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提交 8ddc5faa 编写于 作者: L liaogang
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Update Mac OS X port 

* follow comments to fix bugs
上级 54c37ab7
隐藏空白更改
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Showing with 259 addition and 232 deletion
cmake/cblas.cmake
浏览文件 @ 8ddc5faa
......@@ -44,8 +44,8 @@ set(ATLAS_LIB_SEARCH_PATHS
/usr/lib
/usr/lib/blas/atlas
/usr/lib/atlas
/usr/lib/atlas-base) # special for ubuntu 14.04.
/usr/lib/atlas-base # special for ubuntu 14.04.
)
find_path(ATLAS_INC_DIR NAMES cblas.h
PATHS ${ATLAS_INCLUDE_SEARCH_PATHS})
find_library(ATLAS_CBLAS_LIB NAMES cblas libcblas.so.3
......
cmake/util.cmake
浏览文件 @ 8ddc5faa
......@@ -24,7 +24,9 @@ function(target_circle_link_libraries TARGET_NAME)
list(APPEND libsInArgn ${arg})
endif()
endforeach()
if("${CMAKE_CXX_COMPILER_ID}" STREQUAL "Clang")
list(APPEND LIBS "-undefined dynamic_lookup")
endif()
list(REVERSE libsInArgn)
target_link_libraries(${TARGET_NAME}
${LIBS}
......
doc/build/build_from_source.md
浏览文件 @ 8ddc5faa
Build and Install
Installing from Sources
=================
* [1. Requirement](#Requirement)
* [2. Build on Ubuntu](#ubuntu)
* [3. Build on Mac OS X](#mac)
* [1. Download and Setup](#download)
* [2. Requirements](#requirements)
* [3. Build on Ubuntu](#ubuntu)
* [4. Build on Mac OS X](#mac)
## <span id="Requirement">Requirement</span>
## <span id="download">Download and Setup</span>
You can download PaddlePaddle from the [github source](https://github.com/gangliao/Paddle).
### Dependents
```bash
git clone https://github.com/baidu/Paddle paddle
```
- **CMake**: required for 2.8+ version
- **g++**: a recent c++ compiler supporting c++11, >= 4.6, < 5
- **BLAS library**: such as openBLAS, MKL, ATLAS
- **protobuf**: required for 2.4+ version, 3.x is not supported
- **python**: currently only 2.7 version is supported
## <span id="requirements">Requirements</span>
### Optional
To compile the source code, your computer must be equipped with GCC >=4.6 or Clang Compiler.
### Dependencies
PaddlePaddle also support some build options, you have to install related libraries.
- **CMake**: version >= 2.8
- **BLAS**: MKL, OpenBlas or ATLAS
- **protobuf**: version >= 2.4, **Note: 3.x is not supported**
- **python**: only python 2.7 is supported currently
- **WITH_GPU**: Compile with gpu mode
- The GPU version works best with Cuda Toolkit 7.5 and cuDNN v5
- Other versions Cuda Toolkit 6.5, 7.0 and cuDNN v2, v3, v4 are also supported
- Note: to utilize cuDNN v5, Cuda Toolkit 7.5 is prerequisite and vice versa
- **WITH_DOUBLE**: Compile with double precision, otherwise use single precision
- **WITH_GLOG**: Compile with glog, otherwise use a log implement internally
- **WITH_GFLAGS**: Compile with gflags, otherwise use a flag implement internally
- **WITH_TESTING**: Compile with gtest and run unittest for PaddlePaddle
- **WITH_DOC**: Compile with documentation
- **WITH_SWIG_PY**: Compile with python predict api
- **WITH_STYLE_CHECK**: Style check for source code
### Options
PaddlePaddle supports some build options. To enable it, first you need to install the related libraries.
