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提交 1fb5f12f 编写于 作者: C caoying03
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Merge branch 'develop' into softmax_with_cross_entropy_op

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浏览文件 @ 1fb5f12f
......@@ -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
......
CMakeLists.txt
浏览文件 @ 1fb5f12f
......@@ -67,6 +67,9 @@ endif()
if(ANDROID)
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 @ 1fb5f12f
......@@ -6,13 +6,14 @@ RUN /bin/bash -c 'if [[ -n ${UBUNTU_MIRROR} ]]; then sed -i 's#http://archive.ub
# ENV variables
ARG ANDROID_ABI
ARG ANDROID_API
ENV ANDROID_ABI=${ANDROID_ABI:-"armeabi-v7a"}
ENV ANDROID_API=${ANDROID_API:-21}
ENV HOME=/root \
ANDROID_NDK_HOME=/opt/android-ndk-linux \
ANDROID_ARM_STANDALONE_TOOLCHAIN=/opt/arm-toolchain \
ANDROID_ARM64_STANDALONE_TOOLCHAIN=/opt/arm64-toolchain
ANDROID_TOOLCHAINS_DIR=/opt/toolchains
RUN apt-get update && \
apt-get install -y \
......@@ -42,14 +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} && \
${ANDROID_NDK_HOME}/build/tools/make-standalone-toolchain.sh --arch=arm --platform=android-23 --install-dir=${ANDROID_ARM_STANDALONE_TOOLCHAIN} && \
${ANDROID_NDK_HOME}/build/tools/make-standalone-toolchain.sh --arch=arm64 --platform=android-23 --install-dir=${ANDROID_ARM64_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 @ 1fb5f12f
......@@ -26,9 +26,9 @@ set(IGNORE_PATTERN
.*ImportanceSampler.*
.*cblas\\.h.*
.*\\.pb\\.txt
.*LtrDataProvider.*
.*MultiDataProvider.*
.*pb.*)
.*pb.*
.*pybind.h)
# add_style_check_target
#
......
cmake/external/gflags.cmake
浏览文件 @ 1fb5f12f
......@@ -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 @ 1fb5f12f
......@@ -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 @ 1fb5f12f
......@@ -73,6 +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 @ 1fb5f12f
......@@ -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 @ 1fb5f12f
......@@ -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/if_else_op.md
浏览文件 @ 1fb5f12f
IfOp should have only one branch. An IfOp operator takes a `cond` variable whose value must be a vector of N boolean elements. Its return value has M (M<=N) instances, each corresponds to a true element in `cond`.
```python
import paddle as pd
x = var()
y = var()
cond = var()
b = pd.create_ifop(inputs=[x], output_num=1)
with b.true_block():
x = b.inputs(0)
z = operator.add(x, y)
b.set_output(0, operator.softmax(z))
out = b(cond)
```
If we want the output still has N instances, we can use IfElseOp with a default value, whose minibatch size must be N:
IfOp should have only one branch. An IfOp operator takes a `cond` variable whose value must be a vector of N boolean elements. Its return value has N instances. If cond[i] == True, input instance input[i] will go through true_block() and generate output[i]; otherwise it will produce output from false_bloack().
```python
import paddle as pd
......@@ -39,7 +21,7 @@ with b.false_block():
out = b(cond)
```
If only true_block is set in an IfElseOp, we can have a default value for false as:
If only true_block is set in an IfElseOp, a special case is that we can have a default value for false as:
```python
import paddle as pd
......
doc/design/ops/images/2_level_rnn.dot 0 → 100644
浏览文件 @ 1fb5f12f
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 @ 1fb5f12f
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 @ 1fb5f12f
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 @ 1fb5f12f
# 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/howto/dev/new_op_cn.md
浏览文件 @ 1fb5f12f
......@@ -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
`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
......@@ -367,3 +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。
paddle/capi/CMakeLists.txt
浏览文件 @ 1fb5f12f
......@@ -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/framework/lod_tensor.h
浏览文件 @ 1fb5f12f
......@@ -51,18 +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.
......@@ -104,7 +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 @ 1fb5f12f
......@@ -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
浏览文件 @ 1fb5f12f
......@@ -26,18 +26,16 @@ __global__ void test(size_t* a, int size) {
}
TEST(LoDTensor, LoDInGPU) {
paddle::framework::Tensor tensor;
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});
tensor.Resize({14, 16});
tensor.mutable_data<float>(place);
lod_tensor.Resize({14, 16});
lod_tensor.mutable_data<float>(place);
lod_tensor.set_lod(src_lod);
lod_tensor.set_tensor(&tensor);
CHECK_EQ(lod_tensor.lod_element(0, 2), 4);
CHECK_EQ(lod_tensor.lod_element(0, 4), 8);
......
paddle/framework/operator.cc
浏览文件 @ 1fb5f12f
......@@ -186,6 +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 @ 1fb5f12f
......@@ -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"
......@@ -326,11 +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;
......@@ -363,9 +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_impl.h
浏览文件 @ 1fb5f12f
......@@ -22,7 +22,7 @@ namespace framework {
template <typename T>
inline void Tensor::check_memory_size() const {
PADDLE_ENFORCE_NOT_NULL(
holder_, "Tenosr holds no memory. Call Tensor::mutable_data first.");
holder_, "Tensor holds no memory. Call Tensor::mutable_data first.");
PADDLE_ENFORCE_GE(
holder_->size(), numel() * sizeof(T) + offset_,
"Tensor's dims_ is out of bound. Call Tensor::mutable_data "
......
paddle/framework/tensor_test.cc
浏览文件 @ 1fb5f12f
......@@ -36,7 +36,7 @@ TEST(Tensor, DataAssert) {
} catch (paddle::platform::EnforceNotMet err) {
caught = true;
std::string msg =
"holder_ should not be null\nTenosr holds no memory. Call "
"holder_ should not be null\nTensor holds no memory. Call "
"Tensor::mutable_data first.";
const char* what = err.what();
for (size_t i = 0; i < msg.length(); ++i) {
......@@ -112,7 +112,7 @@ TEST(Tensor, ShareDataWith) {
} catch (paddle::platform::EnforceNotMet err) {
caught = true;
std::string msg =
"holder_ should not be null\nTenosr holds no memory. Call "
"holder_ should not be null\nTensor holds no memory. Call "
"Tensor::mutable_data first.";
const char* what = err.what();
for (size_t i = 0; i < msg.length(); ++i) {
......@@ -274,4 +274,4 @@ TEST(Tensor, ReshapeToMatrix) {
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/neon/NeonDepthwiseConv.h
浏览文件 @ 1fb5f12f
......@@ -594,7 +594,7 @@ struct StridePadding {
float32x4_t s1 = vdupq_n_f32(0.f);
for (int s = 0; s < step; s++) {
float32x4_t s0 = vld1q_f32(input);
float32x4x2_t v = {s0, s1};
float32x4x2_t v = {{s0, s1}};
vst2q_f32(inputPadding, v);
input += 4;
inputPadding += 8;
......
paddle/gserver/layers/MKLDNNConvLayer.cpp 0 → 100644
浏览文件 @ 1fb5f12f
/* 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
paddle/gserver/layers/MKLDNNConvLayer.h 0 → 100644
浏览文件 @ 1fb5f12f
/* 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. */
#pragma once
#include "MKLDNNLayer.h"
#include "mkldnn.hpp"
namespace paddle {
typedef mkldnn::convolution_forward conv_fwd;
typedef mkldnn::convolution_backward_weights conv_bwdWgt;
typedef mkldnn::convolution_backward_data conv_bwdData;
/**
* @brief A subclass of MKLDNNLayer conv layer.
*
* The config file api is mkldnn_conv
*/
class MKLDNNConvLayer : public MKLDNNLayer {
protected:
// padding height and width
int ph_, pw_;
// stride height and width
int sh_, sw_;
// dilation height and width
int dh_, dw_;
// filter(kenerl) height and width
int fh_, fw_;
// group number
int gp_;
// in resetBwdData, the format of wgtValBwdData_ is different with wgtVal_
MKLDNNMatrixPtr wgtValBwdData_;
// convert handle from wgtVal_ to wgtValBwdData_
std::shared_ptr<mkldnn::reorder> cvtWgtVal_;
// save forward primitive_desc, which can be used backward
std::shared_ptr<conv_fwd::primitive_desc> fwdPD_;
// MKLDNNMatrixPtr which should be created from CPU Device
MKLDNNMatrixPtr cpuInVal_;
MKLDNNMatrixPtr cpuInGrad_;
MKLDNNMatrixPtr cpuOutVal_;
MKLDNNMatrixPtr cpuOutGrad_;
// convert handle between CPU device and MKLDNN device
std::shared_ptr<mkldnn::reorder> cvtInVal_;
std::shared_ptr<mkldnn::reorder> cvtInGrad_;
std::shared_ptr<mkldnn::reorder> cvtOutVal_;
std::shared_ptr<mkldnn::reorder> cvtOutGrad_;
// whether the weight has been init
bool hasInitedWgt_;
// true by default, which impact the calculation of output image size.
// details can refer to mathUtil.h
bool caffeMode_;
// weight and bias
std::unique_ptr<Weight> weight_;
std::unique_ptr<Weight> biases_;
public:
explicit MKLDNNConvLayer(const LayerConfig& config)
: MKLDNNLayer(config), hasInitedWgt_(false), caffeMode_(true) {}
~MKLDNNConvLayer() {}
bool init(const LayerMap& layerMap,
const ParameterMap& parameterMap) override;
void reshape(
int& bs, int& ic, int& ih, int& iw, int oc, int& oh, int& ow) override;
void resetFwd(std::vector<mkldnn::primitive>& pipeline,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) override;
void resetBwd(std::vector<mkldnn::primitive>& pipeline,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out) override;
void updateInputData() override;
void updateWeights(const UpdateCallback& callback) override;
void convertWeightsFromPaddle() override;
void convertWeightsToPaddle() override;
void printSizeInfo() override {
MKLDNNLayer::printSizeInfo();
VLOG(MKLDNN_SIZES) << getName() << ": fh: " << fh_ << ", fw: " << fw_
<< ": ph: " << ph_ << ", pw: " << pw_ << ", sh: " << sh_
<< ", sw: " << sw_ << ", dh: " << dh_ << ", dw: " << dw_;
}
void printValueFormatFlow() override {
if (cpuInVal_) {
VLOG(MKLDNN_FMTS) << cpuInVal_->getFormat() << " >>>";
}
MKLDNNLayer::printValueFormatFlow();
if (cpuOutVal_) {
VLOG(MKLDNN_FMTS) << " >>> " << cpuOutVal_->getFormat();
}
}
void printGradFormatFlow() override {
if (cpuInGrad_) {
VLOG(MKLDNN_FMTS) << cpuInGrad_->getFormat() << " <<<";
}
MKLDNNLayer::printGradFormatFlow();
if (cpuOutGrad_) {
VLOG(MKLDNN_FMTS) << " <<< " << cpuOutGrad_->getFormat();
}
}
protected:
/**
* load the dims settings of this conv
*/
void loadConvSettings(mkldnn::memory::dims& wgt,
mkldnn::memory::dims& bias,
mkldnn::memory::dims& stride,
mkldnn::memory::dims& dilation,
mkldnn::memory::dims& padL,
mkldnn::memory::dims& padR);
/**
* reset the forward primitive descriptor.
*/
void resetFwdPD(std::shared_ptr<conv_fwd::primitive_desc>& pd);
/**
* reset the MKLDNNMatrix buffers used in forward.
*/
void resetFwdBuffers(std::shared_ptr<conv_fwd::primitive_desc>& pd,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out);
/**
* reset the forward pipeline.
*/
void resetFwdPipeline(std::vector<mkldnn::primitive>& pipeline,
std::shared_ptr<conv_fwd::primitive_desc>& pd,
MKLDNNMatrixPtr& in,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias,
MKLDNNMatrixPtr& out);
/**
* reset MKLDNNMatrix of input value
*/
void resetInValue(std::shared_ptr<conv_fwd::primitive_desc>& pd,
MKLDNNMatrixPtr& in);
/**
* reset MKLDNNMatrix of weight and bias value
*/
void resetWgtBiasValue(std::shared_ptr<conv_fwd::primitive_desc>& pd,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias);
/**
* reset MKLDNNMatrix of output value
*/
void resetOutValue(std::shared_ptr<conv_fwd::primitive_desc>& pd,
MKLDNNMatrixPtr& out);
/**
* reset the backward weight primitive descriptor.
*/
void resetBwdWgtPD(std::shared_ptr<conv_bwdWgt::primitive_desc>& pd);
/**
* reset the backward data primitive descriptor.
*/
void resetBwdDataPD(std::shared_ptr<conv_bwdData::primitive_desc>& pd);
/**
* reset the MKLDNNMatrix buffers used in backward.
*/
void 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);
/**
* reset the backward pipeline.
*/
void resetBwdPipeline(std::vector<mkldnn::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);
/**
* reset MKLDNNMatrix of output grad
*/
void resetOutGrad(std::shared_ptr<conv_bwdWgt::primitive_desc>& wgtPD,
MKLDNNMatrixPtr& out);
/**
* reset MKLDNNMatrix of weight and bias grad
*/
void resetWgtBiasGrad(std::shared_ptr<conv_bwdWgt::primitive_desc>& wgtPD,
MKLDNNMatrixPtr& wgt,
MKLDNNMatrixPtr& bias);
/**
* reset MKLDNNMatrix of input grad
*/
void resetInGrad(std::shared_ptr<conv_bwdData::primitive_desc>& dataPD,
MKLDNNMatrixPtr& in);
/**
* reset MKLDNNMatrix of weight value for backward data
* since the primitive_desc would be different with wgtVal_
*/
void resetWgtValBwdData(std::shared_ptr<conv_bwdData::primitive_desc>& dataPD,
MKLDNNMatrixPtr& wgt);
/**
* get padding_r according to
* https://github.com/01org/mkl-dnn/blob/master/tests/gtests/
* test_convolution_forward_common.hpp
* @note: mkldnn dilation start from 0 while paddle start from 1
*/
mkldnn::memory::dims getPaddingR() const {
mkldnn::memory::dims padR = {ph_, pw_};
for (int i = 0; i < 2; ++i) {
if ((ih_ - ((fh_ - 1) * dh_ + 1) + ph_ + padR[0]) / sh_ + 1 != oh_) {
++padR[0];
}
if ((iw_ - ((fw_ - 1) * dw_ + 1) + pw_ + padR[1]) / sw_ + 1 != ow_) {
++padR[1];
}
}
return padR;
}
};
} // namespace paddle
paddle/gserver/tests/test_MKLDNN.cpp
浏览文件 @ 1fb5f12f
......@@ -17,6 +17,7 @@ limitations under the License. */
#include <vector>
#include "MKLDNNTester.h"
#include "ModelConfig.pb.h"
#include "paddle/math/MathUtils.h"
using namespace paddle; // NOLINT
......@@ -63,6 +64,83 @@ TEST(MKLDNNLayer, FcLayer) {
testFcLayer({/*bs*/ 15, /*ic*/ 3, /*oc*/ 6, /*ih*/ 16, /*iw*/ 16});
}
struct testConvDesc {
int bs, gp;
int ic, ih, iw;
int oc, oh, ow;
int fh, fw;
int ph, pw;
int sh, sw;
int dh, dw;
};
void testConvLayer(const testConvDesc& pm) {
const std::string compareTypes[] = {"mkldnn_conv", "exconv"};
TestConfig cfg;
cfg.layerConfig.set_type(compareTypes[0]);
cfg.layerConfig.set_num_filters(pm.oc);
cfg.layerConfig.set_size(pm.oc * pm.oh * pm.ow);
// cfg.layerConfig.set_partial_sum(1); // TODO: check it
cfg.layerConfig.set_shared_biases(true);
cfg.inputDefs.push_back(
{INPUT_DATA,
"layer_0",
/* size of input layer= */ size_t(pm.ic * pm.ih * pm.iw),
/* size of weight= */ size_t(pm.oc * pm.ic * pm.fh * pm.fw / pm.gp)});
LayerInputConfig* input = cfg.layerConfig.add_inputs();
ConvConfig* conv = input->mutable_conv_conf();
conv->set_groups(pm.gp);
conv->set_img_size(pm.iw);
conv->set_img_size_y(pm.ih);
conv->set_output_x(pm.ow);
conv->set_output_y(pm.oh);
conv->set_filter_size(pm.fw);
conv->set_filter_size_y(pm.fh);
conv->set_channels(pm.ic);
conv->set_padding(pm.pw);
conv->set_padding_y(pm.ph);
conv->set_stride(pm.sw);
conv->set_stride_y(pm.sh);
conv->set_dilation(pm.dw);
conv->set_dilation_y(pm.dh);
conv->set_caffe_mode(true);
conv->set_filter_channels(conv->channels() / conv->groups());
CHECK_EQ(conv->filter_channels() * pm.gp, conv->channels())
<< "it is indivisible";
int fh = (pm.fh - 1) * pm.dh + 1;
int fw = (pm.fw - 1) * pm.dw + 1;
int ow = outputSize(pm.iw, fw, pm.pw, pm.sw, true);
int oh = outputSize(pm.ih, fh, pm.ph, pm.sh, true);
CHECK_EQ(ow, pm.ow) << "output size check failed";
CHECK_EQ(oh, pm.oh) << "output size check failed";
MKLDNNTester tester;
for (auto biasSize : {pm.oc, 0}) {
cfg.biasSize = biasSize;
TestConfig ref = cfg;
ref.layerConfig.set_type(compareTypes[1]);
for (auto bs : {pm.bs, 1}) {
tester.run(cfg, ref, bs, pm.ih, pm.iw);
}
}
}
TEST(MKLDNNLayer, ConvLayer) {
/* bs, gp, ic, ih, iw, oc, oh, ow, fh, fw, ph, pw, sh, sw, dh, dw */
testConvLayer({2, 1, 3, 32, 32, 16, 32, 32, 3, 3, 1, 1, 1, 1, 1, 1});
testConvLayer({2, 1, 8, 16, 16, 8, 16, 16, 3, 3, 1, 1, 1, 1, 1, 1});
testConvLayer({3, 1, 16, 32, 32, 3, 32, 32, 3, 3, 1, 1, 1, 1, 1, 1});
testConvLayer({8, 1, 16, 18, 18, 32, 18, 18, 3, 3, 1, 1, 1, 1, 1, 1});
testConvLayer({16, 1, 1, 42, 31, 32, 23, 11, 4, 5, 3, 2, 2, 3, 1, 1});
testConvLayer({2, 1, 8, 16, 16, 8, 8, 8, 3, 3, 1, 1, 2, 2, 1, 1});
testConvLayer({3, 1, 8, 13, 13, 8, 7, 7, 3, 3, 1, 1, 2, 2, 1, 1});
// with groups
testConvLayer({2, 2, 4, 5, 5, 8, 5, 5, 3, 3, 1, 1, 1, 1, 1, 1});
testConvLayer({2, 3, 3, 5, 5, 3, 5, 5, 3, 3, 1, 1, 1, 1, 1, 1});
testConvLayer({4, 4, 16, 3, 3, 16, 3, 3, 3, 3, 1, 1, 1, 1, 1, 1});
}
// TODO(TJ): add branch test
int main(int argc, char** argv) {
......
