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CMakeLists.txt
浏览文件 @ 82d31503
......@@ -47,48 +47,85 @@ find_package(Threads REQUIRED)
include(simd)
################################ Configurations #######################################
################################ Exposed Configurations #######################################
option(WITH_GPU "Compile PaddlePaddle with NVIDIA GPU" ${CUDA_FOUND})
option(WITH_AMD_GPU "Compile PaddlePaddle with AMD GPU" OFF)
option(WITH_DSO "Compile PaddlePaddle with dynamic linked CUDA" ON)
option(WITH_AVX "Compile PaddlePaddle with AVX intrinsics" ${AVX_FOUND})
option(WITH_PYTHON "Compile PaddlePaddle with python interpreter" ON)
option(WITH_TESTING "Compile PaddlePaddle with unit testing" OFF)
option(WITH_MKL "Compile PaddlePaddle with MKL support." ${AVX_FOUND})
option(WITH_SYSTEM_BLAS "Use system blas library" OFF)
option(WITH_DISTRIBUTE "Compile with distributed support" OFF)
option(WITH_BRPC_RDMA "Use brpc rdma as the rpc protocal" OFF)
option(ON_INFER "Turn on inference optimization." OFF)
################################ Internal Configurations #######################################
option(WITH_ANAKIN "Compile with Anakin library" OFF)
option(WITH_AMD_GPU "Compile PaddlePaddle with AMD GPU" OFF)
option(WITH_NGRAPH "Compile PaddlePaddle with nGraph support." OFF)
option(WITH_DSO "Compile PaddlePaddle with dynamic linked CUDA" ON)
option(WITH_TESTING "Compile PaddlePaddle with unit testing" OFF)
option(WITH_PYTHON "Compile PaddlePaddle with python interpreter" ON)
option(WITH_PROFILER "Compile PaddlePaddle with GPU profiler and gperftools" OFF)
option(WITH_JEMALLOC "Compile PaddlePaddle with jemalloc" OFF)
option(WITH_COVERAGE "Compile PaddlePaddle with code coverage" OFF)
option(COVERALLS_UPLOAD "Package code coverage data to coveralls" OFF)
option(WITH_DISTRIBUTE "Compile with distributed support" OFF)
option(WITH_PSLIB "Compile with pslib support" OFF)
option(WITH_CONTRIB "Compile the third-party contributation" OFF)
option(REPLACE_ENFORCE_GLOG "Replace PADDLE_ENFORCE with glog/CHECK for better debug." OFF)
# TODO(Superjomn) Remove WITH_ANAKIN option if not needed latter.
option(WITH_ANAKIN "Compile with Anakin library" OFF)
option(ANAKIN_BUILD_FAT_BIN "Build anakin cuda fat-bin lib for all device plantform, ignored when WITH_ANAKIN=OFF" OFF)
option(ANAKIN_BUILD_CROSS_PLANTFORM "Build anakin lib for any nvidia device plantform. ignored when WITH_ANAKIN=OFF" ON)
option(WITH_GRPC "Use grpc as the default rpc framework" ${WITH_DISTRIBUTE})
option(WITH_BRPC_RDMA "Use brpc rdma as the rpc protocal" OFF)
option(ON_INFER "Turn on inference optimization." OFF)
option(WITH_INFERENCE_API_TEST "Test fluid inference high-level api interface" OFF)
option(WITH_SYSTEM_BLAS "Use system blas library" OFF)
option(WITH_INFERENCE_API_TEST "Test fluid inference C++ high-level api interface" OFF)
option(WITH_HIGH_LEVEL_API_TEST "Test fluid python high-level api interface" OFF)
option(PY_VERSION "Compile PaddlePaddle with python3 support" ${PY_VERSION})
option(WITH_FAST_MATH "Make use of fast math library, might affect the precision to some extent" ON)
option(WITH_WBAES "Compile PaddlePaddle with WBAES support" ON)
option(WITH_DGC "Use DGC(Deep Gradient Compression) or not" ON)
# PY_VERSION
if(NOT PY_VERSION)
set(PY_VERSION 2.7)
endif()
set(PYBIND11_PYTHON_VERSION ${PY_VERSION})
# for lite, both server and mobile framework.
option(LITE_WITH_CUDA "Enable CUDA in lite mode" ON)
option(LITE_WITH_X86 "Enable X86 in lite mode" ON)
option(LITE_WITH_LIGHT_WEIGHT_FRAMEWORK "Enable light-weight framework" ON)
set(THIRD_PARTY_PATH "${CMAKE_BINARY_DIR}/third_party" CACHE STRING
"A path setting third party libraries download & build directories.")
set(THIRD_PARTY_BUILD_TYPE Release)
# CMAKE_BUILD_TYPE
if(NOT CMAKE_BUILD_TYPE)
set(CMAKE_BUILD_TYPE "RelWithDebInfo" CACHE STRING
"Choose the type of build, options are: Debug Release RelWithDebInfo MinSizeRel"
FORCE)
"Choose the type of build, options are: Debug Release RelWithDebInfo MinSizeRel"
FORCE)
endif()
include_directories("${PADDLE_SOURCE_DIR}")
# for mobile
if (WITH_LITE AND LITE_WITH_LIGHT_WEIGHT_FRAMEWORK)
message(STATUS "Building the mobile framework")
# include the necessary thirdparty dependencies
include(external/gflags) # download, build, install gflags
include(external/glog) # download, build, install glog
include(external/gtest) # download, build, install gtest
include(external/zlib) # download, build, install gtest
include(external/protobuf) # download, build, install protobuf
include(external/eigen) # download eigen3
include(generic) # simplify cmake module
include(configure) # add paddle env configuration
add_definitions(-std=c++11)
add_subdirectory(paddle)
return()
endif()
# PY_VERSION
if(NOT PY_VERSION)
set(PY_VERSION 2.7)
endif()
set(PYBIND11_PYTHON_VERSION ${PY_VERSION})
if (APPLE)
set(WITH_MKL OFF CACHE STRING
......@@ -100,16 +137,12 @@ if (WIN32)
"Disable DISTRIBUTE when compiling for Windows" FORCE)
endif()
set(THIRD_PARTY_PATH "${CMAKE_BINARY_DIR}/third_party" CACHE STRING
"A path setting third party libraries download & build directories.")
set(FLUID_INSTALL_DIR "${CMAKE_BINARY_DIR}/fluid_install_dir" CACHE STRING
"A path setting fluid shared and static libraries")
set(FLUID_INFERENCE_INSTALL_DIR "${CMAKE_BINARY_DIR}/fluid_inference_install_dir" CACHE STRING
"A path setting fluid inference shared and static libraries")
set(THIRD_PARTY_BUILD_TYPE Release)
set(WITH_MKLML ${WITH_MKL})
if (NOT DEFINED WITH_MKLDNN)
......@@ -149,7 +182,6 @@ include(external/dlpack)
include(external/snappy) # download snappy
include(external/snappystream) # download snappystream
include(external/warpctc) # download, build, install warpctc
include(external/wbaes) # download wbaes
if (NOT WIN32)
# there is no official support of nccl, cupti in windows
......@@ -184,11 +216,6 @@ if(WITH_BRPC_RDMA)
endif()
endif()
# for lite
option(LITE_WITH_CUDA "Enable CUDA in lite mode" ON)
option(LITE_WITH_X86 "Enable X86 in lite mode" ON)
option(LITE_WITH_LIGHT_WEIGHT_FRAMEWORK "Enable light-weight framework" ON)
include(external/threadpool)
include(flags) # set paddle compile flags
include(cudnn) # set cudnn libraries, must before configure
......@@ -200,9 +227,14 @@ if(WITH_GPU)
include(anakin_subgraph)
endif()
if(WITH_GPU AND NOT WIN32)
if(WIN32 OR APPLE OR NOT WITH_GPU OR ON_INFER)
set(WITH_DGC OFF)
endif()
if(WITH_DGC)
message(STATUS "add dgc lib.")
include(external/dgc)
add_definitions(-DPADDLE_WITH_DGC)
endif()
if(WITH_MKL OR WITH_MKLML)
......@@ -232,7 +264,6 @@ include(coveralls) # set code coverage
include(inference_lib) # add paddle fluid inference libraries
include_directories("${PADDLE_SOURCE_DIR}")
if(WITH_AMD_GPU)
find_package(HIP)
......
README.md
浏览文件 @ 82d31503
......@@ -3,8 +3,8 @@
English | [简体中文](./README_cn.md)
[![Build Status](https://travis-ci.org/PaddlePaddle/Paddle.svg?branch=develop)](https://travis-ci.org/PaddlePaddle/Paddle)
[![Documentation Status](https://img.shields.io/badge/docs-latest-brightgreen.svg?style=flat)](http://paddlepaddle.org/documentation/docs/en/1.3/beginners_guide/index_en.html)
[![Documentation Status](https://img.shields.io/badge/中文文档-最新-brightgreen.svg)](http://paddlepaddle.org/documentation/docs/zh/1.3/beginners_guide/index.html)
[![Documentation Status](https://img.shields.io/badge/docs-latest-brightgreen.svg?style=flat)](http://www.paddlepaddle.org/documentation/docs/en/1.4/beginners_guide/index_en.html)
[![Documentation Status](https://img.shields.io/badge/中文文档-最新-brightgreen.svg)](http://www.paddlepaddle.org/documentation/docs/zh/1.4/beginners_guide/index_cn.html)
[![Release](https://img.shields.io/github/release/PaddlePaddle/Paddle.svg)](https://github.com/PaddlePaddle/Paddle/releases)
[![License](https://img.shields.io/badge/license-Apache%202-blue.svg)](LICENSE)
......@@ -18,7 +18,7 @@ learning to many products at Baidu.
Our vision is to enable deep learning for everyone via PaddlePaddle.
Please refer to our [release announcement](https://github.com/PaddlePaddle/Paddle/releases) to track the latest feature of PaddlePaddle.
### Latest PaddlePaddle Release: [Fluid 1.3.0](https://github.com/PaddlePaddle/Paddle/tree/release/1.3)
### Latest PaddlePaddle Release: [Fluid 1.4.1](https://github.com/PaddlePaddle/Paddle/tree/release/1.4)
### Install Latest Stable Release:
```
# Linux CPU
......@@ -26,9 +26,9 @@ pip install paddlepaddle
# Linux GPU cuda9cudnn7
pip install paddlepaddle-gpu
# Linux GPU cuda8cudnn7
pip install paddlepaddle-gpu==1.3.0.post87
pip install paddlepaddle-gpu==1.4.1.post87
# Linux GPU cuda8cudnn5
pip install paddlepaddle-gpu==1.3.0.post85
pip install paddlepaddle-gpu==1.4.1.post85
# For installation on other platform, refer to http://paddlepaddle.org/
```
......@@ -75,26 +75,26 @@ pip install paddlepaddle-gpu==1.3.0.post85
## Installation
It is recommended to read [this doc](http://paddlepaddle.org/documentation/docs/en/1.3/beginners_guide/index_en.html) on our website.
It is recommended to read [this doc](http://www.paddlepaddle.org/documentation/docs/en/1.4/beginners_guide/index_en.html) on our website.
## Documentation
We provide [English](http://paddlepaddle.org/documentation/docs/en/1.3/beginners_guide/index_en.html) and
[Chinese](http://paddlepaddle.org/documentation/docs/zh/1.3/beginners_guide/index.html) documentation.
We provide [English](http://www.paddlepaddle.org/documentation/docs/en/1.4/beginners_guide/index_en.html) and
[Chinese](http://www.paddlepaddle.org/documentation/docs/zh/1.4/beginners_guide/install/index_cn.html) documentation.
- [Deep Learning 101](https://github.com/PaddlePaddle/book)
You might want to start from this online interactive book that can run in a Jupyter Notebook.
- [Distributed Training](http://paddlepaddle.org/documentation/docs/en/1.3/user_guides/howto/training/multi_node_en.html)
- [Distributed Training](http://paddlepaddle.org/documentation/docs/en/1.4/user_guides/howto/training/multi_node_en.html)
You can run distributed training jobs on MPI clusters.
- [Python API](http://paddlepaddle.org/documentation/docs/en/1.3/api/index_en.html)
- [Python API](http://paddlepaddle.org/documentation/docs/en/1.4/api/index_en.html)
Our new API enables much shorter programs.
- [How to Contribute](http://paddlepaddle.org/documentation/docs/en/1.3/advanced_usage/development/contribute_to_paddle/index_en.html)
- [How to Contribute](http://paddlepaddle.org/documentation/docs/en/1.4/advanced_usage/development/contribute_to_paddle/index_en.html)
We appreciate your contributions!
......
README_cn.md
浏览文件 @ 82d31503
......@@ -3,8 +3,8 @@
[English](./README.md) | 简体中文
[![Build Status](https://travis-ci.org/PaddlePaddle/Paddle.svg?branch=develop)](https://travis-ci.org/PaddlePaddle/Paddle)
[![Documentation Status](https://img.shields.io/badge/docs-latest-brightgreen.svg?style=flat)](http://paddlepaddle.org/documentation/docs/en/1.3/beginners_guide/index_en.html)
[![Documentation Status](https://img.shields.io/badge/中文文档-最新-brightgreen.svg)](http://paddlepaddle.org/documentation/docs/zh/1.3/beginners_guide/index.html)
[![Documentation Status](https://img.shields.io/badge/docs-latest-brightgreen.svg?style=flat)](http://www.paddlepaddle.org/documentation/docs/en/1.4/beginners_guide/index_en.html)
[![Documentation Status](https://img.shields.io/badge/中文文档-最新-brightgreen.svg)](http://www.paddlepaddle.org/documentation/docs/zh/1.4/beginners_guide/index_cn.html)
[![Release](https://img.shields.io/github/release/PaddlePaddle/Paddle.svg)](https://github.com/PaddlePaddle/Paddle/releases)
[![License](https://img.shields.io/badge/license-Apache%202-blue.svg)](LICENSE)
......@@ -16,7 +16,7 @@ PaddlePaddle (PArallel Distributed Deep LEarning) 是一个简单易用、高效
跟进PaddlePaddle最新特性请参考我们的[版本说明](https://github.com/PaddlePaddle/Paddle/releases)
### PaddlePaddle最新版本: [Fluid 1.3.0](https://github.com/PaddlePaddle/Paddle/tree/release/1.3)
### PaddlePaddle最新版本: [Fluid 1.4.1](https://github.com/PaddlePaddle/Paddle/tree/release/1.4)
### 安装最新稳定版本:
```
# Linux CPU
......@@ -24,9 +24,9 @@ pip install paddlepaddle
# Linux GPU cuda9cudnn7
pip install paddlepaddle-gpu
# Linux GPU cuda8cudnn7
pip install paddlepaddle-gpu==1.3.0.post87
pip install paddlepaddle-gpu==1.4.1.post87
# Linux GPU cuda8cudnn5
pip install paddlepaddle-gpu==1.3.0.post85
pip install paddlepaddle-gpu==1.4.1.post85
# 其他平台上的安装指引请参考 http://paddlepaddle.org/
```
......@@ -57,26 +57,26 @@ pip install paddlepaddle-gpu==1.3.0.post85
## 安装
推荐阅读官网上的[安装说明](http://paddlepaddle.org/documentation/docs/zh/1.3/beginners_guide/install/index_cn.html)
推荐阅读官网上的[安装说明](http://www.paddlepaddle.org/documentation/docs/zh/1.4/beginners_guide/install/index_cn.html)
## 文档
我们提供[英文](http://paddlepaddle.org/documentation/docs/en/1.3/beginners_guide/index_en.html)和
[中文](http://paddlepaddle.org/documentation/docs/zh/1.3/beginners_guide/index.html) 文档
我们提供[英文](http://www.paddlepaddle.org/documentation/docs/en/1.4/beginners_guide/index_en.html)和
[中文](http://www.paddlepaddle.org/documentation/docs/zh/1.4/beginners_guide/install/index_cn.html) 文档
- [深度学习101](https://github.com/PaddlePaddle/book)
或许您想从这个在线交互式书籍开始，可以在Jupyter Notebook中运行
- [分布式训练](http://paddlepaddle.org/documentation/docs/zh/1.3/user_guides/howto/training/multi_node.html)
- [分布式训练](http://paddlepaddle.org/documentation/docs/zh/1.4/user_guides/howto/training/multi_node.html)
可以在MPI集群上运行分布式训练任务
- [Python API](http://paddlepaddle.org/documentation/docs/zh/1.3/api_cn/index_cn.html)
- [Python API](http://paddlepaddle.org/documentation/docs/zh/1.4/api_cn/index_cn.html)
新的API支持代码更少更简洁的程序
- [贡献方式](http://paddlepaddle.org/documentation/docs/zh/1.3/advanced_usage/development/contribute_to_paddle/index_cn.html)
- [贡献方式](http://paddlepaddle.org/documentation/docs/zh/1.4/advanced_usage/development/contribute_to_paddle/index_cn.html)
欢迎您的贡献!
......
RELEASE.cn.md 已删除 100644 → 0
浏览文件 @ 5d81651a
# v0.11.0版本
## PaddlePaddle Fluid
- PaddlePaddle发布版本v0.11.0包含一个新的特性*PaddlePaddle Fluid*. Fluid 是设计用来让用户像Pytorch和Tensorflow Eager Execution一样执行程序。在这些系统中，不再有*模型*这个概念，应用也不再包含一个用于描述Operator图或者一系列层的符号描述，而是像通用程序那样描述训练或者预测的过程。而Fluid与PyTorch或Eager Execution的区别在于Fluid不依赖Python提供的控制流，例如 if-else-then或者for，而是提供了基于C++实现的控制流并暴露了对应的用with语法实现的Python接口。例如：
https://github.com/PaddlePaddle/Paddle/blob/3df78ed2a98d37f7ae6725894cc7514effd5664b/python/paddle/v2/fluid/tests/test_while_op.py#L36-L44
- 在v0.11.0版本中，我们提供了一个C++类`Executor`用于运行一个Fluid程序。Executor类似一个解释器。在未来的版本中，我们将提升和优化Executor成为一个调试器，就像GDB。并可能提供一些编译器，这个编译器会读取一个上文所描述的应用然后编译成一个等价的
源代码，这个源代码可以被nvcc编译成可以使用CUDA的二进制，或者被icc编译成可以充分利用Intel CPU的二进制。
## 新特点
* 发布 `PaddlePaddle Fluid`。
* 增加了用于模型预测的C-API。
* 用Fluid API实现了一个简单的GAN的例子。
* 增加了关于性能调优的文档。
* 为`paddle.v2.dataset`下载数据集提供了重试机制.
* C++中使用protobuf-lite替换protobuf减少了二进制的大小。
* 发布了新特性 [Elastic Deep Learning (EDL)](https://github.com/PaddlePaddle/cloud/tree/develop/doc/autoscale/experiment).
* 基于Bazel API利用cmake实现了一个的新的构建系统函数库。
* 当使用编译选项`WITH_MKL=ON`时自动下载和编译Intel® [MKLML](https://github.com/01org/mkl-dnn/releases/download/v0.11/mklml_lnx_2018.0.1.20171007.tgz) 函数库.
