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提交 310ef226 编写于 作者: Z zhangchao41
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Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into my-paddle

上级 297b3d0f 8be9930f
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doc/howto/dev/write_docs_cn.rst
浏览文件 @ 310ef226
......@@ -5,15 +5,13 @@
PaddlePaddle的文档包括英文文档 ``doc`` 和中文文档 ``doc_cn`` 两个部分。文档都是通过 `cmake`_ 驱动 `sphinx`_ 编译生成，生成后的文档分别存储在编译目录的 ``doc`` 和 ``doc_cn`` 两个子目录下。
如何构建PaddlePaddle的文档
==========================
如何构建文档
============
PaddlePaddle的文档构建有直接构建和基于Docker构建两种方式，我们提供了一个构建脚本build_docs.sh来进行构建。
PaddlePaddle文档需要准备的环境相对较复杂，所以我们推荐使用基于Docker来构建PaddlePaddle的文档。
PaddlePaddle的文档构建有两种方式。
使用Docker构建PaddlePaddle的文档
--------------------------------
使用Docker构建
--------------
使用Docker构建PaddlePaddle的文档，需要在系统里先安装好Docker工具包。Docker安装请参考 `Docker的官网 <https://docs.docker.com/>`_ 。安装好Docker之后可以使用源码目录下的脚本构建文档，即
......@@ -21,58 +19,46 @@ PaddlePaddle文档需要准备的环境相对较复杂，所以我们推荐使
cd TO_YOUR_PADDLE_CLONE_PATH
cd paddle/scripts/tools/build_docs
bash build_docs.sh with_docker
编译完成后，会在当前目录生成两个子目录\:
* doc 英文文档目录
* doc_cn 中文文档目录
sh build_docs.sh
编译完成之后，会在当前目录生成两个子目录\: doc(英文文档目录)和 doc_cn(中文文档目录)。
打开浏览器访问对应目录下的index.html即可访问本地文档。
直接构建PaddlePaddle的文档
--------------------------
因为PaddlePaddle的v2 api文档生成过程依赖于py_paddle Python包，用户需要首先确认py_paddle包已经安装。
.. code-block:: bash
python -c "import py_paddle"
如果提示错误，那么用户需要在本地编译安装PaddlePaddle，请参考 `源码编译文档 <http://doc.paddlepaddle.org/develop/doc/getstarted/build_and_install/build_from_source_en.html>`_ 。
注意，用户在首次编译安装PaddlePaddle时，请将WITH_DOC选项关闭。在编译安装正确之后，请再次确认py_paddle包已经安装，即可进行下一步操作。
直接构建
--------
如果提示正确，可以执行以下命令编译生成文档，即
.. code-block:: bash
cd TO_YOUR_PADDLE_CLONE_PATH
cd paddle/scripts/tools/build_docs
bash build_docs.sh local
编译完成之后，会在当前目录生成两个子目录\:
* doc 英文文档目录
* doc_cn 中文文档目录
mkdir -p build
cd build
cmake .. -DCMAKE_BUILD_TYPE=Debug -DWITH_GPU=OFF -DWITH_MKLDNN=OFF -DWITH_MKLML=OFF -DWITH_DOC=ON
make gen_proto_py
make paddle_docs paddle_docs_cn
编译完成之后，会在当前目录生成两个子目录\: doc(英文文档目录)和 doc_cn(中文文档目录)。
打开浏览器访问对应目录下的index.html即可访问本地文档。
如何书写PaddlePaddle的文档
==========================
如何书写文档
============
PaddlePaddle文档使用 `sphinx`_ 自动生成，用户可以参考sphinx教程进行书写。
如何更新www.paddlepaddle.org文档
================================
如何更新文档主题
================
PaddlePaddle文档主题在 `TO_YOUR_PADDLE_CLONE_PATH/doc_theme` 文件夹下，包含所有和前端网页设计相关的文件。
开发者给PaddlePaddle代码增加的注释以PR的形式提交到github中，提交方式可参见 `贡献文档 <http://doc.paddlepaddle.org/develop/doc_cn/howto/dev/contribute_to_paddle_cn.html>`_ 。
如何更新doc.paddlepaddle.org
============================
更新的文档以PR的形式提交到github中，提交方式参见 `贡献文档 <http://doc.paddlepaddle.org/develop/doc_cn/howto/dev/contribute_to_paddle_cn.html>`_ 。
目前PaddlePaddle的develop分支的文档是自动触发更新的，用户可以分别查看最新的 `中文文档 <http://doc.paddlepaddle.org/develop/doc_cn/>`_ 和
`英文文档 <http://doc.paddlepaddle.org/develop/doc/>`_ 。
.. _cmake: https://cmake.org/
.. _sphinx: http://www.sphinx-doc.org/en/1.4.8/
paddle/framework/CMakeLists.txt
浏览文件 @ 310ef226
......@@ -9,6 +9,7 @@ cc_test(eigen_test SRCS eigen_test.cc DEPS tensor)
cc_library(lod_tensor SRCS lod_tensor.cc DEPS ddim place tensor)
cc_test(lod_tensor_test SRCS lod_tensor_test.cc DEPS lod_tensor)
nv_test(lod_tensor_gpu_test SRCS lod_tensor_test.cu DEPS lod_tensor)
cc_test(variable_test SRCS variable_test.cc)
......
paddle/framework/lod_tensor.h
浏览文件 @ 310ef226
......@@ -18,8 +18,10 @@
#ifndef PADDLE_ONLY_CPU
#include <thrust/device_vector.h>
#include <thrust/host_vector.h>
#include <thrust/system/cuda/experimental/pinned_allocator.h>
#endif
#include <glog/logging.h>
#include "paddle/framework/ddim.h"
#include "paddle/framework/tensor.h"
#include "paddle/platform/enforce.h"
......@@ -32,7 +34,8 @@ template <typename T>
using Vector = std::vector<T>;
#else
template <typename T>
using Vector = thrust::host_vector<T>;
using Vector = thrust::host_vector<
T, thrust::system::cuda::experimental::pinned_allocator<T>>;
#endif
using LoD = std::vector<Vector<size_t>>;
......
