提交 3553576e 编写于 作者: Q qijun

Merge remote-tracking branch 'baidu/develop' into feature/sppnet

......@@ -38,10 +38,21 @@ addons:
- curl
- lcov
- graphviz
- swig
before_install:
- |
if [ ${JOB} == "BUILD_AND_TEST" ]; then
if [ "$TRAVIS_PULL_REQUEST" != "false" ]; then
TRAVIS_COMMIT_RANGE="FETCH_HEAD...$TRAVIS_BRANCH"
fi
git diff --name-only $TRAVIS_COMMIT_RANGE | grep -qvE '(\.md$)' || {
echo "Only markdown docs were updated, stopping build process."
exit
}
fi
- if [[ "$TRAVIS_OS_NAME" == "linux" ]]; then sudo paddle/scripts/travis/before_install.linux.sh; fi
- if [[ "$TRAVIS_OS_NAME" == "osx" ]]; then paddle/scripts/travis/before_install.osx.sh; fi
- pip install wheel protobuf sphinx breathe recommonmark
- pip install wheel protobuf sphinx breathe recommonmark virtualenv numpy
script:
- paddle/scripts/travis/main.sh
notifications:
......
......@@ -95,11 +95,26 @@ if(NOT WITH_GPU)
add_definitions(-DHPPL_STUB_FUNC)
list(APPEND CMAKE_CXX_SOURCE_FILE_EXTENSIONS cu)
else()
if(${CUDA_VERSION_MAJOR} GREATER 6)
if(COMPILER_SUPPORT_CXX11)
LIST(APPEND CUDA_NVCC_FLAGS -std=c++11)
endif()
endif()
# TODO(yuyang18): Change it to remove std=c++11 in cuda compile.
set(CUDA_PROPAGATE_HOST_FLAGS OFF)
if(NOT CUDNN_FOUND)
message(FATAL_ERROR "Paddle need cudnn to compile")
endif()
set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS} "-g -O3 --use_fast_math")
if(WITH_AVX)
if(AVX_FOUND)
set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS} "-Xcompiler -mavx")
endif(AVX_FOUND)
else(WITH_AVX)
set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS} "-Xcompiler -msse3")
endif(WITH_AVX)
if(WITH_DSO)
set(CUDA_LIBRARIES "")
......
......@@ -14,7 +14,7 @@ developed by Baidu scientists and engineers for the purpose of applying deep
learning to many products at Baidu.
Our vision is to enable deep learning for everyone via PaddlePaddle.
Please refer to our [release log](https://github.com/baidu/Paddle/releases) to track the latest feature of PaddlePaddle.
Please refer to our [release announcement](https://github.com/baidu/Paddle/releases) to track the latest feature of PaddlePaddle.
## Features
......
......@@ -188,14 +188,6 @@ macro(add_simple_unittest TARGET_NAME)
add_unittest(${TARGET_NAME} ${TARGET_NAME}.cpp)
endmacro()
macro(add_paddle_culib TARGET_NAME)
set(NVCC_FLAG ${CUDA_NVCC_FLAGS})
set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS};--use_fast_math)
cuda_add_library(${TARGET_NAME} STATIC ${ARGN})
set(CUDA_NVCC_FLAGS ${NVCC_FLAG})
endmacro()
# Creates C resources file from files in given resource file
function(create_resources res_file output)
# Create empty output file
......
......@@ -21,14 +21,21 @@
set -e
export LC_ALL=C
UNAME_STR=`uname`
if [[ ${UNAME_STR} == 'Linux' ]]; then
SHUF_PROG='shuf'
else
SHUF_PROG='gshuf'
fi
mkdir -p data/tmp
python preprocess.py -i data/reviews_Electronics_5.json.gz
# uniq and shuffle
cd data/tmp
echo 'uniq and shuffle...'
cat pos_*|sort|uniq|shuf> pos.shuffed
cat neg_*|sort|uniq|shuf> neg.shuffed
cat pos_*|sort|uniq|${SHUF_PROG}> pos.shuffed
cat neg_*|sort|uniq|${SHUF_PROG}> neg.shuffed
min_len=`sed -n '$=' neg.shuffed`
test_num=$((min_len/10))
......@@ -42,8 +49,8 @@ head -n$train_num neg.shuffed >train.neg
tail -n$test_num pos.shuffed >test.pos
tail -n$test_num neg.shuffed >test.neg
cat train.pos train.neg|shuf>../train.txt
cat test.pos test.neg|shuf>../test.txt
cat train.pos train.neg | ${SHUF_PROG} >../train.txt
cat test.pos test.neg | ${SHUF_PROG} >../test.txt
cd -
echo 'data/train.txt' > data/train.list
......
