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# 构建iOS平台上的PaddlePaddle库
交叉编译iOS平台上适用的PaddlePaddle库，需要在MacOS系统上进行。本文的将介绍在MacOS上，从源码交叉编译iOS平台上适用的PaddlePaddle库。
## 准备交叉编译环境
Apple官方为iOS开发提供了完整的交叉编译工具和集成开发环境，用户从App Store下载安装Xcode即可。也可自行前往官网下载，[Xcode](https://developer.apple.com/cn/xcode/)。安装完成之后，可在命令行执行`xcodebuild -version`，判断是否安装成功。
```bash
$ xcodebuild -version
Xcode 9.0
Build version 9A235
```
## 配置交叉编译参数
PaddlePaddle为交叉编译提供了工具链配置文档[cmake/cross_compiling/ios.cmake](https://github.com/PaddlePaddle/Paddle/blob/develop/cmake/cross_compiling/ios.cmake)，以提供一些默认的编译器和编译参数配置。
交叉编译iOS版本的PaddlePaddle库时，有一些必须配置的参数：
- `CMAKE_SYSTEM_NAME`，CMake编译的目标平台，必须设置为`iOS`。在设置`CMAKE_SYSTEM_NAME=iOS`后，PaddlePaddle的CMake系统会自动编译所有的第三方依赖库，并且强制设置一些PaddlePaddle参数的值（`WITH_C_API=ON`、`WITH_GPU=OFF`、`WITH_AVX=OFF`、`WITH_PYTHON=OFF`、`WITH_RDMA=OFF`）。
- `WITH_C_API`，是否编译C-API预测库，必须设置为ON。在iOS平台上只支持使用C-API来预测。
- `WITH_SWIG_PY`，必须设置为ON。在iOS平台上不支持通过swig调用来训练或者预测。
iOS平台可选配置参数：
- `IOS_PLATFORM`，可设置为`OS/SIMULATOR`，默认值为`OS`。
- `OS`，构建目标为`arm`架构的iPhone或者iPad等物理设备。
- `SIMULATOR`，构建目标为`x86`架构的模拟器平台。
- `IOS_ARCH`，目标架构。针对不同的`IOS_PLATFORM`，可设置的目标架构如下表所示：
| IOS_PLATFORM | IOS_ARCH |
|--------------|----------------------|
| OS | armv7, armv7s, arm64 (默认) |
| SIMULATOR | i386, x86_64 (默认) |
- `IOS_DEPLOYMENT_TARGET`，最小的iOS部署版本，默认值为`7.0`。
- `IOS_ENABLE_BITCODE`，是否使能[Bitcode](https://developer.apple.com/library/content/documentation/IDEs/Conceptual/AppDistributionGuide/AppThinning/AppThinning.html#//apple_ref/doc/uid/TP40012582-CH35-SW3)，可设置`ON/OFF`，默认值为`ON`。
- `IOS_USE_VECLIB_FOR_BLAS`，是否使用[vecLib](https://developer.apple.com/documentation/accelerate/veclib)框架进行BLAS矩阵计算，可设置`ON/OFF`，默认值为`OFF`。
- `IOS_DEVELOPMENT_ROOT`，`Developer`目录，可显式指定为`/path/to/platform/Developer`。若未显式指定，PaddlePaddle将会根据`IOS_PLATFORM`自动选择`Xcode`对应`platform`的`Developer`目录。
- `IOS_SDK_ROOT`，所使用`SDK`的根目录，可显式指定为`/path/to/platform/Developer/SDKs/SDK`。若未显式指定，PaddlePaddle将会自动选择`IOS_DEVELOPMENT_ROOT`目录下最新的`SDK`版本。
其他配置参数：
- `USE_EIGEN_FOR_BLAS`，是否使用Eigen库进行矩阵计算，在`IOS_USE_VECLIB_FOR_BLAS=OFF`时有效。可设置`ON/OFF`，默认值为`OFF`。
- `HOST_C/CXX_COMPILER`，宿主机的C/C++编译器。默认值为环境变量`CC/CXX`的值；若环境变量`CC/CXX`未设置，则使用`cc/c++`编译器。
常用的cmake配置如下：
```bash
cmake -DCMAKE_SYSTEM_NAME=iOS \
-DIOS_PLATFORM=OS \
-DIOS_ARCH="arm64" \
-DIOS_ENABLE_BITCODE=ON \
-DIOS_USE_VECLIB_FOR_BLAS=ON \
-DCMAKE_INSTALL_PREFIX=your/path/to/install \
-DWITH_C_API=ON \
-DWITH_TESTING=OFF \
-DWITH_SWIG_PY=OFF \
..
```
```bash
cmake -DCMAKE_SYSTEM_NAME=iOS \
-DIOS_PLATFORM=SIMULATOR \
-DIOS_ARCH="x86_64" \
-DIOS_USE_VECLIB_FOR_BLAS=ON \
-DCMAKE_INSTALL_PREFIX=your/path/to/install \
-DWITH_C_API=ON \
-DWITH_TESTING=OFF \
-DWITH_SWIG_PY=OFF \
..
```
用户还可根据自己的需求设置其他编译参数。比如希望最小化生成库的大小，可以设置`CMAKE_BUILD_TYPE`为`MinSizeRel`；若希望得到最快的执行速度，则可设置`CMAKE_BUILD_TYPE`为`Release`。亦可以通过手动设置`CMAKE_C/CXX_FLAGS`来影响PaddlePaddle的编译过程。
**性能TIPS**，为了达到最快的计算速度，在CMake参数配置上，有以下建议：
- 设置`CMAKE_BUILD_TYPE`为`Release`
- 设置`IOS_USE_VECLIB_FOR_BLAS=ON`，调用`vecLib`框架提供的BLAS函数进行矩阵计算。
## 编译和安装
CMake配置完成后，执行以下命令，PaddlePaddle将自动下载和编译所有第三方依赖库、编译和安装PaddlePaddle预测库。
```
$ make
$ make install
```
注意：如果你曾在源码目录下编译过其他平台的PaddlePaddle库，请先使用`rm -rf`命令删除`third_party`目录和`build`目录，以确保所有的第三方依赖库和PaddlePaddle代码都是针对新的CMake配置重新编译的。
执行完安装命令后，`your/path/to/install`目录中会包含以下内容：
- `include`目录，其中包含所有C-API的头文件
- `lib`目录，其中包含PaddlePaddle的C-API静态库
- `third_party`目录，其中包含所依赖的所有第三方库
注意，不同架构的PaddlePaddle库建议安装到不同的目录下，然后使用`lipo`工具将多个静态库合并成一个支持多个架构的fat库。
自此，PaddlePaddle库已经安装完成，用户可将合成的fat库用于深度学习相关的iOS App中，调用方法见C-API文档。
doc/howto/cross_compiling/cross_compiling_for_raspberry_cn.md
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......@@ -59,4 +59,4 @@ make install
注意：如果你曾经在源码目录下编译过其他平台的PaddlePaddle库，请先使用`rm -rf`命令删除`third_party`目录和`build`目录，以确保所有的第三方依赖库和PaddlePaddle代码都是针对新的CMake配置重新编译的。
执行完安装命令后，，`your/path/to/install`目录中会包含`include`和`lib`目录，其中`include`中包含C-API的头文件，`lib`中包含一个Raspberry Pi版本的库。
执行完安装命令后，`your/path/to/install`目录中会包含`include`和`lib`目录，其中`include`中包含C-API的头文件，`lib`中包含一个Raspberry Pi版本的库。
doc/howto/cross_compiling/cross_compiling_for_raspberry_en.md
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......@@ -44,7 +44,7 @@ cmake -DCMAKE_SYSTEM_NAME=RPi \
..
```
To build the inference library, please set the argument WITH_API to ON: `WITH_C_API=ON`.
To build the inference library, please set the argument WITH\_C\_API to ON: `WITH_C_API=ON`.
You can add more arguments. For example, to minimize the size of the generated inference library, you may use `CMAKE_BUILD_TYPE=MinSizeRel`. For performance optimization, you may use `CMAKE_BUILD_TYPE=Release`.
......
doc/howto/usage/cluster/cluster_train_cn.md
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......@@ -19,7 +19,7 @@
* [启动集群作业](#启动集群作业-1)
* [在Kubernetes集群中提交训练作业](#在kubernetes集群中提交训练作业)
# 概述
## 概述
本文将介绍如何使用PaddlePaddle在不同的集群框架下完成分布式训练。分布式训练架构如下图所示：
<img src="https://user-images.githubusercontent.com/13348433/31772175-5f419eca-b511-11e7-9db7-5231fe3d9ccb.png" width="500">
......@@ -32,7 +32,7 @@
在使用同步SGD训练神经网络时，PaddlePaddle使用同步屏障（barrier），使梯度的提交和参数的更新按照顺序方式执行。在异步SGD中，则并不会等待所有trainer提交梯度才更新参数，这样极大地提高了计算的并行性：参数服务器之间不相互依赖，并行地接收梯度和更新参数，参数服务器也不会等待计算节点全部都提交梯度之后才开始下一步，计算节点之间也不会相互依赖，并行地执行模型的训练。可以看出，虽然异步SGD方式会提高参数更新并行度, 但是并不能保证参数同步更新，在任意时间某一台参数服务器上保存的参数可能比另一台要更新，与同步SGD相比，梯度会有噪声。
# 环境准备
## 环境准备
1. 准备您的计算集群。计算集群通常由一组（几台到几千台规模）的Linux服务器组成。服务器之间可以通过局域网（LAN）联通，每台服务器具有集群中唯一的IP地址（或者可被DNS解析的主机名）。集群中的每台计算机通常被成为一个“节点”。
1. 我们需要在集群的所有节点上安装 PaddlePaddle。 如果要启用GPU，还需要在节点上安装对应的GPU驱动以及CUDA。PaddlePaddle的安装可以参考[build_and_install](https://github.com/PaddlePaddle/Paddle/tree/develop/doc/getstarted/build_and_install)的多种安装方式。我们推荐使用[Docker](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/getstarted/build_and_install/docker_install_cn.rst)安装方式来快速安装PaddlePaddle。
......@@ -51,8 +51,8 @@ PaddlePaddle 0.10.0, compiled with
下面以`doc/howto/usage/cluster/src/word2vec`中的代码作为实例，介绍使用PaddlePaddle v2 API完成分布式训练。
# 启动参数说明
## 启动参数服务器
## 启动参数说明
### 启动参数服务器
执行以下的命令启动一个参数服务器并等待和计算节点的数据交互
```bash
$ paddle pserver --port=7164 --ports_num=1 --ports_num_for_sparse=1 --num_gradient_servers=1
......@@ -70,7 +70,7 @@ $ stdbuf -oL /usr/bin/nohup paddle pserver --port=7164 --ports_num=1 --ports_num
| ports_num_for_sparse | 必选 | 1 | 用于稀疏类型参数通信的端口个数 |
| num_gradient_servers | 必选 | 1 | 当前训练任务pserver总数 |
## 启动计算节点
### 启动计算节点
执行以下命令启动使用python编写的trainer程序（文件名为任意文件名，如train.py）
```bash
$ python train.py
......@@ -117,7 +117,7 @@ paddle.init(
| pservers | 必选 | 127.0.0.1 | 当前训练任务启动的pserver的IP列表，多个IP使用“,”隔开 |
## 准备数据集
### 准备数据集
参考样例数据准备脚本[prepare.py](https://github.com/PaddlePaddle/Paddle/tree/develop/doc/howto/usage/cluster/src/word2vec/prepare.py)，准备训练数据和验证数据集，我们使用paddle.dataset.imikolov数据集，并根据分布式训练并发数（trainer节点个数），在`prepare.py`开头部分指定`SPLIT_COUNT`将数据切分成多份。
......@@ -149,7 +149,7 @@ test.txt-00002
对于不同的训练任务，训练数据格式和训练程序的`reader()`会大不相同，所以开发者需要根据自己训练任务的实际场景完成训练数据的分割和`reader()`的编写。
## 准备训练程序
### 准备训练程序
我们会对每个训练任务都会在每个节点上创建一个工作空间（workspace），其中包含了用户的训练程序、程序依赖、挂载或下载的训练数据分片。
......@@ -184,7 +184,7 @@ test.txt-00002
- `train_data_dir`：包含训练数据的目录，可以是从分布式存储挂载过来的，也可以是在任务启动前下载到本地的。
- `test_data_dir`：包含测试数据集的目录。
# 使用分布式计算平台或工具
## 使用分布式计算平台或工具
PaddlePaddle可以使用多种分布式计算平台构建分布式计算任务，包括：
- [Kubernetes](http://kubernetes.io) Google开源的容器集群的调度框架，支持大规模集群生产环境的完整集群方案。
......@@ -195,12 +195,12 @@ PaddlePaddle可以使用多种分布式计算平台构建分布式计算任务
在使用分布式计算平台进行训练时，任务被调度在集群中时，分布式计算平台通常会通过API或者环境变量提供任务运行需要的参数，比如节点的ID、IP和任务节点个数等。
## 使用Fabric启动集群作业
### 使用Fabric启动集群作业
### 准备一个Linux集群
#### 准备一个Linux集群
可以在`paddle/scripts/cluster_train_v2/fabric/docker_cluster`目录下，执行`kubectl -f ssh_servers.yaml`启动一个测试集群，并使用`kubectl get po -o wide`获得这些节点的IP地址。
### 启动集群作业
#### 启动集群作业
`paddle.py` 提供了自动化脚本来启动不同节点中的所有 PaddlePaddle 集群进程。默认情况下，所有命令行选项可以设置为 `paddle.py` 命令选项并且 `paddle.py` 将透明、自动地将这些选项应用到 PaddlePaddle 底层进程。
......@@ -216,10 +216,10 @@ sh run.sh
集群作业将会在几秒后启动。
### 终止集群作业
#### 终止集群作业
`paddle.py`能获取`Ctrl + C` SIGINT 信号来自动终止它启动的所有进程。只需中断 `paddle.py` 任务来终止集群作业。如果程序崩溃你也可以手动终止。
### 检查集群训练结果
#### 检查集群训练结果
详细信息请检查 $workspace/log 里的日志，每一个节点都有相同的日志结构。
`paddle_trainer.INFO`
......@@ -234,13 +234,13 @@ sh run.sh
`train.log`
提供训练过程的 stderr 和 stdout。训练失败时可以检查错误日志。
### 检查模型输出
#### 检查模型输出
运行完成后，模型文件将被写入节点 0 的 `output` 目录中。
工作空间中的 `nodefile` 表示当前集群作业的节点 ID。
## 在OpenMPI集群中提交训练作业
### 在OpenMPI集群中提交训练作业
### 准备OpenMPI集群
#### 准备OpenMPI集群
执行下面的命令以启动3个节点的OpenMPI集群和一个"head"节点：
......@@ -252,7 +252,7 @@ kubectl create -f mpi-nodes.yaml
然后可以从head节点ssh无密码登录到OpenMPI的每个节点上。
### 启动集群作业
#### 启动集群作业
您可以按照下面的步骤在OpenMPI集群中提交paddle训练任务：
......@@ -280,6 +280,6 @@ scp train.txt-00002 test.txt-00002 [node3IP]:/home/tutorial
mpirun -hostfile machines -n 3 /home/tutorial/start_mpi_train.sh
```
## 在Kubernetes集群中提交训练作业
### 在Kubernetes集群中提交训练作业
此部分的使用方法可以参考[here](../k8s/k8s_distributed_cn.md)。
doc/howto/usage/cluster/cluster_train_en.md
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......@@ -19,7 +19,7 @@
* [Launching Cluster Job](#launching-cluster-job-1)
* [Cluster Training Using Kubernetes](#cluster-training-using-kubernetes)
# Introduction
## Introduction
In this article, we'll explain how to run distributed training jobs with PaddlePaddle on different types of clusters. The diagram below shows the main architecture of a distributed trainning job:
......@@ -33,7 +33,7 @@ PaddlePaddle can support both synchronize stochastic gradient descent (SGD) and
When training with synchronize SGD, PaddlePaddle uses an internal "synchronize barrier" which makes gradients update and parameter download in strict order. On the other hand, asynchronous SGD won't wait for all trainers to finish upload at a single step, this will increase the parallelism of distributed training: parameter servers do not depend on each other, they'll do parameter optimization concurrently. Parameter servers will not wait for trainers, so trainers will also do their work concurrently. But asynchronous SGD will introduce more randomness and noises in the gradient.
# Preparations
## Preparations
1. Prepare your computer cluster. It's normally a bunch of Linux servers connected by LAN. Each server will be assigned a unique IP address. The computers in the cluster can be called "nodes".
2. Install PaddlePaddle on every node. If you are going to take advantage of GPU cards, you'll also need to install proper driver and CUDA libraries. To install PaddlePaddle please read [this build and install](https://github.com/PaddlePaddle/Paddle/tree/develop/doc/getstarted/build_and_install) document. We strongly recommend using [Docker installation](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/getstarted/build_and_install/docker_install_en.rst).
......@@ -52,9 +52,9 @@ PaddlePaddle 0.10.0rc, compiled with
We'll take `doc/howto/usage/cluster/src/word2vec` as an example to introduce distributed training using PaddlePaddle v2 API.
# Command-line arguments
## Command-line arguments
## Starting parameter server
### Starting parameter server
Type the below command to start a parameter server which will wait for trainers to connect:
......@@ -74,7 +74,7 @@ $ stdbuf -oL /usr/bin/nohup paddle pserver --port=7164 --ports_num=1 --ports_num
| ports_num_for_sparse | required | 1 | number of ports which serves sparse parameter update |
| num_gradient_servers | required | 1 | total number of gradient servers |
## Starting trainer
### Starting trainer
Type the command below to start the trainer(name the file whatever you want, like "train.py")
```bash
......@@ -122,7 +122,7 @@ paddle.init(
| trainer_id | required | 0 | ID for every trainer, start from 0 |
| pservers | required | 127.0.0.1 | list of IPs of parameter servers, separated by "," |
## Prepare Training Dataset
### Prepare Training Dataset
Here's some example code [prepare.py](https://github.com/PaddlePaddle/Paddle/tree/develop/doc/howto/usage/cluster/src/word2vec/prepare.py), it will download public `imikolov` dataset and split it into multiple files according to job parallelism(trainers count). Modify `SPLIT_COUNT` at the begining of `prepare.py` to change the count of output files.
......@@ -155,7 +155,7 @@ When job started, every trainer needs to get it's own part of data. In some dist
Different training jobs may have different data format and `reader()` function, developers may need to write different data prepare scripts and `reader()` functions for their job.
## Prepare Training program
### Prepare Training program
We'll create a *workspace* directory on each node, storing your training program, dependencies, mounted or downloaded dataset directory.
......@@ -191,7 +191,7 @@ Your workspace may looks like:
- `train_data_dir`: containing training data. Mount from storage service or copy trainning data to here.
- `test_data_dir`: containing testing data.
# Use cluster platforms or cluster management tools
## Use cluster platforms or cluster management tools
PaddlePaddle supports running jobs on several platforms including:
- [Kubernetes](http://kubernetes.io) open-source system for automating deployment, scaling, and management of containerized applications from Google.
......@@ -202,13 +202,13 @@ We'll introduce cluster job management on these platforms. The examples can be f
These cluster platforms provide API or environment variables for training processes, when the job is dispatched to different nodes. Like node ID, IP or total number of nodes etc.
## Cluster Training Using Fabric
### Cluster Training Using Fabric
### Prepare a Linux cluster
#### Prepare a Linux cluster
Run `kubectl -f ssh_servers.yaml` under the directory: `paddle/scripts/cluster_train_v2/fabric/docker_cluster` will launch a demo cluster. Run `kubectl get po -o wide` to get IP addresses of these nodes.
### Launching Cluster Job
#### Launching Cluster Job
`paddle.py` provides automatical scripts to start all PaddlePaddle cluster processes in different nodes. By default, all command line options can be set as `paddle.py` command options and `paddle.py` will transparently and automatically set these options to PaddlePaddle lower level processes.
`paddle.py`provides two distinguished command option for easy job launching.
......@@ -224,10 +224,10 @@ sh run.sh
The cluster Job will start in several seconds.
### Kill Cluster Job
#### Kill Cluster Job
`paddle.py` can capture `Ctrl + C` SIGINT signal to automatically kill all processes launched by it. So just stop `paddle.py` to kill cluster job. You should manually kill the job if the program crashed.
### Check Cluster Training Result
#### Check Cluster Training Result
Check log in $workspace/log for details, each node owns same log structure.
`paddle_trainer.INFO`
......@@ -242,13 +242,13 @@ It provides stderr and stdout of parameter server process. Check error log if tr
`train.log`
It provides stderr and stdout of trainer process. Check error log if training crashes.
### Check Model Output
#### Check Model Output
After one pass finished, model files will be written in `output` directory in node 0.