## <span id="ubuntu">Building on Ubuntu14.04</span>
Optional | Description
------------ | :-----------
**WITH_GPU** | Compile with GPU mode.
**WITH_DOUBLE** | Compile with double precision floating-point, default: single precision. |
**WITH_GLOG** | Compile with glog. If not found, default: an internal log implementation.
**WITH_GFLAGS** | Compile with gflags. If not found, default: an internal flag implementation.
**WITH_TESTING** | Compile with gtest for PaddlePaddle's unit testing.
**WITH_DOC** | Compile to generate PaddlePaddle's docs, default: disabled (OFF).
**WITH_SWIG_PY** | Compile with python predict API, default: disabled (OFF).
**WITH_STYLE_CHECK**| Compile with code style check, default: enabled (ON).
|
### Install Dependencies
**Note:**
- The GPU version works best with Cuda Toolkit 7.5 and cuDNN v5.
- Other versions like Cuda Toolkit 6.5, 7.0, 8.0 and cuDNN v2, v3, v4 are also supported.
- **To utilize cuDNN v5, Cuda Toolkit 7.5 is prerequisite and vice versa.**
- **CPU Dependencies**
As a simple example, consider the following:
```bash
# necessary
sudo apt-get update
sudo apt-get install -y g++ make cmake build-essential libatlas-base-dev python python-pip libpython-dev m4 libprotobuf-dev protobuf-compiler python-protobuf python-numpy git
# optional
sudo apt-get install libgoogle-glog-dev
sudo apt-get install libgflags-dev
sudo apt-get install libgtest-dev
sudo pip install wheel
pushd /usr/src/gtest
cmake .
make
sudo cp *.a /usr/lib
popd
```
1. **Python Dependencies(optional)**
- **GPU Dependencies(optional)**
To compile PaddlePaddle with python predict API, make sure swig installed and set `-DWITH_SWIG_PY=ON` as follows:
If you need to build GPU version, the first thing you need is a machine that has GPU and CUDA installed.
And you also need to install cuDNN.
```bash
# install swig on ubuntu
sudo apt-get install swig
# install swig on Mac OS X
brew install swig
You can download CUDA toolkit and cuDNN from nvidia website:
```bash
https://developer.nvidia.com/cuda-downloads
https://developer.nvidia.com/cudnn
```
You can copy cuDNN files into the CUDA toolkit directory, such as:
# active swig in cmake
cmake .. -DWITH_SWIG_PY=ON
```
```bash
sudo tar -xzf cudnn-7.5-linux-x64-v5.1.tgz -C /usr/local
sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn*
```
Then you need to set LD\_LIBRARY\_PATH, CUDA\_HOME and PATH environment variables in ~/.bashrc.
2. **Doc Dependencies(optional)**
```bash
export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH
export CUDA_HOME=/usr/local/cuda
export PATH=/usr/local/cuda/bin:$PATH
```
- **Python Dependencies(optional)**
To generate PaddlePaddle's documentation, install dependencies and set `-DWITH_DOC=ON` as follows:
If you want to compile PaddlePaddle with python predict api, you need to add -DWITH_SWIG_PY=ON in cmake command and install these first:
```bash
pip install 'sphinx>=1.4.0'
pip install sphinx_rtd_theme breathe recommonmark
```bash
sudo apt-get install swig
```
# install doxygen on Ubuntu
sudo apt-get install doxygen
# install doxygen on Mac OS X
brew install doxygen
- **Doc Dependencies(optional)**
# active docs in cmake
cmake .. -DWITH_DOC=ON`
```
If you want to compile PaddlePaddle with doc, you need to add -DWITH_DOC=ON in cmake command and install these first:
## <span id="ubuntu">Build on Ubuntu 14.04</span>
```bash
pip install 'sphinx>=1.4.0'
pip install sphinx_rtd_theme breathe recommonmark
sudo apt-get install doxygen
```
### Install Dependencies
### Build and Install
- **CPU Dependencies**
CMake will find dependent libraries in system default paths first. After installing some optional libraries, corresponding build option will automatically be on(such as glog, gtest and gflags). And if libraries are not found, you have to set following variables manually in cmake command(CUDNN_ROOT, ATLAS_ROOT, MKL_ROOT, OPENBLAS_ROOT).
