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体验新版 GitCode,发现更多精彩内容 >>
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06aa23b0
编写于
5月 21, 2018
作者:
T
tangwei12
浏览文件
操作
浏览文件
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差异文件
Merge branch 'develop' of github.com:PaddlePaddle/Paddle into checkpoint
上级
be050565
35e55636
变更
32
隐藏空白更改
内联
并排
Showing
32 changed file
with
635 addition
and
275 deletion
+635
-275
Dockerfile
Dockerfile
+1
-1
doc/fluid/howto/optimization/cpu_profiling_cn.md
doc/fluid/howto/optimization/cpu_profiling_cn.md
+3
-1
doc/fluid/howto/optimization/cpu_profiling_en.md
doc/fluid/howto/optimization/cpu_profiling_en.md
+3
-1
doc/v2/build_and_install/pip_install_cn.rst
doc/v2/build_and_install/pip_install_cn.rst
+5
-6
doc/v2/build_and_install/pip_install_en.rst
doc/v2/build_and_install/pip_install_en.rst
+5
-6
paddle/fluid/framework/data_type_transform.cc
paddle/fluid/framework/data_type_transform.cc
+6
-0
paddle/fluid/framework/details/multi_devices_graph_builder.cc
...le/fluid/framework/details/multi_devices_graph_builder.cc
+1
-1
paddle/fluid/inference/tests/test_helper.h
paddle/fluid/inference/tests/test_helper.h
+3
-4
paddle/fluid/operators/CMakeLists.txt
paddle/fluid/operators/CMakeLists.txt
+1
-0
paddle/fluid/operators/beam_search_op.h
paddle/fluid/operators/beam_search_op.h
+0
-4
paddle/fluid/operators/is_empty_op.cc
paddle/fluid/operators/is_empty_op.cc
+30
-27
paddle/fluid/operators/is_empty_op.h
paddle/fluid/operators/is_empty_op.h
+37
-0
paddle/fluid/operators/pool_mkldnn_op.cc
paddle/fluid/operators/pool_mkldnn_op.cc
+153
-76
paddle/fluid/platform/CMakeLists.txt
paddle/fluid/platform/CMakeLists.txt
+1
-1
paddle/fluid/platform/mkldnn_helper.h
paddle/fluid/platform/mkldnn_helper.h
+10
-0
paddle/fluid/platform/profiler.cc
paddle/fluid/platform/profiler.cc
+7
-4
paddle/fluid/platform/profiler.h
paddle/fluid/platform/profiler.h
+2
-0
paddle/fluid/pybind/protobuf.cc
paddle/fluid/pybind/protobuf.cc
+1
-0
paddle/scripts/docker/build.sh
paddle/scripts/docker/build.sh
+1
-1
paddle/scripts/paddle_build.sh
paddle/scripts/paddle_build.sh
+7
-5
python/paddle/fluid/framework.py
python/paddle/fluid/framework.py
+2
-0
python/paddle/fluid/layers/control_flow.py
python/paddle/fluid/layers/control_flow.py
+38
-0
python/paddle/fluid/layers/detection.py
python/paddle/fluid/layers/detection.py
+96
-45
python/paddle/fluid/tests/book/high-level-api/CMakeLists.txt
python/paddle/fluid/tests/book/high-level-api/CMakeLists.txt
+1
-0
python/paddle/fluid/tests/book/high-level-api/image_classification/CMakeLists.txt
...s/book/high-level-api/image_classification/CMakeLists.txt
+7
-0
python/paddle/fluid/tests/book/high-level-api/image_classification/cifar10_small_test_set.py
...-level-api/image_classification/cifar10_small_test_set.py
+82
-0
python/paddle/fluid/tests/book/high-level-api/image_classification/test_image_classification_resnet.py
.../image_classification/test_image_classification_resnet.py
+31
-20
python/paddle/fluid/tests/book/high-level-api/image_classification/test_image_classification_vgg.py
...api/image_classification/test_image_classification_vgg.py
+31
-22
python/paddle/fluid/tests/book/high-level-api/word2vec/test_word2vec_new_api.py
...sts/book/high-level-api/word2vec/test_word2vec_new_api.py
+38
-19
python/paddle/fluid/tests/test_detection.py
python/paddle/fluid/tests/test_detection.py
+18
-0
python/paddle/fluid/tests/unittests/test_is_empty_op.py
python/paddle/fluid/tests/unittests/test_is_empty_op.py
+12
-30
python/paddle/fluid/transpiler/memory_optimization_transpiler.py
...paddle/fluid/transpiler/memory_optimization_transpiler.py
+2
-1
未找到文件。
Dockerfile
浏览文件 @
06aa23b0
...
...
@@ -70,7 +70,7 @@ RUN localedef -i en_US -f UTF-8 en_US.UTF-8
# specify sphinx version as 1.5.6 and remove -U option for [pip install -U
# sphinx-rtd-theme] since -U option will cause sphinx being updated to newest
# version(1.7.1 for now), which causes building documentation failed.
RUN
pip
install
--upgrade
pip
==
9.0.3
&&
\
RUN
easy_install
-U
pip
&&
\
pip
install
-U
wheel
&&
\
pip
install
-U
docopt PyYAML
sphinx
==
1.5.6
&&
\
pip
install
sphinx-rtd-theme
==
0.1.9 recommonmark
...
...
doc/fluid/howto/optimization/cpu_profiling_cn.md
浏览文件 @
06aa23b0
# CPU性能调优
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。
...
...
@@ -8,7 +10,7 @@ PaddlePaddle 用户一般通过调用 Python API 编写深度学习程序。大
*
Python 与 C++ 混合代码的性能分析
# Python代码的性能分析
#
#
Python代码的性能分析
### 生成性能分析文件
...
...
doc/fluid/howto/optimization/cpu_profiling_en.md
浏览文件 @
06aa23b0
# Tune CPU performance
This tutorial introduces techniques we use to profile and tune the
CPU performance of PaddlePaddle. We will use Python packages
`cProfile`
and
`yep`
, and Google's
`perftools`
.
...
...
@@ -14,7 +16,7 @@ the profiling and tuning of
1.
the Python code and
1.
the mixture of Python and C++ code.
# Profiling the Python Code
#
#
Profiling the Python Code
### Generate the Performance Profiling File
...
...
doc/v2/build_and_install/pip_install_cn.rst
浏览文件 @
06aa23b0
...
...
@@ -37,12 +37,11 @@ PaddlePaddle可以使用常用的Python包管理工具
:header: "版本说明", "cp27-cp27mu", "cp27-cp27m"
:widths: 1, 3, 3
"cpu_avx_mkl", "`paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxCp27cp27mu/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxCp27cp27mu/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl>`_"
"cpu_avx_openblas", "`paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxOpenblas/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxOpenblas/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl>`_"
"cpu_noavx_openblas", "`paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuNoavxOpenblas/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuNoavxOpenblas/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl>`_"
"cuda7.5_cudnn5_avx_mkl", "`paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda75cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda75cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl>`_"
"cuda8.0_cudnn5_avx_mkl", "`paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda80cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda80cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl>`_"
"cuda8.0_cudnn7_avx_mkl", "`paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda8cudnn7cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda8cudnn7cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl>`_"
"cpu_avx_mkl", "`paddlepaddle-latest-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxCp27cp27mu/.lastSuccessful/paddlepaddle-latest-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle-latest-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxCp27cp27mu/.lastSuccessful/paddlepaddle-latest-cp27-cp27m-linux_x86_64.whl>`_"
"cpu_avx_openblas", "`paddlepaddle-latest-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxOpenblas/.lastSuccessful/paddlepaddle-latest-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle-latest-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxOpenblas/.lastSuccessful/paddlepaddle-latest-cp27-cp27m-linux_x86_64.whl>`_"
"cpu_noavx_openblas", "`paddlepaddle-latest-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuNoavxOpenblas/.lastSuccessful/paddlepaddle-latest-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle-latest-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuNoavxOpenblas/.lastSuccessful/paddlepaddle-latest-cp27-cp27m-linux_x86_64.whl>`_"
"cuda8.0_cudnn5_avx_mkl", "`paddlepaddle_gpu-latest-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda80cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-latest-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle_gpu-latest-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda80cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-latest-cp27-cp27m-linux_x86_64.whl>`_"
"cuda8.0_cudnn7_avx_mkl", "`paddlepaddle_gpu-latest-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda8cudnn7cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-latest-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle_gpu-latest-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda8cudnn7cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-latest-cp27-cp27m-linux_x86_64.whl>`_"
.. _pip_dependency:
...
...
doc/v2/build_and_install/pip_install_en.rst
浏览文件 @
06aa23b0
...
...