paddle/framework/lod_tensor_test.cu 0 → 100644
浏览文件 @ 310ef226
/*
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 <cuda.h>
#include <cuda_runtime.h>
#include "paddle/framework/lod_tensor.h"
#include "paddle/platform/assert.h"
#include <gtest/gtest.h>
__global__ void test(size_t* a, int size) {
for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < size;
i += blockDim.x * gridDim.x) {
a[i] *= 2;
}
}
TEST(LoDTensor, LoDInGPU) {
paddle::framework::Tensor tensor;
paddle::framework::LoDTensor lod_tensor;
paddle::platform::GPUPlace place(0);
paddle::framework::LoD src_lod;
src_lod.push_back(std::vector<size_t>{0, 2, 4, 6, 8, 10, 12, 14});
tensor.Resize({14, 16});
tensor.mutable_data<float>(place);
lod_tensor.set_lod(src_lod);
lod_tensor.set_tensor(&tensor);
CHECK_EQ(lod_tensor.lod_element(0, 2), 4);
CHECK_EQ(lod_tensor.lod_element(0, 4), 8);
auto lod = lod_tensor.lod();
test<<<1, 8>>>(lod[0].data(), lod[0].size());
cudaDeviceSynchronize();
for (size_t i = 0; i < src_lod[0].size(); ++i) {
CHECK_EQ(lod[0].data()[i], src_lod[0].data()[i] * 2);
}
}
paddle/gserver/layers/Layer.h
浏览文件 @ 310ef226
......@@ -49,6 +49,12 @@ struct LayerState {
};
typedef std::shared_ptr<LayerState> LayerStatePtr;
/// Paddle device ID, MKLDNN is -2, CPU is -1
enum PADDLE_DEVICE_ID {
MKLDNN_DEVICE = -2,
CPU_DEVICE = -1,
};
/**
* @brief Base class for layer.
* Define necessary variables and functions for every layer.
......@@ -59,11 +65,6 @@ protected:
LayerConfig config_;
/// whether to use GPU
bool useGpu_;
/// Paddle device ID, MKLDNN is -2, CPU is -1
enum PADDLE_DEVICE_ID {
MKLDNN_DEVICE = -2,
CPU_DEVICE = -1,
};
/// Device Id. MKLDNN is -2, CPU is -1, and GPU is 0, 1, 2 ...
int deviceId_;
/// Input layers
......
paddle/gserver/layers/MKLDNNFcLayer.cpp
浏览文件 @ 310ef226
......@@ -14,7 +14,6 @@ limitations under the License. */
#include "MKLDNNFcLayer.h"
#include "paddle/utils/Logging.h"
#include "paddle/utils/Stat.h"
using namespace mkldnn; // NOLINT
typedef memory::format format;
......@@ -40,6 +39,8 @@ bool MKLDNNFcLayer::init(const LayerMap& layerMap,
oc_ = getSize();
oh_ = 1;
ow_ = 1;
ih_ = 1;
iw_ = 1;
// input size can not change in FC
iLayerSize_ = inputLayers_[0]->getSize();
......@@ -77,67 +78,53 @@ void MKLDNNFcLayer::convertWeightsToPaddle() {
wgtVal_->reorderDataTo(wgtVal_, dstFmt, targetDim);
}
void MKLDNNFcLayer::reshape() {
const Argument& input = getInput(0, getPrev(0)->getDeviceId());
int batchSize = input.getBatchSize();
if (bs_ == batchSize) {
return;
}
bs_ = batchSize;
ih_ = input.getFrameHeight();
iw_ = input.getFrameWidth();
if (ih_ == 0) {
ih_ = 1;
}
if (iw_ == 0) {
iw_ = 1;
}
CHECK_EQ(iLayerSize_, inputLayers_[0]->getSize());
ic_ = iLayerSize_ / (ih_ * iw_);
CHECK_EQ(size_t(ic_ * ih_ * iw_), iLayerSize_) << "not divisible";
CHECK_EQ(size_t(oc_), getSize());
printSizeInfo();
void MKLDNNFcLayer::reshape(
int& bs, int& ic, int& ih, int& iw, int oc, int& oh, int& ow) {
reshapeInput(bs, ih, iw);
// reset output
output_.setFrameHeight(oh_);
output_.setFrameWidth(ow_);
resetOutput(bs_, oc_);
CHECK_EQ(iLayerSize_, inputLayers_[0]->getSize());
ic = iLayerSize_ / (ih * iw);
CHECK_EQ(size_t(ic * ih * iw), iLayerSize_) << "not divisible";
CHECK_EQ(size_t(oc), getSize());
// reset mkldnn forward
resetFwd();
needResetBwd_ = true;
reshapeOutput(oh, ow);
resizeOutput(bs, oc);
convertWeightsFromPaddle();
printSizeInfo();
}
void MKLDNNFcLayer::resetFwd() {
void MKLDNNFcLayer::resetFwd(std::vector<mkldnn::primitive>& pipeline,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) {
pipeline.clear();
bool hasBias = biases_ && biases_->getW();
const MatrixPtr& wgt = weight_->getW();
const MatrixPtr& bias = hasBias ? biases_->getW() : nullptr;
const MatrixPtr& out = output_.value;
const MatrixPtr& wgtVal = weight_->getW();
const MatrixPtr& biasVal = hasBias ? biases_->getW() : nullptr;
const MatrixPtr& outVal = output_.value;
if (inputIsOnlyMKLDNN()) {
const MatrixPtr& in = getInputValue(0);
inVal_ = std::dynamic_pointer_cast<MKLDNNMatrix>(in);
CHECK(inVal_) << "Input should be MKLDNNMatrix";
const MatrixPtr& inVal = getInputValue(0);
in = std::dynamic_pointer_cast<MKLDNNMatrix>(inVal);
CHECK(in) << "Input should be MKLDNNMatrix";
} else {
CHECK_EQ(getPrev(0)->getDeviceId(), CPU_DEVICE) << "Only support CPU yet";
const MatrixPtr& in = getInputValue(0, CPU_DEVICE);
inVal_ = MKLDNNMatrix::create(
in, memory::dims{bs_, ic_, ih_, iw_}, format::nchw, engine_);
}
inVal_->downSpatial();
wgtVal_ = MKLDNNMatrix::create(
wgt, memory::dims{oc_, ic_, ih_, iw_}, format::oihw, engine_);
wgtVal_->downSpatial();
biasVal_ =