paddle/math/MKLDNNMatrix.cpp
浏览文件 @ 1fb5f12f
......@@ -49,6 +49,27 @@ MKLDNNMatrixPtr MKLDNNMatrix::create(MatrixPtr m,
return create(m, memory::primitive_desc(memory::desc(dims, dtype, fmt), eg));
}
std::shared_ptr<reorder> MKLDNNMatrix::createReorder(const MKLDNNMatrixPtr& src,
const MKLDNNMatrixPtr& dst,
bool checkData) {
if (src == dst || src->getPrimitiveDesc() == dst->getPrimitiveDesc()) {
return nullptr;
}
if (checkData && (src->getData() == dst->getData())) {
LOG(FATAL) << "can not create reorder with inplace data";
return nullptr;
}
memory::dims srcDims = src->getDims();
memory::dims dstDims = dst->getDims();
CHECK_EQ(srcDims.size(), dstDims.size());
for (size_t i = 0; i < srcDims.size(); ++i) {
CHECK_EQ(srcDims[i], dstDims[i]);
}
return std::make_shared<reorder>(*src, *dst);
}
void MKLDNNMatrix::reorderDataFrom(const MKLDNNMatrixPtr& m,
memory::format srcFmt,
memory::dims targetDim) {
......
paddle/math/MKLDNNMatrix.h
浏览文件 @ 1fb5f12f
......@@ -52,6 +52,31 @@ public:
mkldnn::engine& eg,
mkldnn::memory::data_type dtype = mkldnn::memory::data_type::f32);
/**
* Create Memory descriptor.
* default with any format and f32 dtype
*/
static mkldnn::memory::desc createMemoryDesc(
const mkldnn::memory::dims& dims,
const mkldnn::memory::format& fmt = mkldnn::memory::format::any,
const mkldnn::memory::data_type& dtype = mkldnn::memory::data_type::f32) {
return mkldnn::memory::desc(dims, dtype, fmt);
}
/**
* Create reorder primitive.
* Create a mkldnn::reorder handle for converting src MKLDNNMatrix to dst.
* checkData: for whether to check the data handle of src and dst is the same.
* if true, means check it and do not want support inplace reorder;
* otherwise do not check data which means the created reorder
* maybe inplace buffer and do not guarantee the logical is correct
* since not all format or conversion support inplace.
*/
static std::shared_ptr<mkldnn::reorder> createReorder(
const MKLDNNMatrixPtr& src,
const MKLDNNMatrixPtr& dst,
bool checkData = true);
public:
/**
* Reorder this MKLDNNMatrix from other format.
......
paddle/memory/memcpy.cc
浏览文件 @ 1fb5f12f
......@@ -62,6 +62,24 @@ void Copy<platform::GPUPlace, platform::GPUPlace>(platform::GPUPlace dst_place,
}
}
template <>
void Copy<platform::CPUPlace, platform::GPUPlace>(platform::CPUPlace dst_place,
void* dst,
platform::GPUPlace src_place,
const void* src, size_t num) {
platform::SetDeviceId(src_place.device);
platform::GpuMemcpySync(dst, src, num, cudaMemcpyDeviceToHost);
}
template <>
void Copy<platform::GPUPlace, platform::CPUPlace>(platform::GPUPlace dst_place,
void* dst,
platform::CPUPlace src_place,
const void* src, size_t num) {
platform::SetDeviceId(dst_place.device);
platform::GpuMemcpySync(dst, src, num, cudaMemcpyHostToDevice);
}
#endif // PADDLE_ONLY_CPU
} // namespace memory
......
paddle/operators/CMakeLists.txt
浏览文件 @ 1fb5f12f
file(GLOB GENERAL_OPS RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "*_op.cc")
string(REPLACE ".cc" "" GENERAL_OPS "${GENERAL_OPS}")
set(pybind_file ${PADDLE_SOURCE_DIR}/paddle/pybind/pybind.h)
file(WRITE ${pybind_file} "// Generated by the paddle/operator/CMakeLists.txt. DO NOT EDIT!\n\n")
function(op_library TARGET)
# op_library is a function to create op library. The interface is same as
# cc_library. But it handle split GPU/CPU code and link some common library
......@@ -7,10 +9,11 @@ function(op_library TARGET)
set(OP_LIBRARY ${TARGET} ${OP_LIBRARY} PARENT_SCOPE)
set(cc_srcs)
set(cu_srcs)
set(op_common_deps operator op_registry)
set(op_common_deps operator op_registry math_function)
set(options "")
set(oneValueArgs "")
set(multiValueArgs SRCS DEPS)
set(pybind_flag 0)
cmake_parse_arguments(op_library "${options}" "${oneValueArgs}"
"${multiValueArgs}" ${ARGN})
......@@ -46,24 +49,42 @@ function(op_library TARGET)
cc_library(${TARGET} SRCS ${cc_srcs} DEPS ${op_library_DEPS}
${op_common_deps})
endif()
# net_op doesn't need pybind
if ("${TARGET}" STREQUAL "net_op")
set(pybind_flag 1)
endif()
# pybind USE_NO_KERNEL_OP
file(READ ${TARGET}.cc TARGET_CONTENT)
string(REGEX MATCH "OperatorWithKernel" regex_result "${TARGET_CONTENT}")
string(REPLACE "_op" "" TARGET "${TARGET}")
if (${pybind_flag} EQUAL 0 AND regex_result STREQUAL "")
file(APPEND ${pybind_file} "USE_NO_KERNEL_OP(${TARGET});\n")
set(pybind_flag 1)
endif()
# pybind USE_CPU_ONLY_OP
list(LENGTH cu_srcs cu_srcs_len)
if (${pybind_flag} EQUAL 0 AND ${cu_srcs_len} EQUAL 0)
file(APPEND ${pybind_file} "USE_CPU_ONLY_OP(${TARGET});\n")
set(pybind_flag 1)
endif()
# pybind USE_OP
if (${pybind_flag} EQUAL 0)
file(APPEND ${pybind_file} "USE_OP(${TARGET});\n")
endif()
endfunction()
add_subdirectory(math)
set(DEPS_OPS
identity_op
minus_op
mul_op
recurrent_op
scale_op
softmax_op)
op_library(identity_op DEPS scale_op)
op_library(minus_op DEPS scale_op)
op_library(mul_op DEPS math_function)
op_library(softmax_op DEPS softmax_function)
cond_op)
op_library(recurrent_op SRCS recurrent_op.cc rnn/recurrent_op_utils.cc
DEPS framework_proto tensor operator net_op)
op_library(scale_op DEPS net_op)
DEPS framework_proto tensor net_op)
op_library(cond_op SRCS cond_op.cc DEPS framework_proto tensor operator net_op)
list(REMOVE_ITEM GENERAL_OPS ${DEPS_OPS})
foreach(src ${GENERAL_OPS})
......
paddle/operators/accuracy_op.cc 0 → 100644
浏览文件 @ 1fb5f12f
/* 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 "paddle/operators/accuracy_op.h"
namespace paddle {
namespace operators {
class AccuracyOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Inference"),
"Input of Inference must be initialized.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Label"),
"Input of Inference must be initialized.");
auto *inference = ctx.Input<framework::Tensor>("Inference");
auto *label = ctx.Input<framework::Tensor>("Label");
PADDLE_ENFORCE_EQ(label->dims().size(), 1, "label must be a vector");
PADDLE_ENFORCE_EQ(inference->dims()[0], label->dims()[0],
"inference size must be the same as label size");
ctx.Output<framework::LoDTensor>("Accuracy")->Resize({1});
}
};
class AccuracyOpMaker : public framework::OpProtoAndCheckerMaker {
public:
AccuracyOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
// TODO(typhoonzero): support both inference value and indices.
AddInput("Inference", "topk(indices) the network output");
AddInput("Label", "Label of the training data");
// TODO(typhoonzero): AddInput("Weight", ...
AddOutput("Accuracy", "The accuracy of current batch");
AddComment(
R"DOC(Accuracy. It will print accuracy rate for classification.
The accuracy is:
.. math::
accuracy = \\frac{NumOfCorrectPredicts}{NumOfAllSamples})DOC");
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_WITHOUT_GRADIENT(accuracy, ops::AccuracyOp, ops::AccuracyOpMaker);
REGISTER_OP_CPU_KERNEL(accuracy,
ops::AccuracyKernel<paddle::platform::CPUPlace, float>);
paddle/operators/accuracy_op.cu 0 → 100644
浏览文件 @ 1fb5f12f
/* 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 "paddle/operators/accuracy_op.h"
namespace paddle {
namespace operators {
__global__ void AccuracySingleKernel(const int N, const int D, const int top_k,
const int* Xdata, const int* labelData,
float* accuracy) {
int correct = 0;
for (int row = 0; row < N; row++) {
const int label = labelData[row];
for (int col = 0; col < D; col++) {
const int pred = Xdata[row * D + col];
if (pred == label) {
++correct;
break;
}
}
}
*accuracy = static_cast<float>(correct) / static_cast<float>(N);
}
template <typename T>
class AccuracyOpCUDAKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()),
"It must use GPUPlace.");
auto* inference = ctx.Input<Tensor>("Inference");
auto* label = ctx.Input<Tensor>("Label");
auto* accuracy = ctx.Output<Tensor>("Accuracy");
// FIXME(typhoonzero): only support indices currently
// if add support for output values, how to detect the data type?
const int* inference_data = inference->data<int>();
const int* label_data = label->data<int>();
float* accuracy_data = accuracy->mutable_data<float>(ctx.GetPlace());
size_t num_samples = inference->dims()[0];
size_t infer_width = inference->dims()[1];
cudaMemset((void**)&accuracy_data, 0, sizeof(float));
if (num_samples == 0) {
return;
}
AccuracySingleKernel<<<1, 1>>>(num_samples, infer_width, 1, inference_data,
label_data, accuracy_data);
}
};
} // namespace operators
} // namespace paddle
REGISTER_OP_GPU_KERNEL(accuracy,
paddle::operators::AccuracyOpCUDAKernel<float>);
paddle/operators/accuracy_op.h 0 → 100644
浏览文件 @ 1fb5f12f
/* 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 <algorithm>
#include "paddle/framework/eigen.h"
#include "paddle/framework/op_registry.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenScalar = framework::EigenScalar<T, MajorType, IndexType>;
template <typename Place, typename T>
class AccuracyKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* inference = ctx.Input<Tensor>("Inference");
auto* label = ctx.Input<Tensor>("Label");
auto* accuracy = ctx.Output<Tensor>("Accuracy");
float* accuracy_data = accuracy->mutable_data<float>(ctx.GetPlace());
const T* inference_data = inference->data<T>();
const T* label_data = label->data<T>();
size_t num_samples = inference->dims()[0];
size_t class_dim = inference->dims()[1];
*accuracy_data = 0.0f;
if (num_samples == 0) {
return;
}
int num_correct = 0;
// assume inference is already the topk of the output
for (size_t i = 0; i < num_samples; ++i) {
PADDLE_ENFORCE_GE(label_data[i], 0, "label must >= 0");
for (size_t j = 0; j < class_dim; ++j) {
if (inference_data[i * class_dim + j] == label_data[i]) {
++num_correct;
break;
}
}
}
// FIXME(typhoonzero): we don't accumulate the accuracy for now.
*accuracy_data =
static_cast<float>(num_correct) / static_cast<float>(num_samples);
}
};
} // namespace operators
} // namespace paddle
paddle/operators/add_op.cc
浏览文件 @ 1fb5f12f
......@@ -26,7 +26,8 @@ class AddOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE_EQ(ctx.Input<Tensor>("X")->dims(),
ctx.Input<Tensor>("Y")->dims(),
"Two input of Add Op's dimension must be same.");
ctx.Output<Tensor>("Out")->Resize(ctx.Input<Tensor>("X")->dims());
ctx.Output<framework::LoDTensor>("Out")->Resize(
ctx.Input<Tensor>("X")->dims());
}
};
......
paddle/operators/concat_op.cc
浏览文件 @ 1fb5f12f
......@@ -26,7 +26,7 @@ class ConcatOp : public framework::OperatorWithKernel {
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
auto ins = ctx.MultiInput<framework::Tensor>("X");
auto *out = ctx.Output<framework::Tensor>("Out");
auto *out = ctx.Output<framework::LoDTensor>("Out");
size_t axis = static_cast<size_t>(ctx.Attr<int>("axis"));
size_t n = ins.size();
......