`nodefile` in workspace indicates the node id of current cluster job.
## Cluster Training Using OpenMPI
### Cluster Training Using OpenMPI
### Prepare an OpenMPI cluster
#### Prepare an OpenMPI cluster
Run the following command to start a 3-node MPI cluster and one "head" node.
......@@ -260,7 +260,7 @@ kubectl create -f mpi-nodes.yaml
Then you can log in to every OpenMPI node using ssh without input any passwords.
### Launching Cluster Job
#### Launching Cluster Job
Follow the steps to launch a PaddlePaddle training job in OpenMPI cluster:\
......@@ -288,6 +288,6 @@ scp train.txt-00002 test.txt-00002 [node3IP]:/home/tutorial
mpirun -hostfile machines -n 3 /home/tutorial/start_mpi_train.sh
```
## Cluster Training Using Kubernetes
### Cluster Training Using Kubernetes
The details can be found [here](../k8s/k8s_cn.md)
paddle/framework/attribute.cc
浏览文件 @ 83b48ebc
......@@ -19,7 +19,7 @@ limitations under the License. */
namespace paddle {
namespace framework {
Attribute GetAttrValue(const OpDesc::Attr& attr_desc, ProgramDesc* program) {
Attribute GetAttrValue(const OpDesc::Attr& attr_desc) {
switch (attr_desc.type()) {
case framework::AttrType::BOOLEAN: {
return attr_desc.b();
......@@ -61,13 +61,9 @@ Attribute GetAttrValue(const OpDesc::Attr& attr_desc, ProgramDesc* program) {
}
return val;
}
case framework::AttrType::BLOCK: {
PADDLE_ENFORCE(program != nullptr,
"Need to specify ProgramDesc when get a block attr");
return program->mutable_blocks(attr_desc.block_idx());
}
default:
PADDLE_THROW("Unsupport attr type %d", attr_desc.type());
}
PADDLE_ENFORCE(false, "Unknown OpDesc::AttrDesc::type !");
return boost::blank();
}
......
paddle/framework/attribute.h
浏览文件 @ 83b48ebc
......@@ -32,7 +32,7 @@ inline AttrType AttrTypeID() {
return static_cast<AttrType>(tmp.which() - 1);
}
Attribute GetAttrValue(const OpDesc::Attr& attr_desc, ProgramDesc* desc);
Attribute GetAttrValue(const OpDesc::Attr& attr_desc);
class AttrReader {
public:
......
paddle/framework/backward.cc
浏览文件 @ 83b48ebc
......@@ -18,6 +18,7 @@
#include <deque>
#include <list>
#include <memory>
#include <unordered_set>
#include "paddle/framework/block_desc.h"
#include "paddle/framework/op_registry.h"
......@@ -285,6 +286,15 @@ static bool AllGradInSet(const std::vector<std::string>& names,
return true;
}
static std::string FwdName(const std::string& grad_name) {
auto pos = grad_name.find("@GRAD");
if (pos == std::string::npos) {
return "";
} else {
return grad_name.substr(0, pos);
}
}
static void CreateGradVarInBlock(
size_t grad_op_start_index,
const std::unordered_map<std::string, std::string>& param_name_map,
......@@ -294,6 +304,7 @@ static void CreateGradVarInBlock(
for (size_t op_index = grad_op_start_index; op_index < ops.size();
++op_index) {
bool need_infer_shape = false;
std::unordered_set<std::string> new_vars;
ForEachVarName(ops[op_index]->Outputs(),
[&](const std::string& grad_var_name) {
if (block_desc->HasVar(grad_var_name)) {
......@@ -301,8 +312,7 @@ static void CreateGradVarInBlock(
}
need_infer_shape = true;
auto var = block_desc->Var(grad_var_name);
// FIXME(qiao) infer the datatype
var->SetDataType(framework::DataType::FP32);
new_vars.insert(var->Name());
auto it = param_name_map.find(grad_var_name);
if (it == param_name_map.end()) {
return false;
......@@ -316,6 +326,21 @@ static void CreateGradVarInBlock(
});
if (need_infer_shape) {
ops[op_index]->InferVarType(block_desc);
for (auto& arg : ops[op_index]->OutputArgumentNames()) {
if (new_vars.find(arg) == new_vars.end()) {
continue;
}
auto pname = FwdName(arg);
auto* param = block_desc->FindVar(pname);
auto* grad = block_desc->FindVar(arg);
if (param == nullptr) {
LOG(WARNING) << "Cannot find forward variable of " << arg
<< ". Set its gradient to FP32";
grad->SetDataType(DataType::FP32);
} else {
grad->SetDataType(param->GetDataType());
}
}
ops[op_index]->InferShape(*block_desc);
}
}
......@@ -368,7 +393,7 @@ std::vector<std::unique_ptr<OpDescBind>> MakeBlockBackward(
ProgramDescBind& program_desc, int block_idx,
std::unordered_set<std::string>* no_grad_vars,
std::unordered_map<std::string, std::string>* grad_to_var) {
BlockDescBind* cur_block = program_desc.Block(block_idx);
BlockDescBind* cur_block = program_desc.MutableBlock(block_idx);
std::vector<OpDescBind*> op_descs = cur_block->AllOps();
std::unordered_map<std::string, std::vector<size_t>> dup_out_ops;
size_t grad_desc_idx = 0;
......@@ -443,7 +468,7 @@ ParamGradInfoMap AppendBackward(
}
const int root_block_idx = 0;
auto root_block = program_desc.Block(root_block_idx);
auto root_block = program_desc.MutableBlock(root_block_idx);
// insert fill one op for target
// TODO(qiao) add some check to the target.
......@@ -492,7 +517,7 @@ ParamGradInfoMap AppendBackward(
CreateGradVarInBlock(forward_op_num, grad_to_var, root_block, &retv);
for (size_t block_index = forward_block_num;
block_index < program_desc.Size(); ++block_index) {
CreateGradVarInBlock(0, grad_to_var, program_desc.Block(block_index),
CreateGradVarInBlock(0, grad_to_var, program_desc.MutableBlock(block_index),
&retv);
}
return retv;
......
paddle/framework/backward_test.cc
浏览文件 @ 83b48ebc
......@@ -499,7 +499,7 @@ TEST(Backward, linear_net_intermediate_variable_has_no_grad) {
TEST(Backward, simple_single_op) {
f::ProgramDescBind program;
f::BlockDescBind *block = program.Block(0);
f::BlockDescBind *block = program.MutableBlock(0);
f::OpDescBind *op = block->AppendOp();
op->SetType("rowwise_add");
......@@ -535,7 +535,7 @@ TEST(Backward, simple_single_op) {
TEST(Backward, default_attribute) {
f::ProgramDescBind program;
f::BlockDescBind *block = program.Block(0);
f::BlockDescBind *block = program.MutableBlock(0);
f::OpDescBind *op = block->AppendOp();
op->SetType("mul");
op->SetInput("X", {"x"});
......@@ -561,7 +561,7 @@ TEST(Backward, default_attribute) {
TEST(Backward, simple_mult_op) {
f::ProgramDescBind program;
f::BlockDescBind *block = program.Block(0);
f::BlockDescBind *block = program.MutableBlock(0);
f::OpDescBind *op1 = block->AppendOp();
op1->SetType("rowwise_add");
op1->SetInput("X", {"x1"});
......@@ -644,7 +644,7 @@ TEST(Backward, simple_mult_op) {
TEST(Backward, intermedia_var_no_grad) {
f::ProgramDescBind program;
f::BlockDescBind *block = program.Block(0);
f::BlockDescBind *block = program.MutableBlock(0);
f::OpDescBind *op1 = block->AppendOp();
op1->SetType("rowwise_add");
op1->SetInput("X", {"x1"});
......@@ -714,7 +714,7 @@ TEST(Backward, intermedia_var_no_grad) {
TEST(Backward, var_no_grad) {
f::ProgramDescBind program;
f::BlockDescBind *block = program.Block(0);
f::BlockDescBind *block = program.MutableBlock(0);
f::OpDescBind *op1 = block->AppendOp();
op1->SetType("mult_in_out");
op1->SetInput("X", {"x1"});
......@@ -790,7 +790,7 @@ TEST(Backward, var_no_grad) {
TEST(Backward, shared_var) {
f::ProgramDescBind program;
f::BlockDescBind *block = program.Block(0);
f::BlockDescBind *block = program.MutableBlock(0);
f::OpDescBind *op1 = block->AppendOp();
op1->SetType("rowwise_add");
op1->SetInput("X", {"x1"});
......@@ -880,7 +880,7 @@ TEST(Backward, shared_var) {
TEST(Backward, half_backward) {
f::ProgramDescBind program;
f::BlockDescBind *block = program.Block(0);
f::BlockDescBind *block = program.MutableBlock(0);
auto *op1 = block->AppendOp();
op1->SetType("minus");
op1->SetInput("X", {"a"});
......
paddle/framework/block_desc.cc
浏览文件 @ 83b48ebc
......@@ -113,7 +113,7 @@ BlockDescBind *BlockDescBind::ParentBlock() const {
if (this->desc_->parent_idx() == kNoneBlockIndex) {
return nullptr;
}
return prog_->Block(static_cast<size_t>(this->desc_->parent_idx()));
return prog_->MutableBlock(static_cast<size_t>(this->desc_->parent_idx()));
}
BlockDesc *BlockDescBind::Proto() {
......
paddle/framework/executor.cc
浏览文件 @ 83b48ebc
......@@ -73,33 +73,32 @@ static void CreateTensor(Variable* var, VarDesc::VarType var_type) {
}
}
void Executor::Run(const ProgramDesc& pdesc, Scope* scope, int block_id) {
void Executor::Run(const ProgramDescBind& pdesc, Scope* scope, int block_id) {
// TODO(tonyyang-svail):
// - only runs on the first device (i.e. no interdevice communication)
// - will change to use multiple blocks for RNN op and Cond Op
PADDLE_ENFORCE_GT(pdesc.blocks_size(), block_id);
auto& block = pdesc.blocks(block_id);
PADDLE_ENFORCE_LT(block_id, pdesc.Size());
auto& block = pdesc.Block(block_id);
auto& device = device_contexts_[0];
Scope& local_scope = scope->NewScope();
for (auto& var : block.vars()) {
if (var.persistable()) {
auto* ptr = scope->Var(var.name());
CreateTensor(ptr, var.type());
VLOG(3) << "Create Variable " << var.name()
for (auto& var : block.AllVars()) {
if (var->Persistable()) {
auto* ptr = scope->Var(var->Name());
CreateTensor(ptr, var->GetType());
VLOG(3) << "Create Variable " << var->Name()
<< " global, which pointer is " << ptr;
} else {
auto* ptr = local_scope.Var(var.name());
CreateTensor(ptr, var.type());
VLOG(3) << "Create Variable " << var.name()
auto* ptr = local_scope.Var(var->Name());
CreateTensor(ptr, var->GetType());
VLOG(3) << "Create Variable " << var->Name()
<< " locally, which pointer is " << ptr;
}
}
for (auto& op_desc : block.ops()) {
auto op = paddle::framework::OpRegistry::CreateOp(
op_desc, const_cast<ProgramDesc*>(&pdesc));
for (auto& op_desc : block.AllOps()) {
auto op = paddle::framework::OpRegistry::CreateOp(*op_desc);
op->Run(local_scope, *device);
}
......
paddle/framework/executor.h
浏览文件 @ 83b48ebc
......@@ -14,8 +14,8 @@ limitations under the License. */
#pragma once
#include "paddle/framework/framework.pb.h"
#include "paddle/framework/op_info.h"
#include "paddle/framework/program_desc.h"
#include "paddle/framework/scope.h"
#include "paddle/framework/tensor.h"
......@@ -34,7 +34,7 @@ class Executor {
* ProgramDesc
* Scope
*/
void Run(const ProgramDesc&, Scope*, int);
void Run(const ProgramDescBind&, Scope*, int);
private:
std::vector<platform::DeviceContext*> device_contexts_;
......
paddle/framework/lod_tensor_test.cu
浏览文件 @ 83b48ebc
......@@ -36,8 +36,8 @@ TEST(LoDTensor, LoDInGPU) {
lod_tensor.mutable_data<float>(place);
lod_tensor.set_lod(src_lod);
CHECK_EQ(lod_tensor.lod_element(0, 2).first, 4UL);
CHECK_EQ(lod_tensor.lod_element(0, 4).first, 8UL);
EXPECT_EQ(lod_tensor.lod_element(0, 2).first, 4UL);
EXPECT_EQ(lod_tensor.lod_element(0, 4).first, 8UL);
auto lod = lod_tensor.lod();
......@@ -45,6 +45,6 @@ TEST(LoDTensor, LoDInGPU) {
cudaDeviceSynchronize();
for (size_t i = 0; i < src_lod[0].size(); ++i) {
CHECK_EQ(lod[0].data()[i], src_lod[0].data()[i] * 2);
EXPECT_EQ(lod[0].data()[i], src_lod[0].data()[i] * 2);
}
}
\ No newline at end of file
}
paddle/framework/op_desc.cc
浏览文件 @ 83b48ebc
......@@ -52,6 +52,22 @@ class CompileTimeInferShapeContext : public InferShapeContext {
const std::vector<std::string> &Outputs(
const std::string &name) const override;
void ShareLoD(const std::string &in, const std::string &out, size_t i = 0,
size_t j = 0) const override {
PADDLE_ENFORCE_LT(i, Inputs(in).size());
PADDLE_ENFORCE_LT(j, Outputs(out).size());
auto *in_var = block_.FindVarRecursive(Inputs(in)[i]);
auto *out_var = block_.FindVarRecursive(Outputs(out)[j]);
if (in_var->GetType() != VarDesc::LOD_TENSOR) {
VLOG(3) << "input " << in << "is not LodTensor";
return;
}
PADDLE_ENFORCE_EQ(in_var->GetType(), VarDesc::LOD_TENSOR,
"The %d-th output of Output(%s) must be LoDTensor.", j,
out);
in_var->SetLoDLevel(out_var->GetLodLevel());
}
private:
DDim GetDim(const std::string &name) const override;
......@@ -98,7 +114,12 @@ OpDescBind::OpDescBind(const OpDesc &desc, ProgramDescBind *prog)
// restore attrs_
for (const OpDesc::Attr &attr : desc_.attrs()) {
std::string attr_name = attr.name();
attrs_[attr_name] = GetAttrValue(attr, prog->Proto());
if (attr.type() != AttrType::BLOCK) {
attrs_[attr_name] = GetAttrValue(attr);
} else {
auto bid = attr.block_idx();
attrs_[attr_name] = prog->MutableBlock(bid);
}
}
}
......@@ -172,8 +193,7 @@ void OpDescBind::SetAttr(const std::string &name, const Attribute &v) {
}
void OpDescBind::SetBlockAttr(const std::string &name, BlockDescBind &block) {
BlockDesc *desc = block.Proto();
this->attrs_[name] = desc;
this->attrs_[name] = &block;
need_update_ = true;
}
......@@ -192,7 +212,7 @@ Attribute OpDescBind::GetAttr(const std::string &name) const {
int OpDescBind::GetBlockAttr(const std::string &name) const {
auto it = attrs_.find(name);
PADDLE_ENFORCE(it != attrs_.end(), "Attribute %s is not found", name);
return boost::get<BlockDesc *>(it->second)->idx();
return boost::get<BlockDescBind *>(it->second)->ID();
}
const std::unordered_map<std::string, Attribute> &OpDescBind::GetAttrMap()
......
paddle/framework/op_registry.cc
浏览文件 @ 83b48ebc
......@@ -43,13 +43,15 @@ static VariableNameMap ConvertOpDescVarsToVarNameMap(
return ret_val;
}
std::unique_ptr<OperatorBase> OpRegistry::CreateOp(const OpDesc& op_desc,
ProgramDesc* program) {
std::unique_ptr<OperatorBase> OpRegistry::CreateOp(const OpDesc& op_desc) {
VLOG(1) << "CreateOp directly from OpDesc is deprecated. It should only be"
"used in unit tests. Use CreateOp(const OpDescBind& op_desc) "
"instead.";
VariableNameMap inputs = ConvertOpDescVarsToVarNameMap(op_desc.inputs());
VariableNameMap outputs = ConvertOpDescVarsToVarNameMap(op_desc.outputs());
AttributeMap attrs;
for (auto& attr : op_desc.attrs()) {
attrs[attr.name()] = GetAttrValue(attr, program);
attrs[attr.name()] = GetAttrValue(attr);
}
return CreateOp(op_desc.type(), inputs, outputs, attrs);
......
paddle/framework/op_registry.h
浏览文件 @ 83b48ebc
......@@ -77,8 +77,7 @@ class OpRegistry {
const VariableNameMap& outputs,
AttributeMap attrs);
static std::unique_ptr<OperatorBase> CreateOp(const OpDesc& op_desc,
ProgramDesc* program);
static std::unique_ptr<OperatorBase> CreateOp(const OpDesc& op_desc);
static std::unique_ptr<OperatorBase> CreateOp(const OpDescBind& op_desc);
};
......
paddle/framework/op_registry_test.cc
浏览文件 @ 83b48ebc
......@@ -74,7 +74,7 @@ TEST(OpRegistry, CreateOp) {
attr->set_type(paddle::framework::AttrType::FLOAT);
attr->set_f(scale);
auto op = paddle::framework::OpRegistry::CreateOp(op_desc, nullptr);
auto op = paddle::framework::OpRegistry::CreateOp(op_desc);
paddle::framework::Scope scope;
paddle::platform::CPUDeviceContext dev_ctx;
op->Run(scope, dev_ctx);
......@@ -95,7 +95,7 @@ TEST(OpRegistry, IllegalAttr) {
bool caught = false;
try {
paddle::framework::OpRegistry::CreateOp(op_desc, nullptr);
paddle::framework::OpRegistry::CreateOp(op_desc);
} catch (paddle::platform::EnforceNotMet err) {
caught = true;
std::string msg = "larger_than check fail";
......@@ -115,7 +115,7 @@ TEST(OpRegistry, DefaultValue) {
ASSERT_TRUE(op_desc.IsInitialized());
auto op = paddle::framework::OpRegistry::CreateOp(op_desc, nullptr);
auto op = paddle::framework::OpRegistry::CreateOp(op_desc);
paddle::framework::Scope scope;
paddle::platform::CPUDeviceContext dev_ctx;
op->Run(scope, dev_ctx);
......@@ -131,7 +131,7 @@ TEST(OpRegistry, CustomChecker) {
// attr 'test_attr' is not set
bool caught = false;
try {
paddle::framework::OpRegistry::CreateOp(op_desc, nullptr);
paddle::framework::OpRegistry::CreateOp(op_desc);
} catch (paddle::platform::EnforceNotMet err) {
caught = true;
std::string msg = "Attribute 'test_attr' is required!";
......@@ -149,7 +149,7 @@ TEST(OpRegistry, CustomChecker) {
attr->set_i(3);
caught = false;
try {
paddle::framework::OpRegistry::CreateOp(op_desc, nullptr);
paddle::framework::OpRegistry::CreateOp(op_desc);
} catch (paddle::platform::EnforceNotMet err) {
caught = true;
std::string msg = "'test_attr' must be even!";
......@@ -166,7 +166,7 @@ TEST(OpRegistry, CustomChecker) {
attr->set_name("test_attr");
attr->set_type(paddle::framework::AttrType::INT);
attr->set_i(4);
auto op = paddle::framework::OpRegistry::CreateOp(op_desc, nullptr);
auto op = paddle::framework::OpRegistry::CreateOp(op_desc);
paddle::platform::CPUDeviceContext dev_ctx;
paddle::framework::Scope scope;
op->Run(scope, dev_ctx);
......
paddle/framework/operator.cc
浏览文件 @ 83b48ebc
......@@ -351,6 +351,20 @@ class RuntimeInferShapeContext : public InferShapeContext {
return op_.Outputs(name);
}
void ShareLoD(const std::string& in, const std::string& out, size_t i = 0,
size_t j = 0) const override {
PADDLE_ENFORCE_LT(i, Inputs(in).size());
PADDLE_ENFORCE_LT(j, Outputs(out).size());
Variable* in_var = scope_.FindVar(Inputs(in)[i]);
Variable* out_var = scope_.FindVar(Outputs(out)[j]);
if (!in_var->IsType<LoDTensor>()) return;
PADDLE_ENFORCE(out_var->IsType<LoDTensor>(),
"The %d-th output of Output(%s) must be LoDTensor.", j, out);
auto in_tensor = in_var->Get<LoDTensor>();
auto* out_tensor = out_var->GetMutable<LoDTensor>();
out_tensor->set_lod(in_tensor.lod());
}
private:
DDim GetDim(const std::string& name) const override {
Variable* var = scope_.FindVar(name);
......
