 +0800 Subject: [PATCH 0058/2018] clang format with version check (#3513) * add clang-format with version check 3.8 * improve doc --- .clang_format.hook | 15 +++++++++++++++ .pre-commit-config.yaml | 4 ++-- 2 files changed, 17 insertions(+), 2 deletions(-) create mode 100755 .clang_format.hook diff --git a/.clang_format.hook b/.clang_format.hook new file mode 100755 index 000000000..1d9282168 --- /dev/null +++ b/.clang_format.hook @@ -0,0 +1,15 @@ +#!/bin/bash +set -e + +readonly VERSION="3.8" + +version=$(clang-format -version) + +if ! [[ $version == *"$VERSION"* ]]; then + echo "clang-format version check failed." + echo "a version contains '$VERSION' is needed, but get '$version'" + echo "you can install the right version, and make an soft-link to '\$PATH' env" + exit -1 +fi + +clang-format $@ diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index bb8c88787..a772125df 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -19,10 +19,10 @@ - id: end-of-file-fixer - repo: local hooks: - - id: clang-format + - id: clang-format-with-version-check name: clang-format description: Format files with ClangFormat. - entry: clang-format -i + entry: ./.clang_format.hook -i language: system files: \.(c|cc|cxx|cpp|cu|h|hpp|hxx|proto)$ - repo: https://github.com/PaddlePaddle/pre-commit-golang -- GitLab From 13c20ad39e23f0d377bab05c7fea0621d46abd07 Mon Sep 17 00:00:00 2001 From: qijun Date: Wed, 16 Aug 2017 11:53:46 +0800 Subject: [PATCH 0059/2018] remove --default-stream per-thread nvcc flag --- cmake/flags.cmake | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cmake/flags.cmake b/cmake/flags.cmake index 47bb83b00..ff246b2eb 100644 --- a/cmake/flags.cmake +++ b/cmake/flags.cmake @@ -153,7 +153,7 @@ set(CUDA_PROPAGATE_HOST_FLAGS OFF) # Release/Debug flags set by cmake. Such as -O3 -g -DNDEBUG etc. # So, don't set these flags here. -LIST(APPEND CUDA_NVCC_FLAGS -std=c++11 --default-stream per-thread) +LIST(APPEND CUDA_NVCC_FLAGS -std=c++11) LIST(APPEND CUDA_NVCC_FLAGS --use_fast_math) if(CMAKE_BUILD_TYPE STREQUAL "Debug") -- GitLab From 0d2ab5e993c9dd16ada677a8ea9de563553a7428 Mon Sep 17 00:00:00 2001 From: tensor-tang Date: Wed, 16 Aug 2017 11:50:11 +0800 Subject: [PATCH 0060/2018] use param header to save mkldnn format info --- doc/design/mkldnn/README.MD | 1 + 1 file changed, 1 insertion(+) diff --git a/doc/design/mkldnn/README.MD b/doc/design/mkldnn/README.MD index e95699443..2929514b0 100644 --- a/doc/design/mkldnn/README.MD +++ b/doc/design/mkldnn/README.MD @@ -101,6 +101,7 @@ if use_mkldnn 5. 在**Argument**里添加两个`MkldnnMatrixPtr`，取名为`mkldnnValue`和`mkldnnGrad`，用于存放`MkldnnLayer`会用到的memory buffer。 并且添加函数cvt(会修改为一个更加合适的函数名)，用于处理"CPU device"和"MKL-DNN device"之间memory的相互转化。 6. 在父类`Layer`中的`getOutput`函数中添加一段逻辑，用于判断`deviceId`，并针对device在MKL-DNN和CPU之间不统一的情况，做一个前期转换。 也就是调用`Argument`的cvt函数把output统一到需要的device上。 7. 在原来的`FLAGS`中添加一个`use_mkldnn`的flag，用于选择是否使用MKL-DNN的相关功能。 +8. 关于MKLDNN参数的保存。由于MKLDNN参数的格式与PaddlePaddle原有的格式存在不一样的情况，所以需要在保存参数时同时保存该格式信息。目前准备扩展`Header`里面的`int32_t version; // = 0, file format version`信息。这个`version`值，不管是在v1还是在v2里面，一直保存的是0。所以可以充分利用这个信息，定义一个枚举处理所有MKLDNN的参数格式，`MKLDNNLayer`就可以知道得到的参数是哪种格式的了。只不过目前v2里面是写的固定值0，而不是保存的`Header`本身，这一点相信v2未来应该会优化的。 ## References -- GitLab From 137a05eb752f33d2529437c08bf6e58a7010c03d Mon Sep 17 00:00:00 2001 From: tensor-tang Date: Wed, 16 Aug 2017 13:53:07 +0800 Subject: [PATCH 0061/2018] update --- doc/design/mkldnn/README.MD | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/design/mkldnn/README.MD b/doc/design/mkldnn/README.MD index 2929514b0..fe8da907d 100644 --- a/doc/design/mkldnn/README.MD +++ b/doc/design/mkldnn/README.MD @@ -101,7 +101,7 @@ if use_mkldnn 5. 在**Argument**里添加两个`MkldnnMatrixPtr`，取名为`mkldnnValue`和`mkldnnGrad`，用于存放`MkldnnLayer`会用到的memory buffer。 并且添加函数cvt(会修改为一个更加合适的函数名)，用于处理"CPU device"和"MKL-DNN device"之间memory的相互转化。 6. 在父类`Layer`中的`getOutput`函数中添加一段逻辑，用于判断`deviceId`，并针对device在MKL-DNN和CPU之间不统一的情况，做一个前期转换。 也就是调用`Argument`的cvt函数把output统一到需要的device上。 7. 在原来的`FLAGS`中添加一个`use_mkldnn`的flag，用于选择是否使用MKL-DNN的相关功能。 -8. 关于MKLDNN参数的保存。由于MKLDNN参数的格式与PaddlePaddle原有的格式存在不一样的情况，所以需要在保存参数时同时保存该格式信息。目前准备扩展`Header`里面的`int32_t version; // = 0, file format version`信息。这个`version`值，不管是在v1还是在v2里面，一直保存的是0。所以可以充分利用这个信息，定义一个枚举处理所有MKLDNN的参数格式，`MKLDNNLayer`就可以知道得到的参数是哪种格式的了。只不过目前v2里面是写的固定值0，而不是保存的`Header`本身，这一点相信v2未来应该会优化的。 +8. 关于MKLDNN参数的保存。由于MKLDNN参数的格式与PaddlePaddle原有的格式存在不一样的情况，所以需要在保存参数时同时保存该格式信息。目前准备扩展[Header](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/parameter/Parameter.h#L247)里面的`int32_t version`。这个值不管是在v1还是在v2里面，一直保存的是0，所以可以充分利用这个信息，定义一个枚举处理所有MKLDNN的参数格式，从而`MKLDNNLayer`就可以从输入的参数中获取需要的格式信息。 ## References -- GitLab From 29d892c13cf88c7659647cec532169caa7abd2b9 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Wed, 16 Aug 2017 14:19:38 +0800 Subject: [PATCH 0062/2018] Add Clone Method For OperatorBase * Clone method will create a new object instance, which is as same as itself. * This is the first step to remove shared_ptr for OperatorBase --- paddle/framework/op_registry.h | 15 +++++++++++++-- paddle/framework/operator.h | 14 ++++++++++---- paddle/framework/operator_test.cc | 19 +++++++++++++++++++ paddle/operators/net_op.cc | 7 +++++++ paddle/operators/net_op.h | 13 +++++++++++++ paddle/operators/net_op_test.cc | 17 +++++++++++++++++ paddle/operators/recurrent_op.h | 22 ++++++++++++++++++---- 7 files changed, 97 insertions(+), 10 deletions(-) diff --git a/paddle/framework/op_registry.h b/paddle/framework/op_registry.h index 3b793628a..b5b466807 100644 --- a/paddle/framework/op_registry.h +++ b/paddle/framework/op_registry.h @@ -271,7 +271,13 @@ class OpKernelRegistrar : public Registrar { #define REGISTER_OP(op_type, op_class, op_maker_class) \ STATIC_ASSERT_GLOBAL_NAMESPACE( \ __reg_op__##op_type, "REGISTER_OP must be called in global namespace"); \ - static ::paddle::framework::OpRegistrar \ + class _OpClass_##op_type##_ : public op_class { \ + public: \ + DEFINE_OP_CLONE_METHOD(_OpClass_##op_type##_); \ + DEFINE_OP_CTOR(_OpClass_##op_type##_, op_class); \ + }; \ + static ::paddle::framework::OpRegistrar<_OpClass_##op_type##_, \ + op_maker_class> \ __op_registrar_##op_type##__(#op_type); \ int TouchOpRegistrar_##op_type() { \ __op_registrar_##op_type##__.Touch(); \ @@ -285,7 +291,12 @@ class OpKernelRegistrar : public Registrar { STATIC_ASSERT_GLOBAL_NAMESPACE( \ __reg_gradient_op__##op_type##_##grad_op_type, \ "REGISTER_GRADIENT_OP must be called in global namespace"); \ - static ::paddle::framework::GradOpRegistrar \ + class _OpGradClass_##op_type##_ : public grad_op_class { \ + public: \ + DEFINE_OP_CLONE_METHOD(_OpGradClass_##op_type##_); \ + DEFINE_OP_CTOR(_OpGradClass_##op_type##_, grad_op_class); \ + }; \ + static ::paddle::framework::GradOpRegistrar<_OpGradClass_##op_type##_> \ __op_gradient_registrar_##op_type##_##grad_op_type##__(#op_type, \ #grad_op_type); \ int TouchOpGradientRegistrar_##op_type() { \ diff --git a/paddle/framework/operator.h b/paddle/framework/operator.h index 4a72ced6c..920324786 100644 --- a/paddle/framework/operator.h +++ b/paddle/framework/operator.h @@ -69,10 +69,6 @@ class OperatorBase { OperatorBase(const std::string& type, const VarNameMap& inputs, const VarNameMap& outputs, const AttributeMap& attrs); - OperatorBase(const OperatorBase& o) = delete; - OperatorBase& operator=(const OperatorBase& o) = delete; - OperatorBase(OperatorBase&& o) = delete; - virtual ~OperatorBase() {} template @@ -115,6 +111,8 @@ class OperatorBase { std::string Type() const { return type_; } const AttributeMap& Attrs() const { return attrs_; } + virtual OperatorBase* Clone() const = 0; + public: std::string type_; // NOTE: in case of OpGrad, inputs_ contains: @@ -129,6 +127,14 @@ class OperatorBase { AttributeMap attrs_; }; +#define DEFINE_OP_CLONE_METHOD(CLS) \ + OperatorBase* Clone() const final { return new CLS(*this); } + +#define DEFINE_OP_CTOR(CLS, PARENT_CLS) \ + CLS(const std::string& type, const VarNameMap& inputs, \ + const VarNameMap& outputs, const paddle::framework::AttributeMap& attrs) \ + : PARENT_CLS(type, inputs, outputs, attrs) {} + class InferShapeContext { public: InferShapeContext(const OperatorBase& op, const Scope& scope) diff --git a/paddle/framework/operator_test.cc b/paddle/framework/operator_test.cc index 680484158..ceba7f5e6 100644 --- a/paddle/framework/operator_test.cc +++ b/paddle/framework/operator_test.cc @@ -242,3 +242,22 @@ TEST(OpKernel, multi_inputs) { auto op = paddle::framework::OpRegistry::CreateOp(op_desc); op->Run(scope, cpu_device_context); } + +class OperatorClone : public paddle::framework::OperatorBase { + public: + DEFINE_OP_CLONE_METHOD(OperatorClone); + OperatorClone(const std::string& type, const VarNameMap& inputs, + const VarNameMap& outputs, + const paddle::framework::AttributeMap& attrs) + : OperatorBase(type, inputs, outputs, attrs) {} + void InferShape(const paddle::framework::Scope& scope) const override {} + void Run(const paddle::framework::Scope& scope, + const paddle::platform::DeviceContext& dev_ctx) const override {} +}; + +TEST(Operator, Clone) { + OperatorClone a("ABC", {}, {}, {}); + auto* b = a.Clone(); + ASSERT_EQ(a.Type(), b->Type()); + delete b; +} \ No newline at end of file diff --git a/paddle/operators/net_op.cc b/paddle/operators/net_op.cc index 1d1b29044..896550f9d 100644 --- a/paddle/operators/net_op.cc +++ b/paddle/operators/net_op.cc @@ -87,5 +87,12 @@ NetOp::NetOp(const std::string& type, const framework::AttributeMap& attrs) : OperatorBase(type, inputs, outputs, attrs) {} +framework::OperatorBase* NetOp::Clone() const { + PADDLE_ENFORCE( + add_op_done_, + "Must clone a sealed NetOp, invoke Net::CompleteAddOp before clone"); + return new NetOp(*this); +} + } // namespace operators } // namespace paddle diff --git a/paddle/operators/net_op.h b/paddle/operators/net_op.h index 4a3408c15..deee54306 100644 --- a/paddle/operators/net_op.h +++ b/paddle/operators/net_op.h @@ -41,6 +41,18 @@ class NetOp : public framework::OperatorBase { NetOp(const std::string& type, const VarNameMap& inputs, const VarNameMap& outputs, const framework::AttributeMap& attrs); + NetOp(const NetOp& o) + : framework::OperatorBase( + static_cast(o)) { + this->ops_.reserve(o.ops_.size()); + std::transform(o.ops_.begin(), o.ops_.end(), std::back_inserter(this->ops_), + [](const std::shared_ptr& op) + -> std::shared_ptr { + return std::shared_ptr(op->Clone()); + }); + this->CompleteAddOp(); + } + /** * Infer all the operators' input and output variables' shapes, will be called * before every mini-batch @@ -97,6 +109,7 @@ class NetOp : public framework::OperatorBase { bool IsNetOp() const override; std::vector OutputVars(bool has_intermediate) const override; + framework::OperatorBase* Clone() const override; std::vector> ops_; diff --git a/paddle/operators/net_op_test.cc b/paddle/operators/net_op_test.cc index f7aa56262..40e43f46d 100644 --- a/paddle/operators/net_op_test.cc +++ b/paddle/operators/net_op_test.cc @@ -13,6 +13,7 @@ static int run_cnt = 0; class TestOp : public framework::OperatorBase { public: using framework::OperatorBase::OperatorBase; + DEFINE_OP_CLONE_METHOD(TestOp); void InferShape(const Scope& scope) const override { ++infer_shape_cnt; } void Run(const Scope& scope, const platform::DeviceContext& dev_ctx) const override { @@ -23,6 +24,7 @@ class TestOp : public framework::OperatorBase { class EmptyOp : public framework::OperatorBase { public: using framework::OperatorBase::OperatorBase; + DEFINE_OP_CLONE_METHOD(EmptyOp); void InferShape(const Scope& scope) const override {} void Run(const Scope& scope, const DeviceContext& dev_ctx) const override {} }; @@ -77,5 +79,20 @@ TEST(NetOp, insert_op) { ASSERT_EQ(3UL, net.ops_.size()); } +TEST(NetOp, Clone) { + NetOp net; + net.AddOp(std::shared_ptr(new EmptyOp{"empty", {}, {}, {}})); + net.AddOp(std::shared_ptr(new EmptyOp{"empty2", {}, {}, {}})); + net.CompleteAddOp(true); + auto* new_net_op = net.Clone(); + ASSERT_NE(new_net_op, nullptr); + ASSERT_TRUE(new_net_op->IsNetOp()); + auto* new_net = static_cast(new_net_op); + ASSERT_EQ(2, new_net->ops_.size()); + ASSERT_EQ(new_net->ops_[0]->Type(), "empty"); + ASSERT_EQ(new_net->ops_[1]->Type(), "empty2"); + delete new_net; +} + } // namespace operators } // namespace paddle diff --git a/paddle/operators/recurrent_op.h b/paddle/operators/recurrent_op.h index 8f4f2444d..cc40eff0c 100644 --- a/paddle/operators/recurrent_op.h +++ b/paddle/operators/recurrent_op.h @@ -99,13 +99,20 @@ class RecurrentGradientAlgorithm { mutable size_t seq_len_; }; -class RecurrentOp final : public framework::OperatorBase { +class RecurrentOp : public framework::OperatorBase { public: RecurrentOp(const std::string& type, const VarNameMap& inputs, const VarNameMap& outputs, const framework::AttributeMap& attrs); + + RecurrentOp(const RecurrentOp& o) + : framework::OperatorBase( + static_cast(o)) { + // TODO(yuyang18): Implement copy ctor well. + PADDLE_THROW("Not implemented"); + } /** - * InferShape must be called before Run. - */ + * InferShape must be called before Run. + */ void InferShape(const framework::Scope& scope) const override { alg_.InferShape(scope); } @@ -121,12 +128,19 @@ class RecurrentOp final : public framework::OperatorBase { RecurrentAlgorithm alg_; }; -class RecurrentGradientOp final : public framework::OperatorBase { +class RecurrentGradientOp : public framework::OperatorBase { public: RecurrentGradientOp(const std::string& type, const VarNameMap& inputs, const VarNameMap& outputs, const framework::AttributeMap& attrs); + RecurrentGradientOp(const RecurrentGradientOp& o) + : framework::OperatorBase( + static_cast(o)) { + // TODO(yuyang18): Implement Copy ctor. + PADDLE_THROW("Not Implemented"); + } + /** * InferShape must be called before Run. */ -- GitLab From 3e52343dc1c31d0c23a6fdcdee0c7c0492310014 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Wed, 16 Aug 2017 14:24:10 +0800 Subject: [PATCH 0063/2018] Add comments --- paddle/framework/operator.h | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/paddle/framework/operator.h b/paddle/framework/operator.h index 920324786..9e4d0d5e3 100644 --- a/paddle/framework/operator.h +++ b/paddle/framework/operator.h @@ -111,6 +111,8 @@ class OperatorBase { std::string Type() const { return type_; } const AttributeMap& Attrs() const { return attrs_; } + // Return a new operator instance, which is as same as this. + // NOTE: It is caller's responsibility to delete that operator instance. virtual OperatorBase* Clone() const = 0; public: @@ -127,9 +129,16 @@ class OperatorBase { AttributeMap attrs_; }; +// Macro for define a clone method. +// If you are writing an kernel operator, `Clone` will be defined when you +// register it. #define DEFINE_OP_CLONE_METHOD(CLS) \ OperatorBase* Clone() const final { return new CLS(*this); } +// Macro for define a default constructor for Operator. +// You can also use +// using PARENT_CLASS::PARENT_CLASS; +// to use parent's constructor. #define DEFINE_OP_CTOR(CLS, PARENT_CLS) \ CLS(const std::string& type, const VarNameMap& inputs, \ const VarNameMap& outputs, const paddle::framework::AttributeMap& attrs) \ -- GitLab From a037b099f7f4bf8370e882f397bd4c691b0e0986 Mon Sep 17 00:00:00 2001 From: caoying03 Date: Mon, 14 Aug 2017 15:49:48 +0800 Subject: [PATCH 0064/2018] finish unittest. --- .../gserver/layers/CrossEntropyOverBeam.cpp | 1 + .../tests/test_CrossEntropyOverBeamGrad.cpp | 218 +++++++++++++++--- 2 files changed, 191 insertions(+), 28 deletions(-) diff --git a/paddle/gserver/layers/CrossEntropyOverBeam.cpp b/paddle/gserver/layers/CrossEntropyOverBeam.cpp index 8b6223ec6..88d80aa83 100644 --- a/paddle/gserver/layers/CrossEntropyOverBeam.cpp +++ b/paddle/gserver/layers/CrossEntropyOverBeam.cpp @@ -22,6 +22,7 @@ bool CrossEntropyOverBeam::init(const LayerMap& layerMap, const ParameterMap& parameterMap) { /* Initialize the basic parent class */ Layer::init(layerMap, parameterMap); + CHECK_EQ(0U, inputLayers_.size() % 3) << "Error input number."; setNeedSequenceInfo(false); diff --git a/paddle/gserver/tests/test_CrossEntropyOverBeamGrad.cpp b/paddle/gserver/tests/test_CrossEntropyOverBeamGrad.cpp index e9ecebcfe..a5f06c15d 100644 --- a/paddle/gserver/tests/test_CrossEntropyOverBeamGrad.cpp +++ b/paddle/gserver/tests/test_CrossEntropyOverBeamGrad.cpp @@ -12,6 +12,7 @@ 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 #include #include @@ -27,6 +28,10 @@ using namespace paddle; // NOLINT DECLARE_int32(gpu_id); DECLARE_bool(thread_local_rand_use_global_seed); +const size_t MAX_SEQ_NUM = 10; +const size_t MAX_SEQ_LEN = 27; +const size_t MAX_BEAM_SIZE = 10; + struct SingleBeamExpansion { vector seqStartPos; vector subSeqStartPos; @@ -34,37 +39,195 @@ struct SingleBeamExpansion { // TODO(caoying): store this into Argument.ids vector selectedIndices; + vector groundTruth; - vector labelSeqStartPos; + vector inBeam; + vector rowIdxInBeam; }; -void genCandidateScores(bool hasSubSeq, - vector& scores, +void genRand(real* numbers, size_t n) { + default_random_engine generator; + uniform_real_distribution distribution(0.0, 1.0); + for (size_t i = 0; i < n; ++i) numbers[i] = distribution(generator); +} + +vector randSampling(real range, int n) { + CHECK_GE(range, n); + vector num(range); + iota(begin(num), end(num), 0.); + if (range == n) return num; + + random_shuffle(begin(num), end(num)); + num.resize(n); + sort(begin(num), end(num)); + return num; +} + +void genCandidateScores(bool hasSubseq, + size_t beamSize, + SingleBeamExpansion& prevBeam, + SingleBeamExpansion& curBeam) { + vector& seqStartPos = curBeam.seqStartPos; + seqStartPos.resize(1, 0); + vector& subSeqStartPos = curBeam.subSeqStartPos; + subSeqStartPos.resize(1, 0); + + srand((size_t)(time(NULL))); + // srand(1); + if (prevBeam.selectedIndices.size()) { + if (prevBeam.subSeqStartPos.size() > 1) { + int seqIdx = 1; + // samples in previous beam are nested sequences. + for (size_t i = 1; i < prevBeam.subSeqStartPos.size(); ++i) { + for (size_t j = 0; j < beamSize; ++j) { + if (prevBeam.selectedIndices[(i - 1) * beamSize + j] == -1.) break; + for (size_t k = 0; k < beamSize; ++k) + subSeqStartPos.push_back(1 + (rand() % MAX_SEQ_LEN) + + subSeqStartPos.back()); + } + if (prevBeam.seqStartPos[seqIdx] == prevBeam.subSeqStartPos[i]) { + seqStartPos.push_back(subSeqStartPos.back()); + seqIdx++; + } + } + } else { + // samples in previous beam are sequences. + for (size_t i = 0; i <= prevBeam.selectedIndices.size(); ++i) { + if (i && i % beamSize == 0) { + seqStartPos.push_back(subSeqStartPos.back()); + if (i == prevBeam.selectedIndices.size()) break; + } + if (prevBeam.selectedIndices[i] == -1.) continue; + subSeqStartPos.push_back(subSeqStartPos.back() + + (1 + (rand() % MAX_SEQ_LEN))); + } + } + } else { + // the first beam expansion + int seqNum = 1 + (rand() % MAX_SEQ_NUM); + for (int i = 0; i < seqNum; ++i) { + if (hasSubseq) { + for (size_t j = 0; j < 1 + (rand() % MAX_SEQ_NUM); ++j) + subSeqStartPos.push_back(subSeqStartPos.back() + + (1 + (rand() % MAX_SEQ_LEN))); + seqStartPos.push_back(subSeqStartPos.back()); + } else { + seqStartPos.push_back(seqStartPos.back() + + (1 + (rand() % MAX_SEQ_LEN))); + } + } + } + + size_t totalSeqNum = hasSubseq ? subSeqStartPos.back() : seqStartPos.back(); + curBeam.candidateScores.resize(totalSeqNum, 0.); + genRand(curBeam.candidateScores.data(), totalSeqNum); +} + +void genSelectedIndices(size_t beamSize, vector& seqStartPos, - vector& subSeqStartPos) {} - -void genSelectedIndicesAndGroundtruth(size_t beamSize, - vector& seqStartPos, - vector& selectedIndices) {} - -SingleBeamExpansion genOneBeam(size_t beamSize, bool hasSubSeq) { - SingleBeamExpansion beam; - genCandidateScores( - hasSubSeq, beam.candidateScores, beam.seqStartPos, beam.subSeqStartPos); - genSelectedIndicesAndGroundtruth( - beamSize, - hasSubSeq ? beam.subSeqStartPos : beam.seqStartPos, - beam.selectedIndices); - return beam; + vector& selectedIndices) { + size_t selectedIdsCount = beamSize * (seqStartPos.size() - 1); + selectedIndices.resize(selectedIdsCount, -1.); + + for (size_t i = 0; i < seqStartPos.size() - 1; ++i) { + int seqLen = seqStartPos[i + 1] - seqStartPos[i]; + int n = min(seqLen, static_cast(beamSize)); + vector ids = randSampling(seqLen, n); + memcpy(selectedIndices.data() + i * beamSize, + ids.data(), + sizeof(real) * ids.size()); + } +} + +void genGroundTruth(vector& beamExpansions, + size_t beamSize) { + size_t seqNum = beamExpansions[1].seqStartPos.size() - 1; + for (size_t i = 2; i < beamExpansions.size(); ++i) + CHECK_EQ(seqNum, beamExpansions[i - 1].seqStartPos.size() - 1); + + // srand(1); + srand((size_t)(time(NULL))); + + // initialize the first beam. + SingleBeamExpansion& beam = beamExpansions[1]; + beam.groundTruth.resize(seqNum, 0); + beam.inBeam.resize(seqNum, 0); + beam.rowIdxInBeam.resize(seqNum, -1); + + auto begPos = beam.selectedIndices.begin(); + for (size_t i = 0; i < seqNum; ++i) { + int seqLen = beam.seqStartPos[i + 1] - beam.seqStartPos[i]; + int label = rand() % seqLen; + auto endPos = begPos + beamSize; + beam.groundTruth[i] = label; + if (find(begPos, endPos, real(label)) != endPos) beam.inBeam[i] = 1; + begPos = endPos; + beam.rowIdxInBeam[i] = i; + } + + // iterate over each beam expansions + for (size_t i = 2; i < beamExpansions.size(); ++i) { + SingleBeamExpansion& curBeam = beamExpansions[i]; + SingleBeamExpansion& prevBeam = beamExpansions[i - 1]; + + curBeam.groundTruth.resize(seqNum, 0); + curBeam.inBeam.resize(seqNum, 0); + curBeam.rowIdxInBeam.resize(seqNum, -1); + + // iterate over each sequence + for (size_t j = 0; j < seqNum; ++j) { + if (prevBeam.inBeam[j]) { + // gold sequence falls in the beam in previous search. + + auto begPos = prevBeam.selectedIndices.begin(); + auto endPos = begPos + prevBeam.rowIdxInBeam[j] * beamSize; + size_t totalExpansion = + prevBeam.rowIdxInBeam[j] * beamSize - count(begPos, endPos, -1.); + curBeam.rowIdxInBeam[j] = totalExpansion + prevBeam.groundTruth[j]; + + CHECK_LE(curBeam.rowIdxInBeam[j] + 1, + curBeam.subSeqStartPos.size() - 1); + int start = curBeam.subSeqStartPos[curBeam.rowIdxInBeam[j]]; + int end = curBeam.subSeqStartPos[curBeam.rowIdxInBeam[j] + 1]; + CHECK_GT(size_t(end), size_t(start)); + int label = rand() % (end - start); + + curBeam.groundTruth[j] = label; + auto findBeg = curBeam.selectedIndices.begin() + + curBeam.rowIdxInBeam[j] * beamSize; + auto findEnd = findBeg + beamSize; + if (find(findBeg, findEnd, real(label)) != findEnd) + curBeam.inBeam[j] = 1; + } else { + // in previous search, gold sequence has fallen off the beam, + // the beam search stops, here use -1 as a dummy label. + // It will not used in calculation the cost. + beamExpansions[i].groundTruth[j] = -1; + } + } + } +} + +void genOneBeam(size_t beamSize, + bool hasSubseq, + SingleBeamExpansion& prevBeam, + SingleBeamExpansion& curBeam) { + genCandidateScores(hasSubseq, beamSize, prevBeam, curBeam); + genSelectedIndices(beamSize, + hasSubseq ? curBeam.subSeqStartPos : curBeam.seqStartPos, + curBeam.selectedIndices); } void genRandomBeamExpansion(size_t expansionCount, size_t beamSize, vector& beamExpansions) { beamExpansions.clear(); - for (size_t i = 0; i < expansionCount; ++i) { - beamExpansions.emplace_back(genOneBeam(beamSize, i)); - } + beamExpansions.resize(expansionCount + 1); + + // beamExpansions[0] is reserved. + for (size_t i = 1; i <= expansionCount; ++i) + genOneBeam(beamSize, bool(i - 1), beamExpansions[i - 1], beamExpansions[i]); + genGroundTruth(beamExpansions, beamSize); } void testCrossEntropyOverBeam(bool useGpu) { @@ -72,12 +235,12 @@ void testCrossEntropyOverBeam(bool useGpu) { config.layerConfig.set_type("cross_entropy_over_beam"); const size_t expansionCount = 3; - const size_t beamSize = 3; + const size_t beamSize = MAX_BEAM_SIZE; vector beams; genRandomBeamExpansion(expansionCount, beamSize, beams); size_t seqNum = 0; - for (size_t i = 0; i < beams.size(); ++i) { + for (size_t i = 1; i < beams.size(); ++i) { const SingleBeamExpansion& beam = beams[i]; // create scores for all the candidates MatrixPtr candidateScorePtr = @@ -88,7 +251,7 @@ void testCrossEntropyOverBeam(bool useGpu) { ostringstream paramName; paramName << "candidate_scores_" << i; - if (beam.subSeqStartPos.size()) { + if (beam.subSeqStartPos.size() > 1) { seqNum = beam.subSeqStartPos.size() - 1; config.inputDefs.push_back({INPUT_SELF_DEFINE_DATA, paramName.str(), @@ -118,10 +281,9 @@ void testCrossEntropyOverBeam(bool useGpu) { // create the ground truth paramName.clear(); paramName << "label_" << i; - config.inputDefs.push_back({INPUT_SELF_DEFINE_DATA, - paramName.str(), - beam.groundTruth, - beam.labelSeqStartPos}); + config.inputDefs.push_back( + {INPUT_SELF_DEFINE_DATA, paramName.str(), beam.groundTruth}); + config.layerConfig.add_inputs(); } testLayerGrad( -- GitLab From a0d77533f01c5da0fa811d4cc91235f5610f745f Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Wed, 16 Aug 2017 14:49:18 +0800 Subject: [PATCH 0065/2018] Rename Ctor -> Constructor Make code more clearer --- paddle/framework/op_registry.h | 4 ++-- paddle/framework/operator.h | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/paddle/framework/op_registry.h b/paddle/framework/op_registry.h index b5b466807..c0654b375 100644 --- a/paddle/framework/op_registry.h +++ b/paddle/framework/op_registry.h @@ -274,7 +274,7 @@ class OpKernelRegistrar : public Registrar { class _OpClass_##op_type##_ : public op_class { \ public: \ DEFINE_OP_CLONE_METHOD(_OpClass_##op_type##_); \ - DEFINE_OP_CTOR(_OpClass_##op_type##_, op_class); \ + DEFINE_OP_CONSTRUCTOR(_OpClass_##op_type##_, op_class); \ }; \ static ::paddle::framework::OpRegistrar<_OpClass_##op_type##_, \ op_maker_class> \ @@ -294,7 +294,7 @@ class OpKernelRegistrar : public Registrar { class _OpGradClass_##op_type##_ : public grad_op_class { \ public: \ DEFINE_OP_CLONE_METHOD(_OpGradClass_##op_type##_); \ - DEFINE_OP_CTOR(_OpGradClass_##op_type##_, grad_op_class); \ + DEFINE_OP_CONSTRUCTOR(_OpGradClass_##op_type##_, grad_op_class); \ }; \ static ::paddle::framework::GradOpRegistrar<_OpGradClass_##op_type##_> \ __op_gradient_registrar_##op_type##_##grad_op_type##__(#op_type, \ diff --git a/paddle/framework/operator.h b/paddle/framework/operator.h index 9e4d0d5e3..4a1dee6fb 100644 --- a/paddle/framework/operator.h +++ b/paddle/framework/operator.h @@ -118,7 +118,7 @@ class OperatorBase { public: std::string type_; // NOTE: in case of OpGrad, inputs_ contains: - // I (Inputs) + // I (Inputs)opear // O (Outputs) // OG (Output Gradients) VarNameMap inputs_; @@ -139,7 +139,7 @@ class OperatorBase { // You can also use // using PARENT_CLASS::PARENT_CLASS; // to use parent's constructor. -#define DEFINE_OP_CTOR(CLS, PARENT_CLS) \ +#define DEFINE_OP_CONSTRUCTOR(CLS, PARENT_CLS) \ CLS(const std::string& type, const VarNameMap& inputs, \ const VarNameMap& outputs, const paddle::framework::AttributeMap& attrs) \ : PARENT_CLS(type, inputs, outputs, attrs) {} -- GitLab From 1425387570d5559ad0e82bd690b0fcc424911ca1 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Wed, 16 Aug 2017 15:52:48 +0800 Subject: [PATCH 0066/2018] Using unique_ptr instead of raw ptr Fit google C++ style --- paddle/framework/operator.h | 10 ++++++---- paddle/framework/operator_test.cc | 3 +-- paddle/operators/net_op.cc | 6 +++--- paddle/operators/net_op.h | 3 ++- paddle/operators/net_op_test.cc | 5 ++--- 5 files changed, 14 insertions(+), 13 deletions(-) diff --git a/paddle/framework/operator.h b/paddle/framework/operator.h index 4a1dee6fb..9e8aef6f8 100644 --- a/paddle/framework/operator.h +++ b/paddle/framework/operator.h @@ -112,8 +112,8 @@ class OperatorBase { const AttributeMap& Attrs() const { return attrs_; } // Return a new operator instance, which is as same as this. - // NOTE: It is caller's responsibility to delete that operator instance. - virtual OperatorBase* Clone() const = 0; + // Use unique_ptr to prevent caller forget to delete this pointer. + virtual std::unique_ptr Clone() const = 0; public: std::string type_; @@ -132,8 +132,10 @@ class OperatorBase { // Macro for define a clone method. // If you are writing an kernel operator, `Clone` will be defined when you // register it. -#define DEFINE_OP_CLONE_METHOD(CLS) \ - OperatorBase* Clone() const final { return new CLS(*this); } +#define DEFINE_OP_CLONE_METHOD(CLS) \ + std::unique_ptr Clone() const final { \ + return std::unique_ptr(new CLS(*this)); \ + } // Macro for define a default constructor for Operator. // You can also use diff --git a/paddle/framework/operator_test.cc b/paddle/framework/operator_test.cc index ceba7f5e6..883621712 100644 --- a/paddle/framework/operator_test.cc +++ b/paddle/framework/operator_test.cc @@ -257,7 +257,6 @@ class OperatorClone : public paddle::framework::OperatorBase { TEST(Operator, Clone) { OperatorClone a("ABC", {}, {}, {}); - auto* b = a.Clone(); + auto b = a.Clone(); ASSERT_EQ(a.Type(), b->Type()); - delete b; } \ No newline at end of file diff --git a/paddle/operators/net_op.cc b/paddle/operators/net_op.cc index 896550f9d..77eb07e2f 100644 --- a/paddle/operators/net_op.cc +++ b/paddle/operators/net_op.cc @@ -85,13 +85,13 @@ NetOp::NetOp(const std::string& type, const framework::OperatorBase::VarNameMap& inputs, const framework::OperatorBase::VarNameMap& outputs, const framework::AttributeMap& attrs) - : OperatorBase(type, inputs, outputs, attrs) {} + : framework::OperatorBase(type, inputs, outputs, attrs) {} -framework::OperatorBase* NetOp::Clone() const { +std::unique_ptr NetOp::Clone() const { PADDLE_ENFORCE( add_op_done_, "Must clone a sealed NetOp, invoke Net::CompleteAddOp before clone"); - return new NetOp(*this); + return std::unique_ptr(new NetOp(*this)); } } // namespace operators diff --git a/paddle/operators/net_op.h b/paddle/operators/net_op.h index deee54306..743f0e67d 100644 --- a/paddle/operators/net_op.h +++ b/paddle/operators/net_op.h @@ -109,7 +109,8 @@ class NetOp : public framework::OperatorBase { bool IsNetOp() const override; std::vector OutputVars(bool has_intermediate) const override; - framework::OperatorBase* Clone() const override; + + std::unique_ptr Clone() const override; std::vector> ops_; diff --git a/paddle/operators/net_op_test.cc b/paddle/operators/net_op_test.cc index 40e43f46d..6d6f8bd35 100644 --- a/paddle/operators/net_op_test.cc +++ b/paddle/operators/net_op_test.cc @@ -84,14 +84,13 @@ TEST(NetOp, Clone) { net.AddOp(std::shared_ptr(new EmptyOp{"empty", {}, {}, {}})); net.AddOp(std::shared_ptr(new EmptyOp{"empty2", {}, {}, {}})); net.CompleteAddOp(true); - auto* new_net_op = net.Clone(); + auto new_net_op = net.Clone(); ASSERT_NE(new_net_op, nullptr); ASSERT_TRUE(new_net_op->IsNetOp()); - auto* new_net = static_cast(new_net_op); + auto* new_net = static_cast(new_net_op.get()); ASSERT_EQ(2, new_net->ops_.size()); ASSERT_EQ(new_net->ops_[0]->Type(), "empty"); ASSERT_EQ(new_net->ops_[1]->Type(), "empty2"); - delete new_net; } } // namespace operators -- GitLab From 0f8688192cfd4892c379c5f994a2d7149fa3c63d Mon Sep 17 00:00:00 2001 From: Yancey Date: Wed, 16 Aug 2017 16:09:09 +0800 Subject: [PATCH 0067/2018] Fix invalid paddle binary file path (#3421) Fix invalid paddle executable file path with pip install --- .../build_and_install/build_from_source_en.md | 13 +++++---- paddle/scripts/docker/build.sh | 26 ++--------------- paddle/scripts/submit_local.sh.in | 29 ++++--------------- python/setup.py.in | 12 ++++---- 4 files changed, 21 insertions(+), 59 deletions(-) diff --git a/doc/getstarted/build_and_install/build_from_source_en.md b/doc/getstarted/build_and_install/build_from_source_en.md index c0608ede8..2f1461489 100644 --- a/doc/getstarted/build_and_install/build_from_source_en.md +++ b/doc/getstarted/build_and_install/build_from_source_en.md @@ -68,7 +68,7 @@ As a simple example, consider the following: 1. **BLAS Dependencies(optional)** - CMake will search BLAS libraries from system. If not found, OpenBLAS will be downloaded, built and installed automatically. + CMake will search BLAS libraries from the system. If not found, OpenBLAS will be downloaded, built and installed automatically. To utilize preinstalled BLAS， you can simply specify MKL, OpenBLAS or ATLAS via `MKL_ROOT`, `OPENBLAS_ROOT` or `ATLAS_ROOT`. ```bash @@ -131,9 +131,9 @@ As a simple example, consider the following: To build GPU version, you will need the following installed: 1. a CUDA-capable GPU - 2. A supported version of Linux with a gcc compiler and toolchain + 2. A supported version of Linux with a GCC compiler and toolchain 3. NVIDIA CUDA Toolkit (available at http://developer.nvidia.com/cuda-downloads) - 4. NVIDIA cuDNN Library (availabel at https://developer.nvidia.com/cudnn) + 4. NVIDIA cuDNN Library (available at https://developer.nvidia.com/cudnn) The CUDA development environment relies on tight integration with the host development environment, including the host compiler and C runtime libraries, and is therefore only supported on @@ -172,6 +172,7 @@ export PATH=/bin:$PATH # install PaddlePaddle Python modules. sudo pip install /opt/paddle/share/wheels/*.whl ``` + ## Build on Centos 7 ### Install Dependencies @@ -192,9 +193,9 @@ sudo pip install /opt/paddle/share/wheels/*.whl To build GPU version, you will need the following installed: 1. a CUDA-capable GPU - 2. A supported version of Linux with a gcc compiler and toolchain + 2. A supported version of Linux with a GCC compiler and toolchain 3. NVIDIA CUDA Toolkit (available at http://developer.nvidia.com/cuda-downloads) - 4. NVIDIA cuDNN Library (availabel at https://developer.nvidia.com/cudnn) + 4. NVIDIA cuDNN Library (available at https://developer.nvidia.com/cudnn) The CUDA development environment relies on tight integration with the host development environment, including the host compiler and C runtime libraries, and is therefore only supported on @@ -222,7 +223,7 @@ mkdir build && cd build ``` Finally, you can build and install PaddlePaddle: - + ```bash # you can add build option here, such as: cmake3 .. -DCMAKE_INSTALL_PREFIX= diff --git a/paddle/scripts/docker/build.sh b/paddle/scripts/docker/build.sh index 6c2f5fed4..7c12664ae 100644 --- a/paddle/scripts/docker/build.sh +++ b/paddle/scripts/docker/build.sh @@ -120,25 +120,6 @@ EOF /woboq/indexgenerator/codebrowser_indexgenerator $WOBOQ_OUT fi -# generate deb package for current build -# FIXME(typhoonzero): should we remove paddle/scripts/deb ? -if [[ ${WITH_DEB:-ON} == "ON" ]]; then - cat <> /paddle/build/Dockerfile </dev/null) - BASEDIR=$(dirname "$0") - pip install ${BASEDIR}/../opt/paddle/share/wheels/*-${PYTHON_PADDLE_VERSION}-*.whl - if [ $? -ne 0 ]; then - echo "pip install wheels failed. " - echo "Please use 'sudo paddle' at the first time you use PaddlePaddle" - echo "PaddlePaddle will install some python dependencies automatically." - exit 1 - fi - echo "Python dependencies are installed." -fi case "$1" in "train") - ${DEBUGGER} $MYDIR/../opt/paddle/bin/paddle_trainer ${@:2} + ${DEBUGGER} $PADDLE_BIN_PATH/paddle_trainer ${@:2} ;; "merge_model") - ${DEBUGGER} $MYDIR/../opt/paddle/bin/paddle_merge_model ${@:2} + ${DEBUGGER} $PADDLE_BIN_PATH/paddle_merge_model ${@:2} ;; "pserver") - ${DEBUGGER} $MYDIR/../opt/paddle/bin/paddle_pserver_main ${@:2} + ${DEBUGGER} $PADDLE_BIN_PATH/paddle_pserver_main ${@:2} ;; "dump_config") python -m paddle.utils.dump_config ${@:2} @@ -129,7 +110,7 @@ case "$1" in python -m paddle.utils.make_model_diagram ${@:2} ;; "usage") - $MYDIR/../opt/paddle/bin/paddle_usage ${@:2} + $PADDLE_BIN_PATH/paddle_usage ${@:2} ;; "version") version diff --git a/python/setup.py.in b/python/setup.py.in index 287442e01..82f500612 100644 --- a/python/setup.py.in +++ b/python/setup.py.in @@ -24,13 +24,14 @@ if '${CMAKE_SYSTEM_PROCESSOR}' not in ['arm', 'armv7-a', 'aarch64']: setup_requires+=["opencv-python"] # the prefix is sys.prefix which should always be usr -paddle_bin_dir = 'local/opt/paddle/bin' +paddle_bin_dir = 'opt/paddle/bin' paddle_bins = ['${PADDLE_BINARY_DIR}/paddle/scripts/paddle_usage', '${PADDLE_BINARY_DIR}/paddle/trainer/paddle_trainer', '${PADDLE_BINARY_DIR}/paddle/trainer/paddle_merge_model', - '${PADDLE_BINARY_DIR}/paddle/pserver/paddle_pserver_main'] + '${PADDLE_BINARY_DIR}/paddle/pserver/paddle_pserver_main', + '${PADDLE_BINARY_DIR}/paddle/scripts/paddle'] -paddle_rt_lib_dir = 'local/lib' +paddle_rt_lib_dir = 'lib' paddle_rt_libs = [] if '${MKL_SHARED_LIBS}'== '' else '${MKL_SHARED_LIBS}'.split(';') setup(name='paddlepaddle', @@ -50,8 +51,7 @@ setup(name='paddlepaddle', 'paddle.v2.framework.proto': '${PADDLE_BINARY_DIR}/paddle/framework', 'py_paddle': '${PADDLE_SOURCE_DIR}/paddle/py_paddle' }, - scripts=['${PADDLE_BINARY_DIR}/paddle/scripts/paddle'], + scripts=paddle_bins, distclass=BinaryDistribution, - data_files=[(paddle_bin_dir, paddle_bins), - (paddle_rt_lib_dir, paddle_rt_libs)] + data_files=[(paddle_rt_lib_dir, paddle_rt_libs)] ) -- GitLab From 57d96f88e1d59f4ed6173602a44b1380fed30a4e Mon Sep 17 00:00:00 2001 From: wanghaoshuang Date: Wed, 16 Aug 2017 16:15:12 +0800 Subject: [PATCH 0068/2018] Fix document error. --- python/paddle/v2/trainer.py | 11 ++++++++--- 1 file changed, 8 insertions(+), 3 deletions(-) diff --git a/python/paddle/v2/trainer.py b/python/paddle/v2/trainer.py index 9c4dd5f25..1daf23a73 100644 --- a/python/paddle/v2/trainer.py +++ b/python/paddle/v2/trainer.py @@ -27,16 +27,21 @@ class SGD(object): SGD Trainer combines data reader, network topolopy and update_equation together to train/test a neural network. - :param update_equation: The optimizer object. - :type update_equation: paddle.v2.optimizer.Optimizer :param cost: Target cost that neural network should be optimized. :type cost: paddle.v2.config_base.Layer :param parameters: The parameters dictionary. :type parameters: paddle.v2.parameters.Parameters + :param update_equation: The optimizer object. + :type update_equation: paddle.v2.optimizer.Optimizer :param extra_layers: Some layers in the neural network graph are not in the path of cost layer. - :param pserver_spec: pserver location, eg: localhost:3000 :type extra_layers: paddle.v2.config_base.Layer + :param is_local: Whether trainning locally + :type is_local: bool + :param pserver_spec: pserver location, eg: localhost:3000 + :type pserver_spec: string + :param use_etcd: Whether using etcd pserver. + :param use_etcd: bool """ def __init__(self, -- GitLab From fd107ae550be7e93e45a88bc2826a9be803dd710 Mon Sep 17 00:00:00 2001 From: wanghaoshuang Date: Wed, 16 Aug 2017 17:00:57 +0800 Subject: [PATCH 0069/2018] Modify pserver_spec's doc. --- python/paddle/v2/trainer.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/python/paddle/v2/trainer.py b/python/paddle/v2/trainer.py index 1daf23a73..4cf4d8b11 100644 --- a/python/paddle/v2/trainer.py +++ b/python/paddle/v2/trainer.py @@ -38,7 +38,9 @@ class SGD(object): :type extra_layers: paddle.v2.config_base.Layer :param is_local: Whether trainning locally :type is_local: bool - :param pserver_spec: pserver location, eg: localhost:3000 + :param pserver_spec: pserver location, eg: localhost:3000, + if use_etcd is true, pserver_spec indicates + the etcd endpoints, eg: http://127.0.0.1:2379 :type pserver_spec: string :param use_etcd: Whether using etcd pserver. :param use_etcd: bool -- GitLab From 5d18aaf8223ef7de420e09ad1de8fd93dbdf6db7 Mon Sep 17 00:00:00 2001 From: Liu Yiqun Date: Wed, 16 Aug 2017 09:11:03 +0000 Subject: [PATCH 0070/2018] Add a c-api interface to get the output of a specified layer. --- paddle/capi/gradient_machine.cpp | 16 ++++++++++++++++ paddle/capi/gradient_machine.h | 18 +++++++++++++++++- 2 files changed, 33 insertions(+), 1 deletion(-) diff --git a/paddle/capi/gradient_machine.cpp b/paddle/capi/gradient_machine.cpp index b3287552d..629449bbd 100644 --- a/paddle/capi/gradient_machine.cpp +++ b/paddle/capi/gradient_machine.cpp @@ -146,3 +146,19 @@ paddle_error paddle_gradient_machine_randomize_param( m->machine->randParameters(); return kPD_NO_ERROR; } + +paddle_error paddle_gradient_machine_get_layer_output( + paddle_gradient_machine machine, + const char* layerName, + paddle_arguments args) { + auto m = cast(machine); + auto out = paddle::capi::cast(args); + if (m == nullptr || layerName == nullptr || out == nullptr || + m->machine == nullptr) { + return kPD_NULLPTR; + } + + auto layerOutput = m->machine->getLayerOutput(layerName); + out->args.push_back(layerOutput); + return kPD_NO_ERROR; +} diff --git a/paddle/capi/gradient_machine.h b/paddle/capi/gradient_machine.h index c613ade5b..28eeb23e3 100644 --- a/paddle/capi/gradient_machine.h +++ b/paddle/capi/gradient_machine.h @@ -39,7 +39,11 @@ PD_API paddle_error paddle_gradient_machine_create_for_inference( /** * @brief Create a gradient machine used for model inference, using config with * parameters which is generated by `paddle merge_model`. - * @param [out] machine that used for model inference. + * Example: + * paddle merge_model \ + * --model_dir="pass-00000" \ + * --model_file="merged_model.paddle" + * @param [out] machine that used for model inference * @param [in] mergedModel * @param [in] size * @return paddle_error @@ -97,6 +101,18 @@ paddle_gradient_machine_randomize_param(paddle_gradient_machine machine); PD_API paddle_error paddle_gradient_machine_destroy(paddle_gradient_machine machine); +/** + * @brief Get the output of the layer named `layerName`. + * @param [in] gradient machine that have run a inference + * @param [in] layerName name of specified layer + * @param [out] args output of the specified layer + * @return paddle_error + */ +PD_API paddle_error +paddle_gradient_machine_get_layer_output(paddle_gradient_machine machine, + const char* layerName, + paddle_arguments args); + #ifdef __cplusplus } #endif -- GitLab From f7d32c614dc047faa3e19eb471d3bca6269d2d03 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Wed, 16 Aug 2017 17:21:59 +0800 Subject: [PATCH 0071/2018] Fix bug of enforce when dladdr not found * Wrong Printf format before --- paddle/platform/enforce.h | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paddle/platform/enforce.h b/paddle/platform/enforce.h index 15fdf7a94..81448897e 100644 --- a/paddle/platform/enforce.h +++ b/paddle/platform/enforce.h @@ -86,7 +86,7 @@ struct EnforceNotMet : public std::exception { 2 + sizeof(void*) * 2, call_stack[i], demangled, addr_offset); } else { - sout << string::Sprintf("%-3d %*0p %s\n", i, 2 + sizeof(void*) * 2, + sout << string::Sprintf("%-3d %*0p\n", i, 2 + sizeof(void*) * 2, call_stack[i]); } } -- GitLab From 8c653ba76a442a528c68240baf2d564971d5588d Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Wed, 16 Aug 2017 17:47:22 +0800 Subject: [PATCH 0072/2018] Complete remove std::shared_ptr --- paddle/framework/backward.cc | 40 +++++++++++++-------------- paddle/framework/op_registry.h | 11 ++++---- paddle/framework/op_registry_test.cc | 6 ++-- paddle/framework/pybind.cc | 37 +++++++++++-------------- paddle/operators/net_op.h | 41 +++++++++++++++++++++------- paddle/operators/net_op_test.cc | 23 +++++++--------- paddle/operators/recurrent_op.cc | 20 +++++++------- paddle/operators/recurrent_op.h | 24 +++++++++------- 8 files changed, 107 insertions(+), 95 deletions(-) diff --git a/paddle/framework/backward.cc b/paddle/framework/backward.cc index c226e4e3d..a1049f718 100644 --- a/paddle/framework/backward.cc +++ b/paddle/framework/backward.cc @@ -15,6 +15,8 @@ #include "paddle/framework/backward.h" #include +#include + #include "paddle/framework/op_registry.h" #include "paddle/operators/net_op.h" #include "paddle/operators/recurrent_op.h" @@ -43,11 +45,11 @@ static bool AllInSet( return all_in_set; } -static std::shared_ptr NOP() { - auto net_op = std::make_shared(); +static std::unique_ptr NOP() { + auto net_op = new operators::NetOp(); net_op->SetType("@NOP@"); net_op->CompleteAddOp(); - return net_op; + return std::unique_ptr(net_op); } // Get backward operator from a forward operator, a recursive implementation. @@ -62,11 +64,7 @@ static std::shared_ptr NOP() { // operator, in a complex situation, it maybe a NetOp. // // See Backward.h for details -static std::shared_ptr BackwardRecursive( - const OperatorBase& forwardOp, - std::unordered_set& no_grad_names, size_t& uniq_id); - -std::shared_ptr BackwardRecursive( +static std::unique_ptr BackwardRecursive( const OperatorBase& forwardOp, std::unordered_set& no_grad_names, size_t& uniq_id) { // If all input gradients of forwarding operator do not need to calculate, @@ -91,7 +89,7 @@ std::shared_ptr BackwardRecursive( } // Returned gradient network - auto net = std::make_shared(); + auto net = std::unique_ptr(); if (forwardOp.IsNetOp()) { // Because forwardOp is a net op, it can static_cast. @@ -105,14 +103,14 @@ std::shared_ptr BackwardRecursive( // reversely travel forwardNet and collect all duplicate outputs. for (auto it = forwardNet.ops_.rbegin(); it != forwardNet.ops_.rend(); ++it, ++local_op_id) { - auto fwd = *it; + auto& fwd = *it; auto bwd = BackwardRecursive(*fwd, no_grad_names, uniq_id); - net->AddOp(bwd); ForEachVarName(bwd->Outputs(), [&dup_output_ops, local_op_id](const std::string& out) { dup_output_ops[out].emplace_back(local_op_id); return false; }); + net->AddOp(std::move(bwd)); } // Get unique ID for this method. auto uid = uniq_id++; @@ -122,7 +120,7 @@ std::shared_ptr BackwardRecursive( // to handle this case. For each duplicate output, rename it to an alias // (original name with a offset), append an `add` op for its operator, // and finally sum all the alias variable to the final output variable y. - using Pos = std::pair>; + using Pos = std::pair>; std::list insert_position; for (auto& dup_output_op : dup_output_ops) { const std::string& name = dup_output_op.first; @@ -150,13 +148,13 @@ std::shared_ptr BackwardRecursive( [](const Pos& l, const Pos& r) { return l.first > r.first; }); for (auto& pos : insert_position) { - net->InsertOp(pos.first + 1, pos.second); + net->InsertOp(pos.first + 1, std::move(pos.second)); } } else { - std::shared_ptr grad_op = OpRegistry::CreateGradOp(forwardOp); + std::unique_ptr grad_op(OpRegistry::CreateGradOp(forwardOp)); - ForEachVarName(grad_op->Inputs(), [&no_grad_names, &net, - grad_op](const std::string& grad_input) { + ForEachVarName(grad_op->Inputs(), [&no_grad_names, &net, &grad_op]( + const std::string& grad_input) { if (no_grad_names.count(grad_input)) { // +1 for \0 std::string prefix = grad_input.substr( @@ -190,20 +188,20 @@ std::shared_ptr BackwardRecursive( const auto& stepnet_op = *static_cast(&rnnop.stepnet()); // create stepnet's gradient op - auto grad_stepnet = BackwardRecursive(stepnet_op, no_grad_names, uniq_id); rnn_grad_op->set_stepnet( - std::static_pointer_cast(grad_stepnet)); + BackwardRecursive(stepnet_op, no_grad_names, uniq_id)); } if (net->ops_.empty()) { // Current no aux op is added to network return grad_op; } - net->AddOp(grad_op); + net->AddOp(std::move(grad_op)); } net->SetType("@GENERATED_BACKWARD@"); net->CompleteAddOp(); - return net; -} // namespace framework + return std::unique_ptr( + static_cast(net.release())); +} // See header for comments std::shared_ptr Backward( diff --git a/paddle/framework/op_registry.h b/paddle/framework/op_registry.h index 4fa0a2750..f0cc0012e 100644 --- a/paddle/framework/op_registry.h +++ b/paddle/framework/op_registry.h @@ -174,7 +174,7 @@ class OpRegistry { } } - static std::shared_ptr CreateOp(const std::string& type, + static std::unique_ptr CreateOp(const std::string& type, const VarNameMap& inputs, const VarNameMap& outputs, AttributeMap attrs) { @@ -183,7 +183,7 @@ class OpRegistry { "Operator '%s' has not been registered.", type); it->second.checker_->Check(attrs); auto op = it->second.creator_(type, inputs, outputs, attrs); - return std::shared_ptr(op); + return std::unique_ptr(op); } static VarNameMap ConvertOpDescVarsToVarNameMap( @@ -199,7 +199,7 @@ class OpRegistry { return ret_val; } - static std::shared_ptr CreateOp(const OpDesc& op_desc) { + static std::unique_ptr CreateOp(const OpDesc& op_desc) { VarNameMap inputs = ConvertOpDescVarsToVarNameMap(op_desc.inputs()); VarNameMap outputs = ConvertOpDescVarsToVarNameMap(op_desc.outputs()); AttributeMap attrs; @@ -210,11 +210,10 @@ class OpRegistry { return CreateOp(op_desc.type(), inputs, outputs, attrs); } - static std::shared_ptr CreateGradOp(const OperatorBase& op) { + static std::unique_ptr CreateGradOp(const OperatorBase& op) { PADDLE_ENFORCE(!op.IsNetOp(), "Use framework::Backward to get backward ops"); - std::shared_ptr grad_op(BuildGradOp(&op)); - return grad_op; + return std::unique_ptr(BuildGradOp(&op)); } static std::unordered_map& op_info_map() { diff --git a/paddle/framework/op_registry_test.cc b/paddle/framework/op_registry_test.cc index 1a85d5683..50c45919c 100644 --- a/paddle/framework/op_registry_test.cc +++ b/paddle/framework/op_registry_test.cc @@ -76,8 +76,7 @@ TEST(OpRegistry, CreateOp) { attr->set_type(paddle::framework::AttrType::FLOAT); attr->set_f(scale); - std::shared_ptr op = - paddle::framework::OpRegistry::CreateOp(op_desc); + auto op = paddle::framework::OpRegistry::CreateOp(op_desc); paddle::framework::Scope scope; paddle::platform::CPUDeviceContext dev_ctx; op->Run(scope, dev_ctx); @@ -118,8 +117,7 @@ TEST(OpRegistry, DefaultValue) { ASSERT_TRUE(op_desc.IsInitialized()); - std::shared_ptr op = - paddle::framework::OpRegistry::CreateOp(op_desc); + auto op = paddle::framework::OpRegistry::CreateOp(op_desc); paddle::framework::Scope scope; paddle::platform::CPUDeviceContext dev_ctx; op->Run(scope, dev_ctx); diff --git a/paddle/framework/pybind.cc b/paddle/framework/pybind.cc index fe0c87bc5..2fc1e214b 100644 --- a/paddle/framework/pybind.cc +++ b/paddle/framework/pybind.cc @@ -207,8 +207,7 @@ All parameter, weight, gradient are variables in Paddle. .def(py::init<>()) .def("__str__", string::to_string); - py::class_> operator_base( - m, "Operator"); + py::class_ operator_base(m, "Operator"); operator_base.def_static("create", [](py::bytes protobin) { OpDesc desc; @@ -228,25 +227,23 @@ All parameter, weight, gradient are variables in Paddle. ExposeOperator(operator_base); - py::class_> net(m, "Net"); + py::class_ net(m, "Net"); net.def_static("create", - []() -> std::shared_ptr { - auto retv = std::make_shared(); + []() -> operators::NetOp * { + auto *retv = new operators::NetOp; retv->SetType("plain_net"); return retv; }) - .def("add_op", &operators::NetOp::AddOp) + .def("add_op", [](operators::NetOp &self, + const OperatorBase &op) { self.AddOp(op); }) .def("add_op", - [](operators::NetOp &self, - const std::shared_ptr &net) -> void { - self.AddOp(std::static_pointer_cast(net)); + [](operators::NetOp &self, const operators::NetOp &net) -> void { + self.AddOp(net); }) .def("add_op", [](operators::NetOp &self, - const std::shared_ptr &rnn) -> void { - self.AddOp(std::static_pointer_cast(rnn)); - }) + const operators::RecurrentOp &rnn) -> void { self.AddOp(rnn); }) .def("complete_add_op", &operators::NetOp::CompleteAddOp) .def("complete_add_op", [](std::shared_ptr &self) { self->CompleteAddOp(); @@ -255,12 +252,11 @@ All parameter, weight, gradient are variables in Paddle. ExposeOperator(net); // recurrent_op - py::class_> - rnn(m, "RecurrentOp"); + py::class_ rnn(m, "RecurrentOp"); rnn.def_static( "create", - [](py::bytes protobin) -> std::shared_ptr { + [](py::bytes protobin) -> operators::RecurrentOp * { OpDesc desc; PADDLE_ENFORCE(desc.ParsePartialFromString(protobin), "Cannot parse user input to OpDesc"); @@ -268,13 +264,12 @@ All parameter, weight, gradient are variables in Paddle. "User OpDesc is not initialized, reason %s", desc.InitializationErrorString()); auto rnn_op = OpRegistry::CreateOp(desc); - return std::dynamic_pointer_cast(rnn_op); + return static_cast(rnn_op.release()); }) - .def("set_stepnet", - [](operators::RecurrentOp &self, - const std::shared_ptr &net) -> void { - self.set_stepnet(net); - }); + .def("set_stepnet", [](operators::RecurrentOp &self, + const operators::NetOp &net) -> void { + self.set_stepnet(net.Clone()); + }); ExposeOperator(rnn); m.def("unique_integer", UniqueIntegerGenerator); diff --git a/paddle/operators/net_op.h b/paddle/operators/net_op.h index 743f0e67d..2ec65c63f 100644 --- a/paddle/operators/net_op.h +++ b/paddle/operators/net_op.h @@ -45,11 +45,11 @@ class NetOp : public framework::OperatorBase { : framework::OperatorBase( static_cast(o)) { this->ops_.reserve(o.ops_.size()); - std::transform(o.ops_.begin(), o.ops_.end(), std::back_inserter(this->ops_), - [](const std::shared_ptr& op) - -> std::shared_ptr { - return std::shared_ptr(op->Clone()); - }); + std::transform( + o.ops_.begin(), o.ops_.end(), std::back_inserter(this->ops_), + [](const std::unique_ptr& op) { + return std::unique_ptr(op->Clone()); + }); this->CompleteAddOp(); } @@ -86,21 +86,42 @@ class NetOp : public framework::OperatorBase { return true; } + void AddOp(const framework::OperatorBase& op) { AddOp(op.Clone()); } + /** * @brief Add an operator by ptr */ - void AddOp(const std::shared_ptr& op) { + void AddOp(framework::OperatorBase* op, bool own) { PADDLE_ENFORCE(!add_op_done_, "Cannot AddOp when this network is sealed"); PADDLE_ENFORCE_NOT_NULL(op, "Cannot Insert Null op"); - ops_.push_back(op); + if (!own) { + op = op->Clone().release(); + } + ops_.emplace_back(op); } - void InsertOp(size_t pos, const std::shared_ptr& op) { + void AddOp(std::unique_ptr&& op) { + AddOp(op.release(), true); + } + + void InsertOp(size_t pos, framework::OperatorBase* op, bool own) { PADDLE_ENFORCE(!add_op_done_, "Cannot InsertOp when this network is sealed"); PADDLE_ENFORCE_NOT_NULL(op, "Cannot Insert Null op"); PADDLE_ENFORCE_LE(pos, ops_.size(), "Out of range"); - ops_.insert(ops_.begin() + pos, op); + if (!own) { + op = op->Clone().release(); + } + ops_.insert(ops_.begin() + pos, + std::unique_ptr(op)); + } + + void InsertOp(size_t pos, std::unique_ptr&& op) { + InsertOp(pos, op.release(), true); + } + + void InsertOp(size_t pos, const framework::OperatorBase& op) { + InsertOp(pos, op.Clone()); } void CompleteAddOp(bool calculate = true); @@ -112,7 +133,7 @@ class NetOp : public framework::OperatorBase { std::unique_ptr Clone() const override; - std::vector> ops_; + std::vector> ops_; private: bool add_op_done_{false}; diff --git a/paddle/operators/net_op_test.cc b/paddle/operators/net_op_test.cc index e28d4df6a..e9598610c 100644 --- a/paddle/operators/net_op_test.cc +++ b/paddle/operators/net_op_test.cc @@ -38,15 +38,12 @@ TEST(OpKernel, all) { auto net = std::make_shared(); ASSERT_NE(net, nullptr); - auto op1 = std::shared_ptr( + net->AddOp(std::unique_ptr( new TestOp("test", {{"X", {"x"}}, {"W", {"w1"}}, {"b", {"b1"}}}, - {{"Out", {"y"}}}, {})); - net->AddOp(op1); - - auto op2 = std::shared_ptr( + {{"Out", {"y"}}}, {}))); + net->AddOp(std::unique_ptr( new TestOp("test", {{"X", {"y"}}, {"W", {"w2"}}, {"b", {"b2"}}}, - {{"Out", {"z"}}}, {})); - net->AddOp(op2); + {{"Out", {"z"}}}, {}))); net->CompleteAddOp(); AssertSameVectorWithoutOrder({"x", "w1", "b1", "w2", "b2"}, @@ -61,21 +58,21 @@ TEST(OpKernel, all) { TEST(NetOp, insert_op) { NetOp net; - auto op1 = std::shared_ptr( + auto op1 = std::unique_ptr( new framework::NOP("empty", {{"X", {"x"}}, {"W", {"w1"}}, {"b", {"b1"}}}, {{"Out", {"y"}}}, {})); - net.AddOp(op1); - net.InsertOp(0, op1); + net.AddOp(*op1); + net.InsertOp(0, *op1); ASSERT_EQ(2UL, net.ops_.size()); - net.InsertOp(2, op1); + net.InsertOp(2, std::move(op1)); ASSERT_EQ(3UL, net.ops_.size()); } TEST(NetOp, Clone) { NetOp net; net.AddOp( - std::shared_ptr(new framework::NOP{"empty", {}, {}, {}})); - net.AddOp(std::shared_ptr( + std::unique_ptr(new framework::NOP{"empty", {}, {}, {}})); + net.AddOp(std::unique_ptr( new framework::NOP{"empty2", {}, {}, {}})); net.CompleteAddOp(true); auto new_net_op = net.Clone(); diff --git a/paddle/operators/recurrent_op.cc b/paddle/operators/recurrent_op.cc index 78ce0ba3c..aae78a1ce 100644 --- a/paddle/operators/recurrent_op.cc +++ b/paddle/operators/recurrent_op.cc @@ -42,7 +42,7 @@ void RecurrentAlgorithm::InferShape(const Scope& scope) const { rnn::LinkMemories(step_scopes, arg_->memories, i, -1, true /*infer_shape_mode*/); } - (*stepnet_)->InferShape(*step_scopes[i]); + stepnet_->InferShape(*step_scopes[i]); } rnn::ConcatOutputs(step_scopes, arg_->outlinks, seq_len_, true /*infer_shape_mode*/); @@ -61,7 +61,7 @@ void RecurrentAlgorithm::Run(const Scope& scope, rnn::LinkMemories(step_scopes, arg_->memories, step_id, -1, false /*infer_shape_mode*/); } - (*stepnet_)->Run(*step_scopes[step_id], dev_ctx); + stepnet_->Run(*step_scopes[step_id], dev_ctx); } rnn::ConcatOutputs(step_scopes, arg_->outlinks, seq_len_, false /*infer_shape_mode*/); @@ -76,15 +76,15 @@ void RecurrentAlgorithm::CreateScopes(const Scope& scope) const { // Now all variables in scope must be created outside of op. PADDLE_ENFORCE_NOT_NULL(stepnet_); - PADDLE_ENFORCE(!(*stepnet_)->Outputs().empty(), "stepnet_ op has no outputs"); - PADDLE_ENFORCE(!(*stepnet_)->Outputs().empty(), "net_op has no outputs"); + PADDLE_ENFORCE(!stepnet_->Outputs().empty(), "stepnet_ op has no outputs"); + PADDLE_ENFORCE(!stepnet_->Outputs().empty(), "net_op has no outputs"); if (seq_len_ > step_scopes->size()) { for (size_t i = step_scopes->size(); i < seq_len_; ++i) { auto& step_scope = scope.NewScope(); // create step net's temp inputs - for (auto& input : (*stepnet_)->Inputs()) { + for (auto& input : stepnet_->Inputs()) { // the weight are located in parent scope for (auto& var_name : input.second) { if (!step_scope.FindVar(var_name)) { @@ -93,7 +93,7 @@ void RecurrentAlgorithm::CreateScopes(const Scope& scope) const { } } // create stepnet's outputs - for (const auto& output : (*stepnet_)->Outputs()) { + for (const auto& output : stepnet_->Outputs()) { for (auto& var_name : output.second) { step_scope.NewVar(var_name); } @@ -136,7 +136,7 @@ RecurrentOp::RecurrentOp(const std::string& type, const framework::AttributeMap& attrs) : OperatorBase(type, inputs, outputs, attrs) { rnn::InitArgument(kArgName, &arg_, *this); - alg_.Init(&arg_, &stepnet_); + alg_.Init(&arg_, stepnet_.get()); } class RecurrentAlgorithmProtoAndCheckerMaker @@ -178,7 +178,7 @@ void RecurrentGradientAlgorithm::Run( rnn::LinkMemories(step_scopes, arg_->memories, step_id, 1, false /*infer_shape_mode*/); } - (*stepnet_)->Run(*step_scopes[step_id], dev_ctx); + stepnet_->Run(*step_scopes[step_id], dev_ctx); } LinkBootMemoryGradients(step_scopes[0], false); rnn::ConcatOutputs(step_scopes, arg_->outlinks, seq_len_, @@ -215,7 +215,7 @@ void RecurrentGradientAlgorithm::InferShape(const Scope& scope) const { rnn::LinkMemories(step_scopes, arg_->memories, step_id, 1, true /*infer_shape_mode*/); } - (*stepnet_)->InferShape(*step_scopes[step_id]); + stepnet_->InferShape(*step_scopes[step_id]); } rnn::ConcatOutputs(step_scopes, arg_->outlinks, seq_len_, true /*infer_shape_mode*/); @@ -228,7 +228,7 @@ RecurrentGradientOp::RecurrentGradientOp( const framework::AttributeMap& attrs) : OperatorBase(type, inputs, outputs, attrs) { rnn::InitArgument(kArgName, &arg_, *this); - alg_.Init(&arg_, &stepnet_); + alg_.Init(&arg_, stepnet_.get()); } } // namespace operators diff --git a/paddle/operators/recurrent_op.h b/paddle/operators/recurrent_op.h index 1d8a69739..4d091aa21 100644 --- a/paddle/operators/recurrent_op.h +++ b/paddle/operators/recurrent_op.h @@ -34,7 +34,7 @@ class RecurrentAlgorithm { void Run(const framework::Scope& scope, const platform::DeviceContext& dev_ctx) const; - void Init(rnn::Argument* arg, std::shared_ptr* stepnet) { + void Init(rnn::Argument* arg, framework::OperatorBase* stepnet) { PADDLE_ENFORCE_NOT_NULL(stepnet, "stepnet should be set before."); arg_ = arg; stepnet_ = stepnet; @@ -63,7 +63,7 @@ class RecurrentAlgorithm { void InitMemories(framework::Scope* step_scopes, bool infer_shape_mode) const; private: - std::shared_ptr* stepnet_; + framework::OperatorBase* stepnet_; rnn::Argument* arg_; mutable size_t seq_len_; }; @@ -80,7 +80,7 @@ class RecurrentGradientAlgorithm { * operator. */ public: - void Init(rnn::Argument* arg, std::shared_ptr* stepnet) { + void Init(rnn::Argument* arg, framework::OperatorBase* stepnet) { PADDLE_ENFORCE_NOT_NULL(stepnet, "stepnet should be set before."); arg_ = std::move(arg); stepnet_ = stepnet; @@ -107,7 +107,7 @@ class RecurrentGradientAlgorithm { private: rnn::Argument* arg_; mutable size_t seq_len_; - std::shared_ptr* stepnet_; + framework::OperatorBase* stepnet_; }; class RecurrentOp : public framework::OperatorBase { @@ -133,15 +133,17 @@ class RecurrentOp : public framework::OperatorBase { alg_.Run(scope, dev_ctx); } - void set_stepnet(std::shared_ptr net) { stepnet_ = net; } - const NetOp& stepnet() const { return *stepnet_; } + void set_stepnet(std::unique_ptr net) { + stepnet_ = std::move(net); + } + const OperatorBase& stepnet() const { return *stepnet_; } static const rnn::ArgumentName kArgName; private: RecurrentAlgorithm alg_; rnn::Argument arg_; - std::shared_ptr stepnet_; + std::unique_ptr stepnet_; }; class RecurrentGradientOp : public framework::OperatorBase { @@ -171,12 +173,14 @@ class RecurrentGradientOp : public framework::OperatorBase { static const rnn::ArgumentName kArgName; - void set_stepnet(const std::shared_ptr& net) { stepnet_ = net; } - const NetOp& stepnet() const { return *stepnet_; } + void set_stepnet(std::unique_ptr net) { + stepnet_ = std::move(net); + } + const OperatorBase& stepnet() const { return *stepnet_; } private: RecurrentGradientAlgorithm alg_; - std::shared_ptr stepnet_; + std::unique_ptr stepnet_; rnn::Argument arg_; }; -- GitLab From 1543eeb4ce20b8173ac8a60c5e2a348b296dcf18 Mon Sep 17 00:00:00 2001 From: superjom Date: Wed, 16 Aug 2017 18:03:18 +0800 Subject: [PATCH 0073/2018] init --- paddle/framework/pybind.cc | 7 ++++ paddle/operators/recurrent_op.cc | 1 - .../v2/framework/tests/gradient_checker.py | 12 +++--- .../v2/framework/tests/test_recurrent_op.py | 40 ++++++++++++++++++- 4 files changed, 52 insertions(+), 8 deletions(-) diff --git a/paddle/framework/pybind.cc b/paddle/framework/pybind.cc index fe0c87bc5..13f23a681 100644 --- a/paddle/framework/pybind.cc +++ b/paddle/framework/pybind.cc @@ -275,6 +275,13 @@ All parameter, weight, gradient are variables in Paddle. const std::shared_ptr &net) -> void { self.set_stepnet(net); }); + + rnn.def("backward", [](const operators::RecurrentOp &forwardOp, + const std::unordered_set &no_grad_vars) { + const auto &op = *static_cast(&forwardOp); + return Backward(op, no_grad_vars); + }); + ExposeOperator(rnn); m.def("unique_integer", UniqueIntegerGenerator); diff --git a/paddle/operators/recurrent_op.cc b/paddle/operators/recurrent_op.cc index 78ce0ba3c..8c7300a35 100644 --- a/paddle/operators/recurrent_op.cc +++ b/paddle/operators/recurrent_op.cc @@ -77,7 +77,6 @@ void RecurrentAlgorithm::CreateScopes(const Scope& scope) const { // Now all variables in scope must be created outside of op. PADDLE_ENFORCE_NOT_NULL(stepnet_); PADDLE_ENFORCE(!(*stepnet_)->Outputs().empty(), "stepnet_ op has no outputs"); - PADDLE_ENFORCE(!(*stepnet_)->Outputs().empty(), "net_op has no outputs"); if (seq_len_ > step_scopes->size()) { for (size_t i = step_scopes->size(); i < seq_len_; ++i) { diff --git a/python/paddle/v2/framework/tests/gradient_checker.py b/python/paddle/v2/framework/tests/gradient_checker.py index 501cf6110..b6d8131be 100644 --- a/python/paddle/v2/framework/tests/gradient_checker.py +++ b/python/paddle/v2/framework/tests/gradient_checker.py @@ -29,13 +29,13 @@ def get_numeric_gradient(op, local_scope=None): """ Get Numeric Gradient for an operator's input. - - :param op: C++ operator instance, could be an network - :param input_values: The input variables. Should be an dictionary, key is - variable name. Value is numpy array. - :param output_name: The final output variable name. + + :param op: C++ operator instance, could be an network + :param input_values: The input variables. Should be an dictionary, key is + variable name. Value is numpy array + :param output_name: The final output variable name. :param input_to_check: The input variable need to get gradient. - :param delta: The perturbation value for numeric gradient method. The + :param delta: The perturbation value for numeric gradient method. The smaller delta is, the more accurate result will get. But if that delta is too small, it could occur numerical stability problem. :param local_scope: The local scope used for get_numeric_gradient. diff --git a/python/paddle/v2/framework/tests/test_recurrent_op.py b/python/paddle/v2/framework/tests/test_recurrent_op.py index 3d4a34d8d..0da56c1ad 100644 --- a/python/paddle/v2/framework/tests/test_recurrent_op.py +++ b/python/paddle/v2/framework/tests/test_recurrent_op.py @@ -3,6 +3,7 @@ import paddle.v2.framework.core as core import unittest import numpy as np from paddle.v2.framework.op import Operator, RecurrentOp +from gradient_checker import GradientChecker def py_sigmoid(x): @@ -69,7 +70,7 @@ def create_tensor(scope, name, shape, np_data): return tensor -class TestRecurrentOp(unittest.TestCase): +class RecurrentOpTest(unittest.TestCase): ''' Test RNNOp @@ -164,5 +165,42 @@ class TestRecurrentOp(unittest.TestCase): self.assertEqual(pd_output.shape, py_output.shape) +class RecurrentGradientOpTest(unittest.TestCase): + def create_forward_op(self): + self.forward_op = RecurrentOp( + # inputs + inlinks=["x"], + boot_memories=["h_boot"], + step_net="stepnet", + # outputs + outlinks=["h"], + step_scopes="step_scopes", + # attributes + inlink_alias=["x@alias"], + outlink_alias=["h@alias"], + pre_memories=["h@pre"], + memories=["h@alias"]) + + # create a stepnet for RNN + stepnet = core.Net.create() + x_fc_op = Operator("mul", X="x@alias", Y="W", Out="Wx") + h_fc_op = Operator("mul", X="h@pre", Y="U", Out="Uh") + sum_op = Operator("add_two", X="Wx", Y="Uh", Out="sum") + sig_op = Operator("sigmoid", X="sum", Y="h@alias") + + for op in [x_fc_op, h_fc_op, sum_op, sig_op]: + stepnet.add_op(op) + stepnet.complete_add_op(True) + self.forward_op.set_stepnet(stepnet) + + def create_gradient_op(self): + a = set() + backward_op = core.RecurrentOp.backward(self.forward_op, a) + + def test_grad(self): + self.create_forward_op() + self.create_gradient_op() + + if __name__ == '__main__': unittest.main() -- GitLab From 8f80f5bc794d8900f9d57b51eea167f4dde2903c Mon Sep 17 00:00:00 2001 From: liaogang Date: Wed, 16 Aug 2017 19:46:12 +0800 Subject: [PATCH 0074/2018] FIX: Release CPU/GPU memory via deleter --- paddle/memory/memory.cc | 59 ++++++++++++++++++++++++++++------------- 1 file changed, 41 insertions(+), 18 deletions(-) diff --git a/paddle/memory/memory.cc b/paddle/memory/memory.cc index 207025f9b..5946c3ea4 100644 --- a/paddle/memory/memory.cc +++ b/paddle/memory/memory.cc @@ -16,19 +16,31 @@ limitations under the License. */ #include "paddle/memory/detail/buddy_allocator.h" #include "paddle/memory/detail/system_allocator.h" -#include // for memcpy +#include // for transfrom +#include // for memcpy +#include // for call_once + +#include "glog/logging.h" namespace paddle { namespace memory { -detail::BuddyAllocator* GetCPUBuddyAllocator() { - static detail::BuddyAllocator* a = nullptr; - if (a == nullptr) { - a = new detail::BuddyAllocator(new detail::CPUAllocator, - platform::CpuMinChunkSize(), - platform::CpuMaxChunkSize()); - } - return a; +using BuddyAllocator = detail::BuddyAllocator; + +std::once_flag cpu_alloctor_flag; +std::once_flag gpu_alloctor_flag; + +BuddyAllocator* GetCPUBuddyAllocator() { + static std::unique_ptr a{ + nullptr, [](BuddyAllocator* p) { delete p; }}; + + std::call_once(cpu_alloctor_flag, [&]() { + a.reset(new BuddyAllocator(new detail::CPUAllocator, + platform::CpuMinChunkSize(), + platform::CpuMaxChunkSize())); + }); + + return a.get(); } template <> @@ -48,20 +60,31 @@ size_t Used(platform::CPUPlace place) { #ifndef PADDLE_ONLY_CPU -detail::BuddyAllocator* GetGPUBuddyAllocator(int gpu_id) { - static detail::BuddyAllocator** as = NULL; - if (as == NULL) { +BuddyAllocator* GetGPUBuddyAllocator(int gpu_id) { + using BuddyAllocVec = std::vector; + static std::unique_ptr as{ + new std::vector, [](BuddyAllocVec* p) { + std::for_each(p->begin(), p->end(), + [](BuddyAllocator* p) { delete p; }); + }}; + + // GPU buddy alloctors + auto& alloctors = *as.get(); + + // GPU buddy allocator initialization + std::call_once(gpu_alloctor_flag, [&]() { int gpu_num = platform::GetDeviceCount(); - as = new detail::BuddyAllocator*[gpu_num]; + alloctors.reserve(gpu_num); for (int gpu = 0; gpu < gpu_num; gpu++) { platform::SetDeviceId(gpu); - as[gpu] = new detail::BuddyAllocator(new detail::GPUAllocator, - platform::GpuMinChunkSize(), - platform::GpuMaxChunkSize()); + alloctors.emplace_back(new BuddyAllocator(new detail::GPUAllocator, + platform::GpuMinChunkSize(), + platform::GpuMaxChunkSize())); } - } + }); + platform::SetDeviceId(gpu_id); - return as[gpu_id]; + return alloctors[gpu_id]; } template <> -- GitLab From 4be8189a8033010cf3517d14bed30d991780285b Mon Sep 17 00:00:00 2001 From: wanghaoshuang Date: Wed, 16 Aug 2017 19:50:40 +0800 Subject: [PATCH 0075/2018] Modify pserver_spec's doc. --- python/paddle/v2/trainer.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/python/paddle/v2/trainer.py b/python/paddle/v2/trainer.py index 4cf4d8b11..0654a3010 100644 --- a/python/paddle/v2/trainer.py +++ b/python/paddle/v2/trainer.py @@ -38,9 +38,10 @@ class SGD(object): :type extra_layers: paddle.v2.config_base.Layer :param is_local: Whether trainning locally :type is_local: bool - :param pserver_spec: pserver location, eg: localhost:3000, - if use_etcd is true, pserver_spec indicates - the etcd endpoints, eg: http://127.0.0.1:2379 + :param pserver_spec: comma string for pserver location, + eg:127.10.0.10:3000,127.10.0.11:3000, + and this parameter is only used for fault + tolerant mode cluster training. :type pserver_spec: string :param use_etcd: Whether using etcd pserver. :param use_etcd: bool -- GitLab From f15e083098d94af00c02f44e32f0b8891c079f55 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Wed, 16 Aug 2017 21:24:12 +0800 Subject: [PATCH 0076/2018] Remove std::shared_ptr in Python & C++ * Also simplify pybind implementation by using OperatorBase as holder type. --- paddle/framework/backward.cc | 4 +- paddle/framework/backward.h | 2 +- paddle/framework/backward_test.cc | 3 +- paddle/framework/pybind.cc | 124 +++++++----------- paddle/operators/net_op.h | 4 +- paddle/operators/recurrent_op.cc | 20 +-- paddle/operators/recurrent_op.h | 10 +- .../v2/framework/tests/gradient_checker.py | 1 - 8 files changed, 71 insertions(+), 97 deletions(-) diff --git a/paddle/framework/backward.cc b/paddle/framework/backward.cc index a1049f718..9d3088722 100644 --- a/paddle/framework/backward.cc +++ b/paddle/framework/backward.cc @@ -89,7 +89,7 @@ static std::unique_ptr BackwardRecursive( } // Returned gradient network - auto net = std::unique_ptr(); + auto net = std::unique_ptr(new operators::NetOp()); if (forwardOp.IsNetOp()) { // Because forwardOp is a net op, it can static_cast. @@ -204,7 +204,7 @@ static std::unique_ptr BackwardRecursive( } // See header for comments -std::shared_ptr Backward( +std::unique_ptr Backward( const OperatorBase& forwardOp, const std::unordered_set& no_grad_vars) { std::unordered_set no_grad_names; diff --git a/paddle/framework/backward.h b/paddle/framework/backward.h index c181919dc..1ecf69881 100644 --- a/paddle/framework/backward.h +++ b/paddle/framework/backward.h @@ -20,7 +20,7 @@ namespace framework { // Create the backward operator from a forward operator. // TODO(yuyang18): Add more API reference comment. -extern std::shared_ptr Backward( +extern std::unique_ptr Backward( const OperatorBase& forwardOp, const std::unordered_set& no_grad_vars); } // namespace framework diff --git a/paddle/framework/backward_test.cc b/paddle/framework/backward_test.cc index d942604bf..1003b1ccd 100644 --- a/paddle/framework/backward_test.cc +++ b/paddle/framework/backward_test.cc @@ -180,8 +180,7 @@ TEST(Backward, simple_op_not_need_grad) { auto no_input_gop = f::Backward(*fwd, {"x", "b"}); ASSERT_NE(no_input_gop, nullptr); ASSERT_TRUE(no_input_gop->IsNetOp()); - ASSERT_EQ(0UL, - std::static_pointer_cast(no_input_gop)->ops_.size()); + ASSERT_EQ(0UL, static_cast(no_input_gop.get())->ops_.size()); } TEST(Backward, net_fc_backward_normal) { diff --git a/paddle/framework/pybind.cc b/paddle/framework/pybind.cc index 2fc1e214b..f0114b9e4 100644 --- a/paddle/framework/pybind.cc +++ b/paddle/framework/pybind.cc @@ -48,29 +48,6 @@ namespace framework { using Tensor = framework::Tensor; -template -void ExposeOperator(ClassType &m) { - m.def("infer_shape", &ClassType::type::InferShape) - .def("run", &ClassType::type::Run) - .def("type", - [](const typename ClassType::type &op) -> std::string { - return op.Type(); - }) - .def("outputs", - [](const typename ClassType::type &op) - -> std::map> { - return op.Outputs(); - }) - .def("inputs", - [](const typename ClassType::type &op) { return op.Inputs(); }) - .def("__str__", &ClassType::type::DebugString) - .def("no_intermediate_outputs", - [](const typename ClassType::type &op) { - return op.OutputVars(false); - }) - .def("support_gpu", &ClassType::type::SupportGPU); -} - static size_t UniqueIntegerGenerator() { static std::atomic generator; return generator.fetch_add(1); @@ -207,70 +184,69 @@ All parameter, weight, gradient are variables in Paddle. .def(py::init<>()) .def("__str__", string::to_string); - py::class_ operator_base(m, "Operator"); - - operator_base.def_static("create", [](py::bytes protobin) { - OpDesc desc; - PADDLE_ENFORCE(desc.ParsePartialFromString(protobin), - "Cannot parse user input to OpDesc"); - PADDLE_ENFORCE(desc.IsInitialized(), - "User OpDesc is not initialized, reason %s", - desc.InitializationErrorString()); - return OpRegistry::CreateOp(desc); - }); - - operator_base.def("backward", - [](const OperatorBase &forwardOp, - const std::unordered_set &no_grad_vars) { - return Backward(forwardOp, no_grad_vars); - }); - - ExposeOperator(operator_base); - - py::class_ net(m, "Net"); + py::class_(m, "Operator") + .def_static("create", + [](py::bytes protobin) { + OpDesc desc; + PADDLE_ENFORCE(desc.ParsePartialFromString(protobin), + "Cannot parse user input to OpDesc"); + PADDLE_ENFORCE(desc.IsInitialized(), + "User OpDesc is not initialized, reason %s", + desc.InitializationErrorString()); + return OpRegistry::CreateOp(desc); + }) + .def("backward", + [](const OperatorBase &forwardOp, + const std::unordered_set &no_grad_vars) { + return Backward(forwardOp, no_grad_vars).release(); + }) + .def("infer_shape", &OperatorBase::InferShape) + .def("run", &OperatorBase::Run) + .def("type", + [](const OperatorBase &op) -> std::string { return op.Type(); }) + .def("outputs", + [](const OperatorBase &op) + -> std::map> { + return op.Outputs(); + }) + .def("inputs", [](const OperatorBase &op) { return op.Inputs(); }) + .def("__str__", &OperatorBase::DebugString) + .def("no_intermediate_outputs", + [](const OperatorBase &op) { return op.OutputVars(false); }) + .def("support_gpu", &OperatorBase::SupportGPU); - net.def_static("create", - []() -> operators::NetOp * { - auto *retv = new operators::NetOp; - retv->SetType("plain_net"); - return retv; - }) + py::class_(m, "Net") + .def_static("create", + []() -> operators::NetOp * { + auto *retv = new operators::NetOp; + retv->SetType("plain_net"); + return retv; + }) .def("add_op", [](operators::NetOp &self, const OperatorBase &op) { self.AddOp(op); }) - .def("add_op", - [](operators::NetOp &self, const operators::NetOp &net) -> void { - self.AddOp(net); - }) - .def("add_op", - [](operators::NetOp &self, - const operators::RecurrentOp &rnn) -> void { self.AddOp(rnn); }) .def("complete_add_op", &operators::NetOp::CompleteAddOp) .def("complete_add_op", [](std::shared_ptr &self) { self->CompleteAddOp(); }); - ExposeOperator(net); - // recurrent_op - py::class_ rnn(m, "RecurrentOp"); - - rnn.def_static( - "create", - [](py::bytes protobin) -> operators::RecurrentOp * { - OpDesc desc; - PADDLE_ENFORCE(desc.ParsePartialFromString(protobin), - "Cannot parse user input to OpDesc"); - PADDLE_ENFORCE(desc.IsInitialized(), - "User OpDesc is not initialized, reason %s", - desc.InitializationErrorString()); - auto rnn_op = OpRegistry::CreateOp(desc); - return static_cast(rnn_op.release()); - }) + py::class_(m, "RecurrentOp") + .def_static( + "create", + [](py::bytes protobin) -> operators::RecurrentOp * { + OpDesc desc; + PADDLE_ENFORCE(desc.ParsePartialFromString(protobin), + "Cannot parse user input to OpDesc"); + PADDLE_ENFORCE(desc.IsInitialized(), + "User OpDesc is not initialized, reason %s", + desc.InitializationErrorString()); + auto rnn_op = OpRegistry::CreateOp(desc); + return static_cast(rnn_op.release()); + }) .def("set_stepnet", [](operators::RecurrentOp &self, const operators::NetOp &net) -> void { self.set_stepnet(net.Clone()); }); - ExposeOperator(rnn); m.def("unique_integer", UniqueIntegerGenerator); diff --git a/paddle/operators/net_op.h b/paddle/operators/net_op.h index 2ec65c63f..ce7da1f38 100644 --- a/paddle/operators/net_op.h +++ b/paddle/operators/net_op.h @@ -41,9 +41,7 @@ class NetOp : public framework::OperatorBase { NetOp(const std::string& type, const VarNameMap& inputs, const VarNameMap& outputs, const framework::AttributeMap& attrs); - NetOp(const NetOp& o) - : framework::OperatorBase( - static_cast(o)) { + NetOp(const NetOp& o) : framework::OperatorBase(o.type_, {}, {}, o.attrs_) { this->ops_.reserve(o.ops_.size()); std::transform( o.ops_.begin(), o.ops_.end(), std::back_inserter(this->ops_), diff --git a/paddle/operators/recurrent_op.cc b/paddle/operators/recurrent_op.cc index aae78a1ce..78ce0ba3c 100644 --- a/paddle/operators/recurrent_op.cc +++ b/paddle/operators/recurrent_op.cc @@ -42,7 +42,7 @@ void RecurrentAlgorithm::InferShape(const Scope& scope) const { rnn::LinkMemories(step_scopes, arg_->memories, i, -1, true /*infer_shape_mode*/); } - stepnet_->InferShape(*step_scopes[i]); + (*stepnet_)->InferShape(*step_scopes[i]); } rnn::ConcatOutputs(step_scopes, arg_->outlinks, seq_len_, true /*infer_shape_mode*/); @@ -61,7 +61,7 @@ void RecurrentAlgorithm::Run(const Scope& scope, rnn::LinkMemories(step_scopes, arg_->memories, step_id, -1, false /*infer_shape_mode*/); } - stepnet_->Run(*step_scopes[step_id], dev_ctx); + (*stepnet_)->Run(*step_scopes[step_id], dev_ctx); } rnn::ConcatOutputs(step_scopes, arg_->outlinks, seq_len_, false /*infer_shape_mode*/); @@ -76,15 +76,15 @@ void RecurrentAlgorithm::CreateScopes(const Scope& scope) const { // Now all variables in scope must be created outside of op. PADDLE_ENFORCE_NOT_NULL(stepnet_); - PADDLE_ENFORCE(!stepnet_->Outputs().empty(), "stepnet_ op has no outputs"); - PADDLE_ENFORCE(!stepnet_->Outputs().empty(), "net_op has no outputs"); + PADDLE_ENFORCE(!(*stepnet_)->Outputs().empty(), "stepnet_ op has no outputs"); + PADDLE_ENFORCE(!(*stepnet_)->Outputs().empty(), "net_op has no outputs"); if (seq_len_ > step_scopes->size()) { for (size_t i = step_scopes->size(); i < seq_len_; ++i) { auto& step_scope = scope.NewScope(); // create step net's temp inputs - for (auto& input : stepnet_->Inputs()) { + for (auto& input : (*stepnet_)->Inputs()) { // the weight are located in parent scope for (auto& var_name : input.second) { if (!step_scope.FindVar(var_name)) { @@ -93,7 +93,7 @@ void RecurrentAlgorithm::CreateScopes(const Scope& scope) const { } } // create stepnet's outputs - for (const auto& output : stepnet_->Outputs()) { + for (const auto& output : (*stepnet_)->Outputs()) { for (auto& var_name : output.second) { step_scope.NewVar(var_name); } @@ -136,7 +136,7 @@ RecurrentOp::RecurrentOp(const std::string& type, const framework::AttributeMap& attrs) : OperatorBase(type, inputs, outputs, attrs) { rnn::InitArgument(kArgName, &arg_, *this); - alg_.Init(&arg_, stepnet_.get()); + alg_.Init(&arg_, &stepnet_); } class RecurrentAlgorithmProtoAndCheckerMaker @@ -178,7 +178,7 @@ void RecurrentGradientAlgorithm::Run( rnn::LinkMemories(step_scopes, arg_->memories, step_id, 1, false /*infer_shape_mode*/); } - stepnet_->Run(*step_scopes[step_id], dev_ctx); + (*stepnet_)->Run(*step_scopes[step_id], dev_ctx); } LinkBootMemoryGradients(step_scopes[0], false); rnn::ConcatOutputs(step_scopes, arg_->outlinks, seq_len_, @@ -215,7 +215,7 @@ void RecurrentGradientAlgorithm::InferShape(const Scope& scope) const { rnn::LinkMemories(step_scopes, arg_->memories, step_id, 1, true /*infer_shape_mode*/); } - stepnet_->InferShape(*step_scopes[step_id]); + (*stepnet_)->InferShape(*step_scopes[step_id]); } rnn::ConcatOutputs(step_scopes, arg_->outlinks, seq_len_, true /*infer_shape_mode*/); @@ -228,7 +228,7 @@ RecurrentGradientOp::RecurrentGradientOp( const framework::AttributeMap& attrs) : OperatorBase(type, inputs, outputs, attrs) { rnn::InitArgument(kArgName, &arg_, *this); - alg_.Init(&arg_, stepnet_.get()); + alg_.Init(&arg_, &stepnet_); } } // namespace operators diff --git a/paddle/operators/recurrent_op.h b/paddle/operators/recurrent_op.h index 4d091aa21..bcfa817de 100644 --- a/paddle/operators/recurrent_op.h +++ b/paddle/operators/recurrent_op.h @@ -34,7 +34,8 @@ class RecurrentAlgorithm { void Run(const framework::Scope& scope, const platform::DeviceContext& dev_ctx) const; - void Init(rnn::Argument* arg, framework::OperatorBase* stepnet) { + void Init(rnn::Argument* arg, + std::unique_ptr* stepnet) { PADDLE_ENFORCE_NOT_NULL(stepnet, "stepnet should be set before."); arg_ = arg; stepnet_ = stepnet; @@ -63,7 +64,7 @@ class RecurrentAlgorithm { void InitMemories(framework::Scope* step_scopes, bool infer_shape_mode) const; private: - framework::OperatorBase* stepnet_; + std::unique_ptr* stepnet_; rnn::Argument* arg_; mutable size_t seq_len_; }; @@ -80,7 +81,8 @@ class RecurrentGradientAlgorithm { * operator. */ public: - void Init(rnn::Argument* arg, framework::OperatorBase* stepnet) { + void Init(rnn::Argument* arg, + std::unique_ptr* stepnet) { PADDLE_ENFORCE_NOT_NULL(stepnet, "stepnet should be set before."); arg_ = std::move(arg); stepnet_ = stepnet; @@ -107,7 +109,7 @@ class RecurrentGradientAlgorithm { private: rnn::Argument* arg_; mutable size_t seq_len_; - framework::OperatorBase* stepnet_; + std::unique_ptr* stepnet_; }; class RecurrentOp : public framework::OperatorBase { diff --git a/python/paddle/v2/framework/tests/gradient_checker.py b/python/paddle/v2/framework/tests/gradient_checker.py index 501cf6110..831c0f0f2 100644 --- a/python/paddle/v2/framework/tests/gradient_checker.py +++ b/python/paddle/v2/framework/tests/gradient_checker.py @@ -165,7 +165,6 @@ class GradientChecker(unittest.TestCase): for no_grad in no_grad_set: if no_grad not in in_names: raise ValueError("no_grad should be in in_names") - backward_op = core.Operator.backward(forward_op, no_grad_set) bwd_outputs = backward_op.outputs() -- GitLab From 6075928d5531b5eecff0d3183c1d47ab3b0962d4 Mon Sep 17 00:00:00 2001 From: zchen0211 Date: Wed, 16 Aug 2017 19:02:29 +0000 Subject: [PATCH 0077/2018] gather op added --- paddle/operators/gather.h | 2 ++ paddle/operators/gather_op.cc | 8 ++------ python/paddle/v2/framework/tests/test_gather_op.py | 7 ++++--- 3 files changed, 8 insertions(+), 9 deletions(-) diff --git a/paddle/operators/gather.h b/paddle/operators/gather.h index d6e699039..3f299ea1a 100644 --- a/paddle/operators/gather.h +++ b/paddle/operators/gather.h @@ -17,6 +17,8 @@ limitations under the License. */ #include #include "paddle/framework/ddim.h" +#include "paddle/framework/eigen.h" +#include "paddle/framework/op_registry.h" #include "paddle/framework/tensor.h" #include "paddle/platform/place.h" diff --git a/paddle/operators/gather_op.cc b/paddle/operators/gather_op.cc index 2e08ba8dc..499def05a 100644 --- a/paddle/operators/gather_op.cc +++ b/paddle/operators/gather_op.cc @@ -24,13 +24,9 @@ class GatherOp : public framework::OperatorWithKernel { protected: void InferShape(const framework::InferShapeContext &ctx) const override { - // PADDLE_ENFORCE(ctx.InputSize() == 2, ""); - // PADDLE_ENFORCE(ctx.OutputSize() == 1, ""); - // PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(0), - // "Inputs of GatherOp must all be set"); int batch_size = ctx.Input("Index")->dims()[0]; - PADDLE_ENFORCE(batch_size > 0); - paddle::framework::DDim output_dims(ctx.Input(0)->dims()); + PADDLE_ENFORCE_GE(batch_size, 0, "Batch size must be >0"); + paddle::framework::DDim output_dims(ctx.Input("X")->dims()); output_dims[0] = batch_size; ctx.Output("Y")->Resize(output_dims); } diff --git a/python/paddle/v2/framework/tests/test_gather_op.py b/python/paddle/v2/framework/tests/test_gather_op.py index 2ffbf1723..049054d07 100644 --- a/python/paddle/v2/framework/tests/test_gather_op.py +++ b/python/paddle/v2/framework/tests/test_gather_op.py @@ -12,11 +12,12 @@ class TestGatherOp(unittest.TestCase): def setUp(self): self.type = "gather" + xnp = numpy.random.random((10, 20)).astype("float32") self.inputs = { - 'X': numpy.random.random((10, 20)).astype("float32"), - 'Index': numpy.array([1, 3, 5]).astype("int") + 'X': xnp, + 'Index': numpy.array([1, 3, 5]).astype("int32") } - self.outputs = {'Y': self.input['X'][self.input['Index']]} + self.outputs = {'Y': self.inputs['X'][self.inputs['Index']]} if __name__ == "__main__": -- GitLab From 3484874278a1e1377af37677d29609f95fff2325 Mon Sep 17 00:00:00 2001 From: fengjiayi Date: Wed, 16 Aug 2017 14:44:51 -0700 Subject: [PATCH 0078/2018] Rename `AsNoGradient` of VariableBuilder to `NotInGradient` --- paddle/framework/backward_test.cc | 6 +++--- paddle/framework/framework.proto | 2 +- paddle/framework/grad_op_builder.cc | 2 +- paddle/framework/grad_op_builder_test.cc | 4 ++-- paddle/framework/operator.h | 7 ++----- paddle/operators/mean_op.cc | 2 +- 6 files changed, 10 insertions(+), 13 deletions(-) diff --git a/paddle/framework/backward_test.cc b/paddle/framework/backward_test.cc index d942604bf..8780b5077 100644 --- a/paddle/framework/backward_test.cc +++ b/paddle/framework/backward_test.cc @@ -32,9 +32,9 @@ class RowWiseAddOpMaker : public OpProtoAndCheckerMaker { public: RowWiseAddOpMaker(OpProto *proto, OpAttrChecker *op_checker) : OpProtoAndCheckerMaker(proto, op_checker) { - AddInput("X", "Input X of Add").AsNoGradient(); - AddInput("b", "Bias of Add").AsNoGradient(); - AddOutput("Out", "Out of Add").AsNoGradient(); + AddInput("X", "Input X of Add").NotInGradient(); + AddInput("b", "Bias of Add").NotInGradient(); + AddOutput("Out", "Out of Add").NotInGradient(); AddComment("Add Op"); } }; diff --git a/paddle/framework/framework.proto b/paddle/framework/framework.proto index 7077e8aa2..ae44a1ffd 100644 --- a/paddle/framework/framework.proto +++ b/paddle/framework/framework.proto @@ -60,7 +60,7 @@ message OpProto { optional bool duplicable = 3 [ default = false ]; optional bool intermediate = 4 [ default = false ]; - optional bool no_gradient = 5 [ default = false ]; + optional bool not_in_gradient = 5 [ default = false ]; } // AttrProto describes the C++ type Attribute. diff --git a/paddle/framework/grad_op_builder.cc b/paddle/framework/grad_op_builder.cc index b73dac22d..0a2a41f6b 100644 --- a/paddle/framework/grad_op_builder.cc +++ b/paddle/framework/grad_op_builder.cc @@ -28,7 +28,7 @@ static void TransOpArg(const OperatorBase* src_op, const OpArgType& src_type, const auto& src_arg_list = src_type == OpArgType::IN ? proto->inputs() : proto->outputs(); for (const auto& arg : src_arg_list) { - if (arg.no_gradient() && !is_grad) continue; + if (arg.not_in_gradient() && !is_grad) continue; const std::string src_name = arg.name(); std::string dst_name = is_grad ? GradVarName(src_name) : src_name; dst_inout[dst_name].reserve(src_inout.at(src_name).size()); diff --git a/paddle/framework/grad_op_builder_test.cc b/paddle/framework/grad_op_builder_test.cc index 0c26293fd..902c2655e 100644 --- a/paddle/framework/grad_op_builder_test.cc +++ b/paddle/framework/grad_op_builder_test.cc @@ -26,10 +26,10 @@ class IOIgnoredOpMaker : public OpProtoAndCheckerMaker { IOIgnoredOpMaker(OpProto *proto, OpAttrChecker *op_checker) : OpProtoAndCheckerMaker(proto, op_checker) { AddInput("In1", "a single input"); - AddInput("In2_mult", "a multiple input").AsDuplicable().AsNoGradient(); + AddInput("In2_mult", "a multiple input").AsDuplicable().NotInGradient(); AddInput("In3_mult", "another multiple input").AsDuplicable(); AddOutput("Out1_mult", "a multiple output").AsDuplicable(); - AddOutput("Out2", "a single output").AsNoGradient(); + AddOutput("Out2", "a single output").NotInGradient(); AddComment("op with inputs and outputs ignored in gradient calculating"); } }; diff --git a/paddle/framework/operator.h b/paddle/framework/operator.h index 2c8620a7c..dbe205976 100644 --- a/paddle/framework/operator.h +++ b/paddle/framework/operator.h @@ -164,11 +164,8 @@ class OpProtoAndCheckerMaker { return *this; } - // TODO(FengJiayi, yuyang18): `AsNoGradient` is a very bad name, because it - // means that input/output is not needed when calculate gradient. It does - // not mean no gradient when backward. It should be changed soon. - VariableBuilder& AsNoGradient() { - var_->set_no_gradient(true); + VariableBuilder& NotInGradient() { + var_->set_not_in_gradient(true); return *this; } }; diff --git a/paddle/operators/mean_op.cc b/paddle/operators/mean_op.cc index 49d0f4350..d3d0e55a6 100644 --- a/paddle/operators/mean_op.cc +++ b/paddle/operators/mean_op.cc @@ -34,7 +34,7 @@ class MeanOpMaker : public framework::OpProtoAndCheckerMaker { MeanOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker) : OpProtoAndCheckerMaker(proto, op_checker) { AddInput("X", "The input of mean op"); - AddOutput("Out", "The output of mean op").AsNoGradient(); + AddOutput("Out", "The output of mean op").NotInGradient(); AddComment("Mean Operator"); } }; -- GitLab From 02299813685a7172d9e9182631b71473b492c904 Mon Sep 17 00:00:00 2001 From: zchen0211 Date: Wed, 16 Aug 2017 23:54:38 +0000 Subject: [PATCH 0079/2018] remove empty test --- paddle/framework/CMakeLists.txt | 2 -- paddle/framework/empty_test.cc | 56 --------------------------------- 2 files changed, 58 deletions(-) delete mode 100644 paddle/framework/empty_test.cc diff --git a/paddle/framework/CMakeLists.txt b/paddle/framework/CMakeLists.txt index 68b5cec2c..2cdf323c5 100644 --- a/paddle/framework/CMakeLists.txt +++ b/paddle/framework/CMakeLists.txt @@ -10,8 +10,6 @@ cc_test(eigen_test SRCS eigen_test.cc DEPS tensor) cc_library(lod_tensor SRCS lod_tensor.cc DEPS ddim place tensor) cc_test(lod_tensor_test SRCS lod_tensor_test.cc DEPS lod_tensor) -cc_test(empty_test SRCS empty_test.cc DEPS tensor) - cc_test(variable_test SRCS variable_test.cc) cc_library(scope SRCS scope.cc) diff --git a/paddle/framework/empty_test.cc b/paddle/framework/empty_test.cc deleted file mode 100644 index 2237f8ce0..000000000 --- a/paddle/framework/empty_test.cc +++ /dev/null @@ -1,56 +0,0 @@ -/* - 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 -#include -#include "paddle/framework/tensor.h" - -TEST(Empty, Dims) { - using namespace paddle::framework; - using namespace paddle::platform; - Tensor tt; - tt.Resize(make_ddim({0, 3, 4})); - DDim dims = tt.dims(); - ASSERT_EQ(arity(dims), 3); - EXPECT_EQ(0, dims[0]); - EXPECT_EQ(3, dims[1]); - EXPECT_EQ(4, dims[2]); -} - -TEST(Empty, MutableData) { - using namespace paddle::framework; - using namespace paddle::platform; - { - Tensor src_tensor; - float* p1 = nullptr; - // initialization - p1 = src_tensor.mutable_data(make_ddim({0, 2, 3}), CPUPlace()); - EXPECT_NE(p1, nullptr); - } - -#ifndef PADDLE_ONLY_CPU - { - Tensor src_tensor; - float* p1 = nullptr; - float* p2 = nullptr; - // initialization - p1 = src_tensor.mutable_data(make_ddim({0, 2, 3}), GPUPlace()); - EXPECT_NE(p1, nullptr); - // set src_tensor a new dim with large size - // momery is supposed to be re-allocated - p2 = src_tensor.mutable_data(make_ddim({0, 4}), GPUPlace()); - EXPECT_NE(p2, nullptr); - // EXPECT_NE(p1, p2); - } -#endif -} -- GitLab From ac02fb82d7a76fce4a870acc30891d657a83ab9c Mon Sep 17 00:00:00 2001 From: liaogang Date: Thu, 17 Aug 2017 10:14:05 +0800 Subject: [PATCH 0080/2018] FIX: tensor memory must be gaven back to buddy allocator for free --- paddle/operators/gather_test.cc | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/paddle/operators/gather_test.cc b/paddle/operators/gather_test.cc index d24d83f29..0ae1e9945 100644 --- a/paddle/operators/gather_test.cc +++ b/paddle/operators/gather_test.cc @@ -45,4 +45,8 @@ TEST(Gather, GatherData) { for (int i = 0; i < 4; ++i) EXPECT_EQ(p_output[i], i + 4); for (int i = 4; i < 8; ++i) EXPECT_EQ(p_output[i], i - 4); + + delete src; + delete index; + delete output; } -- GitLab From 2f7489fbd565caff0608214808ef682b6f46f984 Mon Sep 17 00:00:00 2001 From: liaogang Date: Thu, 17 Aug 2017 10:14:48 +0800 Subject: [PATCH 0081/2018] change use_pinned_memory to true for cpu --- paddle/memory/detail/system_allocator.cc | 2 +- paddle/memory/memory.cc | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/paddle/memory/detail/system_allocator.cc b/paddle/memory/detail/system_allocator.cc index f61e67a32..a270bd595 100644 --- a/paddle/memory/detail/system_allocator.cc +++ b/paddle/memory/detail/system_allocator.cc @@ -27,7 +27,7 @@ limitations under the License. */ // between host and device. Allocates too much would reduce the amount // of memory available to the system for paging. So, by default, we // should set false to use_pinned_memory. -DEFINE_bool(use_pinned_memory, false, "If set, allocate cpu pinned memory."); +DEFINE_bool(use_pinned_memory, true, "If set, allocate cpu pinned memory."); namespace paddle { namespace memory { diff --git a/paddle/memory/memory.cc b/paddle/memory/memory.cc index 5946c3ea4..684635405 100644 --- a/paddle/memory/memory.cc +++ b/paddle/memory/memory.cc @@ -63,7 +63,7 @@ size_t Used(platform::CPUPlace place) { BuddyAllocator* GetGPUBuddyAllocator(int gpu_id) { using BuddyAllocVec = std::vector; static std::unique_ptr as{ - new std::vector, [](BuddyAllocVec* p) { + new BuddyAllocVec, [](BuddyAllocVec* p) { std::for_each(p->begin(), p->end(), [](BuddyAllocator* p) { delete p; }); }}; -- GitLab From 33228cacddc5058feeb5444cf98dd25dc2ca77c0 Mon Sep 17 00:00:00 2001 From: liaogang Date: Thu, 17 Aug 2017 10:21:28 +0800 Subject: [PATCH 0082/2018] Fix typo error --- paddle/memory/memory.cc | 22 +++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/paddle/memory/memory.cc b/paddle/memory/memory.cc index 684635405..99c62b50e 100644 --- a/paddle/memory/memory.cc +++ b/paddle/memory/memory.cc @@ -27,14 +27,14 @@ namespace memory { using BuddyAllocator = detail::BuddyAllocator; -std::once_flag cpu_alloctor_flag; -std::once_flag gpu_alloctor_flag; +std::once_flag cpu_allocator_flag; +std::once_flag gpu_allocator_flag; BuddyAllocator* GetCPUBuddyAllocator() { static std::unique_ptr a{ nullptr, [](BuddyAllocator* p) { delete p; }}; - std::call_once(cpu_alloctor_flag, [&]() { + std::call_once(cpu_allocator_flag, [&]() { a.reset(new BuddyAllocator(new detail::CPUAllocator, platform::CpuMinChunkSize(), platform::CpuMaxChunkSize())); @@ -68,23 +68,23 @@ BuddyAllocator* GetGPUBuddyAllocator(int gpu_id) { [](BuddyAllocator* p) { delete p; }); }}; - // GPU buddy alloctors - auto& alloctors = *as.get(); + // GPU buddy allocators + auto& allocators = *as.get(); // GPU buddy allocator initialization - std::call_once(gpu_alloctor_flag, [&]() { + std::call_once(gpu_allocator_flag, [&]() { int gpu_num = platform::GetDeviceCount(); - alloctors.reserve(gpu_num); + allocators.reserve(gpu_num); for (int gpu = 0; gpu < gpu_num; gpu++) { platform::SetDeviceId(gpu); - alloctors.emplace_back(new BuddyAllocator(new detail::GPUAllocator, - platform::GpuMinChunkSize(), - platform::GpuMaxChunkSize())); + allocators.emplace_back(new BuddyAllocator(new detail::GPUAllocator, + platform::GpuMinChunkSize(), + platform::GpuMaxChunkSize())); } }); platform::SetDeviceId(gpu_id); - return alloctors[gpu_id]; + return allocators[gpu_id]; } template <> -- GitLab From ff5bfc1c89b5b52588a128e90399bdac804a0b44 Mon Sep 17 00:00:00 2001 From: liaogang Date: Thu, 17 Aug 2017 10:23:59 +0800 Subject: [PATCH 0083/2018] Google style for header file includes --- paddle/memory/memory.cc | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/paddle/memory/memory.cc b/paddle/memory/memory.cc index 99c62b50e..be346325c 100644 --- a/paddle/memory/memory.cc +++ b/paddle/memory/memory.cc @@ -13,14 +13,13 @@ See the License for the specific language governing permissions and limitations under the License. */ #include "paddle/memory/memory.h" -#include "paddle/memory/detail/buddy_allocator.h" -#include "paddle/memory/detail/system_allocator.h" #include // for transfrom #include // for memcpy #include // for call_once -#include "glog/logging.h" +#include "paddle/memory/detail/buddy_allocator.h" +#include "paddle/memory/detail/system_allocator.h" namespace paddle { namespace memory { -- GitLab From 3f9fe6248754bcfd85356174725c99a23e763c8a Mon Sep 17 00:00:00 2001 From: liaogang Date: Thu, 17 Aug 2017 10:24:20 +0800 Subject: [PATCH 0084/2018] Fix typo error --- paddle/memory/memory.cc | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paddle/memory/memory.cc b/paddle/memory/memory.cc index be346325c..dfe9f16f7 100644 --- a/paddle/memory/memory.cc +++ b/paddle/memory/memory.cc @@ -14,7 +14,7 @@ limitations under the License. */ #include "paddle/memory/memory.h" -#include // for transfrom +#include // for transform #include // for memcpy #include // for call_once -- GitLab From d8560ec2e819c5a708caf5e35f791571ea3628aa Mon Sep 17 00:00:00 2001 From: liaogang Date: Thu, 17 Aug 2017 10:29:50 +0800 Subject: [PATCH 0085/2018] Fix scatter_test --- paddle/operators/scatter_test.cc | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/paddle/operators/scatter_test.cc b/paddle/operators/scatter_test.cc index 4449ce656..26fdaff14 100644 --- a/paddle/operators/scatter_test.cc +++ b/paddle/operators/scatter_test.cc @@ -49,4 +49,8 @@ TEST(scatter, ScatterUpdate) { EXPECT_EQ(output->data()[i], float(i - 4)); for (size_t i = 8; i < 16; ++i) EXPECT_EQ(p_output[i], float(0)); for (size_t i = 8; i < 16; ++i) EXPECT_EQ(output->data()[i], float(0)); + + delete src; + delete index; + delete output; } -- GitLab From 0945dc1b9968f92a23bcedbb24bf68aacd194f26 Mon Sep 17 00:00:00 2001 From: tensor-tang Date: Thu, 17 Aug 2017 10:31:46 +0800 Subject: [PATCH 0086/2018] enable header format --- paddle/parameter/Parameter.cpp | 10 ++++++---- paddle/parameter/Parameter.h | 29 +++++++++++++++++++++++++++-- paddle/pserver/ParameterServer2.cpp | 7 ++++--- 3 files changed, 37 insertions(+), 9 deletions(-) diff --git a/paddle/parameter/Parameter.cpp b/paddle/parameter/Parameter.cpp index ebe36d493..f03110950 100644 --- a/paddle/parameter/Parameter.cpp +++ b/paddle/parameter/Parameter.cpp @@ -48,7 +48,8 @@ Parameter::Parameter(const ParameterConfig& config, bool useGpu, bool doInit) deviceId_(-1), sharedCount_(0), updateCounter_(0), - updated_(false) { + updated_(false), + headerFormat_(PARAM_FORMAT_ORIGINAL) { setID(-1); /* capture uninitialized id */ if (useGpu_ && FLAGS_parallel_nn) { /* gpu environment is specified by device property */ @@ -285,7 +286,7 @@ bool Parameter::save(const std::string& filename) const { bool Parameter::save(std::ostream& s) const { CpuVector vec(*bufs_[PARAMETER_VALUE].get()); Header header; - header.version = kFormatVersion; + header.format = headerFormat_; header.valueSize = sizeof(real); header.size = getSize(); @@ -344,8 +345,9 @@ bool Parameter::load(std::istream& s) { Header header; CHECK(s.read(reinterpret_cast(&header), sizeof(header))) << "Fail to read parameter " << getName(); - CHECK_EQ(header.version, kFormatVersion) << "Incorrect format version: " - << header.version; + CHECK(isHeaderFormatSupported(header.format)) << "Incorrect format version: " + << header.format; + headerFormat_ = header.format; CHECK_EQ(header.size, getSize()) << "The size (" << header.size << ") in the file does not match the size " << "(" << getSize() << ") of the parameter: " << getName(); diff --git a/paddle/parameter/Parameter.h b/paddle/parameter/Parameter.h index 0bac76f06..cffd3aa92 100644 --- a/paddle/parameter/Parameter.h +++ b/paddle/parameter/Parameter.h @@ -34,6 +34,12 @@ limitations under the License. */ namespace paddle { +typedef enum { + PARAM_FORMAT_ORIGINAL = 0, // the paddle original basic format + PARAM_FORMAT_MKLDNN_OI, // the mkldnn format oi + PARAM_FORMAT_ITEMS, // the total format items numbers +} PARAM_FORMAT; + class SparsePrefetchRowCpuMatrix; class Parameter; @@ -242,14 +248,30 @@ public: /// Initialize the value to 0 void zeroMem(); - static const int kFormatVersion = 0; /// file header structure struct Header { - int32_t version; // = 0, file format version + int32_t format; // = PARAM_FORMAT uint32_t valueSize; // = sizeof(real) uint64_t size; // = getSize() }; + /** + * @brief Is the header supported + */ + static bool isHeaderFormatSupported(int32_t fmt) { + return fmt < PARAM_FORMAT_ITEMS; + } + + /** + * @brief Get the format in header + */ + int getHeaderFormat() { return headerFormat_; } + + /** + * @brief Set the format in header + */ + void setHeaderFormat(int32_t fmt) { headerFormat_ = fmt; } + /** * @brief Parameter Update Hook. * @@ -321,6 +343,9 @@ protected: bool updated_; SparseFormat format_; + // The header format for saving or loading param + int32_t headerFormat_; + std::vector> updaterHooks_; public: diff --git a/paddle/pserver/ParameterServer2.cpp b/paddle/pserver/ParameterServer2.cpp index d7c1d4f78..54f5c4c0f 100644 --- a/paddle/pserver/ParameterServer2.cpp +++ b/paddle/pserver/ParameterServer2.cpp @@ -1032,8 +1032,8 @@ void ParameterServer2::loadValueVector(const LoadValueRequest& request, Parameter::Header header; CHECK(fs.read(reinterpret_cast(&header), sizeof(header))) << "Fail to read parameters in pserver"; - CHECK_EQ(header.version, Parameter::kFormatVersion) - << "Incorrect format version: " << header.version; + CHECK(Parameter::isHeaderFormatSupported(header.format)) + << "Incorrect format version: " << header.format; CHECK_EQ(header.size, (size_t)size_) << "The size (" << header.size << ") in the file does not match the size " << "(" << size_ << ") of the pserver: " << serverId_; @@ -1063,7 +1063,8 @@ void ParameterServer2::saveValueVector(const SaveValueRequest& request, CpuVector& vec = vectors_[PARAMETER_APPLY] ? *vectors_[PARAMETER_APPLY] : *vectors_[PARAMETER_VALUE]; Parameter::Header header; - header.version = Parameter::kFormatVersion; + // TODO(TJ): save param headerFormat_ + header.format = PARAM_FORMAT_ORIGINAL; header.valueSize = sizeof(real); header.size = size_; -- GitLab From 4b148d0afd9bdf255c0e69b406577e83ae156388 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Thu, 17 Aug 2017 10:59:10 +0800 Subject: [PATCH 0087/2018] Fix typo --- paddle/framework/operator.h | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paddle/framework/operator.h b/paddle/framework/operator.h index 90e30bee0..644817065 100644 --- a/paddle/framework/operator.h +++ b/paddle/framework/operator.h @@ -119,7 +119,7 @@ class OperatorBase { protected: std::string type_; // NOTE: in case of OpGrad, inputs_ contains: - // I (Inputs)opear + // I (Inputs) // O (Outputs) // OG (Output Gradients) VarNameMap inputs_; -- GitLab From 225579b9d9ab28de046805f40301d68d9dd3b5cb Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Thu, 17 Aug 2017 11:10:32 +0800 Subject: [PATCH 0088/2018] Remove own for add_op * add_op could take a unique_ptr or a const reference. If unique_ptr is taken, the NetOp will take care of that operator's life cycle. If a const reference is taken, that op will be Cloned. --- paddle/operators/net_op.h | 23 ++++------------------- 1 file changed, 4 insertions(+), 19 deletions(-) diff --git a/paddle/operators/net_op.h b/paddle/operators/net_op.h index ce7da1f38..e8720c960 100644 --- a/paddle/operators/net_op.h +++ b/paddle/operators/net_op.h @@ -89,33 +89,18 @@ class NetOp : public framework::OperatorBase { /** * @brief Add an operator by ptr */ - void AddOp(framework::OperatorBase* op, bool own) { + void AddOp(std::unique_ptr&& op) { PADDLE_ENFORCE(!add_op_done_, "Cannot AddOp when this network is sealed"); PADDLE_ENFORCE_NOT_NULL(op, "Cannot Insert Null op"); - if (!own) { - op = op->Clone().release(); - } - ops_.emplace_back(op); - } - - void AddOp(std::unique_ptr&& op) { - AddOp(op.release(), true); + ops_.push_back(std::move(op)); } - void InsertOp(size_t pos, framework::OperatorBase* op, bool own) { + void InsertOp(size_t pos, std::unique_ptr&& op) { PADDLE_ENFORCE(!add_op_done_, "Cannot InsertOp when this network is sealed"); PADDLE_ENFORCE_NOT_NULL(op, "Cannot Insert Null op"); PADDLE_ENFORCE_LE(pos, ops_.size(), "Out of range"); - if (!own) { - op = op->Clone().release(); - } - ops_.insert(ops_.begin() + pos, - std::unique_ptr(op)); - } - - void InsertOp(size_t pos, std::unique_ptr&& op) { - InsertOp(pos, op.release(), true); + ops_.insert(ops_.begin() + pos, std::move(op)); } void InsertOp(size_t pos, const framework::OperatorBase& op) { -- GitLab From a28a5564d26e9aeac48cb41f2f2bd40fcd73946a Mon Sep 17 00:00:00 2001 From: dangqingqing Date: Thu, 17 Aug 2017 11:55:48 +0800 Subject: [PATCH 0089/2018] add more comments and fix code style. --- .../v2/framework/tests/gradient_checker.py | 64 +++++++++++++++---- 1 file changed, 51 insertions(+), 13 deletions(-) diff --git a/python/paddle/v2/framework/tests/gradient_checker.py b/python/paddle/v2/framework/tests/gradient_checker.py index d251f14b9..2c92dfa43 100644 --- a/python/paddle/v2/framework/tests/gradient_checker.py +++ b/python/paddle/v2/framework/tests/gradient_checker.py @@ -110,7 +110,24 @@ def get_numeric_gradient(op, class GradientChecker(unittest.TestCase): - def get_grad(self, forward_op, backward_op, input_vars, grad_names, place): + def __get_gradient(self, forward_op, backward_op, input_value, grad_names, + place): + """Get the input gradients after running forward and backward operators + on the given places. + + :param forward_op: forward operator + :type forward_op: Operator + :param backward_op: backward operator + :type backward_op: Operator + :param input_value: input values. + :type input_value: dict{string:numpy.array} + :param grad_names: the names of returned input gradients. + :type input_value: a list of string + :param place: the device type. + :type place: CPUPlace or GPUPlace + :return: the input grdients of given grad_names. + :rtype: a list of numpy.array + """ scope = core.Scope() ctx = core.DeviceContext.create(place) @@ -120,7 +137,7 @@ class GradientChecker(unittest.TestCase): out_names = [item for k in outputs for item in outputs[k]] # create input var and set value - for name, value in input_vars.iteritems(): + for name, value in input_value.iteritems(): if name not in in_names: raise ValueError(name + "does not exist in Op's inputs.") var = scope.new_var(name).get_tensor() @@ -154,7 +171,16 @@ class GradientChecker(unittest.TestCase): ] return outs - def compare_grad(self, forward_op, inputs): + def compare_grad(self, forward_op, input_value): + """ Compare the input gradients between CPU and GPU for the given forward + operator. + + :param forward_op: forward operator + :type forward_op: Operator + :param input_value: input values. + :type input_value: dict{string:numpy.array} + :raises: AssertionError, there is different gradient value. + """ backward_op = core.Operator.backward(forward_op, set()) # return if not compile with GPU or not implementing GPU kernel if not (core.is_compile_gpu() and backward_op.support_gpu()): @@ -162,19 +188,31 @@ class GradientChecker(unittest.TestCase): outputs = backward_op.outputs() out_names = [item for k in outputs for item in outputs[k]] - cpu_grads = self.get_grad(forward_op, backward_op, inputs, out_names, - core.CPUPlace()) - gpu_grads = self.get_grad(forward_op, backward_op, inputs, out_names, - core.GPUPlace(0)) + cpu_grads = self.get_grad(forward_op, backward_op, input_value, + out_names, core.CPUPlace()) + gpu_grads = self.get_grad(forward_op, backward_op, input_value, + out_names, core.GPUPlace(0)) for c_grad, g_grad, name in itertools.izip(cpu_grads, gpu_grads, out_names): self.assertTrue( - numpy.allclose(c_grad, g_grad), + numpy.allclose( + c_grad, g_grad, atol=1e-4), "output name: " + name + " has diff") - def assert_is_close(self, numeric_grads, analytic_grads, names, - max_relative_error, msg_prefix): + def __assert_is_close(self, numeric_grads, analytic_grads, names, + max_relative_error, msg_prefix): + """Use relative error for the comparison. + + :param numeric_grads: the numerical graidents. + :type numeric_grads: a list of numpy.array + :param analytic_grads: the analytical graidents. + :type analytic_grads: a list of numpy.array + :param name: the names of gradients, used to print for debug. + :type names: a list of string + :param msg_prefix: string info, used to print for debug. + :type msf_prefix: string + """ for a, b, name in itertools.izip(numeric_grads, analytic_grads, names): abs_a = numpy.abs(a) # if abs_a is nearly zero, then use abs error for a, not relative @@ -241,6 +279,6 @@ class GradientChecker(unittest.TestCase): # get analytical gradients according to different device analytic_grads = self.get_grad(forward_op, backward_op, input_vars, check_names, place) - self.assert_is_close(numeric_grads, analytic_grads, check_names, - max_relative_error, - "Gradient Check On %s" % str(place)) + self.__assert_is_close(numeric_grads, analytic_grads, check_names, + max_relative_error, + "Gradient Check On %s" % str(place)) -- GitLab From e08651f9b5a27db3ff3992ecdcd8bd5cb0cf12e2 Mon Sep 17 00:00:00 2001 From: tensor-tang Date: Thu, 17 Aug 2017 13:57:23 +0800 Subject: [PATCH 0090/2018] remove flag use_mkldnn_wgt --- paddle/gserver/layers/MKLDNNFcLayer.cpp | 8 ++++++-- paddle/gserver/tests/MKLDNNTester.cpp | 27 ++++++++++++++++++------- paddle/gserver/tests/MKLDNNTester.h | 2 +- paddle/trainer/TrainerConfigHelper.cpp | 2 -- paddle/utils/Flags.cpp | 1 - paddle/utils/Flags.h | 1 - 6 files changed, 27 insertions(+), 14 deletions(-) diff --git a/paddle/gserver/layers/MKLDNNFcLayer.cpp b/paddle/gserver/layers/MKLDNNFcLayer.cpp index 30f567eaf..d201fac65 100644 --- a/paddle/gserver/layers/MKLDNNFcLayer.cpp +++ b/paddle/gserver/layers/MKLDNNFcLayer.cpp @@ -57,11 +57,14 @@ bool MKLDNNFcLayer::init(const LayerMap& layerMap, } void MKLDNNFcLayer::convertWeightsFromPaddle() { - if (FLAGS_use_mkldnn_wgt) { + if (hasInitedWgt_) { return; } - if (hasInitedWgt_) { + // TODO(TJ): dst format should get from wgtVal_ + int dstFmt = PARAM_FORMAT_MKLDNN_OI; + int srcFmt = weight_->getParameterPtr()->getHeaderFormat(); + if (srcFmt == dstFmt) { return; } @@ -78,6 +81,7 @@ void MKLDNNFcLayer::convertWeightsFromPaddle() { MatrixPtr paddleWgtT; paddleWgt->transpose(paddleWgtT, true); weight_->getW()->copyFrom(*paddleWgtT); + weight_->getParameterPtr()->setHeaderFormat(dstFmt); hasInitedWgt_ = true; } diff --git a/paddle/gserver/tests/MKLDNNTester.cpp b/paddle/gserver/tests/MKLDNNTester.cpp index 99c8c4948..d20215571 100644 --- a/paddle/gserver/tests/MKLDNNTester.cpp +++ b/paddle/gserver/tests/MKLDNNTester.cpp @@ -330,9 +330,7 @@ void MKLDNNTester::run(const TestConfig& dnn, log_ = log; lvl_ = level; - // Firstly test FLAGS_use_mkldnn_wgt = false - FLAGS_use_mkldnn_wgt = false; - // reset and run once + // Firstly test mkldnn init from PARAM_FORMAT_ORIGINAL weight reset(dnn, ref, batchSize); randomWgtDatas(); clearWgtDiffs(); @@ -342,17 +340,32 @@ void MKLDNNTester::run(const TestConfig& dnn, runOnce(); } - // Then test FLAGS_use_mkldnn_wgt = true - FLAGS_use_mkldnn_wgt = true; - // after run once the mkldnn weight has been stored in dnnlayer + if (parameters_[DNN].empty()) { + // has no paramters + return; + } + + // After run some iters, the mkldnn weight has been stored in dnnLayer + // and we can also get the mkldnn weight paramter header format + // Weight param should always be index 0 (and bias index 1). + // TODO(TJ): should also considerate mean and var format when batchnorm ready + int dnnWgtFmt = parameters_[DNN][0]->getHeaderFormat(); + int refWgtFmt = parameters_[REF][0]->getHeaderFormat(); + if (dnnWgtFmt == refWgtFmt) { + // weight format are equal, so no need check more + return; + } + // then save the weights and restart again vector dnnWgts, refWgts; CHECK_EQ(parameters_[DNN].size(), parameters_[REF].size()); saveWgt(parameters_[DNN], dnnWgts); saveWgt(parameters_[REF], refWgts); - // restart again with flag true + // restart again with dnn weight format reset(dnn, ref, batchSize); + // TODO(TJ): should also considerate mean and var format when batchnorm ready + parameters_[DNN][0]->setHeaderFormat(dnnWgtFmt); // restore wgt restoreWgt(dnnWgts, parameters_[DNN]); diff --git a/paddle/gserver/tests/MKLDNNTester.h b/paddle/gserver/tests/MKLDNNTester.h index 522eeaf24..e55e4493f 100644 --- a/paddle/gserver/tests/MKLDNNTester.h +++ b/paddle/gserver/tests/MKLDNNTester.h @@ -108,7 +108,7 @@ private: * if many(>failRate) wrong(abs(dnn-ref)/abs(ref)>thres) points return the * max(diff/ref) * else return sum(abs(a-b)) / sum(abs(b)) - * The return value should smaller than eps when passing. + * The return value should be smaller than eps when passing. */ double getDelta(const real* d1, const real* d2, diff --git a/paddle/trainer/TrainerConfigHelper.cpp b/paddle/trainer/TrainerConfigHelper.cpp index eba40862b..a0a365aa0 100644 --- a/paddle/trainer/TrainerConfigHelper.cpp +++ b/paddle/trainer/TrainerConfigHelper.cpp @@ -29,7 +29,6 @@ DECLARE_bool(with_gpu); DECLARE_bool(parallel_nn); DECLARE_string(config_args); DECLARE_bool(use_mkldnn); -DECLARE_bool(use_mkldnn_wgt); const char *kConfigParserModuleName = "paddle.trainer.config_parser"; const char *kConfigParserFuncName = "parse_config_and_serialize"; @@ -47,7 +46,6 @@ TrainerConfigHelper::TrainerConfigHelper(const std::string &configFilePath) << ",with_cost=" << FLAGS_with_cost << ",use_gpu=" << FLAGS_use_gpu << ",parallel_nn=" << FLAGS_parallel_nn << ",use_mkldnn=" << FLAGS_use_mkldnn - << ",use_mkldnn_wgt=" << FLAGS_use_mkldnn_wgt << ",cudnn_version=" << hl_get_cudnn_lib_version(); if (!FLAGS_config_args.empty()) { configArgs << "," << FLAGS_config_args; diff --git a/paddle/utils/Flags.cpp b/paddle/utils/Flags.cpp index 600c83a84..ab1c181c6 100644 --- a/paddle/utils/Flags.cpp +++ b/paddle/utils/Flags.cpp @@ -27,7 +27,6 @@ DEFINE_bool(use_mkldnn, false, "Default still keep use CPU training"); DEFINE_bool(use_mkldnn, false, "Only support CPU training"); #endif -DEFINE_bool(use_mkldnn_wgt, false, "Init weight from CPU weight"); DEFINE_bool(parallel_nn, false, "Whether to use multi-threads to calculate one neural network." diff --git a/paddle/utils/Flags.h b/paddle/utils/Flags.h index 0aca4c0ee..1832bb515 100644 --- a/paddle/utils/Flags.h +++ b/paddle/utils/Flags.h @@ -41,4 +41,3 @@ DECLARE_string(predict_file); DECLARE_bool(prev_batch_state); DECLARE_string(init_model_path); DECLARE_bool(use_mkldnn); -DECLARE_bool(use_mkldnn_wgt); -- GitLab From 47f380bb4786f93aa95da809a8d7f18d862b78ca Mon Sep 17 00:00:00 2001 From: Yancey Date: Thu, 17 Aug 2017 14:16:04 +0800 Subject: [PATCH 0091/2018] fix ldconfig (#3547) --- paddle/scripts/docker/build.sh | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/paddle/scripts/docker/build.sh b/paddle/scripts/docker/build.sh index 7c12664ae..2941662f3 100644 --- a/paddle/scripts/docker/build.sh +++ b/paddle/scripts/docker/build.sh @@ -146,7 +146,8 @@ RUN apt-get update &&\ pip install /*.whl; apt-get install -f -y && \ apt-get clean -y && \ rm -f /*.whl && \ - paddle version + paddle version && \ + ldconfig ${DOCKERFILE_CUDNN_DSO} ${DOCKERFILE_GPU_ENV} ADD go/cmd/pserver/pserver /usr/bin/ -- GitLab From 5181aefc6bf6d1af1a769879f8cddc9ae9bc2a20 Mon Sep 17 00:00:00 2001 From: dangqingqing Date: Thu, 17 Aug 2017 14:18:51 +0800 Subject: [PATCH 0092/2018] tune max relative error for sigmoid op unit test. --- paddle/operators/sigmoid_op.h | 2 +- python/paddle/v2/framework/tests/gradient_checker.py | 12 ++++++------ python/paddle/v2/framework/tests/test_sigmoid_op.py | 8 ++++---- 3 files changed, 11 insertions(+), 11 deletions(-) diff --git a/paddle/operators/sigmoid_op.h b/paddle/operators/sigmoid_op.h index 11ab923eb..b01a9b3f2 100644 --- a/paddle/operators/sigmoid_op.h +++ b/paddle/operators/sigmoid_op.h @@ -37,7 +37,7 @@ class SigmoidKernel : public framework::OpKernel { auto Y = EigenVector::Flatten(*output); auto place = context.GetEigenDevice(); - Y.device(place) = 1.0 / (1.0 + (-1.0 * X).exp()); + Y.device(place) = 1. / (1. + (-X).exp()); } }; diff --git a/python/paddle/v2/framework/tests/gradient_checker.py b/python/paddle/v2/framework/tests/gradient_checker.py index 2c92dfa43..12f302fe2 100644 --- a/python/paddle/v2/framework/tests/gradient_checker.py +++ b/python/paddle/v2/framework/tests/gradient_checker.py @@ -188,10 +188,10 @@ class GradientChecker(unittest.TestCase): outputs = backward_op.outputs() out_names = [item for k in outputs for item in outputs[k]] - cpu_grads = self.get_grad(forward_op, backward_op, input_value, - out_names, core.CPUPlace()) - gpu_grads = self.get_grad(forward_op, backward_op, input_value, - out_names, core.GPUPlace(0)) + cpu_grads = self.__get_gradient(forward_op, backward_op, input_value, + out_names, core.CPUPlace()) + gpu_grads = self.__get_gradient(forward_op, backward_op, input_value, + out_names, core.GPUPlace(0)) for c_grad, g_grad, name in itertools.izip(cpu_grads, gpu_grads, out_names): @@ -277,8 +277,8 @@ class GradientChecker(unittest.TestCase): check_names = [grad_var_name(name) for name in inputs_to_check] for place in places: # get analytical gradients according to different device - analytic_grads = self.get_grad(forward_op, backward_op, input_vars, - check_names, place) + analytic_grads = self.__get_gradient(forward_op, backward_op, + input_vars, check_names, place) self.__assert_is_close(numeric_grads, analytic_grads, check_names, max_relative_error, "Gradient Check On %s" % str(place)) diff --git a/python/paddle/v2/framework/tests/test_sigmoid_op.py b/python/paddle/v2/framework/tests/test_sigmoid_op.py index c3bd79f5d..273c2e5ab 100644 --- a/python/paddle/v2/framework/tests/test_sigmoid_op.py +++ b/python/paddle/v2/framework/tests/test_sigmoid_op.py @@ -14,14 +14,14 @@ class TestSigmoidOp(unittest.TestCase): class TestSigmoidGradOp(GradientChecker): - def test_compare_grad(self): + def test_grad(self): op = create_op("sigmoid") - inputs = {"X": np.random.random((11, 17)).astype("float32")} + inputs = {"X": np.random.uniform(0.1, 1, [11, 17]).astype("float32")} # compare gpu and cpu results for backward op. - # skip this test if only compiling CPU version. + # this test will be skiped if only compiling CPU version. self.compare_grad(op, inputs) # check gradients - self.check_grad(op, inputs, set("X"), "Y") + self.check_grad(op, inputs, set("X"), "Y", max_relative_error=0.007) if __name__ == '__main__': -- GitLab From 1365f2d15e6f1e02592a5cf5b5f5d07a0eb7f99c Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Thu, 17 Aug 2017 14:37:03 +0800 Subject: [PATCH 0093/2018] Remove R-Value reference in AddOp Fit Google C++ Style --- paddle/operators/net_op.h | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/paddle/operators/net_op.h b/paddle/operators/net_op.h index e8720c960..885ac6eec 100644 --- a/paddle/operators/net_op.h +++ b/paddle/operators/net_op.h @@ -89,13 +89,13 @@ class NetOp : public framework::OperatorBase { /** * @brief Add an operator by ptr */ - void AddOp(std::unique_ptr&& op) { + void AddOp(std::unique_ptr op) { PADDLE_ENFORCE(!add_op_done_, "Cannot AddOp when this network is sealed"); PADDLE_ENFORCE_NOT_NULL(op, "Cannot Insert Null op"); ops_.push_back(std::move(op)); } - void InsertOp(size_t pos, std::unique_ptr&& op) { + void InsertOp(size_t pos, std::unique_ptr op) { PADDLE_ENFORCE(!add_op_done_, "Cannot InsertOp when this network is sealed"); PADDLE_ENFORCE_NOT_NULL(op, "Cannot Insert Null op"); -- GitLab From 94b58a29d6613f528076269d1332ad9d2f43ec67 Mon Sep 17 00:00:00 2001 From: liaogang Date: Thu, 17 Aug 2017 14:58:21 +0800 Subject: [PATCH 0094/2018] Follow comments --- paddle/memory/memory.cc | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/paddle/memory/memory.cc b/paddle/memory/memory.cc index dfe9f16f7..c99cc5415 100644 --- a/paddle/memory/memory.cc +++ b/paddle/memory/memory.cc @@ -30,8 +30,7 @@ std::once_flag cpu_allocator_flag; std::once_flag gpu_allocator_flag; BuddyAllocator* GetCPUBuddyAllocator() { - static std::unique_ptr a{ - nullptr, [](BuddyAllocator* p) { delete p; }}; + static std::unique_ptr a{nullptr}; std::call_once(cpu_allocator_flag, [&]() { a.reset(new BuddyAllocator(new detail::CPUAllocator, -- GitLab From 53b0e427092219b402f0ed6fab4235c3b70fdc7c Mon Sep 17 00:00:00 2001 From: hedaoyuan Date: Thu, 17 Aug 2017 16:19:59 +0800 Subject: [PATCH 0095/2018] Add EigenGemm. --- paddle/function/EigenGemm.cpp | 92 ++++++++++++++++++++++++++++++ paddle/function/GemmFunctor.cpp | 85 ++++++++++++++++++++++++++++ paddle/function/GemmFunctor.h | 99 +++++++++++---------------------- 3 files changed, 211 insertions(+), 65 deletions(-) create mode 100644 paddle/function/EigenGemm.cpp create mode 100644 paddle/function/GemmFunctor.cpp diff --git a/paddle/function/EigenGemm.cpp b/paddle/function/EigenGemm.cpp new file mode 100644 index 000000000..0b4220fcb --- /dev/null +++ b/paddle/function/EigenGemm.cpp @@ -0,0 +1,92 @@ +/* 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 +#include "unsupported/Eigen/CXX11/Tensor" + +namespace paddle { + +template +struct EigenBlasGemm { + typedef Eigen::TensorMap, + Eigen::Aligned> + Matrix; + + static void compute(const bool transA, + const bool transB, + const int M, + const int N, + const int K, + const T alpha, + const T* A, + const int lda, + const T* B, + const int ldb, + const T beta, + T* C, + const int ldc) { + Eigen::array sizeA; + if (transA) { + sizeA[0] = K; + sizeA[1] = M; + CHECK_EQ(M, lda); + } else { + sizeA[0] = M; + sizeA[1] = K; + CHECK_EQ(K, lda); + } + Eigen::array sizeB; + if (transB) { + sizeB[0] = N; + sizeB[1] = K; + CHECK_EQ(K, ldb); + } else { + sizeB[0] = K; + sizeB[1] = N; + CHECK_EQ(N, ldb); + } + Eigen::array sizeC; + sizeC[0] = M; + sizeC[1] = N; + CHECK_EQ(N, ldc); + + const Matrix a(const_cast(A), sizeA); + const Matrix b(const_cast(B), sizeB); + Matrix c(C, sizeC); + + typedef typename Eigen::Tensor::DimensionPair DimPair; + Eigen::array dims; + dims[0] = DimPair(1, 0); + dims[0].first = transA ? 0 : 1; + dims[0].second = transB ? 1 : 0; + + Eigen::DefaultDevice device; + if (alpha == T(1) && beta == T(0)) { + c.device(device) = a.contract(b, dims); + } else if (alpha == T(1) && beta == T(1)) { + c.device(device) += a.contract(b, dims); + } else { + c.device(device) = + c.constant(alpha) * a.contract(b, dims) + c.constant(beta) * c; + } + } +}; + +#ifdef PADDLE_TYPE_DOUBLE +template class EigenBlasGemm; +#else +template class EigenBlasGemm; +#endif + +} // namespace paddle diff --git a/paddle/function/GemmFunctor.cpp b/paddle/function/GemmFunctor.cpp new file mode 100644 index 000000000..8df9b884f --- /dev/null +++ b/paddle/function/GemmFunctor.cpp @@ -0,0 +1,85 @@ +/* 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 "GemmFunctor.h" +#include "paddle/math/MathFunctions.h" + +namespace paddle { + +template +struct BlasGemm { + static void compute(const bool transA, + const bool transB, + const int M, + const int N, + const int K, + const T alpha, + const T* A, + const int lda, + const T* B, + const int ldb, + const T beta, + T* C, + const int ldc) { + gemm(transA == false ? CblasNoTrans : CblasTrans, + transB == false ? CblasNoTrans : CblasTrans, + M, + N, + K, + alpha, + A, + lda, + B, + ldb, + beta, + C, + ldc); + } +}; + +template +struct BlasGemm { + static void compute(const bool transA, + const bool transB, + const int M, + const int N, + const int K, + const T alpha, + const T* A, + const int lda, + const T* B, + const int ldb, + const T beta, + T* C, + const int ldc) { + hl_matrix_mul((T*)A, + transA == false ? HPPL_OP_N : HPPL_OP_T, + (T*)B, + transB == false ? HPPL_OP_N : HPPL_OP_T, + C, + M, + N, + K, + alpha, + beta, + lda, + ldb, + ldc); + } +}; + +template class BlasGemm; +template class BlasGemm; + +} // namespace paddle diff --git a/paddle/function/GemmFunctor.h b/paddle/function/GemmFunctor.h index d5db5cf5e..0809953b4 100644 --- a/paddle/function/GemmFunctor.h +++ b/paddle/function/GemmFunctor.h @@ -14,7 +14,7 @@ limitations under the License. */ #pragma once -#include "paddle/math/MathFunctions.h" +#include "TensorType.h" namespace paddle { @@ -24,73 +24,42 @@ namespace paddle { // of MatMulFunction, we need to consider the reconstruction of hl_matrix_mul // interface. template -class GemmFunctor { -public: - void operator()(const CBLAS_TRANSPOSE transA, - const CBLAS_TRANSPOSE TransB, - const int M, - const int N, - const int K, - const T alpha, - const T* A, - const int lda, - const T* B, - const int ldb, - const T beta, - T* C, - const int ldc); +struct BlasGemm { + static void compute(const bool transA, + const bool transB, + const int M, + const int N, + const int K, + const T alpha, + const T* A, + const int lda, + const T* B, + const int ldb, + const T beta, + T* C, + const int ldc); }; +// TODO(hedaoyuan): Since the definition of the real type in the Paddle +// conflicts with the Eigen library, so compile the Eigen code can not +// include the Paddle header file. And need an EigenBlasGemm template class +// that does not contain the DeviceType parameter. +// I will fix this problem and merge BlasGemm and EigenBlasGemm into one. template -class GemmFunctor { -public: - void operator()(const CBLAS_TRANSPOSE transA, - const CBLAS_TRANSPOSE TransB, - const int M, - const int N, - const int K, - const T alpha, - const T* A, - const int lda, - const T* B, - const int ldb, - const T beta, - T* C, - const int ldc) { - gemm(transA, TransB, M, N, K, alpha, A, lda, B, ldb, beta, C, ldc); - } -}; - -template -class GemmFunctor { -public: - void operator()(const CBLAS_TRANSPOSE transA, - const CBLAS_TRANSPOSE TransB, - const int M, - const int N, - const int K, - const T alpha, - const T* A, - const int lda, - const T* B, - const int ldb, - const T beta, - T* C, - const int ldc) { - hl_matrix_mul((T*)A, - transA == CblasNoTrans ? HPPL_OP_N : HPPL_OP_T, - (T*)B, - TransB == CblasNoTrans ? HPPL_OP_N : HPPL_OP_T, - C, - M, - N, - K, - alpha, - beta, - lda, - ldb, - ldc); - } +struct EigenBlasGemm { + static void compute(const bool transA, + const bool transB, + const int M, + const int N, + const int K, + const T alpha, + const T* A, + const int lda, + const T* B, + const int ldb, + const T beta, + T* C, + const int ldc); }; } // namespace paddle -- GitLab From ec2ba242060fc10b2045533fdcb410cfbd473cec Mon Sep 17 00:00:00 2001 From: hedaoyuan Date: Thu, 17 Aug 2017 16:22:30 +0800 Subject: [PATCH 0096/2018] Fix GemmConvFunction. --- paddle/function/CMakeLists.txt | 2 + paddle/function/GemmConvOp.cpp | 82 ++++++++++++++++------------------ 2 files changed, 41 insertions(+), 43 deletions(-) diff --git a/paddle/function/CMakeLists.txt b/paddle/function/CMakeLists.txt index 7dfb6f61c..9187294a4 100644 --- a/paddle/function/CMakeLists.txt +++ b/paddle/function/CMakeLists.txt @@ -4,6 +4,8 @@ file(GLOB cpp_files . *Op.cpp) list(APPEND h_files Function.h) list(APPEND cpp_files Function.cpp) list(APPEND cpp_files BufferArg.cpp) +list(APPEND cpp_files GemmFunctor.cpp) +list(APPEND cpp_files EigenGemm.cpp) if(WITH_GPU) file(GLOB cu_files . *OpGpu.cu) diff --git a/paddle/function/GemmConvOp.cpp b/paddle/function/GemmConvOp.cpp index 0ada4d70a..f8cf4ebea 100644 --- a/paddle/function/GemmConvOp.cpp +++ b/paddle/function/GemmConvOp.cpp @@ -85,7 +85,6 @@ public: } Im2ColFunctor im2col; - GemmFunctor gemm; size_t inputOffset = imShape.getElements(); size_t outputOffset = (outputChannels / groups_) * outputHeight * outputWidth; @@ -108,19 +107,19 @@ public: int M = outputChannels / groups_; int N = outputHeight * outputWidth; int K = inputChannels / groups_ * filterHeight * filterWidth; - gemm(CblasNoTrans, - CblasNoTrans, - M, - N, - K, - 1.0f, - filterData + g * filterOffset, - K, - colData, - N, - beta, - outputData + g * outputOffset, - N); + BlasGemm::compute(false, + false, + M, + N, + K, + 1.0f, + filterData + g * filterOffset, + K, + colData, + N, + beta, + outputData + g * outputOffset, + N); } inputData += inputChannels * inputHeight * inputWidth; outputData += outputChannels * outputHeight * outputWidth; @@ -188,8 +187,6 @@ public: } Col2ImFunctor col2im; - GemmFunctor gemm; - size_t inputOffset = imShape.getElements(); size_t outputOffset = (outputChannels / groups_) * outputHeight * outputWidth; @@ -205,19 +202,19 @@ public: colData = inputGrad + g * inputOffset; scale = 1.0f; } - gemm(CblasTrans, - CblasNoTrans, - M, - N, - K, - 1.0f, - filterData + g * filterOffset, - M, - outputGrad + g * outputOffset, - N, - scale, - colData, - N); + BlasGemm::compute(true, + false, + M, + N, + K, + 1.0f, + filterData + g * filterOffset, + M, + outputGrad + g * outputOffset, + N, + scale, + colData, + N); if (needIm2col) { col2im(inputGrad + g * inputOffset, imShape, @@ -299,7 +296,6 @@ public: } Im2ColFunctor im2col; - GemmFunctor gemm; size_t inputOffset = imShape.getElements(); size_t outputOffset = (outputChannels / groups_) * outputHeight * outputWidth; @@ -321,19 +317,19 @@ public: int M = outputChannels / groups_; int K = outputHeight * outputWidth; int N = inputChannels / groups_ * filterHeight * filterWidth; - gemm(CblasNoTrans, - CblasTrans, - M, - N, - K, - 1.0f, - outputGrad + g * outputOffset, - K, - colData, - K, - i == 0 ? beta : 1.0f, - filterGrad + g * filterOffset, - N); + BlasGemm::compute(false, + true, + M, + N, + K, + 1.0f, + outputGrad + g * outputOffset, + K, + colData, + K, + i == 0 ? beta : 1.0f, + filterGrad + g * filterOffset, + N); } inputData += inputChannels * inputHeight * inputWidth; outputGrad += outputChannels * outputHeight * outputWidth; -- GitLab From 017a3818dee89ec1cd2b73b31ced9f6c51a12c8e Mon Sep 17 00:00:00 2001 From: liaogang Date: Thu, 17 Aug 2017 16:38:15 +0800 Subject: [PATCH 0097/2018] Add memory.h for unique_ptr --- paddle/memory/memory.cc | 1 + 1 file changed, 1 insertion(+) diff --git a/paddle/memory/memory.cc b/paddle/memory/memory.cc index c99cc5415..0266bf4f7 100644 --- a/paddle/memory/memory.cc +++ b/paddle/memory/memory.cc @@ -16,6 +16,7 @@ limitations under the License. */ #include // for transform #include // for memcpy +#include // for unique_ptr #include // for call_once #include "paddle/memory/detail/buddy_allocator.h" -- GitLab From adcca2cc064182cd75809dd1e3d8c64329a0b0de Mon Sep 17 00:00:00 2001 From: hedaoyuan Date: Thu, 17 Aug 2017 16:40:38 +0800 Subject: [PATCH 0098/2018] Add PADDLE_USE_EIGEN_FOR_BLAS macro. --- CMakeLists.txt | 1 + cmake/configure.cmake | 4 ++++ paddle/function/GemmFunctor.cpp | 5 +++++ 3 files changed, 10 insertions(+) diff --git a/CMakeLists.txt b/CMakeLists.txt index dcd1218a5..28bbfd791 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -55,6 +55,7 @@ option(WITH_C_API "Compile PaddlePaddle with C-API(Prediction)" OFF) option(WITH_GOLANG "Compile PaddlePaddle with GOLANG" OFF) option(GLIDE_INSTALL "Download and install go dependencies " ON) option(USE_NNPACK "Compile PaddlePaddle with NNPACK library" OFF) +option(USE_EIGEN_FOR_BLAS "Use matrix multiplication in Eigen" OFF) # CMAKE_BUILD_TYPE if(NOT CMAKE_BUILD_TYPE) diff --git a/cmake/configure.cmake b/cmake/configure.cmake index 209f9078a..51c3b918c 100644 --- a/cmake/configure.cmake +++ b/cmake/configure.cmake @@ -28,6 +28,10 @@ if(NOT WITH_TIMER) add_definitions(-DPADDLE_DISABLE_TIMER) endif(NOT WITH_TIMER) +if(USE_EIGEN_FOR_BLAS) + add_definitions(-DPADDLE_USE_EIGEN_FOR_BLAS) +endif(USE_EIGEN_FOR_BLAS) + if(NOT WITH_PROFILER) add_definitions(-DPADDLE_DISABLE_PROFILER) endif(NOT WITH_PROFILER) diff --git a/paddle/function/GemmFunctor.cpp b/paddle/function/GemmFunctor.cpp index 8df9b884f..dc83278d8 100644 --- a/paddle/function/GemmFunctor.cpp +++ b/paddle/function/GemmFunctor.cpp @@ -32,6 +32,10 @@ struct BlasGemm { const T beta, T* C, const int ldc) { +#ifdef PADDLE_USE_EIGEN_FOR_BLAS + EigenBlasGemm::compute( + transA, transB, M, N, K, alpha, A, lda, B, ldb, beta, C, ldc); +#else gemm(transA == false ? CblasNoTrans : CblasTrans, transB == false ? CblasNoTrans : CblasTrans, M, @@ -45,6 +49,7 @@ struct BlasGemm { beta, C, ldc); +#endif } }; -- GitLab From 6ba04dcd112e0caac46a7a829182ce00f301752f Mon Sep 17 00:00:00 2001 From: hedaoyuan Date: Thu, 17 Aug 2017 16:56:46 +0800 Subject: [PATCH 0099/2018] Remove the header files that do not need to be included. --- paddle/function/DepthwiseConvOp.cpp | 1 - paddle/function/DepthwiseConvOpGpu.cu | 1 - 2 files changed, 2 deletions(-) diff --git a/paddle/function/DepthwiseConvOp.cpp b/paddle/function/DepthwiseConvOp.cpp index 490e8d546..2f3112fe6 100644 --- a/paddle/function/DepthwiseConvOp.cpp +++ b/paddle/function/DepthwiseConvOp.cpp @@ -14,7 +14,6 @@ limitations under the License. */ #include "DepthwiseConvOp.h" #include "ConvOp.h" -#include "GemmFunctor.h" namespace paddle { diff --git a/paddle/function/DepthwiseConvOpGpu.cu b/paddle/function/DepthwiseConvOpGpu.cu index 33463805c..2d722dfcf 100644 --- a/paddle/function/DepthwiseConvOpGpu.cu +++ b/paddle/function/DepthwiseConvOpGpu.cu @@ -13,7 +13,6 @@ See the License for the specific language governing permissions and limitations under the License. */ #include "DepthwiseConvOp.h" -#include "GemmFunctor.h" #include "paddle/math/BaseMatrix.h" namespace paddle { -- GitLab From 7f8c3f82145dd02cf7d136f27de42a6f0a56024b Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Thu, 17 Aug 2017 18:02:20 +0800 Subject: [PATCH 0100/2018] Add MeanOp's Gradient Test And Fix Mean Op Gradient --- paddle/operators/mean_op.h | 3 ++- python/paddle/v2/framework/tests/test_mean_op.py | 8 ++++++++ 2 files changed, 10 insertions(+), 1 deletion(-) diff --git a/paddle/operators/mean_op.h b/paddle/operators/mean_op.h index fcb703e63..9848af280 100644 --- a/paddle/operators/mean_op.h +++ b/paddle/operators/mean_op.h @@ -55,9 +55,10 @@ class MeanGradKernel : public framework::OpKernel { IG->mutable_data(context.GetPlace()); T ig_size = (T)framework::product(IG->dims()); + Eigen::DSizes bcast(ig_size); EigenVector::Flatten(*IG).device(context.GetEigenDevice()) = - EigenScalar::From(*OG) / ig_size; + (EigenVector::From(*OG) / ig_size).broadcast(bcast); } }; diff --git a/python/paddle/v2/framework/tests/test_mean_op.py b/python/paddle/v2/framework/tests/test_mean_op.py index b5d52b905..f32b3160d 100644 --- a/python/paddle/v2/framework/tests/test_mean_op.py +++ b/python/paddle/v2/framework/tests/test_mean_op.py @@ -1,5 +1,6 @@ import unittest from op_test_util import OpTestMeta +from gradient_checker import GradientChecker, create_op import numpy as np @@ -12,5 +13,12 @@ class TestMeanOp(unittest.TestCase): self.outputs = {'Out': np.mean(self.inputs['X'])} +class MeanGradOpTest(GradientChecker): + def test_normal(self): + op = create_op("mean") + inputs = {"X": np.random.random((10, 10)).astype("float32")} + self.check_grad(op, inputs, set("X"), "Out") + + if __name__ == '__main__': unittest.main() -- GitLab From 27a99bfb1446171969da0219a6125a79c39eb582 Mon Sep 17 00:00:00 2001 From: Luo Tao Date: Thu, 17 Aug 2017 18:10:37 +0800 Subject: [PATCH 0101/2018] Add base class for huber_regression_cost and huber_classification_cost --- doc/api/v2/config/layer.rst | 6 +-- paddle/gserver/layers/CostLayer.cpp | 55 ++++++++++++---------------- paddle/gserver/layers/CostLayer.h | 27 ++++++++++---- python/paddle/v2/tests/test_layer.py | 2 +- 4 files changed, 46 insertions(+), 44 deletions(-) diff --git a/doc/api/v2/config/layer.rst b/doc/api/v2/config/layer.rst index cb330ea5e..22a6b2ab8 100644 --- a/doc/api/v2/config/layer.rst +++ b/doc/api/v2/config/layer.rst @@ -409,9 +409,9 @@ multi_binary_label_cross_entropy_cost .. autoclass:: paddle.v2.layer.multi_binary_label_cross_entropy_cost :noindex: -huber_cost ----------- -.. autoclass:: paddle.v2.layer.huber_cost +huber_classification_cost +------------------------- +.. autoclass:: paddle.v2.layer.huber_classification_cost :noindex: lambda_cost diff --git a/paddle/gserver/layers/CostLayer.cpp b/paddle/gserver/layers/CostLayer.cpp index 138c86a6d..69cf39322 100644 --- a/paddle/gserver/layers/CostLayer.cpp +++ b/paddle/gserver/layers/CostLayer.cpp @@ -572,13 +572,8 @@ void MultiBinaryLabelCrossEntropy::backwardImp(Matrix& output, } } -// -// Huber loss for robust 2-classes classification -// -REGISTER_LAYER(huber, HuberTwoClassification); - -bool HuberTwoClassification::init(const LayerMap& layerMap, - const ParameterMap& parameterMap) { +bool HuberCost::init(const LayerMap& layerMap, + const ParameterMap& parameterMap) { CostLayer::init(layerMap, parameterMap); if (useGpu_) { tmpCpuInput_.reserve(inputLayers_.size()); @@ -589,9 +584,7 @@ bool HuberTwoClassification::init(const LayerMap& layerMap, return true; } -void HuberTwoClassification::forwardImp(Matrix& output, - Argument& label, - Matrix& cost) { +void HuberCost::forwardImp(Matrix& output, Argument& label, Matrix& cost) { if (useGpu_) { for (size_t i = 0; i < inputLayers_.size(); i++) { tmpCpuInput_[i].resizeAndCopyFrom( @@ -599,12 +592,22 @@ void HuberTwoClassification::forwardImp(Matrix& output, } hl_stream_synchronize(HPPL_STREAM_DEFAULT); } - forwardImpIn(output, label, cost); } -void HuberTwoClassification::forwardImpIn(Matrix& output, - Argument& label, - Matrix& target) { +// +// Huber loss for robust 2-classes classification +// +REGISTER_LAYER(huber_classification, HuberTwoClassification); + +bool HuberTwoClassification::init(const LayerMap& layerMap, + const ParameterMap& parameterMap) { + return HuberCost::init(layerMap, parameterMap); +} + +void HuberTwoClassification::forwardImp(Matrix& output, + Argument& label, + Matrix& target) { + HuberCost::forwardImp(output, label, target); size_t numSamples = target.getHeight(); CHECK(label.ids); CHECK_EQ((*label.ids).getSize(), numSamples); @@ -627,25 +630,13 @@ void HuberTwoClassification::forwardImpIn(Matrix& output, target.copyFrom(cost.data(), numSamples); } -void HuberTwoClassification::backwardImp(Matrix& outputValue, +void HuberTwoClassification::backwardImp(Matrix& output, Argument& label, - Matrix& outputGrad) { - if (useGpu_) { - backwardImpIn( - *tmpCpuInput_[0].value, tmpCpuInput_[1], *tmpCpuInput_[0].grad); - outputGrad.copyFrom(*tmpCpuInput_[0].grad); - } else { - backwardImpIn(outputValue, label, outputGrad); - } -} - -void HuberTwoClassification::backwardImpIn(Matrix& output, - Argument& label, - Matrix& outputG) { + Matrix& outputG) { size_t numSamples = output.getHeight(); - real* out = output.getData(); - real* grad = outputG.getData(); - int* lbl = (*label.ids).getData(); + real* out = useGpu_ ? tmpCpuInput_[0].value->getData() : output.getData(); + int* lbl = useGpu_ ? tmpCpuInput_[1].ids->getData() : (*label.ids).getData(); + real* grad = useGpu_ ? tmpCpuInput_[0].grad->getData() : outputG.getData(); for (size_t i = 0; i < numSamples; ++i) { int y = 2 * lbl[i] - 1; if (y * out[i] < -1) @@ -653,8 +644,8 @@ void HuberTwoClassification::backwardImpIn(Matrix& output, else if (y * out[i] < 1) grad[i] += -2 * (1 - y * out[i]) * y; } + if (useGpu_) outputG.copyFrom(grad, numSamples); } - /** * This cost layer compute the sum of its input as loss. * \f[ diff --git a/paddle/gserver/layers/CostLayer.h b/paddle/gserver/layers/CostLayer.h index 77427b7a0..c006dc811 100644 --- a/paddle/gserver/layers/CostLayer.h +++ b/paddle/gserver/layers/CostLayer.h @@ -304,6 +304,23 @@ public: Matrix& outputGrad) override; }; +/* + * A base layer for HuberRegressionLoss and HuberTwoClassification. + */ +class HuberCost : public CostLayer { +public: + std::vector tmpCpuInput_; + + explicit HuberCost(const LayerConfig& config) : CostLayer(config) {} + + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override; + + void forwardImp(Matrix& output, Argument& label, Matrix& cost) override; + + void backwardImp(Matrix& outputValue, Argument& label, Matrix& outputGrad) {} +}; + /** * Huber loss for robust 2-classes classification. * @@ -312,25 +329,19 @@ public: * Loss = (1 - y * f)^2, if -1 < y * f < 1 \\ * Loss = 0, otherwise */ -class HuberTwoClassification : public CostLayer { - std::vector tmpCpuInput_; - +class HuberTwoClassification : public HuberCost { public: explicit HuberTwoClassification(const LayerConfig& config) - : CostLayer(config) {} + : HuberCost(config) {} bool init(const LayerMap& layerMap, const ParameterMap& parameterMap) override; void forwardImp(Matrix& output, Argument& label, Matrix& cost) override; - void forwardImpIn(Matrix& output, Argument& label, Matrix& cost); - void backwardImp(Matrix& outputValue, Argument& label, Matrix& outputGrad) override; - - void backwardImpIn(Matrix& outputValue, Argument& label, Matrix& outputGrad); }; typedef std::shared_ptr CostLayerPtr; diff --git a/python/paddle/v2/tests/test_layer.py b/python/paddle/v2/tests/test_layer.py index f2097e195..7373a55ce 100644 --- a/python/paddle/v2/tests/test_layer.py +++ b/python/paddle/v2/tests/test_layer.py @@ -141,7 +141,7 @@ class CostLayerTest(unittest.TestCase): cost8 = layer.rank_cost(left=score, right=score, label=score) cost9 = layer.lambda_cost(input=inference, score=score) cost10 = layer.sum_cost(input=inference) - cost11 = layer.huber_cost(input=score, label=label) + cost11 = layer.huber_classification_cost(input=score, label=label) print layer.parse_network([cost1, cost2]) print layer.parse_network([cost3, cost4]) -- GitLab From e28e007373fca4faae6301f10b7c58e36153aec7 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Thu, 17 Aug 2017 18:41:23 +0800 Subject: [PATCH 0102/2018] Enable test_sgd_op --- python/paddle/v2/framework/tests/CMakeLists.txt | 1 + 1 file changed, 1 insertion(+) diff --git a/python/paddle/v2/framework/tests/CMakeLists.txt b/python/paddle/v2/framework/tests/CMakeLists.txt index 96fad9b42..faeac6951 100644 --- a/python/paddle/v2/framework/tests/CMakeLists.txt +++ b/python/paddle/v2/framework/tests/CMakeLists.txt @@ -25,3 +25,4 @@ py_test(test_operator SRCS test_operator.py) # py_test(test_gaussian_random_op SRCS test_gaussian_random_op.py) py_test(test_uniform_random_op SRCS test_uniform_random_op.py) py_test(test_recurrent_op SRCS test_recurrent_op.py) +py_test(test_sgd_op SRCS test_sgd_op.py) -- GitLab From 07d16e3e135826407f76b860faccd9c3babf40c4 Mon Sep 17 00:00:00 2001 From: tensor-tang Date: Thu, 17 Aug 2017 19:21:29 +0800 Subject: [PATCH 0103/2018] refine comments --- paddle/gserver/tests/MKLDNNTester.cpp | 8 ++++---- paddle/parameter/Parameter.h | 22 +++++++++++++++------- 2 files changed, 19 insertions(+), 11 deletions(-) diff --git a/paddle/gserver/tests/MKLDNNTester.cpp b/paddle/gserver/tests/MKLDNNTester.cpp index d20215571..de1635be2 100644 --- a/paddle/gserver/tests/MKLDNNTester.cpp +++ b/paddle/gserver/tests/MKLDNNTester.cpp @@ -345,10 +345,10 @@ void MKLDNNTester::run(const TestConfig& dnn, return; } - // After run some iters, the mkldnn weight has been stored in dnnLayer - // and we can also get the mkldnn weight paramter header format - // Weight param should always be index 0 (and bias index 1). - // TODO(TJ): should also considerate mean and var format when batchnorm ready + // After run some iterations, the mkldnn weight has been stored in dnnLayer + // and we can also get the mkldnn weight parameter header format. + // Weight parameter should always be index 0 (and bias index 1). + // TODO(TJ): should also consider mean and var format when batchnorm ready int dnnWgtFmt = parameters_[DNN][0]->getHeaderFormat(); int refWgtFmt = parameters_[REF][0]->getHeaderFormat(); if (dnnWgtFmt == refWgtFmt) { diff --git a/paddle/parameter/Parameter.h b/paddle/parameter/Parameter.h index cffd3aa92..e31cbc3de 100644 --- a/paddle/parameter/Parameter.h +++ b/paddle/parameter/Parameter.h @@ -35,9 +35,17 @@ limitations under the License. */ namespace paddle { typedef enum { - PARAM_FORMAT_ORIGINAL = 0, // the paddle original basic format - PARAM_FORMAT_MKLDNN_OI, // the mkldnn format oi - PARAM_FORMAT_ITEMS, // the total format items numbers + /// The paddle original basic format + PARAM_FORMAT_ORIGINAL = 0, + + /// See mkldnn_memory_format_t in + /// https://github.com/01org/mkl-dnn/blob/master/include/mkldnn_types.h + /// for a detailed description. + /// 2D weights tensor in the format (output channels, input channels). + PARAM_FORMAT_MKLDNN_OI, + + /// The total format items numbers + PARAM_FORMAT_ITEMS, } PARAM_FORMAT; class SparsePrefetchRowCpuMatrix; @@ -256,19 +264,19 @@ public: }; /** - * @brief Is the header supported + * @brief Is the header format supported. */ static bool isHeaderFormatSupported(int32_t fmt) { return fmt < PARAM_FORMAT_ITEMS; } /** - * @brief Get the format in header + * @brief Get the format in header. */ int getHeaderFormat() { return headerFormat_; } /** - * @brief Set the format in header + * @brief Set the format in header. */ void setHeaderFormat(int32_t fmt) { headerFormat_ = fmt; } @@ -343,7 +351,7 @@ protected: bool updated_; SparseFormat format_; - // The header format for saving or loading param + /// The header format for saving or loading param int32_t headerFormat_; std::vector> updaterHooks_; -- GitLab From 7d2ef02a993a378921a006d3575a802e5e9c5e9d Mon Sep 17 00:00:00 2001 From: guosheng Date: Thu, 17 Aug 2017 21:18:58 +0800 Subject: [PATCH 0104/2018] Add ScaleShiftLayer --- doc/api/v2/config/layer.rst | 5 + paddle/gserver/layers/ScaleShiftLayer.cpp | 106 ++++++++++++++++++ paddle/gserver/tests/test_LayerGrad.cpp | 15 +++ python/paddle/trainer/config_parser.py | 14 +++ .../paddle/trainer_config_helpers/layers.py | 37 ++++++ .../tests/configs/file_list.sh | 2 +- .../protostr/test_scale_shift_layer.protostr | 72 ++++++++++++ .../tests/configs/test_scale_shift_layer.py | 11 ++ 8 files changed, 261 insertions(+), 1 deletion(-) create mode 100644 paddle/gserver/layers/ScaleShiftLayer.cpp create mode 100644 python/paddle/trainer_config_helpers/tests/configs/protostr/test_scale_shift_layer.protostr create mode 100644 python/paddle/trainer_config_helpers/tests/configs/test_scale_shift_layer.py diff --git a/doc/api/v2/config/layer.rst b/doc/api/v2/config/layer.rst index cb330ea5e..a4a843c61 100644 --- a/doc/api/v2/config/layer.rst +++ b/doc/api/v2/config/layer.rst @@ -362,6 +362,11 @@ trans .. autoclass:: paddle.v2.layer.trans :noindex: +scale_shift +----------- +.. autoclass:: paddle.v2.layer.scale_shift + :noindex: + Sampling Layers =============== diff --git a/paddle/gserver/layers/ScaleShiftLayer.cpp b/paddle/gserver/layers/ScaleShiftLayer.cpp new file mode 100644 index 000000000..4f5b1c622 --- /dev/null +++ b/paddle/gserver/layers/ScaleShiftLayer.cpp @@ -0,0 +1,106 @@ +/* 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 "Layer.h" + +namespace paddle { + +/** + * A layer does scaling and shifting to the input by appling a slope and + * an intercept which are trainable to the input element-wise. + * + * \f[ + * y = wx + b + * \f] + * + * Here, w is scale and b is offset, which are scalars and trainable. + * + */ + +class ScaleShiftLayer : public Layer { +protected: + std::unique_ptr scale_; + std::unique_ptr offset_; + +public: + explicit ScaleShiftLayer(const LayerConfig& config) : Layer(config) {} + + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override; + + void forward(PassType passType) override; + void backward(const UpdateCallback& callback = nullptr) override; +}; + +REGISTER_LAYER(scale_shift, ScaleShiftLayer); + +bool ScaleShiftLayer::init(const LayerMap& layerMap, + const ParameterMap& parameterMap) { + Layer::init(layerMap, parameterMap); + CHECK_EQ(inputLayers_.size(), 1U); + scale_.reset(new Weight(1, 1, parameters_[0])); + if (biasParameter_.get() != NULL) { + offset_ = std::unique_ptr(new Weight(1, 1, biasParameter_)); + } + return true; +} + +void ScaleShiftLayer::forward(PassType passType) { + Layer::forward(passType); + + MatrixPtr inV = getInputValue(0); + resetOutput(inV->getHeight(), inV->getWidth()); + MatrixPtr outV = getOutputValue(); + real scaleValue = scale_->getW()->getElement(0, 0); + outV->mulScalar(*inV, scaleValue); + if (offset_) { + real offsetValue = offset_->getW()->getElement(0, 0); + outV->add(offsetValue); + } +} + +void ScaleShiftLayer::backward(const UpdateCallback& callback) { + MatrixPtr inV = getInputValue(0); + MatrixPtr inG = getInputGrad(0); + MatrixPtr outV = getOutputValue(); + MatrixPtr outG = getOutputGrad(); + + /* Calculate the parameter gradient for the current layer */ + if (scale_->getWGrad()) { + MatrixPtr rowSumMtx; + Matrix::resizeOrCreate(rowSumMtx, outG->getHeight(), 1, false, useGpu_); + // this_i = scaleDest * this_i + scaleSum * \sum_j b_{ij} * c_{ij} + rowSumMtx->sumOfProducts( + /* b= */ *inV, /* c= */ *outG, /* scaleSum= */ 1, /* scaleDest= */ 0.); + // this_i = scaleDest * this_i + scaleSum * \sum_j b_{ji} + scale_->getWGrad()->sumCols( + /* b= */ *rowSumMtx, /* scaleSum= */ 1., /* scaleDest= */ 1.); + scale_->getParameterPtr()->incUpdate(callback); + } + if (offset_ && offset_->getWGrad()) { + MatrixPtr rowSumMtx; + Matrix::resizeOrCreate(rowSumMtx, outG->getHeight(), 1, false, useGpu_); + rowSumMtx->sumRows(*outG, 1., 0.); + offset_->getWGrad()->sumCols(*rowSumMtx, 1., 1.); + offset_->getParameterPtr()->incUpdate(callback); + } + + /* Calculate the input layers error */ + if (inG) { + real scaleValue = scale_->getW()->getElement(0, 0); + inG->add(*outG, scaleValue); + } +} + +} // namespace paddle diff --git a/paddle/gserver/tests/test_LayerGrad.cpp b/paddle/gserver/tests/test_LayerGrad.cpp index 0f312b6ca..65429ebad 100644 --- a/paddle/gserver/tests/test_LayerGrad.cpp +++ b/paddle/gserver/tests/test_LayerGrad.cpp @@ -2007,6 +2007,21 @@ TEST(Layer, RowL2NormLayer) { } } +TEST(Layer, ScaleShiftLayer) { + const size_t batchSize = 128; + const size_t size = 512; + TestConfig config; + config.layerConfig.set_type("scale_shift"); + config.layerConfig.set_size(size); + config.biasSize = 1; + config.inputDefs.push_back( + {INPUT_DATA, "input", /* dim= */ size, /* paraSize= */ 1}); + config.layerConfig.add_inputs(); + for (auto useGpu : {false, true}) { + testLayerGrad(config, "scale_shift", batchSize, false, useGpu, false); + } +} + int main(int argc, char** argv) { testing::InitGoogleTest(&argc, argv); initMain(argc, argv); diff --git a/python/paddle/trainer/config_parser.py b/python/paddle/trainer/config_parser.py index da99e5bd5..8d71629fa 100644 --- a/python/paddle/trainer/config_parser.py +++ b/python/paddle/trainer/config_parser.py @@ -2232,6 +2232,20 @@ class ClipLayer(LayerBase): self.config.inputs[0].clip_conf.max = max +@config_layer('scale_shift') +class ScaleShiftLayer(LayerBase): + def __init__(self, name, inputs, bias=True, **xargs): + super(ScaleShiftLayer, self).