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提交 09a13f6c 编写于 作者: Y yangyaming
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Using LoDTensor and adapt to new unittest. 

Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into fix-3789
上级 696b1f5f 13d00053
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Showing with 9177 addition and 2499 deletion
.gitignore
浏览文件 @ 09a13f6c
......@@ -22,6 +22,7 @@ cmake-build-*
# generated while compiling
python/paddle/v2/framework/core.so
paddle/pybind/pybind.h
CMakeFiles
cmake_install.cmake
paddle/.timestamp
......
.travis.yml
浏览文件 @ 09a13f6c
......@@ -4,7 +4,6 @@ cache:
- $HOME/.ccache
- $HOME/.cache/pip
- $TRAVIS_BUILD_DIR/build/third_party
- $TRAVIS_BUILD_DIR/build_android/third_party
sudo: required
dist: trusty
os:
......@@ -12,7 +11,6 @@ os:
env:
- JOB=build_doc
- JOB=check_style
- JOB=build_android
addons:
apt:
packages:
......@@ -23,7 +21,6 @@ addons:
- python
- python-pip
- python2.7-dev
- python-numpy
- python-wheel
- libboost-dev
- curl
......@@ -37,8 +34,8 @@ before_install:
- if [[ "$JOB" == "check_style" ]]; then sudo ln -s /usr/bin/clang-format-3.8 /usr/bin/clang-format; fi
# Paddle is using protobuf 3.1 currently. Protobuf 3.2 breaks the compatibility. So we specify the python
# protobuf version.
- pip install -r $TRAVIS_BUILD_DIR/python/requirements.txt
- pip install wheel sphinx==1.5.6 recommonmark sphinx-rtd-theme==0.1.9 virtualenv pre-commit LinkChecker
- sudo pip install -r $TRAVIS_BUILD_DIR/python/requirements.txt
- sudo pip install wheel sphinx==1.5.6 recommonmark sphinx-rtd-theme==0.1.9 virtualenv pre-commit LinkChecker
- curl https://glide.sh/get | bash
- eval "$(GIMME_GO_VERSION=1.8.3 gimme)"
- go get -u github.com/alecthomas/gometalinter
......
CMakeLists.txt
浏览文件 @ 09a13f6c
......@@ -65,8 +65,11 @@ if(NOT CMAKE_BUILD_TYPE)
endif()
if(ANDROID)
if(${CMAKE_SYSTEM_VERSION} VERSION_LESS "21")
message(FATAL_ERROR "Unsupport standalone toolchains with Android API level lower than 21")
if(${CMAKE_SYSTEM_VERSION} VERSION_LESS "16")
message(FATAL_ERROR "Unsupport standalone toolchains with Android API level lower than 16")
elseif(${CMAKE_SYSTEM_VERSION} VERSION_LESS "21")
# TODO: support glog for Android api 16 ~ 19 in the future
message(WARNING "Using the unofficial git repository <https://github.com/Xreki/glog.git> instead")
endif()
set(WITH_GPU OFF CACHE STRING
......
Dockerfile.android
浏览文件 @ 09a13f6c
......@@ -4,9 +4,16 @@ MAINTAINER PaddlePaddle Authors <paddle-dev@baidu.com>
ARG UBUNTU_MIRROR
RUN /bin/bash -c 'if [[ -n ${UBUNTU_MIRROR} ]]; then sed -i 's#http://archive.ubuntu.com/ubuntu#${UBUNTU_MIRROR}#g' /etc/apt/sources.list; fi'
# ENV variables
ARG ANDROID_ABI
ARG ANDROID_API
ENV ANDROID_ABI=${ANDROID_ABI:-"armeabi-v7a"}
ENV ANDROID_API=${ANDROID_API:-21}
ENV HOME=/root \
ANDROID_NDK_HOME=/opt/android-ndk-linux \
ANDROID_STANDALONE_TOOLCHAIN=/opt/android-toolchain-gcc
ANDROID_TOOLCHAINS_DIR=/opt/toolchains
RUN apt-get update && \
apt-get install -y \
......@@ -15,12 +22,11 @@ RUN apt-get update && \
apt-get clean -y
# Install Go and glide
RUN wget -O go.tgz https://storage.googleapis.com/golang/go1.8.1.linux-amd64.tar.gz && \
tar -C /usr/local -xzf go.tgz && \
RUN wget -qO- go.tgz https://storage.googleapis.com/golang/go1.8.1.linux-amd64.tar.gz | \
tar -xz -C /usr/local && \
mkdir /root/gopath && \
mkdir /root/gopath/bin && \
mkdir /root/gopath/src && \
rm go.tgz
mkdir /root/gopath/src
ENV GOROOT=/usr/local/go GOPATH=/root/gopath
# should not be in the same line with GOROOT definition, otherwise docker build could not find GOROOT.
ENV PATH=${PATH}:${GOROOT}/bin:${GOPATH}/bin
......@@ -37,13 +43,12 @@ RUN pip install --upgrade pip && \
pip install pre-commit
# Android NDK
RUN mkdir /opt/android-ndk-tmp && \
RUN mkdir -p ${ANDROID_TOOLCHAINS_DIR} && \
mkdir -p /opt/android-ndk-tmp && \
cd /opt/android-ndk-tmp && \
wget -q https://dl.google.com/android/repository/android-ndk-r14b-linux-x86_64.zip && \
unzip -q android-ndk-r14b-linux-x86_64.zip && \
mv android-ndk-r14b ${ANDROID_NDK_HOME} && \
${ANDROID_NDK_HOME}/build/tools/make-standalone-toolchain.sh --arch=arm --platform=android-21 --install-dir=${ANDROID_STANDALONE_TOOLCHAIN} && \
rm -rf /opt/android-ndk-tmp && \
rm -rf ${ANDROID_NDK_HOME}
rm -rf /opt/android-ndk-tmp
CMD ["bash", "/paddle/paddle/scripts/docker/build_android.sh"]
cmake/cpplint.cmake
浏览文件 @ 09a13f6c
......@@ -26,9 +26,9 @@ set(IGNORE_PATTERN
.*ImportanceSampler.*
.*cblas\\.h.*
.*\\.pb\\.txt
.*LtrDataProvider.*
.*MultiDataProvider.*
.*pb.*)
.*pb.*
.*pybind.h)
# add_style_check_target
#
......
cmake/cross_compiling/android.cmake
浏览文件 @ 09a13f6c
......@@ -20,6 +20,7 @@
# The supported variables are listed belows:
#
# ANDROID_STANDALONE_TOOLCHAIN
# ANDROID_TOOLCHAIN
# ANDROID_ABI
# ANDROID_NATIVE_API_LEVEL
# ANDROID_ARM_MODE
......@@ -57,6 +58,10 @@ IF(NOT DEFINED CMAKE_SYSTEM_VERSION AND ANDROID_NATIVE_API_LEVEL)
ENDIF()
ENDIF()
IF(NOT DEFINED ANDROID_TOOLCHAIN)
SET(ANDROID_TOOLCHAIN clang)
ENDIF()
IF(NOT DEFINED ANDROID_ABI)
SET(ANDROID_ABI "armeabi-v7a")
ENDIF()
......@@ -82,6 +87,7 @@ IF("${CMAKE_VERSION}" VERSION_LESS "3.7.0")
"${CMAKE_VERSION}), when cross-compiling for Android.")
IF(ANDROID_STANDALONE_TOOLCHAIN)
# Use standalone toolchain
SET(CMAKE_SYSROOT "${ANDROID_STANDALONE_TOOLCHAIN}/sysroot")
IF(NOT CMAKE_SYSTEM_VERSION)
......@@ -96,26 +102,44 @@ IF("${CMAKE_VERSION}" VERSION_LESS "3.7.0")
ENDIF()
# Toolchain
SET(ANDROID_TOOLCHAIN "gcc")
SET(ANDROID_TOOLCHAIN_ROOT ${ANDROID_STANDALONE_TOOLCHAIN})
IF(ANDROID_ABI MATCHES "^armeabi(-v7a)?$")
SET(ANDROID_TOOLCHAIN_NAME arm-linux-androideabi)
IF(ANDROID_ABI STREQUAL "armeabi")
SET(CMAKE_SYSTEM_PROCESSOR armv5te)
ELSEIF(ANDROID_ABI STREQUAL "armeabi-v7a")
SET(CMAKE_SYSTEM_PROCESSOR armv7-a)
ENDIF()
ENDIF()
IF(ANDROID_ABI STREQUAL "arm64-v8a")
SET(ANDROID_TOOLCHAIN_NAME aarch64-linux-android)
SET(CMAKE_SYSTEM_PROCESSOR aarch64)
ELSE(ANDROID_NDK)
# TODO: use android ndk
ENDIF()
IF(ANDROID_ABI MATCHES "^armeabi(-v7a)?$")
SET(ANDROID_TOOLCHAIN_NAME arm-linux-androideabi)
IF(ANDROID_ABI STREQUAL "armeabi")
SET(CMAKE_SYSTEM_PROCESSOR armv5te)
SET(ANDROID_CLANG_TRIPLE armv5te-none-linux-androideabi)
ELSEIF(ANDROID_ABI STREQUAL "armeabi-v7a")
SET(CMAKE_SYSTEM_PROCESSOR armv7-a)
SET(ANDROID_CLANG_TRIPLE armv7-none-linux-androideabi)
ENDIF()
SET(ANDROID_TOOLCHAIN_PREFIX "${ANDROID_TOOLCHAIN_ROOT}/bin/${ANDROID_TOOLCHAIN_NAME}-")
ELSEIF(ANDROID_ABI STREQUAL "arm64-v8a")
SET(ANDROID_TOOLCHAIN_NAME aarch64-linux-android)
SET(CMAKE_SYSTEM_PROCESSOR aarch64)
SET(ANDROID_CLANG_TRIPLE aarch64-none-linux-android)
ELSE()
MESSAGE(FATAL_ERROR "Invalid Android ABI: ${ANDROID_ABI}.")
ENDIF()
SET(ANDROID_TOOLCHAIN_PREFIX "${ANDROID_TOOLCHAIN_ROOT}/bin/${ANDROID_TOOLCHAIN_NAME}-")
IF(ANDROID_TOOLCHAIN STREQUAL clang)
SET(ANDROID_C_COMPILER_NAME clang)
SET(ANDROID_CXX_COMPILER_NAME clang++)
SET(CMAKE_C_COMPILER_TARGET ${ANDROID_CLANG_TRIPLE})
SET(CMAKE_CXX_COMPILER_TARGET ${ANDROID_CLANG_TRIPLE})
ELSEIF(ANDROID_TOOLCHAIN STREQUAL gcc)
SET(ANDROID_C_COMPILER_NAME gcc)
SET(ANDROID_CXX_COMPILER_NAME g++)
ELSE()
MESSAGE(FATAL_ERROR "Invalid Android toolchain: ${ANDROID_TOOLCHAIN}")
ENDIF()
# C compiler
IF(NOT CMAKE_C_COMPILER)
SET(ANDROID_C_COMPILER "${ANDROID_TOOLCHAIN_PREFIX}gcc")
SET(ANDROID_C_COMPILER "${ANDROID_TOOLCHAIN_PREFIX}${ANDROID_C_COMPILER_NAME}")
ELSE()
GET_FILENAME_COMPONENT(ANDROID_C_COMPILER ${CMAKE_C_COMPILER} PROGRAM)
ENDIF()
......@@ -125,7 +149,7 @@ IF("${CMAKE_VERSION}" VERSION_LESS "3.7.0")
# CXX compiler
IF(NOT CMAKE_CXX_COMPILER)
SET(ANDROID_CXX_COMPILER "${ANDROID_TOOLCHAIN_PREFIX}g++")
SET(ANDROID_CXX_COMPILER "${ANDROID_TOOLCHAIN_PREFIX}${ANDROID_CXX_COMPILER_NAME}")
ELSE()
GET_FILENAME_COMPONENT(ANDROID_CXX_COMPILER ${CMAKE_CXX_COMPILER} PROGRAM)
ENDIF()
......@@ -137,7 +161,7 @@ IF("${CMAKE_VERSION}" VERSION_LESS "3.7.0")
SET(CMAKE_CXX_COMPILER ${ANDROID_CXX_COMPILER} CACHE PATH "CXX compiler" FORCE)
# Toolchain and ABI specific flags.
SET(ANDROID_COMPILER_FLAGS "-ffunction-sections -fdata-sections -finline-limit=64")
SET(ANDROID_COMPILER_FLAGS "-ffunction-sections -fdata-sections")
SET(ANDROID_LINKER_FLAGS "-Wl,--gc-sections")
IF(ANDROID_ABI STREQUAL "armeabi")
......@@ -145,8 +169,7 @@ IF("${CMAKE_VERSION}" VERSION_LESS "3.7.0")
-march=armv5te
-mtune=xscale
-msoft-float)
ENDIF()
IF(ANDROID_ABI STREQUAL "armeabi-v7a")
ELSEIF(ANDROID_ABI STREQUAL "armeabi-v7a")
LIST(APPEND ANDROID_COMPILER_FLAGS
-march=armv7-a
-mfloat-abi=softfp)
......@@ -156,6 +179,8 @@ IF("${CMAKE_VERSION}" VERSION_LESS "3.7.0")
LIST(APPEND ANDROID_COMPILER_FLAGS -mfpu=vfpv3-d16)
ENDIF()
LIST(APPEND ANDROID_LINKER_FLAGS -Wl,--fix-cortex-a8)
ELSEIF(ANDROID_ABI STREQUAL "arm64-v8a")
LIST(APPEND ANDROID_COMPILER_FLAGS -march=armv8-a)
ENDIF()
IF(ANDROID_ABI MATCHES "^armeabi(-v7a)?$")
......@@ -164,10 +189,18 @@ IF("${CMAKE_VERSION}" VERSION_LESS "3.7.0")
ELSE()
LIST(APPEND ANDROID_COMPILER_FLAGS -mthumb)
ENDIF()
IF(ANDROID_TOOLCHAIN STREQUAL clang)
# Disable integrated-as for better compatibility.
LIST(APPEND ANDROID_COMPILER_FLAGS -fno-integrated-as)
ENDIF()
ENDIF()
IF(ANDROID_ABI STREQUAL "arm64-v8a")
LIST(APPEND ANDROID_COMPILER_FLAGS -march=armv8-a)
IF(ANDROID_TOOLCHAIN STREQUAL clang)
# CMake automatically forwards all compiler flags to the linker,
# and clang doesn't like having -Wa flags being used for linking.
# To prevent CMake from doing this would require meddling with
# the CMAKE_<LANG>_COMPILE_OBJECT rules, which would get quite messy.
LIST(APPEND ANDROID_LINKER_FLAGS -Qunused-arguments)
ENDIF()
STRING(REPLACE ";" " " ANDROID_COMPILER_FLAGS "${ANDROID_COMPILER_FLAGS}")
......
cmake/external/gflags.cmake
浏览文件 @ 09a13f6c
......@@ -18,9 +18,9 @@ SET(GFLAGS_SOURCES_DIR ${THIRD_PARTY_PATH}/gflags)
SET(GFLAGS_INSTALL_DIR ${THIRD_PARTY_PATH}/install/gflags)
SET(GFLAGS_INCLUDE_DIR "${GFLAGS_INSTALL_DIR}/include" CACHE PATH "gflags include directory." FORCE)
IF(WIN32)
set(GFLAGS_LIBRARIES "${GFLAGS_INSTALL_DIR}/lib/gflags.lib" CACHE FILEPATH "GFLAGS_LIBRARIES" FORCE)
set(GFLAGS_LIBRARIES "${GFLAGS_INSTALL_DIR}/lib/gflags.lib" CACHE FILEPATH "GFLAGS_LIBRARIES" FORCE)
ELSE(WIN32)
set(GFLAGS_LIBRARIES "${GFLAGS_INSTALL_DIR}/lib/libgflags.a" CACHE FILEPATH "GFLAGS_LIBRARIES" FORCE)
set(GFLAGS_LIBRARIES "${GFLAGS_INSTALL_DIR}/lib/libgflags.a" CACHE FILEPATH "GFLAGS_LIBRARIES" FORCE)
ENDIF(WIN32)
INCLUDE_DIRECTORIES(${GFLAGS_INCLUDE_DIR})
......@@ -56,3 +56,12 @@ SET_PROPERTY(TARGET gflags PROPERTY IMPORTED_LOCATION ${GFLAGS_LIBRARIES})
ADD_DEPENDENCIES(gflags extern_gflags)
LIST(APPEND external_project_dependencies gflags)
IF(WITH_C_API)
INSTALL(DIRECTORY ${GFLAGS_INCLUDE_DIR} DESTINATION third_party/gflags)
IF(ANDROID)
INSTALL(FILES ${GFLAGS_LIBRARIES} DESTINATION third_party/gflags/lib/${ANDROID_ABI})
ELSE()
INSTALL(FILES ${GFLAGS_LIBRARIES} DESTINATION third_party/gflags/lib)
ENDIF()
ENDIF()
cmake/external/glog.cmake
浏览文件 @ 09a13f6c
......@@ -19,9 +19,9 @@ SET(GLOG_INSTALL_DIR ${THIRD_PARTY_PATH}/install/glog)
SET(GLOG_INCLUDE_DIR "${GLOG_INSTALL_DIR}/include" CACHE PATH "glog include directory." FORCE)
IF(WIN32)
SET(GLOG_LIBRARIES "${GLOG_INSTALL_DIR}/lib/libglog.lib" CACHE FILEPATH "glog library." FORCE)
SET(GLOG_LIBRARIES "${GLOG_INSTALL_DIR}/lib/libglog.lib" CACHE FILEPATH "glog library." FORCE)
ELSE(WIN32)
SET(GLOG_LIBRARIES "${GLOG_INSTALL_DIR}/lib/libglog.a" CACHE FILEPATH "glog library." FORCE)
SET(GLOG_LIBRARIES "${GLOG_INSTALL_DIR}/lib/libglog.a" CACHE FILEPATH "glog library." FORCE)
ENDIF(WIN32)
INCLUDE_DIRECTORIES(${GLOG_INCLUDE_DIR})
......@@ -56,3 +56,12 @@ ADD_DEPENDENCIES(glog extern_glog gflags)
LINK_LIBRARIES(glog gflags)
LIST(APPEND external_project_dependencies glog)
IF(WITH_C_API)
INSTALL(DIRECTORY ${GLOG_INCLUDE_DIR} DESTINATION third_party/glog)
IF(ANDROID)
INSTALL(FILES ${GLOG_LIBRARIES} DESTINATION third_party/glog/lib/${ANDROID_ABI})
ELSE()
INSTALL(FILES ${GLOG_LIBRARIES} DESTINATION third_party/glog/lib)
ENDIF()
ENDIF()
