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提交 192c00a7 编写于 作者: G guosheng
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Merge branch 'develop' of https://github.com/PaddlePaddle/paddle into enhance-include-pool

上级 fe6af6b6 1238706d
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cmake/external/grpc.cmake
浏览文件 @ 192c00a7
......@@ -24,9 +24,9 @@ SET(GRPC_INSTALL_DIR ${THIRD_PARTY_PATH}/install/grpc)
SET(GRPC_INCLUDE_DIR "${GRPC_INSTALL_DIR}/include/" CACHE PATH "grpc include directory." FORCE)
SET(GRPC_CPP_PLUGIN "${GRPC_INSTALL_DIR}/bin/grpc_cpp_plugin" CACHE FILEPATH "GRPC_CPP_PLUGIN" FORCE)
IF(APPLE)
SET(BUILD_CMD make -n | sed "s/-Werror//g" | sh)
SET(BUILD_CMD make -n HAS_SYSTEM_PROTOBUF=false -s -j8 static grpc_cpp_plugin | sed "s/-Werror//g" | sh)
ELSE()
SET(BUILD_CMD make)
SET(BUILD_CMD make HAS_SYSTEM_PROTOBUF=false -s -j8 static grpc_cpp_plugin)
ENDIF()
ExternalProject_Add(
......@@ -42,7 +42,7 @@ ExternalProject_Add(
# Disable -Werror, otherwise the compile will fail in MacOS.
# It seems that we cannot configure that by make command.
# Just dry run make command and remove `-Werror`, then use a shell to run make commands
BUILD_COMMAND ${BUILD_CMD} HAS_SYSTEM_PROTOBUF=false -s -j8 static grpc_cpp_plugin
BUILD_COMMAND ${BUILD_CMD}
INSTALL_COMMAND make prefix=${GRPC_INSTALL_DIR} install
)
......
cmake/generic.cmake
浏览文件 @ 192c00a7
......@@ -227,8 +227,8 @@ function(cc_test TARGET_NAME)
set(multiValueArgs SRCS DEPS)
cmake_parse_arguments(cc_test "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
add_executable(${TARGET_NAME} ${cc_test_SRCS})
target_link_libraries(${TARGET_NAME} ${cc_test_DEPS} gtest gtest_main)
add_dependencies(${TARGET_NAME} ${cc_test_DEPS} gtest gtest_main)
target_link_libraries(${TARGET_NAME} ${cc_test_DEPS} paddle_gtest_main paddle_memory gtest gflags)
add_dependencies(${TARGET_NAME} ${cc_test_DEPS} paddle_gtest_main paddle_memory gtest gflags)
add_test(NAME ${TARGET_NAME} COMMAND ${TARGET_NAME} WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR})
endif()
endfunction(cc_test)
......@@ -288,8 +288,8 @@ function(nv_test TARGET_NAME)
set(multiValueArgs SRCS DEPS)
cmake_parse_arguments(nv_test "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
cuda_add_executable(${TARGET_NAME} ${nv_test_SRCS})
target_link_libraries(${TARGET_NAME} ${nv_test_DEPS} gtest gtest_main)
add_dependencies(${TARGET_NAME} ${nv_test_DEPS} gtest gtest_main)
target_link_libraries(${TARGET_NAME} ${nv_test_DEPS} paddle_gtest_main paddle_memory gtest gflags)
add_dependencies(${TARGET_NAME} ${nv_test_DEPS} paddle_gtest_main paddle_memory gtest gflags)
add_test(${TARGET_NAME} ${TARGET_NAME})
endif()
endfunction(nv_test)
......
doc/design/float16.md
浏览文件 @ 192c00a7
......@@ -28,6 +28,51 @@ The goal of float16 is to serve as a key for the executor to find and run the co
- [Eigen](https://github.com/RLovelett/eigen) >= 3.3 supports float16 calculation on both GPU and CPU using the `Eigen::half` class. It is mostly useful for Nvidia GPUs because of the overloaded arithmetic operators using cuda intrinsics. It falls back to using software emulation on CPU for calculation and there is no special treatment to ARM processors.
- [ARM compute library](https://github.com/ARM-software/ComputeLibrary) >= 17.02.01 supports NEON FP16 kernels (requires ARMv8.2-A CPU).
### CUDA version issue
There are currently three versions of CUDA that supports `__half` data type, namely, CUDA 7.5, 8.0, and 9.0.
CUDA 7.5 and 8.0 define `__half` as a simple struct that has a `uint16_t` data (see [`cuda_fp16.h`](https://github.com/ptillet/isaac/blob/9212ab5a3ddbe48f30ef373f9c1fb546804c7a8c/include/isaac/external/CUDA/cuda_fp16.h)) as follows:
```
typedef struct __align__(2) {
unsigned short x;
} __half;
typedef __half half;
```
This struct does not define any overloaded arithmetic operators. So you have to directly use `__hadd` instead of `+` to correctly add two half types:
```
__global__ void Add() {
half a, b, c;
c = __hadd(a, b); // correct
c = a + b; // compiler error: no operator "+" matches these operands
}
```
CUDA 9.0 provides a major update to the half data type. The related code can be found in the updated [`cuda_fp16.h`](https://github.com/ptillet/isaac/blob/master/include/isaac/external/CUDA/cuda_fp16.h) and the newly added [`cuda_fp16.hpp`](https://github.com/ptillet/isaac/blob/master/include/isaac/external/CUDA/cuda_fp16.hpp).
Essentially, CUDA 9.0 renames the original `__half` type in 7.5 and 8.0 as `__half_raw`, and defines a new `__half` class type that has constructors, conversion operators, and also provides overloaded arithmetic operators such as follows:
```
typedef struct __CUDA_ALIGN__(2) {
unsigned short x;
} __half_raw;
struct __CUDA_ALIGN__(2) __half {
protected:
unsigned short __x;
public:
// constructors and conversion operators from/to
// __half_raw and other built-in data types
}
typedef __half half;
__device__ __forceinline__
__half operator+(const __half &lh, const __half &rh) {
return __hadd(lh, rh);
}
// Other overloaded operators
```
This new design makes `c = a + b` work correctly for CUDA half data type.
## Implementation
The float16 class holds a 16-bit `uint16_t` data internally.
......
doc/howto/optimization/cpu_profiling.md
浏览文件 @ 192c00a7
此教程会介绍如何使用Python的cProfile包，与Python库yep，google perftools来运行性能分析(Profiling)与调优。
This tutorial introduces techniques we used to profile and tune the
CPU performance of PaddlePaddle. We will use Python packages
`cProfile` and `yep`, and Google `perftools`.
运行性能分析可以让开发人员科学的，有条不紊的对程序进行性能优化。性能分析是性能调优的基础。因为在程序实际运行中，真正的瓶颈可能和程序员开发过程中想象的瓶颈相去甚远。
Profiling is the process that reveals the performance bottlenecks,
which could be very different from what's in the developers' mind.
Performance tuning is to fix the bottlenecks. Performance optimization
repeats the steps of profiling and tuning alternatively.
性能优化的步骤，通常是循环重复若干次『性能分析 --> 寻找瓶颈 ---> 调优瓶颈 --> 性能分析确认调优效果』。其中性能分析是性能调优的至关重要的量化指标。
PaddlePaddle users program AI by calling the Python API, which calls
into `libpaddle.so.` written in C++. In this tutorial, we focus on
the profiling and tuning of
Paddle提供了Python语言绑定。用户使用Python进行神经网络编程，训练，测试。Python解释器通过`pybind`和`swig`调用Paddle的动态链接库，进而调用Paddle C++部分的代码。所以Paddle的性能分析与调优分为两个部分:
1. the Python code and
1. the mixture of Python and C++ code.
* Python代码的性能分析
* Python与C++混合代码的性能分析
## Profiling the Python Code
### Generate the Performance Profiling File
## Python代码的性能分析
### 生成性能分析文件
Python标准库中提供了性能分析的工具包，[cProfile](https://docs.python.org/2/library/profile.html)。生成Python性能分析的命令如下:
We can use Python standard
package, [`cProfile`](https://docs.python.org/2/library/profile.html),
to generate Python profiling file. For example:
```bash
python -m cProfile -o profile.out main.py
```
其中`-o`标识了一个输出的文件名，用来存储本次性能分析的结果。如果不指定这个文件，`cProfile`会打印一些统计信息到`stdout`。这不方便我们进行后期处理(进行`sort`, `split`, `cut`等等)。
### 查看性能分析文件
where `main.py` is the program we are going to profile, `-o` specifies
the output file. Without `-o`, `cProfile` would outputs to standard
output.
当main.py运行完毕后，性能分析结果文件`profile.out`就生成出来了。我们可以使用[cprofilev](https://github.com/ymichael/cprofilev)来查看性能分析结果。`cprofilev`是一个Python的第三方库。使用它会开启一个HTTP服务，将性能分析结果以网页的形式展示出来。
### Look into the Profiling File
使用`pip install cprofilev`安装`cprofilev`工具。安装完成后，使用如下命令开启HTTP服务
`cProfile` generates `profile.out` after `main.py` completes. We can
use [`cprofilev`](https://github.com/ymichael/cprofilev) to look into
the details:
```bash
cprofilev -a 0.0.0.0 -p 3214 -f profile.out main.py
```
其中`-a`标识HTTP服务绑定的IP。使用`0.0.0.0`允许外网访问这个HTTP服务。`-p`标识HTTP服务的端口。`-f`标识性能分析的结果文件。`main.py`标识被性能分析的源文件。
where `-a` specifies the HTTP IP, `-p` specifies the port, `-f`
specifies the profiling file, and `main.py` is the source file.
访问对应网址，即可显示性能分析的结果。性能分析结果格式如下:
Open the Web browser and points to the local IP and the specifies
port, we will see the output like the following:
```text
```
ncalls tottime percall cumtime percall filename:lineno(function)
1 0.284 0.284 29.514 29.514 main.py:1(<module>)
4696 0.128 0.000 15.748 0.003 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/fluid/executor.py:20(run)
......@@ -44,23 +54,23 @@ cprofilev -a 0.0.0.0 -p 3214 -f profile.out main.py
1 0.144 0.144 6.534 6.534 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/__init__.py:14(<module>)
```
每一列的含义是:
where each line corresponds to Python function, and the meaning of
each column is as follows:
| 列名 | 含义 |
| column | meaning |
| --- | --- |
| ncalls | 函数的调用次数 |
| tottime | 函数实际使用的总时间。该时间去除掉本函数调用其他函数的时间 |
| percall | tottime的每次调用平均时间 |
| cumtime | 函数总时间。包含这个函数调用其他函数的时间 |
| percall | cumtime的每次调用平均时间 |
| filename:lineno(function) | 文件名, 行号，函数名 |
| ncalls | the number of calls into a function |
| tottime | the total execution time of the function, not including the
execution time of other functions called by the function |
| percall | tottime divided by ncalls |
| cumtime | the total execution time of the function, including the execution time of other functions being called |
| percall | cumtime divided by ncalls |
| filename:lineno(function) | where the function is defined |
### Identify Performance Bottlenecks
### 寻找性能瓶颈
通常`tottime`和`cumtime`是寻找瓶颈的关键指标。这两个指标代表了某一个函数真实的运行时间。
将性能分析结果按照tottime排序，效果如下:
Usually, `tottime` and the related `percall` time is what we want to
focus on. We can sort above profiling file by tottime:
```text
4696 12.040 0.003 12.040 0.003 {built-in method run}
......@@ -68,12 +78,15 @@ cprofilev -a 0.0.0.0 -p 3214 -f profile.out main.py
107991 0.676 0.000 1.519 0.000 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/fluid/framework.py:219(__init__)
4697 0.626 0.000 2.291 0.000 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/fluid/framework.py:428(sync_with_cpp)
1 0.618 0.618 0.618 0.618 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/fluid/__init__.py:1(<module>)
```
可以看到最耗时的函数是C++端的`run`函数。这需要联合我们第二节`Python与C++混合代码的性能分析`来进行调优。而`sync_with_cpp`函数的总共耗时很长，每次调用的耗时也很长。于是我们可以点击`sync_with_cpp`的详细信息，了解其调用关系。
We can see that the most time-consuming function is the `built-in
method run`, which is a C++ function in `libpaddle.so`. We will
explain how to profile C++ code in the next section. At the right
moment, let's look into the third function `sync_with_cpp`, which is a
Python function. We can click it to understand more about it:
```text
```
Called By:
Ordered by: internal time
......@@ -92,72 +105,93 @@ Called:
List reduced from 4497 to 2 due to restriction <'sync_with_cpp'>
```
通常观察热点函数间的调用关系，和对应行的代码，就可以了解到问题代码在哪里。当我们做出性能修正后，再次进行性能分析(profiling)即可检查我们调优后的修正是否能够改善程序的性能。
The lists of the callers of `sync_with_cpp` might help us understand
how to improve the function definition.
