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提交 82530e24 编写于 作者: X Xi Chen
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Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into aws-benchmark

上级 07b31b80 2bc5b08d
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浏览文件 @ 82530e24
......@@ -25,12 +25,3 @@ third_party/
# clion workspace.
cmake-build-*
# generated while compiling
paddle/pybind/pybind.h
CMakeFiles
cmake_install.cmake
paddle/.timestamp
python/paddlepaddle.egg-info/
paddle/fluid/pybind/pybind.h
python/paddle/version.py
cmake/external/mklml.cmake
浏览文件 @ 82530e24
......@@ -28,7 +28,7 @@ INCLUDE(ExternalProject)
SET(MKLML_PROJECT "extern_mklml")
SET(MKLML_VER "mklml_lnx_2018.0.1.20171007")
SET(MKLML_URL "https://github.com/01org/mkl-dnn/releases/download/v0.11/${MKLML_VER}.tgz")
SET(MKLML_URL "http://paddlepaddledeps.bj.bcebos.com/${MKLML_VER}.tgz")
SET(MKLML_SOURCE_DIR "${THIRD_PARTY_PATH}/mklml")
SET(MKLML_DOWNLOAD_DIR "${MKLML_SOURCE_DIR}/src/${MKLML_PROJECT}")
SET(MKLML_DST_DIR "mklml")
......
cmake/external/snappystream.cmake
浏览文件 @ 82530e24
......@@ -54,5 +54,7 @@ add_library(snappystream STATIC IMPORTED GLOBAL)
set_property(TARGET snappystream PROPERTY IMPORTED_LOCATION
"${SNAPPYSTREAM_INSTALL_DIR}/lib/libsnappystream.a")
include_directories(${SNAPPYSTREAM_INCLUDE_DIR})
include_directories(${SNAPPYSTREAM_INCLUDE_DIR}) # For snappysteam to include its own headers.
include_directories(${THIRD_PARTY_PATH}/install) # For Paddle to include snappy stream headers.
add_dependencies(snappystream extern_snappystream)
cmake/external/warpctc.cmake
浏览文件 @ 82530e24
......@@ -62,7 +62,8 @@ ExternalProject_Add(
)
MESSAGE(STATUS "warp-ctc library: ${WARPCTC_LIBRARIES}")
INCLUDE_DIRECTORIES(${WARPCTC_INCLUDE_DIR})
INCLUDE_DIRECTORIES(${WARPCTC_INCLUDE_DIR}) # For warpctc code to include its headers.
INCLUDE_DIRECTORIES(${THIRD_PARTY_PATH}/install) # For Paddle code to include warpctc headers.
ADD_LIBRARY(warpctc SHARED IMPORTED GLOBAL)
SET_PROPERTY(TARGET warpctc PROPERTY IMPORTED_LOCATION ${WARPCTC_LIBRARIES})
......
cmake/external/zlib.cmake
浏览文件 @ 82530e24
......@@ -25,7 +25,8 @@ ELSE(WIN32)
SET(ZLIB_LIBRARIES "${ZLIB_INSTALL_DIR}/lib/libz.a" CACHE FILEPATH "zlib library." FORCE)
ENDIF(WIN32)
INCLUDE_DIRECTORIES(${ZLIB_INCLUDE_DIR})
INCLUDE_DIRECTORIES(${ZLIB_INCLUDE_DIR}) # For zlib code to include its own headers.
INCLUDE_DIRECTORIES(${THIRD_PARTY_PATH}/install) # For Paddle code to include zlib.h.
ExternalProject_Add(
extern_zlib
......
cmake/generic.cmake
浏览文件 @ 82530e24
......@@ -244,14 +244,14 @@ function(cc_test TARGET_NAME)
cmake_parse_arguments(cc_test "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
add_executable(${TARGET_NAME} ${cc_test_SRCS})
# Support linking flags: --whole-archive (Linux) / -force_load (MacOS)
target_circle_link_libraries(${TARGET_NAME} ${cc_test_DEPS} paddle_gtest_main paddle_memory gtest gflags glog)
target_circle_link_libraries(${TARGET_NAME} ${cc_test_DEPS} paddle_gtest_main memory gtest gflags glog)
if("${cc_test_DEPS}" MATCHES "ARCHIVE_START")
list(REMOVE_ITEM cc_test_DEPS ARCHIVE_START ARCHIVE_END)
endif()
add_dependencies(${TARGET_NAME} ${cc_test_DEPS} paddle_gtest_main paddle_memory gtest gflags glog)
add_dependencies(${TARGET_NAME} ${cc_test_DEPS} paddle_gtest_main memory gtest gflags glog)
add_test(NAME ${TARGET_NAME}
COMMAND ${TARGET_NAME} ${cc_test_ARGS}
WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR})
WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR})
endif()
endfunction(cc_test)
......@@ -311,8 +311,8 @@ function(nv_test TARGET_NAME)
set(multiValueArgs SRCS DEPS)
cmake_parse_arguments(nv_test "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
cuda_add_executable(${TARGET_NAME} ${nv_test_SRCS})
target_link_libraries(${TARGET_NAME} ${nv_test_DEPS} paddle_gtest_main paddle_memory gtest gflags glog)
add_dependencies(${TARGET_NAME} ${nv_test_DEPS} paddle_gtest_main paddle_memory gtest gflags glog)
target_link_libraries(${TARGET_NAME} ${nv_test_DEPS} paddle_gtest_main memory gtest gflags glog)
add_dependencies(${TARGET_NAME} ${nv_test_DEPS} paddle_gtest_main memory gtest gflags glog)
add_test(${TARGET_NAME} ${TARGET_NAME})
endif()
endfunction(nv_test)
......@@ -387,8 +387,8 @@ function(hip_test TARGET_NAME)
endif()
add_executable(${TARGET_NAME} ${_cmake_options} ${_generated_files} ${_sources})
set_target_properties(${TARGET_NAME} PROPERTIES LINKER_LANGUAGE HIP)
target_link_libraries(${TARGET_NAME} ${hip_test_DEPS} paddle_gtest_main paddle_memory gtest gflags)
add_dependencies(${TARGET_NAME} ${hip_test_DEPS} paddle_gtest_main paddle_memory gtest gflags)
target_link_libraries(${TARGET_NAME} ${hip_test_DEPS} paddle_gtest_main memory gtest gflags)
add_dependencies(${TARGET_NAME} ${hip_test_DEPS} paddle_gtest_main memory gtest gflags)
add_test(${TARGET_NAME} ${TARGET_NAME})
endif()
endfunction(hip_test)
......@@ -561,9 +561,9 @@ function(py_test TARGET_NAME)
set(multiValueArgs SRCS DEPS ARGS ENVS)
cmake_parse_arguments(py_test "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
add_test(NAME ${TARGET_NAME}
COMMAND env PYTHONPATH=${PADDLE_PYTHON_BUILD_DIR}/lib-python ${py_test_ENVS}
COMMAND env PYTHONPATH=${PADDLE_BINARY_DIR}/python ${py_test_ENVS}
${PYTHON_EXECUTABLE} -u ${py_test_SRCS} ${py_test_ARGS}
WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR})
WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR})
endif()
endfunction()
......
doc/design/file_manager/README.md 已删除 100644 → 0
浏览文件 @ 07b31b80
# FileManager设计文档
## 目标
在本文档中，我们设计说明了名为FileManager系统，方便用户上传自己的训练数据以进行分布式训练
主要功能包括：
- 提供常用的命令行管理命令管理文件和目录
- 支持大文件的断点上传、下载
## 名词解释
- PFS：是`Paddlepaddle cloud File System`的缩写，是对用户文件存储空间的抽象，与之相对的是local filesystem。目前我们用CephFS来搭建。
- [CephFS](http://docs.ceph.com/docs/master/cephfs/)：一个POSIX兼容的文件系统。
- Chunk：逻辑划上文件分块的单位。
## 模块
### 架构图
<image src=./src/filemanager.png width=900>
### PFSClient
- 功能： 详细设计[link](./pfs/pfsclient.md)
- 提供用户管理文件的命令
- 需要可以跨平台执行
- 双向验证
PFSClient需要和Ingress之间做双向验证<sup>[tls](#tls)</sup>，所以用户需要首先在`cloud.paddlepaddle.org`上注册一下，申请用户空间，并且把系统生成的CA(certificate authority)、Key、CRT(CA signed certificate)下载到本地，然后才能使用PFSClient。
### [Ingress](https://kubernetes.io/docs/concepts/services-networking/ingress/)
- 功能：
提供七层协议的反向代理、基于粘性会话的负载均衡功能。
- 透传用户身份的办法
Ingress需要把PFSClient的身份信息传给PFSServer，配置的方法参考[link](http://www.integralist.co.uk/posts/clientcertauth.html#3)
### PFSServer
PFSServer提供RESTful API接口，接收处理PFSClient端的文件管理请求，并且把结果返回PFSClient端。
RESTful API
- /api/v1/files
- `GET /api/v1/files`: Get metadata of files or directories.
- `POST /api/v1/files`: Create files or directories.
- `PATCH /api/v1/files`: Update files or directories.
- `DELETE /api/v1/files`: Delete files or directories.
- /api/v1/file/chunks
- `GET /api/v1/storage/file/chunks`: Get chunks's metadata of a file.
- /api/v1/storage/files
- `GET /api/v1/storage/files`: Download files or directories.
- `POST /api/v1/storage/files`: Upload files or directories.
- /api/v1/storage/file/chunks
- `GET /api/v1/storage/file/chunks`: Download chunks's data.
- `POST /api/v1/storage/file/chunks`: Upload chunks's data.
## 文件传输优化
### 分块文件传输
用户文件可能是比较大的，上传到Cloud或者下载到本地的时间可能比较长，而且在传输的过程中也可能出现网络不稳定的情况。为了应对以上的问题，我们提出了Chunk的概念，一个Chunk由所在的文件偏移、数据、数据长度及校验值组成。文件的上传和下载都是通过对Chunk的操作来实现的。由于Chunk比较小（默认256K），完成一个传输动作完成的时间也比较短，不容易出错。PFSClient需要在传输完毕最后一个Chunk的时候检查destination文件的MD5值是否和source文件一致。
一个典型的Chunk如下所示：
```
type Chunk struct {
fileOffset int64
checksum uint32
len uint32
data []byte
}
```
### 生成sparse文件
当destination文件不存在或者大小和source文件不一致时，可以用[Fallocate](https://Go.org/pkg/syscall/#Fallocate)生成sparse文件，然后就可以并发写入多个Chunk。
### 覆盖不一致的部分
文件传输的的关键在于需要PFSClient端对比source和destination的文件Chunks的checksum是否保持一致，不一致的由PFSClient下载或者传输Chunk完成。这样已经传输成功的部分就不用重新传输了。
## 用户使用流程
参考[link](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/cluster_train/data_dispatch.md)
## 框架生成
用[swagger](https://github.com/swagger-api/swagger-codegen)生成PFSClient和PFSServer的框架部分，以便我们可以把更多的精力放到逻辑本身上。
## 参考文档
- <a name=tls></a>[TLS complete guide](https://github.com/k8sp/tls/blob/master/tls.md)
- [aws.s3](http://docs.aws.amazon.com/cli/latest/reference/s3/)
- [linux man document](https://linux.die.net/man/)
doc/design/file_manager/pfs/pfsclient.md 已删除 100644 → 0
浏览文件 @ 07b31b80
# PFSClient
## Description
The `pfs` command is a Command Line Interface to manage your files on PaddlePaddle Cloud
## Synopsis
```
paddle [options] pfs <subcommand> [parameters]
```
## Options
```
--profile (string)
Use a specific profile from your credential file.
--help (string)
Display more information about command
--version
Output version information and exit
--debug
Show detailed debugging log
--only-show-errors (boolean)
Only errors and warnings are displayed. All other output is suppressed.
```
## Path Arguments
When using a command, we need to specify path arguments. There are two path argument type: `localpath` and `pfspath`.
A `pfspath` begin with `/pfs`, eg: `/pfs/$DATACENTER/home/$USER/folder`.
[Here](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/cluster_train/data_dispatch.md#上传训练文件) is how to config datacenters.
## order of Path Arguments
Commonly, if there are two path arguments, the first is the source, and the second is the destination.
## Subcommonds
- rm - remove files or directories
```
Synopsis:
rm [-r] [-v] <PFSPath> ...
Options:
-r
Remove directories and their contents recursively
-v
Cause rm to be verbose, showing files after they are removed.
Examples:
paddle pfs rm /pfs/$DATACENTER/home/$USER/file
paddle pfs rm -r /pfs/$DATACENTER/home/$USER/folder
```
- mv - move (rename) files
```
Synopsis:
mv [-f | -n] [-v] <LocalPath> <PFSPath>
mv [-f | -n] [-v] <LocalPath> ... <PFSPath>
mv [-f | -n] [-v] <PFSPath> <LocalPath>
mv [-f | -n] [-v] <PFSPath> ... <LocalPath>
mv [-f | -n] [-v] <PFSPath> <PFSPath>
mv [-f | -n] [-v] <PFSPath> ... <PFSPath>
Options:
-f
Do not prompt for confirmation before overwriting the destination path. (The -f option overrides previous -n options.)
-n
Do not overwrite an existing file. (The -n option overrides previous -f options.)
-v
Cause mv to be verbose, showing files after they are moved.
Examples:
paddle pfs mv ./text1.txt /pfs/$DATACENTER/home/$USER/text1.txt
```
- cp - copy files or directories
```
Synopsis:
cp [-r] [-f | -n] [-v] [--preserve--links] <LocalPath> <PFSPath>
cp [-r] [-f | -n] [-v] [--preserve--links] <LocalPath> ... <PFSPath>
cp [-r] [-f | -n] [-v] [--preserve--links] <PFSPath> <LocalPath>
cp [-r] [-f | -n] [-v] [--preserve--links] <PFSPath> ... <LocalPath>
cp [-r] [-f | -n] [-v] [--preserve--links] <PFSPath> <PFSPath>
cp [-r] [-f | -n] [-v] [--preserve--links] <PFSPath> ... <PFSPath>
Options:
-r
Copy directories recursively
-f
Do not prompt for confirmation before overwriting the destination path. (The -f option overrides previous -n options.)
-n
Do not overwrite an existing file. (The -n option overrides previous -f options.)
-v
Cause cp to be verbose, showing files after they are copied.
--preserve--links
Reserve links when copy links
Examples:
paddle pfs cp ./file /pfs/$DATACENTER/home/$USER/file
paddle pfs cp /pfs/$DATACENTER/home/$USER/file ./file
```
- ls- list files
```
Synopsis:
ls [-r] <PFSPath> ...
Options:
-R
List directory(ies) recursively
Examples:
paddle pfs ls /pfs/$DATACENTER/home/$USER/file
paddle pfs ls /pfs/$DATACENTER/home/$USER/folder
```
- mkdir - mkdir directory(ies)
Create intermediate directory(ies) as required.
```
Synopsis:
mkdir <PFSPath> ...
Examples:
paddle pfs mkdir /pfs/$DATACENTER/home/$USER/folder
```
doc/fluid/CMakeLists.txt
浏览文件 @ 82530e24
......@@ -27,7 +27,7 @@ sphinx_add_target(paddle_fluid_docs
${CMAKE_CURRENT_SOURCE_DIR}
${SPHINX_HTML_DIR_EN})
add_dependencies(paddle_fluid_docs gen_proto_py)
add_dependencies(paddle_fluid_docs gen_proto_py paddle_python)
# configured documentation tools and intermediate build results
set(BINARY_BUILD_DIR_CN "${CMAKE_CURRENT_BINARY_DIR}/cn/_build")
......@@ -50,6 +50,6 @@ sphinx_add_target(paddle_fluid_docs_cn
${CMAKE_CURRENT_SOURCE_DIR}
${SPHINX_HTML_DIR_CN})
add_dependencies(paddle_fluid_docs_cn gen_proto_py)
add_dependencies(paddle_fluid_docs_cn gen_proto_py paddle_python)
add_subdirectory(api)
doc/fluid/api/CMakeLists.txt
浏览文件 @ 82530e24
......@@ -19,4 +19,4 @@ sphinx_add_target(paddle_fluid_apis
${CMAKE_CURRENT_SOURCE_DIR}
${SPHINX_HTML_DIR_EN})
add_dependencies(paddle_fluid_apis gen_proto_py framework_py_proto copy_paddle_pybind)
add_dependencies(paddle_fluid_apis gen_proto_py framework_py_proto copy_paddle_pybind paddle_python)
doc/fluid/design/algorithm/parameter_average.md
浏览文件 @ 82530e24
......@@ -5,9 +5,11 @@ In a large scale machine learning setup where the size of the training data is h
Polyak and Juditsky (1992) showed that the test performance of simple average of parameters obtained by Stochastic Gradient Descent (SGD) is as good as that of parameter values that are obtained by training the model over and over again, over the training dataset.
Hence, to accelerate the speed of Stochastic Gradient Descent, Averaged Stochastic Gradient Descent (ASGD) was proposed in Polyak and Juditsky (1992). For ASGD, the running average of parameters obtained by SGD, is used as the estimator for <img src="./images/theta_star.gif"/><br/> . The averaging is done as follows:
Hence, to accelerate the speed of Stochastic Gradient Descent, Averaged Stochastic Gradient Descent (ASGD) was proposed in Polyak and Juditsky (1992). For ASGD, the running average of parameters obtained by SGD, is used as the estimator for <img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/theta_star.gif"/><br/> . The averaging is done as follows:
![](./images/asgd.gif)
<p align="center">
<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/asgd.gif"><br />
</p>
We propose averaging for any optimizer similar to how ASGD performs it, as mentioned above.
......
doc/fluid/design/concurrent/channel.md
浏览文件 @ 82530e24
......@@ -2,7 +2,7 @@
## Introduction
A Channel is a data structure that allows for synchronous interprocess
A Channel is a data structure that allows for synchronous interprocess
communication via message passing. It is a fundemental component of CSP
(communicating sequential processes), and allows for users to pass data
between threads without having to worry about synchronization.
......@@ -18,7 +18,7 @@ Creates a new channel that takes in variables of a specific dtype.
- **fluid.make_channel(dtype, capacity=0)**
- **dtype**: The data type of variables being sent/received through channel
- **capacity**: The capacity of the channel. A capacity of 0 represents
- **capacity**: The capacity of the channel. A capacity of 0 represents
an unbuffered channel. Capacity > 0 represents a buffered channel
```
......@@ -40,8 +40,8 @@ fluid.channel_close(ch)
### Send data to a channel
Sends a variable to a channel. Currently, variables of dtype `LoDTensor`,
`LoDRankTable`, `LoDTensorArray`, `SelectedRows`, `ReaderHolder`, and
Sends a variable to a channel. Currently, variables of dtype `LoDTensor`,
`LoDRankTable`, `LoDTensorArray`, `SelectedRows`, `ReaderHolder`, and
`ChannelHolder` are supported.
By default, the data of the Variable is moved from the sender to the receiver,
......@@ -52,7 +52,7 @@ however the user can optionally copy the data before performing the send.
- **variable**: The variable to send to the channel
- **is_copy**: If set to True, channel_send will perform a variable assign
to copy the source variable to a new variable to be sent.
```
ch = fluid.make_channel(dtype=core.VarDesc.VarType.LOD_TENSOR)
var = fill_constant(shape=[1],dtype=core.VarDesc.VarType.INT32, value=100)
......@@ -68,7 +68,7 @@ receiving variable.
- **channel**: The channel to receive the variable from
- **return_variable**: The destination variable used to store the data of the
variable received from the channel
```
ch = fluid.make_channel(dtype=core.VarDesc.VarType.LOD_TENSOR)
var = fill_constant(shape=[1],dtype=core.VarDesc.VarType.INT32, value=-1)
......@@ -84,9 +84,9 @@ internal queues, locks, and conditional variables.
### QueueMessage
QueueMessage encapsulates the state of the channel send/receive operation to be
put in the **sendq/recvq**. It contains a condition variable used to lock the
put in the **sendq/recvq**. It contains a condition variable used to lock the
thread (when there are no available sends/receives). In addition, it contains
a callback function to notify a thread when the QueueMessage is being
a callback function to notify a thread when the QueueMessage is being
processed by the channel.
### Queues
......@@ -108,21 +108,21 @@ channel_recv operation will put a new QueueMessage on the recvq and block the
current thread under two conditions:
1. The channel is buffered and there is no data on the buff_
2. The channel is unbuffered and does not have a sender
### State diagram
#### Channel Send
<p align="center">
<img src="./images/channel_send.png"/><br/>
<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/channel_send.png"/><br/>
</p>
#### Channel Receive
<p align="center">
<img src="./images/channel_recv.png"/><br/>
<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/channel_recv.png"/><br/>
</p>
## Limitations and Considerations
### Variable Copy
......@@ -135,5 +135,5 @@ be sent before it is sent.
Please note that this is acheived by adding an **assign** operator and creating
a temporary variable that is sent in place of the original variable. Please
note that **assign** operator has limited support for only certain variables
note that **assign** operator has limited support for only certain variables
datatypes.
doc/fluid/design/concurrent/concurrent_programming.md
浏览文件 @ 82530e24
......@@ -23,21 +23,25 @@ The following table compares concepts in Fluid and Go
<td>user-defined functions </td>
<td>
<a href="https://github.com/PaddlePaddle/Paddle/tree/develop/python/paddle/fluid">layers</a></td>
<td></td>
</tr>
<tr>
<td>control-flow and built-in functions </td>
<td>
<a href="https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/operators">intrinsics/operators</a></td>
<td></td>
</tr>
<tr>
<td>goroutines, channels </td>
<td>
<a href="https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/framework/thread_pool.h">class ThreadPool</a></td>
<td></td>
</tr>
<tr>
<td>runtime </td>
<td>
<a href="https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/executor.h">class Executor</a></td>
<td></td>
</tr>
</tbody>
</table>
......
doc/fluid/design/concurrent/select_op.md
浏览文件 @ 82530e24
......@@ -2,13 +2,13 @@
## Introduction
In golang, the [**select**](https://golang.org/ref/spec#Select_statements)
statement lets a goroutine wait on multiple communication operations at the
same time. The **select** blocks until one of its cases can run, then
executes the case. If multiple cases are ready to run, then one case is
In golang, the [**select**](https://golang.org/ref/spec#Select_statements)
statement lets a goroutine wait on multiple communication operations at the
same time. The **select** blocks until one of its cases can run, then
executes the case. If multiple cases are ready to run, then one case is
choosen at random to be executed.
With the introduction of CSP for Paddle, we mimic this behavior by
With the introduction of CSP for Paddle, we mimic this behavior by
creating a ***select_op***.
## How to use it
......@@ -17,11 +17,11 @@ The **select_op** is available as a c++ operator. However most users
will prefer to use the much simplier Python API.
- **fluid.Select()**: Creates a select operator and adds it to the current
block within the main program. Also creates a sub block and adds it to the
main program. This sub block is used to hold all variables and operators
block within the main program. Also creates a sub block and adds it to the
main program. This sub block is used to hold all variables and operators
used by the case statements.
Within the select block, users can add cases by
Within the select block, users can add cases by
calling **select.case** or **select.default** method.
- **fluid.Select.case(channel_action, channel, result_variable)**: Represents
......@@ -37,13 +37,13 @@ execute.
```
ch1 = fluid.make_channel(dtype=core.VarDesc.VarType.LOD_TENSOR)
quit_ch = fluid.make_channel(dtype=core.VarDesc.VarType.LOD_TENSOR)
x = fill_constant(shape=[1], dtype=core.VarDesc.VarType.INT32, value=0)
y = fill_constant(shape=[1], dtype=core.VarDesc.VarType.INT32, value=1)
while_cond = fill_constant(shape=[1], dtype=core.VarDesc.VarType.BOOL, value=True)
while_op = While(cond=while_cond)
with while_op.block():
with fluid.Select() as select:
with select.case(fluid.channel_send, channel, x):
......@@ -99,17 +99,17 @@ blocks {
}
}
// Create "select" operator.
// inputs:
// inputs:
// X: All input variables used by operators within the select block
// case_to_execute: Variable filled in by select_op when it determines
// which case to execute.
//
// outputs:
// Out: All output variables referenced by operators within select block.
//
// Out: All output variables referenced by operators within select block.
//
// attrs:
// sub_block: The block id containing the select "cases"
// cases: Serialized list of all cases in the select op.
// cases: Serialized list of all cases in the select op.
// Each case is serialized as: '<index>,<type>,<channel>,<value>'
// where type is 0 for default, 1 for send, and 2 for receive.
// No channel and values are needed for default cases.
......@@ -150,7 +150,7 @@ into **X**. It will also create a temp variable called **case_to_execute**. Th
filled in by the select_op after it has completed processing the case statements.
If there are no available cases to execute (ie: all cases are blocked on channel operations, and
there is no default statement), then the select_op will block the current thread. The thread will
there is no default statement), then the select_op will block the current thread. The thread will
unblock once there is a channel operation affecting one of the case statements, at which point, the
**select_op** will set the **case_to_execute** variable to the index of the case to execute.
......@@ -247,17 +247,17 @@ blocks {
```
Cases are represented by a **conditional_block operator**, whose's condition is set as the output of
equal(**case_to_execute**, **case_index**). Since each case index is unique in this sub-block,
Cases are represented by a **conditional_block operator**, whose's condition is set as the output of
equal(**case_to_execute**, **case_index**). Since each case index is unique in this sub-block,
only one case will be executed.
### select_op flow
<p align="center">
<img src="./images/select_op_workflow.png"/><br/>
<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/select_op_workflow.png"/><br/>
</p>
The select algorithm is inspired by golang's select routine. Please refer to
The select algorithm is inspired by golang's select routine. Please refer to
http://www.tapirgames.com/blog/golang-concurrent-select-implementation for more information.
## Backward Pass
......
doc/fluid/design/dist_train/distributed_architecture.md
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......@@ -40,11 +40,11 @@ computation is only specified in Python code which sits outside of PaddlePaddle,
Similar to how a compiler uses an intermediate representation (IR) so that the programmer does not need to manually optimize their code for most of the cases, we can have an intermediate representation in PaddlePaddle as well. The compiler optimizes the IR as follows:
<img src="src/compiler.png"/>
<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/compiler.png"/>
PaddlePaddle can support model parallelism by converting the IR so that the user no longer needs to manually perform the computation and operations in the Python component:
<img src="src/paddle-compile.png"/>
<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/paddle-compile.png"/>
The IR for PaddlePaddle after refactoring is called a `Block`, it specifies the computation dependency graph and the variables used in the computation.
......@@ -60,7 +60,7 @@ For a detailed explanation, refer to this document -
The revamped distributed training architecture can address the above discussed limitations. Below is the illustration of how it does so:
<img src="src/distributed_architecture.png"/>
<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/distributed_architecture.png"/>
The major components are: *Python API*, *Distribute Transpiler* and *Remote Executor*.
......@@ -152,7 +152,7 @@ for data in train_reader():
`JobDesc` object describe the distributed job resource specification to run on
Cluster environment.
<img src="src/remote_executor.png" width="500" align="center" />
<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/remote_executor.png" width="500" align="center" />
`RemoteExecutor.run` sends the `ProgramDesc` and
[TrainingJob](https://github.com/PaddlePaddle/cloud/blob/unreleased-tpr/doc/autoscale/README.md#training-job-resource)
......@@ -171,7 +171,7 @@ In the future, a more general placement algorithm should be implemented, which m
The local training architecture will be the same as the distributed training architecture, the difference is that everything runs locally, and there is just one PaddlePaddle runtime:
<img src="src/local_architecture.png"/>
<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/local_architecture.png"/>
### Training Data
......
doc/fluid/design/dist_train/multi_cpu.md
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......@@ -8,11 +8,11 @@ Op graph to a multi-CPU Op graph, and run `ParallelDo` Op to run the graph.
## Transpiler
<img src="src/multi-threads/single-thread@3x.png" width="300">
<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/single-thread@3x.png" width="300">
After converted:
<img src="src/multi-threads/multi-threads@3x.png" width="1000">
<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/multi-threads@3x.png" width="1000">
## Implement
......
doc/fluid/design/dist_train/parameter_server.md
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......@@ -41,11 +41,11 @@ We will need these OPs: *Send*, *Recv*, *Enqueue*, *Dequeue*.
Below is an example of converting the user defined graph to the
subgraphs for the trainer and the parameter server:
<img src="src/local-graph.png" width="300"/>
<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/local-graph.png" width="300"/>
After converting:
<img src="src/dist-graph.png" width="700"/>
<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/dist-graph.png" width="700"/>
1. The parameter variable W and its optimizer program are placed on the parameter server.
1. Operators are added to the program.
......@@ -69,8 +69,7 @@ In Fluid, we introduce [SelectedRows](../selected_rows.md) to represent a list o
non-zero gradient data. So when we do parameter optimization both locally and remotely,
we only need to send those non-zero rows to the optimizer operators:
<img src="src/sparse_update.png" width="700" />
<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/sparse_update.png" width="700" />
### Benefits
- Model parallelism becomes easier to implement: it is an extension to
......
doc/fluid/design/dynamic_rnn/rnn.md
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......@@ -5,7 +5,7 @@ This document describes the RNN (Recurrent Neural Network) operator and how it i
## RNN Algorithm Implementation
<p align="center">
<img src="./rnn.jpg"/>
<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/rnn.jpg"/>
</p>
The above diagram shows an RNN unrolled into a full network.
......@@ -22,7 +22,7 @@ There are several important concepts here:
There could be local variables defined in each step-net. PaddlePaddle runtime realizes these variables in *step-scopes* which are created for each step.
<p align="center">
<img src="./rnn.png"/><br/>
<img src="https://github.com/PaddlePaddle/Paddle/tree/develop/doc/fluid/images/rnn.png"/><br/>
Figure 2 illustrates the RNN's data flow
</p>
......@@ -93,7 +93,7 @@ For example, we could have a 2-level RNN, where the top level corresponds to par
The following figure illustrates feeding in text into the lower level, one sentence at a step, and the feeding in step outputs to the top level. The final top level output is about the whole text.
<p align="center">
<img src="./2_level_rnn.png"/>
<img src="https://github.com/PaddlePaddle/Paddle/tree/develop/doc/fluid/images/2_level_rnn.png"/>
</p>
```python
......@@ -149,5 +149,5 @@ If the `output_all_steps` is set to False, it will only output the final time st
<p align="center">
<img src="./rnn_2level_data.png"/>
<img src="https://github.com/PaddlePaddle/Paddle/tree/develop/doc/fluid/images/rnn_2level_data.png"/>
</p>
doc/fluid/design/modules/batch_norm_op.md
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......@@ -2,7 +2,7 @@
## What is batch normalization
Batch normalization is a frequently-used method in deep network training. It adjusts the mean and variance of a layer's output, and make the data distribution easier for next layer's training.
Batch normalization is a frequently-used method in deep network training. It adjusts the mean and variance of a layer's output, and make the data distribution easier for next layer's training.
The principle of batch normalization can be summarized into a simple function:
......@@ -66,7 +66,7 @@ As most C++ operators do, `batch_norm_op` is defined by inputs, outputs, attribu
The following graph showes the training computational process of `batch_norm_op`:
<img src="../images/batch_norm_op_kernel.png" width="800"/>
<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/batch_norm_op_kernel.png" width="800"/>
cudnn provides APIs to finish the whole series of computation, we can use them in our GPU kernel.
......@@ -74,13 +74,13 @@ cudnn provides APIs to finish the whole series of computation, we can use them i
`batch_norm_op` is warpped as a layer in Python:
```python
def batch_norm_layer(net,
```python
def batch_norm_layer(net,
input,
output,
scale,
bias,
use_global_est = False,
output,
scale,
bias,
use_global_est = False,
epsilon = 1e-6,
momentum = 0.99):
mean_cache = scope.new_var(name = 'estimated_mean', trainable = False)
......@@ -119,15 +119,15 @@ for pass_id in range(PASS_NUM):
if pass_id % 100 == 0:
net.infer(test_image) # run inferencing model
# ...
```
```
`is_infer` is an attribute. Once an operator is created, its attributes can not be changed. It suggests us that we shall maintain two `batch_norm_op` in the model, one's `is_infer` is `True`(we call it `infer_batch_norm_op`) and the other one's is `False`(we call it `train_batch_norm_op`). They share all parameters and variables, but be placed in two different branches. That is to say, if a network contains a `batch_norm_op`, it will fork into two branches, one go through `train_batch_norm_op` and the other one go through `infer_batch_norm_op`:
<div align=center>
<img src="../images/batch_norm_fork.png" width="500"/>
<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/batch_norm_fork.png" width="500"/>
</div>
Just like what is shown in the above graph, the net forks before `batch_norm_op` and will never merge again. All the operators after `batch_norm_op` will duplicate.
Just like what is shown in the above graph, the net forks before `batch_norm_op` and will never merge again. All the operators after `batch_norm_op` will duplicate.
When the net runs in training mode, the end of the left branch will be set as the running target, so the dependency tracking process will ignore right branch automatically. When the net runs in inferencing mode, the process is reversed.
......
doc/fluid/design/modules/regularization.md
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......@@ -6,23 +6,23 @@ A central problem in machine learning is how to design an algorithm that will pe
### Parameter Norm Penalties
Most common regularization approaches in deep learning are based on limiting the capacity of the models by adding a parameter norm penalty to the objective function `J`. This is given as follows:
<img src="./images/loss_equation.png" align="center"/><br/>
<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/loss_equation.png" align="center"/><br/>
The parameter `alpha` is a hyperparameter that weights the relative contribution of the norm penalty term, `omega`, relative to the standard objective function `J`.
The most commonly used norm penalties are the L2 norm penalty and the L1 norm penalty. These are given as follows:
##### L2 Regularization:
<img src="./images/l2_regularization.png" align="center"/><br/>
<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/l2_regularization.png" align="center"/><br/>
##### L1 Regularization
<img src="./images/l1_regularization.png" align="center"/><br/>
<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/l1_regularization.png" align="center"/><br/>
A much more detailed mathematical background of regularization can be found [here](http://www.deeplearningbook.org/contents/regularization.html).
## Regularization Survey
A detailed survey of regularization in various deep learning frameworks can be found [here](https://github.com/PaddlePaddle/Paddle/wiki/Regularization-Survey).
A detailed survey of regularization in various deep learning frameworks can be found [here](https://github.com/PaddlePaddle/Paddle/wiki/Regularization-Survey).
## Proposal for Regularization in PaddlePaddle
......@@ -32,41 +32,35 @@ In the new design, we propose to create new operations for regularization. For n
- L2_regularization_op
- L1_regularization_op
These ops can be like any other ops with their own CPU/GPU implementations either using Eigen or separate CPU and GPU kernels. As the initial implementation, we can implement their kernels using Eigen following the abstraction pattern implemented for [Activation Ops](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/accuracy_op.h). This abstraction pattern can make it very easy to implement new regularization schemes other than L1 and L2 norm penalties.
These ops can be like any other ops with their own CPU/GPU implementations either using Eigen or separate CPU and GPU kernels. As the initial implementation, we can implement their kernels using Eigen following the abstraction pattern implemented for [Activation Ops](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/accuracy_op.h). This abstraction pattern can make it very easy to implement new regularization schemes other than L1 and L2 norm penalties.
The idea of building ops for regularization is in sync with the refactored Paddle philosophy of using operators to represent any computation unit. The way these ops will be added to the computation graph, will be decided by the [layer functions](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/python_api.md#layer-function) in Python API.
The idea of building ops for regularization is in sync with the refactored Paddle philosophy of using operators to represent any computation unit. The way these ops will be added to the computation graph, will be decided by the [layer functions](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/python_api.md#layer-function) in Python API.
### Computation Graph
Below is an example of a really simple feed forward neural network.
<img src="./images/feed_forward.png" align="center"/><br/>
<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/feed_forward.png" align="center"/><br/>
The Python API will modify this computation graph to add regularization operators. The modified computation graph will look as follows:
<img src="./images/feed_forward_regularized.png" align="center"/><br/>
<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/feed_forward_regularized.png" align="center"/><br/>
   
### Python API implementation for Regularization
Using the low level ops, `L2_regularization_op` and `L1_regularization_op`, any user can add regularization to their computation graphs. However, this will require a lot of lines of code and we should design Python APIs that support regularization. An example of such an API can be seen in [Keras](https://keras.io/regularizers/). As per the PaddlePaddle [Python API design](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/python_api.md), the layer functions are responsible for creating operators, operator parameters and variables. Since regularization is a property of parameters, it makes sense to create these in the layer functions.
Using the low level ops, `L2_regularization_op` and `L1_regularization_op`, any user can add regularization to their computation graphs. However, this will require a lot of lines of code and we should design Python APIs that support regularization. An example of such an API can be seen in [Keras](https://keras.io/regularizers/). As per the PaddlePaddle [Python API design](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/python_api.md), the layer functions are responsible for creating operators, operator parameters and variables. Since regularization is a property of parameters, it makes sense to create these in the layer functions.
#### Creation of Regularization ops
There are two possibilities for creating the regularization ops:
1. We create these ops immediately while building the computation graph.
2. We add these ops in a lazy manner, just before the backward, similar to the way the optimization ops are added.
1. We create these ops immediately while building the computation graph.
2. We add these ops in a lazy manner, just before the backward, similar to the way the optimization ops are added.
The proposal is to add these ops in a lazy manner just before the backward pass.
The proposal is to add these ops in a lazy manner just before the backward pass.
#### Storage of Regularization attributes
Since we want to create the regularization ops in a lazy manner, the regularization attributes (type of regularization and weight of regularization penalty) can be stored as attributes of the [`Parameter`](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/v2/framework/framework.py#L421) class. This is because regularization is a property of the parameters and storing regularization properties with Parameters also allows for shared parameters.
Since we want to create the regularization ops in a lazy manner, the regularization attributes (type of regularization and weight of regularization penalty) can be stored as attributes of the [`Parameter`](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/v2/framework/framework.py#L421) class. This is because regularization is a property of the parameters and storing regularization properties with Parameters also allows for shared parameters.
#### High-level API
In PaddlePaddle Python API, users will primarily rely on [layer functions](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/python_api.md#layer-function) to create neural network layers. Hence, we also need to provide regularization functionality in layer functions. The design of these APIs can be postponed for later right now. A good reference for these APIs can be found in [Keras](https://keras.io/regularizers/) and also by looking at Tensorflow in [`tf.contrib.layers`](https://www.tensorflow.org/api_guides/python/contrib.layers).
doc/fluid/design/network/deep_speech_2.md
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......@@ -116,7 +116,7 @@ The classical DS2 network contains 15 layers (from bottom to top):
- **One** CTC-loss layer
<div align="center">
<img src="images/ds2_network.png" width=350><br/>
<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/ds2_network.png" width=350><br/>
Figure 1. Archetecture of Deep Speech 2 Network.
</div>
......@@ -142,7 +142,7 @@ Key ingredients about the layers:
- **Batch Normalization Layers**:
- Added to all above layers (except for data and loss layer).
- Sequence-wise normalization for RNNs: BatchNorm only performed on input-state projection and not state-state projection, for efficiency consideration.
<table>
<thead>
<tr>
......@@ -208,7 +208,7 @@ TODO by Assignees
### Beam Search with CTC and LM
<div align="center">
<img src="images/beam_search.png" width=600><br/>
<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/beam_search.png" width=600><br/>
Figure 2. Algorithm for CTC Beam Search Decoder.
</div>
......
doc/fluid/design/network/sequence_decoder.md
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......@@ -199,7 +199,7 @@ Packing the `selected_generation_scores` will get a `LoDTensor`, and each tail i
## LoD and shape changes during decoding
<p align="center">
<img src="./images/LOD-and-shape-changes-during-decoding.jpg"/>
<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/LOD-and-shape-changes-during-decoding.jpg"/>
</p>
According to the image above, the only phase that changes the LoD is beam search.
......
doc/fluid/design/others/gan_api.md
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# Design for GAN
GAN (General Adversarial Net [https://arxiv.org/abs/1406.2661]) is an important model for unsupervised learning and widely used in many areas.
GAN (General Adversarial Net [https://arxiv.org/abs/1406.2661]) is an important model for unsupervised learning and widely used in many areas.
It applies several important concepts in machine learning system design, including building and running subgraphs, dependency tracing, different optimizers in one executor and so forth.
In our GAN design, we wrap it as a user-friendly easily customized python API to design different models. We take the conditional DC-GAN (Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks [https://arxiv.org/abs/1511.06434]) as an example due to its good performance on image generation.
<p align="center">
<img src="./test.dot.png" width = "35%" align="center"/><br/>
<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/test.dot.png" width = "35%" align="center"/><br/>
Figure 1. The overall running logic of GAN. The black solid arrows indicate the forward pass; the green dashed arrows indicate the backward pass of generator training; the red dashed arrows indicate the backward pass of the discriminator training. The BP pass of the green (red) arrow should only update the parameters in the green (red) boxes. The diamonds indicate the data providers. d\_loss and g\_loss marked in red and green are the two targets we would like to run.
</p>
The operators, layers and functions required/optional to build a GAN demo is summarized in https://github.com/PaddlePaddle/Paddle/issues/4563.
<p align="center">
<img src="./dcgan.png" width = "90%" align="center"/><br/>
<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/dcgan.png" width = "90%" align="center"/><br/>
Figure 2. Photo borrowed from the original DC-GAN paper.
</p>
## The Conditional-GAN might be a class.
## The Conditional-GAN might be a class.
This design we adopt the popular open source design in https://github.com/carpedm20/DCGAN-tensorflow and https://github.com/rajathkmp/DCGAN. It contains following data structure:
- DCGAN(object): which contains everything required to build a GAN model. It provides following member functions methods as API:
......@@ -29,7 +29,7 @@ This design we adopt the popular open source design in https://github.com/carped
Returns a generated image.
- discriminator(image):
Given an image, decide if it is from a real source or a fake one.
Given an image, decide if it is from a real source or a fake one.
Returns a 0/1 binary label.
- build_model(self):
......@@ -47,7 +47,7 @@ To be more detailed, we introduce our design of DCGAN as following:
```python
class DCGAN(object):
def __init__(self, y_dim=None):
# hyper parameters
self.y_dim = y_dim # conditional gan or not
self.batch_size = 100
......@@ -82,18 +82,18 @@ class DCGAN(object):
# input z: the random noise
# input y: input data label (optional)
# output G_im: generated fake images
if not self.y_dim:
z = pd.layer.concat(1, [z, y])
G_h0 = pd.layer.fc(z, self.G_w0, self.G_b0)
G_h0_bn = pd.layer.batch_norm(G_h0)
G_h0_relu = pd.layer.relu(G_h0_bn)
G_h1 = pd.layer.deconv(G_h0_relu, self.G_w1, self.G_b1)
G_h1_bn = pd.layer.batch_norm(G_h1)
G_h1_relu = pd.layer.relu(G_h1_bn)
G_h2 = pd.layer.deconv(G_h1_relu, self.G_W2, self.G_b2))
G_im = pd.layer.tanh(G_im)
return G_im
......@@ -111,11 +111,11 @@ class DCGAN(object):
D_h0 = pd.layer.conv2d(image, w=self.D_w0, b=self.D_b0)
D_h0_bn = pd.layer.batchnorm(h0)
D_h0_relu = pd.layer.lrelu(h0_bn)
D_h1 = pd.layer.conv2d(D_h0_relu, w=self.D_w1, b=self.D_b1)
D_h1_bn = pd.layer.batchnorm(D_h1)
D_h1_relu = pd.layer.lrelu(D_h1_bn)
D_h2 = pd.layer.fc(D_h1_relu, w=self.D_w2, b=self.D_b2)
return D_h2
```
......@@ -123,7 +123,7 @@ class DCGAN(object):
### Class member function: Build the model
- Define data readers as placeholders to hold the data;
- Build generator and discriminators;
- Define two training losses for discriminator and generator, respectively.
- Define two training losses for discriminator and generator, respectively.
If we have execution dependency engine to back-trace all tensors, the module building our GAN model will be like this:
```python
class DCGAN(object):
......@@ -133,7 +133,7 @@ class DCGAN(object):
self.images = pd.data(pd.float32, [self.batch_size, self.im_size, self.im_size])
self.faked_images = pd.data(pd.float32, [self.batch_size, self.im_size, self.im_size])
self.z = pd.data(tf.float32, [None, self.z_size])
# step 1: generate images by generator, classify real/fake images with discriminator
if self.y_dim: # if conditional GAN, includes label
self.G = self.generator(self.z, self.y)
......@@ -147,12 +147,12 @@ class DCGAN(object):
# generate fake images
self.sampled = self.sampler(self.z)
self.D_f = self.discriminator(self.images)
# step 2: define the two losses
self.d_loss_real = pd.reduce_mean(pd.cross_entropy(self.D_t, np.ones(self.batch_size))
self.d_loss_fake = pd.reduce_mean(pd.cross_entropy(self.D_f, np.zeros(self.batch_size))
self.d_loss = self.d_loss_real + self.d_loss_fake
self.g_loss = pd.reduce_mean(pd.cross_entropy(self.D_f, np.ones(self.batch_szie))
```
......@@ -176,7 +176,7 @@ class DCGAN(object):
self.G = self.generator(self.z)
self.D_g = self.discriminator(self.G, self.y)
self.g_loss = pd.reduce_mean(pd.cross_entropy(self.D_g, np.ones(self.batch_szie))
with pd.default_block().d_block():
if self.y_dim: # if conditional GAN, includes label
self.D_t = self.discriminator(self.images, self.y)
......@@ -217,7 +217,7 @@ if __name__ == "__main__":
# load mnist data
data_X, data_y = self.load_mnist()
# Two subgraphs required!!!
with pd.block().d_block():
d_optim = pd.train.Adam(lr = .001, beta= .1)
......@@ -228,7 +228,7 @@ if __name__ == "__main__":
# executor
sess = pd.executor()
# training
for epoch in xrange(10000):
for batch_id in range(N / batch_size):
......@@ -239,7 +239,7 @@ if __name__ == "__main__":
batch_z = np.random.uniform(-1., 1., [batch_size, z_dim])
if batch_id % 2 == 0:
sess.run(d_step,
sess.run(d_step,
feed_dict = {dcgan.images: batch_im,
dcgan.y: batch_label,
dcgan.z: batch_z})
......
doc/fluid/dev/index_cn.rst
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......@@ -9,5 +9,5 @@
use_eigen_cn.md
name_convention.md
support_new_device.md
releasing_process.md
releasing_process_cn.md
op_markdown_format.md
doc/fluid/dev/index_en.rst
浏览文件 @ 82530e24
......@@ -9,5 +9,5 @@ Development
use_eigen_en.md
name_convention.md
support_new_device.md
releasing_process.md
releasing_process_en.md
op_markdown_format.md
doc/fluid/dev/releasing_process.md → doc/fluid/dev/releasing_process_cn.md
浏览文件 @ 82530e24
......@@ -10,19 +10,10 @@ PaddlePaddle每次发新的版本，遵循以下流程:
* 使用Regression Test List作为检查列表，测试本次release的正确性。
* 如果失败，记录下所有失败的例子，在这个`release/版本号`分支中，修复所有bug后，Patch号加一，到第二步
* 修改`python/setup.py.in`中的版本信息,并将`istaged`字段设为`True`。
* 编译这个版本的python wheel包，并发布到pypi。
* 由于pypi.python.org目前遵循[严格的命名规范PEP 513](https://www.python.org/dev/peps/pep-0513)，在使用twine上传之前，需要重命名wheel包中platform相关的后缀，比如将`linux_x86_64`修改成`manylinux1_x86_64`。
* pypi上的package名称为paddlepaddle和paddlepaddle_gpu，如果要上传GPU版本的包，需要修改build/python/setup.py中，name: "paddlepaddle_gpu"并重新打包wheel包：`python setup.py bdist_wheel`。
* 上传方法：
```
cd build/python
pip install twine
twine upload dist/[package to upload]
```
* 编译这个版本的Docker发行镜像，发布到dockerhub。如果失败，修复Docker编译镜像问题，Patch号加一，返回第二步
1. 第三步完成后，将`release/版本号`分支合入master分支，并删除`release/版本号`分支。将master分支的合入commit打上tag，tag为`版本号`。同时再将`master`分支合入`develop`分支。最后删除`release/版本号`分支。
1. 协同完成Release Note的书写
* 将这个版本的python wheel包发布到pypi。
* 更新Docker镜像（参考后面的操作细节）。
1. 第三步完成后，将`release/版本号`分支合入master分支，将master分支的合入commit打上tag，tag为`版本号`。同时再将`master`分支合入`develop`分支。
1. 协同完成Release Note的书写。
需要注意的是:
......@@ -31,13 +22,18 @@ PaddlePaddle每次发新的版本，遵循以下流程:
## 发布wheel包到pypi
使用[PaddlePaddle CI](https://paddleci.ngrok.io/project.html?projectId=Manylinux1&tab=projectOverview)
1. 使用[PaddlePaddle CI](https://paddleci.ngrok.io/project.html?projectId=Manylinux1&tab=projectOverview)
完成自动化二进制编译，参考下图，选择需要发布的版本（通常包含一个CPU版本和一个GPU版本），点击"run"右侧的"..."按钮，可以
弹出下面的选择框，在第二个tab (Changes)里选择需要发布的分支，这里选择0.11.0，然后点击"Run Build"按钮。等待编译完成后
可以在此页面的"Artifacts"下拉框中找到生成的3个二进制文件，分别对应CAPI，`cp27m`和`cp27mu`的版本。然后按照上述的方法
使用`twine`工具上传即可。
<img src="ci_build_whl.png">
弹出下面的选择框，在第二个tab (Changes)里选择需要发布的分支，这里选择0.11.0，然后点击"Run Build"按钮。
<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/ci_build_whl.png">
1. 等待编译完成后可以在此页面的"Artifacts"下拉框中找到生成的3个二进制文件，分别对应CAPI，`cp27m`和`cp27mu`的版本。
1. 由于pypi.python.org目前遵循[严格的命名规范PEP 513](https://www.python.org/dev/peps/pep-0513)，在使用twine上传之前，需要重命名wheel包中platform相关的后缀，比如将`linux_x86_64`修改成`manylinux1_x86_64`。
1. 上传：
```
cd build/python
pip install twine
twine upload dist/[package to upload]
```
* 注：CI环境使用 https://github.com/PaddlePaddle/buildtools 这里的DockerImage作为编译环境以支持更多的Linux
发型版，如果需要手动编译，也可以使用这些镜像。这些镜像也可以从 https://hub.docker.com/r/paddlepaddle/paddle_manylinux_devel/tags/ 下载得到。
......@@ -48,10 +44,20 @@ PaddlePaddle每次发新的版本，遵循以下流程:
上述PaddlePaddle CI编译wheel完成后会自动将Docker镜像push到DockerHub，所以，发布Docker镜像只需要对自动push的镜像打上
版本号对应的tag即可：
1. 进入 https://hub.docker.com/r/paddlepaddle/paddle/tags/ 查看latest tag的更新时间是否在上述编译wheel包完成后是否最新。
1. 执行 `docker pull paddlepaddle/paddle:[latest tag]`，latest tag可以是latest或latest-gpu等。
1. 执行 `docker tag paddlepaddle/paddle:[latest tag] paddlepaddle/paddle:[version]`
1. 执行 `docker push paddlepaddle/paddle:[version]`
```
docker pull [镜像]:latest
docker tag [镜像]:latest [镜像]:[version]
docker push [镜像]:[version]
```
需要更新的镜像tag包括：
* `[version]`: CPU版本
* `[version]-openblas`: openblas版本
* `[version]-gpu`: GPU版本（CUDA 8.0 cudnn 5）
* `[version]-gpu-[cudaver]-[cudnnver]`: 不同cuda, cudnn版本的镜像
之后可进入 https://hub.docker.com/r/paddlepaddle/paddle/tags/ 查看是否发布成功。
## PaddlePaddle 分支规范
......@@ -76,7 +82,7 @@ PaddlePaddle开发过程使用[git-flow](http://nvie.com/posts/a-successful-git-
### PaddlePaddle Book中所有章节
PaddlePaddle每次发版本首先要保证PaddlePaddle Book中所有章节功能的正确性。功能的正确性包括验证PaddlePaddle目前的`paddle_trainer`训练和纯使用`Python`训练模型正确性。
PaddlePaddle每次发版本首先要保证PaddlePaddle Book中所有章节功能的正确性。功能的正确性包括验证PaddlePaddle目前的`paddle_trainer`训练和纯使用`Python`训练（V2和Fluid）模型正确性。
<table>
<thead>
......
doc/fluid/dev/releasing_process_en.md 0 → 100644
浏览文件 @ 82530e24
# PaddlePaddle Releasing Process
PaddlePaddle manages its branches using "git-flow branching model", and [Semantic Versioning](http://semver.org/) as it's version number semantics.
Each time we release a new PaddlePaddle version, we should follow the below steps:
1. Fork a new branch from `develop` named `release/[version]`, e.g. `release/0.10.0`.
1. Push a new tag on the release branch, the tag name should be like `[version]rc.patch`. The
first tag should be `0.10.0rc1`, and the second should be `0.10.0.rc2` and so on.