* [Intel® MKL-DNN on PaddlePaddle](https://github.com/PaddlePaddle/Paddle/tree/develop/doc/design/mkldnn):
- 完成了 11个 MKL-DNN 层: Convolution, Fully connectivity, Pooling, ReLU, Tanh, ELU, Softmax, BatchNorm, AddTo, Concat, LRN。
- 完成了 3个 MKL-DNN 网络: VGG-19, ResNet-50, GoogleNet
- 基于Intel Skylake 6148 CPU的[性能测试](https://github.com/PaddlePaddle/Paddle/blob/develop/benchmark/IntelOptimizedPaddle.md) : 相对于MKLML有2~3倍的训练加速。
* 增加 [softsign activation](http://www.paddlepaddle.org/docs/develop/documentation/zh/api/v2/config/activation.html#softsign)
* 增加 [dot product layer](http://www.paddlepaddle.org/docs/develop/documentation/zh/api/v2/config/layer.html#dot-prod)
* 增加 [L2 distance layer](http://www.paddlepaddle.org/docs/develop/documentation/zh/api/v2/config/layer.html#l2-distance)
* 增加 [sub-nested sequence layer](http://www.paddlepaddle.org/docs/develop/documentation/zh/api/v2/config/layer.html#sub-nested-seq)
* 增加 [kmax sequence score layer](http://www.paddlepaddle.org/docs/develop/documentation/zh/api/v2/config/layer.html#kmax-sequence-score)
* 增加 [sequence slice layer](http://www.paddlepaddle.org/docs/develop/documentation/zh/api/v2/config/layer.html#seq-slice)
* 增加 [row convolution layer](http://www.paddlepaddle.org/docs/develop/documentation/zh/api/v2/config/layer.html#row-conv)
* 增加移动端友好的网页
## 改进
* 使用一个Python`whl`包即可安装.
* [V2 API可以实现用户定制化评估](https://github.com/PaddlePaddle/models/tree/develop/ltr#训练过程中输出自定义评估指标)。
* 将 `PADDLE_ONLY_CPU` 改为 `PADDLE_WITH_GPU`, 因为我们会支持多种设备。
* 删除了有一些bug的BarrierStat。
* 清理和删除了paddle::Parameter中未使用的函数。
* 删除了ProtoDataProvider。
* Huber loss同时支持回归和分类。
* 为sequence pooling 层增加`stride`参数。
* v2 API自动使用cudnn batch normalization。
* 可以使用一个固定的参数名共享BN层的参数。
* 2D convolution operation支持variable-dimension input特性。
* 重构cmake中关于CUDA的部分并实现自动检测GPU架构的功能。
* 优化网页导航。
## 错误修复
* 修复ROI pooling的Bug. cc9a761
* 修复当label是dense vector是AUC变成0的问题. #5274
* 修复WarpCTC 层的Bug.
# v0.10.0版本
我们非常高兴发布了PaddlePaddle V0.10.0版，并开发了新的[Python API](http://research.baidu.com/paddlepaddles-new-api-simplifies-deep-learning-programs/)。
- 旧的Python API由于难以学习和使用已经过时了。使用旧版本的API至少需要两份python文件，分别是定义数据生成器和定义网络拓扑结构的文件。用户通过运行`paddle_trainer`的C++程序来启动PaddlePaddle任务，该程序调用Python解释器来运行定义网络拓扑结构的文件，然后通过迭代加载数据生成器提供的小批量数据启动训练循环。这与Python的现代编辑方式不符，比如Jupyter Notebook。
- 新版的API被称为 *V2 API*，允许我们在单个.py文件中，通过编辑更短的Python程序来定义网络结构和数据。此外，该Python程序也可以在Jupyter Notebook中运行，因为PaddlePaddle可以作为共享库来被Python程序加载和使用。
基于新的API，我们提供了一个在线的学习文档 [Deep Learning 101](http://book.paddlepaddle.org/index.en.html) 及其[中文版本](http://book.paddlepaddle.org/)。
我们还致力于迭代更新新版API的在线文档，并将新版API引入分布式集群（包括MPI和Kubernetes）训练中。我们将在下一个版本中发布更多的内容。
## 新特点
* 发布新版[Python API](http://research.baidu.com/paddlepaddles-new-api-simplifies-deep-learning-programs/)。
* 发布深度学习系列课程 [Deep Learning 101](http://book.paddlepaddle.org/index.en.html) 及其[中文版本](http://book.paddlepaddle.org/)。
* 支持矩形输入的CNN。
* 为seqlastin和seqfirstin提供stride pooling。
* 在`trainer_config_helpers`中暴露`seq_concat_layer/seq_reshape_layer`。
* 添加公共数据集包：CIFAR，MNIST，IMDB，WMT14，CONLL05，movielens，imikolov。
* 针对Single Shot Multibox Detection增加 Prior box layer。
* 增加光滑的L1损失。
* 在V2 API中增加 data reader 创建器和修饰器。
* 增加cmrnorm投影的CPU实现。
## 改进
* 提供`paddle_trainer`的Python virtualenv支持。
* 增加代码自动格式化的pre-commit hooks。
* 升级protobuf到3.x版本。
* 在Python数据生成器中提供一个检测数据类型的选项。
* 加速GPU中average层的后向反馈计算。
* 细化文档。
* 使用Travis-CI检查文档中的死链接。
* 增加解释`sparse_vector`的示例。
* 在layer_math.py中添加ReLU。
* 简化Quick Start示例中的数据处理流程。
* 支持CUDNN Deconv。
* 在v2 API中增加数据feeder。
* 在情感分析示例的演示中增加对标准输入流中样本的预测。
* 提供图像预处理的多进程接口。
* 增加V1 API的基准文档。
* 在`layer_math.py`中增加ReLU。
* 提供公共数据集的自动下载包。
* 将`Argument::sumCost`重新命名为`Argument::sum`，并暴露给python。
* 为矩阵相关的表达式评估增加一个新的`TensorExpression`实现。
* 增加延迟分配来优化批处理多表达式计算。
* 增加抽象的类函数及其实现：
* `PadFunc` 和 `PadGradFunc`。
* `ContextProjectionForwardFunc` 和 `ContextProjectionBackwardFunc`。
* `CosSimBackward` 和 `CosSimBackwardFunc`。
* `CrossMapNormalFunc` 和 `CrossMapNormalGradFunc`。
* `MulFunc`。
* 增加`AutoCompare`和`FunctionCompare`类，使得编写比较gpu和cpu版本函数的单元测试更容易。
* 生成`libpaddle_test_main.a`并删除测试文件内的主函数。
* 支持PyDataProvider2中numpy的稠密向量。
* 清理代码库，删除一些复制粘贴的代码片段：
* 增加`SparseRowMatrix`的抽样类`RowBuffer`。
* 清理`GradientMachine`的接口。
* 在layer中增加`override`关键字。
* 简化`Evaluator::create`，使用`ClassRegister`来创建`Evaluator`。
* 下载演示的数据集时检查MD5校验。
* 添加`paddle::Error`，用于替代Paddle中的`LOG(FATAL)`。
## 错误修复
* 检查`recurrent_group`的layer输入类型。
* 不要用.cu源文件运行`clang-format`。
* 修复`LogActivation`的使用错误。
* 修复运行`test_layerHelpers`多次的错误。
* 修复seq2seq示例超出消息大小限制的错误。
* 修复在GPU模式下dataprovider转换的错误。
* 修复`GatedRecurrentLayer`中的错误。
* 修复在测试多个模型时`BatchNorm`的错误。
* 修复paramRelu在单元测试时崩溃的错误。
* 修复`CpuSparseMatrix`编译时相关的警告。
* 修复`MultiGradientMachine`在`trainer_count > batch_size`时的错误。
* 修复`PyDataProvider2`阻止异步加载数据的错误。
RELEASE.md
浏览文件 @ 82d31503
# Release v0.11.0
# Release Note
## PaddlePaddle Fluid
- Release 0.11.0 includes a new feature *PaddlePaddle Fluid*. Fluid is
designed to allow users to program like PyTorch and TensorFlow Eager Execution.
In these systems, there is no longer the concept *model* and applications
do not include a symbolic description of a graph of operators nor a sequence
of layers. Instead, applications look exactly like a usual program that
describes a process of training or inference. The difference between
Fluid and PyTorch or Eager Execution is that Fluid doesn't rely on Python's
control-flow, `if-then-else` nor `for`. Instead, Fluid provides its
C++ implementations and their Python binding using the `with` statement. For an example
https://github.com/PaddlePaddle/Paddle/blob/3df78ed2a98d37f7ae6725894cc7514effd5664b/python/paddle/v2/fluid/tests/test_while_op.py#L36-L44
- In 0.11.0, we provides a C++ class `Executor` to run a Fluid program.
Executor works like an interpreter. In future version, we will improve
`Executor` into a debugger like GDB, and we might provide some compilers,
which, for example, takes an application like the above one, and outputs
an equivalent C++ source program, which can be compiled using
[`nvcc`](http://docs.nvidia.com/cuda/cuda-compiler-driver-nvcc/index.html)
to generate binaries that use CUDA, or using
[`icc`](https://software.intel.com/en-us/c-compilers) to generate binaries
that make full use of Intel CPUs.
## New Features
* Release `PaddlePaddle Fluid`.
* Add C-API for model inference
* Use fluid API to create a simple GAN demo.
* Add develop guide about performance tunning.
* Add retry when download `paddle.v2.dataset`.
* Linking protobuf-lite not protobuf in C++. Reduce the binary size.
* Feature [Elastic Deep Learning (EDL)](https://github.com/PaddlePaddle/cloud/tree/develop/doc/autoscale/experiment) released.
* A new style cmake functions for Paddle. It is based on Bazel API.
* Automatically download and compile with Intel® [MKLML](https://github.com/01org/mkl-dnn/releases/download/v0.11/mklml_lnx_2018.0.1.20171007.tgz) library as CBLAS when build `WITH_MKL=ON`.
* [Intel® MKL-DNN on PaddlePaddle](https://github.com/PaddlePaddle/Paddle/tree/develop/doc/design/mkldnn):
- Complete 11 MKL-DNN layers: Convolution, Fully connectivity, Pooling, ReLU, Tanh, ELU, Softmax, BatchNorm, AddTo, Concat, LRN.
- Complete 3 MKL-DNN networks: VGG-19, ResNet-50, GoogleNet
- [Benchmark](https://github.com/PaddlePaddle/Paddle/blob/develop/benchmark/IntelOptimizedPaddle.md) on Intel Skylake 6148 CPU: 2~3x training speedup compared with MKLML.
* Add the [`softsign` activation](http://www.paddlepaddle.org/docs/develop/documentation/zh/api/v2/config/activation.html#softsign).
* Add the [dot product layer](http://www.paddlepaddle.org/docs/develop/documentation/zh/api/v2/config/layer.html#dot-prod).
* Add the [L2 distance layer](http://www.paddlepaddle.org/docs/develop/documentation/zh/api/v2/config/layer.html#l2-distance).
* Add the [sub-nested sequence layer](http://www.paddlepaddle.org/docs/develop/documentation/zh/api/v2/config/layer.html#sub-nested-seq).
* Add the [kmax sequence score layer](http://www.paddlepaddle.org/docs/develop/documentation/zh/api/v2/config/layer.html#kmax-sequence-score).
* Add the [sequence slice layer](http://www.paddlepaddle.org/docs/develop/documentation/zh/api/v2/config/layer.html#seq-slice).
* Add the [row convolution layer](http://www.paddlepaddle.org/docs/develop/documentation/zh/api/v2/config/layer.html#row-conv)
* Add mobile friendly webpages.
## Improvements
* Build and install using a single `whl` package.
* [Custom evaluating in V2 API](https://github.com/PaddlePaddle/models/tree/develop/ltr#训练过程中输出自定义评估指标).
* Change `PADDLE_ONLY_CPU` to `PADDLE_WITH_GPU`, since we will support many kinds of devices.
* Remove buggy BarrierStat.
* Clean and remove unused functions in paddle::Parameter.
* Remove ProtoDataProvider.
* Huber loss supports both regression and classification.
* Add the `stride` parameter for sequence pooling layers.
* Enable v2 API use cudnn batch normalization automatically.
* The BN layer's parameter can be shared by a fixed the parameter name.
* Support variable-dimension input feature for 2D convolution operation.
* Refine cmake about CUDA to automatically detect GPU architecture.
* Improved website navigation.
## Bug Fixes
* Fix bug in ROI pooling. cc9a761
* Fix AUC is zero when label is dense vector. #5274
* Fix bug in WarpCTC layer.
# Release v0.10.0
We are glad to release version 0.10.0. In this version, we are happy to release the new
[Python API](http://research.baidu.com/paddlepaddles-new-api-simplifies-deep-learning-programs/).
- Our old Python API is kind of out of date. It's hard to learn and hard to
use. To write a PaddlePaddle program using the old API, we'd have to write
at least two Python files: one `data provider` and another one that defines
the network topology. Users start a PaddlePaddle job by running the
`paddle_trainer` C++ program, which calls Python interpreter to run the
network topology configuration script and then start the training loop,
which iteratively calls the data provider function to load minibatches.
This prevents us from writing a Python program in a modern way, e.g., in the
Jupyter Notebook.
- The new API, which we often refer to as the *v2 API*, allows us to write
much shorter Python programs to define the network and the data in a single
.py file. Also, this program can run in Jupyter Notebook, since the entry
point is in Python program and PaddlePaddle runs as a shared library loaded
and invoked by this Python program.
Basing on the new API, we delivered an online interative
book, [Deep Learning 101](http://book.paddlepaddle.org/index.en.html)
and [its Chinese version](http://book.paddlepaddle.org/).
We also worked on updating our online documentation to describe the new API.
But this is an ongoing work. We will release more documentation improvements
in the next version.
We also worked on bring the new API to distributed model training (via MPI and
Kubernetes). This work is ongoing. We will release more about it in the next
version.
## New Features
* We release [new Python API](http://research.baidu.com/paddlepaddles-new-api-simplifies-deep-learning-programs/).
* Deep Learning 101 book in [English](http://book.paddlepaddle.org/index.en.html) and [Chinese](http://book.paddlepaddle.org/).
* Support rectangle input for CNN.
* Support stride pooling for seqlastin and seqfirstin.
* Expose `seq_concat_layer/seq_reshape_layer` in `trainer_config_helpers`.
* Add dataset package: CIFAR, MNIST, IMDB, WMT14, CONLL05, movielens, imikolov.
* Add Priorbox layer for Single Shot Multibox Detection.
* Add smooth L1 cost.
* Add data reader creator and data reader decorator for v2 API.
* Add the CPU implementation of cmrnorm projection.
## Improvements
* Support Python virtualenv for `paddle_trainer`.
* Add pre-commit hooks, used for automatically format our code.
* Upgrade protobuf to version 3.x.
* Add an option to check data type in Python data provider.
* Speedup the backward of average layer on GPU.
* Documentation refinement.
* Check dead links in documents using Travis-CI.
* Add a example for explaining `sparse_vector`.
* Add ReLU in layer_math.py
* Simplify data processing flow for Quick Start.
* Support CUDNN Deconv.
* Add data feeder in v2 API.
* Support predicting the samples from sys.stdin for sentiment demo.
* Provide multi-proccess interface for image preprocessing.
* Add benchmark document for v1 API.
* Add ReLU in `layer_math.py`.
* Add packages for automatically downloading public datasets.
* Rename `Argument::sumCost` to `Argument::sum` since class `Argument` is nothing with cost.
* Expose Argument::sum to Python
* Add a new `TensorExpression` implementation for matrix-related expression evaluations.
* Add lazy assignment for optimizing the calculation of a batch of multiple expressions.
* Add abstract calss `Function` and its implementation:
* `PadFunc` and `PadGradFunc`.
* `ContextProjectionForwardFunc` and `ContextProjectionBackwardFunc`.
* `CosSimBackward` and `CosSimBackwardFunc`.
* `CrossMapNormalFunc` and `CrossMapNormalGradFunc`.
* `MulFunc`.
* Add class `AutoCompare` and `FunctionCompare`, which make it easier to write unit tests for comparing gpu and cpu version of a function.
* Generate `libpaddle_test_main.a` and remove the main function inside the test file.
* Support dense numpy vector in PyDataProvider2.
* Clean code base, remove some copy-n-pasted code snippets:
* Extract `RowBuffer` class for `SparseRowMatrix`.
* Clean the interface of `GradientMachine`.
* Use `override` keyword in layer.
* Simplify `Evaluator::create`, use `ClassRegister` to create `Evaluator`s.
* Check MD5 checksum when downloading demo's dataset.
* Add `paddle::Error` which intentially replace `LOG(FATAL)` in Paddle.
## Bug Fixes
* Check layer input types for `recurrent_group`.
* Don't run `clang-format` with .cu source files.
* Fix bugs with `LogActivation`.
* Fix the bug that runs `test_layerHelpers` multiple times.
* Fix the bug that the seq2seq demo exceeds protobuf message size limit.
* Fix the bug in dataprovider converter in GPU mode.
* Fix a bug in `GatedRecurrentLayer`.
* Fix bug for `BatchNorm` when testing more than one models.
* Fix broken unit test of paramRelu.
* Fix some compile-time warnings about `CpuSparseMatrix`.
* Fix `MultiGradientMachine` error when `trainer_count > batch_size`.
* Fix bugs that prevents from asynchronous data loading in `PyDataProvider2`.
# Release v0.9.0
## New Features:
* New Layers
* bilinear interpolation layer.
* spatial pyramid-pool layer.
* de-convolution layer.
* maxout layer.
* Support rectangle padding, stride, window and input for Pooling Operation.
* Add —job=time in trainer, which can be used to print time info without compiler option -WITH_TIMER=ON.
* Expose cost_weight/nce_layer in `trainer_config_helpers`
* Add FAQ, concepts, h-rnn docs.
* Add Bidi-LSTM and DB-LSTM to quick start demo @alvations
* Add usage track scripts.
## Improvements
* Add Travis-CI for Mac OS X. Enable swig unittest in Travis-CI. Skip Travis-CI when only docs are changed.
* Add code coverage tools.
* Refine convolution layer to speedup and reduce GPU memory.
* Speed up PyDataProvider2
* Add ubuntu deb package build scripts.
* Make Paddle use git-flow branching model.
* PServer support no parameter blocks.
## Bug Fixes
* add zlib link to py_paddle
* add input sparse data check for sparse layer at runtime
* Bug fix for sparse matrix multiplication
* Fix floating-point overflow problem of tanh
* Fix some nvcc compile options
* Fix a bug in yield dictionary in DataProvider
* Fix SRL hang when exit.
# Release v0.8.0beta.1
New features:
* Mac OSX is supported by source code. #138
* Both GPU and CPU versions of PaddlePaddle are supported.
* Support CUDA 8.0
* Enhance `PyDataProvider2`
* Add dictionary yield format. `PyDataProvider2` can yield a dictionary with key is data_layer's name, value is features.
* Add `min_pool_size` to control memory pool in provider.
* Add `deb` install package & docker image for no_avx machines.
* Especially for cloud computing and virtual machines
* Automatically disable `avx` instructions in cmake when machine's CPU don't support `avx` instructions.
* Add Parallel NN api in trainer_config_helpers.
* Add `travis ci` for Github
Bug fixes:
* Several bugs in trainer_config_helpers. Also complete the unittest for trainer_config_helpers
* Check if PaddlePaddle is installed when unittest.