paddle/operators/cond_op.cc 0 → 100644
浏览文件 @ 1fb5f12f
/* 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 "paddle/operators/cond_op.h"
#include <cstring>
#include <sstream>
#include "paddle/framework/op_registry.h"
#include "paddle/operators/gather.h"
#include "paddle/operators/net_op.h"
#include "paddle/operators/scatter.h"
namespace paddle {
namespace operators {
using Scope = framework::Scope;
using Variable = framework::Variable;
using Tensor = framework::Tensor;
using LoDTensor = framework::LoDTensor;
using DDim = framework::DDim;
void CondOp::CreateScope(const Scope& scope) const {
auto sub_scopes_var = scope.FindVar("SubScopes");
PADDLE_ENFORCE(sub_scopes_var != nullptr, "");
auto sub_scopes = sub_scopes_var->GetMutable<std::vector<Scope*>>();
auto& sub_scope = scope.NewScope();
sub_scopes->push_back(&sub_scope);
}
void CondOp::CreateIndexTensor(const Scope& scope) const {
auto index_tensors_var = scope.FindVar("IndexTensors");
PADDLE_ENFORCE(index_tensors_var != nullptr, "");
auto& index_tensors =
*index_tensors_var->GetMutable<std::vector<LoDTensor>>();
index_tensors.push_back(LoDTensor());
}
void CondOp::InferShape(const Scope& scope) const {
auto sub_scopes_var = scope.FindVar("SubScopes");
PADDLE_ENFORCE_NOT_NULL(sub_scopes_var);
auto& sub_scopes = *sub_scopes_var->GetMutable<std::vector<Scope*>>();
for (int i = 0; i < 2; ++i) {
// Create two sub scopes for true and false branches
// sub_scopes[0] for the true branch and sub_scopes[1] for the false
// branch
CreateScope(scope);
// Create two tensors for true and false indices
// index_tensors[0] for the true branch and index_tensors[1] for the false
// branch
CreateIndexTensor(scope);
PADDLE_ENFORCE(!Inputs("Xs").empty(), "Inputs can't be empty");
for (auto& input : Inputs("Xs")) {
// Create a new tensor in sub-scope for input-type tensor
Variable* v = sub_scopes[i]->NewVar(input);
LoDTensor* sub_input = v->GetMutable<LoDTensor>();
sub_input->Resize(scope.FindVar(input)->GetMutable<LoDTensor>()->dims());
}
for (auto& output : (*sub_net_op_[i]).Outputs()) {
for (auto& var_name : output.second) {
sub_scopes[i]->NewVar(var_name);
}
}
// each net calls InferShape
sub_net_op_[i]->InferShape(*sub_scopes[i]);
}
for (auto& output : Outputs("Outs")) {
LoDTensor* tensor_t_out =
sub_scopes[0]->FindVar(output)->GetMutable<LoDTensor>();
PADDLE_ENFORCE_NOT_NULL(tensor_t_out, "True output should not be NULL");
LoDTensor* tensor_f_out =
sub_scopes[1]->FindVar(output)->GetMutable<LoDTensor>();
PADDLE_ENFORCE_NOT_NULL(tensor_f_out, "False output should not be NULL");
auto* tensor_out_var = scope.FindVar(output);
PADDLE_ENFORCE_NOT_NULL(tensor_out_var, "Output not found");
LoDTensor* tensor_out = tensor_out_var->GetMutable<LoDTensor>();
PADDLE_ENFORCE_NOT_NULL(tensor_t_out,
"True output tensor should not be NULL");
// check output size should be same
PADDLE_ENFORCE_EQ(tensor_t_out->dims(), tensor_f_out->dims(),
"Outputs not of the same shape");
tensor_out->Resize(tensor_t_out->dims());
// tensor_out->mutable_data<float>(tensor_out->dims(),
// platform::CPUPlace());
tensor_out->mutable_data<float>(platform::CPUPlace());
}
}
void CondOp::Run(const Scope& scope,
const platform::DeviceContext& dev_ctx) const {
auto* sub_scopes_var = scope.FindVar("SubScopes");
auto sub_scopes = sub_scopes_var->Get<std::vector<Scope*>>();
auto* index_tensors_var = scope.FindVar("IndexTensors");
auto index_tensors = index_tensors_var->Get<std::vector<LoDTensor>>();
std::string cond_name = Input("Cond");
Variable* cond_var = scope.FindVar(cond_name);
PADDLE_ENFORCE_NOT_NULL(cond_var);
const LoDTensor* cond = cond_var->GetMutable<LoDTensor>();
// Step 1: get the true/false index at runtime
// index_[0]: vector<int>, contains all index for cond[i] == true
// index_[1]: vector<int>, contains all index for cond[i] == false
for (int i = 0; i < 2; ++i) index_[i].clear();
const int* cond_data = cond->data<int>();
for (int i = 0; i < cond->dims()[0]; ++i) {
if (cond_data[i])
index_[0].push_back(i);
else
index_[1].push_back(i);
}
// put index_[0] and index_[1] into two tensors:
// index_tensor_[0] and index_tensor_[1]
DDim dim = paddle::framework::make_ddim({0});
for (int i = 0; i < 2; ++i) {
dim[0] = index_[i].size();
int* tmp_ptr =
index_tensors[i].mutable_data<int>(dim, platform::CPUPlace());
index_tensors[i].Resize(dim);
memcpy(tmp_ptr, index_[i].data(), dim[0] * sizeof(int));
}
// Step 2: collect data by calling gather
for (int i = 0; i < 2; ++i) {
// i= 0/i for True and False branches respectively
for (auto& input : Inputs("Xs")) {
// find Tensor
Variable* v = scope.FindVar(input);
PADDLE_ENFORCE_NOT_NULL(v);
LoDTensor* tensor_parent = v->GetMutable<LoDTensor>();
v = sub_scopes[i]->FindVar(input);
PADDLE_ENFORCE_NOT_NULL(v);
LoDTensor* tensor_child = v->GetMutable<LoDTensor>();
// Resize child
DDim dim = tensor_child->dims();
dim[0] = index_[i].size();
tensor_child->Resize(dim);
tensor_child->mutable_data<float>(dim, platform::CPUPlace());
Gather<float>(dev_ctx.GetPlace(), tensor_parent, &index_tensors[i],
tensor_child);
}
}
// Step 3: run
for (int i = 0; i < 2; ++i) {
sub_net_op_[i]->Run(*sub_scopes[i], dev_ctx);
}
// Step 4: merge output results
for (int i = 0; i < 2; ++i) {
// i= 0/i for True and False branches respectively
for (auto& output : Outputs("Outs")) {
// find Tensor
Variable* v = scope.FindVar(output);
PADDLE_ENFORCE_NOT_NULL(v);
LoDTensor* tensor_parent = v->GetMutable<LoDTensor>();
v = sub_scopes[i]->FindVar(output);
PADDLE_ENFORCE_NOT_NULL(v);
LoDTensor* tensor_child = v->GetMutable<LoDTensor>();
ScatterUpdate<float>(dev_ctx.GetPlace(), tensor_child, &index_tensors[i],
tensor_parent);
}
}
}
class CondOpProtoAndCheckerMaker : public framework::OpProtoAndCheckerMaker {
public:
CondOpProtoAndCheckerMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("Cond", "The condition, which is a bool vector");
AddInput("Xs", "Inputs of Subnets").AsDuplicable();
AddOutput("Outs", "Outputs of Cond_Op after merge").AsDuplicable();
AddOutput("SubScopes", "sub scopes for true and false branches");
AddOutput("IndexTensors", "Index Tensors contains indices for true/false");
AddComment(R"DOC(
Sample dependent Cond Operator:
Given Cond[i] as a 1/0 vector to indicate true/false
The equation is:
Out[i] = subnet_t[i], if Cond[i] == true
Out[i] = subnet_t[i], if Cond[i] == false
)DOC");
}
};
} // namespace operators
} // namespace paddle
REGISTER_OP_WITHOUT_GRADIENT(cond, paddle::operators::CondOp,
paddle::operators::CondOpProtoAndCheckerMaker);
paddle/operators/cond_op.h 0 → 100644
浏览文件 @ 1fb5f12f
/* 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 <vector>
#include "glog/logging.h"
#include "paddle/framework/ddim.h"
#include "paddle/framework/eigen.h"
#include "paddle/framework/operator.h"
#include "paddle/framework/tensor.h"
#include "paddle/operators/net_op.h"
namespace paddle {
namespace operators {
/*
* @brief CondOp is a dynamic if-else Operator
*
* It has a input tensor named cond indicating which netop each instance will
* run.
*
* if cond == 1, it will run true_net, which is a NetOp.
*
* if cond == 0, it will run false_net, which is another NetOp.
*/
class CondOp : public framework::OperatorBase {
public:
CondOp(const std::string& type, const framework::VariableNameMap& inputs,
const framework::VariableNameMap& outputs,
const framework::AttributeMap& attrs)
: OperatorBase(type, inputs, outputs, attrs) {
index_.resize(2);
sub_net_op_.resize(2);
}
CondOp(const CondOp& o)
: framework::OperatorBase(
static_cast<const framework::OperatorBase&>(o)) {
// TODO(yuyang18): Implement copy ctor well.
PADDLE_THROW("Not implemented");
}
void CreateScope(const framework::Scope& scope) const;
void CreateIndexTensor(const framework::Scope& scope) const;
/*
* InferShape must be called before Run.
*/
void InferShape(const framework::Scope& scope) const override;
/*
* Set True Block
*/
void set_truenet(std::unique_ptr<OperatorBase>&& net) {
sub_net_op_[0] = std::move(net);
}
/*
* Set False Block
*/
void set_falsenet(std::unique_ptr<OperatorBase>&& net) {
sub_net_op_[1] = std::move(net);
}
void Run(const framework::Scope& scope,
const platform::DeviceContext& dev_ctx) const override;
private:
// sub_net_op_[0]: subnet_t
// sub_net_op_[1]: subnet_f
std::vector<std::unique_ptr<framework::OperatorBase>> sub_net_op_;
// index_[0]: True_index;
// index_[1]: False_index;
mutable std::vector<std::vector<int>> index_;
};
} // namespace operators
} // namespace paddle
paddle/operators/cos_sim_op.cc
浏览文件 @ 1fb5f12f
......@@ -25,16 +25,30 @@ class CosSimOp : public framework::OperatorWithKernel {
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
// notnull check
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) must not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Y"), "Input(Y) must not be null.");
PADDLE_ENFORCE_EQ(ctx.Input<Tensor>("X")->dims(),
ctx.Input<Tensor>("Y")->dims(),
"Dimensions of Input(X) and Input(Y) must be the same.");
auto dims = ctx.Input<Tensor>("X")->dims();
ctx.Output<Tensor>("Out")->Resize({dims[0], 1});
ctx.Output<Tensor>("XNorm")->Resize({dims[0], 1});
ctx.Output<Tensor>("YNorm")->Resize({dims[0], 1});
// shape check
auto x_dims = ctx.Input<Tensor>("X")->dims();
auto y_dims = ctx.Input<Tensor>("Y")->dims();
PADDLE_ENFORCE_EQ(x_dims.size(), y_dims.size(),
"Ranks of Input(X) and Input(Y) must be equal.");
PADDLE_ENFORCE_GE(x_dims.size(), 2,
"Rank of Input(X) must not be less than 2.");
PADDLE_ENFORCE_EQ(framework::slice_ddim(x_dims, 1, x_dims.size()),
framework::slice_ddim(y_dims, 1, y_dims.size()),
"All dimensions except the 1st of Input(X) and Input(Y) "
"must be equal.");
PADDLE_ENFORCE(x_dims[0] == y_dims[0] || y_dims[0] == 1,
"The 1st dimension of Input(Y) must be equal to Input(X) or"
" just 1 (which will be broadcasted to match Input(X)).");
// resize tensor
ctx.Output<framework::LoDTensor>("Out")->Resize({x_dims[0], 1});
ctx.Output<framework::LoDTensor>("XNorm")->Resize({x_dims[0], 1});
ctx.Output<framework::LoDTensor>("YNorm")->Resize({y_dims[0], 1});
}
};
......@@ -42,16 +56,27 @@ class CosSimOpMaker : public framework::OpProtoAndCheckerMaker {
public:
CosSimOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "The first input of cos_sim op.");
AddInput("Y", "The second input of cos_sim op.");
AddInput("X", "The 1st input of cos_sim op.");
AddInput("Y", "The 2nd input of cos_sim op.");
AddOutput("Out", "The output of cos_sim op.");
AddOutput("XNorm", "Row norm of the first input.").AsIntermediate();
AddOutput("YNorm", "Row norm of the second input.").AsIntermediate();
AddOutput("XNorm",
"Norm of the first input, reduced along the 1st "
"dimension.")
.AsIntermediate();
AddOutput("YNorm",
"Norm of the second input, reduced along the 1st "
"dimension.")
.AsIntermediate();
AddComment(R"DOC(
Cosine Similarity Operator.
The equation is: Out = X^T * Y / (sqrt(X^T * X) * sqrt(Y^T * Y))
The equation is: Out = X^T * Y / (sqrt(X^T * X) * sqrt(Y^T * Y)).
Input(X) and Input(Y) must have the same shape, except that the 1st dimension
of Input(Y) could be just 1 (different from Input(X)), which will be
broadcasted to match the shape of Input(X) before computing their cosine
similarity.
)DOC");
}
};
......@@ -62,34 +87,54 @@ class CosSimOpGrad : public framework::OperatorWithKernel {
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
// notnull check
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) must not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Y"), "Input(Y) must not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("XNorm"),
"Input(XNorm) must not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("YNorm"),
"Input(YNorm) must not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Out"),
"Input(Out) must not be null.");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
"Input(Out@GRAD) must not be null.");
// shape check
auto x_dims = ctx.Input<Tensor>("X")->dims();
auto y_dims = ctx.Input<Tensor>("Y")->dims();
auto xnorm_dims = ctx.Input<Tensor>("XNorm")->dims();
auto ynorm_dims = ctx.Input<Tensor>("YNorm")->dims();
auto out_dims = ctx.Input<Tensor>(framework::GradVarName("Out"))->dims();
PADDLE_ENFORCE_EQ(x_dims, y_dims,
"Dimensions of Input(X) and Input(Y) must be the same.");
PADDLE_ENFORCE_EQ(xnorm_dims[0], x_dims[0],
"1st dimension of XNorm must equal that of Input(X).");
PADDLE_ENFORCE_EQ(xnorm_dims[1], 1, "2st dimension of XNorm must be one.");
PADDLE_ENFORCE_EQ(ynorm_dims[0], y_dims[0],
"1st dimension of YNorm must equal that of Input(Y).");
PADDLE_ENFORCE_EQ(ynorm_dims[1], 1, "2st dimension of YNorm must be one.");
PADDLE_ENFORCE_EQ(out_dims[0], x_dims[0],
"1st dimension of Out@GRAD must equal that of Input(X)");
PADDLE_ENFORCE_EQ(out_dims[1], 1, "1st dimension of Out@GRAD must be one.");
auto *x_grad = ctx.Output<Tensor>(framework::GradVarName("X"));
auto *y_grad = ctx.Output<Tensor>(framework::GradVarName("Y"));
auto out_dims = ctx.Input<Tensor>("Out")->dims();
auto out_grad_dims =
ctx.Input<Tensor>(framework::GradVarName("Out"))->dims();
PADDLE_ENFORCE_GE(x_dims.size(), y_dims.size(),
"Ranks of Input(X) and Input(Y) must be equal.");
PADDLE_ENFORCE_GE(x_dims.size(), 2,
"Rank of Input(X) must not be less than 2.");
PADDLE_ENFORCE_EQ(framework::slice_ddim(x_dims, 1, x_dims.size()),
framework::slice_ddim(y_dims, 1, y_dims.size()),
"All dimensions except the 1st of Input(X) and Input(Y) "
"must be equal.");
PADDLE_ENFORCE(x_dims[0] == y_dims[0] || y_dims[0] == 1,
"The 1st dimension of Input(Y) must be equal to Input(X) or"
" just 1 (which will be broadcasted to match Input(X)).");
auto target_xnorm_dims = framework::make_ddim({x_dims[0], 1});
auto target_ynorm_dims = framework::make_ddim({y_dims[0], 1});
PADDLE_ENFORCE_EQ(xnorm_dims, target_xnorm_dims,
"Shape of Input(XNorm) must be [X.Dim(0), 1].");
PADDLE_ENFORCE_EQ(ynorm_dims, target_ynorm_dims,
"Shape of Input(YNorm) must be [Y.Dim(0), 1].");
PADDLE_ENFORCE_EQ(out_dims, target_xnorm_dims,
"Shape of Input(Out) must be [X.Dim(0), 1].");
PADDLE_ENFORCE_EQ(out_grad_dims, target_xnorm_dims,
"Shape of Input(Out@Grad) must be [X.Dim(0), 1].");
// resize tensor
auto *x_grad =
ctx.Output<framework::LoDTensor>(framework::GradVarName("X"));
auto *y_grad =
ctx.Output<framework::LoDTensor>(framework::GradVarName("Y"));
if (x_grad) x_grad->Resize(x_dims);
if (y_grad) y_grad->Resize(y_dims);
}
......