1. After that, we should do:
* Run all regression test on the Regression Test List (see PaddlePaddle TeamCity CI), to confirm
that this release has no major bugs.
* If regression test fails, we must fix those bugs and create a new `release/[version]`
branch from previous release branch.
* Modify `python/setup.py.in`, change the version number and change `ISTAGED` to `True`.
* Publish PaddlePaddle release wheel packages to pypi (see below instructions for detail).
* Update the Docker images (see below instructions for detail).
1. After above step, merge `release/[version]` branch to master and push a tag on the master commit,
then merge `master` to `develop`.
1. Update the Release Note.
***NOTE:***
* Do ***NOT*** merge commits from develop branch to release branches to keep the release branch contain
features only for current release, so that we can test on that version.
* If we want to fix bugs on release branches, we must merge the fix to master, develop and release branch.
## Publish Wheel Packages to pypi
1. Use our [CI tool](https://paddleci.ngrok.io/project.html?projectId=Manylinux1&tab=projectOverview)
to build all wheel packages needed to publish. As shown in the following picture, choose a build
version, click "..." button on the right side of "Run" button, and switch to the second tab in the
pop-up box, choose the current release branch and click "Run Build" button. You may repeat this
step to start different versions of builds.
<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/ci_build_whl.png">
1. After the build succeeds, download the outputs under "Artifacts" including capi, `cp27m` and `cp27mu`.
1. Since pypi.python.org follows [PEP 513](https://www.python.org/dev/peps/pep-0513), before we
upload the package using `twine`, we need to rename the package from `linux_x86_64` to
`manylinux1_x86_64`.
1. Start the upload:
```
cd build/python
pip install twine
twine upload dist/[package to upload]
```
* NOTE: We use a special Docker image to build our releases to support more Linux distributions, you can
download it from https://hub.docker.com/r/paddlepaddle/paddle_manylinux_devel/tags/, or build it using
scripts under `tools/manylinux1`.
* pypi does not allow overwrite the already uploaded version of wheel package, even if you delete the
old version. you must change the version number before upload a new one.
## Publish Docker Images
Our CI tool will push latest images to DockerHub, so we only need to push a version tag like:
```
docker pull [image]:latest
docker tag [image]:latest [image]:[version]
docker push [image]:[version]
```
Tags that need to be updated are:
* `[version]`: CPU only version image
* `[version]-openblas`: openblas version image
* `[version]-gpu`: GPU version（using CUDA 8.0 cudnn 5）
* `[version]-gpu-[cudaver]-[cudnnver]`: tag for different cuda, cudnn versions
You can then checkout the latest pushed tags at https://hub.docker.com/r/paddlepaddle/paddle/tags/.
## Branching Model
We use [git-flow](http://nvie.com/posts/a-successful-git-branching-model/) as our branching model,
with some modifications:
* `master` branch is the stable branch. Each version on the master branch is tested and guaranteed.
* `develop` branch is for development. Each commit on develop branch has passed CI unit test, but no
regression tests are run.
* `release/[version]` branch is used to publish each release. Latest release version branches have
bugfix only for that version, but no feature updates.
* Developer forks are not required to follow
[git-flow](http://nvie.com/posts/a-successful-git-branching-model/)
branching model, all forks is like a feature branch.
* Advise: developer fork's develop branch is used to sync up with main repo's develop branch.
* Advise: developer use it's fork's develop branch to for new branch to start developing.
* Use that branch on developer's fork to create pull requests and start reviews.
* developer can push new commits to that branch when the pull request is open.
* Bug fixes are also started from developers forked repo. And, bug fixes branch can merge to
`master`, `develop` and `releases`.
## PaddlePaddle Regression Test List
### All Chapters of PaddlePaddle Book
We need to guarantee that all the chapters of PaddlePaddle Book can run correctly. Including
V1 (`paddle_trainer` training) and V2 training and Fluid training.
<table>
<thead>
<tr>
<th></th>
<th>Linear Regression</th>
<th>Recognize Digits</th>
<th>Image Classification</th>
<th>Word2Vec</th>
<th>Personalized Recommendation</th>
<th>Sentiment Analysis</th>
<th>Semantic Role Labeling</th>
<th>Machine Translation</th>
</tr>
</thead>
<tbody>
<tr>
<td>API.V2 + Docker + GPU </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr>
<td> API.V2 + Docker + CPU </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr>
<td>`paddle_trainer` + Docker + GPU </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr>
<td>`paddle_trainer` + Docker + CPU </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr>
<td> API.V2 + Ubuntu + GPU</td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr>
<td>API.V2 + Ubuntu + CPU </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr>
<td> `paddle_trainer` + Ubuntu + GPU</td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
</tr>
<tr>
<td> `paddle_trainer` + Ubuntu + CPU</td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
<td> </td>
</tr>
</tbody>
</table>
doc/fluid/howto/performance/profiler.md
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......@@ -23,7 +23,7 @@ But how to record the time for the mixed C++ and CUDA program? There many C++ A
The overall flow is shown as the following figure.
<img src="./images/profiler.png" align="center"/><br/>
<img src="https://raw.githubusercontent.com/PaddlePaddle/Paddle/develop/doc/fluid/images/profiler.png" align="center"/><br/>
### Event
......@@ -36,10 +36,10 @@ enum EventKind {
kPopRange};
```
- kMark: only a marker without time range.
- kPushRange: mark the starting event for time range.
- kPushRange: mark the starting event for time range.
- kPopRange: mark the ending event for time range.
For the CPU code, the events only need to record the current time. For the CUDA code, the [event management functions of CUDA](http://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__EVENT.html#group__CUDART__EVENT) are used. For many pieces of code, an event lists are used to record each piece.
For the CPU code, the events only need to record the current time. For the CUDA code, the [event management functions of CUDA](http://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__EVENT.html#group__CUDART__EVENT) are used. For many pieces of code, an event lists are used to record each piece.
```c++
class Event {
......@@ -66,11 +66,11 @@ struct EventList {
};
```
As mentioned above, there is no need to record the timeline when disabling the profiler. So there is a global state to enable or disable the profiler.
As mentioned above, there is no need to record the timeline when disabling the profiler. So there is a global state to enable or disable the profiler.
```c++
enum ProfilerState {
kDisabled,
kDisabled,
kCPU,
kCUDA
};
......
doc/fluid/images/2_level_rnn.dot 0 → 100644
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digraph G {
rnn [label="1st level RNN" shape=box]
subgraph cluster0 {
label = "time step 0"
sent0 [label="sentence"]
sent1 [label="sentence"]
rnn1 [label="2nd level RNN" shape=box]
sent0 -> rnn1
sent1 -> rnn1
}
subgraph cluster1 {
label = "time step 1"
sent2 [label="sentence"]
sent3 [label="sentence"]
rnn2 [label="2nd level RNN" shape=box]
sent2 -> rnn2
sent3 -> rnn2
}
subgraph cluster2 {
label = "time step 2"
sent4 [label="sentence"]
sent5 [label="sentence"]
rnn3 [label="2nd level RNN" shape=box]
sent4 -> rnn3
sent5 -> rnn3
}
para0 [label="paragraph info 0"]
para1 [label="paragraph info 1"]
para2 [label="paragraph info 2"]
rnn1 -> para0
rnn2 -> para1
rnn3 -> para2
para0 -> rnn
para1 -> rnn
para2 -> rnn
chapter [label="chapter info"]
rnn -> chapter
}
doc/fluid/images/batch_norm_fork.dot 0 → 100644
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digraph ImageBatchNormForkGragh {
subgraph cluster_before {
Prev [label="...", shape=plaintext];
Rnn [label="rnn_op", shape=box];
BatchNorm [label="batch_norm_op", shape=box];
Fc [label="fc_op", shape=box];
After [label="...", shape=plaintext];
Prev -> Rnn -> BatchNorm -> Fc -> After;
label="original";
}
subgraph cluster_after {
Prev2 [label="...", shape=plaintext];
Rnn2 [label="rnn_op", shape=box];
BatchNorm2_1 [label="train_batch_norm_op", shape=box];
BatchNorm2_2 [label="infer_batch_norm_op", shape=box];
Fc2_1 [label="fc_op", shape=box];
Fc2_2 [label="fc_op", shape=box];
After2_1 [label="...", shape=plaintext];
After2_2 [label="...", shape=plaintext];
Prev2 -> Rnn2 -> BatchNorm2_1 -> Fc2_1 -> After2_1;
Rnn2 -> BatchNorm2_2 ->Fc2_2 ->After2_2
label="forked";
}
}
doc/fluid/images/graph_construction_example.bash 0 → 100755
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cat ./graph_construction_example.dot | \
sed 's/color=red/color=red, style=invis/g' | \
sed 's/color=green/color=green, style=invis/g' | \
dot -Tpng > graph_construction_example_forward_only.png
cat ./graph_construction_example.dot | \
sed 's/color=green/color=green, style=invis/g' | \
dot -Tpng > graph_construction_example_forward_backward.png
cat ./graph_construction_example.dot | \
dot -Tpng > graph_construction_example_all.png
doc/fluid/images/graph_construction_example.dot 0 → 100644
浏览文件 @ 82530e24
digraph ImageClassificationGraph {
///////// The forward part /////////
FeedX [label="Feed", color=blue, shape=box];
FeedY [label="Feed", color=blue, shape=box];
InitW [label="Init", color=blue, shape=diamond];
Initb [label="Init", color=blue, shape=diamond];
FC [label="FC", color=blue, shape=box];
MSE [label="MSE", color=blue, shape=box];
x [label="x", color=blue, shape=oval];
l [label="l", color=blue, shape=oval];
y [label="y", color=blue, shape=oval];
W [label="W", color=blue, shape=doublecircle];
b [label="b", color=blue, shape=doublecircle];
cost [label="cost", color=blue, shape=oval];
FeedX -> x -> FC -> y -> MSE -> cost [color=blue];
FeedY -> l [color=blue];
InitW -> W [color=blue];
Initb -> b [color=blue];
W -> FC [color=blue];
b -> FC [color=blue];
l -> MSE [color=blue];
////////// The backward part /////////
MSE_Grad [label="MSE_grad", color=red, shape=box];
FC_Grad [label="FC_grad", color=red, shape=box];
d_cost [label="d cost", color=red, shape=oval];
d_y [label="d y", color=red, shape=oval];
d_b [label="d b", color=red, shape=oval];
d_W [label="d W", color=red, shape=oval];
cost -> MSE_Grad [color=red];
d_cost -> MSE_Grad [color=red];
l -> MSE_Grad [color=red];
y -> MSE_Grad -> d_y [color=red];
x -> FC_Grad [color=red];
y -> FC_Grad [color=red];
d_y -> FC_Grad [color=red];
W -> FC_Grad -> d_W [color=red];
b -> FC_Grad -> d_b [color=red];
////////// The optimizaiton part //////////
OPT_W [label="SGD", color=green, shape=box];
OPT_b [label="SGD", color=green, shape=box];
W -> OPT_W [color=green];
b -> OPT_b [color=green];
d_W -> OPT_W -> W [color=green];
d_b -> OPT_b -> b [color=green];
////////// Groupings //////////
subgraph clusterMSE {
style=invis;
MSE;
MSE_Grad;
}
subgraph clusterFC {
style=invis;
FC;
FC_Grad;
}
}
doc/fluid/images/rnn.dot 0 → 100644
浏览文件 @ 82530e24
digraph G {
label = "simple RNN implementation"
ranksep=2;
//graph [nodesep=1, ranksep=1];
node[nodesep=1]
subgraph cluster0 {
label = "global scope"
rankdir = TB
W
boot_memory
input
output
}
subgraph cluster1 {
label = "step-scope 0"
rankdir = TB
memory0[label="memory"]
prememory0[label="pre-memory"]
step_input0[label="step input"]
step_output0[label="step output"]
}
subgraph cluster2 {
label = "step-scope 1"
rankdir = TB
memory1[label="memory"]
prememory1[label="pre-memory"]
step_input1[label="step input"]
step_output1[label="step output"]
}
subgraph cluster3 {
label = "step-scope 2"
rankdir = TB
memory2[label="memory"]
prememory2[label="pre-memory"]
step_input2[label="step input"]
step_output2[label="step output"]
}
stepnet [shape=box]
stepnet0 [shape=box, style=dashed]
stepnet1 [shape=box, style=dashed]
stepnet2 [shape=box, style=dashed]
edge[color=blue]
boot_memory -> prememory0 [label="init" color="blue"]
memory0 -> prememory1 [label="copy/reference" color="blue"]
memory1 -> prememory2 [label="copy/reference" color="blue"]
edge[color=black]
W -> stepnet0[constraint=false, style=dashed]
W -> stepnet1[constraint=false, style=dashed]
W -> stepnet2[constraint=false, style=dashed]
memory0 -> stepnet0[style=dashed]
prememory0 -> stepnet0 -> step_output0[style=dashed]
memory1 -> stepnet1[style=dashed]
prememory1 -> stepnet1 -> step_output1[style=dashed]
memory2 -> stepnet2[style=dashed]
prememory2 -> stepnet2 -> step_output2[style=dashed]
input -> step_input0
input -> step_input1
input -> step_input2
step_input0 -> stepnet0 [style=dashed]
step_input1 -> stepnet1[style=dashed]
step_input2 -> stepnet2[style=dashed]
step_output0 -> output
step_output1 -> output
step_output2 -> output
stepnet0 -> stepnet[style=dashed]
stepnet1 -> stepnet[style=dashed]
stepnet2 -> stepnet[style=dashed]
}
doc/fluid/images/rnn_2level_data.dot 0 → 100644
浏览文件 @ 82530e24
digraph G {
chapter [label="chapter"]
subgraph cluster0 {
label = "paragraph 0"
top_rnn0[label="top rnn step 0" shape=box]
p0 [label="paragraph 0"]
p1 [label="paragraph 1"]
}
subgraph cluster1{
label = "paragraph 1"
top_rnn1[label="top rnn step 1" shape=box]
p2 [label="paragraph 0"]
p3 [label="paragraph 1"]
}
subgraph cluster_p0 {
label = "sentence 0"
low_rnn0 [label="low rnn step 0" shape=box]
s00 [label="sentence 0"]
s01 [label="sentence 1"]
low_rnn0 -> s00
low_rnn0 -> s01
}
subgraph cluster_p1 {
label = "sentence 1"
low_rnn1 [label="low rnn step 1" shape=box]
s10 [label="sentence 0"]
s11 [label="sentence 1"]
low_rnn1 -> s10
low_rnn1 -> s11
}
subgraph cluster_p2 {
label = "sentence 1"
low_rnn2 [label="low rnn step 0" shape=box]
s20 [label="sentence 0"]
s21 [label="sentence 1"]
low_rnn2 -> s20
low_rnn2 -> s21
}
subgraph cluster_p3 {
label = "sentence 1"
low_rnn3 [label="low rnn step 1" shape=box]
s30 [label="sentence 0"]
s31 [label="sentence 1"]
low_rnn3 -> s30
low_rnn3 -> s31
}
chapter -> top_rnn0
chapter -> top_rnn1
top_rnn0 -> p0
top_rnn0 -> p1
top_rnn1 -> p2
top_rnn1 -> p3
p0 -> low_rnn0
p1 -> low_rnn1
p2 -> low_rnn2
p3 -> low_rnn3
}
doc/fluid/images/test.dot 0 → 100644
浏览文件 @ 82530e24
digraph Test {
z -> generator -> G_img;
G_img -> discriminator -> D_f -> d_loss_f;
label0 -> d_loss_f -> d_loss;
img -> discriminator -> D_t -> d_loss_t;
label1 -> d_loss_t -> d_loss;
d_loss -> d_loss_t[color=red, style=dashed];
d_loss -> d_loss_f[color=red, style=dashed];
d_loss_t -> D_t[color=red, style=dashed];
d_loss_f -> D_f[color=red, style=dashed];
D_t -> discriminator[color=red, style=dashed];
D_f -> discriminator[color=red, style=dashed];
D_f -> g_loss;
label2 -> g_loss;
g_loss -> D_f[color=green, style=dashed];
D_f -> discriminator[color=green, style=dashed];
discriminator -> G_img[color=green, style=dashed];
G_img -> generator[color=green, style=dashed];
discriminator [color=red, shape=box];
generator [color=green, shape=box];
z [shape=diamond];
img [shape=diamond];
label0 [shape=diamond];
label1 [shape=diamond];
label2 [shape=diamond];
d_loss [color=red];
g_loss [color=green];
}
doc/templates/conf.py.cn.in
浏览文件 @ 82530e24
......@@ -13,7 +13,7 @@
# serve to show the default.
import sys
import os, subprocess
sys.path.insert(0, os.path.abspath('@PADDLE_SOURCE_DIR@/python'))
sys.path.insert(0, os.path.abspath('@PADDLE_BINARY_DIR@/python'))
import shlex
from recommonmark import parser, transform
import paddle
......
doc/templates/conf.py.en.in
浏览文件 @ 82530e24
......@@ -13,7 +13,7 @@
# serve to show the default.
import sys
import os, subprocess
sys.path.insert(0, os.path.abspath('@PADDLE_SOURCE_DIR@/python'))
sys.path.insert(0, os.path.abspath('@PADDLE_BINARY_DIR@/python'))
import shlex
from recommonmark import parser, transform
import paddle
......
doc/v2/CMakeLists.txt
浏览文件 @ 82530e24
......@@ -27,7 +27,7 @@ sphinx_add_target(paddle_v2_docs
${CMAKE_CURRENT_SOURCE_DIR}
${SPHINX_HTML_DIR_EN})
add_dependencies(paddle_v2_docs gen_proto_py)
add_dependencies(paddle_v2_docs gen_proto_py paddle_python)
# configured documentation tools and intermediate build results
set(BINARY_BUILD_DIR_CN "${CMAKE_CURRENT_BINARY_DIR}/cn/_build")
......@@ -50,6 +50,6 @@ sphinx_add_target(paddle_v2_docs_cn
${CMAKE_CURRENT_SOURCE_DIR}
${SPHINX_HTML_DIR_CN})
add_dependencies(paddle_v2_docs_cn gen_proto_py)
add_dependencies(paddle_v2_docs_cn gen_proto_py paddle_python)
add_subdirectory(api)
doc/v2/api/CMakeLists.txt
浏览文件 @ 82530e24
......@@ -19,4 +19,4 @@ sphinx_add_target(paddle_v2_apis
${CMAKE_CURRENT_SOURCE_DIR}
${SPHINX_HTML_DIR_EN})
add_dependencies(paddle_v2_apis gen_proto_py framework_py_proto copy_paddle_pybind)
add_dependencies(paddle_v2_apis gen_proto_py framework_py_proto copy_paddle_pybind paddle_python)
paddle/api/CMakeLists.txt
浏览文件 @ 82530e24
......@@ -89,16 +89,17 @@ SWIG_LINK_LIBRARIES(swig_paddle
${START_END}
)
add_custom_command(OUTPUT ${PADDLE_SOURCE_DIR}/paddle/py_paddle/_swig_paddle.so
COMMAND cp ${CMAKE_CURRENT_BINARY_DIR}/swig_paddle.py ${PADDLE_SOURCE_DIR}/paddle/py_paddle
COMMAND cp ${CMAKE_CURRENT_BINARY_DIR}/_swig_paddle.so ${PADDLE_SOURCE_DIR}/paddle/py_paddle
COMMAND ${CMAKE_COMMAND} -E touch .timestamp
add_custom_command(OUTPUT ${PADDLE_BINARY_DIR}/python/py_paddle/_swig_paddle.so
COMMAND ${CMAKE_COMMAND} -E make_directory ${PADDLE_BINARY_DIR}/python/py_paddle
COMMAND cp ${CMAKE_CURRENT_BINARY_DIR}/swig_paddle.py ${PADDLE_BINARY_DIR}/python/py_paddle
COMMAND cp ${CMAKE_CURRENT_BINARY_DIR}/_swig_paddle.so ${PADDLE_BINARY_DIR}/python/py_paddle
COMMAND ${CMAKE_COMMAND} -E touch ${PADDLE_BINARY_DIR}/.timestamp
WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle
DEPENDS _swig_paddle
)
# TODO(yuyang18) : make wheel name calculated by cmake
add_custom_target(python_api_wheel ALL DEPENDS ${PADDLE_SOURCE_DIR}/paddle/py_paddle/_swig_paddle.so)
add_custom_target(python_api_wheel ALL DEPENDS ${PADDLE_BINARY_DIR}/python/py_paddle/_swig_paddle.so)
if(WITH_TESTING)
IF(NOT PY_PIP_FOUND)
......
paddle/api/test/CMakeLists.txt
浏览文件 @ 82530e24
add_custom_command(OUTPUT ${CMAKE_CURRENT_BINARY_DIR}/testTrain.py
COMMAND cp -r ${CMAKE_CURRENT_SOURCE_DIR}/*.py ${CMAKE_CURRENT_BINARY_DIR}
)
add_custom_target(copy_api_test ALL DEPENDS testTrain.py)
py_test(testTrain SRCS testTrain.py)
py_test(testMatrix SRCS testMatrix.py)
py_test(testVector SRCS testVector.py)
......
paddle/fluid/framework/CMakeLists.txt
浏览文件 @ 82530e24
......@@ -7,9 +7,9 @@ cc_test(ddim_test SRCS ddim_test.cc DEPS ddim)
nv_test(dim_test SRCS dim_test.cu DEPS ddim)
if(WITH_GPU)
nv_library(tensor SRCS tensor.cc tensor_util.cu DEPS ddim place paddle_memory device_context framework_proto)
nv_library(tensor SRCS tensor.cc tensor_util.cu DEPS ddim place memory device_context framework_proto)
else()
cc_library(tensor SRCS tensor.cc tensor_util.cc DEPS ddim place paddle_memory device_context framework_proto)
cc_library(tensor SRCS tensor.cc tensor_util.cc DEPS ddim place memory device_context framework_proto)
endif()
cc_test(tensor_test SRCS tensor_test.cc DEPS tensor)
......@@ -21,9 +21,9 @@ endif()
cc_test(eigen_test SRCS eigen_test.cc DEPS tensor)
nv_test(mixed_vector_test SRCS mixed_vector_test.cu DEPS place paddle_memory device_context init)
nv_test(mixed_vector_test SRCS mixed_vector_test.cu DEPS place memory device_context init)
cc_library(lod_tensor SRCS lod_tensor.cc DEPS ddim place tensor framework_proto recordio)
cc_test(lod_tensor_test SRCS lod_tensor_test.cc DEPS lod_tensor paddle_memory)
cc_test(lod_tensor_test SRCS lod_tensor_test.cc DEPS lod_tensor memory)
nv_test(lod_tensor_gpu_test SRCS lod_tensor_test.cu DEPS lod_tensor init)
cc_library(reader SRCS reader.cc DEPS lod_tensor ddim)
......@@ -74,8 +74,8 @@ py_proto_compile(framework_py_proto SRCS framework.proto)
add_custom_target(framework_py_proto_init ALL COMMAND ${CMAKE_COMMAND} -E touch __init__.py)
add_dependencies(framework_py_proto framework_py_proto_init)
add_custom_command(TARGET framework_py_proto POST_BUILD
COMMAND ${CMAKE_COMMAND} -E make_directory ${PADDLE_SOURCE_DIR}/python/paddle/fluid/proto
COMMAND cp *.py ${PADDLE_SOURCE_DIR}/python/paddle/fluid/proto/
COMMAND ${CMAKE_COMMAND} -E make_directory ${PADDLE_BINARY_DIR}/python/paddle/fluid/proto
COMMAND cp *.py ${PADDLE_BINARY_DIR}/python/paddle/fluid/proto/
COMMENT "Copy generated python proto into directory paddle/fluid/proto."
WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR})
......
paddle/fluid/framework/block_desc.h
浏览文件 @ 82530e24
......@@ -17,6 +17,7 @@ limitations under the License. */
#include <deque>
#include <memory>
#include <set>
#include <string>
#include <unordered_map>
#include <vector>
......@@ -96,6 +97,8 @@ class BlockDesc {
*/
void RemoveOp(size_t s, size_t e);
void RemoveVar(const std::string &name) { vars_.erase(name); }
std::vector<OpDesc *> AllOps() const;
size_t OpSize() const { return ops_.size(); }
......
paddle/fluid/framework/channel.h
浏览文件 @ 82530e24
......@@ -14,8 +14,8 @@ limitations under the License. */
#pragma once
#include <stddef.h> // for size_t
#include <condition_variable>
#include <stddef.h> // for size_t
#include <condition_variable> // NOLINT
#include <typeindex>
#include "paddle/fluid/platform/enforce.h"
......@@ -216,7 +216,8 @@ class ChannelHolder {
template <typename T>
struct PlaceholderImpl : public Placeholder {
PlaceholderImpl(size_t buffer_size) : type_(std::type_index(typeid(T))) {
explicit PlaceholderImpl(size_t buffer_size)
: type_(std::type_index(typeid(T))) {
channel_.reset(MakeChannel<T>(buffer_size));
}
......
paddle/fluid/framework/channel_impl.h
浏览文件 @ 82530e24
......@@ -15,7 +15,7 @@ limitations under the License. */
#pragma once
#include <stddef.h> // for size_t
#include <atomic>
#include <condition_variable>
#include <condition_variable> // NOLINT
#include <deque>
#include "paddle/fluid/framework/channel.h"
#include "paddle/fluid/platform/enforce.h"
......@@ -38,7 +38,7 @@ class ChannelImpl : public paddle::framework::Channel<T> {
virtual void Unlock();
virtual bool IsClosed();
virtual void Close();
ChannelImpl(size_t);
explicit ChannelImpl(size_t);
virtual ~ChannelImpl();
virtual void AddToSendQ(const void *referrer, T *data,
......@@ -60,7 +60,7 @@ class ChannelImpl : public paddle::framework::Channel<T> {
const void *referrer; // TODO(thuan): figure out better way to do this
std::function<bool(ChannelAction)> callback;
QueueMessage(T *item)
explicit QueueMessage(T *item)
: data(item), cond(std::make_shared<std::condition_variable_any>()) {}
QueueMessage(T *item, std::shared_ptr<std::condition_variable_any> cond)
......@@ -88,15 +88,15 @@ class ChannelImpl : public paddle::framework::Channel<T> {
}
std::shared_ptr<QueueMessage> get_first_message(
std::deque<std::shared_ptr<QueueMessage>> &queue, ChannelAction action) {
while (!queue.empty()) {
std::deque<std::shared_ptr<QueueMessage>> *queue, ChannelAction action) {
while (!queue->empty()) {
// Check whether this message was added by Select
// If this was added by Select then execute the callback
// to check if you can execute this message. The callback
// can return false if some other case was executed in Select.
// In that case just discard this QueueMessage and process next.
std::shared_ptr<QueueMessage> m = queue.front();
queue.pop_front();
std::shared_ptr<QueueMessage> m = queue->front();
queue->pop_front();
if (m->callback == nullptr || m->callback(action)) return m;
}
return nullptr;
......@@ -147,7 +147,7 @@ void ChannelImpl<T>::Send(T *item) {
// to send to the receiver, bypassing the channel buffer if any
if (!recvq.empty()) {
std::shared_ptr<QueueMessage> m =
get_first_message(recvq, ChannelAction::SEND);
get_first_message(&recvq, ChannelAction::SEND);
if (m != nullptr) {
*(m->data) = std::move(*item);
......@@ -198,7 +198,7 @@ bool ChannelImpl<T>::Receive(T *item) {
// buffer and move front of send queue to the buffer
if (!sendq.empty()) {
std::shared_ptr<QueueMessage> m =
get_first_message(sendq, ChannelAction::RECEIVE);
get_first_message(&sendq, ChannelAction::RECEIVE);
if (buf_.size() > 0) {
// Case 1 : Channel is Buffered
// Do Data transfer from front of buffer
......@@ -219,8 +219,9 @@ bool ChannelImpl<T>::Receive(T *item) {
if (m != nullptr) {
*item = std::move(*(m->data));
m->Notify();
} else
} else {
return recv_return(Receive(item));
}
}
return recv_return(true);
}
......
paddle/fluid/framework/channel_test.cc
浏览文件 @ 82530e24
......@@ -14,8 +14,8 @@ limitations under the License. */
#include "paddle/fluid/framework/channel.h"
#include <chrono>
#include <thread>
#include <chrono> // NOLINT
#include <thread> // NOLINT
#include "gtest/gtest.h"
using paddle::framework::Channel;
......@@ -166,9 +166,9 @@ TEST(Channel, ConcurrentSendNonConcurrentReceiveWithSufficientBufferSize) {
std::thread t([&]() {
// Try to write more than buffer size.
for (size_t i = 0; i < 2 * buffer_size; ++i) {
if (i < buffer_size)
if (i < buffer_size) {
ch->Send(&i); // should block after 10 iterations
else {
} else {
bool is_exception = false;
try {
ch->Send(&i);
......@@ -212,12 +212,12 @@ TEST(Channel, RecevingOrderEqualToSendingOrderWithBufferedChannel3) {
}
void ChannelCloseUnblocksReceiversTest(Channel<int> *ch) {
size_t num_threads = 5;
std::thread t[num_threads];
bool thread_ended[num_threads];
const size_t kNumThreads = 5;
std::thread t[kNumThreads];
bool thread_ended[kNumThreads];
// Launches threads that try to read and are blocked because of no writers
for (size_t i = 0; i < num_threads; i++) {
for (size_t i = 0; i < kNumThreads; i++) {
thread_ended[i] = false;
t[i] = std::thread(
[&](bool *p) {
......@@ -230,7 +230,7 @@ void ChannelCloseUnblocksReceiversTest(Channel<int> *ch) {
std::this_thread::sleep_for(std::chrono::milliseconds(200)); // wait 0.2 sec
// Verify that all the threads are blocked
for (size_t i = 0; i < num_threads; i++) {
for (size_t i = 0; i < kNumThreads; i++) {
EXPECT_EQ(thread_ended[i], false);
}
......@@ -241,21 +241,21 @@ void ChannelCloseUnblocksReceiversTest(Channel<int> *ch) {
std::this_thread::sleep_for(std::chrono::milliseconds(200)); // wait 0.2 sec
// Verify that all threads got unblocked
for (size_t i = 0; i < num_threads; i++) {
for (size_t i = 0; i < kNumThreads; i++) {
EXPECT_EQ(thread_ended[i], true);
}
for (size_t i = 0; i < num_threads; i++) t[i].join();
for (size_t i = 0; i < kNumThreads; i++) t[i].join();
}
void ChannelCloseUnblocksSendersTest(Channel<int> *ch, bool isBuffered) {
size_t num_threads = 5;
std::thread t[num_threads];
bool thread_ended[num_threads];
bool send_success[num_threads];
const size_t kNumThreads = 5;
std::thread t[kNumThreads];
bool thread_ended[kNumThreads];
bool send_success[kNumThreads];
// Launches threads that try to write and are blocked because of no readers
for (size_t i = 0; i < num_threads; i++) {
for (size_t i = 0; i < kNumThreads; i++) {
thread_ended[i] = false;
send_success[i] = false;
t[i] = std::thread(
......@@ -277,13 +277,13 @@ void ChannelCloseUnblocksSendersTest(Channel<int> *ch, bool isBuffered) {
if (isBuffered) {
// If ch is Buffered, atleast 4 threads must be blocked.
int ct = 0;
for (size_t i = 0; i < num_threads; i++) {
for (size_t i = 0; i < kNumThreads; i++) {
if (!thread_ended[i]) ct++;
}
EXPECT_GE(ct, 4);
} else {
// If ch is UnBuffered, all the threads should be blocked.
for (size_t i = 0; i < num_threads; i++) {
for (size_t i = 0; i < kNumThreads; i++) {
EXPECT_EQ(thread_ended[i], false);
}
}
......@@ -294,21 +294,21 @@ void ChannelCloseUnblocksSendersTest(Channel<int> *ch, bool isBuffered) {
std::this_thread::sleep_for(std::chrono::milliseconds(200)); // wait
// Verify that all threads got unblocked
for (size_t i = 0; i < num_threads; i++) {
for (size_t i = 0; i < kNumThreads; i++) {
EXPECT_EQ(thread_ended[i], true);
}
if (isBuffered) {
// Verify that only 1 send was successful
int ct = 0;
for (size_t i = 0; i < num_threads; i++) {
for (size_t i = 0; i < kNumThreads; i++) {
if (send_success[i]) ct++;
}
// Only 1 send must be successful
EXPECT_EQ(ct, 1);
}
for (size_t i = 0; i < num_threads; i++) t[i].join();
for (size_t i = 0; i < kNumThreads; i++) t[i].join();
}
// This tests that closing a buffered channel also unblocks
......@@ -409,13 +409,13 @@ TEST(Channel, UnbufferedMoreReceiveLessSendTest) {
// This tests that destroying a channel unblocks
// any senders waiting for channel to have write space
void ChannelDestroyUnblockSenders(Channel<int> *ch, bool isBuffered) {
size_t num_threads = 5;
std::thread t[num_threads];
bool thread_ended[num_threads];
bool send_success[num_threads];
const size_t kNumThreads = 5;
std::thread t[kNumThreads];
bool thread_ended[kNumThreads];
bool send_success[kNumThreads];
// Launches threads that try to write and are blocked because of no readers
for (size_t i = 0; i < num_threads; i++) {
for (size_t i = 0; i < kNumThreads; i++) {
thread_ended[i] = false;
send_success[i] = false;
t[i] = std::thread(
......@@ -438,14 +438,14 @@ void ChannelDestroyUnblockSenders(Channel<int> *ch, bool isBuffered) {
if (isBuffered) {
// If channel is buffered, verify that atleast 4 threads are blocked
int ct = 0;
for (size_t i = 0; i < num_threads; i++) {
for (size_t i = 0; i < kNumThreads; i++) {
if (thread_ended[i] == false) ct++;
}
// Atleast 4 threads must be blocked
EXPECT_GE(ct, 4);
} else {
// Verify that all the threads are blocked
for (size_t i = 0; i < num_threads; i++) {
for (size_t i = 0; i < kNumThreads; i++) {
EXPECT_EQ(thread_ended[i], false);
}
}
......@@ -454,13 +454,13 @@ void ChannelDestroyUnblockSenders(Channel<int> *ch, bool isBuffered) {
std::this_thread::sleep_for(std::chrono::milliseconds(200)); // wait
// Verify that all threads got unblocked
for (size_t i = 0; i < num_threads; i++) {
for (size_t i = 0; i < kNumThreads; i++) {
EXPECT_EQ(thread_ended[i], true);
}
// Count number of successful sends
int ct = 0;
for (size_t i = 0; i < num_threads; i++) {
for (size_t i = 0; i < kNumThreads; i++) {
if (send_success[i]) ct++;
}
......@@ -473,18 +473,18 @@ void ChannelDestroyUnblockSenders(Channel<int> *ch, bool isBuffered) {
}
// Join all threads
for (size_t i = 0; i < num_threads; i++) t[i].join();
for (size_t i = 0; i < kNumThreads; i++) t[i].join();
}
// This tests that destroying a channel also unblocks
// any receivers waiting on the channel
void ChannelDestroyUnblockReceivers(Channel<int> *ch) {
size_t num_threads = 5;
std::thread t[num_threads];
bool thread_ended[num_threads];
const size_t kNumThreads = 5;
std::thread t[kNumThreads];
bool thread_ended[kNumThreads];
// Launches threads that try to read and are blocked because of no writers
for (size_t i = 0; i < num_threads; i++) {
for (size_t i = 0; i < kNumThreads; i++) {
thread_ended[i] = false;
t[i] = std::thread(
[&](bool *p) {
......@@ -498,18 +498,18 @@ void ChannelDestroyUnblockReceivers(Channel<int> *ch) {
std::this_thread::sleep_for(std::chrono::milliseconds(100)); // wait
// Verify that all threads are blocked
for (size_t i = 0; i < num_threads; i++) {
for (size_t i = 0; i < kNumThreads; i++) {
EXPECT_EQ(thread_ended[i], false);
}
// delete the channel
delete ch;
std::this_thread::sleep_for(std::chrono::milliseconds(200)); // wait
// Verify that all threads got unblocked
for (size_t i = 0; i < num_threads; i++) {
for (size_t i = 0; i < kNumThreads; i++) {
EXPECT_EQ(thread_ended[i], true);
}
for (size_t i = 0; i < num_threads; i++) t[i].join();
for (size_t i = 0; i < kNumThreads; i++) t[i].join();
}
TEST(Channel, BufferedChannelDestroyUnblocksReceiversTest) {
......@@ -679,12 +679,12 @@ TEST(ChannelHolder, TypeMismatchReceiveTest) {
}
void ChannelHolderCloseUnblocksReceiversTest(ChannelHolder *ch) {
size_t num_threads = 5;
std::thread t[num_threads];
bool thread_ended[num_threads];
const size_t kNumThreads = 5;
std::thread t[kNumThreads];
bool thread_ended[kNumThreads];
// Launches threads that try to read and are blocked because of no writers
for (size_t i = 0; i < num_threads; i++) {
for (size_t i = 0; i < kNumThreads; i++) {
thread_ended[i] = false;
t[i] = std::thread(
[&](bool *p) {
......@@ -697,7 +697,7 @@ void ChannelHolderCloseUnblocksReceiversTest(ChannelHolder *ch) {
std::this_thread::sleep_for(std::chrono::milliseconds(200)); // wait 0.2 sec
// Verify that all the threads are blocked
for (size_t i = 0; i < num_threads; i++) {
for (size_t i = 0; i < kNumThreads; i++) {
EXPECT_EQ(thread_ended[i], false);
}
......@@ -708,21 +708,21 @@ void ChannelHolderCloseUnblocksReceiversTest(ChannelHolder *ch) {
std::this_thread::sleep_for(std::chrono::milliseconds(200)); // wait 0.2 sec
// Verify that all threads got unblocked
for (size_t i = 0; i < num_threads; i++) {
for (size_t i = 0; i < kNumThreads; i++) {
EXPECT_EQ(thread_ended[i], true);
}
for (size_t i = 0; i < num_threads; i++) t[i].join();
for (size_t i = 0; i < kNumThreads; i++) t[i].join();
}
void ChannelHolderCloseUnblocksSendersTest(ChannelHolder *ch, bool isBuffered) {
size_t num_threads = 5;
std::thread t[num_threads];
bool thread_ended[num_threads];
bool send_success[num_threads];
const size_t kNumThreads = 5;
std::thread t[kNumThreads];
bool thread_ended[kNumThreads];
bool send_success[kNumThreads];
// Launches threads that try to write and are blocked because of no readers
for (size_t i = 0; i < num_threads; i++) {
for (size_t i = 0; i < kNumThreads; i++) {
thread_ended[i] = false;
send_success[i] = false;
t[i] = std::thread(
......@@ -744,13 +744,13 @@ void ChannelHolderCloseUnblocksSendersTest(ChannelHolder *ch, bool isBuffered) {
if (isBuffered) {
// If ch is Buffered, atleast 4 threads must be blocked.
int ct = 0;
for (size_t i = 0; i < num_threads; i++) {
for (size_t i = 0; i < kNumThreads; i++) {
if (!thread_ended[i]) ct++;
}
EXPECT_GE(ct, 4);
} else {
// If ch is UnBuffered, all the threads should be blocked.
for (size_t i = 0; i < num_threads; i++) {
for (size_t i = 0; i < kNumThreads; i++) {
EXPECT_EQ(thread_ended[i], false);
}
}
......@@ -761,21 +761,21 @@ void ChannelHolderCloseUnblocksSendersTest(ChannelHolder *ch, bool isBuffered) {
std::this_thread::sleep_for(std::chrono::milliseconds(200)); // wait
// Verify that all threads got unblocked
for (size_t i = 0; i < num_threads; i++) {
for (size_t i = 0; i < kNumThreads; i++) {
EXPECT_EQ(thread_ended[i], true);
}
if (isBuffered) {
// Verify that only 1 send was successful
int ct = 0;
for (size_t i = 0; i < num_threads; i++) {
for (size_t i = 0; i < kNumThreads; i++) {
if (send_success[i]) ct++;
}
// Only 1 send must be successful
EXPECT_EQ(ct, 1);
}
for (size_t i = 0; i < num_threads; i++) t[i].join();
for (size_t i = 0; i < kNumThreads; i++) t[i].join();
}
// This tests that closing a channelholder unblocks
......@@ -813,13 +813,13 @@ TEST(Channel, ChannelHolderCloseUnblocksSendersTest) {
// This tests that destroying a channelholder unblocks
// any senders waiting for channel
void ChannelHolderDestroyUnblockSenders(ChannelHolder *ch, bool isBuffered) {
size_t num_threads = 5;
std::thread t[num_threads];
bool thread_ended[num_threads];
bool send_success[num_threads];
const size_t kNumThreads = 5;
std::thread t[kNumThreads];
bool thread_ended[kNumThreads];
bool send_success[kNumThreads];
// Launches threads that try to write and are blocked because of no readers
for (size_t i = 0; i < num_threads; i++) {
for (size_t i = 0; i < kNumThreads; i++) {
thread_ended[i] = false;
send_success[i] = false;
t[i] = std::thread(
......@@ -841,14 +841,14 @@ void ChannelHolderDestroyUnblockSenders(ChannelHolder *ch, bool isBuffered) {
if (isBuffered) {
// If channel is buffered, verify that atleast 4 threads are blocked
int ct = 0;
for (size_t i = 0; i < num_threads; i++) {
for (size_t i = 0; i < kNumThreads; i++) {
if (thread_ended[i] == false) ct++;
}
// Atleast 4 threads must be blocked
EXPECT_GE(ct, 4);
} else {
// Verify that all the threads are blocked
for (size_t i = 0; i < num_threads; i++) {
for (size_t i = 0; i < kNumThreads; i++) {
EXPECT_EQ(thread_ended[i], false);
}
}
......@@ -857,13 +857,13 @@ void ChannelHolderDestroyUnblockSenders(ChannelHolder *ch, bool isBuffered) {
std::this_thread::sleep_for(std::chrono::milliseconds(200)); // wait
// Verify that all threads got unblocked
for (size_t i = 0; i < num_threads; i++) {
for (size_t i = 0; i < kNumThreads; i++) {
EXPECT_EQ(thread_ended[i], true);
}
// Count number of successfuld sends
int ct = 0;
for (size_t i = 0; i < num_threads; i++) {
for (size_t i = 0; i < kNumThreads; i++) {
if (send_success[i]) ct++;
}
......@@ -876,18 +876,18 @@ void ChannelHolderDestroyUnblockSenders(ChannelHolder *ch, bool isBuffered) {
}
// Join all threads
for (size_t i = 0; i < num_threads; i++) t[i].join();
for (size_t i = 0; i < kNumThreads; i++) t[i].join();
}
// This tests that destroying a channelholder also unblocks
// any receivers waiting on the channel
void ChannelHolderDestroyUnblockReceivers(ChannelHolder *ch) {
size_t num_threads = 5;
std::thread t[num_threads];
bool thread_ended[num_threads];
const size_t kNumThreads = 5;
std::thread t[kNumThreads];
bool thread_ended[kNumThreads];
// Launches threads that try to read and are blocked because of no writers
for (size_t i = 0; i < num_threads; i++) {
for (size_t i = 0; i < kNumThreads; i++) {
thread_ended[i] = false;
t[i] = std::thread(
[&](bool *p) {
......@@ -901,18 +901,18 @@ void ChannelHolderDestroyUnblockReceivers(ChannelHolder *ch) {
std::this_thread::sleep_for(std::chrono::milliseconds(200)); // wait
// Verify that all threads are blocked
for (size_t i = 0; i < num_threads; i++) {
for (size_t i = 0; i < kNumThreads; i++) {
EXPECT_EQ(thread_ended[i], false);
}
// delete the channel
delete ch;
std::this_thread::sleep_for(std::chrono::milliseconds(200)); // wait
// Verify that all threads got unblocked
for (size_t i = 0; i < num_threads; i++) {
for (size_t i = 0; i < kNumThreads; i++) {
EXPECT_EQ(thread_ended[i], true);
}
for (size_t i = 0; i < num_threads; i++) t[i].join();
for (size_t i = 0; i < kNumThreads; i++) t[i].join();
}
TEST(ChannelHolder, ChannelHolderDestroyUnblocksReceiversTest) {
......@@ -945,12 +945,12 @@ TEST(ChannelHolder, ChannelHolderDestroyUnblocksSendersTest) {
// This tests that closing a channelholder many times.
void ChannelHolderManyTimesClose(ChannelHolder *ch) {
const int num_threads = 15;
std::thread t[num_threads];
bool thread_ended[num_threads];
const int kNumThreads = 15;
std::thread t[kNumThreads];
bool thread_ended[kNumThreads];
// Launches threads that try to send data to channel.
for (size_t i = 0; i < num_threads / 3; i++) {
for (size_t i = 0; i < kNumThreads / 3; i++) {
thread_ended[i] = false;
t[i] = std::thread(
[&](bool *ended) {
......@@ -962,7 +962,7 @@ void ChannelHolderManyTimesClose(ChannelHolder *ch) {
}
// Launches threads that try to receive data to channel.
for (size_t i = num_threads / 3; i < 2 * num_threads / 3; i++) {
for (size_t i = kNumThreads / 3; i < 2 * kNumThreads / 3; i++) {
thread_ended[i] = false;
t[i] = std::thread(
[&](bool *p) {
......@@ -976,7 +976,7 @@ void ChannelHolderManyTimesClose(ChannelHolder *ch) {
}
// Launches threads that try to close the channel.
for (size_t i = 2 * num_threads / 3; i < num_threads; i++) {
for (size_t i = 2 * kNumThreads / 3; i < kNumThreads; i++) {
thread_ended[i] = false;
t[i] = std::thread(
[&](bool *p) {
......@@ -991,13 +991,13 @@ void ChannelHolderManyTimesClose(ChannelHolder *ch) {
std::this_thread::sleep_for(std::chrono::milliseconds(100)); // wait
// Verify that all threads are unblocked
for (size_t i = 0; i < num_threads; i++) {
for (size_t i = 0; i < kNumThreads; i++) {
EXPECT_EQ(thread_ended[i], true);
}
EXPECT_TRUE(ch->IsClosed());
// delete the channel
delete ch;
for (size_t i = 0; i < num_threads; i++) t[i].join();
for (size_t i = 0; i < kNumThreads; i++) t[i].join();
}
TEST(ChannelHolder, ChannelHolderManyTimesCloseTest) {
......
paddle/fluid/framework/details/CMakeLists.txt
浏览文件 @ 82530e24
......@@ -16,6 +16,6 @@ else()
endif()
cc_library(multi_devices_graph_builder SRCS multi_devices_graph_builder.cc DEPS ssa_graph_builder computation_op_handle
scale_loss_grad_op_handle ${multi_devices_graph_builder_deps})
cc_library(ssa_graph_executor SRCS ssa_graph_executor.cc DEPS ssa_graph)
cc_library(ssa_graph_executor SRCS ssa_graph_executor.cc DEPS ssa_graph framework_proto)
cc_library(threaded_ssa_graph_executor SRCS threaded_ssa_graph_executor.cc DEPS fetch_op_handle ssa_graph_executor scope
simple_threadpool device_context)
paddle/fluid/framework/details/multi_devices_graph_builder.cc
浏览文件 @ 82530e24
......@@ -21,6 +21,9 @@
#include "paddle/fluid/framework/details/nccl_all_reduce_op_handle.h"
#endif
#include <string>
#include <vector>
namespace paddle {
namespace framework {
namespace details {
......@@ -168,6 +171,11 @@ std::unique_ptr<SSAGraph> MultiDevSSAGraphBuilder::Build(
*/
PolishGraphToSupportDataHazards(&result);
/*
* Only variables should be the leaves of graph.
*/
AddOutputToLeafOps(&result);
if (VLOG_IS_ON(10)) {
std::ostringstream sout;
PrintGraphviz(*graph, sout);
......
paddle/fluid/framework/details/ssa_graph_builder.cc
浏览文件 @ 82530e24
......@@ -136,6 +136,17 @@ void SSAGraphBuilder::PrintGraphviz(const SSAGraph &graph, std::ostream &sout) {
sout << "}\n";
}
void SSAGraphBuilder::AddOutputToLeafOps(SSAGraph *graph) {
for (auto &op : graph->ops_) {
if (!op->outputs_.empty()) {
continue;
}
auto *dummy_leaf = new DummyVarHandle();
graph->dep_vars_.emplace(dummy_leaf);
op->AddOutput(dummy_leaf);
}
}
} // namespace details
} // namespace framework
} // namespace paddle
paddle/fluid/framework/details/ssa_graph_builder.h
浏览文件 @ 82530e24
......@@ -14,13 +14,13 @@
#pragma once
#include <memory>
#include <string>
#include "paddle/fluid/framework/details/ssa_graph.h"
#include "paddle/fluid/framework/program_desc.h"
#include "paddle/fluid/platform/place.h"
#include <memory>
#include <string>
namespace paddle {
namespace framework {
namespace details {
......@@ -52,6 +52,8 @@ class SSAGraphBuilder {
const std::string &each_var_name,
const platform::Place &place, size_t place_offset);
static void AddOutputToLeafOps(SSAGraph *graph);
static void PrintGraphviz(const SSAGraph &graph, std::ostream &sout);
};
} // namespace details
......
paddle/fluid/framework/details/threaded_ssa_graph_executor.cc
浏览文件 @ 82530e24
......@@ -87,7 +87,6 @@ FeedFetchList ThreadedSSAGraphExecutor::Run(
// Step 2. Insert FetchOps
std::vector<std::unique_ptr<FetchOpHandle>> fetch_ops;
std::vector<DummyVarHandle> dummy_vars;
FeedFetchList fetch_data(fetch_tensors.size());
std::unordered_map<std::string, std::vector<VarHandleBase *>> fetched_vars;
......@@ -101,13 +100,13 @@ FeedFetchList ThreadedSSAGraphExecutor::Run(
}
}
std::unordered_set<std::unique_ptr<VarHandleBase>> fetch_dependencies;
for (size_t i = 0; i < fetch_tensors.size(); ++i) {
auto &var_name = fetch_tensors[i];
auto &vars = fetched_vars.at(var_name);
auto *op = new FetchOpHandle(&fetch_data, i, &local_scopes_);
fetch_ops.emplace_back(op);
// FIXME: Use new device context
for (auto &p : places_) {
op->dev_ctxes_[p] = fetch_ctxs_.Get(p);
}
......@@ -115,6 +114,11 @@ FeedFetchList ThreadedSSAGraphExecutor::Run(
for (auto *var : vars) {
op->AddInput(var);
}
auto *fetch_dummy = new DummyVarHandle();
op->AddOutput(fetch_dummy);
fetch_dependencies.emplace(fetch_dummy);
InsertPendingVar(*fetch_dummy);
InsertPendingOp(*op);
}
......
paddle/fluid/framework/lod_tensor.h
浏览文件 @ 82530e24
......@@ -142,6 +142,7 @@ class LoDTensor : public Tensor {
return (lod_)[level].size() - 1;
}
// Split LoDTensor and copy to each place specified in places.
std::vector<LoDTensor> SplitLoDTensor(
const std::vector<platform::Place> places) const;
......
paddle/fluid/framework/operator.cc
浏览文件 @ 82530e24
......@@ -35,6 +35,17 @@ std::vector<std::tuple<platform::Place, LibraryType>> kKernelPriority = {
std::make_tuple(platform::CPUPlace(), LibraryType::kPlain),
};
proto::VarType::Type GetDataTypeOfVar(const Variable* var) {
if (var->IsType<framework::LoDTensor>()) {
return framework::ToDataType(var->Get<framework::LoDTensor>().type());
} else if (var->IsType<framework::SelectedRows>()) {
return framework::ToDataType(
var->Get<framework::SelectedRows>().value().type());
} else {
PADDLE_THROW("Var should be LoDTensor or SelectedRows");
}
}
static DDim GetDims(const Scope& scope, const std::string& name) {
Variable* var = scope.FindVar(name);
if (var == nullptr) {
......
paddle/fluid/framework/operator.h
浏览文件 @ 82530e24
......@@ -61,6 +61,8 @@ inline std::string GradVarName(const std::string& var_name) {
return var_name + kGradVarSuffix;
}
proto::VarType::Type GetDataTypeOfVar(const Variable* var);
class OperatorBase;
class ExecutionContext;
......
paddle/fluid/framework/parallel_executor.cc
浏览文件 @ 82530e24
......@@ -150,13 +150,30 @@ void ParallelExecutor::BCastParamsToGPUs(
#endif
}
void ParallelExecutor::Run(const std::vector<std::string> &fetch_tensors,
const std::string &fetched_var_name) {
void ParallelExecutor::Run(
const std::vector<std::string> &fetch_tensors,
const std::string &fetched_var_name,
const std::unordered_map<std::string, LoDTensor> &feed_tensors) {
platform::RecordBlock b(0);
SplitTensorToPlaces(feed_tensors);
auto fetch_data = member_->executor_->Run(fetch_tensors);
*member_->global_scope_->Var(fetched_var_name)->GetMutable<FeedFetchList>() =
fetch_data;
}
void ParallelExecutor::SplitTensorToPlaces(
const std::unordered_map<std::string, LoDTensor> &feed_tensors) {
for (auto it : feed_tensors) {
auto lod_tensors = it.second.SplitLoDTensor(member_->places_);
for (size_t j = 0; j < member_->places_.size(); ++j) {
// TODO(panxy0718): Do I need to delete this var?
member_->local_scopes_[j]
->Var(it.first)
->GetMutable<LoDTensor>()
->ShareDataWith(lod_tensors[j]);
}
}
}
} // namespace framework
} // namespace paddle
paddle/fluid/framework/parallel_executor.h
浏览文件 @ 82530e24
......@@ -42,9 +42,13 @@ class ParallelExecutor {
bool allow_op_delay);
void Run(const std::vector<std::string>& fetch_tensors,
const std::string& fetched_var_name = "fetched_var");
const std::string& fetched_var_name,
const std::unordered_map<std::string, LoDTensor>& feed_tensors);
private:
void SplitTensorToPlaces(
const std::unordered_map<std::string, LoDTensor>& feed_tensors);
ParallelExecutorPrivate* member_;
void BCastParamsToGPUs(const ProgramDesc& startup_program) const;
......
paddle/fluid/framework/scope.cc
浏览文件 @ 82530e24
......@@ -15,7 +15,6 @@ limitations under the License. */
#include "paddle/fluid/framework/scope.h"
#include <memory> // for unique_ptr
#include <mutex> // for call_once
#include <set>
#include "glog/logging.h"
#include "paddle/fluid/framework/threadpool.h"
......@@ -39,6 +38,7 @@ Scope::~Scope() {
}
Scope& Scope::NewScope() const {
std::unique_lock<std::mutex> lock(mutex_);
kids_.push_back(new Scope(this));
return *kids_.back();
}
......@@ -92,6 +92,7 @@ std::vector<std::string> Scope::LocalVarNames() const {
}
void Scope::DeleteScope(Scope* scope) {
std::unique_lock<std::mutex> lock(mutex_);
auto it = std::find(this->kids_.begin(), this->kids_.end(), scope);
PADDLE_ENFORCE(it != this->kids_.end(), "Cannot find %p as kid scope", scope);
this->kids_.erase(it);
......@@ -103,7 +104,7 @@ void Scope::DeleteScope(Scope* scope) {
}
}
void Scope::EraseVars(std::vector<std::string>& var_names) {
void Scope::EraseVars(const std::vector<std::string>& var_names) {
std::set<std::string> var_set(var_names.begin(), var_names.end());
for (auto it = vars_.begin(); it != vars_.end();) {
if (var_set.find(it->first) != var_set.end()) {
......
paddle/fluid/framework/scope.h
浏览文件 @ 82530e24
......@@ -15,6 +15,7 @@ limitations under the License. */
#pragma once
#include <list>
#include <mutex> // NOLINT
#include <string>
#include <unordered_map>
#include <vector>
......@@ -51,7 +52,7 @@ class Scope {
/// Create a variable with a scope-unique name.