* Fix bugs in GTX series GPU
* Fix bug in MultinomialSampler
Also more documentation was written since last release.
# Release v0.8.0beta.0
PaddlePaddle v0.8.0beta.0 release. The install package is not stable yet and it's a pre-release version.
Please turn to [here](https://github.com/PaddlePaddle/Paddle/releases) for release note.
cmake/external/ngraph.cmake
浏览文件 @ 82d31503
......@@ -37,7 +37,7 @@ INCLUDE(GNUInstallDirs)
INCLUDE(ExternalProject)
SET(NGRAPH_PROJECT "extern_ngraph")
SET(NGRAPH_GIT_TAG "a444f7a959b7d87f2c117c9b57a4c387759e481e")
SET(NGRAPH_GIT_TAG "127e0dedfaac8c6f2b148cc03bf5f67ac5fbe6fe")
SET(NGRAPH_SOURCES_DIR ${THIRD_PARTY_PATH}/ngraph)
SET(NGRAPH_INSTALL_DIR ${THIRD_PARTY_PATH}/install/ngraph)
SET(NGRAPH_INC_DIR ${NGRAPH_INSTALL_DIR}/include)
......
cmake/external/protobuf.cmake
浏览文件 @ 82d31503
......@@ -221,6 +221,7 @@ FUNCTION(build_protobuf TARGET_NAME BUILD_FOR_HOST)
-DCMAKE_BUILD_TYPE=${THIRD_PARTY_BUILD_TYPE}
-DCMAKE_INSTALL_PREFIX=${PROTOBUF_INSTALL_DIR}
-DCMAKE_INSTALL_LIBDIR=lib
-DBUILD_SHARED_LIBS=OFF
CMAKE_CACHE_ARGS
-DCMAKE_INSTALL_PREFIX:PATH=${PROTOBUF_INSTALL_DIR}
-DCMAKE_BUILD_TYPE:STRING=${THIRD_PARTY_BUILD_TYPE}
......
cmake/external/wbaes.cmake 已删除 100644 → 0
浏览文件 @ 5d81651a
# Copyright (c) 2019 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.
IF(NOT ${WITH_WBAES})
return()
ENDIF(NOT ${WITH_WBAES})
INCLUDE(ExternalProject)
SET(WBAES_DST_DIR "wbaes")
SET(WBAES_INSTALL_ROOT "${THIRD_PARTY_PATH}/install")
SET(WBAES_INSTALL_DIR ${WBAES_INSTALL_ROOT}/${WBAES_DST_DIR})
SET(WBAES_ROOT ${WBAES_INSTALL_DIR})
SET(WBAES_INC_DIR ${WBAES_ROOT}/include)
SET(WBAES_LIB_DIR ${WBAES_ROOT}/lib)
SET(CMAKE_INSTALL_RPATH "${CMAKE_INSTALL_RPATH}" "${WBAES_ROOT}/lib")
SET(CMAKE_INSTALL_RPATH_USE_LINK_PATH TRUE)
IF(APPLE)
SET(WBAES_TAG "v1.0.0" CACHE STRING "" FORCE)
SET(WBAES_URL "http://paddlepaddledeps.bj.bcebos.com/wbaes-sdk.mac.${WBAES_TAG}.tgz" CACHE STRING "" FORCE)
SET(WBAES_LIB ${WBAES_LIB_DIR}/libwbaes.dylib)
SET(WBAES_SHARED_LIB ${WBAES_LIB_DIR}/libwbaes.dylib)
ELSEIF(WIN32)
SET(WBAES_TAG "v1.0.0" CACHE STRING "" FORCE)
SET(WBAES_URL "http://paddlepaddledeps.bj.bcebos.com/wbaes-sdk.windows-x64.${WBAES_TAG}.tgz" CACHE STRING "" FORCE)
SET(WBAES_LIB ${WBAES_LIB_DIR}/libwbaes.lib)
SET(WBAES_SHARED_LIB ${WBAES_LIB_DIR}/libwbaes.dll)
ELSE()
SET(WBAES_TAG "v1.0.2" CACHE STRING "" FORCE)
SET(WBAES_URL "http://paddlepaddledeps.bj.bcebos.com/wbaes-sdk.linux-x86_64.${WBAES_TAG}.tgz" CACHE STRING "" FORCE)
SET(WBAES_LIB ${WBAES_LIB_DIR}/libwbaes.so)
SET(WBAES_SHARED_LIB ${WBAES_LIB_DIR}/libwbaes.so)
ENDIF()
SET(WBAES_PROJECT "extern_wbaes")
MESSAGE(STATUS "WBAES_URL: ${WBAES_URL}, WBAES_LIB: ${WBAES_LIB}")
SET(WBAES_SOURCE_DIR "${THIRD_PARTY_PATH}/wbaes")
SET(WBAES_DOWNLOAD_DIR "${WBAES_SOURCE_DIR}/src/${WBAES_PROJECT}")
ExternalProject_Add(
${WBAES_PROJECT}
${EXTERNAL_PROJECT_LOG_ARGS}
PREFIX ${WBAES_SOURCE_DIR}
URL ${WBAES_URL}
DOWNLOAD_DIR ${WBAES_DOWNLOAD_DIR}
DOWNLOAD_NO_PROGRESS 1
CONFIGURE_COMMAND ""
BUILD_COMMAND ""
INSTALL_COMMAND ""
${CMAKE_COMMAND} -E copy_directory ${WBAES_DOWNLOAD_DIR}/include ${WBAES_INC_DIR} &&
${CMAKE_COMMAND} -E copy_directory ${WBAES_DOWNLOAD_DIR}/lib ${WBAES_LIB_DIR}
)
INCLUDE_DIRECTORIES(${WBAES_INC_DIR})
ADD_LIBRARY(wbaes SHARED IMPORTED GLOBAL)
SET_PROPERTY(TARGET wbaes PROPERTY IMPORTED_LOCATION ${WBAES_LIB})
SET_PROPERTY(TARGET wbaes PROPERTY IMPORTED_NO_SONAME 1)
ADD_DEPENDENCIES(wbaes ${WBAES_PROJECT})
cmake/generic.cmake
浏览文件 @ 82d31503
......@@ -264,14 +264,6 @@ function(cc_library TARGET_NAME)
list(REMOVE_ITEM cc_library_DEPS warpctc)
add_dependencies(${TARGET_NAME} warpctc)
endif()
# Only deps libwbaes.so, not link
if("${cc_library_DEPS};" MATCHES "wbaes;")
list(REMOVE_ITEM cc_library_DEPS wbaes)
if(NOT "${TARGET_NAME}" MATCHES "dynload_wbaes")
list(APPEND cc_library_DEPS dynload_wbaes)
endif()
add_dependencies(${TARGET_NAME} wbaes)
endif()
# Only deps libmklml.so, not link
if("${cc_library_DEPS};" MATCHES "mklml;")
list(REMOVE_ITEM cc_library_DEPS mklml)
......
cmake/inference_lib.cmake
浏览文件 @ 82d31503
......@@ -131,15 +131,6 @@ elseif (NOT CBLAS_FOUND OR WIN32)
)
endif ()
if (WITH_GPU AND NOT WIN32)
set(dgc_dir "${FLUID_INSTALL_DIR}/third_party/install/dgc")
copy(dgc_lib
SRCS ${DGC_INSTALL_DIR}/lib ${DGC_INSTALL_DIR}/include
DSTS ${dgc_dir} ${dgc_dir}
DEPS dgc)
endif()
if (WITH_MKLDNN)
set(dst_dir "${FLUID_INSTALL_DIR}/third_party/install/mkldnn")
copy(mkldnn_lib
......@@ -170,14 +161,6 @@ copy(snappystream_lib
DSTS ${dst_dir} ${dst_dir}/lib
DEPS snappystream)
if (WITH_WBAES)
set(dst_dir "${FLUID_INSTALL_DIR}/third_party/install/wbaes")
copy(wbaes_lib
SRCS ${WBAES_INC_DIR} ${WBAES_LIB}
DSTS ${dst_dir} ${dst_dir}/lib
DEPS wbaes)
endif ()
set(dst_dir "${FLUID_INSTALL_DIR}/third_party/install/zlib")
copy(zlib_lib
SRCS ${ZLIB_INCLUDE_DIR} ${ZLIB_LIBRARIES}
......
paddle/CMakeLists.txt
浏览文件 @ 82d31503
add_subdirectory(scripts)
add_subdirectory(testing)
set(PYTHON_TESTS_DIR ${PADDLE_BINARY_DIR}/python/paddle/fluid/tests CACHE INTERNAL "python tests directory")
# to limit the mobile dependencies
if (NOT LITE_WITH_LIGHT_WEIGHT_FRAMEWORK)
add_subdirectory(scripts)
add_subdirectory(testing)
set(PYTHON_TESTS_DIR ${PADDLE_BINARY_DIR}/python/paddle/fluid/tests CACHE INTERNAL "python tests directory")
endif()
add_subdirectory(fluid)
paddle/fluid/API.spec
浏览文件 @ 82d31503
此差异已折叠。
paddle/fluid/CMakeLists.txt
浏览文件 @ 82d31503
if (LITE_WITH_LIGHT_WEIGHT_FRAMEWORK) # for mobile
add_subdirectory(lite)
return()
endif()
add_subdirectory(memory)
add_subdirectory(platform)
add_subdirectory(framework)
......@@ -7,6 +11,5 @@ add_subdirectory(string)
add_subdirectory(recordio)
add_subdirectory(pybind)
add_subdirectory(train)
add_subdirectory(lite)
# NOTE: please add subdirectory inference at last.
add_subdirectory(inference)
paddle/fluid/framework/data_feed.cc
浏览文件 @ 82d31503
......@@ -72,7 +72,6 @@ bool DataFeed::PickOneFile(std::string* filename) {
}
VLOG(3) << "file_idx_=" << *file_idx_;
*filename = filelist_[(*file_idx_)++];
// LOG(ERROR) << "pick file:" << *filename;
return true;
}
......@@ -242,6 +241,11 @@ void InMemoryDataFeed<T>::SetTrainerNum(int trainer_num) {
trainer_num_ = trainer_num;
}
template <typename T>
void InMemoryDataFeed<T>::SetFleetSendBatchSize(int64_t size) {
fleet_send_batch_size_ = size;
}
template <typename T>
void InMemoryDataFeed<T>::PutInsToChannel(const std::string& ins_str) {
#ifdef _LINUX
......@@ -361,8 +365,13 @@ void InMemoryDataFeed<T>::GlobalShuffle() {
VLOG(3) << "GlobalShuffle() begin, thread_id=" << thread_id_;
auto fleet_ptr = FleetWrapper::GetInstance();
std::vector<std::vector<T*>> send_vec(trainer_num_);
std::vector<int> send_index(trainer_num_);
uint64_t reserve_len = fleet_send_batch_size_ / trainer_num_;
for (auto& vec : send_vec) {
vec.reserve(fleet_send_batch_size_);
vec.reserve(reserve_len);
}
for (int i = 0; i < trainer_num_; ++i) {
send_index[i] = i;
}
std::vector<std::future<int32_t>> total_status;
auto interval = GetMemoryDataInterval();
......@@ -375,7 +384,10 @@ void InMemoryDataFeed<T>::GlobalShuffle() {
int64_t node_id = random_num % trainer_num_;
send_vec[node_id].push_back(&((*memory_data_)[i]));
if (i % fleet_send_batch_size_ == 0 && i != 0) {
for (int j = 0; j < send_vec.size(); ++j) {
// shuffle the sequence of sending to avoid network timeout error
std::random_shuffle(send_index.begin(), send_index.end());
for (int index = 0; index < send_index.size(); ++index) {
int j = send_index[index];
std::string send_str;
SerializeIns(send_vec[j], &send_str);
VLOG(3) << "send str_length=" << send_str.length()
......@@ -388,7 +400,10 @@ void InMemoryDataFeed<T>::GlobalShuffle() {
}
}
}
for (int j = 0; j < send_vec.size(); ++j) {
// shuffle the sequence of sending to avoid network timeout error
std::random_shuffle(send_index.begin(), send_index.end());
for (int index = 0; index < send_index.size(); ++index) {
int j = send_index[index];
if (send_vec[j].size() != 0) {
std::string send_str;
SerializeIns(send_vec[j], &send_str);
......@@ -450,6 +465,17 @@ void MultiSlotDataFeed::Init(
if (slot.is_used()) {
use_slots_.push_back(all_slots_[i]);
use_slots_is_dense_.push_back(slot.is_dense());
std::vector<int> local_shape;
if (slot.is_dense()) {
// for batch size holder if is_dense
if (slot.shape(0) > 0) {
local_shape.push_back(0);
}
}
for (size_t i = 0; i < slot.shape_size(); ++i) {
local_shape.push_back(slot.shape(i));
}
use_slots_shape_.push_back(local_shape);
}
}
feed_vec_.resize(use_slots_.size());
......@@ -505,7 +531,7 @@ bool MultiSlotDataFeed::CheckFile(const char* filename) {
char* endptr = const_cast<char*>(str);
int len = line.length();
for (size_t i = 0; i < all_slots_.size(); ++i) {
int num = strtol(endptr, &endptr, 10);
auto num = strtol(endptr, &endptr, 10);
if (num < 0) {
VLOG(0) << "error: the number of ids is a negative number: " << num;
VLOG(0) << "please check line<" << instance_cout << "> in file<"
......@@ -736,8 +762,8 @@ void MultiSlotDataFeed::PutToFeedVec(
LoD data_lod{offset};
feed_vec_[i]->set_lod(data_lod);
if (use_slots_is_dense_[i]) {
int dim = total_instance / batch_size_;
feed_vec_[i]->Resize({batch_size_, dim});
use_slots_shape_[i][0] = batch_size_;
feed_vec_[i]->Resize(framework::make_ddim(use_slots_shape_[i]));
}
}
#endif
......@@ -769,6 +795,16 @@ void MultiSlotInMemoryDataFeed::Init(
if (slot.is_used()) {
use_slots_.push_back(all_slots_[i]);
use_slots_is_dense_.push_back(slot.is_dense());
std::vector<int> local_shape;
if (slot.is_dense()) {
if (slot.shape(0) > 0) {
local_shape.push_back(0);
}
}
for (size_t i = 0; i < slot.shape_size(); ++i) {
local_shape.push_back(slot.shape(i));
}
use_slots_shape_.push_back(local_shape);
}
}
feed_vec_.resize(use_slots_.size());
......@@ -924,8 +960,8 @@ void MultiSlotInMemoryDataFeed::PutToFeedVec(
LoD data_lod{offset};
feed_vec_[i]->set_lod(data_lod);
if (use_slots_is_dense_[i]) {
int dim = total_instance / batch_size_;
feed_vec_[i]->Resize({batch_size_, dim});
use_slots_shape_[i][0] = batch_size_;
feed_vec_[i]->Resize(framework::make_ddim(use_slots_shape_[i]));
}
}
#endif
......
paddle/fluid/framework/data_feed.h
浏览文件 @ 82d31503
......@@ -94,6 +94,8 @@ class DataFeed {
virtual void SetThreadNum(int thread_num) {}
// This function will do nothing at default
virtual void SetTrainerNum(int trainer_num) {}
// This function will do nothing at default
virtual void SetFleetSendBatchSize(int64_t size) {}
virtual void SetFileListMutex(std::mutex* mutex) {
mutex_for_pick_file_ = mutex;
}
......@@ -140,6 +142,7 @@ class DataFeed {
// object)
std::vector<std::string> all_slots_;
std::vector<std::string> all_slots_type_;
std::vector<std::vector<int>> use_slots_shape_;
std::vector<int>
use_slots_index_; // -1: not used; >=0: the index of use_slots_
......@@ -212,6 +215,7 @@ class InMemoryDataFeed : public PrivateQueueDataFeed<T> {
virtual void SetThreadId(int thread_id);
virtual void SetThreadNum(int thread_num);
virtual void SetTrainerNum(int trainer_num);
virtual void SetFleetSendBatchSize(int64_t size);
virtual void PutInsToChannel(const std::string& ins_str);
virtual void FillMemoryDataToChannel();
virtual void FillChannelToMemoryData();
......
paddle/fluid/framework/data_feed.proto
浏览文件 @ 82d31503
......@@ -19,6 +19,7 @@ message Slot {
required string type = 2;
optional bool is_dense = 3 [ default = false ];
optional bool is_used = 4 [ default = false ];
repeated int32 shape = 5; // we can define N-D Tensor
}
message MultiSlotDesc { repeated Slot slots = 1; }
......
paddle/fluid/framework/data_set.cc
浏览文件 @ 82d31503
......@@ -64,6 +64,17 @@ void DatasetImpl<T>::SetTrainerNum(int trainer_num) {
}
}
// if you run distributed, and want to do global shuffle,
// set this before global shuffle.
