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2a21d8b3
编写于
11月 23, 2016
作者:
D
dangqingqing
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差异文件
Merge branch 'develop' of
https://github.com/baidu/Paddle
into cn_doc
上级
24cfc5ab
85f0e184
变更
387
展开全部
隐藏空白更改
内联
并排
Showing
387 changed file
with
11013 addition
and
8006 deletion
+11013
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.pre-commit-config.yaml
.pre-commit-config.yaml
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README.md
README.md
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doc/build/build_from_source.md
doc/build/build_from_source.md
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doc_cn/build_and_install/cmake/cblas_settings.csv
doc_cn/build_and_install/cmake/cblas_settings.csv
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doc_cn/build_and_install/cmake/compile_options.csv
doc_cn/build_and_install/cmake/compile_options.csv
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doc_cn/build_and_install/cmake/compile_options.rst
doc_cn/build_and_install/cmake/compile_options.rst
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doc_cn/howto/how_to_write_docs/index.rst
doc_cn/howto/how_to_write_docs/index.rst
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paddle/api/Arguments.cpp
paddle/api/Arguments.cpp
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paddle/api/ConfigParser.cpp
paddle/api/ConfigParser.cpp
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paddle/api/GradientMachine.cpp
paddle/api/GradientMachine.cpp
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paddle/api/Internal.h
paddle/api/Internal.h
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paddle/api/Matrix.cpp
paddle/api/Matrix.cpp
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paddle/api/PaddleAPI.h
paddle/api/PaddleAPI.h
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paddle/api/Parameter.cpp
paddle/api/Parameter.cpp
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paddle/api/ParameterOptimizer.cpp
paddle/api/ParameterOptimizer.cpp
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paddle/api/SequenceGenerator.cpp
paddle/api/SequenceGenerator.cpp
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paddle/api/Trainer.cpp
paddle/api/Trainer.cpp
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paddle/api/Util.cpp
paddle/api/Util.cpp
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paddle/api/Vector.cpp
paddle/api/Vector.cpp
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paddle/cuda/include/hl_activation_functions.h
paddle/cuda/include/hl_activation_functions.h
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paddle/cuda/include/hl_aggregate.h
paddle/cuda/include/hl_aggregate.h
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paddle/cuda/include/hl_avx_functions.h
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paddle/cuda/include/hl_base.h
paddle/cuda/include/hl_base.h
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paddle/cuda/include/hl_batch_transpose.h
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paddle/cuda/include/hl_cnn.h
paddle/cuda/include/hl_cnn.h
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paddle/cuda/include/hl_cuda.h
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paddle/cuda/include/hl_cuda_cudnn.h
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paddle/cuda/include/hl_gpu.h
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paddle/cuda/include/hl_lstm.h
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paddle/cuda/include/hl_matrix.h
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paddle/cuda/include/hl_sequence.h
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paddle/gserver/activations/ActivationFunction.cpp
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paddle/gserver/activations/ActivationFunction.h
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paddle/gserver/dataproviders/MultiDataProvider.cpp
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paddle/gserver/dataproviders/MultiDataProvider.h
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paddle/gserver/dataproviders/ProtoDataProvider.cpp
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paddle/gserver/dataproviders/ProtoDataProvider.h
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paddle/gserver/dataproviders/ProtoReader.h
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paddle/gserver/dataproviders/PyDataProvider.cpp
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paddle/gserver/dataproviders/PyDataProvider.h
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paddle/gserver/dataproviders/PyDataProvider2.cpp
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paddle/gserver/evaluators/CTCErrorEvaluator.cpp
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paddle/gserver/evaluators/Evaluator.cpp
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paddle/gserver/evaluators/Evaluator.h
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paddle/gserver/gradientmachines/ParallelNeuralNetwork.cpp
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paddle/gserver/layers/TransposedFullMatrixProjection.cpp
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未找到文件。
.pre-commit-config.yaml
浏览文件 @
2a21d8b3
...
...
@@ -7,18 +7,14 @@
hooks
:
-
id
:
yapf
-
repo
:
https://github.com/pre-commit/pre-commit-hooks
sha
:
4ef03c4223ad322c7adaa6c6c0efb26b57df3b71
sha
:
7539d8bd1a00a3c1bfd34cdb606d3a6372e83469
hooks
:
-
id
:
check-added-large-files
-
id
:
check-merge-conflict
-
id
:
check-symlinks
-
id
:
detect-private-key
-
id
:
end-of-file-fixer
# TODO(yuyang): trailing whitespace has some bugs on markdown
# files now, please not add it to pre-commit hook now
# - id: trailing-whitespace
#
# TODO(yuyang): debug-statements not fit for Paddle, because
# not all of our python code is runnable. Some are used for
# documenation
# - id: debug-statements
-
repo
:
https://github.com/PaddlePaddle/clang-format-pre-commit-hook.git
sha
:
28c0ea8a67a3e2dbbf4822ef44e85b63a0080a29
hooks
:
-
id
:
clang-formater
README.md
浏览文件 @
2a21d8b3
# PaddlePaddle
[
![Build Status
](
https://travis-ci.org/PaddlePaddle/Paddle.svg?branch=develop
)
](https://travis-ci.org/
baidu
/Paddle)
[
![Build Status
](
https://travis-ci.org/PaddlePaddle/Paddle.svg?branch=develop
)
](https://travis-ci.org/
PaddlePaddle
/Paddle)
[
![Documentation Status
](
https://img.shields.io/badge/docs-latest-brightgreen.svg?style=flat
)
](http://www.paddlepaddle.org/)
[
![Documentation Status
](
https://img.shields.io/badge/中文文档-最新-brightgreen.svg
)
](http://www.paddlepaddle.org/cn/index.html)
[
![Coverage Status
](
https://coveralls.io/repos/github/PaddlePaddle/Paddle/badge.svg?branch=develop
)
](https://coveralls.io/github/
baidu
/Paddle?branch=develop)
[
![Release
](
https://img.shields.io/github/release/
baidu/Paddle.svg?colorB=fedcba
)
](https://github.com/baidu
/Paddle/releases)
[
![Coverage Status
](
https://coveralls.io/repos/github/PaddlePaddle/Paddle/badge.svg?branch=develop
)
](https://coveralls.io/github/
PaddlePaddle
/Paddle?branch=develop)
[
![Release
](
https://img.shields.io/github/release/
PaddlePaddle/Paddle.svg
)
](https://github.com/PaddlePaddle
/Paddle/releases)
[
![License
](
https://img.shields.io/badge/license-Apache%202-blue.svg
)
](LICENSE)
...
...
@@ -17,7 +17,7 @@ developed by Baidu scientists and engineers for the purpose of applying deep
learning to many products at Baidu.
Our vision is to enable deep learning for everyone via PaddlePaddle.
Please refer to our
[
release announcement
](
https://github.com/
baidu
/Paddle/releases
)
to track the latest feature of PaddlePaddle.
Please refer to our
[
release announcement
](
https://github.com/
PaddlePaddle
/Paddle/releases
)
to track the latest feature of PaddlePaddle.
## Features
...
...
@@ -92,7 +92,7 @@ Both [English Docs](http://paddlepaddle.org/doc/) and [Chinese Docs](http://padd
## Ask Questions
You are welcome to submit questions and bug reports as
[
Github Issues
](
https://github.com/
baidu/p
addle/issues
)
.
You are welcome to submit questions and bug reports as
[
Github Issues
](
https://github.com/
PaddlePaddle/P
addle/issues
)
.
## Copyright and License
PaddlePaddle is provided under the
[
Apache-2.0 license
](
LICENSE
)
.
doc/build/build_from_source.md
浏览文件 @
2a21d8b3
...
...
@@ -6,10 +6,10 @@ Installing from Sources
*
[
3. Build on Ubuntu
](
#ubuntu
)
## <span id="download">Download and Setup</span>
You can download PaddlePaddle from the
[
github source
](
https://github.com/
gangliao
/Paddle
)
.
You can download PaddlePaddle from the
[
github source
](
https://github.com/
PaddlePaddle
/Paddle
)
.
```
bash
git clone https://github.com/
baidu
/Paddle paddle
git clone https://github.com/
PaddlePaddle
/Paddle paddle
cd
paddle
```
...
...
doc_cn/build_and_install/cmake/cblas_settings.csv
浏览文件 @
2a21d8b3
MKL_ROOT,mkl的路径,在${MKL_ROOT}/include下需要包含mkl.h,在${MKL_ROOT}/lib目录下需要包含 mkl_core,mkl_sequential和mkl_intel_lp64三个库
ATLAS_ROOT,ATLAS库的路径,在${ATLAS_ROOT}/include下需要包含cblas.h,而在${ATLAS_ROOT}/lib下需要包含cblas和atlas两个库
OPENBLAS_ROOT,在${OPENBLAS_ROOT}/include下需要包含cblas.h,而在${OPENBLAS_ROOT}/lib下需要包含openblas库
REFERENCE_CBLAS_ROOT,在${REFERENCE_CBLAS_ROOT}/include下需要包含cblas.h,在${REFERENCE_CBLAS_ROOT}/lib下需要包含cblas库
\ No newline at end of file
编译选项,描述,注意
MKL_ROOT,MKL的路径,${MKL_ROOT}/include下需要包含mkl.h,${MKL_ROOT}/lib目录下需要包含mkl_core,mkl_sequential和mkl_intel_lp64三个库。
ATLAS_ROOT,ATLAS的路径,${ATLAS_ROOT}/include下需要包含cblas.h,${ATLAS_ROOT}/lib下需要包含cblas和atlas两个库。
OPENBLAS_ROOT,OpenBLAS的路径,${OPENBLAS_ROOT}/include下需要包含cblas.h,${OPENBLAS_ROOT}/lib下需要包含openblas库。
REFERENCE_CBLAS_ROOT,REFERENCE BLAS的路径,${REFERENCE_CBLAS_ROOT}/include下需要包含cblas.h,${REFERENCE_CBLAS_ROOT}/lib下需要包含cblas库。
\ No newline at end of file
doc_cn/build_and_install/cmake/compile_options.csv
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2a21d8b3
选项,说明,默认值
WITH_GPU,是否编译GPU支持。,是否寻找到cuda工具链
WITH_DOUBLE,是否使用双精度浮点数。,否
WITH_DSO,是否使用运行时动态加载cuda动态库,而非静态加载cuda动态库。,是
WITH_AVX,是否编译含有AVX指令集的PaddlePaddle二进制,是
WITH_PYTHON,是否内嵌python解释器。可以方便嵌入式工作。,是
WITH_STYLE_CHECK,是否编译时进行代码风格检查,是
WITH_RDMA,是否开启RDMA支持,否
WITH_GLOG,是否使用GLOG,如果不使用则会使用一个简化版的日志实现。可以方便嵌入式工作。,取决于是否寻找到GLOG
WITH_GFLAGS,是否使用GFLAGS,如果不使用则会使用一个简化版的命令行参数解析。可以方便嵌入式工作。,取决于是否寻找到GFLAGS
WITH_TIMER,是否开启计时功能开启计时功能会导致运行略慢,打印的日志变多。但是方便调试和benchmark,否
WITH_TESTING,是否开启单元测试,取决于是否寻找到gtest
WITH_DOC,是否编译英文文档,否
WITH_DOC_CN,是否编译中文文档,否
WITH_SWIG_PY,是否编译python的swig接口,python的swig接口可以方便进行预测和定制化训练,取决于是否找到swig
选项,说明,默认值
WITH_GPU,是否支持GPU。,取决于是否寻找到CUDA工具链
WITH_DOUBLE,是否使用双精度浮点数。,否
WITH_DSO,是否运行时动态加载CUDA动态库,而非静态加载CUDA动态库。,是
WITH_AVX,是否编译含有AVX指令集的PaddlePaddle二进制文件,是
WITH_PYTHON,是否内嵌PYTHON解释器。方便今后的嵌入式移植工作。,是
WITH_STYLE_CHECK,是否编译时进行代码风格检查,是
WITH_RDMA,是否开启RDMA,否
WITH_GLOG,是否开启GLOG。如果不开启,则会使用一个简化版的日志,同时方便今后的嵌入式移植工作。,取决于是否寻找到GLOG
WITH_GFLAGS,是否使用GFLAGS。如果不开启,则会使用一个简化版的命令行参数解析器,同时方便今后的嵌入式移植工作。,取决于是否寻找到GFLAGS
WITH_TIMER,是否开启计时功能。如果开启会导致运行略慢,打印的日志变多,但是方便调试和测Benchmark,否
WITH_TESTING,是否开启单元测试,取决于是否寻找到GTEST
WITH_DOC,是否编译中英文文档,否
WITH_SWIG_PY,是否编译PYTHON的SWIG接口,该接口可用于预测和定制化训练,取决于是否寻找到SWIG
\ No newline at end of file
doc_cn/build_and_install/cmake/compile_options.rst
浏览文件 @
2a21d8b3
设置PaddlePaddle的编译选项
==========================
PaddlePaddle的编译选项可以在调用cmake的时候设置。cmake是一个跨平台的编译脚本,调用
cmake可以将cmake项目文件,生成各个平台的makefile。详细的cmake使用方法可以参考
`cmake的官方文档 <https://cmake.org/cmake-tutorial>`_ 。
PaddlePaddle的编译选项是可以控制PaddlePaddle生成CPU/GPU版本二进制,链接何种blas等等。所有的
编译选项列表如下
PaddlePaddle的编译选项
----------------------
bool型的编译选项
++++++++++++++++
设置下列编译选项时,可以在cmake的命令行设置。使用 -D命令即可。例如
:code:`cmake -D WITH_GPU=OFF`
.. csv-table:: PaddlePaddle的bool型编译选项
:widths: 1, 7, 2
:file: compile_options.csv
blas相关的编译选项
++++++++++++++++++
PaddlePaddle可以使用 `MKL <https://software.intel.com/en-us/intel-mkl>`_ ,
`Atlas <http://math-atlas.sourceforge.net/>`_ ,
`OpenBlas <http://www.openblas.net/>`_ 和
`refference Blas <http://www.netlib.org/blas/>`_ ,任意一种cblas实现。
通过编译时指定路径来实现引用各种blas。
cmake编译时会首先在系统路径(/usr/lib\:/usr/local/lib)中寻找这些blas的实现。同时
也会读取相关路径变量来进行搜索。路径变量为\:
.. csv-table:: PaddlePaddle的cblas编译选项
:widths: 1, 9
:header: "编译选项", "描述"
:file: cblas_settings.csv
这些变量均可以使用 -D命令指定。例如 :code:`cmake -D MKL_ROOT=/opt/mkl/`。这些变
量也可以通过调用cmake命令前通过环境变量指定。例如
.. code-block:: bash
export MKL_ROOT=/opt/mkl
cmake
需要注意的是,这些变量只在第一次cmake的时候有效。如果在第一次cmake之后想要重新设
置这些变量,推荐清理( :code:`rm -rf` )掉编译目录后,再指定。
cuda/cudnn相关的编译选项
++++++++++++++++++++++++
PaddlePaddle可以使用 cudnn v2之后的任何一个cudnn版本来编译运行。但需要注意的是编译和
运行使用的cudnn尽量是同一个版本。推荐使用最新版本的cudnn v5.1。
在cmake配置时可以使用 :code:`CUDNN_ROOT` 来配置CUDNN的安装路径。使用的命令也是
-D,例如 :code:`cmake -D CUDNN_ROOT=/opt/cudnnv5` 。
需要注意的是,这些变量只在第一次cmake的时候有效。如果在第一次cmake之后想要重新设
置这些变量,推荐清理( :code:`rm -rf` )掉编译目录后,再指定。
PaddlePaddle的编译选项
======================
PaddlePaddle的编译选项,包括生成CPU/GPU二进制文件、链接何种BLAS库等。用户可在调用cmake的时候设置它们,详细的cmake使用方法可以参考 `官方文档 <https://cmake.org/cmake-tutorial>`_ 。
Bool型的编译选项
----------------
用户可在cmake的命令行中,通过使用 ``-D`` 命令设置该类编译选项,例如
.. code-block:: bash
cmake .. -DWITH_GPU=OFF
.. csv-table:: Bool型的编译选项
:widths: 1, 7, 2
:file: compile_options.csv
BLAS/CUDA/Cudnn的编译选项
--------------------------
BLAS
+++++
PaddlePaddle支持以下任意一种BLAS库:`MKL <https://software.intel.com/en-us/intel-mkl>`_ ,`ATLAS <http://math-atlas.sourceforge.net/>`_ ,`OpenBlAS <http://www.openblas.net/>`_ 和 `REFERENCE BLAS <http://www.netlib.org/blas/>`_ 。
.. csv-table:: BLAS路径相关的编译选项
:widths: 1, 2, 7
:file: cblas_settings.csv
CUDA/Cudnn
+++++++++++
PaddlePaddle可以使用cudnn v2之后的任何一个版本来编译运行,但尽量请保持编译和运行使用的cudnn是同一个版本。 我们推荐使用最新版本的cudnn v5.1。
编译选项的设置
++++++++++++++
PaddePaddle通过编译时指定路径来实现引用各种BLAS/CUDA/Cudnn库。cmake编译时,首先在系统路径(/usr/lib\:/usr/local/lib)中搜索这几个库,同时也会读取相关路径变量来进行搜索。 通过使用 ``-D`` 命令可以设置,例如
.. code-block:: bash
cmake .. -DMKL_ROOT=/opt/mkl/ -DCUDNN_ROOT=/opt/cudnnv5
注意:这几个编译选项的设置,只在第一次cmake的时候有效。如果之后想要重新设置,推荐清理整个编译目录(``rm -rf``)后,再指定。
\ No newline at end of file
doc_cn/howto/how_to_write_docs/index.rst
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2a21d8b3
...
...
@@ -2,32 +2,19 @@
如何贡献/修改PaddlePaddle的文档
###############################
PaddlePaddle的文档
使用 `cmake`_ 驱动 `sphinx`_ 生成。公有两个文档,:code:`doc` 和 :code:`doc_cn` 。这两者会在 `cmake`_ 中进行编译,生成后的文档会存储在服务器的 :code:`doc` 和 :code:`doc_cn` 两个
目录下。
PaddlePaddle的文档
包括英文文档 ``doc`` 和中文文档 ``doc_cn`` 两个部分。文档都是通过 `cmake`_ 驱动 `sphinx`_ 编译生成,生成后的文档分别存储在编译目录的 ``doc`` 和 ``doc_cn`` 两个子
目录下。
下面分几个部分介绍一下PaddlePaddle文档的贡献方法。
如何书写PaddlePaddle的文档
==========================
TBD
如何构建PaddlePaddle的文档
==========================
构建PaddlePaddle文档,需要使用构建Paddle的全部环境。准备这个环境相对来说比较复杂,所以本文档提供两种方式构建PaddlePaddle的文档,即
* 使用Docker构建PaddlePaddle的文档
* 直接构建PaddlePaddle的文档。
并且,我们推荐使用Docker来构建PaddlePaddle的文档。
PaddlePaddle的文档构建有直接构建和基于Docker构建两种方式。构建PaddlePaddle文档需要准备的环境相对较复杂,所以我们推荐使用基于Docker来构建PaddlePaddle的文档。
使用Docker构建PaddlePaddle的文档
--------------------------------
使用Docker构建PaddlePaddle的文档,首先要求在系统里安装好Docker工具包。安装Docker请参考 `Docker的官网 <https://docs.docker.com/>`_ 。
安装好Docker之后可以使用源码目录下的脚本构建文档,即
使用Docker构建PaddlePaddle的文档,需要在系统里先安装好Docker工具包。Docker安装请参考 `Docker的官网 <https://docs.docker.com/>`_ 。安装好Docker之后可以使用源码目录下的脚本构建文档,即
.. code-block:: bash
...
...
@@ -35,10 +22,10 @@ TBD
cd paddle/scripts/tools/build_docs
bash build_docs.sh
执行完这个脚本后,该目录下会生成两个目录,分别是
\:
编译完成后,该目录下会生成如下两个子目录
\:
* doc
目录,英文文档地址
* doc_cn
目录,中文文档地址
* doc
英文文档目录
* doc_cn
中文文档目录
打开浏览器访问对应目录下的index.html即可访问本地文档。
...
...
@@ -52,6 +39,10 @@ TBD
TBD
如何书写PaddlePaddle的文档
==========================
TBD
如何更新www.paddlepaddle.org文档
================================
...
...
paddle/api/Arguments.cpp
浏览文件 @
2a21d8b3
...
...
@@ -12,7 +12,6 @@ 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 "PaddleAPI.h"
#include "PaddleAPIPrivate.h"
...
...
@@ -112,7 +111,7 @@ void Arguments::setSlotSequenceStartPositions(size_t idx,
}
void
Arguments
::
setSlotSubSequenceStartPositions
(
size_t
idx
,
IVector
*
vec
)
throw
(
RangeError
)
{
size_t
idx
,
IVector
*
vec
)
throw
(
RangeError
)
{
auto
&
a
=
m
->
getArg
(
idx
);
auto
&
v
=
m
->
cast
<
paddle
::
IVector
>
(
vec
->
getSharedPtr
());
a
.
subSequenceStartPositions
=
std
::
make_shared
<
paddle
::
ICpuGpuVector
>
(
v
);
...
...
paddle/api/ConfigParser.cpp
浏览文件 @
2a21d8b3
...
...
@@ -12,7 +12,6 @@ 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 "PaddleAPI.h"
#include "PaddleAPIPrivate.h"
#include "paddle/trainer/Trainer.h"
...
...
@@ -44,8 +43,7 @@ TrainerConfig* TrainerConfig::createFromTrainerConfigFile(
return
retv
;
}
TrainerConfig
*
TrainerConfig
::
createFromProtoString
(
const
std
::
string
&
str
)
{
TrainerConfig
*
TrainerConfig
::
createFromProtoString
(
const
std
::
string
&
str
)
{
auto
retv
=
new
TrainerConfig
();
paddle
::
TrainerConfig
trainerConfigProto
;
auto
conf
=
std
::
make_shared
<
paddle
::
TrainerConfigHelper
>
(
trainerConfigProto
);
...
...
paddle/api/GradientMachine.cpp
浏览文件 @
2a21d8b3
...
...
@@ -12,7 +12,6 @@ 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 "PaddleAPI.h"
#include "PaddleAPIPrivate.h"
...
...
@@ -27,7 +26,8 @@ GradientMachine::GradientMachine() : m(new GradientMachinePrivate()) {}
GradientMachine
::~
GradientMachine
()
{
delete
m
;
}
GradientMachine
*
GradientMachine
::
createFromPaddleModelPtr
(
const
void
*
confPtr
,
GradientMatchineCreateMode
mode
,
const
void
*
confPtr
,
GradientMatchineCreateMode
mode
,
const
std
::
vector
<
int
>&
types
)
{
auto
&
conf
=
*
(
const
paddle
::
ModelConfig
*
)(
confPtr
);
std
::
vector
<
ParameterType
>
realTypes
;
...
...
@@ -44,7 +44,8 @@ GradientMachine* GradientMachine::createFromPaddleModelPtr(
}
GradientMachine
*
GradientMachine
::
createByConfigProtoStr
(
const
std
::
string
&
protoStr
,
GradientMatchineCreateMode
mode
,
const
std
::
string
&
protoStr
,
GradientMatchineCreateMode
mode
,
const
std
::
vector
<
int
>&
types
)
{
paddle
::
ModelConfig
conf
;
conf
.
