From 7dc584f5c437f76383a9b83b6208c25bfaf4a82e Mon Sep 17 00:00:00 2001 From: xzl Date: Mon, 20 Nov 2017 11:54:44 +0800 Subject: [PATCH 01/58] add upsample layer --- paddle/cuda/include/hl_cnn.h | 42 ++++++ paddle/cuda/include/stub/hl_cnn_stub.h | 18 +++ paddle/cuda/src/hl_cuda_cnn.cu | 76 +++++++++++ paddle/gserver/layers/UpsampleLayer.cpp | 107 +++++++++++++++ paddle/gserver/layers/UpsampleLayer.h | 54 ++++++++ paddle/math/Matrix.cpp | 126 ++++++++++++++++++ paddle/math/Matrix.h | 52 ++++++++ proto/ModelConfig.proto | 11 ++ python/paddle/trainer/config_parser.py | 44 ++++++ .../paddle/trainer_config_helpers/layers.py | 77 +++++++++++ 10 files changed, 607 insertions(+) create mode 100644 paddle/gserver/layers/UpsampleLayer.cpp create mode 100644 paddle/gserver/layers/UpsampleLayer.h diff --git a/paddle/cuda/include/hl_cnn.h b/paddle/cuda/include/hl_cnn.h index 89c1f48eda..c8dd3eb91e 100644 --- a/paddle/cuda/include/hl_cnn.h +++ b/paddle/cuda/include/hl_cnn.h @@ -366,4 +366,46 @@ extern void hl_maxout_backward(real* inGrad, size_t featLen, size_t groups); +/** + * @brief Upsample forward. + * @param[in] inputData input data. + * @param[out] maskData the mask data from MaxPoolWithMaskLayer. + * @param[out] batchSize the batch size of the input. + * @param[in] imgSizeH image height. + * @param[in] imgSizeW image width. + * @param[in] channels the input channels. + * @param[in] outputH the output height. + * @param[in] outputW the output widht. + * @param[out] outputData output data. + */ +extern void hl_upsample_forward(real *inputData, real *maskData, + size_t batchSize, + size_t imgSizeH, + size_t imgSizeW, + size_t channels, + size_t outputH, + size_t outputW, + real *outputData); + +/** + * @brief Upsample backward. + * @param[in] outputGradData the output grad data. + * @param[out] maskData the mask data from MaxPoolWithMaskLayer. + * @param[out] batchSize the batch size of the input. + * @param[in] imgSizeH image height. + * @param[in] imgSizeW image width. + * @param[in] channels the input channels. + * @param[in] outputH the output height. + * @param[in] outputW the output widht. + * @param[out] inputGradData the input grad data. + */ +extern void hl_upsample_backward(real *outputGradData, real *maskData, + size_t batchSize, + size_t imgSizeH, + size_t imgSizeW, + size_t channels, + size_t outputH, + size_t outputW, + real *inputGradData); + #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 968ed4840f..ef1f67980e 100644 --- a/paddle/cuda/include/stub/hl_cnn_stub.h +++ b/paddle/cuda/include/stub/hl_cnn_stub.h @@ -222,4 +222,22 @@ inline void hl_maxout_backward(real* inGrad, size_t featLen, size_t group) {} +inline void hl_upsample_forward(real *inputData, real *maskData, + size_t batchSize, + size_t imgSizeH, + size_t imgSizeW, + size_t channels, + size_t outputH, + size_t outputW, + real *outputData) {} + +inline void hl_upsample_backward(real *outputGradData, real *maskData, + size_t batchSize, + size_t imgSizeH, + size_t imgSizeW, + size_t channels, + size_t outputH, + size_t outputW, + real *inputGradData) {} + #endif // HL_CNN_STUB_H_ diff --git a/paddle/cuda/src/hl_cuda_cnn.cu b/paddle/cuda/src/hl_cuda_cnn.cu index 3699b1e8ae..966c406a86 100644 --- a/paddle/cuda/src/hl_cuda_cnn.cu +++ b/paddle/cuda/src/hl_cuda_cnn.cu @@ -1020,3 +1020,79 @@ void hl_maxout_backward(real* inGrad, num_kernels, inGrad, outGrad, idData, size, featLen, groups); CHECK_SYNC("hl_maxout_backward failed"); } + +__global__ void upsampleForwardCompute(real* input_data, + real* mask_data, + size_t nthreads, + size_t in_h, + size_t in_w, + size_t out_h, + size_t out_w, + real* output_data) { + int index = blockIdx.x * blockDim.x + threadIdx.x; + if (index < nthreads) { + int offset = index / (in_w * in_h) * out_h * out_w; + int upsample_idx = static_cast(mask_data[index]); + output_data[offset + upsample_idx] = input_data[index]; + } +} + +__global__ void upsampleBackwardCompute(real* out_grad, + real* mask_data, + size_t nthreads, + size_t in_h, + size_t in_w, + size_t out_h, + size_t out_w, + real* input_grad) { + int index = blockIdx.x * blockDim.x + threadIdx.x; + if (index < nthreads) { + int offset = index / (in_w * in_h) * out_h * out_w; + int upsample_idx = static_cast(mask_data[index]); + input_grad[index] = out_grad[offset + upsample_idx]; + } +} + +void hl_upsample_forward(real* inputData, + real* maskData, + size_t batchSize, + size_t imgSizeH, + size_t imgSizeW, + size_t channels, + size_t outputH, + size_t outputW, + real* outputData) { + int num_kernels = batchSize * imgSizeH * imgSizeW * channels; + int blocks = (num_kernels + 1024 - 1) / 1024; + upsampleForwardCompute<<>>(inputData, + maskData, + num_kernels, + imgSizeH, + imgSizeW, + outputH, + outputW, + outputData); + CHECK_SYNC("hl_upsample_forward failed"); +} + +void hl_upsample_backward(real* outputGradData, + real* maskData, + size_t batchSize, + size_t imgSizeH, + size_t imgSizeW, + size_t channels, + size_t outputH, + size_t outputW, + real* inputGradData) { + int num_kernels = batchSize * imgSizeH * imgSizeW * channels; + int blocks = (num_kernels + 1024 - 1) / 1024; + upsampleBackwardCompute<<>>(outputGradData, + maskData, + num_kernels, + imgSizeH, + imgSizeW, + outputH, + outputW, + inputGradData); + CHECK_SYNC("hl_upsample_backward failed"); +} diff --git a/paddle/gserver/layers/UpsampleLayer.cpp b/paddle/gserver/layers/UpsampleLayer.cpp new file mode 100644 index 0000000000..300bb82d68 --- /dev/null +++ b/paddle/gserver/layers/UpsampleLayer.cpp @@ -0,0 +1,107 @@ +/* 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 "UpsampleLayer.h" +#include "iostream" + +namespace paddle { + +REGISTER_LAYER(upsample, UpsampleLayer); + +size_t UpsampleLayer::getOutputSize() { + if (upsampleSize_ == 0) { + upsampleSize_ = imgSize_ * scale_ - static_cast(padOutX_); + upsampleSizeY_ = imgSizeY_ * scaleY_ - static_cast(padOutY_); + } + return upsampleSize_ * upsampleSizeY_ * channels_; +} + +bool UpsampleLayer::init(const LayerMap& layerMap, + const ParameterMap& parameterMap) { + Layer::init(layerMap, parameterMap); + CHECK_EQ(inputLayers_.size(), 2U); + CHECK_EQ(config_.inputs_size(), 2); + const auto& conf = config_.inputs(0).upsample_conf(); + const auto& img_conf = conf.image_conf(); + + imgSizeY_ = + img_conf.has_img_size_y() ? img_conf.img_size_y() : img_conf.img_size(); + imgSize_ = img_conf.img_size(); + channels_ = img_conf.channels(); + + CHECK((conf.has_upsample_size()) || (conf.has_scale())) + << "scale or upsample_size is required."; + + if (conf.has_upsample_size()) { + upsampleSize_ = conf.upsample_size(); + upsampleSizeY_ = upsampleSize_; + if (conf.has_upsample_size_y()) { + upsampleSizeY_ = conf.upsample_size_y(); + } + } else { + if (!conf.has_scale_y()) { + scale_ = scaleY_ = conf.scale_y(); + CHECK_GT(static_cast(scale_), 1); + } else { + scale_ = conf.scale(); + scaleY_ = conf.scale_y(); + } + padOutX_ = conf.pad_out_x(); + padOutY_ = conf.pad_out_y(); + CHECK(!padOutX_ || scale_ == 2) + << "Output height padding compensation requires scale_ == 2"; + CHECK(!padOutY_ || scaleY_ == 2) + << "Output width padding compensation requires scaleY_ == 2"; + upsampleSize_ = upsampleSizeY_ = 0; + } + return true; +} + +void UpsampleLayer::forward(PassType passType) { + Layer::forward(passType); + + MatrixPtr input = getInputValue(0); + MatrixPtr mask = inputLayers_[1]->getOutput("mask").value; + + size_t batchSize = input->getHeight(); + size_t outSize = getOutputSize(); + + CHECK_EQ(input->getWidth(), mask->getWidth()); + CHECK_EQ(mask->getHeight(), batchSize); + resetOutput(batchSize, outSize); + + MatrixPtr output = getOutputValue(); + output->upsampleForward(*input, + *mask, + imgSize_, + imgSizeY_, + channels_, + upsampleSize_, + upsampleSizeY_); +} + +void UpsampleLayer::backward(const UpdateCallback& callback) { + MatrixPtr mask = inputLayers_[1]->getOutput("mask").value; + MatrixPtr inputGrad = getInputGrad(0); + MatrixPtr outputGrad = getOutputGrad(); + inputGrad->upsampleBackward(*outputGrad, + *mask, + imgSize_, + imgSizeY_, + channels_, + upsampleSize_, + upsampleSizeY_); +} + +} // namespace paddle diff --git a/paddle/gserver/layers/UpsampleLayer.h b/paddle/gserver/layers/UpsampleLayer.h new file mode 100644 index 0000000000..2ae9363439 --- /dev/null +++ b/paddle/gserver/layers/UpsampleLayer.h @@ -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. */ + +#pragma once + +#include +#include "Layer.h" +#include "paddle/math/Matrix.h" +#include "paddle/utils/Logging.h" +#include "paddle/utils/Stat.h" + +namespace paddle { + +/** + * This layer transpose the pooling process. + * It takes two input, the first input is the input data, and + * the second is the mask data from the max-pool-with-mask layer. + * + */ + +class UpsampleLayer : public Layer { +public: + explicit UpsampleLayer(const LayerConfig& config) : Layer(config) {} + + ~UpsampleLayer() {} + + bool init(const LayerMap& layerMap, + const ParameterMap& parameterMap) override; + + void forward(PassType passType) override; + void backward(const UpdateCallback& callback) override; + + size_t getOutputSize(); + +protected: + size_t scale_, scaleY_; + size_t upsampleSize_, upsampleSizeY_; + size_t padOutX_, padOutY_; + size_t imgSize_, imgSizeY_; + size_t channels_; +}; + +} // namespace paddle diff --git a/paddle/math/Matrix.cpp b/paddle/math/Matrix.cpp index 88e9180690..1f6458a288 100644 --- a/paddle/math/Matrix.cpp +++ b/paddle/math/Matrix.cpp @@ -1023,6 +1023,64 @@ void GpuMatrix::check(std::ostream& os, Matrix& refMat, bool printDiff) { LOG(INFO) << "the diffCnt is " << diffCnt; } +void GpuMatrix::upsampleForward(Matrix& input, + Matrix& mask, + size_t imgSizeH, + size_t imgSizeW, + size_t channels, + size_t outputH, + size_t outputW) { + CHECK(input.useGpu_ == true) << "Matrix type are not equal"; + CHECK(mask.useGpu_ == true) << "Matrix type are not equal"; + + real *inputData = input.getData(); + real *maskData = mask.getData(); + real *outData = data_; + + size_t batch = input.getHeight(); + + CHECK(imgSizeH * imgSizeW * channels == input.getWidth()); + CHECK(imgSizeH * imgSizeW * channels == mask.getWidth()); + CHECK_EQ(batch, this->getHeight()); + CHECK(width_ == outputH * outputW * channels); + hl_upsample_forward(inputData, maskData, + batch, + imgSizeH, + imgSizeW, + channels, + outputH, + outputW, + outData); +} + +void GpuMatrix::upsampleBackward(Matrix& outputGrad, + Matrix& mask, + size_t imgSizeH, + size_t imgSizeW, + size_t channels, + size_t outputH, + size_t outputW) { + CHECK(outputGrad.useGpu_ == true) << "Matrix type are not equal"; + CHECK(mask.useGpu_ == true) << "Matrix type are not equal"; + + real *outputGradData = outputGrad.getData(); + real *maskData = mask.getData(); + real *inputGradData = data_; + size_t batch = outputGrad.getHeight(); + + CHECK(imgSizeH * imgSizeW == this->getWidth()/channels); + CHECK_EQ(batch, this->getHeight()); + CHECK_EQ(channels * outputH * outputW, outputGrad.getWidth()); + hl_upsample_backward(outputGradData, maskData, + batch, + imgSizeH, + imgSizeW, + channels, + outputH, + outputW, + inputGradData); +} + void GpuMatrix::maxPoolForward(Matrix& inputMat, size_t imgSizeH, size_t imgSizeW, @@ -1981,6 +2039,74 @@ void CpuMatrix::inverse(MatrixPtr& matInv, bool memAlloc) { CHECK_EQ(info, 0); } +void CpuMatrix::upsampleForward(Matrix& input, + Matrix& mask, + size_t imgSizeH, + size_t imgSizeW, + size_t channels, + size_t outputH, + size_t outputW) { + real *inputData = input.getData(); + real *maskData = mask.getData(); + real *outData = data_; + size_t inLength = imgSizeH * imgSizeW; + size_t outLength = outputH * outputW; + size_t batch = input.getHeight(); + CHECK(inLength == input.getWidth() / channels); + CHECK_EQ(batch, this->getHeight()); + CHECK_EQ(channels * outLength, this->getWidth()); + + for (size_t k = 0; k < batch; k++) { + for (size_t c = 0; c < channels; c++) { + for (size_t i = 0; i < inLength; i++) { + size_t out_index = static_cast(maskData[i]); + if (out_index >= outLength) { + LOG(FATAL) << "upsample index " << out_index + << " out of range."; + } + outData[out_index] = inputData[i]; + } + inputData += inLength; + maskData += inLength; + outData += outLength; + } + } +} + +void CpuMatrix::upsampleBackward(Matrix& outputGrad, + Matrix& mask, + size_t imgSizeH, + size_t imgSizeW, + size_t channels, + size_t outputH, + size_t outputW) { + real *outputGradData = outputGrad.getData(); + real *maskData = mask.getData(); + real *inputGradData = data_; + size_t inLength = imgSizeH * imgSizeW; + size_t outLength = outputH * outputW; + size_t batch = outputGrad.getHeight(); + CHECK(inLength == this->getWidth()/channels); + CHECK_EQ(batch, this->getHeight()); + CHECK_EQ(channels * outLength, outputGrad.getWidth()); + + for (size_t k = 0; k < batch; k++) { + for (size_t c = 0; c < channels; c++) { + for (size_t i = 0; i < inLength; i++) { + size_t out_index = static_cast(maskData[i]); + if (out_index >= outLength) { + LOG(FATAL) << "upsample index " << out_index + << " out of range."; + } + inputGradData[i] = outputGradData[out_index]; + } + inputGradData += inLength; + maskData += inLength; + outputGradData += outLength; + } + } +} + void CpuMatrix::maxPoolForward(Matrix& inputMat, size_t imgSizeH, size_t imgSizeW, diff --git a/paddle/math/Matrix.h b/paddle/math/Matrix.h index e273f11236..b4fcf05cb2 100644 --- a/paddle/math/Matrix.h +++ b/paddle/math/Matrix.h @@ -859,6 +859,26 @@ public: LOG(FATAL) << "Not implemented"; } + virtual void upsampleForward(Matrix& input, + Matrix& mask, + size_t imgSizeH, + size_t imgSizeW, + size_t channels, + size_t outputH, + size_t outputW) { + LOG(FATAL) << "Not implemeted"; + } + + virtual void upsampleBackward(Matrix& outputGrad, + Matrix& mask, + size_t imgSizeH, + size_t imgSizeW, + size_t channels, + size_t outputH, + size_t outputW) { + LOG(FATAL) << "Not implemeted"; + } + /** * Pooling forward operation, pick out the largest element * in the sizeX of value, if the maskMatP is not NULL, it will @@ -1417,6 +1437,22 @@ public: void classificationError(Matrix& output, IVector& label, size_t topkSize = 1); + void upsampleForward(Matrix& input, + Matrix& mask, + size_t imgSizeH, + size_t imgSizeW, + size_t channels, + size_t outputH, + size_t outputW); + + void upsampleBackward(Matrix& outputGrad, + Matrix& mask, + size_t imgSizeH, + size_t imgSizeW, + size_t channels, + size_t outputH, + size_t outputW); + void maxPoolForward(Matrix& inputMat, size_t imgSizeH, size_t imgSizeW, @@ -1689,6 +1725,22 @@ public: MatrixPtr clone(size_t height, size_t width, bool useGpu = false); + void upsampleForward(Matrix& input, + Matrix& mask, + size_t imgSizeH, + size_t imgSizeW, + size_t channels, + size_t outputH, + size_t outputW); + + void upsampleBackward(Matrix& outputGrad, + Matrix& mask, + size_t imgSizeH, + size_t imgSizeW, + size_t channels, + size_t outputH, + size_t outputW); + void maxPoolForward(Matrix& inputMat, size_t imgSizeH, size_t imgSizeW, diff --git a/proto/ModelConfig.proto b/proto/ModelConfig.proto index 2c2cc62459..2cff25d095 100644 --- a/proto/ModelConfig.proto +++ b/proto/ModelConfig.proto @@ -321,6 +321,16 @@ message ClipConfig { required double max = 2; } +message UpsampleConfig { + required ImageConfig image_conf = 1; + optional uint32 scale = 2 [ default = 2 ]; + optional uint32 scale_y = 3 [ default = 2 ]; + optional bool pad_out_x = 4 [ default = false ]; + optional bool pad_out_y = 5 [ default = false ]; + optional uint32 upsample_size = 6; + optional uint32 upsample_size_y = 7; +} + message ROIPoolConfig { required uint32 pooled_width = 1; required uint32 pooled_height = 2; @@ -357,6 +367,7 @@ message LayerInputConfig { optional ClipConfig clip_conf = 18; optional ScaleSubRegionConfig scale_sub_region_conf = 19; optional ROIPoolConfig roi_pool_conf = 20; + optional UpsampleConfig upsample_conf = 21; } message LayerConfig { diff --git a/python/paddle/trainer/config_parser.py b/python/paddle/trainer/config_parser.py index 5bd68e211a..067ca21d32 100644 --- a/python/paddle/trainer/config_parser.py +++ b/python/paddle/trainer/config_parser.py @@ -466,6 +466,7 @@ class Input(Cfg): maxout=None, spp=None, pad=None, + upsample=None, format=None, nnz=None, is_static=None, @@ -977,6 +978,11 @@ class Pad(Cfg): def __init__(self, channels, pad_c, pad_h, pad_w): self.add_keys(locals()) +@config_class +class Upsample(Cfg): + def __init__(self, scale, scale_y, pad_out_x, pad_out_y, upsample_size, + upsample_size_y): + self.add_keys(locals()) @config_class class Norm(Cfg): @@ -2387,6 +2393,44 @@ class SpatialPyramidPoolLayer(LayerBase): output_x = (pow(4, spp_conf.pyramid_height) - 1) / (4 - 1) self.set_cnn_layer(name, 1, output_x, spp_conf.image_conf.channels) +@config_layer('upsample') +class UpsampleLayer(LayerBase): + def __init__(self, name, inputs, **xargs): + super(UpsampleLayer, self).__init__( + name, 'upsample', 0, inputs=inputs, **xargs) + + input_layer = self.get_input_layer(0) + image_conf = self.config.inputs[0].upsample_conf.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) + + upsample = self.inputs[0].upsample + output_x = 0 + output_y = 0 + output_size = 0 + if upsample.scale: + self.config.inputs[0].upsample_conf.scale = upsample.scale + self.config.inputs[0].upsample_conf.scale_y = upsample.scale_y + output_x = input_layer.width * upsample.scale + output_y = input_layer.height * upsample.scale_y + self.config.inputs[0].upsample_conf.pad_out_x = upsample.pad_out_x + self.config.inputs[0].upsample_conf.pad_out_y = upsample.pad_out_y + if upsample.upsample_size: + self.config.inputs[ + 0].upsample_conf.upsample_size = upsample.upsample_size + self.config.inputs[ + 0].upsample_conf.upsample_size_y = upsample.upsample_size_y + output_x = upsample.upsample_size + output_y = upsample.upsample_size_y + + output_size = image_conf.channels * output_x * output_y + + + self.set_layer_height_width(output_y, output_x) + self.set_layer_depth(input_layer.depth) + self.set_layer_size(output_size) @config_layer('pad') class PadLayer(LayerBase): diff --git a/python/paddle/trainer_config_helpers/layers.py b/python/paddle/trainer_config_helpers/layers.py index 5de1c18950..95369000bb 100644 --- a/python/paddle/trainer_config_helpers/layers.py +++ b/python/paddle/trainer_config_helpers/layers.py @@ -146,6 +146,7 @@ __all__ = [ 'resize_layer', 'sub_seq_layer', 'scale_sub_region_layer', + 'upsample_layer', ] @@ -163,6 +164,7 @@ class LayerType(object): SEQUENCE_RESHAPE = 'seqreshape' POOLING_MAX = 'max' POOLING_AVG = 'average' + UPSAMPLE_LAYER = 'upsample' FC_LAYER = 'fc' COST = 'cost' COSINE_SIM_VEC = 'cos_vm' @@ -2879,6 +2881,81 @@ def img_pool3d_layer(input, num_filters=num_channels, size=l.config.size) +@wrap_name_default("upsample") +@layer_support() +def upsample_layer(input, + name=None, + scale=None, + scale_y=None, + upsample_size=None, + upsample_size_y=None, + pad_out_x=False, + pad_out_y=False, + layer_attr=None): + """ + The DePooling process. + Inputs should be a list of length 2. The first input is a layer, + and the second input should be the MaxWithMaskPoolingLayer + + The example usage is: + + .. code-block:: python + pool1 = paddle.v2.layer.img_pool(input=input, pool_size=2, stride=2, + pool_type=paddle.pooling.MaxWithMask()) + upsample = paddle.v2.layer.upsample(input=[layer1, pool1]) + + :param name: The name of this layer. It is optional. + :type name: basestring + :param input: contains an input layer and a MaxWithMaskPoolingLayer + :type input: list | tuple | collections.Sequence + :param scale: outputSize = scale * inputSize + :type scale: int | list | tuple | . + :param scale_y: scale_y will be equal to scale, if it's value is None, + :type scale: int | None. + :param upsample_size: specify the outputSize. + :type upsample_size: int | list | tuple. + :param upsample_size_y: specify the y dimension outputSize. + :type upsample_size_y: int. + :param pad_out_x: specify exact x dimension size. This parameter only works when scale is 2 + :type pad_out_x: bool. + :param pad_out_y: specify exact y dimension size. This parameter only works when scale is 2 + :type pad_out_y: bool. + :param layer_attr: Extra Layer Attribute. + :type layer_attr: ExtraLayerAttribute + :return: LayerOutput object. + :rtype: LayerOutput + """ + + assert (scale is not None) or (upsample_size is not None), \ + 'scale or upsample_size, there must be one to be designated' + + assert len(input) == 2, 'layer input size must be 2' + assert input[1].layer_type == LayerType.POOL_LAYER, \ + 'the second input should be the MaxPoolWithMaskLayer' + + scale_y = scale \ + if scale is not None else scale_y + upsample_size_y = upsample_size \ + if upsample_size is not None else upsample_size_y + + layer_type = LayerType.UPSAMPLE_LAYER + + layer = Layer( + name=name, + type=layer_type, + inputs=[ + Input( + input[0].name, + upsample=Upsample(scale, scale_y, pad_out_x, pad_out_y, + upsample_size, upsample_size_y)), + Input(input[1].name) + ], + **ExtraLayerAttribute.to_kwargs(layer_attr)) + + sz = layer.config.size + + return LayerOutput(name, layer_type=layer_type, parents=input, size=sz) + @wrap_name_default("spp") @layer_support() From 6da00da7b5b2449d6668a84728708e43ec030433 Mon Sep 17 00:00:00 2001 From: xzl Date: Mon, 20 Nov 2017 11:58:42 +0800 Subject: [PATCH 02/58] code format check --- paddle/cuda/include/hl_cnn.h | 34 +-- paddle/cuda/include/stub/hl_cnn_stub.h | 36 +-- paddle/gserver/layers/UpsampleLayer.cpp | 1 + paddle/gserver/layers/UpsampleLayer.h | 1 - paddle/math/Matrix.cpp | 220 +++++++++--------- paddle/math/Matrix.h | 72 +++--- python/paddle/trainer/config_parser.py | 8 +- .../paddle/trainer_config_helpers/layers.py | 2 + 8 files changed, 192 insertions(+), 182 deletions(-) diff --git a/paddle/cuda/include/hl_cnn.h b/paddle/cuda/include/hl_cnn.h index c8dd3eb91e..8d0822471b 100644 --- a/paddle/cuda/include/hl_cnn.h +++ b/paddle/cuda/include/hl_cnn.h @@ -378,14 +378,15 @@ extern void hl_maxout_backward(real* inGrad, * @param[in] outputW the output widht. * @param[out] outputData output data. */ -extern void hl_upsample_forward(real *inputData, real *maskData, - size_t batchSize, - size_t imgSizeH, - size_t imgSizeW, - size_t channels, - size_t outputH, - size_t outputW, - real *outputData); +extern void hl_upsample_forward(real* inputData, + real* maskData, + size_t batchSize, + size_t imgSizeH, + size_t imgSizeW, + size_t channels, + size_t outputH, + size_t outputW, + real* outputData); /** * @brief Upsample backward. @@ -399,13 +400,14 @@ extern void hl_upsample_forward(real *inputData, real *maskData, * @param[in] outputW the output widht. * @param[out] inputGradData the input grad data. */ -extern void hl_upsample_backward(real *outputGradData, real *maskData, - size_t batchSize, - size_t imgSizeH, - size_t imgSizeW, - size_t channels, - size_t outputH, - size_t outputW, - real *inputGradData); +extern void hl_upsample_backward(real* outputGradData, + real* maskData, + size_t batchSize, + size_t imgSizeH, + size_t imgSizeW, + size_t channels, + size_t outputH, + size_t outputW, + real* inputGradData); #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 ef1f67980e..e83db71bb7 100644 --- a/paddle/cuda/include/stub/hl_cnn_stub.h +++ b/paddle/cuda/include/stub/hl_cnn_stub.h @@ -222,22 +222,24 @@ inline void hl_maxout_backward(real* inGrad, size_t featLen, size_t group) {} -inline void hl_upsample_forward(real *inputData, real *maskData, - size_t batchSize, - size_t imgSizeH, - size_t imgSizeW, - size_t channels, - size_t outputH, - size_t outputW, - real *outputData) {} - -inline void hl_upsample_backward(real *outputGradData, real *maskData, - size_t batchSize, - size_t imgSizeH, - size_t imgSizeW, - size_t channels, - size_t outputH, - size_t outputW, - real *inputGradData) {} +inline void hl_upsample_forward(real* inputData, + real* maskData, + size_t batchSize, + size_t imgSizeH, + size_t imgSizeW, + size_t channels, + size_t outputH, + size_t outputW, + real* outputData) {} + +inline void hl_upsample_backward(real* outputGradData, + real* maskData, + size_t batchSize, + size_t imgSizeH, + size_t imgSizeW, + size_t channels, + size_t outputH, + size_t outputW, + real* inputGradData) {} #endif // HL_CNN_STUB_H_ diff --git a/paddle/gserver/layers/UpsampleLayer.cpp b/paddle/gserver/layers/UpsampleLayer.cpp index 300bb82d68..3ff5332e64 100644 --- a/paddle/gserver/layers/UpsampleLayer.cpp +++ b/paddle/gserver/layers/UpsampleLayer.cpp @@ -30,6 +30,7 @@ size_t UpsampleLayer::getOutputSize() { bool UpsampleLayer::init(const LayerMap& layerMap, const ParameterMap& parameterMap) { Layer::init(layerMap, parameterMap); + CHECK_EQ(inputLayers_.size(), 2U); CHECK_EQ(config_.inputs_size(), 2); const auto& conf = config_.inputs(0).upsample_conf(); diff --git a/paddle/gserver/layers/UpsampleLayer.h b/paddle/gserver/layers/UpsampleLayer.h index 2ae9363439..25efbac5e9 100644 --- a/paddle/gserver/layers/UpsampleLayer.h +++ b/paddle/gserver/layers/UpsampleLayer.h @@ -32,7 +32,6 @@ namespace paddle { class UpsampleLayer : public Layer { public: explicit UpsampleLayer(const LayerConfig& config) : Layer(config) {} - ~UpsampleLayer() {} bool init(const LayerMap& layerMap, diff --git a/paddle/math/Matrix.cpp b/paddle/math/Matrix.cpp index 1f6458a288..ad9a73a2bf 100644 --- a/paddle/math/Matrix.cpp +++ b/paddle/math/Matrix.cpp @@ -1024,61 +1024,63 @@ void GpuMatrix::check(std::ostream& os, Matrix& refMat, bool printDiff) { } void GpuMatrix::upsampleForward(Matrix& input, - Matrix& mask, - size_t imgSizeH, - size_t imgSizeW, - size_t channels, - size_t outputH, - size_t outputW) { - CHECK(input.useGpu_ == true) << "Matrix type are not equal"; - CHECK(mask.useGpu_ == true) << "Matrix type are not equal"; - - real *inputData = input.getData(); - real *maskData = mask.getData(); - real *outData = data_; - - size_t batch = input.getHeight(); - - CHECK(imgSizeH * imgSizeW * channels == input.getWidth()); - CHECK(imgSizeH * imgSizeW * channels == mask.getWidth()); - CHECK_EQ(batch, this->getHeight()); - CHECK(width_ == outputH * outputW * channels); - hl_upsample_forward(inputData, maskData, - batch, - imgSizeH, - imgSizeW, - channels, - outputH, - outputW, - outData); + Matrix& mask, + size_t imgSizeH, + size_t imgSizeW, + size_t channels, + size_t outputH, + size_t outputW) { + CHECK(input.useGpu_ == true) << "Matrix type are not equal"; + CHECK(mask.useGpu_ == true) << "Matrix type are not equal"; + + real* inputData = input.getData(); + real* maskData = mask.getData(); + real* outData = data_; + + size_t batch = input.getHeight(); + + CHECK(imgSizeH * imgSizeW * channels == input.getWidth()); + CHECK(imgSizeH * imgSizeW * channels == mask.getWidth()); + CHECK_EQ(batch, this->getHeight()); + CHECK(width_ == outputH * outputW * channels); + hl_upsample_forward(inputData, + maskData, + batch, + imgSizeH, + imgSizeW, + channels, + outputH, + outputW, + outData); } void GpuMatrix::upsampleBackward(Matrix& outputGrad, - Matrix& mask, - size_t imgSizeH, - size_t imgSizeW, - size_t channels, - size_t outputH, - size_t outputW) { - CHECK(outputGrad.useGpu_ == true) << "Matrix type are not equal"; - CHECK(mask.useGpu_ == true) << "Matrix type are not equal"; - - real *outputGradData = outputGrad.getData(); - real *maskData = mask.getData(); - real *inputGradData = data_; - size_t batch = outputGrad.getHeight(); - - CHECK(imgSizeH * imgSizeW == this->getWidth()/channels); - CHECK_EQ(batch, this->getHeight()); - CHECK_EQ(channels * outputH * outputW, outputGrad.getWidth()); - hl_upsample_backward(outputGradData, maskData, - batch, - imgSizeH, - imgSizeW, - channels, - outputH, - outputW, - inputGradData); + Matrix& mask, + size_t imgSizeH, + size_t imgSizeW, + size_t channels, + size_t outputH, + size_t outputW) { + CHECK(outputGrad.useGpu_ == true) << "Matrix type are not equal"; + CHECK(mask.useGpu_ == true) << "Matrix type are not equal"; + + real* outputGradData = outputGrad.getData(); + real* maskData = mask.getData(); + real* inputGradData = data_; + size_t batch = outputGrad.getHeight(); + + CHECK(imgSizeH * imgSizeW == this->getWidth() / channels); + CHECK_EQ(batch, this->getHeight()); + CHECK_EQ(channels * outputH * outputW, outputGrad.getWidth()); + hl_upsample_backward(outputGradData, + maskData, + batch, + imgSizeH, + imgSizeW, + channels, + outputH, + outputW, + inputGradData); } void GpuMatrix::maxPoolForward(Matrix& inputMat, @@ -2040,71 +2042,69 @@ void CpuMatrix::inverse(MatrixPtr& matInv, bool memAlloc) { } void CpuMatrix::upsampleForward(Matrix& input, - Matrix& mask, - size_t imgSizeH, - size_t imgSizeW, - size_t channels, - size_t outputH, - size_t outputW) { - real *inputData = input.getData(); - real *maskData = mask.getData(); - real *outData = data_; - size_t inLength = imgSizeH * imgSizeW; - size_t outLength = outputH * outputW; - size_t batch = input.getHeight(); - CHECK(inLength == input.getWidth() / channels); - CHECK_EQ(batch, this->getHeight()); - CHECK_EQ(channels * outLength, this->getWidth()); - - for (size_t k = 0; k < batch; k++) { - for (size_t c = 0; c < channels; c++) { - for (size_t i = 0; i < inLength; i++) { - size_t out_index = static_cast(maskData[i]); - if (out_index >= outLength) { - LOG(FATAL) << "upsample index " << out_index - << " out of range."; - } - outData[out_index] = inputData[i]; - } - inputData += inLength; - maskData += inLength; - outData += outLength; + Matrix& mask, + size_t imgSizeH, + size_t imgSizeW, + size_t channels, + size_t outputH, + size_t outputW) { + real* inputData = input.getData(); + real* maskData = mask.getData(); + real* outData = data_; + size_t inLength = imgSizeH * imgSizeW; + size_t outLength = outputH * outputW; + size_t batch = input.getHeight(); + CHECK(inLength == input.getWidth() / channels); + CHECK_EQ(batch, this->getHeight()); + CHECK_EQ(channels * outLength, this->getWidth()); + + for (size_t k = 0; k < batch; k++) { + for (size_t c = 0; c < channels; c++) { + for (size_t i = 0; i < inLength; i++) { + size_t out_index = static_cast(maskData[i]); + if (out_index >= outLength) { + LOG(FATAL) << "upsample index " << out_index << " out of range."; } + outData[out_index] = inputData[i]; + } + inputData += inLength; + maskData += inLength; + outData += outLength; } + } } void CpuMatrix::upsampleBackward(Matrix& outputGrad, - Matrix& mask, - size_t imgSizeH, - size_t imgSizeW, - size_t channels, - size_t outputH, - size_t outputW) { - real *outputGradData = outputGrad.getData(); - real *maskData = mask.getData(); - real *inputGradData = data_; - size_t inLength = imgSizeH * imgSizeW; - size_t outLength = outputH * outputW; - size_t batch = outputGrad.getHeight(); - CHECK(inLength == this->getWidth()/channels); - CHECK_EQ(batch, this->getHeight()); - CHECK_EQ(channels * outLength, outputGrad.getWidth()); - - for (size_t k = 0; k < batch; k++) { - for (size_t c = 0; c < channels; c++) { - for (size_t i = 0; i < inLength; i++) { - size_t out_index = static_cast(maskData[i]); - if (out_index >= outLength) { - LOG(FATAL) << "upsample index " << out_index - << " out of range."; - } - inputGradData[i] = outputGradData[out_index]; - } - inputGradData += inLength; - maskData += inLength; - outputGradData += outLength; + Matrix& mask, + size_t imgSizeH, + size_t imgSizeW, + size_t channels, + size_t outputH, + size_t outputW) { + real* outputGradData = outputGrad.getData(); + real* maskData = mask.getData(); + real* inputGradData = data_; + size_t inLength = imgSizeH * imgSizeW; + size_t outLength = outputH * outputW; + size_t batch = outputGrad.getHeight(); + CHECK(inLength == this->getWidth() / channels); + CHECK_EQ(batch, this->getHeight()); + CHECK_EQ(channels * outLength, outputGrad.getWidth()); + + for (size_t k = 0; k < batch; k++) { + for (size_t c = 0; c < channels; c++) { + for (size_t i = 0; i < inLength; i++) { + size_t out_index = static_cast(maskData[i]); + if (out_index >= outLength) { + LOG(FATAL) << "upsample index " << out_index << " out of range."; } + inputGradData[i] = outputGradData[out_index]; + } + inputGradData += inLength; + maskData += inLength; + outputGradData += outLength; } + } } void CpuMatrix::maxPoolForward(Matrix& inputMat, diff --git a/paddle/math/Matrix.h b/paddle/math/Matrix.h index b4fcf05cb2..6e9ea04d66 100644 --- a/paddle/math/Matrix.h +++ b/paddle/math/Matrix.h @@ -860,22 +860,22 @@ public: } virtual void upsampleForward(Matrix& input, - Matrix& mask, - size_t imgSizeH, - size_t imgSizeW, - size_t channels, - size_t outputH, - size_t outputW) { + Matrix& mask, + size_t imgSizeH, + size_t imgSizeW, + size_t channels, + size_t outputH, + size_t outputW) { LOG(FATAL) << "Not implemeted"; } virtual void upsampleBackward(Matrix& outputGrad, - Matrix& mask, - size_t imgSizeH, - size_t imgSizeW, - size_t channels, - size_t outputH, - size_t outputW) { + Matrix& mask, + size_t imgSizeH, + size_t imgSizeW, + size_t channels, + size_t outputH, + size_t outputW) { LOG(FATAL) << "Not implemeted"; } @@ -1438,20 +1438,20 @@ public: void classificationError(Matrix& output, IVector& label, size_t topkSize = 1); void upsampleForward(Matrix& input, - Matrix& mask, - size_t imgSizeH, - size_t imgSizeW, - size_t channels, - size_t outputH, - size_t outputW); + Matrix& mask, + size_t imgSizeH, + size_t imgSizeW, + size_t channels, + size_t outputH, + size_t outputW); void upsampleBackward(Matrix& outputGrad, - Matrix& mask, - size_t imgSizeH, - size_t imgSizeW, - size_t channels, - size_t outputH, - size_t outputW); + Matrix& mask, + size_t imgSizeH, + size_t imgSizeW, + size_t channels, + size_t outputH, + size_t outputW); void maxPoolForward(Matrix& inputMat, size_t imgSizeH, @@ -1726,20 +1726,20 @@ public: MatrixPtr clone(size_t height, size_t width, bool useGpu = false); void upsampleForward(Matrix& input, - Matrix& mask, - size_t imgSizeH, - size_t imgSizeW, - size_t channels, - size_t outputH, - size_t outputW); + Matrix& mask, + size_t imgSizeH, + size_t imgSizeW, + size_t channels, + size_t outputH, + size_t outputW); void upsampleBackward(Matrix& outputGrad, - Matrix& mask, - size_t imgSizeH, - size_t imgSizeW, - size_t channels, - size_t outputH, - size_t outputW); + Matrix& mask, + size_t imgSizeH, + size_t imgSizeW, + size_t channels, + size_t outputH, + size_t outputW); void maxPoolForward(Matrix& inputMat, size_t imgSizeH, diff --git a/python/paddle/trainer/config_parser.py b/python/paddle/trainer/config_parser.py index 067ca21d32..7563368ad7 100644 --- a/python/paddle/trainer/config_parser.py +++ b/python/paddle/trainer/config_parser.py @@ -978,12 +978,14 @@ class Pad(Cfg): def __init__(self, channels, pad_c, pad_h, pad_w): self.add_keys(locals()) + @config_class class Upsample(Cfg): def __init__(self, scale, scale_y, pad_out_x, pad_out_y, upsample_size, upsample_size_y): self.add_keys(locals()) + @config_class class Norm(Cfg): def __init__(self, @@ -2393,6 +2395,7 @@ class SpatialPyramidPoolLayer(LayerBase): output_x = (pow(4, spp_conf.pyramid_height) - 1) / (4 - 1) self.set_cnn_layer(name, 1, output_x, spp_conf.image_conf.channels) + @config_layer('upsample') class UpsampleLayer(LayerBase): def __init__(self, name, inputs, **xargs): @@ -2407,9 +2410,10 @@ class UpsampleLayer(LayerBase): input_layer.height) upsample = self.inputs[0].upsample - output_x = 0 + output_x = 0 output_y = 0 output_size = 0 + if upsample.scale: self.config.inputs[0].upsample_conf.scale = upsample.scale self.config.inputs[0].upsample_conf.scale_y = upsample.scale_y @@ -2427,11 +2431,11 @@ class UpsampleLayer(LayerBase): output_size = image_conf.channels * output_x * output_y - self.set_layer_height_width(output_y, output_x) self.set_layer_depth(input_layer.depth) self.set_layer_size(output_size) + @config_layer('pad') class PadLayer(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 95369000bb..1ce603389d 100644 --- a/python/paddle/trainer_config_helpers/layers.py +++ b/python/paddle/trainer_config_helpers/layers.py @@ -2881,6 +2881,7 @@ def img_pool3d_layer(input, num_filters=num_channels, size=l.config.size) + @wrap_name_default("upsample") @layer_support() def upsample_layer(input, @@ -2930,6 +2931,7 @@ def upsample_layer(input, 'scale or upsample_size, there must be one to be designated' assert len(input) == 2, 'layer input size must be 2' + assert input[1].layer_type == LayerType.POOL_LAYER, \ 'the second input should be the MaxPoolWithMaskLayer' From 76941d90b1c38b121d711a6e4455f73dfba8f14f Mon Sep 17 00:00:00 2001 From: xzl Date: Wed, 13 Dec 2017 16:31:52 +0800 Subject: [PATCH 03/58] add upsample cpu&gpu forward&backward compare test --- paddle/gserver/tests/CMakeLists.txt | 1 + paddle/gserver/tests/test_Upsample.cpp | 152 +++++++++++++++++++++++++ 2 files changed, 153 insertions(+) create mode 100644 paddle/gserver/tests/test_Upsample.cpp diff --git a/paddle/gserver/tests/CMakeLists.txt b/paddle/gserver/tests/CMakeLists.txt index c295ea19c9..5ef2726764 100644 --- a/paddle/gserver/tests/CMakeLists.txt +++ b/paddle/gserver/tests/CMakeLists.txt @@ -28,6 +28,7 @@ gserver_test(test_BatchNorm) gserver_test(test_KmaxSeqScore) gserver_test(test_Expand) gserver_test(test_MaxPoolingWithMaskOutput) +gserver_test(test_Upsample) ########## test_MKLDNN layers and activations ########## if(WITH_MKLDNN) diff --git a/paddle/gserver/tests/test_Upsample.cpp b/paddle/gserver/tests/test_Upsample.cpp new file mode 100644 index 0000000000..9d6fa1d130 --- /dev/null +++ b/paddle/gserver/tests/test_Upsample.cpp @@ -0,0 +1,152 @@ +/* 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 + +#include "LayerGradUtil.h" +#include "paddle/math/MathUtils.h" +#include "paddle/testing/TestUtil.h" + +using namespace paddle; + +void setPoolConfig(TestConfig* config, + PoolConfig* pool, + const string& poolType) { + (*config).biasSize = 0; + (*config).layerConfig.set_type("pool"); + (*config).layerConfig.set_num_filters(1); + + int kw = 2, kh = 2; + int pw = 0, ph = 0; + int sw = 2, sh = 2; + pool->set_pool_type(poolType); + pool->set_channels(2); + pool->set_size_x(kw); + pool->set_size_y(kh); + pool->set_start(0); + pool->set_padding(pw); + pool->set_padding_y(ph); + pool->set_stride(sw); + pool->set_stride_y(sh); + + int ow = outputSize(pool->img_size(), kw, pw, sw, /* caffeMode */ false); + int oh = outputSize(pool->img_size_y(), kh, ph, sh, /* caffeMode */ false); + pool->set_output_x(ow); + pool->set_output_y(oh); +} + +LayerPtr doOneUpsampleTest(MatrixPtr& inputMat, + const string& poolType, + bool use_gpu, + real* tempGradData) { + /* prepare maxPoolWithMaskLayer */ + TestConfig config; + config.inputDefs.push_back({INPUT_DATA, "layer_0", 128, 0}); + LayerInputConfig* input = config.layerConfig.add_inputs(); + PoolConfig* pool = input->mutable_pool_conf(); + + pool->set_img_size(8); + pool->set_img_size_y(8); + setPoolConfig(&config, pool, "max-pool-with-mask"); + config.layerConfig.set_size(pool->output_x() * pool->output_y() * + pool->channels()); + + config.layerConfig.set_name("MaxPoolWithMask"); + + std::vector dataLayers; + LayerMap layerMap; + vector datas; + + initDataLayer(config, + &dataLayers, + &datas, + &layerMap, + "MaxPoolWithMask", + 1, + false, + use_gpu); + + dataLayers[0]->getOutputValue()->copyFrom(*inputMat); + + FLAGS_use_gpu = use_gpu; + std::vector parameters; + LayerPtr maxPoolingWithMaskOutputLayer; + initTestLayer(config, &layerMap, ¶meters, &maxPoolingWithMaskOutputLayer); + maxPoolingWithMaskOutputLayer->forward(PASS_GC); + + /* prepare the upsample layer */ + LayerConfig upsampleLayerConfig; + upsampleLayerConfig.set_type("upsample"); + LayerInputConfig* input1 = upsampleLayerConfig.add_inputs(); + upsampleLayerConfig.add_inputs(); + + UpsampleConfig* upsampleConfig = input1->mutable_upsample_conf(); + upsampleConfig->set_scale(2); + ImageConfig* imageConfig = upsampleConfig->mutable_image_conf(); + imageConfig->set_channels(2); + imageConfig->set_img_size(4); + imageConfig->set_img_size_y(4); + upsampleLayerConfig.set_size(2 * 8 * 8); + upsampleLayerConfig.set_name("upsample"); + + for (size_t i = 0; i < 2; i++) { + LayerInputConfig& inputTemp = *(upsampleLayerConfig.mutable_inputs(i)); + inputTemp.set_input_layer_name("MaxPoolWithMask"); + } + + LayerPtr upsampleLayer; + ParameterMap parameterMap; + upsampleLayer = Layer::create(upsampleLayerConfig); + layerMap[upsampleLayerConfig.name()] = upsampleLayer; + upsampleLayer->init(layerMap, parameterMap); + upsampleLayer->setNeedGradient(true); + upsampleLayer->forward(PASS_GC); + upsampleLayer->getOutputGrad()->copyFrom(tempGradData, 128); + upsampleLayer->backward(); + + return upsampleLayer; +} + +TEST(Layer, maxPoolingWithMaskOutputLayerFwd) { + bool useGpu = false; + MatrixPtr inputMat; + MatrixPtr inputGPUMat; + MatrixPtr tempGradMat; + + inputMat = Matrix::create(1, 128, false, useGpu); + inputMat->randomizeUniform(); + + tempGradMat = Matrix::create(1, 128, false, useGpu); + tempGradMat->randomizeUniform(); + real* data = inputMat->getData(); + real* tempGradData = tempGradMat->getData(); + + LayerPtr upsampleLayerCPU = + doOneUpsampleTest(inputMat, "max-pool-with-mask", useGpu, tempGradData); + +#ifdef PADDLE_WITH_CUDA + useGpu = true; + inputGPUMat = Matrix::create(1, 128, false, useGpu); + inputGPUMat->copyFrom(data, 128); + LayerPtr upsampleLayerGPU = doOneUpsampleTest( + inputGPUMat, "max-pool-with-mask", useGpu, tempGradData); + checkMatrixEqual(upsampleLayerCPU->getOutput("").value, + upsampleLayerGPU->getOutput("").value); + + checkMatrixEqual(upsampleLayerCPU->getPrev(0)->getOutputGrad(), + upsampleLayerGPU->getPrev(0)->getOutputGrad()); +#endif +} From 1ab4fcb5e705c5e03c0fea3fbca3c00b5e67ff85 Mon Sep 17 00:00:00 2001 From: typhoonzero Date: Mon, 26 Mar 2018 19:55:46 +0800 Subject: [PATCH 04/58] prepare pserver executor --- paddle/fluid/framework/executor.cc | 15 +++++++++++++ paddle/fluid/framework/executor.h | 3 +++ paddle/fluid/operators/listen_and_serv_op.cc | 22 ++++++++++++-------- 3 files changed, 31 insertions(+), 9 deletions(-) diff --git a/paddle/fluid/framework/executor.cc b/paddle/fluid/framework/executor.cc index 0b171e1dcf..5279eb42cd 100644 --- a/paddle/fluid/framework/executor.cc +++ b/paddle/fluid/framework/executor.cc @@ -279,6 +279,21 @@ std::unique_ptr Executor::Prepare( return std::unique_ptr(ctx); } +std::vector> Prepare( + const ProgramDesc& program, const std::vector& block_ids) { + std::vector> result; + for (auto& bid : block_ids) { + auto* ctx = new ExecutorPrepareContext(program, bid); + PADDLE_ENFORCE_LT(static_cast(bid), program.Size()); + auto& block = program.Block(bid); + for (auto& op_desc : block.AllOps()) { + ctx->ops_.push_back(OpRegistry::CreateOp(*op_desc)); + } + result.push_back(std::shared_ptr(ctx)); + } + return result; +} + void Executor::RunPreparedContext(ExecutorPrepareContext* ctx, Scope* scope, bool create_local_scope, bool create_vars) { auto& block = ctx->prog_.Block(ctx->block_id_); diff --git a/paddle/fluid/framework/executor.h b/paddle/fluid/framework/executor.h index d8dd82469a..756f3c7e5a 100644 --- a/paddle/fluid/framework/executor.h +++ b/paddle/fluid/framework/executor.h @@ -60,6 +60,9 @@ class Executor { static std::unique_ptr Prepare( const ProgramDesc& program, int block_id); + static std::vector> Prepare( + const ProgramDesc& program, const std::vector& block_ids); + void RunPreparedContext(ExecutorPrepareContext* ctx, Scope* scope, bool create_local_scope = true, bool create_vars = true); diff --git a/paddle/fluid/operators/listen_and_serv_op.cc b/paddle/fluid/operators/listen_and_serv_op.cc index 08b83375dd..6bae993f61 100644 --- a/paddle/fluid/operators/listen_and_serv_op.cc +++ b/paddle/fluid/operators/listen_and_serv_op.cc @@ -93,6 +93,10 @@ class ListenAndServOp : public framework::OperatorBase { "server program should have at least 2 blocks"); framework::Executor executor(dev_place); + std::vector block_list; + for (int blkid = 1; blkid < num_blocks; ++blkid) + block_list.push_back(blkid); + auto prepared = executor.Prepare(*program, block_list); // TODO(typhoonzero): change this to a while_op for every cluster-batch. bool exit_flag = false; @@ -143,11 +147,12 @@ class ListenAndServOp : public framework::OperatorBase { std::vector> fs; // block0 contains only listen_and_serv op, start run from block1. for (int blkid = 1; blkid < num_blocks - 1; ++blkid) { - fs.push_back( - framework::Async([&executor, &program, &recv_scope, blkid]() { + fs.push_back(framework::Async( + [&executor, &program, &recv_scope, &prepared, blkid]() { int run_block = blkid; // thread local try { - executor.Run(*program, &recv_scope, run_block, false, false); + executor.RunPreparedContext(prepared[run_block].get(), + &recv_scope, false, false); } catch (std::exception &e) { LOG(ERROR) << "run sub program error " << e.what(); } @@ -157,7 +162,9 @@ class ListenAndServOp : public framework::OperatorBase { // Run global block at final step, or block1 if there are only 2 blocks if (num_blocks >= 2) { try { - executor.Run(*program, &recv_scope, num_blocks - 1, false, false); + // executor.Run(program, &recv_scope, num_blocks - 1, false, false); + executor.RunPreparedContext(prepared[num_blocks - 1].get(), + &recv_scope, false, false); } catch (std::exception &e) { LOG(ERROR) << "run sub program error " << e.what(); } @@ -172,14 +179,11 @@ class ListenAndServOp : public framework::OperatorBase { var->GetMutable()->mutable_rows()->clear(); } rpc_service_->SetCond(1); - // FIXME(typhoonzero): use another condition to sync wait clients get. + // NOTE: does not consider barrier request retry in here, we may use + // global barrier id to resolve this. rpc_service_->WaitClientGet(fan_in); sparse_vars.clear(); } // while(true) - - // for (int i = 0; i < num_blocks; ++i) { - // delete blk_ctx_list[i]; - // } } protected: From 18eb77303d24a53f0d8312385527d4e9c3a674a3 Mon Sep 17 00:00:00 2001 From: chengduoZH Date: Mon, 26 Mar 2018 19:12:05 +0800 Subject: [PATCH 05/58] add CUDAPinnedPlace --- paddle/fluid/framework/tensor.h | 29 +++---- paddle/fluid/framework/tensor_impl.h | 23 ++--- .../fluid/memory/detail/system_allocator.cc | 16 ++-- paddle/fluid/memory/detail/system_allocator.h | 4 +- paddle/fluid/memory/memory.cc | 85 ++++++++++--------- paddle/fluid/memory/memory.h | 1 + paddle/fluid/platform/device_context.h | 12 +++ paddle/fluid/platform/place.cc | 11 ++- paddle/fluid/platform/place.h | 30 ++++++- 9 files changed, 125 insertions(+), 86 deletions(-) diff --git a/paddle/fluid/framework/tensor.h b/paddle/fluid/framework/tensor.h index f7a6b5ba84..6eb678e301 100644 --- a/paddle/fluid/framework/tensor.h +++ b/paddle/fluid/framework/tensor.h @@ -45,11 +45,10 @@ class Tensor { friend struct EigenVector; public: - Tensor() : offset_(0), is_pinned_(false) {} + Tensor() : offset_(0) {} /*! Constructor with place should only be used in pybind. */ - explicit Tensor(const platform::Place& place) - : offset_(0), is_pinned_(false) { + explicit Tensor(const platform::Place& place) : offset_(0) { holder_->set_place(place); } @@ -70,12 +69,11 @@ class Tensor { * @note If not exist, then allocation. */ template - inline T* mutable_data(platform::Place place, bool is_pinned = false); + inline T* mutable_data(platform::Place place); - inline void* mutable_data(platform::Place place, std::type_index type, - bool is_pinned = false); + inline void* mutable_data(platform::Place place, std::type_index type); - inline void* mutable_data(platform::Place place, bool is_pinned = false); + inline void* mutable_data(platform::Place place); /** * @brief Return a pointer to mutable memory block. @@ -86,8 +84,7 @@ class Tensor { * @note If not exist, then allocation. */ template - inline T* mutable_data(DDim dims, platform::Place place, - bool is_pinned = false); + inline T* mutable_data(DDim dims, platform::Place place); /*! Return the dimensions of the memory block. */ inline const DDim& dims() const; @@ -152,14 +149,12 @@ class Tensor { template struct PlaceholderImpl : public Placeholder { - PlaceholderImpl(Place place, size_t size, std::type_index type, - bool is_pinned = false) - : ptr_(static_cast(memory::Alloc(place, size, is_pinned)), - memory::PODDeleter(place, is_pinned)), + PlaceholderImpl(Place place, size_t size, std::type_index type) + : ptr_(static_cast(memory::Alloc(place, size)), + memory::PODDeleter(place)), place_(place), size_(size), - type_(type), - is_pinned_(is_pinned) { + type_(type) { PADDLE_ENFORCE_NOT_NULL(ptr_, "Insufficient %s memory to allocation.", (is_cpu_place(place_) ? "CPU" : "GPU")); } @@ -182,9 +177,6 @@ class Tensor { /* the current type of memory */ std::type_index type_; - - /*! use pinned memory or not. */ - bool is_pinned_; }; /*! holds the memory block if allocated. */ @@ -219,7 +211,6 @@ class Tensor { * PlaceHolder::ptr_ and where the tensor data really begins. */ size_t offset_; - bool is_pinned_; }; inline void Tensor::switch_place(platform::Place new_place) { diff --git a/paddle/fluid/framework/tensor_impl.h b/paddle/fluid/framework/tensor_impl.h index 113814971e..7a48390440 100644 --- a/paddle/fluid/framework/tensor_impl.h +++ b/paddle/fluid/framework/tensor_impl.h @@ -101,21 +101,19 @@ inline T* Tensor::data() { } template -inline T* Tensor::mutable_data(DDim dims, platform::Place place, - bool is_pinned) { +inline T* Tensor::mutable_data(DDim dims, platform::Place place) { static_assert(std::is_pod::value, "T must be POD"); Resize(dims); - return mutable_data(place, is_pinned); + return mutable_data(place); } template -inline T* Tensor::mutable_data(platform::Place place, bool is_pinned) { +inline T* Tensor::mutable_data(platform::Place place) { static_assert(std::is_pod::value, "T must be POD"); - return reinterpret_cast(mutable_data(place, typeid(T), is_pinned)); + return reinterpret_cast(mutable_data(place, typeid(T))); } -inline void* Tensor::mutable_data(platform::Place place, std::type_index type, - bool is_pinned) { +inline void* Tensor::mutable_data(platform::Place place, std::type_index type) { if (holder_ != nullptr) { holder_->set_type(type); } @@ -129,27 +127,26 @@ inline void* Tensor::mutable_data(platform::Place place, std::type_index type, holder_->size() < size + offset_) { if (platform::is_cpu_place(place)) { holder_.reset(new PlaceholderImpl( - boost::get(place), size, type, is_pinned)); + boost::get(place), size, type)); } else if (platform::is_gpu_place(place)) { #ifndef PADDLE_WITH_CUDA PADDLE_THROW("'CUDAPlace' is not supported in CPU only device."); } #else holder_.reset(new PlaceholderImpl( - boost::get(place), size, type, is_pinned)); + boost::get(place), size, type)); } #endif offset_ = 0; - is_pinned_ = is_pinned; } return reinterpret_cast(reinterpret_cast(holder_->ptr()) + offset_); } -inline void* Tensor::mutable_data(platform::Place place, bool is_pinned) { +inline void* Tensor::mutable_data(platform::Place place) { PADDLE_ENFORCE(this->holder_ != nullptr, "Cannot invoke mutable data if current hold nothing"); - return mutable_data(place, holder_->type(), is_pinned); + return mutable_data(place, holder_->type()); } inline Tensor& Tensor::ShareDataWith(const Tensor& src) { @@ -191,8 +188,6 @@ inline const DDim& Tensor::dims() const { return dims_; } inline int64_t Tensor::numel() const { return product(dims_); } -inline bool Tensor::isPinned() const { return is_pinned_; } - inline Tensor ReshapeToMatrix(const Tensor& src, int num_col_dims) { Tensor res; res.ShareDataWith(src); diff --git a/paddle/fluid/memory/detail/system_allocator.cc b/paddle/fluid/memory/detail/system_allocator.cc index 71d28dcbad..d20c5c8682 100644 --- a/paddle/fluid/memory/detail/system_allocator.cc +++ b/paddle/fluid/memory/detail/system_allocator.cc @@ -123,20 +123,20 @@ bool GPUAllocator::UseGpu() const { return true; } // memory. It’s locked to a physical address. void* CUDAPinnedAllocator::Alloc(size_t& index, size_t size) { if (size <= 0) return nullptr; - void* p; - // NOTE: here, we use GpuMaxAllocSize() as the maximum memory size + + // NOTE: here, we use CpuMaxAllocSize()/2 as the maximum memory size // of host pinned allocation. Allocates too much would reduce // the amount of memory available to the underlying system for paging. - - size_t usable = paddle::platform::GpuMaxAllocSize() - fallback_alloc_size_; + size_t usable = CpuMaxAllocSize() / 2 - cuda_pinnd_alloc_size_; if (size > usable) return nullptr; // PINNED memory is visible to all CUDA contexts. cudaError_t result = cudaMallocHost(&p, size); + if (result == cudaSuccess) { - index = 1; - fallback_alloc_size_ += size; + index = 1; // PINNED memory + cuda_pinnd_alloc_size_ += size; return p; } @@ -147,8 +147,8 @@ void CUDAPinnedAllocator::Free(void* p, size_t size, size_t index) { cudaError_t err; PADDLE_ASSERT(index == 1); - PADDLE_ASSERT(fallback_alloc_size_ >= size); - fallback_alloc_size_ -= size; + PADDLE_ASSERT(cuda_pinnd_alloc_size_ >= size); + cuda_pinnd_alloc_size_ -= size; err = cudaFreeHost(p); // Purposefully allow cudaErrorCudartUnloading, because diff --git a/paddle/fluid/memory/detail/system_allocator.h b/paddle/fluid/memory/detail/system_allocator.h index 3e024125fa..c2f474f4b6 100644 --- a/paddle/fluid/memory/detail/system_allocator.h +++ b/paddle/fluid/memory/detail/system_allocator.h @@ -59,9 +59,7 @@ class CUDAPinnedAllocator : public SystemAllocator { virtual bool UseGpu() const; private: - size_t gpu_alloc_size_ = - 0; // TODO(zcd): how to define the upper limit of CUDAPinnedMemory? - size_t fallback_alloc_size_ = 0; + size_t cuda_pinnd_alloc_size_ = 0; }; #endif diff --git a/paddle/fluid/memory/memory.cc b/paddle/fluid/memory/memory.cc index f2d5f250bf..6da9f00656 100644 --- a/paddle/fluid/memory/memory.cc +++ b/paddle/fluid/memory/memory.cc @@ -38,8 +38,7 @@ BuddyAllocator* GetCPUBuddyAllocator() { } template <> -void* Alloc(platform::CPUPlace place, size_t size, - bool is_pinned) { +void* Alloc(platform::CPUPlace place, size_t size) { VLOG(10) << "Allocate " << size << " bytes on " << platform::Place(place); void* p = GetCPUBuddyAllocator()->Alloc(size); VLOG(10) << " pointer=" << p; @@ -47,8 +46,7 @@ void* Alloc(platform::CPUPlace place, size_t size, } template <> -void Free(platform::CPUPlace place, void* p, - bool is_pinned) { +void Free(platform::CPUPlace place, void* p) { VLOG(10) << "Free pointer=" << p << " on " << platform::Place(place); GetCPUBuddyAllocator()->Free(p); } @@ -85,27 +83,13 @@ BuddyAllocator* GetGPUBuddyAllocator(int gpu_id) { } BuddyAllocator* GetCUDAPinnedBuddyAllocator(int gpu_id) { - static BuddyAllocator** as = NULL; + static BuddyAllocator* as = NULL; if (as == NULL) { - int gpu_num = platform::GetCUDADeviceCount(); - as = new BuddyAllocator*[gpu_num]; - for (int gpu = 0; gpu < gpu_num; gpu++) { - as[gpu] = nullptr; - } - } - platform::SetDeviceId(gpu_id); - if (!as[gpu_id]) { - as[gpu_id] = new BuddyAllocator(new detail::CUDAPinnedAllocator, - platform::GpuMinChunkSize(), - platform::GpuMaxChunkSize()); - VLOG(10) << "\n\nNOTE: each GPU device use " - << FLAGS_fraction_of_gpu_memory_to_use * 100 - << "% of GPU memory.