@@ -40,12 +40,11 @@ If the links below shows up the login form, just click "Log in as guest" to star
:header: "version", "cp27-cp27mu", "cp27-cp27m"
:widths: 1, 3, 3
"cpu_avx_mkl", "`paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxCp27cp27mu/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxCp27cp27mu/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl>`_"
"cpu_avx_openblas", "`paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxOpenblas/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxOpenblas/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl>`_"
"cpu_noavx_openblas", "`paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuNoavxOpenblas/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuNoavxOpenblas/.lastSuccessful/paddlepaddle-0.11.0-cp27-cp27m-linux_x86_64.whl>`_"
"cuda7.5_cudnn5_avx_mkl", "`paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda75cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda75cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl>`_"
"cuda8.0_cudnn5_avx_mkl", "`paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda80cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda80cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl>`_"
"cuda8.0_cudnn7_avx_mkl", "`paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda8cudnn7cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda8cudnn7cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-0.11.0-cp27-cp27m-linux_x86_64.whl>`_"
"cpu_avx_mkl", "`paddlepaddle-latest-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxCp27cp27mu/.lastSuccessful/paddlepaddle-latest-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle-latest-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxCp27cp27mu/.lastSuccessful/paddlepaddle-latest-cp27-cp27m-linux_x86_64.whl>`_"
"cpu_avx_openblas", "`paddlepaddle-latest-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxOpenblas/.lastSuccessful/paddlepaddle-latest-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle-latest-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuAvxOpenblas/.lastSuccessful/paddlepaddle-latest-cp27-cp27m-linux_x86_64.whl>`_"
"cpu_noavx_openblas", "`paddlepaddle-latest-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuNoavxOpenblas/.lastSuccessful/paddlepaddle-latest-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle-latest-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_CpuNoavxOpenblas/.lastSuccessful/paddlepaddle-latest-cp27-cp27m-linux_x86_64.whl>`_"
"cuda8.0_cudnn5_avx_mkl", "`paddlepaddle_gpu-latest-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda80cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-latest-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle_gpu-latest-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda80cudnn5cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-latest-cp27-cp27m-linux_x86_64.whl>`_"
"cuda8.0_cudnn7_avx_mkl", "`paddlepaddle_gpu-latest-cp27-cp27mu-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda8cudnn7cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-latest-cp27-cp27mu-linux_x86_64.whl>`_", "`paddlepaddle_gpu-latest-cp27-cp27m-linux_x86_64.whl <https://guest:@paddleci.ngrok.io/repository/download/Manylinux1_Cuda8cudnn7cp27cp27mu/.lastSuccessful/paddlepaddle_gpu-latest-cp27-cp27m-linux_x86_64.whl>`_"
.. _pip_dependency:
...
...
paddle/fluid/framework/data_type_transform.cc
浏览文件 @
06aa23b0
...
...
@@ -91,6 +91,12 @@ void TransDataType(const OpKernelType& kernel_type_for_var,
case
proto
::
VarType
::
BOOL
:
framework
::
VisitDataType
(
dst_type
,
CastDataType
<
bool
>
(
in
,
out
,
ctx
));
break
;
case
proto
::
VarType
::
INT16
:
framework
::
VisitDataType
(
dst_type
,
CastDataType
<
bool
>
(
in
,
out
,
ctx
));
break
;
case
proto
::
VarType
::
UINT8
:
framework
::
VisitDataType
(
dst_type
,
CastDataType
<
bool
>
(
in
,
out
,
ctx
));
break
;
default:
PADDLE_THROW
(
"Not support type %d"
,
src_type
);
}
...
...
paddle/fluid/framework/details/multi_devices_graph_builder.cc
浏览文件 @
06aa23b0
...
...
@@ -98,7 +98,7 @@ bool MultiDevSSAGraphBuilder::IsDistTrainOp(const OpDesc &op,
return
false
;
};
if
(
op
.
Type
()
==
"split"
)
{
if
(
op
.
Type
()
==
"split"
||
op
.
Type
()
==
"split_byref"
)
{
return
checker
(
op
.
OutputArgumentNames
(),
send_op
->
InputArgumentNames
());
}
else
if
(
op
.
Type
()
==
"concat"
)
{
return
checker
(
op
.
InputArgumentNames
(),
send_op
->
OutputArgumentNames
());
...
...
paddle/fluid/inference/tests/test_helper.h
浏览文件 @
06aa23b0
...
...
@@ -149,7 +149,7 @@ void TestInference(const std::string& dirname,
state
=
paddle
::
platform
::
ProfilerState
::
kCPU
;
}
else
{
#ifdef PADDLE_WITH_CUDA
state
=
paddle
::
platform
::
ProfilerState
::
k
CUDA
;
state
=
paddle
::
platform
::
ProfilerState
::
k
All
;
// The default device_id of paddle::platform::CUDAPlace is 0.
// Users can get the device_id using:
// int device_id = place.GetDeviceId();
...
...
@@ -172,7 +172,7 @@ void TestInference(const std::string& dirname,
}
// Disable the profiler and print the timing information
paddle
::
platform
::
DisableProfiler
(
paddle
::
platform
::
EventSortingKey
::
kDefault
,
"load_program_profiler
.txt
"
);
"load_program_profiler"
);
paddle
::
platform
::
ResetProfiler
();
// 3. Get the feed_target_names and fetch_target_names
...
...
@@ -236,8 +236,7 @@ void TestInference(const std::string& dirname,
// Disable the profiler and print the timing information
paddle
::
platform
::
DisableProfiler
(
paddle
::
platform
::
EventSortingKey
::
kDefault
,
"run_inference_profiler.txt"
);
paddle
::
platform
::
EventSortingKey
::
kDefault
,
"run_inference_profiler"
);
paddle
::
platform
::
ResetProfiler
();
}
...
...
paddle/fluid/operators/CMakeLists.txt
浏览文件 @
06aa23b0
...
...
@@ -204,6 +204,7 @@ if(WITH_DISTRIBUTE)
set_source_files_properties
(
send_recv_op_test.cc PROPERTIES COMPILE_FLAGS
${
DISTRIBUTE_COMPILE_FLAGS
}
)
cc_test
(
test_send_recv SRCS send_recv_op_test.cc DEPS prefetch_op send_op listen_and_serv_op sum_op executor
)
if
(
WITH_GPU
)
set_source_files_properties
(
test_send_nccl_id.cc PROPERTIES COMPILE_FLAGS
${
DISTRIBUTE_COMPILE_FLAGS
}
)
cc_test
(
test_send_nccl_id SRCS test_send_nccl_id.cc DEPS send_op listen_and_serv_op executor
)
op_library
(
gen_nccl_id_op DEPS nccl_common sendrecvop_grpc
)
set_source_files_properties
(
gen_nccl_id_op.cc PROPERTIES COMPILE_FLAGS
${
DISTRIBUTE_COMPILE_FLAGS
}
)
...
...
paddle/fluid/operators/beam_search_op.h
浏览文件 @
06aa23b0
...
...
@@ -14,10 +14,6 @@ limitations under the License. */
#pragma once
#ifdef PADDLE_WITH_TESTING
#include "gtest/gtest.h"
#endif
#include <string>
#include <vector>
#include "paddle/fluid/framework/lod_tensor.h"
...
...
paddle/fluid/operators/is_empty_op.cc
浏览文件 @
06aa23b0
...
...
@@ -12,45 +12,41 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/is_empty_op.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/operator.h"
namespace
paddle
{
namespace
operators
{
constexpr
char
kInput
[]
=
"X"
;
constexpr
char
kOutput
[]
=
"Out"
;
class
IsEmptyOp
:
public
framework
::
OperatorBase
{
class
IsEmptyOp
:
public
framework
::
OperatorWithKernel
{
public:
IsEmptyOp
(
const
std
::
string
&
type
,
const
framework
::
VariableNameMap
&
inputs
,
const
framework
::
VariableNameMap
&
outputs
,
const
framework
::
AttributeMap
&
attrs
)
:
OperatorBase
(
type
,
inputs
,
outputs
,
attrs
)
{}
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
private:
void
RunImpl
(
const
framework
::
Scope
&
scope
,
const
platform
::
Place
&
place
)
const
override
{
// get input
auto
*
var
=
scope
.
FindVar
(
Input
(
kInput
));
PADDLE_ENFORCE_NOT_NULL
(
var
);
auto
&
tensor
=
var
->
Get
<
framework
::
LoDTensor
>
();
// get output
auto
*
out
=
scope
.
FindVar
(
Output
(
kOutput
));
PADDLE_ENFORCE_NOT_NULL
(
out
);
auto
*
out_tensor
=
out
->
GetMutable
<
framework
::
LoDTensor
>
();
protected:
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"Input(X) of IsEmptyOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"Out"
),
"Output(Out) of IsEmptyOp should not be null."
);
ctx
->
SetOutputDim
(
"Out"
,
{
1
});
}
out_tensor
->
Resize
({
1
});
out_tensor
->
mutable_data
<
bool
>
(
platform
::
CPUPlace
())[
0
]
=
framework
::
product
(
tensor
.
dims
())
==
0
;
framework
::
OpKernelType
GetExpectedKernelType
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
framework
::
OpKernelType
kt
=
framework
::
OpKernelType
(
framework
::
ToDataType
(
ctx
.
Input
<
framework
::
LoDTensor
>
(
"X"
)
->
type
()),
platform
::
CPUPlace
());
return
kt
;
}
};
class
IsEmptyOp
Proto
Maker
:
public
framework
::
OpProtoAndCheckerMaker
{
class
IsEmptyOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
void
Make
()
override
{
AddInput
(
kInput
,
"(Tensor) Tensor which is to be checked."
);
AddOutput
(
kOutput
,
"(Tensor) a boolean Tensor that indicate empty or not."
);
AddInput
(
"X"
,
"(LoDTensor) Tensor which is to be checked."
);
AddOutput
(
"Out"
,
"(LoDTensor) a boolean Tensor that indicate empty or not."
);
AddComment
(
R"DOC(
IsEmpty Operator which checks whether a tensor is empty.
...
...