paddle/operators/cos_sim_op.h
浏览文件 @ 1fb5f12f
......@@ -31,30 +31,38 @@ template <typename Place, typename T>
class CosSimKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto* input_x = context.Input<Tensor>("X");
auto* input_y = context.Input<Tensor>("Y");
auto* output_z = context.Output<Tensor>("Out");
auto* output_x_norm = context.Output<Tensor>("XNorm");
auto* output_y_norm = context.Output<Tensor>("YNorm");
// get Tensor
auto* in_x = context.Input<Tensor>("X");
auto* in_y = context.Input<Tensor>("Y");
auto* out_z = context.Output<Tensor>("Out");
auto* out_x_norm = context.Output<Tensor>("XNorm");
auto* out_y_norm = context.Output<Tensor>("YNorm");
out_z->mutable_data<T>(context.GetPlace());
out_x_norm->mutable_data<T>(context.GetPlace());
out_y_norm->mutable_data<T>(context.GetPlace());
output_z->mutable_data<T>(context.GetPlace());
output_x_norm->mutable_data<T>(context.GetPlace());
output_y_norm->mutable_data<T>(context.GetPlace());
auto dims = input_x->dims();
int64_t size = input_x->numel();
auto new_dims = framework::make_ddim({dims[0], size / dims[0]});
auto x = EigenMatrix<T>::From(*input_x, new_dims);
auto y = EigenMatrix<T>::From(*input_y, new_dims);
auto z = EigenVector<T>::Flatten(*output_z);
auto x_norm = EigenVector<T>::Flatten(*output_x_norm);
auto y_norm = EigenVector<T>::Flatten(*output_y_norm);
// convert Tensor to Eigen Tensor
int rows_x = in_x->dims()[0];
int rows_y = in_y->dims()[0];
auto x = EigenMatrix<T>::Reshape(*in_x, 1);
auto y = EigenMatrix<T>::Reshape(*in_y, 1);
auto z = EigenVector<T>::Flatten(*out_z);
auto x_norm = EigenVector<T>::Flatten(*out_x_norm);
auto y_norm = EigenVector<T>::Flatten(*out_y_norm);
// compute
auto place = context.GetEigenDevice<Place>();
auto xy = (x * y).sum(Eigen::array<int, 1>({{1}}));
x_norm.device(place) = x.square().sum(Eigen::array<int, 1>({{1}})).sqrt();
y_norm.device(place) = y.square().sum(Eigen::array<int, 1>({{1}})).sqrt();
z.device(place) = xy / x_norm / y_norm;
auto row_along = Eigen::array<int, 1>({{1}});
x_norm.device(place) = x.square().sum(row_along).sqrt();
y_norm.device(place) = y.square().sum(row_along).sqrt();
if (rows_x == rows_y) {
auto xy = (x * y).sum(Eigen::array<int, 1>({{1}}));
z.device(place) = xy / x_norm / y_norm;
} else {
Eigen::DSizes<int, 2> bcast(rows_x, 1);
auto xy = (x * y.broadcast(bcast)).sum(row_along);
z.device(place) = xy / x_norm / y_norm.broadcast(bcast);
}
}
};
......@@ -62,43 +70,72 @@ template <typename Place, typename T>
class CosSimGradKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto* input_x = context.Input<Tensor>("X");
auto* input_y = context.Input<Tensor>("Y");
auto* input_z = context.Input<Tensor>("Out");
auto* input_x_norm = context.Input<Tensor>("XNorm");
auto* input_y_norm = context.Input<Tensor>("YNorm");
auto* output_grad_x = context.Output<Tensor>(framework::GradVarName("X"));
auto* output_grad_y = context.Output<Tensor>(framework::GradVarName("Y"));
auto* input_grad_z = context.Input<Tensor>(framework::GradVarName("Out"));
// get Tensor
auto* in_x = context.Input<Tensor>("X");
auto* in_y = context.Input<Tensor>("Y");
auto* in_z = context.Input<Tensor>("Out");
auto* in_x_norm = context.Input<Tensor>("XNorm");
auto* in_y_norm = context.Input<Tensor>("YNorm");
auto* out_grad_x = context.Output<Tensor>(framework::GradVarName("X"));
auto* out_grad_y = context.Output<Tensor>(framework::GradVarName("Y"));
auto* in_grad_z = context.Input<Tensor>(framework::GradVarName("Out"));
auto dims = input_x->dims();
int64_t size = input_x->numel();
auto new_dims = framework::make_ddim({dims[0], size / dims[0]});
auto x = EigenMatrix<T>::From(*input_x, new_dims);
auto y = EigenMatrix<T>::From(*input_y, new_dims);
auto z = EigenMatrix<T>::From(*input_z);
auto x_norm = EigenMatrix<T>::From(*input_x_norm);
auto y_norm = EigenMatrix<T>::From(*input_y_norm);
auto dz = EigenMatrix<T>::From(*input_grad_z);
// convert Tensor to Eigen Tensor
auto x = EigenMatrix<T>::Reshape(*in_x, 1);
auto y = EigenMatrix<T>::Reshape(*in_y, 1);
auto z = EigenMatrix<T>::Reshape(*in_z, 1);
auto x_norm = EigenMatrix<T>::Reshape(*in_x_norm, 1);
auto y_norm = EigenMatrix<T>::Reshape(*in_y_norm, 1);
auto dz = EigenMatrix<T>::Reshape(*in_grad_z, 1);
Eigen::DSizes<int, 2> bcast(1, new_dims[1]);
auto z_bcast = z.broadcast(bcast);
auto dz_bcast = dz.broadcast(bcast);
// compute gradident
int rows_x = in_x->dims()[0];
int rows_y = in_y->dims()[0];
int cols = framework::product(in_x->dims()) / rows_x;
Eigen::DSizes<int, 2> bcast_cols(1, cols);
auto z_bcast = z.broadcast(bcast_cols);
auto dz_bcast = dz.broadcast(bcast_cols);
auto x_snorm_bcast = x_norm.square().eval().broadcast(bcast_cols);
auto place = context.GetEigenDevice<Place>();
auto x_snorm_bcast = x_norm.square().eval().broadcast(bcast);
auto y_snorm_bcast = y_norm.square().eval().broadcast(bcast);
auto norm_prod_bcast = (x_norm * y_norm).eval().broadcast(bcast);
if (output_grad_x) {
output_grad_x->mutable_data<T>(context.GetPlace());
auto dx = EigenMatrix<T>::From(*output_grad_x, new_dims);
dx.device(place) =
dz_bcast * (y / norm_prod_bcast - z_bcast * x / x_snorm_bcast);
}
if (output_grad_y) {
output_grad_y->mutable_data<T>(context.GetPlace());
auto dy = EigenMatrix<T>::From(*output_grad_y, new_dims);
dy.device(place) =
dz_bcast * (x / norm_prod_bcast - z_bcast * y / y_snorm_bcast);
if (rows_x == rows_y) {
auto y_snorm_bcast = y_norm.square().eval().broadcast(bcast_cols);
auto norm_prod_bcast = (x_norm * y_norm).eval().broadcast(bcast_cols);
// compute dx
if (out_grad_x) {
out_grad_x->mutable_data<T>(context.GetPlace());
auto dx = EigenMatrix<T>::Reshape(*out_grad_x, 1);
auto grad = y / norm_prod_bcast - z_bcast * x / x_snorm_bcast;
dx.device(place) = dz_bcast * grad;
}
// compute dy
if (out_grad_y) {
out_grad_y->mutable_data<T>(context.GetPlace());
auto dy = EigenMatrix<T>::Reshape(*out_grad_y, 1);
auto grad = x / norm_prod_bcast - z_bcast * y / y_snorm_bcast;
dy.device(place) = dz_bcast * grad;
}
} else {
Eigen::DSizes<int, 2> bcast_rows(rows_x, 1);
Eigen::DSizes<int, 2> bcast_rows_cols(rows_x, cols);
auto y_bcast = y.broadcast(bcast_rows);
auto y_snorm_bcast = y_norm.square().eval().broadcast(bcast_rows_cols);
auto norm_prod_bcast = (x_norm * y_norm.eval().broadcast(bcast_rows))
.eval()
.broadcast(bcast_cols);
// compute dx
if (out_grad_x) {
out_grad_x->mutable_data<T>(context.GetPlace());
auto dx = EigenMatrix<T>::Reshape(*out_grad_x, 1);
auto grad = y_bcast / norm_prod_bcast - z_bcast * x / x_snorm_bcast;
dx.device(place) = dz_bcast * grad;
}
// compute dy
if (out_grad_y) {
out_grad_y->mutable_data<T>(context.GetPlace());
auto dy = EigenMatrix<T>::Reshape(*out_grad_y, 1);
auto grad = x / norm_prod_bcast - z_bcast * y_bcast / y_snorm_bcast;
dy.device(place) = (dz_bcast * grad).sum(Eigen::array<int, 1>({{0}}));
}
}
}
};
......
paddle/operators/elementwise_mul_op.cc
浏览文件 @ 1fb5f12f
......@@ -31,7 +31,7 @@ class ElementWiseMulOp : public framework::OperatorWithKernel {
auto y_dim = ctx.Input<Tensor>("Y")->dims();
PADDLE_ENFORCE_GE(x_dim.size(), y_dim.size(),
"Rank of first input must >= rank of second input.")
ctx.Output<Tensor>("Out")->Resize(x_dim);
ctx.Output<framework::LoDTensor>("Out")->Resize(x_dim);
}
};
......@@ -80,8 +80,10 @@ class ElementWiseMulOpGrad : public framework::OperatorWithKernel {
auto x_dims = ctx.Input<Tensor>("X")->dims();
auto y_dims = ctx.Input<Tensor>("Y")->dims();
auto out_dims = ctx.Input<Tensor>(framework::GradVarName("Out"))->dims();
auto *x_grad = ctx.Output<Tensor>(framework::GradVarName("X"));
auto *y_grad = ctx.Output<Tensor>(framework::GradVarName("Y"));
auto *x_grad =
ctx.Output<framework::LoDTensor>(framework::GradVarName("X"));
auto *y_grad =
ctx.Output<framework::LoDTensor>(framework::GradVarName("Y"));
PADDLE_ENFORCE_GE(x_dims.size(), y_dims.size(),
"Rank of first input must >= rank of second input.")
......
paddle/operators/elementwise_mul_op.h
浏览文件 @ 1fb5f12f
......@@ -13,10 +13,8 @@
limitations under the License. */
#pragma once
#include <iostream>
#include "paddle/framework/eigen.h"
#include "paddle/framework/op_registry.h"
#include "paddle/operators/math/math_function.h"
namespace paddle {
namespace operators {
......
paddle/operators/fill_zeros_like_op.cc
浏览文件 @ 1fb5f12f
......@@ -23,7 +23,7 @@ class FillZerosLikeOp : public framework::OperatorWithKernel {
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
ctx.Output<framework::Tensor>("Dst")->Resize(
ctx.Output<framework::LoDTensor>("Dst")->Resize(
ctx.Input<framework::Tensor>("Src")->dims());
}
};
......
paddle/operators/gather_op.cc
浏览文件 @ 1fb5f12f
......@@ -28,7 +28,7 @@ class GatherOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE_GE(batch_size, 0, "Batch size must be >0");
framework::DDim output_dims(ctx.Input<Tensor>("X")->dims());
output_dims[0] = batch_size;
ctx.Output<Tensor>("Out")->Resize(output_dims);
ctx.Output<framework::LoDTensor>("Out")->Resize(output_dims);
}
};
......@@ -38,7 +38,7 @@ class GatherGradOp : public framework::OperatorWithKernel {
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
auto X_grad = ctx.Output<Tensor>(framework::GradVarName("X"));
auto X_grad = ctx.Output<framework::LoDTensor>(framework::GradVarName("X"));
auto X = ctx.Input<Tensor>("X");
X_grad->Resize(X->dims());
......
paddle/operators/gaussian_random_op.cc
浏览文件 @ 1fb5f12f
......@@ -44,7 +44,7 @@ class GaussianRandomOp : public framework::OperatorWithKernel {
protected:
void InferShape(const framework::InferShapeContext& context) const override {
auto* tensor = context.Output<framework::Tensor>("Out");
auto* tensor = context.Output<framework::LoDTensor>("Out");
auto dims = Attr<std::vector<int>>("dims");
std::vector<int64_t> temp;
temp.reserve(dims.size());
......
paddle/operators/lookup_table_op.cc
浏览文件 @ 1fb5f12f
......@@ -25,7 +25,7 @@ class LookupTableOp : public framework::OperatorWithKernel {
void InferShape(const framework::InferShapeContext &context) const override {
auto table_t = context.Input<Tensor>("W");
auto ids_t = context.Input<Tensor>("Ids");
auto output_t = context.Output<Tensor>("Out");
auto output_t = context.Output<framework::LoDTensor>("Out");
output_t->Resize({ids_t->dims()[0], table_t->dims()[1]});
}
......@@ -56,7 +56,8 @@ class LookupTableOpGrad : public framework::OperatorWithKernel {
protected:
void InferShape(const framework::InferShapeContext &context) const override {
auto table = context.Input<Tensor>("W");
auto d_table = context.Output<Tensor>(framework::GradVarName("W"));
auto d_table =
context.Output<framework::LoDTensor>(framework::GradVarName("W"));
d_table->Resize(table->dims());
}
};
......
paddle/operators/mean_op.cc
浏览文件 @ 1fb5f12f
......@@ -25,7 +25,7 @@ class MeanOp : public framework::OperatorWithKernel {
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"),
"Input of MeanOp must be initialized.");
ctx.Output<Tensor>("Out")->Resize({1});
ctx.Output<framework::LoDTensor>("Out")->Resize({1});
}
};
......@@ -45,7 +45,7 @@ class MeanGradOp : public framework::OperatorWithKernel {
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
ctx.Output<Tensor>(framework::GradVarName("X"))
ctx.Output<framework::LoDTensor>(framework::GradVarName("X"))
->Resize(ctx.Input<Tensor>("X")->dims());
}
};
......
paddle/operators/minus_op.cc
浏览文件 @ 1fb5f12f
......@@ -33,7 +33,7 @@ class MinusOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE_EQ(
left_tensor->numel(), right_tensor->numel(),
"Minus operator must take two tensor with same num of elements");
ctx.Output<framework::Tensor>("Out")->Resize(left_tensor->dims());
ctx.Output<framework::LoDTensor>("Out")->Resize(left_tensor->dims());
}
};
......@@ -77,8 +77,6 @@ class MinusGradOp : public NetOp {
} // namespace operators
} // namespace paddle
USE_OP(scale);
USE_NO_KERNEL_OP(identity);
namespace ops = paddle::operators;
REGISTER_OP(minus, ops::MinusOp, ops::MinusOpMaker, minus_grad,
ops::MinusGradOp<float>);
......
paddle/operators/mul_op.cc
浏览文件 @ 1fb5f12f
......@@ -18,6 +18,7 @@ namespace paddle {
namespace operators {
using framework::Tensor;
using framework::LoDTensor;
class MulOp : public framework::OperatorWithKernel {
public:
......@@ -45,7 +46,8 @@ class MulOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE_EQ(
x_mat_dims[1], y_mat_dims[0],
"First matrix's width must be equal with second matrix's height.");
ctx.Output<Tensor>("Out")->Resize({x_mat_dims[0], y_mat_dims[1]});
ctx.Output<framework::LoDTensor>("Out")->Resize(
{x_mat_dims[0], y_mat_dims[1]});
}
};
......@@ -94,8 +96,10 @@ class MulOpGrad : public framework::OperatorWithKernel {
auto x_dims = ctx.Input<Tensor>("X")->dims();
auto y_dims = ctx.Input<Tensor>("Y")->dims();
auto out_dims = ctx.Input<Tensor>(framework::GradVarName("Out"))->dims();
auto *x_grad = ctx.Output<Tensor>(framework::GradVarName("X"));
auto *y_grad = ctx.Output<Tensor>(framework::GradVarName("Y"));
auto *x_grad =
ctx.Output<framework::LoDTensor>(framework::GradVarName("X"));
auto *y_grad =
ctx.Output<framework::LoDTensor>(framework::GradVarName("Y"));
auto x_mat_dims =
framework::flatten_to_2d(x_dims, Attr<int>("x_num_col_dims"));
......
paddle/operators/name_convention.md
浏览文件 @ 1fb5f12f
......@@ -38,9 +38,11 @@ public:
AccumulateOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "(Tensor) The input tensor that has to be accumulated to the output tensor. If the output size is not the same as input size, the output tensor is first reshaped and initialized to zero, and only then, accumulation is done.");
AddInput("X", "(Tensor) The input tensor that has to be accumulated to the output tensor.
If the output size is not the same as input size,
the output tensor is first reshaped and initialized to zero, and only then, accumulation is done.");
AddOutput("Out", "(Tensor) Accumulated output tensor");
AddAttr<float>("gamma", "(float, default 1.0) Accumulation multiplier");
AddAttr<float>("gamma", "(float, default 1.0) Accumulation multiplier").SetDefault(1.0f);
AddComment(R"DOC(
Accumulate operator accumulates the input tensor to the output tensor. If the
output tensor already has the right size, we add to it; otherwise, we first
......@@ -51,7 +53,7 @@ Accumulation is done as shown:
Out = 1*X + gamma*Out
where X is the input tensor, Y is the output tensor and gamma is the multiplier
where X is the input tensor, Out is the output tensor and gamma is the multiplier
argument.