ParseFromString
(
protoStr
);
...
...
@@ -56,13 +57,15 @@ GradientMachine* GradientMachine::createByConfigProtoStr(
}
GradientMachine
*
GradientMachine
::
createByModelConfig
(
ModelConfig
*
conf
,
GradientMatchineCreateMode
mode
,
ModelConfig
*
conf
,
GradientMatchineCreateMode
mode
,
const
std
::
vector
<
int
>&
types
)
{
auto
confPtr
=
&
conf
->
m
->
conf
->
getModelConfig
();
return
GradientMachine
::
createFromPaddleModelPtr
(
confPtr
,
mode
,
types
);
}
void
GradientMachine
::
forward
(
const
Arguments
&
inArgs
,
Arguments
*
outArgs
,
void
GradientMachine
::
forward
(
const
Arguments
&
inArgs
,
Arguments
*
outArgs
,
PassType
passType
)
{
auto
&
in
=
m
->
cast
<
std
::
vector
<
paddle
::
Argument
>>
(
inArgs
.
getInternalArgumentsPtr
());
...
...
@@ -99,7 +102,8 @@ void GradientMachine::backward(const UpdateCallback& callback) {
}
void
GradientMachine
::
forwardBackward
(
const
Arguments
&
inArgs
,
Arguments
*
outArgs
,
PassType
passType
,
Arguments
*
outArgs
,
PassType
passType
,
const
UpdateCallback
&
callback
)
{
auto
&
in
=
m
->
cast
<
std
::
vector
<
paddle
::
Argument
>>
(
inArgs
.
getInternalArgumentsPtr
());
...
...
@@ -129,7 +133,7 @@ Parameter* GradientMachine::getParameter(size_t i) throw(RangeError) {
void
GradientMachine
::
randParameters
()
{
m
->
machine
->
randParameters
();
}
Matrix
*
GradientMachine
::
getLayerOutput
(
const
std
::
string
&
layerName
)
const
throw
(
UnsupportError
)
{
throw
(
UnsupportError
)
{
auto
nn
=
std
::
dynamic_pointer_cast
<
paddle
::
NeuralNetwork
>
(
m
->
machine
);
if
(
nn
)
{
auto
mat
=
nn
->
getLayerOutput
(
layerName
);
...
...
@@ -140,8 +144,11 @@ Matrix* GradientMachine::getLayerOutput(const std::string& layerName) const
}
SequenceGenerator
*
GradientMachine
::
asSequenceGenerator
(
const
std
::
vector
<
std
::
string
>&
dict
,
size_t
begin_id
,
size_t
end_id
,
size_t
max_length
,
size_t
beam_size
)
{
const
std
::
vector
<
std
::
string
>&
dict
,
size_t
begin_id
,
size_t
end_id
,
size_t
max_length
,
size_t
beam_size
)
{
SequenceGenerator
*
r
=
SequenceGenerator
::
createByGradientMachineSharedPtr
(
&
m
->
machine
);
r
->
setDict
(
dict
);
...
...
paddle/api/Internal.h
浏览文件 @
2a21d8b3
...
...
@@ -12,7 +12,6 @@ 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 "PaddleAPI.h"
...
...
@@ -23,7 +22,8 @@ limitations under the License. */
template
<
typename
T1
,
typename
T2
>
void
staticCastVector
(
std
::
vector
<
T2
>*
dest
,
const
std
::
vector
<
T1
>&
src
)
{
dest
->
resize
(
src
.
size
());
std
::
transform
(
src
.
begin
(),
src
.
end
(),
dest
->
begin
(),
[](
T1
t
){
return
static_cast
<
T2
>
(
t
);
});
std
::
transform
(
src
.
begin
(),
src
.
end
(),
dest
->
begin
(),
[](
T1
t
)
{
return
static_cast
<
T2
>
(
t
);
});
}
paddle/api/Matrix.cpp
浏览文件 @
2a21d8b3
...
...
@@ -12,7 +12,6 @@ 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 "PaddleAPI.h"
#include "paddle/math/Matrix.h"
#include "paddle/math/SparseMatrix.h"
...
...
@@ -44,17 +43,21 @@ Matrix* Matrix::createZero(size_t height, size_t width, bool useGpu) {
return
m
;
}
Matrix
*
Matrix
::
createDense
(
const
std
::
vector
<
float
>&
data
,
size_t
height
,
size_t
width
,
bool
useGpu
)
{
Matrix
*
Matrix
::
createDense
(
const
std
::
vector
<
float
>&
data
,
size_t
height
,
size_t
width
,
bool
useGpu
)
{
auto
m
=
new
Matrix
();
m
->
m
->
mat
=
paddle
::
Matrix
::
create
(
height
,
width
,
useGpu
);
m
->
m
->
mat
->
copyFrom
(
data
.
data
(),
data
.
size
());
return
m
;
}
Matrix
*
Matrix
::
createDenseFromNumpy
(
float
*
data
,
int
dim1
,
int
dim2
,
bool
copy
,
bool
useGpu
)
throw
(
UnsupportError
)
{
Matrix
*
Matrix
::
createDenseFromNumpy
(
float
*
data
,
int
dim1
,
int
dim2
,
bool
copy
,
bool
useGpu
)
throw
(
UnsupportError
)
{
if
(
useGpu
)
{
/// Gpu mode only supports copy=True
if
(
!
copy
)
{
...
...
@@ -66,7 +69,9 @@ Matrix* Matrix::createDenseFromNumpy(float* data, int dim1, int dim2,
}
}
Matrix
*
Matrix
::
createCpuDenseFromNumpy
(
float
*
data
,
int
dim1
,
int
dim2
,
Matrix
*
Matrix
::
createCpuDenseFromNumpy
(
float
*
data
,
int
dim1
,
int
dim2
,
bool
copy
)
{
auto
m
=
new
Matrix
();
if
(
copy
)
{
...
...
@@ -85,12 +90,20 @@ Matrix* Matrix::createGpuDenseFromNumpy(float* data, int dim1, int dim2) {
return
m
;
}
Matrix
*
Matrix
::
createSparse
(
size_t
height
,
size_t
width
,
size_t
nnz
,
bool
isNonVal
,
bool
isTrans
,
bool
useGpu
)
{
Matrix
*
Matrix
::
createSparse
(
size_t
height
,
size_t
width
,
size_t
nnz
,
bool
isNonVal
,
bool
isTrans
,
bool
useGpu
)
{
auto
m
=
new
Matrix
();
m
->
m
->
mat
=
paddle
::
Matrix
::
createSparseMatrix
(
height
,
width
,
nnz
,
isNonVal
?
paddle
::
NO_VALUE
:
paddle
::
FLOAT_VALUE
,
isTrans
,
useGpu
);
height
,
width
,
nnz
,
isNonVal
?
paddle
::
NO_VALUE
:
paddle
::
FLOAT_VALUE
,
isTrans
,
useGpu
);
return
m
;
}
...
...
@@ -221,7 +234,8 @@ FloatArray Matrix::getData() const {
}
void
Matrix
::
sparseCopyFrom
(
const
std
::
vector
<
int
>&
rows
,
const
std
::
vector
<
int
>&
cols
,
const
std
::
vector
<
int
>&
rows
,
const
std
::
vector
<
int
>&
cols
,
const
std
::
vector
<
float
>&
vals
)
throw
(
UnsupportError
)
{
auto
cpuSparseMat
=
std
::
dynamic_pointer_cast
<
paddle
::
CpuSparseMatrix
>
(
m
->
mat
);
...
...
@@ -240,7 +254,8 @@ void Matrix::sparseCopyFrom(
void
*
Matrix
::
getSharedPtr
()
const
{
return
&
m
->
mat
;
}
void
Matrix
::
toNumpyMatInplace
(
float
**
view_data
,
int
*
dim1
,
void
Matrix
::
toNumpyMatInplace
(
float
**
view_data
,
int
*
dim1
,
int
*
dim2
)
throw
(
UnsupportError
)
{
auto
cpuMat
=
std
::
dynamic_pointer_cast
<
paddle
::
CpuMatrix
>
(
m
->
mat
);
if
(
cpuMat
)
{
...
...
@@ -251,7 +266,8 @@ void Matrix::toNumpyMatInplace(float** view_data, int* dim1,
throw
UnsupportError
();
}
}
void
Matrix
::
copyToNumpyMat
(
float
**
view_m_data
,
int
*
dim1
,
void
Matrix
::
copyToNumpyMat
(
float
**
view_m_data
,
int
*
dim1
,
int
*
dim2
)
throw
(
UnsupportError
)
{
static_assert
(
sizeof
(
paddle
::
real
)
==
sizeof
(
float
),
"Currently PaddleAPI only support for single "
...
...
@@ -269,8 +285,8 @@ void Matrix::copyToNumpyMat(float** view_m_data, int* dim1,
}
else
if
(
auto
gpuMat
=
dynamic_cast
<
paddle
::
GpuMatrix
*>
(
m
->
mat
.
get
()))
{
auto
src
=
gpuMat
->
getData
();
auto
dest
=
*
view_m_data
;
hl_memcpy_device2host
(
dest
,
src
,
sizeof
(
paddle
::
real
)
*
(
*
dim1
)
*
(
*
dim2
));
hl_memcpy_device2host
(
dest
,
src
,
sizeof
(
paddle
::
real
)
*
(
*
dim1
)
*
(
*
dim2
));
}
else
{
LOG
(
WARNING
)
<<
"Unexpected Situation"
;
throw
UnsupportError
();
...
...
@@ -278,7 +294,8 @@ void Matrix::copyToNumpyMat(float** view_m_data, int* dim1,
}
}
void
Matrix
::
copyFromNumpyMat
(
float
*
data
,
int
dim1
,
void
Matrix
::
copyFromNumpyMat
(
float
*
data
,
int
dim1
,
int
dim2
)
throw
(
UnsupportError
,
RangeError
)
{
if
(
isSparse
())
{
throw
UnsupportError
();
...
...
paddle/api/PaddleAPI.h
浏览文件 @
2a21d8b3
...
...
@@ -12,7 +12,6 @@ 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 <stddef.h>
...
...
@@ -61,8 +60,8 @@ class RangeError {};
/// Not support Error, such as access GPU memory directly, etc.
class
UnsupportError
:
public
std
::
runtime_error
{
public:
UnsupportError
()
:
std
::
runtime_error
(
" "
)
{};
UnsupportError
(
const
std
::
string
&
message
)
:
std
::
runtime_error
(
message
)
{};
UnsupportError
()
:
std
::
runtime_error
(
" "
){};
UnsupportError
(
const
std
::
string
&
message
)
:
std
::
runtime_error
(
message
){};
};
/// This type will map to python's list of float.
...
...
@@ -112,7 +111,8 @@ public:
/**
* Create A Matrix with height,width, which is filled by zero.
*/
static
Matrix
*
createZero
(
size_t
height
,
size_t
width
,
static
Matrix
*
createZero
(
size_t
height
,
size_t
width
,
bool
useGpu
=
isUsingGpu
());
/**
...
...
@@ -124,8 +124,11 @@ public:
*
* @note the default sparse type is SPARSE_CSR.
*/
static
Matrix
*
createSparse
(
size_t
height
,
size_t
width
,
size_t
nnz
,
bool
isNonVal
=
true
,
bool
trans
=
false
,
static
Matrix
*
createSparse
(
size_t
height
,
size_t
width
,
size_t
nnz
,
bool
isNonVal
=
true
,
bool
trans
=
false
,
bool
useGpu
=
isUsingGpu
());
/**
...
...
@@ -134,13 +137,17 @@ public:
* @param data list of float should be passed in python.
* @note the value will be copy into a new matrix.
*/
static
Matrix
*
createDense
(
const
std
::
vector
<
float
>&
data
,
size_t
height
,
size_t
width
,
bool
useGpu
=
isUsingGpu
());
static
Matrix
*
createDenseFromNumpy
(
float
*
data
,
int
dim1
,
int
dim2
,
bool
copy
=
true
,
bool
useGpu
=
isUsingGpu
())
throw
(
UnsupportError
);
static
Matrix
*
createDense
(
const
std
::
vector
<
float
>&
data
,
size_t
height
,
size_t
width
,
bool
useGpu
=
isUsingGpu
());
static
Matrix
*
createDenseFromNumpy
(
float
*
data
,
int
dim1
,
int
dim2
,
bool
copy
=
true
,
bool
useGpu
=
isUsingGpu
())
throw
(
UnsupportError
);
/**
* Create Cpu Dense Matrix from numpy matrix, dtype=float32
...
...
@@ -151,7 +158,9 @@ public:
* @param copy true if copy into a new matrix, false will create
* matrix inplace.
*/
static
Matrix
*
createCpuDenseFromNumpy
(
float
*
data
,
int
dim1
,
int
dim2
,
static
Matrix
*
createCpuDenseFromNumpy
(
float
*
data
,
int
dim1
,
int
dim2
,
bool
copy
=
false
);
/// Create Gpu Dense Matrix from numpy matrix, dtype=float32
...
...
@@ -171,11 +180,13 @@ public:
* numpy_mat = m.toNumpyMat()
* @endcode
*/
void
toNumpyMatInplace
(
float
**
view_data
,
int
*
dim1
,
void
toNumpyMatInplace
(
float
**
view_data
,
int
*
dim1
,
int
*
dim2
)
throw
(
UnsupportError
);
/// Copy To numpy mat.
void
copyToNumpyMat
(
float
**
view_m_data
,
int
*
dim1
,
void
copyToNumpyMat
(
float
**
view_m_data
,
int
*
dim1
,
int
*
dim2
)
throw
(
UnsupportError
);
/// Copy From Numpy Mat
...
...
@@ -248,15 +259,18 @@ public:
static
Vector
*
create
(
const
std
::
vector
<
float
>&
data
,
bool
useGpu
=
isUsingGpu
());
static
Vector
*
createVectorFromNumpy
(
float
*
data
,
int
dim
,
bool
copy
=
true
,
bool
useGpu
=
isUsingGpu
())
throw
(
UnsupportError
);
static
Vector
*
createVectorFromNumpy
(
float
*
data
,
int
dim
,
bool
copy
=
true
,
bool
useGpu
=
isUsingGpu
())
throw
(
UnsupportError
);
/**
* Create Cpu Vector from numpy array, which dtype=float32
*
* If copy is false, it will create vector inplace.
*/
static
Vector
*
createCpuVectorFromNumpy
(
float
*
data
,
int
dim
,
static
Vector
*
createCpuVectorFromNumpy
(
float
*
data
,
int
dim
,
bool
copy
=
false
);
/// Create Gpu Vector from numpy array, which dtype=float32
...
...
@@ -312,16 +326,19 @@ public:
static
IVector
*
create
(
const
std
::
vector
<
int
>&
data
,
bool
useGpu
=
isUsingGpu
());
static
IVector
*
createVectorFromNumpy
(
int
*
data
,
int
dim
,
bool
copy
=
true
,
bool
useGpu
=
isUsingGpu
())
throw
(
UnsupportError
);
static
IVector
*
createVectorFromNumpy
(
int
*
data
,
int
dim
,
bool
copy
=
true
,
bool
useGpu
=
isUsingGpu
())
throw
(
UnsupportError
);
/**
* Create Cpu IVector from numpy array, which dtype=int32
*
* If copy is false, it will create vector inplace
*/
static
IVector
*
createCpuVectorFromNumpy
(
int
*
data
,
int
dim
,
static
IVector
*
createCpuVectorFromNumpy
(
int
*
data
,
int
dim
,
bool
copy
=
false
);
/**
* Create Gpu IVector from numpy array, which dtype=int32
...
...
@@ -605,7 +622,8 @@ class ParameterTraverseCallback {
public:
~
ParameterTraverseCallback
();
void
apply
(
const
std
::
vector
<
Vector
*>&
vecs
,
const
ParameterConfig
&
config
,
void
apply
(
const
std
::
vector
<
Vector
*>&
vecs
,
const
ParameterConfig
&
config
,
size_t
sparseId
);
private:
...
...
@@ -638,7 +656,8 @@ public:
void
finishBatch
();
void
update
(
const
std
::
vector
<
Vector
*>&
vecs
,
const
ParameterConfig
&
conf
,
void
update
(
const
std
::
vector
<
Vector
*>&
vecs
,
const
ParameterConfig
&
conf
,
size_t
sparseId
=
NO_SPARSE_ID
);
std
::
vector
<
int
>
getParameterTypes
()
const
;
...
...
@@ -678,7 +697,8 @@ public:
* model config by TrainerConfig
*/
static
GradientMachine
*
createByModelConfig
(
ModelConfig
*
conf
,
GradientMatchineCreateMode
mode
=
CREATE_MODE_NORMAL
,
ModelConfig
*
conf
,
GradientMatchineCreateMode
mode
=
CREATE_MODE_NORMAL
,
const
std
::
vector
<
int
>&
parameterTypes
=
defaultParamTypes
);
/**
...
...
@@ -701,7 +721,8 @@ public:
/**
* Combine forward/backward
*/
void
forwardBackward
(
const
Arguments
&
inArgs
,
Arguments
*
outArgs
,
void
forwardBackward
(
const
Arguments
&
inArgs
,
Arguments
*
outArgs
,
PassType
passType
,
const
UpdateCallback
&
callback
=
UpdateCallback
());
...
...
@@ -722,14 +743,17 @@ public:
*/
SequenceGenerator
*
asSequenceGenerator
(
const
std
::
vector
<
std
::
string
>&
dict
=
std
::
vector
<
std
::
string
>
(),
size_t
begin_id
=
0UL
,
size_t
end_id
=
0UL
,
size_t
max_length
=
100UL
,
size_t
begin_id
=
0UL
,
size_t
end_id
=
0UL
,
size_t
max_length
=
100UL
,
size_t
beam_size
=
-
1UL
);
private:
GradientMachinePrivate
*
m
;
static
GradientMachine
*
createFromPaddleModelPtr
(
const
void
*
confPtr
,
GradientMatchineCreateMode
mode
,
const
void
*
confPtr
,
GradientMatchineCreateMode
mode
,
const
std
::
vector
<
int
>&
types
);
// Not to use c++ 11 init-list, so we use static var as function default arg.
...
...
@@ -751,8 +775,8 @@ public:
/// Create A Trainer By TrainerConfig. using paddle command line.
static
Trainer
*
createByCommandLine
()
throw
(
IOError
);
static
Trainer
*
create
(
TrainerConfig
*
optConfig
,
GradientMachine
*
gm
)
throw
(
IOError
);
static
Trainer
*
create
(
TrainerConfig
*
optConfig
,
GradientMachine
*
gm
)
throw
(
IOError
);
/// Start training
void
startTrain
();
...
...
paddle/api/Parameter.cpp
浏览文件 @
2a21d8b3
...
...
@@ -12,7 +12,6 @@ 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 "PaddleAPI.h"
#include "paddle/parameter/Parameter.h"
...
...
paddle/api/ParameterOptimizer.cpp
浏览文件 @
2a21d8b3
...
...
@@ -12,7 +12,6 @@ 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 "PaddleAPI.h"
#include "PaddleAPIPrivate.h"
#include "paddle/parameter/ParameterOptimizer.h"
...
...
@@ -32,17 +31,21 @@ struct ParameterTraverseCallbackPrivate {
const
paddle
::
ParameterOptimizer
::
TraverseCallback
&
callback
)
:
callback
(
callback
)
{}
void
apply
(
const
std
::
vector
<
Vector
*>&
vecs
,
const
ParameterConfig
&
conf
,
void
apply
(
const
std
::
vector
<
Vector
*>&
vecs
,
const
ParameterConfig
&
conf
,
size_t
sparseId
)
{
std
::
vector
<
paddle
::
VectorPtr
>
real_vecs
;
real_vecs
.
resize
(
vecs
.
size
());
std
::
transform
(
vecs
.
begin
(),
vecs
.
end
(),
real_vecs
.
begin
(),
[](
Vector
*
v
)
{
if
(
v
)
{
return
*
(
paddle
::
VectorPtr
*
)(
v
->
getSharedPtr
());
}
else
{
return
paddle
::
VectorPtr
();
}
});
std
::
transform
(
vecs
.
begin
(),
vecs
.
end
(),
real_vecs
.
begin
(),
[](
Vector
*
v
)
{
if
(
v
)
{
return
*
(
paddle
::
VectorPtr
*
)(
v
->
getSharedPtr
());
}
else
{
return
paddle
::
VectorPtr
();
}
});
paddle
::
ParameterConfig
&
real_conf
=
*
(
paddle
::
ParameterConfig
*
)(
const_cast
<
ParameterConfig
&>
(
conf
)
...
...
@@ -86,10 +89,12 @@ void ParameterOptimizer::startBatch(size_t numSamplesProcessed) {
void
ParameterOptimizer
::
finishBatch
()
{
m
->
optimizer
->
finishBatch
();
}
void
ParameterOptimizer
::
update
(
const
std
::
vector
<
Vector
*>&
vecs
,
const
ParameterConfig
&
conf
,
size_t
sparseId
)
{
ParameterTraverseCallbackPrivate
invoker
([
&
](
const
paddle
::
VectorPtr
_vecs
[],
const
paddle
::
ParameterConfig
&
config
,
size_t
sid
=
-
1UL
)
{
m
->
optimizer
->
update
(
_vecs
,
config
,
sid
);
});
const
ParameterConfig
&
conf
,
size_t
sparseId
)
{
ParameterTraverseCallbackPrivate
invoker
(
[
&
](
const
paddle
::
VectorPtr
_vecs
[],
const
paddle
::
ParameterConfig
&
config
,
size_t
sid
=
-
1UL
)
{
m
->
optimizer
->
update
(
_vecs
,
config
,
sid
);
});
invoker
.
apply
(
vecs
,
conf
,
sparseId
);
}
...
...
@@ -116,8 +121,9 @@ void ParameterTraverseCallback::apply(const std::vector<Vector*>& vecs,
ParameterTraverseCallback
*
ParameterOptimizer
::
needSpecialTraversal
(
const
ParameterConfig
&
config
)
const
{
auto
&
param_config
=
*
(
paddle
::
ParameterConfig
*
)
const_cast
<
ParameterConfig
&>
(
config
).
getRawPtr
();
auto
&
param_config
=
*
(
paddle
::
ParameterConfig
*
)
const_cast
<
ParameterConfig
&>
(
config
)
.
getRawPtr
();
auto
callback
=
m
->
optimizer
->
needSpecialTraversal
(
param_config
);
if
(
callback
)
{
auto
retCallback
=
new
ParameterTraverseCallback
();
...
...
paddle/api/SequenceGenerator.cpp
浏览文件 @
2a21d8b3
...
...
@@ -12,7 +12,6 @@ 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 "PaddleAPI.h"
#include "paddle/gserver/gradientmachines/GradientMachine.h"
#include "paddle/parameter/Argument.h"
...
...