@@ -62,5 +58,12 @@ It will just return product(tensor.ddims()) > 0;
}
// namespace operators
}
// namespace paddle
REGISTER_OP_WITHOUT_GRADIENT
(
is_empty
,
paddle
::
operators
::
IsEmptyOp
,
paddle
::
operators
::
IsEmptyOpProtoMaker
);
namespace
ops
=
paddle
::
operators
;
REGISTER_OPERATOR
(
is_empty
,
ops
::
IsEmptyOp
,
ops
::
IsEmptyOpMaker
,
paddle
::
framework
::
EmptyGradOpMaker
);
REGISTER_OP_CPU_KERNEL
(
is_empty
,
ops
::
IsEmptyOpKernel
<
paddle
::
platform
::
CPUDeviceContext
,
float
>
,
ops
::
IsEmptyOpKernel
<
paddle
::
platform
::
CPUDeviceContext
,
double
>
,
ops
::
IsEmptyOpKernel
<
paddle
::
platform
::
CPUDeviceContext
,
int
>
,
ops
::
IsEmptyOpKernel
<
paddle
::
platform
::
CPUDeviceContext
,
int64_t
>
);
paddle/fluid/operators/is_empty_op.h
0 → 100644
浏览文件 @
06aa23b0
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/operator.h"
namespace
paddle
{
namespace
operators
{
template
<
typename
DeviceContext
,
typename
T
>
class
IsEmptyOpKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
// get input
auto
*
input_tensor
=
context
.
Input
<
framework
::
LoDTensor
>
(
"X"
);
// get output
auto
*
output_tensor
=
context
.
Output
<
framework
::
LoDTensor
>
(
"Out"
);
output_tensor
->
mutable_data
<
bool
>
(
platform
::
CPUPlace
())[
0
]
=
framework
::
product
(
input_tensor
->
dims
())
==
0
;
}
};
}
// namespace operators
}
// namespace paddle
paddle/fluid/operators/pool_mkldnn_op.cc
浏览文件 @
06aa23b0
...
...
@@ -18,6 +18,26 @@ limitations under the License. */
namespace
paddle
{
namespace
operators
{
using
mkldnn
::
memory
;
// Note: paddle has also "memory" namespace
using
mkldnn
::
pooling_forward
;
using
mkldnn
::
pooling_backward
;
// Generate keys for storing/retriving primitives for this operator
// TODO(jczaja): Make hashing function more optimial
static
std
::
string
gethash
(
memory
::
dims
&
input_dims
,
std
::
string
&
pooling_type
,
std
::
vector
<
int
>&
ksize
,
std
::
vector
<
int
>&
strides
,
std
::
vector
<
int
>&
paddings
,
std
::
string
suffix
)
{
auto
dims2str
=
[](
memory
::
dims
&
operand_dims
)
{
std
::
string
dstr
=
""
;
for
(
size_t
i
=
0
;
i
<
operand_dims
.
size
();
++
i
)
{
dstr
+=
std
::
to_string
(
operand_dims
[
i
])
+
"-"
;
}
return
dstr
;
};
return
dims2str
(
input_dims
)
+
dims2str
(
ksize
)
+
dims2str
(
strides
)
+
dims2str
(
paddings
)
+
pooling_type
+
suffix
;
}
template
<
typename
T
>
class
PoolMKLDNNOpKernel
:
public
paddle
::
framework
::
OpKernel
<
T
>
{
public:
...
...
@@ -34,10 +54,6 @@ class PoolMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
// Get an unique name from "argument" name of "Out" variable
// This name will be used as key when saving info into device context
const
std
::
string
key
=
ctx
.
op
().
Output
(
"Out"
);
const
std
::
string
key_pool_pd
=
key
+
"@pool_pd"
;
const
std
::
string
key_pool_workspace_memory
=
key
+
"@pool_workspace_memory"
;
std
::
string
pooling_type
=
ctx
.
Attr
<
std
::
string
>
(
"pooling_type"
);
std
::
vector
<
int
>
ksize
=
ctx
.
Attr
<
std
::
vector
<
int
>>
(
"ksize"
);
...
...
@@ -63,37 +79,71 @@ class PoolMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
std
::
vector
<
int
>
src_tz
=
paddle
::
framework
::
vectorize2int
(
input
->
dims
());
std
::
vector
<
int
>
dst_tz
=
paddle
::
framework
::
vectorize2int
(
output
->
dims
());
// TODO(pzelazko-intel): support more formats
auto
src_md
=
platform
::
MKLDNNMemDesc
(
src_tz
,
mkldnn
::
memory
::
f32
,
mkldnn
::
memory
::
format
::
nchw
);
auto
dst_md
=
platform
::
MKLDNNMemDesc
(
dst_tz
,
mkldnn
::
memory
::
f32
,
mkldnn
::
memory
::
format
::
nchw
);
std
::
shared_ptr
<
mkldnn
::
pooling_forward
::
primitive_desc
>
pool_pd
=
CreatePrimitiveDesc
(
src_md
,
dst_md
,
strides
,
paddings
,
ksize
,
pooling_type
,
mkldnn_engine
);
// save pool_pd into global device context to be referred in backward path
dev_ctx
.
SetBlob
(
key_pool_pd
,
pool_pd
);
std
::
shared_ptr
<
mkldnn
::
memory
>
workspace_memory
=
CreateWorkspaceMemory
(
pool_pd
,
pooling_type
,
mkldnn_engine
);
// save pool_workspace_memory to be referred in backward path
dev_ctx
.
SetBlob
(
key_pool_workspace_memory
,
workspace_memory
);
auto
src_memory
=
mkldnn
::
memory
({
src_md
,
mkldnn_engine
},
static_cast
<
void
*>
(
const_cast
<
T
*>
(
input_data
)));
auto
dst_memory
=
mkldnn
::
memory
({
dst_md
,
mkldnn_engine
},
static_cast
<
void
*>
(
const_cast
<
T
*>
(
output_data
)));
const
std
::
string
key
=
gethash
(
src_tz
,
pooling_type
,
ksize
,
strides
,
paddings
,
ctx
.
op
().
Output
(
"Out"
));
const
std
::
string
key_pool_p
=
key
+
"@pool_p"
;
const
std
::
string
key_pool_pd
=
key
+
"@pool_pd"
;
const
std
::
string
key_pool_src_mem_p
=
key
+
"@pool_src_mem_p"
;
const
std
::
string
key_pool_dst_mem_p
=
key
+
"@pool_dst_mem_p"
;
const
std
::
string
key_pool_workspace_memory
=
key
+
"@pool_workspace_memory"
;
auto
pool_prim
=
mkldnn
::
pooling_forward
(
*
pool_pd
,
src_memory
,
dst_memory
,
*
workspace_memory
);
auto
pool_p
=
std
::
static_pointer_cast
<
pooling_forward
>
(
dev_ctx
.
GetBlob
(
key_pool_p
));
if
(
pool_p
==
nullptr
)
{
// TODO(pzelazko-intel): support more formats
auto
src_md
=
platform
::
MKLDNNMemDesc
(
src_tz
,
platform
::
MKLDNNGetDataType
<
T
>
(),
mkldnn
::
memory
::
format
::
nchw
);
auto
dst_md
=
platform
::
MKLDNNMemDesc
(
dst_tz
,
platform
::
MKLDNNGetDataType
<
T
>
(),
mkldnn
::
memory
::
format
::
nchw
);
std
::
shared_ptr
<
pooling_forward
::
primitive_desc
>
pool_pd
=
CreatePrimitiveDesc
(
src_md
,
dst_md
,
strides
,
paddings
,
ksize
,
pooling_type
,
mkldnn_engine
);
// save pool_pd into global device context to be referred in backward path
dev_ctx
.
SetBlob
(
key_pool_pd
,
pool_pd
);
std
::
shared_ptr
<
mkldnn
::
memory
>
workspace_memory
=
CreateWorkspaceMemory
(
pool_pd
,
pooling_type
,
mkldnn_engine
);
// save pool_workspace_memory to be referred in backward path
dev_ctx
.
SetBlob
(
key_pool_workspace_memory
,
workspace_memory
);
auto
pool_src_memory_p
=
std
::
make_shared
<
memory
>
(
memory
::
primitive_desc
{
src_md
,
mkldnn_engine
},
static_cast
<
void
*>
(
const_cast
<
T
*>
(
input_data
)));
dev_ctx
.
SetBlob
(
key_pool_src_mem_p
,
pool_src_memory_p
);
auto
pool_dst_memory_p
=
std
::
make_shared
<
memory
>
(
memory
::
primitive_desc
{
dst_md
,
mkldnn_engine
},
static_cast
<
void
*>
(
output_data
));
dev_ctx
.
SetBlob
(
key_pool_dst_mem_p
,
pool_dst_memory_p
);
pool_p
=
std
::
make_shared
<
pooling_forward
>
(
*
pool_pd
,
*
(
pool_src_memory_p
.
get
()),
*
(
pool_dst_memory_p
.
get
()),
*
workspace_memory
);
dev_ctx
.
SetBlob
(
key_pool_p
,
pool_p
);
}
else
{
// Primitives already exist
auto
pool_src_memory_p
=
std
::
static_pointer_cast
<
memory
>
(
dev_ctx
.
GetBlob
(
key_pool_src_mem_p
));
PADDLE_ENFORCE
(
pool_src_memory_p
!=
nullptr
,
"Fail to find pooling src mem_p in device context"
);
auto
pool_dst_memory_p
=
std
::
static_pointer_cast
<
memory
>
(
dev_ctx
.
GetBlob
(
key_pool_dst_mem_p
));
PADDLE_ENFORCE
(
pool_dst_memory_p
!=
nullptr
,
"Fail to find pooling dst mem_p in device context"
);
pool_src_memory_p
->
set_data_handle
(
reinterpret_cast
<
void
*>
(
const_cast
<
T
*>
(
input_data
)));
pool_dst_memory_p
->
set_data_handle
(
output_data
);
}
// push primitive to stream and wait until it's executed
std
::
vector
<
mkldnn
::
primitive
>
pipeline
{
pool_prim
};
std
::
vector
<
mkldnn
::
primitive
>
pipeline
{
*
(
pool_p
.
get
())
};
mkldnn
::
stream
(
mkldnn
::
stream
::
kind
::
eager
).
submit
(
pipeline
).
wait
();
}
...
