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经典的线性回归任务
==================
PaddlePaddle是源于百度的一个深度学习平台。这份简短的介绍将向你展示如何利用PaddlePaddle来解决一个经典的线性回归问题。
任务简介
--------
我们展示如何用PaddlePaddle解决 `单变量的线性回归 <https://www.baidu.com/s?wd=单变量线性回归>`_ 问题。线性回归的输入是一批点 `(x, y)` ，其中 `y = wx + b + ε`， 而 ε 是一个符合高斯分布的随机变量。线性回归的输出是从这批点估计出来的参数 `w` 和 `b` 。
一个例子是房产估值。我们假设房产的价格（y）是其大小（x）的一个线性函数，那么我们可以通过收集市场上房子的大小和价格，用来估计线性函数的参数w 和 b。
准备数据
-----------
假设变量 `x` 和 `y` 的真实关系为： `y = 2x + 0.3 + ε`，这里展示如何使用观测数据来拟合这一线性关系。首先，Python代码将随机产生2000个观测点，作为线性回归的输入。下面脚本符合PaddlePaddle期待的读取数据的Python程序的模式。
.. code-block:: python
# dataprovider.py
from paddle.trainer.PyDataProvider2 import *
import random
# 定义输入数据的类型: 2个浮点数
@provider(input_types=[dense_vector(1), dense_vector(1)],use_seq=False)
def process(settings, input_file):
for i in xrange(2000):
x = random.random()
yield [x], [2*x+0.3]
训练模型
-----------
为了还原 `y = 2x + 0.3`，我们先从一条随机的直线 `y' = wx + b` 开始，然后利用观测数据调整 `w` 和 `b` 使得 `y'` 和 `y` 的差距不断减小，最终趋于接近。这个过程就是模型的训练过程，而 `w` 和 `b` 就是模型的参数，即我们的训练目标。
在PaddlePaddle里，该模型的网络配置如下。
.. code-block:: python
# trainer_config.py
from paddle.trainer_config_helpers import *
# 1. 定义数据来源，调用上面的process函数获得观测数据
data_file = 'empty.list'
with open(data_file, 'w') as f: f.writelines(' ')
define_py_data_sources2(train_list=data_file, test_list=None,
module='dataprovider', obj='process',args={})
# 2. 学习算法。控制如何改变模型参数 w 和 b
settings(batch_size=12, learning_rate=1e-3, learning_method=MomentumOptimizer())
# 3. 神经网络配置
x = data_layer(name='x', size=1)
y = data_layer(name='y', size=1)
# 线性计算网络层: ȳ = wx + b
ȳ = fc_layer(input=x, param_attr=ParamAttr(name='w'), size=1, act=LinearActivation(), bias_attr=ParamAttr(name='b'))
# 计算误差函数，即 ȳ 和真实 y 之间的距离
cost = square_error_cost(input= ȳ, label=y)
outputs(cost)
这段简短的配置展示了PaddlePaddle的基本用法：
- 第一部分定义了数据输入。一般情况下，PaddlePaddle先从一个文件列表里获得数据文件地址，然后交给用户自定义的函数（例如上面的 `process`函数）进行读入和预处理从而得到真实输入。本文中由于输入数据是随机生成的不需要读输入文件，所以放一个空列表（`empty.list`）即可。
- 第二部分主要是选择学习算法，它定义了模型参数改变的规则。PaddlePaddle提供了很多优秀的学习算法，这里使用一个基于momentum的随机梯度下降(SGD)算法，该算法每批量(batch)读取12个采样数据进行随机梯度计算来更新更新。
- 最后一部分是神经网络的配置。由于PaddlePaddle已经实现了丰富的网络层，所以很多时候你需要做的只是定义正确的网络层并把它们连接起来。这里使用了三种网络单元：
- **数据层**：数据层 `data_layer` 是神经网络的入口，它读入数据并将它们传输到接下来的网络层。这里数据层有两个，分别对应于变量 `x` 和 `y`。
- **全连接层**：全连接层 `fc_layer` 是基础的计算单元，这里利用它建模变量之间的线性关系。计算单元是神经网络的核心，PaddlePaddle支持大量的计算单元和任意深度的网络连接，从而可以拟合任意的函数来学习复杂的数据关系。
- **回归误差代价层**：回归误差代价层 `square_error_cost` 是众多误差代价函数层的一种，它们在训练过程作为网络的出口，用来计算模型的误差，是模型参数优化的目标函数。
定义了网络结构并保存为 `trainer_config.py` 之后，运行以下训练命令：
.. code-block:: bash
paddle train --config=trainer_config.py --save_dir=./output --num_passes=30
PaddlePaddle将在观测数据集上迭代训练30轮，并将每轮的模型结果存放在 `./output` 路径下。从输出日志可以看到，随着轮数增加误差代价函数的输出在不断的减小，这意味着模型在训练数据上不断的改进，直到逼近真实解：` y = 2x + 0.3 `
模型检验
-----------
训练完成后，我们希望能够检验模型的好坏。一种常用的做法是用学习的模型对另外一组测试数据进行预测，评价预测的效果。在这个例子中，由于已经知道了真实答案，我们可以直接观察模型的参数是否符合预期来进行检验。
PaddlePaddle将每个模型参数作为一个numpy数组单独存为一个文件，所以可以利用如下方法读取模型的参数。
.. code-block:: python
import numpy as np
import os
def load(file_name):
with open(file_name, 'rb') as f:
f.read(16) # skip header for float type.
return np.fromfile(f, dtype=np.float32)
print 'w=%.6f, b=%.6f' % (load('output/pass-00029/w'), load('output/pass-00029/b'))
# w=1.999743, b=0.300137
.. image:: ./parameters.png
:align: center
:scale: 80 %
从图中可以看到，虽然 `w` 和 `b` 都使用随机值初始化，但在起初的几轮训练中它们都在快速逼近真实值，并且后续仍在不断改进，使得最终得到的模型几乎与真实模型一致。
这样，我们用PaddlePaddle解决了单变量线性回归问题， 包括数据输入、模型训练和最后的结果验证。
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Simple Linear Regression
========================
PaddlePaddle is a deep learning platform open-sourced by Baidu. With PaddlePaddle, you can easily train a classic neural network within a couple lines of configuration, or you can build sophisticated models that provide state-of-the-art performance on difficult learning tasks like sentiment analysis, machine translation, image caption and so on.
Problem Background
------------------
Now, to give you a hint of what using PaddlePaddle looks like, let's start with a fundamental learning problem - `simple linear regression <https://en.wikipedia.org/wiki/Simple_linear_regression>`_: you have observed a set of two-dimensional data points of ``X`` and ``Y``, where ``X`` is an explanatory variable and ``Y`` is corresponding dependent variable, and you want to recover the underlying correlation between ``X`` and ``Y``. Linear regression can be used in many practical scenarios. For example, ``X`` can be a variable about house size, and ``Y`` a variable about house price. You can build a model that captures relationship between them by observing real estate markets.
Prepare the Data
-----------------
Suppose the true relationship can be characterized as ``Y = 2X + 0.3``, let's see how to recover this pattern only from observed data. Here is a piece of python code that feeds synthetic data to PaddlePaddle. The code is pretty self-explanatory, the only extra thing you need to add for PaddlePaddle is a definition of input data types.
.. code-block:: python
# dataprovider.py
from paddle.trainer.PyDataProvider2 import *
import random
# define data types of input: 2 real numbers
@provider(input_types=[dense_vector(1), dense_vector(1)],use_seq=False)
def process(settings, input_file):
for i in xrange(2000):
x = random.random()
yield [x], [2*x+0.3]
Train a NeuralNetwork
----------------------
To recover this relationship between ``X`` and ``Y``, we use a neural network with one layer of linear activation units and a square error cost layer. Don't worry if you are not familiar with these terminologies, it's just saying that we are starting from a random line ``Y' = wX + b`` , then we gradually adapt ``w`` and ``b`` to minimize the difference between ``Y'`` and ``Y``. Here is what it looks like in PaddlePaddle:
.. code-block:: python
# trainer_config.py
from paddle.trainer_config_helpers import *
# 1. read data. Suppose you saved above python code as dataprovider.py
data_file = 'empty.list'
with open(data_file, 'w') as f: f.writelines(' ')
define_py_data_sources2(train_list=data_file, test_list=None,
module='dataprovider', obj='process',args={})
# 2. learning algorithm
settings(batch_size=12, learning_rate=1e-3, learning_method=MomentumOptimizer())
# 3. Network configuration
x = data_layer(name='x', size=1)
y = data_layer(name='y', size=1)
y_predict = fc_layer(input=x, param_attr=ParamAttr(name='w'), size=1, act=LinearActivation(), bias_attr=ParamAttr(name='b'))
cost = square_error_cost(input=y_predict, label=y)
outputs(cost)
Some of the most fundamental usages of PaddlePaddle are demonstrated:
- The first part shows how to feed data into PaddlePaddle. In general cases, PaddlePaddle reads raw data from a list of files, and then do some user-defined process to get real input. In this case, we only need to create a placeholder file since we are generating synthetic data on the fly.
- The second part describes learning algorithm. It defines in what ways adjustments are made to model parameters. PaddlePaddle provides a rich set of optimizers, but a simple momentum based optimizer will suffice here, and it processes 12 data points each time.
- Finally, the network configuration. It usually is as simple as "stacking" layers. Three kinds of layers are used in this configuration:
- **Data Layer**: a network always starts with one or more data layers. They provide input data to the rest of the network. In this problem, two data layers are used respectively for ``X`` and ``Y``.
- **FC Layer**: FC layer is short for Fully Connected Layer, which connects all the input units to current layer and does the actual computation specified as activation function. Computation layers like this are the fundamental building blocks of a deeper model.
- **Cost Layer**: in training phase, cost layers are usually the last layers of the network. They measure the performance of current model, and provide guidence to adjust parameters.
Now that everything is ready, you can train the network with a simple command line call:
.. code-block:: bash
paddle train --config=trainer_config.py --save_dir=./output --num_passes=30
This means that PaddlePaddle will train this network on the synthectic dataset for 30 passes, and save all the models under path ``./output``. You will see from the messages printed out during training phase that the model cost is decreasing as time goes by, which indicates we are getting a closer guess.
Evaluate the Model
-------------------
Usually, a different dataset that left out during training phase should be used to evalute the models. However, we are lucky enough to know the real answer: ``w=2, b=0.3``, thus a better option is to check out model parameters directly.
In PaddlePaddle, training is just to get a collection of model parameters, which are ``w`` and ``b`` in this case. Each parameter is saved in an individual file in the popular ``numpy`` array format. Here is the code that reads parameters from last pass.
.. code-block:: python
import numpy as np
import os
def load(file_name):
with open(file_name, 'rb') as f:
f.read(16) # skip header for float type.
return np.fromfile(f, dtype=np.float32)
print 'w=%.6f, b=%.6f' % (load('output/pass-00029/w'), load('output/pass-00029/b'))
# w=1.999743, b=0.300137
.. image:: parameters.png
:align: center
Although starts from a random guess, you can see that value of ``w`` changes quickly towards 2 and ``b`` changes quickly towards 0.3. In the end, the predicted line is almost identical with real answer.
There, you have recovered the underlying pattern between ``X`` and ``Y`` only from observed data.
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从源码编译PaddlePaddle
======================
.. _build_step:
编译方法
----------------
PaddlePaddle主要使用 `CMake <https://cmake.org>`_ 以及GCC, G++作为编译工具。
我们推荐您使用PaddlePaddle编译环境镜像完成编译，这样可以免去单独安装编译依赖的步骤，可选的不同编译环境
可以在 `这里 <https://hub.docker.com/r/paddlepaddle/paddle_manylinux_devel/tags/>`_ 找到。
编译PaddlePaddle，需要执行：
.. code-block:: bash
git clone https://github.com/PaddlePaddle/Paddle.git
cd Paddle
# 如果使用Docker编译环境，执行下面的命令编译CPU-Only的二进制
docker run -it -v $PWD:/paddle -e "WITH_GPU=OFF" -e "WITH_TESTING=OFF" paddlepaddle/paddle_manylinux_devel:cuda8.0_cudnn5 bash -x paddle/scripts/docker/build.sh
# 如果不使用Docker编译环境，执行下面的命令
mkdir build
cd build
cmake -DWITH_GPU=OFF -DWITH_TESTING=OFF ..
make
编译完成后会在build/python/dist目录下生成输出的whl包，可以选在在当前机器安装也可以拷贝到目标机器安装：
.. code-block:: bash
pip install python/dist/*.whl
.. _build_step:
编译依赖
----------------
PaddlePaddle编译需要使用到下面的依赖（包含但不限于），其他的依赖软件，会自动在编译时下载。
.. csv-table:: PaddlePaddle编译依赖
:header: "依赖", "版本", "说明"
:widths: 10, 15, 30
"CMake", ">=3.5", ""
"GCC", "4.8.2", "推荐使用CentOS的devtools2"
"Python", "2.7.x", "依赖libpython2.7.so"
"pip", ">=9.0", ""
"numpy", "", ""
"SWIG", ">=2.0", ""
"Go", ">=1.8", "可选"
.. _build_options:
编译选项
----------------
PaddlePaddle的编译选项，包括生成CPU/GPU二进制文件、链接何种BLAS库等。
用户可在调用cmake的时候设置它们，详细的cmake使用方法可以参考
`官方文档 <https://cmake.org/cmake-tutorial>`_ 。
在cmake的命令行中，通过使用 ``-D`` 命令设置该类编译选项，例如：
.. code-block:: bash
cmake .. -DWITH_GPU=OFF
.. csv-table:: 编译选项说明
:header: "选项", "说明", "默认值"
:widths: 1, 7, 2
"WITH_GPU", "是否支持GPU", "ON"
"WITH_C_API", "是否仅编译CAPI", "OFF"
"WITH_DOUBLE", "是否使用双精度浮点数", "OFF"
"WITH_DSO", "是否运行时动态加载CUDA动态库，而非静态加载CUDA动态库。", "ON"
"WITH_AVX", "是否编译含有AVX指令集的PaddlePaddle二进制文件", "ON"
"WITH_PYTHON", "是否内嵌PYTHON解释器", "ON"
"WITH_STYLE_CHECK", "是否编译时进行代码风格检查", "ON"
"WITH_TESTING", "是否开启单元测试", "ON"
"WITH_DOC", "是否编译中英文文档", "OFF"
"WITH_SWIG_PY", "是否编译PYTHON的SWIG接口，该接口可用于预测和定制化训练", "Auto"
"WITH_GOLANG", "是否编译go语言的可容错parameter server", "ON"
"WITH_MKL", "是否使用MKL数学库，如果为否则是用OpenBLAS", "ON"
BLAS
+++++
PaddlePaddle支持 `MKL <https://software.intel.com/en-us/intel-mkl>`_ 和
`OpenBlAS <http://www.openblas.net/>`_ 两种BLAS库。默认使用MKL。如果使用MKL并且机器含有AVX2指令集，
还会下载MKL-DNN数学库，详细参考 `这里 <https://github.com/PaddlePaddle/Paddle/tree/develop/doc/design/mkldnn#cmake>`_ 。
如果关闭MKL，则会使用OpenBLAS作为BLAS库。
CUDA/cuDNN
+++++++++++
PaddlePaddle在编译时/运行时会自动找到系统中安装的CUDA和cuDNN库进行编译和执行。
使用参数 :code:`-DCUDA_ARCH_NAME=Auto` 可以指定开启自动检测SM架构，加速编译。
PaddlePaddle可以使用cuDNN v5.1之后的任何一个版本来编译运行，但尽量请保持编译和运行使用的cuDNN是同一个版本。
我们推荐使用最新版本的cuDNN。
编译选项的设置
++++++++++++++
PaddePaddle通过编译时指定路径来实现引用各种BLAS/CUDA/cuDNN库。cmake编译时，首先在系统路径（ :code:`/usr/lib:/usr/local/lib` ）中搜索这几个库，同时也会读取相关路径变量来进行搜索。 通过使用 ``-D`` 命令可以设置，例如
.. code-block:: bash
cmake .. -DWITH_GPU=ON -DWITH_TESTING=OFF -DCUDNN_ROOT=/opt/cudnnv5
**注意：这几个编译选项的设置，只在第一次cmake的时候有效。如果之后想要重新设置，推荐清理整个编译目录（** :code:`rm -rf` ）**后，再指定。**
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Installing from Sources
==========================
* [1. Download and Setup](#download)
* [2. Requirements](#requirements)
* [3. Build on Ubuntu](#ubuntu)
* [4. Build on Centos](#centos)
## <span id="download">Download and Setup</span>
You can download PaddlePaddle from the [github source](https://github.com/PaddlePaddle/Paddle).
```bash
git clone https://github.com/PaddlePaddle/Paddle paddle
cd paddle
```
## <span id="requirements">Requirements</span>
To compile the source code, your computer must be equipped with the following dependencies.
- **Compiler**: GCC >= 4.8 or Clang >= 3.3 (AppleClang >= 5.1) and gfortran compiler
- **CMake**: CMake >= 3.0 (at least CMake 3.4 on Mac OS X)
- **BLAS**: MKL, OpenBlas or ATLAS
- **Python**: only support Python 2.7
- **Go**
**Note:** For CUDA 7.0 and CUDA 7.5, GCC 5.0 and up are not supported!
For CUDA 8.0, GCC versions later than 5.3 are not supported!
### Options
PaddlePaddle supports some build options.
<html>
<table>
<thead>
<tr>
<th scope="col" class="left">Optional</th>
<th scope="col" class="left">Description</th>
</tr>
</thead>
<tbody>
<tr><td class="left">WITH_GPU</td><td class="left">Compile PaddlePaddle with NVIDIA GPU</td></tr>
<tr><td class="left">WITH_AVX</td><td class="left">Compile PaddlePaddle with AVX intrinsics</td></tr>
<tr><td class="left">WITH_DSO</td><td class="left">Compile PaddlePaddle with dynamic linked CUDA</td></tr>
<tr><td class="left">WITH_TESTING</td><td class="left">Compile PaddlePaddle with unit testing</td></tr>
<tr><td class="left">WITH_SWIG_PY</td><td class="left">Compile PaddlePaddle with inference api</td></tr>
<tr><td class="left">WITH_STYLE_CHECK</td><td class="left">Compile PaddlePaddle with style check</td></tr>
<tr><td class="left">WITH_PYTHON</td><td class="left">Compile PaddlePaddle with python interpreter</td></tr>
<tr><td class="left">WITH_DOUBLE</td><td class="left">Compile PaddlePaddle with double precision</td></tr>
<tr><td class="left">WITH_RDMA</td><td class="left">Compile PaddlePaddle with RDMA support</td></tr>
<tr><td class="left">WITH_TIMER</td><td class="left">Compile PaddlePaddle with stats timer</td></tr>
<tr><td class="left">WITH_PROFILER</td><td class="left">Compile PaddlePaddle with GPU profiler</td></tr>
<tr><td class="left">WITH_DOC</td><td class="left">Compile PaddlePaddle with documentation</td></tr>
<tr><td class="left">WITH_COVERAGE</td><td class="left">Compile PaddlePaddle with code coverage</td></tr>
<tr><td class="left">COVERALLS_UPLOAD</td><td class="left">Package code coverage data to coveralls</td></tr>
<tr><td class="left">ON_TRAVIS</td><td class="left">Exclude special unit test on Travis CI</td></tr>
</tbody>
</table>
</html>
**Note:**
- The GPU version works best with Cuda Toolkit 8.0 and cuDNN v5.
- Other versions like Cuda Toolkit 7.0, 7.5 and cuDNN v3, v4 are also supported.
- **To utilize cuDNN v5, Cuda Toolkit 7.5 is prerequisite and vice versa.**
As a simple example, consider the following:
1. **BLAS Dependencies(optional)**
CMake will search BLAS libraries from the system. If not found, OpenBLAS will be downloaded, built and installed automatically.
To utilize preinstalled BLAS， you can simply specify MKL, OpenBLAS or ATLAS via `MKL_ROOT`, `OPENBLAS_ROOT` or `ATLAS_ROOT`.
```bash
# specify MKL
cmake .. -DMKL_ROOT=<mkl_path>
# or specify OpenBLAS
cmake .. -DOPENBLAS_ROOT=<openblas_path>
```
2. **Doc Dependencies(optional)**
To generate PaddlePaddle's documentation, install dependencies and set `-DWITH_DOC=ON` as follows:
```bash
pip install 'sphinx>=1.4.0'
pip install sphinx_rtd_theme recommonmark
# install doxygen on Ubuntu
sudo apt-get install doxygen
# install doxygen on Mac OS X
brew install doxygen
# active docs in cmake
cmake .. -DWITH_DOC=ON`
```
## <span id="ubuntu">Build on Ubuntu 14.04</span>
### Install Dependencies
- **Paddle Dependencies**
```bash
# necessary
sudo apt-get update
sudo apt-get install -y git curl gcc g++ gfortran make build-essential automake
sudo apt-get install -y python python-pip python-numpy libpython-dev bison
sudo pip install 'protobuf==3.1.0.post1'
# Install Go
# You can follow https://golang.org/doc/install for a detailed explanation.
wget -O go.tgz https://storage.googleapis.com/golang/go1.8.1.linux-amd64.tar.gz && \
tar -C $HOME -xzf go.tgz && \
mkdir $HOME/gopath && \
rm go.tgz
# Setup environment variables
export GOROOT=$HOME/go
export GOPATH=$HOME/gopath
export PATH=$PATH:$GOROOT/bin
# install cmake 3.4
curl -sSL https://cmake.org/files/v3.4/cmake-3.4.1.tar.gz | tar -xz && \
cd cmake-3.4.1 && ./bootstrap && make -j4 && sudo make install && \
cd .. && rm -rf cmake-3.4.1
```
- **GPU Dependencies (optional)**
To build GPU version, you will need the following installed:
1. a CUDA-capable GPU
2. A supported version of Linux with a GCC compiler and toolchain
3. NVIDIA CUDA Toolkit (available at http://developer.nvidia.com/cuda-downloads)
4. NVIDIA cuDNN Library (available at https://developer.nvidia.com/cudnn)
The CUDA development environment relies on tight integration with the host development environment,
including the host compiler and C runtime libraries, and is therefore only supported on
distribution versions that have been qualified for this CUDA Toolkit release.
After downloading cuDNN library, issue the following commands:
```bash
sudo tar -xzf cudnn-7.5-linux-x64-v5.1.tgz -C /usr/local
sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn*
```
Then you need to set LD\_LIBRARY\_PATH, PATH environment variables in ~/.bashrc.
```bash
export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH
export PATH=/usr/local/cuda/bin:$PATH
```
### Build and Install
As usual, the best option is to create build folder under paddle project directory.
```bash
mkdir build && cd build
```
Finally, you can build and install PaddlePaddle:
```bash
# you can add build option here, such as:
cmake .. -DCMAKE_INSTALL_PREFIX=<path to install>
# please use sudo make install, if you want to install PaddlePaddle into the system
make -j `nproc` && make install
# set PaddlePaddle installation path in ~/.bashrc
export PATH=<path to install>/bin:$PATH
# install PaddlePaddle Python modules.
sudo pip install <path to install>/opt/paddle/share/wheels/*.whl
```
## <span id="centos">Build on Centos 7</span>
### Install Dependencies
- **CPU Dependencies**
```bash
# necessary
sudo yum update
sudo yum install -y epel-release
sudo yum install -y make cmake3 python-devel python-pip gcc-gfortran swig git
sudo pip install wheel numpy
sudo pip install 'protobuf>=3.0.0'
```
- **GPU Dependencies (optional)**
To build GPU version, you will need the following installed:
1. a CUDA-capable GPU
2. A supported version of Linux with a GCC compiler and toolchain
3. NVIDIA CUDA Toolkit (available at http://developer.nvidia.com/cuda-downloads)
4. NVIDIA cuDNN Library (available at https://developer.nvidia.com/cudnn)
The CUDA development environment relies on tight integration with the host development environment,
including the host compiler and C runtime libraries, and is therefore only supported on
distribution versions that have been qualified for this CUDA Toolkit release.
After downloading cuDNN library, issue the following commands:
```bash
sudo tar -xzf cudnn-7.5-linux-x64-v5.1.tgz -C /usr/local
sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn*
```
Then you need to set LD\_LIBRARY\_PATH, PATH environment variables in ~/.bashrc.
```bash
export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH
export PATH=/usr/local/cuda/bin:$PATH
```
### Build and Install
As usual, the best option is to create build folder under paddle project directory.
```bash
mkdir build && cd build
```
Finally, you can build and install PaddlePaddle:
```bash
# you can add build option here, such as:
cmake3 .. -DCMAKE_INSTALL_PREFIX=<path to install>
# please use sudo make install, if you want to install PaddlePaddle into the system
make -j `nproc` && make install
# set PaddlePaddle installation path in ~/.bashrc
export PATH=<path to install>/bin:$PATH
# install PaddlePaddle Python modules.
sudo pip install <path to install>/opt/paddle/share/wheels/*.whl
```
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Build PaddlePaddle from Sources
==========================
.. _build_step:
How To Build
----------------
PaddlePaddle mainly uses `CMake <https://cmake.org>`_ and GCC, G++ as compile
tools. We recommend you to use our pre-built Docker image to run the build
to avoid installing dependencies by yourself. We have several build environment
Docker images `here <https://hub.docker.com/r/paddlepaddle/paddle_manylinux_devel/tags/>`_.