## Profiling Python and C++ Code
### Generate the Profiling File
## Python与C++混合代码的性能分析
To profile a mixture of Python and C++ code, we can use a Python
package, `yep`, that can work with Google's `perftools`, which is a
commonly-used profiler for C/C++ code.
### 生成性能分析文件
C++的性能分析工具非常多。常见的包括`gprof`, `valgrind`, `google-perftools`。但是调试Python中使用的动态链接库与直接调试原始二进制相比增加了很多复杂度。幸而Python的一个第三方库`yep`提供了方便的和`google-perftools`交互的方法。于是这里使用`yep`进行Python与C++混合代码的性能分析
使用`yep`前需要安装`google-perftools`与`yep`包。ubuntu下安装命令为
In Ubuntu systems, we can install `yep` and `perftools` by running the
following commands:
```bash
apt update
apt install libgoogle-perftools-dev
pip install yep
```
安装完毕后，我们可以通过
Then we can run the following command
```bash
python -m yep -v main.py
```
生成性能分析文件。生成的性能分析文件为`main.py.prof`。
to generate the profiling file. The default filename is
`main.py.prof`.
Please be aware of the `-v` command line option, which prints the
analysis results after generating the profiling file. By taking a
glance at the print result, we'd know that if we stripped debug
information from `libpaddle.so` at build time. The following hints
help make sure that the analysis results are readable:
命令行中的`-v`指定在生成性能分析文件之后，在命令行显示分析结果。我们可以在命令行中简单的看一下生成效果。因为C++与Python不同，编译时可能会去掉调试信息，运行时也可能因为多线程产生混乱不可读的性能分析结果。为了生成更可读的性能分析结果，可以采取下面几点措施:
1. Use GCC command line option `-g` when building `libpaddle.so` so to
include the debug information. The standard building system of
PaddlePaddle is CMake, so you might want to set
`CMAKE_BUILD_TYPE=RelWithDebInfo`.
1. 编译时指定`-g`生成调试信息。使用cmake的话，可以将CMAKE_BUILD_TYPE指定为`RelWithDebInfo`。
2. 编译时一定要开启优化。单纯的`Debug`编译性能会和`-O2`或者`-O3`有非常大的差别。`Debug`模式下的性能测试是没有意义的。
3. 运行性能分析的时候，先从单线程开始，再开启多线程，进而多机。毕竟如果单线程调试更容易。可以设置`OMP_NUM_THREADS=1`这个环境变量关闭openmp优化。
1. Use GCC command line option `-O2` or `-O3` to generate optimized
binary code. It doesn't make sense to profile `libpaddle.so`
without optimization, because it would anyway run slowly.
### 查看性能分析文件
1. Profiling the single-threaded binary file before the
multi-threading version, because the latter often generates tangled
profiling analysis result. You might want to set environment
variable `OMP_NUM_THREADS=1` to prevents OpenMP from automatically
starting multiple threads.
在运行完性能分析后，会生成性能分析结果文件。我们可以使用[pprof](https://github.com/google/pprof)来显示性能分析结果。注意，这里使用了用`Go`语言重构后的`pprof`，因为这个工具具有web服务界面，且展示效果更好。
### Look into the Profiling File
安装`pprof`的命令和一般的`Go`程序是一样的，其命令如下:
The tool we used to look into the profiling file generated by
`perftools` is [`pprof`](https://github.com/google/pprof), which
provides a Web-based GUI like `cprofilev`.
We can rely on the standard Go toolchain to retrieve the source code
of `pprof` and build it:
```bash
go get github.com/google/pprof
```
进而我们可以使用如下命令开启一个HTTP服务:
Then we can use it to profile `main.py.prof` generated in the previous
section:
```bash
pprof -http=0.0.0.0:3213 `which python` ./main.py.prof
```
这行命令中，`-http`指开启HTTP服务。`which python`会产生当前Python二进制的完整路径，进而指定了Python可执行文件的路径。`./main.py.prof`输入了性能分析结果。
访问对应的网址，我们可以查看性能分析的结果。结果如下图所示:
Where `-http` specifies the IP and port of the HTTP service.
Directing our Web browser to the service, we would see something like
the following:
![result](./pprof_1.png)
### Identifying the Performance Bottlenecks
### 寻找性能瓶颈
与寻找Python代码的性能瓶颈类似，寻找Python与C++混合代码的性能瓶颈也是要看`tottime`和`cumtime`。而`pprof`展示的调用图也可以帮助我们发现性能中的问题。
例如下图中，
Similar to how we work with `cprofilev`, we'd focus on `tottime` and
`cumtime`.
![kernel_perf](./pprof_2.png)
在一次训练中，乘法和乘法梯度的计算占用2%-4%左右的计算时间。而`MomentumOp`占用了17%左右的计算时间。显然，`MomentumOp`的性能有问题。
在`pprof`中，对于性能的关键路径都做出了红色标记。先检查关键路径的性能问题，再检查其他部分的性能问题，可以更有次序的完成性能的优化。
## 总结
We can see that the execution time of multiplication and the computing
of the gradient of multiplication takes 2% to 4% of the total running
time, and `MomentumOp` takes about 17%. Obviously, we'd want to
optimize `MomentumOp`.
至此，两种性能分析的方式都介绍完毕了。希望通过这两种性能分析的方式，Paddle的开发人员和使用人员可以有次序的，科学的发现和解决性能问题。
`pprof` would mark performance critical parts of the program in
red. It's a good idea to follow the hint.
doc/howto/optimization/cpu_profiling_cn.md 0 → 100644
浏览文件 @ 192c00a7
此教程会介绍如何使用Python的cProfile包、Python库yep、Google perftools来进行性能分析 (profiling) 与调优（performance tuning）。
Profling 指发现性能瓶颈。系统中的瓶颈可能和程序员开发过程中想象的瓶颈相去甚远。Tuning 指消除瓶颈。性能优化的过程通常是不断重复地 profiling 和 tuning。
PaddlePaddle 用户一般通过调用 Python API 编写深度学习程序。大部分 Python API 调用用 C++ 写的 libpaddle.so。所以 PaddlePaddle 的性能分析与调优分为两个部分:
* Python 代码的性能分析
* Python 与 C++ 混合代码的性能分析
## Python代码的性能分析
### 生成性能分析文件
Python标准库中提供了性能分析的工具包，[cProfile](https://docs.python.org/2/library/profile.html)。生成Python性能分析的命令如下:
```bash
python -m cProfile -o profile.out main.py
```
其中 `main.py` 是我们要分析的程序，`-o`标识了一个输出的文件名，用来存储本次性能分析的结果。如果不指定这个文件，`cProfile`会打印到标准输出。
### 查看性能分析文件
`cProfile` 在main.py 运行完毕后输出`profile.out`。我们可以使用[`cprofilev`](https://github.com/ymichael/cprofilev)来查看性能分析结果。`cprofilev`是一个Python的第三方库。使用它会开启一个HTTP服务，将性能分析结果以网页的形式展示出来：
```bash
cprofilev -a 0.0.0.0 -p 3214 -f profile.out main.py
```
其中`-a`标识HTTP服务绑定的IP。使用`0.0.0.0`允许外网访问这个HTTP服务。`-p`标识HTTP服务的端口。`-f`标识性能分析的结果文件。`main.py`标识被性能分析的源文件。
用Web浏览器访问对应网址，即可显示性能分析的结果：
```
ncalls tottime percall cumtime percall filename:lineno(function)
1 0.284 0.284 29.514 29.514 main.py:1(<module>)
4696 0.128 0.000 15.748 0.003 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/fluid/executor.py:20(run)
4696 12.040 0.003 12.040 0.003 {built-in method run}
1 0.144 0.144 6.534 6.534 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/__init__.py:14(<module>)
```
每一列的含义是:
| 列名 | 含义 |
| --- | --- |
| ncalls | 函数的调用次数 |
| tottime | 函数实际使用的总时间。该时间去除掉本函数调用其他函数的时间 |
| percall | tottime的每次调用平均时间 |
| cumtime | 函数总时间。包含这个函数调用其他函数的时间 |
| percall | cumtime的每次调用平均时间 |
| filename:lineno(function) | 文件名, 行号，函数名 |
### 寻找性能瓶颈
通常`tottime`和`cumtime`是寻找瓶颈的关键指标。这两个指标代表了某一个函数真实的运行时间。
将性能分析结果按照tottime排序，效果如下:
```text
4696 12.040 0.003 12.040 0.003 {built-in method run}
300005 0.874 0.000 1.681 0.000 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/dataset/mnist.py:38(reader)
107991 0.676 0.000 1.519 0.000 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/fluid/framework.py:219(__init__)
4697 0.626 0.000 2.291 0.000 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/fluid/framework.py:428(sync_with_cpp)
1 0.618 0.618 0.618 0.618 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/fluid/__init__.py:1(<module>)
```
可以看到最耗时的函数是C++端的`run`函数。这需要联合我们第二节`Python`与`C++`混合代码的性能分析来进行调优。而`sync_with_cpp`函数的总共耗时很长，每次调用的耗时也很长。于是我们可以点击`sync_with_cpp`的详细信息，了解其调用关系。
```text
Called By:
Ordered by: internal time
List reduced from 4497 to 2 due to restriction <'sync_with_cpp'>
Function was called by...
