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aad585b1
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aad585b1
编写于
8月 03, 2017
作者:
Y
Yu Yang
浏览文件
操作
浏览文件
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差异文件
Merge branch 'develop' of github.com:baidu/Paddle into feature/add_empty_to_default_no_grad_set
上级
57f57ead
f4f0119c
变更
64
隐藏空白更改
内联
并排
Showing
64 changed file
with
944 addition
and
362 deletion
+944
-362
CMakeLists.txt
CMakeLists.txt
+2
-2
Dockerfile
Dockerfile
+1
-1
README.md
README.md
+1
-1
cmake/external/mkldnn.cmake
cmake/external/mkldnn.cmake
+21
-26
cmake/external/mklml.cmake
cmake/external/mklml.cmake
+11
-7
cmake/flags.cmake
cmake/flags.cmake
+5
-0
doc/api/v2/config/layer.rst
doc/api/v2/config/layer.rst
+10
-0
paddle/cuda/src/hl_cuda_cudnn.cc
paddle/cuda/src/hl_cuda_cudnn.cc
+9
-0
paddle/framework/op_registry.h
paddle/framework/op_registry.h
+8
-0
paddle/framework/operator.cc
paddle/framework/operator.cc
+4
-4
paddle/framework/operator.h
paddle/framework/operator.h
+1
-1
paddle/framework/tensor.h
paddle/framework/tensor.h
+1
-1
paddle/framework/tensor_impl.h
paddle/framework/tensor_impl.h
+4
-3
paddle/function/ConvOp.h
paddle/function/ConvOp.h
+7
-0
paddle/function/GemmConvOp.cpp
paddle/function/GemmConvOp.cpp
+83
-48
paddle/gserver/layers/ClipLayer.cpp
paddle/gserver/layers/ClipLayer.cpp
+79
-0
paddle/gserver/layers/RowL2NormLayer.cpp
paddle/gserver/layers/RowL2NormLayer.cpp
+98
-0
paddle/gserver/tests/test_LayerGrad.cpp
paddle/gserver/tests/test_LayerGrad.cpp
+30
-0
paddle/math/BaseMatrix.cu
paddle/math/BaseMatrix.cu
+7
-0
paddle/math/BaseMatrix.h
paddle/math/BaseMatrix.h
+7
-0
paddle/operators/CMakeLists.txt
paddle/operators/CMakeLists.txt
+2
-7
paddle/operators/add_op.cc
paddle/operators/add_op.cc
+0
-4
paddle/operators/add_op.cu
paddle/operators/add_op.cu
+1
-0
paddle/operators/add_op.h
paddle/operators/add_op.h
+7
-4
paddle/operators/cross_entropy_op.cu
paddle/operators/cross_entropy_op.cu
+1
-0
paddle/operators/mean_op.cc
paddle/operators/mean_op.cc
+11
-1
paddle/operators/mean_op.cu
paddle/operators/mean_op.cu
+1
-0
paddle/operators/mean_op.h
paddle/operators/mean_op.h
+22
-2
paddle/operators/mul_op.cu
paddle/operators/mul_op.cu
+1
-0
paddle/operators/mul_op.h
paddle/operators/mul_op.h
+9
-4
paddle/operators/recurrent_op.cc
paddle/operators/recurrent_op.cc
+96
-120
paddle/operators/recurrent_op.h
paddle/operators/recurrent_op.h
+9
-6
paddle/operators/recurrent_op_test.cc
paddle/operators/recurrent_op_test.cc
+23
-30
paddle/operators/rowwise_add_op.cu
paddle/operators/rowwise_add_op.cu
+1
-0
paddle/operators/rowwise_add_op.h
paddle/operators/rowwise_add_op.h
+1
-1
paddle/operators/sgd_op.cu
paddle/operators/sgd_op.cu
+1
-0
paddle/operators/sgd_op.h
paddle/operators/sgd_op.h
+6
-2
paddle/operators/sigmoid_op.cu
paddle/operators/sigmoid_op.cu
+1
-0
paddle/operators/sigmoid_op.h
paddle/operators/sigmoid_op.h
+5
-3
paddle/operators/softmax_op.cu
paddle/operators/softmax_op.cu
+1
-0
paddle/operators/softmax_op.h
paddle/operators/softmax_op.h
+2
-2
paddle/operators/type_alias.h
paddle/operators/type_alias.h
+1
-0
paddle/platform/enforce.h
paddle/platform/enforce.h
+6
-6
paddle/pybind/CMakeLists.txt
paddle/pybind/CMakeLists.txt
+2
-2
paddle/pybind/pybind.cc
paddle/pybind/pybind.cc
+62
-10
paddle/pybind/tensor_bind.h
paddle/pybind/tensor_bind.h
+36
-12
paddle/scripts/docker/build.sh
paddle/scripts/docker/build.sh
+3
-3
paddle/scripts/travis/build_doc.sh
paddle/scripts/travis/build_doc.sh
+2
-2
proto/ModelConfig.proto
proto/ModelConfig.proto
+6
-0
python/paddle/trainer/config_parser.py
python/paddle/trainer/config_parser.py
+24
-0
python/paddle/trainer_config_helpers/layers.py
python/paddle/trainer_config_helpers/layers.py
+73
-0
python/paddle/trainer_config_helpers/tests/configs/file_list.sh
.../paddle/trainer_config_helpers/tests/configs/file_list.sh
+1
-1
python/paddle/trainer_config_helpers/tests/configs/protostr/test_clip_layer.protostr
...g_helpers/tests/configs/protostr/test_clip_layer.protostr
+31
-0
python/paddle/trainer_config_helpers/tests/configs/protostr/test_row_l2_norm_layer.protostr
...rs/tests/configs/protostr/test_row_l2_norm_layer.protostr
+27
-0
python/paddle/trainer_config_helpers/tests/configs/test_clip_layer.py
...e/trainer_config_helpers/tests/configs/test_clip_layer.py
+6
-0
python/paddle/trainer_config_helpers/tests/configs/test_row_l2_norm_layer.py
...er_config_helpers/tests/configs/test_row_l2_norm_layer.py
+6
-0
python/paddle/v2/framework/tests/CMakeLists.txt
python/paddle/v2/framework/tests/CMakeLists.txt
+0
-1
python/paddle/v2/framework/tests/op_test_util.py
python/paddle/v2/framework/tests/op_test_util.py
+32
-27
python/paddle/v2/framework/tests/test_add_two_op.py
python/paddle/v2/framework/tests/test_add_two_op.py
+16
-3
python/paddle/v2/framework/tests/test_fc_op.py
python/paddle/v2/framework/tests/test_fc_op.py
+6
-4
python/paddle/v2/framework/tests/test_mul_op.py
python/paddle/v2/framework/tests/test_mul_op.py
+2
-2
python/paddle/v2/framework/tests/test_rowwise_add_op.py
python/paddle/v2/framework/tests/test_rowwise_add_op.py
+2
-2
python/paddle/v2/framework/tests/test_sgd_op.py
python/paddle/v2/framework/tests/test_sgd_op.py
+2
-2
python/paddle/v2/framework/tests/test_tensor.py
python/paddle/v2/framework/tests/test_tensor.py
+8
-5
未找到文件。
CMakeLists.txt
浏览文件 @
aad585b1
...
...
@@ -36,8 +36,8 @@ include(simd)
################################ Configurations #######################################
option
(
WITH_GPU
"Compile PaddlePaddle with NVIDIA GPU"
${
CUDA_FOUND
}
)
option
(
WITH_AVX
"Compile PaddlePaddle with AVX intrinsics"
${
AVX_FOUND
}
)
option
(
WITH_MKLDNN
"Compile PaddlePaddle with mkl-dnn support."
OFF
)
option
(
WITH_MKLML
"Compile PaddlePaddle with mklml package."
OFF
)
option
(
WITH_MKLDNN
"Compile PaddlePaddle with mkl-dnn support."
${
AVX_FOUND
}
)
option
(
WITH_MKLML
"Compile PaddlePaddle with mklml package."
${
AVX_FOUND
}
)
option
(
WITH_DSO
"Compile PaddlePaddle with dynamic linked CUDA"
ON
)
option
(
WITH_TESTING
"Compile PaddlePaddle with unit testing"
ON
)
option
(
WITH_SWIG_PY
"Compile PaddlePaddle with inference api"
ON
)
...
...
Dockerfile
浏览文件 @
aad585b1
...
...
@@ -27,7 +27,7 @@ RUN apt-get update && \
git python-pip python-dev openssh-server bison
\
wget unzip unrar
tar
xz-utils bzip2
gzip
coreutils ntp
\
curl
sed grep
graphviz libjpeg-dev zlib1g-dev
\
python-numpy python-matplotlib gcc
g++
\
python-numpy python-matplotlib gcc
-4.8 g++-4.8
\
automake locales clang-format-3.8 swig doxygen cmake
\
liblapack-dev liblapacke-dev libboost-dev
\
clang-3.8 llvm-3.8 libclang-3.8-dev
\
...
...
README.md
浏览文件 @
aad585b1
...
...
@@ -72,7 +72,7 @@ We provide [English](http://doc.paddlepaddle.org/develop/doc/) and
-
[
Deep Learning 101
](
http://book.paddlepaddle.org/index.html
)
You might want to start from th
e th
is online interactive book that can run in Jupyter Notebook.
You might want to start from this online interactive book that can run in Jupyter Notebook.
-
[
Distributed Training
](
http://doc.paddlepaddle.org/develop/doc/howto/usage/cluster/cluster_train_en.html
)
...
...
cmake/external/mkldnn.cmake
浏览文件 @
aad585b1
...
...
@@ -20,34 +20,30 @@ INCLUDE(ExternalProject)
SET
(
MKLDNN_PROJECT
"extern_mkldnn"
)
SET
(
MKLDNN_SOURCES_DIR
${
THIRD_PARTY_PATH
}
/mkldnn
)
SET
(
MKLDNN_INSTALL_ROOT
${
CMAKE_INSTALL_PREFIX
}
)
IF
(
NOT
"$ENV{HOME}"
STREQUAL
"/root"
)
SET
(
MKLDNN_INSTALL_ROOT
"$ENV{HOME}"
)
ENDIF
()
SET
(
MKLDNN_INSTALL_DIR
"
${
MKLDNN_INSTALL_ROOT
}
/opt/paddle/third_party/mkldnn"
)
SET
(
MKLDNN_INCLUDE_DIR
"
${
MKLDNN_INSTALL_DIR
}
/include"
CACHE PATH
"mkldnn include directory."
FORCE
)
SET
(
MKLDNN_INSTALL_DIR
${
THIRD_PARTY_PATH
}
/install/mkldnn
)
SET
(
MKLDNN_INC_DIR
"
${
MKLDNN_INSTALL_DIR
}
/include"
CACHE PATH
"mkldnn include directory."
FORCE
)
IF
(
WIN32
)
MESSAGE
(
WARNING
"It is not supported compiling with mkldnn in windows Paddle yet."
"Force WITH_MKLDNN=OFF"
)
SET
(
WITH_MKLDNN OFF
)
IF
(
WIN32 OR APPLE
)
MESSAGE
(
WARNING
"Windows or Mac is not supported with MKLDNN in Paddle yet."
"Force WITH_MKLDNN=OFF"
)
SET
(
WITH_MKLDNN OFF CACHE STRING
"Disable MKLDNN in Windows and MacOS"
FORCE
)
return
()
ELSE
(
WIN32
)
SET
(
MKLDNN_LIBRARY
"
${
MKLDNN_INSTALL_DIR
}
/lib/libmkldnn.so"
CACHE FILEPATH
"mkldnn library."
FORCE
)
MESSAGE
(
STATUS
"Set
${
MKLDNN_INSTALL_DIR
}
/lib to runtime path"
)
SET
(
CMAKE_INSTALL_RPATH_USE_LINK_PATH TRUE
)
#SET(CMAKE_MACOSX_RPATH 1) # hold for MacOS
SET
(
CMAKE_INSTALL_RPATH
"
${
CMAKE_INSTALL_RPATH
}
"
"
${
MKLDNN_INSTALL_DIR
}
/lib"
)
ENDIF
(
WIN32
)
ENDIF
()
SET
(
MKLDNN_LIB
"
${
MKLDNN_INSTALL_DIR
}
/lib/libmkldnn.so"
CACHE FILEPATH
"mkldnn library."
FORCE
)
MESSAGE
(
STATUS
"Set
${
MKLDNN_INSTALL_DIR
}
/lib to runtime path"
)
SET
(
CMAKE_INSTALL_RPATH_USE_LINK_PATH TRUE
)
SET
(
CMAKE_INSTALL_RPATH
"
${
CMAKE_INSTALL_RPATH
}
"
"
${
MKLDNN_INSTALL_DIR
}
/lib"
)
INCLUDE_DIRECTORIES
(
${
MKLDNN_INC
LUDE
_DIR
}
)
INCLUDE_DIRECTORIES
(
${
MKLDNN_INC_DIR
}
)
IF
(
${
CBLAS_PROVIDER
}
STREQUAL
"MKLML"
)
SET
(
MKLDNN_DEPENDS
${
MKLML_PROJECT
}
)
SET
(
MKLDNN_MKLROOT
${
MKLML_ROOT
}
)
SET
(
MKLDNN_IOMP_LIB
${
MKLML_IOMP_LIB
}
)
SET
(
MKLDNN_IOMP_DIR
${
MKLML_LIB_DIR
}
)
MESSAGE
(
STATUS
"Build MKLDNN with
${
MKLDNN_MKLROOT
}
"
)
ENDIF
()
ExternalProject_Add
(
...
...
@@ -57,16 +53,15 @@ ExternalProject_Add(
GIT_REPOSITORY
"https://github.com/01org/mkl-dnn.git"
GIT_TAG
"v0.9"
PREFIX
${
MKLDNN_SOURCES_DIR
}
CONFIGURE_COMMAND mkdir -p <SOURCE_DIR>/build
BUILD_COMMAND cd <SOURCE_DIR>/build
&& cmake .. -DCMAKE_INSTALL_PREFIX=
${
MKLDNN_INSTALL_DIR
}
-DMKLROOT=
${
MKLDNN_MKLROOT
}
&& $
(
MAKE
)
INSTALL_COMMAND cd <SOURCE_DIR>/build && $
(
MAKE
)
install
UPDATE_COMMAND
""
CMAKE_ARGS -DCMAKE_INSTALL_PREFIX=
${
MKLDNN_INSTALL_DIR
}
CMAKE_ARGS -DMKLROOT=
${
MKLDNN_MKLROOT
}
CMAKE_CACHE_ARGS -DCMAKE_INSTALL_PREFIX:PATH=
${
MKLDNN_INSTALL_DIR
}
-DMKLROOT:PATH=
${
MKLDNN_MKLROOT
}
)
ADD_LIBRARY
(
mkldnn SHARED IMPORTED GLOBAL
)
SET_PROPERTY
(
TARGET mkldnn PROPERTY IMPORTED_LOCATION
${
MKLDNN_LIB
RARY
}
)
SET_PROPERTY
(
TARGET mkldnn PROPERTY IMPORTED_LOCATION
${
MKLDNN_LIB
}
)
ADD_DEPENDENCIES
(
mkldnn
${
MKLDNN_PROJECT
}
)
MESSAGE
(
STATUS
"Mkldnn library:
${
MKLDNN_LIB
RARY
}
"
)
MESSAGE
(
STATUS
"Mkldnn library:
${
MKLDNN_LIB
}
"
)
LIST
(
APPEND external_project_dependencies mkldnn
)
cmake/external/mklml.cmake
浏览文件 @
aad585b1
...