paddle/framework/operator_test.cc
浏览文件 @ 83b48ebc
......@@ -83,7 +83,7 @@ TEST(OperatorBase, all) {
paddle::platform::CPUDeviceContext device_context;
paddle::framework::Scope scope;
auto op = paddle::framework::OpRegistry::CreateOp(op_desc, nullptr);
auto op = paddle::framework::OpRegistry::CreateOp(op_desc);
scope.Var("OUT1");
ASSERT_EQ(paddle::framework::op_run_num, 0);
op->Run(scope, device_context);
......@@ -208,7 +208,7 @@ TEST(OpKernel, all) {
paddle::platform::CPUDeviceContext cpu_device_context;
paddle::framework::Scope scope;
auto op = paddle::framework::OpRegistry::CreateOp(op_desc, nullptr);
auto op = paddle::framework::OpRegistry::CreateOp(op_desc);
ASSERT_EQ(paddle::framework::cpu_kernel_run_num, 0);
op->Run(scope, cpu_device_context);
ASSERT_EQ(paddle::framework::cpu_kernel_run_num, 1);
......@@ -244,7 +244,7 @@ TEST(OpKernel, multi_inputs) {
scope.Var("y0")->GetMutable<LoDTensor>();
scope.Var("y1")->GetMutable<LoDTensor>();
auto op = paddle::framework::OpRegistry::CreateOp(op_desc, nullptr);
auto op = paddle::framework::OpRegistry::CreateOp(op_desc);
op->Run(scope, cpu_device_context);
}
......
paddle/framework/program_desc.h
浏览文件 @ 83b48ebc
......@@ -37,7 +37,9 @@ class ProgramDescBind {
BlockDescBind *AppendBlock(const BlockDescBind &parent);
BlockDescBind *Block(size_t idx) { return blocks_[idx].get(); }
BlockDescBind *MutableBlock(size_t idx) { return blocks_[idx].get(); }
const BlockDescBind &Block(size_t idx) const { return *blocks_[idx]; }
size_t Size() const { return blocks_.size(); }
......
paddle/framework/program_desc_test.cc
浏览文件 @ 83b48ebc
......@@ -20,7 +20,7 @@ namespace paddle {
namespace framework {
TEST(ProgramDesc, copy_ctor) {
ProgramDescBind program;
auto* global_block = program.Block(0);
auto* global_block = program.MutableBlock(0);
auto* x = global_block->Var("X");
x->SetType(VarDesc_VarType_LOD_TENSOR);
x->SetLoDLevel(0);
......@@ -44,7 +44,7 @@ TEST(ProgramDesc, copy_ctor) {
ProgramDescBind program_copy(program);
auto* global_block_copy = program_copy.Block(0);
auto* global_block_copy = program_copy.MutableBlock(0);
ASSERT_NE(global_block, global_block_copy);
auto assert_same_var = [&](const std::string& name, VarDescBind* var_before) {
......@@ -82,7 +82,7 @@ TEST(ProgramDesc, copy_ctor) {
TEST(ProgramDescBind, serialize_and_deserialize) {
ProgramDescBind program_origin;
auto* global_block = program_origin.Block(0);
auto* global_block = program_origin.MutableBlock(0);
auto* x = global_block->Var("X");
x->SetType(VarDesc_VarType_LOD_TENSOR);
x->SetLoDLevel(0);
......@@ -108,7 +108,7 @@ TEST(ProgramDescBind, serialize_and_deserialize) {
program_origin.Proto()->SerializeToString(&binary_str);
ProgramDescBind program_restored(binary_str);
auto* global_block_restored = program_restored.Block(0);
auto* global_block_restored = program_restored.MutableBlock(0);
ASSERT_NE(global_block, global_block_restored);
auto assert_same_var = [&](const std::string& name, VarDescBind* var_before) {
......
paddle/framework/prune_test.cc
浏览文件 @ 83b48ebc
......@@ -52,7 +52,7 @@ void AddOp(const std::string &type, const f::VariableNameMap &inputs,
TEST(Prune, one_operator) {
f::ProgramDescBind program;
f::BlockDescBind *block = program.Block(0);
f::BlockDescBind *block = program.MutableBlock(0);
AddOp("one_one", {{"input", {"a"}}}, {{"output", {"b"}}}, {}, block);
......@@ -69,7 +69,7 @@ TEST(Prune, one_operator) {
TEST(Prune, forward) {
f::ProgramDescBind program;
f::BlockDescBind *block = program.Block(0);
f::BlockDescBind *block = program.MutableBlock(0);
AddOp("one_one", {{"input", {"a"}}}, {{"output", {"b"}}}, {}, block);
AddOp("one_one", {{"input", {"b"}}}, {{"output", {"c"}}}, {}, block);
......@@ -88,7 +88,7 @@ TEST(Prune, forward) {
TEST(Prune, multi_input_op) {
f::ProgramDescBind program;
f::BlockDescBind *block = program.Block(0);
f::BlockDescBind *block = program.MutableBlock(0);
AddOp("one_one", {{"input", {"a0"}}}, {{"output", {"b0"}}}, {}, block);
AddOp("one_one", {{"input", {"a1"}}}, {{"output", {"b1"}}}, {}, block);
......@@ -106,7 +106,7 @@ TEST(Prune, multi_input_op) {
TEST(Prune, multi_output_op) {
f::ProgramDescBind program;
f::BlockDescBind *block = program.Block(0);
f::BlockDescBind *block = program.MutableBlock(0);
AddOp("one_two", {{"input", {"a"}}}, {{"output", {"b", "c"}}}, {}, block);
AddOp("one_one", {{"input", {"b"}}}, {{"output", {"b1"}}}, {}, block);
......@@ -122,7 +122,7 @@ TEST(Prune, multi_output_op) {
TEST(Prune, multi_target) {
f::ProgramDescBind program;
f::BlockDescBind *block = program.Block(0);
f::BlockDescBind *block = program.MutableBlock(0);
AddOp("one_two", {{"input", {"a"}}}, {{"output", {"b", "c"}}}, {}, block);
AddOp("one_one", {{"input", {"b"}}}, {{"output", {"b1"}}}, {}, block);
......
paddle/framework/shape_inference.cc
浏览文件 @ 83b48ebc
......@@ -28,9 +28,6 @@ void InferShapeContext::SetOutputsDim(
SetDims(names, dims);
}
void InferShapeContext::ShareLoD(const std::string &in, const std::string &out,
size_t i, size_t j) const {}
std::vector<framework::DDim> InferShapeContext::GetDims(
const std::vector<std::string> &names) const {
std::vector<framework::DDim> ret;
......
paddle/framework/shape_inference.h
浏览文件 @ 83b48ebc
......@@ -43,9 +43,8 @@ class InferShapeContext {
virtual const std::vector<std::string> &Outputs(
const std::string &name) const = 0;
// TODO(qiao) implement this function
void ShareLoD(const std::string &in, const std::string &out, size_t i = 0,
size_t j = 0) const;
virtual void ShareLoD(const std::string &in, const std::string &out,
size_t i = 0, size_t j = 0) const = 0;
protected:
virtual framework::DDim GetDim(const std::string &name) const = 0;
......
paddle/framework/type_defs.h
浏览文件 @ 83b48ebc
......@@ -36,7 +36,7 @@ using VariableNameMap = std::map<std::string, std::vector<std::string>>;
using Attribute =
boost::variant<boost::blank, int, float, std::string, std::vector<int>,
std::vector<float>, std::vector<std::string>, bool,
std::vector<bool>, BlockDesc*>;
std::vector<bool>, BlockDescBind*>;
using AttributeMap = std::unordered_map<std::string, Attribute>;
......
paddle/framework/var_type_inference_test.cc
浏览文件 @ 83b48ebc
......@@ -63,41 +63,43 @@ namespace framework {
TEST(InferVarType, sum_op) {
ProgramDescBind prog;
auto *op = prog.Block(0)->AppendOp();
auto *op = prog.MutableBlock(0)->AppendOp();
op->SetType("sum");
op->SetInput("X", {"test_a", "test_b", "test_c"});
op->SetOutput("Out", {"test_out"});
prog.Block(0)->Var("test_a")->SetType(VarDesc::SELECTED_ROWS);
prog.Block(0)->Var("test_b")->SetType(VarDesc::SELECTED_ROWS);
prog.Block(0)->Var("test_c")->SetType(VarDesc::SELECTED_ROWS);
prog.Block(0)->Var("test_out");
prog.MutableBlock(0)->Var("test_a")->SetType(VarDesc::SELECTED_ROWS);
prog.MutableBlock(0)->Var("test_b")->SetType(VarDesc::SELECTED_ROWS);
prog.MutableBlock(0)->Var("test_c")->SetType(VarDesc::SELECTED_ROWS);
prog.MutableBlock(0)->Var("test_out");
op->InferVarType(prog.Block(0));
op->InferVarType(prog.MutableBlock(0));
ASSERT_EQ(VarDesc::SELECTED_ROWS, prog.Block(0)->Var("test_out")->GetType());
ASSERT_EQ(VarDesc::SELECTED_ROWS,
prog.MutableBlock(0)->Var("test_out")->GetType());
prog.Block(0)->Var("test_b")->SetType(VarDesc::LOD_TENSOR);
op->InferVarType(prog.Block(0));
ASSERT_EQ(VarDesc::LOD_TENSOR, prog.Block(0)->Var("test_out")->GetType());
prog.MutableBlock(0)->Var("test_b")->SetType(VarDesc::LOD_TENSOR);
op->InferVarType(prog.MutableBlock(0));
ASSERT_EQ(VarDesc::LOD_TENSOR,
prog.MutableBlock(0)->Var("test_out")->GetType());
}
TEST(InferVarType, sum_op_without_infer_var_type) {
ProgramDescBind prog;
auto *op = prog.Block(0)->AppendOp();
auto *op = prog.MutableBlock(0)->AppendOp();
op->SetType("sum_without_infer_var_type");
op->SetInput("X", {"test2_a", "test2_b", "test2_c"});
op->SetOutput("Out", {"test2_out"});
prog.Block(0)->Var("test2_a")->SetType(VarDesc::SELECTED_ROWS);
prog.Block(0)->Var("test2_b")->SetType(VarDesc::SELECTED_ROWS);
prog.Block(0)->Var("test2_c")->SetType(VarDesc::SELECTED_ROWS);
prog.Block(0)->Var("test2_out");
prog.MutableBlock(0)->Var("test2_a")->SetType(VarDesc::SELECTED_ROWS);
prog.MutableBlock(0)->Var("test2_b")->SetType(VarDesc::SELECTED_ROWS);
prog.MutableBlock(0)->Var("test2_c")->SetType(VarDesc::SELECTED_ROWS);
prog.MutableBlock(0)->Var("test2_out");
op->InferVarType(prog.Block(0));
op->InferVarType(prog.MutableBlock(0));
ASSERT_EQ(VarDesc_VarType_LOD_TENSOR,
prog.Block(0)->Var("test2_out")->GetType());
prog.MutableBlock(0)->Var("test2_out")->GetType());
}
} // namespace framework
......
paddle/gserver/layers/SequenceReshapeLayer.cpp
浏览文件 @ 83b48ebc
......@@ -70,11 +70,23 @@ void SequenceReshapeLayer::forward(PassType passType) {
size_t outDim = getSize();
size_t numSequences = input.getNumSequences();
auto startPositions = input.sequenceStartPositions->getVector(false);
const int* starts = startPositions->getData();
CHECK_EQ(starts[numSequences], input.getBatchSize());
CHECK_EQ(numSequences, startPositions->getSize() - 1);
// by default, we assume each instance as a sequence
IVectorPtr seqStarts;
IVector::resizeOrCreate(seqStarts, input.getBatchSize() + 1, false);
int* startsData = seqStarts->getData();
for (int i = 0; i < input.getBatchSize() + 1; i++) {
startsData[i] = i;
}
const int* starts = startsData;
// if there is sequence, then use start positions
if (input.sequenceStartPositions) {
auto startPositions = input.sequenceStartPositions->getVector(false);
starts = startPositions->getData();
CHECK_EQ(starts[numSequences], input.getBatchSize());
CHECK_EQ(numSequences, startPositions->getSize() - 1);
}
for (size_t seqID = 0; seqID < numSequences; seqID++) {
size_t inNumIns = starts[seqID + 1] - starts[seqID];
......
paddle/memory/detail/buddy_allocator.cc
浏览文件 @ 83b48ebc
......@@ -27,11 +27,11 @@ BuddyAllocator::BuddyAllocator(SystemAllocator* system_allocator,
system_allocator_(std::move(system_allocator)) {}
BuddyAllocator::~BuddyAllocator() {
VLOG(3) << "BuddyAllocator Disconstructor makes sure that all of these "
"have actually been freed";
VLOG(10) << "BuddyAllocator Disconstructor makes sure that all of these "
"have actually been freed";
while (!pool_.empty()) {
auto block = static_cast<MemoryBlock*>(std::get<2>(*pool_.begin()));
VLOG(3) << "Free from block (" << block << ", " << max_chunk_size_ << ")";
VLOG(10) << "Free from block (" << block << ", " << max_chunk_size_ << ")";
system_allocator_->Free(block, max_chunk_size_, block->index(cache_));
cache_.invalidate(block);
......@@ -51,11 +51,12 @@ void* BuddyAllocator::Alloc(size_t unaligned_size) {
// acquire the allocator lock
std::lock_guard<std::mutex> lock(mutex_);
VLOG(3) << "Allocate " << unaligned_size << " bytes from chunk size " << size;
VLOG(10) << "Allocate " << unaligned_size << " bytes from chunk size "
<< size;
// if the allocation is huge, send directly to the system allocator
if (size > max_chunk_size_) {
VLOG(3) << "Allocate from system allocator.";
VLOG(10) << "Allocate from system allocator.";
return SystemAlloc(size);
}
......@@ -70,9 +71,9 @@ void* BuddyAllocator::Alloc(size_t unaligned_size) {
return nullptr;
}
} else {
VLOG(3) << "Allocation from existing memory block " << std::get<2>(*it)
<< " at address "
<< reinterpret_cast<MemoryBlock*>(std::get<2>(*it))->data();
VLOG(10) << "Allocation from existing memory block " << std::get<2>(*it)
<< " at address "
<< reinterpret_cast<MemoryBlock*>(std::get<2>(*it))->data();
}
total_used_ += size;
......@@ -89,10 +90,10 @@ void BuddyAllocator::Free(void* p) {
// Acquire the allocator lock
std::lock_guard<std::mutex> lock(mutex_);
VLOG(3) << "Free from address " << block;
VLOG(10) << "Free from address " << block;
if (block->type(cache_) == MemoryBlock::HUGE_CHUNK) {
VLOG(3) << "Free directly from system allocator";
VLOG(10) << "Free directly from system allocator";
system_allocator_->Free(block, block->total_size(cache_),
block->index(cache_));
......@@ -109,8 +110,8 @@ void BuddyAllocator::Free(void* p) {
// Trying to merge the right buddy
if (block->has_right_buddy(cache_)) {
VLOG(3) << "Merging this block " << block << " with its right buddy "
<< block->right_buddy(cache_);
VLOG(10) << "Merging this block " << block << " with its right buddy "
<< block->right_buddy(cache_);
auto right_buddy = block->right_buddy(cache_);
......@@ -127,8 +128,8 @@ void BuddyAllocator::Free(void* p) {
// Trying to merge the left buddy
if (block->has_left_buddy(cache_)) {
VLOG(3) << "Merging this block " << block << " with its left buddy "
<< block->left_buddy(cache_);
VLOG(10) << "Merging this block " << block << " with its left buddy "
<< block->left_buddy(cache_);
auto left_buddy = block->left_buddy(cache_);
......@@ -144,8 +145,8 @@ void BuddyAllocator::Free(void* p) {
}
// Dumping this block into pool
VLOG(3) << "Inserting free block (" << block << ", "
<< block->total_size(cache_) << ")";
VLOG(10) << "Inserting free block (" << block << ", "
<< block->total_size(cache_) << ")";
pool_.insert(
IndexSizeAddress(block->index(cache_), block->total_size(cache_), block));
......@@ -164,7 +165,7 @@ void* BuddyAllocator::SystemAlloc(size_t size) {
size_t index = 0;
void* p = system_allocator_->Alloc(index, size);
VLOG(3) << "Allocated " << p << " from system allocator.";
VLOG(10) << "Allocated " << p << " from system allocator.";
if (p == nullptr) return nullptr;
......@@ -190,8 +191,8 @@ BuddyAllocator::PoolSet::iterator BuddyAllocator::RefillPool() {
if (p == nullptr) return pool_.end();
VLOG(3) << "Creating and inserting new block " << p
<< " from system allocator";
VLOG(10) << "Creating and inserting new block " << p
<< " from system allocator";
static_cast<MemoryBlock*>(p)->init(cache_, MemoryBlock::FREE_CHUNK, index,
max_chunk_size_, nullptr, nullptr);
......@@ -235,19 +236,19 @@ void* BuddyAllocator::SplitToAlloc(BuddyAllocator::PoolSet::iterator it,
auto block = static_cast<MemoryBlock*>(std::get<2>(*it));
pool_.erase(it);
VLOG(3) << "Split block (" << block << ", " << block->total_size(cache_)
<< ") into";
VLOG(10) << "Split block (" << block << ", " << block->total_size(cache_)
<< ") into";
block->split(cache_, size);
VLOG(3) << "Left block (" << block << ", " << block->total_size(cache_)
<< ")";
VLOG(10) << "Left block (" << block << ", " << block->total_size(cache_)
<< ")";
block->set_type(cache_, MemoryBlock::ARENA_CHUNK);
// the rest of memory if exist
if (block->has_right_buddy(cache_)) {
if (block->right_buddy(cache_)->type(cache_) == MemoryBlock::FREE_CHUNK) {
VLOG(3) << "Insert right block (" << block->right_buddy(cache_) << ", "
<< block->right_buddy(cache_)->total_size(cache_) << ")";
VLOG(10) << "Insert right block (" << block->right_buddy(cache_) << ", "
<< block->right_buddy(cache_)->total_size(cache_) << ")";
pool_.insert(
IndexSizeAddress(block->right_buddy(cache_)->index(cache_),
......@@ -274,7 +275,7 @@ void BuddyAllocator::CleanIdleFallBackAlloc() {
return;
}
VLOG(3) << "Return block " << block << " to fallback allocator.";
VLOG(10) << "Return block " << block << " to fallback allocator.";
system_allocator_->Free(block, max_chunk_size_, block->index(cache_));
cache_.invalidate(block);
......@@ -310,7 +311,7 @@ void BuddyAllocator::CleanIdleNormalAlloc() {
MemoryBlock* block = static_cast<MemoryBlock*>(std::get<2>(*pool));
VLOG(3) << "Return block " << block << " to base allocator.";
VLOG(10) << "Return block " << block << " to base allocator.";
system_allocator_->Free(block, max_chunk_size_, block->index(cache_));
cache_.invalidate(block);
......
paddle/memory/detail/meta_cache.cc
浏览文件 @ 83b48ebc
......@@ -30,7 +30,7 @@ Metadata MetadataCache::load(const MemoryBlock* block) {
return existing_metadata->second;
} else {
auto* meta = reinterpret_cast<const Metadata*>(block);
VLOG(3) << "Load MetaData type=" << meta->type;
VLOG(10) << "Load MetaData type=" << meta->type;
PADDLE_ASSERT(meta->check_guards());
return *reinterpret_cast<const Metadata*>(block);
}
......
paddle/memory/detail/system_allocator.cc
浏览文件 @ 83b48ebc
......@@ -41,7 +41,16 @@ void* CPUAllocator::Alloc(size_t& index, size_t size) {
index = 0; // unlock memory
void* p = malloc(size);
void* p;
#ifdef PADDLE_USE_MKLDNN
// refer to https://github.com/01org/mkl-dnn/blob/master/include/mkldnn.hpp
// memory alignment
PADDLE_ENFORCE_EQ(posix_memalign(&p, 4096ul, size), 0);
#else
PADDLE_ENFORCE_EQ(posix_memalign(&p, 32ul, size), 0);
#endif
PADDLE_ENFORCE(p, "Fail to allocate CPU memory: size = %d .", size);
if (p != nullptr) {
if (FLAGS_use_pinned_memory) {
......
paddle/memory/memory.cc
浏览文件 @ 83b48ebc
......@@ -39,15 +39,15 @@ BuddyAllocator* GetCPUBuddyAllocator() {
template <>
void* Alloc<platform::CPUPlace>(platform::CPUPlace place, size_t size) {
VLOG(3) << "Allocate " << size << " bytes on " << platform::Place(place);
VLOG(10) << "Allocate " << size << " bytes on " << platform::Place(place);
void* p = GetCPUBuddyAllocator()->Alloc(size);
VLOG(3) << " pointer=" << p;
VLOG(10) << " pointer=" << p;
return p;
}
template <>
void Free<platform::CPUPlace>(platform::CPUPlace place, void* p) {
VLOG(3) << "Free pointer=" << p << " on " << platform::Place(place);
VLOG(10) << "Free pointer=" << p << " on " << platform::Place(place);
GetCPUBuddyAllocator()->Free(p);
}
......@@ -69,11 +69,12 @@ BuddyAllocator* GetGPUBuddyAllocator(int gpu_id) {
platform::GpuMinChunkSize(),
platform::GpuMaxChunkSize());
}
VLOG(3) << "\n\nNOTE: each GPU device use "
<< FLAGS_fraction_of_gpu_memory_to_use * 100 << "% of GPU memory.\n"
<< "You can set environment variable '"
<< platform::kEnvFractionGpuMemoryToUse
<< "' to change the fraction of GPU usage.\n\n";
VLOG(10) << "\n\nNOTE: each GPU device use "
<< FLAGS_fraction_of_gpu_memory_to_use * 100
<< "% of GPU memory.\n"
<< "You can set environment variable '"
<< platform::kEnvFractionGpuMemoryToUse
<< "' to change the fraction of GPU usage.\n\n";
}
platform::SetDeviceId(gpu_id);
return as[gpu_id];
......
paddle/operators/dynamic_recurrent_op_test.cc
浏览文件 @ 83b48ebc
......@@ -51,7 +51,7 @@ class RNNAlgorithmTestHelper : public ::testing::Test {
CreateGlobalVariables();
auto op_desc = CreateOpDesc();
op = paddle::framework::OpRegistry::CreateOp(op_desc, nullptr);
op = paddle::framework::OpRegistry::CreateOp(op_desc);
dop = &(dynamic_cast<DynamicRecurrentOp*>(op.get())->rnn);
InitCacheManually();
InitStepNet();
......
paddle/operators/gaussian_random_op.cc
浏览文件 @ 83b48ebc
......@@ -45,14 +45,14 @@ class GaussianRandomOp : public framework::OperatorWithKernel {
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of GaussianRandomOp should not be null.");
auto dims = ctx->Attrs().Get<std::vector<int>>("dims");
auto shape = ctx->Attrs().Get<std::vector<int>>("shape");
std::vector<int64_t> temp;
temp.reserve(dims.size());
for (auto dim : dims) {
temp.reserve(shape.size());
for (auto dim : shape) {
temp.push_back(static_cast<int64_t>(dim));
}
PADDLE_ENFORCE(dims.size() > 0UL,
"dims can be one int or array. dims must be set.");
PADDLE_ENFORCE(shape.size() > 0UL,
"shape can be one int or array. shape must be set.");
ctx->SetOutputDim("Out", framework::make_ddim(temp));
}
......@@ -74,7 +74,7 @@ GaussianRandom operator.