hasBias ? MKLDNNMatrix::create(bias, {oc_}, format::x, engine_) : nullptr;
outVal_ = MKLDNNMatrix::create(out, {bs_, oc_}, format::nc, engine_);
const MatrixPtr& inVal = getInputValue(0, CPU_DEVICE);
in = MKLDNNMatrix::create(
inVal, memory::dims{bs_, ic_, ih_, iw_}, format::nchw, engine_);
}
in->downSpatial();
wgt = MKLDNNMatrix::create(
wgtVal, memory::dims{oc_, ic_, ih_, iw_}, format::oihw, engine_);
wgt->downSpatial();
bias = hasBias ? MKLDNNMatrix::create(biasVal, {oc_}, format::x, engine_)
: nullptr;
out = MKLDNNMatrix::create(outVal, {bs_, oc_}, format::nc, engine_);
// change original output value to mkldnn output value
output_.value = std::dynamic_pointer_cast<Matrix>(outVal_);
output_.value = std::dynamic_pointer_cast<Matrix>(out);
if (!outputIsOnlyMKLDNN()) {
copyOutputInfoToOtherDevice();
// fc cpu output value do not need create convert
// just share point
getOutput(CPU_DEVICE).value->setData(output_.value->getData());
......@@ -146,27 +133,31 @@ void MKLDNNFcLayer::resetFwd() {
// create forward handle
prop_kind pk = prop_kind::forward;
fc_fwd::desc fwdDesc = hasBias ? fc_fwd::desc(pk,
inVal_->getMemoryDesc(),
wgtVal_->getMemoryDesc(),
biasVal_->getMemoryDesc(),
outVal_->getMemoryDesc())
in->getMemoryDesc(),
wgt->getMemoryDesc(),
bias->getMemoryDesc(),
out->getMemoryDesc())
: fc_fwd::desc(pk,
inVal_->getMemoryDesc(),
wgtVal_->getMemoryDesc(),
outVal_->getMemoryDesc());
in->getMemoryDesc(),
wgt->getMemoryDesc(),
out->getMemoryDesc());
fc_fwd::primitive_desc fwdPD = fc_fwd::primitive_desc(fwdDesc, engine_);
if (hasBias) {
fwd_.reset(new fc_fwd(fwdPD, *inVal_, *wgtVal_, *biasVal_, *outVal_));
fwd_.reset(new fc_fwd(fwdPD, *in, *wgt, *bias, *out));
} else {
fwd_.reset(new fc_fwd(fwdPD, *inVal_, *wgtVal_, *outVal_));
fwd_.reset(new fc_fwd(fwdPD, *in, *wgt, *out));
}
printValueFormatFlow();
pipelineFwd_.clear();
pipelineFwd_.push_back(*fwd_);
pipeline.push_back(*fwd_);
}
void MKLDNNFcLayer::resetBwd() {
void MKLDNNFcLayer::resetBwd(std::vector<mkldnn::primitive>& pipeline,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) {
pipeline.clear();
if (!needResetBwd_) {
return;
}
......@@ -175,8 +166,8 @@ void MKLDNNFcLayer::resetBwd() {
/// backward weight
CHECK(inVal_) << "Should have input value";
const MatrixPtr& wgt = weight_->getWGrad();
const MatrixPtr& bias = hasBias ? biases_->getWGrad() : nullptr;
const MatrixPtr& wgtGrad = weight_->getWGrad();
const MatrixPtr& biasGrad = hasBias ? biases_->getWGrad() : nullptr;
// TODO(TJ): merge outgrad
int device = outputIsOnlyMKLDNN() ? MKLDNN_DEVICE : CPU_DEVICE;
......@@ -187,107 +178,66 @@ void MKLDNNFcLayer::resetBwd() {
// for CPU device:
// fc do not need to convert from cpu device since output is always nc format
// only need create from cpu device
const MatrixPtr& out = getOutput(device).grad;
outGrad_ = MKLDNNMatrix::create(out, outVal_->getPrimitiveDesc());
wgtGrad_ = MKLDNNMatrix::create(wgt, wgtVal_->getPrimitiveDesc());
biasGrad_ = hasBias ? MKLDNNMatrix::create(bias, biasVal_->getPrimitiveDesc())
: nullptr;
const MatrixPtr& outGrad = getOutput(device).grad;
out = MKLDNNMatrix::create(outGrad, outVal_->getPrimitiveDesc());
wgt = MKLDNNMatrix::create(wgtGrad, wgtVal_->getPrimitiveDesc());
bias = hasBias ? MKLDNNMatrix::create(biasGrad, biasVal_->getPrimitiveDesc())
: nullptr;
// create memory primitive desc
fc_fwd::desc fwdDesc = fc_fwd::desc(prop_kind::forward,
inVal_->getMemoryDesc(),
wgtGrad_->getMemoryDesc(),
outGrad_->getMemoryDesc());
wgt->getMemoryDesc(),
out->getMemoryDesc());
fc_fwd::primitive_desc fwdPD = fc_fwd::primitive_desc(fwdDesc, engine_);
fc_bwdWgt::desc bwdWgtDesc = hasBias
? fc_bwdWgt::desc(inVal_->getMemoryDesc(),
wgtGrad_->getMemoryDesc(),
biasGrad_->getMemoryDesc(),
outGrad_->getMemoryDesc())
wgt->getMemoryDesc(),
bias->getMemoryDesc(),
out->getMemoryDesc())
: fc_bwdWgt::desc(inVal_->getMemoryDesc(),
wgtGrad_->getMemoryDesc(),
outGrad_->getMemoryDesc());
wgt->getMemoryDesc(),
out->getMemoryDesc());
fc_bwdWgt::primitive_desc bwdWgtPD =
fc_bwdWgt::primitive_desc(bwdWgtDesc, engine_, fwdPD);
if (hasBias) {
bwdWgt_.reset(
new fc_bwdWgt(bwdWgtPD, *inVal_, *outGrad_, *wgtGrad_, *biasGrad_));
bwdWgt_.reset(new fc_bwdWgt(bwdWgtPD, *inVal_, *out, *wgt, *bias));
} else {
bwdWgt_.reset(new fc_bwdWgt(bwdWgtPD, *inVal_, *outGrad_, *wgtGrad_));
bwdWgt_.reset(new fc_bwdWgt(bwdWgtPD, *inVal_, *out, *wgt));
}
pipelineBwd_.clear();
pipelineBwd_.push_back(*bwdWgt_);
pipeline.push_back(*bwdWgt_);
/// backward data
const MatrixPtr& in = inputLayers_[0]->getOutput().grad;
if (in == nullptr) {
const MatrixPtr& inGrad = inputLayers_[0]->getOutput().grad;