// be sure you call CreateReaders before SetFleetSendBatchSize
template <typename T>
void DatasetImpl<T>::SetFleetSendBatchSize(int64_t size) {
fleet_send_batch_size_ = size;
for (auto reader : readers_) {
reader->SetFleetSendBatchSize(size);
}
}
template <typename T>
void DatasetImpl<T>::SetHdfsConfig(const std::string& fs_name,
const std::string& fs_ugi) {
......
paddle/fluid/framework/data_set.h
浏览文件 @ 82d31503
......@@ -47,6 +47,8 @@ class Dataset {
virtual void SetThreadNum(int thread_num) = 0;
// set workers' num
virtual void SetTrainerNum(int trainer_num) = 0;
// set fleet send batch size
virtual void SetFleetSendBatchSize(int64_t size) = 0;
// set fs name and ugi
virtual void SetHdfsConfig(const std::string& fs_name,
const std::string& fs_ugi) = 0;
......@@ -59,6 +61,8 @@ class Dataset {
virtual int GetThreadNum() = 0;
// get worker num
virtual int GetTrainerNum() = 0;
// get fleet send batch size
virtual int64_t GetFleetSendBatchSize() = 0;
// get hdfs config
virtual std::pair<std::string, std::string> GetHdfsConfig() = 0;
// get data fedd desc
......@@ -98,6 +102,7 @@ class DatasetImpl : public Dataset {
virtual void SetFileList(const std::vector<std::string>& filelist);
virtual void SetThreadNum(int thread_num);
virtual void SetTrainerNum(int trainer_num);
virtual void SetFleetSendBatchSize(int64_t size);
virtual void SetHdfsConfig(const std::string& fs_name,
const std::string& fs_ugi);
virtual void SetDataFeedDesc(const std::string& data_feed_desc_str);
......@@ -105,6 +110,7 @@ class DatasetImpl : public Dataset {
virtual const std::vector<std::string>& GetFileList() { return filelist_; }
virtual int GetThreadNum() { return thread_num_; }
virtual int GetTrainerNum() { return trainer_num_; }
virtual int64_t GetFleetSendBatchSize() { return fleet_send_batch_size_; }
virtual std::pair<std::string, std::string> GetHdfsConfig() {
return std::make_pair(fs_name_, fs_ugi_);
}
......@@ -137,6 +143,7 @@ class DatasetImpl : public Dataset {
std::string fs_name_;
std::string fs_ugi_;
unsigned int rand_seed;
int64_t fleet_send_batch_size_;
};
// use std::vector<MultiSlotType> as data type
......
paddle/fluid/framework/details/CMakeLists.txt
浏览文件 @ 82d31503
......@@ -14,6 +14,9 @@ cc_library(multi_devices_graph_check_pass SRCS multi_devices_graph_check_pass.cc
cc_library(alloc_continuous_space_for_grad_pass SRCS alloc_continuous_space_for_grad_pass.cc DEPS graph graph_helper)
cc_library(fuse_adam_op_pass SRCS fuse_adam_op_pass.cc fuse_optimizer_op_pass.cc DEPS graph graph_helper)
cc_library(fuse_sgd_op_pass SRCS fuse_sgd_op_pass.cc fuse_optimizer_op_pass.cc DEPS graph graph_helper)
cc_library(fuse_momentum_op_pass SRCS fuse_momentum_op_pass.cc fuse_optimizer_op_pass.cc DEPS graph graph_helper)
cc_library(record_skip_memory_opt_vars_pass SRCS record_skip_memory_opt_vars_pass.cc DEPS graph graph_helper)
cc_library(variable_visitor SRCS variable_visitor.cc DEPS lod_tensor selected_rows)
......@@ -24,15 +27,19 @@ if(WITH_DISTRIBUTE)
endif()
endif()
set(all_reduce_deps all_reduce_op_handle)
if(WITH_GPU)
set(dgc_deps "")
if(NOT WIN32)
set(dgc_deps dgc)
endif()
nv_library(all_reduce_op_handle SRCS all_reduce_op_handle.cc DEPS op_handle_base scope lod_tensor ddim memory
dynload_cuda variable_visitor ${dgc_deps})
dynload_cuda variable_visitor)
nv_library(fused_all_reduce_op_handle SRCS fused_all_reduce_op_handle.cc DEPS op_handle_base scope lod_tensor ddim memory
dynload_cuda variable_visitor)
if(WITH_DGC)
nv_library(sparse_all_reduce_op_handle SRCS sparse_all_reduce_op_handle.cc DEPS op_handle_base scope
lod_tensor ddim memory dynload_cuda variable_visitor dgc all_reduce_op_handle)
set(all_reduce_deps sparse_all_reduce_op_handle)
endif()
if(WITH_DISTRIBUTE)
nv_library(reduce_op_handle SRCS reduce_op_handle.cc DEPS op_handle_base variable_visitor scope
ddim dynload_cuda selected_rows_functor sendrecvop_rpc)
......@@ -80,7 +87,7 @@ cc_library(sequential_execution_pass SRCS sequential_execution_pass.cc DEPS grap
cc_library(all_reduce_deps_pass SRCS all_reduce_deps_pass.cc DEPS graph graph_helper pass)
cc_library(multi_devices_graph_pass SRCS multi_devices_graph_pass.cc DEPS multi_devices_helper computation_op_handle
scale_loss_grad_op_handle rpc_op_handle fetch_barrier_op_handle all_reduce_op_handle reduce_op_handle broadcast_op_handle fused_broadcast_op_handle)
scale_loss_grad_op_handle rpc_op_handle fetch_barrier_op_handle ${all_reduce_deps} reduce_op_handle broadcast_op_handle fused_broadcast_op_handle)
cc_library(fuse_all_reduce_op_pass SRCS fuse_all_reduce_op_pass.cc DEPS graph graph_helper fused_all_reduce_op_handle)
......@@ -120,4 +127,5 @@ cc_library(build_strategy SRCS build_strategy.cc DEPS
fuse_relu_depthwise_conv_pass
memory_optimize_pass lock_free_optimize_pass
alloc_continuous_space_for_grad_pass fuse_all_reduce_op_pass
fuse_adam_op_pass fuse_sgd_op_pass)
fuse_adam_op_pass fuse_sgd_op_pass fuse_momentum_op_pass
record_skip_memory_opt_vars_pass)
paddle/fluid/framework/details/all_reduce_deps_pass.cc
浏览文件 @ 82d31503
......@@ -13,125 +13,186 @@
// limitations under the License.
#include <algorithm>
#include <memory>
#include <map>
#include <string>
#include <unordered_map>
#include <unordered_set>
#include <utility>
#include <vector>
#include "paddle/fluid/framework/details/all_reduce_deps_pass.h"
#include "paddle/fluid/framework/details/all_reduce_op_handle.h"
#include "paddle/fluid/framework/details/container_cast.h"
#include "paddle/fluid/framework/details/multi_devices_helper.h"
#include "paddle/fluid/framework/details/op_graph_view.h"
#include "paddle/fluid/framework/details/var_handle.h"
#include "paddle/fluid/framework/ir/graph.h"
#include "paddle/fluid/framework/ir/graph_helper.h"
#include "paddle/fluid/framework/ir/pass.h"
#include "paddle/fluid/framework/op_proto_maker.h"
namespace paddle {
namespace framework {
namespace details {
VarHandle* GetValidInput(const OpHandleBase* a) {
for (auto p : a->Inputs()) {
VarHandle* b = dynamic_cast<VarHandle*>(p);
if (b) {
return b;
class AllReduceDepsPass : public ir::Pass {
protected:
void ApplyImpl(ir::Graph* graph) const override {
std::vector<AllReduceOpHandle*> all_reduce_op_handles =
GetSortedAllReduceOps(*graph);
for (size_t i = 1; i < all_reduce_op_handles.size(); ++i) {
auto* dep_var = new DummyVarHandle(graph->CreateControlDepVar());
graph->Get<GraphDepVars>(kGraphDepVars).emplace(dep_var);
all_reduce_op_handles[i - 1]->AddOutput(dep_var);
all_reduce_op_handles[i]->AddInput(dep_var);
}
}
return nullptr;
}
void AllReduceDepsPass::ApplyImpl(ir::Graph* graph) const {
auto graph_ops = ir::FilterByNodeWrapper<OpHandleBase>(*graph);
// get vars order
int order = 0;
std::unordered_map<std::string, int> vars;
// TODO(gongwb): use graph topology sort to find the order of operators.
// Note that must assert topology sort is stable
auto& ops = graph->Get<const std::vector<OpDesc*>>(kStaleProgramOpDescs);
for (auto* op_desc : ops) {
try {
bool is_bk_op =
static_cast<bool>(boost::get<int>(op_desc->GetAttr(
OpProtoAndCheckerMaker::OpRoleAttrName())) &
static_cast<int>(OpRole::kBackward));
if (!is_bk_op) continue;
auto backward_vars =
boost::get<std::vector<std::string>>(op_desc->GetNullableAttr(
OpProtoAndCheckerMaker::OpRoleVarAttrName()));
PADDLE_ENFORCE_EQ(backward_vars.size() % 2, 0);
auto outputs = op_desc->Outputs();
for (auto& o_it : outputs) {
for (auto& v : o_it.second) { // values
vars[v] = order;
VLOG(10) << "in all_reduce_deps_pass:" << v;
}
}
order++;
} catch (boost::bad_get e) {
if (VLOG_IS_ON(10)) {
DebugString(*graph, all_reduce_op_handles);
}
}
std::vector<OpHandleBase*> dist_ops;
// get allreduce ops.
for (auto& op : graph_ops) {
// FIXME(gongwb):add broad cast.
if (op->Name() == "all_reduce" || op->Name() == "reduce") {
dist_ops.push_back(op);
std::vector<AllReduceOpHandle*> GetSortedAllReduceOps(
const ir::Graph& graph) const {
std::vector<AllReduceOpHandle*> all_reduce_op_handles;
std::unordered_map<OpHandleBase*, size_t> pending_ops;
std::unordered_set<OpHandleBase*> ready_ops;
std::unordered_set<OpHandleBase*> next_ready_ops;
auto op_handles = ir::FilterByNodeWrapper<OpHandleBase>(graph);
size_t num_of_ops = op_handles.size();
for (OpHandleBase* op : op_handles) {
size_t not_ready_vars = op->NotReadyInputSize();
if (not_ready_vars) {
pending_ops.insert({op, not_ready_vars});
} else {
ready_ops.insert(op);
}
}
}
VLOG(10) << "dist_ops size:" << dist_ops.size()
<< ", outputs size:" << vars.size() << ", ops size:" << ops.size();
std::sort(dist_ops.begin(), dist_ops.end(), [&](OpHandleBase* op1,
OpHandleBase* op2) {
VarHandle* i0 = dynamic_cast<VarHandle*>(GetValidInput(op1));
VarHandle* i1 = dynamic_cast<VarHandle*>(GetValidInput(op2));
PADDLE_ENFORCE(i0 != nullptr && i1 != nullptr, "%s convert to %s error",
op1->DebugString(), op2->DebugString());
auto l_it = vars.find(i0->name());
auto r_it = vars.find(i1->name());
PADDLE_ENFORCE(l_it != vars.end() && r_it != vars.end(),
"can't find var's name %s and %s in opdesc", i0->name(),
i1->name());
if (l_it->second < r_it->second) return true;
GetSortedAllReduceOps(ready_ops, &all_reduce_op_handles);
size_t has_run_ops = ready_ops.size();
while (has_run_ops != num_of_ops) {
for (auto* op : ready_ops) {
for (auto& ready_var : op->Outputs()) {
for (auto* pend_op : ready_var->PendingOps()) {
auto& deps = --pending_ops[pend_op];
if (deps == 0) {
next_ready_ops.insert(pend_op);
}
}
}
}
if (l_it->second == r_it->second) {
return i0->name() < i1->name();
PADDLE_ENFORCE_NE(next_ready_ops.size(), 0, "There maybe have a cycle.");
ready_ops.clear();
std::swap(ready_ops, next_ready_ops);
GetSortedAllReduceOps(ready_ops, &all_reduce_op_handles);
has_run_ops += ready_ops.size();
}
return all_reduce_op_handles;
}
return false;
});
// add dependency.
auto& sorted_ops = dist_ops;
for (size_t i = 1; i < sorted_ops.size(); ++i) {
auto* dep_var = new DummyVarHandle(graph->CreateControlDepVar());
auto* pre_op = sorted_ops[i - 1];
auto* op = sorted_ops[i];
pre_op->AddOutput(dep_var);
op->AddInput(dep_var);
graph->Get<GraphDepVars>(kGraphDepVars).emplace(dep_var);
void GetSortedAllReduceOps(
const std::unordered_set<OpHandleBase*>& ready_ops,
std::vector<AllReduceOpHandle*>* all_reduce_op_handles) const {
std::vector<AllReduceOpHandle*> current_all_reduce_op_handles;
for (auto& op_handle : ready_ops) {
auto all_reduce_op_handle = dynamic_cast<AllReduceOpHandle*>(op_handle);
if (all_reduce_op_handle) {
current_all_reduce_op_handles.emplace_back(all_reduce_op_handle);
}
}
VLOG(10) << "add all_reduce sequential dependencies between " << pre_op
<< " and " << op;
// NOTE(zcd): For distributed training, it is important to keep the order of
// allReduce on each node consistent. Otherwise, hang may occur.
// Sort the current_all_reduce_op_handles according to the name of input.
sort(current_all_reduce_op_handles.begin(),
current_all_reduce_op_handles.end(),
[](const AllReduceOpHandle* left,
const AllReduceOpHandle* right) -> bool {
auto left_in_vars = DynamicCast<VarHandle>(left->Inputs());
auto right_in_vars = DynamicCast<VarHandle>(right->Inputs());
PADDLE_ENFORCE_GT(left_in_vars.size(), 0);
PADDLE_ENFORCE_EQ(left_in_vars.size(), right_in_vars.size());
return left_in_vars[0]->Name() > right_in_vars[0]->Name();
});
all_reduce_op_handles->insert(all_reduce_op_handles->end(),
current_all_reduce_op_handles.begin(),
current_all_reduce_op_handles.end());
}
VLOG(10) << "pre_op:" << pre_op->DebugString()
<< ", op:" << op->DebugString();
void DebugString(
const ir::Graph& graph,
const std::vector<AllReduceOpHandle*>& all_reduce_op_handles) const {
// get vars order
std::map<int, std::vector<std::string>> vars =
GetSoredGradientsFromStaleProgram(graph);
std::stringstream out;
size_t grads_of_stale_program = 0;
out << "Get Order From kStaleProgramOpDescs: ";
for (auto& var : vars) {
out << "Order " << var.first << " [";
for (auto& var_name : var.second) {
out << var_name << ", ";
++grads_of_stale_program;
}
out << "], ";
}
VLOG(10) << out.str();
std::stringstream out2;
out2 << "Get Order From Topological order: ";
for (auto& op : all_reduce_op_handles) {
bool find_valid_input = false;
for (auto& in_var : op->Inputs()) {
if (dynamic_cast<VarHandle*>(in_var)) {
out2 << in_var->Name() << ", ";
find_valid_input = true;
break;
}
}
PADDLE_ENFORCE(find_valid_input, "Doesn't find valid input.");
}
VLOG(10) << out2.str();
if (grads_of_stale_program != all_reduce_op_handles.size()) {
VLOG(10)
<< "The gradients number of stale program and graph is not equal.";
}
}
}
std::map<int, std::vector<std::string>> GetSoredGradientsFromStaleProgram(
const ir::Graph& graph) const {
std::map<int, std::vector<std::string>> vars;
auto ops = graph.Get<const std::vector<OpDesc*>>(kStaleProgramOpDescs);
int order = 0;
for (auto* op_desc : ops) {
try {
bool is_bk_op =
static_cast<bool>(boost::get<int>(op_desc->GetAttr(
OpProtoAndCheckerMaker::OpRoleAttrName())) &
static_cast<int>(OpRole::kBackward));
if (!is_bk_op) continue;
auto backward_vars =
boost::get<std::vector<std::string>>(op_desc->GetNullableAttr(
OpProtoAndCheckerMaker::OpRoleVarAttrName()));
if (backward_vars.empty()) continue;
PADDLE_ENFORCE_EQ(backward_vars.size() % 2, 0);
for (size_t i = 1; i < backward_vars.size(); i += 2) {
vars[order].emplace_back(backward_vars[i]);
VLOG(1) << "get parameter and gradient: " << backward_vars[i - 1]
<< ", " << backward_vars[i];
}
order++;
} catch (boost::bad_get e) {
}
}
return vars;
}
};
} // namespace details
} // namespace framework
} // namespace paddle
......
paddle/fluid/framework/details/all_reduce_op_handle.cc
浏览文件 @ 82d31503
......@@ -17,18 +17,13 @@
#include "paddle/fluid/framework/details/reduce_and_gather.h"
#include "paddle/fluid/framework/details/variable_visitor.h"
#include "paddle/fluid/framework/operator.h"
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
#include "dgc/dgc.h"
#endif
#include "paddle/fluid/platform/gpu_info.h"
#include "paddle/fluid/platform/profiler.h"
// asynchronous nccl allreduce or synchronous issue:
// https://github.com/PaddlePaddle/Paddle/issues/15049
DEFINE_bool(
sync_nccl_allreduce, false,
sync_nccl_allreduce, true,
"If set true, will call `cudaStreamSynchronize(nccl_stream)`"
"after allreduce, this mode can get better performance in some scenarios.");
......@@ -40,14 +35,11 @@ namespace details {
AllReduceOpHandle::AllReduceOpHandle(ir::Node *node,
const std::vector<Scope *> &local_scopes,
const std::vector<platform::Place> &places,
const platform::NCCLContextMap *ctxs,
bool is_encoded, int nranks)
const platform::NCCLContextMap *ctxs)
: OpHandleBase(node),
local_scopes_(local_scopes),
places_(places),
nccl_ctxs_(ctxs),
is_encoded_(is_encoded),
nranks_(nranks) {
nccl_ctxs_(ctxs) {
if (nccl_ctxs_) {
for (auto &p : places_) {
this->SetDeviceContext(p, nccl_ctxs_->DevCtx(p));
......@@ -62,92 +54,8 @@ AllReduceOpHandle::AllReduceOpHandle(ir::Node *node,
#endif
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
void AllReduceOpHandle::RunImplEncoded() {
platform::RecordEvent record_event(Name());
WaitInputVarGenerated();
auto in_var_handles = DynamicCast<VarHandle>(this->Inputs());
auto out_var_handles = DynamicCast<VarHandle>(this->Outputs());
PADDLE_ENFORCE_EQ(
in_var_handles.size(), places_.size(),
"The NoDummyInputSize should be equal to the number of places.");
PADDLE_ENFORCE_EQ(
in_var_handles.size(), out_var_handles.size(),
"The NoDummyInputSize and NoDummyOutputSize should be equal.");
std::vector<const LoDTensor *> ins;
std::vector<LoDTensor *> outs;
int k = -1;
for (size_t i = 0; i < local_scopes_.size(); ++i) {
auto &local_scope =
local_scopes_[i]->FindVar(kLocalExecScopeName)->Get<Scope *>();
auto original_name =
paddle::framework::GradOriginalVarName(in_var_handles[i]->name());
auto encode_var_name = original_name + g_dgc_encoded;
auto *in_var = local_scope->FindVar(encode_var_name);
PADDLE_ENFORCE_NOT_NULL(in_var);
auto &in = in_var->Get<LoDTensor>();
ins.emplace_back(&in);
auto *out = local_scope->FindVar(out_var_handles[i]->name())
->GetMutable<LoDTensor>();
outs.emplace_back(out);
if (k < 0) {
k = GetKValue(in_var_handles[i]->name());
}
}
PADDLE_ENFORCE(platform::is_gpu_place(ins[0]->place()));
PADDLE_ENFORCE(platform::is_gpu_place(outs[0]->place()));
PADDLE_ENFORCE(nccl_ctxs_, "nccl_ctxs should not be nullptr.");
int dtype = -1;
size_t in_numel = 0;
size_t out_numel = 0;
PADDLE_ENFORCE(nranks_ > 1);
std::vector<std::function<void()>> all_reduce_calls;
for (size_t i = 0; i < local_scopes_.size(); ++i) {
auto &place = places_[i];
auto &in = *ins[i];
void *in_tensor_buf = const_cast<void *>(in.data<void>());
auto &out = *outs[i];
float *out_tensor_buf = out.data<float>();
dtype = (dtype == -1) ? platform::ToNCCLDataType(in.type()) : dtype;
in_numel = (in_numel == 0) ? static_cast<size_t>(in.numel()) : in_numel;
PADDLE_ENFORCE(in_numel % 2 == 0);
PADDLE_ENFORCE(in_numel / 2 == static_cast<size_t>(k));
out_numel = (out_numel == 0) ? static_cast<size_t>(out.numel()) : out_numel;
int dev_id = boost::get<platform::CUDAPlace>(place).device;
auto &nccl_ctx = nccl_ctxs_->at(dev_id);
auto stream = nccl_ctx.stream();
auto comm = nccl_ctx.comm_;
auto &allocator =
platform::DeviceTemporaryAllocator::Instance().Get(place, stream);
int encode_size = 2 * k * sizeof(int);
// dgc use ncclAllGather to get all the encoded data
// so the buffer need nranks.