Use to initialize tensor with gaussian random generator.
)DOC");
AddAttr<std::vector<int>>("dims", "The dimension of random tensor.");
AddAttr<std::vector<int>>("shape", "The dimension of random tensor.");
AddAttr<float>("mean", "mean of random tensor.").SetDefault(.0f);
AddAttr<float>("std", "std of random tensor.").SetDefault(1.0f);
AddAttr<int>("seed",
......
paddle/operators/lookup_table_op.cc
浏览文件 @ 83b48ebc
......@@ -43,7 +43,7 @@ class LookupTableOp : public framework::OperatorWithKernel {
protected:
framework::DataType IndicateDataType(
const framework::ExecutionContext& ctx) const override {
return framework::ToDataType(ctx.Input<Tensor>("W")->type());
return framework::ToDataType(ctx.Input<LoDTensor>("W")->type());
}
};
......@@ -93,7 +93,7 @@ class LookupTableOpGrad : public framework::OperatorWithKernel {
protected:
framework::DataType IndicateDataType(
const framework::ExecutionContext& ctx) const override {
return framework::ToDataType(ctx.Input<Tensor>("W")->type());
return framework::ToDataType(ctx.Input<LoDTensor>("W")->type());
}
};
......
paddle/operators/lookup_table_op.cu
浏览文件 @ 83b48ebc
......@@ -61,16 +61,16 @@ template <typename T>
class LookupTableCUDAKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto table_t = context.Input<Tensor>("W");
auto ids_t = context.Input<Tensor>("Ids");
auto output_t = context.Output<Tensor>("Out");
auto* table_t = context.Input<LoDTensor>("W");
auto* ids_t = context.Input<LoDTensor>("Ids");
auto* output_t = context.Output<LoDTensor>("Out");
size_t N = table_t->dims()[0];
size_t D = table_t->dims()[1];
size_t K = ids_t->numel();
auto ids = ids_t->data<int64_t>();
auto table = table_t->data<T>();
auto output = output_t->mutable_data<T>(context.GetPlace());
auto* ids = ids_t->data<int64_t>();
auto* table = table_t->data<T>();
auto* output = output_t->mutable_data<T>(context.GetPlace());
dim3 threads(128, 8);
dim3 grids(8, 1);
......@@ -87,9 +87,9 @@ class LookupTableGradCUDAKernel : public framework::OpKernel<T> {
void Compute(const framework::ExecutionContext& context) const override {
bool is_sparse = context.Attr<bool>("is_sparse");
if (is_sparse) {
auto* ids = context.Input<Tensor>("Ids");
auto* table = context.Input<Tensor>("W");
auto* d_output = context.Input<Tensor>(framework::GradVarName("Out"));
auto* ids = context.Input<LoDTensor>("Ids");
auto* table = context.Input<LoDTensor>("W");
auto* d_output = context.Input<LoDTensor>(framework::GradVarName("Out"));
auto* d_table = context.Output<SelectedRows>(framework::GradVarName("W"));
auto* ids_data = ids->data<int64_t>();
......@@ -116,12 +116,12 @@ class LookupTableGradCUDAKernel : public framework::OpKernel<T> {
auto* d_output_data = d_output->data<T>();
PADDLE_ENFORCE_EQ(d_table_value->dims(), d_output->dims());
memory::Copy(gpu_place, d_table_data, gpu_place, d_output_data,
d_output->numel(), stream);
d_output->numel() * sizeof(T), stream);
} else {
auto ids_t = context.Input<Tensor>("Ids");
auto d_output_t = context.Input<Tensor>(framework::GradVarName("Out"));
auto d_table_t = context.Output<Tensor>(framework::GradVarName("W"));
auto ids_t = context.Input<LoDTensor>("Ids");
auto d_output_t = context.Input<LoDTensor>(framework::GradVarName("Out"));
auto d_table_t = context.Output<LoDTensor>(framework::GradVarName("W"));
int N = d_table_t->dims()[0];
int D = d_table_t->dims()[1];
......
paddle/operators/lookup_table_op.h
浏览文件 @ 83b48ebc
......@@ -19,22 +19,22 @@
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
using LoDTensor = framework::LoDTensor;
using SelectedRows = framework::SelectedRows;
template <typename T>
class LookupTableKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto table_t = context.Input<Tensor>("W"); // float tensor
auto ids_t = context.Input<Tensor>("Ids"); // int tensor
auto output_t = context.Output<Tensor>("Out"); // float tensor
auto* table_t = context.Input<LoDTensor>("W"); // float tensor
auto* ids_t = context.Input<LoDTensor>("Ids"); // int tensor
auto* output_t = context.Output<LoDTensor>("Out"); // float tensor
int N = table_t->dims()[0];
int D = table_t->dims()[1];
auto ids = ids_t->data<int64_t>();
auto table = table_t->data<T>();
auto output = output_t->mutable_data<T>(context.GetPlace());
auto* ids = ids_t->data<int64_t>();
auto* table = table_t->data<T>();
auto* output = output_t->mutable_data<T>(context.GetPlace());
for (int64_t i = 0; i < ids_t->numel(); ++i) {
PADDLE_ENFORCE_LT(ids[i], N);
PADDLE_ENFORCE_GE(ids[i], 0);
......@@ -49,9 +49,9 @@ class LookupTableGradKernel : public framework::OpKernel<T> {
void Compute(const framework::ExecutionContext& context) const override {
bool is_sparse = context.Attr<bool>("is_sparse");
if (is_sparse) {
auto* ids = context.Input<Tensor>("Ids");
auto* table = context.Input<Tensor>("W");
auto* d_output = context.Input<Tensor>(framework::GradVarName("Out"));
auto* ids = context.Input<LoDTensor>("Ids");
auto* table = context.Input<LoDTensor>("W");
auto* d_output = context.Input<LoDTensor>(framework::GradVarName("Out"));
auto* d_table = context.Output<SelectedRows>(framework::GradVarName("W"));
auto* ids_data = ids->data<int64_t>();
......@@ -76,10 +76,10 @@ class LookupTableGradKernel : public framework::OpKernel<T> {
PADDLE_ENFORCE_EQ(d_table_value->dims(), d_output->dims());
memcpy(d_table_data, d_output_data, sizeof(T) * d_output->numel());
} else {
auto* ids = context.Input<Tensor>("Ids");
auto* d_output = context.Input<Tensor>(framework::GradVarName("Out"));
auto* d_table = context.Output<Tensor>(framework::GradVarName("W"));
auto* table = context.Input<Tensor>("W");
auto* ids = context.Input<LoDTensor>("Ids");
auto* d_output = context.Input<LoDTensor>(framework::GradVarName("Out"));
auto* d_table = context.Output<LoDTensor>(framework::GradVarName("W"));
auto* table = context.Input<LoDTensor>("W");
auto* ids_data = ids->data<int64_t>();
auto ids_dim = ids->dims();
......
paddle/operators/lstm_op.cc
浏览文件 @ 83b48ebc
......@@ -21,7 +21,6 @@ class LSTMOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("Input"),
"Input(Input) of LSTM should not be null.");
......@@ -29,9 +28,13 @@ class LSTMOp : public framework::OperatorWithKernel {
"Output(Hidden) of LSTM should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Cell"),
"Output(Cell) of LSTM should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("BatchGate"),
"Output(BatchGate) of LSTM should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("BatchCellPreAct"),
"Output(BatchGate) of LSTM should not be null.");
auto x_dims = ctx->GetInputDim("Input");
PADDLE_ENFORCE_EQ(x_dims.size(), 2, "Input(X)'s rank must be 2.");
auto in_dims = ctx->GetInputDim("Input");
PADDLE_ENFORCE_EQ(in_dims.size(), 2, "Input(X)'s rank must be 2.");
if (ctx->HasInput("H0")) {
PADDLE_ENFORCE(ctx->HasInput("C0"),
......@@ -44,7 +47,7 @@ class LSTMOp : public framework::OperatorWithKernel {
"should be the same.");
}
int frame_size = x_dims[1] / 4;
int frame_size = in_dims[1] / 4;
auto w_dims = ctx->GetInputDim("Weight");
PADDLE_ENFORCE_EQ(w_dims.size(), 2,
"The rank of Input(Weight) should be 2.");
......@@ -71,12 +74,21 @@ class LSTMOp : public framework::OperatorWithKernel {
"4 * %d if disable peepholes connection",
frame_size);
}
ctx->SetOutputDim("Hidden", {x_dims[0], frame_size});
ctx->SetOutputDim("Cell", {x_dims[0], frame_size});
ctx->SetOutputDim("BatchGate", x_dims);
framework::DDim out_dims({in_dims[0], frame_size});
ctx->SetOutputDim("Hidden", out_dims);
ctx->SetOutputDim("Cell", out_dims);
ctx->SetOutputDim("BatchGate", in_dims);
ctx->SetOutputDim("BatchCellPreAct", out_dims);
ctx->ShareLoD("Input", "Hidden");
ctx->ShareLoD("Input", "Cell");
}
protected:
framework::DataType IndicateDataType(
const framework::ExecutionContext& ctx) const override {
return framework::ToDataType(
ctx.Input<framework::LoDTensor>("Input")->type());
}
};
class LSTMOpMaker : public framework::OpProtoAndCheckerMaker {
......@@ -86,16 +98,18 @@ class LSTMOpMaker : public framework::OpProtoAndCheckerMaker {
AddInput("Input",
"(LoDTensor) the first input is a LodTensor, which support "
"variable-time length input sequence. The underlying tensor in "
"this LoDTensor is a matrix with shape (T X 4D), where, T is the "
"this LoDTensor is a matrix with shape (T X 4D), where T is the "
"total time steps in this mini-batch, D is the hidden size.");
AddInput("H0",
"(Tensor, optional) the initial hidden state is an optional "
"input. This is a tensor with shape (N x D), where N is the "
"batch size, D is the hidden size.");
"batch size, D is the hidden size.")
.AsDispensable();
AddInput("C0",
"(Tensor, optional) the initial cell state is an optional "
"input. This is a tensor with shape (N x D), where N is the "
"batch size. `H0` and `C0` can be NULL but only at the same time");
"batch size. `H0` and `C0` can be NULL but only at the same time")
.AsDispensable();
AddInput("Weight",
"(Tensor) the learnable hidden-hidden weights."
" - The shape is (D x 4D), where D is the hidden size. "
......@@ -109,22 +123,27 @@ class LSTMOpMaker : public framework::OpProtoAndCheckerMaker {
" - Bias = {b_c, b_i, b_f, b_o}."
"2. `usePeepholes = True` "
" - The shape is (1 x 7D). "
" - Bias = {b_c, b_i, b_f, b_o, W_ic, W_fc, W_oc}.");
" - Bias = {b_c, b_i, b_f, b_o, W_ic, W_fc, W_oc}.")
.AsDispensable();
AddOutput("Hidden",
"(LoDTensor) the hidden state of LSTM operator. "
"The shape is (T x D), and lod is the same with the `Input`.");
AddOutput("Cell",
"(LoDTensor) the cell state of LSTM operator. "
"The shape is (T x D), and lod is the same with the `Input`.");
AddOutput("BatchGate",
"(LoDTensor) This LoDTensor contains input gate, forget gate "
"and output gate after the nonlinear computation. This "
"LoDTensor has the same shape with the reorganized input, which "
"was also be called batch input. The LoD size is 2. The first "
"is also be called batch input. The LoD size is 2. The first "
"LoD is the batch offsets and the second LoD contains the "
"indexes, which denote the position of reorganized sequence "
"in the raw input.")
.AsIntermediate();
AddOutput("Hidden",
"(LoDTensor) the hidden state lod tensor of LSTM operator. "
"The shape and lod is the same with the `Input`.");
AddOutput("Cell",
"(LoDTensor) the cell state lod tensor of LSTM operator. "
"The shape and lod is the same with the `Input`.");
AddOutput("BatchCellPreAct",
"(LoDTensor) This LoDTensor is got in the forward and used "
"in the backward.")
.AsIntermediate();
AddAttr<bool>("usePeepholes",
"(bool, defalut: True) "
"whether to enable diagonal/peephole connections.")
......@@ -202,15 +221,37 @@ class LSTMGradOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Hidden")),
"Input(Hidden@GRAD) should not be null");
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Cell")),
"Input(Cell@GRAD) should not be null");
ctx->SetOutputDim(framework::GradVarName("Weight"),
ctx->GetInputDim("Weight"));
ctx->SetOutputDim(framework::GradVarName("Bias"), ctx->GetInputDim("Bias"));
PADDLE_ENFORCE(ctx->HasInput("Input"),
"Input(Input) of LSTM should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Hidden"),
"Input(Hidden) of LSTM should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Cell"),
"Input(Cell) of LSTM should not be null.");
PADDLE_ENFORCE(ctx->HasInput("BatchGate"),
"Input(BatchGate) of LSTM should not be null.");
PADDLE_ENFORCE(ctx->HasInput("BatchCellPreAct"),
"Input(BatchGate) of LSTM should not be null.");
auto in_g_name = framework::GradVarName("Input");
if (ctx->HasOutput(in_g_name))
ctx->SetOutputDim(in_g_name, ctx->GetInputDim("Input"));
auto w_g_name = framework::GradVarName("Weight");
if (ctx->HasOutput(w_g_name))
ctx->SetOutputDim(w_g_name, ctx->GetInputDim("Weight"));
auto b_g_name = framework::GradVarName("Bias");
if (ctx->HasOutput(b_g_name))
ctx->SetOutputDim(b_g_name, ctx->GetInputDim("Bias"));
}
protected:
framework::DataType IndicateDataType(
const framework::ExecutionContext& ctx) const override {
return framework::ToDataType(
ctx.Input<framework::LoDTensor>("Input")->type());
}
};
......
paddle/operators/lstm_op.h
浏览文件 @ 83b48ebc
......@@ -21,8 +21,9 @@ limitations under the License. */
namespace paddle {
namespace operators {
using framework::LoDTensor;
using framework::Tensor;
using LoDTensor = framework::LoDTensor;
using Tensor = framework::Tensor;
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
......@@ -31,15 +32,15 @@ template <typename Place, typename T>
class LSTMKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* input = ctx.Input<framework::LoDTensor>("Input");
auto* weight = ctx.Input<framework::Tensor>("Weight");
auto* bias = ctx.Input<framework::Tensor>("Bias");
auto* input = ctx.Input<LoDTensor>("Input");
auto* weight = ctx.Input<Tensor>("Weight");
auto* bias = ctx.Input<Tensor>("Bias");
auto* batch_gate = ctx.Output<framework::LoDTensor>("BatchGate");
auto* batch_gate = ctx.Output<LoDTensor>("BatchGate");
batch_gate->mutable_data<T>(ctx.GetPlace());
auto* hidden_out = ctx.Output<framework::LoDTensor>("Hidden");
auto* hidden_out = ctx.Output<LoDTensor>("Hidden");
hidden_out->mutable_data<T>(ctx.GetPlace());
auto* cell_out = ctx.Output<framework::LoDTensor>("Cell");
auto* cell_out = ctx.Output<LoDTensor>("Cell");
cell_out->mutable_data<T>(ctx.GetPlace());
// Now the function ShareLoD in InferShape is not implemented.
......@@ -49,7 +50,8 @@ class LSTMKernel : public framework::OpKernel<T> {
bool is_reverse = ctx.Attr<bool>("isReverse");
math::LoDTensor2BatchFunctor<Place, T> to_batch;
to_batch(ctx.device_context(), *input, *batch_gate, is_reverse);
auto& device_ctx = ctx.device_context();
to_batch(device_ctx, *input, *batch_gate, true, is_reverse);
auto in_dims = input->dims();
int frame_size = static_cast<int>(in_dims[1] / 4);
......@@ -69,17 +71,26 @@ class LSTMKernel : public framework::OpKernel<T> {
}
math::LstmMetaValue<T> lstm_value;
T* bias_data = const_cast<T*>(bias->data<T>());
// the code style in LstmMetaValue will be updated later.
lstm_value.checkIg = bias_data + 4 * frame_size;
lstm_value.checkFg = lstm_value.checkIg + frame_size;
lstm_value.checkOg = lstm_value.checkFg + frame_size;
if (bias) {
T* bias_data = const_cast<T*>(bias->data<T>());
// the code style in LstmMetaValue will be updated later.
lstm_value.checkIg = bias_data + 4 * frame_size;
lstm_value.checkFg = lstm_value.checkIg + frame_size;
lstm_value.checkOg = lstm_value.checkFg + frame_size;
} else {
lstm_value.checkIg = nullptr;
lstm_value.checkFg = nullptr;
lstm_value.checkOg = nullptr;
}
lstm_value.prevStateValue = nullptr;
framework::LoDTensor batch_out, batch_cell, batch_cell_pre_act;
batch_out.mutable_data<T>(dims, ctx.GetPlace());
// Use the local variable as here.