Then run:
.. code-block:: bash
git clone https://github.com/PaddlePaddle/Paddle.git
cd Paddle
# run the following command to build CPU-Only binaries if you are using docker
docker run -it -v $PWD:/paddle -e "WITH_GPU=OFF" -e "WITH_TESTING=OFF" paddlepaddle/paddle_manylinux_devel:cuda8.0_cudnn5 bash -x paddle/scripts/docker/build.sh
# else run these commands
mkdir build
cd build
cmake -DWITH_GPU=OFF -DWITH_TESTING=OFF ..
make
When the compile finishes, you can get the output whl package under
build/python/dist, then you can choose to install the whl on local
machine or copy it to the target machine.
.. code-block:: bash
pip install python/dist/*.whl
.. _build_step:
Compile Dependencies
----------------
PaddlePaddle need the following dependencies when compiling, other dependencies
will be downloaded automatically.
.. csv-table:: PaddlePaddle Compile Dependencies
:header: "Dependency", "Version", "Description"
:widths: 10, 15, 30
"CMake", ">=3.5", ""
"GCC", "4.8.2", "Recommend devtools2 for CentOS"
"Python", "2.7.x", "Need libpython2.7.so"
"pip", ">=9.0", ""
"numpy", "", ""
"SWIG", ">=2.0", ""
"Go", ">=1.8", "Optional"
.. _build_options:
Build Options
----------------
Build options include whether build binaries for CPU or GPU, which BLAS
library to use etc. You may pass these settings when running cmake.
For detailed cmake tutorial please refer to `here <https://cmake.org/cmake-tutorial>`_ 。
.. _build_options_bool:
Bool Type Options
----------------
You can add :code:`-D` argument to pass such options, like:
.. code-block:: bash
cmake .. -DWITH_GPU=OFF
.. csv-table:: Bool Type Options
:header: "Option", "Description", "Default"
:widths: 1, 7, 2
"WITH_GPU", "Build with GPU support", "ON"
"WITH_C_API", "Build only CAPI", "OFF"
"WITH_DOUBLE", "Build with double precision", "OFF"
"WITH_DSO", "Dynamically load CUDA libraries", "ON"
"WITH_AVX", "Build with AVX support", "ON"
"WITH_PYTHON", "Build with integrated Python interpreter", "ON"
"WITH_STYLE_CHECK", "Check code style when building", "ON"
"WITH_TESTING", "Build unit tests", "ON"
"WITH_DOC", "Build documentaions", "OFF"
"WITH_SWIG_PY", "Build Python SWIG interface for V2 API", "Auto"
"WITH_GOLANG", "Build fault-tolerant parameter server written in go", "ON"
"WITH_MKL", "Use MKL as BLAS library, else use OpenBLAS", "ON"
BLAS
+++++
PaddlePaddle supports `MKL <https://software.intel.com/en-us/intel-mkl>`_ and
`OpenBlAS <http://www.openblas.net/>`_ as BLAS library。By default it uses MKL.
If you are using MKL and your machine supports AVX2, MKL-DNN will also be downloaded
and used, for more `details <https://github.com/PaddlePaddle/Paddle/tree/develop/doc/design/mkldnn#cmake>`_ .
If you choose not to use MKL, then OpenBlAS will be used.
CUDA/cuDNN
+++++++++++
PaddlePaddle will automatically find CUDA and cuDNN when compiling and running.
parameter :code:`-DCUDA_ARCH_NAME=Auto` can be used to detect SM architecture
automatically in order to speed up the build.
PaddlePaddle can build with any version later than cuDNN v5.1, and we intend to
keep on with latest cuDNN versions. Be sure to run with the same version of cuDNN
you built.
Pass Compile Options
++++++++++++++
You can pass compile options to use intended BLAS/CUDA/Cudnn libraries.
When running cmake command, it will search system paths like
:code:`/usr/lib:/usr/local/lib` and then search paths that you
passed to cmake, i.e.
.. code-block:: bash
cmake .. -DWITH_GPU=ON -DWITH_TESTING=OFF -DCUDNN_ROOT=/opt/cudnnv5
**NOTE: These options only take effect when running cmake for the first time, you need to clean the cmake cache or clean the build directory (** :code:`rm -rf` **) if you want to change it.**
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PaddlePaddle的编译选项
======================
PaddlePaddle的编译选项，包括生成CPU/GPU二进制文件、链接何种BLAS库等。用户可在调用cmake的时候设置它们，详细的cmake使用方法可以参考 `官方文档 <https://cmake.org/cmake-tutorial>`_ 。
Bool型的编译选项
----------------
用户可在cmake的命令行中，通过使用 ``-D`` 命令设置该类编译选项，例如
.. code-block:: bash
cmake .. -DWITH_GPU=OFF
.. csv-table:: Bool型的编译选项
:widths: 1, 7, 2
:file: compile_options.csv
BLAS/CUDA/Cudnn的编译选项
--------------------------
BLAS
+++++
PaddlePaddle支持以下任意一种BLAS库：`MKL <https://software.intel.com/en-us/intel-mkl>`_ ，`ATLAS <http://math-atlas.sourceforge.net/>`_ ，`OpenBlAS <http://www.openblas.net/>`_ 和 `REFERENCE BLAS <http://www.netlib.org/blas/>`_ 。
.. csv-table:: BLAS路径相关的编译选项
:widths: 1, 2, 7
:file: cblas_settings.csv
CUDA/Cudnn
+++++++++++
PaddlePaddle可以使用cudnn v2之后的任何一个版本来编译运行，但尽量请保持编译和运行使用的cudnn是同一个版本。 我们推荐使用最新版本的cudnn v5.1。
编译选项的设置
++++++++++++++
PaddePaddle通过编译时指定路径来实现引用各种BLAS/CUDA/Cudnn库。cmake编译时，首先在系统路径(/usr/lib\:/usr/local/lib)中搜索这几个库，同时也会读取相关路径变量来进行搜索。 通过使用 ``-D`` 命令可以设置，例如
.. code-block:: bash
cmake .. -DMKL_ROOT=/opt/mkl/ -DCUDNN_ROOT=/opt/cudnnv5
注意：这几个编译选项的设置，只在第一次cmake的时候有效。如果之后想要重新设置，推荐清理整个编译目录（``rm -rf``）后，再指定。
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编译选项,描述,注意
MKL_ROOT,MKL的路径,${MKL_ROOT}/include下需要包含mkl.h，${MKL_ROOT}/lib目录下需要包含mkl_core，mkl_sequential和mkl_intel_lp64三个库。
ATLAS_ROOT,ATLAS的路径,${ATLAS_ROOT}/include下需要包含cblas.h，${ATLAS_ROOT}/lib下需要包含cblas和atlas两个库。
OPENBLAS_ROOT,OpenBLAS的路径,${OPENBLAS_ROOT}/include下需要包含cblas.h，${OPENBLAS_ROOT}/lib下需要包含openblas库。
REFERENCE_CBLAS_ROOT,REFERENCE BLAS的路径,${REFERENCE_CBLAS_ROOT}/include下需要包含cblas.h，${REFERENCE_CBLAS_ROOT}/lib下需要包含cblas库。
\ No newline at end of file
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选项,说明,默认值
WITH_GPU,是否支持GPU。,取决于是否寻找到CUDA工具链
WITH_DOUBLE,是否使用双精度浮点数。,否
WITH_DSO,是否运行时动态加载CUDA动态库，而非静态加载CUDA动态库。,是
WITH_AVX,是否编译含有AVX指令集的PaddlePaddle二进制文件,是
WITH_PYTHON,是否内嵌PYTHON解释器。方便今后的嵌入式移植工作。,是
WITH_STYLE_CHECK,是否编译时进行代码风格检查,是
WITH_RDMA,是否开启RDMA,否
WITH_TIMER,是否开启计时功能。如果开启会导致运行略慢，打印的日志变多，但是方便调试和测Benchmark,否
WITH_TESTING,是否开启单元测试,取决于是否寻找到GTEST
WITH_DOC,是否编译中英文文档,否
WITH_SWIG_PY,是否编译PYTHON的SWIG接口，该接口可用于预测和定制化训练,取决于是否寻找到SWIG
\ No newline at end of file
doc/getstarted/build_and_install/docker_install_cn.rst
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PaddlePaddle的Docker容器使用方式
使用Docker安装运行PaddlePaddle
================================
PaddlePaddle目前唯一官方支持的运行的方式是Docker容器。因为Docker能在所有主要操作系统（包括Linux，Mac OS X和Windows）上运行。 请注意，您需要更改 `Dockers设置 <https://github.com/PaddlePaddle/Paddle/issues/627>`_ 才能充分利用Mac OS X和Windows上的硬件资源。
使用Docker安装和运行PaddlePaddle可以无需考虑依赖环境即可运行。并且也可以在Windows的docker中运行。
您可以在 `Docker官网 <https://docs.docker.com/get-started/>`_ 获得基本的Docker安装和使用方法。
Docker使用入门
------------------------------
几个基础的概念帮助理解和使用Docker：
如果您在使用Windows，可以参考
`这篇 <https://docs.docker.com/toolbox/toolbox_install_windows/>`_
教程，完成在Windows上安装和使用Docker。
- *镜像*：一个Docker镜像是一个打包好的软件。它包含了这个软件本身和它所依赖的运行环境。PaddlePaddle的Docker镜像就包含了PaddlePaddle的Python库以及其依赖的多个Python库。这样我们可以直接在Docker中运行需要的程序而不需要安装后在执行。可以执行：
在了解Docker的基本使用方法之后，即可开始下面的步骤：
.. code-block:: bash
.. _docker_pull:
docker images
获取PaddlePaddle的Docker镜像
------------------------------
来列出当前系统中的所有镜像，同样可以执行：
执行下面的命令获取最新的PaddlePaddle Docker镜像
.. code-block:: bash
docker pull paddlepaddle/paddle:0.10.0
来下载Docker镜像，paddlepaddle/paddle是从官方镜像源Dockerhub.com下载的，推荐国内用户使用docker.paddlepaddle.org/paddle下载。
docker pull paddlepaddle/paddle
- *容器*： 如果说一个Docker镜像就是一个程序，那容器就是这个程序运行时产生的“进程”。
实际上，一个容器就是一个操作系统的进程，但是是运行在独立的进程空间，文件系统以及网络之上。
可以执行：
对于国内用户，我们提供了加速访问的镜像源：
.. code-block:: bash
docker run paddlepaddle/paddle:0.10.0
docker pull docker.paddlepaddle.org/paddle
来使用一个镜像启动一个容器。
- 默认情况下，Docker容器会运行在独立的文件系统空间之上，我们无法在Docker容器中
访问到主机上的文件。可以通过*挂载Volume*的方式，将主机上的文件或目录挂载到
Docker容器中。下面的命令把当前目录挂载到了容器中的 /data 目录下，容器使用
debian镜像，并且启动后执行 :code:`ls /data`。
下载GPU版本的Docker镜像：
.. code-block:: bash
docker run --rm -v $(pwd):/data debian ls /data
PaddlePaddle发布的Docker镜像使用说明
------------------------------
我们把PaddlePaddle的编译环境打包成一个镜像，称为开发镜像，里面涵盖了
PaddlePaddle需要的所有编译工具。把编译出来的PaddlePaddle也打包成一个镜
像，称为生产镜像，里面涵盖了PaddlePaddle运行所需的所有环境。每次
PaddlePaddle发布新版本的时候都会发布对应版本的生产镜像以及开发镜像。运
行镜像包括纯CPU版本和GPU版本以及其对应的非AVX版本。我们会在
`dockerhub.com <https://hub.docker.com/r/paddlepaddle/paddle/tags/>`_
和国内镜像`docker.paddlepaddle.org` 提供最新
的Docker镜像，可以在"tags"标签下找到最新的Paddle镜像版本。
**注意：为了方便在国内的开发者下载Docker镜像，我们提供了国内的镜像服务器供大家使用。如果您在国内，请把文档里命令中的paddlepaddle/paddle替换成docker.paddlepaddle.org/paddle。**
1. 开发镜像：:code:`paddlepaddle/paddle:0.10.0-dev`
这个镜像包含了Paddle相关的开发工具以及编译和运行环境。用户可以使用开发镜像代替配置本地环境，完成开发，编译，发布，
文档编写等工作。由于不同的Paddle的版本可能需要不同的依赖和工具，所以如果需要自行配置开发环境需要考虑版本的因素。
开发镜像包含了以下工具：
- gcc/clang
- nvcc
- Python
- sphinx
- woboq
- sshd
很多开发者会使用远程的安装有GPU的服务器工作，用户可以使用ssh登录到这台服务器上并执行 :code:`docker exec`进入开发镜像并开始工作，
也可以在开发镜像中启动一个SSHD服务，方便开发者直接登录到镜像中进行开发:
以交互容器方式运行开发镜像：
.. code-block:: bash
docker run -it --rm -v $(pwd):/paddle paddlepaddle/paddle:0.10.0-dev /bin/bash
或者，可以以后台进程方式运行容器：
.. code-block:: bash
docker run -d -p 2202:22 -p 8888:8888 -v $(pwd):/paddle paddlepaddle/paddle:0.10.0-dev /usr/sbin/sshd -D
然后用密码 :code:`root` SSH进入容器：
.. code-block:: bash
ssh -p 2202 root@localhost
SSH方式的一个优点是我们可以从多个终端进入容器。比如，一个终端运行vi，另一个终端运行Python。另一个好处是我们可以把PaddlePaddle容器运行在远程服务器上，并在笔记本上通过SSH与其连接。
2. 生产镜像：根据CPU、GPU和非AVX区分了如下4个镜像：
- GPU/AVX：:code:`paddlepaddle/paddle:<version>-gpu`
- GPU/no-AVX：:code:`paddlepaddle/paddle:<version>-gpu-noavx`
- CPU/AVX：:code:`paddlepaddle/paddle:<version>`
- CPU/no-AVX：:code:`paddlepaddle/paddle:<version>-noavx`
纯CPU镜像以及GPU镜像都会用到AVX指令集，但是2008年之前生产的旧电脑不支持AVX。以下指令能检查Linux电脑是否支持AVX：
.. code-block:: bash
if cat /proc/cpuinfo | grep -i avx; then echo Yes; else echo No; fi
如果输出是No，就需要选择使用no-AVX的镜像
**注：在0.10.0之后的版本，PaddlePaddle都可以自动判断硬件是否支持AVX，所以无需判断AVX即可使用**
docker pull paddlepaddle/paddle:latest-gpu
docker pull docker.paddlepaddle.org/paddle:latest-gpu
以上方法在GPU镜像里也能用，只是请不要忘记提前在物理机上安装GPU最新驱动。
为了保证GPU驱动能够在镜像里面正常运行，我们推荐使用[nvidia-docker](https://github.com/NVIDIA/nvidia-docker)来运行镜像。
选择下载使用不同的BLAS库的Docker镜像：
.. code-block:: bash
nvidia-docker run -it --rm paddledev/paddle:0.10.0-gpu /bin/bash
.. code-block:: bash
注意: 如果使用nvidia-docker存在问题，你也许可以尝试更老的方法，具体如下，但是我们并不推荐这种方法。：
# 默认是使用MKL的镜像
docker pull paddlepaddle/paddle
# 使用OpenBLAS的镜像
docker pull paddlepaddle/paddle:latest-openblas
.. code-block:: bash
下载指定版本的Docker镜像，可以从 `DockerHub网站 <https://hub.docker.com/r/paddlepaddle/paddle/tags/>`_ 获取可选的tag，并执行下面的命令：
export CUDA_SO="$(\ls /usr/lib64/libcuda* | xargs -I{} echo '-v {}:{}') $(\ls /usr/lib64/libnvidia* | xargs -I{} echo '-v {}:{}')"
export DEVICES=$(\ls /dev/nvidia* | xargs -I{} echo '--device {}:{}')
docker run ${CUDA_SO} ${DEVICES} -it paddledev/paddle:0.10.0-gpu
.. code-block:: bash
3. 运行以及发布您的AI程序
docker pull paddlepaddle/paddle:[tag]
# 比如：
docker pull docker.paddlepaddle.org/paddle:0.10.0-gpu
假设您已经完成了一个AI训练的python程序 :code:`a.py`，这个程序是您在开发机上使用开发镜像完成开发。此时您可以运行这个命令在开发机上进行测试运行：
.. _docker_run:
.. code-block:: bash
在Docker中执行PaddlePaddle训练程序
------------------------------
docker run -it -v $PWD:/work paddle /work/a.py
假设您已经在当前目录（比如在/home/work）编写了一个PaddlePaddle的程序 :code:`train.py` （可以参考
`PaddlePaddleBook <http://www.paddlepaddle.org/docs/develop/book/01.fit_a_line/index.cn.html>`_
编写），就可以使用下面的命令开始执行训练：
如果要使用GPU，请运行：
.. code-block:: bash
.. code-block:: bash
cd /home/work
docker run -it -v $PWD:/work paddlepaddle/paddle /work/train.py
上述命令中， :code:`-it` 参数说明容器已交互式运行； :code:`-v $PWD:/work`
指定将当前路径（Linux中$PWD变量会展开为当前路径的绝对路径）挂载到容器内部的 :code:`/work`
目录； :code:`paddlepaddle/paddle` 指定需要使用的容器； 最后 :code:`/work/train.py`
为容器内执行的命令，即运行训练程序。
nvidia-docker run -it -v $PWD:/work paddle /work/a.py
当然，您也可以进入到Docker容器中，以交互式的方式执行或调试您的代码：
.. code-block:: bash
docker run -it -v $PWD:/work paddlepaddle/paddle /bin/bash
cd /work
python train.py
这里`a.py`包含的所有依赖假设都可以在Paddle的运行容器中。如果需要包含更多的依赖、或者需要发布您的应用的镜像，可以编写`Dockerfile`使用`FROM paddledev/paddle:0.10.0`
创建和发布自己的AI程序镜像。
**注：PaddlePaddle Docker镜像为了减小体积，默认没有安装vim，您可以在容器中执行** :code:`apt-get install -y vim` **安装后，在容器中编辑代码。**
运行PaddlePaddle Book
---------------------
.. _docker_run_book:
Jupyter Notebook是一个开源的web程序，大家可以通过它制作和分享带有代码、公式、图表、文字的交互式文档。用户可以通过网页浏览文档。
使用Docker启动PaddlePaddle Book教程
------------------------------
使用Docker可以快速在本地启动一个包含了PaddlePaddle官方Book教程的Jupyter Notebook，可以通过网页浏览。
PaddlePaddle Book是为用户和开发者制作的一个交互式的Jupyter Notebook。
如果您想要更深入了解deep learning，PaddlePaddle Book一定是您最好的选择。
大家可以通过它阅读教程，或者制作和分享带有代码、公式、图表、文字的交互式文档。
我们提供可以直接运行PaddlePaddle Book的Docker镜像，直接运行：
.. code-block:: bash
.. code-block:: bash
docker run -p 8888:8888 paddlepaddle/book
docker run -p 8888:8888 paddlepaddle/book
然后在浏览器中输入以下网址：
.. code-block:: text
.. code-block:: text
http://localhost:8888/
http://localhost:8888/
就这么简单，享受您的旅程！
通过Docker容器开发PaddlePaddle
------------------------------
开发人员可以在Docker开发镜像中开发PaddlePaddle。这样开发人员可以以一致的方式在不同的平台上工作 - Linux，Mac OS X和Windows。
.. _docker_run_gpu:
1. 制作PaddlePaddle开发镜像
PaddlePaddle每次发布新版本都会发布对应的开发镜像供开发者直接使用。这里介绍如生成造这个开发镜像。
生成Docker镜像的方式有两个，一个是直接把一个容器转换成镜像，另一个是创建Dockerfile并运行docker build指令按照Dockerfile生成镜像。第一个方法的好处是简单快捷，适合自己实验，可以快速迭代。第二个方法的好处是Dockerfile可以把整个生成流程描述很清楚，其他人很容易看懂镜像生成过程，持续集成系统也可以简单地复现这个过程。我们采用第二个方法。Dockerfile位于PaddlePaddle repo的根目录。生成生产镜像只需要运行：
.. code-block:: bash
git clone https://github.com/PaddlePaddle/Paddle.git
cd Paddle
docker build -t paddle:dev .
docker build这个命令的-t指定了生成的镜像的名字，这里我们用paddle:dev。到此，PaddlePaddle开发镜像就被构建完毕了。
使用Docker执行GPU训练
------------------------------
2. 制作PaddlePaddle生产镜像
为了保证GPU驱动能够在镜像里面正常运行，我们推荐使用
`nvidia-docker <https://github.com/NVIDIA/nvidia-docker>`_ 来运行镜像。
请不要忘记提前在物理机上安装GPU最新驱动。
生产镜像的生成分为两步，第一步是运行：
.. code-block:: bash
.. code-block:: bash
docker run -v $(pwd):/paddle -e "WITH_GPU=OFF" -e "WITH_AVX=OFF" -e "WITH_TEST=ON" paddle:dev
nvidia-docker run -it -v $PWD:/work paddledev/paddle:latest-gpu /bin/bash
以上命令会编译PaddlePaddle，生成运行程序，以及生成创建生产镜像的Dockerfile。所有生成的的文件都在build目录下。“WITH_GPU”控制生成的生产镜像是否支持GPU，“WITH_AVX”控制生成的生产镜像是否支持AVX，”WITH_TEST“控制是否生成单元测试。
**注: 如果没有安装nvidia-docker，可以尝试以下的方法，将CUDA库和Linux设备挂载到Docker容器内：**
第二步是运行：
.. code-block:: bash
.. code-block:: bash
docker build -t paddle:prod -f build/Dockerfile ./build
export CUDA_SO="$(\ls /usr/lib64/libcuda* | xargs -I{} echo '-v {}:{}') $(\ls /usr/lib64/libnvidia* | xargs -I{} echo '-v {}:{}')"
export DEVICES=$(\ls /dev/nvidia* | xargs -I{} echo '--device {}:{}')
docker run ${CUDA_SO} ${DEVICES} -it paddledev/paddle:latest-gpu
以上命令会按照生成的Dockerfile把生成的程序拷贝到生产镜像中并做相应的配置，最终生成名为paddle:prod的生产镜像。
**关于AVX：**
3. 运行单元测试
AVX是一种CPU指令集，可以加速PaddlePaddle的计算。最新的PaddlePaddle Docker镜像默认
是开启AVX编译的，所以，如果您的电脑不支持AVX，需要单独
`编译 <./build_from_source_cn.rst>`_ PaddlePaddle为no-avx版本。
运行以下指令：
以下指令能检查Linux电脑是否支持AVX：
.. code-block:: bash
docker run -it -v $(pwd):/paddle paddle:dev bash -c "cd /paddle/build && ctest"
文档
----
Paddle的Docker开发镜像带有一个通过 `woboq code browser
<https://github.com/woboq/woboq_codebrowser>`_ 生成的HTML版本的C++源代码，便于用户浏览C++源码。
只要在Docker里启动PaddlePaddle的时候给它一个名字，就可以再运行另一个Nginx Docker镜像来服务HTML代码：
.. code-block:: bash
docker run -d --name paddle-cpu-doc paddle:0.10.0-dev
docker run -d --volumes-from paddle-cpu-doc -p 8088:80 nginx
if cat /proc/cpuinfo | grep -i avx; then echo Yes; else echo No; fi
接着我们就能够打开浏览器在 http://localhost:8088/paddle/ 浏览代码。
如果输出是No，就需要选择使用no-AVX的镜像
doc/getstarted/build_and_install/docker_install_en.rst
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PaddlePaddle in Docker Containers
=================================
Docker container is currently the only officially-supported way to
running PaddlePaddle. This is reasonable as Docker now runs on all
major operating systems including Linux, Mac OS X, and Windows.
Please be aware that you will need to change `Dockers settings
<https://github.com/PaddlePaddle/Paddle/issues/627>`_ to make full use
of your hardware resource on Mac OS X and Windows.
Run PaddlePaddle in Docker container so that you don't need to care about
runtime dependencies, also you can run under Windows system. You can get
tutorials at `here <https://docs.docker.com/get-started/>`_ .
Working With Docker
-------------------
If you are using Windows, please refer to
`this <https://docs.docker.com/toolbox/toolbox_install_windows/>`_
tutorial to start running docker under windows.
Docker is simple as long as we understand a few basic concepts:
After you've read above tutorials you may proceed the following steps.
- *image*: A Docker image is a pack of software. It could contain one or more programs and all their dependencies. For example, the PaddlePaddle's Docker image includes pre-built PaddlePaddle and Python and many Python packages. We can run a Docker image directly, other than installing all these software. We can type
.. _docker_pull:
.. code-block:: bash
docker images
Pull PaddlePaddle Docker Image
------------------------------
to list all images in the system. We can also run
Run the following command to download the latest Docker images:
.. code-block:: bash
docker pull paddlepaddle/paddle:0.10.0
to download a Docker image, paddlepaddle/paddle in this example,
from Dockerhub.com.
docker pull paddlepaddle/paddle
- *container*: considering a Docker image a program, a container is a
"process" that runs the image. Indeed, a container is exactly an
operating system process, but with a virtualized filesystem, network
port space, and other virtualized environment. We can type
For users in China, we provide a faster mirror:
.. code-block:: bash
docker run paddlepaddle/paddle:0.10.0
docker pull docker.paddlepaddle.org/paddle
to start a container to run a Docker image, paddlepaddle/paddle in this example.