if (inGrad == nullptr) {
return;
}
if (getInput(0, MKLDNN_DEVICE).getAllCount() > 1) {
// TODO(TJ): use outputMaps_ ways to get the inGrad_ when merge outgrad done
} else {
inGrad_ = MKLDNNMatrix::create(in, inVal_->getPrimitiveDesc());
in = MKLDNNMatrix::create(inGrad, inVal_->getPrimitiveDesc());
}
fc_bwdData::desc bwdDataDesc = fc_bwdData::desc(inVal_->getMemoryDesc(),
wgtGrad_->getMemoryDesc(),
outGrad_->getMemoryDesc());
fc_bwdData::desc bwdDataDesc = fc_bwdData::desc(
inVal_->getMemoryDesc(), wgt->getMemoryDesc(), out->getMemoryDesc());
fc_bwdData::primitive_desc bwdDataPD =
fc_bwdData::primitive_desc(bwdDataDesc, engine_, fwdPD);
CHECK(wgtVal_) << "Should have weight memory";
bwdData_.reset(new fc_bwdData(bwdDataPD, *outGrad_, *wgtVal_, *inGrad_));
bwdData_.reset(new fc_bwdData(bwdDataPD, *out, *wgtVal_, *in));
printGradFormatFlow();
pipelineBwd_.push_back(*bwdData_);
pipeline.push_back(*bwdData_);
}
void MKLDNNFcLayer::updateInputData() {
if (inputLayers_[0]->getType() != "data") {
return;
}
real* iData = getInputValue(0, CPU_DEVICE)->getData();
inVal_->setData(iData);
inVal_->setData(getInputValue(0, CPU_DEVICE)->getData());
}
void MKLDNNFcLayer::forward(PassType passType) {
Layer::forward(passType);
reshape();
{
REGISTER_TIMER_INFO("mkldnn_FwdTimer", getName().c_str());
updateInputData();
// just submit forward pipeline
stream_->submit(pipelineFwd_);
}
/* activation */ {
REGISTER_TIMER_INFO("FwActTimer", getName().c_str());
forwardActivation();
}
}
void MKLDNNFcLayer::backward(const UpdateCallback& callback) {
/* Do derivation */ {
REGISTER_TIMER_INFO("BpActTimer", getName().c_str());
backwardActivation();
}
{
REGISTER_TIMER_INFO("mkldnn_bwdTimer", getName().c_str());
resetBwd();
// just sumbmit backward pipeline
stream_->submit(pipelineBwd_);
}
{
REGISTER_TIMER_INFO("WeightUpdate", getName().c_str());
weight_->getParameterPtr()->incUpdate(callback);
if (biases_ && biases_->getWGrad()) {
biases_->getParameterPtr()->incUpdate(callback);
}
void MKLDNNFcLayer::updateWeights(const UpdateCallback& callback) {
weight_->getParameterPtr()->incUpdate(callback);
if (biases_ && biases_->getWGrad()) {
biases_->getParameterPtr()->incUpdate(callback);
}
}
} // namespace paddle
paddle/gserver/layers/MKLDNNFcLayer.h
浏览文件 @ 310ef226
......@@ -45,35 +45,28 @@ public:
bool init(const LayerMap& layerMap,
const ParameterMap& parameterMap) override;
void convertWeightsFromPaddle() override;
void convertWeightsToPaddle() override;
void reshape(
int& bs, int& ic, int& ih, int& iw, int oc, int& oh, int& ow) override;
void forward(PassType passType) override;
void resetFwd(std::vector<mkldnn::primitive>& pipeline,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) override;
void backward(const UpdateCallback& callback) override;
void resetBwd(std::vector<mkldnn::primitive>& pipeline,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) override;
void updateInputData() override;
protected:
/**
* reshape the input image sizes
* and reset output buffer size
* and reset mkldnn forward
*/
void reshape();
/**
* reset the forward primitve and memory
* only would be called when input size changes
*/
void resetFwd();
/**
* reset the backward primitve and memory for mkldnn fc
* only would be called when needed
*/
void resetBwd();
void updateWeights(const UpdateCallback& callback) override;
void convertWeightsFromPaddle() override;
void convertWeightsToPaddle() override;
};
} // namespace paddle
paddle/gserver/layers/MKLDNNLayer.h
浏览文件 @ 310ef226
......@@ -19,6 +19,7 @@ limitations under the License. */
#include "MKLDNNBase.h"
#include "mkldnn.hpp"
#include "paddle/math/MKLDNNMatrix.h"
#include "paddle/utils/Stat.h"
DECLARE_bool(use_mkldnn);
......@@ -33,6 +34,8 @@ typedef std::shared_ptr<MKLDNNLayer> MKLDNNLayerPtr;
*/
class MKLDNNLayer : public Layer {
protected:
// input value element count
size_t inputElemenCnt_;
// batch size
int bs_;
// input image channel, height and width
......@@ -52,7 +55,7 @@ protected:
std::vector<mkldnn::primitive> pipelineFwd_;
std::vector<mkldnn::primitive> pipelineBwd_;
// MKLDNNMatrixPtr
// MKLDNNMatrixPtr with internal format
MKLDNNMatrixPtr inVal_;
MKLDNNMatrixPtr inGrad_;
MKLDNNMatrixPtr outVal_;
......@@ -65,6 +68,7 @@ protected:
public:
explicit MKLDNNLayer(const LayerConfig& config)
: Layer(config),
inputElemenCnt_(0),
bs_(0),
ic_(0),
ih_(0),
......@@ -95,12 +99,104 @@ public:
if (!Layer::init(layerMap, parameterMap)) {
return false;
}
checkCPUOutputsNumber();
stream_.reset(new MKLDNNStream());
engine_ = CPUEngine::Instance().getEngine();
return true;
}
void forward(PassType passType) override {