@@ -42,8 +41,10 @@ struct Path {
// position
static
void
findNBest
(
paddle
::
GradientMachine
*
gradMachine
,
std
::
vector
<
paddle
::
Argument
>&
inArgs
,
std
::
vector
<
Path
>&
finalPaths
,
size_t
bos_id
,
size_t
eos_id
,
size_t
max_length
)
{
std
::
vector
<
Path
>&
finalPaths
,
size_t
bos_id
,
size_t
eos_id
,
size_t
max_length
)
{
std
::
vector
<
Path
>
paths
;
Path
emptyPath
;
paths
.
push_back
(
emptyPath
);
...
...
@@ -166,7 +167,8 @@ public:
if
(
id
<
getSize
())
{
Path
&
p
=
(
*
path_
)[
id
];
std
::
ostringstream
sout
;
std
::
transform
(
p
.
ids
.
begin
(),
p
.
ids
.
end
(),
std
::
transform
(
p
.
ids
.
begin
(),
p
.
ids
.
end
(),
std
::
ostream_iterator
<
std
::
string
>
(
sout
,
split
?
" "
:
""
),
[
&
](
int
id
)
{
return
(
*
dict_
)[
id
];
});
return
sout
.
str
();
...
...
paddle/api/Trainer.cpp
浏览文件 @
2a21d8b3
...
...
@@ -64,12 +64,11 @@ Trainer* Trainer::createByCommandLine() throw(IOError) {
Trainer
::
Trainer
(
TrainerConfig
*
config
,
GradientMachine
*
gm
)
:
m
(
new
TrainerPrivate
())
{
m
->
init
(
config
->
m
->
conf
,
/* testing= */
false
,
gm
?
gm
->
m
->
machine
:
nullptr
);
m
->
init
(
config
->
m
->
conf
,
/* testing= */
false
,
gm
?
gm
->
m
->
machine
:
nullptr
);
}
Trainer
*
Trainer
::
create
(
TrainerConfig
*
config
,
GradientMachine
*
gm
)
throw
(
IOError
)
{
Trainer
*
Trainer
::
create
(
TrainerConfig
*
config
,
GradientMachine
*
gm
)
throw
(
IOError
)
{
auto
retv
=
new
Trainer
(
config
,
gm
);
if
(
retv
->
m
->
getConfig
().
IsInitialized
())
{
return
retv
;
...
...
@@ -134,15 +133,17 @@ void Trainer::finishTestPeriod() { m->finishTestPeriod(); }
Matrix
*
Trainer
::
getLayerOutput
(
const
std
::
string
&
layerName
)
{
auto
nn
=
std
::
dynamic_pointer_cast
<
paddle
::
NeuralNetwork
>
(
this
->
m
->
getGradientMachine
());
this
->
m
->
getGradientMachine
());
CHECK
(
nn
)
<<
"trainerInternal_.getGradientMachine() is not NeuralNetwork"
;
auto
m
=
nn
->
getLayerOutput
(
layerName
);
return
Matrix
::
createByPaddleMatrixPtr
(
&
m
);
}
void
Trainer
::
forwardOneBatch
(
size_t
batchSize
)
{
m
->
forwardOneBatch
(
batchSize
);
}
void
Trainer
::
forwardOneBatch
(
size_t
batchSize
)
{
m
->
forwardOneBatch
(
batchSize
);
}
bool
TrainerPrivate
::
forwardOneBatch
(
size_t
batchSize
)
{
bool
TrainerPrivate
::
forwardOneBatch
(
size_t
batchSize
)
{
CHECK
(
dataProvider_
)
<<
"data_provider is not specified"
;
paddle
::
DataBatch
dataBatch
;
int
num
=
dataProvider_
->
getNextBatch
(
batchSize
,
&
dataBatch
);
...
...
@@ -156,7 +157,6 @@ bool TrainerPrivate::forwardOneBatch(size_t batchSize) {
void
TrainerPrivate
::
forwardOneDataBatch
(
const
std
::
vector
<
paddle
::
Argument
>&
inArgs
)
{
std
::
vector
<
paddle
::
Argument
>&
outArgs
=
forwardOutput_
;
if
(
config_
->
getOptConfig
().
use_sparse_remote_updater
())
{
...
...
paddle/api/Util.cpp
浏览文件 @
2a21d8b3
...
...
@@ -37,13 +37,15 @@ FloatArray::FloatArray(const float* b, const size_t l)
IntArray
::
IntArray
(
const
int
*
b
,
const
size_t
l
,
bool
f
)
:
buf
(
b
),
length
(
l
),
needFree
(
f
)
{}
IntWithFloatArray
::
IntWithFloatArray
(
const
float
*
v
,
const
int
*
i
,
size_t
l
,
IntWithFloatArray
::
IntWithFloatArray
(
const
float
*
v
,
const
int
*
i
,
size_t
l
,
bool
f
)
:
valBuf
(
v
),
idxBuf
(
i
),
length
(
l
),
needFree
(
f
)
{}
bool
isUsingGpu
()
{
return
FLAGS_use_gpu
;
}
bool
isUsingGpu
()
{
return
FLAGS_use_gpu
;
}
void
setUseGpu
(
bool
useGpu
)
{
FLAGS_use_gpu
=
useGpu
;
}
void
setUseGpu
(
bool
useGpu
)
{
FLAGS_use_gpu
=
useGpu
;
}
bool
isGpuVersion
()
{
#ifdef PADDLE_ONLY_CPU
...
...
paddle/api/Vector.cpp
浏览文件 @
2a21d8b3
...
...
@@ -12,7 +12,6 @@ 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 "PaddleAPI.h"
#include "paddle/math/Vector.h"
...
...
@@ -39,8 +38,10 @@ IVector* IVector::create(const std::vector<int>& data, bool useGpu) {
return
v
;
}
IVector
*
IVector
::
createVectorFromNumpy
(
int
*
data
,
int
dim
,
bool
copy
,
bool
useGpu
)
throw
(
UnsupportError
){
IVector
*
IVector
::
createVectorFromNumpy
(
int
*
data
,
int
dim
,
bool
copy
,
bool
useGpu
)
throw
(
UnsupportError
)
{
if
(
useGpu
)
{
/// if use gpu only copy=true is supported
if
(
!
copy
)
{
...
...
@@ -137,8 +138,8 @@ void IVector::copyToNumpyArray(int** view_m_data, int* dim1) {
if
(
auto
cpuVec
=
dynamic_cast
<
paddle
::
CpuIVector
*>
(
m
->
vec
.
get
()))
{
std
::
memcpy
(
*
view_m_data
,
cpuVec
->
getData
(),
sizeof
(
int
)
*
(
*
dim1
));
}
else
if
(
auto
gpuVec
=
dynamic_cast
<
paddle
::
GpuIVector
*>
(
m
->
vec
.
get
()))
{
hl_memcpy_device2host
(
*
view_m_data
,
gpuVec
->
getData
(),
sizeof
(
int
)
*
(
*
dim1
));
hl_memcpy_device2host
(
*
view_m_data
,
gpuVec
->
getData
(),
sizeof
(
int
)
*
(
*
dim1
));
}
else
{
LOG
(
INFO
)
<<
"Unexpected situation"
;
}
...
...
@@ -201,8 +202,10 @@ Vector* Vector::createByPaddleVectorPtr(void* ptr) {
}
}
Vector
*
Vector
::
createVectorFromNumpy
(
float
*
data
,
int
dim
,
bool
copy
,
bool
useGpu
)
throw
(
UnsupportError
){
Vector
*
Vector
::
createVectorFromNumpy
(
float
*
data
,
int
dim
,
bool
copy
,
bool
useGpu
)
throw
(
UnsupportError
)
{
if
(
useGpu
)
{
/// if use gpu only copy=True is supported
if
(
!
copy
)
{
...
...
@@ -251,8 +254,8 @@ void Vector::copyToNumpyArray(float** view_m_data, int* dim1) {
if
(
auto
cpuVec
=
dynamic_cast
<
paddle
::
CpuVector
*>
(
m
->
vec
.
get
()))
{
std
::
memcpy
(
*
view_m_data
,
cpuVec
->
getData
(),
sizeof
(
float
)
*
(
*
dim1
));
}
else
if
(
auto
gpuVec
=
dynamic_cast
<
paddle
::
CpuVector
*>
(
m
->
vec
.
get
()))
{
hl_memcpy_device2host
(
*
view_m_data
,
gpuVec
->
getData
(),
sizeof
(
float
)
*
(
*
dim1
));
hl_memcpy_device2host
(
*
view_m_data
,
gpuVec
->
getData
(),
sizeof
(
float
)
*
(
*
dim1
));
}
else
{
LOG
(
INFO
)
<<
"Unexpected situation"
;
}
...
...
paddle/cuda/include/hl_activation_functions.h
浏览文件 @
2a21d8b3
...
...
@@ -12,7 +12,6 @@ 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. */
#ifndef HL_ACTIVATION_FUNCTIONS_H_
#define HL_ACTIVATION_FUNCTIONS_H_
...
...
@@ -21,11 +20,8 @@ limitations under the License. */
/**
* Active functions: sigmoid, relu, tanh and linear.
*/
#define HPPL_ACTIVE_FUNCTION {hppl::sigmoid, \
hppl::relu, \
hppl::tanh, \
hppl::linear \
}
#define HPPL_ACTIVE_FUNCTION \
{ hppl::sigmoid, hppl::relu, hppl::tanh, hppl::linear }
namespace
hppl
{
...
...
@@ -42,18 +38,18 @@ public:
#ifdef __NVCC__
namespace
gpu
{
static
__device__
Active
<
real
>::
forward
forward
[]
=
HPPL_ACTIVE_FUNCTION
;
static
__device__
Active
<
real
>::
forward
forward
[]
=
HPPL_ACTIVE_FUNCTION
;
static
__device__
Active
<
real
>::
backward
backward
[]
=
HPPL_ACTIVE_FUNCTION
;
}
#else
namespace
cpu
{
static
Active
<
real
>::
forward
forward
[]
=
HPPL_ACTIVE_FUNCTION
;
static
Active
<
real
>::
forward
forward
[]
=
HPPL_ACTIVE_FUNCTION
;
static
Active
<
real
>::
backward
backward
[]
=
HPPL_ACTIVE_FUNCTION
;
}
#ifdef __AVX__
namespace
avx
{
static
Active
<
__m256
>::
forward
forward
[]
=
HPPL_ACTIVE_FUNCTION
;
static
Active
<
__m256
>::
forward
forward
[]
=
HPPL_ACTIVE_FUNCTION
;
static
Active
<
__m256
>::
backward
backward
[]
=
HPPL_ACTIVE_FUNCTION
;
}
#endif
...
...
paddle/cuda/include/hl_aggregate.h
浏览文件 @
2a21d8b3
...
...
@@ -12,7 +12,6 @@ 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. */
#ifndef HL_AGGREGATE_H_
#define HL_AGGREGATE_H_
...
...
paddle/cuda/include/hl_avx_functions.h
浏览文件 @
2a21d8b3
...
...
@@ -12,22 +12,21 @@ 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. */
#ifndef HL_AVX_FUNCTIONS_H_
#define HL_AVX_FUNCTIONS_H_
#include <immintrin.h>
namespace
hppl
{
__m256
relu
(
const
__m256
a
);
__m256
sigmoid
(
const
__m256
a
);
__m256
tanh
(
const
__m256
a
);
__m256
linear
(
const
__m256
a
);
__m256
relu
(
const
__m256
a
,
const
__m256
b
);
__m256
sigmoid
(
const
__m256
a
,
const
__m256
b
);
__m256
tanh
(
const
__m256
a
,
const
__m256
b
);
__m256
linear
(
const
__m256
a
,
const
__m256
b
);
__m256
relu
(
const
__m256
a
);
__m256
sigmoid
(
const
__m256
a
);
__m256
tanh
(
const
__m256
a
);
__m256
linear
(
const
__m256
a
);
__m256
relu
(
const
__m256
a
,
const
__m256
b
);
__m256
sigmoid
(
const
__m256
a
,
const
__m256
b
);
__m256
tanh
(
const
__m256
a
,
const
__m256
b
);
__m256
linear
(
const
__m256
a
,
const
__m256
b
);
}
// namespace hppl
#endif // HL_AVX_FUNCTIONS_H_
paddle/cuda/include/hl_base.h
浏览文件 @
2a21d8b3
...
...
@@ -12,8 +12,6 @@ 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. */
#ifndef HL_BASE_H_
#define HL_BASE_H_
...
...
@@ -33,36 +31,36 @@ limitations under the License. */
* HPPL_STREAM_DEFAULT is HPPL default stream.
*/
typedef
enum
{
HPPL_STREAM_DEFAULT
=
0
,
/* Thread Default Stream*/
HPPL_STREAM_1
=
1
,
HPPL_STREAM_2
=
2
,
HPPL_STREAM_3
=
3
,
HPPL_STREAM_4
=
4
,
HPPL_THREAD_STREAM_1
=
5
,
HPPL_THREAD_STREAM_2
=
6
,
HPPL_THREAD_STREAM_3
=
7
,
HPPL_THREAD_STREAM_4
=
8
,
HPPL_STREAM_END
HPPL_STREAM_DEFAULT
=
0
,
/* Thread Default Stream*/
HPPL_STREAM_1
=
1
,
HPPL_STREAM_2
=
2
,
HPPL_STREAM_3
=
3
,
HPPL_STREAM_4
=
4
,
HPPL_THREAD_STREAM_1
=
5
,
HPPL_THREAD_STREAM_2
=
6
,
HPPL_THREAD_STREAM_3
=
7
,
HPPL_THREAD_STREAM_4
=
8
,
HPPL_STREAM_END
}
hl_stream_t
;
/**
* @brief HPPL activation mode.
*/
typedef
enum
{
HL_ACTIVATION_SIGMOID
=
0
,
HL_ACTIVATION_RELU
=
1
,
HL_ACTIVATION_TANH
=
2
,
HL_ACTIVATION_LINEAR
=
3
,
HL_ACTIVATION_END
HL_ACTIVATION_SIGMOID
=
0
,
HL_ACTIVATION_RELU
=
1
,
HL_ACTIVATION_TANH
=
2
,
HL_ACTIVATION_LINEAR
=
3
,
HL_ACTIVATION_END
}
hl_activation_mode_t
;
/**
* @brief Transpose type.
*/
typedef
enum
{
HPPL_OP_N
=
0
,
/* transpose */
HPPL_OP_T
=
1
,
/* non transpose */
HPPL_OP_END
HPPL_OP_N
=
0
,
/* transpose */
HPPL_OP_T
=
1
,
/* non transpose */
HPPL_OP_END
}
hl_trans_op_t
;
/**
...
...
@@ -148,23 +146,21 @@ typedef struct {
* @brief Sparse matrix value type.
*/
typedef
enum
{
HL_NO_VALUE
=
0
,
/* matrix values only 0 or 1 */
HL_FLOAT_VALUE
=
1
,
HL_VALUE_END
HL_NO_VALUE
=
0
,
/* matrix values only 0 or 1 */
HL_FLOAT_VALUE
=
1
,
HL_VALUE_END
}
hl_matrix_value_t
;
/**
* @brief HPPL matrix format.
*/
typedef
enum
{
HL_SPARSE_CSR
=
0
,
HL_SPARSE_CSC
=
1
,
HL_SPARSE_END
HL_SPARSE_CSR
=
0
,
HL_SPARSE_CSC
=
1
,
HL_SPARSE_END
}
hl_matrix_format_t
;
typedef
struct
_hl_matrix_s
*
hl_matrix_s
;
typedef
struct
_hl_matrix_s
*
hl_matrix_s
;
/**
* @brief HPPL sparse matrix.
...
...
@@ -177,12 +173,12 @@ typedef struct _hl_matrix_s * hl_matrix_s;
* @param nnz nonzero values of sparse matrix.
*/
typedef
struct
{
hl_matrix_s
matrix
;
hl_matrix_format_t
format
;
hl_matrix_value_t
type
;
int
rows
;
int
cols
;
size_t
nnz
;
hl_matrix_s
matrix
;
hl_matrix_format_t
format
;
hl_matrix_value_t
type
;
int
rows
;
int
cols
;
size_t
nnz
;
}
_hl_sparse_matrix_s
,
*
hl_sparse_matrix_s
;
#ifndef PADDLE_TYPE_DOUBLE
...
...
@@ -195,7 +191,7 @@ typedef struct {
*
* HL_FLOAT_MIN: 1.17549435e-38F
*/
#define HL_FLOAT_MAX
3.40282347e+38F
#define HL_FLOAT_MAX 3.40282347e+38F
/**
* if real == double
*
...
...
@@ -203,20 +199,18 @@ typedef struct {
*
* HL_FLOAT_MIN: 2.2250738585072014e-308
*/
#define HL_FLOAT_MIN
1.17549435e-38F
#define HL_FLOAT_MIN 1.17549435e-38F
#else
#define HL_FLOAT_MAX
1.7976931348623157e+308
#define HL_FLOAT_MIN
2.2250738585072014e-308
#define HL_FLOAT_MAX 1.7976931348623157e+308
#define HL_FLOAT_MIN 2.2250738585072014e-308
#endif
/**
* The maximum input value for exp, used to avoid overflow problem.
*
* Currently only used for tanh function.
*/
#define EXP_MAX_INPUT 40.0
#define EXP_MAX_INPUT 40.0
/**
* @brief DIVUP(x, y) is similar to ceil(x / y).
...
...
@@ -224,7 +218,7 @@ typedef struct {
* the size of blockDim.
*/
#ifndef DIVUP
#define DIVUP(x, y) (((x) + (y)
-
1) / (y))
#define DIVUP(x, y) (((x) + (y)
-
1) / (y))
#endif
#ifdef __NVCC__
...
...
@@ -233,7 +227,7 @@ typedef struct {
#include "hl_cuda.h"
#include "cuda_runtime.h"
extern
__thread
bool
g_sync_flag
;
extern
__thread
bool
g_sync_flag
;
extern
__thread
cudaStream_t
default_stream
;
#define STREAM_DEFAULT default_stream
...
...
@@ -241,16 +235,15 @@ extern __thread cudaStream_t default_stream;
* @brief Check cuda kernel execution.
* @param msg error string
*/
#define CHECK_SYNC(msg) \
if (true == g_sync_flag) { \
hl_stream_synchronize(HPPL_STREAM_DEFAULT); \
cudaError_t err \
= (cudaError_t)hl_get_device_last_error(); \
CHECK_EQ(cudaSuccess, err) << "[" << msg << "] " \
<< "CUDA error: " \
<< hl_get_device_error_string((size_t)err); \
#define CHECK_SYNC(msg) \
if (true == g_sync_flag) { \
hl_stream_synchronize(HPPL_STREAM_DEFAULT); \
cudaError_t err = (cudaError_t)hl_get_device_last_error(); \
CHECK_EQ(cudaSuccess, err) \
<< "[" << msg << "] " \
<< "CUDA error: " << hl_get_device_error_string((size_t)err); \
}
#endif
/* __NVCC__ */
#endif
/* __NVCC__ */
#endif
/* HL_BASE_H_ */
#endif
/* HL_BASE_H_ */
paddle/cuda/include/hl_batch_transpose.h
浏览文件 @
2a21d8b3
...
...
@@ -12,7 +12,6 @@ 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. */
#ifndef HL_BATCH_TRANSPOSE_H_
#define HL_BATCH_TRANSPOSE_H_
...
...
@@ -31,10 +30,7 @@ limitations under the License. */
* order. Each batch has height * width data, which are
* arranged in height-first (or row-first) manner.
*/
extern
void
batchTranspose
(
const
real
*
input
,
real
*
output
,
int
width
,
int
height
,
int
batchSize
);
extern
void
batchTranspose
(
const
real
*
input
,
real
*
output
,
int
width
,
int
height
,
int
batchSize
);
#endif // HL_BATCH_TRANSPOSE_H_
paddle/cuda/include/hl_cnn.h
浏览文件 @
2a21d8b3
...
...
@@ -12,7 +12,6 @@ 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. */
#ifndef HL_CNN_H_
#define HL_CNN_H_
...
...
@@ -37,15 +36,21 @@ limitations under the License. */
* @param[in] alpha
* @param[in] beta
*/
extern
void
hl_shrink_col2feature
(
const
real
*
dataCol
,
size_t
channels
,
size_t
height
,
size_t
width
,
size_t
blockH
,
size_t
blockW
,
size_t
strideH
,
size_t
strideW
,
size_t
paddingH
,
size_t
paddingW
,
size_t
outputH
,
size_t
outputW
,
real
*
dataIm
,
real
alpha
=
1
.
0
f
,
real
beta
=
0
.
0
f
);
extern
void
hl_shrink_col2feature
(
const
real
*
dataCol
,
size_t
channels
,
size_t
height
,
size_t
width
,
size_t
blockH
,
size_t
blockW
,
size_t
strideH
,
size_t
strideW
,
size_t
paddingH
,
size_t
paddingW
,
size_t
outputH
,
size_t
outputW
,
real
*
dataIm
,
real
alpha
=
1
.
0
f
,
real
beta
=
0
.
0
f
);
/**
* @brief Expand feature to column.
...
...
@@ -65,14 +70,19 @@ extern void hl_shrink_col2feature(
* @param[out] dataCol expand data.
*
*/
extern
void
hl_expand_feature2col
(
const
real
*
dataIm
,
size_t
channels
,
size_t
height
,
size_t
width
,
size_t
blockH
,
size_t
blockW
,
size_t
strideH
,
size_t
strideW
,
size_t
paddingH
,
size_t
paddingW
,
size_t
outputH
,
size_t
outputW
,
real
*
dataCol
);
extern
void
hl_expand_feature2col
(
const
real
*
dataIm
,
size_t
channels
,
size_t
height
,
size_t
width
,
size_t
blockH
,
size_t
blockW
,
size_t
strideH
,
size_t
strideW
,
size_t
paddingH
,
size_t
paddingW
,
size_t
outputH
,
size_t
outputW
,
real
*
dataCol
);
/**
* @brief Maximum pool forward.
...
...
@@ -94,15 +104,21 @@ extern void hl_expand_feature2col(
* @param[in] tgtStride stride between output data samples.
*
*/
extern
void
hl_maxpool_forward
(
const
int
frameCnt
,
const
real
*
inputData
,
const
int
channels
,
const
int
height
,
const
int
width
,
const
int
pooledH
,
const
int
pooledW
,
const
int
sizeX
,
const
int
sizeY
,
const
int
strideH
,
const
int
strideW
,
const
int
paddingH
,
const
int
paddingW
,
real
*
tgtData
,
const
int
tgtStride
);
extern
void
hl_maxpool_forward
(
const
int
frameCnt
,
const
real
*
inputData
,
const
int
channels
,
const
int
height
,
const
int
width
,
const
int
pooledH
,
const
int
pooledW
,
const
int
sizeX
,
const
int
sizeY
,
const
int
strideH
,
const
int
strideW
,
const
int
paddingH
,
const
int
paddingW
,
real
*
tgtData
,
const
int
tgtStride
);
/**
* @brief Maximum pool backward.
...
...
@@ -125,20 +141,28 @@ extern void hl_maxpool_forward(
* @param[in] paddingH padding height.
* @param[in] paddingW padding width.
* @param[out] targetGrad output grad.
* @param[in] outStride stride between output data samples.
* @param[in] outStride stride between output data samples.