...
@@ -120,9 +170,10 @@ class PoolMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
mkldnn
::
memory
::
primitive_desc
workspace_md
=
pooling_type
==
"max"
?
pool_pd
->
workspace_primitive_desc
()
:
mkldnn
::
memory
::
primitive_desc
(
{{},
mkldnn
::
memory
::
f32
,
mkldnn
::
memory
::
format
::
nchw
},
engine
);
:
mkldnn
::
memory
::
primitive_desc
({{},
platform
::
MKLDNNGetDataType
<
T
>
(),
mkldnn
::
memory
::
format
::
nchw
},
engine
);
auto
p_workspace_memory
=
new
mkldnn
::
memory
(
workspace_md
);
return
std
::
unique_ptr
<
mkldnn
::
memory
>
(
p_workspace_memory
);
...
...
@@ -140,13 +191,6 @@ class PoolMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
const
Tensor
*
out_grad
=
ctx
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Out"
));
Tensor
*
in_x_grad
=
ctx
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"X"
));
// Get an unique name from "argument" name of "Out" variable
// This name will be used as key when referring info from device context
const
std
::
string
key
=
ctx
.
op
().
Input
(
"Out"
);
const
std
::
string
key_pool_pd
=
key
+
"@pool_pd"
;
const
std
::
string
key_pool_workspace_memory
=
key
+
"@pool_workspace_memory"
;
std
::
string
pooling_type
=
ctx
.
Attr
<
std
::
string
>
(
"pooling_type"
);
std
::
vector
<
int
>
ksize
=
ctx
.
Attr
<
std
::
vector
<
int
>>
(
"ksize"
);
std
::
vector
<
int
>
strides
=
ctx
.
Attr
<
std
::
vector
<
int
>>
(
"strides"
);
...
...
@@ -171,43 +215,76 @@ class PoolMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
std
::
vector
<
int
>
diff_dst_tz
=
paddle
::
framework
::
vectorize2int
(
out_grad
->
dims
());
auto
diff_src_md
=
platform
::
MKLDNNMemDesc
(
diff_src_tz
,
mkldnn
::
memory
::
f32
,
mkldnn
::
memory
::
format
::
nchw
);
auto
diff_dst_md
=
platform
::
MKLDNNMemDesc
(
diff_dst_tz
,
mkldnn
::
memory
::
f32
,
mkldnn
::
memory
::
format
::
nchw
);
// Retrieve pool_pd/pool_workspace_memory from device context
auto
pool_pd
=
std
::
static_pointer_cast
<
mkldnn
::
pooling_forward
::
primitive_desc
>
(
dev_ctx
.
GetBlob
(
key_pool_pd
));
PADDLE_ENFORCE
(
pool_pd
!=
nullptr
,
"Fail to find pool_pd in device context"
);
auto
workspace_memory
=
std
::
static_pointer_cast
<
mkldnn
::
memory
>
(
dev_ctx
.
GetBlob
(
key_pool_workspace_memory
));
PADDLE_ENFORCE
(
workspace_memory
!=
nullptr
,
"Fail to find workspace_memory in device context"
);
auto
pool_bwd_desc
=
mkldnn
::
pooling_backward
::
desc
(
pooling_type
==
"max"
?
mkldnn
::
algorithm
::
pooling_max
:
mkldnn
::
algorithm
::
pooling_avg
,
diff_src_md
,
diff_dst_md
,
strides
,
ksize
,
paddings
,
paddings
,
mkldnn
::
padding_kind
::
zero
);
auto
pool_bwd_pd
=
mkldnn
::
pooling_backward
::
primitive_desc
(
pool_bwd_desc
,
mkldnn_engine
,
*
pool_pd
);
auto
diff_src_memory
=
mkldnn
::
memory
({
diff_src_md
,
mkldnn_engine
},
static_cast
<
void
*>
(
const_cast
<
T
*>
(
in_x_grad_data
)));
auto
diff_dst_memory
=
mkldnn
::
memory
({
diff_dst_md
,
mkldnn_engine
},
static_cast
<
void
*>
(
const_cast
<
T
*>
(
out_grad_data
)));
// Get an unique name from "argument" name of "Out" variable
// This name will be used as key when referring info from device context
const
std
::
string
key
=
gethash
(
diff_src_tz
,
pooling_type
,
ksize
,
strides
,
paddings
,
ctx
.
op
().
Input
(
"Out"
));
const
std
::
string
key_pool_bwd_p
=
key
+
"@pool_bwd_p"
;
const
std
::
string
key_pool_diff_src_mem_p
=
key
+
"@pool_diff_src_mem_p"
;
const
std
::
string
key_pool_diff_dst_mem_p
=
key
+
"@pool_diff_dst_mem_p"
;
const
std
::
string
key_pool_pd
=
key
+
"@pool_pd"
;
const
std
::
string
key_pool_workspace_memory
=
key
+
"@pool_workspace_memory"
;
auto
bwd_prim
=
mkldnn
::
pooling_backward
(
pool_bwd_pd
,
diff_dst_memory
,
*
workspace_memory
,
diff_src_memory
);
auto
pool_bwd_p
=
std
::
static_pointer_cast
<
pooling_backward
>
(
dev_ctx
.
GetBlob
(
key_pool_bwd_p
));
if
(
pool_bwd_p
==
nullptr
)
{
auto
diff_src_md
=
platform
::
MKLDNNMemDesc
(
diff_src_tz
,
platform
::
MKLDNNGetDataType
<
T
>
(),
mkldnn
::
memory
::
format
::
nchw
);
auto
diff_dst_md
=
platform
::
MKLDNNMemDesc
(
diff_dst_tz
,
platform
::
MKLDNNGetDataType
<
T
>
(),
mkldnn
::
memory
::
format
::
nchw
);
// Retrieve pool_pd/pool_workspace_memory from device context
auto
pool_pd
=
std
::
static_pointer_cast
<
mkldnn
::
pooling_forward
::
primitive_desc
>
(
dev_ctx
.
GetBlob
(
key_pool_pd
));
PADDLE_ENFORCE
(
pool_pd
!=
nullptr
,
"Fail to find pool_pd in device context"
);
auto
workspace_memory
=
std
::
static_pointer_cast
<
mkldnn
::
memory
>
(
dev_ctx
.
GetBlob
(
key_pool_workspace_memory
));
PADDLE_ENFORCE
(
workspace_memory
!=
nullptr
,
"Fail to find workspace_memory in device context"
);
auto
pool_diff_src_memory_p
=
std
::
make_shared
<
memory
>
(
memory
(
{
diff_src_md
,
mkldnn_engine
},
static_cast
<
void
*>
(
in_x_grad_data
)));
dev_ctx
.
SetBlob
(
key_pool_diff_src_mem_p
,
pool_diff_src_memory_p
);
auto
pool_diff_dst_memory_p
=
std
::
make_shared
<
memory
>
(
memory
({
diff_dst_md
,
mkldnn_engine
},
static_cast
<
void
*>
(
const_cast
<
T
*>
(
out_grad_data
))));
dev_ctx
.
SetBlob
(
key_pool_diff_dst_mem_p
,
pool_diff_dst_memory_p
);
auto
pool_bwd_desc
=
mkldnn
::
pooling_backward
::
desc
(
pooling_type
==
"max"
?
mkldnn
::
algorithm
::
pooling_max
:
mkldnn
::
algorithm
::
pooling_avg
,
diff_src_md
,
diff_dst_md
,
strides
,
ksize
,
paddings
,
paddings
,
mkldnn
::
padding_kind
::
zero
);
auto
pool_bwd_pd
=
mkldnn
::
pooling_backward
::
primitive_desc
(
pool_bwd_desc
,
mkldnn_engine
,
*
pool_pd
);
pool_bwd_p
=
std
::
make_shared
<
pooling_backward
>
(
pool_bwd_pd
,
*
(
pool_diff_dst_memory_p
.
get
()),
*
workspace_memory
,
*
(
pool_diff_src_memory_p
));
dev_ctx
.
SetBlob
(
key_pool_bwd_p
,
pool_bwd_p
);
}
else
{
// Primitives already exist
auto
pool_diff_src_memory_p
=
std
::
static_pointer_cast
<
memory
>
(
dev_ctx
.
GetBlob
(
key_pool_diff_src_mem_p
));
PADDLE_ENFORCE
(
pool_diff_src_memory_p
!=
nullptr
,
"Fail to find pooling src mem_p in device context"
);
auto
pool_diff_dst_memory_p
=
std
::
static_pointer_cast
<
memory
>
(
dev_ctx
.
GetBlob
(
key_pool_diff_dst_mem_p
));
PADDLE_ENFORCE
(
pool_diff_dst_memory_p
!=
nullptr
,
"Fail to find pooling dst mem_p in device context"
);
pool_diff_src_memory_p
->
set_data_handle
(
reinterpret_cast
<
void
*>
(
in_x_grad_data
));
pool_diff_dst_memory_p
->
set_data_handle
(
const_cast
<
T
*>
(
out_grad_data
));
}
// push primitive to stream and wait until it's executed
std
::
vector
<
mkldnn
::
primitive
>
pipeline
{
bwd_prim
};
std
::
vector
<
mkldnn
::
primitive
>
pipeline
{
*
(
pool_bwd_p
.
get
())
};
mkldnn
::
stream
(
mkldnn
::
stream
::
kind
::
eager
).
submit
(
pipeline
).
wait
();
}
// Compute()
};
...