ncalls tottime cumtime
/home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/fluid/framework.py:428(sync_with_cpp) <- 4697 0.626 2.291 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/fluid/framework.py:562(sync_with_cpp)
/home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/fluid/framework.py:562(sync_with_cpp) <- 4696 0.019 2.316 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/fluid/framework.py:487(clone)
1 0.000 0.001 /home/yuyang/perf_test/.env/lib/python2.7/site-packages/paddle/v2/fluid/framework.py:534(append_backward)
Called:
Ordered by: internal time
List reduced from 4497 to 2 due to restriction <'sync_with_cpp'>
```
通常观察热点函数间的调用关系，和对应行的代码，就可以了解到问题代码在哪里。当我们做出性能修正后，再次进行性能分析(profiling)即可检查我们调优后的修正是否能够改善程序的性能。
## Python与C++混合代码的性能分析
### 生成性能分析文件
C++的性能分析工具非常多。常见的包括`gprof`, `valgrind`, `google-perftools`。但是调试Python中使用的动态链接库与直接调试原始二进制相比增加了很多复杂度。幸而Python的一个第三方库`yep`提供了方便的和`google-perftools`交互的方法。于是这里使用`yep`进行Python与C++混合代码的性能分析
使用`yep`前需要安装`google-perftools`与`yep`包。ubuntu下安装命令为
```bash
apt update
apt install libgoogle-perftools-dev
pip install yep
```
安装完毕后，我们可以通过
```bash
python -m yep -v main.py
```
生成性能分析文件。生成的性能分析文件为`main.py.prof`。
命令行中的`-v`指定在生成性能分析文件之后，在命令行显示分析结果。我们可以在命令行中简单的看一下生成效果。因为C++与Python不同，编译时可能会去掉调试信息，运行时也可能因为多线程产生混乱不可读的性能分析结果。为了生成更可读的性能分析结果，可以采取下面几点措施:
1. 编译时指定`-g`生成调试信息。使用cmake的话，可以将CMAKE_BUILD_TYPE指定为`RelWithDebInfo`。
2. 编译时一定要开启优化。单纯的`Debug`编译性能会和`-O2`或者`-O3`有非常大的差别。`Debug`模式下的性能测试是没有意义的。
3. 运行性能分析的时候，先从单线程开始，再开启多线程，进而多机。毕竟单线程调试更容易。可以设置`OMP_NUM_THREADS=1`这个环境变量关闭openmp优化。
### 查看性能分析文件
在运行完性能分析后，会生成性能分析结果文件。我们可以使用[`pprof`](https://github.com/google/pprof)来显示性能分析结果。注意，这里使用了用`Go`语言重构后的`pprof`，因为这个工具具有web服务界面，且展示效果更好。
安装`pprof`的命令和一般的`Go`程序是一样的，其命令如下:
```bash
go get github.com/google/pprof
```
进而我们可以使用如下命令开启一个HTTP服务:
```bash
pprof -http=0.0.0.0:3213 `which python` ./main.py.prof
```
这行命令中，`-http`指开启HTTP服务。`which python`会产生当前Python二进制的完整路径，进而指定了Python可执行文件的路径。`./main.py.prof`输入了性能分析结果。
访问对应的网址，我们可以查看性能分析的结果。结果如下图所示:
![result](./pprof_1.png)
### 寻找性能瓶颈
与寻找Python代码的性能瓶颈类似，寻找Python与C++混合代码的性能瓶颈也是要看`tottime`和`cumtime`。而`pprof`展示的调用图也可以帮助我们发现性能中的问题。
例如下图中，
![kernel_perf](./pprof_2.png)
在一次训练中，乘法和乘法梯度的计算占用2%-4%左右的计算时间。而`MomentumOp`占用了17%左右的计算时间。显然，`MomentumOp`的性能有问题。
在`pprof`中，对于性能的关键路径都做出了红色标记。先检查关键路径的性能问题，再检查其他部分的性能问题，可以更有次序的完成性能的优化。
paddle/framework/op_desc.cc
浏览文件 @ 192c00a7
......@@ -65,7 +65,7 @@ class CompileTimeInferShapeContext : public InferShapeContext {
PADDLE_ENFORCE_EQ(in_var->GetType(), VarDesc::LOD_TENSOR,
"The %d-th output of Output(%s) must be LoDTensor.", j,
out);
in_var->SetLoDLevel(out_var->GetLodLevel());
out_var->SetLoDLevel(in_var->GetLodLevel());
}
bool IsRuntime() const override;
......
paddle/gserver/tests/CMakeLists.txt
浏览文件 @ 192c00a7
......@@ -62,11 +62,11 @@ if(NOT WITH_DOUBLE AND NOT MOBILE_INFERENCE)
endif()
if(NOT MOBILE_INFERENCE)
################## test_Evaluator #######################
################## test_Evaluator #######################
add_unittest(test_Evaluator
test_Evaluator.cpp)
############### test_RecurrentGradientMachine ###############
############### test_RecurrentGradientMachine ###############
# TODO(yuyang18): There is some bug in test_RecurrentGradientMachine
# I will fix it.
add_unittest_without_exec(test_RecurrentGradientMachine
......@@ -77,7 +77,7 @@ if(NOT MOBILE_INFERENCE)
${CMAKE_CURRENT_BINARY_DIR}/test_RecurrentGradientMachine
WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle)
############### test_NetworkCompare ###############
############### test_NetworkCompare ###############
add_unittest_without_exec(test_NetworkCompare
test_NetworkCompare.cpp)
if(WITH_GPU)
......@@ -89,34 +89,33 @@ if(NOT MOBILE_INFERENCE)
COMMAND .set_python_path.sh -d ${PADDLE_SOURCE_DIR}/python ${CMAKE_CURRENT_BINARY_DIR}/test_NetworkCompare --use_gpu=false
WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle)
endif()
endif()
add_unittest_without_exec(test_PyDataProvider2
test_PyDataProvider2.cpp)
add_test(NAME test_PyDataProvider2
COMMAND .set_python_path.sh -d ${PADDLE_SOURCE_DIR}/paddle/gserver/tests:${PADDLE_SOURCE_DIR}/python ${CMAKE_CURRENT_BINARY_DIR}/test_PyDataProvider2
WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle
)
################# test_CompareSparse ##################
add_unittest_without_exec(test_CompareSparse
################# test_CompareSparse ##################
add_unittest_without_exec(test_CompareSparse
test_CompareSparse.cpp)
if(NOT ON_TRAVIS)
if(NOT ON_TRAVIS)
add_test(NAME test_CompareSparse
COMMAND ${PADDLE_SOURCE_DIR}/paddle/.set_python_path.sh -d
${PADDLE_SOURCE_DIR}/python:${PADDLE_SOURCE_DIR}/paddle/gserver/tests
./.set_port.sh -p port -n 6
${CMAKE_CURRENT_BINARY_DIR}/test_CompareSparse
WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle/)
endif()
endif()
################ test_CompareTwoNets ######################
add_unittest_without_exec(test_CompareTwoNets
################ test_CompareTwoNets ######################
add_unittest_without_exec(test_CompareTwoNets
test_CompareTwoNets.cpp)
add_test(NAME test_CompareTwoNets
add_test(NAME test_CompareTwoNets
COMMAND ${PADDLE_SOURCE_DIR}/paddle/.set_python_path.sh -d
${PADDLE_SOURCE_DIR}/python:${PADDLE_SOURCE_DIR}/paddle/gserver/tests
${CMAKE_CURRENT_BINARY_DIR}/test_CompareTwoNets
WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle/)
endif()
################ test_PyDataProvider2 ######################
add_unittest_without_exec(test_PyDataProvider2
test_PyDataProvider2.cpp)
add_test(NAME test_PyDataProvider2
COMMAND .set_python_path.sh -d ${PADDLE_SOURCE_DIR}/paddle/gserver/tests:${PADDLE_SOURCE_DIR}/python ${CMAKE_CURRENT_BINARY_DIR}/test_PyDataProvider2
WORKING_DIRECTORY ${PADDLE_SOURCE_DIR}/paddle
)
paddle/memory/memory.cc
浏览文件 @ 192c00a7
......@@ -81,18 +81,33 @@ BuddyAllocator* GetGPUBuddyAllocator(int gpu_id) {
}
template <>
void* Alloc<platform::GPUPlace>(platform::GPUPlace place, size_t size) {
return GetGPUBuddyAllocator(place.device)->Alloc(size);
size_t Used<platform::GPUPlace>(platform::GPUPlace place) {
return GetGPUBuddyAllocator(place.device)->Used();
}
template <>
void Free<platform::GPUPlace>(platform::GPUPlace place, void* p) {
GetGPUBuddyAllocator(place.device)->Free(p);
void* Alloc<platform::GPUPlace>(platform::GPUPlace 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);
LOG(WARNING) << "Cannot allocate " << size << " bytes in GPU "
<< place.device << ", available " << avail << " bytes";
LOG(WARNING) << "total " << total;
LOG(WARNING) << "GpuMinChunkSize " << platform::GpuMinChunkSize();
LOG(WARNING) << "GpuMaxChunkSize " << platform::GpuMaxChunkSize();
LOG(WARNING) << "GPU memory used: " << Used<platform::GPUPlace>(place);
platform::SetDeviceId(cur_dev);
}
return ptr;
}
template <>
size_t Used<platform::GPUPlace>(platform::GPUPlace place) {
return GetGPUBuddyAllocator(place.device)->Used();
void Free<platform::GPUPlace>(platform::GPUPlace place, void* p) {
GetGPUBuddyAllocator(place.device)->Free(p);
}
#endif
......
paddle/operators/CMakeLists.txt
浏览文件 @ 192c00a7
......@@ -191,6 +191,7 @@ set(DEPS_OPS
sum_op
pool_op
maxout_op
unpool_op
pool_with_index_op
conv_op
conv_transpose_op
......@@ -235,6 +236,7 @@ op_library(adagrad_op DEPS selected_rows_functor)
op_library(conv_op DEPS vol2col)
op_library(pool_op DEPS pooling)
op_library(maxout_op DEPS maxouting)
op_library(unpool_op DEPS unpooling)
op_library(pool_with_index_op DEPS pooling)
op_library(lod_rank_table_op SRCS lod_rank_table_op.cc DEPS lod_rank_table)
op_library(lod_tensor_to_array_op SRCS lod_tensor_to_array_op.cc DEPS lod_rank_table_op)
......
paddle/operators/conv_op.cc
浏览文件 @ 192c00a7
......@@ -97,7 +97,7 @@ Conv2DOpMaker::Conv2DOpMaker(framework::OpProto* proto,
.SetDefault({0, 0});
AddAttr<int>(
"groups",
"(int default:1), the group size of convolution operator. "
"(int default:1), the groups number of the convolution operator. "
"According to grouped convolution in Alex Krizhevsky's Deep CNN paper: "
"when group=2, the first half of the filters is only connected to the "
"first half of the input channels, while the second half of the filters "
......@@ -112,23 +112,29 @@ Conv2DOpMaker::Conv2DOpMaker(framework::OpProto* proto,
Convolution Operator.
The convolution operation calculates the output based on the input, filter
and strides, paddings, groups, dilations parameters. The size of each dimension of the
and strides, paddings, dilations, groups parameters. The size of each dimension of the
parameters is checked in the infer-shape.
Input(Input, Filter) and output(Output) are in NCHW format. Where N is batch
Input(Input) and Output(Output) are in NCHW format. Where N is batch
size, C is the number of channels, H is the height of the feature, and W is
the width of the feature. Parameters(ksize, strides, paddings, dilations) are two elements.
These two elements represent height and width, respectively.
the width of the feature.
Filters(Input) is MCHW format. Where M is the number of output image channels, C is
the number of input image channels, H is the height of the filter, and W
is the width of the filter.
Parameters(strides, paddings, dilations) are two elements. These two elements represent
height and width, respectively.
The input(X) size and output(Out) size may be different.