- By default docker container have an isolated file system namespace,
we can not see the files in the host file system. By using *volume*,
mounted files in host will be visible inside docker container.
Following command will mount current dirctory into /data inside
docker container, run docker container from debian image with
command :code:`ls /data`.
Download GPU version images:
.. code-block:: bash
docker run --rm -v $(pwd):/data debian ls /data
Usage of CPU-only and GPU Images
----------------------------------
We package PaddlePaddle's compile environment into a Docker image,
called the develop image, it contains all compiling tools that
PaddlePaddle needs. We package compiled PaddlePaddle program into a
Docker image as well, called the production image, it contains all
runtime environment that running PaddlePaddle needs. For each version
of PaddlePaddle, we release both of them. Production image includes
CPU-only version and a CUDA GPU version and their no-AVX versions.
We put the docker images on `dockerhub.com
<https://hub.docker.com/r/paddlepaddle/paddle/tags/>`_. You can find the
latest versions under "tags" tab at dockerhub.com.
** NOTE: If you are in China, you can use our Docker image registry mirror to speed up the download process. To use it, please replace all paddlepaddle/paddle in the commands to docker.paddlepaddle.org/paddle.**
1. development image :code:`paddlepaddle/paddle:<version>-dev`
This image has packed related develop tools and runtime
environment. Users and developers can use this image instead of
their own local computer to accomplish development, build,
releasing, document writing etc. While different version of paddle
may depends on different version of libraries and tools, if you
want to setup a local environment, you must pay attention to the
versions. The development image contains:
- gcc/clang
- nvcc
- Python
- sphinx
- woboq
- sshd
Many developers use servers with GPUs, they can use ssh to login to
the server and run :code:`docker exec` to enter the docker
container and start their work. Also they can start a development
docker image with SSHD service, so they can login to the container
and start work.
2. Production images, this image might have multiple variants:
- GPU/AVX：:code:`paddlepaddle/paddle:<version>-gpu`
- GPU/no-AVX：:code:`paddlepaddle/paddle:<version>-gpu-noavx`
- CPU/AVX：:code:`paddlepaddle/paddle:<version>`
- CPU/no-AVX：:code:`paddlepaddle/paddle:<version>-noavx`
Please be aware that the CPU-only and the GPU images both use the
AVX instruction set, but old computers produced before 2008 do not
support AVX. The following command checks if your Linux computer
supports AVX:
.. code-block:: bash
if cat /proc/cpuinfo | grep -i avx; then echo Yes; else echo No; fi
**NOTE：versions after 0.10.0 will automatically detect system AVX support, so manual detect is not needed in this case.**
To run the CPU-only image as an interactive container:
.. code-block:: bash
docker run -it --rm paddlepaddle/paddle:0.10.0 /bin/bash
Above method work with the GPU image too -- the recommended way is
using `nvidia-docker <https://github.com/NVIDIA/nvidia-docker>`_.
Please install nvidia-docker first following this `tutorial
<https://github.com/NVIDIA/nvidia-docker#quick-start>`_.
Now you can run a GPU image:
.. code-block:: bash
nvidia-docker run -it --rm paddlepaddle/paddle:0.10.0-gpu /bin/bash
Train Model Using Python API
----------------------------
Our official docker image provides a runtime for PaddlePaddle
programs. The typical workflow will be as follows:
Create a directory as workspace:
.. code-block:: bash
mkdir ~/workspace
Edit a PaddlePaddle python program using your favourite editor
.. code-block:: bash
emacs ~/workspace/example.py
Run the program using docker:
.. code-block:: bash
docker run --rm -v ~/workspace:/workspace paddlepaddle/paddle:0.10.0 python /workspace/example.py
Or if you are using GPU for training:
docker pull paddlepaddle/paddle:latest-gpu
docker pull docker.paddlepaddle.org/paddle:latest-gpu
.. code-block:: bash
Choose between different BLAS version:
nvidia-docker run --rm -v ~/workspace:/workspace paddlepaddle/paddle:0.10.0-gpu python /workspace/example.py
Above commands will start a docker container by running :code:`python
/workspace/example.py`. It will stop once :code:`python
/workspace/example.py` finishes.
Another way is to tell docker to start a :code:`/bin/bash` session and
run PaddlePaddle program interactively:
.. code-block:: bash
docker run -it -v ~/workspace:/workspace paddlepaddle/paddle:0.10.0 /bin/bash
# now we are inside docker container
cd /workspace
python example.py
Running with GPU is identical:
.. code-block:: bash
nvidia-docker run -it -v ~/workspace:/workspace paddlepaddle/paddle:0.10.0-gpu /bin/bash
# now we are inside docker container
cd /workspace
python example.py
Develop PaddlePaddle or Train Model Using C++ API
---------------------------------------------------
We will be using PaddlePaddle development image since it contains all
compiling tools and dependencies.
.. code-block:: bash
1. Build PaddlePaddle develop image
# image using MKL by default
docker pull paddlepaddle/paddle
# image using OpenBLAS
docker pull paddlepaddle/paddle:latest-openblas
Use following command to build PaddlePaddle develop image:
.. code-block:: bash
If you want to use legacy versions, choose a tag from
`DockerHub <https://hub.docker.com/r/paddlepaddle/paddle/tags/>`_
and run:
git clone https://github.com/PaddlePaddle/Paddle.git && cd Paddle
docker build -t paddle:dev .
2. Build PaddlePaddle production image
.. code-block:: bash
There are two steps for building production image, the first step is to run:
docker pull paddlepaddle/paddle:[tag]
# i.e.
docker pull docker.paddlepaddle.org/paddle:0.10.0-gpu
.. code-block:: bash
.. _docker_run:
docker run -v $(pwd):/paddle -e "WITH_GPU=OFF" -e "WITH_AVX=OFF" -e "WITH_TEST=ON" paddle:dev
Launch your training program in Docker
------------------------------
The above command will compile PaddlePaddle and create a Dockerfile for building production image. All the generated files are in the build directory. "WITH_GPU" controls if the generated production image supports GPU. "WITH_AVX" controls if the generated production image supports AVX. "WITH_TEST" controls if the unit test will be generated.
Assume that you have already written a PaddlePaddle program
named :code:`train.py` under directory :code:`/home/work` (refer to
`PaddlePaddleBook <http://www.paddlepaddle.org/docs/develop/book/01.fit_a_line/index.cn.html>`_
for more samples), then run the following command:
The second step is to run:
.. code-block:: bash
.. code-block:: bash
cd /home/work
docker run -it -v $PWD:/work paddlepaddle/paddle /work/train.py
docker build -t paddle:prod -f build/Dockerfile ./build
In the above command, :code:`-it` means run the container interactively;
:code:`-v $PWD:/work` means mount the current directory ($PWD will expand
to current absolute path in Linux) under :code:`/work` in the container.
:code:`paddlepaddle/paddle` to specify image to use; finnally
:code:`/work/train.py` is the command to run inside docker.
The above command will generate the production image by copying the compiled PaddlePaddle program into the image.
Also, you can go into the container shell, run or debug your code
interactively:
3. Run unit test
.. code-block:: bash
docker run -it -v $PWD:/work paddlepaddle/paddle /bin/bash
cd /work
python train.py
Following command will run unit test:
**NOTE: We did not install vim in the default docker image to reduce the image size, you can run** :code:`apt-get install -y vim` **to install it if you need to edit python files.**
.. code-block:: bash
docker run -it -v $(pwd):/paddle paddle:dev bash -c "cd /paddle/build && ctest"
.. _docker_run_book:
PaddlePaddle Book
------------------
The Jupyter Notebook is an open-source web application that allows
you to create and share documents that contain live code, equations,
visualizations and explanatory text in a single browser.
PaddlePaddle Book is an interactive Jupyter Notebook for users and developers.
We already exposed port 8888 for this book. If you want to
You can create a container serving PaddlePaddle Book using Jupyter Notebook in
one minute using Docker. PaddlePaddle Book is an interactive Jupyter Notebook
for users and developers.If you want to
dig deeper into deep learning, PaddlePaddle Book definitely is your best choice.
We provide a packaged book image, simply issue the command:
.. code-block:: bash
.. code-block:: bash
docker run -p 8888:8888 paddlepaddle/book
docker run -p 8888:8888 paddlepaddle/book
Then, you would back and paste the address into the local browser:
.. code-block:: text
.. code-block:: text
http://localhost:8888/
http://localhost:8888/
That's all. Enjoy your journey!
.. _docker_run_gpu:
Documentation
-------------
Train with Docker with GPU
------------------------------
Paddle Docker images include an HTML version of C++ source code
generated using `woboq code browser
<https://github.com/woboq/woboq_codebrowser>`_. This makes it easy
for users to browse and understand the C++ source code.
We recommend using
`nvidia-docker <https://github.com/NVIDIA/nvidia-docker>`_
to run GPU training jobs. Please ensure you have latest
GPU driver installed before move on.
As long as we give the Paddle Docker container a name, we can run an
additional Nginx Docker container to serve the volume from the Paddle
container:
.. code-block:: bash
.. code-block:: bash
nvidia-docker run -it -v $PWD:/work paddledev/paddle:latest-gpu /bin/bash
docker run -d --name paddle-cpu-doc paddle:<version>
docker run -d --volumes-from paddle-cpu-doc -p 8088:80 nginx
**NOTE: If you don't have nvidia-docker installed, try the following method to mount CUDA libs and devices into the container.**
.. code-block:: bash
Then we can direct our Web browser to the HTML version of source code
at http://localhost:8088/paddle/
export CUDA_SO="$(\ls /usr/lib64/libcuda* | xargs -I{} echo '-v {}:{}') $(\ls /usr/lib64/libnvidia* | xargs -I{} echo '-v {}:{}')"
export DEVICES=$(\ls /dev/nvidia* | xargs -I{} echo '--device {}:{}')
docker run ${CUDA_SO} ${DEVICES} -it paddledev/paddle:latest-gpu
**About AVX:**
AVX is a kind of CPU instruction can accelerate PaddlePaddle's calculations.
The latest PaddlePaddle Docker image turns AVX on by default, so, if your
computer doesn't support AVX, you'll probably need to
`build <./build_from_source_en.rst>`_ with :code:`WITH_AVX=OFF`.
The following command will tell you whether your computer supports AVX.
.. code-block:: bash
if cat /proc/cpuinfo | grep -i avx; then echo Yes; else echo No; fi
doc/getstarted/build_and_install/index_cn.rst
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......@@ -6,12 +6,13 @@
安装流程
++++++++
PaddlePaddle提供Docker镜像来部署环境。
PaddlePaddle提供pip和Docker的安装方式：
.. toctree::
:maxdepth: 1
docker_install_cn.rst
pip_install_cn.rst
docker_install_cn.rst
编译流程
......@@ -19,9 +20,14 @@ PaddlePaddle提供Docker镜像来部署环境。
.. warning::
编译流程主要推荐高级用户查看，普通用户请走安装流程。
建议直接使用上述安装流程，方便快速安装。只有在遇到需要独立定制的二进制时才需要编译。
.. toctree::
:maxdepth: 1
cmake/build_from_source_cn.rst
build_from_source_cn.rst
常见问题解答
++++++++++
`常见问题解答 <http://www.paddlepaddle.org/docs/develop/documentation/zh/faq/build_and_install/index_cn.html>`_
doc/getstarted/build_and_install/index_en.rst
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Install and Build
=================
Install PaddlePaddle
----------------------
.. _install_steps:
.. toctree::
:maxdepth: 1
Install Steps
++++++++
You can choose either pip or Docker to complete your install:
.. toctree::
:maxdepth: 1
pip_install_en.rst
docker_install_en.rst
docker_install_en.rst
Build from Source
-----------------
.. warning::
Please use :code:`docker` image to install paddle. The building guide is used for hacking or contributing PaddlePaddle source code.
We recommend to directly install via above installation steps, you'll only need to build PaddlePaddle from source when you need a modifed binary.
.. toctree::
:maxdepth: 1
build_from_source_en.md
FAQ
++++++++++
`FAQ <http://www.paddlepaddle.org/docs/develop/documentation/zh/faq/build_and_install/index_en.html>`_
doc/getstarted/build_and_install/pip_install_cn.rst 0 → 100644
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使用pip安装PaddlePaddle
================================
PaddlePaddle可以使用常用的Python包管理工具
`pip <https://pip.pypa.io/en/stable/installing/>`_
完成安装，并可以在大多数主流的Linux操作系统以及MacOS上执行。
.. _pip_install:
使用pip安装
------------------------------
执行下面的命令即可在当前机器上安装PaddlePaddle的运行时环境，并自动下载安装依赖软件。
.. code-block:: bash
pip install paddlepaddle
如果需要安装支持GPU的版本，需要执行：
.. code-block:: bash
pip install paddlepaddle-gpu
如果需要获取并安装最新的（开发分支）PaddlePaddle，可以从我们的CI系统中下载最新的whl安装包和c-api开发包并安装，
您可以从下面的表格中找到需要的版本：
如果在点击下面链接时出现如下登陆界面，点击“Log in as guest”即可开始下载：
.. image:: paddleci.png
:scale: 50 %
:align: center
.. csv-table:: 各个版本最新的whl包
:header: "版本说明", "cp27-cp27mu", "cp27-cp27mu", "C-API"
:widths: 1, 3, 3, 3
"cpu_avx_mkl", "`paddlepaddle-0.10.0-cp27-cp27mu-linux_x86_64.whl <http://guest@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxCp27cp27mu/.lastSuccessful/paddlepaddle-0.10.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle-0.10.0-cp27-cp27m-linux_x86_64.whl <http://guest@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxCp27cp27mu/.lastSuccessful/paddlepaddle-0.10.0-cp27-cp27m-linux_x86_64.whl>`_", "`paddle.tgz <http://guest@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxCp27cp27mu/.lastSuccessful/paddle.tgz>`_"
"cpu_avx_openblas", "`paddlepaddle-0.10.0-cp27-cp27mu-linux_x86_64.whl <http://guest@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxOpenblas/.lastSuccessful/paddlepaddle-0.10.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle-0.10.0-cp27-cp27m-linux_x86_64.whl <http://guest@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxOpenblas/.lastSuccessful/paddlepaddle-0.10.0-cp27-cp27m-linux_x86_64.whl>`_", "暂无"
"cuda7.5_cudnn5_avx_mkl", "`paddlepaddle-0.10.0-cp27-cp27mu-linux_x86_64.whl <http://guest@paddleci.ngrok.io/repository/download/Manylinux1_Cuda75cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle-0.10.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle-0.10.0-cp27-cp27m-linux_x86_64.whl <http://guest@paddleci.ngrok.io/repository/download/Manylinux1_Cuda75cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle-0.10.0-cp27-cp27m-linux_x86_64.whl>`_", "`paddle.tgz <http://guest@paddleci.ngrok.io/repository/download/Manylinux1_Cuda75cudnn5cp27cp27mu/.lastSuccessful/paddle.tgz>`_"
"cuda8.0_cudnn5_avx_mkl", "`paddlepaddle-0.10.0-cp27-cp27mu-linux_x86_64.whl <http://guest@paddleci.ngrok.io/repository/download/Manylinux1_Cuda80cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle-0.10.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle-0.10.0-cp27-cp27m-linux_x86_64.whl <http://guest@paddleci.ngrok.io/repository/download/Manylinux1_Cuda80cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle-0.10.0-cp27-cp27m-linux_x86_64.whl>`_", "`paddle.tgz <http://guest@paddleci.ngrok.io/repository/download/Manylinux1_Cuda80cudnn5cp27cp27mu/.lastSuccessful/paddle.tgz>`_"
"cuda8.0_cudnn7_avx_mkl", "`paddlepaddle-0.10.0-cp27-cp27mu-linux_x86_64.whl <http://guest@paddleci.ngrok.io/repository/download/Manylinux1_Cuda8cudnn7cp27cp27mu/.lastSuccessful/paddlepaddle-0.10.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle-0.10.0-cp27-cp27m-linux_x86_64.whl <http://guest@paddleci.ngrok.io/repository/download/Manylinux1_Cuda8cudnn7cp27cp27mu/.lastSuccessful/paddlepaddle-0.10.0-cp27-cp27m-linux_x86_64.whl>`_", "`paddle.tgz <http://guest@paddleci.ngrok.io/repository/download/Manylinux1_Cuda8cudnn7cp27cp27mu/.lastSuccessful/paddle.tgz>`_"
.. _pip_dependency:
运行环境依赖
------------------------------
PaddlePaddle安装包由于不仅仅包含.py程序，而且包含了C++编写的部分，所以我们确保发布的二进制包可以支持主流的Linux操作系统，比如CentOS 6以上，Ubuntu 14.04以上，MacOS 10.12以上。
PaddlePaddle发布的安装包会尽量对齐 `manylinux1 <https://www.python.org/dev/peps/pep-0513/#the-manylinux1-policy>`_ 标准，通常使用CentOS 5作为编译环境。但由于CUDA库通常需要CentOS 6以上，而且CentOS 5即将停止维护，所以我们默认使用CentOS 6作为标准编译环境。
.. csv-table:: PaddlePaddle环境依赖
:header: "依赖", "版本", "说明"
:widths: 10, 15, 30
"操作系统", "Linux, MacOS", "CentOS 6以上，Ubuntu 14.04以上，MacOS 10.12以上"
"Python", "2.7.x", "暂时不支持Python3"
"libc.so", "GLIBC_2.7", "glibc至少包含GLIBC_2.7以上的符号"
"libstdc++.so", "GLIBCXX_3.4.11, CXXABI_1.3.3", "至少包含GLIBCXX_3.4.11, CXXABI_1.3.3以上的符号"
"libgcc_s.so", "GCC_3.3", "至少包含GCC_3.3以上的符号"
.. _pip_faq:
安装常见问题和解决方法
------------------------------
- paddlepaddle*.whl is not a supported wheel on this platform.
出现这个问题的主要原因是，没有找到和当前系统匹配的paddlepaddle安装包。请检查Python版本是否为2.7系列。另外最新的pip官方源中的安装包默认是manylinux1标准，需要使用最新的pip (>9.0.0) 才可以安装。可以使用下面的命令更新您的pip：
.. code-block:: bash
pip install --upgrade pip
如果仍然存在问题，可以执行：
.. code-block:: bash
python -c "import pip; print(pip.pep425tags.get_supported())"
获取当前系统支持的安装包格式，并检查和需安装的包是否匹配。pypi安装包可以在 `这个 <https://pypi.python.org/pypi/paddlepaddle/0.10.5>`_ 链接中找到。
如果系统支持的是 linux_x86_64 而安装包是 manylinux1_x86_64 ，需要升级pip版本到最新； 如果系统支持 manylinux1_x86_64 而安装包（本地）是 linux_x86_64 ，可以重命名这个whl包为 manylinux1_x86_64 再安装。
\ No newline at end of file
doc/getstarted/build_and_install/pip_install_en.rst 0 → 100644
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Install PaddlePaddle Using pip
================================
You can use current widely used Python package management
tool `pip <https://pip.pypa.io/en/stable/installing/>`_
to install PaddlePaddle. This method can be used in
most of current Linux systems or MacOS.
.. _pip_install:
Install Using pip
------------------------------
Run the following command to install PaddlePaddle on the current
machine, it will also download requirements.
.. code-block:: bash
pip install paddlepaddle
If you wish to install GPU version, just run:
.. code-block:: bash
pip install paddlepaddle-gpu
If you wish to install the latest develop branch PaddlePaddle,
you can download the latest whl package from our CI system. Access
the below links, log in as guest, then click at the "Artifact"
tab, you'll find the download link of whl packages.
If the links below shows up the login form, just click "Log in as guest" to start the download:
.. image:: paddleci.png
:scale: 50 %
:align: center
.. csv-table:: whl package of each version
:header: "version", "cp27-cp27mu", "cp27-cp27mu", "C-API"
:widths: 1, 3, 3, 3
"cpu_avx_mkl", "`paddlepaddle-0.10.0-cp27-cp27mu-linux_x86_64.whl <http://guest@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxCp27cp27mu/.lastSuccessful/paddlepaddle-0.10.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle-0.10.0-cp27-cp27m-linux_x86_64.whl <http://guest@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxCp27cp27mu/.lastSuccessful/paddlepaddle-0.10.0-cp27-cp27m-linux_x86_64.whl>`_", "`paddle.tgz <http://guest@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxCp27cp27mu/.lastSuccessful/paddle.tgz>`_"
"cpu_avx_openblas", "`paddlepaddle-0.10.0-cp27-cp27mu-linux_x86_64.whl <http://guest@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxOpenblas/.lastSuccessful/paddlepaddle-0.10.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle-0.10.0-cp27-cp27m-linux_x86_64.whl <http://guest@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxOpenblas/.lastSuccessful/paddlepaddle-0.10.0-cp27-cp27m-linux_x86_64.whl>`_", "Not Available"
"cuda7.5_cudnn5_avx_mkl", "`paddlepaddle-0.10.0-cp27-cp27mu-linux_x86_64.whl <http://guest@paddleci.ngrok.io/repository/download/Manylinux1_Cuda75cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle-0.10.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle-0.10.0-cp27-cp27m-linux_x86_64.whl <http://guest@paddleci.ngrok.io/repository/download/Manylinux1_Cuda75cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle-0.10.0-cp27-cp27m-linux_x86_64.whl>`_", "`paddle.tgz <http://guest@paddleci.ngrok.io/repository/download/Manylinux1_Cuda75cudnn5cp27cp27mu/.lastSuccessful/paddle.tgz>`_"
"cuda8.0_cudnn5_avx_mkl", "`paddlepaddle-0.10.0-cp27-cp27mu-linux_x86_64.whl <http://guest@paddleci.ngrok.io/repository/download/Manylinux1_Cuda80cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle-0.10.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle-0.10.0-cp27-cp27m-linux_x86_64.whl <http://guest@paddleci.ngrok.io/repository/download/Manylinux1_Cuda80cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle-0.10.0-cp27-cp27m-linux_x86_64.whl>`_", "`paddle.tgz <http://guest@paddleci.ngrok.io/repository/download/Manylinux1_Cuda80cudnn5cp27cp27mu/.lastSuccessful/paddle.tgz>`_"
"cuda8.0_cudnn7_avx_mkl", "`paddlepaddle-0.10.0-cp27-cp27mu-linux_x86_64.whl <http://guest@paddleci.ngrok.io/repository/download/Manylinux1_Cuda8cudnn7cp27cp27mu/.lastSuccessful/paddlepaddle-0.10.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle-0.10.0-cp27-cp27m-linux_x86_64.whl <http://guest@paddleci.ngrok.io/repository/download/Manylinux1_Cuda8cudnn7cp27cp27mu/.lastSuccessful/paddlepaddle-0.10.0-cp27-cp27m-linux_x86_64.whl>`_", "`paddle.tgz <http://guest@paddleci.ngrok.io/repository/download/Manylinux1_Cuda8cudnn7cp27cp27mu/.lastSuccessful/paddle.tgz>`_"
.. _pip_dependency:
Runtime Dependency
------------------------------
PaddlePaddle installation packages (whl) does not only contain .py files,
but also binaries built from C++ code. We ensure that PaddlePaddle can
run on current mainline Linux distributions, like CentOS 6, Ubuntu 14.04
and MacOS 10.12.
PaddlePaddle whl packages are trying to satisfy
`manylinux1 <https://www.python.org/dev/peps/pep-0513/#the-manylinux1-policy>`_
standard, which uses CentOS 5 as default build environment. But CUDA libraries
seems only run on CentOS 6 at least, also, CentOS 5 is about to end its lifetime,
so we use CentOS 6 as default build environment.
.. csv-table:: PaddlePaddle Runtime Deps
:header: "Dependency", "version", "description"
:widths: 10, 15, 30
"OS", "Linux, MacOS", "CentOS 6 or later，Ubuntu 14.04 or later，MacOS 10.12 or later"
"Python", "2.7.x", "Currently Python3 is not supported"
"libc.so", "GLIBC_2.7", "glibc at least include GLIBC_2.7 symbols"
"libstdc++.so", "GLIBCXX_3.4.11, CXXABI_1.3.3", "At least include GLIBCXX_3.4.11, CXXABI_1.3.3 symbols"
"libgcc_s.so", "GCC_3.3", "At least include GCC_3.3 symbols"
.. _pip_faq:
FAQ
------------------------------
- paddlepaddle*.whl is not a supported wheel on this platform.
The main cause of this issue is that your current platform is
not supported. Please check that you are using Python 2.7 series.