*
*/
extern
void
hl_maxpool_backward
(
const
int
frameCnt
,
const
real
*
inputData
,
const
real
*
outData
,
const
real
*
outGrad
,
const
int
channels
,
const
int
height
,
const
int
width
,
const
int
pooledH
,
const
int
pooledW
,
const
int
sizeX
,
const
int
sizeY
,
const
int
strideH
,
const
int
strideW
,
const
int
paddingH
,
const
int
paddingW
,
real
scaleA
,
real
scaleB
,
real
*
targetGrad
,
const
int
outStride
);
extern
void
hl_maxpool_backward
(
const
int
frameCnt
,
const
real
*
inputData
,
const
real
*
outData
,
const
real
*
outGrad
,
const
int
channels
,
const
int
height
,
const
int
width
,
const
int
pooledH
,
const
int
pooledW
,
const
int
sizeX
,
const
int
sizeY
,
const
int
strideH
,
const
int
strideW
,
const
int
paddingH
,
const
int
paddingW
,
real
scaleA
,
real
scaleB
,
real
*
targetGrad
,
const
int
outStride
);
/**
* @brief Averge pool forward.
...
...
@@ -160,15 +184,21 @@ extern void hl_maxpool_backward(
* @param[in] tgtStride stride between output data samples.
*
*/
extern
void
hl_avgpool_forward
(
const
int
frameCnt
,
const
real
*
inputData
,
const
int
channels
,
const
int
height
,
const
int
width
,
const
int
pooledH
,
const
int
pooledW
,
const
int
sizeX
,
const
int
sizeY
,
const
int
strideH
,
const
int
strideW
,
const
int
paddingH
,
const
int
paddingW
,
real
*
tgtData
,
const
int
tgtStride
);
extern
void
hl_avgpool_forward
(
const
int
frameCnt
,
const
real
*
inputData
,
const
int
channels
,
const
int
height
,
const
int
width
,
const
int
pooledH
,
const
int
pooledW
,
const
int
sizeX
,
const
int
sizeY
,
const
int
strideH
,
const
int
strideW
,
const
int
paddingH
,
const
int
paddingW
,
real
*
tgtData
,
const
int
tgtStride
);
/**
* @brief Maximum pool backward.
...
...
@@ -189,19 +219,26 @@ extern void hl_avgpool_forward(
* @param[in] scaleA scale.
* @param[in] scaleB scale.
* @param[out] backGrad output grad.
* @param[in] outStride stride between output data samples.
* @param[in] outStride stride between output data samples.
*
*/
extern
void
hl_avgpool_backward
(
const
int
frameCnt
,
const
real
*
outGrad
,
const
int
channels
,
const
int
height
,
const
int
width
,
const
int
pooledH
,
const
int
pooledW
,
const
int
sizeX
,
const
int
sizeY
,
const
int
strideH
,
const
int
strideW
,
int
paddingH
,
int
paddingW
,
real
scaleA
,
real
scaleB
,
real
*
backGrad
,
const
int
outStride
);
extern
void
hl_avgpool_backward
(
const
int
frameCnt
,
const
real
*
outGrad
,
const
int
channels
,
const
int
height
,
const
int
width
,
const
int
pooledH
,
const
int
pooledW
,
const
int
sizeX
,
const
int
sizeY
,
const
int
strideH
,
const
int
strideW
,
int
paddingH
,
int
paddingW
,
real
scaleA
,
real
scaleB
,
real
*
backGrad
,
const
int
outStride
);
/**
* @brief Cross-map-respose normalize forward.
...
...
@@ -218,10 +255,16 @@ extern void hl_avgpool_backward(
* @param[in] beta scale.
*
*/
extern
void
hl_CMRNorm_forward
(
size_t
frameCnt
,
const
real
*
in
,
real
*
scale
,
real
*
out
,
size_t
channels
,
size_t
height
,
size_t
width
,
size_t
sizeX
,
real
alpha
,
real
beta
);
extern
void
hl_CMRNorm_forward
(
size_t
frameCnt
,
const
real
*
in
,
real
*
scale
,
real
*
out
,
size_t
channels
,
size_t
height
,
size_t
width
,
size_t
sizeX
,
real
alpha
,
real
beta
);
/**
* @brief Cross-map-respose normalize backward.
...
...
@@ -240,11 +283,18 @@ extern void hl_CMRNorm_forward(
* @param[in] beta scale.
*
*/
extern
void
hl_CMRNorm_backward
(
size_t
frameCnt
,
const
real
*
inV
,
const
real
*
scale
,
const
real
*
outV
,
const
real
*
outDiff
,
real
*
inDiff
,
size_t
channels
,
size_t
height
,
size_t
width
,
size_t
sizeX
,
real
alpha
,
real
beta
);
extern
void
hl_CMRNorm_backward
(
size_t
frameCnt
,
const
real
*
inV
,
const
real
*
scale
,
const
real
*
outV
,
const
real
*
outDiff
,
real
*
inDiff
,
size_t
channels
,
size_t
height
,
size_t
width
,
size_t
sizeX
,
real
alpha
,
real
beta
);
/**
* @brief Bilinear interpolation forward.
...
...
@@ -278,24 +328,24 @@ extern void hl_bilinear_forward(const real* inData,
const
real
ratioH
,
const
real
ratioW
);
/**
* @brief Bilinear interpolation backward.
*
* @param[out] inGrad input gradient.
* @param[in] inImgH input image height.
* @param[in] inImgW input image width.
* @param[in] inputH input batchSize.
* @param[in] inputW input image data dim.
* @param[in] outGrad output gradient.
* @param[in] outImgH output image height.
* @param[in] outImgW output image width.
* @param[in] outputH output batchSize.
* @param[in] outputW output image data dim.
* @param[in] numChannels number of channels.
* @param[in] ratioH inImgH / outImgH.
* @param[in] ratioW inImgW / outImgW.
*
*/
/**
* @brief Bilinear interpolation backward.
*
* @param[out] inGrad input gradient.
* @param[in] inImgH input image height.
* @param[in] inImgW input image width.
* @param[in] inputH input batchSize.
* @param[in] inputW input image data dim.
* @param[in] outGrad output gradient.
* @param[in] outImgH output image height.
* @param[in] outImgW output image width.
* @param[in] outputH output batchSize.
* @param[in] outputW output image data dim.
* @param[in] numChannels number of channels.
* @param[in] ratioH inImgH / outImgH.
* @param[in] ratioW inImgW / outImgW.
*
*/
extern
void
hl_bilinear_backward
(
real
*
inGrad
,
const
size_t
inImgH
,
const
size_t
inImgW
,
...
...
@@ -321,9 +371,13 @@ extern void hl_bilinear_backward(real* inGrad,
* @param[in] featLen feature length = image height * image width.
* @param[in] groups number of groups.
*/
extern
void
hl_maxout_forward
(
const
real
*
inData
,
real
*
outData
,
int
*
idData
,
size_t
batchSize
,
size_t
size
,
size_t
featLen
,
size_t
groups
);
extern
void
hl_maxout_forward
(
const
real
*
inData
,
real
*
outData
,
int
*
idData
,
size_t
batchSize
,
size_t
size
,
size_t
featLen
,
size_t
groups
);
/**
* @brief MaxOut backward.
...
...
@@ -336,8 +390,12 @@ extern void hl_maxout_forward(
* @param[in] featLen feature length = image height * image width.
* @param[in] groups number of groups.
*/
extern
void
hl_maxout_backward
(
real
*
inGrad
,
const
real
*
outGrad
,
const
int
*
idData
,
size_t
batchSize
,
size_t
size
,
size_t
featLen
,
size_t
groups
);
extern
void
hl_maxout_backward
(
real
*
inGrad
,
const
real
*
outGrad
,
const
int
*
idData
,
size_t
batchSize
,
size_t
size
,
size_t
featLen
,
size_t
groups
);
#endif
/* HL_CNN_H_ */
paddle/cuda/include/hl_cuda.h
浏览文件 @
2a21d8b3
...
...
@@ -12,7 +12,6 @@ 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. */
#ifndef HL_CUDA_H_
#define HL_CUDA_H_
...
...
@@ -22,8 +21,7 @@ limitations under the License. */
/**
* @brief HPPL event.
*/
typedef
struct
_hl_event_st
*
hl_event_t
;
typedef
struct
_hl_event_st
*
hl_event_t
;
/**
* @brief return cuda runtime api version.
...
...
@@ -42,7 +40,7 @@ extern void hl_start();
* if device is NULL, will start all GPU.
* @param[in] number number of devices.
*/
extern
void
hl_specify_devices_start
(
int
*
device
,
int
number
);
extern
void
hl_specify_devices_start
(
int
*
device
,
int
number
);
/**
* @brief Queries if a device may directly access a peer device's memory.
...
...
@@ -126,7 +124,7 @@ extern int hl_get_device();
*
* @return dest_d pointer to device memory.
*/
extern
void
*
hl_malloc_device
(
size_t
size
);
extern
void
*
hl_malloc_device
(
size_t
size
);
/**
* @brief Free device memory.
...
...
@@ -143,7 +141,7 @@ extern void hl_free_mem_device(void *dest_d);
*
* @return dest_h pointer to host memory.
*/
extern
void
*
hl_malloc_host
(
size_t
size
);
extern
void
*
hl_malloc_host
(
size_t
size
);
/**
* @brief Free host page-lock memory.
...
...
@@ -228,9 +226,9 @@ extern void hl_srand(unsigned int seed);
* @param[in] stream stream id.
*/
extern
void
hl_memcpy_async
(
void
*
dst
,
void
*
src
,
size_t
size
,
hl_stream_t
stream
);
void
*
src
,
size_t
size
,
hl_stream_t
stream
);
/**
* @brief Waits for stream tasks to complete.
...
...
@@ -261,8 +259,7 @@ extern void hl_destroy_event(hl_event_t event);
*
* @return time Time between start and end in ms.
*/
extern
float
hl_event_elapsed_time
(
hl_event_t
start
,
hl_event_t
end
);
extern
float
hl_event_elapsed_time
(
hl_event_t
start
,
hl_event_t
end
);
/**
* @brief Records an event.
...
...
@@ -300,7 +297,7 @@ extern void hl_set_device_flags_block();
/**
* @brief Returns the last error string from a cuda runtime call.
*/
extern
const
char
*
hl_get_device_error_string
();
extern
const
char
*
hl_get_device_error_string
();
/**
* @brief Returns the last error string from a cuda runtime call.
...
...
@@ -309,7 +306,7 @@ extern const char* hl_get_device_error_string();
*
* @see hl_get_device_last_error()
*/
extern
const
char
*
hl_get_device_error_string
(
size_t
err
);
extern
const
char
*
hl_get_device_error_string
(
size_t
err
);
/**
* @brief Returns the last error number.
...
...
paddle/cuda/include/hl_cuda_cublas.h
浏览文件 @
2a21d8b3
...
...
@@ -12,7 +12,6 @@ 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. */
#ifndef HL_CUDA_CUBLAS_H_
#define HL_CUDA_CUBLAS_H_
...
...
@@ -29,12 +28,8 @@ limitations under the License. */
* @param[in] ldc the first dimension of C_d.
*
*/
extern
void
hl_matrix_transpose
(
real
*
A_d
,
real
*
C_d
,
int
dimM
,
int
dimN
,
int
lda
,
int
ldc
);
extern
void
hl_matrix_transpose
(
real
*
A_d
,
real
*
C_d
,
int
dimM
,
int
dimN
,
int
lda
,
int
ldc
);
/*
* @brief Matrix transpose, while lda = dimN, ldc = dimM.
...
...
@@ -45,10 +40,7 @@ extern void hl_matrix_transpose(real *A_d,
* @param[in] dimN matrix width.
*
*/
extern
void
hl_matrix_transpose
(
real
*
A_d
,
real
*
C_d
,
int
dimM
,
int
dimN
);
extern
void
hl_matrix_transpose
(
real
*
A_d
,
real
*
C_d
,
int
dimM
,
int
dimN
);
/*
* @brief Matrix inverse
...
...
@@ -60,11 +52,7 @@ extern void hl_matrix_transpose(real *A_d,
* @param[in] ldc the first dimension of C_d
*
*/
extern
void
hl_matrix_inverse
(
real
*
A_d
,
real
*
C_d
,
int
dimN
,
int
lda
,
int
ldc
);
extern
void
hl_matrix_inverse
(
real
*
A_d
,
real
*
C_d
,
int
dimN
,
int
lda
,
int
ldc
);
/**
* @brief C_d = alpha*(op(A_d) * op(B_d)) + beta*C_d
...
...
@@ -84,12 +72,19 @@ extern void hl_matrix_inverse(real *A_d,
* @param[in] ldc the first dimension of C_d.
*
*/
extern
void
hl_matrix_mul
(
real
*
A_d
,
hl_trans_op_t
transa
,
real
*
B_d
,
hl_trans_op_t
transb
,
extern
void
hl_matrix_mul
(
real
*
A_d
,
hl_trans_op_t
transa
,
real
*
B_d
,
hl_trans_op_t
transb
,
real
*
C_d
,
int
dimM
,
int
dimN
,
int
dimK
,
real
alpha
,
real
beta
,
int
lda
,
int
ldb
,
int
ldc
);
int
dimM
,
int
dimN
,
int
dimK
,
real
alpha
,
real
beta
,
int
lda
,
int
ldb
,
int
ldc
);
/**
* @brief C_d = alpha*(op(A_d) * op(B_d)) + beta*C_d
...
...
@@ -106,11 +101,16 @@ extern void hl_matrix_mul(real *A_d, hl_trans_op_t transa,
* @param[in] beta scalar used for multiplication.
*
*/
extern
void
hl_matrix_mul
(
real
*
A_d
,
hl_trans_op_t
transa
,
real
*
B_d
,
hl_trans_op_t
transb
,
extern
void
hl_matrix_mul
(
real
*
A_d
,
hl_trans_op_t
transa
,
real
*
B_d
,
hl_trans_op_t
transb
,
real
*
C_d
,
int
dimM
,
int
dimN
,
int
dimK
,
real
alpha
,
real
beta
);
int
dimM
,
int
dimN
,
int
dimK
,
real
alpha
,
real
beta
);
/**
* @brief This function performs the matrix-vector multiplication.
...
...
@@ -132,11 +132,17 @@ extern void hl_matrix_mul(real *A_d, hl_trans_op_t transa,
*
*/
extern
void
hl_matrix_mul_vector
(
real
*
A_d
,
hl_trans_op_t
trans
,
real
*
B_d
,
real
*
C_d
,
int
dimM
,
int
dimN
,
real
alpha
,
real
beta
,
int
lda
,
int
incb
,
int
incc
);
extern
void
hl_matrix_mul_vector
(
real
*
A_d
,
hl_trans_op_t
trans
,
real
*
B_d
,
real
*
C_d
,
int
dimM
,
int
dimN
,
real
alpha
,
real
beta
,
int
lda
,
int
incb
,
int
incc
);
/**
* @brief This function performs the matrix-vector multiplication.
...
...
@@ -154,9 +160,13 @@ extern void hl_matrix_mul_vector(real *A_d, hl_trans_op_t trans,
* @param[in] beta scalar used for multiplication.
*
*/
extern
void
hl_matrix_mul_vector
(
real
*
A_d
,
hl_trans_op_t
trans
,
real
*
B_d
,
real
*
C_d
,
int
dimM
,
int
dimN
,
real
alpha
,
real
beta
);
extern
void
hl_matrix_mul_vector
(
real
*
A_d
,
hl_trans_op_t
trans
,
real
*
B_d
,
real
*
C_d
,
int
dimM
,
int
dimN
,
real
alpha
,
real
beta
);
#endif
/* HL_CUDA_CUBLAS_H_ */
paddle/cuda/include/hl_cuda_cudnn.h
浏览文件 @
2a21d8b3
...
...
@@ -12,7 +12,6 @@ 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. */
#ifndef HL_CUDA_CUDNN_H_
#define HL_CUDA_CUDNN_H_
...
...
@@ -22,7 +21,7 @@ limitations under the License. */
* hppl pooling mode
*/
typedef
enum
{
HL_POOLING_MAX
=
0
,
HL_POOLING_MAX
=
0
,
// average includes padded values
HL_POOLING_AVERAGE
=
1
,
// average does not include padded values
...
...
@@ -324,17 +323,16 @@ extern void hl_convolution_forward_add_bias(hl_tensor_descriptor bias,
* @param[in] sizeInBytes gpu workspace size (bytes).
* @param[in] convBwdFilterAlgo backward filter algorithm.
*/
extern
void
hl_convolution_backward_filter
(
hl_tensor_descriptor
input
,
real
*
input_data
,
hl_tensor_descriptor
output
,
real
*
output_grad_data
,
hl_filter_descriptor
filter
,
real
*
filter_grad_data
,
hl_convolution_descriptor
conv
,
void
*
gpuWorkSpace
,
size_t
sizeInBytes
,
int
convBwdFilterAlgo
);
extern
void
hl_convolution_backward_filter
(
hl_tensor_descriptor
input
,
real
*
input_data
,
hl_tensor_descriptor
output
,
real
*
output_grad_data
,
hl_filter_descriptor
filter
,
real
*
filter_grad_data
,
hl_convolution_descriptor
conv
,
void
*
gpuWorkSpace
,
size_t
sizeInBytes
,
int
convBwdFilterAlgo
);
/**
* @brief convolution backward data(calculate input image grad data).
...
...
@@ -350,17 +348,16 @@ extern void hl_convolution_backward_filter(
* @param[in] sizeInBytes gpu workspace size (bytes).
* @param[in] convBwdDataAlgo backward data algorithm.
*/
extern
void
hl_convolution_backward_data
(
hl_tensor_descriptor
input
,
real
*
input_data_grad
,
hl_tensor_descriptor
output
,
real
*
output_grad_data
,
hl_filter_descriptor
filter
,
real
*
filter_data
,
hl_convolution_descriptor
conv
,
void
*
gpuWorkSpace
,
size_t
sizeInBytes
,
int
convBwdDataAlgo
);
extern
void
hl_convolution_backward_data
(
hl_tensor_descriptor
input
,
real
*
input_data_grad
,
hl_tensor_descriptor
output
,
real
*
output_grad_data
,
hl_filter_descriptor
filter
,
real
*
filter_data
,
hl_convolution_descriptor
conv
,
void
*
gpuWorkSpace
,
size_t
sizeInBytes
,
int
convBwdDataAlgo
);
/**
* @brief convolution backward bias(calculate bias grad data).
...
...
@@ -383,8 +380,8 @@ extern void hl_convolution_backward_bias(hl_tensor_descriptor bias,
* @param[in] height matrix height.
* @param[in] width matrix width.
*/
extern
void
hl_softmax_forward
(
real
*
input
,
real
*
output
,
extern
void
hl_softmax_forward
(
real
*
input
,
real
*
output
,
int
height
,
int
width
);
...
...
@@ -396,8 +393,8 @@ extern void hl_softmax_forward(real *input,
* @param[in] height matrix height.
* @param[in] width matrix width.
*/
extern
void
hl_softmax_backward
(
real
*
output_value
,
real
*
output_grad
,
extern
void
hl_softmax_backward
(
real
*
output_value
,
real
*
output_grad
,
int
height
,
int
width
);
...
...
@@ -426,18 +423,18 @@ extern void hl_softmax_backward(real *output_value,
*
*/
extern
void
hl_batch_norm_forward_training
(
hl_tensor_descriptor
inputDesc
,
real
*
input
,
real
*
input
,
hl_tensor_descriptor
outputDesc
,
real
*
output
,
real
*
output
,
hl_tensor_descriptor
bnParamDesc
,
real
*
scale
,
real
*
bias
,
real
*
scale
,
real
*
bias
,
double
factor
,
real
*
runningMean
,
real
*
runningInvVar
,
real
*
runningMean
,
real
*
runningInvVar
,
double
epsilon
,
real
*
savedMean
,
real
*
savedVar
);
real
*
savedMean
,
real
*
savedVar
);
/**
* @brief cudnn batch norm forward.
...
...
@@ -463,14 +460,14 @@ extern void hl_batch_norm_forward_training(hl_tensor_descriptor inputDesc,
*
*/
extern
void
hl_batch_norm_forward_inference
(
hl_tensor_descriptor
inputDesc
,
real
*
input
,
real
*
input
,
hl_tensor_descriptor
outputDesc
,
real
*
output
,
real
*
output
,
hl_tensor_descriptor
bnParamDesc
,
real
*
scale
,
real
*
bias
,
real
*
estimatedMean
,
real
*
estimatedVar
,
real
*
scale
,
real
*
bias
,
real
*
estimatedMean
,
real
*
estimatedVar
,
double
epsilon
);
/**
...
...
@@ -483,7 +480,8 @@ extern void hl_batch_norm_forward_inference(hl_tensor_descriptor inputDesc,
* @param[in] inGradDesc input tensor descriptor desc.
* @param[in] inGrad input data.
* @param[in] dBnParamDesc tensor descriptor desc.
* bnScale, bnBias, running mean/var, save_mean/var.
* bnScale, bnBias, running mean/var,
* save_mean/var.
* @param[in] scale batch normalization scale parameter (in original
* paper scale is referred to as gamma).
* @param[in] scaleGrad batch normalization scale parameter (in original
...
...
@@ -497,17 +495,17 @@ extern void hl_batch_norm_forward_inference(hl_tensor_descriptor inputDesc,
*
*/
extern
void
hl_batch_norm_backward
(
hl_tensor_descriptor
inputDesc
,
real
*
input
,
real
*
input
,
hl_tensor_descriptor
outGradDesc
,
real
*
outGrad
,
real
*
outGrad
,
hl_tensor_descriptor
inGradDesc
,
real
*
inGrad
,
real
*
inGrad
,
hl_tensor_descriptor
dBnParamDesc
,
real
*
scale
,
real
*
scaleGrad
,
real
*
biasGrad
,
real
*
scale
,
real
*
scaleGrad
,
real
*
biasGrad
,
double
epsilon
,
real
*
savedMean
,
real
*
savedInvVar
);
real
*
savedMean
,
real
*
savedInvVar
);
#endif // HL_CUDA_CUDNN_H_
paddle/cuda/include/hl_dso_loader.h
浏览文件 @
2a21d8b3
...
...
@@ -12,7 +12,6 @@ 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. */
#ifndef HL_DSO_LOADER_H_
#define HL_DSO_LOADER_H_
...
...
paddle/cuda/include/hl_functions.h
浏览文件 @
2a21d8b3
...
...
@@ -12,7 +12,6 @@ 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. */
#ifndef HL_FUNCTIONS_H_
#define HL_FUNCTIONS_H_
...
...