Example:
Input:
Input shape: (N, C_in, H_in, W_in)
Filter shape: (C_out, C_in, H_f, W_f)
Input shape: $(N, C_{in}, H_{in}, W_{in})$
Filter shape: $(C_{out}, C_{in}, H_f, W_f)$
Output:
Output shape: (N, C_out, H_out, W_out)
where
H_out = (H_in + 2 * paddings[0] - (dilations[0]*(filter_size[0] - 1) + 1)) / strides[0] + 1;
W_out = (W_in + 2 * paddings[1] - (dilations[1]*(filter_size[1] - 1) + 1)) / strides[1] + 1;
Output shape: $(N, C_{out}, H_{out}, W_{out})$
Where
$$
H_{out}= \frac{(H_{in} + 2 * paddings[0] - (dilations[0] * (H_f - 1) + 1))}{strides[0]}+ 1 \\
W_{out}= \frac{(W_{in} + 2 * paddings[1] - (dilations[1] * (W_f - 1) + 1))}{strides[1]}+ 1
$$
)DOC");
}
......@@ -165,7 +171,7 @@ Conv3DOpMaker::Conv3DOpMaker(framework::OpProto* proto,
.SetDefault({0, 0, 0});
AddAttr<int>(
"groups",
"(int default:1), the group size of convolution operator. "
"(int default:1), the groups number of the convolution operator. "
"According to grouped convolution in Alex Krizhevsky's Deep CNN paper: "
"when group=2, the first half of the filters is only connected to the "
"first half of the input channels, while the second half of the filters "
......@@ -174,32 +180,37 @@ Conv3DOpMaker::Conv3DOpMaker(framework::OpProto* proto,
AddAttr<std::vector<int>>("dilations",
"(vector<int> default:{1, 1, 1}), the "
"dilations(d_dilation, h_dilation, w_dilation) of "
"convolution operator. Currently, conv3d doesn't "
"support dilation.")
"convolution operator.")
.SetDefault({1, 1, 1});
AddComment(R"DOC(
Convolution3D Operator.
The convolution operation calculates the output based on the input, filter
and strides, paddings, groups parameters. The size of each dimension of the
and strides, paddings, dilations, groups parameters. The size of each dimension of the
parameters is checked in the infer-shape.
Input(Input, Filter) and output(Output) are in NCDHW format. Where N is batch
Input(Input) and output(Output) are in NCDHW format, where N is batch
size, C is the number of channels,D is the depth of the feature, H is the height of
the feature, and W is the width of the feature. Parameters(ksize, strides, paddings)
are three elements. These three elements represent depth, height and width, respectively.
the feature, and W is the width of the feature.
Filters(Input) is MCDHW format, where M is the number of output image channels,
C is the number of input image channels, D is the depth of the filter,
H is the height of the filter, and W is the width of the filter.
Parameters(strides, paddings, dilations) are three elements. These three elements
represent depth, height and width, respectively.
The input(X) size and output(Out) size may be different.
Example:
Input:
Input shape: (N, C_in, D_in, H_in, W_in)
Filter shape: (C_out, C_in, D_f, H_f, W_f)
Input shape: $(N, C_{in}, D_{in}, H_{in}, W_{in})$
Filter shape: $(C_{out}, C_{in}, D_f, H_f, W_f)$
Output:
Output shape: (N, C_out, D_out, H_out, W_out)
where
D_out = (D_in - filter_size[0] + 2 * paddings[0]) / strides[0] + 1;
H_out = (H_in - filter_size[1] + 2 * paddings[1]) / strides[1] + 1;
W_out = (W_in - filter_size[2] + 2 * paddings[2]) / strides[2] + 1;
Output shape: $(N, C_{out}, D_{out}, H_{out}, W_{out})$
Where
$$
D_{out}= \frac{(D_{in} + 2 * paddings[0] - (dilations[0] * (D_f - 1) + 1))}{ strides[0]}+ 1 \\
H_{out}= \frac{(H_{in} + 2 * paddings[1] - (dilations[1] * (H_f - 1) + 1))}{ strides[1]}+ 1 \\
W_{out}= \frac{(W_{in} + 2 * paddings[2] - (dilations[2] * (W_f - 1) + 1))}{ strides[2]}+ 1
$$
)DOC");
}
......
paddle/operators/conv_transpose_op.cc
浏览文件 @ 192c00a7
......@@ -39,7 +39,7 @@ void ConvTransposeOp::InferShape(framework::InferShapeContext* ctx) const {
"ConvTransposeOp input dimension and strides dimension should "
"be consistent.");
PADDLE_ENFORCE_EQ(paddings.size(), strides.size(),
"ConvTransposeOp paddings dimension and Conv strides "
"ConvTransposeOp paddings dimension and strides "
"dimension should be the same.");
PADDLE_ENFORCE_EQ(in_dims[1], filter_dims[0],
"In ConvTransposeOp, The input channel should be the same "
......@@ -62,13 +62,14 @@ Conv2DTransposeOpMaker::Conv2DTransposeOpMaker(
"The format of input tensor is NCHW. Where N is batch size, C is the "
"number of input channels, H is the height of the feature, and "
"W is the width of the feature.");
AddInput("Filter",
AddInput(
"Filter",
"(Tensor) The filter tensor of convolution transpose operator. "
"The format of the filter tensor is CMHW, where C is the number of "
"output image channels, M is the number of input image channels, "
"The format of the filter tensor is MCHW, where M is the number of "
"input feature channels, C is the number of "
"output feature channels,"
"H is the height of the filter, and W is the width of the filter. "
"We enforce groups number == 1 and padding == 0 in "
"the convolution transpose scenario.");
"We enforce groups number == 1 in the convolution transpose scenario.");
AddOutput("Output",
"(Tensor) The output tensor of convolution transpose operator. "
"The format of output tensor is also NCHW.");
......@@ -88,21 +89,26 @@ Convolution2D Transpose Operator.
The convolution transpose operation calculates the output based on the input, filter
and strides, paddings, groups parameters. The size of each dimension of the
parameters is checked in the infer-shape.
Input(Input, Filter) and output(Output) are in NCHW format. Where N is batch
size, C is the number of channels, H is the height of the feature, and
W is the width of the feature. Parameters(ksize, strides, paddings) are two elements.
These two elements represent height and width, respectively.
Input(Input) and output(Output) are in NCHW format. Where N is batchsize, C is the
number of channels, H is the height of the feature, and W is the width of the feature.
Filter(Input) is in MCHW format. Where M is the number of input feature channels,
C is the number of output feature channels, H is the height of the filter,
and W is the width of the filter.
Parameters(strides, paddings) are two elements. These two elements represent height
and width, respectively.
The input(X) size and output(Out) size may be different.
Example:
Input:
Input shape: (N, C_in, H_in, W_in)
Filter shape: (C_in, C_out, H_f, W_f)
Input shape: $(N, C_{in}, H_{in}, W_{in})$
Filter shape: $(C_{in}, C_{out}, H_f, W_f)$
Output:
Output shape: (N, C_out, H_out, W_out)
where
H_out = (H_in - 1) * strides[0] - 2 * paddings[0] + H_f;
W_out = (W_in - 1) * strides[1] - 2 * paddings[1] + W_f;
Output shape: $(N, C_{out}, H_{out}, W_{out})$
Where
$$
H_{out} = (H_{in} - 1) * strides[0] - 2 * paddings[0] + H_f \\
W_{out} = (W_{in} - 1) * strides[1] - 2 * paddings[1] + W_f
$$
)DOC");
}
......@@ -117,8 +123,9 @@ Conv3DTransposeOpMaker::Conv3DTransposeOpMaker(
"W is the width of the feature.");
AddInput("Filter",
"(Tensor) The filter tensor of convolution transpose operator."
"The format of the filter tensor is CMDHW, where C is the number of "
"output image channels, M is the number of input image channels, D "
"The format of the filter tensor is MCDHW, where M is the number of "
"input feature channels, C is the number of "
"output feature channels, D "
"is the depth of the filter, H is the height of the filter, and "
"W is the width of the filter."
"We enforce groups number == 1 and padding == 0 in "
......@@ -144,23 +151,28 @@ Convolution3D Transpose Operator.
The convolution transpose operation calculates the output based on the input, filter
and strides, paddings, groups parameters. The size of each dimension of the
parameters is checked in the infer-shape.
Input(Input, Filter) and output(Output) are in NCDHW format. Where N is batch
size, C is the number of channels, D is the depth of the feature,
H is the height of the feature, and W is the width of the feature.
Parameters(ksize, strides, paddings) are three elements.
These three elements represent depth, height and width, respectively.
Input(Input) and output(Output) are in NCDHW format. Where N is batch size, C is the
number of channels, D is the depth of the feature, H is the height of the feature,
and W is the width of the feature.
Filter(Input) is in MCDHW format. Where M is the number of input feature channels,
C is the number of output feature channels, D is the depth of the filter,H is the
height of the filter, and W is the width of the filter.
Parameters(strides, paddings) are three elements. These three elements represent
depth, height and width, respectively.
The input(X) size and output(Out) size may be different.
Example:
Input:
Input shape: (N, C_in, D_in, H_in, W_in)
Filter shape: (C_in, C_out, D_f, H_f, W_f)
Input shape: $(N, C_{in}, D_{in}, H_{in}, W_{in})$
Filter shape: $(C_{in}, C_{out}, D_f, H_f, W_f)$
Output:
Output shape: (N, C_out, D_out, H_out, W_out)
where
D_out = (D_in - 1) * strides[0] - 2 * paddings[0] + D_f;
H_out = (H_in - 1) * strides[1] - 2 * paddings[1] + H_f;
W_out = (W_in - 1) * strides[2] - 2 * paddings[2] + W_f;
Output shape: $(N, C_{out}, D_{out}, H_{out}, W_{out})$
Where
$$
D_{out} = (D_{in} - 1) * strides[0] - 2 * paddings[0] + D_f \\
H_{out} = (H_{in} - 1) * strides[1] - 2 * paddings[1] + H_f \\
W_{out} = (W_{in} - 1) * strides[2] - 2 * paddings[2] + W_f
$$
)DOC");
}
......
paddle/operators/conv_transpose_op.h
浏览文件 @ 192c00a7
......@@ -63,7 +63,6 @@ class GemmConvTransposeKernel : public framework::OpKernel<T> {
std::vector<int> strides = context.Attr<std::vector<int>>("strides");
std::vector<int> paddings = context.Attr<std::vector<int>>("paddings");
// TODO(Zhuoyuan): Paddings can be added in future.
// groups will alway be disabled in conv2dtranspose.
const int batch_size = static_cast<int>(input->dims()[0]);
......
paddle/operators/math/CMakeLists.txt
浏览文件 @ 192c00a7
......@@ -13,8 +13,9 @@ if(WITH_GPU)
nv_library(context_project SRCS context_project.cc context_project.cu DEPS device_context math_function)
nv_library(sequence2batch SRCS sequence2batch.cc sequence2batch.cu DEPS device_context)
nv_library(lstm_compute SRCS lstm_compute.cc lstm_compute.cu DEPS device_context activation_functions)
nv_library(gru_compute SRCS gru_compute.cc gru_compute.cu DEPS device_context activation_functions math_function)
nv_library(maxouting SRCS maxouting.cc maxouting.cu DEPS device_context)
nv_library(unpooling SRCS unpooling.cc unpooling.cu DEPS device_context)
nv_library(gru_compute SRCS gru_compute.cc gru_compute.cu DEPS device_context activation_functions math_function)
else()
cc_library(math_function SRCS math_function.cc im2col.cc DEPS cblas device_context framework_proto)
cc_library(selected_rows_functor SRCS selected_rows_functor.cc DEPS selected_rows math_function)
......@@ -26,8 +27,9 @@ else()
cc_library(context_project SRCS context_project.cc DEPS device_context math_function)
cc_library(sequence2batch SRCS sequence2batch.cc DEPS device_context)
cc_library(lstm_compute SRCS lstm_compute.cc DEPS device_context activation_functions)
cc_library(gru_compute SRCS gru_compute.cc DEPS device_context activation_functions math_function)
cc_library(maxouting SRCS maxouting.cc DEPS device_context)
cc_library(unpooling SRCS unpooling.cc DEPS device_context)
cc_library(gru_compute SRCS gru_compute.cc DEPS device_context activation_functions math_function)
endif()
cc_test(math_function_test SRCS math_function_test.cc DEPS math_function tensor)
......