```bash
# necessary
sudo apt-get update
sudo apt-get install -y g++ make cmake build-essential libatlas-base-dev python python-pip libpython-dev m4 libprotobuf-dev protobuf-compiler python-protobuf python-numpy git
# optional
sudo apt-get install libgoogle-glog-dev
sudo apt-get install libgflags-dev
sudo apt-get install libgtest-dev
sudo pip install wheel
pushd /usr/src/gtest
cmake .
make
sudo cp *.a /usr/lib
popd
```
- **GPU Dependencies (optional)**
Here are some examples of cmake command with different options:
To build GPU version, you will need the following installed:
**only cpu**
1. a CUDA-capable GPU
2. A supported version of Linux with a gcc compiler and toolchain
3. NVIDIA CUDA Toolkit (available at http://developer.nvidia.com/cuda-downloads)
4. NVIDIA cuDNN Library (availabel at https://developer.nvidia.com/cudnn)
```bash
cmake -DWITH_GPU=OFF -DWITH_DOC=OFF
```
The CUDA development environment relies on tight integration with the host development environment,
including the host compiler and C runtime libraries, and is therefore only supported on
distribution versions that have been qualified for this CUDA Toolkit release.
After downloading cuDNN library, issue the following commands:
**gpu**
```bash
sudo tar -xzf cudnn-7.5-linux-x64-v5.1.tgz -C /usr/local
sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn*
```
Then you need to set LD\_LIBRARY\_PATH, CUDA\_HOME and PATH environment variables in ~/.bashrc.
```bash
export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH
export CUDA_HOME=/usr/local/cuda
export PATH=/usr/local/cuda/bin:$PATH
```
### Build and Install
As usual, the best option is to create build folder under paddle project directory.
```bash
cmake -DWITH_GPU=ON -DWITH_DOC=OFF
mkdir build && cd build
cmake ..
```
**gpu with doc and swig**
CMake first check PaddlePaddle's dependecies in system default path. After installing some optional
libraries, corresponding build option will be set automatically (for instance, glog, gtest and gflags).
If still not found, you can manually set it based on CMake error information from your screen.
```bash
cmake -DWITH_GPU=ON -DWITH_DOC=ON -DWITH_SWIG_PY=ON
```
As a simple example, consider the following:
- **Only CPU**
```bash
cmake .. -DWITH_GPU=OFF -DWITH_DOC=OFF
```
- **GPU**
```bash
cmake .. -DWITH_GPU=ON -DWITH_DOC=OFF
```
- **GPU with doc and swig**
```bash
cmake .. -DWITH_GPU=ON -DWITH_DOC=ON -DWITH_SWIG_PY=ON
```
Finally, you can download source code and build:
```bash
git clone https://github.com/baidu/Paddle paddle
cd paddle
mkdir build
cd build
# you can add build option here, such as:
cmake -DWITH_GPU=ON -DWITH_DOC=OFF -DCMAKE_INSTALL_PREFIX=<path to install> ..
cmake .. -DWITH_GPU=ON -DWITH_DOC=OFF -DCMAKE_INSTALL_PREFIX=<path to install>
# please use sudo make install, if you want
# to install PaddlePaddle into the system
make -j `nproc` && make install
# PaddlePaddle installation path
# set PaddlePaddle installation path in ~/.bashrc
export PATH=<path to install>/bin:$PATH
```
**Note**
And if you set WITH_SWIG_PY=ON, you have to install related python predict api at the same time:
**Note:**
If you set `WITH_SWIG_PY=ON`, related python dependencies also need to be installed.
Otherwise, PaddlePaddle will automatically install python dependencies
at first time when user run paddle commands, such as `paddle version`, `paddle train`.
It may require sudo privileges:
```bash
pip install <path to install>/opt/paddle/share/wheels/*.whl
# you can run
sudo pip install <path to install>/opt/paddle/share/wheels/*.whl
# or just run
sudo paddle version
```
## <span id="mac">Building on Mac OS X</span>
### Prerequisites
......@@ -150,7 +191,7 @@ This guide is based on Mac OS X 10.11 (El Capitan). Note that if you are running
you will already have Python 2.7.10 and Numpy 1.8 installed.
The best option is to use the package manager homebrew to handle installations and upgrades for you.