\n" - << "You can set GFlags environment variable '" - << "FLAGS_fraction_of_gpu_memory_to_use" - << "' to change the fraction of GPU usage.\n\n"; + as = new BuddyAllocator(new detail::CUDAPinnedAllocator, + platform::CpuMinChunkSize(), + platform::CpuMaxChunkSize()); } - return as[gpu_id]; + return as; } template <> @@ -114,16 +98,9 @@ size_t Used(platform::CUDAPlace place) { } template <> -void* Alloc(platform::CUDAPlace place, size_t size, - bool is_pinned) { - void* ptr; - if (is_pinned) { - auto* buddy_allocator = GetCUDAPinnedBuddyAllocator(place.device); - ptr = buddy_allocator->Alloc(size); - } else { - auto* buddy_allocator = GetGPUBuddyAllocator(place.device); - ptr = buddy_allocator->Alloc(size); - } +void* Alloc(platform::CUDAPlace place, size_t size) { + auto* buddy_allocator = GetGPUBuddyAllocator(place.device); + void* ptr = buddy_allocator->Alloc(size); if (ptr == nullptr) { int cur_dev = platform::GetCurrentDeviceId(); @@ -142,13 +119,39 @@ void* Alloc(platform::CUDAPlace place, size_t size, } template <> -void Free(platform::CUDAPlace place, void* p, - bool is_pinned) { - if (is_pinned) { - GetCUDAPinnedBuddyAllocator(place.device)->Free(p); - } else { - GetGPUBuddyAllocator(place.device)->Free(p); +void Free(platform::CUDAPlace place, void* p) { + GetGPUBuddyAllocator(place.device)->Free(p); +} + +size_t Used(platform::CUDAPinnedPlace place) { + return GetGPUBuddyAllocator(place.device)->Used(); +} + +template <> +void* Alloc(platform::CUDAPinnedPlace place, + size_t size) { + auto* buddy_allocator = GetCUDAPinnedBuddyAllocator(place.device); + void* ptr = buddy_allocator->Alloc(size); + + if (ptr == nullptr) { + int cur_dev = platform::GetCurrentDeviceId(); + platform::SetDeviceId(place.device); + size_t avail, total; + platform::GpuMemoryUsage(avail, total); + LOG(WARNING) << "Cannot allocate " << size << " bytes in GPU " + << place.device << ", available " << avail << " bytes"; + LOG(WARNING) << "total " << total; + LOG(WARNING) << "GpuMinChunkSize " << platform::GpuMinChunkSize(); + LOG(WARNING) << "GpuMaxChunkSize " << platform::GpuMaxChunkSize(); + LOG(WARNING) << "GPU memory used: " << Used(place); + platform::SetDeviceId(cur_dev); } + return ptr; +} + +template <> +void Free(platform::CUDAPinnedPlace place, void* p) { + GetCUDAPinnedBuddyAllocator(place.device)->Free(p); } #endif @@ -165,6 +168,10 @@ size_t Usage::operator()(const platform::CUDAPlace& gpu) const { #endif } +size_t Usage::operator()(const platform::CUDAPinnedPlace& cuda_pinned) const { + return Used(cuda_pinned); +} + size_t memory_usage(const platform::Place& p) { return boost::apply_visitor(Usage(), p); } diff --git a/paddle/fluid/memory/memory.h b/paddle/fluid/memory/memory.h index 062bfc880e..fba7372e71 100644 --- a/paddle/fluid/memory/memory.h +++ b/paddle/fluid/memory/memory.h @@ -57,6 +57,7 @@ size_t Used(Place place); struct Usage : public boost::static_visitor { size_t operator()(const platform::CPUPlace& cpu) const; size_t operator()(const platform::CUDAPlace& gpu) const; + size_t operator()(const platform::CUDAPinnedPlace& cuda_pinned) const; }; size_t memory_usage(const platform::Place& p); diff --git a/paddle/fluid/platform/device_context.h b/paddle/fluid/platform/device_context.h index 202394c7be..e25cfe60b1 100644 --- a/paddle/fluid/platform/device_context.h +++ b/paddle/fluid/platform/device_context.h @@ -118,6 +118,18 @@ struct DefaultDeviceContextType { using TYPE = CUDADeviceContext; }; +// Currently, CUDAPinnedDeviceContext is only used to data copying. +// class CUDAPinnedDeviceContext : public DeviceContext { +// public: +// CUDAPinnedDeviceContext(); +// explicit CUDAPinnedDeviceContext(CUDAPinnedPlace place); +// +// Place GetPlace() const override; +// +// private: +// CUDAPinnedPlace place_; +//}; + #endif #ifdef PADDLE_WITH_MKLDNN diff --git a/paddle/fluid/platform/place.cc b/paddle/fluid/platform/place.cc index de8f958eb0..11ca87c211 100644 --- a/paddle/fluid/platform/place.cc +++ b/paddle/fluid/platform/place.cc @@ -40,12 +40,19 @@ const Place &get_place() { return the_default_place; } const CUDAPlace default_gpu() { return CUDAPlace(0); } const CPUPlace default_cpu() { return CPUPlace(); } +const CUDAPinnedPlace default_cuda_pinned() { return CUDAPinnedPlace(); } bool is_gpu_place(const Place &p) { return boost::apply_visitor(IsCUDAPlace(), p); } -bool is_cpu_place(const Place &p) { return !is_gpu_place(p); } +bool is_cpu_place(const Place &p) { + return boost::apply_visitor(IsCPUPlace(), p); +} + +bool is_cuda_pinned_place(const Place &p) { + return boost::apply_visitor(IsCUDAPinnedPlace(), p); +} bool places_are_same_class(const Place &p1, const Place &p2) { return p1.which() == p2.which(); @@ -53,7 +60,7 @@ bool places_are_same_class(const Place &p1, const Place &p2) { bool is_same_place(const Place &p1, const Place &p2) { if (places_are_same_class(p1, p2)) { - if (is_cpu_place(p1)) { + if (is_cpu_place(p1) || is_cuda_pinned_place(p1)) { return true; } else { return boost::get(p1) == boost::get(p2); diff --git a/paddle/fluid/platform/place.h b/paddle/fluid/platform/place.h index 4cc8b377b8..8f3acd8df6 100644 --- a/paddle/fluid/platform/place.h +++ b/paddle/fluid/platform/place.h @@ -45,12 +45,33 @@ struct CUDAPlace { int device; }; +struct CUDAPinnedPlace { + CUDAPinnedPlace() {} + + // needed for variant equality comparison + inline bool operator==(const CUDAPinnedPlace &) const { return true; } + inline bool operator!=(const CUDAPinnedPlace &) const { return false; } +}; + struct IsCUDAPlace : public boost::static_visitor { bool operator()(const CPUPlace &) const { return false; } bool operator()(const CUDAPlace &gpu) const { return true; } + bool operator()(const CUDAPinnedPlace &) const { return false; } }; -typedef boost::variant Place; +struct IsCPUPlace : public boost::static_visitor { + bool operator()(const CPUPlace &cpu) const { return true; } + bool operator()(const CUDAPlace &) const { return false; } + bool operator()(const CUDAPinnedPlace &) const { return false; } +}; + +struct IsCUDAPinnedPlace : public boost::static_visitor { + bool operator()(const CPUPlace &) const { return false; } + bool operator()(const CUDAPlace &) const { return false; } + bool operator()(const CUDAPinnedPlace &cuda_pinned) const { return true; } +}; + +typedef boost::variant Place; using PlaceList = std::vector; @@ -59,9 +80,11 @@ const Place &get_place(); const CUDAPlace default_gpu(); const CPUPlace default_cpu(); +const CUDAPinnedPlace default_cuda_pinned(); bool is_gpu_place(const Place &); bool is_cpu_place(const Place &); +bool is_cuda_pinned_place(const Place &); bool places_are_same_class(const Place &, const Place &); bool is_same_place(const Place &, const Place &); @@ -97,6 +120,11 @@ struct PlaceVisitorWrapper return typename Visitor::result_type(); #endif } + + typename Visitor::result_type operator()( + const CUDAPinnedPlace &cuda_pinned) const { + return visitor_(cuda_pinned); + } }; template From 158d6c4d1956225e7aa8ddaa4e4af852060916da Mon Sep 17 00:00:00 2001 From: chengduoZH Date: Mon, 26 Mar 2018 20:36:07 +0800 Subject: [PATCH 06/58] add unit test --- paddle/fluid/framework/tensor.h | 3 - .../fluid/memory/detail/system_allocator.cc | 7 ++- paddle/fluid/memory/memcpy.cc | 39 +++++++++++++ paddle/fluid/memory/memory.cc | 49 +++++++---------- paddle/fluid/memory/memory.h | 10 ++-- paddle/fluid/memory/memory_test.cc | 55 +++++++++++++++++++ paddle/fluid/platform/place.cc | 1 + 7 files changed, 124 insertions(+), 40 deletions(-) diff --git a/paddle/fluid/framework/tensor.h b/paddle/fluid/framework/tensor.h index 6eb678e301..6f878541e6 100644 --- a/paddle/fluid/framework/tensor.h +++ b/paddle/fluid/framework/tensor.h @@ -92,9 +92,6 @@ class Tensor { /*! Return the numel of the memory block. */ inline int64_t numel() const; - /*! Return the numel of the memory block. */ - inline bool isPinned() const; - /*! Resize the dimensions of the memory block. */ inline Tensor& Resize(const DDim& dims); diff --git a/paddle/fluid/memory/detail/system_allocator.cc b/paddle/fluid/memory/detail/system_allocator.cc index d20c5c8682..2f3c10aeb2 100644 --- a/paddle/fluid/memory/detail/system_allocator.cc +++ b/paddle/fluid/memory/detail/system_allocator.cc @@ -14,6 +14,7 @@ limitations under the License. */ #include "paddle/fluid/memory/detail/system_allocator.h" #include "paddle/fluid/platform/assert.h" +#include "paddle/fluid/platform/cpu_info.h" #include "paddle/fluid/platform/enforce.h" #include "paddle/fluid/platform/gpu_info.h" @@ -127,10 +128,12 @@ void* CUDAPinnedAllocator::Alloc(size_t& index, size_t size) { // NOTE: here, we use CpuMaxAllocSize()/2 as the maximum memory size // of host pinned allocation. Allocates too much would reduce // the amount of memory available to the underlying system for paging. - size_t usable = CpuMaxAllocSize() / 2 - cuda_pinnd_alloc_size_; + size_t usable = + paddle::platform::CpuMaxAllocSize() / 2 - cuda_pinnd_alloc_size_; if (size > usable) return nullptr; + void* p; // PINNED memory is visible to all CUDA contexts. cudaError_t result = cudaMallocHost(&p, size); @@ -161,7 +164,7 @@ void CUDAPinnedAllocator::Free(void* p, size_t size, size_t index) { } } -bool CUDAPinnedAllocator::UseGpu() const { return true; } +bool CUDAPinnedAllocator::UseGpu() const { return false; } #endif diff --git a/paddle/fluid/memory/memcpy.cc b/paddle/fluid/memory/memcpy.cc index b991360d04..eddcaab8be 100644 --- a/paddle/fluid/memory/memcpy.cc +++ b/paddle/fluid/memory/memcpy.cc @@ -56,6 +56,45 @@ void Copy( } } +template <> +void Copy( + platform::CPUPlace dst_place, void* dst, + platform::CUDAPinnedPlace src_place, const void* src, size_t num) { + std::memcpy(dst, src, num); +} + +template <> +void Copy( + platform::CUDAPinnedPlace dst_place, void* dst, + platform::CPUPlace src_place, const void* src, size_t num) { + std::memcpy(dst, src, num); +} + +template <> +void Copy( + platform::CUDAPinnedPlace dst_place, void* dst, + platform::CUDAPinnedPlace src_place, const void* src, size_t num) { + std::memcpy(dst, src, num); +} + +template <> +void Copy( + platform::CUDAPinnedPlace dst_place, void* dst, + platform::CUDAPlace src_place, const void* src, size_t num, + cudaStream_t stream) { + platform::SetDeviceId(src_place.device); + platform::GpuMemcpyAsync(dst, src, num, cudaMemcpyDeviceToHost, stream); +} + +template <> +void Copy( + platform::CUDAPlace dst_place, void* dst, + platform::CUDAPinnedPlace src_place, const void* src, size_t num, + cudaStream_t stream) { + platform::SetDeviceId(dst_place.device); + platform::GpuMemcpyAsync(dst, src, num, cudaMemcpyHostToDevice, stream); +} + #endif } // namespace memory diff --git a/paddle/fluid/memory/memory.cc b/paddle/fluid/memory/memory.cc index 6da9f00656..94b43af147 100644 --- a/paddle/fluid/memory/memory.cc +++ b/paddle/fluid/memory/memory.cc @@ -82,16 +82,6 @@ BuddyAllocator* GetGPUBuddyAllocator(int gpu_id) { return as[gpu_id]; } -BuddyAllocator* GetCUDAPinnedBuddyAllocator(int gpu_id) { - static BuddyAllocator* as = NULL; - if (as == NULL) { - as = new BuddyAllocator(new detail::CUDAPinnedAllocator, - platform::CpuMinChunkSize(), - platform::CpuMaxChunkSize()); - } - return as; -} - template <> size_t Used(platform::CUDAPlace place) { return GetGPUBuddyAllocator(place.device)->Used(); @@ -100,8 +90,7 @@ size_t Used(platform::CUDAPlace place) { template <> void* Alloc(platform::CUDAPlace place, size_t size) { auto* buddy_allocator = GetGPUBuddyAllocator(place.device); - void* ptr = buddy_allocator->Alloc(size); - + auto* ptr = buddy_allocator->Alloc(size); if (ptr == nullptr) { int cur_dev = platform::GetCurrentDeviceId(); platform::SetDeviceId(place.device); @@ -123,37 +112,39 @@ void Free(platform::CUDAPlace place, void* p) { GetGPUBuddyAllocator(place.device)->Free(p); } +BuddyAllocator* GetCUDAPinnedBuddyAllocator() { + static BuddyAllocator* ba = NULL; + if (ba == NULL) { + ba = new BuddyAllocator(new detail::CUDAPinnedAllocator, + platform::CpuMinChunkSize(), + platform::CpuMaxChunkSize()); + } + return ba; +} + +template <> size_t Used(platform::CUDAPinnedPlace place) { - return GetGPUBuddyAllocator(place.device)->Used(); + return GetCUDAPinnedBuddyAllocator()->Used(); } template <> void* Alloc(platform::CUDAPinnedPlace place, size_t size) { - auto* buddy_allocator = GetCUDAPinnedBuddyAllocator(place.device); + auto* buddy_allocator = GetCUDAPinnedBuddyAllocator(); void* ptr = buddy_allocator->Alloc(size); - if (ptr == nullptr) { - int cur_dev = platform::GetCurrentDeviceId(); - platform::SetDeviceId(place.device); - size_t avail, total; - platform::GpuMemoryUsage(avail, total); - LOG(WARNING) << "Cannot allocate " << size << " bytes in GPU " - << place.device << ", available " << avail << " bytes"; - LOG(WARNING) << "total " << total; - LOG(WARNING) << "GpuMinChunkSize " << platform::GpuMinChunkSize(); - LOG(WARNING) << "GpuMaxChunkSize " << platform::GpuMaxChunkSize(); - LOG(WARNING) << "GPU memory used: " << Used(place); - platform::SetDeviceId(cur_dev); - } + // if (ptr == nullptr) { + // LOG(WARNING) << "Cannot allocate " << size << " bytes in CUDAPinnedPlace + // " + // << ", available " << avail << " bytes" + // } return ptr; } template <> void Free(platform::CUDAPinnedPlace place, void* p) { - GetCUDAPinnedBuddyAllocator(place.device)->Free(p); + GetCUDAPinnedBuddyAllocator()->Free(p); } - #endif size_t Usage::operator()(const platform::CPUPlace& cpu) const { diff --git a/paddle/fluid/memory/memory.h b/paddle/fluid/memory/memory.h index fba7372e71..3e6bfddd69 100644 --- a/paddle/fluid/memory/memory.h +++ b/paddle/fluid/memory/memory.h @@ -33,7 +33,7 @@ namespace memory { * address is valid or not. */ template -void* Alloc(Place place, size_t size, bool is_pinned = false); +void* Alloc(Place place, size_t size); /** * \brief Free memory block in one place. @@ -43,7 +43,7 @@ void* Alloc(Place place, size_t size, bool is_pinned = false); * */ template -void Free(Place place, void* ptr, bool is_pinned = false); +void Free(Place place, void* ptr); /** * \brief Total size of used memory in one place. @@ -75,13 +75,11 @@ class PODDeleter { static_assert(std::is_pod::value, "T must be POD"); public: - explicit PODDeleter(Place place, bool is_pinned = false) - : place_(place), is_pinned_(is_pinned) {} - void operator()(T* ptr) { Free(place_, static_cast(ptr), is_pinned_); } + explicit PODDeleter(Place place) : place_(place) {} + void operator()(T* ptr) { Free(place_, static_cast(ptr)); } private: Place place_; - bool is_pinned_; }; /** diff --git a/paddle/fluid/memory/memory_test.cc b/paddle/fluid/memory/memory_test.cc index eb27a52b25..5254cd28cc 100644 --- a/paddle/fluid/memory/memory_test.cc +++ b/paddle/fluid/memory/memory_test.cc @@ -141,4 +141,59 @@ TEST(BuddyAllocator, GPUMultAlloc) { } } +size_t align(size_t size, paddle::platform::CUDAPinnedPlace place) { + size += sizeof(paddle::memory::detail::Metadata); + size_t alignment = paddle::platform::CpuMinChunkSize(); + size_t remaining = size % alignment; + return remaining == 0 ? size : size + (alignment - remaining); +} + +TEST(BuddyAllocator, CUDAPinnedAllocator) { + void *p = nullptr; + + EXPECT_EQ(p, nullptr); + + paddle::platform::CUDAPinnedPlace cpu; + p = paddle::memory::Alloc(cpu, 4096); + + EXPECT_NE(p, nullptr); + + paddle::platform::Place place = cpu; + EXPECT_EQ(paddle::memory::Used(cpu), paddle::memory::memory_usage(place)); + + paddle::memory::Free(cpu, p); +} + +TEST(BuddyAllocator, CUDAPinnedMultAllocator) { + paddle::platform::CUDAPinnedPlace cpu; + + std::unordered_map ps; + + size_t total_size = paddle::memory::Used(cpu); + EXPECT_EQ(total_size, 0UL); + + for (auto size : + {0, 128, 256, 1024, 4096, 16384, 65536, 262144, 1048576, 4194304}) { + ps[paddle::memory::Alloc(cpu, size)] = size; + + // Buddy Allocator doesn't manage too large memory chunk + if (paddle::memory::Used(cpu) == total_size) continue; + + size_t aligned_size = align(size, cpu); + total_size += aligned_size; + EXPECT_EQ(total_size, paddle::memory::Used(cpu)); + } + + for (auto p : ps) { + EXPECT_EQ(is_aligned(p.first), true); + paddle::memory::Free(cpu, p.first); + + // Buddy Allocator doesn't manage too large memory chunk + if (paddle::memory::Used(cpu) == total_size) continue; + + size_t aligned_size = align(p.second, cpu); + total_size -= aligned_size; + EXPECT_EQ(total_size, paddle::memory::Used(cpu)); + } +} #endif diff --git a/paddle/fluid/platform/place.cc b/paddle/fluid/platform/place.cc index 11ca87c211..655ce8485d 100644 --- a/paddle/fluid/platform/place.cc +++ b/paddle/fluid/platform/place.cc @@ -26,6 +26,7 @@ class PlacePrinter : public boost::static_visitor<> { void operator()(const CUDAPlace &p) { os_ << "CUDAPlace(" << p.device << ")"; } + void operator()(const CUDAPinnedPlace &p) { os_ << "CUDAPinnedPlace"; } private: std::ostream &os_; From ab601c19c37da50f391de62581e572dbe32d6b7f Mon Sep 17 00:00:00 2001 From: chengduoZH Date: Mon, 26 Mar 2018 22:47:20 +0800 Subject: [PATCH 07/58] Add CUDAPinnedPlace --- paddle/fluid/framework/tensor_util.cc | 5 ++++ paddle/fluid/memory/memory.cc | 4 +++ paddle/fluid/operators/math/math_function.cc | 8 ++++++ paddle/fluid/platform/device_context.cc | 24 +++++++++++++++++ paddle/fluid/platform/device_context.h | 27 ++++++++++++-------- paddle/fluid/platform/place.h | 5 ++++ 6 files changed, 63 insertions(+), 10 deletions(-) diff --git a/paddle/fluid/framework/tensor_util.cc b/paddle/fluid/framework/tensor_util.cc index 8b7533ce71..1d864af011 100644 --- a/paddle/fluid/framework/tensor_util.cc +++ b/paddle/fluid/framework/tensor_util.cc @@ -148,6 +148,11 @@ struct AnyVisitor : public boost::static_visitor { const platform::CPUPlace& cpu) const { return *out.data(); } + + bool GetResult(const framework::Tensor& out, + const platform::CUDAPinnedPlace& cpu) const { + return *out.data(); + } }; template diff --git a/paddle/fluid/memory/memory.cc b/paddle/fluid/memory/memory.cc index 94b43af147..f6cbdaaa97 100644 --- a/paddle/fluid/memory/memory.cc +++ b/paddle/fluid/memory/memory.cc @@ -160,7 +160,11 @@ size_t Usage::operator()(const platform::CUDAPlace& gpu) const { } size_t Usage::operator()(const platform::CUDAPinnedPlace& cuda_pinned) const { +#ifdef PADDLE_WITH_CUDA return Used(cuda_pinned); +#else + PADDLE_THROW("'CUDAPinnedPlace' is not supported in CPU only device."); +#endif } size_t memory_usage(const platform::Place& p) { diff --git a/paddle/fluid/operators/math/math_function.cc b/paddle/fluid/operators/math/math_function.cc index 299a0aed01..44fd739fb1 100644 --- a/paddle/fluid/operators/math/math_function.cc +++ b/paddle/fluid/operators/math/math_function.cc @@ -322,6 +322,14 @@ void set_constant_with_place( TensorSetConstantCPU(tensor, value)); } +template <> +void set_constant_with_place( + const platform::DeviceContext& context, framework::Tensor* tensor, + float value) { + framework::VisitDataType(framework::ToDataType(tensor->type()), + TensorSetConstantCPU(tensor, value)); +} + struct TensorSetConstantWithPlace : public boost::static_visitor { TensorSetConstantWithPlace(const platform::DeviceContext& context, framework::Tensor* tensor, float value) diff --git a/paddle/fluid/platform/device_context.cc b/paddle/fluid/platform/device_context.cc index 59b76a1edb..feb4f36700 100644 --- a/paddle/fluid/platform/device_context.cc +++ b/paddle/fluid/platform/device_context.cc @@ -53,6 +53,16 @@ DeviceContextPool::DeviceContextPool( PADDLE_THROW( "'CUDAPlace' is not supported, Please re-compile with WITH_GPU " "option"); +#endif + } else if (platform::is_cuda_pinned_place(p)) { +#ifdef PADDLE_WITH_CUDA + device_contexts_.emplace( + p, + PtrType(new CUDAPinnedDeviceContext(boost::get(p)))); +#else + PADDLE_THROW( + "'CUDAPlace' is not supported, Please re-compile with WITH_GPU " + "option"); #endif } } @@ -186,6 +196,20 @@ cudnnHandle_t CUDADeviceContext::cudnn_handle() const { return cudnn_handle_; } cudaStream_t CUDADeviceContext::stream() const { return stream_; } +CUDAPinnedDeviceContext::CUDAPinnedDeviceContext() { + eigen_device_.reset(new Eigen::DefaultDevice()); +} + +CUDAPinnedDeviceContext::CUDAPinnedDeviceContext(CUDAPinnedPlace place) + : place_(place) { + eigen_device_.reset(new Eigen::DefaultDevice()); +} + +Eigen::DefaultDevice* CUDAPinnedDeviceContext::eigen_device() const { + return eigen_device_.get(); +} + +Place CUDAPinnedDeviceContext::GetPlace() const { return place_; } #endif #ifdef PADDLE_WITH_MKLDNN diff --git a/paddle/fluid/platform/device_context.h b/paddle/fluid/platform/device_context.h index e25cfe60b1..6b796d92d0 100644 --- a/paddle/fluid/platform/device_context.h +++ b/paddle/fluid/platform/device_context.h @@ -119,17 +119,24 @@ struct DefaultDeviceContextType { }; // Currently, CUDAPinnedDeviceContext is only used to data copying. -// class CUDAPinnedDeviceContext : public DeviceContext { -// public: -// CUDAPinnedDeviceContext(); -// explicit CUDAPinnedDeviceContext(CUDAPinnedPlace place); -// -// Place GetPlace() const override; -// -// private: -// CUDAPinnedPlace place_; -//}; +class CUDAPinnedDeviceContext : public DeviceContext { + public: + CUDAPinnedDeviceContext(); + explicit CUDAPinnedDeviceContext(CUDAPinnedPlace place); + + Place GetPlace() const override; + Eigen::DefaultDevice* eigen_device() const; + + private: + CUDAPinnedPlace place_; + std::unique_ptr eigen_device_; +}; + +template <> +struct DefaultDeviceContextType { + using TYPE = CUDAPinnedDeviceContext; +}; #endif #ifdef PADDLE_WITH_MKLDNN diff --git a/paddle/fluid/platform/place.h b/paddle/fluid/platform/place.h index 8f3acd8df6..d0bdcb0da5 100644 --- a/paddle/fluid/platform/place.h +++ b/paddle/fluid/platform/place.h @@ -123,7 +123,12 @@ struct PlaceVisitorWrapper typename Visitor::result_type operator()( const CUDAPinnedPlace &cuda_pinned) const { +#ifdef PADDLE_WITH_CUDA return visitor_(cuda_pinned); +#else + PADDLE_THROW("Paddle is not compiled with CUDA. Cannot visit cuda_pinned"); + return typename Visitor::result_type(); +#endif } }; From 58a9f9f781f9c51fa89c1e332ccda12c18bc7c03 Mon Sep 17 00:00:00 2001 From: chengduoZH Date: Wed, 28 Mar 2018 11:34:52 +0800 Subject: [PATCH 08/58] set the max size of cudapinned memory --- .../fluid/memory/detail/system_allocator.cc | 4 ++-- paddle/fluid/memory/memory.cc | 4 ++-- paddle/fluid/memory/memory_test.cc | 2 +- paddle/fluid/platform/cpu_info.cc | 21 +++++++++++++++++++ paddle/fluid/platform/cpu_info.h | 9 ++++++++ 5 files changed, 35 insertions(+), 5 deletions(-) diff --git a/paddle/fluid/memory/detail/system_allocator.cc b/paddle/fluid/memory/detail/system_allocator.cc index 2f3c10aeb2..2463fdf483 100644 --- a/paddle/fluid/memory/detail/system_allocator.cc +++ b/paddle/fluid/memory/detail/system_allocator.cc @@ -125,11 +125,11 @@ bool GPUAllocator::UseGpu() const { return true; } void* CUDAPinnedAllocator::Alloc(size_t& index, size_t size) { if (size <= 0) return nullptr; - // NOTE: here, we use CpuMaxAllocSize()/2 as the maximum memory size + // NOTE: here, we use CUDAPinnedMaxAllocSize as the maximum memory size // of host pinned allocation. Allocates too much would reduce // the amount of memory available to the underlying system for paging. size_t usable = - paddle::platform::CpuMaxAllocSize() / 2 - cuda_pinnd_alloc_size_; + paddle::platform::CUDAPinnedMaxAllocSize() - cuda_pinnd_alloc_size_; if (size > usable) return nullptr; diff --git a/paddle/fluid/memory/memory.cc b/paddle/fluid/memory/memory.cc index f6cbdaaa97..7b459fe4d0 100644 --- a/paddle/fluid/memory/memory.cc +++ b/paddle/fluid/memory/memory.cc @@ -116,8 +116,8 @@ BuddyAllocator* GetCUDAPinnedBuddyAllocator() { static BuddyAllocator* ba = NULL; if (ba == NULL) { ba = new BuddyAllocator(new detail::CUDAPinnedAllocator, - platform::CpuMinChunkSize(), - platform::CpuMaxChunkSize()); + platform::CUDAPinnedMinChunkSize(), + platform::CUDAPinnedMaxChunkSize()); } return ba; } diff --git a/paddle/fluid/memory/memory_test.cc b/paddle/fluid/memory/memory_test.cc index 5254cd28cc..03829702a0 100644 --- a/paddle/fluid/memory/memory_test.cc +++ b/paddle/fluid/memory/memory_test.cc @@ -143,7 +143,7 @@ TEST(BuddyAllocator, GPUMultAlloc) { size_t align(size_t size, paddle::platform::CUDAPinnedPlace place) { size += sizeof(paddle::memory::detail::Metadata); - size_t alignment = paddle::platform::CpuMinChunkSize(); + size_t alignment = paddle::platform::CUDAPinnedMinChunkSize(); size_t remaining = size % alignment; return remaining == 0 ? size : size + (alignment - remaining); } diff --git a/paddle/fluid/platform/cpu_info.cc b/paddle/fluid/platform/cpu_info.cc index 8db08edba8..d44f1cadd2 100644 --- a/paddle/fluid/platform/cpu_info.cc +++ b/paddle/fluid/platform/cpu_info.cc @@ -27,6 +27,10 @@ DEFINE_double(fraction_of_cpu_memory_to_use, 1, "Default use 100% of CPU memory for PaddlePaddle," "reserve the rest for page tables, etc"); +DEFINE_double(fraction_of_cuda_pinned_memory_to_use, 0.5, + "Default use 100% of CPU memory for PaddlePaddle," + "reserve the rest for page tables, etc"); + namespace paddle { namespace platform { @@ -62,5 +66,22 @@ size_t CpuMaxChunkSize() { return CpuMaxAllocSize() / 32; } +size_t CUDAPinnedMaxAllocSize() { + // For distributed systems, it requires configuring and limiting + // the fraction of memory to use. + return FLAGS_fraction_of_cuda_pinned_memory_to_use * CpuTotalPhysicalMemory(); +} + +size_t CUDAPinnedMinChunkSize() { + // Allow to allocate the minimum chunk size is 64 KB. + return 1 << 16; +} + +size_t CUDAPinnedMaxChunkSize() { + // Allow to allocate the maximum chunk size is roughly 0.39% of CUDA_PINNED + // memory. + return CUDAPinnedMaxAllocSize() / 256; +} + } // namespace platform } // namespace paddle diff --git a/paddle/fluid/platform/cpu_info.h b/paddle/fluid/platform/cpu_info.h index a930151bd1..f06c2b67fe 100644 --- a/paddle/fluid/platform/cpu_info.h +++ b/paddle/fluid/platform/cpu_info.h @@ -22,11 +22,20 @@ namespace platform { //! Get the maximum allocation size for a machine. size_t CpuMaxAllocSize(); +//! Get the maximum allocation size for a machine. +size_t CUDAPinnedMaxAllocSize(); + //! Get the minimum chunk size for buddy allocator. size_t CpuMinChunkSize(); //! Get the maximum chunk size for buddy allocator. size_t CpuMaxChunkSize(); +//! Get the minimum chunk size for buddy allocator. +size_t CUDAPinnedMinChunkSize(); + +//! Get the maximum chunk size for buddy allocator. +size_t CUDAPinnedMaxChunkSize(); + } // namespace platform } // namespace paddle From 64242c5d71c675d04b035bbfcaf598546d813cb3 Mon Sep 17 00:00:00 2001 From: Yi Wang Date: Thu, 29 Mar 2018 13:10:16 -0700 Subject: [PATCH 09/58] Rename test_serde into serde_test --- paddle/fluid/operators/detail/CMakeLists.txt | 4 ++-- .../fluid/operators/detail/{test_serde.cc => serde_test.cc} | 0 2 files changed, 2 insertions(+), 2 deletions(-) rename paddle/fluid/operators/detail/{test_serde.cc => serde_test.cc} (100%) diff --git a/paddle/fluid/operators/detail/CMakeLists.txt b/paddle/fluid/operators/detail/CMakeLists.txt index 2b19f04489..d59411dfb9 100644 --- a/paddle/fluid/operators/detail/CMakeLists.txt +++ b/paddle/fluid/operators/detail/CMakeLists.txt @@ -2,7 +2,7 @@ if(WITH_DISTRIBUTE) grpc_library(sendrecvop_grpc SRCS bytebuffer_stream.cc sendrecvop_utils.cc grpc_client.cc grpc_server.cc variable_response.cc PROTO send_recv.proto DEPS lod_tensor selected_rows) set(DISTRIBUTE_COMPILE_FLAGS "-Wno-non-virtual-dtor -Wno-error=non-virtual-dtor -Wno-error=delete-non-virtual-dtor") - set_source_files_properties(test_serde.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS}) - cc_test(serde_test SRCS test_serde.cc variable_response.cc DEPS grpc++_unsecure grpc_unsecure gpr + set_source_files_properties(serde_test.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS}) + cc_test(serde_test SRCS serde_test.cc variable_response.cc DEPS grpc++_unsecure grpc_unsecure gpr cares zlib protobuf sendrecvop_grpc) endif() diff --git a/paddle/fluid/operators/detail/test_serde.cc b/paddle/fluid/operators/detail/serde_test.cc similarity index 100% rename from paddle/fluid/operators/detail/test_serde.cc rename to paddle/fluid/operators/detail/serde_test.cc From c1c5e166d108b8c7d57d81e8b375e33b18402b7e Mon Sep 17 00:00:00 2001 From: Yi Wang Date: Thu, 29 Mar 2018 14:13:55 -0700 Subject: [PATCH 10/58] Fix cpplint errors --- .../fluid/operators/detail/bytebuffer_stream.cc | 2 +- .../fluid/operators/detail/bytebuffer_stream.h | 8 +++++--- paddle/fluid/operators/detail/grpc_client.cc | 16 ++++++++++------ paddle/fluid/operators/detail/grpc_client.h | 15 +++++++-------- paddle/fluid/operators/detail/grpc_server.cc | 7 +++++-- paddle/fluid/operators/detail/grpc_server.h | 9 ++++++--- paddle/fluid/operators/detail/grpc_service.h | 2 +- .../operators/detail/proto_encoder_helper.h | 10 ++++++---- .../fluid/operators/detail/sendrecvop_utils.cc | 12 +++++++----- paddle/fluid/operators/detail/sendrecvop_utils.h | 2 +- paddle/fluid/operators/detail/serde_test.cc | 8 ++++---- .../fluid/operators/detail/simple_block_queue.h | 4 ++-- .../fluid/operators/detail/variable_response.cc | 14 +++++++++++--- .../fluid/operators/detail/variable_response.h | 6 ++++-- 14 files changed, 70 insertions(+), 45 deletions(-) diff --git a/paddle/fluid/operators/detail/bytebuffer_stream.cc b/paddle/fluid/operators/detail/bytebuffer_stream.cc index 741dd51de9..a14171563e 100644 --- a/paddle/fluid/operators/detail/bytebuffer_stream.cc +++ b/paddle/fluid/operators/detail/bytebuffer_stream.cc @@ -17,7 +17,7 @@ limitations under the License. */ // file and did some modifications so that we can send gRPC // requests without too much copying of the tensor data. -#include "bytebuffer_stream.h" +#include "paddle/fluid/operators/detail/bytebuffer_stream.h" namespace paddle { namespace operators { diff --git a/paddle/fluid/operators/detail/bytebuffer_stream.h b/paddle/fluid/operators/detail/bytebuffer_stream.h index 1791a48aab..054dd4ff29 100644 --- a/paddle/fluid/operators/detail/bytebuffer_stream.h +++ b/paddle/fluid/operators/detail/bytebuffer_stream.h @@ -19,9 +19,11 @@ limitations under the License. */ #pragma once -#include +#include + #include "google/protobuf/io/coded_stream.h" #include "google/protobuf/io/zero_copy_stream.h" +#include "grpc++/grpc++.h" namespace grpc { // A ZeroCopyInputStream that reads from grpc_byte_buffer @@ -56,7 +58,7 @@ class GrpcBufferReader final *data = GRPC_SLICE_START_PTR(slice_) + GRPC_SLICE_LENGTH(slice_) - backup_count_; GPR_CODEGEN_ASSERT(backup_count_ <= INT_MAX); - *size = (int)backup_count_; + *size = static_cast(backup_count_); backup_count_ = 0; return true; } @@ -68,7 +70,7 @@ class GrpcBufferReader final *data = GRPC_SLICE_START_PTR(slice_); // On win x64, int is only 32bit GPR_CODEGEN_ASSERT(GRPC_SLICE_LENGTH(slice_) <= INT_MAX); - byte_count_ += * size = (int)GRPC_SLICE_LENGTH(slice_); + byte_count_ += * size = static_cast(GRPC_SLICE_LENGTH(slice_)); return true; } diff --git a/paddle/fluid/operators/detail/grpc_client.cc b/paddle/fluid/operators/detail/grpc_client.cc index 03b789f326..4660f91542 100644 --- a/paddle/fluid/operators/detail/grpc_client.cc +++ b/paddle/fluid/operators/detail/grpc_client.cc @@ -12,8 +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. */ -#include "grpc_client.h" +#include "paddle/fluid/operators/detail/grpc_client.h" + #include + +#include + #include "paddle/fluid/framework/threadpool.h" namespace paddle { @@ -52,7 +56,7 @@ bool RPCClient::AsyncSendVariable(const std::string& ep, auto call = s->stub_g_.PrepareUnaryCall( s->context_.get(), "/sendrecv.SendRecvService/SendVariable", req, &cq_); call->StartCall(); - call->Finish(&s->reply_, &s->status_, (void*)s); + call->Finish(&s->reply_, &s->status_, reinterpret_cast(s)); }); req_count_++; @@ -64,7 +68,7 @@ void ProcGetResponse(const VarHandle& var_h, // const sendrecv::VariableMessage& ret_msg) { const ::grpc::ByteBuffer& ret_msg) { framework::Variable* outvar = NULL; - DeserializeFromByteBuffer(ret_msg, *var_h.ctx, var_h.scope, outvar); + DeserializeFromByteBuffer(ret_msg, *var_h.ctx, var_h.scope, &outvar); } template @@ -109,7 +113,7 @@ bool RPCClient::AsyncGetVariable(const std::string& ep, auto call = s->stub_g_.PrepareUnaryCall( s->context_.get(), "/sendrecv.SendRecvService/GetVariable", buf, &cq_); call->StartCall(); - call->Finish(&s->reply_, &s->status_, (void*)s); + call->Finish(&s->reply_, &s->status_, reinterpret_cast(s)); }); req_count_++; @@ -126,7 +130,7 @@ void RPCClient::AsyncSendBatchBarrier(const std::string& ep, int64_t time_out) { sendrecv::VariableMessage req; req.set_varname(BATCH_BARRIER_MESSAGE); auto rpc = s->stub_->AsyncSendVariable(s->context_.get(), req, &cq_); - rpc->Finish(&s->reply_, &s->status_, (void*)s); + rpc->Finish(&s->reply_, &s->status_, reinterpret_cast(s)); req_count_++; } @@ -138,7 +142,7 @@ void RPCClient::AsyncSendFetchBarrier(const std::string& ep, int64_t time_out) { sendrecv::VariableMessage req; req.set_varname(FETCH_BARRIER_MESSAGE); auto rpc = s->stub_->AsyncGetVariable(s->context_.get(), req, &cq_); - rpc->Finish(&s->reply_, &s->status_, (void*)s); + rpc->Finish(&s->reply_, &s->status_, reinterpret_cast(s)); req_count_++; } diff --git a/paddle/fluid/operators/detail/grpc_client.h b/paddle/fluid/operators/detail/grpc_client.h index 8216ac52fb..3cfc87bbbb 100644 --- a/paddle/fluid/operators/detail/grpc_client.h +++ b/paddle/fluid/operators/detail/grpc_client.h @@ -14,10 +14,9 @@ limitations under the License. */ #pragma once -#include -#include #include -#include + +#include // NOLINT #include #include #include @@ -25,11 +24,11 @@ limitations under the License. */ #include #include -#include -#include -#include -#include - +#include "grpc++/generic/generic_stub.h" +#include "grpc++/grpc++.h" +#include "grpc++/support/byte_buffer.h" +#include "grpc++/support/slice.h" +#include "grpc/support/log.h" #include "paddle/fluid/framework/data_type.h" #include "paddle/fluid/framework/lod_tensor.h" #include "paddle/fluid/framework/scope.h" diff --git a/paddle/fluid/operators/detail/grpc_server.cc b/paddle/fluid/operators/detail/grpc_server.cc index 9691d1e86b..3001543760 100644 --- a/paddle/fluid/operators/detail/grpc_server.cc +++ b/paddle/fluid/operators/detail/grpc_server.cc @@ -14,6 +14,9 @@ limitations under the License. */ #include "paddle/fluid/operators/detail/grpc_server.h" +#include +#include + using ::grpc::ServerAsyncResponseWriter; namespace paddle { @@ -205,7 +208,7 @@ void AsyncGRPCServer::TryToRegisterNewGetOne() { // FIXME(typhoonzero): change cq_name to enum. void AsyncGRPCServer::HandleRequest(::grpc::ServerCompletionQueue* cq, - std::string cq_name, + const std::string& cq_name, std::function TryToRegisterNewOne) { TryToRegisterNewOne(); @@ -222,7 +225,7 @@ void AsyncGRPCServer::HandleRequest(::grpc::ServerCompletionQueue* cq, if (cq_name == "cq_get") WaitCond(1); if (cq_name == "cq_send") WaitCond(0); - RequestBase* base = (RequestBase*)tag; + RequestBase* base = reinterpret_cast(tag); // reference: // https://github.com/tensorflow/tensorflow/issues/5596 // https://groups.google.com/forum/#!topic/grpc-io/xftlRy-IQwM diff --git a/paddle/fluid/operators/detail/grpc_server.h b/paddle/fluid/operators/detail/grpc_server.h index 10e6dd45a9..0fc9740ccb 100644 --- a/paddle/fluid/operators/detail/grpc_server.h +++ b/paddle/fluid/operators/detail/grpc_server.h @@ -14,9 +14,11 @@ limitations under the License. */ #pragma once -#include -#include +#include +#include // NOLINT +#include +#include "grpc++/grpc++.h" #include "paddle/fluid/framework/lod_tensor.h" #include "paddle/fluid/framework/scope.h" #include "paddle/fluid/framework/selected_rows.h" @@ -62,7 +64,8 @@ class AsyncGRPCServer final { void ShutDown(); protected: - void HandleRequest(::grpc::ServerCompletionQueue *cq, std::string cq_name, + void HandleRequest(::grpc::ServerCompletionQueue *cq, + const std::string &cq_name, std::function TryToRegisterNewOne); void TryToRegisterNewSendOne(); void TryToRegisterNewGetOne(); diff --git a/paddle/fluid/operators/detail/grpc_service.h b/paddle/fluid/operators/detail/grpc_service.h index ae6f9db3bd..acaefd92a2 100644 --- a/paddle/fluid/operators/detail/grpc_service.h +++ b/paddle/fluid/operators/detail/grpc_service.h @@ -114,5 +114,5 @@ class GrpcService final { }; } // namespace detail -} // namespace operator +} // namespace operators } // namespace paddle diff --git a/paddle/fluid/operators/detail/proto_encoder_helper.h b/paddle/fluid/operators/detail/proto_encoder_helper.h index 4a7bfb8bd5..d91d054b25 100644 --- a/paddle/fluid/operators/detail/proto_encoder_helper.h +++ b/paddle/fluid/operators/detail/proto_encoder_helper.h @@ -19,7 +19,9 @@ limitations under the License. */ #pragma once -#include +#include + +#include "grpc++/grpc++.h" #include "paddle/fluid/platform/enforce.h" namespace paddle { @@ -142,6 +144,6 @@ class ProtoEncodeHelper { char* limit_; // Just for CHECKs }; -} // detail -} // operators -} // paddle +} // namespace detail +} // namespace operators +} // namespace paddle diff --git a/paddle/fluid/operators/detail/sendrecvop_utils.cc b/paddle/fluid/operators/detail/sendrecvop_utils.cc index 7e3f015dab..f8576d01b1 100644 --- a/paddle/fluid/operators/detail/sendrecvop_utils.cc +++ b/paddle/fluid/operators/detail/sendrecvop_utils.cc @@ -13,8 +13,10 @@ See the License for the specific language governing permissions and limitations under the License. */ #include "paddle/fluid/operators/detail/sendrecvop_utils.h" + #include -#include +#include // NOLINT + #include "google/protobuf/io/coded_stream.h" #include "google/protobuf/io/zero_copy_stream.h" #include "paddle/fluid/framework/data_type.h" @@ -42,7 +44,7 @@ void SerializeToByteBuffer(const std::string& name, framework::Variable* var, void* buf = malloc(1024); void* payload = nullptr; size_t payload_size; - ProtoEncodeHelper e((char*)buf, 1024); + ProtoEncodeHelper e(static_cast(buf), 1024); e.WriteString(VarMsg::kVarnameFieldNumber, name); if (var->IsType()) { e.WriteUint64(VarMsg::kTypeFieldNumber, 0); @@ -152,7 +154,7 @@ void SerializeToByteBuffer(const std::string& name, framework::Variable* var, framework::proto::VarType_Type_SELECTED_ROWS) { auto* slr = var->GetMutable(); - ProtoEncodeHelper e2((char*)buf, 128); + ProtoEncodeHelper e2(static_cast(buf), 128); // NOTE: rows is of type int64_t size_t rows_memory_size = slr->rows().size() * framework::SizeOfType(typeid(int64_t)); @@ -181,10 +183,10 @@ void SerializeToByteBuffer(const std::string& name, framework::Variable* var, void DeserializeFromByteBuffer(const ::grpc::ByteBuffer& msg, const platform::DeviceContext& ctx, const framework::Scope* scope, - framework::Variable*& var) { + framework::Variable** var) { operators::detail::VariableResponse resp(scope, &ctx); PADDLE_ENFORCE(resp.Parse(msg) == 0, "parse bytebuffer to tensor error!"); - var = resp.GetVar(); + *var = resp.GetVar(); } } // namespace detail diff --git a/paddle/fluid/operators/detail/sendrecvop_utils.h b/paddle/fluid/operators/detail/sendrecvop_utils.h index b3b2b8469c..d795444084 100644 --- a/paddle/fluid/operators/detail/sendrecvop_utils.h +++ b/paddle/fluid/operators/detail/sendrecvop_utils.h @@ -51,7 +51,7 @@ void SerializeToByteBuffer(const std::string& name, framework::Variable* var, void DeserializeFromByteBuffer(const ::grpc::ByteBuffer& msg, const platform::DeviceContext& ctx, const framework::Scope* scope, - framework::Variable*& var); + framework::Variable** var); inline std::type_index ToTypeIndex(sendrecv::VariableMessage::Type type) { switch (type) { diff --git a/paddle/fluid/operators/detail/serde_test.cc b/paddle/fluid/operators/detail/serde_test.cc index ea1670e56f..6fb2369e6e 100644 --- a/paddle/fluid/operators/detail/serde_test.cc +++ b/paddle/fluid/operators/detail/serde_test.cc @@ -14,9 +14,9 @@ limitations under the License. */ #include #include -#include +#include // NOLINT -#include +#include "google/protobuf/text_format.h" #include "gtest/gtest.h" #include "paddle/fluid/framework/lod_tensor.h" #include "paddle/fluid/framework/tensor_util.h" @@ -102,12 +102,12 @@ void RunSerdeTestSelectedRows(platform::Place place) { } else { tensor_data2 = const_cast(tensor2->data()); } - const int64_t* rows_data2 = rows2->data(); + const size_t* rows_data2 = rows2->data(); for (int i = 0; i < tensor_numel; ++i) { EXPECT_FLOAT_EQ(tensor_data2[i], 32.7); } - for (int i = 0; i < rows2->size(); ++i) { + for (size_t i = 0; i < rows2->size(); ++i) { EXPECT_EQ(rows_data2[i], i); } EXPECT_EQ(slr2->height(), 1000); diff --git a/paddle/fluid/operators/detail/simple_block_queue.h b/paddle/fluid/operators/detail/simple_block_queue.h index 36b58b0c67..69773e05df 100644 --- a/paddle/fluid/operators/detail/simple_block_queue.h +++ b/paddle/fluid/operators/detail/simple_block_queue.h @@ -14,9 +14,9 @@ limitations under the License. */ #pragma once -#include +#include // NOLINT #include -#include +#include // NOLINT namespace paddle { namespace operators { diff --git a/paddle/fluid/operators/detail/variable_response.cc b/paddle/fluid/operators/detail/variable_response.cc index f59c9b50bb..7703658899 100644 --- a/paddle/fluid/operators/detail/variable_response.cc +++ b/paddle/fluid/operators/detail/variable_response.cc @@ -13,7 +13,13 @@ // limitations under the License. #include "paddle/fluid/operators/detail/variable_response.h" + #include + +#include +#include +#include + #include "paddle/fluid/operators/detail/send_recv.pb.h" #include "paddle/fluid/operators/detail/sendrecvop_utils.h" @@ -108,7 +114,8 @@ bool ReadRaw(::google::protobuf::io::CodedInputStream* input, bool VariableResponse::CopyLodTensorData( ::google::protobuf::io::CodedInputStream* input, - const platform::DeviceContext& ctx, framework::DDim& dims, int length) { + const platform::DeviceContext& ctx, const framework::DDim& dims, + int length) { auto var = scope_->FindVar(meta_.varname()); auto* tensor = var->GetMutable(); tensor->Resize(dims); @@ -144,14 +151,15 @@ inline framework::DDim GetDims( bool VariableResponse::CopySelectRowsTensorData( ::google::protobuf::io::CodedInputStream* input, - const platform::DeviceContext& ctx, framework::DDim& dims, int length) { + const platform::DeviceContext& ctx, const framework::DDim& dims, + int length) { auto var = scope_->FindVar(meta_.varname()); auto* slr = var->GetMutable(); slr->set_height(meta_.slr_height()); auto* tensor = slr->mutable_value(); tensor->Resize(dims); PADDLE_ENFORCE_EQ( - tensor->numel(), + static_cast(tensor->numel()), length / framework::SizeOfType( paddle::operators::detail::ToTypeIndex(meta_.data_type()))); void* tensor_data = tensor->mutable_data( diff --git a/paddle/fluid/operators/detail/variable_response.h b/paddle/fluid/operators/detail/variable_response.h index e121ed7bce..050b6b8401 100644 --- a/paddle/fluid/operators/detail/variable_response.h +++ b/paddle/fluid/operators/detail/variable_response.h @@ -14,6 +14,8 @@ #pragma once +#include + #include "paddle/fluid/framework/data_type.h" #include "paddle/fluid/framework/lod_tensor.h" #include "paddle/fluid/framework/scope.h" @@ -60,14 +62,14 @@ class VariableResponse { private: bool CopySelectRowsTensorData(::google::protobuf::io::CodedInputStream* input, const platform::DeviceContext& ctx, - framework::DDim& dims, int length); + const framework::DDim& dims, int length); bool CopySelectRowsData(::google::protobuf::io::CodedInputStream* input, const platform::DeviceContext& ctx, int length); bool CopyLodTensorData(::google::protobuf::io::CodedInputStream* input, const platform::DeviceContext& ctx, - framework::DDim& dims, int length); + const framework::DDim& dims, int length); private: const framework::Scope* scope_; From ffa63974b91ba89bdc4d0dcc95b78ed5bc1c68aa Mon Sep 17 00:00:00 2001 From: chengduoZH Date: Thu, 29 Mar 2018 18:51:09 +0800 Subject: [PATCH 11/58] compare the performance of unpinned memory and pinned memory --- paddle/fluid/memory/CMakeLists.txt | 20 +-- paddle/fluid/memory/pinned_memory_test.cu | 144 ++++++++++++++++++++++ 2 files changed, 156 insertions(+), 8 deletions(-) create mode 100644 paddle/fluid/memory/pinned_memory_test.cu diff --git a/paddle/fluid/memory/CMakeLists.txt b/paddle/fluid/memory/CMakeLists.txt index 1a61c48482..53fd8626fa 100644 --- a/paddle/fluid/memory/CMakeLists.txt +++ b/paddle/fluid/memory/CMakeLists.txt @@ -4,13 +4,17 @@ cc_library(memory SRCS memory.cc DEPS place enforce) cc_library(memcpy SRCS memcpy.cc DEPS place) cc_library(paddle_memory - DEPS - memory - memcpy - meta_data - meta_cache - memory_block - buddy_allocator - system_allocator) + DEPS + memory + memcpy + meta_data + meta_cache + memory_block + buddy_allocator + system_allocator) cc_test(memory_test SRCS memory_test.cc DEPS place paddle_memory) + +if (WITH_GPU) + nv_test(pinned_memory_test SRCS pinned_memory_test.cu DEPS place paddle_memory) +endif() diff --git a/paddle/fluid/memory/pinned_memory_test.cu b/paddle/fluid/memory/pinned_memory_test.cu new file mode 100644 index 0000000000..cba39cd407 --- /dev/null +++ b/paddle/fluid/memory/pinned_memory_test.cu @@ -0,0 +1,144 @@ +/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#include "paddle/fluid/memory/detail/memory_block.h" +#include "paddle/fluid/memory/detail/meta_data.h" +#include "paddle/fluid/memory/memcpy.h" +#include "paddle/fluid/memory/memory.h" + +#include "paddle/fluid/platform/cpu_info.h" +#include "paddle/fluid/platform/gpu_info.h" +#include "paddle/fluid/platform/place.h" + +#include +#include + +template +__global__ void Kernel(T* output, int dim) { + int tid = blockIdx.x * blockDim.x + threadIdx.x; + if (tid < dim) { + output[tid] = output[tid] * output[tid] / 100; + } +} + +template +void test_pinned_memory() { + Place cpu_place; + paddle::platform::CUDAPlace cuda_place; + + const int data_size = 4096; + const int iteration = 10; + + // create event start and end + cudaEvent_t start_e, stop_e, copying_e; + float elapsedTime = 0; + cudaEventCreate(&start_e); + cudaEventCreate(&stop_e); + cudaEventCreate(©ing_e); + + // create computation stream, data copying stream + cudaStream_t computation_stream, copying_stream; + cudaStreamCreate(&computation_stream); + cudaStreamCreate(©ing_stream); + + // create record event, pinned memory, gpu memory + std::vector record_event(iteration); + std::vector input_pinned_mem(iteration); + std::vector gpu_mem(iteration); + std::vector output_pinned_mem(iteration); + + // initial data + for (int j = 0; j < iteration; ++j) { + cudaEventCreateWithFlags(&record_event[j], cudaEventDisableTiming); + cudaEventCreate(&(record_event[j])); + input_pinned_mem[j] = static_cast( + paddle::memory::Alloc(cpu_place, data_size * sizeof(float))); + output_pinned_mem[j] = static_cast( + paddle::memory::Alloc(cpu_place, data_size * sizeof(float))); + gpu_mem[j] = static_cast( + paddle::memory::Alloc(cuda_place, data_size * sizeof(float))); + + for (int k = 0; k < data_size; ++k) { + input_pinned_mem[j][k] = k; + } + } + + cudaEventRecord(start_e, computation_stream); + + // computation + for (int m = 0; m < 30; ++m) { + for (int i = 0; i < iteration; ++i) { + // cpu -> GPU on computation stream. + // note: this operation is async for pinned memory. + paddle::memory::Copy(cuda_place, gpu_mem[i], cpu_place, + input_pinned_mem[i], data_size * sizeof(float), + computation_stream); + + // call kernel on computation stream. + Kernel<<<4, 1024, 0, computation_stream>>>(gpu_mem[i], data_size); + + // record event_computation on computation stream + cudaEventRecord(record_event[i], computation_stream); + + // wait event_computation on copy stream. + // note: this operation is async. + cudaStreamWaitEvent(copying_stream, record_event[i], 0); + + // copy data GPU->CPU, on copy stream. + // note: this operation is async for pinned memory. + paddle::memory::Copy(cpu_place, output_pinned_mem[i], cuda_place, + gpu_mem[i], data_size * sizeof(float), + copying_stream); + } + } + + cudaEventRecord(copying_e, copying_stream); + cudaStreamWaitEvent(computation_stream, copying_e, 0); + + cudaEventRecord(stop_e, computation_stream); + + cudaEventSynchronize(start_e); + cudaEventSynchronize(stop_e); + cudaEventElapsedTime(&elapsedTime, start_e, stop_e); + + std::cout << cpu_place << " " + << "time consume:" << elapsedTime / 30 << std::endl; + + for (int l = 0; l < iteration; ++l) { + for (int k = 0; k < data_size; ++k) { + float temp = input_pinned_mem[l][k]; + temp = temp * temp / 100; + EXPECT_FLOAT_EQ(temp, output_pinned_mem[l][k]); + } + } + + // destroy resource + cudaEventDestroy(copying_e); + cudaEventDestroy(start_e); + cudaEventDestroy(stop_e); + for (int j = 0; j < 10; ++j) { + cudaEventDestroy((record_event[j])); + paddle::memory::Free(cpu_place, input_pinned_mem[j]); + paddle::memory::Free(cpu_place, output_pinned_mem[j]); + paddle::memory::Free(cuda_place, gpu_mem[j]); + } +} + +TEST(CPUANDCUDAPinned, CPUAllocator) { + test_pinned_memory(); +} + +TEST(CPUANDCUDAPinned, CUDAPinnedAllocator) { + test_pinned_memory(); +} \ No newline at end of file From 2945a98eb3355f5441862ab843bdbc25abb2d5c3 Mon Sep 17 00:00:00 2001 From: fengjiayi Date: Sun, 1 Apr 2018 18:04:52 +0800 Subject: [PATCH 12/58] Make MultipleReader thread-safe --- .../fluid/operators/reader/open_files_op.cc | 35 +++++++++++++------ 1 file changed, 25 insertions(+), 10 deletions(-) diff --git a/paddle/fluid/operators/reader/open_files_op.cc b/paddle/fluid/operators/reader/open_files_op.cc index b6ac7b21d5..eacedeea88 100644 --- a/paddle/fluid/operators/reader/open_files_op.cc +++ b/paddle/fluid/operators/reader/open_files_op.cc @@ -21,6 +21,22 @@ namespace reader { class MultipleReader : public framework::ReaderBase { public: + class ThreadBufferMap { + public: + std::vector& operator[]( + const std::thread::id& thread_id) { + std::lock_guard lock(mutex_); + return buffer_[thread_id]; + } + + void Clear() { buffer_.clear(); } + + private: + std::mutex mutex_; + std::unordered_map> + buffer_; + }; + MultipleReader(const std::vector& file_names, const std::vector& dims, size_t thread_num) : file_names_(file_names), dims_(dims) { @@ -47,28 +63,27 @@ class MultipleReader : public framework::ReaderBase { framework::Channel* waiting_file_idx_; framework::Channel* available_thread_idx_; framework::Channel>* buffer_; - mutable std::vector local_buffer_; + mutable ThreadBufferMap thread_buffer_map_; }; void MultipleReader::ReadNext(std::vector* out) { if (!HasNext()) { PADDLE_THROW("There is no next data!"); } - - if (local_buffer_.empty()) { - buffer_->Receive(&local_buffer_); - } - *out = local_buffer_; - local_buffer_.clear(); + auto& thread_local_buffer = thread_buffer_map_[std::this_thread::get_id()]; + *out = thread_local_buffer; + thread_local_buffer.clear(); } bool MultipleReader::HasNext() const { - return local_buffer_.empty() ? buffer_->Receive(&local_buffer_) : true; + auto& thread_local_buffer = thread_buffer_map_[std::this_thread::get_id()]; + return thread_local_buffer.empty() ? buffer_->Receive(&thread_local_buffer) + : true; } void MultipleReader::ReInit() { EndScheduler(); - local_buffer_.clear(); + thread_buffer_map_.Clear(); StartNewScheduler(); } @@ -176,7 +191,7 @@ class OpenFilesOp : public framework::OperatorBase { const auto& ranks = Attr>("ranks"); PADDLE_ENFORCE(!shape_concat.empty() && !ranks.empty()); PADDLE_ENFORCE_EQ(std::accumulate(ranks.begin(), ranks.end(), 0), - int(shape_concat.size()), + static_cast(shape_concat.size()), "The accumulate of all ranks should be equal to the " "shape concat's length."); const auto& file_names = Attr>("file_names"); From de5e56bee8cdc92f4a9417c3b91dd6084ac86b79 Mon Sep 17 00:00:00 2001 From: chengduoZH Date: Mon, 2 Apr 2018 13:06:46 +0800 Subject: [PATCH 13/58] add og has been broadcasted --- .../fluid/framework/details/multi_devices_graph_builder.cc | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/paddle/fluid/framework/details/multi_devices_graph_builder.cc b/paddle/fluid/framework/details/multi_devices_graph_builder.cc index a1b913a863..1aa33768c8 100644 --- a/paddle/fluid/framework/details/multi_devices_graph_builder.cc +++ b/paddle/fluid/framework/details/multi_devices_graph_builder.cc @@ -55,6 +55,7 @@ std::unique_ptr MultiDevSSAGraphBuilder::Build( const ProgramDesc &program) const { auto graph = new SSAGraph(); SSAGraph &result = *graph; + std::unordered_set og_has_bc; result.vars_.resize(places_.size()); bool is_forwarding = true; @@ -123,8 +124,10 @@ std::unique_ptr MultiDevSSAGraphBuilder::Build( if (!is_forwarding) { auto var_names = op->OutputArgumentNames(); for (auto &og : var_names) { - if (grad_names_.count(og) != 0) { // is param grad - // Insert NCCL AllReduce Op + if (grad_names_.count(og) != 0 && + og_has_bc.count(og) == 0) { // is param grad + // Insert NCCL AllReduce Op + og_has_bc.insert(og); #ifdef PADDLE_WITH_CUDA result.ops_.emplace_back( new NCCLAllReduceOpHandle(local_scopes_, places_, *nccl_ctxs_)); From 19b4a2a5169afe597745f9543d6d4e5af45aa2f1 Mon Sep 17 00:00:00 2001 From: weixing02 <564445201@qq.com> Date: Mon, 2 Apr 2018 13:20:23 +0800 Subject: [PATCH 14/58] Fix some dead links for cn version --- doc/fluid/dev/index_cn.rst | 6 +++--- doc/fluid/dev/index_en.rst | 2 +- doc/fluid/dev/{new_op_kernel_en.md => new_op_kernel.md} | 0 3 files changed, 4 insertions(+), 4 deletions(-) rename doc/fluid/dev/{new_op_kernel_en.md => new_op_kernel.md} (100%) diff --git a/doc/fluid/dev/index_cn.rst b/doc/fluid/dev/index_cn.rst index e70bf5dff3..f627437f35 100644 --- a/doc/fluid/dev/index_cn.rst +++ b/doc/fluid/dev/index_cn.rst @@ -4,9 +4,9 @@ .. toctree:: :maxdepth: 1 - new_op_en.md - new_op_kernel_en.md - use_eigen_en.md + new_op_cn.md + new_op_kernel.md + use_eigen_cn.md name_convention.md support_new_device.md releasing_process.md diff --git a/doc/fluid/dev/index_en.rst b/doc/fluid/dev/index_en.rst index f0e9afcfcc..0b65fed67a 100644 --- a/doc/fluid/dev/index_en.rst +++ b/doc/fluid/dev/index_en.rst @@ -5,7 +5,7 @@ Development :maxdepth: 1 new_op_en.md - new_op_kernel_en.md + new_op_kernel.md use_eigen_en.md name_convention.md support_new_device.md diff --git a/doc/fluid/dev/new_op_kernel_en.md b/doc/fluid/dev/new_op_kernel.md similarity index 100% rename from doc/fluid/dev/new_op_kernel_en.md rename to doc/fluid/dev/new_op_kernel.md From 9b6c5397c5c39864c453a19bdd2dc6ab21cee26b Mon Sep 17 00:00:00 2001 From: weixing02 <564445201@qq.com> Date: Mon, 2 Apr 2018 13:27:47 +0800 Subject: [PATCH 15/58] Merge branch develop --- .travis.yml | 2 +- paddle/fluid/operators/detail/CMakeLists.txt | 3 +- paddle/fluid/operators/detail/grpc_client.cc | 3 +- paddle/fluid/operators/detail/grpc_server.cc | 61 +++++++- paddle/fluid/operators/detail/grpc_server.h | 15 ++ .../operators/detail/grpc_server_test.cc | 51 +++++++ paddle/fluid/operators/detail/grpc_service.h | 3 + paddle/fluid/operators/detail/send_recv.proto | 2 + paddle/fluid/operators/reshape_op.cc | 130 ++++++++---------- paddle/fluid/operators/reshape_op.h | 127 ++++++++++++++++- python/paddle/fluid/executor.py | 73 +++------- python/paddle/fluid/layers/detection.py | 21 ++- python/paddle/fluid/layers/nn.py | 98 +++++++++++++ python/paddle/fluid/layers/ops.py | 1 - .../paddle/fluid/tests/unittests/op_test.py | 8 +- .../unittests/test_mine_hard_examples_op.py | 0 .../fluid/tests/unittests/test_reshape_op.py | 101 ++++++++++++-- .../tests/unittests/test_target_assign_op.py | 0 18 files changed, 529 insertions(+), 170 deletions(-) create mode 100644 paddle/fluid/operators/detail/grpc_server_test.cc mode change 100755 => 100644 python/paddle/fluid/tests/unittests/test_mine_hard_examples_op.py mode change 100755 => 100644 python/paddle/fluid/tests/unittests/test_target_assign_op.py diff --git a/.travis.yml b/.travis.yml index bf6a41d13c..929c847bd3 100644 --- a/.travis.yml +++ b/.travis.yml @@ -34,7 +34,7 @@ addons: - automake - libtool - ccache - ssh_known_hosts: 52.76.173.135 + ssh_known_hosts: 13.229.163.131 before_install: - if [[ "$JOB" == "check_style" ]]; then sudo ln -s /usr/bin/clang-format-3.8 /usr/bin/clang-format; fi # Paddle is using protobuf 3.1 currently. Protobuf 3.2 breaks the compatibility. So we specify the python diff --git a/paddle/fluid/operators/detail/CMakeLists.txt b/paddle/fluid/operators/detail/CMakeLists.txt index d59411dfb9..f8cd2852f3 100644 --- a/paddle/fluid/operators/detail/CMakeLists.txt +++ b/paddle/fluid/operators/detail/CMakeLists.txt @@ -2,7 +2,8 @@ if(WITH_DISTRIBUTE) grpc_library(sendrecvop_grpc SRCS bytebuffer_stream.cc sendrecvop_utils.cc grpc_client.cc grpc_server.cc variable_response.cc PROTO send_recv.proto DEPS lod_tensor selected_rows) set(DISTRIBUTE_COMPILE_FLAGS "-Wno-non-virtual-dtor -Wno-error=non-virtual-dtor -Wno-error=delete-non-virtual-dtor") - set_source_files_properties(serde_test.