...
@@ -16,19 +16,23 @@ IF(NOT ${WITH_MKLML})
return
()
ENDIF
(
NOT
${
WITH_MKLML
}
)
IF
(
WIN32 OR APPLE
)
MESSAGE
(
WARNING
"Windows or Mac is not supported with MKLML in Paddle yet."
"Force WITH_MKLML=OFF"
)
SET
(
WITH_MKLML OFF CACHE STRING
"Disable MKLML package in Windows and MacOS"
FORCE
)
return
()
ENDIF
()
INCLUDE
(
ExternalProject
)
SET
(
MKLML_PROJECT
"extern_mklml"
)
SET
(
MKLML_VER
"mklml_lnx_2018.0.20170
425
"
)
SET
(
MKLML_VER
"mklml_lnx_2018.0.20170
720
"
)
SET
(
MKLML_URL
"https://github.com/01org/mkl-dnn/releases/download/v0.9/
${
MKLML_VER
}
.tgz"
)
SET
(
MKLML_SOURCE_DIR
"
${
THIRD_PARTY_PATH
}
/mklml"
)
SET
(
MKLML_DOWNLOAD_DIR
"
${
MKLML_SOURCE_DIR
}
/src/
${
MKLML_PROJECT
}
"
)
SET
(
MKLML_DST_DIR
"opt/paddle/third_party/mklml"
)
SET
(
MKLML_INSTALL_ROOT
"
${
CMAKE_INSTALL_PREFIX
}
"
)
IF
(
NOT
"$ENV{HOME}"
STREQUAL
"/root"
)
SET
(
MKLML_INSTALL_ROOT
"$ENV{HOME}"
)
ENDIF
()
SET
(
MKLML_DST_DIR
"mklml"
)
SET
(
MKLML_INSTALL_ROOT
"
${
THIRD_PARTY_PATH
}
/install"
)
SET
(
MKLML_INSTALL_DIR
${
MKLML_INSTALL_ROOT
}
/
${
MKLML_DST_DIR
}
)
SET
(
MKLML_ROOT
${
MKLML_INSTALL_DIR
}
/
${
MKLML_VER
}
)
SET
(
MKLML_INC_DIR
${
MKLML_ROOT
}
/include
)
...
...
cmake/flags.cmake
浏览文件 @
aad585b1
...
...
@@ -9,6 +9,11 @@ function(CheckCompilerCXX11Flag)
if
(
${
CMAKE_CXX_COMPILER_VERSION
}
VERSION_LESS 4.8
)
message
(
FATAL_ERROR
"Unsupported GCC version. GCC >= 4.8 required."
)
endif
()
# TODO(qijun) gcc 4.9 or later versions raise SEGV due to the optimization problem.
# Use Debug mode instead for now.
if
(
CMAKE_CXX_COMPILER_VERSION VERSION_GREATER 4.9 OR CMAKE_CXX_COMPILER_VERSION VERSION_EQUAL 4.9
)
set
(
CMAKE_BUILD_TYPE
"Debug"
CACHE STRING
""
FORCE
)
endif
()
elseif
(
CMAKE_CXX_COMPILER_ID STREQUAL
"AppleClang"
OR CMAKE_CXX_COMPILER_ID STREQUAL
"Clang"
)
# cmake >= 3.0 compiler id "AppleClang" on Mac OS X, otherwise "Clang"
# Apple Clang is a different compiler than upstream Clang which havs different version numbers.
...
...
doc/api/v2/config/layer.rst
浏览文件 @
aad585b1
...
...
@@ -104,6 +104,11 @@ cross_channel_norm
------------------
.. autoclass:: paddle.v2.layer.cross_channel_norm
:noindex:
row_l2_norm
-----------
.. autoclass:: paddle.v2.layer.row_l2_norm
:noindex:
Recurrent Layers
================
...
...
@@ -320,6 +325,11 @@ scaling
.. autoclass:: paddle.v2.layer.scaling
:noindex:
clip
----
.. autoclass:: paddle.v2.layer.clip
:noindex:
slope_intercept
---------------
.. autoclass:: paddle.v2.layer.slope_intercept
...
...
paddle/cuda/src/hl_cuda_cudnn.cc
浏览文件 @
aad585b1
...
...
@@ -1022,6 +1022,15 @@ void hl_batch_norm_forward_inference(hl_tensor_descriptor inputDesc,
real
alpha
=
1.0
f
;
real
beta
=
1.0
f
;
cudnnBatchNormMode_t
mode
=
CUDNN_BATCHNORM_SPATIAL
;
int
batch_size
=
((
cudnn_tensor_descriptor
)
inputDesc
)
->
batch_size
;
if
(
batch_size
>
1024
&&
g_cudnn_lib_version
<
6000
)
{
LOG
(
INFO
)
<<
" To process current batch data with size "
<<
batch_size
<<
" (>1024), cudnnBatchNorm requires cuDNN version >= 6000."
<<
" If there is an error complaining CUDNN_STATUS_NOT_SUPPORTED,"
<<
" just recompile PaddlePaddle with cuDNN >= 6000, replacing"
<<
" current version "
<<
g_cudnn_lib_version
;
}
CHECK_CUDNN
(
dynload
::
cudnnBatchNormalizationForwardInference
(
t_resource
.
cudnn_handle
,
mode
,
...
...
paddle/framework/op_registry.h
浏览文件 @
aad585b1
...
...
@@ -400,6 +400,14 @@ class GradOpRegisterHelper {
return 0; \
}
/**
* Macro to Forbid user register Gradient Operator.
*/
#define NO_GRADIENT(__op_type) \
STATIC_ASSERT_GLOBAL_NAMESPACE( \
__reg_gradient_op__##__op_type##__op_type##_grad, \
"NO_GRADIENT must be in global namespace")
/**
* Macro to Register OperatorKernel.
*/
...
...
paddle/framework/operator.cc
浏览文件 @
aad585b1
...
...
@@ -20,16 +20,16 @@ namespace paddle {
namespace
framework
{
template
<
>
Eigen
::
DefaultDevice
*
ExecutionContext
::
GetEigenDevice
<
Eigen
::
DefaultDevice
&
ExecutionContext
::
GetEigenDevice
<
platform
::
CPUPlace
,
Eigen
::
DefaultDevice
>
()
const
{
return
device_context_
.
get_eigen_device
<
Eigen
::
DefaultDevice
>
();
return
*
device_context_
.
get_eigen_device
<
Eigen
::
DefaultDevice
>
();
}
#ifndef PADDLE_ONLY_CPU
template
<
>
Eigen
::
GpuDevice
*
Eigen
::
GpuDevice
&
ExecutionContext
::
GetEigenDevice
<
platform
::
GPUPlace
,
Eigen
::
GpuDevice
>
()
const
{
return
device_context_
.
get_eigen_device
<
Eigen
::
GpuDevice
>
();
return
*
device_context_
.
get_eigen_device
<
Eigen
::
GpuDevice
>
();
}
#endif
...
...
paddle/framework/operator.h
浏览文件 @
aad585b1
...
...
@@ -253,7 +253,7 @@ class ExecutionContext : public OperatorContext {
template
<
typename
PlaceType
,
typename
DeviceType
=
typename
EigenDeviceConverter
<
PlaceType
>::
EigenDeviceType
>
DeviceType
*
GetEigenDevice
()
const
;
DeviceType
&
GetEigenDevice
()
const
;
platform
::
Place
GetPlace
()
const
{
return
device_context_
.
GetPlace
();
}
...
...
paddle/framework/tensor.h
浏览文件 @
aad585b1
...
...
@@ -167,4 +167,4 @@ class Tensor {
}
// namespace framework
}
// namespace paddle
#include "paddle/framework/
detail/tensor-in
l.h"
#include "paddle/framework/
tensor_imp
l.h"
paddle/framework/
detail/tensor-in
l.h
→
paddle/framework/
tensor_imp
l.h
浏览文件 @
aad585b1
...
...
@@ -13,7 +13,6 @@ See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "paddle/memory/memcpy.h"
namespace
paddle
{
...
...
@@ -62,9 +61,11 @@ inline T* Tensor::mutable_data(platform::Place place) {
if
(
platform
::
is_cpu_place
(
place
))
{
holder_
.
reset
(
new
PlaceholderImpl
<
T
,
platform
::
CPUPlace
>
(
boost
::
get
<
platform
::
CPUPlace
>
(
place
),
size
));
}
else
if
(
platform
::
is_gpu_place
(
place
))
{
#ifdef PADDLE_ONLY_CPU
PADDLE_THROW
(
"'GPUPlace' is not supported in CPU only device."
);
}
#ifndef PADDLE_ONLY_CPU
else
if
(
platform
::
is_gpu_place
(
place
))
{
#else
holder_
.
reset
(
new
PlaceholderImpl
<
T
,
platform
::
GPUPlace
>
(
boost
::
get
<
platform
::
GPUPlace
>
(
place
),
size
));
}
...
...
paddle/function/ConvOp.h
浏览文件 @
aad585b1
...
...
@@ -109,6 +109,13 @@ protected:
return
filter
[
filter
.
ndims
()
-
1
];
}
// determine whether im2col needs to be performed
inline
bool
isNeedIm2col
(
const
TensorShape
&
filter
)
const
{
return
!
(
getFilterHeight
(
filter
)
==
1
&&
getFilterWidth
(
filter
)
==
1
&&
strideH
()
==
1
&&
strideW
()
==
1
&&
paddingH
()
==
0
&&
paddingW
()
==
0
);
}
std
::
vector
<
size_t
>
strides_
;
std
::
vector
<
size_t
>
paddings_
;
...
...
paddle/function/GemmConvOp.cpp
浏览文件 @
aad585b1
...
...
@@ -66,16 +66,23 @@ public:
real
*
inputData
=
inputs
[
0
].
data
<
real
>
();
real
*
filterData
=
inputs
[
1
].
data
<
real
>
();
real
*
outputData
=
outputs
[
0
].
data
<
real
>
();
bool
needIm2col
=
isNeedIm2col
(
filter
);
TensorShape
imShape
=
TensorShape
({
inputChannels
/
groups_
,
inputHeight
,
inputWidth
});
TensorShape
colShape
=
TensorShape
({
inputChannels
/
groups_
,
filterHeight
,
filterWidth
,
outputHeight
,
outputWidth
});
resizeBuffer
<
Device
>
(
colShape
.
getElements
());
real
*
colData
=
reinterpret_cast
<
real
*>
(
memory_
->
getBuf
());
TensorShape
colShape
;
real
*
colData
=
NULL
;
if
(
needIm2col
)
{
colShape
=
TensorShape
({
inputChannels
/
groups_
,
filterHeight
,
filterWidth
,
outputHeight
,
outputWidth
});
resizeBuffer
<
Device
>
(
colShape
.
getElements
());
colData
=
reinterpret_cast
<
real
*>
(
memory_
->
getBuf
());
}
Im2ColFunctor
<
kCFO
,
Device
,
real
>
im2col
;
GemmFunctor
<
Device
,
real
>
gemm
;
...
...
@@ -86,15 +93,18 @@ public:
for
(
size_t
i
=
0
;
i
<
batchSize
;
i
++
)
{
for
(
size_t
g
=
0
;
g
<
groups_
;
g
++
)
{
im2col
(
inputData
+
g
*
inputOffset
,
imShape
,
colData
,
colShape
,
strideH
(),
strideW
(),
paddingH
(),
paddingW
());
if
(
needIm2col
)
{
im2col
(
inputData
+
g
*
inputOffset
,
imShape
,
colData
,
colShape
,
strideH
(),
strideW
(),
paddingH
(),
paddingW
());
}
else
{
colData
=
inputData
+
g
*
inputOffset
;
}
int
M
=
outputChannels
/
groups_
;
int
N
=
outputHeight
*
outputWidth
;
int
K
=
inputChannels
/
groups_
*
filterHeight
*
filterWidth
;
...
...
@@ -159,19 +169,27 @@ public:
real
*
outputGrad
=
inputs
[
0
].
data
<
real
>
();
real
*
filterData
=
inputs
[
1
].
data
<
real
>
();
real
*
inputGrad
=
outputs
[
0
].
data
<
real
>
();
bool
needIm2col
=
isNeedIm2col
(
filter
);
TensorShape
imShape
=
TensorShape
({
inputChannels
/
groups_
,
inputHeight
,
inputWidth
});
TensorShape
colShape
=
TensorShape
({
inputChannels
/
groups_
,
filterHeight
,
filterWidth
,
outputHeight
,
outputWidth
});
resizeBuffer
<
Device
>
(
colShape
.
getElements
());
real
*
colData
=
reinterpret_cast
<
real
*>
(
memory_
->
getBuf
());
TensorShape
colShape
;
real
*
colData
=
NULL
;
if
(
needIm2col
)
{
colShape
=
TensorShape
({
inputChannels
/
groups_
,
filterHeight
,
filterWidth
,
outputHeight
,
outputWidth
});
resizeBuffer
<
Device
>
(
colShape
.
getElements
());
colData
=
reinterpret_cast
<
real
*>
(
memory_
->
getBuf
());
}
Col2ImFunctor
<
kCFO
,
Device
,
real
>
col2im
;
GemmFunctor
<
Device
,
real
>
gemm
;
size_t
inputOffset
=
imShape
.
getElements
();
size_t
outputOffset
=
(
outputChannels
/
groups_
)
*
outputHeight
*
outputWidth
;
...
...
@@ -182,6 +200,11 @@ public:
int
K
=
outputChannels
/
groups_
;
int
N
=
outputHeight
*
outputWidth
;
int
M
=
inputChannels
/
groups_
*
filterHeight
*
filterWidth
;
real
scale
=
0.0
f
;
if
(
!
needIm2col
)
{
colData
=
inputGrad
+
g
*
inputOffset
;
scale
=
1.0
f
;
}
gemm
(
CblasTrans
,
CblasNoTrans
,
M
,
...
...
@@ -192,17 +215,19 @@ public:
M
,
outputGrad
+
g
*
outputOffset
,
N
,
0.0
f
,
scale
,
colData
,
N
);
col2im
(
inputGrad
+
g
*
inputOffset
,
imShape
,
colData
,
colShape
,
strideH
(),
strideW
(),
paddingH
(),
paddingW
());
if
(
needIm2col
)
{
col2im
(
inputGrad
+
g
*
inputOffset
,
imShape
,
colData
,
colShape
,
strideH
(),
strideW
(),
paddingH
(),
paddingW
());
}
}
inputGrad
+=
inputChannels
*
inputHeight
*
inputWidth
;
outputGrad
+=
outputChannels
*
outputHeight
*
outputWidth
;
...
...
@@ -255,16 +280,23 @@ public:
real
*
outputGrad
=
inputs
[
0
].
data
<
real
>
();
real
*
inputData
=
inputs
[
1
].
data
<
real
>
();
real
*
filterGrad
=
outputs
[
0
].
data
<
real
>
();
bool
needIm2col
=
isNeedIm2col
(
filter
);
TensorShape
imShape
=
TensorShape
({
inputChannels
/
groups_
,
inputHeight
,
inputWidth
});
TensorShape
colShape
=
TensorShape
({
inputChannels
/
groups_
,
filterHeight
,
filterWidth
,
outputHeight
,
outputWidth
});
resizeBuffer
<
Device
>
(
colShape
.
getElements
());
real
*
colData
=
reinterpret_cast
<
real
*>
(
memory_
->
getBuf
());
TensorShape
colShape
;
real
*
colData
=
NULL
;
if
(
needIm2col
)
{
colShape
=
TensorShape
({
inputChannels
/
groups_
,
filterHeight
,
filterWidth
,
outputHeight
,
outputWidth
});
resizeBuffer
<
Device
>
(
colShape
.
getElements
());
colData
=
reinterpret_cast
<
real
*>
(
memory_
->
getBuf
());
}
Im2ColFunctor
<
kCFO
,
Device
,
real
>
im2col
;
GemmFunctor
<
Device
,
real
>
gemm
;
...