To install homebrew, first open a terminal window (you can find Terminal in the Utilities folder in Applications), and issue the command:
To install [homebrew](http://brew.sh/), first open a terminal window (you can find Terminal in the Utilities folder in Applications), and issue the command:
```bash
# install brew
......@@ -163,109 +204,103 @@ easy_install pip
- **CPU Dependencies**
```bash
# Install fundamental dependents
brew install glog gflags cmake protobuf openblas
# Install google test on Mac OS X
# Download gtest 1.7.0
wget https://github.com/google/googletest/archive/release-1.7.0.tar.gz
tar -xvf googletest-release-1.7.0.tar.gz && cd googletest-release-1.7.0
# Build gtest
mkdir build && cmake ..
make
# Install gtest library
sudo cp -r ../include/gtest /usr/local/include/
sudo cp lib*.a /usr/local/lib
```
```bash
# Install fundamental dependents
brew install glog gflags cmake protobuf openblas
# Install google test on Mac OS X
# Download gtest 1.7.0
wget https://github.com/google/googletest/archive/release-1.7.0.tar.gz
tar -xvf googletest-release-1.7.0.tar.gz && cd googletest-release-1.7.0
# Build gtest
mkdir build && cmake ..
make
# Install gtest library
sudo cp -r ../include/gtest /usr/local/include/
sudo cp lib*.a /usr/local/lib
```
- **GPU Dependencies(optional)**
If you need to build GPU version, the first thing you need is a machine that has NVIDIA GPU and CUDA installed.
And you also need to install cuDNN.
To build GPU version, you will need the following installed:
You can download CUDA toolkit and cuDNN from nvidia website:
```bash
https://developer.nvidia.com/cuda-downloads
https://developer.nvidia.com/cudnn
```
You can copy cuDNN files into the CUDA toolkit directory, for instance:
```bash
sudo tar -xzf cudnn-7.5-osx-x64-v5.0-ga.tgz -C /usr/local
sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn*
```
Then you need to set DYLD\_LIBRARY\_PATH, CUDA\_HOME and PATH environment variables in ~/.bashrc.
1. a CUDA-capable GPU
2. Mac OS X 10.11 or later
2. the Clang compiler and toolchain installed using Xcode
3. NVIDIA CUDA Toolkit (available at http://developer.nvidia.com/cuda-downloads)
4. NVIDIA cuDNN Library (availabel at https://developer.nvidia.com/cudnn)
```bash
export DYLD_LIBRARY_PATH=/usr/local/cuda/lib:$DYLD_LIBRARY_PATH
export PATH=/usr/local/cuda/bin:$PATH
```
- **Python Dependencies(optional)**
The CUDA development environment relies on tight integration with the host development environment,
including the host compiler and C runtime libraries, and is therefore only supported on
distribution versions that have been qualified for this CUDA Toolkit release.
1. After downloading cuDNN library, issue the following commands:
If you want to compile PaddlePaddle with python predict API, you need to add -DWITH_SWIG_PY=ON in cmake command and install these first:
```bash
sudo tar -xzf cudnn-7.5-osx-x64-v5.0-ga.tgz -C /usr/local
sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn*
```
2. Then you need to set DYLD\_LIBRARY\_PATH, CUDA\_HOME and PATH environment variables in ~/.bashrc.
```bash
brew install swig
```
```bash
export DYLD_LIBRARY_PATH=/usr/local/cuda/lib:$DYLD_LIBRARY_PATH
export PATH=/usr/local/cuda/bin:$PATH
```
- **Doc Dependencies(optional)**
### Build and Install
If you want to compile PaddlePaddle with doc, you need to add -DWITH_DOC=ON in cmake command and install these first:
As usual, the best option is to create build folder under paddle project directory.
```bash
pip install 'sphinx>=1.4.0'
pip install sphinx_rtd_theme breathe recommonmark
brew install doxygen
mkdir build && cd build
cmake ..
```
### Build and Install
CMake can find dependent libraries in system default paths firstly.
After installing some optional libraries, corresponding build option will be on automatically (for instance, glog, gtest and gflags).