@@ -21,30 +20,30 @@ limitations under the License. */
/**
* sigmoid threshold maximum
*/
#define
SIGMOID_THRESHOLD_MIN
-40.0
#define
SIGMOID_THRESHOLD_MIN
-40.0
/**
* sigmoid threshold minimum
*/
#define
SIGMOID_THRESHOLD_MAX
13.0
#define
SIGMOID_THRESHOLD_MAX
13.0
#ifndef __NVCC__
namespace
hppl
{
/*
* forward activation
*/
real
relu
(
const
real
a
);
real
sigmoid
(
const
real
a
);
real
tanh
(
const
real
a
);
real
linear
(
const
real
a
);
/*
* backward activation
*/
real
relu
(
const
real
a
,
const
real
b
);
real
sigmoid
(
const
real
a
,
const
real
b
);
real
tanh
(
const
real
a
,
const
real
b
);
real
linear
(
const
real
a
,
const
real
b
);
/*
* forward activation
*/
real
relu
(
const
real
a
);
real
sigmoid
(
const
real
a
);
real
tanh
(
const
real
a
);
real
linear
(
const
real
a
);
/*
* backward activation
*/
real
relu
(
const
real
a
,
const
real
b
);
real
sigmoid
(
const
real
a
,
const
real
b
);
real
tanh
(
const
real
a
,
const
real
b
);
real
linear
(
const
real
a
,
const
real
b
);
}
// namespace hppl
#ifdef __AVX__
...
...
paddle/cuda/include/hl_gpu.h
浏览文件 @
2a21d8b3
...
...
@@ -12,7 +12,6 @@ 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. */
#ifndef HL_GPU_H_
#define HL_GPU_H_
...
...
paddle/cuda/include/hl_lstm.h
浏览文件 @
2a21d8b3
...
...
@@ -12,7 +12,6 @@ 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. */
#ifndef HL_LSTM_H_
#define HL_LSTM_H_
...
...
paddle/cuda/include/hl_matrix.h
浏览文件 @
2a21d8b3
...
...
@@ -12,7 +12,6 @@ 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. */
#ifndef HL_MATRIX_H_
#define HL_MATRIX_H_
...
...
@@ -30,13 +29,8 @@ limitations under the License. */
* @param[in] beta scalar used for addition.
*
*/
extern
void
hl_matrix_add
(
real
*
A_d
,
real
*
B_d
,
real
*
C_d
,
int
dimM
,
int
dimN
,
real
alpha
,
real
beta
);
extern
void
hl_matrix_add
(
real
*
A_d
,
real
*
B_d
,
real
*
C_d
,
int
dimM
,
int
dimN
,
real
alpha
,
real
beta
);
/**
* @brief Matrix Softmax.
*
...
...
@@ -46,7 +40,7 @@ extern void hl_matrix_add(real* A_d,
* @param[in] dimN matrix width.
*
*/
extern
void
hl_matrix_softmax
(
real
*
A_d
,
real
*
C_d
,
int
dimM
,
int
dimN
);
extern
void
hl_matrix_softmax
(
real
*
A_d
,
real
*
C_d
,
int
dimM
,
int
dimN
);
/**
* @brief Matrix softmax derivative.
...
...
@@ -58,11 +52,8 @@ extern void hl_matrix_softmax(real *A_d, real *C_d, int dimM, int dimN);
* @param[in] dimN matrix width.
*
*/
extern
void
hl_matrix_softmax_derivative
(
real
*
grad_d
,
real
*
output_d
,
real
*
sftmaxSum_d
,
int
dimM
,
int
dimN
);
extern
void
hl_matrix_softmax_derivative
(
real
*
grad_d
,
real
*
output_d
,
real
*
sftmaxSum_d
,
int
dimM
,
int
dimN
);
/**
* @brief Sequence softmax.
...
...
@@ -73,8 +64,8 @@ extern void hl_matrix_softmax_derivative(real* grad_d,
* @param[in] numSequence sequence number.
*
*/
extern
void
hl_sequence_softmax_forward
(
real
*
A_d
,
real
*
C_d
,
extern
void
hl_sequence_softmax_forward
(
real
*
A_d
,
real
*
C_d
,
const
int
*
index
,
int
numSequence
);
...
...
@@ -88,11 +79,8 @@ extern void hl_sequence_softmax_forward(real *A_d,
* @param[in] dimN matrix width.
*
*/
extern
void
hl_matrix_classification_error
(
real
*
A_d
,
int
*
B_d
,
real
*
C_d
,
int
dimM
,
int
dimN
);
extern
void
hl_matrix_classification_error
(
real
*
A_d
,
int
*
B_d
,
real
*
C_d
,
int
dimM
,
int
dimN
);
/**
* @brief Matrix cross entropy.
...
...
@@ -104,11 +92,8 @@ extern void hl_matrix_classification_error(real* A_d,
* @param[in] dimN matrix width.
*
*/
extern
void
hl_matrix_cross_entropy
(
real
*
A_d
,
real
*
C_d
,
int
*
label_d
,
int
dimM
,
int
dimN
);
extern
void
hl_matrix_cross_entropy
(
real
*
A_d
,
real
*
C_d
,
int
*
label_d
,
int
dimM
,
int
dimN
);
/**
* @brief Matrix cross entropy back propagation.
...
...
@@ -120,11 +105,8 @@ extern void hl_matrix_cross_entropy(real* A_d,
* @param[in] dimN matrix width.
*
*/
extern
void
hl_matrix_cross_entropy_bp
(
real
*
grad_d
,
real
*
output_d
,
int
*
label_d
,
int
dimM
,
int
dimN
);
extern
void
hl_matrix_cross_entropy_bp
(
real
*
grad_d
,
real
*
output_d
,
int
*
label_d
,
int
dimM
,
int
dimN
);
/**
* @brief Matrix multi-binary label cross entropy
...
...
@@ -135,11 +117,8 @@ extern void hl_matrix_cross_entropy_bp(real* grad_d,
* @param[in] dimM matrix height.
* @param[in] dimN matrix width.
*/
extern
void
hl_matrix_multi_binary_cross_entropy
(
real
*
output
,
real
*
entropy
,
hl_sparse_matrix_s
mat
,
int
dimM
,
int
dimN
);
extern
void
hl_matrix_multi_binary_cross_entropy
(
real
*
output
,
real
*
entropy
,
hl_sparse_matrix_s
mat
,
int
dimM
,
int
dimN
);
/**
* @brief Matrix multi-binary label cross entropy backprop
...
...
@@ -150,11 +129,8 @@ extern void hl_matrix_multi_binary_cross_entropy(real* output,
* @param[in] dimM matrix height.
* @param[in] dimN matrix width.
*/
extern
void
hl_matrix_multi_binary_cross_entropy_bp
(
real
*
output
,
real
*
grad
,
hl_sparse_matrix_s
mat
,
int
dimM
,
int
dimN
);
extern
void
hl_matrix_multi_binary_cross_entropy_bp
(
real
*
output
,
real
*
grad
,
hl_sparse_matrix_s
mat
,
int
dimM
,
int
dimN
);
/**
* @brief Matrix zero memory.
...
...
@@ -176,12 +152,8 @@ extern void hl_matrix_zero_mem(real* data, int num);
* @param[in] partial_sum
*/
extern
void
hl_param_relu_forward
(
real
*
output
,
real
*
input
,
real
*
w
,
int
width
,
int
height
,
int
partial_sum
);
extern
void
hl_param_relu_forward
(
real
*
output
,
real
*
input
,
real
*
w
,
int
width
,
int
height
,
int
partial_sum
);
/**
* @brief parameter relu backward w
*
...
...
paddle/cuda/include/hl_sequence.h
浏览文件 @
2a21d8b3
...
...
@@ -12,7 +12,6 @@ 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. */
#ifndef HL_SEQUENCE_H_
#define HL_SEQUENCE_H_
...
...
@@ -32,7 +31,7 @@ limitations under the License. */
extern
void
hl_max_sequence_forward
(
real
*
input
,
const
int
*
sequence
,
real
*
output
,
int
*
index
,
int
*
index
,
int
numSequences
,
int
dim
);
...
...
@@ -46,11 +45,8 @@ extern void hl_max_sequence_forward(real* input,
* @param[in] dim input dimension.
*
*/
extern
void
hl_max_sequence_backward
(
real
*
outputGrad
,
int
*
index
,
real
*
inputGrad
,
int
numSequences
,
int
dim
);
extern
void
hl_max_sequence_backward
(
real
*
outputGrad
,
int
*
index
,
real
*
inputGrad
,
int
numSequences
,
int
dim
);
/**
* @brief Context projection forward.
...
...
@@ -63,7 +59,8 @@ extern void hl_max_sequence_backward(real* outputGrad,
* @param[in] inputDim input sequence dimension.
* @param[in] contextLength context length.
* @param[in] contextStart context start.
* @param[in] beginPad number of extra timesteps added at the beginning.
* @param[in] beginPad number of extra timesteps added at the
* beginning.
* @param[in] isPadding trainable padding.
*
*/
...
...
@@ -109,7 +106,8 @@ extern void hl_context_projection_backward_data(real* outputGrad,
* @param[in] totalPad number of extra timesteps.
* @param[in] contextLength context length.
* @param[in] contextStart context start.
* @param[in] beginPad number of extra timesteps added at the beginning.
* @param[in] beginPad number of extra timesteps added at the
* beginning.
*
*/
extern
void
hl_context_projection_backward_weight
(
real
*
outputGrad
,
...
...
@@ -141,9 +139,9 @@ extern void hl_context_projection_backward_weight(real* outputGrad,
* @param[in] seq2batch copy direction.
*
*/
extern
void
hl_sequence2batch_copy
(
real
*
batch
,
real
*
sequence
,
const
int
*
batchIndex
,
extern
void
hl_sequence2batch_copy
(
real
*
batch
,
real
*
sequence
,
const
int
*
batchIndex
,
int
seqWidth
,
int
batchCount
,
bool
seq2batch
);
...
...
@@ -167,9 +165,9 @@ extern void hl_sequence2batch_copy(real *batch,
* @param[in] seq2batch copy direction.
*
*/
extern
void
hl_sequence2batch_add
(
real
*
batch
,
real
*
sequence
,
int
*
batchIndex
,
extern
void
hl_sequence2batch_add
(
real
*
batch
,
real
*
sequence
,
int
*
batchIndex
,
int
seqWidth
,
int
batchCount
,
bool
seq2batch
);
...
...
paddle/cuda/include/hl_sparse.h
浏览文件 @
2a21d8b3
...
...
@@ -12,7 +12,6 @@ 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. */
#ifndef HL_SPARSE_H_
#define HL_SPARSE_H_
...
...
@@ -31,7 +30,7 @@ limitations under the License. */
*/
extern
void
hl_malloc_sparse_matrix
(
hl_sparse_matrix_s
*
A_d
,
hl_matrix_format_t
format
,
hl_matrix_value_t
value_type
,
hl_matrix_value_t
value_type
,
int
dimM
,
int
dimN
,
int
nnz
);
...
...
@@ -60,10 +59,10 @@ extern void hl_free_sparse_matrix(hl_sparse_matrix_s A_d);
*
*/
extern
void
hl_construct_sparse_matrix
(
hl_sparse_matrix_s
*
A_d
,
void
*
dest_d
,
void
*
dest_d
,
size_t
size
,
hl_matrix_format_t
format
,
hl_matrix_value_t
value_type
,
hl_matrix_value_t
value_type
,
int
dimM
,
int
dimN
,
int
nnz
);
...
...
@@ -94,11 +93,11 @@ extern void hl_construct_sparse_matrix(hl_sparse_matrix_s *A_d,
*
*/
extern
void
hl_construct_sparse_matrix
(
hl_sparse_matrix_s
*
A_d
,
real
*
value_d
,
int
*
rows_d
,
int
*
cols_d
,
real
*
value_d
,
int
*
rows_d
,
int
*
cols_d
,
hl_matrix_format_t
format
,
hl_matrix_value_t
value_type
,
hl_matrix_value_t
value_type
,
int
dimM
,
int
dimN
,
int
nnz
);
...
...
@@ -259,10 +258,14 @@ extern void hl_matrix_csr_mul_dense(hl_sparse_matrix_s A_d,
*/
extern
void
hl_matrix_csc_mul_dense
(
hl_sparse_matrix_s
A_d
,
hl_trans_op_t
transa
,
real
*
B_d
,
hl_trans_op_t
transb
,
real
*
B_d
,
hl_trans_op_t
transb
,
real
*
C_d
,
int
dimM
,
int
dimN
,
int
dimK
,
real
alpha
,
real
beta
);
int
dimM
,
int
dimN
,
int
dimK
,
real
alpha
,
real
beta
);
/**
* @brief C_d = alpha*(op(A_d) * op(B_d)) + beta*C_d.
...
...
@@ -311,11 +314,16 @@ extern void hl_matrix_dense_mul_csc(real *A_d,
* @note transb is not support HPPL_OP_T.
*
*/
extern
void
hl_sparse_matrix_mul
(
real
*
A_d
,
hl_trans_op_t
transa
,
real
*
B_d
,
hl_trans_op_t
transb
,
extern
void
hl_sparse_matrix_mul
(
real
*
A_d
,
hl_trans_op_t
transa
,
real
*
B_d
,
hl_trans_op_t
transb
,
hl_sparse_matrix_s
C_d
,
int
dimM
,
int
dimN
,
int
dimK
,
real
alpha
,
real
beta
);
int
dimM
,
int
dimN
,
int
dimK
,
real
alpha
,
real
beta
);
/**
* @brief C_d = alpha*(op(A_d) * op(B_d)) + beta*C_d
...
...
@@ -336,12 +344,16 @@ extern void hl_sparse_matrix_mul(real* A_d, hl_trans_op_t transa,
* @note transa is not support HPPL_OP_T.
*
*/
extern
void
hl_matrix_dense_mul_csr
(
real
*
A_d
,
hl_trans_op_t
transa
,
extern
void
hl_matrix_dense_mul_csr
(
real
*
A_d
,
hl_trans_op_t
transa
,
hl_sparse_matrix_s
B_d
,
hl_trans_op_t
transb
,
real
*
C_d
,
int
dimM
,
int
dimN
,
int
dimK
,
real
alpha
,
real
beta
);
int
dimM
,
int
dimN
,
int
dimK
,
real
alpha
,
real
beta
);
/**
* @brief Memcpy csc_matrix to host.
...
...
@@ -412,7 +424,6 @@ extern void hl_memcpy_from_csr_matrix(real *csr_val,
hl_sparse_matrix_s
csr_matrix
,
hl_stream_t
stream
);
/**
* @brief A_d[j] += B_d[i,j] for i in range(height)
*
...
...
@@ -423,19 +434,13 @@ extern void hl_memcpy_from_csr_matrix(real *csr_val,
* @param[in] scale scale of B_d
*
*/
extern
void
hl_sparse_matrix_column_sum
(
real
*
A_d
,
hl_sparse_matrix_s
B_d
,
int
dimM
,
int
dimN
,
real
scale
);
extern
void
hl_sparse_matrix_column_sum
(
real
*
A_d
,
hl_sparse_matrix_s
B_d
,
int
dimM
,
int
dimN
,
real
scale
);
/**
* @brief implementation of csr sparse matrix in hl_sparse_matirx_column_sum
*/
extern
void
hl_matrix_csr_column_sum
(
real
*
A_d
,
hl_sparse_matrix_s
B_d
,
int
dimM
,
int
dimN
,
real
scale
);
extern
void
hl_matrix_csr_column_sum
(
real
*
A_d
,
hl_sparse_matrix_s
B_d
,
int
dimM
,
int
dimN
,
real
scale
);
/**
* @brief A_d[i,j] += B_d[j]
...
...
@@ -446,13 +451,13 @@ extern void hl_matrix_csr_column_sum(real* A_d,
*
*/
extern
void
hl_sparse_matrix_add_bias
(
hl_sparse_matrix_s
A_d
,
real
*
B_d
,
real
*
B_d
,
real
scale
);
/**
* @brief implementation of csr sparse matrix in hl_sparse_matrix_add_bias
*/
extern
void
hl_matrix_csr_add_bias
(
hl_sparse_matrix_s
A_d
,
real
*
B_d
,
real
*
B_d
,
real
scale
);
/**
...
...
@@ -470,7 +475,7 @@ extern void hl_matrix_csr_add_bias(hl_sparse_matrix_s A_d,
*
*/
extern
void
hl_sparse_matrix_add_dense
(
hl_sparse_matrix_s
A_d
,
real
*
B_d
,
real
*
B_d
,
int
dimM
,
int
dimN
,
real
alpha
,
...
...
@@ -479,7 +484,7 @@ extern void hl_sparse_matrix_add_dense(hl_sparse_matrix_s A_d,
* @brief implementation of csr sparse matrix in hl_sparse_matrix_add_dense
*/
extern
void
hl_matrix_csr_add_dense
(
hl_sparse_matrix_s
A_d
,
real
*
B_d
,
real
*
B_d
,
int
dimM
,
int
dimN
,
real
alpha
,
...
...
@@ -493,7 +498,7 @@ extern void hl_matrix_csr_add_dense(hl_sparse_matrix_s A_d,
* @return return rows pointer, which is gpu address
*
*/
extern
int
*
hl_sparse_matrix_get_rows
(
hl_sparse_matrix_s
sMat
);
extern
int
*
hl_sparse_matrix_get_rows
(
hl_sparse_matrix_s
sMat
);
/**
* @brief get cols pionter of GpuSparseMatrix
...
...
@@ -503,7 +508,7 @@ extern int* hl_sparse_matrix_get_rows(hl_sparse_matrix_s sMat);
* @return return cols pointer, which is gpu address
*
*/
extern
int
*
hl_sparse_matrix_get_cols
(
hl_sparse_matrix_s
sMat
);
extern
int
*
hl_sparse_matrix_get_cols
(
hl_sparse_matrix_s
sMat
);
/**
* @brief get value pionter of GpuSparseMatrix
...
...
@@ -513,7 +518,6 @@ extern int* hl_sparse_matrix_get_cols(hl_sparse_matrix_s sMat);
* @return return value pointer, which is gpu address
*
*/
extern
real
*
hl_sparse_matrix_get_value
(
hl_sparse_matrix_s
sMat
);
extern
real
*
hl_sparse_matrix_get_value
(
hl_sparse_matrix_s
sMat
);
#endif
/* HL_SPARSE_H_ */
paddle/cuda/include/hl_table_apply.h
浏览文件 @
2a21d8b3
...
...
@@ -12,7 +12,6 @@ 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. */
#ifndef HL_TABLE_APPLY_H_
#define HL_TABLE_APPLY_H_
...
...
@@ -31,8 +30,10 @@ limitations under the License. */
* @param[in] dim width of table.
*
*/
extern
void
hl_matrix_select_rows
(
real
*
output
,
int
ldo
,
real
*
table
,
int
ldt
,
extern
void
hl_matrix_select_rows
(
real
*
output
,
int
ldo
,
real
*
table
,
int
ldt
,
int
*
ids
,
int
numSamples
,
int
tableSize
,
...
...
@@ -53,8 +54,10 @@ extern void hl_matrix_select_rows(real* output, int ldo,
* @param[in] dim width of table.
*
*/
extern
void
hl_matrix_add_to_rows
(
real
*
table
,
int
ldt
,
real
*
input
,
int
ldi
,
extern
void
hl_matrix_add_to_rows
(
real
*
table
,
int
ldt
,
real
*
input
,
int
ldi
,
int
*
ids
,
int
numSamples
,
int
tableSize
,
...
...
@@ -72,8 +75,7 @@ extern void hl_matrix_add_to_rows(real* table, int ldt,
*
*/
template
<
class
T
>
extern
void
hl_vector_select_from
(
T
*
dst
,
int
sized
,
const
T
*
src
,
int
sizes
,
const
int
*
ids
,
int
sizei
);
extern
void
hl_vector_select_from
(
T
*
dst
,
int
sized
,
const
T
*
src
,
int
sizes
,
const
int
*
ids
,
int
sizei
);
#endif
/* HL_TABLE_APPLY_H_ */
#endif
/* HL_TABLE_APPLY_H_ */
paddle/cuda/include/hl_time.h
浏览文件 @
2a21d8b3
...
...
@@ -12,7 +12,6 @@ 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. */
#ifndef HL_TIME_H_
#define HL_TIME_H_
...
...
paddle/cuda/include/hl_top_k.h
浏览文件 @
2a21d8b3
...
...
@@ -12,7 +12,6 @@ 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. */
#ifndef HL_TOP_K_H_
#define HL_TOP_K_H_
...
...
@@ -31,9 +30,11 @@ limitations under the License. */
* @param[in] numSamples height of input value.
*
*/
extern
void
hl_matrix_top_k
(
real
*
topVal
,
int
ldv
,
int
*
topIds
,
real
*
src
,
int
lds
,
extern
void
hl_matrix_top_k
(
real
*
topVal
,
int
ldv
,
int
*
topIds
,
real
*
src
,
int
lds
,
int
dim
,
int
beamSize
,
int
numSamples
);
...
...
@@ -50,8 +51,9 @@ extern void hl_matrix_top_k(real* topVal, int ldv,
*
* @note Only support HL_SPARSE_CSR format.
*/
extern
void
hl_sparse_matrix_top_k
(
real
*
topVal
,
int
ldv
,
int
*
topIds
,
extern
void
hl_sparse_matrix_top_k
(
real
*
topVal
,
int
ldv
,
int
*
topIds
,
hl_sparse_matrix_s
src
,
int
beamSize
,
int
numSamples
);
...
...
paddle/cuda/include/stub/hl_aggregate_stub.h
浏览文件 @
2a21d8b3
...
...
@@ -12,29 +12,22 @@ 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. */
#ifndef HL_AGGREGATE_STUB_H_
#define HL_AGGREGATE_STUB_H_
#include "hl_aggregate.h"
inline
void
hl_matrix_row_sum
(
real
*
A_d
,
real
*
C_d
,
int
dimM
,
int
dimN
)
{}
inline
void
hl_matrix_row_sum
(
real
*
A_d
,
real
*
C_d
,
int
dimM
,
int
dimN
)
{}
inline
void
hl_matrix_row_max
(
real
*
A_d
,
real
*
C_d
,
int
dimM
,
int
dimN
)
{}
inline
void
hl_matrix_row_max
(
real
*
A_d
,
real
*
C_d
,
int
dimM
,
int
dimN
)
{}
inline
void
hl_matrix_row_min
(
real
*
A_d
,
real
*
C_d
,
int
dimM
,
int
dimN
)
{}
inline
void
hl_matrix_row_min
(
real
*
A_d
,
real
*
C_d
,
int
dimM
,
int
dimN
)
{}
inline
void
hl_matrix_column_sum
(
real
*
A_d
,
real
*
C_d
,
int
dimM
,
int
dimN
)
{}
inline
void
hl_matrix_column_sum
(
real
*
A_d
,
real
*
C_d
,
int
dimM
,
int
dimN
)
{}
inline
void
hl_matrix_column_max
(
real
*
A_d
,
real
*
C_d
,
int
dimM
,
int
dimN
)
{}
inline
void
hl_matrix_column_max
(
real
*
A_d
,
real
*
C_d
,
int
dimM
,
int
dimN
)
{}
inline
void
hl_matrix_column_min
(
real
*
A_d
,
real
*
C_d
,
int
dimM
,
int
dimN
)
{}
inline
void
hl_matrix_column_min
(
real
*
A_d
,
real
*
C_d
,
int
dimM
,
int
dimN
)
{}
inline
void
hl_vector_sum
(
real
*
A_d
,
real
*
C_h
,
int
dimM
)
{}
...
...
paddle/cuda/include/stub/hl_cnn_stub.h
浏览文件 @
2a21d8b3
...
...