...
@@ -274,15 +306,18 @@ public:
size_t
filterOffset
=
filter
.
getElements
()
/
groups_
;
for
(
size_t
i
=
0
;
i
<
batchSize
;
i
++
)
{
for
(
size_t
g
=
0
;
g
<
groups_
;
g
++
)
{
im2col
(
inputData
+
g
*
inputOffset
,
imShape
,
colData
,
colShape
,
strideH
(),
strideW
(),
paddingH
(),
paddingW
());
if
(
needIm2col
)
{
im2col
(
inputData
+
g
*
inputOffset
,
imShape
,
colData
,
colShape
,
strideH
(),
strideW
(),
paddingH
(),
paddingW
());
}
else
{
colData
=
inputData
+
g
*
inputOffset
;
}
int
M
=
outputChannels
/
groups_
;
int
K
=
outputHeight
*
outputWidth
;
int
N
=
inputChannels
/
groups_
*
filterHeight
*
filterWidth
;
...
...
paddle/gserver/layers/ClipLayer.cpp
0 → 100644
浏览文件 @
aad585b1
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "Layer.h"
namespace
paddle
{
/**
* A layer for clipping the input value by the threshold.
* \f[
* out[i] = \min\left(\max\left(in[i],p_{1}\right),p_{2}\right)
* \f]
*/
class
ClipLayer
:
public
Layer
{
protected:
double
min_
;
double
max_
;
public:
explicit
ClipLayer
(
const
LayerConfig
&
config
)
:
Layer
(
config
)
{}
bool
init
(
const
LayerMap
&
layerMap
,
const
ParameterMap
&
parameterMap
)
override
;
void
forward
(
PassType
passType
)
override
;
void
backward
(
const
UpdateCallback
&
callback
=
nullptr
)
override
;
};
REGISTER_LAYER
(
clip
,
ClipLayer
);
bool
ClipLayer
::
init
(
const
LayerMap
&
layerMap
,
const
ParameterMap
&
parameterMap
)
{
Layer
::
init
(
layerMap
,
parameterMap
);
CHECK_EQ
(
inputLayers_
.
size
(),
1U
);
auto
layerConf
=
config_
.
inputs
(
0
).
clip_conf
();
min_
=
layerConf
.
min
();
max_
=
layerConf
.
max
();
CHECK_LT
(
min_
,
max_
);
return
true
;
}
void
ClipLayer
::
forward
(
PassType
passType
)
{
Layer
::
forward
(
passType
);
MatrixPtr
inV
=
getInputValue
(
0
);
resetOutput
(
inV
->
getHeight
(),
inV
->
getWidth
());
MatrixPtr
outV
=
getOutputValue
();
outV
->
copyFrom
(
*
inV
);
outV
->
clip
(
min_
,
max_
);
}
void
ClipLayer
::
backward
(
const
UpdateCallback
&
callback
)
{
MatrixPtr
inV
=
getInputValue
(
0
);
MatrixPtr
inG
=
getInputGrad
(
0
);
if
(
inG
)
{
MatrixPtr
outV
=
getOutputValue
();
MatrixPtr
outG
=
getOutputGrad
();
MatrixPtr
tmpMtx
;
Matrix
::
resizeOrCreate
(
tmpMtx
,
outG
->
getHeight
(),
outG
->
getWidth
(),
false
,
useGpu_
);
tmpMtx
->
clipDerivative
(
*
inV
,
min_
,
max_
);
inG
->
addDotMul
(
*
outG
,
*
tmpMtx
,
1
,
1
);
}
}
}
// namespace paddle
paddle/gserver/layers/RowL2NormLayer.cpp
0 → 100644
浏览文件 @
aad585b1
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "Layer.h"
namespace
paddle
{
/**
* A layer for L2 normalization in each row,
* \f[
* out[i] = \frac{in[i]}{\sqrt{\sum_{k=1}^N in[k]^{2}}}
* \f]
* where the size of \f$in\f$ is (batchSize x dataDim),
* and the size of \f$out\f$ is (batchSize x dataDim).
*/
class
RowL2NormLayer
:
public
Layer
{
protected:
MatrixPtr
inSquare_
;
MatrixPtr
l2NormReciprocal_
;
MatrixPtr
dotSum_
;
public:
explicit
RowL2NormLayer
(
const
LayerConfig
&
config
)
:
Layer
(
config
)
{}
bool
init
(
const
LayerMap
&
layerMap
,
const
ParameterMap
&
parameterMap
)
override
;
void
forward
(
PassType
passType
)
override
;
void
backward
(
const
UpdateCallback
&
callback
=
nullptr
)
override
;
};
REGISTER_LAYER
(
row_l2_norm
,
RowL2NormLayer
);
bool
RowL2NormLayer
::
init
(
const
LayerMap
&
layerMap
,
const
ParameterMap
&
parameterMap
)
{
Layer
::
init
(
layerMap
,
parameterMap
);
CHECK_EQ
(
inputLayers_
.
size
(),
1U
);
return
true
;
}
void
RowL2NormLayer
::
forward
(
PassType
passType
)
{
Layer
::
forward
(
passType
);
MatrixPtr
inV
=
getInputValue
(
0
);
/* malloc memory for the output_ if necessary */
size_t
batchSize
=
inV
->
getHeight
();
size_t
dataDim
=
getSize
();
CHECK_EQ
(
dataDim
,
inV
->
getWidth
());
resetOutput
(
batchSize
,
dataDim
);
MatrixPtr
outV
=
getOutputValue
();
Matrix
::
resizeOrCreate
(
inSquare_
,
batchSize
,
dataDim
,
false
,
useGpu_
);
inV
->
square2
(
*
inSquare_
);
Matrix
::
resizeOrCreate
(
l2NormReciprocal_
,
batchSize
,
1
,
false
,
useGpu_
);
inSquare_
->
rowSum
(
*
l2NormReciprocal_
);
l2NormReciprocal_
->
sqrt2
(
*
l2NormReciprocal_
);
l2NormReciprocal_
->
scalarDiv
(
*
l2NormReciprocal_
,
1.0
);
outV
->
rowScale
(
0
,
*
inV
,
*
l2NormReciprocal_
);
}
void
RowL2NormLayer
::
backward
(
const
UpdateCallback
&
callback
)
{
MatrixPtr
inV
=
getInputValue
(
0
);
MatrixPtr
inG
=
getInputGrad
(
0
);
MatrixPtr
outV
=
getOutputValue
();
MatrixPtr
outG
=
getOutputGrad
();
size_t
batchSize
=
inV
->
getHeight
();
// inG[ij] += outG[ij] / l2NormReciprocal
// inG[ij] += -inV[ij] * l2NormReciprocal * l2NormReciprocal * DotMul(outG[i],
// inV[i])
if
(
inG
)
{
Matrix
::
resizeOrCreate
(
dotSum_
,
batchSize
,
1
,
false
,
useGpu_
);
dotSum_
->
zeroMem
();
dotSum_
->
rowDotMul
(
0
,
*
outG
,
*
outV
);
dotSum_
->
dotMul
(
*
dotSum_
,
*
l2NormReciprocal_
);
dotSum_
->
dotMul
(
*
dotSum_
,
*
l2NormReciprocal_
);
inSquare_
->
rowScale
(
0
,
*
inV
,
*
dotSum_
);
inG
->
sub
(
*
inSquare_
);
inG
->
addRowScale
(
0
,
*
outG
,
*
l2NormReciprocal_
);
}
}
}
// namespace paddle
paddle/gserver/tests/test_LayerGrad.cpp
浏览文件 @
aad585b1
...
...
@@ -1899,6 +1899,36 @@ TEST(Layer, CropLayer) {
}
}
TEST
(
Layer
,
ClipLayer
)
{
const
size_t
batchSize
=
128
;
const
size_t
size
=
512
;
TestConfig
config
;
config
.
layerConfig
.
set_type
(
"clip"
);
config
.
inputDefs
.
push_back
({
INPUT_DATA
,
"input"
,
size
,
0
});
LayerInputConfig
*
input
=
config
.
layerConfig
.
add_inputs
();
ClipConfig
*
layerConf
=
input
->
mutable_clip_conf
();
double
p1
=
std
::
rand
()
/
(
double
)
RAND_MAX
;
double
p2
=
std
::
rand
()
/
(
double
)
RAND_MAX
;
layerConf
->
set_min
(
std
::
min
(
p1
,
p2
));
layerConf
->
set_max
(
std
::
max
(
p1
,
p2
));
for
(
auto
useGpu
:
{
false
,
true
})
{
testLayerGrad
(
config
,
"clip"
,
batchSize
,
false
,
useGpu
,
false
);
}
}
TEST
(
Layer
,
RowL2NormLayer
)
{
const
size_t
batchSize
=
128
;
const
size_t
size
=
512
;
TestConfig
config
;
config
.
layerConfig
.
set_type
(
"row_l2_norm"
);
config
.
layerConfig
.
set_size
(
size
);
config
.
inputDefs
.
push_back
({
INPUT_DATA
,
"input"
,
size
,
0
});
config
.
layerConfig
.
add_inputs
();
for
(
auto
useGpu
:
{
false
,
true
})
{
testLayerGrad
(
config
,
"row_l2_norm"
,
batchSize
,
false
,
useGpu
,
false
);
}
}
int
main
(
int
argc
,
char
**
argv
)
{
testing
::
InitGoogleTest
(
&
argc
,
argv
);
initMain
(
argc
,
argv
);
...
...
paddle/math/BaseMatrix.cu
浏览文件 @
aad585b1
...
...
@@ -442,6 +442,13 @@ DEFINE_MATRIX_UNARY_PARAMETER_OP(Clip, TWO_PARAMETER,
template
<
class
T
>
void
BaseMatrixT
<
T
>::
clip
(
T
p1
,
T
p2
)
{
applyUnary
(
unary
::
Clip
<
T
>
(
p1
,
p2
));
}
DEFINE_MATRIX_BINARY_PARAMETER_OP
(
ClipDerivative
,
TWO_PARAMETER
,
a
=
b
<
p1
?
0
:
(
b
>
p2
?
0
:
1
));
template
<
class
T
>
void
BaseMatrixT
<
T
>::
clipDerivative
(
BaseMatrixT
&
b
,
T
p1
,
T
p2
)
{
applyBinary
(
binary
::
ClipDerivative
<
T
>
(
p1
,
p2
),
b
);
}
DEFINE_MATRIX_UNARY_PARAMETER_OP
(
BiggerThanScalar
,
ONE_PARAMETER
,
a
=
a
>
p
?
1.0
f
:
0.0
f
);
template
<
class
T
>
...
...
paddle/math/BaseMatrix.h
浏览文件 @
aad585b1
...
...
@@ -488,6 +488,13 @@ public:
*/
void
clip
(
T
p1
,
T
p2
);
/**
* this = b < low ? 0 : 1
*
* this = b > high ? 0 : 1
*/
void
clipDerivative
(
BaseMatrixT
&
b
,
T
p1
,
T
p2
);
/**
* @code
* a = a > p ? 1.0f : 0.0f
...
...
paddle/operators/CMakeLists.txt
浏览文件 @
aad585b1
...
...
@@ -60,10 +60,5 @@ op_library(sgd_op SRCS sgd_op.cc sgd_op.cu)
op_library
(
fc_op
SRCS fc_op.cc
DEPS mul_op rowwise_add_op sigmoid_op softmax_op net
)
op_library
(
recurrent_network_op
SRCS recurrent_network_op.cc
DEPS op_desc tensor net
)
cc_test
(
recurrent_network_op_test
SRCS recurrent_network_op_test.cc
DEPS recurrent_network_op mul_op add_op
)
op_library
(
recurrent_op SRCS recurrent_op.cc DEPS op_desc tensor op_registry operator net
)
cc_test
(
recurrent_op_test SRCS recurrent_op_test.cc DEPS recurrent_op gtest mul_op add_op
)
paddle/operators/add_op.cc
浏览文件 @
aad585b1
...
...
@@ -50,10 +50,6 @@ The equation is: Out = X + Y
class
AddOpGrad
:
public
OperatorWithKernel
{
protected:
void
InferShape
(
const
InferShapeContext
&
ctx
)
const
override
{}
std
::
string
DebugString
()
const
override
{
LOG
(
INFO
)
<<
"AddOpGrad"
;
return
""
;
}
};
}
// namespace operators
...
...
paddle/operators/add_op.cu
浏览文件 @
aad585b1
#define EIGEN_USE_GPU
#include "paddle/framework/op_registry.h"
#include "paddle/operators/add_op.h"
...
...
paddle/operators/add_op.h
浏览文件 @
aad585b1
...
...
@@ -28,10 +28,13 @@ public:
output
->
mutable_data
<
T
>
(
context
.
GetPlace
());
EigenVector
<
T
>::
Flatten
(
*
output
).
device
(
*
(
context
.
GetEigenDevice
<
Place
>
()))
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
input0
)
+
framework
::
EigenVector
<
T
>::
Flatten
(
*
input1
);
auto
X
=
EigenVector
<
T
>::
Flatten
(
*
input0
);
auto
Y
=
EigenVector
<
T
>::
Flatten
(
*
input1
);
auto
Z
=
EigenVector
<
T
>::
Flatten
(
*
output
);
auto
place
=
context
.
GetEigenDevice
<
Place
>
();
Z
.
device
(
place
)
=
X
+
Y
;
}
};
...
...
paddle/operators/cross_entropy_op.cu
浏览文件 @
aad585b1
#define EIGEN_USE_GPU
#include "paddle/operators/cross_entropy_op.h"
REGISTER_OP_GPU_KERNEL
(
onehot_cross_entropy
,
...
...
paddle/operators/mean_op.cc
浏览文件 @
aad585b1
...
...
@@ -33,13 +33,23 @@ public:
MeanOpMaker
(
OpProto
*
proto
,
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"X"
,
"The input of mean op"
);
AddOutput
(
"Out"
,
"The output of mean op"
);
AddOutput
(
"Out"
,
"The output of mean op"
)
.
IgnoreGradient
()
;
AddComment
(
"Mean Operator"
);
}
};
class
MeanGradOp
:
public
OperatorWithKernel
{
protected:
void
InferShape
(
const
InferShapeContext
&
ctx
)
const
override
{
ctx
.
Output
<
Tensor
>
(
"X"
+
GRAD_VAR_SUFFIX
())
->
Resize
(
ctx
.
Input
<
Tensor
>
(
"X"
)
->
dims
());
}
};
}
// namespace operators
}
// namespace paddle
REGISTER_OP
(
mean
,
ops
::
MeanOp
,
ops
::
MeanOpMaker
);
REGISTER_OP_CPU_KERNEL
(
mean
,
ops
::
MeanKernel
<
ops
::
CPUPlace
,
float
>
);
REGISTER_GRADIENT_OP
(
mean
,
mean_grad
,
ops
::
MeanGradOp
);
REGISTER_OP_CPU_KERNEL
(
mean_grad
,
ops
::
MeanGradKernel
<
ops
::
CPUPlace
,
float
>
);
paddle/operators/mean_op.cu
浏览文件 @
aad585b1
...