......@@ -134,9 +134,8 @@ define_py_data_sources2(train_list='data/train.list',
* obj="process": 指定生成数据的函数
* args={"dictionary": word_dict}: 额外的参数,这里指定词典
更详细用例请参考文档<a href = "../../../doc/ui/data_provider/python_case.html">Python Use Case</a>
数据格式和详细文档请参考<a href = "../../../doc/ui/data_provider/pydataprovider2.html">
PyDataProviderWrapper</a>
更详细数据格式和用例请参考<a href = "../../ui/data_provider/pydataprovider2.html">
PyDataProvider2</a>
## 网络结构(Network Architecture)
本节我们将专注于网络结构的介绍。
......
......@@ -177,3 +177,40 @@ PaddlePaddle的参数使用名字 :code:`name` 作为参数的ID,相同名字
pip install --upgrade pip
8. python相关的单元测试都过不了
--------------------------------
如果出现以下python相关的单元测试都过不了的情况:
.. code-block:: bash
24 - test_PyDataProvider (Failed)
26 - test_RecurrentGradientMachine (Failed)
27 - test_NetworkCompare (Failed)
28 - test_PyDataProvider2 (Failed)
32 - test_Prediction (Failed)
33 - test_Compare (Failed)
34 - test_Trainer (Failed)
35 - test_TrainerOnePass (Failed)
36 - test_CompareTwoNets (Failed)
37 - test_CompareTwoOpts (Failed)
38 - test_CompareSparse (Failed)
39 - test_recurrent_machine_generation (Failed)
40 - test_PyDataProviderWrapper (Failed)
41 - test_config_parser (Failed)
42 - test_swig_api (Failed)
43 - layers_test (Failed)
并且查询PaddlePaddle单元测试的日志,提示:
.. code-block:: bash
paddle package is already in your PYTHONPATH. But unittest need a clean environment.
Please uninstall paddle package before start unittest. Try to 'pip uninstall paddle'.
解决办法是:卸载paddle包 :code:`pip uninstall paddle`。
原因是:单元测试使用了一个旧版本的python包,而没有测试到代码中实际修改的python包。即单元测试需要一个干净的环境:
* 如果paddle包已经在python的site-packages里面了,那么单元测试时使用的paddle包,就是site-packages里面的python包,而不是源码目录里 :code:`/python` 目录下的python包。
* 即便设置了 :code:`PYTHONPATH` 到 :code:`/python` 也没用,因为python的搜索路径是优先已经安装的python包。
\ No newline at end of file
PADDLE_BUILD_DIR="@CMAKE_CURRENT_BINARY_DIR@/../"
WITH_GPU="@WITH_GPU@"
PROTOBUF_LIB="@PROTOBUF_LIBRARY@"
ZLIB_LIB="@ZLIB_LIBRARIES@"
CMAKE_THREAD_LIB="@CMAKE_THREAD_LIBS_INIT@"
CMAKE_DL_LIBS="@CMAKE_DL_LIBS@"
......@@ -15,3 +16,4 @@ GFLAGS_LOCATION="@GFLAGS_LOCATION@"
CBLAS_LIBRARIES="@CBLAS_LIBS@"
CUDA_LIBRARIES="@CUDA_LIBRARIES@"
WITH_COVERALLS="@ON_COVERALLS@"
......@@ -38,6 +38,7 @@ try:
self.paddle_build_dir = os.path.abspath(self.paddle_build_dir)
self.with_gpu = PaddleLDFlag.cmake_bool(WITH_GPU)
self.protolib = PROTOBUF_LIB
self.zlib = ZLIB_LIB
self.thread = CMAKE_THREAD_LIB
self.dl_libs = CMAKE_DL_LIBS
self.with_python = PaddleLDFlag.cmake_bool(WITH_PYTHON)
......@@ -47,6 +48,7 @@ try:
self.glog_libs = LIBGLOG_LIBRARY
self.with_gflags = PaddleLDFlag.cmake_bool(WITH_GFLAGS)
self.with_coverage = PaddleLDFlag.cmake_bool(WITH_COVERALLS)
self.gflags_libs = GFLAGS_LIBRARIES
self.gflags_location = GFLAGS_LOCATION
self.cblas_libs = CBLAS_LIBRARIES
......@@ -64,7 +66,7 @@ try:
def parent_dir_str(self):
libdirs = PARENT_LIB_DIRS