Besides, pypi only supports manylinux1 standard, you'll need to
upgrade your pip to >9.0.0. Then run the below command:
.. code-block:: bash
pip install --upgrade pip
If the problem still exists, run the following command:
.. code-block:: bash
python -c "import pip; print(pip.pep425tags.get_supported())"
Then you'll get supported package suffixes, then check if it matches
the file name of the whl package. You can find default whl package at
`here <https://pypi.python.org/pypi/paddlepaddle/0.10.5>`_
If your system supports linux_x86_64 but the whl package is manylinux1_x86_64,
you'll need to update pip to the latest version; If your system supports
manylinux1_x86_64 but the whl package is linux_x86_64 you can rename the
file to manylinux1_x86_64 suffix and then install.
doc/getstarted/index_cn.rst
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新手入门
============
.. _quick_install:
快速安装
++++++++
PaddlePaddle支持使用pip快速安装，目前支持CentOS 6以上, Ubuntu 14.04以及MacOS 10.12，并安装有Python2.7。
执行下面的命令完成快速安装：
.. code-block:: bash
pip install paddlepaddle
如果需要安装支持GPU的版本，需要执行：
.. code-block:: bash
pip install paddlepaddle-gpu
更详细的安装和编译方法参考：
.. toctree::
:maxdepth: 1
build_and_install/index_cn.rst
concepts/use_concepts_cn.rst
- `深度学习入门课程 <http://book.paddlepaddle.org/index.cn.html>`_
.. _quick_start:
快速开始
++++++++
创建一个 housing.py 并粘贴此Python代码：
.. code-block:: python
import paddle.v2 as paddle
# Initialize PaddlePaddle.
paddle.init(use_gpu=False, trainer_count=1)
# Configure the neural network.
x = paddle.layer.data(name='x', type=paddle.data_type.dense_vector(13))
y_predict = paddle.layer.fc(input=x, size=1, act=paddle.activation.Linear())
# Infer using provided test data.
probs = paddle.infer(
output_layer=y_predict,
parameters=paddle.dataset.uci_housing.model(),
input=[item for item in paddle.dataset.uci_housing.test()()])
for i in xrange(len(probs)):
print 'Predicted price: ${:,.2f}'.format(probs[i][0] * 1000)
执行 :code:`python housing.py` 瞧！ 它应该打印出预测住房数据的清单。
.. toctree::
:maxdepth: 1
concepts/use_concepts_cn.rst
doc/getstarted/index_en.rst
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GET STARTED
============
.. _quick_install:
Quick Install
----------------------
You can use pip to install PaddlePaddle with a single command, supports
CentOS 6 above, Ubuntu 14.04 above or MacOS 10.12, with Python 2.7 installed.
Simply run the following command to install:
.. code-block:: bash
pip install paddlepaddle
If you need to install GPU version, run:
.. code-block:: bash
pip install paddlepaddle-gpu
For more details about installation and build:
.. toctree::
:maxdepth: 1
build_and_install/index_en.rst
- `Deep Learning 101 <http://book.paddlepaddle.org/index.html>`_
.. _quick_start:
Quick Start
++++++++
Create a new file called housing.py, and paste this Python
code:
.. code-block:: python
import paddle.v2 as paddle
# Initialize PaddlePaddle.
paddle.init(use_gpu=False, trainer_count=1)
# Configure the neural network.
x = paddle.layer.data(name='x', type=paddle.data_type.dense_vector(13))
y_predict = paddle.layer.fc(input=x, size=1, act=paddle.activation.Linear())
# Infer using provided test data.
probs = paddle.infer(
output_layer=y_predict,
parameters=paddle.dataset.uci_housing.model(),
input=[item for item in paddle.dataset.uci_housing.test()()])
for i in xrange(len(probs)):
print 'Predicted price: ${:,.2f}'.format(probs[i][0] * 1000)
Run :code:`python housing.py` and voila! It should print out a list of predictions
for the test housing data.
doc/howto/optimization/cpu_profiling.md 0 → 100644
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此教程会介绍如何使用Python的cProfile包，与Python库yep，google perftools来运行性能分析(Profiling)与调优。
运行性能分析可以让开发人员科学的，有条不紊的对程序进行性能优化。性能分析是性能调优的基础。因为在程序实际运行中，真正的瓶颈可能和程序员开发过程中想象的瓶颈相去甚远。
性能优化的步骤，通常是循环重复若干次『性能分析 --> 寻找瓶颈 ---> 调优瓶颈 --> 性能分析确认调优效果』。其中性能分析是性能调优的至关重要的量化指标。
Paddle提供了Python语言绑定。用户使用Python进行神经网络编程，训练，测试。Python解释器通过`pybind`和`swig`调用Paddle的动态链接库，进而调用Paddle C++部分的代码。所以Paddle的性能分析与调优分为两个部分:
* Python代码的性能分析
* Python与C++混合代码的性能分析
## Python代码的性能分析
### 生成性能分析文件
Python标准库中提供了性能分析的工具包，[cProfile](https://docs.python.org/2/library/profile.html)。生成Python性能分析的命令如下:
```bash
python -m cProfile -o profile.out main.py
```
其中`-o`标识了一个输出的文件名，用来存储本次性能分析的结果。如果不指定这个文件，`cProfile`会打印一些统计信息到`stdout`。这不方便我们进行后期处理(进行`sort`, `split`, `cut`等等)。
### 查看性能分析文件
当main.py运行完毕后，性能分析结果文件`profile.out`就生成出来了。我们可以使用[cprofilev](https://github.com/ymichael/cprofilev)来查看性能分析结果。`cprofilev`是一个Python的第三方库。使用它会开启一个HTTP服务，将性能分析结果以网页的形式展示出来。
使用`pip install cprofilev`安装`cprofilev`工具。安装完成后，使用如下命令开启HTTP服务
```bash
cprofilev -a 0.0.0.0 -p 3214 -f profile.out main.py
```
其中`-a`标识HTTP服务绑定的IP。使用`0.0.0.0`允许外网访问这个HTTP服务。`-p`标识HTTP服务的端口。`-f`标识性能分析的结果文件。`main.py`标识被性能分析的源文件。
访问对应网址，即可显示性能分析的结果。性能分析结果格式如下:
```text
ncalls tottime percall cumtime percall filename:lineno(function)
1 0.284 0.284 29.514 29.514 main.py:1(<module>)
4696 0.128 0.000 15.748 0.003 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/fluid/executor.py:20(run)
4696 12.040 0.003 12.040 0.003 {built-in method run}
1 0.144 0.144 6.534 6.534 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/__init__.py:14(<module>)
```
每一列的含义是:
| 列名 | 含义 |
| --- | --- |
| ncalls | 函数的调用次数 |
| tottime | 函数实际使用的总时间。该时间去除掉本函数调用其他函数的时间 |
| percall | tottime的每次调用平均时间 |
| cumtime | 函数总时间。包含这个函数调用其他函数的时间 |
| percall | cumtime的每次调用平均时间 |
| filename:lineno(function) | 文件名, 行号，函数名 |
### 寻找性能瓶颈
通常`tottime`和`cumtime`是寻找瓶颈的关键指标。这两个指标代表了某一个函数真实的运行时间。
将性能分析结果按照tottime排序，效果如下:
```text
4696 12.040 0.003 12.040 0.003 {built-in method run}
300005 0.874 0.000 1.681 0.000 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/dataset/mnist.py:38(reader)
107991 0.676 0.000 1.519 0.000 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/fluid/framework.py:219(__init__)
4697 0.626 0.000 2.291 0.000 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/fluid/framework.py:428(sync_with_cpp)
1 0.618 0.618 0.618 0.618 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/fluid/__init__.py:1(<module>)
```
可以看到最耗时的函数是C++端的`run`函数。这需要联合我们第二节`Python与C++混合代码的性能分析`来进行调优。而`sync_with_cpp`函数的总共耗时很长，每次调用的耗时也很长。于是我们可以点击`sync_with_cpp`的详细信息，了解其调用关系。
```text
Called By:
Ordered by: internal time
List reduced from 4497 to 2 due to restriction <'sync_with_cpp'>
Function was called by...
ncalls tottime cumtime
/home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/fluid/framework.py:428(sync_with_cpp) <- 4697 0.626 2.291 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/fluid/framework.py:562(sync_with_cpp)
/home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/fluid/framework.py:562(sync_with_cpp) <- 4696 0.019 2.316 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/fluid/framework.py:487(clone)
1 0.000 0.001 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/fluid/framework.py:534(append_backward)
Called:
Ordered by: internal time
List reduced from 4497 to 2 due to restriction <'sync_with_cpp'>
```
通常观察热点函数间的调用关系，和对应行的代码，就可以了解到问题代码在哪里。当我们做出性能修正后，再次进行性能分析(profiling)即可检查我们调优后的修正是否能够改善程序的性能。
## Python与C++混合代码的性能分析
### 生成性能分析文件
C++的性能分析工具非常多。常见的包括`gprof`, `valgrind`, `google-perftools`。但是调试Python中使用的动态链接库与直接调试原始二进制相比增加了很多复杂度。幸而Python的一个第三方库`yep`提供了方便的和`google-perftools`交互的方法。于是这里使用`yep`进行Python与C++混合代码的性能分析
使用`yep`前需要安装`google-perftools`与`yep`包。ubuntu下安装命令为
```bash
apt install libgoogle-perftools-dev
pip install yep
```
安装完毕后，我们可以通过
```bash
python -m yep -v main.py
```
生成性能分析文件。生成的性能分析文件为`main.py.prof`。
命令行中的`-v`指定在生成性能分析文件之后，在命令行显示分析结果。我们可以在命令行中简单的看一下生成效果。因为C++与Python不同，编译时可能会去掉调试信息，运行时也可能因为多线程产生混乱不可读的性能分析结果。为了生成更可读的性能分析结果，可以采取下面几点措施:
1. 编译时指定`-g`生成调试信息。使用cmake的话，可以将CMAKE_BUILD_TYPE指定为`RelWithDebInfo`。
2. 编译时一定要开启优化。单纯的`Debug`编译性能会和`-O2`或者`-O3`有非常大的差别。`Debug`模式下的性能测试是没有意义的。
3. 运行性能分析的时候，先从单线程开始，再开启多线程，进而多机。毕竟如果单线程调试更容易。可以设置`OMP_NUM_THREADS=1`这个环境变量关闭openmp优化。
### 查看性能分析文件
在运行完性能分析后，会生成性能分析结果文件。我们可以使用[pprof](https://github.com/google/pprof)来显示性能分析结果。注意，这里使用了用`Go`语言重构后的`pprof`，因为这个工具具有web服务界面，且展示效果更好。
安装`pprof`的命令和一般的`Go`程序是一样的，其命令如下:
```bash
go get github.com/google/pprof
```
进而我们可以使用如下命令开启一个HTTP服务:
```bash
pprof -http=0.0.0.0:3213 `which python` ./main.py.prof
```
这行命令中，`-http`指开启HTTP服务。`which python`会产生当前Python二进制的完整路径，进而指定了Python可执行文件的路径。`./main.py.prof`输入了性能分析结果。
访问对应的网址，我们可以查看性能分析的结果。结果如下图所示:
![result](./pprof_1.png)
### 寻找性能瓶颈
与寻找Python代码的性能瓶颈类似，寻找Python与C++混合代码的性能瓶颈也是要看`tottime`和`cumtime`。而`pprof`展示的调用图也可以帮助我们发现性能中的问题。
例如下图中，
![kernel_perf](./pprof_2.png)
在一次训练中，乘法和乘法梯度的计算占用2%-4%左右的计算时间。而`MomentumOp`占用了17%左右的计算时间。显然，`MomentumOp`的性能有问题。
在`pprof`中，对于性能的关键路径都做出了红色标记。先检查关键路径的性能问题，再检查其他部分的性能问题，可以更有次序的完成性能的优化。
## 总结
至此，两种性能分析的方式都介绍完毕了。希望通过这两种性能分析的方式，Paddle的开发人员和使用人员可以有次序的，科学的发现和解决性能问题。
paddle/gserver/layers/ROIPoolLayer.cpp
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......@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "ROIPoolLayer.h"
#include <cfloat>
namespace paddle {
......@@ -126,10 +127,8 @@ void ROIPoolLayer::forward(PassType passType) {
bool isEmpty = (hend <= hstart) || (wend <= wstart);
size_t poolIndex = ph * pooledWidth_ + pw;
if (isEmpty) {
outputData[poolIndex] = 0;
argmaxData[poolIndex] = -1;
}
outputData[poolIndex] = isEmpty ? 0 : -FLT_MAX;
argmaxData[poolIndex] = -1;
for (size_t h = hstart; h < hend; ++h) {
for (size_t w = wstart; w < wend; ++w) {
......
paddle/operators/CMakeLists.txt
浏览文件 @ 322d69f2
......@@ -73,6 +73,13 @@ function(op_library TARGET)
file(APPEND ${pybind_file} "USE_OP(conv2d);\n")
endif()
# conv_cudnn_op contains several operators
if ("${TARGET}" STREQUAL "conv_cudnn_op")
set(pybind_flag 1)
# It's enough to just adding one operator to pybind
file(APPEND ${pybind_file} "USE_OP(conv2d_cudnn);\n")
endif()
# pool_op contains several operators
if ("${TARGET}" STREQUAL "pool_op")
set(pybind_flag 1)
......@@ -193,6 +200,7 @@ set(DEPS_OPS
lod_rank_table_op
lod_tensor_to_array_op
array_to_lod_tensor_op
max_sequence_len_op
lstm_op
tensor_array_read_write_op
gru_op
......@@ -215,6 +223,7 @@ op_library(pool_with_index_op DEPS pooling)
op_library(lod_rank_table_op SRCS lod_rank_table_op.cc DEPS lod_rank_table)
op_library(lod_tensor_to_array_op SRCS lod_tensor_to_array_op.cc DEPS lod_rank_table_op)
op_library(array_to_lod_tensor_op SRCS array_to_lod_tensor_op.cc DEPS lod_rank_table_op)
op_library(max_sequence_len_op SRCS max_sequence_len_op.cc DEPS lod_rank_table)
op_library(tensor_array_read_write_op SRCS tensor_array_read_write_op.cc)
if(WITH_GPU)
op_library(nccl_op DEPS nccl_common)
......
paddle/operators/conv_cudnn_op.cc
浏览文件 @ 322d69f2
......@@ -17,10 +17,10 @@
namespace paddle {
namespace operators {
class CudnnConvOpMaker : public Conv2DOpMaker {
class CudnnConv2DOpMaker : public Conv2DOpMaker {
public:
CudnnConvOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
CudnnConv2DOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
: Conv2DOpMaker(proto, op_checker) {
AddAttr<int>("workspace_size_MB",
"workspace size for cudnn, in MB, "
......@@ -32,16 +32,43 @@ class CudnnConvOpMaker : public Conv2DOpMaker {
}
};
class CudnnConv3DOpMaker : public Conv3DOpMaker {
public:
CudnnConv3DOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
: Conv3DOpMaker(proto, op_checker) {
AddAttr<int>("workspace_size_MB",
"workspace size for cudnn, in MB, "
"workspace is a section of GPU memory which will be "
"allocated/freed each time the operator runs, larger "
"workspace size can increase performance but also requires "
"better hardware. This size should be chosen carefully.")
.SetDefault(4096);
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP(conv_cudnn, ops::ConvOp, ops::CudnnConvOpMaker, conv_cudnn_grad,
ops::ConvOpGrad);
REGISTER_OP(conv2d_cudnn, ops::ConvOp, ops::CudnnConv2DOpMaker,
conv2d_cudnn_grad, ops::ConvOpGrad);
REGISTER_OP(conv3d_cudnn, ops::ConvOp, ops::CudnnConv3DOpMaker,
conv3d_cudnn_grad, ops::ConvOpGrad);
REGISTER_OP_CPU_KERNEL(conv2d_cudnn,
ops::GemmConvKernel<paddle::platform::CPUPlace, float>,
ops::GemmConvKernel<paddle::platform::CPUPlace, double>);
REGISTER_OP_CPU_KERNEL(
conv2d_cudnn_grad,
ops::GemmConvGradKernel<paddle::platform::CPUPlace, float>,
ops::GemmConvGradKernel<paddle::platform::CPUPlace, double>);
REGISTER_OP_CPU_KERNEL(conv_cudnn,
REGISTER_OP_CPU_KERNEL(conv3d_cudnn,
ops::GemmConvKernel<paddle::platform::CPUPlace, float>,
ops::GemmConvKernel<paddle::platform::CPUPlace, double>);
REGISTER_OP_CPU_KERNEL(
conv_cudnn_grad, ops::GemmConvGradKernel<paddle::platform::CPUPlace, float>,
conv3d_cudnn_grad,
ops::GemmConvGradKernel<paddle::platform::CPUPlace, float>,
ops::GemmConvGradKernel<paddle::platform::CPUPlace, double>);
paddle/operators/conv_cudnn_op.cu.cc
浏览文件 @ 322d69f2
......@@ -56,6 +56,21 @@ class CudnnConvOpKernel : public framework::OpKernel<T> {
ScopedFilterDescriptor filter_desc;
ScopedConvolutionDescriptor conv_desc;
DataLayout layout = DataLayout::kNCHW;
if (input->dims().size() == 5) {
layout = DataLayout::kNCDHW;
}
cudnnConvolutionDescriptor_t cudnn_conv_desc =
conv_desc.descriptor<T>(paddings, strides, dilations);
#if CUDNN_VERSION_MIN(7, 0, 0)
// cudnn 7 can support groups, no need to do it mannually
// FIXME(typhoonzero): find a better way to disable groups
// rather than setting it to 1.
PADDLE_ENFORCE(platform::dynload::cudnnSetConvolutionGroupCount(
cudnn_conv_desc, groups));
groups = 1;
#endif
cudnnTensorDescriptor_t cudnn_input_desc = input_desc.descriptor<T>(
layout, framework::vectorize2int(input->dims()), groups);
......@@ -63,19 +78,34 @@ class CudnnConvOpKernel : public framework::OpKernel<T> {
layout, framework::vectorize2int(output->dims()), groups);
cudnnFilterDescriptor_t cudnn_filter_desc = filter_desc.descriptor<T>(
layout, framework::vectorize2int(filter->dims()), groups);
cudnnConvolutionDescriptor_t cudnn_conv_desc =
conv_desc.descriptor<T>(paddings, strides, dilations);
int input_channels = input->dims()[1];
int input_height = input->dims()[2];
int input_width = input->dims()[3];
int output_channels = output->dims()[1];
int output_height = output->dims()[2];
int output_width = output->dims()[3];
int input_height, input_width, input_depth;
if (input->dims().size() == 5) {
input_depth = input->dims()[2];
input_height = input->dims()[3];
input_width = input->dims()[4];
} else { // dim size is enforced in InferShape
input_depth = 1;
input_height = input->dims()[2];
input_width = input->dims()[3];
}
int output_channels = filter->dims()[0];
int output_height, output_width, output_depth;
if (output->dims().size() == 5) {
output_depth = output->dims()[2];
output_height = output->dims()[3];
output_width = output->dims()[4];
} else {
output_depth = 1;
output_height = output->dims()[2];
output_width = output->dims()[3];
}
int group_offset_in = input_channels / groups * input_height * input_width;
int group_offset_in =
input_channels / groups * input_height * input_width * input_depth;
int group_offset_out =
output_channels / groups * output_height * output_width;
output_channels / groups * output_height * output_width * output_depth;
int group_offset_filter = filter->numel() / groups;
// ------------------- cudnn conv workspace ---------------------
void* cudnn_workspace = nullptr;
......@@ -138,12 +168,26 @@ class CudnnConvGradOpKernel : public framework::OpKernel<T> {
// ------------------- cudnn descriptors ---------------------
ScopedTensorDescriptor input_desc;
ScopedTensorDescriptor output_grad_desc;
ScopedTensorDescriptor input_grad_desc;
ScopedFilterDescriptor filter_desc;
ScopedFilterDescriptor filter_grad_desc;
ScopedConvolutionDescriptor conv_desc;
DataLayout layout = DataLayout::kNCHW;
if (input->dims().size() == 5) {
layout = DataLayout::kNCDHW;
}
cudnnConvolutionDescriptor_t cudnn_conv_desc =
conv_desc.descriptor<T>(paddings, strides, dilations);
#if CUDNN_VERSION_MIN(7, 0, 0)
// cudnn 7 can support groups, no need to do it mannually
// FIXME(typhoonzero): find a better way to disable groups
// rather than setting it to 1.
PADDLE_ENFORCE(platform::dynload::cudnnSetConvolutionGroupCount(
cudnn_conv_desc, groups));
groups = 1;
#endif
cudnnTensorDescriptor_t cudnn_input_desc = input_desc.descriptor<T>(
layout, framework::vectorize2int(input->dims()), groups);
......@@ -152,22 +196,35 @@ class CudnnConvGradOpKernel : public framework::OpKernel<T> {
layout, framework::vectorize2int(output_grad->dims()), groups);
cudnnFilterDescriptor_t cudnn_filter_desc = filter_desc.descriptor<T>(
layout, framework::vectorize2int(filter->dims()), groups);
cudnnTensorDescriptor_t cudnn_input_grad_desc = nullptr;
cudnnFilterDescriptor_t cudnn_filter_grad_desc = nullptr;
cudnnConvolutionDescriptor_t cudnn_conv_desc =
conv_desc.descriptor<T>(paddings, strides, dilations);
int input_channels = input->dims()[1];
int input_height = input->dims()[2];
int input_width = input->dims()[3];
int input_height, input_width, input_depth;
if (input->dims().size() == 5) {
input_depth = input->dims()[2];
input_height = input->dims()[3];
input_width = input->dims()[4];
} else { // dim size is enforced in InferShape
input_depth = 1;
input_height = input->dims()[2];
input_width = input->dims()[3];
}
int output_grad_channels = filter->dims()[0];
int output_grad_height = output_grad->dims()[2];
int output_grad_width = output_grad->dims()[3];
int output_grad_height, output_grad_width, output_grad_depth;
if (input->dims().size() == 5) {
output_grad_depth = output_grad->dims()[2];
output_grad_height = output_grad->dims()[3];
output_grad_width = output_grad->dims()[4];
} else {
output_grad_depth = 1;
output_grad_height = output_grad->dims()[2];
output_grad_width = output_grad->dims()[3];
}
int group_offset_in = input_channels / groups * input_height * input_width;
int group_offset_out =
output_grad_channels / groups * output_grad_height * output_grad_width;
int group_offset_in =
input_channels / groups * input_height * input_width * input_depth;
int group_offset_out = output_grad_channels / groups * output_grad_height *
output_grad_width * output_grad_depth;
int group_offset_filter = filter->numel() / groups;
// ------------------- cudnn backward algorithm ---------------------
cudnnConvolutionBwdDataAlgo_t data_algo;
......@@ -180,8 +237,6 @@ class CudnnConvGradOpKernel : public framework::OpKernel<T> {
auto handle = ctx.cuda_device_context().cudnn_handle();
if (input_grad) {
cudnn_input_grad_desc = input_grad_desc.descriptor<T>(
layout, framework::vectorize2int(input_grad->dims()), groups);
PADDLE_ENFORCE(
platform::dynload::cudnnGetConvolutionBackwardDataAlgorithm(
handle, cudnn_filter_desc,
......@@ -190,19 +245,17 @@ class CudnnConvGradOpKernel : public framework::OpKernel<T> {
cudnn_output_grad_desc, cudnn_conv_desc,
// dxDesc: Handle to the previously initialized output tensor
// descriptor.
cudnn_input_grad_desc,
cudnn_input_desc,
CUDNN_CONVOLUTION_BWD_DATA_SPECIFY_WORKSPACE_LIMIT,
workspace_size_limit, &data_algo));
PADDLE_ENFORCE(
platform::dynload::cudnnGetConvolutionBackwardDataWorkspaceSize(
handle, cudnn_filter_desc, cudnn_output_grad_desc,
cudnn_conv_desc, cudnn_input_grad_desc, data_algo, &tmp_size));
cudnn_conv_desc, cudnn_input_desc, data_algo, &tmp_size));
workspace_size_in_bytes = std::max(workspace_size_in_bytes, tmp_size);
}
if (filter_grad) {
cudnn_filter_grad_desc = filter_grad_desc.descriptor<T>(
layout, framework::vectorize2int(filter_grad->dims()), groups);
PADDLE_ENFORCE(
platform::dynload::cudnnGetConvolutionBackwardFilterAlgorithm(
handle, cudnn_input_desc, cudnn_output_grad_desc, cudnn_conv_desc,
......@@ -222,7 +275,6 @@ class CudnnConvGradOpKernel : public framework::OpKernel<T> {
platform::GPUPlace gpu = boost::get<platform::GPUPlace>(ctx.GetPlace());
cudnn_workspace = paddle::memory::Alloc(gpu, workspace_size_in_bytes);
// ------------------- cudnn conv backward data ---------------------
// FIXME(typhoonzero): template type T may not be the same as cudnn call.