If not found, you have to set following variables manually via CMake command (CUDNN_ROOT, ATLAS_ROOT, MKL_ROOT, OPENBLAS_ROOT).
Here are some examples of CMake command with different options:
CMake first check PaddlePaddle's dependecies in system default path. After installing some optional
libraries, corresponding build option will be set automatically (for instance, glog, gtest and gflags).
If still not found, you can manually set it based on CMake error information from your screen.
**only cpu**
As a simple example, consider the following:
```bash
cmake -DWITH_GPU=OFF -DWITH_DOC=OFF
```
- **Only CPU**
**gpu**
```bash
cmake .. -DWITH_GPU=OFF -DWITH_DOC=OFF
```
- **GPU**
```bash
cmake -DWITH_GPU=ON -DWITH_DOC=OFF
```
```bash
cmake .. -DWITH_GPU=ON -DWITH_DOC=OFF
```
**gpu with doc and swig**
- **GPU with doc and swig**
```bash
cmake -DWITH_GPU=ON -DWITH_DOC=ON -DWITH_SWIG_PY=ON
```
```bash
cmake .. -DWITH_GPU=ON -DWITH_DOC=ON -DWITH_SWIG_PY=ON
```
Finally, you can download source code and build:
Finally, you can build PaddlePaddle:
```bash
git clone https://github.com/baidu/Paddle paddle
cd paddle
mkdir build
cd build
# you can add build option here, such as:
cmake -DWITH_GPU=ON -DWITH_DOC=OFF -DCMAKE_INSTALL_PREFIX=<path to install> ..
# please use sudo make install, if you want
# to install PaddlePaddle into the system
cmake .. -DWITH_GPU=ON -DWITH_DOC=OFF -DCMAKE_INSTALL_PREFIX=<installation path>
# please use sudo make install, if you want to install PaddlePaddle into the system
make -j `nproc` && make install
# PaddlePaddle installation path
export PATH=<path to install>/bin:$PATH
# set PaddlePaddle installation path in ~/.bashrc
export PATH=<installation path>/bin:$PATH
```
**Note**
And if you set WITH_SWIG_PY=ON, you have to install related python predict api at the same time:
**Note:**
If you set `WITH_SWIG_PY=ON`, related python dependencies also need to be installed.
Otherwise, PaddlePaddle will automatically install python dependencies
at first time when user run paddle commands, such as `paddle version`, `paddle train`.
It may require sudo privileges:
```bash
# you can run
sudo pip install <path to install>/opt/paddle/share/wheels/*.whl
# or just run
sudo paddle version
```
\ No newline at end of file
paddle/api/Matrix.cpp
浏览文件 @ 8ddc5faa
......@@ -95,7 +95,7 @@ float Matrix::get(size_t x, size_t y) const throw(RangeError) {
}
void Matrix::set(size_t x, size_t y, float val) throw(RangeError,
UnsupportError) {
UnsupportError) {
if (x > this->getWidth() || y > this->getHeight()) {
RangeError e;
throw e;
......@@ -239,7 +239,7 @@ void Matrix::toNumpyMatInplace(float** view_data, int* dim1,
}
void Matrix::copyToNumpyMat(float** view_m_data, int* dim1,
int* dim2) throw(UnsupportError) {
static_assert(sizeof(float) == sizeof(float),
static_assert(sizeof(paddle::real) == sizeof(float),
"Currently PaddleAPI only support for single "
"precision version of paddle.");
if (this->isSparse()) {
......@@ -251,12 +251,12 @@ void Matrix::copyToNumpyMat(float** view_m_data, int* dim1,
if (auto cpuMat = dynamic_cast<paddle::CpuMatrix*>(m->mat.get())) {
auto src = cpuMat->getData();
auto dest = *view_m_data;
std::memcpy(dest, src, sizeof(float) * (*dim1) * (*dim2));
std::memcpy(dest, src, sizeof(paddle::real) * (*dim1) * (*dim2));
} else if (auto gpuMat = dynamic_cast<paddle::GpuMatrix*>(m->mat.get())) {
auto src = gpuMat->getData();
auto dest = *view_m_data;
hl_memcpy_device2host(dest, src,
sizeof(float) * (*dim1) * (*dim2));
sizeof(paddle::real) * (*dim1) * (*dim2));
} else {
LOG(WARNING) << "Unexpected Situation";
throw UnsupportError();
......