Variable* Var(std::string* name = nullptr);
void EraseVars(std::vector<std::string>& var_names);
void EraseVars(const std::vector<std::string>& var_names);
/// Find a variable in the scope or any of its ancestors. Returns
/// nullptr if cannot find.
......@@ -88,6 +89,9 @@ class Scope {
Scope const* parent_{nullptr};
DISABLE_COPY_AND_ASSIGN(Scope);
private:
mutable std::mutex mutex_;
};
} // namespace framework
} // namespace paddle
paddle/fluid/framework/selected_rows.cc
浏览文件 @ 82530e24
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
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.
......@@ -13,6 +16,7 @@ limitations under the License. */
namespace paddle {
namespace framework {
void SerializeToStream(std::ostream& os, const SelectedRows& selected_rows,
const platform::DeviceContext& dev_ctx) {
{ // the 1st field, uint32_t version
......
paddle/fluid/framework/selected_rows.h
浏览文件 @ 82530e24
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
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.
......@@ -47,6 +50,15 @@ class SelectedRows {
void set_rows(const Vector<int64_t>& rows) { rows_ = rows; }
/**
* get the index of id in rows
*/
int64_t index(int64_t id) const {
auto it = std::find(rows_.begin(), rows_.end(), id);
PADDLE_ENFORCE(it != rows_.end(), "id should be in rows");
return static_cast<int64_t>(std::distance(rows_.begin(), it));
}
DDim GetCompleteDims() const {
std::vector<int64_t> dims = vectorize(value_->dims());
dims[0] = height_;
......
paddle/fluid/framework/tensor.h
浏览文件 @ 82530e24
......@@ -45,11 +45,10 @@ class Tensor {
friend struct EigenVector;
public:
Tensor() : offset_(0), is_pinned_(false) {}
Tensor() : offset_(0) {}
/*! Constructor with place should only be used in pybind. */
explicit Tensor(const platform::Place& place)
: offset_(0), is_pinned_(false) {
explicit Tensor(const platform::Place& place) : offset_(0) {
holder_->set_place(place);
}
......@@ -70,12 +69,11 @@ class Tensor {
* @note If not exist, then allocation.
*/
template <typename T>
inline T* mutable_data(platform::Place place, bool is_pinned = false);
inline T* mutable_data(platform::Place place);
inline void* mutable_data(platform::Place place, std::type_index type,
bool is_pinned = false);
inline void* mutable_data(platform::Place place, std::type_index type);
inline void* mutable_data(platform::Place place, bool is_pinned = false);
inline void* mutable_data(platform::Place place);
/**
* @brief Return a pointer to mutable memory block.
......@@ -86,8 +84,7 @@ class Tensor {
* @note If not exist, then allocation.
*/
template <typename T>
inline T* mutable_data(DDim dims, platform::Place place,
bool is_pinned = false);
inline T* mutable_data(DDim dims, platform::Place place);
/*! Return the dimensions of the memory block. */
inline const DDim& dims() const;
......@@ -95,9 +92,6 @@ class Tensor {
/*! Return the numel of the memory block. */
inline int64_t numel() const;
/*! Return the numel of the memory block. */
inline bool isPinned() const;
/*! Resize the dimensions of the memory block. */
inline Tensor& Resize(const DDim& dims);
......@@ -152,14 +146,12 @@ class Tensor {
template <typename Place>
struct PlaceholderImpl : public Placeholder {
PlaceholderImpl(Place place, size_t size, std::type_index type,
bool is_pinned = false)
: ptr_(static_cast<uint8_t*>(memory::Alloc(place, size, is_pinned)),
memory::PODDeleter<uint8_t, Place>(place, is_pinned)),
PlaceholderImpl(Place place, size_t size, std::type_index type)
: ptr_(static_cast<uint8_t*>(memory::Alloc(place, size)),
memory::PODDeleter<uint8_t, Place>(place)),
place_(place),
size_(size),
type_(type),
is_pinned_(is_pinned) {
type_(type) {
PADDLE_ENFORCE_NOT_NULL(ptr_, "Insufficient %s memory to allocation.",
(is_cpu_place(place_) ? "CPU" : "GPU"));
}
......@@ -182,9 +174,6 @@ class Tensor {
/* the current type of memory */
std::type_index type_;
/*! use pinned memory or not. */
bool is_pinned_;
};
/*! holds the memory block if allocated. */
......@@ -219,7 +208,6 @@ class Tensor {
* PlaceHolder::ptr_ and where the tensor data really begins.
*/
size_t offset_;
bool is_pinned_;
};
inline void Tensor::switch_place(platform::Place new_place) {
......
paddle/fluid/framework/tensor_impl.h
浏览文件 @ 82530e24
......@@ -101,21 +101,19 @@ inline T* Tensor::data() {
}
template <typename T>
inline T* Tensor::mutable_data(DDim dims, platform::Place place,
bool is_pinned) {
inline T* Tensor::mutable_data(DDim dims, platform::Place place) {
static_assert(std::is_pod<T>::value, "T must be POD");
Resize(dims);
return mutable_data<T>(place, is_pinned);
return mutable_data<T>(place);
}
template <typename T>
inline T* Tensor::mutable_data(platform::Place place, bool is_pinned) {
inline T* Tensor::mutable_data(platform::Place place) {
static_assert(std::is_pod<T>::value, "T must be POD");
return reinterpret_cast<T*>(mutable_data(place, typeid(T), is_pinned));
return reinterpret_cast<T*>(mutable_data(place, typeid(T)));
}
inline void* Tensor::mutable_data(platform::Place place, std::type_index type,
bool is_pinned) {
inline void* Tensor::mutable_data(platform::Place place, std::type_index type) {
if (holder_ != nullptr) {
holder_->set_type(type);
}
......@@ -129,27 +127,33 @@ inline void* Tensor::mutable_data(platform::Place place, std::type_index type,
holder_->size() < size + offset_) {
if (platform::is_cpu_place(place)) {
holder_.reset(new PlaceholderImpl<platform::CPUPlace>(
boost::get<platform::CPUPlace>(place), size, type, is_pinned));
} else if (platform::is_gpu_place(place)) {
boost::get<platform::CPUPlace>(place), size, type));
} else if (platform::is_gpu_place(place) ||
platform::is_cuda_pinned_place(place)) {
#ifndef PADDLE_WITH_CUDA
PADDLE_THROW("'CUDAPlace' is not supported in CPU only device.");
PADDLE_THROW(
"CUDAPlace or CUDAPinnedPlace is not supported in CPU-only mode.");
}
#else
holder_.reset(new PlaceholderImpl<platform::CUDAPlace>(
boost::get<platform::CUDAPlace>(place), size, type, is_pinned));
if (platform::is_gpu_place(place)) {
holder_.reset(new PlaceholderImpl<platform::CUDAPlace>(
boost::get<platform::CUDAPlace>(place), size, type));
} else if (platform::is_cuda_pinned_place(place)) {
holder_.reset(new PlaceholderImpl<platform::CUDAPinnedPlace>(
boost::get<platform::CUDAPinnedPlace>(place), size, type));
}
}
#endif
offset_ = 0;
is_pinned_ = is_pinned;
}
return reinterpret_cast<void*>(reinterpret_cast<uintptr_t>(holder_->ptr()) +
offset_);
}
inline void* Tensor::mutable_data(platform::Place place, bool is_pinned) {
inline void* Tensor::mutable_data(platform::Place place) {
PADDLE_ENFORCE(this->holder_ != nullptr,
"Cannot invoke mutable data if current hold nothing");
return mutable_data(place, holder_->type(), is_pinned);
"Cannot invoke mutable data if current hold nothing.");
return mutable_data(place, holder_->type());
}
inline Tensor& Tensor::ShareDataWith(const Tensor& src) {
......@@ -191,8 +195,6 @@ inline const DDim& Tensor::dims() const { return dims_; }
inline int64_t Tensor::numel() const { return product(dims_); }
inline bool Tensor::isPinned() const { return is_pinned_; }
inline Tensor ReshapeToMatrix(const Tensor& src, int num_col_dims) {
Tensor res;
res.ShareDataWith(src);
......
paddle/fluid/framework/tensor_util.cc
浏览文件 @ 82530e24
......@@ -148,6 +148,11 @@ struct AnyVisitor : public boost::static_visitor<bool> {
const platform::CPUPlace& cpu) const {
return *out.data<bool>();
}
bool GetResult(const framework::Tensor& out,
const platform::CUDAPinnedPlace& cpu) const {
return *out.data<bool>();
}
};
template <typename Predicate>
......
paddle/fluid/framework/tuple.h
浏览文件 @ 82530e24
......@@ -35,24 +35,25 @@ class Tuple {
public:
using ElementVars = std::vector<ElementVar>;
Tuple(std::vector<ElementVar>& var, std::vector<VarDesc>& var_desc)
Tuple(const std::vector<ElementVar>& var,
const std::vector<VarDesc>& var_desc)
: var_(var), var_desc_(var_desc) {}
Tuple(std::vector<ElementVar>& var) : var_(var) {}
explicit Tuple(std::vector<ElementVar>& var) : var_(var) {}
ElementVar get(int idx) const { return var_[idx]; };
ElementVar get(int idx) const { return var_[idx]; }
ElementVar& get(int idx) { return var_[idx]; };
ElementVar& get(int idx) { return var_[idx]; }
bool isSameType(Tuple& t) const;
bool isSameType(const Tuple& t) const;
size_t getSize() const { return var_.size(); };
size_t getSize() const { return var_.size(); }
private:
ElementVars var_;
std::vector<VarDesc> var_desc_;
};
bool Tuple::isSameType(Tuple& t) const {
bool Tuple::isSameType(const Tuple& t) const {
size_t tuple_size = getSize();
if (tuple_size != t.getSize()) {
return false;
......
paddle/fluid/inference/CMakeLists.txt
浏览文件 @ 82530e24
set(FLUID_CORE_MODULES proto_desc paddle_memory lod_tensor executor prune init)
set(FLUID_CORE_MODULES proto_desc memory lod_tensor executor prune init)
cc_library(paddle_fluid_api
SRCS io.cc
......
paddle/fluid/inference/io.cc
浏览文件 @ 82530e24
......@@ -41,8 +41,7 @@ bool IsPersistable(const framework::VarDesc* var) {
return false;
}
void LoadPersistables(framework::Executor& executor,
framework::Scope& scope,
void LoadPersistables(framework::Executor& executor, framework::Scope& scope,
const framework::ProgramDesc& main_program,
const std::string& dirname,
const std::string& param_filename) {
......@@ -108,10 +107,8 @@ std::unique_ptr<framework::ProgramDesc> Load(framework::Executor& executor,
}
std::unique_ptr<framework::ProgramDesc> Load(
framework::Executor& executor,
framework::Scope& scope,
const std::string& prog_filename,
const std::string& param_filename) {
framework::Executor& executor, framework::Scope& scope,
const std::string& prog_filename, const std::string& param_filename) {
std::string model_filename = prog_filename;
std::string program_desc_str;
ReadBinaryFile(model_filename, program_desc_str);
......
paddle/fluid/inference/io.h
浏览文件 @ 82530e24
......@@ -24,8 +24,7 @@ limitations under the License. */
namespace paddle {
namespace inference {
void LoadPersistables(framework::Executor& executor,
framework::Scope& scope,
void LoadPersistables(framework::Executor& executor, framework::Scope& scope,
const framework::ProgramDesc& main_program,
const std::string& dirname,
const std::string& param_filename);
......
paddle/fluid/inference/tests/book/CMakeLists.txt
浏览文件 @ 82530e24
......@@ -4,7 +4,7 @@ function(inference_test TARGET_NAME)
set(multiValueArgs ARGS)
cmake_parse_arguments(inference_test "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
set(PYTHON_TESTS_DIR ${PADDLE_SOURCE_DIR}/python/paddle/fluid/tests)
set(PYTHON_TESTS_DIR ${PADDLE_BINARY_DIR}/python/paddle/fluid/tests)
set(arg_list "")
if(inference_test_ARGS)
foreach(arg ${inference_test_ARGS})
......
paddle/fluid/inference/tests/book/test_inference_fit_a_line.cc
浏览文件 @ 82530e24
......@@ -9,8 +9,8 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <gtest/gtest.h>
#include "gflags/gflags.h"
#include "gtest/gtest.h"
#include "paddle/fluid/inference/tests/test_helper.h"
DEFINE_string(dirname, "", "Directory of the inference model.");
......@@ -30,8 +30,8 @@ TEST(inference, fit_a_line) {
// The second dim of the input tensor should be 13
// The input data should be >= 0
int64_t batch_size = 10;
SetupTensor<float>(
input, {batch_size, 13}, static_cast<float>(0), static_cast<float>(10));
SetupTensor<float>(&input, {batch_size, 13}, static_cast<float>(0),
static_cast<float>(10));
std::vector<paddle::framework::LoDTensor*> cpu_feeds;
cpu_feeds.push_back(&input);
......
paddle/fluid/inference/tests/book/test_inference_image_classification.cc
浏览文件 @ 82530e24
......@@ -12,8 +12,8 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <gtest/gtest.h>
#include "gflags/gflags.h"
#include "gtest/gtest.h"
#include "paddle/fluid/inference/tests/test_helper.h"
DEFINE_string(dirname, "", "Directory of the inference model.");
......@@ -35,10 +35,8 @@ TEST(inference, image_classification) {
paddle::framework::LoDTensor input;
// Use normilized image pixels as input data,
// which should be in the range [0.0, 1.0].
SetupTensor<float>(input,
{FLAGS_batch_size, 3, 32, 32},
static_cast<float>(0),
static_cast<float>(1));
SetupTensor<float>(&input, {FLAGS_batch_size, 3, 32, 32},
static_cast<float>(0), static_cast<float>(1));
std::vector<paddle::framework::LoDTensor*> cpu_feeds;
cpu_feeds.push_back(&input);
......@@ -48,8 +46,8 @@ TEST(inference, image_classification) {
// Run inference on CPU
LOG(INFO) << "--- CPU Runs: ---";
TestInference<paddle::platform::CPUPlace>(
dirname, cpu_feeds, cpu_fetchs1, FLAGS_repeat);
TestInference<paddle::platform::CPUPlace>(dirname, cpu_feeds, cpu_fetchs1,
FLAGS_repeat);
LOG(INFO) << output1.dims();
#ifdef PADDLE_WITH_CUDA
......@@ -59,8 +57,8 @@ TEST(inference, image_classification) {
// Run inference on CUDA GPU
LOG(INFO) << "--- GPU Runs: ---";
TestInference<paddle::platform::CUDAPlace>(
dirname, cpu_feeds, cpu_fetchs2, FLAGS_repeat);
TestInference<paddle::platform::CUDAPlace>(dirname, cpu_feeds, cpu_fetchs2,
FLAGS_repeat);
LOG(INFO) << output2.dims();
CheckError<float>(output1, output2);
......
paddle/fluid/inference/tests/book/test_inference_label_semantic_roles.cc
浏览文件 @ 82530e24
......@@ -12,8 +12,8 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <gtest/gtest.h>
#include "gflags/gflags.h"
#include "gtest/gtest.h"
#include "paddle/fluid/inference/tests/test_helper.h"
DEFINE_string(dirname, "", "Directory of the inference model.");
......@@ -36,37 +36,21 @@ TEST(inference, label_semantic_roles) {
int64_t predicate_dict_len = 3162;
int64_t mark_dict_len = 2;
SetupLoDTensor(word,
lod,
static_cast<int64_t>(0),
SetupLoDTensor(&word, lod, static_cast<int64_t>(0),
static_cast<int64_t>(word_dict_len - 1));
SetupLoDTensor(predicate,
lod,
static_cast<int64_t>(0),
SetupLoDTensor(&predicate, lod, static_cast<int64_t>(0),
static_cast<int64_t>(predicate_dict_len - 1));
SetupLoDTensor(ctx_n2,
lod,
static_cast<int64_t>(0),
SetupLoDTensor(&ctx_n2, lod, static_cast<int64_t>(0),
static_cast<int64_t>(word_dict_len - 1));
SetupLoDTensor(ctx_n1,
lod,
static_cast<int64_t>(0),
SetupLoDTensor(&ctx_n1, lod, static_cast<int64_t>(0),
static_cast<int64_t>(word_dict_len - 1));
SetupLoDTensor(ctx_0,
lod,
static_cast<int64_t>(0),
SetupLoDTensor(&ctx_0, lod, static_cast<int64_t>(0),
static_cast<int64_t>(word_dict_len - 1));
SetupLoDTensor(ctx_p1,
lod,
static_cast<int64_t>(0),
SetupLoDTensor(&ctx_p1, lod, static_cast<int64_t>(0),
static_cast<int64_t>(word_dict_len - 1));
SetupLoDTensor(ctx_p2,
lod,
static_cast<int64_t>(0),
SetupLoDTensor(&ctx_p2, lod, static_cast<int64_t>(0),
static_cast<int64_t>(word_dict_len - 1));
SetupLoDTensor(mark,
lod,
static_cast<int64_t>(0),
SetupLoDTensor(&mark, lod, static_cast<int64_t>(0),
static_cast<int64_t>(mark_dict_len - 1));
std::vector<paddle::framework::LoDTensor*> cpu_feeds;
......
paddle/fluid/inference/tests/book/test_inference_recognize_digits.cc
浏览文件 @ 82530e24
......@@ -12,8 +12,8 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <gtest/gtest.h>
#include "gflags/gflags.h"
#include "gtest/gtest.h"
#include "paddle/fluid/inference/tests/test_helper.h"
DEFINE_string(dirname, "", "Directory of the inference model.");
......@@ -35,10 +35,8 @@ TEST(inference, recognize_digits) {
paddle::framework::LoDTensor input;
// Use normilized image pixels as input data,
// which should be in the range [-1.0, 1.0].
SetupTensor<float>(input,
{FLAGS_batch_size, 1, 28, 28},
static_cast<float>(-1),
static_cast<float>(1));
SetupTensor<float>(&input, {FLAGS_batch_size, 1, 28, 28},
static_cast<float>(-1), static_cast<float>(1));
std::vector<paddle::framework::LoDTensor*> cpu_feeds;
cpu_feeds.push_back(&input);
......@@ -49,8 +47,8 @@ TEST(inference, recognize_digits) {
// Run inference on CPU
LOG(INFO) << "--- CPU Runs: is_combined=" << is_combined << " ---";
TestInference<paddle::platform::CPUPlace>(
dirname, cpu_feeds, cpu_fetchs1, FLAGS_repeat, is_combined);
TestInference<paddle::platform::CPUPlace>(dirname, cpu_feeds, cpu_fetchs1,
FLAGS_repeat, is_combined);
LOG(INFO) << output1.dims();
#ifdef PADDLE_WITH_CUDA
......@@ -60,8 +58,8 @@ TEST(inference, recognize_digits) {
// Run inference on CUDA GPU
LOG(INFO) << "--- GPU Runs: is_combined=" << is_combined << " ---";
TestInference<paddle::platform::CUDAPlace>(
dirname, cpu_feeds, cpu_fetchs2, FLAGS_repeat, is_combined);
TestInference<paddle::platform::CUDAPlace>(dirname, cpu_feeds, cpu_fetchs2,
FLAGS_repeat, is_combined);
LOG(INFO) << output2.dims();
CheckError<float>(output1, output2);
......
paddle/fluid/inference/tests/book/test_inference_recommender_system.cc
浏览文件 @ 82530e24
......@@ -12,8 +12,8 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <gtest/gtest.h>
#include "gflags/gflags.h"
#include "gtest/gtest.h"
#include "paddle/fluid/inference/tests/test_helper.h"
DEFINE_string(dirname, "", "Directory of the inference model.");
......@@ -36,25 +36,25 @@ TEST(inference, recommender_system) {
// Use the first data from paddle.dataset.movielens.test() as input
std::vector<int64_t> user_id_data = {1};
SetupTensor<int64_t>(user_id, {batch_size, 1}, user_id_data);
SetupTensor<int64_t>(&user_id, {batch_size, 1}, user_id_data);
std::vector<int64_t> gender_id_data = {1};
SetupTensor<int64_t>(gender_id, {batch_size, 1}, gender_id_data);
SetupTensor<int64_t>(&gender_id, {batch_size, 1}, gender_id_data);
std::vector<int64_t> age_id_data = {0};
SetupTensor<int64_t>(age_id, {batch_size, 1}, age_id_data);
SetupTensor<int64_t>(&age_id, {batch_size, 1}, age_id_data);
std::vector<int64_t> job_id_data = {10};
SetupTensor<int64_t>(job_id, {batch_size, 1}, job_id_data);
SetupTensor<int64_t>(&job_id, {batch_size, 1}, job_id_data);
std::vector<int64_t> movie_id_data = {783};
SetupTensor<int64_t>(movie_id, {batch_size, 1}, movie_id_data);
SetupTensor<int64_t>(&movie_id, {batch_size, 1}, movie_id_data);
std::vector<int64_t> category_id_data = {10, 8, 9};
SetupLoDTensor<int64_t>(category_id, {3, 1}, {{0, 3}}, category_id_data);
SetupLoDTensor<int64_t>(&category_id, {3, 1}, {{0, 3}}, category_id_data);
std::vector<int64_t> movie_title_data = {1069, 4140, 2923, 710, 988};
SetupLoDTensor<int64_t>(movie_title, {5, 1}, {{0, 5}}, movie_title_data);
SetupLoDTensor<int64_t>(&movie_title, {5, 1}, {{0, 5}}, movie_title_data);
std::vector<paddle::framework::LoDTensor*> cpu_feeds;
cpu_feeds.push_back(&user_id);
......
paddle/fluid/inference/tests/book/test_inference_rnn_encoder_decoder.cc
浏览文件 @ 82530e24
......@@ -12,8 +12,8 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <gtest/gtest.h>
#include "gflags/gflags.h"
#include "gtest/gtest.h"
#include "paddle/fluid/inference/tests/test_helper.h"
DEFINE_string(dirname, "", "Directory of the inference model.");
......@@ -32,10 +32,10 @@ TEST(inference, rnn_encoder_decoder) {
paddle::framework::LoDTensor word_data, trg_word;
paddle::framework::LoD lod{{0, 4, 10}};
SetupLoDTensor(
word_data, lod, static_cast<int64_t>(0), static_cast<int64_t>(1));
SetupLoDTensor(
trg_word, lod, static_cast<int64_t>(0), static_cast<int64_t>(1));
SetupLoDTensor(&word_data, lod, static_cast<int64_t>(0),
static_cast<int64_t>(1));
SetupLoDTensor(&trg_word, lod, static_cast<int64_t>(0),
static_cast<int64_t>(1));
std::vector<paddle::framework::LoDTensor*> cpu_feeds;
cpu_feeds.push_back(&word_data);
......
paddle/fluid/inference/tests/book/test_inference_understand_sentiment.cc
浏览文件 @ 82530e24
......@@ -12,8 +12,8 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <gtest/gtest.h>
#include "gflags/gflags.h"
#include "gtest/gtest.h"
#include "paddle/fluid/inference/tests/test_helper.h"
DEFINE_string(dirname, "", "Directory of the inference model.");
......@@ -33,9 +33,7 @@ TEST(inference, understand_sentiment) {
paddle::framework::LoD lod{{0, 4, 10}};
int64_t word_dict_len = 5147;
SetupLoDTensor(words,
lod,
static_cast<int64_t>(0),
SetupLoDTensor(&words, lod, static_cast<int64_t>(0),
static_cast<int64_t>(word_dict_len - 1));
std::vector<paddle::framework::LoDTensor*> cpu_feeds;
......
paddle/fluid/inference/tests/book/test_inference_word2vec.cc
浏览文件 @ 82530e24
......@@ -12,8 +12,8 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <gtest/gtest.h>
#include "gflags/gflags.h"
#include "gtest/gtest.h"
#include "paddle/fluid/inference/tests/test_helper.h"
DEFINE_string(dirname, "", "Directory of the inference model.");
......@@ -33,10 +33,10 @@ TEST(inference, word2vec) {
paddle::framework::LoD lod{{0, 1}};
int64_t dict_size = 2073; // The size of dictionary
SetupLoDTensor(first_word, lod, static_cast<int64_t>(0), dict_size - 1);
SetupLoDTensor(second_word, lod, static_cast<int64_t>(0), dict_size - 1);
SetupLoDTensor(third_word, lod, static_cast<int64_t>(0), dict_size - 1);
SetupLoDTensor(fourth_word, lod, static_cast<int64_t>(0), dict_size - 1);
SetupLoDTensor(&first_word, lod, static_cast<int64_t>(0), dict_size - 1);
SetupLoDTensor(&second_word, lod, static_cast<int64_t>(0), dict_size - 1);
SetupLoDTensor(&third_word, lod, static_cast<int64_t>(0), dict_size - 1);
SetupLoDTensor(&fourth_word, lod, static_cast<int64_t>(0), dict_size - 1);
std::vector<paddle::framework::LoDTensor*> cpu_feeds;
cpu_feeds.push_back(&first_word);
......
paddle/fluid/inference/tests/test_helper.h
浏览文件 @ 82530e24
......@@ -11,59 +11,59 @@ distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <map>
#include <random>
#include <string>
#include <vector>
#include <time.h>
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/inference/io.h"
#include "paddle/fluid/platform/profiler.h"
template <typename T>
void SetupTensor(paddle::framework::LoDTensor& input,
paddle::framework::DDim dims,
T lower,
T upper) {
srand(time(0));
T* input_ptr = input.mutable_data<T>(dims, paddle::platform::CPUPlace());
for (int i = 0; i < input.numel(); ++i) {
input_ptr[i] =
(static_cast<T>(rand()) / static_cast<T>(RAND_MAX)) * (upper - lower) +
lower;
void SetupTensor(paddle::framework::LoDTensor* input,
paddle::framework::DDim dims, T lower, T upper) {
std::mt19937 rng(100); // An arbitrarily chosen but fixed seed.
std::uniform_real_distribution<double> uniform_dist(0, 1);
T* input_ptr = input->mutable_data<T>(dims, paddle::platform::CPUPlace());
for (int i = 0; i < input->numel(); ++i) {
input_ptr[i] = static_cast<T>(uniform_dist(rng) * (upper - lower) + lower);
}
}
template <typename T>
void SetupTensor(paddle::framework::LoDTensor& input,
paddle::framework::DDim dims,
std::vector<T>& data) {
void SetupTensor(paddle::framework::LoDTensor* input,
paddle::framework::DDim dims, const std::vector<T>& data) {
CHECK_EQ(paddle::framework::product(dims), static_cast<int64_t>(data.size()));
T* input_ptr = input.mutable_data<T>(dims, paddle::platform::CPUPlace());
memcpy(input_ptr, data.data(), input.numel() * sizeof(T));
T* input_ptr = input->mutable_data<T>(dims, paddle::platform::CPUPlace());
memcpy(input_ptr, data.data(), input->numel() * sizeof(T));
}
template <typename T>
void SetupLoDTensor(paddle::framework::LoDTensor& input,
paddle::framework::LoD& lod,
T lower,
T upper) {
input.set_lod(lod);
void SetupLoDTensor(paddle::framework::LoDTensor* input,
const paddle::framework::LoD& lod, T lower, T upper) {
input->set_lod(lod);
int dim = lod[0][lod[0].size() - 1];
SetupTensor<T>(input, {dim, 1}, lower, upper);
}
template <typename T>
void SetupLoDTensor(paddle::framework::LoDTensor& input,
void SetupLoDTensor(paddle::framework::LoDTensor* input,
paddle::framework::DDim dims,
paddle::framework::LoD lod,
std::vector<T>& data) {
const paddle::framework::LoD lod,
const std::vector<T>& data) {
const size_t level = lod.size() - 1;
CHECK_EQ(dims[0], static_cast<int64_t>((lod[level]).back()));
input.set_lod(lod);
input->set_lod(lod);
SetupTensor<T>(input, dims, data);
}
template <typename T>
void CheckError(paddle::framework::LoDTensor& output1,
paddle::framework::LoDTensor& output2) {
void CheckError(const paddle::framework::LoDTensor& output1,
const paddle::framework::LoDTensor& output2) {
// Check lod information
EXPECT_EQ(output1.lod(), output2.lod());
......@@ -91,9 +91,8 @@ void CheckError(paddle::framework::LoDTensor& output1,
template <typename Place>
void TestInference(const std::string& dirname,
const std::vector<paddle::framework::LoDTensor*>& cpu_feeds,
std::vector<paddle::framework::LoDTensor*>& cpu_fetchs,
const int repeat = 1,
const bool is_combined = false) {
const std::vector<paddle::framework::LoDTensor*>& cpu_fetchs,
const int repeat = 1, const bool is_combined = false) {
// 1. Define place, executor, scope
auto place = Place();
auto executor = paddle::framework::Executor(place);
......@@ -132,11 +131,9 @@ void TestInference(const std::string& dirname,
// `fluid.io.save_inference_model`.
std::string prog_filename = "__model_combined__";
std::string param_filename = "__params_combined__";
inference_program =
paddle::inference::Load(executor,
*scope,
dirname + "/" + prog_filename,
dirname + "/" + param_filename);
inference_program = paddle::inference::Load(
executor, *scope, dirname + "/" + prog_filename,
dirname + "/" + param_filename);
} else {
// Parameters are saved in separate files sited in the specified
// `dirname`.
......
paddle/fluid/memory/.clang-format 已删除 100644 → 0
浏览文件 @ 07b31b80
---
Language: Cpp
BasedOnStyle: Google
Standard: Cpp11
...
paddle/fluid/memory/CMakeLists.txt
浏览文件 @ 82530e24
add_subdirectory(detail)
cc_library(memory SRCS memory.cc DEPS place enforce)
cc_library(malloc SRCS malloc.cc DEPS buddy_allocator place enforce)
cc_library(memcpy SRCS memcpy.cc DEPS place)
cc_library(paddle_memory
DEPS
memory
memcpy
meta_data
meta_cache
memory_block
buddy_allocator
system_allocator)
cc_library(memory
DEPS
malloc
memcpy)
cc_test(memory_test SRCS memory_test.cc DEPS place paddle_memory)
cc_test(malloc_test SRCS malloc_test.cc DEPS malloc)
#if (WITH_GPU)
# nv_test(pinned_memory_test SRCS pinned_memory_test.cu DEPS place memory)
#endif()
paddle/fluid/memory/detail/CMakeLists.txt
浏览文件 @ 82530e24
cc_library(memory_block SRCS memory_block.cc memory_block_desc.cc meta_cache.cc)
if(${WITH_GPU})
nv_library(system_allocator SRCS system_allocator.cc DEPS gflags cpu_info gpu_info)
else(${WITH_GPU})
......@@ -6,10 +8,4 @@ endif(${WITH_GPU})
cc_test(system_allocator_test SRCS system_allocator_test.cc DEPS system_allocator)
cc_library(meta_data SRCS meta_data.cc)
cc_library(meta_cache SRCS meta_cache.cc)
cc_library(memory_block SRCS memory_block.cc)
cc_library(buddy_allocator SRCS buddy_allocator.cc DEPS glog)
cc_library(buddy_allocator SRCS buddy_allocator.cc DEPS memory_block system_allocator glog)
paddle/fluid/memory/detail/buddy_allocator.cc
浏览文件 @ 82530e24
......@@ -46,7 +46,8 @@ inline size_t align(size_t size, size_t alignment) {
void* BuddyAllocator::Alloc(size_t unaligned_size) {
// adjust allocation alignment
size_t size = align(unaligned_size + sizeof(Metadata), min_chunk_size_);
size_t size =
align(unaligned_size + sizeof(MemoryBlock::Desc), min_chunk_size_);
// acquire the allocator lock
std::lock_guard<std::mutex> lock(mutex_);
......@@ -103,7 +104,7 @@ void BuddyAllocator::Free(void* p) {
return;
}
block->mark_as_free(cache_);
block->mark_as_free(&cache_);
total_used_ -= block->total_size(cache_);
total_free_ += block->total_size(cache_);
......@@ -122,7 +123,7 @@ void BuddyAllocator::Free(void* p) {
right_buddy));
// merge its right buddy to the block
block->merge(cache_, right_buddy);
block->merge(&cache_, right_buddy);
}
}
......@@ -139,7 +140,7 @@ void BuddyAllocator::Free(void* p) {
left_buddy->total_size(cache_), left_buddy));
// merge the block to its left buddy
left_buddy->merge(cache_, block);
left_buddy->merge(&cache_, block);
block = left_buddy;
}
}
......@@ -163,13 +164,13 @@ size_t BuddyAllocator::Used() { return total_used_; }
void* BuddyAllocator::SystemAlloc(size_t size) {
size_t index = 0;
void* p = system_allocator_->Alloc(index, size);
void* p = system_allocator_->Alloc(&index, size);
VLOG(10) << "Allocated " << p << " from system allocator.";
if (p == nullptr) return nullptr;
static_cast<MemoryBlock*>(p)->init(cache_, MemoryBlock::HUGE_CHUNK, index,
static_cast<MemoryBlock*>(p)->init(&cache_, MemoryBlock::HUGE_CHUNK, index,
size, nullptr, nullptr);
return static_cast<MemoryBlock*>(p)->data();
......@@ -187,14 +188,14 @@ BuddyAllocator::PoolSet::iterator BuddyAllocator::RefillPool() {
// Allocate a new maximum sized block
size_t index = 0;
void* p = system_allocator_->Alloc(index, max_chunk_size_);
void* p = system_allocator_->Alloc(&index, max_chunk_size_);
if (p == nullptr) return pool_.end();
VLOG(10) << "Creating and inserting new block " << p
<< " from system allocator";
static_cast<MemoryBlock*>(p)->init(cache_, MemoryBlock::FREE_CHUNK, index,
static_cast<MemoryBlock*>(p)->init(&cache_, MemoryBlock::FREE_CHUNK, index,
max_chunk_size_, nullptr, nullptr);
// gpu fallback allocation
......@@ -238,11 +239,11 @@ void* BuddyAllocator::SplitToAlloc(BuddyAllocator::PoolSet::iterator it,
VLOG(10) << "Split block (" << block << ", " << block->total_size(cache_)
<< ") into";
block->split(cache_, size);
block->split(&cache_, size);
VLOG(10) << "Left block (" << block << ", " << block->total_size(cache_)
<< ")";
block->set_type(cache_, MemoryBlock::ARENA_CHUNK);
block->set_type(&cache_, MemoryBlock::ARENA_CHUNK);
// the rest of memory if exist
if (block->has_right_buddy(cache_)) {
......
paddle/fluid/memory/detail/buddy_allocator.h
浏览文件 @ 82530e24
......@@ -14,18 +14,18 @@ limitations under the License. */
#pragma once
#include "paddle/fluid/memory/detail/meta_cache.h"
#include "paddle/fluid/memory/detail/meta_data.h"
#include <mutex> // NOLINT
#include <set>
#include <tuple>
#include <unordered_map>
#include <vector>
#include "paddle/fluid/memory/detail/memory_block.h"
#include "paddle/fluid/memory/detail/system_allocator.h"
#include "paddle/fluid/platform/assert.h"
#include "paddle/fluid/platform/cpu_info.h"
#include "paddle/fluid/platform/gpu_info.h"
#include <mutex>
#include <set>
#include <unordered_map>
#include <vector>
namespace paddle {
namespace memory {
namespace detail {
......
paddle/fluid/memory/detail/memory_block.cc
浏览文件 @ 82530e24
......@@ -13,143 +13,142 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/memory/detail/memory_block.h"
#include "paddle/fluid/memory/detail/meta_cache.h"
#include "paddle/fluid/memory/detail/meta_data.h"
#include "paddle/fluid/platform/assert.h"
namespace paddle {
namespace memory {
namespace detail {
void MemoryBlock::init(MetadataCache& cache, Type t, size_t index, size_t size,
void MemoryBlock::init(MetadataCache* cache, Type t, size_t index, size_t size,
void* left_buddy, void* right_buddy) {
cache.store(this, Metadata(t, index, size - sizeof(Metadata), size,
static_cast<MemoryBlock*>(left_buddy),
static_cast<MemoryBlock*>(right_buddy)));
cache->save(
this, MemoryBlock::Desc(t, index, size - sizeof(MemoryBlock::Desc), size,
static_cast<MemoryBlock*>(left_buddy),
static_cast<MemoryBlock*>(right_buddy)));
}
MemoryBlock::Type MemoryBlock::type(MetadataCache& cache) const {
MemoryBlock::Type MemoryBlock::type(const MetadataCache& cache) const {
return cache.load(this).type;
}
size_t MemoryBlock::size(MetadataCache& cache) const {
size_t MemoryBlock::size(const MetadataCache& cache) const {
return cache.load(this).size;
}
size_t MemoryBlock::total_size(MetadataCache& cache) const {
size_t MemoryBlock::index(const MetadataCache& cache) const {
return cache.load(this).index;
}
size_t MemoryBlock::total_size(const MetadataCache& cache) const {
return cache.load(this).total_size;
}
MemoryBlock* MemoryBlock::left_buddy(MetadataCache& cache) const {
bool MemoryBlock::has_left_buddy(const MetadataCache& cache) const {
return left_buddy(cache) != nullptr;
}
bool MemoryBlock::has_right_buddy(const MetadataCache& cache) const {
return right_buddy(cache) != nullptr;
}
MemoryBlock* MemoryBlock::left_buddy(const MetadataCache& cache) const {
return cache.load(this).left_buddy;
}
MemoryBlock* MemoryBlock::right_buddy(MetadataCache& cache) const {
MemoryBlock* MemoryBlock::right_buddy(const MetadataCache& cache) const {
return cache.load(this).right_buddy;
}
void MemoryBlock::split(MetadataCache& cache, size_t size) {
void MemoryBlock::split(MetadataCache* cache, size_t size) {
// make sure the split fits
PADDLE_ASSERT(total_size(cache) >= size);
PADDLE_ASSERT(total_size(*cache) >= size);
// bail out if there is no room for another partition
if (total_size(cache) - size <= sizeof(Metadata)) {
if (total_size(*cache) - size <= sizeof(MemoryBlock::Desc)) {
return;
}
// find the position of the split
void* right_partition = reinterpret_cast<uint8_t*>(this) + size;
size_t remaining_size = total_size(cache) - size;
size_t remaining_size = total_size(*cache) - size;
// Add the new block as a buddy
auto metadata = cache.load(this);
auto metadata = cache->load(this);
// Write the metadata for the new block
auto new_block_right_buddy = metadata.right_buddy;
cache.store(
static_cast<MemoryBlock*>(right_partition),
Metadata(FREE_CHUNK, index(cache), remaining_size - sizeof(Metadata),
remaining_size, this, new_block_right_buddy));
cache->save(static_cast<MemoryBlock*>(right_partition),
MemoryBlock::Desc(FREE_CHUNK, index(*cache),
remaining_size - sizeof(MemoryBlock::Desc),
remaining_size, this, new_block_right_buddy));
metadata.right_buddy = static_cast<MemoryBlock*>(right_partition);
metadata.size = size - sizeof(Metadata);
metadata.size = size - sizeof(MemoryBlock::Desc);
metadata.total_size = size;
cache.store(this, metadata);
cache->save(this, metadata);
// Write metadata for the new block's right buddy
if (new_block_right_buddy != nullptr) {
auto buddy_metadata = cache.load(new_block_right_buddy);
auto buddy_metadata = cache->load(new_block_right_buddy);
buddy_metadata.left_buddy = static_cast<MemoryBlock*>(right_partition);
cache.store(new_block_right_buddy, buddy_metadata);
cache->save(new_block_right_buddy, buddy_metadata);
}
}
void MemoryBlock::merge(MetadataCache& cache, MemoryBlock* right_buddy) {
void MemoryBlock::merge(MetadataCache* cache, MemoryBlock* right_buddy) {
// only free blocks can be merged
PADDLE_ASSERT(type(cache) == FREE_CHUNK);
PADDLE_ASSERT(right_buddy->type(cache) == FREE_CHUNK);
PADDLE_ASSERT(type(*cache) == FREE_CHUNK);
PADDLE_ASSERT(right_buddy->type(*cache) == FREE_CHUNK);
auto metadata = cache.load(this);
auto metadata = cache->load(this);
// link this->buddy's buddy
metadata.right_buddy = right_buddy->right_buddy(cache);
metadata.right_buddy = right_buddy->right_buddy(*cache);
// link buddy's buddy -> this
if (metadata.right_buddy != nullptr) {
auto buddy_metadata = cache.load(metadata.right_buddy);
auto buddy_metadata = cache->load(metadata.right_buddy);
buddy_metadata.left_buddy = this;
cache.store(metadata.right_buddy, buddy_metadata);
cache->save(metadata.right_buddy, buddy_metadata);
}
metadata.size += right_buddy->total_size(cache);
metadata.total_size += right_buddy->total_size(cache);
metadata.size += right_buddy->total_size(*cache);
metadata.total_size += right_buddy->total_size(*cache);
cache.store(this, metadata);
cache.store(right_buddy, Metadata(INVALID_CHUNK, 0, 0, 0, nullptr, nullptr));
cache->save(this, metadata);
cache->save(right_buddy,
MemoryBlock::Desc(INVALID_CHUNK, 0, 0, 0, nullptr, nullptr));
}
void MemoryBlock::mark_as_free(MetadataCache& cache) {
void MemoryBlock::mark_as_free(MetadataCache* cache) {
// check for double free or corruption
PADDLE_ASSERT(type(cache) != FREE_CHUNK);
PADDLE_ASSERT(type(cache) != INVALID_CHUNK);
PADDLE_ASSERT(type(*cache) != FREE_CHUNK);
PADDLE_ASSERT(type(*cache) != INVALID_CHUNK);
set_type(cache, FREE_CHUNK);
}
void MemoryBlock::set_type(MetadataCache& cache, Type t) {
auto metadata = cache.load(this);
void MemoryBlock::set_type(MetadataCache* cache, Type t) {
auto metadata = cache->load(this);
metadata.type = t;
cache.store(this, metadata);
}
bool MemoryBlock::has_left_buddy(MetadataCache& cache) const {
return left_buddy(cache) != nullptr;
}
bool MemoryBlock::has_right_buddy(MetadataCache& cache) const {
return right_buddy(cache) != nullptr;
}
size_t MemoryBlock::index(MetadataCache& cache) const {
return cache.load(this).index;
cache->save(this, metadata);
}
void* MemoryBlock::data() const {
return const_cast<Metadata*>(reinterpret_cast<const Metadata*>(this)) + 1;
return const_cast<MemoryBlock::Desc*>(
reinterpret_cast<const MemoryBlock::Desc*>(this)) +
1;
}
MemoryBlock* MemoryBlock::metadata() const {
return const_cast<MemoryBlock*>(reinterpret_cast<const MemoryBlock*>(
reinterpret_cast<const Metadata*>(this) - 1));
reinterpret_cast<const MemoryBlock::Desc*>(this) - 1));
}
} // namespace detail
......
paddle/fluid/memory/detail/memory_block.h
浏览文件 @ 82530e24
......@@ -11,21 +11,21 @@ distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <cstddef>
#include <cstdint>
#include <unordered_map>
namespace paddle {
namespace memory {
namespace detail {
// Forward Declarations
// Forward declaration.
class MetadataCache;
/*! \brief A class used to interpret the contents of a memory block */
class MemoryBlock {
public:
// MemoryBlock represents Each allocated memory block, which contains
// MemoryBlock::Desc and the payload.
struct MemoryBlock {
enum Type {
FREE_CHUNK, // memory is free and idle
ARENA_CHUNK, // memory is being occupied
......@@ -33,57 +33,96 @@ class MemoryBlock {
INVALID_CHUNK // memory is invalid
};
public:
void init(MetadataCache& cache, Type t, size_t index, size_t size,
// init saves the MemoryBlock::Desc of the memory block in a MetadataCache.
// If it is a CPU memory block, the MetadataCache writes the
// MemoryBlock::Desc to the beginning of the block; or, if it is a GPU memory
// block, the MetadataCache writes the Meatadata to a std::map in
// the CPU.
void init(MetadataCache* cache, Type t, size_t index, size_t size,
void* left_buddy, void* right_buddy);
public:
/*! \brief The type of the allocation */
Type type(MetadataCache& cache) const;
/*! \brief The size of the data region */
size_t size(MetadataCache& cache) const;
// All these accessors returns fields in the MemoryBlock::Desc of the memory
// block. They all need a MetadataCache instance as their first
// parameter because they read the MemoryBlock::Desc from the cache.
Type type(const MetadataCache& cache) const;
size_t size(const MetadataCache& cache) const;
size_t index(const MetadataCache& cache) const;
size_t total_size(const MetadataCache& cache) const;
bool has_left_buddy(const MetadataCache& cache) const;
bool has_right_buddy(const MetadataCache& cache) const;
MemoryBlock* left_buddy(const MetadataCache& cache) const;
MemoryBlock* right_buddy(const MetadataCache& cache) const;
/*! \brief An index to track the allocator */
size_t index(MetadataCache& cache) const;
// Split the allocation into left/right blocks.
void split(MetadataCache* cache, size_t size);
/*! \brief The total size of the block */
size_t total_size(MetadataCache& cache) const;
// Merge left and right blocks together.
void merge(MetadataCache* cache, MemoryBlock* right_buddy);
/*! \brief Check the left buddy of the block */
bool has_left_buddy(MetadataCache& cache) const;
// Mark the allocation as free.
void mark_as_free(MetadataCache* cache);
/*! \brief Check the right buddy of the block */
bool has_right_buddy(MetadataCache& cache) const;
/*! \brief Get the left buddy */
MemoryBlock* left_buddy(MetadataCache& cache) const;
/*! \brief Get the right buddy */
MemoryBlock* right_buddy(MetadataCache& cache) const;
public:
/*! \brief Split the allocation into left/right blocks */
void split(MetadataCache& cache, size_t size);
// Change the type of the allocation.
void set_type(MetadataCache* cache, Type t);
/*! \brief Merge left and right blocks together */
void merge(MetadataCache& cache, MemoryBlock* right_buddy);
/*! \brief Mark the allocation as free */
void mark_as_free(MetadataCache& cache);
/*! \brief Change the type of the allocation */
void set_type(MetadataCache& cache, Type t);
public:
/*! \brief Get a pointer to the memory block's data */
void* data() const;
/*! \brief Get a pointer to the memory block's metadata */
MemoryBlock* metadata() const;
// MemoryBlock::Desc describes a MemoryBlock.
struct Desc {
Desc(MemoryBlock::Type t, size_t i, size_t s, size_t ts, MemoryBlock* l,
MemoryBlock* r);
Desc();
// Updates guard_begin and guard_end by hashes of the Metadata object.
void update_guards();
// Checks that guard_begin and guard_end are hashes of the Metadata object.
bool check_guards() const;
// TODO(gangliao): compress this
size_t guard_begin = 0;
MemoryBlock::Type type = MemoryBlock::INVALID_CHUNK;
size_t index = 0;
size_t size = 0;
size_t total_size = 0;
MemoryBlock* left_buddy = nullptr;
MemoryBlock* right_buddy = nullptr;
size_t guard_end = 0;
};
};
// A cache for accessing memory block meta-data that may be expensive
// to access directly. This class exists to unify the
// MemoryBlock::Desc format between GPU and CPU allocations. It should
// be removed when the CPU can access all GPU allocations directly via
// UVM.
class MetadataCache {
public:
static size_t overhead();
explicit MetadataCache(bool uses_gpu);
// Disable copying and assignment.
MetadataCache(const MetadataCache&) = delete;
MetadataCache& operator=(const MetadataCache&) = delete;
// Returns the MemoryBlock::Desc for a memory block. When MetadataCache is
// used to manage CPU memory, the MemoryBlock::Desc resides at the beginning
// of the memory block; when used to manage GPU memory, the
// Meatadata resides in CPU memory indexed by cache_.