paddle/operators/math/unpooling.cc 0 → 100644
浏览文件 @ 192c00a7
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/operators/math/unpooling.h"
namespace paddle {
namespace operators {
namespace math {
template <typename T>
class Unpool2dMaxFunctor<platform::CPUPlace, T> {
public:
void operator()(const platform::DeviceContext& context,
const framework::Tensor& input,
const framework::Tensor& indices, framework::Tensor* output) {
const int batch_size = input.dims()[0];
const int input_height = input.dims()[2];
const int input_width = input.dims()[3];
const int output_channels = output->dims()[1];
const int output_height = output->dims()[2];
const int output_width = output->dims()[3];
int input_feasize = input_height * input_width;
int output_feasize = output_height * output_width;
const T* input_data = input.data<T>();
const int* indices_data = indices.data<int>();
T* output_data = output->mutable_data<T>(context.GetPlace());
for (int b = 0; b < batch_size; ++b) {
for (int c = 0; c < output_channels; ++c) {
for (int i = 0; i < input_feasize; ++i) {
int index = indices_data[i];
PADDLE_ENFORCE(index < output_feasize, "err index in unpooling!");
output_data[index] = input_data[i];
}
input_data += input_feasize;
indices_data += input_feasize;
output_data += output_feasize;
}
}
}
};
template <class T>
class Unpool2dMaxGradFunctor<platform::CPUPlace, T> {
public:
void operator()(const platform::DeviceContext& context,
const framework::Tensor& input,
const framework::Tensor& indices,
const framework::Tensor& output,
const framework::Tensor& output_grad,
framework::Tensor* input_grad) {
const int batch_size = input.dims()[0];
const int input_height = input.dims()[2];
const int input_width = input.dims()[3];
const int output_channels = output.dims()[1];
const int output_height = output.dims()[2];
const int output_width = output.dims()[3];
int input_feasize = input_height * input_width;
int output_feasize = output_height * output_width;
const int* indices_data = indices.data<int>();
const T* output_grad_data = output_grad.data<T>();
T* input_grad_data = input_grad->mutable_data<T>(context.GetPlace());
for (int b = 0; b < batch_size; ++b) {
for (int c = 0; c < output_channels; ++c) {
for (int i = 0; i < input_feasize; ++i) {
int index = indices_data[i];
PADDLE_ENFORCE(index < output_feasize, "err index in unpooling!");
input_grad_data[i] = output_grad_data[index];
}
input_grad_data += input_feasize;
indices_data += input_feasize;
output_grad_data += output_feasize;
}
}
}
};
template class Unpool2dMaxGradFunctor<platform::CPUPlace, float>;
template class Unpool2dMaxGradFunctor<platform::CPUPlace, double>;
template class Unpool2dMaxFunctor<platform::CPUPlace, float>;
template class Unpool2dMaxFunctor<platform::CPUPlace, double>;
} // namespace math
} // namespace operators
} // namespace paddle
paddle/operators/math/unpooling.cu 0 → 100644
浏览文件 @ 192c00a7
/* Copyright (c) 2016 paddlepaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/operators/math/unpooling.h"
#include "paddle/platform/cuda_helper.h"
namespace paddle {
namespace operators {
namespace math {
template <typename T>
__global__ void KernelUnpool2dMax(const int nthreads, const T* input_data,
const int* indices_data,
const int input_height, const int input_width,
const int channels, T* output_data,
const int output_height,
const int output_width) {
int in_n_stride = input_height * input_width * channels;
int in_c_stride = input_height * input_width;
int out_n_stride = output_height * output_width * channels;
int out_c_stride = output_height * output_width;
int index = blockIdx.x * blockDim.x + threadIdx.x;
int offset = blockDim.x * gridDim.x;
for (int i = index; i < nthreads; i += offset) {
int bidx = i / in_n_stride;
int boffset = i % in_n_stride;
int cidx = boffset / in_c_stride;
int out_offset = bidx * out_n_stride + cidx * out_c_stride;
int out_index = indices_data[i];
PADDLE_ASSERT(out_index < out_c_stride);
output_data[out_offset + out_index] = input_data[i];
}
}
template <typename T>
__global__ void KernelUnpool2dMaxGrad(
const int nthreads, const T* input_data, const int* indices_data,
const int input_height, const int input_width, const int channels,
const T* output_data, const T* output_grad, const int output_height,
const int output_width, T* input_grad) {
int in_n_stride = input_height * input_width * channels;
int in_c_stride = input_height * input_width;
int out_n_stride = output_height * output_width * channels;
int out_c_stride = output_height * output_width;
int index = blockIdx.x * blockDim.x + threadIdx.x;
int offset = blockDim.x * gridDim.x;
for (int i = index; i < nthreads; i += offset) {
int bidx = i / in_n_stride;
int boffset = i % in_n_stride;
int cidx = boffset / in_c_stride;
int out_offset = bidx * out_n_stride + cidx * out_c_stride;
int out_index = indices_data[i];
PADDLE_ASSERT(out_index < out_c_stride);
input_grad[i] = output_grad[out_offset + out_index];
}
}
/*
* All tensors are in NCHW format.
*/
template <typename T>
class Unpool2dMaxFunctor<platform::GPUPlace, T> {
public:
void operator()(const platform::DeviceContext& context,
const framework::Tensor& input,
const framework::Tensor& indices, framework::Tensor* output) {
const int batch_size = input.dims()[0];
const int input_height = input.dims()[2];
const int input_width = input.dims()[3];
const int output_channels = output->dims()[1];
const int output_height = output->dims()[2];
const int output_width = output->dims()[3];
const T* input_data = input.data<T>();
const int* indices_data = indices.data<int>();
T* output_data = output->mutable_data<T>(context.GetPlace());
int threads = 1024;
int grid = (input.numel() + threads - 1) / threads;
KernelUnpool2dMax<
T><<<grid, threads, 0,
reinterpret_cast<const platform::CUDADeviceContext&>(context)
.stream()>>>(input.numel(), input_data, indices_data,
input_height, input_width, output_channels,
output_data, output_height, output_width);
}
};
/*
* All tensors are in NCHW format.
*/
template <typename T>
class Unpool2dMaxGradFunctor<platform::GPUPlace, T> {
public:
void operator()(const platform::DeviceContext& context,
const framework::Tensor& input,
const framework::Tensor& indices,
const framework::Tensor& output,
const framework::Tensor& output_grad,
framework::Tensor* input_grad) {
const int batch_size = input.dims()[0];
const int input_height = input.dims()[2];
const int input_width = input.dims()[3];
const int output_channels = output.dims()[1];
const int output_height = output.dims()[2];
const int output_width = output.dims()[3];
const T* input_data = input.data<T>();
const int* indices_data = indices.data<int>();
const T* output_data = output.data<T>();
const T* output_grad_data = output_grad.data<T>();
T* input_grad_data = input_grad->mutable_data<T>(context.GetPlace());
int threads = 1024;
int grid = (input.numel() + threads - 1) / threads;
KernelUnpool2dMaxGrad<
T><<<grid, threads, 0,
reinterpret_cast<const platform::CUDADeviceContext&>(context)
.stream()>>>(input.numel(), input_data, indices_data,
input_height, input_width, output_channels,
output_data, output_grad_data, output_height,
output_width, input_grad_data);
}
};
template class Unpool2dMaxGradFunctor<platform::GPUPlace, float>;
template class Unpool2dMaxGradFunctor<platform::GPUPlace, double>;
template class Unpool2dMaxFunctor<platform::GPUPlace, float>;
template class Unpool2dMaxFunctor<platform::GPUPlace, double>;
} // namespace math
} // namespace operators
} // namespace paddle
paddle/operators/math/unpooling.h 0 → 100644
浏览文件 @ 192c00a7
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "paddle/framework/tensor.h"
namespace paddle {
namespace operators {
namespace math {
template <typename Place, typename T>
class Unpool2dMaxFunctor {
public:
void operator()(const platform::DeviceContext& context,
const framework::Tensor& input,
const framework::Tensor& indices, framework::Tensor* output);
};
template <typename Place, class T>
class Unpool2dMaxGradFunctor {
public:
void operator()(const platform::DeviceContext& context,
const framework::Tensor& input,
const framework::Tensor& indices,
const framework::Tensor& output,
const framework::Tensor& output_grad,
framework::Tensor* input_grad);
};
} // namespace math
} // namespace operators
} // namespace paddle
paddle/operators/pool_op.cc
浏览文件 @ 192c00a7
......@@ -105,7 +105,7 @@ Pool2dOpMaker::Pool2dOpMaker(framework::OpProto *proto,
// TypedAttrChecker don't support vector type.)
AddAttr<std::vector<int>>(
"paddings",
"(vector<int>, defalut {0,0}), paddings(height, width) of pooling "
"(vector<int>, default {0,0}), paddings(height, width) of pooling "
"operator."
"If global_pooling = true, paddings and ksize will be ignored.")
.SetDefault({0, 0}); // TODO(Chengduo): Add checker. (Currently,
......@@ -127,10 +127,10 @@ Example:
X shape: $(N, C, H_{in}, W_{in})$
Output:
Out shape: $(N, C, H_{out}, W_{out})$
where
Where
$$
H_{out} = (H_{in} - ksize[0] + 2 * paddings[0]) / strides[0] + 1 \\
W_{out} = (W_{in} - ksize[1] + 2 * paddings[1]) / strides[1] + 1
H_{out} = \frac{(H_{in} - ksize[0] + 2 * paddings[0])}{strides[0]} + 1 \\
W_{out} = \frac{(W_{in} - ksize[1] + 2 * paddings[1])}{strides[1]} + 1
$$
)DOC");
......@@ -177,7 +177,7 @@ Pool3dOpMaker::Pool3dOpMaker(framework::OpProto *proto,
// TypedAttrChecker don't support vector type.)
AddAttr<std::vector<int>>(
"paddings",
"(vector<int>, defalut {0,0,0}), paddings(depth, height, "
"(vector<int>, default {0,0,0}), paddings(depth, height, "
"width) of pooling operator. "
"If global_pooling = true, ksize and paddings will be ignored.")
.SetDefault({0, 0, 0}); // TODO(Chengduo): Add checker. (Currently,
......@@ -199,11 +199,11 @@ Example:
X shape: $(N, C, D_{in}, H_{in}, W_{in})$
Output:
Out shape: $(N, C, D_{out}, H_{out}, W_{out})$
where
Where
$$
D_{out} = (D_{in} - ksize[0] + 2 * paddings[0]) / strides[0] + 1 \\
H_{out} = (H_{in} - ksize[1] + 2 * paddings[1]) / strides[1] + 1 \\
W_{out} = (W_{in} - ksize[2] + 2 * paddings[2]) / strides[2] + 1
D_{out} = \frac{(D_{in} - ksize[0] + 2 * paddings[0])}{strides[0]} + 1 \\
H_{out} = \frac{(H_{in} - ksize[1] + 2 * paddings[1])}{strides[1]} + 1 \\
W_{out} = \frac{(W_{in} - ksize[2] + 2 * paddings[2])}{strides[2]} + 1
$$
)DOC");
......
paddle/operators/pool_with_index_op.cc
浏览文件 @ 192c00a7
......@@ -142,7 +142,7 @@ class MaxPool2dWithIndexOpMaker : public framework::OpProtoAndCheckerMaker {
// TypedAttrChecker don't support vector type.)