passType_ = passType;
{
REGISTER_TIMER_INFO("mkldnn_FwdTimer", getName().c_str());
CHECK(!inputLayers_.empty());
copySeqInfoToOutputs();
size_t elemenCnt = inputLayers_[0]->getOutput().value->getElementCnt();
if (inputElemenCnt_ != elemenCnt) {
// reset when input total sizes changed, not only the batchsize
inputElemenCnt_ = elemenCnt;
reshape(bs_, ic_, ih_, iw_, oc_, oh_, ow_);
resetFwd(pipelineFwd_, inVal_, wgtVal_, biasVal_, outVal_);
convertWeightsFromPaddle();
needResetBwd_ = true;
}
if (inputLayers_[0]->getType() == "data") {
updateInputData();
}
stream_->submit(pipelineFwd_);
}
/* activation */ {
REGISTER_TIMER_INFO("FwActTimer", getName().c_str());
forwardActivation();
}
}
void backward(const UpdateCallback& callback) override {
/* Do derivation */ {
REGISTER_TIMER_INFO("BpActTimer", getName().c_str());
backwardActivation();
}
{
REGISTER_TIMER_INFO("mkldnn_bwdTimer", getName().c_str());
if (needResetBwd_) {
resetBwd(pipelineBwd_, inGrad_, wgtGrad_, biasGrad_, outGrad_);
needResetBwd_ = false;
}
stream_->submit(pipelineBwd_);
}
{
REGISTER_TIMER_INFO("WeightUpdate", getName().c_str());
updateWeights(callback);
}
}
/**
* reshape the input image sizes
* and reset output image and buffer size
* output channel can not be changed
*/
virtual void reshape(
int& bs, int& ic, int& ih, int& iw, int oc, int& oh, int& ow) = 0;
/**
* reset the mkldnn forward primitve and memory
* only would be called when input size changes
*/
virtual void resetFwd(std::vector<mkldnn::primitive>& pipeline,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) = 0;
/**
* reset the mkldnn backward primitve and memory for mkldnn fc
* only would be called when needed
*/
virtual void resetBwd(std::vector<mkldnn::primitive>& pipeline,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) = 0;
/**
* Update input value data when input layer is "data" type.
* Since the input value data address might be changed.
*/
virtual void updateInputData() {}
/**
* Update weights and biases if necessary.
*/
virtual void updateWeights(const UpdateCallback& callback) {}
/**
* convert weight from paddle format to mkldnn format
* weight_ will be override
......@@ -114,10 +210,38 @@ public:
virtual void convertWeightsToPaddle() {}
/**
* Update input value data when input layer is "data" type.
* Since the input value data address might be changed.
* add this interface as public for unit test
*/
virtual void updateInputData() {}
void addOutputArgument(int deviceId) { Layer::addOutputArgument(deviceId); }
protected:
/**
* reshape the input image sizes and input batchsize
*/
virtual void reshapeInput(int& batchsize, int& height, int& width) {
const Argument& input = inputLayers_[0]->getOutput();
batchsize = input.getBatchSize();
int h = input.getFrameHeight();
int w = input.getFrameWidth();
if (h != 0) {
height = h;
}
if (w != 0) {
width = w;
}
}
/**
* reshape output image sizes
*/
virtual void reshapeOutput(size_t height, size_t width) {
output_.setFrameHeight(height);
output_.setFrameWidth(width);
for (size_t i = 0; i < outputOtherDevice_.size(); i++) {
outputOtherDevice_[i].setFrameHeight(height);
outputOtherDevice_[i].setFrameWidth(width);
}
}
/**
* print info about sizes
......@@ -133,8 +257,8 @@ public:
*/
virtual void printValueFormatFlow() {
if (inVal_ && outVal_) {
VLOG(MKLDNN_FMTS) << "value format flow --- " << inVal_->getFormat()
<< " >>> " << outVal_->getFormat();
VLOG(MKLDNN_FMTS) << inVal_->getFormat() << " >>> "
<< outVal_->getFormat();
}
}
......@@ -143,36 +267,12 @@ public:
*/
virtual void printGradFormatFlow() {
if (inGrad_ && outGrad_) {
VLOG(MKLDNN_FMTS) << "grad format flow --- " << inGrad_->getFormat()
<< " <<< " << outGrad_->getFormat();
VLOG(MKLDNN_FMTS) << inGrad_->getFormat() << " <<< "
<< outGrad_->getFormat();
}
}
protected:
/**
* copy image size and sequence info to other device
* @note: can not directly use Layer::copyOutputToOtherDevice since here only
* copy base info and do not copy data value
*/
void copyOutputInfoToOtherDevice() {
int cnt = 0;
for (size_t i = 0; i < outputOtherDevice_.size(); i++) {
outputOtherDevice_[i].setFrameHeight(output_.getFrameHeight());
outputOtherDevice_[i].setFrameWidth(output_.getFrameWidth());
outputOtherDevice_[i].sequenceStartPositions =
output_.sequenceStartPositions;
outputOtherDevice_[i].subSequenceStartPositions =
output_.subSequenceStartPositions;
outputOtherDevice_[i].cpuSequenceDims = output_.cpuSequenceDims;
if (outputOtherDevice_[i].deviceId == CPU_DEVICE) {
++cnt;
}
}
if (cnt > 1) {
LOG(WARNING) << "should not have more than one CPU devie";
}
}
/**
* If input only has MKLDNN device.