int buf_size = nranks_ * encode_size;
auto tmp_ious_data = allocator.Allocate(buf_size);
void *gather_buff = reinterpret_cast<void *>(tmp_ious_data->ptr());
VLOG(10) << "in_numel:" << in_numel << ", out_numel:" << out_numel
<< ", nranks:" << nranks_ << ", gather_buf size:" << buf_size
<< ", k:" << k << ", place:" << place << ", dtype:" << dtype;
all_reduce_calls.emplace_back([=] {
PADDLE_ENFORCE(paddle::communication::dgc::sparseAllGReduce(
in_tensor_buf, gather_buff, k, out_tensor_buf, out_numel, comm,
stream));
});
}
void AllReduceOpHandle::RunAllReduceFuncs(
const std::vector<std::function<void()>> &all_reduce_calls) {
this->RunAndRecordEvent([&] {
if (all_reduce_calls.size() == 1UL) {
// Do not use NCCLGroup when manage NCCL by per thread per device
......@@ -178,68 +86,9 @@ void AllReduceOpHandle::RunImplEncoded() {
}
}
}
int AllReduceOpHandle::GetKValue(const std::string &grad_name) {
auto original_name = paddle::framework::GradOriginalVarName(grad_name);
auto var_name = original_name + g_dgc_k;
PADDLE_ENFORCE(local_scopes_.size() > 0);
auto *scope = local_scopes_[0];
auto &local_scope = scope->FindVar(kLocalExecScopeName)->Get<Scope *>();
auto var = local_scope->FindVar(var_name);
PADDLE_ENFORCE_NOT_NULL(var);
auto tensor = var->Get<LoDTensor>().data<float>();
return *tensor;
}
#endif
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
bool AllReduceOpHandle::IsEncoded() {
if (!is_encoded_) {
return false;
}
auto counter_name = g_dgc_counter_name;
auto step_name = g_dgc_rampup_begin_step;
PADDLE_ENFORCE(local_scopes_.size() > 0);
auto *scope = local_scopes_[0];
auto &local_scope = scope->FindVar(kLocalExecScopeName)->Get<Scope *>();
auto count_var = local_scope->FindVar(counter_name);
auto step_var = local_scope->FindVar(step_name);
if (count_var == nullptr || step_var == nullptr) {
PADDLE_THROW("not find count_var:%s or step_var:%s", counter_name,
step_var);
}
float count = *count_var->Get<LoDTensor>().data<float>();
float step = *step_var->Get<LoDTensor>().data<float>();
if (static_cast<int>(count) < static_cast<int>(step)) {
VLOG(10) << "in all_reduce currentstep:" << count
<< " < rampup_begin_step:" << step
<< " so not use sparse all reduce";
return false;
}
return true;
}
#else
bool AllReduceOpHandle::IsEncoded() { return false; }
#endif
void AllReduceOpHandle::RunImpl() {
if (!IsEncoded()) {
RunImplNormal();
return;
}
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
RunImplEncoded();
#else
PADDLE_THROW("Not compiled with CUDA");
#endif
}
void AllReduceOpHandle::RunImplNormal() {
platform::RecordEvent record_event(Name());
WaitInputVarGenerated();
......@@ -300,27 +149,7 @@ void AllReduceOpHandle::RunImplNormal() {
comm, stream));
});
}
this->RunAndRecordEvent([&] {
if (all_reduce_calls.size() == 1UL) {
// Do not use NCCLGroup when manage NCCL by per thread per device
all_reduce_calls[0]();
} else {
platform::NCCLGroupGuard guard;
for (auto &call : all_reduce_calls) {
call();
}
}
});
if (FLAGS_sync_nccl_allreduce) {
for (auto &p : places_) {
int dev_id = boost::get<platform::CUDAPlace>(p).device;
auto &nccl_ctx = nccl_ctxs_->at(dev_id);
auto stream = nccl_ctx.stream();
cudaStreamSynchronize(stream);
}
}
RunAllReduceFuncs(all_reduce_calls);
#else
PADDLE_THROW("Not compiled with CUDA");
#endif
......
paddle/fluid/framework/details/all_reduce_op_handle.h
浏览文件 @ 82d31503
......@@ -28,19 +28,12 @@ namespace paddle {
namespace framework {
namespace details {
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
constexpr char g_dgc_counter_name[] = "__g_dgc_counter__";
constexpr char g_dgc_rampup_begin_step[] = "__g_rampup_begin_step__";
constexpr char g_dgc_encoded[] = "__dgc_encoded__";
constexpr char g_dgc_k[] = "__dgc_k__";
#endif
struct AllReduceOpHandle : public OpHandleBase {
class AllReduceOpHandle : public OpHandleBase {
public:
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
AllReduceOpHandle(ir::Node *node, const std::vector<Scope *> &local_scopes,
const std::vector<platform::Place> &places,
const platform::NCCLContextMap *ctxs,
bool is_encoded = false, int nranks = -1);
const platform::NCCLContextMap *ctxs);
#else
AllReduceOpHandle(ir::Node *node, const std::vector<Scope *> &local_scopes,
const std::vector<platform::Place> &places);
......@@ -54,18 +47,13 @@ struct AllReduceOpHandle : public OpHandleBase {
protected:
void RunImpl() override;
private:
std::vector<Scope *> local_scopes_;
std::vector<platform::Place> places_;
#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
void RunImplEncoded();
void RunAllReduceFuncs(
const std::vector<std::function<void()>> &all_reduce_calls);
const platform::NCCLContextMap *nccl_ctxs_;
bool is_encoded_{false};
int nranks_{-1};
int GetKValue(const std::string &grad_name);
#endif
void RunImplNormal();
bool IsEncoded();
};
} // namespace details
......
paddle/fluid/framework/details/fuse_adam_op_pass.h → paddle/fluid/framework/details/alloc_continuous_space_for_grad_pass.h
浏览文件 @ 82d31503
......@@ -11,43 +11,67 @@
// 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 <algorithm>
#include <string>
#include <unordered_map>
#include <utility>
#include <vector>
#include "paddle/fluid/framework/details/build_strategy.h"
#include "paddle/fluid/framework/details/fuse_optimizer_op_pass.h"
#include "paddle/fluid/framework/details/multi_devices_helper.h"
#include "paddle/fluid/framework/ir/graph.h"
#include "paddle/fluid/framework/ir/graph_helper.h"
namespace paddle {
namespace framework {
namespace details {
class FuseAdamOpPass : public FuseOptimizerOpPass {
private:
virtual const std::string GetOpType() const;
void SetFuseParameterGroupsSize(int group_size);
int GetFuseParameterGroupsSize();
void SetFuseParameterMemorySize(uint64_t memory_size);
uint64_t GetFuseParameterMemorySize();
class AllocContinuousSpaceForGradPass : public ir::Pass {
protected:
void ApplyImpl(ir::Graph *graph) const override;
template <typename AttrType>
void ResetAttribute(const std::string &attr_name, ir::Graph *graph) const;
virtual const std::vector<std::string> GetAuxiliaryVarNames() const;
void SetGroupGradsAndParams(
const std::unordered_map<std::string, ir::Node *> &var_nodes,
const ParamsAndGrads &params_grads,
GroupGradsAndParams *group_grads_params) const;
void SetGroupAccordingToLayers(
const std::unordered_map<std::string, ir::Node *> &var_nodes,
const ParamsAndGrads &params_grads,
GroupGradsAndParams *group_grads_params) const;
void SetGroupAccordingToMemorySize(
const std::unordered_map<std::string, ir::Node *> &var_nodes,
GroupGradsAndParams *group_grads_params) const;
void SetGroupAccordingToGroupSize(
const std::unordered_map<std::string, ir::Node *> &var_nodes,
GroupGradsAndParams *group_grads_params) const;
private:
bool IsSupportedVarType(const proto::VarType::Type &type) const;
// Fuse Adam Ops and Scale Ops which are used to update "Beta1Pow", "Beta2Pow"
virtual void FuseOptimizerOps(
const std::unordered_map<std::string, std::vector<std::string>> &vars_set,
const std::unordered_map<std::string, std::string> &fused_vars_name,
const std::vector<ir::Node *> &adam_ops, ir::Graph *graph) const;
void RecordParamsAndGrads(ir::Node *node, ParamsAndGrads *params_grads) const;
void FuseAdamOps(
const std::unordered_map<std::string, std::vector<std::string>> &vars_set,
const std::unordered_map<std::string, std::string> &fused_vars_name,
const std::vector<ir::Node *> &adam_ops, ir::Graph *graph) const;
void InitFusedVarsAndAllocSpaceForVars(
const std::vector<platform::Place> &places,
const std::vector<Scope *> &local_scopes,
const std::unordered_map<std::string, ir::Node *> &vars,
const std::string &fused_var_name,
const ParamsAndGrads &params_grads) const;
void FuseScaleOps(const std::vector<std::string> &aux_var_set,
const std::string &fused_var_name,
const std::vector<ir::Node *> &adam_ops,
ir::Graph *graph) const;
void AppendAllocSpaceForVarsOp(const std::vector<std::string> &params_name,
const std::vector<std::string> &grads_name,
const std::string &fused_var_name,
BlockDesc *global_block) const;
};
} // namespace details
......
paddle/fluid/framework/details/async_ssa_graph_executor.cc
浏览文件 @ 82d31503
......@@ -64,9 +64,12 @@ void ProcessGraph(std::vector<ir::Graph *> graphs, Scope *scope) {
node->Op()->GetNullableAttr("epmap"));
auto height_section = boost::get<std::vector<int64_t>>(
node->Op()->GetNullableAttr("sections"));
auto trainer_id =
boost::get<int>(node->Op()->GetNullableAttr("trainer_id"));
send_varname_to_ctx[send_var_name] =
operators::distributed::RpcContext(send_var_name, send_varnames,
epmap, height_section);
epmap, height_section,
trainer_id);
VLOG(3) << "find and init an send op: "
<< send_varname_to_ctx[send_var_name];
} else if (node->Name() == "recv") {
......@@ -75,9 +78,11 @@ void ProcessGraph(std::vector<ir::Graph *> graphs, Scope *scope) {
node->Op()->GetNullableAttr("recv_varnames"));
auto epmap = boost::get<std::vector<std::string>>(
node->Op()->GetNullableAttr("epmap"));
auto trainer_id =
boost::get<int>(node->Op()->GetNullableAttr("trainer_id"));
recv_varname_to_ctx[recv_var_name] =
operators::distributed::RpcContext(recv_var_name, recv_varnames,
epmap, {});
epmap, {}, trainer_id);
nodes_to_delete.push_back(node);
VLOG(3) << "find and remove an recv op: "
<< recv_varname_to_ctx[recv_var_name];
......
paddle/fluid/framework/details/build_strategy.cc
浏览文件 @ 82d31503
......@@ -53,8 +53,11 @@ class ParallelExecutorPassBuilder : public ir::PassBuilder {
viz_pass->Set<std::string>("graph_viz_path", new std::string(graph_path));
}
// Note(zcd): record_skip_memory_opt_vars_pass should be the first pass.
AppendPass("record_skip_memory_opt_vars_pass");
if (strategy_.enable_sequential_execution_) {
VLOG(10) << "Add sequential_execution_pass";
VLOG(5) << "Add sequential_execution_pass";
AppendPass("sequential_execution_pass");
}
......@@ -65,7 +68,7 @@ class ParallelExecutorPassBuilder : public ir::PassBuilder {
// Add op fusion.
if (strategy.fuse_relu_depthwise_conv_) {
VLOG(10) << "Add fuse_relu_depthwise_conv_pass";
VLOG(5) << "Add fuse_relu_depthwise_conv_pass";
AppendPass("fuse_relu_depthwise_conv_pass");
}
......@@ -77,19 +80,19 @@ class ParallelExecutorPassBuilder : public ir::PassBuilder {
// Add automatically inplace.
if (strategy_.enable_inplace_) {
VLOG(10) << "Add inplace_pass";
VLOG(5) << "Add inplace_pass";
AppendPass("inplace_pass");
}
if (strategy_.fuse_elewise_add_act_ops_) {
VLOG(10) << "Add fuse_elewise_add_act_pass";
VLOG(5) << "Add fuse_elewise_add_act_pass";
AppendPass("fuse_elewise_add_act_pass");
}
// for single card training, fuse_all_reduce_ops is unnecessary.
// alloc_continuous_space_for_grad_pass should be before of MultiDevPass.
if (strategy_.fuse_all_reduce_ops_) {
VLOG(10) << "Add alloc_continuous_space_for_grad_pass";
VLOG(5) << "Add alloc_continuous_space_for_grad_pass";
AppendPass("alloc_continuous_space_for_grad_pass");
}
......@@ -101,15 +104,15 @@ class ParallelExecutorPassBuilder : public ir::PassBuilder {
"mode.";
strategy_.fuse_all_optimizer_ops_ = false;
} else {
VLOG(10) << "Add alloc_continuous_space_for_grad_pass";
AppendPass("alloc_continuous_space_for_grad_pass");
// NOTE: fuse_all_xx_ops will count the number of xx operator first,
// if the number is zero, fuse_all_reduce_ops will do nothing.
// Currently, only one type of optimization algorithm can be fused.
VLOG(10) << "Add fuse_adam_op_pass";
VLOG(5) << "Add fuse_adam_op_pass";
AppendPass("fuse_adam_op_pass");
VLOG(10) << "Add fuse_sgd_op_pass";
VLOG(5) << "Add fuse_sgd_op_pass";
AppendPass("fuse_sgd_op_pass");
VLOG(5) << "Add fuse_momentum_op_pass";
AppendPass("fuse_momentum_op_pass");
}
}
......@@ -138,16 +141,29 @@ class ParallelExecutorPassBuilder : public ir::PassBuilder {
// A side-effect of that, memory optimize cannot forsee the fetched vars
// , so fetchlist should be set persistable before call the Run interface.
if (strategy_.memory_optimize_) {
VLOG(10) << "Add memory_optimize_pass";
VLOG(5) << "Add memory_optimize_pass";
AppendPass("memory_optimize_pass");
}
// runtime_context_cache pass should be the last pass to enable the attr of
// all original and fused operators. But no operators can be enabled this
// attr if putting it after MultiDevPass.
if (strategy_.cache_runtime_context_) {
VLOG(5) << "Add runtime_context_cache_pass";
AppendPass("runtime_context_cache_pass");
}
if (strategy_.cache_expected_kernel_) {
VLOG(10) << "Add expected_kernel_cache_pass";
AppendPass("expected_kernel_cache_pass");
}
AppendMultiDevPass(strategy_);
if (strategy_.fuse_all_reduce_ops_) {
// NOTE: fuse_all_reduce_ops will count the number of all_reduce operator
// first, if the number is zero, fuse_all_reduce_ops will do nothing.
VLOG(10) << "Add fuse_all_reduce_op_pass";
VLOG(5) << "Add fuse_all_reduce_op_pass";
AppendPass("fuse_all_reduce_op_pass");
}
......@@ -163,22 +179,22 @@ class ParallelExecutorPassBuilder : public ir::PassBuilder {
"graph_printer", new details::GraphvizSSAGraphPrinter);
}
// Verify that the graph is correct for multi-device executor.
AppendPass("multi_devices_check_pass");
if (VLOG_IS_ON(2)) {
AppendPass("all_reduce_deps_pass");
}
if (SeqOnlyAllReduceOps(strategy_)) {
VLOG(10) << "Add all_reduce_deps_pass";
// experimental shows that the program will be faster if append
// all_reduce_deps_pass here.
if (!strategy_.enable_parallel_graph_ &&
(SeqOnlyAllReduceOps(strategy_) ||
strategy.reduce_ == BuildStrategy::ReduceStrategy::kAllReduce)) {
VLOG(5) << "Add all_reduce_deps_pass";
AppendPass("all_reduce_deps_pass");
}
if (strategy_.remove_unnecessary_lock_) {
VLOG(10) << "Add modify_op_lock_and_record_event_pass";
VLOG(5) << "Add modify_op_lock_and_record_event_pass";
AppendPass("modify_op_lock_and_record_event_pass");
}
// Verify that the graph is correct for multi-device executor.
AppendPass("multi_devices_check_pass");
}
// Convert graph to run on multi-devices.
......@@ -188,16 +204,16 @@ class ParallelExecutorPassBuilder : public ir::PassBuilder {
if (strategy_.async_mode_) {
multi_devices_pass = AppendPass("async_multi_devices_pass").get();
} else if (strategy_.is_distribution_) {
VLOG(10)
VLOG(5)
<< "Add dist_multi_devices_pass, multi device parameter server mode";
multi_devices_pass = AppendPass("dist_multi_devices_pass").get();
} else {
if (strategy.reduce_ == BuildStrategy::ReduceStrategy::kAllReduce) {
VLOG(10) << "Add all_reduce_mode_multi_devices_pass";
VLOG(5) << "Add all_reduce_mode_multi_devices_pass";
multi_devices_pass =
AppendPass("all_reduce_mode_multi_devices_pass").get();
} else if (strategy.reduce_ == BuildStrategy::ReduceStrategy::kReduce) {
VLOG(10) << "Add reduce_mode_multi_devices_pass";
VLOG(5) << "Add reduce_mode_multi_devices_pass";
multi_devices_pass = AppendPass("reduce_mode_multi_devices_pass").get();
} else {
PADDLE_THROW("Unknown reduce strategy.");
......@@ -243,7 +259,7 @@ ir::Graph *BuildStrategy::Apply(ir::Graph *graph,
CreatePassesFromStrategy(false);
for (std::shared_ptr<ir::Pass> &pass : pass_builder_->AllPasses()) {
VLOG(3) << "apply " << pass->Type();
VLOG(3) << "BuildStrategy::Apply pass:" << pass->Type();
if (IsMultiDevPass(pass->Type())) {
pass->Erase(kPlaces);
pass->SetNotOwned<const std::vector<platform::Place>>(kPlaces, &places);
......@@ -263,6 +279,7 @@ ir::Graph *BuildStrategy::Apply(ir::Graph *graph,
} else if (pass->Type() == "alloc_continuous_space_for_grad_pass" ||
pass->Type() == "fuse_adam_op_pass" ||
pass->Type() == "fuse_sgd_op_pass" ||
pass->Type() == "fuse_momentum_op_pass" ||
pass->Type() == "fuse_all_reduce_op_pass") {
pass->Erase(kPlaces);
pass->SetNotOwned<const std::vector<platform::Place>>(kPlaces, &places);
......@@ -294,6 +311,9 @@ ir::Graph *BuildStrategy::Apply(ir::Graph *graph,
"GPU, skipped.";
continue;
}
} else if (pass->Type() == "inplace_pass") {
pass->Erase(kUseCuda);
pass->Set<bool>(kUseCuda, new bool(use_cuda));
}
VLOG(3) << "Start Apply Pass " << pass->Type();
graph = pass->Apply(graph);
......@@ -327,4 +347,8 @@ USE_PASS(alloc_continuous_space_for_grad_pass);
USE_PASS(graph_to_program_pass);
USE_PASS(fuse_adam_op_pass);
USE_PASS(fuse_sgd_op_pass);
USE_PASS(fuse_momentum_op_pass);
USE_PASS(fuse_all_reduce_op_pass);
USE_PASS(runtime_context_cache_pass);
USE_PASS(expected_kernel_cache_pass);
USE_PASS(record_skip_memory_opt_vars_pass);
paddle/fluid/framework/details/build_strategy.h
浏览文件 @ 82d31503
......@@ -83,15 +83,19 @@ struct BuildStrategy {
bool sync_batch_norm_{false};
bool memory_optimize_{true};
// TODO(dzhwinter):
// make enable_inplace, memory_optimize_
// memory_early_delete_ true by default
bool enable_inplace_{true};
// FIXME(liuwei1031) disable memory_optimzie and enable_inplace in 1.4
// to open them by default, we need to solve the fetch variable issue
bool memory_optimize_{false};
bool enable_inplace_{false};
bool enable_sequential_execution_{false};
bool fuse_broadcast_op_{false};
// NOTE(zcd): In reduce mode, fusing broadcast ops may make the program
// faster. Because fusing broadcast OP equals delaying the execution of all
// broadcast Ops, in this case, all nccl streams are used only for reduce
// operations for a period of time.
bool fuse_broadcast_ops_{false};
// FIXME(zcd): is_distribution_ is a temporary field, because in pserver mode,
// num_trainers is 1, so the current fields of build_strategy doesn't tell if
......@@ -103,6 +107,9 @@ struct BuildStrategy {
std::vector<std::string> trainers_endpoints_;
bool remove_unnecessary_lock_{true};
bool cache_runtime_context_{false};
bool cache_expected_kernel_{true};
// NOTE:
// Before you add new options, think if it's a general strategy that works
// with other strategy. If not, the strategy should be created through
......
paddle/fluid/framework/details/all_reduce_deps_pass.h → paddle/fluid/framework/details/dgc_const_values.h
浏览文件 @ 82d31503
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
// 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.