...
@@ -3,3 +3,4 @@
#include "paddle/operators/mean_op.h"
REGISTER_OP_GPU_KERNEL
(
mean
,
ops
::
MeanKernel
<
ops
::
GPUPlace
,
float
>
);
REGISTER_OP_GPU_KERNEL
(
mean_grad
,
ops
::
MeanGradKernel
<
ops
::
GPUPlace
,
float
>
);
\ No newline at end of file
paddle/operators/mean_op.h
浏览文件 @
aad585b1
...
...
@@ -27,8 +27,28 @@ public:
output
->
mutable_data
<
T
>
(
context
.
GetPlace
());
EigenScalar
<
T
>::
From
(
*
output
).
device
(
*
(
context
.
GetEigenDevice
<
Place
>
()))
=
EigenVector
<
T
>::
Flatten
(
*
input
).
mean
();
auto
X
=
EigenVector
<
T
>::
Flatten
(
*
input
);
auto
y
=
EigenScalar
<
T
>::
From
(
*
output
);
auto
place
=
context
.
GetEigenDevice
<
Place
>
();
y
.
device
(
place
)
=
X
.
mean
();
}
};
template
<
typename
Place
,
typename
T
>
class
MeanGradKernel
:
public
OpKernel
{
public:
void
Compute
(
const
ExecutionContext
&
context
)
const
override
{
auto
OG
=
context
.
Input
<
Tensor
>
(
"Out"
+
OperatorBase
::
GRAD_VAR_SUFFIX
());
PADDLE_ENFORCE
(
framework
::
product
(
OG
->
dims
())
==
1
,
"Mean Gradient should be scalar"
);
auto
IG
=
context
.
Output
<
Tensor
>
(
"X"
+
OperatorBase
::
GRAD_VAR_SUFFIX
());
IG
->
mutable_data
<
T
>
(
context
.
GetPlace
());
T
ig_size
=
(
T
)
framework
::
product
(
IG
->
dims
());
EigenVector
<
T
>::
Flatten
(
*
IG
).
device
(
context
.
GetEigenDevice
<
Place
>
())
=
EigenScalar
<
T
>::
From
(
*
OG
)
/
ig_size
;
}
};
...
...
paddle/operators/mul_op.cu
浏览文件 @
aad585b1
...
...
@@ -12,6 +12,7 @@
See the License for the specific language governing permissions and
limitations under the License. */
#define EIGEN_USE_GPU
#include "paddle/operators/mul_op.h"
REGISTER_OP_GPU_KERNEL
(
mul
,
ops
::
MulKernel
<
ops
::
GPUPlace
,
float
>
);
\ No newline at end of file
paddle/operators/mul_op.h
浏览文件 @
aad585b1
...
...
@@ -26,13 +26,18 @@ public:
Eigen
::
array
<
Eigen
::
IndexPair
<
Eigen
::
DenseIndex
>
,
1
>
dim_pair
=
{
{
Eigen
::
IndexPair
<
Eigen
::
DenseIndex
>
(
1
,
0
)}};
auto
input0
=
context
.
Input
<
Tensor
>
(
"X"
);
auto
input1
=
context
.
Input
<
Tensor
>
(
"Y"
);
auto
output
=
context
.
Output
<
Tensor
>
(
0
);
output
->
mutable_data
<
T
>
(
context
.
GetPlace
());
EigenMatrix
<
T
>::
From
(
*
output
).
device
(
*
(
context
.
GetEigenDevice
<
Place
>
()))
=
EigenMatrix
<
T
>::
From
(
*
context
.
Input
<
Tensor
>
(
"X"
))
.
contract
(
EigenMatrix
<
T
>::
From
(
*
context
.
Input
<
Tensor
>
(
"Y"
)),
dim_pair
);
auto
X
=
EigenMatrix
<
T
>::
From
(
*
input0
);
auto
Y
=
EigenMatrix
<
T
>::
From
(
*
input1
);
auto
Z
=
EigenMatrix
<
T
>::
From
(
*
output
);
auto
place
=
context
.
GetEigenDevice
<
Place
>
();
Z
.
device
(
place
)
=
X
.
contract
(
Y
,
dim_pair
);
}
};
}
// namespace operators
...
...
paddle/operators/recurrent_
network_
op.cc
→
paddle/operators/recurrent_op.cc
浏览文件 @
aad585b1
...
...
@@ -12,7 +12,7 @@
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/operators/recurrent_
network_
op.h"
#include "paddle/operators/recurrent_op.h"
#include <glog/logging.h>
#include <cstring>
...
...
@@ -29,11 +29,15 @@ namespace rnn {
void
SegmentInputs
(
const
std
::
vector
<
Scope
*>&
step_scopes
,
const
std
::
vector
<
Link
>&
inlinks
,
const
size_t
seq_len
)
{
const
size_t
seq_len
,
bool
infer_shape_mode
)
{
PADDLE_ENFORCE
(
!
inlinks
.
empty
(),
"no in links are provided."
);
for
(
size_t
i
=
0
;
i
<
inlinks
.
size
();
++
i
)
{
Tensor
*
input
=
step_scopes
[
0
]
->
FindVar
(
inlinks
[
i
].
external
)
->
GetMutable
<
Tensor
>
();
auto
input_var
=
step_scopes
[
0
]
->
FindVar
(
inlinks
[
i
].
external
);
PADDLE_ENFORCE
(
input_var
!=
nullptr
,
"input link [%s] is not in scope."
,
inlinks
[
i
].
external
);
Tensor
*
input
=
input_var
->
GetMutable
<
Tensor
>
();
DDim
dims
=
input
->
dims
();
PADDLE_ENFORCE
(
static_cast
<
size_t
>
(
dims
[
0
])
==
seq_len
,
"all the inlinks must have same length"
);
...
...
@@ -41,7 +45,9 @@ void SegmentInputs(const std::vector<Scope*>& step_scopes,
for
(
size_t
j
=
0
;
j
<
seq_len
;
j
++
)
{
Tensor
*
step_input
=
step_scopes
[
j
]
->
NewVar
(
inlinks
[
i
].
internal
)
->
GetMutable
<
Tensor
>
();
*
step_input
=
input
->
Slice
<
float
>
(
j
,
j
+
1
);
if
(
!
infer_shape_mode
)
{
*
step_input
=
input
->
Slice
<
float
>
(
j
,
j
+
1
);
}
step_input
->
Resize
(
step_dims
);
}
}
...
...
@@ -49,36 +55,41 @@ void SegmentInputs(const std::vector<Scope*>& step_scopes,
void
ConcatOutputs
(
const
std
::
vector
<
Scope
*>&
step_scopes
,
const
std
::
vector
<
Link
>&
outlinks
,
const
size_t
seq_len
)
{
const
size_t
seq_len
,
bool
infer_shape_mode
)
{
for
(
size_t
i
=
0
;
i
<
outlinks
.
size
();
i
++
)
{
Tensor
*
output
=
step_scopes
[
0
]
->
FindVar
(
outlinks
[
i
].
external
)
->
GetMutable
<
Tensor
>
();
// TODO(qingiqng) remove following code after adding
// InferShape in RecurrentGradientOp
DDim
step_dims
=
step_scopes
[
0
]
->
FindVar
(
outlinks
[
i
].
internal
)
->
GetMutable
<
Tensor
>
()
->
dims
();
std
::
vector
<
int
>
dims_vec
=
vectorize
(
step_dims
);
dims_vec
.
insert
(
dims_vec
.
begin
(),
seq_len
);
output
->
mutable_data
<
float
>
(
make_ddim
(
dims_vec
),
platform
::
CPUPlace
());
for
(
size_t
j
=
0
;
j
<
seq_len
;
j
++
)
{
Tensor
*
step_output
=
step_scopes
[
j
]
->
FindVar
(
outlinks
[
i
].
internal
)
->
GetMutable
<
Tensor
>
();
// TODO(luotao02) data type and platform::DeviceContext() should set
// correctly
(
output
->
Slice
<
float
>
(
j
,
j
+
1
))
.
CopyFrom
<
float
>
(
*
step_output
,
platform
::
CPUPlace
());
auto
output_var
=
step_scopes
[
0
]
->
FindVar
(
outlinks
[
i
].
external
);
PADDLE_ENFORCE
(
output_var
!=
nullptr
,
"output link [%s] is not in scope."
,
outlinks
[
i
].
external
);
Tensor
*
output
=
output_var
->
GetMutable
<
Tensor
>
();
if
(
infer_shape_mode
)
{
DDim
step_dims
=
step_scopes
[
0
]
->
FindVar
(
outlinks
[
i
].
internal
)
->
GetMutable
<
Tensor
>
()
->
dims
();
std
::
vector
<
int
>
dims_vec
=
vectorize
(
step_dims
);
dims_vec
.
insert
(
dims_vec
.
begin
(),
seq_len
);
output
->
Resize
(
make_ddim
(
dims_vec
));
}
else
{
output
->
mutable_data
<
float
>
(
platform
::
CPUPlace
());
for
(
size_t
j
=
0
;
j
<
seq_len
;
j
++
)
{
Tensor
*
step_output
=
step_scopes
[
j
]
->
FindVar
(
outlinks
[
i
].
internal
)
->
GetMutable
<
Tensor
>
();
// TODO(luotao02) data type and platform::DeviceContext() should set
// correctly
(
output
->
Slice
<
float
>
(
j
,
j
+
1
))
.
CopyFrom
<
float
>
(
*
step_output
,
platform
::
CPUPlace
());
}
}
}
}
void
LinkMemories
(
const
std
::
vector
<
Scope
*>&
scopes
,
const
std
::
vector
<
rnn
::
MemoryAttr
>&
memories
,
size_t
step_id
,
int
offset
)
{
const
size_t
step_id
,
const
int
offset
,
bool
infer_shape_mode
)
{
PADDLE_ENFORCE
(
step_id
<
scopes
.
size
(),
"step [%d] is out of range of step scopes' size [%d]"
,
step_id
,
...
...
@@ -95,18 +106,13 @@ void LinkMemories(const std::vector<Scope*>& scopes,
auto
scope
=
scopes
[
step_id
];
auto
linked_scope
=
scopes
[
step_id
+
offset
];
for
(
auto
&
attr
:
memories
)
{
auto
mem
=
scope
->
NewVar
(
attr
.
pre_var
)
->
GetMutable
<
Tensor
>
();
// maybe share variable is better?
auto
mem
=
scope
->
FindVar
(
attr
.
pre_var
)
->
GetMutable
<
Tensor
>
();
auto
linked_mem
=
linked_scope
->
FindVar
(
attr
.
var
)
->
GetMutable
<
Tensor
>
();
mem
->
ShareDataWith
<
float
>
(
*
linked_mem
);
// TODO(qingqing) remove following code
// the memory of current step should be allocated in step net
auto
m
=
scope
->
NewVar
(
attr
.
var
)
->
GetMutable
<
Tensor
>
();
// for unit test, as addOp and mulOp are null currently, if not
// mutable_data, mem.data() in output will be error. We will
// remove this line after merge the correct addOp and mulOp.
m
->
mutable_data
<
float
>
(
mem
->
dims
(),
platform
::
CPUPlace
());
if
(
infer_shape_mode
)
{
mem
->
Resize
(
linked_mem
->
dims
());
}
else
{
mem
->
ShareDataWith
<
float
>
(
*
linked_mem
);
}
}
}
...
...
@@ -175,60 +181,39 @@ void RecurrentAlgorithm::InferShape(const Scope& scope) const {
->
dims
()[
0
];
CreateScopes
(
scope
);
auto
step_scopes
=
GetStepScopes
(
scope
);
// SegmentInputs is called in InferShape. The input must hold memory in
// SegmentInputs. But the other op only set dimension for the output in
// InferShape. That's a problem. Wether the RNN op needs InferShape or not?
// Wether the following functions (SegmentInputs, InitMemories, ...) need
// to rewrite for RNN op?
rnn
::
SegmentInputs
(
step_scopes
,
arg_
->
inlinks
,
seq_len_
);
InitMemories
(
step_scopes
[
0
]);
PADDLE_ENFORCE
(
scope
.
FindVar
(
arg_
->
step_net
)
!=
nullptr
,
"stepnet [%s] is not in scope."
,
arg_
->
step_net
);
rnn
::
SegmentInputs
(
step_scopes
,
arg_
->
inlinks
,
seq_len_
,
true
/*infer_shape_mode*/
);
InitMemories
(
step_scopes
[
0
],
true
/*infer_shape_mode*/
);
Variable
*
net
=
scope
.
FindVar
(
arg_
->
step_net
);
PADDLE_ENFORCE
(
net
!=
nullptr
,
"failed to get step net"
);
// If the InferShape is called in OperatorBase's run function,
// the rnn op only needs to do InferShape for the first time step
for
(
size_t
i
=
0
;
i
<
seq_len_
;
i
++
)
{
if
(
i
>
0
)
{
rnn
::
LinkMemories
(
step_scopes
,
arg_
->
memories
,
i
,
-
1
);
rnn
::
LinkMemories
(
step_scopes
,
arg_
->
memories
,
i
,
-
1
,
true
/*infer_shape_mode*/
);
}
net
->
GetMutable
<
NetOp
>
()
->
InferShape
(
*
step_scopes
[
i
]);
}
auto
outlinks
=
arg_
->
outlinks
;
for
(
size_t
i
=
0
;
i
<
outlinks
.
size
();
i
++
)
{
DDim
step_dims
=
step_scopes
[
0
]
->
FindVar
(
outlinks
[
i
].
internal
)
->
GetMutable
<
Tensor
>
()
->
dims
();
std
::
vector
<
int
>
dims_vec
=
vectorize
(
step_dims
);
// now only support fixed length
dims_vec
.
insert
(
dims_vec
.
begin
(),
seq_len_
);
Tensor
*
output
=
step_scopes
[
0
]
->
FindVar
(
outlinks
[
i
].
external
)
->
GetMutable
<
Tensor
>
();
output
->
Resize
(
make_ddim
(
dims_vec
));
}
rnn
::
ConcatOutputs
(
step_scopes
,
arg_
->
outlinks
,
seq_len_
,
true
/*infer_shape_mode*/
);
}
void
RecurrentAlgorithm
::
Run
(
const
Scope
&
scope
,
const
platform
::
DeviceContext
&
dev_ctx
)
const
{
auto
step_scopes
=
GetStepScopes
(
scope
);
rnn
::
SegmentInputs
(
step_scopes
,
arg_
->
inlinks
,
seq_len_
,
false
/*infer_shape_mode*/
);
InitMemories
(
step_scopes
[
0
],
false
/*infer_shape_mode*/
);
Variable
*
net
=
scope
.
FindVar
(
arg_
->
step_net
);
for
(
size_t
step_id
=
0
;
step_id
<
seq_len_
;
step_id
++
)
{
// the link memory is done in InferShape
// maybe remove following code after testing
if
(
step_id
>
0
)
{
rnn
::
LinkMemories
(
step_scopes
,
arg_
->
memories
,
step_id
,
-
1
);
rnn
::
LinkMemories
(
step_scopes
,
arg_
->
memories
,
step_id
,
-
1
,
false
/*infer_shape_mode*/
);
}
net
->
GetMutable
<
NetOp
>
()
->
Run
(
*
step_scopes
[
step_id
],
dev_ctx
);
}
rnn
::
ConcatOutputs
(
step_scopes
,
arg_
->
outlinks
,
seq_len_
);
rnn
::
ConcatOutputs
(
step_scopes
,
arg_
->
outlinks
,
seq_len_
,
false
/*infer_shape_mode*/
);
}
void
RecurrentAlgorithm
::
CreateScopes
(
const
Scope
&
scope
)
const
{
...