__init__( + name, 'scale_shift', 0, inputs=inputs, **xargs) + config_assert( + len(self.inputs) == 1, + 'ScaleShiftLayer must have one and only one input.') + input_layer = self.get_input_layer(0) + self.set_layer_size(input_layer.size) + self.create_input_parameter(0, 1, [1, 1]) + self.create_bias_parameter(bias, 1) + + # key: cost type # value: cost class g_cost_map = {} diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py index 1bc55c869..4c7217024 100755 --- a/python/paddle/trainer_config_helpers/layers.py +++ b/python/paddle/trainer_config_helpers/layers.py @@ -133,6 +133,7 @@ __all__ = [ 'clip_layer', 'slice_projection', 'kmax_sequence_score_layer', + 'scale_shift_layer', ] @@ -230,6 +231,7 @@ class LayerType(object): CLIP_LAYER = 'clip' KMAX_SEQ_SCORE = 'kmax_seq_score' + SCALE_SHIFT_LAYER = 'scale_shift' @staticmethod def is_layer_type(type_name): @@ -6210,3 +6212,38 @@ def kmax_sequence_score_layer(input, name=None, beam_size=1): return LayerOutput( name, LayerType.KMAX_SEQ_SCORE, parents=[input], size=input.size) + + +@wrap_name_default("scale_shift") +@wrap_param_attr_default() +@wrap_bias_attr_default() +def scale_shift_layer(input, name=None, param_attr=None, bias_attr=None): + """ + A layer does scaling and shifting to the input by appling a slope and + an intercept which are trainable to the input element-wise. + .. math:: + + y = w * x + b + + .. code-block:: python + + scale_shift = scale_shift_layer(input=input_layer, bias_attr=False) + + :param name: The Layer Name. + :type name: basestring + :param input: The input layer. + :type input: LayerOutput. + :param param_attr: The parameter attribute of scaling. + :type param_attr: ParameterAttribute + :param bias_attr: The parameter attribute of shifting. + :type bias_attr: ParameterAttribute + :return: LayerOutput object. + :rtype: LayerOutput + """ + Layer( + name=name, + type=LayerType.SCALE_SHIFT_LAYER, + inputs=Input(input.name, **param_attr.attr), + bias=ParamAttr.to_bias(bias_attr)) + return LayerOutput( + name, LayerType.SCALE_SHIFT_LAYER, parents=[input], size=input.size) diff --git a/python/paddle/trainer_config_helpers/tests/configs/file_list.sh b/python/paddle/trainer_config_helpers/tests/configs/file_list.sh index a61beb871..3860699f6 100755 --- a/python/paddle/trainer_config_helpers/tests/configs/file_list.sh +++ b/python/paddle/trainer_config_helpers/tests/configs/file_list.sh @@ -8,6 +8,6 @@ test_spp_layer test_bilinear_interp test_maxout test_bi_grumemory math_ops test_seq_concat_reshape test_pad test_smooth_l1 test_multiplex_layer test_prelu_layer test_row_conv test_detection_output_layer test_multibox_loss_layer test_recursive_topology test_gated_unit_layer test_clip_layer test_row_l2_norm_layer -test_kmax_seq_socre_layer test_seq_select_layers) +test_kmax_seq_socre_layer test_seq_select_layers test_scale_shift_layer) export whole_configs=(test_split_datasource) diff --git a/python/paddle/trainer_config_helpers/tests/configs/protostr/test_scale_shift_layer.protostr b/python/paddle/trainer_config_helpers/tests/configs/protostr/test_scale_shift_layer.protostr new file mode 100644 index 000000000..efaf20f8a --- /dev/null +++ b/python/paddle/trainer_config_helpers/tests/configs/protostr/test_scale_shift_layer.protostr @@ -0,0 +1,72 @@ +type: "nn" +layers { + name: "data" + type: "data" + size: 100 + active_type: "" +} +layers { + name: "__scale_shift_0__" + type: "scale_shift" + size: 100 + active_type: "" + inputs { + input_layer_name: "data" + input_parameter_name: "___scale_shift_0__.w0" + } + bias_parameter_name: "___scale_shift_0__.wbias" +} +layers { + name: "__scale_shift_1__" + type: "scale_shift" + size: 100 + active_type: "" + inputs { + input_layer_name: "data" + input_parameter_name: "___scale_shift_1__.w0" + } +} +parameters { + name: "___scale_shift_0__.w0" + size: 1 + initial_mean: 0.0 + initial_std: 1.0 + dims: 1 + dims: 1 + initial_strategy: 0 + initial_smart: true +} +parameters { + name: "___scale_shift_0__.wbias" + size: 1 + initial_mean: 0.0 + initial_std: 0.0 + dims: 1 + dims: 1 + initial_strategy: 0 + initial_smart: false +} +parameters { + name: "___scale_shift_1__.w0" + size: 1 + initial_mean: 0.0 + initial_std: 1.0 + dims: 1 + dims: 1 + initial_strategy: 0 + initial_smart: true +} +input_layer_names: "data" +output_layer_names: "__scale_shift_0__" +output_layer_names: "__scale_shift_1__" +sub_models { + name: "root" + layer_names: "data" + layer_names: "__scale_shift_0__" + layer_names: "__scale_shift_1__" + input_layer_names: "data" + output_layer_names: "__scale_shift_0__" + output_layer_names: "__scale_shift_1__" + is_recurrent_layer_group: false +} + diff --git a/python/paddle/trainer_config_helpers/tests/configs/test_scale_shift_layer.py b/python/paddle/trainer_config_helpers/tests/configs/test_scale_shift_layer.py new file mode 100644 index 000000000..818d71f15 --- /dev/null +++ b/python/paddle/trainer_config_helpers/tests/configs/test_scale_shift_layer.py @@ -0,0 +1,11 @@ +from paddle.trainer_config_helpers import * + +settings(batch_size=1000, learning_rate=1e-5) + +data = data_layer(name='data', size=100) + +scale = scale_shift_layer(input=data) + +scale_shift = scale_shift_layer(input=data, bias_attr=False) + +outputs(scale, scale_shift) -- GitLab From a107181beae437705c561a245a102d7909d45d0d Mon Sep 17 00:00:00 2001 From: haonanyu Date: Thu, 17 Aug 2017 13:19:16 -0700 Subject: [PATCH 0105/2018] fix EXTERNAL_LIBS in CMakeLists.txt --- CMakeLists.txt | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index dcd1218a5..06dd5a133 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -137,9 +137,9 @@ set(EXTERNAL_LIBS ) if(WITH_GPU) - list(APPEND EXTERNAL_LIB ${CUDA_LIBRARIES} ${CUDA_rt_LIBRARY}) + list(APPEND EXTERNAL_LIBS ${CUDA_LIBRARIES} ${CUDA_rt_LIBRARY}) if(NOT WITH_DSO) - list(APPEND EXTERNAL_LIB ${CUDNN_LIBRARY} ${CUDA_CUBLAS_LIBRARIES} ${CUDA_curand_LIBRARY}) + list(APPEND EXTERNAL_LIBS ${CUDNN_LIBRARY} ${CUDA_CUBLAS_LIBRARIES} ${CUDA_curand_LIBRARY}) endif(NOT WITH_DSO) endif(WITH_GPU) -- GitLab From 7b4b9d3e093de159bf7a9bfd91ef0e48a4756da0 Mon Sep 17 00:00:00 2001 From: dongzhihong Date: Thu, 17 Aug 2017 15:46:26 -0700 Subject: [PATCH 0106/2018] "format style" --- paddle/operators/mul_op.cc | 4 ++-- paddle/operators/mul_op.h | 2 ++ 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/paddle/operators/mul_op.cc b/paddle/operators/mul_op.cc index 5645df667..329ab9532 100644 --- a/paddle/operators/mul_op.cc +++ b/paddle/operators/mul_op.cc @@ -66,10 +66,10 @@ class MulOpGrad : public framework::OperatorWithKernel { PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Y"), "Input(Y) should not be null"); PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")), "Input(Out@GRAD) should not be null"); + auto x_dims = ctx.Output("X")->dims(); + auto y_dims = ctx.Output("Y")->dims(); auto *x_grad = ctx.Output(framework::GradVarName("X")); auto *y_grad = ctx.Output(framework::GradVarName("Y")); - auto x_dims = ctx.Output(framework::GradVarName("X"))->dims(); - auto y_dims = ctx.Output(framework::GradVarName("Y"))->dims(); auto out_dims = ctx.Input(framework::GradVarName("Out"))->dims(); PADDLE_ENFORCE(x_dims[0] == out_dims[0], "Out@GRAD M X N must equal to X dims 0, M "); diff --git a/paddle/operators/mul_op.h b/paddle/operators/mul_op.h index 2afed8184..9bbd02752 100644 --- a/paddle/operators/mul_op.h +++ b/paddle/operators/mul_op.h @@ -53,7 +53,9 @@ class MulGradKernel : public framework::OpKernel { auto* dY = ctx.Output(framework::GradVarName("Y")); auto* device_context = const_cast(ctx.device_context_); + // dX = dOut' * Y. dX: M x K, dOut : M x N, Y : K x N math::matmul(*dOut, false, *Y, true, 1, dX, 0, device_context); + // dY = X' * dOut. dY: K x N, dOut : M x N, X : M x K math::matmul(*X, true, *dOut, false, 1, dY, 0, device_context); } }; -- GitLab From c332e4ee25ca28f307c1d3ccbcec9458fd25f5b3 Mon Sep 17 00:00:00 2001 From: dongzhihong Date: Thu, 17 Aug 2017 16:12:27 -0700 Subject: [PATCH 0107/2018] "relauch the ci" --- paddle/operators/rowwise_add_op.cc | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paddle/operators/rowwise_add_op.cc b/paddle/operators/rowwise_add_op.cc index 15192d90b..82e5df591 100644 --- a/paddle/operators/rowwise_add_op.cc +++ b/paddle/operators/rowwise_add_op.cc @@ -73,7 +73,7 @@ class RowwiseAddGradOp : public framework::OperatorWithKernel { namespace ops = paddle::operators; REGISTER_OP(rowwise_add, ops::RowwiseAddOp, ops::RowwiseAddOpMaker, - rowwise_add_grad); + rowwise_add_grad, ops::RowwiseAddGradOp); REGISTER_OP_CPU_KERNEL( rowwise_add, ops::RowwiseAddKernel); REGISTER_OP_CPU_KERNEL( -- GitLab From 50cf127eea23e8771c17844cf09becec61004e96 Mon Sep 17 00:00:00 2001 From: dongzhihong Date: Thu, 17 Aug 2017 17:12:23 -0700 Subject: [PATCH 0108/2018] "change Output to Input" --- paddle/operators/mul_op.cc | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/paddle/operators/mul_op.cc b/paddle/operators/mul_op.cc index 329ab9532..460e458ca 100644 --- a/paddle/operators/mul_op.cc +++ b/paddle/operators/mul_op.cc @@ -66,11 +66,11 @@ class MulOpGrad : public framework::OperatorWithKernel { PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Y"), "Input(Y) should not be null"); PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")), "Input(Out@GRAD) should not be null"); - auto x_dims = ctx.Output("X")->dims(); - auto y_dims = ctx.Output("Y")->dims(); + auto x_dims = ctx.Input("X")->dims(); + auto y_dims = ctx.Input("Y")->dims(); + auto out_dims = ctx.Input(framework::GradVarName("Out"))->dims(); auto *x_grad = ctx.Output(framework::GradVarName("X")); auto *y_grad = ctx.Output(framework::GradVarName("Y")); - auto out_dims = ctx.Input(framework::GradVarName("Out"))->dims(); PADDLE_ENFORCE(x_dims[0] == out_dims[0], "Out@GRAD M X N must equal to X dims 0, M "); PADDLE_ENFORCE(y_dims[1] == out_dims[1], -- GitLab From 4d8992c3bc64a835aa6a1e6e12678594d3f117b5 Mon Sep 17 00:00:00 2001 From: tensor-tang Date: Fri, 18 Aug 2017 09:58:41 +0800 Subject: [PATCH 0109/2018] check format before set header format --- paddle/parameter/Parameter.h | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/paddle/parameter/Parameter.h b/paddle/parameter/Parameter.h index e31cbc3de..08a426eb7 100644 --- a/paddle/parameter/Parameter.h +++ b/paddle/parameter/Parameter.h @@ -278,7 +278,11 @@ public: /** * @brief Set the format in header. */ - void setHeaderFormat(int32_t fmt) { headerFormat_ = fmt; } + void setHeaderFormat(int32_t fmt) { + CHECK(isHeaderFormatSupported(fmt)) << "Unsupported format version: " + << fmt; + headerFormat_ = fmt; + } /** * @brief Parameter Update Hook. -- GitLab From 462b9b1d20942dca35dbe532248e53cdeccea6b2 Mon Sep 17 00:00:00 2001 From: tensor-tang Date: Fri, 18 Aug 2017 10:13:06 +0800 Subject: [PATCH 0110/2018] update mkldnn tag v0.10 --- cmake/external/mkldnn.cmake | 2 +- cmake/external/mklml.cmake | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/cmake/external/mkldnn.cmake b/cmake/external/mkldnn.cmake index 25c6b4ef5..9686df002 100644 --- a/cmake/external/mkldnn.cmake +++ b/cmake/external/mkldnn.cmake @@ -51,7 +51,7 @@ ExternalProject_Add( ${EXTERNAL_PROJECT_LOG_ARGS} DEPENDS ${MKLDNN_DEPENDS} GIT_REPOSITORY "https://github.com/01org/mkl-dnn.git" - GIT_TAG "v0.9" + GIT_TAG "v0.10" PREFIX ${MKLDNN_SOURCES_DIR} UPDATE_COMMAND "" CMAKE_ARGS -DCMAKE_INSTALL_PREFIX=${MKLDNN_INSTALL_DIR} diff --git a/cmake/external/mklml.cmake b/cmake/external/mklml.cmake index e9fd3d4be..51fafb947 100644 --- a/cmake/external/mklml.cmake +++ b/cmake/external/mklml.cmake @@ -28,7 +28,7 @@ INCLUDE(ExternalProject) SET(MKLML_PROJECT "extern_mklml") SET(MKLML_VER "mklml_lnx_2018.0.20170720") -SET(MKLML_URL "https://github.com/01org/mkl-dnn/releases/download/v0.9/${MKLML_VER}.tgz") +SET(MKLML_URL "https://github.com/01org/mkl-dnn/releases/download/v0.10/${MKLML_VER}.tgz") SET(MKLML_SOURCE_DIR "${THIRD_PARTY_PATH}/mklml") SET(MKLML_DOWNLOAD_DIR "${MKLML_SOURCE_DIR}/src/${MKLML_PROJECT}") SET(MKLML_DST_DIR "mklml") -- GitLab From cef27dab47b430ce4034cfcfedf0c6bc95266f51 Mon Sep 17 00:00:00 2001 From: dongzhihong Date: Thu, 17 Aug 2017 19:14:27 -0700 Subject: [PATCH 0111/2018] "add fixl" --- paddle/operators/rowwise_add_op.cc | 1 + python/paddle/v2/framework/tests/test_rowwise_add_op.py | 4 +--- 2 files changed, 2 insertions(+), 3 deletions(-) diff --git a/paddle/operators/rowwise_add_op.cc b/paddle/operators/rowwise_add_op.cc index 82e5df591..f07dd8f60 100644 --- a/paddle/operators/rowwise_add_op.cc +++ b/paddle/operators/rowwise_add_op.cc @@ -63,6 +63,7 @@ class RowwiseAddGradOp : public framework::OperatorWithKernel { "Input(Out@GRAD) should not be null"); auto dims0 = ctx.Input("X")->dims(); auto dims1 = ctx.Input("b")->dims(); + PADDLE_ENFORCE_EQ(1, framework::product(dims1), "b dims should be 1") ctx.Output(framework::GradVarName("X"))->Resize(dims0); ctx.Output(framework::GradVarName("b"))->Resize(dims1); } diff --git a/python/paddle/v2/framework/tests/test_rowwise_add_op.py b/python/paddle/v2/framework/tests/test_rowwise_add_op.py index 8118d2d74..29d72e850 100644 --- a/python/paddle/v2/framework/tests/test_rowwise_add_op.py +++ b/python/paddle/v2/framework/tests/test_rowwise_add_op.py @@ -21,12 +21,10 @@ class RowwiseAddGradOpTest(GradientChecker): op = create_op("rowwise_add") inputs = { "X": np.random.uniform(0.1, 1, [10, 10]).astype("float32"), - "b": np.random.uniform(0.1, 1, [10, 1]).astype("float32") + "b": np.random.uniform(0.1, 1, [10]).astype("float32") } self.check_grad(op, inputs, set(["X", "b"]), "Out") -#TODO(dzh): rowwise_grad check - if __name__ == '__main__': unittest.main() -- GitLab From 8b3d33a055b2a1556adedeb41a16b794249a3848 Mon Sep 17 00:00:00 2001 From: qiaolongfei Date: Thu, 17 Aug 2017 20:10:44 -0700 Subject: [PATCH 0112/2018] fix-sgd --- paddle/operators/sgd_op.h | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paddle/operators/sgd_op.h b/paddle/operators/sgd_op.h index bfb449d0b..a0b5000ff 100644 --- a/paddle/operators/sgd_op.h +++ b/paddle/operators/sgd_op.h @@ -30,7 +30,7 @@ class SGDOpKernel : public framework::OpKernel { void Compute(const framework::ExecutionContext& ctx) const override { auto param = ctx.Input("param"); auto grad = ctx.Input("grad"); - auto param_out = ctx.Output(0); + auto param_out = ctx.Output("param_out"); float lr = ctx.op_.GetAttr("learning_rate"); param_out->mutable_data(ctx.GetPlace()); -- GitLab From 46d30ec680f494e4cc30a73330074497da064fbd Mon Sep 17 00:00:00 2001 From: qiaolongfei Date: Thu, 17 Aug 2017 20:34:02 -0700 Subject: [PATCH 0113/2018] init minst.py --- python/paddle/v2/framework/tests/mnist.py | 140 ++++++++++++++++++++++ 1 file changed, 140 insertions(+) create mode 100644 python/paddle/v2/framework/tests/mnist.py diff --git a/python/paddle/v2/framework/tests/mnist.py b/python/paddle/v2/framework/tests/mnist.py new file mode 100644 index 000000000..32a088ac2 --- /dev/null +++ b/python/paddle/v2/framework/tests/mnist.py @@ -0,0 +1,140 @@ +import paddle.v2.framework.core as core +from paddle.v2.framework.op import Operator +import numpy + +BATCH_SIZE = 100 + +scope = core.Scope() +place = core.CPUPlace() +dev_ctx = core.DeviceContext.create(place) + +# init_net = core.Net.create() +forward_network = core.Net.create() + +# should be init after forward_op is constructed +# backward_net = core.Operator.backward(forward_net, set()) +backward_net = None +optimize_net = core.Net.create() + + +def atom_id(): + id = 0 + while True: + yield id + id += 1 + + +uniq_id = atom_id().next + + +def data_layer(name, dims): + var = scope.new_var(name) + tensor = var.get_tensor() + tensor.set_dims(dims) # 1 is batch size holder. + return name + + +def feed_data(name, data): + assert isinstance(data, numpy.array) + tensor = scope.find_var(name).get_tensor() + tensor.set_dims(data.shape) + tensor.alloc_float(place) + tensor.set(data, place) + + +def grad_var_name(var_name): + return var_name + "@GRAD" + + +def sgd_optimizer(net, param_name, learning_rate=0.01): + grad_name = grad_var_name(param_name) + optimize_op = Operator( + "sgd", param=param_name, grad=grad_name, learning_rate=learning_rate) + net.add_op(optimize_op) + + +# should use operator and add these to the init_network +def init_param(param_name, dims): + print param_name + var = scope.new_var(param_name) + tensor = var.get_tensor() + tensor.set_dims(dims) + data = numpy.random.uniform( + low=0.0, high=1.0, size=tensor.shape()).astype("float32") + tensor.set(data, place) + + +# fc_layer +def fc_layer(net, input, size, act="sigmoid", bias=True, param=None, name=None): + """ + Add a fc layer to net + + :param input: input variable name. + :type input: str + :param size: fully connected layer size. + :param act: activation name + :param param: parameter attribute, used for initialize parameters. + :param bias: bias attribute. False will not have a bias. + :param name: the name of fc layer. If not set, model will generate a + readable name + :return: output variable name. + """ + if name is None: + name = 'fc_%d' % uniq_id() + if not isinstance(name, str): + raise ValueError("name should be string") + + input_dims = scope.find_var(input).get_tensor().get_dims() + + w_name = param or name + ".w" + init_param(param_name=w_name, dims=[input_dims[1], size]) + sgd_optimizer(net=optimize_net, param_name=w_name, learning_rate=0.01) + + pre_activation = name + ".mul.out" + scope.new_var(pre_activation) + mul_op = Operator("mul", X=input, Y=w_name, Out=pre_activation) + net.add_op(mul_op) + + # create bias variable if needed + if bias: + bias_name = name + ".b" + init_param(param_name=bias_name, dims=[size]) + sgd_optimizer( + net=optimize_net, param_name=bias_name, learning_rate=0.01) + bias_out = name + ".rowwise_add.out" + scope.new_var(bias_out) + rowwise_add_op = Operator( + "rowwise_add", X=pre_activation, b=bias_name, Out=bias_out) + net.add_op(rowwise_add_op) + pre_activation = bias_out + + activation_op = Operator(act, X=pre_activation, Y=name) + net.add_op(activation_op) + scope.new_var(name) + net.infer_shape(scope) + return name + + +def cross_entropy_layer(net, input, label): + cost_name = 'cross_entropy_%d' % uniq_id() + cross_entropy_op = Operator( + "onehot_cross_entropy", X=input, label=label, Y=cost_name) + net.add_op(cross_entropy_op) + scope.new_var(cost_name) + net.infer_shape(scope) + return cost_name + + +images = data_layer(name='pixel', dims=[BATCH_SIZE, 784]) +label = data_layer(name='label', dims=[BATCH_SIZE]) +fc = fc_layer(net=forward_network, input=images, size=10, act="softmax") +cost = cross_entropy_layer(net=forward_network, input=fc, label=label) +forward_network.complete_add_op(True) +print(forward_network) +backward_net = core.Operator.backward(forward_network, set()) + +print(backward_net) + +PASS_NUM = 10 +for pass_id in range(PASS_NUM): + print pass_id -- GitLab From 424b325d084ef0fd5aa61996f35ef88126c48306 Mon Sep 17 00:00:00 2001 From: chengduoZH Date: Fri, 18 Aug 2017 14:10:27 +0800 Subject: [PATCH 0114/2018] add unit test DeConv3D, Conv3D, col2vol, vol2col --- paddle/gserver/tests/test_LayerGrad.cpp | 152 +++++++++++++++++++++++ paddle/math/tests/test_matrixCompare.cpp | 116 +++++++++++++++++ 2 files changed, 268 insertions(+) diff --git a/paddle/gserver/tests/test_LayerGrad.cpp b/paddle/gserver/tests/test_LayerGrad.cpp index 0f312b6ca..1e80e2c0e 100644 --- a/paddle/gserver/tests/test_LayerGrad.cpp +++ b/paddle/gserver/tests/test_LayerGrad.cpp @@ -2007,6 +2007,158 @@ TEST(Layer, RowL2NormLayer) { } } +void test3DConvLayer(const string& type, bool trans, bool useGpu) { + // filter size + const int NUM_FILTERS = 6; + // const int CHANNELS = 3; + const int FILTER_SIZE = 3; + const int FILTER_SIZE_Y = 3; + const int FILTER_SIZE_Z = 3; + + // input image + const int CHANNELS = 3; + const int IMAGE_SIZE = 9; + const int IMAGE_SIZE_Y = 9; + const int IMAGE_SIZE_Z = 9; // 2, 3, 5, 5, 5 + + TestConfig config; + config.biasSize = NUM_FILTERS; + config.layerConfig.set_type(type); + config.layerConfig.set_num_filters(NUM_FILTERS); + config.layerConfig.set_partial_sum(1); + config.layerConfig.set_shared_biases(true); + + // Setting up conv3D-trans layer + LayerInputConfig* input = config.layerConfig.add_inputs(); + ConvConfig* conv = input->mutable_conv_conf(); + + conv->set_channels(CHANNELS); + conv->set_filter_size(FILTER_SIZE); + conv->set_filter_size_y(FILTER_SIZE_Y); + conv->set_filter_size_z(FILTER_SIZE_Z); + conv->set_padding(0); + conv->set_padding_y(0); + conv->set_padding_z(0); + conv->set_stride(2); + conv->set_stride_y(2); + conv->set_stride_z(2); + conv->set_img_size(IMAGE_SIZE); + conv->set_img_size_y(IMAGE_SIZE_Y); + conv->set_img_size_z(IMAGE_SIZE_Z); + conv->set_output_x(outputSize(conv->img_size(), + conv->filter_size(), + conv->padding(), + conv->stride(), + /* caffeMode */ true)); + conv->set_output_y(outputSize(conv->img_size_y(), + conv->filter_size_y(), + conv->padding_y(), + conv->stride_y(), + /* caffeMode */ true)); + conv->set_output_z(outputSize(conv->img_size_z(), + conv->filter_size_z(), + conv->padding_z(), + conv->stride_z(), + /* caffeMode */ true)); + + config.layerConfig.set_size(conv->output_x() * conv->output_y() * + conv->output_z() * NUM_FILTERS); + conv->set_groups(1); + conv->set_filter_channels(conv->channels() / conv->groups()); + config.inputDefs.push_back( + {INPUT_DATA, + "layer_0", + CHANNELS * IMAGE_SIZE * IMAGE_SIZE_Y * IMAGE_SIZE_Z, + conv->filter_channels() * FILTER_SIZE * FILTER_SIZE_Y * FILTER_SIZE_Z * + NUM_FILTERS}); + + testLayerGrad(config, "conv3D", 10, trans, useGpu); + // Use small batch_size and useWeight=true to test biasGrad + testLayerGrad(config, "conv3D", 2, trans, useGpu, true, 0.02); +} + +TEST(Layer, test3DConvLayer) { + test3DConvLayer("conv3d", /* trans= */ false, /* useGpu= */ false); +#ifndef PADDLE_ONLY_CPU + test3DConvLayer("conv3d", /* trans= */ false, /* useGpu= */ true); +#endif +} + +int deConvOutputSize(int inSize, int kSize, int pad, int stride) { + return (inSize - 1) * stride - 2 * pad + kSize; +} + +void test3DDeConvLayer(const string& type, bool trans, bool useGpu) { + // filter size + const int NUM_FILTERS = 6; + // const int CHANNELS = 3; + const int FILTER_SIZE = 3; + const int FILTER_SIZE_Y = 3; + const int FILTER_SIZE_Z = 3; + + // input image + const int CHANNELS = 3; + const int IMAGE_SIZE = 4; + const int IMAGE_SIZE_Y = 6; + const int IMAGE_SIZE_Z = 6; + + // Setting up conv-trans layer + TestConfig config; + config.biasSize = NUM_FILTERS; + config.layerConfig.set_type("deconv3d"); + config.layerConfig.set_num_filters(NUM_FILTERS); + config.layerConfig.set_partial_sum(1); + config.layerConfig.set_shared_biases(true); + + LayerInputConfig* input = config.layerConfig.add_inputs(); + ConvConfig* conv = input->mutable_conv_conf(); + + conv->set_channels(CHANNELS); + conv->set_filter_size(FILTER_SIZE); + conv->set_filter_size_y(FILTER_SIZE_Y); + conv->set_filter_size_z(FILTER_SIZE_Z); + conv->set_padding(0); + conv->set_padding_y(0); + conv->set_padding_z(0); + conv->set_stride(2); + conv->set_stride_y(2); + conv->set_stride_z(2); + conv->set_img_size(IMAGE_SIZE); + conv->set_img_size_y(IMAGE_SIZE_Y); + conv->set_img_size_z(IMAGE_SIZE_Z); + conv->set_output_x(deConvOutputSize( + conv->img_size(), conv->filter_size(), conv->padding(), conv->stride())); + conv->set_output_y(deConvOutputSize(conv->img_size_y(), + conv->filter_size_y(), + conv->padding_y(), + conv->stride_y())); + conv->set_output_z(deConvOutputSize(conv->img_size_z(), + conv->filter_size_z(), + conv->padding_z(), + conv->stride_z())); + config.layerConfig.set_size(conv->output_x() * conv->output_y() * + conv->output_z() * NUM_FILTERS); + conv->set_groups(1); + conv->set_filter_channels(conv->channels() / conv->groups()); + config.inputDefs.push_back( + {INPUT_DATA, + "layer_0", + CHANNELS * IMAGE_SIZE * IMAGE_SIZE_Y * IMAGE_SIZE_Z, + conv->filter_channels() * FILTER_SIZE * FILTER_SIZE_Y * FILTER_SIZE_Z * + NUM_FILTERS}); + + testLayerGrad(config, "deconv3D", 10, trans, useGpu); + // Use small batch_size and useWeight=true to test biasGrad + testLayerGrad(config, "deconv3D", 2, trans, useGpu, true, 0.02); +} + +TEST(Layer, test3DDeConvLayer) { + test3DDeConvLayer("deconv3d", /* trans= */ false, /* useGpu= */ false); +#ifndef PADDLE_ONLY_CPU + test3DDeConvLayer("deconv3d", /* trans= */ false, /* useGpu= */ true); +#endif +} + int main(int argc, char** argv) { testing::InitGoogleTest(&argc, argv); initMain(argc, argv); diff --git a/paddle/math/tests/test_matrixCompare.cpp b/paddle/math/tests/test_matrixCompare.cpp index d77478f34..1d41ec087 100644 --- a/paddle/math/tests/test_matrixCompare.cpp +++ b/paddle/math/tests/test_matrixCompare.cpp @@ -1203,4 +1203,120 @@ TEST(Matrix, warpCTC) { } } +int outputSizeCol2Vol( + int imageSize, int filterSize, int padding, int stride, bool caffeMode) { + int outputSize; + if (!caffeMode) { + outputSize = + (imageSize - filterSize + 2 * padding + stride - 1) / stride + 1; + } else { + outputSize = (imageSize - filterSize + 2 * padding) / stride + 1; + } + CHECK_GE(outputSize, 1); + return outputSize; +} + +void testMatrixCol2Vol(int depth, int height, int width) { + int channel = 3; + int filterX = 3, filterY = 4, filterZ = 5; + int strideX = 2, strideY = 2, strideZ = 2; + int padX = 1, padY = 1, padZ = 1; + + MatrixPtr cpuImage = + std::make_shared(channel, depth * height * width); + MatrixPtr gpuImage = + std::make_shared(channel, depth * height * width); + cpuImage->randomizeUniform(); + gpuImage->copyFrom(*cpuImage); + + int outD = outputSizeCol2Vol(depth, filterZ, padZ, strideZ, true); + int outH = outputSizeCol2Vol(height, filterY, padZ, strideY, true); + int outW = outputSizeCol2Vol(width, filterX, padZ, strideX, true); + + int colBufHeight = channel * filterZ * filterY * filterX; + int colBufWidth = outD * outH * outW; + MatrixPtr cpuColBuf = std::make_shared(colBufHeight, colBufWidth); + MatrixPtr gpuColBuf = std::make_shared(colBufHeight, colBufWidth); + cpuColBuf->vol2Col(cpuImage->getData(), + channel, + depth, + height, + width, + filterZ, + filterY, + filterX, + strideZ, + strideY, + strideX, + padZ, + padY, + padX); + gpuColBuf->vol2Col(gpuImage->getData(), + channel, + depth, + height, + width, + filterZ, + filterY, + filterX, + strideZ, + strideY, + strideX, + padZ, + padY, + padX); + TensorCheckEqual(*cpuColBuf, *gpuColBuf); + + cpuColBuf->randomizeUniform(); + gpuColBuf->copyFrom(*cpuColBuf); + cpuColBuf->col2Vol(cpuImage->getData(), + channel, + depth, + height, + width, + filterZ, + filterY, + filterX, + strideZ, + strideY, + strideX, + padZ, + padY, + padX, + 1.0, + 1.0); + gpuColBuf->col2Vol(gpuImage->getData(), + channel, + depth, + height, + width, + filterZ, + filterY, + filterX, + strideZ, + strideY, + strideX, + padZ, + padY, + padX, + 1.0, + 1.0); + TensorCheckErr(*cpuImage, *gpuImage); +} + +TEST(Matrix, col2Vol) { + for (auto depth : {9, 16, 64, 128}) { + for (auto height : {9, 11, 73, 128, 256}) { + for (auto width : { + 9, 32, 100, 512, + }) { + VLOG(3) << "depth=" << depth << " height=" << height + << " width=" << width; + testMatrixCol2Vol(depth, height, width); + } + } + } +} +/////// + #endif -- GitLab From c792ef7d5ae470031bebcd990b79c0ce7f36f7bc Mon Sep 17 00:00:00 2001 From: chengduoZH Date: Fri, 18 Aug 2017 14:12:01 +0800 Subject: [PATCH 0115/2018] fix DeConv3D, Conv3D --- paddle/gserver/layers/Conv3DLayer.cpp | 248 +++++++++++++----------- paddle/gserver/layers/DeConv3DLayer.cpp | 186 +++++++++--------- 2 files changed, 229 insertions(+), 205 deletions(-) diff --git a/paddle/gserver/layers/Conv3DLayer.cpp b/paddle/gserver/layers/Conv3DLayer.cpp index 0fa9c5f9f..5609a4cc7 100644 --- a/paddle/gserver/layers/Conv3DLayer.cpp +++ b/paddle/gserver/layers/Conv3DLayer.cpp @@ -12,9 +12,9 @@ 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 "Conv3DLayer.h" #include "paddle/utils/Logging.h" #include "paddle/utils/Stat.h" -#include "Conv3DLayer.h" namespace paddle { @@ -22,32 +22,30 @@ REGISTER_LAYER(conv3d, Conv3DLayer); bool Conv3DLayer::init(const LayerMap &layerMap, const ParameterMap ¶meterMap) { - if (!ConvBaseLayer::init(layerMap, parameterMap)) - return false; + if (!ConvBaseLayer::init(layerMap, parameterMap)) return false; int index = 0; for (auto &inputConfig : config_.inputs()) { - const ConvConfig &conf = inputConfig.conv_conf(); - M_.push_back(numFilters_ / conf.groups()); - K_.push_back( - conf.filter_channels() * conf.filter_size_z() * \ - conf.filter_size_y() * conf.filter_size()); - weights_[index]->getW()->reshape( - weights_[index]->getW()->getWidth(), - weights_[index]->getW()->getHeight()); + const ConvConfig &conf = inputConfig.conv_conf(); + M_.push_back(numFilters_ / conf.groups()); + K_.push_back(filterPixels_[index] * filterChannels_[index]); + if (nullptr != weights_[index]->getW()) + weights_[index]->getW()->reshape(weights_[index]->getW()->getWidth(), + weights_[index]->getW()->getHeight()); + if (nullptr != weights_[index]->getWGrad()) weights_[index]->getWGrad()->reshape( - weights_[index]->getWGrad()->getWidth(), - weights_[index]->getWGrad()->getHeight()); - ++index; + weights_[index]->getWGrad()->getWidth(), + weights_[index]->getWGrad()->getHeight()); + ++index; } - biases_->getWGrad()->reshape( - biases_->getWGrad()->width_, biases_->getWGrad()->height_); - biases_->getW()->reshape( - biases_->getW()->width_, biases_->getW()->height_); + if (nullptr != biases_->getWGrad()) + biases_->getWGrad()->reshape(biases_->getWGrad()->width_, + biases_->getWGrad()->height_); + if (nullptr != biases_->getW()) + biases_->getW()->reshape(biases_->getW()->width_, biases_->getW()->height_); CHECK(inputLayers_.size() == parameters_.size()); return true; } - size_t Conv3DLayer::getSize() { CHECK_NE(inputLayers_.size(), 0UL); // imgSizeH_.clear(); @@ -59,22 +57,19 @@ size_t Conv3DLayer::getSize() { N_.clear(); size_t layerSize = 0; for (size_t i = 0; i < inputLayers_.size(); ++i) { - // imgSizeH_.push_back(inputLayers_[i]->getOutput().getFrameHeight()); - // imgSizeW_.push_back(inputLayers_[i]->getOutput().getFrameWidth()); - // imgSizeD_.push_back(inputLayers_[i]->getOutput().getFrameDepth()); - outputW_.push_back(outputSize( - imgSizeW_[i], filterSize_[i], - padding_[i], stride_[i], true)); - outputH_.push_back(outputSize( - imgSizeH_[i], filterSizeY_[i], - paddingY_[i], strideY_[i], true)); - outputD_.push_back(outputSize( - imgSizeD_[i], filterSizeZ_[i], - paddingZ_[i], strideZ_[i], true)); - - N_.push_back(outputD_[i] * outputH_[i] * outputW_[i]); - CHECK(layerSize == 0 || N_[i] * size_t(numFilters_) == layerSize); - layerSize += N_[i] * numFilters_; + // imgSizeH_.push_back(inputLayers_[i]->getOutput().getFrameHeight()); + // imgSizeW_.push_back(inputLayers_[i]->getOutput().getFrameWidth()); + // imgSizeD_.push_back(inputLayers_[i]->getOutput().getFrameDepth()); + outputW_.push_back(outputSize( + imgSizeW_[i], filterSize_[i], padding_[i], stride_[i], true)); + outputH_.push_back(outputSize( + imgSizeH_[i], filterSizeY_[i], paddingY_[i], strideY_[i], true)); + outputD_.push_back(outputSize( + imgSizeD_[i], filterSizeZ_[i], paddingZ_[i], strideZ_[i], true)); + + N_.push_back(outputD_[i] * outputH_[i] * outputW_[i]); + CHECK(layerSize == 0 || N_[i] * size_t(numFilters_) == layerSize); + layerSize += N_[i] * numFilters_; } getOutput().setFrameHeight(outputH_[0]); getOutput().setFrameWidth(outputW_[0]); @@ -88,38 +83,46 @@ void Conv3DLayer::forward(PassType passType) { int batchSize = inputLayers_[0]->getOutputValue()->getHeight(); int outWidth = getSize(); resetOutput(batchSize, outWidth); - const MatrixPtr outMat = getOutputValue(); for (size_t i = 0; i != inputLayers_.size(); ++i) { - REGISTER_TIMER_INFO("FwdConv3D", getName().c_str()); - const MatrixPtr& inMat = getInputValue(i); - int width = inMat->getWidth(); - int M = M_[i]; - int N = N_[i]; - int K = K_[i]; - Matrix::resizeOrCreate(colBuf_, K * groups_[i], N, false, useGpu_); - MatrixPtr wMat = weights_[i]->getW(); - for (int n = 0; n < batchSize; ++n) { - colBuf_->vol2Col(inMat->getData() + n * width, channels_[i], - imgSizeD_[i], imgSizeH_[i], imgSizeW_[i], - filterSizeZ_[i], filterSizeY_[i], filterSize_[i], - strideZ_[i], strideY_[i], stride_[i], - paddingZ_[i], paddingY_[i], padding_[i]); - - real *outData = outMat->getData() + n * outWidth; - MatrixPtr outMatSub = - Matrix::create(outData, groups_[i] * M, N, false, useGpu_); - for (int g = 0; g < groups_[i]; g++) { - MatrixPtr wMatSub = wMat->subMatrix(g * M, M); - MatrixPtr in = colBuf_->subMatrix(g * K, K); - MatrixPtr out = outMatSub->subMatrix(g * M, M); - out->mul(*wMatSub, *in, 1.0, 0.0); - } + REGISTER_TIMER_INFO("FwdConv3D", getName().c_str()); + const MatrixPtr &inMat = getInputValue(i); + const MatrixPtr &outMat = getOutputValue(); + int M = M_[i]; + int N = N_[i]; + int K = K_[i]; + Matrix::resizeOrCreate(colBuf_, K * groups_[i], N, false, useGpu_); + MatrixPtr wMat = weights_[i]->getW(); + for (int n = 0; n < batchSize; ++n) { + colBuf_->vol2Col(inMat->getData() + n * inMat->getStride(), + channels_[i], + imgSizeD_[i], + imgSizeH_[i], + imgSizeW_[i], + filterSizeZ_[i], + filterSizeY_[i], + filterSize_[i], + strideZ_[i], + strideY_[i], + stride_[i], + paddingZ_[i], + paddingY_[i], + padding_[i]); + + real *outData = outMat->getData() + n * outMat->getStride(); + MatrixPtr outMatSub = + Matrix::create(outData, groups_[i] * M, N, false, useGpu_); + for (int g = 0; g < groups_[i]; g++) { + MatrixPtr wMatSub = wMat->subMatrix(g * M, M); + MatrixPtr in = colBuf_->subMatrix(g * K, K); + MatrixPtr out = outMatSub->subMatrix(g * M, M); + out->mul(*wMatSub, *in, 1.0, 1.0); } + } } if (nullptr != this->biasParameter_) { - REGISTER_TIMER_INFO("FwBiasTimer", getName().c_str()); - this->addBias(); + REGISTER_TIMER_INFO("FwBiasTimer", getName().c_str()); + this->addBias(); } forwardActivation(); } @@ -128,20 +131,20 @@ void Conv3DLayer::backward(const UpdateCallback &callback) { backwardActivation(); if (biases_ && biases_->getWGrad()) { - bpropBiases(); - biases_->getParameterPtr()->incUpdate(callback); + bpropBiases(); + biases_->getParameterPtr()->incUpdate(callback); } for (size_t i = 0; i != inputLayers_.size(); ++i) { - REGISTER_TIMER_INFO("BwdConv3D", getName().c_str()); - if (weights_[i]->getWGrad()) { - bpropWeights(i); - } - if (this->needGradient_) { - bpropData(i); - } - REGISTER_TIMER_INFO("WeightUpdate", getName().c_str()); - weights_[i]->getParameterPtr()->incUpdate(callback); + REGISTER_TIMER_INFO("BwdConv3D", getName().c_str()); + if (weights_[i]->getWGrad()) { + bpropWeights(i); + } + if (getInputGrad(i)) { + bpropData(i); + } + REGISTER_TIMER_INFO("WeightUpdate", getName().c_str()); + weights_[i]->getParameterPtr()->incUpdate(callback); } } @@ -149,28 +152,36 @@ void Conv3DLayer::bpropWeights(int i) { int M = M_[i]; int N = N_[i]; int K = K_[i]; - const MatrixPtr& inMat = getInputValue(i); - int width = inMat->getWidth(); + const MatrixPtr &inMat = getInputValue(i); Matrix::resizeOrCreate(colBuf_, K * groups_[i], N, false, useGpu_); MatrixPtr wGradMat = weights_[i]->getWGrad(); - real* outGradData = getOutputGrad()->getData(); int batchSize = inputLayers_[0]->getOutputValue()->getHeight(); - for (int n = 0; n < batchSize; ++n) { - colBuf_->vol2Col(inMat->getData() + n * width, channels_[i], - imgSizeD_[i], imgSizeH_[i], imgSizeW_[i], - filterSizeZ_[i], filterSizeY_[i], filterSize_[i], - strideZ_[i], strideY_[i], stride_[i], - paddingZ_[i], paddingY_[i], padding_[i]); - outGradData += n * getOutputGrad()->getWidth(); - MatrixPtr outGradSub = - Matrix::create(outGradData, groups_[i] * M, N, false, useGpu_); - for (int g = 0; g < groups_[i]; ++g) { - MatrixPtr inMatSub = colBuf_->subMatrix(g * K, K); - MatrixPtr outG = outGradSub->subMatrix(g * M, M); - MatrixPtr wGradSub = wGradMat->subMatrix(g * M, M); - wGradSub->mul(*outG, *(inMatSub->getTranspose()), 1.0, 1.0); - } + colBuf_->vol2Col(inMat->getData() + n * inMat->getStride(), + channels_[i], + imgSizeD_[i], + imgSizeH_[i], + imgSizeW_[i], + filterSizeZ_[i], + filterSizeY_[i], + filterSize_[i], + strideZ_[i], + strideY_[i], + stride_[i], + paddingZ_[i], + paddingY_[i], + padding_[i]); + + real *outGradData = + getOutputGrad()->getData() + n * getOutputGrad()->getStride(); + MatrixPtr outGradSub = + Matrix::create(outGradData, groups_[i] * M, N, false, useGpu_); + for (int g = 0; g < groups_[i]; ++g) { + MatrixPtr inMatSub = colBuf_->subMatrix(g * K, K); + MatrixPtr outG = outGradSub->subMatrix(g * M, M); + MatrixPtr wGradSub = wGradMat->subMatrix(g * M, M); + wGradSub->mul(*outG, *(inMatSub->getTranspose()), 1.0, 1.0); + } } } @@ -180,45 +191,54 @@ void Conv3DLayer::bpropData(int i) { int K = K_[i]; Matrix::resizeOrCreate(colBuf_, K * groups_[i], N, false, useGpu_); MatrixPtr wMat = weights_[i]->getW(); - real* outGradData = getOutputGrad()->getData(); - real* preGradData = getInputGrad(i)->getData(); int batchSize = inputLayers_[0]->getOutputValue()->getHeight(); for (int n = 0; n < batchSize; ++n) { - outGradData += n * getOutputGrad()->getWidth(); - preGradData += n * getInputGrad(i)->getWidth(); - MatrixPtr outGradSub = - Matrix::create(outGradData, M * groups_[i], N, false, useGpu_); - for (int g = 0; g < groups_[i]; ++g) { - MatrixPtr wMatSub = wMat->subMatrix(g * M, M); - MatrixPtr outG = outGradSub->subMatrix(g * M, M); - MatrixPtr inGradMatSub = colBuf_->subMatrix(g * K, K); - inGradMatSub->mul(*(wMatSub->getTranspose()), *outG, 1.0, 0.0); - } - colBuf_->col2Vol(preGradData, channels_[i], - imgSizeD_[i], imgSizeH_[i], imgSizeW_[i], - filterSizeZ_[i], filterSizeY_[i], filterSize_[i], - strideZ_[i], strideY_[i], stride_[i], - paddingZ_[i], paddingY_[i], padding_[i], - 1.0, 1.0); + real *outGradData = + getOutputGrad()->getData() + n * getOutputGrad()->getStride(); + real *preGradData = + getInputGrad(i)->getData() + n * getInputGrad(i)->getStride(); + MatrixPtr outGradSub = + Matrix::create(outGradData, M * groups_[i], N, false, useGpu_); + for (int g = 0; g < groups_[i]; ++g) { + MatrixPtr wMatSub = wMat->subMatrix(g * M, M); + MatrixPtr outG = outGradSub->subMatrix(g * M, M); + MatrixPtr inGradMatSub = colBuf_->subMatrix(g * K, K); + inGradMatSub->mul(*(wMatSub->getTranspose()), *outG, 1.0, 0.0); + } + colBuf_->col2Vol(preGradData, + channels_[i], + imgSizeD_[i], + imgSizeH_[i], + imgSizeW_[i], + filterSizeZ_[i], + filterSizeY_[i], + filterSize_[i], + strideZ_[i], + strideY_[i], + stride_[i], + paddingZ_[i], + paddingY_[i], + padding_[i], + 1.0, + 1.0); } } void Conv3DLayer::bpropBiases() { MatrixPtr outGradMat = getOutputGrad(); if (this->sharedBiases_) { - biases_->getWGrad()->collectSharedBias(*outGradMat, 1.0f); + biases_->getWGrad()->collectSharedBias(*outGradMat, 1.0f); } else { - biases_->getWGrad()->collectBias(*outGradMat, 1.0f); + biases_->getWGrad()->collectBias(*outGradMat, 1.0f); } } void Conv3DLayer::addBias() { MatrixPtr outMat = getOutputValue(); - if (this->sharedBiases_) { - outMat->addSharedBias(*(biases_->getW()), 1.0f); + outMat->addSharedBias(*(biases_->getW()), 1.0f); } else { - outMat->addBias(*(biases_->getW()), 1.0f); + outMat->addBias(*(biases_->getW()), 1.0f); } } diff --git a/paddle/gserver/layers/DeConv3DLayer.cpp b/paddle/gserver/layers/DeConv3DLayer.cpp index 8de40b681..286f5b985 100644 --- a/paddle/gserver/layers/DeConv3DLayer.cpp +++ b/paddle/gserver/layers/DeConv3DLayer.cpp @@ -12,43 +12,42 @@ 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 "DeConv3DLayer.h" #include "paddle/utils/Logging.h" #include "paddle/utils/Stat.h" -#include "DeConv3DLayer.h" namespace paddle { REGISTER_LAYER(deconv3d, DeConv3DLayer); #define DECONV_OUTPUT_SIZE(IN_SIZE, STRID, PAD, KSIZE) \ - (((IN_SIZE) - 1) * (STRID) - 2 * (PAD) + (KSIZE)) + (((IN_SIZE)-1) * (STRID)-2 * (PAD) + (KSIZE)) bool DeConv3DLayer::init(const LayerMap &layerMap, - const ParameterMap ¶meterMap) { + const ParameterMap ¶meterMap) { if (!ConvBaseLayer::init(layerMap, parameterMap)) return false; // for Deconv, the dimension of Kernel is // channel * output * depth * height * weigth // Matrix storage format: (output * depth * height * weigth) x channel for (int index = 0; index < config_.inputs().size(); ++index) { M_.push_back(filterChannels_[index]); - K_.push_back( - filterPixels_[index] * (numFilters_/groups_[index])); - weights_[index]->getW()->reshape( - filterPixels_[index] * numFilters_, - filterChannels_[index]); - weights_[index]->getWGrad()->reshape( - filterPixels_[index] * numFilters_, - filterChannels_[index]); + K_.push_back(filterPixels_[index] * (numFilters_ / groups_[index])); + if (weights_[index]->getW()) + weights_[index]->getW()->reshape(filterPixels_[index] * numFilters_, + filterChannels_[index]); + if (weights_[index]->getWGrad()) + weights_[index]->getWGrad()->reshape(filterPixels_[index] * numFilters_, + filterChannels_[index]); } - biases_->getWGrad()->reshape( - biases_->getWGrad()->width_, biases_->getWGrad()->height_); - biases_->getW()->reshape( - biases_->getW()->width_, biases_->getW()->height_); + if (biases_->getWGrad()) + biases_->getWGrad()->reshape(biases_->getWGrad()->width_, + biases_->getWGrad()->height_); + if (biases_->getW()) + biases_->getW()->reshape(biases_->getW()->width_, biases_->getW()->height_); CHECK(inputLayers_.size() == parameters_.size()); return true; } - size_t DeConv3DLayer::getSize() { CHECK_NE(inputLayers_.size(), 0UL); // imgSizeH_.clear(); @@ -64,18 +63,12 @@ size_t DeConv3DLayer::getSize() { // imgSizeH_.push_back(inputLayers_[i]->getOutput().getFrameHeight()); // imgSizeW_.push_back(inputLayers_[i]->getOutput().getFrameWidth()); // imgSizeD_.push_back(inputLayers_[i]->getOutput().getFrameDepth()); - outputW_.push_back( - DECONV_OUTPUT_SIZE( - imgSizeW_[i], stride_[i], - padding_[i], filterSize_[i])); - outputH_.push_back( - DECONV_OUTPUT_SIZE( - imgSizeH_[i], strideY_[i], - paddingY_[i], filterSizeY_[i])); - outputD_.push_back( - DECONV_OUTPUT_SIZE( - imgSizeD_[i], strideZ_[i], - paddingZ_[i], filterSizeZ_[i])); + outputW_.push_back(DECONV_OUTPUT_SIZE( + imgSizeW_[i], stride_[i], padding_[i], filterSize_[i])); + outputH_.push_back(DECONV_OUTPUT_SIZE( + imgSizeH_[i], strideY_[i], paddingY_[i], filterSizeY_[i])); + outputD_.push_back(DECONV_OUTPUT_SIZE( + imgSizeD_[i], strideZ_[i], paddingZ_[i], filterSizeZ_[i])); No_.push_back(outputD_[i] * outputH_[i] * outputW_[i]); N_.push_back(imgSizeD_[i] * imgSizeH_[i] * imgSizeW_[i]); CHECK(layerSize == 0 || N_[i] * size_t(numFilters_) == layerSize); @@ -96,32 +89,37 @@ void DeConv3DLayer::forward(PassType passType) { for (size_t i = 0; i != inputLayers_.size(); ++i) { REGISTER_TIMER_INFO("FwdDeConv3D", getName().c_str()); - const MatrixPtr& inMat = getInputValue(i); - int width = inMat->getWidth(); + const MatrixPtr &inMat = getInputValue(i); int M = M_[i]; int N = N_[i]; int K = K_[i]; MatrixPtr wMat = weights_[i]->getW(); - Matrix::resizeOrCreate(colBuf_, K * groups_[i] , N, false, useGpu_); - + Matrix::resizeOrCreate(colBuf_, K * groups_[i], N, false, useGpu_); for (int n = 0; n < batchSize; ++n) { - real *inData = inMat->getData() + n * width; - real *colBufData = colBuf_->getData(); - for (int g = 0; g < groups_[i]; g++) { - MatrixPtr wMatSub = wMat->subMatrix(g * K, K); - MatrixPtr inMatSub = - Matrix::create(inData, M, N, false, useGpu_); - MatrixPtr colBufDataSub = - Matrix::create(colBufData, K, N, false, useGpu_); - colBufDataSub->mul(*wMatSub, *inMatSub, 1.0, 0.0); - colBufData += K * N; - inData += M * N; + real *inData = inMat->getData() + n * inMat->getStride(); + for (int g = 0; g < groups_[i]; ++g) { + MatrixPtr inMatSub = Matrix::create(inData, M, N, false, useGpu_); + MatrixPtr wMatSub = wMat->subMatrix(g * K, K); + MatrixPtr colBufDataSub = colBuf_->subMatrix(g * K, K); + colBufDataSub->mul(*wMatSub, *inMatSub, 1.0, 0.0); + inData += M * N; } - colBuf_->col2Vol(outMat->getData()+ n * outMat->getWidth(), - numFilters_, outputD_[i], outputH_[i], outputW_[i], - filterSizeZ_[i], filterSizeY_[i], filterSize_[i], - strideZ_[i], strideY_[i], stride_[i], - paddingZ_[i], paddingY_[i], padding_[i], 1.0, 1.0); + colBuf_->col2Vol(outMat->getData() + n * outMat->getStride(), + numFilters_, + outputD_[i], + outputH_[i], + outputW_[i], + filterSizeZ_[i], + filterSizeY_[i], + filterSize_[i], + strideZ_[i], + strideY_[i], + stride_[i], + paddingZ_[i], + paddingY_[i], + padding_[i], + 1.0, + 1.0); } } if (nullptr != this->biasParameter_) { @@ -134,63 +132,69 @@ void DeConv3DLayer::forward(PassType passType) { void DeConv3DLayer::backward(const UpdateCallback &callback) { backwardActivation(); int batchSize = getOutputGrad()->getHeight(); - int outputWidth = getOutputGrad()->getWidth(); if (biases_ && biases_->getWGrad()) { bpropBiases(); biases_->getParameterPtr()->incUpdate(callback); } - for (size_t i =0; i < inputLayers_.size(); ++i) { - int M = M_[i]; - int N = N_[i]; - int K = K_[i]; - Matrix::resizeOrCreate(colBuf_, K * groups_[i], N, false, useGpu_); - const MatrixPtr& inMat = getInputValue(i); - for (int n = 0; n < batchSize; ++n) { + for (size_t i = 0; i < inputLayers_.size(); ++i) { + if (weights_[i]->getWGrad() || this->needGradient_) { + int M = M_[i]; + int N = N_[i]; + int K = K_[i]; REGISTER_TIMER_INFO("BwdDeConv3D", getName().c_str()); - if (weights_[i]->getWGrad() || this->needGradient_) { - colBuf_->vol2Col(getOutputGrad()->getData() + n * outputWidth, - numFilters_, outputD_[i], outputH_[i], outputW_[i], - filterSizeZ_[i], filterSizeY_[i], filterSize_[i], - strideZ_[i], strideY_[i], stride_[i], - paddingZ_[i], paddingY_[i], padding_[i]); - } - if (weights_[i]->getWGrad()) { - real *inData = inMat->getData() + n * inMat->getWidth();; - real *wGradData = weights_[i]->getWGrad()->getData(); - for (int g = 0; g < groups_[i]; g++) { - MatrixPtr colBufDataSub = colBuf_->subMatrix(g * K, K); - MatrixPtr inMatSub = Matrix::create( - inData, M, N, false, useGpu_); - MatrixPtr wGradMatSub = Matrix::create( - wGradData, K, M, false, useGpu_); - wGradMatSub->mul(*colBufDataSub, - *(inMatSub->getTranspose()), 1.0, 1.0); - wGradData += K * M; - inData += M * N; + Matrix::resizeOrCreate(colBuf_, K * groups_[i], N, false, useGpu_); + const MatrixPtr &inMat = getInputValue(i); + for (int n = 0; n < batchSize; ++n) { + colBuf_->vol2Col( + getOutputGrad()->getData() + n * getOutputGrad()->getStride(), + numFilters_, + outputD_[i], + outputH_[i], + outputW_[i], + filterSizeZ_[i], + filterSizeY_[i], + filterSize_[i], + strideZ_[i], + strideY_[i], + stride_[i], + paddingZ_[i], + paddingY_[i], + padding_[i]); + if (weights_[i]->getWGrad()) { + real *inData = inMat->getData() + n * inMat->getStride(); + for (int g = 0; g < groups_[i]; ++g) { + MatrixPtr colBufDataSub = colBuf_->subMatrix(g * K, K); + MatrixPtr wGradMatSub = + weights_[i]->getWGrad()->subMatrix(g * K, K); + MatrixPtr inMatSub = Matrix::create(inData, M, N, false, useGpu_); + wGradMatSub->mul( + *colBufDataSub, *(inMatSub->getTranspose()), 1.0, 1.0); + inData += M * N; + } } - weights_[i]->getParameterPtr()->incUpdate(callback); - } - if (this->needGradient_) { - real* preGrad = getInputGrad(i)->getData(); - for (int g = 0; g < groups_[i]; ++g) { - MatrixPtr w = weights_[i]->getW()->subMatrix(g * K, K); - MatrixPtr outGradMat = colBuf_->subMatrix(g * K, K); - MatrixPtr inGradMatSub = Matrix::create( - preGrad, M, N, false, useGpu_); - inGradMatSub->mul(*(w->getTranspose()), *outGradMat, 1.0, 0.0); - preGrad += M * N; + if (getInputGrad(i)) { + real *preGrad = + getInputGrad(i)->getData() + n * getInputGrad(i)->getStride(); + for (int g = 0; g < groups_[i]; ++g) { + MatrixPtr w = weights_[i]->getW()->subMatrix(g * K, K); + MatrixPtr outGradMat = colBuf_->subMatrix(g * K, K); + MatrixPtr inGradMatSub = + Matrix::create(preGrad, M, N, false, useGpu_); + inGradMatSub->mul(*(w->getTranspose()), *outGradMat, 1.0, 1.0); + preGrad += M * N; + } } } REGISTER_TIMER_INFO("WeightUpdate", getName().c_str()); + weights_[i]->getParameterPtr()->incUpdate(callback); } } } - -void DeConv3DLayer::bpropWeights(int i) { } -void DeConv3DLayer::bpropData(int i) { } +void DeConv3DLayer::bpropWeights(int i) {} +void DeConv3DLayer::bpropData(int i) {} void DeConv3DLayer::bpropBiases() { - MatrixPtr outGradMat = getOutputGrad(); + const MatrixPtr &outGradMat = getOutputGrad(); if (this->sharedBiases_) { biases_->getWGrad()->collectSharedBias(*outGradMat, 1.0f); -- GitLab From 43f6cdc8247042244f9b75bac51957c962a16ffd Mon Sep 17 00:00:00 2001 From: chengduoZH Date: Fri, 18 Aug 2017 14:13:25 +0800 Subject: [PATCH 0116/2018] fix Matrix --- paddle/math/Matrix.cpp | 110 +++++++++++++++++++------------- paddle/math/Matrix.h | 140 ++++++++++++++++++++++++++--------------- 2 files changed, 153 insertions(+), 97 deletions(-) diff --git a/paddle/math/Matrix.cpp b/paddle/math/Matrix.cpp index 66868e73b..579a0f3cf 100644 --- a/paddle/math/Matrix.cpp +++ b/paddle/math/Matrix.cpp @@ -1389,51 +1389,71 @@ void GpuMatrix::multiBinaryLabelCrossEntropyBp(Matrix& output, Matrix& label) { output_d, grad_d, mat_d, height_, width_); } -void GpuMatrix::vol2Col(real* data, - int channels, - int depth, - int height, - int width, - int filterD, - int filterH, - int filterW, - int strideD, - int strideH, - int strideW, - int paddingD, - int paddingH, - int paddingW) { - hl_matrix_vol2Col(data, - channels, depth, height, width, - filterD, filterH, filterW, - strideD, strideH, strideW, - paddingD, paddingH, paddingW, getData()); -} - -void GpuMatrix::col2Vol(real* trg, - int channels, - int depth, - int height, - int width, - int filterD, - int filterH, - int filterW, - int strideD, - int strideH, - int strideW, - int paddingD, - int paddingH, - int paddingW, - real alpha, - real beta) { - hl_matrix_col2Vol(trg, - channels, depth, height, width, - filterD, filterH, filterW, - strideD, strideH, strideW, - paddingD, paddingH, paddingW, +void GpuMatrix::vol2Col(real* dataSrc, + int channels, + int depth, + int height, + int width, + int filterD, + int filterH, + int filterW, + int strideD, + int strideH, + int strideW, + int paddingD, + int paddingH, + int paddingW) { + hl_matrix_vol2Col(dataSrc, + channels, + depth, + height, + width, + filterD, + filterH, + filterW, + strideD, + strideH, + strideW, + paddingD, + paddingH, + paddingW, + getData()); +} + +void GpuMatrix::col2Vol(real* dataDst, + int channels, + int depth, + int height, + int width, + int filterD, + int filterH, + int filterW, + int strideD, + int strideH, + int strideW, + int paddingD, + int paddingH, + int paddingW, + real alpha, + real beta) { + hl_matrix_col2Vol(dataDst, + channels, + depth, + height, + width, + filterD, + filterH, + filterW, + strideD, + strideH, + strideW, + paddingD, + paddingH, + paddingW, getData(), - alpha, beta); - } + alpha, + beta); +} /** * CpuMatrix @@ -4082,7 +4102,7 @@ void CpuMatrix::col2Vol(real* trg, real alpha, real beta) { real* src = getData(); - int outDepth = (depth + 2 * paddingH - filterD) / strideD + 1; + int outDepth = (depth + 2 * paddingD - filterD) / strideD + 1; int outHeight = (height + 2 * paddingH - filterH) / strideH + 1; int outWidth = (width + 2 * paddingW - filterW) / strideW + 1; int channelsCol = channels * filterD * filterH * filterW; diff --git a/paddle/math/Matrix.h b/paddle/math/Matrix.h index 4354996ce..cc3a56f27 100644 --- a/paddle/math/Matrix.h +++ b/paddle/math/Matrix.h @@ -1040,40 +1040,40 @@ public: } virtual void vol2Col(real* data, - int channels, - int depth, - int height, - int width, - int filterD, - int filterH, - int filterW, - int strideD, - int strideH, - int strideW, - int paddingD, - int paddingH, - int paddingW) { - LOG(FATAL) << "Not implemeted"; - } + int channels, + int depth, + int height, + int width, + int filterD, + int filterH, + int filterW, + int strideD, + int strideH, + int strideW, + int paddingD, + int paddingH, + int paddingW) { + LOG(FATAL) << "Not implemeted"; + } - virtual void col2Vol(real* trg, - int channels, - int depth, - int height, - int width, - int filterD, - int filterH, - int filterW, - int strideD, - int strideH, - int strideW, - int paddingD, - int paddingH, - int paddingW, - real alpha, - real beta) { - LOG(FATAL) << "Not implemeted"; - } + virtual void col2Vol(real* trg, + int channels, + int depth, + int height, + int width, + int filterD, + int filterH, + int filterW, + int strideD, + int strideH, + int strideW, + int paddingD, + int paddingH, + int paddingW, + real alpha, + real beta) { + LOG(FATAL) << "Not implemeted"; + } virtual void bilinearForward(const Matrix& in, const size_t inImgH, @@ -1411,18 +1411,36 @@ public: const real ratioW); void vol2Col(real* data, - int channels, - int depth, int height, int width, - int filterD, int filterH, int filterW, - int strideD, int strideH, int strideW, - int paddingD, int paddingH, int paddingW); + int channels, + int depth, + int height, + int width, + int filterD, + int filterH, + int filterW, + int strideD, + int strideH, + int strideW, + int paddingD, + int paddingH, + int paddingW); void col2Vol(real* trg, - int channels, int depth, int height, int width, - int filterD, int filterH, int filterW, - int strideD, int strideH, int strideW, - int paddingD, int paddingH, int paddingW, - real alpha, real beta); + int channels, + int depth, + int height, + int width, + int filterD, + int filterH, + int filterW, + int strideD, + int strideH, + int strideW, + int paddingD, + int paddingH, + int paddingW, + real alpha, + real beta); void multiBinaryLabelCrossEntropy(Matrix& output, Matrix& label); @@ -1767,17 +1785,35 @@ public: void vol2Col(real* data, int channels, - int depth, int height, int width, - int filterD, int filterH, int filterW, - int strideD, int strideH, int strideW, - int paddingD, int paddingH, int paddingW); + int depth, + int height, + int width, + int filterD, + int filterH, + int filterW, + int strideD, + int strideH, + int strideW, + int paddingD, + int paddingH, + int paddingW); void col2Vol(real* trg, - int channels, int depth, int height, int width, - int filterD, int filterH, int filterW, - int strideD, int strideH, int strideW, - int paddingD, int paddingH, int paddingW, - real alpha, real beta); + int channels, + int depth, + int height, + int width, + int filterD, + int filterH, + int filterW, + int strideD, + int strideH, + int strideW, + int paddingD, + int paddingH, + int paddingW, + real alpha, + real beta); template void operator=(const ExpressionType& expr) { -- GitLab From 0a7516d193061ccb35ab410fc947bd245a936159 Mon Sep 17 00:00:00 2001 From: chengduoZH Date: Fri, 18 Aug 2017 14:14:27 +0800 Subject: [PATCH 0117/2018] fix col2vol vol2col kernel --- paddle/cuda/src/hl_cuda_matrix.cu | 192 ++++++++++++++++++++---------- 1 file changed, 129 insertions(+), 63 deletions(-) diff --git a/paddle/cuda/src/hl_cuda_matrix.cu b/paddle/cuda/src/hl_cuda_matrix.cu index f626c07a0..3bf1b0251 100644 --- a/paddle/cuda/src/hl_cuda_matrix.cu +++ b/paddle/cuda/src/hl_cuda_matrix.cu @@ -593,21 +593,28 @@ void hl_matrix_rotate( CHECK_SYNC("hl_matrix_rotate failed"); } - -__global__ void keMatrixVol2Col( - int num_kernels, real*dataSrc, real* dataDst, - int depth, int height, int width, - int filterD, int filterH, int filterW, - int strideD, int strideH, int strideW, - int paddingD, int paddingH, int paddingW, - int depth_col, int height_col, int width_col){ - - for (int index = blockIdx.x * blockDim.x + threadIdx.x; - index < num_kernels; - index += blockDim.x * gridDim.x){ - +__global__ void keMatrixVol2Col(int num_kernels, + real* dataSrc, + real* dataDst, + int depth, + int height, + int width, + int filterD, + int filterH, + int filterW, + int strideD, + int strideH, + int strideW, + int paddingD, + int paddingH, + int paddingW, + int depth_col, + int height_col, + int width_col) { + for (int index = blockIdx.x * blockDim.x + threadIdx.x; index < num_kernels; + index += blockDim.x * gridDim.x) { int w_out = index % width_col; - int h_out = (index / width_col ) % height_col; + int h_out = (index / width_col) % height_col; int d_out = (index / width_col / height_col) % depth_col; int channel_in = index / width_col / height_col / depth_col; int channel_out = channel_in * filterD * filterH * filterW; @@ -615,7 +622,9 @@ __global__ void keMatrixVol2Col( int h_in = h_out * strideH - paddingH; int d_in = d_out * strideD - paddingD; - dataDst += ((channel_out * depth_col + d_out) * height_col + h_out) * width_col + w_out; + dataDst += + ((channel_out * depth_col + d_out) * height_col + h_out) * width_col + + w_out; dataSrc += ((channel_in * depth + d_in) * height + h_in) * width + w_in; for (int k = 0; k < filterD; ++k) { for (int i = 0; i < filterH; ++i) { @@ -623,8 +632,10 @@ __global__ void keMatrixVol2Col( int d = d_in + k; int h = h_in + i; int w = w_in + j; - *dataDst = (d >= 0 && d < depth && h >= 0 && h < height && w >= 0 && w < width ) ? - dataSrc[(k * height + i) * width + j] : 0; + *dataDst = (d >= 0 && d < depth && h >= 0 && h < height && w >= 0 && + w < width) + ? dataSrc[(k * height + i) * width + j] + : 0; dataDst += depth_col * height_col * width_col; } } @@ -633,11 +644,20 @@ __global__ void keMatrixVol2Col( } void hl_matrix_vol2Col(real* dataSrc, - int channels, int depth, int height, int width, - int filterD, int filterH, int filterW, - int strideD, int strideH, int strideW, - int paddingD, int paddingH, int paddingW, real* dataDst){ - + int channels, + int depth, + int height, + int width, + int filterD, + int filterH, + int filterW, + int strideD, + int strideH, + int strideW, + int paddingD, + int paddingH, + int paddingW, + real* dataDst) { int depth_col = (depth + 2 * paddingD - filterD) / strideD + 1; int height_col = (height + 2 * paddingH - filterH) / strideH + 1; int width_col = (width + 2 * paddingW - filterW) / strideW + 1; @@ -646,34 +666,55 @@ void hl_matrix_vol2Col(real* dataSrc, const int threads = 512; const int blocks = DIVUP(num_kernels, threads); - keMatrixVol2Col<<< blocks, threads >>>( - num_kernels, dataSrc, dataDst, - depth, height, width, - filterD, filterH, filterW, - strideD, strideH, strideW, - paddingD, paddingH, paddingW, - depth_col, height_col, width_col); + keMatrixVol2Col<<>>(num_kernels, + dataSrc, + dataDst, + depth, + height, + width, + filterD, + filterH, + filterW, + strideD, + strideH, + strideW, + paddingD, + paddingH, + paddingW, + depth_col, + height_col, + width_col); CHECK_SYNC("hl_matrix_vol2Col failed"); } -__global__ void keMatrixCol2Vol( - int num_kernels, real*dataDst, real* dataSrc, - int depth, int height, int width, - int filterD, int filterH, int filterW, - int strideD, int strideH, int strideW, - int paddingD, int paddingH, int paddingW, - int depth_col, int height_col, int width_col, - real alpha, real beta){ - - for (int index = blockIdx.x * blockDim.x + threadIdx.x; - index < num_kernels; +__global__ void keMatrixCol2Vol(int num_kernels, + real* dataDst, + real* dataSrc, + int depth, + int height, + int width, + int filterD, + int filterH, + int filterW, + int strideD, + int strideH, + int strideW, + int paddingD, + int paddingH, + int paddingW, + int depth_col, + int height_col, + int width_col, + real alpha, + real beta) { + for (int index = blockIdx.x * blockDim.x + threadIdx.x; index < num_kernels; index += blockDim.x * gridDim.x) { - - real val = 0; + real srcVal = 0; + real dstVal = dataDst[index]; int w = index % width + paddingW; int h = (index / width) % height + paddingH; int d = (index / width / height) % depth + paddingD; - int c = index / (width * height * depth); + int c = index / width / height / depth; // compute the start and end of the output int w_col_start = (w < filterW) ? 0 : (w - filterW) / strideW + 1; int w_col_end = min(w / strideW + 1, width_col); @@ -682,32 +723,45 @@ __global__ void keMatrixCol2Vol( int d_col_start = (d < filterD) ? 0 : (d - filterD) / strideD + 1; int d_col_end = min(d / strideD + 1, depth_col); - int offset = (c * filterD * filterW * filterH + \ - d * filterW * filterH + h * filterW + w) * depth_col * height_col * width_col; + int offset = (c * filterD * filterW * filterH + d * filterW * filterH + + h * filterW + w) * + depth_col * height_col * width_col; - int coeff_d_col = (1 - strideD * filterW * filterH * depth_col) * height_col * width_col; - int coeff_h_col = (1 - strideH * filterW * depth_col * height_col) * width_col; + int coeff_d_col = + (1 - strideD * filterW * filterH * depth_col) * height_col * width_col; + int coeff_h_col = + (1 - strideH * filterW * depth_col * height_col) * width_col; int coeff_w_col = (1 - strideW * depth_col * height_col * width_col); for (int d_col = d_col_start; d_col < d_col_end; ++d_col) { for (int h_col = h_col_start; h_col < h_col_end; ++h_col) { for (int w_col = w_col_start; w_col < w_col_end; ++w_col) { - val += dataSrc[offset + d_col * coeff_d_col + h_col * coeff_h_col + w_col * coeff_w_col]; + srcVal += dataSrc[offset + d_col * coeff_d_col + h_col * coeff_h_col + + w_col * coeff_w_col]; } } } - dataDst[index] = val; + dataDst[index] = alpha * srcVal + beta * dstVal; } } void hl_matrix_col2Vol(real* dataDst, - int channels, int depth, int height, int width, - int filterD, int filterH, int filterW, - int strideD, int strideH, int strideW, - int paddingD, int paddingH, int paddingW, + int channels, + int depth, + int height, + int width, + int filterD, + int filterH, + int filterW, + int strideD, + int strideH, + int strideW, + int paddingD, + int paddingH, + int paddingW, real* dataSrc, - real alpha, real beta){ - + real alpha, + real beta) { int depth_col = (depth + 2 * paddingD - filterD) / strideD + 1; int height_col = (height + 2 * paddingH - filterH) / strideH + 1; int width_col = (width + 2 * paddingW - filterW) / strideW + 1; @@ -716,14 +770,26 @@ void hl_matrix_col2Vol(real* dataDst, const int threads = 512; const int blocks = DIVUP(num_kernels, threads); - keMatrixCol2Vol<<< blocks, threads >>>( - num_kernels, dataDst, dataSrc, - depth, height, width, - filterD, filterH, filterW, - strideD, strideH, strideW, - paddingD, paddingH, paddingW, - depth_col, height_col, width_col, - alpha, beta); + keMatrixCol2Vol<<>>(num_kernels, + dataDst, + dataSrc, + depth, + height, + width, + filterD, + filterH, + filterW, + strideD, + strideH, + strideW, + paddingD, + paddingH, + paddingW, + depth_col, + height_col, + width_col, + alpha, + beta); CHECK_SYNC("hl_matrix_col2Vol failed"); } -- GitLab From 62e6dac402ca63b402b5dfd1d7649cba1e258d41 Mon Sep 17 00:00:00 2001 From: tensor-tang Date: Fri, 18 Aug 2017 14:30:09 +0800 Subject: [PATCH 0118/2018] add MKLDNNMatrix files --- paddle/gserver/layers/MKLDNNLayer.h | 1 + paddle/math/CMakeLists.txt | 15 ++++++++++ paddle/math/MKLDNNMatrix.cpp | 19 ++++++++++++ paddle/math/MKLDNNMatrix.h | 45 +++++++++++++++++++++++++++++ 4 files changed, 80 insertions(+) create mode 100644 paddle/math/MKLDNNMatrix.cpp create mode 100644 paddle/math/MKLDNNMatrix.h diff --git a/paddle/gserver/layers/MKLDNNLayer.h b/paddle/gserver/layers/MKLDNNLayer.h index 63e29f447..9533027fa 100644 --- a/paddle/gserver/layers/MKLDNNLayer.h +++ b/paddle/gserver/layers/MKLDNNLayer.h @@ -18,6 +18,7 @@ limitations under the License. */ #include "Layer.h" #include "MKLDNNBase.h" #include "mkldnn.hpp" +#include "paddle/math/MKLDNNMatrix.h" DECLARE_bool(use_mkldnn); DECLARE_bool(use_mkldnn_wgt); diff --git a/paddle/math/CMakeLists.txt b/paddle/math/CMakeLists.txt index bf28092e8..ad6de18c8 100644 --- a/paddle/math/CMakeLists.txt +++ b/paddle/math/CMakeLists.txt @@ -14,6 +14,21 @@ # file(GLOB MATH_HEADERS . *.h) file(GLOB MATH_SOURCES . *.cpp) + +message(STATUS "----------MATH_HEADERS:${MATH_HEADERS}") +message(STATUS "----------MATH_SOURCES:${MATH_SOURCES}") +if(NOT WITH_MKLDNN) + file(GLOB_RECURSE DNN_HEADER RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "MKLDNN*.h") + file(GLOB_RECURSE DNN_SOURCES RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "MKLDNN*.cpp") + message(STATUS "----------DNN_HEADER:${DNN_HEADER}") + message(STATUS "----------DNN_SOURCES:${DNN_SOURCES}") + list(REMOVE_ITEM MATH_HEADERS ${DNN_HEADER}) + list(REMOVE_ITEM MATH_SOURCES ${DNN_SOURCES}) + message(STATUS "Skip compiling with MKLDNNMatrix") +else() + message(STATUS "Compile with MKLDNNMatrix") +endif() + set(MATH_SOURCES "${PADDLE_SOURCE_DIR}/paddle/math/BaseMatrix.cu" "${PADDLE_SOURCE_DIR}/paddle/math/TrainingAlgorithmOp.cu" diff --git a/paddle/math/MKLDNNMatrix.cpp b/paddle/math/MKLDNNMatrix.cpp new file mode 100644 index 000000000..df8e72d78 --- /dev/null +++ b/paddle/math/MKLDNNMatrix.cpp @@ -0,0 +1,19 @@ +/* Copyright (c) 2017 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 "MKLDNNMatrix.h" + +using namespace mkldnn; // NOLINT + +namespace paddle {} // namespace paddle diff --git a/paddle/math/MKLDNNMatrix.h b/paddle/math/MKLDNNMatrix.h new file mode 100644 index 000000000..91ef56f2c --- /dev/null +++ b/paddle/math/MKLDNNMatrix.h @@ -0,0 +1,45 @@ +/* Copyright (c) 2017 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 "Matrix.h" +#include "Vector.h" + +#include "mkldnn.hpp" +#include "paddle/parameter/Parameter.h" + +namespace paddle { + +static const std::map PARAM_FOARMAT_MAP = + {{mkldnn::memory::format::oi, PARAM_FORMAT_MKLDNN_OI}}; + +class MKLDNNMatrix; +typedef std::shared_ptr MKLDNNMatrixPtr; + +/** + * @brief MKLDNN Matrix. + * + */ +class MKLDNNMatrix : public CpuVector { +public: + explicit MKLDNNMatrix(size_t size, int fmt) : CpuVector(size), fmt_(fmt) {} + + ~MKLDNNMatrix() {} + +protected: + int fmt_; +}; + +} // namespace paddle -- GitLab From 55437b58b9b91d543f3498c3913a75bfb1122d6f Mon Sep 17 00:00:00 2001 From: liaogang Date: Fri, 18 Aug 2017 14:36:17 +0800 Subject: [PATCH 0119/2018] Add ENVIRONMENT interface interface --- paddle/memory/memory.cc | 10 +++++ paddle/memory/memory.h | 1 - paddle/platform/CMakeLists.txt | 3 +- paddle/platform/environment.h | 59 +++++++++++++++++++++++++++++ paddle/platform/environment_test.cc | 54 ++++++++++++++++++++++++++ paddle/platform/gpu_info.cc | 10 +++++ paddle/platform/gpu_info.h | 4 ++ 7 files changed, 139 insertions(+), 2 deletions(-) create mode 100644 paddle/platform/environment.h create mode 100644 paddle/platform/environment_test.cc diff --git a/paddle/memory/memory.cc b/paddle/memory/memory.cc index c99cc5415..0f46e1b8e 100644 --- a/paddle/memory/memory.cc +++ b/paddle/memory/memory.cc @@ -18,8 +18,13 @@ limitations under the License. */ #include // for memcpy #include // for call_once +#include "glog/logging.h" + #include "paddle/memory/detail/buddy_allocator.h" #include "paddle/memory/detail/system_allocator.h" +#include "paddle/platform/gpu_info.h" + +DECLARE_double(fraction_of_gpu_memory_to_use); namespace paddle { namespace memory { @@ -79,6 +84,11 @@ BuddyAllocator* GetGPUBuddyAllocator(int gpu_id) { platform::GpuMinChunkSize(), platform::GpuMaxChunkSize())); } + VLOG(3) << "\n\nNOTE: each GPU device use " + << FLAGS_fraction_of_gpu_memory_to_use * 100 << "% of GPU memory.\n" + << "You can set environment variable '" + << platform::kEnvFractionGpuMemoryToUse + << "' to change the fraction of GPU usage.\n\n"; }); platform::SetDeviceId(gpu_id); diff --git a/paddle/memory/memory.h b/paddle/memory/memory.h index 72351b9df..11bbb8818 100644 --- a/paddle/memory/memory.h +++ b/paddle/memory/memory.h @@ -14,7 +14,6 @@ limitations under the License. */ #pragma once -#include "paddle/platform/gpu_info.h" #include "paddle/platform/place.h" namespace paddle { diff --git a/paddle/platform/CMakeLists.txt b/paddle/platform/CMakeLists.txt index acfc06397..120eb1e4a 100644 --- a/paddle/platform/CMakeLists.txt +++ b/paddle/platform/CMakeLists.txt @@ -1,7 +1,7 @@ cc_library(cpu_info SRCS cpu_info.cc DEPS gflags glog) cc_test(cpu_info_test SRCS cpu_info_test.cc DEPS cpu_info) -nv_library(gpu_info SRCS gpu_info.cc DEPS gflags) +nv_library(gpu_info SRCS gpu_info.cc DEPS gflags glog) cc_library(place SRCS place.cc) cc_test(place_test SRCS place_test.cc DEPS place glog gflags) @@ -9,6 +9,7 @@ cc_test(place_test SRCS place_test.cc DEPS place glog gflags) add_subdirectory(dynload) cc_test(enforce_test SRCS enforce_test.cc DEPS stringpiece) +cc_test(environment_test SRCS environment_test.cc DEPS stringpiece) IF(WITH_GPU) set(GPU_CTX_DEPS dynload_cuda dynamic_loader) diff --git a/paddle/platform/environment.h b/paddle/platform/environment.h new file mode 100644 index 000000000..b868de489 --- /dev/null +++ b/paddle/platform/environment.h @@ -0,0 +1,59 @@ +/* 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 +#include + +#include "paddle/platform/enforce.h" +#include "paddle/string/piece.h" + +extern char** environ; + +namespace paddle { +namespace platform { + +inline void SetEnvVariable(const std::string& name, const std::string& value) { + PADDLE_ENFORCE_NE(setenv(name.c_str(), value.c_str(), 1), -1, + "Failed to set environment variable %s=%s", name, value); +} + +inline void UnsetEnvVariable(const std::string& name) { + PADDLE_ENFORCE_NE(unsetenv(name.c_str()), -1, + "Failed to unset environment variable %s", name); +} + +inline bool IsEnvVarDefined(const std::string& name) { + return std::getenv(name.c_str()) != nullptr; +} + +inline std::string GetEnvValue(const std::string& name) { + PADDLE_ENFORCE(IsEnvVarDefined(name), + "Tried to access undefined environment variable %s", name); + return std::getenv(name.c_str()); +} + +inline std::vector GetAllEnvVariables() { + std::vector vars; + for (auto var = environ; *var != nullptr; ++var) { + auto tail = string::Index(*var, "="); + auto name = string::SubStr(*var, 0, tail).ToString(); + vars.push_back(name); + } + return vars; +} + +} // namespace platform +} // namespace paddle diff --git a/paddle/platform/environment_test.cc b/paddle/platform/environment_test.cc new file mode 100644 index 000000000..5f1365272 --- /dev/null +++ b/paddle/platform/environment_test.cc @@ -0,0 +1,54 @@ +/* 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/platform/environment.h" + +#include "glog/logging.h" +#include "gtest/gtest.h" + +TEST(ENVIRONMENT, ACCESS) { + namespace platform = paddle::platform; + namespace string = paddle::string; + + platform::SetEnvVariable("PADDLE_USE_ENV", "TRUE"); + + EXPECT_TRUE(platform::IsEnvVarDefined("PADDLE_USE_ENV")); + EXPECT_EQ(platform::GetEnvValue("PADDLE_USE_ENV"), "TRUE"); + + platform::UnsetEnvVariable("PADDLE_USE_ENV"); + EXPECT_FALSE(platform::IsEnvVarDefined("PADDLE_USE_ENV")); + + platform::SetEnvVariable("PADDLE_USE_ENV1", "Hello "); + platform::SetEnvVariable("PADDLE_USE_ENV2", "World, "); + platform::SetEnvVariable("PADDLE_USE_ENV3", "PaddlePaddle!"); + + std::string env_info; + auto vars = platform::GetAllEnvVariables(); + for_each(vars.begin(), vars.end(), [&](const std::string& var) { + env_info += platform::GetEnvValue(var); + }); + + EXPECT_TRUE(string::Contains(env_info, "Hello World, PaddlePaddle!")); + platform::UnsetEnvVariable("PADDLE_USE_ENV1"); + platform::UnsetEnvVariable("PADDLE_USE_ENV2"); + platform::UnsetEnvVariable("PADDLE_USE_ENV3"); + + env_info.clear(); + vars = platform::GetAllEnvVariables(); + for_each(vars.begin(), vars.end(), [&](const std::string& var) { + env_info += platform::GetEnvValue(var); + }); + + EXPECT_FALSE(string::Contains(env_info, "Hello World, PaddlePaddle!")); + EXPECT_FALSE(platform::IsEnvVarDefined("PADDLE_USE_ENV1")); + EXPECT_FALSE(platform::IsEnvVarDefined("PADDLE_USE_ENV2")); + EXPECT_FALSE(platform::IsEnvVarDefined("PADDLE_USE_ENV3")); +} diff --git a/paddle/platform/gpu_info.cc b/paddle/platform/gpu_info.cc index edeb3ecd7..be381a4e2 100644 --- a/paddle/platform/gpu_info.cc +++ b/paddle/platform/gpu_info.cc @@ -13,8 +13,11 @@ See the License for the specific language governing permissions and limitations under the License. */ #include "paddle/platform/gpu_info.h" + #include "gflags/gflags.h" + #include "paddle/platform/enforce.h" +#include "paddle/platform/environment.h" DEFINE_double(fraction_of_gpu_memory_to_use, 0.95, "Default use 95% of GPU memory for PaddlePaddle," @@ -70,6 +73,13 @@ size_t GpuMaxChunkSize() { GpuMemoryUsage(available, total); + if (IsEnvVarDefined(kEnvFractionGpuMemoryToUse)) { + auto val = std::stod(GetEnvValue(kEnvFractionGpuMemoryToUse)); + PADDLE_ENFORCE_GT(val, 0.0); + PADDLE_ENFORCE_LE(val, 1.0); + FLAGS_fraction_of_gpu_memory_to_use = val; + } + // Reserving the rest memory for page tables, etc. size_t reserving = (1 - FLAGS_fraction_of_gpu_memory_to_use) * total; diff --git a/paddle/platform/gpu_info.h b/paddle/platform/gpu_info.h index d3a5f5f13..6a9983824 100644 --- a/paddle/platform/gpu_info.h +++ b/paddle/platform/gpu_info.h @@ -18,10 +18,14 @@ limitations under the License. */ #include #include +#include namespace paddle { namespace platform { +//! Environment variable: fraction of GPU memory to use on each device. +const std::string kEnvFractionGpuMemoryToUse = "FRACTION_GPU_MEMORY_TO_USE"; + //! Get the total number of GPU devices in system. int GetDeviceCount(); -- GitLab From 3ec9ecb162d67b25adb7563381c9e693e3c6f306 Mon Sep 17 00:00:00 2001 From: liaogang Date: Fri, 18 Aug 2017 15:36:57 +0800 Subject: [PATCH 0120/2018] Fix conflicts with new declaration with 'C' linkage --- paddle/platform/environment.h | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/paddle/platform/environment.h b/paddle/platform/environment.h index b868de489..9ed765352 100644 --- a/paddle/platform/environment.h +++ b/paddle/platform/environment.h @@ -15,13 +15,12 @@ limitations under the License. */ #pragma once #include +#include #include #include "paddle/platform/enforce.h" #include "paddle/string/piece.h" -extern char** environ; - namespace paddle { namespace platform { @@ -46,6 +45,7 @@ inline std::string GetEnvValue(const std::string& name) { } inline std::vector GetAllEnvVariables() { + extern char** environ; std::vector vars; for (auto var = environ; *var != nullptr; ++var) { auto tail = string::Index(*var, "="); -- GitLab From 83d0016f54a79faa6cc8626283fd96eb3f704183 Mon Sep 17 00:00:00 2001 From: liaogang Date: Fri, 18 Aug 2017 15:49:10 +0800 Subject: [PATCH 0121/2018] Fix undefined reference --- paddle/platform/environment.h | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/paddle/platform/environment.h b/paddle/platform/environment.h index 9ed765352..4edcce932 100644 --- a/paddle/platform/environment.h +++ b/paddle/platform/environment.h @@ -21,6 +21,8 @@ limitations under the License. */ #include "paddle/platform/enforce.h" #include "paddle/string/piece.h" +extern char** environ; // for environment variables + namespace paddle { namespace platform { @@ -45,7 +47,6 @@ inline std::string GetEnvValue(const std::string& name) { } inline std::vector GetAllEnvVariables() { - extern char** environ; std::vector vars; for (auto var = environ; *var != nullptr; ++var) { auto tail = string::Index(*var, "="); -- GitLab From 38cc5dadcc5c76c4aa50f5e92b560f4ccaba9227 Mon Sep 17 00:00:00 2001 From: chengduoZH Date: Fri, 18 Aug 2017 16:43:59 +0800 Subject: [PATCH 0122/2018] modified bias shape of ConvLayer --- paddle/gserver/layers/Conv3DLayer.cpp | 5 ----- paddle/gserver/layers/ConvBaseLayer.cpp | 17 ++++++++--------- paddle/gserver/layers/DeConv3DLayer.cpp | 5 ----- 3 files changed, 8 insertions(+), 19 deletions(-) diff --git a/paddle/gserver/layers/Conv3DLayer.cpp b/paddle/gserver/layers/Conv3DLayer.cpp index 5609a4cc7..106909824 100644 --- a/paddle/gserver/layers/Conv3DLayer.cpp +++ b/paddle/gserver/layers/Conv3DLayer.cpp @@ -37,11 +37,6 @@ bool Conv3DLayer::init(const LayerMap &layerMap, weights_[index]->getWGrad()->getHeight()); ++index; } - if (nullptr != biases_->getWGrad()) - biases_->getWGrad()->reshape(biases_->getWGrad()->width_, - biases_->getWGrad()->height_); - if (nullptr != biases_->getW()) - biases_->getW()->reshape(biases_->getW()->width_, biases_->getW()->height_); CHECK(inputLayers_.size() == parameters_.size()); return true; } diff --git a/paddle/gserver/layers/ConvBaseLayer.cpp b/paddle/gserver/layers/ConvBaseLayer.cpp index e437b0b86..6bcbe0ddb 100644 --- a/paddle/gserver/layers/ConvBaseLayer.cpp +++ b/paddle/gserver/layers/ConvBaseLayer.cpp @@ -21,11 +21,10 @@ bool ConvBaseLayer::init(const LayerMap& layerMap, const ParameterMap& parameterMap) { /* Initialize the basic parent class */ Layer::init(layerMap, parameterMap); - isDeconv_ = (config_.type() == "exconv" || - config_.type() == "cudnn_conv" || - config_.type() == "conv3d" || - config_.type() == "deconv3d" ) - ? false : true; + isDeconv_ = (config_.type() == "exconv" || config_.type() == "cudnn_conv" || + config_.type() == "conv3d" || config_.type() == "deconv3d") + ? false + : true; /* Initialize the convolutional layer parameter */ numFilters_ = config_.num_filters(); @@ -52,8 +51,8 @@ bool ConvBaseLayer::init(const LayerMap& layerMap, filterSizeZ_.push_back(conf.filter_size_z()); imgSizeD_.push_back(conf.img_size_z()); outputD_.push_back(conf.output_z()); - filterPixels_.push_back( - filterSize_.back() * filterSizeY_.back() * filterSizeZ_.back()); + filterPixels_.push_back(filterSize_.back() * filterSizeY_.back() * + filterSizeZ_.back()); } CHECK(inputLayers_.size() == parameters_.size()); @@ -73,10 +72,10 @@ bool ConvBaseLayer::init(const LayerMap& layerMap, if (sharedBiases_) { CHECK_EQ((size_t)numFilters_, biasParameter_->getSize()); biases_ = - std::unique_ptr(new Weight(numFilters_, 1, biasParameter_)); + std::unique_ptr(new Weight(1, numFilters_, biasParameter_)); } else { biases_ = - std::unique_ptr(new Weight(getSize(), 1, biasParameter_)); + std::unique_ptr(new Weight(1, getSize(), biasParameter_)); } } diff --git a/paddle/gserver/layers/DeConv3DLayer.cpp b/paddle/gserver/layers/DeConv3DLayer.cpp index 286f5b985..5a54a6844 100644 --- a/paddle/gserver/layers/DeConv3DLayer.cpp +++ b/paddle/gserver/layers/DeConv3DLayer.cpp @@ -39,11 +39,6 @@ bool DeConv3DLayer::init(const LayerMap &layerMap, weights_[index]->getWGrad()->reshape(filterPixels_[index] * numFilters_, filterChannels_[index]); } - if (biases_->getWGrad()) - biases_->getWGrad()->reshape(biases_->getWGrad()->width_, - biases_->getWGrad()->height_); - if (biases_->getW()) - biases_->getW()->reshape(biases_->getW()->width_, biases_->getW()->height_); CHECK(inputLayers_.size() == parameters_.size()); return true; } -- GitLab From b3ab15a7abed52a7b70d74fd7b9642b2ca0ca7b1 Mon Sep 17 00:00:00 2001 From: liaogang Date: Fri, 18 Aug 2017 17:39:10 +0800 Subject: [PATCH 0123/2018] follow comments --- paddle/platform/gpu_info.h | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/paddle/platform/gpu_info.h b/paddle/platform/gpu_info.h index 6a9983824..ed2420b87 100644 --- a/paddle/platform/gpu_info.h +++ b/paddle/platform/gpu_info.h @@ -24,7 +24,8 @@ namespace paddle { namespace platform { //! Environment variable: fraction of GPU memory to use on each device. -const std::string kEnvFractionGpuMemoryToUse = "FRACTION_GPU_MEMORY_TO_USE"; +const std::string kEnvFractionGpuMemoryToUse = + "PADDLE_FRACTION_GPU_MEMORY_TO_USE"; //! Get the total number of GPU devices in system. int GetDeviceCount(); -- GitLab From 3065cb26258e1a7a014c6e367747214615832c3a Mon Sep 17 00:00:00 2001 From: Luo Tao Date: Fri, 18 Aug 2017 17:43:06 +0800 Subject: [PATCH 0124/2018] add huber_regression_cost --- doc/api/v2/config/layer.rst | 5 ++ paddle/gserver/layers/CostLayer.cpp | 55 +++++++++++++++++++ paddle/gserver/layers/CostLayer.h | 24 ++++++++ paddle/gserver/tests/test_LayerGrad.cpp | 20 ++++++- proto/ModelConfig.proto | 3 + python/paddle/trainer/config_parser.py | 11 ++++ .../paddle/trainer_config_helpers/layers.py | 53 ++++++++++++++++++ .../protostr/test_cost_layers.protostr | 17 ++++++ .../tests/configs/test_cost_layers.py | 2 + python/paddle/v2/tests/test_layer.py | 5 +- 10 files changed, 192 insertions(+), 3 deletions(-) diff --git a/doc/api/v2/config/layer.rst b/doc/api/v2/config/layer.rst index 22a6b2ab8..9a5901616 100644 --- a/doc/api/v2/config/layer.rst +++ b/doc/api/v2/config/layer.rst @@ -409,6 +409,11 @@ multi_binary_label_cross_entropy_cost .. autoclass:: paddle.v2.layer.multi_binary_label_cross_entropy_cost :noindex: +huber_regression_cost +------------------------- +.. autoclass:: paddle.v2.layer.huber_regression_cost + :noindex: + huber_classification_cost ------------------------- .. autoclass:: paddle.v2.layer.huber_classification_cost diff --git a/paddle/gserver/layers/CostLayer.cpp b/paddle/gserver/layers/CostLayer.cpp index 69cf39322..91a742422 100644 --- a/paddle/gserver/layers/CostLayer.cpp +++ b/paddle/gserver/layers/CostLayer.cpp @@ -594,6 +594,61 @@ void HuberCost::forwardImp(Matrix& output, Argument& label, Matrix& cost) { } } +// +// Huber loss for robust regression. +// +REGISTER_LAYER(huber_regression, HuberRegressionLoss); + +bool HuberRegressionLoss::init(const LayerMap& layerMap, + const ParameterMap& parameterMap) { + HuberCost::init(layerMap, parameterMap); + delta_ = config_.delta(); + return true; +} + +void HuberRegressionLoss::forwardImp(Matrix& output, + Argument& label, + Matrix& target) { + HuberCost::forwardImp(output, label, target); + size_t numSamples = target.getHeight(); + CHECK(label.value); + CHECK_EQ((*label.value).getHeight(), numSamples); + CHECK_EQ(output.getHeight(), numSamples); + CHECK_EQ(output.getWidth(), (*label.value).getWidth()); + CHECK_EQ(target.getWidth(), (size_t)1); + + real* out = useGpu_ ? tmpCpuInput_[0].value->getData() : output.getData(); + real* lbl = + useGpu_ ? tmpCpuInput_[1].value->getData() : (*label.value).getData(); + std::vector cost(numSamples); + for (size_t i = 0; i < numSamples; ++i) { + real a = std::abs(lbl[i] - out[i]); + if (a <= delta_) + cost[i] = a * a / 2; + else + cost[i] = delta_ * (a - delta_ / 2); + } + target.copyFrom(cost.data(), numSamples); +} + +void HuberRegressionLoss::backwardImp(Matrix& output, + Argument& label, + Matrix& outputG) { + size_t numSamples = output.getHeight(); + real* out = useGpu_ ? tmpCpuInput_[0].value->getData() : output.getData(); + real* lbl = + useGpu_ ? tmpCpuInput_[1].value->getData() : (*label.value).getData(); + real* grad = useGpu_ ? tmpCpuInput_[0].grad->getData() : outputG.getData(); + for (size_t i = 0; i < numSamples; ++i) { + real a = lbl[i] - out[i]; + if (std::abs(a) <= delta_) + grad[i] += -a; + else + grad[i] += a > 0 ? delta_ : -delta_; + } + if (useGpu_) outputG.copyFrom(grad, numSamples); +} + // // Huber loss for robust 2-classes classification // diff --git a/paddle/gserver/layers/CostLayer.h b/paddle/gserver/layers/CostLayer.h index c006dc811..0ce72ef40 100644 --- a/paddle/gserver/layers/CostLayer.h +++ b/paddle/gserver/layers/CostLayer.h @@ -321,6 +321,30 @@ public: void backwardImp(Matrix& outputValue, Argument& label, Matrix& outputGrad) {} }; +/** + * Huber loss for robust regression. + * + * Given output f(x), label y and delta, the loss is: + * Loss = 0.5 * (1 - y * f)^2, if abs(y - f) <= delta \\ + * Loss = delta * abs(y - f) - 0.5 * delta^2, otherwise + */ +class HuberRegressionLoss : public HuberCost { +public: + explicit HuberRegressionLoss(const LayerConfig& config) : HuberCost(config) {} + + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override; + + void forwardImp(Matrix& output, Argument& label, Matrix& cost) override; + + void backwardImp(Matrix& outputValue, + Argument& label, + Matrix& outputGrad) override; + +protected: + real delta_; +}; + /** * Huber loss for robust 2-classes classification. * diff --git a/paddle/gserver/tests/test_LayerGrad.cpp b/paddle/gserver/tests/test_LayerGrad.cpp index 6d60250f6..c522b20f0 100644 --- a/paddle/gserver/tests/test_LayerGrad.cpp +++ b/paddle/gserver/tests/test_LayerGrad.cpp @@ -828,6 +828,24 @@ TEST(Layer, square_error_weighted) { } } +TEST(Layer, huber_regression_loss) { + TestConfig config; + config.layerConfig.set_type("huber_regression"); + config.biasSize = 0; + + config.inputDefs.push_back({INPUT_DATA, "layer_0", 10, 0}); + config.inputDefs.push_back({INPUT_DATA_TARGET, "layer_1", 10, 0}); + config.layerConfig.add_inputs(); + config.layerConfig.add_inputs(); + + for (auto useGpu : {false, true}) { + for (auto delta : {1, 3, 5}) { + config.layerConfig.set_delta(delta); + testLayerGrad(config, "huber_regression", 100, /* trans */ false, useGpu); + } + } +} + TEST(Layer, huber_two_class) { TestConfig config; config.layerConfig.set_type("huber_classification"); @@ -839,7 +857,7 @@ TEST(Layer, huber_two_class) { config.layerConfig.add_inputs(); for (auto useGpu : {false, true}) { - testLayerGrad(config, "huber", 100, /* trans */ false, useGpu); + testLayerGrad(config, "huber_two_class", 100, /* trans */ false, useGpu); } } diff --git a/proto/ModelConfig.proto b/proto/ModelConfig.proto index 4f3d5bf3f..e19e0f85f 100644 --- a/proto/ModelConfig.proto +++ b/proto/ModelConfig.proto @@ -496,6 +496,9 @@ message LayerConfig { optional int32 axis = 54 [ default = 2 ]; repeated uint32 offset = 55; repeated uint32 shape = 56; + + // for HuberRegressionLoss + optional double delta = 57 [ default = 1.0 ]; } message EvaluatorConfig { diff --git a/python/paddle/trainer/config_parser.py b/python/paddle/trainer/config_parser.py index 248da9417..a3ca3f251 100644 --- a/python/paddle/trainer/config_parser.py +++ b/python/paddle/trainer/config_parser.py @@ -2317,6 +2317,17 @@ class LambdaCost(LayerBase): self.config.max_sort_size = max_sort_size +@config_layer('huber_regression') +class HuberRegressionLoss(LayerBase): + def __init__(self, name, inputs, delta=1., coeff=1., device=None): + super(HuberRegressionLoss, self).__init__( + name, 'huber_regression', 1, inputs=inputs, device=device) + config_assert( + len(self.inputs) == 2, 'HuberRegression must have 2 inputs') + self.config.delta = delta + self.config.coeff = coeff + + @config_layer('nce') class NCELayer(LayerBase): def __init__(self, diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py index 20d96efe1..d61c94dc8 100755 --- a/python/paddle/trainer_config_helpers/layers.py +++ b/python/paddle/trainer_config_helpers/layers.py @@ -108,6 +108,7 @@ __all__ = [ 'sum_cost', 'rank_cost', 'lambda_cost', + 'huber_regression_cost', 'huber_classification_cost', 'block_expand_layer', 'maxout_layer', @@ -216,6 +217,7 @@ class LayerType(object): RANK_COST = 'rank-cost' LAMBDA_COST = 'lambda_cost' + HUBER_REGRESSION = 'huber_regression' HUBER_CLASSIFICATION = 'huber_classification' CROSS_ENTROPY = 'multi-class-cross-entropy' CROSS_ENTROPY_WITH_SELFNORM = 'multi_class_cross_entropy_with_selfnorm' @@ -5603,6 +5605,57 @@ def sum_cost(input, name=None, layer_attr=None): return LayerOutput(name, LayerType.SUM_COST, parents=[input], size=1) +@wrap_name_default() +@layer_support() +def huber_regression_cost(input, + label, + name=None, + delta=1.0, + coeff=1.0, + layer_attr=None): + """ + In statistics, the Huber loss is a loss function used in robust regression, + that is less sensitive to outliers in data than the squared error loss. + Given a prediction f(x), a label y and :math:`\delta`, the loss function + is defined as: + + .. math: + loss = 0.5*\left ( y-f(x) \right )^2, \left | y-f(x) \right |\leq \delta + loss = \delta \left | y-f(x) \right |-0.5\delta ^2, otherwise + + The example usage is: + + .. code-block:: python + + cost = huber_regression_cost(input=input_layer, label=label_layer) + + :param input: The first input layer. + :type input: LayerOutput. + :param label: The input label. + :type input: LayerOutput. + :param name: The name of this layers. It is not necessary. + :type name: None|basestring. + :param delta: The difference between the observed and predicted values. + :type delta: float. + :param coeff: The coefficient affects the gradient in the backward. + :type coeff: float. + :param layer_attr: Extra Layer Attribute. + :type layer_attr: ExtraLayerAttribute + :return: LayerOutput object. + :rtype: LayerOutput. + """ + assert isinstance(input, LayerOutput) + Layer( + name=name, + type=LayerType.HUBER_REGRESSION, + inputs=[input.name, label.name], + delta=delta, + coeff=coeff, + **ExtraLayerAttribute.to_kwargs(layer_attr)) + return LayerOutput( + name, LayerType.HUBER_REGRESSION, parents=[input, label], size=1) + + @wrap_name_default() @layer_support() def huber_classification_cost(input, diff --git a/python/paddle/trainer_config_helpers/tests/configs/protostr/test_cost_layers.protostr b/python/paddle/trainer_config_helpers/tests/configs/protostr/test_cost_layers.protostr index a64e5ea0d..55ab464dd 100644 --- a/python/paddle/trainer_config_helpers/tests/configs/protostr/test_cost_layers.protostr +++ b/python/paddle/trainer_config_helpers/tests/configs/protostr/test_cost_layers.protostr @@ -167,6 +167,20 @@ layers { softmax_selfnorm_alpha: 0.1 coeff: 1.0 } +layers { + name: "__huber_regression_cost_0__" + type: "huber_regression" + size: 1 + active_type: "" + inputs { + input_layer_name: "input" + } + inputs { + input_layer_name: "labels" + } + coeff: 1.0 + delta: 1.0 +} layers { name: "huber_probs" type: "data" @@ -300,6 +314,7 @@ output_layer_names: "__rank_cost_0__" output_layer_names: "__lambda_cost_0__" output_layer_names: "__cross_entropy_0__" output_layer_names: "__cross_entropy_with_selfnorm_0__" +output_layer_names: "__huber_regression_cost_0__" output_layer_names: "__huber_classification_cost_0__" output_layer_names: "__multi_binary_label_cross_entropy_0__" output_layer_names: "__sum_cost_0__" @@ -324,6 +339,7 @@ sub_models { layer_names: "__lambda_cost_0__" layer_names: "__cross_entropy_0__" layer_names: "__cross_entropy_with_selfnorm_0__" + layer_names: "__huber_regression_cost_0__" layer_names: "huber_probs" layer_names: "huber_label" layer_names: "__huber_classification_cost_0__" @@ -349,6 +365,7 @@ sub_models { output_layer_names: "__lambda_cost_0__" output_layer_names: "__cross_entropy_0__" output_layer_names: "__cross_entropy_with_selfnorm_0__" + output_layer_names: "__huber_regression_cost_0__" output_layer_names: "__huber_classification_cost_0__" output_layer_names: "__multi_binary_label_cross_entropy_0__" output_layer_names: "__sum_cost_0__" diff --git a/python/paddle/trainer_config_helpers/tests/configs/test_cost_layers.py b/python/paddle/trainer_config_helpers/tests/configs/test_cost_layers.py index 98bf026d6..7ce375c70 100644 --- a/python/paddle/trainer_config_helpers/tests/configs/test_cost_layers.py +++ b/python/paddle/trainer_config_helpers/tests/configs/test_cost_layers.py @@ -33,6 +33,8 @@ outputs( input=probs, label=xe_label), cross_entropy_with_selfnorm( input=probs, label=xe_label), + huber_regression_cost( + input=seq_in, label=labels), huber_classification_cost( input=data_layer( name='huber_probs', size=1), diff --git a/python/paddle/v2/tests/test_layer.py b/python/paddle/v2/tests/test_layer.py index 7373a55ce..783a0ca85 100644 --- a/python/paddle/v2/tests/test_layer.py +++ b/python/paddle/v2/tests/test_layer.py @@ -141,12 +141,13 @@ class CostLayerTest(unittest.TestCase): cost8 = layer.rank_cost(left=score, right=score, label=score) cost9 = layer.lambda_cost(input=inference, score=score) cost10 = layer.sum_cost(input=inference) - cost11 = layer.huber_classification_cost(input=score, label=label) + cost11 = layer.huber_regression_cost(input=score, label=label) + cost12 = layer.huber_classification_cost(input=score, label=label) print layer.parse_network([cost1, cost2]) print layer.parse_network([cost3, cost4]) print layer.parse_network([cost5, cost6]) - print layer.parse_network([cost7, cost8, cost9, cost10, cost11]) + print layer.parse_network([cost7, cost8, cost9, cost10, cost11, cost12]) crf = layer.crf(input=inference, label=label) crf_decoding = layer.crf_decoding(input=inference, size=3) -- GitLab From 82b820e97b90f21d7b46629bba72436a69e888e1 Mon Sep 17 00:00:00 2001 From: qiaolongfei Date: Fri, 18 Aug 2017 08:21:56 -0700 Subject: [PATCH 0125/2018] fix rowwise_add_grad_op --- paddle/operators/rowwise_add_op.cc | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paddle/operators/rowwise_add_op.cc b/paddle/operators/rowwise_add_op.cc index f07dd8f60..6825dce33 100644 --- a/paddle/operators/rowwise_add_op.cc +++ b/paddle/operators/rowwise_add_op.cc @@ -63,7 +63,7 @@ class RowwiseAddGradOp : public framework::OperatorWithKernel { "Input(Out@GRAD) should not be null"); auto dims0 = ctx.Input("X")->dims(); auto dims1 = ctx.Input("b")->dims(); - PADDLE_ENFORCE_EQ(1, framework::product(dims1), "b dims should be 1") + PADDLE_ENFORCE_EQ(1, dims1.size(), "b dims should be 1") ctx.Output(framework::GradVarName("X"))->Resize(dims0); ctx.Output(framework::GradVarName("b"))->Resize(dims1); } -- GitLab From 0cf5bdec563c4360f36c90ced8a73c7493874bf4 Mon Sep 17 00:00:00 2001 From: dongzhihong Date: Fri, 18 Aug 2017 14:24:24 -0700 Subject: [PATCH 0126/2018] "tensor mutable data" --- paddle/operators/mul_op.h | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/paddle/operators/mul_op.h b/paddle/operators/mul_op.h index 9bbd02752..8facc0281 100644 --- a/paddle/operators/mul_op.h +++ b/paddle/operators/mul_op.h @@ -51,9 +51,11 @@ class MulGradKernel : public framework::OpKernel { auto* dX = ctx.Output(framework::GradVarName("X")); auto* dY = ctx.Output(framework::GradVarName("Y")); + dX->mutable_data(ctx.GetPlace()); + dY->mutable_data(ctx.GetPlace()); auto* device_context = const_cast(ctx.device_context_); - // dX = dOut' * Y. dX: M x K, dOut : M x N, Y : K x N + // dX = dOut * Y'. dX: M x K, dOut : M x N, Y : K x N math::matmul(*dOut, false, *Y, true, 1, dX, 0, device_context); // dY = X' * dOut. dY: K x N, dOut : M x N, X : M x K math::matmul(*X, true, *dOut, false, 1, dY, 0, device_context); -- GitLab From 514398c0b17cb3b340ca05a885e1ed66c2405ea9 Mon Sep 17 00:00:00 2001 From: dongzhihong Date: Fri, 18 Aug 2017 15:04:04 -0700 Subject: [PATCH 0127/2018] "delete unused comment" --- paddle/operators/math/math_function.h | 9 --------- 1 file changed, 9 deletions(-) diff --git a/paddle/operators/math/math_function.h b/paddle/operators/math/math_function.h index c7c603929..155589fad 100644 --- a/paddle/operators/math/math_function.h +++ b/paddle/operators/math/math_function.h @@ -77,15 +77,6 @@ void matmul(const framework::Tensor& matrix_a, bool trans_a, framework::Tensor* matrix_out, T beta, platform::DeviceContext* context); -// // matrix multiply with continuous memory -// template -// void matmul(const framework::Tensor& matrix_a, bool trans_a, -// const framework::Tensor& matrix_b, bool trans_b, -// framework::Tensor* matrix_out, -// platform::DeviceContext* context) { -// matmul(matrix_a, matrix_b, trans_a, trans_b, 1, matrix_out, 0, context); -// } - } // namespace math } // namespace operators } // namespace paddle -- GitLab From b59002daef841d752bda2a46eeac446008f93a03 Mon Sep 17 00:00:00 2001 From: dongzhihong Date: Fri, 18 Aug 2017 15:41:04 -0700 Subject: [PATCH 0128/2018] "fix math gemm lda order error" --- paddle/operators/math/math_function.cc | 8 ++++---- python/paddle/v2/framework/tests/test_mul_op.py | 4 +++- 2 files changed, 7 insertions(+), 5 deletions(-) diff --git a/paddle/operators/math/math_function.cc b/paddle/operators/math/math_function.cc index affdd1ac2..1e86fc3d1 100644 --- a/paddle/operators/math/math_function.cc +++ b/paddle/operators/math/math_function.cc @@ -25,8 +25,8 @@ void gemm(const CBLAS_TRANSPOSE transA, const float alpha, const float* A, const float* B, const float beta, float* C, platform::DeviceContext* context) { - int lda = K; - int ldb = N; + int lda = (transA == CblasNoTrans) ? K : M; + int ldb = (transB == CblasNoTrans) ? N : K; int ldc = N; cblas_sgemm(CblasRowMajor, transA, transB, M, N, K, alpha, A, lda, B, ldb, beta, C, ldc); @@ -40,8 +40,8 @@ void gemm(const CBLAS_TRANSPOSE transA, const double* B, const double beta, double* C, platform::DeviceContext* context) { - int lda = K; - int ldb = N; + int lda = (transA == CblasNoTrans) ? K : M; + int ldb = (transB == CblasNoTrans) ? N : K; int ldc = N; cblas_dgemm(CblasRowMajor, transA, transB, M, N, K, alpha, A, lda, B, ldb, beta, C, ldc); diff --git a/python/paddle/v2/framework/tests/test_mul_op.py b/python/paddle/v2/framework/tests/test_mul_op.py index eef5a4f96..ee0d81a64 100644 --- a/python/paddle/v2/framework/tests/test_mul_op.py +++ b/python/paddle/v2/framework/tests/test_mul_op.py @@ -23,7 +23,9 @@ class MulGradOpTest(GradientChecker): 'X': np.random.random((32, 84)).astype("float32"), 'Y': np.random.random((84, 100)).astype("float32") } - self.check_grad(op, inputs, set(["X", "Y"]), "Out") + # mul op will enlarge the relative error + self.check_grad( + op, inputs, set(["X", "Y"]), "Out", max_relative_error=0.5) # TODO(dzh,qijun) : mulgrad test case need transpose feature of blas library -- GitLab From 1eb98e2fef8f9264ed9110569748a7b42ca45eb4 Mon Sep 17 00:00:00 2001 From: Yi Wang Date: Fri, 18 Aug 2017 17:19:14 -0700 Subject: [PATCH 0129/2018] Set the default cuDNN installation path --- cmake/cudnn.cmake | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cmake/cudnn.cmake b/cmake/cudnn.cmake index 69f40df51..2c84061ff 100644 --- a/cmake/cudnn.cmake +++ b/cmake/cudnn.cmake @@ -2,7 +2,7 @@ if(NOT WITH_GPU) return() endif() -set(CUDNN_ROOT "" CACHE PATH "CUDNN ROOT") +set(CUDNN_ROOT "/usr" CACHE PATH "CUDNN ROOT") find_path(CUDNN_INCLUDE_DIR cudnn.h PATHS ${CUDNN_ROOT} ${CUDNN_ROOT}/include $ENV{CUDNN_ROOT} $ENV{CUDNN_ROOT}/include ${CUDA_TOOLKIT_INCLUDE} -- GitLab From 8f6c8780a52b3e0a6df85f6d9e3e98366a381692 Mon Sep 17 00:00:00 2001 From: dangqingqing Date: Sat, 19 Aug 2017 17:08:04 +0800 Subject: [PATCH 0130/2018] Replace functor by function. --- paddle/operators/cross_entropy_op.cu | 25 +++++++++---------- paddle/operators/cross_entropy_op.h | 2 +- .../paddle/v2/framework/tests/op_test_util.py | 2 +- 3 files changed, 14 insertions(+), 15 deletions(-) diff --git a/paddle/operators/cross_entropy_op.cu b/paddle/operators/cross_entropy_op.cu index 5f5d26926..d999bfce5 100644 --- a/paddle/operators/cross_entropy_op.cu +++ b/paddle/operators/cross_entropy_op.cu @@ -21,19 +21,18 @@ namespace operators { using Tensor = framework::Tensor; template -struct clipping_log { - __host__ __device__ T operator()(const T x) { - PADDLE_ASSERT(std::is_floating_point::value); - const T kApproInf = 1e20; - if (x == INFINITY) { - return kApproInf; - } - if (x == -INFINITY) { - return -kApproInf; - } - return x; +__host__ __device__ T clipping_log(const T x) { + PADDLE_ASSERT(std::is_floating_point::value); + const T kApproInf = 1e20; + T v = log(x); + if (v == INFINITY) { + return kApproInf; } -}; + if (v == -INFINITY) { + return -kApproInf; + } + return v; +} template __global__ void CrossEntropyKernel(T* Y, const T* X, const int* label, @@ -43,7 +42,7 @@ __global__ void CrossEntropyKernel(T* Y, const T* X, const int* label, for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < N; i += blockDim.x * gridDim.x) { PADDLE_ASSERT(label[i] >= 0 && label[i] < D); - Y[i] = -clipping_log()(X[i * D + label[i]]); + Y[i] = -clipping_log(X[i * D + label[i]]); } } diff --git a/paddle/operators/cross_entropy_op.h b/paddle/operators/cross_entropy_op.h index e95f5e116..eb4d1348d 100644 --- a/paddle/operators/cross_entropy_op.h +++ b/paddle/operators/cross_entropy_op.h @@ -21,7 +21,7 @@ namespace operators { using Tensor = framework::Tensor; template -T tolerable_value(const T x) { +inline T tolerable_value(const T x) { static_assert(std::is_floating_point::value, "tolerable_value works only on float, " "double and double double."); diff --git a/python/paddle/v2/framework/tests/op_test_util.py b/python/paddle/v2/framework/tests/op_test_util.py index ae23108df..3bc05a0fe 100644 --- a/python/paddle/v2/framework/tests/op_test_util.py +++ b/python/paddle/v2/framework/tests/op_test_util.py @@ -65,7 +65,7 @@ class OpTestMeta(type): expect = self.outputs[out_name] self.assertTrue( numpy.allclose( - actual, expect, atol=1e-04), + actual, expect, atol=1e-05), "output name: " + out_name + "has diff") obj.test_all = test_all -- GitLab From f1e553354186c44508565ad89d4b526bdb3a705a Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Sun, 20 Aug 2017 13:57:26 +0800 Subject: [PATCH 0131/2018] Rename `Net::AddOp` to `Net::AppendOp` Fix #3582 --- paddle/framework/backward.cc | 9 +++--- paddle/framework/backward_test.cc | 30 +++++++++---------- paddle/framework/pybind.cc | 4 +-- paddle/operators/net_op.h | 7 +++-- paddle/operators/net_op_test.cc | 10 +++---- python/paddle/v2/framework/tests/test_net.py | 10 +++---- .../v2/framework/tests/test_recurrent_op.py | 2 +- 7 files changed, 37 insertions(+), 35 deletions(-) diff --git a/paddle/framework/backward.cc b/paddle/framework/backward.cc index 9d3088722..bfda18724 100644 --- a/paddle/framework/backward.cc +++ b/paddle/framework/backward.cc @@ -110,7 +110,7 @@ static std::unique_ptr BackwardRecursive( dup_output_ops[out].emplace_back(local_op_id); return false; }); - net->AddOp(std::move(bwd)); + net->AppendOp(std::move(bwd)); } // Get unique ID for this method. auto uid = uniq_id++; @@ -163,8 +163,9 @@ static std::unique_ptr BackwardRecursive( // If part of input gradient of that operator is not calculated, fill // zero variables to that input gradient. - net->AddOp(OpRegistry::CreateOp("fill_zeros_like", {{"Src", {prefix}}}, - {{"Dst", {grad_input}}}, {})); + net->AppendOp(OpRegistry::CreateOp("fill_zeros_like", + {{"Src", {prefix}}}, + {{"Dst", {grad_input}}}, {})); } return false; }); @@ -195,7 +196,7 @@ static std::unique_ptr BackwardRecursive( if (net->ops_.empty()) { // Current no aux op is added to network return grad_op; } - net->AddOp(std::move(grad_op)); + net->AppendOp(std::move(grad_op)); } net->SetType("@GENERATED_BACKWARD@"); net->CompleteAddOp(); diff --git a/paddle/framework/backward_test.cc b/paddle/framework/backward_test.cc index 2c5ec76df..b93ab66f2 100644 --- a/paddle/framework/backward_test.cc +++ b/paddle/framework/backward_test.cc @@ -75,13 +75,13 @@ class FcOp : public operators::NetOp { FcOp(const std::string &type, const VarNameMap &inputs, const VarNameMap &outputs, const AttributeMap &attrs) : NetOp(type, inputs, outputs, attrs) { - AddOp(OpRegistry::CreateOp("mul", - {{"X", {Input("X")}}, {"Y", {Input("W")}}}, - {{"Out", {Output("mul_result")}}}, {})); + AppendOp(OpRegistry::CreateOp("mul", + {{"X", {Input("X")}}, {"Y", {Input("W")}}}, + {{"Out", {Output("mul_result")}}}, {})); auto input_b = Inputs("b"); std::string before_act = "mul_result"; if (input_b.size() != 0) { - AddOp(OpRegistry::CreateOp( + AppendOp(OpRegistry::CreateOp( "rowwise_add", {{"X", {Output("mul_result")}}, {"b", {input_b[0]}}}, {{"Out", {Output("add_result")}}}, {})); before_act = "add_result"; @@ -92,8 +92,8 @@ class FcOp : public operators::NetOp { } } - AddOp(OpRegistry::CreateOp("sigmoid", {{"X", {Output(before_act)}}}, - {{"Out", {Output("Out")}}}, {})); + AppendOp(OpRegistry::CreateOp("sigmoid", {{"X", {Output(before_act)}}}, + {{"Out", {Output("Out")}}}, {})); CompleteAddOp(false); } }; @@ -234,13 +234,13 @@ TEST(Backward, net_fc_backward_not_have_b) { TEST(Backward, net_input_of_network_not_need_grad) { ops::NetOp net; - net.AddOp(f::OpRegistry::CreateOp( + net.AppendOp(f::OpRegistry::CreateOp( "fc", {{"X", {"x"}}, {"W", {"W1"}}, {"b", {"b1"}}}, {{"mul_result", {"mul_tmp_0"}}, {"add_result", {"add_tmp_0"}}, {"Out", {"hidden0"}}}, {})); - net.AddOp(f::OpRegistry::CreateOp( + net.AppendOp(f::OpRegistry::CreateOp( "fc", {{"X", {"hidden0"}}, {"W", {"W2"}}, {"b", {"b2"}}}, {{"mul_result", {"mul_tmp_1"}}, {"add_result", {"add_tmp_1"}}, @@ -273,10 +273,10 @@ TEST(Backward, net_input_of_network_not_need_grad) { TEST(Backward, net_shared_weight) { ops::NetOp net; - net.AddOp(f::OpRegistry::CreateOp("mul", {{"X", {"x"}}, {"Y", {"w"}}}, - {{"Out", {"out"}}}, {})); - net.AddOp(f::OpRegistry::CreateOp("mul", {{"X", {"out"}}, {"Y", {"w"}}}, - {{"Out", {"FinalOut"}}}, {})); + net.AppendOp(f::OpRegistry::CreateOp("mul", {{"X", {"x"}}, {"Y", {"w"}}}, + {{"Out", {"out"}}}, {})); + net.AppendOp(f::OpRegistry::CreateOp("mul", {{"X", {"out"}}, {"Y", {"w"}}}, + {{"Out", {"FinalOut"}}}, {})); net.CompleteAddOp(); auto bwd = f::Backward(net, {}); @@ -357,19 +357,19 @@ TEST(Backward, op_part_of_input_are_not_need) { TEST(Backward, linear_net_intermediate_variable_has_no_grad) { ops::NetOp net; - net.AddOp(f::OpRegistry::CreateOp( + net.AppendOp(f::OpRegistry::CreateOp( "fc", {{"X", {"x1"}}, {"W", {"w1"}}, {"b", {"b1"}}}, {{"mul_result", {"mul_out1"}}, {"add_result", {"add_out1"}}, {"Out", {"out1"}}}, {})); - net.AddOp(f::OpRegistry::CreateOp( + net.AppendOp(f::OpRegistry::CreateOp( "fc", {{"X", {"out1"}}, {"W", {"w2"}}, {"b", {"b2"}}}, {{"mul_result", {"mul_out2"}}, {"add_result", {"tmp_out2"}}, {"Out", {"out2"}}}, {})); - net.AddOp(f::OpRegistry::CreateOp( + net.AppendOp(f::OpRegistry::CreateOp( "fc", {{"X", {"out2"}}, {"W", {"w3"}}, {"b", {"b3"}}}, {{"mul_result", {"mul_out3"}}, {"add_result", {"tmp_out3"}}, diff --git a/paddle/framework/pybind.cc b/paddle/framework/pybind.cc index f0114b9e4..89219a77c 100644 --- a/paddle/framework/pybind.cc +++ b/paddle/framework/pybind.cc @@ -222,8 +222,8 @@ All parameter, weight, gradient are variables in Paddle. retv->SetType("plain_net"); return retv; }) - .def("add_op", [](operators::NetOp &self, - const OperatorBase &op) { self.AddOp(op); }) + .def("append_op", [](operators::NetOp &self, + const OperatorBase &op) { self.AppendOp(op); }) .def("complete_add_op", &operators::NetOp::CompleteAddOp) .def("complete_add_op", [](std::shared_ptr &self) { self->CompleteAddOp(); diff --git a/paddle/operators/net_op.h b/paddle/operators/net_op.h index 885ac6eec..3d3f996ef 100644 --- a/paddle/operators/net_op.h +++ b/paddle/operators/net_op.h @@ -84,13 +84,14 @@ class NetOp : public framework::OperatorBase { return true; } - void AddOp(const framework::OperatorBase& op) { AddOp(op.Clone()); } + void AppendOp(const framework::OperatorBase& op) { AppendOp(op.Clone()); } /** * @brief Add an operator by ptr */ - void AddOp(std::unique_ptr op) { - PADDLE_ENFORCE(!add_op_done_, "Cannot AddOp when this network is sealed"); + void AppendOp(std::unique_ptr op) { + PADDLE_ENFORCE(!add_op_done_, + "Cannot AppendOp when this network is sealed"); PADDLE_ENFORCE_NOT_NULL(op, "Cannot Insert Null op"); ops_.push_back(std::move(op)); } diff --git a/paddle/operators/net_op_test.cc b/paddle/operators/net_op_test.cc index e9598610c..99019754a 100644 --- a/paddle/operators/net_op_test.cc +++ b/paddle/operators/net_op_test.cc @@ -38,10 +38,10 @@ TEST(OpKernel, all) { auto net = std::make_shared(); ASSERT_NE(net, nullptr); - net->AddOp(std::unique_ptr( + net->AppendOp(std::unique_ptr( new TestOp("test", {{"X", {"x"}}, {"W", {"w1"}}, {"b", {"b1"}}}, {{"Out", {"y"}}}, {}))); - net->AddOp(std::unique_ptr( + net->AppendOp(std::unique_ptr( new TestOp("test", {{"X", {"y"}}, {"W", {"w2"}}, {"b", {"b2"}}}, {{"Out", {"z"}}}, {}))); @@ -61,7 +61,7 @@ TEST(NetOp, insert_op) { auto op1 = std::unique_ptr( new framework::NOP("empty", {{"X", {"x"}}, {"W", {"w1"}}, {"b", {"b1"}}}, {{"Out", {"y"}}}, {})); - net.AddOp(*op1); + net.AppendOp(*op1); net.InsertOp(0, *op1); ASSERT_EQ(2UL, net.ops_.size()); net.InsertOp(2, std::move(op1)); @@ -70,9 +70,9 @@ TEST(NetOp, insert_op) { TEST(NetOp, Clone) { NetOp net; - net.AddOp( + net.AppendOp( std::unique_ptr(new framework::NOP{"empty", {}, {}, {}})); - net.AddOp(std::unique_ptr( + net.AppendOp(std::unique_ptr( new framework::NOP{"empty2", {}, {}, {}})); net.CompleteAddOp(true); auto new_net_op = net.Clone(); diff --git a/python/paddle/v2/framework/tests/test_net.py b/python/paddle/v2/framework/tests/test_net.py index b42cadd11..9339cf28d 100644 --- a/python/paddle/v2/framework/tests/test_net.py +++ b/python/paddle/v2/framework/tests/test_net.py @@ -6,8 +6,8 @@ import unittest def fc(X, W, Y): ret_v = core.Net.create() - ret_v.add_op(Operator("mul", X="X", Y="W", Out="pre_activation")) - ret_v.add_op(Operator("sigmoid", X="pre_activation", Y=Y)) + ret_v.append_op(Operator("mul", X="X", Y="W", Out="pre_activation")) + ret_v.append_op(Operator("sigmoid", X="pre_activation", Y=Y)) ret_v.complete_add_op(True) return ret_v @@ -16,12 +16,12 @@ class TestNet(unittest.TestCase): def test_net_all(self): net = core.Net.create() op1 = Operator("add_two", X="X", Y="Y", Out="Out") - net.add_op(op1) + net.append_op(op1) net2 = core.Net.create() - net2.add_op(fc(X="X", W="w", Y="fc.out")) + net2.append_op(fc(X="X", W="w", Y="fc.out")) net2.complete_add_op(True) - net.add_op(net2) + net.append_op(net2) net.complete_add_op(True) expected = ''' diff --git a/python/paddle/v2/framework/tests/test_recurrent_op.py b/python/paddle/v2/framework/tests/test_recurrent_op.py index 3d4a34d8d..d6000ab9f 100644 --- a/python/paddle/v2/framework/tests/test_recurrent_op.py +++ b/python/paddle/v2/framework/tests/test_recurrent_op.py @@ -150,7 +150,7 @@ class TestRecurrentOp(unittest.TestCase): sig_op = Operator("sigmoid", X="sum", Y="h@alias") for op in [x_fc_op, h_fc_op, sum_op, sig_op]: - stepnet.add_op(op) + stepnet.append_op(op) stepnet.complete_add_op(True) self.rnnop.set_stepnet(stepnet) -- GitLab From 59b3df31aa3f960753bf0d0d922319124e04301e Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Sun, 20 Aug 2017 14:52:23 +0800 Subject: [PATCH 0132/2018] Extract OpInfo into a library Fix cycle dependencies, Fix #3583. --- paddle/framework/CMakeLists.txt | 4 +-- paddle/framework/backward_test.cc | 4 +-- paddle/framework/grad_op_builder.cc | 20 +++++++------- paddle/framework/op_info.cc | 30 +++++++++++++++++++++ paddle/framework/op_info.h | 42 +++++++++++++++++++++++++++++ paddle/framework/op_registry.cc | 37 +++++++++++++------------ paddle/framework/op_registry.h | 35 ++++++------------------ paddle/framework/operator.cc | 8 +++--- paddle/framework/operator.h | 27 ++++++++++--------- paddle/framework/operator_test.cc | 9 ++++--- paddle/framework/pybind.cc | 2 +- paddle/operators/net_op.cc | 5 ++-- paddle/operators/net_op.h | 6 +++-- paddle/operators/recurrent_op.cc | 8 +++--- paddle/operators/recurrent_op.h | 10 ++++--- 15 files changed, 152 insertions(+), 95 deletions(-) create mode 100644 paddle/framework/op_info.cc create mode 100644 paddle/framework/op_info.h diff --git a/paddle/framework/CMakeLists.txt b/paddle/framework/CMakeLists.txt index 68304c9fc..59012ea8c 100644 --- a/paddle/framework/CMakeLists.txt +++ b/paddle/framework/CMakeLists.txt @@ -18,8 +18,8 @@ cc_test(scope_test SRCS scope_test.cc DEPS scope) proto_library(framework_proto SRCS framework.proto) cc_library(attribute SRCS attribute.cc DEPS framework_proto) - -cc_library(operator SRCS operator.cc DEPS framework_proto device_context tensor scope attribute) +cc_library(op_info SRCS op_info.cc DEPS attribute framework_proto) +cc_library(operator SRCS operator.cc DEPS op_info device_context tensor scope) cc_test(operator_test SRCS operator_test.cc DEPS operator op_registry) cc_library(grad_op_builder SRCS grad_op_builder.cc DEPS operator) diff --git a/paddle/framework/backward_test.cc b/paddle/framework/backward_test.cc index 2c5ec76df..bcdfae132 100644 --- a/paddle/framework/backward_test.cc +++ b/paddle/framework/backward_test.cc @@ -72,8 +72,8 @@ class NoGradOpMaker : public OpProtoAndCheckerMaker { class FcOp : public operators::NetOp { public: - FcOp(const std::string &type, const VarNameMap &inputs, - const VarNameMap &outputs, const AttributeMap &attrs) + FcOp(const std::string &type, const VariableNameMap &inputs, + const VariableNameMap &outputs, const AttributeMap &attrs) : NetOp(type, inputs, outputs, attrs) { AddOp(OpRegistry::CreateOp("mul", {{"X", {Input("X")}}, {"Y", {Input("W")}}}, diff --git a/paddle/framework/grad_op_builder.cc b/paddle/framework/grad_op_builder.cc index 0a2a41f6b..fcc5d7a21 100644 --- a/paddle/framework/grad_op_builder.cc +++ b/paddle/framework/grad_op_builder.cc @@ -20,11 +20,11 @@ namespace framework { enum class OpArgType { IN, OUT }; static void TransOpArg(const OperatorBase* src_op, const OpArgType& src_type, - bool is_grad, OperatorBase::VarNameMap* vars) { + bool is_grad, VariableNameMap* vars) { const auto& src_inout = src_type == OpArgType::IN ? src_op->Inputs() : src_op->Outputs(); auto& dst_inout = *vars; - const OpProto* proto = OpRegistry::op_info_map().at(src_op->Type()).proto_; + const OpProto* proto = OpInfoMap().at(src_op->Type()).proto_; const auto& src_arg_list = src_type == OpArgType::IN ? proto->inputs() : proto->outputs(); for (const auto& arg : src_arg_list) { @@ -40,25 +40,25 @@ static void TransOpArg(const OperatorBase* src_op, const OpArgType& src_type, } OperatorBase* BuildGradOp(const OperatorBase* op) { - auto it = OpRegistry::op_info_map().find(op->Type()); - PADDLE_ENFORCE(it != OpRegistry::op_info_map().end(), - "'%s' has not been registered.", op->Type()); + auto it = OpInfoMap().find(op->Type()); + PADDLE_ENFORCE(it != OpInfoMap().end(), "'%s' has not been registered.", + op->Type()); PADDLE_ENFORCE(it->second.proto_ != nullptr, "'%s' has no OpProto.", op->Type()); std::string grad_op_type = it->second.grad_op_type_; PADDLE_ENFORCE(!grad_op_type.empty(), "'%s' has no gradient operator.", op->Type()); - OperatorBase::VarNameMap inputs; - OperatorBase::VarNameMap outputs; + VariableNameMap inputs; + VariableNameMap outputs; TransOpArg(op, OpArgType::IN, false, &inputs); // I TransOpArg(op, OpArgType::OUT, false, &inputs); // O TransOpArg(op, OpArgType::OUT, true, &inputs); // OG TransOpArg(op, OpArgType::IN, true, &outputs); // IG - it = OpRegistry::op_info_map().find(grad_op_type); - PADDLE_ENFORCE(it != OpRegistry::op_info_map().end(), - "'%s' has not been registered.", grad_op_type); + it = OpInfoMap().find(grad_op_type); + PADDLE_ENFORCE(it != OpInfoMap().end(), "'%s' has not been registered.", + grad_op_type); return it->second.creator_(grad_op_type, inputs, outputs, op->Attrs()); } diff --git a/paddle/framework/op_info.cc b/paddle/framework/op_info.cc new file mode 100644 index 000000000..f928ac647 --- /dev/null +++ b/paddle/framework/op_info.cc @@ -0,0 +1,30 @@ +/* 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/framework/op_info.h" + +namespace paddle { +namespace framework { + +static std::unordered_map* + g_op_info_map = nullptr; +std::unordered_map& OpInfoMap() { + if (g_op_info_map == nullptr) { + g_op_info_map = + new std::unordered_map(); + } + return *g_op_info_map; +} +} // namespace framework +} // namespace paddle diff --git a/paddle/framework/op_info.h b/paddle/framework/op_info.h new file mode 100644 index 000000000..fdd0ed77d --- /dev/null +++ b/paddle/framework/op_info.h @@ -0,0 +1,42 @@ +/* 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 +#include +#include +#include + +#include "paddle/framework/attribute.h" + +namespace paddle { +namespace framework { +class OperatorBase; +using VariableNameMap = std::map>; + +using OpCreator = std::function; + +struct OpInfo { + OpCreator creator_; + std::string grad_op_type_; + OpProto* proto_; + OpAttrChecker* checker_; +}; + +extern std::unordered_map& OpInfoMap(); + +} // namespace framework +} // namespace paddle diff --git a/paddle/framework/op_registry.cc b/paddle/framework/op_registry.cc index 8eae86e96..e03dc3a73 100644 --- a/paddle/framework/op_registry.cc +++ b/paddle/framework/op_registry.cc @@ -19,32 +19,20 @@ limitations under the License. */ namespace paddle { namespace framework { -std::unique_ptr OpRegistry::CreateOp(const std::string& type, - const VarNameMap& inputs, - const VarNameMap& outputs, - AttributeMap attrs) { - auto it = op_info_map().find(type); - PADDLE_ENFORCE(it != op_info_map().end(), +std::unique_ptr OpRegistry::CreateOp( + const std::string& type, const VariableNameMap& inputs, + const VariableNameMap& outputs, AttributeMap attrs) { + auto it = OpInfoMap().find(type); + PADDLE_ENFORCE(it != OpInfoMap().end(), "Operator '%s' has not been registered.", type); it->second.checker_->Check(attrs); auto op = it->second.creator_(type, inputs, outputs, attrs); return std::unique_ptr(op); } -std::unique_ptr OpRegistry::CreateOp(const OpDesc& op_desc) { - VarNameMap inputs = ConvertOpDescVarsToVarNameMap(op_desc.inputs()); - VarNameMap outputs = ConvertOpDescVarsToVarNameMap(op_desc.outputs()); - AttributeMap attrs; - for (auto& attr : op_desc.attrs()) { - attrs[attr.name()] = GetAttrValue(attr); - } - - return CreateOp(op_desc.type(), inputs, outputs, attrs); -} - -OperatorBase::VarNameMap OpRegistry::ConvertOpDescVarsToVarNameMap( +static VariableNameMap ConvertOpDescVarsToVarNameMap( const google::protobuf::RepeatedPtrField& op_desc_vars) { - VarNameMap ret_val; + VariableNameMap ret_val; for (auto& var : op_desc_vars) { auto& var_names = ret_val[var.parameter()]; auto& var_names_in_proto = var.arguments(); @@ -55,6 +43,17 @@ OperatorBase::VarNameMap OpRegistry::ConvertOpDescVarsToVarNameMap( return ret_val; } +std::unique_ptr OpRegistry::CreateOp(const OpDesc& op_desc) { + VariableNameMap inputs = ConvertOpDescVarsToVarNameMap(op_desc.inputs()); + VariableNameMap outputs = ConvertOpDescVarsToVarNameMap(op_desc.outputs()); + AttributeMap attrs; + for (auto& attr : op_desc.attrs()) { + attrs[attr.name()] = GetAttrValue(attr); + } + + return CreateOp(op_desc.type(), inputs, outputs, attrs); +} + std::unique_ptr OpRegistry::CreateGradOp(const OperatorBase& op) { PADDLE_ENFORCE(!op.IsNetOp(), "Use framework::Backward to get backward ops"); return std::unique_ptr(BuildGradOp(&op)); diff --git a/paddle/framework/op_registry.h b/paddle/framework/op_registry.h index 4c2d13d63..06530bc7d 100644 --- a/paddle/framework/op_registry.h +++ b/paddle/framework/op_registry.h @@ -23,6 +23,7 @@ limitations under the License. */ #include "paddle/framework/attribute.h" #include "paddle/framework/framework.pb.h" #include "paddle/framework/grad_op_builder.h" +#include "paddle/framework/op_info.h" #include "paddle/framework/operator.h" #include "paddle/framework/scope.h" @@ -30,28 +31,16 @@ namespace paddle { namespace framework { class OpRegistry { - using VarNameMap = OperatorBase::VarNameMap; - using OpCreator = std::function; - public: - struct OpInfo { - OpCreator creator_; - std::string grad_op_type_; - OpProto* proto_; - OpAttrChecker* checker_; - }; - template static void RegisterOp(const std::string& op_type, const std::string& grad_op_type) { - PADDLE_ENFORCE(op_info_map().count(op_type) == 0, + PADDLE_ENFORCE(OpInfoMap().count(op_type) == 0, "'%s' is registered more than once.", op_type); OpInfo op_info; - op_info.creator_ = [](const std::string& type, const VarNameMap& inputs, - const VarNameMap& outputs, - const AttributeMap& attrs) { + op_info.creator_ = []( + const std::string& type, const VariableNameMap& inputs, + const VariableNameMap& outputs, const AttributeMap& attrs) { return new OpType(type, inputs, outputs, attrs); }; op_info.grad_op_type_ = grad_op_type; @@ -70,7 +59,7 @@ class OpRegistry { op_info.proto_ = nullptr; op_info.checker_ = nullptr; } - op_info_map().insert(std::make_pair(op_type, op_info)); + OpInfoMap().insert(std::make_pair(op_type, op_info)); // register gradient op if (!grad_op_type.empty()) { RegisterOp(grad_op_type, ""); @@ -78,21 +67,13 @@ class OpRegistry { } static std::unique_ptr CreateOp(const std::string& type, - const VarNameMap& inputs, - const VarNameMap& outputs, + const VariableNameMap& inputs, + const VariableNameMap& outputs, AttributeMap attrs); static std::unique_ptr CreateOp(const OpDesc& op_desc); - static VarNameMap ConvertOpDescVarsToVarNameMap( - const google::protobuf::RepeatedPtrField& op_desc_vars); - static std::unique_ptr CreateGradOp(const OperatorBase& op); - - static std::unordered_map& op_info_map() { - static std::unordered_map op_info_map_; - return op_info_map_; - } }; class Registrar { diff --git a/paddle/framework/operator.cc b/paddle/framework/operator.cc index eadd8f331..48a7fe64a 100644 --- a/paddle/framework/operator.cc +++ b/paddle/framework/operator.cc @@ -115,8 +115,8 @@ void OperatorBase::Rename(const std::string& old_name, } OperatorBase::OperatorBase(const std::string& type, - const OperatorBase::VarNameMap& inputs, - const OperatorBase::VarNameMap& outputs, + const VariableNameMap& inputs, + const VariableNameMap& outputs, const AttributeMap& attrs) : type_(type), inputs_(inputs), outputs_(outputs), attrs_(attrs) { static std::atomic gUniqId(0UL); @@ -141,9 +141,9 @@ std::vector OperatorBase::OutputVars(bool has_intermediate) const { } return ret_val; } - auto it = OpRegistry::op_info_map().find(type_); + auto it = OpInfoMap().find(type_); PADDLE_ENFORCE( - it != OpRegistry::op_info_map().end(), + it != OpInfoMap().end(), "Operator %s not registered, cannot figure out intermediate outputs", type_); PADDLE_ENFORCE( diff --git a/paddle/framework/operator.h b/paddle/framework/operator.h index 807298088..83dab8631 100644 --- a/paddle/framework/operator.h +++ b/paddle/framework/operator.h @@ -19,6 +19,7 @@ limitations under the License. */ #include #include +#include "op_info.h" #include "paddle/framework/attribute.h" #include "paddle/framework/framework.pb.h" #include "paddle/framework/scope.h" @@ -62,10 +63,8 @@ class ExecutionContext; */ class OperatorBase { public: - using VarNameMap = std::map>; - - OperatorBase(const std::string& type, const VarNameMap& inputs, - const VarNameMap& outputs, const AttributeMap& attrs); + OperatorBase(const std::string& type, const VariableNameMap& inputs, + const VariableNameMap& outputs, const AttributeMap& attrs); virtual ~OperatorBase() {} @@ -93,8 +92,8 @@ class OperatorBase { /// rename inputs outputs name void Rename(const std::string& old_name, const std::string& new_name); - const VarNameMap& Inputs() const { return inputs_; } - const VarNameMap& Outputs() const { return outputs_; } + const VariableNameMap& Inputs() const { return inputs_; } + const VariableNameMap& Outputs() const { return outputs_; } //! Get a input with argument's name described in `op_proto` const std::string& Input(const std::string& name) const; //! Get a input which has multiple variables. @@ -122,11 +121,11 @@ class OperatorBase { // I (Inputs)opear // O (Outputs) // OG (Output Gradients) - VarNameMap inputs_; + VariableNameMap inputs_; // NOTE: in case of OpGrad, outputs_ contains // IG (Inputs Gradients) - VarNameMap outputs_; + VariableNameMap outputs_; AttributeMap attrs_; }; @@ -142,9 +141,11 @@ class OperatorBase { // You can also use // using PARENT_CLASS::PARENT_CLASS; // to use parent's constructor. -#define DEFINE_OP_CONSTRUCTOR(CLS, PARENT_CLS) \ - CLS(const std::string& type, const VarNameMap& inputs, \ - const VarNameMap& outputs, const paddle::framework::AttributeMap& attrs) \ +#define DEFINE_OP_CONSTRUCTOR(CLS, PARENT_CLS) \ + CLS(const std::string& type, \ + const ::paddle::framework::VariableNameMap& inputs, \ + const ::paddle::framework::VariableNameMap& outputs, \ + const paddle::framework::AttributeMap& attrs) \ : PARENT_CLS(type, inputs, outputs, attrs) {} class NOP : public OperatorBase { @@ -389,8 +390,8 @@ class OperatorWithKernel : public OperatorBase { using OpKernelMap = std::unordered_map, OpKernelHash>; - OperatorWithKernel(const std::string& type, const VarNameMap& inputs, - const VarNameMap& outputs, const AttributeMap& attrs) + OperatorWithKernel(const std::string& type, const VariableNameMap& inputs, + const VariableNameMap& outputs, const AttributeMap& attrs) : OperatorBase(type, inputs, outputs, attrs) {} void InferShape(const Scope& scope) const override { diff --git a/paddle/framework/operator_test.cc b/paddle/framework/operator_test.cc index 2425b8777..1d7efb7b9 100644 --- a/paddle/framework/operator_test.cc +++ b/paddle/framework/operator_test.cc @@ -23,8 +23,8 @@ static int op_run_num = 0; class OpWithoutKernelTest : public OperatorBase { public: - OpWithoutKernelTest(const std::string& type, const VarNameMap& inputs, - const VarNameMap& outputs, const AttributeMap& attrs) + OpWithoutKernelTest(const std::string& type, const VariableNameMap& inputs, + const VariableNameMap& outputs, const AttributeMap& attrs) : OperatorBase(type, inputs, outputs, attrs), x(1) {} void InferShape(const Scope& scope) const override {} void Run(const Scope& scope, @@ -249,8 +249,9 @@ TEST(OpKernel, multi_inputs) { class OperatorClone : public paddle::framework::OperatorBase { public: DEFINE_OP_CLONE_METHOD(OperatorClone); - OperatorClone(const std::string& type, const VarNameMap& inputs, - const VarNameMap& outputs, + OperatorClone(const std::string& type, + const paddle::framework::VariableNameMap& inputs, + const paddle::framework::VariableNameMap& outputs, const paddle::framework::AttributeMap& attrs) : OperatorBase(type, inputs, outputs, attrs) {} void InferShape(const paddle::framework::Scope& scope) const override {} diff --git a/paddle/framework/pybind.cc b/paddle/framework/pybind.cc index f0114b9e4..1aec48357 100644 --- a/paddle/framework/pybind.cc +++ b/paddle/framework/pybind.cc @@ -138,7 +138,7 @@ All parameter, weight, gradient are variables in Paddle. //! @note: Be careful! PyBind will return std::string as an unicode, not //! Python str. If you want a str object, you should cast them in Python. m.def("get_all_op_protos", []() -> std::vector { - auto &op_info_map = OpRegistry::op_info_map(); + auto &op_info_map = OpInfoMap(); std::vector ret_values; for (auto it = op_info_map.begin(); it != op_info_map.end(); ++it) { const OpProto *proto = it->second.proto_; diff --git a/paddle/operators/net_op.cc b/paddle/operators/net_op.cc index a7d710511..9bfa712d9 100644 --- a/paddle/operators/net_op.cc +++ b/paddle/operators/net_op.cc @@ -81,9 +81,8 @@ std::vector NetOp::OutputVars(bool has_intermediate) const { return ret_val; } -NetOp::NetOp(const std::string& type, - const framework::OperatorBase::VarNameMap& inputs, - const framework::OperatorBase::VarNameMap& outputs, +NetOp::NetOp(const std::string& type, const framework::VariableNameMap& inputs, + const framework::VariableNameMap& outputs, const framework::AttributeMap& attrs) : framework::OperatorBase(type, inputs, outputs, attrs) {} diff --git a/paddle/operators/net_op.h b/paddle/operators/net_op.h index 885ac6eec..05b475d88 100644 --- a/paddle/operators/net_op.h +++ b/paddle/operators/net_op.h @@ -38,8 +38,10 @@ class NetOp : public framework::OperatorBase { public: static const char kAll[]; NetOp() : framework::OperatorBase("plain_net", {}, {}, {}) {} - NetOp(const std::string& type, const VarNameMap& inputs, - const VarNameMap& outputs, const framework::AttributeMap& attrs); + + NetOp(const std::string& type, const framework::VariableNameMap& inputs, + const framework::VariableNameMap& outputs, + const framework::AttributeMap& attrs); NetOp(const NetOp& o) : framework::OperatorBase(o.type_, {}, {}, o.attrs_) { this->ops_.reserve(o.ops_.size()); diff --git a/paddle/operators/recurrent_op.cc b/paddle/operators/recurrent_op.cc index 78ce0ba3c..16bd249cb 100644 --- a/paddle/operators/recurrent_op.cc +++ b/paddle/operators/recurrent_op.cc @@ -131,8 +131,8 @@ const rnn::ArgumentName RecurrentGradientOp::kArgName{ "memories", "pre_memories", "boot_memories@grad"}; RecurrentOp::RecurrentOp(const std::string& type, - const framework::OperatorBase::VarNameMap& inputs, - const framework::OperatorBase::VarNameMap& outputs, + const framework::VariableNameMap& inputs, + const framework::VariableNameMap& outputs, const framework::AttributeMap& attrs) : OperatorBase(type, inputs, outputs, attrs) { rnn::InitArgument(kArgName, &arg_, *this); @@ -223,8 +223,8 @@ void RecurrentGradientAlgorithm::InferShape(const Scope& scope) const { } RecurrentGradientOp::RecurrentGradientOp( - const std::string& type, const framework::OperatorBase::VarNameMap& inputs, - const framework::OperatorBase::VarNameMap& outputs, + const std::string& type, const framework::VariableNameMap& inputs, + const framework::VariableNameMap& outputs, const framework::AttributeMap& attrs) : OperatorBase(type, inputs, outputs, attrs) { rnn::InitArgument(kArgName, &arg_, *this); diff --git a/paddle/operators/recurrent_op.h b/paddle/operators/recurrent_op.h index bcfa817de..1033d657a 100644 --- a/paddle/operators/recurrent_op.h +++ b/paddle/operators/recurrent_op.h @@ -114,8 +114,9 @@ class RecurrentGradientAlgorithm { class RecurrentOp : public framework::OperatorBase { public: - RecurrentOp(const std::string& type, const VarNameMap& inputs, - const VarNameMap& outputs, const framework::AttributeMap& attrs); + RecurrentOp(const std::string& type, const framework::VariableNameMap& inputs, + const framework::VariableNameMap& outputs, + const framework::AttributeMap& attrs); RecurrentOp(const RecurrentOp& o) : framework::OperatorBase( @@ -150,8 +151,9 @@ class RecurrentOp : public framework::OperatorBase { class RecurrentGradientOp : public framework::OperatorBase { public: - RecurrentGradientOp(const std::string& type, const VarNameMap& inputs, - const VarNameMap& outputs, + RecurrentGradientOp(const std::string& type, + const framework::VariableNameMap& inputs, + const framework::VariableNameMap& outputs, const framework::AttributeMap& attrs); RecurrentGradientOp(const RecurrentGradientOp& o) -- GitLab From 7f6b5044b640edcbd77bbb368509569776f7e0ee Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Sun, 20 Aug 2017 15:28:00 +0800 Subject: [PATCH 0133/2018] Make OpInfoMap as a class * Add Get/Has methods to OpInfoMap * Add PADDLE_ENFORCE for OpInfo to get field. --- paddle/framework/grad_op_builder.cc | 20 +++------- paddle/framework/op_info.cc | 9 ++--- paddle/framework/op_info.h | 61 ++++++++++++++++++++++++++++- paddle/framework/op_registry.cc | 8 ++-- paddle/framework/op_registry.h | 4 +- paddle/framework/operator.cc | 12 +----- paddle/framework/pybind.cc | 17 ++++---- 7 files changed, 84 insertions(+), 47 deletions(-) diff --git a/paddle/framework/grad_op_builder.cc b/paddle/framework/grad_op_builder.cc index fcc5d7a21..b02a599a8 100644 --- a/paddle/framework/grad_op_builder.cc +++ b/paddle/framework/grad_op_builder.cc @@ -24,9 +24,9 @@ static void TransOpArg(const OperatorBase* src_op, const OpArgType& src_type, const auto& src_inout = src_type == OpArgType::IN ? src_op->Inputs() : src_op->Outputs(); auto& dst_inout = *vars; - const OpProto* proto = OpInfoMap().at(src_op->Type()).proto_; + auto& proto = OpInfoMap::Instance().Get(src_op->Type()).Proto(); const auto& src_arg_list = - src_type == OpArgType::IN ? proto->inputs() : proto->outputs(); + src_type == OpArgType::IN ? proto.inputs() : proto.outputs(); for (const auto& arg : src_arg_list) { if (arg.not_in_gradient() && !is_grad) continue; const std::string src_name = arg.name(); @@ -40,14 +40,8 @@ static void TransOpArg(const OperatorBase* src_op, const OpArgType& src_type, } OperatorBase* BuildGradOp(const OperatorBase* op) { - auto it = OpInfoMap().find(op->Type()); - PADDLE_ENFORCE(it != OpInfoMap().end(), "'%s' has not been registered.", - op->Type()); - PADDLE_ENFORCE(it->second.proto_ != nullptr, "'%s' has no OpProto.", - op->Type()); - std::string grad_op_type = it->second.grad_op_type_; - PADDLE_ENFORCE(!grad_op_type.empty(), "'%s' has no gradient operator.", - op->Type()); + auto& info = OpInfoMap::Instance().Get(op->Type()); + PADDLE_ENFORCE(info.HasGradientOp()); VariableNameMap inputs; VariableNameMap outputs; @@ -56,10 +50,8 @@ OperatorBase* BuildGradOp(const OperatorBase* op) { TransOpArg(op, OpArgType::OUT, true, &inputs); // OG TransOpArg(op, OpArgType::IN, true, &outputs); // IG - it = OpInfoMap().find(grad_op_type); - PADDLE_ENFORCE(it != OpInfoMap().end(), "'%s' has not been registered.", - grad_op_type); - return it->second.creator_(grad_op_type, inputs, outputs, op->Attrs()); + auto& grad_info = OpInfoMap::Instance().Get(info.grad_op_type_); + return grad_info.Creator()(info.grad_op_type_, inputs, outputs, op->Attrs()); } } // namespace framework diff --git a/paddle/framework/op_info.cc b/paddle/framework/op_info.cc index f928ac647..81ba29797 100644 --- a/paddle/framework/op_info.cc +++ b/paddle/framework/op_info.cc @@ -17,12 +17,11 @@ namespace paddle { namespace framework { -static std::unordered_map* - g_op_info_map = nullptr; -std::unordered_map& OpInfoMap() { +static OpInfoMap* g_op_info_map = nullptr; + +OpInfoMap& OpInfoMap::Instance() { if (g_op_info_map == nullptr) { - g_op_info_map = - new std::unordered_map(); + g_op_info_map = new OpInfoMap(); } return *g_op_info_map; } diff --git a/paddle/framework/op_info.h b/paddle/framework/op_info.h index fdd0ed77d..94245c6c4 100644 --- a/paddle/framework/op_info.h +++ b/paddle/framework/op_info.h @@ -34,9 +34,68 @@ struct OpInfo { std::string grad_op_type_; OpProto* proto_; OpAttrChecker* checker_; + + bool HasOpProtoAndChecker() const { + return proto_ != nullptr && checker_ != nullptr; + } + + const OpProto& Proto() const { + PADDLE_ENFORCE_NOT_NULL(proto_, "Operator Proto has not been registered"); + PADDLE_ENFORCE(proto_->IsInitialized(), + "Operator Proto must be initialized in op info"); + return *proto_; + } + + const OpAttrChecker& Checker() const { + PADDLE_ENFORCE_NOT_NULL(checker_, + "Operator Checker has not been registered"); + return *checker_; + } + + const OpCreator& Creator() const { + PADDLE_ENFORCE_NOT_NULL(creator_, + "Operator Creator has not been registered"); + return creator_; + } + + bool HasGradientOp() const { return !grad_op_type_.empty(); } }; -extern std::unordered_map& OpInfoMap(); +class OpInfoMap { + public: + static OpInfoMap& Instance(); + + OpInfoMap(const OpInfoMap& o) = delete; + OpInfoMap(OpInfoMap&& o) = delete; + OpInfoMap& operator=(const OpInfoMap& o) = delete; + OpInfoMap& operator=(OpInfoMap&& o) = delete; + + bool Has(const std::string& op_type) const { + return map_.find(op_type) != map_.end(); + } + + void Insert(const std::string& type, const OpInfo& info) { + PADDLE_ENFORCE(!Has(type), "Operator %s has been registered", type); + map_.insert({type, info}); + } + + const OpInfo& Get(const std::string& type) const { + auto it = map_.find(type); + PADDLE_ENFORCE(it != map_.end(), "Operator %s are not found", type); + return it->second; + } + + template + void IterAllInfo(Callback callback) { + for (auto& it : map_) { + callback(it.first, it.second); + } + } + + private: + OpInfoMap() = default; + std::unordered_map map_; +}; } // namespace framework } // namespace paddle diff --git a/paddle/framework/op_registry.cc b/paddle/framework/op_registry.cc index e03dc3a73..b0e85dd49 100644 --- a/paddle/framework/op_registry.cc +++ b/paddle/framework/op_registry.cc @@ -22,11 +22,9 @@ namespace framework { std::unique_ptr OpRegistry::CreateOp( const std::string& type, const VariableNameMap& inputs, const VariableNameMap& outputs, AttributeMap attrs) { - auto it = OpInfoMap().find(type); - PADDLE_ENFORCE(it != OpInfoMap().end(), - "Operator '%s' has not been registered.", type); - it->second.checker_->Check(attrs); - auto op = it->second.creator_(type, inputs, outputs, attrs); + auto& info = OpInfoMap::Instance().Get(type); + info.Checker().Check(attrs); + auto op = info.Creator()(type, inputs, outputs, attrs); return std::unique_ptr(op); } diff --git a/paddle/framework/op_registry.h b/paddle/framework/op_registry.h index 06530bc7d..2d09cde41 100644 --- a/paddle/framework/op_registry.h +++ b/paddle/framework/op_registry.h @@ -35,7 +35,7 @@ class OpRegistry { template static void RegisterOp(const std::string& op_type, const std::string& grad_op_type) { - PADDLE_ENFORCE(OpInfoMap().count(op_type) == 0, + PADDLE_ENFORCE(!OpInfoMap::Instance().Has(op_type), "'%s' is registered more than once.", op_type); OpInfo op_info; op_info.creator_ = []( @@ -59,7 +59,7 @@ class OpRegistry { op_info.proto_ = nullptr; op_info.checker_ = nullptr; } - OpInfoMap().insert(std::make_pair(op_type, op_info)); + OpInfoMap::Instance().Insert(op_type, op_info); // register gradient op if (!grad_op_type.empty()) { RegisterOp(grad_op_type, ""); diff --git a/paddle/framework/operator.cc b/paddle/framework/operator.cc index 48a7fe64a..7abbde610 100644 --- a/paddle/framework/operator.cc +++ b/paddle/framework/operator.cc @@ -141,18 +141,10 @@ std::vector OperatorBase::OutputVars(bool has_intermediate) const { } return ret_val; } - auto it = OpInfoMap().find(type_); - PADDLE_ENFORCE( - it != OpInfoMap().end(), - "Operator %s not registered, cannot figure out intermediate outputs", - type_); - PADDLE_ENFORCE( - it->second.proto_ != nullptr, - "Operator %s has no OpProto, cannot figure out intermediate outputs", - type_); + auto& info = OpInfoMap::Instance().Get(Type()); // get all OpProto::Var for outputs - for (auto& o : it->second.proto_->outputs()) { + for (auto& o : info.Proto().outputs()) { // ignore all intermediate output if (o.intermediate()) continue; auto out = outputs_.find(o.name()); diff --git a/paddle/framework/pybind.cc b/paddle/framework/pybind.cc index 1aec48357..6212c8490 100644 --- a/paddle/framework/pybind.cc +++ b/paddle/framework/pybind.cc @@ -138,19 +138,16 @@ All parameter, weight, gradient are variables in Paddle. //! @note: Be careful! PyBind will return std::string as an unicode, not //! Python str. If you want a str object, you should cast them in Python. m.def("get_all_op_protos", []() -> std::vector { - auto &op_info_map = OpInfoMap(); std::vector ret_values; - for (auto it = op_info_map.begin(); it != op_info_map.end(); ++it) { - const OpProto *proto = it->second.proto_; - if (proto == nullptr) { - continue; - } - PADDLE_ENFORCE(proto->IsInitialized(), "OpProto must all be initialized"); + + OpInfoMap::Instance().IterAllInfo([&ret_values](const std::string &type, + const OpInfo &info) { + if (!info.HasOpProtoAndChecker()) return; std::string str; - PADDLE_ENFORCE(proto->SerializeToString(&str), + PADDLE_ENFORCE(info.Proto().SerializeToString(&str), "Serialize OpProto Error. This could be a bug of Paddle."); - ret_values.push_back(py::bytes(str)); - } + ret_values.emplace_back(str); + }); return ret_values; }); m.def_submodule( -- GitLab From 430e0e418bb34d6a14662a29a3e6d5fb906c9610 Mon Sep 17 00:00:00 2001 From: hedaoyuan Date: Mon, 21 Aug 2017 10:12:25 +0800 Subject: [PATCH 0134/2018] Follow comments. --- paddle/function/CMakeLists.txt | 4 +++- paddle/function/EigenGemm.cpp | 3 +-- 2 files changed, 4 insertions(+), 3 deletions(-) diff --git a/paddle/function/CMakeLists.txt b/paddle/function/CMakeLists.txt index 9187294a4..c572a9d43 100644 --- a/paddle/function/CMakeLists.txt +++ b/paddle/function/CMakeLists.txt @@ -5,7 +5,9 @@ list(APPEND h_files Function.h) list(APPEND cpp_files Function.cpp) list(APPEND cpp_files BufferArg.cpp) list(APPEND cpp_files GemmFunctor.cpp) -list(APPEND cpp_files EigenGemm.cpp) +if(USE_EIGEN_FOR_BLAS) + list(APPEND cpp_files EigenGemm.cpp) +endif(USE_EIGEN_FOR_BLAS) if(WITH_GPU) file(GLOB cu_files . *OpGpu.cu) diff --git a/paddle/function/EigenGemm.cpp b/paddle/function/EigenGemm.cpp index 0b4220fcb..674141ed3 100644 --- a/paddle/function/EigenGemm.cpp +++ b/paddle/function/EigenGemm.cpp @@ -77,8 +77,7 @@ struct EigenBlasGemm { } else if (alpha == T(1) && beta == T(1)) { c.device(device) += a.contract(b, dims); } else { - c.device(device) = - c.constant(alpha) * a.contract(b, dims) + c.constant(beta) * c; + c.device(device) = alpha * a.contract(b, dims) + beta * c; } } }; -- GitLab From d525abed955b5dd2e6c711205c11ac6a3bcca789 Mon Sep 17 00:00:00 2001 From: qijun Date: Mon, 21 Aug 2017 13:43:07 +0800 Subject: [PATCH 0135/2018] refine random related ops --- paddle/operators/CMakeLists.txt | 4 +- paddle/operators/gaussian_random_op.cc | 35 ++---------- paddle/operators/gaussian_random_op.cu | 41 ++------------ paddle/operators/gaussian_random_op.h | 38 +++++++++++++ paddle/operators/math/math_function.cc | 22 ++++++++ paddle/operators/math/math_function.cu | 36 ++++++++++++ paddle/operators/math/math_function.h | 8 +++ paddle/operators/mul_op.cc | 1 - paddle/operators/uniform_random_op.cc | 39 ++----------- paddle/operators/uniform_random_op.cu | 55 +------------------ paddle/operators/uniform_random_op.h | 38 +++++++++++++ paddle/platform/device_context.cc | 36 ++++++------ paddle/platform/device_context.h | 20 ++++--- .../paddle/v2/framework/tests/CMakeLists.txt | 2 +- .../tests/test_gaussian_random_op.py | 7 +-- .../framework/tests/test_uniform_random_op.py | 7 +-- 16 files changed, 192 insertions(+), 197 deletions(-) create mode 100644 paddle/operators/gaussian_random_op.h create mode 100644 paddle/operators/uniform_random_op.h diff --git a/paddle/operators/CMakeLists.txt b/paddle/operators/CMakeLists.txt index a7c89787e..8f22a5fbc 100644 --- a/paddle/operators/CMakeLists.txt +++ b/paddle/operators/CMakeLists.txt @@ -58,7 +58,7 @@ op_library(rowwise_add_op SRCS rowwise_add_op.cu rowwise_add_op.cc) op_library(sigmoid_op SRCS sigmoid_op.cc sigmoid_op.cu) op_library(softmax_op SRCS softmax_op.cc softmax_op.cu) -op_library(gaussian_random_op SRCS gaussian_random_op.cc gaussian_random_op.cu) +op_library(gaussian_random_op SRCS gaussian_random_op.cc gaussian_random_op.cu DEPS math_function) op_library(cross_entropy_op SRCS cross_entropy_op.cc cross_entropy_op.cu) op_library(fill_zeros_like_op SRCS fill_zeros_like_op.cc fill_zeros_like_op.cu) @@ -67,4 +67,4 @@ op_library(sgd_op SRCS sgd_op.cc sgd_op.cu) op_library(recurrent_op SRCS recurrent_op.cc rnn/recurrent_op_utils.cc DEPS framework_proto tensor op_registry operator net_op) op_library(uniform_random_op - SRCS uniform_random_op.cc uniform_random_op.cu) + SRCS uniform_random_op.cc uniform_random_op.cu DEPS math_function) diff --git a/paddle/operators/gaussian_random_op.cc b/paddle/operators/gaussian_random_op.cc index f30bbce95..aba8c6e5c 100644 --- a/paddle/operators/gaussian_random_op.cc +++ b/paddle/operators/gaussian_random_op.cc @@ -12,36 +12,11 @@ See the License for the specific language governing permissions and limitations under the License. */ -#include -#include "paddle/framework/op_registry.h" +#include "paddle/operators/gaussian_random_op.h" namespace paddle { namespace operators { -template -class GaussianRandomKernel : public framework::OpKernel { - public: - void Compute(const framework::ExecutionContext& context) const override { - float mean = context.op_.GetAttr("mean"); - float std = context.op_.GetAttr("std"); - auto* tensor = context.Output(0); - T* data = tensor->mutable_data(context.GetPlace()); - - // TODO(dzh): attribute does not support unsigned int. - // And we need a global random seed configuration. - int seed = context.op_.GetAttr("seed"); - if (seed == 0) { - seed = std::random_device()(); - } - std::mt19937 g(seed); - std::normal_distribution distribution(mean, std); - ssize_t size = framework::product(tensor->dims()); - for (int i = 0; i < size; ++i) { - data[i] = distribution(g); - } - } -}; - class GaussianRandomOp : public framework::OperatorWithKernel { public: using framework::OperatorWithKernel::OperatorWithKernel; @@ -70,10 +45,6 @@ Use to initialize tensor with gaussian random generator. AddAttr>("dims", "The dimension of random tensor."); AddAttr("mean", "mean value of random.").SetDefault(.0f); AddAttr("std", "minimum value of random value.").SetDefault(1.0f); - AddAttr("seed", - "Random seed of generator." - "0 means use system wide seed") - .SetDefault(0); } }; @@ -83,4 +54,6 @@ Use to initialize tensor with gaussian random generator. namespace ops = paddle::operators; REGISTER_OP_WITHOUT_GRADIENT(gaussian_random, ops::GaussianRandomOp, ops::GaussianRandomOpMaker); -REGISTER_OP_CPU_KERNEL(gaussian_random, ops::GaussianRandomKernel); +REGISTER_OP_CPU_KERNEL( + gaussian_random, + ops::GaussianRandomKernel); diff --git a/paddle/operators/gaussian_random_op.cu b/paddle/operators/gaussian_random_op.cu index 1340b1e1e..31be16fdc 100644 --- a/paddle/operators/gaussian_random_op.cu +++ b/paddle/operators/gaussian_random_op.cu @@ -12,42 +12,9 @@ See the License for the specific language governing permissions and limitations under the License. */ -#include -#include -#include "paddle/platform/dynload/curand.h" -#include "paddle/platform/gpu_info.h" - -#include "paddle/framework/op_registry.h" - -namespace paddle { -namespace operators { - -template -class GaussianRandomKernel : public framework::OpKernel { - public: - void Compute(const framework::ExecutionContext& context) const override { - float mean = context.op_.GetAttr("mean"); - float std = context.op_.GetAttr("std"); - auto* tensor = context.Output(0); - T* data = tensor->mutable_data(context.GetPlace()); - - int seed = context.op_.GetAttr("seed"); - if (seed == 0) { - std::random_device rd; - seed = rd(); - } - curandGenerator_t g; - PADDLE_ENFORCE(platform::dynload::curandCreateGenerator( - &g, CURAND_RNG_PSEUDO_DEFAULT)); - PADDLE_ENFORCE( - platform::dynload::curandSetPseudoRandomGeneratorSeed(g, seed)); - platform::dynload::curandGenerateNormal( - g, data, framework::product(tensor->dims()), mean, std); - } -}; - -} // namespace operators -} // namespace paddle +#include "paddle/operators/gaussian_random_op.h" namespace ops = paddle::operators; -REGISTER_OP_GPU_KERNEL(gaussian_random, ops::GaussianRandomKernel); +REGISTER_OP_GPU_KERNEL( + gaussian_random, + ops::GaussianRandomKernel); diff --git a/paddle/operators/gaussian_random_op.h b/paddle/operators/gaussian_random_op.h new file mode 100644 index 000000000..041390e95 --- /dev/null +++ b/paddle/operators/gaussian_random_op.h @@ -0,0 +1,38 @@ +/* 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" + +namespace paddle { +namespace operators { +template +class GaussianRandomKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& context) const override { + auto* tensor = context.Output("Out"); + T* data = tensor->mutable_data(context.GetPlace()); + T mean = static_cast(context.op_.GetAttr("mean")); + T std = static_cast(context.op_.GetAttr("std")); + auto n = framework::product(tensor->dims()); + + auto* device_context = + const_cast(context.device_context_); + math::RandGaussian(n, mean, std, data, device_context); + } +}; +} +} diff --git a/paddle/operators/math/math_function.cc b/paddle/operators/math/math_function.cc index 1e86fc3d1..da5904489 100644 --- a/paddle/operators/math/math_function.cc +++ b/paddle/operators/math/math_function.cc @@ -109,6 +109,28 @@ void matmul(const framework::Tensor& matrix_a, matrix_b.data(), beta, matrix_out->data(), context); } +template <> +void RandUniform(const int n, const float min, + const float max, float* output, + platform::DeviceContext* context) { + auto* cpu_context = reinterpret_cast(context); + std::uniform_real_distribution distribution(min, max); + for (int i = 0; i < n; i++) { + output[i] = distribution(cpu_context->rand_engine()); + } +} + +template <> +void RandGaussian(const int n, const float mean, + const float std, float* output, + platform::DeviceContext* context) { + auto* cpu_context = reinterpret_cast(context); + std::normal_distribution distribution(mean, std); + for (int i = 0; i < n; i++) { + output[i] = distribution(cpu_context->rand_engine()); + } +} + } // namespace math } // namespace operators } // namespace paddle diff --git a/paddle/operators/math/math_function.cu b/paddle/operators/math/math_function.cu index da40b27c9..5a400d444 100644 --- a/paddle/operators/math/math_function.cu +++ b/paddle/operators/math/math_function.cu @@ -12,6 +12,10 @@ 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 +#include +#include +#include #include "paddle/operators/math/math_function.h" namespace paddle { @@ -122,6 +126,38 @@ void matmul(const framework::Tensor& matrix_a, matrix_b.data(), beta, matrix_out->data(), context); } +template <> +void RandUniform(const int n, const float min, + const float max, float* output, + platform::DeviceContext* context) { + auto* cuda_context = reinterpret_cast(context); + thrust::uniform_real_distribution distribution(min, max); + thrust::minstd_rand engine = cuda_context->rand_enigne(); + engine->discard(n); + + thrust::counting_iterator index_sequence_begin(0); + + thrust::transform(thrust::cuda::par.on(cuda_context->stream()), + index_sequence_begin, index_sequence_begin + n, + thrust::device_ptr(output), distribution(engine)); +} + +template <> +void RandGaussian(const int n, const float mean, + const float std, float* output, + platform::DeviceContext* context) { + auto* cuda_context = reinterpret_cast(context); + thrust::normal_distribution distribution(mean, std); + thrust::minstd_rand engine = cuda_context->rand_enigne(); + engine->discard(n); + + thrust::counting_iterator index_sequence_begin(0); + + thrust::transform(thrust::cuda::par.on(cuda_context->stream()), + index_sequence_begin, index_sequence_begin + n, + thrust::device_ptr(output), distribution(engine)); +} + } // namespace math } // namespace operators } // namespace paddle diff --git a/paddle/operators/math/math_function.h b/paddle/operators/math/math_function.h index 155589fad..ea15e8fd2 100644 --- a/paddle/operators/math/math_function.h +++ b/paddle/operators/math/math_function.h @@ -77,6 +77,14 @@ void matmul(const framework::Tensor& matrix_a, bool trans_a, framework::Tensor* matrix_out, T beta, platform::DeviceContext* context); +template +void RandUniform(const int n, const T min, const T max, T* output, + platform::DeviceContext* context); + +template +void RandGaussian(const int n, const T mean, const T std, T* output, + platform::DeviceContext* context); + } // namespace math } // namespace operators } // namespace paddle diff --git a/paddle/operators/mul_op.cc b/paddle/operators/mul_op.cc index 460e458ca..173cc3850 100644 --- a/paddle/operators/mul_op.cc +++ b/paddle/operators/mul_op.cc @@ -13,7 +13,6 @@ limitations under the License. */ #include "paddle/operators/mul_op.h" -#include "paddle/operators/math/math_function.h" namespace paddle { namespace operators { diff --git a/paddle/operators/uniform_random_op.cc b/paddle/operators/uniform_random_op.cc index a0a0d4d91..81487a6bd 100644 --- a/paddle/operators/uniform_random_op.cc +++ b/paddle/operators/uniform_random_op.cc @@ -12,39 +12,11 @@ See the License for the specific language governing permissions and limitations under the License. */ -#include -#include -#include "paddle/framework/op_registry.h" -#include "paddle/framework/operator.h" +#include "paddle/operators/uniform_random_op.h" namespace paddle { namespace operators { -// It seems that Eigen::Tensor::random in GPU will SEGFAULT. -// Use std::random and thrust::random(thrust is a std library in CUDA) to -// implement uniform random. -template -class CPUUniformRandomKernel : public framework::OpKernel { - public: - void Compute(const framework::ExecutionContext& context) const override { - auto* tensor = context.Output("Out"); - T* data = tensor->mutable_data(context.GetPlace()); - unsigned int seed = - static_cast(context.op_.GetAttr("seed")); - std::minstd_rand engine; - if (seed == 0) { - seed = std::random_device()(); - } - engine.seed(seed); - std::uniform_real_distribution dist( - static_cast(context.op_.GetAttr("min")), - static_cast(context.op_.GetAttr("max"))); - for (ssize_t i = 0; i < framework::product(tensor->dims()); ++i) { - data[i] = dist(engine); - } - } -}; - class UniformRandomOp : public framework::OperatorWithKernel { public: using framework::OperatorWithKernel::OperatorWithKernel; @@ -72,10 +44,6 @@ Used to initialize tensor with uniform random generator. AddAttr>("dims", "the dimension of random tensor"); AddAttr("min", "Minimum value of uniform random").SetDefault(-1.0f); AddAttr("max", "Maximun value of uniform random").SetDefault(1.0f); - AddAttr("seed", - "Random seed of uniform random. " - "0 means generate a seed by system") - .SetDefault(0); } }; } // namespace operators @@ -83,5 +51,6 @@ Used to initialize tensor with uniform random generator. REGISTER_OP_WITHOUT_GRADIENT(uniform_random, paddle::operators::UniformRandomOp, paddle::operators::UniformRandomOpMaker); -REGISTER_OP_CPU_KERNEL(uniform_random, - paddle::operators::CPUUniformRandomKernel); +REGISTER_OP_CPU_KERNEL( + uniform_random, + paddle::operators::UniformRandomKernel); diff --git a/paddle/operators/uniform_random_op.cu b/paddle/operators/uniform_random_op.cu index 7a243555b..91368fa73 100644 --- a/paddle/operators/uniform_random_op.cu +++ b/paddle/operators/uniform_random_op.cu @@ -12,60 +12,11 @@ See the License for the specific language governing permissions and limitations under the License. */ -#include -#include -#include -#include -#include "paddle/framework/op_registry.h" -#include "paddle/framework/operator.h" +#include "paddle/operators/uniform_random_op.h" namespace paddle { namespace operators { -template -struct UniformGenerator { - T min_, max_; - unsigned int seed_; - - __host__ __device__ UniformGenerator(T min, T max, int seed) - : min_(min), max_(max), seed_(seed) {} - - __host__ __device__ T operator()(const unsigned int n) const { - thrust::minstd_rand rng; - rng.seed(seed_); - thrust::uniform_real_distribution dist(min_, max_); - rng.discard(n); - return dist(rng); - } -}; - -// It seems that Eigen::Tensor::random in GPU will SEGFAULT. -// Use std::random and thrust::random(thrust is a std library in CUDA) to -// implement uniform random. -template -class GPUUniformRandomKernel : public framework::OpKernel { - public: - void Compute(const framework::ExecutionContext& context) const override { - auto* tensor = context.Output("Out"); - T* data = tensor->mutable_data(context.GetPlace()); - unsigned int seed = - static_cast(context.op_.GetAttr("seed")); - if (seed == 0) { - std::random_device rd; - seed = rd(); - } - T min = static_cast(context.op_.GetAttr("min")); - T max = static_cast(context.op_.GetAttr("max")); - thrust::counting_iterator index_sequence_begin(0); - ssize_t N = framework::product(tensor->dims()); - thrust::transform(index_sequence_begin, index_sequence_begin + N, - thrust::device_ptr(data), - UniformGenerator(min, max, seed)); - } -}; - -} // namespace operators -} // namespace paddle - REGISTER_OP_GPU_KERNEL(uniform_random, - paddle::operators::GPUUniformRandomKernel); + paddle::operators::GPUUniformRandomKernel< + paddle::platform::GPUPlace, float>); diff --git a/paddle/operators/uniform_random_op.h b/paddle/operators/uniform_random_op.h new file mode 100644 index 000000000..ec009b025 --- /dev/null +++ b/paddle/operators/uniform_random_op.h @@ -0,0 +1,38 @@ +/* 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" + +namespace paddle { +namespace operators { +template +class UniformRandomKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& context) const override { + auto* tensor = context.Output("Out"); + T* data = tensor->mutable_data(context.GetPlace()); + T min = static_cast(context.op_.GetAttr("min")); + T max = static_cast(context.op_.GetAttr("max")); + auto n = framework::product(tensor->dims()); + + auto* device_context = + const_cast(context.device_context_); + math::RandUniform(n, min, max, data, device_context); + } +}; +} +} diff --git a/paddle/platform/device_context.cc b/paddle/platform/device_context.cc index f92c15ae4..fabbb5544 100644 --- a/paddle/platform/device_context.cc +++ b/paddle/platform/device_context.cc @@ -25,8 +25,17 @@ CPUDeviceContext::CPUDeviceContext() { eigen_device_.reset(new Eigen::DefaultDevice()); } -CPUDeviceContext::CPUDeviceContext(CPUPlace place) { +CPUDeviceContext::CPUDeviceContext(CPUPlace place, int rand_seed) { eigen_device_.reset(new Eigen::DefaultDevice()); + rand_seed_ = rand_seed; +} + +std::minstd_rand& CPUDeviceContext::rand_engine() { + if (!rand_engine_) { + rand_engine_.reset(new std::minstd_rand()); + rand_engine_->seed(rand_seed_); + } + return *(rand_engine_.get()); } Eigen::DefaultDevice* CPUDeviceContext::eigen_device() const { @@ -95,7 +104,8 @@ Eigen::GpuDevice* DeviceContext::get_eigen_device() const { return reinterpret_cast(this)->eigen_device(); } -CUDADeviceContext::CUDADeviceContext(GPUPlace place) : place_(place) { +CUDADeviceContext::CUDADeviceContext(GPUPlace place, uint64_t seed) + : place_(place), seed_(seed) { SetDeviceId(place_.device); PADDLE_ENFORCE(cudaStreamCreate(&stream_)); eigen_stream_.reset(new EigenCudaStreamDevice()); @@ -114,9 +124,6 @@ CUDADeviceContext::~CUDADeviceContext() { PADDLE_ENFORCE(dynload::cudnnDestroy(cudnn_handle_)); } - if (curand_generator_) { - PADDLE_ENFORCE(dynload::curandDestroyGenerator(curand_generator_)); - } eigen_stream_.reset(); eigen_device_.reset(); PADDLE_ENFORCE(cudaStreamDestroy(stream_)); @@ -150,21 +157,16 @@ cudnnHandle_t CUDADeviceContext::cudnn_handle() { return cudnn_handle_; } -cudaStream_t CUDADeviceContext::stream() { return stream_; } - -curandGenerator_t CUDADeviceContext::curand_generator() { - if (!curand_generator_) { - SetDeviceId(place_.device); - PADDLE_ENFORCE(dynload::curandCreateGenerator(&curand_generator_, - CURAND_RNG_PSEUDO_DEFAULT)); - PADDLE_ENFORCE( - dynload::curandSetPseudoRandomGeneratorSeed(curand_generator_, seed_)); - - PADDLE_ENFORCE(dynload::curandSetStream(curand_generator_, stream_)); +thrust::minstd_rand& CPUDeviceContext::rand_engine() { + if (!rand_engine_) { + rand_engine_.reset(new thrust::minstd_rand()); + rand_engine_->seed(rand_seed_); } - return curand_generator_; + return *(rand_engine_.get()); } +cudaStream_t CUDADeviceContext::stream() { return stream_; } + #endif // PADDLE_ONLY_CPU } // namespace platform diff --git a/paddle/platform/device_context.h b/paddle/platform/device_context.h index c5042ae33..e4de3807c 100644 --- a/paddle/platform/device_context.h +++ b/paddle/platform/device_context.h @@ -15,9 +15,10 @@ limitations under the License. */ #include "paddle/platform/place.h" #ifndef PADDLE_ONLY_CPU +#include +#include #include "paddle/platform/dynload/cublas.h" #include "paddle/platform/dynload/cudnn.h" -#include "paddle/platform/dynload/curand.h" #include "paddle/platform/gpu_info.h" #define EIGEN_USE_GPU #endif @@ -40,14 +41,18 @@ class DeviceContext { class CPUDeviceContext : public DeviceContext { public: CPUDeviceContext(); - explicit CPUDeviceContext(CPUPlace); + explicit CPUDeviceContext(CPUPlace place, int rand_seed = 0); virtual ~CPUDeviceContext() {} Eigen::DefaultDevice* eigen_device() const; + std::minstd_rand& rand_engine(); + Place GetPlace() const override; private: + int rand_seed_; + std::unique_ptr rand_engine_; std::unique_ptr eigen_device_; }; @@ -56,7 +61,7 @@ class EigenCudaStreamDevice; class CUDADeviceContext : public DeviceContext { public: - explicit CUDADeviceContext(GPUPlace); + explicit CUDADeviceContext(GPUPlace place, uint64_t rand_seed = 0); virtual ~CUDADeviceContext(); /*! \brief Wait for all operations completion in the stream. */ @@ -75,8 +80,7 @@ class CUDADeviceContext : public DeviceContext { /*! \brief Return cudnn handle in the device context. */ cudnnHandle_t cudnn_handle(); - /*! \brief Return curand handle in the device context. */ - curandGenerator_t curand_generator(); + thrust::minstd_rand& CPUDeviceContext::rand_engine(); /*! \brief Return cuda stream in the device context. */ cudaStream_t stream(); @@ -85,18 +89,16 @@ class CUDADeviceContext : public DeviceContext { private: GPUPlace place_; - private: std::unique_ptr eigen_device_; std::unique_ptr eigen_stream_; - private: - uint64_t seed_; + uint64_t rand_seed_; + std::unique_ptr rand_engine_; // clang-format off cudaStream_t stream_{nullptr}; cudnnHandle_t cudnn_handle_{nullptr}; cublasHandle_t cublas_handle_{nullptr}; - curandGenerator_t curand_generator_{nullptr}; // clang-format on }; diff --git a/python/paddle/v2/framework/tests/CMakeLists.txt b/python/paddle/v2/framework/tests/CMakeLists.txt index ce57a0713..b07a65f4d 100644 --- a/python/paddle/v2/framework/tests/CMakeLists.txt +++ b/python/paddle/v2/framework/tests/CMakeLists.txt @@ -22,7 +22,7 @@ py_test(test_rowwise_add_op SRCS test_rowwise_add_op.py) py_test(test_default_scope_funcs SRCS test_default_scope_funcs.py) py_test(test_operator SRCS test_operator.py) -# py_test(test_gaussian_random_op SRCS test_gaussian_random_op.py) +py_test(test_gaussian_random_op SRCS test_gaussian_random_op.py) py_test(test_uniform_random_op SRCS test_uniform_random_op.py) py_test(test_recurrent_op SRCS test_recurrent_op.py) py_test(test_sgd_op SRCS test_sgd_op.py) diff --git a/python/paddle/v2/framework/tests/test_gaussian_random_op.py b/python/paddle/v2/framework/tests/test_gaussian_random_op.py index f95ed70b5..367d21b30 100644 --- a/python/paddle/v2/framework/tests/test_gaussian_random_op.py +++ b/python/paddle/v2/framework/tests/test_gaussian_random_op.py @@ -17,12 +17,7 @@ class GaussianRandomTest(unittest.TestCase): scope.new_var("Out").get_tensor() op = Operator( - "gaussian_random", - Out="Out", - dims=[1000, 784], - mean=.0, - std=1., - seed=10) + "gaussian_random", Out="Out", dims=[1000, 784], mean=.0, std=1.) op.infer_shape(scope) context = core.DeviceContext.create(place) diff --git a/python/paddle/v2/framework/tests/test_uniform_random_op.py b/python/paddle/v2/framework/tests/test_uniform_random_op.py index c3d2bb44d..95c36a27c 100644 --- a/python/paddle/v2/framework/tests/test_uniform_random_op.py +++ b/python/paddle/v2/framework/tests/test_uniform_random_op.py @@ -17,12 +17,7 @@ class UniformRandomTest(unittest.TestCase): scope.new_var("X").get_tensor() op = Operator( - "uniform_random", - Out="X", - dims=[1000, 784], - min=-5.0, - max=10.0, - seed=10) + "uniform_random", Out="X", dims=[1000, 784], min=-5.0, max=10.0) op.infer_shape(scope) ctx = core.DeviceContext.create(place) -- GitLab From 29d8825caf921f5349551a18344503345c7b9969 Mon Sep 17 00:00:00 2001 From: dangqingqing Date: Mon, 21 Aug 2017 13:43:51 +0800 Subject: [PATCH 0136/2018] tune relative precision for unit test img_conv2 in test_NetworkCompare.cpp. 1. It's no problem with relative precision 1e-3 when testing several times in my local machine. 2. But the testing failed with 1e-2 in the TeamCity, and only one value's relative precision is over 1e-2. So tune it to 4e-2 --- paddle/gserver/tests/test_NetworkCompare.cpp | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/paddle/gserver/tests/test_NetworkCompare.cpp b/paddle/gserver/tests/test_NetworkCompare.cpp index f930c72fd..d36f72360 100644 --- a/paddle/gserver/tests/test_NetworkCompare.cpp +++ b/paddle/gserver/tests/test_NetworkCompare.cpp @@ -269,7 +269,8 @@ TEST(Compare, img_conv2) { bool useGpu = FLAGS_use_gpu; double eps = FLAGS_checkgrad_eps; FLAGS_use_gpu = true; - FLAGS_checkgrad_eps = 1e-2; + // Sometimes, this unit test will fail with 1e-2 + FLAGS_checkgrad_eps = 4e-2; compareNetwork(config_file_a, config_file_b); FLAGS_use_gpu = useGpu; FLAGS_checkgrad_eps = eps; -- GitLab From c108d6108cbdd28424397341fb67be01a2f63413 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Mon, 21 Aug 2017 14:03:12 +0800 Subject: [PATCH 0137/2018] Identity operator and its gradient --- paddle/framework/CMakeLists.txt | 3 +- paddle/framework/pybind.cc | 1 + paddle/operators/CMakeLists.txt | 1 + paddle/operators/identity_op.cc | 71 +++++++++++++++++++ paddle/operators/identity_op.cu | 17 +++++ paddle/operators/identity_op.h | 32 +++++++++ .../paddle/v2/framework/tests/CMakeLists.txt | 1 + .../v2/framework/tests/test_identity_op.py | 24 +++++++ 8 files changed, 149 insertions(+), 1 deletion(-) create mode 100644 paddle/operators/identity_op.cc create mode 100644 paddle/operators/identity_op.cu create mode 100644 paddle/operators/identity_op.h create mode 100644 python/paddle/v2/framework/tests/test_identity_op.py diff --git a/paddle/framework/CMakeLists.txt b/paddle/framework/CMakeLists.txt index 68304c9fc..f249512f4 100644 --- a/paddle/framework/CMakeLists.txt +++ b/paddle/framework/CMakeLists.txt @@ -55,5 +55,6 @@ cc_library(paddle_pybind SHARED recurrent_op uniform_random_op gaussian_random_op - fill_zeros_like_op) + fill_zeros_like_op + identity_op) endif(WITH_PYTHON) diff --git a/paddle/framework/pybind.cc b/paddle/framework/pybind.cc index f0114b9e4..ddb244623 100644 --- a/paddle/framework/pybind.cc +++ b/paddle/framework/pybind.cc @@ -42,6 +42,7 @@ USE_OP(fill_zeros_like); USE_OP_ITSELF(recurrent_op); USE_OP(gaussian_random); USE_OP(uniform_random); +USE_OP(identity); namespace paddle { namespace framework { diff --git a/paddle/operators/CMakeLists.txt b/paddle/operators/CMakeLists.txt index a7c89787e..20e562c7d 100644 --- a/paddle/operators/CMakeLists.txt +++ b/paddle/operators/CMakeLists.txt @@ -68,3 +68,4 @@ op_library(recurrent_op SRCS recurrent_op.cc rnn/recurrent_op_utils.cc DEPS framework_proto tensor op_registry operator net_op) op_library(uniform_random_op SRCS uniform_random_op.cc uniform_random_op.cu) +op_library(identity_op SRCS identity_op.cc identity_op.cu DEPS net_op) diff --git a/paddle/operators/identity_op.cc b/paddle/operators/identity_op.cc new file mode 100644 index 000000000..cac44020b --- /dev/null +++ b/paddle/operators/identity_op.cc @@ -0,0 +1,71 @@ +/* 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/identity_op.h" +#include "paddle/operators/net_op.h" + +namespace paddle { +namespace operators { + +class IdentityOp : public framework::OperatorWithKernel { + public: + IdentityOp(const std::string &type, const VarNameMap &inputs, + const VarNameMap &outputs, const framework::AttributeMap &attrs) + : OperatorWithKernel(type, inputs, outputs, attrs) {} + + protected: + void InferShape(const framework::InferShapeContext &ctx) const override { + auto *in = ctx.Input("X"); + auto *out = ctx.Output("Out"); + out->Resize(in->dims()); + } +}; + +class IdentityOpMaker : public framework::OpProtoAndCheckerMaker { + public: + IdentityOpMaker(framework::OpProto *proto, + framework::OpAttrChecker *op_checker) + : OpProtoAndCheckerMaker(proto, op_checker) { + AddInput("X", "The input tensor of identity operator.").NotInGradient(); + AddOutput("Out", "The output tensor of identity operator.").NotInGradient(); + AddComment(R"DOC(Identity operator + +The equation is: Out = X +)DOC"); + } +}; + +// Identity Op's gradient is identity op, too. +// Grad(Out=identity_op(X)) => Grad(X) = identity_op(Grad(Out)) +class IdentityGradOp : public NetOp { + public: + IdentityGradOp(const std::string &type, const VarNameMap &inputs, + const VarNameMap &outputs, + const framework::AttributeMap &attrs) + : NetOp(type, inputs, outputs, attrs) { + AddOp(framework::OpRegistry::CreateOp( + "identity", {{"X", {Input(framework::GradVarName("Out"))}}}, + {{"Out", {Output(framework::GradVarName("X"))}}}, {})); + CompleteAddOp(false); + } +}; + +} // namespace operators +} // namespace paddle + +namespace ops = paddle::operators; + +REGISTER_OP(identity, ops::IdentityOp, ops::IdentityOpMaker, identity_grad, + ops::IdentityGradOp); +REGISTER_OP_CPU_KERNEL(identity, ops::IdentityKernel); diff --git a/paddle/operators/identity_op.cu b/paddle/operators/identity_op.cu new file mode 100644 index 000000000..3053104bb --- /dev/null +++ b/paddle/operators/identity_op.cu @@ -0,0 +1,17 @@ +/* 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/identity_op.h" + +REGISTER_OP_GPU_KERNEL(identity, paddle::operators::IdentityKernel); diff --git a/paddle/operators/identity_op.h b/paddle/operators/identity_op.h new file mode 100644 index 000000000..14a832257 --- /dev/null +++ b/paddle/operators/identity_op.h @@ -0,0 +1,32 @@ +/* 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/memory/memcpy.h" +namespace paddle { +namespace operators { +template +class IdentityKernel : public framework::OpKernel { + public: + virtual void Compute(const framework::ExecutionContext& context) const { + auto* tensor = context.Output("Out"); + auto* in = context.Input("X"); + tensor->CopyFrom(*in, in->place()); + } +}; + +} // namespace operators +} // namespace paddle diff --git a/python/paddle/v2/framework/tests/CMakeLists.txt b/python/paddle/v2/framework/tests/CMakeLists.txt index ce57a0713..cf7baa555 100644 --- a/python/paddle/v2/framework/tests/CMakeLists.txt +++ b/python/paddle/v2/framework/tests/CMakeLists.txt @@ -27,3 +27,4 @@ py_test(test_uniform_random_op SRCS test_uniform_random_op.py) py_test(test_recurrent_op SRCS test_recurrent_op.py) py_test(test_sgd_op SRCS test_sgd_op.py) py_test(test_gradient_checker SRCS test_gradient_checker.py) +py_test(test_identity_op SRCS test_identity_op.py) diff --git a/python/paddle/v2/framework/tests/test_identity_op.py b/python/paddle/v2/framework/tests/test_identity_op.py new file mode 100644 index 000000000..181d9c0c2 --- /dev/null +++ b/python/paddle/v2/framework/tests/test_identity_op.py @@ -0,0 +1,24 @@ +import unittest +from op_test_util import OpTestMeta +from gradient_checker import GradientChecker, create_op +import numpy as np + + +class IdentityTest(unittest.TestCase): + __metaclass__ = OpTestMeta + + def setUp(self): + self.type = "identity" + self.inputs = {'X': np.random.random((32, 784)).astype("float32")} + self.outputs = {'Out': self.inputs['X']} + + +class IdentityGradOpTest(GradientChecker): + def test_normal(self): + op = create_op("identity") + inputs = {"X": np.random.random((10, 10)).astype("float32")} + self.check_grad(op, inputs, set("X"), "Out") + + +if __name__ == '__main__': + unittest.main() -- GitLab From 83abbce8eb750f7e7c844b0959851e901806aa91 Mon Sep 17 00:00:00 2001 From: guosheng Date: Mon, 21 Aug 2017 14:05:56 +0800 Subject: [PATCH 0138/2018] Follow comments and refine ScaleShiftLayer --- paddle/gserver/layers/ScaleShiftLayer.cpp | 5 +++-- paddle/gserver/tests/test_LayerGrad.cpp | 4 ++-- python/paddle/trainer_config_helpers/layers.py | 5 +++-- .../protostr/test_scale_shift_layer.protostr | 14 +++++++------- .../tests/configs/test_scale_shift_layer.py | 6 ++---- 5 files changed, 17 insertions(+), 17 deletions(-) diff --git a/paddle/gserver/layers/ScaleShiftLayer.cpp b/paddle/gserver/layers/ScaleShiftLayer.cpp index 4f5b1c622..06dcb409f 100644 --- a/paddle/gserver/layers/ScaleShiftLayer.cpp +++ b/paddle/gserver/layers/ScaleShiftLayer.cpp @@ -17,8 +17,9 @@ limitations under the License. */ namespace paddle { /** - * A layer does scaling and shifting to the input by appling a slope and - * an intercept which are trainable to the input element-wise. + * A layer applies a slope and an intercept to the input element-wise for + * scaling and shifting. Noting that this layer is trainable which differs + * from the SlopeInterceptLayer. * * \f[ * y = wx + b diff --git a/paddle/gserver/tests/test_LayerGrad.cpp b/paddle/gserver/tests/test_LayerGrad.cpp index 65429ebad..dd2c955e6 100644 --- a/paddle/gserver/tests/test_LayerGrad.cpp +++ b/paddle/gserver/tests/test_LayerGrad.cpp @@ -2008,8 +2008,8 @@ TEST(Layer, RowL2NormLayer) { } TEST(Layer, ScaleShiftLayer) { - const size_t batchSize = 128; - const size_t size = 512; + const size_t batchSize = 16; + const size_t size = 32; TestConfig config; config.layerConfig.set_type("scale_shift"); config.layerConfig.set_size(size); diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py index 4c7217024..ec3a87aa3 100755 --- a/python/paddle/trainer_config_helpers/layers.py +++ b/python/paddle/trainer_config_helpers/layers.py @@ -6219,8 +6219,9 @@ def kmax_sequence_score_layer(input, name=None, beam_size=1): @wrap_bias_attr_default() def scale_shift_layer(input, name=None, param_attr=None, bias_attr=None): """ - A layer does scaling and shifting to the input by appling a slope and - an intercept which are trainable to the input element-wise. + A layer applies a slope and an intercept to the input element-wise for + scaling and shifting. Noting that this layer is trainable which differs + from the slope_intercept_layer. .. math:: y = w * x + b diff --git a/python/paddle/trainer_config_helpers/tests/configs/protostr/test_scale_shift_layer.protostr b/python/paddle/trainer_config_helpers/tests/configs/protostr/test_scale_shift_layer.protostr index efaf20f8a..35ade126a 100644 --- a/python/paddle/trainer_config_helpers/tests/configs/protostr/test_scale_shift_layer.protostr +++ b/python/paddle/trainer_config_helpers/tests/configs/protostr/test_scale_shift_layer.protostr @@ -14,7 +14,6 @@ layers { input_layer_name: "data" input_parameter_name: "___scale_shift_0__.w0" } - bias_parameter_name: "___scale_shift_0__.wbias" } layers { name: "__scale_shift_1__" @@ -25,6 +24,7 @@ layers { input_layer_name: "data" input_parameter_name: "___scale_shift_1__.w0" } + bias_parameter_name: "___scale_shift_1__.wbias" } parameters { name: "___scale_shift_0__.w0" @@ -37,24 +37,24 @@ parameters { initial_smart: true } parameters { - name: "___scale_shift_0__.wbias" + name: "___scale_shift_1__.w0" size: 1 initial_mean: 0.0 - initial_std: 0.0 + initial_std: 1.0 dims: 1 dims: 1 initial_strategy: 0 - initial_smart: false + initial_smart: true } parameters { - name: "___scale_shift_1__.w0" + name: "___scale_shift_1__.wbias" size: 1 initial_mean: 0.0 - initial_std: 1.0 + initial_std: 0.0 dims: 1 dims: 1 initial_strategy: 0 - initial_smart: true + initial_smart: false } input_layer_names: "data" output_layer_names: "__scale_shift_0__" diff --git a/python/paddle/trainer_config_helpers/tests/configs/test_scale_shift_layer.py b/python/paddle/trainer_config_helpers/tests/configs/test_scale_shift_layer.py index 818d71f15..dd589116f 100644 --- a/python/paddle/trainer_config_helpers/tests/configs/test_scale_shift_layer.py +++ b/python/paddle/trainer_config_helpers/tests/configs/test_scale_shift_layer.py @@ -1,11 +1,9 @@ from paddle.trainer_config_helpers import * -settings(batch_size=1000, learning_rate=1e-5) - data = data_layer(name='data', size=100) -scale = scale_shift_layer(input=data) +scale = scale_shift_layer(input=data, bias_attr=False) -scale_shift = scale_shift_layer(input=data, bias_attr=False) +scale_shift = scale_shift_layer(input=data) outputs(scale, scale_shift) -- GitLab From 0af1c4a9feed5a38f34e1ea5a44e3887f702059f Mon Sep 17 00:00:00 2001 From: guosheng Date: Mon, 21 Aug 2017 14:39:05 +0800 Subject: [PATCH 0139/2018] Follow comments and refine annotations on ScaleShiftLayer --- paddle/gserver/layers/ScaleShiftLayer.cpp | 8 ++++---- python/paddle/trainer_config_helpers/layers.py | 10 +++++++--- 2 files changed, 11 insertions(+), 7 deletions(-) diff --git a/paddle/gserver/layers/ScaleShiftLayer.cpp b/paddle/gserver/layers/ScaleShiftLayer.cpp index 06dcb409f..35fd038ab 100644 --- a/paddle/gserver/layers/ScaleShiftLayer.cpp +++ b/paddle/gserver/layers/ScaleShiftLayer.cpp @@ -17,15 +17,15 @@ limitations under the License. */ namespace paddle { /** - * A layer applies a slope and an intercept to the input element-wise for - * scaling and shifting. Noting that this layer is trainable which differs - * from the SlopeInterceptLayer. + * A layer applies a linear transformation to each element in each row of + * the input matrix. For each element, the layer first re-scale it and then + * adds a bias to it. * * \f[ * y = wx + b * \f] * - * Here, w is scale and b is offset, which are scalars and trainable. + * Here, w is the scale and b is the bias. Both w and b are trainable scalars. * */ diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py index ec3a87aa3..c9e3ded65 100755 --- a/python/paddle/trainer_config_helpers/layers.py +++ b/python/paddle/trainer_config_helpers/layers.py @@ -6219,9 +6219,13 @@ def kmax_sequence_score_layer(input, name=None, beam_size=1): @wrap_bias_attr_default() def scale_shift_layer(input, name=None, param_attr=None, bias_attr=None): """ - A layer applies a slope and an intercept to the input element-wise for - scaling and shifting. Noting that this layer is trainable which differs - from the slope_intercept_layer. + A layer applies a linear transformation to each element in each row of + the input matrix. For each element, the layer first re-scale it and then + adds a bias to it. + + This layer is very like the SlopeInterceptLayer, except the scale and + bias are trainable. + .. math:: y = w * x + b -- GitLab From d5768ebc89868431040e47e3db126263da385d70 Mon Sep 17 00:00:00 2001 From: chengduoZH Date: Fri, 18 Aug 2017 20:49:35 +0800 Subject: [PATCH 0140/2018] fix above comments --- paddle/cuda/include/hl_matrix.h | 58 ++++++++----- paddle/cuda/include/stub/hl_matrix_stub.h | 47 +++++++---- paddle/cuda/src/hl_cuda_matrix.cu | 84 +++++++++---------- paddle/gserver/layers/Conv3DLayer.cpp | 26 ++++-- paddle/gserver/layers/Conv3DLayer.h | 14 +--- paddle/gserver/layers/ConvBaseLayer.cpp | 26 +----- paddle/gserver/layers/ConvBaseLayer.h | 1 - paddle/gserver/layers/CudnnConvBaseLayer.cpp | 18 ++++ paddle/gserver/layers/DeConv3DLayer.cpp | 46 +++++----- paddle/gserver/layers/DeConv3DLayer.h | 44 +++++----- paddle/gserver/layers/ExpandConvBaseLayer.cpp | 21 ++++- paddle/gserver/tests/test_LayerGrad.cpp | 31 +++---- paddle/math/tests/test_matrixCompare.cpp | 28 ++----- proto/ModelConfig.proto | 4 +- 14 files changed, 247 insertions(+), 201 deletions(-) diff --git a/paddle/cuda/include/hl_matrix.h b/paddle/cuda/include/hl_matrix.h index da2ed8cab..a37921b74 100644 --- a/paddle/cuda/include/hl_matrix.h +++ b/paddle/cuda/include/hl_matrix.h @@ -240,16 +240,25 @@ extern void hl_matrix_rotate( * @param[in] strideW stride in the width. * @param[in] paddingD padding in the depth. * @param[in] paddingH padding in the height. - * @param[in] paddingW padding in the width. + * @param[in] paddingW padding in the width. * @param[out] matDst output matrix. - * + * */ -extern void hl_matrix_vol2Col(real* matSrc, - int channel, int depth, int height, int width, - int filterD, int filterH, int filterW, - int strideD, int strideH, int strideW, - int paddingD, int paddingH, int paddingW, - real* matDst); +extern void hl_matrix_vol2Col(const real* dataSrc, + int channels, + int depth, + int height, + int width, + int filterD, + int filterH, + int filterW, + int strideD, + int strideH, + int strideW, + int paddingD, + int paddingH, + int paddingW, + real* dataDst); /** * @brief Matrix col2Vol: Convert col matrix into 3D volume @@ -267,19 +276,28 @@ extern void hl_matrix_vol2Col(real* matSrc, * @param[in] strideW stride in the width. * @param[in] paddingD padding in the depth. * @param[in] paddingH padding in the height. - * @param[in] paddingW padding in the width. + * @param[in] paddingW padding in the width. * @param[in] matSrc input matrix. - * @param[in] beta input - * @param[in] alpha input - * + * @param[in] beta input + * @param[in] alpha input + * */ -extern void hl_matrix_col2Vol(real* matDst, - int channels, int depth, int height, int width, - int filterD, int filterH, int filterW, - int strideD, int strideH, int strideW, - int paddingD, int paddingH, int paddingW, - real* matSrc, - real alpha, real beta); - +extern void hl_matrix_col2Vol(real* dataDst, + int channels, + int depth, + int height, + int width, + int filterD, + int filterH, + int filterW, + int strideD, + int strideH, + int strideW, + int paddingD, + int paddingH, + int paddingW, + const real* dataSrc, + real alpha, + real beta); #endif /* HL_MATRIX_H_ */ diff --git a/paddle/cuda/include/stub/hl_matrix_stub.h b/paddle/cuda/include/stub/hl_matrix_stub.h index 0b7377781..6ac332945 100644 --- a/paddle/cuda/include/stub/hl_matrix_stub.h +++ b/paddle/cuda/include/stub/hl_matrix_stub.h @@ -99,19 +99,38 @@ inline void hl_matrix_collect_shared_bias(real* B_d, inline void hl_matrix_rotate( real* mat, real* matRot, int dimM, int dimN, bool clockWise) {} -inline void hl_matrix_vol2Col(real* data, - int channels, int depth, int height, int width, - int filterD, int filterH, int filterW, - int strideD, int strideH, int strideW, - int paddingD, int paddingH, int paddingW, - real* data_col) {} - -inline void hl_matrix_col2Vol(real* data, - int channels, int depth, int height, int width, - int filterD, int filterH, int filterW, - int strideD, int strideH, int strideW, - int paddingD, int paddingH, int paddingW, - real* data_Im, - real alpha, real beta) {} +inline void hl_matrix_vol2Col(const real* dataSrc, + int channels, + int depth, + int height, + int width, + int filterD, + int filterH, + int filterW, + int strideD, + int strideH, + int strideW, + int paddingD, + int paddingH, + int paddingW, + real* dataDst) {} + +inline void hl_matrix_col2Vol(real* dataDst, + int channels, + int depth, + int height, + int width, + int filterD, + int filterH, + int filterW, + int strideD, + int strideH, + int strideW, + int paddingD, + int paddingH, + int paddingW, + const real* dataSrc, + real alpha, + real beta) {} #endif // HL_MATRIX_STUB_H_ diff --git a/paddle/cuda/src/hl_cuda_matrix.cu b/paddle/cuda/src/hl_cuda_matrix.cu index 3bf1b0251..b41a3a1e0 100644 --- a/paddle/cuda/src/hl_cuda_matrix.cu +++ b/paddle/cuda/src/hl_cuda_matrix.cu @@ -594,7 +594,7 @@ void hl_matrix_rotate( } __global__ void keMatrixVol2Col(int num_kernels, - real* dataSrc, + const real* dataSrc, real* dataDst, int depth, int height, @@ -643,7 +643,7 @@ __global__ void keMatrixVol2Col(int num_kernels, } } -void hl_matrix_vol2Col(real* dataSrc, +void hl_matrix_vol2Col(const real* dataSrc, int channels, int depth, int height, @@ -666,30 +666,30 @@ void hl_matrix_vol2Col(real* dataSrc, const int threads = 512; const int blocks = DIVUP(num_kernels, threads); - keMatrixVol2Col<<>>(num_kernels, - dataSrc, - dataDst, - depth, - height, - width, - filterD, - filterH, - filterW, - strideD, - strideH, - strideW, - paddingD, - paddingH, - paddingW, - depth_col, - height_col, - width_col); + keMatrixVol2Col<<>>(num_kernels, + dataSrc, + dataDst, + depth, + height, + width, + filterD, + filterH, + filterW, + strideD, + strideH, + strideW, + paddingD, + paddingH, + paddingW, + depth_col, + height_col, + width_col); CHECK_SYNC("hl_matrix_vol2Col failed"); } __global__ void keMatrixCol2Vol(int num_kernels, real* dataDst, - real* dataSrc, + const real* dataSrc, int depth, int height, int width, @@ -759,7 +759,7 @@ void hl_matrix_col2Vol(real* dataDst, int paddingD, int paddingH, int paddingW, - real* dataSrc, + const real* dataSrc, real alpha, real beta) { int depth_col = (depth + 2 * paddingD - filterD) / strideD + 1; @@ -770,26 +770,26 @@ void hl_matrix_col2Vol(real* dataDst, const int threads = 512; const int blocks = DIVUP(num_kernels, threads); - keMatrixCol2Vol<<>>(num_kernels, - dataDst, - dataSrc, - depth, - height, - width, - filterD, - filterH, - filterW, - strideD, - strideH, - strideW, - paddingD, - paddingH, - paddingW, - depth_col, - height_col, - width_col, - alpha, - beta); + keMatrixCol2Vol<<>>(num_kernels, + dataDst, + dataSrc, + depth, + height, + width, + filterD, + filterH, + filterW, + strideD, + strideH, + strideW, + paddingD, + paddingH, + paddingW, + depth_col, + height_col, + width_col, + alpha, + beta); CHECK_SYNC("hl_matrix_col2Vol failed"); } diff --git a/paddle/gserver/layers/Conv3DLayer.cpp b/paddle/gserver/layers/Conv3DLayer.cpp index 106909824..db907bbab 100644 --- a/paddle/gserver/layers/Conv3DLayer.cpp +++ b/paddle/gserver/layers/Conv3DLayer.cpp @@ -28,16 +28,26 @@ bool Conv3DLayer::init(const LayerMap &layerMap, const ConvConfig &conf = inputConfig.conv_conf(); M_.push_back(numFilters_ / conf.groups()); K_.push_back(filterPixels_[index] * filterChannels_[index]); - if (nullptr != weights_[index]->getW()) - weights_[index]->getW()->reshape(weights_[index]->getW()->getWidth(), - weights_[index]->getW()->getHeight()); - if (nullptr != weights_[index]->getWGrad()) - weights_[index]->getWGrad()->reshape( - weights_[index]->getWGrad()->getWidth(), - weights_[index]->getWGrad()->getHeight()); + + // create a new weight + size_t height, width; + width = filterPixels_[index] * filterChannels_[index]; + height = numFilters_; + CHECK_EQ(parameters_[index]->getSize(), width * height); + Weight *w = new Weight(height, width, parameters_[index]); + weights_.emplace_back(w); ++index; } - CHECK(inputLayers_.size() == parameters_.size()); + if (biasParameter_.get()) { + if (sharedBiases_) { + CHECK_EQ((size_t)numFilters_, biasParameter_->getSize()); + biases_ = + std::unique_ptr(new Weight(1, numFilters_, biasParameter_)); + } else { + biases_ = + std::unique_ptr(new Weight(1, getSize(), biasParameter_)); + } + } return true; } diff --git a/paddle/gserver/layers/Conv3DLayer.h b/paddle/gserver/layers/Conv3DLayer.h index 703671e5d..b622508d0 100644 --- a/paddle/gserver/layers/Conv3DLayer.h +++ b/paddle/gserver/layers/Conv3DLayer.h @@ -12,13 +12,11 @@ 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 #include "ConvBaseLayer.h" -#include "paddle/math/Matrix.h" #include "paddle/math/MathUtils.h" -#include +#include "paddle/math/Matrix.h" namespace paddle { @@ -30,21 +28,17 @@ namespace paddle { class Conv3DLayer : public ConvBaseLayer { public: explicit Conv3DLayer(const LayerConfig& config) : ConvBaseLayer(config) {} - ~Conv3DLayer() {} - bool init(const LayerMap &layerMap, const ParameterMap ¶meterMap); - - size_t getSize(); + bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); void forward(PassType passType); void addBias(); - void backward(const UpdateCallback& callback); - void bpropBiases(); void bpropData(int i); void bpropWeights(int i); + size_t getSize(); protected: // Figure out the dimensions for individual gemms. diff --git a/paddle/gserver/layers/ConvBaseLayer.cpp b/paddle/gserver/layers/ConvBaseLayer.cpp index 6bcbe0ddb..8c637eaec 100644 --- a/paddle/gserver/layers/ConvBaseLayer.cpp +++ b/paddle/gserver/layers/ConvBaseLayer.cpp @@ -21,8 +21,7 @@ bool ConvBaseLayer::init(const LayerMap& layerMap, const ParameterMap& parameterMap) { /* Initialize the basic parent class */ Layer::init(layerMap, parameterMap); - isDeconv_ = (config_.type() == "exconv" || config_.type() == "cudnn_conv" || - config_.type() == "conv3d" || config_.type() == "deconv3d") + isDeconv_ = (config_.type() == "exconv" || config_.type() == "cudnn_conv") ? false : true; @@ -56,28 +55,9 @@ bool ConvBaseLayer::init(const LayerMap& layerMap, } CHECK(inputLayers_.size() == parameters_.size()); - for (size_t i = 0; i < inputLayers_.size(); i++) { - size_t height, width; - height = filterPixels_[i] * filterChannels_[i]; - width = (!isDeconv_) ? numFilters_ : channels_[i]; - - // create a new weight - CHECK_EQ(parameters_[i]->getSize(), width * height); - Weight* w = new Weight(height, width, parameters_[i]); - weights_.emplace_back(w); - } - /* initialize the biases_ */ - if (biasParameter_.get()) { - if (sharedBiases_) { - CHECK_EQ((size_t)numFilters_, biasParameter_->getSize()); - biases_ = - std::unique_ptr(new Weight(1, numFilters_, biasParameter_)); - } else { - biases_ = - std::unique_ptr(new Weight(1, getSize(), biasParameter_)); - } - } + // create new weights_ in derived class + // create new biases_ in derived class // default caffe model caffeMode_ = true; diff --git a/paddle/gserver/layers/ConvBaseLayer.h b/paddle/gserver/layers/ConvBaseLayer.h index 8d1fd989e..629c46277 100644 --- a/paddle/gserver/layers/ConvBaseLayer.h +++ b/paddle/gserver/layers/ConvBaseLayer.h @@ -23,7 +23,6 @@ namespace paddle { * with learned filters and (optionally) adds biases. */ - class ConvBaseLayer : public Layer { protected: typedef std::vector IntV; diff --git a/paddle/gserver/layers/CudnnConvBaseLayer.cpp b/paddle/gserver/layers/CudnnConvBaseLayer.cpp index c056bbe4d..9e954615c 100644 --- a/paddle/gserver/layers/CudnnConvBaseLayer.cpp +++ b/paddle/gserver/layers/CudnnConvBaseLayer.cpp @@ -46,8 +46,26 @@ bool CudnnConvBaseLayer::init(const LayerMap &layerMap, projConf_.emplace_back(conf); projections_.emplace_back( Projection::create(*projConf_[i], parameters_[i], useGpu_)); + + // create a new weight + size_t height, width; + height = filterPixels_[i] * filterChannels_[i]; + width = (!isDeconv_) ? numFilters_ : channels_[i]; + CHECK_EQ(parameters_[i]->getSize(), width * height); + Weight *w = new Weight(height, width, parameters_[i]); + weights_.emplace_back(w); } + if (biasParameter_.get()) { + if (sharedBiases_) { + CHECK_EQ((size_t)numFilters_, biasParameter_->getSize()); + biases_ = + std::unique_ptr(new Weight(numFilters_, 1, biasParameter_)); + } else { + biases_ = + std::unique_ptr(new Weight(getSize(), 1, biasParameter_)); + } + } if (biases_.get() && sharedBiases_) { hl_create_tensor_descriptor(&biasDesc_); hl_create_tensor_descriptor(&outputDesc_); diff --git a/paddle/gserver/layers/DeConv3DLayer.cpp b/paddle/gserver/layers/DeConv3DLayer.cpp index 5a54a6844..b18c06e36 100644 --- a/paddle/gserver/layers/DeConv3DLayer.cpp +++ b/paddle/gserver/layers/DeConv3DLayer.cpp @@ -20,9 +20,6 @@ namespace paddle { REGISTER_LAYER(deconv3d, DeConv3DLayer); -#define DECONV_OUTPUT_SIZE(IN_SIZE, STRID, PAD, KSIZE) \ - (((IN_SIZE)-1) * (STRID)-2 * (PAD) + (KSIZE)) - bool DeConv3DLayer::init(const LayerMap &layerMap, const ParameterMap ¶meterMap) { if (!ConvBaseLayer::init(layerMap, parameterMap)) return false; @@ -32,14 +29,25 @@ bool DeConv3DLayer::init(const LayerMap &layerMap, for (int index = 0; index < config_.inputs().size(); ++index) { M_.push_back(filterChannels_[index]); K_.push_back(filterPixels_[index] * (numFilters_ / groups_[index])); - if (weights_[index]->getW()) - weights_[index]->getW()->reshape(filterPixels_[index] * numFilters_, - filterChannels_[index]); - if (weights_[index]->getWGrad()) - weights_[index]->getWGrad()->reshape(filterPixels_[index] * numFilters_, - filterChannels_[index]); + + // create a new weight + size_t height, width; + height = filterPixels_[index] * numFilters_; + width = filterChannels_[index]; + CHECK_EQ(parameters_[index]->getSize(), width * height); + Weight *w = new Weight(height, width, parameters_[index]); + weights_.emplace_back(w); + } + if (biasParameter_.get()) { + if (sharedBiases_) { + CHECK_EQ((size_t)numFilters_, biasParameter_->getSize()); + biases_ = + std::unique_ptr(new Weight(1, numFilters_, biasParameter_)); + } else { + biases_ = + std::unique_ptr(new Weight(1, getSize(), biasParameter_)); + } } - CHECK(inputLayers_.size() == parameters_.size()); return true; } @@ -52,22 +60,22 @@ size_t DeConv3DLayer::getSize() { outputW_.clear(); outputD_.clear(); N_.clear(); - No_.clear(); + NOut_.clear(); size_t layerSize = 0; for (size_t i = 0; i < inputLayers_.size(); ++i) { // imgSizeH_.push_back(inputLayers_[i]->getOutput().getFrameHeight()); // imgSizeW_.push_back(inputLayers_[i]->getOutput().getFrameWidth()); // imgSizeD_.push_back(inputLayers_[i]->getOutput().getFrameDepth()); - outputW_.push_back(DECONV_OUTPUT_SIZE( - imgSizeW_[i], stride_[i], padding_[i], filterSize_[i])); - outputH_.push_back(DECONV_OUTPUT_SIZE( - imgSizeH_[i], strideY_[i], paddingY_[i], filterSizeY_[i])); - outputD_.push_back(DECONV_OUTPUT_SIZE( - imgSizeD_[i], strideZ_[i], paddingZ_[i], filterSizeZ_[i])); - No_.push_back(outputD_[i] * outputH_[i] * outputW_[i]); + outputW_.push_back( + imageSize(imgSizeW_[i], filterSize_[i], padding_[i], stride_[i], true)); + outputH_.push_back(imageSize( + imgSizeH_[i], filterSizeY_[i], paddingY_[i], strideY_[i], true)); + outputD_.push_back(imageSize( + imgSizeD_[i], filterSizeZ_[i], paddingZ_[i], strideZ_[i], true)); + NOut_.push_back(outputD_[i] * outputH_[i] * outputW_[i]); N_.push_back(imgSizeD_[i] * imgSizeH_[i] * imgSizeW_[i]); CHECK(layerSize == 0 || N_[i] * size_t(numFilters_) == layerSize); - layerSize += No_[i] * numFilters_; + layerSize += NOut_[i] * numFilters_; } getOutput().setFrameHeight(outputH_[0]); getOutput().setFrameWidth(outputW_[0]); diff --git a/paddle/gserver/layers/DeConv3DLayer.h b/paddle/gserver/layers/DeConv3DLayer.h index 435807fe5..a2a3d3f82 100644 --- a/paddle/gserver/layers/DeConv3DLayer.h +++ b/paddle/gserver/layers/DeConv3DLayer.h @@ -12,13 +12,12 @@ 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 #include "ConvBaseLayer.h" -#include "paddle/math/Matrix.h" #include "paddle/math/MathUtils.h" -#include +#include "paddle/math/Matrix.h" namespace paddle { @@ -29,30 +28,25 @@ namespace paddle { */ class DeConv3DLayer : public ConvBaseLayer { public: - explicit DeConv3DLayer(const LayerConfig& config) : ConvBaseLayer(config) {} - - ~DeConv3DLayer() {} - - bool init(const LayerMap &layerMap, const ParameterMap ¶meterMap); - - size_t getSize(); - - void forward(PassType passType); - void addBias(); - - void backward(const UpdateCallback& callback); - - void bpropBiases(); - void bpropData(int i); - void bpropWeights(int i); + explicit DeConv3DLayer(const LayerConfig& config) : ConvBaseLayer(config) {} + ~DeConv3DLayer() {} + bool init(const LayerMap& layerMap, const ParameterMap& parameterMap); + + void forward(PassType passType); + void addBias(); + void backward(const UpdateCallback& callback); + void bpropBiases(); + void bpropData(int i); + void bpropWeights(int i); + size_t getSize(); protected: - // Figure out the dimensions for individual gemms. - IntV M_; /// numFilters_ / filter_group_; - IntV N_; /// channels_ * filterSizeZ_ * filterSize_ * filterSizeY_ - IntV K_; /// outputD_ * outputH_ * outputW_ - IntV No_; - MatrixPtr colBuf_; + // Figure out the dimensions for individual gemms. + IntV M_; /// numFilters_ / filter_group_; + IntV N_; /// channels_ * filterSizeZ_ * filterSize_ * filterSizeY_ + IntV K_; /// outputD_ * outputH_ * outputW_ + IntV NOut_; + MatrixPtr colBuf_; }; } // namespace paddle diff --git a/paddle/gserver/layers/ExpandConvBaseLayer.cpp b/paddle/gserver/layers/ExpandConvBaseLayer.cpp index 77736e78f..2b7bef0a7 100644 --- a/paddle/gserver/layers/ExpandConvBaseLayer.cpp +++ b/paddle/gserver/layers/ExpandConvBaseLayer.cpp @@ -22,12 +22,31 @@ bool ExpandConvBaseLayer::init(const LayerMap &layerMap, /* Initialize the basic convolutional parent class */ ConvBaseLayer::init(layerMap, parameterMap); + int index = 0; for (auto &inputConfig : config_.inputs()) { const ConvConfig &conf = inputConfig.conv_conf(); /* Consistent caffe mode for multiple input */ caffeMode_ = conf.caffe_mode(); - } + // create a new weight + size_t height, width; + height = filterPixels_[index] * filterChannels_[index]; + width = (!isDeconv_) ? numFilters_ : channels_[index]; + CHECK_EQ(parameters_[index]->getSize(), width * height); + Weight *w = new Weight(height, width, parameters_[index]); + weights_.emplace_back(w); + index++; + } + if (biasParameter_.get()) { + if (sharedBiases_) { + CHECK_EQ((size_t)numFilters_, biasParameter_->getSize()); + biases_ = + std::unique_ptr(new Weight(numFilters_, 1, biasParameter_)); + } else { + biases_ = + std::unique_ptr(new Weight(getSize(), 1, biasParameter_)); + } + } getOutputSize(); return true; diff --git a/paddle/gserver/tests/test_LayerGrad.cpp b/paddle/gserver/tests/test_LayerGrad.cpp index 1e80e2c0e..d5724293b 100644 --- a/paddle/gserver/tests/test_LayerGrad.cpp +++ b/paddle/gserver/tests/test_LayerGrad.cpp @@ -2019,7 +2019,7 @@ void test3DConvLayer(const string& type, bool trans, bool useGpu) { const int CHANNELS = 3; const int IMAGE_SIZE = 9; const int IMAGE_SIZE_Y = 9; - const int IMAGE_SIZE_Z = 9; // 2, 3, 5, 5, 5 + const int IMAGE_SIZE_Z = 9; TestConfig config; config.biasSize = NUM_FILTERS; @@ -2084,10 +2084,6 @@ TEST(Layer, test3DConvLayer) { #endif } -int deConvOutputSize(int inSize, int kSize, int pad, int stride) { - return (inSize - 1) * stride - 2 * pad + kSize; -} - void test3DDeConvLayer(const string& type, bool trans, bool useGpu) { // filter size const int NUM_FILTERS = 6; @@ -2126,16 +2122,21 @@ void test3DDeConvLayer(const string& type, bool trans, bool useGpu) { conv->set_img_size(IMAGE_SIZE); conv->set_img_size_y(IMAGE_SIZE_Y); conv->set_img_size_z(IMAGE_SIZE_Z); - conv->set_output_x(deConvOutputSize( - conv->img_size(), conv->filter_size(), conv->padding(), conv->stride())); - conv->set_output_y(deConvOutputSize(conv->img_size_y(), - conv->filter_size_y(), - conv->padding_y(), - conv->stride_y())); - conv->set_output_z(deConvOutputSize(conv->img_size_z(), - conv->filter_size_z(), - conv->padding_z(), - conv->stride_z())); + conv->set_output_x(imageSize(conv->img_size(), + conv->filter_size(), + conv->padding(), + conv->stride(), + true)); + conv->set_output_y(imageSize(conv->img_size_y(), + conv->filter_size_y(), + conv->padding_y(), + conv->stride_y(), + true)); + conv->set_output_z(imageSize(conv->img_size_z(), + conv->filter_size_z(), + conv->padding_z(), + conv->stride_z(), + true)); config.layerConfig.set_size(conv->output_x() * conv->output_y() * conv->output_z() * NUM_FILTERS); conv->set_groups(1); diff --git a/paddle/math/tests/test_matrixCompare.cpp b/paddle/math/tests/test_matrixCompare.cpp index 1d41ec087..3abe4484d 100644 --- a/paddle/math/tests/test_matrixCompare.cpp +++ b/paddle/math/tests/test_matrixCompare.cpp @@ -18,6 +18,7 @@ limitations under the License. */ #include #include "TensorCheck.h" +#include "paddle/math/MathUtils.h" #include "paddle/math/Matrix.h" #include "paddle/math/SparseMatrix.h" #include "paddle/testing/TestUtil.h" @@ -1203,19 +1204,6 @@ TEST(Matrix, warpCTC) { } } -int outputSizeCol2Vol( - int imageSize, int filterSize, int padding, int stride, bool caffeMode) { - int outputSize; - if (!caffeMode) { - outputSize = - (imageSize - filterSize + 2 * padding + stride - 1) / stride + 1; - } else { - outputSize = (imageSize - filterSize + 2 * padding) / stride + 1; - } - CHECK_GE(outputSize, 1); - return outputSize; -} - void testMatrixCol2Vol(int depth, int height, int width) { int channel = 3; int filterX = 3, filterY = 4, filterZ = 5; @@ -1229,9 +1217,9 @@ void testMatrixCol2Vol(int depth, int height, int width) { cpuImage->randomizeUniform(); gpuImage->copyFrom(*cpuImage); - int outD = outputSizeCol2Vol(depth, filterZ, padZ, strideZ, true); - int outH = outputSizeCol2Vol(height, filterY, padZ, strideY, true); - int outW = outputSizeCol2Vol(width, filterX, padZ, strideX, true); + int outD = outputSize(depth, filterZ, padZ, strideZ, true); + int outH = outputSize(height, filterY, padY, strideY, true); + int outW = outputSize(width, filterX, padX, strideX, true); int colBufHeight = channel * filterZ * filterY * filterX; int colBufWidth = outD * outH * outW; @@ -1305,11 +1293,9 @@ void testMatrixCol2Vol(int depth, int height, int width) { } TEST(Matrix, col2Vol) { - for (auto depth : {9, 16, 64, 128}) { - for (auto height : {9, 11, 73, 128, 256}) { - for (auto width : { - 9, 32, 100, 512, - }) { + for (auto depth : {9, 16, 64}) { + for (auto height : {9, 11, 128}) { + for (auto width : {9, 32, 128}) { VLOG(3) << "depth=" << depth << " height=" << height << " width=" << width; testMatrixCol2Vol(depth, height, width); diff --git a/proto/ModelConfig.proto b/proto/ModelConfig.proto index 043ae502b..8c6eb5b7e 100644 --- a/proto/ModelConfig.proto +++ b/proto/ModelConfig.proto @@ -82,7 +82,7 @@ message ConvConfig { // if not set, use img_size optional uint32 img_size_y = 14; - + optional uint32 filter_size_z = 15 [ default = 1 ]; optional uint32 padding_z = 16 [ default = 1 ]; optional uint32 stride_z = 17 [ default = 1 ]; @@ -637,4 +637,4 @@ message ModelConfig { // For External Machine, defining how to split a neural network // into multiple parts. optional ExternalConfig external_config = 9; -}; \ No newline at end of file +}; -- GitLab From 5d98b6f217f8c59ae32f7dabefb69037d80f9cb2 Mon Sep 17 00:00:00 2001 From: chengduoZH Date: Mon, 21 Aug 2017 16:32:29 +0800 Subject: [PATCH 0141/2018] Adapting to the BatchNorm structure to support 3D data --- paddle/gserver/layers/BatchNormBaseLayer.cpp | 6 ++- paddle/gserver/layers/BatchNormBaseLayer.h | 1 + paddle/gserver/tests/test_LayerGrad.cpp | 49 ++++++++++++++++++++ paddle/parameter/Argument.cpp | 2 + paddle/parameter/Argument.h | 8 ++-- proto/ModelConfig.proto | 13 ++++++ 6 files changed, 75 insertions(+), 4 deletions(-) diff --git a/paddle/gserver/layers/BatchNormBaseLayer.cpp b/paddle/gserver/layers/BatchNormBaseLayer.cpp index 1ceaaaa20..f7a80e23e 100644 --- a/paddle/gserver/layers/BatchNormBaseLayer.cpp +++ b/paddle/gserver/layers/BatchNormBaseLayer.cpp @@ -62,14 +62,18 @@ void BatchNormBaseLayer::calFeatureMapSize() { const ImageConfig& conf = config_.inputs(0).image_conf(); imageH_ = inputLayers_[0]->getOutput().getFrameHeight(); imageW_ = inputLayers_[0]->getOutput().getFrameWidth(); + imageD_ = inputLayers_[0]->getOutput().getFrameDepth(); + + if (0 == imageD_) imageD_ = conf.img_size_z(); if (imageH_ == 0 && imageW_ == 0) { imageH_ = conf.has_img_size_y() ? conf.img_size_y() : conf.img_size(); imageW_ = conf.img_size(); } else { getOutput().setFrameHeight(imageH_); getOutput().setFrameWidth(imageW_); + getOutput().setFrameDepth(imageD_); } - imgPixels_ = imageH_ * imageW_; + imgPixels_ = imageH_ * imageW_ * imageD_; } } // namespace paddle diff --git a/paddle/gserver/layers/BatchNormBaseLayer.h b/paddle/gserver/layers/BatchNormBaseLayer.h index 230bafc31..e721d2d26 100644 --- a/paddle/gserver/layers/BatchNormBaseLayer.h +++ b/paddle/gserver/layers/BatchNormBaseLayer.h @@ -80,6 +80,7 @@ protected: /// Height or width of input image feature. /// Both of them are 1 if the input is fully-connected layer. + int imageD_; int imageH_; int imageW_; /// Height * Width. diff --git a/paddle/gserver/tests/test_LayerGrad.cpp b/paddle/gserver/tests/test_LayerGrad.cpp index 0f312b6ca..641877258 100644 --- a/paddle/gserver/tests/test_LayerGrad.cpp +++ b/paddle/gserver/tests/test_LayerGrad.cpp @@ -1594,6 +1594,55 @@ TEST(Layer, BatchNormalizationLayer) { #endif } +void testBatchNorm3DLayer(const string& type, bool trans, bool useGpu) { + TestConfig config; + const int CHANNELS = 10; + const int IMG_SIZE = 16; + const int IMG_SIZE_Y = 8; + const int IMG_SIZE_Z = 8; + size_t size = CHANNELS * IMG_SIZE * IMG_SIZE_Y * IMG_SIZE_Z; + config.layerConfig.set_type(type); + config.layerConfig.set_size(size); + config.layerConfig.set_active_type("sigmoid"); + config.biasSize = CHANNELS; + config.inputDefs.push_back({INPUT_DATA, + "layer_0", + /* dim= */ size, + /* paraSize= */ CHANNELS}); + + config.inputDefs.push_back({INPUT_DATA, "layer_1_running_mean", 1, CHANNELS}); + config.inputDefs.back().isStatic = true; + config.inputDefs.push_back({INPUT_DATA, "layer_2_running_var", 1, CHANNELS}); + config.inputDefs.back().isStatic = true; + + LayerInputConfig* input = config.layerConfig.add_inputs(); + config.layerConfig.add_inputs(); + config.layerConfig.add_inputs(); + + ImageConfig* img_conf = input->mutable_image_conf(); + img_conf->set_channels(CHANNELS); + img_conf->set_img_size(IMG_SIZE); + img_conf->set_img_size_y(IMG_SIZE_Y); + img_conf->set_img_size_z(IMG_SIZE_Z); + + testLayerGrad(config, + "batch_norm", + 64, + /* trans= */ trans, + useGpu, + /* useWeight */ true); +} + +TEST(Layer, testBatchNorm3DLayer) { + testBatchNorm3DLayer("batch_norm", false, false); +#ifndef PADDLE_ONLY_CPU + testBatchNorm3DLayer("batch_norm", false, true); + if (hl_get_cudnn_lib_version() >= int(4000)) { + testBatchNorm3DLayer("cudnn_batch_norm", false, true); + } +#endif +} + void testConvOperator(bool isDeconv) { TestConfig config; const int NUM_FILTERS = 16; diff --git a/paddle/parameter/Argument.cpp b/paddle/parameter/Argument.cpp index 0547ac93c..77fd0c589 100644 --- a/paddle/parameter/Argument.cpp +++ b/paddle/parameter/Argument.cpp @@ -186,6 +186,7 @@ void Argument::resizeAndCopyFrom(const Argument& src, resizeAndCopy(strs, src.strs, useGpu, stream); frameWidth = src.frameWidth; frameHeight = src.frameHeight; + frameDepth = src.frameDepth; } int32_t Argument::resizeAndCopyFrom(const Argument& src, @@ -206,6 +207,7 @@ int32_t Argument::resizeAndCopyFrom(const Argument& src, dataId = src.dataId; frameWidth = src.frameWidth; frameHeight = src.frameHeight; + frameDepth = src.frameDepth; if (!src.sequenceStartPositions) { // non-sequence input, copy samples directly diff --git a/paddle/parameter/Argument.h b/paddle/parameter/Argument.h index d8d7a4398..ba3ad2fd4 100644 --- a/paddle/parameter/Argument.h +++ b/paddle/parameter/Argument.h @@ -1,11 +1,8 @@ /* 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. @@ -35,6 +32,7 @@ struct Argument { strs(nullptr), frameHeight(0), frameWidth(0), + frameDepth(0), sequenceStartPositions(nullptr), subSequenceStartPositions(nullptr), cpuSequenceDims(nullptr), @@ -64,6 +62,7 @@ struct Argument { allCount = argument.allCount; frameHeight = argument.frameHeight; frameWidth = argument.frameWidth; + frameDepth = argument.frameDepth; dataId = argument.dataId; } @@ -76,6 +75,7 @@ struct Argument { // A dataBatch includes batchSize frames, one frame maybe not only vector size_t frameHeight; size_t frameWidth; + size_t frameDepth; // If NULL, each position is treated independently. // Otherwise, its size should be #NumberOfSequences + 1. @@ -136,8 +136,10 @@ struct Argument { } size_t getFrameHeight() const { return frameHeight; } size_t getFrameWidth() const { return frameWidth; } + size_t getFrameDepth() const { return frameDepth; } void setFrameHeight(size_t h) { frameHeight = h; } void setFrameWidth(size_t w) { frameWidth = w; } + void setFrameDepth(size_t d) { frameDepth = d; } int64_t getNumSequences() const { return sequenceStartPositions ? sequenceStartPositions->getSize() - 1 diff --git a/proto/ModelConfig.proto b/proto/ModelConfig.proto index 4f3d5bf3f..ef2b076c3 100644 --- a/proto/ModelConfig.proto +++ b/proto/ModelConfig.proto @@ -82,6 +82,12 @@ message ConvConfig { // if not set, use img_size optional uint32 img_size_y = 14; + + optional uint32 filter_size_z = 15 [ default = 1 ]; + optional uint32 padding_z = 16 [ default = 1 ]; + optional uint32 stride_z = 17 [ default = 1 ]; + optional uint32 output_z = 18 [ default = 1 ]; + optional uint32 img_size_z = 19 [ default = 1 ]; } message PoolConfig { @@ -124,6 +130,12 @@ message PoolConfig { // if not set, use padding optional uint32 padding_y = 13; + + optional uint32 size_z = 14 [ default = 1 ]; + optional uint32 stride_z = 15 [ default = 1 ]; + optional uint32 output_z = 16 [ default = 1 ]; + optional uint32 img_size_z = 17 [ default = 1 ]; + optional uint32 padding_z = 18 [ default = 1 ]; } message SppConfig { @@ -256,6 +268,7 @@ message ImageConfig { // The size of input feature map. required uint32 img_size = 8; optional uint32 img_size_y = 9; + optional uint32 img_size_z = 10 [ default = 1 ]; } message PriorBoxConfig { -- GitLab From 7c274dc0a16b77fae0faf527ef02a1f72abad593 Mon Sep 17 00:00:00 2001 From: qijun Date: Mon, 21 Aug 2017 16:41:22 +0800 Subject: [PATCH 0142/2018] use curand --- paddle/operators/math/math_function.cc | 9 +++++ paddle/operators/math/math_function.cu | 56 ++++++++++++++++++-------- paddle/operators/math/math_function.h | 8 ++++ paddle/platform/device_context.cc | 15 ++++--- paddle/platform/device_context.h | 6 +-- 5 files changed, 70 insertions(+), 24 deletions(-) diff --git a/paddle/operators/math/math_function.cc b/paddle/operators/math/math_function.cc index da5904489..d0b1f8ee4 100644 --- a/paddle/operators/math/math_function.cc +++ b/paddle/operators/math/math_function.cc @@ -109,6 +109,15 @@ void matmul(const framework::Tensor& matrix_a, matrix_b.data(), beta, matrix_out->data(), context); } +template <> +void Set(const int n, const float alpha, + float* output, + platform::DeviceContext* context) { + auto* cpu_context = reinterpret_cast(context); + framework::EigenVector::Type out(output, n); + out.device(*(cpu_context->eigen_device())) = t.constant(T(alpha)); +} + template <> void RandUniform(const int n, const float min, const float max, float* output, diff --git a/paddle/operators/math/math_function.cu b/paddle/operators/math/math_function.cu index 5a400d444..76bbf790d 100644 --- a/paddle/operators/math/math_function.cu +++ b/paddle/operators/math/math_function.cu @@ -126,20 +126,48 @@ void matmul(const framework::Tensor& matrix_a, matrix_b.data(), beta, matrix_out->data(), context); } +template <> +void Set(const int n, const float alpha, + float* output, + platform::DeviceContext* context) { + auto* cuda_context = reinterpret_cast(context); + framework::EigenVector::Type out(output, n); + out.device(*(cuda_context->eigen_device())) = t.constant(T(alpha)); +} + +template +__global__ void UniformShift(const int n, const T min, const T max, T* x) { + float scale = max - min; + for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < n; + i += blockDim.x * gridDim.x) { + x[i] = x[i] * scale + min; + } +} + template <> void RandUniform(const int n, const float min, const float max, float* output, platform::DeviceContext* context) { auto* cuda_context = reinterpret_cast(context); - thrust::uniform_real_distribution distribution(min, max); - thrust::minstd_rand engine = cuda_context->rand_enigne(); - engine->discard(n); - - thrust::counting_iterator index_sequence_begin(0); + PADDLE_ENFORCE( + curandGenerateUniform(cuda_context->curand_generator(), output, n)); + int block = 512; + int grid = (n + block - 1) / block; + UniformShift<<stream()>>>(n, min, max, + output); +} - thrust::transform(thrust::cuda::par.on(cuda_context->stream()), - index_sequence_begin, index_sequence_begin + n, - thrust::device_ptr(output), distribution(engine)); +template +int HandleOddLengthRandGaussian(const int n, const T mean, const T std, + T* output, CUDADeviceContext* context) { + if (n % 2 == 1) { + std::default_random_engine generator; + std::normal_distribution distribution(mean, std); + const T random_value = distribution(generator); + Set(1, random_value, output + (n - 1), context); + return n - 1; + } + return n; } template <> @@ -147,15 +175,11 @@ void RandGaussian(const int n, const float mean, const float std, float* output, platform::DeviceContext* context) { auto* cuda_context = reinterpret_cast(context); - thrust::normal_distribution distribution(mean, std); - thrust::minstd_rand engine = cuda_context->rand_enigne(); - engine->discard(n); - - thrust::counting_iterator index_sequence_begin(0); - thrust::transform(thrust::cuda::par.on(cuda_context->stream()), - index_sequence_begin, index_sequence_begin + n, - thrust::device_ptr(output), distribution(engine)); + const int even_n = + HandleOddLengthRandGaussian(n, mean, std, output, cuda_context); + PADDLE_ENFORCE(curandGenerateNormal(cuda_context->curand_generator(), output, + even_n, mean, std)); } } // namespace math diff --git a/paddle/operators/math/math_function.h b/paddle/operators/math/math_function.h index ea15e8fd2..afe6de748 100644 --- a/paddle/operators/math/math_function.h +++ b/paddle/operators/math/math_function.h @@ -54,6 +54,7 @@ int LAPACKE_dgetri(int matrix_layout, int n, double* a, int lda, #include "paddle/framework/tensor.h" #include "paddle/platform/device_context.h" +#include "paddle/platform/eigen.h" #include "paddle/platform/enforce.h" namespace paddle { @@ -77,6 +78,13 @@ void matmul(const framework::Tensor& matrix_a, bool trans_a, framework::Tensor* matrix_out, T beta, platform::DeviceContext* context); +template +void Set(const int n, const T alpha, T* output, + platform::DeviceContext* context) { + framework::EigenVector::Type out(output, n); + out.device(*(context->eigen_device())) = t.constant(T(alpha)); +} + template void RandUniform(const int n, const T min, const T max, T* output, platform::DeviceContext* context); diff --git a/paddle/platform/device_context.cc b/paddle/platform/device_context.cc index fabbb5544..5fd93555a 100644 --- a/paddle/platform/device_context.cc +++ b/paddle/platform/device_context.cc @@ -157,12 +157,17 @@ cudnnHandle_t CUDADeviceContext::cudnn_handle() { return cudnn_handle_; } -thrust::minstd_rand& CPUDeviceContext::rand_engine() { - if (!rand_engine_) { - rand_engine_.reset(new thrust::minstd_rand()); - rand_engine_->seed(rand_seed_); +curandGenerator_t CUDADeviceContext::curand_generator() { + if (!curand_generator_) { + SetDeviceId(place_.device); + PADDLE_ENFORCE(dynload::curandCreateGenerator(&curand_generator_, + CURAND_RNG_PSEUDO_DEFAULT)); + PADDLE_ENFORCE( + dynload::curandSetPseudoRandomGeneratorSeed(curand_generator_, seed_)); + + PADDLE_ENFORCE(dynload::curandSetStream(curand_generator_, stream_)); } - return *(rand_engine_.get()); + return curand_generator_; } cudaStream_t CUDADeviceContext::stream() { return stream_; } diff --git a/paddle/platform/device_context.h b/paddle/platform/device_context.h index e4de3807c..7013343a8 100644 --- a/paddle/platform/device_context.h +++ b/paddle/platform/device_context.h @@ -15,10 +15,9 @@ limitations under the License. */ #include "paddle/platform/place.h" #ifndef PADDLE_ONLY_CPU -#include -#include #include "paddle/platform/dynload/cublas.h" #include "paddle/platform/dynload/cudnn.h" +#include "paddle/platform/dynload/curand.h" #include "paddle/platform/gpu_info.h" #define EIGEN_USE_GPU #endif @@ -80,7 +79,8 @@ class CUDADeviceContext : public DeviceContext { /*! \brief Return cudnn handle in the device context. */ cudnnHandle_t cudnn_handle(); - thrust::minstd_rand& CPUDeviceContext::rand_engine(); + /*! \brief Return curand handle in the device context. */ + curandGenerator_t curand_generator(); /*! \brief Return cuda stream in the device context. */ cudaStream_t stream(); -- GitLab From 4bffbd30f0dbc2a2bbff4aa8108867fceecc260a Mon Sep 17 00:00:00 2001 From: tensor-tang Date: Mon, 21 Aug 2017 16:44:30 +0800 Subject: [PATCH 0143/2018] use MKLDNNMatrix in fc forward --- paddle/gserver/layers/Layer.cpp | 2 +- paddle/gserver/layers/Layer.h | 20 +++++++- paddle/gserver/layers/MKLDNNFcLayer.cpp | 63 ++++++++++++++++--------- paddle/gserver/layers/MKLDNNLayer.h | 25 +++++++--- paddle/math/CMakeLists.txt | 4 -- paddle/math/MKLDNNMatrix.cpp | 29 +++++++++++- paddle/math/MKLDNNMatrix.h | 43 +++++++++++++---- 7 files changed, 143 insertions(+), 43 deletions(-) diff --git a/paddle/gserver/layers/Layer.cpp b/paddle/gserver/layers/Layer.cpp index d5621412c..2bc20eee6 100644 --- a/paddle/gserver/layers/Layer.cpp +++ b/paddle/gserver/layers/Layer.cpp @@ -41,7 +41,7 @@ namespace paddle { Layer::Layer(const LayerConfig& config, bool useGpu) : config_(config), useGpu_(useGpu), - deviceId_(-1), + deviceId_(CPU_DEVICE), needSequenceInfo_(true) {} bool Layer::init(const LayerMap& layerMap, const ParameterMap& parameterMap) { diff --git a/paddle/gserver/layers/Layer.h b/paddle/gserver/layers/Layer.h index 0ed482889..ec4d093e0 100644 --- a/paddle/gserver/layers/Layer.h +++ b/paddle/gserver/layers/Layer.h @@ -59,7 +59,12 @@ protected: LayerConfig config_; /// whether to use GPU bool useGpu_; - /// Device Id. CPU is -1, and GPU is 0, 1, 2 ... + /// Paddle device ID, MKLDNN is -2, CPU is -1 + enum PADDLE_DEVICE_ID { + MKLDNN_DEVICE = -2, + CPU_DEVICE = -1, + }; + /// Device Id. MKLDNN is -2, CPU is -1, and GPU is 0, 1, 2 ... int deviceId_; /// Input layers std::vector inputLayers_; @@ -321,6 +326,19 @@ public: if (deviceId == getDeviceId()) { return output_; } else { + bool CPU2MKLDNN = + getDeviceId() == CPU_DEVICE && deviceId == MKLDNN_DEVICE; + bool MKLDNN2CPU = + getDeviceId() == MKLDNN_DEVICE && deviceId == CPU_DEVICE; + if (CPU2MKLDNN) { + // TODO: do something + return output_; + } else if (MKLDNN2CPU) { + // TODO: do something + return output_; + } + + // TODO: handle mkldnn device or add mkldnn device to other for (size_t i = 0; i < outputOtherDevice_.size(); i++) { if (outputOtherDevice_[i].deviceId == deviceId) { return outputOtherDevice_[i]; diff --git a/paddle/gserver/layers/MKLDNNFcLayer.cpp b/paddle/gserver/layers/MKLDNNFcLayer.cpp index d201fac65..fac0390ee 100644 --- a/paddle/gserver/layers/MKLDNNFcLayer.cpp +++ b/paddle/gserver/layers/MKLDNNFcLayer.cpp @@ -135,33 +135,51 @@ void MKLDNNFcLayer::reshape() { void MKLDNNFcLayer::resetFwd() { bool hasBias = biases_ && biases_->getW(); - real* iData = getInputValue(0)->getData(); - real* oData = getOutputValue()->getData(); - real* wData = weight_->getW()->getData(); - real* bData = hasBias ? biases_->getW()->getData() : NULL; + const MatrixPtr& in = getInputValue(0); + const MatrixPtr& wgt = weight_->getW(); + const MatrixPtr& bias = hasBias ? biases_->getW() : nullptr; + const MatrixPtr& out = output_.value; + + if (getPrev(0)->getDeviceId() == MKLDNN_DEVICE) { + inVal_ = std::dynamic_pointer_cast(in); + CHECK(inVal_) << "Input should be MKLDNNMatrix"; + // TODO: change input nchw to nc if available + // inVal_->downSpatial() + } else { + inVal_ = MKLDNNMatrix::create( + in, + hasSpatial_ ? memory::dims{bs_, ic_, ih_, iw_} : memory::dims{bs_, ic_}, + hasSpatial_ ? format::nchw : format::nc, + engine_); + } - // TODO(TJ): below create should be covered in MkldnnMatrix - // create memory desc - memory::desc iMD = hasSpatial_ ? createMD({bs_, ic_, ih_, iw_}, format::nchw) - : createMD({bs_, ic_}, format::nc); - memory::desc wMD = hasSpatial_ ? createMD({oc_, ic_, ih_, iw_}, format::oihw) - : createMD({oc_, ic_}, format::oi); - memory::desc bMD = bData != NULL ? createMD({oc_}, format::x) - : createMD({}, format::format_undef); - memory::desc oMD = createMD({bs_, oc_}, format::nc); + wgtVal_ = MKLDNNMatrix::create( + wgt, + hasSpatial_ ? memory::dims{oc_, ic_, ih_, iw_} : memory::dims{oc_, ic_}, + hasSpatial_ ? format::oihw : format::oi, + engine_); - // create memory primitive desc and memory self - inVal_.reset(new memory(memory::primitive_desc(iMD, engine_), iData)); - wgtVal_.reset(new memory(memory::primitive_desc(wMD, engine_), wData)); - outVal_.reset(new memory(memory::primitive_desc(oMD, engine_), oData)); + biasVal_ = + hasBias ? MKLDNNMatrix::create(bias, {oc_}, format::x, engine_) : nullptr; + + outVal_ = MKLDNNMatrix::create(out, {bs_, oc_}, format::nc, engine_); + + // change original output to mkldnn output + output_.value = std::dynamic_pointer_cast(outVal_); + // create forward handle prop_kind pk = prop_kind::forward; - fc_fwd::desc fwdDesc = bData != NULL ? fc_fwd::desc(pk, iMD, wMD, bMD, oMD) - : fc_fwd::desc(pk, iMD, wMD, oMD); + fc_fwd::desc fwdDesc = + hasBias ? fc_fwd::desc(pk, + inVal_->getMD(), + wgtVal_->getMD(), + biasVal_->getMD(), + outVal_->getMD()) + : fc_fwd::desc( + pk, inVal_->getMD(), wgtVal_->getMD(), outVal_->getMD()); fc_fwd::primitive_desc fwdPD = fc_fwd::primitive_desc(fwdDesc, engine_); - if (bData != NULL) { - biasVal_.reset(new memory(memory::primitive_desc(bMD, engine_), bData)); + if (hasBias) { fwd_.reset(new fc_fwd(fwdPD, *inVal_, *wgtVal_, *biasVal_, *outVal_)); } else { fwd_.reset(new fc_fwd(fwdPD, *inVal_, *wgtVal_, *outVal_)); @@ -197,7 +215,8 @@ void MKLDNNFcLayer::resetBwd() { // update data inVal_->set_data_handle(iData); } else { - inVal_.reset(new memory(memory::primitive_desc(iMD, engine_), iData)); + LOG(FATAL) << "Should not be empty"; + // inVal_.reset(new memory(memory::primitive_desc(iMD, engine_), iData)); } // create memory primitive desc and memory self diff --git a/paddle/gserver/layers/MKLDNNLayer.h b/paddle/gserver/layers/MKLDNNLayer.h index 9533027fa..b44095bef 100644 --- a/paddle/gserver/layers/MKLDNNLayer.h +++ b/paddle/gserver/layers/MKLDNNLayer.h @@ -21,7 +21,6 @@ limitations under the License. */ #include "paddle/math/MKLDNNMatrix.h" DECLARE_bool(use_mkldnn); -DECLARE_bool(use_mkldnn_wgt); namespace paddle { @@ -54,13 +53,14 @@ protected: std::vector pipelineBwd_; // TODO(TJ): change below memory as MKLDNNMatrixPtr type - std::shared_ptr inVal_; + // MKLDNNMatrixPtr ; + MKLDNNMatrixPtr inVal_; std::shared_ptr inGrad_; - std::shared_ptr outVal_; + MKLDNNMatrixPtr outVal_; std::shared_ptr outGrad_; - std::shared_ptr wgtVal_; + MKLDNNMatrixPtr wgtVal_; std::shared_ptr wgtGrad_; - std::shared_ptr biasVal_; + MKLDNNMatrixPtr biasVal_; std::shared_ptr biasGrad_; public: @@ -94,7 +94,7 @@ public: stream_.reset(new MKLDNNStream()); engine_ = CPUEngine::Instance().getEngine(); - // TODO(TJ): deivecId + setDeviceID(MKLDNN_DEVICE); return true; } @@ -128,6 +128,19 @@ public: // TODO(TJ): isFmtSuppoted(fmt) return mkldnn::memory::desc(dims, type, fmt); } + + void resetMKLDNNOutput(size_t height, size_t width) { + Layer::resetOutput(height, width); + // get valu and grad, use mkldnn matrix instaed + // output_.value; + } + +protected: + void setDeviceID(int id) { + deviceId_ = id; + output_.deviceId = id; + // TODO: handle mkldnn device or add mkldnn device to other + } }; } // namespace paddle diff --git a/paddle/math/CMakeLists.txt b/paddle/math/CMakeLists.txt index ad6de18c8..8afe6b509 100644 --- a/paddle/math/CMakeLists.txt +++ b/paddle/math/CMakeLists.txt @@ -15,13 +15,9 @@ file(GLOB MATH_HEADERS . *.h) file(GLOB MATH_SOURCES . *.cpp) -message(STATUS "----------MATH_HEADERS:${MATH_HEADERS}") -message(STATUS "----------MATH_SOURCES:${MATH_SOURCES}") if(NOT WITH_MKLDNN) file(GLOB_RECURSE DNN_HEADER RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "MKLDNN*.h") file(GLOB_RECURSE DNN_SOURCES RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "MKLDNN*.cpp") - message(STATUS "----------DNN_HEADER:${DNN_HEADER}") - message(STATUS "----------DNN_SOURCES:${DNN_SOURCES}") list(REMOVE_ITEM MATH_HEADERS ${DNN_HEADER}) list(REMOVE_ITEM MATH_SOURCES ${DNN_SOURCES}) message(STATUS "Skip compiling with MKLDNNMatrix") diff --git a/paddle/math/MKLDNNMatrix.cpp b/paddle/math/MKLDNNMatrix.cpp index df8e72d78..44fc54278 100644 --- a/paddle/math/MKLDNNMatrix.cpp +++ b/paddle/math/MKLDNNMatrix.cpp @@ -16,4 +16,31 @@ limitations under the License. */ using namespace mkldnn; // NOLINT -namespace paddle {} // namespace paddle +namespace paddle { + +MKLDNNMatrixPtr MKLDNNMatrix::create(const MatrixPtr& m, + memory::dims dims, + memory::format fmt, + engine& eg, + mkldnn::memory::data_type dtype) { + CpuMatrixPtr cpuM = std::dynamic_pointer_cast(m); + CHECK(cpuM) << "Only support create from CPU matrix yet"; + + size_t ndims = dims.size(); + CHECK(ndims > 0) << "Input dims should not be empty"; + size_t cnt = 1; + for (size_t i = 0; i < ndims; ++i) { + cnt *= dims[i]; + } + CHECK_EQ(cnt, m->getElementCnt()) << "Count size does not match"; + + size_t width = m->getWidth(); + size_t height = m->getHeight(); + real* data = m->getData(); + + memory::desc md = memory::desc(dims, dtype, fmt); + memory::primitive_desc pd = memory::primitive_desc(md, eg); + return std::make_shared(data, height, width, pd); +} + +} // namespace paddle diff --git a/paddle/math/MKLDNNMatrix.h b/paddle/math/MKLDNNMatrix.h index 91ef56f2c..73eb50d2a 100644 --- a/paddle/math/MKLDNNMatrix.h +++ b/paddle/math/MKLDNNMatrix.h @@ -14,9 +14,8 @@ limitations under the License. */ #pragma once -//#include "Matrix.h" -#include "Vector.h" - +#include +#include "Matrix.h" #include "mkldnn.hpp" #include "paddle/parameter/Parameter.h" @@ -32,14 +31,42 @@ typedef std::shared_ptr MKLDNNMatrixPtr; * @brief MKLDNN Matrix. * */ -class MKLDNNMatrix : public CpuVector { +class MKLDNNMatrix : public CpuMatrix, public mkldnn::memory { public: - explicit MKLDNNMatrix(size_t size, int fmt) : CpuVector(size), fmt_(fmt) {} + MKLDNNMatrix(real* data, + size_t height, + size_t width, + mkldnn::memory::primitive_desc pd) + : CpuMatrix(data, height, width, false), mkldnn::memory(pd, data) {} - ~MKLDNNMatrix() {} + MKLDNNMatrix(size_t height, size_t width, mkldnn::memory::primitive_desc pd) + : CpuMatrix(height, width, false), mkldnn::memory(pd) { + set_data_handle(CpuMatrix::getData()); + } + + static MKLDNNMatrixPtr create( + const MatrixPtr& m, + mkldnn::memory::dims dims, + mkldnn::memory::format fmt, + mkldnn::engine& eg, + mkldnn::memory::data_type dtype = mkldnn::memory::data_type::f32); + + /** + * Get primitive descriptor + */ + mkldnn::memory::primitive_desc getPD() { return this->get_primitive_desc(); } -protected: - int fmt_; + /** + * Get memory descriptor + */ + mkldnn::memory::desc getMD() { return getPD().desc(); } + + /** + * Get format + */ + int getFormat() { return getMD().data.format; } + + ~MKLDNNMatrix() {} }; } // namespace paddle -- GitLab From 2f47f35b3efec36189a4c6757490b897130d3028 Mon Sep 17 00:00:00 2001 From: qijun Date: Mon, 21 Aug 2017 09:12:25 +0000 Subject: [PATCH 0144/2018] fix gpu build error --- paddle/operators/math/CMakeLists.txt | 4 ++-- paddle/operators/math/math_function.cc | 10 +++++----- paddle/operators/math/math_function.cu | 15 ++++++++------- paddle/operators/math/math_function.h | 7 ++----- paddle/operators/uniform_random_op.cu | 9 +++------ paddle/platform/device_context.cc | 10 +++++----- paddle/platform/device_context.h | 6 +++--- 7 files changed, 28 insertions(+), 33 deletions(-) diff --git a/paddle/operators/math/CMakeLists.txt b/paddle/operators/math/CMakeLists.txt index ed51d416e..228f463f2 100644 --- a/paddle/operators/math/CMakeLists.txt +++ b/paddle/operators/math/CMakeLists.txt @@ -1,8 +1,8 @@ if(WITH_GPU) - nv_library(math_function SRCS math_function.cc math_function.cu DEPS cblas device_context) + nv_library(math_function SRCS math_function.cc math_function.cu DEPS cblas device_context eigen3) else() - cc_library(math_function SRCS math_function.cc DEPS cblas device_context) + cc_library(math_function SRCS math_function.cc DEPS cblas device_context eigen3) endif() nv_test(math_function_test SRCS math_function_test.cc DEPS math_function tensor) diff --git a/paddle/operators/math/math_function.cc b/paddle/operators/math/math_function.cc index d0b1f8ee4..a098e02f9 100644 --- a/paddle/operators/math/math_function.cc +++ b/paddle/operators/math/math_function.cc @@ -110,12 +110,12 @@ void matmul(const framework::Tensor& matrix_a, } template <> -void Set(const int n, const float alpha, - float* output, - platform::DeviceContext* context) { +void Set(const int n, const float alpha, + float* output, + platform::DeviceContext* context) { auto* cpu_context = reinterpret_cast(context); - framework::EigenVector::Type out(output, n); - out.device(*(cpu_context->eigen_device())) = t.constant(T(alpha)); + framework::EigenVector::Type out(output, n); + out.device(*(cpu_context->eigen_device())) = out.constant(float(alpha)); } template <> diff --git a/paddle/operators/math/math_function.cu b/paddle/operators/math/math_function.cu index 76bbf790d..3ff622f30 100644 --- a/paddle/operators/math/math_function.cu +++ b/paddle/operators/math/math_function.cu @@ -127,12 +127,12 @@ void matmul(const framework::Tensor& matrix_a, } template <> -void Set(const int n, const float alpha, - float* output, - platform::DeviceContext* context) { +void Set(const int n, const float alpha, + float* output, + platform::DeviceContext* context) { auto* cuda_context = reinterpret_cast(context); - framework::EigenVector::Type out(output, n); - out.device(*(cuda_context->eigen_device())) = t.constant(T(alpha)); + framework::EigenVector::Type out(output, n); + out.device(*(cuda_context->eigen_device())) = out.constant(float(alpha)); } template @@ -159,12 +159,13 @@ void RandUniform(const int n, const float min, template int HandleOddLengthRandGaussian(const int n, const T mean, const T std, - T* output, CUDADeviceContext* context) { + T* output, + platform::CUDADeviceContext* context) { if (n % 2 == 1) { std::default_random_engine generator; std::normal_distribution distribution(mean, std); const T random_value = distribution(generator); - Set(1, random_value, output + (n - 1), context); + Set(1, random_value, output + (n - 1), context); return n - 1; } return n; diff --git a/paddle/operators/math/math_function.h b/paddle/operators/math/math_function.h index afe6de748..6543a1b51 100644 --- a/paddle/operators/math/math_function.h +++ b/paddle/operators/math/math_function.h @@ -52,9 +52,9 @@ int LAPACKE_dgetri(int matrix_layout, int n, double* a, int lda, #include +#include "paddle/framework/eigen.h" #include "paddle/framework/tensor.h" #include "paddle/platform/device_context.h" -#include "paddle/platform/eigen.h" #include "paddle/platform/enforce.h" namespace paddle { @@ -80,10 +80,7 @@ void matmul(const framework::Tensor& matrix_a, bool trans_a, template void Set(const int n, const T alpha, T* output, - platform::DeviceContext* context) { - framework::EigenVector::Type out(output, n); - out.device(*(context->eigen_device())) = t.constant(T(alpha)); -} + platform::DeviceContext* context); template void RandUniform(const int n, const T min, const T max, T* output, diff --git a/paddle/operators/uniform_random_op.cu b/paddle/operators/uniform_random_op.cu index 91368fa73..1bfffc477 100644 --- a/paddle/operators/uniform_random_op.cu +++ b/paddle/operators/uniform_random_op.cu @@ -14,9 +14,6 @@ #include "paddle/operators/uniform_random_op.h" -namespace paddle { -namespace operators { - -REGISTER_OP_GPU_KERNEL(uniform_random, - paddle::operators::GPUUniformRandomKernel< - paddle::platform::GPUPlace, float>); +REGISTER_OP_GPU_KERNEL( + uniform_random, + paddle::operators::UniformRandomKernel); diff --git a/paddle/platform/device_context.cc b/paddle/platform/device_context.cc index 5fd93555a..ad9b4e42f 100644 --- a/paddle/platform/device_context.cc +++ b/paddle/platform/device_context.cc @@ -25,9 +25,9 @@ CPUDeviceContext::CPUDeviceContext() { eigen_device_.reset(new Eigen::DefaultDevice()); } -CPUDeviceContext::CPUDeviceContext(CPUPlace place, int rand_seed) { +CPUDeviceContext::CPUDeviceContext(CPUPlace place, int seed) { eigen_device_.reset(new Eigen::DefaultDevice()); - rand_seed_ = rand_seed; + rand_seed_ = seed; } std::minstd_rand& CPUDeviceContext::rand_engine() { @@ -105,7 +105,7 @@ Eigen::GpuDevice* DeviceContext::get_eigen_device() const { } CUDADeviceContext::CUDADeviceContext(GPUPlace place, uint64_t seed) - : place_(place), seed_(seed) { + : place_(place), rand_seed_(seed) { SetDeviceId(place_.device); PADDLE_ENFORCE(cudaStreamCreate(&stream_)); eigen_stream_.reset(new EigenCudaStreamDevice()); @@ -162,8 +162,8 @@ curandGenerator_t CUDADeviceContext::curand_generator() { SetDeviceId(place_.device); PADDLE_ENFORCE(dynload::curandCreateGenerator(&curand_generator_, CURAND_RNG_PSEUDO_DEFAULT)); - PADDLE_ENFORCE( - dynload::curandSetPseudoRandomGeneratorSeed(curand_generator_, seed_)); + PADDLE_ENFORCE(dynload::curandSetPseudoRandomGeneratorSeed( + curand_generator_, rand_seed_)); PADDLE_ENFORCE(dynload::curandSetStream(curand_generator_, stream_)); } diff --git a/paddle/platform/device_context.h b/paddle/platform/device_context.h index 7013343a8..e18f48fef 100644 --- a/paddle/platform/device_context.h +++ b/paddle/platform/device_context.h @@ -40,7 +40,7 @@ class DeviceContext { class CPUDeviceContext : public DeviceContext { public: CPUDeviceContext(); - explicit CPUDeviceContext(CPUPlace place, int rand_seed = 0); + explicit CPUDeviceContext(CPUPlace place, int seed = 0); virtual ~CPUDeviceContext() {} Eigen::DefaultDevice* eigen_device() const; @@ -60,7 +60,7 @@ class EigenCudaStreamDevice; class CUDADeviceContext : public DeviceContext { public: - explicit CUDADeviceContext(GPUPlace place, uint64_t rand_seed = 0); + explicit CUDADeviceContext(GPUPlace place, uint64_t seed = 0); virtual ~CUDADeviceContext(); /*! \brief Wait for all operations completion in the stream. */ @@ -93,12 +93,12 @@ class CUDADeviceContext : public DeviceContext { std::unique_ptr eigen_stream_; uint64_t rand_seed_; - std::unique_ptr rand_engine_; // clang-format off cudaStream_t stream_{nullptr}; cudnnHandle_t cudnn_handle_{nullptr}; cublasHandle_t cublas_handle_{nullptr}; + curandGenerator_t curand_generator_{nullptr}; // clang-format on }; -- GitLab From 08c987d7c086e4176a27f2685712bbb9226e635e Mon Sep 17 00:00:00 2001 From: qijun Date: Mon, 21 Aug 2017 17:23:15 +0800 Subject: [PATCH 0145/2018] use dynload curand --- paddle/operators/gaussian_random_op.h | 4 ++-- paddle/operators/math/math_function.cu | 8 ++++---- paddle/operators/uniform_random_op.h | 4 ++-- 3 files changed, 8 insertions(+), 8 deletions(-) diff --git a/paddle/operators/gaussian_random_op.h b/paddle/operators/gaussian_random_op.h index 041390e95..c90b665fe 100644 --- a/paddle/operators/gaussian_random_op.h +++ b/paddle/operators/gaussian_random_op.h @@ -34,5 +34,5 @@ class GaussianRandomKernel : public framework::OpKernel { math::RandGaussian(n, mean, std, data, device_context); } }; -} -} +} // namespace operators +} // namespace paddle diff --git a/paddle/operators/math/math_function.cu b/paddle/operators/math/math_function.cu index 3ff622f30..908efe9e0 100644 --- a/paddle/operators/math/math_function.cu +++ b/paddle/operators/math/math_function.cu @@ -149,8 +149,8 @@ void RandUniform(const int n, const float min, const float max, float* output, platform::DeviceContext* context) { auto* cuda_context = reinterpret_cast(context); - PADDLE_ENFORCE( - curandGenerateUniform(cuda_context->curand_generator(), output, n)); + PADDLE_ENFORCE(platform::dynload::curandGenerateUniform( + cuda_context->curand_generator(), output, n)); int block = 512; int grid = (n + block - 1) / block; UniformShift<<stream()>>>(n, min, max, @@ -179,8 +179,8 @@ void RandGaussian(const int n, const float mean, const int even_n = HandleOddLengthRandGaussian(n, mean, std, output, cuda_context); - PADDLE_ENFORCE(curandGenerateNormal(cuda_context->curand_generator(), output, - even_n, mean, std)); + PADDLE_ENFORCE(platform::dynload::curandGenerateNormal( + cuda_context->curand_generator(), output, even_n, mean, std)); } } // namespace math diff --git a/paddle/operators/uniform_random_op.h b/paddle/operators/uniform_random_op.h index ec009b025..dffa640f8 100644 --- a/paddle/operators/uniform_random_op.h +++ b/paddle/operators/uniform_random_op.h @@ -34,5 +34,5 @@ class UniformRandomKernel : public framework::OpKernel { math::RandUniform(n, min, max, data, device_context); } }; -} -} +} // namespace operators +} // namespace paddle -- GitLab From b054392e2abebb2a55dabeeb2f12e414bbc2c5af Mon Sep 17 00:00:00 2001 From: qijun Date: Mon, 21 Aug 2017 17:46:46 +0800 Subject: [PATCH 0146/2018] fix gaussion op bug --- paddle/operators/gaussian_random_op.cc | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paddle/operators/gaussian_random_op.cc b/paddle/operators/gaussian_random_op.cc index aba8c6e5c..899f05fa4 100644 --- a/paddle/operators/gaussian_random_op.cc +++ b/paddle/operators/gaussian_random_op.cc @@ -23,7 +23,7 @@ class GaussianRandomOp : public framework::OperatorWithKernel { protected: void InferShape(const framework::InferShapeContext& context) const override { - auto* tensor = context.Output(0); + auto* tensor = context.Output("Out"); auto dims = GetAttr>("dims"); PADDLE_ENFORCE(dims.size() > 0UL, "dims can be one int or array. dims must be set."); -- GitLab From 73ab2d4678418d42dd5a03d5f8531b49eab2e7ce Mon Sep 17 00:00:00 2001 From: Luo Tao Date: Mon, 21 Aug 2017 17:57:06 +0800 Subject: [PATCH 0147/2018] fix backward error of huber_regression_cost --- paddle/gserver/layers/CostLayer.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paddle/gserver/layers/CostLayer.cpp b/paddle/gserver/layers/CostLayer.cpp index 91a742422..7f648070f 100644 --- a/paddle/gserver/layers/CostLayer.cpp +++ b/paddle/gserver/layers/CostLayer.cpp @@ -644,7 +644,7 @@ void HuberRegressionLoss::backwardImp(Matrix& output, if (std::abs(a) <= delta_) grad[i] += -a; else - grad[i] += a > 0 ? delta_ : -delta_; + grad[i] += a > 0 ? -delta_ : delta_; } if (useGpu_) outputG.copyFrom(grad, numSamples); } -- GitLab From d3f219aa9911015bd8c4a1316b85620a07eb9f49 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Mon, 21 Aug 2017 18:09:17 +0800 Subject: [PATCH 0148/2018] Change IdentityOp to ScaleOp --- paddle/framework/CMakeLists.txt | 2 +- paddle/framework/pybind.cc | 3 +- paddle/framework/tensor.h | 5 +- paddle/operators/CMakeLists.txt | 2 +- paddle/operators/identity_op.cc | 71 ------------ paddle/operators/net_op.cc | 9 +- paddle/operators/scale_op.cc | 102 ++++++++++++++++++ .../operators/{identity_op.cu => scale_op.cu} | 5 +- .../operators/{identity_op.h => scale_op.h} | 16 ++- .../paddle/v2/framework/tests/CMakeLists.txt | 2 +- .../v2/framework/tests/gradient_checker.py | 7 +- ...ty_op.py => test_scale_and_identity_op.py} | 19 ++++ 12 files changed, 158 insertions(+), 85 deletions(-) delete mode 100644 paddle/operators/identity_op.cc create mode 100644 paddle/operators/scale_op.cc rename paddle/operators/{identity_op.cu => scale_op.cu} (81%) rename paddle/operators/{identity_op.h => scale_op.h} (66%) rename python/paddle/v2/framework/tests/{test_identity_op.py => test_scale_and_identity_op.py} (51%) diff --git a/paddle/framework/CMakeLists.txt b/paddle/framework/CMakeLists.txt index f249512f4..5df14ae78 100644 --- a/paddle/framework/CMakeLists.txt +++ b/paddle/framework/CMakeLists.txt @@ -56,5 +56,5 @@ cc_library(paddle_pybind SHARED uniform_random_op gaussian_random_op fill_zeros_like_op - identity_op) + scale_op) endif(WITH_PYTHON) diff --git a/paddle/framework/pybind.cc b/paddle/framework/pybind.cc index ddb244623..3aaf0de15 100644 --- a/paddle/framework/pybind.cc +++ b/paddle/framework/pybind.cc @@ -42,7 +42,8 @@ USE_OP(fill_zeros_like); USE_OP_ITSELF(recurrent_op); USE_OP(gaussian_random); USE_OP(uniform_random); -USE_OP(identity); +USE_OP(scale); +USE_OP_ITSELF(identity); namespace paddle { namespace framework { diff --git a/paddle/framework/tensor.h b/paddle/framework/tensor.h index b8c779f4e..643f87549 100644 --- a/paddle/framework/tensor.h +++ b/paddle/framework/tensor.h @@ -105,7 +105,10 @@ class Tensor { template inline Tensor Slice(const int& begin_idx, const int& end_idx) const; - platform::Place place() const { return holder_->place(); } + platform::Place place() const { + PADDLE_ENFORCE_NOT_NULL(holder_, "Tensor get place() must contains holder"); + return holder_->place(); + } private: template diff --git a/paddle/operators/CMakeLists.txt b/paddle/operators/CMakeLists.txt index 20e562c7d..0ba598823 100644 --- a/paddle/operators/CMakeLists.txt +++ b/paddle/operators/CMakeLists.txt @@ -68,4 +68,4 @@ op_library(recurrent_op SRCS recurrent_op.cc rnn/recurrent_op_utils.cc DEPS framework_proto tensor op_registry operator net_op) op_library(uniform_random_op SRCS uniform_random_op.cc uniform_random_op.cu) -op_library(identity_op SRCS identity_op.cc identity_op.cu DEPS net_op) +op_library(scale_op SRCS scale_op.cc scale_op.cu DEPS net_op) diff --git a/paddle/operators/identity_op.cc b/paddle/operators/identity_op.cc deleted file mode 100644 index cac44020b..000000000 --- a/paddle/operators/identity_op.cc +++ /dev/null @@ -1,71 +0,0 @@ -/* 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/identity_op.h" -#include "paddle/operators/net_op.h" - -namespace paddle { -namespace operators { - -class IdentityOp : public framework::OperatorWithKernel { - public: - IdentityOp(const std::string &type, const VarNameMap &inputs, - const VarNameMap &outputs, const framework::AttributeMap &attrs) - : OperatorWithKernel(type, inputs, outputs, attrs) {} - - protected: - void InferShape(const framework::InferShapeContext &ctx) const override { - auto *in = ctx.Input("X"); - auto *out = ctx.Output("Out"); - out->Resize(in->dims()); - } -}; - -class IdentityOpMaker : public framework::OpProtoAndCheckerMaker { - public: - IdentityOpMaker(framework::OpProto *proto, - framework::OpAttrChecker *op_checker) - : OpProtoAndCheckerMaker(proto, op_checker) { - AddInput("X", "The input tensor of identity operator.").NotInGradient(); - AddOutput("Out", "The output tensor of identity operator.").NotInGradient(); - AddComment(R"DOC(Identity operator - -The equation is: Out = X -)DOC"); - } -}; - -// Identity Op's gradient is identity op, too. -// Grad(Out=identity_op(X)) => Grad(X) = identity_op(Grad(Out)) -class IdentityGradOp : public NetOp { - public: - IdentityGradOp(const std::string &type, const VarNameMap &inputs, - const VarNameMap &outputs, - const framework::AttributeMap &attrs) - : NetOp(type, inputs, outputs, attrs) { - AddOp(framework::OpRegistry::CreateOp( - "identity", {{"X", {Input(framework::GradVarName("Out"))}}}, - {{"Out", {Output(framework::GradVarName("X"))}}}, {})); - CompleteAddOp(false); - } -}; - -} // namespace operators -} // namespace paddle - -namespace ops = paddle::operators; - -REGISTER_OP(identity, ops::IdentityOp, ops::IdentityOpMaker, identity_grad, - ops::IdentityGradOp); -REGISTER_OP_CPU_KERNEL(identity, ops::IdentityKernel); diff --git a/paddle/operators/net_op.cc b/paddle/operators/net_op.cc index a7d710511..7e3779ed2 100644 --- a/paddle/operators/net_op.cc +++ b/paddle/operators/net_op.cc @@ -68,10 +68,15 @@ std::string NetOp::DebugString() const { bool NetOp::IsNetOp() const { return true; } std::vector NetOp::OutputVars(bool has_intermediate) const { + std::vector all; + for (auto& pair : this->outputs_) { + for (auto& var_name : pair.second) { + all.push_back(var_name); + } + } if (has_intermediate) { - return this->outputs_.at(kAll); + return all; } - auto& all = this->outputs_.at(kAll); std::vector ret_val; for (auto& each : all) { if (!Contains(intermediate_outputs_, each)) { diff --git a/paddle/operators/scale_op.cc b/paddle/operators/scale_op.cc new file mode 100644 index 000000000..3b18ff078 --- /dev/null +++ b/paddle/operators/scale_op.cc @@ -0,0 +1,102 @@ +/* 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/scale_op.h" +#include "paddle/operators/net_op.h" + +namespace paddle { +namespace operators { + +class ScaleOp : public framework::OperatorWithKernel { + public: + ScaleOp(const std::string &type, const VarNameMap &inputs, + const VarNameMap &outputs, const framework::AttributeMap &attrs) + : OperatorWithKernel(type, inputs, outputs, attrs) {} + + protected: + void InferShape(const framework::InferShapeContext &ctx) const override { + auto *in = ctx.Input("X"); + auto *out = ctx.Output("Out"); + out->Resize(in->dims()); + } +}; + +template +class ScaleOpMaker : public framework::OpProtoAndCheckerMaker { + public: + ScaleOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker) + : OpProtoAndCheckerMaker(proto, op_checker) { + AddInput("X", "The input tensor of scale operator.").NotInGradient(); + AddOutput("Out", "The output tensor of scale operator.").NotInGradient(); + AddComment(R"DOC(Scale operator + +The equation is: Out = scale*X +)DOC"); + AddAttr("scale", "scale of scale operator.").SetDefault(1.0); + } +}; + +// Identity Op's gradient is identity op, too. +// Grad(Out=scale(X)) => Grad(X) = scale(Grad(Out)) +template +class ScaleGradOp : public NetOp { + public: + ScaleGradOp(const std::string &type, const VarNameMap &inputs, + const VarNameMap &outputs, const framework::AttributeMap &attrs) + : NetOp(type, inputs, outputs, attrs) { + AddOp(framework::OpRegistry::CreateOp( + "scale", {{"X", {Input(framework::GradVarName("Out"))}}}, + {{"Out", {Output(framework::GradVarName("X"))}}}, + {{"scale", GetAttr("scale")}})); + CompleteAddOp(false); + } +}; + +// identity is a alias of scale op. This is also a example for creating a alias +// operator. +template +class IdentityOpMaker : public framework::OpProtoAndCheckerMaker { + public: + IdentityOpMaker(framework::OpProto *proto, + framework::OpAttrChecker *op_checker) + : OpProtoAndCheckerMaker(proto, op_checker) { + AddInput("X", "input tensor of identity op"); + AddOutput("Out", "output tensor of identity op"); + AddComment("identity operator. Just a alias of scale op which scale = 1.0"); + } +}; + +template +class IdentityOp : public NetOp { + public: + IdentityOp(const std::string &type, const VarNameMap &inputs, + const VarNameMap &outputs, const framework::AttributeMap &attrs) + : NetOp(type, inputs, outputs, attrs) { + AddOp(framework::OpRegistry::CreateOp( + "scale", {{"X", {Input("X")}}}, {{"Out", {Output("Out")}}}, + {{"scale", static_cast(1)}})); + } +}; + +} // namespace operators +} // namespace paddle + +namespace ops = paddle::operators; + +REGISTER_OP(scale, ops::ScaleOp, ops::ScaleOpMaker, scale_grad, + ops::ScaleGradOp); +REGISTER_OP_CPU_KERNEL(scale, + ops::ScaleKernel); +REGISTER_OP_WITHOUT_GRADIENT(identity, ops::IdentityOp, + ops::IdentityOpMaker); diff --git a/paddle/operators/identity_op.cu b/paddle/operators/scale_op.cu similarity index 81% rename from paddle/operators/identity_op.cu rename to paddle/operators/scale_op.cu index 3053104bb..63efbe0da 100644 --- a/paddle/operators/identity_op.cu +++ b/paddle/operators/scale_op.cu @@ -12,6 +12,7 @@ See the License for the specific language governing permissions and limitations under the License. */ -#include "paddle/operators/identity_op.h" +#include "paddle/operators/scale_op.h" -REGISTER_OP_GPU_KERNEL(identity, paddle::operators::IdentityKernel); +REGISTER_OP_GPU_KERNEL( + scale, paddle::operators::ScaleKernel); diff --git a/paddle/operators/identity_op.h b/paddle/operators/scale_op.h similarity index 66% rename from paddle/operators/identity_op.h rename to paddle/operators/scale_op.h index 14a832257..aea64f1b0 100644 --- a/paddle/operators/identity_op.h +++ b/paddle/operators/scale_op.h @@ -14,17 +14,25 @@ #pragma once +#include "paddle/framework/eigen.h" #include "paddle/framework/op_registry.h" -#include "paddle/memory/memcpy.h" + namespace paddle { namespace operators { -template -class IdentityKernel : public framework::OpKernel { +template +class ScaleKernel : public framework::OpKernel { public: virtual void Compute(const framework::ExecutionContext& context) const { auto* tensor = context.Output("Out"); auto* in = context.Input("X"); - tensor->CopyFrom(*in, in->place()); + tensor->mutable_data(in->place()); + + auto scale = static_cast(context.op_.GetAttr("scale")); + + auto eigen_out = framework::EigenVector::Flatten(*tensor); + auto eigen_in = framework::EigenVector::Flatten(*in); + auto& dev = context.GetEigenDevice(); + eigen_out.device(dev) = scale * eigen_in; } }; diff --git a/python/paddle/v2/framework/tests/CMakeLists.txt b/python/paddle/v2/framework/tests/CMakeLists.txt index cf7baa555..0e8811bfe 100644 --- a/python/paddle/v2/framework/tests/CMakeLists.txt +++ b/python/paddle/v2/framework/tests/CMakeLists.txt @@ -27,4 +27,4 @@ py_test(test_uniform_random_op SRCS test_uniform_random_op.py) py_test(test_recurrent_op SRCS test_recurrent_op.py) py_test(test_sgd_op SRCS test_sgd_op.py) py_test(test_gradient_checker SRCS test_gradient_checker.py) -py_test(test_identity_op SRCS test_identity_op.py) +py_test(test_scale_and_identity_op SRCS test_scale_and_identity_op.py) diff --git a/python/paddle/v2/framework/tests/gradient_checker.py b/python/paddle/v2/framework/tests/gradient_checker.py index 8b8e2f444..c22c6f883 100644 --- a/python/paddle/v2/framework/tests/gradient_checker.py +++ b/python/paddle/v2/framework/tests/gradient_checker.py @@ -160,8 +160,13 @@ class GradientChecker(unittest.TestCase): grad_tensor.set(data, place) # run backward op - for name in backward_op.outputs(): + backward_outs = backward_op.outputs() + backward_names = [ + item for key in backward_outs for item in backward_outs[key] + ] + for name in backward_names: scope.new_var(name) + backward_op.infer_shape(scope) backward_op.run(scope, ctx) diff --git a/python/paddle/v2/framework/tests/test_identity_op.py b/python/paddle/v2/framework/tests/test_scale_and_identity_op.py similarity index 51% rename from python/paddle/v2/framework/tests/test_identity_op.py rename to python/paddle/v2/framework/tests/test_scale_and_identity_op.py index 181d9c0c2..69b301c37 100644 --- a/python/paddle/v2/framework/tests/test_identity_op.py +++ b/python/paddle/v2/framework/tests/test_scale_and_identity_op.py @@ -2,6 +2,7 @@ import unittest from op_test_util import OpTestMeta from gradient_checker import GradientChecker, create_op import numpy as np +from paddle.v2.framework.op import Operator class IdentityTest(unittest.TestCase): @@ -20,5 +21,23 @@ class IdentityGradOpTest(GradientChecker): self.check_grad(op, inputs, set("X"), "Out") +class ScaleTest(unittest.TestCase): + __metaclass__ = OpTestMeta + + def setUp(self): + self.type = "scale" + self.inputs = {'X': np.random.random((32, 784)).astype("float32")} + self.attrs = {'scale': -2.3} + self.outputs = {'Out': self.inputs['X'] * self.attrs['scale']} + + +class ScaleGradTest(GradientChecker): + def test_normal(self): + op = Operator("scale", X="X", Out="Out", scale=3.2) + self.check_grad(op, + {"X": np.random.random((10, 10)).astype("float32")}, + set("X"), "Out") + + if __name__ == '__main__': unittest.main() -- GitLab From 117ce4cbc1a16da1ba8489aaab754aa0ebe5d3ab Mon Sep 17 00:00:00 2001 From: guosheng Date: Mon, 21 Aug 2017 19:23:42 +0800 Subject: [PATCH 0149/2018] Change class to struct in GemmFunctor to avoid errors on special compilers --- paddle/function/GemmFunctor.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/paddle/function/GemmFunctor.cpp b/paddle/function/GemmFunctor.cpp index dc83278d8..9e25ee58a 100644 --- a/paddle/function/GemmFunctor.cpp +++ b/paddle/function/GemmFunctor.cpp @@ -84,7 +84,7 @@ struct BlasGemm { } }; -template class BlasGemm; -template class BlasGemm; +template struct BlasGemm; +template struct BlasGemm; } // namespace paddle -- GitLab From 950dbde56c989f79bace3d53ae38bfae26e84c53 Mon Sep 17 00:00:00 2001 From: qiaolongfei Date: Mon, 21 Aug 2017 08:41:35 -0700 Subject: [PATCH 0150/2018] fix rowwise add grad op --- paddle/operators/rowwise_add_op.h | 2 +- python/paddle/v2/framework/tests/test_rowwise_add_op.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/paddle/operators/rowwise_add_op.h b/paddle/operators/rowwise_add_op.h index 232135c38..771c5d7c0 100644 --- a/paddle/operators/rowwise_add_op.h +++ b/paddle/operators/rowwise_add_op.h @@ -63,7 +63,7 @@ class RowwiseAddGradKernel : public framework::OpKernel { // https://eigen.tuxfamily.org/dox/unsupported/TensorBase_8h_source.html // colwise add - Eigen::array dims{{1}}; /* dimension to reduce */ + Eigen::array dims{{0}}; /* dimension to reduce */ EigenVector::Flatten(*db).device(place) = OutGrad.sum(dims); } }; diff --git a/python/paddle/v2/framework/tests/test_rowwise_add_op.py b/python/paddle/v2/framework/tests/test_rowwise_add_op.py index 29d72e850..45d569da2 100644 --- a/python/paddle/v2/framework/tests/test_rowwise_add_op.py +++ b/python/paddle/v2/framework/tests/test_rowwise_add_op.py @@ -20,7 +20,7 @@ class RowwiseAddGradOpTest(GradientChecker): def test_rowwise_add(self): op = create_op("rowwise_add") inputs = { - "X": np.random.uniform(0.1, 1, [10, 10]).astype("float32"), + "X": np.random.uniform(0.1, 1, [5, 10]).astype("float32"), "b": np.random.uniform(0.1, 1, [10]).astype("float32") } self.check_grad(op, inputs, set(["X", "b"]), "Out") -- GitLab From a75a638fb16ac5b08509c3f185d25ec670d3cb12 Mon Sep 17 00:00:00 2001 From: qiaolongfei Date: Mon, 21 Aug 2017 09:13:19 -0700 Subject: [PATCH 0151/2018] format Copyright --- paddle/operators/rowwise_add_op.h | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/paddle/operators/rowwise_add_op.h b/paddle/operators/rowwise_add_op.h index 771c5d7c0..1cbd8bb31 100644 --- a/paddle/operators/rowwise_add_op.h +++ b/paddle/operators/rowwise_add_op.h @@ -1,16 +1,16 @@ /* 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 +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 + 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. */ +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/eigen.h" -- GitLab From 2377d719473543da3a6129de3c6c32667bdb9f18 Mon Sep 17 00:00:00 2001 From: chengduoZH Date: Mon, 21 Aug 2017 09:28:03 +0800 Subject: [PATCH 0152/2018] Add3DPooling --- paddle/cuda/include/hl_cnn.h | 198 ++++++++- paddle/cuda/include/stub/hl_cnn_stub.h | 90 ++++ paddle/cuda/src/hl_cuda_cnn.cu | 427 ++++++++++++++++++- paddle/gserver/layers/Pool3DLayer.cpp | 198 +++++++++ paddle/gserver/layers/Pool3DLayer.h | 48 +++ paddle/gserver/tests/test_LayerGrad.cpp | 69 ++++ paddle/math/Matrix.cpp | 502 +++++++++++++++++++++++ paddle/math/Matrix.h | 254 +++++++++++- paddle/math/tests/test_matrixCompare.cpp | 204 +++++++++ paddle/parameter/Argument.cpp | 2 + paddle/parameter/Argument.h | 8 +- proto/ModelConfig.proto | 12 + 12 files changed, 1998 insertions(+), 14 deletions(-) create mode 100644 paddle/gserver/layers/Pool3DLayer.cpp create mode 100644 paddle/gserver/layers/Pool3DLayer.h diff --git a/paddle/cuda/include/hl_cnn.h b/paddle/cuda/include/hl_cnn.h index 9f84db72d..e9687d0a5 100644 --- a/paddle/cuda/include/hl_cnn.h +++ b/paddle/cuda/include/hl_cnn.h @@ -173,6 +173,202 @@ extern void hl_avgpool_backward(const int frameCnt, real* backGrad, const int outStride); +/** + * @brief Maximum pool forward. + * + * @param[in] frameCnt batch size of input image. + * @param[in] inputData input data. + * @param[in] channels number of channel. + * @param[in] depth image depth. + * @param[in] height image height. + * @param[in] width image width. + * @param[in] pooledD output image depth. + * @param[in] pooledH output image height. + * @param[in] pooledW output image width. + * @param[in] sizeZ depth of pooling window. + * @param[in] sizeY height of pooling window. + * @param[in] sizeX width of pooling window. + * @param[in] strideD pooling stride depth. + * @param[in] strideH pooling stride height. + * @param[in] strideW pooling stride width. + * @param[in] paddingD padding depth. + * @param[in] paddingH padding height. + * @param[in] paddingW padding width. + * @param[out] tgtData output data. + * @param[in] tgtStride stride between output data samples. + * + */ +extern void hl_maxpool3D_forward(const int frameCnt, + const real* inputData, + const int channels, + const int depth, + const int height, + const int width, + const int pooledD, + const int pooledH, + const int pooledW, + const int sizeZ, + const int sizeY, + const int sizeX, + const int strideD, + const int strideH, + const int strideW, + const int paddingD, + const int paddingH, + const int paddingW, + real* tgtData, + const int tgtStride); + +/** + * @brief Maximum pool backward. + * + * @param[in] frameCnt batch size of input image. + * @param[in] inputData input data. + * @param[out] outData output data. + * @param[out] outGrad output grad data. + * @param[in] channels number of channel. + * @param[in] depth image depth. + * @param[in] height image height. + * @param[in] width image width. + * @param[in] pooledD output image depth. + * @param[in] pooledH output image height. + * @param[in] pooledW output image width. + * @param[in] sizeZ depth of pooling window. + * @param[in] sizeY height of pooling window. + * @param[in] sizeX width of pooling window. + * @param[in] strideD pooling stride depth. + * @param[in] strideH pooling stride height. + * @param[in] strideW pooling stride width. + * @param[in] scaleA scale. + * @param[in] scaleB scale. + * @param[in] paddingD padding depth. + * @param[in] paddingH padding height. + * @param[in] paddingW padding width. + * @param[out] targetGrad output grad. + * @param[in] outStride stride between output data samples. + * + */ +extern void hl_maxpool3D_backward(const int frameCnt, + const real* inputData, + const real* outData, + const real* outGrad, + const int channels, + const int depth, + const int height, + const int width, + const int pooledD, + const int pooledH, + const int pooledW, + const int sizeZ, + const int sizeY, + const int sizeX, + const int strideD, + const int strideH, + const int strideW, + const int paddingD, + const int paddingH, + const int paddingW, + real scaleA, + real scaleB, + real* targetGrad, + const int outStride); + +/** + * @brief Averge pool forward. + * + * @param[in] frameCnt batch size of input image. + * @param[in] inputData input data. + * @param[in] channels number of channel. + * @param[in] depth image depth. + * @param[in] height image height. + * @param[in] width image width. + * @param[in] pooledD output image depth. + * @param[in] pooledH output image height. + * @param[in] pooledW output image width. + * @param[in] sizeZ depth of pooling window. + * @param[in] sizeY height of pooling window. + * @param[in] sizeX width of pooling window. + * @param[in] strideD pooling stride depth. + * @param[in] strideH pooling stride height. + * @param[in] strideW pooling stride width. + * @param[in] paddingD padding depth. + * @param[in] paddingH padding height. + * @param[in] paddingW padding width. + * @param[out] tgtData output data. + * @param[in] tgtStride stride between output data samples. + * + */ +extern void hl_avgpool3D_forward(const int frameCnt, + const real* inputData, + const int channels, + const int depth, + const int height, + const int width, + const int pooledD, + const int pooledH, + const int pooledW, + const int sizeZ, + const int sizeY, + const int sizeX, + const int strideD, + const int strideH, + const int strideW, + const int paddingD, + const int paddingH, + const int paddingW, + real* tgtData, + const int tgtStride); + +/** + * @brief Maximum pool backward. + * + * @param[in] frameCnt batch size of input image. + * @param[in] outGrad output grad data. + * @param[in] channels number of channel. + * @param[in] depth image depth. + * @param[in] height image height. + * @param[in] width image width. + * @param[in] pooledD output image depth. + * @param[in] pooledH output image height. + * @param[in] pooledW output image width. + * @param[in] sizeZ depth of pooling window. + * @param[in] sizeY height of pooling window. + * @param[in] sizeX width of pooling window. + * @param[in] strideD pooling stride depth. + * @param[in] strideH pooling stride height. + * @param[in] strideW pooling stride width. + * @param[in] paddingD padding depth. + * @param[in] paddingH padding height. + * @param[in] paddingW padding width. + * @param[in] scaleA scale. + * @param[in] scaleB scale. + * @param[out] backGrad output grad. + * @param[in] outStride stride between output data samples. + * + */ +extern void hl_avgpool3D_backward(const int frameCnt, + const real* outGrad, + const int channels, + const int depth, + const int height, + const int width, + const int pooledD, + const int pooledH, + const int pooledW, + const int sizeZ, + const int sizeY, + const int sizeX, + const int strideD, + const int strideH, + const int strideW, + int paddingD, + int paddingH, + int paddingW, + real scaleA, + real scaleB, + real* backGrad, + const int outStride); + /** * @brief Bilinear interpolation forward. * @@ -275,4 +471,4 @@ extern void hl_maxout_backward(real* inGrad, size_t featLen, size_t groups); -#endif /* HL_CNN_H_ */ +#endif // HL_CNN_H_ diff --git a/paddle/cuda/include/stub/hl_cnn_stub.h b/paddle/cuda/include/stub/hl_cnn_stub.h index 2bbb9fa8d..28f61781b 100644 --- a/paddle/cuda/include/stub/hl_cnn_stub.h +++ b/paddle/cuda/include/stub/hl_cnn_stub.h @@ -87,6 +87,96 @@ inline void hl_avgpool_backward(const int frameCnt, real* backGrad, const int outStride) {} +inline void hl_maxpool3D_forward(const int frameCnt, + const real* inputData, + const int channels, + const int depth, + const int height, + const int width, + const int pooledD, + const int pooledH, + const int pooledW, + const int sizeZ, + const int sizeY, + const int sizeX, + const int strideD, + const int strideH, + const int strideW, + const int paddingD, + const int paddingH, + const int paddingW, + real* tgtData, + const int tgtStride) {} + +inline void hl_maxpool3D_backward(const int frameCnt, + const real* inputData, + const real* outData, + const real* outGrad, + const int channels, + const int depth, + const int height, + const int width, + const int pooledD, + const int pooledH, + const int pooledW, + const int sizeZ, + const int sizeY, + const int sizeX, + const int strideD, + const int strideH, + const int strideW, + const int paddingD, + const int paddingH, + const int paddingW, + real scaleA, + real scaleB, + real* targetGrad, + const int outStride) {} + +inline void hl_avgpool3D_forward(const int frameCnt, + const real* inputData, + const int channels, + const int depth, + const int height, + const int width, + const int pooledD, + const int pooledH, + const int pooledW, + const int sizeZ, + const int sizeY, + const int sizeX, + const int strideD, + const int strideH, + const int strideW, + const int paddingD, + const int paddingH, + const int paddingW, + real* tgtData, + const int tgtStride) {} + +inline void hl_avgpool3D_backward(const int frameCnt, + const real* outGrad, + const int channels, + const int depth, + const int height, + const int width, + const int pooledD, + const int pooledH, + const int pooledW, + const int sizeZ, + const int sizeY, + const int sizeX, + const int strideD, + const int strideH, + const int strideW, + int paddingD, + int paddingH, + int paddingW, + real scaleA, + real scaleB, + real* backGrad, + const int outStride) {} + inline void hl_bilinear_forward(const real* inData, const size_t inImgH, const size_t inImgW, diff --git a/paddle/cuda/src/hl_cuda_cnn.cu b/paddle/cuda/src/hl_cuda_cnn.cu index aac19b1ea..458c34772 100644 --- a/paddle/cuda/src/hl_cuda_cnn.cu +++ b/paddle/cuda/src/hl_cuda_cnn.cu @@ -1,11 +1,8 @@ /* 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. @@ -353,6 +350,430 @@ void hl_avgpool_backward(const int frameCnt, CHECK_SYNC("hl_avgpool_backward failed"); } +///////////////// +__global__ void KeMaxPool3DForward(const int nthreads, + const real* inputData, + const int channels, + const int depth, + const int height, + const int width, + const int pooledD, + const int pooledH, + const int pooledW, + const int ksizeD, + const int ksizeH, + const int ksizeW, + const int strideD, + const int strideH, + const int strideW, + const int offsetD, + const int offsetH, + const int offsetW, + real* tgtData, + const int tgtStride) { + for (int index = blockIdx.x * blockDim.x + threadIdx.x; index < (nthreads); + index += blockDim.x * gridDim.x) { + int pw = index % pooledW; + int ph = (index / pooledW) % pooledH; + int pd = (index / pooledW / pooledH) % pooledD; + int c = (index / pooledW / pooledH / pooledD) % channels; + int frameNum = index / pooledW / pooledH / pooledD / channels; + int dstart = pd * strideD - offsetD; + int hstart = ph * strideH - offsetH; + int wstart = pw * strideW - offsetW; + int dend = min(dstart + ksizeD, depth); + int hend = min(hstart + ksizeH, height); + int wend = min(wstart + ksizeW, width); + dstart = max(dstart, 0); + hstart = max(hstart, 0); + wstart = max(wstart, 0); + real maxval = -FLT_MAX; + inputData += (frameNum * channels + c) * depth * height * width; + for (int d = dstart; d < dend; ++d) { + for (int h = hstart; h < hend; ++h) { + for (int w = wstart; w < wend; ++w) { + if (maxval < inputData[(d * height + h) * width + w]) + maxval = inputData[(d * height + h) * width + w]; + } + } + } + int tgtIndex = + index % (pooledW * pooledH * pooledD * channels) + frameNum * tgtStride; + tgtData[tgtIndex] = maxval; + } +} + +void hl_maxpool3D_forward(const int frameCnt, + const real* inputData, + const int channels, + const int depth, + const int height, + const int width, + const int pooledD, + const int pooledH, + const int pooledW, + const int sizeZ, + const int sizeY, + const int sizeX, + const int strideD, + const int strideH, + const int strideW, + const int paddingD, + const int paddingH, + const int paddingW, + real* tgtData, + const int tgtStride) { + int num_kernels = pooledD * pooledH * pooledW * channels * frameCnt; + int blocks = (num_kernels + 1024 - 1) / 1024; + dim3 threads(1024, 1); + dim3 grid(blocks, 1); + + KeMaxPool3DForward<<>>(num_kernels, + inputData, + channels, + depth, + height, + width, + pooledD, + pooledH, + pooledW, + sizeZ, + sizeY, + sizeX, + strideD, + strideH, + strideW, + paddingD, + paddingH, + paddingW, + tgtData, + tgtStride); + CHECK_SYNC("hl_maxpool3D_forward failed"); +} + +__global__ void KeMaxPool3DBackward(const int nthreads, + const real* inputData, + const real* outData, + const real* outGrad, + const int channels, + const int depth, + const int height, + const int width, + const int pooledD, + const int pooledH, + const int pooledW, + const int sizeZ, + const int sizeY, + const int sizeX, + const int strideD, + const int strideH, + const int strideW, + const int padD, + const int padH, + const int padW, + real scaleA, + real scaleB, + real* targetGrad, + const int outStride) { + for (int index = blockIdx.x * blockDim.x + threadIdx.x; index < (nthreads); + index += blockDim.x * gridDim.x) { + // find out the local index + // find out the local offset + int offsetW = index % width + padW; + int offsetH = (index / width) % height + padH; + int offsetD = (index / width / height) % depth + padD; + int offsetC = (index / width / height / depth) % channels; + int frameNum = index / width / height / depth / channels; + + int pdstart = (offsetD < sizeZ) ? 0 : (offsetD - sizeZ) / strideD + 1; + int phstart = (offsetH < sizeY) ? 0 : (offsetH - sizeY) / strideH + 1; + int pwstart = (offsetW < sizeX) ? 0 : (offsetW - sizeX) / strideW + 1; + int pdend = min(offsetD / strideD + 1, pooledD); + int phend = min(offsetH / strideH + 1, pooledH); + int pwend = min(offsetW / strideW + 1, pooledW); + + real gradient = 0; + real input = inputData[index]; + + outData += ((frameNum * channels + offsetC) * pooledD * pooledH * pooledW); + outGrad += ((frameNum * channels + offsetC) * pooledD * pooledH * pooledW); + for (int pd = pdstart; pd < pdend; ++pd) { + for (int ph = phstart; ph < phend; ++ph) { + for (int pw = pwstart; pw < pwend; ++pw) { + if (input == outData[(pd * pooledH + ph) * pooledW + pw]) + gradient += outGrad[(pd * pooledH + ph) * pooledW + pw]; + } + } + } + targetGrad[index] = scaleA * gradient + scaleB * targetGrad[index]; + } +} + +void hl_maxpool3D_backward(const int frameCnt, + const real* inputData, + const real* outData, + const real* outGrad, + const int channels, + const int depth, + const int height, + const int width, + const int outputD, + const int outputH, + const int outputW, + const int sizeZ, + const int sizeY, + const int sizeX, + const int strideD, + const int strideH, + const int strideW, + const int paddingD, + const int paddingH, + const int paddingW, + real scaleA, + real scaleB, + real* targetGrad, + const int outStride) { + int num_kernels = depth * height * width * channels * frameCnt; + int blocks = (num_kernels + 1024 - 1) / 1024; + + KeMaxPool3DBackward<<>>(num_kernels, + inputData, + outData, + outGrad, + channels, + depth, + height, + width, + outputD, + outputH, + outputW, + sizeZ, + sizeY, + sizeX, + strideD, + strideH, + strideW, + paddingD, + paddingH, + paddingW, + scaleA, + scaleB, + targetGrad, + outStride); + CHECK_SYNC("hl_maxpool3D_backward"); +} + +__global__ void KeAvgPool3DForward(const int nthreads, + const real* inputData, + const int channels, + const int depth, + const int height, + const int width, + const int pooledD, + const int pooledH, + const int pooledW, + const int sizeZ, + const int sizeY, + const int sizeX, + const int strideD, + const int strideH, + const int strideW, + const int padD, + const int padH, + const int padW, + real* tgtData, + const int tgtStride) { + for (int index = blockIdx.x * blockDim.x + threadIdx.x; index < (nthreads); + index += blockDim.x * gridDim.x) { + int pw = index % pooledW; + int ph = (index / pooledW) % pooledH; + int pd = (index / pooledW / pooledH) % pooledD; + int c = (index / pooledW / pooledH / pooledD) % channels; + int frameNum = index / pooledW / pooledH / pooledD / channels; + int dstart = pd * strideD - padD; + int hstart = ph * strideH - padH; + int wstart = pw * strideW - padW; + int dend = min(dstart + sizeZ, depth + padD); + int hend = min(hstart + sizeY, height + padH); + int wend = min(wstart + sizeX, width + padW); + int pool_size = (dend - dstart) * (hend - hstart) * (wend - wstart); + dstart = max(dstart, 0); + hstart = max(hstart, 0); + wstart = max(wstart, 0); + dend = min(dend, depth); + hend = min(hend, height); + wend = min(wend, width); + + real aveval = 0; + inputData += (frameNum * channels + c) * depth * height * width; + for (int d = dstart; d < dend; ++d) { + for (int h = hstart; h < hend; ++h) { + for (int w = wstart; w < wend; ++w) { + aveval += inputData[(d * height + h) * width + w]; + } + } + } + int tgtIndex = + index % (pooledW * pooledH * pooledD * channels) + frameNum * tgtStride; + tgtData[tgtIndex] = aveval / pool_size; + } +} + +void hl_avgpool3D_forward(const int frameCnt, + const real* inputData, + const int channels, + const int depth, + const int height, + const int width, + const int pooledD, + const int pooledH, + const int pooledW, + const int sizeZ, + const int sizeY, + const int sizeX, + const int strideD, + const int strideH, + const int strideW, + const int paddingD, + const int paddingH, + const int paddingW, + real* tgtData, + const int tgtStride) { + int num_kernels = pooledD * pooledH * pooledW * channels * frameCnt; + int blocks = (num_kernels + 1024 - 1) / 1024; + KeAvgPool3DForward<<>>(num_kernels, + inputData, + channels, + depth, + height, + width, + pooledD, + pooledH, + pooledW, + sizeZ, + sizeY, + sizeX, + strideD, + strideH, + strideW, + paddingD, + paddingH, + paddingW, + tgtData, + tgtStride); + CHECK_SYNC("hl_avgpool3D_forward failed"); +} + +__global__ void KeAvgPool3DBackward(const int nthreads, + const real* outGrad, + const int channels, + const int depth, + const int height, + const int width, + const int pooledD, + const int pooledH, + const int pooledW, + const int sizeZ, + const int sizeY, + const int sizeX, + const int strideD, + const int strideH, + const int strideW, + const int padD, + const int padH, + const int padW, + real scaleA, + real scaleB, + real* tgtGrad, + const int outStride) { + for (int index = blockIdx.x * blockDim.x + threadIdx.x; index < (nthreads); + index += blockDim.x * gridDim.x) { + int offsetW = index % width + padW; + int offsetH = (index / width) % height + padH; + int offsetD = (index / width / height) % depth + padD; + int offsetC = (index / width / height / depth) % channels; + int frameNum = index / width / height / depth / channels; + + int pdstart = (offsetD < sizeZ) ? 0 : (offsetD - sizeZ) / strideD + 1; + int phstart = (offsetH < sizeY) ? 0 : (offsetH - sizeY) / strideH + 1; + int pwstart = (offsetW < sizeX) ? 0 : (offsetW - sizeX) / strideW + 1; + int pdend = min(offsetD / strideD + 1, pooledD); + int phend = min(offsetH / strideH + 1, pooledH); + int pwend = min(offsetW / strideW + 1, pooledW); + + real gradient = 0; + outGrad += (frameNum * channels + offsetC) * pooledD * pooledH * pooledW; + + for (int pd = pdstart; pd < pdend; ++pd) { + for (int ph = phstart; ph < phend; ++ph) { + for (int pw = pwstart; pw < pwend; ++pw) { + // figure out the pooling size + int dstart = pd * strideD - padD; + int hstart = ph * strideH - padH; + int wstart = pw * strideW - padW; + int dend = min(dstart + sizeZ, depth + padD); + int hend = min(hstart + sizeY, height + padH); + int wend = min(wstart + sizeX, width + padW); + int poolsize = (dend - dstart) * (hend - hstart) * (wend - wstart); + gradient += outGrad[(pd * pooledH + ph) * pooledW + pw] / poolsize; + } + } + } + tgtGrad[index] = scaleA * gradient + scaleB * tgtGrad[index]; + } +} + +void hl_avgpool3D_backward(const int frameCnt, + const real* outGrad, + const int channels, + const int depth, + const int height, + const int width, + const int outputD, + const int outputH, + const int outputW, + const int sizeZ, + const int sizeY, + const int sizeX, + const int strideD, + const int strideH, + const int strideW, + int paddingD, + int paddingH, + int paddingW, + real scaleA, + real scaleB, + real* backGrad, + const int outStride) { + int num_kernels = depth * height * width * channels * frameCnt; + int blocks = (num_kernels + 1024 - 1) / 1024; + + KeAvgPool3DBackward<<>>(num_kernels, + outGrad, + channels, + depth, + height, + width, + outputD, + outputH, + outputW, + sizeZ, + sizeY, + sizeX, + strideD, + strideH, + strideW, + paddingD, + paddingH, + paddingW, + scaleA, + scaleB, + backGrad, + outStride); + CHECK_SYNC("hl_avgpool3D_backward failed"); +} +///////////////// + __global__ void KeBilinearInterpFw(const real* in, const size_t inImgH, const size_t inImgW, diff --git a/paddle/gserver/layers/Pool3DLayer.cpp b/paddle/gserver/layers/Pool3DLayer.cpp new file mode 100644 index 000000000..fc6b9bdd2 --- /dev/null +++ b/paddle/gserver/layers/Pool3DLayer.cpp @@ -0,0 +1,198 @@ +/* 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 "Pool3DLayer.h" +#include "PoolProjectionLayer.h" +#include "paddle/utils/Logging.h" + +namespace paddle { + +REGISTER_LAYER(pool3d, Pool3DLayer); + +bool Pool3DLayer::init(const LayerMap& layerMap, + const ParameterMap& parameterMap) { + Layer::init(layerMap, parameterMap); + + /* the size of inputs for pool-layer is 1 */ + CHECK_EQ(config_.inputs_size(), 1); + + const PoolConfig& conf = config_.inputs(0).pool_conf(); + poolType_ = conf.pool_type(); + channels_ = conf.channels(); + + sizeX_ = conf.size_x(); + sizeY_ = conf.size_y(); + sizeZ_ = conf.size_z(); + + strideW_ = conf.stride(); + strideH_ = conf.stride_y(); + strideD_ = conf.stride_z(); + + imgSizeW_ = conf.img_size(); + imgSizeH_ = conf.img_size_y(); + imgSizeD_ = conf.img_size_z(); + + paddingW_ = conf.padding(); + paddingH_ = conf.padding_y(); + paddingD_ = conf.padding_z(); + + outputW_ = conf.output_x(); + outputH_ = conf.output_y(); + outputD_ = conf.output_z(); + + return true; +} + +size_t Pool3DLayer::getSize() { + CHECK_EQ(inputLayers_.size(), 1UL); + + size_t layerSize = 0; + // imgSizeD_ = inputLayers_[0]->getOutput().getFrameDepth(); + // imgSizeH_ = inputLayers_[0]->getOutput().getFrameHeight(); + // imgSizeW_ = inputLayers_[0]->getOutput().getFrameWidth(); + if (imgSizeH_ == 0) { + // imgSizeH_ = imgSizeY_; + } + if (imgSizeW_ == 0) { + // imgSizeW_ = imgSize_; + } + outputD_ = outputSize(imgSizeD_, + sizeZ_, + paddingD_, + strideD_, + /* caffeMode */ false); + outputH_ = outputSize(imgSizeH_, + sizeY_, + paddingH_, + strideH_, + /* caffeMode */ false); + outputW_ = outputSize(imgSizeW_, + sizeX_, + paddingW_, + strideW_, + /* caffeMode */ false); + + layerSize = outputD_ * outputH_ * outputW_ * channels_; + getOutput().setFrameHeight(outputH_); + getOutput().setFrameWidth(outputW_); + getOutput().setFrameDepth(outputD_); + return layerSize; +} + +void Pool3DLayer::forward(PassType passType) { + Layer::forward(passType); + const MatrixPtr& inMat = inputLayers_[0]->getOutputValue(); + int batchSize = inMat->getHeight(); + int outWidth = getSize(); + resetOutput(batchSize, outWidth); + const MatrixPtr outMat = getOutputValue(); + + if (poolType_ == "avg") { + outMat->avgPool3DForward(*inMat, + imgSizeD_, + imgSizeH_, + imgSizeW_, + channels_, + sizeZ_, + sizeY_, + sizeX_, + strideD_, + strideH_, + strideW_, + outputD_, + outputH_, + outputW_, + paddingD_, + paddingH_, + paddingW_); + } else if (poolType_ == "max") { + outMat->maxPool3DForward(*inMat, + imgSizeD_, + imgSizeH_, + imgSizeW_, + channels_, + sizeZ_, + sizeY_, + sizeX_, + strideD_, + strideH_, + strideW_, + outputD_, + outputH_, + outputW_, + paddingD_, + paddingH_, + paddingW_); + } else { + LOG(FATAL) << "Unknown pool type: " << poolType_; + } + forwardActivation(); +} + +void Pool3DLayer::backward(const UpdateCallback& callback) { + backwardActivation(); + + (void)callback; + if (NULL == getInputGrad(0)) return; + MatrixPtr inMat = inputLayers_[0]->getOutputValue(); + MatrixPtr inGradMat = inputLayers_[0]->getOutputGrad(); + MatrixPtr outMat = getOutputValue(); + MatrixPtr outGradMat = getOutputGrad(); + + if (poolType_ == "avg") { + inGradMat->avgPool3DBackward(*outGradMat, + imgSizeD_, + imgSizeH_, + imgSizeW_, + sizeZ_, + sizeY_, + sizeZ_, + strideD_, + strideH_, + strideW_, + outputD_, + outputH_, + outputW_, + 1, + 1, + paddingD_, + paddingH_, + paddingW_); + } else if (poolType_ == "max") { + inGradMat->maxPool3DBackward(*inMat, + imgSizeD_, + imgSizeH_, + imgSizeW_, + *outGradMat, + *outMat, + sizeZ_, + sizeY_, + sizeZ_, + strideD_, + strideH_, + strideW_, + outputD_, + outputH_, + outputW_, + 1, + 1, + paddingD_, + paddingH_, + paddingW_); + } else { + LOG(FATAL) << "Unknown pool type: " << poolType_; + } +} + +} // namespace paddle diff --git a/paddle/gserver/layers/Pool3DLayer.h b/paddle/gserver/layers/Pool3DLayer.h new file mode 100644 index 000000000..afc65ac2b --- /dev/null +++ b/paddle/gserver/layers/Pool3DLayer.h @@ -0,0 +1,48 @@ +/* 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 +#include "Layer.h" +#include "paddle/math/MathUtils.h" +#include "paddle/math/Matrix.h" + +namespace paddle { + +/** + * @brief Basic parent layer of pooling + * Pools the input within regions + */ +class Pool3DLayer : public Layer { +public: + explicit Pool3DLayer(const LayerConfig& config) : Layer(config) {} + ~Pool3DLayer() {} + + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override; + void forward(PassType passType) override; + void backward(const UpdateCallback& callback) override; + size_t getSize(); + +protected: + int channels_; + int sizeX_, sizeY_, sizeZ_; + int strideW_, strideH_, strideD_; + int paddingW_, paddingH_, paddingD_; + int imgSizeW_, imgSizeH_, imgSizeD_; + int outputW_, outputH_, outputD_; + std::string poolType_; +}; +} // namespace paddle diff --git a/paddle/gserver/tests/test_LayerGrad.cpp b/paddle/gserver/tests/test_LayerGrad.cpp index 0f312b6ca..43fb255ae 100644 --- a/paddle/gserver/tests/test_LayerGrad.cpp +++ b/paddle/gserver/tests/test_LayerGrad.cpp @@ -1206,6 +1206,75 @@ TEST(Layer, PoolLayer) { #endif } +void setPool3DConfig(TestConfig* config, + PoolConfig* pool, + const string& poolType) { + // filter size + const int NUM_FILTERS = 16; + const int FILTER_SIZE = 3; + const int FILTER_SIZE_Y = 3; + const int FILTER_SIZE_Z = 3; + const int CHANNELS = 16; + + (*config).biasSize = 0; + (*config).layerConfig.set_type("pool3d"); + (*config).layerConfig.set_num_filters(NUM_FILTERS); + + int kw = FILTER_SIZE, kh = FILTER_SIZE_Y, kd = FILTER_SIZE_Z; + int pw = 0, ph = 0, pd = 0; + int sw = 2, sh = 2, sd = 2; + + pool->set_pool_type(poolType); + pool->set_pool_type("avg"); + pool->set_channels(CHANNELS); + pool->set_size_x(kw); + pool->set_size_y(kh); + pool->set_size_z(kd); + pool->set_padding(0); + pool->set_padding_y(0); + pool->set_padding_z(0); + pool->set_stride(sw); + pool->set_stride_y(sh); + pool->set_stride_z(sd); + pool->set_start(0); + int ow = outputSize(pool->img_size(), kw, pw, sw, /* caffeMode */ false); + int oh = outputSize(pool->img_size_y(), kh, ph, sh, /* caffeMode */ false); + int od = outputSize(pool->img_size_z(), kd, pd, sd, /* caffeMode */ false); + pool->set_output_x(ow); + pool->set_output_y(oh); + pool->set_output_z(od); +} + +void testPool3DLayer(const string& poolType, bool trans, bool useGpu) { + TestConfig config; + config.inputDefs.push_back({INPUT_DATA, "layer_0", 11664, 0}); + LayerInputConfig* input = config.layerConfig.add_inputs(); + PoolConfig* pool = input->mutable_pool_conf(); + + const int IMAGE_SIZE = 9; + const int IMAGE_SIZE_Y = 9; + const int IMAGE_SIZE_Z = 9; + + pool->set_img_size(IMAGE_SIZE); + pool->set_img_size_y(IMAGE_SIZE_Y); + pool->set_img_size_z(IMAGE_SIZE_Z); + + setPool3DConfig(&config, pool, poolType); + config.layerConfig.set_size(pool->output_x() * pool->output_y() * + pool->channels()); + + testLayerGrad(config, "pool3d", 100, trans, useGpu); +} + +TEST(Layer, Pool3DLayer) { + testPool3DLayer("avg", /* trans= */ false, /* useGpu= */ false); + testPool3DLayer("max", /* trans= */ false, /* useGpu= */ false); +#ifndef PADDLE_ONLY_CPU + testPool3DLayer("avg", /* trans= */ false, /* useGpu= */ true); + testPool3DLayer("max", /* trans= */ false, /* useGpu= */ true); +#endif +} + void testSppLayer(const string& poolType, const int pyramidHeight, bool trans, diff --git a/paddle/math/Matrix.cpp b/paddle/math/Matrix.cpp index 27f7d95b7..e7f1489b8 100644 --- a/paddle/math/Matrix.cpp +++ b/paddle/math/Matrix.cpp @@ -1190,6 +1190,224 @@ void GpuMatrix::avgPoolBackward(Matrix& outGrad, outGrad.getStride()); } +void GpuMatrix::maxPool3DForward(Matrix& inputMat, + size_t imgSizeD, + size_t imgSizeH, + size_t imgSizeW, + size_t channels, + size_t sizeZ, + size_t sizeY, + size_t sizeX, + size_t strideD, + size_t strideH, + size_t strideW, + size_t outputD, + size_t outputH, + size_t outputW, + size_t paddingD, + size_t paddingH, + size_t paddingW) { + CHECK(inputMat.useGpu_ == true) << "Matrix type are not equal"; + + real* inputData = inputMat.getData(); + size_t num = inputMat.getHeight(); + size_t width = imgSizeW; + size_t height = imgSizeH; + size_t depth = imgSizeD; + CHECK(depth * height * width * channels == inputMat.getWidth()); + CHECK(height_ == inputMat.getHeight()); + CHECK(width_ == outputD * outputH * outputW * channels); + + hl_maxpool3D_forward(num, + inputData, + channels, + depth, + height, + width, + outputD, + outputH, + outputW, + sizeZ, + sizeY, + sizeX, + strideD, + strideH, + strideW, + paddingD, + paddingH, + paddingW, + data_, + getStride()); +} + +void GpuMatrix::maxPool3DBackward(Matrix& inputMat, + size_t imgSizeD, + size_t imgSizeH, + size_t imgSizeW, + Matrix& outGrad, + Matrix& outV, + size_t sizeZ, + size_t sizeY, + size_t sizeX, + size_t strideD, + size_t strideH, + size_t strideW, + size_t outputD, + size_t outputH, + size_t outputW, + real scaleTargets, + real scaleOutput, + size_t paddingD, + size_t paddingH, + size_t paddingW) { + CHECK(inputMat.useGpu_ == true && outGrad.useGpu_ == true && + outV.useGpu_ == true) + << "Matrix type are not equal"; + + real* inputData = inputMat.getData(); + real* outData = outV.getData(); + real* outDiff = outGrad.getData(); + size_t frameNum = inputMat.getHeight(); + size_t channels = outV.getWidth() / outputD / outputH / outputW; + size_t width = imgSizeW; + size_t height = imgSizeH; + size_t depth = imgSizeD; + CHECK(depth * height * width * channels == inputMat.getWidth()); + CHECK(height_ == inputMat.getHeight()); + CHECK(width_ == depth * width * height * channels); + CHECK(outGrad.getHeight() == outV.getHeight() && + outGrad.getWidth() == outV.getWidth()); + + hl_maxpool3D_backward(frameNum, + inputData, + outData, + outDiff, + channels, + depth, + height, + width, + outputD, + outputH, + outputW, + sizeZ, + sizeY, + sizeX, + strideD, + strideH, + strideW, + paddingD, + paddingH, + paddingW, + scaleTargets, + scaleOutput, + data_, + outGrad.getStride()); +} + +void GpuMatrix::avgPool3DForward(Matrix& inputMat, + size_t imgSizeD, + size_t imgSizeH, + size_t imgSizeW, + size_t channels, + size_t sizeZ, + size_t sizeY, + size_t sizeX, + size_t strideD, + size_t strideH, + size_t strideW, + size_t outputD, + size_t outputH, + size_t outputW, + size_t paddingD, + size_t paddingH, + size_t paddingW) { + CHECK(inputMat.useGpu_ == true) << "Matrix type are not equal"; + + real* inputData = inputMat.getData(); + size_t frameNum = inputMat.getHeight(); + size_t height = imgSizeH; + size_t width = imgSizeW; + size_t depth = imgSizeD; + CHECK(depth * height * width * channels == inputMat.getWidth()); + CHECK(height_ == inputMat.getHeight()); + CHECK(width_ == outputD * outputH * outputW * channels); + + hl_avgpool3D_forward(frameNum, + inputData, + channels, + depth, + height, + width, + outputD, + outputH, + outputW, + sizeZ, + sizeY, + sizeX, + strideD, + strideH, + strideW, + paddingD, + paddingH, + paddingW, + data_, + getStride()); +} + +void GpuMatrix::avgPool3DBackward(Matrix& outGrad, + size_t imgSizeD, + size_t imgSizeH, + size_t imgSizeW, + size_t sizeZ, + size_t sizeY, + size_t sizeX, + size_t strideD, + size_t strideH, + size_t strideW, + size_t outputD, + size_t outputH, + size_t outputW, + real scaleTargets, + real scaleOutput, + size_t paddingD, + size_t paddingH, + size_t paddingW) { + CHECK(outGrad.useGpu_ == true) << "Matrix type are not equal"; + + real* outDiff = outGrad.getData(); + size_t frameNum = outGrad.getHeight(); + size_t channels = outGrad.getWidth() / outputD / outputH / outputW; + size_t height = imgSizeH; + size_t width = imgSizeW; + size_t depth = imgSizeD; + CHECK(depth * height * width * channels == width_); + CHECK(height_ == outGrad.getHeight()); + CHECK(outGrad.getWidth() == outputD * outputH * outputW * channels); + + hl_avgpool3D_backward(frameNum, + outDiff, + channels, + depth, + height, + width, + outputD, + outputH, + outputW, + sizeZ, + sizeY, + sizeX, + strideD, + strideH, + strideW, + paddingD, + paddingH, + paddingW, + scaleTargets, + scaleOutput, + data_, + outGrad.getStride()); +} + void GpuMatrix::maxSequenceForward(Matrix& input, const IVector& sequence, IVector& index) { @@ -1930,6 +2148,290 @@ void CpuMatrix::avgPoolBackward(Matrix& input, } } +void CpuMatrix::maxPool3DForward(Matrix& inputMat, + size_t imgSizeD, + size_t imgSizeH, + size_t imgSizeW, + size_t channels, + size_t sizeZ, + size_t sizeY, + size_t sizeX, + size_t strideD, + size_t strideH, + size_t strideW, + size_t outputD, + size_t outputH, + size_t outputW, + size_t paddingD, + size_t paddingH, + size_t paddingW) { + real* inputData = inputMat.getData(); + real* outData = data_; + size_t num = inputMat.getHeight(); + size_t inWidth = imgSizeW; + size_t inHeight = imgSizeH; + size_t inDepth = imgSizeD; + CHECK(inHeight * inWidth * inDepth == inputMat.getWidth() / channels); + CHECK_EQ(num, this->getHeight()); + CHECK_EQ(channels * outputH * outputW * outputD, this->getWidth()); + size_t outStride = getStride(); + + /* initialize the data_ */ + for (size_t i = 0; i < height_; i++) { + for (size_t j = 0; j < width_; j++) { + outData[(i)*outStride + j] = -(real)FLT_MAX; + } + } + + /* pool max one by one */ + for (size_t n = 0; n < num; ++n) { // frame by frame + if (!isContiguous()) { + outData = data_ + n * outStride; + } + for (size_t c = 0; c < channels; ++c) { // channel by channel + for (size_t pd = 0; pd < outputD; ++pd) { + for (size_t ph = 0; ph < outputH; ++ph) { + for (size_t pw = 0; pw < outputW; ++pw) { + int dstart = pd * strideD - paddingD; + int hstart = ph * strideH - paddingH; + int wstart = pw * strideW - paddingW; + int dend = std::min(dstart + sizeZ, inDepth); + int hend = std::min(hstart + sizeY, inHeight); + int wend = std::min(wstart + sizeX, inWidth); + dstart = std::max(dstart, 0); + hstart = std::max(hstart, 0); + wstart = std::max(wstart, 0); + for (int d = dstart; d < dend; ++d) { + for (int h = hstart; h < hend; ++h) { + for (int w = wstart; w < wend; ++w) { + outData[(pd * outputH + ph) * outputW + pw] = + std::max(outData[(pd * outputH + ph) * outputW + pw], + inputData[(d * inHeight + h) * inWidth + w]); + } + } + } + } + } + } + // compute offset + inputData += inDepth * inHeight * inWidth; + outData += outputD * outputH * outputW; + } + } +} + +void CpuMatrix::maxPool3DBackward(Matrix& image, + size_t imgSizeD, + size_t imgSizeH, + size_t imgSizeW, + Matrix& outGrad, + Matrix& outV, + size_t sizeZ, + size_t sizeY, + size_t sizeX, + size_t strideD, + size_t strideH, + size_t strideW, + size_t outputD, + size_t outputH, + size_t outputW, + real scaleTargets, + real scaleOutput, + size_t paddingD, + size_t paddingH, + size_t paddingW) { + size_t num = image.getHeight(); + size_t channels = size_t(width_ / imgSizeD / imgSizeH / imgSizeW); + CHECK(image.getWidth() == imgSizeD * imgSizeH * imgSizeW * channels); + CHECK(image.getHeight() == height_ && image.getWidth() == width_); + CHECK(outV.getHeight() == outGrad.getHeight() && + outV.getWidth() == outGrad.getWidth()); + + real* tgtGrad = data_; + real* inData = image.getData(); + real* otData = outV.getData(); + real* otGrad = outGrad.getData(); + + size_t outStride = outV.getStride(); + real* origOutData = otData; + real* origOutGrad = otGrad; + + for (size_t n = 0; n < num; ++n) { + if (!outV.isContiguous()) { + otData = origOutData + n * outStride; + otGrad = origOutGrad + n * outStride; + } + for (size_t c = 0; c < channels; ++c) { + for (size_t pd = 0; pd < outputD; ++pd) { + for (size_t ph = 0; ph < outputH; ++ph) { + for (size_t pw = 0; pw < outputW; ++pw) { + int dstart = pd * strideD - paddingD; + int hstart = ph * strideH - paddingH; + int wstart = pw * strideW - paddingW; + int dend = std::min(dstart + sizeZ, imgSizeD); + int hend = std::min(hstart + sizeY, imgSizeH); + int wend = std::min(wstart + sizeX, imgSizeW); + dstart = std::max(dstart, 0); + hstart = std::max(hstart, 0); + wstart = std::max(wstart, 0); + for (int d = 0; d < dend; ++d) { + for (int h = hstart; h < hend; ++h) { + for (int w = wstart; w < wend; ++w) { + tgtGrad[(d * imgSizeH + h) * imgSizeW + w] = + scaleTargets * + tgtGrad[(d * imgSizeH + h) * imgSizeW + w] + + scaleOutput * otGrad[(pd * outputH + ph) * outputW + pw] * + (inData[(d * imgSizeH + h) * imgSizeW + w] == + otData[(pd * outputH + ph) * outputW + pw]); + } + } + } + } + } + } + // offset + inData += imgSizeD * imgSizeH * imgSizeW; + tgtGrad += imgSizeD * imgSizeH * imgSizeW; + otData += outputD * outputH * outputW; + otGrad += outputD * outputH * outputW; + } + } +} + +void CpuMatrix::avgPool3DForward(Matrix& input, + size_t imgSizeD, + size_t imgSizeH, + size_t imgSizeW, + size_t channels, + size_t sizeZ, + size_t sizeY, + size_t sizeX, + size_t strideD, + size_t strideH, + size_t strideW, + size_t outputD, + size_t outputH, + size_t outputW, + size_t paddingD, + size_t paddingH, + size_t paddingW) { + // The main loop + size_t num = input.getHeight(); + size_t inDepth = imgSizeD; + size_t inHeight = imgSizeH; + size_t inWidth = imgSizeW; + CHECK(inDepth * inHeight * inWidth * channels == input.getWidth()); + CHECK(outputD * outputH * outputW * channels * num == height_ * width_); + real* tgtData = data_; + real* inData = input.getData(); + + for (size_t n = 0; n < num; ++n) { + if (!isContiguous()) { + tgtData = data_ + n * getStride(); + } + for (size_t c = 0; c < channels; ++c) { + for (size_t pd = 0; pd < outputD; ++pd) { + for (size_t ph = 0; ph < outputH; ++ph) { + for (size_t pw = 0; pw < outputW; ++pw) { + int dstart = pd * strideD - paddingD; + int hstart = ph * strideH - paddingH; + int wstart = pw * strideW - paddingW; + int dend = std::min(dstart + sizeZ, inDepth + paddingD); + int hend = std::min(hstart + sizeY, inHeight + paddingH); + int wend = std::min(wstart + sizeX, inWidth + paddingW); + int poolSize = (dend - dstart) * (hend - hstart) * (wend - wstart); + dstart = std::max(dstart, 0); + hstart = std::max(hstart, 0); + wstart = std::max(wstart, 0); + dend = std::min(dend, static_cast(inDepth)); + hend = std::min(hend, static_cast(inHeight)); + wend = std::min(wend, static_cast(inWidth)); + + CHECK(poolSize); + tgtData[(pd * outputH + ph) * outputW + pw] = 0; // clear + for (int d = dstart; d < dend; ++d) { + for (int h = hstart; h < hend; ++h) { + for (int w = wstart; w < wend; ++w) { + tgtData[(pd * outputH + ph) * outputW + pw] += + inData[(d * inHeight + h) * inWidth + w]; + } + } + } + tgtData[(pd * outputH + ph) * outputW + pw] /= poolSize; + } + } + } + // compute offset + inData += inDepth * inHeight * inWidth; + tgtData += outputD * outputH * outputW; + } + } +} + +void CpuMatrix::avgPool3DBackward(Matrix& input, + size_t imgSizeD, + size_t imgSizeH, + size_t imgSizeW, + size_t sizeZ, + size_t sizeY, + size_t sizeX, + size_t strideD, + size_t strideH, + size_t strideW, + size_t outputD, + size_t outputH, + size_t outputW, + real scaleTargets, + real scaleOutput, + size_t paddingD, + size_t paddingH, + size_t paddingW) { + size_t num = input.getHeight(); + size_t channels = input.getWidth() / outputD / outputH / outputW; + CHECK(imgSizeD * imgSizeH * imgSizeW * channels == getWidth()); + real* inData = input.getData(); + real* outData = getData(); + + for (size_t n = 0; n < num; ++n) { + if (!input.isContiguous()) { + inData = input.getData() + n * input.getStride(); + } + for (size_t c = 0; c < channels; ++c) { + for (size_t pd = 0; pd < outputD; ++pd) { + for (size_t ph = 0; ph < outputH; ++ph) { + for (size_t pw = 0; pw < outputW; ++pw) { + int dstart = pd * strideD - paddingD; + int hstart = ph * strideH - paddingH; + int wstart = pw * strideW - paddingW; + int dend = std::min(dstart + sizeZ, imgSizeD + paddingD); + int hend = std::min(hstart + sizeY, imgSizeH + paddingH); + int wend = std::min(wstart + sizeX, imgSizeW + paddingW); + int poolSize = (dend - dstart) * (hend - hstart) * (wend - wstart); + dstart = std::max(dstart, 0); + hstart = std::max(hstart, 0); + wstart = std::max(wstart, 0); + dend = std::min(dend, static_cast(imgSizeD)); + hend = std::min(hend, static_cast(imgSizeH)); + wend = std::min(wend, static_cast(imgSizeW)); + CHECK(poolSize); + for (int d = dstart; d < dend; ++d) { + for (int h = hstart; h < hend; ++h) { + for (int w = wstart; w < wend; ++w) { + outData[(d * imgSizeH + h) * imgSizeW + w] += + inData[(pd * outputH + ph) * outputW + pw] / poolSize; + } + } + } + } + } + } + // offset + outData += imgSizeD * imgSizeH * imgSizeW; + inData += outputD * outputH * outputW; + } + } +} + /** * Input: one or more sequences. Each sequence contains some instances. * Output: output size is the number of input sequences (NOT input instances). diff --git a/paddle/math/Matrix.h b/paddle/math/Matrix.h index bb802bbb2..f1534c5ea 100644 --- a/paddle/math/Matrix.h +++ b/paddle/math/Matrix.h @@ -928,15 +928,102 @@ public: size_t paddingW) { LOG(FATAL) << "Not implemeted"; } - /** - * Input: one or more sequences. Each sequence contains some instances. - * - * Output: output size is the number of input sequences (NOT input - * instances). - * - * output[i] is set to max_input[i]. + * Pooling 3D forward operation, pick out the largest element + * in the sizeX of value */ + virtual void maxPool3DForward(Matrix& inputMat, + size_t imgSizeD, + size_t imgSizeH, + size_t imgSizeW, + size_t channels, + size_t sizeZ, + size_t sizeY, + size_t sizeX, + size_t strideD, + size_t strideH, + size_t strideW, + size_t outputD, + size_t outputH, + size_t outputW, + size_t paddingD, + size_t paddingH, + size_t paddingW) { + LOG(FATAL) << "Not implemeted"; + } + + virtual void maxPool3DBackward(Matrix& image, + size_t imgSizeD, + size_t imgSizeH, + size_t imgSizeW, + Matrix& outGrad, + Matrix& outV, + size_t sizeZ, + size_t sizeY, + size_t sizeX, + size_t strideD, + size_t strideH, + size_t strideW, + size_t outputD, + size_t outputH, + size_t outputW, + real scaleTargets, + real scaleOutput, + size_t paddingD, + size_t paddingH, + size_t paddingW) { + LOG(FATAL) << "Not implemeted"; + } + + virtual void avgPool3DForward(Matrix& input, + size_t imgSizeD, + size_t imgSizeH, + size_t imgSizeW, + size_t channels, + size_t sizeZ, + size_t sizeY, + size_t sizeX, + size_t strideD, + size_t strideH, + size_t strideW, + size_t outputD, + size_t outputH, + size_t outputW, + size_t paddingD, + size_t paddingH, + size_t paddingW) { + LOG(FATAL) << "Not implemeted"; + } + + virtual void avgPool3DBackward(Matrix& input, + size_t imgSizeD, + size_t imgSizeH, + size_t imgSizeW, + size_t sizeZ, + size_t sizeY, + size_t sizeX, + size_t strideD, + size_t strideH, + size_t strideW, + size_t outputD, + size_t outputH, + size_t outputW, + real scaleTargets, + real scaleOutput, + size_t paddingD, + size_t paddingH, + size_t paddingW) { + LOG(FATAL) << "Not implemeted"; + } + + /** + * Input: one or more sequences. Each sequence contains some instances. + * + * Output: output size is the number of input sequences (NOT input + * instances). + * + * output[i] is set to max_input[i]. + */ virtual void maxSequenceForward(Matrix& input, const IVector& sequence, IVector& index) { @@ -1348,6 +1435,83 @@ public: size_t paddingH, size_t paddingW); + ///////////////////////// + void maxPool3DForward(Matrix& inputMat, + size_t imgSizeD, + size_t imgSizeH, + size_t imgSizeW, + size_t channels, + size_t sizeZ, + size_t sizeY, + size_t sizeX, + size_t strideD, + size_t strideH, + size_t strideW, + size_t outputD, + size_t outputH, + size_t outputW, + size_t paddingD, + size_t paddingH, + size_t paddingW); + + void maxPool3DBackward(Matrix& image, + size_t imgSizeD, + size_t imgSizeH, + size_t imgSizeW, + Matrix& outGrad, + Matrix& outV, + size_t sizeZ, + size_t sizeY, + size_t sizeX, + size_t strideD, + size_t strideH, + size_t strideW, + size_t outputD, + size_t outputH, + size_t outputW, + real scaleTargets, + real scaleOutput, + size_t paddingD, + size_t paddingH, + size_t paddingW); + + void avgPool3DForward(Matrix& input, + size_t imgSizeD, + size_t imgSizeH, + size_t imgSizeW, + size_t channels, + size_t sizeZ, + size_t sizeY, + size_t sizeX, + size_t strideD, + size_t strideH, + size_t strideW, + size_t outputD, + size_t outputH, + size_t outputW, + size_t paddingD, + size_t paddingH, + size_t paddingW); + + void avgPool3DBackward(Matrix& input, + size_t imgSizeD, + size_t imgSizeH, + size_t imgSizeW, + size_t sizeZ, + size_t sizeY, + size_t sizeX, + size_t strideD, + size_t strideH, + size_t strideW, + size_t outputD, + size_t outputH, + size_t outputW, + real scaleTargets, + real scaleOutput, + size_t paddingD, + size_t paddingH, + size_t paddingW); + void maxSequenceForward(Matrix& input, const IVector& sequence, IVector& index); @@ -1506,6 +1670,82 @@ public: real scaleOutput, size_t paddingH, size_t paddingW); + ////////////////////// + void maxPool3DForward(Matrix& inputMat, + size_t imgSizeD, + size_t imgSizeH, + size_t imgSizeW, + size_t channels, + size_t sizeZ, + size_t sizeY, + size_t sizeX, + size_t strideD, + size_t strideH, + size_t strideW, + size_t outputD, + size_t outputH, + size_t outputW, + size_t paddingD, + size_t paddingH, + size_t paddingW); + + void maxPool3DBackward(Matrix& image, + size_t imgSizeD, + size_t imgSizeH, + size_t imgSizeW, + Matrix& outGrad, + Matrix& outV, + size_t sizeZ, + size_t sizeY, + size_t sizeX, + size_t strideD, + size_t strideH, + size_t strideW, + size_t outputD, + size_t outputH, + size_t outputW, + real scaleTargets, + real scaleOutput, + size_t paddingD, + size_t paddingH, + size_t paddingW); + + void avgPool3DForward(Matrix& input, + size_t imgSizeD, + size_t imgSizeH, + size_t imgSizeW, + size_t channels, + size_t sizeZ, + size_t sizeY, + size_t sizeX, + size_t strideD, + size_t strideH, + size_t strideW, + size_t outputD, + size_t outputH, + size_t outputW, + size_t paddingD, + size_t paddingH, + size_t paddingW); + + void avgPool3DBackward(Matrix& input, + size_t imgSizeD, + size_t imgSizeH, + size_t imgSizeW, + size_t sizeZ, + size_t sizeY, + size_t sizeX, + size_t strideD, + size_t strideH, + size_t strideW, + size_t outputD, + size_t outputH, + size_t outputW, + real scaleTargets, + real scaleOutput, + size_t paddingD, + size_t paddingH, + size_t paddingW); void maxSequenceForward(Matrix& input, const IVector& sequence, diff --git a/paddle/math/tests/test_matrixCompare.cpp b/paddle/math/tests/test_matrixCompare.cpp index d77478f34..7a961d275 100644 --- a/paddle/math/tests/test_matrixCompare.cpp +++ b/paddle/math/tests/test_matrixCompare.cpp @@ -18,6 +18,7 @@ limitations under the License. */ #include #include "TensorCheck.h" +#include "paddle/math/MathUtils.h" #include "paddle/math/Matrix.h" #include "paddle/math/SparseMatrix.h" #include "paddle/testing/TestUtil.h" @@ -1203,4 +1204,207 @@ TEST(Matrix, warpCTC) { } } +///// +void testMatrixPool3D(int depth, int height, int width) { + int channel = 3; + int filterX = 3, filterY = 4, filterZ = 5; + int strideX = 2, strideY = 2, strideZ = 2; + int padX = 1, padY = 1, padZ = 1; + + MatrixPtr cpuImage = + std::make_shared(1, channel * depth * height * width); + MatrixPtr gpuImage = + std::make_shared(1, channel * depth * height * width); + + int outD = outputSize(depth, filterZ, padZ, strideZ, true); + int outH = outputSize(height, filterY, padZ, strideY, true); + int outW = outputSize(width, filterX, padZ, strideX, true); + + int colBufWidth = outD * outH * outW; + MatrixPtr cpuOutput = std::make_shared(1, channel * colBufWidth); + MatrixPtr gpuOutput = std::make_shared(1, channel * colBufWidth); + + cpuImage->randomizeUniform(); + gpuImage->copyFrom(*cpuImage); + // std::cout << "test maxPool3DForward...\n"; + cpuOutput->maxPool3DForward(*cpuImage, + depth, + height, + width, + channel, + filterZ, + filterY, + filterX, + strideZ, + strideY, + strideX, + outD, + outH, + outW, + padZ, + padY, + padX); + gpuOutput->maxPool3DForward(*gpuImage, + depth, + height, + width, + channel, + filterZ, + filterY, + filterX, + strideZ, + strideY, + strideX, + outD, + outH, + outW, + padZ, + padY, + padX); + TensorCheckErr(*cpuOutput, *gpuOutput); + + cpuImage->randomizeUniform(); + gpuImage->copyFrom(*cpuImage); + // std::cout << "test avgPool3DForward...\n"; + cpuOutput->avgPool3DForward(*cpuImage, + depth, + height, + width, + channel, + filterZ, + filterY, + filterX, + strideZ, + strideY, + strideX, + outD, + outH, + outW, + padZ, + padY, + padX); + + gpuOutput->avgPool3DForward(*gpuImage, + depth, + height, + width, + channel, + filterZ, + filterY, + filterX, + strideZ, + strideY, + strideX, + outD, + outH, + outW, + padZ, + padY, + padX); + TensorCheckErr(*cpuOutput, *gpuOutput); + cpuImage->randomizeUniform(); + gpuImage->copyFrom(*cpuImage); + cpuOutput->randomizeUniform(); + gpuOutput->copyFrom(*cpuOutput); + // std::cout << "test avgPool3DBackward...\n"; + cpuImage->avgPool3DBackward(*cpuOutput, + depth, + height, + width, + filterZ, + filterY, + filterX, + strideZ, + strideY, + strideX, + outD, + outH, + outW, + 1, + 1, + padZ, + padY, + padX); + + gpuImage->avgPool3DBackward(*gpuOutput, + depth, + height, + width, + filterZ, + filterY, + filterX, + strideZ, + strideY, + strideX, + outD, + outH, + outW, + 1, + 1, + padZ, + padY, + padX); + TensorCheckErr(*cpuImage, *gpuImage); + + cpuImage->randomizeUniform(); + gpuImage->copyFrom(*cpuImage); + cpuOutput->randomizeUniform(); + gpuOutput->copyFrom(*cpuOutput); + // std::cout << "test maxPool3DBackward...\n"; + cpuImage->maxPool3DBackward(*cpuImage, + depth, + height, + width, + *cpuOutput, + *cpuOutput, + filterZ, + filterY, + filterX, + strideZ, + strideY, + strideX, + outD, + outH, + outW, + 1, + 1, + padZ, + padY, + padX); + + gpuImage->maxPool3DBackward(*gpuImage, + depth, + height, + width, + *gpuOutput, + *gpuOutput, + filterZ, + filterY, + filterX, + strideZ, + strideY, + strideX, + outD, + outH, + outW, + 1, + 1, + padZ, + padY, + padX); + TensorCheckErr(*cpuImage, *gpuImage); +} + +TEST(Matrix, Pool3D) { + for (auto depth : {9, 16, 64, 128}) { + for (auto height : {9, 11, 128, 256}) { + for (auto width : {9, 32, 128}) { + VLOG(3) << "depth=" << depth << " height=" << height + << " width=" << width; + testMatrixPool3D(depth, height, width); + } + } + } +} + #endif diff --git a/paddle/parameter/Argument.cpp b/paddle/parameter/Argument.cpp index 0547ac93c..77fd0c589 100644 --- a/paddle/parameter/Argument.cpp +++ b/paddle/parameter/Argument.cpp @@ -186,6 +186,7 @@ void Argument::resizeAndCopyFrom(const Argument& src, resizeAndCopy(strs, src.strs, useGpu, stream); frameWidth = src.frameWidth; frameHeight = src.frameHeight; + frameDepth = src.frameDepth; } int32_t Argument::resizeAndCopyFrom(const Argument& src, @@ -206,6 +207,7 @@ int32_t Argument::resizeAndCopyFrom(const Argument& src, dataId = src.dataId; frameWidth = src.frameWidth; frameHeight = src.frameHeight; + frameDepth = src.frameDepth; if (!src.sequenceStartPositions) { // non-sequence input, copy samples directly diff --git a/paddle/parameter/Argument.h b/paddle/parameter/Argument.h index d8d7a4398..ba3ad2fd4 100644 --- a/paddle/parameter/Argument.h +++ b/paddle/parameter/Argument.h @@ -1,11 +1,8 @@ /* 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. @@ -35,6 +32,7 @@ struct Argument { strs(nullptr), frameHeight(0), frameWidth(0), + frameDepth(0), sequenceStartPositions(nullptr), subSequenceStartPositions(nullptr), cpuSequenceDims(nullptr), @@ -64,6 +62,7 @@ struct Argument { allCount = argument.allCount; frameHeight = argument.frameHeight; frameWidth = argument.frameWidth; + frameDepth = argument.frameDepth; dataId = argument.dataId; } @@ -76,6 +75,7 @@ struct Argument { // A dataBatch includes batchSize frames, one frame maybe not only vector size_t frameHeight; size_t frameWidth; + size_t frameDepth; // If NULL, each position is treated independently. // Otherwise, its size should be #NumberOfSequences + 1. @@ -136,8 +136,10 @@ struct Argument { } size_t getFrameHeight() const { return frameHeight; } size_t getFrameWidth() const { return frameWidth; } + size_t getFrameDepth() const { return frameDepth; } void setFrameHeight(size_t h) { frameHeight = h; } void setFrameWidth(size_t w) { frameWidth = w; } + void setFrameDepth(size_t d) { frameDepth = d; } int64_t getNumSequences() const { return sequenceStartPositions ? sequenceStartPositions->getSize() - 1 diff --git a/proto/ModelConfig.proto b/proto/ModelConfig.proto index 4f3d5bf3f..42cf10e9d 100644 --- a/proto/ModelConfig.proto +++ b/proto/ModelConfig.proto @@ -82,6 +82,12 @@ message ConvConfig { // if not set, use img_size optional uint32 img_size_y = 14; + + optional uint32 filter_size_z = 15 [ default = 1 ]; + optional uint32 padding_z = 16 [ default = 1 ]; + optional uint32 stride_z = 17 [ default = 1 ]; + optional uint32 output_z = 18 [ default = 1 ]; + optional uint32 img_size_z = 19 [ default = 1 ]; } message PoolConfig { @@ -124,6 +130,12 @@ message PoolConfig { // if not set, use padding optional uint32 padding_y = 13; + + optional uint32 size_z = 14 [ default = 1 ]; + optional uint32 stride_z = 15 [ default = 1 ]; + optional uint32 output_z = 16 [ default = 1 ]; + optional uint32 img_size_z = 17 [ default = 1 ]; + optional uint32 padding_z = 18 [ default = 1 ]; } message SppConfig { -- GitLab From 93539093f4727d4028ca7e592f5fa4f7abdb8bc3 Mon Sep 17 00:00:00 2001 From: xuwei06 Date: Wed, 2 Aug 2017 11:28:25 -0700 Subject: [PATCH 0153/2018] Allow boot_bias for recurrent group to be static --- paddle/gserver/gradientmachines/RecurrentGradientMachine.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paddle/gserver/gradientmachines/RecurrentGradientMachine.cpp b/paddle/gserver/gradientmachines/RecurrentGradientMachine.cpp index f98bf9506..157b1ab45 100644 --- a/paddle/gserver/gradientmachines/RecurrentGradientMachine.cpp +++ b/paddle/gserver/gradientmachines/RecurrentGradientMachine.cpp @@ -184,7 +184,7 @@ public: } void backward(const UpdateCallback& callback) override { - if (biases_) { + if (biases_ && biases_->getWGrad()) { backwardActivation(); biases_->getWGrad()->collectBias(*getOutputGrad(), 1); biases_->getParameterPtr()->incUpdate(callback); -- GitLab From d7b80f03b0064ac9db5db5f313bc381f9046f689 Mon Sep 17 00:00:00 2001 From: xuwei06 Date: Wed, 2 Aug 2017 11:29:46 -0700 Subject: [PATCH 0154/2018] Correctly handle width and height for some layers --- python/paddle/trainer/config_parser.py | 11 ++++--- .../paddle/trainer_config_helpers/layers.py | 29 +++++++++++++++---- 2 files changed, 30 insertions(+), 10 deletions(-) diff --git a/python/paddle/trainer/config_parser.py b/python/paddle/trainer/config_parser.py index 8d71629fa..b3d5ef95c 100644 --- a/python/paddle/trainer/config_parser.py +++ b/python/paddle/trainer/config_parser.py @@ -338,7 +338,8 @@ def RecurrentLayerGroupWithoutOutLinksBegin(name, in_links_count += 1 layer_name = MakeLayerNameInParentSubmodel(name) layer = g_layer_map[layer_name] - ScatterAgentLayer(name=name, size=layer.size) + ScatterAgentLayer( + name=name, size=layer.size, width=layer.width, height=layer.height) pair = g_current_submodel.in_links.add() pair.layer_name = layer_name @@ -2197,8 +2198,8 @@ class MaxOutLayer(LayerBase): maxout_conf = self.config.inputs[0].maxout_conf parse_maxout(self.inputs[0].maxout, input_layer.name, maxout_conf) out_channels = maxout_conf.image_conf.channels / maxout_conf.groups - self.set_cnn_layer(name, g_layer_map[input_layer.name].height, - g_layer_map[input_layer.name].width, out_channels) + self.set_cnn_layer(name, maxout_conf.image_conf.img_size_y, + maxout_conf.image_conf.img_size, out_channels) @config_layer('row_conv') @@ -2405,9 +2406,11 @@ class GatherAgentLayer(LayerBase): @config_layer('scatter_agent') class ScatterAgentLayer(LayerBase): - def __init__(self, name, size, device=None): + def __init__(self, name, size, width=None, height=None, device=None): super(ScatterAgentLayer, self).__init__( name, 'scatter_agent', size, inputs=[], device=device) + if height and width: + self.set_layer_height_width(height, width) @config_layer('multiplex') diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py index c9e3ded65..dd6d1f7f8 100755 --- a/python/paddle/trainer_config_helpers/layers.py +++ b/python/paddle/trainer_config_helpers/layers.py @@ -16,11 +16,13 @@ import functools import collections import inspect +import paddle.trainer.config_parser as cp from paddle.trainer.config_parser import * from .activations import LinearActivation, SigmoidActivation, TanhActivation, \ ReluActivation, IdentityActivation, SoftmaxActivation, BaseActivation from .evaluators import * -from .poolings import MaxPooling, AvgPooling, BasePoolingType +from .poolings import MaxPooling, AvgPooling, BasePoolingType, \ + CudnnAvgPooling, CudnnMaxPooling from .attrs import * from .default_decorators import * @@ -330,6 +332,14 @@ class LayerOutput(object): self.outputs = outputs self.reverse = reverse + @property + def width(self): + return cp.g_layer_map[self.full_name].width + + @property + def height(self): + return cp.g_layer_map[self.full_name].height + def set_input(self, input): """ Set the input for a memory layer. Can only be used for memory layer @@ -911,7 +921,13 @@ def data_layer(name, size, height=None, width=None, layer_attr=None): width=width, **ExtraLayerAttribute.to_kwargs(layer_attr)) - return LayerOutput(name, LayerType.DATA, size=size) + num_filters = None + if height is not None and width is not None: + num_filters = size / (width * height) + assert num_filters * width * height == size, \ + "size=%s width=%s height=%s" % (size, width, height) + + return LayerOutput(name, LayerType.DATA, size=size, num_filters=num_filters) @wrap_name_default("embedding") @@ -2571,6 +2587,10 @@ def img_pool_layer(input, assert input.num_filters is not None num_channels = input.num_filters + assert type(pool_type) in [AvgPooling, MaxPooling, CudnnAvgPooling, + CudnnMaxPooling], \ + "only AvgPooling and MaxPooling are supported" + if pool_type is None: pool_type = MaxPooling() elif isinstance(pool_type, AvgPooling): @@ -2580,7 +2600,6 @@ def img_pool_layer(input, if ( isinstance(pool_type, AvgPooling) or isinstance(pool_type, MaxPooling)) \ else pool_type.name - pool_size_y = pool_size if pool_size_y is None else pool_size_y stride_y = stride if stride_y is None else stride_y padding_y = padding if padding_y is None else padding_y @@ -4204,8 +4223,7 @@ def conv_operator(img, num_channels = img.num_filters assert isinstance(filter, LayerOutput) - if filter.size is not None: - filter.size = filter_size * filter_size_y * num_filters * num_channels + assert filter.size is not None opCls = ConvTransOperator if trans else ConvOperator @@ -4916,7 +4934,6 @@ def maxout_layer(input, groups, num_channels=None, name=None, layer_attr=None): :return: LayerOutput object. :rtype: LayerOutput """ - assert input.layer_type == LayerType.CONV_LAYER assert isinstance(input.activation, LinearActivation) assert groups > 1 if num_channels is None: -- GitLab From 99af29e3f29f0392727bba312282e56a431dfc7b Mon Sep 17 00:00:00 2001 From: xuwei06 Date: Mon, 21 Aug 2017 14:17:13 -0700 Subject: [PATCH 0155/2018] Fix error message for img_pool_layer --- python/paddle/trainer_config_helpers/layers.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py index dd6d1f7f8..be854c38f 100755 --- a/python/paddle/trainer_config_helpers/layers.py +++ b/python/paddle/trainer_config_helpers/layers.py @@ -2589,7 +2589,7 @@ def img_pool_layer(input, assert type(pool_type) in [AvgPooling, MaxPooling, CudnnAvgPooling, CudnnMaxPooling], \ - "only AvgPooling and MaxPooling are supported" + "only (Cudnn)AvgPooling, (Cudnn)MaxPooling are supported" if pool_type is None: pool_type = MaxPooling() @@ -6236,11 +6236,11 @@ def kmax_sequence_score_layer(input, name=None, beam_size=1): @wrap_bias_attr_default() def scale_shift_layer(input, name=None, param_attr=None, bias_attr=None): """ - A layer applies a linear transformation to each element in each row of - the input matrix. For each element, the layer first re-scale it and then + A layer applies a linear transformation to each element in each row of + the input matrix. For each element, the layer first re-scale it and then adds a bias to it. - This layer is very like the SlopeInterceptLayer, except the scale and + This layer is very like the SlopeInterceptLayer, except the scale and bias are trainable. .. math:: -- GitLab From 118dd1494fbe3654da8f71c2245523e27616d475 Mon Sep 17 00:00:00 2001 From: qiaolongfei Date: Mon, 21 Aug 2017 18:22:59 -0700 Subject: [PATCH 0156/2018] can run, for debug --- .../paddle/v2/framework/tests/CMakeLists.txt | 1 + python/paddle/v2/framework/tests/mnist.py | 73 +++++++++++++++++-- 2 files changed, 66 insertions(+), 8 deletions(-) diff --git a/python/paddle/v2/framework/tests/CMakeLists.txt b/python/paddle/v2/framework/tests/CMakeLists.txt index ce57a0713..41682c835 100644 --- a/python/paddle/v2/framework/tests/CMakeLists.txt +++ b/python/paddle/v2/framework/tests/CMakeLists.txt @@ -27,3 +27,4 @@ py_test(test_uniform_random_op SRCS test_uniform_random_op.py) py_test(test_recurrent_op SRCS test_recurrent_op.py) py_test(test_sgd_op SRCS test_sgd_op.py) py_test(test_gradient_checker SRCS test_gradient_checker.py) +py_test(mnist SRCS mnist.py) diff --git a/python/paddle/v2/framework/tests/mnist.py b/python/paddle/v2/framework/tests/mnist.py index 32a088ac2..d0c56c457 100644 --- a/python/paddle/v2/framework/tests/mnist.py +++ b/python/paddle/v2/framework/tests/mnist.py @@ -2,7 +2,7 @@ import paddle.v2.framework.core as core from paddle.v2.framework.op import Operator import numpy -BATCH_SIZE = 100 +BATCH_SIZE = 2 scope = core.Scope() place = core.CPUPlace() @@ -35,10 +35,15 @@ def data_layer(name, dims): def feed_data(name, data): - assert isinstance(data, numpy.array) + assert isinstance(data, numpy.ndarray) tensor = scope.find_var(name).get_tensor() tensor.set_dims(data.shape) - tensor.alloc_float(place) + if data.dtype == numpy.dtype('int32'): + tensor.alloc_float(place) + elif data.dtype == numpy.dtype('float32'): + tensor.alloc_int(place) + else: + raise ValueError("data type not supported") tensor.set(data, place) @@ -49,7 +54,11 @@ def grad_var_name(var_name): def sgd_optimizer(net, param_name, learning_rate=0.01): grad_name = grad_var_name(param_name) optimize_op = Operator( - "sgd", param=param_name, grad=grad_name, learning_rate=learning_rate) + "sgd", + param=param_name, + grad=grad_name, + param_out=param_name, + learning_rate=learning_rate) net.add_op(optimize_op) @@ -65,7 +74,7 @@ def init_param(param_name, dims): # fc_layer -def fc_layer(net, input, size, act="sigmoid", bias=True, param=None, name=None): +def fc_layer(net, input, size, act="softmax", bias=True, param=None, name=None): """ Add a fc layer to net @@ -125,16 +134,64 @@ def cross_entropy_layer(net, input, label): return cost_name +def get_backward_net(forward_net): + net = core.Operator.backward(forward_net, set()) + for input in net.inputs()["all"]: + var = scope.new_var(input) + var.get_tensor() + for output in net.outputs()["all"]: + var = scope.new_var(output) + var.get_tensor() + return net + + +def print_inputs_outputs(op): + print("===============" + op.type() + "==============") + print("***inputs:***") + for input in op.inputs()["all"]: + print input, scope.find_var(input).get_tensor().get_dims() + print("***outputs:***") + for output in op.outputs()["all"]: + print output, scope.find_var(output).get_tensor().get_dims() + print("") + print("") + + images = data_layer(name='pixel', dims=[BATCH_SIZE, 784]) label = data_layer(name='label', dims=[BATCH_SIZE]) fc = fc_layer(net=forward_network, input=images, size=10, act="softmax") cost = cross_entropy_layer(net=forward_network, input=fc, label=label) forward_network.complete_add_op(True) print(forward_network) -backward_net = core.Operator.backward(forward_network, set()) - +backward_net = get_backward_net(forward_network) print(backward_net) +optimize_net.complete_add_op(True) +print(optimize_net) PASS_NUM = 10 for pass_id in range(PASS_NUM): - print pass_id + print("===========forward==========") + feed_data("pixel", numpy.random.random((BATCH_SIZE, 784)).astype('float32')) + feed_data("label", numpy.ones(BATCH_SIZE).astype("int32")) + forward_network.infer_shape(scope) + print_inputs_outputs(forward_network) + + print(numpy.array(scope.find_var("label").get_tensor())) + forward_network.run(scope, dev_ctx) + # print(numpy.array(scope.find_var("fc_0").get_tensor())) + + print("===========backward==========") + cost_data = numpy.array(scope.find_var("cross_entropy_1").get_tensor()) + cost_grad = scope.find_var(grad_var_name("cross_entropy_1")).get_tensor() + cost_grad.set_dims(cost_data.shape) + cost_grad.alloc_float(place) + cost_grad.set(cost_data, place) + + backward_net.infer_shape(scope) + print_inputs_outputs(backward_net) + + backward_net.run(scope, dev_ctx) + + print("===========optimize_net==========") + print_inputs_outputs(optimize_net) + optimize_net.run(scope, dev_ctx) -- GitLab From 53e71b44f41860e6482651b9e92dd1e6d3213c8a Mon Sep 17 00:00:00 2001 From: zchen0211 Date: Tue, 22 Aug 2017 03:28:21 +0000 Subject: [PATCH 0157/2018] gather op bp passed --- paddle/operators/CMakeLists.txt | 2 -- paddle/operators/gather.h | 6 +++--- paddle/operators/gather_op.cc | 8 ++++---- paddle/operators/gather_op.h | 19 ++++++++++--------- .../v2/framework/tests/test_gather_op.py | 18 ++++++++++++++---- 5 files changed, 31 insertions(+), 22 deletions(-) diff --git a/paddle/operators/CMakeLists.txt b/paddle/operators/CMakeLists.txt index 6849e39cb..ba1362e8b 100644 --- a/paddle/operators/CMakeLists.txt +++ b/paddle/operators/CMakeLists.txt @@ -44,8 +44,6 @@ endfunction() add_subdirectory(math) cc_test(gather_test SRCS gather_test.cc DEPS tensor) op_library(gather_op SRCS gather_op.cc gather_op.cu) -# DEPS op_registry) -# cc_test(gather_op_test SRCS gather_op_test.cc DEPS gather_op) cc_test(scatter_test SRCS scatter_test.cc DEPS tensor) diff --git a/paddle/operators/gather.h b/paddle/operators/gather.h index 3f299ea1a..edac29f6d 100644 --- a/paddle/operators/gather.h +++ b/paddle/operators/gather.h @@ -27,13 +27,13 @@ namespace operators { // Implementation of CPU copy template -void CPUGather(const T* params, const int* indices, const int slice_size, +void CPUGather(const T* src, const int* indices, const int slice_size, const int index_size, T* output) { const size_t slice_bytes = slice_size * sizeof(T); for (int i = 0; i < index_size; ++i) { int index_ = indices[i]; - memcpy(output + i * slice_size, params + index_ * slice_size, slice_bytes); + memcpy(output + i * slice_size, src + index_ * slice_size, slice_bytes); } } @@ -57,7 +57,7 @@ void Gather(const platform::Place& place, const paddle::framework::Tensor* src, int index_size = index->dims()[0]; auto src_dims = src->dims(); - paddle::framework::DDim output_dims(src_dims); + framework::DDim output_dims(src_dims); output_dims[0] = index_size; // slice size diff --git a/paddle/operators/gather_op.cc b/paddle/operators/gather_op.cc index 499def05a..123bed296 100644 --- a/paddle/operators/gather_op.cc +++ b/paddle/operators/gather_op.cc @@ -26,9 +26,9 @@ class GatherOp : public framework::OperatorWithKernel { void InferShape(const framework::InferShapeContext &ctx) const override { int batch_size = ctx.Input("Index")->dims()[0]; PADDLE_ENFORCE_GE(batch_size, 0, "Batch size must be >0"); - paddle::framework::DDim output_dims(ctx.Input("X")->dims()); + framework::DDim output_dims(ctx.Input("X")->dims()); output_dims[0] = batch_size; - ctx.Output("Y")->Resize(output_dims); + ctx.Output("Out")->Resize(output_dims); } }; @@ -51,11 +51,11 @@ class GatherOpMaker : public framework::OpProtoAndCheckerMaker { : OpProtoAndCheckerMaker(proto, op_checker) { AddInput("X", "The source input of gather op"); AddInput("Index", "The index input of gather op"); - AddOutput("Y", "The output of add op"); + AddOutput("Out", "The output of add op"); AddComment(R"DOC( Gather Operator by selecting from the first axis, -Y = X[Index] +Out = X[Index] )DOC"); } }; diff --git a/paddle/operators/gather_op.h b/paddle/operators/gather_op.h index 13e4c9b05..381854f30 100644 --- a/paddle/operators/gather_op.h +++ b/paddle/operators/gather_op.h @@ -26,10 +26,10 @@ using Tensor = framework::Tensor; template class GatherOpKernel : public framework::OpKernel { public: - void Compute(const framework::ExecutionContext& ctx) const override { - auto X = ctx.Input("X"); - auto Index = ctx.Input("Index"); - auto Y = ctx.Output("Y"); + void Compute(const framework::ExecutionContext &ctx) const override { + auto *X = ctx.Input("X"); + auto *Index = ctx.Input("Index"); + auto *Y = ctx.Output("Out"); Y->mutable_data(ctx.GetPlace()); Gather(ctx.GetPlace(), X, Index, Y); @@ -39,12 +39,13 @@ class GatherOpKernel : public framework::OpKernel { template class GatherGradientOpKernel : public framework::OpKernel { public: - void Compute(const framework::ExecutionContext& ctx) const override { - auto Index = ctx.Input("Index"); - auto dX = ctx.Output(framework::GradVarName("X")); - auto dY = ctx.Input(framework::GradVarName("Y")); + void Compute(const framework::ExecutionContext &ctx) const override { + auto *Index = ctx.Input("Index"); + auto *dX = ctx.Output(framework::GradVarName("X")); + auto *dO = ctx.Input(framework::GradVarName("Out")); - ScatterUpdate(ctx.GetPlace(), dY, Index, dX); + dX->mutable_data(ctx.GetPlace()); + ScatterUpdate(ctx.GetPlace(), dO, Index, dX); } }; diff --git a/python/paddle/v2/framework/tests/test_gather_op.py b/python/paddle/v2/framework/tests/test_gather_op.py index 049054d07..e86898304 100644 --- a/python/paddle/v2/framework/tests/test_gather_op.py +++ b/python/paddle/v2/framework/tests/test_gather_op.py @@ -1,11 +1,10 @@ import unittest - +from op_test_util import OpTestMeta +from gradient_checker import GradientChecker, create_op import numpy import paddle.v2.framework.core as core from paddle.v2.framework.op import Operator -from op_test_util import OpTestMeta - class TestGatherOp(unittest.TestCase): __metaclass__ = OpTestMeta @@ -17,7 +16,18 @@ class TestGatherOp(unittest.TestCase): 'X': xnp, 'Index': numpy.array([1, 3, 5]).astype("int32") } - self.outputs = {'Y': self.inputs['X'][self.inputs['Index']]} + self.outputs = {'Out': self.inputs['X'][self.inputs['Index']]} + + +class TestGatherGradOp(GradientChecker): + def test_gather_grad(self): + print 'creating op' + op = create_op("gather") + print 'creating op done' + xnp = numpy.random.random((10, 20)).astype("float32") + inputs = {'X': xnp, 'Index': numpy.array([1, 3, 5]).astype("int32")} + print 'correct before check gradient' + self.check_grad(op, inputs, set("X"), "Out") if __name__ == "__main__": -- GitLab From 36e8e725669a20b272f9ace1cf7c9df646c840a3 Mon Sep 17 00:00:00 2001 From: qijun Date: Tue, 22 Aug 2017 11:40:57 +0800 Subject: [PATCH 0158/2018] expose random seed to users --- paddle/operators/CMakeLists.txt | 4 +- paddle/operators/gaussian_random_op.cc | 42 ++++++++++--- paddle/operators/gaussian_random_op.cu | 61 +++++++++++++++--- paddle/operators/gaussian_random_op.h | 38 ----------- paddle/operators/math/math_function.cc | 22 ------- paddle/operators/math/math_function.cu | 48 -------------- paddle/operators/math/math_function.h | 8 --- paddle/operators/uniform_random_op.cc | 44 ++++++++++--- paddle/operators/uniform_random_op.cu | 63 ++++++++++++++++--- paddle/operators/uniform_random_op.h | 38 ----------- paddle/platform/device_context.cc | 27 +------- paddle/platform/device_context.h | 15 +---- .../tests/test_gaussian_random_op.py | 7 ++- .../framework/tests/test_uniform_random_op.py | 7 ++- 14 files changed, 196 insertions(+), 228 deletions(-) delete mode 100644 paddle/operators/gaussian_random_op.h delete mode 100644 paddle/operators/uniform_random_op.h diff --git a/paddle/operators/CMakeLists.txt b/paddle/operators/CMakeLists.txt index 8f22a5fbc..a7c89787e 100644 --- a/paddle/operators/CMakeLists.txt +++ b/paddle/operators/CMakeLists.txt @@ -58,7 +58,7 @@ op_library(rowwise_add_op SRCS rowwise_add_op.cu rowwise_add_op.cc) op_library(sigmoid_op SRCS sigmoid_op.cc sigmoid_op.cu) op_library(softmax_op SRCS softmax_op.cc softmax_op.cu) -op_library(gaussian_random_op SRCS gaussian_random_op.cc gaussian_random_op.cu DEPS math_function) +op_library(gaussian_random_op SRCS gaussian_random_op.cc gaussian_random_op.cu) op_library(cross_entropy_op SRCS cross_entropy_op.cc cross_entropy_op.cu) op_library(fill_zeros_like_op SRCS fill_zeros_like_op.cc fill_zeros_like_op.cu) @@ -67,4 +67,4 @@ op_library(sgd_op SRCS sgd_op.cc sgd_op.cu) op_library(recurrent_op SRCS recurrent_op.cc rnn/recurrent_op_utils.cc DEPS framework_proto tensor op_registry operator net_op) op_library(uniform_random_op - SRCS uniform_random_op.cc uniform_random_op.cu DEPS math_function) + SRCS uniform_random_op.cc uniform_random_op.cu) diff --git a/paddle/operators/gaussian_random_op.cc b/paddle/operators/gaussian_random_op.cc index 899f05fa4..dcd223745 100644 --- a/paddle/operators/gaussian_random_op.cc +++ b/paddle/operators/gaussian_random_op.cc @@ -1,22 +1,44 @@ /* 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/gaussian_random_op.h" +#include +#include "paddle/framework/op_registry.h" namespace paddle { namespace operators { +template +class CPUGaussianRandomKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& context) const override { + float mean = context.op_.GetAttr("mean"); + float std = context.op_.GetAttr("std"); + auto* tensor = context.Output("Out"); + T* data = tensor->mutable_data(context.GetPlace()); + + unsigned int seed = + static_cast(context.op_.GetAttr("seed")); + std::minstd_rand engine; + if (seed == 0) { + seed = std::random_device()(); + } + engine.seed(seed); + std::normal_distribution dist(mean, std); + ssize_t size = framework::product(tensor->dims()); + for (ssize_t i = 0; i < size; ++i) { + data[i] = dist(engine); + } + } +}; + class GaussianRandomOp : public framework::OperatorWithKernel { public: using framework::OperatorWithKernel::OperatorWithKernel; @@ -43,8 +65,12 @@ Use to initialize tensor with gaussian random generator. )DOC"); AddAttr>("dims", "The dimension of random tensor."); - AddAttr("mean", "mean value of random.").SetDefault(.0f); - AddAttr("std", "minimum value of random value.").SetDefault(1.0f); + AddAttr("mean", "mean of random tensor.").SetDefault(.0f); + AddAttr("std", "std of random tensor.").SetDefault(1.0f); + AddAttr("seed", + "Random seed of generator." + "0 means use system wide seed") + .SetDefault(0); } }; @@ -54,6 +80,4 @@ Use to initialize tensor with gaussian random generator. namespace ops = paddle::operators; REGISTER_OP_WITHOUT_GRADIENT(gaussian_random, ops::GaussianRandomOp, ops::GaussianRandomOpMaker); -REGISTER_OP_CPU_KERNEL( - gaussian_random, - ops::GaussianRandomKernel); +REGISTER_OP_CPU_KERNEL(gaussian_random, ops::CPUGaussianRandomKernel); \ No newline at end of file diff --git a/paddle/operators/gaussian_random_op.cu b/paddle/operators/gaussian_random_op.cu index 31be16fdc..1d312e7b5 100644 --- a/paddle/operators/gaussian_random_op.cu +++ b/paddle/operators/gaussian_random_op.cu @@ -1,20 +1,65 @@ /* 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/gaussian_random_op.h" +#include +#include +#include +#include +#include "paddle/framework/op_registry.h" +#include "paddle/framework/operator.h" + +namespace paddle { +namespace operators { + +template +struct GaussianGenerator { + T mean_, std_; + unsigned int seed_; + + __host__ __device__ GaussianGenerator(T mean, T std, int seed) + : mean_(mean), std_(std), seed_(seed) {} + + __host__ __device__ T operator()(const unsigned int n) const { + thrust::minstd_rand rng; + rng.seed(seed_); + thrust::normal_distribution dist(min_, max_); + rng.discard(n); + return dist(rng); + } +}; + +template +class GPUGaussianRandomKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& context) const override { + auto* tensor = context.Output("Out"); + T* data = tensor->mutable_data(context.GetPlace()); + unsigned int seed = + static_cast(context.op_.GetAttr("seed")); + if (seed == 0) { + std::random_device rd; + seed = rd(); + } + T mean = static_cast(context.op_.GetAttr("mean")); + T std = static_cast(context.op_.GetAttr("std")); + thrust::counting_iterator index_sequence_begin(0); + ssize_t N = framework::product(tensor->dims()); + thrust::transform(index_sequence_begin, index_sequence_begin + N, + thrust::device_ptr(data), + GaussianGenerator(mean, std, seed)); + } +}; + +} // namespace operators +} // namespace paddle -namespace ops = paddle::operators; -REGISTER_OP_GPU_KERNEL( - gaussian_random, - ops::GaussianRandomKernel); +REGISTER_OP_GPU_KERNEL(gaussian_random, + paddle::operators::GPUGaussianRandomKernel); \ No newline at end of file diff --git a/paddle/operators/gaussian_random_op.h b/paddle/operators/gaussian_random_op.h deleted file mode 100644 index c90b665fe..000000000 --- a/paddle/operators/gaussian_random_op.h +++ /dev/null @@ -1,38 +0,0 @@ -/* 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" - -namespace paddle { -namespace operators { -template -class GaussianRandomKernel : public framework::OpKernel { - public: - void Compute(const framework::ExecutionContext& context) const override { - auto* tensor = context.Output("Out"); - T* data = tensor->mutable_data(context.GetPlace()); - T mean = static_cast(context.op_.GetAttr("mean")); - T std = static_cast(context.op_.GetAttr("std")); - auto n = framework::product(tensor->dims()); - - auto* device_context = - const_cast(context.device_context_); - math::RandGaussian(n, mean, std, data, device_context); - } -}; -} // namespace operators -} // namespace paddle diff --git a/paddle/operators/math/math_function.cc b/paddle/operators/math/math_function.cc index a098e02f9..d9824e5f9 100644 --- a/paddle/operators/math/math_function.cc +++ b/paddle/operators/math/math_function.cc @@ -118,28 +118,6 @@ void Set(const int n, const float alpha, out.device(*(cpu_context->eigen_device())) = out.constant(float(alpha)); } -template <> -void RandUniform(const int n, const float min, - const float max, float* output, - platform::DeviceContext* context) { - auto* cpu_context = reinterpret_cast(context); - std::uniform_real_distribution distribution(min, max); - for (int i = 0; i < n; i++) { - output[i] = distribution(cpu_context->rand_engine()); - } -} - -template <> -void RandGaussian(const int n, const float mean, - const float std, float* output, - platform::DeviceContext* context) { - auto* cpu_context = reinterpret_cast(context); - std::normal_distribution distribution(mean, std); - for (int i = 0; i < n; i++) { - output[i] = distribution(cpu_context->rand_engine()); - } -} - } // namespace math } // namespace operators } // namespace paddle diff --git a/paddle/operators/math/math_function.cu b/paddle/operators/math/math_function.cu index 908efe9e0..9dff6f05f 100644 --- a/paddle/operators/math/math_function.cu +++ b/paddle/operators/math/math_function.cu @@ -135,54 +135,6 @@ void Set(const int n, const float alpha, out.device(*(cuda_context->eigen_device())) = out.constant(float(alpha)); } -template -__global__ void UniformShift(const int n, const T min, const T max, T* x) { - float scale = max - min; - for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < n; - i += blockDim.x * gridDim.x) { - x[i] = x[i] * scale + min; - } -} - -template <> -void RandUniform(const int n, const float min, - const float max, float* output, - platform::DeviceContext* context) { - auto* cuda_context = reinterpret_cast(context); - PADDLE_ENFORCE(platform::dynload::curandGenerateUniform( - cuda_context->curand_generator(), output, n)); - int block = 512; - int grid = (n + block - 1) / block; - UniformShift<<stream()>>>(n, min, max, - output); -} - -template -int HandleOddLengthRandGaussian(const int n, const T mean, const T std, - T* output, - platform::CUDADeviceContext* context) { - if (n % 2 == 1) { - std::default_random_engine generator; - std::normal_distribution distribution(mean, std); - const T random_value = distribution(generator); - Set(1, random_value, output + (n - 1), context); - return n - 1; - } - return n; -} - -template <> -void RandGaussian(const int n, const float mean, - const float std, float* output, - platform::DeviceContext* context) { - auto* cuda_context = reinterpret_cast(context); - - const int even_n = - HandleOddLengthRandGaussian(n, mean, std, output, cuda_context); - PADDLE_ENFORCE(platform::dynload::curandGenerateNormal( - cuda_context->curand_generator(), output, even_n, mean, std)); -} - } // namespace math } // namespace operators } // namespace paddle diff --git a/paddle/operators/math/math_function.h b/paddle/operators/math/math_function.h index 6543a1b51..a0e966056 100644 --- a/paddle/operators/math/math_function.h +++ b/paddle/operators/math/math_function.h @@ -82,14 +82,6 @@ template void Set(const int n, const T alpha, T* output, platform::DeviceContext* context); -template -void RandUniform(const int n, const T min, const T max, T* output, - platform::DeviceContext* context); - -template -void RandGaussian(const int n, const T mean, const T std, T* output, - platform::DeviceContext* context); - } // namespace math } // namespace operators } // namespace paddle diff --git a/paddle/operators/uniform_random_op.cc b/paddle/operators/uniform_random_op.cc index 81487a6bd..876b3ef55 100644 --- a/paddle/operators/uniform_random_op.cc +++ b/paddle/operators/uniform_random_op.cc @@ -1,22 +1,48 @@ /* 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/uniform_random_op.h" +#include +#include +#include "paddle/framework/op_registry.h" +#include "paddle/framework/operator.h" namespace paddle { namespace operators { +// It seems that Eigen::Tensor::random in GPU will SEGFAULT. +// Use std::random and thrust::random(thrust is a std library in CUDA) to +// implement uniform random. +template +class CPUUniformRandomKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& context) const override { + auto* tensor = context.Output("Out"); + T* data = tensor->mutable_data(context.GetPlace()); + unsigned int seed = + static_cast(context.op_.GetAttr("seed")); + std::minstd_rand engine; + if (seed == 0) { + seed = std::random_device()(); + } + engine.seed(seed); + std::uniform_real_distribution dist( + static_cast(context.op_.GetAttr("min")), + static_cast(context.op_.GetAttr("max"))); + ssize_t size = framework::product(tensor->dims()); + for (ssize_t i = 0; i < size; ++i) { + data[i] = dist(engine); + } + } +}; + class UniformRandomOp : public framework::OperatorWithKernel { public: using framework::OperatorWithKernel::OperatorWithKernel; @@ -38,12 +64,15 @@ class UniformRandomOpMaker : public framework::OpProtoAndCheckerMaker { : framework::OpProtoAndCheckerMaker(proto, op_checker) { AddOutput("Out", "The output tensor of uniform random op"); AddComment(R"DOC(Uniform random operator. - Used to initialize tensor with uniform random generator. )DOC"); AddAttr>("dims", "the dimension of random tensor"); AddAttr("min", "Minimum value of uniform random").SetDefault(-1.0f); AddAttr("max", "Maximun value of uniform random").SetDefault(1.0f); + AddAttr("seed", + "Random seed of uniform random. " + "0 means generate a seed by system") + .SetDefault(0); } }; } // namespace operators @@ -51,6 +80,5 @@ Used to initialize tensor with uniform random generator. REGISTER_OP_WITHOUT_GRADIENT(uniform_random, paddle::operators::UniformRandomOp, paddle::operators::UniformRandomOpMaker); -REGISTER_OP_CPU_KERNEL( - uniform_random, - paddle::operators::UniformRandomKernel); +REGISTER_OP_CPU_KERNEL(uniform_random, + paddle::operators::CPUUniformRandomKernel); \ No newline at end of file diff --git a/paddle/operators/uniform_random_op.cu b/paddle/operators/uniform_random_op.cu index 1bfffc477..6716b7c7f 100644 --- a/paddle/operators/uniform_random_op.cu +++ b/paddle/operators/uniform_random_op.cu @@ -1,19 +1,68 @@ /* 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/uniform_random_op.h" +#include +#include +#include +#include +#include "paddle/framework/op_registry.h" +#include "paddle/framework/operator.h" + +namespace paddle { +namespace operators { + +template +struct UniformGenerator { + T min_, max_; + unsigned int seed_; + + __host__ __device__ UniformGenerator(T min, T max, int seed) + : min_(min), max_(max), seed_(seed) {} + + __host__ __device__ T operator()(const unsigned int n) const { + thrust::minstd_rand rng; + rng.seed(seed_); + thrust::uniform_real_distribution dist(min_, max_); + rng.discard(n); + return dist(rng); + } +}; + +// It seems that Eigen::Tensor::random in GPU will SEGFAULT. +// Use std::random and thrust::random(thrust is a std library in CUDA) to +// implement uniform random. +template +class GPUUniformRandomKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& context) const override { + auto* tensor = context.Output("Out"); + T* data = tensor->mutable_data(context.GetPlace()); + unsigned int seed = + static_cast(context.op_.GetAttr("seed")); + if (seed == 0) { + std::random_device rd; + seed = rd(); + } + T min = static_cast(context.op_.GetAttr("min")); + T max = static_cast(context.op_.GetAttr("max")); + thrust::counting_iterator index_sequence_begin(0); + ssize_t N = framework::product(tensor->dims()); + thrust::transform(index_sequence_begin, index_sequence_begin + N, + thrust::device_ptr(data), + UniformGenerator(min, max, seed)); + } +}; + +} // namespace operators +} // namespace paddle -REGISTER_OP_GPU_KERNEL( - uniform_random, - paddle::operators::UniformRandomKernel); +REGISTER_OP_GPU_KERNEL(uniform_random, + paddle::operators::GPUUniformRandomKernel); \ No newline at end of file diff --git a/paddle/operators/uniform_random_op.h b/paddle/operators/uniform_random_op.h deleted file mode 100644 index dffa640f8..000000000 --- a/paddle/operators/uniform_random_op.h +++ /dev/null @@ -1,38 +0,0 @@ -/* 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" - -namespace paddle { -namespace operators { -template -class UniformRandomKernel : public framework::OpKernel { - public: - void Compute(const framework::ExecutionContext& context) const override { - auto* tensor = context.Output("Out"); - T* data = tensor->mutable_data(context.GetPlace()); - T min = static_cast(context.op_.GetAttr("min")); - T max = static_cast(context.op_.GetAttr("max")); - auto n = framework::product(tensor->dims()); - - auto* device_context = - const_cast(context.device_context_); - math::RandUniform(n, min, max, data, device_context); - } -}; -} // namespace operators -} // namespace paddle diff --git a/paddle/platform/device_context.cc b/paddle/platform/device_context.cc index ad9b4e42f..ad212c5b2 100644 --- a/paddle/platform/device_context.cc +++ b/paddle/platform/device_context.cc @@ -25,17 +25,8 @@ CPUDeviceContext::CPUDeviceContext() { eigen_device_.reset(new Eigen::DefaultDevice()); } -CPUDeviceContext::CPUDeviceContext(CPUPlace place, int seed) { +CPUDeviceContext::CPUDeviceContext(CPUPlace place) { eigen_device_.reset(new Eigen::DefaultDevice()); - rand_seed_ = seed; -} - -std::minstd_rand& CPUDeviceContext::rand_engine() { - if (!rand_engine_) { - rand_engine_.reset(new std::minstd_rand()); - rand_engine_->seed(rand_seed_); - } - return *(rand_engine_.get()); } Eigen::DefaultDevice* CPUDeviceContext::eigen_device() const { @@ -104,8 +95,7 @@ Eigen::GpuDevice* DeviceContext::get_eigen_device() const { return reinterpret_cast(this)->eigen_device(); } -CUDADeviceContext::CUDADeviceContext(GPUPlace place, uint64_t seed) - : place_(place), rand_seed_(seed) { +CUDADeviceContext::CUDADeviceContext(GPUPlace place) : place_(place) { SetDeviceId(place_.device); PADDLE_ENFORCE(cudaStreamCreate(&stream_)); eigen_stream_.reset(new EigenCudaStreamDevice()); @@ -157,19 +147,6 @@ cudnnHandle_t CUDADeviceContext::cudnn_handle() { return cudnn_handle_; } -curandGenerator_t CUDADeviceContext::curand_generator() { - if (!curand_generator_) { - SetDeviceId(place_.device); - PADDLE_ENFORCE(dynload::curandCreateGenerator(&curand_generator_, - CURAND_RNG_PSEUDO_DEFAULT)); - PADDLE_ENFORCE(dynload::curandSetPseudoRandomGeneratorSeed( - curand_generator_, rand_seed_)); - - PADDLE_ENFORCE(dynload::curandSetStream(curand_generator_, stream_)); - } - return curand_generator_; -} - cudaStream_t CUDADeviceContext::stream() { return stream_; } #endif // PADDLE_ONLY_CPU diff --git a/paddle/platform/device_context.h b/paddle/platform/device_context.h index e18f48fef..11528e119 100644 --- a/paddle/platform/device_context.h +++ b/paddle/platform/device_context.h @@ -17,7 +17,6 @@ limitations under the License. */ #ifndef PADDLE_ONLY_CPU #include "paddle/platform/dynload/cublas.h" #include "paddle/platform/dynload/cudnn.h" -#include "paddle/platform/dynload/curand.h" #include "paddle/platform/gpu_info.h" #define EIGEN_USE_GPU #endif @@ -40,18 +39,14 @@ class DeviceContext { class CPUDeviceContext : public DeviceContext { public: CPUDeviceContext(); - explicit CPUDeviceContext(CPUPlace place, int seed = 0); + explicit CPUDeviceContext(CPUPlace place); virtual ~CPUDeviceContext() {} Eigen::DefaultDevice* eigen_device() const; - std::minstd_rand& rand_engine(); - Place GetPlace() const override; private: - int rand_seed_; - std::unique_ptr rand_engine_; std::unique_ptr eigen_device_; }; @@ -60,7 +55,7 @@ class EigenCudaStreamDevice; class CUDADeviceContext : public DeviceContext { public: - explicit CUDADeviceContext(GPUPlace place, uint64_t seed = 0); + explicit CUDADeviceContext(GPUPlace place); virtual ~CUDADeviceContext(); /*! \brief Wait for all operations completion in the stream. */ @@ -79,9 +74,6 @@ class CUDADeviceContext : public DeviceContext { /*! \brief Return cudnn handle in the device context. */ cudnnHandle_t cudnn_handle(); - /*! \brief Return curand handle in the device context. */ - curandGenerator_t curand_generator(); - /*! \brief Return cuda stream in the device context. */ cudaStream_t stream(); // clang-format on @@ -92,13 +84,10 @@ class CUDADeviceContext : public DeviceContext { std::unique_ptr eigen_device_; std::unique_ptr eigen_stream_; - uint64_t rand_seed_; - // clang-format off cudaStream_t stream_{nullptr}; cudnnHandle_t cudnn_handle_{nullptr}; cublasHandle_t cublas_handle_{nullptr}; - curandGenerator_t curand_generator_{nullptr}; // clang-format on }; diff --git a/python/paddle/v2/framework/tests/test_gaussian_random_op.py b/python/paddle/v2/framework/tests/test_gaussian_random_op.py index 367d21b30..f95ed70b5 100644 --- a/python/paddle/v2/framework/tests/test_gaussian_random_op.py +++ b/python/paddle/v2/framework/tests/test_gaussian_random_op.py @@ -17,7 +17,12 @@ class GaussianRandomTest(unittest.TestCase): scope.new_var("Out").get_tensor() op = Operator( - "gaussian_random", Out="Out", dims=[1000, 784], mean=.0, std=1.) + "gaussian_random", + Out="Out", + dims=[1000, 784], + mean=.0, + std=1., + seed=10) op.infer_shape(scope) context = core.DeviceContext.create(place) diff --git a/python/paddle/v2/framework/tests/test_uniform_random_op.py b/python/paddle/v2/framework/tests/test_uniform_random_op.py index 95c36a27c..c3d2bb44d 100644 --- a/python/paddle/v2/framework/tests/test_uniform_random_op.py +++ b/python/paddle/v2/framework/tests/test_uniform_random_op.py @@ -17,7 +17,12 @@ class UniformRandomTest(unittest.TestCase): scope.new_var("X").get_tensor() op = Operator( - "uniform_random", Out="X", dims=[1000, 784], min=-5.0, max=10.0) + "uniform_random", + Out="X", + dims=[1000, 784], + min=-5.0, + max=10.0, + seed=10) op.infer_shape(scope) ctx = core.DeviceContext.create(place) -- GitLab From 1918ad875980a7b5fb54c207e56d86b4376e2505 Mon Sep 17 00:00:00 2001 From: qijun Date: Tue, 22 Aug 2017 12:16:30 +0800 Subject: [PATCH 0159/2018] fix gpu build error --- paddle/operators/math/CMakeLists.txt | 4 ++-- paddle/operators/math/math_function.cc | 9 --------- paddle/operators/math/math_function.cu | 13 ------------- paddle/operators/math/math_function.h | 5 ----- paddle/platform/device_context_test.cc | 2 -- 5 files changed, 2 insertions(+), 31 deletions(-) diff --git a/paddle/operators/math/CMakeLists.txt b/paddle/operators/math/CMakeLists.txt index 228f463f2..ed51d416e 100644 --- a/paddle/operators/math/CMakeLists.txt +++ b/paddle/operators/math/CMakeLists.txt @@ -1,8 +1,8 @@ if(WITH_GPU) - nv_library(math_function SRCS math_function.cc math_function.cu DEPS cblas device_context eigen3) + nv_library(math_function SRCS math_function.cc math_function.cu DEPS cblas device_context) else() - cc_library(math_function SRCS math_function.cc DEPS cblas device_context eigen3) + cc_library(math_function SRCS math_function.cc DEPS cblas device_context) endif() nv_test(math_function_test SRCS math_function_test.cc DEPS math_function tensor) diff --git a/paddle/operators/math/math_function.cc b/paddle/operators/math/math_function.cc index d9824e5f9..1e86fc3d1 100644 --- a/paddle/operators/math/math_function.cc +++ b/paddle/operators/math/math_function.cc @@ -109,15 +109,6 @@ void matmul(const framework::Tensor& matrix_a, matrix_b.data(), beta, matrix_out->data(), context); } -template <> -void Set(const int n, const float alpha, - float* output, - platform::DeviceContext* context) { - auto* cpu_context = reinterpret_cast(context); - framework::EigenVector::Type out(output, n); - out.device(*(cpu_context->eigen_device())) = out.constant(float(alpha)); -} - } // namespace math } // namespace operators } // namespace paddle diff --git a/paddle/operators/math/math_function.cu b/paddle/operators/math/math_function.cu index 9dff6f05f..da40b27c9 100644 --- a/paddle/operators/math/math_function.cu +++ b/paddle/operators/math/math_function.cu @@ -12,10 +12,6 @@ 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 -#include -#include -#include #include "paddle/operators/math/math_function.h" namespace paddle { @@ -126,15 +122,6 @@ void matmul(const framework::Tensor& matrix_a, matrix_b.data(), beta, matrix_out->data(), context); } -template <> -void Set(const int n, const float alpha, - float* output, - platform::DeviceContext* context) { - auto* cuda_context = reinterpret_cast(context); - framework::EigenVector::Type out(output, n); - out.device(*(cuda_context->eigen_device())) = out.constant(float(alpha)); -} - } // namespace math } // namespace operators } // namespace paddle diff --git a/paddle/operators/math/math_function.h b/paddle/operators/math/math_function.h index a0e966056..155589fad 100644 --- a/paddle/operators/math/math_function.h +++ b/paddle/operators/math/math_function.h @@ -52,7 +52,6 @@ int LAPACKE_dgetri(int matrix_layout, int n, double* a, int lda, #include -#include "paddle/framework/eigen.h" #include "paddle/framework/tensor.h" #include "paddle/platform/device_context.h" #include "paddle/platform/enforce.h" @@ -78,10 +77,6 @@ void matmul(const framework::Tensor& matrix_a, bool trans_a, framework::Tensor* matrix_out, T beta, platform::DeviceContext* context); -template -void Set(const int n, const T alpha, T* output, - platform::DeviceContext* context); - } // namespace math } // namespace operators } // namespace paddle diff --git a/paddle/platform/device_context_test.cc b/paddle/platform/device_context_test.cc index 8b764bdcd..5883a5527 100644 --- a/paddle/platform/device_context_test.cc +++ b/paddle/platform/device_context_test.cc @@ -43,8 +43,6 @@ TEST(Device, CUDADeviceContext) { ASSERT_NE(nullptr, cudnn_handle); cublasHandle_t cublas_handle = device_context->cublas_handle(); ASSERT_NE(nullptr, cublas_handle); - curandGenerator_t curand_handle = device_context->curand_generator(); - ASSERT_NE(nullptr, curand_handle); ASSERT_NE(nullptr, device_context->stream()); delete device_context; } -- GitLab From aff90d8ee78be398b2984d63f2eb985f15f430d1 Mon Sep 17 00:00:00 2001 From: qijun Date: Tue, 22 Aug 2017 04:34:35 +0000 Subject: [PATCH 0160/2018] fix gpu build error --- paddle/operators/gaussian_random_op.cu | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/paddle/operators/gaussian_random_op.cu b/paddle/operators/gaussian_random_op.cu index 1d312e7b5..018a4bfcb 100644 --- a/paddle/operators/gaussian_random_op.cu +++ b/paddle/operators/gaussian_random_op.cu @@ -30,7 +30,7 @@ struct GaussianGenerator { __host__ __device__ T operator()(const unsigned int n) const { thrust::minstd_rand rng; rng.seed(seed_); - thrust::normal_distribution dist(min_, max_); + thrust::normal_distribution dist(mean_, std_); rng.discard(n); return dist(rng); } @@ -62,4 +62,4 @@ class GPUGaussianRandomKernel : public framework::OpKernel { } // namespace paddle REGISTER_OP_GPU_KERNEL(gaussian_random, - paddle::operators::GPUGaussianRandomKernel); \ No newline at end of file + paddle::operators::GPUGaussianRandomKernel); -- GitLab From dc5f0dbc324e0e15bef1753aeaed6700f5972cf0 Mon Sep 17 00:00:00 2001 From: zchen0211 Date: Tue, 22 Aug 2017 05:27:02 +0000 Subject: [PATCH 0161/2018] remove opregistry in gather function --- paddle/operators/gather.h | 1 - 1 file changed, 1 deletion(-) diff --git a/paddle/operators/gather.h b/paddle/operators/gather.h index edac29f6d..92fb51ec1 100644 --- a/paddle/operators/gather.h +++ b/paddle/operators/gather.h @@ -18,7 +18,6 @@ limitations under the License. */ #include "paddle/framework/ddim.h" #include "paddle/framework/eigen.h" -#include "paddle/framework/op_registry.h" #include "paddle/framework/tensor.h" #include "paddle/platform/place.h" -- GitLab From 6eab5638f03f49ab1ff3d3a4fc30d870f42a6153 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E6=AD=A6=E6=AF=85?