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS}) + set_source_files_properties(serde_test.cc grpc_server_test PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS}) cc_test(serde_test SRCS serde_test.cc variable_response.cc DEPS grpc++_unsecure grpc_unsecure gpr cares zlib protobuf sendrecvop_grpc) + cc_test(grpc_server_test SRCS grpc_server_test.cc DEPS sendrecvop_grpc grpc++_unsecure grpc_unsecure gpr cares zlib protobuf) endif() diff --git a/paddle/fluid/operators/detail/grpc_client.cc b/paddle/fluid/operators/detail/grpc_client.cc index 9652bb888b..ba9882ce24 100644 --- a/paddle/fluid/operators/detail/grpc_client.cc +++ b/paddle/fluid/operators/detail/grpc_client.cc @@ -150,7 +150,8 @@ bool RPCClient::AsyncPrefetchVariable(const std::string& ep, s->response_call_back_ = ProcGetResponse; auto call = s->stub_g_.PrepareUnaryCall( - s->context_.get(), "/sendrecv.SendRecvService/GetVariable", req, &cq_); + s->context_.get(), "/sendrecv.SendRecvService/PrefetchVariable", req, + &cq_); call->StartCall(); call->Finish(&s->reply_, &s->status_, (void*)s); }); diff --git a/paddle/fluid/operators/detail/grpc_server.cc b/paddle/fluid/operators/detail/grpc_server.cc index 109c762e74..591b3e334a 100644 --- a/paddle/fluid/operators/detail/grpc_server.cc +++ b/paddle/fluid/operators/detail/grpc_server.cc @@ -128,6 +128,47 @@ class RequestGet final : public RequestBase { SimpleBlockQueue* queue_; }; +class RequestPrefetch final : public RequestBase { + public: + explicit RequestPrefetch(GrpcService::AsyncService* service, + ::grpc::ServerCompletionQueue* cq, + framework::Scope* scope, + const platform::DeviceContext* dev_ctx, + framework::Executor* executor, + framework::ProgramDesc* program, int blkid) + : RequestBase(service, cq, dev_ctx), + responder_(&ctx_), + scope_(scope), + executor_(executor), + program_(program), + blkid_(blkid) { + int method_id = static_cast(detail::GrpcMethod::kPrefetchVariable); + service_->RequestAsyncUnary(method_id, &ctx_, &request_, &responder_, cq_, + cq_, this); + } + + virtual ~RequestPrefetch() {} + + virtual std::string GetReqName() { return request_.varname(); } + + virtual void Process() { + // prefetch process... + ::grpc::ByteBuffer reply; + // TODO(Yancey1989): execute the Block which containers prefetch ops + + responder_.Finish(reply, ::grpc::Status::OK, this); + status_ = FINISH; + } + + protected: + sendrecv::VariableMessage request_; + ServerAsyncResponseWriter<::grpc::ByteBuffer> responder_; + framework::Scope* scope_; + framework::Executor* executor_; + framework::ProgramDesc* program_; + int blkid_; +}; + void AsyncGRPCServer::WaitClientGet(int count) { int fetch_barriers = 0; while (fetch_barriers < count) { @@ -147,6 +188,7 @@ void AsyncGRPCServer::RunSyncUpdate() { cq_send_ = builder.AddCompletionQueue(); cq_get_ = builder.AddCompletionQueue(); + cq_prefetch_ = builder.AddCompletionQueue(); server_ = builder.BuildAndStart(); LOG(INFO) << "Server listening on " << address_ << std::endl; @@ -155,6 +197,8 @@ void AsyncGRPCServer::RunSyncUpdate() { std::bind(&AsyncGRPCServer::TryToRegisterNewSendOne, this); std::function get_register = std::bind(&AsyncGRPCServer::TryToRegisterNewGetOne, this); + std::function prefetch_register = + std::bind(&AsyncGRPCServer::TryToRegisterNewPrefetchOne, this); t_send_.reset( new std::thread(std::bind(&AsyncGRPCServer::HandleRequest, this, @@ -163,11 +207,14 @@ void AsyncGRPCServer::RunSyncUpdate() { t_get_.reset( new std::thread(std::bind(&AsyncGRPCServer::HandleRequest, this, cq_get_.get(), "cq_get", get_register))); - + t_prefetch_.reset(new std::thread( + std::bind(&AsyncGRPCServer::HandleRequest, this, cq_prefetch_.get(), + "cq_prefetch", prefetch_register))); // wait server server_->Wait(); t_send_->join(); t_get_->join(); + t_prefetch_->join(); } void AsyncGRPCServer::ShutdownQueue() { @@ -203,6 +250,18 @@ void AsyncGRPCServer::TryToRegisterNewGetOne() { VLOG(4) << "Create RequestGet status:" << get->Status(); } +void AsyncGRPCServer::TryToRegisterNewPrefetchOne() { + std::unique_lock lock(cq_mutex_); + if (is_shut_down_) { + return; + } + RequestPrefetch* prefetch = + new RequestPrefetch(&service_, cq_prefetch_.get(), scope_, dev_ctx_, + executor_, program_, prefetch_blk_id_); + + VLOG(4) << "Create RequestPrefetch status:" << prefetch->Status(); +} + // FIXME(typhoonzero): change cq_name to enum. void AsyncGRPCServer::HandleRequest(::grpc::ServerCompletionQueue* cq, std::string cq_name, diff --git a/paddle/fluid/operators/detail/grpc_server.h b/paddle/fluid/operators/detail/grpc_server.h index 10e6dd45a9..dd5cf4b377 100644 --- a/paddle/fluid/operators/detail/grpc_server.h +++ b/paddle/fluid/operators/detail/grpc_server.h @@ -17,7 +17,9 @@ limitations under the License. */ #include #include +#include "paddle/fluid/framework/executor.h" #include "paddle/fluid/framework/lod_tensor.h" +#include "paddle/fluid/framework/program_desc.h" #include "paddle/fluid/framework/scope.h" #include "paddle/fluid/framework/selected_rows.h" #include "paddle/fluid/framework/var_type.h" @@ -53,6 +55,12 @@ class AsyncGRPCServer final { void SetDevCtx(const platform::DeviceContext *dev_ctx) { dev_ctx_ = dev_ctx; } + void SetProgram(framework::ProgramDesc *program) { program_ = program; } + + void SetPrefetchBlkdId(int blkid) { prefetch_blk_id_ = blkid; } + + void SetExecutor(framework::Executor *executor) { executor_ = executor; } + const ReceivedMessage Get() { return this->var_recv_queue_.Pop(); } void Push(const std::string &msg_name) { @@ -66,6 +74,7 @@ class AsyncGRPCServer final { std::function TryToRegisterNewOne); void TryToRegisterNewSendOne(); void TryToRegisterNewGetOne(); + void TryToRegisterNewPrefetchOne(); void ShutdownQueue(); private: @@ -73,6 +82,7 @@ class AsyncGRPCServer final { volatile bool is_shut_down_ = false; std::unique_ptr<::grpc::ServerCompletionQueue> cq_send_; std::unique_ptr<::grpc::ServerCompletionQueue> cq_get_; + std::unique_ptr<::grpc::ServerCompletionQueue> cq_prefetch_; GrpcService::AsyncService service_; std::unique_ptr<::grpc::Server> server_; @@ -92,6 +102,11 @@ class AsyncGRPCServer final { std::unique_ptr t_send_; std::unique_ptr t_get_; + std::unique_ptr t_prefetch_; + + int prefetch_blk_id_; + framework::ProgramDesc *program_; + framework::Executor *executor_; }; }; // namespace detail diff --git a/paddle/fluid/operators/detail/grpc_server_test.cc b/paddle/fluid/operators/detail/grpc_server_test.cc new file mode 100644 index 0000000000..5773748106 --- /dev/null +++ b/paddle/fluid/operators/detail/grpc_server_test.cc @@ -0,0 +1,51 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#include +#include +#include + +#include "gtest/gtest.h" +#include "paddle/fluid/operators/detail/grpc_client.h" +#include "paddle/fluid/operators/detail/grpc_server.h" + +namespace framework = paddle::framework; +namespace platform = paddle::platform; +namespace detail = paddle::operators::detail; + +std::unique_ptr rpc_service_; + +void StartServer(const std::string& endpoint) { + rpc_service_.reset(new detail::AsyncGRPCServer(endpoint)); +} + +TEST(PREFETCH, CPU) { + // start up a server instance backend + // TODO(Yancey1989): Need to start a server with optimize blocks and + // prefetch blocks. + std::thread server_thread(StartServer, "127.0.0.1:8889"); + framework::Scope scope; + platform::CPUPlace place; + platform::CPUDeviceContext ctx(place); + // create var on local scope + std::string var_name("tmp_0"); + auto var = scope.Var(var_name); + auto tensor = var->GetMutable(); + tensor->Resize({10, 10}); + + detail::RPCClient client; + client.AsyncPrefetchVariable("127.0.0.1:8889", ctx, scope, var_name, ""); + server_thread.join(); + rpc_service_.reset(nullptr); +} diff --git a/paddle/fluid/operators/detail/grpc_service.h b/paddle/fluid/operators/detail/grpc_service.h index ae6f9db3bd..879e21933b 100644 --- a/paddle/fluid/operators/detail/grpc_service.h +++ b/paddle/fluid/operators/detail/grpc_service.h @@ -76,6 +76,7 @@ namespace detail { enum class GrpcMethod { kSendVariable, kGetVariable, + kPrefetchVariable, }; static const int kGrpcNumMethods = @@ -87,6 +88,8 @@ inline const char* GrpcMethodName(GrpcMethod id) { return "/sendrecv.SendRecvService/SendVariable"; case GrpcMethod::kGetVariable: return "/sendrecv.SendRecvService/GetVariable"; + case GrpcMethod::kPrefetchVariable: + return "/sendrecv.SendREcvService/PrefetchVariable"; } // Shouldn't be reached. diff --git a/paddle/fluid/operators/detail/send_recv.proto b/paddle/fluid/operators/detail/send_recv.proto index 2d33f026e4..fc12e82a7e 100644 --- a/paddle/fluid/operators/detail/send_recv.proto +++ b/paddle/fluid/operators/detail/send_recv.proto @@ -21,6 +21,8 @@ service SendRecvService { rpc SendVariable(VariableMessage) returns (VoidMessage) {} // Argument VariableMessage for GetVariable should only contain varname. rpc GetVariable(VariableMessage) returns (VariableMessage) {} + // Prefetch variable by Ids + rpc PrefetchVariable(VariableMessage) returns (VariableMessage) {} } // VariableMessage is serialized paddle variable message. diff --git a/paddle/fluid/operators/reshape_op.cc b/paddle/fluid/operators/reshape_op.cc index 832509641c..b87b8e6b26 100644 --- a/paddle/fluid/operators/reshape_op.cc +++ b/paddle/fluid/operators/reshape_op.cc @@ -17,90 +17,66 @@ limitations under the License. */ namespace paddle { namespace operators { -class ReshapeOp : public framework::OperatorWithKernel { - public: - ReshapeOp(const std::string &type, const framework::VariableNameMap &inputs, - const framework::VariableNameMap &outputs, - const framework::AttributeMap &attrs) - : OperatorWithKernel(type, inputs, outputs, attrs) {} - - void InferShape(framework::InferShapeContext *ctx) const override { - // input check - PADDLE_ENFORCE(ctx->HasInput("X"), - "Input(X) of ReshapeOp should not be null."); - PADDLE_ENFORCE(ctx->HasOutput("Out"), - "Output(Out) of ReshapeOp should not be null."); - - auto shape = ctx->Attrs().Get>("shape"); - PADDLE_ENFORCE(shape.size() > 0, "Attr(shape) shouldn't be empty."); - auto x_dims = ctx->GetInputDim("X"); - - std::vector neg_dims_idx; - // set some dimension to -1 if it is unknown - const int unknown_size = -1; - for (size_t i = 0; i < shape.size(); ++i) { - PADDLE_ENFORCE(shape[i] > 0 || shape[i] == unknown_size, - "Each dimension of Attr(shape) must be positive or %d.", - unknown_size); - if (shape[i] == unknown_size) { - neg_dims_idx.push_back(i); - PADDLE_ENFORCE(neg_dims_idx.size() <= 1, - "Only one dimension of Attr(shape) can be unknown."); - } - } - - int64_t capacity = - std::accumulate(shape.begin(), shape.end(), 1, std::multiplies()); - int64_t in_size = framework::product(x_dims); - if (neg_dims_idx.size() == 1) { - // dim infer - shape[neg_dims_idx[0]] = in_size / (-capacity); - // recalculate capacity - capacity = shape[neg_dims_idx[0]] * (-capacity); - } - // capacity check - PADDLE_ENFORCE(capacity == in_size, - "The size of Input(X) mismatches with Attr(shape)."); - // resize output - std::vector shape_int64(shape.size(), 0); - std::transform(shape.begin(), shape.end(), shape_int64.begin(), - [](int a) { return static_cast(a); }); - auto out_dims = framework::make_ddim(shape_int64); - ctx->SetOutputDim("Out", out_dims); - if (shape[0] == x_dims[0]) { - // Only pass LoD when the first dimension is equal between - // output and input. - ctx->ShareLoD("X", /*->*/ "Out"); - } - } -}; - class ReshapeOpMaker : public framework::OpProtoAndCheckerMaker { public: ReshapeOpMaker(OpProto *proto, OpAttrChecker *op_checker) : OpProtoAndCheckerMaker(proto, op_checker) { - AddInput("X", "The input tensor of reshape operator."); - AddOutput("Out", "The output tensor of reshape operator."); - AddAttr>("shape", - "(vector) " - "Target shape of reshape operator."); + AddInput("X", "(Tensor). The input tensor of reshape operator."); + AddInput("Shape", + "(Tensor, optional). If provided, reshape according to " + "this given shape. That is to say it has a higher priority than " + "the shape attribute, while the shape attribute still should be " + "set correctly to gurantee shape inference in compile time.") + .AsDispensable(); + AddOutput("Out", "(Tensor). The output tensor of reshape operator."); + AddAttr>( + "shape", "(std::vector) Target shape of reshape operator."); AddAttr("inplace", - "Change the source tensor's shape without copy memory.") - .SetDefault(true); + "(default: false) Change the source tensor's shape without " + "memory copy. When Attr(inplace) is set true, the output " + "tensor shares memory with Input(X), otherwise, a new output " + "tensor is created, and its data are copied from Input(x).") + .SetDefault(false); AddComment(R"DOC( Reshape Operator. -Reshape Input(X) into the shape specified by Attr(shape). +Reshape Input(X) into the shape specified by Attr(shape) or Input(Shape). The +data in Input(X) are unchanged. + +Examples: -An example: -Given a 2-D tensor X with 2 rows and 2 columns : [[1, 2], [3, 4]] +1. Given a 3-D tensor Input(X) with a shape [2, 4, 6], and the target shape +specified by Attr(shape) is [6, 8], the reshape operator will transform Input(X) +into a 2-D tensor with shape [6, 8] and leaving Input(X)'s data unchanged. -and target shape = [1, 4], the reshape operator will transform -the tensor X into a 2-D tensor: [[1, 2, 3, 4]] +2. Given a 3-D tensor Input(X) with a shape [2, 4, 6], and the target shape +specified by Attr(shape) is [2, 3, -1, 2], the reshape operator will transform +Input(X) into a 4-D tensor with shape [2, 3, 4, 2] and leaving Input(X)'s data +unchanged. In this case, one and only dimension of Attr(shape) can be set to -1, +the value of this dimension is inferred from the total element number of +Input(X) and remaining dimensions. + +3. Given a 3-D tensor Input(X) with a shape [2, 4, 6], and the target shape +specified by Attr(shape) is [-1, 0, 3, 2], the reshape operator will transform +Input(X) into a 4-D tensor with shape [2, 4, 3, 2] and leaving Input(X)'s data +unchanged. In this case, besides -1, 0 means the actual dimension value is going +to be copied from the corresponding dimension of Input(X). + +Note: + +1. One and only one dimension in Attr(shape) can be set -1. In this case, +the actual dimension value will be infered from the total element number of +Input(X) and remaining dimensions. + +2. More than one dimensions in Attr(shape) can be set to 0, which means the real +dimension value will be copied from Input(X) at runtime. Note that the index of +0 can not exceed Rank(X). For example, Input(X) is a 3-D tensor with shape +[2, 3, 4], Attr(shape) = [2, 3, 2, 0] is an invalid input. + +3. Input(Shape) has a higher priority than Attr(shape) if it is provided, while +Attr(shape) still should be set correctly to gurantee shape inference in +compile-time. -One dimension in the target shape can be set -1, representing that its -size is unknown. In this case, the real dimension will be infered from -the original shape of Input(X) and other dimensions in the target shape. )DOC"); } }; @@ -119,6 +95,14 @@ class ReshapeGradOp : public framework::OperatorWithKernel { "Input(Out@GRAD) shouldn't be null."); ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X")); } + + protected: + framework::OpKernelType GetExpectedKernelType( + const framework::ExecutionContext &ctx) const override { + return framework::OpKernelType( + framework::ToDataType(ctx.Input("X")->type()), + ctx.device_context()); + } }; } // namespace operators diff --git a/paddle/fluid/operators/reshape_op.h b/paddle/fluid/operators/reshape_op.h index eacb0a0cf2..871b4d38d5 100644 --- a/paddle/fluid/operators/reshape_op.h +++ b/paddle/fluid/operators/reshape_op.h @@ -20,17 +20,129 @@ limitations under the License. */ namespace paddle { namespace operators { +class ReshapeOp : public framework::OperatorWithKernel { + public: + ReshapeOp(const std::string &type, const framework::VariableNameMap &inputs, + const framework::VariableNameMap &outputs, + const framework::AttributeMap &attrs) + : OperatorWithKernel(type, inputs, outputs, attrs) {} + + void InferShape(framework::InferShapeContext *ctx) const override { + PADDLE_ENFORCE(ctx->HasInput("X"), + "Input(X) of ReshapeOp should not be null."); + PADDLE_ENFORCE(ctx->HasOutput("Out"), + "Output(Out) of ReshapeOp should not be null."); + + const std::vector &shape = ctx->Attrs().Get>("shape"); + PADDLE_ENFORCE(!shape.empty(), + "The shape information must be set by Attr(shape)."); + + if (ctx->HasInput("Shape") && ctx->IsRuntime()) { + // If true, set the shape of Output(Out) according to Input(Shape) in + // ReshapeKernel with ExecutionContext. Also check LoD in ReshapeKernel. + ctx->ShareLoD("X", /*->*/ "Out"); + return; + } + + auto x_dims = ctx->GetInputDim("X"); + auto out_dims = ValidateShape(shape, x_dims); + ctx->SetOutputDim("Out", out_dims); + if (x_dims[0] == out_dims[0]) { + // Only pass LoD when the first dimension of output and Input(X) + // are the same. + ctx->ShareLoD("X", /*->*/ "Out"); + } + } + + static framework::DDim ValidateShape(const std::vector shape, + const framework::DDim &in_dims) { + const int64_t in_size = framework::product(in_dims); + // only one dimension canbe set to -1, whose size will be automatically + // infered. + const int64_t unk_dim_val = -1; + const int64_t copy_dim_val = 0; + + std::vector output_shape(shape.size(), 0); + int64_t capacity = 1; + int unk_dim_idx = -1; + for (size_t i = 0; i < shape.size(); ++i) { + if (shape[i] == unk_dim_val) { + PADDLE_ENFORCE( + unk_dim_idx == -1, + "Only one input dimension of Attr(shape) can be unknown."); + unk_dim_idx = i; + } else if (shape[i] == copy_dim_val) { + PADDLE_ENFORCE( + static_cast(i) < in_dims.size(), + "The index of dimension to copy from input shape must be less " + "than the size of input shape."); + } else { + PADDLE_ENFORCE( + shape[i] > 0, + "Each input dimension of Attr(shape) must not be negtive except " + "one unknown dimension."); + } + + capacity *= (shape[i] ? shape[i] : in_dims[i]); + output_shape[i] = + (shape[i] ? static_cast(shape[i]) : in_dims[i]); + } + + if (unk_dim_idx != -1) { + output_shape[unk_dim_idx] = -in_size / capacity; + PADDLE_ENFORCE_EQ(output_shape[unk_dim_idx] * capacity, -in_size, + "Invalid shape is given."); + } else { + PADDLE_ENFORCE_EQ(capacity, in_size, "Invalid shape is given."); + } + return framework::make_ddim(output_shape); + } + + protected: + framework::OpKernelType GetExpectedKernelType( + const framework::ExecutionContext &ctx) const override { + return framework::OpKernelType( + framework::ToDataType(ctx.Input("X")->type()), + ctx.device_context()); + } +}; + template class ReshapeKernel : public framework::OpKernel { public: - void Compute(const framework::ExecutionContext& ctx) const { - auto* out = ctx.Output("Out"); - auto* in = ctx.Input("X"); + void Compute(const framework::ExecutionContext &ctx) const { + auto *out = ctx.Output("Out"); + auto *in = ctx.Input("X"); + auto *shape_tensor = ctx.Input("Shape"); + + framework::DDim out_dims = out->dims(); + if (shape_tensor) { + auto *shape_data = shape_tensor->data(); + if (platform::is_gpu_place(ctx.GetPlace())) { + framework::Tensor cpu_shape_tensor; + TensorCopy(*shape_tensor, platform::CPUPlace(), ctx.device_context(), + &cpu_shape_tensor); + shape_data = cpu_shape_tensor.data(); + } + auto shape = + std::vector(shape_data, shape_data + shape_tensor->numel()); + out_dims = ReshapeOp::ValidateShape(shape, in->dims()); + } + if (!in->lod().empty()) { + PADDLE_ENFORCE_EQ( + out_dims[0], in->dims()[0], + "Reshape operator cannot reshape an input sequence batch " + "into an output sequence batch that has a different " + "number of time steps. Please consider using " + "sequence_reshape op."); + } + bool inplace = ctx.Attr("inplace"); - auto out_dims = out->dims(); + out->Resize(out_dims); if (!inplace) { out->mutable_data(ctx.GetPlace()); framework::TensorCopy(*in, ctx.GetPlace(), ctx.device_context(), out); + // TensorCopy will resize to in_dims. out->Resize(out_dims); } else { out->ShareDataWith(*in); @@ -42,9 +154,10 @@ class ReshapeKernel : public framework::OpKernel { template class ReshapeGradKernel : public framework::OpKernel { public: - void Compute(const framework::ExecutionContext& ctx) const { - auto* d_out = ctx.Input(framework::GradVarName("Out")); - auto* d_x = ctx.Output(framework::GradVarName("X")); + void Compute(const framework::ExecutionContext &ctx) const { + auto *d_out = ctx.Input(framework::GradVarName("Out")); + auto *d_x = ctx.Output(framework::GradVarName("X")); + d_x->mutable_data(ctx.GetPlace()); bool inplace = ctx.Attr("inplace"); diff --git a/python/paddle/fluid/executor.py b/python/paddle/fluid/executor.py index 2612fb1ae4..54d0a12bcd 100644 --- a/python/paddle/fluid/executor.py +++ b/python/paddle/fluid/executor.py @@ -48,8 +48,7 @@ def as_numpy(tensor): assert isinstance(tensor, core.LoDTensor) lod = tensor.lod() if len(lod) > 0: - raise RuntimeError( - "Some of your featched tensors hold LoD information. \ + raise RuntimeError("Some of your fetched tensors hold LoD information. \ They can not be completely cast to Python ndarray. \ Please set the parameter 'return_numpy' as 'False' to \ return LoDTensor itself directly.") @@ -180,60 +179,24 @@ def get_program_cache_key(feed, fetch_list): class Executor(object): - def __init__(self, places): - if not isinstance(places, list) and not isinstance(places, tuple): - places = [places] - - act_places = [] - for each in places: - p = core.Place() - p.set_place(each) - act_places.append(p) - - # TODO(dzhwinter) : only use the first place - self.executor = core.Executor(act_places[0]) - self.places = places + def __init__(self, place): + self.place = place + p = core.Place() + p.set_place(place) + self.executor = core.Executor(p) self.program_caches = dict() - def aslodtensor(self, data): - def accumulate(data): - if not isinstance(data, list): - return 1 - return sum([accumulate(sub) for sub in data]) - - def parselod(data): - seq_lens = [accumulate(seq) for seq in data] - cur_len = 0 - lod = [cur_len] - for l in seq_lens: - cur_len += l - lod.append(cur_len) - return lod - - assert len(self.places) != 0 - if not isinstance(data, list): - # pure tensor case - tensor = core.LoDTensor() - tensor.set(data, self.places[0]) - return tensor - else: - raise RuntimeError("Current implementation lacks unittests") - # lodtensor case - lod = [] - if not isinstance(data[0], list): - lod.append(parselod(data)) - flattened_data = np.concatenate(data, axis=0).astype("int64") - else: - while isinstance(data[0], list): - lod.append(parselod(seq)) - flattened_data = [item for seq in data for item in seq] - data = flattened_data - flattened_data = np.concatenate(data, axis=0).astype("int64") - flattened_data = flattened_data.reshape([len(flattened_data), 1]) - tensor = core.LoDTensor() - tensor.set(flattened_data, self.places[0]) - tensor.set_lod(lod) - return tensor + def as_lodtensor(self, data): + if isinstance(data, list): + raise RuntimeError("Some of your feed data hold LoD information. \ + They can not be completely cast from a list of Python \ + ndarray to LoDTensor. Please convert data to LoDTensor \ + directly before feeding the data.\ + ") + # single tensor case + tensor = core.LoDTensor() + tensor.set(data, self.place) + return tensor def _get_program_cache(self, program_cache_key): return self.program_caches.get(program_cache_key, None) @@ -293,7 +256,7 @@ class Executor(object): feed_target_name = op.desc.output('Out')[0] cur_feed = feed[feed_target_name] if not isinstance(cur_feed, core.LoDTensor): - cur_feed = self.aslodtensor(cur_feed) + cur_feed = self.as_lodtensor(cur_feed) idx = op.desc.attr('col') core.set_feed_variable(scope, cur_feed, feed_var_name, idx) else: diff --git a/python/paddle/fluid/layers/detection.py b/python/paddle/fluid/layers/detection.py index 3e649dc5fd..a5938fe494 100644 --- a/python/paddle/fluid/layers/detection.py +++ b/python/paddle/fluid/layers/detection.py @@ -19,7 +19,6 @@ from layer_function_generator import generate_layer_fn from layer_function_generator import autodoc from ..layer_helper import LayerHelper import tensor -import ops import nn import math @@ -58,7 +57,7 @@ def detection_output(loc, This operation is to get the detection results by performing following two steps: - + 1. Decode input bounding box predictions according to the prior boxes. 2. Get the final detection results by applying multi-class non maximum suppression (NMS). @@ -130,9 +129,9 @@ def detection_output(loc, target_box=loc, code_type='decode_center_size') old_shape = scores.shape - scores = ops.reshape(x=scores, shape=(-1, old_shape[-1])) + scores = nn.reshape(x=scores, shape=(-1, old_shape[-1])) scores = nn.softmax(input=scores) - scores = ops.reshape(x=scores, shape=old_shape) + scores = nn.reshape(x=scores, shape=old_shape) scores = nn.transpose(scores, perm=[0, 2, 1]) scores.stop_gradient = True nmsed_outs = helper.create_tmp_variable(dtype=decoded_box.dtype) @@ -463,7 +462,7 @@ def ssd_loss(location, num, num_prior, num_class = confidence.shape def __reshape_to_2d(var): - return ops.reshape(x=var, shape=[-1, var.shape[-1]]) + return nn.reshape(x=var, shape=[-1, var.shape[-1]]) # 1. Find matched boundding box by prior box. # 1.1 Compute IOU similarity between ground-truth boxes and prior boxes. @@ -474,7 +473,7 @@ def ssd_loss(location, # 2. Compute confidence for mining hard examples # 2.1. Get the target label based on matched indices - gt_label = ops.reshape(x=gt_label, shape=gt_label.shape + (1, )) + gt_label = nn.reshape(x=gt_label, shape=gt_label.shape + (1, )) gt_label.stop_gradient = True target_label, _ = target_assign( gt_label, matched_indices, mismatch_value=background_label) @@ -487,7 +486,7 @@ def ssd_loss(location, conf_loss = nn.softmax_with_cross_entropy(confidence, target_label) # 3. Mining hard examples - conf_loss = ops.reshape(x=conf_loss, shape=(num, num_prior)) + conf_loss = nn.reshape(x=conf_loss, shape=(num, num_prior)) conf_loss.stop_gradient = True neg_indices = helper.create_tmp_variable(dtype='int32') dtype = matched_indices.dtype @@ -556,7 +555,7 @@ def ssd_loss(location, # 5.3 Compute overall weighted loss. loss = conf_loss_weight * conf_loss + loc_loss_weight * loc_loss # reshape to [N, Np], N is the batch size and Np is the prior box number. - loss = ops.reshape(x=loss, shape=[-1, num_prior]) + loss = nn.reshape(x=loss, shape=[-1, num_prior]) loss = nn.reduce_sum(loss, dim=1, keep_dim=True) if normalize: normalizer = nn.reduce_sum(target_loc_weight) @@ -709,7 +708,7 @@ def multi_box_head(inputs, new_shape = [ -1, reduce(lambda x, y: x * y, input.shape[axis:len(input.shape)]) ] - out = ops.reshape(x=input, shape=new_shape) + out = nn.reshape(x=input, shape=new_shape) return out def _is_list_or_tuple_(data): @@ -803,7 +802,7 @@ def multi_box_head(inputs, mbox_loc.shape[0], mbox_loc.shape[1] * mbox_loc.shape[2] * mbox_loc.shape[3] / 4, 4 ] - mbox_loc_flatten = ops.reshape(mbox_loc, shape=new_shape) + mbox_loc_flatten = nn.reshape(mbox_loc, shape=new_shape) mbox_locs.append(mbox_loc_flatten) # get conf @@ -819,7 +818,7 @@ def multi_box_head(inputs, conf_loc.shape[0], conf_loc.shape[1] * conf_loc.shape[2] * conf_loc.shape[3] / num_classes, num_classes ] - conf_loc_flatten = ops.reshape(conf_loc, shape=new_shape) + conf_loc_flatten = nn.reshape(conf_loc, shape=new_shape) mbox_confs.append(conf_loc_flatten) if len(box_results) == 1: diff --git a/python/paddle/fluid/layers/nn.py b/python/paddle/fluid/layers/nn.py index 0332556f62..e59ee25120 100644 --- a/python/paddle/fluid/layers/nn.py +++ b/python/paddle/fluid/layers/nn.py @@ -73,6 +73,7 @@ __all__ = [ 'smooth_l1', 'one_hot', 'autoincreased_step_counter', + 'reshape', 'lod_reset', 'lrn', ] @@ -3265,6 +3266,8 @@ def one_hot(input, depth): The one-hot tensor or LodTensor, same as input. Examples: + .. code-block:: python + X is a LoDTensor: X.lod = [[0, 1, 4]] X.shape = [4, 1] @@ -3319,6 +3322,101 @@ def autoincreased_step_counter(counter_name=None, begin=1, step=1): return counter +def reshape(x, shape, actual_shape=None, act=None, inplace=True, name=None): + """ + Gives a new shape to the input Tensor without changing its data. + + The target shape can be given by :attr:`shape` or :attr:`actual_shape`. + :attr:`shape` is a list of integer while :attr:`actual_shape` is a tensor + variable. :attr:`actual_shape` has a higher priority than :attr:`shape` + if it is provided, while :attr:`shape` still should be set correctly to + gurantee shape inference in compile-time. + + Some tricks exist when specifying the target shape. + + 1. -1 means the value of this dimension is inferred from the total element + number of x and remaining dimensions. Thus one and only one dimension can + be set -1. + + 2. 0 means the actual dimension value is going to be copied from the + corresponding dimension of x. The indice of 0s in shape can not exceed + Rank(X). + + Here are some examples to explain it. + + 1. Given a 3-D tensor x with a shape [2, 4, 6], and the target shape + is [6, 8], the reshape operator will transform x into a 2-D tensor with + shape [6, 8] and leaving x's data unchanged. + + 2. Given a 3-D tensor x with a shape [2, 4, 6], and the target shape + specified is [2, 3, -1, 2], the reshape operator will transform x into a + 4-D tensor with shape [2, 3, 4, 2] and leaving x's data unchanged. In this + case, one dimension of the target shape is set to -1, the value of this + dimension is inferred from the total element number of x and remaining + dimensions. + + 3. Given a 3-D tensor x with a shape [2, 4, 6], and the target shape + is [-1, 0, 3, 2], the reshape operator will transform x into a 4-D tensor + with shape [2, 4, 3, 2] and leaving x's data unchanged. In this case, + besides -1, 0 means the actual dimension value is going to be copied from + the corresponding dimension of x. + + Args: + input(variable): The input tensor. + shape(list): The new shape. At most one dimension of the new shape can + be -1. + actual_shape(variable): An optional input. If provided, reshape + according to this given shape rather than + :attr:`shape` specifying shape. That is to + say :attr:`actual_shape` has a higher priority + than :attr:`shape`. + act (str): The non-linear activation to be applied to output variable. + inplace(bool): If this flag is set true, a new output tensor is created + whose data is copied from input x, otherwise the output + shares data with input without copying. + + Returns(variable): The output tensor. + + Examples: + .. code-block:: python + data = fluid.layers.data( + name='data', shape=[2, 4, 6], dtype='float32') + reshaped = fluid.layers.reshape( + x=data, shape=[-1, 0, 3, 2], act='tanh', inplace=True) + """ + + if not (isinstance(shape, list) or isinstance(shape, tuple)): + raise ValueError("Input shape must be a python lsit or tuple.") + + # Validate the shape + unk_dim_idx = -1 + for dim_idx, dim_size in enumerate(shape): + if dim_size == -1: + assert unk_dim_idx == -1, ( + "Only one dimension in shape can be unknown.") + unk_dim_idx = dim_idx + elif dim_size == 0: + assert dim_idx < len(x.shape), ( + "The indice of 0s in shape can not exceed Rank(X).") + else: + assert dim_size > 0, ( + "Each dimension size given in shape must not be negtive " + "except one unknown dimension.") + + helper = LayerHelper("reshape", **locals()) + reshaped = helper.create_tmp_variable(dtype=x.dtype) + helper.append_op( + type="reshape", + inputs={"X": x, + "Shape": actual_shape} + if isinstance(actual_shape, Variable) else {"X": x}, + attrs={"shape": shape, + "inplace": inplace}, + outputs={"Out": reshaped}) + + return helper.append_activation(reshaped) + + def lod_reset(x, y=None, target_lod=None): """ LoD Reset Operator. Set LoD of **x** to a new one specified by **y** or diff --git a/python/paddle/fluid/layers/ops.py b/python/paddle/fluid/layers/ops.py index 0e5987ee59..a9fe25744c 100644 --- a/python/paddle/fluid/layers/ops.py +++ b/python/paddle/fluid/layers/ops.py @@ -49,7 +49,6 @@ __activations__ = [ __all__ = [ 'mean', 'mul', - 'reshape', 'scale', 'sigmoid_cross_entropy_with_logits', 'elementwise_add', diff --git a/python/paddle/fluid/tests/unittests/op_test.py b/python/paddle/fluid/tests/unittests/op_test.py index 8393f7827b..299ab8e51f 100644 --- a/python/paddle/fluid/tests/unittests/op_test.py +++ b/python/paddle/fluid/tests/unittests/op_test.py @@ -334,7 +334,7 @@ class OpTest(unittest.TestCase): np.allclose( actual_t, expect_t, atol=atol), "Output (" + out_name + ") has diff at " + str(place) + - str(actual_t) + str(expect_t)) + str(actual_t) + "\n" + str(expect_t)) if isinstance(expect, tuple): self.assertListEqual(actual.lod(), expect[1], "Output (" + out_name + @@ -568,6 +568,6 @@ class OpTest(unittest.TestCase): fetch_list = [g for p, g in param_grad_list] executor = Executor(place) - return map( - np.array, - executor.run(prog, feed_dict, fetch_list, return_numpy=False)) + return map(np.array, + executor.run(prog, feed_dict, fetch_list, + return_numpy=False)) diff --git a/python/paddle/fluid/tests/unittests/test_mine_hard_examples_op.py b/python/paddle/fluid/tests/unittests/test_mine_hard_examples_op.py old mode 100755 new mode 100644 diff --git a/python/paddle/fluid/tests/unittests/test_reshape_op.py b/python/paddle/fluid/tests/unittests/test_reshape_op.py index 11f35c74d4..f51b5a7e99 100644 --- a/python/paddle/fluid/tests/unittests/test_reshape_op.py +++ b/python/paddle/fluid/tests/unittests/test_reshape_op.py @@ -14,15 +14,19 @@ import unittest import numpy as np + from op_test import OpTest class TestReshapeOp(OpTest): def setUp(self): + ori_shape = (2, 25) + new_shape = (5, 10) + self.op_type = "reshape" - self.inputs = {'X': np.random.random((10, 20)).astype("float32")} - self.attrs = {'shape': [10 * 20]} - self.outputs = {'Out': self.inputs['X'].reshape(self.attrs['shape'])} + self.inputs = {"X": np.random.random(ori_shape).astype("float32")} + self.attrs = {"shape": new_shape, "inplace": False} + self.outputs = {"Out": self.inputs["X"].reshape(new_shape)} def test_check_output(self): self.check_output() @@ -31,12 +35,33 @@ class TestReshapeOp(OpTest): self.check_grad(["X"], "Out") -class TestReshapeOpDimInfer(OpTest): +class TestReshapeOpDimInfer1(OpTest): def setUp(self): + ori_shape = (5, 10) + new_shape = (5, -1, 5) + self.op_type = "reshape" - self.inputs = {'X': np.random.random((10, 20)).astype("float32")} - self.attrs = {'shape': [4, -1, 5]} - self.outputs = {'Out': self.inputs['X'].reshape(self.attrs['shape'])} + self.inputs = {"X": np.random.random(ori_shape).astype("float32")} + self.attrs = {"shape": new_shape, "inplace": False} + self.outputs = {"Out": self.inputs["X"].reshape(self.attrs["shape"])} + + def test_check_output(self): + self.check_output() + + def test_check_grad(self): + self.check_grad(["X"], "Out") + + +class TestReshapeOpDimInfer2(OpTest): + def setUp(self): + ori_shape = (2, 2, 6) + new_shape = (2, 0, 3, -1) + infered_shape = (2, 2, 3, -1) + + self.op_type = "reshape" + self.inputs = {"X": np.random.random(ori_shape).astype("float32")} + self.attrs = {"shape": new_shape, "inplace": False} + self.outputs = {"Out": self.inputs["X"].reshape(infered_shape)} def test_check_output(self): self.check_output() @@ -47,10 +72,30 @@ class TestReshapeOpDimInfer(OpTest): class TestReshapeOpInplace(OpTest): def setUp(self): + ori_shape = (2, 25) + new_shape = (5, 10) + + self.op_type = "reshape" + self.inputs = {"X": np.random.random(ori_shape).astype("float32")} + self.attrs = {"shape": new_shape} + self.outputs = {"Out": self.inputs["X"].reshape(new_shape)} + + def test_check_output(self): + self.check_output() + + def test_check_grad(self): + self.check_grad(["X"], "Out") + + +class TestReshapeOpDimInferInplace1(OpTest): + def setUp(self): + ori_shape = (5, 10) + new_shape = (5, -1, 5) + self.op_type = "reshape" - self.inputs = {'X': np.random.random((10, 20)).astype("float32")} - self.attrs = {'shape': [10 * 20], 'inplace': True} - self.outputs = {'Out': self.inputs['X'].reshape(self.attrs['shape'])} + self.inputs = {"X": np.random.random(ori_shape).astype("float32")} + self.attrs = {"shape": new_shape} + self.outputs = {"Out": self.inputs["X"].reshape(new_shape)} def test_check_output(self): self.check_output() @@ -59,12 +104,38 @@ class TestReshapeOpInplace(OpTest): self.check_grad(["X"], "Out") -class TestReshapeOpDimInferInplace(OpTest): +class TestReshapeOpDimInferInplace2(OpTest): def setUp(self): + ori_shape = (2, 2, 6) + new_shape = (2, 0, 3, -1) + infered_shape = (2, 2, 3, -1) + + self.op_type = "reshape" + self.inputs = {"X": np.random.random(ori_shape).astype("float32")} + self.attrs = {"shape": new_shape} + self.outputs = {"Out": self.inputs["X"].reshape(infered_shape)} + + def test_check_output(self): + self.check_output() + + def test_check_grad(self): + self.check_grad(["X"], "Out") + + +class TestReshapeOpWithInputShape(OpTest): + def setUp(self): + ori_shape = (6, 5) + new_shape = (0, -1, 5) + actual_shape = (2, 3, 5) + self.op_type = "reshape" - self.inputs = {'X': np.random.random((10, 20)).astype("float32")} - self.attrs = {'shape': [4, -1, 5], 'inplace': True} - self.outputs = {'Out': self.inputs['X'].reshape(self.attrs['shape'])} + self.inputs = { + "X": np.random.random(ori_shape).astype("float32"), + "Shape": np.array( + actual_shape, dtype="int32") + } + self.attrs = {"shape": new_shape} + self.outputs = {"Out": self.inputs["X"].reshape(actual_shape)} def test_check_output(self): self.check_output() @@ -73,5 +144,5 @@ class TestReshapeOpDimInferInplace(OpTest): self.check_grad(["X"], "Out") -if __name__ == '__main__': +if __name__ == "__main__": unittest.main() diff --git a/python/paddle/fluid/tests/unittests/test_target_assign_op.py b/python/paddle/fluid/tests/unittests/test_target_assign_op.py old mode 100755 new mode 100644 From 4c8eef5739ecb64295992a5361a5f52f896895d4 Mon Sep 17 00:00:00 2001 From: guosheng Date: Mon, 2 Apr 2018 14:39:33 +0800 Subject: [PATCH 16/58] Add python wrapper for pad_op --- python/paddle/fluid/layers/nn.py | 58 ++++++++++++++++++++++++++++++++ 1 file changed, 58 insertions(+) diff --git a/python/paddle/fluid/layers/nn.py b/python/paddle/fluid/layers/nn.py index 0332556f62..c6f831c29d 100644 --- a/python/paddle/fluid/layers/nn.py +++ b/python/paddle/fluid/layers/nn.py @@ -75,6 +75,7 @@ __all__ = [ 'autoincreased_step_counter', 'lod_reset', 'lrn', + 'pad', ] @@ -3482,3 +3483,60 @@ def lrn(input, n=5, k=1.0, alpha=1e-4, beta=0.75, name=None): "beta": beta}) return lrn_out + + +def pad(x, paddings, pad_value=0., name=None): + """ + Pads a tensor with a constant value given by :attr:pad_value, and the + padded width is specified by :attr:paddings. + + Specifically, the number of values padded before each dimension + :attr:i is indicated by :attr:paddings[i], and the number of values padded + after each dimension :attr:i is indicated by :attr:paddings[i+1]. + + See below for an example. + + .. code-block:: text + + Given: + x = [[1, 2], [3, 4]] + + paddings = [0, 1, 1, 2] + + pad_value = 0 + + Return: + + out = [[0, 1, 2, 0, 0] + [0, 3, 4, 0, 0] + [0, 0, 0, 0, 0]] + + Args: + x (Variable): The input tensor variable. + paddings (list): A list of integers. Its elements specify the padded + width before and after for each dimension in turn. + The length of :attr:paddings must be + :math:`rank(x) \\times 2`. + pad_value (float): The constant value used to pad. + name(str|None): A name for this layer(optional). If set None, the layer + will be named automatically. + + Returns: + Variable: The padded tensor variable. + + Examples: + .. code-block:: python + # x is a rank 2 tensor variable. + out = fluid.layers.pad( + x=x, paddings=[0, 1, 1, 2], pad_value=0.) + """ + helper = LayerHelper('pad', input=x, **locals()) + dtype = helper.input_dtype() + out = helper.create_tmp_variable(dtype) + helper.append_op( + type='pad', + inputs={'X': x}, + outputs={'Out': out}, + attrs={'paddings': paddings, + 'pad_value': float(pad_value)}) + return out From 75bfdb3a3cfc526dcb5eb664ffcf2d20dd932c3c Mon Sep 17 00:00:00 2001 From: typhoonzero Date: Mon, 2 Apr 2018 17:27:51 +0800 Subject: [PATCH 17/58] refine --- paddle/fluid/framework/executor.cc | 2 +- paddle/fluid/operators/listen_and_serv_op.cc | 42 ++++++++++++-------- 2 files changed, 26 insertions(+), 18 deletions(-) diff --git a/paddle/fluid/framework/executor.cc b/paddle/fluid/framework/executor.cc index 96d9b49c87..16a118090b 100644 --- a/paddle/fluid/framework/executor.cc +++ b/paddle/fluid/framework/executor.cc @@ -279,7 +279,7 @@ std::unique_ptr Executor::Prepare( return std::unique_ptr(ctx); } -std::vector> Prepare( +std::vector> Executor::Prepare( const ProgramDesc& program, const std::vector& block_ids) { std::vector> result; for (auto& bid : block_ids) { diff --git a/paddle/fluid/operators/listen_and_serv_op.cc b/paddle/fluid/operators/listen_and_serv_op.cc index 6094c066f9..d4b0fa3aa1 100644 --- a/paddle/fluid/operators/listen_and_serv_op.cc +++ b/paddle/fluid/operators/listen_and_serv_op.cc @@ -54,20 +54,24 @@ static void CreateTensorFromMessageType(framework::Variable *var, } } -static void ParallelExecuteBlocks(const std::vector ¶llel_blkids, - framework::Executor *executor, - framework::ProgramDesc *program, - framework::Scope *scope) { +static void ParallelExecuteBlocks( + const std::vector ¶llel_blkids, framework::Executor *executor, + const std::vector> + &prepared, + framework::ProgramDesc *program, framework::Scope *scope) { std::vector> fs; for (size_t idx : parallel_blkids) { - fs.push_back(framework::Async([&executor, &program, &scope, idx]() { - int run_block = idx; // thread local - try { - executor->Run(*program, scope, run_block, false, false); - } catch (std::exception &e) { - LOG(ERROR) << "run sub program error " << e.what(); - } - })); + fs.push_back( + framework::Async([&executor, &prepared, &program, &scope, idx]() { + int run_block = idx; // thread local + try { + // executor->Run(*program, scope, run_block, false, false); + executor->RunPreparedContext(prepared[run_block].get(), scope, + false, false); + } catch (std::exception &e) { + LOG(ERROR) << "run sub program error " << e.what(); + } + })); } for (size_t i = 0; i < fs.size(); ++i) fs[i].wait(); } @@ -105,15 +109,18 @@ class ListenAndServOp : public framework::OperatorBase { auto *block = Attr(kOptimizeBlock); auto *program = block->Program(); - int num_blocks = program->Size(); + size_t num_blocks = program->Size(); PADDLE_ENFORCE_GE(num_blocks, 2, "server program should have at least 2 blocks"); framework::Executor executor(dev_place); std::vector block_list; - for (int blkid = 1; blkid < num_blocks; ++blkid) + for (size_t blkid = 1; blkid < num_blocks; ++blkid) block_list.push_back(blkid); auto prepared = executor.Prepare(*program, block_list); + prepared.insert( + prepared.begin(), + std::shared_ptr(nullptr)); // TODO(typhoonzero): change this to a while_op for every cluster-batch. bool exit_flag = false; @@ -161,21 +168,22 @@ class ListenAndServOp : public framework::OperatorBase { // The optimize blocks which have the same parent ID would run parallel // TODO(Yancey1989): need to use ParallelExecutor for future - size_t last_parent_blkid = program->Block(1).Parent(); + int32_t last_parent_blkid = program->Block(1).Parent(); std::vector parallel_blkids; parallel_blkids.push_back(1); double ts = detail::GetTimestamp(); for (size_t blkid = 2; blkid < num_blocks; ++blkid) { if (program->Block(blkid).Parent() != last_parent_blkid) { for (size_t idx : parallel_blkids) VLOG(3) << idx; - ParallelExecuteBlocks(parallel_blkids, &executor, program, + ParallelExecuteBlocks(parallel_blkids, &executor, prepared, program, &recv_scope); parallel_blkids.clear(); last_parent_blkid = program->Block(blkid).Parent(); } parallel_blkids.push_back(blkid); } - ParallelExecuteBlocks(parallel_blkids, &executor, program, &recv_scope); + ParallelExecuteBlocks(parallel_blkids, &executor, prepared, program, + &recv_scope); VLOG(2) << "run all blocks spent (ms) " << detail::GetTimestamp() - ts; From 30adc0b5f867aabc61367d293bd1cabb0216a51a Mon Sep 17 00:00:00 2001 From: chengduoZH Date: Mon, 2 Apr 2018 19:06:57 +0800 Subject: [PATCH 18/58] add notation --- .../framework/details/multi_devices_graph_builder.cc | 12 ++++++++---- 1 file changed, 8 insertions(+), 4 deletions(-) diff --git a/paddle/fluid/framework/details/multi_devices_graph_builder.cc b/paddle/fluid/framework/details/multi_devices_graph_builder.cc index 1aa33768c8..c277bd7cb6 100644 --- a/paddle/fluid/framework/details/multi_devices_graph_builder.cc +++ b/paddle/fluid/framework/details/multi_devices_graph_builder.cc @@ -55,7 +55,7 @@ std::unique_ptr MultiDevSSAGraphBuilder::Build( const ProgramDesc &program) const { auto graph = new SSAGraph(); SSAGraph &result = *graph; - std::unordered_set og_has_bc; + std::unordered_set og_has_been_broadcast; result.vars_.resize(places_.size()); bool is_forwarding = true; @@ -123,11 +123,15 @@ std::unique_ptr MultiDevSSAGraphBuilder::Build( if (!is_forwarding) { auto var_names = op->OutputArgumentNames(); + // Currently, we assume that once gradient is generated, it can be + // broadcast, and each gradient is only broadcast once. But there are no + // other cases, for example, we need to adjust the gradient according to + // the input when we get the gradient, which is not considered at present. for (auto &og : var_names) { if (grad_names_.count(og) != 0 && - og_has_bc.count(og) == 0) { // is param grad - // Insert NCCL AllReduce Op - og_has_bc.insert(og); + og_has_been_broadcast.count(og) == 0) { // is param grad + // Insert NCCL AllReduce Op + og_has_been_broadcast.insert(og); #ifdef PADDLE_WITH_CUDA result.ops_.emplace_back( new NCCLAllReduceOpHandle(local_scopes_, places_, *nccl_ctxs_)); From 2514d70ea77bd770068adc12f99b7eb7f1fcdaf8 Mon Sep 17 00:00:00 2001 From: chengduoZH Date: Mon, 2 Apr 2018 23:19:00 +0800 Subject: [PATCH 19/58] follow comments --- paddle/fluid/memory/detail/system_allocator.cc | 10 +++++++++- paddle/fluid/memory/memory.cc | 9 ++++----- paddle/fluid/platform/cpu_info.cc | 9 +++++---- 3 files changed, 18 insertions(+), 10 deletions(-) diff --git a/paddle/fluid/memory/detail/system_allocator.cc b/paddle/fluid/memory/detail/system_allocator.cc index 2463fdf483..bb6862990a 100644 --- a/paddle/fluid/memory/detail/system_allocator.cc +++ b/paddle/fluid/memory/detail/system_allocator.cc @@ -131,7 +131,12 @@ void* CUDAPinnedAllocator::Alloc(size_t& index, size_t size) { size_t usable = paddle::platform::CUDAPinnedMaxAllocSize() - cuda_pinnd_alloc_size_; - if (size > usable) return nullptr; + if (size > usable) { + LOG(WARNING) << "Cannot malloc " << size / 1024.0 / 1024.0 + << " MB pinned memory." + << ", available " << usable / 1024.0 / 1024.0 << " MB"; + return nullptr; + } void* p; // PINNED memory is visible to all CUDA contexts. @@ -141,6 +146,9 @@ void* CUDAPinnedAllocator::Alloc(size_t& index, size_t size) { index = 1; // PINNED memory cuda_pinnd_alloc_size_ += size; return p; + } else { + LOG(WARNING) << "cudaMallocHost failed."; + return nullptr; } return nullptr; diff --git a/paddle/fluid/memory/memory.cc b/paddle/fluid/memory/memory.cc index 7b459fe4d0..dc6c362728 100644 --- a/paddle/fluid/memory/memory.cc +++ b/paddle/fluid/memory/memory.cc @@ -133,11 +133,10 @@ void* Alloc(platform::CUDAPinnedPlace place, auto* buddy_allocator = GetCUDAPinnedBuddyAllocator(); void* ptr = buddy_allocator->Alloc(size); - // if (ptr == nullptr) { - // LOG(WARNING) << "Cannot allocate " << size << " bytes in CUDAPinnedPlace - // " - // << ", available " << avail << " bytes" - // } + if (ptr == nullptr) { + LOG(WARNING) << "cudaMallocHost Cannot allocate " << size + << " bytes in CUDAPinnedPlace"; + } return ptr; } diff --git a/paddle/fluid/platform/cpu_info.cc b/paddle/fluid/platform/cpu_info.cc index d44f1cadd2..4fc9aae8e3 100644 --- a/paddle/fluid/platform/cpu_info.cc +++ b/paddle/fluid/platform/cpu_info.cc @@ -27,9 +27,10 @@ DEFINE_double(fraction_of_cpu_memory_to_use, 1, "Default use 100% of CPU