return " ".join(map(lambda x: "-L" + os.path.join(self.paddle_build_dir, '..', x),
return " ".join(map(lambda x: "-L" + os.path.join(self.paddle_build_dir, '..', x),
libdirs))
def libs_str(self):
......@@ -82,6 +84,7 @@ try:
"-lpaddle_cuda",
"-lpaddle_api",
self.normalize_flag(self.protolib),
self.normalize_flag(self.zlib),
self.normalize_flag(self.thread),
self.normalize_flag(self.dl_libs),
self.normalize_flag(self.cblas_libs),
......@@ -95,6 +98,8 @@ try:
libs.append(self.normalize_flag(self.gflags_libs))
if self.with_gpu:
libs.append(self.normalize_flag(self.curt))
if self.with_coverage:
libs.append("-fprofile-arcs")
return " ".join(filter(lambda l: len(l) != 0, libs))
def normalize_flag(self, cmake_flag):
......@@ -131,8 +136,14 @@ try:
return False
else:
return True
def c_flag(self):
if self.with_coverage:
return ["-fprofile-arcs", "-ftest-coverage", "-O0", "-g"]
else:
return None
except ImportError:
class PaddleLDFlag(object):
def ldflag_str(self):
pass
def c_flag(self):
pass
......@@ -209,6 +209,15 @@ typedef struct {
#define HL_FLOAT_MIN 2.2250738585072014e-308
#endif
/**
* The maximum input value for exp, used to avoid overflow problem.
*
* Currently only used for tanh function.
*/
#define EXP_MAX_INPUT 40.0
/**
* @brief DIVUP(x, y) is similar to ceil(x / y).
* @note For CUDA, DIVUP will be used to specify
......
......@@ -38,7 +38,9 @@ namespace hppl {
}
__m256 tanh(const __m256 a) {
__m256 max = _mm256_set1_ps(EXP_MAX_INPUT);
__m256 tmp = _mm256_mul_ps(_mm256_set1_ps(-2.0f), a);
tmp = _mm256_min_ps(tmp, max);
tmp = exp(tmp);
return _mm256_sub_ps(
_mm256_div_ps(_mm256_set1_ps(2.0f),
......
......@@ -30,7 +30,9 @@ namespace hppl {
}
real tanh(const real a) {
return (2.0 / (1.0 + exp(-2.0*a))) - 1.0;
real tmp = -2.0 * a;
tmp = (tmp > EXP_MAX_INPUT) ? EXP_MAX_INPUT : tmp;
return (2.0 / (1.0 + exp(tmp))) - 1.0;
}
real linear(const real a) {
......
......@@ -50,7 +50,7 @@ if(NOT WITH_PYTHON)
endif()
if(WITH_GPU)
add_paddle_culib(paddle_gserver ${GSERVER_SOURCES})
cuda_add_library(paddle_gserver ${GSERVER_SOURCES})
else()
add_library(paddle_gserver STATIC
${GSERVER_SOURCES})
......
......@@ -1021,7 +1021,7 @@ TEST(Layer, LstmLayer) {
TestConfig config;
config.layerConfig.set_type("lstmemory");
config.layerConfig.set_size(4);
config.layerConfig.set_active_type("sigmoid");
config.layerConfig.set_active_type("tanh");
config.layerConfig.set_active_state_type("sigmoid");
config.layerConfig.set_active_gate_type("sigmoid");
config.biasSize = 28;
......
......@@ -369,7 +369,7 @@ TEST(Layer, LstmLayer) {
LayerConfig layerConfig;
layerConfig.set_type("lstmemory");
layerConfig.set_active_type("relu");
layerConfig.set_active_state_type("sigmoid");
layerConfig.set_active_state_type("tanh");
layerConfig.set_active_gate_type("sigmoid");
layerConfig.add_inputs();
......