* Otherwise, only support the previous layer using CPU device.
......@@ -205,6 +305,7 @@ protected:
*/
void setDevice(int id) { deviceId_ = id; }
private:
/**
* Set deviceId of the params used in this layer.
*/
......@@ -228,6 +329,42 @@ protected:
parameter->setDevice(id);
}
}
/**
* Check the cpu device number of outputOtherDevice_.
* should have only one at most.
*/
void checkCPUOutputsNumber(int max = 1) {
int cnt = 0;
for (size_t i = 0; i < outputOtherDevice_.size(); i++) {
if (outputOtherDevice_[i].deviceId == CPU_DEVICE) {
++cnt;
}
}
CHECK_LE(cnt, max) << "too much CPU devies";
}
/**
* copy SeqInfo from input layer to this output and other output devices.
* @note: do not use getInput(0) since it used this deviceId_,
* use "inputLayers_[0]->getOutput()" instead.
*/
void copySeqInfoToOutputs() {
if (inputLayers_.empty() || !needSequenceInfo_) {
return;
}
const Argument& input = inputLayers_[0]->getOutput();
output_.sequenceStartPositions = input.sequenceStartPositions;
output_.subSequenceStartPositions = input.subSequenceStartPositions;
output_.cpuSequenceDims = input.cpuSequenceDims;
for (size_t i = 0; i < outputOtherDevice_.size(); i++) {
outputOtherDevice_[i].sequenceStartPositions =
output_.sequenceStartPositions;
outputOtherDevice_[i].subSequenceStartPositions =
output_.subSequenceStartPositions;
outputOtherDevice_[i].cpuSequenceDims = output_.cpuSequenceDims;
}
}
};
} // namespace paddle
paddle/gserver/tests/MKLDNNTester.cpp
浏览文件 @ 310ef226
......@@ -63,8 +63,12 @@ void MKLDNNTester::reset(const TestConfig& dnn,
initTestLayer(
configs_[i], &(layerMaps_[i]), &(parameters_[i]), &(testLayers_[i]));
}
dnnLayer_ = testLayers_[DNN];
refLayer_ = testLayers_[REF];
dnnLayer_ = std::dynamic_pointer_cast<MKLDNNLayer>(testLayers_[DNN]);
CHECK(dnnLayer_);
// for comparison with Paddle reference results,
// need manually add cpu device output for test
dnnLayer_->addOutputArgument(CPU_DEVICE);
EXPECT_EQ(dataLayers_[DNN].size(), dataLayers_[REF].size());
EXPECT_EQ(parameters_[DNN].size(), parameters_[REF].size());
......@@ -109,20 +113,22 @@ void MKLDNNTester::randomBotDatas() {
void MKLDNNTester::randomTopDiffs() {
refLayer_->getOutputGrad()->randomizeUniform();
dnnLayer_->getOutputGrad()->copyFrom(*(refLayer_->getOutputGrad()));
VLOG(lvl_) << "Random dom Backward Input, TopDiff: ";
dnnLayer_->getOutput(CPU_DEVICE)
.grad->copyFrom(*(refLayer_->getOutputGrad()));
VLOG(lvl_) << "Random Backward Input, TopDiff: ";
printMatrix(refLayer_->getOutputGrad());
}
void MKLDNNTester::checkForward() {
printTopDatas();
double delta = compareMatrix(testLayers_[DNN]->getOutputValue(),
testLayers_[REF]->getOutputValue());
VLOG(MKLDNN_ALL) << "Check Forward";
printTopDatas();
double delta = compareMatrix(dnnLayer_->getOutput(-1).value,
refLayer_->getOutputValue());
EXPECT_LE(fabs(delta), eps_);
}
void MKLDNNTester::checkBackwardData() {
VLOG(MKLDNN_ALL) << "Check Backward Data";
// TODO(TJ): uncomment me when batch norm ready
// const bool isBN = dnnLayer_->getType() == "mkldnn_batch_norm";
for (size_t i = 0; i < dataLayers_[DNN].size(); ++i) {
......@@ -144,14 +150,12 @@ void MKLDNNTester::checkBackwardData() {
}
void MKLDNNTester::checkBackwardWgts() {
VLOG(MKLDNN_ALL) << "Check Backward Weight";
CHECK_EQ(parameters_[DNN].size(), parameters_[REF].size());
vector<VectorPtr> dnnWgts; // used to temply save mkldnn weights
saveWgt(parameters_[DNN], dnnWgts);
const MKLDNNLayerPtr dnnlayer =
std::dynamic_pointer_cast<MKLDNNLayer>(dnnLayer_);
CHECK(dnnlayer);
dnnlayer->convertWeightsToPaddle();
dnnLayer_->convertWeightsToPaddle();
for (size_t i = 0; i < parameters_[DNN].size(); ++i) {
const VectorPtr& dnn = parameters_[DNN][i]->getBuf(PARAMETER_VALUE);
const VectorPtr& ref = parameters_[REF][i]->getBuf(PARAMETER_VALUE);
......@@ -189,38 +193,38 @@ void MKLDNNTester::restoreWgt(const vector<VectorPtr>& from,
}
// clear parameters grad
void MKLDNNTester::clearWgtDiffs() {
void MKLDNNTester::clearWgtDiffs(size_t id) {
CHECK_LE(id, parameters_.size());
for (size_t n = 0; n < parameters_.size(); ++n) {
for (size_t i = 0; i < parameters_[n].size(); ++i) {