cmake/external/openblas.cmake
浏览文件 @ 09a13f6c
......@@ -12,6 +12,10 @@
# See the License for the specific language governing permissions and
# limitations under the License.
IF(USE_EIGEN_FOR_BLAS)
return()
ENDIF(USE_EIGEN_FOR_BLAS)
INCLUDE(cblas)
IF(NOT ${CBLAS_FOUND})
......@@ -69,6 +73,26 @@ IF(NOT ${CBLAS_FOUND})
UPDATE_COMMAND ""
CONFIGURE_COMMAND ""
)
IF(WITH_C_API)
INSTALL(DIRECTORY ${CBLAS_INC_DIR} DESTINATION third_party/openblas)
# Because libopenblas.a is a symbolic link of another library, thus need to
# install the whole directory.
IF(ANDROID)
SET(TMP_INSTALL_DIR third_party/openblas/lib/${ANDROID_ABI})
ELSE()
SET(TMP_INSTALL_DIR third_party/openblas/lib)
ENDIF()
INSTALL(CODE "execute_process(
COMMAND ${CMAKE_COMMAND} -E copy_directory ${CBLAS_INSTALL_DIR}/lib
destination ${CMAKE_INSTALL_PREFIX}/${TMP_INSTALL_DIR}
)"
)
INSTALL(CODE "MESSAGE(STATUS \"Installing: \"
\"${CBLAS_INSTALL_DIR}/lib -> ${CMAKE_INSTALL_PREFIX}/${TMP_INSTALL_DIR}\"
)"
)
ENDIF()
ENDIF(NOT ${CBLAS_FOUND})
MESSAGE(STATUS "BLAS library: ${CBLAS_LIBRARIES}")
......
cmake/external/protobuf.cmake
浏览文件 @ 09a13f6c
......@@ -223,6 +223,15 @@ IF(NOT PROTOBUF_FOUND)
SET(PROTOBUF_PROTOC_LIBRARY ${extern_protobuf_PROTOC_LIBRARY}
CACHE FILEPATH "protoc library." FORCE)
IF(WITH_C_API)
INSTALL(DIRECTORY ${PROTOBUF_INCLUDE_DIR} DESTINATION third_party/protobuf)
IF(ANDROID)
INSTALL(FILES ${PROTOBUF_LIBRARY} DESTINATION third_party/protobuf/lib/${ANDROID_ABI})
ELSE()
INSTALL(FILES ${PROTOBUF_LIBRARY} DESTINATION third_party/protobuf/lib)
ENDIF()
ENDIF()
IF(CMAKE_CROSSCOMPILING)
PROMPT_PROTOBUF_LIB(protobuf_host extern_protobuf)
ELSE()
......
cmake/external/zlib.cmake
浏览文件 @ 09a13f6c
......@@ -49,3 +49,12 @@ ExternalProject_Add(
)
LIST(APPEND external_project_dependencies zlib)
IF(WITH_C_API)
INSTALL(DIRECTORY ${ZLIB_INCLUDE_DIR} DESTINATION third_party/zlib)
IF(ANDROID)
INSTALL(FILES ${ZLIB_LIBRARIES} DESTINATION third_party/zlib/lib/${ANDROID_ABI})
ELSE()
INSTALL(FILES ${ZLIB_LIBRARIES} DESTINATION third_party/zlib/lib)
ENDIF()
ENDIF()
doc/design/functions_operators_layers.md
浏览文件 @ 09a13f6c
......@@ -86,12 +86,13 @@ def layer.fc(X):
We'd like to have Python bindings to operators in package `paddle.operator`, and Python compositions of operators in package `paddle.layer`. So we have the following concepts in above illustrative example:
```
| C++ functions/functors | mul | add | | |
|------------------------|--------------|--------------|-------------|----------|
| C++ operator class | mulOp | addOp | FCOp | |
| Python binding | operator.mul | operator.add | operator.fc | |
| Python function | | | | layer.fc |
```
This is how we differentiate layer and operators in PaddlePaddle:
......
doc/design/ops/dist_train.md 0 → 100644
浏览文件 @ 09a13f6c
# Design Doc: Operation Graph Based Parameter Server
## Abstract
We propose an approach to implement the parameter server. In this
approach, there is no fundamental difference between the trainer and
the parameter server: they both run subgraphs, but subgraphs of
different purposes.
## Background
The previous implementations of the parameter server does not run a
subgraph. parameter initialization, optimizer computation, network
communication and checkpointing are implemented twice on both the
trainer and the parameter server.
It would be great if we can write code once and use them on both the
trainer and the parameter server: reduces code duplication and
improves extensibility. Given that after the current refactor, we are
representing everything as a computing graph on the
trainer. Representing everything as a computing graph on the parameter
server becomes a natural extension.
## Design
### Graph Converter
The *graph converter* converts the user-defined operation (OP) graph
into subgraphs to be scheduled on different nodes with the following
steps:
1. OP placement: the OPs will be placed on different nodes according
to heuristic that minimizes estimated total computation
time. Currently we will use a simple heuristic that puts parameter
varable on parameter server workers and everything else on trainer
workers.
1. Add communication OPs to enable the communication between nodes.
We will need these OPs: *Send*, *Recv*, *Enqueue*, *Dequeue*.
Below is an example of converting the user defined graph to the
subgraphs for the trainer and the parameter server:
<img src="src/local-graph.png" width="300"/>
After converting:
<img src="src/dist-graph.png" width="700"/>
1. The parameter variable W and it's optimizer subgraph are placed on the parameter server.
1. Operators are added to the subgraphs.
- *Send* sends data to the connected *Recv* operator. The
scheduler on the receive node will only schedule *Recv* operator
to run when the *Send* operator has ran (the *Send* OP will mark
the *Recv* OP runnable automatically).
- *Enueue* enqueues the input variable, it can block until space
become available in the queue.
- *Dequeue* outputs configurable numbers of tensors from the
queue. It will block until the queue have the required number of
tensors.
### Benefits
- Model parallelism become easier to implement: it's an extension to
the trainer - parameter server approach. we already have the
communication OPs, but need to extend the graph converter's
placement functionality.
- User-defined optimizer is easier to add - user can now express it as
a subgraph.
- No more duplication logic inside the trainer and the parameter
server mentioned in the background section.
### Challenges
- It might be hard for the graph converter to cut a general graph
(without any hint for which subgraph is the optimizer). We may need
to label which subgraph inside the OP graph is the optimizer.
- It's important to balance the parameter shards of on multiple
parameter server. If a single parameter is very big (some
word-embedding, fully connected, softmax layer), we need to
automatically partition the single parameter onto different
parameter servers when possible (only element-wise optimizer depends
on the parameter variable).
### Discussion
- In the "Aync SGD" figure, the "W" variable on the parameter server
could be read and wrote concurrently, what is our locking strategy?
E.g., each variable have a lock cpp method to be invoked by every
OP, or, have a lock OP.
- Can the Enqueue OP be implemented under our current tensor design
(puts the input tensor into the queue tensor)?
- *Dequeue* OP will have variable numbers of output (depends on the
`min_count` attribute), does our current design support it? (similar
question for the *Add* OP)
### References:
[1] [TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems](https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/45166.pdf)
doc/design/ops/images/2_level_rnn.dot 0 → 100644
浏览文件 @ 09a13f6c
digraph G {
rnn [label="1-th level RNN" shape=box]
subgraph cluster0 {
label = "time step 0"
sent0 [label="sentence"]
sent1 [label="sentence"]
rnn1 [label="2-th level RNN" shape=box]
sent0 -> rnn1
sent1 -> rnn1
}
subgraph cluster1 {
label = "time step 1"
sent2 [label="sentence"]
sent3 [label="sentence"]
rnn2 [label="2-th level RNN" shape=box]
sent2 -> rnn2
sent3 -> rnn2
}
subgraph cluster2 {
label = "time step 2"
sent4 [label="sentence"]
sent5 [label="sentence"]
rnn3 [label="2-th level RNN" shape=box]
sent4 -> rnn3
sent5 -> rnn3
}
para0 [label="paragraph info 0"]
para1 [label="paragraph info 1"]
para2 [label="paragraph info 2"]
rnn1 -> para0
rnn2 -> para1
rnn3 -> para2
para0 -> rnn
para1 -> rnn
para2 -> rnn
chapter [label="chapter info"]
rnn -> chapter
}
doc/design/ops/images/rnn.dot 0 → 100644
浏览文件 @ 09a13f6c
digraph G {
label = "simple RNN implementation"
ranksep=2;
//graph [nodesep=1, ranksep=1];
node[nodesep=1]
subgraph cluster0 {
label = "global scope"
rankdir = TB
W
boot_memory
input
output
}
subgraph cluster1 {
label = "step-scope 0"
rankdir = TB
memory0[label="memory"]
prememory0[label="pre-memory"]
step_input0[label="step input"]
step_output0[label="step output"]
}
subgraph cluster2 {
label = "step-scope 1"
rankdir = TB
memory1[label="memory"]
prememory1[label="pre-memory"]
step_input1[label="step input"]
step_output1[label="step output"]
}
subgraph cluster3 {
label = "step-scope 2"
rankdir = TB
memory2[label="memory"]
prememory2[label="pre-memory"]
step_input2[label="step input"]
step_output2[label="step output"]
}
stepnet [shape=box]
stepnet0 [shape=box, style=dashed]
stepnet1 [shape=box, style=dashed]
stepnet2 [shape=box, style=dashed]
edge[color=blue]
boot_memory -> prememory0 [label="init" color="blue"]
memory0 -> prememory1 [label="copy/reference" color="blue"]
memory1 -> prememory2 [label="copy/reference" color="blue"]
edge[color=black]
W -> stepnet0[constraint=false, style=dashed]
W -> stepnet1[constraint=false, style=dashed]
W -> stepnet2[constraint=false, style=dashed]
memory0 -> stepnet0[style=dashed]
prememory0 -> stepnet0 -> step_output0[style=dashed]
memory1 -> stepnet1[style=dashed]
prememory1 -> stepnet1 -> step_output1[style=dashed]
memory2 -> stepnet2[style=dashed]
prememory2 -> stepnet2 -> step_output2[style=dashed]
input -> step_input0
input -> step_input1
input -> step_input2
step_input0 -> stepnet0 [style=dashed]
step_input1 -> stepnet1[style=dashed]
step_input2 -> stepnet2[style=dashed]
step_output0 -> output
step_output1 -> output
step_output2 -> output
stepnet0 -> stepnet[style=dashed]
stepnet1 -> stepnet[style=dashed]
stepnet2 -> stepnet[style=dashed]
}
doc/design/ops/images/rnn_2level_data.dot 0 → 100644
浏览文件 @ 09a13f6c
digraph G {
chapter [label="chapter"]
subgraph cluster0 {
label = "paragraph 0"
top_rnn0[label="top rnn step 0" shape=box]
p0 [label="paragraph 0"]
p1 [label="paragraph 1"]
}
subgraph cluster1{
label = "paragraph 1"
top_rnn1[label="top rnn step 1" shape=box]
p2 [label="paragraph 0"]
p3 [label="paragraph 1"]
}
subgraph cluster_p0 {
label = "sentence 0"
low_rnn0 [label="low rnn step 0" shape=box]
s00 [label="sentence 0"]
s01 [label="sentence 1"]
low_rnn0 -> s00
low_rnn0 -> s01
}
subgraph cluster_p1 {
label = "sentence 1"
low_rnn1 [label="low rnn step 1" shape=box]
s10 [label="sentence 0"]
s11 [label="sentence 1"]
low_rnn1 -> s10
low_rnn1 -> s11
}
subgraph cluster_p2 {
label = "sentence 1"
low_rnn2 [label="low rnn step 0" shape=box]
s20 [label="sentence 0"]
s21 [label="sentence 1"]
low_rnn2 -> s20
low_rnn2 -> s21
}
subgraph cluster_p3 {
label = "sentence 1"
low_rnn3 [label="low rnn step 1" shape=box]
s30 [label="sentence 0"]
s31 [label="sentence 1"]
low_rnn3 -> s30
low_rnn3 -> s31
}
chapter -> top_rnn0
chapter -> top_rnn1
top_rnn0 -> p0
top_rnn0 -> p1
top_rnn1 -> p2
top_rnn1 -> p3
p0 -> low_rnn0
p1 -> low_rnn1
p2 -> low_rnn2
p3 -> low_rnn3
}
doc/design/ops/rnn.md 0 → 100644
浏览文件 @ 09a13f6c
# RNNOp design
This document is about an RNN operator which requires that instances in a mini-batch have the same length. We will have a more flexible RNN operator.
## RNN Algorithm Implementation
<p aligh="center">
<img src="./images/rnn.jpg"/>
</p>
The above diagram shows an RNN unrolled into a full network.
There are several important concepts:
- *step-net*: the sub-graph to run at each step,
- *memory*, $h_t$, the state of the current step,
- *ex-memory*, $h_{t-1}$, the state of the previous step,
- *initial memory value*, the ex-memory of the first step.
### Step-scope
There could be local variables defined in step-nets. PaddlePaddle runtime realizes these variables in *step-scopes* -- scopes created for each step.
<p aligh="center">
<img src="./images/rnn.png"/><br/>
Figure 2 the RNN's data flow
</p>
Please be aware that all steps run the same step-net. Each step
1. creates the step-scope,
2. realizes local variables, including step-outputs, in the step-scope, and
3. runs the step-net, which could use these variables.
The RNN operator will compose its output from step outputs in step scopes.
### Memory and Ex-memory
Let's give more details about memory and ex-memory via a simply example:
$$
h_t = U h_{t-1} + W x_t
$$,
where $h_t$ and $h_{t-1}$ are the memory and ex-memory of step $t$'s respectively.
In the implementation, we can make an ex-memory variable either "refers to" the memory variable of the previous step,
or copy the value of the previous memory value to the current ex-memory variable.
### Usage in Python
For more information on Block, please refer to the [design doc](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/block.md).
We can define an RNN's step-net using Block:
```python
import paddle as pd
X = some_op() # x is some operator's output, and is a LoDTensor
a = some_op()
# declare parameters
W = pd.Variable(shape=[20, 30])
U = pd.Variable(shape=[20, 30])
rnn = pd.create_rnn_op(output_num=1)
with rnn.stepnet():
x = rnn.add_input(X)
# declare a memory (rnn's step)
h = rnn.add_memory(init=a)
# h.pre_state() means previous memory of rnn
new_state = pd.add_two( pd.matmul(W, x) + pd.matmul(U, h.pre_state()))
# update current memory
h.update(new_state)
# indicate that h variables in all step scopes should be merged
rnn.add_outputs(h)
out = rnn()
```
Python API functions in above example:
- `rnn.add_input` indicates the parameter is a variable that will be segmented into step-inputs.
- `rnn.add_memory` creates a variable used as the memory.
- `rnn.add_outputs` mark the variables that will be concatenated across steps into the RNN output.
### Nested RNN and LoDTensor
An RNN whose step-net includes other RNN operators is known as an *nested RNN*.
For example, we could have a 2-level RNN, where the top level corresponds to paragraphs, and the lower level corresponds to sentences.
The following figure illustrates the feeding of text into the lower level, one sentence each step, and the feeding of step outputs to the top level. The final top level output is about the whole text.
<p aligh="center">
<img src="./images/2_level_rnn.png"/>
</p>
```python
import paddle as pd
W = pd.Variable(shape=[20, 30])
U = pd.Variable(shape=[20, 30])
W0 = pd.Variable(shape=[20, 30])
U0 = pd.Variable(shape=[20, 30])
# a is output of some op
a = some_op()
# chapter_data is a set of 128-dim word vectors
# the first level of LoD is sentence
# the second level of LoD is chapter
chapter_data = pd.Variable(shape=[None, 128], type=pd.lod_tensor, level=2)
def lower_level_rnn(paragraph):
'''
x: the input
'''
rnn = pd.create_rnn_op(output_num=1)
with rnn.stepnet():
sentence = rnn.add_input(paragraph, level=0)
h = rnn.add_memory(shape=[20, 30])
h.update(
pd.matmul(W, sentence) + pd.matmul(U, h.pre_state()))
# get the last state as sentence's info
rnn.add_outputs(h)
return rnn
top_level_rnn = pd.create_rnn_op(output_num=1)
with top_level_rnn.stepnet():
paragraph_data = rnn.add_input(chapter_data, level=1)
low_rnn = lower_level_rnn(paragraph_data)
paragraph_out = low_rnn()
h = rnn.add_memory(init=a)
h.update(
pd.matmul(W0, paragraph_data) + pd.matmul(U0, h.pre_state()))
top_level_rnn.add_outputs(h)
# just output the last step
chapter_out = top_level_rnn(output_all_steps=False)
```
in above example, the construction of the `top_level_rnn` calls `lower_level_rnn`. The input is a LoD Tensor. The top level RNN segments input text data into paragraphs, and the lower level RNN segments each paragraph into sentences.