paddle/gserver/gradientmachines/NeuralNetwork.cpp
浏览文件 @ 8ddc5faa
......@@ -385,10 +385,17 @@ void NeuralNetwork::setOutputGrad(const std::vector<Argument>& args) {
}
}
extern NeuralNetwork* newCustomNerualNetwork(
const std::string& name, NeuralNetwork* network) __attribute__((weak));
NeuralNetwork* NeuralNetwork::newNeuralNetwork(
const std::string& name,
NeuralNetwork* rootNetwork) {
return new NeuralNetwork(name, rootNetwork);
if (newCustomNerualNetwork) {
return newCustomNerualNetwork(name, rootNetwork);
} else {
return new NeuralNetwork(name, rootNetwork);
}
}
} // namespace paddle
paddle/trainer/tests/test_Trainer.cpp
浏览文件 @ 8ddc5faa
......@@ -94,7 +94,11 @@ TEST(checkGradient, multi) {
TEST(checkGradient, hsigmoid) { checkGradientTest(configFile2, false, false); }
TEST(checkGradient, chunk) {
#if defined(__APPLE__) || defined (__OSX__)
EXPECT_EQ(0, system("python trainer/tests/gen_proto_data.py"));
#else
EXPECT_EQ(0, system("python2 trainer/tests/gen_proto_data.py"));
#endif
checkGradientTest(configFile3, false, false);
#ifndef PADDLE_ONLY_CPU
checkGradientTest(configFile3, true, true);
......
paddle/utils/PythonUtil.cpp
浏览文件 @ 8ddc5faa
......@@ -144,12 +144,12 @@ PyObjectPtr createPythonClass(
const std::map<std::string, std::string>& kwargs) {
PyGuard guard;
PyObjectPtr pyModule(PyImport_ImportModule(moduleName.c_str()));
// LOG(INFO) << "createPythonClass moduleName.c_str:" << moduleName.c_str();
LOG(INFO) << "createPythonClass moduleName.c_str:" << moduleName.c_str();
CHECK_PY(pyModule) << "Import module " << moduleName << " failed.";
PyObjectPtr pyDict(PyModule_GetDict(pyModule.get()));
CHECK_PY(pyDict) << "Get Dict failed.";
PyObjectPtr pyClass(PyDict_GetItemString(pyDict.get(), className.c_str()));
// LOG(INFO) << "createPythonClass className.c_str():" << className.c_str();
LOG(INFO) << "createPythonClass className.c_str():" << className.c_str();
CHECK_PY(pyClass) << "Import class " << className << " failed.";
PyObjectPtr argsObjectList(PyTuple_New(args.size()));
for (size_t i = 0; i < args.size(); ++i) {
......
paddle/utils/PythonUtil.h
浏览文件 @ 8ddc5faa
......@@ -35,13 +35,6 @@ limitations under the License. */
#include <Python.h>
#include <frameobject.h>
// #ifndef _POSIX_C_SOURCE
// #warning "no _POSIX_C_SOURCE defined in Python.h"
// #endif
// #ifndef _XOPEN_SOURCE
// #warning "no _XOPEN_SOURCE defined in Python.h"
// #endif
#endif
#include "paddle/utils/Util.h"
......
paddle/utils/Stat.cpp
浏览文件 @ 8ddc5faa
......@@ -13,28 +13,12 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "Stat.h"
#include <sys/syscall.h> // for syscall()
#include <sys/types.h>
#include "Util.h"
#include <iomanip>
#include <algorithm>
namespace paddle {
// return the thread id used by glog
pid_t getTID() {
#if defined(__APPLE__) || defined(__OSX__)
pid_t tid = syscall(SYS_thread_selfid);
#else
#ifndef __NR_gettid
#define __NR_gettid 224
#endif
pid_t tid = syscall(__NR_gettid);
#endif
CHECK_NE(tid, -1);
return tid;
}
StatSet globalStat("GlobalStatInfo");
void Stat::addSample(uint64_t value) {
......