......@@ -14,18 +14,18 @@
#pragma once
#include "paddle/fluid/framework/ir/graph.h"
#include "paddle/fluid/framework/ir/pass.h"
#include <string>
namespace paddle {
namespace framework {
namespace details {
// TODO(gongwb): overlap allreduce with backward computation.
class AllReduceDepsPass : public ir::Pass {
protected:
void ApplyImpl(ir::Graph* graph) const override;
};
constexpr char g_dgc_counter_name[] = "__g_dgc_counter__";
constexpr char g_dgc_rampup_begin_step[] = "__g_rampup_begin_step__";
constexpr char g_dgc_u[] = "__dgc_u__";
constexpr char g_dgc_v[] = "__dgc_v__";
constexpr char g_dgc_k[] = "__dgc_k__";
constexpr char g_dgc_encoded[] = "__dgc_encoded__";
} // namespace details
} // namespace framework
......
paddle/fluid/framework/details/eager_deletion_op_handle.cc
浏览文件 @ 82d31503
......@@ -34,7 +34,7 @@ EagerDeletionOpHandle::EagerDeletionOpHandle(
AtomicReferenceCountMap *ref_cnts)
: OpHandleBase(node),
scope_(scope),
var_names_(var_names),
var_names_(var_names.begin(), var_names.end()),
gc_(gc),
ref_cnts_(ref_cnts) {
#ifdef PADDLE_WITH_CUDA
......
paddle/fluid/framework/details/eager_deletion_op_handle.h
浏览文件 @ 82d31503
......@@ -15,7 +15,10 @@
#pragma once
#include <deque>
#include <memory>
#include <string>
#include <unordered_set>
#include <vector>
#include "paddle/fluid/framework/details/op_handle_base.h"
#include "paddle/fluid/framework/details/reference_count_pass_helper.h"
......@@ -37,6 +40,13 @@ class EagerDeletionOpHandle : public OpHandleBase {
std::string Name() const override;
/**
* Currently, EagerDeletionOpHandle has the highest priority.
* This priority settings speed up gc 15% in Transformer
* V100 8-GPU model.
*/
Priority GetPriority() const override { return kHighest; }
protected:
void RunImpl() override;
......@@ -44,7 +54,7 @@ class EagerDeletionOpHandle : public OpHandleBase {
void ClearGarbages(std::deque<std::shared_ptr<memory::Allocation>> *garbages);
const Scope *scope_;
std::unordered_set<std::string> var_names_;
std::vector<std::string> var_names_;
GarbageCollector *gc_; // not own
AtomicReferenceCountMap *ref_cnts_; // not own
#ifdef PADDLE_WITH_CUDA
......
paddle/fluid/framework/details/eager_deletion_pass.cc
浏览文件 @ 82d31503
......@@ -33,6 +33,19 @@ namespace details {
using OpToVarNameSetMap =
std::unordered_map<ComputationOpHandle *, std::unordered_set<std::string>>;
static std::map<size_t, std::unordered_set<std::string>> VarsGroupByScopeIdx(
const OpToVarNameSetMap &map) {
std::map<size_t, std::unordered_set<std::string>> result;
for (auto &pair : map) {
size_t scope_idx = pair.first->GetScopeIdx();
auto &var_set = result[scope_idx];
for (auto &var : pair.second) {
var_set.insert(var);
}
}
return result;
}
// Check whether the variable is LoDTensor based on static VarDesc info
static bool IsLoDTensor(VarDesc *var) {
return var->Proto()->type().type() == proto::VarType::LOD_TENSOR;
......@@ -236,6 +249,14 @@ void EagerDeletionPass::ApplyImpl(ir::Graph *graph) const {
VLOG(10) << "FLAGS_memory_fraction_of_eager_deletion = " << memory_fraction;
VLOG(10) << "Create " << op_vars_map.size() << " EagerDeletionOpHandle(s)";
if (VLOG_IS_ON(10)) {
auto vars_group_by_scope_idx = VarsGroupByScopeIdx(op_vars_map);
for (auto &pair : vars_group_by_scope_idx) {
VLOG(10) << "Scope " << pair.first << " has " << pair.second.size()
<< " vars";
}
}
auto while_op_eager_deletion_pass =
ir::PassRegistry::Instance().Get("while_op_eager_deletion_pass");
while_op_eager_deletion_pass->Apply(graph);
......
paddle/fluid/framework/details/execution_strategy.h
浏览文件 @ 82d31503
......@@ -29,7 +29,7 @@ struct ExecutionStrategy {
// this will loss 15%+ performance.
// Please be aware about this parameters.
size_t num_iteration_per_drop_scope_{1};
ExecutorType type_{kDefault};
ExecutorType type_{kExperimental};
bool dry_run_{false};
size_t num_iteration_per_run_{1}; // only use with async_ssa_graph_executor
// and pyreader with data queue
......
paddle/fluid/framework/details/fast_threaded_ssa_graph_executor.cc
浏览文件 @ 82d31503
......@@ -13,6 +13,7 @@
// limitations under the License.
#include "paddle/fluid/framework/details/fast_threaded_ssa_graph_executor.h"
#include <memory>
#include <queue>
#include <string>
#include <unordered_map>
#include <vector>
......@@ -56,6 +57,7 @@ FeedFetchList FastThreadedSSAGraphExecutor::Run(
fetches.resize(fetch_tensors.size());
std::unordered_map<std::string, std::vector<VarHandleBase *>> fetched_vars;
std::vector<FetchOpHandle *> fetch_ops;
std::vector<OpHandleBase *> ready_fetch_ops;
for (auto &fetch_var_name : fetch_tensors) {
for (auto &var_map : graph_->Get<details::GraphVars>(details::kGraphVars)) {
......@@ -70,8 +72,9 @@ FeedFetchList FastThreadedSSAGraphExecutor::Run(
auto &var_name = fetch_tensors[i];
auto fetched_var_it = fetched_vars.find(var_name);
PADDLE_ENFORCE(fetched_var_it != fetched_vars.end(),
"Cannot find fetched variable.(Perhaps the main_program "
"is not set to ParallelExecutor)");
"Cannot find fetched variable(%s).(Perhaps the main_program "
"is not set to ParallelExecutor)",
var_name);
auto &vars = fetched_var_it->second;
......@@ -88,7 +91,11 @@ FeedFetchList FastThreadedSSAGraphExecutor::Run(
op->AddInput(var);
}
(*op_deps)[op] = static_cast<int>(op->NotReadyInputSize());
int dep = static_cast<int>(op->NotReadyInputSize());
(*op_deps)[op] = dep;
if (dep == 0) {
ready_fetch_ops.emplace_back(op);
}
}
size_t num_complete = 0;
......@@ -97,7 +104,9 @@ FeedFetchList FastThreadedSSAGraphExecutor::Run(
for (auto op : bootstrap_ops_) {
RunOpAsync(op_deps.get(), op, complete_q);
}
for (auto op : ready_fetch_ops) {
RunOpAsync(op_deps.get(), op, complete_q);
}
while (num_complete != op_deps->size()) {
size_t num_comp = complete_q->Pop();
if (num_comp == -1UL) {
......@@ -123,32 +132,53 @@ FeedFetchList FastThreadedSSAGraphExecutor::Run(
return fetches;
}
bool FastThreadedSSAGraphExecutor::RunOp(
OpHandleBase *op, const std::shared_ptr<BlockingQueue<size_t>> &complete_q,
size_t *complete) {
try {
if (LIKELY(!strategy_.dry_run_)) {
op->Run(strategy_.use_cuda_);
}
++(*complete);
return true;
} catch (...) {
exception_.Catch(std::current_exception());
--remaining_;
complete_q->Push(-1UL);
return false;
}
}
void FastThreadedSSAGraphExecutor::RunOpAsync(
std::unordered_map<OpHandleBase *, std::atomic<int>> *op_deps,
OpHandleBase *op,
const std::shared_ptr<BlockingQueue<size_t>> &complete_q) {
++remaining_;
this->pool_.enqueue([=] {
OpHandleBase *op_to_run = op;
std::queue<OpHandleBase *> op_queue;
op_queue.push(op);
size_t complete = 0;
while (op_to_run != nullptr) {
try {
if (LIKELY(!strategy_.dry_run_)) {
op_to_run->Run(strategy_.use_cuda_);
}
++complete;
} catch (...) {
exception_.Catch(std::current_exception());
--remaining_;
complete_q->Push(-1UL);
while (!op_queue.empty()) {
OpHandleBase *op_to_run = op_queue.front();
op_queue.pop();
if (!RunOp(op_to_run, complete_q, &complete)) {
return;
}
auto &outputs = op_to_run->Outputs();
op_to_run = nullptr;
for (auto &output : outputs) {
for (auto &pending_op : output->PendingOps()) {
std::atomic<int> &deps = op_deps->at(pending_op);
if (deps.fetch_sub(1) == 1) { // pending_op ready
if (deps.fetch_sub(1) != 1) continue;
// NOTE(zjl): op with highest priority should run
// first without switching to another thread.
if (pending_op->GetPriority() == OpHandleBase::Priority::kHighest) {
op_queue.push(pending_op);
} else {
if (op_to_run == nullptr) {
op_to_run = pending_op;
} else {
......@@ -157,6 +187,8 @@ void FastThreadedSSAGraphExecutor::RunOpAsync(
}
}
}
if (op_to_run != nullptr) op_queue.push(op_to_run);
}
--remaining_;
complete_q->Push(complete);
......
paddle/fluid/framework/details/fast_threaded_ssa_graph_executor.h
浏览文件 @ 82d31503
......@@ -60,6 +60,10 @@ class FastThreadedSSAGraphExecutor : public SSAGraphExecutor {
::ThreadPool pool_;
::ThreadPool prepare_pool_;
bool RunOp(OpHandleBase *op,
const std::shared_ptr<BlockingQueue<size_t>> &complete_q,
size_t *complete);
void RunOpAsync(std::unordered_map<OpHandleBase *, std::atomic<int>> *op_deps,
OpHandleBase *op,
const std::shared_ptr<BlockingQueue<size_t>> &complete_q);
......
paddle/fluid/framework/details/fetch_op_handle.cc
浏览文件 @ 82d31503
......@@ -13,9 +13,9 @@
// limitations under the License.
#include "paddle/fluid/framework/details/fetch_op_handle.h"
#include <string>
#include <vector>
#include "paddle/fluid/platform/profiler.h"
namespace paddle {
namespace framework {
......@@ -44,6 +44,7 @@ void FetchOpHandle::WaitAndMergeCPUTensors() const {
}
void FetchOpHandle::RunImpl() {
platform::RecordEvent record_event(Name());
WaitInputVarGenerated(platform::CPUPlace());
tensors_.resize(inputs_.size());
......@@ -62,7 +63,8 @@ void FetchOpHandle::RunImpl() {
auto &t = var->Get<framework::LoDTensor>();
if (platform::is_gpu_place(t.place())) {
#ifdef PADDLE_WITH_CUDA
TensorCopySync(t, cpu, &tensors_[i]);
TensorCopy(t, cpu, *dev_ctxes_.at(t.place()), &tensors_[i]);
dev_ctxes_.at(t.place())->Wait();
#endif
} else {
tensors_[i].ShareDataWith(t);
......
paddle/fluid/framework/details/fuse_adam_op_pass.cc
浏览文件 @ 82d31503
......@@ -11,9 +11,15 @@
// 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/framework/details/fuse_adam_op_pass.h"
#include <algorithm>
#include <string>
#include <unordered_map>
#include <utility>
#include <vector>
#include "paddle/fluid/framework/details/build_strategy.h"
#include "paddle/fluid/framework/details/fuse_optimizer_op_pass.h"
#include "paddle/fluid/framework/details/multi_devices_helper.h"
#include "paddle/fluid/framework/ir/graph.h"
#include "paddle/fluid/framework/ir/graph_helper.h"
#include "paddle/fluid/framework/op_registry.h"
......@@ -21,175 +27,182 @@ namespace paddle {
namespace framework {
namespace details {
const std::string FuseAdamOpPass::GetOpType() const { return "adam"; }
const std::vector<std::string> FuseAdamOpPass::GetAuxiliaryVarNames() const {
return {"Param", "Moment1", "Moment2", "Beta1Pow", "Beta2Pow"};
}
void FuseAdamOpPass::FuseOptimizerOps(
const std::unordered_map<std::string, std::vector<std::string>>
&aux_var_set,
const std::unordered_map<std::string, std::string> &fused_vars_name,
const std::vector<ir::Node *> &adam_ops, ir::Graph *graph) const {
FuseAdamOps(aux_var_set, fused_vars_name, adam_ops, graph);
FuseScaleOps(aux_var_set.at("Beta1Pow"), fused_vars_name.at("Beta1Pow"),
adam_ops, graph);
FuseScaleOps(aux_var_set.at("Beta2Pow"), fused_vars_name.at("Beta2Pow"),
adam_ops, graph);
}
void FuseAdamOpPass::FuseAdamOps(
const std::unordered_map<std::string, std::vector<std::string>> &vars_set,
const std::unordered_map<std::string, std::string> &fused_vars_name,
const std::vector<ir::Node *> &adam_ops, ir::Graph *graph) const {
PADDLE_ENFORCE_GT(adam_ops.size(), static_cast<size_t>(0));
// Check attributions
// NOTE: If new attribution is added, the following code maybe need change.
int op_role = boost::get<int>(
adam_ops[0]->Op()->GetAttr(OpProtoAndCheckerMaker::OpRoleAttrName()));
float beta1 = boost::get<float>(adam_ops[0]->Op()->GetAttr("beta1"));
float beta2 = boost::get<float>(adam_ops[0]->Op()->GetAttr("beta2"));
float epsilon = boost::get<float>(adam_ops[0]->Op()->GetAttr("epsilon"));
bool lazy_mode = boost::get<bool>(adam_ops[0]->Op()->GetAttr("lazy_mode"));
int64_t min_row_size_to_use_multithread = boost::get<int64_t>(
adam_ops[0]->Op()->GetAttr("min_row_size_to_use_multithread"));
for (auto &adam_op : adam_ops) {
PADDLE_ENFORCE_EQ(beta1,
boost::get<float>(adam_op->Op()->GetAttr("beta1")));
PADDLE_ENFORCE_EQ(beta2,
boost::get<float>(adam_op->Op()->GetAttr("beta2")));
PADDLE_ENFORCE_EQ(epsilon,
boost::get<float>(adam_op->Op()->GetAttr("epsilon")));
PADDLE_ENFORCE_EQ(lazy_mode,
boost::get<bool>(adam_op->Op()->GetAttr("lazy_mode")));
PADDLE_ENFORCE_EQ(min_row_size_to_use_multithread,
boost::get<int64_t>(adam_op->Op()->GetAttr(
"min_row_size_to_use_multithread")));
PADDLE_ENFORCE_EQ(op_role, boost::get<int>(adam_op->Op()->GetAttr(
OpProtoAndCheckerMaker::OpRoleAttrName())));
class FuseAdamOpPass : public FuseOptimizerOpPass {
private:
const std::string GetOpType() const { return "adam"; }
const std::vector<std::string> GetAuxiliaryVarNames() const {
return {"Moment1", "Moment2", "Beta1Pow", "Beta2Pow"};
}
// NOTE: fused_var is only exist in scope, so the graph doesn't have fused_var
// node.