...
@@ -244,18 +229,19 @@ void RecurrentAlgorithm::CreateScopes(const Scope& scope) const {
// Now all variables in scope must be created outside of op.
auto
net_op
=
scope
.
FindVar
(
arg_
->
step_net
)
->
GetMutable
<
NetOp
>
();
for
(
auto
&
input
:
net_op
->
inputs_
)
{
// the weight are located in parent scope
if
(
!
step_scope
.
FindVar
(
input
))
step_scope
.
NewVar
(
input
);
}
for
(
auto
&
output
:
net_op
->
outputs_
)
{
step_scope
.
NewVar
(
output
);
}
step_scopes
->
emplace_back
(
&
step_scope
);
}
}
}
void
RecurrentAlgorithm
::
InitMemories
(
Scope
*
step_scope
)
const
{
void
RecurrentAlgorithm
::
InitMemories
(
Scope
*
step_scope
,
bool
infer_shape_mode
)
const
{
for
(
auto
&
attr
:
arg_
->
memories
)
{
Tensor
*
pre_mem
=
step_scope
->
NewVar
(
attr
.
pre_var
)
->
GetMutable
<
Tensor
>
();
PADDLE_ENFORCE
(
step_scope
->
FindVar
(
attr
.
boot_var
)
!=
nullptr
,
...
...
@@ -263,13 +249,11 @@ void RecurrentAlgorithm::InitMemories(Scope* step_scope) const {
attr
.
var
,
attr
.
boot_var
);
Tensor
*
boot_mem
=
step_scope
->
FindVar
(
attr
.
boot_var
)
->
GetMutable
<
Tensor
>
();
pre_mem
->
ShareDataWith
<
float
>
(
*
boot_mem
);
// TODO(qingqing) remove following code
// the memory of current step should be allocated in step net
// here for unit test
auto
cur_step_mem
=
step_scope
->
NewVar
(
attr
.
var
)
->
GetMutable
<
Tensor
>
();
cur_step_mem
->
mutable_data
<
float
>
(
boot_mem
->
dims
(),
platform
::
CPUPlace
());
if
(
infer_shape_mode
)
{
pre_mem
->
Resize
(
boot_mem
->
dims
());
}
else
{
pre_mem
->
ShareDataWith
<
float
>
(
*
boot_mem
);
}
}
}
...
...
@@ -307,13 +291,14 @@ public:
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
const
auto
&
name
=
RecurrentOp
::
kArgName
;
// inputs and outputs stored in proto
AddInput
(
name
.
inlinks
,
"the input that need to be segmented for each step."
)
AddInput
(
name
.
inlinks
,
"the inputs that need to be segmented for each step."
)
.
SetMultiple
();
AddInput
(
name
.
boot_memories
,
"variables to initialize memories."
)
.
SetMultiple
();
AddInput
(
name
.
step_net
,
"network shared by all steps."
);
AddOutput
(
name
.
outlinks
,
"the output that need to concated for all steps."
)
AddOutput
(
name
.
outlinks
,
"the output
s
that need to concated for all steps."
)
.
SetMultiple
();
AddOutput
(
name
.
step_scopes
,
"step scopes"
);
...
...
@@ -331,34 +316,39 @@ public:
void
RecurrentGradientAlgorithm
::
Run
(
const
Scope
&
scope
,
const
platform
::
DeviceContext
&
dev_ctx
)
const
{
auto
step_scopes
=
GetStepScopes
(
scope
);
rnn
::
SegmentInputs
(
step_scopes
,
arg_
->
inlinks
,
seq_len_
);
PADDLE_ENFORCE
(
scope
.
FindVar
(
arg_
->
step_net
)
!=
nullptr
,
"step net is not in scope."
);
rnn
::
SegmentInputs
(
step_scopes
,
arg_
->
inlinks
,
seq_len_
,
false
/*infer_shape_mode*/
);
Variable
*
net
=
scope
.
FindVar
(
arg_
->
step_net
);
PADDLE_ENFORCE
(
net
!=
nullptr
,
"failed to get step net"
);
for
(
int
step_id
=
seq_len_
-
1
;
step_id
>=
0
;
--
step_id
)
{
if
(
static_cast
<
size_t
>
(
step_id
)
!=
seq_len_
-
1
)
{
rnn
::
LinkMemories
(
step_scopes
,
arg_
->
memories
,
step_id
,
1
);
rnn
::
LinkMemories
(
step_scopes
,
arg_
->
memories
,
step_id
,
1
,
false
/*infer_shape_mode*/
);
}
net
->
GetMutable
<
NetOp
>
()
->
Run
(
*
step_scopes
[
step_id
],
dev_ctx
);
}
LinkBootMemoryGradients
(
step_scopes
[
0
]);
rnn
::
ConcatOutputs
(
step_scopes
,
arg_
->
outlinks
,
seq_len_
);
LinkBootMemoryGradients
(
step_scopes
[
0
],
false
);
rnn
::
ConcatOutputs
(
step_scopes
,
arg_
->
outlinks
,
seq_len_
,
false
/*infer_shape_mode*/
);
}
void
RecurrentGradientAlgorithm
::
LinkBootMemoryGradients
(
Scope
*
step_scope
)
const
{
Scope
*
step_scope
,
bool
infer_shape_mode
)
const
{
for
(
auto
&
attr
:
arg_
->
memories
)
{
Tensor
*
mem_grad
=
step_scope
->
NewVar
(
attr
.
var
)
->
GetMutable
<
Tensor
>
();
PADDLE_ENFORCE
(
mem_grad
!=
nullptr
,
"boot_tensor should be retrieved before"
);
PADDLE_ENFORCE
(
step_scope
->
FindVar
(
attr
.
var
)
!=
nullptr
,
"memory variable [%s] does not exists"
,
attr
.
var
);
PADDLE_ENFORCE
(
step_scope
->
FindVar
(
attr
.
boot_var
)
!=
nullptr
,
"memory [%s]'s boot variable [%s] not exists"
,
attr
.
var
,
"boot variable [%s] does not exists"
,
attr
.
boot_var
);
Tensor
*
mem_grad
=
step_scope
->
NewVar
(
attr
.
var
)
->
GetMutable
<
Tensor
>
();
Tensor
*
boot_mem_grad
=
step_scope
->
NewVar
(
attr
.
boot_var
)
->
GetMutable
<
Tensor
>
();
boot_mem_grad
->
ShareDataWith
<
float
>
(
*
mem_grad
);
if
(
infer_shape_mode
)
{
boot_mem_grad
->
Resize
(
mem_grad
->
dims
());
}
else
{
boot_mem_grad
->
ShareDataWith
<
float
>
(
*
mem_grad
);
}
}
}
...
...
@@ -367,34 +357,20 @@ void RecurrentGradientAlgorithm::InferShape(const Scope& scope) const {
->
GetMutable
<
Tensor
>
()
->
dims
()[
0
];
auto
step_scopes
=
GetStepScopes
(
scope
);
rnn
::
SegmentInputs
(
step_scopes
,
arg_
->
inlinks
,
seq_len_
);
PADDLE_ENFORCE
(
scope
.
FindVar
(
arg_
->
step_net
)
!=
nullptr
,
"step net is not in scope."
);
rnn
::
SegmentInputs
(
step_scopes
,
arg_
->
inlinks
,
seq_len_
,
true
/*infer_shape_mode*/
);
Variable
*
net
=
scope
.
FindVar
(
arg_
->
step_net
);
PADDLE_ENFORCE
(
net
!=
nullptr
,
"failed to get step net"
);
for
(
int
step_id
=
seq_len_
-
1
;
step_id
>=
0
;
--
step_id
)
{
if
(
static_cast
<
size_t
>
(
step_id
)
!=
seq_len_
-
1
)
{
rnn
::
LinkMemories
(
step_scopes
,
arg_
->
memories
,
step_id
,
1
);
rnn
::
LinkMemories
(
step_scopes
,
arg_
->
memories
,
step_id
,
1
,
true
/*infer_shape_mode*/
);
}
net
->
GetMutable
<
NetOp
>
()
->
InferShape
(
*
step_scopes
[
step_id
]);
}
auto
outlinks
=
arg_
->
outlinks
;
for
(
size_t
i
=
0
;
i
<
outlinks
.
size
();
i
++
)
{
DDim
step_dims
=
step_scopes
[
0
]
->
FindVar
(
outlinks
[
i
].
internal
)
->
GetMutable
<
Tensor
>
()
->
dims
();
std
::
vector
<
int
>
dims_vec
=
vectorize
(
step_dims
);
// now only support fixed length
dims_vec
.
insert
(
dims_vec
.
begin
(),
seq_len_
);
Tensor
*
output
=
step_scopes
[
0
]
->
FindVar
(
outlinks
[
i
].
external
)
->
GetMutable
<
Tensor
>
();
output
->
Resize
(
make_ddim
(
dims_vec
));
}
LinkBootMemoryGradients
(
step_scopes
[
0
]);
rnn
::
ConcatOutputs
(
step_scopes
,
arg_
->
outlinks
,
seq_len_
,
true
/*infer_shape_mode*/
);
LinkBootMemoryGradients
(
step_scopes
[
0
],
true
/*infer_shape_mode*/
);
}
void
RecurrentGradientOp
::
Init
()
{
...
...
paddle/operators/recurrent_
network_
op.h
→
paddle/operators/recurrent_op.h
浏览文件 @
aad585b1
...
...
@@ -72,19 +72,22 @@ struct ArgumentName {
*/
void
SegmentInputs
(
const
std
::
vector
<
Scope
*>&
step_scopes
,
const
std
::
vector
<
Link
>&
inlinks
,
const
size_t
seq_len
);
const
size_t
seq_len
,
bool
infer_shape_mode
);
/**
* Process outputs of step nets and merge to variables.
*/
void
ConcatOutputs
(
const
std
::
vector
<
Scope
*>&
step_scopes
,
const
std
::
vector
<
Link
>&
outlinks
,
const
size_t
seq_len
);
const
size_t
seq_len
,
bool
infer_shape_mode
);
void
LinkMemories
(
const
std
::
vector
<
Scope
*>&
step_scopes
,
const
std
::
vector
<
MemoryAttr
>&
memories
,
size_t
step_id
,
int
offset
);
const
size_t
step_id
,
const
int
offset
,
bool
infer_shape_mode
);
void
InitArgument
(
const
ArgumentName
&
name
,
Argument
*
arg
);
...
...
@@ -122,7 +125,7 @@ protected:
return
*
scope
.
FindVar
(
arg_
->
step_scopes
)
->
GetMutable
<
std
::
vector
<
Scope
*>>
();
}
void
InitMemories
(
Scope
*
step_scopes
)
const
;
void
InitMemories
(
Scope
*
step_scopes
,
bool
infer_shape_mode
)
const
;
private:
std
::
unique_ptr
<
rnn
::
Argument
>
arg_
;
...
...
@@ -145,7 +148,7 @@ public:
void
Run
(
const
Scope
&
scope
,
const
platform
::
DeviceContext
&
dev_ctx
)
const
;
void
LinkBootMemoryGradients
(
Scope
*
step_scopes
)
const
;
void
LinkBootMemoryGradients
(
Scope
*
step_scopes
,
bool
infer_shape_mode
)
const
;
/**
* InferShape must be called before Run.
...
...
paddle/operators/recurrent_
network_
op_test.cc
→
paddle/operators/recurrent_op_test.cc
浏览文件 @
aad585b1
...
...
@@ -18,7 +18,7 @@
#include "paddle/framework/op_registry.h"
#include "paddle/framework/operator.h"
#include "paddle/framework/tensor.h"
#include "paddle/operators/recurrent_
network_
op.h"
#include "paddle/operators/recurrent_op.h"
namespace
paddle
{
namespace
operators
{
...
...
@@ -55,7 +55,7 @@ protected:
w
->
GetMutable
<
Tensor
>
()
->
mutable_data
<
float
>
(
make_ddim
(
std
::
vector
<
int
>
{
30
,
30
}),
platform
::
CPUPlace
());
for
(
auto
boot
:
std
::
vector
<
std
::
string
>
{
"
x_boot"
,
"
h_boot"
})
{
for
(
auto
boot
:
std
::
vector
<
std
::
string
>
{
"h_boot"
})
{
LOG
(
INFO
)
<<
"create global variable "
<<
boot
;
Variable
*
h_boot
=
scope_
.
NewVar
(
boot
);
h_boot
->
GetMutable
<
Tensor
>
()
->
mutable_data
<
float
>
(
...
...
@@ -79,7 +79,6 @@ protected:
op_desc
.
add_inputs
(
"x0"
);
op_desc
.
add_inputs
(
"x1"
);
// boot_memories 3
op_desc
.
add_inputs
(
"x_boot"
);
op_desc
.
add_inputs
(
"h_boot"
);
// step net 5
op_desc
.
add_inputs
(
"step_net"
);
...
...
@@ -91,7 +90,7 @@ protected:
auto
_input_format
=
std
::
vector
<
int
>
{
0
,
// in_link
3
,
// memories
5
// step_net
4
// step_net
};
auto
input_format
=
op_desc
.
add_attrs
();
input_format
->
set_name
(
"input_format"
);
...
...
@@ -129,12 +128,11 @@ protected:
inlink_alias
->
add_strings
(
item
);
}
// pre memories
for
(
const
auto
&
item
:
std
::
vector
<
std
::
string
>
{
"rnn/x@pre"
,
"rnn/h@pre"
})
{
for
(
const
auto
&
item
:
std
::
vector
<
std
::
string
>
{
"rnn/h@pre"
})
{
pre_memories
->
add_strings
(
item
);
}
// memories
for
(
const
auto
&
item
:
std
::
vector
<
std
::
string
>
{
"rnn/
x"
,
"rnn/
h"
})
{
for
(
const
auto
&
item
:
std
::
vector
<
std
::
string
>
{
"rnn/h"
})
{
memories
->
add_strings
(
item
);
}
// output alias
...
...
@@ -151,14 +149,11 @@ protected:
LOG
(
INFO
)
<<
"create variable step_net"
;
Variable
*
var
=
scope_
.
NewVar
(
"step_net"
);
auto
net
=
var
->
GetMutable
<
NetOp
>
();
// rnn/s is net's input or output?
net
->
inputs_
=
{
"rnn/h@pre"
,
"rnn/w"
,
"rnn/x"
};
net
->
inputs_
=
{
"rnn/s"
,
"rnn/h"
};
net
->
AddOp
(
OpRegistry
::
CreateOp
(
"mul"
,
{
"rnn/h@pre"
,
"rnn/w"
},
{
"rnn/s"
},
{}));
net
->
AddOp
(
OpRegistry
::
CreateOp
(
"add_two"
,
{
"
rnn/x
"
,
"rnn/s"
},
{
"rnn/h"
},
{}));
OpRegistry
::
CreateOp
(
"add_two"
,
{
"
x@alias
"
,
"rnn/s"
},
{
"rnn/h"
},
{}));
net
->
CompleteAddOp
();
}
...
...