MemoryBlock::Desc load(const MemoryBlock* memory_block) const;
// Saves the MemoryBlock::Desc of a memory block into the cache. For CPU
// memory block, writes the MemoryBlock::Desc to the beginning of the memory
// block; whereas for GPU memory, writes it to cache_.
void save(MemoryBlock* memory_block, const MemoryBlock::Desc& meta_data);
// For GPU memory block, erases its MemoryBlock::Desc from cache_.
void invalidate(MemoryBlock* memory_block);
private:
typedef std::unordered_map<const MemoryBlock*, MemoryBlock::Desc> MetadataMap;
MetadataMap cache_;
bool uses_gpu_;
};
} // namespace detail
......
paddle/fluid/memory/detail/meta_data.cc → paddle/fluid/memory/detail/memory_block_desc.cc
浏览文件 @ 82530e24
......@@ -12,16 +12,16 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/memory/detail/meta_data.h"
#include <functional>
#include "paddle/fluid/memory/detail/memory_block.h"
namespace paddle {
namespace memory {
namespace detail {
Metadata::Metadata(MemoryBlock::Type t, size_t i, size_t s, size_t ts,
MemoryBlock* l, MemoryBlock* r)
MemoryBlock::Desc::Desc(MemoryBlock::Type t, size_t i, size_t s, size_t ts,
MemoryBlock* l, MemoryBlock* r)
: type(t),
index(i),
size(s),
......@@ -29,7 +29,7 @@ Metadata::Metadata(MemoryBlock::Type t, size_t i, size_t s, size_t ts,
left_buddy(l),
right_buddy(r) {}
Metadata::Metadata()
MemoryBlock::Desc::Desc()
: type(MemoryBlock::INVALID_CHUNK),
index(0),
size(0),
......@@ -37,32 +37,36 @@ Metadata::Metadata()
left_buddy(nullptr),
right_buddy(nullptr) {}
namespace {
template <class T>
inline void hash_combine(std::size_t& seed, const T& v) {
inline void hash_combine(std::size_t* seed, const T& v) {
std::hash<T> hasher;
seed ^= hasher(v) + 0x9e3779b9 + (seed << 6) + (seed >> 2);
(*seed) ^= hasher(v) + 0x9e3779b9 + ((*seed) << 6) + ((*seed) >> 2);
}
inline size_t hash(const Metadata* metadata, size_t initial_seed) {
inline size_t hash(const MemoryBlock::Desc& metadata, size_t initial_seed) {
size_t seed = initial_seed;
hash_combine(seed, (size_t)metadata->type);
hash_combine(seed, metadata->index);
hash_combine(seed, metadata->size);
hash_combine(seed, metadata->total_size);
hash_combine(seed, metadata->left_buddy);
hash_combine(seed, metadata->right_buddy);
hash_combine(&seed, static_cast<size_t>(metadata.type));
hash_combine(&seed, metadata.index);
hash_combine(&seed, metadata.size);
hash_combine(&seed, metadata.total_size);
hash_combine(&seed, metadata.left_buddy);
hash_combine(&seed, metadata.right_buddy);
return seed;
}
void Metadata::update_guards() {
guard_begin = hash(this, 1);
guard_end = hash(this, 2);
} // namespace
void MemoryBlock::Desc::update_guards() {
guard_begin = hash(*this, 1);
guard_end = hash(*this, 2);
}
bool Metadata::check_guards() const {
return guard_begin == hash(this, 1) && guard_end == hash(this, 2);
bool MemoryBlock::Desc::check_guards() const {
return guard_begin == hash(*this, 1) && guard_end == hash(*this, 2);
}
} // namespace detail
......
paddle/fluid/memory/detail/meta_cache.cc
浏览文件 @ 82530e24
......@@ -12,7 +12,6 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/memory/detail/meta_cache.h"
#include "glog/logging.h"
#include "paddle/fluid/memory/detail/memory_block.h"
#include "paddle/fluid/platform/assert.h"
......@@ -23,29 +22,28 @@ namespace detail {
MetadataCache::MetadataCache(bool uses_gpu) : uses_gpu_(uses_gpu) {}
Metadata MetadataCache::load(const MemoryBlock* block) {
MemoryBlock::Desc MetadataCache::load(const MemoryBlock* block) const {
if (uses_gpu_) {
auto existing_metadata = cache_.find(block);
PADDLE_ASSERT(existing_metadata->second.check_guards());
return existing_metadata->second;
auto existing_desc = cache_.find(block);
PADDLE_ASSERT(existing_desc->second.check_guards());
return existing_desc->second;
} else {
auto* meta = reinterpret_cast<const Metadata*>(block);
VLOG(10) << "Load MetaData type=" << meta->type;
PADDLE_ASSERT(meta->check_guards());
return *reinterpret_cast<const Metadata*>(block);
auto* desc = reinterpret_cast<const MemoryBlock::Desc*>(block);
VLOG(10) << "Load MemoryBlock::Desc type=" << desc->type;
PADDLE_ASSERT(desc->check_guards());
return *reinterpret_cast<const MemoryBlock::Desc*>(block);
}
}
void MetadataCache::store(MemoryBlock* block,
const Metadata& original_metadata) {
auto metadata = original_metadata;
metadata.update_guards();
void MetadataCache::save(MemoryBlock* block,
const MemoryBlock::Desc& original_desc) {
auto desc = original_desc;
desc.update_guards();
if (uses_gpu_) {
cache_[block] = metadata;
cache_[block] = desc;
} else {
*reinterpret_cast<Metadata*>(block) = metadata;
*reinterpret_cast<MemoryBlock::Desc*>(block) = desc;
}
}
......
paddle/fluid/memory/detail/meta_cache.h 已删除 100644 → 0
浏览文件 @ 07b31b80
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "paddle/fluid/memory/detail/memory_block.h"
#include "paddle/fluid/memory/detail/meta_data.h"
#include <unordered_map>
namespace paddle {
namespace memory {
namespace detail {
/**
* \brief A cache for accessing memory block meta-data that may be expensive
* to access directly.
*
* \note This class exists to unify the metadata format between GPU and CPU
* allocations. It should be removed when the CPU can access all GPU
* allocations directly via UVM.
*/
class MetadataCache {
public:
explicit MetadataCache(bool uses_gpu);
public:
/*! \brief Load the associated metadata for the specified memory block. */
Metadata load(const MemoryBlock* memory_block);
/*! \brief Store the associated metadata for the specified memory block. */
void store(MemoryBlock* memory_block, const Metadata& meta_data);
/*! \brief Indicate that the specified metadata will no longer be used. */
void invalidate(MemoryBlock* memory_block);
public:
MetadataCache(const MetadataCache&) = delete;
MetadataCache& operator=(const MetadataCache&) = delete;
private:
bool uses_gpu_;
private:
typedef std::unordered_map<const MemoryBlock*, Metadata> MetadataMap;
private:
MetadataMap cache_;
};
} // namespace detail
} // namespace memory
} // namespace paddle
paddle/fluid/memory/detail/system_allocator.cc
浏览文件 @ 82530e24
......@@ -13,15 +13,16 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/memory/detail/system_allocator.h"
#include "paddle/fluid/platform/assert.h"
#include "paddle/fluid/platform/enforce.h"
#include "paddle/fluid/platform/gpu_info.h"
#include <stdlib.h> // for malloc and free
#include <sys/mman.h> // for mlock and munlock
#include <algorithm> // for std::max
#include "gflags/gflags.h"
#include "paddle/fluid/platform/assert.h"
#include "paddle/fluid/platform/cpu_info.h"
#include "paddle/fluid/platform/enforce.h"
#include "paddle/fluid/platform/gpu_info.h"
// If use_pinned_memory is true, CPUAllocator calls mlock, which
// returns pinned and locked memory as staging areas for data exchange
......@@ -34,13 +35,13 @@ namespace paddle {
namespace memory {
namespace detail {
void* CPUAllocator::Alloc(size_t& index, size_t size) {
void* CPUAllocator::Alloc(size_t* index, size_t size) {
// According to http://www.cplusplus.com/reference/cstdlib/malloc/,
// malloc might not return nullptr if size is zero, but the returned
// pointer shall not be dereferenced -- so we make it nullptr.
if (size <= 0) return nullptr;
index = 0; // unlock memory
*index = 0; // unlock memory
void* p;
......@@ -55,7 +56,7 @@ void* CPUAllocator::Alloc(size_t& index, size_t size) {
if (p != nullptr) {
if (FLAGS_use_pinned_memory) {
index = 1;
*index = 1;
mlock(p, size); // lock memory
}
}
......@@ -74,7 +75,7 @@ bool CPUAllocator::UseGpu() const { return false; }
#ifdef PADDLE_WITH_CUDA
void* GPUAllocator::Alloc(size_t& index, size_t size) {
void* GPUAllocator::Alloc(size_t* index, size_t size) {
// CUDA documentation doesn't explain if cudaMalloc returns nullptr
// if size is 0. We just make sure it does.
if (size <= 0) return nullptr;
......@@ -92,7 +93,7 @@ void* GPUAllocator::Alloc(size_t& index, size_t size) {
}
if (result == cudaSuccess) {
index = 0;
*index = 0;
gpu_alloc_size_ += size;
return p;
} else {
......@@ -132,23 +133,33 @@ bool GPUAllocator::UseGpu() const { return true; }
// PINNED memory allows direct DMA transfers by the GPU to and from system
// memory. It’s locked to a physical address.
void* CUDAPinnedAllocator::Alloc(size_t& index, size_t size) {
void* CUDAPinnedAllocator::Alloc(size_t* index, size_t size) {
if (size <= 0) return nullptr;
void* p;
// NOTE: here, we use GpuMaxAllocSize() as the maximum memory size
// NOTE: here, we use CUDAPinnedMaxAllocSize as the maximum memory size
// of host pinned allocation. Allocates too much would reduce
// the amount of memory available to the underlying system for paging.
size_t usable =
paddle::platform::CUDAPinnedMaxAllocSize() - cuda_pinnd_alloc_size_;
size_t usable = paddle::platform::GpuMaxAllocSize() - fallback_alloc_size_;
if (size > usable) return nullptr;
if (size > usable) {
LOG(WARNING) << "Cannot malloc " << size / 1024.0 / 1024.0
<< " MB pinned memory."
<< ", available " << usable / 1024.0 / 1024.0 << " MB";
return nullptr;
}
void* p;
// PINNED memory is visible to all CUDA contexts.
cudaError_t result = cudaMallocHost(&p, size);
if (result == cudaSuccess) {
index = 1;
fallback_alloc_size_ += size;
*index = 1; // PINNED memory
cuda_pinnd_alloc_size_ += size;
return p;
} else {
LOG(WARNING) << "cudaMallocHost failed.";
return nullptr;
}
return nullptr;
......@@ -158,8 +169,8 @@ void CUDAPinnedAllocator::Free(void* p, size_t size, size_t index) {
cudaError_t err;
PADDLE_ASSERT(index == 1);
PADDLE_ASSERT(fallback_alloc_size_ >= size);
fallback_alloc_size_ -= size;
PADDLE_ASSERT(cuda_pinnd_alloc_size_ >= size);
cuda_pinnd_alloc_size_ -= size;
err = cudaFreeHost(p);
// Purposefully allow cudaErrorCudartUnloading, because
......@@ -172,7 +183,7 @@ void CUDAPinnedAllocator::Free(void* p, size_t size, size_t index) {
}
}
bool CUDAPinnedAllocator::UseGpu() const { return true; }
bool CUDAPinnedAllocator::UseGpu() const { return false; }
#endif
......
paddle/fluid/memory/detail/system_allocator.h
浏览文件 @ 82530e24
......@@ -21,21 +21,22 @@ namespace memory {
namespace detail {
/**
* \brief SystemAllocator is the parent class of CPUAllocator and GPUAllocator.
* A BuddyAllocator object uses a SystemAllocator* pointing to the
* \brief SystemAllocator is the parent class of CPUAllocator,
* CUDAPinnedAllocator and GPUAllocator. A BuddyAllocator
* object uses a SystemAllocator* pointing to the
* underlying system allocator.
*/
class SystemAllocator {
public:
virtual ~SystemAllocator() {}
virtual void* Alloc(size_t& index, size_t size) = 0;
virtual void* Alloc(size_t* index, size_t size) = 0;
virtual void Free(void* p, size_t size, size_t index) = 0;
virtual bool UseGpu() const = 0;
};
class CPUAllocator : public SystemAllocator {
public:
virtual void* Alloc(size_t& index, size_t size);
virtual void* Alloc(size_t* index, size_t size);
virtual void Free(void* p, size_t size, size_t index);
virtual bool UseGpu() const;
};
......@@ -45,7 +46,7 @@ class GPUAllocator : public SystemAllocator {
public:
explicit GPUAllocator(int gpu_id) : gpu_id_(gpu_id) {}
virtual void* Alloc(size_t& index, size_t size);
virtual void* Alloc(size_t* index, size_t size);
virtual void Free(void* p, size_t size, size_t index);
virtual bool UseGpu() const;
......@@ -57,14 +58,12 @@ class GPUAllocator : public SystemAllocator {
class CUDAPinnedAllocator : public SystemAllocator {
public:
virtual void* Alloc(size_t& index, size_t size);
virtual void* Alloc(size_t* index, size_t size);
virtual void Free(void* p, size_t size, size_t index);
virtual bool UseGpu() const;
private:
size_t gpu_alloc_size_ =
0; // TODO(zcd): how to define the upper limit of CUDAPinnedMemory?
size_t fallback_alloc_size_ = 0;
size_t cuda_pinnd_alloc_size_ = 0;
};
#endif
......
paddle/fluid/memory/detail/system_allocator_test.cc
浏览文件 @ 82530e24
......@@ -22,11 +22,11 @@ limitations under the License. */
DECLARE_bool(use_pinned_memory);
void TestAllocator(paddle::memory::detail::SystemAllocator& a, size_t size) {
void TestAllocator(paddle::memory::detail::SystemAllocator* a, size_t size) {
bool freed = false;
{
size_t index;
void* p = a.Alloc(index, size);
void* p = a->Alloc(&index, size);
if (size > 0) {
EXPECT_NE(p, nullptr);
} else {
......@@ -36,7 +36,7 @@ void TestAllocator(paddle::memory::detail::SystemAllocator& a, size_t size) {
int* i = static_cast<int*>(p);
std::shared_ptr<int> ptr(i, [&](void* p) {
freed = true;
a.Free(p, size, index);
a->Free(p, size, index);
});
}
EXPECT_TRUE(freed);
......@@ -45,21 +45,21 @@ void TestAllocator(paddle::memory::detail::SystemAllocator& a, size_t size) {
TEST(CPUAllocator, NoLockMem) {
FLAGS_use_pinned_memory = false;
paddle::memory::detail::CPUAllocator a;
TestAllocator(a, 2048);
TestAllocator(a, 0);
TestAllocator(&a, 2048);
TestAllocator(&a, 0);
}
TEST(CPUAllocator, LockMem) {
FLAGS_use_pinned_memory = true;
paddle::memory::detail::CPUAllocator a;
TestAllocator(a, 2048);
TestAllocator(a, 0);
TestAllocator(&a, 2048);
TestAllocator(&a, 0);
}
#ifdef PADDLE_WITH_CUDA
TEST(GPUAllocator, Alloc) {
paddle::memory::detail::GPUAllocator a(0);
TestAllocator(a, 2048);
TestAllocator(a, 0);
TestAllocator(&a, 2048);
TestAllocator(&a, 0);
}
#endif
paddle/fluid/memory/memory.cc → paddle/fluid/memory/malloc.cc
浏览文件 @ 82530e24
......@@ -12,7 +12,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/memory/memory.h"
#include "paddle/fluid/memory/malloc.h"
#include "glog/logging.h"
......@@ -38,8 +38,7 @@ BuddyAllocator* GetCPUBuddyAllocator() {
}
template <>
void* Alloc<platform::CPUPlace>(platform::CPUPlace place, size_t size,
bool is_pinned) {
void* Alloc<platform::CPUPlace>(platform::CPUPlace place, size_t size) {
VLOG(10) << "Allocate " << size << " bytes on " << platform::Place(place);
void* p = GetCPUBuddyAllocator()->Alloc(size);
VLOG(10) << " pointer=" << p;
......@@ -47,8 +46,7 @@ void* Alloc<platform::CPUPlace>(platform::CPUPlace place, size_t size,
}
template <>
void Free<platform::CPUPlace>(platform::CPUPlace place, void* p,
bool is_pinned) {
void Free<platform::CPUPlace>(platform::CPUPlace place, void* p) {
VLOG(10) << "Free pointer=" << p << " on " << platform::Place(place);
GetCPUBuddyAllocator()->Free(p);
}
......@@ -84,52 +82,20 @@ BuddyAllocator* GetGPUBuddyAllocator(int gpu_id) {
return as[gpu_id];
}
BuddyAllocator* GetCUDAPinnedBuddyAllocator(int gpu_id) {
static BuddyAllocator** as = NULL;
if (as == NULL) {
int gpu_num = platform::GetCUDADeviceCount();
as = new BuddyAllocator*[gpu_num];
for (int gpu = 0; gpu < gpu_num; gpu++) {
as[gpu] = nullptr;
}
}
platform::SetDeviceId(gpu_id);
if (!as[gpu_id]) {
as[gpu_id] = new BuddyAllocator(new detail::CUDAPinnedAllocator,
platform::GpuMinChunkSize(),
platform::GpuMaxChunkSize());
VLOG(10) << "\n\nNOTE: each GPU device use "
<< FLAGS_fraction_of_gpu_memory_to_use * 100
<< "% of GPU memory.\n"
<< "You can set GFlags environment variable '"
<< "FLAGS_fraction_of_gpu_memory_to_use"
<< "' to change the fraction of GPU usage.\n\n";
}
return as[gpu_id];
}
template <>
size_t Used<platform::CUDAPlace>(platform::CUDAPlace place) {
return GetGPUBuddyAllocator(place.device)->Used();
}
template <>
void* Alloc<platform::CUDAPlace>(platform::CUDAPlace place, size_t size,
bool is_pinned) {
void* ptr;
if (is_pinned) {
auto* buddy_allocator = GetCUDAPinnedBuddyAllocator(place.device);
ptr = buddy_allocator->Alloc(size);
} else {
auto* buddy_allocator = GetGPUBuddyAllocator(place.device);
ptr = buddy_allocator->Alloc(size);
}
void* Alloc<platform::CUDAPlace>(platform::CUDAPlace place, size_t size) {
auto* buddy_allocator = GetGPUBuddyAllocator(place.device);
auto* ptr = buddy_allocator->Alloc(size);
if (ptr == nullptr) {
int cur_dev = platform::GetCurrentDeviceId();
platform::SetDeviceId(place.device);
size_t avail, total;
platform::GpuMemoryUsage(avail, total);
platform::GpuMemoryUsage(&avail, &total);
LOG(WARNING) << "Cannot allocate " << size << " bytes in GPU "
<< place.device << ", available " << avail << " bytes";
LOG(WARNING) << "total " << total;
......@@ -142,15 +108,42 @@ void* Alloc<platform::CUDAPlace>(platform::CUDAPlace place, size_t size,
}
template <>
void Free<platform::CUDAPlace>(platform::CUDAPlace place, void* p,
bool is_pinned) {
if (is_pinned) {
GetCUDAPinnedBuddyAllocator(place.device)->Free(p);
} else {
GetGPUBuddyAllocator(place.device)->Free(p);
void Free<platform::CUDAPlace>(platform::CUDAPlace place, void* p) {
GetGPUBuddyAllocator(place.device)->Free(p);
}
BuddyAllocator* GetCUDAPinnedBuddyAllocator() {
static BuddyAllocator* ba = NULL;
if (ba == NULL) {
ba = new BuddyAllocator(new detail::CUDAPinnedAllocator,
platform::CUDAPinnedMinChunkSize(),
platform::CUDAPinnedMaxChunkSize());
}
return ba;
}
template <>
size_t Used<platform::CUDAPinnedPlace>(platform::CUDAPinnedPlace place) {
return GetCUDAPinnedBuddyAllocator()->Used();
}
template <>
void* Alloc<platform::CUDAPinnedPlace>(platform::CUDAPinnedPlace place,
size_t size) {
auto* buddy_allocator = GetCUDAPinnedBuddyAllocator();
void* ptr = buddy_allocator->Alloc(size);
if (ptr == nullptr) {
LOG(WARNING) << "cudaMallocHost Cannot allocate " << size
<< " bytes in CUDAPinnedPlace";
}
return ptr;
}
template <>
void Free<platform::CUDAPinnedPlace>(platform::CUDAPinnedPlace place, void* p) {
GetCUDAPinnedBuddyAllocator()->Free(p);
}
#endif
size_t Usage::operator()(const platform::CPUPlace& cpu) const {
......@@ -165,6 +158,14 @@ size_t Usage::operator()(const platform::CUDAPlace& gpu) const {
#endif
}
size_t Usage::operator()(const platform::CUDAPinnedPlace& cuda_pinned) const {
#ifdef PADDLE_WITH_CUDA
return Used(cuda_pinned);
#else
PADDLE_THROW("'CUDAPinnedPlace' is not supported in CPU only device.");
#endif
}
size_t memory_usage(const platform::Place& p) {
return boost::apply_visitor(Usage(), p);
}
......
paddle/fluid/memory/malloc.h 0 → 100644
浏览文件 @ 82530e24
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "paddle/fluid/platform/place.h"
namespace paddle {
namespace memory {
/**
* \brief Allocate memory block in one place.
*
* \param[in] place Allocation place (CPU or GPU).
* \param[in] size Allocation size.
*
* \return Allocated memory block address.
*
* \note If return nullptr, it indicates memory allocation failed
* because insufficient memory in current system. When Alloc
* function is invoked, you must check the returned memory
* address is valid or not.
*/
template <typename Place>
void* Alloc(Place place, size_t size);
/**
* \brief Free memory block in one place.
*
* \param[in] place Allocation place (CPU or GPU).
* \param[in] ptr Memory block address to free.
*
*/
template <typename Place>
void Free(Place place, void* ptr);
/**
* \brief Total size of used memory in one place.
*
* \param[in] place Allocation place (CPU or GPU).
*
*/
template <typename Place>
size_t Used(Place place);
struct Usage : public boost::static_visitor<size_t> {
size_t operator()(const platform::CPUPlace& cpu) const;
size_t operator()(const platform::CUDAPlace& gpu) const;
size_t operator()(const platform::CUDAPinnedPlace& cuda_pinned) const;
};
size_t memory_usage(const platform::Place& p);
/**
* \brief Free memory block in one place.
*
* \note In some cases, custom deleter is used to
* deallocate the memory automatically for
* std::unique_ptr<T> in tensor.h.
*
*/
template <typename T, typename Place>
class PODDeleter {
static_assert(std::is_pod<T>::value, "T must be POD");
public:
explicit PODDeleter(Place place) : place_(place) {}
void operator()(T* ptr) { Free(place_, static_cast<void*>(ptr)); }
private:
Place place_;
};
/**
* \brief Free memory block in one place does not meet POD
*
* \note In some cases, custom deleter is used to
* deallocate the memory automatically for
* std::unique_ptr<T> in tensor.h.
*
*/
template <typename T, typename Place>
class PlainDeleter {
public:
explicit PlainDeleter(Place place) : place_(place) {}
void operator()(T* ptr) { Free(place_, reinterpret_cast<void*>(ptr)); }
private:
Place place_;
};
} // namespace memory
} // namespace paddle
paddle/fluid/memory/memory_test.cc → paddle/fluid/memory/malloc_test.cc
浏览文件 @ 82530e24
......@@ -12,23 +12,22 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/memory/memory.h"
#include "paddle/fluid/memory/detail/memory_block.h"
#include "paddle/fluid/memory/detail/meta_data.h"
#include "paddle/fluid/memory/malloc.h"
#include <unordered_map>
#include "gtest/gtest.h"
#include "paddle/fluid/memory/detail/memory_block.h"
#include "paddle/fluid/platform/cpu_info.h"
#include "paddle/fluid/platform/gpu_info.h"
#include "paddle/fluid/platform/place.h"
#include <gtest/gtest.h>
#include <unordered_map>
inline bool is_aligned(void const *p) {
return 0 == (reinterpret_cast<uintptr_t>(p) & 0x3);
}
size_t align(size_t size, paddle::platform::CPUPlace place) {
size += sizeof(paddle::memory::detail::Metadata);
size += sizeof(paddle::memory::detail::MemoryBlock::Desc);
size_t alignment = paddle::platform::CpuMinChunkSize();
size_t remaining = size % alignment;
return remaining == 0 ? size : size + (alignment - remaining);
......@@ -86,7 +85,7 @@ TEST(BuddyAllocator, CPUMultAlloc) {
#ifdef PADDLE_WITH_CUDA
size_t align(size_t size, paddle::platform::CUDAPlace place) {
size += sizeof(paddle::memory::detail::Metadata);
size += sizeof(paddle::memory::detail::MemoryBlock::Desc);
size_t alignment = paddle::platform::GpuMinChunkSize();
size_t remaining = size % alignment;
return remaining == 0 ? size : size + (alignment - remaining);
......@@ -141,4 +140,59 @@ TEST(BuddyAllocator, GPUMultAlloc) {
}
}
size_t align(size_t size, paddle::platform::CUDAPinnedPlace place) {
size += sizeof(paddle::memory::detail::MemoryBlock::Desc);
size_t alignment = paddle::platform::CUDAPinnedMinChunkSize();
size_t remaining = size % alignment;
return remaining == 0 ? size : size + (alignment - remaining);
}
TEST(BuddyAllocator, CUDAPinnedAllocator) {
void *p = nullptr;
EXPECT_EQ(p, nullptr);
paddle::platform::CUDAPinnedPlace cpu;
p = paddle::memory::Alloc(cpu, 4096);
EXPECT_NE(p, nullptr);
paddle::platform::Place place = cpu;
EXPECT_EQ(paddle::memory::Used(cpu), paddle::memory::memory_usage(place));
paddle::memory::Free(cpu, p);
}
TEST(BuddyAllocator, CUDAPinnedMultAllocator) {
paddle::platform::CUDAPinnedPlace cpu;
std::unordered_map<void *, size_t> ps;
size_t total_size = paddle::memory::Used(cpu);
EXPECT_EQ(total_size, 0UL);
for (auto size :
{0, 128, 256, 1024, 4096, 16384, 65536, 262144, 1048576, 4194304}) {
ps[paddle::memory::Alloc(cpu, size)] = size;
// Buddy Allocator doesn't manage too large memory chunk
if (paddle::memory::Used(cpu) == total_size) continue;
size_t aligned_size = align(size, cpu);
total_size += aligned_size;
EXPECT_EQ(total_size, paddle::memory::Used(cpu));
}
for (auto p : ps) {
EXPECT_EQ(is_aligned(p.first), true);
paddle::memory::Free(cpu, p.first);
// Buddy Allocator doesn't manage too large memory chunk
if (paddle::memory::Used(cpu) == total_size) continue;
size_t aligned_size = align(p.second, cpu);
total_size -= aligned_size;
EXPECT_EQ(total_size, paddle::memory::Used(cpu));
}
}
#endif
paddle/fluid/memory/memcpy.cc
浏览文件 @ 82530e24
......@@ -56,6 +56,45 @@ void Copy<platform::CUDAPlace, platform::CUDAPlace>(
}
}
template <>
void Copy<platform::CPUPlace, platform::CUDAPinnedPlace>(
platform::CPUPlace dst_place, void* dst,
platform::CUDAPinnedPlace src_place, const void* src, size_t num) {
std::memcpy(dst, src, num);
}
template <>
void Copy<platform::CUDAPinnedPlace, platform::CPUPlace>(
platform::CUDAPinnedPlace dst_place, void* dst,
platform::CPUPlace src_place, const void* src, size_t num) {
std::memcpy(dst, src, num);
}
template <>
void Copy<platform::CUDAPinnedPlace, platform::CUDAPinnedPlace>(
platform::CUDAPinnedPlace dst_place, void* dst,
platform::CUDAPinnedPlace src_place, const void* src, size_t num) {
std::memcpy(dst, src, num);
}
template <>
void Copy<platform::CUDAPinnedPlace, platform::CUDAPlace>(
platform::CUDAPinnedPlace dst_place, void* dst,
platform::CUDAPlace src_place, const void* src, size_t num,
cudaStream_t stream) {
platform::SetDeviceId(src_place.device);
platform::GpuMemcpyAsync(dst, src, num, cudaMemcpyDeviceToHost, stream);
}
template <>
void Copy<platform::CUDAPlace, platform::CUDAPinnedPlace>(
platform::CUDAPlace dst_place, void* dst,
platform::CUDAPinnedPlace src_place, const void* src, size_t num,
cudaStream_t stream) {
platform::SetDeviceId(dst_place.device);
platform::GpuMemcpyAsync(dst, src, num, cudaMemcpyHostToDevice, stream);
}
#endif
} // namespace memory
......
paddle/fluid/memory/memory.h
浏览文件 @ 82530e24
......@@ -14,92 +14,5 @@ limitations under the License. */
#pragma once
#include "paddle/fluid/platform/place.h"
namespace paddle {
namespace memory {
/**
* \brief Allocate memory block in one place.
*
* \param[in] place Allocation place (CPU or GPU).
* \param[in] size Allocation size.
*
* \return Allocated memory block address.
*
* \note If return nullptr, it indicates memory allocation failed
* because insufficient memory in current system. When Alloc
* function is invoked, you must check the returned memory
* address is valid or not.
*/
template <typename Place>
void* Alloc(Place place, size_t size, bool is_pinned = false);
/**
* \brief Free memory block in one place.
*
* \param[in] place Allocation place (CPU or GPU).
* \param[in] ptr Memory block address to free.
*
*/
template <typename Place>
void Free(Place place, void* ptr, bool is_pinned = false);
/**
* \brief Total size of used memory in one place.
*
* \param[in] place Allocation place (CPU or GPU).
*
*/
template <typename Place>
size_t Used(Place place);
struct Usage : public boost::static_visitor<size_t> {
size_t operator()(const platform::CPUPlace& cpu) const;
size_t operator()(const platform::CUDAPlace& gpu) const;
};
size_t memory_usage(const platform::Place& p);
/**
* \brief Free memory block in one place.
*
* \note In some cases, custom deleter is used to
* deallocate the memory automatically for
* std::unique_ptr<T> in tensor.h.
*
*/
template <typename T, typename Place>
class PODDeleter {
static_assert(std::is_pod<T>::value, "T must be POD");
public:
explicit PODDeleter(Place place, bool is_pinned = false)
: place_(place), is_pinned_(is_pinned) {}
void operator()(T* ptr) { Free(place_, static_cast<void*>(ptr), is_pinned_); }
private:
Place place_;
bool is_pinned_;
};
/**
* \brief Free memory block in one place does not meet POD
*
* \note In some cases, custom deleter is used to
* deallocate the memory automatically for
* std::unique_ptr<T> in tensor.h.
*
*/
template <typename T, typename Place>
class PlainDeleter {
public:
explicit PlainDeleter(Place place) : place_(place) {}
void operator()(T* ptr) { Free(place_, reinterpret_cast<void*>(ptr)); }
private:
Place place_;
};
} // namespace memory
} // namespace paddle
#include "paddle/fluid/memory/malloc.h"
#include "paddle/fluid/memory/memcpy.h"
paddle/fluid/memory/pinned_memory_test.cu 0 → 100644
浏览文件 @ 82530e24
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <gtest/gtest.h>
#include <unordered_map>
#include "paddle/fluid/memory/detail/memory_block.h"
#include "paddle/fluid/memory/memcpy.h"
#include "paddle/fluid/memory/memory.h"
#include "paddle/fluid/platform/cpu_info.h"
#include "paddle/fluid/platform/gpu_info.h"
#include "paddle/fluid/platform/place.h"
// This unit test is an example comparing the performance between using pinned
// memory and not. In general, using pinned memory will be faster.
template <typename T>
__global__ void Kernel(T* output, int dim) {
int tid = blockIdx.x * blockDim.x + threadIdx.x;
if (tid < dim) {
output[tid] = output[tid] * output[tid] / 100;
}
}
template <typename Place>
float test_pinned_memory() {
Place cpu_place;
paddle::platform::CUDAPlace cuda_place;
const int data_size = 4096;
const int iteration = 10;
// create event start and end
cudaEvent_t start_e, stop_e, copying_e;
float elapsedTime = 0;
cudaEventCreate(&start_e);
cudaEventCreate(&stop_e);
cudaEventCreate(&copying_e);
// create computation stream, data copying stream
cudaStream_t computation_stream, copying_stream;
cudaStreamCreate(&computation_stream);
cudaStreamCreate(&copying_stream);
// create record event, pinned memory, gpu memory
std::vector<cudaEvent_t> record_event(iteration);
std::vector<float*> input_pinned_mem(iteration);
std::vector<float*> gpu_mem(iteration);
std::vector<float*> output_pinned_mem(iteration);
// initial data
for (int j = 0; j < iteration; ++j) {
cudaEventCreateWithFlags(&record_event[j], cudaEventDisableTiming);
cudaEventCreate(&(record_event[j]));
input_pinned_mem[j] = static_cast<float*>(
paddle::memory::Alloc(cpu_place, data_size * sizeof(float)));
output_pinned_mem[j] = static_cast<float*>(
paddle::memory::Alloc(cpu_place, data_size * sizeof(float)));
gpu_mem[j] = static_cast<float*>(
paddle::memory::Alloc(cuda_place, data_size * sizeof(float)));
for (int k = 0; k < data_size; ++k) {
input_pinned_mem[j][k] = k;
}
}
cudaEventRecord(start_e, computation_stream);
// computation
for (int m = 0; m < 30; ++m) {
for (int i = 0; i < iteration; ++i) {
// cpu -> GPU on computation stream.
// note: this operation is async for pinned memory.
paddle::memory::Copy(cuda_place, gpu_mem[i], cpu_place,
input_pinned_mem[i], data_size * sizeof(float),
computation_stream);
// call kernel on computation stream.
Kernel<<<4, 1024, 0, computation_stream>>>(gpu_mem[i], data_size);
// record event_computation on computation stream
cudaEventRecord(record_event[i], computation_stream);
// wait event_computation on copy stream.
// note: this operation is async.
cudaStreamWaitEvent(copying_stream, record_event[i], 0);
// copy data GPU->CPU, on copy stream.
// note: this operation is async for pinned memory.
paddle::memory::Copy(cpu_place, output_pinned_mem[i], cuda_place,
gpu_mem[i], data_size * sizeof(float),
copying_stream);
}
}
cudaEventRecord(copying_e, copying_stream);
cudaStreamWaitEvent(computation_stream, copying_e, 0);
cudaEventRecord(stop_e, computation_stream);
cudaEventSynchronize(start_e);
cudaEventSynchronize(stop_e);
cudaEventElapsedTime(&elapsedTime, start_e, stop_e);
// std::cout << cpu_place << " "
// << "time consume:" << elapsedTime / 30 << std::endl;
for (int l = 0; l < iteration; ++l) {
for (int k = 0; k < data_size; ++k) {
float temp = input_pinned_mem[l][k];
temp = temp * temp / 100;
EXPECT_FLOAT_EQ(temp, output_pinned_mem[l][k]);
}
}
// destroy resource
cudaEventDestroy(copying_e);
cudaEventDestroy(start_e);
cudaEventDestroy(stop_e);
for (int j = 0; j < 10; ++j) {
cudaEventDestroy((record_event[j]));
paddle::memory::Free(cpu_place, input_pinned_mem[j]);
paddle::memory::Free(cpu_place, output_pinned_mem[j]);
paddle::memory::Free(cuda_place, gpu_mem[j]);
}
return elapsedTime / 30;
}
TEST(CPUANDCUDAPinned, CPUAllocatorAndCUDAPinnedAllocator) {
// Generally speaking, operation on pinned_memory is faster than that on
// unpinned-memory, but if this unit test fails frequently, please close this
// test for the time being.
float time1 = test_pinned_memory<paddle::platform::CPUPlace>();
float time2 = test_pinned_memory<paddle::platform::CUDAPinnedPlace>();
EXPECT_GT(time1, time2);
}
paddle/fluid/operators/.clang-format 已删除 100644 → 0
浏览文件 @ 07b31b80
---
Language: Cpp
BasedOnStyle: Google
Standard: Cpp11
...
paddle/fluid/operators/CMakeLists.txt
浏览文件 @ 82530e24
......@@ -3,8 +3,8 @@ string(REPLACE "_mkldnn" "" GENERAL_OPS "${GENERAL_OPS}")
string(REPLACE ".cc" "" GENERAL_OPS "${GENERAL_OPS}")
list(REMOVE_DUPLICATES GENERAL_OPS)
set(DEPS_OPS "")
set(pybind_file ${PADDLE_SOURCE_DIR}/paddle/fluid/pybind/pybind.h)
file(WRITE ${pybind_file} "// Generated by the paddle/operator/CMakeLists.txt. DO NOT EDIT!\n\n")
set(pybind_file ${PADDLE_BINARY_DIR}/paddle/fluid/pybind/pybind.h)
file(WRITE ${pybind_file} "// Generated by the paddle/fluid/operator/CMakeLists.txt. DO NOT EDIT!\n\n")
function(op_library TARGET)
# op_library is a function to create op library. The interface is same as
# cc_library. But it handle split GPU/CPU code and link some common library
......@@ -193,6 +193,7 @@ if(WITH_DISTRIBUTE)
set_source_files_properties(send_vars_op.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
op_library(send_barrier_op DEPS ${DISTRIBUTE_DEPS})
set_source_files_properties(send_barrier_op.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
set_source_files_properties(send_recv_op_test.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
cc_test(test_send_recv SRCS send_recv_op_test.cc DEPS prefetch_op send_op listen_and_serv_op sum_op executor)
else()
set(DEPS_OPS ${DEPS_OPS} send_op prefetch_op recv_op listen_and_serv_op send_vars_op send_barrier_op)
......@@ -262,7 +263,7 @@ cc_test(net_op_test SRCS net_op_test.cc DEPS net_op)
cc_test(scatter_test SRCS scatter_test.cc DEPS tensor)
cc_test(beam_search_decode_op_test SRCS beam_search_decode_op_test.cc DEPS lod_tensor)
cc_test(beam_search_op_test SRCS beam_search_op_test.cc DEPS lod_tensor beam_search_op)
cc_test(strided_memcpy_test SRCS strided_memcpy_test.cc DEPS tensor paddle_memory)
cc_test(strided_memcpy_test SRCS strided_memcpy_test.cc DEPS tensor memory)
cc_test(save_load_op_test SRCS save_load_op_test.cc DEPS save_op load_op)
cc_test(save_load_combine_op_test SRCS save_load_combine_op_test.cc DEPS save_combine_op load_combine_op)
nv_test(nccl_op_test SRCS nccl_op_test.cu.cc DEPS nccl_op gpu_info device_context)
......
paddle/fluid/operators/conv_cudnn_op.cu.cc
浏览文件 @ 82530e24
......@@ -128,10 +128,32 @@ class CUDNNConvOpKernel : public framework::OpKernel<T> {
handle, cudnn_input_desc, cudnn_filter_desc, cudnn_conv_desc,
cudnn_output_desc, CUDNN_CONVOLUTION_FWD_SPECIFY_WORKSPACE_LIMIT,
workspace_size_limit, &algo));
#if CUDA_VERSION >= 9000 && CUDNN_VERSION_MIN(7, 0, 1)
// Tensor core is supported since the volta GPU and
// is only enabled when input and filter data are float16
if (dev_ctx.GetComputeCapability() >= 70 &&
std::type_index(typeid(T)) ==
std::type_index(typeid(platform::float16))) {
PADDLE_ENFORCE(platform::dynload::cudnnSetConvolutionMathType(
cudnn_conv_desc, CUDNN_TENSOR_OP_MATH));
// Currently tensor core is only enabled using this algo
algo = CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_PRECOMP_GEMM;
} else {
PADDLE_ENFORCE(platform::dynload::cudnnSetConvolutionMathType(
cudnn_conv_desc, CUDNN_DEFAULT_MATH));
}
#endif
// get workspace size able to allocate
PADDLE_ENFORCE(platform::dynload::cudnnGetConvolutionForwardWorkspaceSize(
handle, cudnn_input_desc, cudnn_filter_desc, cudnn_conv_desc,
cudnn_output_desc, algo, &workspace_size_in_bytes));
// It is possible for float16 on Volta GPU to allocate more memory than
// the limit because the algo is overrided to use tensor core.
PADDLE_ENFORCE_LE(workspace_size_in_bytes, workspace_size_limit,
"workspace_size to be allocated exceeds the limit");
// Allocate on GPU memory
platform::CUDAPlace gpu = boost::get<platform::CUDAPlace>(ctx.GetPlace());
cudnn_workspace = paddle::memory::Alloc(gpu, workspace_size_in_bytes);
......
paddle/fluid/operators/detail/CMakeLists.txt
浏览文件 @ 82530e24
......@@ -5,5 +5,5 @@ if(WITH_DISTRIBUTE)
set_source_files_properties(serde_test.cc grpc_server_test.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
cc_test(serde_test SRCS serde_test.cc variable_response.cc DEPS grpc++_unsecure grpc_unsecure gpr
cares zlib protobuf sendrecvop_grpc)
cc_test(grpc_server_test SRCS grpc_server_test.cc DEPS sendrecvop_grpc grpc++_unsecure grpc_unsecure gpr cares zlib protobuf)
cc_test(grpc_server_test SRCS grpc_server_test.cc DEPS sendrecvop_grpc grpc++_unsecure grpc_unsecure gpr cares zlib protobuf executor proto_desc lookup_table_op)
endif()
paddle/fluid/operators/detail/grpc_client.cc
浏览文件 @ 82530e24
......@@ -138,7 +138,7 @@ bool RPCClient::AsyncPrefetchVariable(const std::string& ep,
auto* var = p_scope->FindVar(in_var_name_val);
::grpc::ByteBuffer req;
SerializeToByteBuffer(in_var_name_val, var, *p_ctx, &req);
SerializeToByteBuffer(in_var_name_val, var, *p_ctx, &req, out_var_name_val);
// var handle
VarHandle var_h;
......
paddle/fluid/operators/detail/grpc_server.cc
浏览文件 @ 82530e24
......@@ -138,39 +138,48 @@ class RequestPrefetch final : public RequestBase {
framework::Scope* scope,
const platform::DeviceContext* dev_ctx,
framework::Executor* executor,
framework::ProgramDesc* program, int blkid)
framework::ProgramDesc* program,
framework::ExecutorPrepareContext* prefetch_ctx)
: RequestBase(service, cq, dev_ctx),
responder_(&ctx_),
scope_(scope),
executor_(executor),
program_(program),
blkid_(blkid) {
prefetch_ctx_(prefetch_ctx) {
request_.reset(new VariableResponse(scope, dev_ctx_));
int method_id = static_cast<int>(detail::GrpcMethod::kPrefetchVariable);
service_->RequestAsyncUnary(method_id, &ctx_, &request_, &responder_, cq_,
cq_, this);
service_->RequestAsyncUnary(method_id, &ctx_, request_.get(), &responder_,
cq_, cq_, this);
}
virtual ~RequestPrefetch() {}
virtual std::string GetReqName() { return request_.varname(); }
virtual std::string GetReqName() { return request_->Varname(); }
virtual void Process() {
// prefetch process...
::grpc::ByteBuffer reply;
// TODO(Yancey1989): execute the Block which containers prefetch ops
VLOG(3) << "RequestPrefetch Process in";
std::string var_name = request_->OutVarname();
auto var_desc = program_->Block(0).FindVar(var_name);
framework::Scope* local_scope = &scope_->NewScope();
auto* var = local_scope->FindVar(var_name);
InitializeVariable(var, var_desc->GetType());
executor_->RunPreparedContext(prefetch_ctx_, scope_, false, false);
SerializeToByteBuffer(var_name, var, *dev_ctx_, &reply);
responder_.Finish(reply, ::grpc::Status::OK, this);
status_ = FINISH;
}
protected:
sendrecv::VariableMessage request_;
std::shared_ptr<VariableResponse> request_;
ServerAsyncResponseWriter<::grpc::ByteBuffer> responder_;
framework::Scope* scope_;
framework::Executor* executor_;
framework::ProgramDesc* program_;
framework::ExecutorPrepareContext* prefetch_ctx_;
int blkid_;
};
......@@ -186,7 +195,8 @@ void AsyncGRPCServer::WaitClientGet(int count) {
void AsyncGRPCServer::RunSyncUpdate() {
::grpc::ServerBuilder builder;
builder.AddListeningPort(address_, ::grpc::InsecureServerCredentials());
builder.AddListeningPort(address_, ::grpc::InsecureServerCredentials(),
&selected_port_);
builder.SetMaxSendMessageSize(std::numeric_limits<int>::max());
builder.SetMaxReceiveMessageSize(std::numeric_limits<int>::max());
builder.RegisterService(&service_);
......@@ -196,7 +206,8 @@ void AsyncGRPCServer::RunSyncUpdate() {
cq_prefetch_ = builder.AddCompletionQueue();
server_ = builder.BuildAndStart();
LOG(INFO) << "Server listening on " << address_ << std::endl;
LOG(INFO) << "Server listening on " << address_
<< " selected port: " << selected_port_;
std::function<void()> send_register =
std::bind(&AsyncGRPCServer::TryToRegisterNewSendOne, this);
......@@ -266,7 +277,7 @@ void AsyncGRPCServer::TryToRegisterNewPrefetchOne() {
}
RequestPrefetch* prefetch =
new RequestPrefetch(&service_, cq_prefetch_.get(), scope_, dev_ctx_,
executor_, program_, prefetch_blk_id_);
executor_, program_, prefetch_ctx_);
VLOG(4) << "Create RequestPrefetch status:" << prefetch->Status();
}
......
paddle/fluid/operators/detail/grpc_server.h
浏览文件 @ 82530e24
......@@ -63,6 +63,12 @@ class AsyncGRPCServer final {
void SetExecutor(framework::Executor *executor) { executor_ = executor; }
void SetPrefetchPreparedCtx(framework::ExecutorPrepareContext *prepared) {
prefetch_ctx_ = prepared;
}
int GetSelectedPort() { return selected_port_; }
const ReceivedMessage Get() { return this->var_recv_queue_.Pop(); }
void Push(const std::string &msg_name) {
......@@ -109,8 +115,10 @@ class AsyncGRPCServer final {
std::unique_ptr<std::thread> t_prefetch_;
int prefetch_blk_id_;
framework::ExecutorPrepareContext *prefetch_ctx_;
framework::ProgramDesc *program_;
framework::Executor *executor_;
int selected_port_;
};
}; // namespace detail
......
paddle/fluid/operators/detail/grpc_server_test.cc
浏览文件 @ 82530e24
......@@ -20,43 +20,121 @@ limitations under the License. */
#include "paddle/fluid/operators/detail/grpc_client.h"
#include "paddle/fluid/operators/detail/grpc_server.h"
#include "paddle/fluid/framework/block_desc.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/operator.h"
namespace framework = paddle::framework;
namespace platform = paddle::platform;
namespace detail = paddle::operators::detail;
USE_OP(lookup_table);
std::unique_ptr<detail::AsyncGRPCServer> rpc_service_;
framework::BlockDesc* AppendPrefetchBlcok(framework::ProgramDesc* program) {
auto root_block = program->MutableBlock(0);
auto* block = program->AppendBlock(*root_block);
framework::VariableNameMap input({{"W", {"w"}}, {"Ids", {"ids"}}});
framework::VariableNameMap output({{"Output", {"out"}}});
auto op = block->AppendOp();
op->SetType("lookup_table");
op->SetInput("W", {"w"});
op->SetInput("Ids", {"ids"});
op->SetOutput("Out", {"out"});
auto& out = *root_block->Var("out");
out.SetType(framework::proto::VarType::SELECTED_ROWS);
out.SetShape({10, 10});
return block;
}
void CreateVarsOnScope(framework::Scope* scope, platform::CPUPlace* place) {
auto w_var = scope->Var("w");
w_var->GetMutable<framework::SelectedRows>();
auto out_var = scope->Var("out");
out_var->GetMutable<framework::SelectedRows>();
auto ids_var = scope->Var("ids");
ids_var->GetMutable<framework::SelectedRows>();
}
void InitTensorsOnClient(framework::Scope* scope, platform::CPUPlace* place,
int64_t rows_numel) {
CreateVarsOnScope(scope, place);
auto ids_var = scope->Var("ids")->GetMutable<framework::SelectedRows>();
auto rows = ids_var->mutable_rows();
for (int64_t i = 0; i < rows_numel; ++i) rows->push_back(i * 2);
ids_var->mutable_value()->Resize({rows_numel, 1});
ids_var->mutable_value()->mutable_data<float>(*place);
}
void InitTensorsOnServer(framework::Scope* scope, platform::CPUPlace* place,
int64_t rows_numel) {
CreateVarsOnScope(scope, place);
auto w = scope->Var("w")->GetMutable<framework::SelectedRows>();
auto rows = w->mutable_rows();
for (int64_t i = 0; i < rows_numel; ++i) rows->push_back(i);
auto w_value = w->mutable_value();
w_value->Resize({rows_numel, 10});
auto ptr = w_value->mutable_data<float>(*place);
for (int64_t i = 0; i < w_value->numel(); ++i) {
ptr[i] = static_cast<float>(i / 10);
}
}
void StartServer(const std::string& endpoint) {
rpc_service_.reset(new detail::AsyncGRPCServer(endpoint));
framework::ProgramDesc program;
framework::Scope scope;
platform::CPUPlace place;
framework::Executor exe(place);
platform::CPUDeviceContext ctx(place);
auto* block = AppendPrefetchBlcok(&program);
auto prepared = exe.Prepare(program, block->ID());
InitTensorsOnServer(&scope, &place, 10);
rpc_service_->SetProgram(&program);
rpc_service_->SetPrefetchPreparedCtx(prepared.get());
rpc_service_->SetDevCtx(&ctx);
rpc_service_->SetScope(&scope);
rpc_service_->SetExecutor(&exe);
rpc_service_->RunSyncUpdate();
}
TEST(PREFETCH, CPU) {
// start up a server instance backend
// TODO(Yancey1989): Need to start a server with optimize blocks and
// prefetch blocks.
std::thread server_thread(StartServer, "127.0.0.1:8889");
sleep(2);
framework::Scope scope;
platform::CPUPlace place;
platform::CPUDeviceContext ctx(place);
// create var on local scope
std::string in_var_name("in");
int64_t rows_numel = 5;
InitTensorsOnClient(&scope, &place, rows_numel);
std::string in_var_name("ids");
std::string out_var_name("out");
auto* in_var = scope.Var(in_var_name);
auto* in_tensor = in_var->GetMutable<framework::LoDTensor>();
in_tensor->Resize({10, 10});
VLOG(3) << "before mutable_data";
in_tensor->mutable_data<int>(place);
scope.Var(out_var_name);
VLOG(3) << "before fetch";
detail::RPCClient client;
client.AsyncPrefetchVariable("127.0.0.1:8889", ctx, scope, in_var_name,
out_var_name);
client.Wait();
auto var = scope.Var(out_var_name);
auto value = var->GetMutable<framework::SelectedRows>()->value();
auto ptr = value.mutable_data<float>(place);
rpc_service_->ShutDown();
server_thread.join();
rpc_service_.reset(nullptr);
for (int64_t i = 0; i < rows_numel; ++i) {
EXPECT_EQ(ptr[0 + i * value.dims()[1]], static_cast<float>(i * 2));
}
}
paddle/fluid/operators/detail/send_recv.proto
浏览文件 @ 82530e24
......@@ -21,7 +21,7 @@ service SendRecvService {
rpc SendVariable(VariableMessage) returns (VoidMessage) {}
// Argument VariableMessage for GetVariable should only contain varname.
rpc GetVariable(VariableMessage) returns (VariableMessage) {}
// Prefetch variable by Ids
// pre-fetch variable by given variable name and Ids
rpc PrefetchVariable(VariableMessage) returns (VariableMessage) {}
}
......@@ -67,6 +67,8 @@ message VariableMessage {
bytes serialized = 8;
// selected_rows data
bytes rows = 9;
// Look up table block execution output variable name.
string out_varname = 10;
}
message VoidMessage {}
paddle/fluid/operators/detail/sendrecvop_utils.cc
浏览文件 @ 82530e24
......@@ -30,7 +30,8 @@ namespace detail {
void SerializeToByteBuffer(const std::string& name, framework::Variable* var,
const platform::DeviceContext& ctx,
::grpc::ByteBuffer* msg) {
::grpc::ByteBuffer* msg,
const std::string& out_name) {
using VarMsg = sendrecv::VariableMessage;
sendrecv::VariableMessage request;
std::string header;
......@@ -52,6 +53,9 @@ void SerializeToByteBuffer(const std::string& name, framework::Variable* var,
e.WriteUint64(VarMsg::kTypeFieldNumber, 1);
}
if (!out_name.empty()) {
e.WriteString(VarMsg::kOutVarnameFieldNumber, out_name);
}
switch (framework::ToVarType(var->Type())) {
case framework::proto::VarType_Type_LOD_TENSOR: {
auto tensor = var->Get<framework::LoDTensor>();
......
paddle/fluid/operators/detail/sendrecvop_utils.h
浏览文件 @ 82530e24
......@@ -46,7 +46,8 @@ typedef void (*DestroyCallback)(void*);
void SerializeToByteBuffer(const std::string& name, framework::Variable* var,
const platform::DeviceContext& ctx,
::grpc::ByteBuffer* msg);
::grpc::ByteBuffer* msg,
const std::string& out_varname = std::string());
void DeserializeFromByteBuffer(const ::grpc::ByteBuffer& msg,
const platform::DeviceContext& ctx,
......
paddle/fluid/operators/detail/variable_response.cc
浏览文件 @ 82530e24
......@@ -416,6 +416,20 @@ int VariableResponse::Parse(Source* source) {
}
break;
}
case sendrecv::VariableMessage::kOutVarnameFieldNumber: {
uint32_t length;
if ((wt != WIRETYPE_LENGTH_DELIMITED) || !input.ReadVarint32(&length)) {
return tag;
}
std::string temp;
if (!input.ReadString(&temp, length)) {
return tag;
}
meta_.set_out_varname(temp);
break;
}
default: {
// Unknown tag, return unknown error.