AddAttr<std::vector<int>>(
"paddings",
"(vector<int>, defalut:{0, 0}), paddings(height, width) of pooling "
"(vector<int>, default:{0, 0}), paddings(height, width) of pooling "
"operator. "
"If global_pooling = true, paddings and will be ignored.")
.SetDefault({0, 0}); // TODO(Chengduo): Add checker. (Currently,
......@@ -166,10 +166,10 @@ Example:
Output:
Out shape: $(N, C, H_{out}, W_{out})$
Mask shape: $(N, C, H_{out}, W_{out})$
where
Where
$$
H_{out} = (H_{in} - ksize[0] + 2 * paddings[0]) / strides[0] + 1 \\
W_{out} = (W_{in} - ksize[1] + 2 * paddings[1]) / strides[1] + 1
H_{out} = \frac{(H_{in} - ksize[0] + 2 * paddings[0])}{strides[0]} + 1 \\
W_{out} = \frac{(W_{in} - ksize[1] + 2 * paddings[1])}{strides[1]} + 1
$$
)DOC");
......@@ -220,7 +220,7 @@ class MaxPool3dWithIndexOpMaker : public framework::OpProtoAndCheckerMaker {
// TypedAttrChecker don't support vector type.)
AddAttr<std::vector<int>>(
"paddings",
"(vector, defalut {0,0,0}), paddings(depth, "
"(vector, default {0,0,0}), paddings(depth, "
"height, width) of pooling operator. "
"If global_pooling = true, paddings and ksize will be ignored.")
.SetDefault({0, 0, 0}); // TODO(Chengduo): Add checker. (Currently,
......@@ -244,11 +244,11 @@ Example:
Output:
Out shape: $(N, C, D_{out}, H_{out}, W_{out})$
Mask shape: $(N, C, D_{out}, H_{out}, W_{out})$
where
Where
$$
D_{out} = (D_{in} - ksize[0] + 2 * paddings[0]) / strides[0] + 1 \\
H_{out} = (H_{in} - ksize[1] + 2 * paddings[1]) / strides[1] + 1 \\
W_{out} = (W_{in} - ksize[2] + 2 * paddings[2]) / strides[2] + 1
D_{out} = \frac{(D_{in} - ksize[0] + 2 * paddings[0])}{strides[0]} + 1 \\
H_{out} = \frac{(H_{in} - ksize[1] + 2 * paddings[1])}{strides[1]} + 1 \\
W_{out} = \frac{(W_{in} - ksize[2] + 2 * paddings[2])}{strides[2]} + 1
$$
)DOC");
......
paddle/operators/rank_loss_op.cc
浏览文件 @ 192c00a7
......@@ -35,9 +35,10 @@ class RankLossOp : public framework::OperatorWithKernel {
auto right_dims = ctx->GetInputDim("Right");
PADDLE_ENFORCE((label_dims == left_dims) && (left_dims == right_dims),
"All inputs must have the same size");
PADDLE_ENFORCE((label_dims.size() == 2) && (label_dims[1] == 1),
"All inputs must be row vector with size batch_size x 1.");
"All inputs must have the same size.");
PADDLE_ENFORCE(
(label_dims.size() == 2) && (label_dims[1] == 1),
"All inputs must be 2-D tensors with shape [batch_size x 1].");
ctx->SetOutputDim("Out", label_dims);
}
};
......@@ -48,10 +49,17 @@ class RankLossOpMaker : public framework::OpProtoAndCheckerMaker {
framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("Label",
"The label indicating A ranked higher than B or not, row vector.");
AddInput("Left", "The output of RankNet for doc A, vector.");
AddInput("Right", "The output of RankNet for doc B, vetor.");
AddOutput("Out", "The output loss of RankLoss operator, vector.");
"(2-D Tensor with shape [batch_size x 1]) "
"The label indicating A ranked higher than B or not.");
AddInput("Left",
"(2-D Tensor with shape [batch_size x 1]) "
"The output of RankNet for doc A.");
AddInput("Right",
"(2-D Tensor with shape [batch_size x 1]) "
"The output of RankNet for doc B.");
AddOutput("Out",
"(2-D Tensor with shape [batch_size x 1]) "
"The output loss of RankLoss operator.");
AddComment(R"DOC(
RankLoss Operator.
......@@ -65,16 +73,17 @@ P = {0, 1} or {0, 0.5, 1}, where 0.5 means no information about the rank of
the input pair.
The RankLoss operator takes three inputs: Left (o_i), Right (o_j) and Label
(P_{i,j}), which represent the output of RankNet for the two docs and the label,
respectively, and yields the rank loss C_{i,j} using the following equation:
(P_{i,j}), which represent the output score of RankNet for the two docs and
the label respectively, and yields the rank loss C_{i,j} using the following
equation:
\f$$
C_{i,j} = -\tilde{P_{ij}} * o_{i,j} + log(1 + e^{o_{i,j}}) \\
$$
C_{i,j} = -\tilde{P_{ij}} * o_{i,j} + \log(1 + e^{o_{i,j}}) \\
o_{i,j} = o_i - o_j \\
\tilde{P_{i,j}} = \left \{0, 0.5, 1 \right \} \ or \ \left \{0, 1 \right \}
\f$$
$$
The operator can take inputs of one sample or in batch.
The operator can take batch inputs with size batch_size (batch_size >= 1).
)DOC");
}
......
paddle/operators/reshape_op.cc
浏览文件 @ 192c00a7
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
......@@ -38,8 +37,8 @@ class ReshapeOp : public framework::OperatorWithKernel {
// TODO(qiao) change batch_size
for (size_t i = 1; i < shape.size(); ++i) {
PADDLE_ENFORCE(shape[i] > 0,
"Each dimension of shape "
"must be positiv except the first.");
"Each dimension of Attr(shape) "
"must be positive except the first one.");
}
if (shape[0] < 0) {
shape[0] = x_dims[0];
......
paddle/operators/smooth_l1_loss_op.cc
浏览文件 @ 192c00a7
......@@ -22,22 +22,20 @@ class SmoothL1LossOp : public framework::OperatorWithKernel {
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "X must be initialized.");
PADDLE_ENFORCE(ctx->HasInput("Y"), "Y must be initialized.");
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Y"), "Input(Y) should not be null.");
auto x_dims = ctx->GetInputDim("X");
auto y_dims = ctx->GetInputDim("Y");
PADDLE_ENFORCE_EQ(x_dims, y_dims, "The shape of X and Y must be the same.");
PADDLE_ENFORCE_EQ(x_dims, y_dims);
PADDLE_ENFORCE_GE(x_dims.size(), 2,
"The tensor rank of X must be at least 2.");
"The tensor rank of Input(X) should not be less than 2.");
if (ctx->HasInput("InsideWeight")) {
PADDLE_ENFORCE(ctx->HasInput("OutsideWeight"),
"If weights are provided, must specify both "
"inside and outside weights.");
PADDLE_ENFORCE_EQ(ctx->GetInputDim("InsideWeight"), x_dims,
"The shape of InsideWeight must be same as X.");
PADDLE_ENFORCE_EQ(ctx->GetInputDim("OutsideWeight"), x_dims,
"The shape of OutsideWeight must be same as X.");
PADDLE_ENFORCE_EQ(ctx->GetInputDim("InsideWeight"), x_dims);
PADDLE_ENFORCE_EQ(ctx->GetInputDim("OutsideWeight"), x_dims);
}
ctx->SetOutputDim("Diff", x_dims);
......@@ -53,25 +51,29 @@ class SmoothL1LossOpMaker : public framework::OpProtoAndCheckerMaker {
framework::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X",
"The input tensor of smooth l1 loss op."
"The rank should be greater or equal to 2 with shape "
"[batch_size, value_dim1, value_dim2, ..., value_dimN]");
"(Tensor, default Tensor<float>) A tensor with rank at least 2. "
"The input value of smooth l1 loss op with shape "
"[batch_size, dim1, ..., dimN].");
AddInput("Y",
"The target tensor of smooth l1 loss op "
"with the same shape as X.");
"(Tensor, default Tensor<float>) A tensor with rank at least 2. "
"The target value of smooth l1 loss op with same shape as X.");
AddInput("InsideWeight",
"Optional input tensor of smooth l1 loss op with the same shape "
"as X. If provided, the result of (X - Y) will be multiplied "
"(Tensor, default Tensor<float>) A tensor with rank at least 2. "
"This input is optional and should have same shape with X. "
"If provided, the result of (X - Y) will be multiplied "
"by this tensor element by element.")
.AsDispensable();
AddInput("OutsideWeight",
"Optinal input of smooth l1 loss op with the same shape as X."
"If provided, the output smooth l1 loss will be multiplied by "
"this tensor element by element.")
"(Tensor, default Tensor<float>) A tensor with rank at least 2. "
"This input is optional and should have same shape with X. "
"If provided, the out smooth l1 loss will be multiplied by this "
"tensor element by element.")
.AsDispensable();
AddOutput("Diff", "Intermediate variable to cache InsideWeight*(X-Y).")
AddOutput("Diff", "Intermediate variable to cache InsideWeight * (X - Y).")
.AsIntermediate();
AddOutput("Out", "Smooth l1 loss.");
AddOutput("Out",
"(Tensor, default Tensor<float>) A tensor with rank be 2. "
"The output smooth l1 loss with shape [batch_size, 1].");
AddAttr<AttrType>("sigma",
"Hyper parameter of smooth l1 loss op."
"A float scalar with default value 3.0.")
......@@ -79,15 +81,23 @@ class SmoothL1LossOpMaker : public framework::OpProtoAndCheckerMaker {
AddComment(R"DOC(
Smooth L1 Loss Operator.
This operator computes the smooth l1 loss for input and target.
The operator takes the first dimension of input as the batch size.
This operator computes the smooth l1 loss for X and Y.
The operator takes the first dimension of X and Y as batch size.
For each instance, it computes the smooth l1 loss element by element first
and then sums all the losses. So the resulting output shape
is [batch_size, 1].
and then sums all the losses. So the shape of Out is [batch_size, 1].
The equation is:
loss = $$0.5 * (\sigma * (x-y))^2$$ if $$|x - y| < 1 /({\sigma}^2)$$
$$\frac{|x - y| - 0.5}{{\sigma}^2}$$ otherwise
$$
Out_{\sigma}(X, Y)_i = \begin{cases}
0.5 * (\sigma * (X_i - Y_i)) ^ 2
\quad |X_i - Y_i| \lt \frac{1} {{\sigma} ^ 2} \\
\frac{|X_i - Y_i| - 0.5}{{\sigma}^2},
\quad otherwise
\end{cases}
$$
In the above equation, $Out_{\sigma}(X, Y)_i$, $X_i$ and $Y_i$ represent the ith
element of Out, X and Y.
)DOC");
}
......
paddle/operators/unpool_op.cc 0 → 100644
浏览文件 @ 192c00a7
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
Indicesou 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/operators/unpool_op.h"
namespace paddle {
namespace operators {
class Unpool2dOpMaker : public framework::OpProtoAndCheckerMaker {
public:
Unpool2dOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput(
"X",
"(Tensor) The input tensor of unpool operator. "
"The format of input tensor is NCHW. Where N is batch size, C is the "
"number of channels, H and W is the height and width of feature.");
AddInput(
"Indices",
"(Tensor) The input tensor of the indices given out by MaxPool2d. "
"The format of input tensor is NCHW. Where N is batch size, C is the "
"number of channels, H and W is the height and width of feature.");
AddOutput("Out",
"(Tensor) The output tensor of unpool operator."
"The format of output tensor is also NCHW."