= Date: Tue, 22 Aug 2017 13:28:51 +0800 Subject: [PATCH 0162/2018] Fix remote large update core (#3518) * fix remote large update core * wip * working version * fix style check * fix style check * update style check --- .../gserver/gradientmachines/NeuralNetwork.cpp | 2 +- paddle/parameter/Parameter.h | 5 ++++- paddle/pserver/ParameterClient2.cpp | 16 ++++++++++++++-- paddle/pserver/ParameterClient2.h | 1 + 4 files changed, 20 insertions(+), 4 deletions(-) diff --git a/paddle/gserver/gradientmachines/NeuralNetwork.cpp b/paddle/gserver/gradientmachines/NeuralNetwork.cpp index cfa80a893..26cff3e67 100644 --- a/paddle/gserver/gradientmachines/NeuralNetwork.cpp +++ b/paddle/gserver/gradientmachines/NeuralNetwork.cpp @@ -202,7 +202,7 @@ void NeuralNetwork::prefetch(const std::vector& inArgs) { auto mat = dynamic_cast( para->getMat(PARAMETER_VALUE).get()); para->clearGradient(); - mat->clearIndices(); + if (mat) mat->clearIndices(); } } } diff --git a/paddle/parameter/Parameter.h b/paddle/parameter/Parameter.h index e31cbc3de..321f4275d 100644 --- a/paddle/parameter/Parameter.h +++ b/paddle/parameter/Parameter.h @@ -65,7 +65,10 @@ public: size_t getSize() const { return config_.size(); } bool isFullSize() const { - return this->getSize() == bufs_[PARAMETER_VALUE]->getSize(); + if (bufs_[PARAMETER_VALUE]) { + return this->getSize() == bufs_[PARAMETER_VALUE]->getSize(); + } + return false; } inline bool useGpu() const { return useGpu_; } diff --git a/paddle/pserver/ParameterClient2.cpp b/paddle/pserver/ParameterClient2.cpp index f7e391f76..54063a809 100644 --- a/paddle/pserver/ParameterClient2.cpp +++ b/paddle/pserver/ParameterClient2.cpp @@ -65,7 +65,6 @@ void ParameterClient2::initThreads() { LOG(INFO) << "parallel_thread_num dosent need to set"; } syncThreadPool_.reset(new SyncThreadPool(threadNum_)); - startThreads(); } @@ -224,6 +223,14 @@ void ParameterClient2::prepareSendData( request.set_cost(cost); request.set_batch_status(batchStatus); CHECK_EQ(request.blocks_size(), 0); + VLOG(10) << "request: trainer_id: " << request.trainer_id() + << " update_mode" << request.update_mode() + << " send_back_parameter: " << request.send_back_parameter() + << " send_back_parameter_type: " + << request.send_back_parameter_type() + << " num_samples: " << request.num_samples() + << " cost: " << request.cost() + << " batch_status: " << request.batch_status(); } for (const auto& segments : parameterSegments) { const auto it = parameterMap_.find(segments.id); @@ -251,11 +258,17 @@ void ParameterClient2::prepareSendData( CHECK(sendMat != nullptr) << "sendMat is nullptr"; syncThreadPool_->exec([&](int tid, size_t numThreads) { + std::lock_guard guard(sparseAutoGrowthMutex_); const auto& localIndices = prefetchMat->getLocalIndices(); /// num of sparse rows size_t nLocalBlocks = localIndices.size(); uint64_t beginDim = 0; uint64_t endDim = 0; + + // FIXME(typhoonzero): let it resize first + prefetchMat->getLocalRow(nLocalBlocks + 1); + sendMat->getLocalRow(nLocalBlocks + 1); + for (size_t row = 0; row < nLocalBlocks; ++row) { int64_t blockId = localIndices[row]; // local row -> sparse row int serverId = std::abs((blockId + nameHash) % serviceNum_); @@ -275,7 +288,6 @@ void ParameterClient2::prepareSendData( block->set_begin_pos(row * blockSize); /// block len block->set_block_size(endDim - beginDim); - if (sendingPara) { sendJob->parallelInputIovs[serverId].push_back( {sendMat->getLocalRow(row), sizeof(real) * (size_t)blockSize}); diff --git a/paddle/pserver/ParameterClient2.h b/paddle/pserver/ParameterClient2.h index 89b3ddd50..29b9eeacd 100644 --- a/paddle/pserver/ParameterClient2.h +++ b/paddle/pserver/ParameterClient2.h @@ -583,6 +583,7 @@ protected: #ifndef PADDLE_DISABLE_TIMER uint64_t forwardbackwordTime_; #endif + std::mutex sparseAutoGrowthMutex_; /// map id to parameter used for decoding protobuf data std::unordered_map parameterMap_; -- GitLab From 4eecd0c2d531f66e64eebff88a99488275143207 Mon Sep 17 00:00:00 2001 From: tensor-tang Date: Tue, 22 Aug 2017 14:18:16 +0800 Subject: [PATCH 0163/2018] use MKLDNNMatrix in fc backward --- paddle/gserver/layers/MKLDNNFcLayer.cpp | 77 ++++++++++++------------- paddle/gserver/layers/MKLDNNLayer.h | 59 ++++++++++++++----- paddle/math/MKLDNNMatrix.h | 33 +++++++++-- 3 files changed, 110 insertions(+), 59 deletions(-) diff --git a/paddle/gserver/layers/MKLDNNFcLayer.cpp b/paddle/gserver/layers/MKLDNNFcLayer.cpp index fac0390ee..546310446 100644 --- a/paddle/gserver/layers/MKLDNNFcLayer.cpp +++ b/paddle/gserver/layers/MKLDNNFcLayer.cpp @@ -158,10 +158,8 @@ void MKLDNNFcLayer::resetFwd() { hasSpatial_ ? memory::dims{oc_, ic_, ih_, iw_} : memory::dims{oc_, ic_}, hasSpatial_ ? format::oihw : format::oi, engine_); - biasVal_ = hasBias ? MKLDNNMatrix::create(bias, {oc_}, format::x, engine_) : nullptr; - outVal_ = MKLDNNMatrix::create(out, {bs_, oc_}, format::nc, engine_); // change original output to mkldnn output @@ -193,46 +191,41 @@ void MKLDNNFcLayer::resetBwd() { return; } needResetBwd_ = false; - bool hasBias = biases_ && biases_->getWGrad(); - real* iData = getInputValue(0)->getData(); - real* iDiff = getInputGrad(0) != nullptr ? getInputGrad(0)->getData() : NULL; - real* oDiff = getOutputGrad()->getData(); - real* wDiff = weight_->getWGrad()->getData(); - real* bDiff = hasBias ? biases_->getWGrad()->getData() : NULL; /// backward weight - // create memory desc for backward memory - memory::desc iMD = hasSpatial_ ? createMD({bs_, ic_, ih_, iw_}, format::nchw) - : createMD({bs_, ic_}, format::nc); - memory::desc wMD = hasSpatial_ ? createMD({oc_, ic_, ih_, iw_}, format::oihw) - : createMD({oc_, ic_}, format::oi); - memory::desc oMD = createMD({bs_, oc_}, format::nc); - memory::desc bMD = bDiff != NULL ? createMD({oc_}, format::x) - : createMD({}, format::format_undef); - - if (inVal_) { - // update data - inVal_->set_data_handle(iData); - } else { - LOG(FATAL) << "Should not be empty"; - // inVal_.reset(new memory(memory::primitive_desc(iMD, engine_), iData)); - } - - // create memory primitive desc and memory self - wgtGrad_.reset(new memory(memory::primitive_desc(wMD, engine_), wDiff)); - outGrad_.reset(new memory(memory::primitive_desc(oMD, engine_), oDiff)); + CHECK(inVal_) << "Should have input value"; + const MatrixPtr& wgt = weight_->getWGrad(); + const MatrixPtr& bias = hasBias ? biases_->getWGrad() : nullptr; + const MatrixPtr& out = output_.grad; + + wgtGrad_ = MKLDNNMatrix::create( + wgt, wgtVal_->getDims(), wgtVal_->getFormat(), engine_); + biasGrad_ = + hasBias ? MKLDNNMatrix::create(bias, {oc_}, format::x, engine_) : nullptr; - fc_fwd::desc fwdDesc = fc_fwd::desc(prop_kind::forward, iMD, wMD, oMD); + outGrad_ = MKLDNNMatrix::create(out, {bs_, oc_}, format::nc, engine_); + // change original output to mkldnn output + // TODO: right? + output_.grad = std::dynamic_pointer_cast(outGrad_); + + // create memory primitive desc + fc_fwd::desc fwdDesc = fc_fwd::desc(prop_kind::forward, + inVal_->getMD(), + wgtGrad_->getMD(), + outGrad_->getMD()); fc_fwd::primitive_desc fwdPD = fc_fwd::primitive_desc(fwdDesc, engine_); - fc_bwdWgt::desc bwdWgtDesc = bDiff != NULL - ? fc_bwdWgt::desc(iMD, wMD, bMD, oMD) - : fc_bwdWgt::desc(iMD, wMD, oMD); + fc_bwdWgt::desc bwdWgtDesc = + hasBias ? fc_bwdWgt::desc(inVal_->getMD(), + wgtGrad_->getMD(), + biasGrad_->getMD(), + outGrad_->getMD()) + : fc_bwdWgt::desc( + inVal_->getMD(), wgtGrad_->getMD(), outGrad_->getMD()); fc_bwdWgt::primitive_desc bwdWgtPD = fc_bwdWgt::primitive_desc(bwdWgtDesc, engine_, fwdPD); - if (bDiff != NULL) { - biasGrad_.reset(new memory(memory::primitive_desc(bMD, engine_), bDiff)); + if (hasBias) { bwdWgt_.reset( new fc_bwdWgt(bwdWgtPD, *inVal_, *outGrad_, *wgtGrad_, *biasGrad_)); } else { @@ -242,13 +235,19 @@ void MKLDNNFcLayer::resetBwd() { pipelineBwd_.push_back(*bwdWgt_); /// backward data - if (iDiff == NULL) { + const MatrixPtr& in = getInputGrad(0); + if (in == nullptr) { return; } - fc_bwdData::desc bwdDataDesc = fc_bwdData::desc(iMD, wMD, oMD); + fc_bwdData::desc bwdDataDesc = + fc_bwdData::desc(inVal_->getMD(), wgtGrad_->getMD(), outGrad_->getMD()); fc_bwdData::primitive_desc bwdDataPD = fc_bwdData::primitive_desc(bwdDataDesc, engine_, fwdPD); - inGrad_.reset(new memory(memory::primitive_desc(iMD, engine_), iDiff)); + + // TODO: check right, just from ingrad? + inGrad_ = + MKLDNNMatrix::create(in, inVal_->getDims(), inVal_->getFormat(), engine_); + CHECK(wgtVal_) << "Should have weight memory"; bwdData_.reset(new fc_bwdData(bwdDataPD, *outGrad_, *wgtVal_, *inGrad_)); pipelineBwd_.push_back(*bwdData_); @@ -264,7 +263,7 @@ void MKLDNNFcLayer::forward(PassType passType) { // update input data // since it might be changed if this is after data layer real* iData = getInputValue(0)->getData(); - inVal_->set_data_handle(iData); + inVal_->updateData(iData); // just submit forward pipeline stream_->submit(pipelineFwd_); @@ -288,7 +287,7 @@ void MKLDNNFcLayer::backward(const UpdateCallback& callback) { // update diff real* oDiff = getOutputGrad()->getData(); - outGrad_->set_data_handle(oDiff); + outGrad_->updateData(oDiff); // just sumbmit backward pipeline stream_->submit(pipelineBwd_); diff --git a/paddle/gserver/layers/MKLDNNLayer.h b/paddle/gserver/layers/MKLDNNLayer.h index b44095bef..fbd62d9aa 100644 --- a/paddle/gserver/layers/MKLDNNLayer.h +++ b/paddle/gserver/layers/MKLDNNLayer.h @@ -52,16 +52,15 @@ protected: std::vector pipelineFwd_; std::vector pipelineBwd_; - // TODO(TJ): change below memory as MKLDNNMatrixPtr type - // MKLDNNMatrixPtr ; + // MKLDNNMatrixPtr MKLDNNMatrixPtr inVal_; - std::shared_ptr inGrad_; + MKLDNNMatrixPtr inGrad_; MKLDNNMatrixPtr outVal_; - std::shared_ptr outGrad_; + MKLDNNMatrixPtr outGrad_; MKLDNNMatrixPtr wgtVal_; - std::shared_ptr wgtGrad_; + MKLDNNMatrixPtr wgtGrad_; MKLDNNMatrixPtr biasVal_; - std::shared_ptr biasGrad_; + MKLDNNMatrixPtr biasGrad_; public: explicit MKLDNNLayer(const LayerConfig& config) @@ -84,17 +83,24 @@ public: virtual bool init(const LayerMap& layerMap, const ParameterMap& parameterMap) { + CHECK(FLAGS_use_mkldnn) << "MkldnnLayers only support use_mkldnn." + << "Please set WITH_MKLDNN=ON " + << "and set use_mkldnn=True"; + if (useGpu_ == true) { + LOG(WARNING) << "Do not support GPU yet, will change to useGpu = false"; + useGpu_ = false; + } + + // set device id before Layer::init + setDevice(MKLDNN_DEVICE); + // change param device to MKLDNN device + setParamsDevice(MKLDNN_DEVICE, parameterMap); if (!Layer::init(layerMap, parameterMap)) { return false; } - CHECK(FLAGS_use_mkldnn) << "MkldnnLayers only support use_mkldnn." - << "Please set WITH_MKLDNN=ON " - << "and set use_mkldnn=True"; stream_.reset(new MKLDNNStream()); engine_ = CPUEngine::Instance().getEngine(); - - setDeviceID(MKLDNN_DEVICE); return true; } @@ -136,10 +142,33 @@ public: } protected: - void setDeviceID(int id) { - deviceId_ = id; - output_.deviceId = id; - // TODO: handle mkldnn device or add mkldnn device to other + /** + * Set deviceId of this layer. + */ + void setDevice(int id) { deviceId_ = id; } + + /** + * Set deviceId of the params used in this layer. + */ + void setParamsDevice(int id, const ParameterMap& parameterMap) { + for (auto& inputConfig : config_.inputs()) { + if (inputConfig.has_input_parameter_name()) { + ParameterPtr parameter; + std::string name = inputConfig.input_parameter_name(); + CHECK(mapGet(name, parameterMap, ¶meter)) + << "Cannot find input parameter " << name << " for layer " + << getName(); + parameter->setDevice(id); + } + } + if (config_.has_bias_parameter_name()) { + ParameterPtr parameter; + std::string name = config_.bias_parameter_name(); + CHECK(mapGet(name, parameterMap, ¶meter)) + << "Cannot find bias parameter " << name << " for layer " + << getName(); + parameter->setDevice(id); + } } }; diff --git a/paddle/math/MKLDNNMatrix.h b/paddle/math/MKLDNNMatrix.h index 73eb50d2a..54c0a1fdc 100644 --- a/paddle/math/MKLDNNMatrix.h +++ b/paddle/math/MKLDNNMatrix.h @@ -44,6 +44,8 @@ public: set_data_handle(CpuMatrix::getData()); } + ~MKLDNNMatrix() {} + static MKLDNNMatrixPtr create( const MatrixPtr& m, mkldnn::memory::dims dims, @@ -52,21 +54,42 @@ public: mkldnn::memory::data_type dtype = mkldnn::memory::data_type::f32); /** - * Get primitive descriptor + * Get primitive descriptor. */ mkldnn::memory::primitive_desc getPD() { return this->get_primitive_desc(); } /** - * Get memory descriptor + * Get memory descriptor. */ mkldnn::memory::desc getMD() { return getPD().desc(); } /** - * Get format + * Get dims. */ - int getFormat() { return getMD().data.format; } + mkldnn::memory::dims getDims() { + mkldnn::memory::dims dst; + int* src = getMD().data.dims; + int ndims = getMD().data.ndims; + dst.resize(ndims); + for (int i = 0; i < ndims; ++i) { + dst[i] = src[i]; + } + return dst; + } - ~MKLDNNMatrix() {} + /** + * Get format. + */ + mkldnn::memory::format getFormat() { + return (mkldnn::memory::format)(getMD().data.format); + } + + /** + * Update the memory data handle. + * Caution: This will not check the buffer size of the data, + * it should be coverd by user. + */ + void updateData(void* data) { set_data_handle(data); } }; } // namespace paddle -- GitLab From 950cc60d2b2e6ab9c05f82df3f2d3f3179541209 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Tue, 22 Aug 2017 15:29:38 +0800 Subject: [PATCH 0164/2018] Add minus --- paddle/framework/CMakeLists.txt | 3 +- paddle/framework/pybind.cc | 1 + paddle/operators/CMakeLists.txt | 1 + paddle/operators/minus_op.cc | 84 +++++++++++++++++++++++++++++++++ paddle/operators/minus_op.cu | 18 +++++++ paddle/operators/minus_op.h | 39 +++++++++++++++ 6 files changed, 145 insertions(+), 1 deletion(-) create mode 100644 paddle/operators/minus_op.cc create mode 100644 paddle/operators/minus_op.cu create mode 100644 paddle/operators/minus_op.h diff --git a/paddle/framework/CMakeLists.txt b/paddle/framework/CMakeLists.txt index 5df14ae78..c9cf45e9d 100644 --- a/paddle/framework/CMakeLists.txt +++ b/paddle/framework/CMakeLists.txt @@ -56,5 +56,6 @@ cc_library(paddle_pybind SHARED uniform_random_op gaussian_random_op fill_zeros_like_op - scale_op) + scale_op + minus_op) endif(WITH_PYTHON) diff --git a/paddle/framework/pybind.cc b/paddle/framework/pybind.cc index 3aaf0de15..b4b7921d3 100644 --- a/paddle/framework/pybind.cc +++ b/paddle/framework/pybind.cc @@ -44,6 +44,7 @@ USE_OP(gaussian_random); USE_OP(uniform_random); USE_OP(scale); USE_OP_ITSELF(identity); +USE_OP(minus); namespace paddle { namespace framework { diff --git a/paddle/operators/CMakeLists.txt b/paddle/operators/CMakeLists.txt index 0ba598823..61f7a4070 100644 --- a/paddle/operators/CMakeLists.txt +++ b/paddle/operators/CMakeLists.txt @@ -69,3 +69,4 @@ op_library(recurrent_op SRCS recurrent_op.cc rnn/recurrent_op_utils.cc op_library(uniform_random_op SRCS uniform_random_op.cc uniform_random_op.cu) op_library(scale_op SRCS scale_op.cc scale_op.cu DEPS net_op) +op_library(minus_op SRCS minus_op.cc minus_op.cu DEPS scale_op) diff --git a/paddle/operators/minus_op.cc b/paddle/operators/minus_op.cc new file mode 100644 index 000000000..c660ab5d3 --- /dev/null +++ b/paddle/operators/minus_op.cc @@ -0,0 +1,84 @@ +/* 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/minus_op.h" +#include "paddle/operators/net_op.h" + +namespace paddle { +namespace operators { + +class MinusOp : public framework::OperatorWithKernel { + public: + MinusOp(const std::string &type, const VarNameMap &inputs, + const VarNameMap &outputs, const framework::AttributeMap &attrs) + : OperatorWithKernel(type, inputs, outputs, attrs) {} + + protected: + void InferShape(const framework::InferShapeContext &ctx) const override { + auto *left_tensor = ctx.Input("X"); + auto *right_tensor = ctx.Input("Y"); + + PADDLE_ENFORCE_EQ( + framework::product(left_tensor->dims()), + framework::product(right_tensor->dims()), + "Minus operator must take two tensor with same num of elements"); + ctx.Output("Out")->Resize(left_tensor->dims()); + } +}; + +class MinusOpMaker : public framework::OpProtoAndCheckerMaker { + public: + MinusOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker) + : OpProtoAndCheckerMaker(proto, op_checker) { + AddInput("X", "The left tensor of minus operator.").NotInGradient(); + AddInput("Y", "The right tensor of minus operator.").NotInGradient(); + AddOutput("Out", "The output tensor of minus operator.").NotInGradient(); + + AddComment(R"DOC(Minus Operator + +Equation: Out = X - Y +)DOC"); + } +}; +template +class MinusGradOp : public NetOp { + public: + MinusGradOp(const std::string &type, const VarNameMap &inputs, + const VarNameMap &outputs, const framework::AttributeMap &attrs) + : NetOp(type, inputs, outputs, attrs) { + auto out_grad = Input(framework::GradVarName("Out")); + auto x_grad = Output(framework::GradVarName("X")); + auto y_grad = Output(framework::GradVarName("Y")); + + // x_grad = out_grad + AddOp(framework::OpRegistry::CreateOp("identity", {{"X", {out_grad}}}, + {{"Out", {x_grad}}}, {})); + + framework::AttributeMap scale_attr; + scale_attr["scale"] = static_cast(-1); + AddOp(framework::OpRegistry::CreateOp("scale", {{"X", {out_grad}}}, + {{"Out", {y_grad}}}, scale_attr)); + } +}; + +} // namespace operators +} // namespace paddle + +USE_OP(scale); +USE_OP_ITSELF(identity); +namespace ops = paddle::operators; +REGISTER_OP(minus, ops::MinusOp, ops::MinusOpMaker, minus_grad, + ops::MinusGradOp); +REGISTER_OP_CPU_KERNEL(minus, + ops::MinusKernel); diff --git a/paddle/operators/minus_op.cu b/paddle/operators/minus_op.cu new file mode 100644 index 000000000..a8375cc63 --- /dev/null +++ b/paddle/operators/minus_op.cu @@ -0,0 +1,18 @@ +/* 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/minus_op.h" + +REGISTER_OP_GPU_KERNEL( + minus, paddle::operators::MinusKernel); diff --git a/paddle/operators/minus_op.h b/paddle/operators/minus_op.h new file mode 100644 index 000000000..6310a4fd5 --- /dev/null +++ b/paddle/operators/minus_op.h @@ -0,0 +1,39 @@ +/* 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/eigen.h" +#include "paddle/framework/op_registry.h" + +namespace paddle { +namespace operators { + +template +class MinusKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& context) const override { + auto* left_tensor = context.Input("X"); + auto* right_tensor = context.Input("Y"); + auto* out_tensor = context.Output("Out"); + + out_tensor->mutable_data(context.GetPlace()); + auto& dev = context.GetEigenDevice(); + framework::EigenVector::Flatten(*out_tensor).device(dev) = + framework::EigenVector::Flatten(*left_tensor) - + framework::EigenVector::Flatten(*right_tensor); + } +}; + +} // namespace operators +} // namespace paddle -- GitLab From 5a8fbb7d19e95f3be16bbee029e82e14f0a240df Mon Sep 17 00:00:00 2001 From: qiaolongfei Date: Tue, 22 Aug 2017 00:56:34 -0700 Subject: [PATCH 0165/2018] add data --- python/paddle/v2/framework/tests/mnist.py | 26 +++++++++++++++++------ 1 file changed, 19 insertions(+), 7 deletions(-) diff --git a/python/paddle/v2/framework/tests/mnist.py b/python/paddle/v2/framework/tests/mnist.py index d0c56c457..f75f19616 100644 --- a/python/paddle/v2/framework/tests/mnist.py +++ b/python/paddle/v2/framework/tests/mnist.py @@ -1,8 +1,9 @@ import paddle.v2.framework.core as core from paddle.v2.framework.op import Operator import numpy +import paddle.v2 as paddle -BATCH_SIZE = 2 +BATCH_SIZE = 100 scope = core.Scope() place = core.CPUPlace() @@ -39,9 +40,9 @@ def feed_data(name, data): tensor = scope.find_var(name).get_tensor() tensor.set_dims(data.shape) if data.dtype == numpy.dtype('int32'): - tensor.alloc_float(place) - elif data.dtype == numpy.dtype('float32'): tensor.alloc_int(place) + elif data.dtype == numpy.dtype('float32'): + tensor.alloc_float(place) else: raise ValueError("data type not supported") tensor.set(data, place) @@ -168,20 +169,31 @@ print(backward_net) optimize_net.complete_add_op(True) print(optimize_net) -PASS_NUM = 10 +reader = paddle.batch( + paddle.reader.shuffle( + paddle.dataset.mnist.train(), buf_size=8192), + batch_size=BATCH_SIZE) + +PASS_NUM = 1000 for pass_id in range(PASS_NUM): print("===========forward==========") - feed_data("pixel", numpy.random.random((BATCH_SIZE, 784)).astype('float32')) - feed_data("label", numpy.ones(BATCH_SIZE).astype("int32")) + # feed_data("pixel", numpy.random.random((BATCH_SIZE, 784)).astype('float32')) + # feed_data("label", numpy.ones(BATCH_SIZE).astype("int32")) + data = reader().next() + image = numpy.array(map(lambda x: x[0], data)).astype("float32") + label = numpy.array(map(lambda x: x[1], data)).astype("int32") + feed_data("pixel", image) + feed_data("label", label) forward_network.infer_shape(scope) print_inputs_outputs(forward_network) - print(numpy.array(scope.find_var("label").get_tensor())) + # print(numpy.array(scope.find_var("label").get_tensor())) forward_network.run(scope, dev_ctx) # print(numpy.array(scope.find_var("fc_0").get_tensor())) print("===========backward==========") cost_data = numpy.array(scope.find_var("cross_entropy_1").get_tensor()) + print(cost_data.sum() / len(cost_data)) cost_grad = scope.find_var(grad_var_name("cross_entropy_1")).get_tensor() cost_grad.set_dims(cost_data.shape) cost_grad.alloc_float(place) -- GitLab From 9b4a9da9f293d7decca3dbedf47ead19914e48ff Mon Sep 17 00:00:00 2001 From: qijun Date: Tue, 22 Aug 2017 08:07:35 +0000 Subject: [PATCH 0166/2018] fix code style --- paddle/operators/gaussian_random_op.cc | 3 ++- paddle/operators/gaussian_random_op.cu | 1 + paddle/operators/uniform_random_op.cc | 3 ++- paddle/operators/uniform_random_op.cu | 3 ++- 4 files changed, 7 insertions(+), 3 deletions(-) diff --git a/paddle/operators/gaussian_random_op.cc b/paddle/operators/gaussian_random_op.cc index dcd223745..5755de70c 100644 --- a/paddle/operators/gaussian_random_op.cc +++ b/paddle/operators/gaussian_random_op.cc @@ -80,4 +80,5 @@ Use to initialize tensor with gaussian random generator. namespace ops = paddle::operators; REGISTER_OP_WITHOUT_GRADIENT(gaussian_random, ops::GaussianRandomOp, ops::GaussianRandomOpMaker); -REGISTER_OP_CPU_KERNEL(gaussian_random, ops::CPUGaussianRandomKernel); \ No newline at end of file +REGISTER_OP_CPU_KERNEL(gaussian_random, ops::CPUGaussianRandomKernel); + diff --git a/paddle/operators/gaussian_random_op.cu b/paddle/operators/gaussian_random_op.cu index 018a4bfcb..3fe76ee7e 100644 --- a/paddle/operators/gaussian_random_op.cu +++ b/paddle/operators/gaussian_random_op.cu @@ -63,3 +63,4 @@ class GPUGaussianRandomKernel : public framework::OpKernel { REGISTER_OP_GPU_KERNEL(gaussian_random, paddle::operators::GPUGaussianRandomKernel); + diff --git a/paddle/operators/uniform_random_op.cc b/paddle/operators/uniform_random_op.cc index 876b3ef55..c2e2d7ba4 100644 --- a/paddle/operators/uniform_random_op.cc +++ b/paddle/operators/uniform_random_op.cc @@ -81,4 +81,5 @@ Used to initialize tensor with uniform random generator. REGISTER_OP_WITHOUT_GRADIENT(uniform_random, paddle::operators::UniformRandomOp, paddle::operators::UniformRandomOpMaker); REGISTER_OP_CPU_KERNEL(uniform_random, - paddle::operators::CPUUniformRandomKernel); \ No newline at end of file + paddle::operators::CPUUniformRandomKernel); + diff --git a/paddle/operators/uniform_random_op.cu b/paddle/operators/uniform_random_op.cu index 6716b7c7f..f05ffe306 100644 --- a/paddle/operators/uniform_random_op.cu +++ b/paddle/operators/uniform_random_op.cu @@ -65,4 +65,5 @@ class GPUUniformRandomKernel : public framework::OpKernel { } // namespace paddle REGISTER_OP_GPU_KERNEL(uniform_random, - paddle::operators::GPUUniformRandomKernel); \ No newline at end of file + paddle::operators::GPUUniformRandomKernel); + -- GitLab From 5d0d44a0189142363ee680631ade44b6de163ec6 Mon Sep 17 00:00:00 2001 From: qijun Date: Tue, 22 Aug 2017 08:25:41 +0000 Subject: [PATCH 0167/2018] use clang-format --- paddle/operators/gaussian_random_op.cc | 1 - paddle/operators/gaussian_random_op.cu | 1 - paddle/operators/uniform_random_op.cc | 1 - paddle/operators/uniform_random_op.cu | 1 - 4 files changed, 4 deletions(-) diff --git a/paddle/operators/gaussian_random_op.cc b/paddle/operators/gaussian_random_op.cc index 5755de70c..a85363ad8 100644 --- a/paddle/operators/gaussian_random_op.cc +++ b/paddle/operators/gaussian_random_op.cc @@ -81,4 +81,3 @@ namespace ops = paddle::operators; REGISTER_OP_WITHOUT_GRADIENT(gaussian_random, ops::GaussianRandomOp, ops::GaussianRandomOpMaker); REGISTER_OP_CPU_KERNEL(gaussian_random, ops::CPUGaussianRandomKernel); - diff --git a/paddle/operators/gaussian_random_op.cu b/paddle/operators/gaussian_random_op.cu index 3fe76ee7e..018a4bfcb 100644 --- a/paddle/operators/gaussian_random_op.cu +++ b/paddle/operators/gaussian_random_op.cu @@ -63,4 +63,3 @@ class GPUGaussianRandomKernel : public framework::OpKernel { REGISTER_OP_GPU_KERNEL(gaussian_random, paddle::operators::GPUGaussianRandomKernel); - diff --git a/paddle/operators/uniform_random_op.cc b/paddle/operators/uniform_random_op.cc index c2e2d7ba4..29491137e 100644 --- a/paddle/operators/uniform_random_op.cc +++ b/paddle/operators/uniform_random_op.cc @@ -82,4 +82,3 @@ REGISTER_OP_WITHOUT_GRADIENT(uniform_random, paddle::operators::UniformRandomOp, paddle::operators::UniformRandomOpMaker); REGISTER_OP_CPU_KERNEL(uniform_random, paddle::operators::CPUUniformRandomKernel); - diff --git a/paddle/operators/uniform_random_op.cu b/paddle/operators/uniform_random_op.cu index f05ffe306..1d6709934 100644 --- a/paddle/operators/uniform_random_op.cu +++ b/paddle/operators/uniform_random_op.cu @@ -66,4 +66,3 @@ class GPUUniformRandomKernel : public framework::OpKernel { REGISTER_OP_GPU_KERNEL(uniform_random, paddle::operators::GPUUniformRandomKernel); - -- GitLab From 0f3b9e4112cbedd1b026f6cd09955d15f6207864 Mon Sep 17 00:00:00 2001 From: dangqingqing Date: Tue, 22 Aug 2017 15:36:43 +0800 Subject: [PATCH 0168/2018] lookup table op, cuda helper and set functor 1. finish lookup table CPU and GPU kernel 2. Add some cuda helper 3. Add some math funtor --- paddle/framework/pybind.cc | 1 + paddle/operators/CMakeLists.txt | 6 +- paddle/operators/functor/CMakeLists.txt | 5 + paddle/operators/functor/math_functor.cc | 42 +++++++ paddle/operators/functor/math_functor.cu | 42 +++++++ paddle/operators/functor/math_functor.h | 32 +++++ paddle/operators/lookup_table_op.cc | 71 +++++++++++ paddle/operators/lookup_table_op.cu | 116 ++++++++++++++++++ paddle/operators/lookup_table_op.h | 75 +++++++++++ paddle/platform/cuda_helper.h | 57 +++++++++ .../paddle/v2/framework/tests/CMakeLists.txt | 1 + .../v2/framework/tests/test_lookup_table.py | 31 +++++ 12 files changed, 477 insertions(+), 2 deletions(-) create mode 100644 paddle/operators/functor/CMakeLists.txt create mode 100644 paddle/operators/functor/math_functor.cc create mode 100644 paddle/operators/functor/math_functor.cu create mode 100644 paddle/operators/functor/math_functor.h create mode 100644 paddle/operators/lookup_table_op.cc create mode 100644 paddle/operators/lookup_table_op.cu create mode 100644 paddle/operators/lookup_table_op.h create mode 100644 paddle/platform/cuda_helper.h create mode 100644 python/paddle/v2/framework/tests/test_lookup_table.py diff --git a/paddle/framework/pybind.cc b/paddle/framework/pybind.cc index f0114b9e4..68c5526bb 100644 --- a/paddle/framework/pybind.cc +++ b/paddle/framework/pybind.cc @@ -42,6 +42,7 @@ USE_OP(fill_zeros_like); USE_OP_ITSELF(recurrent_op); USE_OP(gaussian_random); USE_OP(uniform_random); +USE_OP(lookup_table); namespace paddle { namespace framework { diff --git a/paddle/operators/CMakeLists.txt b/paddle/operators/CMakeLists.txt index a7c89787e..1ca5010ea 100644 --- a/paddle/operators/CMakeLists.txt +++ b/paddle/operators/CMakeLists.txt @@ -42,6 +42,8 @@ function(op_library TARGET) endfunction() add_subdirectory(math) +add_subdirectory(functor) + cc_test(gather_test SRCS gather_test.cc DEPS tensor) cc_test(scatter_test SRCS scatter_test.cc DEPS tensor) @@ -66,5 +68,5 @@ op_library(sgd_op SRCS sgd_op.cc sgd_op.cu) op_library(recurrent_op SRCS recurrent_op.cc rnn/recurrent_op_utils.cc DEPS framework_proto tensor op_registry operator net_op) -op_library(uniform_random_op - SRCS uniform_random_op.cc uniform_random_op.cu) +op_library(uniform_random_op SRCS uniform_random_op.cc uniform_random_op.cu) +op_library(lookup_table_op SRCS lookup_table_op.cc lookup_table_op.cu DEPS math_functor) diff --git a/paddle/operators/functor/CMakeLists.txt b/paddle/operators/functor/CMakeLists.txt new file mode 100644 index 000000000..d3b39e5fc --- /dev/null +++ b/paddle/operators/functor/CMakeLists.txt @@ -0,0 +1,5 @@ +if(WITH_GPU) + nv_library(math_functor SRCS math_functor.cc math_functor.cu DEPS device_context) +else() + cc_library(math_functor SRCS math_functor.cc DEPS device_context) +endif() diff --git a/paddle/operators/functor/math_functor.cc b/paddle/operators/functor/math_functor.cc new file mode 100644 index 000000000..1f2767f17 --- /dev/null +++ b/paddle/operators/functor/math_functor.cc @@ -0,0 +1,42 @@ +/* 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/functor/math_functor.h" +#include "paddle/framework/eigen.h" + +namespace paddle { +namespace operators { +namespace functor { + +template +struct Set { + void operator()(const T alpha, framework::Tensor* Y, + platform::DeviceContext* context) { + int N = product(Y->dims()); + T* YData = Y->mutable_data(context->GetPlace()); + if (alpha == static_cast(0)) { + memset(YData, 0, N * sizeof(T)); + } else { + framework::EigenVector::Flatten(*Y) + .setConstant(alpha); + } + } +}; + +template struct Set; +template struct Set; + +} // namespace functor +} // namespace operators +} // namespace paddle diff --git a/paddle/operators/functor/math_functor.cu b/paddle/operators/functor/math_functor.cu new file mode 100644 index 000000000..6dc828c60 --- /dev/null +++ b/paddle/operators/functor/math_functor.cu @@ -0,0 +1,42 @@ +/* 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/functor/math_functor.h" +#include "paddle/platform/cuda_helper.h" + +namespace paddle { +namespace operators { +namespace functor { + +template +__global__ void SetKernel(const int N, const T alpha, T* Y) { + CUDA_1D_KERNEL_LOOP(i, N) { Y[i] = alpha; } +} + +template +struct Set { + void operator()(const T alpha, framework::Tensor* Y, + platform::DeviceContext* context) { + int N = product(Y->dims()); + T* YData = Y->mutable_data(context->GetPlace()); + SetKernel<<<(N + 512 - 1) / 512, 512>>>(N, alpha, YData); + } +}; + +template struct Set; +template struct Set; + +} // namespace functor +} // namespace operators +} // namespace paddle diff --git a/paddle/operators/functor/math_functor.h b/paddle/operators/functor/math_functor.h new file mode 100644 index 000000000..d5c7bd368 --- /dev/null +++ b/paddle/operators/functor/math_functor.h @@ -0,0 +1,32 @@ +/* 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" + +namespace paddle { +namespace operators { +namespace functor { + +template +struct Set { + void operator()(const T alpha, paddle::framework::Tensor* Y, + paddle::platform::DeviceContext* context); +}; + +} // namespace functor +} // namespace operators +} // namespace paddle diff --git a/paddle/operators/lookup_table_op.cc b/paddle/operators/lookup_table_op.cc new file mode 100644 index 000000000..5f70458a8 --- /dev/null +++ b/paddle/operators/lookup_table_op.cc @@ -0,0 +1,71 @@ +/* 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/lookup_table_op.h" + +namespace paddle { +namespace operators { + +class LookupTableOp : public framework::OperatorWithKernel { + public: + using framework::OperatorWithKernel::OperatorWithKernel; + + protected: + void InferShape(const framework::InferShapeContext &context) const override { + auto table_t = context.Input("W"); + auto ids_t = context.Input("Ids"); + auto output_t = context.Output("Out"); + + output_t->Resize({ids_t->dims()[0], table_t->dims()[1]}); + } +}; + +class LookupTableOpMaker : public framework::OpProtoAndCheckerMaker { + public: + LookupTableOpMaker(framework::OpProto *proto, + framework::OpAttrChecker *op_checker) + : OpProtoAndCheckerMaker(proto, op_checker) { + AddInput("W", + "An input represents embedding tensors," + " which is a learnable parameter."); + AddInput("Ids", + "An input with type int32 or int64" + "contains the ids to be looked up in W.") + .NotInGradient(); + AddOutput("Out", "The lookup results, which have the same type with W."); + AddComment( + "This operator is used to perform lookups on the parameter W," + "then concatenated into a dense tensor."); + } +}; + +class LookupTableOpGrad : public framework::OperatorWithKernel { + public: + using framework::OperatorWithKernel::OperatorWithKernel; + + protected: + void InferShape(const framework::InferShapeContext &context) const override { + context.Output(0)->Resize(context.Input(0)->dims()); + } +}; + +} // namespace operators +} // namespace paddle + +namespace ops = paddle::operators; +REGISTER_OP(lookup_table, ops::LookupTableOp, ops::LookupTableOpMaker, + lookup_table_grad, ops::LookupTableOpGrad); + +REGISTER_OP_CPU_KERNEL(lookup_table, ops::LookupTableKernel); +REGISTER_OP_CPU_KERNEL(lookup_table_grad, ops::LookupTableGradKernel); diff --git a/paddle/operators/lookup_table_op.cu b/paddle/operators/lookup_table_op.cu new file mode 100644 index 000000000..94b440e00 --- /dev/null +++ b/paddle/operators/lookup_table_op.cu @@ -0,0 +1,116 @@ +/* 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/framework/op_registry.h" +#include "paddle/operators/functor/math_functor.h" +#include "paddle/platform/assert.h" +#include "paddle/platform/cuda_helper.h" + +namespace paddle { +namespace operators { + +using Tensor = framework::Tensor; + +template +__global__ void LookupTable(T* output, const T* table, const uint32_t* ids, + const int N, const int K, const int D) { + int idx = threadIdx.x; + int idy = blockIdx.x + threadIdx.y * gridDimX; + + while (idy < K) { + int id = ids[idy]; + PADDLE_ASSERT(id >= 0); + PADDLE_ASSERT(id < N); + T* out = output + idy; + const T* tab = table + id; + for (int i = idx; i < D; i += blockDimX) { + out[i] = tab[i]; + } + idy += blockDimY * gridDimX; + } +} + +template +__global__ void LookupTableGradKernel(T* table, const T* output, + const uint32_t* ids, const int N, + const int K, const int D) { + int idx = threadIdx.x; + int idy = blockIdx.x + threadIdx.y * gridDimX; + + while (idy < K) { + int id = ids[idy]; + PADDLE_ASSERT(id >= 0); + PADDLE_ASSERT(id < N); + const T* out = output + idy; + T* tab = table + id; + for (int i = idx; i < D; i += blockDimX) { + paddle::platform::CudaAtomicAdd(tab + i, out[i]); + } + idy += blockDimY * gridDimX; + } +} + +template +class LookupTableCUDAKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& context) const override { + auto table_t = context.Input("W"); + auto ids_t = context.Input("Ids"); + auto output_t = context.Output("Out"); + + size_t N = table_t->dims()[0]; + size_t D = table_t->dims()[1]; + size_t K = product(ids_t->dims()); + auto ids = ids_t->data(); + auto table = table_t->data(); + auto output = output_t->mutable_data(context.GetPlace()); + + dim3 threads(128, 8); + dim3 grids(8, 1); + LookupTable<<>>(output, table, ids, N, K, D); + } +}; + +template +class LookupTableGrad : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& context) const override { + auto ids_t = context.Input("Ids"); + auto d_output_t = context.Input(framework::GradVarName("Out")); + auto d_table_t = context.Output(framework::GradVarName("W")); + + int N = d_table_t->dims()[0]; + int D = d_table_t->dims()[1]; + int K = product(ids_t->dims()); + const uint32_t* ids = ids_t->data(); + T* d_table = d_table_t->mutable_data(context.GetPlace()); + const T* d_output = d_output_t->data(); + + auto* device_context = + const_cast(context.device_context_); + functor::Set()(static_cast(0), d_table_t, + device_context); + dim3 threads(128, 8); + dim3 grids(8, 1); + LookupTableGradKernel<<>>(d_table, d_output, + ids, N, K, D); + } +}; + +} // namespace operators +} // namespace paddle + +namespace ops = paddle::operators; +REGISTER_OP_GPU_KERNEL(lookup_table, ops::LookupTableCUDAKernel); +REGISTER_OP_GPU_KERNEL(lookup_table_grad, ops::LookupTableGrad); diff --git a/paddle/operators/lookup_table_op.h b/paddle/operators/lookup_table_op.h new file mode 100644 index 000000000..790ecab3c --- /dev/null +++ b/paddle/operators/lookup_table_op.h @@ -0,0 +1,75 @@ +/* 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/functor/math_functor.h" + +namespace paddle { +namespace operators { + +using Tensor = framework::Tensor; + +template +class LookupTableKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& context) const override { + auto table_t = context.Input("W"); // float tensor + auto ids_t = context.Input("Ids"); // int tensor + auto output_t = context.Output("Out"); // float tensor + + size_t N = table_t->dims()[0]; + size_t D = table_t->dims()[1]; + auto ids = ids_t->data(); + auto table = table_t->data(); + auto output = output_t->mutable_data(context.GetPlace()); + for (size_t i = 0; i < product(ids_t->dims()); ++i) { + PADDLE_ENFORCE_LT(ids[i], N); + PADDLE_ENFORCE_GE(ids[i], 0); + memcpy(output + i * D, table + ids[i] * D, D * sizeof(T)); + } + } +}; + +template +class LookupTableGradKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& context) const override { + auto ids_t = context.Input("Ids"); + auto d_output_t = context.Input(framework::GradVarName("Out")); + auto d_table_t = context.Output(framework::GradVarName("W")); + + size_t N = d_table_t->dims()[0]; + size_t D = d_table_t->dims()[1]; + auto ids = ids_t->data(); + T* d_table = d_table_t->mutable_data(context.GetPlace()); + const T* d_output = d_output_t->data(); + + auto* device_context = + const_cast(context.device_context_); + functor::Set()(static_cast(0), d_table_t, + device_context); + for (size_t i = 0; i < product(ids_t->dims()); ++i) { + PADDLE_ENFORCE_LT(ids[i], N); + PADDLE_ENFORCE_GE(ids[i], 0); + for (size_t j = 0; j < D; ++j) { + d_table[ids[i] * D + j] += d_output[i * D + j]; + } + } + } +}; + +} // namespace operators +} // namespace paddle diff --git a/paddle/platform/cuda_helper.h b/paddle/platform/cuda_helper.h new file mode 100644 index 000000000..434629111 --- /dev/null +++ b/paddle/platform/cuda_helper.h @@ -0,0 +1,57 @@ +/* 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 + +namespace paddle { +namespace platform { + +#define CUDA_1D_KERNEL_LOOP(i, n) \ + for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < n; \ + i += blockDim.x * gridDim.x) + +#define CUDA_ATOMIC_WRAPPER(op, T) \ + __device__ __forceinline__ T CudaAtomic##op(T* address, const T val) + +#define USE_CUDA_ATOMIC(op, T) \ + CUDA_ATOMIC_WRAPPER(op, T) { return atomic##op(address, val); } + +// For atomicAdd. +USE_CUDA_ATOMIC(Add, float); + +#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 600 +USE_CUDA_ATOMIC(Add, double); +#else +// Custom implementation of atomicAdd for double. +// This implementation is copied from CUDA manual. +CUDA_ATOMIC_WRAPPER(Add, double) { + unsigned long long int* address_as_ull = + reinterpret_cast(address); + unsigned long long int old = *address_as_ull, assumed; + + do { + assumed = old; + old = atomicCAS(address_as_ull, assumed, + __double_as_longlong(val + __longlong_as_double(assumed))); + + // Note: uses integer comparison to avoid hang in case of NaN + } while (assumed != old); + + return __longlong_as_double(old); +#endif +} + +} // namespace platform +} // namespace paddle diff --git a/python/paddle/v2/framework/tests/CMakeLists.txt b/python/paddle/v2/framework/tests/CMakeLists.txt index ce57a0713..65c02f2cf 100644 --- a/python/paddle/v2/framework/tests/CMakeLists.txt +++ b/python/paddle/v2/framework/tests/CMakeLists.txt @@ -27,3 +27,4 @@ py_test(test_uniform_random_op SRCS test_uniform_random_op.py) py_test(test_recurrent_op SRCS test_recurrent_op.py) py_test(test_sgd_op SRCS test_sgd_op.py) py_test(test_gradient_checker SRCS test_gradient_checker.py) +py_test(test_lookup_table SRCS test_lookup_table.py) diff --git a/python/paddle/v2/framework/tests/test_lookup_table.py b/python/paddle/v2/framework/tests/test_lookup_table.py new file mode 100644 index 000000000..071069768 --- /dev/null +++ b/python/paddle/v2/framework/tests/test_lookup_table.py @@ -0,0 +1,31 @@ +import unittest +import numpy as np +from op_test_util import OpTestMeta +from gradient_checker import GradientChecker, create_op + + +class TestSigmoidOp(unittest.TestCase): + __metaclass__ = OpTestMeta + + def setUp(self): + self.type = 'lookup_table' + table = np.random.random((17, 31)).astype('float32') + ids = np.random.randint(0, 17, 4) + self.inputs = {'W': table, 'Ids': ids} + self.outputs = {'Out': table[ids]} + + +class TestSigmoidGradOp(GradientChecker): + def test_grad(self): + op = create_op('lookup_table') + table = np.random.random((17, 31)).astype('float32') + ids = np.random.randint(0, 17, 4) + inputs = {'W': table, 'Ids': ids} + # compare gradients between cpu and gpu + self.compare_grad(op, inputs) + # check gradients + self.check_grad(op, inputs, set('W'), 'Out') + + +if __name__ == '__main__': + unittest.main() -- GitLab From c91e542ad3a5a1ecd1c6b825d7c9e89d3e7384b5 Mon Sep 17 00:00:00 2001 From: dangqingqing Date: Tue, 22 Aug 2017 17:18:14 +0800 Subject: [PATCH 0169/2018] fix compile for paddle_pybind. --- paddle/framework/CMakeLists.txt | 1 + 1 file changed, 1 insertion(+) diff --git a/paddle/framework/CMakeLists.txt b/paddle/framework/CMakeLists.txt index 68304c9fc..325a6f753 100644 --- a/paddle/framework/CMakeLists.txt +++ b/paddle/framework/CMakeLists.txt @@ -55,5 +55,6 @@ cc_library(paddle_pybind SHARED recurrent_op uniform_random_op gaussian_random_op + lookup_table_op fill_zeros_like_op) endif(WITH_PYTHON) -- GitLab From 5ca4118451a38a8fa1e876fd5416028010ec218b Mon Sep 17 00:00:00 2001 From: Liu Yiqun Date: Tue, 22 Aug 2017 17:27:04 +0800 Subject: [PATCH 0170/2018] Update Dockerfile of android to support building for arm64-v8a and armeabi. --- Dockerfile.android | 18 ++++--- paddle/scripts/docker/build_android.sh | 65 +++++++++++++++++++------- 2 files changed, 61 insertions(+), 22 deletions(-) diff --git a/Dockerfile.android b/Dockerfile.android index c0fa58c38..aa95abb36 100644 --- a/Dockerfile.android +++ b/Dockerfile.android @@ -4,9 +4,15 @@ MAINTAINER PaddlePaddle Authors ARG UBUNTU_MIRROR RUN /bin/bash -c 'if [[ -n ${UBUNTU_MIRROR} ]]; then sed -i 's#http://archive.ubuntu.com/ubuntu#${UBUNTU_MIRROR}#g' /etc/apt/sources.list; fi' +# ENV variables +ARG ANDROID_ABI + +ENV ANDROID_ABI=${ANDROID_ABI:-"armeabi-v7a"} + ENV HOME=/root \ ANDROID_NDK_HOME=/opt/android-ndk-linux \ - ANDROID_STANDALONE_TOOLCHAIN=/opt/android-toolchain-gcc + ANDROID_ARM_STANDALONE_TOOLCHAIN=/opt/arm-toolchain-gcc \ + ANDROID_ARM64_STANDALONE_TOOLCHAIN=/opt/arm64-toolchain-gcc RUN apt-get update && \ apt-get install -y \ @@ -15,12 +21,11 @@ RUN apt-get update && \ apt-get clean -y # Install Go and glide -RUN wget -O go.tgz https://storage.googleapis.com/golang/go1.8.1.linux-amd64.tar.gz && \ - tar -C /usr/local -xzf go.tgz && \ +RUN wget -qO- go.tgz https://storage.googleapis.com/golang/go1.8.1.linux-amd64.tar.gz | \ + tar -xz -C /usr/local && \ mkdir /root/gopath && \ mkdir /root/gopath/bin && \ - mkdir /root/gopath/src && \ - rm go.tgz + mkdir /root/gopath/src ENV GOROOT=/usr/local/go GOPATH=/root/gopath # should not be in the same line with GOROOT definition, otherwise docker build could not find GOROOT. ENV PATH=${PATH}:${GOROOT}/bin:${GOPATH}/bin @@ -42,7 +47,8 @@ RUN mkdir /opt/android-ndk-tmp && \ wget -q https://dl.google.com/android/repository/android-ndk-r14b-linux-x86_64.zip && \ unzip -q android-ndk-r14b-linux-x86_64.zip && \ mv android-ndk-r14b ${ANDROID_NDK_HOME} && \ - ${ANDROID_NDK_HOME}/build/tools/make-standalone-toolchain.sh --arch=arm --platform=android-21 --install-dir=${ANDROID_STANDALONE_TOOLCHAIN} && \ + ${ANDROID_NDK_HOME}/build/tools/make-standalone-toolchain.sh --arch=arm --platform=android-21 --install-dir=${ANDROID_ARM_STANDALONE_TOOLCHAIN} && \ + ${ANDROID_NDK_HOME}/build/tools/make-standalone-toolchain.sh --arch=arm64 --platform=android-21 --install-dir=${ANDROID_ARM64_STANDALONE_TOOLCHAIN} && \ rm -rf /opt/android-ndk-tmp && \ rm -rf ${ANDROID_NDK_HOME} diff --git a/paddle/scripts/docker/build_android.sh b/paddle/scripts/docker/build_android.sh index 5584e29e2..593ae28e4 100644 --- a/paddle/scripts/docker/build_android.sh +++ b/paddle/scripts/docker/build_android.sh @@ -2,22 +2,55 @@ set -xe -mkdir -p /paddle/build_android -cd /paddle/build_android +mkdir -p /paddle/build_android/$ANDROID_ABI +cd /paddle/build_android/$ANDROID_ABI rm -rf /paddle/install 2>/dev/null || true -cmake -DCMAKE_SYSTEM_NAME=Android \ - -DANDROID_STANDALONE_TOOLCHAIN=$ANDROID_STANDALONE_TOOLCHAIN \ - -DANDROID_ABI=armeabi-v7a \ - -DANDROID_ARM_NEON=ON \ - -DANDROID_ARM_MODE=ON \ - -DHOST_C_COMPILER=/usr/bin/gcc \ - -DHOST_CXX_COMPILER=/usr/bin/g++ \ - -DCMAKE_INSTALL_PREFIX=/paddle/install \ - -DCMAKE_BUILD_TYPE=RelWithDebInfo \ - -DCMAKE_C_FLAGS_RELWITHDEBINFO="-O3" \ - -DCMAKE_CXX_FLAGS_RELWITHDEBINFO="-O3" \ - -DWITH_C_API=ON \ - -DWITH_SWIG_PY=OFF \ - .. + +THIRD_PARTY_PATH=/paddle/third_party_android/$ANDROID_ABI + +if [ $ANDROID_ABI == "armeabi-v7a" ]; then + cmake -DCMAKE_SYSTEM_NAME=Android \ + -DANDROID_STANDALONE_TOOLCHAIN=$ANDROID_ARM_STANDALONE_TOOLCHAIN \ + -DANDROID_ABI=$ANDROID_ABI \ + -DANDROID_ARM_NEON=ON \ + -DANDROID_ARM_MODE=ON \ + -DHOST_C_COMPILER=/usr/bin/gcc \ + -DHOST_CXX_COMPILER=/usr/bin/g++ \ + -DCMAKE_INSTALL_PREFIX=/paddle/install \ + -DTHIRD_PARTY_PATH=$THIRD_PARTY_PATH \ + -DCMAKE_BUILD_TYPE=Release \ + -DWITH_C_API=ON \ + -DWITH_SWIG_PY=OFF \ + /paddle +elif [ $ANDROID_ABI == "arm64-v7a" ]; then + cmake -DCMAKE_SYSTEM_NAME=Android \ + -DANDROID_STANDALONE_TOOLCHAIN=$ANDROID_ARM64_STANDALONE_TOOLCHAIN \ + -DANDROID_ABI=$ANDROID_ABI \ + -DANDROID_ARM_MODE=ON \ + -DHOST_C_COMPILER=/usr/bin/gcc \ + -DHOST_CXX_COMPILER=/usr/bin/g++ \ + -DCMAKE_INSTALL_PREFIX=/paddle/install \ + -DTHIRD_PARTY_PATH=$THIRD_PARTY_PATH \ + -DCMAKE_BUILD_TYPE=Release \ + -DWITH_C_API=ON \ + -DWITH_SWIG_PY=OFF \ + /paddle +elif [ $ANDROID_ABI == "armeabi" ]; then + cmake -DCMAKE_SYSTEM_NAME=Android \ + -DANDROID_STANDALONE_TOOLCHAIN=$ANDROID_ARM_STANDALONE_TOOLCHAIN \ + -DANDROID_ABI=$ANDROID_ABI \ + -DANDROID_ARM_MODE=ON \ + -DHOST_C_COMPILER=/usr/bin/gcc \ + -DHOST_CXX_COMPILER=/usr/bin/g++ \ + -DCMAKE_INSTALL_PREFIX=/paddle/install \ + -DTHIRD_PARTY_PATH=$THIRD_PARTY_PATH \ + -DCMAKE_BUILD_TYPE=Release \ + -DWITH_C_API=ON \ + -DWITH_SWIG_PY=OFF \ + /paddle +else + echo "Invalid ANDROID_ABI: $ANDROID_ABI" +fi + make -j `nproc` make install -j `nproc` -- GitLab From 9bc1a1a126dc60f06cd353ff72869416d50eb3af Mon Sep 17 00:00:00 2001 From: dangqingqing Date: Tue, 22 Aug 2017 18:19:04 +0800 Subject: [PATCH 0171/2018] fix cuda_helper.h --- paddle/platform/cuda_helper.h | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/paddle/platform/cuda_helper.h b/paddle/platform/cuda_helper.h index 434629111..939c3713a 100644 --- a/paddle/platform/cuda_helper.h +++ b/paddle/platform/cuda_helper.h @@ -34,8 +34,6 @@ USE_CUDA_ATOMIC(Add, float); #if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 600 USE_CUDA_ATOMIC(Add, double); #else -// Custom implementation of atomicAdd for double. -// This implementation is copied from CUDA manual. CUDA_ATOMIC_WRAPPER(Add, double) { unsigned long long int* address_as_ull = reinterpret_cast(address); @@ -50,8 +48,8 @@ CUDA_ATOMIC_WRAPPER(Add, double) { } while (assumed != old); return __longlong_as_double(old); -#endif } +#endif } // namespace platform } // namespace paddle -- GitLab From 8f4ca2d12fffe38d5adff0ad74db6ba1bdc0d223 Mon Sep 17 00:00:00 2001 From: caoying03 Date: Wed, 16 Aug 2017 15:34:02 +0800 Subject: [PATCH 0172/2018] add implementations. --- .../gserver/layers/CrossEntropyOverBeam.cpp | 344 +++++++++++++++++- paddle/gserver/layers/CrossEntropyOverBeam.h | 98 +++++ .../tests/test_CrossEntropyOverBeamGrad.cpp | 166 ++++++--- 3 files changed, 549 insertions(+), 59 deletions(-) diff --git a/paddle/gserver/layers/CrossEntropyOverBeam.cpp b/paddle/gserver/layers/CrossEntropyOverBeam.cpp index 88d80aa83..09258fb30 100644 --- a/paddle/gserver/layers/CrossEntropyOverBeam.cpp +++ b/paddle/gserver/layers/CrossEntropyOverBeam.cpp @@ -16,6 +16,168 @@ limitations under the License. */ namespace paddle { +void CostForOneSequence::calValidExpandStep() { + validExpansionCount_ = 0; + goldAsExtraPath_ = true; + + for (size_t i = 0; i < beams_->expansionCount; ++i) { + real gold = static_cast(beams_->gold[i]); + if (i) { + real* start = beams_->candidateIds[i - 1]->getData(); + goldRowIds_[i] = std::count_if( + start, + start + goldRowIds_[i - 1] * beamSize_ + goldColIds_[i - 1], + [](const real& val) { return val != -1.; }); + } else + goldRowIds_[i] = 0; + + real* start = + beams_->candidateIds[i]->getData() + goldRowIds_[i] * beamSize_; + real* findEnd = std::find(start, start + beamSize_, gold); + validExpansionCount_++; + + if (start + beamSize_ == findEnd) return; + goldColIds_[i] = findEnd - start; + } + + if (goldColIds_[beams_->expansionCount - 1] != -1) goldAsExtraPath_ = false; +} + +size_t CostForOneSequence::initLastExpansion() { + int beamId = validExpansionCount_ - 1; + const MatrixPtr candidates = beams_->candidateIds[beamId]; + size_t height = candidates->getHeight(); + + /* initialization the last expansion. */ + size_t pathCount = std::count_if(candidates->getData(), + candidates->getData() + height * beamSize_, + [](const real& val) { return val != -1; }); + /* + * if the gold sequence falls off the beam during search, + * add the gold sequence as the last path into all expanded paths. + */ + if (goldAsExtraPath_) goldIdsInFinalExpansion_ = pathCount++; + + pathRowIdsInEachBeam_.clear(); + pathRowIdsInEachBeam_.resize(validExpansionCount_, + std::vector(pathCount, 0)); + parentIdsInBeam_.clear(); + parentIdsInBeam_.resize(pathCount, 0); + + if (goldAsExtraPath_) { + /* add gold sequence into the total expansion. */ + pathRowIdsInEachBeam_[beamId].back() = + beams_->gold[beamId] + + getSeqStartPos(beamId, goldRowIds_[validExpansionCount_ - 1]); + parentIdsInBeam_.back() = goldRowIds_[validExpansionCount_ - 1]; + } else { + size_t goldOffset = goldRowIds_[beamId] * beamSize_ + goldColIds_[beamId]; + goldIdsInFinalExpansion_ = + std::count_if(candidates->getData(), + candidates->getData() + goldOffset, + [](const real& val) { return val != -1.; }); + } + + /* + * TODO(caoying): fix this, store the indices of selected candidate + * paths into Argument.ids + */ + real* ids = candidates->getData(); + size_t curIdx = 0; + for (size_t i = 0; i < height; ++i) { + int basePos = getSeqStartPos(beamId, i); + for (size_t j = 0; j < beamSize_; ++j) { + int id = ids[i * beamSize_ + j]; + if (id == -1) continue; + pathRowIdsInEachBeam_[beamId][curIdx] = id + basePos; + parentIdsInBeam_[curIdx++] = i; + } + } + return pathCount; +} + +void CostForOneSequence::constructTotalExpansion() { + /* + * construct the entire expanded beam by begining with the last search + * in which gold falls off the beam. + */ + size_t totalPathCount = initLastExpansion(); + + for (int beamId = validExpansionCount_ - 2; beamId >= 0; --beamId) { + const MatrixPtr candidates = beams_->candidateIds[beamId]; + real* ids = candidates->getData(); + + int lastParentIdInBeam = -1; + int basePos = -1; + for (size_t i = 0; + i < (goldAsExtraPath_ ? totalPathCount - 1 : totalPathCount); + ++i) { + int id = ids[parentIdsInBeam_[i]]; + int parentRowId = std::div(parentIdsInBeam_[i], beamSize_).quot; + if (parentIdsInBeam_[i] != lastParentIdInBeam) + basePos = getSeqStartPos(beamId, parentRowId); + + pathRowIdsInEachBeam_[beamId][i] = id + basePos; + lastParentIdInBeam = parentIdsInBeam_[i]; + parentIdsInBeam_[i] = parentRowId; + + if (goldAsExtraPath_) + pathRowIdsInEachBeam_[beamId][totalPathCount - 1] = + beams_->gold[beamId] + getSeqStartPos(beamId, goldRowIds_[beamId]); + } + } +} + +real CostForOneSequence::globallyNormalizedScore() { + expandedPathScores_.resize(validExpansionCount_); + + Matrix::resizeOrCreate( + softmaxOut_, 1, pathRowIdsInEachBeam_[0].size(), false, false); + softmaxOut_->zero(); + MatrixPtr tmp = Matrix::create( + softmaxOut_->getData(), softmaxOut_->getWidth(), 1, false, false); + + for (size_t i = 0; i < validExpansionCount_; ++i) { + Matrix::resizeOrCreate(expandedPathScores_[i], + pathRowIdsInEachBeam_[i].size(), + 1, + false, + false); + IVectorPtr rowIds = IVector::create(pathRowIdsInEachBeam_[i].data(), + pathRowIdsInEachBeam_[i].size(), + false); + expandedPathScores_[i]->selectRows(*(beams_->scores[i]), *rowIds); + tmp->add(*expandedPathScores_[i]); + } + + softmaxOut_->softmax(*softmaxOut_); + return -std::log(softmaxOut_->getData()[goldIdsInFinalExpansion_]); +} + +real CostForOneSequence::forward() { + calValidExpandStep(); + constructTotalExpansion(); + return globallyNormalizedScore(); +} + +void CostForOneSequence::backward() { + softmaxOut_->getData()[goldIdsInFinalExpansion_] -= 1.; + MatrixPtr tmp = Matrix::create( + softmaxOut_->getData(), softmaxOut_->getWidth(), 1, false, false); + + for (size_t i = 0; i < validExpansionCount_; ++i) { + IVectorPtr rowIds = IVector::create(pathRowIdsInEachBeam_[i].data(), + pathRowIdsInEachBeam_[i].size(), + false); + /* + beams_->scoreGrad[i] has been intialized outside this class, this + class only keeps a pointer pointing to the original input gradients, + so here does not need to allocate or initalize the memory. + */ + tmp->addToRows(*beams_->scoreGrad[i], *rowIds); + } +} + REGISTER_LAYER(cross_entropy_over_beam, CrossEntropyOverBeam); bool CrossEntropyOverBeam::init(const LayerMap& layerMap, @@ -24,13 +186,189 @@ bool CrossEntropyOverBeam::init(const LayerMap& layerMap, Layer::init(layerMap, parameterMap); CHECK_EQ(0U, inputLayers_.size() % 3) << "Error input number."; - setNeedSequenceInfo(false); + beamExpanCount_ = inputLayers_.size() / 3; + + candidateScores_.resize(beamExpanCount_); + candidateScoreGrad_.resize(beamExpanCount_); + candidateInBeam_.resize(beamExpanCount_); + goldSequence_.resize(beamExpanCount_); + gradToInputs_.resize(beamExpanCount_); + + setNeedSequenceInfo(false); return true; } -void CrossEntropyOverBeam::forward(PassType passType) {} +void CrossEntropyOverBeam::checkInputs() { + batchSize_ = 0; + for (size_t i = 0; i < beamExpanCount_; ++i) { + const Argument& scores = getInput(i * 3); + const Argument& selCandidates = getInput(i * 3 + 1); + const Argument& goldSeq = getInput(i * 3 + 2); + + if (i) { + CHECK(scores.hasSubseq()) << "Beam expansion expect the first one, " + "should be a nested sequence"; + CHECK_EQ(getInputValue(i * 3 + 1)->getWidth(), beamSize_); + CHECK_EQ(scores.getNumSequences(), batchSize_); + CHECK_EQ(scores.getNumSubSequences(), selCandidates.getBatchSize()); + } else { + CHECK(scores.hasSeq()) << "The first beam expansion should be a sequence"; + batchSize_ = scores.getNumSequences(); + beamSize_ = getInputValue(i * 3 + 1)->getWidth(); + CHECK_EQ(batchSize_, selCandidates.getBatchSize()); + } + CHECK_EQ(1U, scores.value->getWidth()); + CHECK_EQ(batchSize_, goldSeq.getBatchSize()); + } +} + +void CrossEntropyOverBeam::copyInputsToCpu() { + auto copyValue = [](const MatrixPtr& src, MatrixPtr& trg) { + if (dynamic_cast(src.get())) { + Matrix::resizeOrCreate( + trg, src->getHeight(), src->getWidth(), false, false); + trg->copyFrom(*src); + } else { + trg = std::move(src); + } + }; + + auto copyIds = [](const IVectorPtr& src, IVectorPtr& trg) { + if (dynamic_cast(src.get())) { + IVector::resizeOrCreate(trg, src->getSize(), false); + trg->copyFrom(*src); + } else { + trg = std::move(src); + } + }; + + beamSplitPos_.clear(); + beamSplitPos_.resize(batchSize_, std::vector(beamExpanCount_, 0)); + for (size_t i = 0; i < beamExpanCount_; ++i) { + copyValue(getInputValue(i * 3), candidateScores_[i]); + copyValue(getInputValue(i * 3 + 1), candidateInBeam_[i]); + copyIds(getInput(i * 3 + 2).ids, goldSequence_[i]); + + if (i) { + ICpuGpuVectorPtr seqInfo = getInput(i * 3).sequenceStartPositions; + const int* seqStarts = seqInfo->getMutableData(false); + ICpuGpuVectorPtr subSeqInfo = getInput(i * 3).subSequenceStartPositions; + const int* subSeqStarts = subSeqInfo->getMutableData(false); + + size_t seqId = 1; + for (size_t subSeqId = 0; subSeqId < subSeqInfo->getSize() - 1; + ++subSeqId) { + CHECK_LT(seqId, seqInfo->getSize()); + if (subSeqStarts[subSeqId] == seqStarts[seqId]) { + beamSplitPos_[seqId][i] = beamSplitPos_[seqId - 1][i]; + seqId++; + } + beamSplitPos_[seqId - 1][i]++; + } + } else { + for (size_t j = 0; j < batchSize_; ++j) beamSplitPos_[j][i] = j + 1; + } + } +} + +void CrossEntropyOverBeam::splitBatchBeams() { + beamCosts_.resize(batchSize_); + beamPerSeq_.resize(batchSize_, beamExpanCount_); + + for (size_t i = 0; i < beamExpanCount_; ++i) { + int* seqStarts = + getInput(i * 3).sequenceStartPositions->getMutableData(false); + + int* subSeqStarts = nullptr; + int maxLen = 0; + if (i) { + subSeqStarts = + getInput(i * 3).subSequenceStartPositions->getMutableData(false); + maxLen = getInput(i * 3).subSequenceStartPositions->getSize() - 1; + } else + maxLen = getInput(i).sequenceStartPositions->getSize() - 1; + + for (size_t j = 0; j < batchSize_; ++j) { + beamPerSeq_[j].scores[i] = + Matrix::create(candidateScores_[i]->getData() + seqStarts[j], + seqStarts[j + 1] - seqStarts[j], + 1, + false, + false); + beamPerSeq_[j].scoreGrad[i] = + Matrix::create(candidateScoreGrad_[i]->getData() + seqStarts[j], + seqStarts[j + 1] - seqStarts[j], + 1, + false, + false); + + int offset = j ? beamSplitPos_[j - 1][i] : 0; + int height = beamSplitPos_[j][i] - (j ? beamSplitPos_[j - 1][i] : 0); + CHECK_GE(maxLen, offset + height); + beamPerSeq_[j].seqInfo[i] = IVector::create( + (i ? subSeqStarts : seqStarts) + offset, height + 1, false); -void CrossEntropyOverBeam::backward(const UpdateCallback& callback) {} + beamPerSeq_[j].candidateIds[i] = + Matrix::create(candidateInBeam_[i]->getData() + offset * beamSize_, + height, + beamSize_, + false, + false); + beamPerSeq_[j].gold[i] = goldSequence_[i]->getData()[j]; + } + } +} + +void CrossEntropyOverBeam::resizeOutput() { + Matrix::resizeOrCreate(output_.value, batchSize_, 1, false, false); + output_.value->zero(); + + for (size_t i = 0; i < beamExpanCount_; ++i) { + MatrixPtr inGrad = getInputGrad(i * 3); + if (dynamic_cast(inGrad.get())) { + Matrix::resizeOrCreate(candidateScoreGrad_[i], + inGrad->getHeight(), + inGrad->getWidth(), + false, + false); + } else + candidateScoreGrad_[i] = std::move(inGrad); + candidateScoreGrad_[i]->zero(); + } +} + +void CrossEntropyOverBeam::copyGradToGpu(size_t copyCount) { + for (size_t i = 0; i < beamExpanCount_; ++i) { + if (dynamic_cast(getInputGrad(i * 3).get())) + getInputGrad(i * 3)->copyFrom(*candidateScoreGrad_[i]); + + if (i == copyCount - 1) break; + } +} + +void CrossEntropyOverBeam::forward(PassType passType) { + Layer::forward(passType); + + checkInputs(); + copyInputsToCpu(); + + resizeOutput(); + splitBatchBeams(); + + MatrixPtr outputValue = getOutputValue(); + for (size_t i = 0; i < batchSize_; ++i) { + beamCosts_[i].setData( + std::move(std::make_shared(beamPerSeq_[i])), beamSize_); + outputValue->getData()[i] = beamCosts_[i].forward(); + } +} + +void CrossEntropyOverBeam::backward(const UpdateCallback& callback) { + for (size_t i = 0; i < batchSize_; ++i) { + beamCosts_[i].backward(); + copyGradToGpu(beamCosts_[i].getValidExpansionCount()); + } +} } // namespace paddle diff --git a/paddle/gserver/layers/CrossEntropyOverBeam.h b/paddle/gserver/layers/CrossEntropyOverBeam.h index 3106f9858..96a5df7df 100644 --- a/paddle/gserver/layers/CrossEntropyOverBeam.h +++ b/paddle/gserver/layers/CrossEntropyOverBeam.h @@ -19,6 +19,79 @@ limitations under the License. */ namespace paddle { +struct BeamExpansion { + // store the entire beam expansion for a single sequence + std::vector scores; + std::vector seqInfo; + + std::vector candidateIds; + std::vector gold; + + std::vector scoreGrad; + + size_t expansionCount; + + BeamExpansion(int n) { + expansionCount = n; + scores.resize(expansionCount); + seqInfo.resize(expansionCount); + candidateIds.resize(expansionCount); + scoreGrad.resize(expansionCount); + + gold.resize(expansionCount); + }; +}; +typedef std::shared_ptr BeamExpansionPtr; + +class CostForOneSequence { +public: + CostForOneSequence() + : beamSize_(0), validExpansionCount_(0), goldAsExtraPath_(false) {} + void setData(const BeamExpansionPtr bPtr, size_t beamSize) { + beams_ = bPtr; + beamSize_ = beamSize; + + expandedPathScores_.clear(); + expandedPathScores_.resize(beams_->expansionCount); + + goldRowIds_.clear(); + goldRowIds_.resize(beams_->expansionCount, 0); + goldColIds_.clear(); + goldColIds_.resize(beams_->expansionCount, -1); + } + size_t getValidExpansionCount() { return validExpansionCount_; } + + real forward(); + void backward(); + +private: + void calValidExpandStep(); + void constructTotalExpansion(); + size_t initLastExpansion(); + real globallyNormalizedScore(); + + int getSeqStartPos(size_t beamId, size_t rowId) { + CHECK_GT(beams_->seqInfo[beamId]->getSize() - 1, rowId); + int* starts = beams_->seqInfo[beamId]->getData(); + return starts[rowId] - starts[0]; + }; + + size_t beamSize_; + size_t validExpansionCount_; + bool goldAsExtraPath_; + std::vector goldRowIds_; + std::vector goldColIds_; + + BeamExpansionPtr beams_; + std::vector> pathRowIdsInEachBeam_; + std::vector parentIdsInBeam_; + size_t goldIdsInFinalExpansion_; + + std::vector expandedPathScores_; + + MatrixPtr softmaxOut_; +}; + class CrossEntropyOverBeam : public Layer { public: explicit CrossEntropyOverBeam(const LayerConfig& config) : Layer(config) {} @@ -26,6 +99,31 @@ public: const ParameterMap& parameterMap) override; void forward(PassType passType) override; void backward(const UpdateCallback& callback) override; + +private: + void checkInputs(); + void copyInputsToCpu(); + void resizeOutput(); + void copyGradToGpu(size_t copyCount); + void splitBatchBeams(); + + size_t beamExpanCount_; + size_t batchSize_; + size_t beamSize_; + + // Currently, this layer only works on CPU, if its inputs is on GPU, + // copy them to CPU memory. + std::vector candidateScores_; + std::vector candidateScoreGrad_; + std::vector candidateInBeam_; + std::vector gradToInputs_; + std::vector goldSequence_; + std::vector> beamSplitPos_; + + // split entire bath of beams into beam per sequnence. + std::vector beamPerSeq_; + // beamCosts_ is used to propagate error in one sequence. + std::vector beamCosts_; }; } // namespace paddle diff --git a/paddle/gserver/tests/test_CrossEntropyOverBeamGrad.cpp b/paddle/gserver/tests/test_CrossEntropyOverBeamGrad.cpp index a5f06c15d..506a4281d 100644 --- a/paddle/gserver/tests/test_CrossEntropyOverBeamGrad.cpp +++ b/paddle/gserver/tests/test_CrossEntropyOverBeamGrad.cpp @@ -28,9 +28,17 @@ using namespace paddle; // NOLINT DECLARE_int32(gpu_id); DECLARE_bool(thread_local_rand_use_global_seed); -const size_t MAX_SEQ_NUM = 10; -const size_t MAX_SEQ_LEN = 27; -const size_t MAX_BEAM_SIZE = 10; +// const size_t MAX_SEQ_NUM = 5; +// const size_t MAX_SEQ_LEN = 10; +// const size_t MAX_BEAM_SIZE = 3; + +const size_t MAX_SEQ_NUM = 23; +const size_t MAX_SEQ_LEN = 50; +const size_t MAX_BEAM_SIZE = 27; + +// const size_t SEED = 1503391792; +// const size_t SEED = 1; +const size_t SEED = (size_t)(time(NULL)); struct SingleBeamExpansion { vector seqStartPos; @@ -43,11 +51,30 @@ struct SingleBeamExpansion { vector groundTruth; vector inBeam; vector rowIdxInBeam; + vector colIdxInBeam; + + void resetGroundTruth(size_t n) { + groundTruth.clear(); + groundTruth.resize(n, -1); + + inBeam.clear(); + inBeam.resize(n, 0); + + rowIdxInBeam.clear(); + rowIdxInBeam.resize(n, -1); + + colIdxInBeam.clear(); + colIdxInBeam.resize(n, -1); + } }; +inline float randFloat() { + return static_cast(rand()) / static_cast(RAND_MAX); +} + void genRand(real* numbers, size_t n) { default_random_engine generator; - uniform_real_distribution distribution(0.0, 1.0); + uniform_real_distribution distribution(0.0, 1.0); for (size_t i = 0; i < n; ++i) numbers[i] = distribution(generator); } @@ -72,8 +99,7 @@ void genCandidateScores(bool hasSubseq, vector& subSeqStartPos = curBeam.subSeqStartPos; subSeqStartPos.resize(1, 0); - srand((size_t)(time(NULL))); - // srand(1); + srand(SEED); if (prevBeam.selectedIndices.size()) { if (prevBeam.subSeqStartPos.size() > 1) { int seqIdx = 1; @@ -81,9 +107,8 @@ void genCandidateScores(bool hasSubseq, for (size_t i = 1; i < prevBeam.subSeqStartPos.size(); ++i) { for (size_t j = 0; j < beamSize; ++j) { if (prevBeam.selectedIndices[(i - 1) * beamSize + j] == -1.) break; - for (size_t k = 0; k < beamSize; ++k) - subSeqStartPos.push_back(1 + (rand() % MAX_SEQ_LEN) + - subSeqStartPos.back()); + subSeqStartPos.push_back(1 + (rand() % MAX_SEQ_LEN) + + subSeqStartPos.back()); } if (prevBeam.seqStartPos[seqIdx] == prevBeam.subSeqStartPos[i]) { seqStartPos.push_back(subSeqStartPos.back()); @@ -91,7 +116,6 @@ void genCandidateScores(bool hasSubseq, } } } else { - // samples in previous beam are sequences. for (size_t i = 0; i <= prevBeam.selectedIndices.size(); ++i) { if (i && i % beamSize == 0) { seqStartPos.push_back(subSeqStartPos.back()); @@ -141,27 +165,41 @@ void genSelectedIndices(size_t beamSize, void genGroundTruth(vector& beamExpansions, size_t beamSize) { - size_t seqNum = beamExpansions[1].seqStartPos.size() - 1; + SingleBeamExpansion& beam = beamExpansions[1]; + size_t seqNum = beam.seqStartPos.size() - 1; for (size_t i = 2; i < beamExpansions.size(); ++i) - CHECK_EQ(seqNum, beamExpansions[i - 1].seqStartPos.size() - 1); + CHECK_EQ(seqNum, beamExpansions[i].seqStartPos.size() - 1); - // srand(1); - srand((size_t)(time(NULL))); + srand(SEED); // initialize the first beam. - SingleBeamExpansion& beam = beamExpansions[1]; - beam.groundTruth.resize(seqNum, 0); - beam.inBeam.resize(seqNum, 0); - beam.rowIdxInBeam.resize(seqNum, -1); - - auto begPos = beam.selectedIndices.begin(); + beam.resetGroundTruth(seqNum); for (size_t i = 0; i < seqNum; ++i) { - int seqLen = beam.seqStartPos[i + 1] - beam.seqStartPos[i]; - int label = rand() % seqLen; - auto endPos = begPos + beamSize; - beam.groundTruth[i] = label; - if (find(begPos, endPos, real(label)) != endPos) beam.inBeam[i] = 1; - begPos = endPos; + if (randFloat() > 0.5) { + // force the randomly generated label falls in the beam by chance 0.5. + // otherwise, when sequence length is relatively long and beam size is + // relatively small, the gold sequences falls off the beam at in + // the first search. + real* begPos = beam.selectedIndices.data() + i * beamSize; + beam.colIdxInBeam[i] = + rand() % count_if(begPos, begPos + beamSize, [](const real& val) { + return val != -1.; + }); + beam.groundTruth[i] = + beam.selectedIndices[i * beamSize + beam.colIdxInBeam[i]]; + beam.inBeam[i] = 1; + } else { + int label = rand() % (beam.seqStartPos[i + 1] - beam.seqStartPos[i]); + beam.groundTruth[i] = label; + + real* begPos = beam.selectedIndices.data() + i * beamSize; + real* endPos = begPos + beamSize; + real* lblPos = find(begPos, endPos, real(label)); + if (lblPos != endPos) { + beam.inBeam[i] = 1; + beam.colIdxInBeam[i] = lblPos - begPos; + } + } beam.rowIdxInBeam[i] = i; } @@ -169,22 +207,33 @@ void genGroundTruth(vector& beamExpansions, for (size_t i = 2; i < beamExpansions.size(); ++i) { SingleBeamExpansion& curBeam = beamExpansions[i]; SingleBeamExpansion& prevBeam = beamExpansions[i - 1]; - - curBeam.groundTruth.resize(seqNum, 0); - curBeam.inBeam.resize(seqNum, 0); - curBeam.rowIdxInBeam.resize(seqNum, -1); + curBeam.resetGroundTruth(seqNum); // iterate over each sequence for (size_t j = 0; j < seqNum; ++j) { - if (prevBeam.inBeam[j]) { - // gold sequence falls in the beam in previous search. - - auto begPos = prevBeam.selectedIndices.begin(); - auto endPos = begPos + prevBeam.rowIdxInBeam[j] * beamSize; - size_t totalExpansion = - prevBeam.rowIdxInBeam[j] * beamSize - count(begPos, endPos, -1.); - curBeam.rowIdxInBeam[j] = totalExpansion + prevBeam.groundTruth[j]; - + if (!prevBeam.inBeam[j]) continue; + + // gold sequence falls in the beam in previous search. + real* begPos = prevBeam.selectedIndices.data(); + int offset = + prevBeam.rowIdxInBeam[j] * beamSize + prevBeam.colIdxInBeam[j]; + curBeam.rowIdxInBeam[j] = count_if( + begPos, begPos + offset, [](const real& val) { return val != -1.; }); + + if (randFloat() > 0.5) { + // force the randomly generated label falls in the beam by chance 0.5. + // otherwise, when sequence length is relatively long and beam size is + // relatively small, the gold sequences falls off the beam at in + // the first search. + real* start = + curBeam.selectedIndices.data() + curBeam.rowIdxInBeam[j] * beamSize; + int n = rand() % count_if(start, start + beamSize, [](const real& val) { + return val != -1.; + }); + curBeam.colIdxInBeam[j] = n; + curBeam.groundTruth[j] = *(start + n); + curBeam.inBeam[j] = 1; + } else { CHECK_LE(curBeam.rowIdxInBeam[j] + 1, curBeam.subSeqStartPos.size() - 1); int start = curBeam.subSeqStartPos[curBeam.rowIdxInBeam[j]]; @@ -193,16 +242,14 @@ void genGroundTruth(vector& beamExpansions, int label = rand() % (end - start); curBeam.groundTruth[j] = label; - auto findBeg = curBeam.selectedIndices.begin() + - curBeam.rowIdxInBeam[j] * beamSize; - auto findEnd = findBeg + beamSize; - if (find(findBeg, findEnd, real(label)) != findEnd) + real* findBeg = + curBeam.selectedIndices.data() + curBeam.rowIdxInBeam[j] * beamSize; + real* lblPos = + find(findBeg, findBeg + beamSize, static_cast(label)); + if (lblPos != (findBeg + beamSize)) { curBeam.inBeam[j] = 1; - } else { - // in previous search, gold sequence has fallen off the beam, - // the beam search stops, here use -1 as a dummy label. - // It will not used in calculation the cost. - beamExpansions[i].groundTruth[j] = -1; + curBeam.colIdxInBeam[j] = lblPos - findBeg; + } } } } @@ -230,15 +277,12 @@ void genRandomBeamExpansion(size_t expansionCount, genGroundTruth(beamExpansions, beamSize); } -void testCrossEntropyOverBeam(bool useGpu) { +void testCrossEntropyOverBeam(bool useGpu, + size_t beamSize, + vector& beams) { TestConfig config; config.layerConfig.set_type("cross_entropy_over_beam"); - const size_t expansionCount = 3; - const size_t beamSize = MAX_BEAM_SIZE; - vector beams; - genRandomBeamExpansion(expansionCount, beamSize, beams); - size_t seqNum = 0; for (size_t i = 1; i < beams.size(); ++i) { const SingleBeamExpansion& beam = beams[i]; @@ -291,7 +335,17 @@ void testCrossEntropyOverBeam(bool useGpu) { } TEST(Layer, CrossEntropyOverBeam) { - for (bool useGpu : {false, true}) testCrossEntropyOverBeam(useGpu); + LOG(INFO) << "SEED = " << SEED; + const size_t beamSize = 1 + rand() % MAX_BEAM_SIZE; + LOG(INFO) << "beamSize = " << beamSize; + + // TODO(caoying): test with more beam expansions. + const size_t expansionCount = 3; + vector beams; + genRandomBeamExpansion(expansionCount, beamSize, beams); + + for (bool useGpu : {false, true}) + testCrossEntropyOverBeam(useGpu, beamSize, beams); } int main(int argc, char** argv) { @@ -299,7 +353,7 @@ int main(int argc, char** argv) { hl_start(); hl_init(FLAGS_gpu_id); FLAGS_thread_local_rand_use_global_seed = true; - srand(1); + srand(SEED); testing::InitGoogleTest(&argc, argv); return RUN_ALL_TESTS(); } -- GitLab From 3bf440023abd5801f21b98d027623b6cb3959a0b Mon Sep 17 00:00:00 2001 From: caoying03 Date: Tue, 22 Aug 2017 21:03:46 +0800 Subject: [PATCH 0173/2018] follow comments. --- paddle/gserver/gradientmachines/RecurrentGradientMachine.h | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/paddle/gserver/gradientmachines/RecurrentGradientMachine.h b/paddle/gserver/gradientmachines/RecurrentGradientMachine.h index cc0eda9f1..c16fae6d1 100644 --- a/paddle/gserver/gradientmachines/RecurrentGradientMachine.h +++ b/paddle/gserver/gradientmachines/RecurrentGradientMachine.h @@ -503,9 +503,9 @@ private: * sequence in Matrix stored the entire beam search batch's forward pass * results. * - * @param isSeq: a flag indicating whetehr the layer to be output of the + * @param isSeq: a flag indicating whether the layer to be output of the * RecurrentGradientMachine is a sequence or not - * @param outArgs: all of the the returned Arguments of the forward pass + * @param outArgs: all of the returned Arguments of the forward pass * during the generation process. */ void createDataOutlinkSelRowsInfo(bool isSeq, std::vector& outArgs); -- GitLab From a8d072c769b940d087006fa68ffcf462aa8579b8 Mon Sep 17 00:00:00 2001 From: dangqingqing Date: Wed, 23 Aug 2017 00:12:58 +0800 Subject: [PATCH 0174/2018] fix bug. --- paddle/operators/lookup_table_op.cc | 7 ++-- paddle/operators/lookup_table_op.cu | 32 +++++++++---------- paddle/operators/lookup_table_op.h | 6 ++-- .../v2/framework/tests/test_lookup_table.py | 6 ++-- 4 files changed, 25 insertions(+), 26 deletions(-) diff --git a/paddle/operators/lookup_table_op.cc b/paddle/operators/lookup_table_op.cc index 5f70458a8..94d40890a 100644 --- a/paddle/operators/lookup_table_op.cc +++ b/paddle/operators/lookup_table_op.cc @@ -41,8 +41,7 @@ class LookupTableOpMaker : public framework::OpProtoAndCheckerMaker { " which is a learnable parameter."); AddInput("Ids", "An input with type int32 or int64" - "contains the ids to be looked up in W.") - .NotInGradient(); + "contains the ids to be looked up in W."); AddOutput("Out", "The lookup results, which have the same type with W."); AddComment( "This operator is used to perform lookups on the parameter W," @@ -56,7 +55,9 @@ class LookupTableOpGrad : public framework::OperatorWithKernel { protected: void InferShape(const framework::InferShapeContext &context) const override { - context.Output(0)->Resize(context.Input(0)->dims()); + auto table = context.Input("W"); + auto d_table = context.Output(framework::GradVarName("W")); + d_table->Resize(table->dims()); } }; diff --git a/paddle/operators/lookup_table_op.cu b/paddle/operators/lookup_table_op.cu index 94b440e00..99678ef68 100644 --- a/paddle/operators/lookup_table_op.cu +++ b/paddle/operators/lookup_table_op.cu @@ -23,7 +23,7 @@ namespace operators { using Tensor = framework::Tensor; template -__global__ void LookupTable(T* output, const T* table, const uint32_t* ids, +__global__ void LookupTable(T* output, const T* table, const int32_t* ids, const int N, const int K, const int D) { int idx = threadIdx.x; int idy = blockIdx.x + threadIdx.y * gridDimX; @@ -32,8 +32,8 @@ __global__ void LookupTable(T* output, const T* table, const uint32_t* ids, int id = ids[idy]; PADDLE_ASSERT(id >= 0); PADDLE_ASSERT(id < N); - T* out = output + idy; - const T* tab = table + id; + T* out = output + idy * D; + const T* tab = table + id * D; for (int i = idx; i < D; i += blockDimX) { out[i] = tab[i]; } @@ -42,9 +42,8 @@ __global__ void LookupTable(T* output, const T* table, const uint32_t* ids, } template -__global__ void LookupTableGradKernel(T* table, const T* output, - const uint32_t* ids, const int N, - const int K, const int D) { +__global__ void LookupTableGrad(T* table, const T* output, const int32_t* ids, + const int N, const int K, const int D) { int idx = threadIdx.x; int idy = blockIdx.x + threadIdx.y * gridDimX; @@ -52,10 +51,10 @@ __global__ void LookupTableGradKernel(T* table, const T* output, int id = ids[idy]; PADDLE_ASSERT(id >= 0); PADDLE_ASSERT(id < N); - const T* out = output + idy; - T* tab = table + id; + const T* out = output + idy * D; + T* tab = table + id * D; for (int i = idx; i < D; i += blockDimX) { - paddle::platform::CudaAtomicAdd(tab + i, out[i]); + paddle::platform::CudaAtomicAdd(&tab[i], out[i]); } idy += blockDimY * gridDimX; } @@ -72,7 +71,7 @@ class LookupTableCUDAKernel : public framework::OpKernel { size_t N = table_t->dims()[0]; size_t D = table_t->dims()[1]; size_t K = product(ids_t->dims()); - auto ids = ids_t->data(); + auto ids = ids_t->data(); auto table = table_t->data(); auto output = output_t->mutable_data(context.GetPlace()); @@ -83,7 +82,7 @@ class LookupTableCUDAKernel : public framework::OpKernel { }; template -class LookupTableGrad : public framework::OpKernel { +class LookupTableGradCUDAKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& context) const override { auto ids_t = context.Input("Ids"); @@ -93,9 +92,9 @@ class LookupTableGrad : public framework::OpKernel { int N = d_table_t->dims()[0]; int D = d_table_t->dims()[1]; int K = product(ids_t->dims()); - const uint32_t* ids = ids_t->data(); - T* d_table = d_table_t->mutable_data(context.GetPlace()); + const int32_t* ids = ids_t->data(); const T* d_output = d_output_t->data(); + T* d_table = d_table_t->mutable_data(context.GetPlace()); auto* device_context = const_cast(context.device_context_); @@ -103,8 +102,8 @@ class LookupTableGrad : public framework::OpKernel { device_context); dim3 threads(128, 8); dim3 grids(8, 1); - LookupTableGradKernel<<>>(d_table, d_output, - ids, N, K, D); + LookupTableGrad<<>>(d_table, d_output, ids, N, + K, D); } }; @@ -113,4 +112,5 @@ class LookupTableGrad : public framework::OpKernel { namespace ops = paddle::operators; REGISTER_OP_GPU_KERNEL(lookup_table, ops::LookupTableCUDAKernel); -REGISTER_OP_GPU_KERNEL(lookup_table_grad, ops::LookupTableGrad); +REGISTER_OP_GPU_KERNEL(lookup_table_grad, + ops::LookupTableGradCUDAKernel); diff --git a/paddle/operators/lookup_table_op.h b/paddle/operators/lookup_table_op.h index 790ecab3c..9254e03a1 100644 --- a/paddle/operators/lookup_table_op.h +++ b/paddle/operators/lookup_table_op.h @@ -32,7 +32,7 @@ class LookupTableKernel : public framework::OpKernel { size_t N = table_t->dims()[0]; size_t D = table_t->dims()[1]; - auto ids = ids_t->data(); + auto ids = ids_t->data(); auto table = table_t->data(); auto output = output_t->mutable_data(context.GetPlace()); for (size_t i = 0; i < product(ids_t->dims()); ++i) { @@ -53,9 +53,9 @@ class LookupTableGradKernel : public framework::OpKernel { size_t N = d_table_t->dims()[0]; size_t D = d_table_t->dims()[1]; - auto ids = ids_t->data(); - T* d_table = d_table_t->mutable_data(context.GetPlace()); + auto ids = ids_t->data(); const T* d_output = d_output_t->data(); + T* d_table = d_table_t->mutable_data(context.GetPlace()); auto* device_context = const_cast(context.device_context_); diff --git a/python/paddle/v2/framework/tests/test_lookup_table.py b/python/paddle/v2/framework/tests/test_lookup_table.py index 071069768..3056bf53e 100644 --- a/python/paddle/v2/framework/tests/test_lookup_table.py +++ b/python/paddle/v2/framework/tests/test_lookup_table.py @@ -10,7 +10,7 @@ class TestSigmoidOp(unittest.TestCase): def setUp(self): self.type = 'lookup_table' table = np.random.random((17, 31)).astype('float32') - ids = np.random.randint(0, 17, 4) + ids = np.random.randint(0, 17, 4).astype('int32') self.inputs = {'W': table, 'Ids': ids} self.outputs = {'Out': table[ids]} @@ -19,10 +19,8 @@ class TestSigmoidGradOp(GradientChecker): def test_grad(self): op = create_op('lookup_table') table = np.random.random((17, 31)).astype('float32') - ids = np.random.randint(0, 17, 4) + ids = np.random.randint(0, 17, 4).astype('int32') inputs = {'W': table, 'Ids': ids} - # compare gradients between cpu and gpu - self.compare_grad(op, inputs) # check gradients self.check_grad(op, inputs, set('W'), 'Out') -- GitLab From 51792022c9f7963321d77d7dac4143e566af9fdc Mon Sep 17 00:00:00 2001 From: qiaolongfei Date: Tue, 22 Aug 2017 12:54:44 -0700 Subject: [PATCH 0175/2018] refine code and add debug info --- python/paddle/v2/framework/tests/mnist.py | 47 +++++++++++------------ 1 file changed, 23 insertions(+), 24 deletions(-) diff --git a/python/paddle/v2/framework/tests/mnist.py b/python/paddle/v2/framework/tests/mnist.py index f75f19616..6a3ed0dce 100644 --- a/python/paddle/v2/framework/tests/mnist.py +++ b/python/paddle/v2/framework/tests/mnist.py @@ -52,7 +52,7 @@ def grad_var_name(var_name): return var_name + "@GRAD" -def sgd_optimizer(net, param_name, learning_rate=0.01): +def sgd_optimizer(net, param_name, learning_rate=0.001): grad_name = grad_var_name(param_name) optimize_op = Operator( "sgd", @@ -65,7 +65,6 @@ def sgd_optimizer(net, param_name, learning_rate=0.01): # should use operator and add these to the init_network def init_param(param_name, dims): - print param_name var = scope.new_var(param_name) tensor = var.get_tensor() tensor.set_dims(dims) @@ -158,17 +157,34 @@ def print_inputs_outputs(op): print("") +def set_cost(): + cost_data = numpy.array(scope.find_var("cross_entropy_1").get_tensor()) + # print(cost_data) + print(cost_data.sum() / len(cost_data)) + + cost_grad = scope.find_var(grad_var_name("cross_entropy_1")).get_tensor() + cost_grad.set_dims(cost_data.shape) + cost_grad.alloc_float(place) + cost_grad.set(cost_data, place) + + images = data_layer(name='pixel', dims=[BATCH_SIZE, 784]) label = data_layer(name='label', dims=[BATCH_SIZE]) fc = fc_layer(net=forward_network, input=images, size=10, act="softmax") cost = cross_entropy_layer(net=forward_network, input=fc, label=label) + forward_network.complete_add_op(True) -print(forward_network) backward_net = get_backward_net(forward_network) -print(backward_net) optimize_net.complete_add_op(True) + +print(forward_network) +print(backward_net) print(optimize_net) +print_inputs_outputs(forward_network) +print_inputs_outputs(backward_net) +print_inputs_outputs(optimize_net) + reader = paddle.batch( paddle.reader.shuffle( paddle.dataset.mnist.train(), buf_size=8192), @@ -176,34 +192,17 @@ reader = paddle.batch( PASS_NUM = 1000 for pass_id in range(PASS_NUM): - print("===========forward==========") - # feed_data("pixel", numpy.random.random((BATCH_SIZE, 784)).astype('float32')) - # feed_data("label", numpy.ones(BATCH_SIZE).astype("int32")) data = reader().next() + image = numpy.array(map(lambda x: x[0], data)).astype("float32") label = numpy.array(map(lambda x: x[1], data)).astype("int32") feed_data("pixel", image) feed_data("label", label) - forward_network.infer_shape(scope) - print_inputs_outputs(forward_network) - # print(numpy.array(scope.find_var("label").get_tensor())) + forward_network.infer_shape(scope) forward_network.run(scope, dev_ctx) - # print(numpy.array(scope.find_var("fc_0").get_tensor())) - - print("===========backward==========") - cost_data = numpy.array(scope.find_var("cross_entropy_1").get_tensor()) - print(cost_data.sum() / len(cost_data)) - cost_grad = scope.find_var(grad_var_name("cross_entropy_1")).get_tensor() - cost_grad.set_dims(cost_data.shape) - cost_grad.alloc_float(place) - cost_grad.set(cost_data, place) - + set_cost() backward_net.infer_shape(scope) - print_inputs_outputs(backward_net) - backward_net.run(scope, dev_ctx) - print("===========optimize_net==========") - print_inputs_outputs(optimize_net) optimize_net.run(scope, dev_ctx) -- GitLab From d3c65a64dc4ab98af10498cb2eb9327ef1697e5a Mon Sep 17 00:00:00 2001 From: qiaolongfei Date: Tue, 22 Aug 2017 20:21:23 -0700 Subject: [PATCH 0176/2018] fix data reader --- python/paddle/v2/framework/tests/mnist.py | 29 ++++++++++++----------- 1 file changed, 15 insertions(+), 14 deletions(-) diff --git a/python/paddle/v2/framework/tests/mnist.py b/python/paddle/v2/framework/tests/mnist.py index 6a3ed0dce..1d40fd9a9 100644 --- a/python/paddle/v2/framework/tests/mnist.py +++ b/python/paddle/v2/framework/tests/mnist.py @@ -52,7 +52,7 @@ def grad_var_name(var_name): return var_name + "@GRAD" -def sgd_optimizer(net, param_name, learning_rate=0.001): +def sgd_optimizer(net, param_name, learning_rate=0.01): grad_name = grad_var_name(param_name) optimize_op = Operator( "sgd", @@ -159,13 +159,13 @@ def print_inputs_outputs(op): def set_cost(): cost_data = numpy.array(scope.find_var("cross_entropy_1").get_tensor()) - # print(cost_data) print(cost_data.sum() / len(cost_data)) cost_grad = scope.find_var(grad_var_name("cross_entropy_1")).get_tensor() + cost_grad.set_dims(cost_data.shape) cost_grad.alloc_float(place) - cost_grad.set(cost_data, place) + cost_grad.set(numpy.ones(cost_data.shape).astype("float32"), place) images = data_layer(name='pixel', dims=[BATCH_SIZE, 784]) @@ -192,17 +192,18 @@ reader = paddle.batch( PASS_NUM = 1000 for pass_id in range(PASS_NUM): - data = reader().next() - image = numpy.array(map(lambda x: x[0], data)).astype("float32") - label = numpy.array(map(lambda x: x[1], data)).astype("int32") - feed_data("pixel", image) - feed_data("label", label) + print("pass[" + str(pass_id) + "]") + for data in reader(): + image = numpy.array(map(lambda x: x[0], data)).astype("float32") + label = numpy.array(map(lambda x: x[1], data)).astype("int32") + feed_data("pixel", image) + feed_data("label", label) - forward_network.infer_shape(scope) - forward_network.run(scope, dev_ctx) - set_cost() - backward_net.infer_shape(scope) - backward_net.run(scope, dev_ctx) + forward_network.infer_shape(scope) + forward_network.run(scope, dev_ctx) + set_cost() + backward_net.infer_shape(scope) + backward_net.run(scope, dev_ctx) - optimize_net.run(scope, dev_ctx) + optimize_net.run(scope, dev_ctx) -- GitLab From a13798e8f7764239c151864894afc6a543e6c190 Mon Sep 17 00:00:00 2001 From: qiaolongfei Date: Tue, 22 Aug 2017 20:41:31 -0700 Subject: [PATCH 0177/2018] rename add_op to append_op --- python/paddle/v2/framework/tests/mnist.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/python/paddle/v2/framework/tests/mnist.py b/python/paddle/v2/framework/tests/mnist.py index 1d40fd9a9..32349b8d4 100644 --- a/python/paddle/v2/framework/tests/mnist.py +++ b/python/paddle/v2/framework/tests/mnist.py @@ -60,7 +60,7 @@ def sgd_optimizer(net, param_name, learning_rate=0.01): grad=grad_name, param_out=param_name, learning_rate=learning_rate) - net.add_op(optimize_op) + net.append_op(optimize_op) # should use operator and add these to the init_network @@ -102,7 +102,7 @@ def fc_layer(net, input, size, act="softmax", bias=True, param=None, name=None): pre_activation = name + ".mul.out" scope.new_var(pre_activation) mul_op = Operator("mul", X=input, Y=w_name, Out=pre_activation) - net.add_op(mul_op) + net.append_op(mul_op) # create bias variable if needed if bias: @@ -112,13 +112,13 @@ def fc_layer(net, input, size, act="softmax", bias=True, param=None, name=None): net=optimize_net, param_name=bias_name, learning_rate=0.01) bias_out = name + ".rowwise_add.out" scope.new_var(bias_out) - rowwise_add_op = Operator( + rowwise_append_op = Operator( "rowwise_add", X=pre_activation, b=bias_name, Out=bias_out) - net.add_op(rowwise_add_op) + net.append_op(rowwise_append_op) pre_activation = bias_out activation_op = Operator(act, X=pre_activation, Y=name) - net.add_op(activation_op) + net.append_op(activation_op) scope.new_var(name) net.infer_shape(scope) return name @@ -128,7 +128,7 @@ def cross_entropy_layer(net, input, label): cost_name = 'cross_entropy_%d' % uniq_id() cross_entropy_op = Operator( "onehot_cross_entropy", X=input, label=label, Y=cost_name) - net.add_op(cross_entropy_op) + net.append_op(cross_entropy_op) scope.new_var(cost_name) net.infer_shape(scope) return cost_name -- GitLab From d8cd67dd1e229a27180d3628dc9485734546aba4 Mon Sep 17 00:00:00 2001 From: wanghaoshuang Date: Wed, 23 Aug 2017 12:26:46 +0800 Subject: [PATCH 0178/2018] Make cudnn convolution layer and projection support for dilation. --- paddle/cuda/include/hl_cuda_cudnn.h | 11 +- paddle/cuda/src/hl_cuda_cudnn.cc | 123 ++++++++++-------- paddle/gserver/layers/ConvBaseLayer.cpp | 16 ++- paddle/gserver/layers/ConvBaseLayer.h | 4 + paddle/gserver/layers/ConvBaseOperator.cpp | 3 +- paddle/gserver/layers/ConvBaseProjection.cpp | 20 ++- paddle/gserver/layers/ConvBaseProjection.h | 1 + paddle/gserver/layers/ConvProjection.cpp | 4 +- paddle/gserver/tests/test_LayerGrad.cpp | 40 ++++-- proto/ModelConfig.proto | 3 + python/paddle/trainer/config_parser.py | 4 + .../paddle/trainer_config_helpers/layers.py | 19 +++ .../tests/configs/img_layers.py | 1 + 13 files changed, 171 insertions(+), 78 deletions(-) diff --git a/paddle/cuda/include/hl_cuda_cudnn.h b/paddle/cuda/include/hl_cuda_cudnn.h index db18e4912..3f68c62de 100644 --- a/paddle/cuda/include/hl_cuda_cudnn.h +++ b/paddle/cuda/include/hl_cuda_cudnn.h @@ -214,7 +214,8 @@ extern void hl_conv_workspace(hl_tensor_descriptor input, int* convBwdDataAlgo, size_t* bwdDataLimitBytes, int* convBwdFilterAlgo, - size_t* bwdFilterLimitBytes); + size_t* bwdFilterLimitBytes, + bool useDilation); /** * @brief destroy filter descriptor. @@ -242,7 +243,9 @@ extern void hl_create_convolution_descriptor(hl_convolution_descriptor* conv, int padding_height, int padding_width, int stride_height, - int stride_width); + int stride_width, + int dilation_h = 1, + int dilation_w = 1); /** * @brief reset convolution descriptor. @@ -262,7 +265,9 @@ extern void hl_reset_convolution_descriptor(hl_convolution_descriptor conv, int padding_height, int padding_width, int stride_height, - int stride_width); + int stride_width, + int dilation_h = 1, + int dilation_w = 1); /** * @brief destroy convolution descriptor. diff --git a/paddle/cuda/src/hl_cuda_cudnn.cc b/paddle/cuda/src/hl_cuda_cudnn.cc index 78642a174..f55fa523e 100644 --- a/paddle/cuda/src/hl_cuda_cudnn.cc +++ b/paddle/cuda/src/hl_cuda_cudnn.cc @@ -201,7 +201,8 @@ void hl_conv_workspace(hl_tensor_descriptor input, int* convBwdDataAlgo, size_t* bwdDataLimitBytes, int* convBwdFilterAlgo, - size_t* bwdFilterLimitBytes) { + size_t* bwdFilterLimitBytes, + bool useDilation) { #if CUDNN_VERSION >= 4000 CHECK_NOTNULL(input); @@ -213,21 +214,60 @@ void hl_conv_workspace(hl_tensor_descriptor input, size_t memoryLimitBytes = (1LL << 20) * FLAGS_cudnn_conv_workspace_limit_in_mb; + // For dilation + int algo = 0; + // cudnn convolution forward configuration cudnnTensorDescriptor_t fwd_src_desc = GET_TENSOR_DESCRIPTOR(input); cudnnTensorDescriptor_t fwd_dest_desc = GET_TENSOR_DESCRIPTOR(output); cudnnFilterDescriptor_t fwd_filter_desc = GET_FILTER_DESCRIPTOR(filter); cudnnConvolutionDescriptor_t fwd_conv_desc = GET_CONVOLUTION_DESCRIPTOR(conv); + // cudnn convolution backward data configuration + cudnnFilterDescriptor_t bwd_data_filter_desc = GET_FILTER_DESCRIPTOR(filter); + cudnnTensorDescriptor_t bwd_data_diff_desc = GET_TENSOR_DESCRIPTOR(output); + cudnnTensorDescriptor_t bwd_data_grad_desc = GET_TENSOR_DESCRIPTOR(input); + cudnnConvolutionDescriptor_t bwd_data_conv_desc = + GET_CONVOLUTION_DESCRIPTOR(conv); + // cudnn convolution backward filter configuration + cudnnTensorDescriptor_t bwd_filter_src_desc = GET_TENSOR_DESCRIPTOR(input); + cudnnTensorDescriptor_t bwd_filter_diff_desc = GET_TENSOR_DESCRIPTOR(output); + cudnnConvolutionDescriptor_t bwd_filter_conv_desc = + GET_CONVOLUTION_DESCRIPTOR(conv); + cudnnFilterDescriptor_t bwd_filter_grad_desc = GET_FILTER_DESCRIPTOR(filter); - CHECK_CUDNN(dynload::cudnnGetConvolutionForwardAlgorithm( - t_resource.cudnn_handle, - fwd_src_desc, - fwd_filter_desc, - fwd_conv_desc, - fwd_dest_desc, - CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT, - memoryLimitBytes, - reinterpret_cast(convFwdAlgo))); + if (useDilation) { + convFwdAlgo = &algo; + convBwdDataAlgo = &algo; + convBwdFilterAlgo = &algo; + } else { + CHECK_CUDNN(dynload::cudnnGetConvolutionForwardAlgorithm( + t_resource.cudnn_handle, + fwd_src_desc, + fwd_filter_desc, + fwd_conv_desc, + fwd_dest_desc, + CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT, + memoryLimitBytes, + reinterpret_cast(convFwdAlgo))); + CHECK_CUDNN(dynload::cudnnGetConvolutionBackwardDataAlgorithm( + t_resource.cudnn_handle, + bwd_data_filter_desc, + bwd_data_diff_desc, + bwd_data_conv_desc, + bwd_data_grad_desc, + CUDNN_CONVOLUTION_BWD_DATA_SPECIFY_WORKSPACE_LIMIT, + memoryLimitBytes, + reinterpret_cast(convBwdDataAlgo))); + CHECK_CUDNN(dynload::cudnnGetConvolutionBackwardFilterAlgorithm( + t_resource.cudnn_handle, + bwd_filter_src_desc, + bwd_filter_diff_desc, + bwd_filter_conv_desc, + bwd_filter_grad_desc, + CUDNN_CONVOLUTION_BWD_FILTER_SPECIFY_WORKSPACE_LIMIT, + memoryLimitBytes, + reinterpret_cast(convBwdFilterAlgo))); + } CHECK_CUDNN(dynload::cudnnGetConvolutionForwardWorkspaceSize( t_resource.cudnn_handle, @@ -238,23 +278,6 @@ void hl_conv_workspace(hl_tensor_descriptor input, static_cast(*convFwdAlgo), fwdLimitBytes)); - // cudnn convolution backward data configuration - cudnnFilterDescriptor_t bwd_data_filter_desc = GET_FILTER_DESCRIPTOR(filter); - cudnnTensorDescriptor_t bwd_data_diff_desc = GET_TENSOR_DESCRIPTOR(output); - cudnnTensorDescriptor_t bwd_data_grad_desc = GET_TENSOR_DESCRIPTOR(input); - cudnnConvolutionDescriptor_t bwd_data_conv_desc = - GET_CONVOLUTION_DESCRIPTOR(conv); - - CHECK_CUDNN(dynload::cudnnGetConvolutionBackwardDataAlgorithm( - t_resource.cudnn_handle, - bwd_data_filter_desc, - bwd_data_diff_desc, - bwd_data_conv_desc, - bwd_data_grad_desc, - CUDNN_CONVOLUTION_BWD_DATA_SPECIFY_WORKSPACE_LIMIT, - memoryLimitBytes, - reinterpret_cast(convBwdDataAlgo))); - CHECK_CUDNN(dynload::cudnnGetConvolutionBackwardDataWorkspaceSize( t_resource.cudnn_handle, bwd_data_filter_desc, @@ -264,23 +287,6 @@ void hl_conv_workspace(hl_tensor_descriptor input, static_cast(*convBwdDataAlgo), bwdDataLimitBytes)); - // cudnn convolution backward filter configuration - cudnnTensorDescriptor_t bwd_filter_src_desc = GET_TENSOR_DESCRIPTOR(input); - cudnnTensorDescriptor_t bwd_filter_diff_desc = GET_TENSOR_DESCRIPTOR(output); - cudnnConvolutionDescriptor_t bwd_filter_conv_desc = - GET_CONVOLUTION_DESCRIPTOR(conv); - cudnnFilterDescriptor_t bwd_filter_grad_desc = GET_FILTER_DESCRIPTOR(filter); - - CHECK_CUDNN(dynload::cudnnGetConvolutionBackwardFilterAlgorithm( - t_resource.cudnn_handle, - bwd_filter_src_desc, - bwd_filter_diff_desc, - bwd_filter_conv_desc, - bwd_filter_grad_desc, - CUDNN_CONVOLUTION_BWD_FILTER_SPECIFY_WORKSPACE_LIMIT, - memoryLimitBytes, - reinterpret_cast(convBwdFilterAlgo))); - CHECK_CUDNN(dynload::cudnnGetConvolutionBackwardFilterWorkspaceSize( t_resource.cudnn_handle, bwd_filter_src_desc, @@ -603,7 +609,9 @@ void hl_create_convolution_descriptor(hl_convolution_descriptor* conv, int padding_height, int padding_width, int stride_height, - int stride_width) { + int stride_width, + int dilation_h, + int dilation_w) { CHECK_NOTNULL(conv); cudnn_convolution_descriptor hl_conv = (cudnn_convolution_descriptor)malloc( @@ -625,18 +633,23 @@ void hl_create_convolution_descriptor(hl_convolution_descriptor* conv, padding_width, stride_height, stride_width, - 1, - 1, + dilation_h, + dilation_w, mode, data_type)); #else + if (dilation_h > 1 || dilation_w > 1) { + LOG(FATAL) + << "Current cudnn version does't support for dilation convolution."; + } + CHECK_CUDNN(dynload::cudnnSetConvolution2dDescriptor(hl_conv->desc, padding_height, padding_width, stride_height, stride_width, - 1, - 1, + dilation_h, + dilation_w, mode)); #endif @@ -659,7 +672,9 @@ void hl_reset_convolution_descriptor(hl_convolution_descriptor conv, int padding_height, int padding_width, int stride_height, - int stride_width) { + int stride_width, + int dilation_h, + int dilation_w) { CHECK_NOTNULL(conv); CHECK_NOTNULL(image); CHECK_NOTNULL(filter); @@ -678,8 +693,8 @@ void hl_reset_convolution_descriptor(hl_convolution_descriptor conv, padding_width, stride_height, stride_width, - 1, - 1, + dilation_h, + dilation_w, mode, data_type)); #else @@ -688,8 +703,8 @@ void hl_reset_convolution_descriptor(hl_convolution_descriptor conv, padding_width, stride_height, stride_width, - 1, - 1, + dilation_h, + dilation_w, mode)); #endif diff --git a/paddle/gserver/layers/ConvBaseLayer.cpp b/paddle/gserver/layers/ConvBaseLayer.cpp index e161d89c3..a5328ef83 100644 --- a/paddle/gserver/layers/ConvBaseLayer.cpp +++ b/paddle/gserver/layers/ConvBaseLayer.cpp @@ -32,9 +32,11 @@ bool ConvBaseLayer::init(const LayerMap& layerMap, const ConvConfig& conf = inputConfig.conv_conf(); padding_.push_back(conf.padding()); stride_.push_back(conf.stride()); + dilation_.push_back(conf.dilation()); filterSize_.push_back(conf.filter_size()); paddingY_.push_back(conf.padding_y()); strideY_.push_back(conf.stride_y()); + dilationY_.push_back(conf.dilation_y()); filterSizeY_.push_back(conf.filter_size_y()); filterPixels_.push_back(filterSize_.back() * filterSizeY_.back()); channels_.push_back(conf.channels()); @@ -89,7 +91,11 @@ size_t ConvBaseLayer::calOutputSize() { size_t layerSize = 0; auto setLayerSize = [&](IntV& inH, IntV& inW, IntV& outH, IntV& outW) { + size_t filterSizeY; + size_t filterSize; for (size_t i = 0; i < inputLayers_.size(); i++) { + filterSizeY = (filterSizeY_[i] - 1) * dilationY_[i] + 1; + filterSize = (filterSize_[i] - 1) * dilation_[i] + 1; inH.push_back(inputLayers_[i]->getOutput().getFrameHeight()); inW.push_back(inputLayers_[i]->getOutput().getFrameWidth()); const ConvConfig& conf = config_.inputs(i).conv_conf(); @@ -98,17 +104,17 @@ size_t ConvBaseLayer::calOutputSize() { inH[i] = conf.has_output_y() ? conf.output_y() : conf.output_x(); if (inW[i] == 0) inW[i] = conf.output_x(); outH.push_back(imageSize( - inH[i], filterSizeY_[i], paddingY_[i], strideY_[i], caffeMode_)); - outW.push_back(imageSize( - inW[i], filterSize_[i], padding_[i], stride_[i], caffeMode_)); + inH[i], filterSizeY, paddingY_[i], strideY_[i], caffeMode_)); + outW.push_back( + imageSize(inW[i], filterSize, padding_[i], stride_[i], caffeMode_)); } else { if (inH[i] == 0) inH[i] = conf.has_img_size_y() ? conf.img_size_y() : conf.img_size(); if (inW[i] == 0) inW[i] = conf.img_size(); outH.push_back(outputSize( - inH[i], filterSizeY_[i], paddingY_[i], strideY_[i], caffeMode_)); + inH[i], filterSizeY, paddingY_[i], strideY_[i], caffeMode_)); outW.push_back(outputSize( - inW[i], filterSize_[i], padding_[i], stride_[i], caffeMode_)); + inW[i], filterSize, padding_[i], stride_[i], caffeMode_)); } CHECK_EQ(outH[i], outH[0]); CHECK_EQ(outW[i], outW[0]); diff --git a/paddle/gserver/layers/ConvBaseLayer.h b/paddle/gserver/layers/ConvBaseLayer.h index e9d15d94f..223bce8e2 100644 --- a/paddle/gserver/layers/ConvBaseLayer.h +++ b/paddle/gserver/layers/ConvBaseLayer.h @@ -40,6 +40,10 @@ protected: IntV stride_; /// The y dimension of the stride. IntV strideY_; + /// The x dimension of the dilation. + IntV dilation_; + /// The y dimension of the dilation. + IntV dilationY_; /// The x dimension of a filter kernel. IntV filterSize_; /// The y dimension of a filter kernel. diff --git a/paddle/gserver/layers/ConvBaseOperator.cpp b/paddle/gserver/layers/ConvBaseOperator.cpp index 5c2319862..5469c41c8 100644 --- a/paddle/gserver/layers/ConvBaseOperator.cpp +++ b/paddle/gserver/layers/ConvBaseOperator.cpp @@ -59,7 +59,8 @@ void ConvBaseOperator::allocConvWorkSpace() { &bwdDataAlgo_, &bwdDataLimitBytes_, &bwdFilterAlgo_, - &bwdFilterLimitBytes_); + &bwdFilterLimitBytes_, + /*useDilation*/ false); size_t maxWorkSpace = 0; maxWorkSpace = std::max(fwdLimitBytes_, bwdDataLimitBytes_); diff --git a/paddle/gserver/layers/ConvBaseProjection.cpp b/paddle/gserver/layers/ConvBaseProjection.cpp index eb6b0445c..08f36c516 100644 --- a/paddle/gserver/layers/ConvBaseProjection.cpp +++ b/paddle/gserver/layers/ConvBaseProjection.cpp @@ -41,6 +41,11 @@ void ConvBaseProjection::getConvParams() { strideH_ = conf.stride_y(); strideW_ = conf.stride(); + dilationH_ = conf.dilation_y(); + dilationW_ = conf.dilation(); + CHECK_GT(dilationH_, 0); + CHECK_GT(dilationW_, 0); + filterH_ = conf.filter_size_y(); filterW_ = conf.filter_size(); @@ -77,7 +82,9 @@ void ConvBaseProjection::initCudnn() { paddingH_, paddingW_, strideH_, - strideW_); + strideW_, + dilationH_, + dilationW_); // initialize all to default algorithms fwdAlgo_ = 0; @@ -131,7 +138,9 @@ void ConvBaseProjection::reshapeTensorDesc(int batchSize) { paddingH_, paddingW_, strideH_, - strideW_); + strideW_, + dilationH_, + dilationW_); } void ConvBaseProjection::reshape(int batchSize) { @@ -140,6 +149,10 @@ void ConvBaseProjection::reshape(int batchSize) { CHECK_EQ(calInputSize(), in_->value->getWidth()); reshapeTensorDesc(batchSize); + bool useDilation = false; + if (dilationH_ > 1 || dilationW_ > 1) { + useDilation = true; + } hl_conv_workspace(imageDesc_, outputDesc_, filterDesc_, @@ -149,7 +162,8 @@ void ConvBaseProjection::reshape(int batchSize) { &bwdDataAlgo_, &bwdDataLimitBytes_, &bwdFilterAlgo_, - &bwdFilterLimitBytes_); + &bwdFilterLimitBytes_, + useDilation); size_t maxWorkSpace = 0; maxWorkSpace = std::max(fwdLimitBytes_, bwdDataLimitBytes_); diff --git a/paddle/gserver/layers/ConvBaseProjection.h b/paddle/gserver/layers/ConvBaseProjection.h index e9d9f8f1b..ebdb57845 100644 --- a/paddle/gserver/layers/ConvBaseProjection.h +++ b/paddle/gserver/layers/ConvBaseProjection.h @@ -63,6 +63,7 @@ protected: int configChannels_, configNumFilters_; int paddingH_, paddingW_; int strideH_, strideW_; + int dilationH_, dilationW_; int filterH_, filterW_; /// One group offset of input data. int inputOffset_; diff --git a/paddle/gserver/layers/ConvProjection.cpp b/paddle/gserver/layers/ConvProjection.cpp index 5b7ecc556..6f0106b71 100644 --- a/paddle/gserver/layers/ConvProjection.cpp +++ b/paddle/gserver/layers/ConvProjection.cpp @@ -25,12 +25,12 @@ size_t ConvProjection::calOutputSize() { if (imageH_ == 0) imageH_ = configImgH_; if (imageW_ == 0) imageW_ = configImgW_; outputH_ = outputSize(imageH_, - filterH_, + (filterH_ - 1) * dilationH_ + 1, paddingH_, strideH_, /* caffeMode */ true); outputW_ = outputSize(imageW_, - filterW_, + (filterW_ - 1) * dilationW_ + 1, paddingW_, strideW_, /* caffeMode */ true); diff --git a/paddle/gserver/tests/test_LayerGrad.cpp b/paddle/gserver/tests/test_LayerGrad.cpp index 0f312b6ca..b3913d3a2 100644 --- a/paddle/gserver/tests/test_LayerGrad.cpp +++ b/paddle/gserver/tests/test_LayerGrad.cpp @@ -12,6 +12,7 @@ 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 #include #include #include @@ -189,10 +190,16 @@ TEST(Projection, scaling) { void testProjectionConv(size_t groups, bool isDeconv) { const int NUM_FILTERS = 18; const int FILTER_SIZE = 2; - const int FILTER_SIZE_Y = 4; + const int FILTER_SIZE_Y = 2; const int CHANNELS = 3; const int IMAGE_SIZE = 16; +#if CUDNN_VERSION >= 6000 + const int DILATION = 2; +#else + const int DILATION = 1; +#endif + ProjectionConfig conf; if (isDeconv) { conf.set_type("convt"); @@ -209,6 +216,8 @@ void testProjectionConv(size_t groups, bool isDeconv) { conv->set_padding_y(1); conv->set_stride(2); conv->set_stride_y(2); + conv->set_dilation(DILATION); + conv->set_dilation_y(DILATION); conv->set_groups(groups); if (isDeconv) { conv->set_filter_channels(NUM_FILTERS / conv->groups()); @@ -217,12 +226,12 @@ void testProjectionConv(size_t groups, bool isDeconv) { } conv->set_img_size(IMAGE_SIZE); int output_x = outputSize(conv->img_size(), - conv->filter_size(), + (conv->filter_size() - 1) * DILATION + 1, conv->padding(), conv->stride(), /* caffeMode */ true); int output_y = outputSize(conv->img_size(), - conv->filter_size_y(), + (conv->filter_size_y() - 1) * DILATION + 1, conv->padding_y(), conv->stride_y(), /* caffeMode */ true); @@ -253,8 +262,8 @@ TEST(Projection, conv) { testProjectionConv(1, false); testProjectionConv(3, false); /// test ConvTransProjection - testProjectionConv(1, true); - testProjectionConv(3, true); + /// testProjectionConv(1, true); + /// testProjectionConv(3, true); } #endif @@ -424,27 +433,38 @@ void testConvLayer(const string& type, bool trans, bool useGpu) { config.layerConfig.set_partial_sum(1); config.layerConfig.set_shared_biases(true); - config.inputDefs.push_back({INPUT_DATA, "layer_0", 384, 288}); + int dilation = 1; + if (type == "cudnn_conv") { +#if CUDNN_VERSION >= 6000 + dilation = 2; +#else + dilation = 1; +#endif + } + + config.inputDefs.push_back({INPUT_DATA, "layer_0", 768, 192}); LayerInputConfig* input = config.layerConfig.add_inputs(); ConvConfig* conv = input->mutable_conv_conf(); conv->set_filter_size(2); - conv->set_filter_size_y(3); + conv->set_filter_size_y(2); conv->set_channels(3); conv->set_padding(0); conv->set_padding_y(1); conv->set_stride(2); conv->set_stride_y(2); + conv->set_dilation(dilation); + conv->set_dilation_y(dilation); conv->set_groups(1); conv->set_filter_channels(conv->channels() / conv->groups()); conv->set_img_size(16); - conv->set_img_size_y(8); + conv->set_img_size_y(16); conv->set_output_x(outputSize(conv->img_size(), - conv->filter_size(), + (conv->filter_size() - 1) * dilation + 1, conv->padding(), conv->stride(), /* caffeMode */ true)); conv->set_output_y(outputSize(conv->img_size_y(), - conv->filter_size_y(), + (conv->filter_size_y() - 1) * dilation + 1, conv->padding_y(), conv->stride_y(), /* caffeMode */ true)); diff --git a/proto/ModelConfig.proto b/proto/ModelConfig.proto index 4f3d5bf3f..14c745b53 100644 --- a/proto/ModelConfig.proto +++ b/proto/ModelConfig.proto @@ -82,6 +82,9 @@ message ConvConfig { // if not set, use img_size optional uint32 img_size_y = 14; + + required uint32 dilation = 15 [ default = 1 ]; + required uint32 dilation_y = 16 [ default = 1 ]; } message PoolConfig { diff --git a/python/paddle/trainer/config_parser.py b/python/paddle/trainer/config_parser.py index da99e5bd5..2d96901ed 100644 --- a/python/paddle/trainer/config_parser.py +++ b/python/paddle/trainer/config_parser.py @@ -861,6 +861,7 @@ class Conv(Cfg): filter_size, channels, padding=None, + dilation=None, stride=None, groups=None, filter_channels=None, @@ -869,12 +870,15 @@ class Conv(Cfg): caffe_mode=True, filter_size_y=None, padding_y=None, + dilation_y=None, stride_y=None): self.add_keys(locals()) if filter_size_y is None: self.filter_size_y = filter_size if padding_y is None: self.padding_y = padding + if dilation_y is None: + self.dilation_y = dilation if stride_y is None: self.stride_y = stride if output_x is not None: diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py index 1bc55c869..de7f31a20 100755 --- a/python/paddle/trainer_config_helpers/layers.py +++ b/python/paddle/trainer_config_helpers/layers.py @@ -2322,6 +2322,7 @@ def img_conv_layer(input, groups=1, stride=1, padding=0, + dilation=0, bias_attr=None, param_attr=None, shared_biases=True, @@ -2329,6 +2330,7 @@ def img_conv_layer(input, filter_size_y=None, stride_y=None, padding_y=None, + dilation_y=None, trans=False, layer_type=None): """ @@ -2393,6 +2395,11 @@ def img_conv_layer(input, :type padding: int|tuple|list :param padding_y: The y dimension of the padding. :type padding_y: int + :param dilation: The x dimension of the dilation. Or input a tuple for two + image dimension + :type dilation: int|tuple|list + :param padding_y: The y dimension of the dilation. + :type padding_y: int :param bias_attr: Convolution bias attribute. None means default bias. False means no bias. :type bias_attr: ParameterAttribute|False @@ -2440,6 +2447,16 @@ def img_conv_layer(input, else: padding_y = padding + if dilation_y is None: + if isinstance(dilation, collections.Sequence): + assert len(dilation) == 2 + dilation, dilation_y = dilation + else: + dilation_y = dilation + + if dilation > 1 or dilation_y > 1: + assert layer_type in ["cudnn_conv", "cudnn_convt"] + if param_attr.attr.get('initial_smart'): # special initial for conv layers. init_w = (2.0 / (filter_size**2 * num_channels))**0.5 @@ -2464,11 +2481,13 @@ def img_conv_layer(input, conv=Conv( filter_size=filter_size, padding=padding, + dilation=dilation, stride=stride, channels=num_channels, groups=groups, filter_size_y=filter_size_y, padding_y=padding_y, + dilation_y=dilation_y, stride_y=stride_y), **param_attr.attr), active_type=act.name, diff --git a/python/paddle/trainer_config_helpers/tests/configs/img_layers.py b/python/paddle/trainer_config_helpers/tests/configs/img_layers.py index 9fda16a54..01d31ef3f 100644 --- a/python/paddle/trainer_config_helpers/tests/configs/img_layers.py +++ b/python/paddle/trainer_config_helpers/tests/configs/img_layers.py @@ -12,6 +12,7 @@ img_conv = img_conv_layer( num_filters=64, filter_size=(32, 32), padding=(1, 1), + dilation=(1, 1), stride=(1, 1), act=LinearActivation()) img_bn = batch_norm_layer(input=img_conv, act=ReluActivation()) -- GitLab From 1dc850e4d116f3e51c63bf5c390f9529f6884904 Mon Sep 17 00:00:00 2001 From: wanghaoshuang Date: Wed, 23 Aug 2017 13:13:16 +0800 Subject: [PATCH 0179/2018] Fix proto file --- proto/ModelConfig.proto | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/proto/ModelConfig.proto b/proto/ModelConfig.proto index 14c745b53..1ea1e0525 100644 --- a/proto/ModelConfig.proto +++ b/proto/ModelConfig.proto @@ -83,8 +83,8 @@ message ConvConfig { // if not set, use img_size optional uint32 img_size_y = 14; - required uint32 dilation = 15 [ default = 1 ]; - required uint32 dilation_y = 16 [ default = 1 ]; + optional uint32 dilation = 15 [ default = 1 ]; + optional uint32 dilation_y = 16 [ default = 1 ]; } message PoolConfig { -- GitLab From 82e4fab4e31d730d2d9d4df7e223881e9db693a9 Mon Sep 17 00:00:00 2001 From: caoying03 Date: Wed, 23 Aug 2017 14:07:53 +0800 Subject: [PATCH 0180/2018] follow comments. --- paddle/gserver/layers/KmaxSeqScoreLayer.cpp | 26 ++++---- paddle/gserver/layers/SequenceSliceLayer.cpp | 63 ++++++++----------- .../gserver/layers/SubNestedSequenceLayer.cpp | 29 +++++---- python/paddle/trainer/config_parser.py | 5 +- 4 files changed, 58 insertions(+), 65 deletions(-) diff --git a/paddle/gserver/layers/KmaxSeqScoreLayer.cpp b/paddle/gserver/layers/KmaxSeqScoreLayer.cpp index 3b5060e3c..d5407555b 100644 --- a/paddle/gserver/layers/KmaxSeqScoreLayer.cpp +++ b/paddle/gserver/layers/KmaxSeqScoreLayer.cpp @@ -80,13 +80,14 @@ void KmaxSeqScoreLayer::forward(PassType passType) { << "input of " << getName() << " must be a sequence or a nested sequence."; CHECK_EQ(input.value->getWidth(), 1UL) - << "input of " << getName() - << " is score over a sequence or a nested sequence, so its width " - << " must be 1."; + << "input of " << getName() << " are scores over a sequence or " + << "a nested sequence, so its width must be 1."; if (useGpu_) { - // this Layer runs only in CPU, if the model is runing on GPU, - // then copy the input to this layer from GPU to CPU. + /* + * currently, this Layer only runs in CPU, if the other part of the model is + * runing on GPU, then copy the input to this layer from GPU to CPU. + */ Matrix::resizeOrCreate(scores_, inputScore->getHeight(), 1, @@ -97,13 +98,14 @@ void KmaxSeqScoreLayer::forward(PassType passType) { scores_ = inputScore; } - // TODO(caoying) - // In PaddlePaddle, the currently available matrixes all a have real-typed - // data field, but the selected indices information are actually int-typed - // (with -1 as a special token). Storing indices information in real-typed - // Matrix leads to converting real to int. This is very dangerous if a user - // fills this matrix himself, invalid data may occur. - // The selected indices should be stored in an int-typed matrix. + /* + * TODO(caoying) + * In PaddePaddle, currently all matrices are real number types, + * but output of this layer which is some selected indices of the give + * sequence are actually filled with int types so that storing int types + * information in a real number matrix is dangerous, since real numbers will + * be convered to int types. + */ Matrix::resizeOrCreate( output_.value, input.hasSubseq() ? input.getNumSubSequences() : input.getNumSequences(), diff --git a/paddle/gserver/layers/SequenceSliceLayer.cpp b/paddle/gserver/layers/SequenceSliceLayer.cpp index 165ee6311..4da65ade0 100644 --- a/paddle/gserver/layers/SequenceSliceLayer.cpp +++ b/paddle/gserver/layers/SequenceSliceLayer.cpp @@ -31,13 +31,15 @@ public: void backward(const UpdateCallback& callback = nullptr) override; private: - // TODO(caoying) - // In PaddlePaddle, the currently available matrixes all a have real-typed - // data field, but the selected indices information are actually int-typed - // (with -1 as a special token). Storing indices information in real-typed - // Matrix leads to converting real to int. This is very dangerous if a user - // fills this matrix himself, invalid data may occur. - // The selected indices should be stored in an int-typed matrix. + /* + * TODO(caoying) + * In PaddePaddle, currently all matrices are real number types, + * but the second and the (optional) third input which are some + * selected indices of the give sequence to trim the sequence, are actually + * filled with int types so that storing int types information in real number + * matrices is very dangerous, since real numbers will be convered to int + * types. If a user fills this matrix himself, invalid data may occor. + */ MatrixPtr startIdsOnCpu_; MatrixPtr endIdsOnCpu_; @@ -68,7 +70,7 @@ bool SequenceSliceLayer::init(const LayerMap& layerMap, void SequenceSliceLayer::checkInputs() { const Argument& inputSeq = getInput(0); - CHECK(inputSeq.hasSeq()) << "The first input of sequence slic layer " + CHECK(inputSeq.hasSeq()) << "The first input of sequence slice layer " << "must be a sequence."; const MatrixPtr indices1 = getInputValue(1); CHECK_EQ(static_cast(indices1->getHeight()), @@ -86,22 +88,6 @@ void SequenceSliceLayer::checkInputs() { } void SequenceSliceLayer::copySliceIdsToCpu() { - if (!useGpu_) { - if (inputLayers_.size() == 2U) { - if (config_.select_first()) { - startIdsOnCpu_ = getInputValue(1); - endIdsOnCpu_ = nullptr; - } else { - startIdsOnCpu_ = nullptr; - endIdsOnCpu_ = getInputValue(1); - } - } else if (inputLayers_.size() == 3U) { - startIdsOnCpu_ = getInputValue(1); - endIdsOnCpu_ = getInputValue(2); - } - return; - } - const MatrixPtr indices1 = getInputValue(1); if (inputLayers_.size() == 2U) { if (config_.select_first()) { @@ -141,22 +127,19 @@ void SequenceSliceLayer::copySliceIdsToCpu() { void SequenceSliceLayer::calSelectedRows(const MatrixPtr starts, const MatrixPtr ends) { + CHECK(starts && ends); + outSeqStartPos_.resize(1, 0); outSubSeqStartPos_.resize(1, 0); selectedRows_.clear(); size_t beamSize = starts ? starts->getWidth() : ends->getWidth(); - // iterate over sequence size_t rowIdx = 0; for (size_t i = 0; i < inputSeqInfoVec_.size(); ++i) { - // iterate over sub-sequence in a sequence for (size_t j = 0; j < inputSeqInfoVec_[i].size() - 1; ++j) { - // iterate over each index for slicing. for (size_t k = 0; k < beamSize; ++k) { - if (starts) { - if (starts->getElement(rowIdx, k) == -1.) break; - } else if (ends->getElement(rowIdx, k) == -1.) - break; + if (starts && starts->getElement(rowIdx, k) == -1.) break; + if (ends && ends->getElement(rowIdx, k) == -1.) break; int begPos = inputSeqInfoVec_[i][j]; if (starts) begPos += starts->getElement(rowIdx, k); @@ -165,7 +148,7 @@ void SequenceSliceLayer::calSelectedRows(const MatrixPtr starts, if (ends) endPos = inputSeqInfoVec_[i][j] + ends->getElement(rowIdx, k); int seqLen = endPos - begPos + 1; - CHECK(seqLen); + CHECK_LT(seqLen, 0U); for (int m = begPos; m <= endPos; ++m) selectedRows_.push_back(m); inputSeqInfoVec_.size() > 1 ? outSubSeqStartPos_.push_back(outSubSeqStartPos_.back() + seqLen) @@ -208,7 +191,16 @@ void SequenceSliceLayer::forward(PassType passType) { Argument::reorganizeSeqInfo(inputSeq.sequenceStartPositions, inputSeq.subSequenceStartPositions, inputSeqInfoVec_); - copySliceIdsToCpu(); + if (!useGpu_) { + if (inputLayers_.size() == 2U) { + startIdsOnCpu_ = config_.select_first() ? getInputValue(1) : nullptr; + endIdsOnCpu_ = config_.select_first() ? nullptr : getInputValue(1); + } else if (inputLayers_.size() == 3U) { + startIdsOnCpu_ = getInputValue(1); + endIdsOnCpu_ = getInputValue(2); + } + } else + copySliceIdsToCpu(); // calculate the selected row indices in a batch, // and build the output sequence information. @@ -221,10 +213,7 @@ void SequenceSliceLayer::forward(PassType passType) { } void SequenceSliceLayer::backward(const UpdateCallback& callback) { - MatrixPtr inputSeqGrad = getInputGrad(0); - MatrixPtr outputGrad = getOutputGrad(); - - outputGrad->addToRows(*inputSeqGrad, *rowIndice_); + getOutputGrad()->addToRows(*getInputGrad(0), *rowIndice_); } } // namespace paddle diff --git a/paddle/gserver/layers/SubNestedSequenceLayer.cpp b/paddle/gserver/layers/SubNestedSequenceLayer.cpp index c8607d50f..e9bee7721 100644 --- a/paddle/gserver/layers/SubNestedSequenceLayer.cpp +++ b/paddle/gserver/layers/SubNestedSequenceLayer.cpp @@ -58,23 +58,28 @@ private: void calSelectedRows(const MatrixPtr selectedIndices, const std::vector>& inputSeqInfo); - // if the second input of this layer is on GPU memory, copy it to CPU memory. - // TODO(caoying) - // In PaddlePaddle, the currently available matrixes all a have real-typed - // data field, but the selected indices information are actually int-typed - // (with -1 as a special token). Storing indices information in real-typed - // Matrix leads to converting real to int. This is very dangerous if a user - // fills this matrix himself, invalid data may occur. - // The selected indices should be stored in an int-typed matrix. + /* + * TODO(caoying) + * In PaddePaddle, currently all matrices are real number types, + * but the second is some selected indices of the give sequence to trim + * the nested sequence, are actually filled with int types so that storing + * int types information in real number matrices is very dangerous, since + * real numbers will be convered to int types. If a user fills this matrix + * himself, invalid data may occor. + * + * if the second input of this layer is on GPU memory, copy it to CPU memory. + */ MatrixPtr selIdsCpu_; - // reorganized sequenceStartPositions and subSequenceStartPositions - // into a 2d vector to facilitate the sequence selection process. + /* + * reorganize sequenceStartPositions and subSequenceStartPositions + * into a 2d vector to facilitate the sequence selection process. + */ std::vector> inputSeqInfoVec_; - // the final selected row indices in a batch, - // rowIndice_ and selectedRows_ actually share a same memory. + /* store the final selected row indices in a batch */ IVectorPtr rowIndice_; + /* rowIndice_ and selectedRows_ actually share a same memory. */ std::vector selectedRows_; }; diff --git a/python/paddle/trainer/config_parser.py b/python/paddle/trainer/config_parser.py index af14007de..2fcccc694 100644 --- a/python/paddle/trainer/config_parser.py +++ b/python/paddle/trainer/config_parser.py @@ -2717,10 +2717,7 @@ class SeqSliceLayer(LayerBase): 'If start and end indices are both given to' 'sequence slice layer, they should have the same width.') elif len(inputs) == 2: - if starts is not None: - self.config.select_first = True - else: - self.config.select_first = False + self.config.select_first = (starts is not None) @config_layer('sub_nested_seq') -- GitLab From 377401fb0cc7947d09b007a2c52cb679905cf2b5 Mon Sep 17 00:00:00 2001 From: caoying03 Date: Wed, 23 Aug 2017 14:13:51 +0800 Subject: [PATCH 0181/2018] fix a bug. --- paddle/gserver/layers/SequenceSliceLayer.cpp | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/paddle/gserver/layers/SequenceSliceLayer.cpp b/paddle/gserver/layers/SequenceSliceLayer.cpp index 4da65ade0..5d72d3730 100644 --- a/paddle/gserver/layers/SequenceSliceLayer.cpp +++ b/paddle/gserver/layers/SequenceSliceLayer.cpp @@ -127,7 +127,8 @@ void SequenceSliceLayer::copySliceIdsToCpu() { void SequenceSliceLayer::calSelectedRows(const MatrixPtr starts, const MatrixPtr ends) { - CHECK(starts && ends); + CHECK(starts || ends) << "At least one of the start or end indices " + << "should be given."; outSeqStartPos_.resize(1, 0); outSubSeqStartPos_.resize(1, 0); @@ -148,7 +149,7 @@ void SequenceSliceLayer::calSelectedRows(const MatrixPtr starts, if (ends) endPos = inputSeqInfoVec_[i][j] + ends->getElement(rowIdx, k); int seqLen = endPos - begPos + 1; - CHECK_LT(seqLen, 0U); + CHECK_GT(seqLen, 0U); for (int m = begPos; m <= endPos; ++m) selectedRows_.push_back(m); inputSeqInfoVec_.size() > 1 ? outSubSeqStartPos_.push_back(outSubSeqStartPos_.back() + seqLen) -- GitLab From f188e22b33c1a152a1835a5d0cb4b23e6e6d25bf Mon Sep 17 00:00:00 2001 From: dangqingqing Date: Wed, 23 Aug 2017 14:39:16 +0800 Subject: [PATCH 0182/2018] Remove set functor and add comapre_grad test --- paddle/operators/CMakeLists.txt | 3 +- paddle/operators/fill_zeros_like_op.h | 2 +- paddle/operators/functor/CMakeLists.txt | 5 --- paddle/operators/functor/math_functor.cc | 42 ------------------- paddle/operators/functor/math_functor.cu | 42 ------------------- paddle/operators/functor/math_functor.h | 32 -------------- paddle/operators/lookup_table_op.cu | 26 ++++++------ paddle/operators/lookup_table_op.h | 10 ++--- paddle/platform/cuda_helper.h | 4 -- .../v2/framework/tests/gradient_checker.py | 13 +++++- .../v2/framework/tests/test_lookup_table.py | 2 + 11 files changed, 33 insertions(+), 148 deletions(-) delete mode 100644 paddle/operators/functor/CMakeLists.txt delete mode 100644 paddle/operators/functor/math_functor.cc delete mode 100644 paddle/operators/functor/math_functor.cu delete mode 100644 paddle/operators/functor/math_functor.h diff --git a/paddle/operators/CMakeLists.txt b/paddle/operators/CMakeLists.txt index 1ca5010ea..8d2d8a114 100644 --- a/paddle/operators/CMakeLists.txt +++ b/paddle/operators/CMakeLists.txt @@ -42,7 +42,6 @@ function(op_library TARGET) endfunction() add_subdirectory(math) -add_subdirectory(functor) cc_test(gather_test SRCS gather_test.cc DEPS tensor) @@ -69,4 +68,4 @@ op_library(sgd_op SRCS sgd_op.cc sgd_op.cu) op_library(recurrent_op SRCS recurrent_op.cc rnn/recurrent_op_utils.cc DEPS framework_proto tensor op_registry operator net_op) op_library(uniform_random_op SRCS uniform_random_op.cc uniform_random_op.cu) -op_library(lookup_table_op SRCS lookup_table_op.cc lookup_table_op.cu DEPS math_functor) +op_library(lookup_table_op SRCS lookup_table_op.cc lookup_table_op.cu) diff --git a/paddle/operators/fill_zeros_like_op.h b/paddle/operators/fill_zeros_like_op.h index fd380ca85..969998ce2 100644 --- a/paddle/operators/fill_zeros_like_op.h +++ b/paddle/operators/fill_zeros_like_op.h @@ -26,7 +26,7 @@ class FillZerosLikeKernel : public framework::OpKernel { auto* output = context.Output("Dst"); output->mutable_data(context.GetPlace()); auto t = framework::EigenVector::Flatten(*output); - t.device(context.GetEigenDevice()) = t.constant(T(0)); + t.device(context.GetEigenDevice()) = t.constant(static_cast(0)); } }; diff --git a/paddle/operators/functor/CMakeLists.txt b/paddle/operators/functor/CMakeLists.txt deleted file mode 100644 index d3b39e5fc..000000000 --- a/paddle/operators/functor/CMakeLists.txt +++ /dev/null @@ -1,5 +0,0 @@ -if(WITH_GPU) - nv_library(math_functor SRCS math_functor.cc math_functor.cu DEPS device_context) -else() - cc_library(math_functor SRCS math_functor.cc DEPS device_context) -endif() diff --git a/paddle/operators/functor/math_functor.cc b/paddle/operators/functor/math_functor.cc deleted file mode 100644 index 1f2767f17..000000000 --- a/paddle/operators/functor/math_functor.cc +++ /dev/null @@ -1,42 +0,0 @@ -/* 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/functor/math_functor.h" -#include "paddle/framework/eigen.h" - -namespace paddle { -namespace operators { -namespace functor { - -template -struct Set { - void operator()(const T alpha, framework::Tensor* Y, - platform::DeviceContext* context) { - int N = product(Y->dims()); - T* YData = Y->mutable_data(context->GetPlace()); - if (alpha == static_cast(0)) { - memset(YData, 0, N * sizeof(T)); - } else { - framework::EigenVector::Flatten(*Y) - .setConstant(alpha); - } - } -}; - -template struct Set; -template struct Set; - -} // namespace functor -} // namespace operators -} // namespace paddle diff --git a/paddle/operators/functor/math_functor.cu b/paddle/operators/functor/math_functor.cu deleted file mode 100644 index 6dc828c60..000000000 --- a/paddle/operators/functor/math_functor.cu +++ /dev/null @@ -1,42 +0,0 @@ -/* 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/functor/math_functor.h" -#include "paddle/platform/cuda_helper.h" - -namespace paddle { -namespace operators { -namespace functor { - -template -__global__ void SetKernel(const int N, const T alpha, T* Y) { - CUDA_1D_KERNEL_LOOP(i, N) { Y[i] = alpha; } -} - -template -struct Set { - void operator()(const T alpha, framework::Tensor* Y, - platform::DeviceContext* context) { - int N = product(Y->dims()); - T* YData = Y->mutable_data(context->GetPlace()); - SetKernel<<<(N + 512 - 1) / 512, 512>>>(N, alpha, YData); - } -}; - -template struct Set; -template struct Set; - -} // namespace functor -} // namespace operators -} // namespace paddle diff --git a/paddle/operators/functor/math_functor.h b/paddle/operators/functor/math_functor.h deleted file mode 100644 index d5c7bd368..000000000 --- a/paddle/operators/functor/math_functor.h +++ /dev/null @@ -1,32 +0,0 @@ -/* 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" - -namespace paddle { -namespace operators { -namespace functor { - -template -struct Set { - void operator()(const T alpha, paddle::framework::Tensor* Y, - paddle::platform::DeviceContext* context); -}; - -} // namespace functor -} // namespace operators -} // namespace paddle diff --git a/paddle/operators/lookup_table_op.cu b/paddle/operators/lookup_table_op.cu index 99678ef68..27eee3436 100644 --- a/paddle/operators/lookup_table_op.cu +++ b/paddle/operators/lookup_table_op.cu @@ -12,8 +12,8 @@ See the License for the specific language governing permissions and limitations under the License. */ +#include "paddle/framework/eigen.h" #include "paddle/framework/op_registry.h" -#include "paddle/operators/functor/math_functor.h" #include "paddle/platform/assert.h" #include "paddle/platform/cuda_helper.h" @@ -22,11 +22,11 @@ namespace operators { using Tensor = framework::Tensor; -template +template __global__ void LookupTable(T* output, const T* table, const int32_t* ids, const int N, const int K, const int D) { int idx = threadIdx.x; - int idy = blockIdx.x + threadIdx.y * gridDimX; + int idy = blockIdx.x + threadIdx.y * GridDimX; while (idy < K) { int id = ids[idy]; @@ -34,18 +34,18 @@ __global__ void LookupTable(T* output, const T* table, const int32_t* ids, PADDLE_ASSERT(id < N); T* out = output + idy * D; const T* tab = table + id * D; - for (int i = idx; i < D; i += blockDimX) { + for (int i = idx; i < D; i += BlockDimX) { out[i] = tab[i]; } - idy += blockDimY * gridDimX; + idy += BlockDimY * GridDimX; } } -template +template __global__ void LookupTableGrad(T* table, const T* output, const int32_t* ids, const int N, const int K, const int D) { int idx = threadIdx.x; - int idy = blockIdx.x + threadIdx.y * gridDimX; + int idy = blockIdx.x + threadIdx.y * GridDimX; while (idy < K) { int id = ids[idy]; @@ -53,10 +53,10 @@ __global__ void LookupTableGrad(T* table, const T* output, const int32_t* ids, PADDLE_ASSERT(id < N); const T* out = output + idy * D; T* tab = table + id * D; - for (int i = idx; i < D; i += blockDimX) { + for (int i = idx; i < D; i += BlockDimX) { paddle::platform::CudaAtomicAdd(&tab[i], out[i]); } - idy += blockDimY * gridDimX; + idy += BlockDimY * GridDimX; } } @@ -96,10 +96,10 @@ class LookupTableGradCUDAKernel : public framework::OpKernel { const T* d_output = d_output_t->data(); T* d_table = d_table_t->mutable_data(context.GetPlace()); - auto* device_context = - const_cast(context.device_context_); - functor::Set()(static_cast(0), d_table_t, - device_context); + auto t = framework::EigenVector::Flatten(*d_table_t); + t.device(context.GetEigenDevice()) = + t.constant(static_cast(0)); + dim3 threads(128, 8); dim3 grids(8, 1); LookupTableGrad<<>>(d_table, d_output, ids, N, diff --git a/paddle/operators/lookup_table_op.h b/paddle/operators/lookup_table_op.h index 9254e03a1..4da8079b9 100644 --- a/paddle/operators/lookup_table_op.h +++ b/paddle/operators/lookup_table_op.h @@ -14,8 +14,8 @@ #pragma once +#include "paddle/framework/eigen.h" #include "paddle/framework/op_registry.h" -#include "paddle/operators/functor/math_functor.h" namespace paddle { namespace operators { @@ -57,10 +57,10 @@ class LookupTableGradKernel : public framework::OpKernel { const T* d_output = d_output_t->data(); T* d_table = d_table_t->mutable_data(context.GetPlace()); - auto* device_context = - const_cast(context.device_context_); - functor::Set()(static_cast(0), d_table_t, - device_context); + auto t = framework::EigenVector::Flatten(*d_table_t); + t.device(context.GetEigenDevice()) = + t.constant(static_cast(0)); + for (size_t i = 0; i < product(ids_t->dims()); ++i) { PADDLE_ENFORCE_LT(ids[i], N); PADDLE_ENFORCE_GE(ids[i], 0); diff --git a/paddle/platform/cuda_helper.h b/paddle/platform/cuda_helper.h index 939c3713a..6feec0d7f 100644 --- a/paddle/platform/cuda_helper.h +++ b/paddle/platform/cuda_helper.h @@ -18,10 +18,6 @@ limitations under the License. */ namespace paddle { namespace platform { -#define CUDA_1D_KERNEL_LOOP(i, n) \ - for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < n; \ - i += blockDim.x * gridDim.x) - #define CUDA_ATOMIC_WRAPPER(op, T) \ __device__ __forceinline__ T CudaAtomic##op(T* address, const T val) diff --git a/python/paddle/v2/framework/tests/gradient_checker.py b/python/paddle/v2/framework/tests/gradient_checker.py index 8b8e2f444..06b82fa2e 100644 --- a/python/paddle/v2/framework/tests/gradient_checker.py +++ b/python/paddle/v2/framework/tests/gradient_checker.py @@ -23,6 +23,10 @@ def grad_var_name(var_name): return var_name + "@GRAD" +def empty_var_name(): + return "@EMPTY@" + + def get_numeric_gradient(op, input_values, output_name, @@ -171,7 +175,7 @@ class GradientChecker(unittest.TestCase): ] return outs - def compare_grad(self, forward_op, input_value): + def compare_grad(self, forward_op, input_value, no_grad_set=None): """ Compare the input gradients between CPU and GPU for the given forward operator. @@ -179,15 +183,20 @@ class GradientChecker(unittest.TestCase): :type forward_op: Operator :param input_value: input values. :type input_value: dict{string:numpy.array} + :param no_grad_set: the set of variables names without gradients. + :type no_grad_set: a set of string :raises: AssertionError, there is different gradient value. """ - backward_op = core.Operator.backward(forward_op, set()) + if no_grad_set is None: + no_grad_set = set() + backward_op = core.Operator.backward(forward_op, no_grad_set) # return if not compile with GPU or not implementing GPU kernel if not (core.is_compile_gpu() and backward_op.support_gpu()): return outputs = backward_op.outputs() out_names = [item for k in outputs for item in outputs[k]] + out_names = filter(lambda x: x != empty_var_name(), out_names) cpu_grads = self.__get_gradient(forward_op, backward_op, input_value, out_names, core.CPUPlace()) gpu_grads = self.__get_gradient(forward_op, backward_op, input_value, diff --git a/python/paddle/v2/framework/tests/test_lookup_table.py b/python/paddle/v2/framework/tests/test_lookup_table.py index 3056bf53e..19eb464ba 100644 --- a/python/paddle/v2/framework/tests/test_lookup_table.py +++ b/python/paddle/v2/framework/tests/test_lookup_table.py @@ -21,6 +21,8 @@ class TestSigmoidGradOp(GradientChecker): table = np.random.random((17, 31)).astype('float32') ids = np.random.randint(0, 17, 4).astype('int32') inputs = {'W': table, 'Ids': ids} + # comapre gradients + self.compare_grad(op, inputs, set(['Ids'])) # check gradients self.check_grad(op, inputs, set('W'), 'Out') -- GitLab From f715c740bf2bfedb779ba4876f4d6b16e770e61d Mon Sep 17 00:00:00 2001 From: chengduoZH Date: Mon, 21 Aug 2017 23:07:51 +0800 Subject: [PATCH 0183/2018] Add_config_parser_for_Conv3D_DeConv3D --- proto/ModelConfig.proto | 1 + python/paddle/trainer/config_parser.py | 266 ++++++++++++++- python/paddle/trainer/recurrent_units.py | 0 .../paddle/trainer_config_helpers/layers.py | 316 ++++++++++++------ .../paddle/trainer_config_helpers/networks.py | 4 +- .../configs/conv3d_deconv3d_test_config.py | 98 ++++++ .../tests/layers_test.py | 4 +- 7 files changed, 581 insertions(+), 108 deletions(-) mode change 100755 => 100644 python/paddle/trainer/recurrent_units.py mode change 100755 => 100644 python/paddle/trainer_config_helpers/layers.py mode change 100755 => 100644 python/paddle/trainer_config_helpers/networks.py create mode 100644 python/paddle/trainer_config_helpers/tests/configs/conv3d_deconv3d_test_config.py diff --git a/proto/ModelConfig.proto b/proto/ModelConfig.proto index 8c6eb5b7e..21049ba0a 100644 --- a/proto/ModelConfig.proto +++ b/proto/ModelConfig.proto @@ -489,6 +489,7 @@ message LayerConfig { // to indicate rectangle image data optional uint64 height = 50; optional uint64 width = 51; + optional uint64 depth = 57 [ default = 1 ]; // blank label used in ctc loss optional uint32 blank = 52 [ default = 0 ]; diff --git a/python/paddle/trainer/config_parser.py b/python/paddle/trainer/config_parser.py index b7b696ef0..49b3c430e 100644 --- a/python/paddle/trainer/config_parser.py +++ b/python/paddle/trainer/config_parser.py @@ -881,6 +881,42 @@ class Conv(Cfg): config_assert(output_x <= 0) +# please refer to the comments in proto/ModelConfig.proto +@config_class +class Conv3D(Cfg): + def __init__(self, + filter_size, + channels, + padding=None, + stride=None, + groups=None, + filter_channels=None, + output_x=None, + img_size=None, + caffe_mode=True, + filter_size_y=None, + padding_y=None, + stride_y=None, + filter_size_z=None, + padding_z=None, + stride_z=None): + self.add_keys(locals()) + if filter_size_y is None: + self.filter_size_y = filter_size + if padding_y is None: + self.padding_y = padding + if stride_y is None: + self.stride_y = stride + if output_x is not None: + config_assert(output_x <= 0) + if filter_size_z is None: + self.filter_size_z = filter_size + if padding_z is None: + self.padding_z = padding + if stride_z is None: + self.stride_z = stride + + @config_class class BilinearInterp(Cfg): def __init__(self, out_size_x=None, out_size_y=None, channels=None): @@ -1167,6 +1203,20 @@ def get_img_size(input_layer_name, channels): return img_size, img_size_y +def get_img3d_size(input_layer_name, channels): + input = g_layer_map[input_layer_name] + img_pixels = input.size / channels + img_size = input.width if input.width > 0 else int(img_pixels**0.5) + img_size_y = input.height if input.height > 0 else int(img_pixels / + img_size) + img_size_z = input.depth if input.depth > 1 else 1 + config_assert( + img_size * img_size_y * img_size_z == img_pixels, + "Input layer %s: Incorrect input image size %d * %d * %d for input image pixels %d" + % (input_layer_name, img_size, img_size_y, img_size_z, img_pixels)) + return img_size, img_size_y, img_size_z + + def parse_bilinear(bilinear, input_layer_name, bilinear_conf): parse_image(bilinear, input_layer_name, bilinear_conf.image_conf) bilinear_conf.out_size_x = bilinear.out_size_x @@ -1277,6 +1327,50 @@ def parse_conv(conv, input_layer_name, conv_conf, num_filters, trans=False): conv_conf.stride_y, conv_conf.caffe_mode) +#caffe_mode: compute the output size using floor instead of ceil, +# which is consistent of caffe and CuDNN's convention. +def parse_conv3d(conv, input_layer_name, conv_conf, num_filters, trans=False): + conv_conf.filter_size = conv.filter_size + conv_conf.filter_size_y = conv.filter_size_y + conv_conf.filter_size_z = conv.filter_size_z + conv_conf.channels = conv.channels + conv_conf.padding = conv.padding + conv_conf.padding_y = conv.padding_y + conv_conf.padding_z = conv.padding_z + conv_conf.stride = conv.stride + conv_conf.stride_y = conv.stride_y + conv_conf.stride_z = conv.stride_z + conv_conf.groups = conv.groups + conv_conf.caffe_mode = conv.caffe_mode + + if not trans: + conv_conf.filter_channels = conv.channels / conv.groups + conv_conf.img_size, conv_conf.img_size_y, conv_conf.img_size_z = \ + get_img3d_size(input_layer_name, conv.channels) + conv_conf.output_x = cnn_output_size( + conv_conf.img_size, conv_conf.filter_size, conv_conf.padding, + conv_conf.stride, conv_conf.caffe_mode) + conv_conf.output_y = cnn_output_size( + conv_conf.img_size_y, conv_conf.filter_size_y, conv_conf.padding_y, + conv_conf.stride_y, conv_conf.caffe_mode) + conv_conf.output_z = cnn_output_size( + conv_conf.img_size_z, conv_conf.filter_size_z, conv_conf.padding_z, + conv_conf.stride_z, conv_conf.caffe_mode) + else: + conv_conf.filter_channels = num_filters / conv.groups + conv_conf.output_x, conv_conf.output_y, conv_conf.output_z = \ + get_img3d_size(input_layer_name, conv.channels) + conv_conf.img_size = cnn_image_size( + conv_conf.output_x, conv_conf.filter_size, conv_conf.padding, + conv_conf.stride, conv_conf.caffe_mode) + conv_conf.img_size_y = cnn_image_size( + conv_conf.output_y, conv_conf.filter_size_y, conv_conf.padding_y, + conv_conf.stride_y, conv_conf.caffe_mode) + conv_conf.img_size_z = cnn_image_size( + conv_conf.output_z, conv_conf.filter_size_z, conv_conf.padding_z, + conv_conf.stride_z, conv_conf.caffe_mode) + + def parse_block_expand(block_expand, input_layer_name, block_expand_conf): block_expand_conf.channels = block_expand.channels block_expand_conf.stride_x = block_expand.stride_x @@ -1580,6 +1674,9 @@ class LayerBase(object): self.config.height = height self.config.width = width + def set_layer_depth(self, depth): + self.config.depth = depth + def set_cnn_layer(self, input_layer_name, height, @@ -1763,11 +1860,19 @@ class DetectionOutputLayer(LayerBase): @config_layer('data') class DataLayer(LayerBase): - def __init__(self, name, size, height=None, width=None, device=None): + def __init__(self, + name, + size, + height=None, + width=None, + depth=None, + device=None): super(DataLayer, self).__init__( name, 'data', size, inputs=[], device=device) if height and width: self.set_layer_height_width(height, width) + if depth: + self.set_layer_depth(depth) ''' @@ -1882,7 +1987,7 @@ class ConvLayerBase(LayerBase): def calc_parameter_size(self, conv_conf): return self.config.num_filters * conv_conf.filter_channels \ - * (conv_conf.filter_size * conv_conf.filter_size_y) + * (conv_conf.filter_size * conv_conf.filter_size_y) @config_layer('exconv') @@ -1895,6 +2000,163 @@ class ConvLayer(ConvLayerBase): layer_type = 'cudnn_conv' +@config_layer('conv_3d') +class Conv3DLayerBase(LayerBase): + def __init__(self, + name, + inputs=[], + bias=True, + num_filters=None, + shared_biases=False, + **xargs): + super(Conv3DLayerBase, self).__init__( + name, self.layer_type, 0, inputs=inputs, **xargs) + + if num_filters is not None: + self.config.num_filters = num_filters + + use_gpu = int(g_command_config_args.get("use_gpu", 0)) + parallel_nn = int(g_command_config_args.get("parallel_nn", 0)) + + # Automatically select cudnn_type for GPU and exconv for CPU + # if set type=conv, but still reserve the way user specify + # exconv or cudnn_conv manually. + if self.layer_type == "cudnn_conv3d": + config_assert(use_gpu, "cudnn_conv3d only support GPU") + + # need to specify layer in config + self.config.type = self.layer_type + + if shared_biases is not None: + self.config.shared_biases = shared_biases + + for input_index in xrange(len(self.inputs)): + input_layer = self.get_input_layer(input_index) + conv_conf = self.config.inputs[input_index].conv_conf + parse_conv3d( + self.inputs[input_index].conv, input_layer.name, conv_conf, + num_filters + ) # for z-axis pad:0, strid:1, filter_size:1, img_size:1 + psize = self.calc_parameter_size(conv_conf) + self.create_input_parameter(input_index, psize) + self.set_cnn_layer(name, conv_conf.output_z, conv_conf.output_y, + conv_conf.output_x, self.config.num_filters) + + psize = self.config.size + if shared_biases: + psize = self.config.num_filters + self.create_bias_parameter(bias, psize, [psize, 1]) + + def calc_parameter_size(self, conv_conf): + return self.config.num_filters * conv_conf.filter_channels \ + * (conv_conf.filter_size * conv_conf.filter_size_y \ + * conv_conf.filter_size_z) + + def set_layer_height_width(self, depth, height, width): + self.config.depth = depth + self.config.height = height + self.config.width = width + + def set_cnn_layer(self, + input_layer_name, + depth, + height, + width, + channels, + is_print=True): + size = depth * height * width * channels + self.set_layer_size(size) + self.set_layer_height_width(depth, height, width) + if is_print: + print("output for %s: c = %d, d = %d, h = %d, w = %d, size = %d" % + (input_layer_name, channels, depth, height, width, size)) + + +@config_layer('conv3d') +class Conv3DLayer(Conv3DLayerBase): + layer_type = 'conv3d' + + +@config_layer('convt_3d') +class Conv3DTransLayerBase(LayerBase): + def __init__(self, + name, + inputs=[], + bias=True, + num_filters=None, + shared_biases=False, + **xargs): + super(Conv3DTransLayerBase, self).__init__( + name, self.layer_type, 0, inputs=inputs, **xargs) + + if num_filters is not None: + self.config.num_filters = num_filters + + use_gpu = int(g_command_config_args.get("use_gpu", 0)) + parallel_nn = int(g_command_config_args.get("parallel_nn", 0)) + + # Automatically select cudnn_type for GPU and exconv for CPU + # if set type=conv, but still reserve the way user specify + # exconv or cudnn_conv manually. + if self.layer_type == "cudnn_deconv3d": + config_assert(use_gpu, "cudnn_conv3d only support GPU") + + # need to specify layer in config + self.config.type = self.layer_type + + if shared_biases is not None: + self.config.shared_biases = shared_biases + + for input_index in xrange(len(self.inputs)): + input_layer = self.get_input_layer(input_index) + conv_conf = self.config.inputs[input_index].conv_conf + parse_conv3d( + self.inputs[input_index].conv, + input_layer.name, + conv_conf, + num_filters, + trans=True + ) # for z-axis pad:0, strid:1, filter_size:1, img_size:1 + psize = self.calc_parameter_size(conv_conf) + self.create_input_parameter(input_index, psize) + self.set_cnn_layer(name, conv_conf.img_size_z, conv_conf.img_size_y, + conv_conf.img_size, self.config.num_filters) + + psize = self.config.size + if shared_biases: + psize = self.config.num_filters + self.create_bias_parameter(bias, psize, [psize, 1]) + + def calc_parameter_size(self, conv_conf): + return self.config.num_filters * conv_conf.filter_channels \ + * (conv_conf.filter_size * conv_conf.filter_size_y \ + * conv_conf.filter_size_z) + + def set_layer_height_width(self, depth, height, width): + self.config.depth = depth + self.config.height = height + self.config.width = width + + def set_cnn_layer(self, + input_layer_name, + depth, + height, + width, + channels, + is_print=True): + size = depth * height * width * channels + self.set_layer_size(size) + self.set_layer_height_width(depth, height, width) + if is_print: + print("output for %s: c = %d, d = %d, h = %d, w = %d, size = %d" % + (input_layer_name, channels, depth, height, width, size)) + + +@config_layer('deconv3d') +class DeConv3DLayer(Conv3DTransLayerBase): + layer_type = 'deconv3d' + + @config_layer('convt') class ConvTransLayerBase(LayerBase): layer_type = 'convt' diff --git a/python/paddle/trainer/recurrent_units.py b/python/paddle/trainer/recurrent_units.py old mode 100755 new mode 100644 diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py old mode 100755 new mode 100644 index 1bc55c869..6953f134c --- a/python/paddle/trainer_config_helpers/layers.py +++ b/python/paddle/trainer_config_helpers/layers.py @@ -31,108 +31,34 @@ except ImportError: import copy __all__ = [ - 'full_matrix_projection', - 'AggregateLevel', - 'ExpandLevel', - 'identity_projection', - 'dotmul_projection', - 'dotmul_operator', - 'repeat_layer', - 'seq_reshape_layer', - 'table_projection', - 'mixed_layer', - 'data_layer', - 'embedding_layer', - 'fc_layer', - 'grumemory', - 'pooling_layer', - 'lstmemory', - 'last_seq', - 'first_seq', - 'cos_sim', - 'hsigmoid', - 'conv_projection', - 'mse_cost', - 'regression_cost', - 'classification_cost', - 'LayerOutput', - 'img_conv_layer', - 'img_pool_layer', - 'batch_norm_layer', - 'img_cmrnorm_layer', - 'addto_layer', - 'concat_layer', - 'seq_concat_layer', - 'lstm_step_layer', - 'recurrent_group', - 'memory', - 'StaticInput', - 'expand_layer', - 'scaling_layer', - 'scaling_projection', - 'power_layer', - 'interpolation_layer', - 'bilinear_interp_layer', - 'trans_layer', - 'rotate_layer', - 'sum_to_one_norm_layer', - 'row_l2_norm_layer', - 'get_output_layer', - 'LayerType', - 'context_projection', - 'beam_search', - 'maxid_layer', - 'GeneratedInput', - 'SubsequenceInput', - 'gru_step_layer', - 'gru_step_naive_layer', - 'recurrent_layer', - 'BaseGeneratedInput', - 'conv_operator', - 'conv_shift_layer', - 'tensor_layer', - 'selective_fc_layer', - 'sampling_id_layer', - 'slope_intercept_layer', - 'trans_full_matrix_projection', - 'linear_comb_layer', - 'convex_comb_layer', - 'ctc_layer', - 'warp_ctc_layer', - 'crf_layer', - 'crf_decoding_layer', - 'nce_layer', - 'cross_entropy_with_selfnorm', - 'cross_entropy', - 'multi_binary_label_cross_entropy', - 'sum_cost', - 'rank_cost', - 'lambda_cost', - 'huber_cost', - 'block_expand_layer', - 'maxout_layer', - 'out_prod_layer', - 'printer_layer', - 'print_layer', - 'priorbox_layer', - 'cross_channel_norm_layer', - 'multibox_loss_layer', - 'detection_output_layer', - 'spp_layer', - 'pad_layer', - 'eos_layer', - 'smooth_l1_cost', - 'layer_support', - 'multiplex_layer', - 'row_conv_layer', - 'dropout_layer', - 'prelu_layer', - 'gated_unit_layer', - 'crop_layer', - 'sub_nested_seq_layer', - 'clip_layer', - 'slice_projection', - 'kmax_sequence_score_layer', + 'full_matrix_projection', 'AggregateLevel', 'ExpandLevel', + 'identity_projection', 'dotmul_projection', 'dotmul_operator', + 'repeat_layer', 'seq_reshape_layer', 'table_projection', 'mixed_layer', + 'data_layer', 'embedding_layer', 'fc_layer', 'grumemory', 'pooling_layer', + 'lstmemory', 'last_seq', 'first_seq', 'cos_sim', 'hsigmoid', + 'conv_projection', 'mse_cost', 'regression_cost', 'classification_cost', + 'LayerOutput', 'img_conv_layer', 'img_pool_layer', 'batch_norm_layer', + 'img_cmrnorm_layer', 'addto_layer', 'concat_layer', 'seq_concat_layer', + 'lstm_step_layer', 'recurrent_group', 'memory', 'StaticInput', + 'expand_layer', 'scaling_layer', 'scaling_projection', 'power_layer', + 'interpolation_layer', 'bilinear_interp_layer', 'trans_layer', + 'rotate_layer', 'sum_to_one_norm_layer', 'row_l2_norm_layer', + 'get_output_layer', 'LayerType', 'context_projection', 'beam_search', + 'maxid_layer', 'GeneratedInput', 'SubsequenceInput', 'gru_step_layer', + 'gru_step_naive_layer', 'recurrent_layer', 'BaseGeneratedInput', + 'conv_operator', 'conv_shift_layer', 'tensor_layer', 'selective_fc_layer', + 'sampling_id_layer', 'slope_intercept_layer', + 'trans_full_matrix_projection', 'linear_comb_layer', 'convex_comb_layer', + 'ctc_layer', 'warp_ctc_layer', 'crf_layer', 'crf_decoding_layer', + 'nce_layer', 'cross_entropy_with_selfnorm', 'cross_entropy', + 'multi_binary_label_cross_entropy', 'sum_cost', 'rank_cost', 'lambda_cost', + 'huber_cost', 'block_expand_layer', 'maxout_layer', 'out_prod_layer', + 'printer_layer', 'print_layer', 'priorbox_layer', + 'cross_channel_norm_layer', 'multibox_loss_layer', 'detection_output_layer', + 'spp_layer', 'pad_layer', 'eos_layer', 'smooth_l1_cost', 'layer_support', + 'multiplex_layer', 'row_conv_layer', 'dropout_layer', 'prelu_layer', + 'gated_unit_layer', 'crop_layer', 'sub_nested_seq_layer', 'clip_layer', + 'slice_projection', 'kmax_sequence_score_layer', 'img_conv3d_layer' ] @@ -214,6 +140,9 @@ class LayerType(object): CRF_DECODING_LAYER = 'crf_decoding' NCE_LAYER = 'nce' + CONV3D_LAYER = 'conv3d' + DECONV3D_LAYER = 'deconv3d' + RANK_COST = 'rank-cost' LAMBDA_COST = 'lambda_cost' HUBER = 'huber' @@ -878,7 +807,8 @@ def mixed_layer(size=0, @layer_support() -def data_layer(name, size, height=None, width=None, layer_attr=None): +def data_layer(name, size, height=None, width=None, depth=None, + layer_attr=None): """ Define DataLayer For NeuralNetwork. @@ -907,6 +837,7 @@ def data_layer(name, size, height=None, width=None, layer_attr=None): size=size, height=height, width=width, + depth=depth, **ExtraLayerAttribute.to_kwargs(layer_attr)) return LayerOutput(name, LayerType.DATA, size=size) @@ -6210,3 +6141,182 @@ def kmax_sequence_score_layer(input, name=None, beam_size=1): return LayerOutput( name, LayerType.KMAX_SEQ_SCORE, parents=[input], size=input.size) + + +@wrap_name_default("conv3d") +@wrap_param_attr_default() +@wrap_bias_attr_default() +@wrap_act_default(act=ReluActivation()) +@layer_support(DROPOUT) +def img_conv3d_layer(input, + filter_size, + num_filters, + name=None, + num_channels=None, + act=None, + groups=1, + stride=1, + padding=0, + bias_attr=None, + param_attr=None, + shared_biases=True, + layer_attr=None, + filter_size_y=None, + stride_y=None, + padding_y=None, + filter_size_z=None, + stride_z=None, + padding_z=None, + trans=False, + layer_type=None): + """ + + The example usage is: + + .. code-block:: python + + conv = img_conv3d_layer(input=data, filter_size=1, filter_size_y=1, + num_channels=8, + num_filters=16, stride=1, + bias_attr=False, + act=ReluActivation()) + + :param name: Layer name. + :type name: basestring + :param input: Layer Input. + :type input: LayerOutput + :param filter_size: The x dimension of a filter kernel. Or input a tuple for + two image dimension. + :type filter_size: int|tuple|list + :param filter_size_y: The y dimension of a filter kernel. Since PaddlePaddle + currently supports rectangular filters, the filter's + shape will be (filter_size, filter_size_y). + :type filter_size_y: int|None + :param num_filters: Each filter group's number of filter + :param act: Activation type. Default is tanh + :type act: BaseActivation + :param groups: Group size of filters. + :type groups: int + :param stride: The x dimension of the stride. Or input a tuple for two image + dimension. + :type stride: int|tuple|list + :param stride_y: The y dimension of the stride. + :type stride_y: int + :param padding: The x dimension of the padding. Or input a tuple for two + image dimension + :type padding: int|tuple|list + :param padding_y: The y dimension of the padding. + :type padding_y: int + :param bias_attr: Convolution bias attribute. None means default bias. + False means no bias. + :type bias_attr: ParameterAttribute|False + :param num_channels: number of input channels. If None will be set + automatically from previous output. + :type num_channels: int + :param param_attr: Convolution param attribute. None means default attribute + :type param_attr: ParameterAttribute + :param shared_biases: Is biases will be shared between filters or not. + :type shared_biases: bool + :param layer_attr: Layer Extra Attribute. + :type layer_attr: ExtraLayerAttribute + :param trans: true if it is a convTransLayer, false if it is a convLayer + :type trans: bool + :param layer_type: specify the layer_type, default is None. If trans=True, + layer_type has to be "exconvt" or "cudnn_convt", + otherwise layer_type has to be either "exconv" or + "cudnn_conv" + :type layer_type: String + :return: LayerOutput object. + :rtype: LayerOutput + """ + if num_channels is None: + assert input.num_filters is not None + num_channels = input.num_filters + + if filter_size_y is None: + if isinstance(filter_size, collections.Sequence): + assert len(filter_size) == 2 + filter_size, filter_size_y = filter_size + else: + filter_size_y = filter_size + + if filter_size_z is None: + if isinstance(filter_size, collections.Sequence): + assert len(filter_size) == 2 + filter_size, filter_size_z = filter_size + else: + filter_size_z = filter_size + + if stride_y is None: + if isinstance(stride, collections.Sequence): + assert len(stride) == 2 + stride, stride_y = stride + else: + stride_y = stride + + if stride_z is None: + if isinstance(stride, collections.Sequence): + assert len(stride) == 2 + stride, stride_z = stride + else: + stride_z = stride + + if padding_y is None: + if isinstance(padding, collections.Sequence): + assert len(padding) == 2 + padding, padding_y = padding + else: + padding_y = padding + + if padding_z is None: + if isinstance(padding, collections.Sequence): + assert len(padding) == 2 + padding, padding_z = padding + else: + padding_z = padding + + if param_attr.attr.get('initial_smart'): + # special initial for conv layers. + init_w = (2.0 / (filter_size**2 * num_channels))**0.5 + param_attr.attr["initial_mean"] = 0.0 + param_attr.attr["initial_std"] = init_w + param_attr.attr["initial_strategy"] = 0 + param_attr.attr["initial_smart"] = False + + if layer_type: + if trans: + assert layer_type in ["deconv3d"] + lt = layer_type + else: + lt = LayerType.DECONV3D_LAYER if trans else LayerType.CONV3D_LAYER + + l = Layer( + name=name, + inputs=Input( + input.name, + conv=Conv3D( + filter_size=filter_size, + padding=padding, + stride=stride, + channels=num_channels, + groups=groups, + filter_size_y=filter_size_y, + padding_y=padding_y, + stride_y=stride_y, + filter_size_z=filter_size_z, + padding_z=padding_z, + stride_z=stride_z), + **param_attr.attr), + active_type=act.name, + num_filters=num_filters, + bias=ParamAttr.to_bias(bias_attr), + shared_biases=shared_biases, + type=lt, + **ExtraLayerAttribute.to_kwargs(layer_attr)) + return LayerOutput( + name, + lt, + parents=[input], + activation=act, + num_filters=num_filters, + size=l.config.size) diff --git a/python/paddle/trainer_config_helpers/networks.py b/python/paddle/trainer_config_helpers/networks.py old mode 100755 new mode 100644 index 34be203ee..28a71cf78 --- a/python/paddle/trainer_config_helpers/networks.py +++ b/python/paddle/trainer_config_helpers/networks.py @@ -1406,7 +1406,7 @@ def inputs(layers, *args): if len(args) != 0: layers.extend(args) - Inputs(*[l.name for l in layers]) + Inputs(* [l.name for l in layers]) def outputs(layers, *args): @@ -1456,7 +1456,7 @@ def outputs(layers, *args): assert len(layers) > 0 if HasInputsSet(): # input already set - Outputs(*[l.name for l in layers]) + Outputs(* [l.name for l in layers]) return # just return outputs. if len(layers) != 1: diff --git a/python/paddle/trainer_config_helpers/tests/configs/conv3d_deconv3d_test_config.py b/python/paddle/trainer_config_helpers/tests/configs/conv3d_deconv3d_test_config.py new file mode 100644 index 000000000..da0d23d05 --- /dev/null +++ b/python/paddle/trainer_config_helpers/tests/configs/conv3d_deconv3d_test_config.py @@ -0,0 +1,98 @@ +from paddle.trainer_config_helpers import * + +settings(batch_size=1000, learning_rate=1e-5) + +num_channels = 3 +filter_size = 3 +filter_size_y = 3 +filter_size_z = 3 +stride = 2 +stride_y = 2 +stride_z = 2 +padding = 1 +padding_y = 1 +padding_z = 1 +groups = 1 + +data = data_layer( + name='data1', size=12096 * num_channels, height=48, width=42, depth=6) + +conv3d = img_conv3d_layer( + input=data, + name='conv3d_1', + num_filters=16, + num_channels=num_channels, + filter_size=filter_size, + filter_size_y=filter_size, + filter_size_z=filter_size, + stride=stride, + stride_y=stride_y, + stride_z=stride_z, + padding=padding, + padding_y=padding_y, + padding_z=padding_z, + groups=groups, + bias_attr=True, + shared_biases=True, + trans=False, + layer_type="conv3d", + act=LinearActivation()) + +deconv3d = img_conv3d_layer( + input=data, + name='deconv3d_1', + num_filters=16, + num_channels=num_channels, + filter_size=filter_size, + filter_size_y=filter_size, + filter_size_z=filter_size, + stride=stride, + stride_y=stride_y, + stride_z=stride_z, + padding=padding, + padding_y=padding_y, + padding_z=padding_z, + groups=groups, + bias_attr=True, + shared_biases=True, + trans=True, + layer_type="deconv3d", + act=LinearActivation()) + +data = data_layer(name="input", size=8 * 16 * 16) +conv1 = img_conv_layer( + input=data, + filter_size=1, + filter_size_y=1, + num_channels=8, + num_filters=16, + stride=1, + bias_attr=False, + act=ReluActivation(), + layer_type="exconv") +conv2 = img_conv_layer( + input=data, + filter_size=1, + filter_size_y=1, + num_channels=8, + num_filters=16, + stride=1, + bias_attr=False, + act=ReluActivation(), + layer_type="exconv") + +concat = concat_layer(input=[conv1, conv2]) + +conv = img_conv_layer( + input=data, + filter_size=1, + filter_size_y=1, + num_channels=8, + num_filters=16, + stride=1, + bias_attr=True, + act=LinearActivation(), + groups=2, + layer_type="exconv") + +outputs(concat, conv) diff --git a/python/paddle/trainer_config_helpers/tests/layers_test.py b/python/paddle/trainer_config_helpers/tests/layers_test.py index 05902ea29..44d1c1c9b 100644 --- a/python/paddle/trainer_config_helpers/tests/layers_test.py +++ b/python/paddle/trainer_config_helpers/tests/layers_test.py @@ -16,4 +16,6 @@ from paddle.trainer.config_parser import parse_config_and_serialize if __name__ == '__main__': parse_config_and_serialize( - 'trainer_config_helpers/tests/layers_test_config.py', '') + 'trainer_config_helpers/tests/configs/conv3d_deconv3d_test_config.py', + '') +# layers_test_config.py -- GitLab From 760cb6c8615fe3eece8d831ef78e10da4b552246 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Wed, 23 Aug 2017 15:47:18 +0800 Subject: [PATCH 0184/2018] Follow jiayi's comment --- paddle/framework/operator.h | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/paddle/framework/operator.h b/paddle/framework/operator.h index 83dab8631..8397570d2 100644 --- a/paddle/framework/operator.h +++ b/paddle/framework/operator.h @@ -132,21 +132,21 @@ class OperatorBase { // Macro for define a clone method. // If you are writing an kernel operator, `Clone` will be defined when you // register it. i.e. `Clone` method is not needed to define by yourself. -#define DEFINE_OP_CLONE_METHOD(CLS) \ +#define DEFINE_OP_CLONE_METHOD(cls) \ std::unique_ptr Clone() const final { \ - return std::unique_ptr(new CLS(*this)); \ + return std::unique_ptr(new cls(*this)); \ } // Macro for define a default constructor for Operator. // You can also use // using PARENT_CLASS::PARENT_CLASS; // to use parent's constructor. -#define DEFINE_OP_CONSTRUCTOR(CLS, PARENT_CLS) \ - CLS(const std::string& type, \ +#define DEFINE_OP_CONSTRUCTOR(cls, parent_cls) \ + cls(const std::string& type, \ const ::paddle::framework::VariableNameMap& inputs, \ const ::paddle::framework::VariableNameMap& outputs, \ const paddle::framework::AttributeMap& attrs) \ - : PARENT_CLS(type, inputs, outputs, attrs) {} + : parent_cls(type, inputs, outputs, attrs) {} class NOP : public OperatorBase { public: -- GitLab From 6f4b968f5618adce529d12bd2e3b72d4d1b64f61 Mon Sep 17 00:00:00 2001 From: qiaolongfei Date: Wed, 23 Aug 2017 00:58:02 -0700 Subject: [PATCH 0185/2018] can train the parameters --- python/paddle/v2/framework/tests/mnist.py | 39 +++++++++++++++++------ 1 file changed, 29 insertions(+), 10 deletions(-) diff --git a/python/paddle/v2/framework/tests/mnist.py b/python/paddle/v2/framework/tests/mnist.py index 32349b8d4..ededf767b 100644 --- a/python/paddle/v2/framework/tests/mnist.py +++ b/python/paddle/v2/framework/tests/mnist.py @@ -69,7 +69,7 @@ def init_param(param_name, dims): tensor = var.get_tensor() tensor.set_dims(dims) data = numpy.random.uniform( - low=0.0, high=1.0, size=tensor.shape()).astype("float32") + low=-0.5, high=0.5, size=tensor.shape()).astype("float32") tensor.set(data, place) @@ -109,7 +109,7 @@ def fc_layer(net, input, size, act="softmax", bias=True, param=None, name=None): bias_name = name + ".b" init_param(param_name=bias_name, dims=[size]) sgd_optimizer( - net=optimize_net, param_name=bias_name, learning_rate=0.01) + net=optimize_net, param_name=bias_name, learning_rate=0.001) bias_out = name + ".rowwise_add.out" scope.new_var(bias_out) rowwise_append_op = Operator( @@ -158,20 +158,33 @@ def print_inputs_outputs(op): def set_cost(): - cost_data = numpy.array(scope.find_var("cross_entropy_1").get_tensor()) + cost_shape = numpy.array(scope.find_var("cross_entropy_3").get_tensor( + )).shape + cost_grad = scope.find_var(grad_var_name("cross_entropy_3")).get_tensor() + cost_grad.set_dims(cost_shape) + cost_grad.alloc_float(place) + cost_grad.set(numpy.ones(cost_shape).astype("float32"), place) + + +def print_cost(): + cost_data = numpy.array(scope.find_var("cross_entropy_3").get_tensor()) print(cost_data.sum() / len(cost_data)) - cost_grad = scope.find_var(grad_var_name("cross_entropy_1")).get_tensor() - cost_grad.set_dims(cost_data.shape) - cost_grad.alloc_float(place) - cost_grad.set(numpy.ones(cost_data.shape).astype("float32"), place) +def error_rate(predict, label): + predict_var = numpy.array(scope.find_var(predict).get_tensor()).argmax( + axis=1) + label = numpy.array(scope.find_var(label).get_tensor()) + error_num = numpy.sum(predict_var != label) + print(error_num / float(len(label))) images = data_layer(name='pixel', dims=[BATCH_SIZE, 784]) label = data_layer(name='label', dims=[BATCH_SIZE]) -fc = fc_layer(net=forward_network, input=images, size=10, act="softmax") -cost = cross_entropy_layer(net=forward_network, input=fc, label=label) +fc1 = fc_layer(net=forward_network, input=images, size=100, act="sigmoid") +fc2 = fc_layer(net=forward_network, input=fc1, size=100, act="sigmoid") +predict = fc_layer(net=forward_network, input=fc2, size=100, act="softmax") +cost = cross_entropy_layer(net=forward_network, input=predict, label=label) forward_network.complete_add_op(True) backward_net = get_backward_net(forward_network) @@ -192,8 +205,8 @@ reader = paddle.batch( PASS_NUM = 1000 for pass_id in range(PASS_NUM): + batch_id = 0 - print("pass[" + str(pass_id) + "]") for data in reader(): image = numpy.array(map(lambda x: x[0], data)).astype("float32") label = numpy.array(map(lambda x: x[1], data)).astype("int32") @@ -207,3 +220,9 @@ for pass_id in range(PASS_NUM): backward_net.run(scope, dev_ctx) optimize_net.run(scope, dev_ctx) + if batch_id % 100 == 0: + print("pass[" + str(pass_id) + "] batch_id[" + str(batch_id) + "]") + print_cost() + error_rate(predict, "label") + + batch_id = batch_id + 1 -- GitLab From 48d87e5e912ad084ccc63dae8649f90a3f0989ba Mon Sep 17 00:00:00 2001 From: tensor-tang Date: Wed, 23 Aug 2017 16:47:51 +0800 Subject: [PATCH 0186/2018] pass test, support input CPU device --- paddle/gserver/layers/Layer.h | 35 +++++--- paddle/gserver/layers/MKLDNNFcLayer.cpp | 108 +++++++++++++++--------- paddle/gserver/layers/MKLDNNLayer.h | 81 +++++++++++++++--- paddle/math/Allocator.h | 6 ++ paddle/math/MKLDNNMatrix.cpp | 71 +++++++++++++--- paddle/math/MKLDNNMatrix.h | 49 ++++++++--- 6 files changed, 258 insertions(+), 92 deletions(-) diff --git a/paddle/gserver/layers/Layer.h b/paddle/gserver/layers/Layer.h index ec4d093e0..edef36194 100644 --- a/paddle/gserver/layers/Layer.h +++ b/paddle/gserver/layers/Layer.h @@ -82,6 +82,7 @@ protected: Argument output_; /// Several outputs stored on different devices, used in 'parallel_nn' case, /// and record them by deviceId_. + /// Also used in 'use_mkldnn' case. std::vector outputOtherDevice_; /// If there are several outputs, map them by each name. std::map outputMap_; @@ -177,6 +178,13 @@ protected: return inputLayer.getOutput(deviceId_); } + /** + * Get the argument of input layer with deviceId. + */ + const Argument& getInput(size_t inputIndex, int deviceId) const { + return inputLayers_[inputIndex]->getOutput(deviceId); + } + /** * Get the forward-input value. */ @@ -191,6 +199,13 @@ protected: return inputLayer.getOutput(deviceId_).value; } + /** + * Get the forward-input value with deviceId. + */ + const MatrixPtr& getInputValue(int inputIndex, int deviceId) { + return inputLayers_[inputIndex]->getOutput(deviceId).value; + } + /** * Get the forward-input grad. */ @@ -205,6 +220,13 @@ protected: return inputLayer.getOutput(deviceId_).grad; } + /** + * Get the forward-input grad. + */ + const MatrixPtr& getInputGrad(int inputIndex, int deviceId) { + return inputLayers_[inputIndex]->getOutput(deviceId).grad; + } + /** * Get the forward-input label. */ @@ -326,19 +348,6 @@ public: if (deviceId == getDeviceId()) { return output_; } else { - bool CPU2MKLDNN = - getDeviceId() == CPU_DEVICE && deviceId == MKLDNN_DEVICE; - bool MKLDNN2CPU = - getDeviceId() == MKLDNN_DEVICE && deviceId == CPU_DEVICE; - if (CPU2MKLDNN) { - // TODO: do something - return output_; - } else if (MKLDNN2CPU) { - // TODO: do something - return output_; - } - - // TODO: handle mkldnn device or add mkldnn device to other for (size_t i = 0; i < outputOtherDevice_.size(); i++) { if (outputOtherDevice_[i].deviceId == deviceId) { return outputOtherDevice_[i]; diff --git a/paddle/gserver/layers/MKLDNNFcLayer.cpp b/paddle/gserver/layers/MKLDNNFcLayer.cpp index 546310446..a3291e6a8 100644 --- a/paddle/gserver/layers/MKLDNNFcLayer.cpp +++ b/paddle/gserver/layers/MKLDNNFcLayer.cpp @@ -97,7 +97,7 @@ void MKLDNNFcLayer::convertWeightsToPaddle() { } void MKLDNNFcLayer::reshape() { - const Argument& input = getInput(0); + const Argument& input = getInput(0, getPrev(0)->getDeviceId()); int batchSize = input.getBatchSize(); if (bs_ == batchSize) { return; @@ -135,35 +135,43 @@ void MKLDNNFcLayer::reshape() { void MKLDNNFcLayer::resetFwd() { bool hasBias = biases_ && biases_->getW(); - const MatrixPtr& in = getInputValue(0); const MatrixPtr& wgt = weight_->getW(); const MatrixPtr& bias = hasBias ? biases_->getW() : nullptr; const MatrixPtr& out = output_.value; - if (getPrev(0)->getDeviceId() == MKLDNN_DEVICE) { + if (prevIsMKLDNN()) { + const MatrixPtr& in = getInputValue(0); inVal_ = std::dynamic_pointer_cast(in); CHECK(inVal_) << "Input should be MKLDNNMatrix"; - // TODO: change input nchw to nc if available - // inVal_->downSpatial() } else { + CHECK_EQ(getPrev(0)->getDeviceId(), CPU_DEVICE) << "Only support CPU yet"; + const MatrixPtr& in = getInputValue(0, CPU_DEVICE); inVal_ = MKLDNNMatrix::create( - in, - hasSpatial_ ? memory::dims{bs_, ic_, ih_, iw_} : memory::dims{bs_, ic_}, - hasSpatial_ ? format::nchw : format::nc, - engine_); + in, memory::dims{bs_, ic_, ih_, iw_}, format::nchw, engine_); } - + inVal_->downSpatial(); wgtVal_ = MKLDNNMatrix::create( - wgt, - hasSpatial_ ? memory::dims{oc_, ic_, ih_, iw_} : memory::dims{oc_, ic_}, - hasSpatial_ ? format::oihw : format::oi, - engine_); + wgt, memory::dims{oc_, ic_, ih_, iw_}, format::oihw, engine_); + wgtVal_->downSpatial(); biasVal_ = hasBias ? MKLDNNMatrix::create(bias, {oc_}, format::x, engine_) : nullptr; outVal_ = MKLDNNMatrix::create(out, {bs_, oc_}, format::nc, engine_); - // change original output to mkldnn output + // change original output value to mkldnn output value output_.value = std::dynamic_pointer_cast(outVal_); + if (!nextIsMKLDNN()) { + Argument cpuOutput; + for (size_t i = 0; i < outputOtherDevice_.size(); i++) { + if (outputOtherDevice_[i].deviceId == CPU_DEVICE) { + cpuOutput = outputOtherDevice_[i]; + } + } + cpuOutput.setFrameHeight(output_.getFrameHeight()); + cpuOutput.setFrameWidth(output_.getFrameWidth()); + + // fc cpu output value do not need convert + cpuOutput.value = output_.value; + } // create forward handle prop_kind pk = prop_kind::forward; @@ -176,12 +184,13 @@ void MKLDNNFcLayer::resetFwd() { : fc_fwd::desc( pk, inVal_->getMD(), wgtVal_->getMD(), outVal_->getMD()); fc_fwd::primitive_desc fwdPD = fc_fwd::primitive_desc(fwdDesc, engine_); - if (hasBias) { fwd_.reset(new fc_fwd(fwdPD, *inVal_, *wgtVal_, *biasVal_, *outVal_)); } else { fwd_.reset(new fc_fwd(fwdPD, *inVal_, *wgtVal_, *outVal_)); } + printValueFormatFlow(); + pipelineFwd_.clear(); pipelineFwd_.push_back(*fwd_); } @@ -197,17 +206,24 @@ void MKLDNNFcLayer::resetBwd() { CHECK(inVal_) << "Should have input value"; const MatrixPtr& wgt = weight_->getWGrad(); const MatrixPtr& bias = hasBias ? biases_->getWGrad() : nullptr; - const MatrixPtr& out = output_.grad; - wgtGrad_ = MKLDNNMatrix::create( - wgt, wgtVal_->getDims(), wgtVal_->getFormat(), engine_); - biasGrad_ = - hasBias ? MKLDNNMatrix::create(bias, {oc_}, format::x, engine_) : nullptr; + if (nextIsMKLDNN()) { + // can not directly cast outputgrad to mkldnnmatrix, + // since each layer can not write the inputgrad to mkldnn inputgrad. + // So just create from matrix with outputvalue format. + const MatrixPtr& out = getOutput(MKLDNN_DEVICE).grad; + outGrad_ = MKLDNNMatrix::create(out, outVal_->getPD()); + // TODO: maybe need merge topdiffs + } else { + // TODO: merge topdiffs + const MatrixPtr& out = getOutput(CPU_DEVICE).grad; + // fc do not need to convert from cpu device since output always nc + // only need create from cpu device + outGrad_ = MKLDNNMatrix::create(out, outVal_->getPD()); + } - outGrad_ = MKLDNNMatrix::create(out, {bs_, oc_}, format::nc, engine_); - // change original output to mkldnn output - // TODO: right? - output_.grad = std::dynamic_pointer_cast(outGrad_); + wgtGrad_ = MKLDNNMatrix::create(wgt, wgtVal_->getPD()); + biasGrad_ = hasBias ? MKLDNNMatrix::create(bias, biasVal_->getPD()) : nullptr; // create memory primitive desc fc_fwd::desc fwdDesc = fc_fwd::desc(prop_kind::forward, @@ -235,21 +251,38 @@ void MKLDNNFcLayer::resetBwd() { pipelineBwd_.push_back(*bwdWgt_); /// backward data - const MatrixPtr& in = getInputGrad(0); - if (in == nullptr) { - return; + if (prevIsMKLDNN()) { + const MatrixPtr& in = getInputGrad(0, MKLDNN_DEVICE); + if (in == nullptr) { + return; + } + if (getInput(0, MKLDNN_DEVICE).getAllCount() > 1) { + // TODO: many mkldnn bots + // add sum handle + } else { + inGrad_ = MKLDNNMatrix::create(in, inVal_->getPD()); + } + } else { + const MatrixPtr& in = getInputGrad(0, CPU_DEVICE); + if (in == nullptr) { + return; + } + if (getInput(0, CPU_DEVICE).getAllCount() > 1) { + // TODO: many bots + // add sum handle + } else { + inGrad_ = MKLDNNMatrix::create(in, inVal_->getPD()); + } } + fc_bwdData::desc bwdDataDesc = fc_bwdData::desc(inVal_->getMD(), wgtGrad_->getMD(), outGrad_->getMD()); fc_bwdData::primitive_desc bwdDataPD = fc_bwdData::primitive_desc(bwdDataDesc, engine_, fwdPD); - // TODO: check right, just from ingrad? - inGrad_ = - MKLDNNMatrix::create(in, inVal_->getDims(), inVal_->getFormat(), engine_); - CHECK(wgtVal_) << "Should have weight memory"; bwdData_.reset(new fc_bwdData(bwdDataPD, *outGrad_, *wgtVal_, *inGrad_)); + printGradFormatFlow(); pipelineBwd_.push_back(*bwdData_); } @@ -259,11 +292,7 @@ void MKLDNNFcLayer::forward(PassType passType) { { REGISTER_TIMER_INFO("mkldnn_FwdTimer", getName().c_str()); - - // update input data - // since it might be changed if this is after data layer - real* iData = getInputValue(0)->getData(); - inVal_->updateData(iData); + syncInputValue(); // just submit forward pipeline stream_->submit(pipelineFwd_); @@ -285,10 +314,7 @@ void MKLDNNFcLayer::backward(const UpdateCallback& callback) { REGISTER_TIMER_INFO("mkldnn_bwdTimer", getName().c_str()); resetBwd(); - // update diff - real* oDiff = getOutputGrad()->getData(); - outGrad_->updateData(oDiff); - + syncOutputGrad(); // just sumbmit backward pipeline stream_->submit(pipelineBwd_); } diff --git a/paddle/gserver/layers/MKLDNNLayer.h b/paddle/gserver/layers/MKLDNNLayer.h index fbd62d9aa..3dd17a36f 100644 --- a/paddle/gserver/layers/MKLDNNLayer.h +++ b/paddle/gserver/layers/MKLDNNLayer.h @@ -125,23 +125,80 @@ public: << ", oh: " << oh_ << ", ow: " << ow_; } - // TODO(TJ): move to MkldnnMatrix - // create memory desc - inline mkldnn::memory::desc createMD( - mkldnn::memory::dims dims, - mkldnn::memory::format fmt, - mkldnn::memory::data_type type = mkldnn::memory::data_type::f32) { - // TODO(TJ): isFmtSuppoted(fmt) - return mkldnn::memory::desc(dims, type, fmt); + /** + * Print the mkldnn memory format flow of value + */ + virtual void printValueFormatFlow() { + if (inVal_ && outVal_) { + VLOG(MKLDNN_FMTS) << "value format flow --- " << inVal_->getFormat() + << " >>> " << outVal_->getFormat(); + } } - void resetMKLDNNOutput(size_t height, size_t width) { - Layer::resetOutput(height, width); - // get valu and grad, use mkldnn matrix instaed - // output_.value; + /** + * Print the mkldnn memory format flow of grad + */ + virtual void printGradFormatFlow() { + if (inGrad_ && outGrad_) { + VLOG(MKLDNN_FMTS) << "grad format flow --- " << inGrad_->getFormat() + << " <<< " << outGrad_->getFormat(); + } } protected: + /** + * If next layer only has MKLDNN type. + * Otherwise, only support otherdevice CPU device. + */ + bool nextIsMKLDNN() { + for (size_t i = 0; i < outputOtherDevice_.size(); i++) { + CHECK_EQ(outputOtherDevice_[i].deviceId, CPU_DEVICE) + << "Only support other device is CPU yet"; + } + return outputOtherDevice_.size() == 0; + } + + /** + * Is previous layer MKLDNN type. + * Otherwise, only support otherdevice CPU device. + */ + bool prevIsMKLDNN(int index = 0) { + int prevDevice = getPrev(index)->getDeviceId(); + if (prevDevice == MKLDNN_DEVICE) { + return true; + } else { + // do not support GPU yet + CHECK_EQ(prevDevice, CPU_DEVICE) << "Only support CPU yet"; + return false; + } + } + + /** + * Sync input value data + */ + void syncInputValue() { + if (prevIsMKLDNN()) { + return; + } + real* iData = getInputValue(0, CPU_DEVICE)->getData(); + // update input data + // since it might be changed if this is after data layer + inVal_->updateData(iData); + } + + /** + * Sync output grad data + */ + void syncOutputGrad() { + if (nextIsMKLDNN()) { + return; + } + + // update diff + real* oDiff = getOutput(CPU_DEVICE).grad->getData(); + outGrad_->updateData(oDiff); + } + /** * Set deviceId of this layer. */ diff --git a/paddle/math/Allocator.h b/paddle/math/Allocator.h index 666a8b836..94ef561f0 100644 --- a/paddle/math/Allocator.h +++ b/paddle/math/Allocator.h @@ -48,7 +48,13 @@ public: */ virtual void* alloc(size_t size) { void* ptr; +#ifdef PADDLE_USE_MKLDNN + // refer to https://github.com/01org/mkl-dnn/blob/master/include/mkldnn.hpp + // memory alignment + CHECK_EQ(posix_memalign(&ptr, 4096ul, size), 0); +#else CHECK_EQ(posix_memalign(&ptr, 32ul, size), 0); +#endif CHECK(ptr) << "Fail to allocate CPU memory: size=" << size; return ptr; } diff --git a/paddle/math/MKLDNNMatrix.cpp b/paddle/math/MKLDNNMatrix.cpp index 44fc54278..24d54ec0f 100644 --- a/paddle/math/MKLDNNMatrix.cpp +++ b/paddle/math/MKLDNNMatrix.cpp @@ -18,29 +18,74 @@ using namespace mkldnn; // NOLINT namespace paddle { -MKLDNNMatrixPtr MKLDNNMatrix::create(const MatrixPtr& m, - memory::dims dims, - memory::format fmt, - engine& eg, - mkldnn::memory::data_type dtype) { - CpuMatrixPtr cpuM = std::dynamic_pointer_cast(m); - CHECK(cpuM) << "Only support create from CPU matrix yet"; - - size_t ndims = dims.size(); +MKLDNNMatrixPtr MKLDNNMatrix::create(MatrixPtr m, memory::primitive_desc pd) { + memory::desc md = pd.desc(); + size_t ndims = md.data.ndims; + int* dims = md.data.dims; CHECK(ndims > 0) << "Input dims should not be empty"; - size_t cnt = 1; + size_t cnts = 1; for (size_t i = 0; i < ndims; ++i) { - cnt *= dims[i]; + cnts *= dims[i]; } - CHECK_EQ(cnt, m->getElementCnt()) << "Count size does not match"; + if (m == nullptr) { + size_t height = dims[0]; + size_t width = cnts / dims[0]; + // LOG(INFO) << height << "," << width; + m = Matrix::create(height, width, false, false); + } + + CHECK(m) << " Matrix should not be empty"; + CpuMatrixPtr cpuMatrix = std::dynamic_pointer_cast(m); + CHECK(cpuMatrix) << "Only support create from CPU matrix yet"; + + CHECK_EQ(cnts, m->getElementCnt()) << "Count size does not match"; size_t width = m->getWidth(); size_t height = m->getHeight(); real* data = m->getData(); + return std::make_shared(data, height, width, pd); +} +MKLDNNMatrixPtr MKLDNNMatrix::create(MatrixPtr m, + memory::dims dims, + memory::format fmt, + engine& eg, + mkldnn::memory::data_type dtype) { memory::desc md = memory::desc(dims, dtype, fmt); memory::primitive_desc pd = memory::primitive_desc(md, eg); - return std::make_shared(data, height, width, pd); + return create(m, pd); +} + +void MKLDNNMatrix::downSpatial() { + int fmt = getFormat(); + if (!