......
paddle/fluid/operators/detail/variable_response.h
浏览文件 @ 82530e24
......@@ -55,6 +55,7 @@ class VariableResponse {
int Parse(const ::grpc::ByteBuffer& byte_buffer);
inline std::string Varname() { return meta_.varname(); }
inline std::string OutVarname() { return meta_.out_varname(); }
// should call parse first.
framework::Variable* GetVar() { return scope_->FindVar(meta_.varname()); }
......
paddle/fluid/operators/fc_mkldnn_op.cc
浏览文件 @ 82530e24
......@@ -27,8 +27,8 @@ template <typename T>
class MKLDNNMD {
public:
explicit MKLDNNMD(const T* in, const T* w, bool bias)
: in{paddle::framework::vectorize2int(in->dims())},
w{paddle::framework::vectorize2int(w->dims())} {
: in(paddle::framework::vectorize2int(in->dims())),
w(paddle::framework::vectorize2int(w->dims())) {
with_bias_ = bias;
}
......@@ -78,7 +78,7 @@ class MKLDNNMD {
class MKLDNNMemory {
public:
MKLDNNMemory(MKLDNNMD<Tensor>* t, const mkldnn::engine& e)
: md_{t}, engine_{e} {}
: md_(t), engine_(e) {}
virtual ~MKLDNNMemory() = default;
template <typename Output>
......
paddle/fluid/operators/listen_and_serv_op.cc
浏览文件 @ 82530e24
......@@ -12,20 +12,14 @@ 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 <stdint.h>
#include <ostream>
#include <thread>
#include "paddle/fluid/framework/executor.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/threadpool.h"
#include "paddle/fluid/operators/detail/grpc_server.h"
#include "paddle/fluid/operators/listen_and_serv_op.h"
namespace paddle {
namespace operators {
constexpr char kOptimizeBlock[] = "OptimizeBlock";
void RunServer(std::shared_ptr<detail::AsyncGRPCServer> service) {
service->RunSyncUpdate();
VLOG(4) << "RunServer thread end";
......@@ -66,143 +60,138 @@ static void ParallelExecuteBlocks(
for (size_t i = 0; i < fs.size(); ++i) fs[i].wait();
}
class ListenAndServOp : public framework::OperatorBase {
public:
ListenAndServOp(const std::string &type,
const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorBase(type, inputs, outputs, attrs) {
if (!rpc_service_) {
std::string endpoint = Attr<std::string>("endpoint");
rpc_service_.reset(new detail::AsyncGRPCServer(endpoint));
server_thread_.reset(new std::thread(RunServer, rpc_service_));
}
}
ListenAndServOp::ListenAndServOp(const std::string &type,
const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorBase(type, inputs, outputs, attrs) {}
void Stop() override {
rpc_service_->Push(LISTEN_TERMINATE_MESSAGE);
server_thread_->join();
int ListenAndServOp::GetSelectedPort() {
return rpc_service_->GetSelectedPort();
}
void ListenAndServOp::Stop() {
rpc_service_->Push(LISTEN_TERMINATE_MESSAGE);
server_thread_->join();
}
void ListenAndServOp::RunImpl(const framework::Scope &scope,
const platform::Place &dev_place) const {
platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
auto &dev_ctx = *pool.Get(dev_place);
framework::Scope &recv_scope = scope.NewScope();
if (!rpc_service_) {
std::string endpoint = Attr<std::string>("endpoint");
rpc_service_.reset(new detail::AsyncGRPCServer(endpoint));
}
void RunImpl(const framework::Scope &scope,
const platform::Place &dev_place) const override {
platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
auto &dev_ctx = *pool.Get(dev_place);
framework::Scope &recv_scope = scope.NewScope();
// FIXME(Yancey1989): initialize rpc server with lazy mode.
rpc_service_->SetScope(&recv_scope);
rpc_service_->SetDevCtx(&dev_ctx);
auto ins = Inputs("X");
auto fan_in = Attr<int>("Fanin");
auto *block = Attr<framework::BlockDesc *>(kOptimizeBlock);
auto *program = block->Program();
size_t num_blocks = program->Size();
PADDLE_ENFORCE_GE(num_blocks, 2,
"server program should have at least 2 blocks");
framework::Executor executor(dev_place);
std::vector<int> block_list;
for (size_t blkid = 1; blkid < num_blocks; ++blkid)
block_list.push_back(blkid);
auto prepared = executor.Prepare(*program, block_list);
prepared.insert(
prepared.begin(),
std::shared_ptr<framework::ExecutorPrepareContext>(nullptr));
// TODO(qiao) set proper fields for table lookup and update
rpc_service_->SetExecutor(&executor);
rpc_service_->SetPrefetchBlkdId(0);
rpc_service_->SetProgram(program);
// TODO(typhoonzero): change this to a while_op for every cluster-batch.
bool exit_flag = false;
// Record received sparse variables, so that
// we could reset those after execute optimize program
std::vector<framework::Variable *> sparse_vars;
while (!exit_flag) {
// Get from multiple trainers, we don't care about the order in which
// the gradients arrives, just add suffix 0~n and merge the gradient.
rpc_service_->SetCond(0);
size_t recv_var_cnt = 0;
int batch_barrier = 0;
while (batch_barrier != fan_in) {
const detail::ReceivedMessage v = rpc_service_->Get();
auto recv_var_name = v.first;
if (recv_var_name == LISTEN_TERMINATE_MESSAGE) {
LOG(INFO) << "received terminate message and exit";
exit_flag = true;
break;
} else if (recv_var_name == BATCH_BARRIER_MESSAGE) {
VLOG(3) << "recv batch barrier message";
batch_barrier++;
continue;
} else {
VLOG(3) << "received grad: " << recv_var_name;
recv_var_cnt++;
auto var = v.second->GetVar();
if (var == nullptr) {
LOG(ERROR) << "Can not find server side var: " << recv_var_name;
PADDLE_THROW("Can not find server side var");
}
if (var->IsType<framework::SelectedRows>()) {
sparse_vars.push_back(var);
}
}
}
if (exit_flag) {
rpc_service_->SetCond(1);
rpc_service_->ShutDown();
auto ins = Inputs("X");
auto fan_in = Attr<int>("Fanin");
auto *block = Attr<framework::BlockDesc *>(kOptimizeBlock);
auto *program = block->Program();
size_t num_blocks = program->Size();
PADDLE_ENFORCE_GE(num_blocks, 2,
"server program should have at least 2 blocks");
framework::Executor executor(dev_place);
std::vector<int> block_list;
for (size_t blkid = 1; blkid < num_blocks; ++blkid) {
block_list.push_back(blkid);
}
auto prepared = executor.Prepare(*program, block_list);
// Insert placeholder for block0 which holds current op itself.
prepared.insert(prepared.begin(),
std::shared_ptr<framework::ExecutorPrepareContext>(nullptr));
rpc_service_->SetScope(&recv_scope);
rpc_service_->SetDevCtx(&dev_ctx);
// TODO(qiao) set proper fields for table lookup and update
rpc_service_->SetExecutor(&executor);
rpc_service_->SetPrefetchBlkdId(0);
rpc_service_->SetProgram(program);
// start the server listening after all member initialized.
server_thread_.reset(new std::thread(RunServer, rpc_service_));
// FIXME(typhoonzero): do we need to wait until the server port is ready?
sleep(5);
// TODO(typhoonzero): change this to a while_op for every cluster-batch.
bool exit_flag = false;
// Record received sparse variables, so that
// we could reset those after execute optimize program
std::vector<framework::Variable *> sparse_vars;
while (!exit_flag) {
// Get from multiple trainers, we don't care about the order in which
// the gradients arrives, just add suffix 0~n and merge the gradient.
rpc_service_->SetCond(0);
size_t recv_var_cnt = 0;
int batch_barrier = 0;
while (batch_barrier != fan_in) {
const detail::ReceivedMessage v = rpc_service_->Get();
auto recv_var_name = v.first;
if (recv_var_name == LISTEN_TERMINATE_MESSAGE) {
LOG(INFO) << "received terminate message and exit";
exit_flag = true;
break;
}
// NOTE: if is_gpu_place, CUDA kernels are laugched by multiple threads
// and this will still work.
// The optimize blocks which have the same parent ID would run parallel
// TODO(Yancey1989): need to use ParallelExecutor for future
int32_t last_parent_blkid = program->Block(1).Parent();
std::vector<size_t> parallel_blkids;
parallel_blkids.push_back(1);
double ts = detail::GetTimestamp();
for (size_t blkid = 2; blkid < num_blocks; ++blkid) {
if (program->Block(blkid).Parent() != last_parent_blkid) {
for (size_t idx : parallel_blkids) VLOG(3) << idx;
ParallelExecuteBlocks(parallel_blkids, &executor, prepared, program,
&recv_scope);
parallel_blkids.clear();
last_parent_blkid = program->Block(blkid).Parent();
} else if (recv_var_name == BATCH_BARRIER_MESSAGE) {
VLOG(3) << "recv batch barrier message";
batch_barrier++;
continue;
} else {
VLOG(3) << "received grad: " << recv_var_name;
recv_var_cnt++;
auto var = v.second->GetVar();
if (var == nullptr) {
LOG(ERROR) << "Can not find server side var: " << recv_var_name;
PADDLE_THROW("Can not find server side var");
}
if (var->IsType<framework::SelectedRows>()) {
sparse_vars.push_back(var);
}
parallel_blkids.push_back(blkid);
}
ParallelExecuteBlocks(parallel_blkids, &executor, prepared, program,
&recv_scope);
VLOG(3) << "run all blocks spent " << detail::GetTimestamp() - ts
<< "(ms)";
// Reset the received sparse variables, the sum operator would not
// sum the input sparse variables which rows is empty at the next
// mini-batch.
// TODO(Yancey1989): move the reset action into an operator, we couldn't
// have any hide logic in the operator.
for (auto &var : sparse_vars) {
var->GetMutable<framework::SelectedRows>()->mutable_rows()->clear();
}
}
if (exit_flag) {
rpc_service_->SetCond(1);
// NOTE: does not consider barrier request retry in here, we may use
// global barrier id to resolve this.
rpc_service_->WaitClientGet(fan_in);
sparse_vars.clear();
} // while(true)
}
rpc_service_->ShutDown();
break;
}
protected:
std::shared_ptr<detail::AsyncGRPCServer> rpc_service_;
std::shared_ptr<std::thread> server_thread_;
};
// NOTE: if is_gpu_place, CUDA kernels are laugched by multiple threads
// and this will still work.
// The optimize blocks which have the same parent ID would run parallel
// TODO(Yancey1989): need to use ParallelExecutor for future
int32_t last_parent_blkid = program->Block(1).Parent();
std::vector<size_t> parallel_blkids;
parallel_blkids.push_back(1);
double ts = detail::GetTimestamp();
for (size_t blkid = 2; blkid < num_blocks; ++blkid) {
if (program->Block(blkid).Parent() != last_parent_blkid) {
ParallelExecuteBlocks(parallel_blkids, &executor, prepared, program,
&recv_scope);
parallel_blkids.clear();
last_parent_blkid = program->Block(blkid).Parent();
}
parallel_blkids.push_back(blkid);
}
ParallelExecuteBlocks(parallel_blkids, &executor, prepared, program,
&recv_scope);
VLOG(2) << "run all blocks spent " << detail::GetTimestamp() - ts << "(ms)";
// Reset the received sparse variables, the sum operator would not
// sum the input sparse variables which rows is empty at the next
// mini-batch.
// TODO(Yancey1989): move the reset action into an operator, we couldn't
// have any hide logic in the operator.
for (auto &var : sparse_vars) {
var->GetMutable<framework::SelectedRows>()->mutable_rows()->clear();
}
rpc_service_->SetCond(1);
// FIXME(typhoonzero): use another condition to sync wait clients get.
rpc_service_->WaitClientGet(fan_in);
sparse_vars.clear();
} // while(true)
}
class ListenAndServOpMaker : public framework::OpProtoAndCheckerMaker {
public:
......
paddle/fluid/memory/detail/meta_data.h → paddle/fluid/operators/listen_and_serv_op.h
浏览文件 @ 82530e24
......@@ -14,41 +14,40 @@ limitations under the License. */
#pragma once
#include "paddle/fluid/memory/detail/memory_block.h"
#include <stdint.h>
#include <ostream>
#include <stddef.h>
#include "paddle/fluid/framework/executor.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/threadpool.h"
#include "paddle/fluid/operators/detail/grpc_server.h"
namespace paddle {
namespace memory {
namespace detail {
namespace operators {
class Metadata {
public:
Metadata(MemoryBlock::Type t, size_t i, size_t s, size_t ts, MemoryBlock* l,
MemoryBlock* r);
Metadata();
constexpr char kOptimizeBlock[] = "OptimizeBlock";
void RunServer(std::shared_ptr<detail::AsyncGRPCServer> service);
class ListenAndServOp : public framework::OperatorBase {
public:
/*! \brief Update the guards when metadata is changed */
void update_guards();
ListenAndServOp(const std::string &type,
const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs);
/*! \brief Check consistency to previous modification */
bool check_guards() const;
int GetSelectedPort();
public:
// TODO(gangliao): compress this
// clang-format off
size_t guard_begin = 0;
MemoryBlock::Type type = MemoryBlock::INVALID_CHUNK;
size_t index = 0;
size_t size = 0;
size_t total_size = 0;
MemoryBlock* left_buddy = nullptr;
MemoryBlock* right_buddy = nullptr;
size_t guard_end = 0;
// clang-format on
void Stop() override;
void RunImpl(const framework::Scope &scope,
const platform::Place &dev_place) const override;
protected:
mutable std::shared_ptr<detail::AsyncGRPCServer> rpc_service_;
mutable std::shared_ptr<std::thread> server_thread_;
};
} // namespace detail
} // namespace memory
} // namespace operators
} // namespace paddle
paddle/fluid/operators/lookup_table_op.cc
浏览文件 @ 82530e24
......@@ -18,22 +18,6 @@ limitations under the License. */
namespace paddle {
namespace operators {
static inline framework::OpKernelType ExpectedKernelType(
const framework::ExecutionContext& ctx) {
auto* table_var = ctx.InputVar("W");
if (table_var->IsType<LoDTensor>()) {
return framework::OpKernelType(
framework::ToDataType(table_var->Get<LoDTensor>().type()),
ctx.device_context());
} else if (table_var->IsType<SelectedRows>()) {
return framework::OpKernelType(
framework::ToDataType(table_var->Get<SelectedRows>().value().type()),
ctx.device_context());
} else {
PADDLE_THROW("W should be LoDTensor or SelectedRows");
}
}
class LookupTableOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
......@@ -67,7 +51,8 @@ class LookupTableOp : public framework::OperatorWithKernel {
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
return ExpectedKernelType(ctx);
auto data_type = framework::GetDataTypeOfVar(ctx.InputVar("W"));
return framework::OpKernelType(data_type, ctx.device_context());
}
};
......@@ -138,7 +123,8 @@ class LookupTableOpGrad : public framework::OperatorWithKernel {
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
return ExpectedKernelType(ctx);
auto data_type = framework::GetDataTypeOfVar(ctx.InputVar("W"));
return framework::OpKernelType(data_type, ctx.device_context());
}
};
......
paddle/fluid/operators/lookup_table_op.h
浏览文件 @ 82530e24
......@@ -30,13 +30,7 @@ using LoDTensor = framework::LoDTensor;
using SelectedRows = framework::SelectedRows;
using DDim = framework::DDim;
static constexpr int64_t kNoPadding = -1;
inline size_t getIndex(const std::vector<int64_t> &rows, int64_t value) {
auto it = std::find(rows.begin(), rows.end(), value);
PADDLE_ENFORCE(it != rows.end(), "id should be in rows");
return static_cast<size_t>(std::distance(rows.begin(), it));
}
constexpr int64_t kNoPadding = -1;
template <typename T>
class LookupTableKernel : public framework::OpKernel<T> {
......@@ -55,7 +49,9 @@ class LookupTableKernel : public framework::OpKernel<T> {
auto *table_t = context.Input<SelectedRows>("W");
table_dim = table_t->value().dims();
} else {
PADDLE_THROW("table only support LoDTensor and SelectedRows");
PADDLE_THROW(
"The parameter W of a LookupTable "
"must be either LoDTensor or SelectedRows");
}
int64_t *ids;
......@@ -107,7 +103,7 @@ class LookupTableKernel : public framework::OpKernel<T> {
memset(output + i * row_width, 0, row_width * sizeof(T));
} else {
PADDLE_ENFORCE_GE(ids[i], 0);
auto id_index = getIndex(table_t.rows(), ids[i]);
auto id_index = table_t.index(ids[i]);
memcpy(output + i * row_width, table + id_index * row_width,
row_width * sizeof(T));
}
......@@ -128,7 +124,9 @@ class LookupTableGradKernel : public framework::OpKernel<T> {
auto *table_t = context.Input<SelectedRows>("W");
table_dim = table_t->value().dims();
} else {
PADDLE_THROW("table only support LoDTensor and SelectedRows");
PADDLE_THROW(
"The parameter W of a LookupTable "
"must be either LoDTensor or SelectedRows");
}
bool is_sparse = context.Attr<bool>("is_sparse");
......
paddle/fluid/operators/math/math_function.cc
浏览文件 @ 82530e24
......@@ -322,6 +322,14 @@ void set_constant_with_place<platform::CPUPlace>(
TensorSetConstantCPU(tensor, value));
}
template <>
void set_constant_with_place<platform::CUDAPinnedPlace>(
const platform::DeviceContext& context, framework::Tensor* tensor,
float value) {
framework::VisitDataType(framework::ToDataType(tensor->type()),
TensorSetConstantCPU(tensor, value));
}
struct TensorSetConstantWithPlace : public boost::static_visitor<void> {
TensorSetConstantWithPlace(const platform::DeviceContext& context,
framework::Tensor* tensor, float value)
......
paddle/fluid/operators/math/math_function.cu
浏览文件 @ 82530e24
......@@ -39,18 +39,33 @@ void gemm<platform::CUDADeviceContext, float16>(
cublasOperation_t cuTransB =
(transB == CblasNoTrans) ? CUBLAS_OP_N : CUBLAS_OP_T;
const half h_alpha = static_cast<const half>(alpha);
const half h_beta = static_cast<const half>(beta);
const half* h_A = reinterpret_cast<const half*>(A);
const half* h_B = reinterpret_cast<const half*>(B);
half* h_C = reinterpret_cast<half*>(C);
float h_alpha = static_cast<float>(alpha);
float h_beta = static_cast<float>(beta);
// TODO(kexinzhao): add processing code for compute capability < 53 case
PADDLE_ENFORCE_GE(context.GetComputeCapability(), 53,
"cublas Hgemm requires GPU compute capability >= 53");
PADDLE_ENFORCE(platform::dynload::cublasHgemm(
context.cublas_handle(), cuTransB, cuTransA, N, M, K, &h_alpha, h_B, ldb,
h_A, lda, &h_beta, h_C, N));
"cublas fp16 gemm requires GPU compute capability >= 53");
cublasGemmAlgo_t algo = CUBLAS_GEMM_DFALT;
#if CUDA_VERSION >= 9000
if (context.GetComputeCapability() >= 70) {
PADDLE_ENFORCE(platform::dynload::cublasSetMathMode(context.cublas_handle(),
CUBLAS_TENSOR_OP_MATH));
algo = CUBLAS_GEMM_DFALT_TENSOR_OP;
} else {
PADDLE_ENFORCE(platform::dynload::cublasSetMathMode(context.cublas_handle(),
CUBLAS_DEFAULT_MATH));
}
#endif
// cublasHgemm does true FP16 computation which is slow for non-Volta
// GPUs. So use cublasGemmEx instead which does pesudo FP16 computation:
// input/output in fp16, computation in fp32, which can also be accelerated
// using tensor cores in volta GPUs.
PADDLE_ENFORCE(platform::dynload::cublasGemmEx(
context.cublas_handle(), cuTransB, cuTransA, N, M, K, &h_alpha, B,
CUDA_R_16F, ldb, A, CUDA_R_16F, lda, &h_beta, C, CUDA_R_16F, N,
CUDA_R_32F, algo));
}
template <>
......
paddle/fluid/operators/math/softmax.cu
浏览文件 @ 82530e24
......@@ -14,6 +14,8 @@ limitations under the License. */
#define EIGEN_USE_GPU
#include <vector>
#include "paddle/fluid/operators/math/math_function.h"
#include "paddle/fluid/operators/math/softmax.h"
#include "paddle/fluid/operators/math/softmax_impl.h"
......@@ -95,6 +97,7 @@ template class SoftmaxCUDNNFunctor<double>;
template class SoftmaxGradCUDNNFunctor<float>;
template class SoftmaxGradCUDNNFunctor<double>;
template class SoftmaxFunctor<platform::CUDADeviceContext, platform::float16>;
template class SoftmaxFunctor<platform::CUDADeviceContext, float>;
template class SoftmaxFunctor<platform::CUDADeviceContext, double>;
template class SoftmaxGradFunctor<platform::CUDADeviceContext, float>;
......
paddle/fluid/operators/math/softmax_impl.h
浏览文件 @ 82530e24
......@@ -27,7 +27,7 @@ using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
template <typename T>
struct ValueClip {
HOSTDEVICE T operator()(const T& x) const {
const T kThreshold = -64.;
const T kThreshold = static_cast<T>(-64.);
return x < kThreshold ? kThreshold : x;
}
};
......
paddle/fluid/operators/prior_box_op.cc
浏览文件 @ 82530e24
......@@ -73,7 +73,7 @@ class PriorBoxOp : public framework::OperatorWithKernel {
const framework::ExecutionContext& ctx) const override {
return framework::OpKernelType(
framework::ToDataType(ctx.Input<framework::Tensor>("Input")->type()),
platform::CPUPlace());
ctx.device_context());
}
};
......@@ -171,6 +171,5 @@ namespace ops = paddle::operators;
REGISTER_OPERATOR(prior_box, ops::PriorBoxOp, ops::PriorBoxOpMaker,
paddle::framework::EmptyGradOpMaker);
REGISTER_OP_CPU_KERNEL(
prior_box, ops::PriorBoxOpKernel<paddle::platform::CPUPlace, float>,
ops::PriorBoxOpKernel<paddle::platform::CPUPlace, double>);
REGISTER_OP_CPU_KERNEL(prior_box, ops::PriorBoxOpKernel<float>,
ops::PriorBoxOpKernel<double>);
paddle/fluid/operators/prior_box_op.cu 0 → 100644
浏览文件 @ 82530e24
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/prior_box_op.h"
namespace paddle {
namespace operators {
template <typename T>
__device__ inline T clip(T in) {
return min(max(in, 0.), 1.);
}
template <typename T>
__global__ void GenPriorBox(T* out, const T* aspect_ratios, const int height,
const int width, const int im_height,
const int im_width, const int as_num,
const T offset, const T step_width,
const T step_height, const T* min_sizes,
const T* max_sizes, const int min_num,
bool is_clip) {
int num_priors = max_sizes ? as_num * min_num + min_num : as_num * min_num;
int box_num = height * width * num_priors;
for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < box_num;
i += blockDim.x * gridDim.x) {
int h = i / (num_priors * width);
int w = (i / num_priors) % width;
int p = i % num_priors;
int m = max_sizes ? p / (as_num + 1) : p / as_num;
T cx = (w + offset) * step_width;
T cy = (h + offset) * step_height;
T bw, bh;
T min_size = min_sizes[m];
if (max_sizes) {
int s = p % (as_num + 1);
if (s < as_num) {
T ar = aspect_ratios[s];
bw = min_size * sqrt(ar) / 2.;
bh = min_size / sqrt(ar) / 2.;
} else {
T max_size = max_sizes[m];
bw = sqrt(min_size * max_size) / 2.;
bh = bw;
}
} else {
int s = p % as_num;
T ar = aspect_ratios[s];
bw = min_size * sqrt(ar) / 2.;
bh = min_size / sqrt(ar) / 2.;
}
T xmin = (cx - bw) / im_width;
T ymin = (cy - bh) / im_height;
T xmax = (cx + bw) / im_width;
T ymax = (cy + bh) / im_height;
out[i * 4] = is_clip ? clip<T>(xmin) : xmin;
out[i * 4 + 1] = is_clip ? clip<T>(ymin) : ymin;
out[i * 4 + 2] = is_clip ? clip<T>(xmax) : xmax;
out[i * 4 + 3] = is_clip ? clip<T>(ymax) : ymax;
}
}
template <typename T>
__global__ void SetVariance(T* out, const T* var, const int vnum,
const int num) {
for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < num;
i += blockDim.x * gridDim.x) {
out[i] = var[i % vnum];
}
}
template <typename T>
class PriorBoxOpCUDAKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* input = ctx.Input<paddle::framework::Tensor>("Input");
auto* image = ctx.Input<paddle::framework::Tensor>("Image");
auto* boxes = ctx.Output<paddle::framework::Tensor>("Boxes");
auto* vars = ctx.Output<paddle::framework::Tensor>("Variances");
auto min_sizes = ctx.Attr<std::vector<float>>("min_sizes");
auto max_sizes = ctx.Attr<std::vector<float>>("max_sizes");
auto input_aspect_ratio = ctx.Attr<std::vector<float>>("aspect_ratios");
auto variances = ctx.Attr<std::vector<float>>("variances");
auto flip = ctx.Attr<bool>("flip");
auto clip = ctx.Attr<bool>("clip");
std::vector<float> aspect_ratios;
ExpandAspectRatios(input_aspect_ratio, flip, aspect_ratios);
T step_w = static_cast<T>(ctx.Attr<float>("step_w"));
T step_h = static_cast<T>(ctx.Attr<float>("step_h"));
T offset = static_cast<T>(ctx.Attr<float>("offset"));
auto im_width = image->dims()[3];
auto im_height = image->dims()[2];
auto width = input->dims()[3];
auto height = input->dims()[2];
T step_width, step_height;
if (step_w == 0 || step_h == 0) {
step_width = static_cast<T>(im_width) / width;
step_height = static_cast<T>(im_height) / height;
} else {
step_width = step_w;
step_height = step_h;
}
int num_priors = aspect_ratios.size() * min_sizes.size();
if (max_sizes.size() > 0) {
num_priors += max_sizes.size();
}
int min_num = static_cast<int>(min_sizes.size());
int box_num = width * height * num_priors;
int block = 512;
int grid = (box_num + block - 1) / block;
auto stream =
ctx.template device_context<platform::CUDADeviceContext>().stream();
boxes->mutable_data<T>(ctx.GetPlace());
vars->mutable_data<T>(ctx.GetPlace());
framework::Tensor r;
framework::TensorFromVector(aspect_ratios, ctx.device_context(), &r);
framework::Tensor min;
framework::TensorFromVector(min_sizes, ctx.device_context(), &min);
T* max_data = nullptr;
framework::Tensor max;
if (max_sizes.size() > 0) {
framework::TensorFromVector(max_sizes, ctx.device_context(), &max);
max_data = max.data<T>();
}
GenPriorBox<T><<<grid, block, 0, stream>>>(
boxes->data<T>(), r.data<T>(), height, width, im_height, im_width,
aspect_ratios.size(), offset, step_width, step_height, min.data<T>(),
max_data, min_num, clip);
framework::Tensor v;
framework::TensorFromVector(variances, ctx.device_context(), &v);
grid = (box_num * 4 + block - 1) / block;
SetVariance<T><<<grid, block, 0, stream>>>(vars->data<T>(), v.data<T>(),
variances.size(), box_num * 4);
}
}; // namespace operators
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(prior_box, ops::PriorBoxOpCUDAKernel<float>,
ops::PriorBoxOpCUDAKernel<double>);
paddle/fluid/operators/prior_box_op.h
浏览文件 @ 82530e24
......@@ -51,7 +51,7 @@ struct ClipFunctor {
}
};
template <typename Place, typename T>
template <typename T>
class PriorBoxOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
......@@ -106,49 +106,24 @@ class PriorBoxOpKernel : public framework::OpKernel<T> {
int idx = 0;
for (size_t s = 0; s < min_sizes.size(); ++s) {
auto min_size = min_sizes[s];
// first prior: aspect_ratio = 1, size = min_size
box_width = box_height = min_size / 2.;
// xmin
e_boxes(h, w, idx, 0) = (center_x - box_width) / img_width;
// ymin
e_boxes(h, w, idx, 1) = (center_y - box_height) / img_height;
// xmax
e_boxes(h, w, idx, 2) = (center_x + box_width) / img_width;
// ymax
e_boxes(h, w, idx, 3) = (center_y + box_height) / img_height;
idx++;
if (max_sizes.size() > 0) {
auto max_size = max_sizes[s];
// second prior: aspect_ratio = 1,
// size = sqrt(min_size * max_size)
box_width = box_height = sqrt(min_size * max_size) / 2.;
// xmin
// priors with different aspect ratios
for (size_t r = 0; r < aspect_ratios.size(); ++r) {
float ar = aspect_ratios[r];
box_width = min_size * sqrt(ar) / 2.;
box_height = min_size / sqrt(ar) / 2.;
e_boxes(h, w, idx, 0) = (center_x - box_width) / img_width;
// ymin
e_boxes(h, w, idx, 1) = (center_y - box_height) / img_height;
// xmax
e_boxes(h, w, idx, 2) = (center_x + box_width) / img_width;
// ymax
e_boxes(h, w, idx, 3) = (center_y + box_height) / img_height;
idx++;
}
// rest of priors
for (size_t r = 0; r < aspect_ratios.size(); ++r) {
float ar = aspect_ratios[r];
if (fabs(ar - 1.) < 1e-6) {
continue;
}
box_width = min_size * sqrt(ar) / 2.;
box_height = min_size / sqrt(ar) / 2.;
// xmin
if (max_sizes.size() > 0) {
auto max_size = max_sizes[s];
// square prior with size sqrt(minSize * maxSize)
box_width = box_height = sqrt(min_size * max_size) / 2.;
e_boxes(h, w, idx, 0) = (center_x - box_width) / img_width;
// ymin
e_boxes(h, w, idx, 1) = (center_y - box_height) / img_height;
// xmax
e_boxes(h, w, idx, 2) = (center_x + box_width) / img_width;
// ymax
e_boxes(h, w, idx, 3) = (center_y + box_height) / img_height;
idx++;
}
......
paddle/fluid/operators/reader/create_batch_reader_op.cc
浏览文件 @ 82530e24
......@@ -39,10 +39,13 @@ class CreateBatchReaderOp : public framework::OperatorBase {
private:
void RunImpl(const framework::Scope& scope,
const platform::Place& dev_place) const override {
const auto& underlying_reader = scope.FindVar(Input("UnderlyingReader"))
->Get<framework::ReaderHolder>();
auto* out = scope.FindVar(Output("Out"))
->template GetMutable<framework::ReaderHolder>();
if (out->Get() != nullptr) {
return;
}
const auto& underlying_reader = scope.FindVar(Input("UnderlyingReader"))
->Get<framework::ReaderHolder>();
out->Reset(
new BatchReader(underlying_reader.Get(), Attr<int>("batch_size")));
}
......
paddle/fluid/operators/reader/create_double_buffer_reader_op.cc
浏览文件 @ 82530e24
......@@ -99,10 +99,13 @@ class CreateDoubleBufferReaderOp : public framework::OperatorBase {
private:
void RunImpl(const framework::Scope& scope,
const platform::Place& dev_place) const override {
const auto& underlying_reader = scope.FindVar(Input("UnderlyingReader"))
->Get<framework::ReaderHolder>();
auto* out = scope.FindVar(Output("Out"))
->template GetMutable<framework::ReaderHolder>();
if (out->Get() != nullptr) {
return;
}
const auto& underlying_reader = scope.FindVar(Input("UnderlyingReader"))
->Get<framework::ReaderHolder>();
auto place_str = Attr<std::string>("place");
platform::Place place;
......
paddle/fluid/operators/reader/create_multi_pass_reader_op.cc
浏览文件 @ 82530e24
......@@ -62,12 +62,15 @@ class CreateMultiPassReaderOp : public framework::OperatorBase {
private:
void RunImpl(const framework::Scope& scope,
const platform::Place& dev_place) const override {
auto* out = detail::Ref(scope.FindVar(Output("Out")))
.GetMutable<framework::ReaderHolder>();
if (out->Get() != nullptr) {
return;
}
const auto& underlying_reader = scope.FindVar(Input("UnderlyingReader"))
->Get<framework::ReaderHolder>();
auto& out = detail::Ref(scope.FindVar(Output("Out")));
int pass_num = Attr<int>("pass_num");
out.GetMutable<framework::ReaderHolder>()->Reset(
new MultiPassReader(underlying_reader.Get(), pass_num));
out->Reset(new MultiPassReader(underlying_reader.Get(), pass_num));
}
};
......
paddle/fluid/operators/reader/create_shuffle_reader_op.cc
浏览文件 @ 82530e24
......@@ -80,10 +80,14 @@ class CreateShuffleReaderOp : public framework::OperatorBase {
private:
void RunImpl(const framework::Scope& scope,
const platform::Place& dev_place) const override {
auto* out = detail::Ref(scope.FindVar(Output("Out")))
.GetMutable<framework::ReaderHolder>();
if (out->Get() != nullptr) {
return;
}
const auto& underlying_reader = scope.FindVar(Input("UnderlyingReader"))
->Get<framework::ReaderHolder>();
auto& var = detail::Ref(scope.FindVar(Output("Out")));
var.GetMutable<framework::ReaderHolder>()->Reset(
out->Reset(
new ShuffleReader(underlying_reader.Get(),
static_cast<size_t>(Attr<int>("buffer_size"))));
}
......
paddle/fluid/operators/reader/open_files_op.cc
浏览文件 @ 82530e24
......@@ -21,6 +21,22 @@ namespace reader {
class MultipleReader : public framework::ReaderBase {
public:
class ThreadBufferMap {
public:
std::vector<framework::LoDTensor>& operator[](
const std::thread::id& thread_id) {
std::lock_guard<std::mutex> lock(mutex_);
return buffer_[thread_id];
}
void Clear() { buffer_.clear(); }
private:
std::mutex mutex_;
std::unordered_map<std::thread::id, std::vector<framework::LoDTensor>>
buffer_;
};
MultipleReader(const std::vector<std::string>& file_names,
const std::vector<framework::DDim>& dims, size_t thread_num)
: file_names_(file_names), dims_(dims) {
......@@ -47,28 +63,27 @@ class MultipleReader : public framework::ReaderBase {
framework::Channel<size_t>* waiting_file_idx_;
framework::Channel<size_t>* available_thread_idx_;
framework::Channel<std::vector<framework::LoDTensor>>* buffer_;
mutable std::vector<framework::LoDTensor> local_buffer_;
mutable ThreadBufferMap thread_buffer_map_;
};
void MultipleReader::ReadNext(std::vector<framework::LoDTensor>* out) {
if (!HasNext()) {
PADDLE_THROW("There is no next data!");
}
if (local_buffer_.empty()) {
buffer_->Receive(&local_buffer_);
}
*out = local_buffer_;
local_buffer_.clear();
auto& thread_local_buffer = thread_buffer_map_[std::this_thread::get_id()];
*out = thread_local_buffer;
thread_local_buffer.clear();
}
bool MultipleReader::HasNext() const {
return local_buffer_.empty() ? buffer_->Receive(&local_buffer_) : true;
auto& thread_local_buffer = thread_buffer_map_[std::this_thread::get_id()];
return thread_local_buffer.empty() ? buffer_->Receive(&thread_local_buffer)
: true;
}
void MultipleReader::ReInit() {
EndScheduler();
local_buffer_.clear();
thread_buffer_map_.Clear();
StartNewScheduler();
}
......@@ -176,7 +191,7 @@ class OpenFilesOp : public framework::OperatorBase {
const auto& ranks = Attr<std::vector<int>>("ranks");
PADDLE_ENFORCE(!shape_concat.empty() && !ranks.empty());
PADDLE_ENFORCE_EQ(std::accumulate(ranks.begin(), ranks.end(), 0),
int(shape_concat.size()),
static_cast<int>(shape_concat.size()),
"The accumulate of all ranks should be equal to the "
"shape concat's length.");
const auto& file_names = Attr<std::vector<std::string>>("file_names");
......
paddle/fluid/operators/send_recv_op_test.cc
浏览文件 @ 82530e24
......@@ -20,6 +20,7 @@ limitations under the License. */
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/framework/program_desc.h"
#include "paddle/fluid/operators/listen_and_serv_op.h"
#include "paddle/fluid/operators/math/math_function.h"
#include "paddle/fluid/operators/math/selected_rows_functor.h"
#include "paddle/fluid/string/printf.h"
......@@ -34,6 +35,7 @@ namespace m = paddle::operators::math;
// global for simplicity.
std::unique_ptr<f::OperatorBase> listen_and_serv_op;
int selected_port;
void InitTensorsInScope(f::Scope &scope, p::CPUPlace &place) {
p::CPUDeviceContext ctx(place);
......@@ -128,14 +130,16 @@ void StartServerNet(bool is_sparse) {
AddOp("sum", {{"X", {"x0", "x1"}}}, {{"Out", {"Out"}}}, {}, optimize_block);
f::AttributeMap attrs;
attrs.insert({"endpoint", std::string("127.0.0.1:6174")});
attrs.insert({"endpoint", std::string("127.0.0.1:0")});
attrs.insert({"Fanin", 1});
attrs.insert({"ParamList", std::vector<std::string>({"Out"})});
attrs.insert({"GradList", std::vector<std::string>({"x1"})});
attrs.insert({"OptimizeBlock", optimize_block});
listen_and_serv_op =
f::OpRegistry::CreateOp("listen_and_serv", {{"X", {"x1"}}}, {}, attrs);
LOG(INFO) << "selected port before run " << selected_port;
listen_and_serv_op->Run(scope, place);
LOG(INFO) << "server exit";
}
TEST(SendRecvOp, CPUDense) {
......@@ -149,12 +153,19 @@ TEST(SendRecvOp, CPUDense) {
scope.Var("RPC_CLIENT_VAR");
f::AttributeMap attrs;
attrs.insert({"endpoints", std::vector<std::string>({"127.0.0.1:6174"})});
attrs.insert({"epmap", std::vector<std::string>({"127.0.0.1:6174"})});
selected_port = static_cast<paddle::operators::ListenAndServOp *>(
listen_and_serv_op.get())
->GetSelectedPort();
LOG(INFO) << "selected port " << selected_port;
std::string endpoint = paddle::string::Sprintf("127.0.0.1:%d", selected_port);
attrs.insert({"endpoints", std::vector<std::string>({endpoint})});
attrs.insert({"epmap", std::vector<std::string>({endpoint})});
auto send_op = f::OpRegistry::CreateOp(
"send", {{"X", {"x1"}}},
{{"Out", {"Out"}}, {"RPCClient", {"RPC_CLIENT_VAR"}}}, attrs);
LOG(INFO) << "before run " << endpoint;
send_op->Run(scope, place);
LOG(INFO) << "end run";
auto in_var = scope.Var("x1");
auto tensor = in_var->GetMutable<f::LoDTensor>();
......@@ -167,6 +178,7 @@ TEST(SendRecvOp, CPUDense) {
for (int64_t i = 0; i < target->numel(); ++i) {
EXPECT_EQ(expected[i] * 2, actual[i]);
}
LOG(INFO) << "before stop";
listen_and_serv_op->Stop();
server_thread.join();
listen_and_serv_op.reset(nullptr);
......@@ -182,8 +194,13 @@ TEST(SendRecvOp, CPUSparse) {
InitSelectedRowsInScope(scope, place);
scope.Var("RPC_CLIENT_VAR");
f::AttributeMap attrs;
attrs.insert({"endpoints", std::vector<std::string>({"127.0.0.1:6174"})});
attrs.insert({"epmap", std::vector<std::string>({"127.0.0.1:6174"})});
selected_port = static_cast<paddle::operators::ListenAndServOp *>(
listen_and_serv_op.get())
->GetSelectedPort();
LOG(INFO) << "selected port " << selected_port;
std::string endpoint = paddle::string::Sprintf("127.0.0.1:%d", selected_port);
attrs.insert({"endpoints", std::vector<std::string>({endpoint})});
attrs.insert({"epmap", std::vector<std::string>({endpoint})});
auto send_op = f::OpRegistry::CreateOp(
"send", {{"X", {"x1"}}},
{{"Out", {"Out"}}, {"RPCClient", {"RPC_CLIENT_VAR"}}}, attrs);
......
paddle/fluid/operators/sgd_op.cc
浏览文件 @ 82530e24
......@@ -43,9 +43,8 @@ class SGDOp : public framework::OperatorWithKernel {
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
return framework::OpKernelType(
framework::ToDataType(ctx.Input<framework::LoDTensor>("Param")->type()),
ctx.GetPlace());
auto data_type = framework::GetDataTypeOfVar(ctx.InputVar("Param"));
return framework::OpKernelType(data_type, ctx.device_context());
}
};
......@@ -53,10 +52,12 @@ class SGDOpMaker : public framework::OpProtoAndCheckerMaker {
public:
SGDOpMaker(OpProto* proto, OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("Param", "(Tensor) Input parameter");
AddInput("Param", "(Tensor or SelectedRows) Input parameter");
AddInput("LearningRate", "(Tensor) Learning rate of SGD");
AddInput("Grad", "(Tensor) Input gradient");
AddOutput("ParamOut", "(Tensor) Output parameter");
AddInput("Grad", "(Tensor or SelectedRows) Input gradient");
AddOutput("ParamOut",
"(Tensor or SelectedRows, same with Param) "
"Output parameter, should share the same memory with Param");
AddComment(R"DOC(
SGD operator
......
paddle/fluid/operators/sgd_op.h
浏览文件 @ 82530e24
......@@ -23,60 +23,97 @@ namespace operators {
template <typename T>
class SGDOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* param = ctx.Input<framework::Tensor>("Param");
auto* param_out = ctx.Output<framework::Tensor>("ParamOut");
auto* learning_rate = ctx.Input<framework::Tensor>("LearningRate");
auto* grad_var = ctx.InputVar("Grad");
// Actually, all tensors are LoDTensor except SelectedRows.
if (grad_var->IsType<framework::LoDTensor>()) {
param_out->mutable_data<T>(ctx.GetPlace());
auto* grad = ctx.Input<framework::Tensor>("Grad");
auto p = framework::EigenVector<T>::Flatten(*param);
auto g = framework::EigenVector<T>::Flatten(*grad);
auto o = framework::EigenVector<T>::Flatten(*param_out);
auto* lr = learning_rate->data<T>();
o = p - lr[0] * g;
} else if (grad_var->IsType<framework::SelectedRows>()) {
// TODO(qijun): In Sparse SGD operator, in-place update is enforced.
// This manual optimization brings difficulty to track data dependency.
// It's better to find a more elegant solution.
PADDLE_ENFORCE_EQ(param, param_out);
auto* grad = ctx.Input<framework::SelectedRows>("Grad");
void Compute(const framework::ExecutionContext &ctx) const override {
const auto *learning_rate = ctx.Input<framework::Tensor>("LearningRate");
const auto *param_var = ctx.InputVar("Param");
const auto *grad_var = ctx.InputVar("Grad");
if (param_var->IsType<framework::LoDTensor>()) {
const auto *param = ctx.Input<framework::Tensor>("Param");
auto *param_out = ctx.Output<framework::Tensor>("ParamOut");
// Actually, all tensors are LoDTensor except SelectedRows.
if (grad_var->IsType<framework::LoDTensor>()) {
param_out->mutable_data<T>(ctx.GetPlace());
const auto *grad = ctx.Input<framework::Tensor>("Grad");
auto p = framework::EigenVector<T>::Flatten(*param);
auto g = framework::EigenVector<T>::Flatten(*grad);
auto o = framework::EigenVector<T>::Flatten(*param_out);
auto *lr = learning_rate->data<T>();
o = p - lr[0] * g;
} else if (grad_var->IsType<framework::SelectedRows>()) {
// TODO(qijun): In Sparse SGD operator, in-place update is enforced.
// This manual optimization brings difficulty to track data dependency.
// It's better to find a more elegant solution.
PADDLE_ENFORCE_EQ(param, param_out);
const auto *grad = ctx.Input<framework::SelectedRows>("Grad");
// for distributed training, a sparse var may be empty,
// just skip updating.
if (grad->rows().size() == 0) {
return;
}
auto grad_height = grad->height();
auto out_dims = param_out->dims();
PADDLE_ENFORCE_EQ(grad_height, out_dims[0]);
auto &grad_value = grad->value();
auto &grad_rows = grad->rows();
size_t grad_row_numel = grad_value.numel() / grad_rows.size();
PADDLE_ENFORCE_EQ(grad_row_numel, param_out->numel() / grad_height);
auto *grad_data = grad_value.data<T>();
auto *out_data = param_out->data<T>();
auto *lr = learning_rate->data<T>();
for (size_t i = 0; i < grad_rows.size(); i++) {
PADDLE_ENFORCE(grad_rows[i] < grad_height,
"Input rows index should less than height");
for (int64_t j = 0; j < grad_row_numel; j++) {
out_data[grad_rows[i] * grad_row_numel + j] -=
lr[0] * grad_data[i * grad_row_numel + j];
}
}
} else {
PADDLE_THROW("Unsupported Variable Type of Grad");
}
} else if (param_var->IsType<framework::SelectedRows>()) {
PADDLE_ENFORCE(grad_var->IsType<framework::SelectedRows>(),
"when param "
"is SelectedRows, gradient should also be SelectedRows");
const auto &param = param_var->Get<framework::SelectedRows>();
auto *param_out = ctx.Output<framework::SelectedRows>("ParamOut");
const auto &grad = grad_var->Get<framework::SelectedRows>();
// for distributed training, a sparse var may be empty,
// just skip updating.
if (grad->rows().size() == 0) {
if (grad.rows().size() == 0) {
return;
}
auto in_height = grad->height();
auto out_dims = param_out->dims();
PADDLE_ENFORCE_EQ(in_height, out_dims[0]);
auto& in_value = grad->value();
auto& in_rows = grad->rows();
size_t param_row_width = param.value().numel() / param.rows().size();
size_t grad_row_width = grad.value().numel() / grad.rows().size();
PADDLE_ENFORCE_EQ(param_row_width, grad_row_width,
"param_row should have the same size with grad_row");
int64_t in_row_numel = in_value.numel() / in_rows.size();
PADDLE_ENFORCE_EQ(in_row_numel, param_out->numel() / in_height);
auto* in_data = in_value.data<T>();
auto* out_data = param_out->data<T>();
auto* lr = learning_rate->data<T>();
for (size_t i = 0; i < in_rows.size(); i++) {
PADDLE_ENFORCE(in_rows[i] < in_height,
const auto *lr = learning_rate->data<T>();
const auto *grad_data = grad.value().data<T>();
auto *out_data = param_out->mutable_value()->data<T>();
for (size_t i = 0; i < grad.rows().size(); i++) {
PADDLE_ENFORCE(grad.rows()[i] < grad.height(),
"Input rows index should less than height");
for (int64_t j = 0; j < in_row_numel; j++) {
out_data[in_rows[i] * in_row_numel + j] -=
lr[0] * in_data[i * in_row_numel + j];
int64_t id_index = param.index(grad.rows()[i]);
for (int64_t j = 0; j < grad_row_width; j++) {
out_data[id_index * grad_row_width + j] -=
lr[0] * grad_data[i * grad_row_width + j];
}
}
} else {
PADDLE_THROW("Unsupported Variable Type of Grad");
PADDLE_THROW("Unsupported Variable Type of Parameter");
}
}
};
......
paddle/fluid/operators/softmax_op.cc
浏览文件 @ 82530e24
......@@ -13,6 +13,9 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/softmax_op.h"
#include <string>
#ifdef PADDLE_WITH_CUDA
#include "paddle/fluid/platform/cudnn_helper.h"
#endif
......@@ -20,6 +23,7 @@ limitations under the License. */
#ifdef PADDLE_WITH_MKLDNN
#include "paddle/fluid/platform/mkldnn_helper.h"
#endif
namespace paddle {
namespace operators {
......@@ -60,8 +64,8 @@ class SoftmaxOp : public framework::OperatorWithKernel {
auto input_data_type =
framework::ToDataType(ctx.Input<Tensor>("X")->type());
if (input_data_type == framework::proto::VarType::FP16) {
PADDLE_ENFORCE_EQ(library_, framework::LibraryType::kCUDNN,
"float16 can only be used when CUDNN is used");
PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()),
"float16 can only be used on GPU place");
}
std::string data_format = ctx.Attr<std::string>("data_format");
......@@ -70,6 +74,7 @@ class SoftmaxOp : public framework::OperatorWithKernel {
library_);
}
};
class SoftmaxOpMaker : public framework::OpProtoAndCheckerMaker {
public:
SoftmaxOpMaker(OpProto* proto, OpAttrChecker* op_checker)
......
paddle/fluid/operators/softmax_op.cu.cc
浏览文件 @ 82530e24
......@@ -13,11 +13,12 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/softmax_op.h"
#include "paddle/fluid/platform/float16.h"
namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(
softmax, ops::SoftmaxKernel<paddle::platform::CUDADeviceContext, float>);
namespace plat = paddle::platform;
REGISTER_OP_CUDA_KERNEL(
softmax_grad,
ops::SoftmaxGradKernel<paddle::platform::CUDADeviceContext, float>);
softmax, ops::SoftmaxKernel<plat::CUDADeviceContext, float>,
ops::SoftmaxKernel<plat::CUDADeviceContext, plat::float16>);
REGISTER_OP_CUDA_KERNEL(softmax_grad,
ops::SoftmaxGradKernel<plat::CUDADeviceContext, float>);
paddle/fluid/platform/.clang-format 已删除 100644 → 0
浏览文件 @ 07b31b80
---
Language: Cpp
BasedOnStyle: Google
Standard: Cpp11
...
paddle/fluid/platform/CMakeLists.txt
浏览文件 @ 82530e24
......@@ -6,8 +6,8 @@ add_custom_target(profiler_py_proto_init ALL COMMAND ${CMAKE_COMMAND} -E touch _
add_dependencies(profiler_py_proto profiler_py_proto_init)
add_custom_command(TARGET profiler_py_proto POST_BUILD
COMMAND ${CMAKE_COMMAND} -E make_directory ${PADDLE_SOURCE_DIR}/python/paddle/fluid/proto/profiler
COMMAND cp *.py ${PADDLE_SOURCE_DIR}/python/paddle/fluid/proto/profiler
COMMAND ${CMAKE_COMMAND} -E make_directory ${PADDLE_BINARY_DIR}/python/paddle/fluid/proto/profiler
COMMAND cp *.py ${PADDLE_BINARY_DIR}/python/paddle/fluid/proto/profiler
COMMENT "Copy generated python proto into directory paddle/fluid/proto/profiler."
WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR})
......@@ -42,12 +42,12 @@ ENDIF()
# memcpy depends on device_context, here add deps individually for
# avoiding cycle dependencies
cc_library(device_context SRCS device_context.cc DEPS memory buddy_allocator
system_allocator memory_block meta_data meta_cache place eigen3 ${GPU_CTX_DEPS} ${MKLDNN_CTX_DEPS})
cc_library(device_context SRCS device_context.cc DEPS malloc
place eigen3 ${GPU_CTX_DEPS} ${MKLDNN_CTX_DEPS})
nv_test(device_context_test SRCS device_context_test.cu DEPS device_context gpu_info)
nv_test(cudnn_helper_test SRCS cudnn_helper_test.cc DEPS dynload_cuda)
nv_test(transform_test SRCS transform_test.cu DEPS paddle_memory place device_context)
nv_test(transform_test SRCS transform_test.cu DEPS memory place device_context)
cc_library(device_tracer SRCS device_tracer.cc DEPS boost profiler_proto ${GPU_CTX_DEPS})
cc_library(profiler SRCS profiler.cc DEPS device_context device_tracer)
......
paddle/fluid/platform/cpu_info.cc
浏览文件 @ 82530e24
......@@ -27,6 +27,11 @@ DEFINE_double(fraction_of_cpu_memory_to_use, 1,
"Default use 100% of CPU memory for PaddlePaddle,"
"reserve the rest for page tables, etc");
DEFINE_double(
fraction_of_cuda_pinned_memory_to_use, 0.5,
"Default use 50% of CPU memory as the pinned_memory for PaddlePaddle,"
"reserve the rest for page tables, etc");
namespace paddle {
namespace platform {
......@@ -62,5 +67,22 @@ size_t CpuMaxChunkSize() {
return CpuMaxAllocSize() / 32;
}
size_t CUDAPinnedMaxAllocSize() {
// For distributed systems, it requires configuring and limiting
// the fraction of memory to use.
return FLAGS_fraction_of_cuda_pinned_memory_to_use * CpuTotalPhysicalMemory();
}
size_t CUDAPinnedMinChunkSize() {
// Allow to allocate the minimum chunk size is 64 KB.
return 1 << 16;
}
size_t CUDAPinnedMaxChunkSize() {
// Allow to allocate the maximum chunk size is roughly 1/256 of CUDA_PINNED
// memory.
return CUDAPinnedMaxAllocSize() / 256;
}
} // namespace platform
} // namespace paddle
paddle/fluid/platform/cpu_info.h
浏览文件 @ 82530e24
......@@ -22,11 +22,20 @@ namespace platform {
//! Get the maximum allocation size for a machine.
size_t CpuMaxAllocSize();
//! Get the maximum allocation size for a machine.
size_t CUDAPinnedMaxAllocSize();
//! Get the minimum chunk size for buddy allocator.
size_t CpuMinChunkSize();
//! Get the maximum chunk size for buddy allocator.
size_t CpuMaxChunkSize();
//! Get the minimum chunk size for buddy allocator.
size_t CUDAPinnedMinChunkSize();
//! Get the maximum chunk size for buddy allocator.
size_t CUDAPinnedMaxChunkSize();
} // namespace platform
} // namespace paddle
paddle/fluid/platform/cpu_info_test.cc
浏览文件 @ 82530e24
......@@ -12,7 +12,6 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/platform/cpu_info.h"
#include "paddle/fluid/string/printf.h"
#include <ostream>
#include <sstream>
......@@ -20,6 +19,7 @@
#include "gflags/gflags.h"
#include "glog/logging.h"
#include "gtest/gtest.h"
#include "paddle/fluid/string/printf.h"
DECLARE_double(fraction_of_cpu_memory_to_use);
......
paddle/fluid/platform/cudnn_helper.h
浏览文件 @ 82530e24
......@@ -257,9 +257,11 @@ class ScopedConvolutionDescriptor {
}
#endif
cudnnDataType_t compute_type =
(type == CUDNN_DATA_DOUBLE) ? CUDNN_DATA_DOUBLE : CUDNN_DATA_FLOAT;
PADDLE_ENFORCE(dynload::cudnnSetConvolutionNdDescriptor(
desc_, pads.size(), pads.data(), strides.data(), dilations.data(),
CUDNN_CROSS_CORRELATION, type));
CUDNN_CROSS_CORRELATION, compute_type));
return desc_;
}
......
paddle/fluid/platform/device_context.cc
浏览文件 @ 82530e24
......@@ -53,6 +53,16 @@ DeviceContextPool::DeviceContextPool(
PADDLE_THROW(
"'CUDAPlace' is not supported, Please re-compile with WITH_GPU "
"option");
#endif
} else if (platform::is_cuda_pinned_place(p)) {
#ifdef PADDLE_WITH_CUDA
device_contexts_.emplace(
p,
PtrType(new CUDAPinnedDeviceContext(boost::get<CUDAPinnedPlace>(p))));
#else
PADDLE_THROW(
"'CUDAPlace' is not supported, Please re-compile with WITH_GPU "
"option");
#endif
}
}
......@@ -186,6 +196,20 @@ cudnnHandle_t CUDADeviceContext::cudnn_handle() const { return cudnn_handle_; }
cudaStream_t CUDADeviceContext::stream() const { return stream_; }
CUDAPinnedDeviceContext::CUDAPinnedDeviceContext() {
eigen_device_.reset(new Eigen::DefaultDevice());
}
CUDAPinnedDeviceContext::CUDAPinnedDeviceContext(CUDAPinnedPlace place)
: place_(place) {
eigen_device_.reset(new Eigen::DefaultDevice());
}
Eigen::DefaultDevice* CUDAPinnedDeviceContext::eigen_device() const {
return eigen_device_.get();
}
Place CUDAPinnedDeviceContext::GetPlace() const { return place_; }
#endif
#ifdef PADDLE_WITH_MKLDNN
......
paddle/fluid/platform/device_context.h
浏览文件 @ 82530e24
......@@ -118,6 +118,25 @@ struct DefaultDeviceContextType<platform::CUDAPlace> {
using TYPE = CUDADeviceContext;
};
// Currently, CUDAPinnedDeviceContext is only used to data copying.
class CUDAPinnedDeviceContext : public DeviceContext {
public:
CUDAPinnedDeviceContext();
explicit CUDAPinnedDeviceContext(CUDAPinnedPlace place);
Place GetPlace() const override;
Eigen::DefaultDevice* eigen_device() const;
private:
CUDAPinnedPlace place_;
std::unique_ptr<Eigen::DefaultDevice> eigen_device_;
};
template <>
struct DefaultDeviceContextType<platform::CUDAPinnedPlace> {
using TYPE = CUDAPinnedDeviceContext;
};
#endif
#ifdef PADDLE_WITH_MKLDNN
......
paddle/fluid/platform/dynload/cublas.cc
浏览文件 @ 82530e24
......@@ -24,6 +24,10 @@ void *cublas_dso_handle = nullptr;
CUBLAS_BLAS_ROUTINE_EACH(DEFINE_WRAP);
#ifdef CUBLAS_BLAS_ROUTINE_EACH_R2
CUBLAS_BLAS_ROUTINE_EACH_R2(DEFINE_WRAP);
#endif
} // namespace dynload
} // namespace platform
} // namespace paddle
paddle/fluid/platform/dynload/cublas.h
浏览文件 @ 82530e24
/* 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
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
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. */
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <cublas_v2.h>
#include <cuda.h>
#include <dlfcn.h>
#include <mutex>
#include <mutex> // NOLINT
#include "paddle/fluid/platform/dynload/dynamic_loader.h"
namespace paddle {
......@@ -34,18 +35,18 @@ extern void *cublas_dso_handle;
* note: default dynamic linked libs
*/
#ifdef PADDLE_USE_DSO
#define DECLARE_DYNAMIC_LOAD_CUBLAS_WRAP(__name) \
struct DynLoad__##__name { \
template <typename... Args> \
inline cublasStatus_t operator()(Args... args) { \
typedef cublasStatus_t (*cublasFunc)(Args...); \
std::call_once(cublas_dso_flag, \
paddle::platform::dynload::GetCublasDsoHandle, \
&cublas_dso_handle); \
void *p_##__name = dlsym(cublas_dso_handle, #__name); \
return reinterpret_cast<cublasFunc>(p_##__name)(args...); \
} \
}; \
#define DECLARE_DYNAMIC_LOAD_CUBLAS_WRAP(__name) \
struct DynLoad__##__name { \
template <typename... Args> \
inline cublasStatus_t operator()(Args... args) { \
typedef cublasStatus_t (*cublasFunc)(Args...); \
std::call_once(cublas_dso_flag, []() { \
cublas_dso_handle = paddle::platform::dynload::GetCublasDsoHandle(); \
}); \
void *p_##__name = dlsym(cublas_dso_handle, #__name); \
return reinterpret_cast<cublasFunc>(p_##__name)(args...); \
} \
}; \
extern DynLoad__##__name __name
#else
#define DECLARE_DYNAMIC_LOAD_CUBLAS_WRAP(__name) \
......@@ -70,6 +71,7 @@ extern void *cublas_dso_handle;
__macro(cublasDgemm_v2); \
__macro(cublasHgemm); \
__macro(cublasSgemmEx); \
__macro(cublasGemmEx); \
__macro(cublasSgeam_v2); \
__macro(cublasDgeam_v2); \
__macro(cublasCreate_v2); \
......@@ -89,9 +91,15 @@ extern void *cublas_dso_handle;
__macro(cublasSgetrfBatched); \
__macro(cublasSgetriBatched); \
__macro(cublasDgetrfBatched); \
__macro(cublasDgetriBatched)
__macro(cublasDgetriBatched);
CUBLAS_BLAS_ROUTINE_EACH(DECLARE_DYNAMIC_LOAD_CUBLAS_WRAP);
CUBLAS_BLAS_ROUTINE_EACH(DECLARE_DYNAMIC_LOAD_CUBLAS_WRAP)
// APIs available after CUDA 9.0
#if CUDA_VERSION >= 9000
#define CUBLAS_BLAS_ROUTINE_EACH_R2(__macro) __macro(cublasSetMathMode);
CUBLAS_BLAS_ROUTINE_EACH_R2(DECLARE_DYNAMIC_LOAD_CUBLAS_WRAP)
#endif
#undef DECLARE_DYNAMIC_LOAD_CUBLAS_WRAP
} // namespace dynload
......
paddle/fluid/platform/dynload/cudnn.cc
浏览文件 @ 82530e24
......@@ -44,7 +44,8 @@ CUDNN_DNN_ROUTINE_EACH_R7(DEFINE_WRAP);
#ifdef PADDLE_USE_DSO
bool HasCUDNN() {
std::call_once(cudnn_dso_flag, GetCUDNNDsoHandle, &cudnn_dso_handle);
std::call_once(cudnn_dso_flag,
[]() { cudnn_dso_handle = GetCUDNNDsoHandle(); });
return cudnn_dso_handle != nullptr;
}
......
paddle/fluid/platform/dynload/cudnn.h
浏览文件 @ 82530e24
......@@ -16,7 +16,7 @@ limitations under the License. */
#include <cudnn.h>
#include <dlfcn.h>
#include <mutex>
#include <mutex> // NOLINT
#include "paddle/fluid/platform/dynload/dynamic_loader.h"
namespace paddle {
......@@ -30,19 +30,19 @@ extern bool HasCUDNN();
#ifdef PADDLE_USE_DSO
extern void EnforceCUDNNLoaded(const char* fn_name);
#define DECLARE_DYNAMIC_LOAD_CUDNN_WRAP(__name) \
struct DynLoad__##__name { \
template <typename... Args> \
auto operator()(Args... args) -> decltype(__name(args...)) { \
using cudnn_func = decltype(__name(args...)) (*)(Args...); \
std::call_once(cudnn_dso_flag, \
paddle::platform::dynload::GetCUDNNDsoHandle, \
&cudnn_dso_handle); \
EnforceCUDNNLoaded(#__name); \
void* p_##__name = dlsym(cudnn_dso_handle, #__name); \
return reinterpret_cast<cudnn_func>(p_##__name)(args...); \
} \
}; \
#define DECLARE_DYNAMIC_LOAD_CUDNN_WRAP(__name) \
struct DynLoad__##__name { \
template <typename... Args> \
auto operator()(Args... args) -> decltype(__name(args...)) { \
using cudnn_func = decltype(__name(args...)) (*)(Args...); \
std::call_once(cudnn_dso_flag, []() { \
cudnn_dso_handle = paddle::platform::dynload::GetCUDNNDsoHandle(); \
}); \
EnforceCUDNNLoaded(#__name); \
void* p_##__name = dlsym(cudnn_dso_handle, #__name); \
return reinterpret_cast<cudnn_func>(p_##__name)(args...); \
} \
}; \
extern struct DynLoad__##__name __name
#else
......@@ -140,7 +140,8 @@ CUDNN_DNN_ROUTINE_EACH_R5(DECLARE_DYNAMIC_LOAD_CUDNN_WRAP)
#if CUDNN_VERSION >= 7001
#define CUDNN_DNN_ROUTINE_EACH_R7(__macro) \
__macro(cudnnSetConvolutionGroupCount);
__macro(cudnnSetConvolutionGroupCount); \
__macro(cudnnSetConvolutionMathType);
CUDNN_DNN_ROUTINE_EACH_R7(DECLARE_DYNAMIC_LOAD_CUDNN_WRAP)
#endif
......
paddle/fluid/platform/dynload/cupti.h
浏览文件 @ 82530e24
......@@ -11,14 +11,15 @@ distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#ifdef PADDLE_WITH_CUPTI
#include <cuda.h>
#include <cupti.h>
#include <dlfcn.h>
#include <mutex>
#include <mutex> // NOLINT
#include "paddle/fluid/platform/dynload/dynamic_loader.h"
namespace paddle {
......@@ -36,18 +37,18 @@ extern void *cupti_dso_handle;
* note: default dynamic linked libs
*/
#ifdef PADDLE_USE_DSO
#define DECLARE_DYNAMIC_LOAD_CUPTI_WRAP(__name) \
struct DynLoad__##__name { \
template <typename... Args> \
inline CUptiResult CUPTIAPI operator()(Args... args) { \
typedef CUptiResult CUPTIAPI (*cuptiFunc)(Args...); \
std::call_once(cupti_dso_flag, \
paddle::platform::dynload::GetCUPTIDsoHandle, \
&cupti_dso_handle); \
void *p_##__name = dlsym(cupti_dso_handle, #__name); \
return reinterpret_cast<cuptiFunc>(p_##__name)(args...); \
} \
}; \
#define DECLARE_DYNAMIC_LOAD_CUPTI_WRAP(__name) \
struct DynLoad__##__name { \
template <typename... Args> \
inline CUptiResult CUPTIAPI operator()(Args... args) { \
typedef CUptiResult CUPTIAPI (*cuptiFunc)(Args...); \
std::call_once(cupti_dso_flag, []() { \
cupti_dso_handle = paddle::platform::dynload::GetCUPTIDsoHandle(); \
}); \
void *p_##__name = dlsym(cupti_dso_handle, #__name); \
return reinterpret_cast<cuptiFunc>(p_##__name)(args...); \
} \
}; \
extern DynLoad__##__name __name
#else
#define DECLARE_DYNAMIC_LOAD_CUPTI_WRAP(__name) \
......
paddle/fluid/platform/dynload/curand.h
浏览文件 @ 82530e24
......@@ -11,12 +11,13 @@ distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <curand.h>
#include <dlfcn.h>
#include <mutex>
#include <mutex> // NOLINT
#include "paddle/fluid/platform/dynload/dynamic_loader.h"
namespace paddle {
......@@ -25,18 +26,18 @@ namespace dynload {
extern std::once_flag curand_dso_flag;
extern void *curand_dso_handle;
#ifdef PADDLE_USE_DSO
#define DECLARE_DYNAMIC_LOAD_CURAND_WRAP(__name) \
struct DynLoad__##__name { \
template <typename... Args> \
curandStatus_t operator()(Args... args) { \
typedef curandStatus_t (*curandFunc)(Args...); \
std::call_once(curand_dso_flag, \
paddle::platform::dynload::GetCurandDsoHandle, \
&curand_dso_handle); \
void *p_##__name = dlsym(curand_dso_handle, #__name); \
return reinterpret_cast<curandFunc>(p_##__name)(args...); \
} \
}; \
#define DECLARE_DYNAMIC_LOAD_CURAND_WRAP(__name) \
struct DynLoad__##__name { \
template <typename... Args> \
curandStatus_t operator()(Args... args) { \
typedef curandStatus_t (*curandFunc)(Args...); \
std::call_once(curand_dso_flag, []() { \
curand_dso_handle = paddle::platform::dynload::GetCurandDsoHandle(); \
}); \
void *p_##__name = dlsym(curand_dso_handle, #__name); \
return reinterpret_cast<curandFunc>(p_##__name)(args...); \
} \
}; \
extern DynLoad__##__name __name
#else
#define DECLARE_DYNAMIC_LOAD_CURAND_WRAP(__name) \
......
paddle/fluid/platform/dynload/dynamic_loader.cc
浏览文件 @ 82530e24
......@@ -11,12 +11,14 @@ distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/platform/dynload/dynamic_loader.h"
#include <dlfcn.h>
#include <memory>
#include <mutex>
#include <mutex> // NOLINT
#include <string>
#include "gflags/gflags.h"
#include "glog/logging.h"
#include "paddle/fluid/platform/dynload/cupti_lib_path.h"
......@@ -65,22 +67,21 @@ static inline std::string join(const std::string& part1,
return ret;
}
static inline void GetDsoHandleFromDefaultPath(std::string& dso_path,
void** dso_handle,
int dynload_flags) {
static inline void* GetDsoHandleFromDefaultPath(const std::string& dso_path,
int dynload_flags) {
VLOG(3) << "Try to find library: " << dso_path
<< " from default system path.";
// default search from LD_LIBRARY_PATH/DYLD_LIBRARY_PATH
*dso_handle = dlopen(dso_path.c_str(), dynload_flags);
void* dso_handle = dlopen(dso_path.c_str(), dynload_flags);
// DYLD_LIBRARY_PATH is disabled after Mac OS 10.11 to
// bring System Integrity Projection (SIP), if dso_handle
// is null, search from default package path in Mac OS.
#if defined(__APPLE__) || defined(__OSX__)
if (nullptr == *dso_handle) {
dso_path = join("/usr/local/cuda/lib/", dso_path);
*dso_handle = dlopen(dso_path.c_str(), dynload_flags);
if (nullptr == *dso_handle) {
if (nullptr == dso_handle) {
dso_handle =
dlopen(join("/usr/local/cuda/lib/", dso_path).c_str(), dynload_flags);
if (nullptr == dso_handle) {
if (dso_path == "libcudnn.dylib") {
LOG(WARNING) << "Note: [Recommend] copy cudnn into /usr/local/cuda/ \n "
"For instance, sudo tar -xzf "
......@@ -91,28 +92,29 @@ static inline void GetDsoHandleFromDefaultPath(std::string& dso_path,
}
}
#endif
return dso_handle;
}
static inline void GetDsoHandleFromSearchPath(const std::string& search_root,
const std::string& dso_name,
void** dso_handle,
bool throw_on_error = true) {
static inline void* GetDsoHandleFromSearchPath(const std::string& search_root,
const std::string& dso_name,
bool throw_on_error = true) {
int dynload_flags = RTLD_LAZY | RTLD_LOCAL;
*dso_handle = nullptr;
void* dso_handle = nullptr;
std::string dlPath = dso_name;
if (search_root.empty()) {
GetDsoHandleFromDefaultPath(dlPath, dso_handle, dynload_flags);
dso_handle = GetDsoHandleFromDefaultPath(dlPath, dynload_flags);
} else {
// search xxx.so from custom path
dlPath = join(search_root, dso_name);
*dso_handle = dlopen(dlPath.c_str(), dynload_flags);
dso_handle = dlopen(dlPath.c_str(), dynload_flags);
// if not found, search from default path
if (nullptr == *dso_handle) {
if (nullptr == dso_handle) {
LOG(WARNING) << "Failed to find dynamic library: " << dlPath << " ("
<< dlerror() << ")";
dlPath = dso_name;
GetDsoHandleFromDefaultPath(dlPath, dso_handle, dynload_flags);
dso_handle = GetDsoHandleFromDefaultPath(dlPath, dynload_flags);
}
}
auto error_msg =
......@@ -124,70 +126,71 @@ static inline void GetDsoHandleFromSearchPath(const std::string& search_root,
"using the DYLD_LIBRARY_PATH is impossible unless System "
"Integrity Protection (SIP) is disabled.";
if (throw_on_error) {
PADDLE_ENFORCE(nullptr != *dso_handle, error_msg, dlPath, dlerror());
} else if (nullptr == *dso_handle) {
PADDLE_ENFORCE(nullptr != dso_handle, error_msg, dlPath, dlerror());
} else if (nullptr == dso_handle) {
LOG(WARNING) << string::Sprintf(error_msg, dlPath, dlerror());
}
return dso_handle;
}
void GetCublasDsoHandle(void** dso_handle) {
void* GetCublasDsoHandle() {
#if defined(__APPLE__) || defined(__OSX__)
GetDsoHandleFromSearchPath(FLAGS_cuda_dir, "libcublas.dylib", dso_handle);
return GetDsoHandleFromSearchPath(FLAGS_cuda_dir, "libcublas.dylib");
#else
GetDsoHandleFromSearchPath(FLAGS_cuda_dir, "libcublas.so", dso_handle);
return GetDsoHandleFromSearchPath(FLAGS_cuda_dir, "libcublas.so");
#endif
}
void GetCUDNNDsoHandle(void** dso_handle) {
void* GetCUDNNDsoHandle() {
#if defined(__APPLE__) || defined(__OSX__)
GetDsoHandleFromSearchPath(FLAGS_cudnn_dir, "libcudnn.dylib", dso_handle,
false);
return GetDsoHandleFromSearchPath(FLAGS_cudnn_dir, "libcudnn.dylib", false);
#else
GetDsoHandleFromSearchPath(FLAGS_cudnn_dir, "libcudnn.so", dso_handle, false);
return GetDsoHandleFromSearchPath(FLAGS_cudnn_dir, "libcudnn.so", false);
#endif
}
void GetCUPTIDsoHandle(void** dso_handle) {
void* GetCUPTIDsoHandle() {
std::string cupti_path = cupti_lib_path;
if (!FLAGS_cupti_dir.empty()) {
cupti_path = FLAGS_cupti_dir;
}
#if defined(__APPLE__) || defined(__OSX__)
GetDsoHandleFromSearchPath(cupti_path, "libcupti.dylib", dso_handle, false);
return GetDsoHandleFromSearchPath(cupti_path, "libcupti.dylib", false);
#else
GetDsoHandleFromSearchPath(cupti_path, "libcupti.so", dso_handle, false);
return GetDsoHandleFromSearchPath(cupti_path, "libcupti.so", false);
#endif
}
void GetCurandDsoHandle(void** dso_handle) {
void* GetCurandDsoHandle() {
#if defined(__APPLE__) || defined(__OSX__)
GetDsoHandleFromSearchPath(FLAGS_cuda_dir, "libcurand.dylib", dso_handle);
return GetDsoHandleFromSearchPath(FLAGS_cuda_dir, "libcurand.dylib");
#else
GetDsoHandleFromSearchPath(FLAGS_cuda_dir, "libcurand.so", dso_handle);
return GetDsoHandleFromSearchPath(FLAGS_cuda_dir, "libcurand.so");
#endif
}
void GetWarpCTCDsoHandle(void** dso_handle) {
void* GetWarpCTCDsoHandle() {
#if defined(__APPLE__) || defined(__OSX__)
GetDsoHandleFromSearchPath(FLAGS_warpctc_dir, "libwarpctc.dylib", dso_handle);
return GetDsoHandleFromSearchPath(FLAGS_warpctc_dir, "libwarpctc.dylib");
#else
GetDsoHandleFromSearchPath(FLAGS_warpctc_dir, "libwarpctc.so", dso_handle);
return GetDsoHandleFromSearchPath(FLAGS_warpctc_dir, "libwarpctc.so");
#endif
}
void GetLapackDsoHandle(void** dso_handle) {
void* GetLapackDsoHandle() {
#if defined(__APPLE__) || defined(__OSX__)
GetDsoHandleFromSearchPath(FLAGS_lapack_dir, "liblapacke.dylib", dso_handle);
return GetDsoHandleFromSearchPath(FLAGS_lapack_dir, "liblapacke.dylib");
#else
GetDsoHandleFromSearchPath(FLAGS_lapack_dir, "liblapacke.so", dso_handle);
return GetDsoHandleFromSearchPath(FLAGS_lapack_dir, "liblapacke.so");
#endif
}
void GetNCCLDsoHandle(void** dso_handle) {
void* GetNCCLDsoHandle() {
#if defined(__APPLE__) || defined(__OSX__)
GetDsoHandleFromSearchPath(FLAGS_nccl_dir, "libnccl.dylib", dso_handle);
return GetDsoHandleFromSearchPath(FLAGS_nccl_dir, "libnccl.dylib");
#else
GetDsoHandleFromSearchPath(FLAGS_nccl_dir, "libnccl.so", dso_handle);
return GetDsoHandleFromSearchPath(FLAGS_nccl_dir, "libnccl.so");
#endif
}
......
paddle/fluid/platform/dynload/dynamic_loader.h
浏览文件 @ 82530e24
......@@ -18,55 +18,13 @@ namespace paddle {
namespace platform {
namespace dynload {
/**
* @brief load the DSO of CUBLAS
*
* @param **dso_handle dso handler
*
*/
void GetCublasDsoHandle(void** dso_handle);
/**
* @brief load the DSO of CUDNN
*
* @param **dso_handle dso handler
*
*/
void GetCUDNNDsoHandle(void** dso_handle);
void GetCUPTIDsoHandle(void** dso_handle);
/**
* @brief load the DSO of CURAND
*
* @param **dso_handle dso handler
*
*/
void GetCurandDsoHandle(void** dso_handle);
/**
* @brief load the DSO of warp-ctc
*
* @param **dso_handle dso handler
*
*/
void GetWarpCTCDsoHandle(void** dso_handle);
/**
* @brief load the DSO of lapack
*
* @param **dso_handle dso handler
*
*/
void GetLapackDsoHandle(void** dso_handle);
/**
* @brief load the DSO of NVIDIA nccl
*
* @param **dso_handle dso handler
*
*/
void GetNCCLDsoHandle(void** dso_handle);
void* GetCublasDsoHandle();
void* GetCUDNNDsoHandle();
void* GetCUPTIDsoHandle();
void* GetCurandDsoHandle();
void* GetWarpCTCDsoHandle();
void* GetLapackDsoHandle();
void* GetNCCLDsoHandle();
} // namespace dynload
} // namespace platform
......
paddle/fluid/platform/dynload/nccl.cc
浏览文件 @ 82530e24
......@@ -25,11 +25,6 @@ void *nccl_dso_handle;
NCCL_RAND_ROUTINE_EACH(DEFINE_WRAP);
void LoadNCCLDSO() {
platform::call_once(nccl_dso_flag,
[] { GetNCCLDsoHandle(&nccl_dso_handle); });
}
} // namespace dynload
} // namespace platform
} // namespace paddle
paddle/fluid/platform/dynload/nccl.h
浏览文件 @ 82530e24
......@@ -11,12 +11,13 @@ distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <dlfcn.h>
#include <nccl.h>
#include <mutex>
#include <mutex> // NOLINT
#include "paddle/fluid/platform/call_once.h"
#include "paddle/fluid/platform/dynload/dynamic_loader.h"
......@@ -28,18 +29,19 @@ extern std::once_flag nccl_dso_flag;
extern void* nccl_dso_handle;
#ifdef PADDLE_USE_DSO
extern void LoadNCCLDSO();
#define DECLARE_DYNAMIC_LOAD_NCCL_WRAP(__name) \
struct DynLoad__##__name { \
template <typename... Args> \
auto operator()(Args... args) -> decltype(__name(args...)) { \
using nccl_func = decltype(__name(args...)) (*)(Args...); \
paddle::platform::dynload::LoadNCCLDSO(); \
void* p_##__name = dlsym(nccl_dso_handle, #__name); \
return reinterpret_cast<nccl_func>(p_##__name)(args...); \
} \
}; \
#define DECLARE_DYNAMIC_LOAD_NCCL_WRAP(__name) \
struct DynLoad__##__name { \
template <typename... Args> \
auto operator()(Args... args) -> decltype(__name(args...)) { \
using nccl_func = decltype(__name(args...)) (*)(Args...); \
std::call_once(nccl_dso_flag, []() { \
nccl_dso_handle = paddle::platform::dynload::GetNCCLDsoHandle(); \
}); \
void* p_##__name = dlsym(nccl_dso_handle, #__name); \
return reinterpret_cast<nccl_func>(p_##__name)(args...); \
} \
}; \
extern DynLoad__##__name __name
#else
#define DECLARE_DYNAMIC_LOAD_NCCL_WRAP(__name) \
......
paddle/fluid/platform/dynload/warpctc.h
浏览文件 @ 82530e24
......@@ -15,9 +15,10 @@ limitations under the License. */
#pragma once
#include <dlfcn.h>
#include <mutex>
#include "ctc.h"
#include <mutex> // NOLINT
#include "paddle/fluid/platform/dynload/dynamic_loader.h"
#include "warpctc/include/ctc.h"
namespace paddle {
namespace platform {
......@@ -31,18 +32,18 @@ extern void* warpctc_dso_handle;
* (for each function) to dynamic load warpctc routine
* via operator overloading.
*/
#define DYNAMIC_LOAD_WARPCTC_WRAP(__name) \
struct DynLoad__##__name { \
template <typename... Args> \
auto operator()(Args... args) -> decltype(__name(args...)) { \
using warpctcFunc = decltype(__name(args...)) (*)(Args...); \
std::call_once(warpctc_dso_flag, \
paddle::platform::dynload::GetWarpCTCDsoHandle, \
&warpctc_dso_handle); \
void* p_##_name = dlsym(warpctc_dso_handle, #__name); \
return reinterpret_cast<warpctcFunc>(p_##_name)(args...); \
} \
}; \
#define DYNAMIC_LOAD_WARPCTC_WRAP(__name) \
struct DynLoad__##__name { \
template <typename... Args> \
auto operator()(Args... args) -> decltype(__name(args...)) { \
using warpctcFunc = decltype(__name(args...)) (*)(Args...); \
std::call_once(warpctc_dso_flag, []() { \
warpctc_dso_handle = paddle::platform::dynload::GetWarpCTCDsoHandle(); \
}); \
void* p_##_name = dlsym(warpctc_dso_handle, #__name); \
return reinterpret_cast<warpctcFunc>(p_##_name)(args...); \
} \
}; \
extern DynLoad__##__name __name
#define DECLARE_DYNAMIC_LOAD_WARPCTC_WRAP(__name) \
......
paddle/fluid/platform/enforce.h
浏览文件 @ 82530e24
......@@ -16,35 +16,35 @@ limitations under the License. */
#include <dlfcn.h> // for dladdr
#include <execinfo.h> // for backtrace
#ifdef __GNUC__
#include <cxxabi.h> // for __cxa_demangle
#endif // __GNUC__
#ifdef PADDLE_WITH_CUDA
#include <cublas_v2.h>
#include <cudnn.h>
#include <curand.h>
#include <thrust/system/cuda/error.h>
#include <thrust/system_error.h>
#endif // PADDLE_WITH_CUDA
#include <iomanip>
#include <memory>
#include <sstream>
#include <stdexcept>
#include <string>
#include "glog/logging.h"
#include "paddle/fluid/platform/macros.h"
#include "paddle/fluid/string/printf.h"
#include "paddle/fluid/string/to_string.h"
#ifdef __GNUC__
#include <cxxabi.h> // for __cxa_demangle
#endif
#include <glog/logging.h>
#ifdef PADDLE_WITH_CUDA
#include "paddle/fluid/platform/dynload/cublas.h"
#include "paddle/fluid/platform/dynload/cudnn.h"
#include "paddle/fluid/platform/dynload/curand.h"
#include "paddle/fluid/platform/dynload/nccl.h"
#include <cublas_v2.h>
#include <cudnn.h>
#include <curand.h>
#include <thrust/system/cuda/error.h>
#include <thrust/system_error.h>
#endif
namespace paddle {
......@@ -185,7 +185,7 @@ inline typename std::enable_if<sizeof...(Args) != 0, void>::type throw_on_error(
}
}
#endif // PADDLE_ONLY_CPU
#endif // PADDLE_WITH_CUDA
template <typename T>
inline void throw_on_error(T e) {
......
paddle/fluid/platform/enforce_test.cc
浏览文件 @ 82530e24
......@@ -96,7 +96,6 @@ TEST(ENFORCE_GT, FAIL) {
bool caught_exception = false;
try {
PADDLE_ENFORCE_GT(1, 2UL);
} catch (paddle::platform::EnforceNotMet error) {
caught_exception = true;
EXPECT_TRUE(
......@@ -115,7 +114,6 @@ TEST(ENFORCE_GE, FAIL) {
bool caught_exception = false;
try {
PADDLE_ENFORCE_GE(1, 2UL);
} catch (paddle::platform::EnforceNotMet error) {
caught_exception = true;
EXPECT_TRUE(
......@@ -135,7 +133,6 @@ TEST(ENFORCE_LE, FAIL) {
bool caught_exception = false;
try {
PADDLE_ENFORCE_GT(1, 2UL);
} catch (paddle::platform::EnforceNotMet error) {
caught_exception = true;
EXPECT_TRUE(
......@@ -171,7 +168,6 @@ TEST(ENFORCE_NOT_NULL, FAIL) {
try {
int* a = nullptr;
PADDLE_ENFORCE_NOT_NULL(a);
} catch (paddle::platform::EnforceNotMet error) {
caught_exception = true;
EXPECT_TRUE(HasPrefix(StringPiece(error.what()), "a should not be null"));
......
paddle/fluid/platform/float16.h
浏览文件 @ 82530e24
......@@ -15,6 +15,7 @@ limitations under the License. */
#pragma once
#include <stdint.h>
#include <limits>
#ifdef PADDLE_WITH_CUDA
#include <cuda.h>
......@@ -293,39 +294,39 @@ struct PADDLE_ALIGN(2) float16 {
HOSTDEVICE inline explicit operator bool() const { return (x & 0x7fff) != 0; }
HOSTDEVICE inline explicit operator int8_t() const {
return static_cast<int8_t>(float(*this));
return static_cast<int8_t>(static_cast<float>(*this));
}
HOSTDEVICE inline explicit operator uint8_t() const {
return static_cast<uint8_t>(float(*this));
return static_cast<uint8_t>(static_cast<float>(*this));
}
HOSTDEVICE inline explicit operator int16_t() const {
return static_cast<int16_t>(float(*this));
return static_cast<int16_t>(static_cast<float>(*this));
}
HOSTDEVICE inline explicit operator uint16_t() const {
return static_cast<uint16_t>(float(*this));
return static_cast<uint16_t>(static_cast<float>(*this));
}
HOSTDEVICE inline explicit operator int32_t() const {
return static_cast<int32_t>(float(*this));
return static_cast<int32_t>(static_cast<float>(*this));
}
HOSTDEVICE inline explicit operator uint32_t() const {
return static_cast<uint32_t>(float(*this));
return static_cast<uint32_t>(static_cast<float>(*this));
}
HOSTDEVICE inline explicit operator int64_t() const {
return static_cast<int64_t>(float(*this));
return static_cast<int64_t>(static_cast<float>(*this));
}
HOSTDEVICE inline explicit operator uint64_t() const {
return static_cast<uint64_t>(float(*this));
return static_cast<uint64_t>(static_cast<float>(*this));
}
HOSTDEVICE inline explicit operator double() const {
return static_cast<double>(float(*this));
return static_cast<double>(static_cast<float>(*this));
}
private:
......@@ -370,7 +371,7 @@ DEVICE inline half operator+(const half& a, const half& b) {
#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 530
return __hadd(a, b);
#else
float res = float(float16(a)) + float(float16(b));
float res = static_cast<float>(float16(a)) + static_cast<float>(float16(b));
return half(float16(res));
#endif
}
......@@ -379,7 +380,7 @@ DEVICE inline half operator-(const half& a, const half& b) {
#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 530
return __hsub(a, b);
#else
float res = float(float16(a)) - float(float16(b));
float res = static_cast<float>(float16(a)) - static_cast<float>(float16(b));
return half(float16(res));
#endif
}
......@@ -388,7 +389,7 @@ DEVICE inline half operator*(const half& a, const half& b) {
#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 530
return __hmul(a, b);
#else
float res = float(float16(a)) * float(float16(b));
float res = static_cast<float>(float16(a)) * static_cast<float>(float16(b));
return half(float16(res));
#endif
}
......@@ -399,7 +400,7 @@ DEVICE inline half operator/(const half& a, const half& b) {
float denom = __half2float(b);
return __float2half(num / denom);
#else
float res = float(float16(a)) / float(float16(b));
float res = static_cast<float>(float16(a)) / static_cast<float>(float16(b));
return half(float16(res));
#endif
}
......@@ -408,27 +409,27 @@ DEVICE inline half operator-(const half& a) {
#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 530
return __hneg(a);
#else
float res = -float(float16(a));
float res = -static_cast<float>(float16(a));
return half(float16(res));
#endif
}
DEVICE inline half& operator+=(half& a, const half& b) {
DEVICE inline half& operator+=(half& a, const half& b) { // NOLINT
a = a + b;
return a;
}
DEVICE inline half& operator-=(half& a, const half& b) {
DEVICE inline half& operator-=(half& a, const half& b) { // NOLINT
a = a - b;
return a;
}
DEVICE inline half& operator*=(half& a, const half& b) {
DEVICE inline half& operator*=(half& a, const half& b) { // NOLINT
a = a * b;
return a;
}
DEVICE inline half& operator/=(half& a, const half& b) {
DEVICE inline half& operator/=(half& a, const half& b) { // NOLINT
a = a / b;
return a;
}
......@@ -437,7 +438,7 @@ DEVICE inline bool operator==(const half& a, const half& b) {
#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 530
return __heq(a, b);
#else
return float(float16(a)) == float(float16(b));
return static_cast<float>(float16(a)) == static_cast<float>(float16(b));
#endif
}
......@@ -445,7 +446,7 @@ DEVICE inline bool operator!=(const half& a, const half& b) {
#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 530
return __hne(a, b);
#else
return float(float16(a)) != float(float16(b));
return static_cast<float>(float16(a)) != static_cast<float>(float16(b));
#endif
}
......@@ -453,7 +454,7 @@ DEVICE inline bool operator<(const half& a, const half& b) {
#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 530
return __hlt(a, b);
#else
return float(float16(a)) < float(float16(b));
return static_cast<float>(float16(a)) < static_cast<float>(float16(b));
#endif
}
......@@ -461,7 +462,7 @@ DEVICE inline bool operator<=(const half& a, const half& b) {
#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 530
return __hle(a, b);
#else
return float(float16(a)) <= float(float16(b));
return static_cast<float>(float16(a)) <= static_cast<float>(float16(b));
#endif
}
......@@ -469,7 +470,7 @@ DEVICE inline bool operator>(const half& a, const half& b) {
#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 530
return __hgt(a, b);
#else
return float(float16(a)) > float(float16(b));
return static_cast<float>(float16(a)) > static_cast<float>(float16(b));
#endif
}
......@@ -477,7 +478,7 @@ DEVICE inline bool operator>=(const half& a, const half& b) {
#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 530
return __hge(a, b);
#else
return float(float16(a)) >= float(float16(b));
return static_cast<float>(float16(a)) >= static_cast<float>(float16(b));
#endif
}
......@@ -489,7 +490,7 @@ HOSTDEVICE inline float16 operator+(const float16& a, const float16& b) {
#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 530
return float16(__hadd(half(a), half(b)));
#else
return float16(float(a) + float(b));
return float16(static_cast<float>(a) + static_cast<float>(b));
#endif
}
......@@ -497,7 +498,7 @@ HOSTDEVICE inline float16 operator-(const float16& a, const float16& b) {
#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 530
return float16(__hsub(half(a), half(b)));
#else
return float16(float(a) - float(b));
return float16(static_cast<float>(a) - static_cast<float>(b));
#endif
}
......@@ -505,7 +506,7 @@ HOSTDEVICE inline float16 operator*(const float16& a, const float16& b) {
#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 530
return float16(__hmul(half(a), half(b)));
#else
return float16(float(a) * float(b));
return float16(static_cast<float>(a) * static_cast<float>(b));
#endif
}
......@@ -516,7 +517,7 @@ HOSTDEVICE inline float16 operator/(const float16& a, const float16& b) {
float denom = __half2float(half(b));
return float16(num / denom);
#else
return float16(float(a) / float(b));
return float16(static_cast<float>(a) / static_cast<float>(b));
#endif
}
......@@ -530,22 +531,22 @@ HOSTDEVICE inline float16 operator-(const float16& a) {
#endif
}
HOSTDEVICE inline float16& operator+=(float16& a, const float16& b) {
HOSTDEVICE inline float16& operator+=(float16& a, const float16& b) { // NOLINT
a = a + b;
return a;
}
HOSTDEVICE inline float16& operator-=(float16& a, const float16& b) {
HOSTDEVICE inline float16& operator-=(float16& a, const float16& b) { // NOLINT
a = a - b;
return a;
}
HOSTDEVICE inline float16& operator*=(float16& a, const float16& b) {
HOSTDEVICE inline float16& operator*=(float16& a, const float16& b) { // NOLINT
a = a * b;
return a;
}
HOSTDEVICE inline float16& operator/=(float16& a, const float16& b) {
HOSTDEVICE inline float16& operator/=(float16& a, const float16& b) { // NOLINT
a = a / b;
return a;
}
......@@ -554,7 +555,7 @@ HOSTDEVICE inline bool operator==(const float16& a, const float16& b) {
#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 530
return __heq(half(a), half(b));
#else
return float(a) == float(b);
return static_cast<float>(a) == static_cast<float>(b);
#endif
}
......@@ -562,7 +563,7 @@ HOSTDEVICE inline bool operator!=(const float16& a, const float16& b) {
#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 530
return __hne(half(a), half(b));
#else
return float(a) != float(b);
return static_cast<float>(a) != static_cast<float>(b);
#endif
}
......@@ -570,7 +571,7 @@ HOSTDEVICE inline bool operator<(const float16& a, const float16& b) {
#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 530
return __hlt(half(a), half(b));
#else
return float(a) < float(b);
return static_cast<float>(a) < static_cast<float>(b);
#endif
}
......@@ -578,7 +579,7 @@ HOSTDEVICE inline bool operator<=(const float16& a, const float16& b) {
#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 530
return __hle(half(a), half(b));
#else
return float(a) <= float(b);
return static_cast<float>(a) <= static_cast<float>(b);
#endif
}
......@@ -586,7 +587,7 @@ HOSTDEVICE inline bool operator>(const float16& a, const float16& b) {
#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 530
return __hgt(half(a), half(b));
#else
return float(a) > float(b);
return static_cast<float>(a) > static_cast<float>(b);
#endif
}
......@@ -594,7 +595,7 @@ HOSTDEVICE inline bool operator>=(const float16& a, const float16& b) {
#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 530
return __hge(half(a), half(b));
#else
return float(a) >= float(b);
return static_cast<float>(a) >= static_cast<float>(b);
#endif
}
......@@ -679,22 +680,22 @@ inline float16 operator-(const float16& a) {
return res;
}
inline float16& operator+=(float16& a, const float16& b) {
inline float16& operator+=(float16& a, const float16& b) { // NOLINT
a = a + b;
return a;
}
inline float16& operator-=(float16& a, const float16& b) {
inline float16& operator-=(float16& a, const float16& b) { // NOLINT
a = a - b;
return a;
}
inline float16& operator*=(float16& a, const float16& b) {
inline float16& operator*=(float16& a, const float16& b) { // NOLINT
a = a * b;
return a;
}
inline float16& operator/=(float16& a, const float16& b) {
inline float16& operator/=(float16& a, const float16& b) { // NOLINT
a = a / b;
return a;
}
......@@ -784,19 +785,19 @@ inline bool operator>=(const float16& a, const float16& b) {
// Arithmetic operators for float16, software emulated on other CPU
#else
inline float16 operator+(const float16& a, const float16& b) {
return float16(float(a) + float(b));
return float16(static_cast<float>(a) + static_cast<float>(b));
}
inline float16 operator-(const float16& a, const float16& b) {
return float16(float(a) - float(b));
return float16(static_cast<float>(a) - static_cast<float>(b));
}
inline float16 operator*(const float16& a, const float16& b) {
return float16(float(a) * float(b));
return float16(static_cast<float>(a) * static_cast<float>(b));
}
inline float16 operator/(const float16& a, const float16& b) {
return float16(float(a) / float(b));
return float16(static_cast<float>(a) / static_cast<float>(b));
}
inline float16 operator-(const float16& a) {
......@@ -805,51 +806,57 @@ inline float16 operator-(const float16& a) {
return res;
}
inline float16& operator+=(float16& a, const float16& b) {
a = float16(float(a) + float(b));
inline float16& operator+=(float16& a, const float16& b) { // NOLINT
a = float16(static_cast<float>(a) + static_cast<float>(b));
return a;
}
inline float16& operator-=(float16& a, const float16& b) {
a = float16(float(a) - float(b));
inline float16& operator-=(float16& a, const float16& b) { // NOLINT
a = float16(static_cast<float>(a) - static_cast<float>(b));
return a;
}
inline float16& operator*=(float16& a, const float16& b) {
a = float16(float(a) * float(b));
inline float16& operator*=(float16& a, const float16& b) { // NOLINT
a = float16(static_cast<float>(a) * static_cast<float>(b));
return a;
}
inline float16& operator/=(float16& a, const float16& b) {
a = float16(float(a) / float(b));
inline float16& operator/=(float16& a, const float16& b) { // NOLINT
a = float16(static_cast<float>(a) / static_cast<float>(b));
return a;
}
inline bool operator==(const float16& a, const float16& b) {
return float(a) == float(b);
return static_cast<float>(a) == static_cast<float>(b);
}
inline bool operator!=(const float16& a, const float16& b) {
return float(a) != float(b);
return static_cast<float>(a) != static_cast<float>(b);
}
inline bool operator<(const float16& a, const float16& b) {
return float(a) < float(b);
return static_cast<float>(a) < static_cast<float>(b);
}
inline bool operator<=(const float16& a, const float16& b) {
return float(a) <= float(b);
return static_cast<float>(a) <= static_cast<float>(b);
}
inline bool operator>(const float16& a, const float16& b) {
return float(a) > float(b);
return static_cast<float>(a) > static_cast<float>(b);
}
inline bool operator>=(const float16& a, const float16& b) {
return float(a) >= float(b);
return static_cast<float>(a) >= static_cast<float>(b);
}
#endif
HOSTDEVICE inline float16 raw_uint16_to_float16(uint16_t a) {
float16 res;
res.x = a;
return res;
}
HOSTDEVICE inline bool(isnan)(const float16& a) {
#if defined(PADDLE_CUDA_FP16) && defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 530
return __hisnan(half(a));
......@@ -886,28 +893,116 @@ struct is_pod<paddle::platform::float16> {
is_standard_layout<paddle::platform::float16>::value;
};
template <>
struct numeric_limits<paddle::platform::float16> {
static const bool is_specialized = true;
static const bool is_signed = true;
static const bool is_integer = false;
static const bool is_exact = false;
static const bool has_infinity = true;
static const bool has_quiet_NaN = true;
static const bool has_signaling_NaN = true;
static const float_denorm_style has_denorm = denorm_present;
static const bool has_denorm_loss = false;
static const std::float_round_style round_style = std::round_to_nearest;
static const bool is_iec559 = false;
static const bool is_bounded = false;
static const bool is_modulo = false;
static const int digits = 11;
static const int digits10 = 3;
static const int max_digits10 = 5;
static const int radix = 2;
static const int min_exponent = -13;
static const int min_exponent10 = -4;
static const int max_exponent = 16;
static const int max_exponent10 = 4;
static const bool traps = true;
static const bool tinyness_before = false;
static paddle::platform::float16(min)() {
return paddle::platform::raw_uint16_to_float16(0x400);
}
static paddle::platform::float16 lowest() {
return paddle::platform::raw_uint16_to_float16(0xfbff);
}
static paddle::platform::float16(max)() {
return paddle::platform::raw_uint16_to_float16(0x7bff);
}
static paddle::platform::float16 epsilon() {
return paddle::platform::raw_uint16_to_float16(0x0800);
}
static paddle::platform::float16 round_error() {
return paddle::platform::float16(0.5);
}
static paddle::platform::float16 infinity() {
return paddle::platform::raw_uint16_to_float16(0x7c00);
}
static paddle::platform::float16 quiet_NaN() {
return paddle::platform::raw_uint16_to_float16(0x7e00);
}
static paddle::platform::float16 signaling_NaN() {
return paddle::platform::raw_uint16_to_float16(0x7e00);
}
static paddle::platform::float16 denorm_min() {
return paddle::platform::raw_uint16_to_float16(0x1);
}
};
} // namespace std
namespace Eigen {
using float16 = paddle::platform::float16;
template <>
struct NumTraits<float16> : GenericNumTraits<float16> {
enum {
IsSigned = true,
IsInteger = false,
IsComplex = false,
RequireInitialization = false
};
HOSTDEVICE static inline float16 epsilon() {
return paddle::platform::raw_uint16_to_float16(0x0800);
}
HOSTDEVICE static inline float16 dummy_precision() { return float16(1e-2f); }
HOSTDEVICE static inline float16 highest() {
return paddle::platform::raw_uint16_to_float16(0x7bff);
}
HOSTDEVICE static inline float16 lowest() {
return paddle::platform::raw_uint16_to_float16(0xfbff);
}
HOSTDEVICE static inline float16 infinity() {
return paddle::platform::raw_uint16_to_float16(0x7c00);
}
HOSTDEVICE static inline float16 quiet_NaN() {
return paddle::platform::raw_uint16_to_float16(0x7c01);
}
};
namespace numext {
template <>
EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool(isnan)(
const paddle::platform::float16& a) {
HOSTDEVICE inline bool(isnan)(const float16& a) {
return (paddle::platform::isnan)(a);
}
template <>
EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool(isinf)(
const paddle::platform::float16& a) {
HOSTDEVICE inline bool(isinf)(const float16& a) {
return (paddle::platform::isinf)(a);
}
template <>
EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE bool(isfinite)(
const paddle::platform::float16& a) {
HOSTDEVICE inline bool(isfinite)(const float16& a) {
return (paddle::platform::isfinite)(a);
}
template <>
HOSTDEVICE inline float16 exp(const float16& a) {
return float16(::expf(static_cast<float>(a)));
}
} // namespace numext
} // namespace Eigen
paddle/fluid/platform/gpu_info.cc
浏览文件 @ 82530e24
......@@ -14,8 +14,9 @@ limitations under the License. */
#include "paddle/fluid/platform/gpu_info.h"
#include "gflags/gflags.h"
#include <algorithm>
#include "gflags/gflags.h"
#include "paddle/fluid/platform/enforce.h"
DEFINE_double(fraction_of_gpu_memory_to_use, 0.92,
......@@ -77,8 +78,8 @@ void SetDeviceId(int id) {
"cudaSetDevice failed in paddle::platform::SetDeviceId");
}
void GpuMemoryUsage(size_t &available, size_t &total) {
PADDLE_ENFORCE(cudaMemGetInfo(&available, &total),
void GpuMemoryUsage(size_t *available, size_t *total) {
PADDLE_ENFORCE(cudaMemGetInfo(available, total),
"cudaMemGetInfo failed in paddle::platform::GetMemoryUsage");
}
......@@ -86,7 +87,7 @@ size_t GpuMaxAllocSize() {
size_t total = 0;
size_t available = 0;
GpuMemoryUsage(available, total);
GpuMemoryUsage(&available, &total);
// Reserve the rest for page tables, etc.
return static_cast<size_t>(total * FLAGS_fraction_of_gpu_memory_to_use);
......@@ -101,7 +102,7 @@ size_t GpuMaxChunkSize() {
size_t total = 0;
size_t available = 0;
GpuMemoryUsage(available, total);
GpuMemoryUsage(&available, &total);
VLOG(10) << "GPU Usage " << available / 1024 / 1024 << "M/"
<< total / 1024 / 1024 << "M";
size_t reserving = static_cast<size_t>(0.05 * total);
......
paddle/fluid/platform/gpu_info.h
浏览文件 @ 82530e24
......@@ -23,10 +23,6 @@ limitations under the License. */
namespace paddle {
namespace platform {
//! Environment variable: fraction of GPU memory to use on each device.
const std::string kEnvFractionGpuMemoryToUse =
"PADDLE_FRACTION_GPU_MEMORY_TO_USE";
//! Get the total number of GPU devices in system.
int GetCUDADeviceCount();
......@@ -46,7 +42,7 @@ int GetCurrentDeviceId();
void SetDeviceId(int device_id);
//! Get the memory usage of current GPU device.
void GpuMemoryUsage(size_t &available, size_t &total);
void GpuMemoryUsage(size_t *available, size_t *total);
//! Get the maximum allocation size of current GPU device.
size_t GpuMaxAllocSize();
......
paddle/fluid/platform/place.cc
浏览文件 @ 82530e24
......@@ -26,6 +26,7 @@ class PlacePrinter : public boost::static_visitor<> {
void operator()(const CUDAPlace &p) {
os_ << "CUDAPlace(" << p.device << ")";
}
void operator()(const CUDAPinnedPlace &p) { os_ << "CUDAPinnedPlace"; }
private:
std::ostream &os_;
......@@ -40,12 +41,19 @@ const Place &get_place() { return the_default_place; }
const CUDAPlace default_gpu() { return CUDAPlace(0); }
const CPUPlace default_cpu() { return CPUPlace(); }
const CUDAPinnedPlace default_cuda_pinned() { return CUDAPinnedPlace(); }
bool is_gpu_place(const Place &p) {
return boost::apply_visitor(IsCUDAPlace(), p);
}
bool is_cpu_place(const Place &p) { return !is_gpu_place(p); }
bool is_cpu_place(const Place &p) {
return boost::apply_visitor(IsCPUPlace(), p);
}
bool is_cuda_pinned_place(const Place &p) {
return boost::apply_visitor(IsCUDAPinnedPlace(), p);
}
bool places_are_same_class(const Place &p1, const Place &p2) {
return p1.which() == p2.which();
......@@ -53,7 +61,7 @@ bool places_are_same_class(const Place &p1, const Place &p2) {
bool is_same_place(const Place &p1, const Place &p2) {
if (places_are_same_class(p1, p2)) {
if (is_cpu_place(p1)) {
if (is_cpu_place(p1) || is_cuda_pinned_place(p1)) {
return true;
} else {
return boost::get<CUDAPlace>(p1) == boost::get<CUDAPlace>(p2);
......
paddle/fluid/platform/place.h
浏览文件 @ 82530e24
......@@ -11,10 +11,11 @@ distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <iostream>
#include <vector>
#include "paddle/fluid/platform/enforce.h"
#include "paddle/fluid/platform/variant.h"
......@@ -45,12 +46,33 @@ struct CUDAPlace {
int device;
};
struct CUDAPinnedPlace {
CUDAPinnedPlace() {}
// needed for variant equality comparison
inline bool operator==(const CUDAPinnedPlace &) const { return true; }
inline bool operator!=(const CUDAPinnedPlace &) const { return false; }
};
struct IsCUDAPlace : public boost::static_visitor<bool> {
bool operator()(const CPUPlace &) const { return false; }
bool operator()(const CUDAPlace &gpu) const { return true; }
bool operator()(const CUDAPinnedPlace &) const { return false; }
};
typedef boost::variant<CUDAPlace, CPUPlace> Place;
struct IsCPUPlace : public boost::static_visitor<bool> {
bool operator()(const CPUPlace &cpu) const { return true; }
bool operator()(const CUDAPlace &) const { return false; }
bool operator()(const CUDAPinnedPlace &) const { return false; }
};
struct IsCUDAPinnedPlace : public boost::static_visitor<bool> {
bool operator()(const CPUPlace &) const { return false; }
bool operator()(const CUDAPlace &) const { return false; }
bool operator()(const CUDAPinnedPlace &cuda_pinned) const { return true; }
};
typedef boost::variant<CUDAPlace, CPUPlace, CUDAPinnedPlace> Place;
using PlaceList = std::vector<Place>;
......@@ -59,9 +81,11 @@ const Place &get_place();
const CUDAPlace default_gpu();
const CPUPlace default_cpu();
const CUDAPinnedPlace default_cuda_pinned();
bool is_gpu_place(const Place &);
bool is_cpu_place(const Place &);
bool is_cuda_pinned_place(const Place &);
bool places_are_same_class(const Place &, const Place &);
bool is_same_place(const Place &, const Place &);
......@@ -95,6 +119,16 @@ struct PlaceVisitorWrapper
#else
PADDLE_THROW("Paddle is not compiled with CUDA. Cannot visit cuda device");
return typename Visitor::result_type();
#endif
}
typename Visitor::result_type operator()(
const CUDAPinnedPlace &cuda_pinned) const {
#ifdef PADDLE_WITH_CUDA
return visitor_(cuda_pinned);
#else
PADDLE_THROW("Paddle is not compiled with CUDA. Cannot visit cuda_pinned");
return typename Visitor::result_type();
#endif
}
};
......
paddle/fluid/pybind/.clang-format 已删除 100644 → 0
浏览文件 @ 07b31b80
---
Language: Cpp
BasedOnStyle: Google
Standard: Cpp11
...
paddle/fluid/pybind/CMakeLists.txt
浏览文件 @ 82530e24
......@@ -2,17 +2,19 @@ if(WITH_PYTHON)
if(WITH_AMD_GPU)
hip_library(paddle_pybind SHARED
SRCS pybind.cc exception.cc protobuf.cc const_value.cc recordio.cc
DEPS pybind python backward proto_desc paddle_memory executor prune init profiler feed_fetch_method
DEPS pybind python backward proto_desc memory executor prune init profiler feed_fetch_method
parallel_executor
${GLOB_OP_LIB})
else()
cc_library(paddle_pybind SHARED
SRCS pybind.cc exception.cc protobuf.cc const_value.cc recordio.cc
DEPS pybind python backward proto_desc paddle_memory executor prune init profiler feed_fetch_method
DEPS pybind python backward proto_desc memory executor prune init profiler feed_fetch_method
parallel_executor
${GLOB_OP_LIB})
if(NOT APPLE AND NOT ANDROID)
target_link_libraries(paddle_pybind rt)
endif(NOT APPLE AND NOT ANDROID)
endif(WITH_AMD_GPU)
cc_test(tensor_py_test SRCS tensor_py_test.cc DEPS python)
endif(WITH_PYTHON)
paddle/fluid/pybind/const_value.cc
浏览文件 @ 82530e24
......@@ -12,17 +12,17 @@ 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 "const_value.h"
#include "paddle/fluid/pybind/const_value.h"
#include "paddle/fluid/framework/operator.h"
namespace paddle {
namespace pybind {
void BindConstValue(pybind11::module& m) {
m.def("kEmptyVarName", [] { return framework::kEmptyVarName; });
m.def("kTempVarName", [] { return framework::kTempVarName; });
m.def("kGradVarSuffix", [] { return framework::kGradVarSuffix; });
m.def("kZeroVarSuffix", [] { return framework::kZeroVarSuffix; });
void BindConstValue(pybind11::module* m) {
m->def("kEmptyVarName", [] { return framework::kEmptyVarName; });
m->def("kTempVarName", [] { return framework::kTempVarName; });
m->def("kGradVarSuffix", [] { return framework::kGradVarSuffix; });
m->def("kZeroVarSuffix", [] { return framework::kZeroVarSuffix; });
}
} // namespace pybind
......
paddle/fluid/pybind/const_value.h
浏览文件 @ 82530e24
......@@ -11,16 +11,17 @@ distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <Python.h>
#include "paddle/fluid/platform/enforce.h"
#include "pybind11/pybind11.h"
namespace py = pybind11;
namespace paddle {
namespace pybind {
extern void BindConstValue(pybind11::module& m);
void BindConstValue(pybind11::module* m);
} // namespace pybind
} // namespace paddle
paddle/fluid/pybind/exception.cc
浏览文件 @ 82530e24
......@@ -17,8 +17,8 @@ limitations under the License. */
namespace paddle {
namespace pybind {
void BindException(pybind11::module& m) {
static pybind11::exception<platform::EnforceNotMet> exc(m, "EnforceNotMet");
void BindException(pybind11::module* m) {
static pybind11::exception<platform::EnforceNotMet> exc(*m, "EnforceNotMet");
pybind11::register_exception_translator([](std::exception_ptr p) {
try {
if (p) std::rethrow_exception(p);
......@@ -27,7 +27,8 @@ void BindException(pybind11::module& m) {
}
});
m.def("__unittest_throw_exception__", [] { PADDLE_THROW("test exception"); });
m->def("__unittest_throw_exception__",
[] { PADDLE_THROW("test exception"); });
}
} // namespace pybind
......
paddle/fluid/pybind/exception.h
浏览文件 @ 82530e24
......@@ -11,14 +11,17 @@ distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <Python.h>
#include "paddle/fluid/platform/enforce.h"
#include "pybind11/pybind11.h"
namespace paddle {
namespace pybind {
extern void BindException(pybind11::module& m);
void BindException(pybind11::module* m);
} // namespace pybind
} // namespace paddle
paddle/fluid/pybind/protobuf.cc
浏览文件 @ 82530e24
......@@ -11,10 +11,13 @@ distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/pybind/protobuf.h"
#include <deque>
#include <iostream>
#include <string>
#include <tuple>
#include "paddle/fluid/framework/backward.h"
#include "paddle/fluid/framework/block_desc.h"
#include "paddle/fluid/framework/op_desc.h"
......@@ -95,10 +98,11 @@ struct type_caster<boost::variant<Args...>>
namespace paddle {
namespace pybind {
using namespace paddle::framework; // NOLINT
namespace pd = paddle::framework;
template <typename T>
static py::bytes SerializeMessage(T &self) {
static pybind11::bytes SerializeMessage(
T &self) { // NOLINT due to pybind11 convention.