VLOG(10) << "Insert adam to graph ";
OpDesc adam_desc(adam_ops[0]->Op()->Block());
adam_desc.SetType("adam");
adam_desc.SetInput("Param", {fused_vars_name.at("Param")});
adam_desc.SetInput("Grad", {fused_vars_name.at("Grad")});
adam_desc.SetInput("Moment1", {fused_vars_name.at("Moment1")});
adam_desc.SetInput("Moment2", {fused_vars_name.at("Moment2")});
// TODO(zcd): The LearningRate, Beta1Pow, Beta2Pow should be equal.
adam_desc.SetInput("LearningRate", adam_ops[0]->Op()->Input("LearningRate"));
adam_desc.SetInput("Beta1Pow", adam_ops[0]->Op()->Input("Beta1Pow"));
adam_desc.SetInput("Beta2Pow", adam_ops[0]->Op()->Input("Beta2Pow"));
adam_desc.SetOutput("ParamOut", {fused_vars_name.at("Param")});
adam_desc.SetOutput("Moment1Out", {fused_vars_name.at("Moment1")});
adam_desc.SetOutput("Moment2Out", {fused_vars_name.at("Moment2")});
adam_desc.SetAttr("beta1", beta1);
adam_desc.SetAttr("beta2", beta2);
adam_desc.SetAttr("epsilon", epsilon);
adam_desc.SetAttr("lazy_mode", lazy_mode);
adam_desc.SetAttr("min_row_size_to_use_multithread",
min_row_size_to_use_multithread);
adam_desc.SetAttr(OpProtoAndCheckerMaker::OpRoleAttrName(), op_role);
auto adam_node = graph->CreateOpNode(&adam_desc);
InserInputAndOutputForOptOps(adam_ops, adam_node);
}
void FuseAdamOpPass::FuseScaleOps(const std::vector<std::string> &beta_name,
const std::string &fused_var_name,
const std::vector<ir::Node *> &adam_ops,
ir::Graph *graph) const {
PADDLE_ENFORCE_EQ(beta_name.size(), adam_ops.size());
const std::string scale_op_name = "scale";
// Get the scale_ops of dealing the adam's beta var.
std::vector<ir::Node *> scale_ops;
scale_ops.reserve(beta_name.size());
for (size_t i = 0; i < adam_ops.size(); ++i) {
auto &beta_1_pow_name = beta_name[i];
auto beta_pow_iter = std::find_if(
adam_ops[i]->inputs.begin(), adam_ops[i]->inputs.end(),
[&beta_name, &beta_1_pow_name](ir::Node *var_node) -> bool {
return var_node->Var() && var_node->Var()->Name() == beta_1_pow_name;
});
PADDLE_ENFORCE(beta_pow_iter != adam_ops[i]->inputs.end());
auto beta_pow_node = *beta_pow_iter;
auto scale_op_iter = std::find_if(
beta_pow_node->outputs.begin(), beta_pow_node->outputs.end(),
[&scale_op_name](ir::Node *op_node) -> bool {
return op_node->Op() && op_node->Op()->Type() == scale_op_name;
});
PADDLE_ENFORCE(scale_op_iter != beta_pow_node->outputs.end());
scale_ops.emplace_back(*scale_op_iter);
void FuseOptimizerOps(
const std::unordered_map<std::string, std::vector<std::string>>
&aux_var_set,
const std::unordered_map<std::string, std::string> &fused_vars_name,
const std::vector<ir::Node *> &adam_ops, ir::Graph *graph) const {
FuseAdamOps(aux_var_set, fused_vars_name, adam_ops, graph);
FuseScaleOps(aux_var_set.at("Beta1Pow"), fused_vars_name.at("Beta1Pow"),
adam_ops, graph);
FuseScaleOps(aux_var_set.at("Beta2Pow"), fused_vars_name.at("Beta2Pow"),
adam_ops, graph);
}
PADDLE_ENFORCE_EQ(scale_ops.size(), beta_name.size());
// Check attributions
// NOTE: If new attribution is added, the following code maybe need change.
int op_role = boost::get<int>(
scale_ops[0]->Op()->GetAttr(OpProtoAndCheckerMaker::OpRoleAttrName()));
float scale = boost::get<float>(scale_ops[0]->Op()->GetAttr("scale"));
float bias = boost::get<float>(scale_ops[0]->Op()->GetAttr("bias"));
bool bias_after_scale =
boost::get<bool>(scale_ops[0]->Op()->GetAttr("bias_after_scale"));
for (auto &scale_op : scale_ops) {
PADDLE_ENFORCE_EQ(scale,
boost::get<float>(scale_op->Op()->GetAttr("scale")));
PADDLE_ENFORCE_EQ(bias, boost::get<float>(scale_op->Op()->GetAttr("bias")));
PADDLE_ENFORCE_EQ(
bias_after_scale,
boost::get<bool>(scale_op->Op()->GetAttr("bias_after_scale")));
PADDLE_ENFORCE_EQ(op_role, boost::get<int>(scale_op->Op()->GetAttr(
OpProtoAndCheckerMaker::OpRoleAttrName())));
void FuseAdamOps(
const std::unordered_map<std::string, std::vector<std::string>> &vars_set,
const std::unordered_map<std::string, std::string> &fused_vars_name,
const std::vector<ir::Node *> &adam_ops, ir::Graph *graph) const {
PADDLE_ENFORCE_GT(adam_ops.size(), static_cast<size_t>(0));
// Check attributions
// NOTE: If new attribution is added, the following code maybe need change.
int op_role = boost::get<int>(
adam_ops[0]->Op()->GetAttr(OpProtoAndCheckerMaker::OpRoleAttrName()));
float beta1 = boost::get<float>(adam_ops[0]->Op()->GetAttr("beta1"));
float beta2 = boost::get<float>(adam_ops[0]->Op()->GetAttr("beta2"));
float epsilon = boost::get<float>(adam_ops[0]->Op()->GetAttr("epsilon"));
bool lazy_mode = boost::get<bool>(adam_ops[0]->Op()->GetAttr("lazy_mode"));
int64_t min_row_size_to_use_multithread = boost::get<int64_t>(
adam_ops[0]->Op()->GetAttr("min_row_size_to_use_multithread"));
for (auto &adam_op : adam_ops) {
PADDLE_ENFORCE_EQ(beta1,
boost::get<float>(adam_op->Op()->GetAttr("beta1")));
PADDLE_ENFORCE_EQ(beta2,
boost::get<float>(adam_op->Op()->GetAttr("beta2")));
PADDLE_ENFORCE_EQ(epsilon,
boost::get<float>(adam_op->Op()->GetAttr("epsilon")));
PADDLE_ENFORCE_EQ(lazy_mode,
boost::get<bool>(adam_op->Op()->GetAttr("lazy_mode")));
PADDLE_ENFORCE_EQ(min_row_size_to_use_multithread,
boost::get<int64_t>(adam_op->Op()->GetAttr(
"min_row_size_to_use_multithread")));
PADDLE_ENFORCE_EQ(op_role,
boost::get<int>(adam_op->Op()->GetAttr(
OpProtoAndCheckerMaker::OpRoleAttrName())));
}
// NOTE: fused_var is only exist in scope, so the graph doesn't have
// fused_var node.
VLOG(7) << "Insert adam to graph ";
OpDesc adam_desc(adam_ops[0]->Op()->Block());
adam_desc.SetType("adam");
adam_desc.SetInput(kParam, {fused_vars_name.at(kParam)});
adam_desc.SetInput(kGrad, {fused_vars_name.at(kGrad)});
adam_desc.SetInput("Moment1", {fused_vars_name.at("Moment1")});
adam_desc.SetInput("Moment2", {fused_vars_name.at("Moment2")});
// TODO(zcd): The LearningRate, Beta1Pow, Beta2Pow should be equal.
adam_desc.SetInput(kLearningRate, adam_ops[0]->Op()->Input(kLearningRate));
adam_desc.SetInput("Beta1Pow", adam_ops[0]->Op()->Input("Beta1Pow"));
adam_desc.SetInput("Beta2Pow", adam_ops[0]->Op()->Input("Beta2Pow"));
adam_desc.SetOutput("ParamOut", {fused_vars_name.at(kParam)});
adam_desc.SetOutput("Moment1Out", {fused_vars_name.at("Moment1")});
adam_desc.SetOutput("Moment2Out", {fused_vars_name.at("Moment2")});
adam_desc.SetAttr("beta1", beta1);
adam_desc.SetAttr("beta2", beta2);
adam_desc.SetAttr("epsilon", epsilon);
adam_desc.SetAttr("lazy_mode", lazy_mode);
adam_desc.SetAttr("min_row_size_to_use_multithread",
min_row_size_to_use_multithread);
adam_desc.SetAttr(OpProtoAndCheckerMaker::OpRoleAttrName(), op_role);
auto adam_node = graph->CreateOpNode(&adam_desc);
InserInputAndOutputForOptOps(adam_ops, adam_node);
}
// NOTE: fused_var is only exist in scope, so the graph doesn't have fused_var
// node.
VLOG(10) << "Insert fused scale to graph.";
OpDesc scale_desc(scale_ops[0]->Op()->Block());
scale_desc.SetType("scale");
scale_desc.SetInput("X", {fused_var_name});
scale_desc.SetOutput("Out", {fused_var_name});
scale_desc.SetAttr("scale", scale);
scale_desc.SetAttr("bias", bias);
scale_desc.SetAttr("bias_after_scale", bias_after_scale);
scale_desc.SetAttr(OpProtoAndCheckerMaker::OpRoleAttrName(), op_role);
auto scale_node = graph->CreateOpNode(&scale_desc);
for (auto scale_op : scale_ops) {
// set inputs
scale_node->inputs.insert(scale_node->inputs.begin(),
scale_op->inputs.begin(), scale_op->inputs.end());
for (auto &input : scale_op->inputs) {
std::replace(input->outputs.begin(), input->outputs.end(), scale_op,
scale_node);
void FuseScaleOps(const std::vector<std::string> &beta_name,
const std::string &fused_var_name,
const std::vector<ir::Node *> &adam_ops,
ir::Graph *graph) const {
PADDLE_ENFORCE_EQ(beta_name.size(), adam_ops.size());
const std::string scale_op_name = "scale";
// Get the scale_ops of dealing the adam's beta var.
std::vector<ir::Node *> scale_ops;
scale_ops.reserve(beta_name.size());
for (size_t i = 0; i < adam_ops.size(); ++i) {
auto &beta_1_pow_name = beta_name[i];
auto beta_pow_iter = std::find_if(
adam_ops[i]->inputs.begin(), adam_ops[i]->inputs.end(),
[&beta_name, &beta_1_pow_name](ir::Node *var_node) -> bool {
return var_node->Var() &&
var_node->Var()->Name() == beta_1_pow_name;
});
PADDLE_ENFORCE(beta_pow_iter != adam_ops[i]->inputs.end());
auto beta_pow_node = *beta_pow_iter;
auto scale_op_iter = std::find_if(
beta_pow_node->outputs.begin(), beta_pow_node->outputs.end(),
[&scale_op_name](ir::Node *op_node) -> bool {
return op_node->Op() && op_node->Op()->Type() == scale_op_name;
});
PADDLE_ENFORCE(scale_op_iter != beta_pow_node->outputs.end());
scale_ops.emplace_back(*scale_op_iter);
}
// set outputs
scale_node->outputs.insert(scale_node->outputs.begin(),
scale_op->outputs.begin(),
scale_op->outputs.end());
for (auto &output : scale_op->outputs) {
std::replace(output->inputs.begin(), output->inputs.end(), scale_op,
scale_node);
PADDLE_ENFORCE_EQ(scale_ops.size(), beta_name.size());
// Check attributions
// NOTE: If new attribution is added, the following code maybe need change.
int op_role = boost::get<int>(
scale_ops[0]->Op()->GetAttr(OpProtoAndCheckerMaker::OpRoleAttrName()));
float scale = boost::get<float>(scale_ops[0]->Op()->GetAttr("scale"));
float bias = boost::get<float>(scale_ops[0]->Op()->GetAttr("bias"));
bool bias_after_scale =
boost::get<bool>(scale_ops[0]->Op()->GetAttr("bias_after_scale"));
for (auto &scale_op : scale_ops) {
PADDLE_ENFORCE_EQ(scale,
boost::get<float>(scale_op->Op()->GetAttr("scale")));
PADDLE_ENFORCE_EQ(bias,
boost::get<float>(scale_op->Op()->GetAttr("bias")));
PADDLE_ENFORCE_EQ(
bias_after_scale,
boost::get<bool>(scale_op->Op()->GetAttr("bias_after_scale")));
PADDLE_ENFORCE_EQ(op_role,
boost::get<int>(scale_op->Op()->GetAttr(
OpProtoAndCheckerMaker::OpRoleAttrName())));
}
}
// Delete scale_ops
for (auto &scale_op : scale_ops) {
graph->RemoveNode(scale_op);
}
}
// NOTE: fused_var is only exist in scope, so the graph doesn't have
// fused_var node.
VLOG(7) << "Insert fused scale to graph.";
OpDesc scale_desc(scale_ops[0]->Op()->Block());
scale_desc.SetType("scale");
scale_desc.SetInput("X", {fused_var_name});
scale_desc.SetOutput("Out", {fused_var_name});
scale_desc.SetAttr("scale", scale);
scale_desc.SetAttr("bias", bias);
scale_desc.SetAttr("bias_after_scale", bias_after_scale);
scale_desc.SetAttr(OpProtoAndCheckerMaker::OpRoleAttrName(), op_role);
auto scale_node = graph->CreateOpNode(&scale_desc);
for (auto scale_op : scale_ops) {
// set inputs
scale_node->inputs.insert(scale_node->inputs.begin(),
scale_op->inputs.begin(),
scale_op->inputs.end());
for (auto &input : scale_op->inputs) {
std::replace(input->outputs.begin(), input->outputs.end(), scale_op,
scale_node);
}
// set outputs
scale_node->outputs.insert(scale_node->outputs.begin(),
scale_op->outputs.begin(),
scale_op->outputs.end());
for (auto &output : scale_op->outputs) {
std::replace(output->inputs.begin(), output->inputs.end(), scale_op,
scale_node);
}
}
// Delete scale_ops
for (auto &scale_op : scale_ops) {
graph->RemoveNode(scale_op);
}
}
};
} // namespace details
} // namespace framework
} // namespace paddle
......
paddle/fluid/framework/details/fuse_momentum_op_pass.cc 0 → 100644
浏览文件 @ 82d31503
// Copyright (c) 2019 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 <algorithm>
#include <string>
#include <unordered_map>
#include <vector>
#include "paddle/fluid/framework/details/build_strategy.h"
#include "paddle/fluid/framework/details/fuse_optimizer_op_pass.h"
#include "paddle/fluid/framework/ir/graph_helper.h"
#include "paddle/fluid/framework/op_registry.h"
namespace paddle {
namespace framework {
namespace details {
class FuseMomentumOpPass : public FuseOptimizerOpPass {
private:
virtual const std::string GetOpType() const { return "momentum"; }
virtual const std::vector<std::string> GetAuxiliaryVarNames() const {
return {"Velocity"};
}
// Fuse Momentum Ops
virtual void FuseOptimizerOps(
const std::unordered_map<std::string, std::vector<std::string>> &vars_set,
const std::unordered_map<std::string, std::string> &fused_vars_name,
const std::vector<ir::Node *> &momentum_ops, ir::Graph *graph) const {
PADDLE_ENFORCE_GT(momentum_ops.size(), static_cast<size_t>(0));
// Check attributions
// NOTE: If new attribution is added, the following code maybe need change.
int op_role = boost::get<int>(momentum_ops[0]->Op()->GetAttr(
OpProtoAndCheckerMaker::OpRoleAttrName()));
float mu = boost::get<float>(momentum_ops[0]->Op()->GetAttr("mu"));
bool use_nesterov =
boost::get<bool>(momentum_ops[0]->Op()->GetAttr("use_nesterov"));
for (auto &momentum_op : momentum_ops) {
PADDLE_ENFORCE_EQ(mu,
boost::get<float>(momentum_op->Op()->GetAttr("mu")));
PADDLE_ENFORCE_EQ(
use_nesterov,
boost::get<bool>(momentum_op->Op()->GetAttr("use_nesterov")));
PADDLE_ENFORCE_EQ(op_role,
boost::get<int>(momentum_op->Op()->GetAttr(
OpProtoAndCheckerMaker::OpRoleAttrName())));
}
// NOTE: fused_var is only exist in scope, so the graph doesn't have
// fused_var node.
VLOG(7) << "Insert momentum to graph ";
OpDesc momentum_desc(momentum_ops[0]->Op()->Block());
momentum_desc.SetType("momentum");
momentum_desc.SetInput(kParam, {fused_vars_name.at(kParam)});
momentum_desc.SetInput(kGrad, {fused_vars_name.at(kGrad)});
momentum_desc.SetInput("Velocity", {fused_vars_name.at("Velocity")});
// TODO(zcd): The LearningRate should be equal.
momentum_desc.SetInput(kLearningRate,
momentum_ops[0]->Op()->Input(kLearningRate));
momentum_desc.SetOutput("ParamOut", {fused_vars_name.at(kParam)});
momentum_desc.SetOutput("VelocityOut", {fused_vars_name.at("Velocity")});
momentum_desc.SetAttr("mu", mu);
momentum_desc.SetAttr("use_nesterov", use_nesterov);
momentum_desc.SetAttr(OpProtoAndCheckerMaker::OpRoleAttrName(), op_role);
auto momentum_node = graph->CreateOpNode(&momentum_desc);
InserInputAndOutputForOptOps(momentum_ops, momentum_node);
}
};
} // namespace details
} // namespace framework
} // namespace paddle
REGISTER_PASS(fuse_momentum_op_pass,
paddle::framework::details::FuseMomentumOpPass)
.RequirePassAttr(paddle::framework::details::kPlaces)
.RequirePassAttr(paddle::framework::details::kLocalScopes);
paddle/fluid/framework/details/fuse_optimizer_op_pass.cc
浏览文件 @ 82d31503
......@@ -29,7 +29,9 @@ void FuseOptimizerOpPass::ApplyImpl(ir::Graph *graph) const {
auto &local_scopes = Get<const std::vector<Scope *>>(kLocalScopes);
const std::string fuse_op_type = GetOpType();
const std::vector<std::string> aux_var_names = GetAuxiliaryVarNames();
std::vector<std::string> aux_var_names = GetAuxiliaryVarNames();
aux_var_names.emplace_back(kParam);
aux_var_names.emplace_back(kGrad);
// Step 1: Get the specified op and auxiliary variables.
std::vector<ir::Node *> topo_nodes = ir::TopologySortOperations(result);
......@@ -40,15 +42,14 @@ void FuseOptimizerOpPass::ApplyImpl(ir::Graph *graph) const {
&aux_var_set);
}
VLOG(10) << "Find " << fuse_op_type << " operators: " << opt_ops.size();
VLOG(6) << "Find " << fuse_op_type << " operators: " << opt_ops.size();
if (opt_ops.size() == 0) {
return;
}
if (result.Has(kFusedOptType)) {
VLOG(10)
<< "Currently only support fusing one type optimizer op. Has fused "
<< result.Get<FusedOptType>(kFusedOptType);
VLOG(6) << "Currently only support fusing one type optimizer op. Has fused "
<< result.Get<FusedOptType>(kFusedOptType);
return;
} else {
result.Set(kFusedOptType, new FusedOptType);
......@@ -61,53 +62,126 @@ void FuseOptimizerOpPass::ApplyImpl(ir::Graph *graph) const {
result.Set(kFusedVars, new FusedVars);
}
std::unordered_map<std::string, std::string> fused_vars_name;
fused_vars_name.reserve(aux_var_names.size() + 1);
fused_vars_name.reserve(aux_var_names.size());
auto &fused_var_set = result.Get<FusedVars>(kFusedVars);
const std::string prefix(kFusedVarNamePrefix);
// NOTE: the fused_var_name should be unique.
for (auto &var_name : aux_var_names) {
auto fused_var_name = prefix + "_" + fuse_op_type + "_" + var_name + "_" +
aux_var_set[var_name][0];
VLOG(10) << fused_var_name;
VLOG(6) << var_name << ": " << fused_var_name;
fused_vars_name.emplace(var_name, fused_var_name);
PADDLE_ENFORCE_EQ(fused_var_set.count(fused_var_name), 0);
fused_var_set.insert(fused_var_name);
}
// Step 3: Get the fused Gradient's name
auto &params_grads = result.Get<ParamsAndGrads>(kParamsAndGrads);
if (!result.Has(kFusedGrads)) {
PADDLE_THROW(
"The alloc_continuous_space_for_grad_pass should be called before this "
"pass.");
}
auto &fused_grad = result.Get<FusedGrads>(kFusedGrads);
auto &fused_vars = result.Get<FusedVars>(kFusedVars);
auto iter = std::find(fused_vars.begin(), fused_vars.end(), fused_grad);
PADDLE_ENFORCE(iter != fused_vars.end(), "Not find the fused_grad.");
fused_vars_name.emplace("Grad", fused_grad);
// Step 4: Sort the parameters and auxiliary variables according
// to parameters' name to make variables' name correspond correctly.