By default, the `RNNOp` will concatenate the outputs from all the time steps,
if the `output_all_steps` set to False, it will only output the final time step.
<p align="center">
<img src="images/rnn_2level_data.png"/>
</p>
doc/design/var_desc.md 0 → 100644
浏览文件 @ 09a13f6c
## Background
PaddlePaddle divides the description of neural network computation graph into two stages: compile time and runtime.
PaddlePaddle use proto message to describe compile time graph for
1. Computation graph should be able to be saved to a file.
1. In distributed training, the graph will be serialized and send to multiple workers.
The computation graph is constructed by Data Node and Operation Node. The concept to represent them is in the table below.
| |compile time|runtime|
|---|---|---|
|Data|VarDesc(proto)|Variable(cpp)|
|Operation|OpDesc(proto)|Operator(cpp)|
## Definition of VarDesc
A VarDesc should have a name and value, in PaddlePaddle, the value will always be a tensor. Since we use LoDTensor most of the time. We add a LoDTesnorDesc to represent it.
```proto
message VarDesc {
required string name = 1;
optional LoDTensorDesc lod_tensor = 2;
}
```
## Definition of LodTensorDesc
```proto
enum DataType {
BOOL = 0;
INT16 = 1;
INT32 = 2;
INT64 = 3;
FP16 = 4;
FP32 = 5;
FP64 = 6;
}
message LoDTensorDesc {
required DataType data_type = 1;
repeated int32 dims = 2; // [UNK, 640, 480] is saved as [-1, 640, 480]
optional int32 lod_level = 3 [default=0];
}
```
## Definition of Variable in Python
In Python API, layer will take Variable as Input, and return Variable as Output. There should be a class `Variable` in python to help create and manage Variable.
```python
image = Variable(dims=[-1, 640, 480])
# fc1 and fc2 are both Variable
fc1 = layer.fc(input=image, output_size=10)
fc2 = layer.fc(input=fc1, output_size=20)
```
### what should class `Variable` Have
1. `name`.a name of string type is used to mark the value of the Variable.
1. `initializer`. Since our Tensor does not have value. we will always use some Operator to fullfill it when run. So we should have a initialize method to help add the init operator.
1. `operator`. Variable should record which operator produce itself. The reaon is:
- we use pd.eval(targets=[var1, var2]) to run the related ops to get the value of var1 and var2. var.op is used to trace the dependency of the current variable.
In PaddlePaddle, we use Block to describe Computation Graph, so in the code we will use Block but not Graph.
```python
import VarDesc
import LoDTensorDesc
import framework
def AddInitialOperator(variable, initializer):
# add an initialize Operator to block to init this Variable
class Variable(object):
def __init__(self, name, dims, type, initializer):
self._block = get_default_block()
self._name = name
self.op = None
tensor_desc = LoDTensorDesc(data_type=type, dims=dims)
_var_desc = VarDesc(name=name, lod_tensor=tensor_desc)
self._var = framework.CreateVar(_var_desc)
self._block.add_var(self)
# add initial op according to initializer
if initializer is not None:
AddInitialOperator(self, initializer)
def dims(self):
return self._var.dims()
def data_type(self):
return self._var.data_type()
def to_proto(self):
pass
```
Then we can use this Variable to create a fc layer in Python.
```python
import paddle as pd
def flatten_size(X, num_flatten_dims):
prod = 1 # of last num_flatten_dims
for i in xrange(num_flatten_dims):
prod = prod * X.dims[-i-1]
return prod
def layer.fc(X, output_size, num_flatten_dims):
W = Variable(pd.random_uniform(), type=FP32, dims=[flatten_size(X, num_flatten_dims), output_size])
b = Variable(pd.random_uniform(), type=FP32, dims=[output_size])
out = Variable(type=FP32)
y = operator.fc(X, W, b, output=out) # fc will put fc op input into out
pd.InferShape(y)
return out
x = Variable(dims=[-1, 640, 480])
y = layer.fc(x, output_size=100)
z = layer.fc(y, output_size=200)
paddle.eval(targets=[z], ...)
print(z)
```
doc/howto/dev/new_op_cn.md
浏览文件 @ 09a13f6c
......@@ -34,7 +34,7 @@ Kernel实现 | CPU、GPU共享Kernel实现在`.h`文件中，否则，CPU
注册Op | Op注册实现在`.cc`文件；Kernel注册CPU实现在`.cc`文件中，GPU实现在`.cu`文件中
实现新的op都添加至目录[paddle/operators](https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/operators)下，文件命名以`*_op.h`（如有） 、 `*_op.cc` 、`*_op.cu`（如有）结尾。
实现新的op都添加至目录[paddle/operators](https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/operators)下，文件命名以`*_op.h`（如有） 、 `*_op.cc` 、`*_op.cu`（如有）结尾。**系统会根据文件名自动构建op和其对应的Python扩展。**
下面以矩阵乘操作，即[MulOp](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/mul_op.cc)为例来介绍如何写带Kernel的Operator。
......@@ -224,45 +224,15 @@ MulOp(const std::string &type, const framework::VariableNameMap &inputs,
### 5. 编译
- 简单**无特殊依赖**的OP无需修改CMakeList.txt文件。[paddle/operators/CMakeLists.txt](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/CMakeLists.txt) 会自动将 `paddle/operators` 目录下新增的 `*_op.cc` 文件加入编译。
- 较为复杂、**有额外依赖** 的operator仍需要修改[paddle/operators/CMakeLists.txt](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/operators/CMakeLists.txt)。如，`mul_op` 依赖 `math_function`，需要在`CMakeLists.txt`中添加如下内容：
运行下面命令可以进行编译：
```
op_library(mul_op SRCS mul_op.cc mul_op.cu DEPS math_function) +
```
- 运行下面命令可以进行编译：
```
make mul_op
```
```
make mul_op
```
## 绑定Python
- 绑定Python
在 [`paddle/pybind/pybind.cc
`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/pybind/pybind.cc) 使用`USE_OP`告知编译器需要链接的Op，具体解释参考[代码注释](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/framework/op_registry.h#L81)。
```
USE_OP(mul);
```
如果只实现了CPU版本，则使用`USE_CPU_ONLY_OP`:
```
USE_CPU_ONLY_OP(gather);
```
如果OP不带Kernel，则使用`USE_NO_KENREL_OP`:
```
USE_NO_KENREL_OP(recurrent);
```
- 生成库
无需修改 [`paddle/pybind/CMakeLists.txt`](https://github.com/PaddlePaddle/Paddle/blob/develop/paddle/pybind/CMakeLists.txt)文件，`paddle/operators` 目录下新增的 `*_op.cc` 文件会被自动添加链接到生成的lib库中。
系统会对新增的op自动绑定Python，并链接到生成的lib库中。
## 实现单元测试
......@@ -354,11 +324,7 @@ class TestMulGradOp(GradientChecker):
### 编译和执行单元测试
单元测试编写完成之后，在[`python/paddle/v2/framework/tests/CMakeLists.txt`](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/v2/framework/tests/CMakeLists.txt)中添加以下内容，将单元测试加入工程：
```
py_test(test_mul_op SRCS test_mul_op.py)
```
`python/paddle/v2/framework/tests` 目录下新增的 `test_*.py` 单元测试会被自动加入工程进行编译。
请注意，**不同于Op的编译测试，运行单元测试测时需要编译整个工程**，并且编译时需要打开`WITH_TESTING`, 即`cmake paddle_dir -DWITH_TESTING=ON`。编译成功后，执行下面的命令来运行单元测试：
......@@ -371,3 +337,10 @@ make test ARGS="-R test_mul_op -V"
```bash
ctest -R test_mul_op
```
## 注意事项
- 为每个Op创建单独的`*_op.h`（如有）、`*_op.cc`和`*_op.cu`（如有）。不允许一个文件中包含多个Op，这将会导致编译出错。
- 注册Op时的类型名，需要和该Op的名字一样。即不允许在`A_op.cc`里面，注册`REGISTER_OP(B, ...)`等，这将会导致单元测试出错。
- 如果Op没有实现GPU Kernel，请不要创建空的`*_op.cu`，这将会导致单元测试出错。
- 如果多个Op依赖一些共用的函数，可以创建非`*_op.*`格式的文件来存放，如`gather.h`文件。
doc/howto/dev/write_docs_cn.rst
浏览文件 @ 09a13f6c
......@@ -5,15 +5,13 @@
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。
如何构建PaddlePaddle的文档
==========================
如何构建文档
============
PaddlePaddle的文档构建有直接构建和基于Docker构建两种方式，我们提供了一个构建脚本build_docs.sh来进行构建。
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。
PaddlePaddle的文档构建有两种方式。
使用Docker构建PaddlePaddle的文档
--------------------------------
使用Docker构建
--------------
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
......@@ -21,58 +19,46 @@ PaddlePaddle文档需要准备的环境相对较复杂，所以我们推荐使
cd TO_YOUR_PADDLE_CLONE_PATH
cd paddle/scripts/tools/build_docs
bash build_docs.sh with_docker
编译完成后，会在当前目录生成两个子目录\:
* doc 英文文档目录
* doc_cn 中文文档目录
sh build_docs.sh
编译完成之后，会在当前目录生成两个子目录\: doc(英文文档目录)和 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。
直接构建PaddlePaddle的文档
--------------------------
因为PaddlePaddle的v2 api文档生成过程依赖于py_paddle Python包，用户需要首先确认py_paddle包已经安装。
.. code-block:: bash
python -c "import py_paddle"
如果提示错误，那么用户需要在本地编译安装PaddlePaddle，请参考 `源码编译文档 <http://doc.paddlepaddle.org/develop/doc/getstarted/build_and_install/build_from_source_en.html>`_ 。
注意，用户在首次编译安装PaddlePaddle时，请将WITH_DOC选项关闭。在编译安装正确之后，请再次确认py_paddle包已经安装，即可进行下一步操作。
直接构建
--------
如果提示正确，可以执行以下命令编译生成文档，即
.. code-block:: bash
cd TO_YOUR_PADDLE_CLONE_PATH
cd paddle/scripts/tools/build_docs
bash build_docs.sh local
编译完成之后，会在当前目录生成两个子目录\:
* doc 英文文档目录
* doc_cn 中文文档目录
mkdir -p build
cd build
cmake .. -DCMAKE_BUILD_TYPE=Debug -DWITH_GPU=OFF -DWITH_MKLDNN=OFF -DWITH_MKLML=OFF -DWITH_DOC=ON
make gen_proto_py
make paddle_docs paddle_docs_cn
编译完成之后，会在当前目录生成两个子目录\: doc(英文文档目录)和 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。
如何书写PaddlePaddle的文档
==========================
如何书写文档
============
PaddlePaddle文档使用 `sphinx`_ 自动生成，用户可以参考sphinx教程进行书写。
如何更新www.paddlepaddle.org文档
================================
如何更新文档主题
================
PaddlePaddle文档主题在 `TO_YOUR_PADDLE_CLONE_PATH/doc_theme` 文件夹下，包含所有和前端网页设计相关的文件。
开发者给PaddlePaddle代码增加的注释以PR的形式提交到github中，提交方式可参见 `贡献文档 <http://doc.paddlepaddle.org/develop/doc_cn/howto/dev/contribute_to_paddle_cn.html>`_ 。
如何更新doc.paddlepaddle.org
============================
更新的文档以PR的形式提交到github中，提交方式参见 `贡献文档 <http://doc.paddlepaddle.org/develop/doc_cn/howto/dev/contribute_to_paddle_cn.html>`_ 。
目前PaddlePaddle的develop分支的文档是自动触发更新的，用户可以分别查看最新的 `中文文档 <http://doc.paddlepaddle.org/develop/doc_cn/>`_ 和
`英文文档 <http://doc.paddlepaddle.org/develop/doc/>`_ 。
.. _cmake: https://cmake.org/
.. _sphinx: http://www.sphinx-doc.org/en/1.4.8/
paddle/capi/CMakeLists.txt
浏览文件 @ 09a13f6c
......@@ -64,9 +64,29 @@ link_paddle_exe(paddle_capi_shared)
install(FILES ${CAPI_HEADERS} DESTINATION include/paddle)
install(FILES ${CMAKE_CURRENT_BINARY_DIR}/config.h DESTINATION include/paddle)
if(ANDROID)
execute_process(
COMMAND ${GIT_EXECUTABLE} log --pretty=oneline -1
OUTPUT_VARIABLE GIT_COMMITS_LIST
RESULT_VARIABLE GIT_COMMITS_LIST_RESULT
ERROR_QUIET OUTPUT_STRIP_TRAILING_WHITESPACE)
if(${GIT_COMMITS_LIST_RESULT})
set(GIT_COMMITS_LIST "No commits.")
endif()
install(FILES ${CMAKE_CURRENT_BINARY_DIR}/${capi_whole_library}
DESTINATION lib/${ANDROID_ABI})
install(TARGETS paddle_capi_shared DESTINATION lib/${ANDROID_ABI})
install(CODE "FILE(WRITE ${CMAKE_INSTALL_PREFIX}/lib/${ANDROID_ABI}/BUILD.txt
\"Compiler:\n\"
\"\\t${CMAKE_C_COMPILER}\\n\"
\"\\t${CMAKE_CXX_COMPILER}\\n\"
\"Compiler Flags:\\n\"
\"\\t${CMAKE_F_FLAGS}\\n\"
\"\\t${CMAKE_CXX_FLAGS}\\n\"
\"Android API: ${CMAKE_SYSTEM_VERSION}\\n\"
\"Lastest commit:\\n\"
\"\\t${GIT_COMMITS_LIST}\\n\"
)"
)
else(ANDROID)
install(FILES ${CMAKE_CURRENT_BINARY_DIR}/${capi_whole_library} DESTINATION lib)
install(TARGETS paddle_capi_shared DESTINATION lib)
......
paddle/cuda/include/hl_cpu_gru.cuh
浏览文件 @ 09a13f6c
......@@ -18,14 +18,6 @@ limitations under the License. */
#ifndef __NVCC__
#include "paddle/math/MathFunctions.h"
#ifndef PADDLE_TYPE_DOUBLE
#define CBLAS_GEMM paddle::gemm<float>
#else
#define CBLAS_GEMM paddle::gemm<double>
#endif
template<class OpResetOutput>
void hl_naive_gru_forward_reset_output(OpResetOutput opResetOutput,
real *gateValue,
......@@ -210,51 +202,6 @@ inline void forward_final_output(OpFinalOutput opFinalOutput,
}
}
template<class OpResetOutput, class OpFinalOutput>
void hl_cpu_gru_forward(OpResetOutput opResetOutput,
OpFinalOutput opFinalOutput,
hl_gru_value value,
int frameSize,
int batchSize,
hl_activation_mode_t active_node,
hl_activation_mode_t active_gate) {
if (value.prevOutValue) {
CBLAS_GEMM(CblasNoTrans,
CblasNoTrans,
batchSize,
2 * frameSize,
frameSize,
1,
value.prevOutValue,
frameSize,
value.gateWeight,
frameSize * 2,
1,
value.gateValue,
frameSize * 3);
}
forward_reset_output(opResetOutput, value, frameSize, batchSize, active_gate);
if (value.prevOutValue) {
CBLAS_GEMM(CblasNoTrans,
CblasNoTrans,
batchSize,
frameSize,
frameSize,
1,
value.resetOutputValue,
frameSize,
value.stateWeight,
frameSize,
1,
value.gateValue + frameSize * 2,
frameSize * 3);
}
forward_final_output(opFinalOutput, value, frameSize, batchSize, active_node);
}
template<class OpStateGrad>
void hl_naive_gru_backward_state_grad(OpStateGrad opStateGrad,
real *gateValue,
......@@ -525,86 +472,6 @@ inline void backward_reset_grad(OpResetGrad opResetGrad,
}
}
template<class OpStateGrad, class OpResetGrad>
void hl_cpu_gru_backward(OpStateGrad opStateGrad,
OpResetGrad opResetGrad,
hl_gru_value value,
hl_gru_grad grad,
int frameSize,
int batchSize,
hl_activation_mode_t active_node,
hl_activation_mode_t active_gate) {
backward_state_grad(opStateGrad, value, grad,
frameSize, batchSize, active_node);
if (value.prevOutValue && grad.prevOutGrad) {
CBLAS_GEMM(CblasNoTrans,
CblasTrans,
batchSize,
frameSize,
frameSize,
1,
grad.gateGrad + frameSize * 2,
frameSize * 3,
value.stateWeight,
frameSize,
0,
grad.resetOutputGrad,
frameSize);
if (grad.stateWeightGrad) {
CBLAS_GEMM(CblasTrans,
CblasNoTrans,
frameSize,
frameSize,
batchSize,
1,
value.resetOutputValue,
frameSize,
grad.gateGrad + frameSize * 2,
frameSize * 3,
1,
grad.stateWeightGrad,
frameSize);
}
}
backward_reset_grad(opResetGrad, value, grad,
frameSize, batchSize, active_gate);
if (grad.prevOutGrad && value.prevOutValue) {
CBLAS_GEMM(CblasNoTrans,
CblasTrans,
batchSize,
frameSize,
frameSize * 2,
1,
grad.gateGrad,
frameSize * 3,
value.gateWeight,
frameSize * 2,
1,
grad.prevOutGrad,
frameSize);
if (grad.gateWeightGrad) {
CBLAS_GEMM(CblasTrans,
CblasNoTrans,
frameSize,
frameSize * 2,
batchSize,
1,
value.prevOutValue,
frameSize,
grad.gateGrad,
frameSize * 3,
1,
grad.gateWeightGrad,
frameSize * 2);
}
}
}
#endif
#endif // HL_CPU_GRU_CUH_
paddle/framework/CMakeLists.txt
浏览文件 @ 09a13f6c
......@@ -9,6 +9,7 @@ cc_test(eigen_test SRCS eigen_test.cc DEPS tensor)
cc_library(lod_tensor SRCS lod_tensor.cc DEPS ddim place tensor)
cc_test(lod_tensor_test SRCS lod_tensor_test.cc DEPS lod_tensor)
nv_test(lod_tensor_gpu_test SRCS lod_tensor_test.cu DEPS lod_tensor)
cc_test(variable_test SRCS variable_test.cc)
......
paddle/framework/attribute.h
浏览文件 @ 09a13f6c
......@@ -45,7 +45,19 @@ class GreaterThanChecker {
public:
explicit GreaterThanChecker(T lower_bound) : lower_bound_(lower_bound) {}
void operator()(T& value) const {
PADDLE_ENFORCE(value > lower_bound_, "larger_than check fail");
PADDLE_ENFORCE(value > lower_bound_, "larger_than check fails.");
}
private:
T lower_bound_;
};
template <typename T>
class EqualGreaterThanChecker {
public:
explicit EqualGreaterThanChecker(T lower_bound) : lower_bound_(lower_bound) {}
void operator()(T& value) const {
PADDLE_ENFORCE_GE(value, lower_bound_, "equal_larger_than check fails.");
}
private:
......@@ -115,6 +127,11 @@ class TypedAttrChecker {
return *this;
}
TypedAttrChecker& EqualGreaterThan(const T& lower_bound) {
value_checkers_.push_back(EqualGreaterThanChecker<T>(lower_bound));
return *this;
}
// we can add more common limits, like LessThan(), Between()...