...
paddle/fluid/platform/CMakeLists.txt
浏览文件 @
06aa23b0
...
...
@@ -49,7 +49,7 @@ nv_test(device_context_test SRCS device_context_test.cu DEPS device_context gpu_
nv_test
(
cudnn_helper_test SRCS cudnn_helper_test.cc DEPS dynload_cuda
)
nv_test
(
transform_test SRCS transform_test.cu DEPS memory place device_context
)
cc_library
(
device_tracer SRCS device_tracer.cc DEPS boost profiler_proto
${
GPU_CTX_DEPS
}
)
cc_library
(
device_tracer SRCS device_tracer.cc DEPS boost profiler_proto
framework_proto
${
GPU_CTX_DEPS
}
)
cc_library
(
profiler SRCS profiler.cc DEPS device_context device_tracer
)
cc_test
(
profiler_test SRCS profiler_test.cc DEPS profiler
)
...
...
paddle/fluid/platform/mkldnn_helper.h
浏览文件 @
06aa23b0
...
...
@@ -71,5 +71,15 @@ inline bool CanMKLDNNBeUsed(const framework::ExecutionContext& ctx) {
return
use_mkldnn
&&
platform
::
is_cpu_place
(
ctx
.
GetPlace
());
}
template
<
typename
Type
>
mkldnn
::
memory
::
data_type
MKLDNNGetDataType
()
{
return
mkldnn
::
memory
::
data_undef
;
}
template
<
>
inline
mkldnn
::
memory
::
data_type
MKLDNNGetDataType
<
float
>
()
{
return
mkldnn
::
memory
::
f32
;
}
}
// namespace platform
}
// namespace paddle
paddle/fluid/platform/profiler.cc
浏览文件 @
06aa23b0
...
...
@@ -173,8 +173,9 @@ void PopEvent(const std::string& name, const DeviceContext* dev_ctx) {
}
RecordEvent
::
RecordEvent
(
const
std
::
string
&
name
,
const
DeviceContext
*
dev_ctx
)
:
start_ns_
(
PosixInNsec
())
{
:
is_enabled_
(
false
),
start_ns_
(
PosixInNsec
())
{
if
(
g_state
==
ProfilerState
::
kDisabled
)
return
;
is_enabled_
=
true
;
dev_ctx_
=
dev_ctx
;
name_
=
name
;
PushEvent
(
name_
,
dev_ctx_
);
...
...
@@ -183,7 +184,7 @@ RecordEvent::RecordEvent(const std::string& name, const DeviceContext* dev_ctx)
}
RecordEvent
::~
RecordEvent
()
{
if
(
g_state
==
ProfilerState
::
kDisabled
)
return
;
if
(
g_state
==
ProfilerState
::
kDisabled
||
!
is_enabled_
)
return
;
DeviceTracer
*
tracer
=
GetDeviceTracer
();
if
(
tracer
)
{
tracer
->
AddCPURecords
(
CurAnnotation
(),
start_ns_
,
PosixInNsec
(),
...
...
@@ -193,14 +194,16 @@ RecordEvent::~RecordEvent() {
PopEvent
(
name_
,
dev_ctx_
);
}
RecordBlock
::
RecordBlock
(
int
block_id
)
:
start_ns_
(
PosixInNsec
())
{
RecordBlock
::
RecordBlock
(
int
block_id
)
:
is_enabled_
(
false
),
start_ns_
(
PosixInNsec
())
{
if
(
g_state
==
ProfilerState
::
kDisabled
)
return
;
is_enabled_
=
true
;
SetCurBlock
(
block_id
);
name_
=
string
::
Sprintf
(
"block_%d"
,
block_id
);
}
RecordBlock
::~
RecordBlock
()
{
if
(
g_state
==
ProfilerState
::
kDisabled
)
return
;
if
(
g_state
==
ProfilerState
::
kDisabled
||
!
is_enabled_
)
return
;
DeviceTracer
*
tracer
=
GetDeviceTracer
();
if
(
tracer
)
{
// We try to put all blocks at the same nested depth in the
...
...
paddle/fluid/platform/profiler.h
浏览文件 @
06aa23b0
...
...
@@ -74,6 +74,7 @@ struct RecordEvent {
~
RecordEvent
();
bool
is_enabled_
;
uint64_t
start_ns_
;
// The device context is used by Event to get the current cuda stream.
const
DeviceContext
*
dev_ctx_
;
...
...
@@ -89,6 +90,7 @@ struct RecordBlock {
~
RecordBlock
();
private:
bool
is_enabled_
;
std
::
string
name_
;
uint64_t
start_ns_
;
};
...
...
paddle/fluid/pybind/protobuf.cc
浏览文件 @
06aa23b0
...
...
@@ -238,6 +238,7 @@ void BindVarDsec(pybind11::module *m) {
pybind11
::
enum_
<
pd
::
proto
::
VarType
::
Type
>
(
var_desc
,
"VarType"
,
""
)
.
value
(
"BOOL"
,
pd
::
proto
::
VarType
::
BOOL
)
.
value
(
"UINT8"
,
pd
::
proto
::
VarType
::
UINT8
)
.
value
(
"INT16"
,
pd
::
proto
::
VarType
::
INT16
)
.
value
(
"INT32"
,
pd
::
proto
::
VarType
::
INT32
)
.
value
(
"INT64"
,
pd
::
proto
::
VarType
::
INT64
)
...
...
paddle/scripts/docker/build.sh
浏览文件 @
06aa23b0
...
...
@@ -198,7 +198,7 @@ EOF
# run paddle version to install python packages first
RUN apt-get update &&
\
${
NCCL_DEPS
}
\
apt-get install -y wget python-pip dmidecode python-tk &&
pip install -U pip==9.0.3
&&
\
apt-get install -y wget python-pip dmidecode python-tk &&
easy_install -U pip
&&
\
pip install /*.whl; apt-get install -f -y &&
\
apt-get clean -y &&
\
rm -f /*.whl &&
\
...
...
paddle/scripts/paddle_build.sh
浏览文件 @
06aa23b0
...
...
@@ -405,17 +405,19 @@ EOF
function
gen_dockerfile
()
{
# Set BASE_IMAGE according to env variables
CUDA_MAJOR
=
"
$(
echo
$CUDA_VERSION
|
cut
-d
'.'
-f
1
)
.
$(
echo
$CUDA_VERSION
|
cut
-d
'.'
-f
2
)
"
CUDNN_MAJOR
=
$(
echo
$CUDNN_VERSION
|
cut
-d
'.'
-f
1
)
if
[[
${
WITH_GPU
}
==
"ON"
]]
;
then
BASE_IMAGE
=
"nvidia/cuda:8.0-cudnn5
-runtime-ubuntu16.04"
BASE_IMAGE
=
"nvidia/cuda:
${
CUDA_MAJOR
}
-cudnn
${
CUDNN_MAJOR
}
-runtime-ubuntu16.04"
else
BASE_IMAGE
=
"ubuntu:16.04"
BASE_IMAGE
=
"ubuntu:16.04"
fi
DOCKERFILE_GPU_ENV
=
""
DOCKERFILE_CUDNN_DSO
=
""
if
[[
${
WITH_GPU
:-
OFF
}
==
'ON'
]]
;
then
DOCKERFILE_GPU_ENV
=
"ENV LD_LIBRARY_PATH /usr/lib/x86_64-linux-gnu:
\$
{LD_LIBRARY_PATH}"
DOCKERFILE_CUDNN_DSO
=
"RUN ln -s /usr/lib/x86_64-linux-gnu/libcudnn.so.
5
/usr/lib/x86_64-linux-gnu/libcudnn.so"
DOCKERFILE_CUDNN_DSO
=
"RUN ln -s /usr/lib/x86_64-linux-gnu/libcudnn.so.
${
CUDNN_MAJOR
}
/usr/lib/x86_64-linux-gnu/libcudnn.so"
fi
cat
<<
EOF
...
...
@@ -449,7 +451,7 @@ EOF
# run paddle version to install python packages first
RUN apt-get update &&
\
${
NCCL_DEPS
}
\
apt-get install -y wget python-pip dmidecode python-tk &&
pip install -U pip==9.0.3
&&
\
apt-get install -y wget python-pip dmidecode python-tk &&
easy_install -U pip
&&
\
pip install /*.whl; apt-get install -f -y &&
\
apt-get clean -y &&
\
rm -f /*.whl &&
\
...
...
@@ -490,7 +492,7 @@ function gen_fluid_inference_lib() {
Deploying fluid inference library ...
========================================
EOF
make inference_lib_dist
make
-j
`
nproc
`
inference_lib_dist
fi
}
...
...
python/paddle/fluid/framework.py
浏览文件 @
06aa23b0
...
...
@@ -72,6 +72,8 @@ def convert_np_dtype_to_dtype_(np_dtype):
return
core
.
VarDesc
.
VarType
.
INT64
elif
dtype
==
np
.
bool
:
return
core
.
VarDesc
.
VarType
.
BOOL
elif
dtype
==
np
.
uint8
:
return
core
.
VarDesc
.
VarType
.
UINT8
else
:
raise
ValueError
(
"Not supported numpy dtype "
+
str
(
dtype
))
...
...
python/paddle/fluid/layers/control_flow.py
浏览文件 @
06aa23b0
...
...
@@ -49,6 +49,7 @@ __all__ = [
'reorder_lod_tensor_by_rank'
,
'ParallelDo'
,
'Print'
,
'is_empty'
,
]
...
...