@@ -297,7 +292,10 @@ protected:
inlink
.
internal
=
"rnn/x"
;
auto
step_scopes
=
scope_
.
FindVar
(
"step_scopes"
)
->
GetMutable
<
std
::
vector
<
Scope
*>>
();
rnn
::
SegmentInputs
(
*
step_scopes
,
std
::
vector
<
rnn
::
Link
>
{
inlink
},
10
);
rnn
::
SegmentInputs
(
*
step_scopes
,
std
::
vector
<
rnn
::
Link
>
{
inlink
},
10
,
true
/*infer_shape_mode*/
);
}
void
LinkeMemories
()
{
...
...
@@ -311,7 +309,8 @@ protected:
auto
step_scopes
=
scope_
.
FindVar
(
"step_scopes"
)
->
GetMutable
<
std
::
vector
<
Scope
*>>
();
for
(
int
i
=
1
;
i
<
10
;
++
i
)
{
rnn
::
LinkMemories
(
*
step_scopes
,
memories
,
i
,
-
1
);
rnn
::
LinkMemories
(
*
step_scopes
,
memories
,
i
,
-
1
,
true
/*infer_shape_mode*/
);
}
}
...
...
@@ -333,14 +332,14 @@ TEST(RecurrentOp, LinkMemories) {
using
namespace
paddle
::
operators
;
// create and init step scopes
in
t
len
=
10
;
size_
t
len
=
10
;
std
::
vector
<
Scope
*>
step_scopes
;
for
(
in
t
i
=
0
;
i
<
len
;
++
i
)
{
for
(
size_
t
i
=
0
;
i
<
len
;
++
i
)
{
auto
scope
=
new
Scope
();
scope
->
NewVar
(
"pre_h"
);
auto
tensor
=
scope
->
NewVar
(
"h"
)
->
GetMutable
<
Tensor
>
();
float
*
data
=
tensor
->
mutable_data
<
float
>
({
15
,
20
},
CPUPlace
());
for
(
in
t
j
=
0
;
j
<
15
*
20
;
++
j
)
{
for
(
size_
t
j
=
0
;
j
<
15
*
20
;
++
j
)
{
data
[
j
]
=
rand
()
*
(
1.
/
(
double
)
RAND_MAX
);
}
step_scopes
.
push_back
(
scope
);
...
...
@@ -354,24 +353,24 @@ TEST(RecurrentOp, LinkMemories) {
std
::
vector
<
rnn
::
MemoryAttr
>
memories
;
memories
.
push_back
(
mem_attr
);
for
(
in
t
i
=
1
;
i
<
len
;
++
i
)
{
rnn
::
LinkMemories
(
step_scopes
,
memories
,
i
,
-
1
);
for
(
size_
t
i
=
1
;
i
<
len
;
++
i
)
{
rnn
::
LinkMemories
(
step_scopes
,
memories
,
i
,
-
1
,
false
/*infer_shape_mode*/
);
}
// check
for
(
in
t
i
=
0
;
i
<
len
-
1
;
++
i
)
{
for
(
size_
t
i
=
0
;
i
<
len
-
1
;
++
i
)
{
const
float
*
a
=
step_scopes
[
i
]
->
FindVar
(
"h"
)
->
GetMutable
<
Tensor
>
()
->
data
<
float
>
();
const
float
*
b
=
step_scopes
[
i
+
1
]
->
FindVar
(
"pre_h"
)
->
GetMutable
<
Tensor
>
()
->
data
<
float
>
();
for
(
size_t
i
=
0
;
i
<
15
*
20
;
++
i
)
{
ASSERT_FLOAT_EQ
(
a
[
i
],
b
[
i
]);
for
(
size_t
j
=
0
;
j
<
15
*
20
;
++
j
)
{
ASSERT_FLOAT_EQ
(
a
[
j
],
b
[
j
]);
}
}
for
(
int
i
=
len
-
2
;
i
>=
0
;
--
i
)
{
rnn
::
LinkMemories
(
step_scopes
,
memories
,
i
,
1
);
rnn
::
LinkMemories
(
step_scopes
,
memories
,
i
,
1
,
false
/*infer_shape_mode*/
);
}
// check
for
(
int
i
=
len
-
2
;
i
>=
0
;
--
i
)
{
...
...
@@ -379,8 +378,8 @@ TEST(RecurrentOp, LinkMemories) {
step_scopes
[
i
]
->
FindVar
(
"pre_h"
)
->
GetMutable
<
Tensor
>
()
->
data
<
float
>
();
const
float
*
b
=
step_scopes
[
i
+
1
]
->
FindVar
(
"h"
)
->
GetMutable
<
Tensor
>
()
->
data
<
float
>
();
for
(
size_t
i
=
0
;
i
<
15
*
20
;
++
i
)
{
ASSERT_FLOAT_EQ
(
a
[
i
],
b
[
i
]);
for
(
size_t
j
=
0
;
j
<
15
*
20
;
++
j
)
{
ASSERT_FLOAT_EQ
(
a
[
j
],
b
[
j
]);
}
}
...
...
@@ -391,9 +390,3 @@ TEST(RecurrentOp, LinkMemories) {
USE_OP
(
add_two
);
USE_OP
(
mul
);
// int main() {
// //! TODO(yuyang18): Temporary disable this unit-test because implementation
// //! error.
// return 0;
//}
\ No newline at end of file
paddle/operators/rowwise_add_op.cu
浏览文件 @
aad585b1
#define EIGEN_USE_GPU
#include "paddle/operators/rowwise_add_op.h"
REGISTER_OP_GPU_KERNEL
(
rowwise_add
,
...
...
paddle/operators/rowwise_add_op.h
浏览文件 @
aad585b1
...
...
@@ -33,7 +33,7 @@ public:
const
int
rest_size
=
input
.
size
()
/
bias_size
;
Eigen
::
DSizes
<
int
,
1
>
one_d
(
input
.
size
());
Eigen
::
DSizes
<
int
,
1
>
bcast
(
rest_size
);
output
.
reshape
(
one_d
).
device
(
*
(
context
.
GetEigenDevice
<
Place
>
()
))
=
output
.
reshape
(
one_d
).
device
(
context
.
GetEigenDevice
<
Place
>
(
))
=
input
.
reshape
(
one_d
)
+
bias
.
broadcast
(
bcast
).
reshape
(
one_d
);
}
};
...
...
paddle/operators/sgd_op.cu
浏览文件 @
aad585b1
#define EIGEN_USE_GPU
#include "paddle/operators/sgd_op.h"
REGISTER_OP_GPU_KERNEL
(
sgd
,
ops
::
SGDOpKernel
<
ops
::
GPUPlace
,
float
>
);
\ No newline at end of file
paddle/operators/sgd_op.h
浏览文件 @
aad585b1
...
...
@@ -29,8 +29,12 @@ public:
param_out
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
EigenVector
<
T
>::
Flatten
(
*
param_out
).
device
(
*
(
ctx
.
GetEigenDevice
<
Place
>
()))
=
EigenVector
<
T
>::
Flatten
(
*
param
)
-
lr
*
EigenVector
<
T
>::
Flatten
(
*
grad
);
auto
p
=
EigenVector
<
T
>::
Flatten
(
*
param
);
auto
g
=
EigenVector
<
T
>::
Flatten
(
*
grad
);
auto
o
=
EigenVector
<
T
>::
Flatten
(
*
param_out
);
auto
place
=
ctx
.
GetEigenDevice
<
Place
>
();
o
.
device
(
place
)
=
p
-
lr
*
g
;
}
};
...
...
paddle/operators/sigmoid_op.cu
浏览文件 @
aad585b1
#define EIGEN_USE_GPU
#include "paddle/operators/sigmoid_op.h"
REGISTER_OP_GPU_KERNEL
(
sigmoid
,
ops
::
SigmoidKernel
<
ops
::
GPUPlace
,
float
>
);
paddle/operators/sigmoid_op.h
浏览文件 @
aad585b1
...
...
@@ -27,9 +27,11 @@ public:
auto
output
=
context
.
Output
<
Tensor
>
(
0
);
output
->
mutable_data
<
T
>
(
context
.
GetPlace
());
EigenVector
<
T
>::
Flatten
(
*
output
).
device
(
*
(
context
.
GetEigenDevice
<
Place
>
()))
=
1.0
/
(
1.0
+
(
-
1.0
*
EigenVector
<
T
>::
Flatten
(
*
input
)).
exp
());
auto
X
=
EigenVector
<
T
>::
Flatten
(
*
input
);
auto
Y
=
EigenVector
<
T
>::
Flatten
(
*
output
);
auto
place
=
context
.
GetEigenDevice
<
Place
>
();
Y
.
device
(
place
)
=
1.0
/
(
1.0
+
(
-
1.0
*
X
).
exp
());
}
};
}
// namespace operators
...
...
paddle/operators/softmax_op.cu
浏览文件 @
aad585b1
#define EIGEN_USE_GPU
#include "paddle/framework/op_registry.h"
#include "paddle/operators/softmax_op.h"
...
...
paddle/operators/softmax_op.h
浏览文件 @
aad585b1
...
...
@@ -46,9 +46,9 @@ public:
.
reshape
(
batch_by_one
)
.
broadcast
(
one_by_class
));
softmax
.
device
(
*
(
context
.
GetEigenDevice
<
Place
>
()
))
=
shifted_logits
.
exp
();
softmax
.
device
(
context
.
GetEigenDevice
<
Place
>
(
))
=
shifted_logits
.
exp
();
softmax
.
device
(
*
(
context
.
GetEigenDevice
<
Place
>
()
))
=
softmax
.
device
(
context
.
GetEigenDevice
<
Place
>
(
))
=
(
softmax
*
softmax
.
sum
(
along_class
)
.
inverse
()
...
...
paddle/operators/type_alias.h
浏览文件 @
aad585b1
...
...
@@ -51,6 +51,7 @@ using CPUPlace = platform::CPUPlace;
using
GPUPlace
=
platform
::
GPUPlace
;
using
NetOp
=
framework
::
NetOp
;
using
OpRegistry
=
framework
::
OpRegistry
;
using
OperatorBase
=
framework
::
OperatorBase
;
}
// namespace operators
}
// namespace paddle
...
...
paddle/platform/enforce.h
浏览文件 @
aad585b1
...
...
@@ -144,12 +144,12 @@ inline void throw_on_error(T e) {
throw_on_error
(
e
,
""
);
}
#define PADDLE_THROW(...) \
do { \
throw ::paddle::platform::EnforceNotMet( \
std::make_exception_ptr( \
std::runtime_error(string::Sprintf(__VA_ARGS__))), \
__FILE__, __LINE__); \
#define PADDLE_THROW(...)
\
do {
\
throw ::paddle::platform::EnforceNotMet(
\
std::make_exception_ptr(
\
std::runtime_error(
paddle::
string::Sprintf(__VA_ARGS__))), \
__FILE__, __LINE__);
\
} while (0)
#define PADDLE_ENFORCE(...) \
...
...
paddle/pybind/CMakeLists.txt
浏览文件 @
aad585b1
cc_library
(
paddle_pybind SHARED
SRCS pybind.cc
DEPS pybind python
DEPS pybind python
backward
fc_op
sgd_op
add_op
mean_op
cross_entropy_op
recurrent_
network_
op
)
recurrent_op
)
paddle/pybind/pybind.cc
浏览文件 @
aad585b1
...
...
@@ -16,10 +16,13 @@ limitations under the License. */
#include <fstream>
#include <vector>
#include "paddle/framework/backward.h"
#include "paddle/framework/net.h"
#include "paddle/framework/op_registry.h"
#include "paddle/framework/operator.h"
#include "paddle/framework/scope.h"
#include "paddle/platform/enforce.h"
#include "paddle/platform/place.h"
#include "paddle/pybind/tensor_bind.h"
#include "pybind11/numpy.h"
#include "pybind11/pybind11.h"
...
...
@@ -43,6 +46,10 @@ template <typename ClassType>
void
ExposeOperator
(
ClassType
&
m
)
{
m
.
def
(
"infer_shape"
,
&
ClassType
::
type
::
InferShape
)
.
def
(
"run"
,
&
ClassType
::
type
::
Run
)
.
def
(
"type"
,
[](
const
typename
ClassType
::
type
&
op
)
->
std
::
string
{
return
op
.
type_
;
})
.
def
(
"outputs"
,
[](
const
typename
ClassType
::
type
&
op
)
->
std
::
vector
<
std
::
string
>
{
return
op
.
outputs_
;
...
...
@@ -55,6 +62,14 @@ static size_t UniqueIntegerGenerator() {
return
generator
.
fetch_add
(
1
);
}
bool
IsCompileGPU
()
{
#ifdef PADDLE_ONLY_CPU
return
false
;
#else
return
true
;
#endif
}
PYBIND11_PLUGIN
(
core
)
{
py
::
module
m
(
"core"
,
"C++ core of PaddlePaddle"
);
...
...
@@ -69,15 +84,27 @@ PYBIND11_PLUGIN(core) {
self
.
Resize
(
pd
::
make_ddim
(
dim
));
})
.
def
(
"alloc_float"
,
[](
pd
::
Tensor
&
self
)
{
self
.
mutable_data
<
float
>
(
paddle
::
platform
::
CPUPlace
());
[](
pd
::
Tensor
&
self
,
paddle
::
platform
::
GPUPlace
&
place
)
{
self
.
mutable_data
<
float
>
(
place
);
})
.
def
(
"alloc_float"
,
[](
pd
::
Tensor
&
self
,
paddle
::
platform
::
CPUPlace
&
place
)
{
self
.
mutable_data
<
float
>
(
place
);
})
.
def
(
"alloc_int"
,
[](
pd
::
Tensor
&
self
)
{
self
.
mutable_data
<
int
>
(
p
addle
::
platform
::
CPUPlace
()
);
[](
pd
::
Tensor
&
self
,
paddle
::
platform
::
CPUPlace
&
place
)
{
self
.
mutable_data
<
int
>
(
p
lace
);
})
.
def
(
"set"
,
paddle
::
pybind
::
PyTensorSetFromArray
<
float
>
)
.
def
(
"set"
,
paddle
::
pybind
::
PyTensorSetFromArray
<
int
>
)
.
def
(
"alloc_int"
,
[](
pd
::
Tensor
&
self
,
paddle
::
platform
::
GPUPlace
&
place
)
{
self
.
mutable_data
<
int
>
(
place
);
})
.
def
(
"set"
,
paddle
::
pybind
::
PyCPUTensorSetFromArray
<
float
>
)
.
def
(
"set"
,
paddle
::
pybind
::
PyCPUTensorSetFromArray
<
int
>
)
#ifndef PADDLE_ONLY_CPU
.
def
(
"set"
,
paddle
::
pybind
::
PyCUDATensorSetFromArray
<
float
>
)
.
def
(
"set"
,
paddle
::
pybind
::
PyCUDATensorSetFromArray
<
int
>
)
#endif
.
def
(
"shape"
,
[](
pd
::
Tensor
&
self
)
{
return
pd
::
vectorize
(
self
.
dims
());
});
...
...
@@ -136,11 +163,27 @@ All parameter, weight, gradient are variables in Paddle.