TypedAttrChecker& SetDefault(const T& default_value) {
......
paddle/framework/backward.md
浏览文件 @ 09a13f6c
......@@ -2,20 +2,31 @@
## Motivation
In Neural Network, the backpropagation algorithm follows the chain rule, so we need to compound the fundmental gradient operators/expressions together with chain rule . Every forward network need a backward network to construct the full computation graph, the operator/expression's backward pass will be generated respect to forward pass.
## Backward Operator Registry
In Neural Network, many model is solved by the the backpropagation algorithm(known as BP) at present. Technically it caculates the gradient of the loss function, then distributed back through the networks. Follows the chain rule, so we need a module chains the gradient operators/expressions together with to construct the backward pass. Every forward network needs a backward network to construct the full computation graph, the operator/expression's backward pass will be generated respect to forward pass.
A backward network is built up with several backward operators. Backward operators take forward operators' inputs, outputs and output gradients and then calculate its input gradients.
## Implementation
In this design doc, we exported only one API for generating the backward pass.
```c++
std::unique_ptr<OperatorBase> Backward(const OperatorBase& forwardOp,
const std::unordered_set<std::string>& no_grad_vars);
```
The implementation behind it can be divided into two parts, **Backward Operator Creating** and **Backward Operator Building**.
### Backward Operator Registry
A backward network is built up with several backward operators. Backward operators take forward operators' inputs, outputs, and output gradients and then calculate its input gradients.
| | forward operator | backward operator
| ---------------------- | ---------------- |------------------------- |
| **Operator::inputs_** | Inputs | Inputs, Outputs, OutputGradients |
| **Operator::outputs_** | Outputs | InputGradients |
In most cases, there is a one-to-one correspondence between forward and backward operators. These correspondences are recorded by a global hash map(`OpInfoMap`). To follow the philosophy of minimum core and make operators pluggable, the registry mechanism is introduced.
In most cases, there is a one-to-one correspondence between the forward and backward operators. These correspondences are recorded by a global hash map(`OpInfoMap`). To follow the philosophy of minimum core and make operators pluggable, the registry mechanism is introduced.
For example, we have got a `mul_op`, and we can register it's information and corresponding backward operator by the following macro:
For example, we have got a `mul_op`, and we can register its information and corresponding backward operator by the following macro:
```cpp
REGISTER_OP(mul, MulOp, MulOpMaker, mul_grad, MulOpGrad);
......@@ -25,58 +36,65 @@ REGISTER_OP(mul, MulOp, MulOpMaker, mul_grad, MulOpGrad);
`mul_grad` is the type of backward operator, and `MulOpGrad` is its class name.
## Backward Opeartor Creating
### Backward Opeartor Creating
Given a certain forward operator, we can get its corresponding backward opeartor by calling:
Given a certain forward operator, we can get its corresponding backward operator by calling:
```cpp
OperatorBase* bwd_op = BuildGradOp(const OperatorBase* fwd_op);
```
```
The function `BuildGradOp` will sequentially execute following processes:
1. Get the `type_` of given forward operator, and then get the corresponding backward operator's type by looking up the `OpInfoMap`.
2. Build two maps named `inputs` and `outputs` to temporary storage backward operator's inputs and outputs. Copy forward operator's `inputs_` and `outputs_` to map `inputs`, except these are not necessary for gradient computing.
2. Build two maps named `inputs` and `outputs` to temporary storage backward operator's inputs and outputs. Copy forward operator's `inputs_` and `outputs_` to map `inputs`, except these, are not necessary for gradient computing.
3. Add forward inputs' gradient variables into map `output`, adding forward outputs' gradient variables into map `input`.
4. Building backward operator with `inputs`, `outputs` and forward operator's attributes.
## Backward Network Building
### Backward Network Building
A backward network is a series of backward operators. The main idea of building a backward network is creating backward operators in the inverted sequence and put them together.
In our design, the network itself is also a kind of operator. So the operators contained by a big network may be some small network.
given a forward network, it generates the backward network. We only care about the Gradients—`OutputGradients`,`InputGradients`.
A backward network is a series of backward operators. The main idea of building a backward network is creating backward operators in the inverted sequence and append them together one by one. There is some corner case need to process specially.
1. Op
when the input forward network is a Op, return its gradient Operator Immediately.
When the input forward network is an Op, return its gradient Operator Immediately. If all of its outputs are in no gradient set, then return a special `NOP`.
2. NetOp
when the input forward network is a NetOp, it need to call the sub NetOp/Operators backward function recursively. During the process, we need to collect the `OutputGradients` name according to forward NetOp.
In our design, the network itself is also a kind of operator(**NetOp**). So the operators contained by a big network may be some small network. When the input forward network is a NetOp, it needs to call the sub NetOp/Operators backward function recursively. During the process, we need to collect the `OutputGradients` name according to the forward NetOp.
3. RnnOp
RnnOp is a nested stepnet operator. Backward module need to recusively call `Backward` for every stepnet.
4. Sharing Variables
**sharing variables**. As illustrated in the pictures, two operator's share the same variable name of W@GRAD, which will overwrite their sharing input variable.
<p align="center">
<img src="./images/duplicate_op.png" width="50%" ><br/>
**shared variable**. As illustrated in the pictures, two operator's `Output` `Gradient` will overwirte their shared input variable.
​ pic 1. Sharing variables in operators.
<p align="center">
<img src="./images/duplicate_op.png" width="70%" ><br/>
</p>
1. shared variable in two operators.
​ Sharing variable between operators or same input variable used in multiple operators leads to a duplicate gradient variable. As demo show above, we need to rename gradient name recursively and add a generic add operator to replace the overwrite links.
</p>
<p align="center">
<img src="images/duplicate_op2.png" width="40%" ><br/>
Share variable between operators or same input variable used in multiple operators lead to a duplicate gradient variable. As demo show above, we need to rename gradient name recursively, and add a generic add operator replace the overwirte links.
​ pic 2. Replace sharing variable's gradient with `Add` operator.
<p align="center">
<img src="images/duplicate_op2.png" width="90%" ><br/>
</p>
2. replace shared variable gradient with `Add` Operator
​ Because our framework finds variables accord to their names, we need to rename the output links. We add a suffix of number to represent its position in clockwise.
</p>
5. Part of Gradient is Zero.
In the whole graph, there is some case of that one operator's gradient is not needed, but its input's gradient is a dependency link of other operator, we need to fill a same shape gradient matrix in the position. In our implement, we insert a special `fillZeroLike` operator.
​ Then collect the sub graph `OutputGradients`/`InputGradients` as the NetOp's and return it.
Follow these rules above, then collect the sub graph `OutputGradients`/`InputGradients` as the NetOp's and return it.
paddle/framework/ddim.cc
浏览文件 @ 09a13f6c
......@@ -283,5 +283,14 @@ std::ostream& operator<<(std::ostream& os, const DDim& ddim) {
DDim::DDim(std::initializer_list<int64_t> init_list) {
*this = make_ddim(init_list);
}
DDim flatten_to_2d(const DDim& src, int num_col_dims) {
int rank = src.size();
return make_ddim({product(slice_ddim(src, 0, num_col_dims)),
product(slice_ddim(src, num_col_dims, rank))});
}
DDim flatten_to_1d(const DDim& src) { return make_ddim({product(src)}); }
} // namespace framework
} // namespace paddle
paddle/framework/ddim.h
浏览文件 @ 09a13f6c
......@@ -115,6 +115,12 @@ int arity(const DDim& ddim);
std::ostream& operator<<(std::ostream&, const DDim&);
// Reshape a tensor to a matrix. The matrix's first dimension(column length)
// will be the product of tensor's first `num_col_dims` dimensions.
DDim flatten_to_2d(const DDim& src, int num_col_dims);
DDim flatten_to_1d(const DDim& src);
} // namespace framework
} // namespace paddle
......
paddle/framework/eigen.h
浏览文件 @ 09a13f6c
......@@ -63,20 +63,35 @@ struct EigenTensor {
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
struct EigenMatrix : public EigenTensor<T, 2, MajorType, IndexType> {};
struct EigenMatrix : public EigenTensor<T, 2, MajorType, IndexType> {
static typename EigenMatrix::Type Reshape(Tensor& tensor, int num_col_dims) {
int rank = tensor.dims_.size();
PADDLE_ENFORCE(num_col_dims > 0 && num_col_dims < rank,
"`num_col_dims` must be between (0, rank_of_tensor).");
return EigenMatrix::From(tensor,
flatten_to_2d(tensor.dims(), num_col_dims));
}
static typename EigenMatrix::ConstType Reshape(const Tensor& tensor,
int num_col_dims) {
int rank = tensor.dims_.size();
PADDLE_ENFORCE(num_col_dims > 0 && num_col_dims < rank,
"`num_col_dims` must be between (0, rank_of_tensor).");
return EigenMatrix::From(tensor,
flatten_to_2d(tensor.dims(), num_col_dims));
}
};
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
struct EigenVector : public EigenTensor<T, 1, MajorType, IndexType> {
// Flatten reshapes a Tensor into an EigenVector.
static typename EigenVector::Type Flatten(Tensor& tensor) {
return EigenVector::From(
tensor, make_ddim({static_cast<int>(product(tensor.dims_))}));
return EigenVector::From(tensor, {product(tensor.dims_)});
}
static typename EigenVector::ConstType Flatten(const Tensor& tensor) {
return EigenVector::From(
tensor, make_ddim({static_cast<int>(product(tensor.dims_))}));
return EigenVector::From(tensor, {product(tensor.dims_)});
}
};
......
paddle/framework/eigen_test.cc
浏览文件 @ 09a13f6c
......@@ -108,5 +108,24 @@ TEST(Eigen, Matrix) {
}
}
TEST(Eigen, MatrixReshape) {
Tensor t;
float* p = t.mutable_data<float>({2, 3, 6, 4}, platform::CPUPlace());
for (int i = 0; i < 2 * 3 * 6 * 4; ++i) {
p[i] = static_cast<float>(i);
}
EigenMatrix<float>::Type em = EigenMatrix<float>::Reshape(t, 2);
ASSERT_EQ(2 * 3, em.dimension(0));
ASSERT_EQ(6 * 4, em.dimension(1));
for (int i = 0; i < 2 * 3; i++) {
for (int j = 0; j < 6 * 4; j++) {
ASSERT_NEAR(i * 6 * 4 + j, em(i, j), 1e-6f);
}
}
}
} // namespace framework
} // namespace paddle
paddle/framework/lod_tensor.h
浏览文件 @ 09a13f6c
......@@ -18,8 +18,10 @@
#ifndef PADDLE_ONLY_CPU
#include <thrust/device_vector.h>
#include <thrust/host_vector.h>
#include <thrust/system/cuda/experimental/pinned_allocator.h>
#endif
#include <glog/logging.h>
#include "paddle/framework/ddim.h"
#include "paddle/framework/tensor.h"
#include "paddle/platform/enforce.h"
......@@ -32,7 +34,8 @@ template <typename T>
using Vector = std::vector<T>;
#else
template <typename T>
using Vector = thrust::host_vector<T>;
using Vector = thrust::host_vector<
T, thrust::system::cuda::experimental::pinned_allocator<T>>;
#endif
using LoD = std::vector<Vector<size_t>>;
......@@ -48,18 +51,15 @@ bool operator==(const LoD& a, const LoD& b);
* LoDTensor (Level of details Tensor)
* see https://en.wikipedia.org/wiki/Level_of_details for reference.
*/
class LoDTensor {
class LoDTensor : public Tensor {
public:
LoDTensor() {}
LoDTensor(const LoD& lod, Tensor* t) : lod_(lod), tensor_(t) {}
void set_lod(const LoD& lod) { lod_ = lod; }
void set_tensor(Tensor* tensor) { tensor_ = tensor; }
explicit LoDTensor(const LoD& lod) : lod_(lod) {}
Tensor& tensor() { return *tensor_; }
void set_lod(const LoD& lod) { lod_ = lod; }
LoD lod() { return lod_; }
LoD lod() const { return lod_; }
/*
* Get a element from LoD.
......@@ -101,7 +101,6 @@ class LoDTensor {
private:
LoD lod_;
Tensor* tensor_; // not owned
};
} // namespace framework
} // namespace paddle
paddle/framework/lod_tensor_test.cc
浏览文件 @ 09a13f6c
......@@ -36,69 +36,64 @@ class LoDTensorTester : public ::testing::Test {
ASSERT_EQ(lod.size(), 3UL);
tensor.Resize({20 /*batch size*/, 128 /*dim*/});
lod_tensor_.Resize({20 /*batch size*/, 128 /*dim*/});
// malloc memory
tensor.mutable_data<float>(place);
lod_tensor_.mutable_data<float>(place);
lod_tensor.set_lod(lod);
lod_tensor.set_tensor(&tensor);
lod_tensor_.set_lod(lod);
}
protected:
platform::CPUPlace place;
Tensor tensor;
LoDTensor lod_tensor;
LoDTensor lod_tensor_;
};
TEST_F(LoDTensorTester, NumLevels) { ASSERT_EQ(lod_tensor.NumLevels(), 3UL); }
TEST_F(LoDTensorTester, NumLevels) { ASSERT_EQ(lod_tensor_.NumLevels(), 3UL); }
TEST_F(LoDTensorTester, NumElements) {
ASSERT_EQ(lod_tensor.NumElements(0), 2UL);
ASSERT_EQ(lod_tensor.NumElements(1), 4UL);
ASSERT_EQ(lod_tensor.NumElements(2), 8UL);
ASSERT_EQ(lod_tensor_.NumElements(0), 2UL);
ASSERT_EQ(lod_tensor_.NumElements(1), 4UL);
ASSERT_EQ(lod_tensor_.NumElements(2), 8UL);
}
TEST_F(LoDTensorTester, SliceLevels) {
// slice 1 level
for (size_t level = 0; level < 3UL; ++level) {
LoDTensor new_lod_tensor = lod_tensor;
LoDTensor new_lod_tensor = lod_tensor_;
new_lod_tensor.SliceLevels(level, level + 1);
ASSERT_EQ(new_lod_tensor.NumLevels(), 1UL);
ASSERT_EQ(new_lod_tensor.NumElements(0), lod_tensor.NumElements(level));
ASSERT_EQ(new_lod_tensor.tensor().data<float>(),
lod_tensor.tensor().data<float>());
ASSERT_EQ(new_lod_tensor.NumElements(0), lod_tensor_.NumElements(level));
ASSERT_EQ(new_lod_tensor.data<float>(), lod_tensor_.data<float>());
}
// slice 2 level
for (size_t level = 0; level < 2UL; ++level) {
LoDTensor new_lod_tensor = lod_tensor;
LoDTensor new_lod_tensor = lod_tensor_;
new_lod_tensor.SliceLevels(level, level + 2);
ASSERT_EQ(new_lod_tensor.NumLevels(), 2UL);
ASSERT_EQ(new_lod_tensor.NumElements(0), lod_tensor.NumElements(level));
ASSERT_EQ(new_lod_tensor.NumElements(1), lod_tensor.NumElements(level + 1));
ASSERT_EQ(new_lod_tensor.tensor().data<float>(),
lod_tensor.tensor().data<float>());
ASSERT_EQ(new_lod_tensor.NumElements(0), lod_tensor_.NumElements(level));
ASSERT_EQ(new_lod_tensor.NumElements(1),
lod_tensor_.NumElements(level + 1));
ASSERT_EQ(new_lod_tensor.data<float>(), lod_tensor_.data<float>());
}
}
TEST_F(LoDTensorTester, SliceInLevel) {
size_t level = 0;
LoDTensor new_lod_tensor = lod_tensor;
LoDTensor new_lod_tensor = lod_tensor_;
new_lod_tensor.SliceInLevel(level, 0, 2);
EXPECT_EQ(new_lod_tensor.NumLevels(), 3UL);
EXPECT_EQ(new_lod_tensor.NumElements(0), 2UL);
EXPECT_EQ(new_lod_tensor.NumElements(1), 4UL);
EXPECT_EQ(new_lod_tensor.NumElements(2), 8UL);
ASSERT_EQ(new_lod_tensor.tensor().data<float>(),
lod_tensor.tensor().data<float>());
ASSERT_EQ(new_lod_tensor.data<float>(), lod_tensor_.data<float>());
level = 1;
new_lod_tensor = lod_tensor;
new_lod_tensor = lod_tensor_;
new_lod_tensor.SliceInLevel(level, 0, 2);
ASSERT_EQ(new_lod_tensor.NumLevels(), 2UL);
ASSERT_EQ(new_lod_tensor.NumElements(0), 2UL);
ASSERT_EQ(new_lod_tensor.NumElements(1), 4UL);
ASSERT_EQ(new_lod_tensor.tensor().data<float>(),
lod_tensor.tensor().data<float>());
ASSERT_EQ(new_lod_tensor.data<float>(), lod_tensor_.data<float>());
}
} // namespace framework
......