T alpha = 1.0f, beta = 0.0f;
if (input_grad) {
T* input_grad_data = input_grad->mutable_data<T>(ctx.GetPlace());
......@@ -233,21 +285,20 @@ class CudnnConvGradOpKernel : public framework::OpKernel<T> {
handle, &alpha, cudnn_filter_desc,
filter_data + i * group_offset_filter, cudnn_output_grad_desc,
output_grad_data + i * group_offset_out, cudnn_conv_desc, data_algo,
cudnn_workspace, workspace_size_in_bytes, &beta,
cudnn_input_grad_desc, input_grad_data + i * group_offset_in));
cudnn_workspace, workspace_size_in_bytes, &beta, cudnn_input_desc,
input_grad_data + i * group_offset_in));
}
}
// ------------------- cudnn conv backward filter ---------------------
if (filter_grad) {
T* filter_grad_data = filter_grad->mutable_data<T>(ctx.GetPlace());
// Because beta is zero, it is unnecessary to reset filter_grad.
for (int i = 0; i < groups; i++) {
PADDLE_ENFORCE(platform::dynload::cudnnConvolutionBackwardFilter(
handle, &alpha, cudnn_input_desc, input_data + i * group_offset_in,
cudnn_output_grad_desc, output_grad_data + i * group_offset_out,
cudnn_conv_desc, filter_algo, cudnn_workspace,
workspace_size_in_bytes, &beta, cudnn_filter_grad_desc,
workspace_size_in_bytes, &beta, cudnn_filter_desc,
filter_grad_data + i * group_offset_filter));
}
}
......@@ -259,8 +310,16 @@ class CudnnConvGradOpKernel : public framework::OpKernel<T> {
} // namespace operators
} // namespace paddle
REGISTER_OP_GPU_KERNEL(conv_cudnn, paddle::operators::CudnnConvOpKernel<float>,
REGISTER_OP_GPU_KERNEL(conv2d_cudnn,
paddle::operators::CudnnConvOpKernel<float>,
paddle::operators::CudnnConvOpKernel<double>);
REGISTER_OP_GPU_KERNEL(conv2d_cudnn_grad,
paddle::operators::CudnnConvGradOpKernel<float>,
paddle::operators::CudnnConvGradOpKernel<double>);
REGISTER_OP_GPU_KERNEL(conv3d_cudnn,
paddle::operators::CudnnConvOpKernel<float>,
paddle::operators::CudnnConvOpKernel<double>);
REGISTER_OP_GPU_KERNEL(conv_cudnn_grad,
REGISTER_OP_GPU_KERNEL(conv3d_cudnn_grad,
paddle::operators::CudnnConvGradOpKernel<float>,
paddle::operators::CudnnConvGradOpKernel<double>);
paddle/operators/math/selected_rows_functor.cu
浏览文件 @ 322d69f2
......@@ -227,7 +227,6 @@ template struct SelectedRowsAddToTensor<platform::GPUPlace, float>;
template struct SelectedRowsAddToTensor<platform::GPUPlace, double>;
template struct SelectedRowsAddToTensor<platform::GPUPlace, int>;
template struct SelectedRowsAddToTensor<platform::GPUPlace, int64_t>;
} // namespace math
} // namespace operators
} // namespace paddle
paddle/operators/max_sequence_len_op.cc 0 → 100644
浏览文件 @ 322d69f2
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/framework/lod_rank_table.h"
#include "paddle/framework/op_registry.h"
#include "paddle/framework/operator.h"
namespace paddle {
namespace operators {
class MaxSeqenceLenOp : public framework::OperatorBase {
public:
MaxSeqenceLenOp(const std::string &type,
const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
void Run(const framework::Scope &scope,
const platform::DeviceContext &dev_ctx) const override {
auto &rank_table =
scope.FindVar(Input("RankTable"))->Get<framework::LoDRankTable>();
auto *out =
scope.FindVar(Output("Out"))->GetMutable<framework::LoDTensor>();
int64_t *out_ptr = out->mutable_data<int64_t>({1}, platform::CPUPlace());
*out_ptr = rank_table.items()[0].length;
}
};
class MaxSeqenceLenOpProtoMaker : public framework::OpProtoAndCheckerMaker {
public:
MaxSeqenceLenOpProtoMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("RankTable", "The lod_rank_table.");
AddOutput("Out", "The max sequence length.");
AddComment(
R"DOC(Calculate the max sequence length through lod_rank_table.)DOC");
}
};
class MaxSeqenceLenInferShape : public framework::InferShapeBase {
public:
void operator()(framework::InferShapeContext *context) const override {
PADDLE_ENFORCE(context->HasInput("RankTable"));
context->SetOutputDim("Out", {1});
}
};
} // namespace operators
} // namespace paddle
REGISTER_OPERATOR(max_sequence_len, paddle::operators::MaxSeqenceLenOp,
paddle::operators::MaxSeqenceLenOpProtoMaker,
paddle::operators::MaxSeqenceLenInferShape,
paddle::framework::EmptyGradOpMaker);
paddle/platform/cudnn_helper.h
浏览文件 @ 322d69f2
......@@ -116,7 +116,7 @@ inline cudnnTensorFormat_t GetCudnnTensorFormat(
case DataLayout::kNCHW:
return CUDNN_TENSOR_NCHW;
case DataLayout::kNCDHW:
return CUDNN_TENSOR_NCHW; // TODO(chengduoZH) : add CUDNN_TENSOR_NCDHW
return CUDNN_TENSOR_NCHW; // NOTE: cudnn treat NdTensor as the same
default:
PADDLE_THROW("Unknown cudnn equivalent for order");
}
......@@ -143,7 +143,7 @@ class ScopedTensorDescriptor {
strides[i] = dims[i + 1] * strides[i + 1];
}
// Update tensor descriptor dims setting if groups > 1
// FIXME(typhoonzero): Assume using NCHW or NCDHW order
// NOTE: Assume using NCHW or NCDHW order
std::vector<int> dims_with_group(dims.begin(), dims.end()); // copy
if (groups > 1) {
dims_with_group[1] = dims_with_group[1] / groups;
......@@ -186,7 +186,6 @@ class ScopedFilterDescriptor {
// width of the filter.
std::vector<int> kernel_with_group(kernel.begin(), kernel.end());
if (groups > 1) {
// M /= groups
kernel_with_group[0] /= groups;
// NOTE: input filter(C) of the filter is already asserted to be C/groups.
}
......
python/paddle/trainer/config_parser.py
浏览文件 @ 322d69f2
......@@ -2798,19 +2798,18 @@ class AddToLayer(LayerBase):
name, self.layer_type, 0, inputs=inputs, **xargs)
config_assert(len(inputs) > 0, 'inputs cannot be empty for AddToLayer')
if len(self.inputs) > 1:
for input_index in xrange(len(self.inputs)):
assert self.get_input_layer(0).height == self.get_input_layer(
input_index).height
assert self.get_input_layer(0).width == self.get_input_layer(
input_index).width
assert self.get_input_layer(0).depth == self.get_input_layer(
input_index).depth
layer_size = self.get_input_layer(0).size
# To reserve heght, width, depth.
layer_with_hwc = self.get_input_layer(0)
for input_index in xrange(len(self.inputs)):
input_layer = self.get_input_layer(input_index)
assert layer_size == input_layer.size
if input_layer.height and input_layer.height and input_layer.height:
layer_with_hwc = input_layer
self.set_layer_size(self.get_input_layer(0).size)
self.set_layer_height_width(self.get_input_layer(0).height, \
self.get_input_layer(0).width)
self.set_layer_depth(self.get_input_layer(0).depth)
self.set_layer_size(layer_with_hwc.size)
self.set_layer_height_width(layer_with_hwc.height, layer_with_hwc.width)
self.set_layer_depth(layer_with_hwc.depth)
self.create_bias_parameter(bias, self.config.size)
......
python/paddle/v2/fluid/__init__.py
浏览文件 @ 322d69f2
import sys
import core
__all__ = ['proto']
argv = []
if core.is_compile_gpu():
argv = list(sys.argv) + [
"--tryfromenv=fraction_of_gpu_memory_to_use,use_pinned_memory"
]
else:
argv = list(sys.argv) + ["--tryfromenv=use_pinned_memory"]
core.init_gflags(argv)
# import all class inside framework into fluid module
import framework
from framework import *
# import all class inside executor into fluid module
import executor
from executor import *
import io
import evaluator
import initializer
import layers
import nets
import optimizer
import backward
import regularizer
from core import LoDTensor, CPUPlace, GPUPlace
Tensor = LoDTensor
__all__ = framework.__all__ + executor.__all__ + [
'io', 'initializer', 'layers', 'nets', 'optimizer', 'backward',
'regularizer', 'LoDTensor', 'CPUPlace', 'GPUPlace', 'Tensor'
]
def __read_gflags_from_env__():
"""
Enable reading gflags from environment variables.
Returns:
None
"""
import sys
import core
read_env_flags = ['use_pinned_memory']
if core.is_compile_gpu():
read_env_flags.append('fraction_of_gpu_memory_to_use')
core.init_gflags(sys.argv + ["--tryfromenv=" + ",".join(read_env_flags)])
__read_gflags_from_env__()
python/paddle/v2/fluid/evaluator.py
浏览文件 @ 322d69f2
import numpy as np
from paddle.v2.fluid.framework import Program, g_main_program, unique_name, Variable
import paddle.v2.fluid.core as core
import layers
from framework import Program, unique_name, Variable
from layer_helper import LayerHelper
def _clone_var_in_block_(block, var):
__all__ = ['Accuracy']
def _clone_var_(block, var):
assert isinstance(var, Variable)
return block.create_var(
name=var.name,
......@@ -16,175 +20,115 @@ def _clone_var_in_block_(block, var):
class Evaluator(object):
"""
Evalutor Base class.
create metric states
add mini-batch evaluator caculate operator
add increment operator to accumulate the metric states
Base Class for all evaluators
Args:
name(str): The name of evaluator. such as, "accuracy". Used for generate
temporary variable name.
main_program(Program, optional): The evaluator should be added to this
main_program. Default g_main_program
startup_program(Program, optional):The parameter should be added to this
startup_program. Default g_startup_program
Attributes:
states(list): The list of state variables. states will be reset to zero
when `reset` is invoked.
metrics(list): The list of metrics variables. They will be calculate
every mini-batch
"""
def __init__(self, name, **kwargs):
self.states = []
self.metrics = []
self.helper = LayerHelper(name, **kwargs)
def reset(self, executor, reset_program=None):
"""
init the global states
reset metric states at the begin of each pass/user specified batch
"""
self._states = {}
if kwargs.has_key("main_program"):
self._main_program = kwargs.get("main_program")
else:
self._main_program = g_main_program
if reset_program is None:
reset_program = Program()
for var in self.states:
assert isinstance(var, Variable)
g_var = _clone_var_(reset_program.current_block(), var)
layers.fill_constant(
shape=g_var.shape,
value=0.0,
dtype=g_var.dtype,
out=g_var,
main_program=reset_program)
def states(self):
return self._states
executor.run(reset_program)
def _update_ops(self, *args, **kwargs):
def eval(self, executor, eval_program=None):
"""
append update ops to the global states
Evaluate the statistics merged by multiple mini-batches.
"""
raise NotImplementedError()
def reset(self, executor, reset_program=None):
def create_state(self, suffix, dtype, shape):
"""
Clear metric states at the begin of each pass/user specified batch
"""
if reset_program == None:
reset_program = Program()
else:
reset_program = program
block = reset_program.global_block()
for k, var in self._states.iteritems():
g_var = _clone_var_in_block_(block, var)
zeros = block.create_var(dtype="float32", persistable=True)
block.append_op(
type="fill_constant",
outputs={"Out": [zeros]},
attrs={
"shape": g_var.shape,
"value": .0,
"dtype": 5,
})
block.append_op(
type="scale", inputs={"X": zeros}, outputs={"Out": g_var})
executor.run(reset_program, fetch_list=self._states.values())
Create state variable.
NOTE: It is not a public API.
Args:
suffix(str): the state suffix.
dtype(str|core.DataType): the state data type
shape(tuple|list): the shape of state
Returns: State variable
def eval(self, executor, eval_program=None):
"""
Merge the mini-batch statistics to form the evaluation result for multiple mini-batches.
"""
raise NotImplementedError()
state = self.helper.create_variable(
name="_".join([unique_name(self.helper.name), suffix]),
persistable=True,
dtype=dtype,
shape=shape)
self.states.append(state)
return state
class Accuracy(Evaluator):
"""
Accuracy need two state variable Total, Correct
Average Accuracy for multiple mini-batches.
"""
def __init__(self, *args, **kwargs):
def __init__(self, input, label, k=1, **kwargs):
super(Accuracy, self).__init__("accuracy", **kwargs)
block = self._main_program.global_block()
g_total = block.create_var(
name=unique_name("Total"),
persistable=True,
dtype="int64",
shape=[1])
g_correct = block.create_var(
name=unique_name("Correct"),
persistable=True,
dtype="int64",
shape=[1])
self._states["Total"] = g_total
self._states["Correct"] = g_correct
def _update_ops(self, input, label, k=1, **kwargs):
block = self._main_program.global_block()
topk_out = block.create_var(dtype=input.dtype)
topk_indices = block.create_var(dtype="int64")
block.append_op(
type="top_k",
inputs={"X": [input]},
outputs={"Out": [topk_out],
"Indices": [topk_indices]},
attrs={"k": k})
acc_out = block.create_var(dtype=kwargs.get("out_dtype", "float32"))
correct = block.create_var(dtype="int64", persistable=True)
total = block.create_var(dtype="int64", persistable=True)
block.append_op(
type="accuracy",
inputs={
"Out": [topk_out],
"Indices": [topk_indices],
"Label": [label]
},
outputs={
"Accuracy": [acc_out],
"Correct": [correct],
"Total": [total],
})
block.append_op(
type="cast",
inputs={"X": [self._states["Total"]]},
outputs={"Out": [self._states["Total"]]},
attrs={
"in_dtype": 5, # float32
"out_dtype": 2, # int32
})
block.append_op(
type="cast",
inputs={"X": [self._states["Correct"]]},
outputs={"Out": [self._states["Correct"]]},
attrs={
"in_dtype": 5,
"out_dtype": 2,
})
block.append_op(
type="elementwise_add",
inputs={"X": [self._states["Total"]],
"Y": [total]},
outputs={"Out": [self._states["Total"]]})
block.append_op(
type="elementwise_add",
inputs={"X": [self._states["Correct"]],
"Y": [correct]},
outputs={"Out": [self._states["Correct"]]})
return acc_out
main_program = self.helper.main_program
if main_program.current_block().idx != 0:
raise ValueError("You can only invoke Evaluator in root block")
self.total = self.create_state(dtype='int64', shape=[1], suffix='total')
self.correct = self.create_state(
dtype='int64', shape=[1], suffix='correct')
kwargs = {'main_program': main_program}
total = self.helper.create_tmp_variable(dtype='int')
correct = self.helper.create_tmp_variable(dtype='int')
acc = layers.accuracy(
input=input,
label=label,
k=k,
total=total,
correct=correct,
**kwargs)
total = layers.cast(x=total, dtype='int64', **kwargs)
correct = layers.cast(x=correct, dtype='int64', **kwargs)
layers.sums(input=[self.total, total], out=self.total, **kwargs)
layers.sums(input=[self.correct, correct], out=self.correct, **kwargs)
self.metrics.append(acc)
def eval(self, executor, eval_program=None):
if eval_program != None:
eval_program = eval_program
else:
if eval_program is None:
eval_program = Program()
block = eval_program.global_block()
eval_out = block.create_var(dtype=self._states["Total"].dtype)
e_total = _clone_var_in_block_(block, self._states["Total"])
e_correct = _clone_var_in_block_(block, self._states["Correct"])
block.append_op(
type="cast",
inputs={"X": [e_total]},
outputs={"Out": [e_total]},
attrs={
"in_dtype": 2, # int32
"out_dtype": 5, # float32
})
block.append_op(
type="cast",
inputs={"X": [e_correct]},
outputs={"Out": [e_correct]},
attrs={
"in_dtype": 2,
"out_dtype": 5,
})
block.append_op(
type="elementwise_div",
inputs={"X": e_correct,
"Y": e_total},
outputs={"Out": eval_out})
out = executor.run(eval_program, fetch_list=[eval_out])
return np.array(out[0])
def accuracy(*args, **kwargs):
cls = Accuracy(*args, **kwargs)
out = cls._update_ops(*args, **kwargs)
return cls, out
block = eval_program.current_block()
kwargs = {'main_program': eval_program}
total = _clone_var_(block, self.total)
correct = _clone_var_(block, self.correct)
total = layers.cast(total, dtype='float32', **kwargs)
correct = layers.cast(correct, dtype='float32', **kwargs)
out = layers.elementwise_div(x=correct, y=total, **kwargs)
return np.array(executor.run(eval_program, fetch_list=[out])[0])
python/paddle/v2/fluid/executor.py
浏览文件 @ 322d69f2
import paddle.v2.fluid.core as core
from paddle.v2.fluid.framework import Block, Program, g_main_program
import numpy as np
from . import core
from framework import Program, g_main_program
__all__ = ['Executor', 'g_scope']
g_scope = core.Scope()
def as_numpy(tensor):
if isinstance(tensor, list):
return [as_numpy(t) for t in tensor]
assert isinstance(tensor, core.LoDTensor)
lod = tensor.lod()
tensor_data = np.array(tensor)
if len(lod) == 0:
ans = tensor_data
else:
raise RuntimeError("LoD Calculate lacks unit tests and buggy")
# elif len(lod) == 1:
# ans = []
# idx = 0
# while idx < len(lod) - 1:
# ans.append(tensor_data[lod[idx]:lod[idx + 1]])
# idx += 1
# else:
# for l in reversed(lod):
# ans = []
# idx = 0
# while idx < len(l) - 1:
# ans.append(tensor_data[l[idx]:l[idx + 1]])
# idx += 1
# tensor_data = ans
# ans = tensor_data
return ans
class Executor(object):
def __init__(self, places):
if not isinstance(places, list) and not isinstance(places, tuple):
......@@ -16,6 +47,47 @@ class Executor(object):
act_places.append(p)
self.executor = core.Executor(act_places)
self.places = places
def aslodtensor(self, data):
def accumulate(data):
if not isinstance(data, list):
return 1
return sum([accumulate(sub) for sub in data])
def parselod(data):
seq_lens = [accumulate(seq) for seq in data]
cur_len = 0
lod = [cur_len]
for l in seq_lens:
cur_len += l
lod.append(cur_len)
return lod
assert len(self.places) != 0
if not isinstance(data, list):
# pure tensor case
tensor = core.LoDTensor()
tensor.set(data, self.places[0])
return tensor
else:
raise RuntimeError("Current implementation lacks unittests")
# lodtensor case
lod = []
if not isinstance(data[0], list):
lod.append(parselod(data))
flattened_data = np.concatenate(data, axis=0).astype("int64")
else:
while isinstance(data[0], list):
lod.append(parselod(seq))
flattened_data = [item for seq in data for item in seq]
data = flattened_data
flattened_data = np.concatenate(data, axis=0).astype("int64")
flattened_data = flattened_data.reshape([len(flattened_data), 1])
tensor = core.LoDTensor()
tensor.set(flattened_data, self.places[0])
tensor.set_lod(lod)
return tensor
def run(self,
program=None,
......@@ -23,7 +95,8 @@ class Executor(object):
fetch_list=None,
feed_var_name='feed',
fetch_var_name='fetch',
scope=None):
scope=None,
return_numpy=True):
if feed is None:
feed = {}
if fetch_list is None:
......@@ -52,7 +125,10 @@ class Executor(object):
inputs={'X': [feed_var]},
outputs={'Out': [out]},
attrs={'col': i})
core.set_feed_variable(scope, feed[name], feed_var.name, i)
cur_feed = feed[name]
if not isinstance(cur_feed, core.LoDTensor):
cur_feed = self.aslodtensor(cur_feed)
core.set_feed_variable(scope, cur_feed, feed_var.name, i)
fetch_var = global_block.create_var(
name=fetch_var_name,
......@@ -66,7 +142,11 @@ class Executor(object):
attrs={'col': i})
self.executor.run(program.desc, scope, 0, True)
return [
outs = [
core.get_fetch_variable(scope, fetch_var_name, i)
for i in xrange(len(fetch_list))
]
if return_numpy:
outs = as_numpy(outs)
return outs
python/paddle/v2/fluid/framework.py
浏览文件 @ 322d69f2
import paddle.v2.fluid.core as core
import paddle.v2.fluid.proto.framework_pb2 as framework_pb2
import collections
import numpy as np
import copy
from . import core
import proto.framework_pb2 as framework_pb2
__all__ = [
'Block', 'Variable', 'Program', 'Operator', 'default_startup_program',
'default_main_program'
'default_main_program', 'g_startup_program', 'g_main_program'
]
......
python/paddle/v2/fluid/initializer.py
浏览文件 @ 322d69f2
import paddle.v2.fluid.framework as framework
import framework
import numpy as np
__all__ = [
'ConstantInitializer', 'UniformInitializer', 'NormalInitializer',
'XavierInitializer'
]
__all__ = ['Constant', 'Uniform', 'Normal', 'Xavier']
class Initializer(object):
......@@ -368,3 +365,19 @@ class MSRAInitializer(Initializer):
})
var.op = op
return op
# We short the class name, since users will use the initializer with the package
# name. The sample code:
#
# import paddle.fluid as fluid
#
# hidden = fluid.layers.fc(...,
# param_attr=ParamAttr(fluid.initializer.Xavier()))
#
# It is no need to add an `Initializer` as the class suffix
Constant = ConstantInitializer
Uniform = UniformInitializer
Normal = NormalInitializer
Xavier = XavierInitializer
MSRA = MSRAInitializer
python/paddle/v2/fluid/layer_helper.py
浏览文件 @ 322d69f2
import copy
import itertools
from paddle.v2.fluid.framework import Variable, g_main_program, \
g_startup_program, unique_name, Program, dtype_is_floating
from paddle.v2.fluid.initializer import ConstantInitializer, \
UniformInitializer, XavierInitializer
from framework import Variable, g_main_program, \
g_startup_program, unique_name, dtype_is_floating
from paddle.v2.fluid.initializer import Constant, Xavier
class LayerHelper(object):
......@@ -209,7 +208,7 @@ class LayerHelper(object):
def _get_default_initializer(self, dtype):
if dtype is None or dtype_is_floating(dtype) is True:
return XavierInitializer()
return Xavier()
else:
# For integer and boolean types, initialize with all zeros
return ConstantInitializer()
return Constant()
python/paddle/v2/fluid/layers.py
浏览文件 @ 322d69f2
import paddle.v2.fluid.core as core
import paddle.v2.fluid.proto.framework_pb2 as framework_pb2
from paddle.v2.fluid.framework import OpProtoHolder, Variable, Program, \
Operator
from paddle.v2.fluid.initializer import ConstantInitializer, \
NormalInitializer, XavierInitializer
from . import core
import proto.framework_pb2 as framework_pb2
from framework import OpProtoHolder, Variable, Program, Operator
from initializer import Constant, Normal, Xavier
from paddle.v2.fluid.layer_helper import LayerHelper, unique_name
import re
import cStringIO
......@@ -58,10 +56,10 @@ def fc(input,
"""
def _get_default_param_initializer():
return XavierInitializer()
return Xavier()
def _get_default_bias_initializer():
return ConstantInitializer()
return Constant()
helper = LayerHelper('fc', **locals())
......@@ -139,7 +137,7 @@ def embedding(input,
"""
def _get_default_param_initializer():
return XavierInitializer()
return Xavier()
helper = LayerHelper('embedding', **locals())
w = helper.create_parameter(
......@@ -418,6 +416,7 @@ def _create_op_func_(op_type):
_create_op_func_('mean')
_create_op_func_('mul')
_create_op_func_('elementwise_add')
_create_op_func_('elementwise_div')
_create_op_func_('dropout')
_create_op_func_('reshape')
_create_op_func_('sigmoid')
......@@ -457,13 +456,14 @@ def concat(input, axis, main_program=None, startup_program=None):
return out
def sums(input, main_program=None, startup_program=None):
def sums(input, out=None, main_program=None, startup_program=None):
"""
This function takes in the input and performs the sum operation on it
and returns that as the output.
"""
helper = LayerHelper('sum', **locals())
out = helper.create_tmp_variable(dtype=helper.input_dtype())
if out is None:
out = helper.create_tmp_variable(dtype=helper.input_dtype())
helper.append_op(type='sum', inputs={'X': input}, outputs={'Out': out})
return out
......@@ -475,7 +475,7 @@ def linear_chain_crf(input,
main_program=None,
startup_program=None):
def _get_default_param_initializer():
return XavierInitializer()
return Xavier()
helper = LayerHelper('linear_chain_crf', **locals())
size = input.shape[1]
......@@ -606,7 +606,7 @@ def square_error_cost(input, label, **kwargs):
return square_out
def accuracy(input, label, k=1, **kwargs):
def accuracy(input, label, k=1, correct=None, total=None, **kwargs):
"""
This function computes the accuracy using the input and label.
The output is the top_k inputs and their indices.