"Where N is batch size, C is "
"the number of channels, H and W is the height and "
"width of feature.");
AddAttr<std::vector<int>>(
"ksize",
"(vector), the unpooling window size(height, width) "
"of unpooling operator.");
AddAttr<std::vector<int>>("strides",
"(vector, default:{1, 1}), "
"strides (height, width) of unpooling operator.")
.SetDefault({1, 1});
AddAttr<std::vector<int>>("paddings",
"(vector defalut:{0,0}), "
"paddings (height, width) of unpooling operator.")
.SetDefault({0, 0});
AddAttr<std::string>(
"unpooling_type",
"(string), unpooling type, can be \"max\" for max-unpooling ")
.InEnum({"max"});
AddComment(R"DOC(
"Input shape: $(N, C_{in}, H_{in}, W_{in})$
Output shape: $(N, C_{out}, H_{out}, W_{out})$
Where
$$
H_{out} = (H_{in}−1) * strides[0] − 2 * paddings[0] + ksize[0] \\
W_{out} = (W_{in}−1) * strides[1] − 2 * paddings[1] + ksize[1]
$$
Paper: http://www.matthewzeiler.com/wp-content/uploads/2017
/07/iccv2011.pdf
)DOC");
}
};
int OutputSize(int input_size, int ksize, int padding, int stride) {
int output_size = (input_size - 1) * stride - 2 * padding + ksize;
return output_size;
}
class UnpoolOp : public framework::OperatorWithKernel {
protected:
framework::OpKernelType GetKernelType(
const framework::ExecutionContext& ctx) const override {
return framework::OpKernelType(
framework::ToDataType(ctx.Input<framework::Tensor>("X")->type()),
ctx.device_context());
}
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of UnpoolOp"
"should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Indices"),
"Input(Indices) of UnpoolOp"
"should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of UnpoolOp should not be null.");
auto in_x_dims = ctx->GetInputDim("X");
auto in_y_dims = ctx->GetInputDim("Indices");
std::string unpooling_type =
ctx->Attrs().Get<std::string>("unpooling_type");
std::vector<int> ksize = ctx->Attrs().Get<std::vector<int>>("ksize");
std::vector<int> strides = ctx->Attrs().Get<std::vector<int>>("strides");
std::vector<int> paddings = ctx->Attrs().Get<std::vector<int>>("paddings");
PADDLE_ENFORCE(in_x_dims.size() == 4,
"Unpooling intput must be of 4-dimensional.");
PADDLE_ENFORCE_EQ(in_x_dims, in_y_dims);
std::vector<int64_t> output_shape({in_x_dims[0], in_x_dims[1]});
for (size_t i = 0; i < ksize.size(); ++i) {
output_shape.push_back(
OutputSize(in_x_dims[i + 2], ksize[i], paddings[i], strides[i]));
}
ctx->SetOutputDim("Out", framework::make_ddim(output_shape));
}
};
class UnpoolOpGrad : public framework::OperatorWithKernel {
protected:
framework::OpKernelType GetKernelType(
const framework::ExecutionContext& ctx) const override {
return framework::OpKernelType(
framework::ToDataType(ctx.Input<framework::Tensor>("X")->type()),
ctx.device_context());
}
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) must not be null.");
PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("X")),
"Input(X@GRAD) should not be null.");
ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP(unpool, ops::UnpoolOp, ops::Unpool2dOpMaker, unpool_grad,
ops::UnpoolOpGrad);
REGISTER_OP_CPU_KERNEL(unpool,
ops::UnpoolKernel<paddle::platform::CPUPlace, float>,
ops::UnpoolKernel<paddle::platform::CPUPlace, double>);
REGISTER_OP_CPU_KERNEL(
unpool_grad, ops::UnpoolGradKernel<paddle::platform::CPUPlace, float>,
ops::UnpoolGradKernel<paddle::platform::CPUPlace, double>);
paddle/operators/unpool_op.cu.cc 0 → 100644
浏览文件 @ 192c00a7
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
Indicesou 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/operators/unpool_op.h"
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(unpool,
ops::UnpoolKernel<paddle::platform::GPUPlace, float>,
ops::UnpoolKernel<paddle::platform::GPUPlace, double>);
REGISTER_OP_GPU_KERNEL(
unpool_grad, ops::UnpoolGradKernel<paddle::platform::GPUPlace, float>,
ops::UnpoolGradKernel<paddle::platform::GPUPlace, double>);
paddle/operators/unpool_op.h 0 → 100644
浏览文件 @ 192c00a7
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
Indicesou 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/framework/op_registry.h"
#include "paddle/operators/math/math_function.h"
#include "paddle/operators/math/unpooling.h"
namespace paddle {
namespace operators {
template <typename Place, typename T>
class UnpoolKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
const framework::Tensor* in_x = context.Input<framework::Tensor>("X");
const framework::Tensor* in_y = context.Input<framework::Tensor>("Indices");
auto* out = context.Output<framework::Tensor>("Out");
std::string unpooling_type = context.Attr<std::string>("unpooling_type");
std::vector<int> ksize = context.Attr<std::vector<int>>("ksize");
std::vector<int> strides = context.Attr<std::vector<int>>("strides");
std::vector<int> paddings = context.Attr<std::vector<int>>("paddings");
T* output_data = out->mutable_data<T>(context.GetPlace());
if (output_data) {
math::SetConstant<Place, T> set_zero;
set_zero(context.device_context(), out, static_cast<T>(0));
}
math::Unpool2dMaxFunctor<Place, T> unpool2d_max_forward;
unpool2d_max_forward(context.device_context(), *in_x, *in_y, out);
}
};
template <typename Place, typename T>
class UnpoolGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
const framework::Tensor* in_x = context.Input<framework::Tensor>("X");
const framework::Tensor* in_y = context.Input<framework::Tensor>("Indices");
const framework::Tensor* out = context.Input<framework::Tensor>("Out");
const framework::Tensor* out_grad =
context.Input<framework::Tensor>(framework::GradVarName("Out"));
framework::Tensor* in_x_grad =
context.Output<framework::Tensor>(framework::GradVarName("X"));
std::string unpooling_type = context.Attr<std::string>("unpooling_type");
std::vector<int> ksize = context.Attr<std::vector<int>>("ksize");
std::vector<int> strides = context.Attr<std::vector<int>>("strides");
std::vector<int> paddings = context.Attr<std::vector<int>>("paddings");
auto& device_ctx = context.device_context();
math::SetConstant<Place, T> zero;
if (in_x_grad) {
in_x_grad->mutable_data<T>(context.GetPlace());
zero(device_ctx, in_x_grad, static_cast<T>(0));
}
math::Unpool2dMaxGradFunctor<Place, T> unpool2d_max_backward;
unpool2d_max_backward(context.device_context(), *in_x, *in_y, *out,
*out_grad, in_x_grad);
}
};
} // namespace operators
} // namespace paddle
paddle/optimizer/parameter_optimizer_test.cc
浏览文件 @ 192c00a7
......@@ -127,8 +127,3 @@ TEST_F(OptimizerTest, TestGetWeight) { TestGetWeight(); }
TEST_F(OptimizerTest, TestUpdate) { TestUpdate(); }
TEST_F(OptimizerTest, TestCheckPoint) { TestCheckPoint(); }
int main(int argc, char** argv) {
testing::InitGoogleTest(&argc, argv);
return RUN_ALL_TESTS();
}
paddle/optimizer/serialization_test.cc
浏览文件 @ 192c00a7
......@@ -46,8 +46,3 @@ TEST(TensorToProto, Case2) {
EXPECT_EQ(t1[i], t[i]);
}
}
int main(int argc, char** argv) {
testing::InitGoogleTest(&argc, argv);
return RUN_ALL_TESTS();
}
paddle/scripts/docker/build.sh
浏览文件 @ 192c00a7
......@@ -183,6 +183,7 @@ EOF
${DOCKERFILE_GPU_ENV}
ADD go/cmd/pserver/pserver /usr/bin/
ADD go/cmd/master/master /usr/bin/
ADD paddle/pybind/print_operators_doc /usr/bin/
# default command shows the paddle version and exit
CMD ["paddle", "version"]
EOF
......
paddle/testing/CMakeLists.txt
浏览文件 @ 192c00a7
......@@ -5,4 +5,6 @@ if(WITH_TESTING)
add_dependencies(paddle_test_main paddle_proto ${external_project_dependencies})
add_library(paddle_test_util STATIC TestUtil.cpp)
add_dependencies(paddle_test_util paddle_proto ${external_project_dependencies})
add_library(paddle_gtest_main STATIC paddle_gtest_main.cc)
add_dependencies(paddle_gtest_main paddle_memory gtest gflags)
endif()
paddle/testing/paddle_gtest_main.cc 0 → 100644
浏览文件 @ 192c00a7
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <cstring>
#include "gflags/gflags.h"
#include "gtest/gtest.h"
#include "paddle/memory/memory.h"
int main(int argc, char** argv) {
std::vector<char*> new_argv;
std::string gflags_env;
new_argv.push_back(argv[0]);
#ifdef PADDLE_WITH_CUDA
new_argv.push_back(
strdup("--tryfromenv=fraction_of_gpu_memory_to_use,use_pinned_memory"));
#else
new_argv.push_back(strdup("--tryfromenv=use_pinned_memory"));
#endif
int new_argc = static_cast<int>(new_argv.size());
char** new_argv_address = new_argv.data();
google::ParseCommandLineFlags(&new_argc, &new_argv_address, false);
testing::InitGoogleTest(&argc, argv);
paddle::memory::Used(paddle::platform::CPUPlace());
#ifdef PADDLE_WITH_CUDA
paddle::memory::Used(paddle::platform::GPUPlace(0));
#endif
return RUN_ALL_TESTS();
}
python/paddle/v2/fluid/__init__.py
浏览文件 @ 192c00a7
......@@ -36,7 +36,8 @@ def __read_gflags_from_env__():
read_env_flags = ['use_pinned_memory']
if core.is_compile_gpu():
read_env_flags.append('fraction_of_gpu_memory_to_use')
core.init_gflags(sys.argv + ["--tryfromenv=" + ",".join(read_env_flags)])
core.init_gflags([sys.argv[0]] +
["--tryfromenv=" + ",".join(read_env_flags)])
__read_gflags_from_env__()
python/paddle/v2/fluid/framework.py
浏览文件 @ 192c00a7
......@@ -3,10 +3,12 @@ import collections
import numpy as np
from . import core
import proto.framework_pb2 as framework_pb2
import contextlib
__all__ = [
'Block', 'Variable', 'Program', 'Operator', 'default_startup_program',
'default_main_program'
'default_main_program', 'program_guard', 'switch_startup_program',
'switch_main_program'
]
......@@ -659,8 +661,83 @@ _startup_program_ = Program()
def default_startup_program():
"""
Get default startup program. In startup program, Paddle will initialize
parameters, initialize nccl handle, etc.
Returns:
Program: startup program
"""
return _startup_program_
def default_main_program():
"""
Get default main program. The main program is used for training or testing.
Returns:
Program: main program
"""
return _main_program_
def switch_main_program(program):
"""
Switch the main program to a new program.
Args:
program(Program): The new main program
Returns:
Program: The previous main program
"""
global _main_program_
prev_program = _main_program_
_main_program_ = program
return prev_program
def switch_startup_program(program):
"""
Switch the startup program to a new program
Args:
program(Program): The new startup program
Returns:
Program: The previous startup program
"""
global _startup_program_
prev_program = _startup_program_
_startup_program_ = program
return prev_program
@contextlib.contextmanager
def program_guard(main_program, startup_program=None):
"""
Switch program with `with` statement
Examples:
>>> with program_guard(Program()):
>>> data = fluid.layers.data(...)