LoDTensor batch_hidden, batch_cell;
auto* batch_cell_pre_act = ctx.Output<LoDTensor>("BatchCellPreAct");
batch_hidden.mutable_data<T>(dims, ctx.GetPlace());
batch_cell.mutable_data<T>(dims, ctx.GetPlace());
batch_cell_pre_act.mutable_data<T>(dims, ctx.GetPlace());
batch_cell_pre_act->mutable_data<T>(dims, ctx.GetPlace());
auto batch_starts = batch_gate->lod()[0];
size_t num_batch = batch_starts.size() - 1;
......@@ -92,18 +103,18 @@ class LSTMKernel : public framework::OpKernel<T> {
int bend = static_cast<int>(batch_starts[n + 1]);
Tensor gate_t = batch_gate->Slice(bstart, bend);
Tensor out_t = batch_out.Slice(bstart, bend);
Tensor out_t = batch_hidden.Slice(bstart, bend);
Tensor cell_t = batch_cell.Slice(bstart, bend);
Tensor cell_pre_act_t = batch_cell_pre_act.Slice(bstart, bend);
Tensor cell_pre_act_t = batch_cell_pre_act->Slice(bstart, bend);
int cur_batch_size = bend - bstart;
if (n != 0) {
int pre_h_start = static_cast<int>(batch_starts[n - 1]);
int pre_h_end = pre_h_start + cur_batch_size;
auto pre_hidden_t = batch_out.Slice(pre_h_start, pre_h_end);
math::matmul<Place, T>(ctx.device_context(), pre_hidden_t, false,
*weight, false, static_cast<T>(1.0), &gate_t,
auto pre_hidden_t = batch_hidden.Slice(pre_h_start, pre_h_end);
math::matmul<Place, T>(device_ctx, pre_hidden_t, false, *weight, false,
static_cast<T>(1.0), &gate_t,
static_cast<T>(1.0));
}
// else if : FIXME support the initial hidden and cell
......@@ -112,27 +123,186 @@ class LSTMKernel : public framework::OpKernel<T> {
lstm_value.outputValue = out_t.data<T>();
lstm_value.stateValue = cell_t.data<T>();
lstm_value.stateActiveValue = cell_pre_act_t.data<T>();
math::LstmUnitFunctor<Place, T>::compute(ctx.device_context(), lstm_value,
math::LstmUnitFunctor<Place, T>::compute(device_ctx, lstm_value,
frame_size, cur_batch_size,
gate_act, cell_act, cand_act);
lstm_value.prevStateValue = lstm_value.stateValue;
}
math::Batch2LoDTensorFunctor<Place, T> to_seq;
batch_out.set_lod(batch_gate->lod());
batch_hidden.set_lod(batch_gate->lod());
// restore the output hidden in LoDTensor from the batch hidden
to_seq(ctx.device_context(), batch_out, *hidden_out);
to_seq(device_ctx, batch_hidden, *hidden_out);
batch_cell.set_lod(batch_gate->lod());
// restore the output cell state in LoDTensor from the batch cell
to_seq(ctx.device_context(), batch_cell, *cell_out);
to_seq(device_ctx, batch_cell, *cell_out);
}
};
template <typename Place, typename T>
class LSTMGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {}
void Compute(const framework::ExecutionContext& ctx) const override {
auto* input = ctx.Input<LoDTensor>("Input");
auto* weight = ctx.Input<Tensor>("Weight");
auto* bias = ctx.Input<Tensor>("Bias");
auto* hidden_out = ctx.Input<LoDTensor>("Hidden");
auto* cell_out = ctx.Input<LoDTensor>("Cell");
auto* batch_gate = ctx.Input<LoDTensor>("BatchGate");
auto* batch_cell_pre_act = ctx.Input<LoDTensor>("BatchCellPreAct");
auto* hidden_g = ctx.Input<LoDTensor>(framework::GradVarName("Hidden"));
auto* in_g = ctx.Output<LoDTensor>(framework::GradVarName("Input"));
auto* weight_g = ctx.Output<Tensor>(framework::GradVarName("Weight"));
auto* bias_g = ctx.Output<Tensor>(framework::GradVarName("Bias"));
auto& device_ctx = ctx.device_context();
math::SetConstant<Place, T> zero;
if (weight_g) {
weight_g->mutable_data<T>(ctx.GetPlace());
zero(device_ctx, weight_g, static_cast<T>(0.0));
}
auto in_dims = input->dims();
auto out_dims = hidden_g->dims();
int frame_size = static_cast<int>(in_dims[1] / 4);
PADDLE_ENFORCE_EQ(frame_size, out_dims[1]);
math::LstmMetaValue<T> lstm_value;
if (bias) {
T* bias_data = const_cast<T*>(bias->data<T>());
lstm_value.checkIg = bias_data + 4 * frame_size;
lstm_value.checkFg = lstm_value.checkIg + frame_size;
lstm_value.checkOg = lstm_value.checkFg + frame_size;
} else {
lstm_value.checkIg = nullptr;
lstm_value.checkFg = nullptr;
lstm_value.checkOg = nullptr;
}
math::LstmMetaGrad<T> lstm_grad;
if (bias && bias_g) {
T* bias_g_data = const_cast<T*>(bias_g->mutable_data<T>(ctx.GetPlace()));
zero(device_ctx, bias_g, static_cast<T>(0.0));
lstm_grad.checkIgGrad = bias_g_data + 4 * frame_size;
lstm_grad.checkFgGrad = lstm_grad.checkIgGrad + frame_size;
lstm_grad.checkOgGrad = lstm_grad.checkFgGrad + frame_size;
} else {
lstm_grad.checkIgGrad = nullptr;
lstm_grad.checkFgGrad = nullptr;
lstm_grad.checkOgGrad = nullptr;
}
math::LoDTensor2BatchFunctor<Place, T> to_batch;
// use the local variable as here.
LoDTensor batch_hidden;
batch_hidden.mutable_data<T>(out_dims, ctx.GetPlace());
batch_hidden.set_lod(batch_gate->lod());
to_batch(device_ctx, *hidden_out, batch_hidden, false);
LoDTensor batch_hidden_g;
batch_hidden_g.mutable_data<T>(out_dims, ctx.GetPlace());
batch_hidden_g.set_lod(batch_gate->lod());
to_batch(device_ctx, *hidden_g, batch_hidden_g, false);
LoDTensor batch_cell;
batch_cell.mutable_data<T>(out_dims, ctx.GetPlace());
batch_cell.set_lod(batch_gate->lod());
to_batch(device_ctx, *cell_out, batch_cell, false);
LoDTensor batch_cell_g;
batch_cell_g.mutable_data<T>(out_dims, ctx.GetPlace());
batch_cell_g.set_lod(batch_gate->lod());
// TODO(qingqing) support the case output cell has gradient.
// to_batch(device_ctx, *cell_g, batch_cell_g, false);
zero(device_ctx, &batch_cell_g, static_cast<T>(0.0));
LoDTensor batch_gate_g;
batch_gate_g.mutable_data<T>(batch_gate->dims(), ctx.GetPlace());
batch_gate_g.set_lod(batch_gate->lod());
auto gate_act = ctx.Attr<std::string>("gateActivation");
auto cell_act = ctx.Attr<std::string>("cellActivation");
auto cand_act = ctx.Attr<std::string>("candidateActivation");
auto batch_starts = batch_gate->lod()[0];
size_t num_batch = batch_starts.size() - 1;
for (int n = static_cast<int>(num_batch) - 1; n >= 0; n--) {
int bstart = static_cast<int>(batch_starts[n]);
int bend = static_cast<int>(batch_starts[n + 1]);
Tensor gate = batch_gate->Slice(bstart, bend);
Tensor cell = batch_cell.Slice(bstart, bend);
Tensor cell_pre_act = batch_cell_pre_act->Slice(bstart, bend);
lstm_value.gateValue = gate.data<T>();
lstm_value.stateValue = cell.data<T>();
lstm_value.stateActiveValue = cell_pre_act.data<T>();
Tensor out_g = batch_hidden_g.Slice(bstart, bend);
Tensor gate_g = batch_gate_g.Slice(bstart, bend);
Tensor cell_g = batch_cell_g.Slice(bstart, bend);
lstm_grad.stateGrad = cell_g.data<T>();
lstm_grad.gateGrad = gate_g.data<T>();
lstm_grad.outputGrad = out_g.data<T>();
if (n) {
int bstart_pre = static_cast<int>(batch_starts[n - 1]);
Tensor cell_pre = batch_cell.Slice(bstart_pre, bstart);
Tensor cell_pre_g = batch_cell_g.Slice(bstart_pre, bstart);
lstm_value.prevStateValue = cell_pre.data<T>();
lstm_grad.prevStateGrad = cell_pre_g.data<T>();
} else {
lstm_value.prevStateValue = nullptr;
lstm_grad.prevStateGrad = nullptr;
}
int cur_batch_size = bend - bstart;
math::LstmUnitGradFunctor<Place, T>::compute(
device_ctx, lstm_value, lstm_grad, frame_size, cur_batch_size,
gate_act, cell_act, cand_act);
if (n != 0) {
int pre_h_start = static_cast<int>(batch_starts[n - 1]);
int pre_h_end = pre_h_start + cur_batch_size;
auto pre_hidden_g = batch_hidden_g.Slice(pre_h_start, pre_h_end);
math::matmul<Place, T>(device_ctx, gate_g, false, *weight, true,
static_cast<T>(1.0), &pre_hidden_g,
static_cast<T>(1.0));
if (weight_g) {
/* backward weight */
auto pre_hidden = batch_hidden.Slice(pre_h_start, pre_h_end);
math::matmul<Place, T>(device_ctx, pre_hidden, true, gate_g, false,
static_cast<T>(1.0), weight_g,
static_cast<T>(1.0));
}
}
}
math::Batch2LoDTensorFunctor<Place, T> to_seq;
if (in_g) {
/* backward data */
in_g->mutable_data<T>(ctx.GetPlace());
to_seq(device_ctx, batch_gate_g, *in_g);
}
if (bias && bias_g) {
/* backward bias */
int m = static_cast<int>(batch_gate_g.dims()[0]);
int n = static_cast<int>(batch_gate_g.dims()[1]);
Tensor ones;
ones.mutable_data<T>({m}, ctx.GetPlace());
math::SetConstant<Place, T> set;
set(device_ctx, &ones, static_cast<T>(1.0));
math::gemv<Place, T>(device_ctx, true, m, n, 1., batch_gate_g.data<T>(),
ones.data<T>(), 0., bias_g->data<T>());
}
}
};
} // namespace operators
......
paddle/operators/math/detail/lstm_cpu_kernel.h
浏览文件 @ 83b48ebc
......@@ -26,10 +26,7 @@ namespace detail {
template <class T, class Op>
void naive_lstm_forward_one_sequence(Op op, LstmMetaValue<T> value,
int frameSize,
activation_mode_t active_node,
activation_mode_t active_gate,
activation_mode_t active_state) {
int frameSize) {
T rValueIn;
T rValueIg;
T rValueFg;
......@@ -60,10 +57,8 @@ void naive_lstm_forward_one_sequence(Op op, LstmMetaValue<T> value,
rPrevState = value.prevStateValue[i];
}
hppl::cpu::ForwardAct<T> act;
op(rValueIn, rValueIg, rValueFg, rValueOg, rPrevState, rState, rStateAtv,
rOut, rCheckI, rCheckF, rCheckO, act(active_node), act(active_gate),
act(active_state));
rOut, rCheckI, rCheckF, rCheckO);
valueIn[i] = rValueIn;
valueIg[i] = rValueIg;
......@@ -77,10 +72,7 @@ void naive_lstm_forward_one_sequence(Op op, LstmMetaValue<T> value,
template <class T, class Op>
void naive_lstm_backward_one_sequence(Op op, LstmMetaValue<T> value,
LstmMetaGrad<T> grad, int frameSize,
activation_mode_t active_node,
activation_mode_t active_gate,
activation_mode_t active_state) {
LstmMetaGrad<T> grad, int frameSize) {
T rValueIn;
T rValueIg;
T rValueFg;
......@@ -127,11 +119,10 @@ void naive_lstm_backward_one_sequence(Op op, LstmMetaValue<T> value,
rPrevState = value.prevStateValue[i];
}
hppl::cpu::BackwardAct<T> act;
op(rValueIn, rValueIg, rValueFg, rValueOg, rGradIn, rGradIg, rGradFg,
rGradOg, rPrevState, rPrevStateGrad, rState, rStateGrad, rStateAtv,
rOutputGrad, rCheckI, rCheckF, rCheckO, rCheckIGrad, rCheckFGrad,
rCheckOGrad, act(active_node), act(active_gate), act(active_state));
rCheckOGrad);
gradIn[i] = rGradIn;
gradIg[i] = rGradIg;
......@@ -283,8 +274,7 @@ void cpu_lstm_forward(Op op, LstmMetaValue<T> value, int frameSize,
avx_lstm_forward_one_sequence<T>(op, value, frameSize, active_node,
active_gate, active_state);
} else {
naive_lstm_forward_one_sequence<T>(op, value, frameSize, active_node,
active_gate, active_state);
naive_lstm_forward_one_sequence<T>(op, value, frameSize);
}
}
......@@ -297,8 +287,7 @@ void cpu_lstm_backward(Op op, LstmMetaValue<T> value, LstmMetaGrad<T> grad,
avx_lstm_backward_one_sequence<T>(op, value, grad, frameSize, active_node,
active_gate, active_state);
} else {
naive_lstm_backward_one_sequence<T>(op, value, grad, frameSize, active_node,
active_gate, active_state);
naive_lstm_backward_one_sequence<T>(op, value, grad, frameSize);
}
}
......
paddle/operators/math/detail/lstm_gpu_kernel.h
浏览文件 @ 83b48ebc
......@@ -32,9 +32,7 @@ namespace detail {
*/
template <class T, class Op, bool isBatch>
__global__ void KeLstmForward(Op op, LstmMetaValue<T> value, int frameSize,
int batchSize, activation_mode_t active_node,
activation_mode_t active_gate,
activation_mode_t active_state) {
int batchSize) {
const int frameIdx = blockIdx.x * blockDim.x + threadIdx.x;
if (frameIdx >= frameSize) return;
......@@ -70,10 +68,8 @@ __global__ void KeLstmForward(Op op, LstmMetaValue<T> value, int frameSize,
rPrevState = value.prevStateValue[frameIdx];
}
hppl::gpu::ForwardAct<T> act;
op(rValueIn, rValueIg, rValueFg, rValueOg, rPrevState, rState, rStateAtv,
rOut, rCheckI, rCheckF, rCheckO, act(active_node), act(active_gate),
act(active_state));
rOut, rCheckI, rCheckF, rCheckO);
value.gateValue[frameIdx] = rValueIn;
value.gateValue[frameIdx + frameSize] = rValueIg;
......@@ -92,9 +88,7 @@ __global__ void KeLstmForward(Op op, LstmMetaValue<T> value, int frameSize,
template <class T, class Op, bool isBatch>
__global__ void KeLstmBackward(Op op, LstmMetaValue<T> value,
LstmMetaGrad<T> grad, int frameSize,
int batchSize, activation_mode_t active_node,
activation_mode_t active_gate,
activation_mode_t active_state) {
int batchSize) {
const int frameIdx = blockIdx.x * blockDim.x + threadIdx.x;
if (frameIdx >= frameSize) return;
......@@ -145,11 +139,9 @@ __global__ void KeLstmBackward(Op op, LstmMetaValue<T> value,
rPrevState = value.prevStateValue[frameIdx];
}
hppl::gpu::BackwardAct<T> act;
op(rValueIn, rValueIg, rValueFg, rValueOg, rGradIn, rGradIg, rGradFg, rGradOg,
rPrevState, rPrevStateGrad, rState, rStateGrad, rStateAtv, rOutputGrad,
rCheckI, rCheckF, rCheckO, rCheckIGrad, rCheckFGrad, rCheckOGrad,
act(active_node), act(active_gate), act(active_state));
rCheckI, rCheckF, rCheckO, rCheckIGrad, rCheckFGrad, rCheckOGrad);
grad.gateGrad[frameIdx] = rGradIn;
grad.gateGrad[frameIdx + frameSize] = rGradIg;
......@@ -205,13 +197,11 @@ void gpu_lstm_forward(const platform::DeviceContext& context, Op op,
if (batchSize == 1) {
KeLstmForward<T, Op,
/* isBatch= */ false><<<grid, threads, 0, stream>>>(
op, value, frameSize, batchSize, active_node, active_gate,
active_state);
op, value, frameSize, batchSize);
} else {
KeLstmForward<T, Op,
/* isBatch= */ true><<<grid, threads, 0, stream>>>(
op, value, frameSize, batchSize, active_node, active_gate,
active_state);
op, value, frameSize, batchSize);
}
}
......@@ -240,13 +230,11 @@ void gpu_lstm_backward(const platform::DeviceContext& context, Op op,
if (batchSize == 1) {
KeLstmBackward<T, Op,
/* isBatch= */ false><<<grid, threads, 0, stream>>>(
op, value, grad, frameSize, batchSize, active_node, active_gate,
active_state);
op, value, grad, frameSize, batchSize);
} else {
KeLstmBackward<T, Op,
/* isBatch= */ true><<<grid, threads, 0, stream>>>(
op, value, grad, frameSize, batchSize, active_node, active_gate,
active_state);
op, value, grad, frameSize, batchSize);
}
}
......
paddle/operators/math/detail/lstm_kernel.h
浏览文件 @ 83b48ebc
......@@ -24,15 +24,29 @@ namespace detail {
namespace forward {
template <typename T>
DEVICE inline T sigmoid(const T a) {
const T min = SIGMOID_THRESHOLD_MIN;
const T max = SIGMOID_THRESHOLD_MAX;
T tmp = (a < min) ? min : ((a > max) ? max : a);
return static_cast<T>(1.0) / (static_cast<T>(1.0) + exp(-tmp));
}
template <typename T>
DEVICE inline T tanh(const T a) {
T tmp = -2.0 * a;
tmp = (tmp > EXP_MAX_INPUT) ? EXP_MAX_INPUT : tmp;
return (2.0 / (1.0 + exp(tmp))) - 1.0;
}
template <class T>
class lstm {
public:
HOSTDEVICE void operator()(T &valueIn, T &valueIg, T &valueFg, T &valueOg,
T &prevState, T &state, T &stateAtv, T &output,
T &checkI, T &checkF, T &checkO,
typename hppl::ForwardActType<T>::type actInput,
typename hppl::ForwardActType<T>::type actGate,
typename hppl::ForwardActType<T>::type actState) {
T &checkI, T &checkF, T &checkO) {
#if 0
// TODO(qingqing) support to activation speficed by users
valueIn = actInput(valueIn);
valueIg = actGate(valueIg + prevState * checkI);
valueFg = actGate(valueFg + prevState * checkF);
......@@ -40,6 +54,15 @@ class lstm {
valueOg = actGate(valueOg + state * checkO);
stateAtv = actState(state);
output = valueOg * stateAtv;
#else
valueIn = tanh<T>(valueIn);
valueIg = sigmoid<T>(valueIg + prevState * checkI);
valueFg = sigmoid<T>(valueFg + prevState * checkF);
state = valueIn * valueIg + prevState * valueFg;
valueOg = sigmoid<T>(valueOg + state * checkO);
stateAtv = tanh<T>(state);
output = valueOg * stateAtv;
#endif
}
#ifndef __NVCC__
#ifndef __AVX__ // If not compiled with AVX instructs. Disable AVX by default
......@@ -72,6 +95,16 @@ class lstm {
namespace backward {
template <typename T>
DEVICE inline T sigmoid(const T a, const T b) {
return a * b * (1.0 - b);
}
template <typename T>
DEVICE inline T tanh(const T a, const T b) {
return a * (1.0 - b * b);
}
template <class T>
class lstm {
public:
......@@ -80,10 +113,9 @@ class lstm {
T &prevState, T &prevStateGrad, T &state,
T &stateGrad, T &stateAtv, T &outputGrad,
T &checkI, T &checkF, T &checkO, T &checkIGrad,
T &checkFGrad, T &checkOGrad,
typename hppl::BackwardActType<T>::type actInput,
typename hppl::BackwardActType<T>::type actGate,
typename hppl::BackwardActType<T>::type actState) {
T &checkFGrad, T &checkOGrad) {
#if 0
// TODO(qingqing) support to activation speficed by users
gradOg = actGate(outputGrad * stateAtv, valueOg);
stateGrad += actState(outputGrad * valueOg, stateAtv) + gradOg * checkO;
gradIn = actInput(stateGrad * valueIg, valueIn);
......@@ -93,6 +125,17 @@ class lstm {
checkIGrad = gradIg * prevState;
checkFGrad = gradFg * prevState;
checkOGrad = gradOg * state;
#else
gradOg = sigmoid<T>(outputGrad * stateAtv, valueOg);
stateGrad += tanh<T>(outputGrad * valueOg, stateAtv) + gradOg * checkO;
gradIn = tanh<T>(stateGrad * valueIg, valueIn);
gradIg = sigmoid<T>(stateGrad * valueIn, valueIg);
gradFg = sigmoid<T>(stateGrad * prevState, valueFg);
prevStateGrad = gradIg * checkI + gradFg * checkF + stateGrad * valueFg;
checkIGrad = gradIg * prevState;
checkFGrad = gradFg * prevState;
checkOGrad = gradOg * state;
#endif
}
#ifndef __NVCC__
#ifndef __AVX__ // If not compiled with AVX instructs. Disable AVX by default
......