......@@ -620,10 +620,11 @@ def accuracy(input, label, k=1, **kwargs):
outputs={"Out": [topk_out],
"Indices": [topk_indices]},
attrs={"k": k})
acc_out_dtype = kwargs.get("out_dtype", "float32")
acc_out = helper.create_tmp_variable(dtype="float32")
correct = helper.create_tmp_variable(dtype="int64")
total = helper.create_tmp_variable(dtype="int64")
if correct is None:
correct = helper.create_tmp_variable(dtype="int64")
if total is None:
total = helper.create_tmp_variable(dtype="int64")
helper.append_op(
type="accuracy",
inputs={
......@@ -658,10 +659,10 @@ def sequence_conv(input,
"""
def _get_default_bias_initializer():
return ConstantInitializer()
return Constant()
def _get_default_param_initializer():
return XavierInitializer()
return Xavier()
# FIXME(dzh) : want to unify the argument of python layer
# function. So we ignore some unecessary attributes.
......@@ -722,11 +723,11 @@ def conv2d(input,
"""
def _get_default_bias_initializer():
return ConstantInitializer()
return Constant()
def _get_default_param_initializer(filter_size, num_channels):
std = (2.0 / (filter_size[0]**2 * num_channels))**0.5
return NormalInitializer(0.0, std, 0)
return Normal(0.0, std, 0)
helper = LayerHelper('conv2d', **locals())
dtype = helper.input_dtype()
......@@ -875,22 +876,20 @@ def batch_norm(input,
attr=helper.param_attr,
shape=param_shape,
dtype=dtype,
initializer=ConstantInitializer(1.0))
initializer=Constant(1.0))
bias = helper.create_parameter(
attr=helper.param_attr,
shape=param_shape,
dtype=dtype,
initializer=ConstantInitializer(0.0))
initializer=Constant(0.0))
mean = helper.create_global_variable(
dtype=input.dtype, shape=param_shape, persistable=True)
helper.set_variable_initializer(
var=mean, initializer=ConstantInitializer(0.0))
helper.set_variable_initializer(var=mean, initializer=Constant(0.0))
variance = helper.create_global_variable(
dtype=input.dtype, shape=param_shape, persistable=True)
helper.set_variable_initializer(
var=variance, initializer=ConstantInitializer(1.0))
helper.set_variable_initializer(var=variance, initializer=Constant(1.0))
# create output
# mean and mean_out share the same memory
......@@ -1355,6 +1354,33 @@ def lod_rank_table(x, level=0, main_program=None):
return table
def max_sequence_len(rank_table, main_program=None):
"""
This function creates an operator to calculate the length of
max seqence through input rank_table(should be a lod_rank_table)
"""
helper = LayerHelper("max_seqence_len", **locals())
res = helper.create_tmp_variable(dtype="int64")
helper.append_op(
type="max_sequence_len",
inputs={"RankTable": rank_table},
outputs={"Out": res})
return res
def topk(input, k, main_program=None, startup_program=None):
helper = LayerHelper('topk', **locals())
topk_out = helper.create_tmp_variable(dtype=input.data_type)
topk_indices = helper.create_tmp_variable(dtype='int64')
helper.append_op(
type='top_k',
inputs={'X': [input]},
outputs={'Out': [topk_out],
'Indices': [topk_indices]},
attrs={'k': k})
return topk_out, topk_indices
def lod_tensor_to_array(x, table, main_program=None):
"""
This function creates an operator to convert an LOD_Tensor to
......@@ -1388,14 +1414,20 @@ def array_to_lod_tensor(x, table, main_program=None):
return tmp
def fill_constant(shape, dtype, value, main_program=None, startup_program=None):
def fill_constant(shape,
dtype,
value,
out=None,
main_program=None,
startup_program=None):
"""
This function creates a tensor , with shape as mentioned in the input and
specified dtype and fills this up with a constant value that
comes in the input. It also sets the stop_gradient to be True.
"""
helper = LayerHelper("fill_constant", **locals())
out = helper.create_tmp_variable(dtype=dtype)
if out is None:
out = helper.create_tmp_variable(dtype=dtype)
helper.append_op(
type='fill_constant',
inputs={},
......
python/paddle/v2/fluid/nets.py
浏览文件 @ 322d69f2
import paddle.v2.fluid.layers as layers
import layers
__all__ = ["simple_img_conv_pool", "sequence_conv_pool"]
......
python/paddle/v2/fluid/optimizer.py
浏览文件 @ 322d69f2
from collections import defaultdict
import paddle.v2.fluid.framework as framework
from paddle.v2.fluid.framework import unique_name, Program
from paddle.v2.fluid.backward import append_backward_ops
from paddle.v2.fluid.initializer import ConstantInitializer
from paddle.v2.fluid.regularizer import append_regularization_ops
from paddle.v2.fluid.layer_helper import LayerHelper
import framework
from backward import append_backward_ops
from framework import unique_name
from initializer import Constant
from layer_helper import LayerHelper
from regularizer import append_regularization_ops
__all__ = [
'SGDOptimizer', 'MomentumOptimizer', 'AdagradOptimizer', 'AdamOptimizer',
'AdamaxOptimizer', 'DecayedAdagradOptimizer'
]
__all__ = ['SGD', 'Momentum', 'Adagrad', 'Adam', 'Adamax', 'DecayedAdagrad']
class Optimizer(object):
......@@ -48,7 +45,7 @@ class Optimizer(object):
persistable=True)
param_lr = param_lr * self._learning_rate
self.helper.set_variable_initializer(
var=param_lr_var, initializer=ConstantInitializer(param_lr))
var=param_lr_var, initializer=Constant(param_lr))
return param_lr_var
def _create_accumulators(self, block, parameters):
......@@ -96,7 +93,7 @@ class Optimizer(object):
type=param.type,
shape=param.shape)
self.helper.set_variable_initializer(
var, initializer=ConstantInitializer(value=float(fill_value)))
var, initializer=Constant(value=float(fill_value)))
self._accumulators[name][param.name] = var
def _get_accumulator(self, name, param):
......@@ -360,7 +357,7 @@ class AdamOptimizer(Optimizer):
lod_level=0,
persistable=True)
self.helper.set_variable_initializer(
self._beta1_pow_acc, initializer=ConstantInitializer(self._beta1))
self._beta1_pow_acc, initializer=Constant(self._beta1))
self._beta2_pow_acc = self.helper.create_global_variable(
name=unique_name('beta2_pow_acc'),
......@@ -370,7 +367,7 @@ class AdamOptimizer(Optimizer):
persistable=True)
self.helper.set_variable_initializer(
self._beta2_pow_acc, initializer=ConstantInitializer(self._beta2))
self._beta2_pow_acc, initializer=Constant(self._beta2))
# Create accumulator tensors for first and second moments
for p in parameters:
......@@ -462,7 +459,7 @@ class AdamaxOptimizer(Optimizer):
lod_level=0,
persistable=True)
self.helper.set_variable_initializer(
self._beta1_pow_acc, initializer=ConstantInitializer(self._beta1))
self._beta1_pow_acc, initializer=Constant(self._beta1))
# Create accumulator tensors for first moment and infinity norm
for p in parameters:
......@@ -559,3 +556,19 @@ class DecayedAdagradOptimizer(Optimizer):
attrs={"epsilon": self._epsilon})
return decayed_adagrad_op
# We short the class name, since users will use the optimizer with the package
# name. The sample code:
#
# import paddle.fluid as fluid
#
# sgd = fluid.optimizer.SGD(...)
#
# It is no need to add an `Optimizer` as the class suffix
SGD = SGDOptimizer
Momentum = MomentumOptimizer
Adagrad = AdagradOptimizer
Adam = AdamOptimizer
Adamax = AdamaxOptimizer
DecayedAdagrad = DecayedAdagradOptimizer
python/paddle/v2/fluid/regularizer.py
浏览文件 @ 322d69f2
import paddle.v2.fluid.framework as framework
import framework
__all__ = [
'append_regularization_ops', 'L2DecayRegularizer', 'L1DecayRegularizer'
]
__all__ = ['append_regularization_ops', 'L1Decay', 'L2Decay']
def append_regularization_ops(parameters_and_grads):
......@@ -139,3 +137,16 @@ class L1DecayRegularizer(WeightDecayRegularizer):
attrs={"scale": self._regularization_coeff})
return decay
# We short the class name, since users will use the regulaizer with the package
# name. The sample code:
#
# import paddle.fluid as fluid
#
# hidden = fluid.layers.fc(...,
# param_attr=ParamAttr(fluid.regularizer.Xavier()))
#
# It is no need to add a `Regularizer` as the class suffix
L1Decay = L1DecayRegularizer
L2Decay = L2DecayRegularizer
python/paddle/v2/fluid/tests/book/test_fit_a_line.py
浏览文件 @ 322d69f2
import numpy as np
import paddle.v2 as paddle
import paddle.v2.fluid.core as core
import paddle.v2.fluid.framework as framework
import paddle.v2.fluid.layers as layers
from paddle.v2.fluid.executor import Executor
from paddle.v2.fluid.io import save_persistables, load_persistables
from paddle.v2.fluid.optimizer import SGDOptimizer
import paddle.v2.fluid as fluid
x = layers.data(name='x', shape=[13], dtype='float32')
x = fluid.layers.data(name='x', shape=[13], dtype='float32')
y_predict = layers.fc(input=x, size=1, act=None)
y_predict = fluid.layers.fc(input=x, size=1, act=None)
y = layers.data(name='y', shape=[1], dtype='float32')
y = fluid.layers.data(name='y', shape=[1], dtype='float32')
cost = layers.square_error_cost(input=y_predict, label=y)
avg_cost = layers.mean(x=cost)
cost = fluid.layers.square_error_cost(input=y_predict, label=y)
avg_cost = fluid.layers.mean(x=cost)
sgd_optimizer = SGDOptimizer(learning_rate=0.001)
opts = sgd_optimizer.minimize(avg_cost)
sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001)
sgd_optimizer.minimize(avg_cost)
BATCH_SIZE = 20
......@@ -26,32 +21,24 @@ train_reader = paddle.batch(
paddle.dataset.uci_housing.train(), buf_size=500),
batch_size=BATCH_SIZE)
place = core.CPUPlace()
exe = Executor(place)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(framework.default_startup_program())
exe.run(fluid.default_startup_program())
PASS_NUM = 100
for pass_id in range(PASS_NUM):
save_persistables(exe, "./fit_a_line.model/")
load_persistables(exe, "./fit_a_line.model/")
fluid.io.save_persistables(exe, "./fit_a_line.model/")
fluid.io.load_persistables(exe, "./fit_a_line.model/")
for data in train_reader():
x_data = np.array(map(lambda x: x[0], data)).astype("float32")
y_data = np.array(map(lambda x: x[1], data)).astype("float32")
tensor_x = core.LoDTensor()
tensor_x.set(x_data, place)
# print tensor_x.get_dims()
tensor_y = core.LoDTensor()
tensor_y.set(y_data, place)
# print tensor_y.get_dims()
outs = exe.run(framework.default_main_program(),
feed={'x': tensor_x,
'y': tensor_y},
fetch_list=[avg_cost])
out = np.array(outs[0])
if out[0] < 10.0:
x_data = np.array(map(lambda _: _[0], data)).astype("float32")
y_data = np.array(map(lambda _: _[1], data)).astype("float32")
avg_loss_value, = exe.run(fluid.default_main_program(),
feed={'x': x_data,
'y': y_data},
fetch_list=[avg_cost])
if avg_loss_value[0] < 10.0:
exit(0) # if avg cost less than 10.0, we think our code is good.
exit(1)
python/paddle/v2/fluid/tests/book/test_image_classification_train.py
浏览文件 @ 322d69f2
from __future__ import print_function
import numpy as np
import paddle.v2 as paddle
import paddle.v2.fluid.core as core
import paddle.v2.fluid.framework as framework
import paddle.v2.fluid.layers as layers
import paddle.v2.fluid.nets as nets
import paddle.v2.fluid.evaluator as evaluator
from paddle.v2.fluid.io import get_inference_program
from paddle.v2.fluid.executor import Executor
from paddle.v2.fluid.initializer import XavierInitializer
from paddle.v2.fluid.optimizer import AdamOptimizer
import paddle.v2.fluid as fluid
def resnet_cifar10(input, depth=32):
def conv_bn_layer(input, ch_out, filter_size, stride, padding, act='relu'):
tmp = layers.conv2d(
tmp = fluid.layers.conv2d(
input=input,
filter_size=filter_size,
num_filters=ch_out,
......@@ -21,12 +14,11 @@ def resnet_cifar10(input, depth=32):
padding=padding,
act=None,
bias_attr=False)
return layers.batch_norm(input=tmp, act=act)
return fluid.layers.batch_norm(input=tmp, act=act)
def shortcut(input, ch_in, ch_out, stride, program, init_program):
def shortcut(input, ch_in, ch_out, stride):
if ch_in != ch_out:
return conv_bn_layer(input, ch_out, 1, stride, 0, None, program,
init_program)
return conv_bn_layer(input, ch_out, 1, stride, 0, None)
else:
return input
......@@ -34,7 +26,7 @@ def resnet_cifar10(input, depth=32):
tmp = conv_bn_layer(input, ch_out, 3, stride, 1)
tmp = conv_bn_layer(tmp, ch_out, 3, 1, 1, act=None)
short = shortcut(input, ch_in, ch_out, stride)
return layers.elementwise_add(x=tmp, y=short, act='relu')
return fluid.layers.elementwise_add(x=tmp, y=short, act='relu')
def layer_warp(block_func, input, ch_in, ch_out, count, stride):
tmp = block_func(input, ch_in, ch_out, stride)
......@@ -49,14 +41,14 @@ def resnet_cifar10(input, depth=32):
res1 = layer_warp(basicblock, conv1, 16, 16, n, 1)
res2 = layer_warp(basicblock, res1, 16, 32, n, 2)
res3 = layer_warp(basicblock, res2, 32, 64, n, 2)
pool = layers.pool2d(
pool = fluid.layers.pool2d(
input=res3, pool_size=8, pool_type='avg', pool_stride=1)
return pool
def vgg16_bn_drop(input):
def conv_block(input, num_filter, groups, dropouts):
return nets.img_conv_group(
return fluid.nets.img_conv_group(
input=input,
pool_size=2,
pool_stride=2,
......@@ -73,26 +65,20 @@ def vgg16_bn_drop(input):
conv4 = conv_block(conv3, 512, 3, [0.4, 0.4, 0])
conv5 = conv_block(conv4, 512, 3, [0.4, 0.4, 0])
drop = layers.dropout(x=conv5, dropout_prob=0.5)
fc1 = layers.fc(input=drop,
size=512,
act=None,
param_attr={"initializer": XavierInitializer()})
reshape1 = layers.reshape(x=fc1, shape=list(fc1.shape + (1, 1)))
bn = layers.batch_norm(input=reshape1, act='relu')
drop2 = layers.dropout(x=bn, dropout_prob=0.5)
fc2 = layers.fc(input=drop2,
size=512,
act=None,
param_attr={"initializer": XavierInitializer()})
drop = fluid.layers.dropout(x=conv5, dropout_prob=0.5)
fc1 = fluid.layers.fc(input=drop, size=512, act=None)
reshape1 = fluid.layers.reshape(x=fc1, shape=list(fc1.shape + (1, 1)))
bn = fluid.layers.batch_norm(input=reshape1, act='relu')
drop2 = fluid.layers.dropout(x=bn, dropout_prob=0.5)
fc2 = fluid.layers.fc(input=drop2, size=512, act=None)
return fc2
classdim = 10
data_shape = [3, 32, 32]
images = layers.data(name='pixel', shape=data_shape, dtype='float32')
label = layers.data(name='label', shape=[1], dtype='int64')
images = fluid.layers.data(name='pixel', shape=data_shape, dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
# Add neural network config
# option 1. resnet
......@@ -100,35 +86,29 @@ label = layers.data(name='label', shape=[1], dtype='int64')
# option 2. vgg
net = vgg16_bn_drop(images)
# print(program)
predict = fluid.layers.fc(input=net, size=classdim, act='softmax')
cost = fluid.layers.cross_entropy(input=predict, label=label)
avg_cost = fluid.layers.mean(x=cost)
predict = layers.fc(input=net, size=classdim, act='softmax')
cost = layers.cross_entropy(input=predict, label=label)
avg_cost = layers.mean(x=cost)
# optimizer = SGDOptimizer(learning_rate=0.001)
optimizer = AdamOptimizer(learning_rate=0.001)
optimizer = fluid.optimizer.Adam(learning_rate=0.001)
opts = optimizer.minimize(avg_cost)
accuracy, acc_out = evaluator.accuracy(input=predict, label=label)
accuracy = fluid.evaluator.Accuracy(input=predict, label=label)
BATCH_SIZE = 128
PASS_NUM = 1
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.cifar.train10(), buf_size=BATCH_SIZE * 10),
paddle.dataset.cifar.train10(), buf_size=128 * 10),
batch_size=BATCH_SIZE)
test_reader = paddle.batch(paddle.dataset.cifar.test10(), batch_size=BATCH_SIZE)
place = core.CPUPlace()
exe = Executor(place)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(framework.default_startup_program())
exe.run(fluid.default_startup_program())
for pass_id in range(PASS_NUM):
batch_id = 0
accuracy.reset(exe)
for data in train_reader():
img_data = np.array(map(lambda x: x[0].reshape(data_shape),
......@@ -139,56 +119,13 @@ for pass_id in range(PASS_NUM):
batch_size = batch_size * i
y_data = y_data.reshape([batch_size, 1])
tensor_img = core.LoDTensor()
tensor_y = core.LoDTensor()
tensor_img.set(img_data, place)
tensor_y.set(y_data, place)
outs = exe.run(framework.default_main_program(),
feed={"pixel": tensor_img,
"label": tensor_y},
fetch_list=[avg_cost, acc_out])
loss = np.array(outs[0])
acc = np.array(outs[1])
loss, acc = exe.run(fluid.default_main_program(),
feed={"pixel": img_data,
"label": y_data},
fetch_list=[avg_cost] + accuracy.metrics)
pass_acc = accuracy.eval(exe)
batch_id = batch_id + 1
test_accuracy, test_acc_out = evaluator.accuracy(
input=predict, label=label)
test_target = [avg_cost, test_acc_out] + test_accuracy.states().values()
inference_program = get_inference_program(test_target)
test_accuracy.reset(exe)
for data in test_reader():
x_data = np.array(map(lambda x: x[0].reshape(data_shape),
data)).astype("float32")
y_data = np.array(map(lambda x: x[1], data)).astype("int64")
y_data = np.expand_dims(y_data, axis=1)
tensor_x = core.LoDTensor()
tensor_x.set(x_data, place)
tensor_y = core.LoDTensor()
tensor_y.set(y_data, place)
outs = exe.run(inference_program,
feed={'pixel': tensor_x,
'label': tensor_y},
fetch_list=[avg_cost, test_acc_out])
out = np.array(outs[0])
acc = np.array(outs[1])
test_pass_acc = test_accuracy.eval(exe)
print("pass_id:" + str(pass_id) + " batch_id:" + str(batch_id) +
" loss:" + str(loss) + " acc:" + str(acc) + " pass_acc:" + str(
pass_acc) + " test_pass_acc:" + str(test_pass_acc))
if batch_id > 1:
# this model is slow, so if we can train two mini batch, we think it works properly.
exit(0)
print("loss:" + str(loss) + " acc:" + str(acc) + " pass_acc:" + str(
pass_acc))
# this model is slow, so if we can train two mini batch, we think it works properly.
exit(0)
exit(1)
python/paddle/v2/fluid/tests/book/test_label_semantic_roles.py
浏览文件 @ 322d69f2
import numpy as np
import paddle.v2 as paddle
import paddle.v2.dataset.conll05 as conll05
import paddle.v2.fluid.core as core
import paddle.v2.fluid.framework as framework
import paddle.v2.fluid.layers as layers
from paddle.v2.fluid.executor import Executor, g_scope
from paddle.v2.fluid.optimizer import SGDOptimizer
import paddle.v2.fluid as fluid
word_dict, verb_dict, label_dict = conll05.get_dict()
word_dict_len = len(word_dict)
......@@ -34,23 +30,23 @@ def load_parameter(file_name, h, w):
def db_lstm():
# 8 features
word = layers.data(name='word_data', shape=[1], dtype='int64')
predicate = layers.data(name='verb_data', shape=[1], dtype='int64')
ctx_n2 = layers.data(name='ctx_n2_data', shape=[1], dtype='int64')
ctx_n1 = layers.data(name='ctx_n1_data', shape=[1], dtype='int64')
ctx_0 = layers.data(name='ctx_0_data', shape=[1], dtype='int64')
ctx_p1 = layers.data(name='ctx_p1_data', shape=[1], dtype='int64')
ctx_p2 = layers.data(name='ctx_p2_data', shape=[1], dtype='int64')
mark = layers.data(name='mark_data', shape=[1], dtype='int64')
predicate_embedding = layers.embedding(
word = fluid.layers.data(name='word_data', shape=[1], dtype='int64')
predicate = fluid.layers.data(name='verb_data', shape=[1], dtype='int64')
ctx_n2 = fluid.layers.data(name='ctx_n2_data', shape=[1], dtype='int64')
ctx_n1 = fluid.layers.data(name='ctx_n1_data', shape=[1], dtype='int64')
ctx_0 = fluid.layers.data(name='ctx_0_data', shape=[1], dtype='int64')
ctx_p1 = fluid.layers.data(name='ctx_p1_data', shape=[1], dtype='int64')
ctx_p2 = fluid.layers.data(name='ctx_p2_data', shape=[1], dtype='int64')
mark = fluid.layers.data(name='mark_data', shape=[1], dtype='int64')
predicate_embedding = fluid.layers.embedding(
input=predicate,
size=[pred_len, word_dim],
dtype='float32',
is_sparse=IS_SPARSE,
param_attr={'name': 'vemb'})
mark_embedding = layers.embedding(
mark_embedding = fluid.layers.embedding(
input=mark,
size=[mark_dict_len, mark_dim],
dtype='float32',
......@@ -58,7 +54,7 @@ def db_lstm():
word_input = [word, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2]
emb_layers = [
layers.embedding(
fluid.layers.embedding(
size=[word_dict_len, word_dim],
input=x,
param_attr={'name': embedding_name,
......@@ -68,12 +64,12 @@ def db_lstm():
emb_layers.append(mark_embedding)
hidden_0_layers = [
layers.fc(input=emb, size=hidden_dim) for emb in emb_layers
fluid.layers.fc(input=emb, size=hidden_dim) for emb in emb_layers
]
hidden_0 = layers.sums(input=hidden_0_layers)
hidden_0 = fluid.layers.sums(input=hidden_0_layers)
lstm_0 = layers.dynamic_lstm(
lstm_0 = fluid.layers.dynamic_lstm(
input=hidden_0,
size=hidden_dim,
candidate_activation='relu',
......@@ -84,12 +80,12 @@ def db_lstm():
input_tmp = [hidden_0, lstm_0]
for i in range(1, depth):
mix_hidden = layers.sums(input=[
layers.fc(input=input_tmp[0], size=hidden_dim),
layers.fc(input=input_tmp[1], size=hidden_dim)
mix_hidden = fluid.layers.sums(input=[
fluid.layers.fc(input=input_tmp[0], size=hidden_dim),
fluid.layers.fc(input=input_tmp[1], size=hidden_dim)
])
lstm = layers.dynamic_lstm(
lstm = fluid.layers.dynamic_lstm(
input=mix_hidden,
size=hidden_dim,
candidate_activation='relu',
......@@ -99,9 +95,9 @@ def db_lstm():
input_tmp = [mix_hidden, lstm]
feature_out = layers.sums(input=[
layers.fc(input=input_tmp[0], size=label_dict_len),
layers.fc(input=input_tmp[1], size=label_dict_len)
feature_out = fluid.layers.sums(input=[
fluid.layers.fc(input=input_tmp[0], size=label_dict_len),
fluid.layers.fc(input=input_tmp[1], size=label_dict_len)
])
return feature_out
......@@ -116,7 +112,7 @@ def to_lodtensor(data, place):
lod.append(cur_len)
flattened_data = np.concatenate(data, axis=0).astype("int64")
flattened_data = flattened_data.reshape([len(flattened_data), 1])
res = core.LoDTensor()
res = fluid.LoDTensor()
res.set(flattened_data, place)
res.set_lod([lod])
return res
......@@ -125,29 +121,29 @@ def to_lodtensor(data, place):
def main():
# define network topology
feature_out = db_lstm()
target = layers.data(name='target', shape=[1], dtype='int64')
crf_cost = layers.linear_chain_crf(
target = fluid.layers.data(name='target', shape=[1], dtype='int64')
crf_cost = fluid.layers.linear_chain_crf(
input=feature_out,
label=target,
param_attr={"name": 'crfw',
"learning_rate": mix_hidden_lr})
avg_cost = layers.mean(x=crf_cost)
avg_cost = fluid.layers.mean(x=crf_cost)
# TODO(qiao)
# 1. add crf_decode_layer and evaluator
# 2. use other optimizer and check why out will be NAN
sgd_optimizer = SGDOptimizer(learning_rate=0.0001)
opts = sgd_optimizer.minimize(avg_cost)
sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.0001)
sgd_optimizer.minimize(avg_cost)
train_data = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.conll05.test(), buf_size=8192),
batch_size=BATCH_SIZE)
place = core.CPUPlace()
exe = Executor(place)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(framework.default_startup_program())
exe.run(fluid.default_startup_program())
embedding_param = g_scope.find_var(embedding_name).get_tensor()
embedding_param = fluid.g_scope.find_var(embedding_name).get_tensor()
embedding_param.set(
load_parameter(conll05.get_embedding(), word_dict_len, word_dim), place)
......@@ -164,7 +160,7 @@ def main():
mark_data = to_lodtensor(map(lambda x: x[7], data), place)
target = to_lodtensor(map(lambda x: x[8], data), place)
outs = exe.run(framework.default_main_program(),
outs = exe.run(fluid.default_main_program(),
feed={
'word_data': word_data,
'ctx_n2_data': ctx_n2_data,
......