@@ -12,84 +12,134 @@ 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. */
#ifndef HL_CNN_STUB_H_
#define HL_CNN_STUB_H_
#include "hl_cnn.h"
inline
void
hl_shrink_col2feature
(
const
real
*
dataCol
,
size_t
channels
,
size_t
height
,
size_t
width
,
size_t
blockH
,
size_t
blockW
,
size_t
strideH
,
size_t
strideW
,
size_t
paddingH
,
size_t
paddingW
,
size_t
outputH
,
size_t
outputW
,
real
*
dataIm
,
real
alpha
,
real
beta
)
{}
inline
void
hl_expand_feature2col
(
const
real
*
dataIm
,
size_t
channels
,
size_t
height
,
size_t
width
,
size_t
blockH
,
size_t
blockW
,
size_t
strideH
,
size_t
strideW
,
size_t
paddingH
,
size_t
paddingW
,
size_t
outputH
,
size_t
outputW
,
real
*
dataCol
)
{}
inline
void
hl_maxpool_forward
(
const
int
frameCnt
,
const
real
*
inputData
,
const
int
channels
,
const
int
height
,
const
int
width
,
const
int
pooledH
,
const
int
pooledW
,
const
int
sizeX
,
const
int
sizeY
,
const
int
strideH
,
const
int
strideW
,
const
int
paddingH
,
const
int
paddingW
,
real
*
tgtData
,
const
int
tgtStride
)
{}
inline
void
hl_maxpool_backward
(
const
int
frameCnt
,
const
real
*
inputData
,
const
real
*
outData
,
const
real
*
outGrad
,
const
int
channels
,
const
int
height
,
const
int
width
,
const
int
pooledH
,
const
int
pooledW
,
const
int
sizeX
,
const
int
sizeY
,
const
int
strideH
,
const
int
strideW
,
const
int
paddingH
,
const
int
paddingW
,
real
scaleA
,
real
scaleB
,
real
*
targetGrad
,
const
int
outStride
)
{}
inline
void
hl_avgpool_forward
(
const
int
frameCnt
,
const
real
*
inputData
,
const
int
channels
,
const
int
height
,
const
int
width
,
const
int
pooledH
,
const
int
pooledW
,
const
int
sizeX
,
const
int
sizeY
,
const
int
strideH
,
const
int
strideW
,
const
int
paddingH
,
const
int
paddingW
,
real
*
tgtData
,
const
int
tgtStride
)
{}
inline
void
hl_avgpool_backward
(
const
int
frameCnt
,
const
real
*
outGrad
,
const
int
channels
,
const
int
height
,
const
int
width
,
const
int
pooledH
,
const
int
pooledW
,
const
int
sizeX
,
const
int
sizeY
,
const
int
strideH
,
const
int
strideW
,
int
paddingH
,
int
paddingW
,
real
scaleA
,
real
scaleB
,
real
*
backGrad
,
const
int
outStride
)
{}
inline
void
hl_CMRNorm_forward
(
size_t
frameCnt
,
const
real
*
in
,
real
*
scale
,
real
*
out
,
size_t
channels
,
size_t
height
,
size_t
width
,
size_t
sizeX
,
real
alpha
,
real
beta
)
{}
inline
void
hl_CMRNorm_backward
(
size_t
frameCnt
,
const
real
*
inV
,
const
real
*
scale
,
const
real
*
outV
,
const
real
*
outDiff
,
real
*
inDiff
,
size_t
channels
,
size_t
height
,
size_t
width
,
size_t
sizeX
,
real
alpha
,
real
beta
)
{}
inline
void
hl_shrink_col2feature
(
const
real
*
dataCol
,
size_t
channels
,
size_t
height
,
size_t
width
,
size_t
blockH
,
size_t
blockW
,
size_t
strideH
,
size_t
strideW
,
size_t
paddingH
,
size_t
paddingW
,
size_t
outputH
,
size_t
outputW
,
real
*
dataIm
,
real
alpha
,
real
beta
)
{}
inline
void
hl_expand_feature2col
(
const
real
*
dataIm
,
size_t
channels
,
size_t
height
,
size_t
width
,
size_t
blockH
,
size_t
blockW
,
size_t
strideH
,
size_t
strideW
,
size_t
paddingH
,
size_t
paddingW
,
size_t
outputH
,
size_t
outputW
,
real
*
dataCol
)
{}
inline
void
hl_maxpool_forward
(
const
int
frameCnt
,
const
real
*
inputData
,
const
int
channels
,
const
int
height
,
const
int
width
,
const
int
pooledH
,
const
int
pooledW
,
const
int
sizeX
,
const
int
sizeY
,
const
int
strideH
,
const
int
strideW
,
const
int
paddingH
,
const
int
paddingW
,
real
*
tgtData
,
const
int
tgtStride
)
{}
inline
void
hl_maxpool_backward
(
const
int
frameCnt
,
const
real
*
inputData
,
const
real
*
outData
,
const
real
*
outGrad
,
const
int
channels
,
const
int
height
,
const
int
width
,
const
int
pooledH
,
const
int
pooledW
,
const
int
sizeX
,
const
int
sizeY
,
const
int
strideH
,
const
int
strideW
,
const
int
paddingH
,
const
int
paddingW
,
real
scaleA
,
real
scaleB
,
real
*
targetGrad
,
const
int
outStride
)
{}
inline
void
hl_avgpool_forward
(
const
int
frameCnt
,
const
real
*
inputData
,
const
int
channels
,
const
int
height
,
const
int
width
,
const
int
pooledH
,
const
int
pooledW
,
const
int
sizeX
,
const
int
sizeY
,
const
int
strideH
,
const
int
strideW
,
const
int
paddingH
,
const
int
paddingW
,
real
*
tgtData
,
const
int
tgtStride
)
{}
inline
void
hl_avgpool_backward
(
const
int
frameCnt
,
const
real
*
outGrad
,
const
int
channels
,
const
int
height
,
const
int
width
,
const
int
pooledH
,
const
int
pooledW
,
const
int
sizeX
,
const
int
sizeY
,
const
int
strideH
,
const
int
strideW
,
int
paddingH
,
int
paddingW
,
real
scaleA
,
real
scaleB
,
real
*
backGrad
,
const
int
outStride
)
{}
inline
void
hl_CMRNorm_forward
(
size_t
frameCnt
,
const
real
*
in
,
real
*
scale
,
real
*
out
,
size_t
channels
,
size_t
height
,
size_t
width
,
size_t
sizeX
,
real
alpha
,
real
beta
)
{}
inline
void
hl_CMRNorm_backward
(
size_t
frameCnt
,
const
real
*
inV
,
const
real
*
scale
,
const
real
*
outV
,
const
real
*
outDiff
,
real
*
inDiff
,
size_t
channels
,
size_t
height
,
size_t
width
,
size_t
sizeX
,
real
alpha
,
real
beta
)
{}
inline
void
hl_bilinear_forward
(
const
real
*
inData
,
const
size_t
inImgH
,
...
...
@@ -106,25 +156,33 @@ inline void hl_bilinear_forward(const real* inData,
const
real
ratioW
)
{}
inline
void
hl_bilinear_backward
(
real
*
inGrad
,
const
size_t
inImgH
,
const
size_t
inImgW
,
const
size_t
inputH
,
const
size_t
inputW
,
const
real
*
outGrad
,
const
size_t
outImgH
,
const
size_t
outImgW
,
const
size_t
outputH
,
const
size_t
outputW
,
const
size_t
numChannels
,
const
real
ratioH
,
const
real
ratioW
)
{}
inline
void
hl_maxout_forward
(
const
real
*
inData
,
real
*
outData
,
int
*
idData
,
size_t
batchSize
,
size_t
size
,
size_t
featLen
,
size_t
group
)
{}
inline
void
hl_maxout_backward
(
real
*
inGrad
,
const
real
*
outGrad
,
const
int
*
idData
,
size_t
batchSize
,
size_t
size
,
size_t
featLen
,
size_t
group
)
{}
const
size_t
inImgH
,
const
size_t
inImgW
,
const
size_t
inputH
,
const
size_t
inputW
,
const
real
*
outGrad
,
const
size_t
outImgH
,
const
size_t
outImgW
,
const
size_t
outputH
,
const
size_t
outputW
,
const
size_t
numChannels
,
const
real
ratioH
,
const
real
ratioW
)
{}
inline
void
hl_maxout_forward
(
const
real
*
inData
,
real
*
outData
,
int
*
idData
,
size_t
batchSize
,
size_t
size
,
size_t
featLen
,
size_t
group
)
{}
inline
void
hl_maxout_backward
(
real
*
inGrad
,
const
real
*
outGrad
,
const
int
*
idData
,
size_t
batchSize
,
size_t
size
,
size_t
featLen
,
size_t
group
)
{}
#endif // HL_CNN_STUB_H_
paddle/cuda/include/stub/hl_cuda_cublas_stub.h
浏览文件 @
2a21d8b3
...
...
@@ -12,41 +12,42 @@ 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. */
#ifndef HL_CUDA_CUBLAS_STUB_H_
#define HL_CUDA_CUBLAS_STUB_H_
#include "hl_cuda_cublas.h"
inline
void
hl_matrix_transpose
(
real
*
A_d
,
real
*
C_d
,
int
dimM
,
int
dimN
,
int
lda
,
int
ldc
)
{}
inline
void
hl_matrix_transpose
(
real
*
A_d
,
real
*
C_d
,
int
dimM
,
int
dimN
)
{}
inline
void
hl_matrix_inverse
(
real
*
A_d
,
real
*
C_d
,
int
dimN
,
int
lda
,
int
ldc
)
{}
inline
void
hl_matrix_mul
(
real
*
A_d
,
hl_trans_op_t
transa
,
real
*
B_d
,
hl_trans_op_t
transb
,
real
*
C_d
,
int
dimM
,
int
dimN
,
int
dimK
,
real
alpha
,
real
beta
,
int
lda
,
int
ldb
,
int
ldc
)
{}
inline
void
hl_matrix_transpose
(
real
*
A_d
,
real
*
C_d
,
int
dimM
,
int
dimN
,
int
lda
,
int
ldc
)
{}
inline
void
hl_matrix_transpose
(
real
*
A_d
,
real
*
C_d
,
int
dimM
,
int
dimN
)
{}
inline
void
hl_matrix_mul
(
real
*
A_d
,
hl_trans_op_t
transa
,
real
*
B_d
,
hl_trans_op_t
transb
,
inline
void
hl_matrix_inverse
(
real
*
A_d
,
real
*
C_d
,
int
dimN
,
int
lda
,
int
ldc
)
{}
inline
void
hl_matrix_mul
(
real
*
A_d
,
hl_trans_op_t
transa
,
real
*
B_d
,
hl_trans_op_t
transb
,
real
*
C_d
,
int
dimM
,
int
dimN
,
int
dimK
,
real
alpha
,
real
beta
,
int
lda
,
int
ldb
,
int
ldc
)
{}
inline
void
hl_matrix_mul
(
real
*
A_d
,
hl_trans_op_t
transa
,
real
*
B_d
,
hl_trans_op_t
transb
,
real
*
C_d
,
int
dimM
,
int
dimN
,
int
dimK
,
real
alpha
,
real
beta
)
{}
int
dimM
,
int
dimN
,
int
dimK
,
real
alpha
,
real
beta
)
{}
#endif // HL_CUDA_CUBLAS_STUB_H_
paddle/cuda/include/stub/hl_cuda_cudnn_stub.h
浏览文件 @
2a21d8b3
...
...
@@ -12,15 +12,12 @@ 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. */
#ifndef HL_CUDA_CUDNN_STUB_H_
#define HL_CUDA_CUDNN_STUB_H_
#include "hl_cuda_cudnn.h"
inline
int
hl_get_cudnn_lib_version
()
{
return
0
;
}
inline
int
hl_get_cudnn_lib_version
()
{
return
0
;
}
inline
void
hl_create_tensor_descriptor
(
hl_tensor_descriptor
*
image_desc
)
{}
...
...
@@ -68,41 +65,41 @@ inline void hl_pooling_backward(hl_tensor_descriptor input,
hl_pooling_descriptor
pooling
)
{}
inline
void
hl_create_filter_descriptor
(
hl_filter_descriptor
*
filter
,
int
input_feature_maps
,
int
output_feature_maps
,
int
height
,
int
width
)
{}
int
input_feature_maps
,
int
output_feature_maps
,
int
height
,
int
width
)
{}
inline
void
hl_destroy_filter_descriptor
(
hl_filter_descriptor
filter
)
{}
inline
void
hl_create_convolution_descriptor
(
hl_convolution_descriptor
*
conv
,
hl_tensor_descriptor
image
,
hl_filter_descriptor
filter
,
int
padding_height
,
int
padding_width
,
int
stride_height
,
int
stride_width
)
{}
hl_tensor_descriptor
image
,
hl_filter_descriptor
filter
,
int
padding_height
,
int
padding_width
,
int
stride_height
,
int
stride_width
)
{}
inline
void
hl_reset_convolution_descriptor
(
hl_convolution_descriptor
conv
,
hl_tensor_descriptor
image
,
hl_filter_descriptor
filter
,
int
padding_height
,
int
padding_width
,
int
stride_height
,
int
stride_width
)
{}
hl_tensor_descriptor
image
,
hl_filter_descriptor
filter
,
int
padding_height
,
int
padding_width
,
int
stride_height
,
int
stride_width
)
{}
inline
void
hl_destroy_convolution_descriptor
(
hl_convolution_descriptor
conv
)
{}
inline
void
hl_conv_workspace
(
hl_tensor_descriptor
input
,
hl_tensor_descriptor
output
,
hl_filter_descriptor
filter
,
hl_convolution_descriptor
conv
,
int
*
convFwdAlgo
,
size_t
*
fwdLimitBytes
,
int
*
convBwdDataAlgo
,
size_t
*
bwdDataLimitBytes
,
int
*
convBwdFilterAlgo
,
size_t
*
bwdFilterLimitBytes
)
{}
hl_tensor_descriptor
output
,
hl_filter_descriptor
filter
,
hl_convolution_descriptor
conv
,
int
*
convFwdAlgo
,
size_t
*
fwdLimitBytes
,
int
*
convBwdDataAlgo
,
size_t
*
bwdDataLimitBytes
,
int
*
convBwdFilterAlgo
,
size_t
*
bwdFilterLimitBytes
)
{}
inline
void
hl_convolution_forward
(
hl_tensor_descriptor
input
,
real
*
input_data
,
...
...
@@ -116,86 +113,84 @@ inline void hl_convolution_forward(hl_tensor_descriptor input,
int
convFwdAlgo
)
{}
inline
void
hl_convolution_forward_add_bias
(
hl_tensor_descriptor
bias
,
real
*
bias_data
,
hl_tensor_descriptor
output
,
real
*
output_data
)
{}
inline
void
hl_convolution_backward_filter
(
hl_tensor_descriptor
input
,
real
*
input_data
,
hl_tensor_descriptor
output
,
real
*
output_grad_data
,
hl_filter_descriptor
filter
,
real
*
filter_grad_data
,
hl_convolution_descriptor
conv
,
void
*
gpuWorkSpace
,
size_t
sizeInBytes
,
int
convBwdFilterAlgo
)
{}
inline
void
hl_convolution_backward_data
(
hl_tensor_descriptor
input
,
real
*
input_data_grad
,
hl_tensor_descriptor
output
,
real
*
output_grad_data
,
hl_filter_descriptor
filter
,
real
*
filter_data
,
hl_convolution_descriptor
conv
,
void
*
gpuWorkSpace
,
size_t
sizeInBytes
,
int
convBwdDataAlgo
)
{}
real
*
bias_data
,
hl_tensor_descriptor
output
,
real
*
output_data
)
{}
inline
void
hl_convolution_backward_filter
(
hl_tensor_descriptor
input
,
real
*
input_data
,
hl_tensor_descriptor
output
,
real
*
output_grad_data
,
hl_filter_descriptor
filter
,
real
*
filter_grad_data
,
hl_convolution_descriptor
conv
,
void
*
gpuWorkSpace
,
size_t
sizeInBytes
,
int
convBwdFilterAlgo
)
{}
inline
void
hl_convolution_backward_data
(
hl_tensor_descriptor
input
,
real
*
input_data_grad
,
hl_tensor_descriptor
output
,
real
*
output_grad_data
,
hl_filter_descriptor
filter
,
real
*
filter_data
,
hl_convolution_descriptor
conv
,
void
*
gpuWorkSpace
,
size_t
sizeInBytes
,
int
convBwdDataAlgo
)
{}
inline
void
hl_convolution_backward_bias
(
hl_tensor_descriptor
bias
,
real
*
bias_grad_data
,
hl_tensor_descriptor
output
,
real
*
output_grad_data
)
{}
real
*
bias_grad_data
,
hl_tensor_descriptor
output
,
real
*
output_grad_data
)
{}
inline
void
hl_softmax_forward
(
real
*
input
,
real
*
output
,
int
height
,
int
width
)
{}
inline
void
hl_softmax_backward
(
real
*
output_value
,
real
*
output_grad
,
inline
void
hl_softmax_forward
(
real
*
input
,
real
*
output
,
int
height
,
int
width
)
{}
inline
void
hl_softmax_backward
(
real
*
output_value
,
real
*
output_grad
,
int
height
,
int
width
)
{}
inline
void
hl_batch_norm_forward_training
(
hl_tensor_descriptor
inputDesc
,
real
*
input
,
real
*
input
,
hl_tensor_descriptor
outputDesc
,
real
*
output
,
real
*
output
,
hl_tensor_descriptor
bnParamDesc
,
real
*
scale
,
real
*
bias
,
real
*
scale
,
real
*
bias
,
double
factor
,
real
*
runningMean
,
real
*
runningInvVar
,
real
*
runningMean
,
real
*
runningInvVar
,
double
epsilon
,
real
*
savedMean
,
real
*
savedVar
)
{}
real
*
savedMean
,
real
*
savedVar
)
{}
inline
void
hl_batch_norm_forward_inference
(
hl_tensor_descriptor
inputDesc
,
real
*
input
,
real
*
input
,
hl_tensor_descriptor
outputDesc
,
real
*
output
,
real
*
output
,
hl_tensor_descriptor
bnParamDesc
,
real
*
scale
,
real
*
bias
,
real
*
estimatedMean
,
real
*
estimatedVar
,
real
*
scale
,
real
*
bias
,
real
*
estimatedMean
,
real
*
estimatedVar
,
double
epsilon
)
{}
inline
void
hl_batch_norm_backward
(
hl_tensor_descriptor
inputDesc
,
real
*
input
,
real
*
input
,
hl_tensor_descriptor
outGradDesc
,
real
*
outGrad
,
real
*
outGrad
,
hl_tensor_descriptor
inGradDesc
,
real
*
inGrad
,
real
*
inGrad
,
hl_tensor_descriptor
dBnParamDesc
,
real
*
scale
,
real
*
scaleGrad
,
real
*
biasGrad
,
real
*
scale
,
real
*
scaleGrad
,
real
*
biasGrad
,
double
epsilon
,
real
*
savedMean
,
real
*
savedInvVar
)
{}
real
*
savedMean
,
real
*
savedInvVar
)
{}
#endif // HL_CUDA_CUDNN_STUB_H_
paddle/cuda/include/stub/hl_cuda_stub.h
浏览文件 @
2a21d8b3
...
...
@@ -12,7 +12,6 @@ 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. */
#ifndef HL_CUDA_STUB_H_
#define HL_CUDA_STUB_H_
...
...
@@ -24,29 +23,25 @@ inline void hl_specify_devices_start(int *device, int number) {}
inline
void
hl_init
(
int
device
)
{}
inline
int
hl_get_cuda_lib_version
(
int
device
)
{
return
0
;
}
inline
int
hl_get_cuda_lib_version
(
int
device
)
{
return
0
;
}
inline
void
hl_fini
()
{}
inline
void
hl_set_sync_flag
(
bool
flag
)
{}
inline
bool
hl_get_sync_flag
()
{
return
false
;
}
inline
bool
hl_get_sync_flag
()
{
return
false
;
}
inline
int
hl_get_device_count
()
{
return
0
;
}
inline
int
hl_get_device_count
()
{
return
0
;
}
inline
void
hl_set_device
(
int
device
)
{}
inline
int
hl_get_device
()
{
return
0
;
}
inline
int
hl_get_device
()
{
return
0
;
}
inline
void
*
hl_malloc_device
(
size_t
size
)
{
return
NULL
;
}
inline
void
*
hl_malloc_device
(
size_t
size
)
{
return
NULL
;
}
inline
void
hl_free_mem_device
(
void
*
dest_d
)
{}
inline
void
*
hl_malloc_host
(
size_t
size
)
{
return
NULL
;
}
inline
void
*
hl_malloc_host
(
size_t
size
)
{
return
NULL
;
}
inline
void
hl_free_mem_host
(
void
*
dest_h
)
{}
...
...
@@ -64,7 +59,9 @@ inline void hl_rand(real *dest_d, size_t num) {}
inline
void
hl_srand
(
unsigned
int
seed
)
{}
inline
void
hl_memcpy_async
(
void
*
dst
,
void
*
src
,
size_t
size
,
inline
void
hl_memcpy_async
(
void
*
dst
,
void
*
src
,
size_t
size
,
hl_stream_t
stream
)
{}
inline
void
hl_stream_synchronize
(
hl_stream_t
stream
)
{}
...
...
@@ -83,11 +80,11 @@ inline void hl_stream_wait_event(hl_stream_t stream, hl_event_t event) {}
inline
void
hl_event_synchronize
(
hl_event_t
event
)
{}
inline
int
hl_get_device_last_error
()
{
return
0
;
}
inline
int
hl_get_device_last_error
()
{
return
0
;
}
inline
const
char
*
hl_get_device_error_string
()
{
return
NULL
;
}
inline
const
char
*
hl_get_device_error_string
()
{
return
NULL
;
}
inline
const
char
*
hl_get_device_error_string
(
size_t
err
)
{
return
NULL
;
}
inline
const
char
*
hl_get_device_error_string
(
size_t
err
)
{
return
NULL
;
}
inline
bool
hl_cuda_event_is_ready
(
hl_event_t
event
)
{
return
true
;
}
...
...
paddle/cuda/include/stub/hl_lstm_stub.h
浏览文件 @
2a21d8b3
...
...
@@ -12,7 +12,6 @@ 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. */
#ifndef HL_LSTM_STUB_H_
#define HL_LSTM_STUB_H_
...
...
paddle/cuda/include/stub/hl_matrix_stub.h
浏览文件 @
2a21d8b3
...
...
@@ -12,7 +12,6 @@ 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. */
#ifndef HL_MATRIX_STUB_H_
#define HL_MATRIX_STUB_H_
...
...