@@ -1562,3 +1563,40 @@ def reorder_lod_tensor_by_rank(x, rank_table):
'RankTable'
:
[
rank_table
]},
outputs
=
{
'Out'
:
[
out
]})
return
out
def
is_empty
(
x
,
cond
=
None
,
**
ignored
):
"""
**Is Empty**
This layer returns the truth value of whether the variable is empty.
Args:
x(Variable): Operand of *is_empty*
cond(Variable|None): Optional output variable to store the result
of *is_empty*
Returns:
Variable: The tensor variable storing the output of *is_empty*.
Raises:
TypeError: If input cond is not a variable, or cond's dtype is
not bool
Examples:
.. code-block:: python
less = fluid.layers.is_empty(x=input)
"""
helper
=
LayerHelper
(
"is_empty"
,
**
locals
())
if
cond
is
None
:
cond
=
helper
.
create_tmp_variable
(
dtype
=
'bool'
)
cond
.
stop_gradient
=
True
elif
not
isinstance
(
cond
,
Variable
):
raise
TypeError
(
"cond takes a variable"
)
elif
cond
.
dtype
!=
'bool'
:
raise
TypeError
(
"The data type of cond must be bool"
)
helper
.
append_op
(
type
=
'is_empty'
,
inputs
=
{
'X'
:
[
x
]},
outputs
=
{
'Out'
:
[
cond
]})
return
cond
python/paddle/fluid/layers/detection.py
浏览文件 @
06aa23b0
...
...
@@ -23,6 +23,7 @@ import nn
import
math
__all__
=
[
'prior_box'
,
'multi_box_head'
,
'bipartite_match'
,
'target_assign'
,
...
...
@@ -564,6 +565,98 @@ def ssd_loss(location,
return
loss
def
prior_box
(
input
,
image
,
min_sizes
,
max_sizes
=
None
,
aspect_ratios
=
None
,
variance
=
[
0.1
,
0.1
,
0.2
,
0.2
],
flip
=
False
,
clip
=
False
,
steps
=
[
0.0
,
0.0
],
offset
=
0.5
,
name
=
None
):
"""
**Prior box operator**
Generate prior boxes for SSD(Single Shot MultiBox Detector) algorithm.
Each position of the input produce N prior boxes, N is determined by
the count of min_sizes, max_sizes and aspect_ratios, The size of the
box is in range(min_size, max_size) interval, which is generated in
sequence according to the aspect_ratios.
Args:
input(Variable): The Input Variables, the format is NCHW.
image(Variable): The input image data of PriorBoxOp,
the layout is NCHW.
min_sizes(list|tuple): min sizes of generated prior boxes.
max_sizes(list|tuple|None): max sizes of generated prior boxes.
Default: None.
aspect_ratios(list|tuple): the aspect ratios of generated prior
boxes. Default: None.
variance(list|tuple): the variances to be encoded in prior boxes.
Default:[0.1, 0.1, 0.2, 0.2].
flip(bool): Whether to flip aspect ratios. Default:False.
clip(bool): Whether to clip out-of-boundary boxes. Default: False.
step(list|turple): Prior boxes step across weight and height, If
step[0] == 0.0/step[1] == 0.0, the prior boxes step across
height/weight of the input will be automatically calculated.
Default: [0.0]
offset(float): Prior boxes center offset. Default: 0.5
name(str): Name of the prior box op. Default: None.
Returns:
boxes(Variable): the output prior boxes of PriorBox.
The layout is [H, W, num_priors, 4].
H is the height of input, W is the width of input,
num_priors is the total
box count of each position of input.
Variances(Variable): the expanded variances of PriorBox.
The layout is [H, W, num_priors, 4].
H is the height of input, W is the width of input
num_priors is the total
box count of each position of input
Examples:
.. code-block:: python
box, var = prior_box(
input=conv1,
image=images,
min_sizes=[100.],
flip=True,
clip=True)
"""
helper
=
LayerHelper
(
"prior_box"
,
**
locals
())
dtype
=
helper
.
input_dtype
()
attrs
=
{
'min_sizes'
:
min_sizes
,
'aspect_ratios'
:
aspect_ratios
,
'variances'
:
variance
,
'flip'
:
flip
,
'clip'
:
clip
,
'step_w'
:
steps
[
0
],
'step_h'
:
steps
[
1
],
'offset'
:
offset
}
if
max_sizes
is
not
None
and
len
(
max_sizes
)
>
0
and
max_sizes
[
0
]
>
0
:
attrs
[
'max_sizes'
]
=
max_sizes
box
=
helper
.
create_tmp_variable
(
dtype
)
var
=
helper
.
create_tmp_variable
(
dtype
)
helper
.
append_op
(
type
=
"prior_box"
,
inputs
=
{
"Input"
:
input
,
"Image"
:
image
},
outputs
=
{
"Boxes"
:
box
,
"Variances"
:
var
},
attrs
=
attrs
,
)
box
.
stop_gradient
=
True
var
.
stop_gradient
=
True
return
box
,
var
def
multi_box_head
(
inputs
,
image
,
base_size
,
...
...
@@ -660,47 +753,6 @@ def multi_box_head(inputs,
clip=True)
"""
def
_prior_box_
(
input
,
image
,
min_sizes
,
max_sizes
,
aspect_ratios
,
variance
,
flip
=
False
,
clip
=
False
,
step_w
=
0.0
,
step_h
=
0.0
,
offset
=
0.5
,
name
=
None
):
helper
=
LayerHelper
(
"prior_box"
,
**
locals
())
dtype
=
helper
.
input_dtype
()
attrs
=
{
'min_sizes'
:
min_sizes
,
'aspect_ratios'
:
aspect_ratios
,
'variances'
:
variance
,
'flip'
:
flip
,
'clip'
:
clip
,
'step_w'
:
step_w
,
'step_h'
:
step_h
,
'offset'
:
offset
}
if
len
(
max_sizes
)
>
0
and
max_sizes
[
0
]
>
0
:
attrs
[
'max_sizes'
]
=
max_sizes
box
=
helper
.
create_tmp_variable
(
dtype
)
var
=
helper
.
create_tmp_variable
(
dtype
)
helper
.
append_op
(
type
=
"prior_box"
,
inputs
=
{
"Input"
:
input
,
"Image"
:
image
},
outputs
=
{
"Boxes"
:
box
,
"Variances"
:
var
},
attrs
=
attrs
,
)
box
.
stop_gradient
=
True
var
.
stop_gradient
=
True
return
box
,
var
def
_reshape_with_axis_
(
input
,
axis
=
1
):
if
not
(
axis
>
0
and
axis
<
len
(
input
.
shape
)):
raise
ValueError
(
"The axis should be smaller than "
...
...
@@ -777,11 +829,10 @@ def multi_box_head(inputs,
aspect_ratio
=
aspect_ratios
[
i
]
if
not
_is_list_or_tuple_
(
aspect_ratio
):
aspect_ratio
=
[
aspect_ratio
]
step
=
[
step_w
[
i
]
if
step_w
else
0.0
,
step_h
[
i
]
if
step_w
else
0.0
]
box
,
var
=
_prior_box_
(
input
,
image
,
min_size
,
max_size
,
aspect_ratio
,
variance
,
flip
,
clip
,
step_w
[
i
]
if
step_w
else
0.0
,
step_h
[
i
]
if
step_w
else
0.0
,
offset
)
box
,
var
=
prior_box
(
input
,
image
,
min_size
,
max_size
,
aspect_ratio
,
variance
,
flip
,
clip
,
step
,
offset
)
box_results
.
append
(
box
)
var_results
.
append
(
var
)
...
...
python/paddle/fluid/tests/book/high-level-api/CMakeLists.txt
浏览文件 @
06aa23b0
...
...
@@ -8,3 +8,4 @@ endforeach()
add_subdirectory
(
fit_a_line
)
add_subdirectory
(
recognize_digits
)
add_subdirectory
(
image_classification
)
python/paddle/fluid/tests/book/high-level-api/image_classification/CMakeLists.txt
0 → 100644
浏览文件 @
06aa23b0
file
(
GLOB TEST_OPS RELATIVE
"
${
CMAKE_CURRENT_SOURCE_DIR
}
"
"test_*.py"
)
string
(
REPLACE
".py"
""
TEST_OPS
"
${
TEST_OPS
}
"
)
# default test
foreach
(
src
${
TEST_OPS
}
)
py_test
(
${
src
}
SRCS
${
src
}
.py
)
endforeach
()
python/paddle/fluid/tests/book/high-level-api/image_classification/cifar10_small_test_set.py
0 → 100644
浏览文件 @
06aa23b0
# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
CIFAR dataset.
This module will download dataset from
https://www.cs.toronto.edu/~kriz/cifar.html and parse train/test set into
paddle reader creators.
The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes,
with 6000 images per class. There are 50000 training images and 10000 test
images.
The CIFAR-100 dataset is just like the CIFAR-10, except it has 100 classes
containing 600 images each. There are 500 training images and 100 testing
images per class.