python/paddle/v2/fluid/tests/book/test_recognize_digits_conv.py
浏览文件 @ 322d69f2
from __future__ import print_function
import numpy as np
import paddle.v2 as paddle
import paddle.v2.fluid.core as core
import paddle.v2.fluid.evaluator as evaluator
import paddle.v2.fluid.framework as framework
import paddle.v2.fluid.layers as layers
import paddle.v2.fluid.nets as nets
from paddle.v2.fluid.executor import Executor
from paddle.v2.fluid.optimizer import AdamOptimizer
import paddle.v2.fluid as fluid
images = layers.data(name='pixel', shape=[1, 28, 28], dtype='float32')
label = layers.data(name='label', shape=[1], dtype='int64')
conv_pool_1 = nets.simple_img_conv_pool(
images = fluid.layers.data(name='pixel', shape=[1, 28, 28], dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
conv_pool_1 = fluid.nets.simple_img_conv_pool(
input=images,
filter_size=5,
num_filters=20,
pool_size=2,
pool_stride=2,
act="relu")
conv_pool_2 = nets.simple_img_conv_pool(
conv_pool_2 = fluid.nets.simple_img_conv_pool(
input=conv_pool_1,
filter_size=5,
num_filters=50,
......@@ -25,13 +20,13 @@ conv_pool_2 = nets.simple_img_conv_pool(
pool_stride=2,
act="relu")
predict = layers.fc(input=conv_pool_2, size=10, act="softmax")
cost = layers.cross_entropy(input=predict, label=label)
avg_cost = layers.mean(x=cost)
optimizer = AdamOptimizer(learning_rate=0.01, beta1=0.9, beta2=0.999)
opts = optimizer.minimize(avg_cost)
predict = fluid.layers.fc(input=conv_pool_2, size=10, act="softmax")
cost = fluid.layers.cross_entropy(input=predict, label=label)
avg_cost = fluid.layers.mean(x=cost)
optimizer = fluid.optimizer.Adam(learning_rate=0.01)
optimizer.minimize(avg_cost)
accuracy, acc_out = evaluator.accuracy(input=predict, label=label)
accuracy = fluid.evaluator.Accuracy(input=predict, label=label)
BATCH_SIZE = 50
PASS_NUM = 3
......@@ -40,10 +35,10 @@ train_reader = paddle.batch(
paddle.dataset.mnist.train(), buf_size=500),
batch_size=BATCH_SIZE)
place = core.CPUPlace()
exe = Executor(place)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(framework.default_startup_program())
exe.run(fluid.default_startup_program())
for pass_id in range(PASS_NUM):
accuracy.reset(exe)
......@@ -53,17 +48,10 @@ for pass_id in range(PASS_NUM):
y_data = np.array(map(lambda x: x[1], data)).astype("int64")
y_data = y_data.reshape([BATCH_SIZE, 1])
tensor_img = core.LoDTensor()
tensor_y = core.LoDTensor()
tensor_img.set(img_data, place)
tensor_y.set(y_data, place)
outs = exe.run(framework.default_main_program(),
feed={"pixel": tensor_img,
"label": tensor_y},
fetch_list=[avg_cost, acc_out])
loss = np.array(outs[0])
acc = np.array(outs[1])
loss, acc = exe.run(fluid.default_main_program(),
feed={"pixel": img_data,
"label": y_data},
fetch_list=[avg_cost] + accuracy.metrics)
pass_acc = accuracy.eval(exe)
print("pass_id=" + str(pass_id) + " acc=" + str(acc) + " pass_acc=" +
str(pass_acc))
......
python/paddle/v2/fluid/tests/book/test_recognize_digits_mlp.py
浏览文件 @ 322d69f2
from __future__ import print_function
import numpy as np
import paddle.v2 as paddle
import paddle.v2.fluid.core as core
import paddle.v2.fluid.framework as framework
import paddle.v2.fluid.layers as layers
import paddle.v2.fluid.evaluator as evaluator
from paddle.v2.fluid.io import get_inference_program
from paddle.v2.fluid.executor import Executor
from paddle.v2.fluid.initializer import UniformInitializer
from paddle.v2.fluid.optimizer import MomentumOptimizer
from paddle.v2.fluid.regularizer import L2DecayRegularizer
import paddle.v2.fluid as fluid
BATCH_SIZE = 128
image = layers.data(name='x', shape=[784], dtype='float32')
image = fluid.layers.data(name='x', shape=[784], dtype='float32')
param_attr = {
'name': None,
'initializer': UniformInitializer(
low=-1.0, high=1.0),
'regularization': L2DecayRegularizer(0.0005 * BATCH_SIZE)
'regularization': fluid.regularizer.L2Decay(0.0005 * BATCH_SIZE)
}
hidden1 = layers.fc(input=image, size=128, act='relu', param_attr=param_attr)
hidden2 = layers.fc(input=hidden1, size=64, act='relu', param_attr=param_attr)
hidden1 = fluid.layers.fc(input=image,
size=128,
act='relu',
param_attr=param_attr)
hidden2 = fluid.layers.fc(input=hidden1,
size=64,
act='relu',
param_attr=param_attr)
predict = layers.fc(input=hidden2,
size=10,
act='softmax',
param_attr=param_attr)
predict = fluid.layers.fc(input=hidden2,
size=10,
act='softmax',
param_attr=param_attr)
label = layers.data(name='y', shape=[1], dtype='int64')
label = fluid.layers.data(name='y', shape=[1], dtype='int64')
cost = layers.cross_entropy(input=predict, label=label)
avg_cost = layers.mean(x=cost)
cost = fluid.layers.cross_entropy(input=predict, label=label)
avg_cost = fluid.layers.mean(x=cost)
optimizer = MomentumOptimizer(learning_rate=0.001, momentum=0.9)
optimizer = fluid.optimizer.Momentum(learning_rate=0.001, momentum=0.9)
opts = optimizer.minimize(avg_cost)
accuracy, acc_out = evaluator.accuracy(input=predict, label=label)
accuracy = fluid.evaluator.Accuracy(input=predict, label=label)
train_reader = paddle.batch(
paddle.reader.shuffle(
......@@ -45,10 +42,10 @@ train_reader = paddle.batch(
test_reader = paddle.batch(paddle.dataset.mnist.test(), batch_size=128)
place = core.CPUPlace()
exe = Executor(place)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(framework.default_startup_program())
exe.run(fluid.default_startup_program())
PASS_NUM = 100
for pass_id in range(PASS_NUM):
......@@ -58,25 +55,24 @@ for pass_id in range(PASS_NUM):
y_data = np.array(map(lambda x: x[1], data)).astype("int64")
y_data = np.expand_dims(y_data, axis=1)
tensor_x = core.LoDTensor()
tensor_x = fluid.LoDTensor()
tensor_x.set(x_data, place)
tensor_y = core.LoDTensor()
tensor_y = fluid.LoDTensor()
tensor_y.set(y_data, place)
outs = exe.run(framework.default_main_program(),
outs = exe.run(fluid.default_main_program(),
feed={'x': tensor_x,
'y': tensor_y},
fetch_list=[avg_cost, acc_out])
fetch_list=[avg_cost] + accuracy.metrics)
out = np.array(outs[0])
acc = np.array(outs[1])
pass_acc = accuracy.eval(exe)
test_accuracy, test_acc_out = evaluator.accuracy(
input=predict, label=label)
test_accuracy = fluid.evaluator.Accuracy(input=predict, label=label)
test_target = [avg_cost, test_acc_out] + test_accuracy.states().values()
inference_program = get_inference_program(test_target)
test_target = [avg_cost] + test_accuracy.metrics + test_accuracy.states
inference_program = fluid.io.get_inference_program(test_target)
test_accuracy.reset(exe)
for data in test_reader():
......@@ -84,18 +80,10 @@ for pass_id in range(PASS_NUM):
y_data = np.array(map(lambda x: x[1], data)).astype("int64")
y_data = np.expand_dims(y_data, axis=1)
tensor_x = core.LoDTensor()
tensor_x.set(x_data, place)
tensor_y = core.LoDTensor()
tensor_y.set(y_data, place)
outs = exe.run(inference_program,
feed={'x': tensor_x,
'y': tensor_y},
fetch_list=[avg_cost, test_acc_out])
out = np.array(outs[0])
acc = np.array(outs[1])
out, acc = exe.run(inference_program,
feed={'x': x_data,
'y': y_data},
fetch_list=[avg_cost] + test_accuracy.metrics)
test_pass_acc = test_accuracy.eval(exe)
print("pass_id=" + str(pass_id) + " train_cost=" + str(
......
python/paddle/v2/fluid/tests/book/test_understand_sentiment_conv.py
浏览文件 @ 322d69f2
from __future__ import print_function
import numpy as np
import paddle.v2 as paddle
import paddle.v2.fluid.core as core
import paddle.v2.fluid.evaluator as evaluator
import paddle.v2.fluid.framework as framework
import paddle.v2.fluid.layers as layers
import paddle.v2.fluid.nets as nets
from paddle.v2.fluid.executor import Executor
from paddle.v2.fluid.optimizer import AdamOptimizer
import paddle.v2.fluid as fluid
def convolution_net(input_dim, class_dim=2, emb_dim=32, hid_dim=32):
data = layers.data(name="words", shape=[1], dtype="int64")
label = layers.data(name="label", shape=[1], dtype="int64")
data = fluid.layers.data(name="words", shape=[1], dtype="int64")
label = fluid.layers.data(name="label", shape=[1], dtype="int64")
emb = layers.embedding(input=data, size=[input_dim, emb_dim])
conv_3 = nets.sequence_conv_pool(
emb = fluid.layers.embedding(input=data, size=[input_dim, emb_dim])
conv_3 = fluid.nets.sequence_conv_pool(
input=emb,
num_filters=hid_dim,
filter_size=3,
act="tanh",
pool_type="sqrt")
conv_4 = nets.sequence_conv_pool(
conv_4 = fluid.nets.sequence_conv_pool(
input=emb,
num_filters=hid_dim,
filter_size=4,
act="tanh",
pool_type="sqrt")
prediction = layers.fc(input=[conv_3, conv_4],
size=class_dim,
act="softmax")
cost = layers.cross_entropy(input=prediction, label=label)
avg_cost = layers.mean(x=cost)
adam_optimizer = AdamOptimizer(learning_rate=0.002)
opts = adam_optimizer.minimize(avg_cost)
accuracy, acc_out = evaluator.accuracy(input=prediction, label=label)
return avg_cost, accuracy, acc_out
prediction = fluid.layers.fc(input=[conv_3, conv_4],
size=class_dim,
act="softmax")
cost = fluid.layers.cross_entropy(input=prediction, label=label)
avg_cost = fluid.layers.mean(x=cost)
adam_optimizer = fluid.optimizer.Adam(learning_rate=0.002)
adam_optimizer.minimize(avg_cost)
accuracy = fluid.evaluator.Accuracy(input=prediction, label=label)
return avg_cost, accuracy, accuracy.metrics[0]
def to_lodtensor(data, place):
......@@ -46,7 +41,7 @@ def to_lodtensor(data, place):
lod.append(cur_len)
flattened_data = np.concatenate(data, axis=0).astype("int64")
flattened_data = flattened_data.reshape([len(flattened_data), 1])
res = core.LoDTensor()
res = fluid.LoDTensor()
res.set(flattened_data, place)
res.set_lod([lod])
return res
......@@ -67,10 +62,10 @@ def main():
paddle.reader.shuffle(
paddle.dataset.imdb.train(word_dict), buf_size=1000),
batch_size=BATCH_SIZE)
place = core.CPUPlace()
exe = Executor(place)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(framework.default_startup_program())
exe.run(fluid.default_startup_program())
for pass_id in xrange(PASS_NUM):
accuracy.reset(exe)
......@@ -80,15 +75,14 @@ def main():
label = np.array(map(lambda x: x[1], data)).astype("int64")
label = label.reshape([BATCH_SIZE, 1])
tensor_label = core.LoDTensor()
tensor_label = fluid.LoDTensor()
tensor_label.set(label, place)
outs = exe.run(framework.default_main_program(),
feed={"words": tensor_words,
"label": tensor_label},
fetch_list=[cost, acc_out])
cost_val = np.array(outs[0])
acc_val = np.array(outs[1])
cost_val, acc_val = exe.run(
fluid.default_main_program(),
feed={"words": tensor_words,
"label": tensor_label},
fetch_list=[cost, acc_out])
pass_acc = accuracy.eval(exe)
print("cost=" + str(cost_val) + " acc=" + str(acc_val) +
" pass_acc=" + str(pass_acc))
......
python/paddle/v2/fluid/tests/book/test_understand_sentiment_dynamic_lstm.py
浏览文件 @ 322d69f2
import numpy as np
import paddle.v2 as paddle
import paddle.v2.fluid.core as core
import paddle.v2.fluid.evaluator as evaluator
import paddle.v2.fluid.framework as framework
import paddle.v2.fluid.layers as layers
from paddle.v2.fluid.executor import Executor
from paddle.v2.fluid.optimizer import AdamOptimizer
import paddle.v2.fluid as fluid
def stacked_lstm_net(input_dim,
......@@ -14,36 +9,36 @@ def stacked_lstm_net(input_dim,
hid_dim=512,
stacked_num=3):
assert stacked_num % 2 == 1
data = layers.data(name="words", shape=[1], dtype="int64")
label = layers.data(name="label", shape=[1], dtype="int64")
data = fluid.layers.data(name="words", shape=[1], dtype="int64")
label = fluid.layers.data(name="label", shape=[1], dtype="int64")
emb = layers.embedding(input=data, size=[input_dim, emb_dim])
emb = fluid.layers.embedding(input=data, size=[input_dim, emb_dim])
# add bias attr
# TODO(qijun) linear act
fc1 = layers.fc(input=emb, size=hid_dim)
lstm1, cell1 = layers.dynamic_lstm(input=fc1, size=hid_dim)
fc1 = fluid.layers.fc(input=emb, size=hid_dim)
lstm1, cell1 = fluid.layers.dynamic_lstm(input=fc1, size=hid_dim)
inputs = [fc1, lstm1]
for i in range(2, stacked_num + 1):
fc = layers.fc(input=inputs, size=hid_dim)
lstm, cell = layers.dynamic_lstm(
fc = fluid.layers.fc(input=inputs, size=hid_dim)
lstm, cell = fluid.layers.dynamic_lstm(
input=fc, size=hid_dim, is_reverse=(i % 2) == 0)
inputs = [fc, lstm]
fc_last = layers.sequence_pool(input=inputs[0], pool_type='max')
lstm_last = layers.sequence_pool(input=inputs[1], pool_type='max')
fc_last = fluid.layers.sequence_pool(input=inputs[0], pool_type='max')
lstm_last = fluid.layers.sequence_pool(input=inputs[1], pool_type='max')
prediction = layers.fc(input=[fc_last, lstm_last],
size=class_dim,
act='softmax')
cost = layers.cross_entropy(input=prediction, label=label)
avg_cost = layers.mean(x=cost)
adam_optimizer = AdamOptimizer(learning_rate=0.002)
opts = adam_optimizer.minimize(avg_cost)
accuracy, acc_out = evaluator.accuracy(input=prediction, label=label)
return avg_cost, accuracy, acc_out
prediction = fluid.layers.fc(input=[fc_last, lstm_last],
size=class_dim,
act='softmax')
cost = fluid.layers.cross_entropy(input=prediction, label=label)
avg_cost = fluid.layers.mean(x=cost)
adam_optimizer = fluid.optimizer.Adam(learning_rate=0.002)
adam_optimizer.minimize(avg_cost)
accuracy = fluid.evaluator.Accuracy(input=prediction, label=label)
return avg_cost, accuracy, accuracy.metrics[0]
def to_lodtensor(data, place):
......@@ -55,7 +50,7 @@ def to_lodtensor(data, place):
lod.append(cur_len)
flattened_data = np.concatenate(data, axis=0).astype("int64")
flattened_data = flattened_data.reshape([len(flattened_data), 1])
res = core.LoDTensor()
res = fluid.LoDTensor()
res.set(flattened_data, place)
res.set_lod([lod])
return res
......@@ -77,10 +72,10 @@ def main():
paddle.reader.shuffle(
paddle.dataset.imdb.train(word_dict), buf_size=1000),
batch_size=BATCH_SIZE)
place = core.CPUPlace()
exe = Executor(place)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(framework.default_startup_program())
exe.run(fluid.default_startup_program())
for pass_id in xrange(PASS_NUM):
accuracy.reset(exe)
......@@ -90,15 +85,14 @@ def main():
label = np.array(map(lambda x: x[1], data)).astype("int64")
label = label.reshape([BATCH_SIZE, 1])
tensor_label = core.LoDTensor()
tensor_label = fluid.LoDTensor()
tensor_label.set(label, place)
outs = exe.run(framework.default_main_program(),
feed={"words": tensor_words,
"label": tensor_label},
fetch_list=[cost, acc_out])
cost_val = np.array(outs[0])
acc_val = np.array(outs[1])
cost_val, acc_val = exe.run(
fluid.default_main_program(),
feed={"words": tensor_words,
"label": tensor_label},
fetch_list=[cost, acc_out])
pass_acc = accuracy.eval(exe)
print("cost=" + str(cost_val) + " acc=" + str(acc_val) +
" pass_acc=" + str(pass_acc))
......
python/paddle/v2/fluid/tests/book/test_understand_sentiment_lstm.py
浏览文件 @ 322d69f2
import numpy as np
import paddle.v2 as paddle
import paddle.v2.fluid.core as core
import paddle.v2.fluid.framework as framework
import paddle.v2.fluid.layers as layers
from paddle.v2.fluid.executor import Executor
from paddle.v2.fluid.optimizer import AdamOptimizer
import paddle.v2.fluid as fluid
def lstm_net(dict_dim, class_dim=2, emb_dim=32, seq_len=80, batch_size=50):
data = layers.data(
data = fluid.layers.data(
name="words",
shape=[seq_len * batch_size, 1],
append_batch_size=False,
dtype="int64")
label = layers.data(
label = fluid.layers.data(
name="label",
shape=[batch_size, 1],
append_batch_size=False,
dtype="int64")
emb = layers.embedding(input=data, size=[dict_dim, emb_dim])
emb = layers.reshape(x=emb, shape=[batch_size, seq_len, emb_dim])
emb = layers.transpose(x=emb, axis=[1, 0, 2])
emb = fluid.layers.embedding(input=data, size=[dict_dim, emb_dim])
emb = fluid.layers.reshape(x=emb, shape=[batch_size, seq_len, emb_dim])
emb = fluid.layers.transpose(x=emb, axis=[1, 0, 2])
c_pre_init = layers.fill_constant(
c_pre_init = fluid.layers.fill_constant(
dtype=emb.dtype, shape=[batch_size, emb_dim], value=0.0)
layer_1_out = layers.lstm(emb, c_pre_init=c_pre_init, hidden_dim=emb_dim)
layer_1_out = layers.transpose(x=layer_1_out, axis=[1, 0, 2])
layer_1_out = fluid.layers.lstm(
emb, c_pre_init=c_pre_init, hidden_dim=emb_dim)
layer_1_out = fluid.layers.transpose(x=layer_1_out, axis=[1, 0, 2])
prediction = layers.fc(input=layer_1_out, size=class_dim, act="softmax")
cost = layers.cross_entropy(input=prediction, label=label)
prediction = fluid.layers.fc(input=layer_1_out,
size=class_dim,
act="softmax")
cost = fluid.layers.cross_entropy(input=prediction, label=label)
avg_cost = layers.mean(x=cost)
adam_optimizer = AdamOptimizer(learning_rate=0.002)
opts = adam_optimizer.minimize(avg_cost)
acc = layers.accuracy(input=prediction, label=label)
avg_cost = fluid.layers.mean(x=cost)
adam_optimizer = fluid.optimizer.Adam(learning_rate=0.002)
adam_optimizer.minimize(avg_cost)
acc = fluid.layers.accuracy(input=prediction, label=label)
return avg_cost, acc
......@@ -48,7 +47,7 @@ def to_lodtensor(data, place):
lod.append(cur_len)
flattened_data = np.concatenate(data, axis=0).astype("int64")
flattened_data = flattened_data.reshape([len(flattened_data), 1])
res = core.LoDTensor()
res = fluid.LoDTensor()
res.set(flattened_data, place)
res.set_lod([lod])
return res
......@@ -65,7 +64,7 @@ def prepare_feed_data(data, place):
label = np.array(map(lambda x: x[1], data)).astype("int64")
label = label.reshape([len(label), 1])
tensor_label = core.LoDTensor()
tensor_label = fluid.LoDTensor()
tensor_label.set(label, place)
return tensor_words, tensor_label
......@@ -86,17 +85,17 @@ def main():
paddle.reader.shuffle(
paddle.dataset.imdb.train(word_dict), buf_size=BATCH_SIZE * 10),
batch_size=BATCH_SIZE)
place = core.CPUPlace()
exe = Executor(place)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(framework.default_startup_program())
exe.run(fluid.default_startup_program())
for pass_id in xrange(PASS_NUM):
for data in train_data():
chopped_data = chop_data(data)
tensor_words, tensor_label = prepare_feed_data(chopped_data, place)
outs = exe.run(framework.default_main_program(),
outs = exe.run(fluid.default_main_program(),
feed={"words": tensor_words,
"label": tensor_label},
fetch_list=[cost, acc])
......
python/paddle/v2/fluid/tests/book/test_word2vec.py
浏览文件 @ 322d69f2
import numpy as np
import paddle.v2 as paddle
import paddle.v2.fluid.core as core
import paddle.v2.fluid.framework as framework
import paddle.v2.fluid.layers as layers
from paddle.v2.fluid.executor import Executor
from paddle.v2.fluid.optimizer import SGDOptimizer
import paddle.v2.fluid as fluid
PASS_NUM = 100
EMBED_SIZE = 32
......@@ -16,57 +12,57 @@ IS_SPARSE = True
word_dict = paddle.dataset.imikolov.build_dict()
dict_size = len(word_dict)
first_word = layers.data(name='firstw', shape=[1], dtype='int64')
second_word = layers.data(name='secondw', shape=[1], dtype='int64')
third_word = layers.data(name='thirdw', shape=[1], dtype='int64')
forth_word = layers.data(name='forthw', shape=[1], dtype='int64')
next_word = layers.data(name='nextw', shape=[1], dtype='int64')
first_word = fluid.layers.data(name='firstw', shape=[1], dtype='int64')
second_word = fluid.layers.data(name='secondw', shape=[1], dtype='int64')
third_word = fluid.layers.data(name='thirdw', shape=[1], dtype='int64')
forth_word = fluid.layers.data(name='forthw', shape=[1], dtype='int64')
next_word = fluid.layers.data(name='nextw', shape=[1], dtype='int64')
embed_first = layers.embedding(
embed_first = fluid.layers.embedding(
input=first_word,
size=[dict_size, EMBED_SIZE],
dtype='float32',
is_sparse=IS_SPARSE,
param_attr={'name': 'shared_w'})
embed_second = layers.embedding(
embed_second = fluid.layers.embedding(
input=second_word,
size=[dict_size, EMBED_SIZE],
dtype='float32',
is_sparse=IS_SPARSE,
param_attr={'name': 'shared_w'})
embed_third = layers.embedding(
embed_third = fluid.layers.embedding(
input=third_word,
size=[dict_size, EMBED_SIZE],
dtype='float32',
is_sparse=IS_SPARSE,
param_attr={'name': 'shared_w'})
embed_forth = layers.embedding(
embed_forth = fluid.layers.embedding(
input=forth_word,
size=[dict_size, EMBED_SIZE],
dtype='float32',
is_sparse=IS_SPARSE,
param_attr={'name': 'shared_w'})
concat_embed = layers.concat(
concat_embed = fluid.layers.concat(
input=[embed_first, embed_second, embed_third, embed_forth], axis=1)
hidden1 = layers.fc(input=concat_embed, size=HIDDEN_SIZE, act='sigmoid')
predict_word = layers.fc(input=hidden1, size=dict_size, act='softmax')
cost = layers.cross_entropy(input=predict_word, label=next_word)
avg_cost = layers.mean(x=cost)
sgd_optimizer = SGDOptimizer(learning_rate=0.001)
opts = sgd_optimizer.minimize(avg_cost)
hidden1 = fluid.layers.fc(input=concat_embed, size=HIDDEN_SIZE, act='sigmoid')
predict_word = fluid.layers.fc(input=hidden1, size=dict_size, act='softmax')
cost = fluid.layers.cross_entropy(input=predict_word, label=next_word)
avg_cost = fluid.layers.mean(x=cost)
sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001)
sgd_optimizer.minimize(avg_cost)
train_reader = paddle.batch(
paddle.dataset.imikolov.train(word_dict, N), BATCH_SIZE)
place = core.CPUPlace()
exe = Executor(place)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
# fix https://github.com/PaddlePaddle/Paddle/issues/5434 then remove
# below exit line.
exit(0)
exe.run(framework.default_startup_program())
exe.run(fluid.default_startup_program())
for pass_id in range(PASS_NUM):
for data in train_reader():
......@@ -74,36 +70,15 @@ for pass_id in range(PASS_NUM):
input_data = map(lambda x: np.array(x).astype("int64"), input_data)
input_data = map(lambda x: np.expand_dims(x, axis=1), input_data)
first_data = input_data[0]
first_tensor = core.LoDTensor()
first_tensor.set(first_data, place)
second_data = input_data[1]
second_tensor = core.LoDTensor()
second_tensor.set(second_data, place)
third_data = input_data[2]
third_tensor = core.LoDTensor()
third_tensor.set(third_data, place)
forth_data = input_data[3]
forth_tensor = core.LoDTensor()
forth_tensor.set(forth_data, place)
next_data = input_data[4]
next_tensor = core.LoDTensor()
next_tensor.set(next_data, place)
outs = exe.run(framework.default_main_program(),
feed={
'firstw': first_tensor,
'secondw': second_tensor,
'thirdw': third_tensor,
'forthw': forth_tensor,
'nextw': next_tensor
},
fetch_list=[avg_cost])
out = np.array(outs[0])
if out[0] < 10.0:
avg_cost_np = exe.run(fluid.default_main_program(),
feed={
'firstw': input_data[0],
'secondw': input_data[1],
'thirdw': input_data[2],
'forthw': input_data[3],
'nextw': input_data[4]
},
fetch_list=[avg_cost])
if avg_cost_np[0] < 10.0:
exit(0) # if avg cost less than 10.0, we think our code is good.