memory for PaddlePaddle," "reserve the rest for page tables, etc"); -DEFINE_double(fraction_of_cuda_pinned_memory_to_use, 0.5, - "Default use 100% of CPU memory for PaddlePaddle," - "reserve the rest for page tables, etc"); +DEFINE_double( + fraction_of_cuda_pinned_memory_to_use, 0.5, + "Default use 50% of CPU memory as the pinned_memory for PaddlePaddle," + "reserve the rest for page tables, etc"); namespace paddle { namespace platform { @@ -78,7 +79,7 @@ size_t CUDAPinnedMinChunkSize() { } size_t CUDAPinnedMaxChunkSize() { - // Allow to allocate the maximum chunk size is roughly 0.39% of CUDA_PINNED + // Allow to allocate the maximum chunk size is roughly 1/256 of CUDA_PINNED // memory. return CUDAPinnedMaxAllocSize() / 256; } From 0b8534f2a456d786fdc7ef2f252409f0ac0bbca3 Mon Sep 17 00:00:00 2001 From: guosheng Date: Mon, 2 Apr 2018 23:35:54 +0800 Subject: [PATCH 20/58] Refine python wrapper for pad_op --- doc/fluid/api/layers.rst | 6 ++++++ python/paddle/fluid/layers/nn.py | 13 ++++++++----- 2 files changed, 14 insertions(+), 5 deletions(-) diff --git a/doc/fluid/api/layers.rst b/doc/fluid/api/layers.rst index ae35d8c534..22e6fb13d7 100644 --- a/doc/fluid/api/layers.rst +++ b/doc/fluid/api/layers.rst @@ -494,6 +494,12 @@ reshape .. autofunction:: paddle.fluid.layers.reshape :noindex: +pad +--- + +.. autofunction:: paddle.fluid.layers.pad + :noindex: + scale ----- diff --git a/python/paddle/fluid/layers/nn.py b/python/paddle/fluid/layers/nn.py index f96bc6911f..3d13133bf2 100644 --- a/python/paddle/fluid/layers/nn.py +++ b/python/paddle/fluid/layers/nn.py @@ -3380,6 +3380,7 @@ def reshape(x, shape, actual_shape=None, act=None, inplace=True, name=None): Examples: .. code-block:: python + data = fluid.layers.data( name='data', shape=[2, 4, 6], dtype='float32') reshaped = fluid.layers.reshape( @@ -3585,12 +3586,13 @@ def lrn(input, n=5, k=1.0, alpha=1e-4, beta=0.75, name=None): def pad(x, paddings, pad_value=0., name=None): """ - Pads a tensor with a constant value given by :attr:pad_value, and the - padded width is specified by :attr:paddings. + Pads a tensor with a constant value given by :attr:`pad_value`, and the + padded width is specified by :attr:`paddings`. - Specifically, the number of values padded before each dimension - :attr:i is indicated by :attr:paddings[i], and the number of values padded - after each dimension :attr:i is indicated by :attr:paddings[i+1]. + Specifically, the number of values padded before the contents of :attr:`x` + in dimension :attr:`i` is indicated by :attr:`paddings[i]`, and the number + of values padded after the contents of :attr:`x` in dimension :attr:`i` is + indicated by :attr:`paddings[i+1]`. See below for an example. @@ -3624,6 +3626,7 @@ def pad(x, paddings, pad_value=0., name=None): Examples: .. code-block:: python + # x is a rank 2 tensor variable. out = fluid.layers.pad( x=x, paddings=[0, 1, 1, 2], pad_value=0.) From 94eea16e6de736f6b08c2cdf2b99c0057ccfbca5 Mon Sep 17 00:00:00 2001 From: typhoonzero Date: Tue, 3 Apr 2018 15:02:45 +0800 Subject: [PATCH 21/58] fix sendrecv port bind --- paddle/fluid/operators/detail/grpc_server.cc | 9 +- paddle/fluid/operators/detail/grpc_server.h | 3 + paddle/fluid/operators/listen_and_serv_op.cc | 272 ++++++++----------- paddle/fluid/operators/listen_and_serv_op.h | 85 ++++++ paddle/fluid/operators/send_recv_op_test.cc | 27 +- 5 files changed, 233 insertions(+), 163 deletions(-) create mode 100644 paddle/fluid/operators/listen_and_serv_op.h diff --git a/paddle/fluid/operators/detail/grpc_server.cc b/paddle/fluid/operators/detail/grpc_server.cc index 7c978b28b6..b81d374158 100644 --- a/paddle/fluid/operators/detail/grpc_server.cc +++ b/paddle/fluid/operators/detail/grpc_server.cc @@ -186,7 +186,8 @@ void AsyncGRPCServer::WaitClientGet(int count) { void AsyncGRPCServer::RunSyncUpdate() { ::grpc::ServerBuilder builder; - builder.AddListeningPort(address_, ::grpc::InsecureServerCredentials()); + builder.AddListeningPort(address_, ::grpc::InsecureServerCredentials(), + &selected_port_); builder.SetMaxSendMessageSize(std::numeric_limits::max()); builder.SetMaxReceiveMessageSize(std::numeric_limits::max()); builder.RegisterService(&service_); @@ -196,7 +197,8 @@ void AsyncGRPCServer::RunSyncUpdate() { cq_prefetch_ = builder.AddCompletionQueue(); server_ = builder.BuildAndStart(); - LOG(INFO) << "Server listening on " << address_ << std::endl; + LOG(INFO) << "Server listening on " << address_ + << " selected port: " << selected_port_; std::function send_register = std::bind(&AsyncGRPCServer::TryToRegisterNewSendOne, this); @@ -242,6 +244,9 @@ void AsyncGRPCServer::TryToRegisterNewSendOne() { VLOG(3) << "shutdown, do not TryToRegisterNewSendOne"; return; } + while (scope_ == nullptr) { + sleep(0.01); + } RequestSend* send = new RequestSend(&service_, cq_send_.get(), scope_, &var_recv_queue_, dev_ctx_); VLOG(4) << "Create RequestSend status:" << send->Status(); diff --git a/paddle/fluid/operators/detail/grpc_server.h b/paddle/fluid/operators/detail/grpc_server.h index b0596d3cd1..014bbd0e7f 100644 --- a/paddle/fluid/operators/detail/grpc_server.h +++ b/paddle/fluid/operators/detail/grpc_server.h @@ -62,6 +62,8 @@ class AsyncGRPCServer final { void SetExecutor(framework::Executor *executor) { executor_ = executor; } + int GetSelectedPort() { return selected_port_; } + const ReceivedMessage Get() { return this->var_recv_queue_.Pop(); } void Push(const std::string &msg_name) { @@ -109,6 +111,7 @@ class AsyncGRPCServer final { int prefetch_blk_id_; framework::ProgramDesc *program_; framework::Executor *executor_; + int selected_port_; }; }; // namespace detail diff --git a/paddle/fluid/operators/listen_and_serv_op.cc b/paddle/fluid/operators/listen_and_serv_op.cc index b19add24e2..503ddd5d2f 100644 --- a/paddle/fluid/operators/listen_and_serv_op.cc +++ b/paddle/fluid/operators/listen_and_serv_op.cc @@ -12,185 +12,145 @@ 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 "paddle/fluid/framework/executor.h" -#include "paddle/fluid/framework/lod_tensor.h" -#include "paddle/fluid/framework/op_registry.h" -#include "paddle/fluid/framework/threadpool.h" -#include "paddle/fluid/operators/detail/grpc_server.h" +#include "paddle/fluid/operators/listen_and_serv_op.h" namespace paddle { namespace operators { -constexpr char kOptimizeBlock[] = "OptimizeBlock"; - void RunServer(std::shared_ptr service) { service->RunSyncUpdate(); VLOG(4) << "RunServer thread end"; } -static void CreateTensorFromMessageType(framework::Variable *var, - sendrecv::VarType var_type) { - if (var_type == sendrecv::VarType::LOD_TENSOR) { - var->GetMutable(); - } else if (var_type == sendrecv::VarType::SELECTED_ROWS) { - var->GetMutable(); - } else { - PADDLE_THROW( - "VariableMessage type %d is not in " - "[LoDTensor, SelectedRows]", - var_type); - } +ListenAndServOp::ListenAndServOp(const std::string &type, + const framework::VariableNameMap &inputs, + const framework::VariableNameMap &outputs, + const framework::AttributeMap &attrs) + : OperatorBase(type, inputs, outputs, attrs) {} + +int ListenAndServOp::GetSelectedPort() { + return rpc_service_->GetSelectedPort(); } -static void ParallelExecuteBlocks(const std::vector ¶llel_blkids, - framework::Executor *executor, - framework::ProgramDesc *program, - framework::Scope *scope) { - std::vector> fs; - for (size_t idx : parallel_blkids) { - fs.push_back(framework::Async([&executor, &program, &scope, idx]() { - int run_block = idx; // thread local - try { - executor->Run(*program, scope, run_block, false, false); - } catch (std::exception &e) { - LOG(ERROR) << "run sub program error " << e.what(); - } - })); - } - for (size_t i = 0; i < fs.size(); ++i) fs[i].wait(); +void ListenAndServOp::Stop() { + rpc_service_->Push(LISTEN_TERMINATE_MESSAGE); + server_thread_->join(); } -class ListenAndServOp : public framework::OperatorBase { - public: - ListenAndServOp(const std::string &type, - const framework::VariableNameMap &inputs, - const framework::VariableNameMap &outputs, - const framework::AttributeMap &attrs) - : OperatorBase(type, inputs, outputs, attrs) { - if (!rpc_service_) { - std::string endpoint = Attr("endpoint"); - rpc_service_.reset(new detail::AsyncGRPCServer(endpoint)); - server_thread_.reset(new std::thread(RunServer, rpc_service_)); - } - } +void ListenAndServOp::RunImpl(const framework::Scope &scope, + const platform::Place &dev_place) const { + platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance(); + auto &dev_ctx = *pool.Get(dev_place); + framework::Scope &recv_scope = scope.NewScope(); + LOG(INFO) << "created recv scope: " << &recv_scope; - void Stop() override { - rpc_service_->Push(LISTEN_TERMINATE_MESSAGE); - server_thread_->join(); + if (!rpc_service_) { + std::string endpoint = Attr("endpoint"); + rpc_service_.reset(new detail::AsyncGRPCServer(endpoint)); } - void RunImpl(const framework::Scope &scope, - const platform::Place &dev_place) const override { - platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance(); - auto &dev_ctx = *pool.Get(dev_place); - framework::Scope &recv_scope = scope.NewScope(); - - // FIXME(Yancey1989): initialize rpc server with lazy mode. - rpc_service_->SetScope(&recv_scope); - rpc_service_->SetDevCtx(&dev_ctx); - auto ins = Inputs("X"); - auto fan_in = Attr("Fanin"); - - auto *block = Attr(kOptimizeBlock); - auto *program = block->Program(); - int num_blocks = program->Size(); - PADDLE_ENFORCE_GE(num_blocks, 2, - "server program should have at least 2 blocks"); - - framework::Executor executor(dev_place); - - // TODO(qiao) set proper fields for table lookup and update - rpc_service_->SetExecutor(&executor); - rpc_service_->SetPrefetchBlkdId(0); - rpc_service_->SetProgram(program); - - // TODO(typhoonzero): change this to a while_op for every cluster-batch. - bool exit_flag = false; - // Record received sparse variables, so that - // we could reset those after execute optimize program - std::vector sparse_vars; - while (!exit_flag) { - // Get from multiple trainers, we don't care about the order in which - // the gradients arrives, just add suffix 0~n and merge the gradient. - rpc_service_->SetCond(0); - size_t recv_var_cnt = 0; - int batch_barrier = 0; - while (batch_barrier != fan_in) { - const detail::ReceivedMessage v = rpc_service_->Get(); - auto recv_var_name = v.first; - if (recv_var_name == LISTEN_TERMINATE_MESSAGE) { - LOG(INFO) << "received terminate message and exit"; - exit_flag = true; - break; - } else if (recv_var_name == BATCH_BARRIER_MESSAGE) { - VLOG(3) << "recv batch barrier message"; - batch_barrier++; - continue; - } else { - VLOG(3) << "received grad: " << recv_var_name; - recv_var_cnt++; - auto var = v.second->GetVar(); - if (var == nullptr) { - LOG(ERROR) << "Can not find server side var: " << recv_var_name; - PADDLE_THROW("Can not find server side var"); - } - if (var->IsType()) { - sparse_vars.push_back(var); - } - } - } - if (exit_flag) { - rpc_service_->SetCond(1); - rpc_service_->ShutDown(); + auto ins = Inputs("X"); + auto fan_in = Attr("Fanin"); + auto *block = Attr(kOptimizeBlock); + auto *program = block->Program(); + size_t num_blocks = program->Size(); + PADDLE_ENFORCE_GE(num_blocks, 2, + "server program should have at least 2 blocks"); + + framework::Executor executor(dev_place); + + // FIXME(Yancey1989): initialize rpc server with lazy mode. + rpc_service_->SetScope(&recv_scope); + rpc_service_->SetDevCtx(&dev_ctx); + // TODO(qiao) set proper fields for table lookup and update + rpc_service_->SetExecutor(&executor); + rpc_service_->SetPrefetchBlkdId(0); + rpc_service_->SetProgram(program); + // start the server listening after all member initialized. + server_thread_.reset(new std::thread(RunServer, rpc_service_)); + // FIXME(typhoonzero): do we need to wait until the server port is ready? + sleep(5); + + // TODO(typhoonzero): change this to a while_op for every cluster-batch. + bool exit_flag = false; + // Record received sparse variables, so that + // we could reset those after execute optimize program + std::vector sparse_vars; + while (!exit_flag) { + // Get from multiple trainers, we don't care about the order in which + // the gradients arrives, just add suffix 0~n and merge the gradient. + rpc_service_->SetCond(0); + size_t recv_var_cnt = 0; + int batch_barrier = 0; + while (batch_barrier != fan_in) { + const detail::ReceivedMessage v = rpc_service_->Get(); + auto recv_var_name = v.first; + if (recv_var_name == LISTEN_TERMINATE_MESSAGE) { + LOG(INFO) << "received terminate message and exit"; + exit_flag = true; break; - } - - // NOTE: if is_gpu_place, CUDA kernels are laugched by multiple threads - // and this will still work. - - // The optimize blocks which have the same parent ID would run parallel - // TODO(Yancey1989): need to use ParallelExecutor for future - size_t last_parent_blkid = program->Block(1).Parent(); - std::vector parallel_blkids; - parallel_blkids.push_back(1); - double ts = detail::GetTimestamp(); - for (size_t blkid = 2; blkid < num_blocks; ++blkid) { - if (program->Block(blkid).Parent() != last_parent_blkid) { - for (size_t idx : parallel_blkids) VLOG(3) << idx; - ParallelExecuteBlocks(parallel_blkids, &executor, program, - &recv_scope); - parallel_blkids.clear(); - last_parent_blkid = program->Block(blkid).Parent(); + } else if (recv_var_name == BATCH_BARRIER_MESSAGE) { + VLOG(3) << "recv batch barrier message"; + batch_barrier++; + continue; + } else { + VLOG(3) << "received grad: " << recv_var_name; + recv_var_cnt++; + auto var = v.second->GetVar(); + if (var == nullptr) { + LOG(ERROR) << "Can not find server side var: " << recv_var_name; + PADDLE_THROW("Can not find server side var"); + } + if (var->IsType()) { + sparse_vars.push_back(var); } - parallel_blkids.push_back(blkid); - } - ParallelExecuteBlocks(parallel_blkids, &executor, program, &recv_scope); - - VLOG(3) << "run all blocks spent " << detail::GetTimestamp() - ts - << "(ms)"; - - // Reset the received sparse variables, the sum operator would not - // sum the input sparse variables which rows is empty at the next - // mini-batch. - // TODO(Yancey1989): move the reset action into an operator, we couldn't - // have any hide logic in the operator. - for (auto &var : sparse_vars) { - var->GetMutable()->mutable_rows()->clear(); } + } + if (exit_flag) { rpc_service_->SetCond(1); - // FIXME(typhoonzero): use another condition to sync wait clients get. - rpc_service_->WaitClientGet(fan_in); - sparse_vars.clear(); - } // while(true) - } + rpc_service_->ShutDown(); + break; + } - protected: - std::shared_ptr rpc_service_; - std::shared_ptr server_thread_; -}; + // NOTE: if is_gpu_place, CUDA kernels are laugched by multiple threads + // and this will still work. + + // The optimize blocks which have the same parent ID would run parallel + // TODO(Yancey1989): need to use ParallelExecutor for future + int32_t last_parent_blkid = program->Block(1).Parent(); + std::vector parallel_blkids; + parallel_blkids.push_back(1); + double ts = detail::GetTimestamp(); + for (size_t blkid = 2; blkid < num_blocks; ++blkid) { + if (program->Block(blkid).Parent() != last_parent_blkid) { + for (size_t idx : parallel_blkids) VLOG(3) << idx; + ParallelExecuteBlocks(parallel_blkids, &executor, program, &recv_scope); + parallel_blkids.clear(); + last_parent_blkid = program->Block(blkid).Parent(); + } + parallel_blkids.push_back(blkid); + } + ParallelExecuteBlocks(parallel_blkids, &executor, program, &recv_scope); + + VLOG(3) << "run all blocks spent " << detail::GetTimestamp() - ts << "(ms)"; + + // Reset the received sparse variables, the sum operator would not + // sum the input sparse variables which rows is empty at the next + // mini-batch. + // TODO(Yancey1989): move the reset action into an operator, we couldn't + // have any hide logic in the operator. + for (auto &var : sparse_vars) { + var->GetMutable()->mutable_rows()->clear(); + } + rpc_service_->SetCond(1); + // FIXME(typhoonzero): use another condition to sync wait clients get. + rpc_service_->WaitClientGet(fan_in); + sparse_vars.clear(); + } // while(true) +} class ListenAndServOpMaker : public framework::OpProtoAndCheckerMaker { public: diff --git a/paddle/fluid/operators/listen_and_serv_op.h b/paddle/fluid/operators/listen_and_serv_op.h new file mode 100644 index 0000000000..4ebb8c3840 --- /dev/null +++ b/paddle/fluid/operators/listen_and_serv_op.h @@ -0,0 +1,85 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#pragma once + +#include +#include + +#include "paddle/fluid/framework/executor.h" +#include "paddle/fluid/framework/lod_tensor.h" +#include "paddle/fluid/framework/op_registry.h" +#include "paddle/fluid/framework/threadpool.h" +#include "paddle/fluid/operators/detail/grpc_server.h" + +namespace paddle { +namespace operators { + +constexpr char kOptimizeBlock[] = "OptimizeBlock"; + +void RunServer(std::shared_ptr service); + +static void CreateTensorFromMessageType(framework::Variable *var, + sendrecv::VarType var_type) { + if (var_type == sendrecv::VarType::LOD_TENSOR) { + var->GetMutable(); + } else if (var_type == sendrecv::VarType::SELECTED_ROWS) { + var->GetMutable(); + } else { + PADDLE_THROW( + "VariableMessage type %d is not in " + "[LoDTensor, SelectedRows]", + var_type); + } +} + +static void ParallelExecuteBlocks(const std::vector ¶llel_blkids, + framework::Executor *executor, + framework::ProgramDesc *program, + framework::Scope *scope) { + std::vector> fs; + for (size_t idx : parallel_blkids) { + fs.push_back(framework::Async([&executor, &program, &scope, idx]() { + int run_block = idx; // thread local + try { + executor->Run(*program, scope, run_block, false, false); + } catch (std::exception &e) { + LOG(ERROR) << "run sub program error " << e.what(); + } + })); + } + for (size_t i = 0; i < fs.size(); ++i) fs[i].wait(); +} + +class ListenAndServOp : public framework::OperatorBase { + public: + ListenAndServOp(const std::string &type, + const framework::VariableNameMap &inputs, + const framework::VariableNameMap &outputs, + const framework::AttributeMap &attrs); + + int GetSelectedPort(); + + void Stop() override; + + void RunImpl(const framework::Scope &scope, + const platform::Place &dev_place) const override; + + protected: + mutable std::shared_ptr rpc_service_; + mutable std::shared_ptr server_thread_; +}; + +} // namespace operators +} // namespace paddle diff --git a/paddle/fluid/operators/send_recv_op_test.cc b/paddle/fluid/operators/send_recv_op_test.cc index 04392b3e05..542bc3fde2 100644 --- a/paddle/fluid/operators/send_recv_op_test.cc +++ b/paddle/fluid/operators/send_recv_op_test.cc @@ -20,6 +20,7 @@ limitations under the License. */ #include "paddle/fluid/framework/op_registry.h" #include "paddle/fluid/framework/operator.h" #include "paddle/fluid/framework/program_desc.h" +#include "paddle/fluid/operators/listen_and_serv_op.h" #include "paddle/fluid/operators/math/math_function.h" #include "paddle/fluid/operators/math/selected_rows_functor.h" #include "paddle/fluid/string/printf.h" @@ -34,6 +35,7 @@ namespace m = paddle::operators::math; // global for simplicity. std::unique_ptr listen_and_serv_op; +int selected_port; void InitTensorsInScope(f::Scope &scope, p::CPUPlace &place) { p::CPUDeviceContext ctx(place); @@ -128,14 +130,16 @@ void StartServerNet(bool is_sparse) { AddOp("sum", {{"X", {"x0", "x1"}}}, {{"Out", {"Out"}}}, {}, optimize_block); f::AttributeMap attrs; - attrs.insert({"endpoint", std::string("127.0.0.1:6174")}); + attrs.insert({"endpoint", std::string("127.0.0.1:0")}); attrs.insert({"Fanin", 1}); attrs.insert({"ParamList", std::vector({"Out"})}); attrs.insert({"GradList", std::vector({"x1"})}); attrs.insert({"OptimizeBlock", optimize_block}); listen_and_serv_op = f::OpRegistry::CreateOp("listen_and_serv", {{"X", {"x1"}}}, {}, attrs); + LOG(INFO) << "selected port before run " << selected_port; listen_and_serv_op->Run(scope, place); + LOG(INFO) << "server exit"; } TEST(SendRecvOp, CPUDense) { @@ -149,12 +153,19 @@ TEST(SendRecvOp, CPUDense) { scope.Var("RPC_CLIENT_VAR"); f::AttributeMap attrs; - attrs.insert({"endpoints", std::vector({"127.0.0.1:6174"})}); - attrs.insert({"epmap", std::vector({"127.0.0.1:6174"})}); + selected_port = static_cast( + listen_and_serv_op.get()) + ->GetSelectedPort(); + LOG(INFO) << "selected port " << selected_port; + std::string endpoint = paddle::string::Sprintf("127.0.0.1:%d", selected_port); + attrs.insert({"endpoints", std::vector({endpoint})}); + attrs.insert({"epmap", std::vector({endpoint})}); auto send_op = f::OpRegistry::CreateOp( "send", {{"X", {"x1"}}}, {{"Out", {"Out"}}, {"RPCClient", {"RPC_CLIENT_VAR"}}}, attrs); + LOG(INFO) << "before run " << endpoint; send_op->Run(scope, place); + LOG(INFO) << "end run"; auto in_var = scope.Var("x1"); auto tensor = in_var->GetMutable(); @@ -167,6 +178,7 @@ TEST(SendRecvOp, CPUDense) { for (int64_t i = 0; i < target->numel(); ++i) { EXPECT_EQ(expected[i] * 2, actual[i]); } + LOG(INFO) << "before stop"; listen_and_serv_op->Stop(); server_thread.join(); listen_and_serv_op.reset(nullptr); @@ -182,8 +194,13 @@ TEST(SendRecvOp, CPUSparse) { InitSelectedRowsInScope(scope, place); scope.Var("RPC_CLIENT_VAR"); f::AttributeMap attrs; - attrs.insert({"endpoints", std::vector({"127.0.0.1:6174"})}); - attrs.insert({"epmap", std::vector({"127.0.0.1:6174"})}); + selected_port = static_cast( + listen_and_serv_op.get()) + ->GetSelectedPort(); + LOG(INFO) << "selected port " << selected_port; + std::string endpoint = paddle::string::Sprintf("127.0.0.1:%d", selected_port); + attrs.insert({"endpoints", std::vector({endpoint})}); + attrs.insert({"epmap", std::vector({endpoint})}); auto send_op = f::OpRegistry::CreateOp( "send", {{"X", {"x1"}}}, {{"Out", {"Out"}}, {"RPCClient", {"RPC_CLIENT_VAR"}}}, attrs); From d0ac92531dacfa09c8142ad515eb04ee9a8a8ef9 Mon Sep 17 00:00:00 2001 From: Xin Pan Date: Sun, 1 Apr 2018 02:35:07 -0700 Subject: [PATCH 22/58] Improve ParallelExecutor performance --- .../details/nccl_all_reduce_op_handle.cc | 2 +- .../details/nccl_all_reduce_op_handle.h | 5 ++ .../fluid/framework/details/op_handle_base.h | 4 ++ .../details/threaded_ssa_graph_executor.cc | 49 +++++++++++++++---- .../details/threaded_ssa_graph_executor.h | 12 ++++- paddle/fluid/framework/parallel_executor.cc | 2 + python/paddle/fluid/parallel_executor.py | 3 +- 7 files changed, 63 insertions(+), 14 deletions(-) diff --git a/paddle/fluid/framework/details/nccl_all_reduce_op_handle.cc b/paddle/fluid/framework/details/nccl_all_reduce_op_handle.cc index 5ddf331cfc..55b5f11358 100644 --- a/paddle/fluid/framework/details/nccl_all_reduce_op_handle.cc +++ b/paddle/fluid/framework/details/nccl_all_reduce_op_handle.cc @@ -76,7 +76,7 @@ void NCCLAllReduceOpHandle::RunImpl() { } } -std::string NCCLAllReduceOpHandle::Name() const { return "NCCL AllReduce"; } +std::string NCCLAllReduceOpHandle::Name() const { return "nccl_all_reduce"; } } // namespace details } // namespace framework } // namespace paddle diff --git a/paddle/fluid/framework/details/nccl_all_reduce_op_handle.h b/paddle/fluid/framework/details/nccl_all_reduce_op_handle.h index 045070bb6a..3d61fa79f7 100644 --- a/paddle/fluid/framework/details/nccl_all_reduce_op_handle.h +++ b/paddle/fluid/framework/details/nccl_all_reduce_op_handle.h @@ -14,6 +14,9 @@ #pragma once +#include +#include + #include "paddle/fluid/framework/details/op_handle_base.h" #include "paddle/fluid/framework/lod_tensor.h" #include "paddle/fluid/framework/scope.h" @@ -34,6 +37,8 @@ struct NCCLAllReduceOpHandle : public OpHandleBase { std::string Name() const override; + bool IsDelayedOp() override { return true; }; + protected: void RunImpl() override; }; diff --git a/paddle/fluid/framework/details/op_handle_base.h b/paddle/fluid/framework/details/op_handle_base.h index 71672fd24c..54c2d627ff 100644 --- a/paddle/fluid/framework/details/op_handle_base.h +++ b/paddle/fluid/framework/details/op_handle_base.h @@ -13,6 +13,8 @@ // limitations under the License. #pragma once +#include +#include #include "paddle/fluid/framework/details/var_handle.h" #include "paddle/fluid/platform/device_context.h" @@ -53,6 +55,8 @@ class OpHandleBase { void AddOutput(VarHandleBase *out); + virtual bool IsDelayedOp() { return false; } + protected: virtual void RunImpl() = 0; }; diff --git a/paddle/fluid/framework/details/threaded_ssa_graph_executor.cc b/paddle/fluid/framework/details/threaded_ssa_graph_executor.cc index 3f8655147b..075eed4ecc 100644 --- a/paddle/fluid/framework/details/threaded_ssa_graph_executor.cc +++ b/paddle/fluid/framework/details/threaded_ssa_graph_executor.cc @@ -29,17 +29,27 @@ ThreadedSSAGraphExecutor::ThreadedSSAGraphExecutor( local_scopes_(local_scopes), places_(places), fetch_ctxs_(places), - use_event_(use_event) {} + use_event_(use_event), + running_ops_(0) {} + +void ThreadedSSAGraphExecutor::RunDelayedOps( + const std::unordered_set &delayed_ops) { + for (auto op : delayed_ops) { + op->Run(use_event_); + } +} FeedFetchList ThreadedSSAGraphExecutor::Run( const std::vector &fetch_tensors) { std::unordered_map pending_ops; std::unordered_set pending_vars; - BlockingQueue ready_vars; - std::unordered_set ready_ops; + std::unordered_set delayed_ops; + std::unordered_set after_delayed_ops; + std::unordered_set delayed_vars; + auto InsertPendingVar = [&pending_vars, &ready_vars](VarHandleBase &var) { pending_vars.insert(&var); if (var.generated_op_ == nullptr) { @@ -106,7 +116,14 @@ FeedFetchList ThreadedSSAGraphExecutor::Run( auto run_all_ready_ops = [&] { for (auto *op : ready_ops) { - RunOp(ready_vars, op); + if (op->IsDelayedOp()) { + delayed_ops.insert(op); + delayed_vars.insert(op->outputs_.begin(), op->outputs_.end()); + ready_vars.Extend(op->outputs_); + continue; + } + running_ops_++; + RunOp(&ready_vars, op); } ready_ops.clear(); }; @@ -124,7 +141,7 @@ FeedFetchList ThreadedSSAGraphExecutor::Run( // 2. Find ready variable bool timeout; - auto cur_ready_vars = ready_vars.PopAll(1000, &timeout); + auto cur_ready_vars = ready_vars.PopAll(1, &timeout); if (timeout) { if (exception_) { @@ -141,13 +158,24 @@ FeedFetchList ThreadedSSAGraphExecutor::Run( auto &deps = pending_ops[op]; --deps; if (deps == 0) { - ready_ops.insert(op); + if (delayed_vars.find(ready_var) != delayed_vars.end()) { + after_delayed_ops.insert(op); + } else { + ready_ops.insert(op); + } } } } + if (ready_ops.empty() && !delayed_ops.empty() && running_ops_ == 0) { + RunDelayedOps(delayed_ops); + delayed_ops.clear(); + for (auto *op : after_delayed_ops) { + ready_ops.insert(op); + } + after_delayed_ops.clear(); + } // Keep loop until all vars are ready. } - ++computation_count_; auto sync_computation = [&] { @@ -182,12 +210,13 @@ FeedFetchList ThreadedSSAGraphExecutor::Run( } void ThreadedSSAGraphExecutor::RunOp( - BlockingQueue &ready_var_q, details::OpHandleBase *op) { - auto op_run = [&ready_var_q, op, this] { + BlockingQueue *ready_var_q, details::OpHandleBase *op) { + auto op_run = [ready_var_q, op, this] { try { VLOG(10) << op->Name() << " : " << op->DebugString(); op->Run(use_event_); - ready_var_q.Extend(op->outputs_); + running_ops_--; + ready_var_q->Extend(op->outputs_); } catch (platform::EnforceNotMet ex) { exception_.reset(new platform::EnforceNotMet(ex)); } catch (...) { diff --git a/paddle/fluid/framework/details/threaded_ssa_graph_executor.h b/paddle/fluid/framework/details/threaded_ssa_graph_executor.h index 2ea57ac8f9..6193b897e4 100644 --- a/paddle/fluid/framework/details/threaded_ssa_graph_executor.h +++ b/paddle/fluid/framework/details/threaded_ssa_graph_executor.h @@ -14,7 +14,12 @@ #pragma once -#include +#include +#include +#include +#include +#include + #include #include "ThreadPool.h" // ThreadPool in thrird party #include "paddle/fluid/framework/details/ssa_graph_executor.h" @@ -79,9 +84,11 @@ class ThreadedSSAGraphExecutor : public SSAGraphExecutor { ~ThreadedSSAGraphExecutor() {} private: - void RunOp(BlockingQueue &ready_var_q, + void RunOp(BlockingQueue *ready_var_q, details::OpHandleBase *op); + void RunDelayedOps(const std::unordered_set &delayed_ops); + private: std::unique_ptr<::ThreadPool> pool_; std::vector local_scopes_; @@ -89,6 +96,7 @@ class ThreadedSSAGraphExecutor : public SSAGraphExecutor { platform::DeviceContextPool fetch_ctxs_; const bool use_event_; std::unique_ptr exception_; + std::atomic running_ops_; size_t computation_count_{0}; size_t max_async_computation{100}; diff --git a/paddle/fluid/framework/parallel_executor.cc b/paddle/fluid/framework/parallel_executor.cc index 577eea92d2..002a6d362f 100644 --- a/paddle/fluid/framework/parallel_executor.cc +++ b/paddle/fluid/framework/parallel_executor.cc @@ -13,6 +13,7 @@ See the License for the specific language governing permissions and limitations under the License. */ #include "paddle/fluid/framework/parallel_executor.h" +#include "paddle/fluid/platform/profiler.h" #include #include @@ -151,6 +152,7 @@ void ParallelExecutor::BCastParamsToGPUs( void ParallelExecutor::Run(const std::vector &fetch_tensors, const std::string &fetched_var_name) { + platform::RecordBlock b(0); auto fetch_data = member_->executor_->Run(fetch_tensors); *member_->global_scope_->Var(fetched_var_name)->GetMutable() = fetch_data; diff --git a/python/paddle/fluid/parallel_executor.py b/python/paddle/fluid/parallel_executor.py index 5e0588fa73..33e8d3bf21 100644 --- a/python/paddle/fluid/parallel_executor.py +++ b/python/paddle/fluid/parallel_executor.py @@ -16,6 +16,7 @@ import core import multiprocessing import framework import executor +import sys __all__ = ['ParallelExecutor'] @@ -35,7 +36,7 @@ class ParallelExecutor(object): places.append(p) if num_threads is None: - num_threads = min(len(places) * 2, multiprocessing.cpu_count()) + num_threads = len(places) startup = framework.default_startup_program() main = framework.default_main_program() From 46f3a39e91fd422f1b6d5cbaadad9a35456eb36a Mon Sep 17 00:00:00 2001 From: Xin Pan Date: Sun, 1 Apr 2018 18:05:59 -0700 Subject: [PATCH 23/58] polish and add comments. --- .../fluid/framework/details/nccl_all_reduce_op_handle.h | 2 ++ .../framework/details/threaded_ssa_graph_executor.cc | 5 ++++- python/paddle/fluid/parallel_executor.py | 8 ++++++-- 3 files changed, 12 insertions(+), 3 deletions(-) diff --git a/paddle/fluid/framework/details/nccl_all_reduce_op_handle.h b/paddle/fluid/framework/details/nccl_all_reduce_op_handle.h index 3d61fa79f7..bb92625667 100644 --- a/paddle/fluid/framework/details/nccl_all_reduce_op_handle.h +++ b/paddle/fluid/framework/details/nccl_all_reduce_op_handle.h @@ -37,6 +37,8 @@ struct NCCLAllReduceOpHandle : public OpHandleBase { std::string Name() const override; + // Delay and buffer nccl_all_reduce together can significantly increase + // performance. Disable this feature by returning false. bool IsDelayedOp() override { return true; }; protected: diff --git a/paddle/fluid/framework/details/threaded_ssa_graph_executor.cc b/paddle/fluid/framework/details/threaded_ssa_graph_executor.cc index 075eed4ecc..32fc9100ab 100644 --- a/paddle/fluid/framework/details/threaded_ssa_graph_executor.cc +++ b/paddle/fluid/framework/details/threaded_ssa_graph_executor.cc @@ -45,7 +45,10 @@ FeedFetchList ThreadedSSAGraphExecutor::Run( std::unordered_set pending_vars; BlockingQueue ready_vars; std::unordered_set ready_ops; - + // For ops (e.g. nccl_all_reduce) that need to coordinate multiple + // streams from multiple GPUs, it's faster to buffer them and schedule + // together since we currently cannot overlap computation and memcpy streams. + // Should revisit it if overlapping is available. std::unordered_set delayed_ops; std::unordered_set after_delayed_ops; std::unordered_set delayed_vars; diff --git a/python/paddle/fluid/parallel_executor.py b/python/paddle/fluid/parallel_executor.py index 33e8d3bf21..fec7d6899c 100644 --- a/python/paddle/fluid/parallel_executor.py +++ b/python/paddle/fluid/parallel_executor.py @@ -16,7 +16,6 @@ import core import multiprocessing import framework import executor -import sys __all__ = ['ParallelExecutor'] @@ -36,7 +35,12 @@ class ParallelExecutor(object): places.append(p) if num_threads is None: - num_threads = len(places) + if use_cuda: + # Experiments on se-resnext shows that too many threads hurt + # performance. Worth tunning for other models in the future. + num_threads = len(places) + else: + min(len(places) * 2, multiprocessing.cpu_count()) startup = framework.default_startup_program() main = framework.default_main_program() From be1373dcf9c233b6a0c870232adb0e66df64f80c Mon Sep 17 00:00:00 2001 From: Xin Pan Date: Sun, 1 Apr 2018 18:11:52 -0700 Subject: [PATCH 24/58] Polish --- .../framework/details/nccl_all_reduce_op_handle.h | 2 +- paddle/fluid/framework/details/op_handle_base.h | 4 +++- .../framework/details/threaded_ssa_graph_executor.cc | 12 +++++++----- 3 files changed, 11 insertions(+), 7 deletions(-) diff --git a/paddle/fluid/framework/details/nccl_all_reduce_op_handle.h b/paddle/fluid/framework/details/nccl_all_reduce_op_handle.h index bb92625667..ad14a3c5cb 100644 --- a/paddle/fluid/framework/details/nccl_all_reduce_op_handle.h +++ b/paddle/fluid/framework/details/nccl_all_reduce_op_handle.h @@ -39,7 +39,7 @@ struct NCCLAllReduceOpHandle : public OpHandleBase { // Delay and buffer nccl_all_reduce together can significantly increase // performance. Disable this feature by returning false. - bool IsDelayedOp() override { return true; }; + bool IsMultiDeviceTransfer() override { return true; }; protected: void RunImpl() override; diff --git a/paddle/fluid/framework/details/op_handle_base.h b/paddle/fluid/framework/details/op_handle_base.h index 54c2d627ff..d7a541ac4b 100644 --- a/paddle/fluid/framework/details/op_handle_base.h +++ b/paddle/fluid/framework/details/op_handle_base.h @@ -55,7 +55,9 @@ class OpHandleBase { void AddOutput(VarHandleBase *out); - virtual bool IsDelayedOp() { return false; } + // If the Op involves data transfer of multiple devices that + // will likely block other computations. + virtual bool IsMultiDeviceTransfer() { return false; } protected: virtual void RunImpl() = 0; diff --git a/paddle/fluid/framework/details/threaded_ssa_graph_executor.cc b/paddle/fluid/framework/details/threaded_ssa_graph_executor.cc index 32fc9100ab..65fbfb65e1 100644 --- a/paddle/fluid/framework/details/threaded_ssa_graph_executor.cc +++ b/paddle/fluid/framework/details/threaded_ssa_graph_executor.cc @@ -50,7 +50,7 @@ FeedFetchList ThreadedSSAGraphExecutor::Run( // together since we currently cannot overlap computation and memcpy streams. // Should revisit it if overlapping is available. std::unordered_set delayed_ops; - std::unordered_set after_delayed_ops; + std::unordered_set blocked_by_delayed_ops; std::unordered_set delayed_vars; auto InsertPendingVar = [&pending_vars, &ready_vars](VarHandleBase &var) { @@ -119,7 +119,7 @@ FeedFetchList ThreadedSSAGraphExecutor::Run( auto run_all_ready_ops = [&] { for (auto *op : ready_ops) { - if (op->IsDelayedOp()) { + if (op->IsMultiDeviceTransfer()) { delayed_ops.insert(op); delayed_vars.insert(op->outputs_.begin(), op->outputs_.end()); ready_vars.Extend(op->outputs_); @@ -162,20 +162,22 @@ FeedFetchList ThreadedSSAGraphExecutor::Run( --deps; if (deps == 0) { if (delayed_vars.find(ready_var) != delayed_vars.end()) { - after_delayed_ops.insert(op); + blocked_by_delayed_ops.insert(op); } else { ready_ops.insert(op); } } } } + // When there are no other ops to schedule, schedule buffered delayed + // ops and unblock other ops. if (ready_ops.empty() && !delayed_ops.empty() && running_ops_ == 0) { RunDelayedOps(delayed_ops); delayed_ops.clear(); - for (auto *op : after_delayed_ops) { + for (auto *op : blocked_by_delayed_ops) { ready_ops.insert(op); } - after_delayed_ops.clear(); + blocked_by_delayed_ops.clear(); } // Keep loop until all vars are ready. } From b123ce88a17ac18dd24ec396d18c1eac7c832442 Mon Sep 17 00:00:00 2001 From: Xin Pan Date: Mon, 2 Apr 2018 01:10:00 -0700 Subject: [PATCH 25/58] Add enable/disable for delayed ops --- .../details/threaded_ssa_graph_executor.cc | 12 ++++++++---- .../details/threaded_ssa_graph_executor.h | 4 +++- paddle/fluid/framework/parallel_executor.cc | 6 +++--- paddle/fluid/framework/parallel_executor.h | 6 ++++-- paddle/fluid/pybind/pybind.cc | 4 ++-- python/paddle/fluid/parallel_executor.py | 9 +++++++-- .../tests/unittests/test_parallel_executor.py | 16 ++++++++++++++-- 7 files changed, 41 insertions(+), 16 deletions(-) diff --git a/paddle/fluid/framework/details/threaded_ssa_graph_executor.cc b/paddle/fluid/framework/details/threaded_ssa_graph_executor.cc index 65fbfb65e1..1f96b9dc62 100644 --- a/paddle/fluid/framework/details/threaded_ssa_graph_executor.cc +++ b/paddle/fluid/framework/details/threaded_ssa_graph_executor.cc @@ -23,14 +23,15 @@ ThreadedSSAGraphExecutor::ThreadedSSAGraphExecutor( size_t num_threads, bool use_event, const std::vector &local_scopes, const std::vector &places, - std::unique_ptr &&graph) + std::unique_ptr &&graph, bool allow_op_delay) : SSAGraphExecutor(std::move(graph)), pool_(num_threads >= 2 ? new ::ThreadPool(num_threads) : nullptr), local_scopes_(local_scopes), places_(places), fetch_ctxs_(places), use_event_(use_event), - running_ops_(0) {} + running_ops_(0), + allow_op_delay_(allow_op_delay) {} void ThreadedSSAGraphExecutor::RunDelayedOps( const std::unordered_set &delayed_ops) { @@ -119,7 +120,7 @@ FeedFetchList ThreadedSSAGraphExecutor::Run( auto run_all_ready_ops = [&] { for (auto *op : ready_ops) { - if (op->IsMultiDeviceTransfer()) { + if (op->IsMultiDeviceTransfer() && allow_op_delay_) { delayed_ops.insert(op); delayed_vars.insert(op->outputs_.begin(), op->outputs_.end()); ready_vars.Extend(op->outputs_); @@ -138,7 +139,7 @@ FeedFetchList ThreadedSSAGraphExecutor::Run( } // Step 3. Execution - while (!pending_vars.empty()) { + while (!pending_vars.empty() || !ready_ops.empty() || !delayed_ops.empty()) { // 1. Run All Ready ops run_all_ready_ops(); @@ -181,6 +182,9 @@ FeedFetchList ThreadedSSAGraphExecutor::Run( } // Keep loop until all vars are ready. } + PADDLE_ENFORCE(ready_ops.empty()); + PADDLE_ENFORCE(delayed_ops.empty()); + PADDLE_ENFORCE(blocked_by_delayed_ops.empty()); ++computation_count_; auto sync_computation = [&] { diff --git a/paddle/fluid/framework/details/threaded_ssa_graph_executor.h b/paddle/fluid/framework/details/threaded_ssa_graph_executor.h index 6193b897e4..79cfc26b46 100644 --- a/paddle/fluid/framework/details/threaded_ssa_graph_executor.h +++ b/paddle/fluid/framework/details/threaded_ssa_graph_executor.h @@ -75,7 +75,8 @@ class ThreadedSSAGraphExecutor : public SSAGraphExecutor { ThreadedSSAGraphExecutor(size_t num_threads, bool use_event, const std::vector &local_scopes, const std::vector &places, - std::unique_ptr &&graph); + std::unique_ptr &&graph, + bool allow_op_delay); // Run a SSAGraph by a thread pool // Use topological sort algorithm @@ -97,6 +98,7 @@ class ThreadedSSAGraphExecutor : public SSAGraphExecutor { const bool use_event_; std::unique_ptr exception_; std::atomic running_ops_; + bool allow_op_delay_; size_t computation_count_{0}; size_t max_async_computation{100}; diff --git a/paddle/fluid/framework/parallel_executor.cc b/paddle/fluid/framework/parallel_executor.cc index 002a6d362f..1788514324 100644 --- a/paddle/fluid/framework/parallel_executor.cc +++ b/paddle/fluid/framework/parallel_executor.cc @@ -48,7 +48,7 @@ ParallelExecutor::ParallelExecutor( const std::vector &places, const std::unordered_set ¶ms, const ProgramDesc &startup_program, const ProgramDesc &main_program, - const std::string &loss_var_name, Scope *scope) + const std::string &loss_var_name, Scope *scope, bool allow_op_delay) : member_(new ParallelExecutorPrivate(places)) { member_->global_scope_ = scope; @@ -83,8 +83,8 @@ ParallelExecutor::ParallelExecutor( auto graph = builder.Build(main_program); member_->executor_.reset(new details::ThreadedSSAGraphExecutor( - num_threads, use_event, member_->local_scopes_, places, - std::move(graph))); + num_threads, use_event, member_->local_scopes_, places, std::move(graph), + allow_op_delay)); // Step 3. Create vars in each scope; for (auto *scope : member_->local_scopes_) { diff --git a/paddle/fluid/framework/parallel_executor.h b/paddle/fluid/framework/parallel_executor.h index 503efa2e44..964b476234 100644 --- a/paddle/fluid/framework/parallel_executor.h +++ b/paddle/fluid/framework/parallel_executor.h @@ -14,8 +14,9 @@ limitations under the License. */ #pragma once -#include +#include #include +#include #include "paddle/fluid/framework/executor.h" #include "paddle/fluid/framework/op_info.h" #include "paddle/fluid/framework/program_desc.h" @@ -37,7 +38,8 @@ class ParallelExecutor { const std::unordered_set& params, const ProgramDesc& startup_program, const ProgramDesc& main_program, - const std::string& loss_var_name, Scope* scope); + const std::string& loss_var_name, Scope* scope, + bool allow_op_delay); void Run(const std::vector& fetch_tensors, const std::string& fetched_var_name = "fetched_var"); diff --git a/paddle/fluid/pybind/pybind.cc b/paddle/fluid/pybind/pybind.cc index e1b1bbec97..b0a3f06a88 100644 --- a/paddle/fluid/pybind/pybind.cc +++ b/paddle/fluid/pybind/pybind.cc @@ -504,10 +504,10 @@ All parameter, weight, gradient are variables in Paddle. const std::unordered_set ¶ms, const ProgramDesc &startup_program, const ProgramDesc &main_program, const std::string &loss_var_name, - Scope *scope) { + Scope *scope, bool allow_op_delay) { new (&self) ParallelExecutor(num_threads, use_event, places, params, startup_program, main_program, - loss_var_name, scope); + loss_var_name, scope, allow_op_delay); }) .def("run", &ParallelExecutor::Run); diff --git a/python/paddle/fluid/parallel_executor.py b/python/paddle/fluid/parallel_executor.py index fec7d6899c..a2c830b3c9 100644 --- a/python/paddle/fluid/parallel_executor.py +++ b/python/paddle/fluid/parallel_executor.py @@ -21,7 +21,11 @@ __all__ = ['ParallelExecutor'] class ParallelExecutor(object): - def __init__(self, loss_name, use_cuda, num_threads=None): + def __init__(self, + loss_name, + use_cuda, + num_threads=None, + allow_op_delay=False): places = [] if use_cuda: for i in xrange(core.get_cuda_device_count()): @@ -57,7 +61,8 @@ class ParallelExecutor(object): startup.desc, main.desc, loss_name, - scope) + scope, + allow_op_delay) self.scope = scope def run(self, fetch_list): diff --git a/python/paddle/fluid/tests/unittests/test_parallel_executor.py b/python/paddle/fluid/tests/unittests/test_parallel_executor.py index 95d0f9da47..60130298af 100644 --- a/python/paddle/fluid/tests/unittests/test_parallel_executor.py +++ b/python/paddle/fluid/tests/unittests/test_parallel_executor.py @@ -184,7 +184,8 @@ class TestParallelExecutorBase(unittest.TestCase): method, memory_opt=True, iter=10, - batch_size=None): + batch_size=None, + allow_op_delay=False): main = fluid.Program() startup = fluid.Program() with fluid.program_guard(main, startup): @@ -194,7 +195,10 @@ class TestParallelExecutorBase(unittest.TestCase): if memory_opt: fluid.memory_optimize(main) - exe = fluid.ParallelExecutor(loss_name=loss.name, use_cuda=True) + exe = fluid.ParallelExecutor( + loss_name=loss.name, + use_cuda=True, + allow_op_delay=allow_op_delay) if batch_size is not None: batch_size *= fluid.core.get_cuda_device_count() begin = time.time() @@ -236,9 +240,11 @@ class TestMNIST(TestParallelExecutorBase): def test_simple_fc(self): self.check_network_convergence(simple_fc_net) + self.check_network_convergence(simple_fc_net, allow_op_delay=True) def test_batchnorm_fc(self): self.check_network_convergence(fc_with_batchnorm) + self.check_network_convergence(fc_with_batchnorm, allow_op_delay=True) class TestResnet(TestParallelExecutorBase): @@ -268,6 +274,12 @@ class TestResnet(TestParallelExecutorBase): SE_ResNeXt152, batch_size=batch_size), iter=20, batch_size=batch_size) + self.check_network_convergence( + functools.partial( + SE_ResNeXt152, batch_size=batch_size), + iter=20, + batch_size=batch_size, + allow_op_delay=True) class ModelHyperParams(object): From b25a9c488d3d6945157adeeac2c504ca3be977da Mon Sep 17 00:00:00 2001 From: Xin Pan Date: Tue, 3 Apr 2018 00:16:39 -0700 Subject: [PATCH 26/58] Reduce test size to avoid GPU memory issue. --- .../paddle/fluid/tests/unittests/CMakeLists.txt | 2 ++ .../tests/unittests/test_parallel_executor.py | 16 ++++++++-------- 2 files changed, 10 insertions(+), 8 deletions(-) diff --git a/python/paddle/fluid/tests/unittests/CMakeLists.txt b/python/paddle/fluid/tests/unittests/CMakeLists.txt index 0ad273c716..1b2d29a47f 100644 --- a/python/paddle/fluid/tests/unittests/CMakeLists.txt +++ b/python/paddle/fluid/tests/unittests/CMakeLists.txt @@ -29,6 +29,7 @@ function(py_test_modules TARGET_NAME) endfunction() # test time consuming OPs in a separate process for expliot parallism +list(REMOVE_ITEM TEST_OPS test_parallel_executor) list(REMOVE_ITEM TEST_OPS test_warpctc_op) list(REMOVE_ITEM TEST_OPS test_dyn_rnn) list(REMOVE_ITEM TEST_OPS test_mul_op) @@ -64,6 +65,7 @@ else() endif(WITH_FAST_BUNDLE_TEST) # tests with high overhead +py_test_modules(test_parallel_executor MODULES test_parallel_executor) py_test_modules(test_warpctc_op MODULES test_warpctc_op ENVS FLAGS_warpctc_dir=${WARPCTC_LIB_DIR}) py_test_modules(test_train_dyn_rnn MODULES test_dyn_rnn) py_test_modules(test_mul_op MODULES test_mul_op) diff --git a/python/paddle/fluid/tests/unittests/test_parallel_executor.py b/python/paddle/fluid/tests/unittests/test_parallel_executor.py index 60130298af..f132a754a2 100644 --- a/python/paddle/fluid/tests/unittests/test_parallel_executor.py +++ b/python/paddle/fluid/tests/unittests/test_parallel_executor.py @@ -135,18 +135,18 @@ def bottleneck_block(input, num_filters, stride, cardinality, reduction_ratio): return fluid.layers.elementwise_add(x=short, y=scale, act='relu') -def SE_ResNeXt152(batch_size=4): +def SE_ResNeXt152Small(batch_size=2): img = fluid.layers.fill_constant( shape=[batch_size, 3, 224, 224], dtype='float32', value=0.0) label = fluid.layers.fill_constant( shape=[batch_size, 1], dtype='int64', value=0.0) conv = conv_bn_layer( - input=img, num_filters=64, filter_size=3, stride=2, act='relu') + input=img, num_filters=16, filter_size=3, stride=2, act='relu') conv = conv_bn_layer( - input=conv, num_filters=64, filter_size=3, stride=1, act='relu') + input=conv, num_filters=16, filter_size=3, stride=1, act='relu') conv = conv_bn_layer( - input=conv, num_filters=128, filter_size=3, stride=1, act='relu') + input=conv, num_filters=16, filter_size=3, stride=1, act='relu') conv = fluid.layers.pool2d( input=conv, pool_size=3, pool_stride=2, pool_padding=1, pool_type='max') @@ -226,7 +226,7 @@ class TestMNIST(TestParallelExecutorBase): def setUpClass(cls): # Convert mnist to recordio file with fluid.program_guard(fluid.Program(), fluid.Program()): - reader = paddle.batch(mnist.train(), batch_size=32) + reader = paddle.batch(mnist.train(), batch_size=4) feeder = fluid.DataFeeder( feed_list=[ # order is image and label fluid.layers.data( @@ -268,15 +268,15 @@ class TestResnet(TestParallelExecutorBase): def test_resnet(self): import functools - batch_size = 4 + batch_size = 2 self.check_network_convergence( functools.partial( - SE_ResNeXt152, batch_size=batch_size), + SE_ResNeXt152Small, batch_size=batch_size), iter=20, batch_size=batch_size) self.check_network_convergence( functools.partial( - SE_ResNeXt152, batch_size=batch_size), + SE_ResNeXt152Small, batch_size=batch_size), iter=20, batch_size=batch_size, allow_op_delay=True) From cf251eb8cf3051f21306d73c73a48b0f2443ef8d Mon Sep 17 00:00:00 2001 From: Xin Pan Date: Tue, 3 Apr 2018 01:07:19 -0700 Subject: [PATCH 27/58] shrink test size --- .../paddle/fluid/tests/unittests/test_parallel_executor.py | 6 ------ 1 file changed, 6 deletions(-) diff --git a/python/paddle/fluid/tests/unittests/test_parallel_executor.py b/python/paddle/fluid/tests/unittests/test_parallel_executor.py index f132a754a2..a79e4b3e18 100644 --- a/python/paddle/fluid/tests/unittests/test_parallel_executor.py +++ b/python/paddle/fluid/tests/unittests/test_parallel_executor.py @@ -274,12 +274,6 @@ class TestResnet(TestParallelExecutorBase): SE_ResNeXt152Small, batch_size=batch_size), iter=20, batch_size=batch_size) - self.check_network_convergence( - functools.partial( - SE_ResNeXt152Small, batch_size=batch_size), - iter=20, - batch_size=batch_size, - allow_op_delay=True) class ModelHyperParams(object): From 103407aa6027c676cd1bcc13e6f35b2a51f1cc6a Mon Sep 17 00:00:00 2001 From: Luo Tao Date: Tue, 3 Apr 2018 16:09:27 +0800 Subject: [PATCH 28/58] refine sync_with_cpp when remove ops or remove vars --- python/paddle/fluid/framework.py | 18 +++++++++++++++ .../tests/unittests/test_protobuf_descs.py | 23 +++++++++++++++---- 2 files changed, 37 insertions(+), 4 deletions(-) diff --git a/python/paddle/fluid/framework.py b/python/paddle/fluid/framework.py index 3e78788f47..e15456bfc0 100644 --- a/python/paddle/fluid/framework.py +++ b/python/paddle/fluid/framework.py @@ -847,6 +847,11 @@ class Block(object): if not self.has_var(var.name()): self.create_var(name=var.name(), desc=var, type=var.type()) + # sync variables removed from c++ end + for var in self.vars.keys(): + if not self.desc.find_var(var): + self.vars.pop(var) + # sync operators from cpp ops_in_cpp = [] for op_idx in range(0, self.desc.op_size()): @@ -881,6 +886,19 @@ class Block(object): op = Operator(self, op_desc) self.ops.append(op) + # sync ops removed from c++ end + if end_index != -1 and end_index < len(self.ops): + ops_in_cpp_index = 0 + ops_in_python_index = 0 + while ops_in_python_index < len( + self.ops) and ops_in_cpp_index < len(ops_in_cpp): + if self.ops[ops_in_python_index].desc != ops_in_cpp[ + ops_in_cpp_index]: + del self.ops[ops_in_python_index] + else: + ops_in_cpp_index += 1 + ops_in_python_index += 1 + assert len(self.ops) == len(ops_in_cpp) for index in range(len(self.ops)): assert self.ops[index].desc == ops_in_cpp[index] diff --git a/python/paddle/fluid/tests/unittests/test_protobuf_descs.py b/python/paddle/fluid/tests/unittests/test_protobuf_descs.py index da85786d0c..e4cf4a8bce 100644 --- a/python/paddle/fluid/tests/unittests/test_protobuf_descs.py +++ b/python/paddle/fluid/tests/unittests/test_protobuf_descs.py @@ -14,6 +14,7 @@ import unittest import paddle.fluid.core as core +from paddle.fluid.framework import Program class TestOpDesc(unittest.TestCase): @@ -187,32 +188,46 @@ class TestBlockDesc(unittest.TestCase): self.assertEqual(all_ops, [op0, op1, op2]) def test_remove_op(self): - prog = core.ProgramDesc() + program = Program() + prog = program.desc self.assertIsNotNone(prog) block = prog.block(0) self.assertIsNotNone(block) + + op0 = block.append_op() op1 = block.append_op() op2 = block.append_op() + op0.set_type("test") + op1.set_type("test") + op2.set_type("test") + + var0 = block.var("var0") var1 = block.var("var1") var2 = block.var("var2") var3 = block.var("var3") var4 = block.var("var4") var5 = block.var("var5") + + op0.set_input("X", ["var0"]) + op0.set_output("Y", ["var0"]) op1.set_input("X", ["var1", "var2"]) op1.set_output("Y", ["var3", "var4"]) op2.set_input("X", ["var1"]) op2.set_output("Y", ["var4", "var5"]) + program.sync_with_cpp() + # remove op1, its input var2 and output var3 will be removed at the same time, # but its input var1 and output var4 will not be removed since they are used for op2. - block.remove_op(0, 1) + block.remove_op(1, 2) + program.sync_with_cpp() all_ops = [] for idx in xrange(0, block.op_size()): all_ops.append(block.op(idx)) - self.assertEqual(all_ops, [op2]) + self.assertEqual(all_ops, [op0, op2]) all_vars = block.all_vars() - self.assertEqual(set(all_vars), {var1, var4, var5}) + self.assertEqual(set(all_vars), {var0, var1, var4, var5}) if __name__ == '__main__': From 3f3ecae164c13fafc8f5066c53d00eda2a925d45 Mon Sep 17 00:00:00 2001 From: weixing02 <564445201@qq.com> Date: Tue, 3 Apr 2018 17:27:05 +0800 Subject: [PATCH 29/58] Fix tables display error --- benchmark/cluster/README.md | 148 ++++++++++++++-- benchmark/cluster/vgg16/README.md | 160 +++++++++++++++--- .../design/algorithm/parameter_average.md | 2 +- doc/fluid/design/concepts/README.md | 32 +++- doc/fluid/design/concepts/block.md | 61 +++++-- .../concepts/functions_operators_layers.md | 40 ++++- doc/fluid/design/concepts/lod_tensor.md | 38 ++++- doc/fluid/design/concepts/var_desc.md | 25 ++- .../concurrent/concurrent_programming.md | 46 +++-- doc/fluid/design/concurrent/csp.md | 47 +++-- doc/fluid/design/modules/python_api.md | 33 +++- doc/fluid/design/motivation/fluid.md | 36 +++- .../design/motivation/refactorization.md | 36 +++- doc/fluid/design/network/deep_speech_2.md | 123 +++++++++++--- doc/fluid/dev/new_op_cn.md | 33 +++- doc/fluid/dev/new_op_en.md | 29 +++- doc/fluid/dev/releasing_process.md | 126 +++++++++++++- .../concepts/save_model/model_format.md | 68 ++++++-- .../howto/cluster/fluid_cluster_train_cn.md | 58 +++++-- .../howto/optimization/cpu_profiling_cn.md | 42 ++++- .../howto/optimization/cpu_profiling_en.md | 35 ++++ 21 files changed, 1043 insertions(+), 175 deletions(-) diff --git a/benchmark/cluster/README.md b/benchmark/cluster/README.md index b619613ea7..64816098a5 100644 --- a/benchmark/cluster/README.md +++ b/benchmark/cluster/README.md @@ -36,11 +36,41 @@ - Trainer Count: 100 - Metrics: mini-batch / sec -| Batch Size | 32 | 64 | 128 | 256 | -| -- | -- | -- | -- | -- | -| PaddlePaddle Fluid | - | - | - | - | -| PaddlePaddle v2 | - | - | - | - | -| TensorFlow | - | - | - | - | + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Batch Size 3264128 256