......@@ -625,7 +625,10 @@ void BaseMatrixT<T>::squareDerivative(BaseMatrixT& b) {
applyBinary(binary::SquareDerivative<T>(), b);
}
DEFINE_MATRIX_BINARY_OP(Tanh, b = 2.0 / (1.0 + exp(-2 * a)) - 1.0);
DEFINE_MATRIX_BINARY_OP(Tanh,
T tmp = -2.0 * a;
tmp = (tmp > EXP_MAX_INPUT) ? EXP_MAX_INPUT : tmp;
b = 2.0 / (1.0 + std::exp(tmp)) - 1.0);
template<>
void BaseMatrixT<real>::tanh(BaseMatrixT& b) {
applyBinary(binary::Tanh<real>(), b);
......
......@@ -23,7 +23,7 @@ if(NOT WITH_GPU)
add_library(paddle_math STATIC
${MATH_SOURCES})
else()
add_paddle_culib(paddle_math ${MATH_SOURCES})
cuda_add_library(paddle_math ${MATH_SOURCES})
endif()
......
......@@ -200,7 +200,10 @@ void vLog1p(const int n, const T* a, T* r) {
binary::vLog1p<T>(), const_cast<T*>(a), r, 1, n, n, n);
}
DEFINE_MATRIX_BINARY_OP(vTanh, b = 2.0 / (1.0 + std::exp(-2 * a)) - 1.0);
DEFINE_MATRIX_BINARY_OP(vTanh,
T tmp = -2.0 * a;
tmp = (tmp > EXP_MAX_INPUT) ? EXP_MAX_INPUT : tmp;
b = 2.0 / (1.0 + std::exp(tmp)) - 1.0);
template<class T>
void vTanh(const int n, const T* a, T* r) {
hl_cpu_apply_binary_op<T, binary::vTanh<T>, 0, 0>(
......
......@@ -3468,9 +3468,7 @@ void CpuMatrix::tanh(Matrix& output) {
size_t dim = getWidth();
CHECK_EQ(output.getHeight(), numSamples);
CHECK_EQ(output.getWidth(), dim);
errno = 0;
vTanh(numSamples * dim, getData(), output.getData());
CHECK_EQ(errno, 0) << "vTanh error";
}
void CpuMatrix::tanhDerivative(Matrix& output) {
......@@ -3492,10 +3490,8 @@ void CpuMatrix::softrelu(Matrix& output) {
out[j] = x;
}
}
errno = 0;
vExp(numSamples * dim, output.getData(), output.getData());
vLog1p(numSamples * dim, output.getData(), output.getData());
CHECK_EQ(errno, 0) << "vExp+vLog1p error";
}
void CpuMatrix::softreluDerivative(Matrix& output) {
......@@ -3510,9 +3506,7 @@ void CpuMatrix::softreluDerivative(Matrix& output) {
MatrixPtr tmpMat = Matrix::create(numSamples, dim);
real* tmp = tmpMat->getData();
errno = 0;
vExp(size, output.getData(), tmpMat->getData());
CHECK_EQ(errno, 0) << "vExp error";
for (size_t i = 0; i < size; ++i) {
grad[i] *= (1.0 - 1.0 / tmp[i]);
......@@ -3535,10 +3529,7 @@ void CpuMatrix::scaledTanh(Matrix& output, real p1, real p2) {
out[i] = p2 * in[i];
}
// out = tanh(out)
errno = 0;
vTanh(numSamples * dim, out, out);
CHECK_EQ(errno, 0) << "vTanh error";
// out = p1 * out
for (size_t i = 0; i < numSamples * dim; ++i) {
......
......@@ -13,3 +13,4 @@ add_simple_unittest(test_sparseMatrixCompare)
add_simple_unittest(test_perturbation)
add_simple_unittest(test_CpuGpuVector)
add_simple_unittest(test_Allocator)
add_simple_unittest(test_FPException)
/* Copyright (c) 2016 Baidu, Inc. 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. */
/**
* This test is about floating point calculation exception.
* Paddle catches FE_INVALID, FE DIVBYZERO and FE_OVERFLOW exceptions.