const VectorPtr& grad = parameters_[n][i]->getBuf(PARAMETER_GRADIENT);
if (grad) {
grad->zeroMem();
if (id == n || id == parameters_.size()) {
for (size_t i = 0; i < parameters_[n].size(); ++i) {
const VectorPtr& grad = parameters_[n][i]->getBuf(PARAMETER_GRADIENT);
if (grad) {
grad->zeroMem();
}
}
}
}
}
void MKLDNNTester::clearBotDiffs() {
// dnn and ref
void MKLDNNTester::clearBotDiffs(size_t id) {
CHECK_LE(id, dataLayers_.size());
for (size_t n = 0; n < dataLayers_.size(); ++n) {
// all inputs layers
for (size_t i = 0; i < dataLayers_[n].size(); ++i) {
dataLayers_[n][i]->getOutputGrad()->zeroMem();
if (id == n || id == dataLayers_.size()) {
// clear inputs layers of this specific layer
for (size_t i = 0; i < dataLayers_[n].size(); ++i) {
dataLayers_[n][i]->getOutputGrad()->zeroMem();
}
}
}
}
void MKLDNNTester::clearBotDiffs(int n) {
CHECK_LT(n, NUM);
// all inputs layers
for (size_t i = 0; i < dataLayers_[n].size(); ++i) {
dataLayers_[n][i]->getOutputGrad()->zeroMem();
}
}
void MKLDNNTester::clearTopDatas() {
void MKLDNNTester::clearTopDatas(size_t id) {
CHECK_LE(id, testLayers_.size());
for (size_t i = 0; i < testLayers_.size(); ++i) {
testLayers_[i]->getOutputValue()->zeroMem();
if (id == i || id == testLayers_.size()) {
testLayers_[i]->getOutputValue()->zeroMem();
}
}
}
......@@ -300,16 +304,24 @@ void MKLDNNTester::runOnce() {
checkForward();
// test backward
// simple updater
UpdateCallback updateCallback = [](Parameter* para) {
auto& grad = para->getBuf(PARAMETER_GRADIENT);
auto& value = para->getBuf(PARAMETER_VALUE);
real lr = 1e-3;
value->add(*grad, lr);
};
randomTopDiffs();
dnnLayer_->backward(nullptr);
refLayer_->backward(nullptr);
dnnLayer_->backward(updateCallback);
refLayer_->backward(updateCallback);
checkBackwardData();
checkBackwardWgts();
// clear buffers
// ref code will addto the diff, dnn code will writeto it
// and clearTopDatas() and clearWgtDiffs() should be coverd by test layers
// and clearTopDatas(REF) should be coverd by ref layers
clearBotDiffs(REF);
clearWgtDiffs(REF);
}
void MKLDNNTester::run(const TestConfig& dnn,
......
paddle/gserver/tests/MKLDNNTester.h
浏览文件 @ 310ef226
......@@ -18,6 +18,7 @@ limitations under the License. */
#include <vector>
#include "LayerGradUtil.h"
#include "paddle/gserver/layers/MKLDNNBase.h"
#include "paddle/gserver/layers/MKLDNNLayer.h"
namespace paddle {
......@@ -40,7 +41,8 @@ protected:
vector<LayerMap> layerMaps_;
vector<vector<ParameterPtr>> parameters_;
vector<LayerPtr> testLayers_;
LayerPtr dnnLayer_, refLayer_;
LayerPtr refLayer_;
MKLDNNLayerPtr dnnLayer_;
/// run some iterations, all the result should pass
size_t iter_;
......@@ -88,10 +90,10 @@ private:
void checkBackwardData();
void checkBackwardWgts();
void clearWgtDiffs();
void clearBotDiffs();
void clearBotDiffs(int n); // clear specific layer
void clearTopDatas();
// clear specific layer, clear all when id equals NUM
void clearWgtDiffs(size_t id = NUM);
void clearBotDiffs(size_t id = NUM);
void clearTopDatas(size_t id = NUM);
void printTopDatas();
void printMatrix(const MatrixPtr& m);
......
paddle/operators/math/im2col_test.cc
浏览文件 @ 310ef226
......@@ -119,4 +119,4 @@ TEST(math, im2col) {
#ifndef PADDLE_ONLY_CPU
testIm2col<paddle::platform::GPUPlace>();
#endif
}
\ No newline at end of file
}
paddle/pybind/pybind.cc
浏览文件 @ 310ef226
......@@ -17,6 +17,7 @@ limitations under the License. */
#include <vector>
#include "paddle/framework/backward.h"
#include "paddle/framework/lod_tensor.h"
#include "paddle/framework/op_registry.h"
#include "paddle/operators/net_op.h"
#include "paddle/operators/recurrent_op.h"
......@@ -58,6 +59,8 @@ namespace paddle {
namespace framework {
using Tensor = framework::Tensor;
using LoDTensor = framework::LoDTensor;
using LoD = framework::LoD;
static size_t UniqueIntegerGenerator() {
static std::atomic<size_t> generator;
......@@ -117,6 +120,60 @@ PYBIND11_PLUGIN(core) {
return self.data<float>()[offset];
});
py::class_<LoDTensor>(m, "LoDTensor", R"DOC(LoD(Leval of Ddetails) Tensor.
The tensor and LoD info should be created before creating the LoDTensor, then
call the set_tensor and set_lod functions to set them.