exit(1)
python/paddle/v2/fluid/tests/op_test.py
浏览文件 @ 322d69f2
......@@ -261,7 +261,10 @@ class OpTest(unittest.TestCase):
feed_map = self.feed_var(inputs, place)
exe = Executor(place)
outs = exe.run(program, feed=feed_map, fetch_list=fetch_list)
outs = exe.run(program,
feed=feed_map,
fetch_list=fetch_list,
return_numpy=False)
for out_name, out_dup in Operator.get_op_outputs(self.op_type):
if out_name not in self.outputs:
......@@ -500,5 +503,6 @@ class OpTest(unittest.TestCase):
fetch_list = [g for p, g in param_grad_list]
executor = Executor(place)
result = executor.run(prog, feed_dict, fetch_list)
return map(np.array, result)
return map(
np.array,
executor.run(prog, feed_dict, fetch_list, return_numpy=False))
python/paddle/v2/fluid/tests/test_array_read_write_op.py
浏览文件 @ 322d69f2
......@@ -52,15 +52,13 @@ class TestArrayReadWrite(unittest.TestCase):
exe = Executor(cpu)
tensor = core.LoDTensor()
tensor.set(numpy.random.random(size=(100, 100)).astype('float32'), cpu)
outs = map(numpy.array,
exe.run(feed={'x0': tensor,
'x1': tensor,
'x2': tensor},
fetch_list=[a_sum, x_sum],
scope=scope))
tensor = numpy.random.random(size=(100, 100)).astype('float32')
outs = exe.run(feed={'x0': tensor,
'x1': tensor,
'x2': tensor},
fetch_list=[a_sum, x_sum],
scope=scope)
self.assertEqual(outs[0], outs[1])
total_sum = layers.sums(input=[a_sum, x_sum])
......@@ -72,12 +70,11 @@ class TestArrayReadWrite(unittest.TestCase):
[each_x.name + "@GRAD" for each_x in x])
g_out = [
item.sum()
for item in map(
numpy.array,
exe.run(feed={'x0': tensor,
'x1': tensor,
'x2': tensor},
fetch_list=g_vars))
for item in exe.run(
feed={'x0': tensor,
'x1': tensor,
'x2': tensor},
fetch_list=g_vars)
]
g_out_sum = numpy.array(g_out).sum()
......
python/paddle/v2/fluid/tests/test_conditional_block.py
浏览文件 @ 322d69f2
......@@ -21,18 +21,15 @@ class ConditionalBlock(unittest.TestCase):
exe = Executor(cpu)
exe.run(g_startup_program)
x = core.LoDTensor()
x.set(numpy.random.random(size=(10, 1)).astype('float32'), cpu)
x = numpy.random.random(size=(10, 1)).astype('float32')
outs = map(numpy.array, exe.run(feed={'X': x}, fetch_list=[out]))[0]
outs = exe.run(feed={'X': x}, fetch_list=[out])[0]
print outs
loss = layers.mean(x=out)
append_backward_ops(loss=loss)
outs = map(numpy.array,
exe.run(feed={'X': x},
fetch_list=[
g_main_program.block(0).var(data.name + "@GRAD")
]))[0]
outs = exe.run(
feed={'X': x},
fetch_list=[g_main_program.block(0).var(data.name + "@GRAD")])[0]
print outs
......
python/paddle/v2/fluid/tests/test_conv2d_op.py
浏览文件 @ 322d69f2
......@@ -16,8 +16,8 @@ def conv2d_forward_naive(input, filter, group, conv_param):
out_w = 1 + (in_w + 2 * pad[1] - (dilation[1] * (f_w - 1) + 1)) / stride[1]
out = np.zeros((in_n, out_c, out_h, out_w))
d_bolck_w = (dilation[0] * (f_h - 1) + 1)
d_bolck_h = (dilation[1] * (f_w - 1) + 1)
d_bolck_h = (dilation[0] * (f_h - 1) + 1)
d_bolck_w = (dilation[1] * (f_w - 1) + 1)
input_pad = np.pad(input, ((0, ), (0, ), (pad[0], ), (pad[1], )),
mode='constant',
......@@ -167,27 +167,27 @@ class TestWithDilation(TestConv2dOp):
#----------------Conv2dCudnn----------------
class TestCudnn(TestConv2dOp):
def init_op_type(self):
self.op_type = "conv_cudnn"
self.op_type = "conv2d_cudnn"
class TestCudnnWithPad(TestWithPad):
def init_op_type(self):
self.op_type = "conv_cudnn"
self.op_type = "conv2d_cudnn"
class TestCudnnWithStride(TestWithStride):
def init_op_type(self):
self.op_type = "conv_cudnn"
self.op_type = "conv2d_cudnn"
class TestCudnnWithGroup(TestWithGroup):
def init_op_type(self):
self.op_type = "conv_cudnn"
self.op_type = "conv2d_cudnn"
class TestCudnnWith1x1(TestWith1x1):
def init_op_type(self):
self.op_type = "conv_cudnn"
self.op_type = "conv2d_cudnn"
# cudnn v5 does not support dilation conv.
......
python/paddle/v2/fluid/tests/test_conv3d_op.py
浏览文件 @ 322d69f2
......@@ -169,5 +169,31 @@ class TestWithDilation(TestConv3dOp):
self.groups = 3
class TestCudnn(TestConv3dOp):
def init_op_type(self):
self.op_type = "conv3d_cudnn"
class TestWithGroup1Cudnn(TestWithGroup1):
def init_op_type(self):
self.op_type = "conv3d_cudnn"
class TestWithGroup2Cudnn(TestWithGroup2):
def init_op_type(self):
self.op_type = "conv3d_cudnn"
class TestWith1x1Cudnn(TestWith1x1):
def init_op_type(self):
self.op_type = "conv3d_cudnn"
# FIXME(typhoonzero): find a way to determine if
# using cudnn > 6 in python
# class TestWithDilationCudnn(TestWithDilation):
# def init_op_type(self):
# self.op_type = "conv3d_cudnn"
if __name__ == '__main__':
unittest.main()
python/paddle/v2/fluid/tests/test_executor_and_mul.py
浏览文件 @ 322d69f2
import unittest
from paddle.v2.fluid.layers import mul, data
from paddle.v2.fluid.layers import mul, data, sequence_pool
import paddle.v2.fluid.core as core
from paddle.v2.fluid.executor import Executor
from paddle.v2.fluid.framework import g_main_program
......@@ -17,17 +17,13 @@ class TestExecutor(unittest.TestCase):
out = mul(x=a, y=b)
place = core.CPUPlace()
a_np = numpy.random.random((100, 784)).astype('float32')
tensor_a = core.LoDTensor()
tensor_a.set(a_np, place)
b_np = numpy.random.random((784, 100)).astype('float32')
tensor_b = core.LoDTensor()
tensor_b.set(b_np, place)
exe = Executor(place)
outs = exe.run(g_main_program,
feed={'a': tensor_a,
'b': tensor_b},
feed={'a': a_np,
'b': b_np},
fetch_list=[out])
out = numpy.array(outs[0])
out = outs[0]
self.assertEqual((100, 100), out.shape)
self.assertTrue(numpy.allclose(out, numpy.dot(a_np, b_np)))
......
python/paddle/v2/fluid/tests/test_inference_model_io.py
浏览文件 @ 322d69f2
import paddle.v2 as paddle
import paddle.v2.fluid.layers as layers
import unittest
import numpy as np
import paddle.v2.fluid.core as core
import paddle.v2.fluid.optimizer as optimizer
import paddle.v2.fluid.executor as executor
import paddle.v2.fluid.layers as layers
import paddle.v2.fluid.optimizer as optimizer
from paddle.v2.fluid.framework import Program
from paddle.v2.fluid.io import save_inference_model, load_inference_model
import paddle.v2.fluid.executor as executor
import unittest
import numpy as np
class TestBook(unittest.TestCase):
......@@ -44,7 +44,7 @@ class TestBook(unittest.TestCase):
x=cost, main_program=program, startup_program=init_program)
sgd_optimizer = optimizer.SGDOptimizer(learning_rate=0.001)
opts = sgd_optimizer.minimize(avg_cost, init_program)
sgd_optimizer.minimize(avg_cost, init_program)
place = core.CPUPlace()
exe = executor.Executor(place)
......@@ -52,25 +52,20 @@ class TestBook(unittest.TestCase):
exe.run(init_program, feed={}, fetch_list=[])
for i in xrange(100):
x_data = np.array(
tensor_x = np.array(
[[1, 1], [1, 2], [3, 4], [5, 2]]).astype("float32")
y_data = np.array([[-2], [-3], [-7], [-7]]).astype("float32")
tensor_y = np.array([[-2], [-3], [-7], [-7]]).astype("float32")
tensor_x = core.LoDTensor()
tensor_x.set(x_data, place)
tensor_y = core.LoDTensor()
tensor_y.set(y_data, place)
exe.run(program,
feed={'x': tensor_x,
'y': tensor_y},
fetch_list=[avg_cost])
save_inference_model(MODEL_DIR, ["x", "y"], [avg_cost], exe, program)
outs = exe.run(program,
feed={'x': tensor_x,
'y': tensor_y},
fetch_list=[avg_cost])
expected = np.array(outs[0])
expected = exe.run(program,
feed={'x': tensor_x,
'y': tensor_y},
fetch_list=[avg_cost])[0]
reload(executor) # reload to build a new scope
exe = executor.Executor(place)
......@@ -83,7 +78,7 @@ class TestBook(unittest.TestCase):
feed={feed_var_names[0]: tensor_x,
feed_var_names[1]: tensor_y},
fetch_list=fetch_vars)
actual = np.array(outs[0])
actual = outs[0]
self.assertEqual(feed_var_names, ["x", "y"])
self.assertEqual(len(fetch_vars), 1)
......
python/paddle/v2/fluid/tests/test_lod_array_length_op.py
浏览文件 @ 322d69f2
......@@ -13,7 +13,7 @@ class TestLoDArrayLength(unittest.TestCase):
arr_len = layers.array_length(arr)
cpu = core.CPUPlace()
exe = Executor(cpu)
result = numpy.array(exe.run(fetch_list=[arr_len])[0])
result = exe.run(fetch_list=[arr_len])[0]
self.assertEqual(11, result[0])
......
python/paddle/v2/fluid/tests/test_lod_tensor_array_ops.py
浏览文件 @ 322d69f2
......@@ -18,7 +18,11 @@ class TestCPULoDTensorArrayOps(unittest.TestCase):
tensor.set_lod([[0, 3, 9, 10]])
expect = map(lambda x: numpy.array(x).astype('int32'),
[[3, 0, 9], [4, 1], [5, 2], [6], [7], [8]])
self.main(tensor=tensor, expect_array=expect, expect_lod=[] * 6)
self.main(
tensor=tensor,
expect_array=expect,
expect_lod=[] * 6,
expect_max_len=6)
def test_lod_tensor_to_array_level_0_empty_seq(self):
tensor = core.LoDTensor()
......@@ -27,7 +31,11 @@ class TestCPULoDTensorArrayOps(unittest.TestCase):
tensor.set_lod([[0, 3, 9, 9, 10]])
expect = map(lambda x: numpy.array(x).astype('int32'),
[[3, 0, 9], [4, 1], [5, 2], [6], [7], [8]])
self.main(tensor=tensor, expect_array=expect, expect_lod=[] * 6)
self.main(
tensor=tensor,
expect_array=expect,
expect_lod=[] * 6,
expect_max_len=6)
def test_lod_tensor_to_array_level_1(self):
tensor = core.LoDTensor()
......@@ -44,7 +52,11 @@ class TestCPULoDTensorArrayOps(unittest.TestCase):
]
lod = [[[0, 2, 5]], [[0, 6, 12]], [[0, 3]]]
self.main(tensor=tensor, expect_array=expect, expect_lod=lod)
self.main(
tensor=tensor,
expect_array=expect,
expect_lod=lod,
expect_max_len=3)
def test_lod_tensor_to_array_level_1_empty_seq(self):
tensor = core.LoDTensor()
......@@ -63,7 +75,11 @@ class TestCPULoDTensorArrayOps(unittest.TestCase):
]
lod = [[[0, 5, 8, 8, 15]], [[0, 2, 6, 7, 8]], [[0, 2, 6]], [[0, 2]]]
self.main(tensor=tensor, expect_array=expect, expect_lod=lod)
self.main(
tensor=tensor,
expect_array=expect,
expect_lod=lod,
expect_max_len=4)
def test_lod_tensor_to_array_level_2(self):
tensor = core.LoDTensor()
......@@ -80,7 +96,11 @@ class TestCPULoDTensorArrayOps(unittest.TestCase):
]
lod = [[[0, 1, 3, 4], [0, 1, 4, 8, 12]],
[[0, 4, 7], [0, 1, 5, 9, 17, 21, 27, 31]], [[0, 2], [0, 6, 7]]]
self.main(tensor=tensor, expect_array=expect, expect_lod=lod)
self.main(
tensor=tensor,
expect_array=expect,
expect_lod=lod,
expect_max_len=3)
def test_lod_tensor_to_array_level_2_skip_level(self):
tensor = core.LoDTensor()
......@@ -88,14 +108,21 @@ class TestCPULoDTensorArrayOps(unittest.TestCase):
numpy.arange(50).reshape(50, 1).astype('int32'), self.place())
tensor.set_lod([[0, 2, 5, 6], [0, 2, 5, 6, 10, 12, 13],
[0, 3, 7, 11, 17, 21, 22, 23, 27, 31, 39, 45, 46, 50]])
self.main(tensor=tensor, expect_array=None, expect_lod=None, level=1)
def main(self, tensor, expect_array, expect_lod, level=0):
self.main(
tensor=tensor,
expect_array=None,
expect_lod=None,
expect_max_len=4,
level=1)
def main(self, tensor, expect_array, expect_lod, expect_max_len, level=0):
place = self.place()
program = Program()
x = layers.data(name='x', shape=[10], main_program=program)
x.persistable = True
table = layers.lod_rank_table(x, level=level, main_program=program)
max_len = layers.max_sequence_len(table, main_program=program)
max_len.persistable = True
array = layers.lod_tensor_to_array(x, table, main_program=program)
array.persistable = True
......@@ -110,6 +137,10 @@ class TestCPULoDTensorArrayOps(unittest.TestCase):
self.check_array_same(array, expect_array, expect_lod)
self.check_tensor_same(scope.find_var(result.name).get_tensor(), tensor)
self.assertEqual(
numpy.array(scope.find_var(max_len.name).get_tensor())[0],
expect_max_len)
def check_array_same(self, array, expect_tensor, expect_lod):
self.assertEqual(len(expect_tensor), len(array))
for i, exp in enumerate(zip(expect_tensor, expect_lod)):
......@@ -151,10 +182,11 @@ class TestCPULoDTensorArrayOpGrad(unittest.TestCase):
exe = Executor(place)
g_out = [
item.sum()
for item in map(
numpy.array,
exe.run(program, feed={'x': tensor}, fetch_list=[g_vars]))
numpy.array(item).sum()
for item in exe.run(program,
feed={'x': tensor},
fetch_list=[g_vars],
return_numpy=False)
]
g_out_sum = numpy.array(g_out).sum()
......
python/paddle/v2/fluid/tests/test_mnist_if_else_op.py
浏览文件 @ 322d69f2
......@@ -65,17 +65,10 @@ class TestMNISTIfElseOp(unittest.TestCase):
y_data = np.array(map(lambda x: x[1], data)).astype("int64")
y_data = np.expand_dims(y_data, axis=1)
tensor_x = core.LoDTensor()
tensor_x.set(x_data, place)
tensor_y = core.LoDTensor()
tensor_y.set(y_data, place)
outs = map(np.array,
exe.run(kwargs['main_program'],
feed={'x': tensor_x,
'y': tensor_y},
fetch_list=[avg_loss]))
outs = exe.run(kwargs['main_program'],
feed={'x': x_data,
'y': y_data},
fetch_list=[avg_loss])
print outs[0]
if outs[0] < 1.0:
return
......@@ -129,19 +122,12 @@ class TestMNISTIfElseOp(unittest.TestCase):
for data in train_reader():
x_data = np.array(map(lambda x: x[0], data)).astype("float32")
y_data = np.array(map(lambda x: x[1], data)).astype("int64")
y_data = np.expand_dims(y_data, axis=1)
tensor_x = core.LoDTensor()
tensor_x.set(x_data, place)
tensor_y = core.LoDTensor()
tensor_y.set(y_data, place)
y_data = y_data.reshape((y_data.shape[0], 1))
outs = map(np.array,
exe.run(kwargs['main_program'],
feed={'x': tensor_x,
'y': tensor_y},
fetch_list=[avg_loss]))
outs = exe.run(kwargs['main_program'],
feed={'x': x_data,
'y': y_data},
fetch_list=[avg_loss])
print outs[0]
if outs[0] < 1.0:
return
......
python/paddle/v2/fluid/tests/test_parameter.py
浏览文件 @ 322d69f2
......@@ -24,7 +24,7 @@ class TestParameter(unittest.TestCase):
self.assertEqual(0, param.block.idx)
exe = Executor(core.CPUPlace())
p = exe.run(g_main_program, fetch_list=[param])[0]
self.assertTrue(np.allclose(np.array(p), np.ones(shape) * val))
self.assertTrue(np.allclose(p, np.ones(shape) * val))
p = io.get_parameter_value_by_name('fc.w', exe, g_main_program)
self.assertTrue(np.allclose(np.array(p), np.ones(shape) * val))
......
python/paddle/v2/fluid/tests/test_recurrent_op.py
浏览文件 @ 322d69f2
......@@ -156,7 +156,7 @@ class RecurrentOpTest1(unittest.TestCase):
feed=self.feed_map,
fetch_list=[self.output])
return np.array(out[0])
return out[0]
def backward(self):
self.feed_map = {
......@@ -171,7 +171,8 @@ class RecurrentOpTest1(unittest.TestCase):
exe = Executor(self.place)
return exe.run(self.main_program,
feed=self.feed_map,
fetch_list=fetch_list)
fetch_list=fetch_list,
return_numpy=False)
def test_backward(self):
self.check_forward()
......
python/paddle/v2/fluid/tests/test_rnn_memory_helper_op.py
浏览文件 @ 322d69f2
......@@ -7,12 +7,6 @@ import numpy as np
import paddle.v2.fluid.core as core
def create_tensor(np_data, place):
tensor = core.LoDTensor()
tensor.set(np_data, place)
return tensor
class RNNMemoryHelperOpTest(unittest.TestCase):
def setUp(self):
self.program = Program()
......@@ -30,13 +24,13 @@ class RNNMemoryHelperOpTest(unittest.TestCase):
def test_forward(self):
x_np = np.random.normal(size=(2, 3)).astype("float32")
self.feed_map = {'X': create_tensor(x_np, self.place)}
self.feed_map = {'X': x_np}
self.fetch_list = [self.Out]
exe = Executor(self.place)
out = exe.run(self.program,
feed=self.feed_map,
fetch_list=self.fetch_list)
np.isclose(np.array(out[0]), x_np, rtol=1e-5)
self.assertTrue(np.allclose(out[0], x_np, rtol=1e-5))
class RNNMemoryHelperGradOpTest(unittest.TestCase):
......@@ -66,8 +60,7 @@ class RNNMemoryHelperGradOpTest(unittest.TestCase):
def test_backward(self):
self.feed_map = {
name: create_tensor(
np.random.normal(size=(2, 3)).astype("float32"), self.place)
name: np.random.normal(size=(2, 3)).astype("float32")
for name in self.input_names
}
self.fetch_list = [self.output_vars['X@GRAD']]
......@@ -76,7 +69,7 @@ class RNNMemoryHelperGradOpTest(unittest.TestCase):
out = exe.run(self.program,
feed=self.feed_map,
fetch_list=self.fetch_list)
np.isclose(np.array(out[0]), self.feed_map['Out@GRAD'], rtol=1e-5)
np.isclose(out[0], self.feed_map['Out@GRAD'], rtol=1e-5)
class RNNMemoryHelperGradOpWithoutInputTest(unittest.TestCase):
......@@ -110,8 +103,7 @@ class RNNMemoryHelperGradOpWithoutInputTest(unittest.TestCase):
def test_backward(self):
self.feed_map = {
name: create_tensor(
np.random.normal(size=(2, 3)).astype("float32"), self.place)
name: np.random.normal(size=(2, 3)).astype("float32")
for name in ['X', 'Out']
}
self.fetch_list = [self.output_vars['X@GRAD']]
......@@ -120,10 +112,9 @@ class RNNMemoryHelperGradOpWithoutInputTest(unittest.TestCase):
out = exe.run(self.program,
feed=self.feed_map,
fetch_list=self.fetch_list)
np.isclose(
np.array(out[0]),
np.zeros(shape=(2, 3)).astype("float32"),
rtol=1e-5)
self.assertTrue(
np.allclose(
out[0], np.zeros(shape=(2, 3)).astype("float32"), rtol=1e-5))
if __name__ == '__main__':
......
python/paddle/v2/fluid/tests/test_shrink_rnn_memory.py
浏览文件 @ 322d69f2
......@@ -27,19 +27,16 @@ class TestShrinkRNNMemory(unittest.TestCase):
tensor_np = numpy.random.random(size=(3, 100)).astype('float32')
tensor.set(tensor_np, cpu)
exe = Executor(cpu)
outs = map(numpy.array,
exe.run(feed={'x': tensor}, fetch_list=[mem1, mem2, mem3]))
outs = exe.run(feed={'x': tensor}, fetch_list=[mem1, mem2, mem3])
self.assertTrue(numpy.allclose(tensor_np[0:3], outs[0]))
self.assertTrue(numpy.allclose(tensor_np[0:2], outs[1]))
self.assertTrue(numpy.allclose(tensor_np[0:1], outs[2]))
mem3_mean = layers.mean(x=mem3)
append_backward_ops(loss=mem3_mean)
x_grad = map(numpy.array,
exe.run(feed={'x': tensor},
fetch_list=[
g_main_program.global_block().var('x@GRAD')
]))[0]
x_grad = exe.run(
feed={'x': tensor},
fetch_list=[g_main_program.global_block().var('x@GRAD')])[0]
self.assertAlmostEqual(1.0, x_grad.sum(), delta=0.1)
......
python/paddle/v2/fluid/tests/test_split_and_merge_lod_tensor_op.py
浏览文件 @ 322d69f2
......@@ -98,7 +98,11 @@ class TestCPULoDTensorArrayOps(unittest.TestCase):
exe = Executor(place)
scope = core.Scope()
exe.run(program, feed={'x': tensor, 'y': mask}, scope=scope)
exe.run(program,
feed={'x': tensor,
'y': mask},
scope=scope,
return_numpy=False)
var_true = scope.find_var(out_true.name).get_tensor()
......@@ -169,7 +173,8 @@ class TestCPUSplitMergeLoDTensorGrad(unittest.TestCase):
feed={'x': tensor,
'y': mask},
fetch_list=[g_vars],
scope=scope))
scope=scope,
return_numpy=False))
]
g_out_sum = np.array(g_out).sum()
......
python/paddle/v2/fluid/tests/test_while_op.py
浏览文件 @ 322d69f2
......@@ -55,19 +55,10 @@ class TestWhileOp(unittest.TestCase):
for i in xrange(3):
d.append(numpy.random.random(size=[10]).astype('float32'))
d_tensor = []
for item in d:
t = core.LoDTensor()
t.set(item, cpu)
d_tensor.append(t)
outs = map(numpy.array,
exe.run(feed={
'd0': d_tensor[0],
'd1': d_tensor[1],
'd2': d_tensor[2]
},
fetch_list=[sum_result]))
outs = exe.run(feed={'d0': d[0],
'd1': d[1],
'd2': d[2]},
fetch_list=[sum_result])
self.assertAlmostEqual(numpy.sum(d), numpy.sum(outs[0]), delta=0.01)
......
python/paddle/v2/framework/tests/test_elementwise_mod_op.py 已删除 100644 → 0
浏览文件 @ 6cf2dcbc
import unittest
import numpy as np
from op_test import OpTest
class ElementwiseModOp(OpTest):
def setUp(self):
self.op_type = "elementwise_mod"
""" Warning
CPU gradient check error!
'X': np.random.randint((32,84)).astype("int32"),
'Y': np.random.randint((32,84)).astype("int32")
"""
self.inputs = {
'X': np.random.randint(1, 10, [13, 17]).astype("int32"),
'Y': np.random.randint(1, 10, [13, 17]).astype("int32")
}
self.outputs = {'Out': np.mod(self.inputs['X'], self.inputs['Y'])}
def test_check_output(self):
self.check_output()
def test_check_grad_normal(self):
self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.05)
def test_check_grad_ingore_x(self):
self.check_grad(
['Y'], 'Out', max_relative_error=0.05, no_grad_set=set("X"))
def test_check_grad_ingore_y(self):
self.check_grad(
['X'], 'Out', max_relative_error=0.05, no_grad_set=set('Y'))
if __name__ == '__main__':
unittest.main()
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