paddle/framework/lod_tensor_test.cu 0 → 100644
浏览文件 @ 09a13f6c
/*
Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
*/
#include <cuda.h>
#include <cuda_runtime.h>
#include "paddle/framework/lod_tensor.h"
#include "paddle/platform/assert.h"
#include <gtest/gtest.h>
__global__ void test(size_t* a, int size) {
for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < size;
i += blockDim.x * gridDim.x) {
a[i] *= 2;
}
}
TEST(LoDTensor, LoDInGPU) {
paddle::framework::LoDTensor lod_tensor;
paddle::platform::GPUPlace place(0);
paddle::framework::LoD src_lod;
src_lod.push_back(std::vector<size_t>{0, 2, 4, 6, 8, 10, 12, 14});
lod_tensor.Resize({14, 16});
lod_tensor.mutable_data<float>(place);
lod_tensor.set_lod(src_lod);
CHECK_EQ(lod_tensor.lod_element(0, 2), 4);
CHECK_EQ(lod_tensor.lod_element(0, 4), 8);
auto lod = lod_tensor.lod();
test<<<1, 8>>>(lod[0].data(), lod[0].size());
cudaDeviceSynchronize();
for (size_t i = 0; i < src_lod[0].size(); ++i) {
CHECK_EQ(lod[0].data()[i], src_lod[0].data()[i] * 2);
}
}
paddle/framework/operator.cc
浏览文件 @ 09a13f6c
......@@ -123,6 +123,15 @@ OperatorBase::OperatorBase(const std::string& type,
CheckAllInputOutputSet();
}
std::vector<std::string> OperatorBase::InputVars() const {
std::vector<std::string> ret_val;
for (auto& o : outputs_) {
ret_val.reserve(ret_val.size() + o.second.size());
ret_val.insert(ret_val.end(), o.second.begin(), o.second.end());
}
return ret_val;
}
std::vector<std::string> OperatorBase::OutputVars(bool has_intermediate) const {
std::vector<std::string> ret_val;
if (has_intermediate) {
......@@ -177,6 +186,48 @@ void OperatorBase::GenerateTemporaryNames() {
}
}
template <>
const Tensor* InferShapeContext::Input<Tensor>(const std::string& name) const {
auto* var = InputVar(name);
return var == nullptr ? nullptr : GetTensorFromVar(var);
}
template <>
const std::vector<const Tensor*> InferShapeContext::MultiInput<Tensor>(
const std::string& name) const {
auto names = op().Inputs(name);
std::vector<const Tensor*> res;
res.reserve(names.size());
std::transform(names.begin(), names.end(), std::back_inserter(res),
[&](const std::string& sub_name) {
auto var = scope_.FindVar(sub_name);
return var == nullptr ? nullptr : GetTensorFromVar(var);
});
return res;
}
template <>
Tensor* ExecutionContext::Output<Tensor>(const std::string& name) const {
auto* var = OutputVar(name);
return var == nullptr ? nullptr : const_cast<Tensor*>(GetTensorFromVar(var));
}
template <>
std::vector<Tensor*> ExecutionContext::MultiOutput<Tensor>(
const std::string& name) const {
auto names = op().Outputs(name);
std::vector<Tensor*> res;
res.reserve(names.size());
std::transform(names.begin(), names.end(), std::back_inserter(res),
[&](const std::string& sub_name) {
auto var = scope().FindVar(sub_name);
return var == nullptr
? nullptr
: const_cast<Tensor*>(GetTensorFromVar(var));
});
return res;
}
void OpProtoAndCheckerMaker::Validate() {
validated_ = true;
CheckNoDuplicatedInOutAttrs();
......
paddle/framework/operator.h
浏览文件 @ 09a13f6c
......@@ -22,6 +22,7 @@ limitations under the License. */
#include "op_info.h"
#include "paddle/framework/attribute.h"
#include "paddle/framework/framework.pb.h"
#include "paddle/framework/lod_tensor.h"
#include "paddle/framework/scope.h"
#include "paddle/framework/tensor.h"
#include "paddle/platform/device_context.h"
......@@ -94,11 +95,14 @@ class OperatorBase {
const VariableNameMap& Inputs() const { return inputs_; }
const VariableNameMap& Outputs() const { return outputs_; }
//! Get a input with argument's name described in `op_proto`
std::string Input(const std::string& name) const;
//! Get a input which has multiple variables.
const std::vector<std::string>& Inputs(const std::string& name) const;
std::vector<std::string> InputVars() const;
//! Get a output with argument's name described in `op_proto`
std::string Output(const std::string& name) const;
//! Get an output which has multiple variables.
......@@ -311,9 +315,9 @@ class InferShapeContext {
}
template <typename T>
std::vector<const T*> MultiOutput(const std::string& name) const {
std::vector<T*> MultiOutput(const std::string& name) const {
auto names = op_.Outputs(name);
std::vector<const T*> res;
std::vector<T*> res;
res.reserve(names.size());
std::transform(names.begin(), names.end(), std::back_inserter(res),
[&](const std::string& sub_name) {
......@@ -323,11 +327,27 @@ class InferShapeContext {
return res;
}
const Tensor* GetTensorFromVar(const Variable* var) const {
if (var->IsType<LoDTensor>()) {
return &var->Get<LoDTensor>();
}
PADDLE_ENFORCE(var->IsType<Tensor>(),
"The Input(%s) must be LoDTensor or Tensor.");
return &var->Get<Tensor>();
}
private:
const OperatorBase& op_;
const Scope& scope_;
};
template <>
const Tensor* InferShapeContext::Input<Tensor>(const std::string& name) const;
template <>
const std::vector<const Tensor*> InferShapeContext::MultiInput<Tensor>(
const std::string& name) const;
template <typename T>
struct EigenDeviceConverter;
......@@ -360,9 +380,37 @@ class ExecutionContext : public InferShapeContext {
return device_context_;
}
// redefine Output function,
// use Variable::Get instead of Variable::GetMutable
template <typename T>
T* Output(const std::string& name) const {
auto var = OutputVar(name);
return var == nullptr ? nullptr : const_cast<T*>(&var->Get<T>());
}
// redefine MultiOutput function.
// use Variable::Get instead of Variable::GetMutable
template <typename T>
std::vector<T*> MultiOutput(const std::string& name) const {
auto names = op().Outputs(name);
std::vector<T*> res;
res.reserve(names.size());
std::transform(
names.begin(), names.end(), std::back_inserter(res),
[&](const std::string& sub_name) { return Output<T>(sub_name); });
return res;
}
const platform::DeviceContext* device_context_;
};
template <>
Tensor* ExecutionContext::Output<Tensor>(const std::string& name) const;
template <>
std::vector<Tensor*> ExecutionContext::MultiOutput<Tensor>(
const std::string& name) const;
class OpKernel {
public:
/**
......
paddle/framework/tensor.h
浏览文件 @ 09a13f6c
......@@ -43,6 +43,9 @@ class Tensor {
template <typename T, size_t D, int MajorType, typename IndexType>
friend struct EigenTensor;
template <typename T, int MajorType, typename IndexType>
friend struct EigenMatrix;
template <typename T, int MajorType, typename IndexType>
friend struct EigenVector;
......@@ -78,6 +81,9 @@ class Tensor {
/*! Return the dimensions of the memory block. */
inline const DDim& dims() const;
/*! Return the numel of the memory block. */
inline int64_t numel() const;
/*! Resize the dimensions of the memory block. */
inline Tensor& Resize(const DDim& dims);
......@@ -159,6 +165,12 @@ class Tensor {
/*! points to dimensions of memory block. */
DDim dims_;
/**
* A cache of the number of elements in a tensor.
* Would be 0 for an uninitialized tensor.
*/
int64_t numel_;
/**
* @brief A PlaceHolder may be shared by more than one tensor.
*
......
paddle/framework/tensor_impl.h
浏览文件 @ 09a13f6c
......@@ -24,7 +24,7 @@ inline void Tensor::check_memory_size() const {
PADDLE_ENFORCE_NOT_NULL(
holder_, "Tenosr holds no memory. Call Tensor::mutable_data first.");
PADDLE_ENFORCE_GE(
holder_->size(), product(dims_) * sizeof(T) + offset_,
holder_->size(), numel() * sizeof(T) + offset_,
"Tensor's dims_ is out of bound. Call Tensor::mutable_data "
"first to re-allocate memory.\n"
"or maybe the required data-type mismatches the data already stored.");
......@@ -54,11 +54,11 @@ inline T* Tensor::mutable_data(DDim dims, platform::Place place) {
template <typename T>
inline T* Tensor::mutable_data(platform::Place place) {
static_assert(std::is_pod<T>::value, "T must be POD");
PADDLE_ENFORCE_GT(product(dims_), 0,
PADDLE_ENFORCE_GT(numel(), 0,
"Tensor's numel must be larger than zero to call "
"Tensor::mutable_data. Call Tensor::set_dim first.");
/* some versions of boost::variant don't have operator!= */
int64_t size = product(dims_) * sizeof(T);
int64_t size = numel() * sizeof(T);
if (holder_ == nullptr || !(holder_->place() == place) ||
holder_->size() < size + offset_) {
if (platform::is_cpu_place(place)) {
......@@ -97,7 +97,7 @@ inline void Tensor::CopyFrom(const Tensor& src,
auto dst_ptr = static_cast<void*>(mutable_data<T>(dst_place));
auto size = product(src.dims_) * sizeof(T);
auto size = src.numel() * sizeof(T);
if (platform::is_cpu_place(src_place) && platform::is_cpu_place(dst_place)) {
memory::Copy(boost::get<platform::CPUPlace>(dst_place), dst_ptr,
......@@ -131,7 +131,7 @@ inline Tensor Tensor::Slice(const int& begin_idx, const int& end_idx) const {
PADDLE_ENFORCE_LT(begin_idx, end_idx,
"Begin index must be less than end index.");
PADDLE_ENFORCE_NE(dims_[0], 1, "Can not slice a tensor with dims_[0] = 1.");
size_t base = product(dims_) / dims_[0];
size_t base = numel() / dims_[0];
Tensor dst;
dst.holder_ = holder_;
DDim dst_dims = dims_;
......@@ -143,10 +143,21 @@ inline Tensor Tensor::Slice(const int& begin_idx, const int& end_idx) const {
inline Tensor& Tensor::Resize(const DDim& dims) {
dims_ = dims;
numel_ = product(dims_);
return *this;
}
inline const DDim& Tensor::dims() const { return dims_; }
inline int64_t Tensor::numel() const { return numel_; }
template <typename T>
inline Tensor ReshapeToMatrix(const Tensor& src, int num_col_dims) {
Tensor res;
res.ShareDataWith<T>(src);
res.Resize(flatten_to_2d(src.dims(), num_col_dims));
return res;
}
} // namespace framework
} // namespace paddle
paddle/framework/tensor_test.cc
浏览文件 @ 09a13f6c
......@@ -262,3 +262,16 @@ TEST(Tensor, CopyFrom) {
}
#endif
}
TEST(Tensor, ReshapeToMatrix) {
using namespace paddle::framework;
using namespace paddle::platform;
Tensor src;
int* src_ptr = src.mutable_data<int>({2, 3, 4, 9}, CPUPlace());
for (int i = 0; i < 2 * 3 * 4 * 9; ++i) {
src_ptr[i] = i;
}
Tensor res = ReshapeToMatrix<int>(src, 2);
ASSERT_EQ(res.dims()[0], 2 * 3);
ASSERT_EQ(res.dims()[1], 4 * 9);
}
\ No newline at end of file
paddle/function/CMakeLists.txt
浏览文件 @ 09a13f6c
......@@ -44,6 +44,7 @@ if(WITH_GPU)
add_simple_unittest(RowConvOpTest)
add_simple_unittest(BlockExpandOpTest)
add_simple_unittest(CropOpTest)
add_simple_unittest(SwitchOpTest)
endif()
add_simple_unittest(Im2ColTest)
......
paddle/function/EigenGemm.cpp
浏览文件 @ 09a13f6c
......@@ -83,9 +83,9 @@ struct EigenBlasGemm {
};
#ifdef PADDLE_TYPE_DOUBLE
template class EigenBlasGemm<double>;
template struct EigenBlasGemm<double>;
#else
template class EigenBlasGemm<float>;
template struct EigenBlasGemm<float>;
#endif
} // namespace paddle
paddle/function/GruFunctor.h 0 → 100644
浏览文件 @ 09a13f6c
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "GemmFunctor.h"
#include "hl_cpu_gru.cuh"
namespace paddle {
template <DeviceType Device, class T>
struct GruFunctor {
template <class OpResetOutput, class OpFinalOutput>
static void compute(OpResetOutput opResetOutput,
OpFinalOutput opFinalOutput,
hl_gru_value value,
int frameSize,
int batchSize,
hl_activation_mode_t active_node,
hl_activation_mode_t active_gate) {
#ifndef __NVCC__
if (value.prevOutValue) {
BlasGemm<Device, T>::compute(false,
false,
batchSize,
2 * frameSize,
frameSize,
1,
value.prevOutValue,
frameSize,
value.gateWeight,
frameSize * 2,
1,
value.gateValue,
frameSize * 3);
}
forward_reset_output(
opResetOutput, value, frameSize, batchSize, active_gate);
if (value.prevOutValue) {
BlasGemm<Device, T>::compute(false,
false,
batchSize,
frameSize,
frameSize,
1,
value.resetOutputValue,
frameSize,
value.stateWeight,
frameSize,
1,
value.gateValue + frameSize * 2,
frameSize * 3);
}
forward_final_output(
opFinalOutput, value, frameSize, batchSize, active_node);
#endif
}
};
template <DeviceType Device, class T>
struct GruGradFunctor {
template <class OpStateGrad, class OpResetGrad>
static void compute(OpStateGrad opStateGrad,
OpResetGrad opResetGrad,
hl_gru_value value,
hl_gru_grad grad,
int frameSize,
int batchSize,
hl_activation_mode_t active_node,
hl_activation_mode_t active_gate) {
#ifndef __NVCC__
backward_state_grad(
opStateGrad, value, grad, frameSize, batchSize, active_node);
if (value.prevOutValue && grad.prevOutGrad) {
BlasGemm<Device, T>::compute(false,
true,
batchSize,
frameSize,
frameSize,
1,
grad.gateGrad + frameSize * 2,
frameSize * 3,
value.stateWeight,
frameSize,
0,
grad.resetOutputGrad,
frameSize);
if (grad.stateWeightGrad) {
BlasGemm<Device, T>::compute(true,
false,
frameSize,
frameSize,
batchSize,
1,
value.resetOutputValue,
frameSize,
grad.gateGrad + frameSize * 2,
frameSize * 3,
1,
grad.stateWeightGrad,
frameSize);
}
}
backward_reset_grad(
opResetGrad, value, grad, frameSize, batchSize, active_gate);
if (grad.prevOutGrad && value.prevOutValue) {
BlasGemm<Device, T>::compute(false,
true,
batchSize,
frameSize,
frameSize * 2,
1,
grad.gateGrad,
frameSize * 3,
value.gateWeight,
frameSize * 2,
1,
grad.prevOutGrad,
frameSize);
if (grad.gateWeightGrad) {
BlasGemm<Device, T>::compute(true,
false,
frameSize,
frameSize * 2,
batchSize,
1,
value.prevOutValue,
frameSize,
grad.gateGrad,
frameSize * 3,
1,
grad.gateWeightGrad,
frameSize * 2);
}
}
#endif
}
};
} // namespace paddle
paddle/function/Im2Col.h
浏览文件 @ 09a13f6c
......@@ -94,95 +94,4 @@ public:
int paddingWidth);
};
template <class T>
struct Padding {
static void run(const T* src,
T* dest,
int channels,
int inputHeight,
int inputWidth,
int paddingHeight,
int paddingWidth) {
const int destWidth = inputWidth + 2 * paddingWidth;
for (int c = 0; c < channels; c++) {
if (paddingHeight > 0) {
memset(dest, 0, destWidth * paddingHeight * sizeof(T));
dest += destWidth * paddingHeight;
}
for (int i = 0; i < inputHeight; i++) {
// padding head
for (int j = 0; j < paddingWidth; j++) {
*dest++ = T(0);
}
memcpy(dest, src, inputWidth * sizeof(T));
dest += inputWidth;
src += inputWidth;
// padding tail
for (int j = 0; j < paddingWidth; j++) {
*dest++ = T(0);
}
}
if (paddingHeight > 0) {
memset(dest, 0, destWidth * paddingHeight * sizeof(T));
dest += destWidth * paddingHeight;
}
}
}
};
#if defined(__ARM_NEON__) || defined(__ARM_NEON)
template <>
struct Padding<float> {
static void run(const float* src,
float* dest,
int channels,
int inputHeight,
int inputWidth,
int paddingHeight,
int paddingWidth) {
const int destWidth = inputWidth + 2 * paddingWidth;
for (int c = 0; c < channels; c++) {
if (paddingHeight > 0) {
memset(dest, 0, destWidth * paddingHeight * sizeof(float));
dest += destWidth * paddingHeight;
}
for (int i = 0; i < inputHeight; i++) {
// padding head
for (int j = 0; j < paddingWidth; j++) {
*dest++ = float(0);
}
int step = inputWidth >> 2;
int remain = inputWidth & 3;
for (int s = 0; s < step; s++) {
float32x4_t s0 = vld1q_f32(src);
vst1q_f32(dest, s0);
src += 4;
dest += 4;
}
for (int r = 0; r < remain; r++) {
*dest++ = *src++;
}
// padding tail
for (int j = 0; j < paddingWidth; j++) {
*dest++ = float(0);
}
}
if (paddingHeight > 0) {
memset(dest, 0, destWidth * paddingHeight * sizeof(float));
dest += destWidth * paddingHeight;
}
}
}
};
#endif
} // namespace paddle
paddle/function/MulOp.cpp
浏览文件 @ 09a13f6c
......@@ -13,18 +13,10 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "MulOp.h"
/// todo(tianbing), delete it
#include <iostream>
#include "paddle/math/MathFunctions.h"
#include "GemmFunctor.h"
#include "paddle/math/SIMDFunctions.h"
#include "paddle/utils/ThreadLocal.h"
#ifndef PADDLE_TYPE_DOUBLE
#define GEMM paddle::gemm<float>
#else
#define GEMM paddle::gemm<double>
#endif
namespace {
inline void vecAddTo(real* a, const real* b, real scaleB, size_t len) {
for (unsigned int i = 0; i < len; ++i) {
......@@ -114,19 +106,20 @@ void MulOp<DEVICE_TYPE_CPU>(CpuMatrix& out,
real scaleT,
bool aTrans,
bool bTrans) {
GEMM(aTrans ? CblasTrans : CblasNoTrans,
bTrans ? CblasTrans : CblasNoTrans,
out.getHeight(),
out.getWidth(),
!aTrans ? a.getWidth() : a.getHeight(),
scaleAB,
a.getData(),
a.getStride(),
b.getData(),
b.getStride(),
scaleT,
out.getData(),
out.getStride());
BlasGemm<DEVICE_TYPE_CPU, real>::compute(
aTrans,
bTrans,
out.getHeight(),
out.getWidth(),
!aTrans ? a.getWidth() : a.getHeight(),
scaleAB,
a.getData(),
a.getStride(),
b.getData(),
b.getStride(),
scaleT,
out.getData(),
out.getStride());
}
/// dense matrix (+)= sparse matrix * dense matrix
......