PADDLE_ENFORCE(result.Has(kParamsAndGrads), "Does't find kParamsAndGrads.");
PADDLE_ENFORCE_EQ(params_grads.size(), aux_var_set.begin()->second.size(),
"The size of params_grads and aux_var_set are not equal.");
SortParametersAndAuxVars(params_grads, &aux_var_set, &opt_ops);
// Step 5: Alloc continuous space for Parameters and AuxiliaryVar(e.g.
bool grad_fused = false;
if (result.Has(kParamsAndGrads)) {
auto &params_grads = result.Get<ParamsAndGrads>(kParamsAndGrads);
PADDLE_ENFORCE_EQ(
params_grads.size(), aux_var_set.at(kGrad).size(),
"The number of gradients and optimizer ops is not equal.");
std::unordered_set<std::string> opt_grad_set(aux_var_set.at(kGrad).begin(),
aux_var_set.at(kGrad).end());
size_t same_grad_num = 0;
for (auto &p_g : params_grads) {
if (opt_grad_set.count(p_g.second)) {
++same_grad_num;
}
}
// NOTE(zcd): the gradient of kParamsAndGrads may be different with the
// kGrad.
if (same_grad_num == aux_var_set.at(kGrad).size()) {
if (!result.Has(kFusedGrads)) {
PADDLE_THROW(
"The alloc_continuous_space_for_grad_pass should be called before "
"this pass.");
}
auto &fused_grad = result.Get<FusedGrads>(kFusedGrads);
auto &fused_vars = result.Get<FusedVars>(kFusedVars);
auto iter = std::find(fused_vars.begin(), fused_vars.end(), fused_grad);
PADDLE_ENFORCE(iter != fused_vars.end(), "Not find the fused_grad.");
fused_vars_name[kGrad] = fused_grad;
// Sort the parameters and auxiliary variables according
// to parameters' name to make variables' name correspond correctly.
SortParametersAndAuxVars(params_grads, &aux_var_set, &opt_ops);
grad_fused = true;
}
}
// Step 4: Alloc continuous space for Parameters and AuxiliaryVar(e.g.
// Moment1, Moment2, Beta1Pow, Beta2Pow) of all the optimizer ops separately.
aux_var_names.pop_back();
if (!grad_fused) {
InitFusedGradsAndAllocSpaceForGrads(
places, local_scopes, aux_var_set.at(kParam), aux_var_set.at(kGrad),
fused_vars_name.at(kGrad), &result);
}
InitFusedVarsAndAllocSpaceForVars(places, local_scopes, aux_var_names,
aux_var_set, fused_vars_name);
// Step 6: Fuse optimizer Ops and Scale Ops
// Step 5: Fuse optimizer Ops and Scale Ops
FuseOptimizerOps(aux_var_set, fused_vars_name, opt_ops, &result);
// Step 7: Remove optimizer Ops
// Step 6: Remove optimizer Ops
for (auto &opt_op : opt_ops) {
graph->RemoveNode(opt_op);
}
}
void FuseOptimizerOpPass::InitFusedGradsAndAllocSpaceForGrads(
const std::vector<platform::Place> &places,
const std::vector<Scope *> &local_scopes,
const std::vector<std::string> &params,
const std::vector<std::string> &grads, const std::string &fused_grad_name,
ir::Graph *result) const {
// Get Var Nodes
std::unordered_map<std::string, ir::Node *> vars;
for (ir::Node *node : result->Nodes()) {
if (node->IsVar() && node->Var()) {
// Note: The graph may have the same name node. For example, parameter
// is the input of operator and it also is the output of optimizer;
vars.emplace(node->Var()->Name(), node);
}
}
// Set Gradients as Persistable to prevent this var becoming reusable.
for (auto &grad_var_name : grads) {
auto iter = vars.find(grad_var_name);
PADDLE_ENFORCE(iter != vars.end());
PADDLE_ENFORCE_NOT_NULL(iter->second->Var());
PADDLE_ENFORCE(iter->second->Var()->GetType() == proto::VarType::LOD_TENSOR,
"Currently the gradient type only should be LoDTensor when "
"fusing optimizer ops.");
iter->second->Var()->SetPersistable(true);
}
// Init Grads
for (auto it = local_scopes.rbegin(); it != local_scopes.rend(); ++it) {
auto &scope = *it;
VLOG(6) << "Init: " << fused_grad_name;
PADDLE_ENFORCE(scope->FindVar(fused_grad_name) == nullptr,
"%s has existed in scope.", fused_grad_name);
scope->Var(fused_grad_name)->GetMutable<LoDTensor>();
for (auto &grad_var_name : grads) {
auto iter = vars.find(grad_var_name);
PADDLE_ENFORCE(iter != vars.end());
PADDLE_ENFORCE_NOT_NULL(iter->second->Var());
scope->Var(grad_var_name)->GetMutable<LoDTensor>();
}
}
// Define Ops
ProgramDesc program_desc;
auto *global_block = program_desc.MutableBlock(0);
AppendAllocContinuousSpace(params, grads, fused_grad_name, global_block,
false, false);
// Run Ops
RunInitOps(places, local_scopes, *global_block);
}
void FuseOptimizerOpPass::InitFusedVarsAndAllocSpaceForVars(
const std::vector<platform::Place> &places,
const std::vector<Scope *> &local_scopes,
......@@ -115,37 +189,48 @@ void FuseOptimizerOpPass::InitFusedVarsAndAllocSpaceForVars(
const std::unordered_map<std::string, std::vector<std::string>>
&aux_var_set,
const std::unordered_map<std::string, std::string> &fused_vars_name) const {
VLOG(10) << "Init FusedVars.";
// Alloc parameters and auxiliary vars in the respective scope.
size_t idx = local_scopes.size();
for (auto iter = local_scopes.rbegin(); iter != local_scopes.rend();
++iter, --idx) {
auto &scope = *iter;
for (auto &var_name : aux_var_names) {
auto fused_var_name = fused_vars_name.at(var_name);
VLOG(10) << "Init " << fused_var_name;
PADDLE_ENFORCE(scope->FindVar(fused_var_name) == nullptr,
"%s has exist in scope[%d]", fused_var_name, idx);
scope->Var(fused_var_name)->GetMutable<LoDTensor>();
}
// Init Vars
for (auto &var_name : aux_var_names) {
auto &fused_var_name = fused_vars_name.at(var_name);
InitVars(local_scopes, fused_var_name);
}
// Define Ops
ProgramDesc program_desc;
auto *global_block = program_desc.MutableBlock(0);
for (auto &var_name : aux_var_names) {
AppendAllocContinuousSpace(aux_var_set.at(var_name),
fused_vars_name.at(var_name), true,
global_block);
AppendAllocContinuousSpace(
aux_var_set.at(var_name), aux_var_set.at(var_name),
fused_vars_name.at(var_name), global_block, true);
}
// Run Ops
RunInitOps(places, local_scopes, *global_block);
}
void FuseOptimizerOpPass::RunInitOps(const std::vector<platform::Place> &places,
const std::vector<Scope *> &local_scopes,
const BlockDesc &global_block) const {
for (size_t i = 0; i < local_scopes.size(); ++i) {
for (auto &op_desc : global_block->AllOps()) {
for (auto &op_desc : global_block.AllOps()) {
auto op = OpRegistry::CreateOp(*op_desc);
op->Run(*local_scopes[i], places[i]);
}
}
}
void FuseOptimizerOpPass::InitVars(const std::vector<Scope *> &local_scopes,
const std::string &fused_var_name) const {
// Alloc parameters and auxiliary vars in the respective scope.
size_t idx = local_scopes.size();
for (auto iter = local_scopes.rbegin(); iter != local_scopes.rend();
++iter, --idx) {
auto &scope = *iter;
VLOG(6) << "Init: " << fused_var_name;
PADDLE_ENFORCE(scope->FindVar(fused_var_name) == nullptr,
"%s has exist in scope[%d]", fused_var_name, idx);
scope->Var(fused_var_name)->GetMutable<LoDTensor>();
}
}
void FuseOptimizerOpPass::SortParametersAndAuxVars(
const std::vector<std::pair<std::string, std::string>> &params_grads,
std::unordered_map<std::string, std::vector<std::string>> *aux_vars_set,
......@@ -175,7 +260,7 @@ void FuseOptimizerOpPass::SortParametersAndAuxVars(
for (auto &var_name : aux_vars.second) {
out << var_name << " ";
}
VLOG(10) << aux_vars.first << ": " << out.str();
VLOG(6) << aux_vars.first << ": " << out.str();
}
std::vector<ir::Node *> sorted_ops;
......@@ -193,25 +278,28 @@ void FuseOptimizerOpPass::GetSpecifiedOpsAndVars(
const {
if (node->Op()->Type() != op_type) return;
std::stringstream out;
for (auto &var_n : aux_vars_name) {
auto arg_names = node->Op()->Input(var_n);
PADDLE_ENFORCE_EQ(arg_names.size(), static_cast<size_t>(1));
(*aux_args_name)[var_n].emplace_back(arg_names[0]);
VLOG(10) << var_n << ", " << arg_names[0];
out << var_n << ", " << arg_names[0] << "; ";
}
VLOG(7) << out.str();
ops->emplace_back(node);
}
void FuseOptimizerOpPass::AppendAllocContinuousSpace(
const std::vector<std::string> &args, const std::string &out_arg,
bool copy_data, BlockDesc *global_block) const {
const std::vector<std::string> &in_args,
const std::vector<std::string> &out_args, const std::string &fused_out_arg,
BlockDesc *global_block, bool copy_data, bool check_name) const {
auto op_desc = global_block->AppendOp();
op_desc->SetType("alloc_continuous_space");
op_desc->SetInput("Input", args);
op_desc->SetOutput("Output", args);
op_desc->SetOutput("FusedOutput", {out_arg});
op_desc->SetInput("Input", in_args);
op_desc->SetOutput("Output", out_args);
op_desc->SetOutput("FusedOutput", {fused_out_arg});
op_desc->SetAttr("copy_data", copy_data);
op_desc->SetAttr("check_name", true);
op_desc->SetAttr("check_name", check_name);
}
void FuseOptimizerOpPass::InserInputAndOutputForOptOps(
......
paddle/fluid/framework/details/fuse_optimizer_op_pass.h
浏览文件 @ 82d31503
......@@ -27,6 +27,10 @@ namespace paddle {
namespace framework {
namespace details {
constexpr char kGrad[] = "Grad";
constexpr char kParam[] = "Param";
constexpr char kLearningRate[] = "LearningRate";
class FuseOptimizerOpPass : public ir::Pass {
protected:
void ApplyImpl(ir::Graph *graph) const override;
......@@ -56,9 +60,18 @@ class FuseOptimizerOpPass : public ir::Pass {
std::unordered_map<std::string, std::vector<std::string>> *aux_args_name)
const;
void AppendAllocContinuousSpace(const std::vector<std::string> &args,
const std::string &out_arg, bool copy_data,
BlockDesc *global_block) const;
void AppendAllocContinuousSpace(const std::vector<std::string> &in_args,
const std::vector<std::string> &out_args,
const std::string &fused_out_arg,
BlockDesc *global_block, bool copy_data,
bool check_name = true) const;
void InitFusedGradsAndAllocSpaceForGrads(
const std::vector<platform::Place> &places,
const std::vector<Scope *> &local_scopes,
const std::vector<std::string> &params,
const std::vector<std::string> &grads, const std::string &fused_grad_name,
ir::Graph *result) const;
void InitFusedVarsAndAllocSpaceForVars(
const std::vector<platform::Place> &places,
......@@ -68,6 +81,13 @@ class FuseOptimizerOpPass : public ir::Pass {
&aux_var_set,
const std::unordered_map<std::string, std::string> &fused_vars_name)
const;
void RunInitOps(const std::vector<platform::Place> &places,
const std::vector<Scope *> &local_scopes,
const BlockDesc &global_block) const;
void InitVars(const std::vector<Scope *> &local_scopes,
const std::string &fused_var_name) const;
};
} // namespace details
......
paddle/fluid/framework/details/fuse_sgd_op_pass.cc
浏览文件 @ 82d31503
......@@ -11,60 +11,61 @@
// 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/framework/details/fuse_sgd_op_pass.h"
#include <algorithm>
#include <string>
#include <unordered_map>
#include <utility>
#include <vector>
#include "paddle/fluid/framework/details/build_strategy.h"
#include "paddle/fluid/framework/details/fuse_optimizer_op_pass.h"
#include "paddle/fluid/framework/details/multi_devices_helper.h"
#include "paddle/fluid/framework/ir/graph.h"
#include "paddle/fluid/framework/ir/graph_helper.h"
#include "paddle/fluid/framework/op_registry.h"
namespace paddle {
namespace framework {
namespace details {
const std::string FuseSgdOpPass::GetOpType() const { return "sgd"; }
const std::vector<std::string> FuseSgdOpPass::GetAuxiliaryVarNames() const {
return {"Param"};
}
void FuseSgdOpPass::FuseOptimizerOps(
const std::unordered_map<std::string, std::vector<std::string>>
&aux_var_set,
const std::unordered_map<std::string, std::string> &fused_vars_name,
const std::vector<ir::Node *> &sgd_ops, ir::Graph *graph) const {
FuseSgdOps(aux_var_set, fused_vars_name, sgd_ops, graph);
}
class FuseSgdOpPass : public FuseOptimizerOpPass {
private:
virtual const std::string GetOpType() const { return "sgd"; }
void FuseSgdOpPass::FuseSgdOps(
const std::unordered_map<std::string, std::vector<std::string>> &vars_set,
const std::unordered_map<std::string, std::string> &fused_vars_name,
const std::vector<ir::Node *> &sgd_ops, ir::Graph *graph) const {
PADDLE_ENFORCE_GT(sgd_ops.size(), static_cast<size_t>(0));
virtual const std::vector<std::string> GetAuxiliaryVarNames() const {
return {};
}
// NOTE: fused_var is only exist in scope, so the graph doesn't have fused_var
// node.
// Fuse Sgd Ops
virtual void FuseOptimizerOps(
const std::unordered_map<std::string, std::vector<std::string>> &vars_set,
const std::unordered_map<std::string, std::string> &fused_vars_name,
const std::vector<ir::Node *> &sgd_ops, ir::Graph *graph) const {
PADDLE_ENFORCE_GT(sgd_ops.size(), static_cast<size_t>(0));
int op_role = boost::get<int>(
sgd_ops[0]->Op()->GetAttr(OpProtoAndCheckerMaker::OpRoleAttrName()));
VLOG(10) << "Insert sgd to graph ";
// Add fused scale
OpDesc Sgd_desc(sgd_ops[0]->Op()->Block());
Sgd_desc.SetType("sgd");
Sgd_desc.SetInput("Param", {fused_vars_name.at("Param")});
Sgd_desc.SetInput("Grad", {fused_vars_name.at("Grad")});
Sgd_desc.SetOutput("ParamOut", {fused_vars_name.at("Param")});
// NOTE: fused_var is only exist in scope, so the graph doesn't have
// fused_var node.
// TODO(zcd): The LearningRate, Beta1Pow, Beta2Pow should be equal.
Sgd_desc.SetInput("LearningRate", sgd_ops[0]->Op()->Input("LearningRate"));
int op_role = boost::get<int>(
sgd_ops[0]->Op()->GetAttr(OpProtoAndCheckerMaker::OpRoleAttrName()));
VLOG(7) << "Insert sgd to graph ";
// Add fused scale
OpDesc Sgd_desc(sgd_ops[0]->Op()->Block());
Sgd_desc.SetType("sgd");
Sgd_desc.SetInput(kParam, {fused_vars_name.at(kParam)});
Sgd_desc.SetInput(kGrad, {fused_vars_name.at(kGrad)});
Sgd_desc.SetOutput("ParamOut", {fused_vars_name.at(kParam)});
// NOTE: multi_devices_pass requires that every op should have a role.
Sgd_desc.SetAttr(OpProtoAndCheckerMaker::OpRoleAttrName(), op_role);
// TODO(zcd): The LearningRate should be equal.
Sgd_desc.SetInput(kLearningRate, sgd_ops[0]->Op()->Input(kLearningRate));
auto sgd_node = graph->CreateOpNode(&Sgd_desc);
// NOTE: multi_devices_pass requires that every op should have a role.
Sgd_desc.SetAttr(OpProtoAndCheckerMaker::OpRoleAttrName(), op_role);
InserInputAndOutputForOptOps(sgd_ops, sgd_node);
}
auto sgd_node = graph->CreateOpNode(&Sgd_desc);
InserInputAndOutputForOptOps(sgd_ops, sgd_node);
}
};
} // namespace details
} // namespace framework
} // namespace paddle
......
paddle/fluid/framework/details/inplace_op_pass.h 已删除 100644 → 0
浏览文件 @ 5d81651a
// 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 abtain 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 <map>
#include <memory>
#include <string>
#include <unordered_map>
#include <unordered_set>
#include <utility>
#include <vector>
#include "paddle/fluid/framework/details/memory_optimize_helper.h"
#include "paddle/fluid/framework/ir/graph.h"
#include "paddle/fluid/framework/ir/pass.h"
namespace paddle {
namespace framework {
namespace details {
class GraphView {
public:
GraphView() = default;
void Build(ir::Graph* g);
const std::vector<ir::Node*>& AllOps();
ir::Node* GetNodeByName(const std::string& name,
const std::vector<ir::Node*>& nodes) const;
std::vector<ir::Node*> PendingOpsOnVar(ir::Node* var);
// Will Deperated in the future.
// NOTE(dzhwinter) :
// 1. Python memory optimize will reuse
// memory based var name, so different op output may
// have the same variable name. enable inplace on such node
// will generate a circle in ssa graph.
// 2. DistributeTranspiler will use unique name to
// map the parameter and gradient, must be skipped.
bool InSkipSet(const std::string& var) const;
bool CheckDeps(ir::Node* var, ir::Node* current_op) const;
bool CheckOpDeps(ir::Node* op1, ir::Node* op2) const;
void TopoSort(ir::Graph* g);
private:
std::vector<ir::Node*> ops_;
std::unordered_set<std::string> dup_nodes_; // mem opt affect nodes
std::map<ir::Node*, std::unordered_set<ir::Node*>> adj_list_;
std::unordered_map<ir::Node*, uint32_t> op_level_;
};
// swap pairs in sequence
typedef std::vector<std::pair<ir::Node*, ir::Node*>> NodeSwapQueue;
class InplacePass : public ir::Pass {
public:
InplacePass();
protected:
void ApplyImpl(ir::Graph* graph) const override;
void InitSSAGraphNodes() const;
private:
const NodeSwapQueue TryInplaceModifyVar(const std::string& var,
const std::string& cache_var,
const size_t& idx,
ir::Graph* graph) const;
void CommitModify(const NodeSwapQueue&, ir::Graph* graph) const;
void WithdrawModify(const NodeSwapQueue& nodes, ir::Graph* graph) const;
void InplaceModifyDesc(const std::string& in_var, const std::string& out_var,
const size_t& idx) const;
void TryInplaceOpInputOutput(ir::Node* op, ir::Graph* graph) const;
mutable std::map<std::string, std::vector<ir::Node*>> var_nodes_;
mutable std::unordered_set<std::string> whitelist_;
mutable GraphView view_;
};
} // namespace details
} // namespace framework
} // namespace paddle
paddle/fluid/framework/details/memory_optimize_pass.h
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// 1. scan op with subblock and collect the output/input vars.
// while, while_grad, conditional_block
// 2. scan distributed ops and collect the output/input vars
void CollectSkipVarsSet(const std::unordered_set<ir::Node*>&) const;
// 3. op_role_vars
void CollectSkipVarsSet(ir::Graph* graph) const;
private:
// Reuse Node Pool, Owned.
......
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else()
cc_library(fleet_wrapper SRCS fleet_wrapper.cc DEPS framework_proto variable_helper scope)
endif(WITH_PSLIB)
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