PaddlePaddle Fluid-- - -
PaddlePaddle v2 - - - -
TensorFlow - - - -
### Measure the Performance for Different PServer Count @@ -48,11 +78,41 @@ - Batch Size: 64 - Metrics: mini-batch / sec -| PServer Count | 10 | 20 | 40 | 60 | -| -- | -- | -- | -- | -- | -| PaddlePaddle Fluid | - | - | - | - | -| PaddlePaddle v2 | - | - | - | - | -| TensorFlow | - | - | - | - | + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
PServer Count 102040 60
PaddlePaddle Fluid-- - -
PaddlePaddle v2 - - - -
TensorFlow - - - -
### Measure Parallel Efficiency By Increasing Trainer Count @@ -67,11 +127,69 @@ The parallel efficiency is: $E = \div(S, N)$ -| Trainer Counter | 1 | 10 | 20 | 30 | 40 | 50 | 60 | 70 | 80 | 90 | 100 | -| -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -| PaddlePaddle Fluid | - | - | - | - | - | - | - | - | - | - | - | -| PaddlePaddle v2 | - | - | - | - | - | - | - | - | - | - | - | - | -| TensorFlow | - | - | - | - | - | - | - | - | - | - | - | - | - | + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Trainer Counter 11020 30405060 708090100
PaddlePaddle Fluid-- - - -- - - -- -
PaddlePaddle v2 - - - - -- - - -- -
TensorFlow - - - - -- - - -- -
+ ## Reproduce the benchmark diff --git a/benchmark/cluster/vgg16/README.md b/benchmark/cluster/vgg16/README.md index cd681a1a28..d56a912b9b 100644 --- a/benchmark/cluster/vgg16/README.md +++ b/benchmark/cluster/vgg16/README.md @@ -16,11 +16,41 @@ Setting environment variable: `MKL_NUM_THREADS=1`. - Metrics: samples / sec -| Batch Size | 32 | 64 | 128 | 256 | -| -- | -- | -- | -- | -- | -| PaddlePaddle Fluid | 15.44 | 16.32 | 16.74 | 16.79 | -| PaddlePaddle v2 | 15.97 | 17.04 | 17.60 | 17.83 | -| TensorFlow | 9.09 | 9.10 | 9.24 | 8.66 | + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Batch Size 3264128 256
PaddlePaddle Fluid 15.44 16.32 16.74 16.79
PaddlePaddle v2 15.97 17.04 17.60 17.83
TensorFlow 9.09 9.10 9.24 8.66
+ ### Different Batch Size @@ -28,12 +58,40 @@ Setting environment variable: `MKL_NUM_THREADS=1`. - Trainer Count: 20 - Metrics: samples / sec -| Batch Size | 32 | 64 | 128 | 256 | -| -- | -- | -- | -- | -- | -| PaddlePaddle Fluid | 190.20 | 222.15 | 247.40 | 258.18 | -| PaddlePaddle v2 | 170.96 | 233.71 | 256.14 | 329.23 | -| TensorFlow | - | - | - | - | - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Batch Size 3264128 256
PaddlePaddle Fluid 190.20 222.15 247.40 258.18
PaddlePaddle v2 170.96 233.71 256.14 329.23
TensorFlow - - - -
### Accelerate Rate @@ -41,11 +99,41 @@ Setting environment variable: `MKL_NUM_THREADS=1`. - Batch Size: 128 - Metrics: samples / sec -| Trainer Count | 20 | 40 | 80 | 100 | -| -- | -- | -- | -- | -- | -| PaddlePaddle Fluid | 263.29 (78.64%) | 518.80 (77.47%) | 836.26 (62.44%) | 1019.29 (60.89%) | -| PaddlePaddle v2 (need more tests) | 326.85 (92.85%) | 534.58 (75.93%) | 853.30 (60.60%) | 1041.99 (59.20%) | -| TensorFlow | - | - | - | - | + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Trainer Count 204080100
PaddlePaddle Fluid 263.29 (78.64%) 518.80 (77.47%) 836.26 (62.44%) 1019.29 (60.89%)
PaddlePaddle v2 (need more tests) 326.85 (92.85%) 534.58 (75.93%) 853.30 (60.60%) 1041.99 (59.20%)
TensorFlow - - - -
+ ### Different Pserver Count @@ -53,11 +141,41 @@ Setting environment variable: `MKL_NUM_THREADS=1`. - Batch Size: 128 - Metrics: samples/ sec -| PServer Count | 3 | 6 |10 | 20 | -| -- | -- | -- | -- | -- | -| PaddlePaddle Fluid(should fix in next PR) | 589.1 | 592.6 | 656.4 | 655.8 | -| PaddlePaddle v2 | 593.4 | 791.3 | 729.7 | 821.7 | -| TensorFlow | - | - | - | - | + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
PServer Count 361020
PaddlePaddle Fluid(should fix in next PR) 589.1 592.6 656.4 655.8
PaddlePaddle v2 (need more tests) 593.4 791.3 729.7 821.7
TensorFlow - - - -
+ *The performance gap between Fuild and v2 comes from the network interference.* diff --git a/doc/fluid/design/algorithm/parameter_average.md b/doc/fluid/design/algorithm/parameter_average.md index 2c4edee9fe..53d601d3a9 100644 --- a/doc/fluid/design/algorithm/parameter_average.md +++ b/doc/fluid/design/algorithm/parameter_average.md @@ -7,7 +7,7 @@ Polyak and Juditsky (1992) showed that the test performance of simple average of Hence, to accelerate the speed of Stochastic Gradient Descent, Averaged Stochastic Gradient Descent (ASGD) was proposed in Polyak and Juditsky (1992). For ASGD, the running average of parameters obtained by SGD, is used as the estimator for
. The averaging is done as follows: -
+![](./images/asgd.gif) We propose averaging for any optimizer similar to how ASGD performs it, as mentioned above. diff --git a/doc/fluid/design/concepts/README.md b/doc/fluid/design/concepts/README.md index ed3f5aab28..8ded0ad22f 100644 --- a/doc/fluid/design/concepts/README.md +++ b/doc/fluid/design/concepts/README.md @@ -6,11 +6,33 @@ Here are some initial thoughts. Your comments are welcome! I think we need only the following few CMake functions to make a project description mean and clean: -| C++ | CUDA C++ | Go | -|---|---|---| -| cc_library | nv_library | go_library | -| cc_binary | nv_binary | go_binary | -| cc_test | nv_test | go_test | + + + + + + + + + + + + + + + + + + + + + + + + + +
C++CUDA C++Go
cc_library nv_library go_library
cc_binary nv_binary go_binary
cc_test nv_test go_test
+ - The `_library` functions generate .a files from source code. - The `_binary` functions generate executable binary files. diff --git a/doc/fluid/design/concepts/block.md b/doc/fluid/design/concepts/block.md index 907a2def55..3b626bd89c 100644 --- a/doc/fluid/design/concepts/block.md +++ b/doc/fluid/design/concepts/block.md @@ -14,11 +14,29 @@ In programming languages, a block is a pair of curly braces that includes local Blocks work with control flow structures like `if`, `else`, and `for`, which have equivalents in deep learning: -| programming languages | PaddlePaddle | -|-----------------------|-----------------------| -| for, while loop | RNN, WhileOp | -| if, if-else, switch | IfElseOp, SwitchOp | -| sequential execution | a sequence of layers | + + + + + + + + + + + + + + + + + + + + + +
programming languagesPaddlePaddle
for, while loop RNN, WhileOp
if, if-else, switch IfElseOp, SwitchOp
sequential execution a sequence of layers
+ A key difference is that a C++ program describes a one pass computation, whereas a deep learning program describes both the forward and backward passes. @@ -26,12 +44,33 @@ A key difference is that a C++ program describes a one pass computation, whereas The existence of the backward pass makes the execution of a block of PaddlePaddle different from traditional programs: -| programming languages | PaddlePaddle | -|-----------------------|---------------------------------| -| stack | scope hierarchy | -| stack frame | scope | -| push at entering block| push at entering block | -| pop at leaving block | destroy when minibatch completes| + + + + + + + + + + + + + + + + + + + + + + + + + +
programming languagesPaddlePaddle
stack scope hierarchy
stack frame scope
push at entering block push at entering block
pop at leaving block destroy when minibatch completes
+ 1. In traditional programs: diff --git a/doc/fluid/design/concepts/functions_operators_layers.md b/doc/fluid/design/concepts/functions_operators_layers.md index 984b59f4c6..30bc488a18 100644 --- a/doc/fluid/design/concepts/functions_operators_layers.md +++ b/doc/fluid/design/concepts/functions_operators_layers.md @@ -86,12 +86,40 @@ def layer.fc(X): We'd like to have Python bindings to operators in package `paddle.operator`, and Python compositions of operators in package `paddle.layer`. So we have the following concepts in above illustrative example: - -| C++ functions/functors | mul | add | | | -|------------------------|--------------|--------------|-------------|----------| -| C++ operator class | mulOp | addOp | FCOp | | -| Python binding | operator.mul | operator.add | operator.fc | | -| Python function | | | | layer.fc | + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
C++ functions/functorsmuladd
C++ operator class mulOpaddOp FCOp
Python binding operator.mul operator.add operator.fc
Python function layer.fc
This is how we differentiate layer and operators in PaddlePaddle: diff --git a/doc/fluid/design/concepts/lod_tensor.md b/doc/fluid/design/concepts/lod_tensor.md index 10a8a7867f..a88292e788 100644 --- a/doc/fluid/design/concepts/lod_tensor.md +++ b/doc/fluid/design/concepts/lod_tensor.md @@ -2,12 +2,38 @@ Like other deep learning systems, PaddlePaddle supports training models from sequence data. Also, like other systems, PaddlePaddle represent a mini-batch of sequences as a Tensor. What is different is that PaddlePaddle doesn't require all sequences in a mini-batch to be of the same length. Thus no need for padding zeros. -| | TensorFlow | PaddlePaddle | -|-----------------------|------------|--------------| -| RNN | Support | Support | -| recursive RNN | Support | Support | -| padding zeros | Must | No need | -| blob data type | Tensor | LoDTensor | + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
TensorFlowPaddlePaddle
RNN Support Support
recursive RNN Support Support
padding zeros Must No need
blob data type Tensor LoDTensor
+ PaddlePaddle achieves this flexibility by passing through a new data type, *LoD Tensor*, which is a Tensor attached with segmentation index known as *LoD*, between operators. The LoD index doesn't only segment a tensor, but also recursively segments sub-sequences. This document presents the design of LoD and LoDTensor. diff --git a/doc/fluid/design/concepts/var_desc.md b/doc/fluid/design/concepts/var_desc.md index fcba08c07f..6750323c01 100644 --- a/doc/fluid/design/concepts/var_desc.md +++ b/doc/fluid/design/concepts/var_desc.md @@ -10,10 +10,27 @@ PaddlePaddle uses proto message to describe compile time program because : The computation `Program` consists of nested `Blocks`. Each `Block` will consist of data(i.e. `Variable`) and `Operations`. The concept to represent them is in the table below. -| |compile time|runtime| -|---|---|---| -|Data|VarDesc(proto)|Variable(cpp)| -|Operation|OpDesc(proto)|Operator(cpp)| + + + + + + + + + + + + + + + + + + + + +
compile timeruntime
Data VarDesc(proto) Variable(cpp)
Operation OpDesc(proto) Operator(cpp)
## Definition of VarType diff --git a/doc/fluid/design/concurrent/concurrent_programming.md b/doc/fluid/design/concurrent/concurrent_programming.md index f022e67fd3..6460216606 100644 --- a/doc/fluid/design/concurrent/concurrent_programming.md +++ b/doc/fluid/design/concurrent/concurrent_programming.md @@ -10,12 +10,38 @@ The answer relies on the fact that a `ProgramDesc` is similar to an abstract syn The following table compares concepts in Fluid and Go -| Go | Fluid | -|----|-------| -|user-defined functions | [layers](https://github.com/PaddlePaddle/Paddle/tree/develop/python/paddle/fluid) | -| control-flow and built-in functions | [intrinsics/operators](https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/operators) | -| goroutines, channels | [class ThreadPool](https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/framework/thread_pool.h) | -| runtime | [class Executor](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/executor.h) | + + + + + + + + + + + + + + + + + + + + + + + + + + +
GoFluid
user-defined functions +layers
control-flow and built-in functions +intrinsics/operators
goroutines, channels +class ThreadPool
runtime +class Executor
+ ## An Example Concurrent Program @@ -77,11 +103,11 @@ message ProgramDesc { read(output = X) kube_get_workers_addrs(output = L) Y = tensor_array(len(L)) - parallel_for(input = X, output = Y, + parallel_for(input = X, output = Y, attrs = {L, block_id(1)}) # referring to block 1 ] } - + block[1] = Block { parent = 0, vars = [x, y, index], @@ -102,7 +128,7 @@ func main() { //// block 0 X = fluid.read(...) L = fluid.k8s.get_worker_addrs() Y = fluid.tensor_array(len(L)) - fluid.parallel_for(X, L, + fluid.parallel_for(X, L, func(index int) { //// block 1 x = X[index] fluid.send(L[index], x) @@ -116,7 +142,7 @@ An explanation of the above program: - `fluid.k8s` is a package that provides access to Kubernetes API. - `fluid.k8s.get_worker_addrs` returns the list of IP and ports of all pods of the current job except for the current one (the master pod). -- `fluid.tensor_array` creates a [tensor array](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/lod_tensor_array.h). `fluid.parallel_for` creates a `ParallelFor` intrinsic, which, when executed, +- `fluid.tensor_array` creates a [tensor array](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/lod_tensor_array.h). `fluid.parallel_for` creates a `ParallelFor` intrinsic, which, when executed, 1. creates `len(L)` scopes, each for the concurrent running of the sub-block (block 1 in this case), and initializes a variable named "index" in the scope to an integer value in the range `[0, len(L)-1]`, and 2. creates `len(L)` threads by calling into the `ThreadPool` singleton, each thread diff --git a/doc/fluid/design/concurrent/csp.md b/doc/fluid/design/concurrent/csp.md index 10d936860f..66d19f44ba 100644 --- a/doc/fluid/design/concurrent/csp.md +++ b/doc/fluid/design/concurrent/csp.md @@ -13,14 +13,41 @@ Most DL systems, including TensorFlow, Caffe2, and MxNet, can asynchronously exe There were many concurrent programming models, implemented in various forms: -| concurrent programming model | implementation | -|-----|-----| -| mutex | types and functions in standard libraries | -| semaphore | types and functions in standard libraries | -| communicating sequential processes (CSP) | Go programming language | -| actor model | Erlang programming language | -| message passing | MPI | -| bulk synchronous parallel (BSP) | Pregel distributed programming framework | + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
concurrent programming modelimplementation
mutex types and functions in standard libraries
semaphore types and functions in standard libraries
communicating sequential processes (CSP) Go programming language
actor model Erlang programming language
message passing MPI
bulk synchronous parallel (BSP) Pregel distributed programming framework
+ Since Fluid was designed to be a programming language, we would like to implement CSP in Fluid. @@ -118,9 +145,9 @@ There are four types of actions with a channel: ```go close(ch) ``` - + Please be aware that a closed channel is not a nil channel, which is `var ch chan int`. - + There are some [axioms with channels](https://dave.cheney.net/2014/03/19/channel-axioms): 1. A send to a nil channel blocks forever diff --git a/doc/fluid/design/modules/python_api.md b/doc/fluid/design/modules/python_api.md index 73f6d7b90c..f83ad3b6a4 100644 --- a/doc/fluid/design/modules/python_api.md +++ b/doc/fluid/design/modules/python_api.md @@ -2,12 +2,33 @@ Due to the refactorization of the PaddlePaddle core, we need Python classes to construct corresponding protobuf messages that describe a DL program. -| Python classes | Protobuf messages | -| --- | --- | -| Program | ProgramDesc | -| Block | BlockDesc | -| Operator | OpDesc | -| Variable | VarDesc | + + + + + + + + + + + + + + + + + + + + + + + + + +
Python classesProtobuf messages
Program ProgramDesc
Block BlockDesc
Operator OpDesc
Variable VarDesc
+ Please be aware that these Python classes need to maintain some construction-time information, which are not part of the protobuf messages. diff --git a/doc/fluid/design/motivation/fluid.md b/doc/fluid/design/motivation/fluid.md index 110b7d78bf..5e147f8263 100644 --- a/doc/fluid/design/motivation/fluid.md +++ b/doc/fluid/design/motivation/fluid.md @@ -10,11 +10,37 @@ Fluid is the answer. Fluid is similar to PyTorch and TensorFlow Eager Execution Deep learning infrastructure is one of the fastest evolving technologies. Within four years, there have already been three generations of technologies invented. -| Existed since | model as sequence of layers | model as graph of operators | No model | -|--|--|--|--| -| 2013 | Caffe, Theano, Torch, PaddlePaddle | | | -| 2015 | | TensorFlow, MxNet, Caffe2, ONNX, n-graph | | -| 2016 | | | PyTorch, TensorFlow Eager Execution, PaddlePaddle Fluid | + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Existed sincemodel as sequence of layersmodel as graph of operatorsNo model
2013 Caffe, Theano, Torch, PaddlePaddle
2015 TensorFlow, MxNet, Caffe2, ONNX, n-graph
2016 PyTorch, TensorFlow Eager Execution, PaddlePaddle Fluid
+ From the above table, we see that the deep learning technology is evolving towards getting rid of the concept of a model. To understand the reasons behind this direction, a comparison of the *programming paradigms* or the ways to program deep learning applications using these systems, would be helpful. The following section goes over these. diff --git a/doc/fluid/design/motivation/refactorization.md b/doc/fluid/design/motivation/refactorization.md index 7c39fabcc6..f199cc892f 100644 --- a/doc/fluid/design/motivation/refactorization.md +++ b/doc/fluid/design/motivation/refactorization.md @@ -36,11 +36,37 @@ At compile time, the Python program generates a protobuf message representation At runtime, the C++ program realizes the graph and runs it. -| | Representation (protobuf messages) | Realization (C++ class objects) | -|---|---|---| -|Data|[VarDesc](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/framework.proto#L107)|[Variable](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/variable.h#L24)| -|Operation|[OpDesc](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/framework.proto#L35)|[Operator](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/operator.h#L64)| -|Block|BlockDesc|Block| + + + + + + + + + + + + + + + + + + + + + + + + + +
Representation (protobuf messages)Realization (C++ class objects)
Data +VarDesc +Variable
Operation +OpDesc +Operator
Block BlockDesc Block
+ The word *graph* is interchangeable with *block* in this document. A graph consists of computation steps and local variables similar to a C++/Java program block, or a pair of parentheses(`{` and `}`). diff --git a/doc/fluid/design/network/deep_speech_2.md b/doc/fluid/design/network/deep_speech_2.md index af0c6ef36f..7f5dcf55f9 100644 --- a/doc/fluid/design/network/deep_speech_2.md +++ b/doc/fluid/design/network/deep_speech_2.md @@ -1,4 +1,4 @@ -# DeepSpeech2 on PaddlePaddle: Design Doc +# DeepSpeech2 on PaddlePaddle: Design Doc We are planning to build Deep Speech 2 (DS2) \[[1](#references)\], a powerful Automatic Speech Recognition (ASR) engine, on PaddlePaddle. For the first-stage plan, we have the following short-term goals: @@ -68,11 +68,33 @@ We roughly break down the project into 14 tasks: Tasks parallelizable within phases: -Roadmap | Description | Parallelizable Tasks ------------ | :------------------------------------ | :-------------------- -Phase I | Simplified model & components | *Task 1* ~ *Task 8* -Phase II | Standard model & benchmarking & profiling | *Task 9* ~ *Task 12* -Phase III | Documentations | *Task13* ~ *Task14* + + + + + + + + + + + + + + + + + + + + + + + + + +
RoadmapDescription Parallelizable Tasks
Phase I Simplified model & components Task 1 ~ Task 8
Phase II Standard model & benchmarking & profilingTask 9 ~ Task 12
Phase III Documentations Task13 ~ Task14
+ Issue for each task will be created later. Contributions, discussions and comments are all highly appreciated and welcomed! @@ -102,37 +124,82 @@ We don't have to persist on this 2-3-7-1-1-1 depth \[[2](#references)\]. Similar Key ingredients about the layers: -- **Data Layers**: +- **Data Layers**: - Frame sequences data of audio **spectrogram** (with FFT). - - Token sequences data of **transcription** text (labels). + - Token sequences data of **transcription** text (labels). - These two type of sequences do not have the same lengthes, thus a CTC-loss layer is required. -- **2D Convolution Layers**: +- **2D Convolution Layers**: - Not only temporal convolution, but also **frequency convolution**. Like a 2D image convolution, but with a variable dimension (i.e. temporal dimension). - With striding for only the first convlution layer. - No pooling for all convolution layers. -- **Uni-directional RNNs** +- **Uni-directional RNNs** - Uni-directional + row convolution: for low-latency inference. - Bi-direcitional + without row convolution: if we don't care about the inference latency. - **Row convolution**: - For looking only a few steps ahead into the feature, instead of looking into a whole sequence in bi-directional RNNs. - - Not nessesary if with bi-direcitional RNNs. + - Not nessesary if with bi-direcitional RNNs. - "**Row**" means convolutions are done within each frequency dimension (row), and no convolution kernels shared across. - **Batch Normalization Layers**: - Added to all above layers (except for data and loss layer). - Sequence-wise normalization for RNNs: BatchNorm only performed on input-state projection and not state-state projection, for efficiency consideration. - - -Required Components | PaddlePaddle Support | Need to Develop -:------------------------------------- | :-------------------------------------- | :----------------------- -Data Layer I (Spectrogram) | Not supported yet. | TBD (Task 3) -Data Layer II (Transcription) | `paddle.data_type.integer_value_sequence` | - -2D Convolution Layer | `paddle.layer.image_conv_layer` | - -DataType Converter (vec2seq) | `paddle.layer.block_expand` | - -Bi-/Uni-directional RNNs | `paddle.layer.recurrent_group` | - -Row Convolution Layer | Not supported yet. | TBD (Task 4) -CTC-loss Layer | `paddle.layer.warp_ctc` | - -Batch Normalization Layer | `paddle.layer.batch_norm` | - -CTC-Beam search | Not supported yet. | TBD (Task 6) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Required Components PaddlePaddle Support Need to Develop
Data Layer I (Spectrogram) Not supported yet.TBD (Task 3)
Data Layer II (Transcription) paddle.data_type.integer_value_sequence -
2D Convolution Layer paddle.layer.image_conv_layer -
DataType Converter (vec2seq) paddle.layer.block_expand -
Bi-/Uni-directional RNNs paddle.layer.recurrent_group -
Row Convolution Layer Not supported yet.TBD (Task 4)
CTC-loss Layer paddle.layer.warp_ctc -
Batch Normalization Layer paddle.layer.batch_norm -
CTC-Beam search Not supported yet. TBD (Task 6)
+ ### Row Convolution @@ -145,14 +212,14 @@ TODO by Assignees Figure 2. Algorithm for CTC Beam Search Decoder. -- The **Beam Search Decoder** for DS2 CTC-trained network follows the similar approach in \[[3](#references)\] as shown in Figure 2, with two important modifications for the ambiguous parts: - - 1) in the iterative computation of probabilities, the assignment operation is changed to accumulation for one prefix may comes from different paths; +- The **Beam Search Decoder** for DS2 CTC-trained network follows the similar approach in \[[3](#references)\] as shown in Figure 2, with two important modifications for the ambiguous parts: + - 1) in the iterative computation of probabilities, the assignment operation is changed to accumulation for one prefix may comes from different paths; - 2) the if condition ```if l^+ not in A_prev then``` after probabilities' computation is deprecated for it is hard to understand and seems unnecessary. - An **external scorer** would be passed into the decoder to evaluate a candidate prefix during decoding whenever a white space appended in English decoding and any character appended in Mandarin decoding. - Such external scorer consists of language model, word count or any other custom scorers. - The **language model** is built from Task 5, with parameters should be carefully tuned to achieve minimum WER/CER (c.f. Task 7) -- This decoder needs to perform with **high efficiency** for the convenience of parameters tuning and speech recognition in reality. - +- This decoder needs to perform with **high efficiency** for the convenience of parameters tuning and speech recognition in reality. + ## Future Work diff --git a/doc/fluid/dev/new_op_cn.md b/doc/fluid/dev/new_op_cn.md index 9299658567..0c3f88d9c3 100644 --- a/doc/fluid/dev/new_op_cn.md +++ b/doc/fluid/dev/new_op_cn.md @@ -26,13 +26,32 @@ 依据是否包含kernel，可以将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`OperatorWithKernel`，后者继承自`OperatorBase`。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： - - 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、CUDA共享Kernel实现在`.h`文件中，否则，CPU 实现在`.cc`文件中，CUDA 实现在`.cu`文件中。 -注册Op | Op注册实现在`.cc`文件；Kernel注册CPU实现在`.cc`文件中，CUDA实现在`.cu`文件中 + + + + + + + + + + + + + + + + + + + + + + + + + +
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、CUDA共享Kernel实现在`.h`文件中，否则，CPU 实现在`.cc`文件中，CUDA 实现在`.cu`文件中。
注册Op Op注册实现在`.cc`文件；Kernel注册CPU实现在`.cc`文件中，CUDA实现在`.cu`文件中
实现新的op都添加至目录[paddle/operators](https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/operators)下，文件命名以`*_op.h`（如有） 、 `*_op.cc` 、`*_op.cu`（如有）结尾。**系统会根据文件名自动构建op和其对应的Python扩展。** diff --git a/doc/fluid/dev/new_op_en.md b/doc/fluid/dev/new_op_en.md index da8b1bdd10..a566a09131 100644 --- a/doc/fluid/dev/new_op_en.md +++ b/doc/fluid/dev/new_op_en.md @@ -33,6 +33,33 @@ Op definition | `.cc` files Kernel implementation | The kernel methods shared between CPU and CUDA are defined in `.h` files. CPU-specific kernels live in `.cc` files, while CUDA-specific kernels are implemented in `.cu`files. Registering the Op | Ops are registered in `.cc` files; For Kernel registration, `.cc` files contain the CPU implementation, while `.cu` files contain the CUDA implementation. + + + + + + + + + + + + + + + + + + + + + + + + + +
Information Where is it defined
OpProtoMake definition `.cc`files, Backward Op does not need an OpProtoMake interface.
Op definition `.cc` files
Kernel implementation The kernel methods shared between CPU and CUDA are defined in `.h` files. CPU-specific kernels live in `.cc` files, while CUDA-specific kernels are implemented in `.cu`files.
Registering the Op Ops are registered in `.cc` files; For Kernel registration, `.cc` files contain the CPU implementation, while `.cu` files contain the CUDA implementation.
+ New Operator implementations are added to the list [paddle/operators](https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/operators), with file names in the format `*_op.h` (if applicable), `*_op.cc`, `*_op.cu` (if applicable).** The system will use the naming scheme to automatically build operators and their corresponding Python extensions.** @@ -279,7 +306,7 @@ A forward operator unit test inherits `unittest.TestCase` and defines metaclass def test_check_output(self): self.check_output() - + def test_check_grad_normal(self): self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.5) diff --git a/doc/fluid/dev/releasing_process.md b/doc/fluid/dev/releasing_process.md index b978726109..addd474b69 100644 --- a/doc/fluid/dev/releasing_process.md +++ b/doc/fluid/dev/releasing_process.md @@ -66,7 +66,7 @@ PaddlePaddle开发过程使用[git-flow](http://nvie.com/posts/a-successful-git- * 建议，开发者fork的版本库使用`develop`分支同步主版本库的`develop`分支 * 建议，开发者fork的版本库中，再基于`develop`版本fork出自己的功能分支。 * 当功能分支开发完毕后，向PaddlePaddle的主版本库提交`Pull Reuqest`，进而进行代码评审。 - * 在评审过程中，开发者修改自己的代码，可以继续在自己的功能分支提交代码。 + * 在评审过程中，开发者修改自己的代码，可以继续在自己的功能分支提交代码。 * BugFix分支也是在开发者自己的fork版本库维护，与功能分支不同的是，BugFix分支需要分别给主版本库的`master`、`develop`与可能有的`release/版本号`分支，同时提起`Pull Request`。 @@ -78,13 +78,137 @@ PaddlePaddle开发过程使用[git-flow](http://nvie.com/posts/a-successful-git- PaddlePaddle每次发版本首先要保证PaddlePaddle Book中所有章节功能的正确性。功能的正确性包括验证PaddlePaddle目前的`paddle_trainer`训练和纯使用`Python`训练模型正确性。 + | | 新手入门章节 | 识别数字 | 图像分类 | 词向量 | 情感分析 | 语意角色标注 | 机器翻译 | 个性化推荐 | + | --- | --- | --- | --- | --- | --- | --- | --- | --- | + | API.V2 + Docker + GPU | | | | | | | | | + | API.V2 + Docker + CPU | | | | | | | | | + | `paddle_trainer` + Docker + GPU | | | | | | | | | + | `paddle_trainer` + Docker + CPU | | | | | | | | | + | API.V2 + Ubuntu + GPU | | | | | | | | | + | API.V2 + Ubuntu + CPU | | | | | | | | | + | `paddle_trainer` + Ubuntu + GPU | | | | | | | | | + | `paddle_trainer` + Ubuntu + CPU | | | | | | | | | + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
新手入门章节 识别数字 图像分类词向量 情感分析语意角色标注 机器翻译个性化推荐
API.V2 + Docker + GPU
API.V2 + Docker + CPU
`paddle_trainer` + Docker + GPU
`paddle_trainer` + Docker + CPU
API.V2 + Ubuntu + GPU
API.V2 + Ubuntu + CPU
`paddle_trainer` + Ubuntu + GPU
`paddle_trainer` + Ubuntu + CPU
diff --git a/doc/fluid/getstarted/concepts/save_model/model_format.md b/doc/fluid/getstarted/concepts/save_model/model_format.md index e29129fddf..1f12ba0497 100644 --- a/doc/fluid/getstarted/concepts/save_model/model_format.md +++ b/doc/fluid/getstarted/concepts/save_model/model_format.md @@ -4,30 +4,70 @@ A model is an output of the training process. One complete model consists of two parts, the **topology** and the **parameters**. In order to support industrial deployment, the model format must be self-complete and must not expose any training source code. -As a result, In PaddlePaddle, the **topology** is represented as a [ProgramDesc](https://github.com/PaddlePaddle/Paddle/blob/1c0a4c901c9fc881d120249c703b15d1c50dae7d/doc/design/program.md), which describes the model structure. The **parameters** contain all the trainable weights in the model. We must support large size parameters and efficient serialization/deserialization of parameters. +As a result, In PaddlePaddle, the **topology** is represented as a [ProgramDesc](https://github.com/PaddlePaddle/Paddle/blob/1c0a4c901c9fc881d120249c703b15d1c50dae7d/doc/design/program.md), which describes the model structure. The **parameters** contain all the trainable weights in the model. We must support large size parameters and efficient serialization/deserialization of parameters. ## Implementation -The topology is saved as a plain text in a detailed self-contain protobuf file. +The topology is saved as a plain text in a detailed self-contain protobuf file. The parameters are saved as a binary file. As we all know, the protobuf message has a limit of [64M size](https://developers.google.com/protocol-buffers/docs/reference/cpp/google.protobuf.io.coded_stream#CodedInputStream.SetTotalBytesLimit.details). We have done a [benchmark experiment](https://github.com/PaddlePaddle/Paddle/pull/4610), which shows that protobuf is not fit for the task. -As a result, we design a particular format for tensor serialization. By default, an arbitrary tensor in Paddle is a [LoDTensor](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/lod_tensor.md), and has a description information proto of [LoDTensorDesc](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/framework.proto#L99). We save the DescProto as the byte string header. It contains all the necessary information, such as the `dims`, and the `LoD` information in [LoDTensor](https://github.com/PaddlePaddle/Paddle/blob/1c0a4c901c9fc881d120249c703b15d1c50dae7d/paddle/framework/lod_tensor.md). A tensor stores values in a continuous memory buffer. For speed we dump the raw memory to disk and save it as the byte string content. So, the binary format of one tensor is, +As a result, we design a particular format for tensor serialization. By default, an arbitrary tensor in Paddle is a [LoDTensor](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/lod_tensor.md), and has a description information proto of [LoDTensorDesc](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/framework.proto#L99). We save the DescProto as the byte string header. It contains all the necessary information, such as the `dims`, and the `LoD` information in [LoDTensor](https://github.com/PaddlePaddle/Paddle/blob/1c0a4c901c9fc881d120249c703b15d1c50dae7d/paddle/framework/lod_tensor.md). A tensor stores values in a continuous memory buffer. For speed we dump the raw memory to disk and save it as the byte string content. So, the binary format of one tensor is, The table below shows a tensor's byte view in detail. Note that all the signed values are written in the little-endian format. -|field name | type | description | -| --- | --- | --- | -| version | uint32_t | Version of saved file. Always 0 now. | -| tensor desc length | uint32_t | TensorDesc(Protobuf message) length in bytes. | -| tensor desc | void* | TensorDesc protobuf binary message | -| tensor data | void* | Tensor's data in binary format. The length of `tensor_data` is decided by `TensorDesc.dims()` and `TensorDesc.data_type()` | -| lod_level | uint64_t | Level of LoD | -| length of lod[0] | uint64_t | [Optional] length of lod[0] in bytes. | -| data of lod[0] | uint64_t* | [Optional] lod[0].data() | -| ... | ... | ... | - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
field nametype description
version uint32_t Version of saved file. Always 0 now.
tensor desc length uint32_t TensorDesc(Protobuf message) length in bytes.
tensor desc void* TensorDesc protobuf binary message
tensor data void* Tensor's data in binary format. The length of `tensor_data` is decided by `TensorDesc.dims()` and `TensorDesc.data_type()`
lod_level uint64_t Level of LoD
length of lod[0] uint64_t [Optional] length of lod[0] in bytes.
data of lod[0] uint64_t* [Optional] lod[0].data()
... ... ...
## Summary diff --git a/doc/fluid/howto/cluster/fluid_cluster_train_cn.md b/doc/fluid/howto/cluster/fluid_cluster_train_cn.md index 1b6f767869..b99b90056b 100644 --- a/doc/fluid/howto/cluster/fluid_cluster_train_cn.md +++ b/doc/fluid/howto/cluster/fluid_cluster_train_cn.md @@ -65,10 +65,10 @@ exit(1) **因此，在分布式的Fluid环境中，我们有两个角色需要创建，分别是Parameter Server和Trainer。** -### 分布式训练 +### 分布式训练 Fliud专门提供了工具[Distributed Transpiler](https://github.com/PaddlePaddle/Paddle/blob/ba65d54d9d3b41cd3c5171b00f476d4e60133ddb/doc/fluid/design/dist_train/distributed_architecture.md#distributed-transpiler)用于将单机版的训练程序转换为分布式版本的训练程序。工具背后的理念是找出程序的优化算子和梯度参数，将他们分隔为两部分，通过send/recv 操作算子进行连接,优化算子和梯度参数可以在优化器的minimize函数的返回值中获取到。 ```python -optimize_ops, params_grads = sgd_optimizer.minimize(avg_cost) +optimize_ops, params_grads = sgd_optimizer.minimize(avg_cost) ``` 将Distributed Transpiler、优化算子和梯度函数放在一个代码中如下： ```python @@ -99,15 +99,51 @@ for pass_id in range(100): ### 分布式训练脚本运行说明 分布式任务的运行需要将表格中说明的多个参数进行赋值: -| 参数名 | 值类型 | 说明 | 示例 | -|:-------------|:------|:---------------------------------------|:-------------| -| trainer_id | int | 当前训练节点的ID，训练节点ID编号为0 - n-1， n为trainers的值 | 0/1/2/3 | -| pservers | str | parameter server 列表 | 127.0.0.1:6710,127.0.0.1:6711 | -| trainers | int | 训练节点的总个数，>0的数字 | 4 | -| server_endpoint | str | 当前所起的服务节点的IP:PORT | 127.0.0.1:8789 | -| training_role | str | 节点角色， TRAINER/PSERVER | PSERVER | - -**注意：** ```training_role```是用来区分当前所起服务的角色的，用于训练程序中，用户可根据需要自行定义，其他参数为fluid.DistributeTranspiler的transpile函数所需要，需要在调用函数前进行定义，样例如下： + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
参数名 值类型说明 示例
trainer_id int 当前训练节点的ID，训练节点ID编号为0 - n-1， n为trainers的值 0/1/2/3
pservers str parameter server 列表 127.0.0.1:6710,127.0.0.1:6711
trainers int 训练节点的总个数，>0的数字 4
server_endpoint str 当前所起的服务节点的IP:PORT 127.0.0.1:8789
training_rolestr 节点角色， TRAINER/PSERVER PSERVER
+ + +**注意：** ```training_role```是用来区分当前所起服务的角色的，用于训练程序中，用户可根据需要自行定义，其他参数为fluid.DistributeTranspiler的transpile函数所需要，需要在调用函数前进行定义，样例如下： ```python t = fluid.DistributeTranspiler() diff --git a/doc/fluid/howto/optimization/cpu_profiling_cn.md b/doc/fluid/howto/optimization/cpu_profiling_cn.md index 17f895573a..8266dec3c6 100644 --- a/doc/fluid/howto/optimization/cpu_profiling_cn.md +++ b/doc/fluid/howto/optimization/cpu_profiling_cn.md @@ -42,14 +42,40 @@ cprofilev -a 0.0.0.0 -p 3214 -f profile.out main.py 每一列的含义是: -| 列名 | 含义 | -| --- | --- | -| ncalls | 函数的调用次数 | -| tottime | 函数实际使用的总时间。该时间去除掉本函数调用其他函数的时间 | -| percall | tottime的每次调用平均时间 | -| cumtime | 函数总时间。包含这个函数调用其他函数的时间 | -| percall | cumtime的每次调用平均时间 | -| filename:lineno(function) | 文件名, 行号，函数名 | + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
列名含义
ncalls 函数的调用次数
tottime 函数实际使用的总时间。该时间去除掉本函数调用其他函数的时间
percall tottime的每次调用平均时间
cumtime 函数总时间。包含这个函数调用其他函数的时间
percall cumtime的每次调用平均时间
filename:lineno(function) 文件名, 行号，函数名
### 寻找性能瓶颈 diff --git a/doc/fluid/howto/optimization/cpu_profiling_en.md b/doc/fluid/howto/optimization/cpu_profiling_en.md index abe4493c17..4447db252f 100644 --- a/doc/fluid/howto/optimization/cpu_profiling_en.md +++ b/doc/fluid/howto/optimization/cpu_profiling_en.md @@ -66,6 +66,41 @@ each column is as follows: | percall | cumtime divided by ncalls | | filename:lineno(function) | where the function is defined | + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
columnmeaning
ncalls the number of calls into a function
tottime the total execution time of the function, not including the execution time of other functions called by the function
percall tottime divided by ncalls
cumtime the total execution time of the function, including the execution time of other functions being called
percall cumtime divided by ncalls
filename:lineno(function) where the function is define
+ ### Identify Performance Bottlenecks Usually, `tottime` and the related `percall` time is what we want to From 6a7cba417432a750391c832ad18e805bbf03ab3e Mon Sep 17 00:00:00 2001 From: weixing Date: Tue, 3 Apr 2018 17:31:33 +0800 Subject: [PATCH 30/58] Update cpu_profiling_en.md --- doc/fluid/howto/optimization/cpu_profiling_en.md | 9 --------- 1 file changed, 9 deletions(-) diff --git a/doc/fluid/howto/optimization/cpu_profiling_en.md b/doc/fluid/howto/optimization/cpu_profiling_en.md index 4447db252f..e95556dd60 100644 --- a/doc/fluid/howto/optimization/cpu_profiling_en.md +++ b/doc/fluid/howto/optimization/cpu_profiling_en.md @@ -57,15 +57,6 @@ port, we will see the output like the following: where each line corresponds to Python function, and the meaning of each column is as follows: -| column | meaning | -| --- | --- | -| ncalls | the number of calls into a function | -| tottime | the total execution time of the function, not including the execution time of other functions called by the function | -| percall | tottime divided by ncalls | -| cumtime | the total execution time of the function, including the execution time of other functions being called | -| percall | cumtime divided by ncalls | -| filename:lineno(function) | where the function is defined | - From e81b140437395c6d137adb2a5664ea4390d3f3a0 Mon Sep 17 00:00:00 2001 From: weixing Date: Tue, 3 Apr 2018 17:32:10 +0800 Subject: [PATCH 31/58] Update releasing_process.md --- doc/fluid/dev/releasing_process.md | 21 --------------------- 1 file changed, 21 deletions(-) diff --git a/doc/fluid/dev/releasing_process.md b/doc/fluid/dev/releasing_process.md index addd474b69..0810765b85 100644 --- a/doc/fluid/dev/releasing_process.md +++ b/doc/fluid/dev/releasing_process.md @@ -78,27 +78,6 @@ PaddlePaddle开发过程使用[git-flow](http://nvie.com/posts/a-successful-git- PaddlePaddle每次发版本首先要保证PaddlePaddle Book中所有章节功能的正确性。功能的正确性包括验证PaddlePaddle目前的`paddle_trainer`训练和纯使用`Python`训练模型正确性。 - -| | 新手入门章节 | 识别数字 | 图像分类 | 词向量 | 情感分析 | 语意角色标注 | 机器翻译 | 个性化推荐 | - -| --- | --- | --- | --- | --- | --- | --- | --- | --- | - -| API.V2 + Docker + GPU | | | | | | | | | - -| API.V2 + Docker + CPU | | | | | | | | | - -| `paddle_trainer` + Docker + GPU | | | | | | | | | - -| `paddle_trainer` + Docker + CPU | | | | | | | | | - -| API.V2 + Ubuntu + GPU | | | | | | | | | - -| API.V2 + Ubuntu + CPU | | | | | | | | | - -| `paddle_trainer` + Ubuntu + GPU | | | | | | | | | - -| `paddle_trainer` + Ubuntu + CPU | | | | | | | | | -
From 00f8e63b8d4577c77ebafb14909e9ca20e57bb3b Mon Sep 17 00:00:00 2001 From: typhoonzero Date: Tue, 3 Apr 2018 17:35:02 +0800 Subject: [PATCH 32/58] update --- paddle/fluid/operators/CMakeLists.txt | 1 + paddle/fluid/operators/detail/grpc_server.cc | 3 -- paddle/fluid/operators/listen_and_serv_op.cc | 33 +++++++++++++++++++- paddle/fluid/operators/listen_and_serv_op.h | 32 ------------------- 4 files changed, 33 insertions(+), 36 deletions(-) diff --git a/paddle/fluid/operators/CMakeLists.txt b/paddle/fluid/operators/CMakeLists.txt index 9ed79453b9..952ac8b1dc 100644 --- a/paddle/fluid/operators/CMakeLists.txt +++ b/paddle/fluid/operators/CMakeLists.txt @@ -193,6 +193,7 @@ if(WITH_DISTRIBUTE) set_source_files_properties(send_vars_op.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS}) op_library(send_barrier_op DEPS ${DISTRIBUTE_DEPS}) set_source_files_properties(send_barrier_op.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS}) + set_source_files_properties(send_recv_op_test.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS}) cc_test(test_send_recv SRCS send_recv_op_test.cc DEPS prefetch_op send_op listen_and_serv_op sum_op executor) else() set(DEPS_OPS ${DEPS_OPS} send_op prefetch_op recv_op listen_and_serv_op send_vars_op send_barrier_op) diff --git a/paddle/fluid/operators/detail/grpc_server.cc b/paddle/fluid/operators/detail/grpc_server.cc index b81d374158..1515004d97 100644 --- a/paddle/fluid/operators/detail/grpc_server.cc +++ b/paddle/fluid/operators/detail/grpc_server.cc @@ -244,9 +244,6 @@ void AsyncGRPCServer::TryToRegisterNewSendOne() { VLOG(3) << "shutdown, do not TryToRegisterNewSendOne"; return; } - while (scope_ == nullptr) { - sleep(0.01); - } RequestSend* send = new RequestSend(&service_, cq_send_.get(), scope_, &var_recv_queue_, dev_ctx_); VLOG(4) << "Create RequestSend status:" << send->Status(); diff --git a/paddle/fluid/operators/listen_and_serv_op.cc b/paddle/fluid/operators/listen_and_serv_op.cc index 503ddd5d2f..611457e6d6 100644 --- a/paddle/fluid/operators/listen_and_serv_op.cc +++ b/paddle/fluid/operators/listen_and_serv_op.cc @@ -25,6 +25,38 @@ void RunServer(std::shared_ptr service) { VLOG(4) << "RunServer thread end"; } +static void CreateTensorFromMessageType(framework::Variable *var, + sendrecv::VarType var_type) { + if (var_type == sendrecv::VarType::LOD_TENSOR) { + var->GetMutable(); + } else if (var_type == sendrecv::VarType::SELECTED_ROWS) { + var->GetMutable(); + } else { + PADDLE_THROW( + "VariableMessage type %d is not in " + "[LoDTensor, SelectedRows]", + var_type); + } +} + +static void ParallelExecuteBlocks(const std::vector ¶llel_blkids, + framework::Executor *executor, + framework::ProgramDesc *program, + framework::Scope *scope) { + std::vector> fs; + for (size_t idx : parallel_blkids) { + fs.push_back(framework::Async([&executor, &program, &scope, idx]() { + int run_block = idx; // thread local + try { + executor->Run(*program, scope, run_block, false, false); + } catch (std::exception &e) { + LOG(ERROR) << "run sub program error " << e.what(); + } + })); + } + for (size_t i = 0; i < fs.size(); ++i) fs[i].wait(); +} + ListenAndServOp::ListenAndServOp(const std::string &type, const framework::VariableNameMap &inputs, const framework::VariableNameMap &outputs, @@ -62,7 +94,6 @@ void ListenAndServOp::RunImpl(const framework::Scope &scope, framework::Executor executor(dev_place); - // FIXME(Yancey1989): initialize rpc server with lazy mode. rpc_service_->SetScope(&recv_scope); rpc_service_->SetDevCtx(&dev_ctx); // TODO(qiao) set proper fields for table lookup and update diff --git a/paddle/fluid/operators/listen_and_serv_op.h b/paddle/fluid/operators/listen_and_serv_op.h index 4ebb8c3840..0da87afc96 100644 --- a/paddle/fluid/operators/listen_and_serv_op.h +++ b/paddle/fluid/operators/listen_and_serv_op.h @@ -30,38 +30,6 @@ constexpr char kOptimizeBlock[] = "OptimizeBlock"; void RunServer(std::shared_ptr service); -static void CreateTensorFromMessageType(framework::Variable *var, - sendrecv::VarType var_type) { - if (var_type == sendrecv::VarType::LOD_TENSOR) { - var->GetMutable(); - } else if (var_type == sendrecv::VarType::SELECTED_ROWS) { - var->GetMutable(); - } else { - PADDLE_THROW( - "VariableMessage type %d is not in " - "[LoDTensor, SelectedRows]", - var_type); - } -} - -static void ParallelExecuteBlocks(const std::vector ¶llel_blkids, - framework::Executor *executor, - framework::ProgramDesc *program, - framework::Scope *scope) { - std::vector> fs; - for (size_t idx : parallel_blkids) { - fs.push_back(framework::Async([&executor, &program, &scope, idx]() { - int run_block = idx; // thread local - try { - executor->Run(*program, scope, run_block, false, false); - } catch (std::exception &e) { - LOG(ERROR) << "run sub program error " << e.what(); - } - })); - } - for (size_t i = 0; i < fs.size(); ++i) fs[i].wait(); -} - class ListenAndServOp : public framework::OperatorBase { public: ListenAndServOp(const std::string &type, From 44c29abdbf76ddaa8c0fb9fc4a060f034b8d4a13 Mon Sep 17 00:00:00 2001 From: typhoonzero Date: Tue, 3 Apr 2018 17:40:47 +0800 Subject: [PATCH 33/58] remove comments --- paddle/fluid/operators/listen_and_serv_op.cc | 1 - 1 file changed, 1 deletion(-) diff --git a/paddle/fluid/operators/listen_and_serv_op.cc b/paddle/fluid/operators/listen_and_serv_op.cc index d4b0fa3aa1..11eab6f78f 100644 --- a/paddle/fluid/operators/listen_and_serv_op.cc +++ b/paddle/fluid/operators/listen_and_serv_op.cc @@ -65,7 +65,6 @@ static void ParallelExecuteBlocks( framework::Async([&executor, &prepared, &program, &scope, idx]() { int run_block = idx; // thread local try { - // executor->Run(*program, scope, run_block, false, false); executor->RunPreparedContext(prepared[run_block].get(), scope, false, false); } catch (std::exception &e) { From 2811ea4440c8dd3ecaedc7476f9a7c0cb1519a2c Mon Sep 17 00:00:00 2001 From: mozga-intel Date: Wed, 28 Mar 2018 10:45:52 +0200 Subject: [PATCH 34/58] Implementation of MKLDNN FC --- paddle/fluid/operators/CMakeLists.txt | 29 +- paddle/fluid/operators/fc_mkldnn_op.cc | 410 ++++++++++++++++++ paddle/fluid/operators/fc_mkldnn_op.h | 47 ++ python/paddle/fluid/layers/nn.py | 32 +- .../fluid/tests/unittests/test_fc_op.py | 99 +++++ 5 files changed, 603 insertions(+), 14 deletions(-) create mode 100644 paddle/fluid/operators/fc_mkldnn_op.cc create mode 100644 paddle/fluid/operators/fc_mkldnn_op.h create mode 100644 python/paddle/fluid/tests/unittests/test_fc_op.py diff --git a/paddle/fluid/operators/CMakeLists.txt b/paddle/fluid/operators/CMakeLists.txt index 9ed79453b9..6c79998f07 100644 --- a/paddle/fluid/operators/CMakeLists.txt +++ b/paddle/fluid/operators/CMakeLists.txt @@ -1,6 +1,14 @@ file(GLOB GENERAL_OPS RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "*_op.cc") -string(REPLACE "_mkldnn" "" GENERAL_OPS "${GENERAL_OPS}") string(REPLACE ".cc" "" GENERAL_OPS "${GENERAL_OPS}") +if(WITH_MKLDNN) + string(REPLACE "_mkldnn" "" GENERAL_OPS "${GENERAL_OPS}") +else() + foreach(item ${GENERAL_OPS}) + if(${item} MATCHES ".*_mkldnn_op") + list(REMOVE_ITEM GENERAL_OPS ${item}) + endif() + endforeach(item) +endif() list(REMOVE_DUPLICATES GENERAL_OPS) set(DEPS_OPS "") set(pybind_file ${PADDLE_SOURCE_DIR}/paddle/fluid/pybind/pybind.h) @@ -80,7 +88,12 @@ function(op_library TARGET) endif() list(LENGTH cc_srcs cc_srcs_len) - if (${cc_srcs_len} EQUAL 0) + if(WITH_MKLDNN) + list(LENGTH mkldnn_cc_srcs mkldnn_cc_srcs_len) + if (${cc_srcs_len} EQUAL 0 AND ${mkldnn_cc_srcs_len} EQUAL 0) + message(FATAL_ERROR "The op library ${TARGET} should contains at least one .cc file") + endif() + elseif(${cc_srcs_len} EQUAL 0) message(FATAL_ERROR "The op library ${TARGET} should contains at least one .cc file") endif() @@ -109,7 +122,16 @@ function(op_library TARGET) # The registration of USE_OP, please refer to paddle/fluid/framework/op_registry.h. # Note that it's enough to just adding one operator to pybind in a *_op.cc file. # And for detail pybind information, please see generated paddle/pybind/pybind.h. - file(READ ${TARGET}.cc TARGET_CONTENT) + # This replacing is needed, when the CPU's kernel doesn't exist. + string(REPLACE "_op" "_mkldnn_op" target_mkldnn_file "${TARGET}") + if(EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/${TARGET}.cc) + file(READ ${TARGET}.cc TARGET_CONTENT) + elseif(WITH_MKLDNN AND EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/${target_mkldnn_file}.cc) + file(READ ${target_mkldnn_file}.cc TARGET_CONTENT) + else() + message(FATAL_ERROR "Cannot read the ${TARGET} file from ${CMAKE_CURRENT_SOURCE_DIR}") + endif() + string(REGEX MATCH "REGISTER_OP\\(.