paddle/operators/math/math_function.cc
浏览文件 @ 83b48ebc
......@@ -211,6 +211,26 @@ void batched_gemm<platform::CPUPlace, double>(
}
#endif
template <>
void gemv<platform::CPUPlace, float>(const platform::DeviceContext& context,
const bool trans_a, const int M,
const int N, const float alpha,
const float* A, const float* B,
const float beta, float* C) {
CBLAS_TRANSPOSE transA = (trans_a == false) ? CblasNoTrans : CblasTrans;
cblas_sgemv(CblasRowMajor, transA, M, N, alpha, A, N, B, 1, beta, C, 1);
}
template <>
void gemv<platform::CPUPlace, double>(const platform::DeviceContext& context,
const bool trans_a, const int M,
const int N, const double alpha,
const double* A, const double* B,
const double beta, double* C) {
CBLAS_TRANSPOSE transA = (trans_a == false) ? CblasNoTrans : CblasTrans;
cblas_dgemv(CblasRowMajor, transA, M, N, alpha, A, N, B, 1, beta, C, 1);
}
template struct SetConstant<platform::CPUPlace, float>;
} // namespace math
......
paddle/operators/math/math_function.cu
浏览文件 @ 83b48ebc
......@@ -203,6 +203,33 @@ void batched_gemm<platform::GPUPlace, double>(
&beta, C, ldc, strideC, batchCount));
}
template <>
void gemv<platform::GPUPlace, float>(const platform::DeviceContext& context,
const bool trans_a, const int M,
const int N, const float alpha,
const float* A, const float* B,
const float beta, float* C) {
cublasOperation_t cuTransA = (trans_a == false) ? CUBLAS_OP_T : CUBLAS_OP_N;
PADDLE_ENFORCE(platform::dynload::cublasSgemv(
reinterpret_cast<const platform::CUDADeviceContext&>(context)
.cublas_handle(),
cuTransA, N, M, &alpha, A, N, B, 1, &beta, C, 1));
}
template <>
void gemv<platform::GPUPlace, double>(const platform::DeviceContext& context,
const bool trans_a, const int M,
const int N, const double alpha,
const double* A, const double* B,
const double beta, double* C) {
cublasOperation_t cuTransA = (trans_a == false) ? CUBLAS_OP_T : CUBLAS_OP_N;
PADDLE_ENFORCE(platform::dynload::cublasDgemv(
reinterpret_cast<const platform::CUDADeviceContext&>(context)
.cublas_handle(),
cuTransA, N, M, &alpha, A, N, B, 1, &beta, C, 1));
}
template struct SetConstant<platform::GPUPlace, float>;
} // namespace math
......
paddle/operators/math/math_function.h
浏览文件 @ 83b48ebc
......@@ -93,6 +93,11 @@ void batched_gemm(const platform::DeviceContext& context,
const T* A, const T* B, const T beta, T* C,
const int batchCount, const int strideA, const int strideB);
template <typename Place, typename T>
void gemv(const platform::DeviceContext& context, const bool trans_a,
const int M, const int N, const T alpha, const T* A, const T* B,
const T beta, T* C);
template <typename Place, typename T>
struct SetConstant {
void operator()(const platform::DeviceContext& context,
......
paddle/operators/math/math_function_test.cc
浏览文件 @ 83b48ebc
......@@ -89,3 +89,53 @@ TEST(math_function, zero) {
EXPECT_EQ(t[2], 1);
EXPECT_EQ(t[3], 1);
}
template <typename T>
void GemvTest(int m, int n, bool trans) {
paddle::framework::Tensor mat_a;
paddle::framework::Tensor vec_b;
paddle::framework::Tensor vec_c;
auto* cpu_place = new paddle::platform::CPUPlace();
int b_num = trans ? m : n;
int c_num = trans ? n : m;
T* data_a = mat_a.mutable_data<T>({m, n}, *cpu_place);
T* data_b = vec_b.mutable_data<T>({b_num}, *cpu_place);
T* data_c = vec_c.mutable_data<T>({c_num}, *cpu_place);
for (int i = 0; i < mat_a.numel(); ++i) {
data_a[i] = static_cast<T>(i);
}
for (int i = 0; i < vec_b.numel(); ++i) {
data_b[i] = static_cast<T>(i);
}
paddle::platform::CPUDeviceContext context(*cpu_place);
paddle::operators::math::gemv<paddle::platform::CPUPlace, T>(
context, trans, static_cast<int>(m), static_cast<int>(n), 1., data_a,
data_b, 0., data_c);
if (!trans) {
for (int i = 0; i < m; ++i) {
T sum = 0.0;
for (int j = 0; j < n; ++j) {
sum += data_a[i * n + j] * data_b[j];
}
ASSERT_FLOAT_EQ(data_c[i], sum);
}
} else {
for (int i = 0; i < n; ++i) {
T sum = 0.0;
for (int j = 0; j < m; ++j) {
sum += data_a[j * n + i] * data_b[j];
}
ASSERT_FLOAT_EQ(data_c[i], sum);
}
}
}
TEST(math_function, gemv) {
GemvTest<float>(3, 13, false);
GemvTest<double>(4, 5, false);
GemvTest<float>(12, 7, true);
GemvTest<double>(7, 9, true);
}
paddle/operators/math/math_function_test.cu
浏览文件 @ 83b48ebc
......@@ -177,3 +177,65 @@ TEST(math_function, gemm_trans_cublas) {
EXPECT_EQ(input3_ptr[7], 99);
delete gpu_place;
}
template <typename T>
void GemvTest(int m, int n, bool trans) {
paddle::framework::Tensor mat_a;
paddle::framework::Tensor vec_b;
paddle::framework::Tensor vec_c;
auto* cpu_place = new paddle::platform::CPUPlace();
T* data_a = mat_a.mutable_data<T>({m, n}, *cpu_place);
T* data_b = vec_b.mutable_data<T>({trans ? m : n}, *cpu_place);
T* data_c = vec_c.mutable_data<T>({trans ? n : m}, *cpu_place);
auto* gpu_place = new paddle::platform::GPUPlace(0);
paddle::framework::Tensor g_mat_a;
paddle::framework::Tensor g_vec_b;
paddle::framework::Tensor g_vec_c;
T* g_data_a = g_mat_a.mutable_data<T>(mat_a.dims(), *gpu_place);
T* g_data_b = g_vec_b.mutable_data<T>(vec_b.dims(), *gpu_place);
T* g_data_c = g_vec_c.mutable_data<T>(vec_c.dims(), *gpu_place);
for (int i = 0; i < mat_a.numel(); ++i) {
data_a[i] = static_cast<T>(i);
}
for (int i = 0; i < vec_b.numel(); ++i) {
data_b[i] = static_cast<T>(i);
}
paddle::platform::CUDADeviceContext context(*gpu_place);
g_mat_a.CopyFrom(mat_a, *gpu_place, context);
g_vec_b.CopyFrom(vec_b, *gpu_place, context);
paddle::operators::math::gemv<paddle::platform::GPUPlace, T>(
context, trans, static_cast<int>(m), static_cast<int>(n), 1., g_data_a,
g_data_b, 0., g_data_c);
vec_c.CopyFrom(g_vec_c, paddle::platform::CPUPlace(), context);
if (!trans) {
for (int i = 0; i < m; ++i) {
T sum = 0.0;
for (int j = 0; j < n; ++j) {
sum += data_a[i * n + j] * data_b[j];
}
ASSERT_FLOAT_EQ(data_c[i], sum);
}
} else {
for (int i = 0; i < n; ++i) {
T sum = 0.0;
for (int j = 0; j < m; ++j) {
sum += data_a[j * n + i] * data_b[j];
}
ASSERT_FLOAT_EQ(data_c[i], sum);
}
}
}
TEST(math_function, gemv) {
GemvTest<float>(3, 13, false);
GemvTest<double>(3, 13, false);
GemvTest<float>(3, 13, true);
GemvTest<double>(3, 13, true);
}
paddle/operators/math/sequence2batch.h
浏览文件 @ 83b48ebc
......@@ -21,128 +21,6 @@ namespace paddle {
namespace operators {
namespace math {
// template <typename Place, typename T>
// class CopyMatrixRowsFunctor {
// public:
// // If is_src_index is true,
// // copy the indexed rows of input src to the output dst.
// // If is_src_index is false,
// // copy the input src to the indexed rows of output dst.
// // The indexed rows are based on the input index.
// void operator()(const platform::DeviceContext& context,
// const framework::LoDTensor& src, const size_t* index,
// framework::LoDTensor& dst, bool is_src_index);
// };
// template <typename Place, typename T>
// class LoDTensor2BatchFunctor {
// // Calculate the length of each sequence and
// // sort sequence index by the length.
// // example: sequences = {s0, s1, s2}
// // s0: 0 0 0 0, s1: 1 1 1 1 1, s2: 2 2 2
// // seq_info[3] = {(4, 5, 1), (0, 4, 0), (9, 3, 2)}
// //
// struct SeqInfo {
// SeqInfo(int start, int length, int seq_idx)
// : start(start), length(length), seq_idx(seq_idx) {}
// int start;
// int length;
// int seq_idx;
// };
// public:
// void operator()(const platform::DeviceContext& context,
// const framework::LoDTensor& lod_tensor,
// framework::LoDTensor& batch, bool is_reverse) const {
// auto lods = lod_tensor.lod();
// PADDLE_ENFORCE_EQ(lods.size(), 1UL, "Only support one level sequence
// now.");
// auto lod = lods[0];
// std::vector<SeqInfo> seq_info;
// for (size_t seq_id = 0; seq_id < lod.size() - 1; ++seq_id) {
// int length = lod[seq_id + 1] - lod[seq_id];
// seq_info.emplace_back(lod[seq_id], length, seq_id);
// }
// std::sort(seq_info.begin(), seq_info.end(),
// [](SeqInfo a, SeqInfo b) { return a.length > b.length; });
// // calculate the start position of each batch
// // (numBatch equal the maxLength of sequences)
// // example: sequences = {s0, s1, s2}
// // s0: 0 0 0 0, s1: 1 1 1 1 1, s2: 2 2 2
// // num_batch = 5,
// // batchIndex = {b0, b1, b2, b3, b4}
// // b0: 1 0 2, b1: 1 0 2, b2: 1 0 2, b3: 1 0, b4: 1
// // batch_start_positions[6] = {0, 3, 6, 9, 11, 12}
// // batch_start_positions[0] = len(b0)
// // batch_start_positions[1] = len(b0) + len(b1)
// // batch_start_positions[2] = len(b0) + len(b1) + len(b2)
// // ...
// // seq2batch_idx[12] = {4, 0, 9,
// // 5, 1, 10,
// // 6, 2, 11,
// // 7, 3,
// // 8}
// // The batch number represents batch size after rearranging the
// // input LodTensor. It is also the maximum length of input sequence.
// paddle::framework::LoD batch_lods;
// batch_lods.emplace_back(std::vector<size_t>{0});
// batch_lods.emplace_back(std::vector<size_t>{0});
// // batch_lods[0] is the start positions for batch LoDTensor
// int num_batch = seq_info[0].length;
// batch_lods[0].resize(static_cast<size_t>(num_batch + 1));
// // batch_lods[1] is the raw index in the input LoDTensor
// auto dims = lod_tensor.dims();
// batch_lods[1].resize(static_cast<size_t>(dims[0]));
// size_t* batch_starts = batch_lods[0].data();
// size_t* seq2batch_idx = batch_lods[1].data();
// batch_starts[0] = 0;
// for (size_t n = 0; n < num_batch; n++) {
// auto batch_id = static_cast<int>(batch_starts[n]);
// for (size_t i = 0; i < seq_info.size(); ++i) {
// size_t seq_len = seq_info[i].length;
// int start = seq_info[i].start;
// if (n < seq_len) {
// seq2batch_idx[batch_id] =
// is_reverse ? start + seq_len - 1 - n : start + n;
// batch_id++;
// } else {
// break;
// }
// }
// batch_starts[n + 1] = static_cast<size_t>(batch_id);
// }
// batch.set_lod(batch_lods);
// CopyMatrixRowsFunctor<Place, T> to_batch;
// to_batch(context, lod_tensor, seq2batch_idx, batch, true);
// }
// };
// template <typename Place, typename T>
// class Batch2LoDTensorFunctor {
// public:
// void operator()(const platform::DeviceContext& context,
// const framework::LoDTensor& batch,
// framework::LoDTensor& lod_tensor) const {
// auto in_lod = batch.lod();
// PADDLE_ENFORCE_EQ(in_lod.size(), 2UL,
// "The LoD size of input `batch` should be 2.");
// auto out_lod = lod_tensor.lod()[0];
// auto num = out_lod[out_lod.size() - 1];
// PADDLE_ENFORCE_EQ(num, lod_tensor.dims()[0]);
// PADDLE_ENFORCE_EQ(num, in_lod[1].size());
// PADDLE_ENFORCE_EQ(num, batch.dims()[0]);
// CopyMatrixRowsFunctor<Place, T> to_seq;
// size_t* index = in_lod[1].data();
// to_seq(context, batch, index, lod_tensor, false);
// }
// };
template <typename Place, typename T>
class CopyMatrixRowsFunctor {
public:
......@@ -175,8 +53,8 @@ class LoDTensor2BatchFunctor {
public:
void operator()(const platform::DeviceContext& context,
const framework::LoDTensor& lod_tensor,
framework::LoDTensor& batch, bool is_reverse = false,
bool is_cal_batch_lod = true) const {
framework::LoDTensor& batch, bool is_cal_batch_lod,
bool is_reverse = false) const {
if (!is_cal_batch_lod) {
auto lods = batch.lod();
PADDLE_ENFORCE_EQ(lods.size(), 2UL);
......
paddle/operators/nccl_op_test.cu
浏览文件 @ 83b48ebc
......@@ -185,7 +185,7 @@ TEST_F(NCCLTester, ncclAllReduceOp) {
recv_tensor.numel() * sizeof(float),
static_cast<p::CUDADeviceContext *>(dev_ctxs[i])->stream());
for (size_t j = 0; j < f::product(kDims); ++j) {
for (int64_t j = 0; j < f::product(kDims); ++j) {
ASSERT_NEAR(ct[j], result, 1e-5);
}
}
......@@ -234,7 +234,7 @@ TEST_F(NCCLTester, ncclReduceOp) {
recv_tensor.numel() * sizeof(float),
static_cast<p::CUDADeviceContext *>(dev_ctxs[kRoot])->stream());
for (int j = 0; j < f::product(kDims); ++j) {
for (int64_t j = 0; j < f::product(kDims); ++j) {
ASSERT_NEAR(ct[j], result, 1e-5);
}
}
......@@ -282,7 +282,7 @@ TEST_F(NCCLTester, ncclBcastOp) {
recv_tensor.numel() * sizeof(float),
static_cast<p::CUDADeviceContext *>(dev_ctxs[idx])->stream());
for (size_t j = 0; j < f::product(kDims); ++j) {
for (int64_t j = 0; j < f::product(kDims); ++j) {
ASSERT_NEAR(ct[j], result, 1e-5);
}
}
......
paddle/operators/reshape_op.cc
浏览文件 @ 83b48ebc
......@@ -36,7 +36,7 @@ class ReshapeOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE(shape.size() > 0, "Attr(shape) shouldn't be empty.");
auto x_dims = ctx->GetInputDim("X");
// TODO(qiao) change batch_size
for (int i = 1; i < shape.size(); ++i) {
for (size_t i = 1; i < shape.size(); ++i) {
PADDLE_ENFORCE(shape[i] > 0,
"Each dimension of shape "
"must be positiv except the first.");
......
paddle/operators/save_load_op_test.cc
浏览文件 @ 83b48ebc
......@@ -34,7 +34,7 @@ TEST(SaveLoadOp, CPU) {
tensor->set_lod(expect_lod);
int* expect = tensor->mutable_data<int>(place);
for (size_t i = 0; i < paddle::framework::product(tensor->dims()); ++i) {
for (int64_t i = 0; i < tensor->numel(); ++i) {
expect[i] = static_cast<int>(i);
}
paddle::framework::AttributeMap attrs;
......@@ -50,7 +50,7 @@ TEST(SaveLoadOp, CPU) {
"load", {}, {{"Out", {"out_var"}}}, attrs);
load_op->Run(scope, ctx);
int* actual = target->data<int>();
for (size_t i = 0; i < paddle::framework::product(tensor->dims()); ++i) {
for (int64_t i = 0; i < tensor->numel(); ++i) {
EXPECT_EQ(expect[i], actual[i]);
}
auto& actual_lod = target->lod();
......@@ -60,4 +60,4 @@ TEST(SaveLoadOp, CPU) {
EXPECT_EQ(expect_lod[i][j], actual_lod[i][j]);
}
}
}
\ No newline at end of file
}
paddle/operators/sequence_conv_op.cc
浏览文件 @ 83b48ebc
......@@ -89,7 +89,7 @@ class SequenceConvGradOp : public framework::OperatorWithKernel {
}
if (ctx->HasOutput(framework::GradVarName("X"))) {
ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
ctx->ShareLoD(framework::GradVarName("X"), "X");
ctx->ShareLoD("X", framework::GradVarName("X"));
}
if (ctx->HasOutput(framework::GradVarName("Filter"))) {
ctx->SetOutputDim(framework::GradVarName("Filter"),
......
paddle/operators/sequence_pool_op.cc
浏览文件 @ 83b48ebc
......@@ -39,15 +39,14 @@ class SequencePoolOpMaker : public framework::OpProtoAndCheckerMaker {
AddOutput("Out",
"(Tensor), output of SequencePoolOp, which does not contain LoD "
"infomation.");
AddAttr<int>(
"strategy",
"(int, default AVERAGE) the pooling strategy of SequencePoolOp.")
.SetDefault(AVERAGE)
.InEnum({AVERAGE, SUM, SQRT, MAX, LAST, FIRST});
AddAttr<std::string>(
"pooltype",
"(int, default AVERAGE) the pooling pooltype of SequencePoolOp.")
.SetDefault("AVERAGE");
AddComment(R"DOC(
SequencePoolOp pools features of all time-steps of each instance.
It supports six pooling strategy:
It supports six pooling pooltype:
- AVERAGE: Out[i] = average_{for each instance in i-th sequence}{X[i]}
- SUM: Out[i] = sum_{for each instance in i-th sequence}{X[i]}
- SQRT: Out[i] = sum_{for each instance in i-th sequence}{X[i]}
......@@ -63,7 +62,7 @@ class SequencePoolOpMaker : public framework::OpProtoAndCheckerMaker {
and the value of X = [[1, 3], [2, 4, 6], [5, 1]].
Thus, Out is a [3,1,1] Tensor without LoD infomation.