)DOC");
}
......
paddle/operators/cross_entropy_op.cc → paddle/operators/onehot_cross_entropy_op.cc
浏览文件 @ 1fb5f12f
......@@ -12,7 +12,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/operators/cross_entropy_op.h"
#include "paddle/operators/onehot_cross_entropy_op.h"
namespace paddle {
namespace operators {
......@@ -29,7 +29,7 @@ class OnehotCrossEntropyOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE_EQ(X->dims().size(), 2, "X's dimension must be 2.");
PADDLE_ENFORCE_EQ(label->dims().size(), 1, "label's dimension must be 1.");
PADDLE_ENFORCE_EQ(X->dims()[0], label->dims()[0]);
ctx.Output<Tensor>("Y")->Resize({X->dims()[0]});
ctx.Output<framework::LoDTensor>("Y")->Resize({X->dims()[0], 1});
}
};
......@@ -39,7 +39,7 @@ class OnehotCrossEntropyGradientOp : public framework::OperatorWithKernel {
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
auto dX = ctx.Output<Tensor>(framework::GradVarName("X"));
auto dX = ctx.Output<framework::LoDTensor>(framework::GradVarName("X"));
auto X = ctx.Input<Tensor>("X");
dX->Resize(X->dims());
......
paddle/operators/pad_op.cc 0 → 100644
浏览文件 @ 1fb5f12f
/* 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 "paddle/operators/pad_op.h"
namespace paddle {
namespace operators {
using framework::Tensor;
class PadOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
auto x_dim = ctx.Input<Tensor>("X")->dims();
auto paddings = Attr<std::vector<int>>("paddings");
PADDLE_ENFORCE_EQ(x_dim.size() * 2, int64_t(paddings.size()),
"Size of paddings should be equal to 2 * dimension size "
"of input tensor.");
std::vector<int64_t> out_dims(x_dim.size());
for (int i = 0; i < x_dim.size(); ++i) {
out_dims[i] = x_dim[i] + paddings[i * 2] + paddings[i * 2 + 1];
}
ctx.Output<framework::LoDTensor>("Out")->Resize(
framework::make_ddim(out_dims));
}
};
class PadOpMaker : public framework::OpProtoAndCheckerMaker {
public:
PadOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X",
"The input of pad op. "
"The input should be a k-D tensor(k > 0 and k < 7)");
AddOutput("Out",
"The output of pad op."
"A tensor with the same shape as X.")
.NotInGradient();
AddComment(R"DOC(
Pad input into output, as specified by paddings and pad_value. The input should be a k-D tensor(k > 0 and k < 7). As an example:
Given:
X = [[1, 2],
[3, 4]]
and
paddings = [0, 1, 1, 2]
and
pad_value = 0
then we get
Out = [[0, 1, 2, 0, 0]
[0, 3, 4, 0, 0]
[0, 0, 0, 0, 0]]
)DOC");
AddAttr<std::vector<int>>(
"paddings",
"A list<int> to describes padding rules for each dimension."
" For 2-D image tensor, paddings=[0, 1, 2, 3] means"
" padding 0 row to top, 1 row to bottom, 2 columns to left"
" and 3 columns to right.Size of paddings should be equal to"
" 2 * dimension size of input tensor.");
AddAttr<float>("pad_value",
"(float) default to 0; "
"The value to fill padded areas.")
.SetDefault(0.0f);
}
};
class PadOpGrad : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) should not be null");
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
"Input(Out@GRAD) should not be null");
auto x_dims = ctx.Input<Tensor>("X")->dims();
auto *x_g = ctx.Output<framework::LoDTensor>(framework::GradVarName("X"));
if (x_g != nullptr) {
x_g->Resize(x_dims);
}
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP(pad, ops::PadOp, ops::PadOpMaker, pad_grad, ops::PadOpGrad);
REGISTER_OP_CPU_KERNEL(pad, ops::PadKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(pad_grad,
ops::PadGradKernel<paddle::platform::CPUPlace, float>);
paddle/operators/pad_op.cu 0 → 100644
浏览文件 @ 1fb5f12f
/* 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. */
#define EIGEN_USE_GPU
#include "paddle/operators/pad_op.h"
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(pad, ops::PadKernel<paddle::platform::GPUPlace, float>);
REGISTER_OP_GPU_KERNEL(pad_grad,
ops::PadGradKernel<paddle::platform::GPUPlace, float>);
paddle/operators/pad_op.h 0 → 100644
浏览文件 @ 1fb5f12f
/* 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 "paddle/framework/eigen.h"
#include "paddle/framework/op_registry.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
template <typename T, size_t D, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenTensor = framework::EigenTensor<T, D, MajorType, IndexType>;
template <typename Place, typename T, size_t D>
void PadFunction(const framework::ExecutionContext& context) {
auto pads = context.Attr<std::vector<int>>("paddings");
Eigen::array<std::pair<int, int>, D> paddings;
for (size_t i = 0; i < paddings.size(); ++i) {
paddings[i].first = pads[i * 2];
paddings[i].second = pads[i * 2 + 1];
}
T pad_value = context.Attr<T>("pad_value");
auto* x = context.Input<Tensor>("X");
auto* out = context.Output<Tensor>("Out");
out->mutable_data<T>(context.GetPlace());
auto x_tensor = EigenTensor<T, D>::From(*x);
auto out_tensor = EigenTensor<T, D>::From(*out);
auto place = context.GetEigenDevice<Place>();
out_tensor.device(place) = x_tensor.pad(paddings, pad_value);
}
template <typename Place, typename T>
class PadKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& context) const override {
int rank = context.Input<Tensor>("X")->dims().size();
switch (rank) {
case 1:
PadFunction<Place, T, 1>(context);
break;
case 2:
PadFunction<Place, T, 2>(context);
break;
case 3:
PadFunction<Place, T, 3>(context);
break;
case 4:
PadFunction<Place, T, 4>(context);
break;
case 5:
PadFunction<Place, T, 5>(context);
break;
case 6:
PadFunction<Place, T, 6>(context);
break;
default:
PADDLE_THROW(
"PadOp only support tensors with no more than 6 dimensions.");
}
}
};
template <typename Place, typename T, size_t D>
void PadGradFunction(const framework::ExecutionContext& context) {
auto pads = context.Attr<std::vector<int>>("paddings");
Eigen::array<std::pair<int, int>, D> paddings;
for (size_t i = 0; i < paddings.size(); ++i) {
paddings[i].first = -pads[i * 2];
paddings[i].second = -pads[i * 2 + 1];
}
auto* d_out = context.Input<Tensor>(framework::GradVarName("Out"));
auto* d_x = context.Output<Tensor>(framework::GradVarName("X"));
if (d_x != nullptr) {
d_x->mutable_data<T>(context.GetPlace());
auto d_x_tensor = EigenTensor<T, D>::From(*d_x);
auto d_out_tensor = EigenTensor<T, D>::From(*d_out);
auto place = context.GetEigenDevice<Place>();
d_x_tensor.device(place) = d_out_tensor.pad(paddings, 0);
}
}
template <typename Place, typename T>
class PadGradKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& context) const override {
size_t rank =
context.Input<Tensor>(framework::GradVarName("Out"))->dims().size();
switch (rank) {
case 1:
PadGradFunction<Place, T, 1>(context);
break;
case 2:
PadGradFunction<Place, T, 2>(context);
break;
case 3:
PadGradFunction<Place, T, 3>(context);
break;
case 4:
PadGradFunction<Place, T, 4>(context);
break;
case 5:
PadGradFunction<Place, T, 5>(context);
break;
case 6:
PadGradFunction<Place, T, 6>(context);
break;
default:
PADDLE_THROW(
"PadOp only support tensors with no more than 6 dimensions.");
}
}
};
} // namespace operators
} // namespace paddle
paddle/operators/recurrent_op.cc
浏览文件 @ 1fb5f12f
......@@ -26,10 +26,11 @@ namespace operators {
using Scope = framework::Scope;
using Variable = framework::Variable;
using Tensor = framework::Tensor;
using LoDTensor = framework::LoDTensor;
void RecurrentAlgorithm::InferShape(const Scope& scope) const {
seq_len_ = scope.FindVar((arg_->inlinks[0]).external)
->GetMutable<Tensor>()
->GetMutable<LoDTensor>()
->dims()[0];
CreateScopes(scope);
auto step_scopes = GetStepScopes(scope);
......@@ -88,7 +89,7 @@ void RecurrentAlgorithm::CreateScopes(const Scope& scope) const {
// the weight are located in parent scope
for (auto& var_name : input.second) {
if (!step_scope.FindVar(var_name)) {
step_scope.NewVar(var_name)->GetMutable<Tensor>();
step_scope.NewVar(var_name)->GetMutable<LoDTensor>();
}
}
}
......@@ -106,11 +107,12 @@ void RecurrentAlgorithm::CreateScopes(const Scope& scope) const {
void RecurrentAlgorithm::InitMemories(Scope* step_scope,
bool infer_shape_mode) const {
for (auto& attr : arg_->memories) {
Tensor* pre_mem = step_scope->NewVar(attr.pre_var)->GetMutable<Tensor>();
auto* pre_mem = step_scope->NewVar(attr.pre_var)->GetMutable<LoDTensor>();
PADDLE_ENFORCE(step_scope->FindVar(attr.boot_var) != nullptr,
"memory [%s]'s boot variable [%s] not exists", attr.var,
attr.boot_var);
Tensor* boot_mem = step_scope->FindVar(attr.boot_var)->GetMutable<Tensor>();
auto* boot_mem =
step_scope->FindVar(attr.boot_var)->GetMutable<LoDTensor>();
if (infer_shape_mode) {
pre_mem->Resize(boot_mem->dims());
PADDLE_ENFORCE_EQ(pre_mem->dims().size(), 2);
......@@ -192,9 +194,9 @@ void RecurrentGradientAlgorithm::LinkBootMemoryGradients(
"memory variable [%s] does not exists", attr.var);
PADDLE_ENFORCE(step_scope->FindVar(attr.boot_var) != nullptr,
"boot variable [%s] does not exists", attr.boot_var);
Tensor* mem_grad = step_scope->NewVar(attr.var)->GetMutable<Tensor>();
Tensor* boot_mem_grad =
step_scope->NewVar(attr.boot_var)->GetMutable<Tensor>();
auto* mem_grad = step_scope->NewVar(attr.var)->GetMutable<LoDTensor>();
auto* boot_mem_grad =
step_scope->NewVar(attr.boot_var)->GetMutable<LoDTensor>();
if (infer_shape_mode) {
boot_mem_grad->Resize(mem_grad->dims());
} else {
......@@ -205,7 +207,7 @@ void RecurrentGradientAlgorithm::LinkBootMemoryGradients(
void RecurrentGradientAlgorithm::InferShape(const Scope& scope) const {
seq_len_ = scope.FindVar((arg_->inlinks[0]).external)
->GetMutable<Tensor>()
->GetMutable<LoDTensor>()
->dims()[0];
auto step_scopes = GetStepScopes(scope);
rnn::SegmentInputs(step_scopes, arg_->inlinks, seq_len_,
......
paddle/operators/reshape_op.cc
浏览文件 @ 1fb5f12f
......@@ -46,7 +46,7 @@ class ReshapeOp : public framework::OperatorWithKernel {
std::transform(shape.begin(), shape.end(), shape_int64.begin(),
[](int a) { return static_cast<int64_t>(a); });
auto out_dims = framework::make_ddim(shape_int64);
ctx.Output<framework::Tensor>("Out")->Resize(out_dims);
ctx.Output<framework::LoDTensor>("Out")->Resize(out_dims);
}
};
......@@ -90,7 +90,7 @@ class ReshapeGradOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
"Input(Out@GRAD) shouldn't be null.");
auto dims = ctx.Input<framework::Tensor>("X")->dims();
auto *d_in = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
auto *d_in = ctx.Output<framework::LoDTensor>(framework::GradVarName("X"));
d_in->Resize(dims);
}
};
......
paddle/operators/rnn/recurrent_op_utils.cc
浏览文件 @ 1fb5f12f
......@@ -21,6 +21,7 @@ namespace rnn {
namespace f = paddle::framework;
using Tensor = framework::Tensor;
using LoDTensor = framework::LoDTensor;
void SegmentInputs(const std::vector<Scope*>& step_scopes,
const std::vector<Link>& inlinks, const size_t seq_len,
......@@ -31,7 +32,7 @@ void SegmentInputs(const std::vector<Scope*>& step_scopes,
PADDLE_ENFORCE(input_var != nullptr, "input link [%s] is not in scope.",
inlinks[i].external);
Tensor* input = input_var->GetMutable<Tensor>();
LoDTensor* input = input_var->GetMutable<LoDTensor>();
f::DDim dims = input->dims();
PADDLE_ENFORCE(static_cast<size_t>(dims[0]) == seq_len,
"all the inlinks must have same length");
......@@ -40,6 +41,8 @@ void SegmentInputs(const std::vector<Scope*>& step_scopes,
Tensor* step_input =
step_scopes[j]->NewVar(inlinks[i].internal)->GetMutable<Tensor>();
if (!infer_shape_mode) {
// The input of operators of each step is Tensor here.
// Maybe need to modify Slice function.
*step_input = input->Slice<float>(j, j + 1);
}
step_input->Resize(step_dims);
......@@ -54,21 +57,23 @@ void ConcatOutputs(const std::vector<Scope*>& step_scopes,
auto output_var = step_scopes[0]->FindVar(outlinks[i].external);
PADDLE_ENFORCE(output_var != nullptr, "output link [%s] is not in scope.",
outlinks[i].external);
Tensor* output = output_var->GetMutable<Tensor>();
LoDTensor* output = output_var->GetMutable<LoDTensor>();
if (infer_shape_mode) {
auto step_scope_var = step_scopes[0]->FindVar(outlinks[i].internal);
PADDLE_ENFORCE(step_scope_var != nullptr, "%s not in scope",
outlinks[i].internal);
f::DDim step_dims = step_scope_var->template GetMutable<Tensor>()->dims();
f::DDim step_dims =
step_scope_var->template GetMutable<LoDTensor>()->dims();
std::vector<int64_t> dims_vec = vectorize(step_dims);
dims_vec.insert(dims_vec.begin(), seq_len);
output->Resize(f::make_ddim(dims_vec));
} else {
output->mutable_data<float>(platform::CPUPlace());
for (size_t j = 0; j < seq_len; j++) {
Tensor* step_output =
step_scopes[j]->FindVar(outlinks[i].internal)->GetMutable<Tensor>();
LoDTensor* step_output = step_scopes[j]
->FindVar(outlinks[i].internal)
->GetMutable<LoDTensor>();
// TODO(luotao02) data type and platform::DeviceContext() should set
// correctly
(output->Slice<float>(j, j + 1))
......@@ -94,8 +99,8 @@ void LinkMemories(const std::vector<Scope*>& scopes,
auto scope = scopes[step_id];
auto linked_scope = scopes[step_id + offset];
for (auto& attr : memories) {
auto mem = scope->FindVar(attr.pre_var)->GetMutable<Tensor>();
auto linked_mem = linked_scope->FindVar(attr.var)->GetMutable<Tensor>();
auto mem = scope->FindVar(attr.pre_var)->GetMutable<LoDTensor>();
auto linked_mem = linked_scope->FindVar(attr.var)->GetMutable<LoDTensor>();
if (infer_shape_mode) {
mem->Resize(linked_mem->dims());
} else {
......
paddle/operators/rowwise_add_op.cc
浏览文件 @ 1fb5f12f
......@@ -37,7 +37,7 @@ class RowwiseAddOp : public framework::OperatorWithKernel {
framework::slice_ddim(x_dims, num_col_dims, x_dims.size()), b_dims,
"The width of two operands must be same");
PADDLE_ENFORCE_EQ(ctx.OutputSize("Out"), 1, "The output size must be 1");
ctx.Output<Tensor>("Out")->Resize(x_dims);
ctx.Output<framework::LoDTensor>("Out")->Resize(x_dims);
}
};
......@@ -76,8 +76,8 @@ class RowwiseAddGradOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE_EQ(
framework::slice_ddim(x_dims, num_col_dims, x_dims.size()), b_dims,
"The width of two operands must be same");
auto *dx = ctx.Output<Tensor>(framework::GradVarName("X"));
auto *db = ctx.Output<Tensor>(framework::GradVarName("b"));
auto *dx = ctx.Output<framework::LoDTensor>(framework::GradVarName("X"));
auto *db = ctx.Output<framework::LoDTensor>(framework::GradVarName("b"));
if (dx) dx->Resize(x_dims);
if (db) db->Resize(b_dims);
}
......
paddle/operators/scale_op.cc
浏览文件 @ 1fb5f12f
......@@ -28,7 +28,7 @@ class ScaleOp : public framework::OperatorWithKernel {
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
auto *in = ctx.Input<framework::Tensor>("X");
auto *out = ctx.Output<framework::Tensor>("Out");
auto *out = ctx.Output<framework::LoDTensor>("Out");
out->Resize(in->dims());
}
};
......
paddle/operators/scatter_op.cc
浏览文件 @ 1fb5f12f
......@@ -35,7 +35,8 @@ class ScatterOp : public framework::OperatorWithKernel {
framework::DDim data_dim(ctx.Input<Tensor>("Updates")->dims());
for (int i = 1; i < data_dim.size(); ++i)
PADDLE_ENFORCE_EQ(data_dim[i], ctx.Input<Tensor>("Updates")->dims()[i]);
ctx.Output<Tensor>("Out")->Resize(ctx.Input<Tensor>("Ref")->dims());
ctx.Output<framework::LoDTensor>("Out")->Resize(
ctx.Input<Tensor>("Ref")->dims());
}
};
......@@ -45,9 +46,11 @@ class ScatterGradOp : public framework::OperatorWithKernel {
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
auto *dUpdates = ctx.Output<Tensor>(framework::GradVarName("Updates"));
auto *dUpdates =
ctx.Output<framework::LoDTensor>(framework::GradVarName("Updates"));
auto *Updates = ctx.Input<Tensor>("Updates");
auto *dRef = ctx.Output<Tensor>(framework::GradVarName("Ref"));
auto *dRef =
ctx.Output<framework::LoDTensor>(framework::GradVarName("Ref"));
auto *Ref = ctx.Input<Tensor>("Ref");
dRef->Resize(Ref->dims());
......
paddle/operators/sequence_avg_pool_op.cc 0 → 100644
浏览文件 @ 1fb5f12f
/* 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 "paddle/operators/sequence_avg_pool_op.h"
namespace paddle {
namespace operators {
class SequenceAvgPoolOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext& ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"),
"Input of SequenceAvgPoolOp"
"must be initialized.");
auto* x = ctx.Input<framework::LoDTensor>("X");
auto dims = x->dims();
auto lod = x->lod();
PADDLE_ENFORCE_EQ(lod.size(), 1UL, "Only support one level sequence now.");
PADDLE_ENFORCE_GE(
dims[0],
/*batch size = */ static_cast<int64_t>(lod[0].size() - 1),
"The first dimension of Input(X) must be large than batch size.");
dims[0] = lod[0].size() - 1;
ctx.Output<framework::LoDTensor>("Out")->Resize({dims});
}
};
class SequenceAvgPoolOpMaker : public framework::OpProtoAndCheckerMaker {
public:
SequenceAvgPoolOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "Input of SequenceAvgPoolOp.");
AddOutput("Out", "The output of SequenceAvgPoolOp.");
AddComment(R"DOC(
SequenceAvgPoolOp averages features of all time-steps of each instance.