)DOC")
.def("__init__",
[](LoDTensor &instance,
const std::vector<std::vector<size_t>> &lod,
Tensor *t) {
#ifdef PADDLE_ONLY_CPU
new (&instance) LoDTensor(lod, t);
#else
paddle::framework::LoD new_lod;
new_lod.reserve(lod.size());
std::copy(lod.begin(), lod.end(), std::back_inserter(new_lod));
new (&instance) LoDTensor(new_lod, t);
#endif
})
.def("set_tensor",
[](LoDTensor &self, Tensor *tensor) { self.set_tensor(tensor); })
.def("set_lod",
[](LoDTensor &self, const std::vector<std::vector<size_t>> &lod) {
#ifdef PADDLE_ONLY_CPU
self.set_lod(lod);
#else
paddle::framework::LoD new_lod;
new_lod.reserve(lod.size());
std::copy(lod.begin(), lod.end(), std::back_inserter(new_lod));
self.set_lod(new_lod);
#endif
})
.def("tensor",
[](LoDTensor &self) -> Tensor & { return self.tensor(); },
py::return_value_policy::reference)
.def("lod", [](LoDTensor &self) -> std::vector<std::vector<size_t>> {
#ifdef PADDLE_ONLY_CPU
return self.lod();
#else
auto lod = self.lod();
std::vector<std::vector<size_t>> new_lod;
new_lod.reserve(lod.size());
std::transform(lod.begin(), lod.end(), std::back_inserter(new_lod),
[](paddle::framework::Vector<size_t> item) ->
std::vector<size_t> {
std::vector<size_t> v;
v.reserve(item.size());
std::copy(item.begin(), item.end(), std::back_inserter(v));
return v;
});
return new_lod;
#endif
});
py::class_<Variable>(m, "Variable", R"DOC(Variable Class.
All parameter, weight, gradient are variables in Paddle.
......@@ -128,6 +185,11 @@ All parameter, weight, gradient are variables in Paddle.
.def("get_tensor",
[](Variable &self) -> Tensor * { return self.GetMutable<Tensor>(); },
py::return_value_policy::reference)
.def("get_lod_tensor",
[](Variable &self) -> LoDTensor * {
return self.GetMutable<LoDTensor>();
},
py::return_value_policy::reference)
.def("get_net",
[](Variable &self) -> operators::NetOp * {
return self.GetMutable<operators::NetOp>();
......
python/paddle/v2/framework/tests/test_tensor.py
浏览文件 @ 310ef226
......@@ -3,7 +3,7 @@ import unittest
import numpy
class TestScope(unittest.TestCase):
class TestTensor(unittest.TestCase):
def test_int_tensor(self):
scope = core.Scope()
var = scope.new_var("test_tensor")
......@@ -20,8 +20,8 @@ class TestScope(unittest.TestCase):
tensor.set(tensor_array, place)
tensor_array_2 = numpy.array(tensor)
self.assertEqual(1.0, tensor_array_2[3, 9])
self.assertEqual(2.0, tensor_array_2[19, 11])
self.assertEqual(1, tensor_array_2[3, 9])
self.assertEqual(2, tensor_array_2[19, 11])
def test_float_tensor(self):
scope = core.Scope()
......@@ -43,6 +43,84 @@ class TestScope(unittest.TestCase):
self.assertAlmostEqual(1.0, tensor_array_2[3, 9])
self.assertAlmostEqual(2.0, tensor_array_2[19, 11])
def test_int_lod_tensor(self):
places = [core.CPUPlace(), core.GPUPlace(0)]
for place in places:
scope = core.Scope()
var = scope.new_var("test_tensor")
var_lod = scope.new_var("test_lod_tensor")
tensor = var.get_tensor()
lod_tensor = var_lod.get_lod_tensor()
tensor.set_dims([4, 4, 6])
tensor.alloc_int(place)
array = numpy.array(tensor)
array[0, 0, 0] = 3
array[3, 3, 5] = 10
tensor.set(array, place)
lod_tensor.set_tensor(tensor)
lod_tensor.set_lod([[0, 2, 4]])
lod_v = numpy.array(lod_tensor.tensor())
self.assertTrue(numpy.alltrue(array == lod_v))
lod = lod_tensor.lod()
self.assertEqual(0, lod[0][0])
self.assertEqual(2, lod[0][1])
self.assertEqual(4, lod[0][2])
def test_float_lod_tensor(self):
places = [core.CPUPlace(), core.GPUPlace(0)]
for place in places:
scope = core.Scope()
var = scope.new_var("test_tensor")
var_lod = scope.new_var("test_lod_tensor")
tensor = var.get_tensor()
lod_tensor = var_lod.get_lod_tensor()
tensor.set_dims([5, 2, 3, 4])
tensor.alloc_float(place)
tensor_array = numpy.array(tensor)
self.assertEqual((5, 2, 3, 4), tensor_array.shape)
tensor_array[0, 0, 0, 0] = 1.0
tensor_array[0, 0, 0, 1] = 2.0
tensor.set(tensor_array, place)
lod_tensor.set_tensor(tensor)
lod_v = numpy.array(lod_tensor.tensor())
self.assertAlmostEqual(1.0, lod_v[0, 0, 0, 0])
self.assertAlmostEqual(2.0, lod_v[0, 0, 0, 1])
self.assertEqual(len(lod_tensor.lod()), 0)
lod_py = [[0, 2, 5], [0, 2, 4, 5]]
lod_tensor.set_lod(lod_py)
lod = lod_tensor.lod()
self.assertListEqual(lod_py, lod)
def test_lod_tensor_init(self):
scope = core.Scope()
var = scope.new_var("test_tensor")
place = core.CPUPlace()
tensor = var.get_tensor()
tensor.set_dims([5, 2, 3, 4])
tensor.alloc_float(place)
tensor_array = numpy.array(tensor)
tensor_array[0, 0, 0, 0] = 1.0
tensor_array[0, 0, 0, 1] = 2.0
tensor.set(tensor_array, place)
lod_py = [[0, 2, 5], [0, 2, 4, 5]]
lod_tensor = core.LoDTensor(lod_py, tensor)
lod_v = numpy.array(lod_tensor.tensor())
self.assertAlmostEqual(1.0, lod_v[0, 0, 0, 0])
self.assertAlmostEqual(2.0, lod_v[0, 0, 0, 1])
self.assertListEqual(lod_py, lod_tensor.lod())
if __name__ == '__main__':
unittest.main()
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