"""
import
cPickle
import
itertools
import
numpy
import
paddle.v2.dataset.common
import
tarfile
__all__
=
[
'train10'
]
URL_PREFIX
=
'https://www.cs.toronto.edu/~kriz/'
CIFAR10_URL
=
URL_PREFIX
+
'cifar-10-python.tar.gz'
CIFAR10_MD5
=
'c58f30108f718f92721af3b95e74349a'
def
reader_creator
(
filename
,
sub_name
,
batch_size
=
None
):
def
read_batch
(
batch
):
data
=
batch
[
'data'
]
labels
=
batch
.
get
(
'labels'
,
batch
.
get
(
'fine_labels'
,
None
))
assert
labels
is
not
None
for
sample
,
label
in
itertools
.
izip
(
data
,
labels
):
yield
(
sample
/
255.0
).
astype
(
numpy
.
float32
),
int
(
label
)
def
reader
():
with
tarfile
.
open
(
filename
,
mode
=
'r'
)
as
f
:
names
=
(
each_item
.
name
for
each_item
in
f
if
sub_name
in
each_item
.
name
)
batch_count
=
0
for
name
in
names
:
batch
=
cPickle
.
load
(
f
.
extractfile
(
name
))
for
item
in
read_batch
(
batch
):
if
isinstance
(
batch_size
,
int
)
and
batch_count
>
batch_size
:
break
batch_count
+=
1
yield
item
return
reader
def
train10
(
batch_size
=
None
):
"""
CIFAR-10 training set creator.
It returns a reader creator, each sample in the reader is image pixels in
[0, 1] and label in [0, 9].
:return: Training reader creator
:rtype: callable
"""
return
reader_creator
(
paddle
.
v2
.
dataset
.
common
.
download
(
CIFAR10_URL
,
'cifar'
,
CIFAR10_MD5
),
'data_batch'
,
batch_size
=
batch_size
)
python/paddle/fluid/tests/book/high-level-api/image_classification/
no
test_image_classification_resnet.py
→
python/paddle/fluid/tests/book/high-level-api/image_classification/test_image_classification_resnet.py
浏览文件 @
06aa23b0
...
...
@@ -17,6 +17,7 @@ from __future__ import print_function
import
paddle
import
paddle.fluid
as
fluid
import
numpy
import
cifar10_small_test_set
def
resnet_cifar10
(
input
,
depth
=
32
):
...
...
@@ -81,46 +82,50 @@ def train_network():
cost
=
fluid
.
layers
.
cross_entropy
(
input
=
predict
,
label
=
label
)
avg_cost
=
fluid
.
layers
.
mean
(
cost
)
accuracy
=
fluid
.
layers
.
accuracy
(
input
=
predict
,
label
=
label
)
return
avg_cost
,
accuracy
return
[
avg_cost
,
accuracy
]
def
train
(
use_cuda
,
save_path
):
def
train
(
use_cuda
,
train_program
,
save_dirname
):
BATCH_SIZE
=
128
EPOCH_NUM
=
1
train_reader
=
paddle
.
batch
(
paddle
.
reader
.
shuffle
(
paddle
.
dataset
.
cifar
.
train10
(
),
buf_size
=
128
*
10
),
cifar10_small_test_set
.
train10
(
batch_size
=
10
),
buf_size
=
128
*
10
),
batch_size
=
BATCH_SIZE
)
test_reader
=
paddle
.
batch
(
paddle
.
dataset
.
cifar
.
test10
(),
batch_size
=
BATCH_SIZE
)
def
event_handler
(
event
):
if
isinstance
(
event
,
fluid
.
End
Iteration
):
if
(
event
.
batch_id
%
10
)
==
0
:
avg_cost
,
accuracy
=
trainer
.
test
(
reader
=
test_reader
)
if
isinstance
(
event
,
fluid
.
End
StepEvent
):
avg_cost
,
accuracy
=
trainer
.
test
(
reader
=
test_reader
,
feed_order
=
[
'pixel'
,
'label'
]
)
print
(
'BatchID {1:04}, Loss {2:2.2}, Acc {3:2.2}'
.
format
(
event
.
batch_id
+
1
,
avg_cost
,
accuracy
))
print
(
'Loss {0:2.2}, Acc {1:2.2}'
.
format
(
avg_cost
,
accuracy
))
if
accuracy
>
0.01
:
# Low threshold for speeding up CI
trainer
.
params
.
save
(
save_path
)
return
if
accuracy
>
0.01
:
# Low threshold for speeding up CI
if
save_dirname
is
not
None
:
trainer
.
save_params
(
save_dirname
)
return
place
=
fluid
.
CUDAPlace
(
0
)
if
use_cuda
else
fluid
.
CPUPlace
()
trainer
=
fluid
.
Trainer
(
train_
network
,
train_
func
=
train_program
,
optimizer
=
fluid
.
optimizer
.
Adam
(
learning_rate
=
0.001
),
place
=
place
,
event_handler
=
event_handler
)
trainer
.
train
(
train_reader
,
EPOCH_NUM
,
event_handler
=
event_handler
)
place
=
place
)
trainer
.
train
(
reader
=
train_reader
,
num_epochs
=
EPOCH_NUM
,
event_handler
=
event_handler
,
feed_order
=
[
'pixel'
,
'label'
])
def
infer
(
use_cuda
,
save_path
):
params
=
fluid
.
Params
(
save_path
)
def
infer
(
use_cuda
,
inference_program
,
save_dirname
=
None
):
place
=
fluid
.
CUDAPlace
(
0
)
if
use_cuda
else
fluid
.
CPUPlace
()
inferencer
=
fluid
.
Inferencer
(
inference_network
,
params
,
place
=
place
)
inferencer
=
fluid
.
Inferencer
(
infer_func
=
inference_program
,
param_path
=
save_dirname
,
place
=
place
)
# The input's dimension of conv should be 4-D or 5-D.
# Use normilized image pixels as input data, which should be in the range
...
...
@@ -135,8 +140,14 @@ def main(use_cuda):
if
use_cuda
and
not
fluid
.
core
.
is_compiled_with_cuda
():
return
save_path
=
"image_classification_resnet.inference.model"
train
(
use_cuda
,
save_path
)
infer
(
use_cuda
,
save_path
)
train
(
use_cuda
=
use_cuda
,
train_program
=
train_network
,
save_dirname
=
save_path
)
infer
(
use_cuda
=
use_cuda
,
inference_program
=
inference_network
,
save_dirname
=
save_path
)
if
__name__
==
'__main__'
:
...
...
python/paddle/fluid/tests/book/high-level-api/image_classification/
no
test_image_classification_vgg.py
→
python/paddle/fluid/tests/book/high-level-api/image_classification/test_image_classification_vgg.py
浏览文件 @
06aa23b0
...
...
@@ -17,6 +17,7 @@ from __future__ import print_function
import
paddle
import
paddle.fluid
as
fluid
import
numpy
import
cifar10_small_test_set
def
vgg16_bn_drop
(
input
):
...
...
@@ -60,46 +61,48 @@ def train_network():
cost
=
fluid
.
layers
.
cross_entropy
(
input
=
predict
,
label
=
label
)
avg_cost
=
fluid
.
layers
.
mean
(
cost
)
accuracy
=
fluid
.
layers
.
accuracy
(
input
=
predict
,
label
=
label
)
return
avg_cost
,
accuracy
return
[
avg_cost
,
accuracy
]
def
train
(
use_cuda
,
save_path
):
def
train
(
use_cuda
,
train_program
,
save_dirname
):
BATCH_SIZE
=
128
EPOCH_NUM
=
1
train_reader
=
paddle
.
batch
(
paddle
.
reader
.
shuffle
(
paddle
.
dataset
.
cifar
.
train10
(
),
buf_size
=
128
*
10
),
cifar10_small_test_set
.
train10
(
batch_size
=
10
),
buf_size
=
128
*
10
),
batch_size
=
BATCH_SIZE
)
test_reader
=
paddle
.
batch
(
paddle
.
dataset
.
cifar
.
test10
(),
batch_size
=
BATCH_SIZE
)
def
event_handler
(
event
):
if
isinstance
(
event
,
fluid
.
End
Iteration
):
if
(
event
.
batch_id
%
10
)
==
0
:
avg_cost
,
accuracy
=
trainer
.
test
(
reader
=
test_reader
)
if
isinstance
(
event
,
fluid
.
End
StepEvent
):
avg_cost
,
accuracy
=
trainer
.
test
(
reader
=
test_reader
,
feed_order
=
[
'pixel'
,
'label'
]
)
print
(
'BatchID {1:04}, Loss {2:2.2}, Acc {3:2.2}'
.
format
(
event
.
batch_id
+
1
,
avg_cost
,
accuracy
))
print
(
'Loss {0:2.2}, Acc {1:2.2}'
.
format
(
avg_cost
,
accuracy
))
if
accuracy
>
0.01
:
# Low threshold for speeding up CI
trainer
.
params
.
save
(
save_path
)
return
if
accuracy
>
0.01
:
# Low threshold for speeding up CI
if
save_dirname
is
not
None
:
trainer
.
save_params
(
save_dirname
)
return
place
=
fluid
.
CUDAPlace
(
0
)
if
use_cuda
else
fluid
.
CPUPlace
()
trainer
=
fluid
.
Trainer
(
train_network
,
optimizer
=
fluid
.
optimizer
.
Adam
(
learning_rate
=
0.001
),
train_func
=
train_program
,
place
=
place
,
event_handler
=
event_handler
)
trainer
.
train
(
train_reader
,
EPOCH_NUM
,
event_handler
=
event_handler
)
optimizer
=
fluid
.
optimizer
.
Adam
(
learning_rate
=
0.001
))
trainer
.
train
(
reader
=
train_reader
,
num_epochs
=
1
,
event_handler
=
event_handler
,
feed_order
=
[
'pixel'
,
'label'
])
def
infer
(
use_cuda
,
save_path
):
params
=
fluid
.
Params
(
save_path
)
def
infer
(
use_cuda
,
inference_program
,
save_dirname
=
None
):
place
=
fluid
.
CUDAPlace
(
0
)
if
use_cuda
else
fluid
.
CPUPlace
()
inferencer
=
fluid
.
Inferencer
(
inference_network
,
params
,
place
=
place
)
inferencer
=
fluid
.