"The module will return special predefined variable name in Paddle"
)
.
def
(
"empty"
,
pd
::
OperatorBase
::
EMPTY_VAR_NAME
)
.
def
(
"temp"
,
pd
::
OperatorBase
::
TMP_VAR_NAME
);
// clang-format off
py
::
class_
<
paddle
::
platform
::
DeviceContext
>
(
m
,
"DeviceContext"
)
.
def_static
(
"cpu_context"
,
[]()
->
paddle
::
platform
::
DeviceContext
*
{
return
new
paddle
::
platform
::
CPUDeviceContext
();
});
.
def_static
(
"create"
,
[](
paddle
::
platform
::
CPUPlace
&
place
)
->
paddle
::
platform
::
DeviceContext
*
{
return
new
paddle
::
platform
::
CPUDeviceContext
();
})
.
def_static
(
"create"
,
[](
paddle
::
platform
::
GPUPlace
&
place
)
->
paddle
::
platform
::
DeviceContext
*
{
#ifdef PADDLE_ONLY_CPU
PADDLE_THROW
(
"GPUPlace is not supported in CPU device."
);
#else
return
new
paddle
::
platform
::
CUDADeviceContext
(
place
);
#endif
});
// clang-format on
py
::
class_
<
paddle
::
platform
::
GPUPlace
>
(
m
,
"GPUPlace"
).
def
(
py
::
init
<
int
>
());
py
::
class_
<
paddle
::
platform
::
CPUPlace
>
(
m
,
"CPUPlace"
).
def
(
py
::
init
<>
());
py
::
class_
<
pd
::
OperatorBase
,
std
::
shared_ptr
<
pd
::
OperatorBase
>>
operator_base
(
m
,
"Operator"
);
...
...
@@ -154,6 +197,13 @@ All parameter, weight, gradient are variables in Paddle.
desc
.
InitializationErrorString
());
return
pd
::
OpRegistry
::
CreateOp
(
desc
);
});
operator_base
.
def
(
"backward"
,
[](
const
pd
::
OperatorBase
&
forwardOp
,
const
std
::
unordered_set
<
std
::
string
>&
no_grad_vars
)
{
return
pd
::
Backward
(
forwardOp
,
no_grad_vars
);
});
ExposeOperator
(
operator_base
);
py
::
class_
<
pd
::
NetOp
,
std
::
shared_ptr
<
pd
::
NetOp
>>
net
(
m
,
"Net"
);
...
...
@@ -176,5 +226,7 @@ All parameter, weight, gradient are variables in Paddle.
m
.
def
(
"unique_integer"
,
UniqueIntegerGenerator
);
m
.
def
(
"is_compile_gpu"
,
IsCompileGPU
);
return
m
.
ptr
();
}
paddle/pybind/tensor_bind.h
浏览文件 @
aad585b1
...
...
@@ -13,9 +13,11 @@
limitations under the License. */
#pragma once
#include <paddle/framework/tensor.h>
#include <pybind11/numpy.h>
#include <pybind11/pybind11.h>
#include <string>
#include "paddle/framework/tensor.h"
#include "paddle/memory/memcpy.h"
#include "pybind11/numpy.h"
#include "pybind11/pybind11.h"
namespace
py
=
pybind11
;
...
...
@@ -40,9 +42,6 @@ template <size_t I, typename... ARGS>
struct
CastToPyBufferImpl
<
true
,
I
,
ARGS
...
>
{
using
CUR_TYPE
=
typename
std
::
tuple_element
<
I
,
std
::
tuple
<
ARGS
...
>>::
type
;
py
::
buffer_info
operator
()(
framework
::
Tensor
&
tensor
)
{
PADDLE_ENFORCE
(
paddle
::
platform
::
is_cpu_place
(
tensor
.
holder_
->
place
()),
"Only CPU tensor can cast to numpy array"
);
if
(
std
::
type_index
(
typeid
(
CUR_TYPE
))
==
tensor
.
holder_
->
type
())
{
auto
dim_vec
=
framework
::
vectorize
(
tensor
.
dims
());
std
::
vector
<
size_t
>
dims_outside
;
...
...
@@ -56,12 +55,17 @@ struct CastToPyBufferImpl<true, I, ARGS...> {
strides
[
i
-
1
]
=
sizeof
(
CUR_TYPE
)
*
prod
;
prod
*=
dims_outside
[
i
-
1
];
}
framework
::
Tensor
dst_tensor
;
if
(
paddle
::
platform
::
is_gpu_place
(
tensor
.
holder_
->
place
()))
{
dst_tensor
.
CopyFrom
<
CUR_TYPE
>
(
tensor
,
platform
::
CPUPlace
());
}
else
if
(
paddle
::
platform
::
is_cpu_place
(
tensor
.
holder_
->
place
()))
{
dst_tensor
=
tensor
;
}
return
py
::
buffer_info
(
tensor
.
mutable_data
<
CUR_TYPE
>
(
tensor
.
holder_
->
place
()),
dst_tensor
.
mutable_data
<
CUR_TYPE
>
(
dst_
tensor
.
holder_
->
place
()),
sizeof
(
CUR_TYPE
),
py
::
format_descriptor
<
CUR_TYPE
>::
format
(),
(
size_t
)
framework
::
arity
(
tensor
.
dims
()),
(
size_t
)
framework
::
arity
(
dst_
tensor
.
dims
()),
dims_outside
,
strides
);
}
else
{
...
...
@@ -77,9 +81,10 @@ inline py::buffer_info CastToPyBuffer(framework::Tensor &tensor) {
}
template
<
typename
T
>
void
PyTensorSetFromArray
(
void
Py
CPU
TensorSetFromArray
(
framework
::
Tensor
&
self
,
py
::
array_t
<
T
,
py
::
array
::
c_style
|
py
::
array
::
forcecast
>
array
)
{
py
::
array_t
<
T
,
py
::
array
::
c_style
|
py
::
array
::
forcecast
>
array
,
paddle
::
platform
::
CPUPlace
&
place
)
{
std
::
vector
<
int
>
dims
;
dims
.
reserve
(
array
.
ndim
());
for
(
size_t
i
=
0
;
i
<
array
.
ndim
();
++
i
)
{
...
...
@@ -87,9 +92,28 @@ void PyTensorSetFromArray(
}
self
.
Resize
(
framework
::
make_ddim
(
dims
));
auto
*
dst
=
self
.
mutable_data
<
T
>
(
p
addle
::
platform
::
CPUPlace
()
);
auto
*
dst
=
self
.
mutable_data
<
T
>
(
p
lace
);
std
::
memcpy
(
dst
,
array
.
data
(),
sizeof
(
T
)
*
array
.
size
());
}
#ifndef PADDLE_ONLY_CPU
template
<
typename
T
>
void
PyCUDATensorSetFromArray
(
framework
::
Tensor
&
self
,
py
::
array_t
<
T
,
py
::
array
::
c_style
|
py
::
array
::
forcecast
>
array
,
paddle
::
platform
::
GPUPlace
&
place
)
{
std
::
vector
<
int
>
dims
;
dims
.
reserve
(
array
.
ndim
());
for
(
size_t
i
=
0
;
i
<
array
.
ndim
();
++
i
)
{
dims
.
push_back
((
int
)
array
.
shape
()[
i
]);
}
self
.
Resize
(
framework
::
make_ddim
(
dims
));
auto
*
dst
=
self
.
mutable_data
<
T
>
(
place
);
paddle
::
platform
::
GpuMemcpySync
(
dst
,
array
.
data
(),
sizeof
(
T
)
*
array
.
size
(),
cudaMemcpyHostToDevice
);
}
#endif
}
// namespace pybind
}
// namespace paddle
paddle/scripts/docker/build.sh
浏览文件 @
aad585b1
...
...
@@ -69,7 +69,7 @@ cat <<EOF
Installing ...
========================================
EOF
make
install
make
install
-j
`
nproc
`
pip
install
/usr/local/opt/paddle/share/wheels/
*
.whl
paddle version
...
...
@@ -122,7 +122,7 @@ cat <<EOF
Generating .deb package ...
========================================
EOF
cpack
-D
CPACK_GENERATOR
=
'DEB'
..
cpack
-D
CPACK_GENERATOR
=
'DEB'
-j
`
nproc
`
..
cat
<<
EOF
...
...
@@ -148,7 +148,7 @@ cat >> /paddle/build/Dockerfile <<EOF
ADD *.deb /
# run paddle version to install python packages first
RUN apt-get update &&
\
apt-get install -y python-pip && pip install -U pip &&
\
apt-get install -y
wget
python-pip && pip install -U pip &&
\
dpkg -i /*.deb ; apt-get install -f -y &&
\
apt-get clean -y &&
\
rm -f /*.deb &&
\
...
...
paddle/scripts/travis/build_doc.sh
浏览文件 @
aad585b1
...
...
@@ -6,14 +6,14 @@ mkdir -p $TRAVIS_BUILD_DIR/build
cd
$TRAVIS_BUILD_DIR
/build
# Compile paddle binaries first
cmake ..
-DCMAKE_BUILD_TYPE
=
Debug
-DWITH_GPU
=
OFF
-DWITH_DOC
=
OFF
-DWITH_GOLANG
=
ON
-DWITH_STYLE_CHECK
=
OFF
cmake ..
-DCMAKE_BUILD_TYPE
=
Debug
-DWITH_GPU
=
OFF
-DWITH_DOC
=
OFF
-DWITH_
MKLDNN
=
OFF
-DWITH_MKLML
=
OFF
-DWITH_
GOLANG
=
ON
-DWITH_STYLE_CHECK
=
OFF
mkdir
output
make
-j
`
nproc
`
find ..
-name
'*whl'
| xargs pip
install
# install all wheels.
rm
-rf
*
# Compile Documentation only.
cmake ..
-DCMAKE_BUILD_TYPE
=
Debug
-DWITH_GPU
=
OFF
-DWITH_DOC
=
ON
cmake ..
-DCMAKE_BUILD_TYPE
=
Debug
-DWITH_GPU
=
OFF
-DWITH_
MKLDNN
=
OFF
-DWITH_MKLML
=
OFF
-DWITH_
DOC
=
ON
make
-j
`
nproc
`
paddle_docs paddle_docs_cn
# check websites for broken links
...
...
proto/ModelConfig.proto
浏览文件 @
aad585b1
...
...
@@ -298,6 +298,11 @@ message DetectionOutputConfig {
optional
uint32
width
=
9
[
default
=
1
];
}
message
ClipConfig
{
required
double
min
=
1
;
required
double
max
=
2
;
}
message
LayerInputConfig
{
required
string
input_layer_name
=
1
;
optional
string
input_parameter_name
=
2
;
...
...
@@ -318,6 +323,7 @@ message LayerInputConfig {
optional
RowConvConfig
row_conv_conf
=
15
;
optional
MultiBoxLossConfig
multibox_loss_conf
=
16
;
optional
DetectionOutputConfig
detection_output_conf
=
17
;
optional
ClipConfig
clip_conf
=
18
;
}
message
LayerConfig
{
...
...
python/paddle/trainer/config_parser.py
浏览文件 @
aad585b1
...
...
@@ -2198,6 +2198,20 @@ class RowConvLayer(LayerBase):
self
.
create_input_parameter
(
0
,
psize
,
dims
)
@
config_layer
(
'clip'
)
class
ClipLayer
(
LayerBase
):
def
__init__
(
self
,
name
,
inputs
,
min
,
max
,
**
xargs
):
super
(
ClipLayer
,
self
).
__init__
(
name
,
'clip'
,
0
,
inputs
=
inputs
,
**
xargs
)
config_assert
(
len
(
self
.
inputs
)
==
1
,
'ClipLayer must have one and only one input.'
)
config_assert
(
min
<
max
,
'min must be less than max.'
)
input_layer
=
self
.
get_input_layer
(
0
)
self
.
set_layer_size
(
input_layer
.
size
)
self
.
config
.
inputs
[
0
].
clip_conf
.
min
=
min
self
.
config
.
inputs
[
0
].
clip_conf
.
max
=
max
# key: cost type
# value: cost class
g_cost_map
=
{}
...
...
@@ -2754,6 +2768,16 @@ class SumToOneNormLayer(LayerBase):
self
.
set_layer_size
(
input_layer0
.
size
)
@
config_layer
(
'row_l2_norm'
)
class
RowL2NormLayer
(
LayerBase
):
def
__init__
(
self
,
name
,
inputs
,
**
xargs
):
super
(
RowL2NormLayer
,
self
).
__init__
(
name
,
'row_l2_norm'
,
0
,
inputs
=
inputs
,
**
xargs
)
config_assert
(
len
(
self
.
inputs
)
==
1
,
'RowL2NormLayer must have 1 input'
)
input_layer
=
self
.
get_input_layer
(
0
)
self
.
set_layer_size
(
input_layer
.
size
)
@
config_layer
(
'cos_vm'
)
class
CosSimVecMatLayer
(
LayerBase
):
def
__init__
(
self
,
name
,
size
,
inputs
,
cos_scale
=
1.0
,
device
=
None
):
...
...
python/paddle/trainer_config_helpers/layers.py
浏览文件 @
aad585b1
...
...
@@ -76,6 +76,7 @@ __all__ = [
'trans_layer'
,
'rotate_layer'
,
'sum_to_one_norm_layer'
,
'row_l2_norm_layer'
,
'get_output_layer'
,
'LayerType'
,
'context_projection'
,
...
...
@@ -128,6 +129,7 @@ __all__ = [
'prelu_layer'
,
'gated_unit_layer'
,
'crop_layer'
,
'clip_layer'
,
'slice_projection'
,
]
...
...
@@ -160,6 +162,7 @@ class LayerType(object):
BATCH_NORM_LAYER
=
'batch_norm'
NORM_LAYER
=
'norm'
SUM_TO_ONE_NORM_LAYER
=
'sum_to_one_norm'
ROW_L2_NORM_LAYER
=
'row_l2_norm'
ADDTO_LAYER
=
'addto'
CONCAT_LAYER
=
'concat'
...
...
@@ -221,6 +224,7 @@ class LayerType(object):
PRELU
=
'prelu'
CROP_LAYER
=
'crop'
CLIP_LAYER
=
'clip'
@
staticmethod
def
is_layer_type
(
type_name
):
...
...
@@ -2889,6 +2893,42 @@ def sum_to_one_norm_layer(input, name=None, layer_attr=None):
name
,
LayerType
.
SUM_TO_ONE_NORM_LAYER
,
parents
=
[
input
],
size
=
input
.
size
)
@
wrap_name_default
()
@
layer_support
()
def
row_l2_norm_layer
(
input
,
name
=
None
,
layer_attr
=
None
):
"""
A layer for L2-normalization in each row.
.. math::
out[i] =
\f
rac{in[i]}{\sqrt{\sum_{k=1}^N in[k]^{2}}}
where the size of :math:`in` is (batchSize x dataDim) ,
and the size of :math:`out` is a (batchSize x dataDim) .
The example usage is:
.. code-block:: python
row_l2_norm_layer = row_l2_norm_layer(input=layer)
:param input: Input layer.
:type input: LayerOutput
:param name: Layer name.
:type name: basestring
:param layer_attr: extra layer attributes.
:type layer_attr: ExtraLayerAttribute.
:return: LayerOutput object.
:rtype: LayerOutput
"""
Layer
(
name
=
name
,
type
=
LayerType
.
ROW_L2_NORM_LAYER
,
inputs
=
[
input
.
name
],
**
ExtraAttr
.
to_kwargs
(
layer_attr
))
return
LayerOutput
(
name
,
LayerType
.
ROW_L2_NORM_LAYER
,
parents
=
[
input
],
size
=
input
.
size
)
@
wrap_name_default
(
"addto"
)
@
wrap_act_default
(
act
=
LinearActivation
())
@
wrap_bias_attr_default
(
has_bias
=
False
)
...
...