More detailed comments will be added later.
)DOC");
}
};
class SequenceAvgPoolGradOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext& ctx) const override {
PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
"Gradient of Out should not be null");
auto og_dims =
ctx.Input<framework::LoDTensor>(framework::GradVarName("Out"))->dims();
auto x_dims = ctx.Input<framework::LoDTensor>("X")->dims();
PADDLE_ENFORCE_EQ(og_dims.size(), x_dims.size(),
"The rank of output grad must equal to Input(X).");
for (int64_t i = 1; i < og_dims.size(); ++i) {
PADDLE_ENFORCE_EQ(og_dims[i], x_dims[i], "The dimension mismatch.");
}
auto* x_grad =
ctx.Output<framework::LoDTensor>(framework::GradVarName("X"));
x_grad->Resize(x_dims);
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP(sequence_avg_pool, ops::SequenceAvgPoolOp,
ops::SequenceAvgPoolOpMaker, sequence_avg_pool_grad,
ops::SequenceAvgPoolGradOp);
REGISTER_OP_CPU_KERNEL(
sequence_avg_pool,
ops::SequenceAvgPoolKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(
sequence_avg_pool_grad,
ops::SequenceAvgPoolGradKernel<paddle::platform::CPUPlace, float>);
paddle/operators/sequence_avg_pool_op.cu 0 → 100644
浏览文件 @ 1fb5f12f
/* 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. */
#define EIGEN_USE_GPU
#include "paddle/operators/sequence_avg_pool_op.h"
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(
sequence_avg_pool,
ops::SequenceAvgPoolKernel<paddle::platform::GPUPlace, float>);
REGISTER_OP_GPU_KERNEL(
sequence_avg_pool_grad,
ops::SequenceAvgPoolGradKernel<paddle::platform::GPUPlace, float>);
paddle/operators/sequence_avg_pool_op.h 0 → 100644
浏览文件 @ 1fb5f12f
/* 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 "paddle/framework/eigen.h"
#include "paddle/framework/op_registry.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
using LoDTensor = framework::LoDTensor;
template <typename T, int MajorType = Eigen::RowMajor,
typename IndexType = Eigen::DenseIndex>
using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
template <typename Place, typename T>
class SequenceAvgPoolKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto* in = context.Input<LoDTensor>("X");
auto* out = context.Output<LoDTensor>("Out");
auto dims = in->dims();
auto lod = in->lod();
int64_t w = in->numel() / dims[0];
out->mutable_data<T>(context.GetPlace());
auto place = context.GetEigenDevice<Place>();
for (int i = 0; i < static_cast<int>(lod[0].size()) - 1; ++i) {
Tensor in_t = in->Slice<T>(static_cast<int>(lod[0][i]),
static_cast<int>(lod[0][i + 1]));
Tensor out_t = out->Slice<T>(i, i + 1);
int64_t h = static_cast<int64_t>(lod[0][i + 1] - lod[0][i]);
auto in_e = EigenMatrix<T>::From(in_t, {h, w});
auto out_e = EigenMatrix<T>::From(out_t, {h, w});
out_e.device(place) = in_e.mean(Eigen::array<int, 1>({{0}}));
}
}
};
template <typename Place, typename T>
class SequenceAvgPoolGradKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto* in = context.Output<LoDTensor>("X");
auto* in_g = context.Output<LoDTensor>(framework::GradVarName("X"));
auto* out_g = context.Input<LoDTensor>(framework::GradVarName("Out"));
auto dims = in->dims();
auto lod = in->lod();
int64_t w = in->numel() / dims[0];
in_g->mutable_data<T>(context.GetPlace());
auto place = context.GetEigenDevice<Place>();
for (int i = 0; i < static_cast<int>(lod[0].size()) - 1; ++i) {
auto in_g_t = in_g->Slice<T>(static_cast<int>(lod[0][i]),
static_cast<int>(lod[0][i + 1]));
auto out_g_t = out_g->Slice<T>(i, i + 1);
int64_t h = static_cast<int64_t>(lod[0][i + 1] - lod[0][i]);
auto in_g_e = EigenMatrix<T>::From(in_g_t, {h, w});
auto out_g_e = EigenMatrix<T>::From(out_g_t, {1, w});
Eigen::DSizes<int, 2> bcast(h, w);
in_g_e.device(place) = (out_g_e / static_cast<T>(h)).broadcast(bcast);
}
}
};
} // namespace operators
} // namespace paddle
paddle/operators/sgd_op.cc
浏览文件 @ 1fb5f12f
......@@ -23,10 +23,11 @@ class SGDOp : public framework::OperatorWithKernel {
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE(
ctx.Input<Tensor>("param")->dims() == ctx.Input<Tensor>("grad")->dims(),
"Two input of SGD Op's dimension must be same.");
ctx.Output<Tensor>("param_out")->Resize(ctx.Input<Tensor>("param")->dims());
PADDLE_ENFORCE_EQ(ctx.Input<Tensor>("param")->dims(),
ctx.Input<Tensor>("grad")->dims(),
"Two input of SGD Op's dimension must be same.");
ctx.Output<framework::LoDTensor>("param_out")
->Resize(ctx.Input<Tensor>("param")->dims());
}
};
......
paddle/operators/sigmoid_op.cc
浏览文件 @ 1fb5f12f
......@@ -23,7 +23,8 @@ class SigmoidOp : public framework::OperatorWithKernel {
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
ctx.Output<Tensor>("Y")->Resize(ctx.Input<Tensor>("X")->dims());
ctx.Output<framework::LoDTensor>("Y")->Resize(
ctx.Input<Tensor>("X")->dims());
}
};
......@@ -44,7 +45,7 @@ class SigmoidOpGrad : public framework::OperatorWithKernel {
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
ctx.Output<Tensor>(framework::GradVarName("X"))
ctx.Output<framework::LoDTensor>(framework::GradVarName("X"))
->Resize(ctx.Input<Tensor>("Y")->dims());
}
};
......
paddle/operators/softmax_op.cc
浏览文件 @ 1fb5f12f
......@@ -25,7 +25,8 @@ class SoftmaxOp : public framework::OperatorWithKernel {
void InferShape(const framework::InferShapeContext &ctx) const override {
PADDLE_ENFORCE(ctx.Input<Tensor>("X")->dims().size() == 2UL,
"The input of softmax op must be a matrix.");
ctx.Output<Tensor>("Y")->Resize(ctx.Input<Tensor>("X")->dims());
ctx.Output<framework::LoDTensor>("Y")->Resize(
ctx.Input<Tensor>("X")->dims());
}
};
......@@ -71,7 +72,7 @@ class SoftmaxOpGrad : public framework::OperatorWithKernel {
ctx.Input<Tensor>(framework::GradVarName("Y"))->dims(),
"Input(Y) and its gradients should have a same shape.");
ctx.Output<Tensor>(framework::GradVarName("X"))
ctx.Output<framework::LoDTensor>(framework::GradVarName("X"))
->Resize(ctx.Input<Tensor>("X")->dims());
}
};
......
paddle/operators/squared_l2_distance_op.cc
浏览文件 @ 1fb5f12f
......@@ -48,9 +48,9 @@ class SquaredL2DistanceOp : public framework::OperatorWithKernel {
"First dimension of target must be equal to input "
"or to 1.");
ctx.Output<Tensor>("sub_result")
ctx.Output<framework::LoDTensor>("sub_result")
->Resize({x_dims[0], x->numel() / x_dims[0]});
ctx.Output<Tensor>("Out")->Resize({x_dims[0], 1});
ctx.Output<framework::LoDTensor>("Out")->Resize({x_dims[0], 1});
}
};
......@@ -94,8 +94,10 @@ class SquaredL2DistanceGradOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE_EQ(out_dims[1], 1,
"Second dimension of output gradient "
"must be 1.");
auto* x_grad = ctx.Output<Tensor>(framework::GradVarName("X"));
auto* y_grad = ctx.Output<Tensor>(framework::GradVarName("Y"));
auto* x_grad =
ctx.Output<framework::LoDTensor>(framework::GradVarName("X"));
auto* y_grad =
ctx.Output<framework::LoDTensor>(framework::GradVarName("Y"));
if (x_grad) x_grad->Resize(x_dims);
if (y_grad) y_grad->Resize(y_dims);
}
......
paddle/operators/sum_op.cc
浏览文件 @ 1fb5f12f
......@@ -23,7 +23,7 @@ class SumOp : public framework::OperatorWithKernel {
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
auto ins = ctx.MultiInput<framework::Tensor>("X");
auto *out = ctx.Output<framework::Tensor>("Out");
auto *out = ctx.Output<framework::LoDTensor>("Out");
int N = ins.size();
auto in_dim = ins[0]->dims();
......@@ -55,7 +55,8 @@ class SumGradOp : public framework::OperatorWithKernel {
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
auto outputs = ctx.MultiOutput<Tensor>(framework::GradVarName("X"));
auto outputs =
ctx.MultiOutput<framework::LoDTensor>(framework::GradVarName("X"));
auto dims = ctx.Input<Tensor>(framework::GradVarName("Out"))->dims();
for (auto output : outputs) {
output->Resize(dims);
......
paddle/operators/top_k_op.cc
浏览文件 @ 1fb5f12f
......@@ -35,8 +35,8 @@ class TopkOp : public framework::OperatorWithKernel {
framework::DDim dims = input->dims();
dims[dims.size() - 1] = k;
ctx.Output<Tensor>("Out")->Resize(dims);
ctx.Output<Tensor>("Indices")->Resize(dims);
ctx.Output<framework::LoDTensor>("Out")->Resize(dims);
ctx.Output<framework::LoDTensor>("Indices")->Resize(dims);
}
};
......
paddle/operators/uniform_random_op.cc
浏览文件 @ 1fb5f12f
......@@ -50,7 +50,7 @@ class UniformRandomOp : public framework::OperatorWithKernel {
void InferShape(const framework::InferShapeContext& ctx) const override {
PADDLE_ENFORCE(Attr<float>("min") < Attr<float>("max"),
"uniform_random's min must less then max");
auto* tensor = ctx.Output<framework::Tensor>("Out");
auto* tensor = ctx.Output<framework::LoDTensor>("Out");
auto dims = Attr<std::vector<int>>("dims");
std::vector<int64_t> temp;
temp.reserve(dims.size());
......
paddle/platform/CMakeLists.txt
浏览文件 @ 1fb5f12f
......@@ -24,3 +24,4 @@ cc_library(device_context SRCS device_context.cc DEPS memory buddy_allocator
nv_test(device_context_test SRCS device_context_test.cc DEPS device_context gpu_info)
nv_test(cudnn_helper_test SRCS cudnn_helper_test.cc DEPS dynload_cuda)
nv_test(transform_test SRCS transform_test.cu DEPS paddle_memory place)
paddle/operators/concat_op.cu → paddle/platform/details/device_ptr_cast.h
浏览文件 @ 1fb5f12f
......@@ -4,7 +4,7 @@ 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
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,
......@@ -12,8 +12,45 @@ 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. */
#define EIGEN_USE_GPU
#include "paddle/operators/concat_op.h"
#pragma once
namespace ops = paddle::operators;
// TODO(Yancey1989) Add GPU kernel
#ifndef __NVCC__
#error device_ptr_cast must be include by .cu file
#endif
#include <thrust/device_ptr.h>
namespace paddle {
namespace platform {
namespace details {
template <typename T, bool is_ptr>
struct DevicePtrCast;
template <typename T>
struct DevicePtrCast<T, true> {
using ELEM = typename std::remove_pointer<T>::type;
using RTYPE = thrust::device_ptr<ELEM>;
inline thrust::device_ptr<ELEM> operator()(ELEM* ele) const {
return thrust::device_pointer_cast(ele);
}
};
template <typename T>
struct DevicePtrCast<T, false> {
using RTYPE = T;
inline RTYPE operator()(RTYPE it) const { return it; }
};
// Cast T to thrust::device_ptr if T is a pointer.
// Otherwise, e.g., T is a iterator, return T itself.
template <typename T>
auto DevPtrCast(T t) ->
typename DevicePtrCast<T, std::is_pointer<T>::value>::RTYPE {
DevicePtrCast<T, std::is_pointer<T>::value> cast;
return cast(t);
}
} // namespace details
} // namespace platform
} // namespace paddle
paddle/platform/enforce.h
浏览文件 @ 1fb5f12f
......@@ -25,6 +25,10 @@ limitations under the License. */
#include "paddle/string/printf.h"
#include "paddle/string/to_string.h"
#ifdef __GNUC__
#include <cxxabi.h> // for __cxa_demangle
#endif
#ifndef PADDLE_ONLY_CPU
#include "paddle/platform/dynload/cublas.h"
......@@ -42,6 +46,19 @@ limitations under the License. */
namespace paddle {
namespace platform {
namespace {
#ifdef __GNUC__
inline std::string demangle(std::string name) {
int status = -4; // some arbitrary value to eliminate the compiler warning
std::unique_ptr<char, void (*)(void*)> res{
abi::__cxa_demangle(name.c_str(), NULL, NULL, &status), std::free};
return (status == 0) ? res.get() : name;
}
#else
inline std::string demangle(std::string name) { return name; }
#endif
}
struct EnforceNotMet : public std::exception {
std::exception_ptr exp_;
std::string err_str_;
......@@ -61,8 +78,8 @@ struct EnforceNotMet : public std::exception {
Dl_info info;
for (int i = 0; i < size; ++i) {
if (dladdr(call_stack[i], &info)) {
auto demangled = info.dli_sname;
if (dladdr(call_stack[i], &info) && info.dli_sname) {
auto demangled = demangle(info.dli_sname);
auto addr_offset = static_cast<char*>(call_stack[i]) -
static_cast<char*>(info.dli_saddr);
sout << string::Sprintf("%-3d %*0p %s + %zd\n", i,
......
paddle/platform/transform.h 0 → 100644
浏览文件 @ 1fb5f12f
/* 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 "paddle/platform/enforce.h"
#include "paddle/platform/hostdevice.h"
#include "paddle/platform/place.h"
#include <algorithm>
#include <type_traits>
#ifdef __NVCC__
#include <thrust/transform.h>
#include "paddle/platform/details/device_ptr_cast.h"
#endif
namespace paddle {
namespace platform {
// Transform on host or device. It provides the same API in std library.