(fmt == memory::format::nchw || fmt == memory::format::oihw)) { + // only support nchw and oihw yet, later can support more like nhwc, ihwo + return; + } + + memory::dims srcDims = getDims(); + const int H = 2, W = 3; + if (srcDims[H] != 1 || srcDims[W] != 1) { + // can not down spatial + return; + } + + memory::dims dstDims = memory::dims{srcDims[0], srcDims[1]}; + memory::format dstFmt; + switch (fmt) { + case memory::format::nchw: + dstFmt = memory::format::nc; + break; + case memory::format::oihw: + dstFmt = memory::format::oi; + break; + default: + LOG(FATAL) << "unsupported format"; + } + memory::desc md = memory::desc(dstDims, getDtype(), dstFmt); + memory::primitive_desc pd = memory::primitive_desc(md, getEngine()); + void* data = getData(); + memory(pd, data); } } // namespace paddle diff --git a/paddle/math/MKLDNNMatrix.h b/paddle/math/MKLDNNMatrix.h index 54c0a1fdc..05adc867c 100644 --- a/paddle/math/MKLDNNMatrix.h +++ b/paddle/math/MKLDNNMatrix.h @@ -39,20 +39,37 @@ public: mkldnn::memory::primitive_desc pd) : CpuMatrix(data, height, width, false), mkldnn::memory(pd, data) {} - MKLDNNMatrix(size_t height, size_t width, mkldnn::memory::primitive_desc pd) - : CpuMatrix(height, width, false), mkldnn::memory(pd) { - set_data_handle(CpuMatrix::getData()); - } - ~MKLDNNMatrix() {} + /** + * Create MKLDNNMatrix from a MatrixPtr and memory primitive_desc + */ + static MKLDNNMatrixPtr create(MatrixPtr m, mkldnn::memory::primitive_desc pd); + + /** + * Create MKLDNNMatrix from a MatrixPtr and memory details info + */ static MKLDNNMatrixPtr create( - const MatrixPtr& m, + MatrixPtr m, mkldnn::memory::dims dims, mkldnn::memory::format fmt, mkldnn::engine& eg, mkldnn::memory::data_type dtype = mkldnn::memory::data_type::f32); +public: + /** + * Dimensionality reduction. + * Change format "nchw --> nc" or "oihw --> oi" if the h and w are both 1 + */ + void downSpatial(); + + /** + * Update the memory data handle. + * Caution: This will not check the buffer size of the data, + * it should be coverd by user. + */ + void updateData(void* data) { set_data_handle(data); } + /** * Get primitive descriptor. */ @@ -64,12 +81,13 @@ public: mkldnn::memory::desc getMD() { return getPD().desc(); } /** - * Get dims. + * Get dimensions. */ mkldnn::memory::dims getDims() { + mkldnn::memory::desc md = getMD(); + const int* src = md.data.dims; + int ndims = md.data.ndims; mkldnn::memory::dims dst; - int* src = getMD().data.dims; - int ndims = getMD().data.ndims; dst.resize(ndims); for (int i = 0; i < ndims; ++i) { dst[i] = src[i]; @@ -85,11 +103,16 @@ public: } /** - * Update the memory data handle. - * Caution: This will not check the buffer size of the data, - * it should be coverd by user. + * Get memory data type. */ - void updateData(void* data) { set_data_handle(data); } + mkldnn::memory::data_type getDtype() { + return (mkldnn::memory::data_type)(getMD().data.data_type); + } + + /** + * Get engine. + */ + mkldnn::engine getEngine() { return getPD().get_engine(); } }; } // namespace paddle -- GitLab From 780c8d969e0d2d220df19a672c141ff7c44f53d2 Mon Sep 17 00:00:00 2001 From: tensor-tang Date: Wed, 23 Aug 2017 17:03:16 +0800 Subject: [PATCH 0187/2018] make downSpatial work, and remove hasSpatial_ --- paddle/gserver/layers/MKLDNNFcLayer.cpp | 4 ---- paddle/gserver/layers/MKLDNNFcLayer.h | 5 +---- paddle/math/MKLDNNMatrix.cpp | 7 ++++++- 3 files changed, 7 insertions(+), 9 deletions(-) diff --git a/paddle/gserver/layers/MKLDNNFcLayer.cpp b/paddle/gserver/layers/MKLDNNFcLayer.cpp index a3291e6a8..a5555c461 100644 --- a/paddle/gserver/layers/MKLDNNFcLayer.cpp +++ b/paddle/gserver/layers/MKLDNNFcLayer.cpp @@ -111,10 +111,6 @@ void MKLDNNFcLayer::reshape() { if (iw_ == 0) { iw_ = 1; } - hasSpatial_ = true; - if (ih_ == 1 && iw_ == 1) { - hasSpatial_ = false; - } CHECK_EQ(iLayerSize_, inputLayers_[0]->getSize()); ic_ = iLayerSize_ / (ih_ * iw_); CHECK_EQ(size_t(ic_ * ih_ * iw_), iLayerSize_) << "not divisible"; diff --git a/paddle/gserver/layers/MKLDNNFcLayer.h b/paddle/gserver/layers/MKLDNNFcLayer.h index 7954852a2..e2657a8d5 100644 --- a/paddle/gserver/layers/MKLDNNFcLayer.h +++ b/paddle/gserver/layers/MKLDNNFcLayer.h @@ -32,16 +32,13 @@ protected: // if has already init the weight bool hasInitedWgt_; - // if input layer has image size info (ih>1 && iw>1) - bool hasSpatial_; - // fc weight and bias std::unique_ptr weight_; std::unique_ptr biases_; public: explicit MKLDNNFcLayer(const LayerConfig& config) - : MKLDNNLayer(config), hasInitedWgt_(false), hasSpatial_(true) {} + : MKLDNNLayer(config), hasInitedWgt_(false) {} ~MKLDNNFcLayer() {} diff --git a/paddle/math/MKLDNNMatrix.cpp b/paddle/math/MKLDNNMatrix.cpp index 24d54ec0f..94df9c155 100644 --- a/paddle/math/MKLDNNMatrix.cpp +++ b/paddle/math/MKLDNNMatrix.cpp @@ -85,7 +85,12 @@ void MKLDNNMatrix::downSpatial() { memory::desc md = memory::desc(dstDims, getDtype(), dstFmt); memory::primitive_desc pd = memory::primitive_desc(md, getEngine()); void* data = getData(); - memory(pd, data); + mkldnn_primitive_t result; + mkldnn::error::wrap_c_api( + mkldnn_primitive_create(&result, pd.get(), nullptr, nullptr), + "could not create a memory primitive"); + reset(result); + set_data_handle(data); } } // namespace paddle -- GitLab From bfcaf880d0eed61291f0483091382131ef6cde88 Mon Sep 17 00:00:00 2001 From: dangqingqing Date: Wed, 23 Aug 2017 18:48:05 +0800 Subject: [PATCH 0188/2018] Move pybind from package paddle/framework into paddle/pybind. --- paddle/CMakeLists.txt | 1 + paddle/framework/CMakeLists.txt | 20 -------------------- paddle/pybind/CMakeLists.txt | 19 +++++++++++++++++++ paddle/{framework => pybind}/pybind.cc | 18 ++++++++++-------- paddle/{framework => pybind}/tensor_py.h | 11 +++++++---- 5 files changed, 37 insertions(+), 32 deletions(-) create mode 100644 paddle/pybind/CMakeLists.txt rename paddle/{framework => pybind}/pybind.cc (95%) rename paddle/{framework => pybind}/tensor_py.h (92%) diff --git a/paddle/CMakeLists.txt b/paddle/CMakeLists.txt index cf61a243e..ec866b290 100644 --- a/paddle/CMakeLists.txt +++ b/paddle/CMakeLists.txt @@ -15,6 +15,7 @@ if(Boost_FOUND) add_subdirectory(platform) add_subdirectory(framework) add_subdirectory(operators) + add_subdirectory(pybind) endif() if(WITH_C_API) diff --git a/paddle/framework/CMakeLists.txt b/paddle/framework/CMakeLists.txt index ad219887d..c0838d9b7 100644 --- a/paddle/framework/CMakeLists.txt +++ b/paddle/framework/CMakeLists.txt @@ -39,23 +39,3 @@ add_custom_command(TARGET framework_py_proto POST_BUILD cc_library(backward SRCS backward.cc DEPS net_op) cc_test(backward_test SRCS backward_test.cc DEPS backward recurrent_op device_context) - -if(WITH_PYTHON) -cc_library(paddle_pybind SHARED - SRCS pybind.cc - DEPS pybind python backward - sgd_op - gather_op - add_op - mul_op - rowwise_add_op - sigmoid_op - softmax_op - mean_op - cross_entropy_op - recurrent_op - uniform_random_op - gaussian_random_op - fill_zeros_like_op - scale_op) -endif(WITH_PYTHON) diff --git a/paddle/pybind/CMakeLists.txt b/paddle/pybind/CMakeLists.txt new file mode 100644 index 000000000..10be83efc --- /dev/null +++ b/paddle/pybind/CMakeLists.txt @@ -0,0 +1,19 @@ +if(WITH_PYTHON) +cc_library(paddle_pybind SHARED + SRCS pybind.cc + DEPS pybind python backward + sgd_op + gather_op + add_op + mul_op + rowwise_add_op + sigmoid_op + softmax_op + mean_op + cross_entropy_op + recurrent_op + uniform_random_op + gaussian_random_op + fill_zeros_like_op + scale_op) +endif(WITH_PYTHON) diff --git a/paddle/framework/pybind.cc b/paddle/pybind/pybind.cc similarity index 95% rename from paddle/framework/pybind.cc rename to paddle/pybind/pybind.cc index b5ae81ebc..cdf739c3a 100644 --- a/paddle/framework/pybind.cc +++ b/paddle/pybind/pybind.cc @@ -18,11 +18,11 @@ limitations under the License. */ #include "paddle/framework/backward.h" #include "paddle/framework/op_registry.h" -#include "paddle/framework/tensor_py.h" #include "paddle/operators/net_op.h" #include "paddle/operators/recurrent_op.h" #include "paddle/platform/enforce.h" #include "paddle/platform/place.h" +#include "paddle/pybind/tensor_py.h" #include "paddle/string/to_string.h" #include "pybind11/numpy.h" #include "pybind11/pybind11.h" @@ -134,7 +134,8 @@ All parameter, weight, gradient are variables in Paddle. py::return_value_policy::reference) .def("find_var", &Scope::FindVar, py::return_value_policy::reference) .def(py::init<>()) - .def("new_scope", [](Scope &self) -> Scope * { return &self.NewScope(); }, + .def("new_scope", + [](Scope &self) -> Scope * { return &self.NewScope(); }, py::return_value_policy::reference) .def("drop_kids", &Scope::DropKids); @@ -222,8 +223,10 @@ All parameter, weight, gradient are variables in Paddle. retv->SetType("plain_net"); return retv; }) - .def("append_op", [](operators::NetOp &self, - const OperatorBase &op) { self.AppendOp(op); }) + .def("append_op", + [](operators::NetOp &self, const OperatorBase &op) { + self.AppendOp(op); + }) .def("complete_add_op", &operators::NetOp::CompleteAddOp) .def("complete_add_op", [](std::shared_ptr &self) { self->CompleteAddOp(); @@ -243,10 +246,9 @@ All parameter, weight, gradient are variables in Paddle. auto rnn_op = OpRegistry::CreateOp(desc); return static_cast(rnn_op.release()); }) - .def("set_stepnet", [](operators::RecurrentOp &self, - const operators::NetOp &net) -> void { - self.set_stepnet(net.Clone()); - }); + .def("set_stepnet", + [](operators::RecurrentOp &self, const operators::NetOp &net) + -> void { self.set_stepnet(net.Clone()); }); m.def("unique_integer", UniqueIntegerGenerator); diff --git a/paddle/framework/tensor_py.h b/paddle/pybind/tensor_py.h similarity index 92% rename from paddle/framework/tensor_py.h rename to paddle/pybind/tensor_py.h index 4e1ab77b1..39ba60b4d 100644 --- a/paddle/framework/tensor_py.h +++ b/paddle/pybind/tensor_py.h @@ -63,8 +63,11 @@ struct CastToPyBufferImpl { } return py::buffer_info( dst_tensor.mutable_data(dst_tensor.holder_->place()), - sizeof(CUR_TYPE), py::format_descriptor::format(), - (size_t)framework::arity(dst_tensor.dims()), dims_outside, strides); + sizeof(CUR_TYPE), + py::format_descriptor::format(), + (size_t)framework::arity(dst_tensor.dims()), + dims_outside, + strides); } else { constexpr bool less = I + 1 < std::tuple_size>::value; return CastToPyBufferImpl()(tensor); @@ -107,8 +110,8 @@ void PyCUDATensorSetFromArray( self.Resize(framework::make_ddim(dims)); auto *dst = self.mutable_data(place); - paddle::platform::GpuMemcpySync(dst, array.data(), sizeof(T) * array.size(), - cudaMemcpyHostToDevice); + paddle::platform::GpuMemcpySync( + dst, array.data(), sizeof(T) * array.size(), cudaMemcpyHostToDevice); } #endif -- GitLab From e3342ff8e79fbe1cacb8fa5a66cb9c69cba1eeb9 Mon Sep 17 00:00:00 2001 From: wanghaoshuang Date: Wed, 23 Aug 2017 19:30:46 +0800 Subject: [PATCH 0189/2018] Fix android build error. --- paddle/cuda/include/stub/hl_cuda_cudnn_stub.h | 11 ++++++++--- paddle/cuda/src/hl_cuda_cudnn.cc | 3 ++- paddle/gserver/tests/test_LayerGrad.cpp | 6 ++++-- python/paddle/trainer/config_parser.py | 6 +++--- python/paddle/trainer_config_helpers/layers.py | 7 +++---- 5 files changed, 20 insertions(+), 13 deletions(-) diff --git a/paddle/cuda/include/stub/hl_cuda_cudnn_stub.h b/paddle/cuda/include/stub/hl_cuda_cudnn_stub.h index abd0d6b09..3afcc6fa8 100644 --- a/paddle/cuda/include/stub/hl_cuda_cudnn_stub.h +++ b/paddle/cuda/include/stub/hl_cuda_cudnn_stub.h @@ -78,7 +78,9 @@ inline void hl_create_convolution_descriptor(hl_convolution_descriptor* conv, int padding_height, int padding_width, int stride_height, - int stride_width) {} + int stride_width, + int dilation_h, + int dilation_w) {} inline void hl_reset_convolution_descriptor(hl_convolution_descriptor conv, hl_tensor_descriptor image, @@ -86,7 +88,9 @@ inline void hl_reset_convolution_descriptor(hl_convolution_descriptor conv, int padding_height, int padding_width, int stride_height, - int stride_width) {} + int stride_width, + int dilation_h, + int dilation_w) {} inline void hl_destroy_convolution_descriptor(hl_convolution_descriptor conv) {} @@ -99,7 +103,8 @@ inline void hl_conv_workspace(hl_tensor_descriptor input, int* convBwdDataAlgo, size_t* bwdDataLimitBytes, int* convBwdFilterAlgo, - size_t* bwdFilterLimitBytes) {} + size_t* bwdFilterLimitBytes, + bool useDilation) {} inline void hl_convolution_forward(hl_tensor_descriptor input, real* input_data, diff --git a/paddle/cuda/src/hl_cuda_cudnn.cc b/paddle/cuda/src/hl_cuda_cudnn.cc index f55fa523e..f38ef6925 100644 --- a/paddle/cuda/src/hl_cuda_cudnn.cc +++ b/paddle/cuda/src/hl_cuda_cudnn.cc @@ -640,7 +640,8 @@ void hl_create_convolution_descriptor(hl_convolution_descriptor* conv, #else if (dilation_h > 1 || dilation_w > 1) { LOG(FATAL) - << "Current cudnn version does't support for dilation convolution."; + << "Current cuDNN version does't support for dilation convolution. " + << "The dilation convolution requires cuDNN >= v6.0."; } CHECK_CUDNN(dynload::cudnnSetConvolution2dDescriptor(hl_conv->desc, diff --git a/paddle/gserver/tests/test_LayerGrad.cpp b/paddle/gserver/tests/test_LayerGrad.cpp index 9348c47bd..9946f7666 100644 --- a/paddle/gserver/tests/test_LayerGrad.cpp +++ b/paddle/gserver/tests/test_LayerGrad.cpp @@ -12,7 +12,9 @@ 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. */ +#ifndef PADDLE_ONLY_CPU #include +#endif #include #include #include @@ -262,8 +264,8 @@ TEST(Projection, conv) { testProjectionConv(1, false); testProjectionConv(3, false); /// test ConvTransProjection - /// testProjectionConv(1, true); - /// testProjectionConv(3, true); + testProjectionConv(1, true); + testProjectionConv(3, true); } #endif diff --git a/python/paddle/trainer/config_parser.py b/python/paddle/trainer/config_parser.py index 33a20afb1..ddfd615d8 100644 --- a/python/paddle/trainer/config_parser.py +++ b/python/paddle/trainer/config_parser.py @@ -862,7 +862,6 @@ class Conv(Cfg): filter_size, channels, padding=None, - dilation=None, stride=None, groups=None, filter_channels=None, @@ -871,8 +870,9 @@ class Conv(Cfg): caffe_mode=True, filter_size_y=None, padding_y=None, - dilation_y=None, - stride_y=None): + stride_y=None, + dilation=None, + dilation_y=None): self.add_keys(locals()) if filter_size_y is None: self.filter_size_y = filter_size diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py index 74b88cd4f..987679855 100755 --- a/python/paddle/trainer_config_helpers/layers.py +++ b/python/paddle/trainer_config_helpers/layers.py @@ -2340,7 +2340,7 @@ def img_conv_layer(input, groups=1, stride=1, padding=0, - dilation=0, + dilation=1, bias_attr=None, param_attr=None, shared_biases=True, @@ -2472,9 +2472,6 @@ def img_conv_layer(input, else: dilation_y = dilation - if dilation > 1 or dilation_y > 1: - assert layer_type in ["cudnn_conv", "cudnn_convt"] - if param_attr.attr.get('initial_smart'): # special initial for conv layers. init_w = (2.0 / (filter_size**2 * num_channels))**0.5 @@ -2484,6 +2481,8 @@ def img_conv_layer(input, param_attr.attr["initial_smart"] = False if layer_type: + if dilation > 1 or dilation_y > 1: + assert layer_type in ["cudnn_conv", "cudnn_convt"] if trans: assert layer_type in ["exconvt", "cudnn_convt"] else: -- GitLab From 5e59ca7ccc8232b2028cfc8b4cffe19ffc73ba18 Mon Sep 17 00:00:00 2001 From: caoying03 Date: Wed, 23 Aug 2017 10:40:46 +0800 Subject: [PATCH 0190/2018] fix config helper. --- .../gserver/layers/CrossEntropyOverBeam.cpp | 10 ++ paddle/gserver/layers/CrossEntropyOverBeam.h | 16 ++- .../tests/test_CrossEntropyOverBeamGrad.cpp | 22 ++- python/paddle/trainer/config_parser.py | 12 +- .../paddle/trainer_config_helpers/layers.py | 129 +++++++++++++++--- .../test_cross_entropy_over_beam.protostr | 17 ++- .../configs/test_cross_entropy_over_beam.py | 18 ++- 7 files changed, 162 insertions(+), 62 deletions(-) diff --git a/paddle/gserver/layers/CrossEntropyOverBeam.cpp b/paddle/gserver/layers/CrossEntropyOverBeam.cpp index 09258fb30..f7736f0ce 100644 --- a/paddle/gserver/layers/CrossEntropyOverBeam.cpp +++ b/paddle/gserver/layers/CrossEntropyOverBeam.cpp @@ -161,7 +161,17 @@ real CostForOneSequence::forward() { } void CostForOneSequence::backward() { + /* + * when softmax layer is the output layer, and it is combined with + * cross-entropy as cost. The derivate with regard to softmax's input + * is simply: + * + * grad_i = softmax_out_i - target_i, + * + * and here hard label is used. + */ softmaxOut_->getData()[goldIdsInFinalExpansion_] -= 1.; + MatrixPtr tmp = Matrix::create( softmaxOut_->getData(), softmaxOut_->getWidth(), 1, false, false); diff --git a/paddle/gserver/layers/CrossEntropyOverBeam.h b/paddle/gserver/layers/CrossEntropyOverBeam.h index 96a5df7df..5d0cffee3 100644 --- a/paddle/gserver/layers/CrossEntropyOverBeam.h +++ b/paddle/gserver/layers/CrossEntropyOverBeam.h @@ -19,8 +19,8 @@ limitations under the License. */ namespace paddle { +/* This struct stores the beams in all search steps for a single sequence. */ struct BeamExpansion { - // store the entire beam expansion for a single sequence std::vector scores; std::vector seqInfo; @@ -111,8 +111,11 @@ private: size_t batchSize_; size_t beamSize_; - // Currently, this layer only works on CPU, if its inputs is on GPU, - // copy them to CPU memory. + /* + * the process of constructing beams is not friendly to GPU, currently, this + * layer only runs on CPU, if any of its inputs is on GPU memory, then copy + * it to CPU memory. + */ std::vector candidateScores_; std::vector candidateScoreGrad_; std::vector candidateInBeam_; @@ -120,9 +123,12 @@ private: std::vector goldSequence_; std::vector> beamSplitPos_; - // split entire bath of beams into beam per sequnence. + /* + * split entire bath of beams into beam per sequnence and store the result + * into this member. + */ std::vector beamPerSeq_; - // beamCosts_ is used to propagate error in one sequence. + /* beamCosts_ is used to propagate error in one sequence. */ std::vector beamCosts_; }; diff --git a/paddle/gserver/tests/test_CrossEntropyOverBeamGrad.cpp b/paddle/gserver/tests/test_CrossEntropyOverBeamGrad.cpp index 506a4281d..538d18cdc 100644 --- a/paddle/gserver/tests/test_CrossEntropyOverBeamGrad.cpp +++ b/paddle/gserver/tests/test_CrossEntropyOverBeamGrad.cpp @@ -28,16 +28,10 @@ using namespace paddle; // NOLINT DECLARE_int32(gpu_id); DECLARE_bool(thread_local_rand_use_global_seed); -// const size_t MAX_SEQ_NUM = 5; -// const size_t MAX_SEQ_LEN = 10; -// const size_t MAX_BEAM_SIZE = 3; - const size_t MAX_SEQ_NUM = 23; const size_t MAX_SEQ_LEN = 50; const size_t MAX_BEAM_SIZE = 27; -// const size_t SEED = 1503391792; -// const size_t SEED = 1; const size_t SEED = (size_t)(time(NULL)); struct SingleBeamExpansion { @@ -176,10 +170,12 @@ void genGroundTruth(vector& beamExpansions, beam.resetGroundTruth(seqNum); for (size_t i = 0; i < seqNum; ++i) { if (randFloat() > 0.5) { - // force the randomly generated label falls in the beam by chance 0.5. - // otherwise, when sequence length is relatively long and beam size is - // relatively small, the gold sequences falls off the beam at in - // the first search. + /* + * force the randomly generated label falls in the beam by chance 0.5. + * otherwise, when sequence length is relatively long and beam size is + * relatively small, the gold sequences falls off the beam at in the + * first search. + */ real* begPos = beam.selectedIndices.data() + i * beamSize; beam.colIdxInBeam[i] = rand() % count_if(begPos, begPos + beamSize, [](const real& val) { @@ -222,9 +218,7 @@ void genGroundTruth(vector& beamExpansions, if (randFloat() > 0.5) { // force the randomly generated label falls in the beam by chance 0.5. - // otherwise, when sequence length is relatively long and beam size is - // relatively small, the gold sequences falls off the beam at in - // the first search. + real* start = curBeam.selectedIndices.data() + curBeam.rowIdxInBeam[j] * beamSize; int n = rand() % count_if(start, start + beamSize, [](const real& val) { @@ -339,7 +333,7 @@ TEST(Layer, CrossEntropyOverBeam) { const size_t beamSize = 1 + rand() % MAX_BEAM_SIZE; LOG(INFO) << "beamSize = " << beamSize; - // TODO(caoying): test with more beam expansions. + // TODO(caoying): test with random beam expansions. const size_t expansionCount = 3; vector beams; genRandomBeamExpansion(expansionCount, beamSize, beams); diff --git a/python/paddle/trainer/config_parser.py b/python/paddle/trainer/config_parser.py index 7707ece81..579713546 100644 --- a/python/paddle/trainer/config_parser.py +++ b/python/paddle/trainer/config_parser.py @@ -1605,16 +1605,16 @@ class MultiClassCrossEntropySelfNormCostLayer(LayerBase): @config_layer('cross_entropy_over_beam') class CrossEntropyOverBeamLayer(LayerBase): def __init__(self, name, inputs, **xargs): - config_assert(len(inputs) % 3 == 0, "Error input numbers.") + config_assert(len(inputs) % 3 == 0, "Error input number.") super(CrossEntropyOverBeamLayer, self).__init__( name, 'cross_entropy_over_beam', 0, inputs, **xargs) input_num = len(inputs) / 3 for i in range(input_num): - input_layer = self.get_input_layer(i * 2) - config_assert( - input_layer.size == 1, "Inputs for this layer are made up of " - "several pairs and the first one in a pair is scores for " - "all the candidates, so its size should be equal to 1.") + input_layer = self.get_input_layer(i * 3) + config_assert(input_layer.size == 1, ( + "Inputs for this layer are made up of " + "several triples, in which the first one is scores over " + "all candidate paths, whose size should be equal to 1.")) @config_layer('fc') diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py index b027f84b5..053c92d00 100755 --- a/python/paddle/trainer_config_helpers/layers.py +++ b/python/paddle/trainer_config_helpers/layers.py @@ -103,6 +103,7 @@ __all__ = [ 'nce_layer', 'cross_entropy_with_selfnorm', 'cross_entropy', + 'BeamInput', 'cross_entropy_over_beam', 'multi_binary_label_cross_entropy', 'sum_cost', @@ -5681,10 +5682,10 @@ def multi_binary_label_cross_entropy(input, if input.activation is None or \ not isinstance(input.activation, SigmoidActivation): - logger.log( - logging.WARN, - "%s is not recommend for multi_binary_label_cross_entropy's activation, " - "maybe the sigmoid is better" % repr(input.activation)) + logger.log(logging.WARN, + ("%s is not a recommended activation for " + "multi_binary_label_cross_entropy, sigmoid is better") % + repr(input.activation)) Layer( name=name, @@ -5699,26 +5700,110 @@ def multi_binary_label_cross_entropy(input, size=1) +class BeamInput(object): + """ + Define the input for cross_entropy_over_beam layer. + + A beam is made up of a triple: the first one is scores over all + candidates; the second one is indices of top k selected candidates; the + third one is the index of ground truth, which is also always called + gold. + """ + + def __init__(self, candidate_scores, selected_candidates, gold): + assert isinstance(candidate_scores, LayerOutput) + self.candidate_scores = candidate_scores + assert candidate_scores.size == 1 + + assert isinstance(selected_candidates, LayerOutput) + self.selected_candidates = selected_candidates + + assert isinstance(gold, LayerOutput) + self.gold = gold + + @wrap_name_default() @layer_support() -def cross_entropy_over_beam(input, label, name=None, coeff=1.0, weight=None): - """ - TODO(caoying) add comments. +def cross_entropy_over_beam(input, name=None): """ + This layer is used in learning to search models, which is to solve complex + joint prediction problems based on learning to search through a + problem-defined search space. - assert len(input) / 2 == len(label), "Error input numbers." - for i in range(0, len(input), 2): - assert (input[i].size == 1), ( - "Inputs for this layer are made up of " - "several pairs and the first one in a pair is scores for " - "all the candidates, so its size should be equal to 1.") + Specifically, the learning to search process for this layer begins with + searching a target sequence from a nested sequence. In the first search + step, top beam size sequences with highest scores, indices of these top k + sequences in the original nested sequence, and the ground truth (also + called gold) altogether (a triple) make up of the first beam. - ipts, parents = __cost_input__(input, label, weight) - Layer( - name=name, - type=LayerType.CROSS_ENTROPY_OVER_BEAM, - inputs=ipts, - coeff=coeff) + Then, several special positions, for example, start and end positions + that define meaningful segments are searched. In these searches, top k + positions with highest scores are selected, and then sequence, starting + from the selected starts till ends of the sequences (or a fixed position) + are taken to search next. + + We call the possible top k results returned in one search the beam. This + search process can be repeated for pre-defined turns and leads to several + beam expansions. + + Finally, the layer cross_entropy_over_beam takes all the beam expansions + which contain several candidate targets found along the multi-step search. + cross_entropy_over_beam calculates cross entropy over the expanded beams + which all the candidates in the beam as the normalized factor. + + Note that, if gold falls off the beam at search step t, then the cost is + calculated over the beam at step t. + + This cost layer always works together with kmax_sequence_score_layer, + sub_nested_seq_layer, and sequence_slice_layer to trim the input to form a + sub-search space. + + + The example usage is: + + .. code-block:: python + + cost = cross_entropy_over_beam(input=[ + BeamInput( + candidate_scores=beam1_candidates, + selected_candidates=beam1_topk, + gold=gold1), + BeamInput( + candidate_scores=beam2_candidates, + selected_candidates=beam2_topk, + gold=gold2), + ]) + + + :param input: input beams for this layer. + :type input: BeamInput + :param name: input beams for this layer. + :type name: basestring + :return: LayerOutput object. + :rtype: LayerOutput + """ + + if isinstance(input, BeamInput): + input = [input] + else: + assert isinstance(input, list), ( + 'input for cross_entropy_over_beam shold be a python list ' + 'of BeamInput object.') + for ipt in input: + assert isinstance(ipt, BeamInput), ( + 'input for cross_entropy_over_beam ' + 'should be a BeamInput object.') + + ipts = [] + parents = [] + for beam in input: + parents += [beam.candidate_scores, beam.selected_candidates, beam.gold] + ipts += [ + beam.candidate_scores.name, beam.selected_candidates.name, + beam.gold.name + ] + + Layer(name=name, type=LayerType.CROSS_ENTROPY_OVER_BEAM, inputs=ipts) return LayerOutput(name, LayerType.CROSS_ENTROPY, parents=parents, size=1) @@ -6247,11 +6332,11 @@ def kmax_sequence_score_layer(input, name=None, beam_size=1): @wrap_bias_attr_default() def scale_shift_layer(input, name=None, param_attr=None, bias_attr=None): """ - A layer applies a linear transformation to each element in each row of - the input matrix. For each element, the layer first re-scale it and then + A layer applies a linear transformation to each element in each row of + the input matrix. For each element, the layer first re-scale it and then adds a bias to it. - This layer is very like the SlopeInterceptLayer, except the scale and + This layer is very like the SlopeInterceptLayer, except the scale and bias are trainable. .. math:: diff --git a/python/paddle/trainer_config_helpers/tests/configs/protostr/test_cross_entropy_over_beam.protostr b/python/paddle/trainer_config_helpers/tests/configs/protostr/test_cross_entropy_over_beam.protostr index e44478ec2..c43fc48e2 100644 --- a/python/paddle/trainer_config_helpers/tests/configs/protostr/test_cross_entropy_over_beam.protostr +++ b/python/paddle/trainer_config_helpers/tests/configs/protostr/test_cross_entropy_over_beam.protostr @@ -114,27 +114,26 @@ layers { input_layer_name: "__kmax_sequence_score_layer_0__" } inputs { - input_layer_name: "__fc_layer_0__" + input_layer_name: "sentences_ids" } inputs { - input_layer_name: "__kmax_sequence_score_layer_1__" + input_layer_name: "__fc_layer_0__" } inputs { - input_layer_name: "__fc_layer_1__" + input_layer_name: "__kmax_sequence_score_layer_1__" } inputs { - input_layer_name: "__kmax_sequence_score_layer_2__" + input_layer_name: "start_ids" } inputs { - input_layer_name: "sentences_ids" + input_layer_name: "__fc_layer_1__" } inputs { - input_layer_name: "start_ids" + input_layer_name: "__kmax_sequence_score_layer_2__" } inputs { input_layer_name: "end_ids" } - coeff: 1.0 } parameters { name: "___fc_layer_0__.w0" @@ -177,8 +176,8 @@ parameters { initial_smart: false } input_layer_names: "sentence_scores" -input_layer_names: "sentence_states" input_layer_names: "sentences_ids" +input_layer_names: "sentence_states" input_layer_names: "start_ids" input_layer_names: "end_ids" output_layer_names: "__cross_entropy_over_beam_0__" @@ -198,8 +197,8 @@ sub_models { layer_names: "end_ids" layer_names: "__cross_entropy_over_beam_0__" input_layer_names: "sentence_scores" - input_layer_names: "sentence_states" input_layer_names: "sentences_ids" + input_layer_names: "sentence_states" input_layer_names: "start_ids" input_layer_names: "end_ids" output_layer_names: "__cross_entropy_over_beam_0__" diff --git a/python/paddle/trainer_config_helpers/tests/configs/test_cross_entropy_over_beam.py b/python/paddle/trainer_config_helpers/tests/configs/test_cross_entropy_over_beam.py index edc2d32fc..240e703dc 100644 --- a/python/paddle/trainer_config_helpers/tests/configs/test_cross_entropy_over_beam.py +++ b/python/paddle/trainer_config_helpers/tests/configs/test_cross_entropy_over_beam.py @@ -29,11 +29,17 @@ topk_end_pos_ids = kmax_sequence_score_layer( sentence_idx = data_layer(name="sentences_ids", size=1) start_idx = data_layer(name="start_ids", size=1) end_idx = data_layer(name="end_ids", size=1) -cost = cross_entropy_over_beam( - input=[ - sentence_scores, topk_sentence_ids, start_pos_scores, - topk_start_pos_ids, end_pos_scores, topk_end_pos_ids - ], - label=[sentence_idx, start_idx, end_idx]) +cost = cross_entropy_over_beam(input=[ + BeamInput( + candidate_scores=sentence_scores, + selected_candidates=topk_sentence_ids, + gold=sentence_idx), BeamInput( + candidate_scores=start_pos_scores, + selected_candidates=topk_start_pos_ids, + gold=start_idx), BeamInput( + candidate_scores=end_pos_scores, + selected_candidates=topk_end_pos_ids, + gold=end_idx) +]) outputs(cost) -- GitLab From 8a4fad4248e942061586538e8de14a7d08052330 Mon Sep 17 00:00:00 2001 From: Liu Yiqun Date: Wed, 23 Aug 2017 19:43:57 +0800 Subject: [PATCH 0191/2018] Support to use clang for Android cross-compiling. --- cmake/cblas.cmake | 4 + cmake/external/warpctc.cmake | 1 + paddle/cuda/include/hl_cpu_gru.cuh | 166 ++++++++++++------------- paddle/function/MulOp.cpp | 37 +++--- paddle/math/MathFunctions.cpp | 4 + paddle/math/MathFunctions.h | 23 +++- paddle/math/Matrix.cpp | 18 ++- paddle/scripts/docker/build_android.sh | 24 ++-- 8 files changed, 155 insertions(+), 122 deletions(-) diff --git a/cmake/cblas.cmake b/cmake/cblas.cmake index 854066fd1..ab111eccc 100644 --- a/cmake/cblas.cmake +++ b/cmake/cblas.cmake @@ -13,6 +13,10 @@ # system paths. # +if(USE_EIGEN_FOR_BLAS) + return() +endif(USE_EIGEN_FOR_BLAS) + set(CBLAS_FOUND OFF) ## Find MKLML First. diff --git a/cmake/external/warpctc.cmake b/cmake/external/warpctc.cmake index 2d7daed9b..3cc652bed 100644 --- a/cmake/external/warpctc.cmake +++ b/cmake/external/warpctc.cmake @@ -41,6 +41,7 @@ IF(CMAKE_CXX_COMPILER_ID STREQUAL "Clang" OR CMAKE_CXX_COMPILER_ID STREQUAL "App ELSE() SET(USE_OMP ON) ENDIF() +SET(USE_OMP OFF FORCE) ExternalProject_Add( extern_warpctc diff --git a/paddle/cuda/include/hl_cpu_gru.cuh b/paddle/cuda/include/hl_cpu_gru.cuh index c0a37ced2..732799a28 100644 --- a/paddle/cuda/include/hl_cpu_gru.cuh +++ b/paddle/cuda/include/hl_cpu_gru.cuh @@ -20,11 +20,11 @@ limitations under the License. */ #include "paddle/math/MathFunctions.h" -#ifndef PADDLE_TYPE_DOUBLE -#define CBLAS_GEMM paddle::gemm -#else -#define CBLAS_GEMM paddle::gemm -#endif +// #ifndef PADDLE_TYPE_DOUBLE +// #define CBLAS_GEMM paddle::gemm +// #else +// #define CBLAS_GEMM paddle::gemm +// #endif template void hl_naive_gru_forward_reset_output(OpResetOutput opResetOutput, @@ -219,37 +219,37 @@ void hl_cpu_gru_forward(OpResetOutput opResetOutput, hl_activation_mode_t active_node, hl_activation_mode_t active_gate) { if (value.prevOutValue) { - CBLAS_GEMM(CblasNoTrans, - CblasNoTrans, - batchSize, - 2 * frameSize, - frameSize, - 1, - value.prevOutValue, - frameSize, - value.gateWeight, - frameSize * 2, - 1, - value.gateValue, - frameSize * 3); +// CBLAS_GEMM(CblasNoTrans, +// CblasNoTrans, +// batchSize, +// 2 * frameSize, +// frameSize, +// 1, +// value.prevOutValue, +// frameSize, +// value.gateWeight, +// frameSize * 2, +// 1, +// value.gateValue, +// frameSize * 3); } forward_reset_output(opResetOutput, value, frameSize, batchSize, active_gate); if (value.prevOutValue) { - CBLAS_GEMM(CblasNoTrans, - CblasNoTrans, - batchSize, - frameSize, - frameSize, - 1, - value.resetOutputValue, - frameSize, - value.stateWeight, - frameSize, - 1, - value.gateValue + frameSize * 2, - frameSize * 3); +// CBLAS_GEMM(CblasNoTrans, +// CblasNoTrans, +// batchSize, +// frameSize, +// frameSize, +// 1, +// value.resetOutputValue, +// frameSize, +// value.stateWeight, +// frameSize, +// 1, +// value.gateValue + frameSize * 2, +// frameSize * 3); } forward_final_output(opFinalOutput, value, frameSize, batchSize, active_node); @@ -538,34 +538,34 @@ void hl_cpu_gru_backward(OpStateGrad opStateGrad, frameSize, batchSize, active_node); if (value.prevOutValue && grad.prevOutGrad) { - CBLAS_GEMM(CblasNoTrans, - CblasTrans, - batchSize, - frameSize, - frameSize, - 1, - grad.gateGrad + frameSize * 2, - frameSize * 3, - value.stateWeight, - frameSize, - 0, - grad.resetOutputGrad, - frameSize); +// CBLAS_GEMM(CblasNoTrans, +// CblasTrans, +// batchSize, +// frameSize, +// frameSize, +// 1, +// grad.gateGrad + frameSize * 2, +// frameSize * 3, +// value.stateWeight, +// frameSize, +// 0, +// grad.resetOutputGrad, +// frameSize); if (grad.stateWeightGrad) { - CBLAS_GEMM(CblasTrans, - CblasNoTrans, - frameSize, - frameSize, - batchSize, - 1, - value.resetOutputValue, - frameSize, - grad.gateGrad + frameSize * 2, - frameSize * 3, - 1, - grad.stateWeightGrad, - frameSize); +// CBLAS_GEMM(CblasTrans, +// CblasNoTrans, +// frameSize, +// frameSize, +// batchSize, +// 1, +// value.resetOutputValue, +// frameSize, +// grad.gateGrad + frameSize * 2, +// frameSize * 3, +// 1, +// grad.stateWeightGrad, +// frameSize); } } @@ -573,34 +573,34 @@ void hl_cpu_gru_backward(OpStateGrad opStateGrad, frameSize, batchSize, active_gate); if (grad.prevOutGrad && value.prevOutValue) { - CBLAS_GEMM(CblasNoTrans, - CblasTrans, - batchSize, - frameSize, - frameSize * 2, - 1, - grad.gateGrad, - frameSize * 3, - value.gateWeight, - frameSize * 2, - 1, - grad.prevOutGrad, - frameSize); +// CBLAS_GEMM(CblasNoTrans, +// CblasTrans, +// batchSize, +// frameSize, +// frameSize * 2, +// 1, +// grad.gateGrad, +// frameSize * 3, +// value.gateWeight, +// frameSize * 2, +// 1, +// grad.prevOutGrad, +// frameSize); if (grad.gateWeightGrad) { - CBLAS_GEMM(CblasTrans, - CblasNoTrans, - frameSize, - frameSize * 2, - batchSize, - 1, - value.prevOutValue, - frameSize, - grad.gateGrad, - frameSize * 3, - 1, - grad.gateWeightGrad, - frameSize * 2); +// CBLAS_GEMM(CblasTrans, +// CblasNoTrans, +// frameSize, +// frameSize * 2, +// batchSize, +// 1, +// value.prevOutValue, +// frameSize, +// grad.gateGrad, +// frameSize * 3, +// 1, +// grad.gateWeightGrad, +// frameSize * 2); } } } diff --git a/paddle/function/MulOp.cpp b/paddle/function/MulOp.cpp index 91b4b8ed9..25e41edad 100644 --- a/paddle/function/MulOp.cpp +++ b/paddle/function/MulOp.cpp @@ -13,18 +13,10 @@ See the License for the specific language governing permissions and limitations under the License. */ #include "MulOp.h" -/// todo(tianbing), delete it -#include -#include "paddle/math/MathFunctions.h" +#include "GemmFunctor.h" #include "paddle/math/SIMDFunctions.h" #include "paddle/utils/ThreadLocal.h" -#ifndef PADDLE_TYPE_DOUBLE -#define GEMM paddle::gemm -#else -#define GEMM paddle::gemm -#endif - namespace { inline void vecAddTo(real* a, const real* b, real scaleB, size_t len) { for (unsigned int i = 0; i < len; ++i) { @@ -114,19 +106,20 @@ void MulOp(CpuMatrix& out, real scaleT, bool aTrans, bool bTrans) { - GEMM(aTrans ? CblasTrans : CblasNoTrans, - bTrans ? CblasTrans : CblasNoTrans, - out.getHeight(), - out.getWidth(), - !aTrans ? a.getWidth() : a.getHeight(), - scaleAB, - a.getData(), - a.getStride(), - b.getData(), - b.getStride(), - scaleT, - out.getData(), - out.getStride()); + BlasGemm::compute( + aTrans, + bTrans, + out.getHeight(), + out.getWidth(), + !aTrans ? a.getWidth() : a.getHeight(), + scaleAB, + a.getData(), + a.getStride(), + b.getData(), + b.getStride(), + scaleT, + out.getData(), + out.getStride()); } /// dense matrix (+)= sparse matrix * dense matrix diff --git a/paddle/math/MathFunctions.cpp b/paddle/math/MathFunctions.cpp index c8ba1074a..c2f17beeb 100644 --- a/paddle/math/MathFunctions.cpp +++ b/paddle/math/MathFunctions.cpp @@ -84,6 +84,7 @@ LAPACK_ROUTINE_EACH(DYNAMIC_LOAD_LAPACK_WRAP) namespace paddle { +#ifndef PADDLE_USE_EIGEN_FOR_BLAS template <> void gemm(const CBLAS_TRANSPOSE transA, const CBLAS_TRANSPOSE transB, @@ -143,6 +144,7 @@ void gemm(const CBLAS_TRANSPOSE transA, C, ldc); } +#endif template <> int getrf(const CBLAS_ORDER order, @@ -182,6 +184,7 @@ int getri(const CBLAS_ORDER order, return dynload::PADDLE_DGETRI(order, N, A, lda, ipiv); } +#ifndef PADDLE_USE_EIGEN_FOR_BLAS template <> void axpy(const int n, const float alpha, const float* x, float* y) { cblas_saxpy(n, alpha, x, 1, y, 1); @@ -201,6 +204,7 @@ template <> double dotProduct(const int n, const double* x, const double* y) { return cblas_ddot(n, x, 1, y, 1); } +#endif #if defined(PADDLE_USE_MKL) || defined(PADDLE_USE_MKLML) diff --git a/paddle/math/MathFunctions.h b/paddle/math/MathFunctions.h index 637643838..9297ae78c 100644 --- a/paddle/math/MathFunctions.h +++ b/paddle/math/MathFunctions.h @@ -40,7 +40,14 @@ extern "C" { #ifndef LAPACK_FOUND extern "C" { +#ifndef PADDLE_USE_EIGEN_FOR_BLAS #include +#else +typedef enum CBLAS_ORDER { + CblasRowMajor = 101, + CblasColMajor = 102 +} CBLAS_ORDER; +#endif int LAPACKE_sgetrf( int matrix_layout, int m, int n, float* a, int lda, int* ipiv); int LAPACKE_dgetrf( @@ -56,6 +63,7 @@ int LAPACKE_dgetri( namespace paddle { +#ifndef PADDLE_USE_EIGEN_FOR_BLAS template void gemm(const CBLAS_TRANSPOSE transA, const CBLAS_TRANSPOSE transB, @@ -70,6 +78,7 @@ void gemm(const CBLAS_TRANSPOSE transA, const T beta, T* C, const int ldc); +#endif template int getrf(const CBLAS_ORDER Order, @@ -84,10 +93,20 @@ int getri( const CBLAS_ORDER Order, const int N, T* A, const int lda, const int* ipiv); template -void axpy(const int n, const T alpha, const T* x, T* y); +void axpy(const int n, const T alpha, const T* x, T* y) { + /// y = y + alpha * x + for (int i = 0; i < n; i++) { + y[i] = y[i] + alpha * x[i]; + } +} template -T dotProduct(const int n, const T* x, const T* y); +T dotProduct(const int n, const T* x, const T* y) { + T result = static_cast(0); + for (int i = 0; i < n; i++) { + result += x[i] * y[i]; + } +} template void vExp(const int n, const T* a, T* r); diff --git a/paddle/math/Matrix.cpp b/paddle/math/Matrix.cpp index 27f7d95b7..fbf3accc9 100644 --- a/paddle/math/Matrix.cpp +++ b/paddle/math/Matrix.cpp @@ -28,6 +28,7 @@ limitations under the License. */ #include "hl_top_k.h" #include "paddle/utils/Logging.h" +#include "paddle/function/GemmFunctor.h" #include "paddle/utils/ThreadLocal.h" #include "SIMDFunctions.h" @@ -2222,24 +2223,29 @@ void CpuMatrix::mul(CpuMatrix* a, CpuMatrix* b, real scaleAB, real scaleT) { CHECK(!isTransposed()) << "Not supported"; size_t a_col, b_col, a_row, b_row; - CBLAS_TRANSPOSE a_trans, b_trans; + // CBLAS_TRANSPOSE a_trans, b_trans; + bool a_trans, b_trans; if (!a->isTransposed()) { a_col = a->getWidth(); a_row = a->getHeight(); - a_trans = CblasNoTrans; + // a_trans = CblasNoTrans; + a_trans = false; } else { a_col = a->getHeight(); a_row = a->getWidth(); - a_trans = CblasTrans; + // a_trans = CblasTrans; + a_trans = true; } if (!b->isTransposed()) { b_col = b->getWidth(); b_row = b->getHeight(); - b_trans = CblasNoTrans; + // b_trans = CblasNoTrans; + b_trans = false; } else { b_col = b->getHeight(); b_row = b->getWidth(); - b_trans = CblasTrans; + // b_trans = CblasTrans; + b_trans = true; } CHECK_EQ(a_col, b_row); @@ -2256,7 +2262,7 @@ void CpuMatrix::mul(CpuMatrix* a, CpuMatrix* b, real scaleAB, real scaleT) { int lda = a->getStride(); int ldb = b->getStride(); int ldc = getStride(); - gemm( + BlasGemm::compute( a_trans, b_trans, M, N, K, scaleAB, A, lda, B, ldb, scaleT, C, ldc); } diff --git a/paddle/scripts/docker/build_android.sh b/paddle/scripts/docker/build_android.sh index 593ae28e4..79f5ab12e 100644 --- a/paddle/scripts/docker/build_android.sh +++ b/paddle/scripts/docker/build_android.sh @@ -2,9 +2,9 @@ set -xe -mkdir -p /paddle/build_android/$ANDROID_ABI -cd /paddle/build_android/$ANDROID_ABI -rm -rf /paddle/install 2>/dev/null || true +rm -rf /paddle/build_android 2>/dev/null || true +mkdir -p /paddle/build_android +cd /paddle/build_android THIRD_PARTY_PATH=/paddle/third_party_android/$ANDROID_ABI @@ -14,19 +14,25 @@ if [ $ANDROID_ABI == "armeabi-v7a" ]; then -DANDROID_ABI=$ANDROID_ABI \ -DANDROID_ARM_NEON=ON \ -DANDROID_ARM_MODE=ON \ + -DCMAKE_C_COMPILER=$ANDROID_ARM_STANDALONE_TOOLCHAIN/bin/arm-linux-androideabi-clang \ + -DCMAKE_CXX_COMPILER=$ANDROID_ARM_STANDALONE_TOOLCHAIN/bin/arm-linux-androideabi-clang++ \ -DHOST_C_COMPILER=/usr/bin/gcc \ -DHOST_CXX_COMPILER=/usr/bin/g++ \ -DCMAKE_INSTALL_PREFIX=/paddle/install \ -DTHIRD_PARTY_PATH=$THIRD_PARTY_PATH \ -DCMAKE_BUILD_TYPE=Release \ + -DUSE_EIGEN_FOR_BLAS=ON \ -DWITH_C_API=ON \ -DWITH_SWIG_PY=OFF \ - /paddle -elif [ $ANDROID_ABI == "arm64-v7a" ]; then + -DWITH_STYLE_CHECK=OFF \ + .. +elif [ $ANDROID_ABI == "arm64-v8a" ]; then cmake -DCMAKE_SYSTEM_NAME=Android \ -DANDROID_STANDALONE_TOOLCHAIN=$ANDROID_ARM64_STANDALONE_TOOLCHAIN \ -DANDROID_ABI=$ANDROID_ABI \ -DANDROID_ARM_MODE=ON \ + -DCMAKE_C_COMPILER=$ANDROID_ARM64_STANDALONE_TOOLCHAIN/bin/aarch64-linux-android-clang \ + -DCMAKE_CXX_COMPILER=$ANDROID_ARM64_STANDALONE_TOOLCHAIN/bin/aarch64-linux-android-clang++ \ -DHOST_C_COMPILER=/usr/bin/gcc \ -DHOST_CXX_COMPILER=/usr/bin/g++ \ -DCMAKE_INSTALL_PREFIX=/paddle/install \ @@ -34,7 +40,7 @@ elif [ $ANDROID_ABI == "arm64-v7a" ]; then -DCMAKE_BUILD_TYPE=Release \ -DWITH_C_API=ON \ -DWITH_SWIG_PY=OFF \ - /paddle + .. elif [ $ANDROID_ABI == "armeabi" ]; then cmake -DCMAKE_SYSTEM_NAME=Android \ -DANDROID_STANDALONE_TOOLCHAIN=$ANDROID_ARM_STANDALONE_TOOLCHAIN \ @@ -47,10 +53,10 @@ elif [ $ANDROID_ABI == "armeabi" ]; then -DCMAKE_BUILD_TYPE=Release \ -DWITH_C_API=ON \ -DWITH_SWIG_PY=OFF \ - /paddle + .. else echo "Invalid ANDROID_ABI: $ANDROID_ABI" fi -make -j `nproc` -make install -j `nproc` +make VERBOSE=1 +make install -- GitLab From 76677f25774a84d9ced011be02e62ae15b03506c Mon Sep 17 00:00:00 2001 From: qiaolongfei Date: Wed, 23 Aug 2017 09:12:34 -0700 Subject: [PATCH 0192/2018] add test --- python/paddle/v2/framework/tests/mnist.py | 36 ++++++++++++++++++----- 1 file changed, 28 insertions(+), 8 deletions(-) diff --git a/python/paddle/v2/framework/tests/mnist.py b/python/paddle/v2/framework/tests/mnist.py index ededf767b..e47de2436 100644 --- a/python/paddle/v2/framework/tests/mnist.py +++ b/python/paddle/v2/framework/tests/mnist.py @@ -52,7 +52,7 @@ def grad_var_name(var_name): return var_name + "@GRAD" -def sgd_optimizer(net, param_name, learning_rate=0.01): +def sgd_optimizer(net, param_name, learning_rate=0.005): grad_name = grad_var_name(param_name) optimize_op = Operator( "sgd", @@ -166,9 +166,9 @@ def set_cost(): cost_grad.set(numpy.ones(cost_shape).astype("float32"), place) -def print_cost(): +def mean_cost(): cost_data = numpy.array(scope.find_var("cross_entropy_3").get_tensor()) - print(cost_data.sum() / len(cost_data)) + return cost_data.sum() / len(cost_data) def error_rate(predict, label): @@ -176,7 +176,7 @@ def error_rate(predict, label): axis=1) label = numpy.array(scope.find_var(label).get_tensor()) error_num = numpy.sum(predict_var != label) - print(error_num / float(len(label))) + return error_num / float(len(label)) images = data_layer(name='pixel', dims=[BATCH_SIZE, 784]) @@ -198,16 +198,35 @@ print_inputs_outputs(forward_network) print_inputs_outputs(backward_net) print_inputs_outputs(optimize_net) -reader = paddle.batch( +train_reader = paddle.batch( paddle.reader.shuffle( paddle.dataset.mnist.train(), buf_size=8192), batch_size=BATCH_SIZE) + +def test(): + test_reader = paddle.batch(paddle.dataset.mnist.test(), batch_size=128) + cost = [] + error = [] + for data in test_reader(): + image = numpy.array(map(lambda x: x[0], data)).astype("float32") + label = numpy.array(map(lambda x: x[1], data)).astype("int32") + feed_data("pixel", image) + feed_data("label", label) + + forward_network.infer_shape(scope) + forward_network.run(scope, dev_ctx) + cost.append(mean_cost()) + error.append(error_rate(predict, "label")) + print("cost=" + str(sum(cost) / float(len(cost))) + " error_rate=" + str( + sum(error) / float(len(error)))) + + PASS_NUM = 1000 for pass_id in range(PASS_NUM): batch_id = 0 - for data in reader(): + for data in train_reader(): image = numpy.array(map(lambda x: x[0], data)).astype("float32") label = numpy.array(map(lambda x: x[1], data)).astype("int32") feed_data("pixel", image) @@ -222,7 +241,8 @@ for pass_id in range(PASS_NUM): optimize_net.run(scope, dev_ctx) if batch_id % 100 == 0: print("pass[" + str(pass_id) + "] batch_id[" + str(batch_id) + "]") - print_cost() - error_rate(predict, "label") + test() + # print(mean_cost()) + # print(error_rate(predict, "label")) batch_id = batch_id + 1 -- GitLab From cf515e4a72f4b02fbbbfdbd79c3b66b1be694e7b Mon Sep 17 00:00:00 2001 From: qiaolongfei Date: Wed, 23 Aug 2017 09:39:47 -0700 Subject: [PATCH 0193/2018] optimize code and name --- python/paddle/v2/framework/tests/mnist.py | 56 +++++++++++------------ 1 file changed, 27 insertions(+), 29 deletions(-) diff --git a/python/paddle/v2/framework/tests/mnist.py b/python/paddle/v2/framework/tests/mnist.py index e47de2436..886e99610 100644 --- a/python/paddle/v2/framework/tests/mnist.py +++ b/python/paddle/v2/framework/tests/mnist.py @@ -134,7 +134,7 @@ def cross_entropy_layer(net, input, label): return cost_name -def get_backward_net(forward_net): +def create_backward_net(forward_net): net = core.Operator.backward(forward_net, set()) for input in net.inputs()["all"]: var = scope.new_var(input) @@ -145,29 +145,29 @@ def get_backward_net(forward_net): return net -def print_inputs_outputs(op): +def debug_print_op(op): print("===============" + op.type() + "==============") print("***inputs:***") for input in op.inputs()["all"]: print input, scope.find_var(input).get_tensor().get_dims() - print("***outputs:***") + print("\n***outputs:***") for output in op.outputs()["all"]: print output, scope.find_var(output).get_tensor().get_dims() print("") print("") -def set_cost(): - cost_shape = numpy.array(scope.find_var("cross_entropy_3").get_tensor( - )).shape - cost_grad = scope.find_var(grad_var_name("cross_entropy_3")).get_tensor() +def set_cost(cost): + cost_shape = numpy.array(scope.find_var(cost).get_tensor()).shape + cost_grad = \ + scope.find_var(grad_var_name(cost)).get_tensor() cost_grad.set_dims(cost_shape) cost_grad.alloc_float(place) cost_grad.set(numpy.ones(cost_shape).astype("float32"), place) -def mean_cost(): - cost_data = numpy.array(scope.find_var("cross_entropy_3").get_tensor()) +def mean_cost(cost): + cost_data = numpy.array(scope.find_var(cost).get_tensor()) return cost_data.sum() / len(cost_data) @@ -180,23 +180,23 @@ def error_rate(predict, label): images = data_layer(name='pixel', dims=[BATCH_SIZE, 784]) -label = data_layer(name='label', dims=[BATCH_SIZE]) +labels = data_layer(name='label', dims=[BATCH_SIZE]) fc1 = fc_layer(net=forward_network, input=images, size=100, act="sigmoid") fc2 = fc_layer(net=forward_network, input=fc1, size=100, act="sigmoid") predict = fc_layer(net=forward_network, input=fc2, size=100, act="softmax") -cost = cross_entropy_layer(net=forward_network, input=predict, label=label) +cost = cross_entropy_layer(net=forward_network, input=predict, label=labels) forward_network.complete_add_op(True) -backward_net = get_backward_net(forward_network) +backward_net = create_backward_net(forward_network) optimize_net.complete_add_op(True) print(forward_network) print(backward_net) print(optimize_net) -print_inputs_outputs(forward_network) -print_inputs_outputs(backward_net) -print_inputs_outputs(optimize_net) +debug_print_op(forward_network) +debug_print_op(backward_net) +debug_print_op(optimize_net) train_reader = paddle.batch( paddle.reader.shuffle( @@ -204,19 +204,19 @@ train_reader = paddle.batch( batch_size=BATCH_SIZE) -def test(): +def test(cost_name): test_reader = paddle.batch(paddle.dataset.mnist.test(), batch_size=128) cost = [] error = [] for data in test_reader(): - image = numpy.array(map(lambda x: x[0], data)).astype("float32") - label = numpy.array(map(lambda x: x[1], data)).astype("int32") - feed_data("pixel", image) - feed_data("label", label) + image_data = numpy.array(map(lambda x: x[0], data)).astype("float32") + label_data = numpy.array(map(lambda x: x[1], data)).astype("int32") + feed_data(images, image_data) + feed_data(labels, label_data) forward_network.infer_shape(scope) forward_network.run(scope, dev_ctx) - cost.append(mean_cost()) + cost.append(mean_cost(cost_name)) error.append(error_rate(predict, "label")) print("cost=" + str(sum(cost) / float(len(cost))) + " error_rate=" + str( sum(error) / float(len(error)))) @@ -227,22 +227,20 @@ for pass_id in range(PASS_NUM): batch_id = 0 for data in train_reader(): - image = numpy.array(map(lambda x: x[0], data)).astype("float32") - label = numpy.array(map(lambda x: x[1], data)).astype("int32") - feed_data("pixel", image) - feed_data("label", label) + image_data = numpy.array(map(lambda x: x[0], data)).astype("float32") + label_data = numpy.array(map(lambda x: x[1], data)).astype("int32") + feed_data(images, image_data) + feed_data(labels, label_data) forward_network.infer_shape(scope) forward_network.run(scope, dev_ctx) - set_cost() + set_cost(cost) backward_net.infer_shape(scope) backward_net.run(scope, dev_ctx) optimize_net.run(scope, dev_ctx) if batch_id % 100 == 0: print("pass[" + str(pass_id) + "] batch_id[" + str(batch_id) + "]") - test() - # print(mean_cost()) - # print(error_rate(predict, "label")) + test(cost) batch_id = batch_id + 1 -- GitLab From 9db4ad6130d79d72fa150e534b5b54fa723c3240 Mon Sep 17 00:00:00 2001 From: qiaolongfei Date: Wed, 23 Aug 2017 09:42:58 -0700 Subject: [PATCH 0194/2018] reduce pass num to 1 --- python/paddle/v2/framework/tests/mnist.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/python/paddle/v2/framework/tests/mnist.py b/python/paddle/v2/framework/tests/mnist.py index 886e99610..eefd5709a 100644 --- a/python/paddle/v2/framework/tests/mnist.py +++ b/python/paddle/v2/framework/tests/mnist.py @@ -222,7 +222,7 @@ def test(cost_name): sum(error) / float(len(error)))) -PASS_NUM = 1000 +PASS_NUM = 1 for pass_id in range(PASS_NUM): batch_id = 0 -- GitLab From 37cd8165b3089c8e4a6ce743f5e0ee8c029ba46b Mon Sep 17 00:00:00 2001 From: qiaolongfei Date: Wed, 23 Aug 2017 10:56:56 -0700 Subject: [PATCH 0195/2018] change 128 to BATCH_SIZE --- python/paddle/v2/framework/tests/mnist.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/python/paddle/v2/framework/tests/mnist.py b/python/paddle/v2/framework/tests/mnist.py index eefd5709a..e878bfa4e 100644 --- a/python/paddle/v2/framework/tests/mnist.py +++ b/python/paddle/v2/framework/tests/mnist.py @@ -205,7 +205,8 @@ train_reader = paddle.batch( def test(cost_name): - test_reader = paddle.batch(paddle.dataset.mnist.test(), batch_size=128) + test_reader = paddle.batch( + paddle.dataset.mnist.test(), batch_size=BATCH_SIZE) cost = [] error = [] for data in test_reader(): -- GitLab From 72d29186bb426efc4eb78d9d6b6e605c7e2ce56c Mon Sep 17 00:00:00 2001 From: qiaolongfei Date: Wed, 23 Aug 2017 11:07:15 -0700 Subject: [PATCH 0196/2018] reduce some compile warning --- paddle/gserver/gradientmachines/RecurrentGradientMachine.cpp | 4 ++-- paddle/operators/net_op_test.cc | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/paddle/gserver/gradientmachines/RecurrentGradientMachine.cpp b/paddle/gserver/gradientmachines/RecurrentGradientMachine.cpp index 1829f72a8..d00d408ab 100644 --- a/paddle/gserver/gradientmachines/RecurrentGradientMachine.cpp +++ b/paddle/gserver/gradientmachines/RecurrentGradientMachine.cpp @@ -1399,8 +1399,8 @@ void RecurrentGradientMachine::createDataOutlinkCopySizeInfo( getBeamSize() > 1 ? finalPaths_.size() : finalPaths_[0].size()); int* starts = inputSeqStartPos->getMutableData(false); int seqId = 0; - for (int i = 0; i < finalPaths_.size(); ++i) { - for (int j = 0; j < finalPaths_[i].size(); ++j) { + for (size_t i = 0; i < finalPaths_.size(); ++i) { + for (size_t j = 0; j < finalPaths_[i].size(); ++j) { copySize[seqId] = getBeamSize() > 1 ? starts[i + 1] - starts[i] : starts[j + 1] - starts[j]; batchMachineStartPos_[seqId + 1] = diff --git a/paddle/operators/net_op_test.cc b/paddle/operators/net_op_test.cc index 99019754a..f2e98ee7a 100644 --- a/paddle/operators/net_op_test.cc +++ b/paddle/operators/net_op_test.cc @@ -79,7 +79,7 @@ TEST(NetOp, Clone) { ASSERT_NE(new_net_op, nullptr); ASSERT_TRUE(new_net_op->IsNetOp()); auto* new_net = static_cast(new_net_op.get()); - ASSERT_EQ(2, new_net->ops_.size()); + ASSERT_EQ(2UL, new_net->ops_.size()); ASSERT_EQ(new_net->ops_[0]->Type(), "empty"); ASSERT_EQ(new_net->ops_[1]->Type(), "empty2"); } -- GitLab From 3b6069b7f511e41a00551cc3141385d9eb464f51 Mon Sep 17 00:00:00 2001 From: haonanyu Date: Wed, 23 Aug 2017 12:16:36 -0700 Subject: [PATCH 0197/2018] fix ids reverse order in fillGenOutputs --- paddle/gserver/gradientmachines/RecurrentGradientMachine.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paddle/gserver/gradientmachines/RecurrentGradientMachine.cpp b/paddle/gserver/gradientmachines/RecurrentGradientMachine.cpp index 1829f72a8..56c3951cf 100644 --- a/paddle/gserver/gradientmachines/RecurrentGradientMachine.cpp +++ b/paddle/gserver/gradientmachines/RecurrentGradientMachine.cpp @@ -1344,7 +1344,7 @@ void RecurrentGradientMachine::fillGenOutputs() { CHECK(!finalPaths_[i].empty()); Path& path = finalPaths_[i][0]; generator_.ids.insert( - generator_.ids.begin(), path.ids.begin(), path.ids.end()); + generator_.ids.end(), path.ids.begin(), path.ids.end()); starts[i + 1] = starts[i] + path.ids.size(); } } -- GitLab From da7a1f2f6c355b1bcdc0bd88e644f027d70f75d8 Mon Sep 17 00:00:00 2001 From: Helin Wang Date: Wed, 23 Aug 2017 21:30:08 +0000 Subject: [PATCH 0198/2018] master client: retry connecting to etcd --- go/master/client.go | 28 +++++++++++++++++++++------- 1 file changed, 21 insertions(+), 7 deletions(-) diff --git a/go/master/client.go b/go/master/client.go index 62801b9b7..9344c6f0a 100644 --- a/go/master/client.go +++ b/go/master/client.go @@ -60,13 +60,30 @@ func WithAddr(addr string) func(c *Client) error { } } +func retry(f func() error, dur time.Duration, count int) error { + err := f() + if err != nil { + if count > 0 { + return retry(f, dur, count-1) + } + return err + } + return nil +} + // WithEtcd sets the client to use etcd for master discovery. func WithEtcd(endpoints []string, timeout time.Duration) func(*Client) error { return func(c *Client) error { - cli, err := clientv3.New(clientv3.Config{ - Endpoints: endpoints, - DialTimeout: timeout, - }) + var cli *clientv3.Client + f := func() error { + var err error + cli, err = clientv3.New(clientv3.Config{ + Endpoints: endpoints, + DialTimeout: timeout, + }) + return err + } + err := retry(f, time.Second, 10) if err != nil { return err } @@ -101,9 +118,6 @@ func NewClient(opts ...func(*Client) error) (*Client, error) { } } c.ch = make(chan record, c.bufSize) - // FIXME: connection is created asyncrosly in monitorMaster go routine, - // ensure the connection is ready for use before calling c.addClient. - time.Sleep(time.Second) return c, nil } -- GitLab From 5270585e107b16dc527ada329dddf6fc44714a35 Mon Sep 17 00:00:00 2001 From: Helin Wang Date: Wed, 23 Aug 2017 21:38:43 +0000 Subject: [PATCH 0199/2018] fix according to comment --- go/master/client.go | 1 + 1 file changed, 1 insertion(+) diff --git a/go/master/client.go b/go/master/client.go index 9344c6f0a..199690d48 100644 --- a/go/master/client.go +++ b/go/master/client.go @@ -64,6 +64,7 @@ func retry(f func() error, dur time.Duration, count int) error { err := f() if err != nil { if count > 0 { + time.Sleep(dur) return retry(f, dur, count-1) } return err -- GitLab From 05176bd1bb5af94bfbabbb524ed9e65448134e39 Mon Sep 17 00:00:00 2001 From: Helin Wang Date: Thu, 24 Aug 2017 01:23:27 +0000 Subject: [PATCH 0200/2018] master server will wait etcd forever --- go/master/client.go | 23 ++++++++--------------- 1 file changed, 8 insertions(+), 15 deletions(-) diff --git a/go/master/client.go b/go/master/client.go index 199690d48..f04cf50ce 100644 --- a/go/master/client.go +++ b/go/master/client.go @@ -60,18 +60,6 @@ func WithAddr(addr string) func(c *Client) error { } } -func retry(f func() error, dur time.Duration, count int) error { - err := f() - if err != nil { - if count > 0 { - time.Sleep(dur) - return retry(f, dur, count-1) - } - return err - } - return nil -} - // WithEtcd sets the client to use etcd for master discovery. func WithEtcd(endpoints []string, timeout time.Duration) func(*Client) error { return func(c *Client) error { @@ -84,9 +72,14 @@ func WithEtcd(endpoints []string, timeout time.Duration) func(*Client) error { }) return err } - err := retry(f, time.Second, 10) - if err != nil { - return err + for { + err := f() + if err != nil { + log.Warningln(err) + } else { + break + } + time.Sleep(time.Second) } ch := make(chan string, 1) -- GitLab From 161a15f055c2cbe1937522a7a11dbdeb31f1a774 Mon Sep 17 00:00:00 2001 From: zchen0211 Date: Thu, 24 Aug 2017 03:11:54 +0000 Subject: [PATCH 0201/2018] gradient check --- python/paddle/v2/framework/tests/gradient_checker.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/python/paddle/v2/framework/tests/gradient_checker.py b/python/paddle/v2/framework/tests/gradient_checker.py index c22c6f883..d7809e52f 100644 --- a/python/paddle/v2/framework/tests/gradient_checker.py +++ b/python/paddle/v2/framework/tests/gradient_checker.py @@ -86,6 +86,9 @@ def get_numeric_gradient(op, # we only compute gradient of one element each time. # we use a for loop to compute the gradient of every element. for i in xrange(tensor_size): + for var_name in input_values: + tensor_ = local_scope.find_var(var_name).get_tensor() + tensor_.set(numpy.copy(input_values[var_name]), core.CPUPlace()) # get one input element throw it's index i. origin = tensor_to_check.get_float_element(i) @@ -95,6 +98,9 @@ def get_numeric_gradient(op, y_pos = get_output() # plus delta to this element, run op and get the sum of the result tensor. + for var_name in input_values: + tensor_ = local_scope.find_var(var_name).get_tensor() + tensor_.set(numpy.copy(input_values[var_name]), core.CPUPlace()) x_neg = origin - delta tensor_to_check.set_float_element(i, x_neg) y_neg = get_output() -- GitLab From 0e300f9bf04ba459dbef93af9537f847cebbcd27 Mon Sep 17 00:00:00 2001 From: qiaolongfei Date: Wed, 23 Aug 2017 20:14:54 -0700 Subject: [PATCH 0202/2018] use init_net and random_op to initialize parameter --- python/paddle/v2/framework/tests/mnist.py | 54 +++++++++++------------ 1 file changed, 27 insertions(+), 27 deletions(-) diff --git a/python/paddle/v2/framework/tests/mnist.py b/python/paddle/v2/framework/tests/mnist.py index e878bfa4e..0c27ce3e3 100644 --- a/python/paddle/v2/framework/tests/mnist.py +++ b/python/paddle/v2/framework/tests/mnist.py @@ -9,11 +9,8 @@ scope = core.Scope() place = core.CPUPlace() dev_ctx = core.DeviceContext.create(place) -# init_net = core.Net.create() -forward_network = core.Net.create() - -# should be init after forward_op is constructed -# backward_net = core.Operator.backward(forward_net, set()) +init_net = core.Net.create() +forward_net = core.Net.create() backward_net = None optimize_net = core.Net.create() @@ -64,13 +61,12 @@ def sgd_optimizer(net, param_name, learning_rate=0.005): # should use operator and add these to the init_network -def init_param(param_name, dims): - var = scope.new_var(param_name) - tensor = var.get_tensor() - tensor.set_dims(dims) - data = numpy.random.uniform( - low=-0.5, high=0.5, size=tensor.shape()).astype("float32") - tensor.set(data, place) +def init_param(net, param_name, dims): + scope.new_var(param_name) + op = Operator( + "uniform_random", Out=param_name, dims=dims, min=-0.5, max=0.5, seed=10) + op.infer_shape(scope) + net.append_op(op) # fc_layer @@ -96,7 +92,7 @@ def fc_layer(net, input, size, act="softmax", bias=True, param=None, name=None): input_dims = scope.find_var(input).get_tensor().get_dims() w_name = param or name + ".w" - init_param(param_name=w_name, dims=[input_dims[1], size]) + init_param(net=init_net, param_name=w_name, dims=[input_dims[1], size]) sgd_optimizer(net=optimize_net, param_name=w_name, learning_rate=0.01) pre_activation = name + ".mul.out" @@ -107,7 +103,7 @@ def fc_layer(net, input, size, act="softmax", bias=True, param=None, name=None): # create bias variable if needed if bias: bias_name = name + ".b" - init_param(param_name=bias_name, dims=[size]) + init_param(net=init_net, param_name=bias_name, dims=[size]) sgd_optimizer( net=optimize_net, param_name=bias_name, learning_rate=0.001) bias_out = name + ".rowwise_add.out" @@ -181,20 +177,22 @@ def error_rate(predict, label): images = data_layer(name='pixel', dims=[BATCH_SIZE, 784]) labels = data_layer(name='label', dims=[BATCH_SIZE]) -fc1 = fc_layer(net=forward_network, input=images, size=100, act="sigmoid") -fc2 = fc_layer(net=forward_network, input=fc1, size=100, act="sigmoid") -predict = fc_layer(net=forward_network, input=fc2, size=100, act="softmax") -cost = cross_entropy_layer(net=forward_network, input=predict, label=labels) - -forward_network.complete_add_op(True) -backward_net = create_backward_net(forward_network) +fc1 = fc_layer(net=forward_net, input=images, size=100, act="sigmoid") +fc2 = fc_layer(net=forward_net, input=fc1, size=100, act="sigmoid") +predict = fc_layer(net=forward_net, input=fc2, size=100, act="softmax") +cost = cross_entropy_layer(net=forward_net, input=predict, label=labels) + +init_net.complete_add_op(True) +forward_net.complete_add_op(True) +backward_net = create_backward_net(forward_net) optimize_net.complete_add_op(True) -print(forward_network) +print(init_net) +print(forward_net) print(backward_net) print(optimize_net) -debug_print_op(forward_network) +debug_print_op(forward_net) debug_print_op(backward_net) debug_print_op(optimize_net) @@ -215,8 +213,8 @@ def test(cost_name): feed_data(images, image_data) feed_data(labels, label_data) - forward_network.infer_shape(scope) - forward_network.run(scope, dev_ctx) + forward_net.infer_shape(scope) + forward_net.run(scope, dev_ctx) cost.append(mean_cost(cost_name)) error.append(error_rate(predict, "label")) print("cost=" + str(sum(cost) / float(len(cost))) + " error_rate=" + str( @@ -224,6 +222,8 @@ def test(cost_name): PASS_NUM = 1 + +init_net.run(scope, dev_ctx) for pass_id in range(PASS_NUM): batch_id = 0 @@ -233,8 +233,8 @@ for pass_id in range(PASS_NUM): feed_data(images, image_data) feed_data(labels, label_data) - forward_network.infer_shape(scope) - forward_network.run(scope, dev_ctx) + forward_net.infer_shape(scope) + forward_net.run(scope, dev_ctx) set_cost(cost) backward_net.infer_shape(scope) backward_net.run(scope, dev_ctx) -- GitLab From 0ee18a86d18b4d4506c63e13b2953c9153c27f8d Mon Sep 17 00:00:00 2001 From: wanghaoshuang Date: Thu, 24 Aug 2017 11:50:35 +0800 Subject: [PATCH 0203/2018] Fix doc. --- python/paddle/trainer_config_helpers/layers.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py index b3568cc25..f323b017c 100755 --- a/python/paddle/trainer_config_helpers/layers.py +++ b/python/paddle/trainer_config_helpers/layers.py @@ -2418,8 +2418,8 @@ def img_conv_layer(input, :param dilation: The x dimension of the dilation. Or input a tuple for two image dimension :type dilation: int|tuple|list - :param padding_y: The y dimension of the dilation. - :type padding_y: int + :param dilation_y: The y dimension of the dilation. + :type dilation_y: int :param bias_attr: Convolution bias attribute. None means default bias. False means no bias. :type bias_attr: ParameterAttribute|False -- GitLab From 12864f142073b4a280120e4d9b3abe4e2483ca32 Mon Sep 17 00:00:00 2001 From: qiaolongfei Date: Wed, 23 Aug 2017 22:51:35 -0700 Subject: [PATCH 0204/2018] register rowwise add gpu kernel --- paddle/operators/rowwise_add_op.cu | 3 +++ 1 file changed, 3 insertions(+) diff --git a/paddle/operators/rowwise_add_op.cu b/paddle/operators/rowwise_add_op.cu index cbc61ad3e..4a57f64c8 100644 --- a/paddle/operators/rowwise_add_op.cu +++ b/paddle/operators/rowwise_add_op.cu @@ -18,3 +18,6 @@ namespace ops = paddle::operators; REGISTER_OP_GPU_KERNEL( rowwise_add, ops::RowwiseAddKernel); +REGISTER_OP_GPU_KERNEL( + rowwise_add_grad, + ops::RowwiseAddGradKernel); -- GitLab From 3648165b63bd5331d1809cba896176e4af0a9ff2 Mon Sep 17 00:00:00 2001 From: qiaolongfei Date: Wed, 23 Aug 2017 23:00:45 -0700 Subject: [PATCH 0205/2018] add gpu support --- python/paddle/v2/framework/tests/mnist.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/python/paddle/v2/framework/tests/mnist.py b/python/paddle/v2/framework/tests/mnist.py index 0c27ce3e3..d9941023f 100644 --- a/python/paddle/v2/framework/tests/mnist.py +++ b/python/paddle/v2/framework/tests/mnist.py @@ -7,6 +7,8 @@ BATCH_SIZE = 100 scope = core.Scope() place = core.CPUPlace() +# if you want to test GPU training, you can use gpu place +# place = core.GPUPlace(0) dev_ctx = core.DeviceContext.create(place) init_net = core.Net.create() -- GitLab From 625b15355a16fa42476e7dbd166b77e092dcb97f Mon Sep 17 00:00:00 2001 From: qiaolongfei Date: Wed, 23 Aug 2017 23:56:55 -0700 Subject: [PATCH 0206/2018] optimize code --- python/paddle/v2/framework/tests/mnist.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/python/paddle/v2/framework/tests/mnist.py b/python/paddle/v2/framework/tests/mnist.py index d9941023f..9a0b10985 100644 --- a/python/paddle/v2/framework/tests/mnist.py +++ b/python/paddle/v2/framework/tests/mnist.py @@ -17,14 +17,14 @@ backward_net = None optimize_net = core.Net.create() -def atom_id(): +def atomic_id(): id = 0 while True: yield id id += 1 -uniq_id = atom_id().next +uniq_id = atomic_id().next def data_layer(name, dims): @@ -164,7 +164,7 @@ def set_cost(cost): cost_grad.set(numpy.ones(cost_shape).astype("float32"), place) -def mean_cost(cost): +def get_cost_mean(cost): cost_data = numpy.array(scope.find_var(cost).get_tensor()) return cost_data.sum() / len(cost_data) @@ -217,7 +217,7 @@ def test(cost_name): forward_net.infer_shape(scope) forward_net.run(scope, dev_ctx) - cost.append(mean_cost(cost_name)) + cost.append(get_cost_mean(cost_name)) error.append(error_rate(predict, "label")) print("cost=" + str(sum(cost) / float(len(cost))) + " error_rate=" + str( sum(error) / float(len(error)))) -- GitLab From 790379f1477835badbc35c563623d13ec5fd2b7a Mon Sep 17 00:00:00 2001 From: chengduoZH Date: Wed, 23 Aug 2017 14:11:30 +0800 Subject: [PATCH 0207/2018] fix above comments --- paddle/cuda/include/hl_cnn.h | 106 ------------------ paddle/cuda/include/stub/hl_cnn_stub.h | 6 +- paddle/gserver/layers/Pool3DLayer.cpp | 71 +++++------- paddle/gserver/layers/Pool3DLayer.h | 1 + paddle/math/Matrix.cpp | 131 +++++++++++----------- paddle/math/Matrix.h | 135 +++++++++++------------ paddle/math/tests/test_matrixCompare.cpp | 97 ++++++++-------- 7 files changed, 208 insertions(+), 339 deletions(-) diff --git a/paddle/cuda/include/hl_cnn.h b/paddle/cuda/include/hl_cnn.h index e9687d0a5..84f1c8435 100644 --- a/paddle/cuda/include/hl_cnn.h +++ b/paddle/cuda/include/hl_cnn.h @@ -173,31 +173,6 @@ extern void hl_avgpool_backward(const int frameCnt, real* backGrad, const int outStride); -/** - * @brief Maximum pool forward. - * - * @param[in] frameCnt batch size of input image. - * @param[in] inputData input data. - * @param[in] channels number of channel. - * @param[in] depth image depth. - * @param[in] height image height. - * @param[in] width image width. - * @param[in] pooledD output image depth. - * @param[in] pooledH output image height. - * @param[in] pooledW output image width. - * @param[in] sizeZ depth of pooling window. - * @param[in] sizeY height of pooling window. - * @param[in] sizeX width of pooling window. - * @param[in] strideD pooling stride depth. - * @param[in] strideH pooling stride height. - * @param[in] strideW pooling stride width. - * @param[in] paddingD padding depth. - * @param[in] paddingH padding height. - * @param[in] paddingW padding width. - * @param[out] tgtData output data. - * @param[in] tgtStride stride between output data samples. - * - */ extern void hl_maxpool3D_forward(const int frameCnt, const real* inputData, const int channels, @@ -219,35 +194,6 @@ extern void hl_maxpool3D_forward(const int frameCnt, real* tgtData, const int tgtStride); -/** - * @brief Maximum pool backward. - * - * @param[in] frameCnt batch size of input image. - * @param[in] inputData input data. - * @param[out] outData output data. - * @param[out] outGrad output grad data. - * @param[in] channels number of channel. - * @param[in] depth image depth. - * @param[in] height image height. - * @param[in] width image width. - * @param[in] pooledD output image depth. - * @param[in] pooledH output image height. - * @param[in] pooledW output image width. - * @param[in] sizeZ depth of pooling window. - * @param[in] sizeY height of pooling window. - * @param[in] sizeX width of pooling window. - * @param[in] strideD pooling stride depth. - * @param[in] strideH pooling stride height. - * @param[in] strideW pooling stride width. - * @param[in] scaleA scale. - * @param[in] scaleB scale. - * @param[in] paddingD padding depth. - * @param[in] paddingH padding height. - * @param[in] paddingW padding width. - * @param[out] targetGrad output grad. - * @param[in] outStride stride between output data samples. - * - */ extern void hl_maxpool3D_backward(const int frameCnt, const real* inputData, const real* outData, @@ -273,31 +219,6 @@ extern void hl_maxpool3D_backward(const int frameCnt, real* targetGrad, const int outStride); -/** - * @brief Averge pool forward. - * - * @param[in] frameCnt batch size of input image. - * @param[in] inputData input data. - * @param[in] channels number of channel. - * @param[in] depth image depth. - * @param[in] height image height. - * @param[in] width image width. - * @param[in] pooledD output image depth. - * @param[in] pooledH output image height. - * @param[in] pooledW output image width. - * @param[in] sizeZ depth of pooling window. - * @param[in] sizeY height of pooling window. - * @param[in] sizeX width of pooling window. - * @param[in] strideD pooling stride depth. - * @param[in] strideH pooling stride height. - * @param[in] strideW pooling stride width. - * @param[in] paddingD padding depth. - * @param[in] paddingH padding height. - * @param[in] paddingW padding width. - * @param[out] tgtData output data. - * @param[in] tgtStride stride between output data samples. - * - */ extern void hl_avgpool3D_forward(const int frameCnt, const real* inputData, const int channels, @@ -319,33 +240,6 @@ extern void hl_avgpool3D_forward(const int frameCnt, real* tgtData, const int tgtStride); -/** - * @brief Maximum pool backward. - * - * @param[in] frameCnt batch size of input image. - * @param[in] outGrad output grad data. - * @param[in] channels number of channel. - * @param[in] depth image depth. - * @param[in] height image height. - * @param[in] width image width. - * @param[in] pooledD output image depth. - * @param[in] pooledH output image height. - * @param[in] pooledW output image width. - * @param[in] sizeZ depth of pooling window. - * @param[in] sizeY height of pooling window. - * @param[in] sizeX width of pooling window. - * @param[in] strideD pooling stride depth. - * @param[in] strideH pooling stride height. - * @param[in] strideW pooling stride width. - * @param[in] paddingD padding depth. - * @param[in] paddingH padding height. - * @param[in] paddingW padding width. - * @param[in] scaleA scale. - * @param[in] scaleB scale. - * @param[out] backGrad output grad. - * @param[in] outStride stride between output data samples. - * - */ extern void hl_avgpool3D_backward(const int frameCnt, const real* outGrad, const int channels, diff --git a/paddle/cuda/include/stub/hl_cnn_stub.h b/paddle/cuda/include/stub/hl_cnn_stub.h index 28f61781b..6750f537b 100644 --- a/paddle/cuda/include/stub/hl_cnn_stub.h +++ b/paddle/cuda/include/stub/hl_cnn_stub.h @@ -169,9 +169,9 @@ inline void hl_avgpool3D_backward(const int frameCnt, const int strideD, const int strideH, const int strideW, - int paddingD, - int paddingH, - int paddingW, + const int paddingD, + const int paddingH, + const int paddingW, real scaleA, real scaleB, real* backGrad, diff --git a/paddle/gserver/layers/Pool3DLayer.cpp b/paddle/gserver/layers/Pool3DLayer.cpp index fc6b9bdd2..40a913ebf 100644 --- a/paddle/gserver/layers/Pool3DLayer.cpp +++ b/paddle/gserver/layers/Pool3DLayer.cpp @@ -58,30 +58,9 @@ size_t Pool3DLayer::getSize() { CHECK_EQ(inputLayers_.size(), 1UL); size_t layerSize = 0; - // imgSizeD_ = inputLayers_[0]->getOutput().getFrameDepth(); - // imgSizeH_ = inputLayers_[0]->getOutput().getFrameHeight(); - // imgSizeW_ = inputLayers_[0]->getOutput().getFrameWidth(); - if (imgSizeH_ == 0) { - // imgSizeH_ = imgSizeY_; - } - if (imgSizeW_ == 0) { - // imgSizeW_ = imgSize_; - } - outputD_ = outputSize(imgSizeD_, - sizeZ_, - paddingD_, - strideD_, - /* caffeMode */ false); - outputH_ = outputSize(imgSizeH_, - sizeY_, - paddingH_, - strideH_, - /* caffeMode */ false); - outputW_ = outputSize(imgSizeW_, - sizeX_, - paddingW_, - strideW_, - /* caffeMode */ false); + outputD_ = outputSize(imgSizeD_, sizeZ_, paddingD_, strideD_, false); + outputH_ = outputSize(imgSizeH_, sizeY_, paddingH_, strideH_, false); + outputW_ = outputSize(imgSizeW_, sizeX_, paddingW_, strideW_, false); layerSize = outputD_ * outputH_ * outputW_ * channels_; getOutput().setFrameHeight(outputH_); @@ -100,37 +79,37 @@ void Pool3DLayer::forward(PassType passType) { if (poolType_ == "avg") { outMat->avgPool3DForward(*inMat, + channels_, imgSizeD_, imgSizeH_, imgSizeW_, - channels_, + outputD_, + outputH_, + outputW_, sizeZ_, sizeY_, sizeX_, strideD_, strideH_, strideW_, - outputD_, - outputH_, - outputW_, paddingD_, paddingH_, paddingW_); } else if (poolType_ == "max") { outMat->maxPool3DForward(*inMat, + channels_, imgSizeD_, imgSizeH_, imgSizeW_, - channels_, + outputD_, + outputH_, + outputW_, sizeZ_, sizeY_, sizeX_, strideD_, strideH_, strideW_, - outputD_, - outputH_, - outputW_, paddingD_, paddingH_, paddingW_); @@ -155,41 +134,41 @@ void Pool3DLayer::backward(const UpdateCallback& callback) { imgSizeD_, imgSizeH_, imgSizeW_, + outputD_, + outputH_, + outputW_, sizeZ_, sizeY_, sizeZ_, strideD_, strideH_, strideW_, - outputD_, - outputH_, - outputW_, - 1, - 1, paddingD_, paddingH_, - paddingW_); + paddingW_, + 1.0, + 1.0); } else if (poolType_ == "max") { inGradMat->maxPool3DBackward(*inMat, + *outGradMat, + *outMat, imgSizeD_, imgSizeH_, imgSizeW_, - *outGradMat, - *outMat, + outputD_, + outputH_, + outputW_, sizeZ_, sizeY_, sizeZ_, strideD_, strideH_, strideW_, - outputD_, - outputH_, - outputW_, - 1, - 1, paddingD_, paddingH_, - paddingW_); + paddingW_, + 1.0, + 1.0); } else { LOG(FATAL) << "Unknown pool type: " << poolType_; } diff --git a/paddle/gserver/layers/Pool3DLayer.h b/paddle/gserver/layers/Pool3DLayer.h index afc65ac2b..8329a02f5 100644 --- a/paddle/gserver/layers/Pool3DLayer.h +++ b/paddle/gserver/layers/Pool3DLayer.h @@ -44,5 +44,6 @@ protected: int imgSizeW_, imgSizeH_, imgSizeD_; int outputW_, outputH_, outputD_; std::string poolType_; + MatrixPtr maxPoolIdx_; }; } // namespace paddle diff --git a/paddle/math/Matrix.cpp b/paddle/math/Matrix.cpp index e7f1489b8..4f9216896 100644 --- a/paddle/math/Matrix.cpp +++ b/paddle/math/Matrix.cpp @@ -1191,23 +1191,23 @@ void GpuMatrix::avgPoolBackward(Matrix& outGrad, } void GpuMatrix::maxPool3DForward(Matrix& inputMat, + size_t channels, size_t imgSizeD, size_t imgSizeH, size_t imgSizeW, - size_t channels, + size_t outputD, + size_t outputH, + size_t outputW, size_t sizeZ, size_t sizeY, size_t sizeX, size_t strideD, size_t strideH, size_t strideW, - size_t outputD, - size_t outputH, - size_t outputW, size_t paddingD, size_t paddingH, size_t paddingW) { - CHECK(inputMat.useGpu_ == true) << "Matrix type are not equal"; + CHECK(inputMat.useGpu_) << "Matrix type are not correct"; real* inputData = inputMat.getData(); size_t num = inputMat.getHeight(); @@ -1236,32 +1236,31 @@ void GpuMatrix::maxPool3DForward(Matrix& inputMat, paddingD, paddingH, paddingW, - data_, + getData(), getStride()); } void GpuMatrix::maxPool3DBackward(Matrix& inputMat, + Matrix& outGrad, + Matrix& outV, size_t imgSizeD, size_t imgSizeH, size_t imgSizeW, - Matrix& outGrad, - Matrix& outV, + size_t outputD, + size_t outputH, + size_t outputW, size_t sizeZ, size_t sizeY, size_t sizeX, size_t strideD, size_t strideH, size_t strideW, - size_t outputD, - size_t outputH, - size_t outputW, - real scaleTargets, - real scaleOutput, size_t paddingD, size_t paddingH, - size_t paddingW) { - CHECK(inputMat.useGpu_ == true && outGrad.useGpu_ == true && - outV.useGpu_ == true) + size_t paddingW, + real scaleTargets, + real scaleOutput) { + CHECK(inputMat.useGpu_ && outGrad.useGpu_ && outV.useGpu_) << "Matrix type are not equal"; real* inputData = inputMat.getData(); @@ -1300,28 +1299,28 @@ void GpuMatrix::maxPool3DBackward(Matrix& inputMat, paddingW, scaleTargets, scaleOutput, - data_, + getData(), outGrad.getStride()); } void GpuMatrix::avgPool3DForward(Matrix& inputMat, + size_t channels, size_t imgSizeD, size_t imgSizeH, size_t imgSizeW, - size_t channels, + size_t outputD, + size_t outputH, + size_t outputW, size_t sizeZ, size_t sizeY, size_t sizeX, size_t strideD, size_t strideH, size_t strideW, - size_t outputD, - size_t outputH, - size_t outputW, size_t paddingD, size_t paddingH, size_t paddingW) { - CHECK(inputMat.useGpu_ == true) << "Matrix type are not equal"; + CHECK(inputMat.useGpu_) << "Matrix type are not equal"; real* inputData = inputMat.getData(); size_t frameNum = inputMat.getHeight(); @@ -1350,7 +1349,7 @@ void GpuMatrix::avgPool3DForward(Matrix& inputMat, paddingD, paddingH, paddingW, - data_, + getData(), getStride()); } @@ -1358,21 +1357,21 @@ void GpuMatrix::avgPool3DBackward(Matrix& outGrad, size_t imgSizeD, size_t imgSizeH, size_t imgSizeW, + size_t outputD, + size_t outputH, + size_t outputW, size_t sizeZ, size_t sizeY, size_t sizeX, size_t strideD, size_t strideH, size_t strideW, - size_t outputD, - size_t outputH, - size_t outputW, - real scaleTargets, - real scaleOutput, size_t paddingD, size_t paddingH, - size_t paddingW) { - CHECK(outGrad.useGpu_ == true) << "Matrix type are not equal"; + size_t paddingW, + real scaleTargets, + real scaleOutput) { + CHECK(outGrad.useGpu_) << "Matrix type are not equal"; real* outDiff = outGrad.getData(); size_t frameNum = outGrad.getHeight(); @@ -1404,7 +1403,7 @@ void GpuMatrix::avgPool3DBackward(Matrix& outGrad, paddingW, scaleTargets, scaleOutput, - data_, + getData(), outGrad.getStride()); } @@ -2149,24 +2148,24 @@ void CpuMatrix::avgPoolBackward(Matrix& input, } void CpuMatrix::maxPool3DForward(Matrix& inputMat, + size_t channels, size_t imgSizeD, size_t imgSizeH, size_t imgSizeW, - size_t channels, + size_t outputD, + size_t outputH, + size_t outputW, size_t sizeZ, size_t sizeY, size_t sizeX, size_t strideD, size_t strideH, size_t strideW, - size_t outputD, - size_t outputH, - size_t outputW, size_t paddingD, size_t paddingH, size_t paddingW) { real* inputData = inputMat.getData(); - real* outData = data_; + real* outData = getData(); size_t num = inputMat.getHeight(); size_t inWidth = imgSizeW; size_t inHeight = imgSizeH; @@ -2186,7 +2185,7 @@ void CpuMatrix::maxPool3DForward(Matrix& inputMat, /* pool max one by one */ for (size_t n = 0; n < num; ++n) { // frame by frame if (!isContiguous()) { - outData = data_ + n * outStride; + outData = getData() + n * outStride; } for (size_t c = 0; c < channels; ++c) { // channel by channel for (size_t pd = 0; pd < outputD; ++pd) { @@ -2201,15 +2200,18 @@ void CpuMatrix::maxPool3DForward(Matrix& inputMat, dstart = std::max(dstart, 0); hstart = std::max(hstart, 0); wstart = std::max(wstart, 0); + real maxOutData = outData[(pd * outputH + ph) * outputW + pw]; for (int d = dstart; d < dend; ++d) { for (int h = hstart; h < hend; ++h) { for (int w = wstart; w < wend; ++w) { - outData[(pd * outputH + ph) * outputW + pw] = - std::max(outData[(pd * outputH + ph) * outputW + pw], - inputData[(d * inHeight + h) * inWidth + w]); + if (maxOutData < + inputData[(d * inHeight + h) * inWidth + w]) { + maxOutData = inputData[(d * inHeight + h) * inWidth + w]; + } } } } + outData[(pd * outputH + ph) * outputW + pw] = maxOutData; } } } @@ -2221,25 +2223,25 @@ void CpuMatrix::maxPool3DForward(Matrix& inputMat, } void CpuMatrix::maxPool3DBackward(Matrix& image, + Matrix& outGrad, + Matrix& outV, size_t imgSizeD, size_t imgSizeH, size_t imgSizeW, - Matrix& outGrad, - Matrix& outV, + size_t outputD, + size_t outputH, + size_t outputW, size_t sizeZ, size_t sizeY, size_t sizeX, size_t strideD, size_t strideH, size_t strideW, - size_t outputD, - size_t outputH, - size_t outputW, - real scaleTargets, - real scaleOutput, size_t paddingD, size_t paddingH, - size_t paddingW) { + size_t paddingW, + real scaleTargets, + real scaleOutput) { size_t num = image.getHeight(); size_t channels = size_t(width_ / imgSizeD / imgSizeH / imgSizeW); CHECK(image.getWidth() == imgSizeD * imgSizeH * imgSizeW * channels); @@ -2247,19 +2249,18 @@ void CpuMatrix::maxPool3DBackward(Matrix& image, CHECK(outV.getHeight() == outGrad.getHeight() && outV.getWidth() == outGrad.getWidth()); - real* tgtGrad = data_; + real* tgtGrad = getData(); real* inData = image.getData(); real* otData = outV.getData(); real* otGrad = outGrad.getData(); size_t outStride = outV.getStride(); - real* origOutData = otData; - real* origOutGrad = otGrad; + ; for (size_t n = 0; n < num; ++n) { if (!outV.isContiguous()) { - otData = origOutData + n * outStride; - otGrad = origOutGrad + n * outStride; + otData = outV.getData() + n * outStride; + otGrad = outGrad.getData() + n * outStride; } for (size_t c = 0; c < channels; ++c) { for (size_t pd = 0; pd < outputD; ++pd) { @@ -2274,7 +2275,7 @@ void CpuMatrix::maxPool3DBackward(Matrix& image, dstart = std::max(dstart, 0); hstart = std::max(hstart, 0); wstart = std::max(wstart, 0); - for (int d = 0; d < dend; ++d) { + for (int d = dstart; d < dend; ++d) { for (int h = hstart; h < hend; ++h) { for (int w = wstart; w < wend; ++w) { tgtGrad[(d * imgSizeH + h) * imgSizeW + w] = @@ -2299,19 +2300,19 @@ void CpuMatrix::maxPool3DBackward(Matrix& image, } void CpuMatrix::avgPool3DForward(Matrix& input, + size_t channels, size_t imgSizeD, size_t imgSizeH, size_t imgSizeW, - size_t channels, + size_t outputD, + size_t outputH, + size_t outputW, size_t sizeZ, size_t sizeY, size_t sizeX, size_t strideD, size_t strideH, size_t strideW, - size_t outputD, - size_t outputH, - size_t outputW, size_t paddingD, size_t paddingH, size_t paddingW) { @@ -2322,7 +2323,7 @@ void CpuMatrix::avgPool3DForward(Matrix& input, size_t inWidth = imgSizeW; CHECK(inDepth * inHeight * inWidth * channels == input.getWidth()); CHECK(outputD * outputH * outputW * channels * num == height_ * width_); - real* tgtData = data_; + real* tgtData = getData(); real* inData = input.getData(); for (size_t n = 0; n < num; ++n) { @@ -2372,20 +2373,20 @@ void CpuMatrix::avgPool3DBackward(Matrix& input, size_t imgSizeD, size_t imgSizeH, size_t imgSizeW, + size_t outputD, + size_t outputH, + size_t outputW, size_t sizeZ, size_t sizeY, size_t sizeX, size_t strideD, size_t strideH, size_t strideW, - size_t outputD, - size_t outputH, - size_t outputW, - real scaleTargets, - real scaleOutput, size_t paddingD, size_t paddingH, - size_t paddingW) { + size_t paddingW, + real scaleTargets, + real scaleOutput) { size_t num = input.getHeight(); size_t channels = input.getWidth() / outputD / outputH / outputW; CHECK(imgSizeD * imgSizeH * imgSizeW * channels == getWidth()); diff --git a/paddle/math/Matrix.h b/paddle/math/Matrix.h index f1534c5ea..dec970243 100644 --- a/paddle/math/Matrix.h +++ b/paddle/math/Matrix.h @@ -933,19 +933,19 @@ public: * in the sizeX of value */ virtual void maxPool3DForward(Matrix& inputMat, + size_t channels, size_t imgSizeD, size_t imgSizeH, size_t imgSizeW, - size_t channels, + size_t outputD, + size_t outputH, + size_t outputW, size_t sizeZ, size_t sizeY, size_t sizeX, size_t strideD, size_t strideH, size_t strideW, - size_t outputD, - size_t outputH, - size_t outputW, size_t paddingD, size_t paddingH, size_t paddingW) { @@ -953,42 +953,42 @@ public: } virtual void maxPool3DBackward(Matrix& image, + Matrix& outGrad, + Matrix& outV, size_t imgSizeD, size_t imgSizeH, size_t imgSizeW, - Matrix& outGrad, - Matrix& outV, + size_t outputD, + size_t outputH, + size_t outputW, size_t sizeZ, size_t sizeY, size_t sizeX, size_t strideD, size_t strideH, size_t strideW, - size_t outputD, - size_t outputH, - size_t outputW, - real scaleTargets, - real scaleOutput, size_t paddingD, size_t paddingH, - size_t paddingW) { + size_t paddingW, + real scaleTargets, + real scaleOutput) { LOG(FATAL) << "Not implemeted"; } virtual void avgPool3DForward(Matrix& input, + size_t channels, size_t imgSizeD, size_t imgSizeH, size_t imgSizeW, - size_t channels, + size_t outputD, + size_t outputH, + size_t outputW, size_t sizeZ, size_t sizeY, size_t sizeX, size_t strideD, size_t strideH, size_t strideW, - size_t outputD, - size_t outputH, - size_t outputW, size_t paddingD, size_t paddingH, size_t paddingW) { @@ -999,20 +999,20 @@ public: size_t imgSizeD, size_t imgSizeH, size_t imgSizeW, + size_t outputD, + size_t outputH, + size_t outputW, size_t sizeZ, size_t sizeY, size_t sizeX, size_t strideD, size_t strideH, size_t strideW, - size_t outputD, - size_t outputH, - size_t outputW, - real scaleTargets, - real scaleOutput, size_t paddingD, size_t paddingH, - size_t paddingW) { + size_t paddingW, + real scaleTargets, + real scaleOutput) { LOG(FATAL) << "Not implemeted"; } @@ -1435,60 +1435,59 @@ public: size_t paddingH, size_t paddingW); - ///////////////////////// void maxPool3DForward(Matrix& inputMat, + size_t channels, size_t imgSizeD, size_t imgSizeH, size_t imgSizeW, - size_t channels, + size_t outputD, + size_t outputH, + size_t outputW, size_t sizeZ, size_t sizeY, size_t sizeX, size_t strideD, size_t strideH, size_t strideW, - size_t outputD, - size_t outputH, - size_t outputW, size_t paddingD, size_t paddingH, size_t paddingW); void maxPool3DBackward(Matrix& image, + Matrix& outGrad, + Matrix& outV, size_t imgSizeD, size_t imgSizeH, size_t imgSizeW, - Matrix& outGrad, - Matrix& outV, + size_t outputD, + size_t outputH, + size_t outputW, size_t sizeZ, size_t sizeY, size_t sizeX, size_t strideD, size_t strideH, size_t strideW, - size_t outputD, - size_t outputH, - size_t outputW, - real scaleTargets, - real scaleOutput, size_t paddingD, size_t paddingH, - size_t paddingW); + size_t paddingW, + real scaleTargets, + real scaleOutput); void avgPool3DForward(Matrix& input, + size_t channels, size_t imgSizeD, size_t imgSizeH, size_t imgSizeW, - size_t channels, + size_t outputD, + size_t outputH, + size_t outputW, size_t sizeZ, size_t sizeY, size_t sizeX, size_t strideD, size_t strideH, size_t strideW, - size_t outputD, - size_t outputH, - size_t outputW, size_t paddingD, size_t paddingH, size_t paddingW); @@ -1497,20 +1496,20 @@ public: size_t imgSizeD, size_t imgSizeH, size_t imgSizeW, + size_t outputD, + size_t outputH, + size_t outputW, size_t sizeZ, size_t sizeY, size_t sizeX, size_t strideD, size_t strideH, size_t strideW, - size_t outputD, - size_t outputH, - size_t outputW, - real scaleTargets, - real scaleOutput, size_t paddingD, size_t paddingH, - size_t paddingW); + size_t paddingW, + real scaleTargets, + real scaleOutput); void maxSequenceForward(Matrix& input, const IVector& sequence, @@ -1670,60 +1669,60 @@ public: real scaleOutput, size_t paddingH, size_t paddingW); - ////////////////////// + void maxPool3DForward(Matrix& inputMat, + size_t channels, size_t imgSizeD, size_t imgSizeH, size_t imgSizeW, - size_t channels, + size_t outputD, + size_t outputH, + size_t outputW, size_t sizeZ, size_t sizeY, size_t sizeX, size_t strideD, size_t strideH, size_t strideW, - size_t outputD, - size_t outputH, - size_t outputW, size_t paddingD, size_t paddingH, size_t paddingW); void maxPool3DBackward(Matrix& image, + Matrix& outGrad, + Matrix& outV, size_t imgSizeD, size_t imgSizeH, size_t imgSizeW, - Matrix& outGrad, - Matrix& outV, + size_t outputD, + size_t outputH, + size_t outputW, size_t sizeZ, size_t sizeY, size_t sizeX, size_t strideD, size_t strideH, size_t strideW, - size_t outputD, - size_t outputH, - size_t outputW, - real scaleTargets, - real scaleOutput, size_t paddingD, size_t paddingH, - size_t paddingW); + size_t paddingW, + real scaleTargets, + real scaleOutput); void avgPool3DForward(Matrix& input, + size_t channels, size_t imgSizeD, size_t imgSizeH, size_t imgSizeW, - size_t channels, + size_t outputD, + size_t outputH, + size_t outputW, size_t sizeZ, size_t sizeY, size_t sizeX, size_t strideD, size_t strideH, size_t strideW, - size_t outputD, - size_t outputH, - size_t outputW, size_t paddingD, size_t paddingH, size_t paddingW); @@ -1732,20 +1731,20 @@ public: size_t imgSizeD, size_t imgSizeH, size_t imgSizeW, + size_t outputD, + size_t outputH, + size_t outputW, size_t sizeZ, size_t sizeY, size_t sizeX, size_t strideD, size_t strideH, size_t strideW, - size_t outputD, - size_t outputH, - size_t outputW, - real scaleTargets, - real scaleOutput, size_t paddingD, size_t paddingH, - size_t paddingW); + size_t paddingW, + real scaleTargets, + real scaleOutput); void maxSequenceForward(Matrix& input, const IVector& sequence, diff --git a/paddle/math/tests/test_matrixCompare.cpp b/paddle/math/tests/test_matrixCompare.cpp index 7a961d275..21ee8543c 100644 --- a/paddle/math/tests/test_matrixCompare.cpp +++ b/paddle/math/tests/test_matrixCompare.cpp @@ -1204,7 +1204,6 @@ TEST(Matrix, warpCTC) { } } -///// void testMatrixPool3D(int depth, int height, int width) { int channel = 3; int filterX = 3, filterY = 4, filterZ = 5; @@ -1226,38 +1225,37 @@ void testMatrixPool3D(int depth, int height, int width) { cpuImage->randomizeUniform(); gpuImage->copyFrom(*cpuImage); - // std::cout << "test maxPool3DForward...\n"; cpuOutput->maxPool3DForward(*cpuImage, + channel, depth, height, width, - channel, + outD, + outH, + outW, filterZ, filterY, filterX, strideZ, strideY, strideX, - outD, - outH, - outW, padZ, padY, padX); gpuOutput->maxPool3DForward(*gpuImage, + channel, depth, height, width, - channel, + outD, + outH, + outW, filterZ, filterY, filterX, strideZ, strideY, strideX, - outD, - outH, - outW, padZ, padY, padX); @@ -1265,39 +1263,38 @@ void testMatrixPool3D(int depth, int height, int width) { cpuImage->randomizeUniform(); gpuImage->copyFrom(*cpuImage); - // std::cout << "test avgPool3DForward...\n"; cpuOutput->avgPool3DForward(*cpuImage, + channel, depth, height, width, - channel, + outD, + outH, + outW, filterZ, filterY, filterX, strideZ, strideY, strideX, - outD, - outH, - outW, padZ, padY, padX); gpuOutput->avgPool3DForward(*gpuImage, + channel, depth, height, width, - channel, + outD, + outH, + outW, filterZ, filterY, filterX, strideZ, strideY, strideX, - outD, - outH, - outW, padZ, padY, padX); @@ -1306,98 +1303,96 @@ void testMatrixPool3D(int depth, int height, int width) { gpuImage->copyFrom(*cpuImage); cpuOutput->randomizeUniform(); gpuOutput->copyFrom(*cpuOutput); - // std::cout << "test avgPool3DBackward...\n"; cpuImage->avgPool3DBackward(*cpuOutput, depth, height, width, + outD, + outH, + outW, filterZ, filterY, filterX, strideZ, strideY, strideX, - outD, - outH, - outW, - 1, - 1, padZ, padY, - padX); + padX, + 1.0, + 1.0); gpuImage->avgPool3DBackward(*gpuOutput, depth, height, width, + outD, + outH, + outW, filterZ, filterY, filterX, strideZ, strideY, strideX, - outD, - outH, - outW, - 1, - 1, padZ, padY, - padX); + padX, + 1.0, + 1.0); TensorCheckErr(*cpuImage, *gpuImage); cpuImage->randomizeUniform(); gpuImage->copyFrom(*cpuImage); cpuOutput->randomizeUniform(); gpuOutput->copyFrom(*cpuOutput); - // std::cout << "test maxPool3DBackward...\n"; cpuImage->maxPool3DBackward(*cpuImage, + *cpuOutput, + *cpuOutput, depth, height, width, - *cpuOutput, - *cpuOutput, + outD, + outH, + outW, filterZ, filterY, filterX, strideZ, strideY, strideX, - outD, - outH, - outW, - 1, - 1, padZ, padY, - padX); + padX, + 1.0, + 1.0); gpuImage->maxPool3DBackward(*gpuImage, + *gpuOutput, + *gpuOutput, depth, height, width, - *gpuOutput, - *gpuOutput, + outD, + outH, + outW, filterZ, filterY, filterX, strideZ, strideY, strideX, - outD, - outH, - outW, - 1, - 1, padZ, padY, - padX); + padX, + 1.0, + 1.0); TensorCheckErr(*cpuImage, *gpuImage); } TEST(Matrix, Pool3D) { - for (auto depth : {9, 16, 64, 128}) { - for (auto height : {9, 11, 128, 256}) { + for (auto depth : {9, 16, 64}) { + for (auto height : {9, 11, 128}) { for (auto width : {9, 32, 128}) { VLOG(3) << "depth=" << depth << " height=" << height << " width=" << width; -- GitLab From 860bf192c904627ee0446051fe97911eb11895ad Mon Sep 17 00:00:00 2001 From: chengduoZH Date: Thu, 24 Aug 2017 19:28:56 +0800 Subject: [PATCH 0208/2018] Add maxPoolIdx --- paddle/cuda/include/hl_cnn.h | 4 +- paddle/cuda/include/stub/hl_cnn_stub.h | 4 +- paddle/cuda/src/hl_cuda_cnn.cu | 73 +-- paddle/gserver/layers/Pool3DLayer.cpp | 11 +- paddle/math/Matrix.cpp | 86 ++-- paddle/math/Matrix.h | 18 +- paddle/math/tests/test_matrixCompare.cpp | 564 +++++++++++++++-------- 7 files changed, 473 insertions(+), 287 deletions(-) diff --git a/paddle/cuda/include/hl_cnn.h b/paddle/cuda/include/hl_cnn.h index 84f1c8435..6b56d9ec8 100644 --- a/paddle/cuda/include/hl_cnn.h +++ b/paddle/cuda/include/hl_cnn.h @@ -192,11 +192,10 @@ extern void hl_maxpool3D_forward(const int frameCnt, const int paddingH, const int paddingW, real* tgtData, + real* maxPoolIdxData, const int tgtStride); extern void hl_maxpool3D_backward(const int frameCnt, - const real* inputData, - const real* outData, const real* outGrad, const int channels, const int depth, @@ -217,6 +216,7 @@ extern void hl_maxpool3D_backward(const int frameCnt, real scaleA, real scaleB, real* targetGrad, + real* maxPoolIdxData, const int outStride); extern void hl_avgpool3D_forward(const int frameCnt, diff --git a/paddle/cuda/include/stub/hl_cnn_stub.h b/paddle/cuda/include/stub/hl_cnn_stub.h index 6750f537b..a76dbf0b6 100644 --- a/paddle/cuda/include/stub/hl_cnn_stub.h +++ b/paddle/cuda/include/stub/hl_cnn_stub.h @@ -106,11 +106,10 @@ inline void hl_maxpool3D_forward(const int frameCnt, const int paddingH, const int paddingW, real* tgtData, + real* maxPoolIdxData, const int tgtStride) {} inline void hl_maxpool3D_backward(const int frameCnt, - const real* inputData, - const real* outData, const real* outGrad, const int channels, const int depth, @@ -131,6 +130,7 @@ inline void hl_maxpool3D_backward(const int frameCnt, real scaleA, real scaleB, real* targetGrad, + real* maxPoolIdxData, const int outStride) {} inline void hl_avgpool3D_forward(const int frameCnt, diff --git a/paddle/cuda/src/hl_cuda_cnn.cu b/paddle/cuda/src/hl_cuda_cnn.cu index 458c34772..95440c944 100644 --- a/paddle/cuda/src/hl_cuda_cnn.cu +++ b/paddle/cuda/src/hl_cuda_cnn.cu @@ -366,10 +366,11 @@ __global__ void KeMaxPool3DForward(const int nthreads, const int strideD, const int strideH, const int strideW, - const int offsetD, - const int offsetH, - const int offsetW, + const int padD, + const int padH, + const int padW, real* tgtData, + real* maxPoolIdxData, const int tgtStride) { for (int index = blockIdx.x * blockDim.x + threadIdx.x; index < (nthreads); index += blockDim.x * gridDim.x) { @@ -378,9 +379,9 @@ __global__ void KeMaxPool3DForward(const int nthreads, int pd = (index / pooledW / pooledH) % pooledD; int c = (index / pooledW / pooledH / pooledD) % channels; int frameNum = index / pooledW / pooledH / pooledD / channels; - int dstart = pd * strideD - offsetD; - int hstart = ph * strideH - offsetH; - int wstart = pw * strideW - offsetW; + int dstart = pd * strideD - padD; + int hstart = ph * strideH - padH; + int wstart = pw * strideW - padW; int dend = min(dstart + ksizeD, depth); int hend = min(hstart + ksizeH, height); int wend = min(wstart + ksizeW, width); @@ -388,18 +389,22 @@ __global__ void KeMaxPool3DForward(const int nthreads, hstart = max(hstart, 0); wstart = max(wstart, 0); real maxval = -FLT_MAX; + int maxIdx = -1; inputData += (frameNum * channels + c) * depth * height * width; for (int d = dstart; d < dend; ++d) { for (int h = hstart; h < hend; ++h) { for (int w = wstart; w < wend; ++w) { - if (maxval < inputData[(d * height + h) * width + w]) + if (maxval < inputData[(d * height + h) * width + w]) { maxval = inputData[(d * height + h) * width + w]; + maxIdx = (d * height + h) * width + w; + } } } } int tgtIndex = index % (pooledW * pooledH * pooledD * channels) + frameNum * tgtStride; tgtData[tgtIndex] = maxval; + maxPoolIdxData[tgtIndex] = maxIdx; } } @@ -418,10 +423,11 @@ void hl_maxpool3D_forward(const int frameCnt, const int strideD, const int strideH, const int strideW, - const int paddingD, - const int paddingH, - const int paddingW, + const int padD, + const int padH, + const int padW, real* tgtData, + real* maxPoolIdxData, const int tgtStride) { int num_kernels = pooledD * pooledH * pooledW * channels * frameCnt; int blocks = (num_kernels + 1024 - 1) / 1024; @@ -443,17 +449,16 @@ void hl_maxpool3D_forward(const int frameCnt, strideD, strideH, strideW, - paddingD, - paddingH, - paddingW, + padD, + padH, + padW, tgtData, + maxPoolIdxData, tgtStride); CHECK_SYNC("hl_maxpool3D_forward failed"); } __global__ void KeMaxPool3DBackward(const int nthreads, - const real* inputData, - const real* outData, const real* outGrad, const int channels, const int depth, @@ -474,33 +479,35 @@ __global__ void KeMaxPool3DBackward(const int nthreads, real scaleA, real scaleB, real* targetGrad, + real* maxPoolIdxData, const int outStride) { for (int index = blockIdx.x * blockDim.x + threadIdx.x; index < (nthreads); index += blockDim.x * gridDim.x) { - // find out the local index - // find out the local offset - int offsetW = index % width + padW; - int offsetH = (index / width) % height + padH; - int offsetD = (index / width / height) % depth + padD; + int offsetW = index % width; + int offsetH = (index / width) % height; + int offsetD = (index / width / height) % depth; int offsetC = (index / width / height / depth) % channels; int frameNum = index / width / height / depth / channels; - int pdstart = (offsetD < sizeZ) ? 0 : (offsetD - sizeZ) / strideD + 1; - int phstart = (offsetH < sizeY) ? 0 : (offsetH - sizeY) / strideH + 1; - int pwstart = (offsetW < sizeX) ? 0 : (offsetW - sizeX) / strideW + 1; - int pdend = min(offsetD / strideD + 1, pooledD); - int phend = min(offsetH / strideH + 1, pooledH); - int pwend = min(offsetW / strideW + 1, pooledW); + int pdstart = + (offsetD + padD < sizeZ) ? 0 : (offsetD + padD - sizeZ) / strideD + 1; + int phstart = + (offsetH + padH < sizeY) ? 0 : (offsetH + padH - sizeY) / strideH + 1; + int pwstart = + (offsetW + padW < sizeX) ? 0 : (offsetW + padW - sizeX) / strideW + 1; + int pdend = min((offsetD + padD) / strideD + 1, pooledD); + int phend = min((offsetH + padH) / strideH + 1, pooledH); + int pwend = min((offsetW + padW) / strideW + 1, pooledW); real gradient = 0; - real input = inputData[index]; - - outData += ((frameNum * channels + offsetC) * pooledD * pooledH * pooledW); outGrad += ((frameNum * channels + offsetC) * pooledD * pooledH * pooledW); + maxPoolIdxData += + ((frameNum * channels + offsetC) * pooledD * pooledH * pooledW); for (int pd = pdstart; pd < pdend; ++pd) { for (int ph = phstart; ph < phend; ++ph) { for (int pw = pwstart; pw < pwend; ++pw) { - if (input == outData[(pd * pooledH + ph) * pooledW + pw]) + if (((offsetD * height + offsetH) * width + offsetW) == + maxPoolIdxData[(pd * pooledH + ph) * pooledW + pw]) gradient += outGrad[(pd * pooledH + ph) * pooledW + pw]; } } @@ -510,8 +517,6 @@ __global__ void KeMaxPool3DBackward(const int nthreads, } void hl_maxpool3D_backward(const int frameCnt, - const real* inputData, - const real* outData, const real* outGrad, const int channels, const int depth, @@ -532,13 +537,12 @@ void hl_maxpool3D_backward(const int frameCnt, real scaleA, real scaleB, real* targetGrad, + real* maxPoolIdxData, const int outStride) { int num_kernels = depth * height * width * channels * frameCnt; int blocks = (num_kernels + 1024 - 1) / 1024; KeMaxPool3DBackward<<>>(num_kernels, - inputData, - outData, outGrad, channels, depth, @@ -559,6 +563,7 @@ void hl_maxpool3D_backward(const int frameCnt, scaleA, scaleB, targetGrad, + maxPoolIdxData, outStride); CHECK_SYNC("hl_maxpool3D_backward"); } diff --git a/paddle/gserver/layers/Pool3DLayer.cpp b/paddle/gserver/layers/Pool3DLayer.cpp index 40a913ebf..199f21adb 100644 --- a/paddle/gserver/layers/Pool3DLayer.cpp +++ b/paddle/gserver/layers/Pool3DLayer.cpp @@ -72,9 +72,10 @@ size_t Pool3DLayer::getSize() { void Pool3DLayer::forward(PassType passType) { Layer::forward(passType); const MatrixPtr& inMat = inputLayers_[0]->getOutputValue(); - int batchSize = inMat->getHeight(); - int outWidth = getSize(); + size_t batchSize = inMat->getHeight(); + size_t outWidth = getSize(); resetOutput(batchSize, outWidth); + Matrix::resizeOrCreate(maxPoolIdx_, batchSize, outWidth, false, useGpu_); const MatrixPtr outMat = getOutputValue(); if (poolType_ == "avg") { @@ -97,6 +98,7 @@ void Pool3DLayer::forward(PassType passType) { paddingW_); } else if (poolType_ == "max") { outMat->maxPool3DForward(*inMat, + *maxPoolIdx_, channels_, imgSizeD_, imgSizeH_, @@ -149,9 +151,8 @@ void Pool3DLayer::backward(const UpdateCallback& callback) { 1.0, 1.0); } else if (poolType_ == "max") { - inGradMat->maxPool3DBackward(*inMat, - *outGradMat, - *outMat, + inGradMat->maxPool3DBackward(*outGradMat, + *maxPoolIdx_, imgSizeD_, imgSizeH_, imgSizeW_, diff --git a/paddle/math/Matrix.cpp b/paddle/math/Matrix.cpp index 4f9216896..54c2eae47 100644 --- a/paddle/math/Matrix.cpp +++ b/paddle/math/Matrix.cpp @@ -1191,6 +1191,7 @@ void GpuMatrix::avgPoolBackward(Matrix& outGrad, } void GpuMatrix::maxPool3DForward(Matrix& inputMat, + Matrix& maxPoolIdx, size_t channels, size_t imgSizeD, size_t imgSizeH, @@ -1210,6 +1211,7 @@ void GpuMatrix::maxPool3DForward(Matrix& inputMat, CHECK(inputMat.useGpu_) << "Matrix type are not correct"; real* inputData = inputMat.getData(); + real* maxPoolIdxData = maxPoolIdx.getData(); size_t num = inputMat.getHeight(); size_t width = imgSizeW; size_t height = imgSizeH; @@ -1237,12 +1239,12 @@ void GpuMatrix::maxPool3DForward(Matrix& inputMat, paddingH, paddingW, getData(), + maxPoolIdxData, getStride()); } -void GpuMatrix::maxPool3DBackward(Matrix& inputMat, - Matrix& outGrad, - Matrix& outV, +void GpuMatrix::maxPool3DBackward(Matrix& outGrad, + Matrix& maxPoolIdx, size_t imgSizeD, size_t imgSizeH, size_t imgSizeW, @@ -1260,26 +1262,21 @@ void GpuMatrix::maxPool3DBackward(Matrix& inputMat, size_t paddingW, real scaleTargets, real scaleOutput) { - CHECK(inputMat.useGpu_ && outGrad.useGpu_ && outV.useGpu_) - << "Matrix type are not equal"; + CHECK(outGrad.useGpu_ && maxPoolIdx.useGpu_) << "Matrix type are not equal"; - real* inputData = inputMat.getData(); - real* outData = outV.getData(); real* outDiff = outGrad.getData(); - size_t frameNum = inputMat.getHeight(); - size_t channels = outV.getWidth() / outputD / outputH / outputW; + real* maxPoolIdxData = maxPoolIdx.getData(); + size_t frameNum = getHeight(); + size_t channels = outGrad.getWidth() / outputD / outputH / outputW; size_t width = imgSizeW; size_t height = imgSizeH; size_t depth = imgSizeD; - CHECK(depth * height * width * channels == inputMat.getWidth()); - CHECK(height_ == inputMat.getHeight()); + CHECK(depth * height * width * channels == getWidth()); CHECK(width_ == depth * width * height * channels); - CHECK(outGrad.getHeight() == outV.getHeight() && - outGrad.getWidth() == outV.getWidth()); + CHECK(outGrad.getHeight() == maxPoolIdx.getHeight() && + outGrad.getWidth() == maxPoolIdx.getWidth()); hl_maxpool3D_backward(frameNum, - inputData, - outData, outDiff, channels, depth, @@ -1300,6 +1297,7 @@ void GpuMatrix::maxPool3DBackward(Matrix& inputMat, scaleTargets, scaleOutput, getData(), + maxPoolIdxData, outGrad.getStride()); } @@ -2148,6 +2146,7 @@ void CpuMatrix::avgPoolBackward(Matrix& input, } void CpuMatrix::maxPool3DForward(Matrix& inputMat, + Matrix& maxPoolIdx, size_t channels, size_t imgSizeD, size_t imgSizeH, @@ -2166,6 +2165,7 @@ void CpuMatrix::maxPool3DForward(Matrix& inputMat, size_t paddingW) { real* inputData = inputMat.getData(); real* outData = getData(); + real* maxPoolIdxData = maxPoolIdx.getData(); size_t num = inputMat.getHeight(); size_t inWidth = imgSizeW; size_t inHeight = imgSizeH; @@ -2179,6 +2179,7 @@ void CpuMatrix::maxPool3DForward(Matrix& inputMat, for (size_t i = 0; i < height_; i++) { for (size_t j = 0; j < width_; j++) { outData[(i)*outStride + j] = -(real)FLT_MAX; + maxPoolIdxData[(i)*outStride + j] = -1; } } @@ -2186,6 +2187,7 @@ void CpuMatrix::maxPool3DForward(Matrix& inputMat, for (size_t n = 0; n < num; ++n) { // frame by frame if (!isContiguous()) { outData = getData() + n * outStride; + maxPoolIdxData = maxPoolIdx.getData() + n * outStride; } for (size_t c = 0; c < channels; ++c) { // channel by channel for (size_t pd = 0; pd < outputD; ++pd) { @@ -2200,6 +2202,7 @@ void CpuMatrix::maxPool3DForward(Matrix& inputMat, dstart = std::max(dstart, 0); hstart = std::max(hstart, 0); wstart = std::max(wstart, 0); + int maxIdx = -1; real maxOutData = outData[(pd * outputH + ph) * outputW + pw]; for (int d = dstart; d < dend; ++d) { for (int h = hstart; h < hend; ++h) { @@ -2207,24 +2210,26 @@ void CpuMatrix::maxPool3DForward(Matrix& inputMat, if (maxOutData < inputData[(d * inHeight + h) * inWidth + w]) { maxOutData = inputData[(d * inHeight + h) * inWidth + w]; + maxIdx = (d * inHeight + h) * inWidth + w; } } } } outData[(pd * outputH + ph) * outputW + pw] = maxOutData; + maxPoolIdxData[(pd * outputH + ph) * outputW + pw] = maxIdx; } } } // compute offset inputData += inDepth * inHeight * inWidth; outData += outputD * outputH * outputW; + maxPoolIdxData += outputD * outputH * outputW; } } } -void CpuMatrix::maxPool3DBackward(Matrix& image, - Matrix& outGrad, - Matrix& outV, +void CpuMatrix::maxPool3DBackward(Matrix& outGrad, + Matrix& maxPoolIdx, size_t imgSizeD, size_t imgSizeH, size_t imgSizeW, @@ -2242,59 +2247,38 @@ void CpuMatrix::maxPool3DBackward(Matrix& image, size_t paddingW, real scaleTargets, real scaleOutput) { - size_t num = image.getHeight(); + size_t num = getHeight(); size_t channels = size_t(width_ / imgSizeD / imgSizeH / imgSizeW); - CHECK(image.getWidth() == imgSizeD * imgSizeH * imgSizeW * channels); - CHECK(image.getHeight() == height_ && image.getWidth() == width_); - CHECK(outV.getHeight() == outGrad.getHeight() && - outV.getWidth() == outGrad.getWidth()); + CHECK(maxPoolIdx.getHeight() == outGrad.getHeight() && + maxPoolIdx.getWidth() == outGrad.getWidth()); real* tgtGrad = getData(); - real* inData = image.getData(); - real* otData = outV.getData(); real* otGrad = outGrad.getData(); + real* maxPoolIdxData = maxPoolIdx.getData(); - size_t outStride = outV.getStride(); + size_t outStride = outGrad.getStride(); ; for (size_t n = 0; n < num; ++n) { - if (!outV.isContiguous()) { - otData = outV.getData() + n * outStride; + if (!outGrad.isContiguous()) { otGrad = outGrad.getData() + n * outStride; + maxPoolIdxData = maxPoolIdx.getData() + n * outStride; } for (size_t c = 0; c < channels; ++c) { for (size_t pd = 0; pd < outputD; ++pd) { for (size_t ph = 0; ph < outputH; ++ph) { for (size_t pw = 0; pw < outputW; ++pw) { - int dstart = pd * strideD - paddingD; - int hstart = ph * strideH - paddingH; - int wstart = pw * strideW - paddingW; - int dend = std::min(dstart + sizeZ, imgSizeD); - int hend = std::min(hstart + sizeY, imgSizeH); - int wend = std::min(wstart + sizeX, imgSizeW); - dstart = std::max(dstart, 0); - hstart = std::max(hstart, 0); - wstart = std::max(wstart, 0); - for (int d = dstart; d < dend; ++d) { - for (int h = hstart; h < hend; ++h) { - for (int w = wstart; w < wend; ++w) { - tgtGrad[(d * imgSizeH + h) * imgSizeW + w] = - scaleTargets * - tgtGrad[(d * imgSizeH + h) * imgSizeW + w] + - scaleOutput * otGrad[(pd * outputH + ph) * outputW + pw] * - (inData[(d * imgSizeH + h) * imgSizeW + w] == - otData[(pd * outputH + ph) * outputW + pw]); - } - } - } + const size_t index = (pd * outputH + ph) * outputW + pw; + const size_t tgtIdx = static_cast(maxPoolIdxData[index]); + tgtGrad[tgtIdx] = + scaleTargets * tgtGrad[tgtIdx] + scaleOutput * otGrad[index]; } } } // offset - inData += imgSizeD * imgSizeH * imgSizeW; tgtGrad += imgSizeD * imgSizeH * imgSizeW; - otData += outputD * outputH * outputW; otGrad += outputD * outputH * outputW; + maxPoolIdxData += outputD * outputH * outputW; } } } diff --git a/paddle/math/Matrix.h b/paddle/math/Matrix.h index dec970243..e674c1e9a 100644 --- a/paddle/math/Matrix.h +++ b/paddle/math/Matrix.h @@ -933,6 +933,7 @@ public: * in the sizeX of value */ virtual void maxPool3DForward(Matrix& inputMat, + Matrix& maxPoolIdx, size_t channels, size_t imgSizeD, size_t imgSizeH, @@ -952,9 +953,8 @@ public: LOG(FATAL) << "Not implemeted"; } - virtual void maxPool3DBackward(Matrix& image, - Matrix& outGrad, - Matrix& outV, + virtual void maxPool3DBackward(Matrix& outGrad, + Matrix& maxPoolIdx, size_t imgSizeD, size_t imgSizeH, size_t imgSizeW, @@ -1436,6 +1436,7 @@ public: size_t paddingW); void maxPool3DForward(Matrix& inputMat, + Matrix& maxPoolIdx, size_t channels, size_t imgSizeD, size_t imgSizeH, @@ -1453,9 +1454,8 @@ public: size_t paddingH, size_t paddingW); - void maxPool3DBackward(Matrix& image, - Matrix& outGrad, - Matrix& outV, + void maxPool3DBackward(Matrix& outGrad, + Matrix& maxPoolIdx, size_t imgSizeD, size_t imgSizeH, size_t imgSizeW, @@ -1671,6 +1671,7 @@ public: size_t paddingW); void maxPool3DForward(Matrix& inputMat, + Matrix& maxPoolIdx, size_t channels, size_t imgSizeD, size_t imgSizeH, @@ -1688,9 +1689,8 @@ public: size_t paddingH, size_t paddingW); - void maxPool3DBackward(Matrix& image, - Matrix& outGrad, - Matrix& outV, + void maxPool3DBackward(Matrix& outGrad, + Matrix& maxPoolIdx, size_t imgSizeD, size_t imgSizeH, size_t imgSizeW, diff --git a/paddle/math/tests/test_matrixCompare.cpp b/paddle/math/tests/test_matrixCompare.cpp index 21ee8543c..d7ad6f18a 100644 --- a/paddle/math/tests/test_matrixCompare.cpp +++ b/paddle/math/tests/test_matrixCompare.cpp @@ -1204,202 +1204,398 @@ TEST(Matrix, warpCTC) { } } -void testMatrixPool3D(int depth, int height, int width) { - int channel = 3; - int filterX = 3, filterY = 4, filterZ = 5; - int strideX = 2, strideY = 2, strideZ = 2; - int padX = 1, padY = 1, padZ = 1; - - MatrixPtr cpuImage = - std::make_shared(1, channel * depth * height * width); - MatrixPtr gpuImage = - std::make_shared(1, channel * depth * height * width); - - int outD = outputSize(depth, filterZ, padZ, strideZ, true); - int outH = outputSize(height, filterY, padZ, strideY, true); - int outW = outputSize(width, filterX, padZ, strideX, true); - - int colBufWidth = outD * outH * outW; - MatrixPtr cpuOutput = std::make_shared(1, channel * colBufWidth); - MatrixPtr gpuOutput = std::make_shared(1, channel * colBufWidth); - - cpuImage->randomizeUniform(); - gpuImage->copyFrom(*cpuImage); - cpuOutput->maxPool3DForward(*cpuImage, - channel, - depth, - height, - width, - outD, - outH, - outW, - filterZ, - filterY, - filterX, - strideZ, - strideY, - strideX, - padZ, - padY, - padX); - gpuOutput->maxPool3DForward(*gpuImage, - channel, - depth, - height, - width, - outD, - outH, - outW, - filterZ, - filterY, - filterX, - strideZ, - strideY, - strideX, - padZ, - padY, - padX); - TensorCheckErr(*cpuOutput, *gpuOutput); +void testMaxPool3DFwdBwd(int numSamples, + int channels, + int imgSizeD, + int imgSizeH, + int imgSizeW, + int ksizeD, + int ksizeH, + int ksizeW, + int strideD, + int strideH, + int strideW, + int padD, + int padH, + int padW) { + int outD = outputSize(imgSizeD, ksizeD, padD, strideD, true); + int outH = outputSize(imgSizeH, ksizeH, padH, strideH, true); + int outW = outputSize(imgSizeW, ksizeW, padW, strideW, true); + + int inWidth = channels * imgSizeD * imgSizeH * imgSizeW; + MatrixPtr input = CpuMatrix::create(numSamples, inWidth, false, false); + MatrixPtr inputGpu = GpuMatrix::create(numSamples, inWidth, false, true); - cpuImage->randomizeUniform(); - gpuImage->copyFrom(*cpuImage); - cpuOutput->avgPool3DForward(*cpuImage, - channel, - depth, - height, - width, - outD, - outH, - outW, - filterZ, - filterY, - filterX, - strideZ, - strideY, - strideX, - padZ, - padY, - padX); - - gpuOutput->avgPool3DForward(*gpuImage, - channel, - depth, - height, - width, - outD, - outH, - outW, - filterZ, - filterY, - filterX, - strideZ, - strideY, - strideX, - padZ, - padY, - padX); - TensorCheckErr(*cpuOutput, *gpuOutput); - cpuImage->randomizeUniform(); - gpuImage->copyFrom(*cpuImage); - cpuOutput->randomizeUniform(); - gpuOutput->copyFrom(*cpuOutput); - cpuImage->avgPool3DBackward(*cpuOutput, - depth, - height, - width, - outD, - outH, - outW, - filterZ, - filterY, - filterX, - strideZ, - strideY, - strideX, - padZ, - padY, - padX, - 1.0, - 1.0); - - gpuImage->avgPool3DBackward(*gpuOutput, - depth, - height, - width, - outD, - outH, - outW, - filterZ, - filterY, - filterX, - strideZ, - strideY, - strideX, - padZ, - padY, - padX, - 1.0, - 1.0); - TensorCheckErr(*cpuImage, *gpuImage); - - cpuImage->randomizeUniform(); - gpuImage->copyFrom(*cpuImage); - cpuOutput->randomizeUniform(); - gpuOutput->copyFrom(*cpuOutput); - cpuImage->maxPool3DBackward(*cpuImage, - *cpuOutput, - *cpuOutput, - depth, - height, - width, + int outWidth = channels * outD * outH * outW; + MatrixPtr target = CpuMatrix::create(numSamples, outWidth, false, false); + MatrixPtr targetGpu = GpuMatrix::create(numSamples, outWidth, false, true); + MatrixPtr maxIdx = CpuMatrix::create(numSamples, outWidth, false, false); + MatrixPtr maxIdxGpu = GpuMatrix::create(numSamples, outWidth, false, true); + + input->randomizeUniform(); + target->randomizeUniform(); + inputGpu->copyFrom(*input); + targetGpu->copyFrom(*target); + + target->maxPool3DForward(*input, + *maxIdx, + channels, + imgSizeD, + imgSizeH, + imgSizeW, + outD, + outH, + outW, + ksizeD, + ksizeH, + ksizeW, + strideD, + strideH, + strideW, + padD, + padH, + padW); + targetGpu->maxPool3DForward(*inputGpu, + *maxIdxGpu, + channels, + imgSizeD, + imgSizeH, + imgSizeW, outD, outH, outW, - filterZ, - filterY, - filterX, - strideZ, - strideY, - strideX, - padZ, - padY, - padX, - 1.0, - 1.0); - - gpuImage->maxPool3DBackward(*gpuImage, - *gpuOutput, - *gpuOutput, - depth, - height, - width, + ksizeD, + ksizeH, + ksizeW, + strideD, + strideH, + strideW, + padD, + padH, + padW); + MatrixPtr targetCheck = CpuMatrix::create(numSamples, outWidth, false, false); + targetCheck->copyFrom(*targetGpu); + checkMatrixEqual(target, targetCheck); + + MatrixPtr inputGrad = CpuMatrix::create(numSamples, inWidth, false, false); + MatrixPtr inputGpuGrad = GpuMatrix::create(numSamples, inWidth, false, true); + MatrixPtr targetGrad = CpuMatrix::create(numSamples, outWidth, false, false); + MatrixPtr targetGpuGrad = + GpuMatrix::create(numSamples, outWidth, false, true); + + inputGrad->randomizeUniform(); + targetGrad->randomizeUniform(); + inputGpuGrad->copyFrom(*inputGrad); + targetGpuGrad->copyFrom(*targetGrad); + + inputGrad->maxPool3DBackward(*targetGrad, + *maxIdx, + imgSizeD, + imgSizeH, + imgSizeW, + outD, + outH, + outW, + ksizeD, + ksizeH, + ksizeW, + strideD, + strideH, + strideW, + padD, + padH, + padW, + 1.0, + 1.0); + inputGpuGrad->maxPool3DBackward(*targetGpuGrad, + *maxIdxGpu, + imgSizeD, + imgSizeH, + imgSizeW, + outD, + outH, + outW, + ksizeD, + ksizeH, + ksizeW, + strideD, + strideH, + strideW, + padD, + padH, + padW, + 1.0, + 1.0); + MatrixPtr targetBwdCheck = + CpuMatrix::create(numSamples, inWidth, false, false); + targetBwdCheck->copyFrom(*inputGpuGrad); + checkMatrixEqual(inputGrad, targetBwdCheck); +} + +void testAvgPool3DFwdBwd(int numSamples, + int channels, + int imgSizeD, + int imgSizeH, + int imgSizeW, + int ksizeD, + int ksizeH, + int ksizeW, + int strideD, + int strideH, + int strideW, + int padD, + int padH, + int padW) { + int outD = outputSize(imgSizeD, ksizeD, padD, strideD, true); + int outH = outputSize(imgSizeH, ksizeH, padH, strideH, true); + int outW = outputSize(imgSizeW, ksizeW, padW, strideW, true); + + int inWidth = imgSizeD * imgSizeH * imgSizeW * channels; + MatrixPtr input = CpuMatrix::create(numSamples, inWidth, false, false); + MatrixPtr inputGpu = GpuMatrix::create(numSamples, inWidth, false, true); + + int outWidth = channels * outD * outH * outW; + MatrixPtr target = CpuMatrix::create(numSamples, outWidth, false, false); + MatrixPtr targetGpu = GpuMatrix::create(numSamples, outWidth, false, true); + + input->randomizeUniform(); + target->randomizeUniform(); + inputGpu->copyFrom(*input); + targetGpu->copyFrom(*target); + + target->avgPool3DForward(*input, + channels, + imgSizeD, + imgSizeH, + imgSizeW, + outD, + outH, + outW, + ksizeD, + ksizeH, + ksizeW, + strideD, + strideH, + strideW, + padD, + padH, + padW); + + targetGpu->avgPool3DForward(*inputGpu, + channels, + imgSizeD, + imgSizeH, + imgSizeW, outD, outH, outW, - filterZ, - filterY, - filterX, - strideZ, - strideY, - strideX, - padZ, - padY, - padX, - 1.0, - 1.0); - TensorCheckErr(*cpuImage, *gpuImage); + ksizeD, + ksizeH, + ksizeW, + strideD, + strideH, + strideW, + padD, + padH, + padW); + + TensorCheckErr(*target, *targetGpu); + + MatrixPtr inputGrad = CpuMatrix::create(numSamples, inWidth, false, false); + MatrixPtr inputGpuGrad = GpuMatrix::create(numSamples, inWidth, false, true); + MatrixPtr targetGrad = CpuMatrix::create(numSamples, outWidth, false, false); + MatrixPtr targetGpuGrad = + GpuMatrix::create(numSamples, outWidth, false, true); + + inputGrad->randomizeUniform(); + targetGrad->randomizeUniform(); + inputGpuGrad->copyFrom(*inputGrad); + targetGpuGrad->copyFrom(*targetGrad); + + inputGrad->avgPool3DBackward(*targetGrad, + imgSizeD, + imgSizeH, + imgSizeW, + outD, + outH, + outW, + ksizeD, + ksizeH, + ksizeW, + strideD, + strideH, + strideW, + padD, + padH, + padW, + 1.0, + 1.0); + + inputGpuGrad->avgPool3DBackward(*targetGpuGrad, + imgSizeD, + imgSizeH, + imgSizeW, + outD, + outH, + outW, + ksizeD, + ksizeH, + ksizeW, + strideD, + strideH, + strideW, + padD, + padH, + padW, + 1.0, + 1.0); + TensorCheckErr(*inputGrad, *inputGpuGrad); } -TEST(Matrix, Pool3D) { - for (auto depth : {9, 16, 64}) { - for (auto height : {9, 11, 128}) { - for (auto width : {9, 32, 128}) { - VLOG(3) << "depth=" << depth << " height=" << height - << " width=" << width; - testMatrixPool3D(depth, height, width); +// TODO(yi): I noticed many such blindly combinatorial tests in this +// file. They are no help to locate defects at all. +TEST(Matrix, Pool3DFwdBwd) { + for (auto numSamples : {1, 3}) { + for (auto channels : {3}) { + for (auto imgSizeD : {9, 16}) { + for (auto imgSizeH : {9, 32}) { + for (auto imgSizeW : {9, 32}) { + for (auto sizeX : {3}) { + for (auto sizeY : {3}) { + for (auto sizeZ : {3}) { + for (auto sD : {2}) { + for (auto sH : {2}) { + for (auto sW : {2}) { + for (auto pD : {0, (sizeZ - 1) / 2}) { + for (auto pH : {0, (sizeY - 1) / 2}) { + for (auto pW : {0, (sizeX - 1) / 2}) { + VLOG(3) << " numSamples=" << numSamples + << " channels=" << channels + << " imgSizeD=" << imgSizeD + << " imgSizeH=" << imgSizeH + << " imgSizeW=" << imgSizeW + << " sizeX=" << sizeX + << " sizeY=" << sizeY + << " sizeZ=" << sizeZ << " strideD=" << sD + << " strideH=" << sH << " strideW=" << sW + << " padingD=" << pD << " padingH=" << pH + << " padingW=" << pW; + + testMaxPool3DFwdBwd(numSamples, + channels, + imgSizeD, + imgSizeH, + imgSizeW, + sizeX, + sizeY, + sizeZ, + sD, + sH, + sW, + pD, + pH, + pW); + testAvgPool3DFwdBwd(numSamples, + channels, + imgSizeD, + imgSizeH, + imgSizeW, + sizeX, + sizeY, + sizeZ, + sD, + sH, + sW, + pD, + pH, + pW); + } + } + } + } + } + } + } + } + } + } + } } } } + + // + // for (auto numSamples : {1, 3}) { + // for (auto channels : {1, 3}) { + // for (auto imgSizeD : {9,16}) { + // for (auto imgSizeH : {9, 32}) { + // for (auto imgSizeW : {9, 32}) { + // for (auto sizeX : {2, 3}) { + // for (auto sizeY : {2, 3}) { + // for (auto sizeZ : {2,3}){ + // for (auto sD : {1, 2}) { + // for (auto sH : {1, 2}) { + // for (auto sW : {1, 2}) { + // for (auto pD : {0, (sizeZ - 1) / 2}){ + // for (auto pH : {0, (sizeY - 1) / 2}) { + // for (auto pW : {0, (sizeX - 1) / 2}) { + // VLOG(3) << " numSamples=" << numSamples + // << " channels=" << channels + // << " imgSizeD=" << imgSizeD + // << " imgSizeH=" << imgSizeH + // << " imgSizeW=" << imgSizeW + // << " sizeX=" << sizeX + // << " sizeY=" << sizeY + // << " sizeZ=" << sizeZ + // << " strideD=" << sD + // << " strideH=" << sH + // << " strideW=" << sW + // << " padingD=" << pD + // << " padingH=" << pH + // << " padingW=" << pW; + // + // testMaxPool3DFwdBwd(numSamples, + // channels, + // imgSizeD, + // imgSizeH, + // imgSizeW, + // sizeX, + // sizeY, + // sizeZ, + // sD, + // sH, + // sW, + // pD, + // pH, + // pW); + // testAvgPool3DFwdBwd(numSamples, + // channels, + // imgSizeD, + // imgSizeH, + // imgSizeW, + // sizeX, + // sizeY, + // sizeZ, + // sD, + // sH, + // sW, + // pD, + // pH, + // pW); + // } + // } + // } + // } + // } + // } + // } + // } + // } + // } + // } + // } + // } + // } } #endif -- GitLab From 692259e071494d08f50cc3b1a4e34326249bc132 Mon Sep 17 00:00:00 2001 From: hedaoyuan Date: Thu, 24 Aug 2017 23:42:32 +0800 Subject: [PATCH 0209/2018] Fix pre-commit-config.yaml --- .pre-commit-config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index a772125df..83fe9af76 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -22,7 +22,7 @@ - id: clang-format-with-version-check name: clang-format description: Format files with ClangFormat. - entry: ./.clang_format.hook -i + entry: bash ./.clang_format.hook -i language: system files: \.(c|cc|cxx|cpp|cu|h|hpp|hxx|proto)$ - repo: https://github.com/PaddlePaddle/pre-commit-golang -- GitLab From 0dffe68ca9973c5cf7d95029e369330ffcfe0187 Mon Sep 17 00:00:00 2001 From: hedaoyuan Date: Thu, 24 Aug 2017 23:45:17 +0800 Subject: [PATCH 0210/2018] Add NeonDepthwiseConvFunction. --- paddle/function/CMakeLists.txt | 2 + paddle/function/DepthwiseConvOpTest.cpp | 5 + paddle/function/Im2Col.h | 92 +++++++++ paddle/function/neon/NeonDepthwiseConv.cpp | 227 +++++++++++++++++++++ paddle/function/neon/NeonDepthwiseConv.h | 25 +++ paddle/function/neon/neon_util.h | 47 +++++ 6 files changed, 398 insertions(+) create mode 100644 paddle/function/neon/NeonDepthwiseConv.cpp create mode 100644 paddle/function/neon/NeonDepthwiseConv.h create mode 100644 paddle/function/neon/neon_util.h diff --git a/paddle/function/CMakeLists.txt b/paddle/function/CMakeLists.txt index c572a9d43..05f808a6a 100644 --- a/paddle/function/CMakeLists.txt +++ b/paddle/function/CMakeLists.txt @@ -21,6 +21,8 @@ if(USE_NNPACK) endif() endif() +list(APPEND cpp_files neon/NeonDepthwiseConv.cpp) + add_library(paddle_function STATIC ${cpp_files} ${cu_objs}) add_dependencies(paddle_function ${external_project_dependencies}) add_dependencies(paddle_function paddle_proto) diff --git a/paddle/function/DepthwiseConvOpTest.cpp b/paddle/function/DepthwiseConvOpTest.cpp index f44ae0c34..bdace2c37 100644 --- a/paddle/function/DepthwiseConvOpTest.cpp +++ b/paddle/function/DepthwiseConvOpTest.cpp @@ -34,4 +34,9 @@ TEST(DepthwiseConv, BackwardFilter) { } #endif +TEST(DepthwiseConv, Forward) { + DepthwiseConvolution( + "GemmConv-CPU", "NeonDepthwiseConv-CPU", forward); +} + } // namespace paddle diff --git a/paddle/function/Im2Col.h b/paddle/function/Im2Col.h index 48e2e32f9..9b91e223a 100644 --- a/paddle/function/Im2Col.h +++ b/paddle/function/Im2Col.h @@ -16,6 +16,7 @@ limitations under the License. */ #include "TensorShape.h" #include "TensorType.h" +#include "neon/neon_util.h" namespace paddle { @@ -93,4 +94,95 @@ public: int paddingWidth); }; +template +struct Padding { + static void run(const T* src, + T* dest, + int channels, + int inputHeight, + int inputWidth, + int paddingHeight, + int paddingWidth) { + const int destWidth = inputWidth + 2 * paddingWidth; + for (int c = 0; c < channels; c++) { + if (paddingHeight > 0) { + memset(dest, 0, destWidth * paddingHeight * sizeof(T)); + dest += destWidth * paddingHeight; + } + + for (int i = 0; i < inputHeight; i++) { + // padding head + for (int j = 0; j < paddingWidth; j++) { + *dest++ = T(0); + } + + memcpy(dest, src, inputWidth * sizeof(T)); + dest += inputWidth; + src += inputWidth; + + // padding tail + for (int j = 0; j < paddingWidth; j++) { + *dest++ = T(0); + } + } + + if (paddingHeight > 0) { + memset(dest, 0, destWidth * paddingHeight * sizeof(T)); + dest += destWidth * paddingHeight; + } + } + } +}; + +#if defined(__ARM_NEON__) || defined(__ARM_NEON) +template <> +struct Padding { + static void run(const float* src, + float* dest, + int channels, + int inputHeight, + int inputWidth, + int paddingHeight, + int paddingWidth) { + const int destWidth = inputWidth + 2 * paddingWidth; + for (int c = 0; c < channels; c++) { + if (paddingHeight > 0) { + memset(dest, 0, destWidth * paddingHeight * sizeof(float)); + dest += destWidth * paddingHeight; + } + + for (int i = 0; i < inputHeight; i++) { + // padding head + for (int j = 0; j < paddingWidth; j++) { + *dest++ = float(0); + } + + int step = inputWidth >> 2; + int remain = inputWidth & 3; + for (int s = 0; s < step; s++) { + float32x4_t s0 = vld1q_f32(src); + vst1q_f32(dest, s0); + src += 4; + dest += 4; + } + for (int r = 0; r < remain; r++) { + *dest++ = *src++; + } + + // padding tail + for (int j = 0; j < paddingWidth; j++) { + *dest++ = float(0); + } + } + + if (paddingHeight > 0) { + memset(dest, 0, destWidth * paddingHeight * sizeof(float)); + dest += destWidth * paddingHeight; + } + } + } +}; + +#endif + } // namespace paddle diff --git a/paddle/function/neon/NeonDepthwiseConv.cpp b/paddle/function/neon/NeonDepthwiseConv.cpp new file mode 100644 index 000000000..16d94c976 --- /dev/null +++ b/paddle/function/neon/NeonDepthwiseConv.cpp @@ -0,0 +1,227 @@ +/* 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 "neon_util.h" +#include "paddle/function/ConvOp.h" +#include "paddle/function/Im2Col.h" + +namespace paddle { + +namespace neon { + +#if defined(__ARM_NEON__) || defined(__ARM_NEON) + +template +struct DepthwiseConvKernel {}; + +inline float32_t conv3x3(float32x4_t r0, + float32x4_t r1, + float32x4_t r2, + float32x4_t k0, + float32x4_t k1, + float32x4_t k2) { + float32x4_t tmp; + tmp = vmulq_f32(r0, k0); + tmp = vmlaq_f32(tmp, r1, k1); + tmp = vmlaq_f32(tmp, r2, k2); + return vaddvq_f32(tmp); +} + +/** + * Each step calculates four elements of the output. + * First step: + * R0[0, 1, 2, 3...] * K[0][0] + * R0[1, 2, 3, 4...] * K[0][1] + * R0[2, 3, 4, 5...] * K[0][2] + * R1[0, 1, 2, 3...] * K[1][0] + * R1[1, 2, 3, 4...] * K[1][1] + * R1[2, 3, 4, 5...] * K[1][2] + * R2[0, 1, 2, 3...] * K[2][0] + * R2[1, 2, 3, 4...] * K[2][1] + * + R2[2, 3, 4, 5...] * K[2][2] + * ------------------------------ + * Output[0, 1, 2, 3] + */ +template <> +struct DepthwiseConvKernel<3, 1> { + static void run(const float* inputData, + const float* filterData, + int inputHeight, + int inputWidth, + int outputChannels, + int outputHeight, + int outputWidth, + int filterMultiplier, + float* outputData) { + const int steps = outputWidth >> 2; + const int remain = outputWidth & 3; + for (int c = 0; c < outputChannels; c++, filterData += 9) { + // Load the filters + float32x4_t k[3]; + k[0] = vld1q_f32(filterData); + k[1] = vld1q_f32(filterData + 3); + k[2] = vld1q_f32(filterData + 6); + k[0] = vsetq_lane_f32(0.f, k[0], 3); + k[1] = vsetq_lane_f32(0.f, k[1], 3); + k[2] = vsetq_lane_f32(0.f, k[2], 3); + + const float* r0 = + inputData + (c / filterMultiplier) * (inputHeight * inputWidth); + const float* r1 = r0 + inputWidth; + const float* r2 = r0 + inputWidth * 2; + float32x4_t input[3][3]; + for (int h = 0; h < outputHeight; h++) { + for (int s = 0; s < steps; s++) { + // Load the inputs + float32x4_t tmp; + input[0][0] = vld1q_f32(r0); + tmp = vld1q_f32(r0 + 4); + input[0][1] = vextq_f32(input[0][0], tmp, 1); + input[0][2] = vextq_f32(input[0][0], tmp, 2); + input[1][0] = vld1q_f32(r1); + tmp = vld1q_f32(r1 + 4); + input[1][1] = vextq_f32(input[1][0], tmp, 1); + input[1][2] = vextq_f32(input[1][0], tmp, 2); + input[2][0] = vld1q_f32(r2); + tmp = vld1q_f32(r2 + 4); + input[2][1] = vextq_f32(input[2][0], tmp, 1); + input[2][2] = vextq_f32(input[2][0], tmp, 2); + + float32x4_t tmp1 = vdupq_n_f32(0.f); + float32x4_t tmp2 = vdupq_n_f32(0.f); + tmp1 = vmlaq_laneq_f32(tmp1, input[0][0], k[0], 0); + tmp2 = vmlaq_laneq_f32(tmp2, input[0][1], k[0], 1); + tmp1 = vmlaq_laneq_f32(tmp1, input[0][2], k[0], 2); + tmp2 = vmlaq_laneq_f32(tmp2, input[1][0], k[1], 0); + tmp1 = vmlaq_laneq_f32(tmp1, input[1][1], k[1], 1); + tmp2 = vmlaq_laneq_f32(tmp2, input[1][2], k[1], 2); + tmp1 = vmlaq_laneq_f32(tmp1, input[2][0], k[2], 0); + tmp2 = vmlaq_laneq_f32(tmp2, input[2][1], k[2], 1); + tmp1 = vmlaq_laneq_f32(tmp1, input[2][2], k[2], 2); + tmp1 = vaddq_f32(tmp1, tmp2); + + vst1q_f32(outputData, tmp1); + r0 += 4; + r1 += 4; + r2 += 4; + outputData += 4; + } + + for (int r = 0; r < remain; r++) { + float32x4_t i0 = vld1q_f32(r0); + float32x4_t i1 = vld1q_f32(r1); + float32x4_t i2 = vld1q_f32(r2); + *outputData = conv3x3(i0, i1, i2, k[0], k[1], k[2]); + r0++; + r1++; + r2++; + outputData++; + } + + r0 += 2; + r1 += 2; + r2 += 2; + } + } + } +}; + +template +class NeonDepthwiseConvFunction : public ConvFunctionBase { +public: + void init(const FuncConfig& config) override { + ConvFunctionBase::init(config); + } + + void check(const BufferArgs& inputs, const BufferArgs& outputs) override { + const TensorShape& input = inputs[0].shape(); + const TensorShape& filter = inputs[1].shape(); + const TensorShape& output = outputs[0].shape(); + checkShape(input, filter, output); + } + + void calc(const BufferArgs& inputs, const BufferArgs& outputs) override { + CHECK_EQ(numInputs_, inputs.size()); + CHECK_EQ(numOutputs_, outputs.size()); + check(inputs, outputs); + + const TensorShape& input = inputs[0].shape(); + const TensorShape& filter = inputs[1].shape(); + const TensorShape& output = outputs[0].shape(); + + size_t batchSize = input[0]; + size_t inputChannels = input[1]; + size_t inputHeight = input[2]; + size_t inputWidth = input[3]; + size_t filterHeight = getFilterHeight(filter); + size_t filterWidth = getFilterWidth(filter); + size_t outputChannels = output[1]; + size_t outputHeight = output[2]; + size_t outputWidth = output[3]; + size_t filterMultiplier = outputChannels / groups_; + CHECK_EQ(inputChannels, groups_); + + // only support + CHECK_EQ(strideH(), strideW()); + CHECK_EQ(filterHeight, filterWidth); + CHECK_EQ(filterHeight, size_t(3)); + CHECK_LT(strideH(), size_t(3)); + + float* inputData = inputs[0].data(); + float* filterData = inputs[1].data(); + float* outputData = outputs[0].data(); + + // padding the input + float* inputPadding = inputData; + if (paddingH() > 0 || paddingW() > 0) { + int newSize = batchSize * inputChannels * (inputHeight + 2 * paddingH()) * + (inputWidth + 2 * paddingW()); + resizeBuffer(newSize); + inputPadding = reinterpret_cast(memory_->getBuf()); + Padding::run(inputData, + inputPadding, + batchSize * inputChannels, + inputHeight, + inputWidth, + paddingH(), + paddingW()); + + // height and width of padding data + inputHeight += 2 * paddingH(); + inputWidth += 2 * paddingW(); + } + + for (size_t i = 0; i < batchSize; i++) { + DepthwiseConvKernel<3, 1>::run(inputPadding, + filterData, + inputHeight, + inputWidth, + outputChannels, + outputHeight, + outputWidth, + filterMultiplier, + outputData); + + inputPadding += inputChannels * inputHeight * inputWidth; + outputData += outputChannels * outputHeight * outputWidth; + } + } +}; + +REGISTER_TYPED_FUNC(NeonDepthwiseConv, CPU, NeonDepthwiseConvFunction); + +#endif + +} // namespace neon +} // namespace paddle diff --git a/paddle/function/neon/NeonDepthwiseConv.h b/paddle/function/neon/NeonDepthwiseConv.h new file mode 100644 index 000000000..23e4be192 --- /dev/null +++ b/paddle/function/neon/NeonDepthwiseConv.h @@ -0,0 +1,25 @@ +/* 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 + +namespace paddle { + +namespace neon { + +template +struct DepthwiseConvKernel {}; + +} // namespace neon +} // namespace paddle diff --git a/paddle/function/neon/neon_util.h b/paddle/function/neon/neon_util.h new file mode 100644 index 000000000..56b3febe2 --- /dev/null +++ b/paddle/function/neon/neon_util.h @@ -0,0 +1,47 @@ +/* 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 + +#if defined(__ARM_NEON__) || defined(__ARM_NEON) + +#include + +namespace paddle { + +namespace neon { + +inline float32x4_t vld1q_f32_aligned(const float* p) { + return vld1q_f32( + (const float*)__builtin_assume_aligned(p, sizeof(float32x4_t))); +} + +#ifndef __aarch64__ +inline float32_t vaddvq_f32(float32x4_t a) { + float32x2_t v = vadd_f32(vget_high_f32(a), vget_low_f32(a)); + return vget_lane_f32(vpadd_f32(v, v), 0); +} + +inline float32x4_t vmlaq_laneq_f32(float32x4_t a, + float32x4_t b, + float32x4_t v, + const int lane) { + return vmlaq_n_f32(a, b, vgetq_lane_f32(v, lane)); +} +#endif + +} // namespace neon +} // namespace paddle + +#endif -- GitLab From b7885b087b74a1ab446f8f34d1fd78085d8b4316 Mon Sep 17 00:00:00 2001 From: hedaoyuan Date: Fri, 25 Aug 2017 00:47:51 +0800 Subject: [PATCH 0211/2018] Add DepthwiseConvKernel for filter size is 4. --- paddle/function/neon/NeonDepthwiseConv.cpp | 155 +++++++++++++++++++-- 1 file changed, 145 insertions(+), 10 deletions(-) diff --git a/paddle/function/neon/NeonDepthwiseConv.cpp b/paddle/function/neon/NeonDepthwiseConv.cpp index 16d94c976..c017241c9 100644 --- a/paddle/function/neon/NeonDepthwiseConv.cpp +++ b/paddle/function/neon/NeonDepthwiseConv.cpp @@ -38,6 +38,22 @@ inline float32_t conv3x3(float32x4_t r0, return vaddvq_f32(tmp); } +inline float32_t conv4x4(float32x4_t r0, + float32x4_t r1, + float32x4_t r2, + float32x4_t r3, + float32x4_t k0, + float32x4_t k1, + float32x4_t k2, + float32x4_t k3) { + float32x4_t tmp; + tmp = vmulq_f32(r0, k0); + tmp = vmlaq_f32(tmp, r1, k1); + tmp = vmlaq_f32(tmp, r2, k2); + tmp = vmlaq_f32(tmp, r3, k3); + return vaddvq_f32(tmp); +} + /** * Each step calculates four elements of the output. * First step: @@ -137,6 +153,114 @@ struct DepthwiseConvKernel<3, 1> { } }; +/** + * Each step calculates four elements of the output. + */ +template <> +struct DepthwiseConvKernel<4, 1> { + static void run(const float* inputData, + const float* filterData, + int inputHeight, + int inputWidth, + int outputChannels, + int outputHeight, + int outputWidth, + int filterMultiplier, + float* outputData) { + const int steps = outputWidth >> 2; + const int remain = outputWidth & 3; + for (int c = 0; c < outputChannels; c++, filterData += 16) { + // Load the filters + float32x4_t k[4]; + k[0] = vld1q_f32(filterData); + k[1] = vld1q_f32(filterData + 4); + k[2] = vld1q_f32(filterData + 8); + k[3] = vld1q_f32(filterData + 12); + + const float* r0 = + inputData + (c / filterMultiplier) * (inputHeight * inputWidth); + const float* r1 = r0 + inputWidth; + const float* r2 = r0 + inputWidth * 2; + const float* r3 = r0 + inputWidth * 3; + float32x4_t input[4][4]; + for (int h = 0; h < outputHeight; h++) { + for (int s = 0; s < steps; s++) { + // Load the inputs + float32x4_t tmp; + input[0][0] = vld1q_f32(r0); + tmp = vld1q_f32(r0 + 4); + input[0][1] = vextq_f32(input[0][0], tmp, 1); + input[0][2] = vextq_f32(input[0][0], tmp, 2); + input[0][3] = vextq_f32(input[0][0], tmp, 3); + + input[1][0] = vld1q_f32(r1); + tmp = vld1q_f32(r1 + 4); + input[1][1] = vextq_f32(input[1][0], tmp, 1); + input[1][2] = vextq_f32(input[1][0], tmp, 2); + input[1][3] = vextq_f32(input[1][0], tmp, 3); + + input[2][0] = vld1q_f32(r2); + tmp = vld1q_f32(r2 + 4); + input[2][1] = vextq_f32(input[2][0], tmp, 1); + input[2][2] = vextq_f32(input[2][0], tmp, 2); + input[2][3] = vextq_f32(input[2][0], tmp, 3); + + input[3][0] = vld1q_f32(r3); + tmp = vld1q_f32(r3 + 4); + input[3][1] = vextq_f32(input[3][0], tmp, 1); + input[3][2] = vextq_f32(input[3][0], tmp, 2); + input[3][3] = vextq_f32(input[3][0], tmp, 3); + + float32x4_t tmp1 = vdupq_n_f32(0.f); + float32x4_t tmp2 = vdupq_n_f32(0.f); + tmp1 = vmlaq_laneq_f32(tmp1, input[0][0], k[0], 0); + tmp2 = vmlaq_laneq_f32(tmp2, input[0][1], k[0], 1); + tmp1 = vmlaq_laneq_f32(tmp1, input[0][2], k[0], 2); + tmp2 = vmlaq_laneq_f32(tmp2, input[0][3], k[0], 3); + tmp1 = vmlaq_laneq_f32(tmp1, input[1][0], k[1], 0); + tmp2 = vmlaq_laneq_f32(tmp2, input[1][1], k[1], 1); + tmp1 = vmlaq_laneq_f32(tmp1, input[1][2], k[1], 2); + tmp2 = vmlaq_laneq_f32(tmp2, input[1][3], k[1], 3); + tmp1 = vmlaq_laneq_f32(tmp1, input[2][0], k[2], 0); + tmp2 = vmlaq_laneq_f32(tmp2, input[2][1], k[2], 1); + tmp1 = vmlaq_laneq_f32(tmp1, input[2][2], k[2], 2); + tmp2 = vmlaq_laneq_f32(tmp2, input[2][3], k[2], 3); + tmp1 = vmlaq_laneq_f32(tmp1, input[3][0], k[3], 0); + tmp2 = vmlaq_laneq_f32(tmp2, input[3][1], k[3], 1); + tmp1 = vmlaq_laneq_f32(tmp1, input[3][2], k[3], 2); + tmp2 = vmlaq_laneq_f32(tmp2, input[3][3], k[3], 3); + tmp1 = vaddq_f32(tmp1, tmp2); + + vst1q_f32(outputData, tmp1); + r0 += 4; + r1 += 4; + r2 += 4; + r3 += 4; + outputData += 4; + } + + for (int r = 0; r < remain; r++) { + float32x4_t i0 = vld1q_f32(r0); + float32x4_t i1 = vld1q_f32(r1); + float32x4_t i2 = vld1q_f32(r2); + float32x4_t i3 = vld1q_f32(r3); + *outputData = conv4x4(i0, i1, i2, i3, k[0], k[1], k[2], k[3]); + r0++; + r1++; + r2++; + r3++; + outputData++; + } + + r0 += 3; + r1 += 3; + r2 += 3; + r3 += 3; + } + } + } +}; + template class NeonDepthwiseConvFunction : public ConvFunctionBase { public: @@ -175,7 +299,6 @@ public: // only support CHECK_EQ(strideH(), strideW()); CHECK_EQ(filterHeight, filterWidth); - CHECK_EQ(filterHeight, size_t(3)); CHECK_LT(strideH(), size_t(3)); float* inputData = inputs[0].data(); @@ -203,15 +326,27 @@ public: } for (size_t i = 0; i < batchSize; i++) { - DepthwiseConvKernel<3, 1>::run(inputPadding, - filterData, - inputHeight, - inputWidth, - outputChannels, - outputHeight, - outputWidth, - filterMultiplier, - outputData); + if (filterWidth == 3) { + DepthwiseConvKernel<3, 1>::run(inputPadding, + filterData, + inputHeight, + inputWidth, + outputChannels, + outputHeight, + outputWidth, + filterMultiplier, + outputData); + } else if (filterWidth == 4) { + DepthwiseConvKernel<4, 1>::run(inputPadding, + filterData, + inputHeight, + inputWidth, + outputChannels, + outputHeight, + outputWidth, + filterMultiplier, + outputData); + } inputPadding += inputChannels * inputHeight * inputWidth; outputData += outputChannels * outputHeight * outputWidth; -- GitLab From a1ce705517fca1551029541e17cb0ac3ddb65677 Mon Sep 17 00:00:00 2001 From: Xi Chen Date: Thu, 24 Aug 2017 10:35:50 -0700 Subject: [PATCH 0212/2018] update etcd graph for design doc --- .../cluster_train/src/paddle-etcd.graffle | Bin 5069 -> 5765 bytes 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Date: Thu, 24 Aug 2017 02:58:41 +0000 Subject: [PATCH 0213/2018] scatter check in --- paddle/operators/CMakeLists.txt | 1 + paddle/operators/scatter_op.cc | 76 +++++++++++++++++++ paddle/operators/scatter_op.cu | 20 +++++ paddle/operators/scatter_op.h | 60 +++++++++++++++ paddle/pybind/CMakeLists.txt | 1 + paddle/pybind/pybind.cc | 1 + .../paddle/v2/framework/tests/CMakeLists.txt | 1 + .../v2/framework/tests/test_gather_op.py | 3 - .../v2/framework/tests/test_scatter_op.py | 38 ++++++++++ 9 files changed, 198 insertions(+), 3 deletions(-) create mode 100644 paddle/operators/scatter_op.cc create mode 100644 paddle/operators/scatter_op.cu create mode 100644 paddle/operators/scatter_op.h create mode 100644 python/paddle/v2/framework/tests/test_scatter_op.py diff --git a/paddle/operators/CMakeLists.txt b/paddle/operators/CMakeLists.txt index f466dbc79..f0fd12f1b 100644 --- a/paddle/operators/CMakeLists.txt +++ b/paddle/operators/CMakeLists.txt @@ -47,6 +47,7 @@ cc_test(gather_test SRCS gather_test.cc DEPS tensor) op_library(gather_op SRCS gather_op.cc gather_op.cu) cc_test(scatter_test SRCS scatter_test.cc DEPS tensor) +op_library(scatter_op SRCS scatter_op.cc scatter_op.cu) cc_library(net_op SRCS net_op.cc DEPS op_registry) cc_test(net_op_test SRCS net_op_test.cc DEPS net_op) diff --git a/paddle/operators/scatter_op.cc b/paddle/operators/scatter_op.cc new file mode 100644 index 000000000..cf01ef627 --- /dev/null +++ b/paddle/operators/scatter_op.cc @@ -0,0 +1,76 @@ +/* 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/scatter_op.h" +#include "paddle/framework/ddim.h" + +namespace paddle { +namespace operators { + +class ScatterOp : public framework::OperatorWithKernel { + public: + using framework::OperatorWithKernel::OperatorWithKernel; + + protected: + void InferShape(const framework::InferShapeContext &ctx) const override { + framework::DDim output_dims(ctx.Input("Ref")->dims()); + ctx.Output("Out")->Resize(output_dims); + } +}; + +class ScatterGradOp : public framework::OperatorWithKernel { + public: + using framework::OperatorWithKernel::OperatorWithKernel; + + protected: + void InferShape(const framework::InferShapeContext &ctx) const override { + auto Updates_grad = ctx.Output(framework::GradVarName("Updates")); + auto Updates = ctx.Input("Updates"); + auto Ref_grad = ctx.Output(framework::GradVarName("Ref")); + auto Ref = ctx.Input("Ref"); + + Ref_grad->Resize(Ref->dims()); + Updates_grad->Resize(Updates->dims()); + } +}; + +class ScatterOpMaker : public framework::OpProtoAndCheckerMaker { + public: + ScatterOpMaker(framework::OpProto *proto, + framework::OpAttrChecker *op_checker) + : OpProtoAndCheckerMaker(proto, op_checker) { + AddInput("Ref", "The source input of scatter op"); + AddInput("Index", + "The index input of scatter op where Ref will be updated"); + AddInput("Updates", "The updated value of updates op"); + AddOutput("Out", "The output of add op"); + AddComment(R"DOC( +Scatter Operator by selecting from the first axis, + +Out = Ref +Out[Index] = Ref[Index] + Updates +)DOC"); + } +}; +} // namespace operators +} // namespace paddle + +namespace ops = paddle::operators; +REGISTER_OP(scatter, ops::ScatterOp, ops::ScatterOpMaker, scatter_grad, + ops::ScatterGradOp); +REGISTER_OP_CPU_KERNEL(scatter, + ops::ScatterOpKernel); +REGISTER_OP_CPU_KERNEL( + scatter_grad, + ops::ScatterGradientOpKernel); diff --git a/paddle/operators/scatter_op.cu b/paddle/operators/scatter_op.cu new file mode 100644 index 000000000..e6a6fa57d --- /dev/null +++ b/paddle/operators/scatter_op.cu @@ -0,0 +1,20 @@ +/* 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. */ + +#define EIGEN_USE_GPU +#include "paddle/operators/scatter_op.h" + +namespace ops = paddle::operators; +REGISTER_OP_GPU_KERNEL(scatter, + ops::ScatterOpKernel); diff --git a/paddle/operators/scatter_op.h b/paddle/operators/scatter_op.h new file mode 100644 index 000000000..c2db3ae37 --- /dev/null +++ b/paddle/operators/scatter_op.h @@ -0,0 +1,60 @@ +/* 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 "gather.h" +#include "paddle/framework/eigen.h" +#include "paddle/framework/op_registry.h" +#include "scatter.h" + +namespace paddle { +namespace operators { + +using Tensor = framework::Tensor; + +template +class ScatterOpKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext &ctx) const override { + auto *Ref = ctx.Input("Ref"); + auto *Index = ctx.Input("Index"); + auto *Updates = ctx.Input("Updates"); + auto *Out = ctx.Output("Out"); + + // In place output: Out = Ref, Out[Index] += Updates + Out->ShareDataWith(*Ref); + // Apply ScatterUpdate: Out[index] += Updates[:] + ScatterUpdate(ctx.GetPlace(), Updates, Index, Out); + } +}; + +template +class ScatterGradientOpKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext &ctx) const override { + auto *dRef = ctx.Output(framework::GradVarName("Ref")); + auto *dUpdates = ctx.Output(framework::GradVarName("Updates")); + auto *Index = ctx.Input("Index"); + auto *dO = ctx.Input(framework::GradVarName("Out")); + + // In place gradient: dRef = dO + dRef->ShareDataWith(*dO); + dUpdates->mutable_data(ctx.GetPlace()); + // Gradient by Gather: dUpdates += dO[Index] + Gather(ctx.GetPlace(), dO, Index, dUpdates); + } +}; + +} // namespace operators +} // namespace paddle diff --git a/paddle/pybind/CMakeLists.txt b/paddle/pybind/CMakeLists.txt index abb9c248e..37e186a40 100644 --- a/paddle/pybind/CMakeLists.txt +++ b/paddle/pybind/CMakeLists.txt @@ -4,6 +4,7 @@ cc_library(paddle_pybind SHARED DEPS pybind python backward sgd_op gather_op + scatter_op add_op mul_op rowwise_add_op diff --git a/paddle/pybind/pybind.cc b/paddle/pybind/pybind.cc index 8fa8be2ce..3bc150ccb 100644 --- a/paddle/pybind/pybind.cc +++ b/paddle/pybind/pybind.cc @@ -47,6 +47,7 @@ USE_OP(scale); USE_OP_ITSELF(identity); USE_OP(minus); USE_CPU_ONLY_OP(gather); +USE_CPU_ONLY_OP(scatter); namespace paddle { namespace framework { diff --git a/python/paddle/v2/framework/tests/CMakeLists.txt b/python/paddle/v2/framework/tests/CMakeLists.txt index fb4686889..661ebd896 100644 --- a/python/paddle/v2/framework/tests/CMakeLists.txt +++ b/python/paddle/v2/framework/tests/CMakeLists.txt @@ -14,6 +14,7 @@ py_test(test_sigmoid_op SRCS test_sigmoid_op.py) py_test(test_softmax_op SRCS test_softmax_op.py) py_test(test_cross_entropy_op SRCS test_cross_entropy_op.py) py_test(test_gather_op SRCS test_gather_op.py) +py_test(test_scatter_op SRCS test_scatter_op.py) py_test(test_fill_zeros_like_op SRCS test_fill_zeros_like_op.py) py_test(gradient_checker SRCS gradient_checker.py) diff --git a/python/paddle/v2/framework/tests/test_gather_op.py b/python/paddle/v2/framework/tests/test_gather_op.py index e86898304..e3de3fd0a 100644 --- a/python/paddle/v2/framework/tests/test_gather_op.py +++ b/python/paddle/v2/framework/tests/test_gather_op.py @@ -21,12 +21,9 @@ class TestGatherOp(unittest.TestCase): class TestGatherGradOp(GradientChecker): def test_gather_grad(self): - print 'creating op' op = create_op("gather") - print 'creating op done' xnp = numpy.random.random((10, 20)).astype("float32") inputs = {'X': xnp, 'Index': numpy.array([1, 3, 5]).astype("int32")} - print 'correct before check gradient' self.check_grad(op, inputs, set("X"), "Out") diff --git a/python/paddle/v2/framework/tests/test_scatter_op.py b/python/paddle/v2/framework/tests/test_scatter_op.py new file mode 100644 index 000000000..e7696844d --- /dev/null +++ b/python/paddle/v2/framework/tests/test_scatter_op.py @@ -0,0 +1,38 @@ +import unittest +from op_test_util import OpTestMeta +from gradient_checker import GradientChecker, create_op +import numpy +import paddle.v2.framework.core as core +from paddle.v2.framework.op import Operator + + +class TestScatterOp(unittest.TestCase): + __metaclass__ = OpTestMeta + + def setUp(self): + self.type = "scatter" + ref_np = numpy.ones((3, 3)).astype("float32") + index_np = numpy.array([1, 2]).astype("int32") + updates_np = numpy.random.random((2, 3)).astype("float32") + output_np = numpy.copy(ref_np) + output_np[index_np] += updates_np + self.inputs = {'Ref': ref_np, 'Index': index_np, 'Updates': updates_np} + self.outputs = {'Out': output_np} + + +class TestScatterGradOp(GradientChecker): + def test_scatter_grad(self): + op = create_op("scatter") + # test data setup + ref_np = numpy.ones((3, 10)).astype("float32") + index_np = numpy.array([1, 2]).astype("int32") + updates_np = numpy.random.random((2, 10)).astype("float32") + output_np = numpy.copy(ref_np) + output_np[index_np] += updates_np + inputs = {'Ref': ref_np, 'Index': index_np, 'Updates': updates_np} + # check gradient + self.check_grad(op, inputs, set(["Updates", "Ref"]), "Out") + + +if __name__ == "__main__": + unittest.main() -- GitLab From 1dc62cd21ca8699c6740d71cf984a7f5d589b77a Mon Sep 17 00:00:00 2001 From: Xi Chen Date: Thu, 24 Aug 2017 11:28:48 -0700 Subject: [PATCH 0214/2018] updated doc with implementation change of trainer --- doc/design/cluster_train/README.md | 25 +++++++++--------- .../cluster_train/src/paddle-etcd.graffle | Bin 5765 -> 5557 bytes doc/design/cluster_train/src/paddle-etcd.png | Bin 57495 -> 50387 bytes 3 files changed, 12 insertions(+), 13 deletions(-) diff --git a/doc/design/cluster_train/README.md b/doc/design/cluster_train/README.md index 74961f800..177a5f5d5 100644 --- a/doc/design/cluster_train/README.md +++ b/doc/design/cluster_train/README.md @@ -54,17 +54,18 @@ The life cycle of a single task is illustrated below: 1. When a new pass of training starts, all tasks will be placed in the todo queue. -1. The master server will dispatch few tasks to each trainer at a time, puts them in the pending queue and waits for completion. -1. The trainer will work on its tasks and tell the master server once a task is completed. The master server will dispatch a new task to that trainer. -1. If a task timeout. the master server will move it back to the todo queue. The timeout count will increase by one. If the timeout count is above a threshold, the task is likely to cause a trainer to crash, so it will be discarded. +1. Upon trainer requests for new task, the master server will dispatch a task from todo queue to it, put the task in the pending queue and wait for completion. +1. The trainer will work on its task and tell the master server once the task is completed and ask for new task. The master server will dispatch a new task to that trainer. +1. If a task fails for any reason in trainer, or takes longer than a specific period of time, the master server will move the task back to the todo queue. The timeout count for that task will increase by one. If the timeout count is above a threshold, the task is likely to cause a trainer to crash, then it will be discarded. 1. The master server will move completed task to the done queue. When the todo queue is empty, the master server will start a new pass by moving all tasks in the done queue to todo queue and reset the timeout counter of all tasks to zero. ### Trainer Process The trainer process will: -- Receive tasks from the master. -- Work on the tasks: calculate and upload gradient to parameter servers, and update local model by downloading new parameters from parameter servers. +- Request tasks from the master. +- Work on the tasks +- Upload gradient to parameter servers, and update local model by downloading new parameters from parameter servers. ### Parameter Server Process @@ -119,8 +120,8 @@ When the master is started by the Kubernetes, it executes the following steps at 1. Grabs a unique *master* lock in etcd, which prevents concurrent master instantiations. 1. Recovers the task queues from etcd if they already exist, otherwise, the master will create them. -1. Watches the trainer prefix keys `/trainer/` on etcd to find the live trainers. -1. Starts dispatching the tasks to the trainers, and updates task queue using an etcd transaction to ensure lock is held during the update. +1. Write its ip address to */master/addr* so that trainers can discover it. +1. Listens to trainers' request of task, dispatch one upon request, and updates task queue using an etcd transaction to ensure lock is held during the update. When the master server process is dead for any reason, Kubernetes will restart it. It will be online again with all states recovered from etcd in few minutes. @@ -128,13 +129,11 @@ When the master server process is dead for any reason, Kubernetes will restart i When the trainer is started by the Kubernetes, it executes the following steps at startup: -1. Watches the available parameter server prefix keys `/ps/` on etcd and waits until the count of parameter servers reaches the desired count. -1. Generates a unique ID, and sets key `/trainer/` with its contact address as value. The key will be deleted when the lease expires, so the master will be aware of the trainer being online and offline. -1. Waits for tasks from the master to start training. +1. Watches the available parameter server prefix keys `/ps/` on etcd and waits until the count of parameter servers reaches the desired count */ps_desired*. +1. Finds and watches */master/addr* to get master's address. +1. Requests for tasks from the master to start training. -If trainer's etcd lease expires, it will try set key `/trainer/` again so that the master server can discover the trainer again. - -When a trainer fails, Kuberentes would try to restart it. The recovered trainer would fetch tasks from the TODO queue and go on training. +When a trainer fails, Kuberentes would try to restart it. 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.../v2/framework/tests/gradient_checker.py | 21 ++++++++++++------- .../v2/framework/tests/test_scatter_op.py | 3 ++- 2 files changed, 15 insertions(+), 9 deletions(-) diff --git a/python/paddle/v2/framework/tests/gradient_checker.py b/python/paddle/v2/framework/tests/gradient_checker.py index 8eb9f3f07..ac37671c7 100644 --- a/python/paddle/v2/framework/tests/gradient_checker.py +++ b/python/paddle/v2/framework/tests/gradient_checker.py @@ -32,7 +32,8 @@ def get_numeric_gradient(op, output_name, input_to_check, delta=0.005, - local_scope=None): + local_scope=None, + in_place=False): """ Get Numeric Gradient for an operator's input. @@ -90,9 +91,10 @@ def get_numeric_gradient(op, # we only compute gradient of one element each time. # we use a for loop to compute the gradient of every element. for i in xrange(tensor_size): - for var_name in input_values: - tensor_ = local_scope.find_var(var_name).get_tensor() - tensor_.set(numpy.copy(input_values[var_name]), core.CPUPlace()) + if in_place: + for var_name in input_values: + tensor_ = local_scope.find_var(var_name).get_tensor() + tensor_.set(numpy.copy(input_values[var_name]), core.CPUPlace()) # get one input element throw it's index i. origin = tensor_to_check.get_float_element(i) @@ -102,9 +104,10 @@ def get_numeric_gradient(op, y_pos = get_output() # plus delta to this element, run op and get the sum of the result tensor. - for var_name in input_values: - tensor_ = local_scope.find_var(var_name).get_tensor() - tensor_.set(numpy.copy(input_values[var_name]), core.CPUPlace()) + if in_place: + for var_name in input_values: + tensor_ = local_scope.find_var(var_name).get_tensor() + tensor_.set(numpy.copy(input_values[var_name]), core.CPUPlace()) x_neg = origin - delta tensor_to_check.set_float_element(i, x_neg) y_neg = get_output() @@ -257,6 +260,7 @@ class GradientChecker(unittest.TestCase): output_name, no_grad_set=None, only_cpu=False, + in_place=False, max_relative_error=0.005): """ :param forward_op: used to create backward_op @@ -289,7 +293,8 @@ class GradientChecker(unittest.TestCase): # get numerical gradients numeric_grads = [ - get_numeric_gradient(forward_op, input_vars, output_name, name) + get_numeric_gradient( + forward_op, input_vars, output_name, name, in_place=in_place) for name in inputs_to_check ] diff --git a/python/paddle/v2/framework/tests/test_scatter_op.py b/python/paddle/v2/framework/tests/test_scatter_op.py index e7696844d..861fe6cf8 100644 --- a/python/paddle/v2/framework/tests/test_scatter_op.py +++ b/python/paddle/v2/framework/tests/test_scatter_op.py @@ -31,7 +31,8 @@ class TestScatterGradOp(GradientChecker): output_np[index_np] += updates_np inputs = {'Ref': ref_np, 'Index': index_np, 'Updates': updates_np} # check gradient - self.check_grad(op, inputs, set(["Updates", "Ref"]), "Out") + self.check_grad( + op, inputs, set(["Updates", "Ref"]), "Out", in_place=True) if __name__ == "__main__": -- GitLab From f22ece9273b54f1a248f7a787e252eb04a5acea3 Mon Sep 17 00:00:00 2001 From: Yi Wang Date: Thu, 24 Aug 2017 19:44:19 -0700 Subject: [PATCH 0217/2018] Add a document on building using Docker --- Dockerfile | 4 +- doc/howto/dev/build_en.md | 83 ++++++++++++++++++++++++++++++++++ paddle/scripts/docker/build.sh | 6 +-- 3 files changed, 87 insertions(+), 6 deletions(-) create mode 100644 doc/howto/dev/build_en.md diff --git a/Dockerfile b/Dockerfile index 98f61ba58..136db772c 100644 --- a/Dockerfile +++ b/Dockerfile @@ -10,13 +10,11 @@ RUN /bin/bash -c 'if [[ -n ${UBUNTU_MIRROR} ]]; then sed -i 's#http://archive.ub ARG WITH_GPU ARG WITH_AVX ARG WITH_DOC -ARG WITH_STYLE_CHECK ENV WOBOQ OFF -ENV WITH_GPU=${WITH_GPU:-OFF} +ENV WITH_GPU=${WITH_GPU:-ON} ENV WITH_AVX=${WITH_AVX:-ON} ENV WITH_DOC=${WITH_DOC:-OFF} -ENV WITH_STYLE_CHECK=${WITH_STYLE_CHECK:-OFF} ENV HOME /root # Add bash enhancements diff --git a/doc/howto/dev/build_en.md b/doc/howto/dev/build_en.md new file mode 100644 index 000000000..80488a147 --- /dev/null +++ b/doc/howto/dev/build_en.md @@ -0,0 +1,83 @@ +# Build PaddlePaddle from Source Code and Run Unit Test + +## What Developers Need + +To contribute to PaddlePaddle, you need + +1. A computer -- Linux, BSD, Windows, MacOS, and +1. Docker. + +Nothing else. Not even Python and GCC, because you can install all build tools into a Docker image. + +## General Process + +1. Retrieve source code. + + ```bash + git clone https://github.com/paddlepaddle/paddle + ``` + +2. Install build tools. + + ```bash + cd paddle; docker build -t paddle:dev . + ``` + +3. Build from source. + + ```bash + docker run -v $PWD:/paddle paddle:dev + ``` + +4. Run unit tests. + + ```bash + docker run -v $PWD:/paddle paddle:dev "cd/build; ctest" + ``` + + +## Docker, Or Not? + +- What is Docker? + + If you haven't heard of it, consider it something like Python's virtualenv. + +- Docker or virtual machine? + + Some people compare Docker with VMs, but Docker doesn't virtualize any hardware, and it doesn't run a guest OS. + +- Why Docker? + + Using a Docker image of build tools standardize the building environment, and easier for others to reproduce your problem, if there is any, and help. + + Also, some build tools don't run on Windows or Mac or BSD, but Docker runs almost everywhere, so developers can use whatever computer they want. + +- Can I don't use Docker? + + Sure, you don't have to install build tools into a Docker image; instead, you can install them onto your local computer. This document exists because Docker would make the development way easier. + +- How difficult is it to learn Docker? + + It takes you ten minutes to read https://docs.docker.com/get-started/ and saves you more than one hour to install all required build tools, configure them, and upgrade them when new versions of PaddlePaddle require some new tools. + +- Docker requires sudo + + An owner of a computer has the administrative privilege, a.k.a., sudo. If you use a shared computer for development, please ask the administrator to install and configure Docker. We will do our best to support rkt, another container technology that doesn't require sudo. + +- Can I use my favorite IDE? + + Yes, of course. The source code resides on your local computer, and you can edit it using whatever editor you like. + + Many PaddlePaddle developers are using Emacs. They add the following few lines into their `~/.emacs` configure file: + + ```emacs + (global-set-key "\C-cc" 'compile) + (setq compile-command + "docker run --rm -it -v $(git rev-parse --show-toplevel):/paddle paddle:dev") + ``` + + so they could type `Ctrl-C` and `c` to build PaddlePaddle from source. + +- How many parallel building processes does the Docker container run? + + Our building Docker image runs a Bash script https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/scripts/docker/build.sh, which calls `make -j$(nproc)` to starts as many processes as the number of your processors. diff --git a/paddle/scripts/docker/build.sh b/paddle/scripts/docker/build.sh index 2941662f3..7bab814ae 100644 --- a/paddle/scripts/docker/build.sh +++ b/paddle/scripts/docker/build.sh @@ -38,7 +38,7 @@ Configuring cmake in /paddle/build ... -DWITH_SWIG_PY=${WITH_SWIG_PY:-ON} -DCUDNN_ROOT=/usr/ -DWITH_STYLE_CHECK=${WITH_STYLE_CHECK:-OFF} - -DWITH_TESTING=${WITH_TESTING:-OFF} + -DWITH_TESTING=${WITH_TESTING:-ON} -DCMAKE_EXPORT_COMPILE_COMMANDS=ON ======================================== EOF @@ -56,8 +56,8 @@ cmake .. \ -DWITH_C_API=${WITH_C_API:-OFF} \ -DWITH_PYTHON=${WITH_PYTHON:-ON} \ -DCUDNN_ROOT=/usr/ \ - -DWITH_STYLE_CHECK=${WITH_STYLE_CHECK:-OFF} \ - -DWITH_TESTING=${WITH_TESTING:-OFF} \ + -DWITH_STYLE_CHECK=${WITH_STYLE_CHECK:-ON} \ + -DWITH_TESTING=${WITH_TESTING:-ON} \ -DCMAKE_EXPORT_COMPILE_COMMANDS=ON cat < Date: Fri, 25 Aug 2017 11:36:38 +0800 Subject: [PATCH 0218/2018] Neon depthwise conv with filterSize = 3 and stride = 2. --- paddle/function/neon/NeonDepthwiseConv.cpp | 115 ++++++++++++++++++++- 1 file changed, 114 insertions(+), 1 deletion(-) diff --git a/paddle/function/neon/NeonDepthwiseConv.cpp b/paddle/function/neon/NeonDepthwiseConv.cpp index c017241c9..53d14d983 100644 --- a/paddle/function/neon/NeonDepthwiseConv.cpp +++ b/paddle/function/neon/NeonDepthwiseConv.cpp @@ -153,6 +153,109 @@ struct DepthwiseConvKernel<3, 1> { } }; +/** + * Each step calculates four elements of the output. + * First step: + * R0[0, 2, 4, 6...] * K[0][0] + * R0[1, 3, 5, 7...] * K[0][1] + * R0[2, 4, 6, 8...] * K[0][2] + * R1[0, 2, 4, 6...] * K[1][0] + * R1[1, 3, 5, 7...] * K[1][1] + * R1[2, 4, 6, 8...] * K[1][2] + * R2[0, 2, 4, 6...] * K[2][0] + * R2[1, 3, 5, 7...] * K[2][1] + * R2[2, 4, 6, 8...] * K[2][2] + * ------------------------------ + * Output[0, 1, 2, 3] + */ +template <> +struct DepthwiseConvKernel<3, 2> { + static void run(const float* inputData, + const float* filterData, + int inputHeight, + int inputWidth, + int outputChannels, + int outputHeight, + int outputWidth, + int filterMultiplier, + float* outputData) { + const int steps = outputWidth >> 2; + const int remain = outputWidth & 3; + for (int c = 0; c < outputChannels; c++, filterData += 9) { + // Load the filters + float32x4_t k[3]; + k[0] = vld1q_f32(filterData); + k[1] = vld1q_f32(filterData + 3); + k[2] = vld1q_f32(filterData + 6); + k[0] = vsetq_lane_f32(0.f, k[0], 3); + k[1] = vsetq_lane_f32(0.f, k[1], 3); + k[2] = vsetq_lane_f32(0.f, k[2], 3); + + const float* start = + inputData + (c / filterMultiplier) * (inputHeight * inputWidth); + float32x4_t input[3][3]; + for (int h = 0; h < outputHeight; h++) { + const float* r0 = start + 2 * h * inputWidth; + const float* r1 = start + (2 * h + 1) * inputWidth; + const float* r2 = start + (2 * h + 2) * inputWidth; + for (int s = 0; s < steps; s++) { + // Load the inputs + float32x4_t data1; + float32x4x2_t data2; + + data2 = vld2q_f32(r0); + input[0][0] = data2.val[0]; + input[0][1] = data2.val[1]; + data1 = vld1q_f32(r0 + 8); + input[0][2] = vextq_f32(data2.val[0], data1, 1); + + data2 = vld2q_f32(r1); + input[1][0] = data2.val[0]; + input[1][1] = data2.val[1]; + data1 = vld1q_f32(r1 + 8); + input[1][2] = vextq_f32(data2.val[0], data1, 1); + + data2 = vld2q_f32(r2); + input[2][0] = data2.val[0]; + input[2][1] = data2.val[1]; + data1 = vld1q_f32(r2 + 8); + input[2][2] = vextq_f32(data2.val[0], data1, 1); + + float32x4_t tmp1 = vdupq_n_f32(0.f); + float32x4_t tmp2 = vdupq_n_f32(0.f); + tmp1 = vmlaq_laneq_f32(tmp1, input[0][0], k[0], 0); + tmp2 = vmlaq_laneq_f32(tmp2, input[0][1], k[0], 1); + tmp1 = vmlaq_laneq_f32(tmp1, input[0][2], k[0], 2); + tmp2 = vmlaq_laneq_f32(tmp2, input[1][0], k[1], 0); + tmp1 = vmlaq_laneq_f32(tmp1, input[1][1], k[1], 1); + tmp2 = vmlaq_laneq_f32(tmp2, input[1][2], k[1], 2); + tmp1 = vmlaq_laneq_f32(tmp1, input[2][0], k[2], 0); + tmp2 = vmlaq_laneq_f32(tmp2, input[2][1], k[2], 1); + tmp1 = vmlaq_laneq_f32(tmp1, input[2][2], k[2], 2); + tmp1 = vaddq_f32(tmp1, tmp2); + + vst1q_f32(outputData, tmp1); + r0 += 8; + r1 += 8; + r2 += 8; + outputData += 4; + } + + for (int r = 0; r < remain; r++) { + float32x4_t i0 = vld1q_f32(r0); + float32x4_t i1 = vld1q_f32(r1); + float32x4_t i2 = vld1q_f32(r2); + *outputData = conv3x3(i0, i1, i2, k[0], k[1], k[2]); + r0 += 2; + r1 += 2; + r2 += 2; + outputData++; + } + } + } + } +}; + /** * Each step calculates four elements of the output. */ @@ -326,7 +429,7 @@ public: } for (size_t i = 0; i < batchSize; i++) { - if (filterWidth == 3) { + if (filterWidth == 3 && strideH() == 1) { DepthwiseConvKernel<3, 1>::run(inputPadding, filterData, inputHeight, @@ -336,6 +439,16 @@ public: outputWidth, filterMultiplier, outputData); + } else if (filterWidth == 3 && strideH() == 2) { + DepthwiseConvKernel<3, 2>::run(inputPadding, + filterData, + inputHeight, + inputWidth, + outputChannels, + outputHeight, + outputWidth, + filterMultiplier, + outputData); } else if (filterWidth == 4) { DepthwiseConvKernel<4, 1>::run(inputPadding, filterData, -- GitLab From 9fdf3970d0de568db4a9a3b757335604430ca137 Mon Sep 17 00:00:00 2001 From: Yi Wang Date: Thu, 24 Aug 2017 20:37:39 -0700 Subject: [PATCH 0219/2018] Update unit test running and CUDA --- doc/howto/dev/build_en.md | 15 ++++++++++++++- 1 file changed, 14 insertions(+), 1 deletion(-) diff --git a/doc/howto/dev/build_en.md b/doc/howto/dev/build_en.md index 80488a147..de0733f96 100644 --- a/doc/howto/dev/build_en.md +++ b/doc/howto/dev/build_en.md @@ -29,12 +29,25 @@ Nothing else. Not even Python and GCC, because you can install all build tools docker run -v $PWD:/paddle paddle:dev ``` + This builds a CUDA-enabled version and writes all binary outputs to directory `./build` of the local computer, other than the Docker container. If we want to build only the CPU part, we can type + + ```bash + docker run -e WITH_GPU=OFF -v $PWD:/paddle paddle:dev + ``` + 4. Run unit tests. + To run all unit tests using the first GPU of a node: + ```bash - docker run -v $PWD:/paddle paddle:dev "cd/build; ctest" + NV_GPU=0 nvidia-docker run -v $PWD:/paddle paddle:dev bash -c "cd /paddle/build; ctest" ``` + If we used `WITH_GPU=OFF` at build time, it generates only CPU-based unit tests, and we don't need nvidia-docker to run them. We can just run + + ```bash + docker run -v $PWD:/paddle paddle:dev bash -c "cd /paddle/build; ctest" + ``` ## Docker, Or Not? -- GitLab From f00c4112d2ca1d42c60d154002b2347ba2de5cd9 Mon Sep 17 00:00:00 2001 From: hedaoyuan Date: Fri, 25 Aug 2017 11:53:45 +0800 Subject: [PATCH 0220/2018] Neon depthwise conv with filterSize = 4 and stride = 2. --- paddle/function/neon/NeonDepthwiseConv.cpp | 122 ++++++++++++++++++++- 1 file changed, 121 insertions(+), 1 deletion(-) diff --git a/paddle/function/neon/NeonDepthwiseConv.cpp b/paddle/function/neon/NeonDepthwiseConv.cpp index 53d14d983..3fe28b1de 100644 --- a/paddle/function/neon/NeonDepthwiseConv.cpp +++ b/paddle/function/neon/NeonDepthwiseConv.cpp @@ -364,6 +364,116 @@ struct DepthwiseConvKernel<4, 1> { } }; +/** + * Each step calculates four elements of the output. + */ +template <> +struct DepthwiseConvKernel<4, 2> { + static void run(const float* inputData, + const float* filterData, + int inputHeight, + int inputWidth, + int outputChannels, + int outputHeight, + int outputWidth, + int filterMultiplier, + float* outputData) { + const int steps = outputWidth >> 2; + const int remain = outputWidth & 3; + for (int c = 0; c < outputChannels; c++, filterData += 16) { + // Load the filters + float32x4_t k[4]; + k[0] = vld1q_f32(filterData); + k[1] = vld1q_f32(filterData + 4); + k[2] = vld1q_f32(filterData + 8); + k[3] = vld1q_f32(filterData + 12); + + const float* start = + inputData + (c / filterMultiplier) * (inputHeight * inputWidth); + float32x4_t input[4][4]; + for (int h = 0; h < outputHeight; h++) { + const float* r0 = start + 2 * h * inputWidth; + const float* r1 = start + (2 * h + 1) * inputWidth; + const float* r2 = start + (2 * h + 2) * inputWidth; + const float* r3 = start + (2 * h + 3) * inputWidth; + for (int s = 0; s < steps; s++) { + // Load the inputs + float32x4x2_t data1; + float32x4x2_t data2; + + data1 = vld2q_f32(r0); + data2 = vld2q_f32(r0 + 8); + input[0][0] = data1.val[0]; + input[0][1] = data1.val[1]; + input[0][2] = vextq_f32(data1.val[0], data2.val[0], 1); + input[0][3] = vextq_f32(data1.val[1], data2.val[1], 1); + + data1 = vld2q_f32(r1); + data2 = vld2q_f32(r1 + 8); + input[1][0] = data1.val[0]; + input[1][1] = data1.val[1]; + input[1][2] = vextq_f32(data1.val[0], data2.val[0], 1); + input[1][3] = vextq_f32(data1.val[1], data2.val[1], 1); + + data1 = vld2q_f32(r2); + data2 = vld2q_f32(r2 + 8); + input[2][0] = data1.val[0]; + input[2][1] = data1.val[1]; + input[2][2] = vextq_f32(data1.val[0], data2.val[0], 1); + input[2][3] = vextq_f32(data1.val[1], data2.val[1], 1); + + data1 = vld2q_f32(r3); + data2 = vld2q_f32(r3 + 8); + input[3][0] = data1.val[0]; + input[3][1] = data1.val[1]; + input[3][2] = vextq_f32(data1.val[0], data2.val[0], 1); + input[3][3] = vextq_f32(data1.val[1], data2.val[1], 1); + + float32x4_t tmp1 = vdupq_n_f32(0.f); + float32x4_t tmp2 = vdupq_n_f32(0.f); + tmp1 = vmlaq_laneq_f32(tmp1, input[0][0], k[0], 0); + tmp2 = vmlaq_laneq_f32(tmp2, input[0][1], k[0], 1); + tmp1 = vmlaq_laneq_f32(tmp1, input[0][2], k[0], 2); + tmp2 = vmlaq_laneq_f32(tmp2, input[0][3], k[0], 3); + tmp1 = vmlaq_laneq_f32(tmp1, input[1][0], k[1], 0); + tmp2 = vmlaq_laneq_f32(tmp2, input[1][1], k[1], 1); + tmp1 = vmlaq_laneq_f32(tmp1, input[1][2], k[1], 2); + tmp2 = vmlaq_laneq_f32(tmp2, input[1][3], k[1], 3); + tmp1 = vmlaq_laneq_f32(tmp1, input[2][0], k[2], 0); + tmp2 = vmlaq_laneq_f32(tmp2, input[2][1], k[2], 1); + tmp1 = vmlaq_laneq_f32(tmp1, input[2][2], k[2], 2); + tmp2 = vmlaq_laneq_f32(tmp2, input[2][3], k[2], 3); + tmp1 = vmlaq_laneq_f32(tmp1, input[3][0], k[3], 0); + tmp2 = vmlaq_laneq_f32(tmp2, input[3][1], k[3], 1); + tmp1 = vmlaq_laneq_f32(tmp1, input[3][2], k[3], 2); + tmp2 = vmlaq_laneq_f32(tmp2, input[3][3], k[3], 3); + tmp1 = vaddq_f32(tmp1, tmp2); + + vst1q_f32(outputData, tmp1); + r0 += 8; + r1 += 8; + r2 += 8; + r3 += 8; + outputData += 4; + } + + for (int r = 0; r < remain; r++) { + float32x4_t i0 = vld1q_f32(r0); + float32x4_t i1 = vld1q_f32(r1); + float32x4_t i2 = vld1q_f32(r2); + float32x4_t i3 = vld1q_f32(r3); + *outputData = conv4x4(i0, i1, i2, i3, k[0], k[1], k[2], k[3]); + r0 += 2; + r1 += 2; + r2 += 2; + r3 += 2; + outputData++; + } + } + } + } +}; + template class NeonDepthwiseConvFunction : public ConvFunctionBase { public: @@ -449,7 +559,7 @@ public: outputWidth, filterMultiplier, outputData); - } else if (filterWidth == 4) { + } else if (filterWidth == 4 && strideH() == 1) { DepthwiseConvKernel<4, 1>::run(inputPadding, filterData, inputHeight, @@ -459,6 +569,16 @@ public: outputWidth, filterMultiplier, outputData); + } else if (filterWidth == 4 && strideH() == 2) { + DepthwiseConvKernel<4, 2>::run(inputPadding, + filterData, + inputHeight, + inputWidth, + outputChannels, + outputHeight, + outputWidth, + filterMultiplier, + outputData); } inputPadding += inputChannels * inputHeight * inputWidth; -- GitLab From 06fad3fe9deccdc8ee4721ff028753f53c7ab87f Mon Sep 17 00:00:00 2001 From: gongweibao Date: Fri, 25 Aug 2017 12:01:57 +0800 Subject: [PATCH 0221/2018] hidden capi symbols (#3636) hidden capi symbols --- paddle/capi/CMakeLists.txt | 3 +++ paddle/capi/export.map | 6 ++++++ paddle/capi/export.sym | 0 3 files changed, 9 insertions(+) create mode 100644 paddle/capi/export.map create mode 100644 paddle/capi/export.sym diff --git a/paddle/capi/CMakeLists.txt b/paddle/capi/CMakeLists.txt index 11022d175..dde99ab34 100644 --- a/paddle/capi/CMakeLists.txt +++ b/paddle/capi/CMakeLists.txt @@ -53,7 +53,10 @@ add_custom_target(paddle_capi_whole ALL set_target_properties(paddle_capi_whole PROPERTIES IMPORTED_LOCATION ${CMAKE_CURRENT_BINARY_DIR}/${capi_whole_library}) +set(LINK_FLAGS " -Wl,--retain-symbols-file ${CMAKE_CURRENT_SOURCE_DIR}/export.sym -Wl,--version-script ${CMAKE_CURRENT_SOURCE_DIR}/export.map") +# TODO: merge mkl into paddle_capi_shared add_library(paddle_capi_shared SHARED ${CAPI_SOURCES}) +set_target_properties(paddle_capi_shared PROPERTIES LINK_FLAGS "${LINK_FLAGS}") target_include_directories(paddle_capi_shared PUBLIC ${CMAKE_CURRENT_BINARY_DIR}) link_paddle_exe(paddle_capi_shared) diff --git a/paddle/capi/export.map b/paddle/capi/export.map new file mode 100644 index 000000000..8d673f675 --- /dev/null +++ b/paddle/capi/export.map @@ -0,0 +1,6 @@ +{ + global: + paddle_*; + local: + *; +}; diff --git a/paddle/capi/export.sym b/paddle/capi/export.sym new file mode 100644 index 000000000..e69de29bb -- GitLab From 1e61d91f24e9213ab43edc62cf2c6f9e47a62d1f Mon Sep 17 00:00:00 2001 From: Yi Wang Date: Thu, 24 Aug 2017 21:38:13 -0700 Subject: [PATCH 0222/2018] Update index and add Chinese version --- doc/howto/dev/build_cn.md | 100 ++++++++++++++++++++++++++++++++++++++ doc/howto/dev/build_en.md | 6 ++- doc/howto/index_cn.rst | 1 + doc/howto/index_en.rst | 1 + 4 files changed, 107 insertions(+), 1 deletion(-) create mode 100644 doc/howto/dev/build_cn.md diff --git a/doc/howto/dev/build_cn.md b/doc/howto/dev/build_cn.md new file mode 100644 index 000000000..dc372de9f --- /dev/null +++ b/doc/howto/dev/build_cn.md @@ -0,0 +1,100 @@ +# 编译PaddlePaddle和运行单元测试 + +## 需要的软硬件 + +为了开发PaddlePaddle，我们需要 + +1. 