@@ -26,48 +25,30 @@ inline void hl_matrix_add(real* A_d,
real
alpha
,
real
beta
)
{}
inline
void
hl_matrix_softmax
(
real
*
A_d
,
real
*
C_d
,
int
dimM
,
int
dimN
)
{}
inline
void
hl_matrix_softmax
(
real
*
A_d
,
real
*
C_d
,
int
dimM
,
int
dimN
)
{}
inline
void
hl_sequence_softmax_forward
(
real
*
A_d
,
real
*
C_d
,
inline
void
hl_sequence_softmax_forward
(
real
*
A_d
,
real
*
C_d
,
const
int
*
index
,
int
numSequence
)
{}
inline
void
hl_matrix_softmax_derivative
(
real
*
grad_d
,
real
*
output_d
,
real
*
sftmaxSum_d
,
int
dimM
,
int
dimN
)
{}
inline
void
hl_matrix_classification_error
(
real
*
A_d
,
int
*
B_d
,
real
*
C_d
,
int
dimM
,
int
dimN
)
{}
inline
void
hl_matrix_cross_entropy
(
real
*
A_d
,
real
*
C_d
,
int
*
label_d
,
int
dimM
,
int
dimN
)
{}
inline
void
hl_matrix_cross_entropy_bp
(
real
*
grad_d
,
real
*
output_d
,
int
*
label_d
,
int
dimM
,
int
dimN
)
{}
inline
void
hl_matrix_multi_binary_cross_entropy
(
real
*
output
,
real
*
entropy
,
hl_sparse_matrix_s
mat
,
int
dimM
,
int
dimN
)
{}
inline
void
hl_matrix_multi_binary_cross_entropy_bp
(
real
*
output
,
real
*
grad
,
hl_sparse_matrix_s
mat
,
int
dimM
,
int
dimN
)
{}
inline
void
hl_matrix_softmax_derivative
(
real
*
grad_d
,
real
*
output_d
,
real
*
sftmaxSum_d
,
int
dimM
,
int
dimN
)
{}
inline
void
hl_matrix_classification_error
(
real
*
A_d
,
int
*
B_d
,
real
*
C_d
,
int
dimM
,
int
dimN
)
{}
inline
void
hl_matrix_cross_entropy
(
real
*
A_d
,
real
*
C_d
,
int
*
label_d
,
int
dimM
,
int
dimN
)
{}
inline
void
hl_matrix_cross_entropy_bp
(
real
*
grad_d
,
real
*
output_d
,
int
*
label_d
,
int
dimM
,
int
dimN
)
{}
inline
void
hl_matrix_multi_binary_cross_entropy
(
real
*
output
,
real
*
entropy
,
hl_sparse_matrix_s
mat
,
int
dimM
,
int
dimN
)
{}
inline
void
hl_matrix_multi_binary_cross_entropy_bp
(
real
*
output
,
real
*
grad
,
hl_sparse_matrix_s
mat
,
int
dimM
,
int
dimN
)
{}
inline
void
hl_matrix_zero_mem
(
real
*
data
,
int
num
)
{}
...
...
@@ -101,7 +82,6 @@ inline void hl_cossim(real* output,
int
input2_height
,
real
scale
)
{}
inline
void
hl_cossim_derivative
(
real
*
grad
,
real
*
output
,
real
*
prevOutX
,
...
...
paddle/cuda/include/stub/hl_sequence_stub.h
浏览文件 @
2a21d8b3
...
...
@@ -12,7 +12,6 @@ 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. */
#ifndef HL_SEQUENCE_STUB_H_
#define HL_SEQUENCE_STUB_H_
...
...
@@ -21,15 +20,12 @@ limitations under the License. */
inline
void
hl_max_sequence_forward
(
real
*
input
,
const
int
*
sequence
,
real
*
output
,
int
*
index
,
int
*
index
,
int
numSequences
,
int
dim
)
{}
inline
void
hl_max_sequence_backward
(
real
*
outputGrad
,
int
*
index
,
real
*
inputGrad
,
int
numSequences
,
int
dim
)
{}
inline
void
hl_max_sequence_backward
(
real
*
outputGrad
,
int
*
index
,
real
*
inputGrad
,
int
numSequences
,
int
dim
)
{}
inline
void
hl_context_projection_forward
(
real
*
input
,
const
int
*
sequence
,
...
...
@@ -60,16 +56,16 @@ inline void hl_context_projection_backward_weight(real* outputGrad,
int
contextStart
,
int
beginPad
)
{}
inline
void
hl_sequence2batch_copy
(
real
*
batch
,
real
*
sequence
,
const
int
*
batchIndex
,
inline
void
hl_sequence2batch_copy
(
real
*
batch
,
real
*
sequence
,
const
int
*
batchIndex
,
int
seqWidth
,
int
batchCount
,
bool
seq2batch
)
{}
inline
void
hl_sequence2batch_add
(
real
*
batch
,
real
*
sequence
,
int
*
batchIndex
,
inline
void
hl_sequence2batch_add
(
real
*
batch
,
real
*
sequence
,
int
*
batchIndex
,
int
seqWidth
,
int
batchCount
,
bool
seq2batch
)
{}
...
...
paddle/cuda/include/stub/hl_sparse_stub.h
浏览文件 @
2a21d8b3
...
...
@@ -12,7 +12,6 @@ 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. */
#ifndef HL_SPARSE_STUB_H_
#define HL_SPARSE_STUB_H_
...
...
@@ -20,7 +19,7 @@ limitations under the License. */
inline
void
hl_malloc_sparse_matrix
(
hl_sparse_matrix_s
*
A_d
,
hl_matrix_format_t
format
,
hl_matrix_value_t
value_type
,
hl_matrix_value_t
value_type
,
int
dimM
,
int
dimN
,
int
nnz
)
{}
...
...
@@ -28,20 +27,20 @@ inline void hl_malloc_sparse_matrix(hl_sparse_matrix_s *A_d,
inline
void
hl_free_sparse_matrix
(
hl_sparse_matrix_s
A_d
)
{}
inline
void
hl_construct_sparse_matrix
(
hl_sparse_matrix_s
*
A_d
,
void
*
dest_d
,
void
*
dest_d
,
size_t
size
,
hl_matrix_format_t
format
,
hl_matrix_value_t
value_type
,
hl_matrix_value_t
value_type
,
int
dimM
,
int
dimN
,
int
nnz
)
{}
inline
void
hl_construct_sparse_matrix
(
hl_sparse_matrix_s
*
A_d
,
real
*
value_d
,
int
*
rows_d
,
int
*
cols_d
,
real
*
value_d
,
int
*
rows_d
,
int
*
cols_d
,
hl_matrix_format_t
format
,
hl_matrix_value_t
value_type
,
hl_matrix_value_t
value_type
,
int
dimM
,
int
dimN
,
int
nnz
)
{}
...
...
@@ -87,10 +86,14 @@ inline void hl_matrix_csr_mul_dense(hl_sparse_matrix_s A_d,
inline
void
hl_matrix_csc_mul_dense
(
hl_sparse_matrix_s
A_d
,
hl_trans_op_t
transa
,
real
*
B_d
,
hl_trans_op_t
transb
,
real
*
B_d
,
hl_trans_op_t
transb
,
real
*
C_d
,
int
dimM
,
int
dimN
,
int
dimK
,
real
alpha
,
real
beta
)
{}
int
dimM
,
int
dimN
,
int
dimK
,
real
alpha
,
real
beta
)
{}
inline
void
hl_matrix_dense_mul_csc
(
real
*
A_d
,
hl_trans_op_t
transa
,
...
...
@@ -103,18 +106,27 @@ inline void hl_matrix_dense_mul_csc(real *A_d,
real
alpha
,
real
beta
)
{}
inline
void
hl_sparse_matrix_mul
(
real
*
A_d
,
hl_trans_op_t
transa
,
real
*
B_d
,
hl_trans_op_t
transb
,
inline
void
hl_sparse_matrix_mul
(
real
*
A_d
,
hl_trans_op_t
transa
,
real
*
B_d
,
hl_trans_op_t
transb
,
hl_sparse_matrix_s
C_d
,
int
dimM
,
int
dimN
,
int
dimK
,
real
alpha
,
real
beta
)
{}
int
dimM
,
int
dimN
,
int
dimK
,
real
alpha
,
real
beta
)
{}
inline
void
hl_matrix_dense_mul_csr
(
real
*
A_d
,
hl_trans_op_t
transa
,
inline
void
hl_matrix_dense_mul_csr
(
real
*
A_d
,
hl_trans_op_t
transa
,
hl_sparse_matrix_s
B_d
,
hl_trans_op_t
transb
,
real
*
C_d
,
int
dimM
,
int
dimN
,
int
dimK
,
real
alpha
,
real
beta
)
{}
int
dimM
,
int
dimN
,
int
dimK
,
real
alpha
,
real
beta
)
{}
inline
void
hl_memcpy_from_csc_matrix
(
real
*
csc_val
,
size_t
val_size
,
...
...
@@ -134,49 +146,39 @@ inline void hl_memcpy_from_csr_matrix(real *csr_val,
hl_sparse_matrix_s
csr_matrix
,
hl_stream_t
stream
)
{}
inline
void
hl_sparse_matrix_column_sum
(
real
*
A_d
,
hl_sparse_matrix_s
B_d
,
int
dimM
,
int
dimN
,
real
scale
)
{}
inline
void
hl_sparse_matrix_column_sum
(
real
*
A_d
,
hl_sparse_matrix_s
B_d
,
int
dimM
,
int
dimN
,
real
scale
)
{}
inline
void
hl_matrix_csr_column_sum
(
real
*
A_d
,
hl_sparse_matrix_s
B_d
,
int
dimM
,
int
dimN
,
real
scale
)
{}
inline
void
hl_matrix_csr_column_sum
(
real
*
A_d
,
hl_sparse_matrix_s
B_d
,
int
dimM
,
int
dimN
,
real
scale
)
{}
inline
void
hl_sparse_matrix_add_bias
(
hl_sparse_matrix_s
A_d
,
real
*
B_d
,
real
*
B_d
,
real
scale
)
{}
inline
void
hl_matrix_csr_add_bias
(
hl_sparse_matrix_s
A_d
,
real
*
B_d
,
real
*
B_d
,
real
scale
)
{}
inline
void
hl_sparse_matrix_add_dense
(
hl_sparse_matrix_s
A_d
,
real
*
B_d
,
real
*
B_d
,
int
dimM
,
int
dimN
,
real
alpha
,
real
beta
)
{}
inline
void
hl_matrix_csr_add_dense
(
hl_sparse_matrix_s
A_d
,
real
*
B_d
,
real
*
B_d
,
int
dimM
,
int
dimN
,
real
alpha
,
real
beta
)
{}
inline
int
*
hl_sparse_matrix_get_rows
(
hl_sparse_matrix_s
sMat
)
{
return
NULL
;
}
inline
int
*
hl_sparse_matrix_get_rows
(
hl_sparse_matrix_s
sMat
)
{
return
NULL
;
}
inline
int
*
hl_sparse_matrix_get_cols
(
hl_sparse_matrix_s
sMat
)
{
return
NULL
;
}
inline
int
*
hl_sparse_matrix_get_cols
(
hl_sparse_matrix_s
sMat
)
{
return
NULL
;
}
inline
real
*
hl_sparse_matrix_get_value
(
hl_sparse_matrix_s
sMat
)
{
inline
real
*
hl_sparse_matrix_get_value
(
hl_sparse_matrix_s
sMat
)
{
return
NULL
;
}
...
...
paddle/cuda/src/avx_mathfun.h
浏览文件 @
2a21d8b3
此差异已折叠。
点击以展开。
paddle/cuda/src/hl_avx_functions.cc
浏览文件 @
2a21d8b3
...
...
@@ -12,62 +12,58 @@ 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 <immintrin.h>
#include "hl_functions.h"
namespace
hppl
{
extern
__m256
exp
(
__m256
a
);
extern
__m256
exp
(
__m256
a
);
__m256
relu
(
const
__m256
a
)
{
__m256
tmp
=
_mm256_set1_ps
(
0.0
f
);
return
_mm256_max_ps
(
a
,
tmp
);
}
__m256
relu
(
const
__m256
a
)
{
__m256
tmp
=
_mm256_set1_ps
(
0.0
f
);
return
_mm256_max_ps
(
a
,
tmp
);
}
__m256
sigmoid
(
const
__m256
a
)
{
__m256
max
=
_mm256_set1_ps
(
SIGMOID_THRESHOLD_MAX
);
__m256
min
=
_mm256_set1_ps
(
SIGMOID_THRESHOLD_MIN
);
__m256
tmp
=
_mm256_max_ps
(
a
,
min
);
tmp
=
_mm256_min_ps
(
tmp
,
max
);
tmp
=
_mm256_sub_ps
(
_mm256_set1_ps
(
0.0
f
),
tmp
);
tmp
=
exp
(
tmp
);
tmp
=
_mm256_add_ps
(
_mm256_set1_ps
(
1.0
f
),
tmp
);
tmp
=
_mm256_div_ps
(
_mm256_set1_ps
(
1.0
f
),
tmp
);
return
tmp
;
}
__m256
sigmoid
(
const
__m256
a
)
{
__m256
max
=
_mm256_set1_ps
(
SIGMOID_THRESHOLD_MAX
);
__m256
min
=
_mm256_set1_ps
(
SIGMOID_THRESHOLD_MIN
);
__m256
tmp
=
_mm256_max_ps
(
a
,
min
);
tmp
=
_mm256_min_ps
(
tmp
,
max
);
tmp
=
_mm256_sub_ps
(
_mm256_set1_ps
(
0.0
f
),
tmp
);
tmp
=
exp
(
tmp
);
tmp
=
_mm256_add_ps
(
_mm256_set1_ps
(
1.0
f
),
tmp
);
tmp
=
_mm256_div_ps
(
_mm256_set1_ps
(
1.0
f
),
tmp
);
return
tmp
;
}
__m256
tanh
(
const
__m256
a
)
{
__m256
max
=
_mm256_set1_ps
(
EXP_MAX_INPUT
);
__m256
tmp
=
_mm256_mul_ps
(
_mm256_set1_ps
(
-
2.0
f
),
a
);
tmp
=
_mm256_min_ps
(
tmp
,
max
);
tmp
=
exp
(
tmp
);
return
_mm256_sub_ps
(
_mm256_div_ps
(
_mm256_set1_ps
(
2.0
f
),
_mm256_add_ps
(
_mm256_set1_ps
(
1.0
f
),
tmp
)),
_mm256_set1_ps
(
1.0
f
));
}
__m256
tanh
(
const
__m256
a
)
{
__m256
max
=
_mm256_set1_ps
(
EXP_MAX_INPUT
);
__m256
tmp
=
_mm256_mul_ps
(
_mm256_set1_ps
(
-
2.0
f
),
a
);
tmp
=
_mm256_min_ps
(
tmp
,
max
);
tmp
=
exp
(
tmp
);
return
_mm256_sub_ps
(
_mm256_div_ps
(
_mm256_set1_ps
(
2.0
f
),
_mm256_add_ps
(
_mm256_set1_ps
(
1.0
f
),
tmp
)
),
_mm256_set1_ps
(
1.0
f
));
}
__m256
linear
(
const
__m256
a
)
{
return
a
;
}
__m256
linear
(
const
__m256
a
)
{
return
a
;
}
__m256
relu
(
const
__m256
a
,
const
__m256
b
)
{
return
_mm256_mul_ps
(
a
,
__m256
relu
(
const
__m256
a
,
const
__m256
b
)
{
return
_mm256_mul_ps
(
a
,
_mm256_and_ps
(
_mm256_cmp_ps
(
b
,
_mm256_set1_ps
(
0.0
f
),
_CMP_GT_OS
),
_mm256_set1_ps
(
1.0
f
)));
}
_mm256_set1_ps
(
1.0
f
)));
}
__m256
sigmoid
(
const
__m256
a
,
const
__m256
b
)
{
return
_mm256_mul_ps
(
_mm256_mul_ps
(
a
,
b
),
_mm256_sub_ps
(
_mm256_set1_ps
(
1.0
f
),
b
));
}
__m256
sigmoid
(
const
__m256
a
,
const
__m256
b
)
{
return
_mm256_mul_ps
(
_mm256_mul_ps
(
a
,
b
),
_mm256_sub_ps
(
_mm256_set1_ps
(
1.0
f
),
b
));
}
__m256
tanh
(
const
__m256
a
,
const
__m256
b
)
{
return
_mm256_mul_ps
(
a
,
_mm256_sub_ps
(
_mm256_set1_ps
(
1.0
f
),
_mm256_mul_ps
(
b
,
b
)));
}
__m256
tanh
(
const
__m256
a
,
const
__m256
b
)
{
return
_mm256_mul_ps
(
a
,
_mm256_sub_ps
(
_mm256_set1_ps
(
1.0
f
),
_mm256_mul_ps
(
b
,
b
)));
}
__m256
linear
(
const
__m256
a
,
const
__m256
b
)
{
return
a
;
}
__m256
linear
(
const
__m256
a
,
const
__m256
b
)
{
return
a
;
}
}
// namespace hppl
paddle/cuda/src/hl_cpu_functions.cc
浏览文件 @
2a21d8b3
...
...
@@ -12,46 +12,33 @@ 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 <math.h>
#include "hl_functions.h"
namespace
hppl
{
real
relu
(
const
real
a
)
{
return
a
>
0.0
f
?
a
:
0.0
f
;
}
real
sigmoid
(
const
real
a
)
{
const
real
min
=
SIGMOID_THRESHOLD_MIN
;
const
real
max
=
SIGMOID_THRESHOLD_MAX
;
real
tmp
=
(
a
<
min
)
?
min
:
((
a
>
max
)
?
max
:
a
);
return
1.0
/
(
1.0
+
exp
(
-
tmp
));
}
real
tanh
(
const
real
a
)
{
real
tmp
=
-
2.0
*
a
;
tmp
=
(
tmp
>
EXP_MAX_INPUT
)
?
EXP_MAX_INPUT
:
tmp
;
return
(
2.0
/
(
1.0
+
exp
(
tmp
)))
-
1.0
;
}
real
linear
(
const
real
a
)
{
return
a
;
}
real
relu
(
const
real
a
,
const
real
b
)
{
return
a
*
(
b
>
0.0
f
?
1.0
f
:
0.0
f
);
}
real
sigmoid
(
const
real
a
,
const
real
b
)
{
return
a
*
b
*
(
1
-
b
);
}
real
tanh
(
const
real
a
,
const
real
b
)
{
return
a
*
(
1.0
f
-
b
*
b
);
}
real
linear
(
const
real
a
,
const
real
b
)
{
return
a
;
}
real
relu
(
const
real
a
)
{
return
a
>
0.0
f
?
a
:
0.0
f
;
}
real
sigmoid
(
const
real
a
)
{
const
real
min
=
SIGMOID_THRESHOLD_MIN
;
const
real
max
=
SIGMOID_THRESHOLD_MAX
;
real
tmp
=
(
a
<
min
)
?
min
:
((
a
>
max
)
?
max
:
a
);
return
1.0
/
(
1.0
+
exp
(
-
tmp
));
}
real
tanh
(
const
real
a
)
{
real
tmp
=
-
2.0
*
a
;
tmp
=
(
tmp
>
EXP_MAX_INPUT
)
?
EXP_MAX_INPUT
:
tmp
;
return
(
2.0
/
(
1.0
+
exp
(
tmp
)))
-
1.0
;
}
real
linear
(
const
real
a
)
{
return
a
;
}
real
relu
(
const
real
a
,
const
real
b
)
{
return
a
*
(
b
>
0.0
f
?
1.0
f
:
0.0
f
);
}
real
sigmoid
(
const
real
a
,
const
real
b
)
{
return
a
*
b
*
(
1
-
b
);
}
real
tanh
(
const
real
a
,
const
real
b
)
{
return
a
*
(
1.0
f
-
b
*
b
);
}
real
linear
(
const
real
a
,
const
real
b
)
{
return
a
;
}
}
// namespace hppl
paddle/cuda/src/hl_cuda_cublas.cc
浏览文件 @
2a21d8b3
...
...
@@ -12,7 +12,6 @@ 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 <sys/time.h>
#include <mutex>
#include "hl_cuda.h"
...
...
@@ -24,7 +23,7 @@ limitations under the License. */
namespace
dynload
{
std
::
once_flag
cublas_dso_flag
;
void
*
cublas_dso_handle
=
nullptr
;
void
*
cublas_dso_handle
=
nullptr
;
/**
* The following macro definition can generate structs
...
...
@@ -34,31 +33,30 @@ void* cublas_dso_handle = nullptr;
* note: default dynamic linked libs
*/
#ifdef PADDLE_USE_DSO
#define DYNAMIC_LOAD_CUBLAS_WRAP(__name) \
struct DynLoad__##__name { \
template <typename... Args> \
cublasStatus_t operator()(Args... args) { \
typedef cublasStatus_t (*cublasFunc)(Args...); \
std::call_once(cublas_dso_flag, GetCublasDsoHandle, \
&cublas_dso_handle); \
void* p_##__name = dlsym(cublas_dso_handle, #__name); \
return reinterpret_cast<cublasFunc>(p_##__name)(args...); \
} \
#define DYNAMIC_LOAD_CUBLAS_WRAP(__name) \
struct DynLoad__##__name { \
template <typename... Args> \
cublasStatus_t operator()(Args... args) { \
typedef cublasStatus_t (*cublasFunc)(Args...); \
std::call_once(cublas_dso_flag, GetCublasDsoHandle, &cublas_dso_handle); \
void *p_##__name = dlsym(cublas_dso_handle, #__name); \
return reinterpret_cast<cublasFunc>(p_##__name)(args...); \
} \
} __name; // struct DynLoad__##__name
#else
#define DYNAMIC_LOAD_CUBLAS_WRAP(__name)
\
struct DynLoad__##__name {
\
template <typename... Args>
\
cublasStatus_t operator()(Args... args) {
\
return __name(args...);
\
}
\
#define DYNAMIC_LOAD_CUBLAS_WRAP(__name) \
struct DynLoad__##__name {
\
template <typename... Args> \
cublasStatus_t operator()(Args... args) { \
return __name(args...); \
} \
} __name; // struct DynLoad__##__name
#endif
#define DYNAMIC_LOAD_CUBLAS_V2_WRAP(__name) \
DYNAMIC_LOAD_CUBLAS_WRAP(__name)
#define DYNAMIC_LOAD_CUBLAS_V2_WRAP(__name) DYNAMIC_LOAD_CUBLAS_WRAP(__name)
// include all needed cublas functions in HPPL
// clang-format off
#define CUBLAS_BLAS_ROUTINE_EACH(__macro) \
__macro(cublasSgemv) \
__macro(cublasDgemv) \
...
...