// Check IsInitialized in Python
std::string retv;
PADDLE_ENFORCE(self.Proto()->SerializePartialToString(&retv),
......@@ -107,24 +111,24 @@ static py::bytes SerializeMessage(T &self) {
}
// Bind Methods
void BindProgramDesc(py::module &m) {
py::class_<ProgramDesc>(m, "ProgramDesc", "")
.def(py::init<>())
void BindProgramDesc(pybind11::module *m) {
pybind11::class_<pd::ProgramDesc>(*m, "ProgramDesc", "")
.def(pybind11::init<>())
.def("__init__",
[](ProgramDesc &self, const ProgramDesc &other) {
new (&self) ProgramDesc(other);
[](pd::ProgramDesc &self, const pd::ProgramDesc &other) {
new (&self) pd::ProgramDesc(other);
})
.def("__init__",
[](ProgramDesc &self, const py::bytes &binary_str) {
[](pd::ProgramDesc &self, const pybind11::bytes &binary_str) {
std::string str(binary_str);
new (&self) ProgramDesc(str);
new (&self) pd::ProgramDesc(str);
})
.def("append_block", &ProgramDesc::AppendBlock,
py::return_value_policy::reference)
.def("append_block", &pd::ProgramDesc::AppendBlock,
pybind11::return_value_policy::reference)
.def("append_backward",
[](ProgramDesc &program_desc, const VarDesc &target,
[](pd::ProgramDesc &program_desc, const pd::VarDesc &target,
const std::unordered_set<std::string> &no_grad_vars) {
ParamGradInfoMap param_grad_map =
pd::ParamGradInfoMap param_grad_map =
AppendBackward(program_desc, target, no_grad_vars);
std::unordered_map<
std::string, std::tuple<std::string /* grad_var_name */,
......@@ -138,172 +142,184 @@ void BindProgramDesc(py::module &m) {
}
return retv;
})
.def("block", &ProgramDesc::MutableBlock,
py::return_value_policy::reference)
.def("num_blocks", &ProgramDesc::Size)
.def("serialize_to_string", SerializeMessage<ProgramDesc>)
.def("block", &pd::ProgramDesc::MutableBlock,
pybind11::return_value_policy::reference)
.def("num_blocks", &pd::ProgramDesc::Size)
.def("serialize_to_string", SerializeMessage<pd::ProgramDesc>)
.def("parse_from_string",
[](ProgramDesc &program_desc, const std::string &data) {
proto::ProgramDesc *desc = program_desc.Proto();
[](pd::ProgramDesc &program_desc, const std::string &data) {
pd::proto::ProgramDesc *desc = program_desc.Proto();
PADDLE_ENFORCE(desc->ParseFromString(data),
"Fail to parse ProgramDesc from string. This could "
"be a bug of Paddle.");
});
}
void BindBlockDesc(py::module &m) {
py::class_<BlockDesc>(m, "BlockDesc", "")
.def_property_readonly("id", &BlockDesc::ID)
.def_property_readonly("parent", &BlockDesc::Parent)
.def("get_forward_block_idx", &BlockDesc::ForwardBlockID)
.def("set_forward_block_idx", &BlockDesc::SetForwardBlockID)
.def("append_op", &BlockDesc::AppendOp,
py::return_value_policy::reference)
.def("prepend_op", &BlockDesc::PrependOp,
py::return_value_policy::reference)
.def("insert_op", &BlockDesc::InsertOp,
py::return_value_policy::reference)
.def("remove_op", &BlockDesc::RemoveOp)
void BindBlockDesc(pybind11::module *m) {
pybind11::class_<pd::BlockDesc>(*m, "BlockDesc", "")
.def_property_readonly("id", &pd::BlockDesc::ID)
.def_property_readonly("parent", &pd::BlockDesc::Parent)
.def("get_forward_block_idx", &pd::BlockDesc::ForwardBlockID)
.def("set_forward_block_idx", &pd::BlockDesc::SetForwardBlockID)
.def("append_op", &pd::BlockDesc::AppendOp,
pybind11::return_value_policy::reference)
.def("prepend_op", &pd::BlockDesc::PrependOp,
pybind11::return_value_policy::reference)
.def("insert_op", &pd::BlockDesc::InsertOp,
pybind11::return_value_policy::reference)
.def("remove_op", &pd::BlockDesc::RemoveOp)
.def("var",
[](BlockDesc &self, py::bytes byte_name) {
[](pd::BlockDesc &self, pybind11::bytes byte_name) {
std::string name = byte_name;
return self.Var(name);
},
py::return_value_policy::reference)
pybind11::return_value_policy::reference)
.def("has_var",
[](BlockDesc &self, py::bytes byte_name) {
[](pd::BlockDesc &self, pybind11::bytes byte_name) {
std::string name = byte_name;
return self.HasVar(name);
},
py::return_value_policy::reference)
pybind11::return_value_policy::reference)
.def("rename_var",
[](BlockDesc &self, const py::bytes &byte_name,
const py::bytes &byte_name_new) {
[](pd::BlockDesc &self, const pybind11::bytes &byte_name,
const pybind11::bytes &byte_name_new) {
std::string name = byte_name;
std::string new_name = byte_name_new;
self.RenameVar(name, new_name);
})
.def("has_var_recursive",
[](BlockDesc &self, py::bytes byte_name) {
[](pd::BlockDesc &self, pybind11::bytes byte_name) {
std::string name = byte_name;
return self.HasVarRecursive(name);
})
.def("find_var",
[](BlockDesc &self, py::bytes byte_name) {
[](pd::BlockDesc &self, pybind11::bytes byte_name) {
std::string name = byte_name;
return self.FindVar(name);
},
py::return_value_policy::reference)
pybind11::return_value_policy::reference)
.def("find_var_recursive",
[](BlockDesc &self, py::bytes byte_name) {
[](pd::BlockDesc &self, pybind11::bytes byte_name) {
std::string name = byte_name;
return self.FindVarRecursive(name);
},
py::return_value_policy::reference)
.def("all_vars", &BlockDesc::AllVars, py::return_value_policy::reference)
.def("op_size", &BlockDesc::OpSize)
.def("op", &BlockDesc::Op, py::return_value_policy::reference)
.def("serialize_to_string", SerializeMessage<BlockDesc>);
pybind11::return_value_policy::reference)
.def("remove_var",
[](pd::BlockDesc &self, pybind11::bytes byte_name) {
std::string name = byte_name;
return self.RemoveVar(name);
},
pybind11::return_value_policy::reference)
.def("all_vars", &pd::BlockDesc::AllVars,
pybind11::return_value_policy::reference)
.def("op_size", &pd::BlockDesc::OpSize)
.def("op", &pd::BlockDesc::Op, pybind11::return_value_policy::reference)
.def("serialize_to_string", SerializeMessage<pd::BlockDesc>);
}
void BindVarDsec(py::module &m) {
py::class_<VarDesc> var_desc(m, "VarDesc", "");
void BindVarDsec(pybind11::module *m) {
pybind11::class_<pd::VarDesc> var_desc(*m, "VarDesc", "");
var_desc
.def("name",
[](VarDesc &self) {
py::bytes name = self.Name();
[](pd::VarDesc &self) {
pybind11::bytes name = self.Name();
return name;
},
py::return_value_policy::reference)
.def("set_name", &VarDesc::SetName)
.def("set_shape", &VarDesc::SetShape)
.def("set_shapes", &VarDesc::SetShapes)
.def("set_dtype", &VarDesc::SetDataType)
.def("set_dtypes", &VarDesc::SetDataTypes)
.def("set_capacity", &VarDesc::SetCapacity)
.def("shape", &VarDesc::GetShape, py::return_value_policy::reference)
.def("shapes", &VarDesc::GetShapes, py::return_value_policy::reference)
.def("dtype", &VarDesc::GetDataType, py::return_value_policy::reference)
.def("dtypes", &VarDesc::GetDataTypes, py::return_value_policy::reference)
.def("lod_level", &VarDesc::GetLoDLevel)
.def("lod_levels", &VarDesc::GetLoDLevels,
py::return_value_policy::reference)
.def("set_lod_level", &VarDesc::SetLoDLevel)
.def("set_lod_levels", &VarDesc::SetLoDLevels)
.def("type", &VarDesc::GetType)
.def("set_type", &VarDesc::SetType)
.def("serialize_to_string", SerializeMessage<VarDesc>)
.def("persistable", &VarDesc::Persistable)
.def("set_persistable", &VarDesc::SetPersistable);
pybind11::return_value_policy::reference)
.def("set_name", &pd::VarDesc::SetName)
.def("set_shape", &pd::VarDesc::SetShape)
.def("set_shapes", &pd::VarDesc::SetShapes)
.def("set_dtype", &pd::VarDesc::SetDataType)
.def("set_dtypes", &pd::VarDesc::SetDataTypes)
.def("set_capacity", &pd::VarDesc::SetCapacity)
.def("shape", &pd::VarDesc::GetShape,
pybind11::return_value_policy::reference)
.def("shapes", &pd::VarDesc::GetShapes,
pybind11::return_value_policy::reference)
.def("dtype", &pd::VarDesc::GetDataType,
pybind11::return_value_policy::reference)
.def("dtypes", &pd::VarDesc::GetDataTypes,
pybind11::return_value_policy::reference)
.def("lod_level", &pd::VarDesc::GetLoDLevel)
.def("lod_levels", &pd::VarDesc::GetLoDLevels,
pybind11::return_value_policy::reference)
.def("set_lod_level", &pd::VarDesc::SetLoDLevel)
.def("set_lod_levels", &pd::VarDesc::SetLoDLevels)
.def("type", &pd::VarDesc::GetType)
.def("set_type", &pd::VarDesc::SetType)
.def("serialize_to_string", SerializeMessage<pd::VarDesc>)
.def("persistable", &pd::VarDesc::Persistable)
.def("set_persistable", &pd::VarDesc::SetPersistable);
py::enum_<proto::VarType::Type>(var_desc, "VarType", "")
.value("BOOL", proto::VarType::BOOL)
.value("INT16", proto::VarType::INT16)
.value("INT32", proto::VarType::INT32)
.value("INT64", proto::VarType::INT64)
.value("FP16", proto::VarType::FP16)
.value("FP32", proto::VarType::FP32)
.value("FP64", proto::VarType::FP64)
.value("LOD_TENSOR", proto::VarType::LOD_TENSOR)
.value("SELECTED_ROWS", proto::VarType::SELECTED_ROWS)
.value("FEED_MINIBATCH", proto::VarType::FEED_MINIBATCH)
.value("FETCH_LIST", proto::VarType::FETCH_LIST)
.value("STEP_SCOPES", proto::VarType::STEP_SCOPES)
.value("LOD_RANK_TABLE", proto::VarType::LOD_RANK_TABLE)
.value("LOD_TENSOR_ARRAY", proto::VarType::LOD_TENSOR_ARRAY)
.value("CHANNEL", proto::VarType::CHANNEL)
.value("PLACE_LIST", proto::VarType::PLACE_LIST)
.value("READER", proto::VarType::READER)
.value("RAW", proto::VarType::RAW);
pybind11::enum_<pd::proto::VarType::Type>(var_desc, "VarType", "")
.value("BOOL", pd::proto::VarType::BOOL)
.value("INT16", pd::proto::VarType::INT16)
.value("INT32", pd::proto::VarType::INT32)
.value("INT64", pd::proto::VarType::INT64)
.value("FP16", pd::proto::VarType::FP16)
.value("FP32", pd::proto::VarType::FP32)
.value("FP64", pd::proto::VarType::FP64)
.value("LOD_TENSOR", pd::proto::VarType::LOD_TENSOR)
.value("SELECTED_ROWS", pd::proto::VarType::SELECTED_ROWS)
.value("FEED_MINIBATCH", pd::proto::VarType::FEED_MINIBATCH)
.value("FETCH_LIST", pd::proto::VarType::FETCH_LIST)
.value("STEP_SCOPES", pd::proto::VarType::STEP_SCOPES)
.value("LOD_RANK_TABLE", pd::proto::VarType::LOD_RANK_TABLE)
.value("LOD_TENSOR_ARRAY", pd::proto::VarType::LOD_TENSOR_ARRAY)
.value("CHANNEL", pd::proto::VarType::CHANNEL)
.value("PLACE_LIST", pd::proto::VarType::PLACE_LIST)
.value("READER", pd::proto::VarType::READER)
.value("RAW", pd::proto::VarType::RAW);
}
void BindOpDesc(py::module &m) {
py::enum_<proto::AttrType>(m, "AttrType", "")
.value("INT", proto::AttrType::INT)
.value("INTS", proto::AttrType::INTS)
.value("FLOAT", proto::AttrType::FLOAT)
.value("FLOATS", proto::AttrType::FLOATS)
.value("STRING", proto::AttrType::STRING)
.value("STRINGS", proto::AttrType::STRINGS)
.value("BOOL", proto::AttrType::BOOLEAN)
.value("BOOLS", proto::AttrType::BOOLEANS)
.value("BLOCK", proto::AttrType::BLOCK);
void BindOpDesc(pybind11::module *m) {
pybind11::enum_<pd::proto::AttrType>(*m, "AttrType", "")
.value("INT", pd::proto::AttrType::INT)
.value("INTS", pd::proto::AttrType::INTS)
.value("FLOAT", pd::proto::AttrType::FLOAT)
.value("FLOATS", pd::proto::AttrType::FLOATS)
.value("STRING", pd::proto::AttrType::STRING)
.value("STRINGS", pd::proto::AttrType::STRINGS)
.value("BOOL", pd::proto::AttrType::BOOLEAN)
.value("BOOLS", pd::proto::AttrType::BOOLEANS)
.value("BLOCK", pd::proto::AttrType::BLOCK);
py::class_<OpDesc> op_desc(m, "OpDesc", "");
pybind11::class_<pd::OpDesc> op_desc(*m, "OpDesc", "");
op_desc
.def("__init__", [](OpDesc &self) { new (&self) OpDesc(); },
py::return_value_policy::reference)
.def("copy_from", &OpDesc::CopyFrom)
.def("type", &OpDesc::Type)
.def("set_type", &OpDesc::SetType)
.def("input", &OpDesc::Input)
.def("input_names", &OpDesc::InputNames)
.def("output", &OpDesc::Output)
.def("output_names", &OpDesc::OutputNames)
.def("set_input", &OpDesc::SetInput)
.def("set_output", &OpDesc::SetOutput)
.def("input_arg_names", &OpDesc::InputArgumentNames)
.def("output_arg_names", &OpDesc::OutputArgumentNames)
.def("rename_input", &OpDesc::RenameInput)
.def("rename_output", &OpDesc::RenameOutput)
.def("has_attr", &OpDesc::HasAttr)
.def("attr_type", &OpDesc::GetAttrType)
.def("attr_names", &OpDesc::AttrNames)
.def("set_attr", &OpDesc::SetAttr)
.def("attr", &OpDesc::GetAttr)
.def("set_block_attr", &OpDesc::SetBlockAttr)
.def("__init__", [](pd::OpDesc &self) { new (&self) pd::OpDesc(); },
pybind11::return_value_policy::reference)
.def("copy_from", &pd::OpDesc::CopyFrom)
.def("type", &pd::OpDesc::Type)
.def("set_type", &pd::OpDesc::SetType)
.def("input", &pd::OpDesc::Input)
.def("input_names", &pd::OpDesc::InputNames)
.def("output", &pd::OpDesc::Output)
.def("output_names", &pd::OpDesc::OutputNames)
.def("set_input", &pd::OpDesc::SetInput)
.def("set_output", &pd::OpDesc::SetOutput)
.def("input_arg_names", &pd::OpDesc::InputArgumentNames)
.def("output_arg_names", &pd::OpDesc::OutputArgumentNames)
.def("rename_input", &pd::OpDesc::RenameInput)
.def("rename_output", &pd::OpDesc::RenameOutput)
.def("has_attr", &pd::OpDesc::HasAttr)
.def("attr_type", &pd::OpDesc::GetAttrType)
.def("attr_names", &pd::OpDesc::AttrNames)
.def("set_attr", &pd::OpDesc::SetAttr)
.def("attr", &pd::OpDesc::GetAttr)
.def("set_block_attr", &pd::OpDesc::SetBlockAttr)
.def("set_serialized_attr",
[](OpDesc &self, const std::string &name,
const py::bytes &seriralized) {
[](pd::OpDesc &self, const std::string &name,
const pybind11::bytes &seriralized) {
std::string ser(seriralized);
self.SetAttr(name, ser);
})
.def("block_attr", &OpDesc::GetBlockAttr)
.def("check_attrs", &OpDesc::CheckAttrs)
.def("infer_shape", &OpDesc::InferShape)
.def("infer_var_type", &OpDesc::InferVarType)
.def("serialize_to_string", SerializeMessage<OpDesc>)
.def("block", &OpDesc::Block, py::return_value_policy::reference);
.def("block_attr", &pd::OpDesc::GetBlockAttr)
.def("check_attrs", &pd::OpDesc::CheckAttrs)
.def("infer_shape", &pd::OpDesc::InferShape)
.def("infer_var_type", &pd::OpDesc::InferVarType)
.def("serialize_to_string", SerializeMessage<pd::OpDesc>)
.def("block", &pd::OpDesc::Block,
pybind11::return_value_policy::reference);
}
} // namespace pybind
......
paddle/fluid/pybind/protobuf.h
浏览文件 @ 82530e24
......@@ -11,25 +11,25 @@ distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <Python.h>
#include <fstream>
#include <vector>
#include "paddle/fluid/platform/variant.h"
#include "pybind11/numpy.h"
#include "pybind11/pybind11.h"
#include "pybind11/stl.h"
namespace py = pybind11;
namespace paddle {
namespace pybind {
void BindProgramDesc(py::module& m);
void BindBlockDesc(py::module& m);
void BindVarDsec(py::module& m);
void BindOpDesc(py::module& m);
void BindProgramDesc(pybind11::module* m);
void BindBlockDesc(pybind11::module* m);
void BindVarDsec(pybind11::module* m);
void BindOpDesc(pybind11::module* m);
} // namespace pybind
} // namespace paddle
paddle/fluid/pybind/pybind.cc
浏览文件 @ 82530e24
......@@ -11,11 +11,17 @@ 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 <Python.h>
#include <algorithm>
#include <map>
#include <mutex> // NOLINT // for call_once
#include <string>
#include <unordered_map>
#include <utility>
#include <vector>
#include "paddle/fluid/pybind/protobuf.h"
#include <mutex> // for call_once
#include <unordered_map>
#include "paddle/fluid/framework/backward.h"
#include "paddle/fluid/framework/channel.h"
#include "paddle/fluid/framework/executor.h"
......@@ -32,7 +38,6 @@ limitations under the License. */
#include "paddle/fluid/operators/cond_op.h"
#include "paddle/fluid/operators/net_op.h"
#include "paddle/fluid/platform/enforce.h"
#include "paddle/fluid/platform/gpu_info.h"
#include "paddle/fluid/platform/place.h"
#include "paddle/fluid/platform/profiler.h"
#include "paddle/fluid/pybind/const_value.h"
......@@ -69,7 +74,7 @@ PYBIND11_PLUGIN(core) {
// not cause namespace pollution.
using namespace paddle::framework; // NOLINT
BindException(m);
BindException(&m);
py::class_<Tensor>(m, "Tensor", py::buffer_protocol())
.def_buffer(
......@@ -100,6 +105,14 @@ PYBIND11_PLUGIN(core) {
[](Tensor &self, paddle::platform::CUDAPlace &place) {
self.mutable_data<int>(place);
})
.def("alloc_int",
[](Tensor &self, paddle::platform::CUDAPinnedPlace &place) {
self.mutable_data<int>(place);
})
.def("alloc_float",
[](Tensor &self, paddle::platform::CUDAPinnedPlace &place) {
self.mutable_data<float>(place);
})
.def("set", PyCPUTensorSetFromArray<float>)
.def("set", PyCPUTensorSetFromArray<int>)
.def("set", PyCPUTensorSetFromArray<double>)
......@@ -113,6 +126,12 @@ PYBIND11_PLUGIN(core) {
.def("set", PyCUDATensorSetFromArray<int64_t>)
.def("set", PyCUDATensorSetFromArray<bool>)
.def("set", PyCUDATensorSetFromArray<uint16_t>)
.def("set", PyCUDAPinnedTensorSetFromArray<float>)
.def("set", PyCUDAPinnedTensorSetFromArray<int>)
.def("set", PyCUDAPinnedTensorSetFromArray<double>)
.def("set", PyCUDAPinnedTensorSetFromArray<int64_t>)
.def("set", PyCUDAPinnedTensorSetFromArray<bool>)
.def("set", PyCUDAPinnedTensorSetFromArray<uint16_t>)
#endif
.def("shape", [](Tensor &self) { return vectorize(self.dims()); })
.def("set_float_element", TensorSetElement<float>)
......@@ -317,7 +336,17 @@ All parameter, weight, gradient are variables in Paddle.
#else
return new paddle::platform::CUDADeviceContext(place);
#endif
});
})
.def_static("create",
[](paddle::platform::CUDAPinnedPlace& place)
-> paddle::platform::DeviceContext* {
#ifndef PADDLE_WITH_CUDA
PADDLE_THROW(
"CUDAPinnedPlace is not supported in CPU device.");
#else
return new paddle::platform::CUDAPinnedDeviceContext(place);
#endif
});;
// clang-format on
#ifdef PADDLE_WITH_CUDA
py::class_<platform::Communicator>(m, "Communicator").def(py::init<>());
......@@ -330,6 +359,10 @@ All parameter, weight, gradient are variables in Paddle.
.def(py::init<>())
.def("__str__", string::to_string<const platform::CPUPlace &>);
py::class_<paddle::platform::CUDAPinnedPlace>(m, "CUDAPinnedPlace")
.def(py::init<>())
.def("__str__", string::to_string<const platform::CUDAPinnedPlace &>);
py::class_<platform::Place>(m, "Place")
.def(py::init<>())
.def("set_place",
......@@ -339,7 +372,11 @@ All parameter, weight, gradient are variables in Paddle.
.def("set_place",
[](platform::Place &self, const platform::CUDAPlace &gpu_place) {
self = gpu_place;
});
})
.def("set_place", [](platform::Place &self,
const platform::CUDAPinnedPlace &cuda_pinned_place) {
self = cuda_pinned_place;
});
py::class_<OperatorBase>(m, "Operator")
.def_static("create",
......@@ -363,6 +400,11 @@ All parameter, weight, gradient are variables in Paddle.
.def("run",
[](OperatorBase &self, const Scope &scope,
const platform::CUDAPlace &place) { self.Run(scope, place); })
.def("run",
[](OperatorBase &self, const Scope &scope,
const platform::CUDAPinnedPlace &place) {
self.Run(scope, place);
})
.def("type",
[](const OperatorBase &op) -> std::string { return op.Type(); })
.def("outputs",
......@@ -436,11 +478,11 @@ All parameter, weight, gradient are variables in Paddle.
m.def("set_feed_variable", framework::SetFeedVariable);
m.def("get_fetch_variable", framework::GetFetchVariable);
BindProgramDesc(m);
BindBlockDesc(m);
BindVarDsec(m);
BindOpDesc(m);
BindConstValue(m);
BindProgramDesc(&m);
BindBlockDesc(&m);
BindVarDsec(&m);
BindOpDesc(&m);
BindConstValue(&m);
py::class_<framework::LoDRankTable>(m, "LodRankTable")
.def("items", [](framework::LoDRankTable &table) {
......@@ -511,7 +553,7 @@ All parameter, weight, gradient are variables in Paddle.
})
.def("run", &ParallelExecutor::Run);
BindRecordIOWriter(m);
BindRecordIOWriter(&m);
return m.ptr();
}
} // namespace pybind
......
paddle/fluid/pybind/recordio.cc
浏览文件 @ 82530e24
......@@ -13,13 +13,19 @@
// limitations under the License.
#include "paddle/fluid/pybind/recordio.h"
#include <fstream>
#include <string>
#include <vector>
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/recordio/writer.h"
namespace paddle {
namespace pybind {
namespace {
class RecordIOWriter {
public:
RecordIOWriter(const std::string& filename, recordio::Compressor compressor,
......@@ -49,8 +55,10 @@ class RecordIOWriter {
recordio::Writer writer_;
};
void BindRecordIOWriter(py::module& m) {
py::class_<RecordIOWriter> writer(m, "RecordIOWriter", "");
} // namespace
void BindRecordIOWriter(py::module* m) {
py::class_<RecordIOWriter> writer(*m, "RecordIOWriter", "");
py::enum_<recordio::Compressor>(writer, "Compressor", "")
.value("Snappy", recordio::Compressor::kSnappy)
.value("NoCompress", recordio::Compressor::kNoCompress);
......
paddle/fluid/pybind/recordio.h
浏览文件 @ 82530e24
......@@ -21,6 +21,7 @@ namespace py = pybind11;
namespace paddle {
namespace pybind {
extern void BindRecordIOWriter(py::module& m);
void BindRecordIOWriter(py::module* m);
} // namespace pybind
} // namespace paddle
paddle/fluid/pybind/tensor_py.h
浏览文件 @ 82530e24
......@@ -13,7 +13,10 @@ See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <Python.h>
#include <string>
#include <tuple>
#include <vector>
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/memory/memcpy.h"
#include "paddle/fluid/platform/device_context.h"
......@@ -21,12 +24,8 @@ limitations under the License. */
#include "pybind11/numpy.h"
#include "pybind11/pybind11.h"
namespace py = pybind11;
namespace paddle {
namespace pybind {
namespace details {
template <bool less, size_t I, typename... ARGS>
......@@ -34,16 +33,16 @@ struct CastToPyBufferImpl;
template <size_t I, typename... ARGS>
struct CastToPyBufferImpl<false, I, ARGS...> {
py::buffer_info operator()(framework::Tensor &tensor) {
pybind11::buffer_info operator()(const framework::Tensor &tensor) {
PADDLE_THROW("This type of tensor cannot be expose to Python");
return py::buffer_info();
return pybind11::buffer_info();
}
};
template <size_t I, typename... ARGS>
struct CastToPyBufferImpl<true, I, ARGS...> {
using CUR_TYPE = typename std::tuple_element<I, std::tuple<ARGS...>>::type;
py::buffer_info operator()(framework::Tensor &tensor) {
pybind11::buffer_info operator()(const framework::Tensor &tensor) {
if (std::type_index(typeid(CUR_TYPE)) == tensor.type()) {
auto dim_vec = framework::vectorize(tensor.dims());
std::vector<size_t> dims_outside;
......@@ -82,15 +81,15 @@ struct CastToPyBufferImpl<true, I, ARGS...> {
if (std::type_index(typeid(CUR_TYPE)) ==
std::type_index(typeid(platform::float16))) {
return py::buffer_info(dst_tensor.data<CUR_TYPE>(), sizeof(CUR_TYPE),
"e", /* np.dtype('e') == np.float16 */
(size_t)framework::arity(dst_tensor.dims()),
dims_outside, strides);
return pybind11::buffer_info(
dst_tensor.data<CUR_TYPE>(), sizeof(CUR_TYPE),
"e", /* np.dtype('e') == np.float16 */
(size_t)framework::arity(dst_tensor.dims()), dims_outside, strides);
} else {
return py::buffer_info(dst_tensor.data<CUR_TYPE>(), sizeof(CUR_TYPE),
py::format_descriptor<CUR_TYPE>::format(),
(size_t)framework::arity(dst_tensor.dims()),
dims_outside, strides);
return pybind11::buffer_info(
dst_tensor.data<CUR_TYPE>(), sizeof(CUR_TYPE),
pybind11::format_descriptor<CUR_TYPE>::format(),
(size_t)framework::arity(dst_tensor.dims()), dims_outside, strides);
}
} else {
constexpr bool less = I + 1 < std::tuple_size<std::tuple<ARGS...>>::value;
......@@ -101,7 +100,7 @@ struct CastToPyBufferImpl<true, I, ARGS...> {
} // namespace details
inline py::buffer_info CastToPyBuffer(framework::Tensor &tensor) {
inline pybind11::buffer_info CastToPyBuffer(const framework::Tensor &tensor) {
auto buffer_info =
details::CastToPyBufferImpl<true, 0, float, int, double, int64_t, bool,
platform::float16>()(tensor);
......@@ -109,7 +108,7 @@ inline py::buffer_info CastToPyBuffer(framework::Tensor &tensor) {
}
template <typename T>
T TensorGetElement(framework::Tensor &self, size_t offset) {
T TensorGetElement(const framework::Tensor &self, size_t offset) {
if (platform::is_cpu_place(self.place())) {
return self.data<T>()[offset];
} else {
......@@ -121,64 +120,70 @@ T TensorGetElement(framework::Tensor &self, size_t offset) {
// TODO(dzhwinter) : fix the redundent Tensor allocate and free
template <typename T>
void TensorSetElement(framework::Tensor &self, size_t offset, T elem) {
if (platform::is_gpu_place(self.place())) {
void TensorSetElement(framework::Tensor *self, size_t offset, T elem) {
if (platform::is_gpu_place(self->place())) {
std::shared_ptr<framework::Tensor> dst(new framework::Tensor);
framework::TensorCopy(self, platform::CPUPlace(), dst.get());
framework::TensorCopy(*self, platform::CPUPlace(), dst.get());
dst->data<T>()[offset] = elem;
framework::TensorCopy(*dst.get(), self.place(), &self);
framework::TensorCopy(*dst.get(), self->place(), self);
} else if (platform::is_cpu_place(self.place())) {
self.data<T>()[offset] = elem;
} else if (platform::is_cpu_place(self->place())) {
self->data<T>()[offset] = elem;
}
}
template <typename T>
void PyCPUTensorSetFromArray(
framework::Tensor &self,
py::array_t<T, py::array::c_style | py::array::forcecast> array,
paddle::platform::CPUPlace &place) {
framework::Tensor *self,
pybind11::array_t<T, pybind11::array::c_style | pybind11::array::forcecast>
array,
paddle::platform::CPUPlace place) {
std::vector<int64_t> dims;
dims.reserve(array.ndim());
for (size_t i = 0; i < array.ndim(); ++i) {
dims.push_back((int)array.shape()[i]);
dims.push_back(static_cast<int>(array.shape()[i]));
}
self.Resize(framework::make_ddim(dims));
auto *dst = self.mutable_data<T>(place);
self->Resize(framework::make_ddim(dims));
auto *dst = self->mutable_data<T>(place);
std::memcpy(dst, array.data(), sizeof(T) * array.size());
}
template <>
// This following specialization maps uint16_t in the parameter type to
// platform::float16.
void PyCPUTensorSetFromArray(
framework::Tensor &self,
py::array_t<uint16_t, py::array::c_style | py::array::forcecast> array,
paddle::platform::CPUPlace &place) {
framework::Tensor *self,
pybind11::array_t<uint16_t,
pybind11::array::c_style | pybind11::array::forcecast>
array,
paddle::platform::CPUPlace place) {
std::vector<int64_t> dims;
dims.reserve(array.ndim());
for (size_t i = 0; i < array.ndim(); ++i) {
dims.push_back((int)array.shape()[i]);
dims.push_back(static_cast<int>(array.shape()[i]));
}
self.Resize(framework::make_ddim(dims));
auto *dst = self.mutable_data<platform::float16>(place);
self->Resize(framework::make_ddim(dims));
auto *dst = self->mutable_data<platform::float16>(place);
std::memcpy(dst, array.data(), sizeof(uint16_t) * array.size());
}
#ifdef PADDLE_WITH_CUDA
template <typename T>
void PyCUDATensorSetFromArray(
framework::Tensor &self,
py::array_t<T, py::array::c_style | py::array::forcecast> array,
paddle::platform::CUDAPlace &place) {
framework::Tensor *self,
pybind11::array_t<T, pybind11::array::c_style | pybind11::array::forcecast>
array,
paddle::platform::CUDAPlace place) {
std::vector<int64_t> dims;
dims.reserve(array.ndim());
for (size_t i = 0; i < array.ndim(); ++i) {
dims.push_back((int)array.shape()[i]);
dims.push_back(static_cast<int>(array.shape()[i]));
}
self.Resize(framework::make_ddim(dims));
auto *dst = self.mutable_data<T>(place);
self->Resize(framework::make_ddim(dims));
auto *dst = self->mutable_data<T>(place);
platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
auto dev_ctx =
......@@ -188,18 +193,22 @@ void PyCUDATensorSetFromArray(
}
template <>
// This following specialization maps uint16_t in the parameter type to
// platform::float16.
void PyCUDATensorSetFromArray(
framework::Tensor &self,
py::array_t<uint16_t, py::array::c_style | py::array::forcecast> array,
paddle::platform::CUDAPlace &place) {
framework::Tensor *self,
pybind11::array_t<uint16_t,
pybind11::array::c_style | pybind11::array::forcecast>
array,
paddle::platform::CUDAPlace place) {
std::vector<int64_t> dims;
dims.reserve(array.ndim());
for (size_t i = 0; i < array.ndim(); ++i) {
dims.push_back((int)array.shape()[i]);
dims.push_back(static_cast<int>(array.shape()[i]));
}
self.Resize(framework::make_ddim(dims));
auto *dst = self.mutable_data<platform::float16>(place);
self->Resize(framework::make_ddim(dims));
auto *dst = self->mutable_data<platform::float16>(place);
platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
auto dev_ctx =
......@@ -208,6 +217,43 @@ void PyCUDATensorSetFromArray(
sizeof(uint16_t) * array.size(),
cudaMemcpyHostToDevice, dev_ctx->stream());
}
template <typename T>
void PyCUDAPinnedTensorSetFromArray(
framework::Tensor *self,
pybind11::array_t<T, pybind11::array::c_style | pybind11::array::forcecast>
array,
const paddle::platform::CUDAPinnedPlace &place) {
std::vector<int64_t> dims;
dims.reserve(array.ndim());
for (size_t i = 0; i < array.ndim(); ++i) {
dims.push_back(static_cast<int>(array.shape()[i]));
}
self->Resize(framework::make_ddim(dims));
auto *dst = self->mutable_data<T>(place);
std::memcpy(dst, array.data(), sizeof(T) * array.size());
}
template <>
// This following specialization maps uint16_t in the parameter type to
// platform::float16.
void PyCUDAPinnedTensorSetFromArray(
framework::Tensor *self,
pybind11::array_t<uint16_t,
pybind11::array::c_style | pybind11::array::forcecast>
array,
const paddle::platform::CUDAPinnedPlace &place) {
std::vector<int64_t> dims;
dims.reserve(array.ndim());
for (size_t i = 0; i < array.ndim(); ++i) {
dims.push_back(static_cast<int>(array.shape()[i]));
}
self->Resize(framework::make_ddim(dims));
auto *dst = self->mutable_data<platform::float16>(place);
std::memcpy(dst, array.data(), sizeof(uint16_t) * array.size());
}
#endif
} // namespace pybind
......
paddle/fluid/pybind/tensor_py_test.cc 0 → 100644
浏览文件 @ 82530e24
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/pybind/tensor_py.h"
#include <iostream>
#include "gtest/gtest.h"
#include "paddle/fluid/framework/tensor.h"
TEST(TensorPy, CastToPyBufferImpl) {
typedef int ElemType;
paddle::framework::Tensor t;
auto d = paddle::framework::make_ddim({1, 2, 3});
int* p = t.mutable_data<ElemType>(d, paddle::platform::CPUPlace());
for (int i = 0; i < paddle::framework::product(d); ++i) {
p[i] = i;
}
pybind11::buffer_info bi = paddle::pybind::CastToPyBuffer(t);
EXPECT_EQ(bi.itemsize, static_cast<size_t>(sizeof(ElemType)));
EXPECT_EQ(bi.size, static_cast<size_t>(paddle::framework::product(d)));
EXPECT_EQ(bi.ndim, static_cast<size_t>(3)); // 3-dimensional as d.
EXPECT_EQ(bi.shape.size(), 3U); // as Dim d.
EXPECT_EQ(bi.shape[0], static_cast<size_t>(1));
EXPECT_EQ(bi.shape[1], static_cast<size_t>(2));
EXPECT_EQ(bi.shape[2], static_cast<size_t>(3));
EXPECT_EQ(bi.strides.size(), 3U); // 3-dimensional as d.
EXPECT_EQ(bi.strides[2], static_cast<size_t>(sizeof(ElemType)));
EXPECT_EQ(bi.strides[1], static_cast<size_t>(sizeof(ElemType) * 3));
EXPECT_EQ(bi.strides[0], static_cast<size_t>(sizeof(ElemType) * 2 * 3));
}
paddle/fluid/recordio/chunk.cc
浏览文件 @ 82530e24
......@@ -14,11 +14,13 @@
#include "paddle/fluid/recordio/chunk.h"
#include <algorithm>
#include <memory>
#include <sstream>
#include "paddle/fluid/platform/enforce.h"
#include "snappystream.hpp"
#include "zlib.h"
#include "snappy_stream/include/snappystream.hpp"
#include "zlib/include/zlib.h"
namespace paddle {
namespace recordio {
......@@ -58,8 +60,8 @@ static void ReadStreamByBuf(std::istream& in, size_t limit, Callback callback) {
* Copy stream in to another stream
*/
static void PipeStream(std::istream& in, std::ostream& os) {
ReadStreamByBuf(
in, 0, [&os](const char* buf, size_t len) { os.write(buf, len); });
ReadStreamByBuf(in, 0,
[&os](const char* buf, size_t len) { os.write(buf, len); });
}
/**
......@@ -68,8 +70,8 @@ static void PipeStream(std::istream& in, std::ostream& os) {
static uint32_t Crc32Stream(std::istream& in, size_t limit = 0) {
uint32_t crc = static_cast<uint32_t>(crc32(0, nullptr, 0));
ReadStreamByBuf(in, limit, [&crc](const char* buf, size_t len) {
crc = static_cast<uint32_t>(crc32(
crc, reinterpret_cast<const Bytef*>(buf), static_cast<uInt>(len)));
crc = static_cast<uint32_t>(crc32(crc, reinterpret_cast<const Bytef*>(buf),
static_cast<uInt>(len)));
});
return crc;
}
......
paddle/fluid/recordio/chunk.h
浏览文件 @ 82530e24
......@@ -24,7 +24,7 @@ namespace recordio {
// A Chunk contains the Header and optionally compressed records.
class Chunk {
public:
public:
Chunk() : num_bytes_(0) {}
void Add(const std::string& buf) {
num_bytes_ += buf.size();
......@@ -46,7 +46,7 @@ public:
bool Empty() const { return records_.empty(); }
private:
private:
std::vector<std::string> records_;
// sum of record lengths in bytes.
size_t num_bytes_;
......
paddle/fluid/recordio/chunk_test.cc
浏览文件 @ 82530e24
......@@ -18,29 +18,27 @@
#include "gtest/gtest.h"
using namespace paddle::recordio;
TEST(Chunk, SaveLoad) {
Chunk ch;
paddle::recordio::Chunk ch;
ch.Add(std::string("12345", 6));
ch.Add(std::string("123", 4));
std::stringstream ss;
ch.Write(ss, Compressor::kNoCompress);
ch.Write(ss, paddle::recordio::Compressor::kNoCompress);
ss.seekg(0);
ch.Parse(ss);
ASSERT_EQ(ch.NumBytes(), 10U);
}
TEST(Chunk, Compressor) {
Chunk ch;
paddle::recordio::Chunk ch;
ch.Add(std::string("12345", 6));
ch.Add(std::string("123", 4));
ch.Add(std::string("123", 4));
ch.Add(std::string("123", 4));
std::stringstream ss;
ch.Write(ss, Compressor::kSnappy);
ch.Write(ss, paddle::recordio::Compressor::kSnappy);
std::stringstream ss2;
ch.Write(ss2, Compressor::kNoCompress);
ch.Write(ss2, paddle::recordio::Compressor::kNoCompress);
ASSERT_LE(ss.tellp(), ss2.tellp()); // Compress should contain less data;
ch.Clear();
......
paddle/fluid/recordio/header.h
浏览文件 @ 82530e24
......@@ -37,7 +37,7 @@ enum class Compressor : uint32_t {
// Header is the metadata of Chunk
class Header {
public:
public:
Header();
Header(uint32_t num, uint32_t sum, Compressor ct, uint32_t cs);
......@@ -51,7 +51,7 @@ public:
Compressor CompressType() const { return compressor_; }
uint32_t CompressSize() const { return compress_size_; }
private:
private:
uint32_t num_records_;
uint32_t checksum_;
Compressor compressor_;
......
paddle/fluid/recordio/header_test.cc
浏览文件 @ 82530e24
......@@ -18,14 +18,12 @@
#include "gtest/gtest.h"
using namespace paddle::recordio;
TEST(Recordio, ChunkHead) {
Header hdr(0, 1, Compressor::kGzip, 3);
paddle::recordio::Header hdr(0, 1, paddle::recordio::Compressor::kGzip, 3);
std::stringstream ss;
hdr.Write(ss);
ss.seekg(0, std::ios::beg);
Header hdr2;
paddle::recordio::Header hdr2;
hdr2.Parse(ss);
EXPECT_TRUE(hdr == hdr2);
}
paddle/fluid/recordio/scanner.cc
浏览文件 @ 82530e24
......@@ -13,10 +13,14 @@
// limitations under the License.