*REGISTER_OP\\(" multi_register "${TARGET_CONTENT}") string(REGEX MATCH "REGISTER_OP\\([a-z0-9_]*," one_register "${multi_register}") if (one_register STREQUAL "") @@ -224,7 +246,6 @@ op_library(recurrent_op DEPS executor) op_library(warpctc_op DEPS dynload_warpctc sequence_padding sequence_scale) op_library(cos_sim_op DEPS cos_sim_functor) op_library(parallel_do_op DEPS executor) - if (WITH_GPU) op_library(conv_op DEPS vol2col depthwise_conv im2col) else() diff --git a/paddle/fluid/operators/fc_mkldnn_op.cc b/paddle/fluid/operators/fc_mkldnn_op.cc new file mode 100644 index 0000000000..48655d36fc --- /dev/null +++ b/paddle/fluid/operators/fc_mkldnn_op.cc @@ -0,0 +1,410 @@ +/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#include "paddle/fluid/operators/fc_mkldnn_op.h" +#include "paddle/fluid/framework/tensor.h" +#include "paddle/fluid/platform/device_context.h" +#include "paddle/fluid/platform/mkldnn_helper.h" + +namespace paddle { +namespace operators { + +using paddle::framework::Tensor; +using paddle::platform::MKLDNNDeviceContext; + +void FCOp::InferShape(framework::InferShapeContext* ctx) const { + PADDLE_ENFORCE(ctx->HasInput("Input"), + "X(Input) of Fully Connected should not be null."); + PADDLE_ENFORCE(ctx->HasOutput("Out"), + "Out(Output) of Fully Connected should not be null."); + PADDLE_ENFORCE(ctx->HasInput("W"), + "W(Input) of Fully Connected should not be null."); + + auto in_dims = ctx->GetInputDim("Input"); + auto w_dims = ctx->GetInputDim("W"); + std::vector output_shape({in_dims[0], w_dims[1]}); + + PADDLE_ENFORCE(in_dims.size() == 4, + "Fully Connected input should be 4-D tensor."); + + PADDLE_ENFORCE(w_dims.size() == 2, + "Fully Connected input should be 2-D tensor."); + + ctx->SetOutputDim("Out", framework::make_ddim(output_shape)); + ctx->ShareLoD("Input", "Out"); +} + +framework::OpKernelType FCOp::GetExpectedKernelType( + const framework::ExecutionContext& ctx) const { + framework::LibraryType library{framework::LibraryType::kMKLDNN}; + + std::string data_format = ctx.Attr("data_format"); + framework::DataLayout layout = framework::StringToDataLayout(data_format); + + return framework::OpKernelType( + framework::ToDataType(ctx.Input("Input")->type()), ctx.GetPlace(), + layout, library); +} + +void FCOpGrad::InferShape(framework::InferShapeContext* ctx) const { + auto in_dims = ctx->GetInputDim("Input"); + auto w_dims = ctx->GetInputDim("W"); + + if (ctx->HasOutput(framework::GradVarName("Input"))) { + ctx->SetOutputDim(framework::GradVarName("Input"), in_dims); + } + if (ctx->HasOutput(framework::GradVarName("W"))) { + ctx->SetOutputDim(framework::GradVarName("W"), w_dims); + } +} + +framework::OpKernelType FCOpGrad::GetExpectedKernelType( + const framework::ExecutionContext& ctx) const { + framework::LibraryType library{framework::LibraryType::kMKLDNN}; + + std::string data_format = ctx.Attr("data_format"); + framework::DataLayout layout = framework::StringToDataLayout(data_format); + + return framework::OpKernelType( + framework::ToDataType(ctx.Input("Input")->type()), ctx.GetPlace(), + layout, library); +} + +class FCOpMaker : public framework::OpProtoAndCheckerMaker { + public: + FCOpMaker(OpProto* proto, OpAttrChecker* op_checker) + : OpProtoAndCheckerMaker(proto, op_checker) { + AddInput( + "Input", + "(Tensor) The input tensor of fully connected operator. " + "The format of input tensor is NCHW, where N is batch size, C is the " + "number of channels, H is the height of the feature, " + "and W is the width of the feature."); + AddInput("W", "(Tensor), The second input tensor of fc op."); + AddOutput("Out", + "(Tensor) The output tensor of pooling operator. " + "The format of output tensor is also NCHW, " + "where N is batch size, C is the number of channels, " + "H is the height of the feature, " + "and W is the width of the feature."); + AddAttr("use_mkldnn", + "(bool, default false) Only used in mkldnn kernel") + .SetDefault(false); + AddAttr("with_bias", + "(bool, default false) Only used in mkldnn kernel") + .SetDefault(false); + AddAttr( + "data_format", + "(string, default NCHW) Only used in " + "An optional string from: \"NHWC\", \"NCHW\". " + "Defaults to \"NHWC\". Specify the data format of the output data, " + "the input will be transformed automatically. ") + .SetDefault("AnyLayout"); + AddComment(R"DOC( +)DOC"); + } +}; + +struct MKLDNNMatrixSize final { + explicit MKLDNNMatrixSize(const std::vector& in, + const std::vector& w) + : mb{in[0]}, ic{in[1]}, oc{w[1]}, h{in[2]}, w{in[3]} {} + + bool is_spatial() const { return h > 1 && w > 1; } + + const int mb; + const int ic; + const int oc; + const int h, w; +}; + +template +class MKLDNNMD { + public: + explicit MKLDNNMD(const T* in, const T* w, bool bias) + : sz_(std::unique_ptr(new MKLDNNMatrixSize( + paddle::framework::vectorize2int(in->dims()), + paddle::framework::vectorize2int(w->dims())))) { + with_bias_ = bias; + } + + mkldnn::memory::desc dst() const { + return platform::MKLDNNMemDesc({sz_->mb, sz_->oc}, + mkldnn::memory::data_type::f32, + mkldnn::memory::format::nc); + } + + mkldnn::memory::desc src() const { + return sz_->is_spatial() + ? platform::MKLDNNMemDesc({sz_->mb, sz_->ic, sz_->h, sz_->w}, + mkldnn::memory::data_type::f32, + mkldnn::memory::format::nchw) + : platform::MKLDNNMemDesc({sz_->mb, sz_->ic}, + mkldnn::memory::data_type::f32, + mkldnn::memory::format::nc); + } + + mkldnn::memory::desc weights() const { + return sz_->is_spatial() + ? platform::MKLDNNMemDesc({sz_->oc, sz_->ic, sz_->h, sz_->w}, + mkldnn::memory::data_type::f32, + mkldnn::memory::format::oihw) + : platform::MKLDNNMemDesc({sz_->oc, sz_->ic}, + mkldnn::memory::data_type::f32, + mkldnn::memory::format::oi); + } + + mkldnn::memory::desc bias() const { + return with_bias_ + ? platform::MKLDNNMemDesc({sz_->oc}, + mkldnn::memory::data_type::f32, + mkldnn::memory::format::format_undef) + : platform::MKLDNNMemDesc({}, mkldnn::memory::data_type::f32, + mkldnn::memory::format::format_undef); + } + + private: + std::unique_ptr sz_; + bool with_bias_; +}; + +class MKLDNNMemory { + public: + MKLDNNMemory(MKLDNNMD* t, const mkldnn::engine& e) + : md_{t}, engine_{e} {} + virtual ~MKLDNNMemory() = default; + + template + mkldnn::memory dst(const Output* out) { + return mkldnn::memory({md_->dst(), engine_}, + static_cast(const_cast(out))); + } + + template + mkldnn::memory dst(Output* out) { + return mkldnn::memory({md_->dst(), engine_}, out); + } + + template + mkldnn::memory src(const Input* in) { + return mkldnn::memory({md_->src(), engine_}, + static_cast(const_cast(in))); + } + + template + mkldnn::memory weights(const Weight* w) { + return mkldnn::memory({md_->weights(), engine_}, + static_cast(const_cast(w))); + } + + mkldnn::memory bias() { + return mkldnn::memory(mkldnn::memory::primitive_desc(md_->bias(), engine_)); + } + + private: + MKLDNNMD* md_; + const mkldnn::engine& engine_; +}; + +template +class FCMKLDNNOpKernel : public paddle::framework::OpKernel { + void Compute(const paddle::framework::ExecutionContext& ctx) const override { + PADDLE_ENFORCE(paddle::platform::is_cpu_place(ctx.GetPlace()), + "It must use CPUPlace."); + + auto& dev_ctx = ctx.template device_context(); + const auto& mkldnn_engine = dev_ctx.GetEngine(); + + auto input = ctx.Input("Input"); + auto w = ctx.Input("W"); + + PADDLE_ENFORCE(input->dims().size() == 4, + "Input must be with 4 dimensions, i.e. NCHW"); + PADDLE_ENFORCE(w->dims().size() == 2, + "Weights must be with 2 dimensions, i.e. NC"); + + bool with_bias = ctx.Attr("with_bias"); + MKLDNNMD md(input, w, with_bias); + + std::shared_ptr pd = + FcFwdPrimitiveDesc(md.src(), md.weights(), md.dst(), md.bias(), + with_bias, mkldnn_engine); + + const std::string key = ctx.op().Output("Out"); + const std::string key_fc_pd = key + "@fc_pd"; + + dev_ctx.SetBlob(key_fc_pd, pd); + + MKLDNNMemory mem(&md, mkldnn_engine); + + const T* input_data = input->data(); + const T* w_data = w->data(); + + auto output = ctx.Output("Out"); + T* output_data = output->mutable_data(ctx.GetPlace()); + + auto dst_memory = mem.dst(output_data); + auto src_memory = mem.src(input_data); + auto weights_memory = mem.weights(w_data); + auto bias_memory = mem.bias(); + + auto forward = with_bias ? mkldnn::inner_product_forward( + *pd, src_memory, weights_memory, bias_memory, + dst_memory) + : mkldnn::inner_product_forward( + *pd, src_memory, weights_memory, dst_memory); + + std::vector pipeline = {forward}; + mkldnn::stream(mkldnn::stream::kind::eager).submit(pipeline).wait(); + } + + private: + std::unique_ptr + FcFwdPrimitiveDesc(const mkldnn::memory::desc& src, + const mkldnn::memory::desc& weights, + const mkldnn::memory::desc& dst, + const mkldnn::memory::desc& bias, const bool with_bias, + const mkldnn::engine& engine) const { + auto desc = with_bias + ? mkldnn::inner_product_forward::desc( + mkldnn::prop_kind::forward, src, weights, bias, dst) + : mkldnn::inner_product_forward::desc( + mkldnn::prop_kind::forward, src, weights, dst); + + auto pd = new mkldnn::inner_product_forward::primitive_desc(desc, engine); + return std::unique_ptr(pd); + } +}; + +template +class FCMKLDNNGradOpKernel : public paddle::framework::OpKernel { + public: + void Compute(const paddle::framework::ExecutionContext& ctx) const override { + PADDLE_ENFORCE(paddle::platform::is_cpu_place(ctx.GetPlace()), + "It must use CPUPlace."); + + auto& dev_ctx = ctx.template device_context(); + const auto& mkldnn_engine = dev_ctx.GetEngine(); + + T* input_grad_data = nullptr; + T* w_grad_data = nullptr; + + Tensor* input_grad = ctx.Output(framework::GradVarName("Input")); + Tensor* w_grad = ctx.Output(framework::GradVarName("W")); + + if (input_grad) { + input_grad_data = input_grad->mutable_data(ctx.GetPlace()); + } + if (w_grad) { + w_grad_data = w_grad->mutable_data(ctx.GetPlace()); + } + + const Tensor* input = ctx.Input("Input"); + const T* input_data = input->data(); + + const Tensor* w = ctx.Input("W"); + const T* w_data = w->data(); + + const Tensor* out_grad = ctx.Input(framework::GradVarName("Out")); + const T* out_grad_data = out_grad->data(); + + bool with_bias = ctx.Attr("with_bias"); + + MKLDNNMD md(input, w, with_bias); + MKLDNNMemory mem(&md, mkldnn_engine); + + auto dst_memory = mem.dst(out_grad_data); + auto src_memory = mem.src(input_data); + auto weights_memory = mem.weights(w_data); + auto bias_memory = mem.bias(); + + const std::string key = ctx.op().Input("Out"); + const std::string key_fc_pd = key + "@fc_pd"; + + auto pd = + std::static_pointer_cast( + dev_ctx.GetBlob(key_fc_pd)); + + PADDLE_ENFORCE(pd != nullptr, "Fail to find key_fc_pd in device context"); + + if (w_grad) { + auto weights_grad_memory = mem.weights(w_grad_data); + + mkldnn::inner_product_backward_weights::primitive_desc bwd_weight_pd = + FcBwdWeightsPrimitiveDesc(md.src(), md.weights(), md.dst(), md.bias(), + with_bias, *pd, mkldnn_engine); + + auto bwd_weights_prim = mkldnn::inner_product_backward_weights( + bwd_weight_pd, src_memory, dst_memory, weights_grad_memory, + bias_memory); + + std::vector pipeline{bwd_weights_prim}; + mkldnn::stream(mkldnn::stream::kind::eager).submit(pipeline).wait(); + } + + if (input_grad) { + auto src_grad_memory = mem.src(input_grad_data); + + mkldnn::inner_product_backward_data::primitive_desc bwd_data_pd = + FcBwdDataPrimitiveDesc(md.src(), md.weights(), md.dst(), *pd, + mkldnn_engine); + + auto bwd_data_prim = mkldnn::inner_product_backward_data( + bwd_data_pd, dst_memory, weights_memory, src_grad_memory); + + std::vector pipeline{bwd_data_prim}; + mkldnn::stream(mkldnn::stream::kind::eager).submit(pipeline).wait(); + } + } + + private: + mkldnn::inner_product_backward_weights::primitive_desc + FcBwdWeightsPrimitiveDesc( + const mkldnn::memory::desc& src, const mkldnn::memory::desc& diff_weights, + const mkldnn::memory::desc& diff_dst, const mkldnn::memory::desc& bias, + const bool with_bias, + const mkldnn::inner_product_forward::primitive_desc& pd, + const mkldnn::engine& engine) const { + auto bwd_weight_desc = with_bias + ? mkldnn::inner_product_backward_weights::desc( + src, diff_weights, bias, diff_dst) + : mkldnn::inner_product_backward_weights::desc( + src, diff_weights, bias, diff_dst); + + return mkldnn::inner_product_backward_weights::primitive_desc( + bwd_weight_desc, engine, pd); + } + + mkldnn::inner_product_backward_data::primitive_desc FcBwdDataPrimitiveDesc( + const mkldnn::memory::desc& diff_src, const mkldnn::memory::desc& weights, + const mkldnn::memory::desc& diff_dst, + const mkldnn::inner_product_forward::primitive_desc& pd, + const mkldnn::engine& engine) const { + auto bwd_data_desc = + mkldnn::inner_product_backward_data::desc(diff_src, weights, diff_dst); + return mkldnn::inner_product_backward_data::primitive_desc(bwd_data_desc, + engine, pd); + } +}; +} // namespace operators +} // namespace paddle + +REGISTER_OP(fc, paddle::operators::FCOp, paddle::operators::FCOpMaker, fc_grad, + paddle::operators::FCOpGrad); + +REGISTER_OP_KERNEL(fc, MKLDNN, ::paddle::platform::CPUPlace, + paddle::operators::FCMKLDNNOpKernel); + +REGISTER_OP_KERNEL(fc_grad, MKLDNN, ::paddle::platform::CPUPlace, + paddle::operators::FCMKLDNNGradOpKernel); diff --git a/paddle/fluid/operators/fc_mkldnn_op.h b/paddle/fluid/operators/fc_mkldnn_op.h new file mode 100644 index 0000000000..9e6c66491d --- /dev/null +++ b/paddle/fluid/operators/fc_mkldnn_op.h @@ -0,0 +1,47 @@ +/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#pragma once + +#include "paddle/fluid/framework/op_registry.h" + +namespace paddle { +namespace operators { + +using Tensor = framework::Tensor; + +class FCOp : public framework::OperatorWithKernel { + public: + using framework::OperatorWithKernel::OperatorWithKernel; + + void InferShape(framework::InferShapeContext* ctx) const override; + + protected: + framework::OpKernelType GetExpectedKernelType( + const framework::ExecutionContext& ctx) const override; +}; + +class FCOpGrad : public framework::OperatorWithKernel { + public: + using framework::OperatorWithKernel::OperatorWithKernel; + + void InferShape(framework::InferShapeContext* ctx) const override; + + protected: + framework::OpKernelType GetExpectedKernelType( + const framework::ExecutionContext& ctx) const override; +}; + +} // namespace operators +} // namespace paddle diff --git a/python/paddle/fluid/layers/nn.py b/python/paddle/fluid/layers/nn.py index 3d13133bf2..bfae205bcf 100644 --- a/python/paddle/fluid/layers/nn.py +++ b/python/paddle/fluid/layers/nn.py @@ -86,6 +86,7 @@ def fc(input, param_attr=None, bias_attr=None, use_mkldnn=False, + with_bias=False, act=None, name=None): """ @@ -133,6 +134,8 @@ def fc(input, bias_attr (ParamAttr|list of ParamAttr, default None): The parameter attribute for the bias of this layer. If it is set to None, no bias will be added to the output units. act (str, default None): Activation to be applied to the output of this layer. + use_mkldnn(bool): Use mkldnn kernel or not, it is valid only when the mkldnn + library is installed. Default: False name (str, default None): The name of this layer. Returns: @@ -162,16 +165,25 @@ def fc(input, w = helper.create_parameter( attr=param_attr, shape=param_shape, dtype=dtype, is_bias=False) tmp = helper.create_tmp_variable(dtype) - helper.append_op( - type="mul", - inputs={"X": input_var, - "Y": w}, - outputs={"Out": tmp}, - attrs={ - "x_num_col_dims": num_flatten_dims, - "y_num_col_dims": 1, - 'use_mkldnn': use_mkldnn - }) + if use_mkldnn == False: + helper.append_op( + type="mul", + inputs={"X": input_var, + "Y": w}, + outputs={"Out": tmp}, + attrs={ + "x_num_col_dims": num_flatten_dims, + "y_num_col_dims": 1, + 'use_mkldnn': use_mkldnn + }) + else: + helper.append_op( + type="fc", + inputs={"Input": input_var, + "W": w}, + outputs={"Out": tmp}, + attrs={"use_mkldnn": use_mkldnn, + "with_bias": with_bias}) mul_results.append(tmp) # sum diff --git a/python/paddle/fluid/tests/unittests/test_fc_op.py b/python/paddle/fluid/tests/unittests/test_fc_op.py new file mode 100644 index 0000000000..3f547f3c48 --- /dev/null +++ b/python/paddle/fluid/tests/unittests/test_fc_op.py @@ -0,0 +1,99 @@ +# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import unittest +import numpy as np +from op_test import OpTest + + +def fully_connected_naive(input, weights, bias_data=None): + in_n, in_c, in_h, in_w = input.shape + w_h, w_c = weights.shape + + x_data = np.reshape(input, [in_n, in_c * in_h * in_w]) + w_data = np.transpose(np.reshape(weights, (w_c, in_c * in_h * in_w))) + result = None + + if not bias_data: + result = np.dot(x_data, w_data) + else: + result = np.dot(x_data, w_data) + bias_data + + return result + + +class MatrixGenerate: + def __init__(self, mb, ic, oc, h, w): + self.input = np.random.random((mb, ic, h, w)).astype("float32") + self.weights = np.random.random((ic * h * w, oc)).astype("float32") + + +class TestFCMKLDNNOp(OpTest): + def setUp(self): + self.op_type = "fc" + self.use_mkldnn = True + self.with_bias = True + self.matrix = MatrixGenerate(1, 10, 15, 3, 3) + + self.inputs = {'Input': self.matrix.input, 'W': self.matrix.weights} + + self.attrs = { + 'use_mkldnn': self.use_mkldnn, + 'with_bias': self.with_bias + } + + self.outputs = { + 'Out': fully_connected_naive(self.matrix.input, self.matrix.weights) + } + + def test_check_output(self): + self.check_output() + + def test_check_grad_normal(self): + self.check_grad(set(['Input', 'W']), 'Out', max_relative_error=0.9) + + def test_check_grad_no_weight(self): + self.check_grad( + ['Input'], 'Out', max_relative_error=0.5, no_grad_set=set('W')) + + +class TestFCMKLDNNOp1(TestFCMKLDNNOp): + def init_op_type(self): + self.matrix = MatrixGenerate(2, 15, 48, 2, 2) + + +class TestFCMKLDNNOp2(TestFCMKLDNNOp): + def init_op_type(self): + self.matrix = MatrixGenerate(2, 32, 40, 1, 1) + + +class TestFCMKLDNNOp3(TestFCMKLDNNOp): + def init_op_type(self): + self.matrix = MatrixGenerate(2, 2, 4, 1, 1) + + +class TestFCMKLDNNOp4(TestFCMKLDNNOp): + def init_op_type(self): + self.with_bias = False + self.matrix = MatrixGenerate(2, 32, 48, 2, 2) + + +class TestFCMKLDNNOp4(TestFCMKLDNNOp): + def init_op_type(self): + self.with_bias = False + self.matrix = MatrixGenerate(2, 32, 1000, 6, 6) + + +if __name__ == "__main__": + unittest.main() From 34a8084328921d4d043fc3c8308063d38087e62f Mon Sep 17 00:00:00 2001 From: mozga-intel Date: Thu, 29 Mar 2018 20:31:59 +0200 Subject: [PATCH 35/58] Added new fc files, register fc kernel --- paddle/fluid/operators/CMakeLists.txt | 29 +---- paddle/fluid/operators/fc_mkldnn_op.cc | 108 +--------------- paddle/fluid/operators/fc_op.cc | 122 ++++++++++++++++++ .../operators/{fc_mkldnn_op.h => fc_op.h} | 5 + python/paddle/fluid/layers/nn.py | 67 ++++++---- 5 files changed, 178 insertions(+), 153 deletions(-) create mode 100644 paddle/fluid/operators/fc_op.cc rename paddle/fluid/operators/{fc_mkldnn_op.h => fc_op.h} (91%) diff --git a/paddle/fluid/operators/CMakeLists.txt b/paddle/fluid/operators/CMakeLists.txt index 6c79998f07..9ed79453b9 100644 --- a/paddle/fluid/operators/CMakeLists.txt +++ b/paddle/fluid/operators/CMakeLists.txt @@ -1,14 +1,6 @@ file(GLOB GENERAL_OPS RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "*_op.cc") +string(REPLACE "_mkldnn" "" GENERAL_OPS "${GENERAL_OPS}") string(REPLACE ".cc" "" GENERAL_OPS "${GENERAL_OPS}") -if(WITH_MKLDNN) - string(REPLACE "_mkldnn" "" GENERAL_OPS "${GENERAL_OPS}") -else() - foreach(item ${GENERAL_OPS}) - if(${item} MATCHES ".*_mkldnn_op") - list(REMOVE_ITEM GENERAL_OPS ${item}) - endif() - endforeach(item) -endif() list(REMOVE_DUPLICATES GENERAL_OPS) set(DEPS_OPS "") set(pybind_file ${PADDLE_SOURCE_DIR}/paddle/fluid/pybind/pybind.h) @@ -88,12 +80,7 @@ function(op_library TARGET) endif() list(LENGTH cc_srcs cc_srcs_len) - if(WITH_MKLDNN) - list(LENGTH mkldnn_cc_srcs mkldnn_cc_srcs_len) - if (${cc_srcs_len} EQUAL 0 AND ${mkldnn_cc_srcs_len} EQUAL 0) - message(FATAL_ERROR "The op library ${TARGET} should contains at least one .cc file") - endif() - elseif(${cc_srcs_len} EQUAL 0) + if (${cc_srcs_len} EQUAL 0) message(FATAL_ERROR "The op library ${TARGET} should contains at least one .cc file") endif() @@ -122,16 +109,7 @@ function(op_library TARGET) # The registration of USE_OP, please refer to paddle/fluid/framework/op_registry.h. # Note that it's enough to just adding one operator to pybind in a *_op.cc file. # And for detail pybind information, please see generated paddle/pybind/pybind.h. - # This replacing is needed, when the CPU's kernel doesn't exist. - string(REPLACE "_op" "_mkldnn_op" target_mkldnn_file "${TARGET}") - if(EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/${TARGET}.cc) - file(READ ${TARGET}.cc TARGET_CONTENT) - elseif(WITH_MKLDNN AND EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/${target_mkldnn_file}.cc) - file(READ ${target_mkldnn_file}.cc TARGET_CONTENT) - else() - message(FATAL_ERROR "Cannot read the ${TARGET} file from ${CMAKE_CURRENT_SOURCE_DIR}") - endif() - + file(READ ${TARGET}.cc TARGET_CONTENT) string(REGEX MATCH "REGISTER_OP\\(.*REGISTER_OP\\(" multi_register "${TARGET_CONTENT}") string(REGEX MATCH "REGISTER_OP\\([a-z0-9_]*," one_register "${multi_register}") if (one_register STREQUAL "") @@ -246,6 +224,7 @@ op_library(recurrent_op DEPS executor) op_library(warpctc_op DEPS dynload_warpctc sequence_padding sequence_scale) op_library(cos_sim_op DEPS cos_sim_functor) op_library(parallel_do_op DEPS executor) + if (WITH_GPU) op_library(conv_op DEPS vol2col depthwise_conv im2col) else() diff --git a/paddle/fluid/operators/fc_mkldnn_op.cc b/paddle/fluid/operators/fc_mkldnn_op.cc index 48655d36fc..3e006189ef 100644 --- a/paddle/fluid/operators/fc_mkldnn_op.cc +++ b/paddle/fluid/operators/fc_mkldnn_op.cc @@ -12,8 +12,8 @@ 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/fluid/operators/fc_mkldnn_op.h" #include "paddle/fluid/framework/tensor.h" +#include "paddle/fluid/operators/fc_op.h" #include "paddle/fluid/platform/device_context.h" #include "paddle/fluid/platform/mkldnn_helper.h" @@ -23,105 +23,12 @@ namespace operators { using paddle::framework::Tensor; using paddle::platform::MKLDNNDeviceContext; -void FCOp::InferShape(framework::InferShapeContext* ctx) const { - PADDLE_ENFORCE(ctx->HasInput("Input"), - "X(Input) of Fully Connected should not be null."); - PADDLE_ENFORCE(ctx->HasOutput("Out"), - "Out(Output) of Fully Connected should not be null."); - PADDLE_ENFORCE(ctx->HasInput("W"), - "W(Input) of Fully Connected should not be null."); - - auto in_dims = ctx->GetInputDim("Input"); - auto w_dims = ctx->GetInputDim("W"); - std::vector output_shape({in_dims[0], w_dims[1]}); - - PADDLE_ENFORCE(in_dims.size() == 4, - "Fully Connected input should be 4-D tensor."); - - PADDLE_ENFORCE(w_dims.size() == 2, - "Fully Connected input should be 2-D tensor."); - - ctx->SetOutputDim("Out", framework::make_ddim(output_shape)); - ctx->ShareLoD("Input", "Out"); -} - -framework::OpKernelType FCOp::GetExpectedKernelType( - const framework::ExecutionContext& ctx) const { - framework::LibraryType library{framework::LibraryType::kMKLDNN}; - - std::string data_format = ctx.Attr("data_format"); - framework::DataLayout layout = framework::StringToDataLayout(data_format); - - return framework::OpKernelType( - framework::ToDataType(ctx.Input("Input")->type()), ctx.GetPlace(), - layout, library); -} - -void FCOpGrad::InferShape(framework::InferShapeContext* ctx) const { - auto in_dims = ctx->GetInputDim("Input"); - auto w_dims = ctx->GetInputDim("W"); - - if (ctx->HasOutput(framework::GradVarName("Input"))) { - ctx->SetOutputDim(framework::GradVarName("Input"), in_dims); - } - if (ctx->HasOutput(framework::GradVarName("W"))) { - ctx->SetOutputDim(framework::GradVarName("W"), w_dims); - } -} - -framework::OpKernelType FCOpGrad::GetExpectedKernelType( - const framework::ExecutionContext& ctx) const { - framework::LibraryType library{framework::LibraryType::kMKLDNN}; - - std::string data_format = ctx.Attr("data_format"); - framework::DataLayout layout = framework::StringToDataLayout(data_format); - - return framework::OpKernelType( - framework::ToDataType(ctx.Input("Input")->type()), ctx.GetPlace(), - layout, library); -} - -class FCOpMaker : public framework::OpProtoAndCheckerMaker { - public: - FCOpMaker(OpProto* proto, OpAttrChecker* op_checker) - : OpProtoAndCheckerMaker(proto, op_checker) { - AddInput( - "Input", - "(Tensor) The input tensor of fully connected operator. " - "The format of input tensor is NCHW, where N is batch size, C is the " - "number of channels, H is the height of the feature, " - "and W is the width of the feature."); - AddInput("W", "(Tensor), The second input tensor of fc op."); - AddOutput("Out", - "(Tensor) The output tensor of pooling operator. " - "The format of output tensor is also NCHW, " - "where N is batch size, C is the number of channels, " - "H is the height of the feature, " - "and W is the width of the feature."); - AddAttr("use_mkldnn", - "(bool, default false) Only used in mkldnn kernel") - .SetDefault(false); - AddAttr("with_bias", - "(bool, default false) Only used in mkldnn kernel") - .SetDefault(false); - AddAttr( - "data_format", - "(string, default NCHW) Only used in " - "An optional string from: \"NHWC\", \"NCHW\". " - "Defaults to \"NHWC\". Specify the data format of the output data, " - "the input will be transformed automatically. ") - .SetDefault("AnyLayout"); - AddComment(R"DOC( -)DOC"); - } -}; - struct MKLDNNMatrixSize final { explicit MKLDNNMatrixSize(const std::vector& in, const std::vector& w) : mb{in[0]}, ic{in[1]}, oc{w[1]}, h{in[2]}, w{in[3]} {} - bool is_spatial() const { return h > 1 && w > 1; } + bool is_spatial() const { return h > 2 && w > 2; } const int mb; const int ic; @@ -229,12 +136,12 @@ class FCMKLDNNOpKernel : public paddle::framework::OpKernel { auto input = ctx.Input("Input"); auto w = ctx.Input("W"); - PADDLE_ENFORCE(input->dims().size() == 4, - "Input must be with 4 dimensions, i.e. NCHW"); + PADDLE_ENFORCE(input->dims().size() == 4 || input->dims().size() == 2, + "Input must be with 2 or 4 dimensions, i.e. NCHW"); PADDLE_ENFORCE(w->dims().size() == 2, "Weights must be with 2 dimensions, i.e. NC"); - bool with_bias = ctx.Attr("with_bias"); + bool with_bias = ctx.Attr("bias_attr"); MKLDNNMD md(input, w, with_bias); std::shared_ptr pd = @@ -319,7 +226,7 @@ class FCMKLDNNGradOpKernel : public paddle::framework::OpKernel { const Tensor* out_grad = ctx.Input(framework::GradVarName("Out")); const T* out_grad_data = out_grad->data(); - bool with_bias = ctx.Attr("with_bias"); + bool with_bias = ctx.Attr("bias_attr"); MKLDNNMD md(input, w, with_bias); MKLDNNMemory mem(&md, mkldnn_engine); @@ -400,9 +307,6 @@ class FCMKLDNNGradOpKernel : public paddle::framework::OpKernel { } // namespace operators } // namespace paddle -REGISTER_OP(fc, paddle::operators::FCOp, paddle::operators::FCOpMaker, fc_grad, - paddle::operators::FCOpGrad); - REGISTER_OP_KERNEL(fc, MKLDNN, ::paddle::platform::CPUPlace, paddle::operators::FCMKLDNNOpKernel); diff --git a/paddle/fluid/operators/fc_op.cc b/paddle/fluid/operators/fc_op.cc new file mode 100644 index 0000000000..93b59854db --- /dev/null +++ b/paddle/fluid/operators/fc_op.cc @@ -0,0 +1,122 @@ +/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#include "paddle/fluid/operators/fc_op.h" + +namespace paddle { +namespace operators { + +void FCOp::InferShape(framework::InferShapeContext* ctx) const { + PADDLE_ENFORCE(ctx->HasInput("Input"), + "X(Input) of Fully Connected should not be null."); + PADDLE_ENFORCE(ctx->HasOutput("Out"), + "Out(Output) of Fully Connected should not be null."); + PADDLE_ENFORCE(ctx->HasInput("W"), + "W(Input) of Fully Connected should not be null."); + + auto in_dims = ctx->GetInputDim("Input"); + auto w_dims = ctx->GetInputDim("W"); + std::vector output_shape({in_dims[0], w_dims[1]}); + + PADDLE_ENFORCE(in_dims.size() == 4, + "Fully Connected input should be 4-D tensor."); + + PADDLE_ENFORCE(w_dims.size() == 2, + "Fully Connected input should be 2-D tensor."); + + ctx->SetOutputDim("Out", framework::make_ddim(output_shape)); + ctx->ShareLoD("Input", "Out"); +} + +framework::OpKernelType FCOp::GetExpectedKernelType( + const framework::ExecutionContext& ctx) const { + framework::LibraryType library{framework::LibraryType::kMKLDNN}; + framework::DataLayout layout{framework::DataLayout::kAnyLayout}; + + return framework::OpKernelType( + framework::ToDataType(ctx.Input("Input")->type()), ctx.GetPlace(), + layout, library); +} + +void FCOpGrad::InferShape(framework::InferShapeContext* ctx) const { + auto in_dims = ctx->GetInputDim("Input"); + auto w_dims = ctx->GetInputDim("W"); + + if (ctx->HasOutput(framework::GradVarName("Input"))) { + ctx->SetOutputDim(framework::GradVarName("Input"), in_dims); + } + if (ctx->HasOutput(framework::GradVarName("W"))) { + ctx->SetOutputDim(framework::GradVarName("W"), w_dims); + } +} + +framework::OpKernelType FCOpGrad::GetExpectedKernelType( + const framework::ExecutionContext& ctx) const { + framework::LibraryType library{framework::LibraryType::kMKLDNN}; + framework::DataLayout layout{framework::DataLayout::kAnyLayout}; + + return framework::OpKernelType( + framework::ToDataType(ctx.Input("Input")->type()), ctx.GetPlace(), + layout, library); +} + +FCOpMaker::FCOpMaker(OpProto* proto, OpAttrChecker* op_checker) + : OpProtoAndCheckerMaker(proto, op_checker) { + AddInput( + "Input", + "(Tensor) The input tensor of fully connected operator. " + "The format of input tensor is NCHW, where N is batch size, C is the " + "number of channels, H is the height of the feature, " + "and W is the width of the feature."); + AddInput("W", "(Tensor), The second input tensor of fc op."); + AddOutput("Out", + "(Tensor) The output tensor of fully connected operator. " + "The format of output tensor is also NCHW, " + "where N is batch size, C is the number of channels, " + "H is the height of the feature, " + "and W is the width of the feature."); + AddAttr("use_mkldnn", + "(bool, default false) Only used in mkldnn kernel") + .SetDefault(false); + AddAttr("bias_attr", "(bool, default false) Only used in mkldnn kernel") + .SetDefault(false); + AddComment(R"DOC( + Fully Connected Operator. + + The fully connected operation calculates the output based on the input, weights and bias attribute. + The size of each dimension of the parameters checked in the infer-shape. + Input(Input) is NCHW or NC format. Where N is batch size, C is the number of channels, + H is the height of the feature, and W is the width of the feature. + Weights(W) is OIHW or OI format. Where H is the height of the feature, W is the width of the feature, + O is the height of output, and I is the number of channels. + Output(Out) is NC format. Where N is batch size, and C is the number of channels. + The matrix of bias is generated by the mkldnn framework, when the bias_attr is True. + Additional parametrs are use_mkldnn and bias_attr. + The input(X) size and output(Out) size may be diffrent. + +Example: + Input: + Input shape: $(N, C_{in}, H_{in}, W_{in})$ + Weight shape: $(O_{out}, I_{in}, H_{in}, W_{in})$ + Bias shape: $(O_{out})$ + Output: + Output shape: $(N, C_{out})$ +)DOC"); +} + +} // namespace operators +} // namespace paddle + +REGISTER_OP(fc, paddle::operators::FCOp, paddle::operators::FCOpMaker, fc_grad, + paddle::operators::FCOpGrad); diff --git a/paddle/fluid/operators/fc_mkldnn_op.h b/paddle/fluid/operators/fc_op.h similarity index 91% rename from paddle/fluid/operators/fc_mkldnn_op.h rename to paddle/fluid/operators/fc_op.h index 9e6c66491d..70fa96440d 100644 --- a/paddle/fluid/operators/fc_mkldnn_op.h +++ b/paddle/fluid/operators/fc_op.h @@ -43,5 +43,10 @@ class FCOpGrad : public framework::OperatorWithKernel { const framework::ExecutionContext& ctx) const override; }; +class FCOpMaker : public framework::OpProtoAndCheckerMaker { + public: + FCOpMaker(OpProto* proto, OpAttrChecker* op_checker); +}; + } // namespace operators } // namespace paddle diff --git a/python/paddle/fluid/layers/nn.py b/python/paddle/fluid/layers/nn.py index bfae205bcf..40809b304f 100644 --- a/python/paddle/fluid/layers/nn.py +++ b/python/paddle/fluid/layers/nn.py @@ -86,7 +86,6 @@ def fc(input, param_attr=None, bias_attr=None, use_mkldnn=False, - with_bias=False, act=None, name=None): """ @@ -156,16 +155,39 @@ def fc(input, dtype = helper.input_dtype() mul_results = [] - for input_var, param_attr in helper.iter_inputs_and_params(): - input_shape = input_var.shape + if use_mkldnn: + tmp = helper.create_tmp_variable(dtype) + input_shape = input.shape param_shape = [ reduce(lambda a, b: a * b, input_shape[num_flatten_dims:], 1) ] + [size] w = helper.create_parameter( - attr=param_attr, shape=param_shape, dtype=dtype, is_bias=False) - tmp = helper.create_tmp_variable(dtype) - if use_mkldnn == False: + attr=helper.param_attr, + shape=param_shape, + dtype=dtype, + is_bias=False) + bias_attr = False + if bias_attr is not None: + bias_attr = True + helper.append_op( + type="fc", + inputs={"Input": input, + "W": w}, + outputs={"Out": tmp}, + attrs={"use_mkldnn": use_mkldnn, + "bias_attr": bias_attr}) + return helper.append_activation(tmp) + else: + for input_var, param_attr in helper.iter_inputs_and_params(): + input_shape = input_var.shape + param_shape = [ + reduce(lambda a, b: a * b, input_shape[num_flatten_dims:], 1) + ] + [size] + + w = helper.create_parameter( + attr=param_attr, shape=param_shape, dtype=dtype, is_bias=False) + tmp = helper.create_tmp_variable(dtype) helper.append_op( type="mul", inputs={"X": input_var, @@ -174,29 +196,22 @@ def fc(input, attrs={ "x_num_col_dims": num_flatten_dims, "y_num_col_dims": 1, - 'use_mkldnn': use_mkldnn }) + mul_results.append(tmp) + + if len(mul_results) == 1: + pre_bias = mul_results[0] else: + pre_bias = helper.create_tmp_variable(dtype) helper.append_op( - type="fc", - inputs={"Input": input_var, - "W": w}, - outputs={"Out": tmp}, - attrs={"use_mkldnn": use_mkldnn, - "with_bias": with_bias}) - mul_results.append(tmp) - - # sum - if len(mul_results) == 1: - pre_bias = mul_results[0] - else: - pre_bias = helper.create_tmp_variable(dtype) - helper.append_op( - type="sum", inputs={"X": mul_results}, outputs={"Out": pre_bias}) - # add bias - pre_activation = helper.append_bias_op(pre_bias, dim_start=num_flatten_dims) - # add activation - return helper.append_activation(pre_activation) + type="sum", + inputs={"X": mul_results}, + outputs={"Out": pre_bias}) + # add bias + pre_activation = helper.append_bias_op( + pre_bias, dim_start=num_flatten_dims) + # add activation + return helper.append_activation(pre_activation) def embedding(input, From 32f8ac7d3ba1c13ceb8e8bc186ed7c65e6099be6 Mon Sep 17 00:00:00 2001 From: mozga-intel Date: Fri, 30 Mar 2018 20:09:33 +0200 Subject: [PATCH 36/58] Remove additional message --- paddle/fluid/operators/fc_mkldnn_op.cc | 41 ++++++++++---------------- paddle/fluid/operators/fc_op.cc | 17 ++--------- python/paddle/fluid/layers/nn.py | 5 ++-- 3 files changed, 21 insertions(+), 42 deletions(-) diff --git a/paddle/fluid/operators/fc_mkldnn_op.cc b/paddle/fluid/operators/fc_mkldnn_op.cc index 3e006189ef..d0023713b4 100644 --- a/paddle/fluid/operators/fc_mkldnn_op.cc +++ b/paddle/fluid/operators/fc_mkldnn_op.cc @@ -23,67 +23,56 @@ namespace operators { using paddle::framework::Tensor; using paddle::platform::MKLDNNDeviceContext; -struct MKLDNNMatrixSize final { - explicit MKLDNNMatrixSize(const std::vector& in, - const std::vector& w) - : mb{in[0]}, ic{in[1]}, oc{w[1]}, h{in[2]}, w{in[3]} {} - - bool is_spatial() const { return h > 2 && w > 2; } - - const int mb; - const int ic; - const int oc; - const int h, w; -}; - template class MKLDNNMD { public: explicit MKLDNNMD(const T* in, const T* w, bool bias) - : sz_(std::unique_ptr(new MKLDNNMatrixSize( - paddle::framework::vectorize2int(in->dims()), - paddle::framework::vectorize2int(w->dims())))) { + : in{paddle::framework::vectorize2int(in->dims())}, + w{paddle::framework::vectorize2int(w->dims())} { with_bias_ = bias; } mkldnn::memory::desc dst() const { - return platform::MKLDNNMemDesc({sz_->mb, sz_->oc}, + return platform::MKLDNNMemDesc({in[0], w[1]}, mkldnn::memory::data_type::f32, mkldnn::memory::format::nc); } mkldnn::memory::desc src() const { - return sz_->is_spatial() - ? platform::MKLDNNMemDesc({sz_->mb, sz_->ic, sz_->h, sz_->w}, + return is_spatial() + ? platform::MKLDNNMemDesc({in[0], in[1], in[2], in[3]}, mkldnn::memory::data_type::f32, mkldnn::memory::format::nchw) - : platform::MKLDNNMemDesc({sz_->mb, sz_->ic}, + : platform::MKLDNNMemDesc({in[0], in[1]}, mkldnn::memory::data_type::f32, mkldnn::memory::format::nc); } mkldnn::memory::desc weights() const { - return sz_->is_spatial() - ? platform::MKLDNNMemDesc({sz_->oc, sz_->ic, sz_->h, sz_->w}, + return is_spatial() + ? platform::MKLDNNMemDesc({w[1], in[1], in[2], in[3]}, mkldnn::memory::data_type::f32, mkldnn::memory::format::oihw) - : platform::MKLDNNMemDesc({sz_->oc, sz_->ic}, + : platform::MKLDNNMemDesc({w[1], in[1]}, mkldnn::memory::data_type::f32, mkldnn::memory::format::oi); } mkldnn::memory::desc bias() const { return with_bias_ - ? platform::MKLDNNMemDesc({sz_->oc}, - mkldnn::memory::data_type::f32, + ? platform::MKLDNNMemDesc({w[1]}, mkldnn::memory::data_type::f32, mkldnn::memory::format::format_undef) : platform::MKLDNNMemDesc({}, mkldnn::memory::data_type::f32, mkldnn::memory::format::format_undef); } private: - std::unique_ptr sz_; + bool is_spatial() const { return in.size() > 1 && w.size() > 1; } + + std::vector in; + std::vector w; bool with_bias_; + bool is_spatial_; }; class MKLDNNMemory { diff --git a/paddle/fluid/operators/fc_op.cc b/paddle/fluid/operators/fc_op.cc index 93b59854db..41f9c0b8a9 100644 --- a/paddle/fluid/operators/fc_op.cc +++ b/paddle/fluid/operators/fc_op.cc @@ -29,8 +29,8 @@ void FCOp::InferShape(framework::InferShapeContext* ctx) const { auto w_dims = ctx->GetInputDim("W"); std::vector output_shape({in_dims[0], w_dims[1]}); - PADDLE_ENFORCE(in_dims.size() == 4, - "Fully Connected input should be 4-D tensor."); + PADDLE_ENFORCE(in_dims.size() == 4 || in_dims.size() == 2, + "Fully Connected input should be 2-D or 4-D tensor."); PADDLE_ENFORCE(w_dims.size() == 2, "Fully Connected input should be 2-D tensor."); @@ -96,22 +96,11 @@ FCOpMaker::FCOpMaker(OpProto* proto, OpAttrChecker* op_checker) The fully connected operation calculates the output based on the input, weights and bias attribute. The size of each dimension of the parameters checked in the infer-shape. - Input(Input) is NCHW or NC format. Where N is batch size, C is the number of channels, - H is the height of the feature, and W is the width of the feature. - Weights(W) is OIHW or OI format. Where H is the height of the feature, W is the width of the feature, - O is the height of output, and I is the number of channels. - Output(Out) is NC format. Where N is batch size, and C is the number of channels. The matrix of bias is generated by the mkldnn framework, when the bias_attr is True. Additional parametrs are use_mkldnn and bias_attr. The input(X) size and output(Out) size may be diffrent. -Example: - Input: - Input shape: $(N, C_{in}, H_{in}, W_{in})$ - Weight shape: $(O_{out}, I_{in}, H_{in}, W_{in})$ - Bias shape: $(O_{out})$ - Output: - Output shape: $(N, C_{out})$ + The fully connected layer only supports MKLDNN version )DOC"); } diff --git a/python/paddle/fluid/layers/nn.py b/python/paddle/fluid/layers/nn.py index 40809b304f..d2e7d58524 100644 --- a/python/paddle/fluid/layers/nn.py +++ b/python/paddle/fluid/layers/nn.py @@ -167,8 +167,9 @@ def fc(input, shape=param_shape, dtype=dtype, is_bias=False) - bias_attr = False - if bias_attr is not None: + if bias_attr is None or bias_attr is False: + bias_attr = False + else: bias_attr = True helper.append_op( type="fc", From 46e14bbcbb046780f62aa0a41a9ce083ca9677bc Mon Sep 17 00:00:00 2001 From: mozga-intel Date: Tue, 3 Apr 2018 14:57:37 +0200 Subject: [PATCH 37/58] Enforce: 2 and 4 dims, remove information about out in format --- paddle/fluid/operators/fc_mkldnn_op.cc | 6 +++--- paddle/fluid/operators/fc_op.cc | 21 ++++++--------------- 2 files changed, 9 insertions(+), 18 deletions(-) diff --git a/paddle/fluid/operators/fc_mkldnn_op.cc b/paddle/fluid/operators/fc_mkldnn_op.cc index d0023713b4..9c704a2949 100644 --- a/paddle/fluid/operators/fc_mkldnn_op.cc +++ b/paddle/fluid/operators/fc_mkldnn_op.cc @@ -125,10 +125,10 @@ class FCMKLDNNOpKernel : public paddle::framework::OpKernel { auto input = ctx.Input("Input"); auto w = ctx.Input("W"); - PADDLE_ENFORCE(input->dims().size() == 4 || input->dims().size() == 2, + PADDLE_ENFORCE(input->dims().size() == 2 || input->dims().size() == 4, "Input must be with 2 or 4 dimensions, i.e. NCHW"); - PADDLE_ENFORCE(w->dims().size() == 2, - "Weights must be with 2 dimensions, i.e. NC"); + PADDLE_ENFORCE(w->dims().size() == 2 || w->dims().size() == 4, + "Weights must be with 2 or 4 dimensions, i.e. OI or OIHW"); bool with_bias = ctx.Attr("bias_attr"); MKLDNNMD md(input, w, with_bias); diff --git a/paddle/fluid/operators/fc_op.cc b/paddle/fluid/operators/fc_op.cc index 41f9c0b8a9..381771f157 100644 --- a/paddle/fluid/operators/fc_op.cc +++ b/paddle/fluid/operators/fc_op.cc @@ -13,6 +13,7 @@ See the License for the specific language governing permissions and limitations under the License. */ #include "paddle/fluid/operators/fc_op.h" +#include namespace paddle { namespace operators { @@ -29,11 +30,11 @@ void FCOp::InferShape(framework::InferShapeContext* ctx) const { auto w_dims = ctx->GetInputDim("W"); std::vector output_shape({in_dims[0], w_dims[1]}); - PADDLE_ENFORCE(in_dims.size() == 4 || in_dims.size() == 2, + PADDLE_ENFORCE(in_dims.size() == 2 || in_dims.size() == 4, "Fully Connected input should be 2-D or 4-D tensor."); - PADDLE_ENFORCE(w_dims.size() == 2, - "Fully Connected input should be 2-D tensor."); + PADDLE_ENFORCE(w_dims.size() == 2 || w_dims.size() == 4, + "Fully Connected input should be 2-D or 4-D tensor."); ctx->SetOutputDim("Out", framework::make_ddim(output_shape)); ctx->ShareLoD("Input", "Out"); @@ -73,19 +74,9 @@ framework::OpKernelType FCOpGrad::GetExpectedKernelType( FCOpMaker::FCOpMaker(OpProto* proto, OpAttrChecker* op_checker) : OpProtoAndCheckerMaker(proto, op_checker) { - AddInput( - "Input", - "(Tensor) The input tensor of fully connected operator. " - "The format of input tensor is NCHW, where N is batch size, C is the " - "number of channels, H is the height of the feature, " - "and W is the width of the feature."); + AddInput("Input", "(Tensor) The input tensor of fully connected operator. "); AddInput("W", "(Tensor), The second input tensor of fc op."); - AddOutput("Out", - "(Tensor) The output tensor of fully connected operator. " - "The format of output tensor is also NCHW, " - "where N is batch size, C is the number of channels, " - "H is the height of the feature, " - "and W is the width of the feature."); + AddOutput("Out", "(Tensor) The output tensor of fully connected operator. "); AddAttr("use_mkldnn", "(bool, default false) Only used in mkldnn kernel") .SetDefault(false); From 766c74055a892b0b6759f57bc3b9bbbaf523386e Mon Sep 17 00:00:00 2001 From: chengduoZH Date: Tue, 3 Apr 2018 19:25:29 +0800 Subject: [PATCH 38/58] follow comments --- paddle/fluid/memory/CMakeLists.txt | 6 +++--- paddle/fluid/memory/detail/system_allocator.h | 8 ++++++-- paddle/fluid/memory/pinned_memory_test.cu | 18 +++++++++++------- 3 files changed, 20 insertions(+), 12 deletions(-) diff --git a/paddle/fluid/memory/CMakeLists.txt b/paddle/fluid/memory/CMakeLists.txt index 53fd8626fa..66e4f8cdd0 100644 --- a/paddle/fluid/memory/CMakeLists.txt +++ b/paddle/fluid/memory/CMakeLists.txt @@ -15,6 +15,6 @@ cc_library(paddle_memory cc_test(memory_test SRCS memory_test.cc DEPS place paddle_memory) -if (WITH_GPU) - nv_test(pinned_memory_test SRCS pinned_memory_test.cu DEPS place paddle_memory) -endif() +# if (WITH_GPU) +# nv_test(pinned_memory_test SRCS pinned_memory_test.cu DEPS place paddle_memory) +# endif() diff --git a/paddle/fluid/memory/detail/system_allocator.h b/paddle/fluid/memory/detail/system_allocator.h index c2f474f4b6..e3c50ef648 100644 --- a/paddle/fluid/memory/detail/system_allocator.h +++ b/paddle/fluid/memory/detail/system_allocator.h @@ -21,8 +21,9 @@ namespace memory { namespace detail { /** - * \brief SystemAllocator is the parent class of CPUAllocator and GPUAllocator. - * A BuddyAllocator object uses a SystemAllocator* pointing to the + * \brief SystemAllocator is the parent class of CPUAllocator, + * CUDAPinnedAllocator and GPUAllocator. A BuddyAllocator + * object uses a SystemAllocator* pointing to the * underlying system allocator. */ class SystemAllocator { @@ -43,6 +44,8 @@ class CPUAllocator : public SystemAllocator { #ifdef PADDLE_WITH_CUDA class GPUAllocator : public SystemAllocator { public: + explicit GPUAllocator(int gpu_id) : gpu_id_(gpu_id) {} + virtual void* Alloc(size_t& index, size_t size); virtual void Free(void* p, size_t size, size_t index); virtual bool UseGpu() const; @@ -50,6 +53,7 @@ class GPUAllocator : public SystemAllocator { private: size_t gpu_alloc_size_ = 0; size_t fallback_alloc_size_ = 0; + int gpu_id_; }; class CUDAPinnedAllocator : public SystemAllocator { diff --git a/paddle/fluid/memory/pinned_memory_test.cu b/paddle/fluid/memory/pinned_memory_test.cu index cba39cd407..926a5b265b 100644 --- a/paddle/fluid/memory/pinned_memory_test.cu +++ b/paddle/fluid/memory/pinned_memory_test.cu @@ -24,6 +24,8 @@ limitations under the License. */ #include #include +// This unit test is an example comparing the performance between using pinned +// memory and not. In general, using pinned memory will be faster. template __global__ void Kernel(T* output, int dim) { int tid = blockIdx.x * blockDim.x + threadIdx.x; @@ -33,7 +35,7 @@ __global__ void Kernel(T* output, int dim) { } template -void test_pinned_memory() { +float test_pinned_memory() { Place cpu_place; paddle::platform::CUDAPlace cuda_place; @@ -133,12 +135,14 @@ void test_pinned_memory() { paddle::memory::Free(cpu_place, output_pinned_mem[j]); paddle::memory::Free(cuda_place, gpu_mem[j]); } + return elapsedTime / 30; } -TEST(CPUANDCUDAPinned, CPUAllocator) { - test_pinned_memory(); +TEST(CPUANDCUDAPinned, CPUAllocatorAndCUDAPinnedAllocator) { + // Generally speaking, operation on pinned_memory is faster than that on + // unpinned-memory, but if this unit test fails frequently, please close this + // test for the time being. + float time1 = test_pinned_memory(); + float time2 = test_pinned_memory(); + EXPECT_GT(time1, time2) } - -TEST(CPUANDCUDAPinned, CUDAPinnedAllocator) { - test_pinned_memory(); -} \ No newline at end of file From 09b53c086de7011d445758f4dafa875d2d14ed4c Mon Sep 17 00:00:00 2001 From: Luo Tao Date: Tue, 3 Apr 2018 20:51:16 +0800 Subject: [PATCH 39/58] add remove_var from c++ end --- paddle/fluid/framework/block_desc.h | 3 ++ paddle/fluid/pybind/protobuf.cc | 18 +++++--- .../tests/unittests/test_protobuf_descs.py | 42 +++++++++---------- 3 files changed, 37 insertions(+), 26 deletions(-) diff --git a/paddle/fluid/framework/block_desc.h b/paddle/fluid/framework/block_desc.h index 468423e0e8..873969b2a8 100644 --- a/paddle/fluid/framework/block_desc.h +++ b/paddle/fluid/framework/block_desc.h @@ -17,6 +17,7 @@ limitations under the License. */ #include #include #include +#include #include #include @@ -96,6 +97,8 @@ class BlockDesc { */ void RemoveOp(size_t s, size_t e); + void RemoveVar(const std::string &name) { vars_.erase(name); } + std::vector AllOps() const; size_t OpSize() const { return ops_.size(); } diff --git a/paddle/fluid/pybind/protobuf.cc b/paddle/fluid/pybind/protobuf.cc index 45a64f4384..985984983a 100644 --- a/paddle/fluid/pybind/protobuf.cc +++ b/paddle/fluid/pybind/protobuf.cc @@ -15,6 +15,8 @@ limitations under the License. */ #include "paddle/fluid/pybind/protobuf.h" #include #include +#include +#include #include "paddle/fluid/framework/backward.h" #include "paddle/fluid/framework/block_desc.h" #include "paddle/fluid/framework/op_desc.h" @@ -98,7 +100,7 @@ namespace pybind { using namespace paddle::framework; // NOLINT template -static py::bytes SerializeMessage(T &self) { +static py::bytes SerializeMessage(T &self) { // NOLINT // Check IsInitialized in Python std::string retv; PADDLE_ENFORCE(self.Proto()->SerializePartialToString(&retv), @@ -107,7 +109,7 @@ static py::bytes SerializeMessage(T &self) { } // Bind Methods -void BindProgramDesc(py::module &m) { +void BindProgramDesc(py::module &m) { // NOLINT py::class_(m, "ProgramDesc", "") .def(py::init<>()) .def("__init__", @@ -151,7 +153,7 @@ void BindProgramDesc(py::module &m) { }); } -void BindBlockDesc(py::module &m) { +void BindBlockDesc(py::module &m) { // NOLINT py::class_(m, "BlockDesc", "") .def_property_readonly("id", &BlockDesc::ID) .def_property_readonly("parent", &BlockDesc::Parent) @@ -200,13 +202,19 @@ void BindBlockDesc(py::module &m) { return self.FindVarRecursive(name); }, py::return_value_policy::reference) + .def("remove_var", + [](BlockDesc &self, py::bytes byte_name) { + std::string name = byte_name; + return self.RemoveVar(name); + }, + py::return_value_policy::reference) .def("all_vars", &BlockDesc::AllVars, py::return_value_policy::reference) .def("op_size", &BlockDesc::OpSize) .def("op", &BlockDesc::Op, py::return_value_policy::reference) .def("serialize_to_string", SerializeMessage); } -void BindVarDsec(py::module &m) { +void BindVarDsec(py::module &m) { // NOLINT py::class_ var_desc(m, "VarDesc", ""); var_desc .def("name", @@ -257,7 +265,7 @@ void BindVarDsec(py::module &m) { .value("RAW", proto::VarType::RAW); } -void BindOpDesc(py::module &m) { +void BindOpDesc(py::module &m) { // NOLINT py::enum_(m, "AttrType", "") .value("INT", proto::AttrType::INT) .value("INTS", proto::AttrType::INTS) diff --git a/python/paddle/fluid/tests/unittests/test_protobuf_descs.py b/python/paddle/fluid/tests/unittests/test_protobuf_descs.py index e4cf4a8bce..f98a8bbc68 100644 --- a/python/paddle/fluid/tests/unittests/test_protobuf_descs.py +++ b/python/paddle/fluid/tests/unittests/test_protobuf_descs.py @@ -19,9 +19,9 @@ from paddle.fluid.framework import Program class TestOpDesc(unittest.TestCase): def test_op_desc(self): - prog = core.ProgramDesc() - self.assertIsNotNone(prog) - block = prog.block(0) + program_desc = core.ProgramDesc() + self.assertIsNotNone(program_desc) + block = program_desc.block(0) self.assertIsNotNone(block) op = block.append_op() self.assertIsNotNone(op) @@ -67,7 +67,7 @@ class TestOpDesc(unittest.TestCase): self.assertEqual(8, len(op.attr_names())) - op.set_block_attr("block_attr", prog.block(0)) + op.set_block_attr("block_attr", program_desc.block(0)) self.assertEqual(0, op.block_attr("block_attr")) mul_op = block.append_op() @@ -88,20 +88,20 @@ class TestProgramDesc(unittest.TestCase): del program_desc def test_append_block(self): - prog_desc = core.ProgramDesc() - self.assertIsNotNone(prog_desc) - block_root = prog_desc.block(0) + program_desc = core.ProgramDesc() + self.assertIsNotNone(program_desc) + block_root = program_desc.block(0) self.assertIsNotNone(block_root) self.assertEqual(block_root.id, 0) - block1 = prog_desc.append_block(block_root) - block2 = prog_desc.append_block(block1) + block1 = program_desc.append_block(block_root) + block2 = program_desc.append_block(block1) self.assertIsNotNone(block1) self.assertEqual(block1.id, block2.parent) self.assertEqual(block_root.id, block1.parent) - block3 = prog_desc.append_block(block_root) + block3 = program_desc.append_block(block_root) self.assertEqual(block3.parent, block_root.id) - self.assertEqual(prog_desc.block(1).id, 1) - self.assertEqual(4, prog_desc.num_blocks()) + self.assertEqual(program_desc.block(1).id, 1) + self.assertEqual(4, program_desc.num_blocks()) class TestVarDesc(unittest.TestCase): @@ -162,9 +162,9 @@ class TestVarDesc(unittest.TestCase): class TestBlockDesc(unittest.TestCase): def test_add_var(self): - prog = core.ProgramDesc() - self.assertIsNotNone(prog) - block = prog.block(0) + program_desc = core.ProgramDesc() + self.assertIsNotNone(program_desc) + block = program_desc.block(0) self.assertIsNotNone(block) var1 = block.var("var1") var2 = block.var("var2") @@ -175,9 +175,9 @@ class TestBlockDesc(unittest.TestCase): self.assertEqual(var2_re, var2) def test_add_op(self): - prog = core.ProgramDesc() - self.assertIsNotNone(prog) - block = prog.block(0) + program_desc = core.ProgramDesc() + self.assertIsNotNone(program_desc) + block = program_desc.block(0) self.assertIsNotNone(block) op1 = block.append_op() op2 = block.append_op() @@ -189,9 +189,9 @@ class TestBlockDesc(unittest.TestCase): def test_remove_op(self): program = Program() - prog = program.desc - self.assertIsNotNone(prog) - block = prog.block(0) + program_desc = program.desc + self.assertIsNotNone(program_desc) + block = program_desc.block(0) self.assertIsNotNone(block) op0 = block.append_op() From 51c22fe434a7a9ba0db4f3f9497f04387e95563a Mon Sep 17 00:00:00 2001 From: chengduoZH Date: Tue, 3 Apr 2018 22:09:01 +0800 Subject: [PATCH 40/58] follow comments --- paddle/fluid/memory/CMakeLists.txt | 6 +++--- paddle/fluid/memory/pinned_memory_test.cu | 11 +++++------ 2 files changed, 8 insertions(+), 9 deletions(-) diff --git a/paddle/fluid/memory/CMakeLists.txt b/paddle/fluid/memory/CMakeLists.txt index 66e4f8cdd0..8b3043af7a 100644 --- a/paddle/fluid/memory/CMakeLists.txt +++ b/paddle/fluid/memory/CMakeLists.txt @@ -15,6 +15,6 @@ cc_library(paddle_memory cc_test(memory_test SRCS memory_test.cc DEPS place paddle_memory) -# if (WITH_GPU) -# nv_test(pinned_memory_test SRCS pinned_memory_test.cu DEPS place paddle_memory) -# endif() +#if (WITH_GPU) +# nv_test(pinned_memory_test SRCS pinned_memory_test.cu DEPS place paddle_memory) +#endif() diff --git a/paddle/fluid/memory/pinned_memory_test.cu b/paddle/fluid/memory/pinned_memory_test.cu index 926a5b265b..a000001f41 100644 --- a/paddle/fluid/memory/pinned_memory_test.cu +++ b/paddle/fluid/memory/pinned_memory_test.cu @@ -11,6 +11,8 @@ 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/fluid/memory/detail/memory_block.h" #include "paddle/fluid/memory/detail/meta_data.h" @@ -21,9 +23,6 @@ limitations under the License. */ #include "paddle/fluid/platform/gpu_info.h" #include "paddle/fluid/platform/place.h" -#include -#include - // This unit test is an example comparing the performance between using pinned // memory and not. In general, using pinned memory will be faster. template @@ -114,8 +113,8 @@ float test_pinned_memory() { cudaEventSynchronize(stop_e); cudaEventElapsedTime(&elapsedTime, start_e, stop_e); - std::cout << cpu_place << " " - << "time consume:" << elapsedTime / 30 << std::endl; + // std::cout << cpu_place << " " + // << "time consume:" << elapsedTime / 30 << std::endl; for (int l = 0; l < iteration; ++l) { for (int k = 0; k < data_size; ++k) { @@ -144,5 +143,5 @@ TEST(CPUANDCUDAPinned, CPUAllocatorAndCUDAPinnedAllocator) { // test for the time being. float time1 = test_pinned_memory(); float time2 = test_pinned_memory(); - EXPECT_GT(time1, time2) + EXPECT_GT(time1, time2); } From 3ceff3057b5ca1803ee64679fe24a0bf5512707a Mon Sep 17 00:00:00 2001 From: Kevin Zhan Date: Wed, 4 Apr 2018 00:28:49 +0800 Subject: [PATCH 41/58] Update recurrent_group_en.md (#9445) * Update recurrent_group_en.md Fix https://github.com/PaddlePaddle/Paddle/issues/9013 * Update recurrent_group_en.md --- doc/v2/howto/rnn/recurrent_group_en.md | 95 +++++++++++++++++++++++++- 1 file changed, 94 insertions(+), 1 deletion(-) diff --git a/doc/v2/howto/rnn/recurrent_group_en.md b/doc/v2/howto/rnn/recurrent_group_en.md index d264b0a9f8..de6b60f29e 100644 --- a/doc/v2/howto/rnn/recurrent_group_en.md +++ b/doc/v2/howto/rnn/recurrent_group_en.md @@ -1,3 +1,96 @@ # Recurrent Group Tutorial -TBD +## Overview + +Sequential data is common in natural language processing. + +A sentence is a sequence of words and many sentences form a paragraph further. Therefore, a paragraph can be viewed as a nested sequence with two level, where each element of the sequence is another sequence. That is to say, sequential data could be recursive. An example of two-level recursive sequential data is that an article is composed of a sequence of sentences, and each sentence a sequence of words. + +PaddlePaddle and PaddlePaddle v2 support two-level recursive sequential data. The two-level sequence is a very flexible data, which helps us to better describe more complex language data such as discribing paragraphs and several rounds of dialogues. Based on two-level sequence input, we can design and build a flexible, hierarchical RNN model that encodes input data from the word and sentence level. For the support of arbitrary levels, please refer to PaddlePaddle Fluid. + +In PaddlePaddle, `recurrent_group` is an arbitrarily complex RNN unit. The user only needs to define the calculation that the RNN will complete in one time step. PaddlePaddle is responsible for the propagation of information and error in time series. + +Furthermore, `recurrent_group` can also be extended to handle two-level sequence. By defining two nested `recurrent_group` operations at the clause level and the word level respectively, a hierarchical and complex RNN is finally achieved. + +Currently, in the PaddlePaddle, there are `recurrent_group` and some Layers that can process bidirectional sequences. For details, refer to the document: Layers for supporting double-layer sequences as input. + +## Related Concepts + +### Basic Principle +`recurrent_group` is an arbitrarily complex RNN unit supported by PaddlePaddle. The user only needs to focus on the calculations that the RNN is designed to complete within a single time step. The PaddlePaddle is responsible for completing the propagation of information and gradients over time. + +In PaddlePaddle, a simple call to `recurrent_group` is as follows: + +``` python +recurrent_group(step, input, reverse) +``` +- step: A callable function that defines the calculations completed by the RNN unit within a time