@@ -88,41 +86,41 @@ CUBLAS_BLAS_ROUTINE_EACH(DYNAMIC_LOAD_CUBLAS_V2_WRAP)
}
/* namespace dynload */
// clang-format on
#ifndef PADDLE_TYPE_DOUBLE
#define
CUBLAS_GEAM
dynload::cublasSgeam
#define
CUBLAS_GEMV
dynload::cublasSgemv
#define
CUBLAS_GEMM
dynload::cublasSgemm
#define
CUBLAS_GETRF
dynload::cublasSgetrfBatched
#define
CUBLAS_GETRI
dynload::cublasSgetriBatched
#define
CUBLAS_GEAM
dynload::cublasSgeam
#define
CUBLAS_GEMV
dynload::cublasSgemv
#define
CUBLAS_GEMM
dynload::cublasSgemm
#define
CUBLAS_GETRF
dynload::cublasSgetrfBatched
#define
CUBLAS_GETRI
dynload::cublasSgetriBatched
#else
#define
CUBLAS_GEAM
dynload::cublasDgeam
#define
CUBLAS_GEMV
dynload::cublasDgemv
#define
CUBLAS_GEMM
dynload::cublasDgemm
#define
CUBLAS_GETRF
dynload::cublasDgetrfBatched
#define
CUBLAS_GETRI
dynload::cublasDgetriBatched
#define
CUBLAS_GEAM
dynload::cublasDgeam
#define
CUBLAS_GEMV
dynload::cublasDgemv
#define
CUBLAS_GEMM
dynload::cublasDgemm
#define
CUBLAS_GETRF
dynload::cublasDgetrfBatched
#define
CUBLAS_GETRI
dynload::cublasDgetriBatched
#endif
const
char
*
hl_cublas_get_error_string
(
cublasStatus_t
status
)
{
const
char
*
hl_cublas_get_error_string
(
cublasStatus_t
status
)
{
switch
(
status
)
{
case
CUBLAS_STATUS_NOT_INITIALIZED
:
return
"[cublas status]: not initialized"
;
case
CUBLAS_STATUS_ALLOC_FAILED
:
return
"[cublas status]: allocate failed"
;
case
CUBLAS_STATUS_INVALID_VALUE
:
return
"[cublas status]: invalid value"
;
case
CUBLAS_STATUS_ARCH_MISMATCH
:
return
"[cublas status]: arch mismatch"
;
case
CUBLAS_STATUS_MAPPING_ERROR
:
return
"[cublas status]: mapping error"
;
case
CUBLAS_STATUS_EXECUTION_FAILED
:
return
"[cublas status]: execution failed"
;
case
CUBLAS_STATUS_INTERNAL_ERROR
:
return
"[cublas status]: internal error"
;
case
CUBLAS_STATUS_SUCCESS
:
return
"[cublas status]: success"
;
default:
return
"[cublas status]: unknown error"
;
case
CUBLAS_STATUS_NOT_INITIALIZED
:
return
"[cublas status]: not initialized"
;
case
CUBLAS_STATUS_ALLOC_FAILED
:
return
"[cublas status]: allocate failed"
;
case
CUBLAS_STATUS_INVALID_VALUE
:
return
"[cublas status]: invalid value"
;
case
CUBLAS_STATUS_ARCH_MISMATCH
:
return
"[cublas status]: arch mismatch"
;
case
CUBLAS_STATUS_MAPPING_ERROR
:
return
"[cublas status]: mapping error"
;
case
CUBLAS_STATUS_EXECUTION_FAILED
:
return
"[cublas status]: execution failed"
;
case
CUBLAS_STATUS_INTERNAL_ERROR
:
return
"[cublas status]: internal error"
;
case
CUBLAS_STATUS_SUCCESS
:
return
"[cublas status]: success"
;
default:
return
"[cublas status]: unknown error"
;
}
}
...
...
@@ -131,27 +129,21 @@ const char* hl_cublas_get_error_string(cublasStatus_t status) {
* support << operator for more details error info.
*/
cublasStatus_t
g_cublasStat
;
#define CHECK_CUBLAS(cublas_func) \
g_cublasStat = cublas_func; \
CHECK_EQ(CUBLAS_STATUS_SUCCESS, g_cublasStat) \
<< "Cublas Error: " \
<< hl_cublas_get_error_string(g_cublasStat) \
<< " "
#define CHECK_CUBLAS(cublas_func) \
g_cublasStat = cublas_func; \
CHECK_EQ(CUBLAS_STATUS_SUCCESS, g_cublasStat) \
<< "Cublas Error: " << hl_cublas_get_error_string(g_cublasStat) << " "
void
hl_cublas_init
(
cublasHandle_t
*
cublas_handle
,
cudaStream_t
stream
)
{
CHECK_CUBLAS
(
dynload
::
cublasCreate
(
cublas_handle
))
<<
"[cublas init] Cublas create handle faild!"
;
<<
"[cublas init] Cublas create handle faild!"
;
CHECK_CUBLAS
(
dynload
::
cublasSetStream
(
*
cublas_handle
,
stream
))
<<
"[cublas init] Cublas set stream faild!"
;
<<
"[cublas init] Cublas set stream faild!"
;
}
void
hl_matrix_transpose
(
real
*
A_d
,
real
*
C_d
,
int
dimM
,
int
dimN
,
int
lda
,
int
ldc
)
{
void
hl_matrix_transpose
(
real
*
A_d
,
real
*
C_d
,
int
dimM
,
int
dimN
,
int
lda
,
int
ldc
)
{
real
alpha
=
1.0
;
real
beta
=
0.0
;
...
...
@@ -159,11 +151,18 @@ void hl_matrix_transpose(real *A_d,
CHECK_NOTNULL
(
C_d
);
CHECK_CUBLAS
(
CUBLAS_GEAM
(
t_resource
.
handle
,
CUBLAS_OP_T
,
CUBLAS_OP_N
,
dimM
,
dimN
,
&
alpha
,
A_d
,
lda
,
&
beta
,
nullptr
,
dimM
,
C_d
,
ldc
));
CUBLAS_OP_T
,
CUBLAS_OP_N
,
dimM
,
dimN
,
&
alpha
,
A_d
,
lda
,
&
beta
,
nullptr
,
dimM
,
C_d
,
ldc
));
CHECK_SYNC
(
"hl_matrix_transpose failed"
);
}
...
...
@@ -188,13 +187,13 @@ void hl_matrix_inverse(real *A_d, real *C_d, int dimN, int lda, int ldc) {
small-sized matrices. There may be a better way to reconstruct
the API for better performance.
*/
CHECK_CUBLAS
(
CUBLAS_GETRF
(
t_resource
.
handle
,
dimN
,
inout_d
,
lda
,
pivot_d
,
info_d
,
1
));
CHECK_CUBLAS
(
CUBLAS_GETRF
(
t_resource
.
handle
,
dimN
,
inout_d
,
lda
,
pivot_d
,
info_d
,
1
));
int
info_h
;
hl_memcpy
(
&
info_h
,
info_d
,
sizeof
(
int
));
if
(
info_h
!=
0
)
{
LOG
(
FATAL
)
<<
"Factorization of matrix failed: matrix may be singular.
\n
"
;
LOG
(
FATAL
)
<<
"Factorization of matrix failed: matrix may be singular.
\n
"
;
}
/* Step 2: Compute the inverse of the matrix given its LU decomposition */
...
...
@@ -203,12 +202,18 @@ void hl_matrix_inverse(real *A_d, real *C_d, int dimN, int lda, int ldc) {
hl_memcpy
(
out_d
,
out_h
,
sizeof
(
real
*
));
CHECK_CUBLAS
(
CUBLAS_GETRI
(
t_resource
.
handle
,
dimN
,
(
const
real
**
)
inout_d
,
lda
,
pivot_d
,
out_d
,
ldc
,
info_d
,
1
));
dimN
,
(
const
real
**
)
inout_d
,
lda
,
pivot_d
,
out_d
,
ldc
,
info_d
,
1
));
hl_memcpy
(
&
info_h
,
info_d
,
sizeof
(
int
));
if
(
info_h
!=
0
)
{
LOG
(
FATAL
)
<<
"Inversion of matrix failed: matrix may be singular.
\n
"
;
LOG
(
FATAL
)
<<
"Inversion of matrix failed: matrix may be singular.
\n
"
;
}
hl_free_mem_device
(
inout_d
);
...
...
@@ -218,12 +223,19 @@ void hl_matrix_inverse(real *A_d, real *C_d, int dimN, int lda, int ldc) {
CHECK_SYNC
(
"hl_matrix_inverse failed"
);
}
void
hl_matrix_mul
(
real
*
A_d
,
hl_trans_op_t
transa
,
real
*
B_d
,
hl_trans_op_t
transb
,
void
hl_matrix_mul
(
real
*
A_d
,
hl_trans_op_t
transa
,
real
*
B_d
,
hl_trans_op_t
transb
,
real
*
C_d
,
int
dimM
,
int
dimN
,
int
dimK
,
real
alpha
,
real
beta
,
int
lda
,
int
ldb
,
int
ldc
)
{
int
dimM
,
int
dimN
,
int
dimK
,
real
alpha
,
real
beta
,
int
lda
,
int
ldb
,
int
ldc
)
{
CHECK_NOTNULL
(
A_d
);
CHECK_NOTNULL
(
B_d
);
CHECK_NOTNULL
(
C_d
);
...
...
@@ -231,8 +243,8 @@ void hl_matrix_mul(real *A_d, hl_trans_op_t transa,
if
(
dimN
==
1
&&
dimM
!=
1
&&
dimK
!=
1
&&
transb
==
HPPL_OP_N
)
{
int
m
=
(
transa
==
HPPL_OP_N
)
?
dimM
:
dimK
;
int
n
=
(
transa
==
HPPL_OP_N
)
?
dimK
:
dimM
;
hl_matrix_mul_vector
(
A_d
,
transa
,
B_d
,
C_d
,
m
,
n
,
alpha
,
beta
,
lda
,
ldb
,
ldc
);
hl_matrix_mul_vector
(
A_d
,
transa
,
B_d
,
C_d
,
m
,
n
,
alpha
,
beta
,
lda
,
ldb
,
ldc
);
return
;
}
...
...
@@ -240,8 +252,7 @@ void hl_matrix_mul(real *A_d, hl_trans_op_t transa,
int
m
=
(
transb
==
HPPL_OP_N
)
?
dimK
:
dimN
;
int
n
=
(
transb
==
HPPL_OP_N
)
?
dimN
:
dimK
;
hl_trans_op_t
trans
=
(
transb
==
HPPL_OP_N
)
?
HPPL_OP_T
:
HPPL_OP_N
;
hl_matrix_mul_vector
(
B_d
,
trans
,
A_d
,
C_d
,
m
,
n
,
alpha
,
beta
,
ldb
,
1
,
1
);
hl_matrix_mul_vector
(
B_d
,
trans
,
A_d
,
C_d
,
m
,
n
,
alpha
,
beta
,
ldb
,
1
,
1
);
return
;
}
...
...
@@ -250,26 +261,47 @@ void hl_matrix_mul(real *A_d, hl_trans_op_t transa,
stat
=
CUBLAS_GEMM
(
t_resource
.
handle
,
CUBLAS_OP_N
,
CUBLAS_OP_N
,
dimN
,
dimM
,
dimK
,
&
alpha
,
B_d
,
ldb
,
A_d
,
lda
,
&
beta
,
C_d
,
ldc
);
dimN
,
dimM
,
dimK
,
&
alpha
,
B_d
,
ldb
,
A_d
,
lda
,
&
beta
,
C_d
,
ldc
);
}
else
if
((
HPPL_OP_T
==
transa
)
&&
(
HPPL_OP_N
==
transb
))
{
stat
=
CUBLAS_GEMM
(
t_resource
.
handle
,
CUBLAS_OP_N
,
CUBLAS_OP_T
,
dimN
,
dimM
,
dimK
,
&
alpha
,
B_d
,
ldb
,
A_d
,
lda
,
&
beta
,
C_d
,
ldc
);
dimN
,
dimM
,
dimK
,
&
alpha
,
B_d
,
ldb
,
A_d
,
lda
,
&
beta
,
C_d
,
ldc
);
}
else
if
((
HPPL_OP_N
==
transa
)
&&
(
HPPL_OP_T
==
transb
))
{
stat
=
CUBLAS_GEMM
(
t_resource
.
handle
,
CUBLAS_OP_T
,
CUBLAS_OP_N
,
dimN
,
dimM
,
dimK
,
&
alpha
,
B_d
,
ldb
,
A_d
,
lda
,
&
beta
,
C_d
,
ldc
);
dimN
,
dimM
,
dimK
,
&
alpha
,
B_d
,
ldb
,
A_d
,
lda
,
&
beta
,
C_d
,
ldc
);
}
else
{
LOG
(
FATAL
)
<<
"parameter transa error!"
;
}
...
...
@@ -277,24 +309,46 @@ void hl_matrix_mul(real *A_d, hl_trans_op_t transa,
CHECK_SYNC
(
"hl_matrix_mul failed"
);
}
void
hl_matrix_mul
(
real
*
A_d
,
hl_trans_op_t
transa
,
real
*
B_d
,
hl_trans_op_t
transb
,
void
hl_matrix_mul
(
real
*
A_d
,
hl_trans_op_t
transa
,
real
*
B_d
,
hl_trans_op_t
transb
,
real
*
C_d
,
int
dimM
,
int
dimN
,
int
dimK
,
real
alpha
,
real
beta
)
{
int
dimM
,
int
dimN
,
int
dimK
,
real
alpha
,
real
beta
)
{
int
lda
=
(
HPPL_OP_N
==
transa
)
?
dimK
:
dimM
;
int
ldb
=
(
HPPL_OP_N
==
transb
)
?
dimN
:
dimK
;
int
ldc
=
dimN
;
hl_matrix_mul
(
A_d
,
transa
,
B_d
,
transb
,
C_d
,
dimM
,
dimN
,
dimK
,
alpha
,
beta
,
lda
,
ldb
,
ldc
);
hl_matrix_mul
(
A_d
,
transa
,
B_d
,
transb
,
C_d
,
dimM
,
dimN
,
dimK
,
alpha
,
beta
,
lda
,
ldb
,
ldc
);
}
void
hl_matrix_mul_vector
(
real
*
A_d
,
hl_trans_op_t
trans
,
real
*
B_d
,
real
*
C_d
,
int
dimM
,
int
dimN
,
real
alpha
,
real
beta
,
int
lda
,
int
incb
,
int
incc
)
{
void
hl_matrix_mul_vector
(
real
*
A_d
,
hl_trans_op_t
trans
,
real
*
B_d
,
real
*
C_d
,
int
dimM
,
int
dimN
,
real
alpha
,
real
beta
,
int
lda
,
int
incb
,
int
incc
)
{
CHECK_NOTNULL
(
A_d
);
CHECK_NOTNULL
(
B_d
);
CHECK_NOTNULL
(
C_d
);
...
...
@@ -303,21 +357,29 @@ void hl_matrix_mul_vector(real *A_d, hl_trans_op_t trans,
if
(
HPPL_OP_N
==
trans
)
{
stat
=
CUBLAS_GEMV
(
t_resource
.
handle
,
CUBLAS_OP_T
,
dimN
,
dimM
,
dimN
,
dimM
,
&
alpha
,
A_d
,
lda
,
B_d
,
incb
,
A_d
,
lda
,
B_d
,
incb
,
&
beta
,
C_d
,
incc
);
C_d
,
incc
);
}
else
if
(
HPPL_OP_T
==
trans
)
{
stat
=
CUBLAS_GEMV
(
t_resource
.
handle
,
CUBLAS_OP_N
,
dimN
,
dimM
,
dimN
,
dimM
,
&
alpha
,
A_d
,
lda
,
B_d
,
incb
,
A_d
,
lda
,
B_d
,
incb
,
&
beta
,
C_d
,
incc
);
C_d
,
incc
);
}
else
{
LOG
(
FATAL
)
<<
"parameter transa error!"
;
}
...
...
@@ -326,10 +388,14 @@ void hl_matrix_mul_vector(real *A_d, hl_trans_op_t trans,
CHECK_SYNC
(
"hl_matrix_mul_vector"
);
}
void
hl_matrix_mul_vector
(
real
*
A_d
,
hl_trans_op_t
trans
,
real
*
B_d
,
real
*
C_d
,
int
dimM
,
int
dimN
,
real
alpha
,
real
beta
)
{
hl_matrix_mul_vector
(
A_d
,
trans
,
B_d
,
C_d
,
dimM
,
dimN
,
alpha
,
beta
,
dimN
,
1
,
1
);
void
hl_matrix_mul_vector
(
real
*
A_d
,
hl_trans_op_t
trans
,
real
*
B_d
,
real
*
C_d
,
int
dimM
,
int
dimN
,
real
alpha
,
real
beta
)
{
hl_matrix_mul_vector
(
A_d
,
trans
,
B_d
,
C_d
,
dimM
,
dimN
,
alpha
,
beta
,
dimN
,
1
,
1
);
}
paddle/cuda/src/hl_cuda_cudnn.cc
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paddle/cuda/src/hl_cuda_device.cc
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paddle/cuda/src/hl_cudart_wrap.cc
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paddle/cuda/src/hl_dso_loader.cc
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paddle/cuda/src/hl_math.cc
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...
...
@@ -12,24 +12,15 @@ 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 "avx_mathfun.h"
namespace
hppl
{
__m256
exp
(
__m256
a
)
{
return
exp256_ps
(
a
);
}
__m256
exp
(
__m256
a
)
{
return
exp256_ps
(
a
);
}
__m256
log
(
__m256
a
)
{
return
log256_ps
(
a
);
}
__m256
log
(
__m256
a
)
{
return
log256_ps
(
a
);
}
__m256
sin
(
__m256
a
)
{
return
sin256_ps
(
a
);
}
__m256
sin
(
__m256
a
)
{
return
sin256_ps
(
a
);
}
__m256
cos
(
__m256
a
)
{
return
cos256_ps
(
a
);
}
__m256
cos
(
__m256
a
)
{
return
cos256_ps
(
a
);
}
}
// namespace hppl
paddle/cuda/src/hl_time.cc
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...
...
@@ -12,7 +12,6 @@ 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 <chrono>
#include <stdlib.h>
#include <iostream>
...
...
@@ -21,8 +20,7 @@ limitations under the License. */
using
std
::
chrono
::
high_resolution_clock
;
int64_t
getCurrentTimeStick
()
{
high_resolution_clock
::
time_point
tp
=
high_resolution_clock
::
now
();
high_resolution_clock
::
duration
dtn
=
tp
.
time_since_epoch
();
return
dtn
.
count
();
high_resolution_clock
::
time_point
tp
=
high_resolution_clock
::
now
();
high_resolution_clock
::
duration
dtn
=
tp
.
time_since_epoch
();
return
dtn
.
count
();
}
paddle/gserver/activations/ActivationFunction.cpp
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paddle/gserver/activations/ActivationFunction.h
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...
...
@@ -12,7 +12,6 @@ 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 <string>
#include <vector>
...
...
paddle/gserver/dataproviders/DataProvider.cpp
浏览文件 @
2a21d8b3
...
...
@@ -12,7 +12,6 @@ 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 "DataProvider.h"
#include "paddle/utils/Util.h"
...
...
@@ -57,7 +56,7 @@ void BufferBatch::clone(DataBatch* srcBatch, bool useGpu) {
}
}
DoubleBuffer
::
DoubleBuffer
(
DataProvider
*
dataPool
,
DoubleBuffer
::
DoubleBuffer
(
DataProvider
*
dataPool
,
bool
useGpu
,
int64_t
batchSize
)
{
batchSize_
=
batchSize
;
...
...
@@ -155,7 +154,7 @@ void DoubleBuffer::startAsyncLoad() {
}
ClassRegistrar
<
DataProvider
,
DataConfig
,
ModelConfig
,
bool
>
DataProvider
::
registrar_
;
DataProvider
::
registrar_
;
DataProvider
*
DataProvider
::
create
(
const
DataConfig
&
config
,
const
ModelConfig
&
modelConfig
,
...
...
@@ -182,7 +181,8 @@ int64_t DataProvider::getNextBatch(int64_t size, DataBatch* batch) {
for
(
int
i
=
0
;
i
<
config_
.
constant_slots_size
();
++
i
)
{
MemoryHandlePtr
handle
=
constantSlots
[
i
]
?
constantSlots
[
i
]
->
getMemoryHandle
()
:
nullptr
;
Matrix
::
resizeOrCreate
(
constantSlots
[
i
],
batchSize
,
Matrix
::
resizeOrCreate
(
constantSlots
[
i
],
batchSize
,
1
,
// = width
false
,
// = trans
useGpu_
);
// = useGpu
...
...
@@ -216,7 +216,8 @@ void DataProvider::initAsyncLoader() {
}
SimpleDataProviderBase
::
SimpleDataProviderBase
(
const
DataConfig
&
config
,
bool
useGpu
,
bool
withInfo
)
bool
useGpu
,
bool
withInfo
)
:
DataProvider
(
config
,
useGpu
)
{
/* initialize the size of a sample, and the buffer */
sampleDim_
=
config_
.
feat_dim
()
*
(
2
*
config_
.
context_len
()
+
1
);
...
...
@@ -337,7 +338,8 @@ int64_t SimpleDataProviderBase::fillBuffer() {
sampleNumInBuf_
=
n
+
fillBufferImp
(
hInputDataBuf_
->
getData
()
+
n
*
sampleDim_
,
hInputLabelBuf_
->
getData
()
+
n
,
hInputInfoBuf_
->
getData
()
+
n
,
bufferCapacity_
-
n
);
hInputInfoBuf_
->
getData
()
+
n
,
bufferCapacity_
-
n
);
/* for stachastic gradient training */
if
(
!
skipShuffle_
)
{
...
...
@@ -357,11 +359,14 @@ SimpleDataProvider::SimpleDataProvider(const DataConfig& config, bool useGpu)
SimpleDataProvider
::~
SimpleDataProvider
()
{}
int64_t
SimpleDataProvider
::
fillBufferImp
(
real
*
data
,
int
*
label
,
int
*
info
,
int64_t
SimpleDataProvider
::
fillBufferImp
(
real
*
data
,
int
*
label
,
int
*
info
,
int64_t
size
)
{
(
void
)
info
;
int64_t
n
=
std
::
min
<
int64_t
>
(
labels_
.
size
()
-
currentSampleIndex_
,
size
);
memcpy
(
data
,
&
data_
[
currentSampleIndex_
*
sampleDim_
],
memcpy
(
data
,
&
data_
[
currentSampleIndex_
*
sampleDim_
],
n
*
sampleDim_
*
sizeof
(
real
));
memcpy
(
label
,
&
labels_
[
currentSampleIndex_
],
sizeof
(
int
)
*
n
);
currentSampleIndex_
+=
n
;
...
...
paddle/gserver/dataproviders/DataProvider.h
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paddle/gserver/dataproviders/DataProviderGroup.h
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...
...
@@ -12,7 +12,6 @@ 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 "DataProvider.h"
...
...
@@ -65,8 +64,8 @@ void DataProviderGroup<T>::reset() {
provider_
=
nullptr
;
// shuffle file list
std
::
shuffle
(
fileList_
.
begin
(),
fileList_
.
end
(),
ThreadLocalRandomEngine
::
get
());
std
::
shuffle
(
fileList_
.
begin
(),
fileList_
.
end
(),
ThreadLocalRandomEngine
::
get
());
startLoader
();
DataProvider
::
reset
();
...
...
@@ -113,8 +112,9 @@ void DataProviderGroup<T>::startLoader() {
size_t
endPos
=
std
::
min
(
fileList_
.
size
(),
startPos
+
loadFileCount
);
std
::
vector
<
std
::
string
>
fileVec
(
fileList_
.
begin
()
+
startPos
,
fileList_
.
begin
()
+
endPos
);
loader_
->
addJob
([
this
,
fileVec
]()
->
ProviderPtrType
{
return
this
->
loadFile
(
fileVec
);
});
loader_
->
addJob
([
this
,
fileVec
]()
->
ProviderPtrType
{
return
this
->
loadFile
(
fileVec
);
});
}
loader_
->
stopAddJob
();
}
...
...
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