paddle/utils/Thread.h
浏览文件 @ 8ddc5faa
......@@ -13,24 +13,10 @@ See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "Util.h"
#include "Logging.h"
#include <thread>
#include <sys/syscall.h>
#include <unistd.h>
inline pid_t gettid() {
#if defined(__APPLE__) || defined(__OSX__)
pid_t tid = syscall(SYS_thread_selfid);
#else
#ifndef __NR_gettid
#define __NR_gettid 224
#endif
pid_t tid = syscall(__NR_gettid);
#endif
CHECK_NE(tid, -1);
return tid;
}
#include "Queue.h"
#include "ThreadLocal.h"
......@@ -186,7 +172,7 @@ public:
jobFinishBarrier_(numWorkers + 1),
jobFunc_(nullptr),
checkOwner_(checkOwner) {
ownerThreadId_ = ::gettid();
ownerThreadId_ = getTID();
workers_.resize(numWorkers);
start();
}
......@@ -210,7 +196,7 @@ public:
*/
void exec(JobFunc jobFunc, JobFunc ownerFunc = nullptr) {
if (checkOwner_) {
CHECK_EQ(ownerThreadId_, ::gettid())
CHECK_EQ(ownerThreadId_, getTID())
<< "this sync thread pool should be used in one thread";
}
......
paddle/utils/ThreadLocal.cpp
浏览文件 @ 8ddc5faa
......@@ -12,10 +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 "Util.h"
#include "ThreadLocal.h"
#include "Thread.h"
#include "CommandLineParser.h"
P_DEFINE_bool(thread_local_rand_use_global_seed, false,
......@@ -31,11 +29,11 @@ unsigned int* ThreadLocalRand::getSeed() {
if (!p) { // init seed
if (FLAGS_thread_local_rand_use_global_seed) {
p = new unsigned int(defaultSeed_);
} else if (getpid() == gettid()) { // main thread
} else if (getpid() == getTID()) { // main thread
// deterministic, but differs from global srand()
p = new unsigned int(defaultSeed_ - 1);
} else {
p = new unsigned int(defaultSeed_ + gettid());
p = new unsigned int(defaultSeed_ + getTID());
LOG(INFO) << "thread use undeterministic rand seed:" << *p;
}
seed_.set(p);
......@@ -51,7 +49,7 @@ std::default_random_engine& ThreadLocalRandomEngine::get() {
int defaultSeed = ThreadLocalRand::getDefaultSeed();
engine->seed(FLAGS_thread_local_rand_use_global_seed
? defaultSeed
: defaultSeed + gettid());
: defaultSeed + getTID());
engine_.set(engine);
}
return *engine;
......
paddle/utils/Util.cpp
浏览文件 @ 8ddc5faa
......@@ -93,6 +93,19 @@ static void installProfilerSwitch() {}
namespace paddle {
pid_t getTID() {
#if defined(__APPLE__) || defined(__OSX__)
pid_t tid = syscall(SYS_thread_selfid);
#else
#ifndef __NR_gettid
#define __NR_gettid 224
#endif
pid_t tid = syscall(__NR_gettid);
#endif
CHECK_NE(tid, -1);
return tid;
}
static bool g_initialized = false;
typedef std::pair<int, std::function<void()>> PriorityFuncPair;
typedef std::vector<PriorityFuncPair> InitFuncList;
......
paddle/utils/Util.h
浏览文件 @ 8ddc5faa
......@@ -24,6 +24,8 @@ limitations under the License. */
#include <unordered_map>
#include <mutex>
#include <functional>
#include <sys/syscall.h> // for syscall()
#include <sys/types.h>
#include "CommandLineParser.h"
#include "Logging.h"
......@@ -63,6 +65,9 @@ limitations under the License. */
namespace paddle {
// return the thread id used by glog
pid_t getTID();
/**
* return the 1-based index of the highest bit set
*
......
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