@@ -6046,3 +6086,36 @@ def crop_layer(input, offset, axis=2, shape=None, name=None, layer_attr=None):
layer_type
=
LayerType
.
CROP_LAYER
,
parents
=
input
,
size
=
l
.
config
.
size
)
@
wrap_name_default
(
"clip"
)
def
clip_layer
(
input
,
min
,
max
,
name
=
None
):
"""
A layer for clipping the input value by the threshold.
.. math::
out[i] = \min\left(\max\left(in[i],p_{1}
\r
ight),p_{2}
\r
ight)
.. code-block:: python
clip = clip_layer(input=input_layer, min=-10, max=10)
:param name: The Layer Name.
:type name: basestring
:param input: The input layer.
:type input: LayerOutput.
:param min: The lower threshold for clipping.
:type min: double
:param max: The upper threshold for clipping.
:type max: double
:return: LayerOutput
"""
Layer
(
name
=
name
,
type
=
LayerType
.
CLIP_LAYER
,
inputs
=
[
input
.
name
],
min
=
min
,
max
=
max
)
return
LayerOutput
(
name
,
LayerType
.
CLIP_LAYER
,
parents
=
[
input
],
size
=
input
.
size
)
python/paddle/trainer_config_helpers/tests/configs/file_list.sh
浏览文件 @
aad585b1
...
...
@@ -7,6 +7,6 @@ test_rnn_group shared_fc shared_lstm shared_gru test_cost_layers_with_weight
test_spp_layer test_bilinear_interp test_maxout test_bi_grumemory math_ops
test_seq_concat_reshape test_pad test_smooth_l1 test_multiplex_layer
test_prelu_layer test_row_conv test_detection_output_layer test_multibox_loss_layer
test_recursive_topology test_gated_unit_layer
)
test_recursive_topology test_gated_unit_layer
test_clip_layer test_row_l2_norm_layer
)
export
whole_configs
=(
test_split_datasource
)
python/paddle/trainer_config_helpers/tests/configs/protostr/test_clip_layer.protostr
0 → 100644
浏览文件 @
aad585b1
type: "nn"
layers {
name: "input"
type: "data"
size: 300
active_type: ""
}
layers {
name: "__clip_0__"
type: "clip"
size: 300
active_type: ""
inputs {
input_layer_name: "input"
clip_conf {
min: -10
max: 10
}
}
}
input_layer_names: "input"
output_layer_names: "__clip_0__"
sub_models {
name: "root"
layer_names: "input"
layer_names: "__clip_0__"
input_layer_names: "input"
output_layer_names: "__clip_0__"
is_recurrent_layer_group: false
}
python/paddle/trainer_config_helpers/tests/configs/protostr/test_row_l2_norm_layer.protostr
0 → 100644
浏览文件 @
aad585b1
type: "nn"
layers {
name: "input"
type: "data"
size: 300
active_type: ""
}
layers {
name: "__row_l2_norm_layer_0__"
type: "row_l2_norm"
size: 300
active_type: ""
inputs {
input_layer_name: "input"
}
}
input_layer_names: "input"
output_layer_names: "__row_l2_norm_layer_0__"
sub_models {
name: "root"
layer_names: "input"
layer_names: "__row_l2_norm_layer_0__"
input_layer_names: "input"
output_layer_names: "__row_l2_norm_layer_0__"
is_recurrent_layer_group: false
}
python/paddle/trainer_config_helpers/tests/configs/test_clip_layer.py
0 → 100644
浏览文件 @
aad585b1
from
paddle.trainer_config_helpers
import
*
data
=
data_layer
(
name
=
'input'
,
size
=
300
)
clip
=
clip_layer
(
input
=
data
,
min
=-
10
,
max
=
10
)
outputs
(
clip
)
python/paddle/trainer_config_helpers/tests/configs/test_row_l2_norm_layer.py
0 → 100644
浏览文件 @
aad585b1
from
paddle.trainer_config_helpers
import
*
data
=
data_layer
(
name
=
'input'
,
size
=
300
)
row_l2_norm
=
row_l2_norm_layer
(
input
=
data
)
outputs
(
row_l2_norm
)
python/paddle/v2/framework/tests/CMakeLists.txt
浏览文件 @
aad585b1
...
...
@@ -8,7 +8,6 @@ add_python_test(test_framework
test_fc_op.py
test_add_two_op.py
test_sgd_op.py
test_cross_entropy_op.py
test_mul_op.py
test_mean_op.py
test_sigmoid_op.py
...
...
python/paddle/v2/framework/tests/op_test_util.py
浏览文件 @
aad585b1
...
...
@@ -26,40 +26,45 @@ class OpTestMeta(type):
scope
=
core
.
Scope
()
kwargs
=
dict
()
places
=
[]
places
.
append
(
core
.
CPUPlace
())
if
core
.
is_compile_gpu
():
places
.
append
(
core
.
GPUPlace
(
0
))
for
in_name
in
func
.
all_input_args
:
if
hasattr
(
self
,
in_name
):
kwargs
[
in_name
]
=
in_name
var
=
scope
.
new_var
(
in_name
).
get_tensor
()
arr
=
getattr
(
self
,
in_name
)
var
.
set_dims
(
arr
.
shape
)
var
.
set
(
arr
)
else
:
kwargs
[
in_name
]
=
"@EMPTY@"
for
place
in
places
:
for
in_name
in
func
.
all_input_args
:
if
hasattr
(
self
,
in_name
):
kwargs
[
in_name
]
=
in_name
var
=
scope
.
new_var
(
in_name
).
get_tensor
()
arr
=
getattr
(
self
,
in_name
)
var
.
set_dims
(
arr
.
shape
)
var
.
set
(
arr
,
place
)
else
:
kwargs
[
in_name
]
=
"@EMPTY@"
for
out_name
in
func
.
all_output_args
:
if
hasattr
(
self
,
out_name
):
kwargs
[
out_name
]
=
out_name
scope
.
new_var
(
out_name
).
get_tensor
()
for
out_name
in
func
.
all_output_args
:
if
hasattr
(
self
,
out_name
):
kwargs
[
out_name
]
=
out_name
scope
.
new_var
(
out_name
).
get_tensor
()
for
attr_name
in
func
.
all_attr_args
:
if
hasattr
(
self
,
attr_name
):
kwargs
[
attr_name
]
=
getattr
(
self
,
attr_name
)
for
attr_name
in
func
.
all_attr_args
:
if
hasattr
(
self
,
attr_name
):
kwargs
[
attr_name
]
=
getattr
(
self
,
attr_name
)
op
=
func
(
**
kwargs
)
op
=
func
(
**
kwargs
)
op
.
infer_shape
(
scope
)
op
.
infer_shape
(
scope
)
ctx
=
core
.
DeviceContext
.
cpu_context
(
)
op
.
run
(
scope
,
ctx
)
ctx
=
core
.
DeviceContext
.
create
(
place
)
op
.
run
(
scope
,
ctx
)
for
out_name
in
func
.
all_output_args
:
actual
=
numpy
.
array
(
scope
.
find_var
(
out_name
).
get_tensor
())
expect
=
getattr
(
self
,
out_name
)
# TODO(qijun) The default decimal is 7, but numpy.dot and eigen.mul
# has some diff, and could not pass unittest. So I set decimal 3 here.
# And I will check this in future.
numpy
.
testing
.
assert_almost_equal
(
actual
,
expect
,
decimal
=
3
)
for
out_name
in
func
.
all_output_args
:
actual
=
numpy
.
array
(
scope
.
find_var
(
out_name
).
get_tensor
())
expect
=
getattr
(
self
,
out_name
)
# TODO(qijun) The default decimal is 7, but numpy.dot and eigen.mul
# has some diff, and could not pass unittest. So I set decimal 3 here.
# And I will check this in future.
numpy
.
testing
.
assert_almost_equal
(
actual
,
expect
,
decimal
=
3
)
obj
.
test_all
=
test_all
return
obj
python/paddle/v2/framework/tests/test_add_two_op.py
浏览文件 @
aad585b1
import
unittest
from
op_test_util
import
OpTestMeta
import
numpy
import
paddle.v2.framework.core
as
core
import
paddle.v2.framework.create_op_creation_methods
as
creation
from
op_test_util
import
OpTestMeta
class
TestAddOp
(
unittest
.
TestCase
):
...
...
@@ -8,10 +12,19 @@ class TestAddOp(unittest.TestCase):
def
setUp
(
self
):
self
.
type
=
"add_two"
self
.
X
=
numpy
.
random
.
random
((
342
,
34
5
)).
astype
(
"float32"
)
self
.
Y
=
numpy
.
random
.
random
((
342
,
34
5
)).
astype
(
"float32"
)
self
.
X
=
numpy
.
random
.
random
((
102
,
10
5
)).
astype
(
"float32"
)
self
.
Y
=
numpy
.
random
.
random
((
102
,
10
5
)).
astype
(
"float32"
)
self
.
Out
=
self
.
X
+
self
.
Y
class
TestAddGradOp
(
unittest
.
TestCase
):
def
test_add_grad
(
self
):
op
=
creation
.
op_creations
.
add_two
(
X
=
"X"
,
Y
=
"Y"
,
Out
=
"Out"
)
backward_op
=
core
.
Operator
.
backward
(
op
,
set
())
self
.
assertEqual
(
backward_op
.
type
(),
"add_two_grad"
)
expected
=
'''Op(add_two_grad), inputs:(X, Y, Out, Out@GRAD), outputs:(X@GRAD, Y@GRAD).'''
self
.
assertEqual
(
expected
,
str
(
backward_op
))
if
__name__
==
'__main__'
:
unittest
.
main
()
python/paddle/v2/framework/tests/test_fc_op.py
浏览文件 @
aad585b1
...
...
@@ -7,17 +7,19 @@ import paddle.v2.framework.create_op_creation_methods as creation
class
TestFc
(
unittest
.
TestCase
):
def
test_fc
(
self
):
scope
=
core
.
Scope
()
place
=
core
.
CPUPlace
()
x
=
scope
.
new_var
(
"X"
)
x_tensor
=
x
.
get_tensor
()
x_tensor
.
set_dims
([
1000
,
784
])
x_tensor
.
alloc_float
()
x_tensor
.
alloc_float
(
place
)
w
=
scope
.
new_var
(
"W"
)
w_tensor
=
w
.
get_tensor
()
w_tensor
.
set_dims
([
784
,
100
])
w_tensor
.
alloc_float
()
w_tensor
.
alloc_float
(
place
)
w_tensor
.
set
(
numpy
.
random
.
random
((
784
,
100
)).
astype
(
"float32"
))
w_tensor
.
set
(
numpy
.
random
.
random
((
784
,
100
)).
astype
(
"float32"
)
,
place
)
# Set a real numpy array here.
# x_tensor.set(numpy.array([]))
...
...
@@ -32,7 +34,7 @@ class TestFc(unittest.TestCase):
op
.
infer_shape
(
scope
)
self
.
assertEqual
([
1000
,
100
],
tensor
.
shape
())
ctx
=
core
.
DeviceContext
.
c
pu_context
(
)
ctx
=
core
.
DeviceContext
.
c
reate
(
place
)
op
.
run
(
scope
,
ctx
)
...
...
python/paddle/v2/framework/tests/test_mul_op.py
浏览文件 @
aad585b1
...
...
@@ -8,8 +8,8 @@ class TestMulOp(unittest.TestCase):
def
setUp
(
self
):
self
.
type
=
"mul"
self
.
X
=
np
.
random
.
random
((
32
,
7
84
)).
astype
(
"float32"
)
self
.
Y
=
np
.
random
.
random
((
7
84
,
100
)).
astype
(
"float32"
)
self
.
X
=
np
.
random
.
random
((
32
,
84
)).
astype
(
"float32"
)
self
.
Y
=
np
.
random
.
random
((
84
,
100
)).
astype
(
"float32"
)
self
.
Out
=
np
.
dot
(
self
.
X
,
self
.
Y
)
...
...
python/paddle/v2/framework/tests/test_rowwise_add_op.py
浏览文件 @
aad585b1
...
...
@@ -8,8 +8,8 @@ class TestRowwiseAddOp(unittest.TestCase):
def
setUp
(
self
):
self
.
type
=
"rowwise_add"
self
.
X
=
np
.
random
.
random
((
32
,
7
84
)).
astype
(
"float32"
)
self
.
b
=
np
.
random
.
random
(
7
84
).
astype
(
"float32"
)
self
.
X
=
np
.
random
.
random
((
32
,
84
)).
astype
(
"float32"
)
self
.
b
=
np
.
random
.
random
(
84
).
astype
(
"float32"
)
self
.
Out
=
np
.
add
(
self
.
X
,
self
.
b
)
...
...
python/paddle/v2/framework/tests/test_sgd_op.py
浏览文件 @
aad585b1
...
...
@@ -8,8 +8,8 @@ class TestSGD(unittest.TestCase):
def
setUp
(
self
):
self
.
type
=
"sgd"
self
.
param
=
numpy
.
random
.
random
((
342
,
34
5
)).
astype
(
"float32"
)
self
.
grad
=
numpy
.
random
.
random
((
342
,
34
5
)).
astype
(
"float32"
)
self
.
param
=
numpy
.
random
.
random
((
102
,
10
5
)).
astype
(
"float32"
)
self
.
grad
=
numpy
.
random
.
random
((
102
,
10
5
)).
astype
(
"float32"
)
self
.
learning_rate
=
0.1
self
.
param_out
=
self
.
param
-
self
.
learning_rate
*
self
.
grad
...
...
python/paddle/v2/framework/tests/test_tensor.py
浏览文件 @
aad585b1
...
...
@@ -7,16 +7,17 @@ class TestScope(unittest.TestCase):
def
test_int_tensor
(
self
):
scope
=
core
.
Scope
()
var
=
scope
.
new_var
(
"test_tensor"
)
place
=
core
.
CPUPlace
()
tensor
=
var
.
get_tensor
()
tensor
.
set_dims
([
1000
,
784
])
tensor
.
alloc_int
()
tensor
.
alloc_int
(
place
)
tensor_array
=
numpy
.
array
(
tensor
)
self
.
assertEqual
((
1000
,
784
),
tensor_array
.
shape
)
tensor_array
[
3
,
9
]
=
1
tensor_array
[
19
,
11
]
=
2
tensor
.
set
(
tensor_array
)
tensor
.
set
(
tensor_array
,
place
)
tensor_array_2
=
numpy
.
array
(
tensor
)
self
.
assertEqual
(
1.0
,
tensor_array_2
[
3
,
9
])
...
...
@@ -25,16 +26,18 @@ class TestScope(unittest.TestCase):
def
test_float_tensor
(
self
):
scope
=
core
.
Scope
()
var
=
scope
.
new_var
(
"test_tensor"
)
place
=
core
.
CPUPlace
()
tensor
=
var
.
get_tensor
()
tensor
.
set_dims
([
1000
,
784
])
tensor
.
alloc_float
()
tensor
.
alloc_float
(
place
)
tensor_array
=
numpy
.
array
(
tensor
)
self
.
assertEqual
((
1000
,
784
),
tensor_array
.
shape
)
tensor_array
[
3
,
9
]
=
1.0
tensor_array
[
19
,
11
]
=
2.0
tensor
.
set
(
tensor_array
)
tensor
.
set
(
tensor_array
,
place
)
tensor_array_2
=
numpy
.
array
(
tensor
)
self
.
assertAlmostEqual
(
1.0
,
tensor_array_2
[
3
,
9
])
...
...
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