paddle/function/SwitchOp.cpp 0 → 100644
浏览文件 @ 09a13f6c
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "SwitchOp.h"
#include "paddle/math/Vector.h"
namespace paddle {
template <>
void NCHW2NHWC<DEVICE_TYPE_CPU>(real* outputs,
const real* inputs,
const int num,
const int inC,
const int inH,
const int inW,
const int argType) {
for (int n = 0; n < num; ++n) {
for (int c = 0; c < inC; ++c) {
for (int h = 0; h < inH; ++h) {
for (int w = 0; w < inW; ++w) {
if (argType == ADD_TO) {
outputs[((n * inH + h) * inW + w) * inC + c] += *(inputs++);
} else {
outputs[((n * inH + h) * inW + w) * inC + c] = *(inputs++);
}
}
}
}
}
}
template <>
void NHWC2NCHW<DEVICE_TYPE_CPU>(real* outputs,
const real* inputs,
const int num,
const int inH,
const int inW,
const int inC,
const int argType) {
for (int n = 0; n < num; ++n) {
for (int h = 0; h < inH; ++h) {
for (int w = 0; w < inW; ++w) {
for (int c = 0; c < inC; ++c) {
if (argType == ADD_TO) {
outputs[((n * inC + c) * inH + h) * inW + w] += *(inputs++);
} else {
outputs[((n * inC + c) * inH + h) * inW + w] = *(inputs++);
}
}
}
}
}
}
/**
* \brief Switch dimension order of image input.
* The input and output is a 4D tensor. Switch order
* 'batch_size,channels, height, width' to
* order 'batch_size, height, width, channels'.
*
* Argument in this Function:
* \param inputs input data with order 'batch_size,channels, height, width'.
* \param outputs output data with order 'batch_size, height, width, channels'.
*/
template <DeviceType Device>
class NCHW2NHWCFunc : public FunctionBase {
public:
void init(const FuncConfig& config) override {}
void calc(const BufferArgs& inputs, const BufferArgs& outputs) override {
CHECK_EQ(1UL, inputs.size());
CHECK_EQ(1UL, outputs.size());
size_t num = inputs[0].shape()[0];
size_t inC = inputs[0].shape()[1];
size_t inH = inputs[0].shape()[2];
size_t inW = inputs[0].shape()[3];
NCHW2NHWC<Device>(outputs[0].data<real>(),
inputs[0].data<real>(),
num,
inC,
inH,
inW,
outputs[0].getArgType());
}
};
/**
* \brief Switch dimension order of image input.
* The input and output is a 4D tensor. Switch order
* 'batch_size, height, width, channels' to
* order 'batch_size, channels, height, width'.
*
* Argument in this Function:
* \param inputs input data with order 'batch_size, height, width, channels'.
* \param outputs output data with order 'batch_size, channels, height, width'.
*/
template <DeviceType Device>
class NHWC2NCHWFunc : public FunctionBase {
public:
void init(const FuncConfig& config) override {}
void calc(const BufferArgs& inputs, const BufferArgs& outputs) override {
CHECK_EQ(1UL, inputs.size());
CHECK_EQ(1UL, outputs.size());
size_t num = inputs[0].shape()[0];
size_t inH = inputs[0].shape()[1];
size_t inW = inputs[0].shape()[2];
size_t inC = inputs[0].shape()[3];
NHWC2NCHW<Device>(outputs[0].data<real>(),
inputs[0].data<real>(),
num,
inH,
inW,
inC,
outputs[0].getArgType());
}
};
REGISTER_TYPED_FUNC(NCHW2NHWC, CPU, NCHW2NHWCFunc);
REGISTER_TYPED_FUNC(NHWC2NCHW, CPU, NHWC2NCHWFunc);
#ifndef PADDLE_ONLY_CPU
REGISTER_TYPED_FUNC(NCHW2NHWC, GPU, NCHW2NHWCFunc);
REGISTER_TYPED_FUNC(NHWC2NCHW, GPU, NHWC2NCHWFunc);
#endif
} // namespace paddle
paddle/function/SwitchOp.h 0 → 100644
浏览文件 @ 09a13f6c
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "Function.h"
namespace paddle {
/**
* \brief This funtion switch dimension order of image input.
* The input and output is a 4D tensor. Switch order 'batch_size,
*channels, height, width' to
* order 'batch_size, height, width, channels'.
*
* \param[out] outputs save results.
* \param[in] inputs input data.
* \param[in] num batch size of input data.
* \param[in] inC channel number of input data.
* \param[in] inH height of input data.
* \param[in] inH with of input data.
* \param[in] argType type of output argument.
*/
template <DeviceType Device>
void NCHW2NHWC(real* outputs,
const real* inputs,
const int num,
const int inC,
const int inH,
const int inW,
const int argtype);
/**
* \brief This funtion switch dimension order of image input.
* The input and output is a 4D tensor. Switch order 'batch_size,
*height, width, channels' to
* order 'batch_size, channels, height, width'.
*
* \param[out] inGrad gradients of previous layer.
* \param[in] outGrad output gradients.
* \param[in] num batch size of input data.
* \param[in] inH height of input data.
* \param[in] inW with of input data.
* \param[in] inC channel number of input data.
* \param[in] argType type of output argument.
*/
template <DeviceType Device>
void NHWC2NCHW(real* inGrad,
const real* outGrad,
const int num,
const int inH,
const int inW,
const int inC,
const int argType);
} // namespace paddle
paddle/function/SwitchOpGpu.cu 0 → 100644
浏览文件 @ 09a13f6c
/* Copyright (c) 2016 Paddle
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "SwitchOp.h"
#include "hl_base.h"
namespace paddle {
__global__ void KeNCHW2NHWC(real* outputs,
const real* inputs,
int inC,
int inH,
int inW,
int nthreads,
int argType) {
const int idx = threadIdx.x + blockIdx.x * blockDim.x;
if (idx < nthreads) {
const int w = idx % inW;
const int h = (idx / inW) % inH;
const int c = (idx / inW / inH) % inC;
const int n = idx / inW / inH / inC;
const int off = ((n * inH + h) * inW + w) * inC + c;
if (argType == ADD_TO) {
outputs[off] += inputs[idx];
} else {
outputs[off] = inputs[idx];
}
}
}
template <>
void NCHW2NHWC<DEVICE_TYPE_GPU>(real* outputs,
const real* inputs,
const int num,
const int inC,
const int inH,
const int inW,
const int argType) {
size_t nth = num * inC * inH * inW;
int blockSize = 1024;
int gridSize = (nth + 1024 - 1) / 1024;
KeNCHW2NHWC<<<gridSize, blockSize, 0, STREAM_DEFAULT>>>(
outputs, inputs, inC, inH, inW, nth, argType);
CHECK_SYNC("NCHW2NHWC");
}
__global__ void KeNHWC2NCHW(real* outputs,
const real* inputs,
int inH,
int inW,
int inC,
int nthreads,
int argType) {
const int idx = threadIdx.x + blockIdx.x * blockDim.x;
if (idx < nthreads) {
const int c = idx % inC;
const int w = (idx / inC) % inW;
const int h = (idx / inC / inW) % inH;
const int n = idx / inW / inH / inC;
const int off = ((n * inC + c) * inH + h) * inW + w;
if (argType == ADD_TO) {
outputs[off] += inputs[idx];
} else {
outputs[off] = inputs[idx];
}
}
}
template <>
void NHWC2NCHW<DEVICE_TYPE_GPU>(real* outputs,
const real* inputs,
const int num,
const int inH,
const int inW,
const int inC,
const int argType) {
int nth = num * inC * inH * inW;
int blockSize = 1024;
int gridSize = (nth + 1024 - 1) / 1024;
KeNHWC2NCHW<<<gridSize, blockSize, 0, STREAM_DEFAULT>>>(
outputs, inputs, inH, inW, inC, nth, argType);
CHECK_SYNC("NHWC2NCHW");
}
} // namespace paddle
paddle/function/SwitchOpTest.cpp 0 → 100644
浏览文件 @ 09a13f6c
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include <gtest/gtest.h>
#include "FunctionTest.h"
namespace paddle {
TEST(Pad, real) {
for (size_t numSamples : {1, 4, 8, 16}) {
for (size_t channels : {1, 4, 8, 16}) {
for (size_t imgSizeH : {1, 4, 8, 16}) {
for (size_t imgSizeW : {1, 4, 8, 16}) {
VLOG(3) << " numSamples=" << numSamples << " channels=" << channels
<< " imgSizeH=" << imgSizeH << " imgSizeW=" << imgSizeW;
for (bool test_grad : {true, false}) {
CpuGpuFuncCompare compare(test_grad ? "NHWC2NCHW" : "NCHW2NHWC",
FuncConfig());
TensorShape inDims{numSamples, channels, imgSizeH, imgSizeW};
TensorShape outDims{numSamples, imgSizeH, imgSizeW, channels};
compare.addInputs(
BufferArg(VALUE_TYPE_FLOAT, test_grad ? outDims : inDims));
compare.addOutputs(BufferArg(
VALUE_TYPE_FLOAT, test_grad ? inDims : outDims, ASSIGN_TO));
compare.run();
}
}
}
}
}
}
} // namespace paddle
paddle/function/neon/NeonDepthwiseConv.h 0 → 100644
浏览文件 @ 09a13f6c
此差异已折叠。
paddle/function/neon/NeonDepthwiseConvTranspose.cpp 0 → 100644
浏览文件 @ 09a13f6c
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "NeonDepthwiseConv.h"
#include "paddle/function/ConvOp.h"
namespace paddle {
#if defined(__ARM_NEON__) || defined(__ARM_NEON)
template <DeviceType Device>
class NeonDepthwiseConvTransposeFunction : public ConvFunctionBase {
public:
void init(const FuncConfig& config) override {
ConvFunctionBase::init(config);
}
void check(const BufferArgs& inputs, const BufferArgs& outputs) override {
const TensorShape& input = inputs[0].shape();
const TensorShape& filter = inputs[1].shape();
const TensorShape& output = outputs[0].shape();
checkShape(input, filter, output);
}
void calc(const BufferArgs& inputs, const BufferArgs& outputs) override {
CHECK_EQ(numInputs_, inputs.size());
CHECK_EQ(numOutputs_, outputs.size());
check(inputs, outputs);
const TensorShape& input = inputs[0].shape();
const TensorShape& filter = inputs[1].shape();
const TensorShape& output = outputs[0].shape();
int batchSize = input[0];
int inputChannels = input[1];
int inputHeight = input[2];
int inputWidth = input[3];
int filterHeight = getFilterHeight(filter);
int filterWidth = getFilterWidth(filter);
int outputChannels = output[1];
int outputHeight = output[2];
int outputWidth = output[3];
int filterMultiplier = outputChannels / groups_;
CHECK_EQ(inputChannels, groups_);
// only support strideH() == strideW() and filterHeight == filterWidth.
CHECK_EQ(strideH(), strideW());
CHECK_EQ(paddingH(), paddingW());
CHECK_EQ(filterHeight, filterWidth);
float* inputData = inputs[0].data<float>();
float* filterData = inputs[1].data<float>();
float* outputData = outputs[0].data<float>();
// padding the input, input -> inputPadding
float* inputPadding = inputData;
int padInputHeight =
(inputHeight - 1) * strideH() + 2 * filterHeight - 1 - 2 * paddingH();
int padInputWidth =
(inputWidth - 1) * strideW() + 2 * filterWidth - 1 - 2 * paddingW();
if (padInputHeight > inputHeight || padInputWidth > inputWidth) {
int newSize = batchSize * inputChannels * padInputHeight * padInputWidth;
resizeBuffer<Device>(newSize);
inputPadding = reinterpret_cast<float*>(memory_->getBuf());
if (strideH() == 1) {
neon::Padding<float>::run(inputData,
inputPadding,
batchSize * inputChannels,
inputHeight,
inputWidth,
padInputHeight,
padInputWidth);
} else if (strideH() == 2) {
neon::StridePadding::run(inputData,
inputPadding,
batchSize * inputChannels,
inputHeight,
inputWidth,
padInputHeight,
padInputWidth);
} else {
LOG(FATAL) << "Not supported";
}
}
std::function<void(
const float*, const float*, int, int, int, int, int, int, float*)>
DepthWiseConv;
if (filterWidth == 3) {
DepthWiseConv = neon::DepthwiseConvKernel<3, 1>::run;
} else if (filterWidth == 4) {
DepthWiseConv = neon::DepthwiseConvKernel<4, 1>::run;
} else {
LOG(FATAL) << "Not supported";
}
for (int i = 0; i < batchSize; i++) {
DepthWiseConv(inputPadding,
filterData,
padInputHeight,
padInputWidth,
outputChannels,
outputHeight,
outputWidth,
filterMultiplier,
outputData);
inputPadding += inputChannels * padInputHeight * padInputWidth;
outputData += outputChannels * outputHeight * outputWidth;
}
}
};
#ifndef PADDLE_TYPE_DOUBLE
REGISTER_TYPED_FUNC(NeonDepthwiseConvTranspose,
CPU,
NeonDepthwiseConvTransposeFunction);
#endif
#endif
} // namespace paddle
paddle/function/neon/neon_util.h
浏览文件 @ 09a13f6c
......@@ -33,12 +33,8 @@ inline float32_t vaddvq_f32(float32x4_t a) {
return vget_lane_f32(vpadd_f32(v, v), 0);
}
inline float32x4_t vmlaq_laneq_f32(float32x4_t a,
float32x4_t b,
float32x4_t v,
const int lane) {
return vmlaq_n_f32(a, b, vgetq_lane_f32(v, lane));
}
#define vmlaq_laneq_f32(a, b, v, lane) \
vmlaq_n_f32(a, b, vgetq_lane_f32(v, lane))
#endif
} // namespace neon
......
paddle/gserver/layers/BatchNormBaseLayer.cpp
浏览文件 @ 09a13f6c
......@@ -62,14 +62,18 @@ void BatchNormBaseLayer::calFeatureMapSize() {
const ImageConfig& conf = config_.inputs(0).image_conf();
imageH_ = inputLayers_[0]->getOutput().getFrameHeight();
imageW_ = inputLayers_[0]->getOutput().getFrameWidth();
imageD_ = inputLayers_[0]->getOutput().getFrameDepth();
if (0 == imageD_) imageD_ = conf.img_size_z();
if (imageH_ == 0 && imageW_ == 0) {
imageH_ = conf.has_img_size_y() ? conf.img_size_y() : conf.img_size();
imageW_ = conf.img_size();
} else {
getOutput().setFrameHeight(imageH_);
getOutput().setFrameWidth(imageW_);
getOutput().setFrameDepth(imageD_);
}
imgPixels_ = imageH_ * imageW_;
imgPixels_ = imageH_ * imageW_ * imageD_;
}
} // namespace paddle
paddle/gserver/layers/BatchNormBaseLayer.h
浏览文件 @ 09a13f6c
......@@ -80,6 +80,7 @@ protected:
/// Height or width of input image feature.