step +- input: The input must be a single-layer sequence or a double-layer sequence +- reverse: Whether to process the input sequence in reverse order + +The core of using `recurrent_group` is to design the logic of the step function. The step function can be freely combined with various layers supported by PaddlePaddle to complete arbitrary arithmetic logic. The input of `recurrent_group` (input) becomes the input of the step function. Since the step function only focuses on the calculation within one time step of RNN, here `recurrent_group` completes the splitting of the original input data for us. + +### Input +The input sequence processed by `recurrent_group` is mainly divided into the following three types: + +- **Input Data**: When putting a two-level sequence into `recurrent_group`, it will be disassembled into a single-level sequence. When putting a single-level sequence into `recurrent_group`, it will be disassembled into a non-sequence and then passed to the step function. This process is completely transparent to the user. There are two possible types: 1) User input via data_layer; 2) Output from other layers. + +- **Read-only Memory Input**: `StaticInput` defines a read-only Memory. The input specified by `StaticInput` will not be disassembled by `recurrent_group`, and each time step of the `recurrent_group` loop will always be able to reference all inputs. It may be a non-sequence or a single-layer sequence. + +- **Input of Sequence Generation Task**: `GeneratedInput` is only used to specify input data in a sequence generation task. + +### Input Example + +Sequence generation tasks mostly follow the encoder-decoer architecture. The encoder and decoder can be arbitrary neural network units capable of processing sequences and RNN is the most popular choice. + +Given the encoder output and the current word, the decoder predicts the next most likely word each time. In this structure, the decoder accepts two inputs: + +- Target sequence to be generated: a input of the decoder and the basis of the decoder loop. `recurrent_group` will disassemble this input type. + +- Encoder output, an non-sequencce or single-sequence: a unbounded memory. Each time step in the decoder loop will reference the entire result and should not be disassembled. This type of input must be specified via `StaticInput`. For more discussion on Unbounded Memory, please refer to the paper [Neural Turning Machine](https://arxiv.org/abs/1410.5401). + +In a sequence generation task, the decoder RNN always refers to the word vector of the word predicted at the previous moment as the current time input. `GeneratedInput` will automate this process. + +### Output +The `step` function must return the output of one or more Layers. The output of this Layer will be the final output of the entire `recurrent_group`. In the output process, `recurrent_group` will concatenate the output of each time step, which is also transparent to the user. + +### Memory +Memory can only be defined and used in `recurrent_group`. Memory cannot exist independently and must point to a layer defined by PaddlePaddle. Memory is referenced to get a momentary output from this layer, so memory can be interpreted as a delay operation. + +The user can explicitly specify the output of a layer to initialize the memory. When not specified, memory is initialized to 0 by default. + +## Sequence-level RNN Introduction + +`recurrent_group` helps us to split the input sequence, merge the output, and loop through the sequence of computational logic. + +Using this feature, the two nested `recurrent_group` can handle the nested two-level sequences, implementing sequence-level RNN structures at both the word and sentence levels. + +- Word-level RNN: each state corresponds to a word. +- Sequence-level RNN: a sequence-layer RNN consists of multiple word-layer RNNs. Each word-layer RNN (ie, each state of a sequence-layer RNN) has a subsequence. + +For convenience of description, the following takes the NLP task as an example. A paragraph containing a subsequence is defined as a two-level sequence, and a sentence containing a word is defined as a single-layer sequence. Then, the zero-level sequence is a word. + +## Usage of Sequence-level RNN + +### Usage of Training Process +Using `recurrent_group` requires the following conventions: + +- **Single-input Single-output**: Both input and output are single layer sequences. + - If there are multiple inputs, the number of words in different input sequences must be exactly equal. + - A single-layer sequence is output, and the number of words in the output sequence is the same as the input sequence. + - memory: define memory to point to a layer in the step function, get a moment output from this layer by referencing memory to form a recurrent connection. The is_seq parameter of memory must be false. If memory is not defined, the operations within each time step are independent. + - boot_layer: the initial state of memory, set 0 by default. is_seq in memory must be false. + +- **Double-input Double-output**: Both input and output are two-level sequence. + - If there are multiple input sequences, the number of subsequence contained in different inputs must be strictly equal, but the number of words in the subsequence may not be equal. + - output a two-level sequence. The number of subsequence and the number of words are the same as the specified input sequence and the first input is default. + - memory: defining memory in the step function, pointing to a layer, by referring to the memory to get the output of this layer at a time, forming a recurrent connection. The memory defined in the outer `recurrent_group` step function can record the state of the previous subsequence, either as a single-level sequence (only as read-only memory) or as a word. If memory is not defined, the operations between subsequence are independent. + - boot_layer: the initial state of memory. It is either a single-level sequence (only as read-only memory) or a vector. The default is not set, that is, the initial state is 0. + +- **Double-input Single-output**: not support for now, and output the error with "In hierachical RNN, all out links should be from sequences now". + +### Usage of Generation Process +Using `beam_search` need follow those conventions: + +- Word-level RNN: generate the next word from a word. +- Sequence-level RNN: the single-layer RNN generated subsequence is concatenated into a new double-layer sequence. Semantically, there is no case where a subsequence generates the next subseq directly. From 937797849e22b5e0ce642ebc9d673eac0b40e9f5 Mon Sep 17 00:00:00 2001 From: Yi Wang Date: Tue, 3 Apr 2018 10:36:49 -0700 Subject: [PATCH 42/58] Fix serde_test.cc compile error --- paddle/fluid/operators/detail/serde_test.cc | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/paddle/fluid/operators/detail/serde_test.cc b/paddle/fluid/operators/detail/serde_test.cc index 6fb2369e6e..f8cae6b26a 100644 --- a/paddle/fluid/operators/detail/serde_test.cc +++ b/paddle/fluid/operators/detail/serde_test.cc @@ -102,12 +102,12 @@ void RunSerdeTestSelectedRows(platform::Place place) { } else { tensor_data2 = const_cast(tensor2->data()); } - const size_t* rows_data2 = rows2->data(); + const int64_t* rows_data2 = rows2->data(); for (int i = 0; i < tensor_numel; ++i) { EXPECT_FLOAT_EQ(tensor_data2[i], 32.7); } - for (size_t i = 0; i < rows2->size(); ++i) { + for (int64_t i = 0; i < rows2->size(); ++i) { EXPECT_EQ(rows_data2[i], i); } EXPECT_EQ(slr2->height(), 1000); From 54316bdd7ecda394df2c54c8173b7d886506fc39 Mon Sep 17 00:00:00 2001 From: Yi Wang Date: Tue, 3 Apr 2018 11:53:21 -0700 Subject: [PATCH 43/58] Update --- paddle/fluid/operators/conv_op.cc | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/paddle/fluid/operators/conv_op.cc b/paddle/fluid/operators/conv_op.cc index 650bc92be2..695db841a4 100644 --- a/paddle/fluid/operators/conv_op.cc +++ b/paddle/fluid/operators/conv_op.cc @@ -13,6 +13,10 @@ See the License for the specific language governing permissions and limitations under the License. */ #include "paddle/fluid/operators/conv_op.h" + +#include +#include + #ifdef PADDLE_WITH_CUDA #include "paddle/fluid/platform/cudnn_helper.h" #endif From ebcf5fb9d44e581e0f0f56873fb638a9d71447c5 Mon Sep 17 00:00:00 2001 From: Yi Wang Date: Tue, 3 Apr 2018 13:26:12 -0700 Subject: [PATCH 44/58] Fix compare warning --- paddle/fluid/operators/listen_and_serv_op.cc | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/paddle/fluid/operators/listen_and_serv_op.cc b/paddle/fluid/operators/listen_and_serv_op.cc index b19add24e2..6cc468e449 100644 --- a/paddle/fluid/operators/listen_and_serv_op.cc +++ b/paddle/fluid/operators/listen_and_serv_op.cc @@ -96,7 +96,7 @@ class ListenAndServOp : public framework::OperatorBase { auto *block = Attr(kOptimizeBlock); auto *program = block->Program(); - int num_blocks = program->Size(); + size_t num_blocks = program->Size(); PADDLE_ENFORCE_GE(num_blocks, 2, "server program should have at least 2 blocks"); @@ -153,7 +153,7 @@ class ListenAndServOp : public framework::OperatorBase { // The optimize blocks which have the same parent ID would run parallel // TODO(Yancey1989): need to use ParallelExecutor for future - size_t last_parent_blkid = program->Block(1).Parent(); + int32_t last_parent_blkid = program->Block(1).Parent(); std::vector parallel_blkids; parallel_blkids.push_back(1); double ts = detail::GetTimestamp(); From e941914a3377b00400b0a096aa64f544e9311364 Mon Sep 17 00:00:00 2001 From: Yi Wang Date: Tue, 3 Apr 2018 13:37:34 -0700 Subject: [PATCH 45/58] Fix compilation warnings in variable_response.cc --- paddle/fluid/operators/detail/variable_response.cc | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/paddle/fluid/operators/detail/variable_response.cc b/paddle/fluid/operators/detail/variable_response.cc index f59c9b50bb..01eb8acc55 100644 --- a/paddle/fluid/operators/detail/variable_response.cc +++ b/paddle/fluid/operators/detail/variable_response.cc @@ -13,7 +13,11 @@ // limitations under the License. #include "paddle/fluid/operators/detail/variable_response.h" -#include + +#include +#include +#include + #include "paddle/fluid/operators/detail/send_recv.pb.h" #include "paddle/fluid/operators/detail/sendrecvop_utils.h" @@ -151,7 +155,7 @@ bool VariableResponse::CopySelectRowsTensorData( auto* tensor = slr->mutable_value(); tensor->Resize(dims); PADDLE_ENFORCE_EQ( - tensor->numel(), + static_cast(tensor->numel()), length / framework::SizeOfType( paddle::operators::detail::ToTypeIndex(meta_.data_type()))); void* tensor_data = tensor->mutable_data( From 66e0aed700b51b94a751bf6acb885b098685a054 Mon Sep 17 00:00:00 2001 From: Yi Wang Date: Tue, 3 Apr 2018 15:17:00 -0700 Subject: [PATCH 46/58] Fix compilation warnings of fix_split_ids_op.h (#9619) --- paddle/fluid/operators/split_ids_op.h | 9 +++------ 1 file changed, 3 insertions(+), 6 deletions(-) diff --git a/paddle/fluid/operators/split_ids_op.h b/paddle/fluid/operators/split_ids_op.h index 3e750ed2d1..d36ed398eb 100644 --- a/paddle/fluid/operators/split_ids_op.h +++ b/paddle/fluid/operators/split_ids_op.h @@ -30,19 +30,16 @@ class SplitIdsOpKernel : public framework::OpKernel { PADDLE_THROW("SplitIds do not support GPU kernel"); } - const auto* ids_t = ctx.Input("Ids"); - auto& ids_dims = ids_t->dims(); + auto& ids_dims = ctx.Input("Ids")->dims(); + const T* ids = ctx.Input("Ids")->data(); auto outs = ctx.MultiOutput("Out"); - - const T* ids = ids_t->data(); - const size_t shard_num = outs.size(); std::vector> out_ids; out_ids.resize(outs.size()); // split id by their shard_num. - for (size_t i = 0; i < ids_dims[0]; ++i) { + for (int i = 0; i < ids_dims[0]; ++i) { T id = ids[i]; size_t shard_id = static_cast(id) % shard_num; out_ids[shard_id].push_back(id); From 8baf59cebe16e0124e9414c5bddd9d84d06c4ac5 Mon Sep 17 00:00:00 2001 From: weixing02 <564445201@qq.com> Date: Wed, 4 Apr 2018 10:59:32 +0800 Subject: [PATCH 47/58] Upload image sources to github --- .../design/algorithm/parameter_average.md | 4 +- doc/fluid/images/2_level_rnn.dot | 56 +++++++++++ doc/fluid/images/2_level_rnn.png | Bin 0 -> 52666 bytes .../LOD-and-shape-changes-during-decoding.jpg | Bin 0 -> 62624 bytes doc/fluid/images/asgd.gif | Bin 0 -> 620 bytes doc/fluid/images/batch_norm_fork.dot | 25 +++++ doc/fluid/images/batch_norm_fork.png | Bin 0 -> 23873 bytes doc/fluid/images/batch_norm_op_kernel.png | Bin 0 -> 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doc/fluid/images/fluid-compiler.png | Bin 0 -> 124118 bytes .../images/graph_construction_example.bash | 11 +++ .../images/graph_construction_example.dot | 68 ++++++++++++++ .../images/graph_construction_example_all.png | Bin 0 -> 57513 bytes ..._construction_example_forward_backward.png | Bin 0 -> 50107 bytes ...raph_construction_example_forward_only.png | Bin 0 -> 30790 bytes doc/fluid/images/l1_regularization.png | Bin 0 -> 1157 bytes doc/fluid/images/l2_regularization.png | Bin 0 -> 989 bytes doc/fluid/images/local-graph.graffle | Bin 0 -> 2527 bytes doc/fluid/images/local-graph.png | Bin 0 -> 28561 bytes doc/fluid/images/local_architecture.graffle | Bin 0 -> 3109 bytes doc/fluid/images/local_architecture.png | Bin 0 -> 104998 bytes doc/fluid/images/lookup_table.png | Bin 0 -> 24246 bytes doc/fluid/images/lookup_table_training.png | Bin 0 -> 90423 bytes doc/fluid/images/loss_equation.png | Bin 0 -> 1589 bytes doc/fluid/images/multi-threads.graffle | Bin 0 -> 12925 bytes 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doc/fluid/images/rnn_2level_data.dot | 75 +++++++++++++++ doc/fluid/images/rnn_2level_data.png | Bin 0 -> 68929 bytes doc/fluid/images/single-thread@3x.png | Bin 0 -> 78099 bytes doc/fluid/images/sparse_update.graffle | Bin 0 -> 10788 bytes doc/fluid/images/sparse_update.png | Bin 0 -> 122536 bytes doc/fluid/images/test.dot | 35 +++++++ doc/fluid/images/test.dot.png | Bin 0 -> 58935 bytes doc/fluid/images/theta_star.gif | Bin 0 -> 156 bytes doc/fluid/images/timeline.jpeg | Bin 0 -> 70606 bytes doc/fluid/images/tracing.jpeg | Bin 0 -> 30668 bytes 67 files changed, 360 insertions(+), 1 deletion(-) create mode 100644 doc/fluid/images/2_level_rnn.dot create mode 100644 doc/fluid/images/2_level_rnn.png create mode 100644 doc/fluid/images/LOD-and-shape-changes-during-decoding.jpg create mode 100644 doc/fluid/images/asgd.gif create mode 100644 doc/fluid/images/batch_norm_fork.dot create mode 100644 doc/fluid/images/batch_norm_fork.png create mode 100644 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mode 100644 doc/fluid/images/multigpu_allreduce.graffle create mode 100644 doc/fluid/images/multigpu_allreduce.png create mode 100644 doc/fluid/images/multigpu_before_convert.graffle create mode 100644 doc/fluid/images/multigpu_before_convert.png create mode 100644 doc/fluid/images/multiple_reader.png create mode 100644 doc/fluid/images/paddle-compile.graffle create mode 100644 doc/fluid/images/paddle-compile.png create mode 100644 doc/fluid/images/pprof_1.png create mode 100644 doc/fluid/images/pprof_2.png create mode 100644 doc/fluid/images/profiler.png create mode 100644 doc/fluid/images/readers.png create mode 100644 doc/fluid/images/remote_executor.graffle create mode 100644 doc/fluid/images/remote_executor.png create mode 100644 doc/fluid/images/rnn.dot create mode 100644 doc/fluid/images/rnn.jpg create mode 100644 doc/fluid/images/rnn.png create mode 100644 doc/fluid/images/rnn_2level_data.dot create mode 100644 doc/fluid/images/rnn_2level_data.png create mode 100644 doc/fluid/images/single-thread@3x.png create mode 100644 doc/fluid/images/sparse_update.graffle create mode 100644 doc/fluid/images/sparse_update.png create mode 100644 doc/fluid/images/test.dot create mode 100644 doc/fluid/images/test.dot.png create mode 100644 doc/fluid/images/theta_star.gif create mode 100644 doc/fluid/images/timeline.jpeg create mode 100644 doc/fluid/images/tracing.jpeg diff --git a/doc/fluid/design/algorithm/parameter_average.md b/doc/fluid/design/algorithm/parameter_average.md index 53d601d3a9..70c5cdecad 100644 --- a/doc/fluid/design/algorithm/parameter_average.md +++ b/doc/fluid/design/algorithm/parameter_average.md @@ -7,7 +7,9 @@ Polyak and Juditsky (1992) showed that the test performance of simple average of Hence, to accelerate the speed of Stochastic Gradient Descent, Averaged Stochastic Gradient Descent (ASGD) was proposed in Polyak and Juditsky (1992). For ASGD, the running average of parameters obtained by SGD, is used as the estimator for
. The averaging is done as follows: -![](./images/asgd.gif) +

+
+

We propose averaging for any optimizer similar to how ASGD performs it, as mentioned above. diff --git a/doc/fluid/images/2_level_rnn.dot b/doc/fluid/images/2_level_rnn.dot new file mode 100644 index 0000000000..5d77865061 --- /dev/null +++ b/doc/fluid/images/2_level_rnn.dot @@ -0,0 +1,56 @@ +digraph G { + + rnn [label="1st level RNN" shape=box] + + subgraph cluster0 { + label = "time step 0" + + sent0 [label="sentence"] + sent1 [label="sentence"] + + rnn1 [label="2nd level RNN" shape=box] + + sent0 -> rnn1 + sent1 -> rnn1 + } + + subgraph cluster1 { + label = "time step 1" + + sent2 [label="sentence"] + sent3 [label="sentence"] + + rnn2 [label="2nd level RNN" shape=box] + + sent2 -> rnn2 + sent3 -> rnn2 + } + + subgraph cluster2 { + label = "time step 2" + + sent4 [label="sentence"] + sent5 [label="sentence"] + + rnn3 [label="2nd level RNN" shape=box] + + sent4 -> rnn3 + sent5 -> rnn3 + } + + + para0 [label="paragraph info 0"] + para1 [label="paragraph info 1"] + para2 [label="paragraph info 2"] + + rnn1 -> para0 + rnn2 -> para1 + rnn3 -> para2 + + para0 -> rnn + para1 -> rnn + para2 -> rnn + + chapter [label="chapter info"] + rnn -> chapter +} diff --git a/doc/fluid/images/2_level_rnn.png b/doc/fluid/images/2_level_rnn.png new file mode 100644 index 0000000000000000000000000000000000000000..0537a75beb175c0c284717421f7aa908da2a5038 GIT binary patch literal 52666 zcmZ6z1z1#D+doVUB^^o+-6$y_Fw`g|4T=RwgVN0q64G7LT_OqyNH>zwpmazMC^d9{ zYtHk&-~WBib+J_FqT9 zw|;>bQS5j`z8wD^olYh3--jq1$a3OV{o2df<*@$y!xkp|??YH5SfcR^f~QFkWxxOW zSg~v4{_Bu(o4lW5JT`7?{QtdP^uMFn`~Q82Kmy#KQ`?gMzwbOJ?jh~JW{4RR50g%X zgyvH9Ul)v(*N=~lN1{i^a?|T4EJyp16+2%siua>|1F6^KjB%L|`*#=C_uRKsG|nugdl z?RV!1du){ys}5(uA{s)d6EFz$?)ei?a`vi=&+9$K{UH|r>&4rfmPM#B&SjTD^tn%OUk3t`hAsPNxc(6xfL=}9${XhiV!T)`hu`%Gdw~L7rNtsRv zDHAkd;yinRR>Y#0(S44EROU42@^D=9S~gab**}FchosNY?_hl}L446$m zX)%);vixmXeK+?OquZ4Hub1|)&H2XKrh_3Vg2Hy21V1UP36{-_JN(a>Ei)X0tKUv) zWO=Tq9wuBq0*@f5t%vCyKdJPdpZE3I#*-nA{kA@?Z2rcbhEiiEaD?!fs`+^4Z9*vP 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[label="batch_norm_op", shape=box]; + Fc [label="fc_op", shape=box]; + After [label="...", shape=plaintext]; + Prev -> Rnn -> BatchNorm -> Fc -> After; + label="original"; + } + + subgraph cluster_after { + Prev2 [label="...", shape=plaintext]; + Rnn2 [label="rnn_op", shape=box]; + BatchNorm2_1 [label="train_batch_norm_op", shape=box]; + BatchNorm2_2 [label="infer_batch_norm_op", shape=box]; + Fc2_1 [label="fc_op", shape=box]; + Fc2_2 [label="fc_op", shape=box]; + After2_1 [label="...", shape=plaintext]; + After2_2 [label="...", shape=plaintext]; + Prev2 -> Rnn2 -> BatchNorm2_1 -> Fc2_1 -> After2_1; + Rnn2 -> BatchNorm2_2 ->Fc2_2 ->After2_2 + label="forked"; + } +} diff --git a/doc/fluid/images/batch_norm_fork.png b/doc/fluid/images/batch_norm_fork.png new file mode 100644 index 0000000000000000000000000000000000000000..aded62bce5bc268b7a3ef4dc96c89fe21d6ea955 GIT binary patch literal 23873 zcmeFZby!wg^fd|yNQo#2ilj;-2olmDBGN4&B@I&2(xE7d0@4lANOy;zBHj5CDjhE< 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100755 index 0000000000..35e6997abd --- /dev/null +++ b/doc/fluid/images/graph_construction_example.bash @@ -0,0 +1,11 @@ +cat ./graph_construction_example.dot | \ + sed 's/color=red/color=red, style=invis/g' | \ + sed 's/color=green/color=green, style=invis/g' | \ + dot -Tpng > graph_construction_example_forward_only.png + +cat ./graph_construction_example.dot | \ + sed 's/color=green/color=green, style=invis/g' | \ + dot -Tpng > graph_construction_example_forward_backward.png + +cat ./graph_construction_example.dot | \ + dot -Tpng > graph_construction_example_all.png diff --git a/doc/fluid/images/graph_construction_example.dot b/doc/fluid/images/graph_construction_example.dot new file mode 100644 index 0000000000..e115f9844b --- /dev/null +++ b/doc/fluid/images/graph_construction_example.dot @@ -0,0 +1,68 @@ +digraph ImageClassificationGraph { + ///////// The forward part ///////// + FeedX [label="Feed", color=blue, shape=box]; + FeedY [label="Feed", color=blue, shape=box]; + InitW [label="Init", color=blue, shape=diamond]; + Initb [label="Init", color=blue, shape=diamond]; + FC [label="FC", color=blue, shape=box]; + MSE [label="MSE", color=blue, shape=box]; + + x [label="x", color=blue, shape=oval]; + l [label="l", color=blue, shape=oval]; + y [label="y", color=blue, shape=oval]; + W [label="W", color=blue, shape=doublecircle]; + b [label="b", color=blue, shape=doublecircle]; + cost [label="cost", color=blue, shape=oval]; + + FeedX -> x -> FC -> y -> MSE -> cost [color=blue]; + FeedY -> l [color=blue]; + InitW -> W [color=blue]; + Initb -> b [color=blue]; + W -> FC [color=blue]; + b -> FC [color=blue]; + l -> MSE [color=blue]; + + ////////// The backward part ///////// + MSE_Grad [label="MSE_grad", color=red, shape=box]; + FC_Grad [label="FC_grad", color=red, shape=box]; + + d_cost [label="d cost", color=red, shape=oval]; + d_y [label="d y", color=red, shape=oval]; + d_b [label="d b", color=red, shape=oval]; + d_W [label="d W", color=red, shape=oval]; + + cost -> MSE_Grad [color=red]; + d_cost -> MSE_Grad [color=red]; + l -> MSE_Grad [color=red]; + y -> MSE_Grad -> d_y [color=red]; + + x -> FC_Grad [color=red]; + y -> FC_Grad [color=red]; + d_y -> FC_Grad [color=red]; + W -> FC_Grad -> d_W [color=red]; + b -> FC_Grad -> d_b [color=red]; + + ////////// The optimizaiton part ////////// + + OPT_W [label="SGD", color=green, shape=box]; + OPT_b [label="SGD", color=green, shape=box]; + + W -> OPT_W [color=green]; + b -> OPT_b [color=green]; + d_W -> OPT_W -> W [color=green]; + d_b -> OPT_b -> b [color=green]; + + ////////// Groupings ////////// + + subgraph clusterMSE { + style=invis; + MSE; + MSE_Grad; + } + + subgraph clusterFC { + style=invis; + FC; + FC_Grad; + } +} diff --git a/doc/fluid/images/graph_construction_example_all.png b/doc/fluid/images/graph_construction_example_all.png new file mode 100644 index 0000000000000000000000000000000000000000..261611a5721f9aa97874f7e6d897fe48cf667db2 GIT binary patch literal 57513 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zzb)|0$=m16lvnSXy&faH8o!ApB&^%(I#Z7IkBfqa4%f#;rZInRoU994bgfIU?L}kH zv8~IF{6*Jyp$@n)>{i`0pH7T{S;#n@9B5L@P?R5XkSwfu2M-;G-@p@YuaD}dZutG* zA8YLqsL+t4(eWVgP4%mjb0c@}3gg@2TbC-X;?Tgr$i%`SV4<5Z!`k<$BZ#d4 z6NE743mcrNJ$Gp~h6D@89I+kq>Mk|sbKAV@-@gBKH>PG^#{DyXC#~Q9_}kI#)ute; z_HfA@|M+YDd1SReDjQFK{{Q}YFqa3^JxFd)PZ?U>2CwjQBBr&z^j97S1 z5jD7g{<(jBKsX;mo(6<-g!&l@d0;12dBidq*kkTNHoEP z6&wV5_Cs^0XZ5e=rKGRDZ?eF{9(NNjM-3D{d5Hb5O})!xvX prememory0 [label="init" color="blue"] + memory0 -> prememory1 [label="copy/reference" color="blue"] + memory1 -> prememory2 [label="copy/reference" color="blue"] + + edge[color=black] + W -> stepnet0[constraint=false, style=dashed] + W -> stepnet1[constraint=false, style=dashed] + W -> stepnet2[constraint=false, style=dashed] + + memory0 -> stepnet0[style=dashed] + prememory0 -> stepnet0 -> step_output0[style=dashed] + + memory1 -> stepnet1[style=dashed] + prememory1 -> stepnet1 -> step_output1[style=dashed] + + memory2 -> stepnet2[style=dashed] + prememory2 -> stepnet2 -> step_output2[style=dashed] + + input -> step_input0 + input -> step_input1 + input -> step_input2 + + step_input0 -> stepnet0 [style=dashed] + step_input1 -> stepnet1[style=dashed] + step_input2 -> stepnet2[style=dashed] + + step_output0 -> output + step_output1 -> output + step_output2 -> output + + stepnet0 -> stepnet[style=dashed] + stepnet1 -> stepnet[style=dashed] + stepnet2 -> stepnet[style=dashed] + +} diff --git a/doc/fluid/images/rnn.jpg b/doc/fluid/images/rnn.jpg new file mode 100644 index 0000000000000000000000000000000000000000..9867e404cf959df0dce6ded5222b466c788fb840 GIT binary patch literal 44320 zcmdqI1ymg0vM)R_l8^vFgA?3c2MxhJFt{ejV1qNbBoGMhK1hPYAi)NQ5L{*m4ub`E zcYitOy>sqA=YIFxZ@sndTHpHi%$ly6-d(%uSJkzC!L4(cilKZ8z>CJZf$Xd>S5gr^k|SG@cL<^CpaHY3t~|H~kow*fumgvQIz@`dYC- z$Sa5^`?wEq0TO`L@BbL_{}0)G;`msixyc3*zIyHO| zax7CxqgSlCXAKj~>?*WF2kUgVz1da-89q5cvVi^PhTN)S?=1I;Wf$=g}EDKJIj$n5Hc}H@HR+~F*cD&p0@>DaQmK{ooRxy 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[label="sentence 0"] + s11 [label="sentence 1"] + low_rnn1 -> s10 + low_rnn1 -> s11 + } + + subgraph cluster_p2 { + label = "sentence 1" + low_rnn2 [label="low rnn step 0" shape=box] + s20 [label="sentence 0"] + s21 [label="sentence 1"] + low_rnn2 -> s20 + low_rnn2 -> s21 + } + + subgraph cluster_p3 { + label = "sentence 1" + low_rnn3 [label="low rnn step 1" shape=box] + s30 [label="sentence 0"] + s31 [label="sentence 1"] + low_rnn3 -> s30 + low_rnn3 -> s31 + } + + + chapter -> top_rnn0 + chapter -> top_rnn1 + + top_rnn0 -> p0 + top_rnn0 -> p1 + top_rnn1 -> p2 + top_rnn1 -> p3 + + + p0 -> low_rnn0 + p1 -> low_rnn1 + p2 -> low_rnn2 + p3 -> low_rnn3 + +} diff --git a/doc/fluid/images/rnn_2level_data.png b/doc/fluid/images/rnn_2level_data.png new file mode 100644 index 0000000000000000000000000000000000000000..4be81b2430717a6a506342a09fc26899568574c6 GIT binary patch literal 68929 zcmdqJWk8kP)-?ZiWsZXU17pEda2HY7emIx zFA~p)i`;#amW1W!^E|Bv58 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g_loss; + label2 -> g_loss; + + g_loss -> D_f[color=green, style=dashed]; + D_f -> discriminator[color=green, style=dashed]; + discriminator -> G_img[color=green, style=dashed]; + G_img -> generator[color=green, style=dashed]; + + discriminator [color=red, shape=box]; + generator [color=green, shape=box]; + z [shape=diamond]; + img [shape=diamond]; + label0 [shape=diamond]; + label1 [shape=diamond]; + label2 [shape=diamond]; + + d_loss [color=red]; + g_loss [color=green]; +} diff --git a/doc/fluid/images/test.dot.png b/doc/fluid/images/test.dot.png new file mode 100644 index 0000000000000000000000000000000000000000..4e121a40b9f7b2232d7cdda315bad15926446f55 GIT binary patch literal 58935 zcmc$GbyQVb*Zx5X326{%51}9}AkylgC8R}4Km?=&q{~3M6+}w91f&$C1Vma&q`SLI z>Noej?>D}GzCYhFe&ZeEz4so@*?aA^=9=@F&-1JusHP%!k${!}g+g6akjJQ_P*^S~ z)cN=exbR8#U>y(q1J^`J4ud*F{`avuD;kAjMk!!!Yq}<^jJoM-&Yt3JbP|aX1Tdou 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zS*fQd|K8hp%Ov^LhU#CJDA(6Yznb^U`~vIQFKs@hropv}36AU&G7<{YAH6@cz{Ae? zqN9xbj~TatmD24CdkP=C7x6dU-d6gWH!i!BUn%mgYr%;%9z2K4k{A^ld@El5owh%1 zk*7WP9y8DPf2ZdHS96BvKA7K9&-G90Zfx8iyAOR+AD-^M67hVp|CX+sPh2gzj+q8I z*gUx0Kg0XPSKTl9tEykz_u!uW;(o~Yi~Ab%@)y?z-*@}PuybGUSN~e+7fjE7nP0X2 zlD}Y7301>oa%~7GnEzk<>$182i-rAb;6R+#=6!oF`+hKQo$t3bZPUq&3G?rr{Cn}= zRbWxpB7A87XW%%&MBs6nk5*BsD+cky;}Q`2S!3 z`wlog@O2sRU|AGxEGAB2hGs) zP#a?Gk_R5jIvQHUgcL{(=$N3<&>}vxE{%p3(WMJ$x5sGOB0g|{&>|tU GZUO)|OGNnq literal 0 HcmV?d00001 From d988b9a997360b31c5d85a8dd9815320ba409efd Mon Sep 17 00:00:00 2001 From: weixing02 <564445201@qq.com> Date: Wed, 4 Apr 2018 12:05:09 +0800 Subject: [PATCH 48/58] Redirect img paths to github images --- doc/fluid/design/concurrent/channel.md | 28 ++++++------- doc/fluid/design/concurrent/select_op.md | 42 +++++++++---------- .../dist_train/distributed_architecture.md | 10 ++--- doc/fluid/design/dist_train/multi_cpu.md | 4 +- .../design/dist_train/parameter_server.md | 6 +-- doc/fluid/design/dynamic_rnn/rnn.md | 8 ++-- doc/fluid/design/modules/batch_norm_op.md | 22 +++++----- doc/fluid/design/modules/regularization.md | 32 ++++++-------- doc/fluid/design/network/deep_speech_2.md | 6 +-- doc/fluid/design/network/sequence_decoder.md | 2 +- doc/fluid/design/others/gan_api.md | 40 +++++++++--------- doc/fluid/dev/releasing_process.md | 2 +- doc/fluid/howto/performance/profiler.md | 10 ++--- 13 files changed, 103 insertions(+), 109 deletions(-) diff --git a/doc/fluid/design/concurrent/channel.md b/doc/fluid/design/concurrent/channel.md index a00a3325e7..a5cf17faa8 100644 --- a/doc/fluid/design/concurrent/channel.md +++ b/doc/fluid/design/concurrent/channel.md @@ -2,7 +2,7 @@ ## Introduction -A Channel is a data structure that allows for synchronous interprocess +A Channel is a data structure that allows for synchronous interprocess communication via message passing. It is a fundemental component of CSP (communicating sequential processes), and allows for users to pass data between threads without having to worry about synchronization. @@ -18,7 +18,7 @@ Creates a new channel that takes in variables of a specific dtype. - **fluid.make_channel(dtype, capacity=0)** - **dtype**: The data type of variables being sent/received through channel - - **capacity**: The capacity of the channel. A capacity of 0 represents + - **capacity**: The capacity of the channel. A capacity of 0 represents an unbuffered channel. Capacity > 0 represents a buffered channel ``` @@ -40,8 +40,8 @@ fluid.channel_close(ch) ### Send data to a channel -Sends a variable to a channel. Currently, variables of dtype `LoDTensor`, -`LoDRankTable`, `LoDTensorArray`, `SelectedRows`, `ReaderHolder`, and +Sends a variable to a channel. Currently, variables of dtype `LoDTensor`, +`LoDRankTable`, `LoDTensorArray`, `SelectedRows`, `ReaderHolder`, and `ChannelHolder` are supported. By default, the data of the Variable is moved from the sender to the receiver, @@ -52,7 +52,7 @@ however the user can optionally copy the data before performing the send. - **variable**: The variable to send to the channel - **is_copy**: If set to True, channel_send will perform a variable assign to copy the source variable to a new variable to be sent. - + ``` ch = fluid.make_channel(dtype=core.VarDesc.VarType.LOD_TENSOR) var = fill_constant(shape=[1],dtype=core.VarDesc.VarType.INT32, value=100) @@ -68,7 +68,7 @@ receiving variable. - **channel**: The channel to receive the variable from - **return_variable**: The destination variable used to store the data of the variable received from the channel - + ``` ch = fluid.make_channel(dtype=core.VarDesc.VarType.LOD_TENSOR) var = fill_constant(shape=[1],dtype=core.VarDesc.VarType.INT32, value=-1) @@ -84,9 +84,9 @@ internal queues, locks, and conditional variables. ### QueueMessage QueueMessage encapsulates the state of the channel send/receive operation to be -put in the **sendq/recvq**. It contains a condition variable used to lock the +put in the **sendq/recvq**. It contains a condition variable used to lock the thread (when there are no available sends/receives). In addition, it contains -a callback function to notify a thread when the QueueMessage is being +a callback function to notify a thread when the QueueMessage is being processed by the channel. ### Queues @@ -108,21 +108,21 @@ channel_recv operation will put a new QueueMessage on the recvq and block the current thread under two conditions: 1. The channel is buffered and there is no data on the buff_ 2. The channel is unbuffered and does not have a sender - + ### State diagram #### Channel Send

-
+

- + #### Channel Receive

-
+

- + ## Limitations and Considerations ### Variable Copy @@ -135,5 +135,5 @@ be sent before it is sent. Please note that this is acheived by adding an **assign** operator and creating a temporary variable that is sent in place of the original variable. Please -note that **assign** operator has limited support for only certain variables +note that **assign** operator has limited support for only certain variables datatypes. diff --git a/doc/fluid/design/concurrent/select_op.md b/doc/fluid/design/concurrent/select_op.md index 52c226bc94..98dd94a2be 100644 --- a/doc/fluid/design/concurrent/select_op.md +++ b/doc/fluid/design/concurrent/select_op.md @@ -2,13 +2,13 @@ ## Introduction -In golang, the [**select**](https://golang.org/ref/spec#Select_statements) -statement lets a goroutine wait on multiple communication operations at the -same time. The **select** blocks until one of its cases can run, then -executes the case. If multiple cases are ready to run, then one case is +In golang, the [**select**](https://golang.org/ref/spec#Select_statements) +statement lets a goroutine wait on multiple communication operations at the +same time. The **select** blocks until one of its cases can run, then +executes the case. If multiple cases are ready to run, then one case is choosen at random to be executed. -With the introduction of CSP for Paddle, we mimic this behavior by +With the introduction of CSP for Paddle, we mimic this behavior by creating a ***select_op***. ## How to use it @@ -17,11 +17,11 @@ The **select_op** is available as a c++ operator. However most users will prefer to use the much simplier Python API. - **fluid.Select()**: Creates a select operator and adds it to the current -block within the main program. Also creates a sub block and adds it to the -main program. This sub block is used to hold all variables and operators +block within the main program. Also creates a sub block and adds it to the +main program. This sub block is used to hold all variables and operators used by the case statements. - -Within the select block, users can add cases by + +Within the select block, users can add cases by calling **select.case** or **select.default** method. - **fluid.Select.case(channel_action, channel, result_variable)**: Represents @@ -37,13 +37,13 @@ execute. ``` ch1 = fluid.make_channel(dtype=core.VarDesc.VarType.LOD_TENSOR) quit_ch = fluid.make_channel(dtype=core.VarDesc.VarType.LOD_TENSOR) - + x = fill_constant(shape=[1], dtype=core.VarDesc.VarType.INT32, value=0) y = fill_constant(shape=[1], dtype=core.VarDesc.VarType.INT32, value=1) - + while_cond = fill_constant(shape=[1], dtype=core.VarDesc.VarType.BOOL, value=True) while_op = While(cond=while_cond) - + with while_op.block(): with fluid.Select() as select: with select.case(fluid.channel_send, channel, x): @@ -99,17 +99,17 @@ blocks { } } // Create "select" operator. - // inputs: + // inputs: // X: All input variables used by operators within the select block // case_to_execute: Variable filled in by select_op when it determines // which case to execute. // // outputs: - // Out: All output variables referenced by operators within select block. - // + // Out: All output variables referenced by operators within select block. + // // attrs: // sub_block: The block id containing the select "cases" - // cases: Serialized list of all cases in the select op. + // cases: Serialized list of all cases in the select op. // Each case is serialized as: ',,,' // where type is 0 for default, 1 for send, and 2 for receive. // No channel and values are needed for default cases. @@ -150,7 +150,7 @@ into **X**. It will also create a temp variable called **case_to_execute**. Th filled in by the select_op after it has completed processing the case statements. If there are no available cases to execute (ie: all cases are blocked on channel operations, and -there is no default statement), then the select_op will block the current thread. The thread will +there is no default statement), then the select_op will block the current thread. The thread will unblock once there is a channel operation affecting one of the case statements, at which point, the **select_op** will set the **case_to_execute** variable to the index of the case to execute. @@ -247,17 +247,17 @@ blocks { ``` -Cases are represented by a **conditional_block operator**, whose's condition is set as the output of -equal(**case_to_execute**, **case_index**). Since each case index is unique in this sub-block, +Cases are represented by a **conditional_block operator**, whose's condition is set as the output of +equal(**case_to_execute**, **case_index**). Since each case index is unique in this sub-block, only one case will be executed. ### select_op flow

-
+

-The select algorithm is inspired by golang's select routine. Please refer to +The select algorithm is inspired by golang's select routine. Please refer to http://www.tapirgames.com/blog/golang-concurrent-select-implementation for more information. ## Backward Pass diff --git a/doc/fluid/design/dist_train/distributed_architecture.md b/doc/fluid/design/dist_train/distributed_architecture.md index a405cb6aaf..3cd4750bce 100644 --- a/doc/fluid/design/dist_train/distributed_architecture.md +++ b/doc/fluid/design/dist_train/distributed_architecture.md @@ -40,11 +40,11 @@ computation is only specified in Python code which sits outside of PaddlePaddle, Similar to how a compiler uses an intermediate representation (IR) so that the programmer does not need to manually optimize their code for most of the cases, we can have an intermediate representation in PaddlePaddle as well. The compiler optimizes the IR as follows: - + PaddlePaddle can support model parallelism by converting the IR so that the user no longer needs to manually perform the computation and operations in the Python component: - + The IR for PaddlePaddle after refactoring is called a `Block`, it specifies the computation dependency graph and the variables used in the computation. @@ -60,7 +60,7 @@ For a detailed explanation, refer to this document - The revamped distributed training architecture can address the above discussed limitations. Below is the illustration of how it does so: - + The major components are: *Python API*, *Distribute Transpiler* and *Remote Executor*. @@ -152,7 +152,7 @@ for data in train_reader(): `JobDesc` object describe the distributed job resource specification to run on Cluster environment. - + `RemoteExecutor.run` sends the `ProgramDesc` and [TrainingJob](https://github.com/PaddlePaddle/cloud/blob/unreleased-tpr/doc/autoscale/README.md#training-job-resource) @@ -171,7 +171,7 @@ In the future, a more general placement algorithm should be implemented, which m The local training architecture will be the same as the distributed training architecture, the difference is that everything runs locally, and there is just one PaddlePaddle runtime: - + ### Training Data diff --git a/doc/fluid/design/dist_train/multi_cpu.md b/doc/fluid/design/dist_train/multi_cpu.md index a8d8ee0422..586612622a 100644 --- a/doc/fluid/design/dist_train/multi_cpu.md +++ b/doc/fluid/design/dist_train/multi_cpu.md @@ -8,11 +8,11 @@ Op graph to a multi-CPU Op graph, and run `ParallelDo` Op to run the graph. ## Transpiler - + After converted: - + ## Implement diff --git a/doc/fluid/design/dist_train/parameter_server.md b/doc/fluid/design/dist_train/parameter_server.md index 6ce48dfbfc..179b5f8c29 100644 --- a/doc/fluid/design/dist_train/parameter_server.md +++ b/doc/fluid/design/dist_train/parameter_server.md @@ -41,11 +41,11 @@ We will need these OPs: *Send*, *Recv*, *Enqueue*, *Dequeue*. Below is an example of converting the user defined graph to the subgraphs for the trainer and the parameter server: - + After converting: - + 1. The parameter variable W and its optimizer program are placed on the parameter server. 1. Operators are added to the program. @@ -69,7 +69,7 @@ In Fluid, we introduce [SelectedRows](../selected_rows.md) to represent a list o non-zero gradient data. So when we do parameter optimization both locally and remotely, we only need to send those non-zero rows to the optimizer operators: - + ### Benefits diff --git a/doc/fluid/design/dynamic_rnn/rnn.md b/doc/fluid/design/dynamic_rnn/rnn.md index 6f414e5549..9a61cd788a 100644 --- a/doc/fluid/design/dynamic_rnn/rnn.md +++ b/doc/fluid/design/dynamic_rnn/rnn.md @@ -5,7 +5,7 @@ This document describes the RNN (Recurrent Neural Network) operator and how it i ## RNN Algorithm Implementation

- +

The above diagram shows an RNN unrolled into a full network. @@ -22,7 +22,7 @@ There are several important concepts here: There could be local variables defined in each step-net. PaddlePaddle runtime realizes these variables in *step-scopes* which are created for each step.

-
+
Figure 2 illustrates the RNN's data flow

@@ -93,7 +93,7 @@ For example, we could have a 2-level RNN, where the top level corresponds to par The following figure illustrates feeding in text into the lower level, one sentence at a step, and the feeding in step outputs to the top level. The final top level output is about the whole text.

- +

```python @@ -149,5 +149,5 @@ If the `output_all_steps` is set to False, it will only output the final time st

- +

diff --git a/doc/fluid/design/modules/batch_norm_op.md b/doc/fluid/design/modules/batch_norm_op.md index d1392619c4..211e060cc1 100644 --- a/doc/fluid/design/modules/batch_norm_op.md +++ b/doc/fluid/design/modules/batch_norm_op.md @@ -2,7 +2,7 @@ ## What is batch normalization -Batch normalization is a frequently-used method in deep network training. It adjusts the mean and variance of a layer's output, and make the data distribution easier for next layer's training. +Batch normalization is a frequently-used method in deep network training. It adjusts the mean and variance of a layer's output, and make the data distribution easier for next layer's training. The principle of batch normalization can be summarized into a simple function: @@ -66,7 +66,7 @@ As most C++ operators do, `batch_norm_op` is defined by inputs, outputs, attribu The following graph showes the training computational process of `batch_norm_op`: - + cudnn provides APIs to finish the whole series of computation, we can use them in our GPU kernel. @@ -74,13 +74,13 @@ cudnn provides APIs to finish the whole series of computation, we can use them i `batch_norm_op` is warpped as a layer in Python: -```python -def batch_norm_layer(net, +```python +def batch_norm_layer(net, input, - output, - scale, - bias, - use_global_est = False, + output, + scale, + bias, + use_global_est = False, epsilon = 1e-6, momentum = 0.99): mean_cache = scope.new_var(name = 'estimated_mean', trainable = False) @@ -119,15 +119,15 @@ for pass_id in range(PASS_NUM): if pass_id % 100 == 0: net.infer(test_image) # run inferencing model # ... -``` +``` `is_infer` is an attribute. Once an operator is created, its attributes can not be changed. It suggests us that we shall maintain two `batch_norm_op` in the model, one's `is_infer` is `True`(we call it `infer_batch_norm_op`) and the other one's is `False`(we call it `train_batch_norm_op`). They share all parameters and variables, but be placed in two different branches. That is to say, if a network contains a `batch_norm_op`, it will fork into two branches, one go through `train_batch_norm_op` and the other one go through `infer_batch_norm_op`:
- +
-Just like what is shown in the above graph, the net forks before `batch_norm_op` and will never merge again. All the operators after `batch_norm_op` will duplicate. +Just like what is shown in the above graph, the net forks before `batch_norm_op` and will never merge again. All the operators after `batch_norm_op` will duplicate. When the net runs in training mode, the end of the left branch will be set as the running target, so the dependency tracking process will ignore right branch automatically. When the net runs in inferencing mode, the process is reversed. diff --git a/doc/fluid/design/modules/regularization.md b/doc/fluid/design/modules/regularization.md index 21280ac898..ffc3199a84 100644 --- a/doc/fluid/design/modules/regularization.md +++ b/doc/fluid/design/modules/regularization.md @@ -6,23 +6,23 @@ A central problem in machine learning is how to design an algorithm that will pe ### Parameter Norm Penalties Most common regularization approaches in deep learning are based on limiting the capacity of the models by adding a parameter norm penalty to the objective function `J`. This is given as follows: -
+
The parameter `alpha` is a hyperparameter that weights the relative contribution of the norm penalty term, `omega`, relative to the standard objective function `J`. The most commonly used norm penalties are the L2 norm penalty and the L1 norm penalty. These are given as follows: ##### L2 Regularization: -
+
##### L1 Regularization -
+
A much more detailed mathematical background of regularization can be found [here](http://www.deeplearningbook.org/contents/regularization.html). ## Regularization Survey -A detailed survey of regularization in various deep learning frameworks can be found [here](https://github.com/PaddlePaddle/Paddle/wiki/Regularization-Survey). +A detailed survey of regularization in various deep learning frameworks can be found [here](https://github.com/PaddlePaddle/Paddle/wiki/Regularization-Survey). ## Proposal for Regularization in PaddlePaddle @@ -32,41 +32,35 @@ In the new design, we propose to create new operations for regularization. For n - L2_regularization_op - L1_regularization_op -These ops can be like any other ops with their own CPU/GPU implementations either using Eigen or separate CPU and GPU kernels. As the initial implementation, we can implement their kernels using Eigen following the abstraction pattern implemented for [Activation Ops](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/accuracy_op.h). This abstraction pattern can make it very easy to implement new regularization schemes other than L1 and L2 norm penalties. +These ops can be like any other ops with their own CPU/GPU implementations either using Eigen or separate CPU and GPU kernels. As the initial implementation, we can implement their kernels using Eigen following the abstraction pattern implemented for [Activation Ops](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/accuracy_op.h). This abstraction pattern can make it very easy to implement new regularization schemes other than L1 and L2 norm penalties. -The idea of building ops for regularization is in sync with the refactored Paddle philosophy of using operators to represent any computation unit. The way these ops will be added to the computation graph, will be decided by the [layer functions](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/python_api.md#layer-function) in Python API. +The idea of building ops for regularization is in sync with the refactored Paddle philosophy of using operators to represent any computation unit. The way these ops will be added to the computation graph, will be decided by the [layer functions](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/python_api.md#layer-function) in Python API. ### Computation Graph Below is an example of a really simple feed forward neural network. -
+
The Python API will modify this computation graph to add regularization operators. The modified computation graph will look as follows: -
+
    ### Python API implementation for Regularization -Using the low level ops, `L2_regularization_op` and `L1_regularization_op`, any user can add regularization to their computation graphs. However, this will require a lot of lines of code and we should design Python APIs that support regularization. An example of such an API can be seen in [Keras](https://keras.io/regularizers/). As per the PaddlePaddle [Python API design](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/python_api.md), the layer functions are responsible for creating operators, operator parameters and variables. Since regularization is a property of parameters, it makes sense to create these in the layer functions. +Using the low level ops, `L2_regularization_op` and `L1_regularization_op`, any user can add regularization to their computation graphs. However, this will require a lot of lines of code and we should design Python APIs that support regularization. An example of such an API can be seen in [Keras](https://keras.io/regularizers/). As per the PaddlePaddle [Python API design](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/python_api.md), the layer functions are responsible for creating operators, operator parameters and variables. Since regularization is a property of parameters, it makes sense to create these in the layer functions. #### Creation of Regularization ops There are two possibilities for creating the regularization ops: -1. We create these ops immediately while building the computation graph. -2. We add these ops in a lazy manner, just before the backward, similar to the way the optimization ops are added. +1. We create these ops immediately while building the computation graph. +2. We add these ops in a lazy manner, just before the backward, similar to the way the optimization ops are added. -The proposal is to add these ops in a lazy manner just before the backward pass. +The proposal is to add these ops in a lazy manner just before the backward pass. #### Storage of Regularization attributes -Since we want to create the regularization ops in a lazy manner, the regularization attributes (type of regularization and weight of regularization penalty) can be stored as attributes of the [`Parameter`](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/v2/framework/framework.py#L421) class. This is because regularization is a property of the parameters and storing regularization properties with Parameters also allows for shared parameters. +Since we want to create the regularization ops in a lazy manner, the regularization attributes (type of regularization and weight of regularization penalty) can be stored as attributes of the [`Parameter`](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/v2/framework/framework.py#L421) class. This is because regularization is a property of the parameters and storing regularization properties with Parameters also allows for shared parameters. #### High-level API In PaddlePaddle Python API, users will primarily rely on [layer functions](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/python_api.md#layer-function) to create neural network layers. Hence, we also need to provide regularization functionality in layer functions. The design of these APIs can be postponed for later right now. A good reference for these APIs can be found in [Keras](https://keras.io/regularizers/) and also by looking at Tensorflow in [`tf.contrib.layers`](https://www.tensorflow.org/api_guides/python/contrib.layers). - - - - - - diff --git a/doc/fluid/design/network/deep_speech_2.md b/doc/fluid/design/network/deep_speech_2.md index 7f5dcf55f9..d3906143d3 100644 --- a/doc/fluid/design/network/deep_speech_2.md +++ b/doc/fluid/design/network/deep_speech_2.md @@ -116,7 +116,7 @@ The classical DS2 network contains 15 layers (from bottom to top): - **One** CTC-loss layer
-
+
Figure 1. Archetecture of Deep Speech 2 Network.
@@ -142,7 +142,7 @@ Key ingredients about the layers: - **Batch Normalization Layers**: - Added to all above layers (except for data and loss layer). - Sequence-wise normalization for RNNs: BatchNorm only performed on input-state projection and not state-state projection, for efficiency consideration. - + @@ -208,7 +208,7 @@ TODO by Assignees ### Beam Search with CTC and LM
-
+
Figure 2. Algorithm for CTC Beam Search Decoder.
diff --git a/doc/fluid/design/network/sequence_decoder.md b/doc/fluid/design/network/sequence_decoder.md index c4a9bbeeef..a56c1b5bca 100644 --- a/doc/fluid/design/network/sequence_decoder.md +++ b/doc/fluid/design/network/sequence_decoder.md @@ -199,7 +199,7 @@ Packing the `selected_generation_scores` will get a `LoDTensor`, and each tail i ## LoD and shape changes during decoding

- +

According to the image above, the only phase that changes the LoD is beam search. diff --git a/doc/fluid/design/others/gan_api.md b/doc/fluid/design/others/gan_api.md index fb41df8615..8cc7930470 100644 --- a/doc/fluid/design/others/gan_api.md +++ b/doc/fluid/design/others/gan_api.md @@ -1,24 +1,24 @@ # Design for GAN -GAN (General Adversarial Net [https://arxiv.org/abs/1406.2661]) is an important model for unsupervised learning and widely used in many areas. +GAN (General Adversarial Net [https://arxiv.org/abs/1406.2661]) is an important model for unsupervised learning and widely used in many areas. It applies several important concepts in machine learning system design, including building and running subgraphs, dependency tracing, different optimizers in one executor and so forth. In our GAN design, we wrap it as a user-friendly easily customized python API to design different models. We take the conditional DC-GAN (Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks [https://arxiv.org/abs/1511.06434]) as an example due to its good performance on image generation.

-
+
Figure 1. The overall running logic of GAN. The black solid arrows indicate the forward pass; the green dashed arrows indicate the backward pass of generator training; the red dashed arrows indicate the backward pass of the discriminator training. The BP pass of the green (red) arrow should only update the parameters in the green (red) boxes. The diamonds indicate the data providers. d\_loss and g\_loss marked in red and green are the two targets we would like to run.

The operators, layers and functions required/optional to build a GAN demo is summarized in https://github.com/PaddlePaddle/Paddle/issues/4563.

-
+
Figure 2. Photo borrowed from the original DC-GAN paper.