template <typename Place, typename InputIter, typename OutputIter,
typename UnaryOperation>
void Transform(Place place, InputIter first, InputIter last, OutputIter result,
UnaryOperation op) {
if (is_cpu_place(place)) {
std::transform(first, last, result, op);
} else {
#ifdef __NVCC__
using namespace details;
thrust::transform(DevPtrCast(first), DevPtrCast(last), DevPtrCast(result),
op);
#else
PADDLE_THROW("Do not invoke `Transform<GPUPlace>` in .cc file");
#endif
}
}
template <typename Place, typename InputIter1, typename InputIter2,
typename OutputIter, typename BinaryOperation>
void Transform(Place place, InputIter1 first1, InputIter1 last1,
InputIter2 first2, OutputIter result, BinaryOperation op) {
if (is_cpu_place(place)) {
std::transform(first1, last1, first2, result, op);
} else {
#ifdef __NVCC__
using namespace details;
thrust::transform(DevPtrCast(first1), DevPtrCast(last1), DevPtrCast(first2),
DevPtrCast(result), op);
#else
PADDLE_THROW("Do not invoke `Transform<GPUPlace>` in .cc file");
#endif
}
};
} // namespace platform
} // namespace paddle
paddle/platform/transform_test.cu 0 → 100644
浏览文件 @ 1fb5f12f
/* 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 "paddle/memory/memcpy.h"
#include "paddle/memory/memory.h"
#include "paddle/platform/transform.h"
template <typename T>
class Scale {
public:
explicit Scale(const T& scale) : scale_(scale) {}
HOSTDEVICE T operator()(const T& a) const { return a * scale_; }
private:
T scale_;
};
template <typename T>
class Multiply {
public:
HOSTDEVICE T operator()(const T& a, const T& b) const { return a * b; }
};
TEST(Transform, CPUUnary) {
using namespace paddle::platform;
float buf[4] = {0.1, 0.2, 0.3, 0.4};
Transform(CPUPlace(), buf, buf + 4, buf, Scale<float>(10));
for (int i = 0; i < 4; ++i) {
ASSERT_NEAR(buf[i], static_cast<float>(i + 1), 1e-5);
}
}
TEST(Transform, GPUUnary) {
using namespace paddle::platform;
using namespace paddle::memory;
GPUPlace gpu0(0);
float cpu_buf[4] = {0.1, 0.2, 0.3, 0.4};
float* gpu_buf = static_cast<float*>(Alloc(gpu0, sizeof(float) * 4));
Copy(gpu0, gpu_buf, CPUPlace(), cpu_buf, sizeof(cpu_buf));
Transform(gpu0, gpu_buf, gpu_buf + 4, gpu_buf, Scale<float>(10));
Copy(CPUPlace(), cpu_buf, gpu0, gpu_buf, sizeof(cpu_buf));
Free(gpu0, gpu_buf);
for (int i = 0; i < 4; ++i) {
ASSERT_NEAR(cpu_buf[i], static_cast<float>(i + 1), 1e-5);
}
}
TEST(Transform, CPUBinary) {
using namespace paddle::platform;
using namespace paddle::memory;
int buf[4] = {1, 2, 3, 4};
Transform(CPUPlace(), buf, buf + 4, buf, buf, Multiply<int>());
for (int i = 0; i < 4; ++i) {
ASSERT_EQ((i + 1) * (i + 1), buf[i]);
}
}
TEST(Transform, GPUBinary) {
using namespace paddle::platform;
using namespace paddle::memory;
int buf[4] = {1, 2, 3, 4};
GPUPlace gpu0(0);
int* gpu_buf = static_cast<int*>(Alloc(gpu0, sizeof(buf)));
Copy(gpu0, gpu_buf, CPUPlace(), buf, sizeof(buf));
Transform(gpu0, gpu_buf, gpu_buf + 4, gpu_buf, gpu_buf, Multiply<int>());
Copy(CPUPlace(), buf, gpu0, gpu_buf, sizeof(buf));
Free(gpu0, gpu_buf);
for (int i = 0; i < 4; ++i) {
ASSERT_EQ((i + 1) * (i + 1), buf[i]);
}
}
\ No newline at end of file
paddle/pybind/pybind.cc
浏览文件 @ 1fb5f12f
......@@ -19,10 +19,12 @@ limitations under the License. */
#include "paddle/framework/backward.h"
#include "paddle/framework/lod_tensor.h"
#include "paddle/framework/op_registry.h"
#include "paddle/operators/cond_op.h"
#include "paddle/operators/net_op.h"
#include "paddle/operators/recurrent_op.h"
#include "paddle/platform/enforce.h"
#include "paddle/platform/place.h"
#include "paddle/pybind/pybind.h"
#include "paddle/pybind/tensor_py.h"
#include "paddle/string/to_string.h"
#include "pybind11/numpy.h"
......@@ -31,33 +33,6 @@ limitations under the License. */
namespace py = pybind11;
USE_OP(add);
USE_OP(onehot_cross_entropy);
USE_OP(sgd);
USE_OP(mul);
USE_OP(elementwise_mul);
USE_OP(mean);
USE_OP(sigmoid);
USE_OP(softmax);
USE_OP(rowwise_add);
USE_OP(fill_zeros_like);
USE_NO_KERNEL_OP(recurrent);
USE_OP(gaussian_random);
USE_OP(uniform_random);
USE_OP(lookup_table);
USE_OP(scale);
USE_NO_KERNEL_OP(identity);
USE_OP(minus);
USE_OP(cos_sim);
USE_CPU_ONLY_OP(gather);
USE_CPU_ONLY_OP(scatter);
USE_CPU_ONLY_OP(concat);
USE_CPU_ONLY_OP(softmax_with_cross_entropy);
USE_OP(top_k);
USE_OP(squared_l2_distance);
USE_OP(sum);
USE_OP(reshape);
namespace paddle {
namespace framework {
......@@ -123,27 +98,21 @@ PYBIND11_PLUGIN(core) {
return self.data<float>()[offset];
});
py::class_<LoDTensor>(m, "LoDTensor", R"DOC(LoD(Leval of Ddetails) Tensor.
The tensor and LoD info should be created before creating the LoDTensor, then
call the set_tensor and set_lod functions to set them.
)DOC")
.def("__init__",
[](LoDTensor &instance,
const std::vector<std::vector<size_t>> &lod,
Tensor *t) {
py::class_<LoDTensor, Tensor>(m, "LoDTensor")
.def_buffer(
[](Tensor &self) -> py::buffer_info { return CastToPyBuffer(self); })
.def(
"__init__",
[](LoDTensor &instance, const std::vector<std::vector<size_t>> &lod) {
#ifdef PADDLE_ONLY_CPU
new (&instance) LoDTensor(lod, t);
new (&instance) LoDTensor(lod);
#else
paddle::framework::LoD new_lod;
new_lod.reserve(lod.size());
std::copy(lod.begin(), lod.end(), std::back_inserter(new_lod));
new (&instance) LoDTensor(new_lod, t);
new (&instance) LoDTensor(new_lod);
#endif
})
.def("set_tensor",
[](LoDTensor &self, Tensor *tensor) { self.set_tensor(tensor); })
})
.def("set_lod",
[](LoDTensor &self, const std::vector<std::vector<size_t>> &lod) {
#ifdef PADDLE_ONLY_CPU
......@@ -155,9 +124,6 @@ call the set_tensor and set_lod functions to set them.
self.set_lod(new_lod);
#endif
})
.def("tensor",
[](LoDTensor &self) -> Tensor & { return self.tensor(); },
py::return_value_policy::reference)
.def("lod", [](LoDTensor &self) -> std::vector<std::vector<size_t>> {
#ifdef PADDLE_ONLY_CPU
return self.lod();
......@@ -186,9 +152,6 @@ All parameter, weight, gradient are variables in Paddle.
[](Variable &var, int val) -> void { *var.GetMutable<int>() = val; })
.def("get_int", [](const Variable &var) -> int { return var.Get<int>(); })
.def("get_tensor",
[](Variable &self) -> Tensor * { return self.GetMutable<Tensor>(); },
py::return_value_policy::reference)
.def("get_lod_tensor",
[](Variable &self) -> LoDTensor * {
return self.GetMutable<LoDTensor>();
},
......@@ -326,6 +289,28 @@ All parameter, weight, gradient are variables in Paddle.
[](operators::RecurrentOp &self, const operators::NetOp &net)
-> void { self.set_stepnet(net.Clone()); });
// cond_op
py::class_<operators::CondOp, OperatorBase>(m, "CondOp")
.def_static("create",
[](py::bytes protobin) -> operators::CondOp * {
OpDesc desc;
PADDLE_ENFORCE(desc.ParsePartialFromString(protobin),
"Cannot parse user input to OpDesc");
PADDLE_ENFORCE(desc.IsInitialized(),
"User OpDesc is not initialized, reason %s",
desc.InitializationErrorString());
auto cond_op = OpRegistry::CreateOp(desc);
return static_cast<operators::CondOp *>(cond_op.release());
})
.def("set_truenet",
[](operators::CondOp &self, const operators::NetOp &net) -> void {
self.set_truenet(net.Clone());
})
.def("set_falsenet",
[](operators::CondOp &self, const operators::NetOp &net) -> void {
self.set_falsenet(net.Clone());
});
m.def("unique_integer", UniqueIntegerGenerator);
m.def("is_compile_gpu", IsCompileGPU);
......
paddle/scripts/docker/build_android.sh
浏览文件 @ 1fb5f12f
......@@ -2,8 +2,30 @@
set -xe
if [ $ANDROID_ABI == "arm64-v8a" ]; then
ANDROID_ARCH=arm64
else # armeabi, armeabi-v7a
ANDROID_ARCH=arm
fi
ANDROID_STANDALONE_TOOLCHAIN=$ANDROID_TOOLCHAINS_DIR/$ANDROID_ARCH-android-$ANDROID_API
cat <<EOF
============================================
Generating the standalone toolchain ...
${ANDROID_NDK_HOME}/build/tools/make-standalone-toolchain.sh
--arch=$ANDROID_ARCH
--platform=android-$ANDROID_API
--install-dir=${ANDROID_STANDALONE_TOOLCHAIN}
============================================
EOF
${ANDROID_NDK_HOME}/build/tools/make-standalone-toolchain.sh \
--arch=$ANDROID_ARCH \
--platform=android-$ANDROID_API \
--install-dir=$ANDROID_STANDALONE_TOOLCHAIN
BUILD_ROOT=/paddle/build_android
DEST_ROOT=/paddle/install
DEST_ROOT=/paddle/install_android
rm -rf $BUILD_ROOT 2>/dev/null || true
mkdir -p $BUILD_ROOT
......@@ -11,7 +33,7 @@ cd $BUILD_ROOT
if [ $ANDROID_ABI == "armeabi-v7a" ]; then
cmake -DCMAKE_SYSTEM_NAME=Android \
-DANDROID_STANDALONE_TOOLCHAIN=$ANDROID_ARM_STANDALONE_TOOLCHAIN \
-DANDROID_STANDALONE_TOOLCHAIN=$ANDROID_STANDALONE_TOOLCHAIN \
-DANDROID_ABI=$ANDROID_ABI \
-DANDROID_ARM_NEON=ON \
-DANDROID_ARM_MODE=ON \
......@@ -26,7 +48,7 @@ if [ $ANDROID_ABI == "armeabi-v7a" ]; then
..
elif [ $ANDROID_ABI == "arm64-v8a" ]; then
cmake -DCMAKE_SYSTEM_NAME=Android \
-DANDROID_STANDALONE_TOOLCHAIN=$ANDROID_ARM64_STANDALONE_TOOLCHAIN \
-DANDROID_STANDALONE_TOOLCHAIN=$ANDROID_STANDALONE_TOOLCHAIN \
-DANDROID_ABI=$ANDROID_ABI \
-DANDROID_ARM_MODE=ON \
-DHOST_C_COMPILER=/usr/bin/gcc \
......@@ -40,12 +62,12 @@ elif [ $ANDROID_ABI == "arm64-v8a" ]; then
..
elif [ $ANDROID_ABI == "armeabi" ]; then
cmake -DCMAKE_SYSTEM_NAME=Android \
-DANDROID_STANDALONE_TOOLCHAIN=$ANDROID_ARM_STANDALONE_TOOLCHAIN \
-DANDROID_STANDALONE_TOOLCHAIN=$ANDROID_STANDALONE_TOOLCHAIN \
-DANDROID_ABI=$ANDROID_ABI \
-DANDROID_ARM_MODE=ON \
-DHOST_C_COMPILER=/usr/bin/gcc \
-DHOST_CXX_COMPILER=/usr/bin/g++ \
-DCMAKE_INSTALL_PREFIX=/paddle/install \
-DCMAKE_INSTALL_PREFIX=$DEST_ROOT \
-DCMAKE_BUILD_TYPE=Release \
-DWITH_C_API=ON \
-DWITH_SWIG_PY=OFF \
......@@ -55,5 +77,10 @@ else
echo "Invalid ANDROID_ABI: $ANDROID_ABI"
fi
cat <<EOF
============================================
Building in $BUILD_ROOT ...
============================================
EOF
make -j `nproc`
make install -j `nproc`
python/paddle/trainer/config_parser.py
浏览文件 @ 1fb5f12f
......@@ -2055,20 +2055,26 @@ class ConvLayerBase(LayerBase):
if num_filters is not None:
self.config.num_filters = num_filters
use_mkldnn = int(g_command_config_args.get("use_mkldnn", 0))
use_gpu = int(g_command_config_args.get("use_gpu", 0))
parallel_nn = int(g_command_config_args.get("parallel_nn", 0))
# Automatically select cudnn_type for GPU and exconv for CPU
# Automatically select cudnn_type for GPU, exconv for CPU
# and mkldnn_conv for MKLDNN
# if set type=conv, but still reserve the way user specify
# exconv or cudnn_conv manually.
# exconv, mkldnn_conv or cudnn_conv manually.
if self.layer_type == "cudnn_conv":
config_assert(use_gpu, "cudnn_conv only support GPU")
if self.layer_type == "mkldnn_conv":
config_assert(use_mkldnn, "mkldnn_conv only support MKLDNN")
if (use_gpu == 1 and self.layer_type != "exconv" and
self.layer_type != "mkldnn_conv" and
(parallel_nn == 0 or self.config.device > -1)):
self.layer_type = "cudnn_conv"
else:
self.layer_type = "exconv"
self.layer_type = "mkldnn_conv" if use_mkldnn else "exconv"
# need to specify layer in config
self.config.type = self.layer_type
......@@ -2100,6 +2106,11 @@ class ConvLayer(ConvLayerBase):
layer_type = 'exconv'
@config_layer('mkldnn_conv')
class ConvLayer(ConvLayerBase):
layer_type = 'mkldnn_conv'
@config_layer('cudnn_conv')
class ConvLayer(ConvLayerBase):
layer_type = 'cudnn_conv'
......
python/paddle/trainer_config_helpers/layers.py
浏览文件 @ 1fb5f12f
......@@ -169,6 +169,7 @@ class LayerType(object):
EXCONV_LAYER = 'exconv'
EXCONVTRANS_LAYER = 'exconvt'
CUDNNCONV_LAYER = 'cudnn_conv'
CUDNNCONVTRANS_LAYER = 'cudnn_convt'
POOL_LAYER = 'pool'
POOL3D_LAYER = 'pool3d'
BATCH_NORM_LAYER = 'batch_norm'
......
python/paddle/v2/framework/op.py
浏览文件 @ 1fb5f12f
......@@ -97,7 +97,7 @@ class OpDescCreationMethod(object):
new_attr.strings.extend(user_defined_attr)
elif attr.type == framework_pb2.INT_PAIRS:
for p in user_defined_attr:
pair = new_attr.pairs.add()
pair = new_attr.int_pairs.add()
pair.first = p[0]
pair.second = p[1]
else:
......@@ -215,5 +215,27 @@ class __RecurrentOp__(object):
return core.RecurrentOp.create(proto.SerializeToString())
class __CondOp__(object):
__proto__ = None
type = "cond"
def __init__(self):
# cache recurrent_op's proto
if self.__proto__ is None:
for op_proto in get_all_op_protos():
if op_proto.type == self.type:
self.__proto__ = op_proto
def __call__(self, *args, **kwargs):
if self.type not in args and "type" not in kwargs:
kwargs["type"] = self.type
# create proto
create_method = OpDescCreationMethod(self.__proto__)
proto = create_method(*args, **kwargs)
# create condop
return core.CondOp.create(proto.SerializeToString())
Operator = OperatorFactory() # The default global factory
RecurrentOp = __RecurrentOp__()
CondOp = __CondOp__()
python/paddle/v2/framework/tests/op_test.py
浏览文件 @ 1fb5f12f
......@@ -85,7 +85,7 @@ def get_numeric_gradient(scope,
op,
inputs,
input_to_check,
output_name,
output_names,
delta=0.005,
in_place=False):
......@@ -100,8 +100,11 @@ def get_numeric_gradient(scope,
ctx = core.DeviceContext.create(core.CPUPlace())
def get_output():
op.run(scope, ctx)
return np.array(scope.find_var(output_name).get_tensor()).sum()
sum = 0.0
for output_name in output_names:
op.run(scope, ctx)
sum += np.array(scope.find_var(output_name).get_tensor()).sum()
return sum
tensor_to_check = scope.find_var(input_to_check).get_tensor()
tensor_size = product(tensor_to_check.get_dims())
......@@ -227,7 +230,7 @@ class OpTest(unittest.TestCase):
def check_grad(self,
inputs_to_check,
output_name,
output_names,
no_grad_set=None,
in_place=False,
max_relative_error=0.005):
......@@ -239,13 +242,16 @@ class OpTest(unittest.TestCase):
if no_grad_set is None:
no_grad_set = set()
if not type(output_names) is list:
output_names = [output_names]
numeric_grads = [
get_numeric_gradient(
self.scope,
self.op,
self.inputs,
input_to_check,
output_name,
output_names,
in_place=in_place) for input_to_check in inputs_to_check
]
grad_names = [
......