Inferencer
(
infer_func
=
inference_program
,
param_path
=
save_dirname
,
place
=
place
)
# The input's dimension of conv should be 4-D or 5-D.
# Use normilized image pixels as input data, which should be in the range
...
...
@@ -114,8 +117,14 @@ def main(use_cuda):
if
use_cuda
and
not
fluid
.
core
.
is_compiled_with_cuda
():
return
save_path
=
"image_classification_vgg.inference.model"
train
(
use_cuda
,
save_path
)
infer
(
use_cuda
,
save_path
)
train
(
use_cuda
=
use_cuda
,
train_program
=
train_network
,
save_dirname
=
save_path
)
infer
(
use_cuda
=
use_cuda
,
inference_program
=
inference_network
,
save_dirname
=
save_path
)
if
__name__
==
'__main__'
:
...
...
python/paddle/fluid/tests/book/high-level-api/word2vec/
no_
test_word2vec_new_api.py
→
python/paddle/fluid/tests/book/high-level-api/word2vec/test_word2vec_new_api.py
浏览文件 @
06aa23b0
...
...
@@ -90,7 +90,7 @@ def train_program(is_sparse):
return
avg_cost
def
train
(
use_cuda
,
train_program
,
save_
path
):
def
train
(
use_cuda
,
train_program
,
save_
dirname
):
train_reader
=
paddle
.
batch
(
paddle
.
dataset
.
imikolov
.
train
(
word_dict
,
N
),
BATCH_SIZE
)
test_reader
=
paddle
.
batch
(
...
...
@@ -99,27 +99,36 @@ def train(use_cuda, train_program, save_path):
place
=
fluid
.
CUDAPlace
(
0
)
if
use_cuda
else
fluid
.
CPUPlace
()
def
event_handler
(
event
):
if
isinstance
(
event
,
fluid
.
EndEpochEvent
):
outs
=
trainer
.
test
(
reader
=
test_reader
)
if
isinstance
(
event
,
fluid
.
EndStepEvent
):
outs
=
trainer
.
test
(
reader
=
test_reader
,
feed_order
=
[
'firstw'
,
'secondw'
,
'thirdw'
,
'forthw'
,
'nextw'
])
avg_cost
=
outs
[
0
]
print
(
"loss= "
,
avg_cost
)
if
avg_cost
<
5.0
:
trainer
.
save_params
(
save_path
)
return
if
avg_cost
<
10.0
:
trainer
.
save_params
(
save_dirname
)
trainer
.
stop
()
if
math
.
isnan
(
avg_cost
):
sys
.
exit
(
"got NaN loss, training failed."
)
trainer
=
fluid
.
Trainer
(
train_program
,
fluid
.
optimizer
.
SGD
(
learning_rate
=
0.001
),
place
=
place
)
train_func
=
train_program
,
optimizer
=
fluid
.
optimizer
.
SGD
(
learning_rate
=
0.001
),
place
=
place
)
trainer
.
train
(
reader
=
train_reader
,
num_epochs
=
1
,
event_handler
=
event_handler
)
reader
=
train_reader
,
num_epochs
=
1
,
event_handler
=
event_handler
,
feed_order
=
[
'firstw'
,
'secondw'
,
'thirdw'
,
'forthw'
,
'nextw'
])
def
infer
(
use_cuda
,
inference_program
,
save_
path
):
def
infer
(
use_cuda
,
inference_program
,
save_
dirname
=
None
):
place
=
fluid
.
CUDAPlace
(
0
)
if
use_cuda
else
fluid
.
CPUPlace
()
inferencer
=
fluid
.
Inferencer
(
infer_func
=
inference_program
,
param_path
=
save_
path
,
place
=
place
)
infer_func
=
inference_program
,
param_path
=
save_
dirname
,
place
=
place
)
lod
=
[
0
,
1
]
first_word
=
create_random_lodtensor
(
lod
,
place
,
low
=
0
,
high
=
dict_size
-
1
)
...
...
@@ -127,12 +136,14 @@ def infer(use_cuda, inference_program, save_path):
third_word
=
create_random_lodtensor
(
lod
,
place
,
low
=
0
,
high
=
dict_size
-
1
)
fourth_word
=
create_random_lodtensor
(
lod
,
place
,
low
=
0
,
high
=
dict_size
-
1
)
result
=
inferencer
.
infer
({
'firstw'
:
first_word
,
'secondw'
:
second_word
,
'thirdw'
:
third_word
,
'forthw'
:
fourth_word
})
result
=
inferencer
.
infer
(
{
'firstw'
:
first_word
,
'secondw'
:
second_word
,
'thirdw'
:
third_word
,
'forthw'
:
fourth_word
},
return_numpy
=
False
)
print
(
np
.
array
(
result
[
0
]))
...
...
@@ -140,9 +151,17 @@ def main(use_cuda, is_sparse):
if
use_cuda
and
not
fluid
.
core
.
is_compiled_with_cuda
():
return
save_path
=
"word2vec.params"
train
(
use_cuda
,
partial
(
train_program
,
is_sparse
),
save_path
)
infer
(
use_cuda
,
partial
(
inference_program
,
is_sparse
),
save_path
)
save_path
=
"word2vec.inference.model"
train
(
use_cuda
=
use_cuda
,
train_program
=
partial
(
train_program
,
is_sparse
),
save_dirname
=
save_path
)
infer
(
use_cuda
=
use_cuda
,
inference_program
=
partial
(
inference_program
,
is_sparse
),
save_dirname
=
save_path
)
if
__name__
==
'__main__'
:
...
...
python/paddle/fluid/tests/test_detection.py
浏览文件 @
06aa23b0
...
...
@@ -109,6 +109,24 @@ class TestDetection(unittest.TestCase):
print
(
str
(
program
))
class
TestPriorBox
(
unittest
.
TestCase
):
def
test_prior_box
(
self
):
data_shape
=
[
3
,
224
,
224
]
images
=
fluid
.
layers
.
data
(
name
=
'pixel'
,
shape
=
data_shape
,
dtype
=
'float32'
)
conv1
=
fluid
.
layers
.
conv2d
(
images
,
3
,
3
,
2
)
box
,
var
=
layers
.
prior_box
(
input
=
conv1
,
image
=
images
,
min_sizes
=
[
100.0
],
aspect_ratios
=
[
1.
],
flip
=
True
,
clip
=
True
)
assert
len
(
box
.
shape
)
==
4
assert
box
.
shape
==
var
.
shape
assert
box
.
shape
[
3
]
==
4
class
TestMultiBoxHead
(
unittest
.
TestCase
):
def
test_multi_box_head
(
self
):
data_shape
=
[
3
,
224
,
224
]
...
...
python/paddle/fluid/tests/unittests/test_is_empty_op.py
浏览文件 @
06aa23b0
...
...
@@ -14,42 +14,24 @@
import
unittest
import
numpy
as
np
from
paddle.fluid.op
import
Operator
import
paddle.fluid.core
as
core
from
op_test
import
OpTest
def
create_tensor
(
scope
,
name
,
np_data
):
tensor
=
scope
.
var
(
name
).
get_tensor
()
tensor
.
set_dims
(
np_data
.
shape
)
tensor
.
set
(
np_data
,
core
.
CPUPlace
())
return
tensor
class
TestIsEmptyOp
(
unittest
.
TestCase
):
class
TestEmpty
(
OpTest
):
def
setUp
(
self
):
self
.
scope
=
core
.
Scope
()
# create input variables
np_data0
=
np
.
array
([
0
,
1
,
2
])
create_tensor
(
self
.
scope
,
"X0"
,
np_data0
)
np_data1
=
np
.
array
([
1
])
t
=
create_tensor
(
self
.
scope
,
"X1"
,
np_data1
)
t
.
set_dims
([
0
])
self
.
op_type
=
"is_empty"
self
.
inputs
=
{
'X'
:
np
.
array
([
1
,
2
,
3
])}
self
.
outputs
=
{
'Out'
:
np
.
array
([
False
])}
# create output variables
self
.
scope
.
var
(
"out"
)
def
test_check_output
(
self
):
self
.
check_output
(
)
def
test_no_empty
(
self
):
self
.
one_case
(
"X0"
,
False
)
def
test_empty
(
self
):
self
.
one_case
(
"X1"
,
True
)
def
one_case
(
self
,
input
,
target
):
op
=
Operator
(
type
=
"is_empty"
,
X
=
input
,
Out
=
"out"
)
op
.
run
(
self
.
scope
,
core
.
CPUPlace
())
out
=
self
.
scope
.
var
(
"out"
).
get_tensor
()
self
.
assertEqual
(
np
.
array
(
out
)[
0
],
target
)
class
TestNotEmpty
(
TestEmpty
):
def
setUp
(
self
):
self
.
op_type
=
"is_empty"
self
.
inputs
=
{
'X'
:
np
.
array
([])}
self
.
outputs
=
{
'Out'
:
np
.
array
([
True
])}
if
__name__
==
"__main__"
:
...
...
python/paddle/fluid/transpiler/memory_optimization_transpiler.py
浏览文件 @
06aa23b0
...
...
@@ -24,7 +24,8 @@ dtype_to_size = {
core
.
VarDesc
.
VarType
.
INT16
:
2
,
core
.
VarDesc
.
VarType
.
INT32
:
4
,
core
.
VarDesc
.
VarType
.
INT64
:
8
,
core
.
VarDesc
.
VarType
.
BOOL
:
1
core
.
VarDesc
.
VarType
.
BOOL
:
1
,
core
.
VarDesc
.
VarType
.
UINT8
:
1
,
}
SUB_BLOCK_OPS
=
[
...
...
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