>>> hidden = fluid.layers.fc(...)
Args:
main_program(Program): New main program inside `with` statement
startup_program(Program): New startup program inside `with` statement.
None means do not change startup program.
Returns:
None
"""
if not isinstance(main_program, Program):
raise TypeError("main_program should be Program")
main_program = switch_main_program(main_program)
if startup_program is not None:
if not isinstance(startup_program, Program):
raise TypeError("startup_program should be Program")
startup_program = switch_startup_program(startup_program)
yield
switch_main_program(main_program)
if startup_program is not None:
switch_startup_program(startup_program)
python/paddle/v2/fluid/tests/test_layers.py
浏览文件 @ 192c00a7
from __future__ import print_function
import unittest
import paddle.v2.fluid.layers as layers
import paddle.v2.fluid.nets as nets
from paddle.v2.fluid.framework import Program
from paddle.v2.fluid.framework import Program, program_guard
class TestBook(unittest.TestCase):
def test_fit_a_line(self):
program = Program()
x = layers.data(
name='x', shape=[13], dtype='float32', main_program=program)
y_predict = layers.fc(input=x, size=1, act=None, main_program=program)
y = layers.data(
name='y', shape=[1], dtype='float32', main_program=program)
cost = layers.square_error_cost(
input=y_predict, label=y, main_program=program)
avg_cost = layers.mean(x=cost, main_program=program)
with program_guard(program, startup_program=Program()):
x = layers.data(name='x', shape=[13], dtype='float32')
y_predict = layers.fc(input=x, size=1, act=None)
y = layers.data(name='y', shape=[1], dtype='float32')
cost = layers.square_error_cost(input=y_predict, label=y)
avg_cost = layers.mean(x=cost)
self.assertIsNotNone(avg_cost)
program.append_backward(avg_cost)
print str(program)
print(str(program))
def test_recognize_digits_mlp(self):
program = Program()
with program_guard(program, startup_program=Program()):
# Change g_program, so the rest layers use `g_program`
images = layers.data(
name='pixel', shape=[784], dtype='float32', main_program=program)
label = layers.data(
name='label', shape=[1], dtype='int32', main_program=program)
hidden1 = layers.fc(input=images,
size=128,
act='relu',
main_program=program)
hidden2 = layers.fc(input=hidden1,
size=64,
act='relu',
main_program=program)
predict = layers.fc(input=hidden2,
size=10,
act='softmax',
main_program=program)
cost = layers.cross_entropy(
input=predict, label=label, main_program=program)
avg_cost = layers.mean(x=cost, main_program=program)
images = layers.data(name='pixel', shape=[784], dtype='float32')
label = layers.data(name='label', shape=[1], dtype='int32')
hidden1 = layers.fc(input=images, size=128, act='relu')
hidden2 = layers.fc(input=hidden1, size=64, act='relu')
predict = layers.fc(input=hidden2, size=10, act='softmax')
cost = layers.cross_entropy(input=predict, label=label)
avg_cost = layers.mean(x=cost)
self.assertIsNotNone(avg_cost)
print str(program)
print(str(program))
def test_simple_conv2d(self):
program = Program()
images = layers.data(
name='pixel',
shape=[3, 48, 48],
dtype='int32',
main_program=program)
layers.conv2d(
input=images,
num_filters=3,
filter_size=[4, 4],
main_program=program)
with program_guard(program, startup_program=Program()):
images = layers.data(name='pixel', shape=[3, 48, 48], dtype='int32')
layers.conv2d(input=images, num_filters=3, filter_size=[4, 4])
print str(program)
print(str(program))
def test_conv2d_transpose(self):
program = Program()
kwargs = {'main_program': program}
img = layers.data(
name='pixel', shape=[3, 2, 2], dtype='float32', **kwargs)
layers.conv2d_transpose(
input=img, num_filters=10, output_size=28, **kwargs)
print str(program)
with program_guard(program):
img = layers.data(name='pixel', shape=[3, 2, 2], dtype='float32')
layers.conv2d_transpose(input=img, num_filters=10, output_size=28)
print(str(program))
def test_recognize_digits_conv(self):
program = Program()
with program_guard(program, startup_program=Program()):
images = layers.data(
name='pixel',
shape=[1, 28, 28],
dtype='float32',
main_program=program)
label = layers.data(
name='label', shape=[1], dtype='int32', main_program=program)
name='pixel', shape=[1, 28, 28], dtype='float32')
label = layers.data(name='label', shape=[1], dtype='int32')
conv_pool_1 = nets.simple_img_conv_pool(
input=images,
filter_size=5,
num_filters=2,
pool_size=2,
pool_stride=2,
act="relu",
main_program=program)
act="relu")
conv_pool_2 = nets.simple_img_conv_pool(
input=conv_pool_1,
filter_size=5,
num_filters=4,
pool_size=2,
pool_stride=2,
act="relu",
main_program=program)
act="relu")
predict = layers.fc(input=conv_pool_2,
size=10,
act="softmax",
main_program=program)
cost = layers.cross_entropy(
input=predict, label=label, main_program=program)
avg_cost = layers.mean(x=cost, main_program=program)
predict = layers.fc(input=conv_pool_2, size=10, act="softmax")
cost = layers.cross_entropy(input=predict, label=label)
avg_cost = layers.mean(x=cost)
program.append_backward(avg_cost)
print str(program)
print(str(program))
def test_word_embedding(self):
program = Program()
with program_guard(program, startup_program=Program()):
dict_size = 10000
embed_size = 32
first_word = layers.data(
name='firstw', shape=[1], dtype='int64', main_program=program)
second_word = layers.data(
name='secondw', shape=[1], dtype='int64', main_program=program)
third_word = layers.data(
name='thirdw', shape=[1], dtype='int64', main_program=program)
forth_word = layers.data(
name='forthw', shape=[1], dtype='int64', main_program=program)
next_word = layers.data(
name='nextw', shape=[1], dtype='int64', main_program=program)
first_word = layers.data(name='firstw', shape=[1], dtype='int64')
second_word = layers.data(name='secondw', shape=[1], dtype='int64')
third_word = layers.data(name='thirdw', shape=[1], dtype='int64')
forth_word = layers.data(name='forthw', shape=[1], dtype='int64')
next_word = layers.data(name='nextw', shape=[1], dtype='int64')
embed_first = layers.embedding(
input=first_word,
size=[dict_size, embed_size],
dtype='float32',
param_attr='shared_w',
main_program=program)
param_attr='shared_w')
embed_second = layers.embedding(
input=second_word,
size=[dict_size, embed_size],
dtype='float32',
param_attr='shared_w',
main_program=program)
param_attr='shared_w')
embed_third = layers.embedding(
input=third_word,
size=[dict_size, embed_size],
dtype='float32',
param_attr='shared_w',
main_program=program)
param_attr='shared_w')
embed_forth = layers.embedding(
input=forth_word,
size=[dict_size, embed_size],
dtype='float32',
param_attr='shared_w',
main_program=program)
param_attr='shared_w')
concat_embed = layers.concat(
input=[embed_first, embed_second, embed_third, embed_forth],
axis=1,
main_program=program)
axis=1)
hidden1 = layers.fc(input=concat_embed,
size=256,
act='sigmoid',
main_program=program)
hidden1 = layers.fc(input=concat_embed, size=256, act='sigmoid')
predict_word = layers.fc(input=hidden1,
size=dict_size,
act='softmax',
main_program=program)
cost = layers.cross_entropy(
input=predict_word, label=next_word, main_program=program)
avg_cost = layers.mean(x=cost, main_program=program)
act='softmax')
cost = layers.cross_entropy(input=predict_word, label=next_word)
avg_cost = layers.mean(x=cost)
self.assertIsNotNone(avg_cost)
print str(program)
print(str(program))
def test_linear_chain_crf(self):
program = Program()
# Change g_program, so the rest layers use `g_program`
images = layers.data(
name='pixel', shape=[784], dtype='float32', main_program=program)
label = layers.data(
name='label', shape=[1], dtype='int32', main_program=program)
hidden = layers.fc(input=images, size=128, main_program=program)
crf = layers.linear_chain_crf(
input=hidden, label=label, main_program=program)
print str(program)
with program_guard(program, startup_program=Program()):
images = layers.data(name='pixel', shape=[784], dtype='float32')
label = layers.data(name='label', shape=[1], dtype='int32')
hidden = layers.fc(input=images, size=128)
crf = layers.linear_chain_crf(input=hidden, label=label)
self.assertNotEqual(crf, None)
print(str(program))
if __name__ == '__main__':
......
python/paddle/v2/fluid/tests/test_unpool_op.py 0 → 100644
浏览文件 @ 192c00a7
import unittest
import numpy as np
from op_test import OpTest
def unpool2dmax_forward_naive(input, indices, ksize, strides, paddings):
s0, s1, s2, s3 = input.shape
out_hsize = (s2 - 1) * strides[0] - 2 * paddings[0] + ksize[0]
out_wsize = (s2 - 1) * strides[1] - 2 * paddings[1] + ksize[1]
out = np.zeros((s0, s1, out_hsize, out_wsize))
for nidx in xrange(s0):
for cidx in xrange(s1):
for h in xrange(s2):
for w in xrange(s3):
index = indices[nidx, cidx, h, w]
hidx = (index - index % out_wsize) / out_wsize
widx = index % out_wsize
out[nidx, cidx, int(hidx), int(widx)] = \
input[nidx, cidx, h, w]
return out
class TestUnpoolOp(OpTest):
def setUp(self):
self.op_type = "unpool"
self.init_test_case()
pre_input = np.random.random(self.shape).astype("float32")
nsize, csize, hsize, wsize = pre_input.shape
hsize_out = (hsize - self.ksize[0] + 2 * self.paddings[0]) / \
self.strides[0] + 1
wsize_out = (wsize - self.ksize[1] + 2 * self.paddings[1]) / \
self.strides[1] + 1
input = np.zeros((nsize, csize, hsize_out, wsize_out))
indices = np.zeros((nsize, csize, hsize_out, wsize_out))
for i in xrange(hsize_out):
for j in xrange(wsize_out):
r_start = np.max((i * self.strides[0] - self.paddings[0], 0))
r_end = np.min((i * self.strides[0] + self.ksize[0] - \
self.paddings[0], hsize))
c_start = np.max((j * self.strides[1] - self.paddings[1], 0))
c_end = np.min((j * self.strides[1] + self.ksize[1] - \
self.paddings[1], wsize))
for nidx in xrange(nsize):
for cidx in xrange(csize):
x_masked = pre_input[nidx, cidx, r_start:r_end, \
c_start:c_end]
input[nidx, cidx, i, j] = x_masked.max()
arg = x_masked.argmax()
indices[nidx, cidx, i, j] = \
(r_start + arg / self.ksize[1]) * wsize + \
c_start + arg % self.ksize[1]
output = self.unpool2d_forward_naive(input, indices, self.ksize, \
self.strides, self.paddings).astype("float32")
self.inputs = {
'X': input.astype('float32'),
'Indices': indices.astype('int32')
}
self.attrs = {
'strides': self.strides,
'paddings': self.paddings,
'ksize': self.ksize,
'unpooling_type': self.unpooling_type,
}
self.outputs = {'Out': output.astype('float32')}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Out')
def init_test_case(self):
self.unpool2d_forward_naive = unpool2dmax_forward_naive
self.unpooling_type = "max"
self.shape = [6, 4, 5, 5]
self.ksize = [3, 3]
self.strides = [2, 2]
self.paddings = [0, 0]
if __name__ == '__main__':
unittest.main()
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