-## The Conditional-GAN might be a class. +## The Conditional-GAN might be a class. This design we adopt the popular open source design in https://github.com/carpedm20/DCGAN-tensorflow and https://github.com/rajathkmp/DCGAN. It contains following data structure: - DCGAN(object): which contains everything required to build a GAN model. It provides following member functions methods as API: @@ -29,7 +29,7 @@ This design we adopt the popular open source design in https://github.com/carped Returns a generated image. - discriminator(image): -Given an image, decide if it is from a real source or a fake one. +Given an image, decide if it is from a real source or a fake one. Returns a 0/1 binary label. - build_model(self): @@ -47,7 +47,7 @@ To be more detailed, we introduce our design of DCGAN as following: ```python class DCGAN(object): def __init__(self, y_dim=None): - + # hyper parameters self.y_dim = y_dim # conditional gan or not self.batch_size = 100 @@ -82,18 +82,18 @@ class DCGAN(object): # input z: the random noise # input y: input data label (optional) # output G_im: generated fake images - + if not self.y_dim: z = pd.layer.concat(1, [z, y]) - + G_h0 = pd.layer.fc(z, self.G_w0, self.G_b0) G_h0_bn = pd.layer.batch_norm(G_h0) G_h0_relu = pd.layer.relu(G_h0_bn) - + G_h1 = pd.layer.deconv(G_h0_relu, self.G_w1, self.G_b1) G_h1_bn = pd.layer.batch_norm(G_h1) G_h1_relu = pd.layer.relu(G_h1_bn) - + G_h2 = pd.layer.deconv(G_h1_relu, self.G_W2, self.G_b2)) G_im = pd.layer.tanh(G_im) return G_im @@ -111,11 +111,11 @@ class DCGAN(object): D_h0 = pd.layer.conv2d(image, w=self.D_w0, b=self.D_b0) D_h0_bn = pd.layer.batchnorm(h0) D_h0_relu = pd.layer.lrelu(h0_bn) - + D_h1 = pd.layer.conv2d(D_h0_relu, w=self.D_w1, b=self.D_b1) D_h1_bn = pd.layer.batchnorm(D_h1) D_h1_relu = pd.layer.lrelu(D_h1_bn) - + D_h2 = pd.layer.fc(D_h1_relu, w=self.D_w2, b=self.D_b2) return D_h2 ``` @@ -123,7 +123,7 @@ class DCGAN(object): ### Class member function: Build the model - Define data readers as placeholders to hold the data; - Build generator and discriminators; -- Define two training losses for discriminator and generator, respectively. +- Define two training losses for discriminator and generator, respectively. If we have execution dependency engine to back-trace all tensors, the module building our GAN model will be like this: ```python class DCGAN(object): @@ -133,7 +133,7 @@ class DCGAN(object): self.images = pd.data(pd.float32, [self.batch_size, self.im_size, self.im_size]) self.faked_images = pd.data(pd.float32, [self.batch_size, self.im_size, self.im_size]) self.z = pd.data(tf.float32, [None, self.z_size]) - + # step 1: generate images by generator, classify real/fake images with discriminator if self.y_dim: # if conditional GAN, includes label self.G = self.generator(self.z, self.y) @@ -147,12 +147,12 @@ class DCGAN(object): # generate fake images self.sampled = self.sampler(self.z) self.D_f = self.discriminator(self.images) - + # step 2: define the two losses self.d_loss_real = pd.reduce_mean(pd.cross_entropy(self.D_t, np.ones(self.batch_size)) self.d_loss_fake = pd.reduce_mean(pd.cross_entropy(self.D_f, np.zeros(self.batch_size)) self.d_loss = self.d_loss_real + self.d_loss_fake - + self.g_loss = pd.reduce_mean(pd.cross_entropy(self.D_f, np.ones(self.batch_szie)) ``` @@ -176,7 +176,7 @@ class DCGAN(object): self.G = self.generator(self.z) self.D_g = self.discriminator(self.G, self.y) self.g_loss = pd.reduce_mean(pd.cross_entropy(self.D_g, np.ones(self.batch_szie)) - + with pd.default_block().d_block(): if self.y_dim: # if conditional GAN, includes label self.D_t = self.discriminator(self.images, self.y) @@ -217,7 +217,7 @@ if __name__ == "__main__": # load mnist data data_X, data_y = self.load_mnist() - + # Two subgraphs required!!! with pd.block().d_block(): d_optim = pd.train.Adam(lr = .001, beta= .1) @@ -228,7 +228,7 @@ if __name__ == "__main__": # executor sess = pd.executor() - + # training for epoch in xrange(10000): for batch_id in range(N / batch_size): @@ -239,7 +239,7 @@ if __name__ == "__main__": batch_z = np.random.uniform(-1., 1., [batch_size, z_dim]) if batch_id % 2 == 0: - sess.run(d_step, + sess.run(d_step, feed_dict = {dcgan.images: batch_im, dcgan.y: batch_label, dcgan.z: batch_z}) diff --git a/doc/fluid/dev/releasing_process.md b/doc/fluid/dev/releasing_process.md index 0810765b85..d459b54e09 100644 --- a/doc/fluid/dev/releasing_process.md +++ b/doc/fluid/dev/releasing_process.md @@ -37,7 +37,7 @@ PaddlePaddle每次发新的版本，遵循以下流程: 可以在此页面的"Artifacts"下拉框中找到生成的3个二进制文件，分别对应CAPI，`cp27m`和`cp27mu`的版本。然后按照上述的方法 使用`twine`工具上传即可。 - + * 注：CI环境使用 https://github.com/PaddlePaddle/buildtools 这里的DockerImage作为编译环境以支持更多的Linux 发型版，如果需要手动编译，也可以使用这些镜像。这些镜像也可以从 https://hub.docker.com/r/paddlepaddle/paddle_manylinux_devel/tags/ 下载得到。 diff --git a/doc/fluid/howto/performance/profiler.md b/doc/fluid/howto/performance/profiler.md index b20b5efdc1..fe05534be7 100644 --- a/doc/fluid/howto/performance/profiler.md +++ b/doc/fluid/howto/performance/profiler.md @@ -23,7 +23,7 @@ But how to record the time for the mixed C++ and CUDA program? There many C++ A The overall flow is shown as the following figure. -
+
### Event @@ -36,10 +36,10 @@ enum EventKind { kPopRange}; ``` - kMark: only a marker without time range. -- kPushRange: mark the starting event for time range. +- kPushRange: mark the starting event for time range. - kPopRange: mark the ending event for time range. -For the CPU code, the events only need to record the current time. For the CUDA code, the [event management functions of CUDA](http://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__EVENT.html#group__CUDART__EVENT) are used. For many pieces of code, an event lists are used to record each piece. +For the CPU code, the events only need to record the current time. For the CUDA code, the [event management functions of CUDA](http://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__EVENT.html#group__CUDART__EVENT) are used. For many pieces of code, an event lists are used to record each piece. ```c++ class Event { @@ -66,11 +66,11 @@ struct EventList { }; ``` -As mentioned above, there is no need to record the timeline when disabling the profiler. So there is a global state to enable or disable the profiler. +As mentioned above, there is no need to record the timeline when disabling the profiler. So there is a global state to enable or disable the profiler. ```c++ enum ProfilerState { - kDisabled, + kDisabled, kCPU, kCUDA }; From 27533b64237528e0de0166b45a322d4ab6fee276 Mon Sep 17 00:00:00 2001 From: Yu Yang Date: Wed, 4 Apr 2018 12:56:32 +0800 Subject: [PATCH 49/58] Fix Leaf Ops in Graph All leaves must be variables. When all variables are ready, the execution will be completed. If a operator has no output, the `Op::Run` might not be started when the execution of graph has been complete. --- .../framework/details/multi_devices_graph_builder.cc | 8 ++++++++ paddle/fluid/framework/details/ssa_graph_builder.cc | 11 +++++++++++ paddle/fluid/framework/details/ssa_graph_builder.h | 8 +++++--- .../framework/details/threaded_ssa_graph_executor.cc | 8 ++++++-- 4 files changed, 30 insertions(+), 5 deletions(-) diff --git a/paddle/fluid/framework/details/multi_devices_graph_builder.cc b/paddle/fluid/framework/details/multi_devices_graph_builder.cc index c277bd7cb6..128a5344fb 100644 --- a/paddle/fluid/framework/details/multi_devices_graph_builder.cc +++ b/paddle/fluid/framework/details/multi_devices_graph_builder.cc @@ -21,6 +21,9 @@ #include "paddle/fluid/framework/details/nccl_all_reduce_op_handle.h" #endif +#include +#include + namespace paddle { namespace framework { namespace details { @@ -168,6 +171,11 @@ std::unique_ptr MultiDevSSAGraphBuilder::Build( */ PolishGraphToSupportDataHazards(&result); + /* + * Only variables should be the leaves of graph. + */ + AddOutputToLeafOps(&result); + if (VLOG_IS_ON(10)) { std::ostringstream sout; PrintGraphviz(*graph, sout); diff --git a/paddle/fluid/framework/details/ssa_graph_builder.cc b/paddle/fluid/framework/details/ssa_graph_builder.cc index 361ba6d397..0a4febd22f 100644 --- a/paddle/fluid/framework/details/ssa_graph_builder.cc +++ b/paddle/fluid/framework/details/ssa_graph_builder.cc @@ -136,6 +136,17 @@ void SSAGraphBuilder::PrintGraphviz(const SSAGraph &graph, std::ostream &sout) { sout << "}\n"; } + +void SSAGraphBuilder::AddOutputToLeafOps(SSAGraph *graph) { + for (auto &op : graph->ops_) { + if (!op->outputs_.empty()) { + continue; + } + auto *dummy_leaf = new DummyVarHandle(); + graph->dep_vars_.emplace(dummy_leaf); + op->AddOutput(dummy_leaf); + } +} } // namespace details } // namespace framework } // namespace paddle diff --git a/paddle/fluid/framework/details/ssa_graph_builder.h b/paddle/fluid/framework/details/ssa_graph_builder.h index bf20e7164a..be1f0460e4 100644 --- a/paddle/fluid/framework/details/ssa_graph_builder.h +++ b/paddle/fluid/framework/details/ssa_graph_builder.h @@ -14,13 +14,13 @@ #pragma once +#include +#include + #include "paddle/fluid/framework/details/ssa_graph.h" #include "paddle/fluid/framework/program_desc.h" #include "paddle/fluid/platform/place.h" -#include -#include - namespace paddle { namespace framework { namespace details { @@ -52,6 +52,8 @@ class SSAGraphBuilder { const std::string &each_var_name, const platform::Place &place, size_t place_offset); + static void AddOutputToLeafOps(SSAGraph *graph); + static void PrintGraphviz(const SSAGraph &graph, std::ostream &sout); }; } // namespace details diff --git a/paddle/fluid/framework/details/threaded_ssa_graph_executor.cc b/paddle/fluid/framework/details/threaded_ssa_graph_executor.cc index 1f96b9dc62..596e573186 100644 --- a/paddle/fluid/framework/details/threaded_ssa_graph_executor.cc +++ b/paddle/fluid/framework/details/threaded_ssa_graph_executor.cc @@ -87,7 +87,6 @@ FeedFetchList ThreadedSSAGraphExecutor::Run( // Step 2. Insert FetchOps std::vector> fetch_ops; - std::vector dummy_vars; FeedFetchList fetch_data(fetch_tensors.size()); std::unordered_map> fetched_vars; @@ -101,13 +100,13 @@ FeedFetchList ThreadedSSAGraphExecutor::Run( } } + std::unordered_set> fetch_dependencies; for (size_t i = 0; i < fetch_tensors.size(); ++i) { auto &var_name = fetch_tensors[i]; auto &vars = fetched_vars.at(var_name); auto *op = new FetchOpHandle(&fetch_data, i, &local_scopes_); fetch_ops.emplace_back(op); - // FIXME: Use new device context for (auto &p : places_) { op->dev_ctxes_[p] = fetch_ctxs_.Get(p); } @@ -115,6 +114,11 @@ FeedFetchList ThreadedSSAGraphExecutor::Run( for (auto *var : vars) { op->AddInput(var); } + + auto *fetch_dummy = new DummyVarHandle(); + op->AddOutput(fetch_dummy); + fetch_dependencies.emplace(fetch_dummy); + InsertPendingVar(*fetch_dummy); InsertPendingOp(*op); } From 4bbfa9eccb726071f55c14263fd62812cc3f746b Mon Sep 17 00:00:00 2001 From: Xin Pan Date: Tue, 3 Apr 2018 23:14:13 -0700 Subject: [PATCH 50/58] Add feed to ParallelExecutor --- doc/fluid/api/gen_doc.sh | 0 paddle/fluid/framework/lod_tensor.h | 1 + paddle/fluid/framework/parallel_executor.cc | 21 +++++- paddle/fluid/framework/parallel_executor.h | 6 +- python/paddle/fluid/parallel_executor.py | 38 +++++++--- .../tests/unittests/test_parallel_executor.py | 72 ++++++++++++------- 6 files changed, 99 insertions(+), 39 deletions(-) mode change 100755 => 100644 doc/fluid/api/gen_doc.sh diff --git a/doc/fluid/api/gen_doc.sh b/doc/fluid/api/gen_doc.sh old mode 100755 new mode 100644 diff --git a/paddle/fluid/framework/lod_tensor.h b/paddle/fluid/framework/lod_tensor.h index dee505fee0..4f130d2659 100644 --- a/paddle/fluid/framework/lod_tensor.h +++ b/paddle/fluid/framework/lod_tensor.h @@ -142,6 +142,7 @@ class LoDTensor : public Tensor { return (lod_)[level].size() - 1; } + // Split LoDTensor and copy to each place specified in places. std::vector SplitLoDTensor( const std::vector places) const; diff --git a/paddle/fluid/framework/parallel_executor.cc b/paddle/fluid/framework/parallel_executor.cc index 1788514324..7be93fa600 100644 --- a/paddle/fluid/framework/parallel_executor.cc +++ b/paddle/fluid/framework/parallel_executor.cc @@ -150,13 +150,30 @@ void ParallelExecutor::BCastParamsToGPUs( #endif } -void ParallelExecutor::Run(const std::vector &fetch_tensors, - const std::string &fetched_var_name) { +void ParallelExecutor::Run( + const std::vector &fetch_tensors, + const std::string &fetched_var_name, + const std::unordered_map &feed_tensors) { platform::RecordBlock b(0); + SplitTensorToPlaces(feed_tensors); auto fetch_data = member_->executor_->Run(fetch_tensors); *member_->global_scope_->Var(fetched_var_name)->GetMutable() = fetch_data; } +void ParallelExecutor::SplitTensorToPlaces( + const std::unordered_map &feed_tensors) { + for (auto it : feed_tensors) { + auto lod_tensors = it.second.SplitLoDTensor(member_->places_); + for (size_t j = 0; j < member_->places_.size(); ++j) { + // TODO(panxy0718): Do I need to delete this var? + member_->local_scopes_[j] + ->Var(it.first) + ->GetMutable() + ->ShareDataWith(lod_tensors[j]); + } + } +} + } // namespace framework } // namespace paddle diff --git a/paddle/fluid/framework/parallel_executor.h b/paddle/fluid/framework/parallel_executor.h index 964b476234..c7c58b2b80 100644 --- a/paddle/fluid/framework/parallel_executor.h +++ b/paddle/fluid/framework/parallel_executor.h @@ -42,9 +42,13 @@ class ParallelExecutor { bool allow_op_delay); void Run(const std::vector& fetch_tensors, - const std::string& fetched_var_name = "fetched_var"); + const std::string& fetched_var_name, + const std::unordered_map& feed_tensors); private: + void SplitTensorToPlaces( + const std::unordered_map& feed_tensors); + ParallelExecutorPrivate* member_; void BCastParamsToGPUs(const ProgramDesc& startup_program) const; diff --git a/python/paddle/fluid/parallel_executor.py b/python/paddle/fluid/parallel_executor.py index a2c830b3c9..4153049c05 100644 --- a/python/paddle/fluid/parallel_executor.py +++ b/python/paddle/fluid/parallel_executor.py @@ -26,25 +26,29 @@ class ParallelExecutor(object): use_cuda, num_threads=None, allow_op_delay=False): - places = [] + self._places = [] + self._act_places = [] if use_cuda: for i in xrange(core.get_cuda_device_count()): p = core.Place() - p.set_place(core.CUDAPlace(i)) - places.append(p) + self._act_places.append(core.CUDAPlace(i)) + p.set_place(self._act_places[-1]) + self._places.append(p) else: for i in xrange(multiprocessing.cpu_count()): p = core.Place() - p.set_place(core.CPUPlace()) - places.append(p) + self._act_places.append(core.CPUPlace(i)) + p.set_place(self._act_places[-1]) + self._places.append(p) + assert self._places, "no place for execution" if num_threads is None: if use_cuda: # Experiments on se-resnext shows that too many threads hurt # performance. Worth tunning for other models in the future. - num_threads = len(places) + num_threads = len(self._places) else: - min(len(places) * 2, multiprocessing.cpu_count()) + min(len(self._places) * 2, multiprocessing.cpu_count()) startup = framework.default_startup_program() main = framework.default_main_program() @@ -53,7 +57,7 @@ class ParallelExecutor(object): self.executor = core.ParallelExecutor( num_threads, True if use_cuda else False, # use_event - places, + self._places, set([ p.name for p in main.global_block().iter_parameters() if not p.stop_gradient @@ -65,8 +69,22 @@ class ParallelExecutor(object): allow_op_delay) self.scope = scope - def run(self, fetch_list): + def run(self, fetch_list, feed_dict={}): + """ + :param fetch_list: A list of variable names that will be fetched. + :param feed_dict: A dict mapping for feed variable name to LoDTensor + or numpy array. + :return: fetched value list. + """ + feed_tensor_dict = {} + for i, feed_name in enumerate(feed_dict): + feed_tensor = feed_dict[feed_name] + if not isinstance(feed_tensor, core.LoDTensor): + feed_tensor = core.LoDTensor() + feed_tensor.set(feed_dict[feed_name], self._act_places[0]) + feed_tensor_dict[feed_name] = feed_tensor + fetch_var_name = '@FETCHED_VAR_NAME@' - self.executor.run(fetch_list, fetch_var_name) + self.executor.run(fetch_list, fetch_var_name, feed_tensor_dict) arr = self.scope.find_var(fetch_var_name).get_lod_tensor_array() return [arr[i] for i in range(len(arr))] diff --git a/python/paddle/fluid/tests/unittests/test_parallel_executor.py b/python/paddle/fluid/tests/unittests/test_parallel_executor.py index a79e4b3e18..0cbef82e33 100644 --- a/python/paddle/fluid/tests/unittests/test_parallel_executor.py +++ b/python/paddle/fluid/tests/unittests/test_parallel_executor.py @@ -21,13 +21,17 @@ import paddle.dataset.mnist as mnist import paddle.dataset.wmt16 as wmt16 -def simple_fc_net(): - reader = fluid.layers.open_recordio_file( - filename='./mnist.recordio', - shapes=[[-1, 784], [-1, 1]], - lod_levels=[0, 0], - dtypes=['float32', 'int64']) - img, label = fluid.layers.read_file(reader) +def simple_fc_net(use_feed): + if use_feed: + img = fluid.layers.data(name='image', shape=[784], dtype='float32') + label = fluid.layers.data(name='label', shape=[1], dtype='int64') + else: + reader = fluid.layers.open_recordio_file( + filename='./mnist.recordio', + shapes=[[-1, 784], [-1, 1]], + lod_levels=[0, 0], + dtypes=['float32', 'int64']) + img, label = fluid.layers.read_file(reader) hidden = img for _ in xrange(4): hidden = fluid.layers.fc( @@ -42,13 +46,18 @@ def simple_fc_net(): return loss -def fc_with_batchnorm(): - reader = fluid.layers.open_recordio_file( - filename='./mnist.recordio', - shapes=[[-1, 784], [-1, 1]], - lod_levels=[0, 0], - dtypes=['float32', 'int64']) - img, label = fluid.layers.read_file(reader) +def fc_with_batchnorm(use_feed): + if use_feed: + img = fluid.layers.data(name='image', shape=[784], dtype='float32') + label = fluid.layers.data(name='label', shape=[1], dtype='int64') + else: + reader = fluid.layers.open_recordio_file( + filename='./mnist.recordio', + shapes=[[-1, 784], [-1, 1]], + lod_levels=[0, 0], + dtypes=['float32', 'int64']) + img, label = fluid.layers.read_file(reader) + hidden = img for _ in xrange(1): hidden = fluid.layers.fc( @@ -135,7 +144,9 @@ def bottleneck_block(input, num_filters, stride, cardinality, reduction_ratio): return fluid.layers.elementwise_add(x=short, y=scale, act='relu') -def SE_ResNeXt152Small(batch_size=2): +def SE_ResNeXt152Small(batch_size=2, use_feed=False): + assert not use_feed, "SE_ResNeXt doesn't support feed yet" + img = fluid.layers.fill_constant( shape=[batch_size, 3, 224, 224], dtype='float32', value=0.0) label = fluid.layers.fill_constant( @@ -185,30 +196,28 @@ class TestParallelExecutorBase(unittest.TestCase): memory_opt=True, iter=10, batch_size=None, - allow_op_delay=False): + allow_op_delay=False, + feed_dict={}): main = fluid.Program() startup = fluid.Program() with fluid.program_guard(main, startup): - loss = method() + loss = method(use_feed=len(feed_dict) > 0) adam = fluid.optimizer.Adam() adam.minimize(loss) if memory_opt: fluid.memory_optimize(main) - exe = fluid.ParallelExecutor( - loss_name=loss.name, - use_cuda=True, - allow_op_delay=allow_op_delay) + exe = fluid.ParallelExecutor(loss_name=loss.name, use_cuda=True) if batch_size is not None: batch_size *= fluid.core.get_cuda_device_count() begin = time.time() - first_loss, = exe.run([loss.name]) + first_loss, = exe.run([loss.name], feed_dict=feed_dict) first_loss = numpy.array(first_loss) for i in xrange(iter): - exe.run([]) + exe.run([], feed_dict=feed_dict) - last_loss, = exe.run([loss.name]) + last_loss, = exe.run([loss.name], feed_dict=feed_dict) end = time.time() if batch_size is not None: @@ -242,9 +251,19 @@ class TestMNIST(TestParallelExecutorBase): self.check_network_convergence(simple_fc_net) self.check_network_convergence(simple_fc_net, allow_op_delay=True) + img = numpy.zeros(shape=[32, 784], dtype='float32') + label = numpy.ones(shape=[32, 1], dtype='int64') + self.check_network_convergence( + simple_fc_net, feed_dict={"image": img, + "label": label}) + def test_batchnorm_fc(self): self.check_network_convergence(fc_with_batchnorm) - self.check_network_convergence(fc_with_batchnorm, allow_op_delay=True) + img = numpy.zeros(shape=[32, 784], dtype='float32') + label = numpy.ones(shape=[32, 1], dtype='int64') + self.check_network_convergence( + fc_with_batchnorm, feed_dict={"image": img, + "label": label}) class TestResnet(TestParallelExecutorBase): @@ -400,7 +419,8 @@ def prepare_batch_input(insts, src_pad_idx, trg_pad_idx, n_head): import transformer_model -def transformer(): +def transformer(use_feed): + assert not use_feed, "transfomer doesn't support feed yet" return transformer_model.transformer( ModelHyperParams.src_vocab_size + 1, ModelHyperParams.trg_vocab_size + 1, ModelHyperParams.max_length + 1, From 4d1fe5c3ec6b8f346ac1d398b76e97133b0d6b1e Mon Sep 17 00:00:00 2001 From: weixing02 <564445201@qq.com> Date: Wed, 4 Apr 2018 14:37:02 +0800 Subject: [PATCH 51/58] Fix github links error --- doc/fluid/design/algorithm/parameter_average.md | 4 ++-- doc/fluid/design/concurrent/channel.md | 4 ++-- doc/fluid/design/concurrent/concurrent_programming.md | 4 ++++ doc/fluid/design/concurrent/select_op.md | 2 +- .../design/dist_train/distributed_architecture.md | 10 +++++----- doc/fluid/design/dist_train/multi_cpu.md | 4 ++-- doc/fluid/design/dist_train/parameter_server.md | 7 +++---- doc/fluid/design/dynamic_rnn/rnn.md | 2 +- doc/fluid/design/modules/batch_norm_op.md | 4 ++-- doc/fluid/design/modules/regularization.md | 10 +++++----- doc/fluid/design/network/deep_speech_2.md | 4 ++-- doc/fluid/design/network/sequence_decoder.md | 2 +- doc/fluid/design/others/gan_api.md | 4 ++-- doc/fluid/dev/releasing_process.md | 2 +- doc/fluid/howto/performance/profiler.md | 2 +- 15 files changed, 34 insertions(+), 31 deletions(-) diff --git a/doc/fluid/design/algorithm/parameter_average.md b/doc/fluid/design/algorithm/parameter_average.md index 70c5cdecad..940d37fb31 100644 --- a/doc/fluid/design/algorithm/parameter_average.md +++ b/doc/fluid/design/algorithm/parameter_average.md @@ -5,10 +5,10 @@ In a large scale machine learning setup where the size of the training data is h Polyak and Juditsky (1992) showed that the test performance of simple average of parameters obtained by Stochastic Gradient Descent (SGD) is as good as that of parameter values that are obtained by training the model over and over again, over the training dataset. -Hence, to accelerate the speed of Stochastic Gradient Descent, Averaged Stochastic Gradient Descent (ASGD) was proposed in Polyak and Juditsky (1992). For ASGD, the running average of parameters obtained by SGD, is used as the estimator for
. The averaging is done as follows: +Hence, to accelerate the speed of Stochastic Gradient Descent, Averaged Stochastic Gradient Descent (ASGD) was proposed in Polyak and Juditsky (1992). For ASGD, the running average of parameters obtained by SGD, is used as the estimator for
. The averaging is done as follows:

-
+

We propose averaging for any optimizer similar to how ASGD performs it, as mentioned above. diff --git a/doc/fluid/design/concurrent/channel.md b/doc/fluid/design/concurrent/channel.md index a5cf17faa8..df67438bcc 100644 --- a/doc/fluid/design/concurrent/channel.md +++ b/doc/fluid/design/concurrent/channel.md @@ -114,13 +114,13 @@ current thread under two conditions: #### Channel Send

-
+

#### Channel Receive

-
+

## Limitations and Considerations diff --git a/doc/fluid/design/concurrent/concurrent_programming.md b/doc/fluid/design/concurrent/concurrent_programming.md index 6460216606..1859f983e9 100644 --- a/doc/fluid/design/concurrent/concurrent_programming.md +++ b/doc/fluid/design/concurrent/concurrent_programming.md @@ -23,21 +23,25 @@ The following table compares concepts in Fluid and Go + + + +
user-defined functions layers
control-flow and built-in functions intrinsics/operators
goroutines, channels class ThreadPool
runtime class Executor
diff --git a/doc/fluid/design/concurrent/select_op.md b/doc/fluid/design/concurrent/select_op.md index 98dd94a2be..4fcae57cc7 100644 --- a/doc/fluid/design/concurrent/select_op.md +++ b/doc/fluid/design/concurrent/select_op.md @@ -254,7 +254,7 @@ only one case will be executed. ### select_op flow

-
+

The select algorithm is inspired by golang's select routine. Please refer to diff --git a/doc/fluid/design/dist_train/distributed_architecture.md b/doc/fluid/design/dist_train/distributed_architecture.md index 3cd4750bce..229cb47c17 100644 --- a/doc/fluid/design/dist_train/distributed_architecture.md +++ b/doc/fluid/design/dist_train/distributed_architecture.md @@ -40,11 +40,11 @@ computation is only specified in Python code which sits outside of PaddlePaddle, Similar to how a compiler uses an intermediate representation (IR) so that the programmer does not need to manually optimize their code for most of the cases, we can have an intermediate representation in PaddlePaddle as well. The compiler optimizes the IR as follows: - + PaddlePaddle can support model parallelism by converting the IR so that the user no longer needs to manually perform the computation and operations in the Python component: - + The IR for PaddlePaddle after refactoring is called a `Block`, it specifies the computation dependency graph and the variables used in the computation. @@ -60,7 +60,7 @@ For a detailed explanation, refer to this document - The revamped distributed training architecture can address the above discussed limitations. Below is the illustration of how it does so: - + The major components are: *Python API*, *Distribute Transpiler* and *Remote Executor*. @@ -152,7 +152,7 @@ for data in train_reader(): `JobDesc` object describe the distributed job resource specification to run on Cluster environment. - + `RemoteExecutor.run` sends the `ProgramDesc` and [TrainingJob](https://github.com/PaddlePaddle/cloud/blob/unreleased-tpr/doc/autoscale/README.md#training-job-resource) @@ -171,7 +171,7 @@ In the future, a more general placement algorithm should be implemented, which m The local training architecture will be the same as the distributed training architecture, the difference is that everything runs locally, and there is just one PaddlePaddle runtime: - + ### Training Data diff --git a/doc/fluid/design/dist_train/multi_cpu.md b/doc/fluid/design/dist_train/multi_cpu.md index 586612622a..38222d0830 100644 --- a/doc/fluid/design/dist_train/multi_cpu.md +++ b/doc/fluid/design/dist_train/multi_cpu.md @@ -8,11 +8,11 @@ Op graph to a multi-CPU Op graph, and run `ParallelDo` Op to run the graph. ## Transpiler - + After converted: - + ## Implement diff --git a/doc/fluid/design/dist_train/parameter_server.md b/doc/fluid/design/dist_train/parameter_server.md index 179b5f8c29..73c85da5e8 100644 --- a/doc/fluid/design/dist_train/parameter_server.md +++ b/doc/fluid/design/dist_train/parameter_server.md @@ -41,11 +41,11 @@ We will need these OPs: *Send*, *Recv*, *Enqueue*, *Dequeue*. Below is an example of converting the user defined graph to the subgraphs for the trainer and the parameter server: - + After converting: - + 1. The parameter variable W and its optimizer program are placed on the parameter server. 1. Operators are added to the program. @@ -69,8 +69,7 @@ In Fluid, we introduce [SelectedRows](../selected_rows.md) to represent a list o non-zero gradient data. So when we do parameter optimization both locally and remotely, we only need to send those non-zero rows to the optimizer operators: - - + ### Benefits - Model parallelism becomes easier to implement: it is an extension to diff --git a/doc/fluid/design/dynamic_rnn/rnn.md b/doc/fluid/design/dynamic_rnn/rnn.md index 9a61cd788a..7b61b050f6 100644 --- a/doc/fluid/design/dynamic_rnn/rnn.md +++ b/doc/fluid/design/dynamic_rnn/rnn.md @@ -5,7 +5,7 @@ This document describes the RNN (Recurrent Neural Network) operator and how it i ## RNN Algorithm Implementation

- +

The above diagram shows an RNN unrolled into a full network. diff --git a/doc/fluid/design/modules/batch_norm_op.md b/doc/fluid/design/modules/batch_norm_op.md index 211e060cc1..e451ffcc73 100644 --- a/doc/fluid/design/modules/batch_norm_op.md +++ b/doc/fluid/design/modules/batch_norm_op.md @@ -66,7 +66,7 @@ As most C++ operators do, `batch_norm_op` is defined by inputs, outputs, attribu The following graph showes the training computational process of `batch_norm_op`: - + cudnn provides APIs to finish the whole series of computation, we can use them in our GPU kernel. @@ -124,7 +124,7 @@ for pass_id in range(PASS_NUM): `is_infer` is an attribute. Once an operator is created, its attributes can not be changed. It suggests us that we shall maintain two `batch_norm_op` in the model, one's `is_infer` is `True`(we call it `infer_batch_norm_op`) and the other one's is `False`(we call it `train_batch_norm_op`). They share all parameters and variables, but be placed in two different branches. That is to say, if a network contains a `batch_norm_op`, it will fork into two branches, one go through `train_batch_norm_op` and the other one go through `infer_batch_norm_op`:
- +
Just like what is shown in the above graph, the net forks before `batch_norm_op` and will never merge again. All the operators after `batch_norm_op` will duplicate. diff --git a/doc/fluid/design/modules/regularization.md b/doc/fluid/design/modules/regularization.md index ffc3199a84..8cd5ff71d1 100644 --- a/doc/fluid/design/modules/regularization.md +++ b/doc/fluid/design/modules/regularization.md @@ -6,17 +6,17 @@ A central problem in machine learning is how to design an algorithm that will pe ### Parameter Norm Penalties Most common regularization approaches in deep learning are based on limiting the capacity of the models by adding a parameter norm penalty to the objective function `J`. This is given as follows: -
+
The parameter `alpha` is a hyperparameter that weights the relative contribution of the norm penalty term, `omega`, relative to the standard objective function `J`. The most commonly used norm penalties are the L2 norm penalty and the L1 norm penalty. These are given as follows: ##### L2 Regularization: -
+
##### L1 Regularization -
+
A much more detailed mathematical background of regularization can be found [here](http://www.deeplearningbook.org/contents/regularization.html). @@ -40,11 +40,11 @@ The idea of building ops for regularization is in sync with the refactored Paddl Below is an example of a really simple feed forward neural network. -
+
The Python API will modify this computation graph to add regularization operators. The modified computation graph will look as follows: -
+
    ### Python API implementation for Regularization diff --git a/doc/fluid/design/network/deep_speech_2.md b/doc/fluid/design/network/deep_speech_2.md index d3906143d3..f32a5b7e8a 100644 --- a/doc/fluid/design/network/deep_speech_2.md +++ b/doc/fluid/design/network/deep_speech_2.md @@ -116,7 +116,7 @@ The classical DS2 network contains 15 layers (from bottom to top): - **One** CTC-loss layer
-
+
Figure 1. Archetecture of Deep Speech 2 Network.
@@ -208,7 +208,7 @@ TODO by Assignees ### Beam Search with CTC and LM
-
+
Figure 2. Algorithm for CTC Beam Search Decoder.
diff --git a/doc/fluid/design/network/sequence_decoder.md b/doc/fluid/design/network/sequence_decoder.md index a56c1b5bca..f13d30ca9f 100644 --- a/doc/fluid/design/network/sequence_decoder.md +++ b/doc/fluid/design/network/sequence_decoder.md @@ -199,7 +199,7 @@ Packing the `selected_generation_scores` will get a `LoDTensor`, and each tail i ## LoD and shape changes during decoding

- +

According to the image above, the only phase that changes the LoD is beam search. diff --git a/doc/fluid/design/others/gan_api.md b/doc/fluid/design/others/gan_api.md index 8cc7930470..7167470088 100644 --- a/doc/fluid/design/others/gan_api.md +++ b/doc/fluid/design/others/gan_api.md @@ -7,14 +7,14 @@ It applies several important concepts in machine learning system design, includi In our GAN design, we wrap it as a user-friendly easily customized python API to design different models. We take the conditional DC-GAN (Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks [https://arxiv.org/abs/1511.06434]) as an example due to its good performance on image generation.

-
+
Figure 1. The overall running logic of GAN. The black solid arrows indicate the forward pass; the green dashed arrows indicate the backward pass of generator training; the red dashed arrows indicate the backward pass of the discriminator training. The BP pass of the green (red) arrow should only update the parameters in the green (red) boxes. The diamonds indicate the data providers. d\_loss and g\_loss marked in red and green are the two targets we would like to run.

The operators, layers and functions required/optional to build a GAN demo is summarized in https://github.com/PaddlePaddle/Paddle/issues/4563.

-
+
Figure 2. Photo borrowed from the original DC-GAN paper.

diff --git a/doc/fluid/dev/releasing_process.md b/doc/fluid/dev/releasing_process.md index d459b54e09..c5943ccd81 100644 --- a/doc/fluid/dev/releasing_process.md +++ b/doc/fluid/dev/releasing_process.md @@ -37,7 +37,7 @@ PaddlePaddle每次发新的版本，遵循以下流程: 可以在此页面的"Artifacts"下拉框中找到生成的3个二进制文件，分别对应CAPI，`cp27m`和`cp27mu`的版本。然后按照上述的方法 使用`twine`工具上传即可。 - + * 注：CI环境使用 https://github.com/PaddlePaddle/buildtools 这里的DockerImage作为编译环境以支持更多的Linux 发型版，如果需要手动编译，也可以使用这些镜像。这些镜像也可以从 https://hub.docker.com/r/paddlepaddle/paddle_manylinux_devel/tags/ 下载得到。 diff --git a/doc/fluid/howto/performance/profiler.md b/doc/fluid/howto/performance/profiler.md index fe05534be7..ee96e7c74c 100644 --- a/doc/fluid/howto/performance/profiler.md +++ b/doc/fluid/howto/performance/profiler.md @@ -23,7 +23,7 @@ But how to record the time for the mixed C++ and CUDA program? There many C++ A The overall flow is shown as the following figure. -
+
### Event From 39277e9282294dc18b4c2b93aa000a15b58bea5f Mon Sep 17 00:00:00 2001 From: typhoonzero Date: Wed, 4 Apr 2018 14:55:28 +0800 Subject: [PATCH 52/58] fix transpiler condition op in optimize --- python/paddle/fluid/distribute_transpiler.py | 32 ++++++++++++++------ 1 file changed, 22 insertions(+), 10 deletions(-) diff --git a/python/paddle/fluid/distribute_transpiler.py b/python/paddle/fluid/distribute_transpiler.py index 9311fc9904..6d76c1a8d1 100644 --- a/python/paddle/fluid/distribute_transpiler.py +++ b/python/paddle/fluid/distribute_transpiler.py @@ -408,11 +408,16 @@ class DistributeTranspiler: pserver_vars = pserver_program.global_block().vars created_var_map = dict() for _, var in pserver_vars.iteritems(): - tmpvar = s_prog.global_block().create_var( - name=var.name, - persistable=var.persistable, - dtype=var.dtype, - shape=var.shape) + if var.type == core.VarDesc.VarType.STEP_SCOPES: + tmpvar = s_prog.global_block().create_var( + name=var.name, persistable=var.persistable, type=var.type) + else: + tmpvar = s_prog.global_block().create_var( + name=var.name, + persistable=var.persistable, + type=var.type, + dtype=var.dtype, + shape=var.shape) created_var_map[var.name] = tmpvar # 2. rename op outputs @@ -708,11 +713,18 @@ class DistributeTranspiler: varlist = [varlist] for var in varlist: - program.global_block().create_var( - name=var.name, - persistable=var.persistable, - dtype=var.dtype, - shape=var.shape) + print("##### deal var: ", var) + if var.type == core.VarDesc.VarType.STEP_SCOPES: + program.global_block().create_var( + name=var.name, + persistable=var.persistable, + type=var.type) + else: + program.global_block().create_var( + name=var.name, + persistable=var.persistable, + dtype=var.dtype, + shape=var.shape) optimize_block.append_op( type=opt_op.type, From e0b396e7ba80738fe8c87edb80e5743b8d692cb7 Mon Sep 17 00:00:00 2001 From: typhoonzero Date: Wed, 4 Apr 2018 15:14:48 +0800 Subject: [PATCH 53/58] update by comment --- python/paddle/fluid/distribute_transpiler.py | 14 ++------------ 1 file changed, 2 insertions(+), 12 deletions(-) diff --git a/python/paddle/fluid/distribute_transpiler.py b/python/paddle/fluid/distribute_transpiler.py index 6d76c1a8d1..134dbe573a 100644 --- a/python/paddle/fluid/distribute_transpiler.py +++ b/python/paddle/fluid/distribute_transpiler.py @@ -412,12 +412,7 @@ class DistributeTranspiler: tmpvar = s_prog.global_block().create_var( name=var.name, persistable=var.persistable, type=var.type) else: - tmpvar = s_prog.global_block().create_var( - name=var.name, - persistable=var.persistable, - type=var.type, - dtype=var.dtype, - shape=var.shape) + tmpvar = s_prog.global_block().clone_variable(var) created_var_map[var.name] = tmpvar # 2. rename op outputs @@ -713,18 +708,13 @@ class DistributeTranspiler: varlist = [varlist] for var in varlist: - print("##### deal var: ", var) if var.type == core.VarDesc.VarType.STEP_SCOPES: program.global_block().create_var( name=var.name, persistable=var.persistable, type=var.type) else: - program.global_block().create_var( - name=var.name, - persistable=var.persistable, - dtype=var.dtype, - shape=var.shape) + program.global_block().clone_variable(var) optimize_block.append_op( type=opt_op.type, From a16a872783d52d9ba7d32d53848e95cc4ccaefd6 Mon Sep 17 00:00:00 2001 From: typhoonzero Date: Wed, 4 Apr 2018 15:56:21 +0800 Subject: [PATCH 54/58] update --- python/paddle/fluid/distribute_transpiler.py | 14 ++----------- python/paddle/fluid/framework.py | 21 +++++++++++++------- 2 files changed, 16 insertions(+), 19 deletions(-) diff --git a/python/paddle/fluid/distribute_transpiler.py b/python/paddle/fluid/distribute_transpiler.py index 134dbe573a..31bedb592f 100644 --- a/python/paddle/fluid/distribute_transpiler.py +++ b/python/paddle/fluid/distribute_transpiler.py @@ -408,11 +408,7 @@ class DistributeTranspiler: pserver_vars = pserver_program.global_block().vars created_var_map = dict() for _, var in pserver_vars.iteritems(): - if var.type == core.VarDesc.VarType.STEP_SCOPES: - tmpvar = s_prog.global_block().create_var( - name=var.name, persistable=var.persistable, type=var.type) - else: - tmpvar = s_prog.global_block().clone_variable(var) + tmpvar = s_prog.global_block().clone_variable(var) created_var_map[var.name] = tmpvar # 2. rename op outputs @@ -708,13 +704,7 @@ class DistributeTranspiler: varlist = [varlist] for var in varlist: - if var.type == core.VarDesc.VarType.STEP_SCOPES: - program.global_block().create_var( - name=var.name, - persistable=var.persistable, - type=var.type) - else: - program.global_block().clone_variable(var) + program.global_block().clone_variable(var) optimize_block.append_op( type=opt_op.type, diff --git a/python/paddle/fluid/framework.py b/python/paddle/fluid/framework.py index e15456bfc0..39d4017861 100644 --- a/python/paddle/fluid/framework.py +++ b/python/paddle/fluid/framework.py @@ -946,13 +946,20 @@ class Block(object): The new variable cloned from 'var' in current block. """ assert isinstance(var, Variable) - return self.create_var( - name=var.name, - shape=var.shape, - dtype=var.dtype, - type=var.type, - lod_level=var.lod_level, - persistable=True) + ret_var = None + # make STEP_SCOPES var can be safely cloned. + if var.type == core.VarDesc.VarType.STEP_SCOPES: + ret_var = self.create_var( + name=var.name, persistable=var.persistable, type=var.type) + else: + ret_var = self.create_var( + name=var.name, + shape=var.shape, + dtype=var.dtype, + type=var.type, + lod_level=var.lod_level, + persistable=True) + return ret_var class Program(object): From a84a580e65c7008f7e4aa03b5e93057ac65e988a Mon Sep 17 00:00:00 2001 From: qingqing01 Date: Wed, 4 Apr 2018 16:22:28 +0800 Subject: [PATCH 55/58] Add CUDA kernel for prior_box_op. (#9553) --- paddle/fluid/operators/prior_box_op.cc | 7 +- paddle/fluid/operators/prior_box_op.cu | 167 ++++++++++++++++++ paddle/fluid/operators/prior_box_op.h | 45 ++--- .../tests/unittests/test_prior_box_op.py | 56 +++--- 4 files changed, 207 insertions(+), 68 deletions(-) create mode 100644 paddle/fluid/operators/prior_box_op.cu diff --git a/paddle/fluid/operators/prior_box_op.cc b/paddle/fluid/operators/prior_box_op.cc index c22a55bce2..82e54139c8 100644 --- a/paddle/fluid/operators/prior_box_op.cc +++ b/paddle/fluid/operators/prior_box_op.cc @@ -73,7 +73,7 @@ class PriorBoxOp : public framework::OperatorWithKernel { const framework::ExecutionContext& ctx) const override { return framework::OpKernelType( framework::ToDataType(ctx.Input("Input")->type()), - platform::CPUPlace()); + ctx.device_context()); } }; @@ -171,6 +171,5 @@ namespace ops = paddle::operators; REGISTER_OPERATOR(prior_box, ops::PriorBoxOp, ops::PriorBoxOpMaker, paddle::framework::EmptyGradOpMaker); -REGISTER_OP_CPU_KERNEL( - prior_box, ops::PriorBoxOpKernel, - ops::PriorBoxOpKernel); +REGISTER_OP_CPU_KERNEL(prior_box, ops::PriorBoxOpKernel, + ops::PriorBoxOpKernel); diff --git a/paddle/fluid/operators/prior_box_op.cu b/paddle/fluid/operators/prior_box_op.cu new file mode 100644 index 0000000000..76bf2b3b7d --- /dev/null +++ b/paddle/fluid/operators/prior_box_op.cu @@ -0,0 +1,167 @@ +/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#include "paddle/fluid/operators/prior_box_op.h" + +namespace paddle { +namespace operators { + +template +__device__ inline T clip(T in) { + return min(max(in, 0.), 1.); +} + +template +__global__ void GenPriorBox(T* out, const T* aspect_ratios, const int height, + const int width, const int im_height, + const int im_width, const int as_num, + const T offset, const T step_width, + const T step_height, const T* min_sizes, + const T* max_sizes, const int min_num, + bool is_clip) { + int num_priors = max_sizes ? as_num * min_num + min_num : as_num * min_num; + int box_num = height * width * num_priors; + for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < box_num; + i += blockDim.x * gridDim.x) { + int h = i / (num_priors * width); + int w = (i / num_priors) % width; + int p = i % num_priors; + int m = max_sizes ? p / (as_num + 1) : p / as_num; + T cx = (w + offset) * step_width; + T cy = (h + offset) * step_height; + T bw, bh; + T min_size = min_sizes[m]; + if (max_sizes) { + int s = p % (as_num + 1); + if (s < as_num) { + T ar = aspect_ratios[s]; + bw = min_size * sqrt(ar) / 2.; + bh = min_size / sqrt(ar) / 2.; + } else { + T max_size = max_sizes[m]; + bw = sqrt(min_size * max_size) / 2.; + bh = bw; + } + } else { + int s = p % as_num; + T ar = aspect_ratios[s]; + bw = min_size * sqrt(ar) / 2.; + bh = min_size / sqrt(ar) / 2.; + } + T xmin = (cx - bw) / im_width; + T ymin = (cy - bh) / im_height; + T xmax = (cx + bw) / im_width; + T ymax = (cy + bh) / im_height; + out[i * 4] = is_clip ? clip(xmin) : xmin; + out[i * 4 + 1] = is_clip ? clip(ymin) : ymin; + out[i * 4 + 2] = is_clip ? clip(xmax) : xmax; + out[i * 4 + 3] = is_clip ? clip(ymax) : ymax; + } +} + +template +__global__ void SetVariance(T* out, const T* var, const int vnum, + const int num) { + for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < num; + i += blockDim.x * gridDim.x) { + out[i] = var[i % vnum]; + } +} + +template +class PriorBoxOpCUDAKernel : public framework::OpKernel { + public: + void Compute(const framework::ExecutionContext& ctx) const override { + auto* input = ctx.Input("Input"); + auto* image = ctx.Input("Image"); + auto* boxes = ctx.Output("Boxes"); + auto* vars = ctx.Output("Variances"); + + auto min_sizes = ctx.Attr>("min_sizes"); + auto max_sizes = ctx.Attr>("max_sizes"); + auto input_aspect_ratio = ctx.Attr>("aspect_ratios"); + auto variances = ctx.Attr>("variances"); + auto flip = ctx.Attr("flip"); + auto clip = ctx.Attr("clip"); + + std::vector aspect_ratios; + ExpandAspectRatios(input_aspect_ratio, flip, aspect_ratios); + + T step_w = static_cast(ctx.Attr("step_w")); + T step_h = static_cast(ctx.Attr("step_h")); + T offset = static_cast(ctx.Attr("offset")); + + auto im_width = image->dims()[3]; + auto im_height = image->dims()[2]; + + auto width = input->dims()[3]; + auto height = input->dims()[2]; + + T step_width, step_height; + if (step_w == 0 || step_h == 0) { + step_width = static_cast(im_width) / width; + step_height = static_cast(im_height) / height; + } else { + step_width = step_w; + step_height = step_h; + } + + int num_priors = aspect_ratios.size() * min_sizes.size(); + if (max_sizes.size() > 0) { + num_priors += max_sizes.size(); + } + int min_num = static_cast(min_sizes.size()); + int box_num = width * height * num_priors; + + int block = 512; + int grid = (box_num + block - 1) / block; + + auto stream = + ctx.template device_context().stream(); + + boxes->mutable_data(ctx.GetPlace()); + vars->mutable_data(ctx.GetPlace()); + + framework::Tensor r; + framework::TensorFromVector(aspect_ratios, ctx.device_context(), &r); + + framework::Tensor min; + framework::TensorFromVector(min_sizes, ctx.device_context(), &min); + + T* max_data = nullptr; + framework::Tensor max; + if (max_sizes.size() > 0) { + framework::TensorFromVector(max_sizes, ctx.device_context(), &max); + max_data = max.data(); + } + + GenPriorBox<<>>( + boxes->data(), r.data(), height, width, im_height, im_width, + aspect_ratios.size(), offset, step_width, step_height, min.data(), + max_data, min_num, clip); + + framework::Tensor v; + framework::TensorFromVector(variances, ctx.device_context(), &v); + grid = (box_num * 4 + block - 1) / block; + SetVariance<<>>(vars->data(), v.data(), + variances.size(), box_num * 4); + } +}; // namespace operators + +} // namespace operators +} // namespace paddle + +namespace ops = paddle::operators; +REGISTER_OP_CUDA_KERNEL(prior_box, ops::PriorBoxOpCUDAKernel, + ops::PriorBoxOpCUDAKernel); diff --git a/paddle/fluid/operators/prior_box_op.h b/paddle/fluid/operators/prior_box_op.h index 18bb2deb6b..1e4a12aac1 100644 --- a/paddle/fluid/operators/prior_box_op.h +++ b/paddle/fluid/operators/prior_box_op.h @@ -51,7 +51,7 @@ struct ClipFunctor { } }; -template +template class PriorBoxOpKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& ctx) const override { @@ -106,49 +106,24 @@ class PriorBoxOpKernel : public framework::OpKernel { int idx = 0; for (size_t s = 0; s < min_sizes.size(); ++s) { auto min_size = min_sizes[s]; - // first prior: aspect_ratio = 1, size = min_size - box_width = box_height = min_size / 2.; - // xmin - e_boxes(h, w, idx, 0) = (center_x - box_width) / img_width; - // ymin - e_boxes(h, w, idx, 1) = (center_y - box_height) / img_height; - // xmax - e_boxes(h, w, idx, 2) = (center_x + box_width) / img_width; - // ymax - e_boxes(h, w, idx, 3) = (center_y + box_height) / img_height; - - idx++; - if (max_sizes.size() > 0) { - auto max_size = max_sizes[s]; - // second prior: aspect_ratio = 1, - // size = sqrt(min_size * max_size) - box_width = box_height = sqrt(min_size * max_size) / 2.; - // xmin + // priors with different aspect ratios + for (size_t r = 0; r < aspect_ratios.size(); ++r) { + float ar = aspect_ratios[r]; + box_width = min_size * sqrt(ar) / 2.; + box_height = min_size / sqrt(ar) / 2.; e_boxes(h, w, idx, 0) = (center_x - box_width) / img_width; - // ymin e_boxes(h, w, idx, 1) = (center_y - box_height) / img_height; - // xmax e_boxes(h, w, idx, 2) = (center_x + box_width) / img_width; - // ymax e_boxes(h, w, idx, 3) = (center_y + box_height) / img_height; idx++; } - - // rest of priors - for (size_t r = 0; r < aspect_ratios.size(); ++r) { - float ar = aspect_ratios[r]; - if (fabs(ar - 1.) < 1e-6) { - continue; - } - box_width = min_size * sqrt(ar) / 2.; - box_height = min_size / sqrt(ar) / 2.; - // xmin + if (max_sizes.size() > 0) { + auto max_size = max_sizes[s]; + // square prior with size sqrt(minSize * maxSize) + box_width = box_height = sqrt(min_size * max_size) / 2.; e_boxes(h, w, idx, 0) = (center_x - box_width) / img_width; - // ymin e_boxes(h, w, idx, 1) = (center_y - box_height) / img_height; - // xmax e_boxes(h, w, idx, 2) = (center_x + box_width) / img_width; - // ymax e_boxes(h, w, idx, 3) = (center_y + box_height) / img_height; idx++; } diff --git a/python/paddle/fluid/tests/unittests/test_prior_box_op.py b/python/paddle/fluid/tests/unittests/test_prior_box_op.py index c21138c13e..bcbc02a2ba 100644 --- a/python/paddle/fluid/tests/unittests/test_prior_box_op.py +++ b/python/paddle/fluid/tests/unittests/test_prior_box_op.py @@ -28,7 +28,6 @@ class TestPriorBoxOp(OpTest): self.attrs = { 'min_sizes': self.min_sizes, - 'max_sizes': self.max_sizes, 'aspect_ratios': self.aspect_ratios, 'variances': self.variances, 'flip': self.flip, @@ -37,25 +36,28 @@ class TestPriorBoxOp(OpTest): 'step_h': self.step_h, 'offset': self.offset } + if len(self.max_sizes) > 0: + self.attrs['max_sizes'] = self.max_sizes self.outputs = {'Boxes': self.out_boxes, 'Variances': self.out_var} def test_check_output(self): self.check_output() - def test_check_grad(self): - return - def setUp(self): self.op_type = "prior_box" self.set_data() + def set_max_sizes(self): + max_sizes = [5, 10] + self.max_sizes = np.array(max_sizes).astype('float32').tolist() + def init_test_params(self): - self.layer_w = 4 - self.layer_h = 4 + self.layer_w = 32 + self.layer_h = 32 - self.image_w = 20 - self.image_h = 20 + self.image_w = 40 + self.image_h = 40 self.step_w = float(self.image_w) / float(self.layer_w) self.step_h = float(self.image_h) / float(self.layer_h) @@ -66,8 +68,7 @@ class TestPriorBoxOp(OpTest): self.min_sizes = [2, 4] self.min_sizes = np.array(self.min_sizes).astype('float32').tolist() - self.max_sizes = [5, 10] - self.max_sizes = np.array(self.max_sizes).astype('float32').tolist() + self.set_max_sizes() self.aspect_ratios = [2.0, 3.0] self.flip = True self.real_aspect_ratios = [1, 2.0, 1.0 / 2.0, 3.0, 1.0 / 3.0] @@ -79,7 +80,7 @@ class TestPriorBoxOp(OpTest): self.clip = True self.num_priors = len(self.real_aspect_ratios) * len(self.min_sizes) - if len(self.max_sizes) > 1: + if len(self.max_sizes) > 0: self.num_priors += len(self.max_sizes) self.offset = 0.5 @@ -105,35 +106,27 @@ class TestPriorBoxOp(OpTest): idx = 0 for s in range(len(self.min_sizes)): min_size = self.min_sizes[s] - c_w = c_h = min_size / 2. - out_boxes[h, w, idx, :] = [ - (c_x - c_w) / self.image_w, (c_y - c_h) / self.image_h, - (c_x + c_w) / self.image_w, (c_y + c_h) / self.image_h - ] - idx += 1 - - if len(self.max_sizes) > 0: - max_size = self.max_sizes[s] - # second prior: aspect_ratio = 1, - c_w = c_h = math.sqrt(min_size * max_size) / 2 + # rest of priors + for r in range(len(self.real_aspect_ratios)): + ar = self.real_aspect_ratios[r] + c_w = min_size * math.sqrt(ar) / 2 + c_h = (min_size / math.sqrt(ar)) / 2 out_boxes[h, w, idx, :] = [(c_x - c_w) / self.image_w, (c_y - c_h) / self.image_h, (c_x + c_w) / self.image_w, (c_y + c_h) / self.image_h] idx += 1 - # rest of priors - for r in range(len(self.real_aspect_ratios)): - ar = self.real_aspect_ratios[r] - if math.fabs(ar - 1.) < 1e-6: - continue - c_w = min_size * math.sqrt(ar) / 2 - c_h = (min_size / math.sqrt(ar)) / 2 + if len(self.max_sizes) > 0: + max_size = self.max_sizes[s] + # second prior: aspect_ratio = 1, + c_w = c_h = math.sqrt(min_size * max_size) / 2 out_boxes[h, w, idx, :] = [(c_x - c_w) / self.image_w, (c_y - c_h) / self.image_h, (c_x + c_w) / self.image_w, (c_y + c_h) / self.image_h] idx += 1 + # clip the prior's coordidate such that it is within[0, 1] if self.clip: out_boxes = np.clip(out_boxes, 0.0, 1.0) @@ -144,5 +137,10 @@ class TestPriorBoxOp(OpTest): self.out_var = out_var.astype('float32') +class TestPriorBoxOpWithMaxSize(TestPriorBoxOp): + def set_max_sizes(self): + self.max_sizes = [] + + if __name__ == '__main__': unittest.main() From 92e92ceba1e99e07ac485c9538f53125dd9a77a4 Mon Sep 17 00:00:00 2001 From: Xin Pan Date: Wed, 4 Apr 2018 04:24:26 -0700 Subject: [PATCH 56/58] Add type check --- paddle/scripts/docker/build.sh | 0 paddle/scripts/travis/build_doc.sh | 0 python/paddle/fluid/parallel_executor.py | 3 +++ tools/codestyle/cpplint_pre_commit.hook | 0 4 files changed, 3 insertions(+) mode change 100755 => 100644 paddle/scripts/docker/build.sh mode change 100755 => 100644 paddle/scripts/travis/build_doc.sh mode change 100755 => 100644 tools/codestyle/cpplint_pre_commit.hook diff --git a/paddle/scripts/docker/build.sh b/paddle/scripts/docker/build.sh old mode 100755 new mode 100644 diff --git a/paddle/scripts/travis/build_doc.sh b/paddle/scripts/travis/build_doc.sh old mode 100755 new mode 100644 diff --git a/python/paddle/fluid/parallel_executor.py b/python/paddle/fluid/parallel_executor.py index 4153049c05..1b3ba414ec 100644 --- a/python/paddle/fluid/parallel_executor.py +++ b/python/paddle/fluid/parallel_executor.py @@ -76,6 +76,9 @@ class ParallelExecutor(object): or numpy array. :return: fetched value list. """ + if not isinstance(feed_dict, dict): + raise TypeError("feed_dict should be a dict") + feed_tensor_dict = {} for i, feed_name in enumerate(feed_dict): feed_tensor = feed_dict[feed_name] diff --git a/tools/codestyle/cpplint_pre_commit.hook b/tools/codestyle/cpplint_pre_commit.hook old mode 100755 new mode 100644 From bdea5bee7d263212b1c2e367b8d6b61daaecfa18 Mon Sep 17 00:00:00 2001 From: Xin Pan Date: Wed, 4 Apr 2018 04:32:36 -0700 Subject: [PATCH 57/58] polish --- doc/fluid/api/gen_doc.sh | 0 paddle/scripts/docker/build.sh | 0 paddle/scripts/travis/build_doc.sh | 0 tools/codestyle/cpplint_pre_commit.hook | 0 4 files changed, 0 insertions(+), 0 deletions(-) mode change 100644 => 100755 doc/fluid/api/gen_doc.sh mode change 100644 => 100755 paddle/scripts/docker/build.sh mode change 100644 => 100755 paddle/scripts/travis/build_doc.sh mode change 100644 => 100755 tools/codestyle/cpplint_pre_commit.hook diff --git a/doc/fluid/api/gen_doc.sh b/doc/fluid/api/gen_doc.sh old mode 100644 new mode 100755 diff --git a/paddle/scripts/docker/build.sh b/paddle/scripts/docker/build.sh old mode 100644 new mode 100755 diff --git a/paddle/scripts/travis/build_doc.sh b/paddle/scripts/travis/build_doc.sh old mode 100644 new mode 100755 diff --git a/tools/codestyle/cpplint_pre_commit.hook b/tools/codestyle/cpplint_pre_commit.hook old mode 100644 new mode 100755 From f8dd03dced4b725dbd161d1b9ebbe89b97ce6173 Mon Sep 17 00:00:00 2001 From: mozga-intel Date: Wed, 4 Apr 2018 14:04:17 +0200 Subject: [PATCH 58/58] Prepare code for CentOS (#9651) --- paddle/fluid/operators/fc_mkldnn_op.cc | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/paddle/fluid/operators/fc_mkldnn_op.cc b/paddle/fluid/operators/fc_mkldnn_op.cc index 9c704a2949..847b7b0c12 100644 --- a/paddle/fluid/operators/fc_mkldnn_op.cc +++ b/paddle/fluid/operators/fc_mkldnn_op.cc @@ -27,8 +27,8 @@ template class MKLDNNMD { public: explicit MKLDNNMD(const T* in, const T* w, bool bias) - : in{paddle::framework::vectorize2int(in->dims())}, - w{paddle::framework::vectorize2int(w->dims())} { + : in(paddle::framework::vectorize2int(in->dims())), + w(paddle::framework::vectorize2int(w->dims())) { with_bias_ = bias; } @@ -78,7 +78,7 @@ class MKLDNNMD { class MKLDNNMemory { public: MKLDNNMemory(MKLDNNMD* t, const mkldnn::engine& e) - : md_{t}, engine_{e} {} + : md_(t), engine_(e) {} virtual ~MKLDNNMemory() = default; template

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