/// Both of them are 1 if the input is fully-connected layer.
int imageD_;
int imageH_;
int imageW_;
/// Height * Width.
......
paddle/gserver/layers/CudnnBatchNormLayer.cpp
浏览文件 @ 09a13f6c
......@@ -37,7 +37,7 @@ bool CudnnBatchNormLayer::init(const LayerMap& layerMap,
}
void CudnnBatchNormLayer::reshape(int batchSize) {
hl_tensor_reshape(ioDesc_, batchSize, channels_, imageH_, imageW_);
hl_tensor_reshape(ioDesc_, batchSize, channels_, imageH_ * imageD_, imageW_);
}
void CudnnBatchNormLayer::forward(PassType passType) {
......@@ -104,7 +104,7 @@ void CudnnBatchNormLayer::forward(PassType passType) {
EPS,
batchSize,
channels_,
imageH_,
imageH_ * imageD_,
imageW_);
}
}
......
paddle/gserver/layers/DeConv3DLayer.cpp
浏览文件 @ 09a13f6c
......@@ -53,27 +53,27 @@ bool DeConv3DLayer::init(const LayerMap &layerMap,
size_t DeConv3DLayer::getSize() {
CHECK_NE(inputLayers_.size(), 0UL);
outputH_.clear();
outputW_.clear();
outputD_.clear();
imgSizeW_.clear();
imgSizeH_.clear();
imgSizeD_.clear();
N_.clear();
NOut_.clear();
size_t layerSize = 0;
for (size_t i = 0; i < inputLayers_.size(); ++i) {
outputW_.push_back(
imageSize(imgSizeW_[i], filterSize_[i], padding_[i], stride_[i], true));
outputH_.push_back(imageSize(
imgSizeH_[i], filterSizeY_[i], paddingY_[i], strideY_[i], true));
outputD_.push_back(imageSize(
imgSizeD_[i], filterSizeZ_[i], paddingZ_[i], strideZ_[i], true));
NOut_.push_back(outputD_[i] * outputH_[i] * outputW_[i]);
N_.push_back(imgSizeD_[i] * imgSizeH_[i] * imgSizeW_[i]);
imgSizeW_.push_back(
imageSize(outputW_[i], filterSize_[i], padding_[i], stride_[i], true));
imgSizeH_.push_back(imageSize(
outputH_[i], filterSizeY_[i], paddingY_[i], strideY_[i], true));
imgSizeD_.push_back(imageSize(
outputD_[i], filterSizeZ_[i], paddingZ_[i], strideZ_[i], true));
NOut_.push_back(imgSizeD_[i] * imgSizeH_[i] * imgSizeW_[i]);
N_.push_back(outputD_[i] * outputH_[i] * outputW_[i]);
CHECK(layerSize == 0 || N_[i] * size_t(numFilters_) == layerSize);
layerSize += NOut_[i] * numFilters_;
}
getOutput().setFrameHeight(outputH_[0]);
getOutput().setFrameWidth(outputW_[0]);
getOutput().setFrameDepth(outputD_[0]);
getOutput().setFrameHeight(imgSizeH_[0]);
getOutput().setFrameWidth(imgSizeW_[0]);
getOutput().setFrameDepth(imgSizeD_[0]);
return layerSize;
}
......@@ -103,9 +103,9 @@ void DeConv3DLayer::forward(PassType passType) {
}
colBuf_->col2Vol(outMat->getData() + n * outMat->getStride(),
numFilters_,
outputD_[i],
outputH_[i],
outputW_[i],
imgSizeD_[i],
imgSizeH_[i],
imgSizeW_[i],
filterSizeZ_[i],
filterSizeY_[i],
filterSize_[i],
......@@ -144,9 +144,9 @@ void DeConv3DLayer::backward(const UpdateCallback &callback) {
colBuf_->vol2Col(
getOutputGrad()->getData() + n * getOutputGrad()->getStride(),
numFilters_,
outputD_[i],
outputH_[i],
outputW_[i],
imgSizeD_[i],
imgSizeH_[i],
imgSizeW_[i],
filterSizeZ_[i],
filterSizeY_[i],
filterSize_[i],
......
paddle/gserver/layers/DetectionOutputLayer.cpp
浏览文件 @ 09a13f6c
......@@ -139,7 +139,13 @@ void DetectionOutputLayer::forward(PassType passType) {
allDecodedBBoxes,
&allIndices);
resetOutput(numKept, 7);
if (numKept > 0) {
resetOutput(numKept, 7);
} else {
MatrixPtr outV = getOutputValue();
outV = NULL;
return;
}
MatrixPtr outV = getOutputValue();
getDetectionOutput(confBuffer_->getData(),
numKept,
......
paddle/gserver/layers/DetectionUtil.cpp
浏览文件 @ 09a13f6c
......@@ -469,7 +469,7 @@ size_t getDetectionIndices(
const size_t numClasses,
const size_t backgroundId,
const size_t batchSize,
const size_t confThreshold,
const real confThreshold,
const size_t nmsTopK,
const real nmsThreshold,
const size_t keepTopK,
......
paddle/gserver/layers/DetectionUtil.h
浏览文件 @ 09a13f6c
......@@ -275,7 +275,7 @@ size_t getDetectionIndices(
const size_t numClasses,
const size_t backgroundId,
const size_t batchSize,
const size_t confThreshold,
const real confThreshold,
const size_t nmsTopK,
const real nmsThreshold,
const size_t keepTopK,
......
paddle/gserver/layers/GruCompute.cpp
浏览文件 @ 09a13f6c
......@@ -14,6 +14,7 @@ limitations under the License. */
#include "GruCompute.h"
#include "hl_recurrent_apply.cuh"
#include "paddle/function/GruFunctor.h"
#include "paddle/utils/Util.h"
namespace paddle {
......@@ -25,13 +26,13 @@ void GruCompute::init(LayerConfig &config) {
template <>
void GruCompute::forward<0>(hl_gru_value value, int frameSize, int batchSize) {
hl_cpu_gru_forward(hppl::forward::gru_resetOutput(),
hppl::forward::gru_finalOutput(),
value,
frameSize,
batchSize,
activeNode_,
activeGate_);
GruFunctor<DEVICE_TYPE_CPU, real>::compute(hppl::forward::gru_resetOutput(),
hppl::forward::gru_finalOutput(),
value,
frameSize,
batchSize,
activeNode_,
activeGate_);
}
template <>
......@@ -39,14 +40,15 @@ void GruCompute::backward<0>(hl_gru_value value,
hl_gru_grad grad,
int frameSize,
int batchSize) {
hl_cpu_gru_backward(hppl::backward::gru_stateGrad(),
hppl::backward::gru_resetGrad(),
value,
grad,
frameSize,
batchSize,
activeNode_,
activeGate_);
GruGradFunctor<DEVICE_TYPE_CPU, real>::compute(
hppl::backward::gru_stateGrad(),
hppl::backward::gru_resetGrad(),
value,
grad,
frameSize,
batchSize,
activeNode_,
activeGate_);
}
} // namespace paddle
paddle/gserver/layers/Layer.h
浏览文件 @ 09a13f6c
......@@ -49,6 +49,12 @@ struct LayerState {
};
typedef std::shared_ptr<LayerState> LayerStatePtr;
/// Paddle device ID, MKLDNN is -2, CPU is -1
enum PADDLE_DEVICE_ID {
MKLDNN_DEVICE = -2,
CPU_DEVICE = -1,
};
/**
* @brief Base class for layer.
* Define necessary variables and functions for every layer.
......@@ -59,11 +65,6 @@ protected:
LayerConfig config_;
/// whether to use GPU
bool useGpu_;
/// Paddle device ID, MKLDNN is -2, CPU is -1
enum PADDLE_DEVICE_ID {
MKLDNN_DEVICE = -2,
CPU_DEVICE = -1,
};
/// Device Id. MKLDNN is -2, CPU is -1, and GPU is 0, 1, 2 ...
int deviceId_;
/// Input layers
......
paddle/gserver/layers/MKLDNNConvLayer.cpp 0 → 100644
浏览文件 @ 09a13f6c
/* Copyright (c) 2017 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "MKLDNNConvLayer.h"
#include "paddle/math/MathUtils.h"
#include "paddle/utils/Logging.h"
using namespace mkldnn; // NOLINT
typedef memory::format format;
namespace paddle {
REGISTER_LAYER(mkldnn_conv, MKLDNNConvLayer);
bool MKLDNNConvLayer::init(const LayerMap& layerMap,
const ParameterMap& parameterMap) {
if (!MKLDNNLayer::init(layerMap, parameterMap)) {
return false;
}
CHECK_EQ(inputLayers_.size(), 1) << "Only support one input layer yet";
CHECK_EQ(inputLayers_.size(), parameters_.size());
CHECK(config_.shared_biases()) << "Only support shared biases yet";
oc_ = config_.num_filters();
const ConvConfig& conf = config_.inputs(0).conv_conf();
ic_ = conf.channels();
fw_ = conf.filter_size();
fh_ = conf.filter_size_y();
pw_ = conf.padding();
ph_ = conf.padding_y();
dw_ = conf.dilation();
dh_ = conf.dilation_y();
sw_ = conf.stride();
sh_ = conf.stride_y();
gp_ = conf.groups();
oh_ = conf.output_y();
ow_ = conf.output_x();
ih_ = conf.img_size_y();
iw_ = conf.img_size();
caffeMode_ = conf.caffe_mode();
CHECK(caffeMode_) << "Only support caffe mode yet";
CHECK(dh_ == 1 && dw_ == 1) << "Only support dilation 1 yet";
// check group setting
CHECK_EQ((oc_ / gp_) * gp_, oc_) << "group is indivisible for oc";
CHECK_EQ((ic_ / gp_) * gp_, ic_) << "group is indivisible for ic";
// create weight
size_t height = oc_ / gp_;
size_t width = ic_ * fh_ * fw_;
CHECK_EQ(parameters_[0]->getSize(), height * width);
weight_ =
std::unique_ptr<Weight>(new Weight(height, width, parameters_[0], 0));
// create biases
if (biasParameter_.get() != NULL) {
biases_ = std::unique_ptr<Weight>(new Weight(1, oc_, biasParameter_));
}
return true;
}
void MKLDNNConvLayer::convertWeightsFromPaddle() {
if (hasInitedWgt_) {
return;
}
CHECK(wgtVal_) << "should have been initialized";
// the paddle weight format is oihw or goihw
auto targetDim = wgtVal_->getDims();
auto srcFmt = (gp_ == 1) ? memory::format::oihw : memory::format::goihw;
wgtVal_->reorderDataFrom(wgtVal_, srcFmt, targetDim);
hasInitedWgt_ = true;
}
void MKLDNNConvLayer::convertWeightsToPaddle() {
CHECK(wgtVal_) << "should have been initialized";
auto targetDim = wgtVal_->getDims();
auto dstFmt = (gp_ == 1) ? memory::format::oihw : memory::format::goihw;
wgtVal_->reorderDataTo(wgtVal_, dstFmt, targetDim);
}
void MKLDNNConvLayer::reshape(
int& bs, int& ic, int& ih, int& iw, int oc, int& oh, int& ow) {
reshapeInput(bs, ih, iw);
// cal output sizes
// oc can not be changed
int fh = (fh_ - 1) * dh_ + 1;
int fw = (fw_ - 1) * dw_ + 1;
oh = outputSize(ih, fh, ph_, sh_, caffeMode_);
ow = outputSize(iw, fw, pw_, sw_, caffeMode_);
reshapeOutput(oh, ow);
resizeOutput(bs, oc * oh * ow);
printSizeInfo();
}
void MKLDNNConvLayer::resetFwd(std::vector<primitive>& pipeline,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) {
resetFwdPD(fwdPD_);
resetFwdBuffers(fwdPD_, in, wgt, bias, out);
resetFwdPipeline(pipeline, fwdPD_, in, wgt, bias, out);
printValueFormatFlow();
}
void MKLDNNConvLayer::resetBwd(std::vector<primitive>& pipeline,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) {
std::shared_ptr<conv_bwdWgt::primitive_desc> bwdWgtPD;
std::shared_ptr<conv_bwdData::primitive_desc> bwdDataPD;
resetBwdWgtPD(bwdWgtPD);
resetBwdDataPD(bwdDataPD);
resetBwdBuffers(bwdWgtPD, bwdDataPD, in, wgt, bias, out);
resetBwdPipeline(pipeline, bwdWgtPD, bwdDataPD, in, wgt, bias, out);
printGradFormatFlow();
}
void MKLDNNConvLayer::updateInputData() {
cpuInVal_->setData(getInputValue(0, CPU_DEVICE)->getData());
}
void MKLDNNConvLayer::updateWeights(const UpdateCallback& callback) {
weight_->getParameterPtr()->incUpdate(callback);
if (biases_ && biases_->getWGrad()) {
biases_->getParameterPtr()->incUpdate(callback);
}
}
void MKLDNNConvLayer::loadConvSettings(memory::dims& wgt,
memory::dims& bias,
memory::dims& stride,
memory::dims& dilation,
memory::dims& padL,
memory::dims& padR) {
wgt = (gp_ == 1) ? memory::dims{oc_, ic_, fh_, fw_}
: memory::dims{gp_, oc_ / gp_, ic_ / gp_, fh_, fw_};
bias = memory::dims{oc_};
stride = memory::dims{sh_, sw_};
padL = memory::dims{ph_, pw_};
padR = getPaddingR();
// note: mkldnn dilation start from 0
dilation = memory::dims{dh_ - 1, dw_ - 1};
}
void MKLDNNConvLayer::resetFwdPD(
std::shared_ptr<conv_fwd::primitive_desc>& pd) {
// dims for conv
memory::dims inDims = memory::dims{bs_, ic_, ih_, iw_};
memory::dims outDims = memory::dims{bs_, oc_, oh_, ow_};
memory::dims wgtDims, biasDims, strides, dilations, padL, padR;
loadConvSettings(wgtDims, biasDims, strides, dilations, padL, padR);
prop_kind pk = passType_ == PASS_TEST ? prop_kind::forward_scoring
: prop_kind::forward_training;
algorithm algo = algorithm::convolution_direct;
padding_kind padKind = padding_kind::zero;
conv_fwd::desc fwdDesc =
biases_ && biases_->getW()
? conv_fwd::desc(pk,
algo,
MKLDNNMatrix::createMemoryDesc(inDims),
MKLDNNMatrix::createMemoryDesc(wgtDims),
MKLDNNMatrix::createMemoryDesc(biasDims),
MKLDNNMatrix::createMemoryDesc(outDims),
strides,
dilations,
padL,
padR,
padKind)
: conv_fwd::desc(pk,
algo,
MKLDNNMatrix::createMemoryDesc(inDims),
MKLDNNMatrix::createMemoryDesc(wgtDims),
MKLDNNMatrix::createMemoryDesc(outDims),
strides,
dilations,
padL,
padR,
padKind);
pd.reset(new conv_fwd::primitive_desc(fwdDesc, engine_));
}
void MKLDNNConvLayer::resetFwdBuffers(
std::shared_ptr<conv_fwd::primitive_desc>& pd,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) {
CHECK(pd);
resetInValue(pd, in);
resetWgtBiasValue(pd, wgt, bias);
resetOutValue(pd, out);
}
void MKLDNNConvLayer::resetFwdPipeline(
std::vector<primitive>& pipeline,
std::shared_ptr<conv_fwd::primitive_desc>& pd,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) {
pipeline.clear();
if (cvtInVal_) {
pipeline.push_back(*cvtInVal_);
}
if (bias) {
fwd_.reset(new conv_fwd(*pd, *in, *wgt, *bias, *out));
} else {
fwd_.reset(new conv_fwd(*pd, *in, *wgt, *out));
}
pipeline.push_back(*fwd_);
if (cvtOutVal_) {
pipeline.push_back(*cvtOutVal_);
}
}
void MKLDNNConvLayer::resetInValue(
std::shared_ptr<conv_fwd::primitive_desc>& pd, MKLDNNMatrixPtr& in) {
const MatrixPtr& inMat = inputLayers_[0]->getOutput().value;
in = MKLDNNMatrix::create(inMat, pd->src_primitive_desc());
// create buffer and reorder if input value do not match
cpuInVal_ = nullptr;
cvtInVal_ = nullptr;
if (inputIsOnlyMKLDNN()) {
MKLDNNMatrixPtr dnnIn = std::dynamic_pointer_cast<MKLDNNMatrix>(inMat);
CHECK(dnnIn) << "Input should be MKLDNNMatrix";
if (dnnIn->getPrimitiveDesc() != in->getPrimitiveDesc()) {
CHECK_EQ(dnnIn->getFormat(), format::nc);
CHECK(ih_ == 1 && iw_ == 1) << "when input is nc format";
// create a new one with nchw format and same data
memory::dims inDims = memory::dims{bs_, ic_, 1, 1};
dnnIn = MKLDNNMatrix::create(inMat, inDims, format::nchw, engine_);
CHECK(dnnIn->getPrimitiveDesc() == in->getPrimitiveDesc());
}
in = dnnIn;
} else {
const MatrixPtr& cpuIn = getInputValue(0, CPU_DEVICE);
memory::dims inDims = memory::dims{bs_, ic_, ih_, iw_};
cpuInVal_ = MKLDNNMatrix::create(cpuIn, inDims, format::nchw, engine_);
if (cpuInVal_->getPrimitiveDesc() != in->getPrimitiveDesc()) {
// create new mkldnn matrix
in = MKLDNNMatrix::create(nullptr, pd->src_primitive_desc());
cvtInVal_ = MKLDNNMatrix::createReorder(cpuInVal_, in);
CHECK(cvtInVal_) << "should not be emptry";
} else {
in = cpuInVal_;
}
}
}
void MKLDNNConvLayer::resetWgtBiasValue(
std::shared_ptr<conv_fwd::primitive_desc>& pd,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias) {
wgt = MKLDNNMatrix::create(weight_->getW(), pd->weights_primitive_desc());