python/paddle/v2/framework/tests/test_accuracy_op.py 0 → 100644
浏览文件 @ 1fb5f12f
import unittest
import numpy as np
from op_test import OpTest
class TestAccuracyOp(OpTest):
def setUp(self):
self.op_type = "accuracy"
infer = np.random.randint(0, 2, (32, 1)).astype("int")
label = np.random.randint(0, 2, (32, )).astype("int")
self.inputs = {'Inference': infer, "Label": label}
num_correct = 0
for rowid in xrange(32):
for ele in infer[rowid]:
if ele == label[rowid]:
num_correct += 1
break
self.outputs = {'Accuracy': [num_correct / 32.0]}
def test_check_output(self):
self.check_output()
if __name__ == '__main__':
unittest.main()
python/paddle/v2/framework/tests/test_cond_op.py 0 → 100644
浏览文件 @ 1fb5f12f
import logging
import paddle.v2.framework.core as core
import unittest
import numpy as np
from paddle.v2.framework.op import Operator, CondOp
class PySimpleCond(object):
'''
A simple implementation of dynamic if-else based on numpy
'''
def __init__(self):
array = [1] * 10
for i in range(1, 10, 2):
array[i] = 0
self.cond = np.array(array)
self.x = np.ones(shape=(10, 1))
def forward(self):
self.index_t = np.where(self.cond == 1)
self.index_f = np.where(self.cond == 0)
y_t = self.x[self.index_t]
y_f = self.x[self.index_f]
y_t = y_t * 2.
y_f = y_f * (-2.)
output = np.zeros(shape=(10, 1))
output[self.index_t] = y_t
output[self.index_f] = y_f
return output
class PySimpleCondTest(unittest.TestCase):
def setUp(self):
self.condnn = PySimpleCond()
def test_forward(self):
output = self.condnn.forward()
def create_tensor(scope, name, shape, np_data):
tensor = scope.new_var(name).get_tensor()
tensor.set_dims(shape)
tensor.set(np_data, core.CPUPlace())
return tensor
class TestCondOp(unittest.TestCase):
'''
Test CondOp
equation:
cond = [True, False, True, False, ...]
y[index_t] = x[index_t] * 2.
y[index_f] = x[index_f] * -2.
outputs:
y
'''
def setUp(self):
self.py_cond = PySimpleCond()
def forward(self):
self.scope = core.Scope()
self.create_global_variables()
self.create_cond_op()
self.create_sub_net()
ctx = core.DeviceContext.create(core.CPUPlace())
self.condop.infer_shape(self.scope)
self.condop.run(self.scope, ctx)
return np.array(self.scope.find_var("Out").get_tensor())
def create_global_variables(self):
x_np_data = self.py_cond.x
create_tensor(self.scope, "X", [10, 1], x_np_data)
cond_np_data = self.py_cond.cond.astype("int32")
create_tensor(self.scope, "cond", [10, 1], cond_np_data)
self.scope.new_var("SubScopes")
self.scope.new_var("IndexTensors")
self.scope.new_var("Out")
def create_cond_op(self):
self.condop = CondOp(
Cond="cond",
Xs=["X"],
Outs=["Out"],
SubScopes="SubScopes",
IndexTensors="IndexTensors")
def create_sub_net(self):
truenet = core.Net.create()
scale_op_t = Operator("scale", X='X', Out='Out', scale=2.)
truenet.append_op(scale_op_t)
truenet.complete_add_op(True)
self.condop.set_truenet(truenet)
falsenet = core.Net.create()
scale_op_t = Operator("scale", X='X', Out='Out', scale=-2.)
falsenet.append_op(scale_op_t)
falsenet.complete_add_op(True)
self.condop.set_falsenet(falsenet)
def test_forward(self):
print 'test cond op forward'
pd_output = self.forward()
py_output = self.py_cond.forward()
print 'pd_output', pd_output
print
print 'py_output', py_output
self.assertEqual(pd_output.shape, py_output.shape)
print 'test passed'
return 0
if __name__ == "__main__":
unittest.main()
python/paddle/v2/framework/tests/test_cos_sim_op.py
浏览文件 @ 1fb5f12f
......@@ -7,8 +7,8 @@ class TestCosSimOp(OpTest):
def setUp(self):
self.op_type = "cos_sim"
self.inputs = {
'X': np.random.random((10, 5)).astype("float32"),
'Y': np.random.random((10, 5)).astype("float32")
'X': np.random.random((6, 5)).astype("float32"),
'Y': np.random.random((6, 5)).astype("float32")
}
expect_x_norm = np.linalg.norm(self.inputs['X'], axis=1)
expect_y_norm = np.linalg.norm(self.inputs['Y'], axis=1)
......@@ -28,12 +28,66 @@ class TestCosSimOp(OpTest):
def test_check_grad_ingore_x(self):
self.check_grad(
['Y'], 'Out', max_relative_error=0.05, no_grad_set=set('X'))
['Y'], 'Out', max_relative_error=0.05, no_grad_set=set("X"))
def test_check_grad_ignore_y(self):
def test_check_grad_ingore_y(self):
self.check_grad(
['X'], 'Out', max_relative_error=0.05, no_grad_set=set('Y'))
if __name__ == "__main__":
class TestCosSimOp2(TestCosSimOp):
def setUp(self):
self.op_type = "cos_sim"
self.inputs = {
'X': np.random.random((6, 5)).astype("float32"),
'Y': np.random.random((1, 5)).astype("float32")
}
expect_x_norm = np.linalg.norm(self.inputs['X'], axis=1)
expect_y_norm = np.linalg.norm(self.inputs['Y'], axis=1)
expect_out = (self.inputs['X'] * self.inputs['Y']).sum(axis=1) / \
expect_x_norm / expect_y_norm
self.outputs = {
'XNorm': np.expand_dims(expect_x_norm, 1),
'YNorm': np.expand_dims(expect_y_norm, 1),
'Out': np.expand_dims(expect_out, 1)
}
class TestCosSimOp3(TestCosSimOp):
def setUp(self):
self.op_type = "cos_sim"
self.inputs = {
'X': np.random.random((6, 5, 2)).astype("float32"),
'Y': np.random.random((6, 5, 2)).astype("float32")
}
expect_x_norm = np.linalg.norm(self.inputs['X'], axis=(1, 2))
expect_y_norm = np.linalg.norm(self.inputs['Y'], axis=(1, 2))
expect_out = (self.inputs['X'] * self.inputs['Y']).sum(axis=(1, 2)) / \
expect_x_norm / expect_y_norm
self.outputs = {
'XNorm': np.expand_dims(expect_x_norm, 1),
'YNorm': np.expand_dims(expect_y_norm, 1),
'Out': np.expand_dims(expect_out, 1)
}
class TestCosSimOp4(TestCosSimOp):
def setUp(self):
self.op_type = "cos_sim"
self.inputs = {
'X': np.random.random((6, 5, 2)).astype("float32"),
'Y': np.random.random((1, 5, 2)).astype("float32")
}
expect_x_norm = np.linalg.norm(self.inputs['X'], axis=(1, 2))
expect_y_norm = np.linalg.norm(self.inputs['Y'], axis=(1, 2))
expect_out = (self.inputs['X'] * self.inputs['Y']).sum(axis=(1, 2)) / \
expect_x_norm / expect_y_norm
self.outputs = {
'XNorm': np.expand_dims(expect_x_norm, 1),
'YNorm': np.expand_dims(expect_y_norm, 1),
'Out': np.expand_dims(expect_out, 1)
}
if __name__ == '__main__':
unittest.main()
python/paddle/v2/framework/tests/test_cross_entropy_op.py
浏览文件 @ 1fb5f12f
......@@ -9,16 +9,16 @@ class TestCrossEntropy(OpTest):
self.op_type = "onehot_cross_entropy"
batch_size = 30
class_num = 10
X = numpy.random.uniform(0.1, 1.0,
[batch_size, class_num]).astype("float32")
labels = numpy.random.randint(0, class_num, batch_size, dtype="int32")
cross_entropy = numpy.asmatrix(
[[-numpy.log(X[i][labels[i]])] for i in range(X.shape[0])],
dtype="float32")
self.inputs = {"X": X, "label": labels}
Y = []
for i in range(0, batch_size):
Y.append(-numpy.log(X[i][labels[i]]))
self.outputs = {"Y": numpy.array(Y).astype("float32")}
self.outputs = {"Y": cross_entropy}
def test_check_output(self):
self.check_output()
......
python/paddle/v2/framework/tests/test_gradient_checker.py
浏览文件 @ 1fb5f12f
......@@ -12,7 +12,8 @@ class GetNumericGradientTest(unittest.TestCase):
z = x + y
scope = core.Scope()
add_op = create_op(scope, "add", {'X': x, 'Y': y}, {'Out': z}, dict())
arr = get_numeric_gradient(scope, add_op, {'X': x, 'Y': y}, 'X', 'Out')
arr = get_numeric_gradient(scope, add_op, {'X': x,
'Y': y}, 'X', ['Out'])
self.assertAlmostEqual(arr.mean(), 1.0, delta=1e-4)
def test_softmax_op(self):
......
python/paddle/v2/framework/tests/test_pad_op.py 0 → 100644
浏览文件 @ 1fb5f12f
import unittest
import numpy as np
from op_test import OpTest
class TestPadOp(OpTest):
def setUp(self):
self.initTestCase()
self.op_type = "pad"
self.inputs = {'X': np.random.random(self.shape).astype("float32"), }
self.attrs = {}
self.attrs['paddings'] = np.array(self.paddings).flatten()
self.attrs['pad_value'] = self.pad_value
self.outputs = {
'Out': np.pad(self.inputs['X'],
self.paddings,
mode='constant',
constant_values=self.pad_value)
}
def test_check_output(self):
self.check_output()
def test_check_grad_normal(self):
self.check_grad(['X'], 'Out', max_relative_error=0.006)
def initTestCase(self):
self.shape = (16, 16)
self.paddings = [(0, 1), (2, 3)]
self.pad_value = 0
class TestCase1(TestPadOp):
def initTestCase(self):
self.shape = (2, 3, 4, 4)
self.paddings = [(0, 1), (2, 3), (2, 1), (1, 1)]
self.pad_value = 0.5
class TestCase2(TestPadOp):
def initTestCase(self):
self.shape = (2, 2, 2)
self.paddings = [(0, 0), (0, 0), (1, 2)]
self.pad_value = 1
class TestCase3(TestPadOp):
def initTestCase(self):
self.shape = (8)
self.paddings = [(0, 1)]
self.pad_value = 0.9
if __name__ == '__main__':
unittest.main()
python/paddle/v2/framework/tests/test_tensor.py
浏览文件 @ 1fb5f12f
......@@ -44,79 +44,66 @@ class TestTensor(unittest.TestCase):
self.assertAlmostEqual(2.0, tensor_array_2[19, 11])
def test_int_lod_tensor(self):
places = [core.CPUPlace(), core.GPUPlace(0)]
for place in places:
scope = core.Scope()
var = scope.new_var("test_tensor")
var_lod = scope.new_var("test_lod_tensor")
tensor = var.get_tensor()
lod_tensor = var_lod.get_lod_tensor()
tensor.set_dims([4, 4, 6])
tensor.alloc_int(place)
array = numpy.array(tensor)
array[0, 0, 0] = 3
array[3, 3, 5] = 10
tensor.set(array, place)
place = core.CPUPlace()
scope = core.Scope()
var_lod = scope.new_var("test_lod_tensor")
lod_tensor = var_lod.get_tensor()
lod_tensor.set_tensor(tensor)
lod_tensor.set_lod([[0, 2, 4]])
lod_tensor.set_dims([4, 4, 6])
lod_tensor.alloc_int(place)
array = numpy.array(lod_tensor)
array[0, 0, 0] = 3
array[3, 3, 5] = 10
lod_tensor.set(array, place)
lod_tensor.set_lod([[0, 2, 4]])
lod_v = numpy.array(lod_tensor.tensor())
self.assertTrue(numpy.alltrue(array == lod_v))
lod_v = numpy.array(lod_tensor)
self.assertTrue(numpy.alltrue(array == lod_v))
lod = lod_tensor.lod()
self.assertEqual(0, lod[0][0])
self.assertEqual(2, lod[0][1])
self.assertEqual(4, lod[0][2])
lod = lod_tensor.lod()
self.assertEqual(0, lod[0][0])
self.assertEqual(2, lod[0][1])
self.assertEqual(4, lod[0][2])
def test_float_lod_tensor(self):
places = [core.CPUPlace(), core.GPUPlace(0)]
for place in places:
scope = core.Scope()
var = scope.new_var("test_tensor")
var_lod = scope.new_var("test_lod_tensor")
tensor = var.get_tensor()
lod_tensor = var_lod.get_lod_tensor()
tensor.set_dims([5, 2, 3, 4])
tensor.alloc_float(place)
place = core.CPUPlace()
scope = core.Scope()
var_lod = scope.new_var("test_lod_tensor")
tensor_array = numpy.array(tensor)
self.assertEqual((5, 2, 3, 4), tensor_array.shape)
tensor_array[0, 0, 0, 0] = 1.0
tensor_array[0, 0, 0, 1] = 2.0
tensor.set(tensor_array, place)
lod_tensor = var_lod.get_tensor()
lod_tensor.set_dims([5, 2, 3, 4])
lod_tensor.alloc_float(place)
lod_tensor.set_tensor(tensor)
tensor_array = numpy.array(lod_tensor)
self.assertEqual((5, 2, 3, 4), tensor_array.shape)
tensor_array[0, 0, 0, 0] = 1.0
tensor_array[0, 0, 0, 1] = 2.0
lod_tensor.set(tensor_array, place)
lod_v = numpy.array(lod_tensor.tensor())
self.assertAlmostEqual(1.0, lod_v[0, 0, 0, 0])
self.assertAlmostEqual(2.0, lod_v[0, 0, 0, 1])
self.assertEqual(len(lod_tensor.lod()), 0)
lod_v = numpy.array(lod_tensor)
self.assertAlmostEqual(1.0, lod_v[0, 0, 0, 0])
self.assertAlmostEqual(2.0, lod_v[0, 0, 0, 1])
self.assertEqual(len(lod_tensor.lod()), 0)
lod_py = [[0, 2, 5], [0, 2, 4, 5]]
lod_tensor.set_lod(lod_py)
lod = lod_tensor.lod()
self.assertListEqual(lod_py, lod)
lod_py = [[0, 2, 5], [0, 2, 4, 5]]
lod_tensor.set_lod(lod_py)
lod = lod_tensor.lod()
self.assertListEqual(lod_py, lod)
def test_lod_tensor_init(self):
scope = core.Scope()
var = scope.new_var("test_tensor")
place = core.CPUPlace()
tensor = var.get_tensor()
tensor.set_dims([5, 2, 3, 4])
tensor.alloc_float(place)
tensor_array = numpy.array(tensor)
lod_py = [[0, 2, 5], [0, 2, 4, 5]]
lod_tensor = core.LoDTensor(lod_py)
lod_tensor.set_dims([5, 2, 3, 4])
lod_tensor.alloc_float(place)
tensor_array = numpy.array(lod_tensor)
tensor_array[0, 0, 0, 0] = 1.0
tensor_array[0, 0, 0, 1] = 2.0
tensor.set(tensor_array, place)
lod_py = [[0, 2, 5], [0, 2, 4, 5]]
lod_tensor.set(tensor_array, place)
lod_tensor = core.LoDTensor(lod_py, tensor)
lod_v = numpy.array(lod_tensor.tensor())
lod_v = numpy.array(lod_tensor)
self.assertAlmostEqual(1.0, lod_v[0, 0, 0, 0])
self.assertAlmostEqual(2.0, lod_v[0, 0, 0, 1])
self.assertListEqual(lod_py, lod_tensor.lod())
......
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