And for different strategy, the value of Out is as follows:
And for different pooltype, the value of Out is as follows:
- AVERAGE: [2, 4, 3], where 2=(1+3)/2, 4=(2+4+6)/3, 3=(5+1)/2
- SUM: [4, 12, 6], where 4=1+3, 12=2+4+6, 6=5+1
......
paddle/operators/sequence_pool_op.h
浏览文件 @ 83b48ebc
......@@ -29,22 +29,13 @@ template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
enum SeqPoolType {
AVERAGE = 0,
SUM = 1,
SQRT = 2, // square_root_n
MAX = 3,
LAST = 4,
FIRST = 5
};
template <typename Place, typename T>
class SequencePoolKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto* in = context.Input<LoDTensor>("X");
auto* out = context.Output<LoDTensor>("Out");
int strategy = context.Attr<int>("strategy");
std::string pooltype = context.Attr<std::string>("pooltype");
auto dims = in->dims();
auto lod = in->lod();
......@@ -71,28 +62,21 @@ class SequencePoolKernel : public framework::OpKernel<T> {
auto in_e = EigenMatrix<T>::From(in_t, framework::make_ddim({h, w}));
auto out_e = EigenVector<T>::Flatten(out_t);
switch (strategy) {
case AVERAGE:
out_e.device(place) = in_e.mean(Eigen::array<int, 1>({{0}}));
break;
case SUM:
out_e.device(place) = in_e.sum(Eigen::array<int, 1>({{0}}));
break;
case SQRT:
out_e.device(place) = in_e.sum(Eigen::array<int, 1>({{0}})) /
std::sqrt(static_cast<T>(h));
break;
case MAX:
out_e.device(place) = in_e.maximum(Eigen::array<int, 1>({{0}}));
break;
case LAST:
out_e.device(place) = in_e.chip(h - 1, 0);
break;
case FIRST:
out_e.device(place) = in_e.chip(0, 0);
break;
default:
PADDLE_THROW("unsupported pooling strategy");
if (pooltype == "AVERAGE") {
out_e.device(place) = in_e.mean(Eigen::array<int, 1>({{0}}));
} else if (pooltype == "SUM") {
out_e.device(place) = in_e.sum(Eigen::array<int, 1>({{0}}));
} else if (pooltype == "SQRT") {
out_e.device(place) = in_e.sum(Eigen::array<int, 1>({{0}})) /
std::sqrt(static_cast<T>(h));
} else if (pooltype == "MAX") {
out_e.device(place) = in_e.maximum(Eigen::array<int, 1>({{0}}));
} else if (pooltype == "LAST") {
out_e.device(place) = in_e.chip(h - 1, 0);
} else if (pooltype == "FIRST") {
out_e.device(place) = in_e.chip(0, 0);
} else {
PADDLE_THROW("unsupported pooling pooltype");
}
}
}
......@@ -105,15 +89,15 @@ class SequencePoolGradKernel : public framework::OpKernel<T> {
auto* in = context.Input<LoDTensor>("X");
auto* in_g = context.Output<LoDTensor>(framework::GradVarName("X"));
auto* out_g = context.Input<LoDTensor>(framework::GradVarName("Out"));
int strategy = context.Attr<int>("strategy");
std::string pooltype = context.Attr<std::string>("pooltype");
auto dims = in->dims();
auto lod = in->lod()[0];
int64_t w = in->numel() / dims[0];
in_g->mutable_data<T>(context.GetPlace());
if (strategy == LAST || strategy == FIRST) {
// set X@Grad be zero at first when strategy is LAST/FIRST
if (pooltype == "LAST" || pooltype == "FIRST") {
// set X@Grad be zero at first when pooltype is LAST/FIRST
math::SetConstant<Place, T> functor;
functor(context.device_context(), in_g, 0);
}
......@@ -127,41 +111,33 @@ class SequencePoolGradKernel : public framework::OpKernel<T> {
auto out_g_e = EigenMatrix<T>::From(out_g_t, {1, w});
Eigen::DSizes<int, 2> bcast(h, 1);
switch (strategy) {
case AVERAGE:
in_g_e.device(place) = (out_g_e / static_cast<T>(h)).broadcast(bcast);
break;
case SUM:
in_g_e.device(place) = (out_g_e).broadcast(bcast);
break;
case SQRT:
in_g_e.device(place) =
(out_g_e / std::sqrt(static_cast<T>(h))).broadcast(bcast);
break;
case MAX: {
auto in_t =
in->Slice(static_cast<int>(lod[i]), static_cast<int>(lod[i + 1]));
Eigen::Map<const Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic>>
in_t_map(in_t.data<T>(), h, w);
int row_id;
Eigen::array<int, 2> extents{{1, 1}};
for (int col_id = 0; col_id < w; col_id++) {
in_t_map.col(col_id).maxCoeff(&row_id);
Eigen::array<int, 2> in_offsets{{row_id, col_id}};
Eigen::array<int, 2> out_offsets{{0, col_id}};
in_g_e.slice(in_offsets, extents).device(place) =
out_g_e.slice(out_offsets, extents);
}
break;
if (pooltype == "AVERAGE") {
in_g_e.device(place) = (out_g_e / static_cast<T>(h)).broadcast(bcast);
} else if (pooltype == "SUM") {
in_g_e.device(place) = (out_g_e).broadcast(bcast);
} else if (pooltype == "SQRT") {
in_g_e.device(place) =
(out_g_e / std::sqrt(static_cast<T>(h))).broadcast(bcast);
} else if (pooltype == "MAX") {
auto in_t =
in->Slice(static_cast<int>(lod[i]), static_cast<int>(lod[i + 1]));
Eigen::Map<const Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic>>
in_t_map(in_t.data<T>(), h, w);
int row_id;
Eigen::array<int, 2> extents{{1, 1}};
for (int col_id = 0; col_id < w; col_id++) {
in_t_map.col(col_id).maxCoeff(&row_id);
Eigen::array<int, 2> in_offsets{{row_id, col_id}};
Eigen::array<int, 2> out_offsets{{0, col_id}};
in_g_e.slice(in_offsets, extents).device(place) =
out_g_e.slice(out_offsets, extents);
}
case LAST:
in_g_e.chip(h - 1, 0).device(place) = out_g_e;
break;
case FIRST:
in_g_e.chip(0, 0).device(place) = out_g_e;
break;
default:
PADDLE_THROW("unsupported pooling strategy");
} else if (pooltype == "LAST") {
in_g_e.chip(h - 1, 0).device(place) = out_g_e;
} else if (pooltype == "FIRST") {
in_g_e.chip(0, 0).device(place) = out_g_e;
} else {
PADDLE_THROW("unsupported pooling pooltype");
}
}
}
......
paddle/pybind/protobuf.cc
浏览文件 @ 83b48ebc
......@@ -129,7 +129,8 @@ void BindProgramDesc(py::module &m) {
}
return retv;
})
.def("block", &ProgramDescBind::Block, py::return_value_policy::reference)
.def("block", &ProgramDescBind::MutableBlock,
py::return_value_policy::reference)
.def("num_blocks", &ProgramDescBind::Size)
.def("serialize_to_string",
[](ProgramDescBind &program_desc) -> py::bytes {
......
paddle/pybind/pybind.cc
浏览文件 @ 83b48ebc
......@@ -275,7 +275,7 @@ All parameter, weight, gradient are variables in Paddle.
const std::vector<std::array<size_t, 2>> &targets) {
ProgramDescBind prog_with_targets(origin);
for (const auto &t : targets) {
prog_with_targets.Block(t[0])->Op(t[1])->MarkAsTarget();
prog_with_targets.MutableBlock(t[0])->Op(t[1])->MarkAsTarget();
}
ProgramDesc pruned_desc;
Prune(*prog_with_targets.Proto(), &pruned_desc);
......@@ -335,7 +335,7 @@ All parameter, weight, gradient are variables in Paddle.
PADDLE_ENFORCE(desc.IsInitialized(),
"User OpDesc is not initialized, reason %s",
desc.InitializationErrorString());
return OpRegistry::CreateOp(desc, nullptr);
return OpRegistry::CreateOp(desc);
})
.def("backward",
[](const OperatorBase &forwardOp,
......@@ -439,7 +439,7 @@ All parameter, weight, gradient are variables in Paddle.
PADDLE_ENFORCE(desc.IsInitialized(),
"User OpDesc is not initialized, reason %s",
desc.InitializationErrorString());
auto rnn_op = OpRegistry::CreateOp(desc, nullptr);
auto rnn_op = OpRegistry::CreateOp(desc);
return static_cast<operators::RecurrentOp *>(rnn_op.release());
})
.def("set_stepnet", [](operators::RecurrentOp &self,
......@@ -457,7 +457,7 @@ All parameter, weight, gradient are variables in Paddle.
PADDLE_ENFORCE(desc.IsInitialized(),
"User OpDesc is not initialized, reason %s",
desc.InitializationErrorString());
auto rnn_op = OpRegistry::CreateOp(desc, nullptr);
auto rnn_op = OpRegistry::CreateOp(desc);
return static_cast<operators::DynamicRecurrentOp *>(
rnn_op.release());
})
......@@ -484,7 +484,7 @@ All parameter, weight, gradient are variables in Paddle.
PADDLE_ENFORCE(desc.IsInitialized(),
"User OpDesc is not initialized, reason %s",
desc.InitializationErrorString());
auto cond_op = OpRegistry::CreateOp(desc, nullptr);
auto cond_op = OpRegistry::CreateOp(desc);
return static_cast<operators::CondOp *>(cond_op.release());
})
.def("set_truenet",
......@@ -498,10 +498,7 @@ All parameter, weight, gradient are variables in Paddle.
py::class_<framework::Executor>(m, "Executor")
.def(py::init<std::vector<platform::Place> &>())
.def("run", [](Executor &self, ProgramDescBind *program_bind,
Scope *scope, int block_id) {
self.Run(*program_bind->Proto(), scope, block_id);
});
.def("run", &Executor::Run);
m.def("unique_integer", UniqueIntegerGenerator);
m.def("init_gflags", InitGflags);
......
paddle/scripts/docker/build_android.sh
浏览文件 @ 83b48ebc
......@@ -4,6 +4,10 @@ set -xe
if [ $ANDROID_ABI == "arm64-v8a" ]; then
ANDROID_ARCH=arm64
if [ $ANDROID_API -lt 21 ]; then
echo "Warning: arm64-v8a requires ANDROID_API >= 21."
ANDROID_API=21
fi
else # armeabi, armeabi-v7a
ANDROID_ARCH=arm
fi
......
paddle/trainer/MergeModel.cpp
浏览文件 @ 83b48ebc
......@@ -27,6 +27,13 @@ using namespace paddle; // NOLINT
using namespace std; // NOLINT
int main(int argc, char** argv) {
if (FLAGS_model_dir.empty() || FLAGS_config_file.empty() ||
FLAGS_model_file.empty()) {
LOG(INFO) << "Usage: ./paddle_merge_model --model_dir=pass-00000 "
"--config_file=config.py --model_file=out.paddle";
return 0;
}
initMain(argc, argv);
initPython(argc, argv);
......
python/paddle/v2/dataset/imdb.py
浏览文件 @ 83b48ebc
......@@ -116,7 +116,7 @@ def reader_creator(pos_pattern, neg_pattern, word_idx, buffer_size):
yield [word_idx.get(w, UNK) for w in doc], i % 2
doc = qs[i % 2].get()
return reader()
return reader
def train(word_idx):
......
python/paddle/v2/framework/framework.py
浏览文件 @ 83b48ebc
......@@ -354,8 +354,8 @@ class Block(object):
def create_var(self, *args, **kwargs):
var = Variable(self, *args, **kwargs)
if 'init_attr' in kwargs:
self._prepend_initialize_ops_(var, kwargs['init_attr'])
if 'initializer' in kwargs:
kwargs['initializer'](var, self)
return var
def has_var(self, name):
......@@ -364,8 +364,8 @@ class Block(object):
def create_parameter(self, *args, **kwargs):
global_block = self.program.global_block()
param = Parameter(global_block, *args, **kwargs)
if 'init_attr' in kwargs:
self._prepend_initialize_ops_(param, kwargs['init_attr'])
if 'initializer' in kwargs:
kwargs['initializer'](param, self)
return param
def append_op(self, *args, **kwargs):
......@@ -424,17 +424,6 @@ class Block(object):
for index in range(len(self.ops)):
assert self.ops[index].desc == ops_in_cpp[index]
def _prepend_initialize_ops_(self, param, init_attr):
op_type = init_attr['type']
init_attr['shape'] = param.shape
init_attr['data_type'] = int(param.data_type)
op = self.prepend_op(
type=op_type,
inputs=None,
outputs={'Out': [param]},
attrs=init_attr)
param.op = op
class Program(object):
def __init__(self):
......
python/paddle/v2/framework/initializer.py 0 → 100644
浏览文件 @ 83b48ebc
import paddle.v2.framework.framework as framework
__all__ = ['ConstantInitializer', 'UniformInitializer']
class Initializer(object):
"""Base class for variable initializers
Defines the common interface of variable initializers.
They add operations to the init program that are used
to initialize variables. Users should not use this class
directly, but need to use one of its implementations.
"""
def __init_(self):
pass
def __call__(self, param, block):
"""Add corresponding initialization operations to the network
"""
raise NotImplementedError()
class ConstantInitializer(Initializer):
"""Implements the constant initializer
"""
def __init__(self, value=0.0):
"""Constructor for ConstantInitializer
Args:
value: constant value to initialize the variable
"""
assert value is not None
super(ConstantInitializer, self).__init__()
self._value = value
def __call__(self, var, block):
"""Add constant initialization ops for a variable
Args:
var: Variable that needs to be initialized
block: The block in which initialization ops
should be added
Returns:
the initialization op
"""
assert isinstance(var, framework.Variable)
assert isinstance(block, framework.Block)
# Initialization Ops should be prepended and not appended
op = block.prepend_op(
type="fill_constant",
outputs={"Out": var},
attrs={
"shape": var.shape,
"data_type": int(var.data_type),
"value": self._value
})
var.op = op
return op
class UniformInitializer(Initializer):
"""Implements the random uniform distribution initializer
"""
def __init__(self, low=-1.0, high=1.0, seed=0):
"""Constructor for UniformInitializer
Args:
low: lower boundary of the uniform distribution
high: upper boundary of the uniform distribution
seed: random seed
"""
assert low is not None
assert high is not None
assert high >= low
assert seed is not None
super(UniformInitializer, self).__init__()
self._low = low
self._high = high
self._seed = seed
def __call__(self, var, block):
"""Add uniform distribution initialization ops for a variable
Args:
var: Variable that needs to be initialized
block: The block in which initialization ops
should be added
Returns:
the initialization op
"""
assert isinstance(var, framework.Variable)
assert isinstance(block, framework.Block)
# Initialization Ops should be prepended and not appended
op = block.prepend_op(
type="uniform_random",
outputs={"Out": var},
attrs={
"shape": var.shape,
"data_type": int(var.data_type),
"min": self._low,
"max": self._high,
"seed": self._seed
})
var.op = op
return op
class NormalInitializer(Initializer):
"""Implements the random Normal(Gaussian) distribution initializer
"""
def __init__(self, loc=0.0, scale=1.0, seed=0):
"""Constructor for NormalInitializer
Args:
loc: mean of the normal distribution
scale: standard deviation of the normal distribution
seed: random seed
"""
assert loc is not None
assert scale is not None
assert seed is not None
super(NormalInitializer, self).__init__()
self._mean = loc
self._std_dev = scale
self._seed = seed
def __call__(self, var, block):
"""Add normal distribution initialization ops for a variable
Args:
var: Variable that needs to be initialized
block: The block in which initialization ops
should be added
Returns:
the initialization op
"""
assert isinstance(var, framework.Variable)
assert isinstance(block, framework.Block)
# Initialization Ops should be prepended and not appended
op = block.prepend_op(
type="gaussian_random",
outputs={"Out": var},
attrs={
"shape": var.shape,
"data_type": int(var.data_type),
"mean": self._mean,
"std": self._std_dev,
"seed": self._seed
})
var.op = op
return op
python/paddle/v2/framework/layer_helper.py
浏览文件 @ 83b48ebc
......@@ -5,6 +5,8 @@ import paddle.v2.framework.core as core
from paddle.v2.framework.framework import Variable, g_program, \
g_init_program
from paddle.v2.framework.initializer import ConstantInitializer, \
UniformInitializer
def unique_name(prefix):
......@@ -66,14 +68,7 @@ class LayerHelper(object):
@property
def param_attr(self):
default = {
'name': None,
'init_attr': {
'type': 'uniform_random',
'min': -1.0,
'max': 1.0
}
}
default = {'name': None, 'initializer': UniformInitializer()}
actual = self.kwargs.get('param_attr', None)
if actual is None:
actual = default
......@@ -83,13 +78,7 @@ class LayerHelper(object):
return actual
def bias_attr(self):
default = {
'name': None,
'init_attr': {
'type': 'fill_constant',
'value': 0.0
}
}
default = {'name': None, 'initializer': ConstantInitializer()}
bias_attr = self.kwargs.get('bias_attr', None)
if bias_attr is True:
bias_attr = default
......@@ -153,8 +142,24 @@ class LayerHelper(object):
return self.program.global_block().create_var(
*args, persistable=False, **kwargs)
def append_bias_op(self, input_var):
size = list(input_var.shape[1:])
def append_bias_op(self, input_var, num_flatten_dims=None):
"""
Append bias operator and return its output. If the user does not set
bias_attr, append_bias_op will return input_var
:param input_var: the input variable. The len(input_var.shape) is larger
or equal than 2.
:param num_flatten_dims: The input tensor will be flatten as a matrix
when adding bias.
`matrix.shape = product(input_var.shape[0:num_flatten_dims]), product(
input_var.shape[num_flatten_dims:])`
"""
if num_flatten_dims is None:
num_flatten_dims = self.kwargs.get('num_flatten_dims', None)
if num_flatten_dims is None:
num_flatten_dims = 1
size = list(input_var.shape[num_flatten_dims:])
bias_attr = self.bias_attr()
if not bias_attr:
return input_var
......
python/paddle/v2/framework/layers.py
浏览文件 @ 83b48ebc
from paddle.v2.framework.layer_helper import LayerHelper, unique_name
import paddle.v2.framework.core as core
from paddle.v2.framework.framework import OpProtoHolder, Variable, Program
from paddle.v2.framework.initializer import ConstantInitializer
import re
__all__ = [
'fc', 'data', 'cross_entropy', 'conv2d', 'pool2d', 'embedding', 'concat',
'StaticRNN', 'cast', 'sequence_conv', 'sequence_pool', 'accuracy'
'StaticRNN', 'cast', 'sequence_conv', 'sequence_pool', 'sums', 'cos_sim',
'batch_norm', 'accuracy'
]
......@@ -165,18 +167,6 @@ _create_op_func_('dropout')
_create_op_func_('reshape')
def cast(x, data_type, program=None):
helper = LayerHelper('cast', **locals())
out = helper.create_tmp_variable(dtype=data_type)
helper.append_op(
type='cast',
inputs={'X': [x]},
outputs={'Out': [out]},
attrs={'in_data_type': x.data_type,
'out_data_type': out.data_type})
return out
def cast(x, data_type, program=None):
helper = LayerHelper('cast', **locals())
out = helper.create_tmp_variable(dtype=data_type)
......@@ -191,9 +181,7 @@ def cast(x, data_type, program=None):
def concat(input, axis, program=None, init_program=None):
helper = LayerHelper('concat', **locals())
if not isinstance(input, list) and not isinstance(input, tuple):
input = [input]
out = helper.create_tmp_variable(dtype=input[0].data_type)
out = helper.create_tmp_variable(dtype=helper.input_dtype())
helper.append_op(
type='concat',
inputs={'X': input},
......@@ -202,6 +190,28 @@ def concat(input, axis, program=None, init_program=None):
return out
def sums(input, program=None, init_program=None):
helper = LayerHelper('sum', **locals())
out = helper.create_tmp_variable(dtype=helper.input_dtype())
helper.append_op(type='sum', inputs={'X': [input]}, outputs={'Out': out})
return out
def cos_sim(X, Y, program=None, init_program=None):
helper = LayerHelper('cos_sim', **locals())
out = helper.create_tmp_variable(dtype=helper.input_dtype("X"))
xnorm = helper.create_tmp_variable(dtype=helper.input_dtype("X"))
ynorm = helper.create_tmp_variable(dtype=helper.input_dtype("X"))
helper.append_op(
type='cos_sim',
inputs={'X': [X],
'Y': [Y]},
outputs={'Out': [out],
'XNorm': [xnorm],
'YNorm': [ynorm]})
return out, xnorm, ynorm
def cross_entropy(input, label, **kwargs):
helper = LayerHelper('cross_entropy', **kwargs)
out = helper.create_tmp_variable(dtype=input.data_type)
......@@ -254,9 +264,7 @@ def accuracy(input, label, k=1, **kwargs):
def sequence_conv(input,
num_filters,
name=None,
filter_size=3,
act=None,
stride=1,
padding=None,
bias_attr=None,
......@@ -270,7 +278,7 @@ def sequence_conv(input,
helper = LayerHelper('sequence_conv', **locals())
dtype = helper.input_dtype()
filter_shape = [num_filters, filter_size]
filter_shape = [filter_size * input.shape[1], num_filters]
filter = helper.create_parameter(
attr=helper.param_attr, shape=filter_shape, dtype=dtype)
pre_bias = helper.create_tmp_variable(dtype)
......@@ -279,7 +287,7 @@ def sequence_conv(input,
type='sequence_conv',
inputs={
'X': [input],
'Filter': filter,
'Filter': [filter],
},
outputs={"Out": pre_bias},
attrs={
......@@ -287,7 +295,6 @@ def sequence_conv(input,
'context_start': 0,
'context_length': filter_size
})
pre_act = helper.append_bias_op(pre_bias)
return helper.append_activation(pre_act)
......@@ -344,31 +351,21 @@ def conv2d(input,
return helper.append_activation(pre_act)
def sequence_pool(input,
pool_size,
pool_type,
pool_stride=1,
pool_padding=0,
global_pooling=False,
program=None,
init_program=None):
# FIXME(dzh) : want to unify the argument of python layer
# function. So we ignore some unecessary attributes
ENUM_POOL_TYPE = set(["max", "avg", "sqrt", "last", "first"])
if pool_type not in ENUM_POOL_TYPE:
def sequence_pool(input, pool_type, **kwargs):
ENUM_POOL_TYPE = set(["MAX", "AVG", "SQRT", "LAST", "FIRST"])
if pool_type.upper() not in ENUM_POOL_TYPE:
raise ValueError("Unknown pool_type: '%s'. It can only be %s.",
str(pool_type), " ".join(ENUM_POOL_TYPE))
helper = LayerHelper('sequence_pool', **locals())
helper = LayerHelper('sequence_pool', **kwargs)
dtype = helper.input_dtype()
pool_out = helper.create_tmp_variable(dtype)
helper.append_op(
type="sequence_pool",
inputs={"X": [input]},
outputs={"Out": pool_out},
attrs={"strategy": pool_type})
outputs={"Out": [pool_out]},
attrs={"pooltype": pool_type.upper()})
return pool_out
......@@ -433,26 +430,12 @@ def batch_norm(input,
else:
raise ValueError("unsupported data layout:" + data_layout)
def get_init_attr(value):
if not isinstance(value, float):
raise ValueError("attr value should be a float")
return {'type': 'fill_constant', 'value': value}
def prepend_init_op(var, init_attr):
assert isinstance(var, Variable)
op_type = init_attr['type']
init_attr['shape'] = var.shape
init_attr['data_type'] = int(var.data_type)
op = var.block.prepend_op(
type=op_type, inputs=None, outputs={'Out': [var]}, attrs=init_attr)
return op
def create_persistable_var(dtype, shape, init_attr=None):
def create_persistable_var(dtype, shape, initializer=None):
name = unique_name(".".join([helper.name, "xxxx"]))
var = init_program.global_block().create_var(
dtype=dtype, shape=shape, name=name, persistable=True)
if 'init_attr' is not None:
prepend_init_op(var, init_attr)
if initializer is not None:
initializer(var, var.block)
return program.global_block().create_var(
name=name, dtype=dtype, shape=shape, persistable=True)
......@@ -465,8 +448,9 @@ def batch_norm(input,
attr=helper.param_attr, shape=param_shape, dtype=dtype)
# create input
mean = create_persistable_var(dtype, param_shape, get_init_attr(0.0))
variance = create_persistable_var(dtype, param_shape, get_init_attr(1.0))
mean = create_persistable_var(dtype, param_shape, ConstantInitializer(0.0))
variance = create_persistable_var(dtype, param_shape,
ConstantInitializer(1.0))
# create output
# mean and mean_out share the same memory
......