VLOG(MKLDNN_FMTS) << "Weight value format: " << wgt->getFormat();
bias = nullptr;
if (biases_ && biases_->getW()) {
bias = MKLDNNMatrix::create(biases_->getW(), pd->bias_primitive_desc());
}
}
void MKLDNNConvLayer::resetOutValue(
std::shared_ptr<conv_fwd::primitive_desc>& pd, MKLDNNMatrixPtr& out) {
out = MKLDNNMatrix::create(output_.value, pd->dst_primitive_desc());
// change original output value from cpu matrix to mkldnn matrix
output_.value = std::dynamic_pointer_cast<Matrix>(out);
// create reorder if output value has cpu device and pd do not match
cpuOutVal_ = nullptr;
cpuOutVal_ = nullptr;
if (!outputIsOnlyMKLDNN()) {
const MatrixPtr& cpuOut = getOutput(CPU_DEVICE).value;
memory::dims outDims = memory::dims{bs_, oc_, oh_, ow_};
cpuOutVal_ = MKLDNNMatrix::create(cpuOut, outDims, format::nchw, engine_);
if (cpuOutVal_->getPrimitiveDesc() != out->getPrimitiveDesc()) {
cvtOutVal_ = MKLDNNMatrix::createReorder(out, cpuOutVal_);
CHECK(cvtOutVal_) << "should not be emptry";
} else {
// CPU output share the same data of MKLDNN output
cpuOut->setData(out->getData());
cpuOutVal_ = out;
}
}
}
void MKLDNNConvLayer::resetBwdWgtPD(
std::shared_ptr<conv_bwdWgt::primitive_desc>& pd) {
memory::dims wgtDims, biasDims, strides, dilations, padL, padR;
loadConvSettings(wgtDims, biasDims, strides, dilations, padL, padR);
// create backward weight using input, output and weight value memory desc
CHECK(inVal_) << "Should have input value";
CHECK(outVal_) << "Should have output value";
CHECK(wgtVal_) << "Should have weight value";
algorithm algo = algorithm::convolution_direct;
padding_kind padKind = padding_kind::zero;
auto bwdWgtDesc = biasVal_ != nullptr
? conv_bwdWgt::desc(algo,
inVal_->getMemoryDesc(),
wgtVal_->getMemoryDesc(),
biasVal_->getMemoryDesc(),
outVal_->getMemoryDesc(),
strides,
padL,
padR,
padKind)
: conv_bwdWgt::desc(algo,
inVal_->getMemoryDesc(),
wgtVal_->getMemoryDesc(),
outVal_->getMemoryDesc(),
strides,
padL,
padR,
padKind);
pd.reset(new conv_bwdWgt::primitive_desc(bwdWgtDesc, engine_, *fwdPD_));
CHECK(pd->src_primitive_desc() == inVal_->getPrimitiveDesc())
<< "primitive desc of in value should equal";
CHECK(pd->diff_dst_primitive_desc() == outVal_->getPrimitiveDesc())
<< "primitive desc of out grad should equal the out value";
CHECK(pd->diff_weights_primitive_desc() == wgtVal_->getPrimitiveDesc())
<< "primitive desc of weight grad should equal the weight value";
}
void MKLDNNConvLayer::resetBwdDataPD(
std::shared_ptr<conv_bwdData::primitive_desc>& pd) {
if (inputLayers_[0]->getOutput().grad == nullptr) {
return;
}
memory::dims wgtDims, biasDims, strides, dilations, padL, padR;
loadConvSettings(wgtDims, biasDims, strides, dilations, padL, padR);
CHECK(inVal_) << "Should have input value";
CHECK(outVal_) << "Should have output value";
// create backward data using input and output value memory desc
// but using weight memory desc with any format
auto bwdDataDesc = conv_bwdData::desc(algorithm::convolution_direct,
inVal_->getMemoryDesc(),
MKLDNNMatrix::createMemoryDesc(wgtDims),
outVal_->getMemoryDesc(),
strides,
padL,
padR,
padding_kind::zero);
pd.reset(new conv_bwdData::primitive_desc(bwdDataDesc, engine_, *fwdPD_));
CHECK(pd->diff_src_primitive_desc() == inVal_->getPrimitiveDesc())
<< "primitive desc of in grad should equal the in value";
CHECK(pd->diff_dst_primitive_desc() == outVal_->getPrimitiveDesc())
<< "primitive desc of out grad should equal";
}
void MKLDNNConvLayer::resetBwdBuffers(
std::shared_ptr<conv_bwdWgt::primitive_desc>& wgtPD,
std::shared_ptr<conv_bwdData::primitive_desc>& dataPD,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) {
CHECK(wgtPD);
resetOutGrad(wgtPD, out);
resetWgtBiasGrad(wgtPD, wgt, bias);
resetInGrad(dataPD, in);
resetWgtValBwdData(dataPD, wgtValBwdData_);
}
void MKLDNNConvLayer::resetBwdPipeline(
std::vector<primitive>& pipeline,
std::shared_ptr<conv_bwdWgt::primitive_desc>& wgtPD,
std::shared_ptr<conv_bwdData::primitive_desc>& dataPD,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) {
pipeline.clear();
if (cvtOutGrad_) {
pipeline.push_back(*cvtOutGrad_);
}
// add bwdWgt handle
if (bias) {
bwdWgt_.reset(new conv_bwdWgt(*wgtPD, *inVal_, *out, *wgt, *bias));
} else {
bwdWgt_.reset(new conv_bwdWgt(*wgtPD, *inVal_, *out, *wgt));
}
pipeline.push_back(*bwdWgt_);
if (dataPD == nullptr) {
return;
}
if (cvtWgtVal_) {
pipeline.push_back(*cvtWgtVal_);
}
// add bwdData handle
CHECK(wgtValBwdData_) << "Should have weight memory";
bwdData_.reset(new conv_bwdData(*dataPD, *out, *wgtValBwdData_, *in));
pipeline.push_back(*bwdData_);
if (cvtInGrad_) {
pipeline.push_back(*cvtInGrad_);
}
}
void MKLDNNConvLayer::resetOutGrad(
std::shared_ptr<conv_bwdWgt::primitive_desc>& wgtPD, MKLDNNMatrixPtr& out) {
const MatrixPtr& outMat = output_.grad;
out = MKLDNNMatrix::create(outMat, wgtPD->diff_dst_primitive_desc());
CHECK(outVal_ != nullptr &&
out->getPrimitiveDesc() == outVal_->getPrimitiveDesc())
<< "primitive desc of out grad and value should be equal";
// TODO(TJ): merge outgrad
// create reorder if has output grad does not match
cpuOutGrad_ = nullptr;
cvtOutGrad_ = nullptr;
if (!outputIsOnlyMKLDNN()) {
const MatrixPtr& cpuOut = getOutput(CPU_DEVICE).grad;
// same PrimitiveDesc with cpuInVal_
CHECK(cpuOutVal_);
cpuOutGrad_ = MKLDNNMatrix::create(cpuOut, cpuOutVal_->getPrimitiveDesc());
if (cpuOutGrad_->getPrimitiveDesc() == out->getPrimitiveDesc()) {
outMat->setData(cpuOut->getData());
out = cpuOutGrad_;
} else {
cvtOutGrad_ = MKLDNNMatrix::createReorder(cpuOutGrad_, out);
CHECK(cvtOutGrad_);
}
}
}
void MKLDNNConvLayer::resetWgtBiasGrad(
std::shared_ptr<conv_bwdWgt::primitive_desc>& wgtPD,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias) {
wgt = MKLDNNMatrix::create(weight_->getWGrad(),
wgtPD->diff_weights_primitive_desc());
CHECK(nullptr != wgtVal_ &&
wgt->getPrimitiveDesc() == wgtVal_->getPrimitiveDesc())
<< "primitive desc of weight grad and value should be equal";
VLOG(MKLDNN_FMTS) << "weight grad format: " << wgt->getFormat();
if (biasVal_ == nullptr) {
return;
}
bias = MKLDNNMatrix::create(biases_->getWGrad(),
wgtPD->diff_bias_primitive_desc());
CHECK(bias->getPrimitiveDesc() == biasVal_->getPrimitiveDesc())
<< "primitive desc of bias grad should equal the bias value";
}
void MKLDNNConvLayer::resetInGrad(
std::shared_ptr<conv_bwdData::primitive_desc>& dataPD,
MKLDNNMatrixPtr& in) {
if (dataPD == nullptr) {
return;
}
// TODO(TJ): use outputMaps_ ways to get the inGrad_ when merge outgrad done
in = MKLDNNMatrix::create(inputLayers_[0]->getOutput().grad,
dataPD->diff_src_primitive_desc());
CHECK(nullptr != inVal_ &&
in->getPrimitiveDesc() == inVal_->getPrimitiveDesc())
<< "primitive desc of input grad and value should be equal";
// create reorder if has output grad does not match
cpuInGrad_ = nullptr;
cvtInGrad_ = nullptr;
if (!inputIsOnlyMKLDNN()) {
const MatrixPtr& cpuIn = getInputGrad(0, CPU_DEVICE);
// same PrimitiveDesc with cpuInVal_
CHECK(cpuInVal_);
cpuInGrad_ = MKLDNNMatrix::create(cpuIn, cpuInVal_->getPrimitiveDesc());
if (cpuInGrad_->getPrimitiveDesc() != in->getPrimitiveDesc()) {
const MatrixPtr& dnnIn = getInputGrad(0, MKLDNN_DEVICE);
in = MKLDNNMatrix::create(dnnIn, in->getPrimitiveDesc());
cvtInGrad_ = MKLDNNMatrix::createReorder(in, cpuInGrad_);
CHECK(cvtInGrad_);
} else {
in = cpuInGrad_;
}
}
}
void MKLDNNConvLayer::resetWgtValBwdData(
std::shared_ptr<conv_bwdData::primitive_desc>& dataPD,
MKLDNNMatrixPtr& wgt) {
if (dataPD == nullptr) {
return;
}
// create new weight value for backward data, and create reorder if necessary
// since the primitive_desc would be different with wgtVal_
CHECK(wgtVal_) << "should have weight value";
if (dataPD->weights_primitive_desc() != wgtVal_->getPrimitiveDesc()) {
wgtValBwdData_ =
MKLDNNMatrix::create(nullptr, dataPD->weights_primitive_desc());
cvtWgtVal_ = MKLDNNMatrix::createReorder(wgtVal_, wgtValBwdData_);
CHECK(cvtWgtVal_);
} else {
wgtValBwdData_ = wgtVal_;
}
VLOG(MKLDNN_FMTS) << "weight value format for backward data"
<< wgtValBwdData_->getFormat();
}
} // namespace paddle
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paddle/gserver/layers/MKLDNNFcLayer.h
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......@@ -45,35 +45,28 @@ public:
bool init(const LayerMap& layerMap,
const ParameterMap& parameterMap) override;
void convertWeightsFromPaddle() override;
void reshape(
int& bs, int& ic, int& ih, int& iw, int oc, int& oh, int& ow) override;
void convertWeightsToPaddle() override;
void resetFwd(std::vector<mkldnn::primitive>& pipeline,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) override;
void forward(PassType passType) override;
void resetBwd(std::vector<mkldnn::primitive>& pipeline,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) override;
void backward(const UpdateCallback& callback) override;
void updateInputData() override;
protected:
/**
* reshape the input image sizes
* and reset output buffer size
* and reset mkldnn forward
*/
void reshape();
/**
* reset the forward primitve and memory
* only would be called when input size changes
*/
void resetFwd();
/**
* reset the backward primitve and memory for mkldnn fc
* only would be called when needed
*/
void resetBwd();
void convertOutputToOtherDevice() override;
void updateWeights(const UpdateCallback& callback) override;
void convertWeightsFromPaddle() override;
void convertWeightsToPaddle() override;
};
} // namespace paddle
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "SwitchOrderLayer.h"
#include "paddle/utils/Stat.h"
namespace paddle {
REGISTER_LAYER(switch_order, SwitchOrderLayer);
bool SwitchOrderLayer::init(const LayerMap& layerMap,
const ParameterMap& parameterMap) {
/* Initialize the basic parent class */
Layer::init(layerMap, parameterMap);
auto& img_conf = config_.inputs(0).image_conf();
size_t inD = img_conf.img_size_z();
size_t inH =
img_conf.has_img_size_y() ? img_conf.img_size_y() : img_conf.img_size();
size_t inW = img_conf.img_size();
size_t inC = img_conf.channels();
inH = inH * inD;
inDims_ = TensorShape({0, inC, inH, inW});
outDims_ = TensorShape(4);
auto& reshape_conf = config_.reshape_conf();
for (int i = 0; i < reshape_conf.height_axis_size(); i++) {
heightAxis_.push_back(reshape_conf.height_axis(i));
}
for (int i = 0; i < reshape_conf.width_axis_size(); i++) {
widthAxis_.push_back(reshape_conf.width_axis(i));
}
createFunction(nchw2nhwc_, "NCHW2NHWC", FuncConfig());
createFunction(nhwc2nchw_, "NHWC2NCHW", FuncConfig());
return true;
}
void SwitchOrderLayer::setOutDims() {
outDims_.setDim(0, inDims_[0]);
outDims_.setDim(1, inDims_[2]);
outDims_.setDim(2, inDims_[3]);
outDims_.setDim(3, inDims_[1]);
reshapeHeight_ = 1;
for (size_t i = 0; i < heightAxis_.size(); i++) {
reshapeHeight_ *= outDims_[heightAxis_[i]];
}
output_.setFrameHeight(reshapeHeight_);
reshapeWidth_ = 1;
for (size_t i = 0; i < widthAxis_.size(); i++) {
reshapeWidth_ *= outDims_[widthAxis_[i]];
}
output_.setFrameWidth(reshapeWidth_);
}
void SwitchOrderLayer::setInDims() {
MatrixPtr input = inputLayers_[0]->getOutputValue();
size_t batchSize = input->getHeight();
inDims_.setDim(0, batchSize);
int d = inputLayers_[0]->getOutput().getFrameDepth();
d = (d == 0 ? 1 : d);
int h = inputLayers_[0]->getOutput().getFrameHeight();
if (h != 0) inDims_.setDim(2, h * d);
int w = inputLayers_[0]->getOutput().getFrameWidth();
if (w != 0) inDims_.setDim(3, w);
int totalCount = input->getElementCnt();
int channels = totalCount / (inDims_[0] * inDims_[2] * inDims_[3]);
if (channels != 0) inDims_.setDim(1, channels);
}
void SwitchOrderLayer::forward(PassType passType) {
Layer::forward(passType);
setInDims();
setOutDims();
resetOutput(outDims_[0], outDims_[1] * outDims_[2] * outDims_[3]);
if (heightAxis_.size() > 0) {
resetOutput(reshapeHeight_, reshapeWidth_);
}
// switch NCHW to NHWC
BufferArgs inputs;
BufferArgs outputs;
inputs.addArg(*getInputValue(0), inDims_);
outputs.addArg(*getOutputValue(), outDims_);
nchw2nhwc_[0]->calc(inputs, outputs);
forwardActivation();
}
void SwitchOrderLayer::backward(const UpdateCallback& callback) {
(void)callback;
backwardActivation();
// switch NHWC to NCHW
BufferArgs inputs;
BufferArgs outputs;
inputs.addArg(*getOutputGrad(), outDims_);
outputs.addArg(*getInputGrad(0), inDims_, ADD_TO);
nhwc2nchw_[0]->calc(inputs, outputs);
}
} // namespace paddle
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paddle/gserver/tests/MKLDNNTester.h
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paddle/math/MKLDNNMatrix.cpp
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paddle/math/MKLDNNMatrix.h
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paddle/math/MathFunctions.h
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paddle/math/Matrix.cpp
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paddle/math/Matrix.h
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};
class CpuMatrix : public Matrix {
private:
MatrixPtr sftmaxSum_;
MatrixPtr sftmaxDot_;
public:
CpuMatrix(size_t height, size_t width, bool trans = false);
CpuMatrix(real* data, size_t height, size_t width, bool trans = false)
......
paddle/operators/CMakeLists.txt
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paddle/platform/enforce.h
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proto/ModelConfig.proto
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python/paddle/v2/event.py
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