*
* Some exceptions occur in the middle of a set of formulas,
* that can be circumvented by some tricks.
* For example,
* calculate tanh
* b = 2.0 / (1.0 + exp(-2 * a)) - 1.0
*
* If the result of (-2 * a) is too large,
* a FE_OVERFLOW exception occurs when calculating exp.
* But the result of tanh is no overflow problem,
* so we can add some tricks to prevent exp calculate an excessive value.
*
*/
#include <fenv.h>
#include <gtest/gtest.h>
#include "paddle/math/Matrix.h"
#include "paddle/utils/Excepts.h"
using namespace paddle; // NOLINT
void SetTensorValue(Matrix& matrix, real value) {
int height = matrix.getHeight();
int width = matrix.getWidth();
int stride = matrix.getStride();
real* data = matrix.getData();
for (int i = 0; i < height; i++) {
int j = rand() % width; // NOLINT
if (typeid(matrix) == typeid(CpuMatrix)) {
data[i * stride + j] = value;
} else if (typeid(matrix) == typeid(GpuMatrix)) {
hl_memcpy(&data[i * stride + j], &value, sizeof(real));
} else {
LOG(FATAL) << "should not reach here";
}
}
}
template<typename Matrix>
void testTanh(real illegal) {
MatrixPtr A = std::make_shared<Matrix>(10, 10);
MatrixPtr B = std::make_shared<Matrix>(10, 10);
A->randomizeUniform();
B->randomizeUniform();
SetTensorValue(*A, illegal);
A->tanh(*B);
}
template<typename Matrix>
void testSigmoid(real illegal) {
MatrixPtr A = std::make_shared<Matrix>(10, 10);
MatrixPtr B = std::make_shared<Matrix>(10, 10);
A->randomizeUniform();
B->randomizeUniform();
SetTensorValue(*A, illegal);
A->sigmoid(*B);
}
TEST(fp, overflow) {
for (auto illegal : {-90.0, 90.0}) {
LOG(INFO) << " illegal=" << illegal;
testTanh<CpuMatrix>(illegal);
testSigmoid<CpuMatrix>(illegal);
}
}
int main(int argc, char** argv) {
testing::InitGoogleTest(&argc, argv);
initMain(argc, argv);
feenableexcept(FE_INVALID | FE_DIVBYZERO | FE_OVERFLOW);
return RUN_ALL_TESTS();
}
......@@ -3,6 +3,8 @@ source ./common.sh
CMAKE_EXTRA=""
if [[ "$TRAVIS_OS_NAME" == "osx" ]]; then
CMAKE_EXTRA="-DPYTHON_LIBRARY=/usr/local/Cellar/python/2.7.12_1/Frameworks/Python.framework/Versions/2.7/lib/python2.7/config/libpython2.7.dylib"
else
CMAKE_EXTRA="-DWITH_SWIG_PY=ON"
fi
......
......@@ -31,8 +31,8 @@ is_lin = (system == 'linux')
# because generate paddle LDFLAGS is too complicated to do in setup.py
# it just read COMAKE generated LDFLAGS.
extra_links = []
ldflags = api.paddle_ld_flags.PaddleLDFlag()
ldflags = ldflags.ldflag_str()
obj = api.paddle_ld_flags.PaddleLDFlag()
ldflags = obj.ldflag_str()
if ldflags is not None:
extra_links.extend(ldflags.split(" "))
......@@ -51,13 +51,20 @@ elif is_osx == True:
include_dirs = [np.get_include(), "../"] # include numpy and paddle.
extra_c = obj.c_flag()
attr=dict()
if extra_c is not None:
attr["extra_compile_args"] = extra_c
setup(name="py_paddle",
version="@PADDLE_VERSION@",
ext_modules=[
Extension('py_paddle._swig_paddle', # Build SWIG Extension.
['Paddle_wrap.cxx'],
include_dirs = include_dirs,
extra_link_args = extra_links
extra_link_args = extra_links,
**attr
)
],
packages=['py_paddle'],
......
......@@ -13,5 +13,5 @@ for file in $files
do
base_protostr=$protostr/$file
new_protostr=$protostr/$file.unitest
diff $base_protostr $new_protostr
diff $base_protostr $new_protostr -u
done
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