python/paddle/v2/framework/nets.py
浏览文件 @ 83b48ebc
......@@ -101,24 +101,19 @@ def img_conv_group(input,
def sequence_conv_pool(input,
num_filters,
filter_size,
pool_size,
pool_stride,
act,
pool_type="max",
program=None,
init_program=None):
conv_out = layers.sequence_conv(
input=input,
num_filters=num_filters,
filter_size=filter_size,
act=act,
program=program,
init_program=init_program)
pool_out = layers.sequence_pool(
input=conv_out,
pool_size=pool_size,
pool_type='max',
pool_stride=pool_stride,
pool_type=pool_type,
program=program,
init_program=init_program)
return pool_out
python/paddle/v2/framework/tests/test_gaussian_random_op.py
浏览文件 @ 83b48ebc
......@@ -19,7 +19,7 @@ class TestGaussianRandomOp(unittest.TestCase):
op = Operator(
"gaussian_random",
Out='Out',
dims=[1000, 784],
shape=[1000, 784],
mean=.0,
std=1.,
seed=10)
......
python/paddle/v2/framework/tests/test_initializer.py 0 → 100644
浏览文件 @ 83b48ebc
import unittest
import paddle.v2.framework.framework as framework
import paddle.v2.framework.initializer as initializer
DELTA = 0.00001
class TestConstantInitializer(unittest.TestCase):
def test_constant_initializer_default_value(self):
"""Test the constant initializer with default value
"""
program = framework.Program()
block = program.global_block()
block.create_parameter(
dtype="float32",
shape=[5, 10],
lod_level=0,
name="param",
initializer=initializer.ConstantInitializer())
self.assertEqual(len(block.ops), 1)
init_op = block.ops[0]
self.assertEqual(init_op.type, 'fill_constant')
self.assertAlmostEqual(init_op.attr('value'), 0.0, delta=DELTA)
def test_constant_initializer(self):
"""Test constant initializer with supplied value
"""
program = framework.Program()
block = program.global_block()
block.create_parameter(
dtype="float32",
shape=[5, 10],
lod_level=0,
name="param",
initializer=initializer.ConstantInitializer(2.3))
self.assertEqual(len(block.ops), 1)
init_op = block.ops[0]
self.assertEqual(init_op.type, 'fill_constant')
self.assertAlmostEqual(init_op.attr('value'), 2.3, delta=DELTA)
class TestUniformInitializer(unittest.TestCase):
def test_uniform_initializer_default_value(self):
"""Test the uniform initializer with default value
"""
program = framework.Program()
block = program.global_block()
block.create_parameter(
dtype="float32",
shape=[5, 10],
lod_level=0,
name="param",
initializer=initializer.UniformInitializer())
self.assertEqual(len(block.ops), 1)
init_op = block.ops[0]
self.assertEqual(init_op.type, 'uniform_random')
self.assertAlmostEqual(init_op.attr('min'), -1.0, delta=DELTA)
self.assertAlmostEqual(init_op.attr('max'), 1.0, delta=DELTA)
self.assertEqual(init_op.attr('seed'), 0)
def test_uniform_initializer(self):
"""Test uniform initializer with supplied attributes
"""
program = framework.Program()
block = program.global_block()
block.create_parameter(
dtype="float32",
shape=[5, 10],
lod_level=0,
name="param",
initializer=initializer.UniformInitializer(-4.2, 3.1, 123))
self.assertEqual(len(block.ops), 1)
init_op = block.ops[0]
self.assertEqual(init_op.type, 'uniform_random')
self.assertAlmostEqual(init_op.attr('min'), -4.2, delta=DELTA)
self.assertAlmostEqual(init_op.attr('max'), 3.1, delta=DELTA)
self.assertEqual(init_op.attr('seed'), 123)
class TestNormalInitializer(unittest.TestCase):
def test_normal_initializer_default_value(self):
"""Test the normal initializer with default value
"""
program = framework.Program()
block = program.global_block()
block.create_parameter(
dtype="float32",
shape=[5, 10],
lod_level=0,
name="param",
initializer=initializer.NormalInitializer())
self.assertEqual(len(block.ops), 1)
init_op = block.ops[0]
self.assertEqual(init_op.type, 'gaussian_random')
self.assertAlmostEqual(init_op.attr('mean'), 0.0, delta=DELTA)
self.assertAlmostEqual(init_op.attr('std'), 1.0, delta=DELTA)
self.assertEqual(init_op.attr('seed'), 0)
def test_normal_initializer(self):
"""Test normal initializer with supplied attributes
"""
program = framework.Program()
block = program.global_block()
block.create_parameter(
dtype="float32",
shape=[5, 10],
lod_level=0,
name="param",
initializer=initializer.NormalInitializer(2.3, 1.9, 123))
self.assertEqual(len(block.ops), 1)
init_op = block.ops[0]
self.assertEqual(init_op.type, 'gaussian_random')
self.assertAlmostEqual(init_op.attr('mean'), 2.3, delta=DELTA)
self.assertAlmostEqual(init_op.attr('std'), 1.9, delta=DELTA)
self.assertEqual(init_op.attr('seed'), 123)
if __name__ == '__main__':
unittest.main()
python/paddle/v2/framework/tests/test_lstm_op.py
浏览文件 @ 83b48ebc
......@@ -52,7 +52,7 @@ def lstm(
g = np.dot(h_pre, w_h) # 1 x 4D
g = g + x
g = np.reshape(g, (1, g.size))
c_tmp, g_i, g_f, g_o = np.split(g, 4, axis=1)
c, g_i, g_f, g_o = np.split(g, 4, axis=1)
if w_c is None:
g_i = act_gate(g_i) # 1 x D
g_f = act_gate(g_f) # 1 x D
......@@ -60,7 +60,7 @@ def lstm(
w_ic, w_fc, w_oc = np.split(w_c, 3, axis=1)
g_i = act_gate(g_i + w_ic * c_pre) # 1 x D
g_f = act_gate(g_f + w_fc * c_pre) # 1 x D
c = g_f * c_pre + g_i * act_cand(c_tmp) # 1 x D
c = g_f * c_pre + g_i * act_cand(c) # 1 x D
if w_c is None:
g_o = act_gate(g_o) # 1 x D
......@@ -68,8 +68,7 @@ def lstm(
_, _, w_oc = np.split(w_c, 3, axis=1)
g_o = act_gate(g_o + w_oc * c) # 1 x D
h = g_o * act_cell(c)
bg = np.concatenate((act_cand(c_tmp), g_i, g_f, g_o), axis=1)
return h, c, bg
return h, c
def _reverse(x, lod):
y = np.zeros_like(x)
......@@ -82,7 +81,6 @@ def lstm(
batch_size = len(offset) - 1
hidden = []
cell = []
gate = []
input = _reverse(input, offset) if is_reverse else input
if w_b is not None:
input = input + np.tile(w_b, (offset[-1], 1))
......@@ -94,96 +92,109 @@ def lstm(
c_pre = c0[i] # 1 x D
for j in range(seq_len):
# compute one step
h_pre, c_pre, g_pre = _step(x[j], w_h, w_c, h_pre, c_pre, act_gate,
act_cell, act_cand)
h_pre, c_pre = _step(x[j], w_h, w_c, h_pre, c_pre, act_gate,
act_cell, act_cand)
hidden.append(h_pre.flatten())
cell.append(c_pre.flatten())
gate.append(g_pre.flatten())
hidden = np.array(hidden).astype("float64")
cell = np.array(cell).astype("float64")
gate = np.array(gate).astype("float64")
hidden = np.array(hidden).astype('float64')
cell = np.array(cell).astype('float64')
hidden = _reverse(hidden, offset) if is_reverse else hidden
cell = _reverse(cell, offset) if is_reverse else cell
assert gate.shape == input.shape
assert hidden.shape == (input.shape[0], input.shape[1] / 4)
assert cell.shape == (input.shape[0], input.shape[1] / 4)
return hidden, cell, gate
return hidden, cell
class TestLstmOp(OpTest):
def set_data(self):
self.lod = [[0, 2, 6, 9]]
self.D = 64
self.sort_idx = [2, 6, 0, 3, 7, 1, 4, 8, 5]
def set_argument(self):
self.lod = [[0, 2, 5, 7]]
self.D = 16
self.act_gate = "sigmoid"
self.act_cell = "tanh"
self.act_cand = "tanh"
self.act_gate = 'sigmoid'
self.act_cell = 'tanh'
self.act_cand = 'tanh'
self.has_initial_state = True
self.is_reverse = False
def setUp(self):
self.set_data()
self.op_type = "lstm"
self.set_argument()
self.op_type = 'lstm'
T = self.lod[0][-1]
N = len(self.lod[0]) - 1
x = np.random.normal(size=(T, 4 * self.D)).astype("float64")
h0 = np.zeros((N, self.D)).astype("float64")
c0 = np.zeros((N, self.D)).astype("float64")
w = np.random.normal(size=(self.D, 4 * self.D)).astype("float64")
b = np.random.normal(size=(1, 7 * self.D)).astype("float64")
x = np.random.normal(size=(T, 4 * self.D)).astype('float64')
h0 = np.zeros((N, self.D)).astype('float64')
c0 = np.zeros((N, self.D)).astype('float64')
w = np.random.normal(size=(self.D, 4 * self.D)).astype('float64')
b = np.random.normal(size=(1, 7 * self.D)).astype('float64')
w_b = b[:, 0:4 * self.D]
w_c = b[:, 4 * self.D:]
h, c, g = lstm(x, self.lod, h0, c0, w, w_b, w_c, self.is_reverse,
ACTVATION[self.act_gate], ACTVATION[self.act_cell],
ACTVATION[self.act_cand])
g_sort = np.zeros_like(x)
for i, j in enumerate(self.sort_idx):
g_sort[i, :] = g[j, :]
self.inputs = {
'Input': (x, self.lod),
'H0': h0,
'C0': c0,
'Weight': w,
'Bias': b
}
h, c = lstm(x, self.lod, h0, c0, w, w_b, w_c, self.is_reverse,
ACTVATION[self.act_gate], ACTVATION[self.act_cell],
ACTVATION[self.act_cand])
self.inputs = {'Input': (x, self.lod), 'Weight': w, 'Bias': b}
if self.has_initial_state:
self.inputs['H0'] = h0
self.inputs['C0'] = c0
self.outputs = {
'Hidden': (h, self.lod),
'Cell': (c, self.lod),
'BatchGate': g_sort
}
self.attrs = {
'usePeepholes': True,
'isReverse': self.is_reverse,
'gateActivation': 'sigmoid',
'cellActivation': 'tanh',
'candidateActivation': 'tanh'
'gateActivation': self.act_gate,
'cellActivation': self.act_cell,
'candidateActivation': self.act_cand
}
def test_check_output(self):
self.check_output()
self.check_output(atol=1e-8)
#TODO(qingqing) add more unit testing case
def test_check_grad(self):
# TODO(qingqing) remove folowing lines after the check_grad is refined.
N = len(self.lod[0]) - 1
self.outputs['BatchGate'] = np.zeros((N, 4 * self.D)).astype('float64')
self.outputs['BatchCellPreAct'] = np.zeros(
(N, self.D)).astype('float64')
self.check_grad(
['Input', 'Weight', 'Bias'], ['Hidden'], max_relative_error=5e-4)
class TestLstmOpHasNoInitial(TestLstmOp):
def set_argument(self):
self.lod = [[0, 2, 5, 7]]
self.D = 16
self.act_gate = 'sigmoid'
self.act_cell = 'tanh'
self.act_cand = 'tanh'
self.has_initial_state = False
self.is_reverse = True
class TestLstmOpRerverse(TestLstmOp):
def set_data(self):
self.lod = [[0, 2, 6, 9]]
self.D = 64
self.sort_idx = [2, 6, 0, 3, 7, 1, 4, 8, 5]
def set_argument(self):
self.lod = [[0, 2, 5, 7]]
self.D = 16
self.act_gate = "sigmoid"
self.act_cell = "tanh"
self.act_cand = "tanh"
self.act_gate = 'sigmoid'
self.act_cell = 'tanh'
self.act_cand = 'tanh'
self.has_initial_state = True
self.is_reverse = True
if __name__ == "__main__":
if __name__ == '__main__':
unittest.main()
python/paddle/v2/framework/tests/test_recognize_digits_mlp.py
浏览文件 @ 83b48ebc
......@@ -3,9 +3,10 @@ import paddle.v2.framework.layers as layers
import paddle.v2.framework.core as core
import paddle.v2.framework.optimizer as optimizer
from paddle.v2.framework.framework import Program, g_program
from paddle.v2.framework.framework import Program
from paddle.v2.framework.executor import Executor
from paddle.v2.framework.regularizer import L2DecayRegularizer
from paddle.v2.framework.initializer import UniformInitializer
import numpy as np
......@@ -21,11 +22,8 @@ image = layers.data(
param_attr = {
'name': None,
'init_attr': {
'type': 'uniform_random',
'min': -1.0,
'max': 1.0
},
'initializer': UniformInitializer(
low=-1.0, high=1.0),
'regularization': L2DecayRegularizer(0.0005 * BATCH_SIZE)
}
......
python/paddle/v2/framework/tests/test_seq_pool.py
浏览文件 @ 83b48ebc
......@@ -3,15 +3,6 @@ import numpy as np
from op_test import OpTest
class SeqPoolType(OpTest):
AVERAGE = 0
SUM = 1
SQRT = 2
MAX = 3
LAST = 4
FIRST = 5
class TestSeqAvgPool(OpTest):
def set_data(self):
self.op_type = 'sequence_pool'
......@@ -25,7 +16,7 @@ class TestSeqAvgPool(OpTest):
return x, lod, out
def compute(self, x, lod, out):
self.attrs = {'strategy': SeqPoolType.AVERAGE}
self.attrs = {'pooltype': "AVERAGE"}
for i in range(4):
sub_x = x[lod[0][i]:lod[0][i + 1], :]
out[i] = sub_x.mean(axis=0)
......@@ -54,7 +45,7 @@ class TestSeqAvgPool2D(TestSeqAvgPool):
return x, lod, out
def compute(self, x, lod, out):
self.attrs = {'strategy': SeqPoolType.AVERAGE}
self.attrs = {'pooltype': "AVERAGE"}
for i in range(4):
sub_x = np.reshape(x[lod[0][i]:lod[0][i + 1], :], (-1, 3 * 17))
out[i] = np.reshape(sub_x.mean(axis=0), (3, 17))
......@@ -62,7 +53,7 @@ class TestSeqAvgPool2D(TestSeqAvgPool):
class TestSeqSumPool(TestSeqAvgPool):
def compute(self, x, lod, out):
self.attrs = {'strategy': SeqPoolType.SUM}
self.attrs = {'pooltype': "SUM"}
for i in range(4):
sub_x = x[lod[0][i]:lod[0][i + 1], :]
out[i] = sub_x.sum(axis=0)
......@@ -70,7 +61,7 @@ class TestSeqSumPool(TestSeqAvgPool):
class TestSeqSumPool2D(TestSeqAvgPool2D):
def compute(self, x, lod, out):
self.attrs = {'strategy': SeqPoolType.SUM}
self.attrs = {'pooltype': "SUM"}
for i in range(4):
sub_x = np.reshape(x[lod[0][i]:lod[0][i + 1], :], (-1, 3 * 17))
out[i] = np.reshape(sub_x.sum(axis=0), (3, 17))
......@@ -78,7 +69,7 @@ class TestSeqSumPool2D(TestSeqAvgPool2D):
class TestSeqSqrtPool(TestSeqAvgPool):
def compute(self, x, lod, out):
self.attrs = {'strategy': SeqPoolType.SQRT}
self.attrs = {'pooltype': "SQRT"}
for i in range(4):
sub_x = x[lod[0][i]:lod[0][i + 1], :]
len = lod[0][i + 1] - lod[0][i]
......@@ -87,7 +78,7 @@ class TestSeqSqrtPool(TestSeqAvgPool):
class TestSeqSqrtPool2D(TestSeqAvgPool2D):
def compute(self, x, lod, out):
self.attrs = {'strategy': SeqPoolType.SQRT}
self.attrs = {'pooltype': "SQRT"}
for i in range(4):
sub_x = np.reshape(x[lod[0][i]:lod[0][i + 1], :], (-1, 3 * 17))
len = lod[0][i + 1] - lod[0][i]
......@@ -99,7 +90,7 @@ class TestSeqSqrtPool2D(TestSeqAvgPool2D):
class TestSeqMaxPool(TestSeqAvgPool):
def compute(self, x, lod, out):
self.attrs = {'strategy': SeqPoolType.MAX}
self.attrs = {'pooltype': "MAX"}
for i in range(4):
sub_x = x[lod[0][i]:lod[0][i + 1], :]
out[i] = np.amax(sub_x, axis=0)
......@@ -111,7 +102,7 @@ class TestSeqMaxPool(TestSeqAvgPool):
class TestSeqMaxPool2D(TestSeqAvgPool2D):
def compute(self, x, lod, out):
self.attrs = {'strategy': SeqPoolType.MAX}
self.attrs = {'pooltype': "MAX"}
for i in range(4):
sub_x = np.reshape(x[lod[0][i]:lod[0][i + 1], :], (-1, 3 * 17))
out[i] = np.reshape(np.amax(sub_x, axis=0), (3, 17))
......@@ -123,7 +114,7 @@ class TestSeqMaxPool2D(TestSeqAvgPool2D):
class TestSeqLastPool(TestSeqAvgPool):
def compute(self, x, lod, out):
self.attrs = {'strategy': SeqPoolType.LAST}
self.attrs = {'pooltype': "LAST"}
for i in range(4):
sub_x = x[lod[0][i]:lod[0][i + 1], :]
out[i] = sub_x[-1, :]
......@@ -131,7 +122,7 @@ class TestSeqLastPool(TestSeqAvgPool):
class TestSeqLastPool2D(TestSeqAvgPool2D):
def compute(self, x, lod, out):
self.attrs = {'strategy': SeqPoolType.LAST}
self.attrs = {'pooltype': "LAST"}
for i in range(4):
sub_x = np.reshape(x[lod[0][i]:lod[0][i + 1], :], (-1, 3 * 17))
out[i] = np.reshape(sub_x[-1, :], (3, 17))
......@@ -139,7 +130,7 @@ class TestSeqLastPool2D(TestSeqAvgPool2D):
class TestSeqFirstPool(TestSeqAvgPool):
def compute(self, x, lod, out):
self.attrs = {'strategy': SeqPoolType.FIRST}
self.attrs = {'pooltype': "FIRST"}
for i in range(4):
sub_x = x[lod[0][i]:lod[0][i + 1], :]
out[i] = sub_x[0, :]
......@@ -147,7 +138,7 @@ class TestSeqFirstPool(TestSeqAvgPool):
class TestSeqFirstPool2D(TestSeqAvgPool2D):
def compute(self, x, lod, out):
self.attrs = {'strategy': SeqPoolType.FIRST}
self.attrs = {'pooltype': "FIRST"}
for i in range(4):
sub_x = np.reshape(x[lod[0][i]:lod[0][i + 1], :], (-1, 3 * 17))
out[i] = np.reshape(sub_x[0, :], (3, 17))
......
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