#include "paddle/fluid/recordio/scanner.h"
#include <string>
#include "paddle/fluid/platform/enforce.h"
namespace paddle {
namespace recordio {
Scanner::Scanner(std::unique_ptr<std::istream> &&stream)
: stream_(std::move(stream)) {
Reset();
......
paddle/fluid/recordio/scanner.h
浏览文件 @ 82530e24
......@@ -16,12 +16,15 @@
#include <fstream>
#include <memory>
#include <string>
#include "paddle/fluid/recordio/chunk.h"
namespace paddle {
namespace recordio {
class Scanner {
public:
public:
explicit Scanner(std::unique_ptr<std::istream>&& stream);
explicit Scanner(const std::string& filename);
......@@ -32,7 +35,7 @@ public:
bool HasNext() const;
private:
private:
std::unique_ptr<std::istream> stream_;
Chunk cur_chunk_;
size_t offset_;
......
paddle/fluid/recordio/writer.cc
浏览文件 @ 82530e24
......@@ -12,9 +12,14 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/recordio/writer.h"
#include <string>
#include "paddle/fluid/platform/enforce.h"
namespace paddle {
namespace recordio {
void Writer::Write(const std::string& record) {
cur_chunk_.Add(record);
if (cur_chunk_.NumRecords() >= max_num_records_in_chunk_) {
......
paddle/fluid/recordio/writer.h
浏览文件 @ 82530e24
......@@ -11,16 +11,17 @@
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#pragma once
#include <string>
#include "paddle/fluid/recordio/chunk.h"
namespace paddle {
namespace recordio {
class Writer {
public:
Writer(std::ostream* sout,
Compressor compressor,
public:
Writer(std::ostream* sout, Compressor compressor,
size_t max_num_records_in_chunk = 1000)
: stream_(*sout),
max_num_records_in_chunk_(max_num_records_in_chunk),
......@@ -32,7 +33,7 @@ public:
~Writer();
private:
private:
std::ostream& stream_;
size_t max_num_records_in_chunk_;
Chunk cur_chunk_;
......
paddle/fluid/recordio/writer_scanner_test.cc
浏览文件 @ 82530e24
......@@ -12,9 +12,10 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#include "gtest/gtest.h"
#include <sstream>
#include <string>
#include "gtest/gtest.h"
#include "paddle/fluid/recordio/scanner.h"
#include "paddle/fluid/recordio/writer.h"
......@@ -66,4 +67,4 @@ TEST(WriterScanner, TinyChunk) {
ASSERT_EQ(scanner.Next(), "DEFG");
ASSERT_FALSE(scanner.HasNext());
}
}
\ No newline at end of file
}
paddle/fluid/string/.clang-format 已删除 120000 → 0
浏览文件 @ 07b31b80
../framework/.clang-format
\ No newline at end of file
paddle/fluid/string/piece.cc
浏览文件 @ 82530e24
......@@ -12,7 +12,7 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#include "piece.h"
#include "paddle/fluid/string/piece.h"
#include <string.h>
......
paddle/fluid/string/printf.h
浏览文件 @ 82530e24
......@@ -71,6 +71,8 @@
#include <iostream>
#include <sstream>
#include <string>
#include "tinyformat/tinyformat.h" // https://github.com/c42f/tinyformat
namespace paddle {
......
paddle/fluid/string/printf_test.cc
浏览文件 @ 82530e24
......@@ -11,7 +11,8 @@
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "printf.h"
#include "paddle/fluid/string/printf.h"
#include <string>
......@@ -21,7 +22,7 @@ TEST(StringPrintf, StringPrintf) {
std::string weekday = "Wednesday";
const char* month = "July";
size_t day = 27;
long hour = 14;
int hour = 14;
int min = 44;
EXPECT_EQ(std::string("Wednesday, July 27, 14:44"),
paddle::string::Sprintf("%s, %s %d, %.2d:%.2d", weekday, month, day,
......
paddle/fluid/string/to_string_test.cc
浏览文件 @ 82530e24
......@@ -12,7 +12,7 @@ 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 "to_string.h"
#include "paddle/fluid/string/to_string.h"
#include <gtest/gtest.h>
constexpr char kOutputString[] = "User Defined Output";
......@@ -26,14 +26,13 @@ std::ostream& operator<<(std::ostream& s, const UserDefinedClass& ins) {
}
TEST(to_string, normal) {
using namespace paddle::string;
using paddle::string::to_string;
ASSERT_EQ("10", to_string(10));
ASSERT_EQ("abc", to_string("abc"));
ASSERT_EQ("1.2", to_string(1.2));
}
TEST(to_string, user_defined) {
using namespace paddle::string;
UserDefinedClass instance;
ASSERT_EQ(kOutputString, to_string(instance));
ASSERT_EQ(kOutputString, paddle::string::to_string(instance));
}
paddle/gserver/tests/CMakeLists.txt
浏览文件 @ 82530e24
......@@ -14,6 +14,11 @@ function(gserver_test TARGET)
COMMAND ${TARGET})
endfunction()
add_custom_command(OUTPUT ${CMAKE_CURRENT_BINARY_DIR}/concat_dotmul_a.conf
COMMAND cp -r ${CMAKE_CURRENT_SOURCE_DIR}/* ${CMAKE_CURRENT_BINARY_DIR}
)
add_custom_target(copy_gserver_conf ALL DEPENDS concat_dotmul_a.conf)
gserver_test(test_LayerGrad)
gserver_test(test_CRFLayerGrad)
gserver_test(test_CrossEntropyOverBeamGrad)
......@@ -31,12 +36,12 @@ gserver_test(test_Upsample)
set(PYTHON_PATH
${PADDLE_SOURCE_DIR}/paddle/.set_python_path.sh -d
${PADDLE_SOURCE_DIR}/python/:${PADDLE_SOURCE_DIR}/paddle/gserver/tests)
${PADDLE_BINARY_DIR}/python/:${PADDLE_BINARY_DIR}/paddle/gserver/tests)
function(gserver_test_with_python TARGET)
add_unittest_without_exec(${TARGET} ${TARGET}.cpp)
add_test(NAME ${TARGET}
COMMAND ${PYTHON_PATH} ${CMAKE_CURRENT_BINARY_DIR}/${TARGET}
WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle/)
WORKING_DIRECTORY ${PADDLE_BINARY_DIR}/paddle/)
endfunction()
gserver_test_with_python(test_PyDataProvider2)
......@@ -57,7 +62,7 @@ if(WITH_MKLDNN)
LayerGradUtil.cpp)
add_test(NAME test_MKLDNN
COMMAND ${PYTHON_PATH} ${CMAKE_CURRENT_BINARY_DIR}/test_MKLDNN
WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle)
WORKING_DIRECTORY ${PADDLE_BINARY_DIR}/paddle)
endif()
############### test_WarpCTCLayer #######################
......@@ -66,7 +71,7 @@ if(NOT WITH_DOUBLE AND NOT MOBILE_INFERENCE)
test_WarpCTCLayer.cpp)
add_test(NAME test_WarpCTCLayer
COMMAND ${CMAKE_CURRENT_BINARY_DIR}/test_WarpCTCLayer --warpctc_dir=${WARPCTC_LIB_DIR}
WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle)
WORKING_DIRECTORY ${PADDLE_BINARY_DIR}/paddle)
endif()
if(NOT MOBILE_INFERENCE)
......@@ -84,15 +89,15 @@ if(NOT MOBILE_INFERENCE)
endif()
add_test(NAME test_NetworkCompare
COMMAND ${PYTHON_PATH} ${CMAKE_CURRENT_BINARY_DIR}/test_NetworkCompare --use_gpu=${use_gpu}
WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle)
WORKING_DIRECTORY ${PADDLE_BINARY_DIR}/paddle)
############ test_CompareSparse ################
add_unittest_without_exec(test_CompareSparse
test_CompareSparse.cpp)
if(NOT ON_TRAVIS)
add_test(NAME test_CompareSparse
COMMAND ${PYTHON_PATH} ./.set_port.sh -p port -n 6
COMMAND ${PYTHON_PATH} ${PADDLE_SOURCE_DIR}/paddle/.set_port.sh -p port -n 6
${CMAKE_CURRENT_BINARY_DIR}/test_CompareSparse
WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle/)
WORKING_DIRECTORY ${PADDLE_BINARY_DIR}/paddle/)
endif()
endif()
paddle/gserver/tests/test_Upsample.cpp
浏览文件 @ 82530e24
......@@ -20,10 +20,8 @@ limitations under the License. */
#include "paddle/math/MathUtils.h"
#include "paddle/testing/TestUtil.h"
using namespace paddle;
void setPoolConfig(TestConfig* config,
PoolConfig* pool,
void setPoolConfig(paddle::TestConfig* config,
paddle::PoolConfig* pool,
const string& poolType) {
(*config).biasSize = 0;
(*config).layerConfig.set_type("pool");
......@@ -42,21 +40,23 @@ void setPoolConfig(TestConfig* config,
pool->set_stride(sw);
pool->set_stride_y(sh);
int ow = outputSize(pool->img_size(), kw, pw, sw, /* caffeMode */ false);
int oh = outputSize(pool->img_size_y(), kh, ph, sh, /* caffeMode */ false);
int ow =
paddle::outputSize(pool->img_size(), kw, pw, sw, /* caffeMode */ false);
int oh =
paddle::outputSize(pool->img_size_y(), kh, ph, sh, /* caffeMode */ false);
pool->set_output_x(ow);
pool->set_output_y(oh);
}
LayerPtr doOneUpsampleTest(MatrixPtr& inputMat,
const string& poolType,
bool use_gpu,
real* tempGradData) {
paddle::LayerPtr doOneUpsampleTest(const paddle::MatrixPtr& inputMat,
const string& poolType,
bool use_gpu,
real* tempGradData) {
/* prepare maxPoolWithMaskLayer */
TestConfig config;
config.inputDefs.push_back({INPUT_DATA, "layer_0", 128, 0});
LayerInputConfig* input = config.layerConfig.add_inputs();
PoolConfig* pool = input->mutable_pool_conf();
paddle::TestConfig config;
config.inputDefs.push_back({paddle::INPUT_DATA, "layer_0", 128, 0});
paddle::LayerInputConfig* input = config.layerConfig.add_inputs();
paddle::PoolConfig* pool = input->mutable_pool_conf();
pool->set_img_size(8);
pool->set_img_size_y(8);
......@@ -66,9 +66,9 @@ LayerPtr doOneUpsampleTest(MatrixPtr& inputMat,
config.layerConfig.set_name("MaxPoolWithMask");
std::vector<DataLayerPtr> dataLayers;
LayerMap layerMap;
vector<Argument> datas;
std::vector<paddle::DataLayerPtr> dataLayers;
paddle::LayerMap layerMap;
vector<paddle::Argument> datas;
initDataLayer(config,
&dataLayers,
......@@ -82,20 +82,20 @@ LayerPtr doOneUpsampleTest(MatrixPtr& inputMat,
dataLayers[0]->getOutputValue()->copyFrom(*inputMat);
FLAGS_use_gpu = use_gpu;
std::vector<ParameterPtr> parameters;
LayerPtr maxPoolingWithMaskOutputLayer;
std::vector<paddle::ParameterPtr> parameters;
paddle::LayerPtr maxPoolingWithMaskOutputLayer;
initTestLayer(config, &layerMap, &parameters, &maxPoolingWithMaskOutputLayer);
maxPoolingWithMaskOutputLayer->forward(PASS_GC);
maxPoolingWithMaskOutputLayer->forward(paddle::PASS_GC);
/* prepare the upsample layer */
LayerConfig upsampleLayerConfig;
paddle::LayerConfig upsampleLayerConfig;
upsampleLayerConfig.set_type("upsample");
LayerInputConfig* input1 = upsampleLayerConfig.add_inputs();
paddle::LayerInputConfig* input1 = upsampleLayerConfig.add_inputs();
upsampleLayerConfig.add_inputs();
UpsampleConfig* upsampleConfig = input1->mutable_upsample_conf();
paddle::UpsampleConfig* upsampleConfig = input1->mutable_upsample_conf();
upsampleConfig->set_scale(2);
ImageConfig* imageConfig = upsampleConfig->mutable_image_conf();
paddle::ImageConfig* imageConfig = upsampleConfig->mutable_image_conf();
imageConfig->set_channels(2);
imageConfig->set_img_size(4);
imageConfig->set_img_size_y(4);
......@@ -103,17 +103,18 @@ LayerPtr doOneUpsampleTest(MatrixPtr& inputMat,
upsampleLayerConfig.set_name("upsample");
for (size_t i = 0; i < 2; i++) {
LayerInputConfig& inputTemp = *(upsampleLayerConfig.mutable_inputs(i));
paddle::LayerInputConfig& inputTemp =
*(upsampleLayerConfig.mutable_inputs(i));
inputTemp.set_input_layer_name("MaxPoolWithMask");
}
LayerPtr upsampleLayer;
ParameterMap parameterMap;
upsampleLayer = Layer::create(upsampleLayerConfig);
paddle::LayerPtr upsampleLayer;
paddle::ParameterMap parameterMap;
upsampleLayer = paddle::Layer::create(upsampleLayerConfig);
layerMap[upsampleLayerConfig.name()] = upsampleLayer;
upsampleLayer->init(layerMap, parameterMap);
upsampleLayer->setNeedGradient(true);
upsampleLayer->forward(PASS_GC);
upsampleLayer->forward(paddle::PASS_GC);
upsampleLayer->getOutputGrad()->copyFrom(tempGradData, 128);
upsampleLayer->backward();
......@@ -122,31 +123,31 @@ LayerPtr doOneUpsampleTest(MatrixPtr& inputMat,
TEST(Layer, maxPoolingWithMaskOutputLayerFwd) {
bool useGpu = false;
MatrixPtr inputMat;
MatrixPtr inputGPUMat;
MatrixPtr tempGradMat;
paddle::MatrixPtr inputMat;
paddle::MatrixPtr inputGPUMat;
paddle::MatrixPtr tempGradMat;
inputMat = Matrix::create(1, 128, false, useGpu);
inputMat = paddle::Matrix::create(1, 128, false, useGpu);
inputMat->randomizeUniform();
tempGradMat = Matrix::create(1, 128, false, useGpu);
tempGradMat = paddle::Matrix::create(1, 128, false, useGpu);
tempGradMat->randomizeUniform();
real* data = inputMat->getData();
real* tempGradData = tempGradMat->getData();
LayerPtr upsampleLayerCPU =
paddle::LayerPtr upsampleLayerCPU =
doOneUpsampleTest(inputMat, "max-pool-with-mask", useGpu, tempGradData);
#ifdef PADDLE_WITH_CUDA
useGpu = true;
inputGPUMat = Matrix::create(1, 128, false, useGpu);
real* data = inputMat->getData();
inputGPUMat = paddle::Matrix::create(1, 128, false, useGpu);
inputGPUMat->copyFrom(data, 128);
LayerPtr upsampleLayerGPU = doOneUpsampleTest(
paddle::LayerPtr upsampleLayerGPU = doOneUpsampleTest(
inputGPUMat, "max-pool-with-mask", useGpu, tempGradData);
checkMatrixEqual(upsampleLayerCPU->getOutput("").value,
upsampleLayerGPU->getOutput("").value);
paddle::checkMatrixEqual(upsampleLayerCPU->getOutput("").value,
upsampleLayerGPU->getOutput("").value);
checkMatrixEqual(upsampleLayerCPU->getPrev(0)->getOutputGrad(),
upsampleLayerGPU->getPrev(0)->getOutputGrad());
paddle::checkMatrixEqual(upsampleLayerCPU->getPrev(0)->getOutputGrad(),
upsampleLayerGPU->getPrev(0)->getOutputGrad());
#endif
}
paddle/testing/CMakeLists.txt
浏览文件 @ 82530e24
......@@ -6,6 +6,6 @@ if(WITH_TESTING)
add_library(paddle_test_util STATIC TestUtil.cpp)
add_dependencies(paddle_test_util paddle_proto ${external_project_dependencies})
if(NOT MOBILE_INFERENCE)
cc_library(paddle_gtest_main SRCS paddle_gtest_main.cc DEPS init paddle_memory gtest gflags)
cc_library(paddle_gtest_main SRCS paddle_gtest_main.cc DEPS init memory gtest gflags)
endif()
endif()
paddle/trainer/tests/CMakeLists.txt
浏览文件 @ 82530e24
add_custom_command(OUTPUT ${CMAKE_CURRENT_BINARY_DIR}/sample_trainer_config.conf
COMMAND cp -r ${CMAKE_CURRENT_SOURCE_DIR}/* ${CMAKE_CURRENT_BINARY_DIR}
)
add_custom_target(copy_trainer_conf ALL DEPENDS sample_trainer_config.conf)
set(PYTHON_PATH
${PADDLE_SOURCE_DIR}/paddle/.set_python_path.sh -d
${PADDLE_SOURCE_DIR}/python/:${PADDLE_SOURCE_DIR}/paddle/trainer/tests)
${PADDLE_BINARY_DIR}/python/:${PADDLE_BINARY_DIR}/paddle/trainer/tests)
function(trainer_test TARGET)
add_unittest_without_exec(${TARGET} ${TARGET}.cpp)
add_test(NAME ${TARGET}
COMMAND ${PYTHON_PATH} ${CMAKE_CURRENT_BINARY_DIR}/${TARGET}
WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle/)
WORKING_DIRECTORY ${PADDLE_BINARY_DIR}/paddle/)
endfunction()
trainer_test(test_Compare)
......@@ -22,11 +27,11 @@ if(WITH_PYTHON)
add_test(NAME test_TrainerOnePass
COMMAND ${PYTHON_PATH} ${PADDLE_SOURCE_DIR}/paddle/.set_port.sh -p port
${CMAKE_CURRENT_BINARY_DIR}/test_TrainerOnePass
WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle/)
WORKING_DIRECTORY ${PADDLE_BINARY_DIR}/paddle/)
endif()
#################### test_config_parser #########################
add_test(NAME test_config_parser
COMMAND ${PYTHON_PATH} ${PYTHON_EXECUTABLE}
${PADDLE_SOURCE_DIR}/paddle/trainer/tests/config_parser_test.py
WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle/)
WORKING_DIRECTORY ${PADDLE_BINARY_DIR}/paddle/)
paddle/utils/CMakeLists.txt
浏览文件 @ 82530e24
......@@ -2,8 +2,8 @@
file(GLOB UTIL_HEADERS . *.h)
file(GLOB UTIL_SOURCES . *.cpp)
create_resources(${CMAKE_CURRENT_SOURCE_DIR}/enable_virtualenv.py
${CMAKE_CURRENT_SOURCE_DIR}/enable_virtualenv.c)
set(UTIL_RES ${CMAKE_CURRENT_SOURCE_DIR}/enable_virtualenv.c)
${CMAKE_CURRENT_BINARY_DIR}/enable_virtualenv.c)
set(UTIL_RES ${CMAKE_CURRENT_BINARY_DIR}/enable_virtualenv.c)
if(APPLE)
file(GLOB UTIL_ARCH_SOURCES . arch/osx/*.cpp)
......
proto/CMakeLists.txt
浏览文件 @ 82530e24
......@@ -15,13 +15,14 @@ foreach(filename ${proto_filenames})
get_filename_component(ABS_FIL ${filename} ABSOLUTE)
get_filename_component(FIL_WE ${filename} NAME_WE)
set(CUR_PROTO_GEN_PY
${PADDLE_SOURCE_DIR}/paddle/python/paddle/proto/${FIL_WE}_pb2.py)
${PADDLE_BINARY_DIR}/paddle/python/paddle/proto/${FIL_WE}_pb2.py)
set(PROTO_GEN_PY
${CUR_PROTO_GEN_PY}
${PROTO_GEN_PY})
add_custom_command(OUTPUT ${CUR_PROTO_GEN_PY}
COMMAND ${CMAKE_COMMAND} -E make_directory ${PADDLE_BINARY_DIR}/python/paddle/proto
COMMAND ${PROTOBUF_PROTOC_EXECUTABLE}
ARGS "--python_out=${PADDLE_SOURCE_DIR}/python/paddle/proto"
ARGS "--python_out=${PADDLE_BINARY_DIR}/python/paddle/proto"
"-I" ${CMAKE_CURRENT_SOURCE_DIR} ${ABS_FIL}
DEPENDS ${ABS_FIL} protoc)
endforeach()
......
python/CMakeLists.txt
浏览文件 @ 82530e24
......@@ -47,14 +47,16 @@ configure_file(${CMAKE_CURRENT_SOURCE_DIR}/setup.py.in
${CMAKE_CURRENT_BINARY_DIR}/setup.py)
add_custom_command(OUTPUT ${PADDLE_SOURCE_DIR}/python/paddle/fluid/core.so
COMMAND cmake -E copy $<TARGET_FILE:paddle_pybind> ${PADDLE_SOURCE_DIR}/python/paddle/fluid/core.so
add_custom_command(OUTPUT ${PADDLE_BINARY_DIR}/python/paddle/fluid/core.so
COMMAND cmake -E copy $<TARGET_FILE:paddle_pybind> ${PADDLE_BINARY_DIR}/python/paddle/fluid/core.so
DEPENDS paddle_pybind)
add_custom_target(copy_paddle_pybind ALL DEPENDS ${PADDLE_SOURCE_DIR}/python/paddle/fluid/core.so)
add_custom_target(copy_paddle_pybind ALL DEPENDS ${PADDLE_BINARY_DIR}/python/paddle/fluid/core.so)
add_custom_command(OUTPUT ${PADDLE_PYTHON_BUILD_DIR}/.timestamp
COMMAND touch stub.cc
COMMAND ${CMAKE_COMMAND} -E copy_directory ${PADDLE_SOURCE_DIR}/python/paddle ${PADDLE_BINARY_DIR}/python/paddle
COMMAND cp -r ${PADDLE_SOURCE_DIR}/paddle/py_paddle ${PADDLE_BINARY_DIR}/python/
COMMAND env ${py_env} ${PYTHON_EXECUTABLE} setup.py bdist_wheel
COMMAND ${CMAKE_COMMAND} -E touch ${PADDLE_PYTHON_BUILD_DIR}/.timestamp
COMMAND ${CMAKE_COMMAND} -E remove_directory ${PADDLE_PYTHON_BUILD_DIR}/lib-python
......
python/paddle/fluid/__init__.py
浏览文件 @ 82530e24
......@@ -31,7 +31,7 @@ import regularizer
import average
from param_attr import ParamAttr, WeightNormParamAttr
from data_feeder import DataFeeder
from core import LoDTensor, CPUPlace, CUDAPlace
from core import LoDTensor, CPUPlace, CUDAPlace, CUDAPinnedPlace
from distribute_transpiler import DistributeTranspiler
from distribute_transpiler_simple import SimpleDistributeTranspiler
from concurrency import (Go, make_channel, channel_send, channel_recv,
......@@ -57,6 +57,7 @@ __all__ = framework.__all__ + executor.__all__ + concurrency.__all__ + [
'LoDTensor',
'CPUPlace',
'CUDAPlace',
'CUDAPinnedPlace',
'Tensor',
'ParamAttr',
'WeightNormParamAttr',
......
python/paddle/fluid/debuger.py
浏览文件 @ 82530e24
......@@ -16,6 +16,7 @@ import sys
import re
from graphviz import GraphPreviewGenerator
import proto.framework_pb2 as framework_pb2
from google.protobuf import text_format
_vartype2str_ = [
"UNK",
......@@ -100,7 +101,7 @@ def repr_var(vardesc):
def pprint_program_codes(program_desc):
reprs = []
for block_idx in range(program_desc.num_blocks()):
for block_idx in range(program_desc.desc.num_blocks()):
block_desc = program_desc.block(block_idx)
block_repr = pprint_block_codes(block_desc)
reprs.append(block_repr)
......@@ -127,7 +128,7 @@ def pprint_block_codes(block_desc, show_backward=False):
if type(block_desc) is not framework_pb2.BlockDesc:
block_desc = framework_pb2.BlockDesc.FromString(
block_desc.serialize_to_string())
block_desc.desc.serialize_to_string())
var_reprs = []
op_reprs = []
for var in block_desc.vars:
......@@ -237,13 +238,13 @@ def draw_block_graphviz(block, highlights=None, path="./temp.dot"):
# draw parameters and args
vars = {}
for var in desc.vars:
shape = [str(i) for i in var.lod_tensor.tensor.dims]
if not shape:
shape = ['null']
# TODO(gongwb): format the var.type
# create var
if var.persistable:
varn = graph.add_param(
var.name, var.type, shape, highlight=need_highlight(var.name))
var.name,
str(var.type).replace("\n", "<br />", 1),
highlight=need_highlight(var.name))
else:
varn = graph.add_arg(var.name, highlight=need_highlight(var.name))
vars[var.name] = varn
......@@ -268,4 +269,4 @@ def draw_block_graphviz(block, highlights=None, path="./temp.dot"):
for var in op.outputs:
add_op_link_var(opn, var, True)
graph(path, show=True)
graph(path, show=False)
python/paddle/fluid/distribute_transpiler.py
浏览文件 @ 82530e24
......@@ -17,7 +17,7 @@ import framework
from framework import Program, default_main_program, default_startup_program, Parameter, Variable
import optimizer
from layer_helper import LayerHelper
from distributed_spliter import *
import distributed_splitter as splitter
import math
from . import core
import debuger
......@@ -36,7 +36,7 @@ class VarBlock:
class UnionFind(object):
""" Union-find data struct.
Union-find is a data struct that keeps track of a set of elements partitioned
into a number of disjoint (non-overlapping) subsets.
......@@ -138,7 +138,7 @@ class DistributeTranspiler:
program=None,
pservers="127.0.0.1:6174",
trainers=1,
split_method=round_robin):
split_method=splitter.round_robin):
"""
Transpile the program to distributed data-parallelism programs.
The main_program will be transformed to use a remote parameter server
......@@ -303,7 +303,7 @@ class DistributeTranspiler:
# If two ops are connected, we could add these two ops
# into one set.
ufind = self._create_ufind(self.optimize_ops)
# step 4.2
# step 4.2
# Iterate through the ops and append optimize op which
# located on current pserver
opt_op_on_pserver = []
......@@ -312,7 +312,7 @@ class DistributeTranspiler:
opt_op_on_pserver.append(op)
# step 4.3
# Iterate through the ops, and if an op and the optimize ops
# which located on current pserver are in one set, then
# which located on current pserver are in one set, then
# append it into the sub program.
# We try to put optimization program run parallelly, assume
......@@ -408,11 +408,7 @@ class DistributeTranspiler:
pserver_vars = pserver_program.global_block().vars
created_var_map = dict()
for _, var in pserver_vars.iteritems():
tmpvar = s_prog.global_block().create_var(
name=var.name,
persistable=var.persistable,
dtype=var.dtype,
shape=var.shape)
tmpvar = s_prog.global_block().clone_variable(var)
created_var_map[var.name] = tmpvar
# 2. rename op outputs
......@@ -708,11 +704,7 @@ class DistributeTranspiler:
varlist = [varlist]
for var in varlist:
program.global_block().create_var(
name=var.name,
persistable=var.persistable,
dtype=var.dtype,
shape=var.shape)
program.global_block().clone_variable(var)
optimize_block.append_op(
type=opt_op.type,
......@@ -760,7 +752,7 @@ class DistributeTranspiler:
def _is_opt_op(self, op):
# NOTE: It's a HACK implement.
# optimize op: SGDOptimize, MomentumOptimizer, AdamOptimizer and etc...
# optimize op: SGDOptimize, MomentumOptimizer, AdamOptimizer and etc...
if "Param" in op.input_names and \
"LearningRate" in op.input_names:
return True
......
python/paddle/fluid/distributed_spliter.py → python/paddle/fluid/distributed_splitter.py
浏览文件 @ 82530e24
......@@ -17,8 +17,10 @@ def hash_name(varlist, pserver_endpoints):
"""
hash variable names to several endpoints.
:param varlist: a list of Variables
:return: a map of pserver endpoint -> varname
Args:
varlist(list): a list of Variables
Returns(dict): a map of pserver endpoint -> varname
"""
def _hash_block(block_str, total):
......@@ -34,9 +36,14 @@ def hash_name(varlist, pserver_endpoints):
def round_robin(varlist, pserver_endpoints):
"""
distribute variables to several endpoints.
Distribute variables to several endpoints.
Args:
varlist(list): a list of variables
pserver_endpoints(list): a list of pserver endpoints
Returns(list[int]): the endpoint for each variable
"""
assert (len(varlist) > len(pserver_endpoints))
assert (len(varlist) >= len(pserver_endpoints))
eplist = []
pserver_idx = 0
......
python/paddle/fluid/framework.py
浏览文件 @ 82530e24
......@@ -640,6 +640,20 @@ class Operator(object):
"""
return self.desc.block_attr(name)
def all_attrs(self):
"""
Get the attribute dict
Returns(dict): The Operator's attribute dict
"""
attr_names = self.attr_names
attr_map = {}
for n in attr_names:
if n == 'sub_block':
attr_map[n] = self.block_attr(n)
else:
attr_map[n] = self.attr(n)
return attr_map
class Block(object):
def __init__(self, program, idx):
......@@ -838,7 +852,7 @@ class Block(object):
def sync_with_cpp(self):
"""
Sync with the desc on the c++ end.
Sync from the desc on the c++ end.
This method is used to synchronize the c++ desc instance generated by backward.
"""
......@@ -946,13 +960,27 @@ class Block(object):
The new variable cloned from 'var' in current block.
"""
assert isinstance(var, Variable)
return self.create_var(
name=var.name,
shape=var.shape,
dtype=var.dtype,
type=var.type,
lod_level=var.lod_level,
persistable=True)
ret_var = None
# make STEP_SCOPES var can be safely cloned.
if var.type == core.VarDesc.VarType.STEP_SCOPES:
ret_var = self.create_var(
name=var.name, persistable=var.persistable, type=var.type)
elif var.type == core.VarDesc.VarType.SELECTED_ROWS:
ret_var = self.create_var(
name=var.name,
shape=var.shape,
dtype=var.dtype,
type=var.type,
persistable=True)
else:
ret_var = self.create_var(
name=var.name,
shape=var.shape,
dtype=var.dtype,
type=var.type,
lod_level=var.lod_level,
persistable=True)
return ret_var
class Program(object):
......
python/paddle/fluid/graphviz.py
浏览文件 @ 82530e24
......@@ -83,7 +83,7 @@ class Graph(object):
file = open(dot_path, 'w')
file.write(self.__str__())
image_path = os.path.join(
os.path.dirname(__file__), dot_path[:-3] + "pdf")
os.path.dirname(dot_path), dot_path[:-3] + "pdf")
cmd = ["dot", "-Tpdf", dot_path, "-o", image_path]
subprocess.Popen(
cmd,
......@@ -199,7 +199,7 @@ class GraphPreviewGenerator(object):
else:
self.graph.show(path)
def add_param(self, name, data_type, shape, highlight=False):
def add_param(self, name, data_type, highlight=False):
label = '\n'.join([
'<<table cellpadding="5">',
' <tr>',
......@@ -214,11 +214,6 @@ class GraphPreviewGenerator(object):
str(data_type),
' </td>'
' </tr>',
' <tr>',
' <td>',
'[%s]' % 'x'.join(shape),
' </td>'
' </tr>',
'</table>>',
])
return self.graph.node(
......
python/paddle/fluid/layers/io.py
浏览文件 @ 82530e24
......@@ -255,7 +255,32 @@ def _copy_reader_var_(block, var):
new_var.desc.set_shapes(var.desc.shapes())
new_var.desc.set_dtypes(var.desc.dtypes())
new_var.persistable = True
return monkey_patch_reader_methods(new_var)
return new_var
def _copy_reader_create_op_(block, op):
input_param_names = op.input_names
new_input_map = {}
for param_name in input_param_names:
new_input_map[param_name] = []
arg_names = op.input(param_name)
for arg_name in arg_names:
new_input_map[param_name].append(block.var(arg_name))
output_param_names = op.output_names
new_output_map = {}
for param_name in output_param_names:
new_output_map[param_name] = []
arg_names = op.output(param_name)
for arg_name in arg_names:
new_output_map[param_name].append(block.var(arg_name))
new_op = block.append_op(
type=op.type,
inputs=new_input_map,
outputs=new_output_map,
attrs=op.all_attrs())
return new_op
def open_recordio_file(filename, shapes, lod_levels, dtypes):
......@@ -283,8 +308,9 @@ def open_recordio_file(filename, shapes, lod_levels, dtypes):
startup_var.desc.set_dtypes(dtypes)
startup_var.persistable = True
return _copy_reader_var_(default_main_program().current_block(),
startup_var)
main_prog_var = _copy_reader_var_(default_main_program().current_block(),
startup_var)
return monkey_patch_reader_methods(main_prog_var)
def open_files(filenames, thread_num, shapes, lod_levels, dtypes):
......@@ -313,22 +339,25 @@ def open_files(filenames, thread_num, shapes, lod_levels, dtypes):
startup_var.desc.set_dtypes(dtypes)
startup_var.persistable = True
return _copy_reader_var_(default_main_program().current_block(),
startup_var)
main_prog_var = _copy_reader_var_(default_main_program().current_block(),
startup_var)
return monkey_patch_reader_methods(main_prog_var)
def __create_decorated_reader__(op_type, reader, attrs):
var_name = unique_name(op_type)
startup_blk = default_startup_program().current_block()
startup_var = startup_blk.create_var(name=var_name)
startup_blk.append_op(
startop_op = startup_blk.append_op(
type=op_type,
inputs={'UnderlyingReader': reader},
outputs={'Out': [startup_var]},
attrs=attrs)
startup_var.persistable = True
return _copy_reader_var_(default_main_program().current_block(),
startup_var)
main_prog_block = default_main_program().current_block()
main_prog_var = _copy_reader_var_(main_prog_block, startup_var)
_copy_reader_create_op_(main_prog_block, startop_op)
return monkey_patch_reader_methods(main_prog_var)
def create_shuffle_reader(reader, buffer_size):
......
python/paddle/fluid/parallel_executor.py
浏览文件 @ 82530e24
......@@ -26,25 +26,29 @@ class ParallelExecutor(object):
use_cuda,
num_threads=None,
allow_op_delay=False):
places = []
self._places = []
self._act_places = []
if use_cuda:
for i in xrange(core.get_cuda_device_count()):
p = core.Place()
p.set_place(core.CUDAPlace(i))
places.append(p)
self._act_places.append(core.CUDAPlace(i))
p.set_place(self._act_places[-1])
self._places.append(p)
else:
for i in xrange(multiprocessing.cpu_count()):
p = core.Place()
p.set_place(core.CPUPlace())
places.append(p)
self._act_places.append(core.CPUPlace(i))
p.set_place(self._act_places[-1])
self._places.append(p)
assert self._places, "no place for execution"
if num_threads is None:
if use_cuda:
# Experiments on se-resnext shows that too many threads hurt
# performance. Worth tunning for other models in the future.
num_threads = len(places)
num_threads = len(self._places)
else:
min(len(places) * 2, multiprocessing.cpu_count())
min(len(self._places) * 2, multiprocessing.cpu_count())
startup = framework.default_startup_program()
main = framework.default_main_program()
......@@ -53,7 +57,7 @@ class ParallelExecutor(object):
self.executor = core.ParallelExecutor(
num_threads,
True if use_cuda else False, # use_event
places,
self._places,
set([
p.name for p in main.global_block().iter_parameters()
if not p.stop_gradient
......@@ -65,8 +69,25 @@ class ParallelExecutor(object):
allow_op_delay)
self.scope = scope
def run(self, fetch_list):
def run(self, fetch_list, feed_dict={}):
"""
:param fetch_list: A list of variable names that will be fetched.
:param feed_dict: A dict mapping for feed variable name to LoDTensor
or numpy array.
:return: fetched value list.
"""
if not isinstance(feed_dict, dict):
raise TypeError("feed_dict should be a dict")
feed_tensor_dict = {}
for i, feed_name in enumerate(feed_dict):
feed_tensor = feed_dict[feed_name]
if not isinstance(feed_tensor, core.LoDTensor):
feed_tensor = core.LoDTensor()
feed_tensor.set(feed_dict[feed_name], self._act_places[0])
feed_tensor_dict[feed_name] = feed_tensor
fetch_var_name = '@FETCHED_VAR_NAME@'
self.executor.run(fetch_list, fetch_var_name)
self.executor.run(fetch_list, fetch_var_name, feed_tensor_dict)
arr = self.scope.find_var(fetch_var_name).get_lod_tensor_array()
return [arr[i] for i in range(len(arr))]
python/paddle/fluid/tests/unittests/CMakeLists.txt
浏览文件 @ 82530e24
......@@ -22,9 +22,9 @@ function(py_test_modules TARGET_NAME)
set(multiValueArgs MODULES DEPS ARGS ENVS)
cmake_parse_arguments(py_test_modules "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
add_test(NAME ${TARGET_NAME}
COMMAND env PYTHONPATH=${PADDLE_PYTHON_BUILD_DIR}/lib-python ${py_test_modules_ENVS}
COMMAND env PYTHONPATH=${PADDLE_BINARY_DIR}/python ${py_test_modules_ENVS}
${PYTHON_EXECUTABLE} -u -m unittest --verbose ${py_test_modules_MODULES} ${py_test_modules_ARGS}
WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR})
WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR})
endif()
endfunction()
......
python/paddle/fluid/tests/unittests/test_conv2d_op.py
浏览文件 @ 82530e24
......@@ -97,8 +97,11 @@ class TestConv2dOp(OpTest):
}
self.outputs = {'Output': output}
def testcudnn(self):
return core.is_compiled_with_cuda() and self.use_cudnn
def test_check_output(self):
if self.use_cudnn:
if self.testcudnn():
place = core.CUDAPlace(0)
self.check_output_with_place(place, atol=1e-5)
else:
......@@ -107,7 +110,7 @@ class TestConv2dOp(OpTest):
def test_check_grad(self):
if self.dtype == np.float16:
return
if self.use_cudnn:
if self.testcudnn():
place = core.CUDAPlace(0)
self.check_grad_with_place(
place,
......@@ -121,7 +124,7 @@ class TestConv2dOp(OpTest):
def test_check_grad_no_filter(self):
if self.dtype == np.float16:
return
if self.use_cudnn:
if self.testcudnn():
place = core.CUDAPlace(0)
self.check_grad_with_place(
place, ['Input'],
......@@ -138,7 +141,7 @@ class TestConv2dOp(OpTest):
def test_check_grad_no_input(self):
if self.dtype == np.float16:
return
if self.use_cudnn:
if self.testcudnn():
place = core.CUDAPlace(0)
self.check_grad_with_place(
place, ['Filter'],
......
python/paddle/fluid/tests/unittests/test_debugger.py
浏览文件 @ 82530e24
......@@ -51,7 +51,9 @@ class TestDebugger(unittest.TestCase):
outputs={"Out": mul_out},
attrs={"x_num_col_dims": 1})
print(debuger.pprint_program_codes(p.desc))
print(debuger.pprint_program_codes(p))
debuger.draw_block_graphviz(p.block(0), path="./test.dot")
if __name__ == '__main__':
......
python/paddle/fluid/tests/unittests/test_lookup_table_op.py
浏览文件 @ 82530e24
......@@ -115,18 +115,18 @@ class TestLookupTableWIsSelectedRows(OpTest):
w_array = np.ones((len(rows), row_numel)).astype("float32")
for i in range(len(rows)):
w_array[i] *= i
ids_tensor = w_selected_rows.get_tensor()
ids_tensor.set(w_array, place)
w_tensor = w_selected_rows.get_tensor()
w_tensor.set(w_array, place)
# create Out Variable
Out_tensor = scope.var('Out').get_tensor()
out_tensor = scope.var('Out').get_tensor()
# create and run lookup_table operator
lookup_table = Operator("lookup_table", W='W', Ids='Ids', Out='Out')
lookup_table.run(scope, place)
# get result from Out
result_array = np.array(Out_tensor)
result_array = np.array(out_tensor)
# all(): return True if all elements of the iterable are true (or if the iterable is empty)
for idx, row in enumerate(ids_array):
assert (row[0] == result_array[idx]).all()
......
python/paddle/fluid/tests/unittests/test_parallel_executor.py
浏览文件 @ 82530e24
......@@ -21,13 +21,17 @@ import paddle.dataset.mnist as mnist
import paddle.dataset.wmt16 as wmt16
def simple_fc_net():
reader = fluid.layers.open_recordio_file(
filename='./mnist.recordio',
shapes=[[-1, 784], [-1, 1]],
lod_levels=[0, 0],
dtypes=['float32', 'int64'])
img, label = fluid.layers.read_file(reader)
def simple_fc_net(use_feed):
if use_feed:
img = fluid.layers.data(name='image', shape=[784], dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
else:
reader = fluid.layers.open_recordio_file(
filename='./mnist.recordio',
shapes=[[-1, 784], [-1, 1]],
lod_levels=[0, 0],
dtypes=['float32', 'int64'])
img, label = fluid.layers.read_file(reader)
hidden = img
for _ in xrange(4):
hidden = fluid.layers.fc(
......@@ -42,13 +46,18 @@ def simple_fc_net():
return loss
def fc_with_batchnorm():
reader = fluid.layers.open_recordio_file(
filename='./mnist.recordio',
shapes=[[-1, 784], [-1, 1]],
lod_levels=[0, 0],
dtypes=['float32', 'int64'])
img, label = fluid.layers.read_file(reader)
def fc_with_batchnorm(use_feed):
if use_feed:
img = fluid.layers.data(name='image', shape=[784], dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
else:
reader = fluid.layers.open_recordio_file(
filename='./mnist.recordio',
shapes=[[-1, 784], [-1, 1]],
lod_levels=[0, 0],
dtypes=['float32', 'int64'])
img, label = fluid.layers.read_file(reader)
hidden = img
for _ in xrange(1):
hidden = fluid.layers.fc(
......@@ -135,7 +144,9 @@ def bottleneck_block(input, num_filters, stride, cardinality, reduction_ratio):
return fluid.layers.elementwise_add(x=short, y=scale, act='relu')
def SE_ResNeXt152Small(batch_size=2):
def SE_ResNeXt50Small(batch_size=2, use_feed=False):
assert not use_feed, "SE_ResNeXt doesn't support feed yet"
img = fluid.layers.fill_constant(
shape=[batch_size, 3, 224, 224], dtype='float32', value=0.0)
label = fluid.layers.fill_constant(
......@@ -150,9 +161,9 @@ def SE_ResNeXt152Small(batch_size=2):
conv = fluid.layers.pool2d(
input=conv, pool_size=3, pool_stride=2, pool_padding=1, pool_type='max')
cardinality = 64
cardinality = 32
reduction_ratio = 16
depth = [3, 8, 36, 3]
depth = [3, 4, 6, 3]
num_filters = [128, 256, 512, 1024]
for block in range(len(depth)):
......@@ -185,30 +196,28 @@ class TestParallelExecutorBase(unittest.TestCase):
memory_opt=True,
iter=10,
batch_size=None,
allow_op_delay=False):
allow_op_delay=False,
feed_dict={}):
main = fluid.Program()
startup = fluid.Program()
with fluid.program_guard(main, startup):
loss = method()
loss = method(use_feed=len(feed_dict) > 0)
adam = fluid.optimizer.Adam()
adam.minimize(loss)
if memory_opt:
fluid.memory_optimize(main)
exe = fluid.ParallelExecutor(
loss_name=loss.name,
use_cuda=True,
allow_op_delay=allow_op_delay)
exe = fluid.ParallelExecutor(loss_name=loss.name, use_cuda=True)
if batch_size is not None:
batch_size *= fluid.core.get_cuda_device_count()
begin = time.time()
first_loss, = exe.run([loss.name])
first_loss, = exe.run([loss.name], feed_dict=feed_dict)
first_loss = numpy.array(first_loss)
for i in xrange(iter):
exe.run([])
exe.run([], feed_dict=feed_dict)
last_loss, = exe.run([loss.name])
last_loss, = exe.run([loss.name], feed_dict=feed_dict)
end = time.time()
if batch_size is not None:
......@@ -242,9 +251,19 @@ class TestMNIST(TestParallelExecutorBase):
self.check_network_convergence(simple_fc_net)
self.check_network_convergence(simple_fc_net, allow_op_delay=True)
img = numpy.zeros(shape=[32, 784], dtype='float32')
label = numpy.ones(shape=[32, 1], dtype='int64')
self.check_network_convergence(
simple_fc_net, feed_dict={"image": img,
"label": label})
def test_batchnorm_fc(self):
self.check_network_convergence(fc_with_batchnorm)
self.check_network_convergence(fc_with_batchnorm, allow_op_delay=True)
img = numpy.zeros(shape=[32, 784], dtype='float32')
label = numpy.ones(shape=[32, 1], dtype='int64')
self.check_network_convergence(
fc_with_batchnorm, feed_dict={"image": img,
"label": label})
class TestResnet(TestParallelExecutorBase):
......@@ -271,7 +290,7 @@ class TestResnet(TestParallelExecutorBase):
batch_size = 2
self.check_network_convergence(
functools.partial(
SE_ResNeXt152Small, batch_size=batch_size),
SE_ResNeXt50Small, batch_size=batch_size),
iter=20,
batch_size=batch_size)
......@@ -400,7 +419,8 @@ def prepare_batch_input(insts, src_pad_idx, trg_pad_idx, n_head):
import transformer_model
def transformer():
def transformer(use_feed):
assert not use_feed, "transfomer doesn't support feed yet"
return transformer_model.transformer(
ModelHyperParams.src_vocab_size + 1,
ModelHyperParams.trg_vocab_size + 1, ModelHyperParams.max_length + 1,
......
python/paddle/fluid/tests/unittests/test_prior_box_op.py
浏览文件 @ 82530e24
......@@ -28,7 +28,6 @@ class TestPriorBoxOp(OpTest):
self.attrs = {
'min_sizes': self.min_sizes,
'max_sizes': self.max_sizes,
'aspect_ratios': self.aspect_ratios,
'variances': self.variances,
'flip': self.flip,
......@@ -37,25 +36,28 @@ class TestPriorBoxOp(OpTest):
'step_h': self.step_h,
'offset': self.offset
}
if len(self.max_sizes) > 0:
self.attrs['max_sizes'] = self.max_sizes
self.outputs = {'Boxes': self.out_boxes, 'Variances': self.out_var}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
return
def setUp(self):
self.op_type = "prior_box"
self.set_data()
def set_max_sizes(self):
max_sizes = [5, 10]
self.max_sizes = np.array(max_sizes).astype('float32').tolist()
def init_test_params(self):
self.layer_w = 4
self.layer_h = 4
self.layer_w = 32
self.layer_h = 32
self.image_w = 20
self.image_h = 20
self.image_w = 40
self.image_h = 40
self.step_w = float(self.image_w) / float(self.layer_w)
self.step_h = float(self.image_h) / float(self.layer_h)
......@@ -66,8 +68,7 @@ class TestPriorBoxOp(OpTest):
self.min_sizes = [2, 4]
self.min_sizes = np.array(self.min_sizes).astype('float32').tolist()
self.max_sizes = [5, 10]
self.max_sizes = np.array(self.max_sizes).astype('float32').tolist()
self.set_max_sizes()
self.aspect_ratios = [2.0, 3.0]
self.flip = True
self.real_aspect_ratios = [1, 2.0, 1.0 / 2.0, 3.0, 1.0 / 3.0]
......@@ -79,7 +80,7 @@ class TestPriorBoxOp(OpTest):
self.clip = True
self.num_priors = len(self.real_aspect_ratios) * len(self.min_sizes)
if len(self.max_sizes) > 1:
if len(self.max_sizes) > 0:
self.num_priors += len(self.max_sizes)
self.offset = 0.5
......@@ -105,35 +106,27 @@ class TestPriorBoxOp(OpTest):
idx = 0
for s in range(len(self.min_sizes)):
min_size = self.min_sizes[s]
c_w = c_h = min_size / 2.
out_boxes[h, w, idx, :] = [
(c_x - c_w) / self.image_w, (c_y - c_h) / self.image_h,
(c_x + c_w) / self.image_w, (c_y + c_h) / self.image_h
]
idx += 1
if len(self.max_sizes) > 0:
max_size = self.max_sizes[s]
# second prior: aspect_ratio = 1,
c_w = c_h = math.sqrt(min_size * max_size) / 2
# rest of priors
for r in range(len(self.real_aspect_ratios)):
ar = self.real_aspect_ratios[r]
c_w = min_size * math.sqrt(ar) / 2
c_h = (min_size / math.sqrt(ar)) / 2
out_boxes[h, w, idx, :] = [(c_x - c_w) / self.image_w,
(c_y - c_h) / self.image_h,
(c_x + c_w) / self.image_w,
(c_y + c_h) / self.image_h]
idx += 1
# rest of priors
for r in range(len(self.real_aspect_ratios)):
ar = self.real_aspect_ratios[r]
if math.fabs(ar - 1.) < 1e-6:
continue
c_w = min_size * math.sqrt(ar) / 2
c_h = (min_size / math.sqrt(ar)) / 2
if len(self.max_sizes) > 0:
max_size = self.max_sizes[s]
# second prior: aspect_ratio = 1,
c_w = c_h = math.sqrt(min_size * max_size) / 2
out_boxes[h, w, idx, :] = [(c_x - c_w) / self.image_w,
(c_y - c_h) / self.image_h,
(c_x + c_w) / self.image_w,
(c_y + c_h) / self.image_h]
idx += 1
# clip the prior's coordidate such that it is within[0, 1]
if self.clip:
out_boxes = np.clip(out_boxes, 0.0, 1.0)
......@@ -144,5 +137,10 @@ class TestPriorBoxOp(OpTest):
self.out_var = out_var.astype('float32')
class TestPriorBoxOpWithMaxSize(TestPriorBoxOp):
def set_max_sizes(self):
self.max_sizes = []
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/test_protobuf_descs.py
浏览文件 @ 82530e24
......@@ -19,9 +19,9 @@ from paddle.fluid.framework import Program
class TestOpDesc(unittest.TestCase):
def test_op_desc(self):
prog = core.ProgramDesc()
self.assertIsNotNone(prog)
block = prog.block(0)
program_desc = core.ProgramDesc()
self.assertIsNotNone(program_desc)
block = program_desc.block(0)
self.assertIsNotNone(block)
op = block.append_op()
self.assertIsNotNone(op)
......@@ -67,7 +67,7 @@ class TestOpDesc(unittest.TestCase):
self.assertEqual(8, len(op.attr_names()))
op.set_block_attr("block_attr", prog.block(0))
op.set_block_attr("block_attr", program_desc.block(0))
self.assertEqual(0, op.block_attr("block_attr"))
mul_op = block.append_op()
......@@ -88,20 +88,20 @@ class TestProgramDesc(unittest.TestCase):
del program_desc
def test_append_block(self):
prog_desc = core.ProgramDesc()
self.assertIsNotNone(prog_desc)
block_root = prog_desc.block(0)
program_desc = core.ProgramDesc()
self.assertIsNotNone(program_desc)
block_root = program_desc.block(0)
self.assertIsNotNone(block_root)
self.assertEqual(block_root.id, 0)
block1 = prog_desc.append_block(block_root)
block2 = prog_desc.append_block(block1)
block1 = program_desc.append_block(block_root)
block2 = program_desc.append_block(block1)
self.assertIsNotNone(block1)
self.assertEqual(block1.id, block2.parent)
self.assertEqual(block_root.id, block1.parent)
block3 = prog_desc.append_block(block_root)
block3 = program_desc.append_block(block_root)
self.assertEqual(block3.parent, block_root.id)
self.assertEqual(prog_desc.block(1).id, 1)
self.assertEqual(4, prog_desc.num_blocks())
self.assertEqual(program_desc.block(1).id, 1)
self.assertEqual(4, program_desc.num_blocks())
class TestVarDesc(unittest.TestCase):
......@@ -162,9 +162,9 @@ class TestVarDesc(unittest.TestCase):
class TestBlockDesc(unittest.TestCase):
def test_add_var(self):
prog = core.ProgramDesc()
self.assertIsNotNone(prog)
block = prog.block(0)
program_desc = core.ProgramDesc()
self.assertIsNotNone(program_desc)
block = program_desc.block(0)
self.assertIsNotNone(block)
var1 = block.var("var1")
var2 = block.var("var2")
......@@ -175,9 +175,9 @@ class TestBlockDesc(unittest.TestCase):
self.assertEqual(var2_re, var2)
def test_add_op(self):
prog = core.ProgramDesc()
self.assertIsNotNone(prog)
block = prog.block(0)
program_desc = core.ProgramDesc()
self.assertIsNotNone(program_desc)
block = program_desc.block(0)
self.assertIsNotNone(block)
op1 = block.append_op()
op2 = block.append_op()
......@@ -189,9 +189,9 @@ class TestBlockDesc(unittest.TestCase):
def test_remove_op(self):
program = Program()
prog = program.desc
self.assertIsNotNone(prog)
block = prog.block(0)
program_desc = program.desc
self.assertIsNotNone(program_desc)
block = program_desc.block(0)
self.assertIsNotNone(block)
op0 = block.append_op()
......
python/paddle/fluid/tests/unittests/test_recordio_reader.py
浏览文件 @ 82530e24
......@@ -15,8 +15,8 @@
import unittest
import paddle.fluid as fluid
import paddle
import paddle.dataset.mnist as mnist
import paddle.v2 as paddle
import paddle.v2.dataset.mnist as mnist
class TestRecordIO(unittest.TestCase):
......
python/paddle/fluid/tests/unittests/test_sgd_op.py
浏览文件 @ 82530e24
......@@ -97,5 +97,72 @@ class TestSparseSGDOp(unittest.TestCase):
self.check_with_place(place)
class TestSGDOpOptimizeSelectedRows(unittest.TestCase):
def check_with_place(self, place):
scope = core.Scope()
row_width = 12
# create and initialize Grad Variable
grad_height = 10
grad_rows = [0, 4, 7]
grad_selected_rows = scope.var('Grad').get_selected_rows()
grad_selected_rows.set_height(grad_height)
grad_selected_rows.set_rows(grad_rows)
grad_array = np.ones((len(grad_rows), row_width)).astype("float32")
grad_array[0, 0] = 2.0
grad_array[2, 8] = 4.0
grad_tensor = grad_selected_rows.get_tensor()
grad_tensor.set(grad_array, place)
# create and initialize Param Variable
# create and initialize W Variable
param_rows = [0, 1, 2, 3, 4, 5, 6, 7]
# init Param
w_selected_rows = scope.var('Param').get_selected_rows()
w_selected_rows.set_height(len(param_rows))
w_selected_rows.set_rows(param_rows)
w_array = np.ones((len(param_rows), row_width)).astype("float32")
for i in range(len(param_rows)):
w_array[i] *= i
w_tensor = w_selected_rows.get_tensor()
w_tensor.set(w_array, place)
w_before_optimize = np.array(w_tensor)
# create and initialize LeraningRate Variable
lr_value = 0.1
lr = scope.var('LearningRate').get_tensor()
lr_array = np.full((1), lr_value).astype("float32")
lr.set(lr_array, place)
# optimize with Python
w_after_optimize = np.copy(w_before_optimize)
for index, id in enumerate(grad_rows):
w_after_optimize[id] = w_before_optimize[
id] - lr_value * grad_array[index]
# create and run sgd operator
sgd_op = Operator(
"sgd",
Param='Param',
Grad='Grad',
ParamOut='Param',
LearningRate='LearningRate')
sgd_op.run(scope, place)
# get and compare result
result_array = np.array(w_tensor)
assert (result_array == w_after_optimize).all()
def test_sparse_parameter_sgd(self):
places = [core.CPUPlace()]
# do not support GPU kernel currently
for place in places:
self.check_with_place(place)
if __name__ == "__main__":
unittest.main()
python/paddle/fluid/tests/unittests/test_softmax_op.py
浏览文件 @ 82530e24
......@@ -68,6 +68,17 @@ class TestSoftmaxCUDNNOp(TestSoftmaxOp):
self.use_cudnn = True
class TestSoftmaxFP16Op(TestSoftmaxOp):
def init_kernel_type(self):
self.dtype = np.float16
def test_check_output(self):
if core.is_compiled_with_cuda():
place = core.CUDAPlace(0)
if core.is_float16_supported(place):
self.check_output_with_place(place, atol=1e-3)
class TestSoftmaxFP16CUDNNOp(TestSoftmaxOp):
def init_kernel_type(self):
self.use_cudnn = True
......
python/paddle/trainer_config_helpers/tests/CMakeLists.txt
浏览文件 @ 82530e24
#################### test_config_parser #########################
add_test(NAME layers_test
COMMAND ${PADDLE_SOURCE_DIR}/paddle/.set_python_path.sh -d ${PADDLE_SOURCE_DIR}/python/
COMMAND ${PADDLE_SOURCE_DIR}/paddle/.set_python_path.sh -d ${PADDLE_BINARY_DIR}/python/
${PYTHON_EXECUTABLE} ${PADDLE_SOURCE_DIR}/python/paddle/trainer_config_helpers/tests/layers_test.py
WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/python/paddle)
add_test(NAME test_reset_hook
COMMAND ${PADDLE_SOURCE_DIR}/paddle/.set_python_path.sh -d ${PADDLE_SOURCE_DIR}/python/
COMMAND ${PADDLE_SOURCE_DIR}/paddle/.set_python_path.sh -d ${PADDLE_BINARY_DIR}/python/
${PYTHON_EXECUTABLE} ${PADDLE_SOURCE_DIR}/python/paddle/trainer_config_helpers/tests/test_reset_hook.py
WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/python/paddle)
add_paddle_exe(protobuf_equal ProtobufEqualMain.cpp)
add_test(NAME test_layerHelpers
COMMAND
${PADDLE_SOURCE_DIR}/python/paddle/trainer_config_helpers/tests/configs/run_tests.sh ${PYTHON_EXECUTABLE}
COMMAND ${PADDLE_SOURCE_DIR}/paddle/.set_python_path.sh -d ${PADDLE_BINARY_DIR}/python/
${PADDLE_BINARY_DIR}/python/paddle/trainer_config_helpers/tests/configs/run_tests.sh ${PYTHON_EXECUTABLE}
${CMAKE_CURRENT_BINARY_DIR}/protobuf_equal
)
python/paddle/trainer_config_helpers/tests/configs/generate_protostr.sh
浏览文件 @ 82530e24
......@@ -2,7 +2,6 @@
set -e
cd `dirname $0`
export PYTHONPATH=$PWD/../../../../
protostr=$PWD/protostr
. file_list.sh
......
python/setup.py.in
浏览文件 @ 82530e24
......@@ -58,7 +58,7 @@ def mkl():
'istaged': ISTAGED,
'with_mkl': '@WITH_MKL@'})
write_version_py(filename='@PADDLE_SOURCE_DIR@/python/paddle/version.py')
write_version_py(filename='@PADDLE_BINARY_DIR@/python/paddle/version.py')
packages=['paddle',
......@@ -107,9 +107,10 @@ package_dir={
# So that package points to other directory.
'paddle.fluid.proto.profiler': '${PADDLE_BINARY_DIR}/paddle/fluid/platform',
'paddle.fluid.proto': '${PADDLE_BINARY_DIR}/paddle/fluid/framework',
'paddle.fluid': '${PADDLE_BINARY_DIR}/python/paddle/fluid',
}
if '${WITH_FLUID_ONLY}'== 'OFF':
package_dir['py_paddle']='${PADDLE_SOURCE_DIR}/paddle/py_paddle'
package_dir['py_paddle']='${PADDLE_BINARY_DIR}/python/py_paddle'
paddle_rt_lib_dir = 'lib'
......
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