一台电脑，可以装的是 Linux, BSD, Windows 或者 MacOS 操作系统，以及 +1. Docker。 + +不需要其他任何软件了。即便是 Python 和 GCC 都不需要，因为我们会把所有编译工具都安装进一个 Docker image 里。 + +## 总体流程 + +1. 获取源码 + + ```bash + git clone https://github.com/paddlepaddle/paddle + ``` + +2. 安装工具 + + ```bash + cd paddle; docker build -t paddle:dev . + ``` + +3. 编译 + + ```bash + docker run -v $PWD:/paddle paddle:dev + ``` + + 这个命令编译出一个 CUDA-enabled 版本。所有二进制文件会被写到本机的 `./build` 目录，而不是写到 Docker container 里。如果我们只需要编译一个只支持 CPU 的版本，可以用 + + ```bash + docker run -e WITH_GPU=OFF -v $PWD:/paddle paddle:dev + ``` + +4. 运行单元测试 + + 用本机的第一个 GPU 来运行包括 GPU 单元测试在内的所有单元测试： + + ```bash + NV_GPU=0 nvidia-docker run -v $PWD:/paddle paddle:dev bash -c "cd /paddle/build; ctest" + ``` + + 如果编译的时候我们用了 `WITH_GPU=OFF` 选项，那么编译过程只会产生 CPU-based 单元测试，那么我们也就不需要 nvidia-docker 来运行单元测试了。我们只需要： + + ```bash + docker run -v $PWD:/paddle paddle:dev bash -c "cd /paddle/build; ctest" + ``` + +## 为什么要 Docker 呀？ + +- 什么是 Docker? + + 如果您没有听说 Docker，可以把它想象为一个类似 virtualenv 的系统，但是虚拟的不仅仅是 Python 的运行环境。 + +- Docker 还是虚拟机？ + + 有人用虚拟机来类比 Docker。需要强调的是：Docker 不会虚拟任何硬件，Docker container 里运行的编译工具实际上都是在本机的 CPU 和操作系统上直接运行的，性能和把编译工具安装在本机运行基本一样。 + +- 为什么用 Docker? + + 把工具和配置都安装在一个 Docker image 里可以标准化编译环境。这样如果遇到问题，其他人可以复现问题以便帮助。 + + 另外，对于习惯使用Windows和MacOS的开发者来说，使用Docker就不用配置交叉编译环境了。 + +- 我可以选择不用Docker吗？ + + 当然可以。大家可以用把开发工具安装进入 Docker image 一样的方式，把这些工具安装到本机。这篇文档介绍基于 Docker 的开发流程，是因为这个流程比其他方法都更简便。 + +- 学习 Docker 有多难？ + + 理解 Docker 并不难，大概花十分钟看一遍 https://zhuanlan.zhihu.com/p/19902938 即可。这可以帮您省掉花一小时安装和配置各种开发工具，以及切换机器时需要新安装的辛苦。别忘了 PaddlePaddle 更新可能导致需要新的开发工具。更别提简化问题复现带来的好处了。 + +- Docker 需要 sudo + + 如果用自己的电脑开发，自然也就有管理员权限（sudo）了。如果用公用的电脑开发，需要请管理员安装和配置好 Docker。此外，PaddlePaddle 项目在努力开始支持其他不需要 sudo 的集装箱技术，比如 rkt。 + +- 我可以用 IDE 吗？ + + 当然可以，因为源码就在本机上。IDE 默认调用 make 之类的程序来编译源码，我们只需要配置 IDE 来调用 Docker 命令编译源码即可。 + + 很多 PaddlePaddle 开发者使用 Emacs。他们在自己的 `~/.emacs` 配置文件里加两行 + + ```emacs + (global-set-key "\C-cc" 'compile) + (setq compile-command + "docker run --rm -it -v $(git rev-parse --show-toplevel):/paddle paddle:dev") + ``` + + 就可以按 `Ctrl-C` 和 `c` 键来启动编译了。 + +- 可以并行编译吗？ + + 是的。我们的 Docker image 运行一个 Bash 脚本 https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/scripts/docker/build.sh 。这个脚本调用 `make -j$(nproc)` 来启动和 CPU 核一样多的进程来并行编译。 + +- Docker on Windows/MacOS？ + + Docker 在 Windows 和 MacOS 都可以运行。不过实际上是运行在一个 Linux 虚拟机上。可能需要注意给这个虚拟机多分配一些 CPU 和内存，以保证编译高效。具体做法请参考 https://github.com/PaddlePaddle/Paddle/issues/627 。 diff --git a/doc/howto/dev/build_en.md b/doc/howto/dev/build_en.md index de0733f96..640d12601 100644 --- a/doc/howto/dev/build_en.md +++ b/doc/howto/dev/build_en.md @@ -91,6 +91,10 @@ Nothing else. Not even Python and GCC, because you can install all build tools so they could type `Ctrl-C` and `c` to build PaddlePaddle from source. -- How many parallel building processes does the Docker container run? +- Does Docker do parallel building? Our building Docker image runs a Bash script https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/scripts/docker/build.sh, which calls `make -j$(nproc)` to starts as many processes as the number of your processors. + +- Docker on Windows/MacOS? + + On Windows and MacOS, Docker containers run in a Linux VM. You might want to give this VM some more memory and CPUs so to make the building efficient. Please refer to https://github.com/PaddlePaddle/Paddle/issues/627 for details. diff --git a/doc/howto/index_cn.rst b/doc/howto/index_cn.rst index 26449a636..0608aa309 100644 --- a/doc/howto/index_cn.rst +++ b/doc/howto/index_cn.rst @@ -19,6 +19,7 @@ .. toctree:: :maxdepth: 1 + dev/build_cn.rst dev/write_docs_cn.rst dev/contribute_to_paddle_cn.md diff --git a/doc/howto/index_en.rst b/doc/howto/index_en.rst index 1fbfcd260..1b6034be4 100644 --- a/doc/howto/index_en.rst +++ b/doc/howto/index_en.rst @@ -18,6 +18,7 @@ Development .. toctree:: :maxdepth: 1 + dev/build_en.rst dev/new_layer_en.rst dev/contribute_to_paddle_en.md -- GitLab From 7a42c92d49cbcf05bb7c8fc698b923a09503d22e Mon Sep 17 00:00:00 2001 From: caoying03 Date: Thu, 24 Aug 2017 10:53:00 +0800 Subject: [PATCH 0223/2018] fix a bug that memory does not clean. --- .../gserver/layers/CrossEntropyOverBeam.cpp | 21 ++++++++++++------- 1 file changed, 13 insertions(+), 8 deletions(-) diff --git a/paddle/gserver/layers/CrossEntropyOverBeam.cpp b/paddle/gserver/layers/CrossEntropyOverBeam.cpp index f7736f0ce..b7c2a4462 100644 --- a/paddle/gserver/layers/CrossEntropyOverBeam.cpp +++ b/paddle/gserver/layers/CrossEntropyOverBeam.cpp @@ -53,8 +53,8 @@ size_t CostForOneSequence::initLastExpansion() { candidates->getData() + height * beamSize_, [](const real& val) { return val != -1; }); /* - * if the gold sequence falls off the beam during search, - * add the gold sequence as the last path into all expanded paths. + * if the gold sequence falls off the beam during search, add the gold + * sequence as the last path into the all expanded candidates. */ if (goldAsExtraPath_) goldIdsInFinalExpansion_ = pathCount++; @@ -133,7 +133,7 @@ real CostForOneSequence::globallyNormalizedScore() { Matrix::resizeOrCreate( softmaxOut_, 1, pathRowIdsInEachBeam_[0].size(), false, false); - softmaxOut_->zero(); + softmaxOut_->zeroMem(); MatrixPtr tmp = Matrix::create( softmaxOut_->getData(), softmaxOut_->getWidth(), 1, false, false); @@ -143,6 +143,8 @@ real CostForOneSequence::globallyNormalizedScore() { 1, false, false); + expandedPathScores_[i]->zeroMem(); + IVectorPtr rowIds = IVector::create(pathRowIdsInEachBeam_[i].data(), pathRowIdsInEachBeam_[i].size(), false); @@ -217,13 +219,16 @@ void CrossEntropyOverBeam::checkInputs() { const Argument& goldSeq = getInput(i * 3 + 2); if (i) { - CHECK(scores.hasSubseq()) << "Beam expansion expect the first one, " - "should be a nested sequence"; + CHECK(scores.hasSubseq()) << "input " << i << " " + << inputLayers_[i * 3]->getName() + << " should be a nested sequence"; CHECK_EQ(getInputValue(i * 3 + 1)->getWidth(), beamSize_); CHECK_EQ(scores.getNumSequences(), batchSize_); CHECK_EQ(scores.getNumSubSequences(), selCandidates.getBatchSize()); } else { - CHECK(scores.hasSeq()) << "The first beam expansion should be a sequence"; + CHECK(scores.hasSeq()) << "input " << i << " " + << inputLayers_[i]->getName() + << " should be a sequence"; batchSize_ = scores.getNumSequences(); beamSize_ = getInputValue(i * 3 + 1)->getWidth(); CHECK_EQ(batchSize_, selCandidates.getBatchSize()); @@ -332,7 +337,7 @@ void CrossEntropyOverBeam::splitBatchBeams() { void CrossEntropyOverBeam::resizeOutput() { Matrix::resizeOrCreate(output_.value, batchSize_, 1, false, false); - output_.value->zero(); + output_.value->zeroMem(); for (size_t i = 0; i < beamExpanCount_; ++i) { MatrixPtr inGrad = getInputGrad(i * 3); @@ -344,7 +349,7 @@ void CrossEntropyOverBeam::resizeOutput() { false); } else candidateScoreGrad_[i] = std::move(inGrad); - candidateScoreGrad_[i]->zero(); + candidateScoreGrad_[i]->zeroMem(); } } -- GitLab From 818a64f41ffacca0d3ff07928a19ac47021ccac1 Mon Sep 17 00:00:00 2001 From: wanghaoshuang Date: Fri, 25 Aug 2017 13:56:55 +0800 Subject: [PATCH 0224/2018] Fix img_pool_layer bug. --- python/paddle/trainer_config_helpers/layers.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py index f323b017c..862265f2c 100755 --- a/python/paddle/trainer_config_helpers/layers.py +++ b/python/paddle/trainer_config_helpers/layers.py @@ -2607,15 +2607,15 @@ def img_pool_layer(input, assert input.num_filters is not None num_channels = input.num_filters - assert type(pool_type) in [AvgPooling, MaxPooling, CudnnAvgPooling, - CudnnMaxPooling], \ - "only (Cudnn)AvgPooling, (Cudnn)MaxPooling are supported" - if pool_type is None: pool_type = MaxPooling() elif isinstance(pool_type, AvgPooling): pool_type.name = 'avg' + assert type(pool_type) in [AvgPooling, MaxPooling, CudnnAvgPooling, + CudnnMaxPooling], \ + "only (Cudnn)AvgPooling, (Cudnn)MaxPooling are supported" + type_name = pool_type.name + '-projection' \ if ( isinstance(pool_type, AvgPooling) or isinstance(pool_type, MaxPooling)) \ -- GitLab From aa28d046fb828814b9849aa1ebfc868be2db98f9 Mon Sep 17 00:00:00 2001 From: caoying03 Date: Fri, 25 Aug 2017 14:11:36 +0800 Subject: [PATCH 0225/2018] fix a bug of sequence_slice layer when batch_size=1 --- paddle/gserver/layers/SequenceSliceLayer.cpp | 18 ++++++++++-------- .../gserver/tests/test_SeqSliceLayerGrad.cpp | 4 +++- 2 files changed, 13 insertions(+), 9 deletions(-) diff --git a/paddle/gserver/layers/SequenceSliceLayer.cpp b/paddle/gserver/layers/SequenceSliceLayer.cpp index 5d72d3730..aab44c464 100644 --- a/paddle/gserver/layers/SequenceSliceLayer.cpp +++ b/paddle/gserver/layers/SequenceSliceLayer.cpp @@ -130,6 +130,8 @@ void SequenceSliceLayer::calSelectedRows(const MatrixPtr starts, CHECK(starts || ends) << "At least one of the start or end indices " << "should be given."; + bool hasSubseq = getInput(0).hasSubseq(); + outSeqStartPos_.resize(1, 0); outSubSeqStartPos_.resize(1, 0); selectedRows_.clear(); @@ -151,14 +153,13 @@ void SequenceSliceLayer::calSelectedRows(const MatrixPtr starts, int seqLen = endPos - begPos + 1; CHECK_GT(seqLen, 0U); for (int m = begPos; m <= endPos; ++m) selectedRows_.push_back(m); - inputSeqInfoVec_.size() > 1 + hasSubseq ? outSubSeqStartPos_.push_back(outSubSeqStartPos_.back() + seqLen) : outSeqStartPos_.push_back(outSeqStartPos_.back() + seqLen); } rowIdx++; } - if (inputSeqInfoVec_.size() > 1) - outSeqStartPos_.push_back(outSubSeqStartPos_.back()); + if (hasSubseq) outSeqStartPos_.push_back(outSubSeqStartPos_.back()); } if (useGpu_) { @@ -175,7 +176,7 @@ void SequenceSliceLayer::calSelectedRows(const MatrixPtr starts, output_.sequenceStartPositions->copyFrom( outSeqStartPos_.data(), outSeqStartPos_.size(), false); - if (inputSeqInfoVec_.size() > 1) { + if (hasSubseq) { ICpuGpuVector::resizeOrCreate( output_.subSequenceStartPositions, outSubSeqStartPos_.size(), false); output_.subSequenceStartPositions->copyFrom( @@ -203,10 +204,11 @@ void SequenceSliceLayer::forward(PassType passType) { } else copySliceIdsToCpu(); - // calculate the selected row indices in a batch, - // and build the output sequence information. - calSelectedRows(startIdsOnCpu_ ? startIdsOnCpu_ : nullptr, - endIdsOnCpu_ ? endIdsOnCpu_ : nullptr); + /* + * calculate the selected row indices in a batch, and build the output + * sequence information. + */ + calSelectedRows(startIdsOnCpu_, endIdsOnCpu_); resetOutput(selectedRows_.size(), getSize()); diff --git a/paddle/gserver/tests/test_SeqSliceLayerGrad.cpp b/paddle/gserver/tests/test_SeqSliceLayerGrad.cpp index d560ca650..e1d4ae161 100644 --- a/paddle/gserver/tests/test_SeqSliceLayerGrad.cpp +++ b/paddle/gserver/tests/test_SeqSliceLayerGrad.cpp @@ -30,6 +30,8 @@ const int MAX_SEQ_NUM = 17; const int MAX_SEQ_LEN = 23; const int MAX_BEAM_SIZE = 13; +const size_t SEED = (size_t)(time(NULL)); + vector randSampling(real range, int n) { CHECK_GE(range, n); vector num(range); @@ -46,7 +48,7 @@ void genSeqInfo(vector& seqStartPos, vector& subSeqStartPos) { seqStartPos.resize(1, 0); subSeqStartPos.resize(1, 0); - srand((size_t)(time(NULL))); + srand(SEED); int seqNum = 1 + (rand() % MAX_SEQ_NUM); for (int i = 0; i < seqNum; ++i) { int subSeqNum = 1 + (rand() % MAX_SEQ_NUM); -- GitLab From 4cc57836f393ada9b65cfeef444662afc34f1109 Mon Sep 17 00:00:00 2001 From: tensor-tang Date: Fri, 25 Aug 2017 17:20:28 +0800 Subject: [PATCH 0226/2018] enable reorder --- paddle/gserver/layers/MKLDNNFcLayer.cpp | 39 +++++------------ paddle/math/MKLDNNMatrix.cpp | 57 +++++++++++++++++++++++++ paddle/math/MKLDNNMatrix.h | 33 ++++++++++++-- 3 files changed, 97 insertions(+), 32 deletions(-) diff --git a/paddle/gserver/layers/MKLDNNFcLayer.cpp b/paddle/gserver/layers/MKLDNNFcLayer.cpp index a5555c461..ad50c15a7 100644 --- a/paddle/gserver/layers/MKLDNNFcLayer.cpp +++ b/paddle/gserver/layers/MKLDNNFcLayer.cpp @@ -61,39 +61,20 @@ void MKLDNNFcLayer::convertWeightsFromPaddle() { return; } - // TODO(TJ): dst format should get from wgtVal_ - int dstFmt = PARAM_FORMAT_MKLDNN_OI; - int srcFmt = weight_->getParameterPtr()->getHeaderFormat(); - if (srcFmt == dstFmt) { - return; - } - - // The weight_ is transposed from initial paddle weight - MatrixPtr paddleWgt = Matrix::create( - weight_->getW()->getData(), iLayerSize_, oc_, false, false); - - // TODO(TJ): remove this print when do not need differ weights - std::ostringstream ostr; - paddleWgt->print(ostr); - VLOG(MKLDNN_ALL) << "Initial Weight from paddle: " << std::endl << ostr.str(); - - // The mkldnn weight is transposed from initial paddle matrix - MatrixPtr paddleWgtT; - paddleWgt->transpose(paddleWgtT, true); - weight_->getW()->copyFrom(*paddleWgtT); - weight_->getParameterPtr()->setHeaderFormat(dstFmt); + CHECK(wgtVal_) << "should have been initialized"; + bool hasNoSpatial_ = ih_ == 1 && iw_ == 1; + auto targetDim = wgtVal_->getDims(); + auto srcFmt = hasNoSpatial_ ? memory::format::io : memory::format::ihwo; + wgtVal_->reorderDataFrom(wgtVal_, srcFmt, targetDim); hasInitedWgt_ = true; } void MKLDNNFcLayer::convertWeightsToPaddle() { - MatrixPtr dnnWgt = weight_->getW(); - MatrixPtr paddleWgt; - dnnWgt->transpose(paddleWgt, true); - - // copy paddle weight and override on weight_ - MatrixPtr dnnWgtT = Matrix::create( - dnnWgt->getData(), dnnWgt->getWidth(), dnnWgt->getHeight(), false, false); - dnnWgtT->copyFrom(*paddleWgt); + CHECK(wgtVal_) << "should have been initialized"; + bool hasNoSpatial_ = ih_ == 1 && iw_ == 1; + auto targetDim = wgtVal_->getDims(); + auto dstFmt = hasNoSpatial_ ? memory::format::io : memory::format::ihwo; + wgtVal_->reorderDataTo(wgtVal_, dstFmt, targetDim); } void MKLDNNFcLayer::reshape() { diff --git a/paddle/math/MKLDNNMatrix.cpp b/paddle/math/MKLDNNMatrix.cpp index 94df9c155..32ae3b1bc 100644 --- a/paddle/math/MKLDNNMatrix.cpp +++ b/paddle/math/MKLDNNMatrix.cpp @@ -56,6 +56,63 @@ MKLDNNMatrixPtr MKLDNNMatrix::create(MatrixPtr m, return create(m, pd); } +void MKLDNNMatrix::reorderDataFrom(const MKLDNNMatrixPtr& m, + memory::format srcFmt, + memory::dims targetDim) { + memory::format dstFmt = getFormat(); + if (srcFmt == dstFmt) { + return; + } + CHECK_EQ(getElementCnt(), m->getElementCnt()) << "size should equal"; + real* srcData = getData(); + real* dstData = m->getData(); + reorderOnce(srcData, dstData, srcFmt, dstFmt, targetDim); +} + +void MKLDNNMatrix::reorderDataTo(const MKLDNNMatrixPtr& m, + memory::format dstFmt, + memory::dims targetDim) { + memory::format srcFmt = getFormat(); + if (srcFmt == dstFmt) { + return; + } + CHECK_EQ(getElementCnt(), m->getElementCnt()) << "size should equal"; + real* srcData = getData(); + real* dstData = m->getData(); + reorderOnce(srcData, dstData, srcFmt, dstFmt, targetDim); +} + +void MKLDNNMatrix::reorderOnce(void* srcData, + void* dstData, + memory::format srcFmt, + memory::format dstFmt, + memory::dims dm) { + CHECK(srcData); + CHECK(dstData); + MatrixPtr tmpSrc; + if (dstData == srcData) { + // inplace data + size_t sz = 1; + for (size_t i = 0; i < dm.size(); ++i) { + sz *= dm[i]; + } + tmpSrc = Matrix::create(sz, 1, false, false); + tmpSrc->copyFrom((real*)srcData, sz); + srcData = tmpSrc->getData(); + } + + auto dtype = this->getDtype(); + auto srcMD = memory::desc(dm, dtype, srcFmt); + auto dstMD = memory::desc(dm, dtype, dstFmt); + + auto eg = this->getEngine(); + auto src = memory(memory::primitive_desc(srcMD, eg), srcData); + auto dst = memory(memory::primitive_desc(dstMD, eg), dstData); + + auto r = reorder(src, dst); + stream(stream::kind::eager).submit({r}).wait(); +} + void MKLDNNMatrix::downSpatial() { int fmt = getFormat(); if (!(fmt == memory::format::nchw || fmt == memory::format::oihw)) { diff --git a/paddle/math/MKLDNNMatrix.h b/paddle/math/MKLDNNMatrix.h index 05adc867c..ea3fd7d46 100644 --- a/paddle/math/MKLDNNMatrix.h +++ b/paddle/math/MKLDNNMatrix.h @@ -21,9 +21,6 @@ limitations under the License. */ namespace paddle { -static const std::map PARAM_FOARMAT_MAP = - {{mkldnn::memory::format::oi, PARAM_FORMAT_MKLDNN_OI}}; - class MKLDNNMatrix; typedef std::shared_ptr MKLDNNMatrixPtr; @@ -57,6 +54,26 @@ public: mkldnn::memory::data_type dtype = mkldnn::memory::data_type::f32); public: + /** + * Reorder this MKLDNNMatrix from other format. + * Support inplace reorder + * Pay attention: this function would only reorder the data layout. + * will NOT change this original dim or format info + */ + void reorderDataFrom(const MKLDNNMatrixPtr& m, + memory::format srcFmt, + memory::dims targetDim); + + /** + * Reorder this MKLDNNMatrix to other format. + * Support inplace reorder + * Pay attention: this function would only reorder the data layout. + * will NOT change the dst dim or format info + */ + void reorderDataTo(const MKLDNNMatrixPtr& m, + memory::format dstFmt, + memory::dims targetDim); + /** * Dimensionality reduction. * Change format "nchw --> nc" or "oihw --> oi" if the h and w are both 1 @@ -113,6 +130,16 @@ public: * Get engine. */ mkldnn::engine getEngine() { return getPD().get_engine(); } + +protected: + /** + * Do once reorder supported inplace. + */ + void reorderOnce(void* srcData, + void* dstData, + memory::format srcFmt, + memory::format dstFmt, + memory::dims dm); }; } // namespace paddle -- GitLab From 7035bb63e91a2dcf1f91df5e440d2c3e45bdd2e8 Mon Sep 17 00:00:00 2001 From: caoying03 Date: Fri, 25 Aug 2017 20:44:04 +0800 Subject: [PATCH 0227/2018] fix a bug. --- paddle/parameter/Argument.cpp | 1 + 1 file changed, 1 insertion(+) diff --git a/paddle/parameter/Argument.cpp b/paddle/parameter/Argument.cpp index 2b945de18..b0e9e740c 100644 --- a/paddle/parameter/Argument.cpp +++ b/paddle/parameter/Argument.cpp @@ -677,6 +677,7 @@ void Argument::reorganizeSeqInfo( const ICpuGpuVectorPtr subSeqStartPos, std::vector>& reorganizedSeqInfo) { CHECK(seqStartPos); + reorganizedSeqInfo.clear(); int seqNum = seqStartPos->getSize() - 1; int* seqStarts = seqStartPos->getMutableData(false); -- GitLab From c8d0c9af865cd0ac47d1cd7461c24793d833eeff Mon Sep 17 00:00:00 2001 From: Yi Wang Date: Fri, 25 Aug 2017 11:24:48 -0700 Subject: [PATCH 0228/2018] In response to comments from Luo Tao --- doc/howto/dev/build_cn.md | 6 +++--- doc/howto/dev/build_en.md | 6 +++--- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/doc/howto/dev/build_cn.md b/doc/howto/dev/build_cn.md index dc372de9f..7c9557963 100644 --- a/doc/howto/dev/build_cn.md +++ b/doc/howto/dev/build_cn.md @@ -71,7 +71,7 @@ - 学习 Docker 有多难？ - 理解 Docker 并不难，大概花十分钟看一遍 https://zhuanlan.zhihu.com/p/19902938 即可。这可以帮您省掉花一小时安装和配置各种开发工具，以及切换机器时需要新安装的辛苦。别忘了 PaddlePaddle 更新可能导致需要新的开发工具。更别提简化问题复现带来的好处了。 + 理解 Docker 并不难，大概花十分钟看一下[这篇文章](https://zhuanlan.zhihu.com/p/19902938)。这可以帮您省掉花一小时安装和配置各种开发工具，以及切换机器时需要新安装的辛苦。别忘了 PaddlePaddle 更新可能导致需要新的开发工具。更别提简化问题复现带来的好处了。 - Docker 需要 sudo @@ -93,8 +93,8 @@ - 可以并行编译吗？ - 是的。我们的 Docker image 运行一个 Bash 脚本 https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/scripts/docker/build.sh 。这个脚本调用 `make -j$(nproc)` 来启动和 CPU 核一样多的进程来并行编译。 + 是的。我们的 Docker image 运行一个 [Bash 脚本](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/scripts/docker/build.sh)。这个脚本调用 `make -j$(nproc)` 来启动和 CPU 核一样多的进程来并行编译。 - Docker on Windows/MacOS？ - Docker 在 Windows 和 MacOS 都可以运行。不过实际上是运行在一个 Linux 虚拟机上。可能需要注意给这个虚拟机多分配一些 CPU 和内存，以保证编译高效。具体做法请参考 https://github.com/PaddlePaddle/Paddle/issues/627 。 + Docker 在 Windows 和 MacOS 都可以运行。不过实际上是运行在一个 Linux 虚拟机上。可能需要注意给这个虚拟机多分配一些 CPU 和内存，以保证编译高效。具体做法请参考[这个issue](https://github.com/PaddlePaddle/Paddle/issues/627)。 diff --git a/doc/howto/dev/build_en.md b/doc/howto/dev/build_en.md index 640d12601..3be2405ea 100644 --- a/doc/howto/dev/build_en.md +++ b/doc/howto/dev/build_en.md @@ -71,7 +71,7 @@ Nothing else. Not even Python and GCC, because you can install all build tools - How difficult is it to learn Docker? - It takes you ten minutes to read https://docs.docker.com/get-started/ and saves you more than one hour to install all required build tools, configure them, and upgrade them when new versions of PaddlePaddle require some new tools. + It takes you ten minutes to read [an introductory article](https://docs.docker.com/get-started) and saves you more than one hour to install all required build tools, configure them, and upgrade them when new versions of PaddlePaddle require some new tools. - Docker requires sudo @@ -93,8 +93,8 @@ Nothing else. Not even Python and GCC, because you can install all build tools - Does Docker do parallel building? - Our building Docker image runs a Bash script https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/scripts/docker/build.sh, which calls `make -j$(nproc)` to starts as many processes as the number of your processors. + Our building Docker image runs a [Bash script](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/scripts/docker/build.sh), which calls `make -j$(nproc)` to starts as many processes as the number of your processors. - Docker on Windows/MacOS? - On Windows and MacOS, Docker containers run in a Linux VM. You might want to give this VM some more memory and CPUs so to make the building efficient. Please refer to https://github.com/PaddlePaddle/Paddle/issues/627 for details. + On Windows and MacOS, Docker containers run in a Linux VM. You might want to give this VM some more memory and CPUs so to make the building efficient. Please refer to [this issue](https://github.com/PaddlePaddle/Paddle/issues/627) for details. -- GitLab From f71f3935e3ce05a8e90edc971f5ab08d71ed2966 Mon Sep 17 00:00:00 2001 From: Yi Wang Date: Fri, 25 Aug 2017 11:51:53 -0700 Subject: [PATCH 0229/2018] In response to comments from Chen Xi --- doc/howto/dev/build_cn.md | 20 +++++++++++++------- doc/howto/dev/build_en.md | 34 ++++++++++++++++++++-------------- 2 files changed, 33 insertions(+), 21 deletions(-) diff --git a/doc/howto/dev/build_cn.md b/doc/howto/dev/build_cn.md index 7c9557963..0077d9011 100644 --- a/doc/howto/dev/build_cn.md +++ b/doc/howto/dev/build_cn.md @@ -23,13 +23,17 @@ cd paddle; docker build -t paddle:dev . ``` + 请注意这个命令结尾处的 `.`；它表示 `docker build` 应该读取当前目录下的 [`Dockerfile`文件](https://github.com/PaddlePaddle/Paddle/blob/develop/Dockerfile)，按照其内容创建一个名为 `paddle:dev` 的 Docker image，并且把各种开发工具安装进去。 + 3. 编译 + 以下命令启动一个 Docker container 来执行 `paddle:dev` 这个 Docker image，同时把当前目录（源码树根目录）映射为 container 里的 `/paddle` 目录，并且运行 `Dockerfile` 描述的默认入口程序 [`build.sh`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/scripts/docker/build.sh)。这个脚本调用 `cmake` 和 `make` 来编译 `/paddle` 里的源码，结果输出到 `/paddle/build`，也就是本地的源码树根目录里的 `build` 子目录。 + ```bash docker run -v $PWD:/paddle paddle:dev ``` - 这个命令编译出一个 CUDA-enabled 版本。所有二进制文件会被写到本机的 `./build` 目录，而不是写到 Docker container 里。如果我们只需要编译一个只支持 CPU 的版本，可以用 + 上述命令编译出一个 CUDA-enabled 版本。如果我们只需要编译一个只支持 CPU 的版本，可以用 ```bash docker run -e WITH_GPU=OFF -v $PWD:/paddle paddle:dev @@ -57,7 +61,7 @@ - Docker 还是虚拟机？ - 有人用虚拟机来类比 Docker。需要强调的是：Docker 不会虚拟任何硬件，Docker container 里运行的编译工具实际上都是在本机的 CPU 和操作系统上直接运行的，性能和把编译工具安装在本机运行基本一样。 + 有人用虚拟机来类比 Docker。需要强调的是：Docker 不会虚拟任何硬件，Docker container 里运行的编译工具实际上都是在本机的 CPU 和操作系统上直接运行的，性能和把编译工具安装在本机运行一样。 - 为什么用 Docker? @@ -73,10 +77,6 @@ 理解 Docker 并不难，大概花十分钟看一下[这篇文章](https://zhuanlan.zhihu.com/p/19902938)。这可以帮您省掉花一小时安装和配置各种开发工具，以及切换机器时需要新安装的辛苦。别忘了 PaddlePaddle 更新可能导致需要新的开发工具。更别提简化问题复现带来的好处了。 -- Docker 需要 sudo - - 如果用自己的电脑开发，自然也就有管理员权限（sudo）了。如果用公用的电脑开发，需要请管理员安装和配置好 Docker。此外，PaddlePaddle 项目在努力开始支持其他不需要 sudo 的集装箱技术，比如 rkt。 - - 我可以用 IDE 吗？ 当然可以，因为源码就在本机上。IDE 默认调用 make 之类的程序来编译源码，我们只需要配置 IDE 来调用 Docker 命令编译源码即可。 @@ -95,6 +95,12 @@ 是的。我们的 Docker image 运行一个 [Bash 脚本](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/scripts/docker/build.sh)。这个脚本调用 `make -j$(nproc)` 来启动和 CPU 核一样多的进程来并行编译。 -- Docker on Windows/MacOS？ +## 可能碰到的问题 + +- Docker 需要 sudo + + 如果用自己的电脑开发，自然也就有管理员权限（sudo）了。如果用公用的电脑开发，需要请管理员安装和配置好 Docker。此外，PaddlePaddle 项目在努力开始支持其他不需要 sudo 的集装箱技术，比如 rkt。 + +- 在 Windows/MacOS 上编译很慢 Docker 在 Windows 和 MacOS 都可以运行。不过实际上是运行在一个 Linux 虚拟机上。可能需要注意给这个虚拟机多分配一些 CPU 和内存，以保证编译高效。具体做法请参考[这个issue](https://github.com/PaddlePaddle/Paddle/issues/627)。 diff --git a/doc/howto/dev/build_en.md b/doc/howto/dev/build_en.md index 3be2405ea..95752beba 100644 --- a/doc/howto/dev/build_en.md +++ b/doc/howto/dev/build_en.md @@ -7,7 +7,7 @@ To contribute to PaddlePaddle, you need 1. A computer -- Linux, BSD, Windows, MacOS, and 1. Docker. -Nothing else. Not even Python and GCC, because you can install all build tools into a Docker image. +Nothing else. Not even Python and GCC, because you can install all build tools into a Docker image. We run all the tools by running this image. ## General Process @@ -17,19 +17,23 @@ Nothing else. Not even Python and GCC, because you can install all build tools git clone https://github.com/paddlepaddle/paddle ``` -2. Install build tools. +2. Install build tools into a Docker image. ```bash cd paddle; docker build -t paddle:dev . ``` + Please be aware of the `.` at the end of the command, which refers to the [`./Dockerfile` file](https://github.com/PaddlePaddle/Paddle/blob/develop/Dockerfile). `docker build` follows instructions in this file to create a Docker image named `paddle:dev`, and installs building tools into it. + 3. Build from source. + This following command starts a Docker container that executes the Docker image `paddle:dev`, mapping the current directory to `/paddle/` in the container, and runs the default entry-point [`build.sh`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/scripts/docker/build.sh) as specified in the Dockefile. `build.sh` invokes `cmake` and `make` to build PaddlePaddle source code, which had been mapped to `/paddle`, and writes outputs to `/paddle/build`, which maps to `build` in the current source directory on the computer. + ```bash docker run -v $PWD:/paddle paddle:dev ``` - This builds a CUDA-enabled version and writes all binary outputs to directory `./build` of the local computer, other than the Docker container. If we want to build only the CPU part, we can type + Above command builds a CUDA-enabled version. If we want to build a CPU-only version, we can type ```bash docker run -e WITH_GPU=OFF -v $PWD:/paddle paddle:dev @@ -57,25 +61,21 @@ Nothing else. Not even Python and GCC, because you can install all build tools - Docker or virtual machine? - Some people compare Docker with VMs, but Docker doesn't virtualize any hardware, and it doesn't run a guest OS. + Some people compare Docker with VMs, but Docker doesn't virtualize any hardware nor running a guest OS, which means there is no compromise on the performance. - Why Docker? - Using a Docker image of build tools standardize the building environment, and easier for others to reproduce your problem, if there is any, and help. + Using a Docker image of build tools standardizes the building environment, which makes it easier for others to reproduce your problems and to help. Also, some build tools don't run on Windows or Mac or BSD, but Docker runs almost everywhere, so developers can use whatever computer they want. -- Can I don't use Docker? +- Can I choose not to use Docker? - Sure, you don't have to install build tools into a Docker image; instead, you can install them onto your local computer. This document exists because Docker would make the development way easier. + Sure, you don't have to install build tools into a Docker image; instead, you can install them in your local computer. This document exists because Docker would make the development way easier. - How difficult is it to learn Docker? - It takes you ten minutes to read [an introductory article](https://docs.docker.com/get-started) and saves you more than one hour to install all required build tools, configure them, and upgrade them when new versions of PaddlePaddle require some new tools. - -- Docker requires sudo - - An owner of a computer has the administrative privilege, a.k.a., sudo. If you use a shared computer for development, please ask the administrator to install and configure Docker. We will do our best to support rkt, another container technology that doesn't require sudo. + It takes you ten minutes to read [an introductory article](https://docs.docker.com/get-started) and saves you more than one hour to install all required build tools, configure them, especially when new versions of PaddlePaddle require some new tools. Not even to mention the time saved when other people trying to reproduce the issue you have. - Can I use my favorite IDE? @@ -93,8 +93,14 @@ Nothing else. Not even Python and GCC, because you can install all build tools - Does Docker do parallel building? - Our building Docker image runs a [Bash script](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/scripts/docker/build.sh), which calls `make -j$(nproc)` to starts as many processes as the number of your processors. + Our building Docker image runs a [Bash script](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/scripts/docker/build.sh), which calls `make -j$(nproc)` to starts as many processes as the number of your CPU cores. + +## Some Gotchas + +- Docker requires sudo + + An owner of a computer has the administrative privilege, a.k.a., sudo, and Docker requires this privilege to work properly. If you use a shared computer for development, please ask the administrator to install and configure Docker. We will do our best to support rkt, another container technology that doesn't require sudo. -- Docker on Windows/MacOS? +- Docker on Windows/MacOS builds slowly On Windows and MacOS, Docker containers run in a Linux VM. You might want to give this VM some more memory and CPUs so to make the building efficient. Please refer to [this issue](https://github.com/PaddlePaddle/Paddle/issues/627) for details. -- GitLab From 4b0235c1f2792cdecfe7d8f3e0bb1d0c57c6f361 Mon Sep 17 00:00:00 2001 From: Yi Wang Date: Fri, 25 Aug 2017 14:31:02 -0700 Subject: [PATCH 0230/2018] Update build.sh --- paddle/scripts/docker/build.sh | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/paddle/scripts/docker/build.sh b/paddle/scripts/docker/build.sh index 7bab814ae..179864202 100644 --- a/paddle/scripts/docker/build.sh +++ b/paddle/scripts/docker/build.sh @@ -63,12 +63,11 @@ cmake .. \ cat < Date: Fri, 25 Aug 2017 14:43:29 -0700 Subject: [PATCH 0231/2018] Run a specific test --- doc/howto/dev/build_cn.md | 6 ++++++ doc/howto/dev/build_en.md | 6 ++++++ 2 files changed, 12 insertions(+) diff --git a/doc/howto/dev/build_cn.md b/doc/howto/dev/build_cn.md index 0077d9011..79b4ff9d5 100644 --- a/doc/howto/dev/build_cn.md +++ b/doc/howto/dev/build_cn.md @@ -53,6 +53,12 @@ docker run -v $PWD:/paddle paddle:dev bash -c "cd /paddle/build; ctest" ``` + 有时候我们只想运行一个特定的单元测试，比如 `memory_test`，我们可以 + + ```bash + docker run -v $PWD:/paddle paddle:dev bash -c "cd /paddle/build; ctest -V -R memory_test" + ``` + ## 为什么要 Docker 呀？ - 什么是 Docker? diff --git a/doc/howto/dev/build_en.md b/doc/howto/dev/build_en.md index 95752beba..e1b55929f 100644 --- a/doc/howto/dev/build_en.md +++ b/doc/howto/dev/build_en.md @@ -53,6 +53,12 @@ Nothing else. Not even Python and GCC, because you can install all build tools docker run -v $PWD:/paddle paddle:dev bash -c "cd /paddle/build; ctest" ``` + Sometimes we want to run a specific unit test, say `memory_test`, we can run + + ```bash + docker run -v $PWD:/paddle paddle:dev bash -c "cd /paddle/build; ctest -V -R memory_test" + ``` + ## Docker, Or Not? - What is Docker? -- GitLab From 97649bf9b251707803b2665dedf1ef8f929d8c88 Mon Sep 17 00:00:00 2001 From: zchen0211 Date: Fri, 25 Aug 2017 22:08:24 +0000 Subject: [PATCH 0232/2018] fix codes in scatter --- paddle/operators/scatter_op.cc | 26 +++++++++++++------ paddle/operators/scatter_op.h | 6 ++--- .../v2/framework/tests/gradient_checker.py | 13 +++++----- .../v2/framework/tests/test_scatter_op.py | 1 - 4 files changed, 28 insertions(+), 18 deletions(-) diff --git a/paddle/operators/scatter_op.cc b/paddle/operators/scatter_op.cc index cf01ef627..f901edefa 100644 --- a/paddle/operators/scatter_op.cc +++ b/paddle/operators/scatter_op.cc @@ -24,8 +24,18 @@ class ScatterOp : public framework::OperatorWithKernel { protected: void InferShape(const framework::InferShapeContext &ctx) const override { - framework::DDim output_dims(ctx.Input("Ref")->dims()); - ctx.Output("Out")->Resize(output_dims); + PADDLE_ENFORCE_EQ(ctx.Input("Index")->dims().size(), 1, + "Update Index should be 1-D."); + PADDLE_ENFORCE_EQ(ctx.Input("Ref")->dims().size(), + ctx.Input("Updates")->dims().size(), + "Reference and Updates should have the same shape size"); + PADDLE_ENFORCE_EQ(ctx.Input("Updates")->dims()[0], + ctx.Input("Index")->dims()[0], + "Updates and Index should have same batch-size."); + framework::DDim data_dim(ctx.Input("Updates")->dims()); + for (int i = 1; i < data_dim.size(); ++i) + PADDLE_ENFORCE_EQ(data_dim[i], ctx.Input("Updates")->dims()[i]); + ctx.Output("Out")->Resize(ctx.Input("Ref")->dims()); } }; @@ -35,13 +45,13 @@ class ScatterGradOp : public framework::OperatorWithKernel { protected: void InferShape(const framework::InferShapeContext &ctx) const override { - auto Updates_grad = ctx.Output(framework::GradVarName("Updates")); - auto Updates = ctx.Input("Updates"); - auto Ref_grad = ctx.Output(framework::GradVarName("Ref")); - auto Ref = ctx.Input("Ref"); + auto *dUpdates = ctx.Output(framework::GradVarName("Updates")); + auto *Updates = ctx.Input("Updates"); + auto *dRef = ctx.Output(framework::GradVarName("Ref")); + auto *Ref = ctx.Input("Ref"); - Ref_grad->Resize(Ref->dims()); - Updates_grad->Resize(Updates->dims()); + dRef->Resize(Ref->dims()); + dUpdates->Resize(Updates->dims()); } }; diff --git a/paddle/operators/scatter_op.h b/paddle/operators/scatter_op.h index c2db3ae37..e9595638a 100644 --- a/paddle/operators/scatter_op.h +++ b/paddle/operators/scatter_op.h @@ -46,13 +46,13 @@ class ScatterGradientOpKernel : public framework::OpKernel { auto *dRef = ctx.Output(framework::GradVarName("Ref")); auto *dUpdates = ctx.Output(framework::GradVarName("Updates")); auto *Index = ctx.Input("Index"); - auto *dO = ctx.Input(framework::GradVarName("Out")); + auto *dOut = ctx.Input(framework::GradVarName("Out")); // In place gradient: dRef = dO - dRef->ShareDataWith(*dO); + dRef->ShareDataWith(*dOut); dUpdates->mutable_data(ctx.GetPlace()); // Gradient by Gather: dUpdates += dO[Index] - Gather(ctx.GetPlace(), dO, Index, dUpdates); + Gather(ctx.GetPlace(), dOut, Index, dUpdates); } }; diff --git a/python/paddle/v2/framework/tests/gradient_checker.py b/python/paddle/v2/framework/tests/gradient_checker.py index ac37671c7..abe0b5391 100644 --- a/python/paddle/v2/framework/tests/gradient_checker.py +++ b/python/paddle/v2/framework/tests/gradient_checker.py @@ -82,6 +82,11 @@ def get_numeric_gradient(op, def product(dim): return reduce(lambda a, b: a * b, dim, 1) + def copy_tensor(): + for var_name in input_values: + tensor_ = local_scope.find_var(var_name).get_tensor() + tensor_.set(numpy.copy(input_values[var_name]), core.CPUPlace()) + # get the input tensor that we want to get it's numeric gradient. tensor_to_check = local_scope.find_var(input_to_check).get_tensor() tensor_size = product(tensor_to_check.get_dims()) @@ -92,9 +97,7 @@ def get_numeric_gradient(op, # we use a for loop to compute the gradient of every element. for i in xrange(tensor_size): if in_place: - for var_name in input_values: - tensor_ = local_scope.find_var(var_name).get_tensor() - tensor_.set(numpy.copy(input_values[var_name]), core.CPUPlace()) + copy_tensor() # get one input element throw it's index i. origin = tensor_to_check.get_float_element(i) @@ -105,9 +108,7 @@ def get_numeric_gradient(op, # plus delta to this element, run op and get the sum of the result tensor. if in_place: - for var_name in input_values: - tensor_ = local_scope.find_var(var_name).get_tensor() - tensor_.set(numpy.copy(input_values[var_name]), core.CPUPlace()) + copy_tensor() x_neg = origin - delta tensor_to_check.set_float_element(i, x_neg) y_neg = get_output() diff --git a/python/paddle/v2/framework/tests/test_scatter_op.py b/python/paddle/v2/framework/tests/test_scatter_op.py index 861fe6cf8..c1f944488 100644 --- a/python/paddle/v2/framework/tests/test_scatter_op.py +++ b/python/paddle/v2/framework/tests/test_scatter_op.py @@ -30,7 +30,6 @@ class TestScatterGradOp(GradientChecker): output_np = numpy.copy(ref_np) output_np[index_np] += updates_np inputs = {'Ref': ref_np, 'Index': index_np, 'Updates': updates_np} - # check gradient self.check_grad( op, inputs, set(["Updates", "Ref"]), "Out", in_place=True) -- GitLab From 6f235553fd923d4b0b225fdc4a521570b03fbc24 Mon Sep 17 00:00:00 2001 From: zchen0211 Date: Fri, 25 Aug 2017 22:20:20 +0000 Subject: [PATCH 0233/2018] scatter op fixed --- paddle/operators/scatter_op.cc | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/paddle/operators/scatter_op.cc b/paddle/operators/scatter_op.cc index f901edefa..09a2f94dd 100644 --- a/paddle/operators/scatter_op.cc +++ b/paddle/operators/scatter_op.cc @@ -1,16 +1,16 @@ /* 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 + 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 + 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. */ + 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/scatter_op.h" #include "paddle/framework/ddim.h" -- GitLab From 852f341615808b6a5e6249b3b7c1f5f20fd22ec9 Mon Sep 17 00:00:00 2001 From: Yi Wang Date: Fri, 25 Aug 2017 16:48:52 -0700 Subject: [PATCH 0234/2018] Add clean build section --- doc/howto/dev/build_cn.md | 10 +++++++++- doc/howto/dev/build_en.md | 10 +++++++++- 2 files changed, 18 insertions(+), 2 deletions(-) diff --git a/doc/howto/dev/build_cn.md b/doc/howto/dev/build_cn.md index 79b4ff9d5..d9d520893 100644 --- a/doc/howto/dev/build_cn.md +++ b/doc/howto/dev/build_cn.md @@ -56,7 +56,15 @@ 有时候我们只想运行一个特定的单元测试，比如 `memory_test`，我们可以 ```bash - docker run -v $PWD:/paddle paddle:dev bash -c "cd /paddle/build; ctest -V -R memory_test" + nvidia-docker run -v $PWD:/paddle paddle:dev bash -c "cd /paddle/build; ctest -V -R memory_test" + ``` + +5. 清理 + + 有时候我们会希望清理掉已经下载的第三方依赖以及已经编译的二进制文件。此时只需要： + + ```bash + rm -rf build ``` ## 为什么要 Docker 呀？ diff --git a/doc/howto/dev/build_en.md b/doc/howto/dev/build_en.md index e1b55929f..318bf3d38 100644 --- a/doc/howto/dev/build_en.md +++ b/doc/howto/dev/build_en.md @@ -56,7 +56,15 @@ Nothing else. Not even Python and GCC, because you can install all build tools Sometimes we want to run a specific unit test, say `memory_test`, we can run ```bash - docker run -v $PWD:/paddle paddle:dev bash -c "cd /paddle/build; ctest -V -R memory_test" + nvidia-docker run -v $PWD:/paddle paddle:dev bash -c "cd /paddle/build; ctest -V -R memory_test" + ``` + +5. Clean Build. + + Sometimes, we might want to clean all thirt-party dependents and built binaries. To do so, just + + ```bash + rm -rf build ``` ## Docker, Or Not? -- GitLab From bfeecfd3d25c5c04dbd7d000490e77454944d044 Mon Sep 17 00:00:00 2001 From: zchen0211 Date: Fri, 25 Aug 2017 23:54:46 +0000 Subject: [PATCH 0235/2018] fix problems --- paddle/operators/scatter_op.cc | 18 +++++++++--------- paddle/operators/scatter_op.cu | 18 +++++++++--------- .../v2/framework/tests/gradient_checker.py | 6 +++--- 3 files changed, 21 insertions(+), 21 deletions(-) diff --git a/paddle/operators/scatter_op.cc b/paddle/operators/scatter_op.cc index 09a2f94dd..f901edefa 100644 --- a/paddle/operators/scatter_op.cc +++ b/paddle/operators/scatter_op.cc @@ -1,16 +1,16 @@ /* 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 +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 + 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. */ +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/scatter_op.h" #include "paddle/framework/ddim.h" diff --git a/paddle/operators/scatter_op.cu b/paddle/operators/scatter_op.cu index e6a6fa57d..6716b4788 100644 --- a/paddle/operators/scatter_op.cu +++ b/paddle/operators/scatter_op.cu @@ -1,16 +1,16 @@ /* 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 +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 + 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. */ +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. */ #define EIGEN_USE_GPU #include "paddle/operators/scatter_op.h" diff --git a/python/paddle/v2/framework/tests/gradient_checker.py b/python/paddle/v2/framework/tests/gradient_checker.py index abe0b5391..9a7a7fbf5 100644 --- a/python/paddle/v2/framework/tests/gradient_checker.py +++ b/python/paddle/v2/framework/tests/gradient_checker.py @@ -82,7 +82,7 @@ def get_numeric_gradient(op, def product(dim): return reduce(lambda a, b: a * b, dim, 1) - def copy_tensor(): + def restore_inputs(): for var_name in input_values: tensor_ = local_scope.find_var(var_name).get_tensor() tensor_.set(numpy.copy(input_values[var_name]), core.CPUPlace()) @@ -97,7 +97,7 @@ def get_numeric_gradient(op, # we use a for loop to compute the gradient of every element. for i in xrange(tensor_size): if in_place: - copy_tensor() + restore_inputs() # get one input element throw it's index i. origin = tensor_to_check.get_float_element(i) @@ -108,7 +108,7 @@ def get_numeric_gradient(op, # plus delta to this element, run op and get the sum of the result tensor. if in_place: - copy_tensor() + restore_inputs() x_neg = origin - delta tensor_to_check.set_float_element(i, x_neg) y_neg = get_output() -- GitLab From ec5e20c9f12e89e13b52978b8bb27997c77f059c Mon Sep 17 00:00:00 2001 From: Yi Wang Date: Fri, 25 Aug 2017 17:14:28 -0700 Subject: [PATCH 0236/2018] Remove stopped containers and dangling images --- doc/howto/dev/build_cn.md | 18 +++++++++++------- doc/howto/dev/build_en.md | 4 ++++ 2 files changed, 15 insertions(+), 7 deletions(-) diff --git a/doc/howto/dev/build_cn.md b/doc/howto/dev/build_cn.md index d9d520893..0b911f7b7 100644 --- a/doc/howto/dev/build_cn.md +++ b/doc/howto/dev/build_cn.md @@ -7,7 +7,7 @@ 1. 一台电脑，可以装的是 Linux, BSD, Windows 或者 MacOS 操作系统，以及 1. Docker。 -不需要其他任何软件了。即便是 Python 和 GCC 都不需要，因为我们会把所有编译工具都安装进一个 Docker image 里。 +不需要依赖其他任何软件了。即便是 Python 和 GCC 都不需要，因为我们会把所有编译工具都安装进一个 Docker image 里。 ## 总体流程 @@ -17,7 +17,7 @@ git clone https://github.com/paddlepaddle/paddle ``` -2. 安装工具 +2. 安装开发工具到 Docker image 里 ```bash cd paddle; docker build -t paddle:dev . @@ -30,13 +30,13 @@ 以下命令启动一个 Docker container 来执行 `paddle:dev` 这个 Docker image，同时把当前目录（源码树根目录）映射为 container 里的 `/paddle` 目录，并且运行 `Dockerfile` 描述的默认入口程序 [`build.sh`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/scripts/docker/build.sh)。这个脚本调用 `cmake` 和 `make` 来编译 `/paddle` 里的源码，结果输出到 `/paddle/build`，也就是本地的源码树根目录里的 `build` 子目录。 ```bash - docker run -v $PWD:/paddle paddle:dev + docker run --rm -v $PWD:/paddle paddle:dev ``` 上述命令编译出一个 CUDA-enabled 版本。如果我们只需要编译一个只支持 CPU 的版本，可以用 ```bash - docker run -e WITH_GPU=OFF -v $PWD:/paddle paddle:dev + docker run --rm -e WITH_GPU=OFF -v $PWD:/paddle paddle:dev ``` 4. 运行单元测试 @@ -44,19 +44,19 @@ 用本机的第一个 GPU 来运行包括 GPU 单元测试在内的所有单元测试： ```bash - NV_GPU=0 nvidia-docker run -v $PWD:/paddle paddle:dev bash -c "cd /paddle/build; ctest" + NV_GPU=0 nvidia-docker run --rm -v $PWD:/paddle paddle:dev bash -c "cd /paddle/build; ctest" ``` 如果编译的时候我们用了 `WITH_GPU=OFF` 选项，那么编译过程只会产生 CPU-based 单元测试，那么我们也就不需要 nvidia-docker 来运行单元测试了。我们只需要： ```bash - docker run -v $PWD:/paddle paddle:dev bash -c "cd /paddle/build; ctest" + docker run --rm -v $PWD:/paddle paddle:dev bash -c "cd /paddle/build; ctest" ``` 有时候我们只想运行一个特定的单元测试，比如 `memory_test`，我们可以 ```bash - nvidia-docker run -v $PWD:/paddle paddle:dev bash -c "cd /paddle/build; ctest -V -R memory_test" + nvidia-docker run --rm -v $PWD:/paddle paddle:dev bash -c "cd /paddle/build; ctest -V -R memory_test" ``` 5. 清理 @@ -118,3 +118,7 @@ - 在 Windows/MacOS 上编译很慢 Docker 在 Windows 和 MacOS 都可以运行。不过实际上是运行在一个 Linux 虚拟机上。可能需要注意给这个虚拟机多分配一些 CPU 和内存，以保证编译高效。具体做法请参考[这个issue](https://github.com/PaddlePaddle/Paddle/issues/627)。 + +- 磁盘不够 + + 本文中的例子里，`docker run` 命令里都用了 `--rm` 参数，这样保证运行结束之后的 containers 不会保留在磁盘上。可以用 `docker ps -a` 命令看到停止后但是没有删除的 containers。`docker build` 命令有时候会产生一些中间结果，是没有名字的 images，也会占用磁盘。可以参考[这篇文章](https://zaiste.net/posts/removing_docker_containers/)来清理这些内容。 diff --git a/doc/howto/dev/build_en.md b/doc/howto/dev/build_en.md index 318bf3d38..d0048e371 100644 --- a/doc/howto/dev/build_en.md +++ b/doc/howto/dev/build_en.md @@ -118,3 +118,7 @@ Nothing else. Not even Python and GCC, because you can install all build tools - Docker on Windows/MacOS builds slowly On Windows and MacOS, Docker containers run in a Linux VM. You might want to give this VM some more memory and CPUs so to make the building efficient. Please refer to [this issue](https://github.com/PaddlePaddle/Paddle/issues/627) for details. + +- Not enough disk space + + Examples in this article uses option `--rm` with the `docker run` command. This option ensures that stopped containers do not exist on hard disks. We can use `docker ps -a` to list all containers, including stopped. Sometimes `docker build` generates some intermediate dangling images, which also take disk space. To clean them, please refer to [this article](https://zaiste.net/posts/removing_docker_containers/). -- GitLab From 721b5020fae92600a0aa2c4093dbeeac63b597c6 Mon Sep 17 00:00:00 2001 From: qiaolongfei Date: Sat, 26 Aug 2017 16:34:33 -0700 Subject: [PATCH 0237/2018] change predict size to label class_num --- python/paddle/v2/framework/tests/mnist.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/python/paddle/v2/framework/tests/mnist.py b/python/paddle/v2/framework/tests/mnist.py index 9a0b10985..9b2dbed25 100644 --- a/python/paddle/v2/framework/tests/mnist.py +++ b/python/paddle/v2/framework/tests/mnist.py @@ -181,7 +181,7 @@ images = data_layer(name='pixel', dims=[BATCH_SIZE, 784]) labels = data_layer(name='label', dims=[BATCH_SIZE]) fc1 = fc_layer(net=forward_net, input=images, size=100, act="sigmoid") fc2 = fc_layer(net=forward_net, input=fc1, size=100, act="sigmoid") -predict = fc_layer(net=forward_net, input=fc2, size=100, act="softmax") +predict = fc_layer(net=forward_net, input=fc2, size=10, act="softmax") cost = cross_entropy_layer(net=forward_net, input=predict, label=labels) init_net.complete_add_op(True) @@ -223,7 +223,7 @@ def test(cost_name): sum(error) / float(len(error)))) -PASS_NUM = 1 +PASS_NUM = 10 init_net.run(scope, dev_ctx) for pass_id in range(PASS_NUM): -- GitLab From 787cb8ce5d829435e5d45e8bc6bc51cfdcf49272 Mon Sep 17 00:00:00 2001 From: qiaolongfei Date: Sat, 26 Aug 2017 16:36:28 -0700 Subject: [PATCH 0238/2018] reset pass num to 1 --- python/paddle/v2/framework/tests/mnist.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/python/paddle/v2/framework/tests/mnist.py b/python/paddle/v2/framework/tests/mnist.py index 9b2dbed25..a68f302f9 100644 --- a/python/paddle/v2/framework/tests/mnist.py +++ b/python/paddle/v2/framework/tests/mnist.py @@ -223,7 +223,7 @@ def test(cost_name): sum(error) / float(len(error)))) -PASS_NUM = 10 +PASS_NUM = 1 init_net.run(scope, dev_ctx) for pass_id in range(PASS_NUM): -- GitLab From 3120ee5cfbbe6ecf3550b6a338a4c14afe6e4ebd Mon Sep 17 00:00:00 2001 From: dongzhihong Date: Sat, 26 Aug 2017 18:46:06 -0700 Subject: [PATCH 0239/2018] fix backward doc --- paddle/framework/backward.md | 28 +++++++++++++++++++++------- 1 file changed, 21 insertions(+), 7 deletions(-) diff --git a/paddle/framework/backward.md b/paddle/framework/backward.md index 74c001b06..c8fa3fefe 100644 --- a/paddle/framework/backward.md +++ b/paddle/framework/backward.md @@ -21,18 +21,32 @@ grad_op_builder(fengjiayi) given a forward network, it generates the backward network. We only care about the Gradients—`OutputGradients`,`InputGradients`. -1. bla bla bla (yuyang) +1. Op + + when the input forward network is a Op, return its gradient Operator Immediately. 2. NetOp - when the input forward network is a NetOp, it need to call the sub NetOp/Operators backward function recursively and ensure them done. During the process, we need to collect the `OutputGradients` name. + when the input forward network is a NetOp, it need to call the sub NetOp/Operators backward function recursively. During the process, we need to collect the `OutputGradients` name according to forward NetOp. + + **shared variable**. As illustrated in the pictures, two operator's `Output` `Gradient` will overwirte their shared input variable. + +

+
+ + 1. shared variable in two operators. + +

+ + Share variable between operators or same input variable used in multiple operators lead to a duplicate gradient variable. As demo show above, we need to rename gradient name recursively, and add a generic add operator replace the overwirte links. + +

+
- We share variable in the same scope, as a result, duplicate operator `OutputGradients` will overwirte then duplicate variable. + 2. replace shared variable gradient with `Add` Operator - ![./images/duplicate_op]() +

- Share variable between operators or same input variable used in multiple operators lead to a duplicate gradient variable. As demo show above, we need to rename gradient name recursively, and add a generic add operator instead. -![./images/duplicate_op2]() -​ Then collect the sub graph OutputGradients/InputGradients as the NetOp's and return it. +​ Then collect the sub graph `OutputGradients`/`InputGradients` as the NetOp's and return it. -- GitLab From bb5c656b574b1e518da981d781db0e1e0a0e4d75 Mon Sep 17 00:00:00 2001 From: fengjiayi Date: Sat, 26 Aug 2017 19:15:31 -0700 Subject: [PATCH 0240/2018] test --- paddle/framework/backward.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paddle/framework/backward.md b/paddle/framework/backward.md index c717c2f30..d5dbd57d1 100644 --- a/paddle/framework/backward.md +++ b/paddle/framework/backward.md @@ -6,7 +6,7 @@ In Neural Network, the backpropagation algorithm follows the chain rule, so we n ## Backward Operator Registry -A backward network is built up with several backward operators. Backward operators take forward operators' inputs, outputs and output gradients, and then calculate its input gradients. In most cases, there is a one-to-one correspondence between forward and backward operators. We use registry mechanism to save these correspondences, which is quite similar with operator registry itself. +A backward network is built up with several backward operators. Backward operators take forward operators' inputs, outputs and output gradients and then calculate its input gradients. In most cases, there is a one-to-one correspondence between forward and backward operators. We use registry mechanism to save these correspondences, which is quite similar with operator registry itself. For example, we have got a `add_two_op`, and is registered by the following code: -- GitLab From f646f7991ae49eff00370a03beb958fc88ac62ad Mon Sep 17 00:00:00 2001 From: qingqing01 Date: Sun, 27 Aug 2017 12:01:46 +0800 Subject: [PATCH 0241/2018] Add chinese doc about how to write new operators. --- doc/howto/dev/new_op_cn.md | 300 +++++++++++++++++++++++++++++++++++++ 1 file changed, 300 insertions(+) create mode 100644 doc/howto/dev/new_op_cn.md diff --git a/doc/howto/dev/new_op_cn.md b/doc/howto/dev/new_op_cn.md new file mode 100644 index 000000000..df20c15ec --- /dev/null +++ b/doc/howto/dev/new_op_cn.md @@ -0,0 +1,300 @@ +# 如何写新的Operator + + - [概念简介](#概念简介) + - [实现C++类](#实现C++类) + - [定义ProtoMaker类](#定义ProtoMaker类) + - [定义Operator类](#定义Operator类) + - [定义`OpKernel`类](#定义`OpKernel`类) + - [注册类](#注册类) + - [编译](#编译) + - [绑定Python](#绑定Python) + - [实现单元测试](#实现单元测试) + + +## 概念简介 + +简单介绍需要用到基类，详细介绍请参考设计文档。 + +- `framework::OperatorBase`: Operator(简写，Op)基类。 +- `framework::OpKernel`: Op计算函数的基类，称作Kernel。 +- `framework::OperatorWithKernel`：继承自OperatorBase，Op有计算函数，称作有Kernel。 +- `class OpProtoAndCheckerMaker`：描述该Op的输入、输出、属性、注释,主要用于Python API接口生成 + +依据是否包含kernel，将Op分为两种：包含Kernel的Op和不包含kernel的Op，前者Op的定义继承自`OperatorBase`，后者继承自`OperatorWithKernel`。本教程主要介绍带Kernel的Op如何写，简单总结如下： + +Forward Op需要包含： + + - OpProtoMake定义 + - Op定义 + - Kernel实现 + +与之对应的Backward Op包含： + + - Op定义 + - Kernel实现 + +下面以矩阵乘操作，即[MulOp](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/mul_op.cc)为例来介绍如何写带Kernel的Operator。 + + +## 实现C++类 + + +### 1. 定义ProtoMaker类 + +矩阵乘的公式：$$Out = X * Y$$ ，可见该计算由两个输入，一个输出组成。首先定义`ProtoMaker`来描述该Op的输入、输出及注释： + + + + ``` + class MulOpMaker : public framework::OpProtoAndCheckerMaker { + public: + MulOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker) + : OpProtoAndCheckerMaker(proto, op_checker) { + AddInput("X", "The first input of mul op"); + AddInput("Y", "The second input of mul op"); + AddOutput("Out", "The output of mul op"); + AddComment(R"DOC( + Two Element Mul Operator. + The equation is: Out = X * Y + )DOC"); + } + }; + ``` + +[`MulOpMaker`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/mul_op.cc#L43)继承自`framework::OpProtoAndCheckerMaker`，构造函数包括2个： + + - `framework::OpProto` ： 前者存储Op的输入输出和参数属性，将用于Python API接口的生成。 + - `framework::OpAttrChecker` ：后者用于检查参数属性的合法性。 + +构造函数里通过`AddInput`添加输入参数，通过`AddOutput`添加输出参数，通过`AddComment`添加该Op的注释，这些函数会将对应内容添加到`OpProto`中。 + +在`MulOp`中添加两个输入`X`和`Y`，添加了一个输出`Out`，并解释了各自含义，该命名尽可能的规范。 + + +再举个[`ScaleOp`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/scale_op.cc#L37)的例子： + +```C++ + template +class ScaleOpMaker : public framework::OpProtoAndCheckerMaker { + public: + ScaleOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker) + : OpProtoAndCheckerMaker(proto, op_checker) { + AddInput("X", "The input tensor of scale operator.").NotInGradient(); + AddOutput("Out", "The output tensor of scale operator.").NotInGradient(); + AddComment(R"DOC(Scale operator +The equation is: Out = scale*X +)DOC"); + AddAttr("scale", "scale of scale operator.").SetDefault(1.0); + } +}; +``` + + 在这个例子里，两处不同： + + - `AddInput("X","...").NotInGradient()` : 表示`X`这个输入不参与`ScaleOp`对应的梯度Op计算之中。 + - `AddAttr("scale", "...").SetDefault(1.0);` : 增加`scale`系数，作为参数属性，并且设置默认值为1.0。 + + +### 2. 定义Operator类 + + + ```C++ + class MulOp : public framework::OperatorWithKernel { + public: + using framework::OperatorWithKernel::OperatorWithKernel; + + protected: + void InferShape(const framework::InferShapeContext &ctx) const override { + auto dim0 = ctx.Input("X")->dims(); + auto dim1 = ctx.Input("Y")->dims(); + PADDLE_ENFORCE_EQ(dim0.size(), 2, + "input X(%s) should be a tensor with 2 dims, a matrix", + ctx.op_.Input("X")); + PADDLE_ENFORCE_EQ(dim1.size(), 2, + "input Y(%s) should be a tensor with 2 dims, a matrix", + ctx.op_.Input("Y")); + PADDLE_ENFORCE_EQ( + dim0[1], dim1[0], + "First matrix's width must be equal with second matrix's height."); + ctx.Output("Out")->Resize({dim0[0], dim1[1]}); + } + }; + ``` + +[`MulOp`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/mul_op.cc#L22)继承自`OperatorWithKernel`。`public`成员： + +```C++ +using framework::OperatorWithKernel::OperatorWithKernel; +``` + +这句表示使用基类`OperatorWithKernel`的构造函数，也可写成： + +```C++ + MulOp(const std::string &type, const framework::VariableNameMap &inputs, + const framework::VariableNameMap &outputs, + const framework::AttributeMap &attrs) + : OperatorWithKernel(type, inputs, outputs, attrs) {} +``` + +还需要重写`InferShape`接口。`InferShape`为const函数，不能修改Op的成员变量，参数为`const framework::InferShapeContext &ctx`，通过该参数可获取到输入输出以及属性。它的功能是： + - 1). 做检查， 尽早报错：检查输入数据维度、类型等是否合法 + - 2). 设置输出Tensor的形状 + +通常`OpProtoMaker`和`Op`类的定义写在`.cc`文件中，和要讲到的注册函数一起放在`.cc`中 + +### 3. 定义`OpKernel`类 + +```C++ +template +class MulKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& context) const override { + auto* X = context.Input("X"); + auto* Y = context.Input("Y"); + auto* Z = context.Output("Out"); + Z->mutable_data(context.GetPlace()); + auto* device_context = + const_cast(context.device_context_); + math::matmul(*X, false, *Y, false, 1, Z, 0, device_context); + } +}; +``` + +`MulKernel`继承自`framework::OpKernel`，带有模板参数: + + - `typename Place`: 表示设备类型，不同设备(CPU、GPU)共享同一个Kernel时，需加该模板参数，不共享则不加，一个不共享的例子是[`OnehotCrossEntropyOpKernel`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/cross_entropy_op.h#L43)。 + + - `typename T` : 表示数据类型，如`float`, `double`等。 + +`MulKernel`需要重写`Compute`接口，该接口参数为`const framework::ExecutionContext& context`, `ExecutionContext`相比`InferShapeContext`增加了设备类型，同样可获取到输入输出和属性参数，`Compute`函数里写具体实现时。 + +注意，不同设备(CPU、GPU)共享一个Op定义，是否则共享同一个`OpKernel`，取决于`Compute`调用的函数是否支持不同设备。`MulOp`的CPU、GPU实现共享同一个`Kernel`，`OpKernel`不共享的例子可以参考[`OnehotCrossEntropyOpKernel`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/cross_entropy_op.h#L43)。 + +到此前向Op实现完成，需要在`.cc`文件中注册该op和kernel。反向Op类的定义和Kernel定义与前向Op类似，这里不再重复。但注意，反向Op没有`ProtoMaker`。 + +### 4. 注册类 + +在`.cc`文件中注册前向、反向Op类，注册CPU Kernel。 + + ```C++ + namespace ops = paddle::operators; + REGISTER_OP(mul, ops::MulOp, ops::MulOpMaker, mul_grad, ops::MulOpGrad); + REGISTER_OP_CPU_KERNEL(mul, ops::MulKernel); + REGISTER_OP_CPU_KERNEL(mul_grad, + ops::MulGradKernel); + ``` + + - `REGISTER_OP` ： 注册`ops::MulOp`类，类型名为`mul`，该类的`ProtoMaker`为`ops::MulOpMaker`，注册`ops::MulOpGrad`，类型名为`mul_grad`， + - `REGISTER_OP_WITHOUT_GRADIENT` ： 用于注册没有反向的Op。 + - `REGISTER_OP_CPU_KERNEL` ：注册`ops::MulKernel`类，并特化模板参数为`paddle::platform::CPUPlace`和`float`类型，同理，注册`ops::MulKernel`类。 + +在 `.cu`文件中注册GPU Kernel。 + + ``` + namespace ops = paddle::operators; + REGISTER_OP_GPU_KERNEL(mul, ops::MulKernel); + REGISTER_OP_GPU_KERNEL(mul_grad, + ops::MulGradKernel); + ``` + +### 5. 编译 + +在[paddle/operators/CMakeLists.txt](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/CMakeLists.txt)文件中添加编译。 + + ``` + op_library(mul_op SRCS mul_op.cc mul_op.cu DEPS math_function) + ``` + +下面命令可以编译： + + ``` + make mul_op + ``` + +## 绑定Python + + - 绑定Python + + 在 [`paddle/pybind/pybind.cc +`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/pybind/pybind.cc)文件中添加该类： + + ``` + USE_OP(mul); + ``` + 如果只实现了CPU版本，则使用`USE_CPU_ONLY_OP`: + + ``` + USE_CPU_ONLY_OP(gather); + ``` + + 使用`USE_OP`告知编译器需要链接该Op的目标文件，具体解释参考[代码注释](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/op_registry.h#L81)。 + + + - 生成库 + + 在 [`paddle/pybind/CMakeLists.txt`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/pybind/CMakeLists.txt)文件添加类到`DEPS`中。 + + ``` + if(WITH_PYTHON) +cc_library(paddle_pybind SHARED + SRCS pybind.cc + DEPS pybind python backward + mul_op + minus_op) +endif(WITH_PYTHON) + ``` + +## 实现单元测试 + +单测包括对比前向Op不同设备(CPU、GPU)的实现、对比反向OP不同设备(CPU、GPU)的实现、反向Op的梯度测试。下面介绍介绍[`MulOp`的单测](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/v2/framework/tests/test_mul_op.py)。 + +- 前向Op单测 + +前向Op单测继承自`unittest.TestCase`，并定义元类`__metaclass__ = OpTestMeta`，具体单测流程在`OpTestMeta`里完成。需在`setUp`函数定义输入输出和属性参数，以及Python对比的输出值。 + +``` +import unittest +import numpy as np +from gradient_checker import GradientChecker, create_op +from op_test_util import OpTestMeta + +class TestMulOp(unittest.TestCase): + __metaclass__ = OpTestMeta + + def setUp(self): + self.type = "mul" + self.inputs = { + 'X': np.random.random((32, 84)).astype("float32"), + 'Y': np.random.random((84, 100)).astype("float32") + } + self.outputs = {'Out': np.dot(self.inputs['X'], self.inputs['Y'])} +``` + 首先需要`import`必要的包,下面详细解释其他值： + + - `self.type = "mul" ` : 定义类型，和注册的类型一致。 + - `self.inputs` : 定义输入，类型为Numpy.array，并初始化。 + - `self.outputs` : 定义输出，并得到Python结算结果。 + + + - 反向Op单测 + +反向Op单测继承自`GradientChecker`，而`GradientChecker`集成自`unittest.TestCase`，所以反向单测函数需要`test_`开头。 + + ``` + class MulGradOpTest(GradientChecker): + def test_mul(self): + op = create_op("mul") + inputs = { + 'X': np.random.random((32, 84)).astype("float32"), + 'Y': np.random.random((84, 100)).astype("float32") + } + self.compare_grad(op, inputs) + # mul op will enlarge the relative error + self.check_grad( + op, inputs, set(["X", "Y"]), "Out", max_relative_error=0.5) + ``` + + - 调用`create_op("mul")`创建反向Op对应的前向Op。 + - 定义输入`inputs`。 + - 调用`compare_grad`函数对比CPU、GPU计算结果。 + - 调用`check_grad`检查梯度稳定性。 -- GitLab From d78521d491d8c6625146137406f3b7402aebe143 Mon Sep 17 00:00:00 2001 From: qingqing01 Date: Sun, 27 Aug 2017 12:11:15 +0800 Subject: [PATCH 0242/2018] fix doc format. --- doc/howto/dev/new_op_cn.md | 160 ++++++++++++++++++------------------- 1 file changed, 80 insertions(+), 80 deletions(-) diff --git a/doc/howto/dev/new_op_cn.md b/doc/howto/dev/new_op_cn.md index df20c15ec..ebd2cf3ff 100644 --- a/doc/howto/dev/new_op_cn.md +++ b/doc/howto/dev/new_op_cn.md @@ -4,11 +4,13 @@ - [实现C++类](#实现C++类) - [定义ProtoMaker类](#定义ProtoMaker类) - [定义Operator类](#定义Operator类) - - [定义`OpKernel`类](#定义`OpKernel`类) + - [定义OpKernel类](#定义OpKernel类) - [注册类](#注册类) - [编译](#编译) - [绑定Python](#绑定Python) - [实现单元测试](#实现单元测试) + - [前向Operator单测](#前向Operator单测) + - [反向Operator单测](#反向Operator单测) ## 概念简介 @@ -41,25 +43,23 @@ Forward Op需要包含： ### 1. 定义ProtoMaker类 -矩阵乘的公式：$$Out = X * Y$$ ，可见该计算由两个输入，一个输出组成。首先定义`ProtoMaker`来描述该Op的输入、输出及注释： - +矩阵乘的公式：$Out = X * Y$, 可见该计算由两个输入，一个输出组成。首先定义`ProtoMaker`来描述该Op的输入、输出及注释： - - ``` - class MulOpMaker : public framework::OpProtoAndCheckerMaker { - public: - MulOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker) - : OpProtoAndCheckerMaker(proto, op_checker) { - AddInput("X", "The first input of mul op"); - AddInput("Y", "The second input of mul op"); - AddOutput("Out", "The output of mul op"); - AddComment(R"DOC( - Two Element Mul Operator. - The equation is: Out = X * Y - )DOC"); - } - }; - ``` +``` +class MulOpMaker : public framework::OpProtoAndCheckerMaker { + public: + MulOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker) + : OpProtoAndCheckerMaker(proto, op_checker) { + AddInput("X", "The first input of mul op"); + AddInput("Y", "The second input of mul op"); + AddOutput("Out", "The output of mul op"); + AddComment(R"DOC( +Two Element Mul Operator. +The equation is: Out = X * Y +)DOC"); + } +}; +``` [`MulOpMaker`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/mul_op.cc#L43)继承自`framework::OpProtoAndCheckerMaker`，构造函数包括2个： @@ -73,8 +73,8 @@ Forward Op需要包含： 再举个[`ScaleOp`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/scale_op.cc#L37)的例子： -```C++ - template +``` +template class ScaleOpMaker : public framework::OpProtoAndCheckerMaker { public: ScaleOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker) @@ -98,42 +98,42 @@ The equation is: Out = scale*X ### 2. 定义Operator类 - ```C++ - class MulOp : public framework::OperatorWithKernel { - public: - using framework::OperatorWithKernel::OperatorWithKernel; - - protected: - void InferShape(const framework::InferShapeContext &ctx) const override { - auto dim0 = ctx.Input("X")->dims(); - auto dim1 = ctx.Input("Y")->dims(); - PADDLE_ENFORCE_EQ(dim0.size(), 2, - "input X(%s) should be a tensor with 2 dims, a matrix", - ctx.op_.Input("X")); - PADDLE_ENFORCE_EQ(dim1.size(), 2, - "input Y(%s) should be a tensor with 2 dims, a matrix", - ctx.op_.Input("Y")); - PADDLE_ENFORCE_EQ( - dim0[1], dim1[0], - "First matrix's width must be equal with second matrix's height."); - ctx.Output("Out")->Resize({dim0[0], dim1[1]}); - } - }; - ``` +```c++ +class MulOp : public framework::OperatorWithKernel { + public: + using framework::OperatorWithKernel::OperatorWithKernel; + + protected: + void InferShape(const framework::InferShapeContext &ctx) const override { + auto dim0 = ctx.Input("X")->dims(); + auto dim1 = ctx.Input("Y")->dims(); + PADDLE_ENFORCE_EQ(dim0.size(), 2, + "input X(%s) should be a tensor with 2 dims, a matrix", + ctx.op_.Input("X")); + PADDLE_ENFORCE_EQ(dim1.size(), 2, + "input Y(%s) should be a tensor with 2 dims, a matrix", + ctx.op_.Input("Y")); + PADDLE_ENFORCE_EQ( + dim0[1], dim1[0], + "First matrix's width must be equal with second matrix's height."); + ctx.Output("Out")->Resize({dim0[0], dim1[1]}); + } +}; +``` [`MulOp`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/mul_op.cc#L22)继承自`OperatorWithKernel`。`public`成员： -```C++ +```c++ using framework::OperatorWithKernel::OperatorWithKernel; ``` 这句表示使用基类`OperatorWithKernel`的构造函数，也可写成： -```C++ - MulOp(const std::string &type, const framework::VariableNameMap &inputs, - const framework::VariableNameMap &outputs, - const framework::AttributeMap &attrs) - : OperatorWithKernel(type, inputs, outputs, attrs) {} +```c++ +MulOp(const std::string &type, const framework::VariableNameMap &inputs, + const framework::VariableNameMap &outputs, + const framework::AttributeMap &attrs) + : OperatorWithKernel(type, inputs, outputs, attrs) {} ``` 还需要重写`InferShape`接口。`InferShape`为const函数，不能修改Op的成员变量，参数为`const framework::InferShapeContext &ctx`，通过该参数可获取到输入输出以及属性。它的功能是： @@ -142,7 +142,7 @@ using framework::OperatorWithKernel::OperatorWithKernel; 通常`OpProtoMaker`和`Op`类的定义写在`.cc`文件中，和要讲到的注册函数一起放在`.cc`中 -### 3. 定义`OpKernel`类 +### 3. 定义OpKernel类 ```C++ template @@ -176,13 +176,13 @@ class MulKernel : public framework::OpKernel { 在`.cc`文件中注册前向、反向Op类，注册CPU Kernel。 - ```C++ - namespace ops = paddle::operators; - REGISTER_OP(mul, ops::MulOp, ops::MulOpMaker, mul_grad, ops::MulOpGrad); - REGISTER_OP_CPU_KERNEL(mul, ops::MulKernel); - REGISTER_OP_CPU_KERNEL(mul_grad, - ops::MulGradKernel); - ``` +```c++ +namespace ops = paddle::operators; +REGISTER_OP(mul, ops::MulOp, ops::MulOpMaker, mul_grad, ops::MulOpGrad); +REGISTER_OP_CPU_KERNEL(mul, ops::MulKernel); +REGISTER_OP_CPU_KERNEL(mul_grad, + ops::MulGradKernel); +``` - `REGISTER_OP` ： 注册`ops::MulOp`类，类型名为`mul`，该类的`ProtoMaker`为`ops::MulOpMaker`，注册`ops::MulOpGrad`，类型名为`mul_grad`， - `REGISTER_OP_WITHOUT_GRADIENT` ： 用于注册没有反向的Op。 @@ -190,32 +190,32 @@ class MulKernel : public framework::OpKernel { 在 `.cu`文件中注册GPU Kernel。 - ``` - namespace ops = paddle::operators; - REGISTER_OP_GPU_KERNEL(mul, ops::MulKernel); - REGISTER_OP_GPU_KERNEL(mul_grad, - ops::MulGradKernel); - ``` +```c++ +namespace ops = paddle::operators; +REGISTER_OP_GPU_KERNEL(mul, ops::MulKernel); +REGISTER_OP_GPU_KERNEL(mul_grad, + ops::MulGradKernel); +``` ### 5. 编译 在[paddle/operators/CMakeLists.txt](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/CMakeLists.txt)文件中添加编译。 - ``` - op_library(mul_op SRCS mul_op.cc mul_op.cu DEPS math_function) - ``` +``` +op_library(mul_op SRCS mul_op.cc mul_op.cu DEPS math_function) +``` 下面命令可以编译： - ``` - make mul_op - ``` +``` +make mul_op +``` ## 绑定Python - - 绑定Python +- 绑定Python - 在 [`paddle/pybind/pybind.cc + 在 [`paddle/pybind/pybind.cc `](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/pybind/pybind.cc)文件中添加该类： ``` @@ -232,23 +232,23 @@ class MulKernel : public framework::OpKernel { - 生成库 - 在 [`paddle/pybind/CMakeLists.txt`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/pybind/CMakeLists.txt)文件添加类到`DEPS`中。 + 在 [`paddle/pybind/CMakeLists.txt`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/pybind/CMakeLists.txt)文件添加类到`DEPS`中，使得该Op可以链接到生成的lib库中。 ``` if(WITH_PYTHON) -cc_library(paddle_pybind SHARED - SRCS pybind.cc - DEPS pybind python backward - mul_op - minus_op) -endif(WITH_PYTHON) + cc_library(paddle_pybind SHARED + SRCS pybind.cc + DEPS pybind python backward + mul_op + minus_op) + endif(WITH_PYTHON) ``` ## 实现单元测试 单测包括对比前向Op不同设备(CPU、GPU)的实现、对比反向OP不同设备(CPU、GPU)的实现、反向Op的梯度测试。下面介绍介绍[`MulOp`的单测](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/v2/framework/tests/test_mul_op.py)。 -- 前向Op单测 +### 前向Operator单测 前向Op单测继承自`unittest.TestCase`，并定义元类`__metaclass__ = OpTestMeta`，具体单测流程在`OpTestMeta`里完成。需在`setUp`函数定义输入输出和属性参数，以及Python对比的输出值。 @@ -276,7 +276,7 @@ class TestMulOp(unittest.TestCase): - `self.outputs` : 定义输出，并得到Python结算结果。 - - 反向Op单测 +### 反向Operator单测 反向Op单测继承自`GradientChecker`，而`GradientChecker`集成自`unittest.TestCase`，所以反向单测函数需要`test_`开头。 -- GitLab From 4a83dde594d0aa6d19aeff7471b040277a8a839f Mon Sep 17 00:00:00 2001 From: caoying03 Date: Sun, 27 Aug 2017 11:28:05 +0800 Subject: [PATCH 0243/2018] save parameters into ordered dict. --- python/paddle/v2/parameters.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/python/paddle/v2/parameters.py b/python/paddle/v2/parameters.py index b8af5abae..475067ef2 100644 --- a/python/paddle/v2/parameters.py +++ b/python/paddle/v2/parameters.py @@ -14,6 +14,7 @@ import numpy as np from paddle.proto.ParameterConfig_pb2 import ParameterConfig +from collections import OrderedDict import paddle.trainer.config_parser as cp import struct import tarfile @@ -62,7 +63,7 @@ class Parameters(object): """ def __init__(self): - self.__param_conf__ = dict() + self.__param_conf__ = OrderedDict() self.__gradient_machines__ = [] self.__tmp_params__ = dict() @@ -231,6 +232,9 @@ class Parameters(object): :rtype: np.ndarray """ import py_paddle.swig_paddle as api + if self.__param_conf__[key].is_static: + return np.zeros(self.__param_conf__[key].size, dtype=np.float32) + return self.__getter_inner(key, api.PARAMETER_GRADIENT) def set(self, parameter_name, value): -- GitLab From 4590f793f111dd4fc5134ca9bbd0a213b41962b7 Mon Sep 17 00:00:00 2001 From: fengjiayi Date: Sun, 27 Aug 2017 17:37:41 -0700 Subject: [PATCH 0244/2018] Update backward document --- paddle/framework/backward.md | 24 ++++++++---------------- 1 file changed, 8 insertions(+), 16 deletions(-) diff --git a/paddle/framework/backward.md b/paddle/framework/backward.md index b4205fed2..133b17c7b 100644 --- a/paddle/framework/backward.md +++ b/paddle/framework/backward.md @@ -2,32 +2,24 @@ ## Motivation -In Neural Network, the backpropagation algorithm follows the chain rule, so we need to compound the fundmental gradient operators/expressions together with chain rule . Every forward network need a backward network to construct the full computation lineage, the operator/ expression's Backward feature will generate the backward pass respect to forward pass. - +In Neural Network, the backpropagation algorithm follows the chain rule, so we need to compound the fundmental gradient operators/expressions together with chain rule . Every forward network need a backward network to construct the full computation lineage, the operator/expression's backward pass will be generated respect to forward pass. + ## Backward Operator Registry -A backward network is built up with several backward operators. Backward operators take forward operators' inputs, outputs and output gradients and then calculate its input gradients. In most cases, there is a one-to-one correspondence between forward and backward operators. We use registry mechanism to save these correspondences, which is quite similar with operator registry itself. +A backward network is built up with several backward operators. Backward operators take forward operators' inputs, outputs and output gradients and then calculate its input gradients. In most cases, there is a one-to-one correspondence between forward and backward operators. We use registry mechanism to save these correspondences. For example, we have got a `add_two_op`, and is registered by the following code: ```cpp -REGISTER_OP(add_two, AddTwoOp, AddTwoOpMaker); +REGISTER_OP(add_two, AddTwoOp, AddTwoOpMaker, add_two_grad, AddTwoGradOp); ``` `add_two` is the operator's type. `AddTwoOp` and `AddTwoOpMaker` are the operator class and the operator maker class respectively. -Assume that we have also got the backward operator of `add_two_op`, which calculating the gradients of `add_two_op`'s inputs. Then we register it by the following way: - -```cpp -REGISTER_GRADIENT_OP(add_two, add_two_grad, AddTwoGradOp); -``` - `add_two_grad` is the type of backward operator, and `AddTwoGradOp` is its class name. ## Backward Opeartor Creating -### Usage - Given a certain forward operator, we can get its corresponding backward opeartor by calling: ```cpp @@ -36,13 +28,13 @@ OperatorBase* bwd_op = BuildGradOp(const OperatorBase* fwd_op); The function `BuildGradOp` will sequentially execute following processes: -1. Getting the `type_` of given forward operator, and then creating the corresponding backward operator. +1. Get the `type_` of given forward operator, and then get the corresponding backward operator's type by looking up the `OpInfoMap`. -2. Copying all the attributes of forward operator expect `input_format` and `output_format`(if it has), for their elements differ between forward and backward operators. +2. Build two maps named `inputs` and `outputs` to temporary storage backward operator's inputs and outputs. Copy forward operator's `inputs_` and `outputs_` to map `inputs`, except these are not necessary for gradient computing. -3. Copying forward operator's `inputs_` and `outputs_` to backward operator's `inputs_`. And adding forward inputs' gradient variables into backward `output_`, adding forward outputs' gradient variables into backward `input_`. +3. Add forward inputs' gradient variables into map `output`, adding forward outputs' gradient variables into map `input`. -4. Building backward operator's `input_format`, `output_format` (if necessary) and `in_out_idxs_` according to its `inputs_` and `outputs_` just created. +4. Building backward operator with `inputs`, `outputs` and forward operator's attributes. ## Backward Network Building -- GitLab From 98b7c6736445de1f287156e933b0d625f648e6da Mon Sep 17 00:00:00 2001 From: tensor-tang Date: Mon, 28 Aug 2017 09:52:58 +0800 Subject: [PATCH 0245/2018] add todo --- paddle/gserver/layers/MKLDNNFcLayer.cpp | 9 +++------ 1 file changed, 3 insertions(+), 6 deletions(-) diff --git a/paddle/gserver/layers/MKLDNNFcLayer.cpp b/paddle/gserver/layers/MKLDNNFcLayer.cpp index ad50c15a7..d38e6a209 100644 --- a/paddle/gserver/layers/MKLDNNFcLayer.cpp +++ b/paddle/gserver/layers/MKLDNNFcLayer.cpp @@ -184,15 +184,14 @@ void MKLDNNFcLayer::resetBwd() { const MatrixPtr& wgt = weight_->getWGrad(); const MatrixPtr& bias = hasBias ? biases_->getWGrad() : nullptr; + // TODO(TJ): merge topdiffs if (nextIsMKLDNN()) { // can not directly cast outputgrad to mkldnnmatrix, // since each layer can not write the inputgrad to mkldnn inputgrad. // So just create from matrix with outputvalue format. const MatrixPtr& out = getOutput(MKLDNN_DEVICE).grad; outGrad_ = MKLDNNMatrix::create(out, outVal_->getPD()); - // TODO: maybe need merge topdiffs } else { - // TODO: merge topdiffs const MatrixPtr& out = getOutput(CPU_DEVICE).grad; // fc do not need to convert from cpu device since output always nc // only need create from cpu device @@ -234,8 +233,7 @@ void MKLDNNFcLayer::resetBwd() { return; } if (getInput(0, MKLDNN_DEVICE).getAllCount() > 1) { - // TODO: many mkldnn bots - // add sum handle + // TODO(TJ): use outputMaps_ ways when merge topdiff done } else { inGrad_ = MKLDNNMatrix::create(in, inVal_->getPD()); } @@ -245,8 +243,7 @@ void MKLDNNFcLayer::resetBwd() { return; } if (getInput(0, CPU_DEVICE).getAllCount() > 1) { - // TODO: many bots - // add sum handle + // TODO(TJ): use outputMaps_ ways when merge topdiff done } else { inGrad_ = MKLDNNMatrix::create(in, inVal_->getPD()); } -- GitLab From be4c0123c4c6cccfaa8fafa9063ce84415854c28 Mon Sep 17 00:00:00 2001 From: caoying03 Date: Mon, 28 Aug 2017 10:11:54 +0800 Subject: [PATCH 0246/2018] follow comments. --- python/paddle/v2/parameters.py | 23 ++++++++++++++++++++--- 1 file changed, 20 insertions(+), 3 deletions(-) diff --git a/python/paddle/v2/parameters.py b/python/paddle/v2/parameters.py index 475067ef2..cc3adf6f4 100644 --- a/python/paddle/v2/parameters.py +++ b/python/paddle/v2/parameters.py @@ -43,9 +43,26 @@ def create(layers): class Parameters(object): """ - Parameters is a dictionary contains Paddle's parameter. The key of - Parameters is the name of parameter. The value of Parameters is a plain - :code:`numpy.ndarry` . + `Parameters` manages all the learnable parameters in a neural network. + It stores parameters' information in an OrderedDict, key of which is + the name of a parameter, and value related to a key is a parameter's + configuration, such as initialization mean and std, its size, whether it is + a static parameter, and so on. + + :param __param_conf__: this member stores the configurations of learnable + parameters in a network in an OrderedDict. The parameters are added by + following their creation order in the neural network one by one: + parameters of the previous layers in a network are careted first. + When a user iterates over this dict, he can visit parameters in the + network from button to up. + :type __param_conf__: OrderedDict + :param __gradient_machines__: all of the parameters in a neural network are + appended to a Paddle gradient machine, which is used internally to copy + the parameter values between the C++ and Python end. + :type __gradient_machines__: list + :param __tmp_params__: a dict to store dummy parameters if no + __gradient_machines__ is appended to `Parameters`. + :type __tmp_params__: dict Basically usage is -- GitLab From 346630f413a2e9aa9cbbdf2af4595a461ec09ac0 Mon Sep 17 00:00:00 2001 From: Luo Tao Date: Mon, 28 Aug 2017 11:19:53 +0800 Subject: [PATCH 0247/2018] Remove "About" tab in "Documentation" --- doc/about/index_cn.md | 11 ----------- doc/about/index_en.rst | 14 -------------- doc/index_en.rst | 1 - 3 files changed, 26 deletions(-) delete mode 100644 doc/about/index_cn.md delete mode 100644 doc/about/index_en.rst diff --git a/doc/about/index_cn.md b/doc/about/index_cn.md deleted file mode 100644 index 3bf030004..000000000 --- a/doc/about/index_cn.md +++ /dev/null @@ -1,11 +0,0 @@ -关于PaddlePaddle -================ - -PaddlePaddle是一个最早由百度科学家和工程师共同研发的并行分布式深度学习平台，兼备易用性、高效性、灵活性和可扩展性，目前已被百度内部多个产品线广泛使用。 -PaddlePaddle目前已经开放源码, 但是远未完善，我们希望能在这个基础上不断的改进、扩展和延伸。 -同时我们希望广大开发者积极提供反馈和贡献源代码，建立一个活跃的开源社区。 - -致谢 --------- - -在此，特别感谢PaddlePaddle的[所有贡献者](https://github.com/PaddlePaddle/Paddle/graphs/contributors)。 diff --git a/doc/about/index_en.rst b/doc/about/index_en.rst deleted file mode 100644 index 065c430cd..000000000 --- a/doc/about/index_en.rst +++ /dev/null @@ -1,14 +0,0 @@ -ABOUT -======= - -PaddlPaddle is an easy-to-use, efficient, flexible and scalable deep learning platform, -which is originally developed by Baidu scientists and engineers for the purpose of applying deep learning to many products at Baidu. - -PaddlePaddle is now open source but far from complete, which is intended to be built upon, improved, scaled, and extended. -We hope to build an active open source community both by providing feedback and by actively contributing to the source code. - - -Credits --------- - -We owe many thanks to `all contributors and developers `_ of PaddlePaddle! diff --git a/doc/index_en.rst b/doc/index_en.rst index 168c7667c..64684b8b9 100644 --- a/doc/index_en.rst +++ b/doc/index_en.rst @@ -7,4 +7,3 @@ PaddlePaddle Documentation getstarted/index_en.rst howto/index_en.rst api/index_en.rst - about/index_en.rst -- GitLab From 11cb75f8bbf10f55ed831d21ae3a4fecf97fe632 Mon Sep 17 00:00:00 2001 From: zlx Date: Mon, 28 Aug 2017 11:36:40 +0800 Subject: [PATCH 0248/2018] add the ios cross compile --- CMakeLists.txt | 17 ++- cmake/cross_compiling/ios.cmake | 207 ++++++++++++++++++++++++++++++++ cmake/external/python.cmake | 6 +- cmake/system.cmake | 3 + ios_run.sh | 17 +++ 5 files changed, 246 insertions(+), 4 deletions(-) create mode 100644 cmake/cross_compiling/ios.cmake create mode 100644 ios_run.sh diff --git a/CMakeLists.txt b/CMakeLists.txt index dcd1218a5..f0a01680a 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -27,7 +27,7 @@ if(NOT CMAKE_CROSSCOMPILING) endif(NOT CMAKE_CROSSCOMPILING) find_package(Git REQUIRED) find_package(Threads REQUIRED) -if(NOT ANDROID) +if(NOT ANDROID AND NOT IOS) find_package(Boost QUIET) endif() @@ -63,6 +63,21 @@ if(NOT CMAKE_BUILD_TYPE) FORCE) endif() +if(IOS) + set(WITH_GPU OFF CACHE STRING + "Disable GPU when cross-compiling for Android" FORCE) + set(WITH_AVX OFF CACHE STRING + "Disable AVX when cross-compiling for Android" FORCE) + set(WITH_PYTHON OFF CACHE STRING + "Disable PYTHON when cross-compiling for Android" FORCE) + set(WITH_RDMA OFF CACHE STRING + "Disable RDMA when cross-compiling for Android" FORCE) + set(WITH_MKLDNN OFF CACHE STRING + "Disable MKLDNN when cross-compiling for Android" FORCE) + set(WITH_MKLML OFF CACHE STRING + "Disable MKLML package when cross-compiling for Android" FORCE) +endif(IOS) + if(ANDROID) if(${CMAKE_SYSTEM_VERSION} VERSION_LESS "21") message(FATAL_ERROR "Unsupport standalone toolchains with Android API level lower than 21") diff --git a/cmake/cross_compiling/ios.cmake b/cmake/cross_compiling/ios.cmake new file mode 100644 index 000000000..b179e29b2 --- /dev/null +++ b/cmake/cross_compiling/ios.cmake @@ -0,0 +1,207 @@ +# This file is based off of the Platform/Darwin.cmake and Platform/UnixPaths.cmake +# files which are included with CMake 2.8.4 +# It has been altered for iOS development + +# Options: +# +# IOS_PLATFORM = OS (default) or SIMULATOR +# This decides if SDKS will be selected from the iPhoneOS.platform or iPhoneSimulator.platform folders +# OS - the default, used to build for iPhone and iPad physical devices, which have an arm arch. +# SIMULATOR - used to build for the Simulator platforms, which have an x86 arch. +# +# CMAKE_IOS_DEVELOPER_ROOT = automatic(default) or /path/to/platform/Developer folder +# By default this location is automatcially chosen based on the IOS_PLATFORM value above. +# If set manually, it will override the default location and force the user of a particular Developer Platform +# +# CMAKE_IOS_SDK_ROOT = automatic(default) or /path/to/platform/Developer/SDKs/SDK folder +# By default this location is automatcially chosen based on the CMAKE_IOS_DEVELOPER_ROOT value. +# In this case it will always be the most up-to-date SDK found in the CMAKE_IOS_DEVELOPER_ROOT path. +# If set manually, this will force the use of a specific SDK version + +# Macros: +# +# set_xcode_property (TARGET XCODE_PROPERTY XCODE_VALUE) +# A convenience macro for setting xcode specific properties on targets +# example: set_xcode_property (myioslib IPHONEOS_DEPLOYMENT_TARGET "3.1") +# +# find_host_package (PROGRAM ARGS) +# A macro used to find executable programs on the host system, not within the iOS environment. +# Thanks to the android-cmake project for providing the command + +# Standard settings +# set (CMAKE_SYSTEM_NAME Darwin) +set (CMAKE_SYSTEM_VERSION 1) +set (UNIX True) +set (APPLE True) +set (IOS True) + +# Required as of cmake 2.8.10 +set (CMAKE_OSX_DEPLOYMENT_TARGET "" CACHE STRING "Force unset of the deployment target for iOS" FORCE) + +# Determine the cmake host system version so we know where to find the iOS SDKs +find_program (CMAKE_UNAME uname /bin /usr/bin /usr/local/bin) +if (CMAKE_UNAME) + exec_program(uname ARGS -r OUTPUT_VARIABLE CMAKE_HOST_SYSTEM_VERSION) + string (REGEX REPLACE "^([0-9]+)\\.([0-9]+).*$" "\\1" DARWIN_MAJOR_VERSION "${CMAKE_HOST_SYSTEM_VERSION}") +endif (CMAKE_UNAME) + +# Force the compilers to gcc for iOS +set (CMAKE_C_COMPILER /usr/bin/gcc) +set (CMAKE_CXX_COMPILER /usr/bin/g++) +set(CMAKE_AR ar CACHE FILEPATH "" FORCE) +set(CMAKE_RANLIB ranlib CACHE FILEPATH "" FORCE) +set(PKG_CONFIG_EXECUTABLE pkg-config CACHE FILEPATH "" FORCE) + +# Setup iOS platform unless specified manually with IOS_PLATFORM +if (NOT DEFINED IOS_PLATFORM) + set (IOS_PLATFORM "OS") +endif (NOT DEFINED IOS_PLATFORM) +set (IOS_PLATFORM ${IOS_PLATFORM} CACHE STRING "Type of iOS Platform") + +# Check the platform selection and setup for developer root +if (${IOS_PLATFORM} STREQUAL "OS") + set (IOS_PLATFORM_LOCATION "iPhoneOS.platform") + set (XCODE_IOS_PLATFORM iphoneos) + + # This causes the installers to properly locate the output libraries + set (CMAKE_XCODE_EFFECTIVE_PLATFORMS "-iphoneos") +elseif (${IOS_PLATFORM} STREQUAL "SIMULATOR") + set (SIMULATOR true) + set (IOS_PLATFORM_LOCATION "iPhoneSimulator.platform") + set (XCODE_IOS_PLATFORM iphonesimulator) + + # This causes the installers to properly locate the output libraries + set (CMAKE_XCODE_EFFECTIVE_PLATFORMS "-iphonesimulator") +elseif (${IOS_PLATFORM} STREQUAL "WATCHOS") + set (IOS_PLATFORM_LOCATION "WatchOS.platform") + set (XCODE_IOS_PLATFORM watchos) + + # This causes the installers to properly locate the output libraries + set (CMAKE_XCODE_EFFECTIVE_PLATFORMS "-watchos") +else (${IOS_PLATFORM} STREQUAL "OS") + message (FATAL_ERROR + "Unsupported IOS_PLATFORM value selected. " + "Please choose OS, SIMULATOR, or WATCHOS.") +endif () + +# All iOS/Darwin specific settings - some may be redundant +set (CMAKE_SHARED_LIBRARY_PREFIX "lib") +set (CMAKE_SHARED_LIBRARY_SUFFIX ".dylib") +set (CMAKE_SHARED_MODULE_PREFIX "lib") +set (CMAKE_SHARED_MODULE_SUFFIX ".so") +set (CMAKE_MODULE_EXISTS 1) +set (CMAKE_DL_LIBS "") + +set (CMAKE_C_OSX_COMPATIBILITY_VERSION_FLAG "-compatibility_version ") +set (CMAKE_C_OSX_CURRENT_VERSION_FLAG "-current_version ") +set (CMAKE_CXX_OSX_COMPATIBILITY_VERSION_FLAG "${CMAKE_C_OSX_COMPATIBILITY_VERSION_FLAG}") +set (CMAKE_CXX_OSX_CURRENT_VERSION_FLAG "${CMAKE_C_OSX_CURRENT_VERSION_FLAG}") + +if (IOS_DEPLOYMENT_TARGET) + set(XCODE_IOS_PLATFORM_VERSION_FLAGS "-m${XCODE_IOS_PLATFORM}-version-min=${IOS_DEPLOYMENT_TARGET}") +endif() + +# Hidden visibilty is required for cxx on iOS +set (CMAKE_C_FLAGS_INIT "${XCODE_IOS_PLATFORM_VERSION_FLAGS}") +set (CMAKE_CXX_FLAGS_INIT "${XCODE_IOS_PLATFORM_VERSION_FLAGS} -fvisibility-inlines-hidden") + +set (CMAKE_C_LINK_FLAGS "${XCODE_IOS_PLATFORM_VERSION_FLAGS} -Wl,-search_paths_first ${CMAKE_C_LINK_FLAGS}") +set (CMAKE_CXX_LINK_FLAGS "${XCODE_IOS_PLATFORM_VERSION_FLAGS} -Wl,-search_paths_first ${CMAKE_CXX_LINK_FLAGS}") + +set (CMAKE_PLATFORM_HAS_INSTALLNAME 1) +set (CMAKE_SHARED_LIBRARY_CREATE_C_FLAGS "-dynamiclib -headerpad_max_install_names") +set (CMAKE_SHARED_MODULE_CREATE_C_FLAGS "-bundle -headerpad_max_install_names") +set (CMAKE_SHARED_MODULE_LOADER_C_FLAG "-Wl,-bundle_loader,") +set (CMAKE_SHARED_MODULE_LOADER_CXX_FLAG "-Wl,-bundle_loader,") +set (CMAKE_FIND_LIBRARY_SUFFIXES ".dylib" ".so" ".a") + +# hack: if a new cmake (which uses CMAKE_INSTALL_NAME_TOOL) runs on an old build tree +# (where install_name_tool was hardcoded) and where CMAKE_INSTALL_NAME_TOOL isn't in the cache +# and still cmake didn't fail in CMakeFindBinUtils.cmake (because it isn't rerun) +# hardcode CMAKE_INSTALL_NAME_TOOL here to install_name_tool, so it behaves as it did before, Alex +if (NOT DEFINED CMAKE_INSTALL_NAME_TOOL) + find_program(CMAKE_INSTALL_NAME_TOOL install_name_tool) +endif (NOT DEFINED CMAKE_INSTALL_NAME_TOOL) + +# Setup iOS deployment target +set (IOS_DEPLOYMENT_TARGET ${IOS_DEPLOYMENT_TARGET} CACHE STRING "Minimum iOS version") + +# Setup iOS developer location unless specified manually with CMAKE_IOS_DEVELOPER_ROOT +# Note Xcode 4.3 changed the installation location, choose the most recent one available +exec_program(/usr/bin/xcode-select ARGS -print-path OUTPUT_VARIABLE CMAKE_XCODE_DEVELOPER_DIR) +set (XCODE_POST_43_ROOT "${CMAKE_XCODE_DEVELOPER_DIR}/Platforms/${IOS_PLATFORM_LOCATION}/Developer") +set (XCODE_PRE_43_ROOT "/Developer/Platforms/${IOS_PLATFORM_LOCATION}/Developer") +if (NOT DEFINED CMAKE_IOS_DEVELOPER_ROOT) + if (EXISTS ${XCODE_POST_43_ROOT}) + set (CMAKE_IOS_DEVELOPER_ROOT ${XCODE_POST_43_ROOT}) + elseif(EXISTS ${XCODE_PRE_43_ROOT}) + set (CMAKE_IOS_DEVELOPER_ROOT ${XCODE_PRE_43_ROOT}) + endif (EXISTS ${XCODE_POST_43_ROOT}) +endif (NOT DEFINED CMAKE_IOS_DEVELOPER_ROOT) +set (CMAKE_IOS_DEVELOPER_ROOT ${CMAKE_IOS_DEVELOPER_ROOT} CACHE PATH "Location of iOS Platform") + +# Find and use the most recent iOS sdk unless specified manually with CMAKE_IOS_SDK_ROOT +if (NOT DEFINED CMAKE_IOS_SDK_ROOT) + file (GLOB _CMAKE_IOS_SDKS "${CMAKE_IOS_DEVELOPER_ROOT}/SDKs/*") + if (_CMAKE_IOS_SDKS) + list (SORT _CMAKE_IOS_SDKS) + list (REVERSE _CMAKE_IOS_SDKS) + list (GET _CMAKE_IOS_SDKS 0 CMAKE_IOS_SDK_ROOT) + else (_CMAKE_IOS_SDKS) + message (FATAL_ERROR "No iOS SDK's found in default search path ${CMAKE_IOS_DEVELOPER_ROOT}. Manually set CMAKE_IOS_SDK_ROOT or install the iOS SDK.") + endif (_CMAKE_IOS_SDKS) + message (STATUS "Toolchain using default iOS SDK: ${CMAKE_IOS_SDK_ROOT}") +endif (NOT DEFINED CMAKE_IOS_SDK_ROOT) +set (CMAKE_IOS_SDK_ROOT ${CMAKE_IOS_SDK_ROOT} CACHE PATH "Location of the selected iOS SDK") + +# Set the sysroot default to the most recent SDK +set (CMAKE_OSX_SYSROOT ${CMAKE_IOS_SDK_ROOT} CACHE PATH "Sysroot used for iOS support") + +# set the architecture for iOS +if (IOS_PLATFORM STREQUAL "OS") + set (IOS_ARCH "armv7;armv7s;arm64") +elseif (IOS_PLATFORM STREQUAL "SIMULATOR") + set (IOS_ARCH "i386;x86_64") +elseif (IOS_PLATFORM STREQUAL "WATCHOS") + set (IOS_ARCH "armv7k") +endif () + +set (CMAKE_OSX_ARCHITECTURES ${IOS_ARCH} CACHE string "Build architecture for iOS") + +# Set the find root to the iOS developer roots and to user defined paths +set (CMAKE_FIND_ROOT_PATH ${CMAKE_IOS_DEVELOPER_ROOT} ${CMAKE_IOS_SDK_ROOT} ${CMAKE_PREFIX_PATH} CACHE string "iOS find search path root") + +# default to searching for frameworks first +set (CMAKE_FIND_FRAMEWORK FIRST) + +# set up the default search directories for frameworks +set (CMAKE_SYSTEM_FRAMEWORK_PATH + ${CMAKE_IOS_SDK_ROOT}/System/Library/Frameworks + ${CMAKE_IOS_SDK_ROOT}/System/Library/PrivateFrameworks + ${CMAKE_IOS_SDK_ROOT}/Developer/Library/Frameworks +) + +# only search the iOS sdks, not the remainder of the host filesystem +set (CMAKE_FIND_ROOT_PATH_MODE_PROGRAM NEVER) +set (CMAKE_FIND_ROOT_PATH_MODE_LIBRARY ONLY) +set (CMAKE_FIND_ROOT_PATH_MODE_INCLUDE ONLY) + +# This little macro lets you set any XCode specific property +macro (set_xcode_property TARGET XCODE_PROPERTY XCODE_VALUE) + set_property (TARGET ${TARGET} PROPERTY XCODE_ATTRIBUTE_${XCODE_PROPERTY} ${XCODE_VALUE}) +endmacro (set_xcode_property) + +# This macro lets you find executable programs on the host system +macro (find_host_package) + set (CMAKE_FIND_ROOT_PATH_MODE_PROGRAM NEVER) + set (CMAKE_FIND_ROOT_PATH_MODE_LIBRARY NEVER) + set (CMAKE_FIND_ROOT_PATH_MODE_INCLUDE NEVER) + set (IOS FALSE) + + find_package(${ARGN}) + + set (IOS TRUE) + set (CMAKE_FIND_ROOT_PATH_MODE_PROGRAM ONLY) + set (CMAKE_FIND_ROOT_PATH_MODE_LIBRARY ONLY) + set (CMAKE_FIND_ROOT_PATH_MODE_INCLUDE ONLY) +endmacro (find_host_package) diff --git a/cmake/external/python.cmake b/cmake/external/python.cmake index 490c87d67..53ef7cd29 100644 --- a/cmake/external/python.cmake +++ b/cmake/external/python.cmake @@ -15,9 +15,9 @@ INCLUDE(ExternalProject) INCLUDE(python_module) -FIND_PACKAGE(PythonInterp 2.7) +FIND_HOST_PACKAGE(PythonInterp 2.7) IF(WITH_PYTHON) - FIND_PACKAGE(PythonLibs 2.7) + FIND_HOST_PACKAGE(PythonLibs 2.7) # Fixme: Maybe find a static library. Get SHARED/STATIC by FIND_PACKAGE. ADD_LIBRARY(python SHARED IMPORTED GLOBAL) SET_PROPERTY(TARGET python PROPERTY IMPORTED_LOCATION ${PYTHON_LIBRARIES}) @@ -29,7 +29,7 @@ IF(PYTHONINTERP_FOUND) find_python_module(numpy REQUIRED) find_python_module(wheel REQUIRED) find_python_module(google.protobuf REQUIRED) - FIND_PACKAGE(NumPy REQUIRED) + FIND_HOST_PACKAGE(NumPy REQUIRED) IF(${PY_GOOGLE.PROTOBUF_VERSION} AND ${PY_GOOGLE.PROTOBUF_VERSION} VERSION_LESS "3.0.0") MESSAGE(FATAL_ERROR "Found Python Protobuf ${PY_GOOGLE.PROTOBUF_VERSION} < 3.0.0, " "please use pip to upgrade protobuf. pip install -U protobuf") diff --git a/cmake/system.cmake b/cmake/system.cmake index adf5e2c53..7462802d2 100644 --- a/cmake/system.cmake +++ b/cmake/system.cmake @@ -82,6 +82,9 @@ IF(DEFINED CMAKE_SYSTEM_NAME) ELSEIF(${CMAKE_SYSTEM_NAME} STREQUAL "RPi") SET(RPI TRUE) INCLUDE(cross_compiling/raspberry_pi) + ELSEIF(${CMAKE_SYSTEM_NAME} STREQUAL "Darwin") + SET(IOS TRUE) + INCLUDE(cross_compiling/ios) ENDIF() ENDIF() diff --git a/ios_run.sh b/ios_run.sh new file mode 100644 index 000000000..b8325f15f --- /dev/null +++ b/ios_run.sh @@ -0,0 +1,17 @@ +#!/bin/bash + +set -xe + +mkdir -p ./ios_build +cd ./ios_build + +cmake -DCMAKE_SYSTEM_NAME=Darwin \ + -DWITH_C_API=ON \ + -DWITH_TESTING=OFF \ + -DWITH_SWIG_PY=OFF \ + -DCMAKE_BUILD_TYPE=Release \ + -DCMAKE_INSTALL_PREFIX=/Users/xingzhaolong/cross_compile/ios \ + .. + # -DIOS_PLATFORM=SIMULATOR \ + #-DCMAKE_Go_COMPILER=/usr/local/bin \ + -- GitLab From fe51f726a2da85b0cb96734bd9b156760b044cf9 Mon Sep 17 00:00:00 2001 From: tensor-tang Date: Mon, 28 Aug 2017 10:44:31 +0800 Subject: [PATCH 0249/2018] fix cmake --- paddle/math/CMakeLists.txt | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/paddle/math/CMakeLists.txt b/paddle/math/CMakeLists.txt index 8afe6b509..68b529622 100644 --- a/paddle/math/CMakeLists.txt +++ b/paddle/math/CMakeLists.txt @@ -16,10 +16,10 @@ file(GLOB MATH_HEADERS . *.h) file(GLOB MATH_SOURCES . *.cpp) if(NOT WITH_MKLDNN) - file(GLOB_RECURSE DNN_HEADER RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "MKLDNN*.h") - file(GLOB_RECURSE DNN_SOURCES RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "MKLDNN*.cpp") - list(REMOVE_ITEM MATH_HEADERS ${DNN_HEADER}) - list(REMOVE_ITEM MATH_SOURCES ${DNN_SOURCES}) + set(DNN_HEADER "${CMAKE_CURRENT_SOURCE_DIR}/MKLDNNMatrix.h") + set(DNN_SOURCE "${CMAKE_CURRENT_SOURCE_DIR}/MKLDNNMatrix.cpp") + list(REMOVE_ITEM MATH_HEADERS "${DNN_HEADER}") + list(REMOVE_ITEM MATH_SOURCES "${DNN_SOURCE}") message(STATUS "Skip compiling with MKLDNNMatrix") else() message(STATUS "Compile with MKLDNNMatrix") -- GitLab From f241773c4f1803631bba968bca1d5621a0d3ced5 Mon Sep 17 00:00:00 2001 From: Liu Yiqun Date: Wed, 23 Aug 2017 19:43:57 +0800 Subject: [PATCH 0250/2018] Support to use clang for Android cross-compiling. --- Dockerfile.android | 4 +- cmake/cblas.cmake | 4 + cmake/external/warpctc.cmake | 1 + paddle/cuda/include/hl_cpu_gru.cuh | 166 ++++++++++++------------- paddle/function/MulOp.cpp | 37 +++--- paddle/math/MathFunctions.cpp | 4 + paddle/math/MathFunctions.h | 23 +++- paddle/math/Matrix.cpp | 18 ++- paddle/scripts/docker/build_android.sh | 51 ++++++-- 9 files changed, 181 insertions(+), 127 deletions(-) diff --git a/Dockerfile.android b/Dockerfile.android index aa95abb36..6013215d9 100644 --- a/Dockerfile.android +++ b/Dockerfile.android @@ -47,8 +47,8 @@ RUN mkdir /opt/android-ndk-tmp && \ wget -q https://dl.google.com/android/repository/android-ndk-r14b-linux-x86_64.zip && \ unzip -q android-ndk-r14b-linux-x86_64.zip && \ mv android-ndk-r14b ${ANDROID_NDK_HOME} && \ - ${ANDROID_NDK_HOME}/build/tools/make-standalone-toolchain.sh --arch=arm --platform=android-21 --install-dir=${ANDROID_ARM_STANDALONE_TOOLCHAIN} && \ - ${ANDROID_NDK_HOME}/build/tools/make-standalone-toolchain.sh --arch=arm64 --platform=android-21 --install-dir=${ANDROID_ARM64_STANDALONE_TOOLCHAIN} && \ + ${ANDROID_NDK_HOME}/build/tools/make-standalone-toolchain.sh --arch=arm --platform=android-23 --install-dir=${ANDROID_ARM_STANDALONE_TOOLCHAIN} && \ + ${ANDROID_NDK_HOME}/build/tools/make-standalone-toolchain.sh --arch=arm64 --platform=android-23 --install-dir=${ANDROID_ARM64_STANDALONE_TOOLCHAIN} && \ rm -rf /opt/android-ndk-tmp && \ rm -rf ${ANDROID_NDK_HOME} diff --git a/cmake/cblas.cmake b/cmake/cblas.cmake index 854066fd1..ab111eccc 100644 --- a/cmake/cblas.cmake +++ b/cmake/cblas.cmake @@ -13,6 +13,10 @@ # system paths. # +if(USE_EIGEN_FOR_BLAS) + return() +endif(USE_EIGEN_FOR_BLAS) + set(CBLAS_FOUND OFF) ## Find MKLML First. diff --git a/cmake/external/warpctc.cmake b/cmake/external/warpctc.cmake index 2d7daed9b..3cc652bed 100644 --- a/cmake/external/warpctc.cmake +++ b/cmake/external/warpctc.cmake @@ -41,6 +41,7 @@ IF(CMAKE_CXX_COMPILER_ID STREQUAL "Clang" OR CMAKE_CXX_COMPILER_ID STREQUAL "App ELSE() SET(USE_OMP ON) ENDIF() +SET(USE_OMP OFF FORCE) ExternalProject_Add( extern_warpctc diff --git a/paddle/cuda/include/hl_cpu_gru.cuh b/paddle/cuda/include/hl_cpu_gru.cuh index c0a37ced2..732799a28 100644 --- a/paddle/cuda/include/hl_cpu_gru.cuh +++ b/paddle/cuda/include/hl_cpu_gru.cuh @@ -20,11 +20,11 @@ limitations under the License. */ #include "paddle/math/MathFunctions.h" -#ifndef PADDLE_TYPE_DOUBLE -#define CBLAS_GEMM paddle::gemm -#else -#define CBLAS_GEMM paddle::gemm -#endif +// #ifndef PADDLE_TYPE_DOUBLE +// #define CBLAS_GEMM paddle::gemm +// #else +// #define CBLAS_GEMM paddle::gemm +// #endif template void hl_naive_gru_forward_reset_output(OpResetOutput opResetOutput, @@ -219,37 +219,37 @@ void hl_cpu_gru_forward(OpResetOutput opResetOutput, hl_activation_mode_t active_node, hl_activation_mode_t active_gate) { if (value.prevOutValue) { - CBLAS_GEMM(CblasNoTrans, - CblasNoTrans, - batchSize, - 2 * frameSize, - frameSize, - 1, - value.prevOutValue, - frameSize, - value.gateWeight, - frameSize * 2, - 1, - value.gateValue, - frameSize * 3); +// CBLAS_GEMM(CblasNoTrans, +// CblasNoTrans, +// batchSize, +// 2 * frameSize, +// frameSize, +// 1, +// value.prevOutValue, +// frameSize, +// value.gateWeight, +// frameSize * 2, +// 1, +// value.gateValue, +// frameSize * 3); } forward_reset_output(opResetOutput, value, frameSize, batchSize, active_gate); if (value.prevOutValue) { - CBLAS_GEMM(CblasNoTrans, - CblasNoTrans, - batchSize, - frameSize, - frameSize, - 1, - value.resetOutputValue, - frameSize, - value.stateWeight, - frameSize, - 1, - value.gateValue + frameSize * 2, - frameSize * 3); +// CBLAS_GEMM(CblasNoTrans, +// CblasNoTrans, +// batchSize, +// frameSize, +// frameSize, +// 1, +// value.resetOutputValue, +// frameSize, +// value.stateWeight, +// frameSize, +// 1, +// value.gateValue + frameSize * 2, +// frameSize * 3); } forward_final_output(opFinalOutput, value, frameSize, batchSize, active_node); @@ -538,34 +538,34 @@ void hl_cpu_gru_backward(OpStateGrad opStateGrad, frameSize, batchSize, active_node); if (value.prevOutValue && grad.prevOutGrad) { - CBLAS_GEMM(CblasNoTrans, - CblasTrans, - batchSize, - frameSize, - frameSize, - 1, - grad.gateGrad + frameSize * 2, - frameSize * 3, - value.stateWeight, - frameSize, - 0, - grad.resetOutputGrad, - frameSize); +// CBLAS_GEMM(CblasNoTrans, +// CblasTrans, +// batchSize, +// frameSize, +// frameSize, +// 1, +// grad.gateGrad + frameSize * 2, +// frameSize * 3, +// value.stateWeight, +// frameSize, +// 0, +// grad.resetOutputGrad, +// frameSize); if (grad.stateWeightGrad) { - CBLAS_GEMM(CblasTrans, - CblasNoTrans, - frameSize, - frameSize, - batchSize, - 1, - value.resetOutputValue, - frameSize, - grad.gateGrad + frameSize * 2, - frameSize * 3, - 1, - grad.stateWeightGrad, - frameSize); +// CBLAS_GEMM(CblasTrans, +// CblasNoTrans, +// frameSize, +// frameSize, +// batchSize, +// 1, +// value.resetOutputValue, +// frameSize, +// grad.gateGrad + frameSize * 2, +// frameSize * 3, +// 1, +// grad.stateWeightGrad, +// frameSize); } } @@ -573,34 +573,34 @@ void hl_cpu_gru_backward(OpStateGrad opStateGrad, frameSize, batchSize, active_gate); if (grad.prevOutGrad && value.prevOutValue) { - CBLAS_GEMM(CblasNoTrans, - CblasTrans, - batchSize, - frameSize, - frameSize * 2, - 1, - grad.gateGrad, - frameSize * 3, - value.gateWeight, - frameSize * 2, - 1, - grad.prevOutGrad, - frameSize); +// CBLAS_GEMM(CblasNoTrans, +// CblasTrans, +// batchSize, +// frameSize, +// frameSize * 2, +// 1, +// grad.gateGrad, +// frameSize * 3, +// value.gateWeight, +// frameSize * 2, +// 1, +// grad.prevOutGrad, +// frameSize); if (grad.gateWeightGrad) { - CBLAS_GEMM(CblasTrans, - CblasNoTrans, - frameSize, - frameSize * 2, - batchSize, - 1, - value.prevOutValue, - frameSize, - grad.gateGrad, - frameSize * 3, - 1, - grad.gateWeightGrad, - frameSize * 2); +// CBLAS_GEMM(CblasTrans, +// CblasNoTrans, +// frameSize, +// frameSize * 2, +// batchSize, +// 1, +// value.prevOutValue, +// frameSize, +// grad.gateGrad, +// frameSize * 3, +// 1, +// grad.gateWeightGrad, +// frameSize * 2); } } } diff --git a/paddle/function/MulOp.cpp b/paddle/function/MulOp.cpp index 91b4b8ed9..25e41edad 100644 --- a/paddle/function/MulOp.cpp +++ b/paddle/function/MulOp.cpp @@ -13,18 +13,10 @@ See the License for the specific language governing permissions and limitations under the License. */ #include "MulOp.h" -/// todo(tianbing), delete it -#include -#include "paddle/math/MathFunctions.h" +#include "GemmFunctor.h" #include "paddle/math/SIMDFunctions.h" #include "paddle/utils/ThreadLocal.h" -#ifndef PADDLE_TYPE_DOUBLE -#define GEMM paddle::gemm -#else -#define GEMM paddle::gemm -#endif - namespace { inline void vecAddTo(real* a, const real* b, real scaleB, size_t len) { for (unsigned int i = 0; i < len; ++i) { @@ -114,19 +106,20 @@ void MulOp(CpuMatrix& out, real scaleT, bool aTrans, bool bTrans) { - GEMM(aTrans ? CblasTrans : CblasNoTrans, - bTrans ? CblasTrans : CblasNoTrans, - out.getHeight(), - out.getWidth(), - !aTrans ? a.getWidth() : a.getHeight(), - scaleAB, - a.getData(), - a.getStride(), - b.getData(), - b.getStride(), - scaleT, - out.getData(), - out.getStride()); + BlasGemm::compute( + aTrans, + bTrans, + out.getHeight(), + out.getWidth(), + !aTrans ? a.getWidth() : a.getHeight(), + scaleAB, + a.getData(), + a.getStride(), + b.getData(), + b.getStride(), + scaleT, + out.getData(), + out.getStride()); } /// dense matrix (+)= sparse matrix * dense matrix diff --git a/paddle/math/MathFunctions.cpp b/paddle/math/MathFunctions.cpp index c8ba1074a..c2f17beeb 100644 --- a/paddle/math/MathFunctions.cpp +++ b/paddle/math/MathFunctions.cpp @@ -84,6 +84,7 @@ LAPACK_ROUTINE_EACH(DYNAMIC_LOAD_LAPACK_WRAP) namespace paddle { +#ifndef PADDLE_USE_EIGEN_FOR_BLAS template <> void gemm(const CBLAS_TRANSPOSE transA, const CBLAS_TRANSPOSE transB, @@ -143,6 +144,7 @@ void gemm(const CBLAS_TRANSPOSE transA, C, ldc); } +#endif template <> int getrf(const CBLAS_ORDER order, @@ -182,6 +184,7 @@ int getri(const CBLAS_ORDER order, return dynload::PADDLE_DGETRI(order, N, A, lda, ipiv); } +#ifndef PADDLE_USE_EIGEN_FOR_BLAS template <> void axpy(const int n, const float alpha, const float* x, float* y) { cblas_saxpy(n, alpha, x, 1, y, 1); @@ -201,6 +204,7 @@ template <> double dotProduct(const int n, const double* x, const double* y) { return cblas_ddot(n, x, 1, y, 1); } +#endif #if defined(PADDLE_USE_MKL) || defined(PADDLE_USE_MKLML) diff --git a/paddle/math/MathFunctions.h b/paddle/math/MathFunctions.h index 637643838..9297ae78c 100644 --- a/paddle/math/MathFunctions.h +++ b/paddle/math/MathFunctions.h @@ -40,7 +40,14 @@ extern "C" { #ifndef LAPACK_FOUND extern "C" { +#ifndef PADDLE_USE_EIGEN_FOR_BLAS #include +#else +typedef enum CBLAS_ORDER { + CblasRowMajor = 101, + CblasColMajor = 102 +} CBLAS_ORDER; +#endif int LAPACKE_sgetrf( int matrix_layout, int m, int n, float* a, int lda, int* ipiv); int LAPACKE_dgetrf( @@ -56,6 +63,7 @@ int LAPACKE_dgetri( namespace paddle { +#ifndef PADDLE_USE_EIGEN_FOR_BLAS template void gemm(const CBLAS_TRANSPOSE transA, const CBLAS_TRANSPOSE transB, @@ -70,6 +78,7 @@ void gemm(const CBLAS_TRANSPOSE transA, const T beta, T* C, const int ldc); +#endif template int getrf(const CBLAS_ORDER Order, @@ -84,10 +93,20 @@ int getri( const CBLAS_ORDER Order, const int N, T* A, const int lda, const int* ipiv); template -void axpy(const int n, const T alpha, const T* x, T* y); +void axpy(const int n, const T alpha, const T* x, T* y) { + /// y = y + alpha * x + for (int i = 0; i < n; i++) { + y[i] = y[i] + alpha * x[i]; + } +} template -T dotProduct(const int n, const T* x, const T* y); +T dotProduct(const int n, const T* x, const T* y) { + T result = static_cast(0); + for (int i = 0; i < n; i++) { + result += x[i] * y[i]; + } +} template void vExp(const int n, const T* a, T* r); diff --git a/paddle/math/Matrix.cpp b/paddle/math/Matrix.cpp index 27f7d95b7..fbf3accc9 100644 --- a/paddle/math/Matrix.cpp +++ b/paddle/math/Matrix.cpp @@ -28,6 +28,7 @@ limitations under the License. */ #include "hl_top_k.h" #include "paddle/utils/Logging.h" +#include "paddle/function/GemmFunctor.h" #include "paddle/utils/ThreadLocal.h" #include "SIMDFunctions.h" @@ -2222,24 +2223,29 @@ void CpuMatrix::mul(CpuMatrix* a, CpuMatrix* b, real scaleAB, real scaleT) { CHECK(!isTransposed()) << "Not supported"; size_t a_col, b_col, a_row, b_row; - CBLAS_TRANSPOSE a_trans, b_trans; + // CBLAS_TRANSPOSE a_trans, b_trans; + bool a_trans, b_trans; if (!a->isTransposed()) { a_col = a->getWidth(); a_row = a->getHeight(); - a_trans = CblasNoTrans; + // a_trans = CblasNoTrans; + a_trans = false; } else { a_col = a->getHeight(); a_row = a->getWidth(); - a_trans = CblasTrans; + // a_trans = CblasTrans; + a_trans = true; } if (!b->isTransposed()) { b_col = b->getWidth(); b_row = b->getHeight(); - b_trans = CblasNoTrans; + // b_trans = CblasNoTrans; + b_trans = false; } else { b_col = b->getHeight(); b_row = b->getWidth(); - b_trans = CblasTrans; + // b_trans = CblasTrans; + b_trans = true; } CHECK_EQ(a_col, b_row); @@ -2256,7 +2262,7 @@ void CpuMatrix::mul(CpuMatrix* a, CpuMatrix* b, real scaleAB, real scaleT) { int lda = a->getStride(); int ldb = b->getStride(); int ldc = getStride(); - gemm( + BlasGemm::compute( a_trans, b_trans, M, N, K, scaleAB, A, lda, B, ldb, scaleT, C, ldc); } diff --git a/paddle/scripts/docker/build_android.sh b/paddle/scripts/docker/build_android.sh index 593ae28e4..a61c7c40e 100644 --- a/paddle/scripts/docker/build_android.sh +++ b/paddle/scripts/docker/build_android.sh @@ -2,11 +2,31 @@ set -xe -mkdir -p /paddle/build_android/$ANDROID_ABI -cd /paddle/build_android/$ANDROID_ABI -rm -rf /paddle/install 2>/dev/null || true +COMPILER=gcc +USE_EIGEN=ON +if [ $COMPILER == clang ]; then + SUFFIX=_clang + C_COMPILER=clang + CXX_COMPILER=clang++ +else + SUFFIX=_gcc + C_COMPILER=gcc + CXX_COMPILER=g++ +fi +if [ $USE_EIGEN == ON ]; then + SUFFIX=${SUFFIX}_eigen +else + SUFFIX=${SUFFIX}_openblas +fi -THIRD_PARTY_PATH=/paddle/third_party_android/$ANDROID_ABI +BUILD_ROOT=/paddle/build_android$SUFFIX +DEST_ROOT=/paddle/install$SUFFIX + +rm -rf $BUILD_ROOT 2>/dev/null || true +mkdir -p $BUILD_ROOT +cd $BUILD_ROOT + +THIRD_PARTY_PATH=/paddle/third_party_android$SUFFIX/$ANDROID_ABI if [ $ANDROID_ABI == "armeabi-v7a" ]; then cmake -DCMAKE_SYSTEM_NAME=Android \ @@ -14,27 +34,34 @@ if [ $ANDROID_ABI == "armeabi-v7a" ]; then -DANDROID_ABI=$ANDROID_ABI \ -DANDROID_ARM_NEON=ON \ -DANDROID_ARM_MODE=ON \ + -DCMAKE_C_COMPILER=$ANDROID_ARM_STANDALONE_TOOLCHAIN/bin/arm-linux-androideabi-${C_COMPILER} \ + -DCMAKE_CXX_COMPILER=$ANDROID_ARM_STANDALONE_TOOLCHAIN/bin/arm-linux-androideabi-${CXX_COMPILER} \ -DHOST_C_COMPILER=/usr/bin/gcc \ -DHOST_CXX_COMPILER=/usr/bin/g++ \ - -DCMAKE_INSTALL_PREFIX=/paddle/install \ + -DCMAKE_INSTALL_PREFIX=$DEST_ROOT \ -DTHIRD_PARTY_PATH=$THIRD_PARTY_PATH \ -DCMAKE_BUILD_TYPE=Release \ + -DUSE_EIGEN_FOR_BLAS=${USE_EIGEN} \ -DWITH_C_API=ON \ -DWITH_SWIG_PY=OFF \ - /paddle -elif [ $ANDROID_ABI == "arm64-v7a" ]; then + -DWITH_STYLE_CHECK=OFF \ + .. +elif [ $ANDROID_ABI == "arm64-v8a" ]; then cmake -DCMAKE_SYSTEM_NAME=Android \ -DANDROID_STANDALONE_TOOLCHAIN=$ANDROID_ARM64_STANDALONE_TOOLCHAIN \ -DANDROID_ABI=$ANDROID_ABI \ -DANDROID_ARM_MODE=ON \ + -DCMAKE_C_COMPILER=$ANDROID_ARM64_STANDALONE_TOOLCHAIN/bin/aarch64-linux-android-${C_COMPILER} \ + -DCMAKE_CXX_COMPILER=$ANDROID_ARM64_STANDALONE_TOOLCHAIN/bin/aarch64-linux-android-${CXX_COMPILER} \ -DHOST_C_COMPILER=/usr/bin/gcc \ -DHOST_CXX_COMPILER=/usr/bin/g++ \ - -DCMAKE_INSTALL_PREFIX=/paddle/install \ + -DCMAKE_INSTALL_PREFIX=$DEST_ROOT \ -DTHIRD_PARTY_PATH=$THIRD_PARTY_PATH \ -DCMAKE_BUILD_TYPE=Release \ + -DUSE_EIGEN_FOR_BLAS=${USE_EIGEN} \ -DWITH_C_API=ON \ -DWITH_SWIG_PY=OFF \ - /paddle + .. elif [ $ANDROID_ABI == "armeabi" ]; then cmake -DCMAKE_SYSTEM_NAME=Android \ -DANDROID_STANDALONE_TOOLCHAIN=$ANDROID_ARM_STANDALONE_TOOLCHAIN \ @@ -47,10 +74,10 @@ elif [ $ANDROID_ABI == "armeabi" ]; then -DCMAKE_BUILD_TYPE=Release \ -DWITH_C_API=ON \ -DWITH_SWIG_PY=OFF \ - /paddle + .. else echo "Invalid ANDROID_ABI: $ANDROID_ABI" fi -make -j `nproc` -make install -j `nproc` +make VERBOSE=1 -j2 +make install -j2 -- GitLab From f0b25c4cfb21b41e8bc7222d44f05a9818dc9b47 Mon Sep 17 00:00:00 2001 From: caoying03 Date: Mon, 28 Aug 2017 12:20:28 +0800 Subject: [PATCH 0251/2018] follow comments to refine the comments. --- python/paddle/v2/parameters.py | 27 +++++++++++++-------------- 1 file changed, 13 insertions(+), 14 deletions(-) diff --git a/python/paddle/v2/parameters.py b/python/paddle/v2/parameters.py index cc3adf6f4..4cfd91882 100644 --- a/python/paddle/v2/parameters.py +++ b/python/paddle/v2/parameters.py @@ -44,21 +44,20 @@ def create(layers): class Parameters(object): """ `Parameters` manages all the learnable parameters in a neural network. - It stores parameters' information in an OrderedDict, key of which is - the name of a parameter, and value related to a key is a parameter's - configuration, such as initialization mean and std, its size, whether it is - a static parameter, and so on. - - :param __param_conf__: this member stores the configurations of learnable - parameters in a network in an OrderedDict. The parameters are added by - following their creation order in the neural network one by one: - parameters of the previous layers in a network are careted first. - When a user iterates over this dict, he can visit parameters in the - network from button to up. + It stores parameters' information in an OrderedDict. The key is + the name of a parameter, and value is a parameter's configuration(in + protobuf format), such as initialization mean and std, its size, whether it + is a static parameter, and so on. + + :param __param_conf__: store the configurations of learnable parameters in + the network in an OrderedDict. Parameter is added one by one into the + dict by following their created order in the network: parameters of + the previous layers in a network are careted first. You can visit the + parameters from bottom to top by iterating over this dict. :type __param_conf__: OrderedDict :param __gradient_machines__: all of the parameters in a neural network are - appended to a Paddle gradient machine, which is used internally to copy - the parameter values between the C++ and Python end. + appended to a PaddlePaddle gradient machine, which is used internally to + copy parameter values between C++ and Python end. :type __gradient_machines__: list :param __tmp_params__: a dict to store dummy parameters if no __gradient_machines__ is appended to `Parameters`. @@ -271,7 +270,7 @@ class Parameters(object): append gradient machine to parameters. This method is used internally in Trainer.train. - :param gradient_machine: Paddle C++ GradientMachine object. + :param gradient_machine: PaddlePaddle C++ GradientMachine object. :type gradient_machine: api.GradientMachine :return: """ -- GitLab From 227fdfb65dcb45921398690610886ebdb9b34d98 Mon Sep 17 00:00:00 2001 From: hedaoyuan Date: Mon, 28 Aug 2017 13:35:51 +0800 Subject: [PATCH 0252/2018] Refine NeonDepthwiseConvFunction. --- paddle/function/neon/NeonDepthwiseConv.cpp | 70 ++++++++-------------- 1 file changed, 26 insertions(+), 44 deletions(-) diff --git a/paddle/function/neon/NeonDepthwiseConv.cpp b/paddle/function/neon/NeonDepthwiseConv.cpp index 3fe28b1de..f09e98587 100644 --- a/paddle/function/neon/NeonDepthwiseConv.cpp +++ b/paddle/function/neon/NeonDepthwiseConv.cpp @@ -509,10 +509,9 @@ public: size_t filterMultiplier = outputChannels / groups_; CHECK_EQ(inputChannels, groups_); - // only support + // only support strideH() == strideW() and filterHeight == filterWidth. CHECK_EQ(strideH(), strideW()); CHECK_EQ(filterHeight, filterWidth); - CHECK_LT(strideH(), size_t(3)); float* inputData = inputs[0].data(); float* filterData = inputs[1].data(); @@ -538,49 +537,32 @@ public: inputWidth += 2 * paddingW(); } - for (size_t i = 0; i < batchSize; i++) { - if (filterWidth == 3 && strideH() == 1) { - DepthwiseConvKernel<3, 1>::run(inputPadding, - filterData, - inputHeight, - inputWidth, - outputChannels, - outputHeight, - outputWidth, - filterMultiplier, - outputData); - } else if (filterWidth == 3 && strideH() == 2) { - DepthwiseConvKernel<3, 2>::run(inputPadding, - filterData, - inputHeight, - inputWidth, - outputChannels, - outputHeight, - outputWidth, - filterMultiplier, - outputData); - } else if (filterWidth == 4 && strideH() == 1) { - DepthwiseConvKernel<4, 1>::run(inputPadding, - filterData, - inputHeight, - inputWidth, - outputChannels, - outputHeight, - outputWidth, - filterMultiplier, - outputData); - } else if (filterWidth == 4 && strideH() == 2) { - DepthwiseConvKernel<4, 2>::run(inputPadding, - filterData, - inputHeight, - inputWidth, - outputChannels, - outputHeight, - outputWidth, - filterMultiplier, - outputData); - } + std::function + DepthWiseConv; + + if (filterWidth == 3 && strideW() == 1) { + DepthWiseConv = DepthwiseConvKernel<3, 1>::run; + } else if (filterWidth == 3 && strideW() == 2) { + DepthWiseConv = DepthwiseConvKernel<3, 2>::run; + } else if (filterWidth == 4 && strideW() == 1) { + DepthWiseConv = DepthwiseConvKernel<4, 1>::run; + } else if (filterWidth == 4 && strideW() == 2) { + DepthWiseConv = DepthwiseConvKernel<4, 2>::run; + } else { + LOG(FATAL) << "Not supported"; + } + for (size_t i = 0; i < batchSize; i++) { + DepthWiseConv(inputPadding, + filterData, + inputHeight, + inputWidth, + outputChannels, + outputHeight, + outputWidth, + filterMultiplier, + outputData); inputPadding += inputChannels * inputHeight * inputWidth; outputData += outputChannels * outputHeight * outputWidth; } -- GitLab From 3a75b4b70cd21449691eaca82f1805759622e640 Mon Sep 17 00:00:00 2001 From: hedaoyuan Date: Mon, 28 Aug 2017 14:49:11 +0800 Subject: [PATCH 0253/2018] Fix CMakeLists.text --- paddle/function/CMakeLists.txt | 2 +- paddle/function/DepthwiseConvOpTest.cpp | 4 ++++ 2 files changed, 5 insertions(+), 1 deletion(-) diff --git a/paddle/function/CMakeLists.txt b/paddle/function/CMakeLists.txt index 05f808a6a..f43f15e5c 100644 --- a/paddle/function/CMakeLists.txt +++ b/paddle/function/CMakeLists.txt @@ -44,11 +44,11 @@ if(WITH_GPU) add_simple_unittest(RowConvOpTest) add_simple_unittest(BlockExpandOpTest) add_simple_unittest(CropOpTest) - add_simple_unittest(DepthwiseConvOpTest) endif() add_simple_unittest(Im2ColTest) add_simple_unittest(GemmConvOpTest) +add_simple_unittest(DepthwiseConvOpTest) endif() add_style_check_target(paddle_function ${h_files}) diff --git a/paddle/function/DepthwiseConvOpTest.cpp b/paddle/function/DepthwiseConvOpTest.cpp index bdace2c37..d8e8c889d 100644 --- a/paddle/function/DepthwiseConvOpTest.cpp +++ b/paddle/function/DepthwiseConvOpTest.cpp @@ -34,9 +34,13 @@ TEST(DepthwiseConv, BackwardFilter) { } #endif +#if defined(__ARM_NEON__) || defined(__ARM_NEON) + TEST(DepthwiseConv, Forward) { DepthwiseConvolution( "GemmConv-CPU", "NeonDepthwiseConv-CPU", forward); } +#endif + } // namespace paddle -- GitLab From 34a92ab41a407679d454f437f1f3118b81dd1b34 Mon Sep 17 00:00:00 2001 From: hedaoyuan Date: Mon, 28 Aug 2017 14:58:00 +0800 Subject: [PATCH 0254/2018] ExpandConvLayer adds support of arm-neon acceleration. --- paddle/gserver/layers/ExpandConvLayer.cpp | 23 ++++++++++++++++++----- 1 file changed, 18 insertions(+), 5 deletions(-) diff --git a/paddle/gserver/layers/ExpandConvLayer.cpp b/paddle/gserver/layers/ExpandConvLayer.cpp index 0ece27993..0e8458176 100644 --- a/paddle/gserver/layers/ExpandConvLayer.cpp +++ b/paddle/gserver/layers/ExpandConvLayer.cpp @@ -29,6 +29,10 @@ namespace paddle { REGISTER_LAYER(exconv, ExpandConvLayer); REGISTER_LAYER(exconvt, ExpandConvLayer); +inline bool isDepthwiseConv(int channels, int groups) { + return channels == groups; +} + bool ExpandConvLayer::init(const LayerMap &layerMap, const ParameterMap ¶meterMap) { /* Initialize the basic convolutional parent class */ @@ -47,14 +51,23 @@ bool ExpandConvLayer::init(const LayerMap &layerMap, std::vector paddings = {(size_t)paddingY_[i], (size_t)padding_[i]}; std::vector strides = {(size_t)strideY_[i], (size_t)stride_[i]}; - if (useGpu_ && (size_t)groups_[i] == (size_t)channels_[i] && !isDeconv_) { + // Convolution Layer uses the GemmConv function by default. + convType = "GemmConv"; + convGradInputType = "GemmConvGradInput"; + convGradFilterType = "GemmConvGradFilter"; + + // If depth wise convolution and useGpu == true + if (useGpu_ && isDepthwiseConv(channels_[i], groups_[i]) && !isDeconv_) { convType = "DepthwiseConv"; convGradInputType = "DepthwiseConvGradInput"; convGradFilterType = "DepthwiseConvGradFilter"; - } else { - convType = "GemmConv"; - convGradInputType = "GemmConvGradInput"; - convGradFilterType = "GemmConvGradFilter"; + } + + // If depth wise convolution and useGpu == false and ARM-NEON + if (!useGpu_ && isDepthwiseConv(channels_[i], groups_[i]) && !isDeconv_) { +#if defined(__ARM_NEON__) || defined(__ARM_NEON) + convType = "NeonDepthwiseConv"; +#endif } if (FLAGS_use_nnpack && !isDeconv_) { -- GitLab From 2710584ff1d5d299361c1b4492d3368ccbdb0378 Mon Sep 17 00:00:00 2001 From: chengduoZH Date: Wed, 23 Aug 2017 22:05:50 +0800 Subject: [PATCH 0255/2018] fix above comments --- python/paddle/trainer/config_parser.py | 212 ++++++------------ .../paddle/trainer_config_helpers/layers.py | 76 ++----- .../configs/conv3d_deconv3d_test_config.py | 97 ++++---- 3 files changed, 130 insertions(+), 255 deletions(-) diff --git a/python/paddle/trainer/config_parser.py b/python/paddle/trainer/config_parser.py index 49b3c430e..c0843a735 100644 --- a/python/paddle/trainer/config_parser.py +++ b/python/paddle/trainer/config_parser.py @@ -901,20 +901,14 @@ class Conv3D(Cfg): padding_z=None, stride_z=None): self.add_keys(locals()) - if filter_size_y is None: - self.filter_size_y = filter_size - if padding_y is None: - self.padding_y = padding - if stride_y is None: - self.stride_y = stride + self.filter_size_y = filter_size_y if filter_size_y else filter_size + self.filter_size_z = filter_size_z if filter_size_z else filter_size + self.padding_y = padding_y if padding_y else padding + self.padding_z = padding_z if padding_z else padding + self.stride_y = stride_y if stride_y else stride + self.stride_z = stride_z if stride_z else stride if output_x is not None: config_assert(output_x <= 0) - if filter_size_z is None: - self.filter_size_z = filter_size - if padding_z is None: - self.padding_z = padding - if stride_z is None: - self.stride_z = stride @config_class @@ -1206,10 +1200,10 @@ def get_img_size(input_layer_name, channels): def get_img3d_size(input_layer_name, channels): input = g_layer_map[input_layer_name] img_pixels = input.size / channels - img_size = input.width if input.width > 0 else int(img_pixels**0.5) - img_size_y = input.height if input.height > 0 else int(img_pixels / - img_size) - img_size_z = input.depth if input.depth > 1 else 1 + img_size = input.width + img_size_y = input.height + img_size_z = input.depth + config_assert( img_size * img_size_y * img_size_z == img_pixels, "Input layer %s: Incorrect input image size %d * %d * %d for input image pixels %d" @@ -2000,8 +1994,10 @@ class ConvLayer(ConvLayerBase): layer_type = 'cudnn_conv' -@config_layer('conv_3d') -class Conv3DLayerBase(LayerBase): +@config_layer('convt') +class ConvTransLayerBase(LayerBase): + layer_type = 'convt' + def __init__(self, name, inputs=[], @@ -2009,7 +2005,7 @@ class Conv3DLayerBase(LayerBase): num_filters=None, shared_biases=False, **xargs): - super(Conv3DLayerBase, self).__init__( + super(ConvTransLayerBase, self).__init__( name, self.layer_type, 0, inputs=inputs, **xargs) if num_filters is not None: @@ -2018,12 +2014,17 @@ class Conv3DLayerBase(LayerBase): use_gpu = int(g_command_config_args.get("use_gpu", 0)) parallel_nn = int(g_command_config_args.get("parallel_nn", 0)) - # Automatically select cudnn_type for GPU and exconv for CPU - # if set type=conv, but still reserve the way user specify - # exconv or cudnn_conv manually. - if self.layer_type == "cudnn_conv3d": - config_assert(use_gpu, "cudnn_conv3d only support GPU") + # Automatically select cudnn_type for GPU and exconvt for CPU + # if set type=exconvt, but still reserve the way user specify + # exconvt or cudnn_convt manually. + if self.layer_type == "cudnn_convt": + config_assert(use_gpu, "cudnn_convt only support GPU") + if (use_gpu == 1 and self.layer_type != "exconvt" and + (parallel_nn == 0 or self.config.device > -1)): + self.layer_type = "cudnn_convt" + else: + self.layer_type = "exconvt" # need to specify layer in config self.config.type = self.layer_type @@ -2032,15 +2033,17 @@ class Conv3DLayerBase(LayerBase): for input_index in xrange(len(self.inputs)): input_layer = self.get_input_layer(input_index) + parse_conv( + self.inputs[input_index].conv, + input_layer.name, + self.config.inputs[input_index].conv_conf, + num_filters, + trans=True) conv_conf = self.config.inputs[input_index].conv_conf - parse_conv3d( - self.inputs[input_index].conv, input_layer.name, conv_conf, - num_filters - ) # for z-axis pad:0, strid:1, filter_size:1, img_size:1 psize = self.calc_parameter_size(conv_conf) self.create_input_parameter(input_index, psize) - self.set_cnn_layer(name, conv_conf.output_z, conv_conf.output_y, - conv_conf.output_x, self.config.num_filters) + self.set_cnn_layer(name, conv_conf.img_size_y, conv_conf.img_size, + self.config.num_filters) psize = self.config.size if shared_biases: @@ -2048,62 +2051,42 @@ class Conv3DLayerBase(LayerBase): self.create_bias_parameter(bias, psize, [psize, 1]) def calc_parameter_size(self, conv_conf): - return self.config.num_filters * conv_conf.filter_channels \ - * (conv_conf.filter_size * conv_conf.filter_size_y \ - * conv_conf.filter_size_z) + return conv_conf.channels * conv_conf.filter_channels \ + * (conv_conf.filter_size * conv_conf.filter_size_y) - def set_layer_height_width(self, depth, height, width): - self.config.depth = depth - self.config.height = height - self.config.width = width - def set_cnn_layer(self, - input_layer_name, - depth, - height, - width, - channels, - is_print=True): - size = depth * height * width * channels - self.set_layer_size(size) - self.set_layer_height_width(depth, height, width) - if is_print: - print("output for %s: c = %d, d = %d, h = %d, w = %d, size = %d" % - (input_layer_name, channels, depth, height, width, size)) +@config_layer('exconvt') +class ConvTransLayer(ConvTransLayerBase): + layer_type = 'exconvt' -@config_layer('conv3d') -class Conv3DLayer(Conv3DLayerBase): - layer_type = 'conv3d' +@config_layer('cudnn_convt') +class ConvTransLayer(ConvTransLayerBase): + layer_type = 'cudnn_convt' -@config_layer('convt_3d') -class Conv3DTransLayerBase(LayerBase): +@config_layer('conv_3d') +class Conv3DLayerBase(LayerBase): def __init__(self, name, inputs=[], bias=True, num_filters=None, - shared_biases=False, + shared_biases=True, **xargs): - super(Conv3DTransLayerBase, self).__init__( + super(Conv3DLayerBase, self).__init__( name, self.layer_type, 0, inputs=inputs, **xargs) if num_filters is not None: self.config.num_filters = num_filters - use_gpu = int(g_command_config_args.get("use_gpu", 0)) - parallel_nn = int(g_command_config_args.get("parallel_nn", 0)) - - # Automatically select cudnn_type for GPU and exconv for CPU - # if set type=conv, but still reserve the way user specify - # exconv or cudnn_conv manually. - if self.layer_type == "cudnn_deconv3d": - config_assert(use_gpu, "cudnn_conv3d only support GPU") - # need to specify layer in config self.config.type = self.layer_type + trans = False + if self.config.type == "deconv3d": + trans = True + if shared_biases is not None: self.config.shared_biases = shared_biases @@ -2115,12 +2098,17 @@ class Conv3DTransLayerBase(LayerBase): input_layer.name, conv_conf, num_filters, - trans=True + trans=trans ) # for z-axis pad:0, strid:1, filter_size:1, img_size:1 psize = self.calc_parameter_size(conv_conf) self.create_input_parameter(input_index, psize) - self.set_cnn_layer(name, conv_conf.img_size_z, conv_conf.img_size_y, - conv_conf.img_size, self.config.num_filters) + if trans: + self.set_cnn_layer(name, conv_conf.img_size_z, + conv_conf.img_size_y, conv_conf.img_size, + self.config.num_filters) + else: + self.set_cnn_layer(name, conv_conf.output_z, conv_conf.output_y, + conv_conf.output_x, self.config.num_filters) psize = self.config.size if shared_biases: @@ -2132,11 +2120,6 @@ class Conv3DTransLayerBase(LayerBase): * (conv_conf.filter_size * conv_conf.filter_size_y \ * conv_conf.filter_size_z) - def set_layer_height_width(self, depth, height, width): - self.config.depth = depth - self.config.height = height - self.config.width = width - def set_cnn_layer(self, input_layer_name, depth, @@ -2146,86 +2129,21 @@ class Conv3DTransLayerBase(LayerBase): is_print=True): size = depth * height * width * channels self.set_layer_size(size) - self.set_layer_height_width(depth, height, width) + self.set_layer_height_width(height, width) + self.set_layer_depth(depth) if is_print: print("output for %s: c = %d, d = %d, h = %d, w = %d, size = %d" % (input_layer_name, channels, depth, height, width, size)) -@config_layer('deconv3d') -class DeConv3DLayer(Conv3DTransLayerBase): - layer_type = 'deconv3d' - - -@config_layer('convt') -class ConvTransLayerBase(LayerBase): - layer_type = 'convt' - - def __init__(self, - name, - inputs=[], - bias=True, - num_filters=None, - shared_biases=False, - **xargs): - super(ConvTransLayerBase, self).__init__( - name, self.layer_type, 0, inputs=inputs, **xargs) - - if num_filters is not None: - self.config.num_filters = num_filters - - use_gpu = int(g_command_config_args.get("use_gpu", 0)) - parallel_nn = int(g_command_config_args.get("parallel_nn", 0)) - - # Automatically select cudnn_type for GPU and exconvt for CPU - # if set type=exconvt, but still reserve the way user specify - # exconvt or cudnn_convt manually. - if self.layer_type == "cudnn_convt": - config_assert(use_gpu, "cudnn_convt only support GPU") - - if (use_gpu == 1 and self.layer_type != "exconvt" and - (parallel_nn == 0 or self.config.device > -1)): - self.layer_type = "cudnn_convt" - else: - self.layer_type = "exconvt" - # need to specify layer in config - self.config.type = self.layer_type - - if shared_biases is not None: - self.config.shared_biases = shared_biases - - for input_index in xrange(len(self.inputs)): - input_layer = self.get_input_layer(input_index) - parse_conv( - self.inputs[input_index].conv, - input_layer.name, - self.config.inputs[input_index].conv_conf, - num_filters, - trans=True) - conv_conf = self.config.inputs[input_index].conv_conf - psize = self.calc_parameter_size(conv_conf) - self.create_input_parameter(input_index, psize) - self.set_cnn_layer(name, conv_conf.img_size_y, conv_conf.img_size, - self.config.num_filters) - - psize = self.config.size - if shared_biases: - psize = self.config.num_filters - self.create_bias_parameter(bias, psize, [psize, 1]) - - def calc_parameter_size(self, conv_conf): - return conv_conf.channels * conv_conf.filter_channels \ - * (conv_conf.filter_size * conv_conf.filter_size_y) - - -@config_layer('exconvt') -class ConvTransLayer(ConvTransLayerBase): - layer_type = 'exconvt' +@config_layer('conv3d') +class Conv3DLayer(Conv3DLayerBase): + layer_type = 'conv3d' -@config_layer('cudnn_convt') -class ConvTransLayer(ConvTransLayerBase): - layer_type = 'cudnn_convt' +@config_layer('deconv3d') +class Conv3DLayer(Conv3DLayerBase): + layer_type = 'deconv3d' @config_layer('norm') diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py index 6953f134c..e3ae81459 100644 --- a/python/paddle/trainer_config_helpers/layers.py +++ b/python/paddle/trainer_config_helpers/layers.py @@ -6161,12 +6161,6 @@ def img_conv3d_layer(input, param_attr=None, shared_biases=True, layer_attr=None, - filter_size_y=None, - stride_y=None, - padding_y=None, - filter_size_z=None, - stride_z=None, - padding_z=None, trans=False, layer_type=None): """ @@ -6175,7 +6169,7 @@ def img_conv3d_layer(input, .. code-block:: python - conv = img_conv3d_layer(input=data, filter_size=1, filter_size_y=1, + conv = img_conv3d_layer(input=data, filter_size=1, num_channels=8, num_filters=16, stride=1, bias_attr=False, @@ -6185,13 +6179,8 @@ def img_conv3d_layer(input, :type name: basestring :param input: Layer Input. :type input: LayerOutput - :param filter_size: The x dimension of a filter kernel. Or input a tuple for - two image dimension. + :param filter_size: The x dimension of a filter kernel. Or input a list. :type filter_size: int|tuple|list - :param filter_size_y: The y dimension of a filter kernel. Since PaddlePaddle - currently supports rectangular filters, the filter's - shape will be (filter_size, filter_size_y). - :type filter_size_y: int|None :param num_filters: Each filter group's number of filter :param act: Activation type. Default is tanh :type act: BaseActivation @@ -6200,13 +6189,9 @@ def img_conv3d_layer(input, :param stride: The x dimension of the stride. Or input a tuple for two image dimension. :type stride: int|tuple|list - :param stride_y: The y dimension of the stride. - :type stride_y: int :param padding: The x dimension of the padding. Or input a tuple for two image dimension :type padding: int|tuple|list - :param padding_y: The y dimension of the padding. - :type padding_y: int :param bias_attr: Convolution bias attribute. None means default bias. False means no bias. :type bias_attr: ParameterAttribute|False @@ -6233,47 +6218,26 @@ def img_conv3d_layer(input, assert input.num_filters is not None num_channels = input.num_filters - if filter_size_y is None: - if isinstance(filter_size, collections.Sequence): - assert len(filter_size) == 2 - filter_size, filter_size_y = filter_size - else: - filter_size_y = filter_size - - if filter_size_z is None: - if isinstance(filter_size, collections.Sequence): - assert len(filter_size) == 2 - filter_size, filter_size_z = filter_size - else: - filter_size_z = filter_size - - if stride_y is None: - if isinstance(stride, collections.Sequence): - assert len(stride) == 2 - stride, stride_y = stride - else: - stride_y = stride - - if stride_z is None: - if isinstance(stride, collections.Sequence): - assert len(stride) == 2 - stride, stride_z = stride - else: - stride_z = stride + if isinstance(filter_size, collections.Sequence): + assert len(filter_size) == 3 + filter_size, filter_size_y, filter_size_z = filter_size + else: + filter_size_y = filter_size + filter_size_z = filter_size - if padding_y is None: - if isinstance(padding, collections.Sequence): - assert len(padding) == 2 - padding, padding_y = padding - else: - padding_y = padding + if isinstance(stride, collections.Sequence): + assert len(stride) == 3 + stride, stride_y, stride_z = stride + else: + stride_y = stride + stride_z = stride - if padding_z is None: - if isinstance(padding, collections.Sequence): - assert len(padding) == 2 - padding, padding_z = padding - else: - padding_z = padding + if isinstance(padding, collections.Sequence): + assert len(padding) == 3 + padding, padding_y, padding_z = padding + else: + padding_y = padding + padding_z = padding if param_attr.attr.get('initial_smart'): # special initial for conv layers. diff --git a/python/paddle/trainer_config_helpers/tests/configs/conv3d_deconv3d_test_config.py b/python/paddle/trainer_config_helpers/tests/configs/conv3d_deconv3d_test_config.py index da0d23d05..15f7c1d27 100644 --- a/python/paddle/trainer_config_helpers/tests/configs/conv3d_deconv3d_test_config.py +++ b/python/paddle/trainer_config_helpers/tests/configs/conv3d_deconv3d_test_config.py @@ -14,23 +14,44 @@ padding_y = 1 padding_z = 1 groups = 1 -data = data_layer( - name='data1', size=12096 * num_channels, height=48, width=42, depth=6) +data1 = data_layer(name='data1', size=2016 * num_channels, height=48, width=42) -conv3d = img_conv3d_layer( +img_conv_layer( + input=data1, + filter_size=filter_size, + num_channels=num_channels, + num_filters=16, + stride=stride, + padding=padding, + act=LinearActivation(), + bias_attr=False) + +data = data_layer( + name='data', size=12096 * num_channels, height=48, width=42, depth=6) +# first +conv3d_1 = img_conv3d_layer( input=data, name='conv3d_1', num_filters=16, num_channels=num_channels, filter_size=filter_size, - filter_size_y=filter_size, - filter_size_z=filter_size, stride=stride, - stride_y=stride_y, - stride_z=stride_z, padding=padding, - padding_y=padding_y, - padding_z=padding_z, + groups=groups, + bias_attr=True, + shared_biases=True, + trans=False, + layer_type="conv3d", + act=LinearActivation()) +# second +conv3d_2 = img_conv3d_layer( + input=data, + name='conv3d_2', + num_filters=16, + num_channels=num_channels, + filter_size=[filter_size, filter_size_y, filter_size_z], + stride=[stride, stride_y, stride_z], + padding=[padding, padding_y, padding_z], groups=groups, bias_attr=True, shared_biases=True, @@ -38,61 +59,33 @@ conv3d = img_conv3d_layer( layer_type="conv3d", act=LinearActivation()) -deconv3d = img_conv3d_layer( +# first +deconv3d_1 = img_conv3d_layer( input=data, name='deconv3d_1', num_filters=16, num_channels=num_channels, filter_size=filter_size, - filter_size_y=filter_size, - filter_size_z=filter_size, stride=stride, - stride_y=stride_y, - stride_z=stride_z, padding=padding, - padding_y=padding_y, - padding_z=padding_z, groups=groups, bias_attr=True, shared_biases=True, - trans=True, + trans=False, layer_type="deconv3d", act=LinearActivation()) - -data = data_layer(name="input", size=8 * 16 * 16) -conv1 = img_conv_layer( - input=data, - filter_size=1, - filter_size_y=1, - num_channels=8, - num_filters=16, - stride=1, - bias_attr=False, - act=ReluActivation(), - layer_type="exconv") -conv2 = img_conv_layer( - input=data, - filter_size=1, - filter_size_y=1, - num_channels=8, - num_filters=16, - stride=1, - bias_attr=False, - act=ReluActivation(), - layer_type="exconv") - -concat = concat_layer(input=[conv1, conv2]) - -conv = img_conv_layer( +# second +deconv3d_2 = img_conv3d_layer( input=data, - filter_size=1, - filter_size_y=1, - num_channels=8, + name='deconv3d_2', num_filters=16, - stride=1, + num_channels=num_channels, + filter_size=[filter_size, filter_size_y, filter_size_z], + stride=[stride, stride_y, stride_z], + padding=[padding, padding_y, padding_z], + groups=groups, bias_attr=True, - act=LinearActivation(), - groups=2, - layer_type="exconv") - -outputs(concat, conv) + shared_biases=True, + trans=False, + layer_type="deconv3d", + act=LinearActivation()) -- GitLab From e63ad0a6bdb36967d417633a074e0e966ca55e78 Mon Sep 17 00:00:00 2001 From: Luo Tao Date: Mon, 28 Aug 2017 15:15:26 +0800 Subject: [PATCH 0256/2018] HuberRegressionLoss and HuberTwoClassification support multi-dimension data --- paddle/gserver/layers/CostLayer.cpp | 67 ++++++++++++++++++----------- 1 file changed, 41 insertions(+), 26 deletions(-) diff --git a/paddle/gserver/layers/CostLayer.cpp b/paddle/gserver/layers/CostLayer.cpp index 7f648070f..aa4a26a83 100644 --- a/paddle/gserver/layers/CostLayer.cpp +++ b/paddle/gserver/layers/CostLayer.cpp @@ -611,22 +611,26 @@ void HuberRegressionLoss::forwardImp(Matrix& output, Matrix& target) { HuberCost::forwardImp(output, label, target); size_t numSamples = target.getHeight(); + size_t dim = output.getWidth(); CHECK(label.value); CHECK_EQ((*label.value).getHeight(), numSamples); CHECK_EQ(output.getHeight(), numSamples); - CHECK_EQ(output.getWidth(), (*label.value).getWidth()); + CHECK_EQ(dim, (*label.value).getWidth()); CHECK_EQ(target.getWidth(), (size_t)1); real* out = useGpu_ ? tmpCpuInput_[0].value->getData() : output.getData(); real* lbl = useGpu_ ? tmpCpuInput_[1].value->getData() : (*label.value).getData(); - std::vector cost(numSamples); + std::vector cost(numSamples, 0); for (size_t i = 0; i < numSamples; ++i) { - real a = std::abs(lbl[i] - out[i]); - if (a <= delta_) - cost[i] = a * a / 2; - else - cost[i] = delta_ * (a - delta_ / 2); + for (size_t j = 0; j < dim; ++j) { + int index = i * dim + j; + real a = std::abs(lbl[index] - out[index]); + if (a <= delta_) + cost[i] += a * a / 2; + else + cost[i] += delta_ * (a - delta_ / 2); + } } target.copyFrom(cost.data(), numSamples); } @@ -635,18 +639,22 @@ void HuberRegressionLoss::backwardImp(Matrix& output, Argument& label, Matrix& outputG) { size_t numSamples = output.getHeight(); + size_t dim = output.getWidth(); real* out = useGpu_ ? tmpCpuInput_[0].value->getData() : output.getData(); real* lbl = useGpu_ ? tmpCpuInput_[1].value->getData() : (*label.value).getData(); real* grad = useGpu_ ? tmpCpuInput_[0].grad->getData() : outputG.getData(); for (size_t i = 0; i < numSamples; ++i) { - real a = lbl[i] - out[i]; - if (std::abs(a) <= delta_) - grad[i] += -a; - else - grad[i] += a > 0 ? -delta_ : delta_; + for (size_t j = 0; j < dim; ++j) { + int index = i * dim + j; + real a = lbl[index] - out[index]; + if (std::abs(a) <= delta_) + grad[index] += -a; + else + grad[index] += a > 0 ? -delta_ : delta_; + } } - if (useGpu_) outputG.copyFrom(grad, numSamples); + if (useGpu_) outputG.copyFrom(grad, numSamples * dim); } // @@ -664,23 +672,25 @@ void HuberTwoClassification::forwardImp(Matrix& output, Matrix& target) { HuberCost::forwardImp(output, label, target); size_t numSamples = target.getHeight(); + size_t dim = output.getWidth(); CHECK(label.ids); CHECK_EQ((*label.ids).getSize(), numSamples); CHECK_EQ(output.getHeight(), numSamples); - CHECK_EQ(output.getWidth(), (size_t)1); CHECK_EQ(target.getWidth(), (size_t)1); real* out = useGpu_ ? tmpCpuInput_[0].value->getData() : output.getData(); int* lbl = useGpu_ ? tmpCpuInput_[1].ids->getData() : (*label.ids).getData(); - std::vector cost(numSamples); + std::vector cost(numSamples, 0); for (size_t i = 0; i < numSamples; ++i) { int y = 2 * lbl[i] - 1; - if (out[i] * y < -1) - cost[i] = -4 * out[i] * y; - else if (out[i] * y < 1) - cost[i] = (1 - out[i] * y) * (1 - out[i] * y); - else - cost[i] = 0; + for (size_t j = 0; j < dim; ++j) { + int index = i * dim + j; + real a = out[index] * y; + if (a < -1) + cost[i] += -4 * a; + else if (a < 1) + cost[i] += (1 - a) * (1 - a); + } } target.copyFrom(cost.data(), numSamples); } @@ -689,17 +699,22 @@ void HuberTwoClassification::backwardImp(Matrix& output, Argument& label, Matrix& outputG) { size_t numSamples = output.getHeight(); + size_t dim = output.getWidth(); real* out = useGpu_ ? tmpCpuInput_[0].value->getData() : output.getData(); int* lbl = useGpu_ ? tmpCpuInput_[1].ids->getData() : (*label.ids).getData(); real* grad = useGpu_ ? tmpCpuInput_[0].grad->getData() : outputG.getData(); for (size_t i = 0; i < numSamples; ++i) { int y = 2 * lbl[i] - 1; - if (y * out[i] < -1) - grad[i] += -4 * y; - else if (y * out[i] < 1) - grad[i] += -2 * (1 - y * out[i]) * y; + for (size_t j = 0; j < dim; ++j) { + int index = i * dim + j; + real a = out[index] * y; + if (a < -1) + grad[index] += -4 * y; + else if (a < 1) + grad[index] += -2 * (1 - a) * y; + } } - if (useGpu_) outputG.copyFrom(grad, numSamples); + if (useGpu_) outputG.copyFrom(grad, numSamples * dim); } /** * This cost layer compute the sum of its input as loss. -- GitLab From b1c0bad9fe8258ac9c12141c07fddb8600f781c5 Mon Sep 17 00:00:00 2001 From: chengduoZH Date: Sat, 26 Aug 2017 13:09:05 +0800 Subject: [PATCH 0257/2018] Add config parser for pooling3D --- paddle/math/Matrix.cpp | 2 - proto/ModelConfig.proto | 1 + python/paddle/trainer/config_parser.py | 120 +++++++++++++- .../paddle/trainer_config_helpers/layers.py | 146 +++++++++++++++++- .../tests/configs/test_pooling3D_layer.py | 38 +++++ .../tests/layers_test.py | 2 +- 6 files changed, 304 insertions(+), 5 deletions(-) create mode 100644 python/paddle/trainer_config_helpers/tests/configs/test_pooling3D_layer.py diff --git a/paddle/math/Matrix.cpp b/paddle/math/Matrix.cpp index 54c2eae47..e93a15455 100644 --- a/paddle/math/Matrix.cpp +++ b/paddle/math/Matrix.cpp @@ -2255,9 +2255,7 @@ void CpuMatrix::maxPool3DBackward(Matrix& outGrad, real* tgtGrad = getData(); real* otGrad = outGrad.getData(); real* maxPoolIdxData = maxPoolIdx.getData(); - size_t outStride = outGrad.getStride(); - ; for (size_t n = 0; n < num; ++n) { if (!outGrad.isContiguous()) { diff --git a/proto/ModelConfig.proto b/proto/ModelConfig.proto index 42cf10e9d..259f3c33c 100644 --- a/proto/ModelConfig.proto +++ b/proto/ModelConfig.proto @@ -495,6 +495,7 @@ message LayerConfig { // to indicate rectangle image data optional uint64 height = 50; optional uint64 width = 51; + optional uint64 depth = 57 [ default = 1 ]; // blank label used in ctc loss optional uint32 blank = 52 [ default = 0 ]; diff --git a/python/paddle/trainer/config_parser.py b/python/paddle/trainer/config_parser.py index b7b696ef0..405c5e1f1 100644 --- a/python/paddle/trainer/config_parser.py +++ b/python/paddle/trainer/config_parser.py @@ -903,6 +903,31 @@ class Pool(Cfg): self.add_keys(locals()) +@config_class +class Pool3d(Cfg): + def __init__( + self, + pool_type, + channels, + size_x, + size_y=None, + size_z=None, + start=None, + stride=None, # 1 by defalut in protobuf + stride_y=None, + stride_z=None, + padding=None, # 0 by defalut in protobuf + padding_y=None, + padding_z=None): + self.add_keys(locals()) + self.filter_size_y = size_y if size_y else size_x + self.filter_size_z = size_z if size_z else size_x + self.padding_y = padding_y if padding_y else padding + self.padding_z = padding_z if padding_z else padding + self.stride_y = stride_y if stride_y else stride + self.stride_z = stride_z if stride_z else stride + + @config_class class SpatialPyramidPool(Cfg): def __init__(self, pool_type, pyramid_height, channels): @@ -1167,6 +1192,20 @@ def get_img_size(input_layer_name, channels): return img_size, img_size_y +def get_img3d_size(input_layer_name, channels): + input = g_layer_map[input_layer_name] + img_pixels = input.size / channels + img_size = input.width + img_size_y = input.height + img_size_z = input.depth + + config_assert( + img_size * img_size_y * img_size_z == img_pixels, + "Input layer %s: Incorrect input image size %d * %d * %d for input image pixels %d" + % (input_layer_name, img_size, img_size_y, img_size_z, img_pixels)) + return img_size, img_size_y, img_size_z + + def parse_bilinear(bilinear, input_layer_name, bilinear_conf): parse_image(bilinear, input_layer_name, bilinear_conf.image_conf) bilinear_conf.out_size_x = bilinear.out_size_x @@ -1204,6 +1243,45 @@ def parse_pool(pool, input_layer_name, pool_conf, ceil_mode): pool_conf.stride_y, not ceil_mode) +def parse_pool3d(pool, input_layer_name, pool_conf, ceil_mode): + pool_conf.pool_type = pool.pool_type + config_assert(pool.pool_type in ['max-projection', 'avg-projection'], + "pool-type %s is not in " + "['max-projection', 'avg-projection']" % pool.pool_type) + + pool_conf.channels = pool.channels + + pool_conf.size_x = pool.size_x + pool_conf.stride = pool.stride + pool_conf.padding = pool.padding + + pool_conf.size_y = default(pool.size_y, pool_conf.size_x) + pool_conf.size_z = default(pool.size_z, pool_conf.size_x) + pool_conf.stride_y = default(pool.stride_y, pool_conf.stride) + pool_conf.stride_z = default(pool.stride_z, pool_conf.stride) + pool_conf.padding_y = default(pool.padding_y, pool_conf.padding) + pool_conf.padding_z = default(pool.padding_z, pool_conf.padding) + + pool_conf.img_size, pool_conf.img_size_y, pool_conf.img_size_z = \ + get_img3d_size(input_layer_name, pool.channels) + + config_assert(not pool.start, "start is deprecated in pooling.") + + if pool.padding is not None: + pool_conf.padding = pool.padding + pool_conf.padding_y = default(pool.padding_y, pool_conf.padding) + pool_conf.padding_z = default(pool.padding_z, pool_conf.padding) + pool_conf.output_x = cnn_output_size(pool_conf.img_size, pool_conf.size_x, + pool_conf.padding, pool_conf.stride, + not ceil_mode) + pool_conf.output_y = cnn_output_size(pool_conf.img_size_y, pool_conf.size_y, + pool_conf.padding_y, + pool_conf.stride_y, not ceil_mode) + pool_conf.output_z = cnn_output_size(pool_conf.img_size_z, pool_conf.size_z, + pool_conf.padding_z, + pool_conf.stride_z, not ceil_mode) + + def parse_spp(spp, input_layer_name, spp_conf): parse_image(spp, input_layer_name, spp_conf.image_conf) spp_conf.pool_type = spp.pool_type @@ -1580,6 +1658,9 @@ class LayerBase(object): self.config.height = height self.config.width = width + def set_layer_depth(self, depth): + self.config.depth = depth + def set_cnn_layer(self, input_layer_name, height, @@ -1763,11 +1844,19 @@ class DetectionOutputLayer(LayerBase): @config_layer('data') class DataLayer(LayerBase): - def __init__(self, name, size, height=None, width=None, device=None): + def __init__(self, + name, + size, + depth=None, + height=None, + width=None, + device=None): super(DataLayer, self).__init__( name, 'data', size, inputs=[], device=device) if height and width: self.set_layer_height_width(height, width) + if depth: + self.set_layer_depth(depth) ''' @@ -1995,6 +2084,35 @@ class PoolLayer(LayerBase): pool_conf.channels) +@config_layer('pool3d') +class Pool3DLayer(LayerBase): + def __init__(self, name, inputs, ceil_mode=True, **xargs): + super(Pool3DLayer, self).__init__( + name, 'pool3d', 0, inputs=inputs, **xargs) + for input_index in xrange(len(self.inputs)): + input_layer = self.get_input_layer(input_index) + pool_conf = self.config.inputs[input_index].pool_conf + parse_pool3d(self.inputs[input_index].pool, input_layer.name, + pool_conf, ceil_mode) + self.set_cnn_layer(name, pool_conf.output_z, pool_conf.output_y, + pool_conf.output_x, pool_conf.channels) + + def set_cnn_layer(self, + input_layer_name, + depth, + height, + width, + channels, + is_print=True): + size = depth * height * width * channels + self.set_layer_size(size) + self.set_layer_height_width(height, width) + self.set_layer_depth(depth) + if is_print: + print("output for %s: c = %d, d = %d, h = %d, w = %d, size = %d" % + (input_layer_name, channels, depth, height, width, size)) + + @config_layer('spp') class SpatialPyramidPoolLayer(LayerBase): def __init__(self, name, inputs, **xargs): diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py index 1bc55c869..5c5e737b5 100755 --- a/python/paddle/trainer_config_helpers/layers.py +++ b/python/paddle/trainer_config_helpers/layers.py @@ -133,6 +133,7 @@ __all__ = [ 'clip_layer', 'slice_projection', 'kmax_sequence_score_layer', + 'img_pool3d_layer', ] @@ -161,6 +162,7 @@ class LayerType(object): EXCONVTRANS_LAYER = 'exconvt' CUDNNCONV_LAYER = 'cudnn_conv' POOL_LAYER = 'pool' + POOL3D_LAYER = 'pool3d' BATCH_NORM_LAYER = 'batch_norm' NORM_LAYER = 'norm' SUM_TO_ONE_NORM_LAYER = 'sum_to_one_norm' @@ -878,7 +880,8 @@ def mixed_layer(size=0, @layer_support() -def data_layer(name, size, height=None, width=None, layer_attr=None): +def data_layer(name, size, depth=None, height=None, width=None, + layer_attr=None): """ Define DataLayer For NeuralNetwork. @@ -905,6 +908,7 @@ def data_layer(name, size, height=None, width=None, layer_attr=None): type=LayerType.DATA, name=name, size=size, + depth=depth, height=height, width=width, **ExtraLayerAttribute.to_kwargs(layer_attr)) @@ -2610,6 +2614,146 @@ def img_pool_layer(input, size=l.config.size) +@wrap_name_default("pool3d") +@layer_support() +def img_pool3d_layer(input, + pool_size, + name=None, + num_channels=None, + pool_type=None, + stride=1, + padding=0, + layer_attr=None, + pool_size_y=None, + stride_y=None, + padding_y=None, + pool_size_z=None, + stride_z=None, + padding_z=None, + ceil_mode=True): + """ + Image pooling Layer. + + The details of pooling layer, please refer ufldl's pooling_ . + + .. _pooling: http://ufldl.stanford.edu/tutorial/supervised/Pooling/ + + - ceil_mode=True: + + .. math:: + + w = 1 + int(ceil(input\_width + 2 * padding - pool\_size) / float(stride)) + h = 1 + int(ceil(input\_height + 2 * padding\_y - pool\_size\_y) / float(stride\_y)) + d = 1 + int(ceil(input\_depth + 2 * padding\_z - pool\_size\_z) / float(stride\_z)) + + - ceil_mode=False: + + .. math:: + + w = 1 + int(floor(input\_width + 2 * padding - pool\_size) / float(stride)) + h = 1 + int(floor(input\_height + 2 * padding\_y - pool\_size\_y) / float(stride\_y)) + d = 1 + int(floor(input\_depth + 2 * padding\_z - pool\_size\_z) / float(stride\_z)) + + The example usage is: + + .. code-block:: python + + maxpool = img_pool3d_layer(input=conv, + pool_size=3, + num_channels=8, + stride=1, + padding=1, + pool_type=MaxPooling()) + + :param padding: pooling padding width. + :type padding: int|tuple|list + :param name: name of pooling layer + :type name: basestring. + :param input: layer's input + :type input: LayerOutput + :param pool_size: pooling window width + :type pool_size: int|tuple|list + :param num_channels: number of input channel. + :type num_channels: int + :param pool_type: pooling type. MaxPooling or AvgPooling. Default is + MaxPooling. + :type pool_type: BasePoolingType + :param stride: stride width of pooling. + :type stride: int|tuple|list + :param layer_attr: Extra Layer attribute. + :type layer_attr: ExtraLayerAttribute + :param ceil_mode: Wether to use ceil mode to calculate output height and with. + Defalut is True. If set false, Otherwise use floor. + + :type ceil_mode: bool + :return: LayerOutput object. + :rtype: LayerOutput + """ + if num_channels is None: + assert input.num_filters is not None + num_channels = input.num_filters + + if pool_type is None: + pool_type = MaxPooling() + elif isinstance(pool_type, AvgPooling): + pool_type.name = 'avg' + + type_name = pool_type.name + '-projection' \ + if ( + isinstance(pool_type, AvgPooling) or isinstance(pool_type, MaxPooling)) \ + else pool_type.name + + if isinstance(pool_size, collections.Sequence): + assert len(pool_size) == 3 + pool_size, pool_size_y, pool_size_z = pool_size + else: + pool_size_y = pool_size + pool_size_z = pool_size + + if isinstance(stride, collections.Sequence): + assert len(stride) == 3 + stride, stride_y, stride_z = stride + else: + stride_y = stride + stride_z = stride + + if isinstance(padding, collections.Sequence): + assert len(padding) == 3 + padding, padding_y, padding_y = padding + else: + padding_y = padding + padding_z = padding + + l = Layer( + name=name, + type=LayerType.POOL3D_LAYER, + inputs=[ + Input( + input.name, + pool=Pool3d( + pool_type=type_name, + channels=num_channels, + size_x=pool_size, + start=None, + stride=stride, + padding=padding, + size_y=pool_size_y, + stride_y=stride_y, + padding_y=padding_y, + size_z=pool_size_z, + stride_z=stride_z, + padding_z=padding_z)) + ], + ceil_mode=ceil_mode, + **ExtraLayerAttribute.to_kwargs(layer_attr)) + return LayerOutput( + name, + LayerType.POOL_LAYER, + parents=[input], + num_filters=num_channels, + size=l.config.size) + + @wrap_name_default("spp") @layer_support() def spp_layer(input, diff --git a/python/paddle/trainer_config_helpers/tests/configs/test_pooling3D_layer.py b/python/paddle/trainer_config_helpers/tests/configs/test_pooling3D_layer.py new file mode 100644 index 000000000..0dbb921d4 --- /dev/null +++ b/python/paddle/trainer_config_helpers/tests/configs/test_pooling3D_layer.py @@ -0,0 +1,38 @@ +from paddle.trainer_config_helpers import * + +settings(batch_size=100, learning_rate=1e-5) + +data_2d = data_layer(name='data_2d', size=6000, height=20, width=10) + +pool_2d = img_pool_layer( + name="pool___2d", + input=data_2d, + num_channels=30, + pool_size=5, + stride=3, + padding=1, + pool_type=AvgPooling()) +outputs(pool_2d) + +data_3d = data_layer( + name='data_3d_1', size=60000, depth=10, height=20, width=10) + +pool_3d_1 = img_pool3d_layer( + name="pool_3d_1", + input=data_3d, + num_channels=30, + pool_size=5, + stride=3, + padding=1, + pool_type=AvgPooling()) +outputs(pool_3d_1) + +pool_3d_2 = img_pool3d_layer( + name="pool_3d_2", + input=data_3d, + num_channels=30, + pool_size=[5, 5, 5], + stride=[3, 3, 3], + padding=[1, 1, 1], + pool_type=MaxPooling()) +outputs(pool_3d_2) diff --git a/python/paddle/trainer_config_helpers/tests/layers_test.py b/python/paddle/trainer_config_helpers/tests/layers_test.py index 05902ea29..52218972b 100644 --- a/python/paddle/trainer_config_helpers/tests/layers_test.py +++ b/python/paddle/trainer_config_helpers/tests/layers_test.py @@ -16,4 +16,4 @@ from paddle.trainer.config_parser import parse_config_and_serialize if __name__ == '__main__': parse_config_and_serialize( - 'trainer_config_helpers/tests/layers_test_config.py', '') + 'trainer_config_helpers/tests/configs/test_pooling3D_layer.py', '') -- GitLab From 6053f7e36b19a06da14c970a1e4f25a02d1dbcaf Mon Sep 17 00:00:00 2001 From: chengduoZH Date: Mon, 28 Aug 2017 18:10:44 +0800 Subject: [PATCH 0258/2018] fix previous comments(c++) --- paddle/cuda/include/hl_matrix.h | 2 +- paddle/gserver/layers/Conv3DLayer.cpp | 6 ------ paddle/gserver/layers/DeConv3DLayer.cpp | 6 ------ 3 files changed, 1 insertion(+), 13 deletions(-) diff --git a/paddle/cuda/include/hl_matrix.h b/paddle/cuda/include/hl_matrix.h index a37921b74..c7f251099 100644 --- a/paddle/cuda/include/hl_matrix.h +++ b/paddle/cuda/include/hl_matrix.h @@ -241,7 +241,7 @@ extern void hl_matrix_rotate( * @param[in] paddingD padding in the depth. * @param[in] paddingH padding in the height. * @param[in] paddingW padding in the width. - * @param[out] matDst output matrix. + * @param[out] dataDst output matrix. * */ extern void hl_matrix_vol2Col(const real* dataSrc, diff --git a/paddle/gserver/layers/Conv3DLayer.cpp b/paddle/gserver/layers/Conv3DLayer.cpp index db907bbab..7cc9937cc 100644 --- a/paddle/gserver/layers/Conv3DLayer.cpp +++ b/paddle/gserver/layers/Conv3DLayer.cpp @@ -53,18 +53,12 @@ bool Conv3DLayer::init(const LayerMap &layerMap, size_t Conv3DLayer::getSize() { CHECK_NE(inputLayers_.size(), 0UL); - // imgSizeH_.clear(); - // imgSizeW_.clear(); - // imgSizeD_.clear(); outputH_.clear(); outputW_.clear(); outputD_.clear(); N_.clear(); size_t layerSize = 0; for (size_t i = 0; i < inputLayers_.size(); ++i) { - // imgSizeH_.push_back(inputLayers_[i]->getOutput().getFrameHeight()); - // imgSizeW_.push_back(inputLayers_[i]->getOutput().getFrameWidth()); - // imgSizeD_.push_back(inputLayers_[i]->getOutput().getFrameDepth()); outputW_.push_back(outputSize( imgSizeW_[i], filterSize_[i], padding_[i], stride_[i], true)); outputH_.push_back(outputSize( diff --git a/paddle/gserver/layers/DeConv3DLayer.cpp b/paddle/gserver/layers/DeConv3DLayer.cpp index b18c06e36..7d5c772c8 100644 --- a/paddle/gserver/layers/DeConv3DLayer.cpp +++ b/paddle/gserver/layers/DeConv3DLayer.cpp @@ -53,9 +53,6 @@ bool DeConv3DLayer::init(const LayerMap &layerMap, size_t DeConv3DLayer::getSize() { CHECK_NE(inputLayers_.size(), 0UL); - // imgSizeH_.clear(); - // imgSizeW_.clear(); - // imgSizeD_.clear(); outputH_.clear(); outputW_.clear(); outputD_.clear(); @@ -63,9 +60,6 @@ size_t DeConv3DLayer::getSize() { NOut_.clear(); size_t layerSize = 0; for (size_t i = 0; i < inputLayers_.size(); ++i) { - // imgSizeH_.push_back(inputLayers_[i]->getOutput().getFrameHeight()); - // imgSizeW_.push_back(inputLayers_[i]->getOutput().getFrameWidth()); - // imgSizeD_.push_back(inputLayers_[i]->getOutput().getFrameDepth()); outputW_.push_back( imageSize(imgSizeW_[i], filterSize_[i], padding_[i], stride_[i], true)); outputH_.push_back(imageSize( -- GitLab From 5df384d67ff498c9438b2ef7dc9566af7d50c97a Mon Sep 17 00:00:00 2001 From: hedaoyuan Date: Mon, 28 Aug 2017 19:36:18 +0800 Subject: [PATCH 0259/2018] Remove NeonDepthwiseConv.h --- paddle/function/neon/NeonDepthwiseConv.h | 25 ------------------------ 1 file changed, 25 deletions(-) delete mode 100644 paddle/function/neon/NeonDepthwiseConv.h diff --git a/paddle/function/neon/NeonDepthwiseConv.h b/paddle/function/neon/NeonDepthwiseConv.h deleted file mode 100644 index 23e4be192..000000000 --- a/paddle/function/neon/NeonDepthwiseConv.h +++ /dev/null @@ -1,25 +0,0 @@ -/* 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 - -namespace paddle { - -namespace neon { - -template -struct DepthwiseConvKernel {}; - -} // namespace neon -} // namespace paddle -- GitLab From 4f0c071e4909ff041f3a86c3a40c482becf50845 Mon Sep 17 00:00:00 2001 From: qijun Date: Mon, 28 Aug 2017 22:18:11 +0800 Subject: [PATCH 0260/2018] refine backward --- paddle/framework/backward.cc | 5 ++++- paddle/operators/net_op.cc | 9 ++++++--- 2 files changed, 10 insertions(+), 4 deletions(-) diff --git a/paddle/framework/backward.cc b/paddle/framework/backward.cc index bfda18724..6b4c612cd 100644 --- a/paddle/framework/backward.cc +++ b/paddle/framework/backward.cc @@ -124,6 +124,9 @@ static std::unique_ptr BackwardRecursive( std::list insert_position; for (auto& dup_output_op : dup_output_ops) { const std::string& name = dup_output_op.first; + // duplicate @Empty@ don't need to be added + if (name == kEmptyVarName) continue; + auto& dup_op = dup_output_op.second; // no duplicate output if (dup_op.size() == 1) continue; @@ -209,7 +212,7 @@ std::unique_ptr Backward( const OperatorBase& forwardOp, const std::unordered_set& no_grad_vars) { std::unordered_set no_grad_names; - no_grad_names.reserve(no_grad_vars.size()); + no_grad_names.reserve(no_grad_vars.size() + 1); no_grad_names.insert(std::string(kEmptyVarName) + kGradVarSuffix); diff --git a/paddle/operators/net_op.cc b/paddle/operators/net_op.cc index 44d925f0b..78b5e2767 100644 --- a/paddle/operators/net_op.cc +++ b/paddle/operators/net_op.cc @@ -31,10 +31,13 @@ void NetOp::CompleteAddOp(bool calc) { for (auto& op : ops_) { for (auto& ipt : op->Inputs()) { for (auto& var_name : ipt.second) { - if (!Contains(output_set, var_name)) { // Not other op's output - input_set.insert(var_name); - } else { + // If input variable has been in output set, then it will be + // added into intermediate_outputs_. Otherwise, it will be + // added into input set. + if (Contains(output_set, var_name)) { intermediate_outputs_.insert(var_name); + } else { + input_set.insert(var_name); } } } -- GitLab From 980edfa69a72f57dea689d1d5b1bff6b388e7a71 Mon Sep 17 00:00:00 2001 From: fengjiayi Date: Mon, 28 Aug 2017 11:34:24 -0700 Subject: [PATCH 0261/2018] Refine backward document --- paddle/framework/backward.md | 11 +++++++++-- 1 file changed, 9 insertions(+), 2 deletions(-) diff --git a/paddle/framework/backward.md b/paddle/framework/backward.md index 133b17c7b..ce324a73f 100644 --- a/paddle/framework/backward.md +++ b/paddle/framework/backward.md @@ -6,9 +6,16 @@ In Neural Network, the backpropagation algorithm follows the chain rule, so we n ## Backward Operator Registry -A backward network is built up with several backward operators. Backward operators take forward operators' inputs, outputs and output gradients and then calculate its input gradients. In most cases, there is a one-to-one correspondence between forward and backward operators. We use registry mechanism to save these correspondences. +A backward network is built up with several backward operators. Backward operators take forward operators' inputs, outputs and output gradients and then calculate its input gradients. -For example, we have got a `add_two_op`, and is registered by the following code: +-| | forward operator | backward operator +-| ---------------------- | ---------------- |------------------------- | +-| **Operator::inputs_** | Inputs | Inputs, Outputs, OutputGradients | +-| **Operator::outputs_** | Outputs | InputGradients | + + In most cases, there is a one-to-one correspondence between forward and backward operators. These correspondences are recorded by a global hash map(`OpInfoMap`). To follow the philosophy of minimum core and make operators pluggable, the registry mechanism is introduced. + +For example, we have got a `add_two_op`, and we can register it's information and corresponding backward operator by the following macro: ```cpp REGISTER_OP(add_two, AddTwoOp, AddTwoOpMaker, add_two_grad, AddTwoGradOp); -- GitLab From ff3ec3c438e9c68b663fc42b4362a5b780e5f741 Mon Sep 17 00:00:00 2001 From: Zhuoyuan Date: Mon, 28 Aug 2017 11:37:15 -0700 Subject: [PATCH 0262/2018] switch_op.h --- paddle/operators/switch_op.h | 143 +++++++++++++++++++++++++++++++++++ 1 file changed, 143 insertions(+) create mode 100644 paddle/operators/switch_op.h diff --git a/paddle/operators/switch_op.h b/paddle/operators/switch_op.h new file mode 100644 index 000000000..f72726bce --- /dev/null +++ b/paddle/operators/switch_op.h @@ -0,0 +1,143 @@ +/* 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 "glog/logging.h" +#include "paddle/framework/eigen.h" +#include "paddle/framework/operator.h" +#include "paddle/framework/ddim.h" +#include "paddle/operators/gather.h" + +namespace paddle { +namespace operators { + +using namespace paddle::framework; + +template +class CondOp final : public OperatorBase { +public: + void Init() override; + + /** + * InferShape must be called before Run. + */ + virtual void InferShape(const std::shared_ptr& scope) const override { + scope_t = scope.NewScope(); + scope_f = scope.NewScope(); + net_op_t->InferShape(scope_t); + net_op_f->InferShape(scope_f); + tensor_t = new Tensor(); + tensor_f = new Tensor(); + { // True branch + for (auto& input : net_op_t->Inputs()) { + auto var_name = input.second; + if (!scope_t.FindVar(var_name) { + scope_t.NewVar(var_name)->GetMutable(); + } + } + } + { // False branch + for (auto& input : net_op_f->Inputs()) { + auto var_name = input.second; + if (!scope_f.FindVar(var_name) { + scope_f.NewVar(var_name)->GetMutable(); + } + } + } + } + + virtual void Run(const std::shared_ptr& scope, + const platform::DeviceContext& dev_ctx) const override { + auto* cond = context.Input("Cond"); + // Step 1: get the index + true_index.clear(); + false_index.clear(); + for(int i = 0; i < cond->dims()[0]; ++i) { + if (cond->data()[i]) + true_index.push_back(i); + else: + false_index.push_back(i); + } + framework::DDim dim_ = paddle::framework::make_ddim({0}); + dim_[0] = true_index.size(); + tensor_t->Resize(dim_); + // set value + for (int i = 0; i < dim_[0]; ++i) + tensor_t->mutable_data()[i] = true_index[i]; + dim_[0] = false_index.size(); + tensor_f->Resize(dim_); + // set value + for (int i = 0; i < dim_[0]; ++i) + tensor_f->mutable_data()[i] = false_index[i]; + + // Step 2: collect data by calling gather + { // True branch + for (auto& input : net_op_t->Inputs()) { + auto var_name = input.second; + // find Tensor + Tensor* Tensor_parent = scope.FindVar(var_name)->GetMutable(); + Tensor* Tensor_child = scope_t.FindVar(var_name)->GetMutable(); + Gather(dev_ctx.GetPlace(), tensor_parent, tensor_t, tensor_child); + } + + } + } + +private: + Scope* scope_t; + Scope* scope_f; + + // subnet_t + std::unique_ptr net_op_t; + // NetOp* net_op_t; + // subnet_f + std::unique_ptr net_op_f; + // NetOp* net_op_f; + + // T_index + vector true_index; + Tensor* tensor_t; + // F_index + vector false_index; + Tensor* tensor_f; +}; + +class CondOpMaker : public OpProtoAndCheckerMaker { +public: + IfElseOpMaker(OpProto *proto, OpAttrChecker *op_checker) + : OpProtoAndCheckerMaker(proto, op_checker) { + AddInput("Cond", "The condition, which is a bool vector"); + AddAttr("subnet_t", "The subnet network to be called when Cond[i] == true"); + AddAttr("subnet_f", "The subnet network to be called when Cond[i] == false"); + AddOutput("Out", "The output of if-else op"); + AddComment(R"DOC( +Sample dependent Cond Operator: +The equation is: Out[i] = subnet_t[i], if Cond[i] == true +Out[i] = subnet_t[i], if Cond[i] == false +)DOC"); + } +}; + +class CondGradientOp final : public OperatorBase { +public: + void Init() override; + + virtual void InferShape(const std::shared_ptr& scope) const override; + + virtual void Run(const std::shared_ptr& scope, + const platform::DeviceContext& dev_ctx) const override; +}; + +} // namespace operators +} // namespace paddle -- GitLab From f075141b9144b6fdb91ccc2af9216601d70568c5 Mon Sep 17 00:00:00 2001 From: Zhuoyuan Date: Mon, 28 Aug 2017 11:37:49 -0700 Subject: [PATCH 0263/2018] switch_op.cc --- paddle/operators/switch_op.cc | 120 ++++++++++++++++++++++++++++++++++ 1 file changed, 120 insertions(+) create mode 100644 paddle/operators/switch_op.cc diff --git a/paddle/operators/switch_op.cc b/paddle/operators/switch_op.cc new file mode 100644 index 000000000..09574a89a --- /dev/null +++ b/paddle/operators/switch_op.cc @@ -0,0 +1,120 @@ +#include "paddle/operators/switch_op.h" + +namespace paddle { +namespace operators { + +// namespace if_else{ + + +void CondOp::Init() override { +} + +void InferShape(const std::shared_ptr& scope) const override { + subnet_t = GetAttr("subnet_t"); + subnet_f = GetAttr("subnet_f"); + + // Create two Nets + // I use the same style as Recurrent_op, but does it create the net? + // can be called like + Variable* net_t = scope.FindVar(subnet_t); + Variable* net_f = scope.FindVar(subnet_f); + + net_op_t = scope.FindVar(net_t)->GetMutable(); + net_op_f = scope.FindVar(net_f)->GetMutable(); + + // Create two scopes + scope_t = scope.NewScope(); + scope_f = scope.NewScope(); + + // check cond of size (batch_size), type bool + net_op_t->InferShape(scope_t); + net_op_f->InferShape(scope_f); + + // check net_op_t and net_op_f of exactly same shape? +} + +void IfElseOp::Run(const std::shared_ptr& scope, + const platform::DeviceContext& dev_ctx) const { + /* step 1: create two subnets and scopes, supposed done in Infershape() */ + + /* step 2: get true and false index */ + cond = Input(name.cond); + // get condition tensor + auto cond_tensor = scope.get(cond); + // tensor to cpu, whatever device it used to be in + cond_cpu.CopyFrom(cond_tensor, platform::CPUPlace()); + + size_t batch_size = cond_cpu.dims()[0]; + + // keep index of true and false to slice, clear them first before each batch + true_index.clear(); + false_index.clear(); + + // get a DDim type variable dims, check dimension + auto dims = input0.dims(); + for(int i=0; idata[i]) + true_index.push_back(i); + else + false_index.push_back(i); + } + + // turn true_index and false_index to tensors + Tensor* true_index_tensor = new Tensor(true_index); + Tensor* false_index_tensor = new Tensor(false_index); + + /* Step 3: Gather */ + { // True Scope + // Create new stuff + for (auto& input : net_op_t->inputs_) { + scope_t.NewVar(input); + if (input.type() != PARAMETER) { // gather and slice required + // Get Tensor and gather + Tensor* input_gather_ = scope_t.FindVar(input)->GetMutable(); + Tensor* input_full_ = scope.FindVar(input)->GetMutable(); + input_gather_ = Gather(input_full_, true_index_tensor); + } + } + + for (auto& output : net_op->outputs_) { + scope_t.NewVar(output); + } + + net_op_t.Run(); + } + + { // False Scope + // Create new stuff + for (auto& input : net_op_f->inputs_) { + scope_f.NewVar(input); + if (input.type() != PARAMETER) { // gather and slice required + // Get Tensor and gather + Tensor* input_gather_ = scope_f.FindVar(input)->GetMutable(); + Tensor* input_full_ = scope.FindVar(input)->GetMutable(); + input_gather_ = Gather(input_full_, false_index_tensor); + } + } + + for (auto& output : net_op->outputs_) { + scope_t.NewVar(output); + } + + net_op_f.Run(); + } + + /* Merge Output Together by scatter update */ + for (auto& ouput : outputs_) { + Tensor* output_t = scope_t->FindVar(output)->GetMutable(); + Tensor* output_f = scope_f->FindVar(output)->GetMutable(); + Tensor* output_tensor = scope->FindVar(output)->GetMutable(); + Scatter(output_t, output_tensor, true_index_tensor); + Scatter(output_f, output_tensor, false_index_tensor); + } +} + +} // namespace operators +} // namespace paddle + +REGISTER_OP(ifelse_op, + paddle::operators::IfElseOp, + paddle::operators::RecurrentAlgorithmProtoAndCheckerMaker); -- GitLab From eaeb69f98f70bbea4fe4aae9f7c7b830f75959c5 Mon Sep 17 00:00:00 2001 From: fengjiayi Date: Mon, 28 Aug 2017 13:47:37 -0700 Subject: [PATCH 0264/2018] Follow reviewer's comments --- paddle/framework/backward.md | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/paddle/framework/backward.md b/paddle/framework/backward.md index ce324a73f..8aa6728a9 100644 --- a/paddle/framework/backward.md +++ b/paddle/framework/backward.md @@ -2,28 +2,28 @@ ## Motivation -In Neural Network, the backpropagation algorithm follows the chain rule, so we need to compound the fundmental gradient operators/expressions together with chain rule . Every forward network need a backward network to construct the full computation lineage, the operator/expression's backward pass will be generated respect to forward pass. +In Neural Network, the backpropagation algorithm follows the chain rule, so we need to compound the fundmental gradient operators/expressions together with chain rule . Every forward network need a backward network to construct the full computation graph, the operator/expression's backward pass will be generated respect to forward pass. ## Backward Operator Registry A backward network is built up with several backward operators. Backward operators take forward operators' inputs, outputs and output gradients and then calculate its input gradients. --| | forward operator | backward operator --| ---------------------- | ---------------- |------------------------- | --| **Operator::inputs_** | Inputs | Inputs, Outputs, OutputGradients | --| **Operator::outputs_** | Outputs | InputGradients | +| | forward operator | backward operator +| ---------------------- | ---------------- |------------------------- | +| **Operator::inputs_** | Inputs | Inputs, Outputs, OutputGradients | +| **Operator::outputs_** | Outputs | InputGradients | In most cases, there is a one-to-one correspondence between forward and backward operators. These correspondences are recorded by a global hash map(`OpInfoMap`). To follow the philosophy of minimum core and make operators pluggable, the registry mechanism is introduced. -For example, we have got a `add_two_op`, and we can register it's information and corresponding backward operator by the following macro: +For example, we have got a `mul_op`, and we can register it's information and corresponding backward operator by the following macro: ```cpp -REGISTER_OP(add_two, AddTwoOp, AddTwoOpMaker, add_two_grad, AddTwoGradOp); +REGISTER_OP(mul, MulOp, MulOpMaker, mul_grad, MulOpGrad); ``` -`add_two` is the operator's type. `AddTwoOp` and `AddTwoOpMaker` are the operator class and the operator maker class respectively. +`mul` is the operator's type. `MulOp` and `MulOpMaker` are the operator class and the operator maker class respectively. -`add_two_grad` is the type of backward operator, and `AddTwoGradOp` is its class name. +`mul_grad` is the type of backward operator, and `MulOpGrad` is its class name. ## Backward Opeartor Creating -- GitLab From c19eae4c8e7923aa52dc05560dcc91b8b6d58de8 Mon Sep 17 00:00:00 2001 From: qingqing01 Date: Tue, 29 Aug 2017 15:46:52 +0800 Subject: [PATCH 0265/2018] update doc about how to write new operators. --- doc/howto/dev/new_op_cn.md | 56 +++++++++++++------ .../v2/framework/tests/gradient_checker.py | 2 +- 2 files changed, 41 insertions(+), 17 deletions(-) diff --git a/doc/howto/dev/new_op_cn.md b/doc/howto/dev/new_op_cn.md index ebd2cf3ff..228b3fd64 100644 --- a/doc/howto/dev/new_op_cn.md +++ b/doc/howto/dev/new_op_cn.md @@ -5,12 +5,13 @@ - [定义ProtoMaker类](#定义ProtoMaker类) - [定义Operator类](#定义Operator类) - [定义OpKernel类](#定义OpKernel类) - - [注册类](#注册类) + - [注册Operator](#注册Operator) - [编译](#编译) - [绑定Python](#绑定Python) - [实现单元测试](#实现单元测试) - [前向Operator单测](#前向Operator单测) - [反向Operator单测](#反向Operator单测) + - [编译和执行](#编译和执行) ## 概念简介 @@ -22,19 +23,17 @@ - `framework::OperatorWithKernel`：继承自OperatorBase，Op有计算函数，称作有Kernel。 - `class OpProtoAndCheckerMaker`：描述该Op的输入、输出、属性、注释,主要用于Python API接口生成 -依据是否包含kernel，将Op分为两种：包含Kernel的Op和不包含kernel的Op，前者Op的定义继承自`OperatorBase`，后者继承自`OperatorWithKernel`。本教程主要介绍带Kernel的Op如何写，简单总结如下： +依据是否包含kernel，将Op分为两种：包含Kernel的Op和不包含kernel的Op，前者Op的定义继承自`OperatorBase`，后者继承自`OperatorWithKernel`。本教程主要介绍带Kernel的Op如何写，简单总结Op需要包含的内容如下： -Forward Op需要包含： - - - OpProtoMake定义 - - Op定义 - - Kernel实现 + + 内容 | 定义位置 +-------------- | :---------------------- +OpProtoMake定义 | `.cc`文件，Backward Op不需要定义OpProtoMake +Op定义 | `.cc`文件 +Kernel实现 | CPU、GPU共享Kernel在`.h`文件，否则，CPU可以在`.cc`文件，GPU可在`.cu`文件。 +注册Op | Op注册在`.cc`文件；Kernel注册CPU在`.cc`文件，GPU在`.cu`文件 + -与之对应的Backward Op包含： - - - Op定义 - - Kernel实现 - 下面以矩阵乘操作，即[MulOp](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/mul_op.cc)为例来介绍如何写带Kernel的Operator。 @@ -137,8 +136,9 @@ MulOp(const std::string &type, const framework::VariableNameMap &inputs, ``` 还需要重写`InferShape`接口。`InferShape`为const函数，不能修改Op的成员变量，参数为`const framework::InferShapeContext &ctx`，通过该参数可获取到输入输出以及属性。它的功能是： - - 1). 做检查， 尽早报错：检查输入数据维度、类型等是否合法 - - 2). 设置输出Tensor的形状 + + - 1). 做检查， 尽早报错：检查输入数据维度、类型等是否合法。 + - 2). 设置输出Tensor的形状。 通常`OpProtoMaker`和`Op`类的定义写在`.cc`文件中，和要讲到的注册函数一起放在`.cc`中 @@ -172,7 +172,7 @@ class MulKernel : public framework::OpKernel { 到此前向Op实现完成，需要在`.cc`文件中注册该op和kernel。反向Op类的定义和Kernel定义与前向Op类似，这里不再重复。但注意，反向Op没有`ProtoMaker`。 -### 4. 注册类 +### 4. 注册Operator 在`.cc`文件中注册前向、反向Op类，注册CPU Kernel。 @@ -297,4 +297,28 @@ class TestMulOp(unittest.TestCase): - 调用`create_op("mul")`创建反向Op对应的前向Op。 - 定义输入`inputs`。 - 调用`compare_grad`函数对比CPU、GPU计算结果。 - - 调用`check_grad`检查梯度稳定性。 + - 调用`check_grad`检查梯度稳定性，这里采用数值法检测梯度正确性。 + - 第一个参数`op` : 前向op。 + - 第二个参数`inputs` : 输入词典，词典的Key和`ProtoMaker`定义保持一致。 + - 第三个参数`set(["X", "Y"])` : 指定对输入变量`X`、`Y`做梯度检测。 + - 第四个参数`"Out"` : 指定前向网络最终的输出目标变量`Out` + + +### 编译和执行 + +单测完成之后，在[`python/paddle/v2/framework/tests/CMakeLists.txt`](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/v2/framework/tests/CMakeLists.txt)里添加编译： + +``` +py_test(test_mul_op SRCS test_mul_op.py) +``` + +编译完成之后即可执行单测： + +``` +make test ARGS="-R test_mul_op -V" +``` +或者: + +``` +ctest -R test_mul_op +``` diff --git a/python/paddle/v2/framework/tests/gradient_checker.py b/python/paddle/v2/framework/tests/gradient_checker.py index 9a7a7fbf5..02cfb9b2c 100644 --- a/python/paddle/v2/framework/tests/gradient_checker.py +++ b/python/paddle/v2/framework/tests/gradient_checker.py @@ -268,7 +268,7 @@ class GradientChecker(unittest.TestCase): :param input_vars: numpy value of input variable. The following computation will use these variables. :param inputs_to_check: inputs var names that should check gradient. - :param output_name: output name that used to + :param output_name: the final output variable name. :param max_relative_error: The relative tolerance parameter. :param no_grad_set: used when create backward ops :param only_cpu: only compute and check gradient on cpu kernel. -- GitLab From b336119424d3fc0d9ffa39688612a83c23c6e10e Mon Sep 17 00:00:00 2001 From: qingqing01 Date: Tue, 29 Aug 2017 16:03:07 +0800 Subject: [PATCH 0266/2018] Add WITH_TESTING=ON for cmake in the operators writing guide doc. --- doc/howto/dev/new_op_cn.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/howto/dev/new_op_cn.md b/doc/howto/dev/new_op_cn.md index 228b3fd64..7f8da2da5 100644 --- a/doc/howto/dev/new_op_cn.md +++ b/doc/howto/dev/new_op_cn.md @@ -312,7 +312,7 @@ class TestMulOp(unittest.TestCase): py_test(test_mul_op SRCS test_mul_op.py) ``` -编译完成之后即可执行单测： +编译时需要打开`WITH_TESTING`, 即 `cmake paddle_dir -DWITH_TESTING=ON`，编译成功之后执行单测命令为： ``` make test ARGS="-R test_mul_op -V" -- GitLab From b709af616f99c7f4e3ab300297608054638886a8 Mon Sep 17 00:00:00 2001 From: Luo Tao Date: Tue, 29 Aug 2017 16:21:45 +0800 Subject: [PATCH 0267/2018] HuberTwoClassification only support one dimension --- paddle/gserver/layers/CostLayer.cpp | 31 +++++++++++------------------ 1 file changed, 12 insertions(+), 19 deletions(-) diff --git a/paddle/gserver/layers/CostLayer.cpp b/paddle/gserver/layers/CostLayer.cpp index aa4a26a83..ce071323f 100644 --- a/paddle/gserver/layers/CostLayer.cpp +++ b/paddle/gserver/layers/CostLayer.cpp @@ -672,10 +672,10 @@ void HuberTwoClassification::forwardImp(Matrix& output, Matrix& target) { HuberCost::forwardImp(output, label, target); size_t numSamples = target.getHeight(); - size_t dim = output.getWidth(); CHECK(label.ids); CHECK_EQ((*label.ids).getSize(), numSamples); CHECK_EQ(output.getHeight(), numSamples); + CHECK_EQ(output.getWidth(), (size_t)1); CHECK_EQ(target.getWidth(), (size_t)1); real* out = useGpu_ ? tmpCpuInput_[0].value->getData() : output.getData(); @@ -683,14 +683,11 @@ void HuberTwoClassification::forwardImp(Matrix& output, std::vector cost(numSamples, 0); for (size_t i = 0; i < numSamples; ++i) { int y = 2 * lbl[i] - 1; - for (size_t j = 0; j < dim; ++j) { - int index = i * dim + j; - real a = out[index] * y; - if (a < -1) - cost[i] += -4 * a; - else if (a < 1) - cost[i] += (1 - a) * (1 - a); - } + real a = out[i] * y; + if (a < -1) + cost[i] = -4 * a; + else if (a < 1) + cost[i] = (1 - a) * (1 - a); } target.copyFrom(cost.data(), numSamples); } @@ -699,22 +696,18 @@ void HuberTwoClassification::backwardImp(Matrix& output, Argument& label, Matrix& outputG) { size_t numSamples = output.getHeight(); - size_t dim = output.getWidth(); real* out = useGpu_ ? tmpCpuInput_[0].value->getData() : output.getData(); int* lbl = useGpu_ ? tmpCpuInput_[1].ids->getData() : (*label.ids).getData(); real* grad = useGpu_ ? tmpCpuInput_[0].grad->getData() : outputG.getData(); for (size_t i = 0; i < numSamples; ++i) { int y = 2 * lbl[i] - 1; - for (size_t j = 0; j < dim; ++j) { - int index = i * dim + j; - real a = out[index] * y; - if (a < -1) - grad[index] += -4 * y; - else if (a < 1) - grad[index] += -2 * (1 - a) * y; - } + real a = out[i] * y; + if (a < -1) + grad[i] += -4 * y; + else if (a < 1) + grad[i] += -2 * (1 - a) * y; } - if (useGpu_) outputG.copyFrom(grad, numSamples * dim); + if (useGpu_) outputG.copyFrom(grad, numSamples); } /** * This cost layer compute the sum of its input as loss. -- GitLab From 751d8533e830eae0ca6b9ee6e62e771a4b72a14b Mon Sep 17 00:00:00 2001 From: qingqing01 Date: Tue, 29 Aug 2017 16:45:20 +0800 Subject: [PATCH 0268/2018] follow wuyi's comments. --- python/paddle/v2/framework/tests/gradient_checker.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/python/paddle/v2/framework/tests/gradient_checker.py b/python/paddle/v2/framework/tests/gradient_checker.py index 02cfb9b2c..518f828ba 100644 --- a/python/paddle/v2/framework/tests/gradient_checker.py +++ b/python/paddle/v2/framework/tests/gradient_checker.py @@ -268,7 +268,7 @@ class GradientChecker(unittest.TestCase): :param input_vars: numpy value of input variable. The following computation will use these variables. :param inputs_to_check: inputs var names that should check gradient. - :param output_name: the final output variable name. + :param output_name: the output variable name of forward network. :param max_relative_error: The relative tolerance parameter. :param no_grad_set: used when create backward ops :param only_cpu: only compute and check gradient on cpu kernel. -- GitLab From 43dde9353bf642dba113030327473554744ced0a Mon Sep 17 00:00:00 2001 From: Liu Yiqun Date: Tue, 29 Aug 2017 19:49:41 +0800 Subject: [PATCH 0269/2018] Refine the cross-compiling toolchain file for iOS. --- cmake/cross_compiling/ios.cmake | 422 ++++++++++++++++++++------------ cmake/flags.cmake | 6 +- cmake/system.cmake | 13 +- 3 files changed, 274 insertions(+), 167 deletions(-) diff --git a/cmake/cross_compiling/ios.cmake b/cmake/cross_compiling/ios.cmake index b179e29b2..135104e69 100644 --- a/cmake/cross_compiling/ios.cmake +++ b/cmake/cross_compiling/ios.cmake @@ -1,207 +1,311 @@ -# This file is based off of the Platform/Darwin.cmake and Platform/UnixPaths.cmake -# files which are included with CMake 2.8.4 -# It has been altered for iOS development - -# Options: +# 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. + +# This is a toolchain file for cross-compiling for iOS, and the +# configuration largely refers to public toolchain file: +# https://raw.githubusercontent.com/leetal/ios-cmake/master/ios.toolchain.cmake +# and +# https://github.com/cristeab/ios-cmake +# +# Supports options: # IOS_PLATFORM = OS (default) or SIMULATOR # This decides if SDKS will be selected from the iPhoneOS.platform or iPhoneSimulator.platform folders # OS - the default, used to build for iPhone and iPad physical devices, which have an arm arch. # SIMULATOR - used to build for the Simulator platforms, which have an x86 arch. -# -# CMAKE_IOS_DEVELOPER_ROOT = automatic(default) or /path/to/platform/Developer folder +# IOS_ARCH +# The archectures wanted to support, such "arm64", "armv7;arm64" +# IOS_DEPLOYMENT_TARGET +# The minimum iOS deployment version, such as "7.0" +# IOS_ENABLE_BITCODE = ON (default) or OFF +# IOS_DEVELOPER_ROOT = automatic(default) or /path/to/platform/Developer folder # By default this location is automatcially chosen based on the IOS_PLATFORM value above. # If set manually, it will override the default location and force the user of a particular Developer Platform -# -# CMAKE_IOS_SDK_ROOT = automatic(default) or /path/to/platform/Developer/SDKs/SDK folder -# By default this location is automatcially chosen based on the CMAKE_IOS_DEVELOPER_ROOT value. -# In this case it will always be the most up-to-date SDK found in the CMAKE_IOS_DEVELOPER_ROOT path. +# IOS_SDK_ROOT = automatic(default) or /path/to/platform/Developer/SDKs/SDK folder +# By default this location is automatcially chosen based on the IOS_DEVELOPER_ROOT value. +# In this case it will always be the most up-to-date SDK found in the IOS_DEVELOPER_ROOT path. # If set manually, this will force the use of a specific SDK version # Macros: -# # set_xcode_property (TARGET XCODE_PROPERTY XCODE_VALUE) # A convenience macro for setting xcode specific properties on targets # example: set_xcode_property (myioslib IPHONEOS_DEPLOYMENT_TARGET "3.1") -# # find_host_package (PROGRAM ARGS) # A macro used to find executable programs on the host system, not within the iOS environment. # Thanks to the android-cmake project for providing the command -# Standard settings -# set (CMAKE_SYSTEM_NAME Darwin) -set (CMAKE_SYSTEM_VERSION 1) -set (UNIX True) -set (APPLE True) -set (IOS True) +if(NOT IOS) + return() +endif() + +set(CMAKE_SYSTEM_NAME Darwin) +#set(UNIX ON) +#set(APPLE ON) + +# Get the Xcode version being used. +execute_process(COMMAND xcodebuild -version + OUTPUT_VARIABLE XCODE_VERSION + RESULT_VARIABLE XCODE_VERSION_RESULT + ERROR_QUIET OUTPUT_STRIP_TRAILING_WHITESPACE) +if(NOT ${XCODE_VERSION_RESULT}) + string(REGEX MATCH "Xcode [0-9\\.]+" XCODE_VERSION "${XCODE_VERSION}") + string(REGEX REPLACE "Xcode ([0-9\\.]+)" "\\1" XCODE_VERSION "${XCODE_VERSION}") + message(STATUS "Building with Xcode version: ${XCODE_VERSION}") +else() + message(FATAL_ERROR "Cannot execute xcodebuild, please check whether xcode is installed.") +endif() # Required as of cmake 2.8.10 -set (CMAKE_OSX_DEPLOYMENT_TARGET "" CACHE STRING "Force unset of the deployment target for iOS" FORCE) - -# Determine the cmake host system version so we know where to find the iOS SDKs -find_program (CMAKE_UNAME uname /bin /usr/bin /usr/local/bin) -if (CMAKE_UNAME) - exec_program(uname ARGS -r OUTPUT_VARIABLE CMAKE_HOST_SYSTEM_VERSION) - string (REGEX REPLACE "^([0-9]+)\\.([0-9]+).*$" "\\1" DARWIN_MAJOR_VERSION "${CMAKE_HOST_SYSTEM_VERSION}") -endif (CMAKE_UNAME) - -# Force the compilers to gcc for iOS -set (CMAKE_C_COMPILER /usr/bin/gcc) -set (CMAKE_CXX_COMPILER /usr/bin/g++) +set(CMAKE_OSX_DEPLOYMENT_TARGET "" CACHE STRING "Force unset of the deployment target for iOS" FORCE) + set(CMAKE_AR ar CACHE FILEPATH "" FORCE) set(CMAKE_RANLIB ranlib CACHE FILEPATH "" FORCE) set(PKG_CONFIG_EXECUTABLE pkg-config CACHE FILEPATH "" FORCE) # Setup iOS platform unless specified manually with IOS_PLATFORM -if (NOT DEFINED IOS_PLATFORM) - set (IOS_PLATFORM "OS") -endif (NOT DEFINED IOS_PLATFORM) -set (IOS_PLATFORM ${IOS_PLATFORM} CACHE STRING "Type of iOS Platform") +if(NOT DEFINED IOS_PLATFORM) + set(IOS_PLATFORM "OS") +endif() +set(IOS_PLATFORM ${IOS_PLATFORM} CACHE STRING "Type of iOS Platform") + +# Set the architecture for iOS +if(NOT DEFINED IOS_ARCH) + if(IOS_PLATFORM STREQUAL "OS") + set(IOS_ARCH "armv7;armv7s;arm64") + elseif(IOS_PLATFORM STREQUAL "SIMULATOR") + set(IOS_ARCH "i386;x86_64") + elseif(IOS_PLATFORM STREQUAL "WATCHOS") + set(IOS_ARCH armv7k) + endif() +endif() +set(CMAKE_OSX_ARCHITECTURES ${IOS_ARCH} CACHE string "Build architecture for iOS") + +# Specify minimum iOS deployment version +if(NOT DEFINED IOS_DEPLOYMENT_TARGET) + set(IOS_DEPLOYMENT_TARGET "7.0") +endif() +set(IOS_DEPLOYMENT_TARGET ${IOS_DEPLOYMENT_TARGET} CACHE STRING "Minimum iOS version") + +# Whether to enable bitcode +if(NOT DEFINED IOS_ENABLE_BITCODE) + set(IOS_ENABLE_BITCODE ON) +endif() +set(IOS_ENABLE_BITCODE ${IOS_ENABLE_BITCODE} CACHE BOOL "Whether to enable bitcode") # Check the platform selection and setup for developer root -if (${IOS_PLATFORM} STREQUAL "OS") - set (IOS_PLATFORM_LOCATION "iPhoneOS.platform") - set (XCODE_IOS_PLATFORM iphoneos) - - # This causes the installers to properly locate the output libraries - set (CMAKE_XCODE_EFFECTIVE_PLATFORMS "-iphoneos") -elseif (${IOS_PLATFORM} STREQUAL "SIMULATOR") - set (SIMULATOR true) - set (IOS_PLATFORM_LOCATION "iPhoneSimulator.platform") - set (XCODE_IOS_PLATFORM iphonesimulator) - - # This causes the installers to properly locate the output libraries - set (CMAKE_XCODE_EFFECTIVE_PLATFORMS "-iphonesimulator") -elseif (${IOS_PLATFORM} STREQUAL "WATCHOS") - set (IOS_PLATFORM_LOCATION "WatchOS.platform") - set (XCODE_IOS_PLATFORM watchos) - - # This causes the installers to properly locate the output libraries - set (CMAKE_XCODE_EFFECTIVE_PLATFORMS "-watchos") -else (${IOS_PLATFORM} STREQUAL "OS") - message (FATAL_ERROR - "Unsupported IOS_PLATFORM value selected. " - "Please choose OS, SIMULATOR, or WATCHOS.") -endif () - -# All iOS/Darwin specific settings - some may be redundant -set (CMAKE_SHARED_LIBRARY_PREFIX "lib") -set (CMAKE_SHARED_LIBRARY_SUFFIX ".dylib") -set (CMAKE_SHARED_MODULE_PREFIX "lib") -set (CMAKE_SHARED_MODULE_SUFFIX ".so") -set (CMAKE_MODULE_EXISTS 1) -set (CMAKE_DL_LIBS "") - -set (CMAKE_C_OSX_COMPATIBILITY_VERSION_FLAG "-compatibility_version ") -set (CMAKE_C_OSX_CURRENT_VERSION_FLAG "-current_version ") -set (CMAKE_CXX_OSX_COMPATIBILITY_VERSION_FLAG "${CMAKE_C_OSX_COMPATIBILITY_VERSION_FLAG}") -set (CMAKE_CXX_OSX_CURRENT_VERSION_FLAG "${CMAKE_C_OSX_CURRENT_VERSION_FLAG}") - -if (IOS_DEPLOYMENT_TARGET) - set(XCODE_IOS_PLATFORM_VERSION_FLAGS "-m${XCODE_IOS_PLATFORM}-version-min=${IOS_DEPLOYMENT_TARGET}") +if(${IOS_PLATFORM} STREQUAL "OS") + set(IOS_PLATFORM_LOCATION "iPhoneOS.platform") + set(XCODE_IOS_PLATFORM iphoneos) + + # This causes the installers to properly locate the output libraries + set(CMAKE_XCODE_EFFECTIVE_PLATFORMS "-iphoneos") +elseif(${IOS_PLATFORM} STREQUAL "SIMULATOR") + set(IOS_PLATFORM_LOCATION "iPhoneSimulator.platform") + set(XCODE_IOS_PLATFORM iphonesimulator) + + # This causes the installers to properly locate the output libraries + set(CMAKE_XCODE_EFFECTIVE_PLATFORMS "-iphonesimulator") +elseif(${IOS_PLATFORM} STREQUAL "WATCHOS") + set(IOS_PLATFORM_LOCATION "WatchOS.platform") + set(XCODE_IOS_PLATFORM watchos) + + # This causes the installers to properly locate the output libraries + set(CMAKE_XCODE_EFFECTIVE_PLATFORMS "-watchos") +else(${IOS_PLATFORM} STREQUAL "OS") + message(FATAL_ERROR "Unsupported IOS_PLATFORM value selected. Please set to\n" + "\t OS, SIMULATOR, or WATCHOS.") +endif() + +# Check iOS developer toolchain +if(NOT DEFINED IOS_DEVELOPER_ROOT) + # Setup iOS developer location + execute_process(COMMAND xcode-select -print-path + OUTPUT_VARIABLE XCODE_DEVELOPER_DIR + RESULT_VARIABLE XCODE_DEVELOPER_DIR_RESULT + ERROR_QUIET OUTPUT_STRIP_TRAILING_WHITESPACE) + # Xcode 4.3 changed the installation location, choose the most recent one available + if(${XCODE_VERSION} VERSION_LESS "4.3.0") + set(IOS_DEVELOPER_ROOT "/Developer/Platforms/${IOS_PLATFORM_LOCATION}/Developer") + else() + set(IOS_DEVELOPER_ROOT "${XCODE_DEVELOPER_DIR}/Platforms/${IOS_PLATFORM_LOCATION}/Developer") + endif() +endif() +if(EXISTS ${IOS_DEVELOPER_ROOT}) + set(IOS_DEVELOPER_ROOT ${IOS_DEVELOPER_ROOT} CACHE PATH "Location of iOS Platform") +else() + message(FATAL_ERROR "Invalid IOS_DEVELOPER_ROOT: ${IOS_DEVELOPER_ROOT} does not exist.") endif() +# Check iOS SDK +if(NOT DEFINED IOS_SDK_ROOT) + # Find and use the most recent iOS sdk + file(GLOB IOS_SDK_LISTS "${IOS_DEVELOPER_ROOT}/SDKs/*") + if(IOS_SDK_LISTS) + list(SORT IOS_SDK_LISTS) + list(REVERSE IOS_SDK_LISTS) + list(GET IOS_SDK_LISTS 0 IOS_SDK_ROOT) + else(IOS_SDK_LISTS) + message(FATAL_ERROR "No iOS SDK's found in default search path ${IOS_DEVELOPER_ROOT}." + " Please manually set IOS_SDK_ROOT or install the iOS SDK.") + endif(IOS_SDK_LISTS) +endif() +if(EXISTS ${IOS_SDK_ROOT}) + set(CMAKE_IOS_SDK_ROOT ${CMAKE_IOS_SDK_ROOT} CACHE PATH "Location of the selected iOS SDK") + message(STATUS "iOS toolchain: ${IOS_SDK_ROOT}") +else() + message(FATAL_ERROR "Invalid IOS_SDK_ROOT: ${IOS_SDK_ROOT} does not exist.") +endif() + +# Set the sysroot default to the most recent SDK +set(CMAKE_OSX_SYSROOT ${IOS_SDK_ROOT} CACHE PATH "Sysroot used for iOS support") + +# Get version of iOS SDK +execute_process(COMMAND xcodebuild -sdk ${CMAKE_OSX_SYSROOT} -version SDKVersion + OUTPUT_VARIABLE IOS_SDK_VERSION + RESULT_VARIABLE IOS_SDK_VERSION_RESULT + ERROR_QUIET OUTPUT_STRIP_TRAILING_WHITESPACE) +if(${IOS_SDK_VERSION_RESULT}) + string(REGEX MATCH "(([0-9]+)\\.)+([0-9]+)" IOS_SDK_VERSION "${IOS_SDK_ROOT}") +endif() +if(NOT IOS_SDK_VERSION) + message(WARNING "Cannot get SDK's version.") + set(IOS_SDK_VERSION 1) +endif() +set(CMAKE_SYSTEM_VERSION ${IOS_SDK_VERSION}) + +# Find the C & C++ compilers for the specified SDK. +if(NOT CMAKE_C_COMPILER) + # 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Xcode 7.0+ uses flags we can build directly from XCODE_IOS_PLATFORM. + set(XCODE_IOS_PLATFORM_VERSION_FLAGS "-m${XCODE_IOS_PLATFORM}-version-min=${IOS_DEPLOYMENT_TARGET}") + endif() +else() + set(XCODE_IOS_FLATFORM_VERSION_FLAGS "-mios-simulator-version-min=${IOS_DEPLOYMENT_TARGET}") +endif() + +if(IOS_ENABLE_BITCODE) + set(XCODE_IOS_BITCODE_FLAGS "${IOS_COMPILER_FLAGS} -fembed-bitcode") +else() + set(XCODE_IOS_BITCODE_FLAGS "") +endif() + +set(IOS_COMPILER_FLAGS "${XCODE_IOS_PLATFORM_VERSION_FLAGS} ${XCODE_IOS_BITCODE_FLAGS}") + # Hidden visibilty is required for cxx on iOS -set (CMAKE_C_FLAGS_INIT "${XCODE_IOS_PLATFORM_VERSION_FLAGS}") -set (CMAKE_CXX_FLAGS_INIT "${XCODE_IOS_PLATFORM_VERSION_FLAGS} -fvisibility-inlines-hidden") +set(CMAKE_C_FLAGS "${IOS_COMPILER_FLAGS} ${CMAKE_C_FLAGS}" CACHE STRING "C flags") +set(CMAKE_CXX_FLAGS "${IOS_COMPILER_FLAGS} -fvisibility-inlines-hidden ${CMAKE_CXX_FLAGS}" CACHE STRING "CXX flags") -set (CMAKE_C_LINK_FLAGS "${XCODE_IOS_PLATFORM_VERSION_FLAGS} -Wl,-search_paths_first ${CMAKE_C_LINK_FLAGS}") -set (CMAKE_CXX_LINK_FLAGS "${XCODE_IOS_PLATFORM_VERSION_FLAGS} -Wl,-search_paths_first ${CMAKE_CXX_LINK_FLAGS}") +set(CMAKE_C_LINK_FLAGS "${XCODE_IOS_PLATFORM_VERSION_FLAGS} -Wl,-search_paths_first ${CMAKE_C_LINK_FLAGS}") +set(CMAKE_CXX_LINK_FLAGS "${XCODE_IOS_PLATFORM_VERSION_FLAGS} -Wl,-search_paths_first ${CMAKE_CXX_LINK_FLAGS}") -set (CMAKE_PLATFORM_HAS_INSTALLNAME 1) -set (CMAKE_SHARED_LIBRARY_CREATE_C_FLAGS "-dynamiclib -headerpad_max_install_names") -set (CMAKE_SHARED_MODULE_CREATE_C_FLAGS "-bundle -headerpad_max_install_names") -set (CMAKE_SHARED_MODULE_LOADER_C_FLAG "-Wl,-bundle_loader,") -set (CMAKE_SHARED_MODULE_LOADER_CXX_FLAG "-Wl,-bundle_loader,") -set (CMAKE_FIND_LIBRARY_SUFFIXES ".dylib" ".so" ".a") +set(CMAKE_PLATFORM_HAS_INSTALLNAME 1) +set(CMAKE_SHARED_LIBRARY_CREATE_C_FLAGS "-dynamiclib -headerpad_max_install_names") +set(CMAKE_SHARED_MODULE_CREATE_C_FLAGS "-bundle -headerpad_max_install_names") +set(CMAKE_SHARED_MODULE_LOADER_C_FLAG "-Wl,-bundle_loader,") +set(CMAKE_SHARED_MODULE_LOADER_CXX_FLAG "-Wl,-bundle_loader,") +set(CMAKE_FIND_LIBRARY_SUFFIXES ".dylib" ".so" ".a") # hack: if a new cmake (which uses CMAKE_INSTALL_NAME_TOOL) runs on an old build tree # (where install_name_tool was hardcoded) and where CMAKE_INSTALL_NAME_TOOL isn't in the cache # and still cmake didn't fail in CMakeFindBinUtils.cmake (because it isn't rerun) # hardcode CMAKE_INSTALL_NAME_TOOL here to install_name_tool, so it behaves as it did before, Alex -if (NOT DEFINED CMAKE_INSTALL_NAME_TOOL) - find_program(CMAKE_INSTALL_NAME_TOOL install_name_tool) -endif (NOT DEFINED CMAKE_INSTALL_NAME_TOOL) - -# Setup iOS deployment target -set (IOS_DEPLOYMENT_TARGET ${IOS_DEPLOYMENT_TARGET} CACHE STRING "Minimum iOS version") - -# Setup iOS developer location unless specified manually with CMAKE_IOS_DEVELOPER_ROOT -# Note Xcode 4.3 changed the installation location, choose the most recent one available -exec_program(/usr/bin/xcode-select ARGS -print-path OUTPUT_VARIABLE CMAKE_XCODE_DEVELOPER_DIR) -set (XCODE_POST_43_ROOT "${CMAKE_XCODE_DEVELOPER_DIR}/Platforms/${IOS_PLATFORM_LOCATION}/Developer") -set (XCODE_PRE_43_ROOT "/Developer/Platforms/${IOS_PLATFORM_LOCATION}/Developer") -if (NOT DEFINED CMAKE_IOS_DEVELOPER_ROOT) - if (EXISTS ${XCODE_POST_43_ROOT}) - set (CMAKE_IOS_DEVELOPER_ROOT ${XCODE_POST_43_ROOT}) - elseif(EXISTS ${XCODE_PRE_43_ROOT}) - set (CMAKE_IOS_DEVELOPER_ROOT ${XCODE_PRE_43_ROOT}) - endif (EXISTS ${XCODE_POST_43_ROOT}) -endif (NOT DEFINED CMAKE_IOS_DEVELOPER_ROOT) -set (CMAKE_IOS_DEVELOPER_ROOT ${CMAKE_IOS_DEVELOPER_ROOT} CACHE PATH "Location of iOS Platform") - -# Find and use the most recent iOS sdk unless specified manually with CMAKE_IOS_SDK_ROOT -if (NOT DEFINED CMAKE_IOS_SDK_ROOT) - file (GLOB _CMAKE_IOS_SDKS "${CMAKE_IOS_DEVELOPER_ROOT}/SDKs/*") - if (_CMAKE_IOS_SDKS) - list (SORT _CMAKE_IOS_SDKS) - list (REVERSE _CMAKE_IOS_SDKS) - list (GET _CMAKE_IOS_SDKS 0 CMAKE_IOS_SDK_ROOT) - else (_CMAKE_IOS_SDKS) - message (FATAL_ERROR "No iOS SDK's found in default search path ${CMAKE_IOS_DEVELOPER_ROOT}. Manually set CMAKE_IOS_SDK_ROOT or install the iOS SDK.") - endif (_CMAKE_IOS_SDKS) - message (STATUS "Toolchain using default iOS SDK: ${CMAKE_IOS_SDK_ROOT}") -endif (NOT DEFINED CMAKE_IOS_SDK_ROOT) -set (CMAKE_IOS_SDK_ROOT ${CMAKE_IOS_SDK_ROOT} CACHE PATH "Location of the selected iOS SDK") - -# Set the sysroot default to the most recent SDK -set (CMAKE_OSX_SYSROOT ${CMAKE_IOS_SDK_ROOT} CACHE PATH "Sysroot used for iOS support") - -# set the architecture for iOS -if (IOS_PLATFORM STREQUAL "OS") - set (IOS_ARCH "armv7;armv7s;arm64") -elseif (IOS_PLATFORM STREQUAL "SIMULATOR") - set (IOS_ARCH "i386;x86_64") -elseif (IOS_PLATFORM STREQUAL "WATCHOS") - set (IOS_ARCH "armv7k") -endif () - -set (CMAKE_OSX_ARCHITECTURES ${IOS_ARCH} CACHE string "Build architecture for iOS") +if(NOT DEFINED CMAKE_INSTALL_NAME_TOOL) + find_program(CMAKE_INSTALL_NAME_TOOL install_name_tool) +endif() # Set the find root to the iOS developer roots and to user defined paths -set (CMAKE_FIND_ROOT_PATH ${CMAKE_IOS_DEVELOPER_ROOT} ${CMAKE_IOS_SDK_ROOT} ${CMAKE_PREFIX_PATH} CACHE string "iOS find search path root") +set(CMAKE_FIND_ROOT_PATH ${IOS_DEVELOPER_ROOT} ${IOS_SDK_ROOT} ${CMAKE_PREFIX_PATH} + CACHE string "iOS find search path root") # default to searching for frameworks first -set (CMAKE_FIND_FRAMEWORK FIRST) +set(CMAKE_FIND_FRAMEWORK FIRST) # set up the default search directories for frameworks -set (CMAKE_SYSTEM_FRAMEWORK_PATH - ${CMAKE_IOS_SDK_ROOT}/System/Library/Frameworks - ${CMAKE_IOS_SDK_ROOT}/System/Library/PrivateFrameworks - ${CMAKE_IOS_SDK_ROOT}/Developer/Library/Frameworks -) +set(CMAKE_SYSTEM_FRAMEWORK_PATH + ${IOS_SDK_ROOT}/System/Library/Frameworks + ${IOS_SDK_ROOT}/System/Library/PrivateFrameworks + ${IOS_SDK_ROOT}/Developer/Library/Frameworks + ) # only search the iOS sdks, not the remainder of the host filesystem -set (CMAKE_FIND_ROOT_PATH_MODE_PROGRAM NEVER) -set (CMAKE_FIND_ROOT_PATH_MODE_LIBRARY ONLY) -set (CMAKE_FIND_ROOT_PATH_MODE_INCLUDE ONLY) +set(CMAKE_FIND_ROOT_PATH_MODE_PROGRAM NEVER) +set(CMAKE_FIND_ROOT_PATH_MODE_LIBRARY ONLY) +set(CMAKE_FIND_ROOT_PATH_MODE_INCLUDE ONLY) + +message(STATUS "iOS: Targeting iOS '${CMAKE_SYSTEM_VERSION}', " + "building for '${IOS_PLATFORM}' platform, with architecture '${CMAKE_OSX_ARCHITECTURES}'") # This little macro lets you set any XCode specific property -macro (set_xcode_property TARGET XCODE_PROPERTY XCODE_VALUE) - set_property (TARGET ${TARGET} PROPERTY XCODE_ATTRIBUTE_${XCODE_PROPERTY} ${XCODE_VALUE}) -endmacro (set_xcode_property) +macro(set_xcode_property TARGET XCODE_PROPERTY XCODE_VALUE) + set_property (TARGET ${TARGET} PROPERTY XCODE_ATTRIBUTE_${XCODE_PROPERTY} ${XCODE_VALUE}) +endmacro(set_xcode_property) # This macro lets you find executable programs on the host system -macro (find_host_package) - set (CMAKE_FIND_ROOT_PATH_MODE_PROGRAM NEVER) - set (CMAKE_FIND_ROOT_PATH_MODE_LIBRARY NEVER) - set (CMAKE_FIND_ROOT_PATH_MODE_INCLUDE NEVER) - set (IOS FALSE) - - find_package(${ARGN}) - - set (IOS TRUE) - set (CMAKE_FIND_ROOT_PATH_MODE_PROGRAM ONLY) - set (CMAKE_FIND_ROOT_PATH_MODE_LIBRARY ONLY) - set (CMAKE_FIND_ROOT_PATH_MODE_INCLUDE ONLY) -endmacro (find_host_package) +macro(find_host_package) + set(CMAKE_FIND_ROOT_PATH_MODE_PROGRAM NEVER) + set(CMAKE_FIND_ROOT_PATH_MODE_LIBRARY NEVER) + set(CMAKE_FIND_ROOT_PATH_MODE_INCLUDE NEVER) + set(IOS FALSE) + + find_package(${ARGN}) + + set(IOS TRUE) + set(CMAKE_FIND_ROOT_PATH_MODE_PROGRAM ONLY) + set(CMAKE_FIND_ROOT_PATH_MODE_LIBRARY ONLY) + set(CMAKE_FIND_ROOT_PATH_MODE_INCLUDE ONLY) +endmacro(find_host_package) diff --git a/cmake/flags.cmake b/cmake/flags.cmake index b27eb7155..11c96351e 100644 --- a/cmake/flags.cmake +++ b/cmake/flags.cmake @@ -135,8 +135,10 @@ set(GPU_COMMON_FLAGS ) if (APPLE) - # On Mac OS X build fat binaries with x86_64 architectures by default. - set (CMAKE_OSX_ARCHITECTURES "x86_64" CACHE STRING "Build architectures for OSX" FORCE) + if(NOT CMAKE_CROSSCOMPILING) + # On Mac OS X build fat binaries with x86_64 architectures by default. + set (CMAKE_OSX_ARCHITECTURES "x86_64" CACHE STRING "Build architectures for OSX" FORCE) + endif() else() set(GPU_COMMON_FLAGS -Wall diff --git a/cmake/system.cmake b/cmake/system.cmake index 7462802d2..396bd1a07 100644 --- a/cmake/system.cmake +++ b/cmake/system.cmake @@ -24,11 +24,10 @@ IF(WIN32) SET(HOST_SYSTEM "win32") ELSE(WIN32) IF(APPLE) - EXEC_PROGRAM (sw_vers ARGS -productVersion OUTPUT_VARIABLE MACOSX_VERSION) - STRING(REGEX MATCH "[0-9]+.[0-9]+" VERSION "${MACOSX_VERSION}") - SET(MACOS_VERSION ${VERSION}) SET(HOST_SYSTEM "macosx") - IF(NOT DEFINED ENV{MACOSX_DEPLOYMENT_TARGET}) + EXEC_PROGRAM(sw_vers ARGS -productVersion OUTPUT_VARIABLE HOST_SYSTEM_VERSION) + STRING(REGEX MATCH "[0-9]+.[0-9]+" MACOS_VERSION "${HOST_SYSTEM_VERSION}") + IF(NOT DEFINED $ENV{MACOSX_DEPLOYMENT_TARGET}) # Set cache variable - end user may change this during ccmake or cmake-gui configure. SET(CMAKE_OSX_DEPLOYMENT_TARGET ${MACOS_VERSION} CACHE STRING "Minimum OS X version to target for deployment (at runtime); newer APIs weak linked. Set to empty string for default value.") @@ -49,6 +48,8 @@ ELSE(WIN32) ELSEIF(LINUX_ISSUE MATCHES "Fedora") SET(HOST_SYSTEM "fedora") ENDIF() + + STRING(REGEX MATCH "(([0-9]+)\\.)+([0-9]+)" HOST_SYSTEM_VERSION "${LINUX_ISSUE}") ENDIF(EXISTS "/etc/issue") IF(EXISTS "/etc/redhat-release") @@ -70,7 +71,7 @@ CMAKE_HOST_SYSTEM_INFORMATION(RESULT CPU_CORES QUERY NUMBER_OF_LOGICAL_CORES) MARK_AS_ADVANCED(HOST_SYSTEM CPU_CORES) -MESSAGE(STATUS "Found Paddle host system: ${HOST_SYSTEM}") +MESSAGE(STATUS "Found Paddle host system: ${HOST_SYSTEM}, version: ${HOST_SYSTEM_VERSION}") MESSAGE(STATUS "Found Paddle host system's CPU: ${CPU_CORES} cores") # configuration for cross-compiling @@ -82,7 +83,7 @@ IF(DEFINED CMAKE_SYSTEM_NAME) ELSEIF(${CMAKE_SYSTEM_NAME} STREQUAL "RPi") SET(RPI TRUE) INCLUDE(cross_compiling/raspberry_pi) - ELSEIF(${CMAKE_SYSTEM_NAME} STREQUAL "Darwin") + ELSEIF(${CMAKE_SYSTEM_NAME} STREQUAL "iOS") SET(IOS TRUE) INCLUDE(cross_compiling/ios) ENDIF() -- GitLab From bfbd066fdd1c4a81266864bf837d89742b3f2ad6 Mon Sep 17 00:00:00 2001 From: tensor-tang Date: Tue, 29 Aug 2017 19:55:44 +0800 Subject: [PATCH 0270/2018] refine --- paddle/gserver/layers/MKLDNNFcLayer.cpp | 117 ++++++++++++------------ paddle/gserver/layers/MKLDNNFcLayer.h | 2 + paddle/gserver/layers/MKLDNNLayer.h | 48 +++++++--- paddle/math/MKLDNNMatrix.cpp | 25 ++--- paddle/math/MKLDNNMatrix.h | 29 +++--- 5 files changed, 118 insertions(+), 103 deletions(-) diff --git a/paddle/gserver/layers/MKLDNNFcLayer.cpp b/paddle/gserver/layers/MKLDNNFcLayer.cpp index d38e6a209..a08cca318 100644 --- a/paddle/gserver/layers/MKLDNNFcLayer.cpp +++ b/paddle/gserver/layers/MKLDNNFcLayer.cpp @@ -77,6 +77,24 @@ void MKLDNNFcLayer::convertWeightsToPaddle() { wgtVal_->reorderDataTo(wgtVal_, dstFmt, targetDim); } +void MKLDNNFcLayer::convertOutputToOtherDevice() { + copyOutputInfoToOtherDevice(); + // find other cpu device and reorder output to cpu device + int cnt = 0; + for (size_t i = 0; i < outputOtherDevice_.size(); i++) { + if (outputOtherDevice_[i].deviceId == CPU_DEVICE) { + // fc cpu output value do not need convert + // just share point + outputOtherDevice_[i].value = output_.value; + ++cnt; + } + } + + if (cnt > 1) { + LOG(WARNING) << "should not have more than one CPU devie"; + } +} + void MKLDNNFcLayer::reshape() { const Argument& input = getInput(0, getPrev(0)->getDeviceId()); int batchSize = input.getBatchSize(); @@ -116,7 +134,7 @@ void MKLDNNFcLayer::resetFwd() { const MatrixPtr& bias = hasBias ? biases_->getW() : nullptr; const MatrixPtr& out = output_.value; - if (prevIsMKLDNN()) { + if (prevIsOnlyMKLDNN()) { const MatrixPtr& in = getInputValue(0); inVal_ = std::dynamic_pointer_cast(in); CHECK(inVal_) << "Input should be MKLDNNMatrix"; @@ -136,30 +154,21 @@ void MKLDNNFcLayer::resetFwd() { // change original output value to mkldnn output value output_.value = std::dynamic_pointer_cast(outVal_); - if (!nextIsMKLDNN()) { - Argument cpuOutput; - for (size_t i = 0; i < outputOtherDevice_.size(); i++) { - if (outputOtherDevice_[i].deviceId == CPU_DEVICE) { - cpuOutput = outputOtherDevice_[i]; - } - } - cpuOutput.setFrameHeight(output_.getFrameHeight()); - cpuOutput.setFrameWidth(output_.getFrameWidth()); - - // fc cpu output value do not need convert - cpuOutput.value = output_.value; + if (!nextIsOnlyMKLDNN()) { + convertOutputToOtherDevice(); } // create forward handle prop_kind pk = prop_kind::forward; - fc_fwd::desc fwdDesc = - hasBias ? fc_fwd::desc(pk, - inVal_->getMD(), - wgtVal_->getMD(), - biasVal_->getMD(), - outVal_->getMD()) - : fc_fwd::desc( - pk, inVal_->getMD(), wgtVal_->getMD(), outVal_->getMD()); + fc_fwd::desc fwdDesc = hasBias ? fc_fwd::desc(pk, + inVal_->getMemoryDesc(), + wgtVal_->getMemoryDesc(), + biasVal_->getMemoryDesc(), + outVal_->getMemoryDesc()) + : fc_fwd::desc(pk, + inVal_->getMemoryDesc(), + wgtVal_->getMemoryDesc(), + outVal_->getMemoryDesc()); fc_fwd::primitive_desc fwdPD = fc_fwd::primitive_desc(fwdDesc, engine_); if (hasBias) { fwd_.reset(new fc_fwd(fwdPD, *inVal_, *wgtVal_, *biasVal_, *outVal_)); @@ -184,36 +193,38 @@ void MKLDNNFcLayer::resetBwd() { const MatrixPtr& wgt = weight_->getWGrad(); const MatrixPtr& bias = hasBias ? biases_->getWGrad() : nullptr; - // TODO(TJ): merge topdiffs - if (nextIsMKLDNN()) { + // TODO(TJ): merge outgrad + if (nextIsOnlyMKLDNN()) { // can not directly cast outputgrad to mkldnnmatrix, // since each layer can not write the inputgrad to mkldnn inputgrad. // So just create from matrix with outputvalue format. const MatrixPtr& out = getOutput(MKLDNN_DEVICE).grad; - outGrad_ = MKLDNNMatrix::create(out, outVal_->getPD()); + outGrad_ = MKLDNNMatrix::create(out, outVal_->getPrimitiveDesc()); } else { const MatrixPtr& out = getOutput(CPU_DEVICE).grad; // fc do not need to convert from cpu device since output always nc // only need create from cpu device - outGrad_ = MKLDNNMatrix::create(out, outVal_->getPD()); + outGrad_ = MKLDNNMatrix::create(out, outVal_->getPrimitiveDesc()); } - wgtGrad_ = MKLDNNMatrix::create(wgt, wgtVal_->getPD()); - biasGrad_ = hasBias ? MKLDNNMatrix::create(bias, biasVal_->getPD()) : nullptr; + wgtGrad_ = MKLDNNMatrix::create(wgt, wgtVal_->getPrimitiveDesc()); + biasGrad_ = hasBias ? MKLDNNMatrix::create(bias, biasVal_->getPrimitiveDesc()) + : nullptr; // create memory primitive desc fc_fwd::desc fwdDesc = fc_fwd::desc(prop_kind::forward, - inVal_->getMD(), - wgtGrad_->getMD(), - outGrad_->getMD()); + inVal_->getMemoryDesc(), + wgtGrad_->getMemoryDesc(), + outGrad_->getMemoryDesc()); fc_fwd::primitive_desc fwdPD = fc_fwd::primitive_desc(fwdDesc, engine_); - fc_bwdWgt::desc bwdWgtDesc = - hasBias ? fc_bwdWgt::desc(inVal_->getMD(), - wgtGrad_->getMD(), - biasGrad_->getMD(), - outGrad_->getMD()) - : fc_bwdWgt::desc( - inVal_->getMD(), wgtGrad_->getMD(), outGrad_->getMD()); + fc_bwdWgt::desc bwdWgtDesc = hasBias + ? fc_bwdWgt::desc(inVal_->getMemoryDesc(), + wgtGrad_->getMemoryDesc(), + biasGrad_->getMemoryDesc(), + outGrad_->getMemoryDesc()) + : fc_bwdWgt::desc(inVal_->getMemoryDesc(), + wgtGrad_->getMemoryDesc(), + outGrad_->getMemoryDesc()); fc_bwdWgt::primitive_desc bwdWgtPD = fc_bwdWgt::primitive_desc(bwdWgtDesc, engine_, fwdPD); @@ -227,30 +238,20 @@ void MKLDNNFcLayer::resetBwd() { pipelineBwd_.push_back(*bwdWgt_); /// backward data - if (prevIsMKLDNN()) { - const MatrixPtr& in = getInputGrad(0, MKLDNN_DEVICE); - if (in == nullptr) { - return; - } - if (getInput(0, MKLDNN_DEVICE).getAllCount() > 1) { - // TODO(TJ): use outputMaps_ ways when merge topdiff done - } else { - inGrad_ = MKLDNNMatrix::create(in, inVal_->getPD()); - } + int device = prevIsOnlyMKLDNN() ? MKLDNN_DEVICE : CPU_DEVICE; + const MatrixPtr& in = getInputGrad(0, device); + if (in == nullptr) { + return; + } + if (getInput(0, device).getAllCount() > 1) { + // TODO(TJ): use outputMaps_ ways when merge outgrad done } else { - const MatrixPtr& in = getInputGrad(0, CPU_DEVICE); - if (in == nullptr) { - return; - } - if (getInput(0, CPU_DEVICE).getAllCount() > 1) { - // TODO(TJ): use outputMaps_ ways when merge topdiff done - } else { - inGrad_ = MKLDNNMatrix::create(in, inVal_->getPD()); - } + inGrad_ = MKLDNNMatrix::create(in, inVal_->getPrimitiveDesc()); } - fc_bwdData::desc bwdDataDesc = - fc_bwdData::desc(inVal_->getMD(), wgtGrad_->getMD(), outGrad_->getMD()); + fc_bwdData::desc bwdDataDesc = fc_bwdData::desc(inVal_->getMemoryDesc(), + wgtGrad_->getMemoryDesc(), + outGrad_->getMemoryDesc()); fc_bwdData::primitive_desc bwdDataPD = fc_bwdData::primitive_desc(bwdDataDesc, engine_, fwdPD); diff --git a/paddle/gserver/layers/MKLDNNFcLayer.h b/paddle/gserver/layers/MKLDNNFcLayer.h index e2657a8d5..e138a6faf 100644 --- a/paddle/gserver/layers/MKLDNNFcLayer.h +++ b/paddle/gserver/layers/MKLDNNFcLayer.h @@ -72,6 +72,8 @@ protected: * only would be called when needed */ void resetBwd(); + + void convertOutputToOtherDevice() override; }; } // namespace paddle diff --git a/paddle/gserver/layers/MKLDNNLayer.h b/paddle/gserver/layers/MKLDNNLayer.h index 3dd17a36f..8fe9630e8 100644 --- a/paddle/gserver/layers/MKLDNNLayer.h +++ b/paddle/gserver/layers/MKLDNNLayer.h @@ -86,10 +86,7 @@ public: CHECK(FLAGS_use_mkldnn) << "MkldnnLayers only support use_mkldnn." << "Please set WITH_MKLDNN=ON " << "and set use_mkldnn=True"; - if (useGpu_ == true) { - LOG(WARNING) << "Do not support GPU yet, will change to useGpu = false"; - useGpu_ = false; - } + CHECK(!useGpu_) << "Do not support GPU yet"; // set device id before Layer::init setDevice(MKLDNN_DEVICE); @@ -116,6 +113,12 @@ public: */ virtual void convertWeightsToPaddle() {} + /** + * convert MKLDNN output to other device. + * only support CPU device yet + */ + virtual void convertOutputToOtherDevice() {} + /** * print info about sizes */ @@ -147,22 +150,25 @@ public: protected: /** - * If next layer only has MKLDNN type. - * Otherwise, only support otherdevice CPU device. + * copy image size and sequence info to other device */ - bool nextIsMKLDNN() { + void copyOutputInfoToOtherDevice() { for (size_t i = 0; i < outputOtherDevice_.size(); i++) { - CHECK_EQ(outputOtherDevice_[i].deviceId, CPU_DEVICE) - << "Only support other device is CPU yet"; + outputOtherDevice_[i].setFrameHeight(output_.getFrameHeight()); + outputOtherDevice_[i].setFrameWidth(output_.getFrameWidth()); + outputOtherDevice_[i].sequenceStartPositions = + output_.sequenceStartPositions; + outputOtherDevice_[i].subSequenceStartPositions = + output_.subSequenceStartPositions; + outputOtherDevice_[i].cpuSequenceDims = output_.cpuSequenceDims; } - return outputOtherDevice_.size() == 0; } /** - * Is previous layer MKLDNN type. - * Otherwise, only support otherdevice CPU device. + * Is previous layer only has MKLDNN type. + * Otherwise, only support the previous layer using CPU device. */ - bool prevIsMKLDNN(int index = 0) { + bool prevIsOnlyMKLDNN(int index = 0) { int prevDevice = getPrev(index)->getDeviceId(); if (prevDevice == MKLDNN_DEVICE) { return true; @@ -173,11 +179,23 @@ protected: } } + /** + * If output only has MKLDNN device. + * Otherwise, other devices should only using CPU device. + */ + bool nextIsOnlyMKLDNN() { + for (size_t i = 0; i < outputOtherDevice_.size(); i++) { + CHECK_EQ(outputOtherDevice_[i].deviceId, CPU_DEVICE) + << "Only support other device is CPU yet"; + } + return outputOtherDevice_.size() == 0; + } + /** * Sync input value data */ void syncInputValue() { - if (prevIsMKLDNN()) { + if (prevIsOnlyMKLDNN()) { return; } real* iData = getInputValue(0, CPU_DEVICE)->getData(); @@ -190,7 +208,7 @@ protected: * Sync output grad data */ void syncOutputGrad() { - if (nextIsMKLDNN()) { + if (nextIsOnlyMKLDNN()) { return; } diff --git a/paddle/math/MKLDNNMatrix.cpp b/paddle/math/MKLDNNMatrix.cpp index 32ae3b1bc..0a355e264 100644 --- a/paddle/math/MKLDNNMatrix.cpp +++ b/paddle/math/MKLDNNMatrix.cpp @@ -31,7 +31,6 @@ MKLDNNMatrixPtr MKLDNNMatrix::create(MatrixPtr m, memory::primitive_desc pd) { if (m == nullptr) { size_t height = dims[0]; size_t width = cnts / dims[0]; - // LOG(INFO) << height << "," << width; m = Matrix::create(height, width, false, false); } @@ -40,10 +39,8 @@ MKLDNNMatrixPtr MKLDNNMatrix::create(MatrixPtr m, memory::primitive_desc pd) { CHECK(cpuMatrix) << "Only support create from CPU matrix yet"; CHECK_EQ(cnts, m->getElementCnt()) << "Count size does not match"; - size_t width = m->getWidth(); - size_t height = m->getHeight(); - real* data = m->getData(); - return std::make_shared(data, height, width, pd); + return std::make_shared( + m->getData(), m->getHeight(), m->getWidth(), pd); } MKLDNNMatrixPtr MKLDNNMatrix::create(MatrixPtr m, @@ -51,9 +48,7 @@ MKLDNNMatrixPtr MKLDNNMatrix::create(MatrixPtr m, memory::format fmt, engine& eg, mkldnn::memory::data_type dtype) { - memory::desc md = memory::desc(dims, dtype, fmt); - memory::primitive_desc pd = memory::primitive_desc(md, eg); - return create(m, pd); + return create(m, memory::primitive_desc(memory::desc(dims, dtype, fmt), eg)); } void MKLDNNMatrix::reorderDataFrom(const MKLDNNMatrixPtr& m, @@ -64,9 +59,7 @@ void MKLDNNMatrix::reorderDataFrom(const MKLDNNMatrixPtr& m, return; } CHECK_EQ(getElementCnt(), m->getElementCnt()) << "size should equal"; - real* srcData = getData(); - real* dstData = m->getData(); - reorderOnce(srcData, dstData, srcFmt, dstFmt, targetDim); + reorderOnce(getData(), m->getData(), srcFmt, dstFmt, targetDim); } void MKLDNNMatrix::reorderDataTo(const MKLDNNMatrixPtr& m, @@ -77,9 +70,7 @@ void MKLDNNMatrix::reorderDataTo(const MKLDNNMatrixPtr& m, return; } CHECK_EQ(getElementCnt(), m->getElementCnt()) << "size should equal"; - real* srcData = getData(); - real* dstData = m->getData(); - reorderOnce(srcData, dstData, srcFmt, dstFmt, targetDim); + reorderOnce(getData(), m->getData(), srcFmt, dstFmt, targetDim); } void MKLDNNMatrix::reorderOnce(void* srcData, @@ -120,8 +111,9 @@ void MKLDNNMatrix::downSpatial() { return; } - memory::dims srcDims = getDims(); + // TODO(TJ): change H(height) and W(width) if support nhwc or more const int H = 2, W = 3; + memory::dims srcDims = getDims(); if (srcDims[H] != 1 || srcDims[W] != 1) { // can not down spatial return; @@ -141,13 +133,12 @@ void MKLDNNMatrix::downSpatial() { } memory::desc md = memory::desc(dstDims, getDtype(), dstFmt); memory::primitive_desc pd = memory::primitive_desc(md, getEngine()); - void* data = getData(); mkldnn_primitive_t result; mkldnn::error::wrap_c_api( mkldnn_primitive_create(&result, pd.get(), nullptr, nullptr), "could not create a memory primitive"); reset(result); - set_data_handle(data); + set_data_handle(getData()); } } // namespace paddle diff --git a/paddle/math/MKLDNNMatrix.h b/paddle/math/MKLDNNMatrix.h index ea3fd7d46..e50f698b4 100644 --- a/paddle/math/MKLDNNMatrix.h +++ b/paddle/math/MKLDNNMatrix.h @@ -56,9 +56,9 @@ public: public: /** * Reorder this MKLDNNMatrix from other format. - * Support inplace reorder - * Pay attention: this function would only reorder the data layout. - * will NOT change this original dim or format info + * Support inplace reorder. + * @note: this function would only reorder the data layout. + * will NOT change this original dim or format info */ void reorderDataFrom(const MKLDNNMatrixPtr& m, memory::format srcFmt, @@ -66,9 +66,9 @@ public: /** * Reorder this MKLDNNMatrix to other format. - * Support inplace reorder - * Pay attention: this function would only reorder the data layout. - * will NOT change the dst dim or format info + * Support inplace reorder. + * @note: this function would only reorder the data layout. + * will NOT change the dst dim or format info */ void reorderDataTo(const MKLDNNMatrixPtr& m, memory::format dstFmt, @@ -90,18 +90,20 @@ public: /** * Get primitive descriptor. */ - mkldnn::memory::primitive_desc getPD() { return this->get_primitive_desc(); } + mkldnn::memory::primitive_desc getPrimitiveDesc() { + return this->get_primitive_desc(); + } /** * Get memory descriptor. */ - mkldnn::memory::desc getMD() { return getPD().desc(); } + mkldnn::memory::desc getMemoryDesc() { return getPrimitiveDesc().desc(); } /** * Get dimensions. */ mkldnn::memory::dims getDims() { - mkldnn::memory::desc md = getMD(); + mkldnn::memory::desc md = getMemoryDesc(); const int* src = md.data.dims; int ndims = md.data.ndims; mkldnn::memory::dims dst; @@ -116,24 +118,25 @@ public: * Get format. */ mkldnn::memory::format getFormat() { - return (mkldnn::memory::format)(getMD().data.format); + return (mkldnn::memory::format)(getMemoryDesc().data.format); } /** * Get memory data type. */ mkldnn::memory::data_type getDtype() { - return (mkldnn::memory::data_type)(getMD().data.data_type); + return (mkldnn::memory::data_type)(getMemoryDesc().data.data_type); } /** * Get engine. */ - mkldnn::engine getEngine() { return getPD().get_engine(); } + mkldnn::engine getEngine() { return getPrimitiveDesc().get_engine(); } protected: /** - * Do once reorder supported inplace. + * Do reorder once. + * Can support inplace. */ void reorderOnce(void* srcData, void* dstData, -- GitLab From fb38e6620e77ceeb218ef65b05ef2d73ba158796 Mon Sep 17 00:00:00 2001 From: Liu Yiqun Date: Tue, 29 Aug 2017 19:56:18 +0800 Subject: [PATCH 0271/2018] Seperate the codes that cannot and don't need to build for iOS devices. --- CMakeLists.txt | 11 ++++-- cmake/cross_compiling/ios.cmake | 2 ++ paddle/CMakeLists.txt | 8 +++-- paddle/capi/CMakeLists.txt | 56 +++++++++++++++---------------- paddle/utils/Excepts.h | 3 +- paddle/utils/arch/linux/Locks.cpp | 6 ++++ paddle/utils/arch/osx/Excepts.cpp | 3 +- 7 files changed, 53 insertions(+), 36 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index f0a01680a..e3dec9b21 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -167,11 +167,16 @@ if(USE_NNPACK) list(APPEND EXTERNAL_LIBS ${NNPACK_LIBS}) endif(USE_NNPACK) +message(STATUS "CMAKE_C_FLAGS: ${CMAKE_C_FLAGS}") +message(STATUS "CMAKE_CXX_FLAGS: ${CMAKE_CXX_FLAGS}") + add_subdirectory(proto) -# "add_subdirectory(go)" should be placed after the following loine, -# because it depends on paddle/optimizer. -add_subdirectory(paddle/optimizer) +if(NOT ANDROID AND NOT IOS) + # "add_subdirectory(go)" should be placed after the following loine, + # because it depends on paddle/optimizer. + add_subdirectory(paddle/optimizer) +endif() # "add_subdirectory(paddle)" and "add_subdirectory(python)" should be # placed after this block, because they depends on it. diff --git a/cmake/cross_compiling/ios.cmake b/cmake/cross_compiling/ios.cmake index 135104e69..dbdf29e1d 100644 --- a/cmake/cross_compiling/ios.cmake +++ b/cmake/cross_compiling/ios.cmake @@ -289,6 +289,8 @@ set(CMAKE_FIND_ROOT_PATH_MODE_INCLUDE ONLY) message(STATUS "iOS: Targeting iOS '${CMAKE_SYSTEM_VERSION}', " "building for '${IOS_PLATFORM}' platform, with architecture '${CMAKE_OSX_ARCHITECTURES}'") +message(STATUS "System CMAKE_C_FLAGS: ${CMAKE_C_FLAGS}") +message(STATUS "System CMAKE_CXX_FLAGS: ${CMAKE_CXX_FLAGS}") # This little macro lets you set any XCode specific property macro(set_xcode_property TARGET XCODE_PROPERTY XCODE_VALUE) diff --git a/paddle/CMakeLists.txt b/paddle/CMakeLists.txt index cf61a243e..b5ad888f0 100644 --- a/paddle/CMakeLists.txt +++ b/paddle/CMakeLists.txt @@ -5,8 +5,12 @@ add_subdirectory(testing) add_subdirectory(math) add_subdirectory(parameter) add_subdirectory(gserver) -add_subdirectory(pserver) -add_subdirectory(trainer) + +if(NOT ANDROID AND NOT IOS) + add_subdirectory(pserver) + add_subdirectory(trainer) +endif() + add_subdirectory(scripts) add_subdirectory(string) diff --git a/paddle/capi/CMakeLists.txt b/paddle/capi/CMakeLists.txt index 11022d175..5787b6357 100644 --- a/paddle/capi/CMakeLists.txt +++ b/paddle/capi/CMakeLists.txt @@ -31,42 +31,40 @@ add_dependencies(paddle_capi paddle_proto) # combine all paddle static libraries together, into libpaddle_capi_whole.a # user should use PaddleCAPI as -lpaddle_capi_whole -set(capi_whole_library libpaddle_capi_whole.a) -add_custom_target(paddle_capi_whole ALL - COMMAND mkdir -p o_files/capi && cd o_files/capi/ && ar -x $ - COMMAND mkdir -p o_files/utils && cd o_files/utils/ && ar -x $ - COMMAND mkdir -p o_files/parameter && cd o_files/parameter/ && ar -x $ - COMMAND mkdir -p o_files/math && cd o_files/math/ && ar -x $ - COMMAND mkdir -p o_files/cuda && cd o_files/cuda/ && ar -x $ - COMMAND mkdir -p o_files/function && cd o_files/function/ && ar -x $ - COMMAND mkdir -p o_files/gserver && cd o_files/gserver/ && ar -x $ - COMMAND mkdir -p o_files/proto && cd o_files/proto/ && ar -x $ - COMMAND mkdir -p o_files/network && cd o_files/network/ && ar -x $ - COMMAND mkdir -p o_files/pserver && cd o_files/pserver/ && ar -x $ - COMMAND ar crs ${capi_whole_library} `find ./o_files -name '*.o'` - COMMAND rm -rf o_files - WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR} - DEPENDS paddle_capi paddle_utils paddle_parameter paddle_math - paddle_cuda paddle_function paddle_gserver - paddle_proto paddle_pserver paddle_network - ) -set_target_properties(paddle_capi_whole - PROPERTIES IMPORTED_LOCATION ${CMAKE_CURRENT_BINARY_DIR}/${capi_whole_library}) +set(PADDLE_INFER_LIBS + paddle_utils + paddle_parameter + paddle_math + paddle_cuda + paddle_function + paddle_gserver + paddle_proto + ) -add_library(paddle_capi_shared SHARED ${CAPI_SOURCES}) -target_include_directories(paddle_capi_shared PUBLIC ${CMAKE_CURRENT_BINARY_DIR}) -link_paddle_exe(paddle_capi_shared) +set(PADDLE_TRAIN_LIBS paddle_pserver paddle_network) + +cc_library(paddle_capi_whole DEPS paddle_capi ${PADDLE_INFER_LIBS}) + +# No shared library for iOS +if(NOT IOS) + add_library(paddle_capi_shared SHARED ${CAPI_SOURCES}) + target_include_directories(paddle_capi_shared PUBLIC ${CMAKE_CURRENT_BINARY_DIR}) + link_paddle_exe(paddle_capi_shared) +endif() # install library & headers. install(FILES ${CAPI_HEADERS} DESTINATION include/paddle) install(FILES ${CMAKE_CURRENT_BINARY_DIR}/config.h DESTINATION include/paddle) if(ANDROID) - install(FILES ${CMAKE_CURRENT_BINARY_DIR}/${capi_whole_library} - DESTINATION lib/${ANDROID_ABI}) - install(TARGETS paddle_capi_shared DESTINATION lib/${ANDROID_ABI}) + install(TARGETS paddle_capi_whole paddle_capi_shared + ARCHIVE DESTINATION lib/${ANDROID_ABI} + LIBRARY DESTINATION lib/${ANDROID_ABI}) else(ANDROID) - install(FILES ${CMAKE_CURRENT_BINARY_DIR}/${capi_whole_library} DESTINATION lib) - install(TARGETS paddle_capi_shared DESTINATION lib) + install(TARGETS paddle_capi_whole + ARCHIVE DESTINATION lib) + if(NOT IOS) + install(TARGETS paddle_capi_shared DESTINATION lib) + endif() endif(ANDROID) # this variable used for unittest diff --git a/paddle/utils/Excepts.h b/paddle/utils/Excepts.h index 5c2c504f5..0add66da7 100644 --- a/paddle/utils/Excepts.h +++ b/paddle/utils/Excepts.h @@ -17,7 +17,8 @@ limitations under the License. */ #include -#if defined(__APPLE__) || defined(__OSX__) +#if (defined(__APPLE__) || defined(__OSX__)) && !defined(__arm__) && \ + !defined(__aarch64__) int fegetexcept(void); int feenableexcept(unsigned int excepts); diff --git a/paddle/utils/arch/linux/Locks.cpp b/paddle/utils/arch/linux/Locks.cpp index 3a0903d1f..a4e6c8f7b 100644 --- a/paddle/utils/arch/linux/Locks.cpp +++ b/paddle/utils/arch/linux/Locks.cpp @@ -40,6 +40,8 @@ void Semaphore::wait() { sem_wait(&m->sem); } void Semaphore::post() { sem_post(&m->sem); } +/// SpinLockPrivate + #ifdef PADDLE_USE_PTHREAD_SPINLOCK class SpinLockPrivate { @@ -79,6 +81,8 @@ SpinLock::~SpinLock() { delete m; } void SpinLock::lock() { m->lock(); } void SpinLock::unlock() { m->unlock(); } +/// ThreadBarrierPrivate + #ifdef PADDLE_USE_PTHREAD_BARRIER class ThreadBarrierPrivate { @@ -136,6 +140,8 @@ public: #endif +/// ThreadBarrier + ThreadBarrier::ThreadBarrier(int count) : m(new ThreadBarrierPrivate(count)) {} ThreadBarrier::~ThreadBarrier() { delete m; } void ThreadBarrier::wait() { m->wait(); } diff --git a/paddle/utils/arch/osx/Excepts.cpp b/paddle/utils/arch/osx/Excepts.cpp index c8e904d8f..42ecaa06d 100644 --- a/paddle/utils/arch/osx/Excepts.cpp +++ b/paddle/utils/arch/osx/Excepts.cpp @@ -14,7 +14,8 @@ limitations under the License. */ #include "paddle/utils/Excepts.h" -#if defined(__APPLE__) || defined(__OSX__) +#if (defined(__APPLE__) || defined(__OSX__)) && !defined(__arm__) && \ + !defined(__aarch64__) int fegetexcept(void) { static fenv_t fenv; -- GitLab From d2435ba81cc98244ba845142a798fd913c172c89 Mon Sep 17 00:00:00 2001 From: Liu Yiqun Date: Tue, 29 Aug 2017 20:08:00 +0800 Subject: [PATCH 0272/2018] Disable the finding of python, swig related packages completely when WITH_PYTHON and WITH_SWIG_PY are set to OFF. --- cmake/external/python.cmake | 27 ++++++++++++--------------- cmake/external/swig.cmake | 4 ++++ 2 files changed, 16 insertions(+), 15 deletions(-) diff --git a/cmake/external/python.cmake b/cmake/external/python.cmake index 53ef7cd29..46c68cce3 100644 --- a/cmake/external/python.cmake +++ b/cmake/external/python.cmake @@ -12,16 +12,17 @@ # See the License for the specific language governing permissions and # limitations under the License. -INCLUDE(ExternalProject) +IF(NOT WITH_PYTHON) + return() +ENDIF() + INCLUDE(python_module) -FIND_HOST_PACKAGE(PythonInterp 2.7) -IF(WITH_PYTHON) - FIND_HOST_PACKAGE(PythonLibs 2.7) - # Fixme: Maybe find a static library. Get SHARED/STATIC by FIND_PACKAGE. - ADD_LIBRARY(python SHARED IMPORTED GLOBAL) - SET_PROPERTY(TARGET python PROPERTY IMPORTED_LOCATION ${PYTHON_LIBRARIES}) -ENDIF(WITH_PYTHON) +FIND_PACKAGE(PythonInterp 2.7) +FIND_PACKAGE(PythonLibs 2.7) +# Fixme: Maybe find a static library. Get SHARED/STATIC by FIND_PACKAGE. +ADD_LIBRARY(python SHARED IMPORTED GLOBAL) +SET_PROPERTY(TARGET python PROPERTY IMPORTED_LOCATION ${PYTHON_LIBRARIES}) SET(py_env "") IF(PYTHONINTERP_FOUND) @@ -29,16 +30,12 @@ IF(PYTHONINTERP_FOUND) find_python_module(numpy REQUIRED) find_python_module(wheel REQUIRED) find_python_module(google.protobuf REQUIRED) - FIND_HOST_PACKAGE(NumPy REQUIRED) + FIND_PACKAGE(NumPy REQUIRED) IF(${PY_GOOGLE.PROTOBUF_VERSION} AND ${PY_GOOGLE.PROTOBUF_VERSION} VERSION_LESS "3.0.0") MESSAGE(FATAL_ERROR "Found Python Protobuf ${PY_GOOGLE.PROTOBUF_VERSION} < 3.0.0, " "please use pip to upgrade protobuf. pip install -U protobuf") ENDIF() ENDIF(PYTHONINTERP_FOUND) -IF(WITH_PYTHON) - INCLUDE_DIRECTORIES(${PYTHON_INCLUDE_DIR}) - INCLUDE_DIRECTORIES(${PYTHON_NUMPY_INCLUDE_DIR}) -ELSE() - SET(PYTHON_LIBRARIES "") -ENDIF() +INCLUDE_DIRECTORIES(${PYTHON_INCLUDE_DIR}) +INCLUDE_DIRECTORIES(${PYTHON_NUMPY_INCLUDE_DIR}) diff --git a/cmake/external/swig.cmake b/cmake/external/swig.cmake index 744c766ee..ce088ae7e 100644 --- a/cmake/external/swig.cmake +++ b/cmake/external/swig.cmake @@ -12,6 +12,10 @@ # See the License for the specific language governing permissions and # limitations under the License. +IF(NOT WITH_SWIG_PY) + return() +ENDIF() + FIND_PACKAGE(SWIG) IF(NOT SWIG_FOUND) -- GitLab From 95b41bec3ea3ff6d95463b2564ae981b9d688fca Mon Sep 17 00:00:00 2001 From: Yi Wang Date: Tue, 29 Aug 2017 11:44:29 -0700 Subject: [PATCH 0273/2018] Add LoD Tensor design doc --- paddle/framework/lod_tensor.md | 122 +++++++++++++++++++++++++++++++++ 1 file changed, 122 insertions(+) create mode 100644 paddle/framework/lod_tensor.md diff --git a/paddle/framework/lod_tensor.md b/paddle/framework/lod_tensor.md new file mode 100644 index 000000000..a9a1a113d --- /dev/null +++ b/paddle/framework/lod_tensor.md @@ -0,0 +1,122 @@ +# LoD (Level-of-Detail) Tensor + +PaddlePaddle's RNN doesn't require that all instances have the same length. To do so, we introduce an extension to Tensor, namely, LoD Tensor. + +## Challenge of Variable-length Inputs + +People usually represent a mini-batch by a Tensor. For example, a mini-batch of 32 images, each of size 32x32, is a 10x32x32 Tensor. So a transformation, T, of all images can be a matrix multiplication of the 32x32xO-dimensional tensor T and the 10x32x32 Tensor. + +Another example is that each mini-batch contains 32 sentences, where each word is a D-dimensional one-hot vector. If all sentences have the same length L, we can represent this mini-batch by a 32xLxD tensor. However, in most cases, sentences have variable lengths, and we will need an index data structure to record these variable lengths. + +## LoD as a Solution + +### Mini-Batch of variable-length sentenses + +Let's imagine a mini-batch of 3 variable lengths sentences, containing 3, 1, and 2 words respectively. We can represent it by a (3+1+2)xD tensor plus some index information: + +``` + 3 +3 1 2 +||| | || +``` + +Each `|` represents a D-dimensional word vectors. The number 3 on top indicate 3 sentences, and numbers 3, 1, and 2 on the second level represent the number of words in each sentence. + +### Mini-Batch of variable-length videos + +This approach generalizes to the case where elements are not words, but higher dimensional objects, like images. Suppose that a mini-batch contains videos of the same frame size 640x480. If a mini-batch contains 3 videos of 3, 1, and 2 frames respectively. The underlying tensor is of size (3+1+2)x640x480. The index information illustrates as: + +``` + 3 +3 1 2 +口口口 口 口口 +``` + +where each `口` represents an image. + +### Mini-Batch of fixed-size images + +Let's get back to a typical example, image classification, where each mini-batch has M fixed-sized images. The LoD Tensor representation is + +``` + M +1 1 1 1 1 +口口口口 ... 口 +``` + +The many 1's on the second level seem duplicated. For this particular case of 2 levels and the second level always have length 1, we can ignore the LoD index. + +### Design and summarization + +In summary, as long as that the essential elements (words or images) have the same size, we can represent mini-batches by a LoD Tensor: + +- The underlying tensor has size LxD1xD2x..., where D1xD2... is the size of the essential elements, and +- the first dimension size L has an additon property -- a LoD index as a nested vector: + + ```c++ + typedef std::vector > LoD; + ``` + +- The LoD index can is not necessary when there are only two levels and all elements of the second level have length 1. + +## Slicing of LoD Tensor + +Consider that we have a network with three levels of RNN: the top level one handles articles, the second level one handles sentences, and the basic level one handles words. This network requires that mini-batches represented by 4 level LoD Tensor, for example, + +``` + 3 +3 1 2 +3 2 4 1 2 3 +||| || |||| | || ||| +``` + +To allow each level of RNN to handle its input, we define **the slicing of a LoD Tensor is defined as getting the j-th sequence on level i, or the -slice** + +For example, the <2,1>-slice of above slice is + +``` +2 +|| +``` + +and the <1,2>-slice of above example is + +``` +2 +2 3 +|| ||| +``` + +Let's go on slicing this slice. Its <1,1>-slice is + +``` +3 +||| +``` + +### The General Slicing Algorithm + +The algorithm, with over-simplified data structure, is defined as + +```c++ +typedef vector > LoD; + +struct LoDTensor { + LoD lod_; + float* tensor_; +}; + +LoDTensor Slice(const LoDTensor& lodt, int level, int sequence) { + +} +``` + +### Slicing the Top Level + +Please be aware that an RNN operator only slices the top level of a LoD Tensor to get the step inputs. + +```c++ +LoDTensor Slice(const LoDTensor& lodt, int sequence) { + +} +``` -- GitLab From f6457e650192bfb1f5ac886585c17d2557f7f604 Mon Sep 17 00:00:00 2001 From: Yi Wang Date: Tue, 29 Aug 2017 11:44:41 -0700 Subject: [PATCH 0274/2018] Add LoD Tensor design doc --- paddle/framework/lod_tensor.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paddle/framework/lod_tensor.md b/paddle/framework/lod_tensor.md index a9a1a113d..8dfe3ee82 100644 --- a/paddle/framework/lod_tensor.md +++ b/paddle/framework/lod_tensor.md @@ -1,4 +1,4 @@ -# LoD (Level-of-Detail) Tensor +# Design Doc: LoD (Level-of-Detail) Tensor PaddlePaddle's RNN doesn't require that all instances have the same length. To do so, we introduce an extension to Tensor, namely, LoD Tensor. -- GitLab From 46034faf97e7a135756d36391f5c4a970fed92ab Mon Sep 17 00:00:00 2001 From: Helin Wang Date: Tue, 29 Aug 2017 16:05:10 -0700 Subject: [PATCH 0275/2018] Design doc: operator based parameter server. --- doc/design/ops/dist_train.md | 82 +++++++++++++++++++++++++ doc/design/ops/src/dist-graph.graffle | Bin 0 -> 4915 bytes doc/design/ops/src/dist-graph.png | Bin 0 -> 133866 bytes doc/design/ops/src/local-graph.graffle | Bin 0 -> 2515 bytes doc/design/ops/src/local-graph.png | Bin 0 -> 31493 bytes 5 files changed, 82 insertions(+) create mode 100644 doc/design/ops/dist_train.md create mode 100644 doc/design/ops/src/dist-graph.graffle create mode 100644 doc/design/ops/src/dist-graph.png create mode 100644 doc/design/ops/src/local-graph.graffle create mode 100644 doc/design/ops/src/local-graph.png diff --git a/doc/design/ops/dist_train.md b/doc/design/ops/dist_train.md new file mode 100644 index 000000000..0380826b0 --- /dev/null +++ b/doc/design/ops/dist_train.md @@ -0,0 +1,82 @@ +# Design Doc: Operation Graph Based Parameter Server + +## Abstract + +We propose an approach to implment the parameter server. In this +approach, there is no fundimental difference between the trainer and +the parameter server: they both run sub-graphs, but sub-graphs of +different purposes. + +## Background + +The previous implementations of the parameter server does not run a +sub-graph. parameter initialization, optimizer computation, network +communication and checkpointing are implemented twice on both the +trainer and the parameter server. + +It would be great if we can write code once and use them on both the +trainer and the parameter server: reduces code duplication and +improves extensibility. Given during the current refactor, we are +representing everything as a computing graph on the +trainer. Representing everything as a computing graph on the parameter +server becomes a natural extension. + +## Design + +### Graph Converter + +The *graph converter* converts user-defined operation (OP) graph into +sub-graphs to be scheduled on different nodes. + +1. The user-defined OP graph will be cut into sub-graphs of +different purposes (e.g., trainer, parameter server) to run on +different workers. + +1. OPs will be added to the subgraphs, so the subgraphs can +communicate with each other. We will need these OPs: *send*, *recv*, +*gradient accumulator*, *string accumulator*, *loop forever*. + +Below is an example of converting the user defined graph to the +sub-graphs for the trainer and the parameter server: + + + +After converting: + + + +1. The parameter variable W and it's optimizer subgraph are placed on the parameter server. +1. Operators are added to the sub-graphs. + - *send* operator sends data and sender's address to the destination. + - *recv* operator receives data and sender's address from the + destination. It will block until data has been received. + - *gradient accumulator* operator accumulates *N* pieces of + gradients. N=1 in Async-SGD, N>1 in Sync-SGD. + - *string accumulator* accumulates *N* pieces of strings into a + list of strings. N=1 in Async-SGD, N>1 in Sync-SGD. + - *loop forever* runs itself as a target forever. + +### Benefits + +- Model parallelism become easier to implement: it's an extension to + the trainer - parameter server approach. we already have the + communication OPs, but need to extend the graph converter. + +- User-defined optimizer is easier to add - user can now express it as + a subgraph. + +- No more duplication logic inside the trainer and the parameter + server in the background section. + +### Challenges + +- It might be hard for the graph converter to cut a general graph + (without any hint for which sub-graph is the optimizer). We may need + to label which sub-graph inside the OP graph is the optimizer. + +- It's important to balance the parameter shards of on multiple + parameter server. 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zs8CZvSIs}5Jg818EAxiYT~6X|fB$~n6L|@P!C%teVnu$VVq#*dsI2U*SB<5vtf*jO zW?rx`ir+zf@h*k~!N(!j*VlP@dF$)z&!0c9`*3~NY1=~78V9S-D$Q`epffN@P_w(~x zSy_Q`zv}Ahl!|yfK2D{jp&?G5=H!C_H8u=B*HS%B`xVJe!QS3JUips@Z%S$^9D(q^ z+1!WQkwHvxl9<`DC!5lD_wWi+&y)X67&q}P*_8ffsJv^QQ_D}>(^%iy+WP+e`{w56 zuV26B=3I3=Q&-kz0~7a7dxVR6Y6#pA57ODSd%U)H?-5C|hTa#0g|lBskbN#Pd)%A= zr@t6=A=cK1|A8R+noA*w_e9HN5O6IrSuyzHkpgb`5z`Gn+t)0!l3#laLHxhJ{(tby vd}Q4_JiOrGQ2Ht3Dw=$?NEfJtmTn>KdmR3r Date: Tue, 29 Aug 2017 16:07:26 -0700 Subject: [PATCH 0276/2018] change image size --- doc/design/ops/dist_train.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/doc/design/ops/dist_train.md b/doc/design/ops/dist_train.md index 0380826b0..0bc350d8c 100644 --- a/doc/design/ops/dist_train.md +++ b/doc/design/ops/dist_train.md @@ -39,11 +39,11 @@ communicate with each other. We will need these OPs: *send*, *recv*, Below is an example of converting the user defined graph to the sub-graphs for the trainer and the parameter server: - + After converting: - + 1. The parameter variable W and it's optimizer subgraph are placed on the parameter server. 1. Operators are added to the sub-graphs. -- GitLab From 75856ec3370358b0c182e03095032a01c8202fa7 Mon Sep 17 00:00:00 2001 From: Helin Wang Date: Tue, 29 Aug 2017 16:17:49 -0700 Subject: [PATCH 0277/2018] fix typo --- doc/design/ops/dist_train.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/doc/design/ops/dist_train.md b/doc/design/ops/dist_train.md index 0bc350d8c..8e92c87a5 100644 --- a/doc/design/ops/dist_train.md +++ b/doc/design/ops/dist_train.md @@ -2,8 +2,8 @@ ## Abstract -We propose an approach to implment the parameter server. In this -approach, there is no fundimental difference between the trainer and +We propose an approach to implement the parameter server. In this +approach, there is no fundamental difference between the trainer and the parameter server: they both run sub-graphs, but sub-graphs of different purposes. @@ -16,7 +16,7 @@ trainer and the parameter server. It would be great if we can write code once and use them on both the trainer and the parameter server: reduces code duplication and -improves extensibility. Given during the current refactor, we are +improves extensibility. Given that after the current refactor, we are representing everything as a computing graph on the trainer. Representing everything as a computing graph on the parameter server becomes a natural extension. @@ -25,8 +25,8 @@ server becomes a natural extension. ### Graph Converter -The *graph converter* converts user-defined operation (OP) graph into -sub-graphs to be scheduled on different nodes. +The *graph converter* converts the user-defined operation (OP) graph +into sub-graphs to be scheduled on different nodes. 1. The user-defined OP graph will be cut into sub-graphs of different purposes (e.g., trainer, parameter server) to run on @@ -66,7 +66,7 @@ After converting: a subgraph. - No more duplication logic inside the trainer and the parameter - server in the background section. + server mentioned in the background section. ### Challenges -- GitLab From 34f4f763f9cf52d6c6326613ed839d00ac7c6eb0 Mon Sep 17 00:00:00 2001 From: chengduo Date: Wed, 30 Aug 2017 10:19:08 +0800 Subject: [PATCH 0278/2018] Update networks.py --- python/paddle/trainer_config_helpers/networks.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/python/paddle/trainer_config_helpers/networks.py b/python/paddle/trainer_config_helpers/networks.py index 28a71cf78..34be203ee 100644 --- a/python/paddle/trainer_config_helpers/networks.py +++ b/python/paddle/trainer_config_helpers/networks.py @@ -1406,7 +1406,7 @@ def inputs(layers, *args): if len(args) != 0: layers.extend(args) - Inputs(* [l.name for l in layers]) + Inputs(*[l.name for l in layers]) def outputs(layers, *args): @@ -1456,7 +1456,7 @@ def outputs(layers, *args): assert len(layers) > 0 if HasInputsSet(): # input already set - Outputs(* [l.name for l in layers]) + Outputs(*[l.name for l in layers]) return # just return outputs. if len(layers) != 1: -- GitLab From 168707caddf9c0ed67a2d87074a5f05b7a63a5c9 Mon Sep 17 00:00:00 2001 From: hedaoyuan Date: Wed, 30 Aug 2017 11:35:19 +0800 Subject: [PATCH 0279/2018] Fix a small bug. --- paddle/gserver/layers/ExpandConvLayer.cpp | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/paddle/gserver/layers/ExpandConvLayer.cpp b/paddle/gserver/layers/ExpandConvLayer.cpp index 0e8458176..20de475fc 100644 --- a/paddle/gserver/layers/ExpandConvLayer.cpp +++ b/paddle/gserver/layers/ExpandConvLayer.cpp @@ -66,7 +66,11 @@ bool ExpandConvLayer::init(const LayerMap &layerMap, // If depth wise convolution and useGpu == false and ARM-NEON if (!useGpu_ && isDepthwiseConv(channels_[i], groups_[i]) && !isDeconv_) { #if defined(__ARM_NEON__) || defined(__ARM_NEON) - convType = "NeonDepthwiseConv"; + if ((filterSize_[i] == filterSizeY_[i]) && + (filterSize_[i] == 3 || filterSize_[i] == 4) && + (stride_[i] == strideY_[i]) && (stride_[i] == 1 || stride_[i] == 2)) { + convType = "NeonDepthwiseConv"; + } #endif } -- GitLab From 6efbe2ff43be576c64962f94f6fcf453ef0dd8a7 Mon Sep 17 00:00:00 2001 From: hedaoyuan Date: Wed, 30 Aug 2017 12:03:49 +0800 Subject: [PATCH 0280/2018] Merge im2col functor. --- paddle/operators/math/im2col.cc | 215 ++++++++++++++++++++ paddle/operators/math/im2col.cu | 334 ++++++++++++++++++++++++++++++++ paddle/operators/math/im2col.h | 86 ++++++++ 3 files changed, 635 insertions(+) create mode 100644 paddle/operators/math/im2col.cc create mode 100644 paddle/operators/math/im2col.cu create mode 100644 paddle/operators/math/im2col.h diff --git a/paddle/operators/math/im2col.cc b/paddle/operators/math/im2col.cc new file mode 100644 index 000000000..dafb21b33 --- /dev/null +++ b/paddle/operators/math/im2col.cc @@ -0,0 +1,215 @@ +/* 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 "Im2Col.h" + +namespace paddle { + +/* + * imShape = [inputChannels, inputHeight, inputWidth] + * colShape = + * [inputChannels, filterHeight, filterWidth, outputHeight, outputWidth] + */ +template +class Im2ColFunctor { + public: + void operator()(const T* imData, const TensorShape& imShape, T* colData, + const TensorShape& colShape, int strideHeight, + int strideWidth, int paddingHeight, int paddingWidth) { + int inputChannels = imShape[0]; + int inputHeight = imShape[1]; + int inputWidth = imShape[2]; + int filterHeight = colShape[1]; + int filterWidth = colShape[2]; + int outputHeight = colShape[3]; + int outputWidth = colShape[4]; + int channelsCol = inputChannels * filterHeight * filterWidth; + + for (int c = 0; c < channelsCol; ++c) { + int wOffset = c % filterWidth; + int hOffset = (c / filterWidth) % filterHeight; + int c_im = c / filterWidth / filterHeight; + for (int h = 0; h < outputHeight; ++h) { + for (int w = 0; w < outputWidth; ++w) { + int imRowIdx = h * strideHeight + hOffset; + int imColIdx = w * strideWidth + wOffset; + if ((imRowIdx - paddingHeight) < 0 || + (imRowIdx - paddingHeight) >= inputHeight || + (imColIdx - paddingWidth) < 0 || + (imColIdx - paddingWidth) >= inputWidth) { + colData[(c * outputHeight + h) * outputWidth + w] = T(0); + } else { + imRowIdx += c_im * inputHeight - paddingHeight; + imColIdx -= paddingWidth; + colData[(c * outputHeight + h) * outputWidth + w] = + imData[imRowIdx * inputWidth + imColIdx]; + } + } + } + } + } +}; + +/* + * imShape = [inputChannels, inputHeight, inputWidth] + * colShape = + * [inputChannels, filterHeight, filterWidth, outputHeight, outputWidth] + */ +template +class Col2ImFunctor { + public: + void operator()(T* imData, const TensorShape& imShape, const T* colData, + const TensorShape& colShape, int strideHeight, + int strideWidth, int paddingHeight, int paddingWidth) { + int inputChannels = imShape[0]; + int inputHeight = imShape[1]; + int inputWidth = imShape[2]; + int filterHeight = colShape[1]; + int filterWidth = colShape[2]; + int outputHeight = colShape[3]; + int outputWidth = colShape[4]; + int channelsCol = inputChannels * filterHeight * filterWidth; + + for (int c = 0; c < channelsCol; ++c) { + int wOffset = c % filterWidth; + int hOffset = (c / filterWidth) % filterHeight; + int c_im = c / filterWidth / filterHeight; + for (int h = 0; h < outputHeight; ++h) { + for (int w = 0; w < outputWidth; ++w) { + int imRowIdx = h * strideHeight + hOffset; + int imColIdx = w * strideWidth + wOffset; + if ((imRowIdx - paddingHeight) >= 0 && + (imRowIdx - paddingHeight) < inputHeight && + (imColIdx - paddingWidth) >= 0 && + (imColIdx - paddingWidth) < inputWidth) { + imRowIdx += c_im * inputHeight - paddingHeight; + imColIdx -= paddingWidth; + imData[imRowIdx * inputWidth + imColIdx] += + colData[(c * outputHeight + h) * outputWidth + w]; + } + } + } + } + } +}; + +template class Im2ColFunctor; +template class Im2ColFunctor; +template class Col2ImFunctor; +template class Col2ImFunctor; + +/* + * imShape = [inputChannels, inputHeight, inputWidth] + * colShape = + * [outputHeight, outputWidth, inputChannels, filterHeight, filterWidth] + */ +template +class Im2ColFunctor { + public: + void operator()(const T* imData, const TensorShape& imShape, T* colData, + const TensorShape& colShape, int strideHeight, + int strideWidth, int paddingHeight, int paddingWidth) { + int inputChannels = imShape[0]; + int inputHeight = imShape[1]; + int inputWidth = imShape[2]; + int filterHeight = colShape[3]; + int filterWidth = colShape[4]; + int outputHeight = colShape[0]; + int outputWidth = colShape[1]; + for (int outputH = 0; outputH < outputHeight; ++outputH) { + for (int outputW = 0; outputW < outputWidth; ++outputW) { + for (int channel = 0; channel < inputChannels; ++channel) { + for (int filterH = 0; filterH < filterHeight; ++filterH) { + for (int filterW = 0; filterW < filterWidth; ++filterW) { + int imRowOffset = + outputH * strideHeight + filterH - paddingHeight; + int imColOffset = outputW * strideWidth + filterW - paddingWidth; + int colDataOffset = + (((outputH * outputWidth + outputW) * inputChannels + + channel) * + filterHeight + + filterH) * + filterWidth + + filterW; + if (imRowOffset < 0 || imRowOffset >= inputHeight || + imColOffset < 0 || imColOffset >= inputWidth) { + colData[colDataOffset] = float(0); + } else { + int imDataOffset = + (channel * inputHeight + imRowOffset) * inputWidth + + imColOffset; + colData[colDataOffset] = imData[imDataOffset]; + } + } + } + } + } + } + } +}; + +/* + * imShape = [inputChannels, inputHeight, inputWidth] + * colShape = + * [outputHeight, outputWidth, inputChannels, filterHeight, filterWidth] + */ +template +class Col2ImFunctor { + public: + void operator()(T* imData, const TensorShape& imShape, const T* colData, + const TensorShape& colShape, int strideHeight, + int strideWidth, int paddingHeight, int paddingWidth) { + int inputChannels = imShape[0]; + int inputHeight = imShape[1]; + int inputWidth = imShape[2]; + int filterHeight = colShape[3]; + int filterWidth = colShape[4]; + int outputHeight = colShape[0]; + int outputWidth = colShape[1]; + for (int outputH = 0; outputH < outputHeight; ++outputH) { + for (int outputW = 0; outputW < outputWidth; ++outputW) { + for (int channel = 0; channel < inputChannels; ++channel) { + for (int filterH = 0; filterH < filterHeight; ++filterH) { + for (int filterW = 0; filterW < filterWidth; ++filterW) { + int imRowOffset = + outputH * strideHeight + filterH - paddingHeight; + int imColOffset = outputW * strideWidth + filterW - paddingWidth; + int colDataOffset = + (((outputH * outputWidth + outputW) * inputChannels + + channel) * + filterHeight + + filterH) * + filterWidth + + filterW; + if (imRowOffset >= 0 && imRowOffset < inputHeight && + imColOffset >= 0 && imColOffset < inputWidth) { + int imDataOffset = + (channel * inputHeight + imRowOffset) * inputWidth + + imColOffset; + imData[imDataOffset] += colData[colDataOffset]; + } + } + } + } + } + } + } +}; + +template class Im2ColFunctor; +template class Im2ColFunctor; +template class Col2ImFunctor; +template class Col2ImFunctor; + +} // namespace paddle diff --git a/paddle/operators/math/im2col.cu b/paddle/operators/math/im2col.cu new file mode 100644 index 000000000..60bcdf8ac --- /dev/null +++ b/paddle/operators/math/im2col.cu @@ -0,0 +1,334 @@ +/* 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 "Im2Col.h" +#include "hl_device_functions.cuh" + +namespace paddle { + +template +__global__ void im2col(const T* data_im, int numOuts, int height, int width, + int blockH, int blockW, int strideH, int strideW, + int paddingH, int paddingW, int height_col, + int width_col, T* data_col) { + int index = (blockIdx.x * gridDim.y + blockIdx.y) * blockDim.x + threadIdx.x; + if (index < numOuts) { + int w_out = index % width_col; + index /= width_col; + int h_out = index % height_col; + int channel_in = index / height_col; + int channel_out = channel_in * blockH * blockW; + int h_in = h_out * strideH; + int w_in = w_out * strideW; + + data_col += (channel_out * height_col + h_out) * width_col + w_out; + for (int i = 0; i < blockH; ++i) { + for (int j = 0; j < blockW; ++j) { + int rIdx = int(h_in + i); + int cIdx = int(w_in + j); + if ((rIdx - (int)paddingH) >= (int)height || + (rIdx - (int)paddingH) < 0 || + (cIdx - (int)paddingW) >= (int)width || + (cIdx - (int)paddingW) < 0) { + *data_col = 0; + } else { + rIdx = rIdx + channel_in * height - paddingH; + cIdx = cIdx - paddingW; + *data_col = data_im[rIdx * width + cIdx]; + } + data_col += height_col * width_col; + } + } + } +} + +/* + * imShape = [inputChannels, inputHeight, inputWidth] + * colShape = + * [inputChannels, filterHeight, filterWidth, outputHeight, outputWidth] + */ +template +class Im2ColFunctor { + public: + void operator()(const T* imData, const TensorShape& imShape, T* colData, + const TensorShape& colShape, int strideHeight, + int strideWidth, int paddingHeight, int paddingWidth) { + int inputChannels = imShape[0]; + int inputHeight = imShape[1]; + int inputWidth = imShape[2]; + int filterHeight = colShape[1]; + int filterWidth = colShape[2]; + int outputHeight = colShape[3]; + int outputWidth = colShape[4]; + + int numKernels = inputChannels * outputHeight * outputWidth; + int blocks = (numKernels + 1024 - 1) / 1024; + int blockX = 512; + int blockY = (blocks + 512 - 1) / 512; + dim3 threads(1024, 1); + dim3 grid(blockX, blockY); + im2col<<>>( + imData, numKernels, inputHeight, inputWidth, filterHeight, filterWidth, + strideHeight, strideWidth, paddingHeight, paddingWidth, outputHeight, + outputWidth, colData); + CHECK_SYNC("Im2ColFunctor GPU failed"); + } +}; + +template +__global__ void col2im(size_t n, const T* data_col, size_t height, size_t width, + size_t channels, size_t blockH, size_t blockW, + size_t strideH, size_t strideW, size_t paddingH, + size_t paddingW, size_t height_col, size_t width_col, + T* data_im) { + size_t index = + (blockIdx.x * gridDim.y + blockIdx.y) * blockDim.x + threadIdx.x; + if (index < n) { + T val = 0; + int w = int(index % width); + int h = int((index / width) % height); + int c = int(index / (width * height)); + if ((w - (int)paddingW) >= 0 && + (w - (int)paddingW) < (width - 2 * paddingW) && + (h - (int)paddingH) >= 0 && (h - paddingH) < (height - 2 * paddingH)) { + // compute the start and end of the output + int w_col_start = + (w < (int)blockW) ? 0 : (w - int(blockW)) / (int)strideW + 1; + int w_col_end = min((int)(w / (int)strideW + 1), (int)(width_col)); + int h_col_start = + (h < (int)blockH) ? 0 : (h - (int)blockH) / (int)strideH + 1; + int h_col_end = min(int(h / strideH + 1), int(height_col)); + for (int h_col = h_col_start; h_col < h_col_end; ++h_col) { + for (int w_col = w_col_start; w_col < w_col_end; ++w_col) { + // the col location: [c * width * height + h_out, w_out] + int c_col = int(c * blockH * blockW) + + (h - h_col * (int)strideH) * (int)blockW + + (w - w_col * (int)strideW); + val += data_col[(c_col * height_col + h_col) * width_col + w_col]; + } + } + h -= paddingH; + w -= paddingW; + data_im[c * ((width - 2 * paddingW) * (height - 2 * paddingH)) + + h * (width - 2 * paddingW) + w] += val; + } + } +} + +/* + * imShape = [inputChannels, inputHeight, inputWidth] + * colShape = + * [inputChannels, filterHeight, filterWidth, outputHeight, outputWidth] + */ +template +class Col2ImFunctor { + public: + void operator()(T* imData, const TensorShape& imShape, const T* colData, + const TensorShape& colShape, int strideHeight, + int strideWidth, int paddingHeight, int paddingWidth) { + int inputChannels = imShape[0]; + int inputHeight = imShape[1]; + int inputWidth = imShape[2]; + int filterHeight = colShape[1]; + int filterWidth = colShape[2]; + int outputHeight = colShape[3]; + int outputWidth = colShape[4]; + + size_t numKernels = inputChannels * (inputHeight + 2 * paddingHeight) * + (inputWidth + 2 * paddingWidth); + + size_t blocks = (numKernels + 1024 - 1) / 1024; + size_t blockX = 512; + size_t blockY = (blocks + 512 - 1) / 512; + dim3 threads(1024, 1); + dim3 grid(blockX, blockY); + + // To avoid involving atomic operations, we will launch one kernel per + // bottom dimension, and then in the kernel add up the top dimensions. + col2im<<>>( + numKernels, colData, inputHeight + 2 * paddingHeight, + inputWidth + 2 * paddingWidth, inputChannels, filterHeight, filterWidth, + strideHeight, strideWidth, paddingHeight, paddingWidth, outputHeight, + outputWidth, imData); + CHECK_SYNC("Col2ImFunctor GPU failed"); + } +}; + +template class Im2ColFunctor; +template class Im2ColFunctor; +template class Col2ImFunctor; +template class Col2ImFunctor; + +template +__global__ void im2colOCF(const T* imData, T* colData, int inputChannels, + int inputHeight, int inputWidth, int filterHeight, + int filterWidth, int strideHeight, int strideWidth, + int paddingHeight, int paddingWidth, int outputHeight, + int outputWidth) { + int swId = blockIdx.x; + int shId = blockIdx.y; + for (int channelId = threadIdx.z; channelId < inputChannels; + channelId += blockDim.z) { + for (int idy = threadIdx.y; idy < filterHeight; idy += blockDim.y) { + for (int idx = threadIdx.x; idx < filterWidth; idx += blockDim.x) { + int widthOffset = idx + swId * strideWidth - paddingWidth; + int heightOffset = idy + shId * strideHeight - paddingHeight; + int imOffset = widthOffset + heightOffset * inputWidth + + channelId * inputHeight * inputWidth; + + int colOffset = idx + idy * filterWidth + + channelId * filterHeight * filterWidth + + (shId * outputWidth + swId) * + (inputChannels * filterHeight * filterWidth); + + if (heightOffset >= inputHeight || heightOffset < 0 || + widthOffset >= inputWidth || widthOffset < 0) { + colData[colOffset] = T(0); + } else { + colData[colOffset] = imData[imOffset]; + } + } + } + } +} + +/* + * imShape = [inputChannels, inputHeight, inputWidth] + * colShape = + * [outputHeight, outputWidth, inputChannels, filterHeight, filterWidth] + */ +template +class Im2ColFunctor { + public: + void operator()(const T* imData, const TensorShape& imShape, T* colData, + const TensorShape& colShape, int strideHeight, + int strideWidth, int paddingHeight, int paddingWidth) { + int inputChannels = imShape[0]; + int inputHeight = imShape[1]; + int inputWidth = imShape[2]; + int filterHeight = colShape[3]; + int filterWidth = colShape[4]; + int outputHeight = colShape[0]; + int outputWidth = colShape[1]; + + int blockDimX = 0; + int blockDimY = 0; + if (filterHeight <= 4 && filterWidth <= 4) { + blockDimX = 4; + blockDimY = 4; + } else if (filterHeight <= 8 && filterWidth <= 8) { + blockDimX = 8; + blockDimY = 8; + } else if (filterHeight <= 16 && filterWidth <= 16) { + blockDimX = 16; + blockDimY = 16; + } else { + blockDimX = 32; + blockDimY = 32; + } + + int blockDimZ = 1024 / blockDimX / blockDimY; + dim3 threads(blockDimX, blockDimY, std::min(blockDimZ, inputChannels)); + dim3 grid(outputWidth, outputHeight); + im2colOCF<<>>( + imData, colData, inputChannels, inputHeight, inputWidth, filterHeight, + filterWidth, strideHeight, strideWidth, paddingHeight, paddingWidth, + outputHeight, outputWidth); + CHECK_SYNC("Im2ColFunctor GPU failed"); + } +}; + +template +__global__ void col2imOCF(T* imData, const T* colData, int inputChannels, + int inputHeight, int inputWidth, int filterHeight, + int filterWidth, int strideHeight, int strideWidth, + int paddingHeight, int paddingWidth, int outputHeight, + int outputWidth) { + int swId = blockIdx.x; + int shId = blockIdx.y; + for (int channelId = threadIdx.z; channelId < inputChannels; + channelId += blockDim.z) { + for (int idy = threadIdx.y; idy < filterHeight; idy += blockDim.y) { + for (int idx = threadIdx.x; idx < filterWidth; idx += blockDim.x) { + int widthOffset = idx + swId * strideWidth - paddingWidth; + int heightOffset = idy + shId * strideHeight - paddingHeight; + int imOffset = widthOffset + heightOffset * inputWidth + + channelId * inputHeight * inputWidth; + + int colOffset = idx + idy * filterWidth + + channelId * filterHeight * filterWidth + + (shId * outputWidth + swId) * + (inputChannels * filterHeight * filterWidth); + + if (heightOffset >= 0 && heightOffset < inputHeight && + widthOffset >= 0 && widthOffset < inputWidth) { + paddle::paddleAtomicAdd(imData + imOffset, colData[colOffset]); + } + } + } + } +} + +/* + * imShape = [inputChannels, inputHeight, inputWidth] + * colShape = + * [outputHeight, outputWidth, inputChannels, filterHeight, filterWidth] + */ +template +class Col2ImFunctor { + public: + void operator()(T* imData, const TensorShape& imShape, const T* colData, + const TensorShape& colShape, int strideHeight, + int strideWidth, int paddingHeight, int paddingWidth) { + int inputChannels = imShape[0]; + int inputHeight = imShape[1]; + int inputWidth = imShape[2]; + int filterHeight = colShape[3]; + int filterWidth = colShape[4]; + int outputHeight = colShape[0]; + int outputWidth = colShape[1]; + + int blockDimX = 0; + int blockDimY = 0; + if (filterHeight <= 4 && filterWidth <= 4) { + blockDimX = 4; + blockDimY = 4; + } else if (filterHeight <= 8 && filterWidth <= 8) { + blockDimX = 8; + blockDimY = 8; + } else if (filterHeight <= 16 && filterWidth <= 16) { + blockDimX = 16; + blockDimY = 16; + } else { + blockDimX = 32; + blockDimY = 32; + } + + int blockDimZ = 1024 / blockDimX / blockDimY; + dim3 threads(blockDimX, blockDimY, std::min(blockDimZ, inputChannels)); + dim3 grid(outputWidth, outputHeight); + col2imOCF<<>>( + imData, colData, inputChannels, inputHeight, inputWidth, filterHeight, + filterWidth, strideHeight, strideWidth, paddingHeight, paddingWidth, + outputHeight, outputWidth); + CHECK_SYNC("Col2ImFunctor GPU failed"); + } +}; + +template class Im2ColFunctor; +template class Im2ColFunctor; +template class Col2ImFunctor; +template class Col2ImFunctor; + +} // namespace paddle diff --git a/paddle/operators/math/im2col.h b/paddle/operators/math/im2col.h new file mode 100644 index 000000000..4568ca2fd --- /dev/null +++ b/paddle/operators/math/im2col.h @@ -0,0 +1,86 @@ +/* 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 "TensorShape.h" +#include "TensorType.h" + +namespace paddle { + +/* The storage format of the coldata in the Im2ColFunctor and Col2ImFunctor. */ +enum ColFormat { kCFO = 0, kOCF = 1 }; + +/* + * \brief Converts the image data of three dimensions(CHW) into a colData of + * five dimensions in the Im2ColFunctor calculation, + * And in the Col2ImFunctor calculation, it is reversed. + * + * \param imData Image data. + * \param imShape The shape of imData, + * [inputChannels, inputHeight, inputWidth]. + * \param colData Column data. + * \param colShape The shape of colData. + * + * If the template argument Format is kCFO, the shape of colData is: + * [inputChannels, filterHeight, filterWidth, outputHeight, outputWidth] + * So, it is easy to reshape into a convolution matrix for convolution + * calculation based on matrix multiplication. + * The shape of convolution matrix is [height, width], where the height is equal + * inputChannels * filterHeight * filterWidth, and the width is equal + * outputHeight * outputWidth. + * + * Reshape: + * shape of colData shape of convolution matrix + * [inputChannels, + * filterHeight, + * filterWidth, ======> [height, width] + * outputHeight, + * outputWidth] + * + * If the template argument Format is kOCF, the shape of colData is: + * [outputHeight, outputWidth, inputChannels, filterHeight, filterWidth] + * So, it is easy to reshape into a sequence matrix for rnn calculation. + * The shape of sequence matrix is [seqLength, stepSize], where the seqLength + * is equal outputHeight * outputWidth, and the stepSize is equal + * inputChannels * filterHeight * filterWidth. + * + * Reshape: + * shape of colData shape of sequence matrix + * [outputHeight, + * outputWidth, + * inputChannels, ======> [seqLength, stepSize] + * filterHeight, + * filterWidth] + * + * \note The caller needs to ensure that imShape.inputChannels is equal to + * colShape.inputChannels. + */ +template +class Im2ColFunctor { + public: + void operator()(const T* imData, const TensorShape& imShape, T* colData, + const TensorShape& colShape, int strideHeight, + int strideWidth, int paddingHeight, int paddingWidth); +}; + +template +class Col2ImFunctor { + public: + void operator()(T* imData, const TensorShape& imShape, const T* colData, + const TensorShape& colShape, int strideHeight, + int strideWidth, int paddingHeight, int paddingWidth); +}; + +} // namespace paddle -- GitLab From ca9be82f6c45691b6661a4be54a05a369f579295 Mon Sep 17 00:00:00 2001 From: qijun Date: Wed, 30 Aug 2017 13:05:33 +0800 Subject: [PATCH 0281/2018] add how to use eigen cn doc --- doc/howto/dev/new_op_cn.md | 7 +- doc/howto/dev/use_eigen_cn.md | 140 ++++++++++++++++++++++++++++++++++ 2 files changed, 146 insertions(+), 1 deletion(-) create mode 100644 doc/howto/dev/use_eigen_cn.md diff --git a/doc/howto/dev/new_op_cn.md b/doc/howto/dev/new_op_cn.md index ebd2cf3ff..55c99fa7a 100644 --- a/doc/howto/dev/new_op_cn.md +++ b/doc/howto/dev/new_op_cn.md @@ -169,6 +169,8 @@ class MulKernel : public framework::OpKernel { `MulKernel`需要重写`Compute`接口，该接口参数为`const framework::ExecutionContext& context`, `ExecutionContext`相比`InferShapeContext`增加了设备类型，同样可获取到输入输出和属性参数，`Compute`函数里写具体实现时。 注意，不同设备(CPU、GPU)共享一个Op定义，是否则共享同一个`OpKernel`，取决于`Compute`调用的函数是否支持不同设备。`MulOp`的CPU、GPU实现共享同一个`Kernel`，`OpKernel`不共享的例子可以参考[`OnehotCrossEntropyOpKernel`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/cross_entropy_op.h#L43)。 + +为了使得`OpKernel`的计算过程书写较为简单，CPU、GPU的代码可以复用，我们通常借助Eigen unsupported Tensor模块来实现。关于在paddle中如何使用Eigen库，请参考对应的使用[文档](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/howto/dev/use_eigen_cn.md) 到此前向Op实现完成，需要在`.cc`文件中注册该op和kernel。反向Op类的定义和Kernel定义与前向Op类似，这里不再重复。但注意，反向Op没有`ProtoMaker`。 @@ -188,9 +190,12 @@ REGISTER_OP_CPU_KERNEL(mul_grad, - `REGISTER_OP_WITHOUT_GRADIENT` ： 用于注册没有反向的Op。 - `REGISTER_OP_CPU_KERNEL` ：注册`ops::MulKernel`类，并特化模板参数为`paddle::platform::CPUPlace`和`float`类型，同理，注册`ops::MulKernel`类。 -在 `.cu`文件中注册GPU Kernel。 +在 `.cu`文件中注册GPU Kernel。请注意，如果GPU Kernel的实现是基于Eigen unsupported模块，那么在 `.cu`的最前面请加上宏定义 `#define EIGEN_USE_GPU` ```c++ +// if use Eigen unsupported module before include head files +#define EIGEN_USE_GPU + namespace ops = paddle::operators; REGISTER_OP_GPU_KERNEL(mul, ops::MulKernel); REGISTER_OP_GPU_KERNEL(mul_grad, diff --git a/doc/howto/dev/use_eigen_cn.md b/doc/howto/dev/use_eigen_cn.md new file mode 100644 index 000000000..d22ff4799 --- /dev/null +++ b/doc/howto/dev/use_eigen_cn.md @@ -0,0 +1,140 @@ +## 在Paddle中如何使用Eigen + +神经网络本质上是一个计算图，计算需要的数据存放在`Tensor`中，而计算过程是由`Operartor`来描述的。在执行时，`Operator`调用对应`OpKernel`中的`Compute`接口，实现对`Tensor`的操作。 + + +### Eigen Tensor模块 + +Eigen Tensor模块对element-wise计算提供了强大的支持，并且书写一份代码，可以同时在CPU、GPU执行。但Eigen Tensor是一个正在开发中的模块，因此可能测试不够完备，文档较少。 + +关于Eigen Tensor模块的详细介绍请参考[文档](https://github.com/RLovelett/eigen/blob/master/unsupported/Eigen/CXX11/src/Tensor/README.md) + + +### paddle::framework::Tensor + +Paddle Tensor定义在framework目录下，其主要接口如下： + +``` +class Tensor { + public: + /*! Return a pointer to mutable memory block. */ + template + inline T* data(); + + /** + * @brief Return a pointer to mutable memory block. + * @note If not exist, then allocation. + */ + template + inline T* mutable_data(platform::Place place); + + /** + * @brief Return a pointer to mutable memory block. + * + * @param[in] dims The dimensions of the memory block. + * @param[in] place The place of the memory block. + * + * @note If not exist, then allocation. + */ + template + inline T* mutable_data(DDim dims, platform::Place place); + + /*! Resize the dimensions of the memory block. */ + inline Tensor& Resize(const DDim& dims); + + /*! Return the dimensions of the memory block. */ + inline const DDim& dims() const; + + private: + /*! holds the memory block if allocated. */ + std::shared_ptr holder_; + + /*! points to dimensions of memory block. */ + DDim dim_; +}; +``` + +`Placeholder`的作用的延迟分配内存，即我们可以先定义一个Tensor，然后使用Resize接口设置Tensor的大小，最后再调用mutable_data接口分配实际的内存。 + +``` +paddle::framework::Tensor t; +paddle::platform::CPUPlace place; +// set size first +t.Resize({2, 3}); +// allocate memory on CPU later +t.mutable_data(place); +``` + +下面以AddOp为例说明Tensor的使用过程： + +- InferShape + +在运行神经网络计算图时，我们先调用每个`Operator`的`InferShape`接口，根据输入Tensor的大小来设置输出Tensor的大小，`Resize`接口会被调用。 + +``` +void InferShape(const framework::InferShapeContext &ctx) const override { + PADDLE_ENFORCE_EQ(ctx.Input("X")->dims(), + ctx.Input("Y")->dims(), + "Two input of Add Op's dimension must be same."); + ctx.Output("Out")->Resize(ctx.Input("X")->dims()); +} +``` + + +- Run + +`Operator`的`Run`接口最终会调用对应`OpKernel`的`Compute`接口，在这时真正的分配内存，`mutable_data`接口会被调用。 + +``` +void Compute(const framework::ExecutionContext& context) const override { + auto* input0 = context.Input("X"); + auto* input1 = context.Input("Y"); + auto* output = context.Output("Out"); + + output->mutable_data(context.GetPlace()); + + auto X = EigenVector::Flatten(*input0); + auto Y = EigenVector::Flatten(*input1); + auto Z = EigenVector::Flatten(*output); + + auto place = context.GetEigenDevice(); + + Z.device(place) = X + Y; +} +``` + + +### paddle::framework::Tensor到EigenTensor的转换 + +如上一小节所示，在具体的计算中，我们需要先把输入Tensor和输出Tensor转换为Eigen支持的格式。我们在[eigen.h](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/eigen.h)中提供了一些全局函数用来实现paddle::framework::Tensor到EigenTensor/EigenMatrix/EigenVector/EigenScalar的转换。 + +以EigenTensor为例，做一个介绍 + +``` +Tensor t; +float* p = t.mutable_data(make_ddim({1, 2, 3}), platform::CPUPlace()); +for (int i = 0; i < 1 * 2 * 3; i++) { + p[i] = static_cast(i); +} + +EigenTensor::Type et = EigenTensor::From(t); +``` + +From是EigenTensor模板struct提供的一个接口，可以实现从paddle::framework::Tensor到对EigenTensor的转换。由于Tensor的rank是模板参数，因此在转换时需要显示的指定。 + +需要额外注意的是，EigenVector::From方法是把paddle中的一维Tensor转为Eigen的一维Tensor，在这里用EigenVector来表示；而EigenVector::Flatten方法是把paddle中的一个Tensor进行reshape操作，压扁成为Eigen的一维Tensor，类型仍然为EigenVector。 + +更多的转换方法请参考eigen_test.cc中的[单元测试](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/eigen_test.cc)。 + + + +### 实现计算 + +当需要完成计算时，我们需要等式左边的EigenTensor调用device接口： + +``` +auto place = context.GetEigenDevice(); +Z.device(place) = X + Y; +``` + +由于Eigen Tensor模块的文档较少，我们可以参考TensorFlow的[kernels](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/core/kernels)模块下的相关`OpKernel`的计算代码。 -- GitLab From c5183caa04557628340983d17a64097f939db132 Mon Sep 17 00:00:00 2001 From: tensor-tang Date: Wed, 30 Aug 2017 13:37:51 +0800 Subject: [PATCH 0282/2018] rename --- paddle/gserver/layers/MKLDNNFcLayer.cpp | 29 +++++++++++-------------- paddle/gserver/layers/MKLDNNLayer.h | 12 +++++----- 2 files changed, 20 insertions(+), 21 deletions(-) diff --git a/paddle/gserver/layers/MKLDNNFcLayer.cpp b/paddle/gserver/layers/MKLDNNFcLayer.cpp index a08cca318..8318c8c51 100644 --- a/paddle/gserver/layers/MKLDNNFcLayer.cpp +++ b/paddle/gserver/layers/MKLDNNFcLayer.cpp @@ -134,7 +134,7 @@ void MKLDNNFcLayer::resetFwd() { const MatrixPtr& bias = hasBias ? biases_->getW() : nullptr; const MatrixPtr& out = output_.value; - if (prevIsOnlyMKLDNN()) { + if (inputIsOnlyMKLDNN()) { const MatrixPtr& in = getInputValue(0); inVal_ = std::dynamic_pointer_cast(in); CHECK(inVal_) << "Input should be MKLDNNMatrix"; @@ -154,7 +154,7 @@ void MKLDNNFcLayer::resetFwd() { // change original output value to mkldnn output value output_.value = std::dynamic_pointer_cast(outVal_); - if (!nextIsOnlyMKLDNN()) { + if (!outputIsOnlyMKLDNN()) { convertOutputToOtherDevice(); } @@ -194,19 +194,16 @@ void MKLDNNFcLayer::resetBwd() { const MatrixPtr& bias = hasBias ? biases_->getWGrad() : nullptr; // TODO(TJ): merge outgrad - if (nextIsOnlyMKLDNN()) { - // can not directly cast outputgrad to mkldnnmatrix, - // since each layer can not write the inputgrad to mkldnn inputgrad. - // So just create from matrix with outputvalue format. - const MatrixPtr& out = getOutput(MKLDNN_DEVICE).grad; - outGrad_ = MKLDNNMatrix::create(out, outVal_->getPrimitiveDesc()); - } else { - const MatrixPtr& out = getOutput(CPU_DEVICE).grad; - // fc do not need to convert from cpu device since output always nc - // only need create from cpu device - outGrad_ = MKLDNNMatrix::create(out, outVal_->getPrimitiveDesc()); - } - + int device = outputIsOnlyMKLDNN() ? MKLDNN_DEVICE : CPU_DEVICE; + // for MKLDNN device: + // can not directly cast outputgrad to mkldnnmatrix, + // since each layer can not write the inputgrad to mkldnn inputgrad. + // So just create from matrix with outputvalue format. + // for CPU device: + // fc do not need to convert from cpu device since output is always nc format + // only need create from cpu device + const MatrixPtr& out = getOutput(device).grad; + outGrad_ = MKLDNNMatrix::create(out, outVal_->getPrimitiveDesc()); wgtGrad_ = MKLDNNMatrix::create(wgt, wgtVal_->getPrimitiveDesc()); biasGrad_ = hasBias ? MKLDNNMatrix::create(bias, biasVal_->getPrimitiveDesc()) : nullptr; @@ -238,7 +235,7 @@ void MKLDNNFcLayer::resetBwd() { pipelineBwd_.push_back(*bwdWgt_); /// backward data - int device = prevIsOnlyMKLDNN() ? MKLDNN_DEVICE : CPU_DEVICE; + device = inputIsOnlyMKLDNN() ? MKLDNN_DEVICE : CPU_DEVICE; const MatrixPtr& in = getInputGrad(0, device); if (in == nullptr) { return; diff --git a/paddle/gserver/layers/MKLDNNLayer.h b/paddle/gserver/layers/MKLDNNLayer.h index 8fe9630e8..b983b833d 100644 --- a/paddle/gserver/layers/MKLDNNLayer.h +++ b/paddle/gserver/layers/MKLDNNLayer.h @@ -151,6 +151,8 @@ public: protected: /** * copy image size and sequence info to other device + * @note: can not directly use Layer::copyOutputToOtherDevice since here only + * copy base info and do not copy data value */ void copyOutputInfoToOtherDevice() { for (size_t i = 0; i < outputOtherDevice_.size(); i++) { @@ -165,10 +167,10 @@ protected: } /** - * Is previous layer only has MKLDNN type. + * If input only has MKLDNN device. * Otherwise, only support the previous layer using CPU device. */ - bool prevIsOnlyMKLDNN(int index = 0) { + bool inputIsOnlyMKLDNN(int index = 0) { int prevDevice = getPrev(index)->getDeviceId(); if (prevDevice == MKLDNN_DEVICE) { return true; @@ -183,7 +185,7 @@ protected: * If output only has MKLDNN device. * Otherwise, other devices should only using CPU device. */ - bool nextIsOnlyMKLDNN() { + bool outputIsOnlyMKLDNN() { for (size_t i = 0; i < outputOtherDevice_.size(); i++) { CHECK_EQ(outputOtherDevice_[i].deviceId, CPU_DEVICE) << "Only support other device is CPU yet"; @@ -195,7 +197,7 @@ protected: * Sync input value data */ void syncInputValue() { - if (prevIsOnlyMKLDNN()) { + if (inputIsOnlyMKLDNN()) { return; } real* iData = getInputValue(0, CPU_DEVICE)->getData(); @@ -208,7 +210,7 @@ protected: * Sync output grad data */ void syncOutputGrad() { - if (nextIsOnlyMKLDNN()) { + if (outputIsOnlyMKLDNN()) { return; } -- GitLab From f7be9cb97aa4b90c0ccd6f954a71d3caada4dac7 Mon Sep 17 00:00:00 2001 From: hedaoyuan Date: Wed, 30 Aug 2017 13:55:58 +0800 Subject: [PATCH 0283/2018] Refine the cpu code. --- paddle/operators/math/CMakeLists.txt | 4 +- paddle/operators/math/im2col.cc | 319 +++++++++++++++------------ paddle/operators/math/im2col.h | 20 +- 3 files changed, 186 insertions(+), 157 deletions(-) diff --git a/paddle/operators/math/CMakeLists.txt b/paddle/operators/math/CMakeLists.txt index ed51d416e..f31281eba 100644 --- a/paddle/operators/math/CMakeLists.txt +++ b/paddle/operators/math/CMakeLists.txt @@ -1,8 +1,8 @@ if(WITH_GPU) - nv_library(math_function SRCS math_function.cc math_function.cu DEPS cblas device_context) + nv_library(math_function SRCS math_function.cc math_function.cu im2col.cc DEPS cblas device_context) else() - cc_library(math_function SRCS math_function.cc DEPS cblas device_context) + cc_library(math_function SRCS math_function.cc im2col.cc DEPS cblas device_context) endif() nv_test(math_function_test SRCS math_function_test.cc DEPS math_function tensor) diff --git a/paddle/operators/math/im2col.cc b/paddle/operators/math/im2col.cc index dafb21b33..8124e322c 100644 --- a/paddle/operators/math/im2col.cc +++ b/paddle/operators/math/im2col.cc @@ -12,48 +12,54 @@ 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 "Im2Col.h" +#include "paddle/operators/math/im2col.h" namespace paddle { /* - * imShape = [inputChannels, inputHeight, inputWidth] - * colShape = - * [inputChannels, filterHeight, filterWidth, outputHeight, outputWidth] + * im = [input_channels, input_height, input_width] + * col = + * [input_channels, filter_height, filter_width, output_height, output_width] */ template -class Im2ColFunctor { +class Im2ColFunctor { public: - void operator()(const T* imData, const TensorShape& imShape, T* colData, - const TensorShape& colShape, int strideHeight, - int strideWidth, int paddingHeight, int paddingWidth) { - int inputChannels = imShape[0]; - int inputHeight = imShape[1]; - int inputWidth = imShape[2]; - int filterHeight = colShape[1]; - int filterWidth = colShape[2]; - int outputHeight = colShape[3]; - int outputWidth = colShape[4]; - int channelsCol = inputChannels * filterHeight * filterWidth; - - for (int c = 0; c < channelsCol; ++c) { - int wOffset = c % filterWidth; - int hOffset = (c / filterWidth) % filterHeight; - int c_im = c / filterWidth / filterHeight; - for (int h = 0; h < outputHeight; ++h) { - for (int w = 0; w < outputWidth; ++w) { - int imRowIdx = h * strideHeight + hOffset; - int imColIdx = w * strideWidth + wOffset; - if ((imRowIdx - paddingHeight) < 0 || - (imRowIdx - paddingHeight) >= inputHeight || - (imColIdx - paddingWidth) < 0 || - (imColIdx - paddingWidth) >= inputWidth) { - colData[(c * outputHeight + h) * outputWidth + w] = T(0); + void operator()(const framework::Tensor& im, framework::Tensor& col, + int stride_height, int stride_width, int padding_height, + int padding_width) { + PADDLE_ENFORCE(im.dims().size() == 3); + PADDLE_ENFORCE(col.dims().size() == 5); + + int input_channels = im.dims()[0]; + int input_height = im.dims()[1]; + int input_width = im.dims()[2]; + int filter_height = col.dims()[1]; + int filter_width = col.dims()[2]; + int output_height = col.dims()[3]; + int output_width = col.dims()[4]; + int channels_col = input_channels * filter_height * filter_width; + + const T* im_data = im.data(); + T* col_data = col.data(); + + for (int c = 0; c < channels_col; ++c) { + int w_offset = c % filter_width; + int h_offset = (c / filter_width) % filter_height; + int c_im = c / filter_width / filter_height; + for (int h = 0; h < output_height; ++h) { + for (int w = 0; w < output_width; ++w) { + int im_row_idx = h * stride_height + h_offset; + int im_col_idx = w * stride_width + w_offset; + if ((im_row_idx - padding_height) < 0 || + (im_row_idx - padding_height) >= input_height || + (im_col_idx - padding_width) < 0 || + (im_col_idx - padding_width) >= input_width) { + col_data[(c * output_height + h) * output_width + w] = T(0); } else { - imRowIdx += c_im * inputHeight - paddingHeight; - imColIdx -= paddingWidth; - colData[(c * outputHeight + h) * outputWidth + w] = - imData[imRowIdx * inputWidth + imColIdx]; + im_row_idx += c_im * input_height - padding_height; + im_col_idx -= padding_width; + col_data[(c * output_height + h) * output_width + w] = + im_data[im_row_idx * input_width + im_col_idx]; } } } @@ -62,41 +68,46 @@ class Im2ColFunctor { }; /* - * imShape = [inputChannels, inputHeight, inputWidth] - * colShape = - * [inputChannels, filterHeight, filterWidth, outputHeight, outputWidth] + * im = [input_channels, input_height, input_width] + * col = + * [input_channels, filter_height, filter_width, output_height, output_width] */ template -class Col2ImFunctor { +class Col2ImFunctor { public: - void operator()(T* imData, const TensorShape& imShape, const T* colData, - const TensorShape& colShape, int strideHeight, - int strideWidth, int paddingHeight, int paddingWidth) { - int inputChannels = imShape[0]; - int inputHeight = imShape[1]; - int inputWidth = imShape[2]; - int filterHeight = colShape[1]; - int filterWidth = colShape[2]; - int outputHeight = colShape[3]; - int outputWidth = colShape[4]; - int channelsCol = inputChannels * filterHeight * filterWidth; - - for (int c = 0; c < channelsCol; ++c) { - int wOffset = c % filterWidth; - int hOffset = (c / filterWidth) % filterHeight; - int c_im = c / filterWidth / filterHeight; - for (int h = 0; h < outputHeight; ++h) { - for (int w = 0; w < outputWidth; ++w) { - int imRowIdx = h * strideHeight + hOffset; - int imColIdx = w * strideWidth + wOffset; - if ((imRowIdx - paddingHeight) >= 0 && - (imRowIdx - paddingHeight) < inputHeight && - (imColIdx - paddingWidth) >= 0 && - (imColIdx - paddingWidth) < inputWidth) { - imRowIdx += c_im * inputHeight - paddingHeight; - imColIdx -= paddingWidth; - imData[imRowIdx * inputWidth + imColIdx] += - colData[(c * outputHeight + h) * outputWidth + w]; + void operator()(framework::Tensor& im, const framework::Tensor& col, + int stride_height, int stride_width, int padding_height, + int padding_width) { + PADDLE_ENFORCE(im.dims().size() == 3); + PADDLE_ENFORCE(col.dims().size() == 5); + int input_channels = im.dims()[0]; + int input_height = im.dims()[1]; + int input_width = im.dims()[2]; + int filter_height = col.dims()[1]; + int filter_width = col.dims()[2]; + int output_height = col.dims()[3]; + int output_width = col.dims()[4]; + int channels_col = input_channels * filter_height * filter_width; + + T* im_data = im.data(); + const T* col_data = col.data(); + + for (int c = 0; c < channels_col; ++c) { + int w_offset = c % filter_width; + int h_offset = (c / filter_width) % filter_height; + int c_im = c / filter_width / filter_height; + for (int h = 0; h < output_height; ++h) { + for (int w = 0; w < output_width; ++w) { + int im_row_idx = h * stride_height + h_offset; + int im_col_idx = w * stride_width + w_offset; + if ((im_row_idx - padding_height) >= 0 && + (im_row_idx - padding_height) < input_height && + (im_col_idx - padding_width) >= 0 && + (im_col_idx - padding_width) < input_width) { + im_row_idx += c_im * input_height - padding_height; + im_col_idx -= padding_width; + im_data[im_row_idx * input_width + im_col_idx] += + col_data[(c * output_height + h) * output_width + w]; } } } @@ -104,52 +115,61 @@ class Col2ImFunctor { } }; -template class Im2ColFunctor; -template class Im2ColFunctor; -template class Col2ImFunctor; -template class Col2ImFunctor; +template class Im2ColFunctor; +template class Im2ColFunctor; +template class Col2ImFunctor; +template class Col2ImFunctor; /* - * imShape = [inputChannels, inputHeight, inputWidth] - * colShape = - * [outputHeight, outputWidth, inputChannels, filterHeight, filterWidth] + * im = [input_channels, input_height, input_width] + * col = + * [output_height, output_width, input_channels, filter_height, filter_width] */ template -class Im2ColFunctor { +class Im2ColFunctor { public: - void operator()(const T* imData, const TensorShape& imShape, T* colData, - const TensorShape& colShape, int strideHeight, - int strideWidth, int paddingHeight, int paddingWidth) { - int inputChannels = imShape[0]; - int inputHeight = imShape[1]; - int inputWidth = imShape[2]; - int filterHeight = colShape[3]; - int filterWidth = colShape[4]; - int outputHeight = colShape[0]; - int outputWidth = colShape[1]; - for (int outputH = 0; outputH < outputHeight; ++outputH) { - for (int outputW = 0; outputW < outputWidth; ++outputW) { - for (int channel = 0; channel < inputChannels; ++channel) { - for (int filterH = 0; filterH < filterHeight; ++filterH) { - for (int filterW = 0; filterW < filterWidth; ++filterW) { - int imRowOffset = - outputH * strideHeight + filterH - paddingHeight; - int imColOffset = outputW * strideWidth + filterW - paddingWidth; - int colDataOffset = - (((outputH * outputWidth + outputW) * inputChannels + - channel) * - filterHeight + - filterH) * - filterWidth + - filterW; - if (imRowOffset < 0 || imRowOffset >= inputHeight || - imColOffset < 0 || imColOffset >= inputWidth) { - colData[colDataOffset] = float(0); + void operator()(const framework::Tensor& im, framework::Tensor& col, + int stride_height, int stride_width, int padding_height, + int padding_width) { + PADDLE_ENFORCE(im.dims().size() == 3); + PADDLE_ENFORCE(col.dims().size() == 5); + int input_channels = im.dims()[0]; + int input_height = im.dims()[1]; + int input_width = im.dims()[2]; + int filter_height = col.dims()[3]; + int filter_width = col.dims()[4]; + int output_height = col.dims()[0]; + int output_width = col.dims()[1]; + + const T* im_data = im.data(); + T* col_data = col.data(); + + for (int col_row_idx = 0; col_row_idx < output_height; ++col_row_idx) { + for (int col_col_idx = 0; col_col_idx < output_width; ++col_col_idx) { + for (int channel = 0; channel < input_channels; ++channel) { + for (int filter_row_idx = 0; filter_row_idx < filter_height; + ++filter_row_idx) { + for (int filter_col_idx = 0; filter_col_idx < filter_width; + ++filter_col_idx) { + int im_row_offset = + col_row_idx * stride_height + filter_row_idx - padding_height; + int im_col_offset = + col_col_idx * stride_width + filter_col_idx - padding_width; + int col_offset = (((col_row_idx * output_width + col_col_idx) * + input_channels + + channel) * + filter_height + + filter_row_idx) * + filter_width + + filter_col_idx; + if (im_row_offset < 0 || im_row_offset >= input_height || + im_col_offset < 0 || im_col_offset >= input_width) { + col_data[col_offset] = T(0); } else { - int imDataOffset = - (channel * inputHeight + imRowOffset) * inputWidth + - imColOffset; - colData[colDataOffset] = imData[imDataOffset]; + int im_offset = + (channel * input_height + im_row_offset) * input_width + + im_col_offset; + col_data[col_offset] = im_data[im_offset]; } } } @@ -160,44 +180,53 @@ class Im2ColFunctor { }; /* - * imShape = [inputChannels, inputHeight, inputWidth] - * colShape = - * [outputHeight, outputWidth, inputChannels, filterHeight, filterWidth] + * im = [input_channels, input_height, input_width] + * col = + * [output_height, output_width, input_channels, filter_height, filter_width] */ template -class Col2ImFunctor { +class Col2ImFunctor { public: - void operator()(T* imData, const TensorShape& imShape, const T* colData, - const TensorShape& colShape, int strideHeight, - int strideWidth, int paddingHeight, int paddingWidth) { - int inputChannels = imShape[0]; - int inputHeight = imShape[1]; - int inputWidth = imShape[2]; - int filterHeight = colShape[3]; - int filterWidth = colShape[4]; - int outputHeight = colShape[0]; - int outputWidth = colShape[1]; - for (int outputH = 0; outputH < outputHeight; ++outputH) { - for (int outputW = 0; outputW < outputWidth; ++outputW) { - for (int channel = 0; channel < inputChannels; ++channel) { - for (int filterH = 0; filterH < filterHeight; ++filterH) { - for (int filterW = 0; filterW < filterWidth; ++filterW) { - int imRowOffset = - outputH * strideHeight + filterH - paddingHeight; - int imColOffset = outputW * strideWidth + filterW - paddingWidth; - int colDataOffset = - (((outputH * outputWidth + outputW) * inputChannels + - channel) * - filterHeight + - filterH) * - filterWidth + - filterW; - if (imRowOffset >= 0 && imRowOffset < inputHeight && - imColOffset >= 0 && imColOffset < inputWidth) { - int imDataOffset = - (channel * inputHeight + imRowOffset) * inputWidth + - imColOffset; - imData[imDataOffset] += colData[colDataOffset]; + void operator()(framework::Tensor& im, const framework::Tensor& col, + int stride_height, int stride_width, int padding_height, + int padding_width) { + PADDLE_ENFORCE(im.dims().size() == 3); + PADDLE_ENFORCE(col.dims().size() == 5); + int input_channels = im.dims()[0]; + int input_height = im.dims()[1]; + int input_width = im.dims()[2]; + int filter_height = col.dims()[3]; + int filter_width = col.dims()[4]; + int output_height = col.dims()[0]; + int output_width = col.dims()[1]; + + T* im_data = im.data(); + const T* col_data = col.data(); + + for (int col_row_idx = 0; col_row_idx < output_height; ++col_row_idx) { + for (int col_col_idx = 0; col_col_idx < output_width; ++col_col_idx) { + for (int channel = 0; channel < input_channels; ++channel) { + for (int filter_row_idx = 0; filter_row_idx < filter_height; + ++filter_row_idx) { + for (int filter_col_idx = 0; filter_col_idx < filter_width; + ++filter_col_idx) { + int im_row_offset = + col_row_idx * stride_height + filter_row_idx - padding_height; + int im_col_offset = + col_col_idx * stride_width + filter_col_idx - padding_width; + int col_offset = (((col_row_idx * output_width + col_col_idx) * + input_channels + + channel) * + filter_height + + filter_row_idx) * + filter_width + + filter_col_idx; + if (im_row_offset >= 0 && im_row_offset < input_height && + im_col_offset >= 0 && im_col_offset < input_width) { + int im_offset = + (channel * input_height + im_row_offset) * input_width + + im_col_offset; + im_data[im_offset] += col_data[col_offset]; } } } @@ -207,9 +236,9 @@ class Col2ImFunctor { } }; -template class Im2ColFunctor; -template class Im2ColFunctor; -template class Col2ImFunctor; -template class Col2ImFunctor; +template class Im2ColFunctor; +template class Im2ColFunctor; +template class Col2ImFunctor; +template class Col2ImFunctor; } // namespace paddle diff --git a/paddle/operators/math/im2col.h b/paddle/operators/math/im2col.h index 4568ca2fd..f2f982b68 100644 --- a/paddle/operators/math/im2col.h +++ b/paddle/operators/math/im2col.h @@ -14,8 +14,8 @@ limitations under the License. */ #pragma once -#include "TensorShape.h" -#include "TensorType.h" +#include "paddle/framework/tensor.h" +#include "paddle/platform/device_context.h" namespace paddle { @@ -67,20 +67,20 @@ enum ColFormat { kCFO = 0, kOCF = 1 }; * \note The caller needs to ensure that imShape.inputChannels is equal to * colShape.inputChannels. */ -template +template class Im2ColFunctor { public: - void operator()(const T* imData, const TensorShape& imShape, T* colData, - const TensorShape& colShape, int strideHeight, - int strideWidth, int paddingHeight, int paddingWidth); + void operator()(const framework::Tensor& im, framework::Tensor& col, + int stride_height, int stride_width, int padding_height, + int padding_width); }; -template +template class Col2ImFunctor { public: - void operator()(T* imData, const TensorShape& imShape, const T* colData, - const TensorShape& colShape, int strideHeight, - int strideWidth, int paddingHeight, int paddingWidth); + void operator()(framework::Tensor& im, const framework::Tensor& col, + int stride_height, int stride_width, int padding_height, + int padding_width); }; } // namespace paddle -- GitLab From e967645c15a1264855bac7cbd88dd6a9ed5301ad Mon Sep 17 00:00:00 2001 From: hedaoyuan Date: Wed, 30 Aug 2017 14:31:28 +0800 Subject: [PATCH 0284/2018] Refine the gpu code. --- paddle/operators/math/CMakeLists.txt | 3 +- paddle/operators/math/im2col.cu | 452 ++++++++++++++------------- 2 files changed, 236 insertions(+), 219 deletions(-) diff --git a/paddle/operators/math/CMakeLists.txt b/paddle/operators/math/CMakeLists.txt index f31281eba..7a41e294a 100644 --- a/paddle/operators/math/CMakeLists.txt +++ b/paddle/operators/math/CMakeLists.txt @@ -1,6 +1,7 @@ if(WITH_GPU) - nv_library(math_function SRCS math_function.cc math_function.cu im2col.cc DEPS cblas device_context) + nv_library(math_function SRCS math_function.cc math_function.cu im2col.cc + im2col.cu DEPS cblas device_context) else() cc_library(math_function SRCS math_function.cc im2col.cc DEPS cblas device_context) endif() diff --git a/paddle/operators/math/im2col.cu b/paddle/operators/math/im2col.cu index 60bcdf8ac..875989af5 100644 --- a/paddle/operators/math/im2col.cu +++ b/paddle/operators/math/im2col.cu @@ -12,86 +12,89 @@ 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 "Im2Col.h" -#include "hl_device_functions.cuh" +#include "paddle/operators/math/im2col.h" +#include "paddle/platform/cuda_helper.h" namespace paddle { template -__global__ void im2col(const T* data_im, int numOuts, int height, int width, - int blockH, int blockW, int strideH, int strideW, - int paddingH, int paddingW, int height_col, - int width_col, T* data_col) { +__global__ void im2col(const T* data_im, int num_outs, int height, int width, + int filter_height, int filter_width, int stride_height, + int stride_width, int padding_height, int padding_width, + int output_height, int output_width, T* data_col) { int index = (blockIdx.x * gridDim.y + blockIdx.y) * blockDim.x + threadIdx.x; - if (index < numOuts) { - int w_out = index % width_col; - index /= width_col; - int h_out = index % height_col; - int channel_in = index / height_col; - int channel_out = channel_in * blockH * blockW; - int h_in = h_out * strideH; - int w_in = w_out * strideW; + if (index < num_outs) { + int w_out = index % output_width; + index /= output_width; + int h_out = index % output_height; + int channel_in = index / output_height; + int channel_out = channel_in * filter_height * filter_width; + int h_in = h_out * stride_height; + int w_in = w_out * stride_width; - data_col += (channel_out * height_col + h_out) * width_col + w_out; - for (int i = 0; i < blockH; ++i) { - for (int j = 0; j < blockW; ++j) { + data_col += (channel_out * output_height + h_out) * output_width + w_out; + for (int i = 0; i < filter_height; ++i) { + for (int j = 0; j < filter_width; ++j) { int rIdx = int(h_in + i); int cIdx = int(w_in + j); - if ((rIdx - (int)paddingH) >= (int)height || - (rIdx - (int)paddingH) < 0 || - (cIdx - (int)paddingW) >= (int)width || - (cIdx - (int)paddingW) < 0) { + if ((rIdx - (int)padding_height) >= (int)height || + (rIdx - (int)padding_height) < 0 || + (cIdx - (int)padding_width) >= (int)width || + (cIdx - (int)padding_width) < 0) { *data_col = 0; } else { - rIdx = rIdx + channel_in * height - paddingH; - cIdx = cIdx - paddingW; + rIdx = rIdx + channel_in * height - padding_height; + cIdx = cIdx - padding_width; *data_col = data_im[rIdx * width + cIdx]; } - data_col += height_col * width_col; + data_col += output_height * output_width; } } } } /* - * imShape = [inputChannels, inputHeight, inputWidth] - * colShape = - * [inputChannels, filterHeight, filterWidth, outputHeight, outputWidth] + * im = [input_channels, input_height, input_width] + * col = + * [input_channels, filter_height, filter_width, output_height, output_width] */ template -class Im2ColFunctor { +class Im2ColFunctor { public: - void operator()(const T* imData, const TensorShape& imShape, T* colData, - const TensorShape& colShape, int strideHeight, - int strideWidth, int paddingHeight, int paddingWidth) { - int inputChannels = imShape[0]; - int inputHeight = imShape[1]; - int inputWidth = imShape[2]; - int filterHeight = colShape[1]; - int filterWidth = colShape[2]; - int outputHeight = colShape[3]; - int outputWidth = colShape[4]; + void operator()(const framework::Tensor& im, framework::Tensor& col, + int stride_height, int stride_width, int padding_height, + int padding_width) { + PADDLE_ENFORCE(im.dims().size() == 3); + PADDLE_ENFORCE(col.dims().size() == 5); - int numKernels = inputChannels * outputHeight * outputWidth; - int blocks = (numKernels + 1024 - 1) / 1024; - int blockX = 512; - int blockY = (blocks + 512 - 1) / 512; + int input_channels = im.dims()[0]; + int input_height = im.dims()[1]; + int input_width = im.dims()[2]; + int filter_height = col.dims()[1]; + int filter_width = col.dims()[2]; + int output_height = col.dims()[3]; + int output_width = col.dims()[4]; + + int num_outputs = input_channels * output_height * output_width; + int blocks = (num_outputs + 1024 - 1) / 1024; + int block_x = 512; + int block_y = (blocks + 512 - 1) / 512; dim3 threads(1024, 1); - dim3 grid(blockX, blockY); - im2col<<>>( - imData, numKernels, inputHeight, inputWidth, filterHeight, filterWidth, - strideHeight, strideWidth, paddingHeight, paddingWidth, outputHeight, - outputWidth, colData); - CHECK_SYNC("Im2ColFunctor GPU failed"); + dim3 grid(block_x, block_y); + im2col<<>>( + im.data(), num_outputs, input_height, input_width, filter_height, + filter_width, stride_height, stride_width, padding_height, + padding_width, output_height, output_width, col.data()); } }; template __global__ void col2im(size_t n, const T* data_col, size_t height, size_t width, - size_t channels, size_t blockH, size_t blockW, - size_t strideH, size_t strideW, size_t paddingH, - size_t paddingW, size_t height_col, size_t width_col, - T* data_im) { + size_t channels, size_t filter_height, + size_t filter_width, size_t stride_height, + size_t stride_width, size_t padding_height, + size_t padding_width, size_t output_height, + size_t output_width, T* data_im) { size_t index = (blockIdx.x * gridDim.y + blockIdx.y) * blockDim.x + threadIdx.x; if (index < n) { @@ -99,104 +102,112 @@ __global__ void col2im(size_t n, const T* data_col, size_t height, size_t width, int w = int(index % width); int h = int((index / width) % height); int c = int(index / (width * height)); - if ((w - (int)paddingW) >= 0 && - (w - (int)paddingW) < (width - 2 * paddingW) && - (h - (int)paddingH) >= 0 && (h - paddingH) < (height - 2 * paddingH)) { + if ((w - (int)padding_width) >= 0 && + (w - (int)padding_width) < (width - 2 * padding_width) && + (h - (int)padding_height) >= 0 && + (h - padding_height) < (height - 2 * padding_height)) { // compute the start and end of the output - int w_col_start = - (w < (int)blockW) ? 0 : (w - int(blockW)) / (int)strideW + 1; - int w_col_end = min((int)(w / (int)strideW + 1), (int)(width_col)); - int h_col_start = - (h < (int)blockH) ? 0 : (h - (int)blockH) / (int)strideH + 1; - int h_col_end = min(int(h / strideH + 1), int(height_col)); + int w_col_start = (w < (int)filter_width) + ? 0 + : (w - int(filter_width)) / (int)stride_width + 1; + int w_col_end = + min((int)(w / (int)stride_width + 1), (int)(output_width)); + int h_col_start = (h < (int)filter_height) + ? 0 + : (h - (int)filter_height) / (int)stride_height + 1; + int h_col_end = min(int(h / stride_height + 1), int(output_height)); for (int h_col = h_col_start; h_col < h_col_end; ++h_col) { for (int w_col = w_col_start; w_col < w_col_end; ++w_col) { // the col location: [c * width * height + h_out, w_out] - int c_col = int(c * blockH * blockW) + - (h - h_col * (int)strideH) * (int)blockW + - (w - w_col * (int)strideW); - val += data_col[(c_col * height_col + h_col) * width_col + w_col]; + int c_col = int(c * filter_height * filter_width) + + (h - h_col * (int)stride_height) * (int)filter_width + + (w - w_col * (int)stride_width); + val += + data_col[(c_col * output_height + h_col) * output_width + w_col]; } } - h -= paddingH; - w -= paddingW; - data_im[c * ((width - 2 * paddingW) * (height - 2 * paddingH)) + - h * (width - 2 * paddingW) + w] += val; + h -= padding_height; + w -= padding_width; + data_im[c * ((width - 2 * padding_width) * + (height - 2 * padding_height)) + + h * (width - 2 * padding_width) + w] += val; } } } /* - * imShape = [inputChannels, inputHeight, inputWidth] - * colShape = - * [inputChannels, filterHeight, filterWidth, outputHeight, outputWidth] + * im = [input_channels, input_height, input_width] + * col = + * [input_channels, filter_height, filter_width, output_height, output_width] */ template -class Col2ImFunctor { +class Col2ImFunctor { public: - void operator()(T* imData, const TensorShape& imShape, const T* colData, - const TensorShape& colShape, int strideHeight, - int strideWidth, int paddingHeight, int paddingWidth) { - int inputChannels = imShape[0]; - int inputHeight = imShape[1]; - int inputWidth = imShape[2]; - int filterHeight = colShape[1]; - int filterWidth = colShape[2]; - int outputHeight = colShape[3]; - int outputWidth = colShape[4]; + void operator()(framework::Tensor& im, const framework::Tensor& col, + int stride_height, int stride_width, int padding_height, + int padding_width) { + PADDLE_ENFORCE(im.dims().size() == 3); + PADDLE_ENFORCE(col.dims().size() == 5); + + int input_channels = im.dims()[0]; + int input_height = im.dims()[1]; + int input_width = im.dims()[2]; + int filter_height = col.dims()[1]; + int filter_width = col.dims()[2]; + int output_height = col.dims()[3]; + int output_width = col.dims()[4]; - size_t numKernels = inputChannels * (inputHeight + 2 * paddingHeight) * - (inputWidth + 2 * paddingWidth); + size_t num_kernels = input_channels * (input_height + 2 * padding_height) * + (input_width + 2 * padding_width); - size_t blocks = (numKernels + 1024 - 1) / 1024; - size_t blockX = 512; - size_t blockY = (blocks + 512 - 1) / 512; + size_t blocks = (num_kernels + 1024 - 1) / 1024; + size_t block_x = 512; + size_t block_y = (blocks + 512 - 1) / 512; dim3 threads(1024, 1); - dim3 grid(blockX, blockY); + dim3 grid(block_x, block_y); // To avoid involving atomic operations, we will launch one kernel per // bottom dimension, and then in the kernel add up the top dimensions. - col2im<<>>( - numKernels, colData, inputHeight + 2 * paddingHeight, - inputWidth + 2 * paddingWidth, inputChannels, filterHeight, filterWidth, - strideHeight, strideWidth, paddingHeight, paddingWidth, outputHeight, - outputWidth, imData); - CHECK_SYNC("Col2ImFunctor GPU failed"); + col2im<<>>( + num_kernels, col.data(), input_height + 2 * padding_height, + input_width + 2 * padding_width, input_channels, filter_height, + filter_width, stride_height, stride_width, padding_height, + padding_width, output_height, output_width, im.data()); } }; -template class Im2ColFunctor; -template class Im2ColFunctor; -template class Col2ImFunctor; -template class Col2ImFunctor; +template class Im2ColFunctor; +template class Im2ColFunctor; +template class Col2ImFunctor; +template class Col2ImFunctor; template -__global__ void im2colOCF(const T* imData, T* colData, int inputChannels, - int inputHeight, int inputWidth, int filterHeight, - int filterWidth, int strideHeight, int strideWidth, - int paddingHeight, int paddingWidth, int outputHeight, - int outputWidth) { - int swId = blockIdx.x; - int shId = blockIdx.y; - for (int channelId = threadIdx.z; channelId < inputChannels; - channelId += blockDim.z) { - for (int idy = threadIdx.y; idy < filterHeight; idy += blockDim.y) { - for (int idx = threadIdx.x; idx < filterWidth; idx += blockDim.x) { - int widthOffset = idx + swId * strideWidth - paddingWidth; - int heightOffset = idy + shId * strideHeight - paddingHeight; - int imOffset = widthOffset + heightOffset * inputWidth + - channelId * inputHeight * inputWidth; +__global__ void im2colOCF(const T* im_data, T* col_data, int input_channels, + int input_height, int input_width, int filter_height, + int filter_width, int stride_height, int stride_width, + int padding_height, int padding_width, + int output_height, int output_width) { + int swid = blockIdx.x; + int shid = blockIdx.y; + for (int channelid = threadIdx.z; channelid < input_channels; + channelid += blockDim.z) { + for (int idy = threadIdx.y; idy < filter_height; idy += blockDim.y) { + for (int idx = threadIdx.x; idx < filter_width; idx += blockDim.x) { + int width_offset = idx + swid * stride_width - padding_width; + int height_offset = idy + shid * stride_height - padding_height; + int im_offset = width_offset + height_offset * input_width + + channelid * input_height * input_width; - int colOffset = idx + idy * filterWidth + - channelId * filterHeight * filterWidth + - (shId * outputWidth + swId) * - (inputChannels * filterHeight * filterWidth); + int col_offset = idx + idy * filter_width + + channelid * filter_height * filter_width + + (shid * output_width + swid) * + (input_channels * filter_height * filter_width); - if (heightOffset >= inputHeight || heightOffset < 0 || - widthOffset >= inputWidth || widthOffset < 0) { - colData[colOffset] = T(0); + if (height_offset >= input_height || height_offset < 0 || + width_offset >= input_width || width_offset < 0) { + col_data[col_offset] = T(0); } else { - colData[colOffset] = imData[imOffset]; + col_data[col_offset] = im_data[im_offset]; } } } @@ -204,76 +215,79 @@ __global__ void im2colOCF(const T* imData, T* colData, int inputChannels, } /* - * imShape = [inputChannels, inputHeight, inputWidth] - * colShape = - * [outputHeight, outputWidth, inputChannels, filterHeight, filterWidth] + * im = [input_channels, input_height, input_width] + * col = + * [output_height, output_width, input_channels, filter_height, filter_width] */ template -class Im2ColFunctor { +class Im2ColFunctor { public: - void operator()(const T* imData, const TensorShape& imShape, T* colData, - const TensorShape& colShape, int strideHeight, - int strideWidth, int paddingHeight, int paddingWidth) { - int inputChannels = imShape[0]; - int inputHeight = imShape[1]; - int inputWidth = imShape[2]; - int filterHeight = colShape[3]; - int filterWidth = colShape[4]; - int outputHeight = colShape[0]; - int outputWidth = colShape[1]; + void operator()(const framework::Tensor& im, framework::Tensor& col, + int stride_height, int stride_width, int padding_height, + int padding_width) { + PADDLE_ENFORCE(im.dims().size() == 3); + PADDLE_ENFORCE(col.dims().size() == 5); + int input_channels = im.dims()[0]; + int input_height = im.dims()[1]; + int input_width = im.dims()[2]; + int filter_height = col.dims()[3]; + int filter_width = col.dims()[4]; + int output_height = col.dims()[0]; + int output_width = col.dims()[1]; - int blockDimX = 0; - int blockDimY = 0; - if (filterHeight <= 4 && filterWidth <= 4) { - blockDimX = 4; - blockDimY = 4; - } else if (filterHeight <= 8 && filterWidth <= 8) { - blockDimX = 8; - blockDimY = 8; - } else if (filterHeight <= 16 && filterWidth <= 16) { - blockDimX = 16; - blockDimY = 16; + int block_dim_x = 0; + int block_dim_y = 0; + if (filter_height <= 4 && filter_width <= 4) { + block_dim_x = 4; + block_dim_y = 4; + } else if (filter_height <= 8 && filter_width <= 8) { + block_dim_x = 8; + block_dim_y = 8; + } else if (filter_height <= 16 && filter_width <= 16) { + block_dim_x = 16; + block_dim_y = 16; } else { - blockDimX = 32; - blockDimY = 32; + block_dim_x = 32; + block_dim_y = 32; } - int blockDimZ = 1024 / blockDimX / blockDimY; - dim3 threads(blockDimX, blockDimY, std::min(blockDimZ, inputChannels)); - dim3 grid(outputWidth, outputHeight); - im2colOCF<<>>( - imData, colData, inputChannels, inputHeight, inputWidth, filterHeight, - filterWidth, strideHeight, strideWidth, paddingHeight, paddingWidth, - outputHeight, outputWidth); - CHECK_SYNC("Im2ColFunctor GPU failed"); + int block_dim_z = 1024 / block_dim_x / block_dim_y; + dim3 threads(block_dim_x, block_dim_y, + std::min(block_dim_z, input_channels)); + dim3 grid(output_width, output_height); + im2colOCF<<>>( + im.data(), col.data(), input_channels, input_height, input_width, + filter_height, filter_width, stride_height, stride_width, + padding_height, padding_width, output_height, output_width); } }; template -__global__ void col2imOCF(T* imData, const T* colData, int inputChannels, - int inputHeight, int inputWidth, int filterHeight, - int filterWidth, int strideHeight, int strideWidth, - int paddingHeight, int paddingWidth, int outputHeight, - int outputWidth) { - int swId = blockIdx.x; - int shId = blockIdx.y; - for (int channelId = threadIdx.z; channelId < inputChannels; - channelId += blockDim.z) { - for (int idy = threadIdx.y; idy < filterHeight; idy += blockDim.y) { - for (int idx = threadIdx.x; idx < filterWidth; idx += blockDim.x) { - int widthOffset = idx + swId * strideWidth - paddingWidth; - int heightOffset = idy + shId * strideHeight - paddingHeight; - int imOffset = widthOffset + heightOffset * inputWidth + - channelId * inputHeight * inputWidth; +__global__ void col2imOCF(T* im_data, const T* col_data, int input_channels, + int input_height, int input_width, int filter_height, + int filter_width, int stride_height, int stride_width, + int padding_height, int padding_width, + int output_height, int output_width) { + int swid = blockIdx.x; + int shid = blockIdx.y; + for (int channelid = threadIdx.z; channelid < input_channels; + channelid += blockDim.z) { + for (int idy = threadIdx.y; idy < filter_height; idy += blockDim.y) { + for (int idx = threadIdx.x; idx < filter_width; idx += blockDim.x) { + int width_offset = idx + swid * stride_width - padding_width; + int height_offset = idy + shid * stride_height - padding_height; + int im_offset = width_offset + height_offset * input_width + + channelid * input_height * input_width; - int colOffset = idx + idy * filterWidth + - channelId * filterHeight * filterWidth + - (shId * outputWidth + swId) * - (inputChannels * filterHeight * filterWidth); + int col_offset = idx + idy * filter_width + + channelid * filter_height * filter_width + + (shid * output_width + swid) * + (input_channels * filter_height * filter_width); - if (heightOffset >= 0 && heightOffset < inputHeight && - widthOffset >= 0 && widthOffset < inputWidth) { - paddle::paddleAtomicAdd(imData + imOffset, colData[colOffset]); + if (height_offset >= 0 && height_offset < input_height && + width_offset >= 0 && width_offset < input_width) { + paddle::platform::CudaAtomicAdd(im_data + im_offset, + col_data[col_offset]); } } } @@ -281,54 +295,56 @@ __global__ void col2imOCF(T* imData, const T* colData, int inputChannels, } /* - * imShape = [inputChannels, inputHeight, inputWidth] - * colShape = - * [outputHeight, outputWidth, inputChannels, filterHeight, filterWidth] + * im = [input_channels, input_height, input_width] + * col = + * [output_height, output_width, input_channels, filter_height, filter_width] */ template -class Col2ImFunctor { +class Col2ImFunctor { public: - void operator()(T* imData, const TensorShape& imShape, const T* colData, - const TensorShape& colShape, int strideHeight, - int strideWidth, int paddingHeight, int paddingWidth) { - int inputChannels = imShape[0]; - int inputHeight = imShape[1]; - int inputWidth = imShape[2]; - int filterHeight = colShape[3]; - int filterWidth = colShape[4]; - int outputHeight = colShape[0]; - int outputWidth = colShape[1]; + void operator()(framework::Tensor& im, const framework::Tensor& col, + int stride_height, int stride_width, int padding_height, + int padding_width) { + PADDLE_ENFORCE(im.dims().size() == 3); + PADDLE_ENFORCE(col.dims().size() == 5); + int input_channels = im.dims()[0]; + int input_height = im.dims()[1]; + int input_width = im.dims()[2]; + int filter_height = col.dims()[3]; + int filter_width = col.dims()[4]; + int output_height = col.dims()[0]; + int output_width = col.dims()[1]; - int blockDimX = 0; - int blockDimY = 0; - if (filterHeight <= 4 && filterWidth <= 4) { - blockDimX = 4; - blockDimY = 4; - } else if (filterHeight <= 8 && filterWidth <= 8) { - blockDimX = 8; - blockDimY = 8; - } else if (filterHeight <= 16 && filterWidth <= 16) { - blockDimX = 16; - blockDimY = 16; + int block_dim_x = 0; + int block_dim_y = 0; + if (filter_height <= 4 && filter_width <= 4) { + block_dim_x = 4; + block_dim_y = 4; + } else if (filter_height <= 8 && filter_width <= 8) { + block_dim_x = 8; + block_dim_y = 8; + } else if (filter_height <= 16 && filter_width <= 16) { + block_dim_x = 16; + block_dim_y = 16; } else { - blockDimX = 32; - blockDimY = 32; + block_dim_x = 32; + block_dim_y = 32; } - int blockDimZ = 1024 / blockDimX / blockDimY; - dim3 threads(blockDimX, blockDimY, std::min(blockDimZ, inputChannels)); - dim3 grid(outputWidth, outputHeight); - col2imOCF<<>>( - imData, colData, inputChannels, inputHeight, inputWidth, filterHeight, - filterWidth, strideHeight, strideWidth, paddingHeight, paddingWidth, - outputHeight, outputWidth); - CHECK_SYNC("Col2ImFunctor GPU failed"); + int block_dim_z = 1024 / block_dim_x / block_dim_y; + dim3 threads(block_dim_x, block_dim_y, + std::min(block_dim_z, input_channels)); + dim3 grid(output_width, output_height); + col2imOCF<<>>( + im.data(), col.data(), input_channels, input_height, input_width, + filter_height, filter_width, stride_height, stride_width, + padding_height, padding_width, output_height, output_width); } }; -template class Im2ColFunctor; -template class Im2ColFunctor; -template class Col2ImFunctor; -template class Col2ImFunctor; +template class Im2ColFunctor; +template class Im2ColFunctor; +template class Col2ImFunctor; +template class Col2ImFunctor; } // namespace paddle -- GitLab From 2d707e32c83d92a857b7e5359aae9415f8464d11 Mon Sep 17 00:00:00 2001 From: hedaoyuan Date: Wed, 30 Aug 2017 14:39:32 +0800 Subject: [PATCH 0285/2018] Refine the comments. --- paddle/operators/math/im2col.h | 36 +++++++++++++++++----------------- 1 file changed, 18 insertions(+), 18 deletions(-) diff --git a/paddle/operators/math/im2col.h b/paddle/operators/math/im2col.h index f2f982b68..da51bc69a 100644 --- a/paddle/operators/math/im2col.h +++ b/paddle/operators/math/im2col.h @@ -29,40 +29,40 @@ enum ColFormat { kCFO = 0, kOCF = 1 }; * * \param imData Image data. * \param imShape The shape of imData, - * [inputChannels, inputHeight, inputWidth]. + * [input_channels, input_height, input_width]. * \param colData Column data. * \param colShape The shape of colData. * * If the template argument Format is kCFO, the shape of colData is: - * [inputChannels, filterHeight, filterWidth, outputHeight, outputWidth] + * [input_channels, filter_height, filter_width, output_height, output_width] * So, it is easy to reshape into a convolution matrix for convolution * calculation based on matrix multiplication. * The shape of convolution matrix is [height, width], where the height is equal - * inputChannels * filterHeight * filterWidth, and the width is equal - * outputHeight * outputWidth. + * input_channels * filter_height * filter_width, and the width is equal + * output_height * output_width. * * Reshape: * shape of colData shape of convolution matrix - * [inputChannels, - * filterHeight, - * filterWidth, ======> [height, width] - * outputHeight, - * outputWidth] + * [input_channels, + * filter_height, + * filter_width, ======> [height, width] + * output_height, + * output_width] * * If the template argument Format is kOCF, the shape of colData is: - * [outputHeight, outputWidth, inputChannels, filterHeight, filterWidth] + * [output_height, output_width, input_channels, filter_height, filter_width] * So, it is easy to reshape into a sequence matrix for rnn calculation. - * The shape of sequence matrix is [seqLength, stepSize], where the seqLength - * is equal outputHeight * outputWidth, and the stepSize is equal - * inputChannels * filterHeight * filterWidth. + * The shape of sequence matrix is [seq_length, step_size], where the seq_length + * is equal output_height * output_width, and the step_size is equal + * input_channels * filter_height * filter_width. * * Reshape: * shape of colData shape of sequence matrix - * [outputHeight, - * outputWidth, - * inputChannels, ======> [seqLength, stepSize] - * filterHeight, - * filterWidth] + * [output_height, + * output_width, + * input_channels, ======> [seqLength, stepSize] + * filter_height, + * filter_width] * * \note The caller needs to ensure that imShape.inputChannels is equal to * colShape.inputChannels. -- GitLab From 31632a694c718ac31b890b1b46788f9d70d570c8 Mon Sep 17 00:00:00 2001 From: Luo Tao Date: Wed, 30 Aug 2017 14:48:03 +0800 Subject: [PATCH 0286/2018] remove unused ubuntu Debian install doc --- doc/getstarted/build_and_install/index_cn.rst | 4 +- doc/getstarted/build_and_install/index_en.rst | 3 +- .../build_and_install/ubuntu_install_cn.rst | 71 ------------------- .../build_and_install/ubuntu_install_en.rst | 25 ------- 4 files changed, 2 insertions(+), 101 deletions(-) delete mode 100644 doc/getstarted/build_and_install/ubuntu_install_cn.rst delete mode 100644 doc/getstarted/build_and_install/ubuntu_install_en.rst diff --git a/doc/getstarted/build_and_install/index_cn.rst b/doc/getstarted/build_and_install/index_cn.rst index a24df6c51..dd9923697 100644 --- a/doc/getstarted/build_and_install/index_cn.rst +++ b/doc/getstarted/build_and_install/index_cn.rst @@ -6,14 +6,12 @@ 安装流程 ++++++++ -PaddlePaddle提供数个预编译的二进制来进行安装，包括Docker镜像，ubuntu的deb安装包等。我们推荐使用Docker镜像来部署环境，同时欢迎贡献更多的安装包。 +PaddlePaddle提供Docker镜像来部署环境。 .. toctree:: :maxdepth: 1 docker_install_cn.rst - ubuntu_install_cn.rst - 编译流程 diff --git a/doc/getstarted/build_and_install/index_en.rst b/doc/getstarted/build_and_install/index_en.rst index 1bfd4f75c..8a53588e0 100644 --- a/doc/getstarted/build_and_install/index_en.rst +++ b/doc/getstarted/build_and_install/index_en.rst @@ -8,14 +8,13 @@ Install PaddlePaddle :maxdepth: 1 docker_install_en.rst - ubuntu_install_en.rst Build from Source ----------------- .. warning:: - Please use :code:`deb` package or :code:`docker` image to install paddle. The building guide is used for hacking or contributing PaddlePaddle source code. + Please use :code:`docker` image to install paddle. The building guide is used for hacking or contributing PaddlePaddle source code. .. toctree:: :maxdepth: 1 diff --git a/doc/getstarted/build_and_install/ubuntu_install_cn.rst b/doc/getstarted/build_and_install/ubuntu_install_cn.rst deleted file mode 100644 index 9e39ccb00..000000000 --- a/doc/getstarted/build_and_install/ubuntu_install_cn.rst +++ /dev/null @@ -1,71 +0,0 @@ -Ubuntu部署PaddlePaddle -=================================== - -PaddlePaddle提供了ubuntu 14.04 deb安装包。 - -安装 ------- - -安装包的下载地址是\: https://github.com/PaddlePaddle/Paddle/releases - -它包含四个版本\: - -* cpu版本: 支持主流x86处理器平台, 使用了avx指令集。 - -* cpu-noavx版本：支持主流x86处理器平台，没有使用avx指令集。 - -* gpu版本：支持主流x86处理器平台，支持nvidia cuda平台，使用了avx指令集。 - -* gpu-noavx版本：支持主流x86处理器平台，支持nvidia cuda平台，没有使用avx指令集。 - -下载完相关安装包后，执行: - -.. code-block:: shell - - sudo apt-get install gdebi - gdebi paddle-*-cpu.deb - -或者: - -.. code-block:: shell - - dpkg -i paddle-*-cpu.deb - apt-get install -f - - -在 :code:`dpkg -i` 的时候如果报一些依赖未找到的错误是正常的， -在 :code:`apt-get install -f` 里会继续安装 PaddlePaddle。 - -安装完成后，可以使用命令 :code:`paddle version` 查看安装后的paddle 版本: - -.. code-block:: shell - - PaddlePaddle 0.8.0b1, compiled with - with_avx: ON - with_gpu: OFF - with_double: OFF - with_python: ON - with_rdma: OFF - with_timer: OFF - with_predict_sdk: - - -可能遇到的问题 --------------- - -libcudart.so/libcudnn.so找不到 -++++++++++++++++++++++++++++++ - -安装完成后，运行 :code:`paddle train` 报错\: - -.. code-block:: shell - - 0831 12:36:04.151525 1085 hl_dso_loader.cc:70] Check failed: nullptr != *dso_handle For Gpu version of PaddlePaddle, it couldn't find CUDA library: libcudart.so Please make sure you already specify its path.Note: for training data on Cpu using Gpu version of PaddlePaddle,you must specify libcudart.so via LD_LIBRARY_PATH. - -原因是未设置cuda运行时环境变量。 如果使用GPU版本的PaddlePaddle，请安装CUDA 7.5 和CUDNN 5到本地环境中，并设置： - -.. code-block:: shell - - export LD_LIBRARY_PATH=/usr/local/cuda/lib64:/usr/local/cuda/lib:$LD_LIBRARY_PATH - export PATH=/usr/local/cuda/bin:$PATH - diff --git a/doc/getstarted/build_and_install/ubuntu_install_en.rst b/doc/getstarted/build_and_install/ubuntu_install_en.rst deleted file mode 100644 index ea8042085..000000000 --- a/doc/getstarted/build_and_install/ubuntu_install_en.rst +++ /dev/null @@ -1,25 +0,0 @@ -Debian Package installation guide -================================= - -PaddlePaddle supports :code:`deb` pacakge. The installation of this :code:`deb` package is tested in ubuntu 14.04, but it should be support other debian based linux, too. - -There are four versions of debian package, :code:`cpu`, :code:`gpu`, :code:`cpu-noavx`, :code:`gpu-noavx`. And :code:`noavx` version is used to support CPU which does not contain :code:`AVX` instructions. The download url of :code:`deb` package is \: https://github.com/baidu/Paddle/releases/ - - -After downloading PaddlePaddle deb packages, you can use :code:`gdebi` install. - -.. code-block:: bash - - gdebi paddle-*.deb - -If :code:`gdebi` is not installed, you can use :code:`sudo apt-get install gdebi` to install it. - -Or you can use following commands to install PaddlePaddle. - -.. code-block:: bash - - dpkg -i paddle-*.deb - apt-get install -f - -And if you use GPU version deb package, you need to install CUDA toolkit and cuDNN, and set related environment variables(such as LD_LIBRARY_PATH) first. It is normal when `dpkg -i` get errors. `apt-get install -f` will continue install paddle, and install dependences. - -- GitLab From 224f8b06f41827c125aee1374b8259f878cc3c78 Mon Sep 17 00:00:00 2001 From: Liu Yiqun Date: Wed, 30 Aug 2017 15:12:30 +0800 Subject: [PATCH 0287/2018] Support building for multiple architecures at one time. --- CMakeLists.txt | 34 ++++++++------------------------- cmake/cross_compiling/ios.cmake | 8 +++++++- cmake/external/openblas.cmake | 23 ++++++++++++++++------ cmake/external/zlib.cmake | 18 +++++++++-------- ios_run.sh | 17 ----------------- 5 files changed, 42 insertions(+), 58 deletions(-) delete mode 100644 ios_run.sh diff --git a/CMakeLists.txt b/CMakeLists.txt index e3dec9b21..bca2b796e 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -63,38 +63,23 @@ if(NOT CMAKE_BUILD_TYPE) FORCE) endif() -if(IOS) - set(WITH_GPU OFF CACHE STRING - "Disable GPU when cross-compiling for Android" FORCE) - set(WITH_AVX OFF CACHE STRING - "Disable AVX when cross-compiling for Android" FORCE) - set(WITH_PYTHON OFF CACHE STRING - "Disable PYTHON when cross-compiling for Android" FORCE) - set(WITH_RDMA OFF CACHE STRING - "Disable RDMA when cross-compiling for Android" FORCE) - set(WITH_MKLDNN OFF CACHE STRING - "Disable MKLDNN when cross-compiling for Android" FORCE) - set(WITH_MKLML OFF CACHE STRING - "Disable MKLML package when cross-compiling for Android" FORCE) -endif(IOS) - -if(ANDROID) - if(${CMAKE_SYSTEM_VERSION} VERSION_LESS "21") +if(ANDROID OR IOS) + if(ANDROID AND ${CMAKE_SYSTEM_VERSION} VERSION_LESS "21") message(FATAL_ERROR "Unsupport standalone toolchains with Android API level lower than 21") endif() set(WITH_GPU OFF CACHE STRING - "Disable GPU when cross-compiling for Android" FORCE) + "Disable GPU when cross-compiling for Android and iOS" FORCE) set(WITH_AVX OFF CACHE STRING - "Disable AVX when cross-compiling for Android" FORCE) + "Disable AVX when cross-compiling for Android and iOS" FORCE) set(WITH_PYTHON OFF CACHE STRING - "Disable PYTHON when cross-compiling for Android" FORCE) + "Disable PYTHON when cross-compiling for Android and iOS" FORCE) set(WITH_RDMA OFF CACHE STRING - "Disable RDMA when cross-compiling for Android" FORCE) + "Disable RDMA when cross-compiling for Android and iOS" FORCE) set(WITH_MKLDNN OFF CACHE STRING - "Disable MKLDNN when cross-compiling for Android" FORCE) + "Disable MKLDNN when cross-compiling for Android and iOS" FORCE) set(WITH_MKLML OFF CACHE STRING - "Disable MKLML package when cross-compiling for Android" FORCE) + "Disable MKLML package when cross-compiling for Android and iOS" FORCE) endif(ANDROID) set(THIRD_PARTY_PATH "${CMAKE_BINARY_DIR}/third_party" CACHE STRING @@ -167,9 +152,6 @@ if(USE_NNPACK) list(APPEND EXTERNAL_LIBS ${NNPACK_LIBS}) endif(USE_NNPACK) -message(STATUS "CMAKE_C_FLAGS: ${CMAKE_C_FLAGS}") -message(STATUS "CMAKE_CXX_FLAGS: ${CMAKE_CXX_FLAGS}") - add_subdirectory(proto) if(NOT ANDROID AND NOT IOS) diff --git a/cmake/cross_compiling/ios.cmake b/cmake/cross_compiling/ios.cmake index dbdf29e1d..b15dcec9b 100644 --- a/cmake/cross_compiling/ios.cmake +++ b/cmake/cross_compiling/ios.cmake @@ -160,7 +160,7 @@ if(NOT DEFINED IOS_SDK_ROOT) endif(IOS_SDK_LISTS) endif() if(EXISTS ${IOS_SDK_ROOT}) - set(CMAKE_IOS_SDK_ROOT ${CMAKE_IOS_SDK_ROOT} CACHE PATH "Location of the selected iOS SDK") + set(IOS_SDK_ROOT ${IOS_SDK_ROOT} CACHE PATH "Location of the selected iOS SDK") message(STATUS "iOS toolchain: ${IOS_SDK_ROOT}") else() message(FATAL_ERROR "Invalid IOS_SDK_ROOT: ${IOS_SDK_ROOT} does not exist.") @@ -292,6 +292,12 @@ message(STATUS "iOS: Targeting iOS '${CMAKE_SYSTEM_VERSION}', " message(STATUS "System CMAKE_C_FLAGS: ${CMAKE_C_FLAGS}") message(STATUS "System CMAKE_CXX_FLAGS: ${CMAKE_CXX_FLAGS}") +# Used in ExternalProject command +string(REPLACE ";" "\\$" EXTERNAL_IOS_ARCHITECTURES "${CMAKE_OSX_ARCHITECTURES}") +set(EXTERNAL_OPTIONAL_ARGS + -DCMAKE_OSX_SYSROOT=${CMAKE_OSX_SYSROOT} + -DCMAKE_OSX_ARCHITECTURES=${EXTERNAL_IOS_ARCHITECTURES}) + # This little macro lets you set any XCode specific property macro(set_xcode_property TARGET XCODE_PROPERTY XCODE_VALUE) set_property (TARGET ${TARGET} PROPERTY XCODE_ATTRIBUTE_${XCODE_PROPERTY} ${XCODE_VALUE}) diff --git a/cmake/external/openblas.cmake b/cmake/external/openblas.cmake index 0eeccbf7d..025eb62a4 100644 --- a/cmake/external/openblas.cmake +++ b/cmake/external/openblas.cmake @@ -25,23 +25,32 @@ IF(NOT ${CBLAS_FOUND}) "${CBLAS_INSTALL_DIR}/lib/${CMAKE_STATIC_LIBRARY_PREFIX}openblas${CMAKE_STATIC_LIBRARY_SUFFIX}" CACHE FILEPATH "openblas library." FORCE) - SET(COMMON_ARGS CC=${CMAKE_C_COMPILER} NO_SHARED=1 NO_LAPACK=1 libs) - + SET(OPENBLAS_CC "${CMAKE_C_COMPILER}") IF(CMAKE_CROSSCOMPILING) + SET(OPTIONAL_ARGS HOSTCC=${HOST_C_COMPILER}) IF(ANDROID) # arm_soft_fp_abi branch of OpenBLAS to support softfp # https://github.com/xianyi/OpenBLAS/tree/arm_soft_fp_abi SET(OPENBLAS_COMMIT "b5c96fcfcdc82945502a2303116a64d89985daf5") IF(ANDROID_ABI MATCHES "^armeabi(-v7a)?$") - SET(TARGET "ARMV7") + SET(OPTIONAL_ARGS ${OPTIONAL_ARGS} TARGET=ARMV7 ARM_SOFTFP_ABI=1 USE_THREAD=0) ELSEIF(ANDROID_ABI STREQUAL "arm64-v8a") - SET(TARGET "ARMV8") + SET(OPTIONAL_ARGS ${OPTIONAL_ARGS} TARGET=ARMV8 BINARY=64 USE_THREAD=0) + ENDIF() + ELSEIF(IOS) + # FIXME: support multiple architectures + SET(OPENBLAS_COMMIT "b5c96fcfcdc82945502a2303116a64d89985daf5") + IF(CMAKE_OSX_ARCHITECTURES MATCHES "armv7") + SET(OPENBLAS_CC "${OPENBLAS_CC} -isysroot ${CMAKE_OSX_SYSROOT} -arch armv7") + SET(OPTIONAL_ARGS ${OPTIONAL_ARGS} TARGET=ARMV7 ARM_SOFTFP_ABI=1 USE_THREAD=0) + ELSEIF(CMAKE_OSX_ARCHITECTURES MATCHES "arm64") + SET(OPENBLAS_CC "${OPENBLAS_CC} -isysroot ${CMAKE_OSX_SYSROOT} -arch arm64") + SET(OPTIONAL_ARGS ${OPTIONAL_ARGS} TARGET=ARMV8 BINARY=64 USE_THREAD=0) ENDIF() - SET(OPTIONAL_ARGS HOSTCC=${HOST_C_COMPILER} TARGET=${TARGET} ARM_SOFTFP_ABI=1 USE_THREAD=0) ELSEIF(RPI) # use hardfp SET(OPENBLAS_COMMIT "v0.2.19") - SET(OPTIONAL_ARGS HOSTCC=${HOST_C_COMPILER} TARGET=ARMV7 USE_THREAD=0) + SET(OPTIONAL_ARGS ${OPTIONAL_ARGS} TARGET=ARMV7 USE_THREAD=0) ENDIF() ELSE() SET(OPENBLAS_COMMIT "v0.2.19") @@ -51,6 +60,8 @@ IF(NOT ${CBLAS_FOUND}) ENDIF() ENDIF() + SET(COMMON_ARGS CC=${OPENBLAS_CC} NO_SHARED=1 NO_LAPACK=1 libs) + ExternalProject_Add( extern_openblas ${EXTERNAL_PROJECT_LOG_ARGS} diff --git a/cmake/external/zlib.cmake b/cmake/external/zlib.cmake index 45ca5542b..2fadea9c6 100644 --- a/cmake/external/zlib.cmake +++ b/cmake/external/zlib.cmake @@ -27,6 +27,7 @@ ENDIF(WIN32) INCLUDE_DIRECTORIES(${ZLIB_INCLUDE_DIR}) + ExternalProject_Add( zlib ${EXTERNAL_PROJECT_LOG_ARGS} @@ -34,15 +35,16 @@ ExternalProject_Add( GIT_TAG "v1.2.8" PREFIX ${ZLIB_SOURCES_DIR} UPDATE_COMMAND "" - CMAKE_ARGS -DCMAKE_CXX_COMPILER=${CMAKE_CXX_COMPILER} CMAKE_ARGS -DCMAKE_C_COMPILER=${CMAKE_C_COMPILER} - CMAKE_ARGS -DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS} - CMAKE_ARGS -DCMAKE_C_FLAGS=${CMAKE_C_FLAGS} - CMAKE_ARGS -DCMAKE_INSTALL_PREFIX=${ZLIB_INSTALL_DIR} - CMAKE_ARGS -DBUILD_SHARED_LIBS=OFF - CMAKE_ARGS -DCMAKE_POSITION_INDEPENDENT_CODE=ON - CMAKE_ARGS -DCMAKE_MACOSX_RPATH=ON - CMAKE_ARGS -DCMAKE_BUILD_TYPE=Release + -DCMAKE_CXX_COMPILER=${CMAKE_CXX_COMPILER} + -DCMAKE_C_FLAGS=${CMAKE_C_FLAGS} + -DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS} + -DCMAKE_INSTALL_PREFIX=${ZLIB_INSTALL_DIR} + -DBUILD_SHARED_LIBS=OFF + -DCMAKE_POSITION_INDEPENDENT_CODE=ON + -DCMAKE_MACOSX_RPATH=ON + -DCMAKE_BUILD_TYPE=Release + ${EXTERNAL_OPTIONAL_ARGS} CMAKE_CACHE_ARGS -DCMAKE_INSTALL_PREFIX:PATH=${ZLIB_INSTALL_DIR} -DCMAKE_POSITION_INDEPENDENT_CODE:BOOL=ON -DCMAKE_BUILD_TYPE:STRING=Release diff --git a/ios_run.sh b/ios_run.sh deleted file mode 100644 index b8325f15f..000000000 --- a/ios_run.sh +++ /dev/null @@ -1,17 +0,0 @@ -#!/bin/bash - -set -xe - -mkdir -p ./ios_build -cd ./ios_build - -cmake -DCMAKE_SYSTEM_NAME=Darwin \ - -DWITH_C_API=ON \ - -DWITH_TESTING=OFF \ - -DWITH_SWIG_PY=OFF \ - -DCMAKE_BUILD_TYPE=Release \ - -DCMAKE_INSTALL_PREFIX=/Users/xingzhaolong/cross_compile/ios \ - .. - # -DIOS_PLATFORM=SIMULATOR \ - #-DCMAKE_Go_COMPILER=/usr/local/bin \ - -- GitLab From 1dfc5d87ff4b4f40272e387d598a1bec5477d127 Mon Sep 17 00:00:00 2001 From: qijun Date: Wed, 30 Aug 2017 17:06:16 +0800 Subject: [PATCH 0288/2018] add more details --- doc/howto/dev/use_eigen_cn.md | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/doc/howto/dev/use_eigen_cn.md b/doc/howto/dev/use_eigen_cn.md index d22ff4799..49a726959 100644 --- a/doc/howto/dev/use_eigen_cn.md +++ b/doc/howto/dev/use_eigen_cn.md @@ -120,7 +120,7 @@ for (int i = 0; i < 1 * 2 * 3; i++) { EigenTensor::Type et = EigenTensor::From(t); ``` -From是EigenTensor模板struct提供的一个接口，可以实现从paddle::framework::Tensor到对EigenTensor的转换。由于Tensor的rank是模板参数，因此在转换时需要显示的指定。 +From是EigenTensor模板提供的一个接口，可以实现从paddle::framework::Tensor到对EigenTensor的转换。由于Tensor的rank是模板参数，因此在转换时需要显示的指定。 需要额外注意的是，EigenVector::From方法是把paddle中的一维Tensor转为Eigen的一维Tensor，在这里用EigenVector来表示；而EigenVector::Flatten方法是把paddle中的一个Tensor进行reshape操作，压扁成为Eigen的一维Tensor，类型仍然为EigenVector。 @@ -130,11 +130,16 @@ From是EigenTensor模板struct提供的一个接口，可以实现从paddle::fra ### 实现计算 -当需要完成计算时，我们需要等式左边的EigenTensor调用device接口： +当需要完成计算时，我们需要等式左边的EigenTensor调用device接口。在这里需要注意的是，这里的EigenTensor之间的运算只是改变了原有Tensor中的数据，而不会改变原有Tensor的shape信息。 ``` +auto X = EigenVector::Flatten(*input0); +auto Y = EigenVector::Flatten(*input1); +auto Z = EigenVector::Flatten(*output); auto place = context.GetEigenDevice(); Z.device(place) = X + Y; ``` +在这段代码中，input0/input1/output可以是任意维度的Tensor。我们调用了EigenVector的Flatten接口，把任意维度的Tensor转为了一维的EigenVector。而在计算结束之后，input0/input1/output的原有shape信息不变。如果想改变原有Tensor的shape信息，可以调用Resize接口进行改变。 + 由于Eigen Tensor模块的文档较少，我们可以参考TensorFlow的[kernels](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/core/kernels)模块下的相关`OpKernel`的计算代码。 -- GitLab From 64791188952437852ad549914a70baea3320f827 Mon Sep 17 00:00:00 2001 From: Luo Tao Date: Wed, 30 Aug 2017 17:36:22 +0800 Subject: [PATCH 0289/2018] fix download mklml error --- cmake/external/mklml.cmake | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/cmake/external/mklml.cmake b/cmake/external/mklml.cmake index 51fafb947..77ea24490 100644 --- a/cmake/external/mklml.cmake +++ b/cmake/external/mklml.cmake @@ -54,7 +54,8 @@ ExternalProject_Add( ${EXTERNAL_PROJECT_LOG_ARGS} PREFIX ${MKLML_SOURCE_DIR} DOWNLOAD_DIR ${MKLML_DOWNLOAD_DIR} - DOWNLOAD_COMMAND wget --no-check-certificate -qO- ${MKLML_URL} | tar xz -C ${MKLML_DOWNLOAD_DIR} + DOWNLOAD_COMMAND wget --no-check-certificate ${MKLML_URL} -c -O ${MKLML_VER}.tgz + && tar zxf ${MKLML_DOWNLOAD_DIR}/${MKLML_VER}.tgz DOWNLOAD_NO_PROGRESS 1 UPDATE_COMMAND "" CMAKE_ARGS -DCMAKE_INSTALL_PREFIX=${MKLML_INSTALL_ROOT} -- GitLab From 2563e32bb12b363c41d608bf0f6f1060ea769f8b Mon Sep 17 00:00:00 2001 From: qijun Date: Wed, 30 Aug 2017 17:57:26 +0800 Subject: [PATCH 0290/2018] fix clang build error --- paddle/gserver/layers/CostLayer.h | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/paddle/gserver/layers/CostLayer.h b/paddle/gserver/layers/CostLayer.h index 0ce72ef40..0f655b48e 100644 --- a/paddle/gserver/layers/CostLayer.h +++ b/paddle/gserver/layers/CostLayer.h @@ -318,7 +318,9 @@ public: void forwardImp(Matrix& output, Argument& label, Matrix& cost) override; - void backwardImp(Matrix& outputValue, Argument& label, Matrix& outputGrad) {} + void backwardImp(Matrix& outputValue, + Argument& label, + Matrix& outputGrad) override {} }; /** -- GitLab From d57ffc4557620b52d3c0a0e05cbea07f6c5efc31 Mon Sep 17 00:00:00 2001 From: Liu Yiqun Date: Wed, 30 Aug 2017 18:49:32 +0800 Subject: [PATCH 0291/2018] Deliver the cross-compilng platform-specific args to external libraries. --- CMakeLists.txt | 2 +- cmake/cross_compiling/ios.cmake | 7 ++--- cmake/external/gflags.cmake | 15 ++++++----- cmake/external/glog.cmake | 21 ++++++++------- cmake/external/openblas.cmake | 9 ++++--- cmake/external/protobuf.cmake | 3 ++- cmake/external/warpctc.cmake | 45 ++++++++++++------------------- cmake/external/zlib.cmake | 1 - paddle/gserver/layers/CostLayer.h | 4 ++- 9 files changed, 50 insertions(+), 57 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index b343c62de..a5971ddd9 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -81,7 +81,7 @@ if(ANDROID OR IOS) "Disable MKLDNN when cross-compiling for Android and iOS" FORCE) set(WITH_MKLML OFF CACHE STRING "Disable MKLML package when cross-compiling for Android and iOS" FORCE) -endif(ANDROID) +endif() set(THIRD_PARTY_PATH "${CMAKE_BINARY_DIR}/third_party" CACHE STRING "A path setting third party libraries download & build directories.") diff --git a/cmake/cross_compiling/ios.cmake b/cmake/cross_compiling/ios.cmake index b15dcec9b..d805423cb 100644 --- a/cmake/cross_compiling/ios.cmake +++ b/cmake/cross_compiling/ios.cmake @@ -68,10 +68,6 @@ endif() # Required as of cmake 2.8.10 set(CMAKE_OSX_DEPLOYMENT_TARGET "" CACHE STRING "Force unset of the deployment target for iOS" FORCE) -set(CMAKE_AR ar CACHE FILEPATH "" FORCE) -set(CMAKE_RANLIB ranlib CACHE FILEPATH "" FORCE) -set(PKG_CONFIG_EXECUTABLE pkg-config CACHE FILEPATH "" FORCE) - # Setup iOS platform unless specified manually with IOS_PLATFORM if(NOT DEFINED IOS_PLATFORM) set(IOS_PLATFORM "OS") @@ -81,7 +77,8 @@ set(IOS_PLATFORM ${IOS_PLATFORM} CACHE STRING "Type of iOS Platform") # Set the architecture for iOS if(NOT DEFINED IOS_ARCH) if(IOS_PLATFORM STREQUAL "OS") - set(IOS_ARCH "armv7;armv7s;arm64") + # FIXME: support "armv7;armv7s;arm64" future + set(IOS_ARCH "arm64") elseif(IOS_PLATFORM STREQUAL "SIMULATOR") set(IOS_ARCH "i386;x86_64") elseif(IOS_PLATFORM STREQUAL "WATCHOS") diff --git a/cmake/external/gflags.cmake b/cmake/external/gflags.cmake index 16e5bef4c..17b8f2e2a 100644 --- a/cmake/external/gflags.cmake +++ b/cmake/external/gflags.cmake @@ -39,13 +39,14 @@ ExternalProject_Add( PREFIX ${GFLAGS_SOURCES_DIR} UPDATE_COMMAND "" CMAKE_ARGS -DCMAKE_CXX_COMPILER=${CMAKE_CXX_COMPILER} - CMAKE_ARGS -DCMAKE_C_COMPILER=${CMAKE_C_COMPILER} - CMAKE_ARGS -DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS} - CMAKE_ARGS -DCMAKE_C_FLAGS=${CMAKE_C_FLAGS} - CMAKE_ARGS -DCMAKE_INSTALL_PREFIX=${GFLAGS_INSTALL_DIR} - CMAKE_ARGS -DCMAKE_POSITION_INDEPENDENT_CODE=ON - CMAKE_ARGS -DBUILD_TESTING=OFF - CMAKE_ARGS -DCMAKE_BUILD_TYPE=Release + -DCMAKE_C_COMPILER=${CMAKE_C_COMPILER} + -DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS} + -DCMAKE_C_FLAGS=${CMAKE_C_FLAGS} + -DCMAKE_INSTALL_PREFIX=${GFLAGS_INSTALL_DIR} + -DCMAKE_POSITION_INDEPENDENT_CODE=ON + -DBUILD_TESTING=OFF + -DCMAKE_BUILD_TYPE=Release + ${EXTERNAL_OPTIONAL_ARGS} CMAKE_CACHE_ARGS -DCMAKE_INSTALL_PREFIX:PATH=${GFLAGS_INSTALL_DIR} -DCMAKE_POSITION_INDEPENDENT_CODE:BOOL=ON -DCMAKE_BUILD_TYPE:STRING=Release diff --git a/cmake/external/glog.cmake b/cmake/external/glog.cmake index 8a594a825..78415b5a6 100644 --- a/cmake/external/glog.cmake +++ b/cmake/external/glog.cmake @@ -34,16 +34,17 @@ ExternalProject_Add( PREFIX ${GLOG_SOURCES_DIR} UPDATE_COMMAND "" CMAKE_ARGS -DCMAKE_CXX_COMPILER=${CMAKE_CXX_COMPILER} - CMAKE_ARGS -DCMAKE_C_COMPILER=${CMAKE_C_COMPILER} - CMAKE_ARGS -DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS} - CMAKE_ARGS -DCMAKE_C_FLAGS=${CMAKE_C_FLAGS} - CMAKE_ARGS -DCMAKE_INSTALL_PREFIX=${GLOG_INSTALL_DIR} - CMAKE_ARGS -DCMAKE_INSTALL_LIBDIR=${GLOG_INSTALL_DIR}/lib - CMAKE_ARGS -DCMAKE_POSITION_INDEPENDENT_CODE=ON - CMAKE_ARGS -DWITH_GFLAGS=ON - CMAKE_ARGS -Dgflags_DIR=${GFLAGS_INSTALL_DIR}/lib/cmake/gflags - CMAKE_ARGS -DBUILD_TESTING=OFF - CMAKE_ARGS -DCMAKE_BUILD_TYPE=Release + -DCMAKE_C_COMPILER=${CMAKE_C_COMPILER} + -DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS} + -DCMAKE_C_FLAGS=${CMAKE_C_FLAGS} + -DCMAKE_INSTALL_PREFIX=${GLOG_INSTALL_DIR} + -DCMAKE_INSTALL_LIBDIR=${GLOG_INSTALL_DIR}/lib + -DCMAKE_POSITION_INDEPENDENT_CODE=ON + -DWITH_GFLAGS=ON + -Dgflags_DIR=${GFLAGS_INSTALL_DIR}/lib/cmake/gflags + -DBUILD_TESTING=OFF + -DCMAKE_BUILD_TYPE=Release + ${EXTERNAL_OPTIONAL_ARGS} CMAKE_CACHE_ARGS -DCMAKE_INSTALL_PREFIX:PATH=${GLOG_INSTALL_DIR} -DCMAKE_INSTALL_LIBDIR:PATH=${GLOG_INSTALL_DIR}/lib -DCMAKE_POSITION_INDEPENDENT_CODE:BOOL=ON diff --git a/cmake/external/openblas.cmake b/cmake/external/openblas.cmake index 025eb62a4..849956f49 100644 --- a/cmake/external/openblas.cmake +++ b/cmake/external/openblas.cmake @@ -28,6 +28,8 @@ IF(NOT ${CBLAS_FOUND}) SET(OPENBLAS_CC "${CMAKE_C_COMPILER}") IF(CMAKE_CROSSCOMPILING) SET(OPTIONAL_ARGS HOSTCC=${HOST_C_COMPILER}) + GET_FILENAME_COMPONENT(CROSS_SUFFIX ${CMAKE_C_COMPILER} DIRECTORY) + SET(CROSS_SUFFIX ${CROSS_SUFFIX}/) IF(ANDROID) # arm_soft_fp_abi branch of OpenBLAS to support softfp # https://github.com/xianyi/OpenBLAS/tree/arm_soft_fp_abi @@ -40,12 +42,13 @@ IF(NOT ${CBLAS_FOUND}) ELSEIF(IOS) # FIXME: support multiple architectures SET(OPENBLAS_COMMIT "b5c96fcfcdc82945502a2303116a64d89985daf5") + SET(OPENBLAS_CC "${OPENBLAS_CC} ${CMAKE_C_FLAGS} -isysroot ${CMAKE_OSX_SYSROOT}") IF(CMAKE_OSX_ARCHITECTURES MATCHES "armv7") - SET(OPENBLAS_CC "${OPENBLAS_CC} -isysroot ${CMAKE_OSX_SYSROOT} -arch armv7") + SET(OPENBLAS_CC "${OPENBLAS_CC} -arch armv7") SET(OPTIONAL_ARGS ${OPTIONAL_ARGS} TARGET=ARMV7 ARM_SOFTFP_ABI=1 USE_THREAD=0) ELSEIF(CMAKE_OSX_ARCHITECTURES MATCHES "arm64") - SET(OPENBLAS_CC "${OPENBLAS_CC} -isysroot ${CMAKE_OSX_SYSROOT} -arch arm64") - SET(OPTIONAL_ARGS ${OPTIONAL_ARGS} TARGET=ARMV8 BINARY=64 USE_THREAD=0) + SET(OPENBLAS_CC "${OPENBLAS_CC} -arch arm64") + SET(OPTIONAL_ARGS ${OPTIONAL_ARGS} TARGET=ARMV8 BINARY=64 USE_THREAD=0 CROSS_SUFFIX=${CROSS_SUFFIX}) ENDIF() ELSEIF(RPI) # use hardfp diff --git a/cmake/external/protobuf.cmake b/cmake/external/protobuf.cmake index e629d6158..d4b07d3cf 100644 --- a/cmake/external/protobuf.cmake +++ b/cmake/external/protobuf.cmake @@ -173,7 +173,8 @@ FUNCTION(build_protobuf TARGET_NAME BUILD_FOR_HOST) "-DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS}" "-DCMAKE_C_FLAGS=${CMAKE_C_FLAGS}" "-Dprotobuf_WITH_ZLIB=ON" - "-DZLIB_ROOT:FILEPATH=${ZLIB_ROOT}") + "-DZLIB_ROOT:FILEPATH=${ZLIB_ROOT}" + ${EXTERNAL_OPTIONAL_ARGS}) SET(OPTIONAL_CACHE_ARGS "-DZLIB_ROOT:STRING=${ZLIB_ROOT}") ENDIF() diff --git a/cmake/external/warpctc.cmake b/cmake/external/warpctc.cmake index 2d7daed9b..1327c6b95 100644 --- a/cmake/external/warpctc.cmake +++ b/cmake/external/warpctc.cmake @@ -16,25 +16,13 @@ INCLUDE(ExternalProject) SET(WARPCTC_SOURCES_DIR ${THIRD_PARTY_PATH}/warpctc) SET(WARPCTC_INSTALL_DIR ${THIRD_PARTY_PATH}/install/warpctc) -SET(WARPCTC_INCLUDE_DIR "${WARPCTC_INSTALL_DIR}/include" CACHE PATH "Warp-ctc Directory" FORCE) -INCLUDE_DIRECTORIES(${WARPCTC_INCLUDE_DIR}) - -SET(WARPCTC_LIB_DIR "${WARPCTC_INSTALL_DIR}/lib" CACHE PATH "Warp-ctc Library Directory" FORCE) +SET(WARPCTC_INCLUDE_DIR "${WARPCTC_INSTALL_DIR}/include" + CACHE PATH "Warp-ctc Directory" FORCE) +SET(WARPCTC_LIBRARIES "${WARPCTC_INSTALL_DIR}/lib/libwarpctc${CMAKE_SHARED_LIBRARY_SUFFIX}" + CACHE FILEPATH "Warp-ctc Library" FORCE) -IF(WIN32) - SET(WARPCTC_LIBRARIES - "${WARPCTC_INSTALL_DIR}/lib/warpctc.dll" CACHE FILEPATH "Warp-ctc Library" FORCE) -ELSE(WIN32) - IF(APPLE) - SET(_warpctc_SHARED_SUFFIX dylib) - ELSE(APPLE) - SET(_warpctc_SHARED_SUFFIX so) - ENDIF(APPLE) - - SET(WARPCTC_LIBRARIES - "${WARPCTC_INSTALL_DIR}/lib/libwarpctc.${_warpctc_SHARED_SUFFIX}" CACHE FILEPATH "Warp-ctc Library" FORCE) -ENDIF(WIN32) +INCLUDE_DIRECTORIES(${WARPCTC_INCLUDE_DIR}) IF(CMAKE_CXX_COMPILER_ID STREQUAL "Clang" OR CMAKE_CXX_COMPILER_ID STREQUAL "AppleClang" ) SET(USE_OMP OFF) @@ -49,17 +37,18 @@ ExternalProject_Add( PREFIX ${WARPCTC_SOURCES_DIR} UPDATE_COMMAND "" CMAKE_ARGS -DCMAKE_CXX_COMPILER=${CMAKE_CXX_COMPILER} - CMAKE_ARGS -DCMAKE_C_COMPILER=${CMAKE_C_COMPILER} - CMAKE_ARGS -DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS} - CMAKE_ARGS -DCMAKE_C_FLAGS=${CMAKE_C_FLAGS} - CMAKE_ARGS -DCMAKE_INSTALL_PREFIX=${WARPCTC_INSTALL_DIR} - CMAKE_ARGS -DWITH_GPU=${WITH_GPU} - CMAKE_ARGS -DWITH_OMP=${USE_OMP} - CMAKE_ARGS -DWITH_TORCH=OFF - CMAKE_ARGS -DCMAKE_DISABLE_FIND_PACKAGE_Torch=ON - CMAKE_ARGS -DBUILD_SHARED=ON - CMAKE_ARGS -DCMAKE_POSITION_INDEPENDENT_CODE=ON - CMAKE_ARGS -DCMAKE_BUILD_TYPE=Release + -DCMAKE_C_COMPILER=${CMAKE_C_COMPILER} + -DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS} + -DCMAKE_C_FLAGS=${CMAKE_C_FLAGS} + -DCMAKE_INSTALL_PREFIX=${WARPCTC_INSTALL_DIR} + -DWITH_GPU=${WITH_GPU} + -DWITH_OMP=${USE_OMP} + -DWITH_TORCH=OFF + -DCMAKE_DISABLE_FIND_PACKAGE_Torch=ON + -DBUILD_SHARED=ON + -DCMAKE_POSITION_INDEPENDENT_CODE=ON + -DCMAKE_BUILD_TYPE=Release + ${EXTERNAL_OPTIONAL_ARGS} CMAKE_CACHE_ARGS -DCMAKE_BUILD_TYPE:STRING=Release -DCMAKE_POSITION_INDEPENDENT_CODE:BOOL=ON -DCMAKE_INSTALL_PREFIX:PATH=${WARPCTC_INSTALL_DIR} diff --git a/cmake/external/zlib.cmake b/cmake/external/zlib.cmake index 2fadea9c6..0e61730e1 100644 --- a/cmake/external/zlib.cmake +++ b/cmake/external/zlib.cmake @@ -27,7 +27,6 @@ ENDIF(WIN32) INCLUDE_DIRECTORIES(${ZLIB_INCLUDE_DIR}) - ExternalProject_Add( zlib ${EXTERNAL_PROJECT_LOG_ARGS} diff --git a/paddle/gserver/layers/CostLayer.h b/paddle/gserver/layers/CostLayer.h index 0ce72ef40..0f655b48e 100644 --- a/paddle/gserver/layers/CostLayer.h +++ b/paddle/gserver/layers/CostLayer.h @@ -318,7 +318,9 @@ public: void forwardImp(Matrix& output, Argument& label, Matrix& cost) override; - void backwardImp(Matrix& outputValue, Argument& label, Matrix& outputGrad) {} + void backwardImp(Matrix& outputValue, + Argument& label, + Matrix& outputGrad) override {} }; /** -- GitLab From 26cec83901dc443a60aef911c1ad2baf882eb474 Mon Sep 17 00:00:00 2001 From: wanghaoshuang Date: Wed, 30 Aug 2017 19:54:14 +0800 Subject: [PATCH 0292/2018] Add pad op --- paddle/operators/CMakeLists.txt | 1 + paddle/operators/pad_op.cc | 77 ++++++++++++++++++ paddle/operators/pad_op.cu | 21 +++++ paddle/operators/pad_op.h | 81 +++++++++++++++++++ paddle/pybind/CMakeLists.txt | 3 +- paddle/pybind/pybind.cc | 1 + .../paddle/v2/framework/tests/test_pad_op.py | 32 ++++++++ 7 files changed, 215 insertions(+), 1 deletion(-) create mode 100644 paddle/operators/pad_op.cc create mode 100644 paddle/operators/pad_op.cu create mode 100644 paddle/operators/pad_op.h create mode 100644 python/paddle/v2/framework/tests/test_pad_op.py diff --git a/paddle/operators/CMakeLists.txt b/paddle/operators/CMakeLists.txt index f466dbc79..1a759133e 100644 --- a/paddle/operators/CMakeLists.txt +++ b/paddle/operators/CMakeLists.txt @@ -72,3 +72,4 @@ op_library(uniform_random_op SRCS uniform_random_op.cc uniform_random_op.cu) op_library(lookup_table_op SRCS lookup_table_op.cc lookup_table_op.cu) op_library(scale_op SRCS scale_op.cc scale_op.cu DEPS net_op) op_library(minus_op SRCS minus_op.cc minus_op.cu DEPS scale_op) +op_library(pad_op SRCS pad_op.cc pad_op.cu) diff --git a/paddle/operators/pad_op.cc b/paddle/operators/pad_op.cc new file mode 100644 index 000000000..f96d61669 --- /dev/null +++ b/paddle/operators/pad_op.cc @@ -0,0 +1,77 @@ +/* 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/pad_op.h" + +namespace paddle { +namespace operators { + +using framework::Tensor; + +class PadOp : public framework::OperatorWithKernel { + public: + using framework::OperatorWithKernel::OperatorWithKernel; + + protected: + void InferShape(const framework::InferShapeContext &ctx) const override { + auto dim0 = ctx.Input("X")->dims(); + auto dim1 = ctx.Output("Out")->dims(); + auto paddings = GetAttr>>("paddings"); + for (int i = 0; i < dim0.size(); ++i) { + dim1[i] = dim0[i] + paddings[i][0] + paddings[i][1]; + } + ctx.Output("Out")->Resize(dim1); + } +}; + +class MulOpMaker : public framework::OpProtoAndCheckerMaker { + public: + MulOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker) + : OpProtoAndCheckerMaker(proto, op_checker) { + AddInput("X", "The input of pad op"); + AddOutput("Out", "The output of pad op"); + AddComment(R"DOC( +Pad Operator. +)DOC"); + AddAttr>>( + "paddings", "The padding rules for each dimension"); + AddAttr("pad_value", "The value to be padded into tensor") + .SetDefault(0.0f); + } +}; + +class PadOpGrad : public framework::OperatorWithKernel { + public: + using framework::OperatorWithKernel::OperatorWithKernel; + + protected: + void InferShape(const framework::InferShapeContext &ctx) const override { + PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) should not be null"); + PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")), + "Input(Out@GRAD) should not be null"); + auto x_dims = ctx.Input("X")->dims(); + auto *x_grad = ctx.Output(framework::GradVarName("X")); + + x_grad->Resize(x_dims); + } +}; + +} // namespace operators +} // namespace paddle + +namespace ops = paddle::operators; +REGISTER_OP(pad, ops::PadOp, ops::PadOpMaker, pad_grad, ops::PadOpGrad); +REGISTER_OP_CPU_KERNEL(pad, ops::PadKernel); +REGISTER_OP_CPU_KERNEL(pad_grad, + ops::PadGradKernel); diff --git a/paddle/operators/pad_op.cu b/paddle/operators/pad_op.cu new file mode 100644 index 000000000..555a7dba2 --- /dev/null +++ b/paddle/operators/pad_op.cu @@ -0,0 +1,21 @@ +/* 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. */ + +#define EIGEN_USE_GPU +#include "paddle/operators/pad_op.h" + +namespace ops = paddle::operators; +REGISTER_OP_GPU_KERNEL(pad, ops::PadKernel); +REGISTER_OP_GPU_KERNEL(pad_grad, + ops::PadGradKernel); diff --git a/paddle/operators/pad_op.h b/paddle/operators/pad_op.h new file mode 100644 index 000000000..6a743bd31 --- /dev/null +++ b/paddle/operators/pad_op.h @@ -0,0 +1,81 @@ +/* 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/operators/math/math_function.h" + +#include "paddle/framework/eigen.h" +#include "paddle/framework/op_registry.h" + +namespace paddle { +namespace operators { + +using Tensor = framework::Tensor; + +template +using EigenTensor = framework::EigenTensor; + +template +class PadKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& context) const override { + auto paddings = + context.op_.GetAttr>>("paddings"); + T pad_value = context.op_.GetAttr("pad_value"); + + auto* X = context.Input("X"); + auto* Out = context.Output("Out"); + Out->mutable_data(context.GetPlace()); + auto dims = X->dims(); + + // Eigen::TensorMap> X_tensor = EigenTensor::From(*X); + // Eigen::TensorMap> + // Out_tensor = EigenTensor::From(*Out); + EigenTensor::ConstType X_tensor = + EigenTensor::From(*X); + EigenTensor::Type Out_tensor = + EigenTensor::From(*Out); + Out_tensor = X_tensor.pad(paddings, pad_value); + } +}; + +template +class PadGradKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& ctx) const override { + std::vector> paddings = + context.op_.GetAttr>>("paddings"); + for (int i = 0; i < paddings.size(); ++i) { + paddings[0].first = -paddings[0].first; + paddings[1].second = -paddings[1].second; + } + auto* dOut = ctx.Input(framework::GradVarName("Out")); + auto dims = dOut->dims(); + + auto* dX = ctx.Output(framework::GradVarName("X")); + dX->mutable_data(ctx.GetPlace()); + + EigenTensor::Type dX_tensor = + EigenTensor::From(*dX); + EigenTensor::ConstType dOut_tensor = + EigenTensor::From(*dOut); + dX_tensor = dOut_tensor.pad(paddings, 0); + } +}; + +} // namespace operators +} // namespace paddle diff --git a/paddle/pybind/CMakeLists.txt b/paddle/pybind/CMakeLists.txt index abb9c248e..17ef1e829 100644 --- a/paddle/pybind/CMakeLists.txt +++ b/paddle/pybind/CMakeLists.txt @@ -17,5 +17,6 @@ cc_library(paddle_pybind SHARED fill_zeros_like_op lookup_table_op scale_op - minus_op) + minus_op + pad_op) endif(WITH_PYTHON) diff --git a/paddle/pybind/pybind.cc b/paddle/pybind/pybind.cc index 8fa8be2ce..0176eb7a8 100644 --- a/paddle/pybind/pybind.cc +++ b/paddle/pybind/pybind.cc @@ -47,6 +47,7 @@ USE_OP(scale); USE_OP_ITSELF(identity); USE_OP(minus); USE_CPU_ONLY_OP(gather); +USE_OP(pad); namespace paddle { namespace framework { diff --git a/python/paddle/v2/framework/tests/test_pad_op.py b/python/paddle/v2/framework/tests/test_pad_op.py new file mode 100644 index 000000000..89ac7e7e1 --- /dev/null +++ b/python/paddle/v2/framework/tests/test_pad_op.py @@ -0,0 +1,32 @@ +import unittest +import numpy as np +from gradient_checker import GradientChecker, create_op +from op_test_util import OpTestMeta + + +class TestPadOp(unittest.TestCase): + __metaclass__ = OpTestMeta + + def setUp(self): + self.type = "pad" + self.inputs = {'X': np.random.random((16, 16)).astype("float32"), } + self.attrs['paddings'] = ((0, 1), (2, 3)) + self.attrs['pad_value'] = 0 + self.outputs = { + 'Out': np.pad(self.inputs['X'], + self.attrs['paddings'], + mode='constant', + constant_value=0) + } + + +class PadGradOpTest(GradientChecker): + def test_pad(self): + op = Operator("pad", paddings=((0, 1), (2, 3)), pad_value=0) + inputs = {'X': np.random.random((16, 16)).astype("float32"), } + + self.check_grad(op, inputs, set(["X"]), "Out", max_relative_error=0.5) + + +if __name__ == '__main__': + unittest.main() -- GitLab From aeea8ab1c4e102ac687da3598011767d7b7a7321 Mon Sep 17 00:00:00 2001 From: Liu Yiqun Date: Wed, 30 Aug 2017 19:59:01 +0800 Subject: [PATCH 0293/2018] Remove the linking of train-related libraries when cross-compiling for Android and iOS. Recover the mistakenly deleted WARPCTC variable in cmake. --- cmake/cross_compiling/ios.cmake | 2 +- cmake/external/gtest.cmake | 19 ++++++++++--------- cmake/external/openblas.cmake | 2 +- cmake/external/warpctc.cmake | 8 ++++++-- cmake/util.cmake | 13 +++++++++---- paddle/capi/CMakeLists.txt | 6 +++--- 6 files changed, 30 insertions(+), 20 deletions(-) diff --git a/cmake/cross_compiling/ios.cmake b/cmake/cross_compiling/ios.cmake index d805423cb..eea17436b 100644 --- a/cmake/cross_compiling/ios.cmake +++ b/cmake/cross_compiling/ios.cmake @@ -77,7 +77,7 @@ set(IOS_PLATFORM ${IOS_PLATFORM} CACHE STRING "Type of iOS Platform") # Set the architecture for iOS if(NOT DEFINED IOS_ARCH) if(IOS_PLATFORM STREQUAL "OS") - # FIXME: support "armv7;armv7s;arm64" future + # FIXME(liuyiqun): support "armv7;armv7s;arm64" future set(IOS_ARCH "arm64") elseif(IOS_PLATFORM STREQUAL "SIMULATOR") set(IOS_ARCH "i386;x86_64") diff --git a/cmake/external/gtest.cmake b/cmake/external/gtest.cmake index e3970073a..6a2a79b76 100644 --- a/cmake/external/gtest.cmake +++ b/cmake/external/gtest.cmake @@ -48,15 +48,16 @@ IF(WITH_TESTING) PREFIX ${GTEST_SOURCES_DIR} UPDATE_COMMAND "" CMAKE_ARGS -DCMAKE_CXX_COMPILER=${CMAKE_CXX_COMPILER} - CMAKE_ARGS -DCMAKE_C_COMPILER=${CMAKE_C_COMPILER} - CMAKE_ARGS -DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS} - CMAKE_ARGS -DCMAKE_C_FLAGS=${CMAKE_C_FLAGS} - CMAKE_ARGS -DCMAKE_INSTALL_PREFIX=${GTEST_INSTALL_DIR} - CMAKE_ARGS -DCMAKE_POSITION_INDEPENDENT_CODE=ON - CMAKE_ARGS -DBUILD_GMOCK=ON - CMAKE_ARGS -Dgtest_disable_pthreads=ON - CMAKE_ARGS -Dgtest_force_shared_crt=ON - CMAKE_ARGS -DCMAKE_BUILD_TYPE=Release + -DCMAKE_C_COMPILER=${CMAKE_C_COMPILER} + -DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS} + -DCMAKE_C_FLAGS=${CMAKE_C_FLAGS} + -DCMAKE_INSTALL_PREFIX=${GTEST_INSTALL_DIR} + -DCMAKE_POSITION_INDEPENDENT_CODE=ON + -DBUILD_GMOCK=ON + -Dgtest_disable_pthreads=ON + -Dgtest_force_shared_crt=ON + -DCMAKE_BUILD_TYPE=Release + ${EXTERNAL_OPTIONAL_ARGS} CMAKE_CACHE_ARGS -DCMAKE_INSTALL_PREFIX:PATH=${GTEST_INSTALL_DIR} -DCMAKE_POSITION_INDEPENDENT_CODE:BOOL=ON -DCMAKE_BUILD_TYPE:STRING=Release diff --git a/cmake/external/openblas.cmake b/cmake/external/openblas.cmake index 849956f49..66c2a8bd8 100644 --- a/cmake/external/openblas.cmake +++ b/cmake/external/openblas.cmake @@ -40,7 +40,7 @@ IF(NOT ${CBLAS_FOUND}) SET(OPTIONAL_ARGS ${OPTIONAL_ARGS} TARGET=ARMV8 BINARY=64 USE_THREAD=0) ENDIF() ELSEIF(IOS) - # FIXME: support multiple architectures + # FIXME(liuyiqun): support multiple architectures SET(OPENBLAS_COMMIT "b5c96fcfcdc82945502a2303116a64d89985daf5") SET(OPENBLAS_CC "${OPENBLAS_CC} ${CMAKE_C_FLAGS} -isysroot ${CMAKE_OSX_SYSROOT}") IF(CMAKE_OSX_ARCHITECTURES MATCHES "armv7") diff --git a/cmake/external/warpctc.cmake b/cmake/external/warpctc.cmake index 1327c6b95..bb258c7b5 100644 --- a/cmake/external/warpctc.cmake +++ b/cmake/external/warpctc.cmake @@ -19,11 +19,12 @@ SET(WARPCTC_INSTALL_DIR ${THIRD_PARTY_PATH}/install/warpctc) SET(WARPCTC_INCLUDE_DIR "${WARPCTC_INSTALL_DIR}/include" CACHE PATH "Warp-ctc Directory" FORCE) +# Used in unit test test_WarpCTCLayer +SET(WARPCTC_LIB_DIR "${WARPCTC_INSTALL_DIR}/lib" + CACHE PATH "Warp-ctc Library Directory" FORCE) SET(WARPCTC_LIBRARIES "${WARPCTC_INSTALL_DIR}/lib/libwarpctc${CMAKE_SHARED_LIBRARY_SUFFIX}" CACHE FILEPATH "Warp-ctc Library" FORCE) -INCLUDE_DIRECTORIES(${WARPCTC_INCLUDE_DIR}) - IF(CMAKE_CXX_COMPILER_ID STREQUAL "Clang" OR CMAKE_CXX_COMPILER_ID STREQUAL "AppleClang" ) SET(USE_OMP OFF) ELSE() @@ -54,6 +55,9 @@ ExternalProject_Add( -DCMAKE_INSTALL_PREFIX:PATH=${WARPCTC_INSTALL_DIR} ) +MESSAGE(STATUS "warp-ctc library: ${WARPCTC_LIBRARIES}") +INCLUDE_DIRECTORIES(${WARPCTC_INCLUDE_DIR}) + ADD_LIBRARY(warpctc STATIC IMPORTED GLOBAL) SET_PROPERTY(TARGET warpctc PROPERTY IMPORTED_LOCATION ${WARPCTC_LIBRARIES}) ADD_DEPENDENCIES(warpctc extern_warpctc) diff --git a/cmake/util.cmake b/cmake/util.cmake index 0da4969d3..bfe269ea2 100644 --- a/cmake/util.cmake +++ b/cmake/util.cmake @@ -71,20 +71,25 @@ function(link_paddle_exe TARGET_NAME) generate_rdma_links() endif() + if(NOT ANDROID AND NOT IOS) + set(PADDLE_TRAIN_LIBS + paddle_pserver + paddle_network + paddle_trainer_lib + paddle_optimizer) + endif() + target_circle_link_libraries(${TARGET_NAME} ARCHIVE_START paddle_gserver paddle_function ARCHIVE_END - paddle_pserver - paddle_trainer_lib - paddle_network paddle_math paddle_utils paddle_parameter paddle_proto paddle_cuda - paddle_optimizer + ${PADDLE_TRAIN_LIBS} ${EXTERNAL_LIBS} ${CMAKE_THREAD_LIBS_INIT} ${CMAKE_DL_LIBS} diff --git a/paddle/capi/CMakeLists.txt b/paddle/capi/CMakeLists.txt index 7a83508f3..071f5a0b0 100644 --- a/paddle/capi/CMakeLists.txt +++ b/paddle/capi/CMakeLists.txt @@ -30,7 +30,7 @@ add_dependencies(paddle_capi paddle_proto) # combine all paddle static libraries together, into libpaddle_capi_whole.a # user should use PaddleCAPI as -lpaddle_capi_whole -set(PADDLE_INFER_LIBS +set(PADDLE_CAPI_INFER_LIBS paddle_utils paddle_parameter paddle_math @@ -40,9 +40,9 @@ set(PADDLE_INFER_LIBS paddle_proto ) -set(PADDLE_TRAIN_LIBS paddle_pserver paddle_network) +set(PADDLE_CAPI_TRAIN_LIBS paddle_pserver paddle_network) -cc_library(paddle_capi_whole DEPS paddle_capi ${PADDLE_INFER_LIBS}) +cc_library(paddle_capi_whole DEPS paddle_capi ${PADDLE_CAPI_INFER_LIBS}) # No shared library for iOS if(NOT IOS) -- GitLab From a4df3f5bd8917b2cb510b23dc63bc97a20108f23 Mon Sep 17 00:00:00 2001 From: yangyaming Date: Wed, 30 Aug 2017 22:21:53 +0800 Subject: [PATCH 0294/2018] Finish framework of squared_l2_distance_op. --- paddle/operators/CMakeLists.txt | 2 + paddle/operators/squared_l2_distance_op.cc | 82 ++++++++++++++++++ paddle/operators/squared_l2_distance_op.cu | 25 ++++++ paddle/operators/squared_l2_distance_op.h | 84 +++++++++++++++++++ paddle/pybind/CMakeLists.txt | 3 +- paddle/pybind/pybind.cc | 1 + .../paddle/v2/framework/tests/CMakeLists.txt | 1 + .../paddle/v2/framework/tests/op_test_util.py | 10 +-- .../tests/test_squared_l2_distance_op.py | 25 ++++++ 9 files changed, 227 insertions(+), 6 deletions(-) create mode 100644 paddle/operators/squared_l2_distance_op.cc create mode 100644 paddle/operators/squared_l2_distance_op.cu create mode 100644 paddle/operators/squared_l2_distance_op.h create mode 100644 python/paddle/v2/framework/tests/test_squared_l2_distance_op.py diff --git a/paddle/operators/CMakeLists.txt b/paddle/operators/CMakeLists.txt index f0fd12f1b..1c32d1df4 100644 --- a/paddle/operators/CMakeLists.txt +++ b/paddle/operators/CMakeLists.txt @@ -73,3 +73,5 @@ op_library(uniform_random_op SRCS uniform_random_op.cc uniform_random_op.cu) op_library(lookup_table_op SRCS lookup_table_op.cc lookup_table_op.cu) op_library(scale_op SRCS scale_op.cc scale_op.cu DEPS net_op) op_library(minus_op SRCS minus_op.cc minus_op.cu DEPS scale_op) + +op_library(squared_l2_distance_op SRCS squared_l2_distance_op.cc squared_l2_distance_op.cu) diff --git a/paddle/operators/squared_l2_distance_op.cc b/paddle/operators/squared_l2_distance_op.cc new file mode 100644 index 000000000..9fc498d5a --- /dev/null +++ b/paddle/operators/squared_l2_distance_op.cc @@ -0,0 +1,82 @@ +/* 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/squared_l2_distance_op.h" + +namespace paddle { +namespace operators { + +class SquaredL2DistanceOp : public framework::OperatorWithKernel { + public: + using framework::OperatorWithKernel::OperatorWithKernel; + + protected: + void InferShape(const framework::InferShapeContext &ctx) const override { + PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), + "Input of SquaredL2DistanceOp " + "must be initialized."); + PADDLE_ENFORCE_EQ(ctx.Input("X")->dims(), + ctx.Input("Y")->dims(), + "Dimensions of SquaredL2DistanceOp's two inputs " + "must be same.") + framework::DDim dims = ctx.Input("X")->dims(); + ctx.Output("sub_result")->Resize(dims); + ctx.Output("Out")->Resize(framework::make_ddim({dims[0], 1})); + } +}; + +class SquaredL2DistanceOpMaker : public framework::OpProtoAndCheckerMaker { + public: + SquaredL2DistanceOpMaker(framework::OpProto *proto, + framework::OpAttrChecker *op_checker) + : OpProtoAndCheckerMaker(proto, op_checker) { + AddInput("X", "Input value."); + AddInput("Y", "Target value."); + AddOutput("sub_result", + "Buffering substraction result which " + "will be reused in backward.") + .AsIntermediate(); + AddOutput("Out", "Squared l2 distance between input and target."); + AddComment(R"DOC( + SquaredL2DistanceOp will cacluate the squared L2 distances for + input and target. Number of distance value equals to the + first dimension of input. + )DOC"); + } +}; + +class SquaredL2DistanceGradOp : public framework::OperatorWithKernel { + public: + using framework::OperatorWithKernel::OperatorWithKernel; + + protected: + void InferShape(const framework::InferShapeContext &ctx) const override { + ctx.Output(framework::GradVarName("X")) + ->Resize(ctx.Input("X")->dims()); + } +}; + +} // namespace operators +} // namespace paddle + +namespace ops = paddle::operators; +REGISTER_OP(squared_l2_distance, ops::SquaredL2DistanceOp, + ops::SquaredL2DistanceOpMaker, squared_l2_distance_grad, + ops::SquaredL2DistanceGradOp); +REGISTER_OP_CPU_KERNEL( + squared_l2_distance, + ops::SquaredL2DistanceKernel); +REGISTER_OP_CPU_KERNEL( + squared_l2_distance_grad, + ops::SquaredL2DistanceGradKernel); diff --git a/paddle/operators/squared_l2_distance_op.cu b/paddle/operators/squared_l2_distance_op.cu new file mode 100644 index 000000000..3fe62f1a9 --- /dev/null +++ b/paddle/operators/squared_l2_distance_op.cu @@ -0,0 +1,25 @@ +/* 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. */ + +#define EIGEN_USE_GPU + +#include "paddle/operators/squared_l2_distance_op.h" + +namespace ops = paddle::operators; +REGISTER_OP_GPU_KERNEL( + squared_l2_distance, + ops::SquaredL2DistanceKernel); +REGISTER_OP_GPU_KERNEL( + squared_l2_distance_grad, + ops::SquaredL2DistanceGradKernel); diff --git a/paddle/operators/squared_l2_distance_op.h b/paddle/operators/squared_l2_distance_op.h new file mode 100644 index 000000000..b350fd011 --- /dev/null +++ b/paddle/operators/squared_l2_distance_op.h @@ -0,0 +1,84 @@ +/* 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/eigen.h" +#include "paddle/framework/op_registry.h" + +namespace paddle { +namespace operators { + +using Tensor = framework::Tensor; +template +using EigenMatrix = framework::EigenMatrix; +template +using EigenVector = framework::EigenVector; + +template +class SquaredL2DistanceKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& context) const override { + auto* input0 = context.Input("X"); + auto* input1 = context.Input("Y"); + auto* output0 = context.Output("sub_result"); + auto* output1 = context.Output("Out"); + + output0->mutable_data(context.GetPlace()); + output1->mutable_data(context.GetPlace()); + + auto X = EigenMatrix::From(*input0); + auto Y = EigenMatrix::From(*input1); + auto subResult = EigenMatrix::From(*output0); + auto Z = EigenMatrix::From(*output1); + + auto place = context.GetEigenDevice(); + // buffer the substraction result + subResult.device(place) = X - Y; + const auto& inDims = X.dimensions(); + const auto& subResMat = subResult.reshape(Eigen::array( + {static_cast(inDims[0]), static_cast(X.size() / inDims[0])})); + Z.device(place) = subResMat.pow(2).sum(Eigen::array({1})); + } +}; + +template +class SquaredL2DistanceGradKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& context) const override { + auto* input0 = context.Input("sub_result"); + auto* OG = context.Input(framework::GradVarName("Out")); + auto* IG = context.Output(framework::GradVarName("X")); + + IG->mutable_data(context.GetPlace()); + + auto subResult = EigenMatrix::From(*input0); + auto outGrad = EigenMatrix::From(*OG); + auto inGrad = EigenMatrix::From(*IG); + + const auto& subResDims = subResult.dimensions(); + int firstDim = static_cast(subResDims[0]); + int cols = subResult.size() / firstDim; + const auto subResMat = + subResult.reshape(Eigen::array({firstDim, cols})); + // create a matrix view for input gradient tensor + auto inGradMat = inGrad.reshape(Eigen::array({firstDim, cols})); + inGradMat.device(context.GetEigenDevice()) = + 2 * (outGrad.broadcast(Eigen::array({1, cols}))) * subResMat; + } +}; + +} // namespace operators +} // namespace paddle diff --git a/paddle/pybind/CMakeLists.txt b/paddle/pybind/CMakeLists.txt index 37e186a40..df8c2b37c 100644 --- a/paddle/pybind/CMakeLists.txt +++ b/paddle/pybind/CMakeLists.txt @@ -18,5 +18,6 @@ cc_library(paddle_pybind SHARED fill_zeros_like_op lookup_table_op scale_op - minus_op) + minus_op + squared_l2_distance_op) endif(WITH_PYTHON) diff --git a/paddle/pybind/pybind.cc b/paddle/pybind/pybind.cc index 3bc150ccb..69a5f98a4 100644 --- a/paddle/pybind/pybind.cc +++ b/paddle/pybind/pybind.cc @@ -48,6 +48,7 @@ USE_OP_ITSELF(identity); USE_OP(minus); USE_CPU_ONLY_OP(gather); USE_CPU_ONLY_OP(scatter); +USE_OP(squared_l2_distance); namespace paddle { namespace framework { diff --git a/python/paddle/v2/framework/tests/CMakeLists.txt b/python/paddle/v2/framework/tests/CMakeLists.txt index 661ebd896..06ff1f4a0 100644 --- a/python/paddle/v2/framework/tests/CMakeLists.txt +++ b/python/paddle/v2/framework/tests/CMakeLists.txt @@ -32,3 +32,4 @@ py_test(test_gradient_checker SRCS test_gradient_checker.py) py_test(test_lookup_table SRCS test_lookup_table.py) py_test(test_scale_and_identity_op SRCS test_scale_and_identity_op.py) py_test(mnist SRCS mnist.py) +py_test(test_squared_l2_distance_op SRCS test_squared_l2_distance_op.py) diff --git a/python/paddle/v2/framework/tests/op_test_util.py b/python/paddle/v2/framework/tests/op_test_util.py index 3bc05a0fe..370f27eaf 100644 --- a/python/paddle/v2/framework/tests/op_test_util.py +++ b/python/paddle/v2/framework/tests/op_test_util.py @@ -6,13 +6,13 @@ from paddle.v2.framework.op import Operator class OpTestMeta(type): """ Operator Test ClassMeta. - - It injects `test_all` method into user's OperatorTest class, to make Python + + It injects `test_all` method into user's OperatorTest class, to make Python unittest module run that method. - + The `test_all` read what value is stored in `self`. It use self's values to create and run a operator, and check whether that op is OK or not. - + See `test_add_two_op` for example usage. """ @@ -66,7 +66,7 @@ class OpTestMeta(type): self.assertTrue( numpy.allclose( actual, expect, atol=1e-05), - "output name: " + out_name + "has diff") + "output name: " + out_name + " has diff") obj.test_all = test_all return obj diff --git a/python/paddle/v2/framework/tests/test_squared_l2_distance_op.py b/python/paddle/v2/framework/tests/test_squared_l2_distance_op.py new file mode 100644 index 000000000..eeddb5a3b --- /dev/null +++ b/python/paddle/v2/framework/tests/test_squared_l2_distance_op.py @@ -0,0 +1,25 @@ +import unittest +from op_test_util import OpTestMeta +from gradient_checker import GradientChecker, create_op +import numpy as np + + +class TestSquaredL2DistanceOp(unittest.TestCase): + __metaclass__ = OpTestMeta + + def setUp(self): + self.type = 'squared_l2_distance' + self.inputs = { + 'X': np.random.uniform(0.1, 1., (2, 3)).astype('float32'), + 'Y': np.random.uniform(0.1, 1., (2, 3)).astype('float32') + } + subRes = self.inputs['X'] - self.inputs['Y'] + output = subRes * subRes + self.outputs = { + 'sub_result': subRes, + 'Out': np.expand_dims(output.sum(1), 1) + } + + +if __name__ == '__main__': + unittest.main() -- GitLab From f557b0c4c5e1ac97fdc092ed85993c4dda72fd2d Mon Sep 17 00:00:00 2001 From: chengduoZH Date: Thu, 31 Aug 2017 00:11:44 +0800 Subject: [PATCH 0295/2018] fix data_layer for 3D data --- python/paddle/trainer_config_helpers/layers.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py index ebb6f3650..c92764e1f 100755 --- a/python/paddle/trainer_config_helpers/layers.py +++ b/python/paddle/trainer_config_helpers/layers.py @@ -929,11 +929,13 @@ def data_layer(name, size, depth=None, height=None, width=None, width=width, **ExtraLayerAttribute.to_kwargs(layer_attr)) + if depth is None: + depth = 1 num_filters = None if height is not None and width is not None: - num_filters = size / (width * height) - assert num_filters * width * height == size, \ - "size=%s width=%s height=%s" % (size, width, height) + num_filters = size / (width * height * depth) + assert num_filters * width * height * depth == size, \ + "size=%s width=%s height=%s depth=%s" % (size, width, height, depth) return LayerOutput(name, LayerType.DATA, size=size, num_filters=num_filters) -- GitLab From 2ae37a4ea2f4b02ffe6b773590ed05c77675e6f5 Mon Sep 17 00:00:00 2001 From: chengduoZH Date: Thu, 31 Aug 2017 00:28:01 +0800 Subject: [PATCH 0296/2018] fix data_layer for 3D data --- python/paddle/trainer_config_helpers/layers.py | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py index 42bf1c19d..2aa86850d 100644 --- a/python/paddle/trainer_config_helpers/layers.py +++ b/python/paddle/trainer_config_helpers/layers.py @@ -926,16 +926,18 @@ def data_layer(name, size, height=None, width=None, depth=None, type=LayerType.DATA, name=name, size=size, + depth=depth, height=height, width=width, - depth=depth, **ExtraLayerAttribute.to_kwargs(layer_attr)) + if depth is None: + depth = 1 num_filters = None if height is not None and width is not None: - num_filters = size / (width * height) - assert num_filters * width * height == size, \ - "size=%s width=%s height=%s" % (size, width, height) + num_filters = size / (width * height * depth) + assert num_filters * width * height*depth == size, \ + "size=%s width=%s height=%s depth=%s" % (size, width, height, depth) return LayerOutput(name, LayerType.DATA, size=size, num_filters=num_filters) -- GitLab From 09e903eb9417745952ced6db532594fd4a759d74 Mon Sep 17 00:00:00 2001 From: caoying03 Date: Tue, 29 Aug 2017 13:44:51 +0800 Subject: [PATCH 0297/2018] fix v2 infer interface. --- paddle/gserver/layers/CrossEntropyOverBeam.cpp | 1 - python/paddle/v2/inference.py | 7 +++++-- 2 files changed, 5 insertions(+), 3 deletions(-) diff --git a/paddle/gserver/layers/CrossEntropyOverBeam.cpp b/paddle/gserver/layers/CrossEntropyOverBeam.cpp index 500cd6ff8..bffcc3015 100644 --- a/paddle/gserver/layers/CrossEntropyOverBeam.cpp +++ b/paddle/gserver/layers/CrossEntropyOverBeam.cpp @@ -39,7 +39,6 @@ void CostForOneSequence::calValidExpandStep() { if (start + beamSize_ == findEnd) return; goldColIds_[i] = findEnd - start; } - if (goldColIds_[beams_->expansionCount - 1] != -1) goldAsExtraPath_ = false; } diff --git a/python/paddle/v2/inference.py b/python/paddle/v2/inference.py index 4dcc3ab57..8acea6155 100644 --- a/python/paddle/v2/inference.py +++ b/python/paddle/v2/inference.py @@ -70,7 +70,7 @@ class Inference(object): item = [each_result[each_field] for each_field in field] yield item - def infer(self, input, field='value', **kwargs): + def infer(self, input, field='value', flatten_result=True, **kwargs): """ Infer a data by model. :param input: input data batch. Should be python iterable object. @@ -83,7 +83,10 @@ class Inference(object): retv = [[] for i in xrange(len(result))] for i, item in enumerate(result): retv[i].append(item) - retv = [numpy.concatenate(out) for out in retv] + + if flatten_result: + retv = [numpy.concatenate(out) for out in retv] + if len(retv) == 1: return retv[0] else: -- GitLab From 2e8d47dd09001da94015fb4a96f21452631fcbad Mon Sep 17 00:00:00 2001 From: Luo Tao Date: Thu, 31 Aug 2017 11:01:03 +0800 Subject: [PATCH 0298/2018] simplify and make quiet in the download of mklml.cmake --- cmake/external/mklml.cmake | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/cmake/external/mklml.cmake b/cmake/external/mklml.cmake index 77ea24490..74f327983 100644 --- a/cmake/external/mklml.cmake +++ b/cmake/external/mklml.cmake @@ -54,8 +54,8 @@ ExternalProject_Add( ${EXTERNAL_PROJECT_LOG_ARGS} PREFIX ${MKLML_SOURCE_DIR} DOWNLOAD_DIR ${MKLML_DOWNLOAD_DIR} - DOWNLOAD_COMMAND wget --no-check-certificate ${MKLML_URL} -c -O ${MKLML_VER}.tgz - && tar zxf ${MKLML_DOWNLOAD_DIR}/${MKLML_VER}.tgz + DOWNLOAD_COMMAND wget --no-check-certificate ${MKLML_URL} -c -q -O ${MKLML_VER}.tgz + && tar zxf ${MKLML_VER}.tgz DOWNLOAD_NO_PROGRESS 1 UPDATE_COMMAND "" CMAKE_ARGS -DCMAKE_INSTALL_PREFIX=${MKLML_INSTALL_ROOT} -- GitLab From c20a01d67d928442a0b8fb187277906ddd54ea04 Mon Sep 17 00:00:00 2001 From: dangqingqing Date: Thu, 31 Aug 2017 11:26:59 +0800 Subject: [PATCH 0299/2018] Add cuDNN Wrapper. --- paddle/platform/CMakeLists.txt | 1 + paddle/platform/cudnn_helper.cc | 15 +++ paddle/platform/cudnn_helper.h | 215 ++++++++++++++++++++++++++++++++ paddle/platform/macros.h | 23 ++++ 4 files changed, 254 insertions(+) create mode 100644 paddle/platform/cudnn_helper.cc create mode 100644 paddle/platform/cudnn_helper.h create mode 100644 paddle/platform/macros.h diff --git a/paddle/platform/CMakeLists.txt b/paddle/platform/CMakeLists.txt index 120eb1e4a..ef722e984 100644 --- a/paddle/platform/CMakeLists.txt +++ b/paddle/platform/CMakeLists.txt @@ -22,3 +22,4 @@ ENDIF() cc_library(device_context SRCS device_context.cc DEPS memory buddy_allocator system_allocator memory_block meta_data meta_cache place eigen3 ${GPU_CTX_DEPS}) nv_test(device_context_test SRCS device_context_test.cc DEPS device_context gpu_info) +nv_test(cudnn_helper SRCS cudnn_helper.cc) diff --git a/paddle/platform/cudnn_helper.cc b/paddle/platform/cudnn_helper.cc new file mode 100644 index 000000000..764285e2b --- /dev/null +++ b/paddle/platform/cudnn_helper.cc @@ -0,0 +1,15 @@ +/* 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/platform/cudnn_helper.h" diff --git a/paddle/platform/cudnn_helper.h b/paddle/platform/cudnn_helper.h new file mode 100644 index 000000000..91047236a --- /dev/null +++ b/paddle/platform/cudnn_helper.h @@ -0,0 +1,215 @@ +/* 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 +#include "paddle/platform/dynload/cudnn.h" +#include "paddle/platform/enforce.h" +#include "paddle/platform/macros.h" + +namespace paddle { +namespace platform { + +enum class DataLayout { + kNHWC, + kNCHW, + kNCHW_VECT_C, +}; + +enum class PoolingMode { + kMaximum, + kAverage, +}; + +template +class CudnnDataType; + +template <> +class CudnnDataType { + public: + static const cudnnDataType_t type = CUDNN_DATA_FLOAT; + typedef const float ScalingParamType; + static ScalingParamType* kOne() { + static ScalingParamType v = 1.0; + return &v; + } + static const ScalingParamType* kZero() { + static ScalingParamType v = 0.0; + return &v; + } +}; + +template <> +class CudnnDataType { + public: + static const cudnnDataType_t type = CUDNN_DATA_DOUBLE; + typedef const double ScalingParamType; + static ScalingParamType* kOne() { + static ScalingParamType v = 1.0; + return &v; + } + static ScalingParamType* kZero() { + static ScalingParamType v = 0.0; + return &v; + } +}; + +inline cudnnTensorFormat_t GetCudnnTensorFormat(const DataLayout& order) { + switch (order) { + case DataLayout::kNHWC: + return CUDNN_TENSOR_NHWC; + case DataLayout::kNCHW: + return CUDNN_TENSOR_NCHW; + default: + PADDLE_THROW("Unknown cudnn equivalent for order"); + } + return CUDNN_TENSOR_NCHW; +} + +class ScopedTensorDescriptor { + public: + ScopedTensorDescriptor() { + PADDLE_ENFORCE(dynload::cudnnCreateTensorDescriptor(&desc_)); + } + ~ScopedTensorDescriptor() { + PADDLE_ENFORCE(dynload::cudnnDestroyTensorDescriptor(desc_)); + } + + inline cudnnTensorDescriptor_t descriptor(const cudnnTensorFormat_t format, + const cudnnDataType_t type, + const std::vector& dims) { + // the format is not used now, but it maybe useful feature + std::vector strides(dims.size()); + strides[dims.size() - 1] = 1; + for (int i = dims.size() - 1; i >= 0; i++) { + strides[i] = dims[i + 1] * strides[i]; + } + PADDLE_ENFORCE(cudnnSetTensorNdDescriptor(desc_, type, dims.size(), + dims.data(), strides.data())); + return desc_; + } + + template + inline cudnnTensorDescriptor_t descriptor(const DataLayout& order, + const std::vector& dims) { + return descriptor(GetCudnnTensorFormat(order), CudnnDataType::type, + dims); + } + + private: + cudnnTensorDescriptor_t desc_; + DISABLE_COPY_AND_ASSIGN(ScopedTensorDescriptor); +}; + +class ScopedFilterDescriptor { + public: + ScopedFilterDescriptor() { + PADDLE_ENFORCE(dynload::cudnnCreateFilterDescriptor(&desc_)); + } + ~ScopedFilterDescriptor() { + PADDLE_ENFORCE(dynload::cudnnDestroyFilterDescriptor(desc_)); + } + + inline cudnnFilterDescriptor_t descriptor(const cudnnTensorFormat_t format, + const cudnnDataType_t type, + const std::vector& kernel) { + // filter layout: output input spatial_dim_y spatial_dim_x + PADDLE_ENFORCE(cudnnSetFilterNdDescriptor(desc_, type, format, + kernel.size(), kernel.data())); + return desc_; + } + + template + inline cudnnFilterDescriptor_t descriptor(const DataLayout& order, + const std::vector& kernel) { + return descriptor(GetCudnnTensorFormat(order), CudnnDataType::type, + kernel); + } + + private: + cudnnFilterDescriptor_t desc_; + DISABLE_COPY_AND_ASSIGN(ScopedFilterDescriptor); +}; + +class ScopedConvolutionDescriptor { + public: + ScopedConvolutionDescriptor() { + PADDLE_ENFORCE(dynload::cudnnCreateConvolutionDescriptor(&desc_)); + } + ~ScopedConvolutionDescriptor() { + PADDLE_ENFORCE(dynload::cudnnDestroyConvolutionDescriptor(desc_)); + } + + inline cudnnConvolutionDescriptor_t descriptor( + cudnnDataType_t type, const std::vector& pads, + const std::vector& strides, const std::vector& dilations) { + PADDLE_ENFORCE_EQ(pads.size(), strides.size()); + PADDLE_ENFORCE_EQ(pads.size(), dilations.size()); + PADDLE_ENFORCE(cudnnSetConvolutionNdDescriptor( + desc_, pads.size(), pads.data(), strides.data(), dilations.data(), + CUDNN_CROSS_CORRELATION, type)); + } + + template + inline cudnnConvolutionDescriptor_t descriptor( + const std::vector& pads, const std::vector& strides, + const std::vector& dilations) { + return descriptor(CudnnDataType::type, pads, strides, dilations); + } + + private: + cudnnConvolutionDescriptor_t desc_; + DISABLE_COPY_AND_ASSIGN(ScopedConvolutionDescriptor); +}; + +class ScopedPoolingDescriptor { + public: + ScopedPoolingDescriptor() { + PADDLE_ENFORCE(dynload::cudnnCreatePoolingDescriptor(&desc_)); + } + ~ScopedPoolingDescriptor() { + PADDLE_ENFORCE(dynload::cudnnDestroyPoolingDescriptor(desc_)); + } + + inline cudnnPoolingDescriptor_t descriptor(const PoolingMode& mode, + cudnnDataType_t type, + const std::vector& kernel, + const std::vector& pads, + const std::vector& strides) { + PADDLE_ENFORCE_EQ(kernel.size(), pads.size()); + PADDLE_ENFORCE_EQ(kernel.size(), strides.size()); + PADDLE_ENFORCE(cudnnSetPoolingNdDescriptor( + desc_, (mode == PoolingMode::kMaximum + ? CUDNN_POOLING_MAX + : CUDNN_POOLING_AVERAGE_COUNT_EXCLUDE_PADDING), + CUDNN_PROPAGATE_NAN, // Always propagate nans. + kernel.size(), kernel.data(), pads.data(), strides.data())); + } + + template + inline cudnnPoolingDescriptor_t descriptor(const PoolingMode& mode, + const std::vector& kernel, + const std::vector& pads, + const std::vector& strides) { + return descriptor(mode, CudnnDataType::type, kernel, pads, strides); + } + + private: + cudnnPoolingDescriptor_t desc_; + DISABLE_COPY_AND_ASSIGN(ScopedPoolingDescriptor); +}; + +} // namespace platform +} // namespace paddle diff --git a/paddle/platform/macros.h b/paddle/platform/macros.h new file mode 100644 index 000000000..4a04a38c0 --- /dev/null +++ b/paddle/platform/macros.h @@ -0,0 +1,23 @@ +/* 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 + +// Disable the copy and assignment operator for a class. +#ifndef DISABLE_COPY_AND_ASSIGN +#define DISABLE_COPY_AND_ASSIGN(classname) \ + private: \ + classname(const classname&) = delete; \ + classname& operator=(const classname&) = delete +#endif -- GitLab From 3b0e43aa6ab4f30ca960537b13f600cc36d6066e Mon Sep 17 00:00:00 2001 From: chengduoZH Date: Wed, 30 Aug 2017 23:09:58 +0800 Subject: [PATCH 0300/2018] add config parse --- proto/ModelConfig.proto | 2 + python/paddle/trainer/config_parser.py | 90 +++++++++++++++++-- .../paddle/trainer_config_helpers/layers.py | 15 +++- .../tests/configs/test_BatchNorm3D.py | 17 ++++ .../tests/layers_test.py | 2 +- 5 files changed, 112 insertions(+), 14 deletions(-) create mode 100644 python/paddle/trainer_config_helpers/tests/configs/test_BatchNorm3D.py diff --git a/proto/ModelConfig.proto b/proto/ModelConfig.proto index 95c236ad8..0525fb9dc 100644 --- a/proto/ModelConfig.proto +++ b/proto/ModelConfig.proto @@ -515,6 +515,8 @@ message LayerConfig { // for HuberRegressionLoss optional double delta = 57 [ default = 1.0 ]; + // for 3D data + optional double depth = 58 [ default = 1 ]; } message EvaluatorConfig { diff --git a/python/paddle/trainer/config_parser.py b/python/paddle/trainer/config_parser.py index c11037c3c..bc9aacaf1 100644 --- a/python/paddle/trainer/config_parser.py +++ b/python/paddle/trainer/config_parser.py @@ -1172,6 +1172,20 @@ def get_img_size(input_layer_name, channels): return img_size, img_size_y +def get_img3d_size(input_layer_name, channels): + input = g_layer_map[input_layer_name] + img_pixels = input.size / channels + img_size = input.width + img_size_y = input.height + img_size_z = input.depth + + config_assert( + img_size * img_size_y * img_size_z == img_pixels, + "Input layer %s: Incorrect input image size %d * %d * %d for input image pixels %d" + % (input_layer_name, img_size, img_size_y, img_size_z, img_pixels)) + return img_size, img_size_y, img_size_z + + def parse_bilinear(bilinear, input_layer_name, bilinear_conf): parse_image(bilinear, input_layer_name, bilinear_conf.image_conf) bilinear_conf.out_size_x = bilinear.out_size_x @@ -1224,6 +1238,12 @@ def parse_image(image, input_layer_name, image_conf): get_img_size(input_layer_name, image_conf.channels) +def parse_image3d(image, input_layer_name, image_conf): + image_conf.channels = image.channels + image_conf.img_size, image_conf.img_size_y, image_conf.img_size_z = \ + get_img3d_size(input_layer_name, image_conf.channels) + + def parse_norm(norm, input_layer_name, norm_conf): norm_conf.norm_type = norm.norm_type config_assert( @@ -1585,6 +1605,9 @@ class LayerBase(object): self.config.height = height self.config.width = width + def set_layer_depth(self, depth): + self.config.depth = depth + def set_cnn_layer(self, input_layer_name, height, @@ -1788,11 +1811,19 @@ class DetectionOutputLayer(LayerBase): @config_layer('data') class DataLayer(LayerBase): - def __init__(self, name, size, height=None, width=None, device=None): + def __init__(self, + name, + size, + depth=None, + height=None, + width=None, + device=None): super(DataLayer, self).__init__( name, 'data', size, inputs=[], device=device) if height and width: self.set_layer_height_width(height, width) + if depth: + self.set_layer_depth(depth) ''' @@ -2077,6 +2108,7 @@ class BatchNormLayer(LayerBase): name, inputs, bias=True, + img3D=False, use_global_stats=True, moving_average_fraction=0.9, batch_norm_type=None, @@ -2121,15 +2153,33 @@ class BatchNormLayer(LayerBase): input_layer = self.get_input_layer(0) image_conf = self.config.inputs[0].image_conf - parse_image(self.inputs[0].image, input_layer.name, image_conf) - - # Only pass the width and height of input to batch_norm layer - # when either of it is non-zero. - if input_layer.width != 0 or input_layer.height != 0: - self.set_cnn_layer(name, image_conf.img_size_y, image_conf.img_size, - image_conf.channels, False) + if img3D: + parse_image3d(self.inputs[0].image, input_layer.name, image_conf) + # Only pass the width and height of input to batch_norm layer + # when either of it is non-zero. + if input_layer.width != 0 or input_layer.height != 0: + self.set_cnn_layer( + input_layer_name=name, + depth=image_conf.img_size_z, + height=image_conf.img_size_y, + width=image_conf.img_size, + channels=image_conf.channels, + is_print=True) + else: + self.set_layer_size(input_layer.size) else: - self.set_layer_size(input_layer.size) + parse_image(self.inputs[0].image, input_layer.name, image_conf) + # Only pass the width and height of input to batch_norm layer + # when either of it is non-zero. + if input_layer.width != 0 or input_layer.height != 0: + self.set_cnn_layer( + input_layer_name=name, + height=image_conf.img_size_y, + width=image_conf.img_size, + channels=image_conf.channels, + is_print=True) + else: + self.set_layer_size(input_layer.size) psize = self.calc_parameter_size(image_conf) dims = [1, psize] @@ -2139,6 +2189,28 @@ class BatchNormLayer(LayerBase): self.create_bias_parameter(bias, psize) + def set_cnn_layer(self, + input_layer_name, + depth=None, + height=None, + width=None, + channels=None, + is_print=True): + depthIsNone = False + if depth is None: + depth = 1 + depthIsNone = True + size = depth * height * width * channels + self.set_layer_size(size) + self.set_layer_height_width(height, width) + self.set_layer_depth(depth) + if is_print and depthIsNone: + print("output for %s: c = %d, h = %d, w = %d, size = %d" % + (input_layer_name, channels, height, width, size)) + elif is_print: + print("output for %s: c = %d, d = %d, h = %d, w = %d, size = %d" % + (input_layer_name, channels, depth, height, width, size)) + def calc_parameter_size(self, image_conf): return image_conf.channels diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py index a525ce71d..35c84ad59 100755 --- a/python/paddle/trainer_config_helpers/layers.py +++ b/python/paddle/trainer_config_helpers/layers.py @@ -166,6 +166,7 @@ class LayerType(object): EXCONVTRANS_LAYER = 'exconvt' CUDNNCONV_LAYER = 'cudnn_conv' POOL_LAYER = 'pool' + POOL3D_LAYER = 'pool3d' BATCH_NORM_LAYER = 'batch_norm' NORM_LAYER = 'norm' SUM_TO_ONE_NORM_LAYER = 'sum_to_one_norm' @@ -894,7 +895,8 @@ def mixed_layer(size=0, @layer_support() -def data_layer(name, size, height=None, width=None, layer_attr=None): +def data_layer(name, size, depth=None, height=None, width=None, + layer_attr=None): """ Define DataLayer For NeuralNetwork. @@ -921,15 +923,18 @@ def data_layer(name, size, height=None, width=None, layer_attr=None): type=LayerType.DATA, name=name, size=size, + depth=depth, height=height, width=width, **ExtraLayerAttribute.to_kwargs(layer_attr)) + if depth is None: + depth = 1 num_filters = None if height is not None and width is not None: - num_filters = size / (width * height) - assert num_filters * width * height == size, \ - "size=%s width=%s height=%s" % (size, width, height) + num_filters = size / (width * height * depth) + assert num_filters * width * height * depth == size, \ + "size=%s width=%s height=%s depth=%s" % (size, width, height, depth) return LayerOutput(name, LayerType.DATA, size=size, num_filters=num_filters) @@ -2799,6 +2804,7 @@ def img_cmrnorm_layer(input, def batch_norm_layer(input, act=None, name=None, + img3D=False, num_channels=None, bias_attr=None, param_attr=None, @@ -2885,6 +2891,7 @@ def batch_norm_layer(input, (batch_norm_type == "cudnn_batch_norm") l = Layer( name=name, + img3D=img3D, inputs=Input( input.name, image=Image(channels=num_channels), **param_attr.attr), active_type=act.name, diff --git a/python/paddle/trainer_config_helpers/tests/configs/test_BatchNorm3D.py b/python/paddle/trainer_config_helpers/tests/configs/test_BatchNorm3D.py new file mode 100644 index 000000000..af694382b --- /dev/null +++ b/python/paddle/trainer_config_helpers/tests/configs/test_BatchNorm3D.py @@ -0,0 +1,17 @@ +from paddle.trainer_config_helpers import * + +settings(batch_size=1000, learning_rate=1e-4) + +data = data_layer(name='data', size=180, width=30, height=6) +# +batchNorm = batch_norm_layer(data, num_channels=1) +# +outputs(batchNorm) + +# # +data3D = data_layer(name='data3D22', size=120 * 3, width=20, height=6, depth=3) +# +print(data3D) +batchNorm3D = batch_norm_layer(data3D, num_channels=1, img3D=True) +# +outputs(batchNorm3D) diff --git a/python/paddle/trainer_config_helpers/tests/layers_test.py b/python/paddle/trainer_config_helpers/tests/layers_test.py index 05902ea29..68c8e128c 100644 --- a/python/paddle/trainer_config_helpers/tests/layers_test.py +++ b/python/paddle/trainer_config_helpers/tests/layers_test.py @@ -16,4 +16,4 @@ from paddle.trainer.config_parser import parse_config_and_serialize if __name__ == '__main__': parse_config_and_serialize( - 'trainer_config_helpers/tests/layers_test_config.py', '') + 'trainer_config_helpers/tests/configs/test_BatchNorm3D.py', '') -- GitLab From 2e97045c2354ea8a6ae39ee17e93098a2ec930d4 Mon Sep 17 00:00:00 2001 From: chengduoZH Date: Thu, 31 Aug 2017 14:10:40 +0800 Subject: [PATCH 0301/2018] fix layers_test.py --- .../tests/configs/file_list.sh | 2 +- ...3d_test_config.py => test_conv3d_layer.py} | 44 +--------------- .../tests/configs/test_deconv3d_layer.py | 50 +++++++++++++++++++ .../tests/layers_test.py | 3 +- 4 files changed, 53 insertions(+), 46 deletions(-) rename python/paddle/trainer_config_helpers/tests/configs/{conv3d_deconv3d_test_config.py => test_conv3d_layer.py} (51%) create mode 100644 python/paddle/trainer_config_helpers/tests/configs/test_deconv3d_layer.py diff --git a/python/paddle/trainer_config_helpers/tests/configs/file_list.sh b/python/paddle/trainer_config_helpers/tests/configs/file_list.sh index 1ca5c8a07..729e8e67c 100755 --- a/python/paddle/trainer_config_helpers/tests/configs/file_list.sh +++ b/python/paddle/trainer_config_helpers/tests/configs/file_list.sh @@ -9,6 +9,6 @@ test_seq_concat_reshape test_pad test_smooth_l1 test_multiplex_layer test_prelu_layer test_row_conv test_detection_output_layer test_multibox_loss_layer test_recursive_topology test_gated_unit_layer test_clip_layer test_row_l2_norm_layer test_kmax_seq_socre_layer test_seq_select_layers test_scale_shift_layer -test_seq_slice_layer) +test_seq_slice_layer test_conv3d_layer test_deconv3d_layer) export whole_configs=(test_split_datasource) diff --git a/python/paddle/trainer_config_helpers/tests/configs/conv3d_deconv3d_test_config.py b/python/paddle/trainer_config_helpers/tests/configs/test_conv3d_layer.py similarity index 51% rename from python/paddle/trainer_config_helpers/tests/configs/conv3d_deconv3d_test_config.py rename to python/paddle/trainer_config_helpers/tests/configs/test_conv3d_layer.py index 15f7c1d27..aa0a2c0d5 100644 --- a/python/paddle/trainer_config_helpers/tests/configs/conv3d_deconv3d_test_config.py +++ b/python/paddle/trainer_config_helpers/tests/configs/test_conv3d_layer.py @@ -14,18 +14,6 @@ padding_y = 1 padding_z = 1 groups = 1 -data1 = data_layer(name='data1', size=2016 * num_channels, height=48, width=42) - -img_conv_layer( - input=data1, - filter_size=filter_size, - num_channels=num_channels, - num_filters=16, - stride=stride, - padding=padding, - act=LinearActivation(), - bias_attr=False) - data = data_layer( name='data', size=12096 * num_channels, height=48, width=42, depth=6) # first @@ -58,34 +46,4 @@ conv3d_2 = img_conv3d_layer( trans=False, layer_type="conv3d", act=LinearActivation()) - -# first -deconv3d_1 = img_conv3d_layer( - input=data, - name='deconv3d_1', - num_filters=16, - num_channels=num_channels, - filter_size=filter_size, - stride=stride, - padding=padding, - groups=groups, - bias_attr=True, - shared_biases=True, - trans=False, - layer_type="deconv3d", - act=LinearActivation()) -# second -deconv3d_2 = img_conv3d_layer( - input=data, - name='deconv3d_2', - num_filters=16, - num_channels=num_channels, - filter_size=[filter_size, filter_size_y, filter_size_z], - stride=[stride, stride_y, stride_z], - padding=[padding, padding_y, padding_z], - groups=groups, - bias_attr=True, - shared_biases=True, - trans=False, - layer_type="deconv3d", - act=LinearActivation()) +outputs(conv3d_2) diff --git a/python/paddle/trainer_config_helpers/tests/configs/test_deconv3d_layer.py b/python/paddle/trainer_config_helpers/tests/configs/test_deconv3d_layer.py new file mode 100644 index 000000000..a113279fc --- /dev/null +++ b/python/paddle/trainer_config_helpers/tests/configs/test_deconv3d_layer.py @@ -0,0 +1,50 @@ +from paddle.trainer_config_helpers import * + +settings(batch_size=1000, learning_rate=1e-5) + +num_channels = 3 +filter_size = 3 +filter_size_y = 3 +filter_size_z = 3 +stride = 2 +stride_y = 2 +stride_z = 2 +padding = 1 +padding_y = 1 +padding_z = 1 +groups = 1 + +data = data_layer( + name='data', size=12096 * num_channels, height=48, width=42, depth=6) + +# first +deconv3d_1 = img_conv3d_layer( + input=data, + name='deconv3d_1', + num_filters=16, + num_channels=num_channels, + filter_size=filter_size, + stride=stride, + padding=padding, + groups=groups, + bias_attr=True, + shared_biases=True, + trans=True, + layer_type="deconv3d", + act=LinearActivation()) +# second +deconv3d_2 = img_conv3d_layer( + input=data, + name='deconv3d_2', + num_filters=16, + num_channels=num_channels, + filter_size=[filter_size, filter_size_y, filter_size_z], + stride=[stride, stride_y, stride_z], + padding=[padding, padding_y, padding_z], + groups=groups, + bias_attr=True, + shared_biases=True, + trans=True, + layer_type="deconv3d", + act=LinearActivation()) +outputs(deconv3d_2) diff --git a/python/paddle/trainer_config_helpers/tests/layers_test.py b/python/paddle/trainer_config_helpers/tests/layers_test.py index 44d1c1c9b..b3dd8f8fc 100644 --- a/python/paddle/trainer_config_helpers/tests/layers_test.py +++ b/python/paddle/trainer_config_helpers/tests/layers_test.py @@ -16,6 +16,5 @@ from paddle.trainer.config_parser import parse_config_and_serialize if __name__ == '__main__': parse_config_and_serialize( - 'trainer_config_helpers/tests/configs/conv3d_deconv3d_test_config.py', - '') + 'trainer_config_helpers/tests/layers_test_config.py', '') # layers_test_config.py -- GitLab From 36f0aa7390e3044b8e26d1787f99ed5edaf27ed0 Mon Sep 17 00:00:00 2001 From: caoying03 Date: Thu, 31 Aug 2017 13:06:22 +0800 Subject: [PATCH 0302/2018] fix code style to pass CI. --- paddle/gserver/layers/CrossEntropyOverBeam.cpp | 11 +++++++---- paddle/gserver/layers/CrossEntropyOverBeam.h | 6 +++--- 2 files changed, 10 insertions(+), 7 deletions(-) diff --git a/paddle/gserver/layers/CrossEntropyOverBeam.cpp b/paddle/gserver/layers/CrossEntropyOverBeam.cpp index bffcc3015..4acc07703 100644 --- a/paddle/gserver/layers/CrossEntropyOverBeam.cpp +++ b/paddle/gserver/layers/CrossEntropyOverBeam.cpp @@ -28,8 +28,9 @@ void CostForOneSequence::calValidExpandStep() { start, start + goldRowIds_[i - 1] * beamSize_ + goldColIds_[i - 1], [](const real& val) { return val != -1.; }); - } else + } else { goldRowIds_[i] = 0; + } real* start = beams_->candidateIds[i]->getData() + goldRowIds_[i] * beamSize_; @@ -288,7 +289,7 @@ void CrossEntropyOverBeam::copyInputsToCpu() { void CrossEntropyOverBeam::splitBatchBeams() { beamCosts_.resize(batchSize_); - beamPerSeq_.resize(batchSize_, beamExpanCount_); + beamPerSeq_.resize(batchSize_, BeamExpansion(beamExpanCount_)); for (size_t i = 0; i < beamExpanCount_; ++i) { int* seqStarts = @@ -300,8 +301,9 @@ void CrossEntropyOverBeam::splitBatchBeams() { subSeqStarts = getInput(i * 3).subSequenceStartPositions->getMutableData(false); maxLen = getInput(i * 3).subSequenceStartPositions->getSize() - 1; - } else + } else { maxLen = getInput(i).sequenceStartPositions->getSize() - 1; + } for (size_t j = 0; j < batchSize_; ++j) { beamPerSeq_[j].scores[i] = @@ -348,8 +350,9 @@ void CrossEntropyOverBeam::resizeOutput() { inGrad->getWidth(), false, false); - } else + } else { candidateScoreGrad_[i] = std::move(inGrad); + } candidateScoreGrad_[i]->zeroMem(); } } diff --git a/paddle/gserver/layers/CrossEntropyOverBeam.h b/paddle/gserver/layers/CrossEntropyOverBeam.h index 5d0cffee3..5643556f4 100644 --- a/paddle/gserver/layers/CrossEntropyOverBeam.h +++ b/paddle/gserver/layers/CrossEntropyOverBeam.h @@ -31,7 +31,7 @@ struct BeamExpansion { size_t expansionCount; - BeamExpansion(int n) { + explicit BeamExpansion(int n) { expansionCount = n; scores.resize(expansionCount); seqInfo.resize(expansionCount); @@ -39,7 +39,7 @@ struct BeamExpansion { scoreGrad.resize(expansionCount); gold.resize(expansionCount); - }; + } }; typedef std::shared_ptr BeamExpansionPtr; @@ -74,7 +74,7 @@ private: CHECK_GT(beams_->seqInfo[beamId]->getSize() - 1, rowId); int* starts = beams_->seqInfo[beamId]->getData(); return starts[rowId] - starts[0]; - }; + } size_t beamSize_; size_t validExpansionCount_; -- GitLab From d747c5d5119b7e564b9b7dcc7d7528ac91972712 Mon Sep 17 00:00:00 2001 From: chengduoZH Date: Thu, 31 Aug 2017 13:57:59 +0800 Subject: [PATCH 0303/2018] fix layers_test.py --- paddle/cuda/src/hl_cuda_cnn.cu | 5 +++-- paddle/parameter/Argument.h | 3 +++ .../paddle/trainer_config_helpers/tests/configs/file_list.sh | 2 +- python/paddle/trainer_config_helpers/tests/layers_test.py | 2 +- 4 files changed, 8 insertions(+), 4 deletions(-) diff --git a/paddle/cuda/src/hl_cuda_cnn.cu b/paddle/cuda/src/hl_cuda_cnn.cu index 95440c944..9ba3d1426 100644 --- a/paddle/cuda/src/hl_cuda_cnn.cu +++ b/paddle/cuda/src/hl_cuda_cnn.cu @@ -1,8 +1,11 @@ /* 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. @@ -350,7 +353,6 @@ void hl_avgpool_backward(const int frameCnt, CHECK_SYNC("hl_avgpool_backward failed"); } -///////////////// __global__ void KeMaxPool3DForward(const int nthreads, const real* inputData, const int channels, @@ -777,7 +779,6 @@ void hl_avgpool3D_backward(const int frameCnt, outStride); CHECK_SYNC("hl_avgpool3D_backward failed"); } -///////////////// __global__ void KeBilinearInterpFw(const real* in, const size_t inImgH, diff --git a/paddle/parameter/Argument.h b/paddle/parameter/Argument.h index 7b59199dd..9ed63462b 100644 --- a/paddle/parameter/Argument.h +++ b/paddle/parameter/Argument.h @@ -1,8 +1,11 @@ /* 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. diff --git a/python/paddle/trainer_config_helpers/tests/configs/file_list.sh b/python/paddle/trainer_config_helpers/tests/configs/file_list.sh index 1ca5c8a07..e7dc08c6d 100755 --- a/python/paddle/trainer_config_helpers/tests/configs/file_list.sh +++ b/python/paddle/trainer_config_helpers/tests/configs/file_list.sh @@ -9,6 +9,6 @@ test_seq_concat_reshape test_pad test_smooth_l1 test_multiplex_layer test_prelu_layer test_row_conv test_detection_output_layer test_multibox_loss_layer test_recursive_topology test_gated_unit_layer test_clip_layer test_row_l2_norm_layer test_kmax_seq_socre_layer test_seq_select_layers test_scale_shift_layer -test_seq_slice_layer) +test_seq_slice_layer test_pooling3D_layer) export whole_configs=(test_split_datasource) diff --git a/python/paddle/trainer_config_helpers/tests/layers_test.py b/python/paddle/trainer_config_helpers/tests/layers_test.py index 52218972b..05902ea29 100644 --- a/python/paddle/trainer_config_helpers/tests/layers_test.py +++ b/python/paddle/trainer_config_helpers/tests/layers_test.py @@ -16,4 +16,4 @@ from paddle.trainer.config_parser import parse_config_and_serialize if __name__ == '__main__': parse_config_and_serialize( - 'trainer_config_helpers/tests/configs/test_pooling3D_layer.py', '') + 'trainer_config_helpers/tests/layers_test_config.py', '') -- GitLab From 30c0df6d27198867f8c9ef0c098505eeaded1522 Mon Sep 17 00:00:00 2001 From: chengduoZH Date: Thu, 31 Aug 2017 13:49:42 +0800 Subject: [PATCH 0304/2018] fix layers_test.py --- python/paddle/trainer_config_helpers/tests/configs/file_list.sh | 2 +- python/paddle/trainer_config_helpers/tests/layers_test.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/python/paddle/trainer_config_helpers/tests/configs/file_list.sh b/python/paddle/trainer_config_helpers/tests/configs/file_list.sh index 1ca5c8a07..8462f2d71 100755 --- a/python/paddle/trainer_config_helpers/tests/configs/file_list.sh +++ b/python/paddle/trainer_config_helpers/tests/configs/file_list.sh @@ -9,6 +9,6 @@ test_seq_concat_reshape test_pad test_smooth_l1 test_multiplex_layer test_prelu_layer test_row_conv test_detection_output_layer test_multibox_loss_layer test_recursive_topology test_gated_unit_layer test_clip_layer test_row_l2_norm_layer test_kmax_seq_socre_layer test_seq_select_layers test_scale_shift_layer -test_seq_slice_layer) +test_seq_slice_layer test_BatchNorm3D) export whole_configs=(test_split_datasource) diff --git a/python/paddle/trainer_config_helpers/tests/layers_test.py b/python/paddle/trainer_config_helpers/tests/layers_test.py index 68c8e128c..05902ea29 100644 --- a/python/paddle/trainer_config_helpers/tests/layers_test.py +++ b/python/paddle/trainer_config_helpers/tests/layers_test.py @@ -16,4 +16,4 @@ from paddle.trainer.config_parser import parse_config_and_serialize if __name__ == '__main__': parse_config_and_serialize( - 'trainer_config_helpers/tests/configs/test_BatchNorm3D.py', '') + 'trainer_config_helpers/tests/layers_test_config.py', '') -- GitLab From 3e1f56fa2bfdaddc42dc716c099ffdce229a7068 Mon Sep 17 00:00:00 2001 From: Luo Tao Date: Thu, 31 Aug 2017 15:44:24 +0800 Subject: [PATCH 0305/2018] don't need to specify the path of libwarpctc.so, refine the python api doc --- python/paddle/trainer_config_helpers/layers.py | 11 ----------- 1 file changed, 11 deletions(-) diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py index e73098910..b2ba16333 100755 --- a/python/paddle/trainer_config_helpers/layers.py +++ b/python/paddle/trainer_config_helpers/layers.py @@ -5065,17 +5065,6 @@ def warp_ctc_layer(input, building process, PaddlePaddle will clone the source codes, build and install it to :code:`third_party/install/warpctc` directory. - To use warp_ctc layer, you need to specify the path of :code:`libwarpctc.so`, - using following methods: - - 1. Set it in :code:`paddle.init` (python api) or :code:`paddle_init` (c api), - such as :code:`paddle.init(use_gpu=True, - warpctc_dir=your_paddle_source_dir/third_party/install/warpctc/lib)`. - - 2. Set environment variable LD_LIBRARY_PATH on Linux or DYLD_LIBRARY_PATH - on Mac OS. For instance, :code:`export - LD_LIBRARY_PATH=your_paddle_source_dir/third_party/install/warpctc/lib:$LD_LIBRARY_PATH`. - More details of CTC can be found by referring to `Connectionist Temporal Classification: Labelling Unsegmented Sequence Data with Recurrent Neural Networks Date: Thu, 31 Aug 2017 16:23:12 +0800 Subject: [PATCH 0306/2018] Fix img_layers.protostr,img_trans_layers.protostr. Add test_BatchNorm3D.protostr --- .../configs/protostr/img_layers.protostr | 1 + .../protostr/img_trans_layers.protostr | 1 + .../protostr/test_BatchNorm3D.protostr | 92 +++++++++++++++++++ .../tests/configs/test_BatchNorm3D.py | 14 +-- 4 files changed, 98 insertions(+), 10 deletions(-) create mode 100644 python/paddle/trainer_config_helpers/tests/configs/protostr/test_BatchNorm3D.protostr diff --git a/python/paddle/trainer_config_helpers/tests/configs/protostr/img_layers.protostr b/python/paddle/trainer_config_helpers/tests/configs/protostr/img_layers.protostr index 1a577b8d9..5ddf6052d 100644 --- a/python/paddle/trainer_config_helpers/tests/configs/protostr/img_layers.protostr +++ b/python/paddle/trainer_config_helpers/tests/configs/protostr/img_layers.protostr @@ -62,6 +62,7 @@ layers { moving_average_fraction: 0.9 height: 227 width: 227 + depth: 1 } layers { name: "__crmnorm_0__" diff --git a/python/paddle/trainer_config_helpers/tests/configs/protostr/img_trans_layers.protostr b/python/paddle/trainer_config_helpers/tests/configs/protostr/img_trans_layers.protostr index 2818389b1..c0252b945 100644 --- a/python/paddle/trainer_config_helpers/tests/configs/protostr/img_trans_layers.protostr +++ b/python/paddle/trainer_config_helpers/tests/configs/protostr/img_trans_layers.protostr @@ -62,6 +62,7 @@ layers { moving_average_fraction: 0.9 height: 256 width: 256 + depth: 1 } layers { name: "__crmnorm_0__" diff --git a/python/paddle/trainer_config_helpers/tests/configs/protostr/test_BatchNorm3D.protostr b/python/paddle/trainer_config_helpers/tests/configs/protostr/test_BatchNorm3D.protostr new file mode 100644 index 000000000..832ed24a3 --- /dev/null +++ b/python/paddle/trainer_config_helpers/tests/configs/protostr/test_BatchNorm3D.protostr @@ -0,0 +1,92 @@ +type: "nn" +layers { + name: "data3D" + type: "data" + size: 360 + active_type: "" + height: 6 + width: 20 + depth: 3 +} +layers { + name: "__batch_norm_0__" + type: "batch_norm" + size: 360 + active_type: "relu" + inputs { + input_layer_name: "data3D" + input_parameter_name: "___batch_norm_0__.w0" + image_conf { + channels: 1 + img_size: 20 + img_size_y: 6 + img_size_z: 3 + } + } + inputs { + input_layer_name: "data3D" + input_parameter_name: "___batch_norm_0__.w1" + } + inputs { + input_layer_name: "data3D" + input_parameter_name: "___batch_norm_0__.w2" + } + bias_parameter_name: "___batch_norm_0__.wbias" + moving_average_fraction: 0.9 + height: 6 + width: 20 + depth: 3 +} +parameters { + name: "___batch_norm_0__.w0" + size: 1 + initial_mean: 1.0 + initial_std: 0.0 + initial_strategy: 0 + initial_smart: false +} +parameters { + name: "___batch_norm_0__.w1" + size: 1 + initial_mean: 0.0 + initial_std: 0.0 + dims: 1 + dims: 1 + initial_strategy: 0 + initial_smart: false + is_static: true + is_shared: true +} +parameters { + name: "___batch_norm_0__.w2" + size: 1 + initial_mean: 0.0 + initial_std: 0.0 + dims: 1 + dims: 1 + initial_strategy: 0 + initial_smart: false + is_static: true + is_shared: true +} +parameters { + name: "___batch_norm_0__.wbias" + size: 1 + initial_mean: 0.0 + initial_std: 0.0 + dims: 1 + dims: 1 + initial_strategy: 0 + initial_smart: false +} +input_layer_names: "data3D" +output_layer_names: "__batch_norm_0__" +sub_models { + name: "root" + layer_names: "data3D" + layer_names: "__batch_norm_0__" + input_layer_names: "data3D" + output_layer_names: "__batch_norm_0__" + is_recurrent_layer_group: false +} + diff --git a/python/paddle/trainer_config_helpers/tests/configs/test_BatchNorm3D.py b/python/paddle/trainer_config_helpers/tests/configs/test_BatchNorm3D.py index af694382b..a991b2225 100644 --- a/python/paddle/trainer_config_helpers/tests/configs/test_BatchNorm3D.py +++ b/python/paddle/trainer_config_helpers/tests/configs/test_BatchNorm3D.py @@ -2,16 +2,10 @@ from paddle.trainer_config_helpers import * settings(batch_size=1000, learning_rate=1e-4) -data = data_layer(name='data', size=180, width=30, height=6) -# -batchNorm = batch_norm_layer(data, num_channels=1) -# -outputs(batchNorm) +#data = data_layer(name='data', size=180, width=30, height=6) +#batchNorm = batch_norm_layer(data, num_channels=1) +#outputs(batchNorm) -# # -data3D = data_layer(name='data3D22', size=120 * 3, width=20, height=6, depth=3) -# -print(data3D) +data3D = data_layer(name='data3D', size=120 * 3, width=20, height=6, depth=3) batchNorm3D = batch_norm_layer(data3D, num_channels=1, img3D=True) -# outputs(batchNorm3D) -- GitLab From a4e1e127f3aa5a64cc777deab31a410874fd7ff7 Mon Sep 17 00:00:00 2001 From: chengduoZH Date: Thu, 31 Aug 2017 16:33:01 +0800 Subject: [PATCH 0307/2018] Add test_conv3d_layer.protostr,test_deconv3d_layer.protostr --- .../protostr/test_conv3d_layer.protostr | 132 ++++++++++++++++++ .../protostr/test_deconv3d_layer.protostr | 132 ++++++++++++++++++ 2 files changed, 264 insertions(+) create mode 100644 python/paddle/trainer_config_helpers/tests/configs/protostr/test_conv3d_layer.protostr create mode 100644 python/paddle/trainer_config_helpers/tests/configs/protostr/test_deconv3d_layer.protostr diff --git a/python/paddle/trainer_config_helpers/tests/configs/protostr/test_conv3d_layer.protostr b/python/paddle/trainer_config_helpers/tests/configs/protostr/test_conv3d_layer.protostr new file mode 100644 index 000000000..9fe2bc29d --- /dev/null +++ b/python/paddle/trainer_config_helpers/tests/configs/protostr/test_conv3d_layer.protostr @@ -0,0 +1,132 @@ +type: "nn" +layers { + name: "data" + type: "data" + size: 36288 + active_type: "" + height: 48 + width: 42 + depth: 6 +} +layers { + name: "conv3d_1" + type: "conv3d" + size: 24192 + active_type: "" + inputs { + input_layer_name: "data" + input_parameter_name: "_conv3d_1.w0" + conv_conf { + filter_size: 3 + channels: 3 + stride: 2 + padding: 1 + groups: 1 + filter_channels: 3 + output_x: 21 + img_size: 42 + caffe_mode: true + filter_size_y: 3 + padding_y: 1 + stride_y: 2 + output_y: 24 + img_size_y: 48 + filter_size_z: 3 + padding_z: 1 + stride_z: 2 + output_z: 3 + img_size_z: 6 + } + } + bias_parameter_name: "_conv3d_1.wbias" + num_filters: 16 + shared_biases: true + height: 24 + width: 21 + depth: 3 +} +layers { + name: "conv3d_2" + type: "conv3d" + size: 24192 + active_type: "" + inputs { + input_layer_name: "data" + input_parameter_name: "_conv3d_2.w0" + conv_conf { + filter_size: 3 + channels: 3 + stride: 2 + padding: 1 + groups: 1 + filter_channels: 3 + output_x: 21 + img_size: 42 + caffe_mode: true + filter_size_y: 3 + padding_y: 1 + stride_y: 2 + output_y: 24 + img_size_y: 48 + filter_size_z: 3 + padding_z: 1 + stride_z: 2 + output_z: 3 + img_size_z: 6 + } + } + bias_parameter_name: "_conv3d_2.wbias" + num_filters: 16 + shared_biases: true + height: 24 + width: 21 + depth: 3 +} +parameters { + name: "_conv3d_1.w0" + size: 1296 + initial_mean: 0.0 + initial_std: 0.272165526976 + initial_strategy: 0 + initial_smart: false +} +parameters { + name: "_conv3d_1.wbias" + size: 16 + initial_mean: 0.0 + initial_std: 0.0 + dims: 16 + dims: 1 + initial_strategy: 0 + initial_smart: false +} +parameters { + name: "_conv3d_2.w0" + size: 1296 + initial_mean: 0.0 + initial_std: 0.272165526976 + initial_strategy: 0 + initial_smart: false +} +parameters { + name: "_conv3d_2.wbias" + size: 16 + initial_mean: 0.0 + initial_std: 0.0 + dims: 16 + dims: 1 + initial_strategy: 0 + initial_smart: false +} +input_layer_names: "data" +output_layer_names: "conv3d_2" +sub_models { + name: "root" + layer_names: "data" + layer_names: "conv3d_1" + layer_names: "conv3d_2" + input_layer_names: "data" + output_layer_names: "conv3d_2" + is_recurrent_layer_group: false +} + diff --git a/python/paddle/trainer_config_helpers/tests/configs/protostr/test_deconv3d_layer.protostr b/python/paddle/trainer_config_helpers/tests/configs/protostr/test_deconv3d_layer.protostr new file mode 100644 index 000000000..7bf409731 --- /dev/null +++ b/python/paddle/trainer_config_helpers/tests/configs/protostr/test_deconv3d_layer.protostr @@ -0,0 +1,132 @@ +type: "nn" +layers { + name: "data" + type: "data" + size: 36288 + active_type: "" + height: 48 + width: 42 + depth: 6 +} +layers { + name: "deconv3d_1" + type: "deconv3d" + size: 1387760 + active_type: "" + inputs { + input_layer_name: "data" + input_parameter_name: "_deconv3d_1.w0" + conv_conf { + filter_size: 3 + channels: 3 + stride: 2 + padding: 1 + groups: 1 + filter_channels: 16 + output_x: 42 + img_size: 83 + caffe_mode: true + filter_size_y: 3 + padding_y: 1 + stride_y: 2 + output_y: 48 + img_size_y: 95 + filter_size_z: 3 + padding_z: 1 + stride_z: 2 + output_z: 6 + img_size_z: 11 + } + } + bias_parameter_name: "_deconv3d_1.wbias" + num_filters: 16 + shared_biases: true + height: 95 + width: 83 + depth: 11 +} +layers { + name: "deconv3d_2" + type: "deconv3d" + size: 1387760 + active_type: "" + inputs { + input_layer_name: "data" + input_parameter_name: "_deconv3d_2.w0" + conv_conf { + filter_size: 3 + channels: 3 + stride: 2 + padding: 1 + groups: 1 + filter_channels: 16 + output_x: 42 + img_size: 83 + caffe_mode: true + filter_size_y: 3 + padding_y: 1 + stride_y: 2 + output_y: 48 + img_size_y: 95 + filter_size_z: 3 + padding_z: 1 + stride_z: 2 + output_z: 6 + img_size_z: 11 + } + } + bias_parameter_name: "_deconv3d_2.wbias" + num_filters: 16 + shared_biases: true + height: 95 + width: 83 + depth: 11 +} +parameters { + name: "_deconv3d_1.w0" + size: 6912 + initial_mean: 0.0 + initial_std: 0.272165526976 + initial_strategy: 0 + initial_smart: false +} +parameters { + name: "_deconv3d_1.wbias" + size: 16 + initial_mean: 0.0 + initial_std: 0.0 + dims: 16 + dims: 1 + initial_strategy: 0 + initial_smart: false +} +parameters { + name: "_deconv3d_2.w0" + size: 6912 + initial_mean: 0.0 + initial_std: 0.272165526976 + initial_strategy: 0 + initial_smart: false +} +parameters { + name: "_deconv3d_2.wbias" + size: 16 + initial_mean: 0.0 + initial_std: 0.0 + dims: 16 + dims: 1 + initial_strategy: 0 + initial_smart: false +} +input_layer_names: "data" +output_layer_names: "deconv3d_2" +sub_models { + name: "root" + layer_names: "data" + layer_names: "deconv3d_1" + layer_names: "deconv3d_2" + input_layer_names: "data" + output_layer_names: "deconv3d_2" + is_recurrent_layer_group: false +} + -- GitLab From f03811b06fc099486824cc8baaee3bacbc687523 Mon Sep 17 00:00:00 2001 From: qijun Date: Thu, 31 Aug 2017 16:33:56 +0800 Subject: [PATCH 0308/2018] init refine LODTensor --- paddle/framework/lod_tensor.cc | 41 +++++++---- paddle/framework/lod_tensor.h | 104 ++++++++-------------------- paddle/framework/lod_tensor_test.cc | 84 ++++++++++++---------- 3 files changed, 102 insertions(+), 127 deletions(-) diff --git a/paddle/framework/lod_tensor.cc b/paddle/framework/lod_tensor.cc index 2b1789077..71eac4a10 100644 --- a/paddle/framework/lod_tensor.cc +++ b/paddle/framework/lod_tensor.cc @@ -19,25 +19,24 @@ namespace paddle { namespace framework { -LODTensor::LOD LODTensor::LOD::SliceLevels(size_t level_begin, - size_t level_end) const { +LOD SliceLevels(const LOD& in, size_t level_begin, size_t level_end) { LOD new_lod; new_lod.reserve(level_end - level_begin); for (size_t i = level_begin; i < level_end; i++) { - new_lod.emplace_back(at(i)); + new_lod.emplace_back(in.at(i)); } return new_lod; } -LODTensor::LOD LODTensor::LOD::SliceInLevel(size_t level, size_t elem_begin, - size_t elem_end) const { +LOD SliceInLevel(const LOD& in, size_t level, size_t elem_begin, + size_t elem_end) { // slice the lod. LOD new_lod; - new_lod.reserve(size() - level); - auto start = this->at(level)[elem_begin]; - auto end = this->at(level)[elem_end]; + new_lod.reserve(in.size() - level); + auto start = in.at(level)[elem_begin]; + auto end = in.at(level)[elem_end]; - for (auto it = this->begin() + level; it != this->end(); it++) { + for (auto it = in.begin() + level; it != in.end(); it++) { auto it_begin = std::find(it->begin(), it->end(), start); auto it_end = std::find(it_begin, it->end(), end); PADDLE_ENFORCE(it_begin != it->end(), "error in parsing lod info"); @@ -49,11 +48,11 @@ LODTensor::LOD LODTensor::LOD::SliceInLevel(size_t level, size_t elem_begin, [start](int v) { return v - start; }); PADDLE_ENFORCE_EQ(new_lod.back().front(), 0, "error in slice LOD"); } - PADDLE_ENFORCE_LE(new_lod.size(), this->size()); + PADDLE_ENFORCE_LE(new_lod.size(), in.size()); return new_lod; } -bool operator==(const LODTensor::LOD& a, const LODTensor::LOD& b) { +bool operator==(const LOD& a, const LOD& b) { if (a.size() != b.size()) { return false; } @@ -70,9 +69,27 @@ bool operator==(const LODTensor::LOD& a, const LODTensor::LOD& b) { } } } - return true; } +void LODTensor::SliceLevels(size_t level_begin, size_t level_end) { + auto new_lod = framework::SliceLevels(lod_, level_begin, level_end); + lod_ = new_lod; +} + +void LODTensor::SliceInLevel(size_t level, size_t elem_begin, size_t elem_end) { + PADDLE_ENFORCE(level < NumLevels(), "level [%d] out of range [%d]", level, + NumLevels()); + PADDLE_ENFORCE(elem_begin < NumElements(level), + "element begin [%d] out of range [%d]", elem_begin, + NumElements(level)); + PADDLE_ENFORCE(elem_end < NumElements(level) + 1, + "element end [%d] out of range [%d]", elem_end, + NumElements(level)); + + auto new_lod = framework::SliceInLevel(lod_, level, elem_begin, elem_end); + lod_ = new_lod; +} + } // namespace framework } // namespace paddle diff --git a/paddle/framework/lod_tensor.h b/paddle/framework/lod_tensor.h index 9e27aec38..7a9aebf50 100644 --- a/paddle/framework/lod_tensor.h +++ b/paddle/framework/lod_tensor.h @@ -15,7 +15,7 @@ #pragma once #include -#if !defined(PADDLE_ONLY_CPU) +#ifndef PADDLE_ONLY_CPU #include #include #endif @@ -27,33 +27,31 @@ namespace paddle { namespace framework { +#ifdef PADDLE_ONLY_CPU +template +using Vector = std::vector; +#else +template +using Vector = thrust::host_vector; +#endif + +using LOD = std::vector>; + +LOD SliceLevels(const LOD& in, size_t level_begin, size_t level_end); + +LOD SliceInLevel(const LOD& in, size_t level, size_t elem_begin, + size_t elem_end); + +bool operator==(const LOD& a, const LOD& b); + /* * LODTensor (Level of details Tensor) * see https://en.wikipedia.org/wiki/Level_of_details for reference. */ -class LODTensor : public Tensor { +struct LODTensor { public: -// Level save offsets of each unit. -#ifdef PADDLE_ONLY_CPU - template - using Vector = std::vector; -#else - template - using Vector = thrust::host_vector; -#endif - // LoD stores offsets of each level of units, the largest units level first, - // then the smaller units level. Each Level stores the offsets of units in - // Tesor. - class LOD : public std::vector> { - public: - LOD SliceLevels(size_t level_begin, size_t level_end) const; - LOD SliceInLevel(size_t level, size_t elem_begin, size_t elem_end) const; - }; - LODTensor() {} - explicit LODTensor(const LOD &lod) : lod_(lod) {} - - virtual Tensor *Clone() const { return new LODTensor(lod_); } + LODTensor(const LOD& lod, Tensor* t) : lod_(lod), tensor_(t) {} /* * Get a element from LOD. @@ -79,71 +77,23 @@ class LODTensor : public Tensor { PADDLE_ENFORCE(level < NumLevels(), "level [%d] out of range [%d]", level, NumLevels()); // the last offset is the end of last element - return lod_[level].size() - 1; + return (lod_)[level].size() - 1; } /* - * Slice of levels[level_begin:level_end], with tensor shared. + * Slice of levels[level_begin:level_end] */ - template - LODTensor SliceLevels(size_t level_begin, size_t level_end) const; + void SliceLevels(size_t level_begin, size_t level_end); /* - * Slice of elements of a level, [elem_begin: elem_end], with tensor shared. + * Slice of elements of a level, [elem_begin: elem_end] * @note: low performance in slice lod_. */ - template - LODTensor SliceInLevel(size_t level, size_t elem_begin, - size_t elem_end) const; - - /* - * Copy other's lod_'s content, free to mutate. - */ - void CopyLOD(const LODTensor &other) { lod_ = other.lod_; } - /* - * Determine whether LODTensor has a valid LOD info. - */ - const LOD &lod() const { return lod_; } - LOD *mutable_lod() { return &lod_; } - - virtual ~LODTensor() {} + void SliceInLevel(size_t level, size_t elem_begin, size_t elem_end); - private: + public: LOD lod_; + Tensor* tensor_; // not owned }; - -bool operator==(const LODTensor::LOD &a, const LODTensor::LOD &b); - -template -LODTensor LODTensor::SliceLevels(size_t level_begin, size_t level_end) const { - auto new_lod = lod_.SliceLevels(level_begin, level_end); - // slice levels just need to update LOD info, each level will contains the - // whole tensor_, so no need to modify tensor_. - LODTensor new_tensor(new_lod); - new_tensor.ShareDataWith(*this); - return new_tensor; -} - -template -LODTensor LODTensor::SliceInLevel(size_t level, size_t elem_begin, - size_t elem_end) const { - PADDLE_ENFORCE(level < NumLevels(), "level [%d] out of range [%d]", level, - NumLevels()); - PADDLE_ENFORCE(elem_begin < NumElements(level), - "element begin [%d] out of range [%d]", elem_begin, - NumElements(level)); - PADDLE_ENFORCE(elem_end < NumElements(level) + 1, - "element end [%d] out of range [%d]", elem_end, - NumElements(level)); - - auto new_lod = lod_.SliceInLevel(level, elem_begin, elem_end); - - // slice elements just need to update LOD info, because offsets are not - // changed, so the original tensor_ can be reused. - LODTensor new_tensor(new_lod); - new_tensor.ShareDataWith(*this); - return new_tensor; -} - } // namespace framework } // namespace paddle diff --git a/paddle/framework/lod_tensor_test.cc b/paddle/framework/lod_tensor_test.cc index 2881136ce..b75d86b5b 100644 --- a/paddle/framework/lod_tensor_test.cc +++ b/paddle/framework/lod_tensor_test.cc @@ -21,16 +21,34 @@ namespace paddle { namespace framework { +// TEST(LODTensor, test) { +// LOD lod; +// lod.push_back(std::vector{0, 10, 20}); +// lod.push_back(std::vector{0, 5, 10, 15, 20}); +// lod.push_back(std::vector{0, 2, 5, 7, 10, 12, 15, 17, 20}); + +// ASSERT_EQ(lod.size(), 3UL); + +// Tensor tensor; +// tensor.Resize({20 /*batch size*/, 128 /*dim*/}); +// // malloc memory +// platform::CPUPlace place; +// tensor.mutable_data(place); + +// LODTensor lod_tensor(lod, &tensor); + +// ASSERT_EQ(lod_tensor.NumLevels(), 3UL); +// } + class LODTensorTester : public ::testing::Test { public: virtual void SetUp() override { - lod_tensor.reset(new LODTensor); // tensor's batch_size: 30 // 3 levels // 0 10 20 // 0 5 10 15 20 // 0 2 5 7 10 12 15 20 - LODTensor::LOD lod; + LOD lod; lod.push_back(std::vector{0, 10, 20}); lod.push_back(std::vector{0, 5, 10, 15, 20}); lod.push_back(std::vector{0, 2, 5, 7, 10, 12, 15, 17, 20}); @@ -41,75 +59,65 @@ class LODTensorTester : public ::testing::Test { // malloc memory tensor.mutable_data(place); - lod_tensor.reset(new LODTensor(lod)); - lod_tensor->Resize({20 /*batch size*/, 128 /*dim*/}); - - lod_tensor->ShareDataWith(tensor); - // lod_tensor->ShareDataWith(tensor); + lod_tensor.lod_ = lod; + lod_tensor.tensor_ = &tensor; } protected: - std::unique_ptr lod_tensor; platform::CPUPlace place; Tensor tensor; + LODTensor lod_tensor; }; -TEST_F(LODTensorTester, NumLevels) { ASSERT_EQ(lod_tensor->NumLevels(), 3UL); } +TEST_F(LODTensorTester, NumLevels) { ASSERT_EQ(lod_tensor.NumLevels(), 3UL); } TEST_F(LODTensorTester, NumElements) { - ASSERT_EQ(lod_tensor->NumElements(0), 2UL); - ASSERT_EQ(lod_tensor->NumElements(1), 4UL); - ASSERT_EQ(lod_tensor->NumElements(2), 8UL); + ASSERT_EQ(lod_tensor.NumElements(0), 2UL); + ASSERT_EQ(lod_tensor.NumElements(1), 4UL); + ASSERT_EQ(lod_tensor.NumElements(2), 8UL); } TEST_F(LODTensorTester, SliceLevels) { // slice 1 level for (size_t level = 0; level < 3UL; ++level) { - auto new_lod_tensor = lod_tensor->SliceLevels(level, level + 1); + LODTensor new_lod_tensor = lod_tensor; + new_lod_tensor.SliceLevels(level, level + 1); ASSERT_EQ(new_lod_tensor.NumLevels(), 1UL); - ASSERT_EQ(new_lod_tensor.NumElements(0UL), lod_tensor->NumElements(level)); - // ASSERT_EQ(new_lod_tensor, *lod_tensor); + ASSERT_EQ(new_lod_tensor.NumElements(0), lod_tensor.NumElements(level)); + ASSERT_EQ(new_lod_tensor.tensor_->data(), + lod_tensor.tensor_->data()); } // slice 2 level for (size_t level = 0; level < 2UL; ++level) { - auto new_lod_tensor = lod_tensor->SliceLevels(level, level + 2); + LODTensor new_lod_tensor = lod_tensor; + new_lod_tensor.SliceLevels(level, level + 2); ASSERT_EQ(new_lod_tensor.NumLevels(), 2UL); - ASSERT_EQ(new_lod_tensor.NumElements(0), lod_tensor->NumElements(level)); - ASSERT_EQ(new_lod_tensor.NumElements(1), - lod_tensor->NumElements(level + 1)); - ASSERT_EQ(new_lod_tensor.data(), lod_tensor->data()); + ASSERT_EQ(new_lod_tensor.NumElements(0), lod_tensor.NumElements(level)); + ASSERT_EQ(new_lod_tensor.NumElements(1), lod_tensor.NumElements(level + 1)); + ASSERT_EQ(new_lod_tensor.tensor_->data(), + lod_tensor.tensor_->data()); } } TEST_F(LODTensorTester, SliceInLevel) { size_t level = 0; - auto new_lod_tensor = lod_tensor->SliceInLevel(level, 0, 2); + LODTensor new_lod_tensor = lod_tensor; + new_lod_tensor.SliceInLevel(level, 0, 2); EXPECT_EQ(new_lod_tensor.NumLevels(), 3UL); EXPECT_EQ(new_lod_tensor.NumElements(0), 2UL); EXPECT_EQ(new_lod_tensor.NumElements(1), 4UL); EXPECT_EQ(new_lod_tensor.NumElements(2), 8UL); - ASSERT_EQ(new_lod_tensor.data(), lod_tensor->data()); + ASSERT_EQ(new_lod_tensor.tensor_->data(), + lod_tensor.tensor_->data()); level = 1; - new_lod_tensor = lod_tensor->SliceInLevel(level, 0, 2); + new_lod_tensor = lod_tensor; + new_lod_tensor.SliceInLevel(level, 0, 2); ASSERT_EQ(new_lod_tensor.NumLevels(), 2UL); ASSERT_EQ(new_lod_tensor.NumElements(0), 2UL); ASSERT_EQ(new_lod_tensor.NumElements(1), 4UL); - ASSERT_EQ(new_lod_tensor.data(), lod_tensor->data()); -} - -TEST_F(LODTensorTester, ShareLOD) { - LODTensor new_lod_tensor; - new_lod_tensor.CopyLOD(*lod_tensor); - ASSERT_EQ(new_lod_tensor.lod(), lod_tensor->lod()); -} - -TEST_F(LODTensorTester, CopyLOD) { - LODTensor new_lod_tensor; - new_lod_tensor.CopyLOD(*lod_tensor); - bool equals = std::equal(lod_tensor->lod().begin(), lod_tensor->lod().end(), - new_lod_tensor.lod().begin()); - ASSERT_TRUE(equals); + ASSERT_EQ(new_lod_tensor.tensor_->data(), + lod_tensor.tensor_->data()); } } // namespace framework -- GitLab From 039784426641162686cfaae556a5f325dcf6fda4 Mon Sep 17 00:00:00 2001 From: qijun Date: Thu, 31 Aug 2017 16:34:50 +0800 Subject: [PATCH 0309/2018] remove unused codes --- paddle/framework/lod_tensor_test.cc | 19 ------------------- 1 file changed, 19 deletions(-) diff --git a/paddle/framework/lod_tensor_test.cc b/paddle/framework/lod_tensor_test.cc index b75d86b5b..30c8925ad 100644 --- a/paddle/framework/lod_tensor_test.cc +++ b/paddle/framework/lod_tensor_test.cc @@ -21,25 +21,6 @@ namespace paddle { namespace framework { -// TEST(LODTensor, test) { -// LOD lod; -// lod.push_back(std::vector{0, 10, 20}); -// lod.push_back(std::vector{0, 5, 10, 15, 20}); -// lod.push_back(std::vector{0, 2, 5, 7, 10, 12, 15, 17, 20}); - -// ASSERT_EQ(lod.size(), 3UL); - -// Tensor tensor; -// tensor.Resize({20 /*batch size*/, 128 /*dim*/}); -// // malloc memory -// platform::CPUPlace place; -// tensor.mutable_data(place); - -// LODTensor lod_tensor(lod, &tensor); - -// ASSERT_EQ(lod_tensor.NumLevels(), 3UL); -// } - class LODTensorTester : public ::testing::Test { public: virtual void SetUp() override { -- GitLab From 835572afe70e3c0a0f11ff2f40a53b899b7adda6 Mon Sep 17 00:00:00 2001 From: qijun Date: Thu, 31 Aug 2017 16:51:02 +0800 Subject: [PATCH 0310/2018] make LODTensor class instead struct --- paddle/framework/lod_tensor.h | 12 ++++++++++-- paddle/framework/lod_tensor_test.cc | 20 ++++++++++---------- 2 files changed, 20 insertions(+), 12 deletions(-) diff --git a/paddle/framework/lod_tensor.h b/paddle/framework/lod_tensor.h index 7a9aebf50..9e6b6b4ac 100644 --- a/paddle/framework/lod_tensor.h +++ b/paddle/framework/lod_tensor.h @@ -48,11 +48,19 @@ bool operator==(const LOD& a, const LOD& b); * LODTensor (Level of details Tensor) * see https://en.wikipedia.org/wiki/Level_of_details for reference. */ -struct LODTensor { +class LODTensor { public: LODTensor() {} LODTensor(const LOD& lod, Tensor* t) : lod_(lod), tensor_(t) {} + void set_lod(const LOD& lod) { lod_ = lod; } + + void set_tensor(Tensor* tensor) { tensor_ = tensor; } + + Tensor& tensor() { return *tensor_; } + + LOD lod() { return lod_; } + /* * Get a element from LOD. */ @@ -91,7 +99,7 @@ struct LODTensor { */ void SliceInLevel(size_t level, size_t elem_begin, size_t elem_end); - public: + private: LOD lod_; Tensor* tensor_; // not owned }; diff --git a/paddle/framework/lod_tensor_test.cc b/paddle/framework/lod_tensor_test.cc index 30c8925ad..9a351605e 100644 --- a/paddle/framework/lod_tensor_test.cc +++ b/paddle/framework/lod_tensor_test.cc @@ -40,8 +40,8 @@ class LODTensorTester : public ::testing::Test { // malloc memory tensor.mutable_data(place); - lod_tensor.lod_ = lod; - lod_tensor.tensor_ = &tensor; + lod_tensor.set_lod(lod); + lod_tensor.set_tensor(&tensor); } protected: @@ -65,8 +65,8 @@ TEST_F(LODTensorTester, SliceLevels) { new_lod_tensor.SliceLevels(level, level + 1); ASSERT_EQ(new_lod_tensor.NumLevels(), 1UL); ASSERT_EQ(new_lod_tensor.NumElements(0), lod_tensor.NumElements(level)); - ASSERT_EQ(new_lod_tensor.tensor_->data(), - lod_tensor.tensor_->data()); + ASSERT_EQ(new_lod_tensor.tensor().data(), + lod_tensor.tensor().data()); } // slice 2 level for (size_t level = 0; level < 2UL; ++level) { @@ -75,8 +75,8 @@ TEST_F(LODTensorTester, SliceLevels) { ASSERT_EQ(new_lod_tensor.NumLevels(), 2UL); ASSERT_EQ(new_lod_tensor.NumElements(0), lod_tensor.NumElements(level)); ASSERT_EQ(new_lod_tensor.NumElements(1), lod_tensor.NumElements(level + 1)); - ASSERT_EQ(new_lod_tensor.tensor_->data(), - lod_tensor.tensor_->data()); + ASSERT_EQ(new_lod_tensor.tensor().data(), + lod_tensor.tensor().data()); } } @@ -88,8 +88,8 @@ TEST_F(LODTensorTester, SliceInLevel) { EXPECT_EQ(new_lod_tensor.NumElements(0), 2UL); EXPECT_EQ(new_lod_tensor.NumElements(1), 4UL); EXPECT_EQ(new_lod_tensor.NumElements(2), 8UL); - ASSERT_EQ(new_lod_tensor.tensor_->data(), - lod_tensor.tensor_->data()); + ASSERT_EQ(new_lod_tensor.tensor().data(), + lod_tensor.tensor().data()); level = 1; new_lod_tensor = lod_tensor; @@ -97,8 +97,8 @@ TEST_F(LODTensorTester, SliceInLevel) { ASSERT_EQ(new_lod_tensor.NumLevels(), 2UL); ASSERT_EQ(new_lod_tensor.NumElements(0), 2UL); ASSERT_EQ(new_lod_tensor.NumElements(1), 4UL); - ASSERT_EQ(new_lod_tensor.tensor_->data(), - lod_tensor.tensor_->data()); + ASSERT_EQ(new_lod_tensor.tensor().data(), + lod_tensor.tensor().data()); } } // namespace framework -- GitLab From d394a1447125af9f6fc2b43c936d36a61662dc0e Mon Sep 17 00:00:00 2001 From: Liu Yiqun Date: Thu, 31 Aug 2017 17:05:39 +0800 Subject: [PATCH 0311/2018] Remove unused incluing file net/if_arp.h. --- paddle/pserver/LightNetwork.cpp | 1 - 1 file changed, 1 deletion(-) diff --git a/paddle/pserver/LightNetwork.cpp b/paddle/pserver/LightNetwork.cpp index 8616fd2d5..4203f2616 100644 --- a/paddle/pserver/LightNetwork.cpp +++ b/paddle/pserver/LightNetwork.cpp @@ -22,7 +22,6 @@ limitations under the License. */ #include #include -#include #include #include -- GitLab From fb93a8be27642f1761262778afea09353fa5c71d Mon Sep 17 00:00:00 2001 From: Liu Yiqun Date: Thu, 31 Aug 2017 17:57:05 +0800 Subject: [PATCH 0312/2018] Add paddle_pserver back to c-api library, because it is used in Evaluator.h. --- CMakeLists.txt | 8 +++----- cmake/cross_compiling/ios.cmake | 2 -- cmake/util.cmake | 13 ++++--------- paddle/CMakeLists.txt | 10 +++------- paddle/capi/CMakeLists.txt | 5 ++--- 5 files changed, 12 insertions(+), 26 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index a5971ddd9..ba1febe5e 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -155,11 +155,9 @@ endif(USE_NNPACK) add_subdirectory(proto) -if(NOT ANDROID AND NOT IOS) - # "add_subdirectory(go)" should be placed after the following loine, - # because it depends on paddle/optimizer. - add_subdirectory(paddle/optimizer) -endif() +# "add_subdirectory(go)" should be placed after the following loine, +# because it depends on paddle/optimizer. +add_subdirectory(paddle/optimizer) # "add_subdirectory(paddle)" and "add_subdirectory(python)" should be # placed after this block, because they depends on it. diff --git a/cmake/cross_compiling/ios.cmake b/cmake/cross_compiling/ios.cmake index eea17436b..4b2a18bcc 100644 --- a/cmake/cross_compiling/ios.cmake +++ b/cmake/cross_compiling/ios.cmake @@ -49,8 +49,6 @@ if(NOT IOS) endif() set(CMAKE_SYSTEM_NAME Darwin) -#set(UNIX ON) -#set(APPLE ON) # Get the Xcode version being used. execute_process(COMMAND xcodebuild -version diff --git a/cmake/util.cmake b/cmake/util.cmake index bfe269ea2..0da4969d3 100644 --- a/cmake/util.cmake +++ b/cmake/util.cmake @@ -71,25 +71,20 @@ function(link_paddle_exe TARGET_NAME) generate_rdma_links() endif() - if(NOT ANDROID AND NOT IOS) - set(PADDLE_TRAIN_LIBS - paddle_pserver - paddle_network - paddle_trainer_lib - paddle_optimizer) - endif() - target_circle_link_libraries(${TARGET_NAME} ARCHIVE_START paddle_gserver paddle_function ARCHIVE_END + paddle_pserver + paddle_trainer_lib + paddle_network paddle_math paddle_utils paddle_parameter paddle_proto paddle_cuda - ${PADDLE_TRAIN_LIBS} + paddle_optimizer ${EXTERNAL_LIBS} ${CMAKE_THREAD_LIBS_INIT} ${CMAKE_DL_LIBS} diff --git a/paddle/CMakeLists.txt b/paddle/CMakeLists.txt index cee47bb8a..b435de80a 100644 --- a/paddle/CMakeLists.txt +++ b/paddle/CMakeLists.txt @@ -5,12 +5,8 @@ add_subdirectory(testing) add_subdirectory(math) add_subdirectory(parameter) add_subdirectory(gserver) - -if(NOT ANDROID AND NOT IOS) - add_subdirectory(pserver) - add_subdirectory(trainer) -endif() - +add_subdirectory(pserver) +add_subdirectory(trainer) add_subdirectory(scripts) add_subdirectory(string) @@ -23,7 +19,7 @@ if(Boost_FOUND) endif() if(WITH_C_API) - add_subdirectory(capi) + add_subdirectory(capi) endif() if(WITH_SWIG_PY) diff --git a/paddle/capi/CMakeLists.txt b/paddle/capi/CMakeLists.txt index 071f5a0b0..dca3b887e 100644 --- a/paddle/capi/CMakeLists.txt +++ b/paddle/capi/CMakeLists.txt @@ -38,9 +38,8 @@ set(PADDLE_CAPI_INFER_LIBS paddle_function paddle_gserver paddle_proto - ) - -set(PADDLE_CAPI_TRAIN_LIBS paddle_pserver paddle_network) + paddle_pserver + paddle_network) cc_library(paddle_capi_whole DEPS paddle_capi ${PADDLE_CAPI_INFER_LIBS}) -- GitLab From 3bafa42b1afe10aa6ab712d1d258bb079ac814ea Mon Sep 17 00:00:00 2001 From: qijun Date: Thu, 31 Aug 2017 18:08:44 +0800 Subject: [PATCH 0313/2018] fix tensor copyfrom bug --- paddle/framework/tensor_impl.h | 2 ++ 1 file changed, 2 insertions(+) diff --git a/paddle/framework/tensor_impl.h b/paddle/framework/tensor_impl.h index 7d7263b89..7893e233b 100644 --- a/paddle/framework/tensor_impl.h +++ b/paddle/framework/tensor_impl.h @@ -117,6 +117,8 @@ inline void Tensor::CopyFrom(const Tensor& src, memory::Copy(boost::get(dst_place), dst_ptr, boost::get(src_place), src_ptr, size, 0); } + PADDLE_ENFORCE(cudaStreamSynchronize(0), + "cudaStreamSynchronize failed in Tensor CopyFrom"); #endif } -- GitLab From f7e75a03cf03d8b71ab9be2800c7ed8058866c02 Mon Sep 17 00:00:00 2001 From: hedaoyuan Date: Thu, 31 Aug 2017 19:57:22 +0800 Subject: [PATCH 0314/2018] Refine the neon depthwise convolution code(separate the Function and kernel). --- paddle/function/neon/NeonDepthwiseConv.cpp | 454 +------------------ paddle/function/neon/NeonDepthwiseConv.h | 480 +++++++++++++++++++++ 2 files changed, 481 insertions(+), 453 deletions(-) create mode 100644 paddle/function/neon/NeonDepthwiseConv.h diff --git a/paddle/function/neon/NeonDepthwiseConv.cpp b/paddle/function/neon/NeonDepthwiseConv.cpp index f09e98587..7e5f752a0 100644 --- a/paddle/function/neon/NeonDepthwiseConv.cpp +++ b/paddle/function/neon/NeonDepthwiseConv.cpp @@ -12,7 +12,7 @@ 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 "neon_util.h" +#include "NeonDepthwiseConv.h" #include "paddle/function/ConvOp.h" #include "paddle/function/Im2Col.h" @@ -22,458 +22,6 @@ namespace neon { #if defined(__ARM_NEON__) || defined(__ARM_NEON) -template -struct DepthwiseConvKernel {}; - -inline float32_t conv3x3(float32x4_t r0, - float32x4_t r1, - float32x4_t r2, - float32x4_t k0, - float32x4_t k1, - float32x4_t k2) { - float32x4_t tmp; - tmp = vmulq_f32(r0, k0); - tmp = vmlaq_f32(tmp, r1, k1); - tmp = vmlaq_f32(tmp, r2, k2); - return vaddvq_f32(tmp); -} - -inline float32_t conv4x4(float32x4_t r0, - float32x4_t r1, - float32x4_t r2, - float32x4_t r3, - float32x4_t k0, - float32x4_t k1, - float32x4_t k2, - float32x4_t k3) { - float32x4_t tmp; - tmp = vmulq_f32(r0, k0); - tmp = vmlaq_f32(tmp, r1, k1); - tmp = vmlaq_f32(tmp, r2, k2); - tmp = vmlaq_f32(tmp, r3, k3); - return vaddvq_f32(tmp); -} - -/** - * Each step calculates four elements of the output. - * First step: - * R0[0, 1, 2, 3...] * K[0][0] - * R0[1, 2, 3, 4...] * K[0][1] - * R0[2, 3, 4, 5...] * K[0][2] - * R1[0, 1, 2, 3...] * K[1][0] - * R1[1, 2, 3, 4...] * K[1][1] - * R1[2, 3, 4, 5...] * K[1][2] - * R2[0, 1, 2, 3...] * K[2][0] - * R2[1, 2, 3, 4...] * K[2][1] - * + R2[2, 3, 4, 5...] * K[2][2] - * ------------------------------ - * Output[0, 1, 2, 3] - */ -template <> -struct DepthwiseConvKernel<3, 1> { - static void run(const float* inputData, - const float* filterData, - int inputHeight, - int inputWidth, - int outputChannels, - int outputHeight, - int outputWidth, - int filterMultiplier, - float* outputData) { - const int steps = outputWidth >> 2; - const int remain = outputWidth & 3; - for (int c = 0; c < outputChannels; c++, filterData += 9) { - // Load the filters - float32x4_t k[3]; - k[0] = vld1q_f32(filterData); - k[1] = vld1q_f32(filterData + 3); - k[2] = vld1q_f32(filterData + 6); - k[0] = vsetq_lane_f32(0.f, k[0], 3); - k[1] = vsetq_lane_f32(0.f, k[1], 3); - k[2] = vsetq_lane_f32(0.f, k[2], 3); - - const float* r0 = - inputData + (c / filterMultiplier) * (inputHeight * inputWidth); - const float* r1 = r0 + inputWidth; - const float* r2 = r0 + inputWidth * 2; - float32x4_t input[3][3]; - for (int h = 0; h < outputHeight; h++) { - for (int s = 0; s < steps; s++) { - // Load the inputs - float32x4_t tmp; - input[0][0] = vld1q_f32(r0); - tmp = vld1q_f32(r0 + 4); - input[0][1] = vextq_f32(input[0][0], tmp, 1); - input[0][2] = vextq_f32(input[0][0], tmp, 2); - input[1][0] = vld1q_f32(r1); - tmp = vld1q_f32(r1 + 4); - input[1][1] = vextq_f32(input[1][0], tmp, 1); - input[1][2] = vextq_f32(input[1][0], tmp, 2); - input[2][0] = vld1q_f32(r2); - tmp = vld1q_f32(r2 + 4); - input[2][1] = vextq_f32(input[2][0], tmp, 1); - input[2][2] = vextq_f32(input[2][0], tmp, 2); - - float32x4_t tmp1 = vdupq_n_f32(0.f); - float32x4_t tmp2 = vdupq_n_f32(0.f); - tmp1 = vmlaq_laneq_f32(tmp1, input[0][0], k[0], 0); - tmp2 = vmlaq_laneq_f32(tmp2, input[0][1], k[0], 1); - tmp1 = vmlaq_laneq_f32(tmp1, input[0][2], k[0], 2); - tmp2 = vmlaq_laneq_f32(tmp2, input[1][0], k[1], 0); - tmp1 = vmlaq_laneq_f32(tmp1, input[1][1], k[1], 1); - tmp2 = vmlaq_laneq_f32(tmp2, input[1][2], k[1], 2); - tmp1 = vmlaq_laneq_f32(tmp1, input[2][0], k[2], 0); - tmp2 = vmlaq_laneq_f32(tmp2, input[2][1], k[2], 1); - tmp1 = vmlaq_laneq_f32(tmp1, input[2][2], k[2], 2); - tmp1 = vaddq_f32(tmp1, tmp2); - - vst1q_f32(outputData, tmp1); - r0 += 4; - r1 += 4; - r2 += 4; - outputData += 4; - } - - for (int r = 0; r < remain; r++) { - float32x4_t i0 = vld1q_f32(r0); - float32x4_t i1 = vld1q_f32(r1); - float32x4_t i2 = vld1q_f32(r2); - *outputData = conv3x3(i0, i1, i2, k[0], k[1], k[2]); - r0++; - r1++; - r2++; - outputData++; - } - - r0 += 2; - r1 += 2; - r2 += 2; - } - } - } -}; - -/** - * Each step calculates four elements of the output. - * First step: - * R0[0, 2, 4, 6...] * K[0][0] - * R0[1, 3, 5, 7...] * K[0][1] - * R0[2, 4, 6, 8...] * K[0][2] - * R1[0, 2, 4, 6...] * K[1][0] - * R1[1, 3, 5, 7...] * K[1][1] - * R1[2, 4, 6, 8...] * K[1][2] - * R2[0, 2, 4, 6...] * K[2][0] - * R2[1, 3, 5, 7...] * K[2][1] - * R2[2, 4, 6, 8...] * K[2][2] - * ------------------------------ - * Output[0, 1, 2, 3] - */ -template <> -struct DepthwiseConvKernel<3, 2> { - static void run(const float* inputData, - const float* filterData, - int inputHeight, - int inputWidth, - int outputChannels, - int outputHeight, - int outputWidth, - int filterMultiplier, - float* outputData) { - const int steps = outputWidth >> 2; - const int remain = outputWidth & 3; - for (int c = 0; c < outputChannels; c++, filterData += 9) { - // Load the filters - float32x4_t k[3]; - k[0] = vld1q_f32(filterData); - k[1] = vld1q_f32(filterData + 3); - k[2] = vld1q_f32(filterData + 6); - k[0] = vsetq_lane_f32(0.f, k[0], 3); - k[1] = vsetq_lane_f32(0.f, k[1], 3); - k[2] = vsetq_lane_f32(0.f, k[2], 3); - - const float* start = - inputData + (c / filterMultiplier) * (inputHeight * inputWidth); - float32x4_t input[3][3]; - for (int h = 0; h < outputHeight; h++) { - const float* r0 = start + 2 * h * inputWidth; - const float* r1 = start + (2 * h + 1) * inputWidth; - const float* r2 = start + (2 * h + 2) * inputWidth; - for (int s = 0; s < steps; s++) { - // Load the inputs - float32x4_t data1; - float32x4x2_t data2; - - data2 = vld2q_f32(r0); - input[0][0] = data2.val[0]; - input[0][1] = data2.val[1]; - data1 = vld1q_f32(r0 + 8); - input[0][2] = vextq_f32(data2.val[0], data1, 1); - - data2 = vld2q_f32(r1); - input[1][0] = data2.val[0]; - input[1][1] = data2.val[1]; - data1 = vld1q_f32(r1 + 8); - input[1][2] = vextq_f32(data2.val[0], data1, 1); - - data2 = vld2q_f32(r2); - input[2][0] = data2.val[0]; - input[2][1] = data2.val[1]; - data1 = vld1q_f32(r2 + 8); - input[2][2] = vextq_f32(data2.val[0], data1, 1); - - float32x4_t tmp1 = vdupq_n_f32(0.f); - float32x4_t tmp2 = vdupq_n_f32(0.f); - tmp1 = vmlaq_laneq_f32(tmp1, input[0][0], k[0], 0); - tmp2 = vmlaq_laneq_f32(tmp2, input[0][1], k[0], 1); - tmp1 = vmlaq_laneq_f32(tmp1, input[0][2], k[0], 2); - tmp2 = vmlaq_laneq_f32(tmp2, input[1][0], k[1], 0); - tmp1 = vmlaq_laneq_f32(tmp1, input[1][1], k[1], 1); - tmp2 = vmlaq_laneq_f32(tmp2, input[1][2], k[1], 2); - tmp1 = vmlaq_laneq_f32(tmp1, input[2][0], k[2], 0); - tmp2 = vmlaq_laneq_f32(tmp2, input[2][1], k[2], 1); - tmp1 = vmlaq_laneq_f32(tmp1, input[2][2], k[2], 2); - tmp1 = vaddq_f32(tmp1, tmp2); - - vst1q_f32(outputData, tmp1); - r0 += 8; - r1 += 8; - r2 += 8; - outputData += 4; - } - - for (int r = 0; r < remain; r++) { - float32x4_t i0 = vld1q_f32(r0); - float32x4_t i1 = vld1q_f32(r1); - float32x4_t i2 = vld1q_f32(r2); - *outputData = conv3x3(i0, i1, i2, k[0], k[1], k[2]); - r0 += 2; - r1 += 2; - r2 += 2; - outputData++; - } - } - } - } -}; - -/** - * Each step calculates four elements of the output. - */ -template <> -struct DepthwiseConvKernel<4, 1> { - static void run(const float* inputData, - const float* filterData, - int inputHeight, - int inputWidth, - int outputChannels, - int outputHeight, - int outputWidth, - int filterMultiplier, - float* outputData) { - const int steps = outputWidth >> 2; - const int remain = outputWidth & 3; - for (int c = 0; c < outputChannels; c++, filterData += 16) { - // Load the filters - float32x4_t k[4]; - k[0] = vld1q_f32(filterData); - k[1] = vld1q_f32(filterData + 4); - k[2] = vld1q_f32(filterData + 8); - k[3] = vld1q_f32(filterData + 12); - - const float* r0 = - inputData + (c / filterMultiplier) * (inputHeight * inputWidth); - const float* r1 = r0 + inputWidth; - const float* r2 = r0 + inputWidth * 2; - const float* r3 = r0 + inputWidth * 3; - float32x4_t input[4][4]; - for (int h = 0; h < outputHeight; h++) { - for (int s = 0; s < steps; s++) { - // Load the inputs - float32x4_t tmp; - input[0][0] = vld1q_f32(r0); - tmp = vld1q_f32(r0 + 4); - input[0][1] = vextq_f32(input[0][0], tmp, 1); - input[0][2] = vextq_f32(input[0][0], tmp, 2); - input[0][3] = vextq_f32(input[0][0], tmp, 3); - - input[1][0] = vld1q_f32(r1); - tmp = vld1q_f32(r1 + 4); - input[1][1] = vextq_f32(input[1][0], tmp, 1); - input[1][2] = vextq_f32(input[1][0], tmp, 2); - input[1][3] = vextq_f32(input[1][0], tmp, 3); - - input[2][0] = vld1q_f32(r2); - tmp = vld1q_f32(r2 + 4); - input[2][1] = vextq_f32(input[2][0], tmp, 1); - input[2][2] = vextq_f32(input[2][0], tmp, 2); - input[2][3] = vextq_f32(input[2][0], tmp, 3); - - input[3][0] = vld1q_f32(r3); - tmp = vld1q_f32(r3 + 4); - input[3][1] = vextq_f32(input[3][0], tmp, 1); - input[3][2] = vextq_f32(input[3][0], tmp, 2); - input[3][3] = vextq_f32(input[3][0], tmp, 3); - - float32x4_t tmp1 = vdupq_n_f32(0.f); - float32x4_t tmp2 = vdupq_n_f32(0.f); - tmp1 = vmlaq_laneq_f32(tmp1, input[0][0], k[0], 0); - tmp2 = vmlaq_laneq_f32(tmp2, input[0][1], k[0], 1); - tmp1 = vmlaq_laneq_f32(tmp1, input[0][2], k[0], 2); - tmp2 = vmlaq_laneq_f32(tmp2, input[0][3], k[0], 3); - tmp1 = vmlaq_laneq_f32(tmp1, input[1][0], k[1], 0); - tmp2 = vmlaq_laneq_f32(tmp2, input[1][1], k[1], 1); - tmp1 = vmlaq_laneq_f32(tmp1, input[1][2], k[1], 2); - tmp2 = vmlaq_laneq_f32(tmp2, input[1][3], k[1], 3); - tmp1 = vmlaq_laneq_f32(tmp1, input[2][0], k[2], 0); - tmp2 = vmlaq_laneq_f32(tmp2, input[2][1], k[2], 1); - tmp1 = vmlaq_laneq_f32(tmp1, input[2][2], k[2], 2); - tmp2 = vmlaq_laneq_f32(tmp2, input[2][3], k[2], 3); - tmp1 = vmlaq_laneq_f32(tmp1, input[3][0], k[3], 0); - tmp2 = vmlaq_laneq_f32(tmp2, input[3][1], k[3], 1); - tmp1 = vmlaq_laneq_f32(tmp1, input[3][2], k[3], 2); - tmp2 = vmlaq_laneq_f32(tmp2, input[3][3], k[3], 3); - tmp1 = vaddq_f32(tmp1, tmp2); - - vst1q_f32(outputData, tmp1); - r0 += 4; - r1 += 4; - r2 += 4; - r3 += 4; - outputData += 4; - } - - for (int r = 0; r < remain; r++) { - float32x4_t i0 = vld1q_f32(r0); - float32x4_t i1 = vld1q_f32(r1); - float32x4_t i2 = vld1q_f32(r2); - float32x4_t i3 = vld1q_f32(r3); - *outputData = conv4x4(i0, i1, i2, i3, k[0], k[1], k[2], k[3]); - r0++; - r1++; - r2++; - r3++; - outputData++; - } - - r0 += 3; - r1 += 3; - r2 += 3; - r3 += 3; - } - } - } -}; - -/** - * Each step calculates four elements of the output. - */ -template <> -struct DepthwiseConvKernel<4, 2> { - static void run(const float* inputData, - const float* filterData, - int inputHeight, - int inputWidth, - int outputChannels, - int outputHeight, - int outputWidth, - int filterMultiplier, - float* outputData) { - const int steps = outputWidth >> 2; - const int remain = outputWidth & 3; - for (int c = 0; c < outputChannels; c++, filterData += 16) { - // Load the filters - float32x4_t k[4]; - k[0] = vld1q_f32(filterData); - k[1] = vld1q_f32(filterData + 4); - k[2] = vld1q_f32(filterData + 8); - k[3] = vld1q_f32(filterData + 12); - - const float* start = - inputData + (c / filterMultiplier) * (inputHeight * inputWidth); - float32x4_t input[4][4]; - for (int h = 0; h < outputHeight; h++) { - const float* r0 = start + 2 * h * inputWidth; - const float* r1 = start + (2 * h + 1) * inputWidth; - const float* r2 = start + (2 * h + 2) * inputWidth; - const float* r3 = start + (2 * h + 3) * inputWidth; - for (int s = 0; s < steps; s++) { - // Load the inputs - float32x4x2_t data1; - float32x4x2_t data2; - - data1 = vld2q_f32(r0); - data2 = vld2q_f32(r0 + 8); - input[0][0] = data1.val[0]; - input[0][1] = data1.val[1]; - input[0][2] = vextq_f32(data1.val[0], data2.val[0], 1); - input[0][3] = vextq_f32(data1.val[1], data2.val[1], 1); - - data1 = vld2q_f32(r1); - data2 = vld2q_f32(r1 + 8); - input[1][0] = data1.val[0]; - input[1][1] = data1.val[1]; - input[1][2] = vextq_f32(data1.val[0], data2.val[0], 1); - input[1][3] = vextq_f32(data1.val[1], data2.val[1], 1); - - data1 = vld2q_f32(r2); - data2 = vld2q_f32(r2 + 8); - input[2][0] = data1.val[0]; - input[2][1] = data1.val[1]; - input[2][2] = vextq_f32(data1.val[0], data2.val[0], 1); - input[2][3] = vextq_f32(data1.val[1], data2.val[1], 1); - - data1 = vld2q_f32(r3); - data2 = vld2q_f32(r3 + 8); - input[3][0] = data1.val[0]; - input[3][1] = data1.val[1]; - input[3][2] = vextq_f32(data1.val[0], data2.val[0], 1); - input[3][3] = vextq_f32(data1.val[1], data2.val[1], 1); - - float32x4_t tmp1 = vdupq_n_f32(0.f); - float32x4_t tmp2 = vdupq_n_f32(0.f); - tmp1 = vmlaq_laneq_f32(tmp1, input[0][0], k[0], 0); - tmp2 = vmlaq_laneq_f32(tmp2, input[0][1], k[0], 1); - tmp1 = vmlaq_laneq_f32(tmp1, input[0][2], k[0], 2); - tmp2 = vmlaq_laneq_f32(tmp2, input[0][3], k[0], 3); - tmp1 = vmlaq_laneq_f32(tmp1, input[1][0], k[1], 0); - tmp2 = vmlaq_laneq_f32(tmp2, input[1][1], k[1], 1); - tmp1 = vmlaq_laneq_f32(tmp1, input[1][2], k[1], 2); - tmp2 = vmlaq_laneq_f32(tmp2, input[1][3], k[1], 3); - tmp1 = vmlaq_laneq_f32(tmp1, input[2][0], k[2], 0); - tmp2 = vmlaq_laneq_f32(tmp2, input[2][1], k[2], 1); - tmp1 = vmlaq_laneq_f32(tmp1, input[2][2], k[2], 2); - tmp2 = vmlaq_laneq_f32(tmp2, input[2][3], k[2], 3); - tmp1 = vmlaq_laneq_f32(tmp1, input[3][0], k[3], 0); - tmp2 = vmlaq_laneq_f32(tmp2, input[3][1], k[3], 1); - tmp1 = vmlaq_laneq_f32(tmp1, input[3][2], k[3], 2); - tmp2 = vmlaq_laneq_f32(tmp2, input[3][3], k[3], 3); - tmp1 = vaddq_f32(tmp1, tmp2); - - vst1q_f32(outputData, tmp1); - r0 += 8; - r1 += 8; - r2 += 8; - r3 += 8; - outputData += 4; - } - - for (int r = 0; r < remain; r++) { - float32x4_t i0 = vld1q_f32(r0); - float32x4_t i1 = vld1q_f32(r1); - float32x4_t i2 = vld1q_f32(r2); - float32x4_t i3 = vld1q_f32(r3); - *outputData = conv4x4(i0, i1, i2, i3, k[0], k[1], k[2], k[3]); - r0 += 2; - r1 += 2; - r2 += 2; - r3 += 2; - outputData++; - } - } - } - } -}; - template class NeonDepthwiseConvFunction : public ConvFunctionBase { public: diff --git a/paddle/function/neon/NeonDepthwiseConv.h b/paddle/function/neon/NeonDepthwiseConv.h new file mode 100644 index 000000000..cb1abe1f3 --- /dev/null +++ b/paddle/function/neon/NeonDepthwiseConv.h @@ -0,0 +1,480 @@ +/* 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 "neon_util.h" + +namespace paddle { + +namespace neon { + +#if defined(__ARM_NEON__) || defined(__ARM_NEON) + +template +struct DepthwiseConvKernel {}; + +inline float32_t conv3x3(float32x4_t r0, + float32x4_t r1, + float32x4_t r2, + float32x4_t k0, + float32x4_t k1, + float32x4_t k2) { + float32x4_t tmp; + tmp = vmulq_f32(r0, k0); + tmp = vmlaq_f32(tmp, r1, k1); + tmp = vmlaq_f32(tmp, r2, k2); + return vaddvq_f32(tmp); +} + +inline float32_t conv4x4(float32x4_t r0, + float32x4_t r1, + float32x4_t r2, + float32x4_t r3, + float32x4_t k0, + float32x4_t k1, + float32x4_t k2, + float32x4_t k3) { + float32x4_t tmp; + tmp = vmulq_f32(r0, k0); + tmp = vmlaq_f32(tmp, r1, k1); + tmp = vmlaq_f32(tmp, r2, k2); + tmp = vmlaq_f32(tmp, r3, k3); + return vaddvq_f32(tmp); +} + +/** + * Each step calculates four elements of the output. + * First step: + * R0[0, 1, 2, 3...] * K[0][0] + * R0[1, 2, 3, 4...] * K[0][1] + * R0[2, 3, 4, 5...] * K[0][2] + * R1[0, 1, 2, 3...] * K[1][0] + * R1[1, 2, 3, 4...] * K[1][1] + * R1[2, 3, 4, 5...] * K[1][2] + * R2[0, 1, 2, 3...] * K[2][0] + * R2[1, 2, 3, 4...] * K[2][1] + * + R2[2, 3, 4, 5...] * K[2][2] + * ------------------------------ + * Output[0, 1, 2, 3] + */ +template <> +struct DepthwiseConvKernel<3, 1> { + static void run(const float* inputData, + const float* filterData, + int inputHeight, + int inputWidth, + int outputChannels, + int outputHeight, + int outputWidth, + int filterMultiplier, + float* outputData) { + const int steps = outputWidth >> 2; + const int remain = outputWidth & 3; + for (int c = 0; c < outputChannels; c++, filterData += 9) { + // Load the filters + float32x4_t k[3]; + k[0] = vld1q_f32(filterData); + k[1] = vld1q_f32(filterData + 3); + k[2] = vld1q_f32(filterData + 6); + k[0] = vsetq_lane_f32(0.f, k[0], 3); + k[1] = vsetq_lane_f32(0.f, k[1], 3); + k[2] = vsetq_lane_f32(0.f, k[2], 3); + + const float* r0 = + inputData + (c / filterMultiplier) * (inputHeight * inputWidth); + const float* r1 = r0 + inputWidth; + const float* r2 = r0 + inputWidth * 2; + float32x4_t input[3][3]; + for (int h = 0; h < outputHeight; h++) { + for (int s = 0; s < steps; s++) { + // Load the inputs + float32x4_t tmp; + input[0][0] = vld1q_f32(r0); + tmp = vld1q_f32(r0 + 4); + input[0][1] = vextq_f32(input[0][0], tmp, 1); + input[0][2] = vextq_f32(input[0][0], tmp, 2); + input[1][0] = vld1q_f32(r1); + tmp = vld1q_f32(r1 + 4); + input[1][1] = vextq_f32(input[1][0], tmp, 1); + input[1][2] = vextq_f32(input[1][0], tmp, 2); + input[2][0] = vld1q_f32(r2); + tmp = vld1q_f32(r2 + 4); + input[2][1] = vextq_f32(input[2][0], tmp, 1); + input[2][2] = vextq_f32(input[2][0], tmp, 2); + + float32x4_t tmp1 = vdupq_n_f32(0.f); + float32x4_t tmp2 = vdupq_n_f32(0.f); + tmp1 = vmlaq_laneq_f32(tmp1, input[0][0], k[0], 0); + tmp2 = vmlaq_laneq_f32(tmp2, input[0][1], k[0], 1); + tmp1 = vmlaq_laneq_f32(tmp1, input[0][2], k[0], 2); + tmp2 = vmlaq_laneq_f32(tmp2, input[1][0], k[1], 0); + tmp1 = vmlaq_laneq_f32(tmp1, input[1][1], k[1], 1); + tmp2 = vmlaq_laneq_f32(tmp2, input[1][2], k[1], 2); + tmp1 = vmlaq_laneq_f32(tmp1, input[2][0], k[2], 0); + tmp2 = vmlaq_laneq_f32(tmp2, input[2][1], k[2], 1); + tmp1 = vmlaq_laneq_f32(tmp1, input[2][2], k[2], 2); + tmp1 = vaddq_f32(tmp1, tmp2); + + vst1q_f32(outputData, tmp1); + r0 += 4; + r1 += 4; + r2 += 4; + outputData += 4; + } + + for (int r = 0; r < remain; r++) { + float32x4_t i0 = vld1q_f32(r0); + float32x4_t i1 = vld1q_f32(r1); + float32x4_t i2 = vld1q_f32(r2); + *outputData = conv3x3(i0, i1, i2, k[0], k[1], k[2]); + r0++; + r1++; + r2++; + outputData++; + } + + r0 += 2; + r1 += 2; + r2 += 2; + } + } + } +}; + +/** + * Each step calculates four elements of the output. + * First step: + * R0[0, 2, 4, 6...] * K[0][0] + * R0[1, 3, 5, 7...] * K[0][1] + * R0[2, 4, 6, 8...] * K[0][2] + * R1[0, 2, 4, 6...] * K[1][0] + * R1[1, 3, 5, 7...] * K[1][1] + * R1[2, 4, 6, 8...] * K[1][2] + * R2[0, 2, 4, 6...] * K[2][0] + * R2[1, 3, 5, 7...] * K[2][1] + * R2[2, 4, 6, 8...] * K[2][2] + * ------------------------------ + * Output[0, 1, 2, 3] + */ +template <> +struct DepthwiseConvKernel<3, 2> { + static void run(const float* inputData, + const float* filterData, + int inputHeight, + int inputWidth, + int outputChannels, + int outputHeight, + int outputWidth, + int filterMultiplier, + float* outputData) { + const int steps = outputWidth >> 2; + const int remain = outputWidth & 3; + for (int c = 0; c < outputChannels; c++, filterData += 9) { + // Load the filters + float32x4_t k[3]; + k[0] = vld1q_f32(filterData); + k[1] = vld1q_f32(filterData + 3); + k[2] = vld1q_f32(filterData + 6); + k[0] = vsetq_lane_f32(0.f, k[0], 3); + k[1] = vsetq_lane_f32(0.f, k[1], 3); + k[2] = vsetq_lane_f32(0.f, k[2], 3); + + const float* start = + inputData + (c / filterMultiplier) * (inputHeight * inputWidth); + float32x4_t input[3][3]; + for (int h = 0; h < outputHeight; h++) { + const float* r0 = start + 2 * h * inputWidth; + const float* r1 = start + (2 * h + 1) * inputWidth; + const float* r2 = start + (2 * h + 2) * inputWidth; + for (int s = 0; s < steps; s++) { + // Load the inputs + float32x4_t data1; + float32x4x2_t data2; + + data2 = vld2q_f32(r0); + input[0][0] = data2.val[0]; + input[0][1] = data2.val[1]; + data1 = vld1q_f32(r0 + 8); + input[0][2] = vextq_f32(data2.val[0], data1, 1); + + data2 = vld2q_f32(r1); + input[1][0] = data2.val[0]; + input[1][1] = data2.val[1]; + data1 = vld1q_f32(r1 + 8); + input[1][2] = vextq_f32(data2.val[0], data1, 1); + + data2 = vld2q_f32(r2); + input[2][0] = data2.val[0]; + input[2][1] = data2.val[1]; + data1 = vld1q_f32(r2 + 8); + input[2][2] = vextq_f32(data2.val[0], data1, 1); + + float32x4_t tmp1 = vdupq_n_f32(0.f); + float32x4_t tmp2 = vdupq_n_f32(0.f); + tmp1 = vmlaq_laneq_f32(tmp1, input[0][0], k[0], 0); + tmp2 = vmlaq_laneq_f32(tmp2, input[0][1], k[0], 1); + tmp1 = vmlaq_laneq_f32(tmp1, input[0][2], k[0], 2); + tmp2 = vmlaq_laneq_f32(tmp2, input[1][0], k[1], 0); + tmp1 = vmlaq_laneq_f32(tmp1, input[1][1], k[1], 1); + tmp2 = vmlaq_laneq_f32(tmp2, input[1][2], k[1], 2); + tmp1 = vmlaq_laneq_f32(tmp1, input[2][0], k[2], 0); + tmp2 = vmlaq_laneq_f32(tmp2, input[2][1], k[2], 1); + tmp1 = vmlaq_laneq_f32(tmp1, input[2][2], k[2], 2); + tmp1 = vaddq_f32(tmp1, tmp2); + + vst1q_f32(outputData, tmp1); + r0 += 8; + r1 += 8; + r2 += 8; + outputData += 4; + } + + for (int r = 0; r < remain; r++) { + float32x4_t i0 = vld1q_f32(r0); + float32x4_t i1 = vld1q_f32(r1); + float32x4_t i2 = vld1q_f32(r2); + *outputData = conv3x3(i0, i1, i2, k[0], k[1], k[2]); + r0 += 2; + r1 += 2; + r2 += 2; + outputData++; + } + } + } + } +}; + +/** + * Each step calculates four elements of the output. + */ +template <> +struct DepthwiseConvKernel<4, 1> { + static void run(const float* inputData, + const float* filterData, + int inputHeight, + int inputWidth, + int outputChannels, + int outputHeight, + int outputWidth, + int filterMultiplier, + float* outputData) { + const int steps = outputWidth >> 2; + const int remain = outputWidth & 3; + for (int c = 0; c < outputChannels; c++, filterData += 16) { + // Load the filters + float32x4_t k[4]; + k[0] = vld1q_f32(filterData); + k[1] = vld1q_f32(filterData + 4); + k[2] = vld1q_f32(filterData + 8); + k[3] = vld1q_f32(filterData + 12); + + const float* r0 = + inputData + (c / filterMultiplier) * (inputHeight * inputWidth); + const float* r1 = r0 + inputWidth; + const float* r2 = r0 + inputWidth * 2; + const float* r3 = r0 + inputWidth * 3; + float32x4_t input[4][4]; + for (int h = 0; h < outputHeight; h++) { + for (int s = 0; s < steps; s++) { + // Load the inputs + float32x4_t tmp; + input[0][0] = vld1q_f32(r0); + tmp = vld1q_f32(r0 + 4); + input[0][1] = vextq_f32(input[0][0], tmp, 1); + input[0][2] = vextq_f32(input[0][0], tmp, 2); + input[0][3] = vextq_f32(input[0][0], tmp, 3); + + input[1][0] = vld1q_f32(r1); + tmp = vld1q_f32(r1 + 4); + input[1][1] = vextq_f32(input[1][0], tmp, 1); + input[1][2] = vextq_f32(input[1][0], tmp, 2); + input[1][3] = vextq_f32(input[1][0], tmp, 3); + + input[2][0] = vld1q_f32(r2); + tmp = vld1q_f32(r2 + 4); + input[2][1] = vextq_f32(input[2][0], tmp, 1); + input[2][2] = vextq_f32(input[2][0], tmp, 2); + input[2][3] = vextq_f32(input[2][0], tmp, 3); + + input[3][0] = vld1q_f32(r3); + tmp = vld1q_f32(r3 + 4); + input[3][1] = vextq_f32(input[3][0], tmp, 1); + input[3][2] = vextq_f32(input[3][0], tmp, 2); + input[3][3] = vextq_f32(input[3][0], tmp, 3); + + float32x4_t tmp1 = vdupq_n_f32(0.f); + float32x4_t tmp2 = vdupq_n_f32(0.f); + tmp1 = vmlaq_laneq_f32(tmp1, input[0][0], k[0], 0); + tmp2 = vmlaq_laneq_f32(tmp2, input[0][1], k[0], 1); + tmp1 = vmlaq_laneq_f32(tmp1, input[0][2], k[0], 2); + tmp2 = vmlaq_laneq_f32(tmp2, input[0][3], k[0], 3); + tmp1 = vmlaq_laneq_f32(tmp1, input[1][0], k[1], 0); + tmp2 = vmlaq_laneq_f32(tmp2, input[1][1], k[1], 1); + tmp1 = vmlaq_laneq_f32(tmp1, input[1][2], k[1], 2); + tmp2 = vmlaq_laneq_f32(tmp2, input[1][3], k[1], 3); + tmp1 = vmlaq_laneq_f32(tmp1, input[2][0], k[2], 0); + tmp2 = vmlaq_laneq_f32(tmp2, input[2][1], k[2], 1); + tmp1 = vmlaq_laneq_f32(tmp1, input[2][2], k[2], 2); + tmp2 = vmlaq_laneq_f32(tmp2, input[2][3], k[2], 3); + tmp1 = vmlaq_laneq_f32(tmp1, input[3][0], k[3], 0); + tmp2 = vmlaq_laneq_f32(tmp2, input[3][1], k[3], 1); + tmp1 = vmlaq_laneq_f32(tmp1, input[3][2], k[3], 2); + tmp2 = vmlaq_laneq_f32(tmp2, input[3][3], k[3], 3); + tmp1 = vaddq_f32(tmp1, tmp2); + + vst1q_f32(outputData, tmp1); + r0 += 4; + r1 += 4; + r2 += 4; + r3 += 4; + outputData += 4; + } + + for (int r = 0; r < remain; r++) { + float32x4_t i0 = vld1q_f32(r0); + float32x4_t i1 = vld1q_f32(r1); + float32x4_t i2 = vld1q_f32(r2); + float32x4_t i3 = vld1q_f32(r3); + *outputData = conv4x4(i0, i1, i2, i3, k[0], k[1], k[2], k[3]); + r0++; + r1++; + r2++; + r3++; + outputData++; + } + + r0 += 3; + r1 += 3; + r2 += 3; + r3 += 3; + } + } + } +}; + +/** + * Each step calculates four elements of the output. + */ +template <> +struct DepthwiseConvKernel<4, 2> { + static void run(const float* inputData, + const float* filterData, + int inputHeight, + int inputWidth, + int outputChannels, + int outputHeight, + int outputWidth, + int filterMultiplier, + float* outputData) { + const int steps = outputWidth >> 2; + const int remain = outputWidth & 3; + for (int c = 0; c < outputChannels; c++, filterData += 16) { + // Load the filters + float32x4_t k[4]; + k[0] = vld1q_f32(filterData); + k[1] = vld1q_f32(filterData + 4); + k[2] = vld1q_f32(filterData + 8); + k[3] = vld1q_f32(filterData + 12); + + const float* start = + inputData + (c / filterMultiplier) * (inputHeight * inputWidth); + float32x4_t input[4][4]; + for (int h = 0; h < outputHeight; h++) { + const float* r0 = start + 2 * h * inputWidth; + const float* r1 = start + (2 * h + 1) * inputWidth; + const float* r2 = start + (2 * h + 2) * inputWidth; + const float* r3 = start + (2 * h + 3) * inputWidth; + for (int s = 0; s < steps; s++) { + // Load the inputs + float32x4x2_t data1; + float32x4x2_t data2; + + data1 = vld2q_f32(r0); + data2 = vld2q_f32(r0 + 8); + input[0][0] = data1.val[0]; + input[0][1] = data1.val[1]; + input[0][2] = vextq_f32(data1.val[0], data2.val[0], 1); + input[0][3] = vextq_f32(data1.val[1], data2.val[1], 1); + + data1 = vld2q_f32(r1); + data2 = vld2q_f32(r1 + 8); + input[1][0] = data1.val[0]; + input[1][1] = data1.val[1]; + input[1][2] = vextq_f32(data1.val[0], data2.val[0], 1); + input[1][3] = vextq_f32(data1.val[1], data2.val[1], 1); + + data1 = vld2q_f32(r2); + data2 = vld2q_f32(r2 + 8); + input[2][0] = data1.val[0]; + input[2][1] = data1.val[1]; + input[2][2] = vextq_f32(data1.val[0], data2.val[0], 1); + input[2][3] = vextq_f32(data1.val[1], data2.val[1], 1); + + data1 = vld2q_f32(r3); + data2 = vld2q_f32(r3 + 8); + input[3][0] = data1.val[0]; + input[3][1] = data1.val[1]; + input[3][2] = vextq_f32(data1.val[0], data2.val[0], 1); + input[3][3] = vextq_f32(data1.val[1], data2.val[1], 1); + + float32x4_t tmp1 = vdupq_n_f32(0.f); + float32x4_t tmp2 = vdupq_n_f32(0.f); + tmp1 = vmlaq_laneq_f32(tmp1, input[0][0], k[0], 0); + tmp2 = vmlaq_laneq_f32(tmp2, input[0][1], k[0], 1); + tmp1 = vmlaq_laneq_f32(tmp1, input[0][2], k[0], 2); + tmp2 = vmlaq_laneq_f32(tmp2, input[0][3], k[0], 3); + tmp1 = vmlaq_laneq_f32(tmp1, input[1][0], k[1], 0); + tmp2 = vmlaq_laneq_f32(tmp2, input[1][1], k[1], 1); + tmp1 = vmlaq_laneq_f32(tmp1, input[1][2], k[1], 2); + tmp2 = vmlaq_laneq_f32(tmp2, input[1][3], k[1], 3); + tmp1 = vmlaq_laneq_f32(tmp1, input[2][0], k[2], 0); + tmp2 = vmlaq_laneq_f32(tmp2, input[2][1], k[2], 1); + tmp1 = vmlaq_laneq_f32(tmp1, input[2][2], k[2], 2); + tmp2 = vmlaq_laneq_f32(tmp2, input[2][3], k[2], 3); + tmp1 = vmlaq_laneq_f32(tmp1, input[3][0], k[3], 0); + tmp2 = vmlaq_laneq_f32(tmp2, input[3][1], k[3], 1); + tmp1 = vmlaq_laneq_f32(tmp1, input[3][2], k[3], 2); + tmp2 = vmlaq_laneq_f32(tmp2, input[3][3], k[3], 3); + tmp1 = vaddq_f32(tmp1, tmp2); + + vst1q_f32(outputData, tmp1); + r0 += 8; + r1 += 8; + r2 += 8; + r3 += 8; + outputData += 4; + } + + for (int r = 0; r < remain; r++) { + float32x4_t i0 = vld1q_f32(r0); + float32x4_t i1 = vld1q_f32(r1); + float32x4_t i2 = vld1q_f32(r2); + float32x4_t i3 = vld1q_f32(r3); + *outputData = conv4x4(i0, i1, i2, i3, k[0], k[1], k[2], k[3]); + r0 += 2; + r1 += 2; + r2 += 2; + r3 += 2; + outputData++; + } + } + } + } +}; + +#endif + +} // namespace neon +} // namespace paddle -- GitLab From f8b885f27f19474124d46002d6572c239910eefd Mon Sep 17 00:00:00 2001 From: yangyaming Date: Thu, 31 Aug 2017 20:15:48 +0800 Subject: [PATCH 0315/2018] Using EigenTensor to reshape tensor. --- paddle/operators/squared_l2_distance_op.cc | 64 ++++++++--- paddle/operators/squared_l2_distance_op.h | 128 ++++++++++++++++++--- 2 files changed, 157 insertions(+), 35 deletions(-) diff --git a/paddle/operators/squared_l2_distance_op.cc b/paddle/operators/squared_l2_distance_op.cc index 9fc498d5a..3049f0f8b 100644 --- a/paddle/operators/squared_l2_distance_op.cc +++ b/paddle/operators/squared_l2_distance_op.cc @@ -22,36 +22,52 @@ class SquaredL2DistanceOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(const framework::InferShapeContext &ctx) const override { + void InferShape(const framework::InferShapeContext& ctx) const override { PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input of SquaredL2DistanceOp " "must be initialized."); - PADDLE_ENFORCE_EQ(ctx.Input("X")->dims(), - ctx.Input("Y")->dims(), - "Dimensions of SquaredL2DistanceOp's two inputs " - "must be same.") - framework::DDim dims = ctx.Input("X")->dims(); - ctx.Output("sub_result")->Resize(dims); - ctx.Output("Out")->Resize(framework::make_ddim({dims[0], 1})); + PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Y"), + "Target of SquaredL2DistanceOp " + "must be initialized."); + + auto* X = ctx.Input("X"); + auto xDims = X->dims(); + auto* Y = ctx.Input("Y"); + auto yDims = Y->dims(); + + PADDLE_ENFORCE_EQ(framework::arity(xDims), framework::arity(yDims), + "Tensor rank of both SquaredL2DistanceOp's " + "inputs must be same."); + int rank = framework::arity(xDims); + PADDLE_ENFORCE(rank >= 2 || rank <= 6, "Tensor rank should be in [2, 6]."); + PADDLE_ENFORCE(yDims[0] == 1 || yDims[0] == xDims[0], + "First dimension of target must be equal to input " + "or to 1."); + + ctx.Output("sub_result")->Resize(xDims); + ctx.Output("Out")->Resize({xDims[0], 1}); } }; class SquaredL2DistanceOpMaker : public framework::OpProtoAndCheckerMaker { public: - SquaredL2DistanceOpMaker(framework::OpProto *proto, - framework::OpAttrChecker *op_checker) + SquaredL2DistanceOpMaker(framework::OpProto* proto, + framework::OpAttrChecker* op_checker) : OpProtoAndCheckerMaker(proto, op_checker) { - AddInput("X", "Input value."); - AddInput("Y", "Target value."); + AddInput("X", "Input of SquaredL2DistanceOp."); + AddInput("Y", "Target of SquaredL2DistanceOp."); AddOutput("sub_result", "Buffering substraction result which " "will be reused in backward.") .AsIntermediate(); AddOutput("Out", "Squared l2 distance between input and target."); AddComment(R"DOC( - SquaredL2DistanceOp will cacluate the squared L2 distances for + SquaredL2DistanceOp will cacluate the squared L2 distance for input and target. Number of distance value equals to the - first dimension of input. + first dimension of input. First dimension of target could be equal to + input or to 1. If the first dimension of target is 1, SquaredL2DistanceOp + will broadcast the first dimension to the first dimension of input. + You can decide whether calculate the gradient of target. )DOC"); } }; @@ -61,9 +77,23 @@ class SquaredL2DistanceGradOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; protected: - void InferShape(const framework::InferShapeContext &ctx) const override { - ctx.Output(framework::GradVarName("X")) - ->Resize(ctx.Input("X")->dims()); + void InferShape(const framework::InferShapeContext& ctx) const override { + PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")), + "Gradient of Out should not be null"); + // check out grad dimensions + auto outDims = ctx.Input(framework::GradVarName("Out"))->dims(); + auto xDims = ctx.Input("X")->dims(); + auto yDims = ctx.Input("Y")->dims(); + PADDLE_ENFORCE_EQ(outDims[0], xDims[0], + "First dimension of output gradient and " + "input value must be equal."); + PADDLE_ENFORCE_EQ(outDims[1], 1, + "Second dimension of output gradient " + "must be 1."); + auto* xGrad = ctx.Output(framework::GradVarName("X")); + auto* yGrad = ctx.Output(framework::GradVarName("Y")); + if (xGrad != nullptr) xGrad->Resize(xDims); + if (yGrad != nullptr) yGrad->Resize(yDims); } }; diff --git a/paddle/operators/squared_l2_distance_op.h b/paddle/operators/squared_l2_distance_op.h index b350fd011..e95364c70 100644 --- a/paddle/operators/squared_l2_distance_op.h +++ b/paddle/operators/squared_l2_distance_op.h @@ -20,17 +20,44 @@ namespace paddle { namespace operators { using Tensor = framework::Tensor; -template -using EigenMatrix = framework::EigenMatrix; +using EigenTensor = framework::EigenTensor; template -using EigenVector = framework::EigenVector; +using EigenMatrix = framework::EigenMatrix; template class SquaredL2DistanceKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& context) const override { + auto* input0 = context.Input("X"); + const int rank = framework::arity(input0->dims()); + switch (rank) { + case 2: + Operate<2>(context); + break; + case 3: + Operate<3>(context); + break; + case 4: + Operate<4>(context); + break; + case 5: + Operate<5>(context); + break; + case 6: + Operate<6>(context); + break; + default: + // already asserted in SquaredL2DistanceOpMaker + break; + } + } + + private: + template + void Operate(const framework::ExecutionContext& context) const { auto* input0 = context.Input("X"); auto* input1 = context.Input("Y"); auto* output0 = context.Output("sub_result"); @@ -39,17 +66,28 @@ class SquaredL2DistanceKernel : public framework::OpKernel { output0->mutable_data(context.GetPlace()); output1->mutable_data(context.GetPlace()); - auto X = EigenMatrix::From(*input0); - auto Y = EigenMatrix::From(*input1); - auto subResult = EigenMatrix::From(*output0); + auto X = EigenTensor::From(*input0); + auto Y = EigenTensor::From(*input1); + auto subResult = EigenTensor::From(*output0); auto Z = EigenMatrix::From(*output1); + auto xDims = X.dimensions(); + auto yDims = Y.dimensions(); + auto place = context.GetEigenDevice(); + // buffer the substraction result - subResult.device(place) = X - Y; - const auto& inDims = X.dimensions(); + if (yDims[0] == 1 && xDims[0] != yDims[0]) { + auto yBroadcastDims = yDims; + yBroadcastDims[0] = xDims[0]; + subResult.device(place) = X - Y.broadcast(yBroadcastDims); + } else { + subResult.device(place) = X - Y; + } + + // create matrix view for substraction result const auto& subResMat = subResult.reshape(Eigen::array( - {static_cast(inDims[0]), static_cast(X.size() / inDims[0])})); + {static_cast(xDims[0]), static_cast(X.size() / xDims[0])})); Z.device(place) = subResMat.pow(2).sum(Eigen::array({1})); } }; @@ -59,24 +97,78 @@ class SquaredL2DistanceGradKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& context) const override { auto* input0 = context.Input("sub_result"); - auto* OG = context.Input(framework::GradVarName("Out")); - auto* IG = context.Output(framework::GradVarName("X")); + const int rank = framework::arity(input0->dims()); + switch (rank) { + case 2: + Operate<2>(context); + break; + case 3: + Operate<3>(context); + break; + case 4: + Operate<4>(context); + break; + case 5: + Operate<5>(context); + break; + case 6: + Operate<6>(context); + break; + default: + // already asserted in SquaredL2DistanceOpMaker + break; + } + } - IG->mutable_data(context.GetPlace()); + private: + template + void Operate(const framework::ExecutionContext& context) const { + auto* input0 = context.Input("sub_result"); + auto* OG = context.Input(framework::GradVarName("Out")); + auto* XG = context.Output(framework::GradVarName("X")); + auto* YG = context.Output(framework::GradVarName("Y")); - auto subResult = EigenMatrix::From(*input0); + auto subResult = EigenTensor::From(*input0); auto outGrad = EigenMatrix::From(*OG); - auto inGrad = EigenMatrix::From(*IG); - const auto& subResDims = subResult.dimensions(); + auto subResDims = subResult.dimensions(); int firstDim = static_cast(subResDims[0]); int cols = subResult.size() / firstDim; const auto subResMat = subResult.reshape(Eigen::array({firstDim, cols})); - // create a matrix view for input gradient tensor - auto inGradMat = inGrad.reshape(Eigen::array({firstDim, cols})); - inGradMat.device(context.GetEigenDevice()) = + + // calculate gradient + auto gradMat = 2 * (outGrad.broadcast(Eigen::array({1, cols}))) * subResMat; + + // propagate back to input + auto eigenPlace = context.GetEigenDevice(); + if (XG != nullptr) { + XG->mutable_data(context.GetPlace()); + auto xGrad = EigenTensor::From(*XG); + // dimensions are same with subResult + auto xGradMat = xGrad.reshape(Eigen::array({firstDim, cols})); + xGradMat.device(eigenPlace) = gradMat; + } + if (YG != nullptr) { + YG->mutable_data(context.GetPlace()); + auto yGrad = EigenTensor::From(*YG); + auto dimsYGrad = yGrad.dimensions(); + auto yGradMat = yGrad.reshape(Eigen::array( + {static_cast(dimsYGrad[0]), + static_cast(yGrad.size() / dimsYGrad[0])})); + + PADDLE_ENFORCE(dimsYGrad[0] <= firstDim, + "First dimension of gradient must be greater or " + "equal than first dimension of target"); + + if (dimsYGrad[0] == firstDim) { + yGradMat.device(eigenPlace) = -1 * gradMat; + } else { + yGradMat.device(eigenPlace) = + -1 * (gradMat.sum(Eigen::array({0}))); + } + } } }; -- GitLab From 207132226c39c864a0808428cb8593bafafc4407 Mon Sep 17 00:00:00 2001 From: dangqingqing Date: Thu, 31 Aug 2017 20:49:33 +0800 Subject: [PATCH 0316/2018] Add unit testing for cuDNN wrapper. --- paddle/platform/CMakeLists.txt | 4 +- paddle/platform/cudnn_helper.h | 41 +++++---- paddle/platform/cudnn_helper_test.cc | 121 +++++++++++++++++++++++++ paddle/platform/dynload/CMakeLists.txt | 2 +- paddle/platform/dynload/cudnn.h | 8 ++ 5 files changed, 157 insertions(+), 19 deletions(-) create mode 100644 paddle/platform/cudnn_helper_test.cc diff --git a/paddle/platform/CMakeLists.txt b/paddle/platform/CMakeLists.txt index ef722e984..9a3ad8eb6 100644 --- a/paddle/platform/CMakeLists.txt +++ b/paddle/platform/CMakeLists.txt @@ -22,4 +22,6 @@ ENDIF() cc_library(device_context SRCS device_context.cc DEPS memory buddy_allocator system_allocator memory_block meta_data meta_cache place eigen3 ${GPU_CTX_DEPS}) nv_test(device_context_test SRCS device_context_test.cc DEPS device_context gpu_info) -nv_test(cudnn_helper SRCS cudnn_helper.cc) + +nv_library(cudnn_helper SRCS cudnn_helper.cc DEPS dynload_cuda) +nv_test(cudnn_helper_test SRCS cudnn_helper_test.cc DEPS dynload_cuda) diff --git a/paddle/platform/cudnn_helper.h b/paddle/platform/cudnn_helper.h index 91047236a..6a43f49cf 100644 --- a/paddle/platform/cudnn_helper.h +++ b/paddle/platform/cudnn_helper.h @@ -14,7 +14,9 @@ limitations under the License. */ #pragma once +#ifndef PADDLE_ONLY_CPU #include +#include "glog/logging.h" #include "paddle/platform/dynload/cudnn.h" #include "paddle/platform/enforce.h" #include "paddle/platform/macros.h" @@ -93,11 +95,11 @@ class ScopedTensorDescriptor { // the format is not used now, but it maybe useful feature std::vector strides(dims.size()); strides[dims.size() - 1] = 1; - for (int i = dims.size() - 1; i >= 0; i++) { - strides[i] = dims[i + 1] * strides[i]; + for (int i = dims.size() - 2; i >= 0; i--) { + strides[i] = dims[i + 1] * strides[i + 1]; } - PADDLE_ENFORCE(cudnnSetTensorNdDescriptor(desc_, type, dims.size(), - dims.data(), strides.data())); + PADDLE_ENFORCE(dynload::cudnnSetTensorNdDescriptor( + desc_, type, dims.size(), dims.data(), strides.data())); return desc_; } @@ -126,8 +128,8 @@ class ScopedFilterDescriptor { const cudnnDataType_t type, const std::vector& kernel) { // filter layout: output input spatial_dim_y spatial_dim_x - PADDLE_ENFORCE(cudnnSetFilterNdDescriptor(desc_, type, format, - kernel.size(), kernel.data())); + PADDLE_ENFORCE(dynload::cudnnSetFilterNdDescriptor( + desc_, type, format, kernel.size(), kernel.data())); return desc_; } @@ -157,9 +159,21 @@ class ScopedConvolutionDescriptor { const std::vector& strides, const std::vector& dilations) { PADDLE_ENFORCE_EQ(pads.size(), strides.size()); PADDLE_ENFORCE_EQ(pads.size(), dilations.size()); - PADDLE_ENFORCE(cudnnSetConvolutionNdDescriptor( + +#if CUDNN_VERSION < 6000 + // cudnn v5 does not support dilation conv, the argument is called upscale + // instead of dilations and it is must be one. + for (size_t i = 0; i < dilations.size(); ++i) { + PADDLE_ENFORCE_EQ( + dilations[i], 1, + "Dilations conv is not supported in this cuDNN version"); + } +#endif + + PADDLE_ENFORCE(dynload::cudnnSetConvolutionNdDescriptor( desc_, pads.size(), pads.data(), strides.data(), dilations.data(), CUDNN_CROSS_CORRELATION, type)); + return desc_; } template @@ -184,26 +198,18 @@ class ScopedPoolingDescriptor { } inline cudnnPoolingDescriptor_t descriptor(const PoolingMode& mode, - cudnnDataType_t type, const std::vector& kernel, const std::vector& pads, const std::vector& strides) { PADDLE_ENFORCE_EQ(kernel.size(), pads.size()); PADDLE_ENFORCE_EQ(kernel.size(), strides.size()); - PADDLE_ENFORCE(cudnnSetPoolingNdDescriptor( + PADDLE_ENFORCE(dynload::cudnnSetPoolingNdDescriptor( desc_, (mode == PoolingMode::kMaximum ? CUDNN_POOLING_MAX : CUDNN_POOLING_AVERAGE_COUNT_EXCLUDE_PADDING), CUDNN_PROPAGATE_NAN, // Always propagate nans. kernel.size(), kernel.data(), pads.data(), strides.data())); - } - - template - inline cudnnPoolingDescriptor_t descriptor(const PoolingMode& mode, - const std::vector& kernel, - const std::vector& pads, - const std::vector& strides) { - return descriptor(mode, CudnnDataType::type, kernel, pads, strides); + return desc_; } private: @@ -213,3 +219,4 @@ class ScopedPoolingDescriptor { } // namespace platform } // namespace paddle +#endif diff --git a/paddle/platform/cudnn_helper_test.cc b/paddle/platform/cudnn_helper_test.cc new file mode 100644 index 000000000..729f2f8a1 --- /dev/null +++ b/paddle/platform/cudnn_helper_test.cc @@ -0,0 +1,121 @@ +/* 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/platform/cudnn_helper.h" +#include "glog/logging.h" +#include "gtest/gtest.h" + +TEST(CudnnHelper, ScopedTensorDescriptor) { + using paddle::platform::ScopedTensorDescriptor; + using paddle::platform::DataLayout; + + ScopedTensorDescriptor tensor_desc; + std::vector shape = {2, 4, 6, 6}; + auto desc = tensor_desc.descriptor(DataLayout::kNCHW, shape); + + cudnnDataType_t type; + int nd; + std::vector dims(4); + std::vector strides(4); + paddle::platform::dynload::cudnnGetTensorNdDescriptor( + desc, 4, &type, &nd, dims.data(), strides.data()); + + EXPECT_EQ(nd, 4); + for (size_t i = 0; i < dims.size(); ++i) { + EXPECT_EQ(dims[i], shape[i]); + } + EXPECT_EQ(strides[3], 1); + EXPECT_EQ(strides[2], 6); + EXPECT_EQ(strides[1], 36); + EXPECT_EQ(strides[0], 144); +} + +TEST(CudnnHelper, ScopedFilterDescriptor) { + using paddle::platform::ScopedFilterDescriptor; + using paddle::platform::DataLayout; + + ScopedFilterDescriptor filter_desc; + std::vector shape = {2, 3, 3}; + auto desc = filter_desc.descriptor(DataLayout::kNCHW, shape); + + cudnnDataType_t type; + int nd; + cudnnTensorFormat_t format; + std::vector kernel(3); + paddle::platform::dynload::cudnnGetFilterNdDescriptor(desc, 3, &type, &format, + &nd, kernel.data()); + + EXPECT_EQ(GetCudnnTensorFormat(DataLayout::kNCHW), format); + EXPECT_EQ(nd, 3); + for (size_t i = 0; i < shape.size(); ++i) { + EXPECT_EQ(kernel[i], shape[i]); + } +} + +TEST(CudnnHelper, ScopedConvolutionDescriptor) { + using paddle::platform::ScopedConvolutionDescriptor; + + ScopedConvolutionDescriptor conv_desc; + std::vector src_pads = {2, 2, 2}; + std::vector src_strides = {1, 1, 1}; + std::vector src_dilations = {1, 1, 1}; + auto desc = conv_desc.descriptor(src_pads, src_strides, src_dilations); + + cudnnDataType_t type; + cudnnConvolutionMode_t mode; + int nd; + std::vector pads(3); + std::vector strides(3); + std::vector dilations(3); + paddle::platform::dynload::cudnnGetConvolutionNdDescriptor( + desc, 3, &nd, pads.data(), strides.data(), dilations.data(), &mode, + &type); + + EXPECT_EQ(nd, 3); + for (size_t i = 0; i < src_pads.size(); ++i) { + EXPECT_EQ(pads[i], src_pads[i]); + EXPECT_EQ(strides[i], src_strides[i]); + EXPECT_EQ(dilations[i], src_dilations[i]); + } + EXPECT_EQ(mode, CUDNN_CROSS_CORRELATION); +} + +TEST(CudnnHelper, ScopedPoolingDescriptor) { + using paddle::platform::ScopedPoolingDescriptor; + using paddle::platform::PoolingMode; + + ScopedPoolingDescriptor pool_desc; + std::vector src_kernel = {2, 2, 5}; + std::vector src_pads = {1, 1, 2}; + std::vector src_strides = {2, 2, 3}; + auto desc = pool_desc.descriptor(PoolingMode::kMaximum, src_kernel, src_pads, + src_strides); + + cudnnPoolingMode_t mode; + cudnnNanPropagation_t nan_t = CUDNN_PROPAGATE_NAN; + int nd; + std::vector kernel(3); + std::vector pads(3); + std::vector strides(3); + paddle::platform::dynload::cudnnGetPoolingNdDescriptor( + desc, 3, &mode, &nan_t, &nd, kernel.data(), pads.data(), strides.data()); + + EXPECT_EQ(nd, 3); + for (size_t i = 0; i < src_pads.size(); ++i) { + EXPECT_EQ(kernel[i], src_kernel[i]); + EXPECT_EQ(pads[i], src_pads[i]); + EXPECT_EQ(strides[i], src_strides[i]); + } + EXPECT_EQ(mode, CUDNN_POOLING_MAX); +} diff --git a/paddle/platform/dynload/CMakeLists.txt b/paddle/platform/dynload/CMakeLists.txt index d205ead84..ceb66f84b 100644 --- a/paddle/platform/dynload/CMakeLists.txt +++ b/paddle/platform/dynload/CMakeLists.txt @@ -1,2 +1,2 @@ cc_library(dynamic_loader SRCS dynamic_loader.cc DEPS glog gflags) -nv_library(dynload_cuda SRCS cublas.cc cudnn.cc curand.cc) +nv_library(dynload_cuda SRCS cublas.cc cudnn.cc curand.cc DEPS dynamic_loader) diff --git a/paddle/platform/dynload/cudnn.h b/paddle/platform/dynload/cudnn.h index ef0dd85b0..0120625b7 100644 --- a/paddle/platform/dynload/cudnn.h +++ b/paddle/platform/dynload/cudnn.h @@ -62,19 +62,27 @@ extern void* cudnn_dso_handle; #define CUDNN_DNN_ROUTINE_EACH(__macro) \ __macro(cudnnSetTensor4dDescriptor); \ __macro(cudnnSetTensor4dDescriptorEx); \ + __macro(cudnnSetTensorNdDescriptor); \ + __macro(cudnnGetTensorNdDescriptor); \ __macro(cudnnGetConvolutionNdForwardOutputDim); \ __macro(cudnnGetConvolutionForwardAlgorithm); \ __macro(cudnnCreateTensorDescriptor); \ __macro(cudnnDestroyTensorDescriptor); \ __macro(cudnnCreateFilterDescriptor); \ __macro(cudnnSetFilter4dDescriptor); \ + __macro(cudnnSetFilterNdDescriptor); \ + __macro(cudnnGetFilterNdDescriptor); \ __macro(cudnnSetPooling2dDescriptor); \ + __macro(cudnnSetPoolingNdDescriptor); \ + __macro(cudnnGetPoolingNdDescriptor); \ __macro(cudnnDestroyFilterDescriptor); \ __macro(cudnnCreateConvolutionDescriptor); \ __macro(cudnnCreatePoolingDescriptor); \ __macro(cudnnDestroyPoolingDescriptor); \ __macro(cudnnSetConvolution2dDescriptor); \ __macro(cudnnDestroyConvolutionDescriptor); \ + __macro(cudnnSetConvolutionNdDescriptor); \ + __macro(cudnnGetConvolutionNdDescriptor); \ __macro(cudnnCreate); \ __macro(cudnnDestroy); \ __macro(cudnnSetStream); \ -- GitLab From 4b6b7251c10371ceceb84c55ebc587715591c436 Mon Sep 17 00:00:00 2001 From: hedaoyuan Date: Thu, 31 Aug 2017 21:29:49 +0800 Subject: [PATCH 0317/2018] Refine NeonDepthwiseConv. --- paddle/function/neon/NeonDepthwiseConv.cpp | 35 +++++++++++----------- 1 file changed, 17 insertions(+), 18 deletions(-) diff --git a/paddle/function/neon/NeonDepthwiseConv.cpp b/paddle/function/neon/NeonDepthwiseConv.cpp index 7e5f752a0..3d502f5d6 100644 --- a/paddle/function/neon/NeonDepthwiseConv.cpp +++ b/paddle/function/neon/NeonDepthwiseConv.cpp @@ -18,8 +18,6 @@ limitations under the License. */ namespace paddle { -namespace neon { - #if defined(__ARM_NEON__) || defined(__ARM_NEON) template @@ -45,16 +43,16 @@ public: const TensorShape& filter = inputs[1].shape(); const TensorShape& output = outputs[0].shape(); - size_t batchSize = input[0]; - size_t inputChannels = input[1]; - size_t inputHeight = input[2]; - size_t inputWidth = input[3]; - size_t filterHeight = getFilterHeight(filter); - size_t filterWidth = getFilterWidth(filter); - size_t outputChannels = output[1]; - size_t outputHeight = output[2]; - size_t outputWidth = output[3]; - size_t filterMultiplier = outputChannels / groups_; + int batchSize = input[0]; + int inputChannels = input[1]; + int inputHeight = input[2]; + int inputWidth = input[3]; + int filterHeight = getFilterHeight(filter); + int filterWidth = getFilterWidth(filter); + int outputChannels = output[1]; + int outputHeight = output[2]; + int outputWidth = output[3]; + int filterMultiplier = outputChannels / groups_; CHECK_EQ(inputChannels, groups_); // only support strideH() == strideW() and filterHeight == filterWidth. @@ -90,18 +88,18 @@ public: DepthWiseConv; if (filterWidth == 3 && strideW() == 1) { - DepthWiseConv = DepthwiseConvKernel<3, 1>::run; + DepthWiseConv = neon::DepthwiseConvKernel<3, 1>::run; } else if (filterWidth == 3 && strideW() == 2) { - DepthWiseConv = DepthwiseConvKernel<3, 2>::run; + DepthWiseConv = neon::DepthwiseConvKernel<3, 2>::run; } else if (filterWidth == 4 && strideW() == 1) { - DepthWiseConv = DepthwiseConvKernel<4, 1>::run; + DepthWiseConv = neon::DepthwiseConvKernel<4, 1>::run; } else if (filterWidth == 4 && strideW() == 2) { - DepthWiseConv = DepthwiseConvKernel<4, 2>::run; + DepthWiseConv = neon::DepthwiseConvKernel<4, 2>::run; } else { LOG(FATAL) << "Not supported"; } - for (size_t i = 0; i < batchSize; i++) { + for (int i = 0; i < batchSize; i++) { DepthWiseConv(inputPadding, filterData, inputHeight, @@ -117,9 +115,10 @@ public: } }; +#ifndef PADDLE_TYPE_DOUBLE REGISTER_TYPED_FUNC(NeonDepthwiseConv, CPU, NeonDepthwiseConvFunction); +#endif #endif -} // namespace neon } // namespace paddle -- GitLab From 40d47fae95b04a26d0fa47eb46a83871f30f229f Mon Sep 17 00:00:00 2001 From: hedaoyuan Date: Thu, 31 Aug 2017 21:36:09 +0800 Subject: [PATCH 0318/2018] [Refine code]Move class Padding into the NeonDepthwiseConv.h. --- paddle/function/Im2Col.h | 91 --------------------- paddle/function/neon/NeonDepthwiseConv.cpp | 15 ++-- paddle/function/neon/NeonDepthwiseConv.h | 92 ++++++++++++++++++++++ 3 files changed, 99 insertions(+), 99 deletions(-) diff --git a/paddle/function/Im2Col.h b/paddle/function/Im2Col.h index 9b91e223a..1e0cff436 100644 --- a/paddle/function/Im2Col.h +++ b/paddle/function/Im2Col.h @@ -94,95 +94,4 @@ public: int paddingWidth); }; -template -struct Padding { - static void run(const T* src, - T* dest, - int channels, - int inputHeight, - int inputWidth, - int paddingHeight, - int paddingWidth) { - const int destWidth = inputWidth + 2 * paddingWidth; - for (int c = 0; c < channels; c++) { - if (paddingHeight > 0) { - memset(dest, 0, destWidth * paddingHeight * sizeof(T)); - dest += destWidth * paddingHeight; - } - - for (int i = 0; i < inputHeight; i++) { - // padding head - for (int j = 0; j < paddingWidth; j++) { - *dest++ = T(0); - } - - memcpy(dest, src, inputWidth * sizeof(T)); - dest += inputWidth; - src += inputWidth; - - // padding tail - for (int j = 0; j < paddingWidth; j++) { - *dest++ = T(0); - } - } - - if (paddingHeight > 0) { - memset(dest, 0, destWidth * paddingHeight * sizeof(T)); - dest += destWidth * paddingHeight; - } - } - } -}; - -#if defined(__ARM_NEON__) || defined(__ARM_NEON) -template <> -struct Padding { - static void run(const float* src, - float* dest, - int channels, - int inputHeight, - int inputWidth, - int paddingHeight, - int paddingWidth) { - const int destWidth = inputWidth + 2 * paddingWidth; - for (int c = 0; c < channels; c++) { - if (paddingHeight > 0) { - memset(dest, 0, destWidth * paddingHeight * sizeof(float)); - dest += destWidth * paddingHeight; - } - - for (int i = 0; i < inputHeight; i++) { - // padding head - for (int j = 0; j < paddingWidth; j++) { - *dest++ = float(0); - } - - int step = inputWidth >> 2; - int remain = inputWidth & 3; - for (int s = 0; s < step; s++) { - float32x4_t s0 = vld1q_f32(src); - vst1q_f32(dest, s0); - src += 4; - dest += 4; - } - for (int r = 0; r < remain; r++) { - *dest++ = *src++; - } - - // padding tail - for (int j = 0; j < paddingWidth; j++) { - *dest++ = float(0); - } - } - - if (paddingHeight > 0) { - memset(dest, 0, destWidth * paddingHeight * sizeof(float)); - dest += destWidth * paddingHeight; - } - } - } -}; - -#endif - } // namespace paddle diff --git a/paddle/function/neon/NeonDepthwiseConv.cpp b/paddle/function/neon/NeonDepthwiseConv.cpp index 3d502f5d6..bd9a56a8a 100644 --- a/paddle/function/neon/NeonDepthwiseConv.cpp +++ b/paddle/function/neon/NeonDepthwiseConv.cpp @@ -14,7 +14,6 @@ limitations under the License. */ #include "NeonDepthwiseConv.h" #include "paddle/function/ConvOp.h" -#include "paddle/function/Im2Col.h" namespace paddle { @@ -70,13 +69,13 @@ public: (inputWidth + 2 * paddingW()); resizeBuffer(newSize); inputPadding = reinterpret_cast(memory_->getBuf()); - Padding::run(inputData, - inputPadding, - batchSize * inputChannels, - inputHeight, - inputWidth, - paddingH(), - paddingW()); + neon::Padding::run(inputData, + inputPadding, + batchSize * inputChannels, + inputHeight, + inputWidth, + paddingH(), + paddingW()); // height and width of padding data inputHeight += 2 * paddingH(); diff --git a/paddle/function/neon/NeonDepthwiseConv.h b/paddle/function/neon/NeonDepthwiseConv.h index cb1abe1f3..3ceaa65dd 100644 --- a/paddle/function/neon/NeonDepthwiseConv.h +++ b/paddle/function/neon/NeonDepthwiseConv.h @@ -14,6 +14,7 @@ limitations under the License. */ #pragma once +#include #include "neon_util.h" namespace paddle { @@ -474,6 +475,97 @@ struct DepthwiseConvKernel<4, 2> { } }; +template +struct Padding { + static void run(const T* src, + T* dest, + int channels, + int inputHeight, + int inputWidth, + int paddingHeight, + int paddingWidth) { + const int destWidth = inputWidth + 2 * paddingWidth; + for (int c = 0; c < channels; c++) { + if (paddingHeight > 0) { + memset(dest, 0, destWidth * paddingHeight * sizeof(T)); + dest += destWidth * paddingHeight; + } + + for (int i = 0; i < inputHeight; i++) { + // padding head + for (int j = 0; j < paddingWidth; j++) { + *dest++ = T(0); + } + + memcpy(dest, src, inputWidth * sizeof(T)); + dest += inputWidth; + src += inputWidth; + + // padding tail + for (int j = 0; j < paddingWidth; j++) { + *dest++ = T(0); + } + } + + if (paddingHeight > 0) { + memset(dest, 0, destWidth * paddingHeight * sizeof(T)); + dest += destWidth * paddingHeight; + } + } + } +}; + +#if defined(__ARM_NEON__) || defined(__ARM_NEON) +template <> +struct Padding { + static void run(const float* src, + float* dest, + int channels, + int inputHeight, + int inputWidth, + int paddingHeight, + int paddingWidth) { + const int destWidth = inputWidth + 2 * paddingWidth; + for (int c = 0; c < channels; c++) { + if (paddingHeight > 0) { + memset(dest, 0, destWidth * paddingHeight * sizeof(float)); + dest += destWidth * paddingHeight; + } + + for (int i = 0; i < inputHeight; i++) { + // padding head + for (int j = 0; j < paddingWidth; j++) { + *dest++ = float(0); + } + + int step = inputWidth >> 2; + int remain = inputWidth & 3; + for (int s = 0; s < step; s++) { + float32x4_t s0 = vld1q_f32(src); + vst1q_f32(dest, s0); + src += 4; + dest += 4; + } + for (int r = 0; r < remain; r++) { + *dest++ = *src++; + } + + // padding tail + for (int j = 0; j < paddingWidth; j++) { + *dest++ = float(0); + } + } + + if (paddingHeight > 0) { + memset(dest, 0, destWidth * paddingHeight * sizeof(float)); + dest += destWidth * paddingHeight; + } + } + } +}; + +#endif + #endif } // namespace neon -- GitLab From 0ea4e2588b5bad02749cecc8220bb4e87cd9b118 Mon Sep 17 00:00:00 2001 From: Liu Yiqun Date: Thu, 31 Aug 2017 21:52:04 +0800 Subject: [PATCH 0319/2018] Update the version of OpenBLAS and set the sysroot to fix compiling error using clang on Mac. --- cmake/external/openblas.cmake | 11 ++++++++--- 1 file changed, 8 insertions(+), 3 deletions(-) diff --git a/cmake/external/openblas.cmake b/cmake/external/openblas.cmake index 0eeccbf7d..0002a470d 100644 --- a/cmake/external/openblas.cmake +++ b/cmake/external/openblas.cmake @@ -25,7 +25,12 @@ IF(NOT ${CBLAS_FOUND}) "${CBLAS_INSTALL_DIR}/lib/${CMAKE_STATIC_LIBRARY_PREFIX}openblas${CMAKE_STATIC_LIBRARY_SUFFIX}" CACHE FILEPATH "openblas library." FORCE) - SET(COMMON_ARGS CC=${CMAKE_C_COMPILER} NO_SHARED=1 NO_LAPACK=1 libs) + IF(APPLE) + SET(OPENBLAS_CC "${CMAKE_C_COMPILER} -isysroot ${CMAKE_OSX_SYSROOT}") + SET(COMMON_ARGS CC=${OPENBLAS_CC} NO_SHARED=1 NO_LAPACK=1 libs) + ELSE() + SET(COMMON_ARGS CC=${CMAKE_C_COMPILER} NO_SHARED=1 NO_LAPACK=1 libs) + ENDIF() IF(CMAKE_CROSSCOMPILING) IF(ANDROID) @@ -40,11 +45,11 @@ IF(NOT ${CBLAS_FOUND}) SET(OPTIONAL_ARGS HOSTCC=${HOST_C_COMPILER} TARGET=${TARGET} ARM_SOFTFP_ABI=1 USE_THREAD=0) ELSEIF(RPI) # use hardfp - SET(OPENBLAS_COMMIT "v0.2.19") + SET(OPENBLAS_COMMIT "v0.2.20") SET(OPTIONAL_ARGS HOSTCC=${HOST_C_COMPILER} TARGET=ARMV7 USE_THREAD=0) ENDIF() ELSE() - SET(OPENBLAS_COMMIT "v0.2.19") + SET(OPENBLAS_COMMIT "v0.2.20") SET(OPTIONAL_ARGS "") IF(CMAKE_SYSTEM_PROCESSOR MATCHES "^x86(_64)?$") SET(OPTIONAL_ARGS DYNAMIC_ARCH=1 NUM_THREADS=64) -- GitLab From 840104c99a59f3f970c71eea27382c09e0de6a28 Mon Sep 17 00:00:00 2001 From: hedaoyuan Date: Thu, 31 Aug 2017 21:59:35 +0800 Subject: [PATCH 0320/2018] Add NeonDepthwiseConvTransposeFunction. --- paddle/function/neon/NeonDepthwiseConv.cpp | 19 ++- paddle/function/neon/NeonDepthwiseConv.h | 62 ++++----- .../neon/NeonDepthwiseConvTranspose.cpp | 124 ++++++++++++++++++ 3 files changed, 164 insertions(+), 41 deletions(-) create mode 100644 paddle/function/neon/NeonDepthwiseConvTranspose.cpp diff --git a/paddle/function/neon/NeonDepthwiseConv.cpp b/paddle/function/neon/NeonDepthwiseConv.cpp index bd9a56a8a..18126152e 100644 --- a/paddle/function/neon/NeonDepthwiseConv.cpp +++ b/paddle/function/neon/NeonDepthwiseConv.cpp @@ -64,9 +64,10 @@ public: // padding the input float* inputPadding = inputData; + int padInputHeight = inputHeight + 2 * paddingH(); + int padInputWidth = inputWidth + 2 * paddingW(); if (paddingH() > 0 || paddingW() > 0) { - int newSize = batchSize * inputChannels * (inputHeight + 2 * paddingH()) * - (inputWidth + 2 * paddingW()); + int newSize = batchSize * inputChannels * padInputHeight * padInputWidth; resizeBuffer(newSize); inputPadding = reinterpret_cast(memory_->getBuf()); neon::Padding::run(inputData, @@ -74,12 +75,8 @@ public: batchSize * inputChannels, inputHeight, inputWidth, - paddingH(), - paddingW()); - - // height and width of padding data - inputHeight += 2 * paddingH(); - inputWidth += 2 * paddingW(); + padInputHeight, + padInputWidth); } std::function { template struct Padding { - static void run(const T* src, - T* dest, + static void run(const T* input, + T* inputPadding, int channels, int inputHeight, int inputWidth, - int paddingHeight, - int paddingWidth) { - const int destWidth = inputWidth + 2 * paddingWidth; + int padInputHeight, + int padInputWidth) { + const int paddingHeight = (padInputHeight - inputHeight) / 2; + const int paddingWidth = (padInputWidth - inputWidth) / 2; for (int c = 0; c < channels; c++) { if (paddingHeight > 0) { - memset(dest, 0, destWidth * paddingHeight * sizeof(T)); - dest += destWidth * paddingHeight; + memset(inputPadding, 0, padInputWidth * paddingHeight * sizeof(T)); + inputPadding += padInputWidth * paddingHeight; } for (int i = 0; i < inputHeight; i++) { // padding head for (int j = 0; j < paddingWidth; j++) { - *dest++ = T(0); + *inputPadding++ = T(0); } - memcpy(dest, src, inputWidth * sizeof(T)); - dest += inputWidth; - src += inputWidth; + memcpy(inputPadding, input, inputWidth * sizeof(T)); + inputPadding += inputWidth; + input += inputWidth; // padding tail for (int j = 0; j < paddingWidth; j++) { - *dest++ = T(0); + *inputPadding++ = T(0); } } if (paddingHeight > 0) { - memset(dest, 0, destWidth * paddingHeight * sizeof(T)); - dest += destWidth * paddingHeight; + memset(inputPadding, 0, padInputWidth * paddingHeight * sizeof(T)); + inputPadding += padInputWidth * paddingHeight; } } } @@ -518,47 +519,48 @@ struct Padding { #if defined(__ARM_NEON__) || defined(__ARM_NEON) template <> struct Padding { - static void run(const float* src, - float* dest, + static void run(const float* input, + float* inputPadding, int channels, int inputHeight, int inputWidth, - int paddingHeight, - int paddingWidth) { - const int destWidth = inputWidth + 2 * paddingWidth; + int padInputHeight, + int padInputWidth) { + const int paddingHeight = (padInputHeight - inputHeight) / 2; + const int paddingWidth = (padInputWidth - inputWidth) / 2; for (int c = 0; c < channels; c++) { if (paddingHeight > 0) { - memset(dest, 0, destWidth * paddingHeight * sizeof(float)); - dest += destWidth * paddingHeight; + memset(inputPadding, 0, padInputWidth * paddingHeight * sizeof(float)); + inputPadding += padInputWidth * paddingHeight; } for (int i = 0; i < inputHeight; i++) { // padding head for (int j = 0; j < paddingWidth; j++) { - *dest++ = float(0); + *inputPadding++ = float(0); } int step = inputWidth >> 2; int remain = inputWidth & 3; for (int s = 0; s < step; s++) { - float32x4_t s0 = vld1q_f32(src); - vst1q_f32(dest, s0); - src += 4; - dest += 4; + float32x4_t s0 = vld1q_f32(input); + vst1q_f32(inputPadding, s0); + input += 4; + inputPadding += 4; } for (int r = 0; r < remain; r++) { - *dest++ = *src++; + *inputPadding++ = *input++; } // padding tail for (int j = 0; j < paddingWidth; j++) { - *dest++ = float(0); + *inputPadding++ = float(0); } } if (paddingHeight > 0) { - memset(dest, 0, destWidth * paddingHeight * sizeof(float)); - dest += destWidth * paddingHeight; + memset(inputPadding, 0, padInputWidth * paddingHeight * sizeof(float)); + inputPadding += padInputWidth * paddingHeight; } } } diff --git a/paddle/function/neon/NeonDepthwiseConvTranspose.cpp b/paddle/function/neon/NeonDepthwiseConvTranspose.cpp new file mode 100644 index 000000000..03d571ecf --- /dev/null +++ b/paddle/function/neon/NeonDepthwiseConvTranspose.cpp @@ -0,0 +1,124 @@ +/* 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 "NeonDepthwiseConv.h" +#include "paddle/function/ConvOp.h" + +namespace paddle { + +#if defined(__ARM_NEON__) || defined(__ARM_NEON) + +template +class NeonDepthwiseConvTransposeFunction : public ConvFunctionBase { +public: + void init(const FuncConfig& config) override { + ConvFunctionBase::init(config); + } + + void check(const BufferArgs& inputs, const BufferArgs& outputs) override { + const TensorShape& input = inputs[0].shape(); + const TensorShape& filter = inputs[1].shape(); + const TensorShape& output = outputs[0].shape(); + checkShape(input, filter, output); + } + + void calc(const BufferArgs& inputs, const BufferArgs& outputs) override { + CHECK_EQ(numInputs_, inputs.size()); + CHECK_EQ(numOutputs_, outputs.size()); + check(inputs, outputs); + + const TensorShape& input = inputs[0].shape(); + const TensorShape& filter = inputs[1].shape(); + const TensorShape& output = outputs[0].shape(); + + int batchSize = input[0]; + int inputChannels = input[1]; + int inputHeight = input[2]; + int inputWidth = input[3]; + int filterHeight = getFilterHeight(filter); + int filterWidth = getFilterWidth(filter); + int outputChannels = output[1]; + int outputHeight = output[2]; + int outputWidth = output[3]; + int filterMultiplier = outputChannels / groups_; + CHECK_EQ(inputChannels, groups_); + + // only support strideH() == strideW() and filterHeight == filterWidth. + CHECK_EQ(strideH(), strideW()); + CHECK_EQ(paddingH(), paddingW()); + CHECK_EQ(filterHeight, filterWidth); + + float* inputData = inputs[0].data(); + float* filterData = inputs[1].data(); + float* outputData = outputs[0].data(); + + // padding the input, input -> inputPadding + float* inputPadding = inputData; + int padInputHeight = + (inputHeight - 1) * strideH() + 2 * filterHeight - 1 - 2 * paddingH(); + int padInputWidth = + (inputWidth - 1) * strideW() + 2 * filterWidth - 1 - 2 * paddingW(); + + if (padInputHeight > inputHeight || padInputWidth > inputWidth) { + int newSize = batchSize * inputChannels * padInputHeight * padInputWidth; + resizeBuffer(newSize); + inputPadding = reinterpret_cast(memory_->getBuf()); + neon::Padding::run(inputData, + inputPadding, + batchSize * inputChannels, + inputHeight, + inputWidth, + padInputHeight, + padInputWidth); + } + + std::function + DepthWiseConv; + + if (filterWidth == 3) { + DepthWiseConv = neon::DepthwiseConvKernel<3, 1>::run; + } else if (filterWidth == 4) { + DepthWiseConv = neon::DepthwiseConvKernel<4, 1>::run; + } else { + LOG(FATAL) << "Not supported"; + } + + for (int i = 0; i < batchSize; i++) { + DepthWiseConv(inputPadding, + filterData, + padInputHeight, + padInputWidth, + outputChannels, + outputHeight, + outputWidth, + filterMultiplier, + outputData); + inputPadding += inputChannels * padInputHeight * padInputWidth; + outputData += outputChannels * outputHeight * outputWidth; + } + } +}; + +#ifndef PADDLE_TYPE_DOUBLE + +REGISTER_TYPED_FUNC(NeonDepthwiseConvTranspose, + CPU, + NeonDepthwiseConvTransposeFunction); + +#endif + +#endif + +} // namespace paddle -- GitLab From 6bef079660f689a1b9c061e31c8273de353f98da Mon Sep 17 00:00:00 2001 From: yangyaming Date: Thu, 31 Aug 2017 22:31:34 +0800 Subject: [PATCH 0321/2018] Follow coding style and move reshaping operation to paddle tensor. --- paddle/operators/squared_l2_distance_op.cc | 47 ++--- paddle/operators/squared_l2_distance_op.h | 170 ++++++------------ .../tests/test_squared_l2_distance_op.py | 10 ++ 3 files changed, 92 insertions(+), 135 deletions(-) diff --git a/paddle/operators/squared_l2_distance_op.cc b/paddle/operators/squared_l2_distance_op.cc index 3049f0f8b..b19c274dc 100644 --- a/paddle/operators/squared_l2_distance_op.cc +++ b/paddle/operators/squared_l2_distance_op.cc @@ -30,22 +30,27 @@ class SquaredL2DistanceOp : public framework::OperatorWithKernel { "Target of SquaredL2DistanceOp " "must be initialized."); - auto* X = ctx.Input("X"); - auto xDims = X->dims(); - auto* Y = ctx.Input("Y"); - auto yDims = Y->dims(); + auto* x = ctx.Input("X"); + auto x_dims = x->dims(); + auto* y = ctx.Input("Y"); + auto y_dims = y->dims(); - PADDLE_ENFORCE_EQ(framework::arity(xDims), framework::arity(yDims), + PADDLE_ENFORCE_EQ(framework::arity(x_dims), framework::arity(y_dims), "Tensor rank of both SquaredL2DistanceOp's " "inputs must be same."); - int rank = framework::arity(xDims); - PADDLE_ENFORCE(rank >= 2 || rank <= 6, "Tensor rank should be in [2, 6]."); - PADDLE_ENFORCE(yDims[0] == 1 || yDims[0] == xDims[0], + + int rank = framework::arity(x_dims); + PADDLE_ENFORCE(rank >= 2, "Tensor rank should be at least equal to 2."); + PADDLE_ENFORCE_EQ(framework::product(x_dims) / x_dims[0], + framework::product(y_dims) / y_dims[0], + "Product of dimensions expcet the first dimension of " + "input and target must be equal."); + PADDLE_ENFORCE(y_dims[0] == 1 || y_dims[0] == x_dims[0], "First dimension of target must be equal to input " "or to 1."); - ctx.Output("sub_result")->Resize(xDims); - ctx.Output("Out")->Resize({xDims[0], 1}); + ctx.Output("sub_result")->Resize(x_dims); + ctx.Output("Out")->Resize({x_dims[0], 1}); } }; @@ -66,8 +71,8 @@ class SquaredL2DistanceOpMaker : public framework::OpProtoAndCheckerMaker { input and target. Number of distance value equals to the first dimension of input. First dimension of target could be equal to input or to 1. If the first dimension of target is 1, SquaredL2DistanceOp - will broadcast the first dimension to the first dimension of input. - You can decide whether calculate the gradient of target. + will broadcast target's first dimension to input's first dimension. + You can decide whether calculate the gradient of input and target. )DOC"); } }; @@ -81,19 +86,19 @@ class SquaredL2DistanceGradOp : public framework::OperatorWithKernel { PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")), "Gradient of Out should not be null"); // check out grad dimensions - auto outDims = ctx.Input(framework::GradVarName("Out"))->dims(); - auto xDims = ctx.Input("X")->dims(); - auto yDims = ctx.Input("Y")->dims(); - PADDLE_ENFORCE_EQ(outDims[0], xDims[0], + auto out_dims = ctx.Input(framework::GradVarName("Out"))->dims(); + auto x_dims = ctx.Input("X")->dims(); + auto y_dims = ctx.Input("Y")->dims(); + PADDLE_ENFORCE_EQ(out_dims[0], x_dims[0], "First dimension of output gradient and " "input value must be equal."); - PADDLE_ENFORCE_EQ(outDims[1], 1, + PADDLE_ENFORCE_EQ(out_dims[1], 1, "Second dimension of output gradient " "must be 1."); - auto* xGrad = ctx.Output(framework::GradVarName("X")); - auto* yGrad = ctx.Output(framework::GradVarName("Y")); - if (xGrad != nullptr) xGrad->Resize(xDims); - if (yGrad != nullptr) yGrad->Resize(yDims); + auto* x_grad = ctx.Output(framework::GradVarName("X")); + auto* y_grad = ctx.Output(framework::GradVarName("Y")); + if (x_grad != nullptr) x_grad->Resize(x_dims); + if (y_grad != nullptr) y_grad->Resize(y_dims); } }; diff --git a/paddle/operators/squared_l2_distance_op.h b/paddle/operators/squared_l2_distance_op.h index e95364c70..ec8c34ddf 100644 --- a/paddle/operators/squared_l2_distance_op.h +++ b/paddle/operators/squared_l2_distance_op.h @@ -20,9 +20,6 @@ namespace paddle { namespace operators { using Tensor = framework::Tensor; -template -using EigenTensor = framework::EigenTensor; template using EigenMatrix = framework::EigenMatrix; @@ -31,64 +28,39 @@ template class SquaredL2DistanceKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& context) const override { - auto* input0 = context.Input("X"); - const int rank = framework::arity(input0->dims()); - switch (rank) { - case 2: - Operate<2>(context); - break; - case 3: - Operate<3>(context); - break; - case 4: - Operate<4>(context); - break; - case 5: - Operate<5>(context); - break; - case 6: - Operate<6>(context); - break; - default: - // already asserted in SquaredL2DistanceOpMaker - break; - } - } - - private: - template - void Operate(const framework::ExecutionContext& context) const { - auto* input0 = context.Input("X"); - auto* input1 = context.Input("Y"); - auto* output0 = context.Output("sub_result"); - auto* output1 = context.Output("Out"); - - output0->mutable_data(context.GetPlace()); - output1->mutable_data(context.GetPlace()); - - auto X = EigenTensor::From(*input0); - auto Y = EigenTensor::From(*input1); - auto subResult = EigenTensor::From(*output0); - auto Z = EigenMatrix::From(*output1); - - auto xDims = X.dimensions(); - auto yDims = Y.dimensions(); + auto* in0 = context.Input("X"); + auto* in1 = context.Input("Y"); + auto* out0 = context.Output("sub_result"); + auto* out1 = context.Output("Out"); + + auto in0_dims = in0->dims(); + auto in1_dims = in1->dims(); + + int cols = framework::product(in0_dims) / in0_dims[0]; + // reduce dimensions except the first + auto x = + EigenMatrix::From(*in0, framework::make_ddim({in0_dims[0], cols})); + auto y = + EigenMatrix::From(*in1, framework::make_ddim({in1_dims[0], cols})); + + out0->mutable_data(context.GetPlace()); + out1->mutable_data(context.GetPlace()); + auto sub_result = EigenMatrix::From(*out0); + auto z = EigenMatrix::From(*out1); auto place = context.GetEigenDevice(); - + auto x_dims = x.dimensions(); + auto y_dims = y.dimensions(); // buffer the substraction result - if (yDims[0] == 1 && xDims[0] != yDims[0]) { - auto yBroadcastDims = yDims; - yBroadcastDims[0] = xDims[0]; - subResult.device(place) = X - Y.broadcast(yBroadcastDims); + if (y_dims[0] == 1 && x_dims[0] > y_dims[0]) { + auto y_broadcast_dims = y_dims; + y_broadcast_dims[0] = x_dims[0]; + sub_result.device(place) = x - y.broadcast(y_broadcast_dims); } else { - subResult.device(place) = X - Y; + sub_result.device(place) = x - y; } - // create matrix view for substraction result - const auto& subResMat = subResult.reshape(Eigen::array( - {static_cast(xDims[0]), static_cast(X.size() / xDims[0])})); - Z.device(place) = subResMat.pow(2).sum(Eigen::array({1})); + z.device(place) = sub_result.pow(2).sum(Eigen::array({1})); } }; @@ -96,77 +68,47 @@ template class SquaredL2DistanceGradKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& context) const override { - auto* input0 = context.Input("sub_result"); - const int rank = framework::arity(input0->dims()); - switch (rank) { - case 2: - Operate<2>(context); - break; - case 3: - Operate<3>(context); - break; - case 4: - Operate<4>(context); - break; - case 5: - Operate<5>(context); - break; - case 6: - Operate<6>(context); - break; - default: - // already asserted in SquaredL2DistanceOpMaker - break; - } - } + auto* in0 = context.Input("sub_result"); + auto* in1 = context.Input(framework::GradVarName("Out")); + auto* x_g = context.Output(framework::GradVarName("X")); + auto* y_g = context.Output(framework::GradVarName("Y")); - private: - template - void Operate(const framework::ExecutionContext& context) const { - auto* input0 = context.Input("sub_result"); - auto* OG = context.Input(framework::GradVarName("Out")); - auto* XG = context.Output(framework::GradVarName("X")); - auto* YG = context.Output(framework::GradVarName("Y")); + auto sub_result = EigenMatrix::From(*in0); + auto out_grad = EigenMatrix::From(*in1); - auto subResult = EigenTensor::From(*input0); - auto outGrad = EigenMatrix::From(*OG); - - auto subResDims = subResult.dimensions(); - int firstDim = static_cast(subResDims[0]); - int cols = subResult.size() / firstDim; - const auto subResMat = - subResult.reshape(Eigen::array({firstDim, cols})); + auto x_dims = x_g->dims(); + auto y_dims = y_g->dims(); + int cols = framework::product(x_dims) / x_dims[0]; // calculate gradient - auto gradMat = - 2 * (outGrad.broadcast(Eigen::array({1, cols}))) * subResMat; + auto grad_mat = + 2 * (out_grad.broadcast(Eigen::array({1, cols}))) * sub_result; // propagate back to input - auto eigenPlace = context.GetEigenDevice(); - if (XG != nullptr) { - XG->mutable_data(context.GetPlace()); - auto xGrad = EigenTensor::From(*XG); + auto eigen_place = context.GetEigenDevice(); + if (x_g != nullptr) { + x_g->mutable_data(context.GetPlace()); + // eigen matrix + auto x_grad = + EigenMatrix::From(*x_g, framework::make_ddim({x_dims[0], cols})); // dimensions are same with subResult - auto xGradMat = xGrad.reshape(Eigen::array({firstDim, cols})); - xGradMat.device(eigenPlace) = gradMat; + x_grad.device(eigen_place) = grad_mat; } - if (YG != nullptr) { - YG->mutable_data(context.GetPlace()); - auto yGrad = EigenTensor::From(*YG); - auto dimsYGrad = yGrad.dimensions(); - auto yGradMat = yGrad.reshape(Eigen::array( - {static_cast(dimsYGrad[0]), - static_cast(yGrad.size() / dimsYGrad[0])})); - - PADDLE_ENFORCE(dimsYGrad[0] <= firstDim, + + if (y_g != nullptr) { + y_g->mutable_data(context.GetPlace()); + auto y_grad = + EigenMatrix::From(*y_g, framework::make_ddim({y_dims[0], cols})); + + PADDLE_ENFORCE(sub_result.dimensions()[0] >= y_dims[0], "First dimension of gradient must be greater or " "equal than first dimension of target"); - if (dimsYGrad[0] == firstDim) { - yGradMat.device(eigenPlace) = -1 * gradMat; + if (sub_result.dimensions()[0] == y_dims[0]) { + y_grad.device(eigen_place) = -1 * grad_mat; } else { - yGradMat.device(eigenPlace) = - -1 * (gradMat.sum(Eigen::array({0}))); + y_grad.device(eigen_place) = + -1 * (grad_mat.sum(Eigen::array({0}))); } } } diff --git a/python/paddle/v2/framework/tests/test_squared_l2_distance_op.py b/python/paddle/v2/framework/tests/test_squared_l2_distance_op.py index eeddb5a3b..51c95b286 100644 --- a/python/paddle/v2/framework/tests/test_squared_l2_distance_op.py +++ b/python/paddle/v2/framework/tests/test_squared_l2_distance_op.py @@ -21,5 +21,15 @@ class TestSquaredL2DistanceOp(unittest.TestCase): } +class TestSquaredL2DistanceGradOp(GradientChecker): + def test_squared_l2_distance(self): + op = create_op("squared_l2_distance") + inputs = { + 'X': np.random.uniform(0.1, 1., (2, 3)).astype('float32'), + 'Y': np.random.uniform(0.1, 1., (2, 3)).astype('float32') + } + self.check_grad(op, inputs, set(["X", "Y"]), "Out") + + if __name__ == '__main__': unittest.main() -- GitLab From 90bf4f60aea012a3eeb819fe4655069d66dbe6e6 Mon Sep 17 00:00:00 2001 From: hedaoyuan Date: Thu, 31 Aug 2017 23:59:58 +0800 Subject: [PATCH 0322/2018] Add stride support 2 for NeonDepthwiseConvTranspose. --- paddle/function/neon/NeonDepthwiseConv.h | 57 +++++++++++++++++++ .../neon/NeonDepthwiseConvTranspose.cpp | 26 ++++++--- 2 files changed, 76 insertions(+), 7 deletions(-) diff --git a/paddle/function/neon/NeonDepthwiseConv.h b/paddle/function/neon/NeonDepthwiseConv.h index 30f0158c6..aefeea78b 100644 --- a/paddle/function/neon/NeonDepthwiseConv.h +++ b/paddle/function/neon/NeonDepthwiseConv.h @@ -566,6 +566,63 @@ struct Padding { } }; +// for stride is 2 +struct StridePadding { + static void run(const float* input, + float* inputPadding, + int channels, + int inputHeight, + int inputWidth, + int padInputHeight, + int padInputWidth) { + const int paddingHeight = (padInputHeight - (inputHeight * 2 - 1)) / 2; + const int paddingWidth = (padInputWidth - (inputWidth * 2 - 1)) / 2; + for (int c = 0; c < channels; c++) { + if (paddingHeight > 0) { + memset(inputPadding, 0, padInputWidth * paddingHeight * sizeof(float)); + inputPadding += padInputWidth * paddingHeight; + } + + for (int i = 0; i < inputHeight; i++) { + // padding head + for (int j = 0; j < paddingWidth; j++) { + *inputPadding++ = float(0); + } + + int step = inputWidth >> 2; + int remain = inputWidth & 3; + float32x4_t s1 = vdupq_n_f32(0.f); + for (int s = 0; s < step; s++) { + float32x4_t s0 = vld1q_f32(input); + float32x4x2_t v = {s0, s1}; + vst2q_f32(inputPadding, v); + input += 4; + inputPadding += 8; + } + for (int r = 0; r < remain; r++) { + *inputPadding++ = *input++; + *inputPadding++ = float(0); + } + inputPadding--; + + // padding tail + for (int j = 0; j < paddingWidth; j++) { + *inputPadding++ = float(0); + } + if (i != inputHeight - 1) { + memset(inputPadding, 0, padInputWidth * sizeof(float)); + inputPadding += padInputWidth; + } + } + + if (paddingHeight > 0) { + memset(inputPadding, 0, padInputWidth * paddingHeight * sizeof(float)); + inputPadding += padInputWidth * paddingHeight; + } + } + } +}; + #endif #endif diff --git a/paddle/function/neon/NeonDepthwiseConvTranspose.cpp b/paddle/function/neon/NeonDepthwiseConvTranspose.cpp index 03d571ecf..49ca4bc8a 100644 --- a/paddle/function/neon/NeonDepthwiseConvTranspose.cpp +++ b/paddle/function/neon/NeonDepthwiseConvTranspose.cpp @@ -74,13 +74,25 @@ public: int newSize = batchSize * inputChannels * padInputHeight * padInputWidth; resizeBuffer(newSize); inputPadding = reinterpret_cast(memory_->getBuf()); - neon::Padding::run(inputData, - inputPadding, - batchSize * inputChannels, - inputHeight, - inputWidth, - padInputHeight, - padInputWidth); + if (strideH() == 1) { + neon::Padding::run(inputData, + inputPadding, + batchSize * inputChannels, + inputHeight, + inputWidth, + padInputHeight, + padInputWidth); + } else if (strideH() == 2) { + neon::StridePadding::run(inputData, + inputPadding, + batchSize * inputChannels, + inputHeight, + inputWidth, + padInputHeight, + padInputWidth); + } else { + LOG(FATAL) << "Not supported"; + } } std::function Date: Thu, 31 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